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National Bureau of Economic Research (NBER) – Bias and Credibility

Least biased.

These sources have minimal bias and use very few loaded words (wording that attempts to influence an audience by using appeal to emotion or stereotypes).  The reporting is factual and usually sourced.  These are the most credible media sources. See all Least Biased Sources.

• Overall, we rate NBER least biased based on reasonably balanced editorial positions. We also rate them High for factual reporting due to proper sourcing and a clean fact check record.

Detailed Report

Bias Rating: LEAST BIASED Factual Reporting: HIGH Country: USA (45/180 Press Freedom) Media Type: Organization/Foundation Traffic/Popularity: Medium Traffic MBFC Credibility Rating: HIGH CREDIBILITY

Founded in 1920, the NBER is a private, non-profit, non-partisan organization dedicated to conducting economic research and to disseminating research findings among academics, public policy makers, and business professionals. The NBER is the nation’s leading nonprofit economic research organization. Twenty-six Nobel Prize winners in Economics and thirteen past chairs of the President’s Council of Economic Advisers have held NBER affiliations. The current president and CEO is James M. Poterba .

Read our profile on United States government and media.

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The National Bureau of Economic Research (NBER) is a nonprofit funded through donations and grants.

Analysis / Bias

In review, NBER reports news on economic research that is geared toward policymakers. The research includes original working papers such as The Political Economy of Responses to COVID-19 in the U.S.A. and this Industrialization and Urbanization in Nineteenth-Century America .

Editorially, they tend to favor left-leaning policies, but they cover both objectives and attempt to be as least biased as possible.

Failed Fact Checks

• None in the Last 5 years

Overall, we rate NBER least biased based on reasonably balanced editorial positions. We also rate them High for factual reporting due to proper sourcing and a clean fact check record. (D. Van Zandt 3/23/2017) Updated (3/26/2021)

Source:  http://www.nber.org/

Last Updated on June 27, 2023 by Media Bias Fact Check

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National Bureau of Economic Research (NBER): Meaning, Role

Katrina Ávila Munichiello is an experienced editor, writer, fact-checker, and proofreader with more than fourteen years of experience working with print and online publications.

What Is the National Bureau of Economic Research?

The National Bureau of Economic Research (NBER) is a private, non-profit, non-partisan research organization with an aim is to promote a greater understanding of how the economy works. It disseminates economic research among public policymakers, business professionals, and the academic community.

Key Takeaways

• The NBER is a private, non-profit research organization.
• The focus areas for its research are: new statistical measurements, estimating quantitative models of economic behavior, assessing the effects of public policy on the U.S. economy, and projecting the effect of alternative policy proposals.
• NBER's research papers are produced quickly and released as "working papers." They function as talking points among economists interested in new developments within their field.

Understanding National Bureau of Economic Research (NBER)

Hundreds of the nation's leading scholars in economics and business are also NBER researchers who focus on four types of empirical research: developing new statistical measurements, estimating quantitative models of economic behavior, assessing the effects of public policies on the U.S. economy, and projecting the effects of alternative policy proposals. As of 2021, thirty-eight current or past NBER board members and research affiliates have been awarded the Nobel Prize in Economics .

According to the organization, "The National Bureau of Economic Research (NBER) is a private, nonpartisan organization that facilitates cutting-edge investigation and analysis of major economic issues. It disseminates research findings to academics, public and private-sector decision-makers, and the public by posting more than 1,200 working papers and convening more than 120 scholarly conferences each year."

The NBER officially declared an end to the economic expansion in February of 2020 as the U.S. fell into a recession during that year's economic crisis.

Role of NBER In Modern Economics

The more than 1,600 economists who are NBER researchers are the leading scholars in their fields. Most NBER-affiliated researchers are either Faculty Research Fellows (FRFs) or Research Associates (RAs). Faculty Research Fellows are typically junior scholars. Research Associates, whose appointments are approved by the NBER Board of Directors, hold tenured positions at their home institutions.

The NBER is supported by research grants from government agencies and private foundations, by investment income, and by contributions from individuals and corporations.

The group took in $32 million for the year ended June 30, 2020, according to its financial statement.

The economist Paul Krugman, writing in the New York Times, said NBER is "best described, I’d say, as the old-boy network of economics made flesh. There are a couple of NBER offices, but they’re small; what the organization mainly consists of is its associates and what they do. In many sub-fields of economics, just about anyone well-known in the profession is an NBER research associate; it’s normal for these associates to release new research as NBER working papers.

The function of these papers, in turn, is to get research out quickly so other economists can discuss it (which includes criticizing it). For working economists, the NBER WP series provides what amounts to one-stop shopping for new developments in their field."

National Bureau of Economic Research. " About the NBER ." Accessed Oct. 22, 2021.

The Statesman's Yearbook. " National Bureau of Economic Research (NBER) ." Accessed Oct. 22, 2021.

National Bureau of Economic Research. " Nobel Laureates ." Accessed Oct. 22, 2021.

National Bureau of Economic Research. " Business Cycle Dating Committee Announcement June 8, 2020 ." Accessed Oct. 22, 2021.

National Bureau of Economic Research. " Summary Statements for the Fiscal Year Ended June 30th, 2020 ." Accessed Oct. 22, 2021.

The New York Times. " Understanding the NBER ." Accessed Oct. 22, 2021.

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Nber based recession indicators for the united states from the period following the peak through the trough (usrec).

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Data in this graph are copyrighted. Please review the copyright information in the series notes before sharing.

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Units:   +1 or 0 , Not Seasonally Adjusted

Frequency:   Monthly

This time series is an interpretation of US Business Cycle Expansions and Contractions data provided by The National Bureau of Economic Research (NBER). Our time series is composed of dummy variables that represent periods of expansion and recession. The NBER identifies months and quarters of turning points without designating a date within the period that turning points occurred. The dummy variable adopts an arbitrary convention that the turning point occurred at a specific date within the period. The arbitrary convention does not reflect any judgment on this issue by the NBER's Business Cycle Dating Committee. A value of 1 is a recessionary period, while a value of 0 is an expansionary period. For this time series, the recession begins the first day of the period following a peak and ends on the last day of the period of the trough. For more options on recession shading, see the notes and links below. The recession shading data that we provide initially comes from the source as a list of dates that are either an economic peak or trough. We interpret dates into recession shading data using one of three arbitrary methods. All of our recession shading data is available using all three interpretations. The period between a peak and trough is always shaded as a recession. The peak and trough are collectively extrema. Depending on the application, the extrema, both individually and collectively, may be included in the recession period in whole or in part. In situations where a portion of a period is included in the recession, the whole period is deemed to be included in the recession period. The first interpretation, known as the midpoint method, is to show a recession from the midpoint of the peak through the midpoint of the trough for monthly and quarterly data. For daily data, the recession begins on the 15th of the month of the peak and ends on the 15th of the month of the trough. Daily data is a disaggregation of monthly data. For monthly and quarterly data, the entire peak and trough periods are included in the recession shading. This method shows the maximum number of periods as a recession for monthly and quarterly data. The Federal Reserve Bank of St. Louis uses this method in its own publications. One version of this time series is represented using the midpoint method The second interpretation, known as the trough method, is to show a recession from the period following the peak through the trough (i.e. the peak is not included in the recession shading, but the trough is). For daily data, the recession begins on the first day of the first month following the peak and ends on the last day of the month of the trough. Daily data is a disaggregation of monthly data. The trough method is used when displaying data on FRED graphs. The trough method is used for this series. The third interpretation, known as the peak method, is to show a recession from the period of the peak to the trough (i.e. the peak is included in the recession shading, but the trough is not). For daily data, the recession begins on the first day of the month of the peak and ends on the last day of the month preceding the trough. Daily data is a disaggregation of monthly data. Here is an example of this time series represented using the peak method .

Suggested Citation:

Federal Reserve Bank of St. Louis, NBER based Recession Indicators for the United States from the Period following the Peak through the Trough [USREC], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/USREC, April 26, 2024.

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National Bureau of Economic Research (NBER) Working Papers

Full-text of NBER Working Papers from November 1994 to the present.

About this Database

Full-text of NBER Working Papers from November 1994 to the present. Approximately 500 papers are published annually. The NBER is a private, non-profit, non-partisan organization dedicated to conducting economic research and to disseminating research findings among academics, public policy makers, and business professionals. Key focus areas include developing new statistical measurements, estimating quantitative models of economic behavior, and analyzing the effects of public policies.

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How Do Economists Determine Whether the Economy Is in a   Recession?

What is a recession? While some maintain that two consecutive quarters of falling real GDP constitute a recession, that is neither the official definition nor the way economists evaluate the state of the business cycle. Instead, both official determinations of recessions and economists’ assessment of economic activity are based on a holistic look at the data—including the labor market, consumer and business spending, industrial production, and incomes. Based on these data, it is unlikely that the decline in GDP in the first quarter of this year—even if followed by another GDP decline in the second quarter—indicates a recession.

The National Bureau of Economic Research (NBER) Business Cycle Dating Committee —the official recession scorekeeper—defines a recession as “a significant decline in economic activity that is spread across the economy and that lasts more than a few months.” The variables the committee typically tracks include real personal income minus government transfers, employment, various forms of real consumer spending, and industrial production. Notably, there are no fixed rules or thresholds that trigger a determination of decline, although the committee does note that in recent decades, they have given more weight to real personal income less transfers and payroll employment.

Also, because the committee depends on government statistics that are reported at various lags, it cannot officially designate a recession until after it starts. [1] So how might the NBER committee assess the health of the economy? 

Figure 1 shows the trend in four of the NBER committee’s recession-indicator variables—real income minus transfers, real spending, industrial production, and employment—relative to their values in April 2020 (the trough of the last recession, and thus, the month before the current expansion began). All of these indicators have exhibited strong growth in the U.S. economy since the start of the pandemic, and have continued to expand through the first half of this year. And while real income net of transfers has been flat in recent months, industrial production, employment, and real spending have grown this year.  The committee does not directly consider inflation; however, it is embedded in the real income and spending variables it tracks, including those plotted in Figure 1. Those data show that while inflation is highly elevated, real spending is still growing, powered by one of the strongest labor markets on record and an elevated stock of household savings.

The fact that the NBER committee looks for a “significant decline” in activity that is broad-based puts this year’s 1.6 percent rate contraction in first quarter real GDP into context. Far from being a broad contraction, the negative estimate of the growth rate was a function of inventories—one of the noisiest components of GDP growth [2] —and net exports, in part reflecting our economic strength relative to that of our trading partners, as well as less snarled global supply chains. Private domestic final demand—consumer spending and fixed investment (which together make up over 80 percent of nominal GDP)—grew at a 3.0 percent real annualized rate in the first quarter, demonstrating solid, above-trend growth. And payroll employment grew at an even stronger 4.7 percent annualized rate, followed by 3.4 percent in Q2. In fact, the 1.1 million jobs created in the second quarter—an average of around 375,000 jobs per month—is more than three times more jobs created than in any three-month period leading up to a recession.

Finally, although the unemployment rate is not on the committee’s list, the fact that it has held at a historically low 3.6 percent in the past four months also has bearing on the recession question. A widely cited indicator of recessions (the “Sahm rule” named after economist Claudia Sahm) maintains that a recession is likely underway when the three-month moving average of the unemployment rate rises by at least half a percentage point (50 basis points) relative to its lowest point in the previous 12 months. The fact that the Sahm indicator is 0, far below its 50 basis-point threshold, provides yet another indication that the economic expansion is ongoing.

Recession probabilities are never zero, but trends in the data through the first half of this year used to determine a recession are not indicating a downturn.

Looking ahead, we know that the U.S., along with the rest of the global economy, faces significant headwinds—and little relevant data are yet available on the third quarter (2022Q3). At the same time, there is a good chance that the strength of the labor market and of consumer balance sheets help the economy transition from the rapid growth of the last year to steadier and more stable growth. But, whatever path the economy takes, CEA will continue to carefully track these indicators to assess the state of the economic cycle.

[1] In fact, when recessions are short-lived, the committee typically announces them after they are over.

[2] Inventories in the GDP accounts reflect not a level change, as for example, with consumer spending, but a change in a change, i.e., whether inventories were growing or shrinking faster or slower than the previous quarter. In fact, the level of inventories rose in 2022 Q1, just not as fast as in the previous quarter.

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National Bureau of Economic Research

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Founded in 1920, the National Bureau of Economic Research is a private, nonprofit, nonpartisan research organization dedicated to promoting a greater understanding of how the economy works. The NBER is committed to undertaking and disseminating unbiased economic research among public policymakers, business professionals, and the academic community.

Over the years the Bureau's research agenda has encompassed a wide variety of issues that confront our society. The Bureau's early research focused on the aggregate economy, examining in detail the business cycle and long-term economic growth. Simon Kuznets' pioneering work on national income accounting, Wesley Mitchell's influential study of the business cycle, and Milton Friedman's research on the demand for money and the determinants of consumer spending were among the early studies done at the NBER.

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Turnout in U.S. has soared in recent elections but by some measures still trails that of many other countries

Voter turnout in the 2020 U.S. general election soared to  levels not seen in decades , fueled by the bitter campaign between Joe Biden and Donald Trump and facilitated by  pandemic-related changes  to state election rules. More than 158.4 million people voted in that election, according to a Pew Research Center tabulation of official state returns, amounting to 62.8% of people of voting age, using Census Bureau estimates of the 2020 voting-age population.

The 2020 voting surge followed unusually high turnout in the 2018 midterm elections , when about 47.5% of the voting-age population – and 51.8% of voting-age citizens – went to the polls.

This year, some political analysts are  predicting another heavy turnout  in this month’s midterms. According to a recent Center survey , 72% of registered voters say they’re “extremely” or “very” motivated to vote this year, and 65% say it “really matters” which party wins control of Congress – a level roughly on par with the run-up to the 2018 vote.

As the 2022 midterm elections draw near, Pew Research Center decided to revisit its occasional comparisons of U.S. turnout rates with those of other countries.

For our comparison group, we began with the 37 other countries in the  Organization for Economic Cooperation and Development  (OECD), a group of mostly highly developed, mostly democratic states. For greater diversification, we added to that group the six current candidates for OECD membership (Argentina, Brazil, Bulgaria, Croatia, Peru and Romania), as well as six other economically significant electoral democracies (India, Indonesia, the Philippines, South Africa, Taiwan and Uruguay), for an even 50 countries.

Political scientists often define turnout as votes cast divided by the estimated number of  eligible  voters. But eligible-voter estimates are difficult or impossible to find for many nations. So to compare turnout calculations internationally, we used two different denominators – the estimated voting-age population and the total number of registered voters, because they’re readily available for most countries.

Using both denominators, we calculated turnout rates for the most recent national election in each country as of Oct. 31, 2022, except in cases where that election was for a largely ceremonial position (such as president in a parliamentary system) or for European Parliament members, as turnout is often substantially lower in such elections. In countries that elect both a legislature and a head of state, we used the election that attracted the most voters. Voting-age turnout is based on estimates of each country’s voting-age population (VAP) by the  International Institute for Democracy and Electoral Assistance (IDEA). Registered-voter turnout is derived from each country’s reported registration data. (In some countries, IDEA’s VAP estimates are lower than the reported number of registered voters due to methodological differences.)

For most countries, we gathered vote totals from national election authorities or statistical agencies. For the U.S., which has no central elections authority, we compiled the total votes cast in the 2020 presidential election from each state’s election office, and checked them against figures compiled by the  Office of the Clerk of the U.S. House of Representatives (read more about the methodology ). We also drew data on reported registrations from the  U.S. Census Bureau .

One unknown factor, though, is how the many state voting-law changes since 2020 will affect turnout. While some states have  rolled back  early voting, absentee or mail-in voting, and other rule changes that made voting easier in 2020 – or adopted new rules that make voting more difficult or inconvenient – other states have expanded ballot access .

Even if predictions of higher-than-usual turnout come to pass, the United States likely will still trail many of its peers in the developed world in voting-age population turnout. In fact, when comparing turnout among the voting-age population in the 2020 presidential election against recent national elections in 49 other countries, the U.S. ranks 31st – between Colombia (62.5%) and Greece (63.5%).

The Center examined the most recent nationwide election results for 50 countries, mostly with highly developed economies and solid democratic traditions. The clear turnout champion was Uruguay: In the second, decisive round of that nation’s 2019 presidential election, 94.9% of the estimated voting-age population and 90.1% of  registered  voters cast ballots.

Uruguay’s voting-age turnout was followed by Turkey (89% in the 2018 presidential election) and Peru (83.6% in last year’s presidential election). All five countries with the highest voting-age turnout have presidential, as opposed to parliamentary, systems of government, and four of the five have – and enforce – laws making  voting compulsory .

In Switzerland, by contrast, just 36.1% of the voting-age population turned out in the 2019 parliamentary elections, the lowest among the 50 countries in our analysis. But that may have less to do with voter apathy than with demographics: More than a quarter of Switzerland’s permanent resident population (25.7%) are  foreign nationals , and hence ineligible to vote in Swiss elections .

When turnout is calculated as a share of  registered  voters, Swiss turnout rises to 45.1% – still the second-lowest among the 50 countries we examined. In Luxembourg, by comparison, changing the metric makes a dramatic difference: The tiny country’s voting-age turnout was just 48.2% in its 2018 parliamentary election, but 89.7% of registered voters went to the polls. Why?  Nearly half  of the population (47.1%) are foreigners.

Those examples illustrate how turnout comparisons between countries are seldom clean and often tricky. Another complicating factor, besides demographics, is how countries register their voters.

In many countries, the national government takes the lead in getting people’s names on the voter rolls – whether by registering them automatically once they become eligible (as in, for example,  Sweden  or Japan ) or by aggressively encouraging them to do so (as in the  United Kingdom ). In such countries, there’s often little difference in turnout rates among registered voters and the voting-age population as a whole.

In other countries – notably the United States – it’s largely up to individual voters to register themselves. And the U.S. is unusual in that voter registration is not the job of a single national agency, but of individual states, counties and cities. That means the rules can  vary considerably  depending on where a would-be voter lives.

It also means there’s no single, authoritative source for how many people are registered to vote in the U.S. The  Census Bureau  estimates that in 2020, 168.3 million people were registered to vote in 2020 – or at least said they were. Even so, that figure represents only about two-thirds of the total voting-age population (66.7%) and 72.7% of citizens of voting age. By comparison, 91.8% of the UK’s voting-age population was registered to vote in that country’s 2019 parliamentary election; the equivalent rates were 89.1% in Canada, 94.1% in New Zealand and 90.7% in Germany for those countries’ most recent national elections.

In the U.S., there’s a huge gap between voting-age turnout (62.8% in 2020) and registered-voter turnout (94.1% that same year). In essence, registered voters in the U.S. are much more of a self-selected group than in other countries – already more likely to vote because, in most cases, they took the trouble to register themselves.

Some states are trying to reduce that gap. As of this past January, 19 states and the District of Columbia  automatically register  people to vote (unless they opt out) when they interact with the state motor vehicles department or other designated state agencies. Three other states are on track to fully implement automatic registration in the next few years. And  North Dakota  doesn’t require voter registration at all.

Another complicating factor for cross-national turnout comparisons: According to the International Institute for Democracy and Electoral Assistance (IDEA), 27 countries (and one Swiss canton, or member state of the Swiss Confederation) have laws making voting compulsory , including 12 of the 50 countries examined here. Overall, 14 of those 27 countries actively enforce their laws, with penalties including fines, inability to access certain public services, or even imprisonment.

How much difference such laws make is unclear. On the one hand, four of the five countries with the highest turnout rate (whether measured as a share of the total voting-age population or of registered voters) have and enforce such laws. In the eight countries examined that enforce compulsory-voting laws, voting-age turnout averaged 78.2% in the most recent election, compared with 57.6% in the four countries that have such laws on the books but don’t actively enforce them. But in the remaining 38 countries and Switzerland, which have no national compulsory-voting laws, turnout averaged 65%.

Although there aren’t many examples, there’s some indication that too many elections in too short a time can dampen voters’ enthusiasm. Consider Bulgaria, which has had four parliamentary elections in the past 18 months, as the leading parties have repeatedly tried and failed to form a stable governing coalition. Turnout was 58.3% of voting-age Bulgarians in the first election (April 2021), but steadily fell to 45.8% in the most recent one (45.8% earlier this month). And with a  splintered parliament  as yet unable to agree on a new government, weary Bulgarians may yet have to trudge back to the polls sooner rather than later.

Israelis had to go the polls four times between April 2019 and March 2021 before lawmakers were able to agree on a governing coalition; turnout among voting-age Israelis rose from 74.6% in the first election to 77.9% in the third, before falling back to 73.7% in the March 2021 vote. But the coalition that emerged nearly three months after that election fell apart barely a year later , and Israel is holding yet another election today, Nov. 1.

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Drew DeSilver is a senior writer at Pew Research Center

Tuning Out: Americans on the Edge of Politics

Attitudes on an interconnected world, turnout in 2022 house midterms declined from 2018 high, final official returns show, what makes someone a good member of society, most americans say it’s very important to vote to be a good member of society, most popular.

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Business R&D Performance in the United States Tops $600 Billion in 2021

September 28, 2023

Funds spent for business R&D performed in the United States, by type of R&D, source of funds, and size of company: 2018–21

i = more than 50% of the estimate is a combination of imputation and reweighting to account for nonresponse.

a Domestic R&D performance is the cost of R&D paid for and performed by the respondent company and paid for by others outside of the company and performed by the respondent company. b R&D comprises creative and systematic work undertaken in order to increase the stock of knowledge and to devise new applications of available knowledge. This includes (1) activities aimed at acquiring new knowledge or understanding without specific immediate commercial applications or uses (basic research), (2) activities aimed at solving a specific problem or meeting a specific commercial objective (applied research), and (3) systematic work, drawing on research and practical experience and resulting in additional knowledge, which is directed to producing new processes or to improving existing products—goods or services—or processes (development). c Includes foreign subsidiaries of U.S. companies. d Includes companies located inside and outside the United States; U.S. state government agencies and laboratories; U.S. universities, colleges, and academic researchers; and all other organizations located inside and outside the United States. e Includes only companies with 10 or more domestic employees.

Detail may not add to total because of rounding.

National Center for Science and Engineering Statistics and Census Bureau, Business Enterprise Research and Development Survey.

R&D Performance, by Type of R&D, Industry Sector, and Source of Funding

In 2021, of the $602 billion that companies spent on R&D, $40 billion (7%) was spent on basic research, $86 billion (14%) on applied research, and $476 billion (79%) on development ( table 1 ). In 2021, companies in manufacturing industries performed $326 billion (54%) of domestic R&D , defined as R&D performed in the 50 states and Washington, DC ( table 2 ). Most of the funding came from these companies’ own funds (88%). Companies in nonmanufacturing industries performed $276 billion of domestic R&D (46% of total domestic R&D performance), 87% of which was paid for from companies’ own funds.

The U.S. federal government was a large source of external funding for R&D ( also referred to as R&D paid for by others ) across all industries. Of the $75 billion paid for by others, the federal government accounted for $24 billion. Seventy-four percent of federal government funding went to three industry groups: aerospace products and parts (North American Industry Classification System [NAICS] code 3364) ($11 billion), scientific research and development services (NAICS 5417) ($4 billion), and computer and electronic products (NAICS 334) ($3 billion). Companies also received funds from other U.S. companies ($27 billion) and foreign companies—including foreign parent companies of U.S. subsidiaries ($23 billion). Eighteen billion dollars (69%) of all business R&D funded by other U.S. companies was for scientific research and development services (NAICS 5417). The distribution of foreign company R&D funding was spread more broadly across multiple industries ( table 2 ). (See “ Survey Information and Data Availability ” for information on the availability of data tables with full industry detail.)

Funds spent for business R&D performed in the United States, by source of funds, selected industry, and company size: 2021

D = suppressed to avoid disclosure of confidential information; i = more than 50% of the estimate is a combination of imputation and reweighting to account for nonresponse; r = relative standard error is more than 50%.

NAICS = North American Industry Classification System; nec = not elsewhere classified.

a All R&D is the cost of R&D paid for and performed by the respondent company and paid for by others outside of the company and performed by the respondent company. b Includes foreign subsidiaries of U.S. companies ($32.1 billion). c Includes foreign parent companies of U.S. subsidiaries ($20.8 billion) and unaffiliated companies ($2.5 billion). Excludes funds from foreign subsidiaries to U.S. companies paid for through intercompany transactions ($32.1 billion). d Includes U.S. state government agencies and laboratories ($0.3 billion); U.S. universities, colleges, and academic researchers (< $0.01 billion); and all other organizations located inside ($0.7 billion) and outside the United States (< $0.01 billion). e Includes only companies with 10 or more domestic employees.

Detail may not add to total because of rounding. Industry classification was based on the dominant business code for domestic R&D performance, where available. For companies that did not report business codes, the classification used for sampling was assigned. Statistics are representative of companies located in the United States that performed or funded $50,000 or more of R&D.

National Center for Science and Engineering Statistics and Census Bureau, Business Enterprise Research and Development Survey, 2021.

Sales, R&D Intensity, and Employment of Companies That Performed or Funded R&D

U.S. companies that performed or funded R&D reported domestic net sales of $13 trillion in 2021 ( table 3 ). ​ Determining the amount of domestic net sales and operating revenues was left to the reporting company. However, guidance was given to include revenues from foreign operations and subsidiaries and from discontinued operations and to exclude intracompany transfers, returns, allowances, freight charges, and excise, sales, and other revenue-based taxes. For all industries, the R&D intensity (R&D-to-sales ratio) was 4.6%; for manufacturers, 5.0%; and for nonmanufacturers, 4.2%. Manufacturing industries with high levels of R&D intensity in 2021 were pharmaceuticals and medicines (NAICS 3254) (16.1%) and computer and electronic products (NAICS 334) (13.0%). Among the nonmanufacturing industries, industries with high levels of R&D intensity were scientific research and development services (NAICS 5417) (41.2%), software publishers (NAICS 5112) (12.9%), and computer systems design and related services (NAICS 5415) (10.2%).

Businesses that performed or funded R&D employed 23.7 million people in the United States in 2021 ( table 3 ). ​ Employment statistics in this InfoBrief are headcounts unless they are designated as full-time equivalent (FTE) estimates. R&D employees include researchers (defined as R&D scientists and engineers and their managers) and the technicians, technologists, and support staff members who work on R&D or who provide direct support to R&D activities. Approximately 2.1 million (9%) were business R&D employees. ​ The number of persons employed who were assigned full time to R&D plus a prorated number of employees who worked on R&D only part of the time was 1.9 million FTEs, of which 1.3 million FTEs were R&D researchers.

Of the 2.1 million people working on R&D in companies that performed or funded business R&D in 2021, 1.5 million were men and 0.6 million were women; 48% of the men and 45% of the women worked in manufacturing industries ( table 4 ). Researchers—that is, scientists, engineers, and their managers—accounted for 1.4 million of the 2.1 million R&D workers (67%). Of the R&D workers, 130,000 (9%) held PhD degrees. R&D technicians numbered 501,000, and 205,000 were grouped as other supporting staff.

Sales, R&D, R&D intensity, and employment for companies that performed or funded business R&D in the United States, by selected industry and company size: 2021

a Dollar values are for goods sold or services rendered by R&D-performing or R&D-funding companies located in the United States to customers outside of the company, including the U.S. federal government, foreign customers, and the company's foreign subsidiaries. Included are revenues from a company’s foreign operations and subsidiaries and from discontinued operations. If a respondent company is owned by a foreign parent company, sales to the parent company and to affiliates not owned by the respondent company are included. Excluded are intracompany transfers, returns, allowances, freight charges, and excise, sales, and other revenue-based taxes. b All R&D is the cost of R&D paid for and performed by the respondent company and paid for by others outside of the company and performed by the respondent company. c R&D intensity is the cost of domestic R&D paid for by the respondent company and others outside of the company and performed by the company divided by domestic net sales of companies that performed or funded R&D. d Data recorded on 12 March represent employment figures for the year. e Headcounts of researchers, R&D managers, technicians, clerical staff, and others assigned to R&D groups. f Includes only companies with 10 or more domestic employees.

Detail may not add to total because of rounding. Industry classification was based on the dominant business code for domestic R&D performance, where available. For companies that did not report business codes, the classification used for sampling was assigned.

Domestic employment, R&D employment by sex and work activity, R&D researchers by level of education, and full-time equivalent researcher employment for companies that performed or funded business R&D in the United States, by industrial sector: 2021

NAICS = North American Industry Classification System.

a Data recorded on 12 March represent employment figures for the year. b Includes R&D scientists and engineers and their managers. c Includes clerical staff and others assigned to R&D groups. d The number of persons employed who were assigned full time to R&D, plus a prorated number of employees who worked on R&D only part of the time.

Detail may not add to total because of rounding. Industry classification was based on the dominant business code for domestic R&D performance, where available. For companies that did not report business codes, the classification used for sampling was assigned. Excludes data for federally funded research and development centers. Also available in the full set of data tables are statistics on domestic R&D employment, by state; foreign R&D personnel headcounts, by country; and headcounts of leased (i.e., external) R&D personnel, by function.

R&D Performance, by Company Size

Small- and medium-sized companies (10–249 domestic employees) performed 9.8% of the nation’s total business R&D in 2021 ( table 3 ). Frascati Manual ; see Organisation for Economic Co-operation and Development (OECD). 2015. Frascati Manual: Guidelines for Collecting and Reporting Data on Research and Experimental Development. The Measurement of Scientific, Technological, and Innovation Activities . Paris: OECD Publishing. Available at https://www.oecd-ilibrary.org/science-and-technology/frascati-manual-2015_9789264239012-en . Anderson and Kindlon (2019) provide estimates of R&D performance and employment using these new classifications over 2008–15. The authors also compare the trends to those observed in SIRD for the time prior to 2008. The ABS, also cosponsored by NCSES and the Census Bureau, collects R&D data from companies with fewer than 10 employees for 2017 and beyond. See Anderson G, Kindlon A; NCSES. 2019. Indicators of R&D in Small Businesses: Data from the 2009–15 Business R&D and Innovation Survey . InfoBrief NSF 19-316. Alexandria, VA: National Science Foundation. Available at https://www.nsf.gov/statistics/2019/nsf19316/ ." data-bs-content="Company size classifications changed for 2017 and subsequent years in response to the revised Frascati Manual ; see Organisation for Economic Co-operation and Development (OECD). 2015. Frascati Manual: Guidelines for Collecting and Reporting Data on Research and Experimental Development. The Measurement of Scientific, Technological, and Innovation Activities . Paris: OECD Publishing. Available at https://www.oecd-ilibrary.org/science-and-technology/frascati-manual-2015_9789264239012-en . Anderson and Kindlon (2019) provide estimates of R&D performance and employment using these new classifications over 2008–15. The authors also compare the trends to those observed in SIRD for the time prior to 2008. The ABS, also cosponsored by NCSES and the Census Bureau, collects R&D data from companies with fewer than 10 employees for 2017 and beyond. See Anderson G, Kindlon A; NCSES. 2019. Indicators of R&D in Small Businesses: Data from the 2009–15 Business R&D and Innovation Survey . InfoBrief NSF 19-316. Alexandria, VA: National Science Foundation. Available at https://www.nsf.gov/statistics/2019/nsf19316/ ." data-endnote-uuid="bbd761ec-4ed8-45ec-810e-9b53647fe422">​ Company size classifications changed for 2017 and subsequent years in response to the revised Frascati Manual ; see Organisation for Economic Co-operation and Development (OECD). 2015. Frascati Manual: Guidelines for Collecting and Reporting Data on Research and Experimental Development. The Measurement of Scientific, Technological, and Innovation Activities . Paris: OECD Publishing. Available at https://www.oecd-ilibrary.org/science-and-technology/frascati-manual-2015_9789264239012-en . Anderson and Kindlon (2019) provide estimates of R&D performance and employment using these new classifications over 2008–15. The authors also compare the trends to those observed in SIRD for the time prior to 2008. The ABS, also cosponsored by NCSES and the Census Bureau, collects R&D data from companies with fewer than 10 employees for 2017 and beyond. See Anderson G, Kindlon A; NCSES. 2019. Indicators of R&D in Small Businesses: Data from the 2009–15 Business R&D and Innovation Survey . InfoBrief NSF 19-316. Alexandria, VA: National Science Foundation. Available at https://www.nsf.gov/statistics/2019/nsf19316/ . For these companies as a group, the R&D intensity was 8.8%. These companies accounted for 5% of sales and employed 7% of the 23.7 million employees who worked for R&D-performing or R&D-funding companies. They employed 18% of the 2.1 million employees engaged in business R&D in the United States.

Large companies with 250–24,999 domestic employees performed 52% of the nation’s total business R&D in 2021, and their R&D intensity was 4.7%. They accounted for 51% of sales, employed 42% of those who worked for R&D-performing or R&D-funding companies, and employed 51% of R&D employees in the United States.

The largest companies (25,000 or more domestic employees) performed 38% of the nation’s total business R&D in 2021, and their R&D intensity was 4.0%. They accounted for 44% of sales, employed 51% of those who worked for R&D-performing or R&D-funding companies, and employed 31% of business R&D employees in the United States.

R&D Performance, by State

In 2021, of the $602 billion of R&D performed in the United States, businesses in California alone accounted for 35.1% ( table 5 ). Other states with large amounts of business R&D were Washington (8.1% of the national total in 2021), Massachusetts (6.6%), Texas (4.7%), New York (4.4%), and New Jersey (4.2%). Over Half of U.S. Business R&D Performed in 10 Metropolitan Areas in 2015 . InfoBrief NSF 19-322. Alexandria, VA: National Science Foundation. Available at https://www.nsf.gov/statistics/2019/nsf19322/ . Also see Shackelford B, Wolfe R; NCSES. 2020. Businesses Performed 60% of Their U.S. R&D in 10 Metropolitan Areas in 2018 . InfoBrief NSF 21-331. Alexandria, VA: National Science Foundation. Available at https://ncses.nsf.gov/pubs/nsf21331 . Information and statistics on U.S. state trends in R&D, science and engineering education, workforce, patents and publications, and knowledge-intensive industries is also available in the Science and Engineering State Indicators data tool at https://ncses.nsf.gov/indicators/states ." data-bs-content="In addition to statistics for all states and for all states by industry, below-state level statistics are available in the full set of data tables and in other InfoBriefs; see Shackelford B, Wolfe R; NCSES. 2019. Over Half of U.S. Business R&D Performed in 10 Metropolitan Areas in 2015 . InfoBrief NSF 19-322. Alexandria, VA: National Science Foundation. Available at https://www.nsf.gov/statistics/2019/nsf19322/ . Also see Shackelford B, Wolfe R; NCSES. 2020. Businesses Performed 60% of Their U.S. R&D in 10 Metropolitan Areas in 2018 . InfoBrief NSF 21-331. Alexandria, VA: National Science Foundation. Available at https://ncses.nsf.gov/pubs/nsf21331 . Information and statistics on U.S. state trends in R&D, science and engineering education, workforce, patents and publications, and knowledge-intensive industries is also available in the Science and Engineering State Indicators data tool at https://ncses.nsf.gov/indicators/states ." data-endnote-uuid="8051c6cd-6983-4989-9a6c-bbc5713eaaa4">​ In addition to statistics for all states and for all states by industry, below-state level statistics are available in the full set of data tables and in other InfoBriefs; see Shackelford B, Wolfe R; NCSES. 2019. Over Half of U.S. Business R&D Performed in 10 Metropolitan Areas in 2015 . InfoBrief NSF 19-322. Alexandria, VA: National Science Foundation. Available at https://www.nsf.gov/statistics/2019/nsf19322/ . Also see Shackelford B, Wolfe R; NCSES. 2020. Businesses Performed 60% of Their U.S. R&D in 10 Metropolitan Areas in 2018 . InfoBrief NSF 21-331. Alexandria, VA: National Science Foundation. Available at https://ncses.nsf.gov/pubs/nsf21331 . Information and statistics on U.S. state trends in R&D, science and engineering education, workforce, patents and publications, and knowledge-intensive industries is also available in the Science and Engineering State Indicators data tool at https://ncses.nsf.gov/indicators/states .

Funds spent for business R&D performed in the United States, by state and source of funds: 2021

a All R&D is the cost of domestic R&D paid for by the respondent company and others outside of the company and performed by the respondent company. b Includes data reported that were not allocated to a specific state by multi-establishment companies. For single-establishment companies, data reported were allocated to the state in the address used to mail the survey form.

Capital Expenditures

Companies that performed or funded R&D in the United States in 2021 spent $793 billion on capital, that is, assets with expected useful lives of more than 1 year ( table 6 ). Of this amount, $53 billion (7%) was for assets used for domestic R&D operations (i.e., land acquisitions, buildings and land improvement, equipment, capitalized software, and other assets). Companies in manufacturing industries spent $28 billion on capital for domestic R&D, and companies in nonmanufacturing industries spent $24 billion. Industries with high levels of capital expenditures on assets used for domestic R&D in 2021 were pharmaceuticals and medicines (NAICS 3254) ($7.5 billion, or 14% of national capital expenditures on assets used for R&D) and semiconductor and other electronic products (NAICS 3344) ($5 billion, or 9%). Among all types of capital assets, manufacturing industries spent the most on equipment ($15 billion, or 53% of total capital assets used for domestic R&D), and nonmanufacturing industries disbursed the most on capitalized software ($13.7 billion, or 56%).

Capital expenditures in the United States, total and used for domestic R&D, by type of expenditure, industry, and company size: 2021

* = amount < $500,000; i = more than 50% of the estimate is a combination of imputation and reweighting to account for nonresponse; r = relative standard error is more than 50%.

a Domestic R&D is the R&D paid for by the respondent company and others outside of the company and performed by the company. b Capital expenditures are payments by a business for assets that usually have a useful life of more than 1 year. The value of assets acquired or improved through capital expenditures is recorded on a company’s balance sheet. BERD Survey statistics exclude the cost of assets acquired through mergers and acquisitions. c Capital expenditures for long-lived assets used in a company’s R&D operations are not included in its R&D expense, but any depreciation recorded for those assets is included in its R&D expense. For 2021, depreciation associated with domestic R&D paid for and performed by the company was $18.4 billion and with domestic R&D performed by the company and paid for by others was $2.7 billion. d Includes the cost of purchased or improved buildings and other facilities that are fixed to the land. e Includes the cost of other capital expenditures, including purchased patents and other intangible assets, and expenditures not distributed among the categories shown. f Includes only companies with 10 or more domestic employees.

Detail may not add to total because of rounding. Industry classification was based on dominant business code for domestic R&D performance, where available. For companies that did not report business codes, the classification used for sampling was assigned. An estimate range may be displayed in place of a single estimate to avoid disclosing operations of individual companies.

National Center for Science and Engineering Statistics and U.S. Census Bureau, Business Enterprise Research and Development Survey, 2021.

Survey Information and Data Availability

The sample for the BERD Survey was selected to represent all for-profit, nonfarm companies that were publicly or privately held, had 10 or more employees in the United States, and performed or funded R&D either domestically or abroad. The estimates in this InfoBrief are based on responses from a sample of the population and may differ from actual values because of sampling variability or other factors. As a result, apparent differences between the estimates for two or more groups may not be statistically significant. All comparative statements in this InfoBrief have undergone statistical testing and are significant at the 90% confidence level unless otherwise noted. The variances of estimates in this report were calculated using design-based formulas. Also, because the statistics from the survey are based on a sample, they are subject to both sampling and nonsampling errors. (See the 2021 “Technical Notes” at https://ncses.nsf.gov/surveys/business-enterprise-research-development/ .) ​ The Census Bureau reviewed the information in this InfoBrief for unauthorized disclosure of confidential information and approved the disclosure avoidance practices applied (Project No. P-7504682, Disclosure Review Board (DRB) approval number: CBDRB-FY23-0161).

Beginning in survey year 2018, companies that performed or funded less than $50,000 of R&D were excluded from tabulation.

In this InfoBrief, money amounts are expressed in current U.S. dollars and are not adjusted for inflation. A company is defined as a business organization located in the United States, either U.S. owned or a U.S. affiliate of a foreign parent company, of one or more establishments under common ownership or control.

For 2020, a total of 47,500 companies were sampled to represent the population of 1,140,000 companies; for 2021, a total of 47,500 companies were sampled, representing 1,137,000 companies. The actual numbers of reporting units in the sample that remained within the scope of the survey between sample selection and tabulation were 44,500 for 2020 and 44,000 for 2021. These lower counts represent the number of reporting units that were determined to be within the scope of the survey after all data collected were processed. Reasons for the reduced counts include mergers, acquisitions, and instances where companies had fewer than 10 employees in the United States or had gone out of business in the interim. Of these in-scope reporting units, 67% were considered to have met the criteria for a complete response to the 2020 survey; 69% fulfilled the 2021 complete response criteria. Coverage of the previous year’s known positive R&D stratum for 2020 was 92%; the coverage rate for 2021 was also 92%. Industry classification was based on the dominant business activity for domestic R&D performance, where available. For reporting units that did not report business activity codes for R&D, the classification used for sampling was assigned.

The estimation methodology for state estimates in the BERD Survey takes the form of a hybrid estimator, combining the unweighted reported amount, by state, with a weighted amount apportioned (or raked) across states with relevant industrial activity. The hybrid estimator smooths the estimate over states with R&D activity, by industry, and accounts for real observed change within a state. Table 5 shows the adjusted state estimates after this estimation methodology was applied.

The full set of data tables from the 2021 survey will be available at the BERD Survey page . Individual data tables and tables with relative standard errors and imputation rates from the 2021 survey are available from the author in advance of the full release. To minimize reporting burden, survey items are rotated on and off the survey on an odd- and even-numbered year schedule. Statistics on patents, intellectual property, and technology transfer activities were rotated off the survey for 2021. Items rotated on the survey for 2021 include questions on R&D performed by others by type of performer, federal R&D by government agency, and R&D by application area.

The BERD Survey contains confidential data that are protected under Title 13 and Title 26 of the U.S. Code. Restricted microdata can be accessed at the secure Federal Statistical Research Data Centers (FSRDCs) administered by the Census Bureau. FSRDCs are partnerships between federal statistical agencies and leading research institutions. FSRDCs provide secure environments supporting qualified researchers using restricted-access data while protecting respondent confidentiality. Researchers interested in using the microdata can submit a proposal to the Census Bureau, which evaluates proposals based on their benefit to the Census Bureau, scientific merit, feasibility, and risk of disclosure. To learn more about the FSRDCs and how to apply, please visit https://www.census.gov/about/adrm/fsrdc.html .

Suggested Citation

Britt R; National Center for Science and Engineering Statistics (NCSES). 2023. Business R&D Performance in the United States Tops $600 Billion in 2021 . NSF 23-350. Alexandria, VA: National Science Foundation. Available at http://ncses.nsf.gov/pubs/nsf23350 .

1 NSF has cosponsored an annual business R&D survey since 1953. The Survey of Industrial Research and Development (SIRD) collected data for 1953–2007, and its successor, the Business R&D and Innovation Survey (BRDIS), collected data for 2008–16. Beginning with 2017, the collection of innovation data was moved to the Annual Business Survey (ABS), another survey cosponsored with the Census Bureau, and BRDIS became the Business Research and Development Survey (BRDS). Beginning with 2019, the business R&D data collection reported here was renamed the Business Enterprise Research and Development (BERD) Survey for international comparability.

2 Determining the amount of domestic net sales and operating revenues was left to the reporting company. However, guidance was given to include revenues from foreign operations and subsidiaries and from discontinued operations and to exclude intracompany transfers, returns, allowances, freight charges, and excise, sales, and other revenue-based taxes.

3 Employment statistics in this InfoBrief are headcounts unless they are designated as full-time equivalent (FTE) estimates. R&D employees include researchers (defined as R&D scientists and engineers and their managers) and the technicians, technologists, and support staff members who work on R&D or who provide direct support to R&D activities.

4 The number of persons employed who were assigned full time to R&D plus a prorated number of employees who worked on R&D only part of the time was 1.9 million FTEs, of which 1.3 million FTEs were R&D researchers.

5 Company size classifications changed for 2017 and subsequent years in response to the revised Frascati Manual ; see Organisation for Economic Co-operation and Development (OECD). 2015. Frascati Manual: Guidelines for Collecting and Reporting Data on Research and Experimental Development. The Measurement of Scientific, Technological, and Innovation Activities . Paris: OECD Publishing. Available at https://www.oecd-ilibrary.org/science-and-technology/frascati-manual-2015_9789264239012-en . Anderson and Kindlon (2019) provide estimates of R&D performance and employment using these new classifications over 2008–15. The authors also compare the trends to those observed in SIRD for the time prior to 2008. The ABS, also cosponsored by NCSES and the Census Bureau, collects R&D data from companies with fewer than 10 employees for 2017 and beyond. See Anderson G, Kindlon A; NCSES. 2019. Indicators of R&D in Small Businesses: Data from the 2009–15 Business R&D and Innovation Survey . InfoBrief NSF 19-316. Alexandria, VA: National Science Foundation. Available at https://www.nsf.gov/statistics/2019/nsf19316/ .

6 In addition to statistics for all states and for all states by industry, below-state level statistics are available in the full set of data tables and in other InfoBriefs; see Shackelford B, Wolfe R; NCSES. 2019. Over Half of U.S. Business R&D Performed in 10 Metropolitan Areas in 2015 . InfoBrief NSF 19-322. Alexandria, VA: National Science Foundation. Available at https://www.nsf.gov/statistics/2019/nsf19322/ . Also see Shackelford B, Wolfe R; NCSES. 2020. Businesses Performed 60% of Their U.S. R&D in 10 Metropolitan Areas in 2018 . InfoBrief NSF 21-331. Alexandria, VA: National Science Foundation. Available at https://ncses.nsf.gov/pubs/nsf21331 . Information and statistics on U.S. state trends in R&D, science and engineering education, workforce, patents and publications, and knowledge-intensive industries is also available in the Science and Engineering State Indicators data tool at https://ncses.nsf.gov/indicators/states .

7 The Census Bureau reviewed the information in this InfoBrief for unauthorized disclosure of confidential information and approved the disclosure avoidance practices applied (Project No. P-7504682, Disclosure Review Board (DRB) approval number: CBDRB-FY23-0161).

Report Author

Ronda Britt Survey Manager NCSES Tel: (703) 292-7765 E-mail: [email protected]

National Center for Science and Engineering Statistics Directorate for Social, Behavioral and Economic Sciences National Science Foundation 2415 Eisenhower Avenue, Suite W14200 Alexandria, VA 22314 Tel: (703) 292-8780 FIRS: (800) 877-8339 TDD: (800) 281-8749 E-mail: [email protected]

Source Data & Analysis

Data Tables (NSF 23-351)

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The Reporter

The Economics of Generative AI

Artificial intelligence (AI) is not a new field. The term was coined in 1956, but the field has only recently begun having significant effects on the economy.

Research in AI went through three eras. Early work focused primarily on symbolic systems with hand-coded rules and instructions. In the 1980s, expert systems, which consisted of hundreds or thousands of “if…then” rules drawn from interviews with human experts, helped diagnose diseases and make loan recommendations, but with limited commercial success.

Later, the focus shifted to machine learning systems, including “supervised learning” systems trained to make predictions based on large datasets of human-labeled examples. As computational power increased, deep learning algorithms became increasingly successful, leading to an explosion of interest in AI in the 2010s.

More recently, even larger models using unsupervised or self-supervised systems have become a major focus of the field. Large-language models (LLMs) — trained on massive amounts of text to simply predict the next word in a sequence — have astounded the public with their ability to produce meaningful and remarkable output. These systems have been found to outperform humans for a growing range of knowledge-intensive tasks: taking the bar exam, for instance. In addition, studies show that access to LLMs and other types of generative AI tools can help human workers improve their own performance.

In the past year, a growing body of work has explored how new AI tools might impact productivity in applications as diverse as coding, writing, and management consulting. 1

In research with Lindsey Raymond, we analyze the effects of generative AI on worker productivity in the context of technical customer support. 2 Our study is based on data from over 5,179 agents, about 1,300 of whom were given access to an LLM-based assistant that provided real-time suggestions for communicating with customers. The system, trained on millions of examples of successful and unsuccessful conversations, provided suggestions that the agents could use, adapt, or reject. The tool was rolled out in phases, creating quasi-experimental evidence on its causal effects.

We found significant improvements in worker productivity as measured by the number of customer issues workers were able to resolve per hour. Within four months, treated agents were outperforming nontreated agents who had been on the job for over twice as long.

On average, worker productivity increased by 14 percent. These gains were concentrated among the lowest quintile of workers, whether measured by experience or prior productivity, where there were productivity gains of up to 35 percent. In contrast, the top quintile saw negligible gains and, in some cases, even small decreases in the quality of conversations, as measured by customer satisfaction. This pattern is reflective of how the system is trained: by observing successful conversations, the system is able to glean the behavior of the most skilled agents and pass on these behaviors as suggestions to novice workers.

Did the system deskill the workforce? Another natural experiment suggests not. As with most large systems, there were occasional outages when the system unexpectedly became unavailable. Workers who had previously been using the system now had to answer questions without access to it, and nonetheless they continued to outperform those who had never used the system. This suggests that the system helped them learn, and retain, answers.

Our results point to the possibility that — in contrast with earlier waves of information technology that largely benefited higher-skill workers — generative AI technologies could particularly benefit workers at the lower or middle levels of the skills distribution. Drawing on these and other results, David Autor sees opportunities for the recent waves of AI to help rebuild the middle class by increasing the value of output from their labor. 3

Advances in AI technologies and algorithmic design can yield improvements beyond direct measures of productivity. For example, we saw evidence in our study that AI assistance improves the experience of work for treated agents, as measured by the processing of conversation transcripts: customers spoke more kindly to agents and were less likely to ask to speak to a supervisor. These effects were likely driven both by agents’ improved social skills and increased access to technical knowledge as a result of chat assistance.

Indeed, there is growing evidence that generative AI tools may outperform humans in an area traditionally considered a source of strength for humans relative to machines: empathy and social skills. One study of doctors’ responses to patient questions found that an LLM-based chatbot provided answers that were judged by expert human evaluators to be more detailed, higher quality, and 10 times more likely to be considered empathetic. 4

Finally, innovations in AI systems may further improve the functioning of current AI tools. For example, Li, Raymond, and Peter Bergman explore how algorithm design can improve the quality of interview decisions in the context of professional services hiring. They find that while traditional supervised learning systems — which look for workers who match historical patterns of success in the firm’s training data — select higher-quality workers relative to human hiring, they are also far less likely to select applicants who are Black or Hispanic. In contrast, reinforcement learning and contextual bandit models — which value learning about workers who have not traditionally been represented in the firm’s training data — are able to deliver similar improvements in worker quality while also distributing job opportunities more broadly.

While the effects of AI on productivity and work practices are now evident not only in a number of laboratory settings but also in business applications, it may take longer for them to show up in aggregate statistics. Brynjolfsson, Daniel Rock, and Chad Syverson discuss a set of reasons why the effects of AI might not quickly change aggregate productivity numbers. 5

For one thing, labor productivity is typically defined as GDP per hour worked. But GDP as it is traditionally measured may miss many of the benefits of an increasingly digital economy that creates free goods and makes them more widely available while also improving the quality, variety, or convenience of existing goods. An alternative metric, GDP-B, seeks to address these challenges by assessing the benefits of goods and services, not the amount spent. 6

Furthermore, general purpose technologies like AI are likely to experience a lag between their initial adoption and observable improvements in productivity. In a second study, Brynjolfsson, Rock, and Syverson model this “Productivity J-Curve.” 7 As with other types of information technology, the initial phase of AI adoption is characterized by time-consuming complementary investments, including the realignment of business processes, the integration of new technologies into existing workflows, and the upskilling of the workforce. As noted by Brynjolfsson and Lorin Hitt, these adjustments are costly and may create valuable intangible assets, but neither the costs nor the benefits are typically accounted for when measuring a firm’s output. 8 As a result, productivity as it is conventionally measured may initially be seen as stagnating or even falling. However, as these technological and organizational complements are gradually implemented, the productivity benefits of AI begin to materialize, marked by an upward trajectory in the J-curve.

The Productivity J-Curve model implies that productivity metrics fail to capture the full extent of benefits during the initial stages of AI adoption, leading to underestimation of AI’s potential.

The ultimate economic effects of generative AI will depend not only upon how much it boosts productivity and changes work in specific cases, but also on how much of the economy it is likely to affect. As noted by Daron Acemoglu and Autor, occupations can be broken down into specific tasks. 9 Applying this insight, Brynjolfsson, Tom Mitchell, and Rock look at 18,156 tasks in the O-NET taxonomy and find that most occupations include at least some tasks that could be automated or augmented by machine learning, though significant redesign would typically be required to realize the full potential of the technology. 10 Building on this work, Tyna Eloundou, Sam Manning, Pamela Mishkin, and Rock estimate that approximately 80 percent of the US workforce could have at least 10 percent of their work tasks either automated or augmented by the introduction of LLMs, while around 19 percent of workers could see at least half of their tasks affected. 11

Hulten’s theorem states that a first-order approximation of the productivity effects of a technology is the share of the economy affected multiplied by its average productivity impact. There is evidence that both the potential productivity impact and the potential share of the economy affected are significant in the case of generative AI, suggesting that the ultimate effects may be substantial, though, as implied by the Productivity J-Curve, they may take some time to be realized. 12

The field of economics itself is not immune to the effects of generative AI. Students of economics are using the tools to help with their assignments, requiring a rethinking of teaching methods. We and our colleagues are using the tools to help with research and writing; we used LLMs to help with aspects of the preparation of this article. Anton Korinek described six ways that LLMs can assist economists: ideation and feedback, writing, background research, data analysis, coding, and mathematical derivations. 13 Jens Ludwig and Sendhil Mullainathan go further, showing that AI models can be used to make the first stage of the scientific process — hypothesis generation — more systematic. 14

As discussed by Brynjolfsson and Gabriel Unger, important policy choices are emerging regarding AI’s effects on productivity, industrial concentration, and inequality. 15 For instance, on the question of inequality, the distinction between technology used for automation versus augmentation or, more formally, AI that substitutes for rather than complements labor, can have significant effects on the distribution of income and bargaining power. 16 Brynjolfsson has argued that either approach can boost productivity but has noted that a focus on human-like AI can lead to a “Turing Trap” by reducing worker bargaining power. As AI continues to grow in power, so too does the need for economic research to better understand how we can harness its benefits while mitigating its risks.

Researchers

More from nber.

“ Experimental Evidence on the Productivity Effects of Generative Artificial Intelligence, ” Noy S, Zhang W. Science 381(6654), July 2023, pp. 187–192. “ Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality, ” Dell’Acqua F, McFowland III E, Mollick E, Lifshitz-Assaf H, Kellogg KC, Rajendran S, Krayer L, Candelon F, Lakhani KR. Harvard Business School Working Paper No. 24-013, September 2023.

“ Generative AI at Work, ” Brynjolfsson E, Li D, Raymond LR. NBER Working Paper 31161, November 2023.

“ Applying AI to Rebuild Middle Class Jobs, ” Autor D. NBER Working Paper 32140. February 2024.

“ Comparing Physician and Artificial Intelligence Chatbot Responses to Patient Questions Posted to a Public Social Media Forum, ” Ayers JW, Poliak A, Dredze M, Leas EC, Zhu Z, Kelley JB, Faix DJ, Goodman AM, et. al. JAMA Internal Medicine 183(6), April 2023, pp. 589–596.

“ Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics, ” Brynjolfsson E, Rock D, Syverson C. NBER Working Paper 24001, November 2017.

“ GDP-B: Accounting for the Value of New and Free Goods in the Digital Economy, ” Brynjolfsson E, Collis A, Diewert WE, Eggers F, Fox KJ. NBER Working Paper 25695, March 2019.

“ The Productivity J-Curve: How Intangibles Complement General Purpose Technologies, ” Brynjolfsson E, Rock D, Syverson C. NBER Working Paper 25148, January 2020, and American Economic Journal: Macroeconomics 13(1), January 2021, pp. 333–372.

“ Beyond Computation: Information Technology, Organizational Transformation and Business Performance, ” Brynjolfsson E, Hitt LM. Journal of Economic Perspectives , 14(4), Fall 2000, pp. 23–48.

“ Skills, Tasks and Technologies: Implications for Employment and Earnings, ” Acemoglu D, Autor D. NBER Working Paper 16082, June 2010. Published as “Chapter 12 - Skills, Tasks and Technologies: Implications for Employment and Earnings” in Handbook of Labor Economics 4(B), 2011, pp. 1043–1171.

“ What Can Machines Learn, and What Does It Mean for Occupations and the Economy? ” Brynjolfsson E, Mitchell T, Rock D. AEA Papers and Proceedings 108, May 2018, pp. 43–47.

“ GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models, ” Eloundou T, Manning S, Mishkin P, Rock D. arXiv , August 2023.

“ Machines of Mind: The Case for an AI-Powered Productivity Boom, ” Baily MN, Brynjolfsson E, Korinek A. Brookings Institution, May 10, 2023.

“ Generative AI for Economic Research: Use Cases and Implications for Economists, ” Korinek A, Journal of Economic Literature 61(4), December 2023, pp. 1281–1317.

“ Machine Learning as a Tool for Hypothesis Generation, ” Ludwig J, Mullainathan S. NBER Working Paper 31017, March 2023.

“ The Macroeconomics of Artificial Intelligence, ” Brynjolfsson E, Unger G. International Monetary Fund, December 2023.

“ The Turing Trap: The Promise & Peril of Human-Like Artificial Intelligence, ” Brynjolfsson E. Daedalus 151(2), Spring 2022, pp. 272–287. An earlier version of this argument was published as Race Against the Machine: How the Digital Revolution is Accelerating Innovation, Driving Productivity, and Irreversibly Transforming Employment and the Economy , Brynjolfsson E, McAfee A. Digital Frontier Press, 2011.

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