quantitative research on covid 19 pandemic in economic

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Article Contents

I. introduction, ii. construction of the public database, iii. economic impacts of covid-19, iv. evaluation of policy responses to covid-19, v. conclusion, data availability.

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The Economic Impacts of COVID-19: Evidence from a New Public Database Built Using Private Sector Data *

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Raj Chetty, John N Friedman, Michael Stepner, Opportunity Insights Team , The Economic Impacts of COVID-19: Evidence from a New Public Database Built Using Private Sector Data, The Quarterly Journal of Economics , Volume 139, Issue 2, May 2024, Pages 829–889, https://doi.org/10.1093/qje/qjad048

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We build a publicly available database that tracks economic activity in the United States at a granular level in real time using anonymized data from private companies. We report weekly statistics on consumer spending, business revenues, job postings, and employment rates disaggregated by county, sector, and income group. Using the publicly available data, we show how the COVID-19 pandemic affected the economy by analyzing heterogeneity in its effects across subgroups. High-income individuals reduced spending sharply in March 2020, particularly in sectors that require in-person interaction. This reduction in spending greatly reduced the revenues of small businesses in affluent, dense areas. Those businesses laid off many of their employees, leading to widespread job losses, especially among low-wage workers in such areas. High-wage workers experienced a V-shaped recession that lasted a few weeks, whereas low-wage workers experienced much larger, more persistent job losses. Even though consumer spending and job postings had recovered fully by December 2021, employment rates in low-wage jobs remained depressed in areas that were initially hard hit, indicating that the temporary fall in labor demand led to a persistent reduction in labor supply. Building on this diagnostic analysis, we evaluate the effects of fiscal stimulus policies designed to stem the downward spiral in economic activity. Cash stimulus payments led to sharp increases in spending early in the pandemic, but much smaller responses later in the pandemic, especially for high-income households. Real-time estimates of marginal propensities to consume provided better forecasts of the impacts of subsequent rounds of stimulus payments than historical estimates. Overall, our findings suggest that fiscal policies can stem secondary declines in consumer spending and job losses, but cannot restore full employment when the initial shock to consumer spending arises from health concerns. More broadly, our analysis demonstrates how public statistics constructed from private sector data can support many research and real-time policy analyses, providing a new tool for empirical macroeconomics.

Since Kuznets (1941) , macroeconomic policy decisions have been made on the basis of publicly available statistics constructed from recurring surveys of households and businesses conducted by the federal government. Although such statistics have great value for understanding total economic activity, they have two limitations. First, survey-based data typically cannot be used to assess variation across geographies or subgroups; due to relatively small sample sizes, most statistics are typically reported only at the national or state level and breakdowns for demographic subgroups or sectors are unavailable. Second, such statistics are often available only at low frequencies, often with a significant time lag. 1

In this article, we address these challenges by (i) building a public database that measures spending, employment, and other outcomes at a high-frequency, granular level using anonymized transaction data collected by companies in the private sector and (ii) demonstrating how this new database can be used to obtain insights into the effects of the coronavirus pandemic (COVID-19) and policy responses in near real time—within three weeks of the shock or policy change of interest.

We organize the article in three parts. First, we construct statistics on consumer spending, business revenues, employment rates, job postings, and other key indicators—disaggregated by geographic area (county or ZIP code), industry, and income level—by combining data from credit card processors, payroll firms, and financial services firms. The main challenge in using transactional data collected by private companies (which we refer to as “private sector data” in what follows) to measure economic activity is a tension between research value and privacy protection. For research, it is beneficial to use raw, disaggregated data—ideally down to the individual consumer or business level—to maximize precision and flexibility of research designs. But from a privacy perspective, it is preferable to aggregate and mask data to reduce the risk of disclosure of private information. To balance these conflicting interests, one must construct statistics that are sufficiently aggregated and masked to mitigate privacy concerns yet sufficiently granular to support research. Our goal is to demonstrate how one can produce public statistics that deliver insights analogous to those obtained from the underlying confidential microdata, thereby improving the transparency, timeliness, and reproducibility of empirical macroeconomic research ( Miguel et al. 2014 ).

We construct publicly available series suitable for research from raw transactional data in a series of steps. We first develop algorithms to clean the raw data by removing data artifacts and smoothing seasonal fluctuations. Raw transactional data can exhibit sharp fluctuations and noise driven by changes in clientele, platform design, or exogenous events such as holidays ( Leamer 2011 ; McElroy, Monsell, and Hutchinson 2018 ). We systematically examine each series for such artifacts and develop methods to address them. Next, we take steps to limit privacy loss by reporting only changes since January 2020 (rather than raw levels), masking small cells, and pooling data from multiple companies to comply with regulations governing the disclosure of material nonpublic information. After establishing these protocols, we report the final statistics using an automated pipeline that ingests data from businesses and publishes processed data, typically within a week after the relevant transactions occur.

The new data series we construct is a complement to, not a replacement for, existing public statistics obtained from representative surveys. The benefits of our data are their granularity and frequency—providing daily or weekly data for sectors and subgroups down to the county level. The drawback is that there are no ex ante guarantees that the data provide a representative picture of economic activity because any one company’s clients are not necessarily a representative sample of U.S. households or firms. We discuss these trade-offs in greater detail in Section II.C . To address these challenges, we benchmark each series to publicly available statistics from representative surveys and create series that track the survey-based measures closely, making ongoing adjustments to series that diverge from national statistics (e.g., because of changes in a data provider’s clients). Ultimately, the statistics we construct from transactional data provide an additional set of (imperfect) signals on economic activity that can in principle yield better statistical inferences when combined with existing survey-based statistics. 2 Whether these data yield valuable new insights in practice is an empirical question.

In the second part of the article, we evaluate the empirical value of the new data by using them to analyze the economic effects of COVID-19, focusing on the period from March 2020 to December 2021—covering both the decline in economic activity and the recovery to baseline spending levels. To evaluate how far one can get solely with public statistics rather than confidential data, we deliberately conduct our empirical analysis using the aggregate statistics we release publicly. 3

National accounts reveal that GDP fell in the second quarter of 2020 after the COVID-19 shock primarily because of a reduction in consumer spending. We find that spending fell primarily because high-income households started spending much less, using the median household income in the ZIP code where the cardholder lives as a proxy for household income. 4 As of April 2020, 41% of the reduction in total spending since January 2020 came from households in ZIP codes with median income in the top quartile, while 12% came from households in ZIP codes with median income in the bottom quartile. This is because the rich account for a larger share of spending to begin with and because they cut spending more in percentage terms. Spending reductions were concentrated in services that require in-person physical interaction, such as hotels and restaurants, consistent with contemporaneous work by Alexander and Karger (2023) and Cox et al. (2020) . These findings suggest that high-income households reduced spending primarily because of health concerns rather than a reduction in income or wealth.

Next, we leverage geographic variation in the demand shocks businesses face to identify the effects of the consumer spending shock on businesses. In-person services are typically produced by small businesses (such as restaurants) that serve customers in their local area. The revenues of those small businesses in high-income, dense areas (high-rent ZIP codes) fell by 61% between January and mid-April 2020, compared with 31% in the lowest-rent ZIP codes.

As businesses lost revenue, they passed the shock on to their employees, particularly low-wage workers. Postings for jobs with low skill requirements fell sharply in April 2020, with a much larger reduction in high-rent areas than in low-rent areas. Postings for jobs with high skill requirements fell much less and exhibit no cross-sectional gradient with respect to rent. As a result of the labor demand shock, employment rates fell by 39% for workers with wage rates in the bottom quartile of the pre-COVID wage distribution as of April 15, 2020 (the trough of the COVID recession), consistent with results first established using other confidential payroll data sources by Cajner et al. (2020) . For those in the top wage quartile, employment rates fell by 14%. Low-wage people working at small businesses in affluent areas were especially likely to lose their jobs. At small businesses located in the highest-rent ZIP codes, 30% of workers were laid off within two weeks after the COVID crisis began; in the lowest-rent ZIP codes, 15% lost their jobs.

Employment levels for workers in the top wage quartile rebounded quickly, returning to pre-COVID levels by the end of June 2020. In contrast, employment recovered much more slowly for low-wage workers. The total number of jobs in the bottom quartile of the prepandemic wage distribution remained 12% below baseline even as of December 2021 (adjusting for wage growth). Why did employment rates for low-wage workers remain persistently lower? Unlike at the start of the pandemic, the source of lower employment rates at the end of 2021 was not a lack of labor demand: total consumer spending and low-skilled job postings were well above pre-COVID baseline levels throughout 2021. Furthermore, job postings for low-skilled workers were just as high in high-rent areas as they were in low-rent areas by December 2021. However, employment rates for low-wage workers continue to exhibit a sharp gradient with respect to rent, with employment levels (adjusted for wage growth) returning to pre-COVID baseline rates in the lowest-rent areas but remaining 23 percentage points below pre-COVID levels in the highest-rent areas. Employment rates in December 2021 were much more strongly related to the size of the initial shock to economic activity—for example, the change in employment rates as of April 2020—than contemporaneous factors such as COVID case rates or unemployment benefit levels. In short, the initial labor demand shock induced by the reduction in aggregate demand in March 2020 led to a persistent reduction in labor supply among low-wage workers in the hardest-hit areas. As a result, business cycle dynamics during the COVID crisis were not symmetric: on the way down, spending and employment fell in lockstep, but on the way back, they did not rise together, echoing patterns documented in the Great Recession ( Yagan 2019 ).

In the third part of the article, we examine the scope for stabilization policies to break the chain of events documented above. We focus on the effects of stimulus payments, whose goal was to mitigate reductions in economic activity by boosting aggregate spending. The federal government sent households stimulus checks at three points during the crisis: April 15, 2020; January 4, 2021; and March 17, 2021. Using an event study design, we find that the stimulus payments made in April 2020 increased spending uniformly across the household income distribution (again proxying for income based on ZIP code), with low- and high-income households increasing spending substantially in the days after they received checks, consistent with evidence from Baker et al. (forthcoming) and Cox et al. (2020) using individual-level administrative data.

In contrast, the January 2021 payments had highly heterogeneous effects across the income distribution: low-income households continued to spend a substantial fraction of their stimulus checks, but high-income households (those living in the top quartile of ZIP codes by median income) spent virtually none of the money they received. The impacts of the stimulus changed sharply over the course of the recession because of the heterogeneous spending dynamics documented above: high-income households cut spending sharply but did not lose much income, and as a result had built up considerable savings by January 2021, sharply reducing their marginal propensity to consume. Because our spending data are available with a short lag, we were able to establish this result three weeks after the second stimulus payment. These results were cited in policy debates regarding who should receive the March 2021 stimulus payments, which ultimately concluded with policy makers lowering the income threshold for eligibility relative to initial proposals.

Finally, as predicted based on effects of the January 2021 stimulus, we find that the March 2021 stimulus payments increased spending for low-income households, but had little impact on spending for high-income households who remained eligible. Hence, estimates of marginal propensities to consume in January 2021 provided much better forecasts of the effects of the March 2021 stimulus payments than historical estimates from prior recessions, which suggested there would be little heterogeneity in MPCs by income level ( Sahm, Shapiro, and Slemrod 2012 ; Broda and Parker 2014 ), or even estimates from just months earlier in the same recession (April 2020). This example demonstrates how public statistics constructed from private sector data can support a “real time” approach to macroeconomic policy, where policies are adjusted based on current evidence on their effects rather than relying solely on historical predictions from other economic environments.

The data can analogously be used to analyze the effects of other policies (beyond stimulus payments) that were implemented during the COVID-19 crisis. We find that results from our publicly available data match those from studies that use confidential data sources closely. For instance, we find that state-ordered shutdowns and reopenings of economies had modest effects on economic activity ( Goolsbee and Syverson 2021 ) and that loans to small businesses as part of the Paycheck Protection Program (PPP) had small effects on employment rates ( Hubbard and Strain 2020 ; Autor et al. 2022a ; Granja et al. 2022 ). In addition, several studies use the new data constructed here to evaluate the effects of many other policies, from the effects of unemployment benefit changes ( Casado et al. 2020 ) to eviction moratoria ( An, Gabriel, and Tzur-Ilan 2022 ).

We conclude by analyzing whether the combination of government policies was adequate to stem the decline in economic activity set off by the reduction in consumer spending. Consumer spending fell sharply in April 2020 in the dense, affluent areas where many low-wage workers lost their jobs, portending the start of a downward spiral of secondary effects stemming from the initial aggregate demand shock. However, the relationship between consumer spending and the rate of local job loss flattened sharply by July 2020. Spending recovered to baseline levels or above baseline, even in places where many low-wage workers lost their jobs—presumably because of the substantial infusion of income to such areas in the form of fiscal stimulus, unemployment benefits, and other programs that led to an increase in disposable income at the bottom of the distribution ( Blanchet, Saez, and Zucman 2022 ).

Overall, our findings suggest that fiscal policies can be very valuable for limiting secondary declines in consumer spending arising from a loss of income as workers lose their jobs. However, fiscal policy itself does not have the capacity to restore full employment when the initial shock to consumer spending arises from health concerns ( Guerrieri et al. 2022 ). Furthermore, even after health concerns have abated, changes in labor supply among those who lost their jobs may lead to persistent reductions in employment. It may therefore be useful to target reemployment policies to individuals who held low-wage jobs in places that suffered the largest job losses ( Austin, Glaeser, and Summers 2018 ). Our data provide a way to monitor the areas and sectors in which job losses persist, information that can be used to target and evaluate such programs going forward.

Beyond showing how the COVID-19 pandemic affected economic activity, the broader contribution of this study is the construction of a new public database of granular macroeconomic statistics that opens new avenues for empirical macroeconomics, from finer analysis of heterogeneous effects across subgroups and areas to real-time policy fine-tuning. Importantly, such analyses can be conducted by many researchers and policy analysts, not just those who can secure access to confidential data and devote resources to cleaning and harmonizing it. In this sense, the data assembled here provide a prototype for a new system of real-time, granular national accounts that can be refined in future work, much as Kuznets (1941) and Summers and Heston (1984 ; 1991) developed prototypes for national accounts that were refined in subsequent work (e.g., Feenstra, Inklaar, and Timmer 2015 ). Going forward, our intention is to continue to maintain and refine this database in collaboration with researchers at government statistical agencies, with the ultimate goal of creating a complement to survey-based statistics that yield further detail on economic activity.

Our work builds on two literatures: a long-standing literature on macroeconomic measurement and a recent literature on the economics of pandemics. In the macroeconomic measurement literature, our work is most closely related to studies showing that private sector data sources can be used to forecast government statistics (see Abraham et al. 2019 for an overview of this work). In the COVID-19 pandemic literature, numerous papers have used confidential private sector data to analyze consumer spending; see Vavra (2021) and Brodeur et al. (2021) for surveys. The contribution of the present study is to present a comprehensive characterization of how COVID-19 and subsequent stabilization policies affected economic activity by disaggregating data across geographic areas and subgroups at a high frequency. We discuss specific connections with prior work in the context of presenting our results.

The rest of this article is organized as follows. The next section describes how we construct the data series we make public. In Section III , we analyze the effects of COVID-19 on spending, revenue, and employment. Section IV analyzes the effects of stimulus and other government policies enacted to mitigate COVID’s effects. Section V concludes. Technical details are available in the Online Appendix , and the data used to produce the results can be downloaded online .

We use anonymized data from several private companies to construct public indices of consumer spending, small-business revenue, job postings, and employment rates. All of the data series described below can be freely downloaded from the Economic Tracker website .

We release each data series at the highest available frequency using an automated pipeline that ingests data from data providers, constructs the relevant statistics, conducts quality control tests, and outputs the series publicly. Online Appendix  A details the engineering of this pipeline.

II.A. Methods

We disaggregate each series by industrial sector, county, and income quartile wherever feasible. To systematize our approach and facilitate comparisons between series, we adopt the following four principles when constructing each series.

First, we remove artifacts in raw data that arise from changes in data provider coverage or systems. For instance, firms’ clients often change discretely, sometimes leading to discontinuous jumps in series, particularly in small cells. We systematically search for large jumps in series, study their root causes by consulting with the data provider, and address such discontinuities by imposing continuity using series-specific methods described below.

Second, we smooth low- and high-frequency fluctuations in the data. We address high-frequency fluctuations through aggregation, for example, by reporting seven-day moving averages to smooth fluctuations across days of the week. Certain series—most notably consumer spending and business revenue—exhibit strong lower-frequency seasonal fluctuations that are autocorrelated across years (e.g., a surge in spending around the holiday season). We deseasonalize such series by indexing each week’s value in 2020 relative to corresponding values for the same week in 2019.

Third, we take a series of steps to protect the confidentiality of businesses and their clients. Instead of reporting levels of each series, we report indexed values that show percentage changes relative to mean values in January 2020. 5 We suppress small cells and exclude outliers to meet privacy and data protection requirements, with thresholds that vary across data sets as described below. For data obtained from publicly traded firms—whose ability to disclose data is restricted by Securities and Exchange Commission regulations governing the disclosure of material nonpublic information—we combine data from multiple firms so that the statistics we report do not reveal information about any single company’s activities.

Finally, we address the challenge that our data sources capture information about the customers each company serves rather than the general population. Instead of attempting to adjust for this nonrepresentative sampling, we characterize the portion of the economy that each series represents by comparing each sample we use to national benchmarks and label the sector and population subgroup that each series represents.

We follow these four broad principles to construct every public data series that we release, while adapting the data-processing methodology to the specific characteristics of each data source.

II.B. Data Series

This section provides an overview of how we produce each data series. We summarize the data sources and give an overview of our key processing steps in Online Appendix  Table I, and provide summary statistics on sample sizes for each series in Online Appendix  Table II.

1. Consumer Spending

We measure consumer spending using aggregated and anonymized data on credit and debit card spending collected by Affinity Solutions, a company that aggregates consumer credit and debit card spending information to support a variety of financial service products, such as loyalty programs for banks. Affinity Solutions captures nearly 10% of debit and credit card spending in the United States. We obtain raw data from Affinity Solutions disaggregated by county, quartile of ZIP code median income, industry, and day starting from January 1, 2019.

We process the raw Affinity data into an analytical series following the four steps above—removing artifacts and outliers, deseasonalizing, indexing, and benchmarking—and describe each step in detail in Online Appendix  B. As an example of our data-processing methods, we detect and remove discontinuous breaks caused by entry or exit of card providers from the sample. Because these card providers have geographically concentrated customer bases, the number of active cards in a county exhibits a sharp upward or downward spike when the sample of local card providers changes ( Online Appendix  Figure I.A). We identify these sudden changes by analyzing the number of unique cards from each county with at least one transaction in each week, using a supremum Wald test for a structural break at an unknown break point. If we identify a structural break in week t , we impute spending changes in weeks { t − 1, t, t + 1} using the mean week-to-week percent change in spending excluding all counties with a structural break in the same state.

The Affinity series has broad coverage across industries but overrepresents categories in which credit and debit cards are used for purchases (see Online Appendix  Figure II discussed in Online Appendix  B). We therefore view the Affinity series as providing statistics that are representative of total card spending, but not total consumer spending.

2. Small-Business Revenue

We obtain data on small-business transactions and revenues from Womply, a company that aggregates data from several credit card processors to provide analytical insights to small businesses and other clients. Womply receives data from approximately 500,000 small businesses, which corresponds to more than 5% of small businesses with 1–499 employees in the United States in 2020 ( U.S. SBA Office of Advocacy 2020 ). In contrast to the Affinity series on consumer spending, which is a cardholder-based panel covering total spending, Womply is a firm-based panel covering total revenues of small businesses disaggregated by county, sector, and week. Another key distinction is that Womply data measure the location of the business as opposed to where the cardholder lives.

We process this small-business data following each of the four broad steps as with the consumer spending data from Affinity, but we tailor the methodology to the structure of the Womply data, as detailed in Online Appendix  C. To take one example, there are again discontinuous breaks in the number of observed small businesses due to churn in the observed sample of payment processors, analogous to the entry and exit of card providers in consumer spending data. However, unlike the repeated cross sections of consumer spending data, we can address such sample churn more directly using the panel data on small businesses. In each calendar year, we follow the sample of businesses operating during the first week of the year: no new businesses enter the panel midyear. We must still detect cases where a payment processor exits the sample, and we adopt a similar approach to detecting discontinuous breaks as we applied to consumer spending data. We look for sharp drops in businesses operating at the state and national levels ( Online Appendix  Figure I.B). 6 After adjusting for these discontinuous exits, we proceed with the rest of the steps described in Online Appendix C to construct a seasonally adjusted series for total small-business revenue.

Womply revenues are broadly distributed across sectors. A larger share of the Womply revenue data come from industries that have a larger share of small businesses, such as food services, professional services, and other services, as one would expect given that the Womply data only cover small businesses ( Online Appendix  Figure II).

3. Job Postings

We obtain data on job postings from 2007 to the present from Lightcast (formerly known as Burning Glass Technologies). Lightcast aggregates nearly all jobs posted online from approximately 40,000 online job boards in the United States. Lightcast then removes duplicate postings across sites and assigns attributes including geographic locations, required job qualifications, and industry.

We receive raw data from Lightcast on job postings disaggregated by industry, week, job qualifications, and county. 7 We report job postings at the weekly level, expressed as changes in percentage terms relative to the first four complete weeks of 2020.

Lightcast provides a sample that is representative of private sector job postings in the United States. Online Appendix  Figure III shows that the distribution of industries in the Lightcast data is well aligned with the Bureau of Labor Statistics’ Job Openings and Labor Market Turnover Survey (JOLTS), consistent with Carnevale, Jayasundera, and Repnikov (2014) .

4. Employment

We use three data sources to obtain information on employment rates: payroll data from Paychex and Intuit and worker-level data from Earnin. We describe these data sources and then discuss how we construct a weekly series that is broadly representative of private nonfarm employment rates in the United States (see Online Appendix  Tables III and IV).

i. Paychex and Intuit . Paychex provides payroll services to approximately 670,000 small and medium-sized businesses across the United States and pays 8% of U.S. private-sector workers ( Paychex 2020 ). To track how employment changes vary across the wage distribution, we separate employees into four groups based on their hourly wage rates. We split the sample into the four groups whose wages (if they work full-time for the full year) would be above/below 100%, 150%, and 250% of the federal poverty line (FPL). For convenience, we refer to these groups as “wage quartiles” because these thresholds group workers approximately into quartiles before the pandemic. 8 This approach allows us to track the total number of jobs in different parts of the wage distribution, adjusting for inflation over time. We obtain aggregate weekly data on total employment for each hourly wage group by county, industry (two-digit NAICS), firm size bin, and pay frequency.

Intuit offers payroll services to businesses as part of its Quickbooks program, covering approximately 1 million businesses as of January 2020. Businesses that use Quickbooks tend to be very small (fewer than 20 employees). We obtain anonymized, aggregated data on month-on-month and year-on-year changes in total employment (the number of workers paid in the prior month) based on repeated cross sections. We construct a national series from population-weighted averages of state changes in each month.

To protect business privacy and maximize precision, we combine Paychex and Intuit data to construct our primary employment series. We clean this series for analysis following the general principles (see Online Appendix  E). 9 We do not seasonally adjust our employment series because we have incomplete data in 2019; fortunately, seasonal fluctuations in employment are an order of magnitude smaller than those in spending ( Online Appendix  Figure IV) and hence are unlikely to affect our results.

ii. Earnin . Earnin is a financial management application that provides its members with access to their income as they earn it, in advance of their paychecks. Because its users tend to have lower income levels, Earnin primarily provides information on employment for low-wage workers. We obtain anonymized data from Earnin from January 2020 onward, describing the date a paycheck is received, workplace ZIP code, firm size, industry, and amount of pay. Earnin complements the firm-based payroll data sets by providing a worker-level sample with more granular ZIP-level geographic identifiers. However, because workers self-select into the sample when they enter or exit the Earnin customer base, the labor market disruptions of the pandemic generate substantial sample selection over time. We use the Earnin sample only to study the first six months of the COVID pandemic, from March to September 2020, when the sample is relatively stable. We convert the Earnin data into an employment series using an approach similar to that used to construct the combined Paychex and Intuit employment series (detailed in Online Appendix  E).

5. Public Data Sources: UI Records, COVID-19 Incidence, and Google Mobility Reports

In addition to the new private sector data sources, we collect and use three sets of data from public sources to supplement our analysis: data on unemployment benefit claims obtained from the Department of Labor and state government agencies; data on COVID-19 cases and deaths obtained from the New York Times , Johns Hopkins, the Centers for Disease Control and Prevention (CDC), and the U.S. Department of Health and Human Services; and data on the amount of time people spend at home versus other locations obtained from Google’s COVID-19 Community Mobility Reports. More details on these data sources are provided in Online Appendices F to H.

II.C. Limitations

The rest of this article demonstrates how the database assembled here is valuable for uncovering the economic effects of COVID-19. However, these new data also have three important limitations that users should weigh, especially in future applications.

First, each data series we construct necessarily reflects the clientele of the data provider, and thus does not provide guarantees of population representativeness. We take several steps to verify that each data series is nationally representative: we compare the cross-sectional composition of each series against nationally representative statistics in this section, and compare trends in each series during the COVID-19 pandemic to data from publicly available benchmarks in the next section. But it is impossible to verify the representativeness of each level of disaggregation (e.g., county-level consumer spending), precisely because no existing public data sets provide similarly granular and high-frequency data—hence the value of these novel data sources. Given this limitation, it is valuable to verify empirical results using multiple different data series and triangulate findings against whatever data are available from representative surveys at coarser levels of aggregation, as we do in our analysis below.

Second, the series we construct have sampling error from both idiosyncratic variation across firms and households as well as from changing client bases and business closures. The economic shocks associated with COVID-19 were especially large, making their effects easy to detect even in the presence of such errors. In Section III.C , we show that the data series are sufficiently reliable to detect moderate-sized changes in economic activity at local levels (e.g., employment rate changes of 4 percentage points at the commuting zone level for the 50 largest commuting zones). Smaller fluctuations—for example, monthly innovations in employment rates during periods of normal economic growth—will not be distinguishable from sampling noise in these data sets.

Third, while our data cover certain sectors well—such as spending on items covered by credit and debit cards—they entirely exclude other sectors, such as spending on housing and durable goods such as vehicles. In the context of the COVID-19 pandemic, the data series we construct overlap with the sectors that exhibit the largest changes in economic activity (see Section III ), but in other applications that may not be the case.

In light of these limitations, the data constructed here should be used to complement representative survey-based statistics, not as a substitute. Furthermore, the present version of the database is a prototype that can be improved over time. For example, noise in estimates of employment rates from changes in payroll firms’ clientele can be mitigated by chaining together estimates of employment changes from rotating panels of firms instead of relying on repeated cross sections. Adding additional data partners can address gaps in coverage, such as spending on housing. Such refinements could mitigate the limitations, though statistics from representative surveys will remain essential as benchmarks.

According to the U.S. Bureau of Economic Analysis (2020) , GDP fell by |${\$}$| 1.61 trillion (an annualized rate of 29.9%) from the first quarter of 2020 to the second quarter of 2020, shown by the first bar in Online Appendix  Figure V.A. GDP fell primarily because of a reduction in personal consumption expenditures (consumer spending), which fell by |${\$}$| 1.20 trillion. Government purchases and net exports did not change significantly, while private investment fell by |${\$}$| 0.53 trillion. 10 We begin our analysis by studying the determinants of this sharp reduction in consumer spending. Then we turn to examine downstream effects of the reduction in consumer spending on business activity and the labor market.

III.A. Consumer Spending

We analyze consumer spending using data on aggregate credit and debit card spending. National accounts data show that spending that is well captured on credit and debit cards—essentially all spending excluding housing, health care, and motor vehicles—fell by approximately |${\$}$| 0.90 trillion between the first quarter of 2020 and the second quarter of 2020, making up 75% of the total reduction in personal consumption expenditures. 11

1. Benchmarking

Our card spending series is well aligned with the Advance Monthly Retail Trade Survey (MARTS), one of the main inputs used to construct the national accounts. 12   Online Appendix  Figure V.B plots the month-on-month changes in spending on retail services (excluding auto-related expenses) and food services: both series track each other before the pandemic, then food services spending drops rapidly in March and April 2020, while total retail spending fluctuates much less during the pandemic. The root mean square error (RMSE) of the Affinity series relative to the MARTS is 3 to 5 percentage points, which is small relative to the fluctuations induced by COVID, but calls for caution in evaluating smaller shocks. Online Appendix  Figure VI.A expands this analysis to other categories by plotting the change in spending from January to April 2020 in the Affinity spending series against the decline in consumer spending as measured in the MARTS. Despite the fact that the MARTS category definitions are not perfectly aligned with those in the card spending data, the relative declines are generally well aligned across sectors, with a correlation of 0.92. 13

2. Heterogeneity by Income

We begin by examining spending changes by household income. We do not directly observe cardholders’ incomes in our data; instead, we proxy for cardholders’ incomes using the median household income in the ZIP code in which they live (based on data from the 2014–18 American Community Survey). ZIP codes are strong predictors of income because of the degree of income segregation in most U.S. cities; however, they are not a perfect proxy for income and can be prone to bias in certain applications, particularly when studying tail outcomes ( Chetty et al. 2020 ). To evaluate the accuracy of our ZIP code imputation procedure, we compare our estimates to those in contemporaneous work by Cox et al. (2020) , who observe cardholder income directly based on checking account data for clients of JPMorgan Chase. Our estimates are closely aligned with those estimates, suggesting that the ZIP code proxy is reasonably accurate in this application. 14

Figure I , Panel A plots a seven-day moving average of total daily card spending for households in the bottom versus top quartile of ZIP codes based on median household income. Spending fell sharply on March 15, 2020, when the national emergency was declared and the threat of COVID became widely discussed in the United States. Spending fell from |${\$}$| 8.3 billion a day in February 2020 to |${\$}$| 5.5 billion a day between March 25 and April 14, 2020 (a 34% reduction) for high-income households; the corresponding change for low-income households was |${\$}$| 3.5 billion to |${\$}$| 2.7 billion (a 23% reduction).

Changes in Consumer Spending during the COVID Pandemic

This figure disaggregates spending changes by income and sector using debit and credit card data from Affinity Solutions and national accounts (NIPA) data. Panel A plots daily spending levels for consumers in the highest and lowest quartiles of household income by combining total card spending in January 2020 (from NIPA Table 2.3.5) with our Affinity Solutions spending series. See the notes to Online Appendix  Table V for details on this method. Panel B disaggregates the sectoral shares of seasonally adjusted spending changes (left bar) and pre-COVID spending levels (right bar). See Online Appendix  B.3 for the definitions of the sectors plotted in Panel B. Panel C decomposes the change in personal consumption expenditures (PCE) in the Great Recession and the COVID-19 recession using NIPA Table 2.3.6. PCE is defined here as the sum of durable goods, nondurable goods, and services in seasonally adjusted, chained (2012) dollars. The peak to trough declines are calculated from December 2007 to June 2009 for the Great Recession and from January to April 2020 for the COVID-19 recession. Data sources: Affinity Solutions, NIPA.

Because high-income households cut spending more in percentage terms and accounted for a larger share of aggregate spending to begin with, they accounted for a much larger share of the decline in total spending in the United States than low-income households. In Online Appendix  Table V, Panel A, column (2) we estimate that as of mid-April 2020, top-quartile households accounted for 41% of the aggregate spending decline after the COVID shock, while bottom-quartile households accounted for only 12% of the decline.

This gap in spending patterns by income grew even larger over time. By August 2020, spending had returned to 2019 levels among households in the bottom quartile, whereas spending among high-income households remained 8% below baseline levels. Spending then continued to rise gradually in subsequent months and began to exceed pre-COVID levels starting in 2021 for both low- and high-income groups. The degree of heterogeneity in spending changes by income is larger than that observed in previous recessions ( Petev, Pistaferri, and Saporta-Eksten 2011 , figure 6) and played a central role in the downstream impacts of COVID on businesses and the labor market, as we show below.

3. Heterogeneity across Sectors

Next we disaggregate the change in total card spending across categories to understand why households cut spending so rapidly. In particular, we seek to distinguish two channels: reductions in spending due to loss of income versus fears of contracting or spreading COVID.

The left bar in Figure I , Panel B plots the share of the total decline in spending from the pre-COVID period to mid-April 2020 accounted for by various categories. Fifty-seven percent of the reduction in spending came from reduced spending on goods or services that require in-person contact (and thereby carry a risk of COVID infection), such as hotels, transportation, and food services. This is particularly striking given that these goods accounted for less than one-third of total spending in January, as shown by the right bar in Figure I , Panel B. These gaps grew larger as the pandemic progressed, as consumer spending increased above prepandemic levels for goods and remote services by mid-August 2020, but remained sharply depressed for in-person services ( Online Appendix  Table V, Panel B). The fact that the spending reductions vary so sharply across goods in line with their health risks indicates that health concerns (either one’s own health or altruistic concerns about others’ health) rather than a lack of purchasing power drove spending reductions.

These patterns of spending reductions differ markedly from those observed in prior recessions. Figure I , Panel C compares the change in spending across categories in national accounts data in the COVID recession and the Great Recession in 2009–10. In the Great Recession, nearly all of the reduction in consumer spending came from a reduction in spending on goods; spending on services was almost unchanged. In the COVID recession, 71% of the reduction in total spending came from a reduction in spending on services.

4. Heterogeneity by COVID Incidence

To further evaluate the role of health concerns, we examine the association between COVID case rates across areas and changes in spending. Figure II shows that spending fell more in counties with higher rates of COVID infection, in both low- and high-income areas, during the trough in consumer spending from March 25 to April 14, 2020. However, there was a substantial reduction in spending even in areas without high rates of realized COVID infection, consistent with widespread concern about the disease even in areas where outbreaks were less prevalent. To examine the mechanism driving these spending reductions, Online Appendix  Figure VII uses anonymized cell phone data from Google to present a binned scatter plot of the amount of time spent outside home versus COVID case rates, again separately for low- and high-income counties. As in Figure II , there is a strong negative relationship between time spent outside and COVID case rates, with a steeper slope in low-income counties. The reduction in spending on services that require physical, in-person interaction (e.g., restaurants) follows directly from this reduction in time spent outside.

Association between COVID-19 Incidence and Changes in Consumer Spending

This figure presents a county-level binned scatter plot. To construct it, we divide the data into 20 equal-sized bins, ranking by the x -axis variable and weighting by the county’s population, and plot the (population-weighted) means of the y -axis and x -axis variables in each bin. The y -axis presents the change in seasonally adjusted consumer spending from the base period (January 6–February 2, 2020) to the three-week period of March 25 to April 14, 2020 (see Section II.B and Online Appendix  B for details on the construction of our consumer spending series). The x -axis variable is the log of the county’s cumulative COVID case rate per capita as of April 14, 2020; axis labels show the levels on a log scale. We plot values separately for counties in the top and bottom quartiles of median household income (measured using population-weighted 2014–2018 ACS data). Data sources: Affinity Solutions, New York Times .

In sum, disaggregated data on consumer spending reveal that spending in the initial stages of the pandemic fell primarily because of health concerns rather than a loss of current or expected income—consistent with the mechanisms emphasized by Eichenbaum, Rebelo, and Trabandt (2021) . Disposable income ultimately fell relatively little because few high-income individuals lost their jobs (as we show in Section III.C ) and because the income losses of lower-income households who lost their jobs were more than offset by supplemental unemployment benefits, stimulus payments, and other transfers ( Ganong, Noel, and Vavra 2020 ; Blanchet, Saez, and Zucman 2022 ). Next we turn to the effects of the spending reductions induced by these health concerns on businesses and the labor market.

III.B. Business Revenues

Services that are consumed in person (e.g., restaurants) are typically produced by small businesses who serve customers in their local area. 15 The reduced in-person spending by high-income households documented above thus has heterogeneous effects across areas, with businesses located in more affluent areas facing larger spending shocks. We exploit this geographic heterogeneity to identify the effects of the reduction in consumer spending on businesses and their employees, starting by examining effects on small-business revenues. 16

We measure small-business revenues using data from Womply, which records revenues from credit card transactions for small businesses (as defined by the Small Business Administration) at the location where the sale occurs. Because there is no publicly available series on small-business revenues, we compare trends in the Womply data to the Affinity consumer spending data. These series are generally well aligned, especially in sectors with a large share of small businesses, such as food and accommodation services, where the RMSE of the Womply series relative to Affinity is 2.68 percentage points ( Online Appendix  Figure VIII). For retail, where large businesses have a larger market share, the RMSE is 6.72 percentage points.

2. National Trends

In the aggregate time series (plotted in Online Appendix  Figure IX.A), small-business revenues fell by 48% when the pandemic began and then recovered to 11% below pre-COVID levels by July 2020. Small-business revenues then remained at that level until late 2020, reaching pre-COVID levels only in September 2021. The larger fall and slower recovery of small-business revenues relative to total consumer spending is consistent with evidence that consumer spending shifted toward large online retailers during the pandemic ( Alexander and Karger 2023 ). Unfortunately, we lack data on revenues at large businesses, so we cannot examine these impacts directly.

3. Heterogeneity across Areas

To illustrate the data underlying our geographic analysis, we map the change in small-business revenues from January 2020 to the period immediately after the COVID shock (March 23–April 12, 2020) by ZIP code in New York City, Chicago, and San Francisco ( Online Appendix  Figure X). 17 In all three cities, revenue losses were largest in the most affluent neighborhoods (Manhattan in New York and Lincoln Park in Chicago) and in the central business districts in each city. Even in predominantly residential areas, businesses located in more affluent neighborhoods suffered much larger revenue losses.

Figure III generalizes these examples by presenting a binned scatter plot of percent changes in small-business revenue versus median rents (for a two-bedroom apartment) by ZIP code. 18 In the top ventile of ZIP codes by rent, small-business revenues fell by 61%, compared with 31% in the bottom ventile of ZIP codes by rent, consistent with the differences observed in the Affinity consumer spending data across areas. 19

Changes in Small-Business Revenues versus Median Two-Bedroom Rent, by ZIP Code

This figure presents a binned scatter plot showing the relationship between changes in seasonally adjusted small-business revenue in Womply data versus rent at the ZIP code level. The binned scatter plot is constructed as described in Figure II . We measure changes in small-business revenue as the average value of our index at the ZIP code level between March 23 and April 12, 2020 (see Section II.B and Online Appendix  C for details on the construction of our small-business revenue series). The x -axis variable is the ZIP code median rent for a two-bedroom apartment in the 2014–2018 ACS. Data sources: Womply, ACS.

The business revenue loss versus rent gradient is similar when we compare ZIP codes within the same county by regressing revenue changes on rent with county fixed effects ( Table I , Panel A, column 2), or when comparing businesses within the same industry across ZIP codes using sector fixed effects ( Online Appendix  Figure XII.A). It also remains similar when controlling for the (pre-COVID) density of high-wage workers in a ZIP code to account for differences that may arise from shifts to remote work in business districts ( Table I Panel A, column (3)). 20

Association Between Rent and Changes in Business Revenue and Employment

Notes. This table presents estimates from population-weighted OLS regressions at the county and ZIP code level. We regress percentage changes in small-business revenue (using Womply data) and low-wage employment (using Paychex-Intuit and Earnin data) on median two-bedroom rent (as measured in the 2014–2018 ACS). Standard errors are reported in parentheses; county-level regressions use robust standard errors and ZIP-level regressions use standard errors clustered by county. The dependent variable is in percentage point units. The dependent variable in Panel A is the average change in small-business revenue measured from March 23 to April 12, 2020, relative to January 4 to 31, 2020. All regressions in Panel A are at the ZIP code level. The dependent variable in Panel B is the change in low-wage employment measured from June 27 to July 31, 2020, relative to January 4 to 31, 2020. In Panel B, columns (1) and (2) are at the county level using combined Paychex and Intuit data, while column (3) is at the ZIP code level using Earnin data. In both panels, column (1) shows the baseline regression without any controls: this specification corresponds to the estimated slope coefficient and standard error reported in Figure III (small-business revenue) and Figure IV , Panel C (low-wage employment). In Panel A, column (2) adds county fixed effects and column (3) further adds the log of the density of high-wage workers as a control (which is observed using the Census LODES for 92% of ZIP codes representing 99% of the U.S. population). In Panel B, column (2) adds the log of the density of high-wage workers as a control to the baseline county level regression, and column (3) switches to ZIP code–level data for a specification analogous to the one in column (3) of Panel A. Data sources: Womply, Paychex, Intuit, Earnin, ACS, Census LODES.

In sum, businesses in dense, affluent areas lost the most revenue—consistent with the sharp reduction in spending on in-person goods and services by high-income households. Next, we examine how businesses reacted to this loss of revenue, focusing on the incidence of the shock on their employees.

III.C. Labor Market Effects

We begin by analyzing how the loss of revenues affected labor demand using data on job postings from Lightcast. Figure IV , Panel A presents a binned scatter plot of the change in job postings that require minimal education between January 2020 and the April 2020 trough versus median rents by county. Job postings with minimal educational requirements fell much more sharply in high-rent areas than for workers in lower-rent areas (difference = 6.9 percentage points, or 22.8%), consistent with the larger shocks to revenue faced by firms located in high-rent areas. By contrast, postings for jobs that require higher levels of education—which are much more likely to be in tradeable sectors that are less influenced by local conditions (e.g., finance or professional services)—exhibit no relationship with local rents ( Figure IV , Panel B).

Changes in Job Postings and Employment Rates versus Rent

This figure shows binned scatter plots of the relationship between median rents and changes in job postings (Panels A and B) or changes in employment rates (Panel C). The binned scatter plots are constructed as described in Figure II . Solid lines are best-fit lines estimated using OLS. Each panel also displays the slope coefficient and standard error of the corresponding linear OLS regression. In each panel, the x -axis variable is the median rent in a county for a two-bedroom apartment in the 2014–2018 ACS. In Panel A, the y -axis variable is the average value of our job postings series for jobs requiring minimal or some education between March 25 and April 14, 2020 (see Section II.B and Online Appendix  D for more detail on our job postings series). Panel B replicates Panel A with job postings for workers with moderate, considerable, or extensive education. In both Panels A and B, we winsorize our job postings series at the 99th percentile of the (population-weighted) county-level distribution within each level of required education. In Panel C, the y -axis variable is the average value of our bottom wage quartile employment series during July 2020 (see Section II.B and Online Appendix  E for more detail on the construction of our employment series). Data sources: Paychex, Intuit, Lightcast, ACS.

Having established that the pandemic reduced labor demand, especially for lower-skilled workers working in affluent areas, we turn to examine its effects on employment rates using data from payroll companies.

Our payroll-based employment series is broadly aligned with measures from nationally representative statistics. Online Appendix  Figure XIII.A shows that month-on-month changes in employment rates for all workers estimated from combined Paychex and Intuit payroll data generally fall between estimates obtained from the Current Employment Statistics (CES; a survey of businesses) and Current Population Survey (a survey of households). Turning to specific sectors, Online Appendix  Figure XIII.B focuses on month-on-month employment changes in two sectors that experienced very different trajectories: food services, where employment fell heavily, and professional services, where it did not. In both cases, our Paychex-Intuit series closely tracks data from the CES. Online Appendix  Figure XIV.A shows more generally that changes in employment rates across private nonfarm sectors (two-digit NAICS) are very closely aligned in our series and the CES, with a correlation of 0.97 when looking at changes from January to July 2020.

Unlike with spending and business revenues, there are publicly available sources of data on employment rates that can be disaggregated geographically and used to evaluate the representativeness of our data across areas. Our employment series closely matches state-level variation in employment changes during the pandemic in the CES, with a population-weighted correlation of 0.73 when looking at changes from January to July 2020 ( Online Appendix  Figure XIV.B). Our estimates are also well aligned with commuting zone (CZ) level estimates from the Quarterly Census of Employment and Wages (QCEW) ( Online Appendix  Figure XIV.C). Similarly, disaggregating the national data by wage rate, we find that our estimates are closely aligned with estimates based on the Current Population Survey and estimates in Cajner et al. (2020) ( Online Appendix Figure XV).

These comparisons indicate that our combined employment series provides representative estimates of changes in employment rates across wage groups and geographic areas during the COVID pandemic. A natural question going forward is how accurate our local area estimates will be in more typical periods, where the shocks of interest are likely to be far smaller than during the pandemic. To evaluate the accuracy of our data from this broader perspective, we calculate the population-weighted RMSE between our estimates of CZ-level changes in quarterly employment in January to September 2021 and corresponding statistics from the QCEW. We find an RMSE of 1.69 percentage points for the 50 largest CZs and 3.64 percentage points when including all CZs. Because the QCEW statistics are based on unemployment insurance records covering the entire population, the RMSE can be loosely interpreted as the average standard error of our estimate, accounting for noise arising from both sampling error and changes in nonrepresentative sampling. The relatively small MSEs indicate that our data can identify employment shocks considerably smaller than those induced by the pandemic. For instance, the worst-hit quartile of CZs in the United States during the Great Recession had mean employment losses of 8.73 percentage points from 2007 to 2010, while the least-hit quartile of CZs had mean employment gains of 2.59 percentage points; our data would have been sufficiently precise to reliably differentiate those CZs. As another example, Aldy (2014) estimates that the 2010 Gulf Oil spill decreased employment in non-Panhandle Gulf Coast Florida counties by 2.7 percentage points; since the population of this region is equivalent to the fourth-largest CZ (with population of 7 million), our payroll-based series would have been sufficiently precise to detect and monitor this effect in near real time as well.

The key limitation of publicly available employment data is that existing data sources can only be disaggregated either by county or wage level. Our payroll-based data sources allow us to measure changes in employment by county and wage level, which we show proves to be valuable in understanding the effects of the COVID shock. 21

2. Heterogeneity by Wage Rates

Figure V , Panel A plots employment rates by real prepandemic wage quartile. Each series shows the change in the total number of workers employed in jobs with hourly wage rates that fall in the relevant quartile of the pre-COVID wage distribution (with thresholds adjusted over time for inflation as described in Section II.B ) relative to the baseline level in January 2020.

Changes in Employment by Wage Quartile

Panel A plots our combined Paychex-Intuit employment series from January 2020 through December 2021 for each wage quartile. We define moving wage quartile thresholds in each month based on 100%, 150%, and 250% of the federal poverty line for a family of four, adjusted for inflation, then converted into a full-time-equivalent hourly wage by dividing by 2,000 hours (50 weeks of work at 40 hours per week). In January 2020, the thresholds were |${\$}$| 13.10, |${\$}$| 19.65, and |${\$}$| 32.75, and the four bins in ascending order by wage contained 23.4%, 27.4%, 25.7%, and 23.5% of CPS respondents. See Section II.B and Online Appendix  E for details on the construction of this series. In Panel B, we reweight the county-by-industry (two-digit NAICS) distribution of bottom wage quartile employment to match the distribution for top wage quartile employment in January 2020. For each series in Panel B, we restrict the sample to county-by-industry cells with nonzero employment in all four wage quartiles in January 2020; this sample restriction excludes 2.5% of worker-days from the sample. Data sources: Paychex, Intuit.

We find substantial heterogeneity in job losses by wage rate, consistent with the findings of Cajner et al. (2020) in prior work using ADP data. Employment rates fell by 39% around the trough of the recession (April 15, 2020) for workers in the bottom wage quartile (i.e., the total number of jobs paying <  |${\$}$| 13.10/hour in January 2020 was 39% lower as of April 15). By contrast, employment rates fell by only 14% for those in the top wage quartile (those jobs paying more than |${\$}$| 32.75/hour in January 2020) as of April 15.

High-wage workers not only were less likely to lose their jobs to begin with but also recovered their jobs much more quickly. By June 2020—just three months after the recession began—employment for high-wage workers had nearly returned to the pre-COVID baseline. Employment rates in low-wage jobs recovered rapidly to 20% below baseline levels by summer 2020, but then stalled from that point onward.

To identify the mechanisms driving these employment effects, we again exploit geographic variation, studying whether employment fell most in the high-rent areas that faced the largest demand shocks. Figure IV , Panel C plots changes in bottom-wage-quartile employment rates from January to July 2020 versus median rents, by county. Consistent with the larger shocks in high-rent areas to business revenue and labor demand for low-skilled workers, low-wage employment rates fell much more in more affluent counties. Low-wage employment fell by 21.7% in the highest-rent counties, compared with 16.8% in the lowest-rent counties. We find a similar pattern at the ZIP code level using employment data from Earnin ( Online Appendix  Figure XVI.A). Table I , Panel B presents a set of regression estimates quantifying these effects. Low-wage employment rates fell more in higher-rent areas (column (1)), even when controlling for the density of high-wage workers (column (2)) and comparing ZIP codes within the same county (column (3)).

The concentration of employment losses in more affluent areas is a consequence of the specific pattern of demand shocks induced by COVID rather than a general feature of recessions. Online Appendix  Figure XVII shows that in the Great Recession (2007–2010), counties in the bottom quartile of the household median income distribution accounted for 29% of job losses, while those in the top quartile accounted for 21% of job losses. By contrast, in the COVID recession (January–April 2020), counties in the top quartile accounted for a larger share of job losses than counties in the bottom quartile.

In summary, the pandemic led to a short V-shaped recession for high-wage workers, but a prolonged reduction in employment for lower-wage workers that persisted until at least December 2021, the end of our analysis period. Geographic disaggregation reveals that the drop in low-wage employment at the start of the pandemic was driven primarily by a contraction in spending among high-income individuals—which then reduced labor demand for low-skilled workers—rather than voluntary reductions in labor supply (that might have been induced, for example, by health concerns or unemployment benefits). In the next section, we examine why employment rates remained low even as the economy began to recover.

III.D. Recovery

By the middle of 2021, aggregate consumer spending ( Figure I , Panel A) and small-business revenues ( Online Appendix Figure IX.A) had met pre-COVID baseline levels and continued to climb upward. Yet employment rates in jobs that paid wages in the bottom quartile of the prepandemic distribution remained 20% lower even as of December 2021 ( Figure V , Panel A). What explains this “jobless recovery” at the bottom of the wage distribution?

1. Wage Growth

Part of the explanation is real wage growth: wage rates rose faster than the poverty line during the pandemic, leading some workers to move up out of the bottom wage bin (rather than into nonemployment). To quantify the effect of wage growth, we seek to measure how much wage rates changed in a given job. Lacking panel data at the job level, we measure changes in wage rates in detailed industry, occupation, and demographic cells using data from the CPS (see Online Appendix  E.4 for details). Using the estimated wage growth distribution, we estimate that 7.7 percentage points of the reduction in the total number of workers in the lowest wage group as of December 2021 is due to wage growth, leaving 12 percentage points due to changes in employment patterns—either exits into nonemployment or switches to higher-paying jobs.

We assess the contribution of switching to higher-paying jobs using two methods: assessing whether the cross-sectional composition of employment has shifted toward higher-paying jobs and measuring employment rates by prepandemic wage quartile in panel data. To implement the first test, we measure the wage distribution based on pre-COVID (2019) wage rates in each industry × occupation × race × gender × region cell of the CPS. We find that shifts in the job distribution across these cells actually led to an increase in the share of workers in the lowest wage group as of December 2021. To implement the second test, we use the CPS Outgoing Rotation Group panel, consisting of individuals who responded to CPS survey interviews spaced 12 months apart. In this panel, nonemployment rates as of July 2020 to February 2021 are 7.7 percentage points higher for those who started out in the bottom wage quartile pre-COVID (between July 2019 to February 2020) than for those who started out in the top wage quartile. 22 These findings indicate that exits to nonemployment explain most of the remaining reduction in bottom-quartile employment in the cross-sectional data after accounting for wage growth.

In the rest of this section, we analyze why low-wage workers remained out of work at higher rates as of December 2021, distinguishing between labor demand and supply channels.

2. Labor Demand

Although aggregate demand recovered, consumer demand may have shifted across sectors and technologies in ways that reduced labor demand for lower-skilled workers in the United States. For example, consumer demand shifted persistently over the course of the pandemic toward larger companies, online vendors, and certain sectors such as retail trade ( Carman and Nataraj 2020 ; Dunn, Hood, and Driessen 2020 ; Alexander and Karger 2023 ). Such companies might have more capital-intensive production functions or outsource more of their production, leading to a persistent downward shift in the demand for low-skilled labor in the United States. Furthermore, firms may have sought efficiencies in their production processes and economic activity may have shifted to more efficient firms during the recession, potentially further reducing labor demand ( Berger 2012 ; Lazear, Shaw, and Stanton 2016 ; Jaimovich and Siu 2020 ).

To evaluate this demand-side explanation, we first examine the evolution of aggregate job postings over time in Online Appendix  Figure IX.B. Postings for jobs that required minimal or no skills had returned to pre-COVID levels by mid-2020 and were well above pre-COVID levels in most of 2021 as businesses sought to restaff after reducing their payrolls earlier in the pandemic, consistent with the findings of Forsythe et al. (2022) . 23

Furthermore, there is no evidence of mismatch in labor demand relative to the supply of low-wage workers across sectors or places. Figure V , Panel B plots employment for workers in the bottom wage quartile, reweighting the series to match baseline employment shares by county and industry (two-digit NAICS) in the top wage quartile. This reweighting closes very little of the gap between the two series, showing that differences in industry and location do not explain the differences in employment trajectories between low- and high-wage workers. 24 Similarly, we find no evidence of a spatial mismatch between job posts and workers: reweighting job postings across counties by the number of bottom-wage-quartile workers in January 2020 has little effect on the time series of job postings ( Online Appendix Figure IX.B).

We conclude that low-wage workers appear to have had considerable demand for their skills in their own counties, but chose not to take jobs that were available.

3. Labor Supply

Given these findings, we examine mechanisms that may have led to a reduction in labor supply among low-wage workers. We begin by analyzing how the labor market recovery differed in high- versus low-rent counties, building on the analysis in the previous sections. 25

Figure VI , Panel A shows that job postings were approximately 20% above pre-COVID baseline levels in both high-rent and low-rent counties in December 2021. The gradient in job postings with respect to rent that emerged when the pandemic hit ( Figure IV , Panel A) disappeared entirely by December 2021. Yet low-wage employment rates remained substantially lower in high-rent areas than in low-rent areas ( Figure VI , Panel B). In the lowest-rent counties—where the initial reduction in aggregate demand was smallest (as measured by small-business revenues and job postings)—the total number of workers with jobs in the bottom wage quartile as of December 2021 was 9% lower than it was pre-COVID. This 9% reduction is roughly consistent with what we would expect based on wage growth (as discussed already), indicating that employment had roughly fully recovered in places where the pandemic had minimal effects on aggregate demand initially. In contrast, in the highest-rent counties, bottom-wage-quartile employment was 23% lower in December 2021 than it was pre-COVID.

Evolution of the Association between Low-Education Job Postings and Low-Wage Employment with Rent

This figure presents a summary of the results of a set of regressions documenting the relationship between job postings and employment with rent over time. Panel A replicates Figure IV , Panel A, but using the average value of the low-education job postings series in December 2021 instead of April 2020. Panel B replicates Figure IV , Panel C, but using the average value of the Paychex-Intuit employment series in December 2021 instead of July 2020. The binned scatter plots are constructed as described in Figure II . Panel C plots the slope of the best-fit line from a population-weighted regression of low-education job postings on median county rent (as in Panel A) for each month from April 2020 through December 2021. The slopes estimated in Figures IV , Panel A and VI , Panel A are the first and last estimates in this series, respectively. Panel D replicates Panel C for the slope of the bottom wage quartile employment versus median rent (as in Panel B). In Panels C and D, the dashed lines above and below the solid series represent the upper and lower boundaries of the 95% confidence interval for the slope estimated in each month. Panels B and D omit counties from CA, MA, and NY, since these three states raised the minimum wage at some point after July 2020 above our upper threshold for the bottom wage quartile of employment. Data sources: Lightcast, Paychex, Intuit, ACS.

Figure VI , Panels C and D characterize the evolution of the job postings and employment gradients by county-level rents by month from April 2020 to December 2021. They plot slopes from regressions of job postings and low-wage employment rates on rent across counties (weighted by population) by month. The job postings gradient begins to flatten starting in January 2021 and disappears completely by the last quarter of 2021. In stark contrast, the employment gradient steepens over time and never recovers during the period we study. 26

The results in Figure VI suggest that the places that experienced larger demand shocks initially (namely, more affluent, high-rent areas) exhibit persistent declines in employment even as of December 2021, despite the fact that labor demand had recovered fully in those areas by that point. One potential explanation for this hysteresis in employment rates is a change in preferences or commitments that workers made when the pandemic hit that induced persistent changes in labor supply. For example, low-wage workers may have moved to smaller apartments or changed their living arrangements such that they could afford to work less when the pandemic hit, and may have decided that they preferred to retain these arrangements going forward even when labor demand recovered. Another possibility is that low-wage workers’ human capital decayed and made it more difficult for them to obtain available jobs.

In Table II , we contextualize the magnitude of the cross-sectional variation in low-wage employment rates by regressing changes in bottom-wage-quartile employment rates from January 2020 to December 2021 on median rents by county and other covariates that reflect contemporaneous economic conditions. Column (1) replicates the specification in Figure VI , Panel B, showing that employment remains sharply depressed in higher-rent areas (where the initial aggregate demand shock was more severe) relative to lower-rent areas in December 2021. In column (2), we include two variables that measure contemporaneous health and economic conditions—the average COVID case rate from October to December 2021 (a measure of the risk of COVID exposure) and the number of weeks of unemployment insurance (UI) benefits that individuals were eligible for in their state as of December 2021—as well as a set of demographic controls. Including these variables does not affect the relationship between median rents and employment rates significantly.

Mechanisms Underlying the Persistent Reduction in Low-Wage Employment: Hysteresis versus Current Conditions

Notes. This table presents estimates for a set of population-weighted regressions examining the determinants of employment patterns in December 2021 at the county level. Robust standard errors are reported in parentheses. The sample omits California, Massachusetts, and New York due to mismeasurement of low-wage employment changes as a result of minimum wage increases; see Online Appendix  E.2 for more information. Column (1) reports the results of regressing the change in low-wage (i.e., bottom-quartile) employment from January 2020 to December 2021 against the average median two-bedroom rent (as measured in the 2014–2018 ACS) at the county level. Column (2) adds the average COVID-19 case rate in October to December 2021 (a measure of the risk of COVID exposure), the maximum number of weeks of unemployment insurance eligibility in each state, and a set of demographic controls: foreign-born population share, nonwhite population share, share of the population who are working age (25–54), and female population share. Column (3) repeats the specification in column (2), replacing median two-bedroom rent with the size of the initial low-wage employment shock to each county, measured as the change in low-wage employment from January 2020 to July 2020. Column (4) repeats the specification in column (2) using the change in high-wage (i.e., top-quartile) employment from January 2020 to December 2021 as the dependent variable. The bottom two rows of the table report the change in the dependent variable explained by COVID risk exposure and UI extensions, calculated by multiplying the coefficient by the population-weighted mean of the respective variable. Data sources: Paychex, Intuit.

These estimates imply that the ongoing risk of COVID can explain approximately 3.0 percentage points of the 12 percentage point reduction in bottom-wage-quartile employment that is not due to wage growth. Similarly, multiplying the coefficient on the UI benefits variable by the mean number of the weeks of additional UI benefits for which individuals were eligible in December 2021 implies that UI benefit extensions account for less than 1 percentage point of the reduction in bottom-wage-quartile employment. This cross-sectional estimate based on changes in UI policies across states over time is consistent with the quasi-experimental elasticities of employment rates with respect to UI benefit length estimated by Coombs et al. (2022) , which also finds that UI benefits appear to have small effects on employment rates during the pandemic.

Table II , column (3) presents a variant of the specification in column (2), replacing median rent with the change in bottom-wage-quartile employment rates from January to July 2020—the immediate loss in low-wage employment after the shock—as the key independent variable. We find a positive relationship, showing that areas where employment fell more in the immediate aftermath of the pandemic exhibited persistent declines in employment nearly two years later.

Column (4) replicates column 2, replacing the dependent variable with the change in employment rate for jobs that paid wages in the top quartile of the prepandemic wage distribution. We find no relationship between top-quartile employment rates and rents, consistent with the rapid recovery of labor demand and employment for high-skilled workers.

Finally, we evaluate whether changes in the total number of available workers (i.e., the total population of lower-skilled workers in high-rent areas)—rather than changes in labor supply for a specific worker—can explain a significant portion of the shortfall in employment rates. Although the number of immigrants to the United States fell during the pandemic, CPS data show that trends in total low-wage employment rates for immigrants and U.S. citizens aged 16 or older were virtually identical ( Online Appendix  Figure XX.A). Internal migration from high-rent to lower-rent areas in the United States also does not explain a significant share of the larger reduction in employment rates in high-rent areas ( Online Appendix  Figure XX.B). Demographic trends in aging over this short period are also too small to explain the shortfall in employment: the working age population (aged 15–64) grew from 205.7 to 207.1 million between January 2020 and 2022 ( Organisation for Economic Co-operation and Development 2023 ). Finally, the share of individuals who moved to self-employment (and hence were not available to be low-wage employees) is also too small to explain the aggregate shortfall in bottom-wage-quartile employment: the self-employed share of individuals aged 16 or older rose from 6.05% in January 2020 to 6.15% in December 2021 ( U.S. Bureau of Labor Statistics 2023a , 2023b , 2023c ).

In sum, the persistent reduction in low-wage employment is not readily explained by changes in labor demand, changes in contemporaneous incentives to work such as UI benefits or ongoing health risks, or changes in the total number of workers. Rather, the strongest predictor of the cross-sectional variation in employment rates in December 2021 are variables that predict the size of the initial shock to aggregate demand—echoing the findings of Yagan (2019) , who documents hysteresis in the labor markets that were hit hardest in the Great Recession.

In this section, we examine the scope for stabilization policies to break the chain of events documented above: reductions in spending, especially by high-income households, were associated with losses in business revenue and low-wage employment. We begin by evaluating the stimulus payments made to households during the pandemic, illustrating how the public data we construct are useful for real-time policy evaluation. We briefly discuss other examples of policy evaluations and conclude by assessing whether the combination of policy responses was sufficient to stabilize economic activity.

IV.A. Stimulus Payments to Households

The federal government sent a total of |${\$}$| 814.4 billion in stimulus checks to households at three points during the pandemic: April 2020, January 2021, and March 2021 ( Internal Revenue Service 2022 ). Were these stimulus payments successful in boosting consumer spending?

We estimate the causal effect of each stimulus payment on spending in the first month after receipt, focusing in particular on heterogeneity across the income distribution. We focus on a one-month horizon because prior work shows that most of the impact of stimulus payments and tax refunds is concentrated within three months of receipt (e.g., Sahm, Shapiro, and Slemrod 2010 ; Broda and Parker 2014 ). Moreover, spending effects in the first month are highly predictive of spending effects in the first three months across subgroups ( Parker and Souleles 2019 , table 3). 27

1. April 2020

The Coronavirus Aid, Relief, and Economic Security (CARES) Act made direct payments to nearly 160 million people starting in mid-April 2020. Individuals earning less than |${\$}$| 75,000 received a stimulus payment of |${\$}$| 1,200; married couples earning less than |${\$}$| 150,000 received a payment of |${\$}$| 2,400; and households received an additional |${\$}$| 500 for each dependent they claimed. 28 IRS statistics show that 69% of stimulus payments made in April were direct deposited on exactly April 15, 2020, though some households received payments on April 14 ( Bureau of the Fiscal Service 2020 ).

We evaluate the effects of these stimulus payments on consumer spending using a high-frequency difference-in-differences research design applied to our card spending data, comparing daily spending before versus after April 15 in 2020 versus spending on the same calendar date in 2019. To reduce cyclical fluctuations, we residualize daily spending (indexed to average levels in January 2019) with respect to day-of-week fixed effects, which we estimate using data for 2019. We then adjust for a linear pretrend in spending (which we assume to be common across all income quartiles) to capture aggregate shocks in spending during the pandemic in 2020. 29 To capture high-frequency changes in spending, we do not smooth the daily spending using a seven-day moving average, unlike in preceding sections.

Figure VII , Panel A plots the difference in daily spending in 2020 versus 2019 for households who live in ZIP codes with median household income in the bottom quartile of the national distribution (which we call “low-income” households for convenience). Spending increases markedly following the arrival of payments, with particularly high spending in the days when stimulus checks first arrived. To quantify the magnitude of the (short-run) effect of the stimulus on spending, in Online Appendix  Table VI we estimate difference-in-differences models using OLS regressions of daily spending by income quartile (residualized against a common linear pretrend) in the 25 days before and after April 15 on an indicator for being pre- versus poststimulus interacted with calendar year. To capture the nonlinear dynamics evident in the nonparametric event study plots, we estimate separate treatment effects for the first five days starting on April 15 and from the sixth day onward; see Online Appendix K for a more detailed description of our methodology.

Effects of Stimulus Payments on Spending: Event Studies

This figure shows event studies of the effect of stimulus payments on consumer spending. We measure consumer spending using data from Affinity Solutions. To construct each consumer spending time series, we express consumer spending on each day as a percentage change relative to mean daily consumer spending over January 2019, residualize these daily percentage changes with respect to day-of-week fixed effects (estimated out-of-sample using data in 2019), calculate the first difference with respect to values from the corresponding period starting in 2019, and adjust the estimates for a linear pretrend in first differences. Panel A depicts this spending time series for 25 days before and after April 15, 2020 (the modal date for deposits of the CARES Act economic impact payments) for cardholders with residential addresses in the bottom income quartile of ZIP codes. We exclude April 14, 2020, from the preperiod because some households received stimulus payments on this date. Panel B repeats this figure for the top income quartile of ZIP codes. Panel C repeats Panels A and B for the days around January 4, 2021 (the modal date for deposits of the COVID-Related Tax Relief Act economic impact payments), plotting outcomes for both the bottom and top income quartiles. The preperiod in Panel C runs from December 4 to 14, 2020, with the holiday period (December 15, 2020 to January 3, 2021) removed due to high daily volatility in spending levels (see Section IV.A and Online Appendix  Figure XXIII for more details). The postperiod runs from January 4 to 19, 2021, reflecting the data available when this analysis was originally published on January 26, 2021. Due to the omission of the holiday period, we do not remove a linear pretrend as in Panels A and B. Panel D repeats Panel C for the days around March 17, 2021 (the modal date for deposits of the American Rescue Plan Act economic impact payments). We exclude March 13 to 16, 2021 from the preperiod as payments were made starting March 13. In Panels A, B, and D, we interpolate the value for Easter Sunday using the average of adjacent daily values. Data sources: Affinity Solutions, ACS.

Using this approach, we estimate that spending increased by 21 percentage points (std. err. = 3.13) for bottom-income-quartile households in the first month after the stimulus payments. Accounting for the fraction of households who actually received stimulus checks in this group, this estimate translates to an increase in spending of |${\$}$| 442 during the first month after receiving a |${\$}$| 1,200 stimulus check (see Online Appendix K for more details). The estimates remain stable when varying the window used to estimate the treatment effect, with point estimates ranging from |${\$}$| 320 to |${\$}$| 452, as shown in Online Appendix Figure XXII.

Figure VII , Panel B repeats this analysis for high-income households—those who live in ZIP codes with median household income in the top quartile of the distribution. Once again, we see a clear increase in spending in the month after the stimulus payments were made relative to the month before, although there is no immediate spike in spending on the day that the checks were received, as one might expect given that higher-income households are less likely to be liquidity constrained at high frequencies. We estimate that spending for top-income-quartile households increased by 11 percentage points. This smaller percentage point effect is to be expected because higher-income households received smaller stimulus payments both in absolute terms and as a percentage of their total expenditure. Rescaling this effect, we estimate that high-income households spent |${\$}$| 732 per |${\$}$| 1,200 of stimulus payments received in the first month.

The first bar in each set of bars plotted in Figure VIII presents estimates of the impact of the April 2020 stimulus payments on spending over a one-month horizon for the four ZIP-income quartiles. Across the income distribution, households spent a large fraction of their April 2020 stimulus checks in the month immediately after receipt, consistent with evidence from confidential data from JPMorgan Chase account holders subsequently reported by Cox et al. (2020) . 30

Impacts of Stimulus Payments on Spending, by Income Quartile

This figure plots estimates of the marginal propensity to spend out of stimulus payments in the first month after receipt for each of the three rounds of stimulus payments, separately by income quartile (based on median ZIP code income). The estimates are scaled per |${\$}$| 1,200 of stimulus payment and correspond to the “Combined Dollar” estimates reported in Online Appendix  Table VI, column (5). See Section IV.A and Online Appendix  K.3 for details on how these estimates were calculated. We also report p -values testing the null hypothesis of equal effect sizes between each pair of stimulus rounds, for the highest- and lowest-quartile of ZIP-level incomes. These p -values are based on permutation tests reported in Online Appendix  Figures XXIV and XXV. Data source: Affinity Solutions.

2. January 2021

The COVID-Related Tax Relief Act made payments of |${\$}$| 600 per person to most Americans available beginning on January 4, 2021. Eligibility criteria largely followed those for the earlier round of stimulus, with single households eligible for the full stimulus amount up to |${\$}$| 75,000 in income ( ⁠|${\$}$| 150,000 for married households). The stimulus amount fell at higher income levels, with childless households with incomes up to |${\$}$| 87,000 (or |${\$}$| 174,000 if married filing jointly) receiving a payment.

To evaluate whether our data could shed light on this policy’s effect sufficiently rapidly to inform the design of future stimulus payments, we analyzed the effects of the stimulus payments on spending from January 4 to 19, 2021, and released results publicly on January 26, 2021 ( Chetty, Friedman, and Stepner 2021 ). We use the same difference-in-differences design we used to study the first stimulus, except that we use December 4 to 14, 2020, as the preperiod rather than the days immediately preceding the stimulus payments because those days coincide with the Christmas holiday period, when daily spending exhibits 10 times higher variance across days (even when looking at changes across years) than during the first half of December ( Online Appendix Figure XXIII). 31 We also omit pretrends here because of the gap created by omission of the holiday period.

Figure VII , Panel C replicates the series in Panels A and B for the January 2021 stimulus, plotting indexed daily changes in spending in 2021 versus 2020 for bottom- and top-income-quartile households. Low-income households increase spending significantly after the arrival of the January stimulus payments. In contrast, high-income households do not change their spending levels significantly after January 4, 2021, relative to December 2020. Using difference-in-differences models analogous to those before, we estimate that low-income households increased spending over the first month after receiving their stimulus checks by 6 percentage points, while high-income households increased spending by 0.4 percentage points, an estimate that is not significantly different from zero. The middle bars shown in Figure VIII rescale these estimated effects into dollars per |${\$}$| 1,200 to facilitate comparisons across stimulus rounds. While the marginal propensity to consume (MPC) out of these stimulus payments in the first month fell significantly for all income groups in January 2021 relative to April 2020, the drop in the MPC for high-income households was especially large. We estimate that low-income households spent |${\$}$| 187 per |${\$}$| 1,200 of stimulus received, 58% smaller than the |${\$}$| 442 estimated in April 2020. High-income households spent much less of their second stimulus checks—from |${\$}$| 732 per |${\$}$| 1,200 received in April 2020 to just |${\$}$| 35 per |${\$}$| 1,200 in January 2021, a reduction of 95%. 32 These heterogeneous effects on spending across income groups are aligned with results subsequently reported in May 2021 by Greig, Deadman, and Noel (2021 , 20, box 1), using confidential data from JPMorgan Chase.

In short, this analysis demonstrates that one can gauge the (short-term) effects of stimulus payments with just two weeks of data after the payments are made using what are now publicly available data—enabling a rapid feedback loop for subsequent policy changes. Indeed, based on these estimates, we predicted that making further stimulus payments to high-income households would have modest effects on their spending, suggesting that targeting the next round of stimulus toward lower-income households would save substantial resources that could be used to support other programs, with minimal impact on economic activity.

3. March 2021

After extensive debate about whether higher-income households should continue to receive stimulus payments—including discussion of the evidence described above ( Lambert and Sraders 2021 )—Congress passed the American Rescue Plan on March 11, 2021. The final plan continued to pay the full stimulus amount of |${\$}$| 1,400 to households earning up to |${\$}$| 150,000, but phased the payments out more rapidly beyond that threshold than initially proposed, so that households with incomes above |${\$}$| 80,000 (for single filers without children) or |${\$}$| 160,000 (for married couples without children) received no stimulus. These revisions reduced the total amount of stimulus payments made to high-income households by approximately |${\$}$| 17 billion relative to the original proposal in January 2020 ( Watson 2021 ).

Did the March 2021 stimulus payments in fact have lower effects on spending of higher-income households, as predicted based on the January 2021 evidence? Figure VII , Panel D replicates the preceding figures for the 25 days before and after the March 2021 checks were sent out; here, we use exactly the same estimator as in the first stimulus, as there are no holiday-induced fluctuations in the preperiod. Bottom-income-quartile households increased spending considerably in the days following the March payments, while spending for high-income households did not change significantly. The third set of bars in Figure VIII rescale these effects into dollar impacts per |${\$}$| 1,200 of stimulus payment. The estimated effects are much more similar to those observed in January 2021 than in April 2020, with positive effects on spending for lower-income households but near-zero effects on spending for top-quartile households.

4. Discussion

Why did the marginal propensity to consume out of cash windfalls fall sharply over the course of the pandemic, especially for higher-income households? Studies of stimulus payments in prior recessions find little heterogeneity in MPCs by income, but show that households with higher liquid wealth balances exhibit lower MPCs ( Johnson, Parker, and Souleles 2006 ; Broda and Parker 2014 ; Jappelli and Pistaferri 2014 ). In normal times, most households even in the top income quartile tend to have relatively little liquid wealth ( Kaplan and Violante 2014 ), explaining why they exhibit high MPCs out of windfalls in previous recessions. But during the pandemic, households started to accumulate substantial liquid wealth because their incomes remained relatively stable while their spending fell sharply, as discussed in Section III . The national savings rate (measured in NIPA Table 2.6) rose from 7.6% in 2019 to 18.5% on average in Q2–Q4 of 2020. Using confidential data from JPMorgan Chase, Greig, Deadman, and Noel (2021) further show that cash balances in checking accounts rose substantially from January to December 2020, with the largest increases (in dollars) among high-income households. Given this rapid growth in liquid wealth, it is not surprising that high-income households started to spend less of their stimulus payments over time. 33

This analysis illustrates the value of real-time estimation of policy impacts rather than predictions based on historical estimates. Despite being based on a consensus across a large set of studies, historical predictions about the lack of heterogeneity in MPCs by income proved to be inaccurate given the unusual impacts of the pandemic on spending behavior across the income distribution. 34 The core challenge is that parameters such as MPCs are not invariant to the economic and policy environment. By directly estimating such parameters in real time using newly available data, one can make policy decisions that respond transparently—based on publicly available information—to current economic conditions.

IV.B. Effects of Other Policies

The data we make publicly available can also be used to study a range of other policies beyond stimulus payments. For illustration, we briefly discuss four examples of policies that were implemented during the COVID-19 crisis. The first two are based on analyses we conduct ourselves (detailed in Online Appendix  L) and the latter two are analyses conducted by other researchers using our data in combination with other data sources.

1. State-Ordered Shutdowns and Reopenings

Many states enacted stay-at-home orders and shutdowns of businesses in an effort to limit the spread of COVID infections and later reopened their economies by removing these restrictions. Using the card spending and payroll data, we evaluate the effects of these policies using event study designs that compare trends in states that shut down and reopened at different dates. We find that state-ordered shutdowns and reopenings had modest effects on economic activity. Spending and employment remained well below baseline levels even after reopenings, and trended similarly in states that reopened earlier relative to comparable states that reopened later ( Online Appendix  Figures XXVI–XXVII). Spending and employment also fell well before state-level shutdowns were implemented. These findings are consistent with work by Goolsbee and Syverson (2021) and Sears et al. (2023) using cell phone location data as well as Bartik et al. (2020) using timesheet data on hours of work.

2. Paycheck Protection Program

The PPP sought to reduce employment losses by providing forgivable loans worth more than |${\$}$| 800 billion in total to small businesses that maintained sufficiently high employment (relative to precrisis levels). Using our payroll data disaggregated by firm size, we evaluate the impacts of the PPP on employment by comparing employment trends at firms with fewer than 500 employees (which were eligible for PPP assistance) with firms in the same sector that had more than 500 employees (who were ineligible). We find that employment increased by only 2.48 percentage points after the PPP was enacted in April 2020 relative to larger firms that were ineligible for PPP ( Online Appendix  Figure XXVIII). Our point estimates imply that the cost per job saved by the PPP was |${\$}$| 301,863 ( ⁠|${\$}$| 86,201 at the upper bound of the 95% confidence interval); netting out potential UI payments to these potentially unemployed workers reduces this number only slightly to |${\$}$| 283,513 per job saved (see Online Appendix  L.2 for details). Autor et al. (2022a , 2022b) reach similar conclusions using the same research design with microdata from ADP, another large payroll processor. Granja et al. (2022) use a different design, exploiting cross-sectional variation in PPP takeup driven by bank composition, and reach similar conclusions, partly drawing upon the data we make publicly available. Together, all of these studies suggest the PPP had modest marginal impacts on employment in the short run, likely because the vast majority of PPP loans went to inframarginal firms that were not planning to lay off many workers. 35

3. Unemployment Benefit Increases

The Federal Pandemic Unemployment Compensation (FPUC) program paid supplemental unemployment benefits of up to |${\$}$| 600 per week from March to September 2020. Casado et al. (2020) use county-level variation in wage replacement rates resulting from differences in industrial composition to estimate the effect of FPUC payments on aggregate spending. Using our publicly available spending data combined with UI claims data from Illinois, they estimate that a 1% increase in the replacement rate increased county-level spending by 0.167%, which implies that each |${\$}$| 1 of UI benefits increased aggregate spending at the county level by |${\$}$| 1.23. For comparison, our estimates above imply that |${\$}$| 1 of spending in the form of stimulus checks increased household-level spending by an MPC = 0.256 on average. In a standard Keynesian model, an MPC of 0.256 would imply an effect on aggregate spending of |$\frac{ 0.256 }{ 1 - 0.256 } = 0.344$|⁠ , an order of magnitude smaller than the estimated effect of UI benefits. In the pandemic, where some sectors were effectively shut down, theory suggests that the multipliers would be even smaller than the standard Keynesian benchmark ( Guerrieri et al. 2022 ). This comparison suggests that UI benefits targeted to unemployed individuals were a more potent tool to stimulate aggregate spending than stimulus payments to all individuals, especially later in the pandemic as employed households built up a large stock of savings.

4. Eviction Moratoria

Many state and local governments enacted moratoria on tenant eviction during the pandemic to provide stable housing for those who might have lost their jobs. These moratoria were implemented at different times in different states and counties. An, Gabriel, and Tzur-Ilan (2022) exploit variation in the timing of such moratoria to estimate their effects on spending. Using our publicly available data on consumer spending by category coupled with other sources, they estimate that a one-week eviction moratorium is associated with a 1% increase in spending on necessities such as food and groceries. They conclude that eviction moratoria not only reduced housing instability but also boosted spending on other goods and potentially provided an aggregate stimulus as a result.

Methodologically, these examples illustrate that data from private sector sources can be used to evaluate a wide variety of policies rapidly because many policies have heterogeneous effects across geographic areas or other dimensions, such as firm size. Reassuringly, the findings obtained from our public statistics match those obtained from studies with access to the underlying microdata, demonstrating that public statistics constructed from private sector data sources can support many policy analyses. Taken together, these studies suggest that policies targeted directly at households that suffered the largest income losses—such as those who became unemployed or faced eviction—had the largest effects on spending and downstream economic activity in the pandemic.

IV.C. Secondary Effects on Spending

We conclude by stepping back from the effects of specific policies and analyzing whether the combination of government policies—those analyzed already as well as other macroeconomic responses and changes in the economy—was adequate to stem the downward spiral in economic activity set off by the initial reduction in consumer spending documented in Section III . Did the loss of jobs among low-wage workers trigger a secondary reduction in their own spending levels due to a lack of disposable income (rather than health concerns)—potentially setting off further business revenue losses and employment losses? Or was government intervention adequate to prevent such secondary responses? We investigate secondary spending responses among low-income individuals by returning to the geographic heterogeneity in the size of initial consumer demand shocks by local rent levels, as in Section III . In particular, we compare how spending evolved in low-income ZIP codes whose residents worked predominantly in high-rent areas versus those whose residents worked predominantly in low-rent areas.

Figure IX , Panel A presents a binned scatter plot of changes in low-wage employment from January to April 2020 by home (residential) ZIP code versus average workplace rent. We construct this figure by combining ZIP code–level data on employment rates of low-wage workers from Earnin that we make publicly available with public data from the Census LEHD Origin-Destination Employment Statistics (LODES) database, which provides information on the matrix of residential ZIP by work ZIP for low-income workers in the United States in 2017, to compute the average workplace median rent level for each residential ZIP. Figure IX , Panel A shows that low-income individuals who were working in high-rent areas pre-COVID were much less likely to be employed after the shock hit in April 2020—consistent with our findings above. 36

Changes in Employment and Consumer Spending for Low-Income Households versus Workplace Rent

This figure examines the relationship between low-wage employment and consumer spending for individuals living in a home ZIP code z with the average rent in the ZIP codes of the workplaces for low-wage workers who live in home ZIP code z . Panel A presents a binned scatterplot showing the relationship between low-wage employment for workers living in a home ZIP code and the average median rent in the workplace ZIP codes for low-wage workers from that home ZIP code. We measure low-wage employment in each home ZIP code using the Earnin employment series in April 2020. We then match each home ZIP code to the distribution of workplace ZIP codes using the Census LODES data for low-wage workers. We calculate the x -axis variable as the average median rent for a two-bedroom apartment (measured in the 2014–2018 ACS), averaged across workplace ZIP codes using the distribution from the LODES data for each home ZIP code. See Section IV.C for a detailed discussion. Panel B replicates Panel A for a different outcome: average consumer spending between March 25 and April 14, 2020, restricting to ZIP codes in the bottom quartile of median income, as measured in the 2014–2018 ACS. Panel C replicates Panel B with consumer spending instead measured during October 2020. The binned scatter plots are constructed as described in Figure II . Panel D plots the average level of consumer spending for the top quartile of households appearing in Panels B and C ranked on average median workplace rent (i.e., the five right-most dots) in each month from February 2020 through December 2021. Data sources: Earnin, Affinity Solutions, Census LODES, ACS.

Next we analyze how these differential shocks to employment affected spending patterns, taking a step toward mapping the flow of shocks in the economy ( Andersen et al. 2022 ). Figure IX , Panel B replicates Panel A using spending changes on the y -axis, restricting to households living in low-income ZIP codes. 37 Low-income people living in areas where people tend to work in high-rent ZIP codes cut spending by 33% on average from January to April 2020, compared with 23% for those living in areas where people tend to work in low-rent ZIPs ( Figure IX , Panel B). The relationship remains similar in magnitude but is less precisely estimated when we compare ZIP codes in the same county ( Online Appendix Table VII).

Figure IX , Panel B implies that low-income households who lost their jobs at the start of the pandemic reduced their own spending more at the start of the pandemic—portending the start of the downward spiral. However, while employment losses for low-wage workers persisted over time, the reductions in spending did not. Figure IX , Panel C shows that by October 2020, spending in low-income ZIP codes was slightly higher than it was pre-COVID on average, and there was no longer any relationship between workplace rents and spending levels among low-income households despite the persistence of employment losses in higher-rent areas ( Figure VI , Panel D). Figure IX , Panel D plots the evolution of spending in bottom-income-quartile ZIP codes that rank in the top quartile of median workplace rent. Despite the fact that these areas faced the largest and most persistent employment losses, consumer spending recovered very rapidly after falling sharply at the onset of the pandemic, exceeding pre-COVID levels starting in July 2020.

In sum, although a sharp gradient of spending reduction with respect to employment losses emerged early in the pandemic, it vanished within a few months. Total spending in areas where many workers had lost their jobs and remained out of work remained on par with areas where workers had lost less income—indicating that the secondary spending response that could have produced a further downward spiral was effectively shut down shortly after the crisis began. Losses in earned income likely did not translate to further spending reductions because the fiscal response to the crisis (e.g., via extended unemployment benefits and stimulus payments) actually increased the total disposable income of low-income households ( Blanchet, Saez, and Zucman 2022 ; Ganong et al. 2022 ). These results suggest that as a whole, macroeconomic policy responses appear to have been effective in limiting secondary declines in consumer spending as workers lost their jobs—perhaps even going beyond what was necessary—even if they could not address the losses in employment that arose from the initial shock to consumer spending driven by health concerns.

Transactional data held by private companies have great potential for measuring economic activity, but to date have been accessible only through contracts to work with confidential microdata. In this article, we constructed a public database to measure economic activity at a high-frequency, granular level using data from private companies. By systematically cleaning, aggregating, and benchmarking the underlying microdata, we construct series that can be released publicly without disclosing sensitive information.

We use this new public database to analyze the economic effects of COVID-19, demonstrating two ways the data provide a new tool for empirical macroeconomics. First, the data can be used to rapidly diagnose the root factors driving an economic crisis by learning from cross-sectional heterogeneity, since different places and subgroups often face different shocks. In the case of COVID-19, we find that a sharp reduction in spending by high-income individuals due to health concerns led to losses of business revenues and persistent reductions in low-wage employment in affluent areas. Second, the data permit rapid, real-time policy evaluation—as demonstrated by our analyses showing the changing effects of fiscal stimulus payments over the course of the pandemic—opening a path to fine-tuning policy responses based on their observed impacts rather relying solely on historical estimates.

The benefit of constructing a public database to conduct such analyses rather than working directly with private firms’ confidential data is that we centralize the fixed costs of cleaning the data for research purposes. This facilitates transparency and reproducibility and enables researchers to readily access this data to conduct a much broader set of analyses. For example, the data have been used by local policy makers to inform local policy responses and forecast tax revenue impacts (e.g., Maine, Missouri, Kansas, and Texas). They have also been used by congressional staff to design federal policies, for example, predicting the effects and costs of policies targeted based on business revenue losses ( Bennet 2020 ). And they have been used by other researchers to analyze a broad range of issues, from constructing price indices that account for changes in consumption bundles ( Cavallo 2020 ) to analyzing the effects of political views on economic outcomes ( Makridis and Hartley 2020 ).

Although we have focused here on the short-run effects of COVID-19, private sector data can be useful in monitoring effects of economic shocks on long-term outcomes as well. As an illustration, Figure X plots weekly student engagement on Zearn, an online math platform used by nearly 1 million elementary school students as part of their regular school curriculum (see Online Appendix I). Children in high-income areas learned less when the COVID crisis hit and schools shifted to remote instruction, but soon recovered to baseline levels. By contrast, children in lower-income areas completed 41% fewer lessons than they did prepandemic through the end of the school year. These findings—first established in May 2020 and confirmed by subsequent work such as Goldhaber et al. (2022) and Jack et al. (2023) —raise the concern that the pandemic may have long-lasting effects on low-income families not just through persistent reductions in employment documented here but also through effects on the next generation.

Effects of COVID-19 on Educational Progress by Income Group

This figure plots a time series of student engagement on the Zearn Math online platform, splitting schools into quartiles based on the share of students in the school eligible for Free or Reduced Price Lunch (FRPL). We measure student engagement as the average number of students using the Zearn Math application in each week, relative to the mean value of students using the platform in the same classroom during the reference period of January 6 to February 7, 2020. We restrict the sample to classrooms with at least 10 students using Zearn on average and at least 5 students doing so in each week during the reference period. We measure the share of students eligible for FRPL in each school using demographic data from the Common Core data set from MDR Education, a private education data firm. Data sources: Zearn, Common Core.

Over the twentieth century, the Bureau of Economic Analysis built on a prototype developed by Kuznets (1941) to institute surveys of businesses and households that form the basis for today’s National Income and Product Accounts. The database created here provides a prototype for a system of more granular, real-time national accounts built using transactional private sector data. The fact that even this first prototype yields insights that cannot be obtained from existing data suggests that aggregating data from private companies to construct public statistics has great potential for improving our understanding of economic activity and policy making.

The data underlying this article are available in the Harvard Dataverse, https://doi.org/10.7910/DVN/4CFSZW ( Chetty, Friedman, and Stepner 2023 ).

We thank the corporate partners who provided the underlying data used to construct the public database built in this article: Affinity Solutions (especially Atul Chadha and Arun Rajagopal), Lightcast (Anton Libsch and Bledi Taska), CoinOut (Jeff Witten), Earnin (Arun Natesan and Ram Palaniappan), Homebase (Ray Sandza and Andrew Vogeley), Intuit (Christina Foo and Krithika Swaminathan), Kronos (David Gilbertson), Paychex (Mike Nichols and Shadi Sifain), Womply (Derek Doel and Ryan Thorpe), and Zearn (Billy McRae and Shalinee Sharma). We are very grateful to Nathaniel Hendren, who collaborated with us to launch the initial version of the database and helped conduct preliminary analyses for the first draft of this article in spring 2020. We are also grateful to Ryan Rippel of the Gates Foundation for his support in launching this project and to Gregory Bruich for early conversations that helped spark this work. We thank David Autor, Gabriel Chodorow-Reich, Haley O’Donnell, Emmanuel Farhi, Jason Furman, Steven Hamilton, Erik Hurst, Xavier Jaravel, Lawrence Katz, Fabian Lange, Emmanuel Saez, Ludwig Straub, Danny Yagan, and numerous seminar participants for helpful comments. The work was funded by the Chan-Zuckerberg Initiative, Bill & Melinda Gates Foundation, Overdeck Family Foundation, Andrew and Melora Balson, Harvard University, Brown University, JPB Foundation, Smith Richardson Foundation, and the University of Toronto. The project was approved under Harvard University IRB 20-0586. The Opportunity Insights Economic Tracker Team as of July 2023 has consisted of Hamidah Alatas, Camille Baker, Harvey Barnhard, Matt Bell, Gregory Bruich, Tina Chelidze, Lucas Chu, Westley Cineus, Sebi Devlin-Foltz, Michael Droste, Dhruv Gaur, Federico Gonzalez, Rayshauna Gray, Abigail Hiller, Matthew Jacob, Tyler Jacobson, Margaret Kallus, Fiona Kastel, Laura Kincaide, Caitlin Kupsc, Sarah LaBauve, Lucía Lamas, Maddie Marino, Kai Matheson, Jared Miller, Christian Mott, Kate Musen, Danny Onorato, Sarah Oppenheimer, Trina Ott, Lynn Overmann, Max Pienkny, Jeremiah Prince, Sebastian Puerta, Daniel Reuter, Peter Ruhm, Tom Rutter, Emanuel Schertz, Shannon Felton Spence, Krista Stapleford, Kamelia Stavreva, Ceci Steyn, James Stratton, Clare Suter, Elizabeth Thach, Nicolaj Thor, Amanda Wahlers, Kristen Watkins, Alanna Williams, David Williams, Chase Williamson, Shady Yassin, Ruby Zhang, and Austin Zheng.

For example, data on consumer spending disaggregated by geography are only available for selected large metro areas at a biannual level in the Consumer Expenditure Survey (CEX).

Survey-based statistics themselves do not necessarily provide “ground truth” because of sampling error, recall error, and growing nonresponse bias ( Dutz et al. 2021 ; Meyer and Mittag 2021 ). Thus, even for longer-term inferences at the national level, combining information from transactional data with information from surveys can be valuable.

We provide a replication kit that generates all of the results in the article from publicly available data. We use nonpublic data for certain robustness checks and validation analyses reported in the Online Appendix  (as documented in the replication kit).

We verify the quality of our publicly available ZIP code–level proxies for income by showing that our estimates of spending by income group during the pandemic are closely aligned with those of Cox et al. (2020) , who observe household income directly for JPMorgan Chase clients in confidential microdata.

We always index to January 2020 after summing to a given cell (geographic unit, industry, etc.) rather than at the firm or individual level. This dollar-weighted approach overweights bigger firms and higher-income individuals, but leads to smoother series and is more relevant for certain macroeconomic policy questions (e.g., changes in aggregate spending).

We use a higher level of geographic aggregation to detect breaks here than the county-level aggregation used for consumer spending because the number of small businesses is an order of magnitude smaller than the number of active credit and debit cards, and so tests for structural breaks have less power.

Industry is defined using select NAICS supersectors, aggregated from two-digit NAICS classification codes. Job qualifications are defined using ONET job zones, which classify jobs into five groups based on the amount of preparation they require. We also obtain analogous data broken down by educational requirements.

In January 2020, the thresholds were |${\$}$| 13.10, |${\$}$| 19.65, and |${\$}$| 32.75, and the four bins in ascending order by wage contained 23.4%, 27.4%, 25.7%, and 23.5% of CPS respondents. The FPL is updated annually at the beginning of each year. We use the annual FPL to set the thresholds each January and smoothly adjust the thresholds in the year using CPI inflation, as described in Online Appendix E.

As an example of the specific data-processing challenges that we address in constructing the employment series, bunching at integer values in the wage distribution generates discontinuities in the number of workers assigned to each wage group as the thresholds for the groups are updated due to inflation. For example, when the threshold for the lowest wage group crosses |${\$}$| 14/hour, a discrete mass of workers who were previously a part of the second quartile are now defined as being in the bottom quartile, causing a discontinuity in both series. To address this issue, we spread workers out from the whole number wages by adding a random number between −0.5 and 0.5 to their hourly wage, transforming the point mass at the integer wage into a uniform distribution between [ wage − 0.5, wage + 0.5] (see Online Appendix E.2 for details).

Most of the reduction in private investment was driven by a reduction in inventories and equipment investment in the transportation and retail sectors, both of which are plausibly a response to reductions in current and anticipated consumer spending. In the first quarter of 2020, consumer spending accounted for an even larger share of the reduction in GDP, further supporting the view that the initial shock to the economy came from a reduction in consumer spending ( U.S. Bureau of Economic Analysis 2020 ).

The rest of the reduction is largely accounted for by health care expenditures; housing and motor vehicle expenditures did not change significantly.

The series are not perfectly comparable because the category definitions differ slightly across the data sets. For example, we observe food and accommodation services combined together in the card data but only food services in the MARTS. In addition, the MARTS includes corporate card transactions, whereas we exclude them to isolate consumer spending. Hence, we would not expect the series to track each other perfectly even if the card spending data provided a perfect representation of national spending patterns.

One specific source of potential bias in our spending series is that it does not include cash transactions and thus could be biased by potential substitution from cash to credit card purchases. We evaluate this concern using receipts data from CoinOut, which allows us to measure cash spending on groceries (see Online Appendix B.3). In practice, trends in card and cash spending track each other closely ( Online Appendix Figure VI.B). These results—along with the fact that our card spending series closely track estimates from the MARTS—indicate that aggregate fluctuations in card spending do not appear to have been offset by opposite-signed changes in cash spending.

Cox et al. (2020) report an 8 percentage point larger decline in spending for the highest income quartile relative to the lowest income quartile in the second week of April. Our estimate of the gap at that time is also 8 percentage points, although the levels of the declines in our data are slightly smaller in magnitude for both groups.

For example, more than 50% of workers in food and accommodation services (a major nontradeable sector) work in establishments with fewer than 50 employees ( U.S. Census Bureau 2017 ).

We focus on small businesses because their customers are typically located near the business itself; larger businesses’ customers (e.g., large retail chains) are more dispersed, making the geographic location of the business less relevant.

We use 2010 Census ZIP Code Tabulation Areas (ZCTAs) to perform all geographic analyses of ZIP-level data. Throughout the text, we refer to these areas simply as “ZIP codes.”

Rents are a simple measure of the affluence of an area that combine income and population density: the highest-rent ZIP codes tend to be high-income, dense areas such as Manhattan. Plotting small-business revenue against median incomes or population density produces analogous results ( Online Appendix Figure XI).

Part of the reason that revenues fell so sharply in high-rent ZIP codes is that affluent families moved elsewhere during the pandemic. To quantify the relative contribution of such “extensive-margin” mechanisms versus intensive-margin reductions in spending by high-income households who did not leave, we use aggregated mobile phone data from SafeGraph ( Allcott et al. 2020 ) to estimate changes in local population at high frequencies. Although population fell more in high-rent areas, changes in small business revenues as of April 2020 still exhibit a sharp gradient with respect to local rents even conditional on SafeGraph-based estimates of population counts (12.3% per |${\$}$| 1,000 rent, std. err. 0.95).

Of course, households do not restrict their spending solely to businesses in their own ZIP code. We find similar patterns when zooming out to the county level. Counties with larger top 1% income shares experienced larger losses of small-business revenue ( Online Appendix  Figure XII.B). Poverty rates are not strongly associated with revenue losses at the county level ( Online Appendix  Figure XII.C), indicating that it is the presence of the rich in particular (as opposed to the middle class) that is most predictive of economic effects on local businesses.

Another benefit of our payroll-based employment series is the timeliness of its local-area estimates: it matches the county-level granularity of the QCEW (which is released with a lag of six months), but with the timeliness of the monthly employment statistics in the CES that are released at the national level and for 450 metropolitan statistical areas.

We cannot use this panel approach to examine employment beyond February 2021 conditional on pre-COVID wage rates because households responding to the CPS answer the Outgoing Rotation Group panel questions exactly twice, 12 months apart.

The high level of job postings in the second half of 2021 may also reflect a labor supply shortage, as companies had to post more jobs to fill a set of positions.

Online Appendix  Figure XVIII presents a specific example of this result by plotting trends in employment and spending in the retail trade sector. Total retail spending was 25% higher as of December 2021 relative to the pre-COVID baseline. Employment of high-wage workers was 5% above baseline levels, but employment of low-wage workers was still down by 19% in this sector—as in the economy as a whole.

We omit California, Massachusetts, and New York in this cross-sectional analysis because they each raised their minimum wages during our sample, leading to a discrete mechanical reduction in the number of bottom-wage-quartile workers over the course of the pandemic (see Online Appendix  E.2).

The differential changes in employment rates in low-wage jobs across low-versus high-rent areas are not driven by differential changes in wage growth rates or occupational switching. Using the approaches described above at a national level (see Online Appendix E.4 for details), we find that wage growth rates are, if anything, lower in high-rent states than low-rent states and that rates of switching to higher-paying jobs are uncorrelated with state-level rents. Furthermore, the CPS panel shows that employment for workers who started in the bottom wage quartile prepandemic remained lower in high-rent states in February 2021 ( Online Appendix  Figure XIX).

Prior studies benefited from substantial variation in the timing of payments, permitting identification over a longer period of time. In contrast, the stimulus payments we study each largely arrived on a single day, making it challenging to estimate effects over longer horizons without strong assumptions about counterfactual trends.

The payments were reduced at higher levels of income and phased out entirely for households with incomes above |${\$}$| 99,000 (for single filers without children) or |${\$}$| 198,000 (for married couples without children).

We permit pretrends because spending fell rapidly for all income groups in the days immediately preceding the April 15 stimulus payments, as shown in Figure I , Panel A. We assume common trends to maximize precision, as we find no significant differences in pretrends in spending across income quartiles in the 25 days preceding the stimulus payments. The differential changes in spending by income quartile discussed in Section III emerged before that period, immediately after the pandemic began. We also show that not adjusting for pretrends at all yields qualitatively similar conclusions in Online Appendix  Figure XXI.

Disaggregating the spending data by sector, we find that most of the additional spending from the April 2020 stimulus went to durable goods rather than in-person services. The stimulus thus increased the overall level of spending but did not channel money back to the businesses that lost the most revenue due to the COVID shock. These findings provide evidence for the “broken Keynesian cross” mechanism established in Guerrieri et al. (2022) ’s model, where funds are not recirculated back to the sectors shut down by the pandemic, potentially diminishing multiplier effects.

Using the same 25-day preperiod window as was used for the first stimulus yields point estimates that are statistically indistinguishable from those we present, but with much wider confidence intervals due to the greater noise in the preperiod.

Both estimates are significantly lower (with p < .005) than those from April 2020 based on a permutation test; see Online Appendix  Figures XXIV and XXV for the full distribution of placebo estimates.

Summarizing the literature on impacts of stimulus payments, Sahm (2019) observes that “households with low liquid assets relative to their income tend to spend more (and more quickly) out of additional income than those households with ample liquidity.” In normal times, Sahm observes that “targeting current low-income or low-wealth households may not identify the households most likely to spend the stimulus, which could include some wealthy households.” The link between income and liquid wealth changed during the pandemic, making such targeting more feasible.

With the benefit of hindsight, one may have been able to predict that MPCs would begin to fall for high-income households as their liquid savings rose, but it is difficult to gauge ex ante which of the many potential dimensions of heterogeneity and structural change warrant attention.

This analysis focuses solely on short-run employment effects; it remains possible that the PPP may have long-term benefits by reducing permanent business closures, as emphasized by Hubbard and Strain (2020) .

These results are driven by work location rather than sectoral differences in employment across areas: in the Earnin microdata, we find similar results even when comparing workers employed at the same firm (e.g., a chain restaurant). People working in high-rent ZIP codes in January 2020 remained less likely to have a job (anywhere) in April 2020 than their coworkers working in a different establishment of the same firm in lower-rent ZIP codes.

We restrict this figure to households living in low-income ZIPs because we cannot disaggregate the Affinity data by individual-level income. Since the employment data already represent only low-income workers, we do not restrict to low-income ZIPs in the employment analysis; however, the patterns are very similar when restricting to low-income ZIPs in the Earnin data.

Abraham   Katharine G. , Jarmin   Ron S. , Moyer   Brian , Shapiro   Matthew D. , eds., Big Data for 21st Century Economic Statistics, ( Chicago : University of Chicago Press , 2019 ).

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Economic effects of the COVID-19 pandemic on entrepreneurship and small businesses

• Open access
• Published: 12 September 2021
• Volume 58 , pages 593–609, ( 2022 )

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• Maksim Belitski   ORCID: orcid.org/0000-0002-9895-0105 1 , 2 ,
• Christina Guenther 3 ,
• Alexander S. Kritikos 4 , 5 , 6 , 7 &
• Roy Thurik 8 , 9  

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The existential threat to small businesses, based on their crucial role in the economy, is behind the plethora of scholarly studies in 2020, the first year of the COVID-19 pandemic. Examining the 15 contributions of the special issue on the “Economic effects of the COVID-19 pandemic on entrepreneurship and small businesses,” the paper comprises four parts: a systematic review of the literature on the effect on entrepreneurship and small businesses; a discussion of four literature strands based on this review; an overview of the contributions in this special issue; and some ideas for post-pandemic economic research.

Plain English Summary

Responding to COVID-19 involves not just shielding small business jobs, supporting entrepreneurship, and raising government debt but also creating productive entrepreneurship and resilient location-specific entrepreneurial ecosystems. The COVID-19 pandemic is an unprecedented challenge for small businesses that also brings new market opportunities. The papers in this special issue of Small Business Economics Journal aim to shed light on the economic effects of the COVID-19 pandemic by looking at the macro- and microeconomic effects on entrepreneurship and small businesses as well as the role of financial support policies and well-being in both developed and developing countries. Future research should focus on the role of digitization and financial mechanisms supporting small businesses during crises.

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1 Introduction

Epidemics and pandemics do not just come and go, they impact the economy and society. For example, the epidemic in the early 1830s, when France (and other parts of central Europe) was hit hard by cholera with hospitals overwhelmed by patients whose ailments doctors could not explain (O'Sullivan, 2021 ). While the epidemic wiped out at least 3% of Parisians in the first month, it would contribute to an industrial revolution in France. It also increased political instability and social disparity, with the city’s poor being hit hardest by the pandemic, while the wealthier used their savings and resources to relocate from pandemic-impacted cities and reduce their interactions with the community (Economist, 2021 ).

The Spanish flu affected most of Europe and the USA in 1918. While it infected 500 million people—about a third of the world’s population at the time—it killed between 20 and 50 million people across four successive waves, including some 675,000 Americans (History.com, 2020 ). The enforcement of various restrictions varied across the cities and countries: the New York City Health Commissioner, for example, ordered businesses to open and close on staggered shifts to avoid overcrowding on the subway (History.com, 2020 ). In the USA and Europe, businesses were forced to shut down because so many employees were sick. Several authors demonstrate that the Spanish flu pandemic gave way to new businesses, with start-ups booming from 1919 in the middle of the pandemic onward (Beach et al., 2020 ; Karlsson et al., 2014 ).

The COVID-19 pandemic presents an unprecedented challenge in many ways. First, it threatens millions of people’s lives all over the world. It has already taken a death toll of almost four million people worldwide, as of the end of June 2021 (Worldometers, 2021 ). At the same time, the social distancing guidelines, taken to contain the virus, affected the service sector in particular, an area where physical proximity often matters and a sector that depends more on micro and small businesses than the manufacturing sector.

Therefore, COVID-19 directly affected self-employed individuals more than employed individuals (Kritikos et al., 2020 ) and small businesses more than large businesses, both in Europe and the USA (Digitally Driven, 2020 , 2021 ).

A survey conducted by NBER of more than 5800 small businesses in the USA found that 43% of small firms were expected to be closed by December 2020 (Bartik et al., 2020 ). Small firms in hospitality, retail, personal services, entertainment, and the arts were most affected (Bartik et al., 2020 ). A survey conducted by the Connected Commerce Council of more than 5016 European small and medium-sized businesses carried out in November–December 2020 found that practically all SMEs were affected, with an average 20% decrease in sales and a 16% decrease in customer base (Digitally Driven, 2021 ).

Barrero et al., ( 2020 : 17) demonstrate for the USA that, “temporary layoffs and furloughs account for 77% of gross staffing reductions in the first months of crises in the United States,” while the Financial Times ( 2020 ) reports that, “more than 3 m Americans filed for first-time unemployment benefits during a first week of May 2020, taking the number of applications for the first three months of the lockdown to 33.5 million. The number of working business owners in the United States plummeted from 15.0 million in February 2020 to 11.7 million two months later in April” (Fairlie, 2020 ). In the UK, the unemployment rate surged to its highest level since 2017 as the pandemic continued to affect jobs (Thomas, 2020 ). In the long term, the COVID-19 pandemic is expected to become a cleansing process and a large reallocation shock (Caballero and Hammour, 1991 ) for firms of different sizes and industries.

Governments throughout the world responded with support initiatives. In the USA, the largest program providing funds to small businesses is the Paycheck Protection Program (PPP) with a volume of $650 billion during the early stages of the pandemic (Bhutta et al., 2020 ). The Small Business Administration (SBA)–administered program provided loans to small businesses through banks, credit unions, and other financial institutions with the goal of keeping small businesses open and retaining employees on the payroll (Fairlie & Fossen, 2021 ). In the UK, the government implemented the Coronavirus Job Retention Scheme (CJRS) (popularly known as “the Furlough” scheme) for waged workers. The CJRS covers 80% of employee salaries up to a maximum of £2500 per month. More than 8.7 million jobs were furloughed at an estimated total cost of around £60 billion (Yue & Cowling, 2021 ). After initially ignoring the 4.6 million self-employed, the UK government announced the Self-Employment Income Support Scheme, which awarded grants of 70% of average monthly trading profits calculated from tax returns for 2018 and 2019. This scheme only applied to those self-employed who earned less than £50,000 in profit for the relevant period (Yue & Cowling, 2021 ). The measures supported by the German government intended to protect businesses and start-ups affected by the COVID-19 crisis include taxation support, state-supported short-time work compensation schemes, improved measures at guarantee banks, loans and special programs provided by KfW (Kreditanstalt für Wiederaufbau) (PWC, 2020 ), and an emergency aid that offers one-off lump sum payments to self-employed facing substantial revenue declines (Block et al., 2020 ).

In China, measures started in February 2020 when Chinese central bank unblocked extensions or renewals of loans to companies and announced a reduction in the banks’ mandatory reserve ratio. The government presented a package to support the digitalization of SMEs in the context of the crisis. A wide range of policy measures was announced for SMEs at the regional level in China, including deferred tax payments for SMEs, reducing rent costs, waiving administrative fees, subsidizing R&D costs for SMEs, social insurance subsidies, subsidies for training and purchasing teleworking services, and additional funding to spur SME loans (KPMG, 2020 ). The 2020 GEM report mentions that 54 national governments made emergency policy decisions and actions in response to the COVID-19 pandemic (GEM 2020 ). Unprecedented amounts of state aid were channeled into propping up economies around the globe.

Despite the deployment of administrative, fiscal, and monetary tools to counter the fall in employment and demand, it seemed unlikely that these measures will be enough to attain a full offset. The response to COVID-19 requires both top-down and bottom-up approaches, e.g., government and private initiatives to support productive entrepreneurs, instead of dying industries and failing firms.

The shock of the pandemic may further increase inequality in at least two ways: First, female owners of small businesses faced a 35% higher probability of experiencing income losses than their male counterparts with the gender gap among the self-employed being largely explained by the fact that women disproportionately work in industries that are more severely affected by the COVID-19 pandemic (Graeber et al., 2021 ). Second, the consequences of the COVID-19 pandemic may be more pronounced for minorities in developed (Fairlie and Fossen, 2021 ) and developing countries (Maliszewska et al., 2020 ; Pereira & Patel, 2021 ).

More efficient and productive incumbents are likely to grow, with new businesses and industries emerging. The new “Never-Meet-in-Person Era” will change industries, impacting large and small firms in certain industries, such as transport, hospitality, arts and entertainment, and personal services. The weight of hybrid firms, platform-based firms, and platform-matchmakers in the global economy will grow rapidly (Kenney & Zysman, 2020 ).

The emergence of digital technologies has significantly reduced the economic costs of data—search, storage, computation, transmission—and enabled new economic activities during the COVID-19 pandemic and a change in lifestyle. Since the start of the pandemic, small and large firms, able to create a platform-based ecosystem, have become a force of “creative destruction,” value creation, and value appropriation (Acs et al., 2021 ).

The big issue is how the shock and the resulting recession will affect firms, large and small, young and mature, family and non-family firms, community-embedded small firms, and platform-based blitz-scalers not only in the short term but also the mid- and long terms. Will this be different than for any other exogeneous shock?

The potential consequences for businesses may include but are not limited to closed premises, reduced operating hours, job cuts, supply chain disruptions, jeopardizing the R&D processes, cessation of operations, business model changes, loss of key customers, and restrictions on products/services.

News stories highlight the millions of layoffs triggered by the pandemic and lockdown (Barrero et al., 2020 ), while they also relate to examples of large-scale hiring. For example, on April 18, 2020, Walmart reported that it had hired 150,000 new employees, with plans to hire 50,000 more (Nassauer, 2020 ). Fidelity Investments and Fifth Third Bancorp have also been on “hiring sprees,” and hires through Zoom eliminated the worry to be spotted during a job interview lunch by current employers. Will this be the beginning of a new revolution toward large multinational corporate structure, away from micro and small businesses? Businesses may have had different experiences from responding to the previous recessions and other pandemics but can these lessons be useful for small and large firms to respond to COVID-19?

Therefore, the objective of this special issue is to examine the economic effects of the COVID-19 pandemic on entrepreneurship and small businesses as well as help to promote research and economic implications relevant to understanding the nature of the pandemic shock, consequences, and opportunities for SMEs and large firms in the short- and long-term perspectives more broadly.

The present introduction to the special issue is organized as follows. It consists of four parts: a systematic review of the literature on the effect of COVID-19 on entrepreneurship and small businesses; a discussion of four literature strands based on this overview; an overview of the contributions in this special issue; and some ideas about the post-pandemic economic research, organized according to four avenues.

2 Systematic literature review

We start our analysis by performing a “systematic literature review” (Tranfield et al., 2003 ). It is a reliable and efficient method of identifying and evaluating a sizeable literature volume and is widely used in business research (Verma & Gustafsson, 2020 ). The advantage of this method is that it allows for capturing all existing studies on the topic, to incorporate quantitative, qualitative, and mixed-method studies, as well as to identify the state of knowledge regarding theories, special entities, and fields of study.

Based on this systematic and comprehensive literature review, we investigate research gaps and identify areas that require further research using the Scopus and Web of Science database, taking our lead from prior systematic literature reviews of Rousseau et al. ( 2008 ) and Verma and Gustafsson ( 2020 ). Before moving to the systematic literature review on the effect of COVID-19 pandemic on small business and entrepreneurship, we wanted to find out whether there is prior research on the economic effects of historic pandemics, such as the Spanish flu. Therefore, we use the period of 50 years, which resulted in only 60 publications, related to the effect of Spanish flu on small businesses. Interestingly, most papers on the effect of the Spanish flu on small business were published during the COVID-19 pandemic (see Fig.  1 ). Researchers from the USA, UK, and Canada have led this field of research.

Timeline of publications on small business and the Spanish flu. Note: 2021 is an incomplete year since the research was done in May of that year

Our next, and main, step was to review the literature on the economic effects of COVID-19 on small businesses and entrepreneurship. We used the period from December 2019 to June 2021 because it corresponds to the pandemic period. We included all articles, data sets, early-access publications, and data studies in English, yielding 3607 published pieces. Once we applied the selection criteria, including only articles published in international peer-reviewed journals, in English and the area of study, the number of publications dropped to 1789. The distribution of articles by field of science is as follows: social sciences (29.3%), business management (22.6%), economics (12.9%), environmental sciences (10.9%), energy (8.8%), organizational studies (2.3%), arts and humanities (2.0%), psychology (1.9%), and other (9.30%).

In the third stage, we used the field of research exclusion criteria with the aim of retaining publications from relevant fields such as business economics, management, social sciences, and economics. Most of the publications come from the USA, China, and the UK (see Fig.  2 ).

The region of the publications on small business and COVID-19

We excluded the BIOSIS Citation Index, BIOSIS Previews, Medline, Zoological Record, and FSTA. This means that we just kept the Web of Science and Scopus databases, yielding to 285 papers. Based on the keywords, text, and abstracts from these 285 papers, we created the visualization network to identify the themes related to the impact of COVID-19 on small businesses using VOSviewer. Co-word analysis applies text-mining techniques to the papers’ titles, abstracts, keywords, and text. Co-word connections allow for identifying and combining multiple co-occurrences and keywords in the same paper, as well as determining the relationship between different keywords (Verma & Gustafsson, 2020 ).

The outcome of the systemic literature review resulted in a keywords network visualization that required (i) selecting the patterns of topics and (ii) clustering topics theories: digitization and open innovation, resilience and disaster, knowledge creation and learning (dynamic capabilities), including industry effects (e.g., healthcare, information technology, tourisms) (Fig.  3 ). The theories were identified by reading all the abstracts and keywords of the 285 papers. These four theories are further explained in the next section and will be matched to the papers that comprise this special issue. We note that a clear discrimination between these literatures is not always possible.

The keywords network visualization

3 Theories and contributions

Based on the systematic literature review, this section describes how four literatures can be used by scholars to better understand and explain the economic effects of the COVID-19 pandemic on small business across different countries, firm sizes, and the severity of the crisis. First, there is disaster theory literature, which focuses on the financial and physical resources enabling small firms to be more resilient during crises. A body of literature stresses the importance of community-based networks and the role of social capital in helping small businesses to respond to disasters (Bin & Edwards, 2009 ; Torres et al., 2019 ).

Torres et al. ( 2019 ) investigate small business owners’ response to natural disasters and catastrophes through the lens of resources and social capital, drawing a line between resilient small businesses that not only remain operating but also thrive after a disaster and those exiting. Evidence focusing on small businesses shows that they widely engage in disaster relief for their community (Bin & Edwards, 2009 ), clarifying that in addition to governments, entrepreneurs and small businesses also become active (Markman et al., 2019 ). Post-disaster business resilience is the product of many complex decisions resulting from the interaction of individuals, families, businesses, and communities (Marshall & Schrank, 2014 ).

Second, responses to crises and exogeneous shocks is at the heart of resilience theory. The origins of the resilience concept in the business literature go back to Staw et al. ( 1981 ) and Meyer ( 1982 ). Both authors draw upon variation–selection–retention mechanisms posited by evolutionary theory (Campbell 1965) and develop very different propositions regarding how organizations respond to external shocks. Staw et al. ( 1981 ) introduce a theory on how negatively framed situations lead to risk avoidance in the form of “threat-rigidity effects.” Meyer ( 1982 ) extends the resilience framework by studying hospital responses to an unexpected doctors’ strike or “environmental jolt,” contradicting the proposition by Staw et al. ( 1981 ) that an external threat automatically places an organization at risk.

Resilience takes place over time and is related to the recovery of individuals, businesses, communities, and institutions. Most studies consider post-disaster business resilience as a binary stage of open or closed businesses (Marshall & Schrank, 2014 ). By capturing measures and processes that contribute to small business resilience as a disaster response, Tugade and Fredrickson ( 2004 ) provide real world examples, while Torres et al. ( 2019 ) emphasize the role of community and support to entrepreneurs in a post-shock period.

Research on resilience and post-disaster management literature began to comment that there are few avenues to detect whether or not an entrepreneur had “resilience potential,” prior to demonstrating a resilient or non-resilient response (Linnenluecke et al., 2012 ). Furthermore, researchers argue that more attention should be devoted to the period of detecting a threat and activating firm’s response. Conceptualization of organizational resilience broadly fall in three categories: (1) resilience as an outcome, (2) resilience as a process, and (3) resilience capabilities (Bullough et al., 2014 ; Duchek, 2020 ).

In the post-COVID world, agile and resilient new businesses will be able to take advantage of their entrepreneurial orientation and find opportunities in the upheaval that the pandemic has caused globally (Zahra, 2020 ). In an environment characterized by high volatility and uncertainty, the importance of the firms’ dynamic capabilities (DC) to integrate resources in recognizing new opportunities is also further heightened (Battisti & Deakins, 2017 ). The role of DCs and the role of resilience (Bergami et. al, 2021 ; Bullough & Renko, 2013 ; Bullough et al., 2014 ) are differentiators between not just the survival and failure of small businesses and entrepreneurs and also the speed with which new ventures are able to learn, both determining their growth and survival in the long term (Zahra, 2020 ).

Third, there is a literature on the role of knowledge creation and absorptive capacity in addressing the negative effects of disasters and crises. Dynamic capabilities (DC) are the key concept underlying absorptive capacity as the antecedent organizational and strategic routines by which managers alter their resource base—acquire and shed resources, integrate them together, and recombine them—to generate new value-creating strategies (Eisenhardt & Martin, 2000 ; Grant, 1996 ). Teece et al., ( 1997 : 516) defines DCs as “the firm’s ability to integrate, build, and reconfigure internal and external competences to address rapidly changing environments. Dynamic capabilities thus reflect an organization’s ability to achieve new and innovative forms of competitive advantage given path dependencies and market positions.”

Managing uncertainty tends to be the new normal for many companies around the world (i.e., climate change, COVID-19), thus stressing the importance of creating competitive advantage and improving dynamic capabilities that are so important for small business (Arend, 2013 ) and that seem to be the only antidotes to uncertainty during the COVID-19 pandemic (Flammer & Ioannou, 2020 ).

The role of dynamic capabilities was brought forward by Priyono et al. ( 2020 ) in their analysis of how small businesses cope with environmental changes due to the COVID-19 pandemic by pursuing the business model transformation with the change in dynamic capabilities related to adaptation of digital technologies and digital skills.

Dynamic capabilities, which became even more relevant in the digital era (Li et al., 2016 ), enable small businesses to adopt digital tools more quickly and efficiently. This enables stronger response to the COVID-19 pandemic. For example, Audretsch and Belitski ( 2021 ) demonstrate how European small businesses adopt digital technologies and develop strategic, managerial, and digital skills to increase their efficiency.

The DC theory could be relevant in the response to the volatility, velocity, and criticality of COVID-19 effects (Obal & Gao, 2020 ), for instance, by redeploying salespeople to virtual rather than physical sales calls. The literature on dynamic capabilities could draw on prior research in times of high turbulence but is sparse and focuses mainly on financial crises. For example, Fainshmidt and Frazier ( 2017 ) and Makkonen et al. ( 2014 ) find a disconnect between pre-crisis settings and the types of DCs most useful during crisis.

Bartik et al. ( 2020 ) and Kuckertz et al. ( 2020 ) suggest how government initiatives help businesses cope with the COVID-19 pandemic. A further cluster of papers use information gathering surveys (e.g., Bartik et al., 2020 ; Fairlie, 2020 ; Kritikos, et al., 2020 ) and case studies (Kuckertz et al., 2020 ; Robinson & Kengatharan, 2020 ). There is a lack of research on the intersection of the pandemic and DCs.

It is important to understand the boundary conditions explaining whether DCs can benefit small businesses compared to larger firms. Prior research suggests the existence of a positive feedback loop that results in firms with the largest initial capability endowments generating more new capabilities. Taken together, despite prior research on DC in small businesses (Arend, 2013 ; Fainshmidt & Frazier, 2017 ), only a few studies deal with the role of firm size in determining DCs and in response to the COVID-19 shock.

The fourth strand of literature is related to digitization and the role of digital capabilities in adopting new business models, responding to uncertainty, and developing resilience. Behavior of rapidly growing small businesses depends on their business models (Hennart, 2014 ; Kuratko et al., 2020 ), and the role of digitally enabled firms and business models is important in times of volatility (Li et al., 2016 ; Vadana et al., 2019 ). The role of digital capabilities is expected to grow in importance for entrepreneurship and small business research and practice during and after the COVID-19 crisis. Digital capabilities will be able to change business models and introduce business model innovation (Clauss et al., 2019 ). In the entrepreneurship literature, entrepreneurial growth remains an oft-neglected topic of research, as only a few studies (Asemokha et al., 2019 ; Child et al., 2017 ) shed light on the dynamics of business models and growth or performance in entrepreneurship.

There is still a gap with respect to understanding which DCs need to be developed for firms to respond to opportunities of COVID-19, such as digitalization and business model change (Seetharaman, 2020 ).

Works on digitization in small businesses analyze the implementation of business intelligence as part of their efforts to increase competitiveness in a highly dynamic business environment. A better understanding of the adoption levels of innovation by small businesses is relevant due to the important contribution of small businesses to both employment generation and economic growth (Audretsch et al. 2021b ).

Studies commissioned by Google in the USA in 2020 and in Europe in 2021 demonstrate that the so-called Digital Safety Net has empowered millions of small businesses to shift resources, modify business plans, and continually evolve throughout the pandemic (Digitally Driven, 2021 ). The COVID-19 pandemic threatened small businesses globally, but their use of digital tools has acted as a “Digital Safety Net” and saved many of them (Digitally Driven, 2020 , 2021 ).

4 Papers in the present special issue

The papers in this special issue can be divided into four strands by the unit of analysis, policy implications, and the literature used. These strands can be connected to the four literatures distinguished in the previous section. The first strand reveals the macro-economic effects of Covid-19 on entrepreneurship, small businesses, and the role of digital technologies in changing work routines of entrepreneurs, which relates to the literature on disaster management and the role of digital tools and capabilities. The second strand touches upon the economic and socio-psychological impact of the COVID-19 pandemic on entrepreneurship building on resilience literature and literature on the role of dynamic capabilities, in addition raising the issues of inequality and the effects of COVID-19 in developing and developed economies. The third strand deals with financial support to small businesses and entrepreneurship, building on the literature that addresses the negative effects of disasters and crises as well as macroeconomic responses to shocks. Finally, the fourth strand discusses the effect of various policy and well-being issues for small businesses during COVID-19 drawing on resilience and disaster theory literature.

The first strand contains three papers. Addressing the macroeconomic effects of COVID-19 on the way of living and working, a study of Zhang et al. ( 2021 ) “Working from Home: Small Business Performance and the COVID-19 Pandemic” focuses on working from home as an opportunity rather than an activity that leads to frustration, loneliness, and worries about the future (Banerjee & Rai, 2020 ). In this paper, working from home appears to be an opportunity to improve small businesses’ performance in the COVID-19 crisis. The authors built a theoretical framework based on firm profit maximization using daily and weekly data to demonstrate that working from home impacts the industrial structure and peoples’ work behavior.

A study by Meurer et al. ( 2021 ) demonstrates how entrepreneurs can use alternative support sources of communication and business, such as online communities, raising the question of how support is created in such spaces. Drawing on an affordances perspective, the authors investigate how entrepreneurs interact with online communities and base their qualitative analysis on conversation data (76,365 posts) from an online community of entrepreneurs on Reddit during the COVID-19 pandemic. The findings draw out four affordances that online communities offer to entrepreneurs (resolving problems, reframing problems, reflecting on situations, refocusing thinking and efforts), resulting in a framework of entrepreneurial support creation in online communities.

Altogether these two papers demonstrate how small businesses and individual entrepreneurs can adjust to new business conditions by working from home, developing new business models, and seeking social support to leverage the negative impact of the COVID-19 pandemic.

The study of Pedauga et al. ( 2021 ), “Macroeconomic Lockdown and SMEs: The Impact of the COVID-19 Pandemic in Spain,” takes a macroeconomic perspective to empirically test the role of small business in the economy. The authors use a financial social accounting matrix to distinguish between the direct and indirect effects that are transferred from micro, small, medium, and large firms to the rest of the economy during the COVID-19 pandemic. The authors explore the sequence of reactions associated with shocks that arise from the COVID-19 lockdown to small businesses using a structural model for the Spanish economy and identifying the role of businesses of different sizes for the gross domestic product (GDP). Interestingly, small businesses “explain” 43% of the gross domestic product and two-thirds of the unemployment decline caused by the COVID-19 pandemic.

The second strand of studies in this special issue examines the economic and non-economic impact on small business performance of the COVID-19 pandemic. The study of Grözinger et al. ( 2021 ) on “The Power of Positivity: Organizational Psychological Capital and Firm Performance During Exogenous Crisis” investigates how psychological capital in businesses impacts performance and creative innovation through organizational citizenship behavior, solidarity, and cooperation. The authors use structural equation modelling and regression analysis on 379 small businesses to demonstrate that psychological capital positively influences creative innovation and thus performance during crises. This research contributes to the organizational behavior approach of the small business literature by showing that psychological resources of small businesses can strengthen performance in times of crisis and help to prepare for future shocks.

The study by Torrès et al. ( 2021a ), “Risk of Burnout in French Entrepreneurs During the COVID-19 Crisis,” discriminates between three sources of burnout: the threat of becoming ill, having to stay at home due to the lockdown, and having to file for bankruptcy due to the economic downturn. They use seven data sets of French entrepreneurs with a temporal comparison of averages and two data sets of French entrepreneurs with a cross-sectional analysis of individuals. They show that the risk of burnout increased during the pandemic, that all three factors play important roles, and that the financial threat is the dominant one. These findings call for the extension of entrepreneurial support systems beyond the financial by also involving an “entrepreneurship care” aspect, which includes telephone support, webinars, mental help facilities, and other support measures.

The study by Kalenkoski and Wulff Pabilonia ( 2021 ), called “Impacts of COVID-19 on the Self-employed,” uses monthly panel data from the Current Population Survey in the USA and examines the initial impacts of COVID-19 on the employment and hours of unincorporated self-employed workers. The authors find that effects become visible in March 2020 as voluntary social distancing started, peaked in April during the complete shutdown, and were slightly smaller in May. They conclude that self-employed married mothers were hit hardest and were even forced out of the labor force to care for children. Moreover, remote work and working in an essential industry mitigate some of the negative effects on employment and hours worked.

Pereira and Patel ( 2021 ) in their study, “Is the Impact of COVID-19 More Severe on Self-employed of Colour? Large Scale Evidence from Brazil,” complement prior research on self-employed from racial minority groups and use resilience theory to explain how minority self-employed in Brazil responded to the COVID-19 pandemic with lessons for other developing countries (e.g., Sri Lanka) (Robinson & Kengatharan, 2020 ). The paper extends the argument that minorities may face greater adversity from the COVID-19 pandemic in the USA and other developed countries (Buheji et al., 2020 ), while there is little evidence that minority self-employed in a developing country are also significantly affected in the context of the COVID-19 pandemic.

The third strand of studies brings together the role of financing for entrepreneurship and small businesses in crises and a variety of support tools. Studies in this part discuss the role of financial support and other government programs to respond to economic disruption. Various support policies were developed and provided by governments all over the world in response to address their small businesses’ financing needs. In a paper by Liu et al. ( 2021 ), “SMEs’ Line of Credit under the COVID-19: Evidence from China,” the Chinese SMEs’ financing responses to the outbreak of COVID-19 are examined. The study shows the supportive role of Chinese state-owned banks on small businesses’ lines of credit. These policy instruments can be broadly categorized into loan guarantees, direct lending to small businesses, grants and subsidies, and equity instruments. Interestingly, there are considerable differences in supporting small businesses’ financing policies between countries. For example, in the USA, European Union, the UK, and China and Russia, policies to support small businesses during the pandemic were a commonplace. Brazilian and Indian government provided little support to small business.

The study of Fairlie and Fossen ( 2021 ), “Did the Paycheck Protection Program and Economic Injury Disaster Loan Program Get Disbursed to Minority Communities in the Early Stages of COVID-19?,” examines the effect of the US federal government response to help small businesses—the Paycheck Protection Program (PPP) and the related Economic Injury Disaster Loans (EIDL). The program’s stated goal is helping disadvantaged groups. The authors provide the first detailed analysis of how the 2020 PPP and EIDL funds were disbursed across minority communities in the country. The authors find a positive relationship between PPP loan receipt per business and the minority share of the population or businesses, although funds flowed to minority communities later than to communities with lower minority shares. This study acknowledges the importance of financial support through PPP loans of minority communities as a share of the population. The important evidence is that the EIDL program, both in numbers per business and amounts per employee, was positively distributed to minority communities. This is the first study about how loans and advances from these programs were distributed between minority and non-minority communities.

Another study by Atkins et al. ( 2021 ), “Discrimination in Lending? Evidence from the Paycheck Protection Program,” adds to our understanding of the role of race in loans made through the Paycheck Protection Program (PPP). Expanding the paper of Fairlie and Fossen ( 2021 ), the authors argue that the historical record and PPP program design choices made it likely that many Black-owned businesses received smaller PPP loans than White-owned businesses: Black-owned businesses received loans that were approximately 50% smaller than observationally similar White-owned businesses. Interestingly, the effect is marginally smaller in areas with more bank competition and disappeared over time as changes to the PPP program were implemented allowing for entry by fintechs and other non-traditional lenders.

The study by Block et al ( 2021 ), “The Determinants of Bootstrap Financing in Crises: Evidence from Entrepreneurial Ventures in the COVID-19 Pandemic,” investigates the measures that entrepreneurial ventures undertake to preserve liquidity. The authors build on prior research on bootstrap financing as an important enabler for the growth of resource-constrained early-stage ventures. Their work fills the gap about the use of bootstrap financing during COVID-19, during which the preservation of liquidity is particularly salient. The determinants of bootstrap financing are embedded into a “necessity” human capital perspective and an “opportunity” cost perspective. The analyses are based on data of 17,046 German entrepreneurial ventures.

The study of Dörr et al. ( 2021 ), “Small Firms and the COVID-19 Insolvency Gap,” focuses on fiscal policy in rescuing companies short of liquidity from insolvency. The authors show that, in the first months of the crisis, the small businesses that are the backbone of Germany’s economy benefited from large and mainly indiscriminate aid measures. The authors estimate the extent to which the policy response induced an insolvency gap and analyze whether the gap is characterized by firms that were already struggling before the pandemic. They also examined whether this insolvency gap differs with respect to firm size and find that the gap was larger for smaller firms. The theoretical contribution of the paper is in translating Schumpeter’s theory of the cleansing effect in economic crises into an empirical assessment by estimating the size of a policy-induced insolvency gap using firm-specific credit rating data combined with information on insolvency filings.

The fourth strand of studies represents a variety of micro and macro public support and well-being programs aiming to mitigate the negative effects of the COVID-19 crises.

The Lastauskas ( 2021 ) study, called “Lockdown, Employment Adjustment, and Financial Frictions,” examines businesses’ employment adjustments after the imposition of stringent lockdown in March 2020. It uses monthly administrative data and takes value-added tax payment changes as a proxy for the demand shock. The main finding is that all businesses in the manufacturing sector reduced employment more if they had uncovered tax liabilities before the lockdown. Among small businesses, those in the real estate and the service sectors downsized more rapidly. While employment changes are rather modest, this early evidence points to the importance of addressing liquidity needs and specific pre-conditions among capital-intensive and services businesses to avoid employment losses.

The Belghitar et al. ( 2021 ) study, “When the rainy day is the worst hurricane ever: the effects of governmental policies on SMEs during COVID-19,” examines the impact of COVID-19 on 42,401 UK small businesses and how government intervention affected their capability to survive the pandemic. The results show that, without governmental mitigation schemes, 59% of UK small businesses report negative earnings and that their residual life is reduced from 164 to 139 days. This analysis demonstrates that government financial support may reduce the number of small businesses with negative earnings and allows extending the residual life for small businesses with negative earnings up to 194 days. Block et al. ( 2020 ), who analyze the first emergency aid program in Germany, find similar effects among German businesses hit by the crisis. Interestingly, the study of Belghitar et al. ( 2021 ) highlights that—in contrast to Block et al ( 2020 )—those industries that were worst hit by COVID-19 are not those that benefited the most from the government support scheme. The possible reason is that the government scheme does not differentiate between firms that do or do not deserve support.

Finally, the study of Braunerhjelm ( 2021 ) deals with macro-economic stabilization policies and discusses that targeting aggregate demand may not suffice to mitigate the comprehensive effects of the COVID-19 crisis. Entitled “Rethinking Stabilization Policies: Including Supply-side Effects and Entrepreneurial Processes,” it suggests that a more active role for fiscal policies is needed and presents a modified framework for stabilization policies, giving an extended role to supply-side measures and emphasizing policies that can promote entrepreneurial processes and knowledge upgrading efforts. Aligning policies at the micro- and macro-levels can be expected to counteract economic downturns more efficiently as the potential for long-term growth is enhanced. Such a redirection of stabilization policies is argued to strengthen the competitive standing of both firms and individuals.

5 Future research

There are many discussions and arguments proclaiming that nothing in business will be left unchanged: in the post-COVID world, there will be opportunities for entrepreneurs to embark on creating new products and services, with novel business models and business routines arising that are different from traditional ones (Janssen et al., 2021 ). Changes in (the perception of) well-being, the way of consuming, in the way of filtering out the resilient and the agile, the adoption of new digital technologies and learning skills, and much more will all contribute to something that some call the “new normal.” Below, we contribute to this discussion with respect to four dimensions of future research, all connected to the contents of this special issue, initially sparked by our discussions with authors and referees during the online paper-development-workshop organized by the University of Reading on November 20, 2020: caution is warranted as all suffer from a certain degree of speculation.

5.1 Long- and short-term economic effect of COVID-19

The results of several papers in this special issue demonstrate that investigating the long-term effects induced by the policy responses to COVID-19 on turnover, productivity, innovation, and entrepreneurship in developed countries is needed. However, future research may also want to demonstrate a wider economic, political, and societal challenge, including inequality and poverty, unemployment within poor countries, and the gap between rich and poor countries (Bartik et al., 2020 ; Robinson & Kengatharan, 2020 ).

Real wages in certain sectors may rise, such as tourism, hospitality, and restaurants, as the disease reduces the supply of workers, leaving survivors in a stronger bargaining position.

The macro- and microeconomic effects of the COVID-19 shock are different between small and large firms as well as between the self-employed and incorporated business. Smaller businesses are typically disadvantaged in their ability to capture the opportunities that crises have created. It is important to research further the role of local and national governments, public organizations, civil society, and other stakeholders in mitigating the effect of crises.

Forming partnerships between small and large firms, the role of open innovation and knowledge spillovers may emerge as an important conduit for entrepreneurship and for mitigating the effects of COVID-19. Particularly interesting is the dynamics of so-called science, technology, engineering, and mathematics (STEM)–related jobs in the long term.

Further insights are needed to understand economic and psychological drivers of innovation during crises. While previous research demonstrates that context matters (Audretsch et al., 2021a ; Welter, 2011 ; Welter et al., 2019 ), the context of a crisis is a compelling, yet understudied, one. Welter et al. ( 2019 ) outlines three recent and overlapping waves of contextualization in the entrepreneurship field and shows that the discussion has moved from challenging the Silicon Valley model by considering the why, what, and how of entrepreneurship (first wave) to considering more subjective elements in enactment of contexts (second wave), through broadening the domain of entrepreneurship research (third wave).

To quantify the effect of the COVID-19 lockdown on economic activity, it may be possible to consider the links between all three waves (Welter et al., 2019 ) at the idiosyncratic level and their aggregate impact. It is probably not just sectoral issues and those issues related to the labor market or economic growth that play a role, but also deeper mental issues (Torrès et al., 2021a ).

5.2 The use of digital technology, competencies, and robots

Digital skills trends seem to be interacting with the pandemic and its social, political, economic, environmental, and demographic tensions, combining to accelerate the reconfiguration of production and service systems. This reconfiguration of existing skills and adoption of digital skills not only affects employment trends, but also the way we work and experience our mental and physical health, perhaps even long after the crisis is over.

The role of digital technology has significantly increased under COVID-19. For instance, digital technologies affected the way firms do head-hunting during COVID-19 as well as how products and services are manufactured and delivered. During disease outbreaks—Ebola in 2014–2016 and COVID-19 in 2019, among others—the adoption of robot and digital tools accelerates, especially when the health impact is severe and associated with potential economic losses or economic crises.

Entrepreneurship in the post-pandemic world will further fuse with the digital economy. This will take the form of entrepreneurs increasingly selling products on digital platforms, using digital tools like TikTok for marketing and relying on platforms such as Kickstarter for funding. Moreover, we believe that entrepreneurs will further seek to use peers in online communities to develop opportunities, get assistance with problems, and find collaborators. The key implication is that, while entrepreneurs in the past have often physically worked side by side to develop their business locally, in the future such bounds will play a diminishing role. One can start a business in Ghana, work with a programmer in Indonesia, find a marketing specialist in Paris, secure funding over Kickstarter, and sell the product through a digital platform. In other words, COVID-19 fosters the transition of the entrepreneurial economy into a digital, disembodied economy. The next big technology to be adopted at large scale is likely to be 5G. The large-scale use of artificial intelligence is being pushed but may not be relevant until 2025 at the earliest. Quantum computing is also being pushed, but not likely to affect small businesses before 2030.

All small businesses must be prepared for the “new normal” of a digitally driven economy (Meurer et al., 2021 ). Many are well positioned, but others feel uncertain due to challenges accessing capital, tools, and training, as well as with measuring success. During the pandemic, so-called advanced small businesses invested more than twice as much money in digital tools than the so-called uncertain small businesses (Digitally Driven, 2021 ). The working environment changed fundamentally with the digitalization and flexibilization of work receiving a considerable boost. These changes probably make companies more resilient to future shocks.

Even though the self-employed initially were hit harder by the COVID-19 pandemic than larger firms in the USA and Europe (Digitally Driven, 2020 , 2021 ), there is reason to be optimistic because, for the millions of SMEs that still lack skills, technology, and resources, adopting digital tools is within reach with the right mindset, strategy, access to world-class digital technologies, and training. As the working world has become more flexible, it is likely that mixed forms of remote and physical working (especially in teams) will become accepted in the future. However, we also learned that remote work cannot sufficiently replace personal encounters in all cases. Therefore, we believe that society and the working world will learn to appreciate such personal encounters again and that these will be valued differently in the future. Future research may need to better understand the role personal encounters and skills, which, along with new technology, will be valued more in the future.

5.3 Financing for entrepreneurship

As witnessed by several contributions in the present special issue, there are many promising avenues for research regarding what drives the financing of entrepreneurial activity during and after the COVID-19 crisis. For example, we would expect that entrepreneurial motivation may play an important role, along with networks of venture capital and angel investors. A significant share of solo self-employed individuals start their businesses out of necessity (Block et al., 2015 ; Caliendo & Kritikos, 2019 ; De Vries et al., 2020 ; Zwan et al., 2016 ). As policymakers want more high-growth ventures to recover from the crisis, their interest in opportunity-driven entrepreneurs may grow. Human and social capital including networks for entrepreneurship may be important for sourcing entrepreneurial financing. Finally, research should also analyze performance effects and investigate whether and how various sources of finance, beyond bootstrapping during the COVID-19 crisis, may impact long-term entrepreneurial performance, survival, and high growth (Audretsch et al., 2021b ).

Financial support policies are important for supporting small businesses and individual entrepreneurs with the mechanisms and the extent of such support being substantially different between OECD and non-OECD countries. Thus, understanding the causes and consequences of SME financing policies in the COVID-19 era would be intriguing and pivotal for both academic researchers and policymakers. Future research could also examine whether and how the institutional and development stage heterogeneities shape the policy differences related to stakeholders, unit of financing, and form of financing (e.g., grants, loans, equity). In that sense, the pandemic is a natural experiment.

A criticism of the financial support programs is that often there was no data collected on applications for loans that were denied (Fairlie & Fossen, 2021 ). This is an important piece of information that should be collected for future research on public support to small businesses and entrepreneurs to gauge demand and unmet need for these loans, in particular by minority businesses in developed and developing countries. As in the case of the USA, PPP and EIDL funds were allocated to support businesses, and it is crucial to track who receives funding and how it helps small businesses to become more resilient and grow during the crisis.

During the first phase of the pandemic, massive government support slowed firm exits. However, it may be argued that the resources were not spent efficiently and that public support mechanisms slowed down industrial dynamics. Hence, an important challenge for the post-pandemic world is to revitalize entry rates and stimulate technology adaption while also encouraging the adoption of new business models that restore productivity and growth beyond pre-crisis levels. In this context, research in industrial dynamics may help to contribute to the existing long-run challenges faced by modern societies such as digitization, decarbonization, and sustained prosperity.

Looking ahead, government and policymakers may want to design financial policy interventions that dampen the impacts of the pandemic on small businesses. Future research should focus on direct policies, like zero-interest loans, subsidies, and grants. According to Liu et al. ( 2021 ), in this special issue, the measures should target subgroups, firms that heavily rely on supply chains, and small businesses without stable bank relationships.

Understanding the effects of the interplay between liquidity support, on the one hand, and temporary adjustments to insolvency regimes, on the other, will provide an important lesson from the COVID-19 crisis. Further research may focus on the interplay of these two instruments as it is assumed that they may discourage struggling firms from exiting the market.

5.4 Non-economic effects of the COVID-19

An increasing number of studies in the entrepreneurship literature analyzes to what degree entrepreneurs’ mental health influences their activities. Further studies about the perception of burnout or general mental health issues, with a focus on experiences during the COVID-19 pandemic across more countries, industries, and fields, could expand what we know about the response of entrepreneurs during crises and how negative effects (e.g., burnout) could be leveraged.

COVID-19 put a large strain on entrepreneurs, who experienced an unprecedent shock to their businesses (Torrès et al., 2021b ) Without being able to meet physically with investors and clients, some entrepreneurs had to scale down their businesses; others closed their business, and solo entrepreneurs were left more isolated than before. The COVID-19 pandemic has likely been detrimental to the mental health of entrepreneurs. The pandemic forced entrepreneurs to reflect on the importance of their mental health and to actively seek and establish coping techniques. Some entrepreneurs experiencing failure may decide that entrepreneurship is not for them, but we expect that those who continue their entrepreneurial career found ways to cope with high stress levels. For instance, such entrepreneurs will use “time boxing” to become more productive, meditate regularly, or use digital tools to connect with peers. These entrepreneurs will likely also focus more on balancing their working and private lives by creating a working situation that suits their social needs. In that sense, some of the entrepreneurs who suffered during the pandemic may come back mentally stronger and more resilient.

The lockdown likely led to frustration, loneliness, and worries about the future (Kritikos et al., 2020 ), which are also risk factors for mental illnesses (Banerjee & Rai, 2020 ). Future research can focus on the impact of lockdowns and quarantine on small businesses as well as on the link between lockdowns, psychological effects (Brooks et al., 2020 ), and entrepreneurship (Shepherd, 2020 ). Results of future investigations could inspire entrepreneurs to search for novel, more sustainable, and more social forms of entrepreneurship, better understanding failures and successes of small businesses. This knowledge, which is often informal and tacit, represents a source of wealth for dealing with new forms of crisis (both health related and economic).

Protecting and supporting the health of small businesses and entrepreneurs during and after the COVID-19 pandemic is essential because they have a special role in the aftermath of crisis and in the anticipated post-pandemic boom. This aftermath may be predominantly dematerialized with a virtual mode of working and new norms of working from home. The climate and the green agenda would be a priority. A large part of business services would be contactless. Entrepreneurs’ health—both physical and mental—would be acknowledged and recognized as vital, both by the entrepreneurs themselves and by the policy makers.

However, given the length of school closures and the considerable reduction in the availability of childcare centers, the gender gap in entrepreneurship, which was identified at the beginning of this crisis, may widen in the post-pandemic period (Seebauer et al., 2021 ).

In general, economic inequality between and within nations is likely to also increase the likelihood of contracting the coronavirus and dying from it. Developing nations with weak healthcare systems and an inability to practice social distancing also account for the unequal impact. For people of low socio-economic status and economically disadvantaged people in developed countries, COVID-19 also poses higher risks of living in overcrowded accommodations increasing risk of illness (Patel et al., 2020 ). Racial and ethnic minorities experience higher death rates from COVID-19, which has also unequally affected urban residents and foreign migrants around the world. With the closure of schools, nurseries, and other childcare facilities for all but children of essential workers (Blundell et al., 2020 ), parents were typically left with the sole responsibility for caring for their children, including education, which particularly affected the survival of the self-employed. How these growing inequalities affect business dynamics will become an entire field of scholarly research and, hopefully, of compensating policy interventions.

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Belitski, M., Guenther, C., Kritikos, A.S. et al. Economic effects of the COVID-19 pandemic on entrepreneurship and small businesses. Small Bus Econ 58 , 593–609 (2022). https://doi.org/10.1007/s11187-021-00544-y

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• Volume 9, Issue 9
• The global health and economic value of COVID-19 vaccination
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• JP Sevilla 1 , 2 ,
• Daria Burnes 1 ,
• http://orcid.org/0000-0001-5300-4206 Joseph S Knee 1 ,
• Manuela Di Fusco 3 ,
• http://orcid.org/0009-0000-9111-7666 Moe H Kyaw 3 ,
• Jingyan Yang 3 ,
• Jennifer L Nguyen 3 ,
• David E Bloom 2
• 1 Data for Decisions, LLC , Waltham , Massachusetts , USA
• 2 Harvard T H Chan School of Public Health , Boston , Massachusetts , USA
• 3 Pfizer Inc , New York , New York , USA
• Correspondence to Dr JP Sevilla; jsevilla{at}datafordecisions.net

Introduction The COVID-19 pandemic triggered one of the largest global health and economic crises in recent history. COVID-19 vaccination (CV) has been the central tool for global health and macroeconomic recovery, yet estimates of CV’s global health and macroeconomic value remain scarce.

Methods We used regression analyses to measure the impact of CV on gross domestic product (GDP), infections and deaths. We combined regression estimates of vaccine-averted infections and deaths with estimates of quality-adjusted life years (QALY) losses, and direct and indirect costs, to estimate three broad value components: (i) QALY gains, (ii) direct and indirect costs averted and (iii) GDP impacts. The global value is the sum of components over 148 countries between January 2020 and December 2021 for CV generally and for Pfizer-BioNTech specifically.

Results CV’s global value was US$5.2 (95% CI US$4.1 to US$6.2) trillion, with Pfizer-BioNTech’s vaccines contributing over US$1.9 (95% CI US$1.5 to US$2.3) trillion. Varying key parameters results in values 10%–20% higher or lower than the base-case value. The largest value component was GDP impacts, followed by QALY gains, then direct and indirect costs averted. CV provided US$740 of value per dose, while Pfizer-BioNTech specifically provided >US$1600 per dose. We estimated conservative benefit-cost ratios of 13.9 and 30.8 for CV and Pfizer-BioNTech, respectively.

Conclusions We provide the first estimates of the broad value of CV incorporating GDP, QALY and direct and indirect cost impacts. Through December 2021, CV produced significant health and economic value, represented strong value for money and produced significant macroeconomic benefits that should be considered in vaccine evaluation.

• global health
• health economics
• health policy

Data availability statement

Data not subject to license restrictions are available in a public open access repository. The IHME data are not available due to license restrictions. All public data, Stata and Python code, and supplementary results used and generated by this study are freely and publicly available in the following GitHub repository: https://github.com/DataforDecisionsLLC/The-global-health-and-economic-value-of-COVID-19-vaccination .

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/ .

https://doi.org/10.1136/bmjgh-2024-015031

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WHAT IS ALREADY KNOWN ON THIS TOPIC

Only three studies have evaluated the global health and/or macroeconomic impact of COVID-19 vaccination (CV), but (i) none translated global mortality or morbidity impacts into summary health measures such as quality-adjusted life years (QALYs), (ii) studies that estimated the global economic impacts of CV did not assess its impacts on the key macroeconomic indicator, gross domestic product (GDP) and (iii) none of the global studies provided a single comprehensive value measure integrating both health and macroeconomic values, or estimated the value-for-money of CV relating its benefits to its costs.

WHAT THIS STUDY ADDS

Our study addresses all three of the above deficits in the literature and in our understanding of the value of CV, finding that CV provided >US$5 trillion in value globally through December 2021, that GDP gains constituted the largest value component (US$2.6 trillion), QALY gains constituted the second largest component (US$2.1 trillion) and direct and indirect costs averted constituted the smallest component (US$0.4 trillion).

CV provided a value of US$741 per dose, which corresponds to a very strong benefit-cost ratio of almost 14.

The GDP benefits alone yielded a benefit-cost ratio of 6.5, indicating that CV’s costs are justified many times over by GDP benefits alone.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

This study demonstrates the significant value and value-for-money of CV, and the substantial economic and health benefits it provides.

It supports investments in, and prioritisation of, global vaccination to end the pandemics; clarifies that addressing pandemics is as much an economic imperative as a health imperative; and reinforces the need to value pandemic vaccines’ economic benefits alongside their health benefits to achieve optimal levels of global investment.

Introduction

The COVID-19 pandemic has been one of the largest pandemics and global economic crises of the past century. COVID-19 vaccination (CV) has been claimed the single most important policy tool allowing global health and macroeconomic recovery. 1 2 Yet there is limited research quantifying the broad impact of CV on the global economy or global health.

We found only three studies that quantified the global health and/or economic impact of CV. Watson et al 3 estimated the impact of CV solely in terms of averted global COVID-19-related deaths. Bell et al 4 and Yang et al 5 measured both the health (ie, averted global infections and/or deaths) and global economic impacts of CV. Yang et al 5 estimated CV economic impacts solely in terms of averted direct and indirect costs; however, the study did not incorporate the value of unpaid work into its indirect cost estimates. And Bell et al 4 estimated CV’s economic impacts on direct costs and global trade, the latter of which was based on hypothetical CV scenarios as opposed to actual CV data. None of the global studies that estimated the health impacts of CV translated these effects into quality-adjusted life years (QALYs) or disability-adjusted life years (DALYs) gains. And those studies that estimated the global economic impacts of CV did not evaluate such impacts on gross domestic product (GDP). The valuation methods used in the existing literature do not allow direct comparison of the relative magnitudes of global economic and health values. Additionally, none of the global studies derived a single comprehensive value measure spanning its health and macroeconomic values, nor an estimate of the value-for-money (VfM) of CV relating its benefits to its costs. We also found no published estimates of the global health or macroeconomic impact of Pfizer-BioNTech vaccines specifically.

Yet, value assessments are essential for decisions regarding coverage of CV within population vaccination programmes by non-commercial vaccine payers such as national governments, multilaterals like the World Bank and non-profits like Gavi who are motivated by public goals such as population health and well-being. And appropriate value assessments should adopt a societal perspective, reflecting both health sector impacts of CV and broader societal impacts, including on the macroeconomy and productivity. 6 We make progress in this direction and add to the existing literature by quantifying CV’s broad VfM, reflecting its contribution to global GDP recovery, QALY gains and averted direct and indirect costs.

We retrospectively assess the VfM of non-commercial payers’ (NCP) spending on population CV programmes, adopting a societal perspective, and estimating three components of the broad health and economic value (‘broad value’) of CV: (i) health impacts in terms of QALY gains, (ii) averted direct and indirect costs of COVID-19 infections and deaths and (iii) GDP impacts. We estimate these values for each country or territory in our sample (hereafter, ‘country’) and for the globe, and for all vaccine brands combined and for the Pfizer-BioNTech vaccine specifically.

Our focus on the VfM of NCP spending on population CV programmes allows us to ignore CV-related R&D costs, manufacturing costs and vaccine manufacturer profits. In a market economy, it is not the NCP’s responsibility to account for these. Instead, it is the vaccine manufacturer’s burden as a commercial entity to develop a vaccine that is sufficiently valuable to NCPs that negotiated prices cover these costs while allowing for profits. R&D costs, whether private or public, are also irrelevant to NCP coverage decisions because such costs will have already been incurred regardless of whether or not NCPs decide to cover CV. They are therefore not incremental to the coverage decision and drop out of a comparison of vaccination and no vaccination scenarios.

To estimate these broad values, we perform a regression analysis to measure the impact of CV doses on GDP, COVID-19 infections (‘infections’) and COVID-19 deaths (‘deaths’). We combine estimates of vaccine-averted infections and deaths from these regressions with estimates of QALY losses, direct costs per non-fatal infection and death and indirect costs per non-fatal infection to estimate QALY gains and costs averted by vaccination. We value QALY gains at per capita full income. The broad value of CV (VoCV) is the sum of monetised QALY gains, GDP gains and averted direct and indirect costs. The global VoCV sums up country-level broad values, which are themselves the sum of a country’s period-specific broad values.

Box 1 describes the steps of the analysis. The online supplemental appendix provides further details on data sources, variable construction and methods.

Supplemental material

Analysis steps for the valuation.

Estimate infection-, death- and PCGDP regressions.

Compute averted infections, deaths and PCGDP losses from vaccination, relative to a no-vaccination counterfactual, using estimates from (1).

Compute averted non-fatal cases from vaccination by taking the difference between averted infections and deaths from (2).

Estimate QALYs per fatal case and per non-fatal case.

Estimate direct costs per case.

Estimate indirect costs (unpaid work) per non-fatal case.

Estimate population-level QALY gains by combining averted deaths from (2), averted non-fatal cases from (3) and QALY losses per fatal and non-fatal cases from (4).

Estimate population-level averted direct costs by combining averted deaths from (2), averted non-fatal cases from (3) and direct costs per case from (5).

Estimate population-level averted indirect costs by combining averted non-fatal cases from (3) and indirect costs per non-fatal case from (6).

Compute full income.

Compute the monetary value of population-level QALY gains by multiplying population-level QALY gains from (7) by full income from (10).

Compute population-level vaccination benefits by summing the population-level monetary value of QALY gains from (11), averted direct costs from (8) and averted indirect costs from (9).

Compute benefit per dose by dividing population-level vaccination benefits from (12) by vaccine doses.

Assume each vaccine dose costs US$53.19.

Compute BCR as ratio of (13) to (14).

BCR, benefit-cost ratio; PCGDP, per capita gross domestic product; QALY, quality-adjusted life year.

Data sources and summary statistics are provided in table 1 . We rely on licensed Institute for Health Metrics and Evaluation (IHME) COVID-19 Projections data 7 for vaccine doses, infections, deaths, hospitalisations and intensive care unit (ICU) beds. All other data sources are in the public domain.

• View inline

Data sources and summary statistics

We conduct our analysis in Excel, Stata V.18 and Python V.3.11.

Study countries, horizon and currency

Our study population consists of 148 countries with available data and >1 mllion population, covering nearly 98% of the global population ( online supplemental table S1 ).

The highest frequency at which some data are available is quarterly. Thus, the time-period in most of our analysis is the calendar-quarter (‘quarter’). However, in countries where only annual GDP data are available, the time-period in the GDP regression is annual.

Given data limitations, we limit the study horizon to the pre-Omicron period, spanning 2020Q1–2021Q4. Our base currency, to avoid postpandemic volatility, is 2019 US$. See online supplemental appendix for inflation and exchange rate adjustments.

Regression specification

Infections and deaths.

To estimate the impact of vaccinations on infections and deaths, we estimate the following panel regression equations:

We lag all independent variables to avoid reverse causality from dependent variables.

Gross domestic product

For countries with quarterly GDP data, we estimate a panel regression with an identical specification as (1) and (2) except for the dependent variable:

For the 66 countries without quarterly GDP data but with annual GDP data and vaccination starting in either 2020Q4 or 2021Q1, we estimate a cross-sectional regression:

We allow for a lagged GDP shortfall on the right hand side to allow for dynamic convergence, whereby larger initial shortfalls may imply larger ‘rebounds’. We include lagged cumulative per capita infections to represent natural immunity and pandemic trajectory.

Comments on regression specifications

Regression equations (1)–(3) are parsimonious in that they have relatively few explanatory variables. We chose parsimonious specifications to reduce the risk of overfitting chance correlations, preserve the precision of estimates, preserve sample size (ie, avoid dropping countries without the requisite data) and avoid introducing endogenous and collinear variables. Some endogenous variables such as household and firm behaviours, for example, are likely driven by vaccination (eg, households and firms will likely re-engage in workplace-centred or public-facing economic activity or reduce social distancing when vaccination rates are high), so behavioural changes are part of the mechanism for vaccine impact and should not be controlled for when estimating that impact. While we include lagged infections as explanatory variables, we do not also include lagged deaths: deaths are relatively stable proportions of infections, so lagged infections can proxy for, and will be highly collinear with, lagged deaths. Parsimony also reflects scarcity of relevant variables measured on a quarterly basis and available for many countries. For example, measures of health system capacity (eg, hospital beds per capita) are not available on a quarterly basis during the study time horizon. We note that while our regression specifications are linear in variables, they are non-linear in infections and vaccinations, since we allow two lagged terms for each, thus allowing their timing to matter (we tested more complex lag structures, but these did not improve fit).

Vaccination’s impacts

We separately measure the impact of all CVs relative to a ‘no vaccination’ counterfactual and the impact of ‘Pfizer-BioNTech’ vaccines relative to ‘no Pfizer-BioNTech’ counterfactual. These counterfactuals are simulated worlds where no CV is one scenario and no Pfizer-BioNTech is another scenario. We simulate these counterfactuals using the results of our regression analyses as we describe in online supplemental appendix S3 .

Quality-adjusted life years

We compute country-specific QALY losses per fatal and non-fatal infection. QALY losses per fatal infection equal the weighted average of age-specific QALY losses from death, 13 14 with weights reflecting the age distribution of COVID-19 deaths. 15 QALY losses from death are discounted at 3% following WHO recommendations. 16 QALY losses per non-fatal infection are age-invariant and equal the weighted average of QALY losses from infections of different severity levels, 17 with weights reflecting the relative probabilities of those levels (‘severity-weighted average’). The severity levels are asymptomatic, mild (not requiring hospitalisation), severe (requiring hospitalisation without ICU admission) and critical (requiring ICU admission). We allow long COVID to affect QALY losses following severe and critical infections. 18 We compute QALY gains from vaccination from the product of averted fatal infections from vaccination and the QALY loss per fatal infection, as well as the product of averted non-fatal infections from vaccination and the QALY loss per non-fatal infection.

Direct and indirect costs

We estimate country-specific age-invariant direct costs per infection (which we apply equally to fatal and non-fatal infections) as a severity-weighted average of the product of inpatient or outpatient unit costs 19 and severity-specific durations of utilisation (eg, hospital or ICU length of stay, or one outpatient visit per mild infection). 20

Country-specific age-invariant indirect costs per non-fatal infection consist of lost unpaid work related to infection, which is a severity-weighted average of the product of severity-specific workdays lost (our proxy for the days of unpaid work lost), daily hours spent on unpaid work and the hourly wage. We exclude paid work from indirect costs to eliminate double counting given our consideration of GDP impacts. We exclude all indirect costs of fatal cases to eliminate double counting given our monetisation of QALYs.

Full income

We monetise QALYs at country-specific age-invariant full income, equal to the sum of per capita annual income 21 and the per capita value of annual non-market time. Annual non-market time consists of unpaid work and leisure time, 22 23 where each hour is valued at the hourly wage. 24 As discussed below and shown in the online supplemental appendix , full income is a conservative approximation to individuals’ willingness-to-pay (WTP) per QALY.

The broad value formula

We take the broad VoCV to be the sum of monetised QALY gains, GDP gains and averted direct and indirect costs. We theoretically derive and justify this broad value measure more fully in online supplemental appendix S1 . Put simply, when utility is a multiplicative function of health and full income, then the impact of a shock on utility can be approximated by the sum of two terms. The first term is the impact on utility of the shock-induced impact on health holding full income fixed, and the second is the impact on utility of the shock-induced impact on full income holding health fixed. We convert these utility impacts into WTP to avoid such impacts by dividing them by the expected marginal utility of full income. Under certain functional form and simplifying assumptions, we find that the first term can be conservatively approximated by the product of full income (serving as a conservative estimate of WTP per QALY) and the QALY impacts of the policy, while the second term equals the policy-induced impact on full income. (See Murphy and Topel 25 (their equation 13) for an example of full income as we define it being a component of, and a conservative approximation to, WTP for health.) Since the COVID-19 pandemic is a shock and CV is a reduction in the magnitude of the shock, the broad VoCV can be approximated by the resulting form.

Since our broad value measure reflects WTP for the health and economic impacts of policy, our analysis is a form of cost-benefit analysis (CBA). Since we derive our broad value measure from a utility framework in which QALYs represent health-related utilities, our analysis is also a form of cost-utility analysis (CUA). However, given that we value QALYs at individuals’ WTP per QALY, ours is a societal perspective CUA, as opposed to a health payer perspective CUA where QALYs are valued using the shadow price of some exogenously given health payer budget. Our broad value measure can also be understood within a societal-perspective cost of illness (CoI) framework in which costs of illness are defined broadly to encompass health and productivity losses, health losses are valued in terms of individuals’ WTP for health, health is measured by QALYs and the broad value of vaccination equals its averted CoI.

Marginal versus non-marginal risks

The approximations we use to derive the broad value formula are valid for marginal risks, but the risks imposed by COVID-19 are potentially non-marginal. Standard models of WTP for mortality risk reductions suggest that the rate of substitution of wealth for risk reduction declines when the magnitude of the risk reduction grows, 26 raising the prospect that formulas like ours derived for marginal risks will overestimate WTP for non-marginal risks. Despite this concern, we persist with our approximations for three reasons. First, a published calibration exercise suggests such overestimation is modest in the range of risk reductions we consider. Table 1 shows median per capita deaths per calendar quarter of 14/100 000, implying an annualised mortality risk shock of 56/100 000=0.56/1000, which is within the 1/1000 to 1/10 000 range in which overestimation appears modest. 26 Second, the same standard models ignore several salient features of COVID-19—dread, uncertainty, ambiguity and catastrophe—that may double the WTP. 26 This would considerably offset the modest overestimation, suggesting that our approach is likely on balance conservative. Third, WTP for non-marginal risks is significantly understudied, and there are no broadly accepted or well-established empirical estimates of such WTP. 26

Scenario and sensitivity analyses

We perform one-way scenario analyses, using lower and upper bound estimates of infections in place of mean estimates, using confirmed deaths in place of under-reporting adjusted deaths and using 0% and 6% discount rates.

We perform a probabilistic sensitivity analysis (PSA), taking 1000 draws from a multivariate normal distribution constructed using the model coefficients and variance-covariance matrix. For each draw, we calculate the VoCV. We estimate a 95% CI from these 1000 VoCVs.

As shown in table 1 , on average, fatal infections incur vastly larger QALY losses (2.74) than non-fatal infections (0.0074), and indirect costs per non-fatal infection (US$489.33) are an order of magnitude larger than the direct cost per fatal or non-fatal infection (US$46.13).

We present the results of the base-case regression models in table 2 .

Base-case regression results

Vaccination coefficients generally have the correct sign (negative in the infections and deaths regressions, and positive in the GDP regressions) and are statistically significant. We provide our interpretation of the coefficients as infection and mortality risk reductions and percentage-point GDP gains in online supplemental appendix S3 .

We summarise our central findings in tables 3 and 4 . Table 3 presents pandemic outcomes (columns A, B), outcomes in the no vaccination counterfactuals (columns C, D) and attributes the differences to CV (columns E, F). Table 4 converts the aggregated values in table 3 (colums E, F) to per capita and per dose values.

Global VoCV by value component and focal brand (2019 US$)

Global value of COVID-19 vaccination (2019 US$) per capita and per dose by value component

Base-case population-level results

The similarity across (A) and (B) in table 3 indicates that our regressions provide reasonable predictions of infections, deaths and GDP. Table 3 also shows that the pandemic caused US$5.6 trillion in GDP losses, that the absence of all CV and Pfizer-BioNTech vaccines would have raised this loss to US$8.2 and US$6.5 trillion, respectively. Therefore, we estimate that CV as a whole and Pfizer-BioNTech vaccines specifically produced GDP benefits of US$2.6 trillion and US$877.8 billion, respectively.

Our results suggest that the pandemic caused 5.2 billion infections (symptomatic and asymptomatic), that the absence of all CV and Pfizer-BioNTech vaccines would have raised this to 6.9 and 5.5 billion infections, respectively and that therefore CV as a whole and Pfizer-BioNTech vaccines in particular, averted 1.7 billion and 270.7 million infections, respectively. The pandemic caused 15.4 million deaths, the absence of all CV and Pfizer-BioNTech vaccines would have raised this to 19.4 and 16.5 million deaths, respectively, and therefore CV as a whole and Pfizer-BioNTech vaccines in particular averted 4.1 and 1.1 million deaths, respectively.

Attributing differences in predicted and counterfactual outcomes to vaccines in this manner, we find that CV (Pfizer-BioNTech alone) averted 54.7 (11.8) million QALYs whose monetary value is US$2.1 trillion (US$873.3 billion), US$62.6 (US$27.8) billion in direct costs and US$340.9 (US$128.9) billion in indirect costs.

Combining GDP losses, monetised QALY losses and direct and indirect costs, the pandemic caused a total loss of US$10.2 trillion, the absence of all CV and Pfizer-BioNTech vaccines would have raised this to US$15.4 and US$12.2 trillion, respectively, and therefore CV as a whole and Pfizer-BioNTech vaccine specifically produced US$5.2 and US$1.9 trillion in value, respectively.

Considering the postvaccination period (2021Q1–2021Q4), we estimate the pandemic burden to be US$4.98 trillion, which would have been US$10.13 trillion without all CV or US$6.89 trillion without Pfizer-BioNTech specifically. Thus, once the vaccine rollout began, all CV reduced the burden of the pandemic by 50.9% ((US$10.13 trillion−US$4.98 trillion)/US$10.13 trillion) and Pfizer-BioNTech specifically reduced it by 27.7% ((US$6.89 trillion–US$4.98 trillion)/US$6.89 trillion).

Recall that we value QALYs conservatively using full income, implying that our estimated share of health in total burdens and benefits is likewise conservative. Subject to that caveat, we find that GDP losses constitute at least half (54%) the total pandemic burden ( table 3 ), suggesting pandemics are as much a threat to the global economy as they are to global health, and that addressing pandemics is as much an economic imperative as a health imperative. Furthermore, the GDP-related value of vaccination is at least as large as the value of its impact on global population health as measured by monetised QALY gains (51% vs 41% for all CV and 46% vs 46% for Pfizer-BioNTech vaccination). We find that the broader economic value of vaccination (reflecting its impact on global GDP and indirect costs) exceeds its health system-related values (reflecting its impact on global population health and direct costs): economic values comprise 51.2%+6.6%=57.8% of the VoCV while health-related values comprise 40.9%+1.2%=42.1% of its value; economic values comprise 46.0%+6.8%=52.8% of the value of Pfizer-BioNTech vaccination while health-relative values comprise 45.8%+1.5%=47.3%.

Per capita and per dose results

On average, each person in our sample receives US$710 in value from CV and US$269 in value from Pfizer-BioNTech. On average, each dose of CV and Pfizer-BioNTech produces US$741 and US$1640 in value, respectively. However, these values can vary widely by country. For example, the 25th percentile VoCV per dose for all vaccines (Pfizer-BioNTech) is US$93.00 (US$252.16) and the 75th percentile is US$965.92 (US$2482.13) with a few major outliers ( figure 1 ).

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Country-specific broad value per dose for all COVID-19 vaccination (top) and Pfizer-BioNTech specifically.

Ratios of our per capita and per dose values yield a pooled estimate of doses per capita: US$710.06/US$741.16=0.96 and US$269.23/US$1640.28=0.16 doses per capita for CV and Pfizer-BioNTech, respectively. Fewer doses per capita explains why Pfizer-BioNTech has greater value per dose but lesser value per capita.

Benefit-cost ratio

In the USA, the average cost per original monovalent booster dose was US$20.69, and administration costs were between US$25 and US$40 per dose, 27 yielding a per dose cost estimate inclusive of administration costs of US$20.69+(US$25+US$40)/2=US$53.19. We treat this as a simple upper-bound estimate of the cost per dose. Given the per dose benefits, the benefit-cost ratio for CV in general is US$741.16/US$53.19=13.93 and for Pfizer-BioNTech vaccination is US$1640.28/US$53.19=30.84. Thus, vaccination benefits are an order of magnitude larger than costs. Our calculations suggest that macroeconomic benefits alone rationalise CV’s costs multiple times over 4 : macroeconomic benefits per dose are US$347.45/US$53.19=6.5 and US$611.94/US$53.19=11.5 times per dose costs for CV and Pfizer-BioNTech, respectively.

Sensitivity analyses

Our PSA suggests that 95% confidence intervals are given by VoCVs 21% above and below our main estimates ( table 5 ). Using IHME’s lower bound of infections reduces the VoCV by 20.6% for all vaccines and 20.1% for Pfizer-BioNTech. Using the upper bound increases the VoCV by 21.8% and 22.3%, respectively. Using IHME’s unscaled deaths variable reduces the VoCV by 18.2% and 20.7%, respectively. Reducing the annual discount rate to 0% increases the VoCV by 13.3% for all vaccines and 14.4% for Pfizer-BioNTech. Increasing the annual discount rate to 6% decreases it 7.2% and 7.9%, respectively. In summary, varying values for key parameters result in estimates that are about 10%–20% above or below the base-case estimates.

Total Value of vaccination sensitivity analysis results (2019 US$)

Vaccine effectiveness

We estimate vaccine effectiveness against infections and deaths to be 36.5% and 30.6%, respectively, for CV, and 49.3% and 63.2%, respectively, for Pfizer-BioNTech. Thus, Pfizer-BioNTech effectiveness against infections and deaths is at least 50% and 100% higher, respectively, than that of CVs on average (see online supplemental appendix S5.1 for details).

This study uses a regression-based approach to estimate the global health and macroeconomic impact of CV over the first 2 years of the pandemic. Combining GDP losses, monetised QALY losses and direct and indirect costs, we find that the pandemic caused an economic loss of US$10.2 trillion. The absence of all CV and Pfizer-BioNTech vaccines would have raised this loss to US$15.4 and US$12.2 trillion, respectively. Therefore, CV as a whole, and Pfizer-BioNTech vaccines in particular, generated values of US$5.2 and US$1.9 trillion, respectively.

Table 6 compares our results with those of other studies.

Comparisons of our results with those from other selected studies

Our estimates of averted infections are higher than those found in the literature (Di Fusco et al 20 ; Yang et al 5 ); our averted deaths from vaccination are similar to those of other studies (Di Fusco et al 20 ; Steele et al 28 ; Yang et al 5 ) and fall considerably below the least conservative estimates (Watson et al 3 ); our estimates of the GDP loss from the pandemic are similar to those of The Economist 29 and Cutler and Summers, 30 but smaller than less conservative estimates of Walmsley et al 31 ; our monetised QALY losses during the pandemic is smaller than the most comparable estimates we found for the globe 32 and for the USA 30 ; our VoCV is similar to that of Kirson et al 33 for the USA, but much higher than that of Sandmann et al 34 for the UK.

Substantial differences between our study and other studies are likely due to methods (regression vs models, see section S2.1) and data input differences. For example, IHME data on infections (symptomatic and asymptomatic) 35 used in this study far exceed Our World in Data estimates of confirmed cases. 36 While estimates in table 6 vary widely across studies, the literature reveals that the COVID-19 pandemic has imposed profound health and economic burdens. Our study contributes to this literature by estimating the broad loss of the pandemic and the broad benefit of CV.

The following are limitations of our study. Like all regression analyses, our predicted values may underestimate or overestimate observed values for individual country-quarter observations even if such deviations wash out in aggregate, thereby making country-specific projections less reliable than global projections. We also have data limitations: some countries only have annual as opposed to quarterly GDP data; infections, deaths and other important quantities are not age-disaggregated; COVID-19 health utility impacts are inferred from proxy conditions; some variables are missing for some countries, which require extrapolation from other countries. We do not measure potentially important value elements, such as fiscal effects, mental health, education, community transmission reduction and caregiver burdens, which makes our VoCV estimates conservative. We do not address the relationship between COVID-19 and comorbidities. We assume long COVID only affects QALY losses in severe and critical infections, while there is evidence that it could also affect patients with mild infections. 18 We ignore the equity issues raised by cross-country differences in WTP resulting from global inequality in per capita gross domestic product. We are unable to address concerns regarding Nickell bias. And we use formulas derived for marginal risks and do not more fully address the impact on WTP of non-marginal risks, dread, uncertainty, anxiety and catastrophe.

There are many possible extensions of our study. First, a more rigorous treatment of VfM calculations could include country-specific and manufacturer-specific prices. Second, we adopt a CBA approach where every dollar’s worth of benefit has equal value. This implies that when we value QALYs using full income, a rich country’s QALY counts more than a poor country’s QALY (because of the former’s higher full income), and that US$1 of GDP gain counts equally in rich and poor countries, even if such GDP gain may produce larger well-being gains in poor countries given diminishing marginal utility of income. Therefore, future extensions could use more equity-sensitive value frameworks like social welfare functions. Other extensions include the VoCV in controlling variant emergence, the global health and economic costs of CV-related vaccine hesitancy and optimal spending on pandemic preparedness.

We find CV impacts to be empirically large, and though we do not fully investigate CV costs, simple approximations suggest significant VfM. Importantly, we find that the broader economic costs of the pandemic exceed its narrower health-related costs, that the broader economic benefits of CV exceed its narrower health-related benefits and that the macroeconomic benefits alone justify the costs of CV many times over.

These results have implications for perhaps the most important policy debate in vaccine evaluation: whether to value vaccines narrowly (ie, focusing only on health-centric outcomes) or broadly (ie, incorporating broader socio-economic and other outcomes). Our results suggest narrow approaches ignore perhaps half the harm of pandemics, and perhaps half the benefits of pandemic vaccines. This suggests that achieving optimal societal and global investment in CV requires valuing it broadly.

Conclusions

We find that the COVID-19 pandemic had a profound impact on global health as measured by QALY losses, and on the global economy as measured by GDP losses. Our study finds that GDP losses constitute at least half the total health and economic burden of the COVID-19 pandemic, suggesting that addressing pandemics is as much an economic imperative as a health imperative. In per capita terms, the broad VoCV confers considerable value to each person in the world on average. Our benefit-cost ratios suggest that vaccination benefits are an order of magnitude larger than vaccination costs, indicating that CV offers significant value for money. The GDP-related VoCV is at least as large as its QALY-related value, suggesting that the broader economic VoCV exceeds its narrow health-related value, and macroeconomic benefits alone rationalise CV’s costs multiple times over. Perhaps the most important policy debate in vaccine evaluation is whether to value vaccines narrowly (focusing only on health impacts) or broadly (including broader socio-economic impacts). Our findings suggest that narrow approaches neglect at least half the harm of pandemics and at least half the benefits of pandemic vaccines, and that broad valuation is required to achieve optimal societal investment in CV. Our results highlight the profound health and economic impacts of the COVID-19 pandemic and of CV, and the importance of valuing CV from a societal perspective that takes into account economic impacts.

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• Chaudhuri E ,
• Watson OJ ,
• Barnsley G ,
• Toor J , et al
• Brassel S ,
• Oliver E , et al
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• Institute for Health Metrics and Evaluation (IHME)
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• United Nations Population Division, Department of Economic and Social Affairs, World Population Prospects
• Janssen B ,
• Acosta E , COVerAGE-DB project team
• World Health Organization
• Robinson LA ,
• Hastie CE ,
• McAuley A , et al
• Di Fusco M ,
• Marczell K ,
• Deger KA , et al
• International Labour Organization
• ILOSTAT Wages and Working Time Statistics (COND)
• Murphy KM ,
• Steele MK ,
• Couture A ,
• Reed C , et al
• The Economist
• Cutler DM ,
• Walmsley T ,
• John R , et al
• Fann JC-Y ,
• Yang M-C , et al
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• Our World in Data

Handling editor Lei Si

Contributors JPS, MDF, JY and DEB conceptualised the study. MDF and DEB acquired the study’s funding. JPS, DB, JSK and DEB decided on the analytical methodology. MDF provided resources for the study, and JPS, DB, JSK, MDF, MHK and JY contributed to the investigative process. DB and JSK collected, managed and analysed data using their own code. JPS, DB and JSK validated this analysis. JPS, DB, JSK, MDF and DEB participated in project administration roles, and JPS, MDF, MHK, JLN, JY and DEB supervised the study. JPS, DB and JSK wrote the original manuscript, with DB and JSK providing visualisations. All authors reviewed, edited and approved the final draft. All authors have approved the decision to submit for publication and meet the ICMJE criteria for authorship. JPS is the guarantor.

Funding This study was funded by Pfizer Inc. and editorial support was provided by Data for Decisions.

Map disclaimer The inclusion of any map (including the depiction of any boundaries therein), or of any geographic or locational reference, does not imply the expression of any opinion whatsoever on the part of BMJ concerning the legal status of any country, territory, jurisdiction or area or of its authorities. Any such expression remains solely that of the relevant source and is not endorsed by BMJ. Maps are provided without any warranty of any kind, either express or implied.

Competing interests JPS, DB and JSK are employees of Data for Decisions (DfD) and worked on this study in that capacity. JPS and DB have worked on other studies funded by grants from Pfizer Inc. to DfD. DEB is an external consultant to DfD and in that capacity has worked on this and other studies funded by grants from Pfizer Inc. to DfD. JPS and DEB in their personal capacities have received compensation from Pfizer Inc. for providing consulting services and for speaking and participating in meetings and advisory boards. MDF, MK, JLN and JY are employees of Pfizer Inc. and each held Pfizer stock or stock options at the time of the study. Pfizer Inc. employs MDF, MK, JLN and JY, but otherwise played no role in study design, data collection and analysis, decision to publish or preparation of the article.

Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

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Economic Synopses

Fiscal policy and covid-19: insights from a quantitative model.

The ongoing COVID-19 outbreak and subsequent infection-control actions have disrupted economic activity across the globe. In response, fiscal authorities are designing and implementing stabilization packages to mitigate the effects of rising unemployment and business closures. 1  

In this essay, I describe the analysis in a recent working paper (Faria-e-Castro, 2020) that uses a macroeconomic model to think about the effects of some of these fiscal policies in the event of a pandemic. The model is used to evaluate the effects of five types of policies on household income and consumption. I find that increases in unemployment insurance (UI) most effectively mitigate the effect of a pandemic on household income. 

My model features two types of households: (i) worker-borrowers who earn income from employment and (ii) owner-savers who are wealthier agents who own firms and banks and receive profits from those institutions. Workers can either be unemployed, in which case they receive UI, or employed and work in one of two sectors. The first sector is a "quarantine" sector that is susceptible to disruption due to a pandemic. One can think of these jobs as involving close (or actual) physical contact with other people and hence creating a greater chance for workers and customers of contracting an infectious disease (e.g., restaurants or air travel). The other sector is a non-services sector where activity is not directly affected by the outbreak (but there may be indirect effects). 

The Outbreak

In the model, an outbreak of disease shuts down the services sector. Households, afraid of being infected, stop consuming services that involve close physical contact with other people. 2 The shock increases the unemployment rate by 20 percent, in line with some projections of current conditions, with most job loss coming from the services sector. 3 The impact of these losses spills over to the rest of the economy: As services sector workers lose their jobs and income, they have to reduce their consumption of non-services. This, in turn, reduces employment in the other sector. 

Policy Responses

Because the reduction in the demand for services and subsequent slowdown is a consequence of policies—such as social distancing—designed to contain the outbreak, it is not obvious that fiscal policy should be used to offset the effects of this shock on GDP. That is, the government may not want to buy goods or deliver tax cuts to stimulate economic activity if it wants people to stay home precisely to prevent infection. Policy can, however, help contain spillovers to other sectors and stabilize income for affected workers. I consider five types of policies: (i) government purchases, (ii) income tax cuts, (iii) increases in UI, (iv) unconditional transfers, 4 and (v) liquidity assistance to distressed firms. 

The primary negative economic effect of the pandemic is that it sharply raises unemployment. Policies that transfer resources directly to borrowers—policies (ii) through (iv)—are more effective than the others; UI is the best targeted policy of policies (ii) through (iv). Higher UI generates the largest benefit in household income net of transfers per dollar spent by the government. Income tax cuts benefit workers who remain employed, but there are fewer of these workers after the shock. Unconditional transfers benefit everyone, which means that resources are spent on households that do not necessarily need these transfers (i.e., savers). Transfers to the unemployed are, therefore, the best measure because they directly target people who have lost their jobs from the outbreak. Liquidity assistance to affected firms is less effective at stabilizing income (per dollar spent) but is very effective at stabilizing employment in the affected sector. Therefore, governments should consider this instrument to preserve employment. 

One important caveat is that I consider only the effects of conventional stabilization policy and do not explicitly model the effects of government investment in the public health response. In the words of Austan Goolsbee, professor of economics at the University of Chicago and former Chair of the Council of Economic Advisers, "…the difference of virus economics from regular business cycle economics is this paradox that the best thing you can do for the economy has nothing to do with the economy." 5

1 At the time of writing, the U.S. Congress is preparing to pass a $2 trillion aid package.

2 A recent paper by Eichenbaum, Rebelo, and Trabandt (2020) embeds an epidemiology model into a macroeconomic model. This allows the authors to explicitly study the effects of public health interventions, such as social distancing and the introduction of a vaccine.

3 See, for example, this blog post by Federal Reserve Bank of St. Louis President James Bullard: https://www.stlouisfed.org/on-the-economy/2020/march/bullard-expected-us-macroeconomic-performance-pandemic-adjustment-period .

4 For example, sending a check to every person in the United States.

5 National Public Radio. "Medicine for the Economy." Planet Money (podcast), Episode 979. 

Eichenbaum, M.S.; Rebelo, S. and Trabandt, M. "The Macroeconomics of Epidemics." NBER Working Paper No. 26882, National Bureau of Economic Research, 2020.

Faria-e-Castro, M. "Fiscal Policy during a Pandemic." Working Paper 2020-006, Federal Reserve Bank of St. Louis, 2020; https://research.stlouisfed.org/wp/more/2020-006 .

© 2020, Federal Reserve Bank of St. Louis. The views expressed are those of the author(s) and do not necessarily reflect official positions of the Federal Reserve Bank of St. Louis or the Federal Reserve System.

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CONCEPTUAL ANALYSIS article

Covid-19, economic impact, mental health, and coping behaviors: a conceptual framework and future research directions.

• 1 School of Business Administration, Jimei University, Xiamen, China
• 2 Durham University Business School, Durham University, Durham, United Kingdom

The COVID-19 pandemic has caused serious economic and social consequences. Recent research shows that the pandemic has not only caused a physical health crisis but also caused many psychological and mental crises. Based on the contemporary cognitive-behavioral models, this article offers a conceptual analysis of how the pandemic affects individual mental health and coping behaviors from the perspective of individual economic status, individual context, and social context. The analysis shows that (1) the pandemic has led to increased economic uncertainty, increased unemployment and underemployment pressure, increased income uncertainty, and different degrees of employment pressure and economic difficulties; (2) these difficulties have stimulated different levels of mental health problems, ranging from perceived insecurity (environmental, food safety, etc.), worry, fear, to stress, anxiety, depression, etc., and the mental health deterioration varies across different groups, with the symptoms of psychological distress are more obvious among disadvantageous groups; and (3) mental health problems have caused behavior changes, and various stress behaviors such as protective behaviors and resistive behaviors. Future research directions are suggested.

Introduction

The current COVID-19 pandemic is still ongoing, and it is concerning that we still do not know how long it will last and what long-term effects it will have. Despite the successful development of vaccines, the medical capacity to completely treat this disease is still limited. Non-pharmaceutical interventions (NPIs), such as increasing handwashing, reducing physical contact, wearing masks in public places, maintaining social distance, quarantine, and isolation, are still the main strategies for handling this pandemic ( Van Bavel et al., 2020 ; Gössling et al., 2021 ). The social and economic consequences of the pandemic are devastating: almost half of the global workforce is at risk of losing their livelihoods, tens of millions are at risk of falling into extreme poverty, and millions of companies are facing existential threat ( Alauddin et al., 2021 ). In addition to the pandemic itself, the economic impact of the crisis brings heavy psychological stress to individuals, causing mental health problems, and may trigger long-lasting behavior changes. Other pandemic-related factors may also cause psychological distress, including mandatory use of face masks ( Wang et al., 2020a ), lockdowns ( Le et al., 2020 ), lack of access to medical services ( Hao et al., 2020 ; Tee et al., 2021 ), dissatisfaction with health information ( Tee et al., 2021 ), perceived discrimination ( Wang et al., 2021 ), and stress about returning to work ( Tan et al., 2020 ).

Prior behavioral science research focuses on perceived threats, stress, and coping ( Van Bavel et al., 2020 ). In the early stages of the pandemic, the physical health risks associated with the COVID-19 pandemic have received extensive attention from the academic community ( Mehta et al., 2020 ; Odayar et al., 2020 ), and there is growing research attention on the risks of mental health associated with the spread of the pandemic ( Auerbach and Miller, 2020 ; Xiong et al., 2020 ; Wang et al., 2020a ). The focal attention since the outbreak of the pandemic has been the psychological distress as a result of the pandemic itself ( Jungmann and Witthöft, 2020 ) or the adverse economic impact of the pandemic ( Bierman et al., 2021 ). However, it is still unclear how the pandemic control measures cause mental health problems through economic impact ( Murakami et al., 2021 ). Many scholars believe that the measures taken during the pandemic may cause people to suffer more economic losses and fall into economic difficulties, thereby causing serious mental health problems ( Timming et al., 2021 ), while some scholars believe that although the pandemic may cause huge economic losses, people’s mental health status has not decreased ( Murakami et al., 2021 ). Therefore, it is necessary to conduct a conceptual analysis of the economic impact of the pandemic and mental health by synthesizing the relevant findings in existing literature ( Ali et al., 2021 ).

This study aims to develop a conceptual framework linking the pandemic to individual economic problems, unemployment, mental health, and behavior change. The main research questions are (1) what kind of individual economic stress has the pandemic caused? 2) what mental health problems have this individual economic stress caused, and to what extent? 3) does the mental health problem vary by different groups or individuals? 4) what kind of behaviors may be caused by the deterioration of mental health?

Theoretical Background

According to the World Health Organization, mental health includes subjective well-being, self-efficacy, autonomy, ability, intergenerational dependence, intellectual or emotional potential. When there is a problem with mental health, there will be a decline in subjective well-being and various negative emotions (such as fear, nervousness, loneliness, and despair), and symptoms such as mental distress (such as anxiety, depression, and stress) will appear ( Hossain et al., 2020 ). Mental health issues are considered as public health problems that are often affected by factors related to occupation, employment opportunities, and economic stress ( Ali et al., 2021 ). Many scholars have examined the impact of economic poverty and unemployment on mental health ( Jin et al., 1997 ). Disaster mental health research also shows that people generally suffer emotional or psychological distress following a disaster ( Pfefferbaum and North, 2020 ).

Mental Health Amid the Pandemic

The World Health Organization (2020) proposes mental health indicators for the COVID-19 pandemic: painful symptoms and perceived danger. Mental distress is a short-term state of emotional distress, often driven by limited resources to manage stressors and daily life needs ( Patel and Rietveld, 2020 ). The pandemic can become a major source of stress, especially in chronic anxiety and financial stress ( Van Bavel et al., 2020 ). Mental distress has become the focus of research on mental health problems amid a large-scale crisis ( Cheng et al., 2004 ; Wang et al., 2020b ). Preliminary evidence suggests that symptoms of anxiety, depression, and self-reported stress are common psychological responses to the pandemic ( Rajkumar, 2020 ). Salari et al. (2020) reported that the prevalence of stress was between 29.6 and 33.7%. In addition to mental distress, the pandemic and corresponding interventions or preventive measures may make people feel insecure, fearful, uncertain, lonely, or isolated ( Auerbach and Miller, 2020 ), which exacerbates the psychological distress ( Pfefferbaum and North, 2020 ).

Public Health Interventions

Non-medical interventions or control measures during the pandemic may weaken social relationships that can help people to regulate emotions, cope with stress, and maintain adaptability ( Rimé, 2009 ; Jetten et al., 2017 ; Williams et al., 2018 ), exacerbate feelings of loneliness and isolation ( Hawkley and Cacioppo, 2010 ; Holmes et al., 2020 ), and become a risk factor for more serious mental health disorders ( Cacioppo et al., 2006 ). The stresses experienced during the pandemic, especially the economic stress, may cause difficulties in interpersonal relationships, destroy psychological resources, and make normal interactions difficult ( Karney, 2020 ). The impact of the pandemic interventions on mental health vary across different (employment) groups.

Contemporary Cognitive-Behavioral Models and Mental Health

The contemporary cognitive-behavioral models ( Taylor and Asmundson, 2004 ; Asmundson et al., 2010 ) explore the key role of traits, triggering events, cognition, and behaviors in the development and maintenance of health anxiety, which can be used to analyze mental health problems during the pandemic period. Jungmann and Witthöft (2020) believe that during the pandemic, idiosyncratic health anxiety regulates the relationship between excessive online information search and viral anxiety, and adaptive emotions serve as a buffer between the two. The “Role Tension” model explores mental health issues from the perspective of role conflicts. It believes that individuals with multiple social roles may experience role conflicts, resulting in stress and adverse mental health ( Oomens et al., 2007 ). The broader behavioral immune system theory ( McKay et al., 2020 ) explores the specific path of disease anxiety, and believes that disgust tendency and sensitivity, and emotional response are all part of the behavioral immune system.

Conceptual Framework

Figure 1 summarizes the themes from recent research findings in a conceptual framework.

Figure 1 . Conceptual framework.

The Mechanism of COVID-19’s Impact on Mental Health

In addition to the pandemic itself, the lockdown, quarantine, or self-isolation policies that aim at fighting the pandemic, the involuntary underemployment or unemployment have led to individuals’ economic difficulties and mental health problems of varying degrees for many people. The economic impact on individuals seems to further exuberate the suffering from the pandemic ( Bierman et al., 2021 ).

Employment Uncertainty

Employment problems caused by the pandemic include involuntary unemployment ( Piltch-Loeb et al., 2021 ), involuntary underemployment ( Pierce et al., 2020 ; Ferry et al., 2021 ), employment uncertainty and insecurity ( Wilson et al., 2020 ), job instability or inability to work ( Sirviö et al., 2012 ), and others. Studies have shown that involuntary underemployment and/or unemployment are related to poor mental health ( Dooley et al., 2000 ; Pharr et al., 2012 ), especially those who are unemployed during economic crises or recessions ( Uutela, 2010 ; Drydakis, 2015 ; Fiori et al., 2016 ). It is reported that crisis-related unemployment has led to a sharp rise in psychological disorders in low- and middle-income countries ( Uutela, 2010 ). Despite the government measures to limit the economic impact, the involuntary underemployment or unemployment caused by the epidemic is prominent.

The impact of the pandemic on mental health varies ( Pierce et al., 2020 ). Long-term unemployed people are most vulnerable to adverse mental health effects ( Pierce et al., 2020 ), and those who were employed and retired in the months before the pandemic experience worse than expected mental health conditions ( Ferry et al., 2021 ). Reduced work has different effects on the mental health of different groups. People who are in a poor health condition or self-isolated, and those who have their work reduced due to care responsibilities, have a higher degree of psychological distress ( Ferry et al., 2021 ). The higher the work insecurity caused by the pandemic, the more severe the symptoms of depression ( Wilson et al., 2020 ). As the pandemic continues, the fear of the pandemic itself has not increased mental health problems, but the deterioration of the labor market and the increase in the unemployment rate may intensify people’s fear of unemployment, thereby increasing the degree of mental distress ( Timming et al., 2021 ). In addition, due to the lockdown, people’s work routines can be broken. Remote work, interruption of work activities due to lockdown measures, or increased workload due to the needs of the pandemic may also become factors affecting mental health ( Rossi et al., 2020 ).

Economic Uncertainty

The analysis of individual economic distress during the pandemic usually focus on short-term economic distress or economic stress, such as personal income uncertainty, personal financial difficulties, salary reduction and other economic (income) losses ( Piltch-Loeb et al., 2021 ), as well as the expected long-term financial impact, such as depletion of savings and/or retirement funds ( Piltch-Loeb et al., 2021 ).

There are two possible ways in which economic distress mediates the impact of the pandemic on psychological distress. One is the economic hardship or economic threat triggered by the pandemic itself. Individual economic loss, economic hardship, or economic threat was significantly associated with mental health ( Ali et al., 2021 ). The pandemic has led to increased risks of depression, anxiety, stress, despair ( Pettinicchio et al., 2021 ), insomnia ( Hossain and Ali, 2021 ), and other common mental health problems. The negative relationship between economic distress and mental health may be a cumulative process. As exposure to distress extends, the average level of individual sufferings increases ( Bierman et al., 2021 ). At the later stage in the pandemic, economic-related anxiety may be a major predictor of psychological distress ( Timming et al., 2021 ).

Second, the unemployment and employment transition triggered by the pandemic affects the financial situation, which in turn affects psychological distress ( Thomas et al., 2007 ). The economic recession triggered by the pandemic and the increase in economic uncertainty has led to business bankruptcy or downsizing, increased involuntary underemployment or unemployment, increased uncertainty in personal income, and increased likelihood of individuals or families experiencing financial difficulties and economic pressure, consequently triggering large scale mental health problems ( Kimhi et al., 2020 ).

Coping Behaviors

The direct consequence of the pandemic’s impact on mental health is the change of personal behavior and habits. Studies on past epidemics and pandemics have shown that negative emotions such as anxiety and stress during the epidemic may lead to different behavior patterns.

Positive Defensive Behavior

Humans are born with a set of defense systems against ecological threats ( Mobbs et al., 2015 ). The main emotional response during a pandemic is fear. When people feel capable of responding to the threat of the pandemic, fear can cause individual behavior changes, but if people feel powerless, a defensive response occurs. Positive defensive behavior includes protective, defensive (avoidance), and substitution behaviors.

Protective Behavior

Mental health problems, such as high anxiety, during the epidemic may produce protective behaviors or compensatory behaviors ( Wheaton et al., 2012 ), including washing hands frequently, wearing masks, increasing cleaning of items, social distancing, and other restrictions. Protective behavior can be voluntary ( Rubin et al., 2009 ) or compliant with government regulation ( Fragkaki et al., 2021 ). In addition, people actively engage in physical activities to cope with stress and anxiety ( Ai et al., 2021 ).

Defensive (Avoidance) Behavior

Such behavior includes avoiding touching public goods, strangers, keeping a distance from “patients,” avoiding densely populated places and public transport ( Rubin et al., 2009 ), or even resigning from jobs that are perceived to be dangerous ( Yin and Ni, 2021 ).

Substitution Behavior (E.g. Using Technologies)

Service provision based on digital and artificial intelligence technology has become a possible solution to replace human service provision ( Nayal et al., 2021 ), triggering changes in consumer behavior by using technology-mediated services (such as robots) to replace manual services ( Kim et al., 2021 ).

Negative Resistance or Disruptive Behaviors

Resisting behavior.

People with low economic status are more likely to be vigilant about the public health information they receive are less willing to take recommended safety measures and may be more susceptible to “fake news” ( Van Bavel et al., 2020 ). Misunderstandings and worries about the pandemic may also cause the public to refuse to comply with preventive measures ( Prati et al., 2011 ). When people are less worried about the pandemic, they are less likely to engage in hygiene behaviors (such as washing hands), comply with social distance regulations, or be vaccinated if vaccines are available ( Taylor, 2019 ). People also resist or refuse to participate in protective actions proposed by the government when they maintain an optimistic bias about the consequences of the outbreak ( Fragkaki et al., 2021 ).

Panic Consumption Behavior

During the pandemic, a large number of customers stocked up on daily necessities to avoid the expected future threat due to uncertainty and panic arising from perceived scarcity, resulting in panic buying ( Omar et al., 2021 ). People flooded hospitals and clinics unnecessarily when they misunderstood their minor illness as a sign of a serious infection ( Asmundson and Taylor, 2020a , 2020b ).

Negative Idleness or Sabotage Behavior

Anxiety is an important driving force of behavior ( Taylor, 2019 ). Overly anxious individuals may engage in socially disruptive behaviors, especially for frontline service workers who are directly exposed to the outbreak (e.g., hotel staff), and may result in negative idleness (e.g., tardiness and absenteeism) or even disruptive behaviors or sabotage ( Karatepe et al., 2021 ).

Excessive Stress Behavior

Anxiety and depression caused by the economic difficulties and employment difficulties caused by the crisis may result in various excessive stress behaviors, such as alcoholism ( Ahmed et al., 2020 ) drug abuse ( Nagelhout et al., 2017 ), even suicide ( Milner et al., 2013 ), etc.

The Boundary Conditions

Sociodemographic factors.

The impact of economic or employment difficulties caused by the pandemic on mental health may be related to socio-demographic factors, including age, gender, ethnicity, family size, occupation, and income ( Ferry et al., 2021 ). Age is one factor. Young people are more likely to have a higher level of anxiety and stress due to the pandemic and corresponding intervention measures than the elderly ( Mann et al., 2020 ; Salameh et al., 2020 ; Hu and Qian, 2021 ; Ribeiro et al., 2021 ). Young people with mental health problems are especially likely to experience adverse health, well-being, and employment outcomes with long-term consequences ( Bauer et al., 2021 ). However, there are also arguments that the elderly may have greater financial difficulties due to the increase in medical expenses during the epidemic, which may trigger mental health problems ( Van Bavel et al., 2020 ), and the elderly’s negative health consequences have been long-term ones ( Van Bavel et al., 2020 ).

Gender is another one. Studies have shown that women are more likely to have higher levels of anxiety and stress when faced with possible physical health problems ( Salameh et al., 2020 ; Ferry et al., 2021 ; Ribeiro et al., 2021 ). However, when there is the fear of losing their job and the economic anxiety surrounding this possibility, the psychological distress level is more serious for male than female employees ( Timming et al., 2021 ). The third factor is ethnicity. Black and ethnic minority respondents have a higher level of economic anxiety ( Mann et al., 2020 ). The study by Timming et al. (2021) shows that, compared with non-Hispanic respondents, Hispanic respondents are significantly more anxious about losing their jobs. The fourth factor is family size and the number of children. Respondents from families with no children have lower levels of economic anxiety ( Mann et al., 2020 ). People living non-marital life have higher levels of psychological distress ( Ferry et al., 2021 ).

Occupation is the fifth factor. People working at the emergency and customer-facing services, such as doctors, medical staff, police forces, frontline volunteer organizations, and bankers, have a higher risk of infection and subsequent mental stress ( Shammi et al., 2020 ). The mental health of the unemployed, self-employed, and private professionals is worse than that of government professionals ( Ali et al., 2020 ) for increased income (or economic) uncertainty caused by the pandemic ( Patel and Rietveld, 2020 ) or for self-isolation or social distancing measures ( Auerbach and Miller, 2020 ).

The sixth factor is income status. Most studies show that economic hardship resulting from the pandemic may make those disadvantaged groups (e.g., those living in poverty, low-income families, homeless, and refugees) the most vulnerable to experience the corresponding negative consequences ( Van Bavel et al., 2020 ; Długosz, 2021 ; Hu and Qian, 2021 ). The mental health of people with disabilities and chronic diseases ( Pettinicchio et al., 2021 ), living alone, and socially marginalized people is even more hostile ( Kwong et al., 2020 ). However, some studies have suggested that the pandemic has a greater impact on the mental health of employees from high-income families ( Ferry et al., 2021 ).

Personality Traits and Psychological Conditions

Personality traits and psychological conditions play an important role in the formation of mental health. Fisher et al. (2021) suggested that depressed and anxious psychological states during the epidemic were associated with diminished energy, functional efficiency, optimism, creativity, engagement, and the ability to focus and solve problems, all of which are necessary for social and economic participation. During the pandemic, those with low collective self-esteem, low responsibility, and low openness to experience have higher levels of economic anxiety, as do those with high levels of neuroticism, perceived vulnerability to illness, and attribution from large group activities ( Mann et al., 2020 ). People with mental and physical health conditions may have higher levels of depression and anxiety because they are more likely to be unemployed and are prone to have higher levels of depression and anxiety ( Hao et al., 2020 ; Kwong et al., 2020 ; Jung et al., 2021 ). Extreme loneliness is the main cause of psychological distress ( Mikocka-Walus et al., 2021 ).

Emotional responses are part of the behavioral immune system. McKay et al. (2020) suggested that emotional reactions such as aversive tendencies and sensitivities are moderators of people’s disease sensitivity and anxiety. High perceived risks, especially economic risks, are significantly associated with less positive emotions and more negative emotions, leading to more severe mental health problems ( Han et al., 2021 ). The “optimism bias” may help individuals to avoid negative emotions ( Van Bavel et al., 2020 ); however, it may not be conducive for people to engage in behavior change in response to non-pharmacological interventions while individuals with high levels of anxiety and high perceived severity are more likely to be involved in behavior change ( Fragkaki et al., 2021 ).

External Environment

The complex factors of population density, health care capacity, limited resources and existing poverty, environmental factors, social structure, cultural norms, the number of people already infected, and the rapidly occurring community transmission of COVID-19 virus in a country or region can all contribute to public fears, which may lead to higher levels of mental health problems ( Shammi et al., 2020 ).

Level of Economic Development or Socio-Economic Crisis

People in low- and middle-income countries may have higher levels of stress, anxiety, and depression than those in high- and middle-income countries ( Tee et al., 2021 ). In lower-middle-income countries with socio-economic crises, political instability, dense population and limited resources, the stress and anxiety during the pandemic are high ( Salameh et al., 2020 ). Even in high-income countries such as Canada and the United Kingdom, deterioration in mental health has been reported, and are increasing along with the extension of the pandemic period ( Zajacova et al., 2020 ).

Government Economic Intervention Policies or Welfare Policies

Policies that reduce economic stress (e.g., economic interventions such as emergency response benefits) may alleviate the level of mental health deterioration in the early stages of a pandemic by reducing economic hardship and making people less worried about their economic situation ( Zajacova et al., 2020 ). Vaccine-based interventions help to mitigate the economic impact of the outbreak ( Meltzer et al., 1999 ).

Future Research Directions

Mental health management, monitoring and preventive measures.

For policymakers, health authorities and health care professionals, it is very important to understand the impact of health anxiety on behavior. Future research should investigate the monitoring and preventive measures for different industries or different groups so as to help the government, service providers and employers understand the groups that should be given priority in mental health support ( Ferry et al., 2021 ) and better conduct mental health rehabilitation. More studies are needed to examine the risk assessment of the pandemic, reliable risk communication with risk groups, the establishment of a cross-departmental management task force, and other measures.

Social Protection Measures and Relief Programs

Social protection measures include daily demand provision and social support ( Jung et al., 2021 ) and cash transfer programs ( Bauer et al., 2021 ). Future research should examine how to effectively use social protection measures (or relief plans) to solve the short-term and long-term effects of economic uncertainty caused by large-scale epidemics or economic crises on mental health. First, it is necessary to study how to support individual and family cash transfer programs to support young people’s future life opportunities and break the vicious circle between mental illness and poverty that puts many young people at a disadvantage in socio-economic and mental health ( Bauer et al., 2021 ). Second, it is essential to study the physical and mental health of the most economically disadvantaged during economic downturns ( Holmes et al., 2020 ; Bierman et al., 2021 ), and specialized relief measures that target low-income populations ( Shammi et al., 2020 ). Third, future research should consider both material and social supports in the examination of social protection measures (or relief programs). Fourth, future research attention needs to be paid to employee assistance programs, with a particular focus on mental health support for male employees.

Intervention and Rehabilitation Measures

Interventions to reduce economic uncertainty and economic risks should be a focus of future research from two aspects. Future research can be conducted around three aspects: First, to examine how to cultivate an individual’s adaptive mentality to epidemics. Second, to explore individual resilience and psychological rehabilitation during and after a pandemic crisis ( Hjemdal et al., 2011 ). Third, to explore the use of online interaction for social and mental health support. During the pandemic, providing remote mental health services is very important ( Salameh et al., 2020 ). Future studies should examine online interactions to cultivate empathy and a sense of connection to enhance mental health ( Schroeder et al., 2017 ; Waytz and Gray, 2018 ).

Consequences of Mental Health

There are currently few studies on the behavioral consequences of mental health, and more research is needed to understand the behavioral consequences of mental health caused by the epidemic. For example, the current research mainly focuses on panic buying behavior, and other compensatory behaviors can be added in the future, such as increasing the number of purchased goods, increasing specific food consumption, online shopping, and so on. Another example is to understand how individual factors (including health anxiety) specifically affect people’s behavior in response to the pandemic ( Asmundson and Taylor, 2020b ). In addition, more research is required to examine the impact of the economic impact of the epidemic on the long-term behavior of individuals, especially stressful behaviors such as alcohol abuse, drug abuse, and suicide.

Impact of Macro-Environmental Factors

In different cultural contexts (e.g., collectivism vs. individualism), economic distress and non-interventional measures such as social distancing may have different effects on mental health. From an evolutionary psychology perspective, when a group encounters a collective threat, strict rules may help the collective to coordinate and survive ( Roos et al., 2015 ). In the face of a pandemic, a culture that is accustomed to putting freedom above safety can make community coordination difficult. However, currently there is little comparative research on mental health and behavior changes specifically for different cultures, and it is worthy of further thinking in the future.

Ethnic Group

People of different ethnic groups may have different attitudes and behaviors toward the epidemic. Further research is needed to examine the different responses of different ethnic groups to the epidemic ( Rubin et al., 2009 ). Moreover, ethnic groups may have different degrees of xenophobia due to fear of coronavirus, and more research is needed to understand the relationship between coronavirus phobia and coronavirus-related xenophobia, and the possible role of individual difference variables (e.g., susceptibility to disease) within an ethnic group ( Taylor, 2019 ).

Economic Development

Future research may examine the relationships between economic development and the impact of the pandemic on mental health based on the economic status of different countries, and explore solutions to the severe psychosocial health phenomena that may be caused by socio-economic crises in economically underdeveloped countries amid a large scale crisis.

Relatively little research is focused on how psychological distress caused by the pandemic varies across countries. Future studies can compare and analyze the differences in the level of psychological distress in different countries with different economic conditions. As countries have achieved varying degrees of success in controlling the spread of the COVID-19 virus ( Patel and Rietveld, 2020 ). Future research based on international data can further explore the level of psychological distress in countries where government interventions are relatively successful, in comparison with those countries that are not so successful.

Long-Term Effects

As the COVID-19 pandemic continues to evolve, the sources of psychological distress surrounding the pandemic and the degree of psychological distress may change ( Piltch-Loeb et al., 2021 ). The extant research mainly focuses on the early or short-term psychological impact of the pandemic. Long-term longitudinal research should be added in the future to investigate the sources of psychological and mental distress at different time points ( Magnavita et al., 2020 ). Although a large number of studies have found a positive relationship between the economic uncertainty (or difficulties) and mental health problems, other studies do not degree with the relationship between deteriorating mental health and the level of job insecurity and financial impact ( Kwong et al., 2020 ). Further empirical research is needed to understand the interrelationships among various antecedents and how different factors mediate or moderate the relationship between the pandemic and mental health.

This conceptual analysis article explores two mechanisms (i.e., economic distress and employment distress) that lead to the deterioration of individuals’ mental health. The proposed conceptual framework explains how the COVID-19 pandemic and public health interventions affect people’s mental health, the responding coping behaviors. The extant literature provides evidence supporting the hypothesis that the COVID-19 pandemic and its associated measures increase individual economic uncertainty and employment uncertainty, thereby triggering mental health problems and coping behaviors. The findings of most studies support this mechanism from the onset of the pandemic to the emergence of economic distress and employment distress, to the deterioration of mental health, and then to changes in people’s behaviors. Supportive evidence was found in different countries (e.g., the United States, China, Bangladesh, Italy, etc.) and in different groups (elderly, young, disabled, mentally ill, etc.).

Author Contributions

XL: conceptualization, methodology, and writing – original draft preparation. ZL: conceptualization and writing – reviewing and editing. All authors contributed to the article and approved the submitted version.

This research was supported by the Educational Commission of Fujian Province of China (grant no. JAS20129) and the Science Foundation of Jimei University, China.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: COVID-19 pandemic, economic difficulty, employment difficulty, mental health, coping behavior

Citation: Lu X and Lin Z (2021) COVID-19, Economic Impact, Mental Health, and Coping Behaviors: A Conceptual Framework and Future Research Directions. Front. Psychol . 12:759974. doi: 10.3389/fpsyg.2021.759974

Received: 17 August 2021; Accepted: 22 October 2021; Published: 11 November 2021.

Reviewed by:

Copyright © 2021 Lu and Lin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Zhibin Lin, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

What did the Fed do in response to the COVID-19 crisis?

Subscribe to the hutchins roundup and newsletter, eric milstein and eric milstein research analyst - the hutchins center on fiscal and monetary policy david wessel david wessel director - the hutchins center on fiscal and monetary policy , senior fellow - economic studies.

January 2, 2024

• 18 min read

This piece was originally published in March 2020. It has been updated to reflect ongoing developments. Jeffrey Cheng, Tyler Powell, and David Skidmore contributed to earlier versions.

The coronavirus crisis in the United States—and the associated business closures, event cancellations, and work-from-home policies—triggered a deep economic downturn. The sharp contraction and deep uncertainty about the course of the virus and economy sparked a “dash for cash”—a desire to hold deposits and only the most liquid assets—that disrupted financial markets and threatened to make a dire situation much worse. The Federal Reserve stepped in with a broad array of actions to keep credit flowing to limit the economic damage from the pandemic. These included large purchases of U.S. government and mortgage-backed securities and lending to support households, employers, financial market participants, and state and local governments. “We are deploying these lending powers to an unprecedented extent [and] … will continue to use these powers forcefully, proactively, and aggressively until we are confident that we are solidly on the road to recovery,” Jerome Powell, chair of the Federal Reserve Board of Governors,  said in April 2020 . In that same month, Powell discussed the Fed’s goals during a  webinar  at the Brookings’ Hutchins Center on Fiscal and Monetary Policy. This post summarizes the Fed’s actions though the end of 2021.

HOW DID THE FED SUPPORT THE U.S. ECONOMY AND FINANCIAL MARKETS?

Easing monetary policy.

• Federal funds rate:  The Fed cut its  target for the federal funds rate , the rate banks pay to borrow from each other overnight, by a total of 1.5 percentage points at its meetings on March 3 and March 15, 2020. These cuts lowered the funds rate to a range of 0% to 0.25%. The federal funds rate is a benchmark for other short-term rates, and also affects longer-term rates, so this move was aimed at supporting spending by lowering the cost of borrowing for households and businesses.
• Forward guidance:  Using a tool honed during the Great Recession of 2007-09, the Fed offered forward guidance on the future path of interest rates. Initially, it said that it would keep rates near zero “until it is confident that the economy has weathered recent events and is on track to achieve its maximum employment and price stability goals.” In September 2020 , reflecting the Fed’s new  monetary policy framework , it strengthened that guidance, saying that rates would remain low “until labor market conditions have reached levels consistent with the Committee’s assessments of maximum employment and inflation has risen to 2% and is on track to moderately exceed 2% for some time.” By the end of 2021, inflation was well above the Fed’s 2% target and labor markets were nearing the Fed’s “maximum employment” target. At its December 2021 meeting, the Fed’s policy-making committee, the Federal Open Market Committee (FOMC), signaled that most of its members expected to raise interest rates in three one-quarter percentage point moves in 2022.
• Quantitative easing (QE):  The Fed resumed purchasing massive amounts of debt securities, a key tool it employed during the Great Recession. Responding to the acute dysfunction of the Treasury and mortgage-backed securities (MBS) markets after the outbreak of COVID-19, the Fed’s actions initially aimed to restore smooth functioning to these markets, which play a critical role in the flow of credit to the broader economy as benchmarks and sources of liquidity. On March 15, 2020 , the Fed shifted the objective of QE to supporting the economy. It said that it would buy at least $500 billion in Treasury securities and $200 billion in government-guaranteed mortgage-backed securities over “the coming months.” On March 23, 2020 , it made the purchases open-ended, saying it would buy securities “in the amounts needed to support smooth market functioning and effective transmission of monetary policy to broader financial conditions,” expanding the stated purpose of the bond buying to include bolstering the economy. In June 2020 , the Fed set its rate of purchases to at least $80 billion a month in Treasuries and $40 billion in residential and commercial mortgage-backed securities until further notice. The Fed updated its guidance in December 2020 to indicate it would slow these purchases once the economy had made “substantial further progress” toward the Fed’s goals of maximum employment and price stability. In November 2021 , judging that test had been met, the Fed began tapering its pace of asset purchases by $10 billion in Treasuries and $5 billion in MBS each month. At the subsequent FOMC meeting in December 2021 , the Fed doubled its speed of tapering, reducing its bond purchases by $20 billion in Treasuries and $10 billion in MBS each month.

Supporting Financial Markets

• Lending to securities firms:  Through the  Primary Dealer Credit Facility  (PDCF), a program revived from the global financial crisis, the Fed offered low interest rate loans up to 90 days to 24 large financial institutions known as primary dealers . The dealers provided the Fed with various securities as collateral, including commercial paper and municipal bonds. The goal was to help these dealers continue to play their role in keeping credit markets functioning during a time of stress. Early in the pandemic, institutions and individuals were inclined to avoid risky assets and hoard cash, and dealers encountered barriers to financing the rising inventories of securities they accumulated as they made markets. To re-establish the PDCF, the Fed had to obtain the approval of the Treasury Secretary to invoke its emergency lending authority under  Section 13(3) of the Federal Reserve Act  for the first time since the 2007-09 crisis. The program expired on March 31, 2021.
• Backstopping money market mutual funds:  The Fed also re-launched the crisis-era  Money Market Mutual Fund Liquidity Facility  (MMLF). This facility lent to banks against collateral they purchased from prime money market funds, which invest in Treasury securities and corporate short-term IOUs known as commercial paper. At the onset of COVID-19, investors, questioning the value of the private securities these funds held, withdrew from prime money market funds en masse. To meet these outflows, funds attempted to sell their securities, but market disruptions made it difficult to find buyers for even high-quality and shorter-maturity securities. These attempts to sell the securities only drove prices lower (in a “fire sale”) and closed off markets that businesses rely on to raise funds. In response, the Fed set up the MMLF to “assist money market funds in meeting demands for redemptions by households and other investors, enhancing overall market functioning and credit provision to the broader economy.” The Fed invoked Section 13(3) and obtained permission to administer the program from Treasury, which provided $10 billion from its Exchange Stabilization Fund to cover potential losses. Given limited usage, the MMLF expired on March 31, 2021.
• Repo operations:  The Fed vastly expanded the scope of its repurchase agreement (repo) operations to funnel cash to money markets. The repo market is where firms borrow and lend cash and securities short-term, usually overnight. Since  disruptions in the repo market  can affect the federal funds rate, the Fed’s repo operations made cash available to primary dealers in exchange for Treasury and other government-backed securities. Before coronavirus turmoil hit the market, the Fed was offering $100 billion in overnight repo and $20 billion in two-week repo. Throughout the pandemic, the Fed significantly expanded the program—both in the amounts offered and the length of the loans. In July 2021, the Fed established a permanent Standing Repo Facility to backstop money markets during times of stress.
• Foreign and International Monetary Authorities (FIMA) Repo Facility: Sales of U.S. Treasury securities by foreigners who wanted dollars added to strains in money markets. To ensure foreigners had access to dollar funding without selling Treasuries in the market, the Fed in July 2021 established a  new repo facility called FIMA  that offers dollar funding to the considerable number of foreign central banks that do not have established swap lines with the Fed. The Fed makes overnight dollar loans to these central banks, taking Treasury securities as collateral. The central banks can then lend dollars to their domestic financial institutions.
• International swap lines:  Using another tool that was important during the global financial crisis, the Fed made U.S. dollars available to foreign central banks to improve the liquidity of global dollar funding markets and to help those authorities support their domestic banks who needed to raise dollar funding. In exchange, the Fed received foreign currencies and charged interest on the swaps. For the five central banks that have permanent swap lines with the Fed—Canada, England, the Eurozone, Japan, and Switzerland—the Fed lowered its interest rate and extended the maturity of the swaps. It also provided temporary swap lines to the central banks of Australia, Brazil, Denmark, Mexico, New Zealand, Norway, Singapore, South Korea, and Sweden. In June 2021, the Fed extended these temporary swaps until December 31, 2021 .

Encouraging Banks to Lend

• Direct lending to banks:  The Fed lowered the rate that it charges banks for loans from its  discount window  by 2 percentage points, from 2.25% to 0.25%, lower than during the Great Recession. These loans are typically overnight—meaning that they are taken out at the end of one day and repaid the following morning—but the Fed extended the terms to 90 days. At the discount window, banks pledge a wide variety of collateral (securities, loans, etc.) to the Fed in exchange for cash, so the Fed takes little (or no) risk in making these loans. The cash allows banks to keep functioning, since depositors can continue to withdraw money and the banks can make new loans. However, banks are sometimes  reluctant to borrow from the discount window  because they fear that if word leaks out, markets and others will think they are in trouble. To counter this stigma,  eight big banks agreed to borrow from the discount window in March 2020.
• Temporarily relaxing regulatory requirements : The Fed encouraged banks—both the  largest banks and community banks —to dip into their regulatory capital and liquidity buffers to increase lending during the pandemic. Reforms instituted after the financial crisis require banks to hold additional loss-absorbing capital to prevent future failures and bailouts. However, these reforms also include provisions that allow banks to use their capital buffers to support lending in downturns. The Fed supported this lending through a technical change to its  TLAC (total loss-absorbing capacity) requirement —which includes capital and long-term debt—to gradually phase in restrictions associated with shortfalls in TLAC. (To preserve capital,  big banks also suspended buybacks of their shares .) The Fed also eliminated banks’ reserve requirement—the percent of deposits that banks must hold as reserves to meet cash demand—though this was largely irrelevant because banks held far more than the required reserves. The Fed restricted dividends and share buybacks of bank holding companies throughout the pandemic, but lifted these restrictions effective June 30, 2021, for most firms based on stress test results. These stress tests showed that banks had ample capital to support lending even if the economy performed far weaker than anticipated.

Supporting Corporations and Businesses

• Direct lending to major corporate employers:  In a significant step beyond its crisis-era programs, which focused primarily on financial market functioning, the Fed established two new facilities to support the flow of credit to U.S. corporations on March 23, 2020. The  Primary Market Corporate Credit Facility (PMCCF)  allowed the Fed to lend directly to corporations by buying new bond issues and providing loans. Borrowers could defer interest and principal payments for at least the first six months so that they had cash to pay employees and suppliers (but they could not pay dividends or buy back stock). And, under the new  Secondary Market Corporate Credit Facility (SMCCF) , the Fed could purchase existing corporate bonds as well as exchange-traded funds investing in investment-grade corporate bonds. An orderly secondary market was seen as helping businesses access new credit in the primary market. These facilities allowed “companies access to credit so that they are better able to maintain business operations and capacity during the period of dislocations related to the pandemic,” the Fed said. Initially supporting $100 billion in new financing, the Fed announced on April 9, 2020, that the facilities would be increased to backstop a combined $750 billion of corporate debt. And, as with previous facilities, the Fed invoked Section 13(3) of the Federal Reserve Act and received permission from the U.S. Treasury, which provided $75 billion from its  Exchange Stabilization Fund  to cover potential losses. Late in 2020, after the recovery from the pandemic was under way, and despite the Fed’s misgivings, Treasury Secretary Steven Mnuchin decided that the final bond and loan purchases for the corporate credit facilities would take place no later than December 31, 2020. The Fed objected to the cutoff, preferring to keep the facilities available until there was a firmer assurance that financial conditions would not deteriorate again. The Fed said on June 2, 2021  that it would gradually sell off its $13.7 billion portfolio of corporate bonds, which it completed in December 2021.
• Commercial Paper Funding Facility (CPFF):  Commercial paper is a $1.2 trillion market in which firms issue unsecured short-term debt to finance their day-to-day operations.  Through the CPFF , another reinstated crisis-era program, the Fed bought commercial paper, essentially lending directly to corporations for up to three months at a rate 1 to 2 percentage points higher than  overnight lending rates . “By eliminating much of the risk that eligible issuers will not be able to repay investors by rolling over their maturing commercial paper obligations, this facility should encourage investors to once again engage in term lending in the commercial paper market,” the Fed said. “An improved commercial paper market will enhance the ability of businesses to maintain employment and investment as the nation deals with the coronavirus outbreak.” As with other non-bank lending facilities, the Fed invoked Section 13(3) and received permission from the U.S. Treasury, which put $10 billion into the CPFF to cover any losses. The Commercial Paper Funding Facility  lapsed on March 31, 2021 .
• Supporting loans to small- and mid-sized businesses:  The Fed’s  Main Street Lending Program , announced on April 9, 2020, aimed to support businesses too large for the Small Business Administration’s  Paycheck Protection Program (PPP)  and too small for the Fed’s two corporate credit facilities. The program was subsequently expanded and broadened to include more potential borrowers. Through three facilities—the  New Loans Facility ,  Expanded Loans Facility , and Priority Loans Facility —the Fed was prepared to fund up to $600 billion in five-year loans. Businesses with up to 15,000 employees or up to $5 billion in annual revenue could participate. In June 2020 , the Fed lowered the minimum loan size for New Loans and Priority Loans, increased the maximum for all facilities, and extended the repayment period. As with other facilities, the Fed invoked Section 13(3) and received permission from the U.S. Treasury, which through the CARES Act put $75 billion into the three Main Street Programs to cover losses. Borrowers are subject to restrictions on stock buybacks, dividends, and executive compensation. (See  here  for additional operational details.) Secretary Mnuchin, again over the Fed’s objections, decided that the Main Street facility would  stop taking loan submissions on December 14, 2020, as it was set to make its final purchases by January 8, 2021. The Fed also established a  Paycheck Protection Program Liquidity Facility  that facilitated loans made under the PPP. Banks lending to small businesses could borrow from the facility using PPP loans as collateral. The PPP Liquidity Facility closed on July 30, 2021 . According to a December 2023 Government Accountability Office report : Of the 1,830 loans made through the Main Street Lending Program, 1,175 (or 64%) remained outstanding as of the end of August 2023, the most recent data available at the time of the report. These loans total $11.3 billion. Since required interest payments began in August 2021, most borrowers have been making them on time. However, delinquent payments increased to about 7.6% in August 2023. This, the GAO said, may reflect the effects of increased interest payments as rates on Main Street loans rose from less than 0.2% at the program’s start to 5.33% in August 2023. GAO reported that 610 loans (or about 3%) had been fully repaid, and 45 loans (or about 2.5%) had recorded losses.
• Supporting loans to non-profit institutions:  In July 2020, the Fed expanded the Main Street Lending Program to  non-profits , including hospitals, schools, and social service organizations that were in sound financial condition before the pandemic. Borrowers needed at least 10 employees and endowments of no more than $3 billion, among other eligibility conditions. The loans were for five years, but payment of principal was deferred for the first two years. As with loans to businesses, lenders retained 5% of the loans. This addition to the Main Street program lapsed with the rest of the facility on January 8, 2021.

Supporting Households and Consumers

• Term Asset-Backed Securities Loan Facility  (TALF):  Through this facility, reestablished on March 23, 2020, the Fed supported households, consumers, and small businesses by lending to holders of asset-backed securities collateralized by new loans. These loans included student loans, auto loans, credit card loans, and loans guaranteed by the SBA. In a step beyond the crisis-era program, the Fed expanded eligible collateral to include existing commercial mortgage-backed securities and newly issued collateralized loan obligations of the highest quality. Like the programs supporting corporate lending, the Fed said the TALF would initially support up to $100 billion in new credit. To restart it, the Fed invoked Section 13(3) and received permission from the Treasury, which allocated $10 billion from the Exchange Stabilization Fund to finance the program. Without an extension,  this facility stopped making purchases on December 31, 2020 , at Secretary Mnuchin’s order.

Supporting State and Municipal Borrowing

• Direct lending to state and municipal governments:  During the 2007-09 financial crisis, the Fed resisted backstopping municipal and state borrowing, seeing that as the responsibility of the administration and Congress. But in this crisis, the Fed lent directly to state and local governments through the  Municipal Liquidity Facility , which was created on  April 9, 2020 . The Fed expanded the list of eligible borrowers on  April 27  and  June 3, 2020 . The municipal bond market was under  enormous stress in March 2020 , and state and municipal governments found it increasingly hard to borrow as they battled COVID-19. The Fed’s facility offered loans to U.S. states, including the District of Columbia, counties with at least 500,000 residents, and cities with at least 250,000 residents. Through the program, the Fed made $500 billion available to government entities that had investment-grade credit ratings as of April 8, 2020, in exchange for notes tied to future tax revenues with maturities of less than three years. In June 2020,  Illinois  became the first government entity to tap the facility. Under changes announced that month, the Fed allowed governors in states with cities and counties that did not meet the population threshold to designate up to two localities to participate. Governors were also able to designate two revenue bond issuers—airports, toll facilities, utilities, public transit—to be eligible. The  New York Metropolitan Transportation Authority  (MTA) took advantage of this provision in August, borrowing $451 million from the facility. The Fed invoked Section 13(3) with the approval of the U.S. Treasury, which used the CARES Act to provide $35 billion to cover any potential losses. (See  here  for additional details.) The  Municipal Liquidity Facility stopped purchases on December 31, 2020  when it lost Treasury support, per Secretary Mnuchin’s decision. The New York MTA secured a  second loan  from the facility on December 10, 2020, borrowing $2.9 billion before lending halted.
• Supporting municipal bond liquidity:  The Fed also used two of its credit facilities to backstop muni markets. It expanded the eligible collateral for the MMLF to include municipal variable-rate demand notes and highly rated municipal debt with maturities of up to 12 months. The Fed also expanded the eligible collateral of the CPFF to include high-quality commercial paper backed by tax-exempt state and municipal securities. These steps allowed banks to funnel cash into the municipal debt market, where stress had been building due to a lack of liquidity.

WHY WERE THE FED’S ACTIONS IMPORTANT?

Steps taken by federal, state, and local officials to mitigate the spread of the virus limited economic activity, leading to a sudden and deep recession with millions of jobs lost. The Fed’s actions ensured that credit continued to flow to households and businesses, preventing financial market disruptions from intensifying the economic damage.

In many other countries, most credit flows through the banking system. In the U.S., a substantial amount of credit flows through capital markets, so the Fed worked to keep them functioning as smoothly as possible. As one of our colleagues, Don Kohn, former Federal Reserve Vice Chair,  said in March 2020:

“The Treasury market in particular is the foundation for trading in many other securities markets in the U.S. and around the world; if it’s disrupted, the functioning of every market will be impaired. The Fed’s purchase of securities is explicitly aimed at improving the functioning of the Treasury and MBS markets, where market liquidity had been well below par in recent days.”

But targeting the Treasury market proved insufficient, given the severity of the COVID recession and the disruption of flows of credit across other financial markets. So the Fed intervened directly in the markets for corporate and municipal debt to ensure that key economic actors could raise funds to pay workers and avoid bankruptcies. These measures aimed to help businesses survive the crisis and resume hiring and production when the pandemic ebbed.

Banks also needed support to keep credit flowing. When financial markets are clogged, firms tend to draw on bank lines of credit, which can lead banks to pull back on lending or selling Treasury and other securities. The Fed supplied unlimited liquidity to financial institutions so they could meet credit drawdowns and make new loans to businesses and households feeling financial strains.

The authors did not receive financial support from any firm or person for this article or from any firm or person with a financial or political interest in this article. They are not currently an officers, directors, or board members of any organization with a financial or political interest in this article. Prior to his consulting work for Brookings, Dave Skidmore was employed by the Board of Governors of the Federal Reserve System.

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How Quantitative Models Can Help Policy Makers Respond to COVID-19

Good policy making recognizes and adapts to the uncertainty inherent in model building..

• By Lars Peter Hansen
• April 22, 2020
• CBR - Economics
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My work as an economist has made me intimately familiar with uncertainty. I use dynamic models and explore the impacts of uncertainty in a variety of settings. I use tools from statistics and decision theory to investigate both market behavior and implications for policy.

Like the rest of the population, I now find myself in rather surreal and, at times, dire circumstances. I wish I were, but do not claim to be, an expert in epidemiology. Like many, I have been trying to give myself at least some basic knowledge of pandemics and models aimed at understanding how COVID-19 can evolve dramatically over a short period of time.

As an outsider, I find the modeling impacts from epidemiology of considerable interest. In the mornings, I search immediately for updated numbers and predictions, hoping that we will soon see an important turnaround in COVID-19 cases, deaths, human suffering, and the subsequent global socioeconomic turmoil.

The role of quantitative models

Policy makers look to forecasts or projections about the future evolution of the contagion and subsequent fatalities to guide their policy choices. These can be best guesses or warnings about how bad things could become. These considerations factor into their decision-making in at least informal ways.

Epidemiologists no doubt have important insights that we all look to digest. Economists and other social scientists are quick to consider ways by which they can draw upon their current stock of knowledge to incorporate the likely responses of individuals and businesses to various policy alternatives.

Quantitative predictions of disease transmission under alternative policies and the resulting social behaviors, however, bring special challenges. Models require specific assumptions and ingredients that govern how social and economic interactions play out within the models. Subjective judgments are unavoidable. There are unknown parameters to calibrate in the face of limited data. These challenges are pervasive in quantitative modeling that aims to support policy.

Aquinas’s warning

Different researchers or research groups build models with different implications. I have great respect for the scientific-model builders who make revealing attempts at quantifying the uncertainty we face, and for those policy advisors who are willing to accept differences in the outputs and predictions of alternative models. At the same time, I worry when policy makers seemingly embrace models without a full understanding of the underlying assumptions involved, or because those models deliver the findings that they prefer to see.

I find it insightful to think of every model as telling a quantitative story. Each may seek to offer guidance and insight, but alternative models may have different predictions and implications for policy. By nature, each model is an abstraction and necessarily a simplification, and sometimes the approximation can appear to be bold. There are uncertainties within each model having to do with unknown inputs, and there are differences across models in terms of how they aim to depict behavior.

If only we could just “let the data speak”—but that is not how most model building proceeds.

When thinking about using models in a variety of settings, including our current health and economic crisis, I am reminded of an injunction attributed to Thomas Aquinas: “Beware the man of one book.”

We should replace the word book with model when our understanding of the phenomenon in question has such apparent limits. Looking across the predictions of numerous models is a valuable exercise. Substantive expertise can help in weighing the pros and cons of alternative models, but when there are obvious bounds to our understanding, this seldom rules out all but one model.

Looking to data

We look to data to help calibrate inputs, but many concerns have been raised about the quality and reliability of data pertaining to the COVID-19 pandemic. At the most rudimentary level, we are unsure of the actual numbers of contaminated people. Death attributions are challenging because unhealthy people are substantially more vulnerable to the disease. We do not yet know how strong the immunity is of those who have already been affected by the virus and survived.

We can look to evidence from countries with earlier experiences, such as China, where the disease and its initial transmission started. But serious concerns have been raised about the officially quoted numbers there and elsewhere. If only we could just “let the data speak”— but that is not how most model building proceeds .

There has to be some guesswork in determining how best to exploit the evidence we have from previous experiences. Data limitations make it challenging, even for experts, to assess the merits and limits of alternative models and predictions.

Where does economics come in?

Policy-relevant modeling for crises such as COVID-19 isn’t just about epidemiology. Inside the models are individuals making decisions about social interactions and businesses responding to new economic demands and policy restraints. The people “inside the model” respond to changes in their environment and policies that might be implemented along the way. The economist in me has been observing a quantitative macroeconomics literature quickly emerge that incorporates simplified epidemiological specifications of disease within a macroeconomic framework in the face of the crisis. To my colleagues’ credit, they aim to address important policy challenges and to introduce behavioral responses to changing incentives.

They explore the health benefits and economic consequences of quarantining a significant portion of the population, and the best ways to use testing to improve the social and economic outcomes of the current crisis. We know from a variety of experiences that incentives can matter when assessing policies. But it is no small feat to incorporate epidemiological forces in dynamic models of the economy in credible ways, even putting aside how best to confront the overriding uncertainty.

My guess or hope is that much of this quantitative-modeling literature that is coming together in the fields of economics and epidemiology will help us to design policies to confront future pandemics better, as this one is unfolding at a much faster rate than the necessary scientific advances needed to produce new and better integrated models, inclusive of the social sciences. In my view, for these efforts to be successful, it will require that uncertainty be incorporated formally into the modeling and not treated as an afterthought.

Uncertainty and trade-offs

Economists identify and assess trade-offs pertinent to the conduct of prudent policy, which even at a qualitative level is an important contribution. Indeed, there are extremely tricky economic and social trade-offs that policy makers must cope with, although some have suggested naively that we should put them aside.

For instance, we cannot quarantine everyone and leave society without access to food and necessary pharmaceuticals. Exactly where we draw the line entails a trade-off between protecting people from exposure to the virus and making the accessibility of necessary food and medicine easier and less costly. When exactly do we choose to remove restrictions on various social and economic activities as we emerge from this pandemic? Such assessments clearly involve weighing costs and benefits of alternative courses of action.

How we use alternative-model predictions to guide policy also exposes a trade-off that warrants serious consideration. When policy advisors explore alternative courses of action, they are necessarily unsure of the consequences. Various projections get reported in the press about how infections and resulting deaths will evolve in the future. We are keenly interested in when things will turn around.

Existing quantitative models are tools that tell stories we should take seriously when used by experts who are willing to acknowledge limitations.

In sifting through projections reported in the media, we encounter a wide range of outcomes. On more careful inspection, an important reason for some of the differences is that they represent different public-health protocols or conventions. Some projections represent “best guesses” and others represent “worst-case” possibilities. Even the term worst case is a misnomer, as even these forecasts are typically premised on “reasonable” bounds in terms of their model inputs.

Both types of projections can be informative as long as it is understood that they serve different but related purposes. In formal or even informal approaches to addressing urgent social problems, we are confronted with how much weight or attention we should attach to the alternative health trajectories that might play out. How much attention should be paid to our best guesses of how the disease will evolve under alternative policies relative to the more cautious examination of worst-case trajectories whereby the number of infections and deaths are much more severe? There are “in-between” possibilities as well. Simulations of the best-guess and worst-case type, and for that matter, even in-between ones expressed using probabilities, are all revealing. I believe it is the role of the media to do a more balanced job of reporting the options, and reporters should aim to be more transparent with the public about assumptions made for each simulation.

However, these simulations alone do not inform us of the best course of action. This would be true even in a simpler setting in which we could assign probabilities with great confidence. Determining prudent policy choices includes taking a stand on how concerned about or averse to uncertainty we should be. This goes beyond merely assigning probabilities to alternative outcomes. How much attention should we pay to outcomes that are potentially much worse than our best guesses of the disease and fatality forecasts, when exploring alternative courses of action?

Why do I call this a trade-off? Going with the best guesses may leave us vulnerable to bad outcomes. Featuring only so-called worst-case analysis in future policy considerations is not some panacea either. Embracing this approach could potentially induce subsequent social losses when unlikely worst-case outcomes do not emerge. It is these types of considerations that I wish were formally integrated into the economic analysis of policy as it applies here—and to other policy challenges. Policy advisors necessarily confront this trade-off when they look at alternative-model projections.

Miserable uncertainty

I am a firm believer that models can provide useful frameworks for prudent policy design, provided they are used sensibly and without unjustified confidence in their predictions. Existing quantitative models are tools that tell stories we should take seriously when used by experts who are willing to acknowledge limitations. This willingness should be a virtue and not a vice. One modicum of good news is that new information now flows quickly and openly to challenge model predictions. There have been some remarkable changes in model predictions of new infections and fatalities in response to the most recent evidence. The emerging body of evidence will no doubt lead to important modeling advances in the future.

I only wish that I, and other academics, could provide even better quantitative ways to guide policy in this challenging time. Mark Twain observed that “education is the path from cocky ignorance to miserable uncertainty.” We are living in the “miserable uncertainty” to which Twain referred, uncertainty that comes with the bounds to our understanding.

But as scholars with quantitative ambitions seek to distill and process information and insights now unfolding at a rapid pace, I can only applaud sensible policy makers as they weigh the alternative possible outcomes in real time. Such leaders are placed in the hot seats of having to implement sensible policy over the short time frame during which this pandemic is unfolding, and in the face of obvious uncertainty. At the same time, I cringe seeing leaders who seemingly place the cart before the horse by only targeting evidence that supports preordained political agendas. Unfortunately, such agendas often get in the way of sensible policy making.

While economists struggle to come up with the best way to model individual altruism, I can only hope that, at least for this episode, altruism is much more prevalent than it is in the models that economists typically use. Along these lines, I am continually reminded of the contributions of the real heroes from our health-care system, whom we are placing on the front lines of this global crisis, and who are risking their personal health to support that of their communities.

Lars Peter Hansen is the David Rockefeller Distinguished Service Professor at the University of Chicago Departments of Economics and Statistics and at Chicago Booth. He received the Nobel Prize in Economic Sciences in 2013.

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• Published: 16 June 2020

COVID-19 impact on research, lessons learned from COVID-19 research, implications for pediatric research

• Debra L. Weiner 1 , 2 ,
• Vivek Balasubramaniam 3 ,
• Shetal I. Shah 4 &
• Joyce R. Javier 5 , 6

on behalf of the Pediatric Policy Council

Pediatric Research volume  88 ,  pages 148–150 ( 2020 ) Cite this article

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The COVID-19 pandemic has resulted in unprecedented research worldwide. The impact on research in progress at the time of the pandemic, the importance and challenges of real-time pandemic research, and the importance of a pediatrician-scientist workforce are all highlighted by this epic pandemic. As we navigate through and beyond this pandemic, which will have a long-lasting impact on our world, including research and the biomedical research enterprise, it is important to recognize and address opportunities and strategies for, and challenges of research and strengthening the pediatrician-scientist workforce.

The first cases of what is now recognized as SARS-CoV-2 infection, termed COVID-19, were reported in Wuhan, China in December 2019 as cases of fatal pneumonia. By February 26, 2020, COVID-19 had been reported on all continents except Antarctica. As of May 4, 2020, 3.53 million cases and 248,169 deaths have been reported from 210 countries. 1

Impact of COVID-19 on ongoing research

The impact on research in progress prior to COVID-19 was rapid, dramatic, and no doubt will be long term. The pandemic curtailed most academic, industry, and government basic science and clinical research, or redirected research to COVID-19. Most clinical trials, except those testing life-saving therapies, have been paused, and most continuing trials are now closed to new enrollment. Ongoing clinical trials have been modified to enable home administration of treatment and virtual monitoring to minimize participant risk of COVID-19 infection, and to avoid diverting healthcare resources from pandemic response. In addition to short- and long-term patient impact, these research disruptions threaten the careers of physician-scientists, many of whom have had to shift efforts from research to patient care. To protect research in progress, as well as physician-scientist careers and the research workforce, ongoing support is critical. NIH ( https://grants.nih.gov/policy/natural-disasters/corona-virus.htm ), PCORI ( https://www.pcori.org/funding-opportunities/applicant-and-awardee-faqs-related-covid-19 ), and other funders acted swiftly to provide guidance on proposal submission and award management, and implement allowances that enable grant personnel to be paid and time lines to be relaxed. Research institutions have also implemented strategies to mitigate the long-term impact of research disruptions. Support throughout and beyond the pandemic to retain currently well-trained research personnel and research support teams, and to accommodate loss of research assets, including laboratory supplies and study participants, will be required to complete disrupted research and ultimately enable new research.

In the long term, it is likely that the pandemic will force reallocation of research dollars at the expense of research areas funded prior to the pandemic. It will be more important than ever for the pediatric research community to engage in discussion and decisions regarding prioritization of funding goals for dedicated pediatric research and meaningful inclusion of children in studies. The recently released 2020 National Institute of Child Health and Development (NICHD) strategic plan that engaged stakeholders, including scientists and patients, to shape the goals of the Institute, will require modification to best chart a path toward restoring normalcy within pediatric science.

COVID-19 research

This global pandemic once again highlights the importance of research, stable research infrastructure, and funding for public health emergency (PHE)/disaster preparedness, response, and resiliency. The stakes in this worldwide pandemic have never been higher as lives are lost, economies falter, and life has radically changed. Ultimate COVID-19 mitigation and crisis resolution is dependent on high-quality research aligned with top priority societal goals that yields trustworthy data and actionable information. While the highest priority goals are treatment and prevention, biomedical research also provides data critical to manage and restore economic and social welfare.

Scientific and technological knowledge and resources have never been greater and have been leveraged globally to perform COVID-19 research at warp speed. The number of studies related to COVID-19 increases daily, the scope and magnitude of engagement is stunning, and the extent of global collaboration unprecedented. On January 5, 2020, just weeks after the first cases of illness were reported, the genetic sequence, which identified the pathogen as a novel coronavirus, SARS-CoV-2, was released, providing information essential for identifying and developing treatments, vaccines, and diagnostics. As of May 3, 2020 1133 COVID-19 studies, including 148 related to hydroxychloroquine, 13 to remdesivir, 50 to vaccines, and 100 to diagnostic testing, were registered on ClinicalTrials.gov, and 980 different studies on the World Health Organization’s International Clinical Trials Registry Platform (WHO ICTRP), made possible, at least in part, by use of data libraries to inform development of antivirals, immunomodulators, antibody-based biologics, and vaccines. On April 7, 2020, the FDA launched the Coronavirus Treatment Acceleration Program (CTAP) ( https://www.fda.gov/drugs/coronavirus-covid-19-drugs/coronavirus-treatment-acceleration-program-ctap ). On April 17, 2020, NIH announced a partnership with industry to expedite vaccine development ( https://www.nih.gov/news-events/news-releases/nih-launch-public-private-partnership-speed-covid-19-vaccine-treatment-options ). As of May 1, 2020, remdesivir (Gilead), granted FDA emergency use authorization, is the only approved therapeutic for COVID-19. 2

The pandemic has intensified research challenges. In a rush for data already thousands of manuscripts, news reports, and blogs have been published, but to date, there is limited scientifically robust data. Some studies do not meet published clinical trial standards, which now include FDA’s COVID-19-specific standards, 3 , 4 , 5 and/or are published without peer review. Misinformation from studies diverts resources from development and testing of more promising therapeutic candidates and has endangered lives. Ibuprofen, initially reported as unsafe for patients with COVID-19, resulted in a shortage of acetaminophen, endangering individuals for whom ibuprofen is contraindicated. Hydroxychloroquine initially reported as potentially effective for treatment of COVID-19 resulted in shortages for patients with autoimmune diseases. Remdesivir, in rigorous trials, showed decrease in duration of COVID-19, with greater effect given early. 6 Given the limited availability and safety data, the use outside clinical trials is currently approved only for severe disease. Vaccines typically take 10–15 years to develop. As of May 3, 2020, of nearly 100 vaccines in development, 8 are in trial. Several vaccines are projected to have emergency approval within 12–18 months, possibly as early as the end of the year, 7 still an eternity for this pandemic, yet too soon for long-term effectiveness and safety data. Antibody testing, necessary for diagnosis, therapeutics, and vaccine testing, has presented some of the greatest research challenges, including validation, timing, availability and prioritization of testing, interpretation of test results, and appropriate patient and societal actions based on results. 8 Relaxing physical distancing without data regarding test validity, duration, and strength of immunity to different strains of COVID-19 could have catastrophic results. Understanding population differences and disparities, which have been further exposed during this pandemic, is critical for response and long-term pandemic recovery. The “Equitable Data Collection and Disclosure on COVID-19 Act” calls for the CDC (Centers for Disease Control and Prevention) and other HHS (United States Department of Health & Human Services) agencies to publicly release racial and demographic information ( https://bass.house.gov/sites/bass.house.gov/files/Equitable%20Data%20Collection%20and%20Dislosure%20on%20COVID19%20Act_FINAL.pdf )

Trusted sources of up-to-date, easily accessible information must be identified (e.g., WHO https://www.who.int/emergencies/diseases/novel-coronavirus-2019/global-research-on-novel-coronavirus-2019-ncov , CDC https://www.cdc.gov/coronavirus/2019-nCoV/hcp/index.html , and for children AAP (American Academy of Pediatrics) https://www.aappublications.org/cc/covid-19 ) and should comment on quality of data and provide strategies and crisis standards to guide clinical practice.

Long-term, lessons learned from research during this pandemic could benefit the research enterprise worldwide beyond the pandemic and during other PHE/disasters with strategies for balancing multiple novel approaches and high-quality, time-efficient, cost-effective research. This challenge, at least in part, can be met by appropriate study design, collaboration, patient registries, automated data collection, artificial intelligence, data sharing, and ongoing consideration of appropriate regulatory approval processes. In addition, research to develop and evaluate innovative strategies and technologies to improve access to care, management of health and disease, and quality, safety, and cost effectiveness of care could revolutionize healthcare and healthcare systems. During PHE/disasters, crisis standards for research should be considered along with ongoing and just-in-time PHE/disaster training for researchers willing to share information that could be leveraged at time of crisis. A dedicated funded core workforce of PHE/disaster researchers and funded infrastructure should be considered, potentially as a consortium of networks, that includes physician-scientists, basic scientists, social scientists, mental health providers, global health experts, epidemiologists, public health experts, engineers, information technology experts, economists and educators to strategize, consult, review, monitor, interpret studies, guide appropriate clinical use of data, and inform decisions regarding effective use of resources for PHE/disaster research.

Differences between adult and pediatric COVID-19, the need for pediatric research

As reported by the CDC, from February 12 to April 2, 2020, of 149,760 cases of confirmed COVID-19 in the United States, 2572 (1.7%) were children aged <18 years, similar to published rates in China. 9 Severe illness has been rare. Of 749 children for whom hospitalization data is available, 147 (20%) required hospitalization (5.7% of total children), and 15 of 147 required ICU care (2.0%, 0.58% of total). Of the 95 children aged <1 year, 59 (62%) were hospitalized, and 5 (5.3%) required ICU admission. Among children there were three deaths. Despite children being relatively spared by COVID-19, spread of disease by children, and consequences for their health and pediatric healthcare are potentially profound with immediate and long-term impact on all of society.

We have long been aware of the importance and value of pediatric research on children, and society. COVID-19 is no exception and highlights the imperative need for a pediatrician-scientist workforce. Understanding differences in epidemiology, susceptibility, manifestations, and treatment of COVID-19 in children can provide insights into this pathogen, pathogen–host interactions, pathophysiology, and host response for the entire population. Pediatric clinical registries of COVID-infected, COVID-exposed children can provide data and specimens for immediate and long-term research. Of the 1133 COVID-19 studies on ClinicalTrials.gov, 202 include children aged ≤17 years. Sixty-one of the 681 interventional trials include children. With less diagnostic testing and less pediatric research, we not only endanger children, but also adults by not identifying infected children and limiting spread by children.

Pediatric considerations and challenges related to treatment and vaccine research for COVID-19 include appropriate dosing, pediatric formulation, and pediatric specific short- and long-term effectiveness and safety. Typically, initial clinical trials exclude children until safety has been established in adults. But with time of the essence, deferring pediatric research risks the health of children, particularly those with special needs. Considerations specific to pregnant women, fetuses, and neonates must also be addressed. Childhood mental health in this demographic, already struggling with a mental health pandemic prior to COVID-19, is now further challenged by social disruption, food and housing insecurity, loss of loved ones, isolation from friends and family, and exposure to an infodemic of pandemic-related information. Interestingly, at present mental health visits along with all visits to pediatric emergency departments across the United States are dramatically decreased. Understanding factors that mitigate and worsen psychiatric symptoms should be a focus of research, and ideally will result in strategies for prevention and management in the long term, including beyond this pandemic. Social well-being of children must also be studied. Experts note that the pandemic is a perfect storm for child maltreatment given that vulnerable families are now socially isolated, facing unemployment, and stressed, and that children are not under the watch of mandated reporters in schools, daycare, and primary care. 10 Many states have observed a decrease in child abuse reports and an increase in severity of emergency department abuse cases. In the short term and long term, it will be important to study the impact of access to care, missed care, and disrupted education during COVID-19 on physical and cognitive development.

Training and supporting pediatrician-scientists, such as through NIH physician-scientist research training and career development programs ( https://researchtraining.nih.gov/infographics/physician-scientist ) at all stages of career, as well as fostering research for fellows, residents, and medical students willing to dedicate their research career to, or at least understand implications of their research for, PHE/disasters is important for having an ongoing, as well as a just-in-time surge pediatric-focused PHE/disaster workforce. In addition to including pediatric experts in collaborations and consortiums with broader population focus, consideration should be given to pediatric-focused multi-institutional, academic, industry, and/or government consortiums with infrastructure and ongoing funding for virtual training programs, research teams, and multidisciplinary oversight.

The impact of the COVID-19 pandemic on research and research in response to the pandemic once again highlights the importance of research, challenges of research particularly during PHE/disasters, and opportunities and resources for making research more efficient and cost effective. New paradigms and models for research will hopefully emerge from this pandemic. The importance of building sustained PHE/disaster research infrastructure and a research workforce that includes training and funding for pediatrician-scientists and integrates the pediatrician research workforce into high-quality research across demographics, supports the pediatrician-scientist workforce and pipeline, and benefits society.

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Department of Pediatrics, Division of Emergency Medicine, Boston Children’s Hospital, Boston, MA, USA

Debra L. Weiner

Harvard Medical School, Boston, MA, USA

Department of Pediatrics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA

Vivek Balasubramaniam

Department of Pediatrics and Division of Neonatology, Maria Fareri Children’s Hospital at Westchester Medical Center, New York Medical College, Valhalla, NY, USA

Shetal I. Shah

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Joyce R. Javier

Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

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All authors made substantial contributions to conception and design, data acquisition and interpretation, drafting the manuscript, and providing critical revisions. All authors approve this final version of the manuscript.

Pediatric Policy Council

Scott C. Denne, MD, Chair, Pediatric Policy Council; Mona Patel, MD, Representative to the PPC from the Academic Pediatric Association; Jean L. Raphael, MD, MPH, Representative to the PPC from the Academic Pediatric Association; Jonathan Davis, MD, Representative to the PPC from the American Pediatric Society; DeWayne Pursley, MD, MPH, Representative to the PPC from the American Pediatric Society; Tina Cheng, MD, MPH, Representative to the PPC from the Association of Medical School Pediatric Department Chairs; Michael Artman, MD, Representative to the PPC from the Association of Medical School Pediatric Department Chairs; Shetal Shah, MD, Representative to the PPC from the Society for Pediatric Research; Joyce Javier, MD, MPH, MS, Representative to the PPC from the Society for Pediatric Research.

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Weiner, D.L., Balasubramaniam, V., Shah, S.I. et al. COVID-19 impact on research, lessons learned from COVID-19 research, implications for pediatric research. Pediatr Res 88 , 148–150 (2020). https://doi.org/10.1038/s41390-020-1006-3

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The global burden of the covid-19 pandemic: comparing benefit–cost analysis and social welfare analysis.

Published online by Cambridge University Press:  12 September 2024

This article discusses the difference between benefit–cost analysis (BCA) and social welfare analysis in the evaluation of pandemic preparedness policies. Two social welfare approaches are considered: utilitarianism and prioritarianism. BCA sums the individuals’ monetary equivalents of the pandemic impacts. Social welfare analysis aggregates individuals’ well-being impacts. The aggregation rule identifies the normative judgments about what is fair. This article shows that the two methods yield very different estimates of the value of avoiding a future pandemic similar to the COVID-19 one. Compared to BCA, considerations about the distribution of the costs of the hypothetical intervention play a major role in the estimate of both utilitarian and prioritarian pandemic burdens: The more progressive the distribution of the costs is, the larger the net benefits of preventing the pandemic. In contrast, the BCA pandemic burden is indifferent to the distribution of the intervention costs. In addition, BCA tends to underestimate the burden suffered by low-income countries compared to social welfare analysis.

1. Introduction

The COVID-19 pandemic has forced countries to make difficult trade-offs. For example, vaccination prioritization strategies and decisions to ration treatment required weighing the relative health benefits to different people. Decisions about the extent and length of school and business shutdowns required balancing the reductions in deaths and morbidity against socioeconomic costs. Are conventional benefit–cost approaches suitable to make these kinds of trade-offs? This article challenges the use of benefit–cost analysis (BCA) in the economic evaluation of pandemic risks whose impacts are heterogeneously distributed across the population. In particular, this article argues in favor of an alternative method, social welfare analysis (SWA). The main advantage of SWA is that it enables ethical concerns about the distribution of impacts to be incorporated in the evaluation exercise in a transparent way (Adler et al. , Reference Adler, Bradley, Ferranna, Fleurbaey, Hammitt, Turquier, Voorhoeve, Wilkinson and Savulescu 2023 ). Although this article focuses on the economic evaluation of pandemic risks, its implications are wider and relate to any policy with heterogeneous impacts across the population.

This article explores the differences between BCA and SWA in the evaluation of pandemic-related policies. To illustrate the difference between the two methods, I estimate the maximum willingness-to-pay (WTP) for a pandemic preparedness intervention that is designed to prevent a pandemic similar to the COVID-19 one. Pandemic preparedness investments include, e.g. the development of vaccines against pandemic-potential pathogens (Saville et al. , Reference Saville, Cramer, Downham, Hacker, Lurie, Van der Veken, Whelan and Hatchett 2022 ), establishing guidelines for the optimal global distribution of vaccines, therapeutics, protective equipment and other resources (Emanuel et al. , Reference Emanuel, Persad, Kern, Buchanan, Fabre, Halliday, Heath, Herzog, Leland, Lemango, Luna, McCoy, Norheim, Ottersen, Schaefer, Tan, Heath Wellman, Wolff and Richardson 2020 ), and strengthening epidemic surveillance systems. To determine the maximum WTP for pandemic preparedness, I consider the perspective of a supranational benevolent decision maker who aims at improving the well-being of the global population. The social value of preventing a COVID-19-like pandemic depends on the adopted value framework (BCA vs. SWA). In particular, it depends on the method to monetize health impacts and on the procedure to aggregate monetary impacts across the global population.

BCA determines the value of an intervention by converting all its health and non-health impacts into monetary equivalents based on individuals’ WTP to avoid those impacts, and then summing up those monetary equivalents. Estimates of value-per-statistical-life (VSL) are typically used to monetize the value of mortality risk reductions (Hammitt, Reference Hammitt 2000 ). VSL represents the monetary equivalent of saving one (unidentified) life among a group of identical people. VSL estimates are derived from individuals’ stated or revealed willingness to trade small changes in income for small changes in mortality risk (Viscusi, Reference Viscusi 2018 ). For example, if individuals are willing to pay $10 to reduce their risk of premature death by 1 in a million, then the monetary value of saving one statistical life is $10 million. In other words, if each member of a group of 1 million people, identical in all relevant ways, considers it in their best interest to pay $10 to reduce the expected number of deaths within the group by one, then the value of preventing one death is $10 million. In principle, VSL measures are heterogeneous across the population, reflecting the preferences and circumstances of different individuals (e.g., age, income, overall mortality risk) (Viscusi, Reference Viscusi 2010 ). Empirical VSL estimates are available for several countries and they can serve as proxies for countries with unavailable estimates, with appropriate adjustments for differences in income and other characteristics (Hammitt & Robinson, Reference Hammitt and Robinson 2011 ; Viscusi & Masterman, Reference Viscusi and Masterman 2017 ).

To mitigate concerns for discrimination, practical guidelines for the conduct of BCA suggest to use population-average VSLs, independent of individuals’ characteristics such as income or age (Robinson, Reference Robinson 2007 ). However, it is also common practice to use different VSL estimates for different countries based on their national income (Viscusi & Masterman, Reference Viscusi and Masterman 2017 ; Robinson et al. , Reference Robinson, Hammitt and O’Keeffe 2019 ). As the pandemic preparedness example illustrates, that choice matters when evaluating interventions with supranational impacts (e.g., when determining the global value of developing new vaccines). In particular, comparisons of the burden of a pandemic across countries would place a larger value on negative health impacts experienced in high-income countries compared to the same health impacts borne by lower-income countries.

One possible solution is to adopt a global VSL estimate for all countries (Cadarette et al. , Reference Cadarette, Ferranna, Cannon, Abbas, Giannini, Zucker and Bloom 2023 ), similarly to what is typically done at the national level. Although easy to implement, the use of a single VSL estimate at the national or global level neglects potentially ethically justifiable reasons for attaching different moral values to risk reductions accruing to different people. For example, it is often considered ethically and economically reasonable to treat people in different age groups differently (Ferranna et al. , Reference Ferranna, Hammitt, Adler, Bloom, Sousa-Poza and Sunde 2023 ). From a societal perspective, the death of a young person is often considered worse than the death of an old person, and not only because the former has a longer remaining life expectancy than the latter. There exists a relatively large public health literature supporting the “fair innings” principle (Harris, Reference Harris 1985 ; Williams, Reference Williams 1997 ; Bognar, Reference Bognar 2015 ). The principle states that saving the life of a younger person is fairer than saving the life of an older person when each would gain the same extension in longevity because older individuals have already lived a long life and, as such, they have less claim to scarce resources. Footnote 3 This issue is particularly important in the case of COVID-19, since mortality risk increases with age. Therefore, even though the dependence of WTP on income can have unacceptable ethical consequences, the use of a single VSL may also be ethically problematic. Adopting a constant value-per-statistical-life-year (VSLY) instead of a constant VSL addresses differences in remaining life expectancy between young and old people (Hammitt, Reference Hammitt 2007 ). However, we still have the problem of how to select this constant VSLY and whether there should be cross-country differences based on income. Moreover, fair innings concerns would still play no role in the BCA results.

SWA does not face the same ethical challenges as BCA. SWA proceeds by estimating the impact of the pandemic on individuals’ well-being, and then aggregating those impacts through a social welfare function. The aggregation rule identifies the normative assumptions about distributive justice (Adler, Reference Adler 2019 ). For example, the utilitarian social welfare function assumes that total welfare is equal to the sum of individuals’ well-being. If marginal well-being is decreasing in income, the utilitarian approach is sensitive to inequalities in income. Thus, monetary impacts experienced by low-income individuals are morally more important than similar monetary impacts experienced by high-income individuals. Another popular welfare function is the prioritarian one, which assumes that total welfare is equal to the sum of a concave transformation of individuals’ well-being. Thus, the prioritarian approach is concerned with the distribution of well-being across the population, attaching higher value to well-being increments that accrue to the worse-off individuals (e.g., low-income individuals, but also individuals in poor health or young individuals).

There is a long tradition of scholarly work advocating for the use of SWA (or its weighted-BCA approximation) in policy evaluation (e.g., Arrow, Reference Arrow 1963 , Drèze & Stern, Reference Drèze, Stern, Auerbach and Feldstein 1987 , Blackorby & Donaldson, Reference Blackorby and Donaldson 1990 , and, more recently, Adler, Reference Adler 2012 ). One problem with SWA is that it requires an interpersonally comparable measure of individual well-being. As argued in Adler ( Reference Adler 2016 ) and Fleurbaey and Abi-Rafeh ( Reference Fleurbaey and Abi-Rafeh 2016 ), the reluctance to adopt SWA in policy evaluation is likely due to difficulties in measuring and ranking well-being levels across the population, especially when individuals hold heterogeneous preferences. Different approaches have been developed to measure well-being (see Adler & Decancq, Reference Adler, Decancq, Adler and Norheim 2022 for a review), and in this article I will focus on one specific approach (von Neumann-Morgenstern utilities). However, due to data limitations, I will also assume homogeneous preferences across countries and calibrate only one set of preference parameters. The assumption of homogeneous preferences is common in practical applications of SWA (Adler, Reference Adler, Adler and Norheim 2022 ), and it implies interpersonally comparability of individual well-being metrics.

In contrast, BCA does not require a method for comparing well-being across individuals. BCA uses monetary values to compare benefits and costs across individuals, and the sum of individuals’ costs and benefits determines whether the policy yields a potential Pareto improvement (Kaldor–Hicks efficiency criterion). The logic is that, when the net benefits of a policy are positive, then there are enough resources for the “winners” to compensate the “losers,” such that everyone will at the end be better off with the policy than without it. There is a large literature on the advantages and drawbacks of this view (e.g., Arrow, Reference Arrow 1963 ; Harberger, Reference Harberger 1978 ; Kaplow, Reference Kaplow 2004 ; Boadway, Reference Boadway, Adler and Fleurbaey 2016 ; Hammitt, Reference Hammitt 2021 ). One well-recognized issue is that the compensation is hypothetical: There is no evidence that the “losers” are compensated through transfers, adjustments to the tax systems or other policies. In this article, I take the view that it is not possible to separate efficiency and equity in policy evaluation (Adler, Reference Adler 2016 ; Fleurbaey & Abi-Rafeh, Reference Fleurbaey and Abi-Rafeh 2016 ). If the impacts of a policy are heterogeneously distributed across the population, then the policy will have an (intended or unintended) effect on equity. BCA is not agnostic about distributive justice. On the contrary, by looking at the simple sum of individuals’ WTPs, BCA is implicitly taking the moral stance that monetary benefits to the wealthy are as socially valuable as monetary benefits to the less wealthy. Even though SWA is more challenging to perform than BCA, SWA enables ethical concerns about the distribution of impacts to be incorporated in the evaluation exercise.

A few caveats before proceeding. Because of data limitations, this article focuses only on two pandemic impacts: COVID-19 deaths and income losses. The estimated global burden of the COVID-19 pandemic is conservative as additional impacts (e.g., non-COVID-19 deaths, morbidity, mental health and education losses) are ignored. Additionally, the application focuses on between-country differences in pandemic impacts, while within-country inequalities are neglected. This will lead to conservative estimates of the global pandemic burden when using a social welfare approach since the distribution of impacts across the global population is likely to be more unequal than the average distribution of impacts across countries. For example, the adoption of country-average income losses neglects that the pandemic caused some people to fall into poverty. A distribution-sensitive welfare approach would consider this outcome morally wrong, thereby increasing the overall value of preventing the pandemic.

This article is structured as follows. Section 2 summarizes the literature most closely related to this article. Section 3 introduces a stylized model to estimate the global value of preventing a COVID-19-like pandemic and derives three measures of such a value: the BCA value based on country-specific VSL measures, the utilitarian value, and the prioritarian one. Section 4 describes the data required for the analysis and presents the estimation results. I estimate the global value of preventing the pandemic and its distribution across all countries with available data. Section 5 concludes and discusses the main limitations of this article.

2. Related literature

The health, social, and economic costs of the COVID-19 pandemic are staggering. As of September 2023, almost 7 million COVID-19 deaths have been recorded globally (WHO, 2023 ), while estimates of excess mortality (including both estimates of undocumented COVID-19 deaths and deaths from other causes) are in the ballpark of 26 million deaths (The Economist, 2023 ). Footnote 4 Global Gross Domestic Product (GDP) reduced by 3.1% in 2020 (IMF, 2021 ). Almost 1.6 billion students worldwide were affected by school closures because of the COVID-19 pandemic, and average learning loss amounted to roughly 35% of a school year’s worth of learning (Betthäuser et al. , Reference Betthäuser, Bach-Mortensen and Engzell 2023 ). The pandemic drove massive unemployment and permanent business closures (Baek et al. , Reference Baek, McCrory, Messer and Mui 2021 , Fairlie et al. , Reference Fairlie, Fossen, Johnsen and Droboniku 2023 ) and disrupted global supply chains (Guerrieri et al. , Reference Guerrieri, Lorenzoni, Straub and Werning 2022 ). It also imposed a sizable mental health toll due to isolation, fear of contagion and loss of family and friends (Giuntella et al. , Reference Giuntella, Hyde, Saccardo and Sadoff 2021 ).

The impacts of the pandemic have been very unequal across the population. On the one hand, COVID-19 mortality risk sharply increases with age (Pijls et al. , Reference Pijls, Jolani, Atherley, Derckx, Janna, Gregor and Hendricks 2021 ). Due to differences in population age structure, high-income countries typically registered more COVID-19 deaths than lower-income countries. On the other hand, the COVID-19 pandemic has exacerbated existing inequalities through: patterns of infection and death that disproportionately affect disadvantaged populations (e.g., the overproportion of minorities and low-income individuals among essential workers); socioeconomic differences in healthcare access and in ability to invest resources to control the pandemic, including the purchase of vaccines and treatments; and inadequate income relief programs to equitably share the economic burden of the pandemic (Stantcheva, Reference Stantcheva 2022 ). Overall, the pandemic seems to have led to an increase in global income inequality (Mahler et al. , Reference Mahler, Yonzan and Lakner 2022 ). Footnote 5

In the evaluation of pandemic-related policies, two main approaches have emerged: BCA based on VSL measures (e.g., Gollier, Reference Gollier 2020 ; Greenstone & Nigam, Reference Greenstone and Nigam 2020 ; Thunstrom et al. , Reference Thunstrom, Newbold, Finnoff, Ashworth and Shogren 2020 ), and utilitarian social welfare functions (e.g., Hall et al. , Reference Hall, Jones and Klenow 2020 ; Quaas et al. , Reference Quaas, Meya, Schenck, Bos, Drupp and Requate 2021 ; Glover et al. , Reference Glover, Heathcote, Krueger and Ríos-Rull 2023 ). A few papers discuss nonutilitarian social welfare approaches (e.g., Ferranna et al. , Reference Ferranna, Sevilla, Bloom, Adler and Norheim 2022 ; Adler et al. , Reference Adler, Bradley, Ferranna, Fleurbaey, Hammitt, Turquier, Voorhoeve, Wilkinson and Savulescu 2023 ). Here, I review only the articles that estimate the burden of the pandemic. Footnote 6

Cutler and Summers ( Reference Cutler and Summers 2020 ) use a BCA approach and estimate that the expected cost of the COVID-19 pandemic in the United States amounted to more than $16 trillion. That figure is based on a population-average VSL estimate of $7 million, and on the inclusion of losses due to premature death, long-term health impairments, mental health impairments and the economic recession. In a similar vein, Viscusi ( Reference Viscusi 2020 ) adopts income-adjusted VSL measures for different countries and estimates that the global COVID-19 mortality burden in the first half of 2020 amounted to $3.5 trillion, with the United States experiencing 25% of the total deaths but 41% of the monetary burden. The global estimate is found by assuming a population-average VSL of $11 million for the United States and a global income elasticity of 1 (i.e., the ratio of VSL to national income is constant across countries). In particular, the use of income-adjusted VSL measures implies that the mortality burden in the United States is valued more than the mortality burden in other countries. This ethically challenging result is not robust to social welfare evaluation methods.

The closest analysis to this article is Decerf et al. ( Reference Decerf, Francisco, Mahler and Sterck 2021 ), who estimate the global burden of the pandemic by adopting a utilitarian welfare function that depends solely on the number of people alive and on whether they are in poverty or not. The welfare loss of the pandemic is the weighted sum of the number of years lost to premature COVID-19 death and the number of additional years spent in poverty because of the pandemic. The trade-off between mortality and poverty is given by a single normative parameter. The estimated welfare loss depends largely on the increase in poverty caused by the pandemic, especially in lower-income countries. Given that the welfare metric is the number of years of life and each year of life is valued equally, impacts in high-income countries are not inflated compared to similar impacts in lower-income countries. However, the welfare measure is sensitive to differences in life expectancy across countries.

There exists also a small literature focusing on the costs of future pandemics. Fan et al. ( Reference Fan, Jamison and Summers 2018 ) and Glennerster et al. ( Reference Glennerster, Snyder and Tan 2023 ) both employ VSL measures to price pandemic risks and find that the expected global annual losses from pandemic risk range from $500 billion (2013 values) to $700 billion (2021 values), respectively. The larger estimate provided by Glennerster et al. is in part due to risk updates following the COVID-19 pandemic and to the inclusion of educational losses in the calculus. Martin and Pindyck ( Reference Martin and Pindyck 2021 ) consider a utilitarian framework and estimate that the WTP to avoid major pandemics is 10% or more of annual consumption and partly driven by the risk of macroeconomic contractions. With a utilitarian welfare framework, the value of non-marginal health risks positively depends on background income risk under commonly used parameterization of the utility function.

This article focuses on the inability of BCA to account for the distributional impacts of a pandemic. Three other shortcomings of BCA have been highlighted in the literature on pandemic policies. First, morbidity consequences can be sizable (think, e.g., at long-COVID), and it is unclear how to evaluate reductions in morbidity for emerging or previously unexplored health risks (Kniesner & Sullivan, Reference Kniesner and Sullivan 2020 ; Robinson et al. , Reference Robinson, Sullivan and Shogren 2021 ). Second, BCA based on standard VSL estimates overestimates the value of preventing a large number of deaths (Hammitt, Reference Hammitt 2020 ). VSL is the marginal rate of substitution of income for mortality risk, but pandemics entail non-marginal health risk changes. In particular, standard VSL estimates tend to overestimate individuals’ WTP to reduce non-marginal risks. When the risk change is large, income constraints play an important role as individuals might be required to deplete their resources to pay for the risk reduction. Third, pandemic risks are novel and subject to large uncertainties (Berger et al. , Reference Berger, Berger, Bosetti, Gilboa, Hansen, Jarvis, Marinacci and Smith 2021 ). However, issues of deep uncertainty and ambiguity are not featured in BCA. Although all these shortcomings are important, this article focuses exclusively on the distributional aspect.

3. Evaluation frameworks

The section introduces a simplified framework to assess the value of a hypothetical intervention to prevent the impacts of a pandemic similar to the COVID-19 one. The COVID-19 pandemic has affected people’s lives along multiple dimensions, from exposing individuals to severe health risks to disrupting work habits and social relations. Impacts have also been heterogeneously distributed across the population, with older people, minorities and socioeconomic vulnerable populations often bearing the brunt of the pandemic. Notwithstanding the plethora of impacts of the pandemic, in the following I will focus only on two major impacts: deaths and income losses. This choice is dictated by data constraints and it is not meant to downplay the importance of other impacts (e.g., non-COVID-19 deaths, long-COVID, worsening of mental health, undernutrition, disruption to routine immunization and educational losses). Additionally, since consistent data across countries are missing, I disregard the within-country distribution of pandemic impacts, and focus only on between-country differences in deaths and income losses.

3.1. A stylized model

The starting point for the analysis is a measure of individual well-being. Generally speaking, well-being is assumed to depend on a bundle of attributes that are important to individuals (e.g., income, physical and mental health, longevity, quality of the environment and social relations). Since the only COVID-19 impacts included in the analysis are death and income losses, I assume that individuals’ well-being depends only on income and longevity.

Well-being and its dependence of the bundle of attributes can be measured in several ways (Adler & Fleurbaey, Reference Adler and Fleurbaey 2016 ; Adler & Decancq, Reference Adler, Decancq, Adler and Norheim 2022 ), including: reports of life satisfaction or experience of emotions (Layard & De Neve, Reference Layard and De Neve 2023 ); attainment of a list of objective goods or “capabilities” (e.g., being healthy, having meaningful social relations) (Sen, Reference Sen 1999 ; Nussbaum, Reference Nussbaum 2011 ); adjusting individual income by the value of nonmarket attributes based on the individual’s preferences for those attributes (Fleurbaey et al. , Reference Fleurbaey, Luchini, Muller and Schokkaert 2013 ); and employing utility functions that represent individuals’ risk preferences regarding alternative probability distributions of attributes over a lifetime (von Neumann-Morgenstern utility function) (Adler, Reference Adler 2019 ). In this article, I measure well-being through a von Neumann-Morgenstern utility function. Additionally, I assume that individuals have homogeneous preferences, i.e. there exists a single utility function for the whole population. I will not test the robustness of the results to other measures of well-being or to heterogeneous preferences.

Furthermore, I assume that the well-being metric of interest is lifetime well-being rather than sub-lifetime well-being. As a consequence, judgments about who is worse-off in a society are based on individuals’ entire life trajectory and not only on their current or future circumstances. Lifetime well-being is additive in period utility and the marginal utility of income is diminishing, so that a dollar raises the utility of a poor individual more than it does that of a rich individual. To make computations tractable, I assume no time discounting and no economic growth.

3.2. Benefit–cost analysis

BCA determines the social value of preventing the pandemic by summing individuals’ WTP to avoid the pandemic impacts. Given ( 3 ), the BCA value is given by:

3.3. Utilitarian SWA

Let us now consider the value of prevention estimated through a utilitarian social welfare function. The utilitarian aggregation rule sums individuals’ lifetime well-being. Utilitarian welfare without the pandemic is equal to:

Utilitarian welfare with the pandemic is equal to:

In welfare terms, the utilitarian value of preventing the pandemic is given by:

3.4. Prioritarian SWA

In the presence of risk, there exist two different forms of prioritarianism. Ex ante prioritarianism is concerned with the distribution of expected well-being across the population. Generally speaking, from the ex ante point of view, priority is given to individuals who are facing the largest mortality risk. Ex post prioritarianism is concerned with the distribution of realized well-being across the population. Generally speaking, from the ex post point of view, priority is given to individuals who die prematurely. Ex ante prioritarianism violates stochastic dominance, and for that reason it is problematic (Adler, Reference Adler 2019 ). Footnote 15 In this article, I focus on ex post prioritarianism. I will not investigate if the ex ante approach would lead to similar conclusions as those found in this article.

In welfare terms, the prioritarian value of preventing a pandemic is approximately equal to: Footnote 16

4. The global value of pandemic prevention

The section provides estimates of the value of preventing a COVID-19-like pandemic for different countries in the world and for the world as a whole. Pandemic impacts include GDP losses and COVID-19 deaths. I present three different estimates of the value of pandemic prevention. First, I use a conventional BCA approach ( 4 ). The value of prevention is equal to the sum of the GDP loss and the monetary equivalent of the deaths recorded in a country. The value of saving a life is equal to income-adjusted VSLs. Then, I compute the utilitarian value using expression ( 6 ) and the prioritarian value using expression ( 8 ).

For pandemic burdens, economic losses are given by the reduction in GDP experienced by countries during the pandemic. I consider only economic losses experienced in 2020 and 2021 to avoid any confounding with the war in Ukraine (started in February 2022). To project GDP per capita in the absence of the pandemic, I use growth projections from the International Monetary Fund published in October 2019 (IMF, 2019 ). Because of data constraints, I consider only COVID-19 deaths and neglect excess deaths from other causes during the pandemic. The total number of COVID-19 deaths by country is derived from Our World in Data (Mathieu et al. , Reference Mathieu, Ritchie, Rodés-Guirao, Appel, Giattino, Hasell, Macdonald, Dattani, Beltekian, Ortiz-Ospina and Roser 2020 ). I take the latest available figure for each country (August 1, 2023).

4.2. Calibration

In the BCA exercise, the country-specific VSL estimates are derived by adjusting the U.S. estimate for cross-country differences in income, as described in Robinson et al. ( Reference Robinson, Hammitt and O’Keeffe 2019 ). Footnote 18 I assume that the VSL for the United States is $10 million, which is approximately 160 times the projected GDP per capita in 2020 absent the pandemic. Assuming an income elasticity of 1 (i.e., the VSL to income ratio is constant across countries), I derive VSL estimates for the other countries based on their projected income in the non-pandemic scenario. Footnote 19

4.3. Results

Table 1 reports the number of official COVID-19 deaths and the estimated per capita GDP loss for each country with available data. The last three columns in Table 1 summarize the main results of this article. The fourth column reports the BCA value of preventing the COVID-19 pandemic that is derived using country-specific VSL estimates; the fifth column reports the utilitarian value and the last column reports the value computed with a prioritarian welfare function. The row titled “World” summarizes the global economic and health losses of COVID-19 and the global value of preventing a COVID-19-like pandemic.

Table 1. Burden of the pandemic around the world and the value of prevention

In absolute terms, the countries with the largest number of recorded COVID-19 deaths are the United States (1.13 million deaths), Brazil (0.70 million deaths) and India (0.53 million deaths). In per-capita terms, the largest cumulative death rate has been experienced in Peru (650 deaths per 100,000 people). The number of official COVID-19 death rates tends to be larger in higher-income countries than in lower-income countries, but there is a lot of variation within country-income groups ( Figure 1a ). This reflects, among other factors, the older age structure of the population in higher-income countries.

Figure 1. Correlation between pandemic outcomes and 2019 Gross Domestic Product (GDP) per capita.

Notes: In both figures, the x-axis displays the natural logarithm of GDP per capita in 2019 (PPP, constant 2017 international $). In (a), the y-axis represents the number of official COVID-19 deaths per 100,000 people. In (b), the y-axis represents the total GDP loss over the period 2020–2021 as a percentage of 2019 GDP. Each dot represents a country. In (b), two observations were dropped to ease the readability of the graph: Guyana (GDP loss = −102% of 2019 GDP) and Timor-Leste (GDP loss = −49% of 2019 GDP). Countries are divided into income groups based on the 2023 World Bank classification (HI, high-income countries; LI, low-income countries, LMI, lower-middle-income countries, UMI, upper-middle-income countries). Country acronyms: AFG, Afghanistan; BGR, Bulgaria; CPV, Cabo Verde; HUN, Hungary; IRL, Ireland; MDV, Maldives; PAN, Panama; PER, Peru; USA, United States.

Total GDP loss was largest in India ($2.5 trillion), United States ($1.7 trillion), and China ($1.2 trillion). In per-capita terms, the largest GDP loss occurred in Panama ($14,000). As a percentage of GDP, the largest loss was registered in the Maldives (62% of 2019 GDP) ( Figure 1b ). In comparison, the GDP loss experienced in the United States amounted to $5,000 per capita, or 8% of 2019 GDP. Note that GDP loss is estimated compared to IMF growth projections made before the pandemic and they depend on the accuracy of those projections. For example, some countries grew more during the pandemic than what was earlier projected (e.g., Ireland). The estimated GDP loss thus results to be negative. From the adopted data, it is impossible to disentangle whether the growth occurred because of the pandemic or despite the pandemic (e.g., because of bias in the projections or because of local conditions). Likewise, for countries with a positive GDP loss, this loss has to be intended with respect to what was projected in 2019, and it might well underestimate or overestimate the pandemic loss. Additionally, the total estimated GDP loss sums yearly losses. Many countries started the recovery process in 2021 once vaccines became available. The total loss thus may underestimate the economic burden felt in 2020, as well as overestimate the one experienced in 2021.

The global value of preventing a COVID-19-like pandemic amounts to $48 trillion (or about $6,200 per capita) when we evaluate mortality risks using country-specific VSL estimates. Such a measure is sensitive to country-differences in economic conditions. For example, the value of pandemic prevention in the United States is estimated at $13 trillion ($38,400 per capita). This corresponds to 27% of the global value even though only 16% of global deaths and 12% of the global GDP loss were experienced in the country. In contrast, the value of pandemic prevention in India amounts to $3 trillion ($2,200 per capita), or 6% of the global value, even though the country registered 8% of the global number of deaths and 17% of the global GDP loss. Had the mortality burden in India been evaluated at the United States VSL, the total value of prevention in India would have increased to $8 trillion.

Both the utilitarian and the prioritarian values of pandemic prevention are smaller than the one derived using conventional BCA methods. In the utilitarian case, the value of prevention amounts to $4,900 per capita (or $16 trillion), while it equals $4,800 per capita in the prioritarian case. There are two reasons behind this result. The first reason concerns the distribution of the costs of the hypothetical intervention. Standard BCA is indifferent to the distribution of costs: A policy paid by rich individuals has exactly the same net benefits as an identical policy (in terms of total costs and total benefits) that is paid by poor individuals. In contrast, with a distribution-sensitive welfare framework, the distribution of costs matters. The more the costs of the hypothetical intervention are borne by poor countries, the lower is the net social value of the intervention. The proportional-cost rule assumed in the exercise implies that the hypothetical intervention has no impact on relative income inequality. Yet, the mere fact that low-income countries would pay some of the costs tends to reduce the overall value of pandemic prevention.

The second reason concerns the distribution of the pandemic impacts. Both the utilitarian and the prioritarian approaches attach more weight to losses experienced in lower-income countries than comparable losses borne in higher-income countries. Therefore, compared to BCA, both utilitarianism and prioritarianism reduce the value of prevention in high-income countries and increase the value of prevention in low-income countries. For example, from a utilitarian point of view, the benefits of prevention in the U.S. amount to $10,300 per capita, almost a fourth of the BCA estimate ($38,400 per capita). In contrast, the utilitarian benefits of prevention in India are equal to $5,600 per capita, while the BCA estimate amounts to $2,200 per capita. The largest utilitarian value of pandemic prevention is recorded in Peru ($21,500 per capita), the country with the largest death rate. Since COVID-19 mortality and income loss tend to be larger in higher-income countries than in lower-income countries, the utilitarian and prioritarian adjustments both reduce the overall value of pandemic prevention compared to standard BCA. Had the pandemic impacts been more regressive (with larger deaths and income losses in lower-income countries than higher-income countries), then utilitarianism and prioritarianism would increase the overall value of prevention compared to BCA.

In this specific example, there is little difference between the utilitarian and the prioritarian overall value of pandemic prevention. Compared to utilitarianism, prioritarianism tends to attach even more weight to losses experienced in lower income countries on the grounds that individuals living in low-income countries are worst-off in well-being terms. The low well-being ranking is due to both low income and low average longevity. Given the proportional-cost rule and the larger health and economic impacts of the pandemic in higher-income countries, a concern for the worst-off tends to further decrease the prioritarian value of pandemic prevention compared to utilitarianism.

Thus, utilitarianism and prioritarianism both favor policies that reduce inequality across individuals. The utilitarian framework is concerned with inequality in income due to the decreasing marginal utility of income assumption. The prioritarian framework is concerned with inequality in well-being, which, in turn, is affected by inequality in income and inequality in health (here proxied by longevity). The larger the reduction in (income or well-being) inequality brought about by the policy, the larger the value of preventing a pandemic. Given that, in the example, everyone contributes to the policy costs but high-income countries benefit the most from the policy, the prioritarian value is lower than the utilitarian one, which, in turn, is lower than the BCA value. Note that this ranking is driven by the specific assumptions about the distribution of pandemic impacts and policy costs and it will not hold in general.

4.4. Sensitivity analysis

Table 2. The global per-capita value of pandemic prevention under different scenarios

The main policy implication of these results is that investing in pandemic preparedness is a good use of money when the investment is paid mostly by high-income countries. If lower income countries were asked to contribute significantly to the investment, then paying for pandemic preparedness may be more welfare-reducing than the pandemic itself.

The last row of Table 2 shows what would happen if we used different income elasticities of VSL for lower-income and higher-income countries, as recommended in Robinson et al. ( Reference Robinson, Hammitt and O’Keeffe 2019 ). In particular, the income elasticity of VSL is set at 1.5 for low- and middle-income countries and at 0.6 for high-income countries. The latter is based on a recent meta-regression analysis for the United States (Viscusi & Masterman, Reference Viscusi and Masterman 2017 ). The overall value of preventing the pandemic decreases independently of the evaluation framework, but differences across frameworks persist.

5. Discussion and conclusions

In this article, I explore the differences between BCA and SWA in the evaluation of interventions that aim at preventing future pandemics similar to the COVID-19 one. I show that BCA and SWA lead to very different recommendations. BCA determines the burden of pandemics by summing up the individuals’ monetary equivalents of the pandemic impacts. This overall burden is then compared to the total cost of the intervention. Thus, BCA is indifferent to the distribution of both monetary benefits and monetary losses. As a result, pandemic preparedness interventions are ranked independently of who bear the costs of the interventions. Additionally, since VSL typically increases in income, interventions that prevent pandemic-related mortality in high-income countries tend to be ranked higher than interventions that prevent similar losses in lower-income countries. In contrast, both utilitarian and prioritarian welfare frameworks are sensitive to the distribution of monetary benefits and costs, and impacts experienced by high-income individuals are not valued more than similar impacts experienced by low-income individuals only because the former have a higher ability to pay than the latter. Moreover, considerations about the distribution of the costs of the intervention play a major role in the value assessment under a social welfare approach: The more regressive is the distribution of costs, the lower is the value of a given intervention. This implies that, from a welfare perspective, we cannot measure the burden of future pandemics and discuss policies to prevent such a burden without reflecting on who is going to pay for those policies. Cost-sharing issues affect, for example, the funding and development of novel vaccines, as well as the funding of financing mechanisms for enhancing pandemic preparedness in low- and middle-income countries.

The COVID-19 pandemic has sparked renewed interest in pandemic preparedness. A small literature has tackled the issue of how much countries should invest to prepare for future pandemics given potential competing uses of limited resources (e.g., Fan et al. , Reference Fan, Jamison and Summers 2018 ; Glennerster et al. , Reference Glennerster, Snyder and Tan 2023 ). BCA has been the go-to methodology. The main lesson from this article is that the BCA estimates may lead to welfare-reducing choices if the distribution of the intervention costs and of the associated benefits are not carefully accounted for – unless the intervention is accompanied by an appropriate redistribution mechanism that sufficiently reduces inequality in well-being. This article has focused on the estimate of the COVID-19 pandemic burden around the world. Those estimates are informative about the optimal investment in pandemic preparedness to prevent another pandemic similar to COVID-19. Of course, the framework can be extended to reflect about the optimal investment to prevent any type of pandemic.

The analysis has several limitations. First of all, due to data constraints, the application focuses exclusively on the direct mortality and income consequences of the COVID-19 pandemic. I also looked only at short-term economic costs. The estimates are thus quite conservative as I neglected the morbidity consequences of COVID-19, other types of health impacts (e.g., on mental health or non-COVID-19 mortality), as well as long-term health or income losses due to, e.g. long-COVID, educational setbacks, and poor access to healthcare. Additionally, there are not yet enough data to judge the inequality impacts of COVID-19 within different countries, e.g. whether, all things considered, the world’s very poorest have suffered the most or whether the pandemic had any effect on inequalities in longevity or income. Although there is ample evidence of inequality in impacts at both the national and international levels, household-level survey data to run individual-level comparisons are not yet consistently available.

Furthermore, I use approximated formulas to estimate the utilitarian and prioritarian burdens of the pandemic. Because of the simplified model, those approximated formulas have a clean and intuitive interpretation. The choice of using simple approximations is to make the comparison with BCA more straightforward. Had I estimated the welfare burden of the pandemic without the approximation, it would have been more difficult to determine whether the departure from BCA was due to the concern for equity or the reliance on non-marginal risks. Nevertheless, it is questionable whether the marginal risk assumption is appropriate in the case of pandemics (Robinson et al ., Reference Robinson, Eber and Hammitt. 2021 ), even though it has been widely used in the pandemic literature so far (e.g., Fan et al. , Reference Fan, Jamison and Summers 2018 ; Viscusi, Reference Viscusi 2020 ; Glennerster et al. , Reference Glennerster, Snyder and Tan 2023 ). Future work could relax this assumption and investigate the sensitivity of economic evaluation to the chosen value framework. For example, Adler et al. ( Reference Adler, Bradley, Ferranna, Fleurbaey, Hammitt, Turquier, Voorhoeve, Wilkinson and Savulescu 2023 ) pursue this direction by estimating the value of lockdown policies in the United States through a distribution-sensitive welfare framework and without the adoption of the small risk assumption.

In measuring the well-being impacts of the pandemic, I relied on the assumption of homogeneous preferences. The homogeneous preference assumption simplifies the framework, but it neglects that individuals do have different preferences. A proper account of the well-being impacts of the pandemic should allow for the possibility that individuals have different opinions about what matters in life. For example, the willingness to take risks is often found to reduce with age (Dohmen et al. , Reference Dohmen, Falk, Bart, Huffman and Sunde 2017 ), and there seem to be consistent cross-country differences in preferences based on customs and social norms (Falk et al. , Reference Falk, Becker, Dohmen, Enke, Huffman and Sunde 2018 ). With a few exceptions (e.g., Boarini et al. , Reference Boarini, Fleurbaey, Murtin and Schreyer 2022 ), the homogeneity assumption is ubiquitous in the literature estimating differences in welfare across countries.

The choice of well-being measure plays a major role in the conceptualization of welfare and equitable distributions. This article has focused on the standard notion of von Neumann-Morgenstern utility as a measure of individual well-being, with lifetime utility being concave in income but linear in longevity. The results of this article have to be interpreted in reference to the chosen well-being measure. For example, I show that utilitarian weights do not depend on longevity, while prioritarian weights do. Other specifications of lifetime utility and other measures of well-being may lead to different results. In particular, it would be fruitful to explore whether the main results of this article are robust to the specific well-being measure adopted. Moreover, I focused exclusively on an ex post measure of well-being and welfare. Such an ex post approach presupposes that the policy maker cares about the distribution of realized well-being across the population. It would be interesting to investigate whether the global distribution of pandemic burdens is affected by the choice of welfare perspective.

Contrary to BCA, the estimation of the pandemic welfare impacts requires a larger amount of information. Indeed, we need data not only on the aggregate mortality, morbidity and economic losses due to the pandemic, but also on how those impacts are distributed across the population of interest and whether there is any correlation with pre-existing inequities in health, income or other attributes. The stylized model considered in this article was parsimonious in terms of data. More realistic applications will be more data intensive.

Although this article focuses on COVID-19 and the value of pandemic prevention, the issues discussed in this article are not unique to pandemic policies, but they apply to any intervention with unequal impacts across the population. It is often claimed that standard BCA is a methodology to identify the most efficient policy and that equity concerns should play no role in the process. However, the adoption of BCA principles implicitly entails a stance on equity: All individual monetary impacts have the same moral importance independently of who is experiencing the (positive or negative) impacts. SWA provides a valuable and flexible framework to identify policies that increase overall well-being and promote its fair distribution. For this reason, this article argues that SWA should be routinely used in policy evaluation.

Acknowledgments

The author thanks the editor and the three reviewers for their valuable comments. This article builds on a 2022 conference held by the Brocher Foundation, “Healthy, Wealthy, and Wise – The Ethics of Health Valuation,” organized by Nir Eyal (Rutgers University), Samia Hurst (University of Geneva), Lisa A. Robinson (Harvard University) and Daniel Wikler (Harvard University). The author thanks the Brocher Foundation and conference participants for their support and helpful comments. This special issue was supported by the Brocher Foundation, with supplemental funding from the Rutgers University Center for Population-Level Bioethics; the University of Geneva Institute for Ethics, History, and the Humanities and the University of Bergen Centre for Ethics and Priority Setting. More information on the Brocher Foundation is available at: https://fondation-brocher.ch/ .

The author declares none.

1 Surveys and laboratory experiments find a large degree of heterogeneity in distributive justice preferences (Schokkaert & Tarroux, Reference Schokkaert, Tarroux, Adler and Norheim 2022 ). In particular, a preference for income redistribution is not universal, depending on issues such as personal responsibility, perceived social mobility and relative income (Alesina & Angeletos, Reference Alesina and Angeletos 2005 ; Alesina et al. , Reference Alesina, Stantcheva and Teso 2018 ; Almås et al. , Reference Almås, Cappelen and Tungodden 2020 ). On the other hand, equity is a major concern for many people (Kuziemko et al. , Reference Kuziemko, Norton, Saez and Stantcheva 2015 ), and transfers from the richest to the poorest are almost generally accepted (Amiel & Cowell, Reference Amiel and Cowell 1999 ).

2 Suppose there are two individuals with the same preference parameters – so in this sense they “value life” the same – but different incomes. Even though they have the same preferences, the richer person will typically have a larger willingness to pay for mortality risk reductions because of their higher ability to pay.

3 Adler et al. ( Reference Adler, Ferranna, Hammitt and Treich 2021 ) show that the fair innings principle is justified only by prioritarian social welfare functions applied to lifetime well-being. The elicitation of people’s ethical preferences seems to support the notion that the young should receive priority in life saving treatments (Dolan et al. , Reference Dolan, Shaw, Tsuchiya and Williams 2005 ; Dolan & Tsuchiya, Reference Dolan and Tsuchiya 2012 ). However, the support for fair innings is not universal. A substantial fraction of the population seems to hold the deontological view that all lives matter equally independently of expected benefits or individual characteristics (Adler et al. , Reference Adler, Ferranna, de Laubier, Hammitt and Treich 2024 ).

4 Excess mortality is defined as the difference between overall deaths and expected deaths in the absence of the COVID-19 pandemic. Two factors explain the discrepancy between confirmed COVID-19 deaths and excess deaths. First, not all COVID-19 deaths are reported because of limited diagnostic capacity, exacerbation of comorbidities that are recorded as the main cause of death, and limited access to healthcare (Whittaker et al. , Reference Whittaker, Patrick, Alhaffar, Hamlet, Djaafara, Ghani, Ferguson, Dahab, Checchi and Watson 2021 ). Second, the pandemic caused broad negative health impacts, including the disruption of routine immunization and medical screening, delays in treatments, and reduced willingness to seek care due to fear of infection or health insurance loss (Czeisler et al. , Reference Czeisler, Marynak, Clarke, Salah, Shakya, Thierry, Ali, McMillan, Wiley, Weaver, Czeisler, Rajaratnam and Howard 2020 ; Richards et al. , Reference Richards, Anderson, Carter, Ebert and Mossialos 2020 ; Cantor et al. , Reference Cantor, Sood, Bravata, Pera and Whaley 2022 ).

5 The debate about the global inequality impact of the pandemic is still open. Although the within-country unequal burden of COVID-19 is typically uncontested, measures of global inequality are sensitive to the specific inequality metric used in the analysis. For instance, Deaton ( Reference Deaton 2021 ) shows that inequality in GDP across countries has reduced during the pandemic, while inequality in the global distribution of income has increased.

6 As opposed to papers that estimate the benefits and costs of interventions to mitigate the effects of the COVID-19 pandemic (e.g., economic lockdown policies) or that discuss the allocation of vaccines and other resources (e.g., Ferranna et al. , Reference Ferranna, Sevilla, Bloom, Adler and Norheim 2022 ).

7 The framework can be easily extended to multi-periods income losses.

The previous equation can be rewritten as equation ( 2 ).

After rearranging terms, I get equation ( 3 ).

15 While ex ante prioritarianism violates stochastic dominance, ex post prioritarianism violates the ex ante Pareto principle. In addition, ex post prioritarianism gives no importance to how the final outcome came about (e.g., through a fair lottery or an unfair one). The choice between the ex ante and the ex post approach depends on society’s ethical judgments. In this article, I adopt the ex post approach because, although individuals face a mortality risk, there is no aggregate risk (the number of deaths is deterministic). I assume that the decision-maker is interested in preventing a sure number of deaths rather than reducing inequality in individuals’ risks. For a more in-depth discussion of the differences between ex ante and ex post approaches, see Fleurbaey ( Reference Fleurbaey 2010 ) and Adler ( Reference Adler 2012 , Reference Adler 2019 ).

16 The prioritarian value of preventing a pandemic is equal to:

17 Population-average longevity is the average longevity of the population considering the population age structure and the differences in longevity across age groups.

18 Presumably, individual WTP for changes in their own mortality risk includes consideration of the change in income associated with the survival curve shift. This means that there may be double-counting with the labor share of GDP. In the case of COVID, since most deaths are among the elderly (who are unlikely to be in the paid labor force), this double-counting seems unimportant. In the exercise, I assume that the value of the income loss caused by the pandemic is not captured by the VSL estimate.

19 Robinson et al. ( Reference Robinson, Hammitt and O’Keeffe 2019 ) recommend using an income elasticity of 1.5 for lower-income countries and a lower value for higher-income countries (See also Robinson et al ., Reference Robinson, Hammitt, Cecchini, Chalkidou, Claxton, Cropper, Hoang-Vu Eozenou, Ferranti, Deolalikar, Guanais, Jamison, Kwon, Lauer, O’Keeffe, Walker, Whittington, Wilkinson, Wilson and Wong 2019 ). To simplify, I adopt a unique value for all countries and set the elasticity to 1. This value is consistent with meta-analyses of both revealed and stated preferences data, as shown in Viscusi and Masterman ( Reference Viscusi and Masterman 2017 ) and Masterman and Viscusi ( Reference Masterman and Viscusi 2018 ). An income elasticity of 1 was used also in Viscusi ( Reference Viscusi 2020 ) to estimate the global burden of COVID-19.

21 Recent meta-analyses suggest to use values around 1.5 for the elasticity of the marginal utility of income (Groom & Maddison, Reference Groom and Maddison Pr 2019 ; Acland & Greenberg, Reference Acland and Greenberg 2023 ).

Figure 1. Correlation between pandemic outcomes and 2019 Gross Domestic Product (GDP) per capita. Notes: In both figures, the x-axis displays the natural logarithm of GDP per capita in 2019 (PPP, constant 2017 international $). In (a), the y-axis represents the number of official COVID-19 deaths per 100,000 people. In (b), the y-axis represents the total GDP loss over the period 2020–2021 as a percentage of 2019 GDP. Each dot represents a country. In (b), two observations were dropped to ease the readability of the graph: Guyana (GDP loss = −102% of 2019 GDP) and Timor-Leste (GDP loss = −49% of 2019 GDP). Countries are divided into income groups based on the 2023 World Bank classification (HI, high-income countries; LI, low-income countries, LMI, lower-middle-income countries, UMI, upper-middle-income countries). Country acronyms: AFG, Afghanistan; BGR, Bulgaria; CPV, Cabo Verde; HUN, Hungary; IRL, Ireland; MDV, Maldives; PAN, Panama; PER, Peru; USA, United States.

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• Maddalena Ferranna (a1)
• DOI: https://doi.org/10.1017/bca.2024.23

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Open Access

Peer-reviewed

Research Article

The challenges arising from the COVID-19 pandemic and the way people deal with them. A qualitative longitudinal study

Contributed equally to this work with: Dominika Maison, Diana Jaworska, Dominika Adamczyk, Daria Affeltowicz

Roles Conceptualization, Funding acquisition, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing

Affiliation Faculty of Psychology, University of Warsaw, Warsaw, Poland

Roles Formal analysis, Investigation, Writing – original draft, Writing – review & editing

Roles Conceptualization, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Roles Conceptualization, Formal analysis, Investigation, Methodology

• Dominika Maison, 
• Diana Jaworska, 
• Dominika Adamczyk, 
• Daria Affeltowicz

• Published: October 11, 2021
• https://doi.org/10.1371/journal.pone.0258133
• Peer Review
• Reader Comments

The conducted qualitative research was aimed at capturing the biggest challenges related to the beginning of the COVID-19 pandemic. The interviews were carried out in March-June (five stages of the research) and in October (the 6 th stage of the research). A total of 115 in-depth individual interviews were conducted online with 20 respondents, in 6 stages. The results of the analysis showed that for all respondents the greatest challenges and the source of the greatest suffering were: a) limitation of direct contact with people; b) restrictions on movement and travel; c) necessary changes in active lifestyle; d) boredom and monotony; and e) uncertainty about the future.

Citation: Maison D, Jaworska D, Adamczyk D, Affeltowicz D (2021) The challenges arising from the COVID-19 pandemic and the way people deal with them. A qualitative longitudinal study. PLoS ONE 16(10): e0258133. https://doi.org/10.1371/journal.pone.0258133

Editor: Shah Md Atiqul Haq, Shahjalal University of Science and Technology, BANGLADESH

Received: April 6, 2021; Accepted: September 18, 2021; Published: October 11, 2021

Copyright: © 2021 Maison et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files ( S1 Dataset ).

Funding: This work was supported by the Faculty of Psychology, University of Warsaw, Poland from the funds awarded by the Ministry of Science and Higher Education in the form of a subsidy for the maintenance and development of research potential in 2020 (501-D125-01-1250000). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

The coronavirus disease (COVID-19), discovered in December 2019 in China, has reached the level of a pandemic and, till June 2021, it has affected more than 171 million people worldwide and caused more than 3.5 million deaths all over the world [ 1 ]. The COVID-19 pandemic as a major health crisis has caught the attention of many researchers, which has led to the creation of a broad quantitative picture of human behavior during the coronavirus outbreak [ 2 – 4 ]. What has been established so far is, among others, the psychological symptoms that can occur as a result of lockdown [ 2 ], and the most common coping strategies [ 5 ]. However, what we still miss is an in-depth understanding of the changes in the ways of coping with challenges over different stages of the pandemic. In the following study, we used a longitudinal qualitative method to investigate the challenges during the different waves of the coronavirus pandemic as well as the coping mechanisms accompanying them.

In Poland, the first patient was diagnosed with COVID-19 on the 4 th March 2020. Since then, the number of confirmed cases has grown to more than 2.8 million and the number of deaths to more than 73,000 (June 2021) [ 1 ]. From mid-March 2020, the Polish government, similarly to many other countries, began to introduce a number of restrictions to limit the spread of the virus. These restrictions had been changing from week to week, causing diverse reactions in people [ 6 ]. It needs to be noted that the reactions to such a dynamic situation cannot be covered by a single study. Therefore, in our study we used qualitative longitudinal research in order to monitor changes in people’s emotions, attitudes, and behavior. So far, few longitudinal studies have been carried out that investigated the various issues related to the COVID-19 pandemic; however, all of them were quantitative [ 7 – 10 ]. The qualitative approach (and especially the use of enabling and projective techniques) allows for an in-depth exploration of respondents’ reactions that goes beyond respondents’ declarations and captures what they are less aware of or even unconscious of. This study consisted of six stages of interviews that were conducted at key moments for the development of the pandemic situation in Poland. The first stage of the study was carried out at the moment of the most severe lockdown and the biggest restrictions (March 2020) and was focused on exploration how did people react to the new uncertain situation. The second stage of the study was conducted at the time when restrictions were extended and the obligation to cover the mouth and nose everywhere outside the household were introduced (middle of April 2020) and was focused at the way how did people deal with the lack of family gatherings over Easter. The third stage of the study was conducted at the moment of announcing the four stages of lifting the restrictions (April 2020) and was focused on people’s reaction to an emerging vision of getting back to normalcy. The fourth stage of the study was carried out, after the introduction of the second stage of lifting the restrictions: shopping malls, hotels, and cultural institutions were gradually being opened (May 2020). The fifth stage of the study was conducted after all four stages of restriction lifting were in place (June 2020). Only the obligation to cover the mouth and nose in public spaces, an order to maintain social distance, as well as the functioning of public places under a sanitary regime were still in effect. During those 5 stages coping strategies with the changes in restrictions were explored. The sixth and last stage of the study was a return to the respondents after a longer break, at the turn of October and November 2020, when the number of coronavirus cases in Poland began to increase rapidly and the media declared “the second wave of the pandemic”. It was the moment when the restrictions were gradually being reintroduced. A full description of the changes occurring in Poland at the time of the study can be found in S1 Table .

The following study is the first qualitative longitudinal study investigating how people cope with the challenges arising from the COVID-19 pandemic at its different stages. The study, although conducted in Poland, shows the universal psychological relations between the challenges posed by the pandemic (and, even more, the restrictions resulting from the pandemic, which were very similar across different countries, not only European) and the ways of dealing with them.

Literature review

The COVID-19 pandemic has led to a global health crisis with severe economic [ 11 ], social [ 3 ], and psychological consequences [ 4 ]. Despite the fact that there were multiple crises in recent years, such as natural disasters, economic crises, and even epidemics, the coronavirus pandemic is the first in 100 years to severely affect the entire world. The economic effects of the COVID-19 pandemic concern an impending global recession caused by the lockdown of non-essential industries and the disruption of production and supply chains [ 11 ]. Social consequences may be visible in many areas, such as the rise in family violence [ 3 ], the ineffectiveness of remote education, and increased food insecurity among impoverished families due to school closures [ 12 ]. According to some experts, the psychological consequences of COVID-19 are the ones that may persist for the longest and lead to a global mental health crisis [ 13 ]. The coronavirus outbreak is generating increased depressive symptoms, stress, anxiety, insomnia, denial, fear, and anger all over the world [ 2 , 14 ]. The economic, social, and psychological problems that people are currently facing are the consequences of novel challenges that have been posed by the pandemic.

The coronavirus outbreak is a novel, uncharted situation that has shaken the world and completely changed the everyday lives of many individuals. Due to the social distancing policy, many people have switched to remote work—in Poland, almost 75% of white-collar workers were fully or partially working from home from mid-March until the end of May 2020 [ 15 ]. School closures and remote learning imposed a new obligation on parents of supervising education, especially with younger children [ 16 ]. What is more, the government order of self-isolation forced people to spend almost all their time at home and limit or completely abandon human encounters. In addition, the deteriorating economic situation was the cause of financial hardship for many people. All these difficulties and challenges arose in the aura of a new, contagious disease with unexplored, long-lasting health effects and not fully known infectivity and lethality [ 17 ]. Dealing with the situation was not facilitated by the phenomenon of global misinformation, called by some experts as the “infodemic”, which may be defined as an overabundance of information that makes it difficult for people to find trustworthy sources and reliable guidance [ 18 ]. Studies have shown that people have multiple ways of reacting to a crisis: from radical and even violent practices, towards individual solutions and depression [ 19 ]. Not only the challenges arising from the COVID-19 pandemic but also the ways of reacting to it and coping with it are issues of paramount importance that are worth investigating.

The reactions to unusual crisis situations may be dependent on dispositional factors, such as trait anxiety or perceived control [ 20 , 21 ]. A study on reactions to Hurricane Hugo has shown that people with higher trait anxiety are more likely to develop posttraumatic symptoms following a natural disaster [ 20 ]. Moreover, lack of perceived control was shown to be positively related to the level of distress during an earthquake in Turkey [ 21 ]. According to some researchers, the COVID-19 crisis and natural disasters have much in common, as the emotions and behavior they cause are based on the same primal human emotion—fear [ 22 ]. Both pandemics and natural disasters disrupt people’s everyday lives and may have severe economic, social and psychological consequences [ 23 ]. However, despite many similarities to natural disasters, COVID-19 is a unique situation—only in 2020, the current pandemic has taken more lives than the world’s combined natural disasters in any of the past twenty years [ 24 ]. It needs to be noted that natural disasters may pose different challenges than health crises and for this reason, they may provoke disparate reactions [ 25 ]. Research on the reactions to former epidemics has shown that avoidance and safety behaviors, such as avoiding going out, visiting crowded places, and visiting hospitals, are widespread at such times [ 26 ]. When it comes to the ways of dealing with the current COVID-19 pandemic, a substantial part of the quantitative research on this issue focuses on coping mechanisms. Studies have shown that the most prevalent coping strategies are highly problem-focused [ 5 ]. Most people tend to listen to expert advice and behave calmly and appropriately in the face of the coronavirus outbreak [ 5 ]. Problem-focused coping is particularly characteristic of healthcare professionals. A study on Chinese nurses has shown that the closer the problem is to the person and the more fear it evokes, the more problem-focused coping strategy is used to deal with it [ 27 ]. On the other hand, a negative coping style that entails risky or aggressive behaviors, such as drug or alcohol use, is also used to deal with the challenges arising from the COVID-19 pandemic [ 28 ]. The factors that are correlated with negative coping include coronavirus anxiety, impairment, and suicidal ideation [ 28 ]. It is worth emphasizing that social support is a very important component of dealing with crises [ 29 ].

Scientists have attempted to systematize the reactions to difficult and unusual situations. One such concept is the “3 Cs” model created by Reich [ 30 ]. It accounts for the general rules of resilience in situations of stress caused by crises, such as natural disasters. The 3 Cs stand for: control (a belief that personal resources can be accessed to achieve valued goals), coherence (the human desire to make meaning of the world), and connectedness (the need for human contact and support) [ 30 ]. Polizzi and colleagues [ 22 ] reviewed this model from the perspective of the current COVID-19 pandemic. The authors claim that natural disasters and COVID-19 pandemic have much in common and therefore, the principles of resilience in natural disaster situations can also be used in the situation of the current pandemic [ 22 ]. They propose a set of coping behaviors that could be useful in times of the coronavirus outbreak, which include control (e.g., planning activities for each day, getting adequate sleep, limiting exposure to the news, and helping others), coherence (e.g., mindfulness and developing a coherent narrative on the event), and connectedness (e.g., establishing new relationships and caring for existing social bonds) [ 22 ].

Current study

The issue of the challenges arising from the current COVID-19 pandemic and the ways of coping with them is complex and many feelings accompanying these experiences may be unconscious and difficult to verbalize. Therefore, in order to explore and understand it deeply, qualitative methodology was applied. Although there were few qualitative studies on the reaction to the pandemic [e.g., 31 – 33 ], they did not capture the perception of the challenges and their changes that arise as the pandemic develops. Since the situation with the COVID-19 pandemic is very dynamic, the reactions to the various restrictions, orders or bans are evolving. Therefore, it was decided to conduct a qualitative longitudinal study with multiple interviews with the same respondents [ 34 ].

The study investigates the challenges arising from the current pandemic and the way people deal with them. The main aim of the project was to capture people’s reactions to the unusual and unexpected situation of the COVID-19 pandemic. Therefore, the project was largely exploratory in nature. Interviews with the participants at different stages of the epidemic allowed us to see a wide spectrum of problems and ways of dealing with them. The conducted study had three main research questions:

• What are the biggest challenges connected to the COVID-19 pandemic and the resulting restrictions?
• How are people dealing with the pandemic challenges?
• What are the ways of coping with the restrictions resulting from a pandemic change as it continues and develops (perspective of first 6 months)?

The study was approved by the institutional review board of the Faculty of Psychology University of Warsaw, Poland. All participants were provided written and oral information about the study, which included that participation was voluntary, that it was possible to withdraw without any consequences at any time, and the precautions that would be taken to protect data confidentiality. Informed consent was obtained from all participants. To ensure confidentiality, quotes are presented only with gender, age, and family status.

The study was based on qualitative methodology: individual in-depth interviews, s which are the appropriate to approach a new and unknown and multithreaded topic which, at the beginning of 2020, was the COVID-19 pandemic. Due to the need to observe respondents’ reactions to the dynamically changing situation of the COVID-19 pandemic, longitudinal study was used where the moderator met on-line with the same respondent several times, at specific time intervals. A longitudinal study was used to capture the changes in opinions, emotions, and behaviors of the respondents resulting from the changes in the external circumstances (qualitative in-depth interview tracking–[ 34 ]).

The study took place from the end of March to October 2020. Due to the epidemiological situation in the country interviews took place online, using the Google Meets online video platform. The audio was recorded and then transcribed. Before taking part in the project, the respondents were informed about the purpose of the study, its course, and the fact that participation in the project is voluntary, and that they will be able to withdraw from participation at any time. The respondents were not paid for taking part in the project.

Participants.

In total, 115 interviews were conducted with 20 participants (6 interviews with the majority of respondents). Two participants (number 11 and 19, S2 Table ) dropped out of the last two interviews, and one (number 6) dropped out of the last interview. The study was based on a purposive sample and the respondents differed in gender, age, education, family status, and work situation (see S2 Table ). In addition to demographic criteria intended to ensure that the sample was as diverse as possible, an additional criterion was to have a permanent Internet connection and a computer capable of online video interviewing. Study participants were recruited using the snowball method. They were distant acquaintances of acquaintances of individuals involved in the study. None of the moderators knew their interviewees personally.

A total of 10 men and 10 women participated in the study; their age range was: 25–55; the majority had higher education (17 respondents), they were people with different professions and work status, and different family status (singles, couples without children, and families with children). Such diversity of respondents allowed us to obtain information from different life perspectives. A full description of characteristics of study participants can be found in S2 Table .

Each interview took 2 hours on average, which gives around 240 hours of interviews. Subsequent interviews with the same respondents conducted at different intervals resulted from the dynamics of the development of the pandemic and the restrictions introduced in Poland by the government.

The interviews scenario took a semi-structured form. This allowed interviewers freely modify the questions and topics depending on the dynamics of the conversation and adapt the subject matter of the interviews not only to the research purposes but also to the needs of a given respondent. The interview guides were modified from week to week, taking into account the development of the epidemiological situation, while at the same time maintaining certain constant parts that were repeated in each interview. The main parts of the interview topic guide consisted of: (a) experiences from the time of previous interviews: thoughts, feeling, fears, and hopes; (b) everyday life—organization of the day, work, free time, shopping, and eating, etc.; (c) changes—what had changed in the life of the respondent from the time of the last interview; (d) ways of coping with the situation; and (e) media—reception of information appearing in the media. Additionally, in each interview there were specific parts, such as the reactions to the beginning of the pandemic in the first interview or the reaction to the specific restrictions that were introduced.

The interviews were conducted by 5 female interviewers with experience in moderating qualitative interviews, all with a psychological background. After each series of interviews, all the members of the research teams took part in debriefing sessions, which consisted of discussing the information obtained from each respondent, exchanging general conclusions, deciding about the topics for the following interview stage, and adjusting them to the pandemic situation in the country.

Data analysis.

All the interviews were transcribed in Polish by the moderators and then double-checked (each moderator transcribed the interviews of another moderator, and then the interviewer checked the accuracy of the transcription). The whole process of analysis was conducted on the material in Polish (the native language of the authors of the study and respondents). The final page count of the transcript is approximately 1800 pages of text. The results presented below are only a portion of the total data collected during the interviews. While there are about 250 pages of the transcription directly related to the topic of the article, due to the fact that the interview was partly free-form, some themes merge with others and it is not possible to determine the exact number of pages devoted exclusively to analysis related to the topic of the article. Full dataset can be found in S1 Dataset .

Data was then processed into thematic analysis, which is defined as a method of developing qualitative data consisting of the identification, analysis, and description of the thematic areas [ 35 ]. In this type of analysis, a thematic unit is treated as an element related to the research problem that includes an important aspect of data. An important advantage of thematic analysis is its flexibility, which allows for the adoption of the most appropriate research strategy to the phenomenon under analysis. An inductive approach was used to avoid conceptual tunnel vision. Extracting themes from the raw data using an inductive approach precludes the researcher from imposing a predetermined outcome.

As a first step, each moderator reviewed the transcripts of the interviews they had conducted. Each transcript was thematically coded individually from this point during the second and the third reading. In the next step, one of the researchers reviewed the codes extracted by the other members of the research team. Then she made initial interpretations by generating themes that captured the essence of the previously identified codes. The researcher created a list of common themes present in all of the interviews. In the next step, the extracted themes were discussed again with all the moderators conducting the coding in order to achieve consistency. This collaborative process was repeated several times during the analysis. Here, further superordinate (challenges of COVID-19 pandemic) and subordinate (ways of dealing with challenges) themes were created, often by collapsing others together, and each theme listed under a superordinate and subordinate category was checked to ensure they were accurately represented. Through this process of repeated analysis and discussion of emerging themes, it was possible to agree on the final themes that are described below.

Main challenges of the COVID-19 pandemic.

Challenge 1 –limitation of direct contact with people . The first major challenge of the pandemic was that direct contact with other people was significantly reduced. The lockdown forced many people to work from home and limit contact not only with friends but also with close family (parents, children, and siblings). Limiting contact with other people was a big challenge for most of our respondents, especially those who were living alone and for those who previously led an active social life. Depending on their earlier lifestyle profile, for some, the bigger problem was the limitation of contact with the family, for others with friends, and for still others with co-workers.

I think that because I can’t meet up with anyone and that I’m not in a relationship , I miss having sex , and I think it will become even more difficult because it will be increasingly hard to meet anyone . (5 . 3_ M_39_single) . The number In the brackets at the end of the quotes marks the respondent’s number (according to Table 1 ) and the stage of the interview (after the dash), further is information about gender (F/M), age of the respondent and family status. Linguistic errors in the quotes reflect the spoken language of the respondents.

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https://doi.org/10.1371/journal.pone.0258133.t001

Changes over time . Over the course of the 6 months of the study, an evolution in the attitudes to the restriction of face-to-face contact could be seen: from full acceptance, to later questioning its rationale. Initially (March and April), almost all the respondents understood the reasons for the isolation and were compliant. At the beginning, people were afraid of the unknown COVID-19. They were concerned that the tragic situation from Italy, which was intensively covered in the media, could repeat itself in Poland (stage 1–2 of the study). However, with time, the isolation started to bother them more and more, and they started to look for solutions to bypass the isolation guidelines (stage 3–4), both real (simply meeting each other) and mental (treating isolation only as a guideline and not as an order, perceiving the family as being less threatening than acquaintances or strangers in a store). The turning point was the long May weekend that, due to two public holidays (1 st and 3 rd May), has for many years been used as an opportunity to go away with family or friends. Many people broke their voluntary isolation during that time encouraged by information about the coming loosening of restrictions.

During the summer (stage 5 of the survey), practically no one was fully compliant with the isolation recommendations anymore. At that time, a growing familiarity could be observed with COVID-19 and an increasing tendency to talk about it as “one of many diseases”, and to convince oneself that one is not at risk and that COVID-19 is no more threatening than other viruses. Only a small group of people consciously failed to comply with the restrictions of contact with others from the very beginning of the pandemic. This behavior was mostly observed among people who were generally less anxious and less afraid of COVID-19.

I’ve had enough. I’ve had it with sitting at home. Okay, there’s some kind of virus, it’s as though it’s out there somewhere; it’s like I know 2 people who were infected but they’re still alive, nothing bad has happened to anyone. It’s just a tiny portion of people who are dying. And is it really such a tragedy that we have to be locked up at home? Surely there’s an alternative agenda there? (17.4_F_35_Adult and child)

Ways of dealing . In the initial phase, when almost everyone accepted this restriction and submitted to it, the use of communication platforms for social meetings increased (see Ways of dealing with challenges in Table 1 ) . Meetings on communication platforms were seen as an equivalent of the previous face-to-face contact and were often even accompanied by eating or drinking alcohol together. However, over time (at around stage 4–5 of the study) people began to feel that such contact was an insufficient substitute for face-to-face meetings and interest in online meetings began to wane. During this time, however, an interesting phenomenon could be seen, namely, that for many people the family was seen as a safer environment than friends, and definitely safer than strangers. The belief was that family members would be honest about being sick, while strangers not necessarily, and—on an unconscious level—the feeling was that the “family is safe”, and the “family can’t hurt them”.

When it became clear that online communication is an insufficient substitute for face-to-face contacts, people started to meet up in real life. However, a change in many behaviors associated with meeting people is clearly visible, e.g.: refraining from shaking hands, refraining from cheek kissing to greet one another, and keeping a distance during a conversation.

I can’t really say that I could ‘feel’ Good Friday or Holy Saturday. On Sunday, we had breakfast together with my husband’s family and his sister. We were in three different places but we connected over Skype. Later, at noon, we had some coffee with my parents, also over Skype. It’s obvious though that this doesn’t replace face-to-face contact but it’s always some form of conversation. (9.3_F_25_Couple, no children)

Challenge 2 –restrictions on movement and travel . In contrast to the restrictions on contact with other people, the restrictions on movement and the closing of borders were perceived more negatively and posed bigger challenges for some people (especially those who used to do a lot of travelling). In this case, it was less clear why these regulations were introduced (especially travel restrictions within the country). Moreover, travel restrictions, particularly in the case of international travels, were associated with a limitation of civil liberties. The limitation (or complete ban) on travelling abroad in the Polish situation evoked additional connotations with the communist times, that is, with the fact that there was no freedom of movement for Polish citizens (associations with totalitarianism and dictatorship). Interestingly, the lack of acceptance of this restriction was also manifested by people who did not travel much. Thus, it was not just a question of restricting travelling abroad but more of restricting the potential opportunity (“even if I’m not planning on going anywhere, I know I still can”).

Limitations on travelling around the country were particularly negatively felt by families with children, where parents believe that regular exercise and outings are necessary for the proper development of their children. For parents, it was problematic to accept the prohibition of leaving the house and going to the playground (which remained closed until mid-May). Being outdoors was perceived as important for maintaining immunity (exercise as part of a healthy lifestyle), therefore, people could not understand the reason underlying this restriction and, as a consequence, often did not accept it.

I was really bothered by the very awareness that I can’t just jump in my car or get on a plane whenever I want and go wherever I want. It’s not something that I have to do on a daily basis but freedom of movement and travelling are very important for me. (14.2_M_55_Two adults and children)

Changes over time . The travel and movement limitations, although objectively less severe for most people, aroused much greater anger than the restrictions on social contact. This was probably due to a greater sense of misunderstanding as to why these rules were being introduced in the first place. Moreover, they were often communicated inconsistently and chaotically (e.g., a ban on entering forests was introduced while, at the same time, shopping malls remained open and masses were allowed to attend church services). This anger grew over time—from interview to interview, the respondents’ irritation and lack of acceptance of this was evident (culminating in the 3 rd -4 th stage of the study). The limitation of mobility was also often associated with negative consequences for both health and the economy. Many people are convinced that being in the open air (especially accompanied by physical activity) strengthens immunity, therefore, limiting such activity may have negative health consequences. Some respondents pointed out that restricting travelling, the use of hotels and restaurants, especially during the holiday season, will have serious consequences for the existence of the tourism industry.

I can’t say I completely agree with these limitations because it’s treating everything selectively. It’s like the shopping mall is closed, I can’t buy any shoes but I can go to a home improvement store and buy some wallpaper for myself. So I don’t see the difference between encountering people in a home improvement store and a shopping mall. (18.2_F_48_Two adults and children)

Ways of dealing . Since the restriction of movement and travel was more often associated with pleasure-related behaviors than with activities necessary for living, the compensations for these restrictions were usually also from the area of hedonistic behaviors. In the statements of our respondents, terms such as “indulging” or “rewarding oneself” appeared, and behaviors such as throwing small parties at home, buying better alcohol, sweets, and new clothes were observed. There were also increased shopping behaviors related to hobbies (sometimes hobbies that could not be pursued at the given time)–a kind of “post-pandemic” shopping spree (e.g., a new bike or new skis).

Again, the reaction to this restriction also depended on the level of fear of the COVID-19 disease. People who were more afraid of being infected accepted these restrictions more easily as it gave them the feeling that they were doing something constructive to protect themselves from the infection. Conversely, people with less fears and concerns were more likely to rebel and break these bans and guidelines.

Another way of dealing with this challenge was making plans for interesting travel destinations for the post-pandemic period. This was especially salient in respondents with an active lifestyle in the past and especially visible during the 5 th stage of the study.

Today was the first day when I went to the store (due to being in quarantine after returning from abroad). I spent loads of money but I normally would have never spent so much on myself. I bought sweets and confectionery for Easter time, some Easter chocolates, too. I thought I’d do some more baking so I also bought some ingredients to do this. (1.2_ F_25_single)

Challenge 3 –necessary change in active lifestyle . Many of the limitations related to COVID-19 were a challenge for people with an active lifestyle who would regularly go to the cinema, theater, and gym, use restaurants, and do a lot of travelling. For those people, the time of the COVID constraints has brought about huge changes in their lifestyle. Most of their activities were drastically restricted overnight and they suddenly became domesticated by force, especially when it was additionally accompanied by a transition to remote work.

Compulsory spending time at home also had serious consequences for people with school-aged children who had to confront themselves with the distance learning situation of their children. The second challenge for families with children was also finding (or helping find) activities for their children to do in their free time without leaving the house.

I would love to go to a restaurant somewhere. We order food from the restaurant at least once a week, but I’d love to go to the restaurant. Spending time there is a different way of functioning. It is enjoyable and that is what I miss. I would also go to the cinema, to the theater. (13.3_M_46_Two adults and child.)

Changes over time . The nuisance of restrictions connected to an active lifestyle depended on the level of restrictions in place at a given time and the extent to which a given activity could be replaced by an alternative. Moreover, the response to these restrictions depended more on the individual differences in lifestyle rather than on the stage of the interview (except for the very beginning, when the changes in lifestyle and everyday activities were very sudden).

I miss that these restaurants are not open . And it’s not even that I would like to eat something specific . It is in all of this that I miss such freedom the most . It bothers me that I have no freedom . And I am able to get used to it , I can cook at home , I can order from home . But I just wish I had a choice . (2 . 6_F_27_single ).

Ways of dealing . In the initial phase of the pandemic (March-April—stage 1–3 of the study), when most people were afraid of the coronavirus, the acceptance of the restrictions was high. At the same time, efforts were made to find activities that could replace existing ones. Going to the gym was replaced by online exercise, and going to the cinema or theater by intensive use of streaming platforms. In the subsequent stages of the study, however, the respondents’ fatigue with these “substitutes” was noticeable. It was then that more irritation and greater non-acceptance of certain restrictions began to appear. On the other hand, the changes or restrictions introduced during the later stages of the pandemic were less sudden than the initial ones, so they were often easier to get used to.

I bought a small bike and even before that we ordered some resistance bands to work out at home, which replace certain gym equipment and devices. […] I’m considering learning a language. From the other online things, my girlfriend is having yoga classes, for instance. (7.2_M_28_Couple, no children)

Challenge 4 –boredom , monotony . As has already been shown, for many people, the beginning of the pandemic was a huge change in lifestyle, an absence of activities, and a resulting slowdown. It was sometimes associated with a feeling of weariness, monotony, and even of boredom, especially for people who worked remotely, whose days began to be similar to each other and whose working time merged with free time, weekdays with the weekends, and free time could not be filled with previous activities.

In some way, boredom. I can’t concentrate on what I’m reading. I’m trying to motivate myself to do such things as learning a language because I have so much time on my hands, or to do exercises. I don’t have this balance that I’m actually doing something for myself, like reading, working out, but also that I’m meeting up with friends. This balance has gone, so I’ve started to get bored with many things. Yesterday I felt that I was bored and something should start happening. (…) After some time, this lack of events and meetings leads to such immense boredom. (1.5_F_25_single)

Changes over time . The feeling of monotony and boredom was especially visible in stage 1 and 2 of the study when the lockdown was most restrictive and people were knocked out of their daily routines. As the pandemic continued, boredom was often replaced by irritation in some, and by stagnation in others (visible in stages 3 and 4 of the study) while, at the same time, enthusiasm for taking up new activities was waning. As most people were realizing that the pandemic was not going to end any time soon, a gradual adaptation to the new lifestyle (slower and less active) and the special pandemic demands (especially seen in stage 5 and 6 of the study) could be observed.

But I see that people around me , in fact , both family and friends , are slowly beginning to prepare themselves for more frequent stays at home . So actually more remote work , maybe everything will not be closed and we will not be locked in four walls , but this tendency towards isolation or self-isolation , such a deliberate one , appears . I guess we are used to the fact that it has to be this way . (15 . 6_M_43_Two adults and child) .

Ways of dealing . The answer to the monotony of everyday life and to finding different ways of separating work from free time was to stick to certain rituals, such as “getting dressed for work”, even when work was only by a computer at home or, if possible, setting a fixed meal time when the whole family would gather together. For some, the time of the beginning of the pandemic was treated as an extra vacation. This was especially true of people who could not carry out their work during the time of the most severe restrictions (e.g., hairdressers and doctors). For them, provided that they believed that everything would return to normal and that they would soon go back to work, a “vacation mode” was activated wherein they would sleep longer, watch a lot of movies, read books, and generally do pleasant things for which they previously had no time and which they could now enjoy without feeling guilty. Another way of dealing with the monotony and transition to a slower lifestyle was taking up various activities for which there was no time before, such as baking bread at home and cooking fancy dishes.

I generally do have a set schedule. I begin work at eight. Well, and what’s changed is that I can get up last minute, switch the computer on and be practically making my breakfast and coffee during this time. I do some work and then print out some materials for my younger daughter. You know, I have work till four, I keep on going up to the computer and checking my emails. (19.1_F_39_Two adults and children)

Challenge 5 –uncertainty about the future . Despite the difficulties arising from the circumstances and limitations described above, it seems that psychologically, the greatest challenge during a pandemic is the uncertainty of what will happen next. There was a lot of contradictory information in the media that caused a sense of confusion and heightened the feeling of anxiety.

I’m less bothered about the changes that were put in place and more about this concern about what will happen in the future. Right now, it’s like there’s these mood swings. […] Based on what’s going on, this will somehow affect every one of us. And that’s what I’m afraid of. The fact that someone will not survive and I have no way of knowing who this could be—whether it will be me or anyone else, or my dad, if somehow the coronavirus will sneak its way into our home. I simply don’t know. I’m simply afraid of this. (10.1_F_55_Couple, no children)

Changes over time . In the first phase of the pandemic (interviews 1–3), most people felt a strong sense of not being in control of the situation and of their own lives. Not only did the consequences of the pandemic include a change in lifestyle but also, very often, the suspension of plans altogether. In addition, many people felt a strong fear of the future, about what would happen, and even a sense of threat to their own or their loved ones’ lives. Gradually (interview 4), alongside anxiety, anger began to emerge about not knowing what would happen next. At the beginning of the summer (stage 5 of the study), most people had a hope of the pandemic soon ending. It was a period of easing restrictions and of opening up the economy. Life was starting to look more and more like it did before the pandemic, fleetingly giving an illusion that the end of the pandemic was “in sight” and the vision of a return to normal life. Unfortunately, autumn showed that more waves of the pandemic were approaching. In the interviews of the 6 th stage of the study, we could see more and more confusion and uncertainty, a loss of hope, and often a manifestation of disagreement with the restrictions that were introduced.

This is making me sad and angry. More angry, in fact. […] I don’t know what I should do. Up until now, there was nothing like this. Up until now, I was pretty certain of what I was doing in all the decisions I was making. (14.4_M_55_Two adults and children)

Ways of dealing . People reacted differently to the described feeling of insecurity. In order to reduce the emerging fears, some people searched (sometimes even compulsively) for any information that could help them “take control” of the situation. These people searched various sources, for example, information on the number of infected persons and the number of deaths. This knowledge gave them the illusion of control and helped them to somewhat reduce the anxiety evoked by the pandemic. The behavior of this group was often accompanied by very strict adherence to all guidelines and restrictions (e.g., frequent hand sanitization, wearing a face mask, and avoiding contact with others). This behavior increased the sense of control over the situation in these people.

A completely opposite strategy to reducing the feeling of uncertainty which we also observed in some respondents was cutting off information in the media about the scale of the disease and the resulting restrictions. These people, unable to keep up with the changing information and often inconsistent messages, in order to maintain cognitive coherence tried to cut off the media as much as possible, assuming that even if something really significant had happened, they would still find out.

I want to keep up to date with the current affairs. Even if it is an hour a day. How is the pandemic situation developing—is it increasing or decreasing. There’s a bit of propaganda there because I know that when they’re saying that they have the situation under control, they can’t control it anyway. Anyhow, it still has a somewhat calming effect that it’s dying down over here and that things aren’t that bad. And, apart from this, I listen to the news concerning restrictions, what we can and can’t do. (3.1_F_54_single)

Discussion and conclusions

The results of our study showed that the five greatest challenges resulting from the COVID-19 pandemic are: limitations of direct contact with people, restrictions on movement and travel, change in active lifestyle, boredom and monotony, and finally uncertainty about the future. As we can see the spectrum of problems resulting from the pandemic is very wide and some of them have an impact on everyday functioning and lifestyle, some other influence psychological functioning and well-being. Moreover, different people deal with these problems differently and different changes in everyday life are challenging for them. The first challenge of the pandemic COVID-19 problem is the consequence of the limitation of direct contact with others. This regulation has very strong psychological consequences in the sense of loneliness and lack of closeness. Initially, people tried to deal with this limitation through the use of internet communicators. It turned out, however, that this form of contact for the majority of people was definitely insufficient and feelings of deprivation quickly increased. As much data from psychological literature shows, contact with others can have great psychological healing properties [e.g., 29 ]. The need for closeness is a natural need in times of crisis and catastrophes [ 30 ]. Unfortunately, during the COVID-19 pandemic, the ability to meet this need was severely limited by regulations. This led to many people having serious problems with maintaining a good psychological condition.

Another troubling limitation found in our study were the restrictions on movement and travel, and the associated restrictions of most activities, which caused a huge change in lifestyle for many people. As shown in previous studies, travel and diverse leisure activities are important predictors of greater well-being [ 36 ]. Moreover, COVID-19 pandemic movement restrictions may be perceived by some people as a threat to human rights [ 37 ], which can contribute to people’s reluctance to accept lockdown rules.

The problem with accepting these restrictions was also related to the lack of understanding of the reasons behind them. Just as the limitation in contact with other people seemed understandable, the limitations related to physical activity and mobility were less so. Because of these limitations many people lost a sense of understanding of the rules and restrictions being imposed. Inconsistent communication in the media—called by some researchers the ‘infodemic’ [ 18 ], as well as discordant recommendations in different countries, causing an increasing sense of confusion in people.

Another huge challenge posed by the current pandemic is the feeling of uncertainty about the future. This feeling is caused by constant changes in the rules concerning daily functioning during the pandemic and what is prohibited and what is allowed. People lose their sense of being in control of the situation. From the psychological point of view, a long-lasting experience of lack of control can cause so-called learned helplessness, a permanent feeling of having no influence over the situation and no possibility of changing it [ 38 ], which can even result in depression and lower mental and physical wellbeing [ 39 ]. Control over live and the feeling that people have an influence on what happens in their lives is one of the basic rules of crisis situation resilience [ 30 ]. Unfortunately, also in this area, people have huge deficits caused by the pandemic. The obtained results are coherent with previous studies regarding the strategies harnessed to cope with the pandemic [e.g., 5 , 10 , 28 , 33 ]. For example, some studies showed that seeking social support is one of the most common strategies used to deal with the coronavirus pandemic [ 33 , 40 ]. Other ways to deal with this situation include distraction, active coping, and a positive appraisal of the situation [ 41 ]. Furthermore, research has shown that simple coping behaviors such as a healthy diet, not reading too much COVID-19 news, following a daily routine, and spending time outdoors may be protective factors against anxiety and depressive symptoms in times of the coronavirus pandemic [ 41 ].

This study showed that the acceptance of various limitations, and especially the feeling of discomfort associated with them, depended on the person’s earlier lifestyle. The more active and socializing a person was, the more restrictions were burdensome for him/her. The second factor, more of a psychological nature, was the fear of developing COVID-19. In this case, people who were more afraid of getting sick were more likely to submit to the imposed restrictions that, paradoxically, did not reduce their anxiety, and sometimes even heightened it.

Limitations of the study.

While the study shows interesting results, it also has some limitations. The purpose of the study was primarily to capture the first response to problems resulting from a pandemic, and as such its design is not ideal. First, the study participants are not diverse as much as would be desirable. They are mostly college-educated and relatively well off, which may influence how they perceive the pandemic situation. Furthermore, the recruitment was done by searching among the further acquaintances of the people involved in the study, so there is a risk that all the people interviewed come from a similar background. It would be necessary to conduct a study that also describes the reaction of people who are already in a more difficult life situation before the pandemic starts.

Moreover, it would also be worthwhile to pay attention to the interviewers themselves. All of the moderators were female, and although gender effects on the quality of the interviews and differences between the establishment of relationships between women and men were not observed during the debriefing process, the topic of gender effects on the results of qualitative research is frequently addressed in the literature [ 42 , 43 ]. Although the researchers approached the process with reflexivity and self-criticism at all stages, it would have seemed important to involve male moderators in the study to capture any differences in relationship dynamics.

Practical implications.

The study presented has many practical implications. Decision-makers in the state can analyze the COVID-19 pandemic crisis in a way that avoids a critical situation involving other infectious diseases in the future. The results of our study showing the most disruptive effects of the pandemic on people can serve as a basis for developing strategies to deal with the effects of the crisis so that it does not translate into a deterioration of the public’s mental health in the future.

The results of our study can also provide guidance on how to communicate information about restrictions in the future so that they are accepted and respected (for example by giving rational explanations of the reasons for introducing particular restrictions). In addition, the results of our study can also be a source of guidance on how to deal with the limitations that may arise in a recurrent COVID-19 pandemic, as well as other emergencies that could come.

The analysis of the results showed that the COVID-19 pandemic, and especially the lockdown periods, are a particular challenge for many people due to reduced social contact. On the other hand, it is social contacts that are at the same time a way of a smoother transition of crises. This knowledge should prompt decision-makers to devise ways to ensure pandemic safety without drastically limiting social contacts and to create solutions that give people a sense of control (instead of depriving it of). Providing such solutions can reduce the psychological problems associated with a pandemic and help people to cope better with it.

Conclusions

As more and more is said about the fact that the COVID-19 pandemic may not end soon and that we are likely to face more waves of this disease and related lockdowns, it is very important to understand how the different restrictions are perceived, what difficulties they cause and what are the biggest challenges resulting from them. For example, an important element of accepting the restrictions is understanding their sources, i.e., what they result from, what they are supposed to prevent, and what consequences they have for the fight against the pandemic. Moreover, we observed that the more incomprehensible the order was, the more it provoked to break it. This means that not only medical treatment is extremely important in an effective fight against a pandemic, but also appropriate communication.

The results of our study showed also that certain restrictions cause emotional deficits (e.g., loneliness, loss of sense of control) and, consequently, may cause serious problems with psychological functioning. From this perspective, it seems extremely important to understand which restrictions are causing emotional problems and how they can be dealt with in order to reduce the psychological discomfort associated with them.

Supporting information

S1 table. a full description of the changes occurring in poland at the time of the study..

https://doi.org/10.1371/journal.pone.0258133.s001

S2 Table. Characteristics of study participants.

https://doi.org/10.1371/journal.pone.0258133.s002

S1 Dataset. Transcriptions from the interviews.

https://doi.org/10.1371/journal.pone.0258133.s003

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Understanding the Impacts of the COVID-19 Pandemic on Small Businesses and Workers Using Quantitative and Qualitative Methods

Jenna honan.

Department of Community, Environment, and Policy, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA

Maia Ingram

Department of Health Promotion Sciences, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA

Carolina Quijada

Marvin chaires, jocelyn fimbres, catherine ornelas, leah stauber, rachel spitz.

Sonora Environmental Research Institute, Tucson, AZ, USA

Flor Sandoval

Scott carvajal, dean billheimer.

Department of Epidemiology and Biostatitics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA

Ann Marie Wolf

Paloma beamer, associated data.

The data underlying this article cannot be shared publicly due to concern for the privacy of individuals that participated in the study. If a reasonable request for deidentified data is sent to the corresponding author, the data may be shared pending consultation from the University of Arizona IRB and community research partners.

The COVID-19 pandemic has simultaneously exacerbated and elucidated inequities in resource distribution for small businesses across the United States in terms of worker health and the financial stability of both owners and employees. This disparity was further intensified by the constantly changing and sometimes opposing health and safety guidelines and recommendations to businesses from the local, state, and federal government agencies. To better understand how the pandemic has impacted small businesses, a cross-sectional survey was administered to owners, managers, and workers ( n = 45) in the beauty and auto shop sectors from Southern Arizona. The survey identified barriers to safe operation that these businesses faced during the pandemic, illuminated worker concerns about COVID-19, and elicited perceptions of how workplaces have changed since the novel coronavirus outbreak of 2019. A combination of open-ended and close-ended questions explored how businesses adapted to the moving target of pandemic safety recommendations, as well as how the pandemic affected businesses and workers more generally. Almost all the beauty salons surveyed had to close their doors (22/25), either temporarily or permanently, due to COVID-19, while most of the auto repair shops were able to stay open (13/20). Beauty salons were more likely to implement exposure controls meant to limit transmission with customers and coworkers, such as wearing face masks and disallowing walk-ins, and were also more likely to be affected by pandemic-related issues, such as reduced client load and sourcing difficulties. Auto shops, designated by the state of Arizona to be ‘essential’ businesses, were less likely to have experienced financial precarity due to the pandemic. Content analysis of open-ended questions using the social-ecological model documented current and future worker concerns, namely financial hardships from lockdowns and the long-term viability of their business, unwillingness of employees to return to work, uncertainty regarding the progression of the pandemic, conflict over suitable health and safety protocols, and personal or family health and well-being (including anxiety and/or stress). Findings from the survey indicate that small businesses did not have clear guidance from policymakers during the pandemic and that the enacted regulations and guidelines focused on either health and safety or finances, but rarely both. Businesses often improvised and made potentially life-changing decisions with little to no support. This analysis can be used to inform future pandemic preparedness plans for small businesses that are cost-efficient, effective at reducing environmental exposures, and ultimately more likely to be implemented by the workers.

What’s important about this paper?

This study of small businesses—salons and automobile repair shops—used the social-ecological framework to understand responses to the COVID-19 pandemic. Concerns identified were both financial and health related, such as can be mitigated through sustained educational outreach and financial support in future pandemics. This study contributes to the groundwork for future studies and community engagement that will help businesses and policymakers develop and strengthen healthy and safe work practices.

Introduction

The COVID-19 pandemic has simultaneously exacerbated and elucidated inequities in resource distribution for small businesses, with pronounced effects on the health and financial stability of both owners and employees. Small businesses tend to employ people of low socioeconomic status (SES), including minority and immigrant workers ( Acs and Nichols, 2007 ), who have experienced a disproportionate rate of COVID-19 morbidity and mortality throughout the pandemic ( Tai et al. , 2020 ). Latino populations in Arizona faced a roughly two-fold risk of catching COVID-19 paired with a 14–24% increased risk of death when compared to non-Hispanic whites ( Shen et al. , 2021 ). With a Latino/Hispanic population of approximately 44%, these discrepancies are particularly relevant for Tucson, Arizona residents ( U.S. Census Bureau, 2021 ). This is in conjunction with pre-pandemic health disparities associated with other environmental and occupational exposures that cause inflated rates of illness and disease for workers with lower SES ( Okun et al. , 2001 ; Brunette, 2005 ).

Small businesses, which are generally considered those with fewer than 100 employees, are also particularly vulnerable to economic uncertainty ( Lussier, 1996 ). The disruptions that occurred beginning in 2020 across the United States as a result of shelter-in-place orders and social distancing recommendations put forth by local, state, and federal governments created enormous financial burdens for small business owners, most of whom were ill-prepared for interruptions as abrupt and long-lasting as a pandemic. This occurred concurrently with the need to increase spending for personal protective equipment (PPE) and disinfection products, while efforts to secure both PPE and normal industry supplies were hampered by global supply chain issues. Businesses were forced to weigh the competing risks from possible workplace exposures to SARS-CoV-2 against the subsequent loss of income from public health measures meant to minimize viral transmission, such as reducing client load or temporarily closing. We conducted a survey of small businesses in Southern Arizona designed to explore how specific challenges were intensified during the pandemic and to identify strategies that have the potential to increase workplace safety in the long-term.

Challenges for Small Businesses

General challenges for small businesses may include the inability to afford health insurance for workers, the cost of engineering controls to eliminate or minimize workplace hazards, the fees and time commitments of employee training, and expensive PPE ( Black et al., 1993 , 1999 ; Moutray, 2009 ; NIOSH, 2015a ; Feinmann, 2020 ). With limited funds available for management, there is often less oversight of the individual worker, who may inadvertently cut safety corners to meet productivity demands, which in turn may normalize unsafe habits that can permeate the workplace ( Lundell and Marcham, 2018 ). Finally, small businesses are less likely to consult with industrial hygienists or government agencies ( Pedersen and Sieber, 1998 ; Okun et al. , 2001 ) due to high costs and the sometimes-distrustful attitude toward government or unions ( Azaroff et al. , 2011 ). It may also stem from a lack of access to the chronically understaffed and underfunded regulatory agencies ( Rachleff, 2021 ), as well as inadequate knowledge on when and how to contact these organizations on behalf of their business and workers ( Schneider et al. , 2004 ; Sinclair et al. , 2013 ). Finally, some small business owners lack the scientific training needed to access and interpret the relevant data ( Okun et al. , 2001 ; Brunette, 2005 ; Sinclair et al. , 2013 ) or to identify valid sources of information.

These issues were more pronounced for small businesses during the COVID-19 pandemic because they tended to lack the financial or material resources, the business framework, and the legal capacity to rapidly modify their workplace in a way that would meet all the requirements for safe and healthy operation during a pandemic ( Fairlie, 2020 ). This was further intensified by the constantly changing and sometimes opposing health and safety guidelines and recommendations coming from various government sources. Not only were the relevant data gradually being gathered synchronously with the immediate need for decision-making and policy development, but personal politics also played a role in the adoption and dissemination of chosen guidelines by state and local agents.

In terms of COVID-19, small businesses throughout the USA were overwhelmed by the concurrent stressors. Many were forced to either temporarily or permanently close their doors ( Fairlie, 2020 ). Owners, managers, and workers had to make significant and potentially life-altering decisions without all pertinent information at their disposal. For example, businesses had to balance the nebulous odds of virus transmission against the increased health risks associated with the use of volatile cleaning disinfectants, often amid societal pressure to over-sanitize to assuage fears. Again, this unequivocally affected Latino workers because of their high employment rates in the small business sectors that provide in-person services to the public ( Fischer, 2008 ; Noe-Bustamante et al. , 2021 ).

Safety culture in small businesses

One way that businesses can provide healthy workplaces is to foster a culture of safety, defined as ‘the attitudes, beliefs, perceptions and values that employees share in relation to safety’ ( Chib and Kanetkar, 2014 ). Safety culture involves creating norms and policies that emphasize safety as a priority and purveying them to each worker through managerial reiteration.

One aspect of safety culture is to recognize and address the numerous interconnected influences on an individual’s actions. The social-ecological model (SEM) recognizes that health and safety behavior is constantly affected and modified by one’s surroundings ( Kilanowski, 2017 ). The SEM takes a tiered approach, with the innermost level being an individual’s own beliefs and actions. This is followed by interpersonal influences, including interactions with family, peers, coworkers, and even customers who can directly affect an individual’s ideologies and conduct. The next level of influence is organizational, which promotes and/or enforces practices and work environments that enhance safety and wellness. Community is the subsequent level, in which the interplay of all the establishments within an environmental and social context are coalesced. The final level is policy, which impacts the activities of each of the previous levels ( Lee et al. , 2017 ). The SEM is widely applied to develop and promote health interventions in a broad range of settings ( McCloskey et al. , 2011 ; Kilanowski, 2017 ). Our group previously used the SEM to conceptualize how to ensure the health and safety of low-wage workers in small businesses ( Ingram et al. , 2021 ).

Another common and valuable way of evaluating safety in the workplace is through the National Institute for Occupational Safety and Health (NIOSH) Hierarchy of Controls (HoC). The HoC also takes a layered approach toward worker health and safety, ideally moving from most to least effective, starting with the goal of complete elimination of the hazard and concluding with individual protections. The level most reliant on the individual is the use of PPE. This layer necessitates regular and accurate use of the equipment, assuming it is always readily available. The HoC then offers administrative controls, with the goal of minimizing exposures by modifying employee behaviors through rules or work processes. Engineering controls use the building to remove hazards from the work areas, such as HVAC, local exhaust systems, or air purifiers, instead of relying on error-prone people. Substitution, which replaces the hazards with something less hazardous, and elimination, which physically removes it from the workplace, are preferred to engineering controls. The HoC model is applied through standards and enforcements. This includes any overarching guidelines and regulations that are relevant ( NIOSH, 2015b ; Morris and Cannady, 2019 ). The two models are highly comparable in that they use a larger societal framework to affect an individual ( Fig. 1 ). Both emphasize the consequences that each layer has on the others below it and acknowledge that some levels are more effective or feasible than others.

The social-ecological model and hierarchy of controls.

This research uses the SEM and HoC to understand how small businesses have adapted to the previously outlined stressors from the COVID-19 pandemic. Survey questions were designed to explore the barriers that workers encountered, to ask about concerns workers had about COVID-19, and to find any gaps between small businesses and governmental or community organizations’ resource distribution. This analysis will strengthen understanding of the impact of COVID-19 on the immediate needs of businesses while pushing for policy changes that support safer and more sustainable work environments. The goal of this study was to identify these factors and to better recognize and understand the role that each play on the health and economic security of small businesses and their staff.

A survey was implemented as part of a community-engaged research partnership between the University of Arizona, the Sonora Environmental Research Institute (SERI), and El Rio Health Center to address the health and safety of small business owners and workers in Southern Arizona. The survey included both qualitative and quantitative sections, allowing for a mixed method analysis of responses. This study, which was a supplement to an ongoing parent study, sought to identify and develop needed resources to provide to local small businesses during the pandemic. This also allowed us to maintain contact with businesses that had participated in the assessment phase of the parent study, which was put on hold as small businesses responded to the pandemic.

Parent study

The parent study will evaluate a community health worker (CHW) intervention through a cluster-randomized trial aimed at reducing volatile organic compound (VOC) exposures in auto body shops and beauty salons in metropolitan Tucson, Arizona. VOCs can cause several negative health outcomes, such as respiratory irritation, neurological disease, reproductive disorders, or cancer ( Indoor Air Pollution: An Introduction for Health Professionals, 1995 ; Soni et al. , 2018 ; Fimbres et al. , 2021 ). We had initiated business recruitment for this study when the pandemic began, which halted our research temporarily. By focusing on documenting challenges and barriers to these businesses during the pandemic, we hope to transfer our findings to other workplace hazards, including VOCs. Since recruitment for the parent study had recently been initiated before transitioning to the study presented here, there was no overlap in recruitment efforts.

Beauty salons and auto repair shops were the focus of this survey because the parent study had conducted previous research with these businesses prior to the pandemic regarding other workplace hazards. This simultaneously gave us deeper insight into these workplaces and allowed us to maintain and strengthen our ongoing relationships within these industries. It also helped us gain awareness into how other environmental exposures may have changed in response to the pandemic, such as through increased cleaning frequency or the use of stronger disinfectants.

Because beauty salon workers interface more directly with the public than auto repair workers, and because beauty salon workers are predominantly female while auto shop workers are primarily male ( Data USA, 2021 ), responses were expected to vary between the two groups. Additionally, auto shops were designated early on in Arizona as an ‘essential business,’ while beauty salons were contentiously debated regarding their essential status ( Arizona Board of Cosmetology, 2020 ; Office of the Arizona Governor, 2020 ; Polletta and Ruelas, 2020 ). Many clients were willing to postpone making appointments for beauty treatments, whereas vehicle repairs were less suitable for delays. These differences improve the likelihood that the responses represent a broad range of potential reactions from workers within these two business sectors.

Survey design

The survey had three sections: business practices, perceived risks, and impacts on businesses. Questions were adapted from several previous surveys developed to conceptualize risk from other settings and populations ( Cabrera and Leckie, 2009 ) and from a validated survey created specifically to measure risk perceptions regarding COVID-19 ( Conway III et al ., 2020 ). The questions focused on the choices that owners and workers made about modifying workplace practices during the pandemic, such as disinfection frequency or customer interactions, about barriers that they or their businesses faced, and about their worries regarding COVID-19 and the subsequent effects on business practices. The majority of responses were binary (yes/no or true/false), such as, ‘Did your shop close at any time since March 1, 2020 due to COVID-19 or anything related to it?’ Three open-ended questions were included in the survey to allow the participants to share information that may not have been covered by the close-ended questions. These questions asked about any other workplace barriers not previously mentioned, concerns the workers have about the coming year, and anything else they wanted to mention about how the pandemic has affected them or their workplace. The survey was available in English and Spanish.

Participants were recruited via social media, phone calls, mailed flyers, and poster advertisements. Phone calls were made to 656 businesses as direct outreach for recruitment, with 320 (48.8%) of the shops being in the beauty sector and 336 (51.2%) from the auto repair industry. Contact information for the businesses was compiled based on internet searches, social media presence, and driving through targeted neighborhoods looking for relevant storefronts. Surveys were either self-administered online ( n = 23) or asked over the phone ( n = 22) to owners, managers, and workers beginning in April 2021 through November 2021. Responses were de-identified prior to analysis.

Quantitative analysis

Inclusion criteria required that participants be adults (over 18 years old) who worked in Southern Arizona at a beauty salon or auto shop and were English- or Spanish-speaking. Responses were sorted by workplace, which were listed as beauty salon, auto shop, or ‘other.’ Those who selected ‘other’ as their type of work were categorized based on the description of their workplace as either beauty salon or auto shop. Although the survey has longitudinal components, this analysis considered only baseline responses as a means to identify and explore the similarities and differences between the reactions of beauty salon workers and auto repair workers. Data regarding previously implemented safety practices and pandemic-related barriers were then analyzed as one dataset to provide a more generalized look at small business health and safety practices.

Frequencies and percentages were used to describe categorical responses, and descriptive numerical summaries were used for questions that provided numerical values. Comparison of categorical responses between auto shops and beauty salons were evaluated using the Pearson Chi-Square Test of Independence. A P -value of less than 0.05 was considered statistically significant. Graphical displays were assessed to identify visual trends in ordered (categorical) outcomes.

Qualitative analysis

For each open-ended question, any participants with missing values, ‘N/A,’ ‘No,’ or equivalent responses for all three questions were excluded from the thematic analysis. Two researchers independently conducted a contextual analysis and categorized the comments into overarching themes. Each researcher grouped the responses based on similarity of content, created a description of the category, and then met to compare results and create final thematic classifications. Next, the researchers coded the comments to the matching levels of the SEM. Responses that fit into multiple levels of the SEM were coded accordingly. Any discrepancies in the coding were discussed, and comments were recoded, if necessary. Finally, responses were further stratified into beauty salon or auto shop workers to determine if there were any noticeable differences between the two business types.

The study yielded 45 completed surveys for analysis. Responses were nearly evenly split between the sectors, with 20 (44.4%) from auto repair shops or similar businesses, such as headlight repairs or boat maintenance, and 25 (55.6%) from beauty salons, such as hair salons, nail salons, and aesthetician offices ( Table 1 ). The responses were well dispersed based on gender, with 25 (55.6%) females and 20 (44.4%) males, although there was a statistically significant difference in gender between the two shop types. Of the 45 respondents, 21 (46.7%) identified as Hispanic, Latino, or Spanish. The participants ranged from 21 to 71 years old, with a median age of 40.5 years. The shops had an average of about five employees (SD ± 4.6), with the largest company employing 24 workers and the smallest being a single person. Three of the 45 surveys (6.7%) were completed in Spanish, with one of these three participants indicating Latino, Hispanic, or Spanish ethnicity. There were also significant differences in the level of education between the two shop types.

Survey respondent background and demographic characteristics

*Statistically significant difference between auto shops and beauty salons ( P < 0.05).

Quantitative findings

A vast majority of beauty salon workers stated that their shops closed either temporarily or permanently due to COVID-19 at 88% (22/25), while only 35% (7/20) of auto shops said the same. This is likely because auto shops were labeled ‘essential’ on March 23, 2020, by Governor Ducey’s Executive Order 2020-12 ( Ducey, 2020 ). Despite this large difference, beauty salon and auto shop workers reported similar percentages regarding their ability to get financial assistance for their businesses at 44% (11/25) and 40% (8/20), respectively ( Table 2 ).

Survey responses regarding pandemic-related barriers faced since March 2020

Participants were asked about their ability to purchase disinfectants or cleaning supplies, hand soap or sanitizer, and PPE. Disinfectants were the most difficult products to purchase, followed by hand soap or sanitizer for auto shops, and PPE for beauty salons ( Table 2 ).

To determine if there was a difference in vaccination rate between the two business types, we asked participants if they had received at least one dose of a COVID-19 vaccine. For auto repair shops, 80% of the respondents said yes, while for beauty salons the vaccination rate was 84% ( P = 0.526) ( Table 2 ). Vaccination rates in Arizona at the time of this survey administration were approaching 60% ( ADHS, 2021 ).

Information regarding the number of respondents that use various safety practices in their workplace as prevention strategies for COVID-19 transmission is presented in Table 3 . The most often used practices were increased frequency of workplace cleaning and disinfection, requiring hand washing or sanitizing more regularly, and use of masks by staff. The least often used practices included using portable air filters or UV lights for air disinfection. Beauty salons were significantly more likely than auto shops to require the use of face masks for clients ( P = 0.005), face masks for workers ( P = 0.015), and limiting the number of workers inside the business ( P = 0.034) ( Table 3 ).

Safety practices implemented by businesses to prevent transmission of COVID-19

a PPE controls.

b Administrative controls.

c Engineering controls.

d Elimination/substitution controls.

e Standards/enforcement.

† Temperature check, symptom questionnaire, or other.

Owners, managers and workers from both beauty salons and auto shops sought updates primarily from local (31/45) and national (29/45) news media, followed by government websites (27/45). Social media (22/45) and family or peers (20/45) were used less than news outlets and government websites, but far more often than university websites (5/45). Trade groups (14/45) were more popular sources of information for beauty salon workers (13/25) than auto workers (1/20) ( Fig. 2 ). ‘Other’ write-in options included talk radio, emailed updates, news articles, corporate heads, clients, and, interestingly, banks.

Number of participants by shop type that reported using these information sources to receive updates regarding COVID-19.

Qualitative findings

Approximately half (25/45) of the participants responded to one or more of the open-ended questions. Respondents expressed substantial anxiety and stress, including concerns related to finances due to lockdowns and the long-term viability of their business, unwillingness of employees to return to work, uncertainty regarding the progression of the pandemic, conflict over suitable health and safety protocols, and concern about personal or family health and well-being. Auto workers were more likely to discuss financial concerns, while beauty salon workers focused on health and safety.

Illustrative quotes were selected and categorized into the social-ecological framework ( Table 4 ). Most responses were categorized into the interpersonal level, demonstrating respondents’ concerns about their clients and coworkers, emphasizing their anxiety regarding the health of their employees and their customers, and describing conflict over mask protocols or other protective measures. On the organizational level, the financial health of the business and the physical health of the workers were major concerns. At the level of policy, comments reflected on the perceived failure of government measures to adequately alleviate their financial burdens, in particular noting dissatisfaction with disproportionate aide being given to larger corporations.

Selected participant responses from three open-ended questions sorted into social-economic model categories

Our study found that in both beauty salons and auto repair shops in Tucson, Arizona, the pandemic has caused small business owners and workers to struggle financially and emotionally. Limited access to supplies, insufficient economic assistance, and the unremitting possibility of viral exposures led many small businesses to close, either temporarily or permanently. We verified that small businesses struggled to access supplies, particularly for the auto repair shops. Despite the fact that beauty salons were not using their products as quickly due to closures and limited appointments, the percentage of participants that indicated difficulties with ordering supplies was still relatively high. Based on the open-ended responses, workers in businesses who remained open believed they were expected to protect themselves and their customers from COVID-19 without clear protocols, often at their own expense.

These findings are consistent with prior research that evaluated the response of small businesses to the pandemic ( Bartik et al. , 2020 ; Fairlie, 2020 ; Kalogiannidis, 2020 ). Bartik et al. (2020) found that 43% of the small businesses in their study closed temporarily due to COVID-19, and that businesses with in-person services were more negatively affected than those with less person-to-person interactions. About 70% of the businesses in their study expected to receive governmental financial assistance, while our respondents indicated that only 40% of auto shop workers and 44% of beauty salon workers were successful in doing so. In an analysis of the April 2020 Current Population Survey (CPS), Fairlie (2020) found that 22% of small business owners nationwide closed their businesses due to COVID-19. Fairlie also discusses disparities within this percentage, as minority owners tended to be more likely to lose their business. For example, Latinos saw a 32% decrease in business ownership during this time. Kalogiannidis (2020) examines how supply chain issues, social distancing, and travel bans created financial stress for small businesses. In Southern Arizona, the economic impact of closed borders was particularly visible, as the exchange of ‘non-essential’ goods and services between Mexico and the United States was entirely hindered ( Sandin, 2020 ; Uhler, 2020 ; USDHS, 2020 ).

In general, beauty salon workers were more likely to obtain information from almost all listed options than auto shop workers, implying that salon workers were more likely than auto workers to actively seek out news about COVID-19. This is particularly poignant because of the rapid developments regarding recommendations and guidelines for businesses to minimize transmission of SARS-CoV-2. Anchoring bias, which occurs when people rely most heavily on the first information they receive when making decisions, may play a large role in the response of businesses ( Mohamed et al. , 2021 ). Because transmission was initially thought to occur primarily from contact with contaminated surfaces instead of the currently accepted aerosols, some businesses may have been less likely to implement safety practices that are more protective from transmission via contaminated air. This may help explain the low numbers of respondents who used portable air filters or UV lights, which are highly effective at neutralizing airborne viruses. The trends regarding the safety practices used by these businesses to minimize infection over time are generally the same for beauty salons and auto shops, although salon workers were more likely to implement almost all of the practices in their workplace. Significant differences were seen in mask-wearing and limiting number of in-person workers, both of which are controls that effectively minimize aerosol transmission ( Clase et al. , 2020 ; Sun and Zhai, 2020 ; Bazant and Bush, 2021 ; Cheng et al. , 2021 ). Previous studies have found an absence of transmission despite confirmed exposures in hair salons that required mask wearing, reiterating the value of masks in the workplace ( Hendrix et al. , 2020 ; Swaney et al. , 2021 ).

Economic issues were discussed far more often than health and safety concerns. This could be because many of the surveys were completed while respondents were at their workplace, bringing income to the forefront of their minds. If the surveys were instead completed at home, they may have focused more on the health of themselves, their family, or their friends. Additionally, the surveys were distributed beginning mid-year of 2021, more than a year after the start of the pandemic. This may have led to ‘COVID-19 burnout,’ where those who are exposed to prolonged interpersonal stressors, particularly while on the job, become exhausted by continually thinking and talking about health issues ( Maslach and Leiter, 2016 ; Arslan et al. , 2021 ), which could have made them less likely to want to discuss their health concerns. However, occupational safety is of paramount importance during a pandemic and deserves a unique focus.

Further analysis of this discrepancy between responses involving economic versus health concerns showed dissimilarities between auto repair shop and beauty salon responses. Auto shop workers were generally more concerned about financial precarity than were beauty salon workers. As designated ‘essential’ businesses, they were less likely to be closed due to government shutdowns than beauty salons. Despite this, open-ended responses indicated that auto shops showed a much greater concern about economic uncertainty than health and safety. Personal safety may have been more salient for beauty salon workers because they must often be within six feet of their clients for more than 15 min (defined as ‘close contact’ by the CDC), increasing the likelihood of COVID-19 transmission. On the other hand, auto repair workers tend to do their jobs with limited direct contact with the public. This may have given them more time to focus on the COVID-19 related impacts on their finances stemming from minimized travel, low traffic because of the shift to working from home, and the widespread lack of vehicle usage during periods of the pandemic. Auto shop workers described difficulties related to COVID-19 as a top-down issue, concentrating on how COVID-19 safety regulations, such as lockdowns and social distancing, are affecting profits.

Our analysis underscores the importance of applying a social-ecological framework when considering worker health and safety. Many of the survey comments aligned with individual, interpersonal and organizational levels of the SEM, indicative of lacking public health policies that could have protected them against these concerns. For example, one participant mentioned fighting with a coworker about mask protocols while at work. Convoluted policies regarding mask use from local and state governments likely contributed to this type of interpersonal conflict. It is notable that the state and local governments in Arizona were often at odds about mask mandates during this time ( Weissert et al. , 2021 ). Agencies that focus on a population’s well-being, like local nonprofit organizations, industry-specific trade groups, or health clinics, are important community-level resources that can work with businesses to help them disentangle the confusing and opposing views of policy makers.

The most frequently reported COVID-19 mitigation practices fall into the administrative levels of the HoC ( Table 3 ), which rely on individual behavior change. Many strategies minimize transmission, but do not provide personal protection, other than N95 or equivalent respirators. The businesses that incorporated engineering options, such as using portable air filters or UV lights, generally did so at a much lower rate than other options. Changing the air filters on ventilation systems was done more often than other engineering controls, but this response may reflect normal routines unrelated to COVID-19 concerns. Engineering controls are noticeably lacking, potentially because these typically require modifications to the building, which can be expensive to install and maintain. Moreover, business complexes may have one central ventilation system, preventing owners from making modifications to their worksite, especially if the space is rented. This highlights the need to reach these businesses with information resources that emphasize the airborne transmissibility of SARS-CoV-2 and the control options available to minimize that risk, such as UV lights or portable air filters.

Participants reported that they used news media and governmental websites as their primary sources for COVID-19 updates. This is noteworthy when considering effective public health communication. There is a strategic and direct connection between community health and safety and the scientific literacy of citizens, which is ultimately what leads to empowerment, action, and change ( Christensen et al. , 2016 ). According to our survey, the main source of pandemic information is local news, which can and should be used for community-level engagement to encourage use of the upper levels of the HoC and to share resources about how to apply for financial support. This is particularly important at a time when facts are politicized. Data are easily obtained and readily available, but if it is not accessible to the layman and presented without bias, progress in sustaining healthy workplace environments will be hindered. The connection between short-term actions and long-term consequences is a difficult concept to present to the public, particularly when public health advocacy is at odds with economic gains.

A key missing step in keeping people safe is the engagement of the entire community to help guide policymaking and then translate that policy into action. This is emphasized by the qualitative analysis, in which some respondents expressed the desire for action to be taken at the community and policy levels where it is currently lacking. In this sense, the involvement of intermediaries, such as CHWs, would be beneficial to help decipher and translate the information coming from local, state, and federal authorities while also hearing the concerns and understanding the barriers faced by workers and businesses to promote health and safety in the workplace, which can then permeate into the rest of the community members ( Sinclair et al. , 2013 ). The use of CHWs can help bridge the gap between policy and practice ( Koch et al. , 1998 ; Friedman et al. , 2006 ; Rosenthal et al. , 2010 ); however, this does not address the lack of policy mentioned above. Creating an easily interpretable, accessible, and comprehensive preparedness plan that protects worker health while providing an economic safety net will help optimize community adaptability and endurance for future pandemics.

There are several limitations in our survey. The small number of responses ( n = 45) gives limited statistical power for quantitative analyses. While we reached a population that is often understudied, it is still difficult for these workers to participate in research studies. With limited downtime, they are sometimes reprimanded by their employers for diverting their attention away from the business. Our response rate for the survey was only 6.9% (45/656), signifying that nonresponse bias may be present in these results ( Draugalis and Plaza, 2009 ). Additionally, because we reached out to small business workers at their workplace, we are very likely missing input from businesses who were most heavily affected, as they remained closed at the time of our recruitment and therefore could not be reached. Although we did not document the name of the workplace in the survey, it is possible that some of the businesses were part of larger chains that were better situated to endure a pandemic, resulting in survey responses that may not accurately depict the struggles of smaller businesses. Additionally, we did not gather information about whether the businesses were family owned and operated, although it is unclear whether this would skew responses towards economic hardships or health concerns. Finally, information about whether the businesses rented or owned the shop space was not collected but could play a role in the responses, particularly for questions that consider financial standing or the ability of businesses to make changes, such as to ventilation systems. Further studies that can capture these details would be beneficial for understanding the intricacies of small business decision-making. Despite these limitations, our study had many strengths. By allowing both closed- and open-ended questions, we were able to draw upon the experiences of each individual to create a bigger picture of the common workplace practices among small businesses. The qualitative analysis provided a meaningful backdrop against which the quantitative analysis could be better understood.

This study can be used as a foundation for future research regarding factors that lead businesses to develop and strengthen safety cultures for times during and beyond a pandemic. Although our study focused on only two industries, future research can employ similar methods for other commercial sectors to improve our understanding of the operations of small businesses as a whole. These may include businesses that commonly employ other minority populations, businesses with less prominent gender homogeny, or other small businesses that interact with the public in different ways, such as massage parlors or restaurants. As of 2013, the small business sector represented 48% of the American workforce ( SBA, 2016 ). Additional studies should further address ways for small businesses to better understand and apply the HoC framework to protect workers from everyday hazards. Given the limited staff and resources of the regulatory agencies, CHWs may be an important conduit for bringing and translating this information to underserved communities.

Conclusions

In the time since we began conducting our survey, the Delta and Omicron variants have emerged, further underscoring the reality that managing the spread of this infectious disease should remain a public health priority. Our findings on the response of small businesses to the COVID-19 pandemic make clear the importance of providing owners and workers with the tools to protect themselves and their community through sustained educational outreach and support during public health crises. Disseminating reliable sources of information early on can cut back on misinterpretations of facts, which can have lasting impacts on how people view and respond to endemics or pandemics. In the future, sufficient emergency-use funds for small businesses could help prevent polarized reactions that can lead to unsafe work environments. In a time when agreement between policy makers is limited, trusted community leaders, such as CHWs or board members of trade associations, can encourage individuals to make safe, feasible, and sustainable business decisions that are relevant to their specific industries. Not only can they be a source of information, but they can also provide outlets for small business workers to discuss barriers to implementation that can then be considered when developing future control strategies. However, clear and consistent nonpartisan guidelines and policies across all levels of government would be most effective in helping small business workers navigate in an ever-changing world.

Supplementary Material

Wxac048_suppl_supplementary_material, acknowledgements.

We would like to thank the participants of our study for their time and input. We would also like to thank Dr. Denise Moreno Ramírez for her help in creating the visual conceptualization of the SEM and HoC in combination.

Contributor Information

Jenna Honan, Department of Community, Environment, and Policy, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA.

Maia Ingram, Department of Health Promotion Sciences, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA.

Carolina Quijada, Department of Community, Environment, and Policy, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA.

Marvin Chaires, Department of Community, Environment, and Policy, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA.

Jocelyn Fimbres, Department of Community, Environment, and Policy, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA.

Catherine Ornelas, Department of Community, Environment, and Policy, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA.

Sam Sneed, Department of Community, Environment, and Policy, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA.

Leah Stauber, Department of Community, Environment, and Policy, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA.

Rachel Spitz, Sonora Environmental Research Institute, Tucson, AZ, USA.

Flor Sandoval, Sonora Environmental Research Institute, Tucson, AZ, USA.

Scott Carvajal, Department of Health Promotion Sciences, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA.

Dean Billheimer, Department of Epidemiology and Biostatitics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA.

Ann Marie Wolf, Sonora Environmental Research Institute, Tucson, AZ, USA.

Paloma Beamer, Department of Community, Environment, and Policy, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA.

This project is funded by National Institutes of Health grants R01ES028250 and P30ES006694.

Conflict of interest

The authors declare no conflict of interest relating to the material presented in this article. The publication’s contents are solely the responsibility of the authors and do not necessarily represent the official views of the National Institutes of Health.

Data availability

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• Open access
• Published: 09 September 2024

A quantitative content analysis of topical characteristics of the online COVID-19 infodemic in the United States and Japan

• Matthew Seah 1 &
• Miho Iwakuma 1  

BMC Public Health volume  24 , Article number:  2447 ( 2024 ) Cite this article

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Metrics details

The COVID-19 pandemic has spurred the growth of a global infodemic. In order to combat the COVID-19 infodemic, it is necessary to understand what kinds of misinformation are spreading. Furthermore, various local factors influence how the infodemic manifests in different countries. Therefore, understanding how and why infodemics differ between countries is a matter of interest for public health. This study aims to elucidate and compare the types of COVID-19 misinformation produced from the infodemic in the US and Japan.

COVID-19 fact-checking articles were obtained from the two largest publishers of fact-checking articles in each language. 1,743 US articles and 148 Japanese articles in their respective languages were gathered, with articles published between 23 January 2020 and 4 November 2022. Articles were analyzed using the free text mining software KH Coder. Exploration of frequently-occurring words and groups of related words was carried out. Based on agglomeration plots and prior research, eight categories of misinformation were created. Lastly, coding rules were created for these eight categories, and a chi-squared test was performed to compare the two datasets.

Overall, the most frequent words in both languages were related to health-related terms, but the Japan dataset had more words referring to foreign countries. Among the eight categories, differences with chi-squared p  ≤ 0.01 were found after Holm-Bonferroni p value adjustment for the proportions of misinformation regarding statistics (US 40.0% vs. JP 25.7%, ϕ 0.0792); origin of the virus and resultant discrimination (US 7.0% vs. JP 20.3%, ϕ 0.1311); and COVID-19 disease severity, treatment, or testing (US 32.6% vs. JP 45.9%, ϕ 0.0756).

Conclusions

Local contextual factors were found that likely influenced the infodemic in both countries; representations of these factors include societal polarization in the US and the HPV vaccine scare in Japan. It is possible that Japan’s relative resistance to misinformation affects the kinds of misinformation consumed, directing attention away from conspiracy theories and towards health-related issues. However, more studies need to be done to verify whether misinformation resistance affects misinformation consumption patterns this way.

Peer Review reports

Introduction

The COVID-19 pandemic has brought into the spotlight the growing infodemic : the “excessive amount of unfiltered information concerning a problem such that the solution is made more difficult” [ 1 ]. Between the mainstream media, statements made by politicians, social media platforms, instant messaging services, and changing guidelines released by official institutions, the typical person is constantly inundated with a barrage of information that presents both the challenge of discerning reliable information, as well as the option to take fringe or pseudoscientific theories as the truth. This represents a public health concern, as COVID-19 misinformation or “fake news” may spread anti-vaccine views or promote racial discrimination [ 2 ].

A multi-pronged approach is necessary to mitigate the impact of the infodemic, as no single intervention can achieve the breadth required to match the scale of the worldwide flow of information. Eysenbach proposes four pillars of infodemic management in his 2020 paper: infoveillance and infodemiology (surveillance of information supply and demand, as well as its quality); building eHealth literacy; improving the translation of knowledge between academia and larger outlets such as policymakers, mainstream media, and social media; and the peer-review process and fact-checking [ 3 ].

“Fact-checking” refers to the process of evaluating a statement for its factual accuracy or whether it has been framed in a misleading manner due to omission of context. Fact-checking has its origins in American TV segments devoted to checking the accuracy of statements made by American presidential candidates [ 4 ], though most current fact-checking content is produced by websites such as Snopes or FactCheck.org in the form of articles or videos.

Fact-checking alone cannot be the ultimate counter to misinformation – not only does it have limited effects on correcting perceptions of misinformation due to the strong biases and emotions involved when interacting with such information [ 4 , 5 ], the local politics of truth [ 6 ], i.e. the historical and cultural contexts of the region, inform behavior and beliefs to a significant degree; for instance, close-contact burial practices in parts of west Africa stricken by ebola [ 7 ], or vaccine hesitancy in Japan following the HPV vaccine scare in 2013 [ 8 ]. Interventions targeting an infodemic need to take into account the nature and context of the region to be effective.

One of the few extant studies comparing the COVID-19 infodemics and national contexts across countries was published by Zeng et al. [ 9 ], in which they analyzed fact-checking article contents from the US, China, India, Germany, and France. Some key findings included the fact that non-health misinformation (e.g. regarding politics, or the origin of the virus) is nearly twice as common as health misinformation (e.g. COVID-19 being “just a cold”); Germany is relatively resilient to misinformation compared to the US or India owing to its low societal polarization and high trust in the news media; misinformation regarding the spread of COVID-19 or travel restrictions is common in China, likely due to China being the early epicenter of the pandemic as well as large-scale travel movements that occur around Chinese New Year; and wedge-driving misinformation along religious lines is common in India owing to the longstanding conflict between the nation’s Muslim and Hindu populations.

Although there is already an abundance of cross-cultural research between the US and Japan, a comparative study of infodemics in these countries has yet to be done, and much has changed in the time since the publication of the Zeng paper – noteworthy developments including the progress made in global vaccination campaigns [ 10 ], and the emergence of the highly transmissible delta and omicron variants [ 11 ]. Furthermore, the national contexts of the US and Japan differ to a notable extent, in geographical, sociocultural, and historical terms, making it reasonable to expect differences in the types of misinformation that would gather more traction. Therefore, this research aims to provide an updated understanding of the COVID-19 infodemics in the US and Japan through a quantitative content analysis of the types of misinformation that appear in fact-checking articles.

Methodology

Data selection and gathering.

In order to find the types of COVID-19 misinformation that gathered significant traction in the US and Japan, COVID-19 fact-checking articles were gathered from the top two largest fact-checking publishers: Politifact and FactCheck.org for the US, and Buzzfeed and InFact for Japan. All articles were written in their respective countries’ languages (English for the US, Japanese for Japan). A summary of the data sources used is shown in Table  1 below. Articles included were published between 23 January 2020 and 4 November 2022.

Article URLs were scraped from the COVID-19 sections of each source in Python, using the Selenium library in Chrome 108.0.5359.124. Following this, a separate program was used to visit the listed URLs and scrape the article contents using the news-please library [ 16 ]. (Source codes can be accessed at https://github.com/seahmatthew/KyotoU-PublicHealth2023 .)

Data analysis in KH coder

The open-source quantitative text analysis program KH Coder [ 17 ], developed by Koichi Higuchi at Ritsumeikan university, was used to analyze the article contents, with the US and Japan datasets in separate projects. As of January 2023, there are 5,761 published research articles which make use of KH Coder [ 18 ], many of which cover health-related research topics. Its strengths include functions for statistical analysis (e.g., term frequency) of large data files, as well as the KWIC Concordance function [ 19 ] which provides the capability to easily refer to the original data from any given result.

Word Frequency [ 19 ] was used to obtain an overview of the data as a preliminary step. Following this, Hierarchal Cluster Analysis [ 19 ] was used to explore groups of related words, and also to build the lists of terms to force pickup (such as “toilet paper” or “Moderna”) which would not be picked up by default, and irrelevant terms to force ignore (such as “website” or “article”), which introduce noise due to appearing very frequently but not being indicative of any relevant themes. This took a process of trial and error especially when building the force ignore lists, as blocking certain seemingly irrelevant terms would sometimes turn out to hide an otherwise useable article.

After substantive force pickup/ignore lists had been built for each languages, the lists were compared to ensure that relevant keywords were ignored in both languages, although words that appear frequently as syntactic features in each language (such as “pants [on] fire” or “subject”) were not duplicated in the same way.

Next, Hierarchal Cluster Analysis was re-run using the finalized force pickup/ignore lists to gather the terms to form the document coding files. For the U.S. dataset, the minimum Term Frequency (TF) was set to 90, Document Frequency (DF) to 1, and only nouns, proper nouns, and terms from the force pickup list were analyzed to minimize noise. For the Japan dataset, the minimum TF was set to 10, DF to 1, and only nouns, proper nouns, location names, and terms from the force pickup list were analyzed. For both datasets, the Ward method and Jaccard frequency were used, with the number of clusters shown being auto-chosen.

Based on the agglomeration plot turning points from the Hierarchal Cluster analyses, the prior Zeng paper [ 9 ], and familiarity with the data, it was decided to split the data into eight categories. From the categories and keywords found, coding files were built for the US and Japan datasets and applied to obtain the frequencies for each category. Articles could be assigned to multiple categories, and manual sorting was used to classify articles through a first pass after automatic sorting. Articles that failed to be classified in any category after both automatic and manual sorting were assigned to a separate Miscellaneous category.

After the code frequencies for each language had been obtained, chi-squared tests were carried out to test whether there were differences in the frequencies across countries. Holm-Bonferroni adjustment was used to adjust the p values.

The agglomeration plots produced from the Hierarchal Cluster analyses are shown below in Fig.  1 . The turning points show that somewhere in the range of seven categories would be ideal, but considering prior research and familiarity with the data, it was decided to generate eight categories.

Agglomeration plots produced by Hierarchal Cluster Analysis of the US (left) and Japan (right) datasets

The coding files created based on the categories and keywords found are shown in Table  2 . A total of eight categories were created: government policy; resource shortages; statistics; measures to stem the spread of infection; masks and transmission; origin of the virus and resultant discrimination; COVID-19 disease severity, treatment, or testing; and vaccine efficacy, contents, or safety. Each category contains a set of keywords in its respective language that results in close association; for instance, “lockdown”, “quarantine”, and “border” associate highly with articles about measures taken to stem the spread of infection.

A summary of the top 50 words with the highest tf (term frequency) is shown in Table  3 . Both the U.S. and Japan lists are topped by words pertaining to vaccination, masks, cases and testing, likely because these words are likely to appear across a broad range of categories. For instance, words pertaining to vaccination could appear in both articles about supposed deleterious health effects of vaccination, as well as articles about vaccination program plans or vaccine-related conspiracy theories.

A summary of the code frequencies, chi-squared test p values, and relevant excerpts from the data is provided below in Table  4 . Articles that contained none of the eight predetermined codes are grouped in the “Miscellaneous” category. Chi-squared tests were carried out to compare the code frequencies across datasets, and p value correction was done using the Holm-Bonferroni method. Three categories stood out due to their relatively low p values and relatively high effect sizes: statistics, the origin of the virus and resultant discrimination, and COVID-19 severity, treatment, and testing.

Versions of Tables  2 and 3 , and 4 with the original Japanese text are available in Supp_012024.docx.

The effect sizes ϕ for each category are shown below in Table  5 . Only the category on the origin of the virus and resultant discrimination showed an effect size exceeding 0.1, a small effect. The two categories of statistics, and COVID-19 severity, treatment, and testing showed the next-highest effect sizes of > 0.07. Hence, these three categories were chosen for further discussion.

Similarities and differences between US and Japan categories

Selective reading of articles with high tf (term frequency) for the chosen categories produced a handful of similarities and differences. Within the statistics category (which was more common in the US dataset, 40.1% vs. 25.7%, ϕ 0.0792), misinformation from both countries tended to downplay the severity of the COVID-19 mortality rate, or otherwise make factually false statistical assertions. US misinformation tended to make more (invalid) comparisons to influenza, and there were false assertions that the US was performing statistically better in terms of mortality rate than other countries, while Japanese misinformation contained more assertions that vaccines increase mortality rate. Many of the US articles in this category were based on quotes from then-President Donald Trump.

Within the category regarding the origin of the virus and resultant discrimination (which was more common in the Japan dataset, 20.3% vs. 7.0%, ϕ 0.1311), misinformation from both countries asserted that COVID-19 was artificially made in the Wuhan Institute of Virology. However, US misinformation tended to focus on federal funding for the institute, and some articles tied the origin of the pandemic to Chinese meat-eating practices. Japanese misinformation focused more on Chinese people within Japan itself, such as warning of incoming tourist swarms or Chinese nationals taking up space in hospitals.

Within the category of COVID-19 severity, treatment, or testing (which was more common in the Japan dataset, 46.0% vs. 32.6%, ϕ 0.0756), both countries had misinformation about treatments for COVID-19, as well as about testing kits. While both countries mentioned ivermectin, hydroxychloroquine and marijuana as COVID-19 treatments were exclusive to the US dataset, while green tea and hot water were exclusive to the Japan dataset. More US articles tended to downplay the severity of infection by likening it to the flu. There were pieces of misinformation in the US that stemmed from misinterpretation of test kits, while there were Japanese assertions that COVID-19 test kits are faulty or ineffective.

Overall, non-health misinformation appeared more frequently than health misinformation, echoing findings from other studies analyzing fact-checking articles [ 9 ] or social media posts [ 20 ].

In addition, while the category frequencies for masks and transmission did not appear to differ, the contents of articles in these categories showed differences: articles from the US dataset tended to be regarding misinformation on the effectiveness of masks as a means for preventing transmission, while articles from the Japan dataset tended to be on ancillary topics, such as the country of manufacture of masks, or mask shortages. Mask-wearing as a means for preventing disease transmission while sick is an established aspect of Japanese culture [ 21 ].

National contextual factors that affect misinformation consumption

As outlined above, there are some differences in the contents of the COVID-19 misinformation circulating in the US and Japan. A few of the numerous contextual factors that may have influenced these differences will be described further below.

Importantly, it should not be assumed that a cause-and-effect relationship is at play, as a myriad of factors influence consumer (and macro-level) information-seeking habits. For instance, on the micro level, there are consumer culture factors that influence patterns of consumption, such as social influences or social class [ 22 ]; on the macro level, society-level factors such as the quality of official communications can affect attitudes towards health measures [ 23 ]. Some evidence also exists to suggest that in certain countries, the demand for certain kinds of misinformation fluctuates based on the epidemic curve [ 9 ]. While a comprehensive list of every potential influencing factor would be beyond the scope of this research, it can be seen that local context can indeed influence information-seeking habits. Understanding the concerns and mindsets of those grappling with the infodemic should be a priority in determining what countermeasures to take (e.g., targeted messaging, rapid response, etc.).

On the topic of the high prevalence of political figures involved in US misinformation, a survey conducted by the Reuters Institute for the Study of Journalism in 2020 [ 24 ] found that American information-seeking habits surrounding COVID-19 are strongly tied to political affiliation. Left-leaning respondents were likely to trust the news media and unlikely to trust the government; the opposite was true for right-leaning participants. Trump was himself a major direct source of COVID-19 misinformation [ 25 ], and many of the erroneous claims he made are reflected in the data, especially in the Statistics and Origin categories. The significant sway a person’s political beliefs hold over their information-seeking behavior in the US is likely to be associated with the country’s highly polarized political climate. This finding of the high frequency of misinformation from politicians in the US is echoed in the Zeng paper [ 9 ], and the same paper found that this connection between societal polarization and political misinformation was also clear in India.

In the Japanese dataset, articles pertaining to the origin of COVID-19 from China were much more frequent and pointed in general; as opposed to US articles which mostly addressed conspiracy theories of American funding for the Wuhan Institute of Virology or the animal origins of the virus, articles in this category in the Japan dataset tended to focus directly on Chinese nationals, either as disproportionate occupants of Japanese medical institutions, or as spreaders of COVID-19 inbound from China. Japan’s relative geographical proximity to China and popularity as a Chinese tourist destination, as well as existing anti-Chinese sentiment that has been worsening progressively since the 1980s [ 26 ], may explain to some extent the personal nature of Japanese misinformation in this category.

At first glance, it may seem surprising that both the US and Japan have similar proportions of articles discussing vaccine efficacy, contents, or safety, especially given the heavy role US political figures played in leading supporters to act contrary to evidence-based findings [ 27 ]. In an article published in the Japanese journal Chiryo in 2021, the founders of HPV vaccine awareness group MinPapi describe how vaccine hesitancy in Japan may have been exacerbated by the human papillomavirus (HPV) vaccine side effect scare in 2013 [ 28 ]; years later, addressing vaccine hesitancy through their new website CoviNavi continues to be a challenge.

Additionally, a 2021 survey conducted in Japan showed that Japanese respondents were uncertain in general about what sources of COVID-19 information they could trust [ 20 ]. 24.7% of respondents believed there was no information source they could trust, and only 26.0% of respondents felt they could trust health experts. This stands in stark contrast to the results from the aforementioned Reuters study, where over 80% of American respondents on both sides of the political spectrum felt they could trust health experts. This difference in response to the infodemic – picking sides, as opposed to being assailed by uncertainty – may actually help to explain why vaccine misinformation is relatively common in both countries; one possible interpretation is that a limited segment of the American audience consumes vaccine misinformation in greater per capita amounts, while a more general segment of the Japanese audience consumes vaccine misinformation in lower per capita amounts.

Disinformation resilience and its effects on misinformation consumption

In a 2020 paper, Humprecht et al. outline a framework for cross-national comparisons of disinformation (henceforth “misinformation”) resilience : the degree to which online misinformation is likely to receive exposure and be spread [ 29 ]. Political factors limiting misinformation resilience include societal polarization, and frequency of populist communication; media-related factors include low trust in news media, weak public news services, and audience fragmentation; economic factors include a large advertisement market size, and high social media usage. Using this framework in a comparison of the US with 16 other mainly European countries, the authors found that the US scored the lowest in misinformation resilience, owing to its fragmented media landscape, large ad market, low trust in news, highly polarized society, and frequent populist communication.

In comparison to the US, Japan scores notably lower in terms of populist communication [ 30 ]; NHK, the public broadcasting network, attains comparable viewership to other networks [ 31 ] as opposed to American public broadcasters with one- to two-thirds the viewership of major American TV networks [ 32 , 33 ]; major TV news networks in Japan attain roughly two times the viewer share of US TV network providers, with Yahoo! News dominating the online news market with over 50% weekly usage [ 34 ]. While a formal comparison has yet to be done in the literature, these factors suggest that Japan may be more resilient to misinformation than the US. It is possible that this affected the sizes of the datasets that could be obtained, leading to the US dataset being more than ten times as large than the Japan dataset.

While it stands to reason that increased misinformation resilience would lead to lower spread and consumption of misinformation, its effect on the types of misinformation consumed is less clear. In the Zeng study [ 9 ], Germany stood out as one of the studied countries with high misinformation resilience; compared to the other countries which tended to contain high proportions of articles on political conspiracy theories, lockdown measures, or transmission methods, misinformation from Germany was centered on COVID-19 treatment and vaccines, similarly to the Japan dataset used in this report. If we consider the nature of rumors and misinformation as an answer-seeking response to a perceived external threat [ 35 ], one possible interpretation of this pattern is that increased misinformation resilience in the midst of the pandemic contributes to lower distraction with non-key issues – the key issue in this context being the health impact of COVID-19 and how it can be avoided or treated. The “Miscellaneous” category is mostly comprised of articles on these non-key issues , including those bordering on absurdity or conspiracy; while this category was not notably differently sized between the US and Japan datasets, the Japan data had a noticeably lower proportion of misinformation along the lines of the “deity of death” US article.

Strengths and limitations of this study

In comparison to prior studies which used fact-checking articles as data, this study uses a larger sample size for the US dataset and offers a Japanese dataset for the first time. In particular, using KH Coder allowed for multiple categories to be assigned to a single article, which reflects the data more accurately than other studies [ 9 ] that are limited to a single category for each article. Additionally, quantitative content analysis using KH Coder allowed for counting the term frequencies in the large datasets, as well as for referring back to the original data when needed using the KWIK Concordance function.

However, as to the limitations of the study, the span of misinformation covered in this report is limited to that selected by the editorial teams in a “gatekeeping” process [ 36 ] for the four online news sources used; in particular, fact-checking in Japan is a relatively new endeavor, with the InFact team and website notably smaller than established fact-checking organizations from the US. This has negative implications for the generalizability of the Japan data, and a larger future dataset would likely give richer results. In addition, since the categorization processes were carried out automatically, there may be a handful of data points that have not been categorized correctly. More studies should be done to further verify the relationship between the misinformation resistance of a country and the types of misinformation that spread within it. Future studies of this nature will have larger and more varied datasets to work with, whether they are about COVID-19 or any other infodemic. Finally, the effect sizes found for the sections discussed here are all of small magnitude, meaning that it should not be inferred that certain segments of misinformation should receive disproportionate amounts of focus in countries that seem vulnerable to that kind of misinformation.

Practical implications

In combination with aggregated data from other countries, data on the types of misinformation which are comparatively common in the country provides policymakers a reference point when allocating resources to tackling misinformation, through means such as rapid-response messaging [ 37 ]. Of course, this data should be weighed against the actual likely impact of said misinformation spreading in the populace; any given piece vaccine misinformation is likely to do more harm overall than a wild claim of a vaccination center bearing a logo of a “deity of death”.

This research also opens up new avenues for further research – for instance, research to verify whether modifying our taking a culturally-relevant approach to tackling misinformation results in better correction outcomes. One possible example would be altering the tone of messaging to be firmer and more succinct in an environment like Japan, where misinformation likely spreads out of uncertainty instead of certainty in misinformation, while a more indirect approach may be more effective in places like the United States where misinformed beliefs are grounded in certainty.

Using quantitative content analysis, this study shows the similarities and differences in the COVID-19 infodemics in US and Japan since the start of the pandemic. Differences were found in the proportion of articles mentioning statistics, the origin of the virus and resultant discrimination, and COVID-19 severity, treatment and testing, though the effect sizes were seen to be small.

Several facets of national context appear to support the trends seen in the data, such as the history of the HPV vaccine in Japan leading to increased distrust of COVID-19 vaccines. In addition, application of a misinformation resilience framework appears to show that in countries with higher resilience, distracting non-key issues such as conspiracy theories attract less attention compared to key issues , which refer to COVID-19 health impacts and other health information in the context of the pandemic. Understanding the types of misinformation in circulation gives policymakers and educators direction in developing strategies to counter this misinformation.

Lastly, it should be reiterated that fact-checking, even when done through appropriate channels in a culturally relevant manner, cannot be relied upon as the sole measure with which to combat an infodemic. Not only does fact-checking have heavily limited effects on correcting misinformed beliefs [ 4 , 5 ], a deluge of fact-checking information may even backfire by contributing to information overload and avoidance in the intended audience [ 38 ], or by simply acting as a dissemination channel for the misinformation that would not have been spread otherwise [ 36 ]. Fact-checking has a place as one of the pillars of infodemic management – there is a need to uphold journalistic integrity, and to provide a reliable source for a more invested, informed reader subset. The other pillars of infoveillance and infodemiology, the gradual process of building eHealth literacy in the populace, and providing clear, timely translations of scientific findings to actionable messages need to be upheld in tandem as a long-term strategy for decreasing the impact of misinformation [ 3 ].

Data availability

The dataset supporting the conclusions of this article is available in the GitHub repository, https://doi.org/10.5281/zenodo.8282744 at https://github.com/seahmatthew/KyotoU-PublicHealth2023 [ 39 ].

Abbreviations

Coronavirus disease 2019

Human papillomavirus

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Seah, M., Iwakuma, M. A quantitative content analysis of topical characteristics of the online COVID-19 infodemic in the United States and Japan. BMC Public Health 24 , 2447 (2024). https://doi.org/10.1186/s12889-024-19813-y

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