equal pay for equal work research paper

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• Published: 24 November 2022

Within-job gender pay inequality in 15 countries

• Andrew M. Penner 1 ,
• Trond Petersen 2 ,
• Are Skeie Hermansen 3 , 4 ,
• Anthony Rainey 5 ,
• István Boza 6 ,
• Marta M. Elvira 7 ,
• Olivier Godechot 8 , 9 ,
• Martin Hällsten 10 ,
• Lasse Folke Henriksen 11 ,
• Feng Hou 12 ,
• Aleksandra Kanjuo Mrčela 13 ,
• Joe King 14   na1 ,
• Naomi Kodama 14 ,
• Tali Kristal 15 ,
• Alena Křížková 16 ,
• Zoltán Lippényi 17 ,
• Silvia Maja Melzer 14   na1 ,
• Eunmi Mun 18 ,
• Paula Apascaritei 14   na1 ,
• Dustin Avent-Holt 19 ,
• Nina Bandelj 1 ,
• Gergely Hajdu 20 ,
• Jiwook Jung 18 ,
• Andreja Poje 13 ,
• Halil Sabanci 21 ,
• Mirna Safi 8 ,
• Matthew Soener 22 ,
• Donald Tomaskovic-Devey 5 &
• Zaibu Tufail 1  

Nature Human Behaviour volume  7 ,  pages 184–189 ( 2023 ) Cite this article

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An Author Correction to this article was published on 31 January 2023

This article has been updated

Extant research on the gender pay gap suggests that men and women who do the same work for the same employer receive similar pay, so that processes sorting people into jobs are thought to account for the vast majority of the pay gap. Data that can identify women and men who do the same work for the same employer are rare, and research informing this crucial aspect of gender differences in pay is several decades old and from a limited number of countries. Here, using recent linked employer–employee data from 15 countries, we show that the processes sorting people into different jobs account for substantially less of the gender pay differences than was previously believed and that within-job pay differences remain consequential.

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Despite great advances in gender equality, women earn less than men in all advanced industrialized countries. These gender gaps are strongly related to the occupations and establishments in which women and men work. Germinal research highlights that, although there are substantial differences in the overall wages men and women receive, women and men who do the same work for the same employer receive very similar wages 1 , 2 , 3 . The processes involved in sorting women and men into different jobs, and particularly into differentially remunerated male- and female-dominated occupations, are thus viewed as central to understanding gender pay inequality 4 , 5 , 6 .

This understanding of the gender gap has far-reaching policy implications. If there are sizeable differences between the pay that women and men receive when they do the same work for the same employer (that is, within-job inequality), policies mandating equal pay have an important role to play in creating gender equality in the labour market. If, however, differences arise overwhelmingly through sorting women and men into different jobs, policies should focus on the organizational hiring and promotion practices that match people to jobs, as well as on broader societal views regarding whose work is defined as valuable 7 , 8 , 9 .

Most evidence regarding gender pay inequality comes from surveys of individuals that contain occupational data but lack good indicators of firms and jobs. Data that contain detailed occupational information and link individuals to others working for the same employer (that is, linked employer–employee data) are rarely available, so that data that can examine gender differences among those with the same occupation and employer (that is, within-job inequality) are difficult to access. The best evidence on within-job gender pay differences comes from a limited number of countries using linked employer–employee data ranging from 1980 through 1990 to examine within-job gender wage differences 1 , 2 , 3 . In this Article, we contribute to this literature by using linked employer–employee data to provide recent estimates of the levels and change in within-establishment, within-occupation and within-job differences in earnings across 15 countries: Canada, Czechia, Denmark, France, Germany, Hungary, Israel, Japan, the Netherlands, Norway, Slovenia, South Korea, Spain, Sweden and the United States. We show that although much of the gender inequality we observe is accounted for by sorting into establishments, occupations and jobs, within-job gender gaps in earnings remain an important source of differences in all 15 countries. Analyses for the six countries where we can examine the contractual hourly wage rate show that sorting is similarly important for gender differences in wages, suggesting that equal pay policies have an important role to play in creating gender pay equity.

Our core analyses focus on four sets of ordinary least squares regression models. The first model adjusts only for basic individual-level covariates, and provides our baseline estimate of the overall gender pay gap in each country. In subsequent models, we introduce a series of fixed effects so that we compare women and men working in the same establishment (model 2), the same occupation (model 3) and the same job (that is, occupation–establishment unit; model 4). Comparing the results of these four models enables us to see the degree to which gender differences in pay in any given year are accounted for by sorting across establishments, occupations and occupation–establishment units.

Table 1 presents information on gender differences in earnings in our 15 countries. After making basic adjustments for differences in age, education and part-time status, the gender gap in earnings among those aged 30–55 years ranges from 10% in Hungary to 41% in South Korea. Within-job gender gaps are smaller but still substantial, ranging from 7% in Denmark and France to 26% in Japan. Comparing the results in the first and fourth columns (basic adjustment and within-job), we see that within-job gender differences remain a substantial source of the overall earnings gaps in all of our 15 countries. As is visible in the final column, within-job differences typically account for about half of the overall gender differences that we observe in our countries, ranging from just over a third of the overall gap (Israel) to over nine-tenths of the gender earnings gap in Hungary.

The results in the second and third columns of Table 1 report within-establishment and within-occupation gender differences in earnings. Comparing these columns with the results with only basic adjustments highlights the role of sorting into establishments and occupations in creating gender pay differences. Where previous research 1 , 2 , 3 found that sorting into occupations is substantially more important for gender inequality than sorting into establishments, we find evidence that sorting into both occupations and establishments plays an important role in producing gender differences. Our findings thus not only underscore the salience of within-job differences, but also document the importance of processes that differentially sort women and men into high-paying establishments and occupations.

Figure 1 depicts how the within-job and overall gender gaps have changed from 2005 to our most recent year of data (for most countries this represents approximately 10 years; for information on the most recent year that we have data from each country, see Table 1 ). The x axis plots the average annual change in the within-job gender gap for each country, and the y axis plots each country’s average annual change in overall gender gap over this period. In most countries, both the overall gender gap and the within-job gender gap have fallen over time. However, this is not the case in the three Central and Eastern European countries. In Czechia, within-job gender differences decline, but overall gender differences in earnings increase, suggesting that gender differences in earnings in Czechia are increasingly due to processes sorting women and men into different jobs. Gender differences also increase in Hungary and Slovenia, where the increase is due not only to sorting processes, but also to an increase in within-job gender gaps. Of particular note, none of our 15 countries exhibits a decrease in the overall gender earnings gap coupled with an increase in within-job gender earnings gaps (as would be the case if egalitarian sorting processes counteracted rising within-job inequality); this suggests that the processes sorting women and men into different jobs are rarely gender egalitarian.

CA, Canada; CZ, Czechia; DK, Denmark; DE, Germany; ES, Spain; FR, France; HU, Hungary; IL, Israel; JP, Japan; KR, South Korea; NL, the Netherlands; NO, Norway; SI, Slovenia; SE, Sweden; US, United States. The y axis represents the average annual change in the overall gender gap in earnings (accounting only for basic adjustments, and corresponding to the first column of results in Table 1 ), and the x axis reports the average annual change in the within-job gender gap in earnings (corresponding to the fourth column of results in Table 1 ). Larger positive numbers correspond to larger increases in the gender earnings gap across years, while negative numbers correspond to decreases in the gap. We use data from approximately 10 years in each country, beginning in 2005 where possible and continuing through the most recent year available (for information on the most recent year available to us in each country, see Table 1 ). In three countries (the Netherlands, South Korea and Spain), we do not have data from 2005 and so use 2006 as our initial year. See the tables presented in Supplementary Information for the underlying coefficients reporting gender differences for each year. Supplementary figures depict country-specific trends for overall, within-establishment, within-occupation and within-occupation–establishment gender differences in earnings for each country.

Given the rapid expansion of women’s rights around the world, one might expect uniform improvement in women’s pay via both reduced sorting into different jobs and lower levels of within-job inequality. The empirical record is more mixed, with nearly universal improvements in education and labour force participation, continued and sometimes even increased segregation, and little information on what happens within jobs 10 .

Our analyses of linked employer–employee data from 15 countries show that currently both within-job differences and sorting into jobs make substantial contributions to gender pay gaps. Interestingly, the trends we document highlight that sorting is increasingly important, and that within-job differences are shrinking in importance in most countries. Thus, while the conclusions drawn by previous research—that sorting accounts for the vast majority of gender differences, and within job inequality is not a substantial concern—may not accurately summarize the current state of gender pay inequality, if the trends we observe hold, they may describe our future. In the current context, however, our findings suggest that policies focusing on equal pay for equal work and policies attending to hiring, promotion and other job-sorting processes are both vital to establishing gender equality in the labour market.

Limitations

Large-scale comparative analyses contain numerous challenges around data harmonization and ensuring that analytic decisions that are appropriate in some contexts are not problematic in others. Although we sought to ensure that the analyses conducted in each country are comparable, factors like parental leave policies, the availability and prevalence of part-time work, and the relevance of occupations and firms differ across our 15 countries. These differences necessarily mean that the comparisons we make across countries involve comparing contexts with different gender regimes and where paid work is organized very differently. Despite these limitations, we believe that these comparisons are informative, and in our Supplementary Information we report results from analyses where we alter variable definitions, model specifications and sample definitions, showing that the results we present here are remarkably robust.

This study uses linked employer–employee data (that is, data that link individual employees to specific employers) from 15 countries to investigate the extent to which the gender pay gap arises from women and men receiving different pay when doing the same work for the same employer (as opposed to from processes sorting women and men into different occupations and establishments). By allowing us to compare individuals to others working for the same employer, the linked employer–employee data that we use provide important insights into inequality. Below we provide information on our modelling strategy for our core analyses, and we summarize the data available in each of our 15 countries in Table 2 . More information on the data used for each country and results from country-specific robustness checks are included in Supplementary Information , which also presents country-specific results on changes over time, providing a sense of each country’s trends in gender inequality at the overall, establishment, occupation and job (that is, occupation–establishment) levels.

As noted above, our core analyses focus on four sets of ordinary least squares regression models. Our first model adjusts only for basic individual-level covariates, and provides our baseline estimate of the overall gender pay gap in each country. In subsequent models we compare only women and men who work in the same establishment (model 2), only women and men who work in the same occupation (model 3) and only women and men who work in the same job (that is, occupation-establishment unit; model 4). We estimate these models separately by year for each country, allowing us to examine country-specific trends in these gender differences.

The equations estimated for our core models follow the same general form, using four different specifications:

where the subscripts represent i for individuals (or for each employment spell of an individual, depending on the country), f for full-time versus part-time status, o for occupations, e for establishments and t for years. The dependent variable is the logarithm of earnings (ln earnings it ) for individual (or employment spell) i in year t , and the independent variables are collected in the vector x it , which includes a constant, the gender, age and age-squared of individual i , and a series of indicator variables for the education of individual i (except in countries where information on education was not available).

To address concerns regarding the comparability of full-time versus part-time workers, we consider full-time versus part-time status a defining characteristic of a job and include this axis in constructing fixed effects for all of our core models. Thus, model 1 includes the term η ft , a fixed effect (that is, indicator variable) for full-time versus part-time work, so that this basic adjustment model adjusts for age, age-squared, education and full-time versus part-time work. Model 2 includes the covariates in x it (age, age-squared and education), as well as the fixed effects η eft representing the unique units formed by combining the establishment and full-time versus part-time indicators. Model 2 thus provides estimates of the gender gap obtained from comparing women and men who work in the same establishment; for each establishment it can be thought of as estimating the gender gap separately for full-time workers and part-time workers and then taking a weighted average of these two gender gaps across all establishments. Models 3 and 4 are analogous to model 2, but contain the fixed effects η oft and η oeft that refer respectively to the unique units formed by combining full-time versus part-time status with either occupation ( η oft ) or occupation–establishment units ( η oeft ). The analytic sample for each model is restricted to gender-integrated fixed effect units. The subscripts to the θ parameters indicate that these are different coefficients, pertaining to different levels, basic adjustments ( B ), establishment ( E ), occupation ( O ) and occupation–establishment ( OE ).

We use the natural log of earnings as our dependent variable. Following standard conventions, these coefficients are interpreted as the relative difference between the average female and male earnings, but more formally our estimates refer to the difference in relative geometric means for unlogged earnings (which is the absolute difference in the arithmetic means of logged earnings). For an extended discussion of the interpretation of such coefficients, see Petersen 11 .

Data were analysed using STATA versions 14–17 and SAS version 9.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

This paper uses restricted-access data from 15 different countries. As described in Supplementary Information , the data underlying our analyses in each country can be accessed by receiving permissions from the relevant data owners, including Statistics Canada; the Ministry of Labor and Social Affairs of the Czech Republic; Statistics Denmark; the French Comité du Secret Statistique; the German Institute for Employment Research; the Databank of the Centre for Economic and Regional Studies in Hungary; Israel’s Central Bureau of Statistics (CBS); the Japanese Ministry of Health, Labour and Welfare; the Central Bureau of Statistics of the Netherlands; Statistics Norway; the Slovenian Statistical Office; Statistics Korea; the Ministry of Labor, Migration and Social Security of Spain; Statistics Sweden; and the US Census Bureau.

Change history

31 january 2023.

A Correction to this paper has been published: https://doi.org/10.1038/s41562-023-01523-x

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Acknowledgements

This research was supported by the National Science Foundation (Award 0525831; D.A., A.M.P. and D.T.), the Humboldt Foundation (grant number AR8227; D.T.), the Research Council of Norway (grant number 287016; A.S.H.), European Research Council ERC Starting Grant (grant number 851149; A.S.H.), the European Research Council ERC Starting Grant (grant number 677739; T.K.), the French Agence Nationale de la Recherche (grant ANR-17-CE41-0009-01; M. Safi and O.G.), the Independent Research Fund Denmark (grant number 5052-00143b; L.H.), the European Social Fund and state budget of the Czechia (grant number CZ.03.1.51/0.0/0.0/15_009/0003702; A.K.), the Czech NPO Systemic Risk Institute (LX22NPO5101; A.K.), and institutional support (RVO: 68378025; A.K.), the Spanish Ministry of Science and Innovation (grant number PID2020-118807RB-I00/AEI /10.13039/501100011033; M.E.), the Fritz Henkel Stiftung (Endowed PhD Scholarship; HS) and Swedish Forte (grant number 2015-00807; M.H.), Z.L. received support from the European Research Council ERC Advanced Grant (grant number 340045), and A.K.M. was supported by the Slovenian Research Agency (ARRS) under grant no. P5-0193. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Research on the US data was conducted by J.K. while J.K. was working for the US Census Bureau. This paper is released to inform interested parties of research and to encourage discussion. The views expressed are those of the authors and not those of the US Census Bureau. Tabular materials presented in this paper were approved for release by the US Census Bureau’s Disclosure Review Board (CBDRB-FY18-258).

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Authors and Affiliations

Department of Sociology, University of California, Irvine, Irvine, CA, USA

Andrew M. Penner, Nina Bandelj & Zaibu Tufail

Department of Sociology, University of California, Berkeley, Berkeley, CA, USA

Trond Petersen

Department of Sociology and Human Geography, University of Oslo, Oslo, Norway

Are Skeie Hermansen

Swedish Institute for Social Research, Stockholm University, Stockholm, Sweden

Department of Sociology, University of Massachusetts, Amherst, Amherst, MA, USA

Anthony Rainey & Donald Tomaskovic-Devey

Centre for Economic and Regional Studies, Budapest, Hungary

István Boza

Departments of Strategic Management and Managing People in Organizations, IESE Business School, Barcelona, Spain

Marta M. Elvira

CRIS-CNRS, Sciences Po, Paris, France

Olivier Godechot & Mirna Safi

MaxPo, Sciences Po, Paris, France

Olivier Godechot

Department of Sociology, Stockholm University, Stockholm, Sweden

Martin Hällsten

Department of Organization, Copenhagen Business School, Copenhagen, Denmark

Lasse Folke Henriksen

Statistics Canada, Ottawa, Ontario, Canada

Faculty of Social Sciences, University of Ljubljana, Ljubljana, Slovenia

Aleksandra Kanjuo Mrčela & Andreja Poje

Faculty of Economics, Meiji Gakuin University, Tokyo, Japan

Joe King, Naomi Kodama, Silvia Maja Melzer & Paula Apascaritei

Department of Sociology, University of Haifa, Haifa, Israel

Tali Kristal

Institute of Sociology, Czech Academy of Sciences, Prague, Czechia

Alena Křížková

Department of Sociology, University of Groningen, Groningen, the Netherlands

Zoltán Lippényi

School of Labor and Employment Relations, University of Illinois, Urbana-Champaign, Urbana-Champaign, IL, USA

Eunmi Mun & Jiwook Jung

Department of Social Sciences, Augusta University, Augusta, GA, USA

Dustin Avent-Holt

Department of Economics, Vienna University of Economics and Business, Vienna, Austria

Gergely Hajdu

Management Department, Frankfurt School of Finance and Management, Frankfurt, Germany

Halil Sabanci

Department of Sociology, University of Illinois, Urbana-Champaign, Urbana-Champaign, IL, USA

Matthew Soener

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Contributions

A. Penner, T.P., A.S.H., A.R., I.B., M.E., O.G., M.H., L.F.H., F.H., A.K.M., J.K., N.K., T.K., A.K., Z.L., S.M.M., E.M., P.A., D.A.-H., N.B., G.H., J.J., A. Poje, H.S., M. Safi, M. Soener, D.T.-D. and Z.T. designed the analyses, interpreted the results, and wrote the paper. A.S.H. led the analyses comparing results to findings from previous work in Norway and Sweden; Z.L. led the development of weights; and I.B. and O.G. led analyses ensuring the robustness of results to the inclusion of person fixed effects. A.S.H. was responsible for conducting the Norwegian analyses; I.B. and G.H. were responsible for conducting the Hungarian analyses; M.E., H.S. and P.A. were responsible for conducting the Spanish analyses; O.G., M. Safi and M. Soener were responsible for conducting the French analyses; M.H. was responsible for conducting the Swedish analyses; L.F.H. was responsible for conducting the Danish analyses; F.H. was responsible for conducting the Canadian analyses; A.K.M. and A. Poje were responsible for conducting the Slovenian analyses; J.K. was responsible for conducting the US analyses; N.K. was responsible for conducting the Japanese analyses; T.K. was responsible for conducting the Israeli analyses; A.K. was responsible for conducting the Czech analyses; Z.L. was responsible for conducting the Dutch analyses; S.M.M. was responsible for conducting the German analyses; and E.M. and J.J. were responsible for conducting the South Korean analyses.

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Correspondence to Andrew M. Penner .

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Supplementary Discussion, Tables 1–30 and Figs. 1–18.

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Penner, A.M., Petersen, T., Hermansen, A.S. et al. Within-job gender pay inequality in 15 countries. Nat Hum Behav 7 , 184–189 (2023). https://doi.org/10.1038/s41562-022-01470-z

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Equal Pay for Equal Work? Considering the Gender Gap in Illegal Pay

• Original Paper
• Published: 17 March 2021
• Volume 38 , pages 425–458, ( 2022 )

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• Holly Nguyen 1 ,
• Brandy R. Parker 1 &
• Sally S. Simpson 2  

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To provide quantitative attention to the correlates of the gender gap in illegal pay. Guided by the literatures on the gendered nature of offending, illegal earnings, and the gender gap in legal pay, we ask: what factors are associated with the gender gap in illegal pay?

We use the Delaware Decision Making Study, a sample of incarcerated offenders, to unpack the gender gap in illegal pay with the Blinder-Oaxaca decomposition technique.

The gender gap in illegal pay is partly accounted for by criminal analogs—criminal capital and psychosocial attributes—to correlates for the gender gap in legal pay and differences in reward structures. Race also emerges as an important factor.

Conclusions

The disadvantage women face in the legal workforce extends to illegal markets, and our understanding about the gender gap in legal pay can be translated to criminal contexts.

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Decertifying Gender: The Challenge of Equal Pay

Introduction

Equal Pay by Gender and by Nationality: A Comparative Analysis of Switzerland’s Unequal Equal Pay Policy Regimes Across Time*

Others have since built on this concept of time allocation to study criminal choice, including Block and Heineke ( 1975 ), Heineke ( 1978 ), and Schmidt and Witte ( 1984 ).

A few exceptions to the focus on the extensive margins are Brady et al. ( 2015 ) examination of earnings of female prostitutes in India and Daly’s ( 1989 ) documentation of gender differences in illegal earnings from white-collar crime. Brady and associates (2015) found that female sex workers with brokers had significantly more customers but significantly lower weekly earnings. They argued that sex workers with brokers were more likely in an exploitative relationship (see also Moffat & Peters, 2004 ). While Brady et al. ( 2015 ) establish that there is variation in illegal earnings among women, their study does not address gender differences in illegal pay. Daly ( 1989 ), on the other hand, notes that female white-collar defendants are structurally and demographically less privileged than their male counterparts, translating into less access to organizational resources with which to commit crime and substantially lower economic gains from their attempted crimes.

Evidence shows that when men and women work in the same job-cell (i.e., within occupation, within employer) they earn similar wages but, typically, integration is rare (Groshen 1991 ).

How reliable is self-reported illegal pay? Extant research suggests that validity and reliability of self-reported illegal pay is good (e.g., Charest 2004 ; Morselli and Tremblay 2004 ). Most recently, Nguyen and Loughran ( 2017 ) analyzed the validity and reliability of self-reported illegal earnings using the Pathways to Desistance study and the National Supported Work project. The authors reported generally good validity and reliability of self-reported illegal earnings across the two datasets.

We observed a gender gap in illegal pay across four other data sources (see Fig. 1 and Table 6 in " Appendix A ").

Table 7 in " Appendix B " displays the descriptive statistics for males and females in the incarcerated sample by each of the four crime types.

Note that even though the word “counterfactual” is commonly used when describing the method, it does not connote causation.

As shown in Table 8 of " Appendix C ", age of first crime is inversely associated with reporting affirmative to one or more income-generating crime among males but not females, though the association is weak. Number of arrests and impulsivity are positively associated with reporting having engaged in one or more income-generating crime for both males and females. However, number of arrests is weakly associated for both genders and is marginally significant for females. As such, there does not appear to be much selection into reporting any illegal pay among either males or females based on observables.

Criminal capital includes age first crime and number of arrests. Specialization was omitted from the income generating crime decomposition.

Detailed linear probability models by gender are presented in Table 8 " Appendix C ". Overall, criminal capital measures (age first crime and number of arrests) are significantly related to the probability of reporting income generating crimes among males. Impulsivity is significantly related to the probability of reporting income generating crimes among males and females.

Since not all subjects participated in one or more of the four economic crimes queried about, we observe illegal pay for only a subset of the incarcerated sample. Estimating illegal pay only for those who choose to engage in income-generating crime could possibly lead to a nonrandom sample and biased estimates. We address the problem of sample selectivity bias in estimating illegal pay by using the Heckman ( 1979 ) two-equation model where selection can be treated as a form of omitted variable bias. We present our results in " Appendix D ". As Table 10 shows, the gender gap in participating in income-generating crimes in the incarcerated sample is small. The results of the Heckit models are substantively similar to the ones presented here.

PUMS data can be downloaded from the United States Census Bureau website at: https://www.census.gov/programs-surveys/acs/data/pums.html .

Category for weeks worked during past 12 months:0 = N/A (less than 16 y/o or did not work during past 12 months); 1 = 50 to 52 weeks; 2 = 48 to 49 weeks; 3 = 40 to 47 weeks; 4 = 27 to 39 weeks; 5 = 14 to 26 weeks; 6 = 14 weeks or less.

The OLS estimates are available in Table 13 in " Appendix F ". Results are as expected: age, education, and the average hours worked per week are positively associated with weekly wages. Respondents who are black earned less weekly than respondent who are white or other race/ethnicity. Those in retail and service industries and in secondary occupations also earned lower wages. Overall, the results are similar for both genders.

1 = no schooling completed; 2 = nursery school, preschool; 3 = kindergarten; 4 = grade 1; 5 = grade 2; 6 = grade 3; 7 = grade 4; 8 = grade 5; 9 = grade 6; 10 = grade 7; 11 = grade 8; 12 = grade 9; 13 = grade 10; 14 = grade 11; 15 = grade; 12 (no diploma); 16 = high school diploma; 17 = GED or alternative credential; 18 = some college, but less than 1 year; 19 = 1 or more years of college credit, no degree; 20 = associate's degree; 21 = bachelor's degree; 22 = master's degree; 23 = professional degree beyond a bachelor's degree; 24 = doctoral degree.

Agriculture, Forestry, Fishing and Hunting (codes 0170–0290); Mining (codes 0370–0490); Construction (code 0770); Manufacturing (codes 1070–3990); Wholesale Trade (codes 4070–4590); Retail Trade (codes 4670–5790); Transportation and Warehousing (codes 6070–6390); Utilities (codes 0570–0690); Information and Communications (codes 6470–6780); Finance, Insurance, Real Estate, and Rental and Leasing (codes 6870–7190);

Professional, Scientific, Management, Administrative, and Waste Management (codes 7270–7790); Educational, Health and Social Services (codes 7860–8470); Arts, Entertainment, Recreation, Accommodations, and Food Services (codes 8560–8690); Other Services (Except Public Administration; codes 8770–9290); Public Administration (codes 9370–9590); Armed Forces (codes 9670–9870).

Management, Professional and Related Occupations are all classified as not secondary/entry level (i.e., Management Occupations; Business Operations Specialists; Financial Specialists; Computer and Mathematical Occupations; Architecture and Engineering Occupations; Life, Physical, and Social Science Occupations; Community and Social Service Occupations; Education, Training, and Library Occupations; Arts, Design, Entertainment, Sports, and Media Occupations; Healthcare Practitioners and Technical Occupations—codes 10–3540); Service Occupations are mixed classified (Healthcare Support Occupations are all classified as not secondary/entry level—codes 3600–3655; Protective Service Occupations are all classified as not secondary/entry level – codes 3700–3955; Food Preparation and Serving Occupations are mixed classified—codes 4000–4010 = not secondary/entry level and codes 4020–4150 = secondary/entry level; Building and Grounds Cleaning and Maintenance are mixed classified—codes 4200–4210 = not secondary/entry level and codes 4220–4250 = secondary/entry level; Personal Care and Service Occupations are mixed classified—codes 4300–4340, 4410, 4460–4465, 4600–4610, 4640 = not secondary/entry level and codes 4350–4400, 4420–4430, 4500–4540, 4620, 4650 = secondary/entry level); Sales and Office Occupations are mixed classified (Sales Occupations are mixed classified—codes 4700–4710, 4740, 4800–4940, 4965 = not secondary/entry level and codes 4720, 4750–4760, 4950 = secondary/entry level; Office and Administrative Support Occupations are mixed classified—codes 5000–5260, 5310–5610, 5630–5940 = not secondary/entry level and codes 5300, 5620 = secondary/entry level; Farming, Fishing and Forestry Occupations are mixed classified—codes 6005–6040 = not secondary/entry level and codes 6050–6130 = secondary/entry level); Construction, Extraction and Maintenance Occupations are mixed classified (Construction Trades are mixed classified—codes 6200, 6300–6320, 6660, 6740 = not secondary/entry level and codes 6210–6260, 6330–6600, 6700–6730, 6765 = secondary/entry level; Extraction Workers are mixed classified—codes 6800–6840 = not secondary/entry level and codes 6940 = secondary/entry level; Installation, Maintenance, and Repair Workers are mixed classified—codes 7000 = not secondary/entry level and codes 7010–7630 = secondary/entry level); Production, Transportation and Material Moving Occupations are mixed classified (Production Occupations are mixed classified—codes 7700, 7800, 7850, 7900–8200, 8220–8255, 8320, 8340, 8400–8420, 8500, 8530–8540, 8600–8640, 8720–8800, 8830–8860, 8930, 8965 = not secondary/entry level and codes 7710–7750, 7810–7840, 7855, 8210, 8265–8310, 8330, 8350, 8450–8460, 8510, 8550, 8650–8710, 8810, 8910–8920, 8940–8950 = secondary/entry level; Transportation and Material Moving Occupations are mixed classified—codes 9000–9600, 9650–9750 = not secondary/entry level and codes 9610–9640 = secondary/entry level); Military Specific Occupations are all classified as not secondary/entry level (codes 9800–9830).

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We would like to thank Tom Loughran, Lea Pessin, Emily Owens, and the anonymous reviewers for their thoughtful feedback on previous versions of this manuscript.

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Appendix A: Gender Gap in Illegal Pay in Other Data Sources

See Fig.  1 and Table 6 .

Average female to male ratio, unadjusted

Appendix B: Descriptive Statistics by Crime Type and Gender

See Table 7 .

Appendix C: Probability of Income Generating Crime

Appendix d: sample selection.

We address the problem of sample selectivity bias in estimating illegal pay by using the Heckman ( 1979 ) two-equation model where selection can be treated as a form of omitted variable bias.

We use multiple exclusion restrictions (i.e., variables that are theoretically important to explain selection into income-generating crimes, but do not explain the amount of illegal pay). Under the null hypothesis H 0 : ρ = 0 selection is exogenous and Eqs.  2 and 3 can be consistently estimated using OLS. Rejection of H 0 implies a selection problem. We use these corrected estimates in the decomposition model.

Exclusion Restrictions

Earnings Expectations: Because incarcerated persons do not have current legal wage offers, we are interested in their subjective expectations of potential legal earnings relative to potential illegal earnings upon release. Expecting to make more illegally than legally will result in a higher likelihood of selecting into income-generating crimes. When women perceive gender discrimination, they lower their earnings expectations (Orazem et al. 2003 ), which in turn could lead to lower pay and subsequent wage growth. Earnings expectations are asked of all respondents: “How much money do you think you could make illegally compared to a legal job?” Reponses are categorical: 1 = more legally, 2 = about the same and 3 = more illegally. Within the incarcerated sample, on average earners expect to make more illegally than legally than non-earners. This holds across gender, yet females tend to have a greater tendency to expect to earn more illegally than legally than males.

Illegal Reservation Price : This measure is directly analogous to the legal reservation wage. Reservation wages encapsulates all of the relevant information in an individual’s job search behavior and can depend on a number of individual factors such as gender (Killingsworth and Heckman 1986 ), race and ethnicity (Freeman and Holzer 1986 ; Holzer 1986 ), or degree of risk aversion (Devine and Kiefer 1991 ; Pannenberg 2007 ). Just like the legal sector, motherhood theoretically factors into the illegal reservation wage such that females who have children are less likely to engage in offending (Kreager et al. 2010 ; Yule et al. 2015 ). Conversely, local life circumstances, such as addiction to illicit substances can theoretically lower an individual’s illegal reservation price given that addiction fuels the need for quick cash (Uggen and Thompson 2003 ). The higher the illegal reservation price, the less likely an individual will engage in the crime for pay. Illegal reservation price is asked for each of the four crime types (drug selling, burglary, robbery, or forgery): “What is the lowest amount of money you would need to earn to participate in [ crime type]?” The measure was skewed, so we top-coded to a million dollars and then logged.

We account for selection into the four crime types we analyze with several exclusions restrictions. With valid exclusion restrictions, the Heckman method generally performs comparably or significantly better than OLS (Lennox et al. 2012 ; Puhani 2000 ). However, in the case of weak exclusion restrictions or multicollinearity, the Heckman method can produce biased estimates and can be less efficient than OLS. Overall, we observe that the Heckit estimates are substantively similar to the OLS estimates, suggesting that selecting into income-generating crimes among the incarcerated sample does not produce substantial bias.

Appendix E: Race/Ethnicity

See Table 11 .

The gender gap in illegal pay is prevalent for both blacks and whites. Black males have the largest illegal pay premium, with an unadjusted pay of $1,963. Black females have the second highest illegal pay with $1,280 per typical crime. The unadjusted pay gap suggests that black males report 1.53 times higher pay than black females. White males report 1.22 times higher illegal pay, making an average of $681 per typical crime whereas white females report making $556 per typical crime. The race gap appears to be larger than the gender gap; black males report making 2.89 times higher illegal pay than white males and black females report making 2.29 time more than white females. Unfortunately, due to a lack of sufficient cases for females, we are not able to decompose the gender gap in illegal pay stratified by race but discuss avenues for future inquiry in the discussion section.

Appendix F: American Community Survey

Legal Pay : Our measure of legal pay is wages or salary income over the past 12 months. The weekly wage was calculated by dividing the self-reported income over the past 12 months by 51 weeks, using respondents who reported working 50–52 weeks in the last 12 months. Males ($900) earn more than females ($687) on average.

Demographics : Age is measured continuously. Respondents are 45 years of age on average. Education is recorded in categories with higher values denoting more education. Footnote 15 We treat this measure as continuous and note that women are slightly more educated than men. Race/Ethnicity: Categories include white alone (76%), black or African American alone (16%), and other (7%), with the racial/ethnic breakdown being fairly similar across genders.

Average Hours/Week Worked : To control for potential differences between genders in the number of hours working over the past 12 months, we control for usual hours worked per week over the past 12 months. On average, males (41 h) work more hours per week than females (37 h).

Industry : To account for the possibility that industry may account for part of the gender gap in legal pay, we categorize industries based on IND codes. Footnote 16 In particular, we focus on five key industries: construction, manufacturing, retail trade, transportation and warehousing, and other services. While men and women in our analytic sample are fairly evenly involved in retail trade, transportation and warehousing, and other services industries, men are more involved in construction and manufacturing than women.

Occupation : Even within industries, men and women may be employed in differing occupations with differing wage distributions. To account for differences in the types of jobs in which individuals are employed, we classify occupations as secondary/entry level or not based on OCC codes. Footnote 17 Based on our classification scheme, more men are employed in secondary/entry level occupations than women (Table

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Nguyen, H., Parker, B.R. & Simpson, S.S. Equal Pay for Equal Work? Considering the Gender Gap in Illegal Pay. J Quant Criminol 38 , 425–458 (2022). https://doi.org/10.1007/s10940-021-09498-6

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Published : 17 March 2021

Issue Date : June 2022

DOI : https://doi.org/10.1007/s10940-021-09498-6

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The Enduring Grip of the Gender Pay Gap

Table of contents, how the gender pay gap increases with age, mothers with children at home tend to be less engaged with the workplace, while fathers are more active, employed mothers earn about the same as similarly educated women without children at home; both groups earn less than fathers, progress in closing the gender pay gap has slowed despite gains in women’s education, gender pay gap differs widely by race and ethnicity, broader economic forces may impact men’s and women’s earnings in different ways, what’s next for the gender pay gap.

The gender pay gap – the difference between the earnings of men and women – has barely closed in the United States in the past two decades. In 2022, American women typically earned 82 cents for every dollar earned by men. That was about the same as in 2002, when they earned 80 cents to the dollar. The slow pace at which the gender pay gap has narrowed this century contrasts sharply with the progress in the preceding two decades: In 1982, women earned just 65 cents to each dollar earned by men.

There is no single explanation for why progress toward narrowing the pay gap has all but stalled in the 21st century. Women generally begin their careers closer to wage parity with men, but they lose ground as they age and progress through their work lives, a pattern that has remained consistent over time. The pay gap persists even though women today are more likely than men to have graduated from college. In fact, the pay gap between college-educated women and men is not any narrower than the one between women and men who do not have a college degree. This points to the dominant role of other factors that still set women back or give men an advantage.

One of these factors is parenthood. Mothers ages 25 to 44 are less likely to be in the labor force than women of the same age who do not have children at home, and they tend to work fewer hours each week when employed. This can reduce the earnings of some mothers, although evidence suggests the effect is either modest overall or short-lived for many. On the other hand, fathers are more likely to be in the labor force – and to work more hours each week – than men without children at home. This is linked to an increase in the pay of fathers – a phenomenon referred to as the “ fatherhood wage premium ” – and tends to widen the gender pay gap.

Related: Gender pay gap in U.S. hasn’t changed much in two decades

Family needs can also influence the types of jobs women and men pursue , contributing to gender segregation across occupations. Differential treatment of women, including gender stereotypes and discrimination , may also play a role. And the gender wage gap varies widely by race and ethnicity.

Pew Research Center conducted this study to better understand how women’s pay compared with men’s pay in the U.S. in the economic aftermath of the COVID-19 outbreak .

The study is based on the analysis of monthly Current Population Survey (CPS) data from January 1982 to December 2022 monthly files ( IPUMS ). The CPS is the U.S. government’s official source for monthly estimates of unemployment . For a quarter of the sample each month, the CPS also records data on usual hourly earnings for hourly workers and usual weekly earnings and hours worked for other workers. In this report, monthly CPS files were combined to create annual files to boost sample sizes and to analyze the gender pay gap in greater detail.

The comparison between women’s and men’s pay is based on their median hourly earnings. For workers who are not hourly workers, hourly earnings were computed as the ratio of usual weekly earnings to usual weekly hours worked. The samples include employed workers ages 16 and older with positive earnings, working full time or part time, including those for whom earnings were imputed by the Census Bureau . Self-employed workers are excluded because their earnings are not recorded in the CPS.

The COVID-19 outbreak affected data collection efforts by the U.S. government in its surveys, especially in 2020 and 2021, limiting in-person data collection and affecting the response rate. It is possible that some measures of economic outcomes and how they vary across demographic groups are affected by these changes in data collection.

“Mothers” and “fathers” refer to women and men 16 and older who have an own child younger than 18 living in the household.

The U.S. labor force, used interchangeably with the workforce in this analysis, consists of people 16 and older who are either employed or actively looking for work.

White, Black and Asian workers include those who report being only one race and who are not Hispanic. Hispanics are of any race. Asian workers include Pacific Islanders. Other racial and ethnic groups are included in all totals but are not shown separately.

“High school graduate” refers to those who have a high school diploma or its equivalent, such as a General Education Development (GED) certificate, and those who had completed 12th grade, but their diploma status was unclear (those who had finished 12th grade but not received a diploma are excluded). “Some college” include workers with an associate degree and those who attended college but did not obtain a degree.

Younger women – those ages 25 to 34 and early in their work lives – have edged closer to wage parity with men in recent years. Starting in 2007, their earnings have consistently stood at about 90 cents to the dollar or more compared with men of the same age. But even as pay parity might appear in reach for women at the start of their careers, the wage gap tends to increase as they age.

Consider, for example, women who were ages 25 to 34 in 2010. In that year, they earned 92% as much as men their age, compared with 83% for women overall. But by 2022, this group of women, now ages 37 to 46, earned only 84% as much as men of the same age. This pattern repeats itself for groups of women who were ages 25 to 34 in earlier years – say, 2005 or 2000 – and it may well be the future for women entering the workforce now.

A good share of the increase in the gender pay gap takes place when women are between the ages of 35 and 44. In 2022, women ages 25 to 34 earned about 92% as much as men of the same ages, but women ages 35 to 44 and 45 to 54 earned 83% as much. The ratio dropped to 79% among those ages 55 to 64. This general pattern has not changed in at least four decades.

The increase in the pay gap coincides with the age at which women are more likely to have children under 18 at home. In 2022, 40% of employed women ages 25 to 34 had at least one child at home. The same was true for 66% of women ages 35 to 44 but for fewer – 39% – among women ages 45 to 54. Only 6% of employed women ages 55 to 64 had children at home in 2022.

Similarly, the share of employed men with children at home peaks between the ages of 35 to 44, standing at 58% in 2022. This is also when fathers tend to receive higher pay, even as the pay of employed mothers in same age group is unaffected.

Parenthood leads some women to put their careers on hold, whether by choice or necessity, but it has the opposite effect among men. In 2022, 70% of mothers ages 25 to 34 had a job or were looking for one, compared with 84% of women of the same age without children at home. This amounted to the withdrawal of 1.4 million younger mothers from the workforce. Moreover, when they are employed, younger mothers tend to put in a shorter workweek – by two hours per week, on average – than other women their age. Reduced engagement with the workplace among younger mothers is also a long-running phenomenon.

Fathers, however, are more likely to hold a job or be looking for one than men who don’t have children at home, and this is true throughout the prime of their working years , from ages 25 to 54. Among those who do have a job, fathers also work a bit more each week, on average, than men who do not have children at home.

As a result, the gender gap in workplace activity is greater among those who have children at home than among those who do not. For example, among those ages 35 to 44, 94% of fathers are active in the workforce, compared with 75% of mothers – a gap of 19 percentage points. But among those with no children at home in this age group, 84% of men and 78% of women are active in the workforce – a gap of 6 points.

These patterns contribute to the gap in workplace activity between men and women overall. As of 2022, 68% of men ages 16 and older – with or without children at home – are either employed or seeking employment. That compares with 57% of women, a difference of 11 percentage points. This gap was as wide as 24 points in 1982, but it narrowed to 14 points by 2002. Men overall also worked about three hours more per week at a job than women in 2022, on average, down from a gap of about six hours per week in 1982.

Parenthood affects the hourly earnings of employed women and men in unexpected ways. While employed mothers overall appear to earn less than employed women without children at home, the gap is driven mainly by differences in educational attainment between the two groups. Among women with similar levels of education, there is little gap in the earnings of mothers and non-mothers. However, fathers earn more than other workers, including other men without children at home, regardless of education level. This phenomenon – known as the fatherhood wage premium – is one of the main ways that parenthood affects the gender pay gap among employed workers.

Motherhood does have important effects on the potential earnings of women. Women who experience breaks in their careers after becoming mothers sacrifice at least some of their earnings . Some mothers may never work for pay after having children, passing on earnings altogether. But it is difficult to know what the earnings of mothers might have been and, as a result, it is hard to know for certain what the full effect of motherhood is on women’s earnings. Estimates suggest that motherhood may account for much of the current shortfall in the earnings potential of women overall. 1

Among employed men and women, the impact of parenting is felt most among those ages 25 to 54, when they are most likely to have children under 18 at home. In 2022, mothers ages 25 to 34 earned 85% as much as fathers that age, but women without children at home earned 97% as much as fathers. In contrast, employed women ages 35 to 44 – with or without children – both earned about 80% as much as fathers. The table turns for women ages 45 to 54, with mothers earning more than women with no children at home. Among those ages 35 to 44 or 45 to 54, men without children earned only 84% as much as fathers.

But these patterns in the earnings of employed mothers and women with no children at home are influenced greatly by differences in education levels between the two. Among employed women ages 25 to 34, some 61% of women without children at home had a bachelor’s degree or higher level of education in 2022, compared with 37% of mothers. It follows that among women ages 25 to 34, those without children at home (a more highly educated group, on average) earned more than women with at least one child at home. Conversely, employed mothers ages 45 to 54 were more likely than other women to have at least a bachelor’s degree – 58% vs. 42%. For that reason, mothers ages 45 to 54 earned more than women without children. 2

When the earnings of mothers are compared with those of women without children at home who have the same level of education, the differences either narrow or go away. Among employed women ages 25 to 34 with at least a bachelor’s degree, both mothers and women without children at home earned 80% as much as fathers in 2022. Among women ages 25 to 34 with a high school diploma and no further education, mothers earned 79% as much as fathers and women with no children at home earned 84% as much. The narrowing of the gap in earnings of mothers and women without children at home after controlling for education level also extends to other age groups.

Thus, among the employed, the effect of parenthood on the gender pay gap does not seem to be driven by a decrease in mothers’ earnings relative to women without children at home. Instead, the widening of the pay gap with parenthood appears to be driven more by an increase in the earnings of fathers. Fathers ages 25 to 54 not only earn more than mothers the same age, they also earn more than men with no children at home. Nonetheless, men without children at home still earn more than women with or without children at home.

Although there is little gap in the earnings of employed mothers and women with no children at home who have the same level of education, there is a lingering gap in workplace engagement between the two groups. Whether they had at least a bachelor’s degree or were high school graduates, mothers ages 25 to 34 are less likely to hold a job or be looking for one. Similarly, younger mothers on average work fewer hours than women without children at home each week, regardless of their education level. The opposite is true for fathers compared with men without children at home.

The share of women with at least a bachelor’s degree has increased steadily since 1982 – and faster than among men. In 1982, 20% of employed women ages 25 and older had a bachelor’s degree or higher level of education, compared with 26% of employed men. By 2022, 48% of employed women had at least a bachelor’s degree, compared with 41% of men. Still, women did not see the pay gap close to the same extent from 2002 to 2022 as they did from 1982 to 2002.

In part, this may be linked to how the gains from going to college have changed in recent decades, for women and men alike. The college wage premium – the boost in earnings workers get from a college degree – increased rapidly during the 1980s. But the rise in the premium slowed down over time and came to a halt around 2010. This likely reduced the relative growth in the earnings of women.

Although gains in education have raised the average earnings of women and have narrowed the gender pay gap overall, college-educated women are no closer to wage parity with their male counterparts than other women. In 2022, women with at least a bachelor’s degree earned 79% as much as men who were college graduates, and women who were high school graduates earned 81% as much as men with the same level of education. This underscores the challenges faced by women of all education levels in closing the pay gap.

Notably, the gender wage gap has closed more among workers without a four-year college degree than among those who do have a bachelor’s degree or more education. For example, the wage gap for women without a high school diploma narrowed from 62% in 1982 to 83% in 2022 relative to men at the same education level. But it closed only from 69% to 79% among bachelor’s degree holders over the same period. This is because only men with at least a bachelor’s degree experienced positive wage growth from 1982 to 2022; all other men saw their real wages decrease. Meanwhile, the real earnings of women increased regardless of their level of education.

As women have improved their level of education in recent decades, they’ve also increased their share of employment in higher-paying occupations, such as managerial, business and finance, legal, and computer, science and engineering (STEM) occupations. In 1982, women accounted for only 26% of employment in managerial occupations. By 2022, their share had risen to 40%. Women also substantially increased their presence in social, arts and media occupations. Over the same period, the shares of women in several lower-paying fields, such as administrative support jobs and food preparation and serving occupations, fell significantly.

Even so, women are still underrepresented in managerial and STEM occupations – along with construction, repair and production, and transportation occupations – when compared with their share of employment overall. And there has been virtually no change in the degree to which women are over represented in education, health care, and personal care and services occupations – the last of which are lower paying than the average across all occupations. The distribution of women and men across occupations remains one of the drivers of the gender pay gap . But the degree to which this distribution is the result of personal choices or gender stereotypes is not entirely clear.

Looking across racial and ethnic groups, a wide gulf separates the earnings of Black and Hispanic women from the earnings of White men. 3 In 2022, Black women earned 70% as much as White men and Hispanic women earned only 65% as much. The ratio for White women stood at 83%, about the same as the earnings gap overall, while Asian women were closer to parity with White men, making 93% as much.

The pay gap narrowed for all groups of women from 1982 to 2022, but more so for White women than for Black and Hispanic women. The earnings gap for Asian women narrowed by about 17 percentage points from 2002 to 2022, but data for this group is not available for 1982.

To some extent, the gender wage gap varies by race and ethnicity because of differences in education, experience, occupation and other factors that drive the gender wage gap for women overall. But researchers have uncovered new evidence of hiring discrimination against various racial and ethnic groups, along with discrimination against other groups, such as LGBTQ and disabled workers. Discrimination in hiring may feed into differences in earnings by shutting out workers from opportunities.

Changes in the gender pay gap are also shaped by economic factors that sometimes drive men’s and women’s earnings in distinctive ways. Because men and women tend to work in different types of jobs and industries, their earnings may respond differently to external pressures.

More specifically, men’s earnings essentially didn’t change from 1982 to 2002. Potential reasons for that include a more rapid decline in union membership among men, a shift away from jobs calling for more physical skills, and global competition that sharply reduced employment in manufacturing in the 1980s. At the same time, women’s earnings increased substantially as they raised their level of education and shifted toward higher-paying occupations.

But in some ways, the economic climate has proved less favorable for women this century. For reasons that are not entirely clear, women’s employment was slower to recover from the Great Recession of 2007-2009. More recently, the COVID-19 recession took on the moniker “ she-cession ” because of the pressure on jobs disproportionately held by women . Amid a broader slowdown in earnings growth from 2000 to 2015, the increase in women’s earnings from 2002 to 2022 was not much greater than the increase in men’s earnings, limiting the closure in the gender pay gap over the period.

Higher education, a shift to higher-paying occupations and more labor market experience have helped women narrow the gender pay gap since 1982. But even as women have continued to outpace men in educational attainment, the pay gap has been stuck in a holding pattern since 2002, ranging from 80 to 85 cents to the dollar.

More sustained progress in closing the pay gap may depend on deeper changes in societal and cultural norms and in workplace flexibility that affect how men and women balance their careers and family lives . Even in countries that have taken the lead in implementing family-friendly policies, such as Denmark, parenthood continues to drive a significant wedge in the earnings of men and women. New research suggests that family-friendly policies in the U.S. may be keeping the pay gap from closing. Gender stereotypes and discrimination, though difficult to quantify, also appear to be among the “last-mile” hurdles impeding further progress.

What is the gender wage gap in your metropolitan area? Find out with our pay gap calculator

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Equal Pay for Equal Work: Pass the Paycheck Fairness Act

According to the U.S. Census Bureau, in 2013, women who worked full time earned, on average, only 78 cents for every dollar men earned. The figures are even worse for women of color. African American women earned only approximately 64 cents and Latinas only 56 cents for each dollar earned by a white male.

The Paycheck Fairness Act will help secure equal pay for equal work for all Americans. The bill would update the Equal Pay Act of 1963, a law that has not been able to achieve its promise of closing the wage gap because of limited enforcement tools and inadequate remedies. The Paycheck Fairness Act would make critical changes to the law, including:

• requiring employers to demonstrate that wage differentials are based on factors other than sex;
• prohibiting retaliation against workers who inquire about their employers’ wage practices or disclose their own wages;
• permitting reasonable comparisons between employees within clearly defined geographical areas to determine fair wages;
• strengthening penalties for equal pay violations;
• directing the Department of Labor to assist employers and collect wage-related data; and
• authorizing additional training for Equal Employment Opportunity Commission staff to better identify and handle wage disputes.

The time has come to make equal pay a reality. During this climate of unprecedented economic uncertainty, nothing could be more important than ensuring that all workers receive equal pay for equal work.

• Executive Action Needed to End Employment Discrimination (2/6/2014)
• Fulfilling the Promise of the Lilly Ledbetter Fair Pay Act (2/3/2014)
• Lilly Ledbetter: Celebrating a Champion Still Fighting for Us (1/29/2014)
• Celebrating Equal Pay (And Bacon!) at the White House (6/12/2013)
• Fifty Years Later, Fulfilling the Promise of Equal Pay (6/10/2013)
• Happy 50th Birthday, Equal Pay Act! (6/10/2013)
• ACLU and the Equal Pay Act of 1963: Celebrating 50 Years of Advocacy (6/6/2013)
• Celebrate Women’s Equality Day – Support Equal Pay Today! (8/26/2013)
• 18 More Cents… in 50 years (4/9/2013)
• What Would You Do With $11,000? (4/9/2013)
• “Our Journey is Not Complete” – Equal Pay Requires Passage of Paycheck Fairness Act (01/29/2013) crossposted to The Hill
• On the First Anniversary of Wal-Mart v. Dukes: Stand Up or Be Trampled (6/20/2012)
• An Unhappy Anniversary for the Equal Pay Act (6/6/2012)
• The Paycheck Fairness Act: It’s Time to Stop the Catch 22 (6/4/2012)
• Paycheck Fairness Act Is Sorely Needed (5/4/2012)
• We Can’t Wait For Fair Pay (4/17/2012)

ADVOCACY DOCUMENTS <!– Urge President Obama to Ban Retaliation in Federal Contracting –>

• ACLU Factsheet on the Paycheck Fairness Act (Updated March 2015)
• ACLU Letter in Support of Paycheck Fairness Act for Senate Floor Vote September 2014
• ACLU Letter in Support of Paycheck Fairness Act for Senate Floor Vote 2014 (Updated 4/8/2014)
• ACLU Factsheet on Anti-Retaliation Executive Order – April 2014 (4/3/2014)
• ACLU Letter in Support of Paycheck Fairness Act for Senate Floor Vote 2014 (4/3/2014)
• ACLU Letter in Support of the Paycheck Fairness Act (S.84) for Senate HELP Hearing (3/31/2014)
• ACLU Letter to Congress in Support of the Paycheck Fairness Act Reintroduction (1/23/2013)
• ACLU Letter to President Obama on Executive Order Banning Retaliation for Wage Inquiries in Federal Contracting (4/17/2012)
• ACLU Letter on Paycheck Fairness Act Co-Sponsorship (4/7/2011)

<!–

Paycheck Fairness Bill Necessary to Strengthen Equal Pay Protections (7/31/2008) –>

<!– TAKE ACTION Urge President Obama to Ban Retaliation in Federal Contracting. Did you know that you can be fired for disclosing your own wages to a co-worker? And did you know that on the 50th anniversary of President Kennedy’s signing of the Equal Pay Act of 1963, women still, on average, make only 77 cents for every dollar earned by a man? The figures are even more dismal for women of color – in 2011, African-American women only earned approximately 64 cents and Latinas only 55 cents for each dollar earned by a white man. Urge President Obama ban retaliation in federal contracting for wage inquires. Take Action! » –> SPECIAL FEATURE 50th Anniversary of Equal Pay Act This year marks the 50th anniversary of President John F. Kennedy’s signing of the Equal Pay Act of 1963. This landmark piece of federal anti-discrimination law was one of the very first to address gender-based pay disparities. On the day he signed it, President Kennedy called the act a “first step” which “affirms our determination that when women enter the labor force they will find equality in their pay envelopes.” But he noted that “much remains to be done to achieve full equality of economic opportunity.” Learn More »

SPECIAL FEATURE Pay Equity The ACLU works to end discrimination in the workplace and ensure that all workers — regardless of sex, race, national origin, age or disability—are able to bring home every dollar they rightfully earn. As a result of discrimination, including employers’ reliance on gender stereotypes, women lack parity with men in earnings. Learn More »

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Promoting equality, diversity and inclusion in research and funding: reflections from a digital manufacturing research network

• Oliver J. Fisher 1 ,
• Debra Fearnshaw   ORCID: orcid.org/0000-0002-6498-9888 2 ,
• Nicholas J. Watson 3 ,
• Peter Green 4 ,
• Fiona Charnley 5 ,
• Duncan McFarlane 6 &
• Sarah Sharples 2  

Research Integrity and Peer Review volume  9 , Article number:  5 ( 2024 ) Cite this article

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Equal, diverse, and inclusive teams lead to higher productivity, creativity, and greater problem-solving ability resulting in more impactful research. However, there is a gap between equality, diversity, and inclusion (EDI) research and practices to create an inclusive research culture. Research networks are vital to the research ecosystem, creating valuable opportunities for researchers to develop their partnerships with both academics and industrialists, progress their careers, and enable new areas of scientific discovery. A feature of a network is the provision of funding to support feasibility studies – an opportunity to develop new concepts or ideas, as well as to ‘fail fast’ in a supportive environment. The work of networks can address inequalities through equitable allocation of funding and proactive consideration of inclusion in all of their activities.

This study proposes a strategy to embed EDI within research network activities and funding review processes. This paper evaluates 21 planned mitigations introduced to address known inequalities within research events and how funding is awarded. EDI data were collected from researchers engaging in a digital manufacturing network activities and funding calls to measure the impact of the proposed method.

Quantitative analysis indicates that the network’s approach was successful in creating a more ethnically diverse network, engaging with early career researchers, and supporting researchers with care responsibilities. However, more work is required to create a gender balance across the network activities and ensure the representation of academics who declare a disability. Preliminary findings suggest the network’s anonymous funding review process has helped address inequalities in funding award rates for women and those with care responsibilities, more data are required to validate these observations and understand the impact of different interventions individually and in combination.

Conclusions

In summary, this study offers compelling evidence regarding the efficacy of a research network's approach in advancing EDI within research and funding. The network hopes that these findings will inform broader efforts to promote EDI in research and funding and that researchers, funders, and other stakeholders will be encouraged to adopt evidence-based strategies for advancing this important goal.

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Introduction

Achieving equality, diversity, and inclusion (EDI) is an underpinning contributor to human rights, civilisation and society-wide responsibility [ 1 ]. Furthermore, promoting and embedding EDI within research environments is essential to make the advancements required to meet today’s research challenges [ 2 ]. This is evidenced by equal, diverse and inclusive teams leading to higher productivity, creativity and greater problem-solving ability [ 3 ], which increases the scientific impact of research outputs and researchers [ 4 ]. However, there remains a gap between EDI research and the everyday implementation of inclusive practices to achieve change [ 5 ]. This paper presents and reflects on the EDI measures trialled by the UK Engineering and Physical Sciences Research Council (EPSRC) funded digital manufacturing research network, Connected Everything (grant number: EP/S036113/1) [ 6 ]. The EPSRC is a UK research council that funds engineering and physical sciences research. By sharing these reflections, this work aims to contribute to the wider effort of creating an inclusive research culture. The perceptions of equality, diversity, and inclusion may vary among individuals. For the scope of this study, the following definitions are adopted:

Equality: Equality is about ensuring that every individual has an equal opportunity to make the most of their lives and talents. No one should have poorer life chances because of the way they were born, where they come from, what they believe, or whether they have a disability.

Diversity: Diversity concerns understanding that each individual is unique, recognising our differences, and exploring these differences in a safe, positive, and nurturing way to value each other as individuals.

Inclusion: Inclusion is an effort and practice in which groups or individuals with different backgrounds are culturally and socially accepted, welcomed and treated equally. This concerns treating each person as an individual, making them feel valued, and supported and being respectful of who they are.

Research networks have varied goals, but a common purpose is to create new interdisciplinary research communities, by fostering interactions between researchers and appropriate scientific, technological and industrial groups. These networks aim to offer valuable career progression opportunities for researchers, through access to research funding, forming academic and industrial collaborations at network events, personal and professional development, and research dissemination. However, feedback from a 2021 survey of 19 UK research networks, suggests that these research networks are not always diverse, and whilst on the face of it they seem inclusive, they are perceived as less inclusive by minority groups (including non-males, those with disabilities, and ethnic minority respondents) [ 7 ]. The exclusivity of these networks further exacerbates the inequality within the academic community as it prevents certain groups from being able to engage with all aspects of network activities.

Research investigating the causes of inequality and exclusivity has identified several suggestions to make research culture more inclusive, including improving diverse representation within event programmes and panels [ 8 , 9 ]; ensuring events are accessible to all [ 10 ]; providing personalised resources and training to build capacity and increase engagement [ 11 ]; educating institutions and funders to understand and address the barriers to research [ 12 ]; and increasing diversity in peer review and funding panels [ 13 ]. Universities, research institutions and research funding bodies are increasingly taking responsibility to ensure the health of the research and innovation system and to foster inclusion. For example, the EPSRC has set out their own ‘Expectation for EDI’ to promote the formation of a diverse and inclusive research culture [ 14 ]. To drive change, there is an emphasis on the importance of measuring diversity and links to measured outcomes to benchmark future studies on how interventions affect diversity [ 5 ]. Further, collecting and sharing EDI data can also drive aspirations, provide a target for actions, and allow institutions to consider common issues. However, there is a lack of available data regarding the impact of EDI practices on diversity that presents an obstacle, impeding the realisation of these benefits and hampering progress in addressing common issues and fostering diversity and inclusion [ 5 ].

Funding acquisition is important to an academic’s career progression, yet funding may often be awarded in ways that feel unequal and/or non-transparent. The importance of funding in academic career progression means that, if credit for obtaining funding is not recognised appropriately, careers can be damaged, and, as a result of the lack of recognition for those who have been involved in successful research, funding bodies may not have a complete picture of the research community, and are unable to deliver the best value for money [ 15 ]. Awarding funding is often a key research network activity and an area where networks can have a positive impact on the wider research community. It is therefore important that practices are established to embed EDI consideration within the funding process and to ensure that network funding is awarded without bias. Recommendations from the literature to make the funding award process fairer include: ensuring a diverse funding panel; funders instituting reviewer anti-bias training; anonymous review; and/or automatic adjustments to correct for known biases [ 16 ]. In the UK, the government organisation UK Research and Innovation (UKRI), tasked with overseeing research and innovation funding, has pledged to publish data to enhance transparency. This initiative aims to furnish an evidence base for designing interventions and evaluating their efficacy. While the data show some positive signs (e.g., the award rates for male and female PI applicants were equal at 29% in 2020–21), Ottoline Leyser (UKRI Chief Executive) highlights the ‘persistent pernicious disparities for under-represented groups in applying for and winning research funding’ [ 17 ]. This suggests that a more radical approach to rethinking the traditional funding review process may be required.

This paper describes the approach taken by the ‘Connected Everything’ EPSRC-funded Network to embed EDI in all aspects of its research funding process, and evaluates the impact of this ambition, leading to recommendations for embedding EDI in research funding allocation.

Connected everything’s equality diversity and inclusion strategy

Connected Everything aims to create a multidisciplinary community of researchers and industrialists to address key challenges associated with the future of digital manufacturing. The network is managed by an investigator team who are responsible for the strategic planning and, working with the network manager, to oversee the delivery of key activities. The network was first funded between 2016–2019 (grant number: EP/P001246/1) and was awarded a second grant (grant number: EP/S036113/1). The network activities are based around three goals: building partnerships, developing leadership and accelerating impact.

The Connected Everything network represents a broad range of disciplines, including manufacturing, computer science, cybersecurity, engineering, human factors, business, sociology, innovation and design. Some of the subject areas, such as Computer Science and Engineering, tend to be male-dominated (e.g., in 2021/22, a total of 185,42 higher education student enrolments in engineering & technology subjects was broken down as 20.5% Female and 79.5% Male [ 18 ]). The networks also face challenges in terms of accessibility for people with care responsibilities and disabilities. In 2019, Connected Everything committed to embedding EDI in all its network activities and published a guiding principle and goals for improving EDI (see Additional file 1 ). When designing the processes to deliver the second iteration of Connected Everything, the team identified several sources of potential bias/exclusion which have the potential to impact engagement with the network. Based on these identified factors, a series of mitigation interventions were implemented and are outlined in Table  1 .

Connected everything anonymous review process

A key Connected Everything activity is the funding of feasibility studies to enable cross-disciplinary, foresight, speculative and risky early-stage research, with a focus on low technology-readiness levels. Awards are made via a short, written application followed by a pitch to a multidisciplinary diverse panel including representatives from industry. Six- to twelve-month-long projects are funded to a maximum value of £60,000.

The current peer-review process used by funders may reveal the applicants’ identities to the reviewer. This can introduce dilemmas to the reviewer regarding (a) deciding whether to rely exclusively on information present within the application or search for additional information about the applicants and (b) whether or not to account for institutional prestige [ 34 ]. Knowing an applicant’s identity can bias the assessment of the proposal, but by focusing the assessment on the science rather than the researcher, equality is more frequently achieved between award rates (i.e., the proportion of successful applications) [ 15 ]. To progress Connected Everything’s commitment to EDI, the project team created a 2-stage review process, where the applicants’ identity was kept anonymous during the peer review stage. This anonymous process, which is outlined in Fig.  1 , was created for the feasibility study funding calls in 2019 and used for subsequent funding calls.

Connected Everything’s anonymous review process [EDI: Equality, diversity, and inclusion]

To facilitate the anonymous review process, the proposal was submitted in two parts: part A the research idea and part B the capability-to-deliver statement. All proposals were first anonymously reviewed by a random selection of two members from the Connected Everything executive group, which is a diverse group of digital manufacturing experts and peers from academia, industry and research institutions that provide guidance and leadership on Connected Everything activities. The reviewers rated the proposals against the selection criteria (see Additional file 1 , Table 1) and provided overall comments alongside a recommendation on whether or not the applicant should be invited to the panel pitch. This information was summarised and shared with a moderation sift panel, made up of a minimum of two Connected Everything investigators and a minimum of one member of the executive group, that tensioned the reviewers’ comments (i.e. comments and evaluations provided by the peer reviewers are carefully considered and weighed against each other) and ultimately decided which proposals to invite to the panel. This tension process included using the identifying information to ensure the applicants did have the capability to deliver the project. If this remained unclear, the applicants were asked to confirm expertise in an area the moderation sift panel thought was key or asked to bring in additional expertise to the project team during the panel pitch.

During stage two the applicants were invited to pitch their research idea to a panel of experts who were selected to reflect the diversity of the community. The proposals, including applicants’ identities, were shared with the panel at least two weeks ahead of the panel. Individual panel members completed a summary sheet at the end of the pitch session to record how well the proposal met the selection criteria (see Additional file 1 , Table 1). Panel members did not discuss their funding decision until all the pitches had been completed. A panel chair oversaw the process but did not declare their opinion on a specific feasibility study unless the panel could not agree on an outcome. The panel and panel chair were reminded to consider ways to manage their unconscious bias during the selection process.

Due to the positive response received regarding the anonymous review process, Connected Everything extended its use when reviewing other funded activities. As these awards were for smaller grant values (~ £5,000), it was decided that no panel pitch was required, and the researcher’s identity was kept anonymous for the entire process.

Data collection and analysis methods

Data collection.

Equality, diversity and inclusion data were voluntarily collected from applicants for Connected Everything research funding and from participants who won scholarships to attend Connected Everything funded activities. Responses to the EDI data requests were collected from nine Connected Everything coordinated activities between 2019 and 2022. Data requests were sent after the applicant had applied for Connected Everything funding or had attended a Connected Everything funded activity. All data requests were completed voluntarily, with reassurance given that completion of the data requested in no way affected their application. In total 260 responses were received, of which the three feasibility study calls comprised 56.2% of the total responses received. Overall, there was a 73.8% response rate.

To understand the diversity of participants engaging with Connected Everything activities and funding, the data requests asked for details of specific diversity characteristics: gender, transgender, disability, ethnicity, age, and care responsibilities. Although sex and gender are terms that are often used interchangeably, they are two different concepts. To clarify, the definitions used by the UK government describe sex as a set of biological attributes that is generally limited to male or female, and typically attributed to individuals at birth. In contrast, gender identity is a social construction related to behaviours and attributes, and is self-determined based on a person’s internal perception, identification and experience. Transgender is a term used to describe people whose gender identity is not the same as the sex they were registered at birth. Respondents were first asked to identify their gender and then whether their gender was different from their birth sex.

For this study, respondents were asked to (voluntarily) self-declare whether they consider themselves to be disabled or not. Ethnicity within the data requests was based on the 2011 census classification system. When reporting ethnicity data, this study followed the AdvanceHE example to aggregate the census categories into six groups to enable benchmarking against the available academic ethnicity data. AdvanceHE is a UK charity that works to improve the higher education system for staff, students and society. However, it was acknowledged that there were limitations with this grouping, including the assumption that minority ethnic staff or students are a homogenous group [ 16 ]. Therefore, this study made sure to breakdown these groups during the discussion of the results. The six groups are:

Asian: Asian/Asian British: Indian, Pakistani, Bangladeshi, and any other Asian background;

Black: Black/African/Caribbean/Black British: African, Caribbean, and any other Black/African/Caribbean background;

Other ethnic backgrounds, including Arab.

White: all white ethnic groups.

Benchmarking data

Published data from the Higher Education Statistics Agency [ 26 ] (a UK organisation responsible for collecting, analysing, and disseminating data related to higher education institutions and students), UKRI funding data [ 19 , 35 ] and 2011 census data [ 36 ] were used to benchmark the EDI data collected within this study. The responses to the data collected were compared to the engineering and technology cluster of academic disciplines, as this is most represented by Connected Everything’s main funded EPSRC. The Higher Education Statistics Agency defines the engineering and technology cluster as including the following subject areas: general engineering; chemical engineering; mineral, metallurgy & materials engineering; civil engineering; electrical, electronic & computer engineering; mechanical, aero & production engineering and; IT, systems sciences & computer software engineering [ 37 ].

When assessing the equality in funding award rates, previous studies have focused on analysing the success rates of only the principal investigators [ 15 , 16 , 38 ]; however, Connected Everything recognised that writing research proposals is a collaborative task, so requested diversity data from the whole research team. The average of the last six years of published principal investigator and co-investigator diversity data for UKRI and EPSRC funding awards (2015–2021) was used to benchmark the Connected Everything funding data [ 35 ]. The UKRI and EPSRC funding review process includes a peer review stage followed by panel pitch and assessment stage; however, the applicant's track record is assessed during the peer review stage, unlike the Connected Everything review process.

The data collected have been used to evaluate the success of the planned migrations to address EDI factors affecting the higher education research ecosystem, as outlined in Table  1 (" Connected Everything’s Equality Diversity and Inclusion Strategy " Section).

Dominance of small number of research-intensive universities receiving funding from network

The dominance of a small number of research-intensive universities receiving funding from a network can have implications for the field of research, including: the unequal distribution of resources; a lack of diversity of research, limited collaboration opportunities; and impact on innovation and progress. Analysis of published EPSRC funding data between 2015 and 2021 [ 19 ], shows that the funding has been predominately (74.1%, 95% CI [71.%, 76.9%] out of £3.98 billion) awarded to Russell Group universities. The Russell Group is a self-selected association of 24 research-intensive universities (out of the 174 universities) in the UK, established in 1994. Evaluation of the universities that received Connected Everything feasibility study funding between 2016–2019, shows that Connected Everything awarded just over half (54.6%, 95% CI [25.1%, 84.0%] out of 11 awards) to Russell Group universities. Figure  2 shows that the Connected Everything funding awarded to Russell Group universities reduced to 44.4%, 95% CI [12.0%, 76.9%] of 9 awards between 2019–2022.

A comparison of funding awarded by EPSRC (total = £3.98 billion) across Russell Group universities and non-Russell Group universities, alongside the allocations for Connected Everything I (total = £660 k) and Connected Everything II (total = £540 k)

Dominance of successful applications from men

The percentage point difference between the award rates of researchers who identified as female, those who declare a disability, or identified as ethnic minority applicants and carers and their respective counterparts have been plotted in Fig.  3 . Bars to the right of the axis mean that the award rate of the female/declared-disability/ethnic-minority/carer applicants is greater than that of male/non- disability/white/not carer applicants.

Percentage point (PP) differences in award rate by funding provider for gender, disability status, ethnicity and care responsibilities (data not collected by UKRI and EPSRC [ 35 ]). The total number of applicants for each funder are as follows: Connected Everything = 146, EPSRC = 37,960, and UKRI = 140,135. *The numbers of applicants were too small (< 5) to enable a meaningful discussion

Figure  3 (A) shows that between 2015 and 2021 research team applicants who identified as male had a higher award rate than those who identified as female when applying for EPSRC and wider UKRI research council funding. Connected Everything funding applicants who identified as female achieved a higher award rate (19.4%, 95% CI [6.5%, 32.4%] out of 146) compared to male applicants (15.6%, 95% CI [8.8%, 22.4%] out of 146). These data suggest that biases have been reduced by the Connected Everything review process and other mitigation strategies (e.g., visible gender diversity in panel pitch members and publishing CE principal and goals to demonstrate commitment to equality and fairness). This finding aligns with an earlier study that found gender bias during the peer review process, resulting in female investigators receiving less favourable evaluations than their male counterparts [ 15 ].

Over-representation of people identifying as male in engineering and technology academic community

Figure  4 shows the response to the gender question, with 24.2%, 95% CI [19.0%, 29.4%] of 260 responses identifying as female. This aligns with the average for the engineering and technology cluster (21.4%, 95% CI [20.9%, 21.9%] female of 27,740 academic staff), which includes subject areas representative of our main funder, EPSRC [ 22 ]. We also sought to understand the representation of transgender researchers within the network. However, following the rounding policy outlined by UK Government statistics policies and procedures [ 39 ], the number of responses that identified as a different sex to birth was too low (< 5) to enable a meaningful discussion.

Gender question responses from a total of 260 respondents

Dominance of successful applications from white academics

Figure  3 (C) shows that researchers with a minority ethnicity consistently have a lower award rate than white researchers when applying for EPSRC and UKRI funding. Similarly, the results in Fig.  3 (C) indicate that white researchers are more successful (8.0% percentage point, 95% CI [-8.6%, 24.6%]) when applying for Connected Everything funding. These results indicate that more measures should be implemented to support the ethnic minority researchers applying for Connected Everything funding, as well as sense checking there is no unconscious bias in any of the Connected Everything funding processes. The breakdown of the ethnicity diversity of applicants at different stages of the Connected Everything review process (i.e. all applications, applicants invited to panel pitch and awarded feasibility studies) has been plotted in Fig.  5 to help identify where more support is needed. Figure  5 shows an increase in the proportion of white researchers from 54%, 95% CI [45.4%, 61.8%] of all 146 applicants to 66%, 95% CI [52.8%, 79.1%] of the 50 researchers invited to the panel pitch. This suggests that stage 1 of the Connected Everything review process (anonymous review of written applications) may favour white applicants and/or introduce unconscious bias into the process.

Ethnicity questions responses from different stages during the Connected Everything anonymous review process. The total number of applicants is 146, with 50 at the panel stage and 23 ultimately awarded

Under-representation of those from black or minority ethnic backgrounds

Connected Everything appears to have a wide range of ethnic diversity, as shown in Fig.  6 . The ethnicities Asian (18.3%, 95% CI [13.6%, 23.0%]), Black (5.1%, 95% CI [2.4%, 7.7%]), Chinese (12.5%, 95% CI [8.4%, 16.5%]), mixed (3.5%, 95% CI [1.3%, 5.7%]) and other (7.8%, 95% CI [4.5%, 11.1%]) have a higher representation among the 260 individuals engaging with network’s activities, in contrast to both the engineering and technology academic community and the wider UK population. When separating these groups into the original ethnic diversity answers, it becomes apparent that there is no engagement with ‘Black or Black British: Caribbean’, ‘Mixed: White and Black Caribbean’ or ‘Mixed: White and Asian’ researchers within Connected Everything activities. The lack of engagement with researchers from a Caribbean heritage is systemic of a lack of representation within the UK research landscape [ 25 ].

Ethnicity question responses from a total of 260 respondents compared to distribution of the 13,085 UK engineering and technology (E&T) academic staff [ 22 ] and 56 million people recorded in the UK 2011 census data [ 36 ]

Under-representation of disabilities, chronic conditions, invisible illnesses and neurodiversity in funded activities and events.

Figure  7 (A) shows that 5.7%, 95% CI [2.4%, 8.9%] of 194 responses declared a disability. This is higher than the average of engineering and technology academics that identify as disabled (3.4%, 95% CI [3.2%, 3.7%] of 27,730 academics). Between Jan-March 2022, 9.0 million people of working age (16–64) within the UK were identified as disabled by the Office for National Statistics [ 40 ], which is 21% of the working age population [ 27 ]. Considering these statistics, there is a stark under-representation of disabilities, chronic conditions, invisible illnesses and neurodiversity amongst engineering and technology academic staff and those engaging in Connected Everything activities.

Responses to A  Disability and B  Care responsibilities questions colected from a total of 194 respondents

Between 2015 and 2020 academics that declared a disability have been less successful than academics without a disability in attracting UKRI and EPSRC funding, as shown in Fig.  3 (B). While Fig.  3 (B) shows that those who declare a disability have a higher Connected Everything funding award rate, the number of applicants who declared a disability was too small (< 5) to enable a meaningful discussion regarding this result.

Under-representation of those with care responsibilities in funded activities and events

In response to the care responsibilities question, Fig.  7 (B) shows that 27.3%, 95% CI [21.1%, 33.6%] of 194 respondents identified as carers, which is higher than the 6% of adults estimated to be providing informal care across the UK in a UK Government survey of the 2020/2021 financial year [ 41 ]. However, the ‘informal care’ definition used by the 2021 survey includes unpaid care to a friend or family member needing support, perhaps due to illness, older age, disability, a mental health condition or addiction [ 41 ]. The Connected Everything survey included care responsibilities across the spectrum of care that includes partners, children, other relatives, pets, friends and kin. It is important to consider a wide spectrum of care responsibilities, as key academic events, such as conferences, have previously been demonstrably exclusionary sites for academics with care responsibilities [ 42 ]. Breakdown analysis of the responses to care responsibilities by gender in Fig.  8 reveals that 37.8%, 95% CI [25.3%, 50.3%] of 58 women respondents reported care responsibilities, compared to 22.6%, 95% CI [61.1%, 76.7%] of 136 men respondents. Our findings reinforce similar studies that conclude the burden of care falls disproportionately on female academics [ 43 ].

Responses to care responsibilities when grouped by A  136 males and B  58 females

Figure  3 (D) shows that researchers with careering responsibilities applying for Connected Everything funding have a higher award rate than those researchers applying without care responsibilities. These results suggest that the Connected Everything review process is supportive of researchers with care responsibilities, who have faced barriers in other areas of academia.

Reduced opportunities for ECRs

Early-career researchers (ECRs) represent the transition stage between starting a PhD and senior academic positions. EPSRC defines an ECR as someone who is either within eight years of their PhD award, or equivalent professional training or within six years of their first academic appointment [ 44 ]. These periods exclude any career break, for example, due to family care; health reasons; and reasons related to COVID-19 such as home schooling or increased teaching load. The median age for starting a PhD in the UK is 24 to 25, while PhDs usually last between three and four years [ 45 ]. Therefore, these data would imply that the EPSRC median age of ECRs is between 27 and 37 years. It should be noted, however, that this definition is not ideal and excludes ECRs who may have started their research career later in life.

Connected Everything aims to support ECRs via measures that include mentoring support, workshops, summer schools and podcasts. Figure  9 shows a greater representation of researchers engaging with Connected Everything activities that are aged between 30–44 (62.4%, 95% CI [55.6%, 69.2%] of 194 respondents) when compared to the wider engineering and technology academic community (43.7%, 95% CI [43.1%, 44.3%] of 27,780 academics) and UK population (26.9%, 95% CI [26.9%, 26.9%]).

Age question responses from a total of 194 respondents compared to distribution of the 27,780 UK engineering and technology (E&T) academic staff [ 22 ] and 56 million people recorded in the UK 2011 census data [ 36 ]

High competition for funding has a greater impact on ECRs

Figure  10 shows that the largest age bracket applying for and winning Connected Everything funding is 31–45, whereas 72%, CI 95% [70.1%, 74.5%] of 12,075 researchers awarded EPSRC grants between 2015 and 2021 were 40 years or older. These results suggest that measures introduced by Connected Everything has been successful at providing funding opportunities for researchers who are likely to be early-mid career stage.

Age of researchers at applicant and awarded funding stages for A  Connected Everything between 2019–2022 (total of 146 applicants and 23 awarded) and B  EPSRC funding between 2015–2021 [ 35 ] (total of 35,780 applicants and 12,075 awarded)

The results of this paper provide insights into the impact that Connected Everything’s planned mitigations have had on promoting equality, diversity, and inclusion (EDI) in research and funding. Collecting EDI data from individuals who engage with network activities and apply for research funding enabled an evaluation of whether these mitigations have been successful in achieving the intended outcomes outlined at the start of the study, as summarised in Table  2 .

The results in Table  2 indicate that Connected Everything’s approach to EDI has helped achieve the intended outcome to improve representation of women, ECRs, those with a declared disability and black/minority ethnic backgrounds engaging with network events when compared to the engineering and technology academic community. In addition, the network has helped raise awareness of the high presence of researchers with care responsibilities at network events, which can help to track progress towards making future events inclusive and accessible towards these carers. The data highlights two areas for improvement: (1) ensuring a gender balance; and (2) increasing representation of those with declared disabilities. Both these discrepancies are indicative of the wider imbalances and underrepresentation of these groups in the engineering and technology academic community [ 26 ], yet represent areas where networks can strive to make a difference. Possible strategies include: using targeted outreach; promoting greater representation of these groups in event speakers; and going further to create a welcoming and inclusive environment. One barrier that can disproportionately affect women researchers is the need to balance care responsibilities with attending network events [ 46 ]. This was reflected in the Connected Everything data that reported 37.8%, 95% CI [25.3%, 50.3%] of women engaging with network activities had care responsibilities, compared to 22.6%, 95% CI [61.1%, 76.7%] of men. Providing accommodations such as on-site childcare, flexible scheduling, or virtual attendance options can therefore help to promote inclusivity and allow more women researchers to attend.

Only 5.7%, 95% CI [2.4%, 8.9%] of responses engaging with Connected Everything declared a disability, which is higher than the engineering and technology academic community (3.4%, 95% CI [3.2%, 3.7%]) [ 26 ], but unrepresentative of the wider UK population. It has been suggested that academics can be uncomfortable when declaring disabilities because scholarly contributions and institutional citizenship are so prized that they feel they cannot be honest about their issues or health concerns and keep them secret [ 47 ]. In research networks, it is important to be mindful of this hidden group within higher education and ensure that measures are put in place to make the network’s activities inclusive to all. Future considerations for accommodations to improve research events inclusivity include: improving physical accessibility of events; providing assistive technology such as screen readers, audio descriptions, and captioning can help individuals with visual or hearing impairments to access and participate; providing sign language interpreters; offering flexible scheduling options; and the provision of quiet rooms, written materials in accessible formats, and support staff trained to work with individuals with cognitive disabilities.

Connected Everything introduced measures (e.g., anonymised reviewing process, Q&A sessions before funding calls, inclusive design of panel pitch) to help address inequalities in how funding is awarded. Table 2 shows success in reducing the dominance of researchers who identify as male and research-intensive universities in winning research funding and that researchers with care responsibilities were more successful at winning funding than those without care responsibilities. The data revealed that the proposed measures were unable to address the inequality in award rates between white and ethnic minority researchers, which is an area to look to improve. The inequality appears to occur during the anonymous review stage, with a greater proportion of white researchers being invited to panel. Recommendations to make the review process fairer include: ensuring greater diversity of reviewers; reviewer anti-bias training; and automatic adjustments to correct for known biases in writing style [ 16 , 32 ].

When reflecting on the development of a strategy to embed EDI throughout the network, Connected Everything has learned several key lessons that may benefit other networks undergoing a similar activity. These include:

EDI is never ‘done’: There is a constant need to review approaches to EDI to ensure they remain relevant to the network community. Connected Everything could review its principles to include the concept of justice in its approach to diversity and inclusion. The concept of justice concerning EDI refers to the removal of systematic barriers that stop fair and equitable distribution of resources and opportunities among all members of society, regardless of their individual characteristics or backgrounds. The principles and subsequent actions could be reviewed against the EDI expectations [ 14 ], paying particular attention to areas where barriers may still be present. For example, shifting from welcoming people into existing structures and culture to creating new structures and culture together, with specific emphasis on decision or advisory mechanisms within the network. This activity could lend itself to focusing more on tailored support to overcome barriers, thus achieving equity, if it is not within the control of the network to remove the barrier itself (justice).

Widen diversity categories: By collecting data on a broad range of characteristics, we can identify and address disparities and biases that might otherwise be overlooked. A weakness of this dataset is that ignores the experience of those with intersectional identities, across race, ethnicity, gender, class, disability and/ or LGBTQI. The Wellcome Trust noted how little was known about the socio-economic background of scientists and researchers [ 48 ].

Collect data on whole research teams: For the first two calls for feasibility study funding, Connected Everything only asked the Principal Investigator to voluntarily provide their data. We realised that this was a limited approach and, in the third call, asked for the data regarding the whole research team to be shared anonymously. Furthermore, we do not currently measure the diversity of our event speakers, panellists or reviewers. Collecting these data in the future will help to ensure the network is accountable and will ensure that all groups are represented during our activities and in the funding decision-making process.

High response rate: Previous surveys measuring network diversity (e.g., [ 7 ]) have struggled to get responses when surveying their memberships; whereas, this study achieved a response rate of 73.8%. We attribute this high response rate to sending EDI data requests on the point of contact with the network (e.g., on submitting funding proposals or after attending network events), rather than trying to survey the entire network membership at anyone point in time.

Improve administration: The administration associated with collecting EDI data requires a commitment to transparency, inclusivity, and continuous improvement. For example, during the first feasibility funding call, Connected Everything made it clear that the review process would be anonymous, but the application form was not in separate documents. This made anonymising the application forms extremely time-consuming. For the subsequent calls, separate documents were created – Part A for identifying information (Principal Investigator contact details, Project Team and Industry collaborators) and Part B for the research idea.

Accepting that this can be uncomfortable: Trying to improve EDI can be uncomfortable because it often requires challenging our assumptions, biases, and existing systems and structures. However, it is essential if we want to make real progress towards equity and inclusivity. Creating processes to support embedding EDI takes time and Connected Everything has found it is rare to get it right the first time. Connected Everything is sharing its learning as widely as possible both to support others in their approaches and continue our learning as we reflect on how to continually improve, even when it is challenging.

Enabling individual engagement with EDI: During this work, Connected Everything recognised that methods for engaging with such EDI issues in research design and delivery are lacking. Connected Everything, with support from the Future Food Beacon of Excellence at the University of Nottingham, set out to develop a card-based tool [ 49 ] to help researchers and stakeholders identify questions around how their work may promote equity and increase inclusion or have a negative impact towards one or more protected groups and how this can be overcome. The results of this have been shared at conference presentations [ 50 ] and will be published later.

While this study provides insights into how EDI can be improved in research network activities and funding processes, it is essential to acknowledge several limitations that may impact the interpretation of the findings.

Sample size and generalisability: A total of 260 responses were received, which may not be representative of our overall network of 500 + members. Nevertheless, this data provides a sense of the current diversity engaging in Connected Everything activities and funding opportunities, which we can compare with other available data to steer action to further diversify the network.

Handling of missing data: Out of the 260 responses, 66 data points were missing for questions regarding age, disability, and caring responsibilities. These questions were mistakenly omitted from a Connected Everything summer school survey, contributing to 62 missing data points. While we assumed the remainer of missing data to be at random during analysis, it's important to acknowledge it could be related to other factors, potentially introducing bias into our results.

Emphasis on quantitative data: The study relies on using quantitative data to evaluate the impact of the EDI measures introduced by Connected Everything. However, relying solely on quantitative metrics may overlook nuanced aspects of EDI that cannot be easily quantified. For example, EDI encompasses multifaceted issues influenced by historical, cultural, and contextual factors. These nuances may not be fully captured by numbers alone. In addition, some EDI efforts may not yield immediate measurable outcomes but still contribute to a more inclusive environment.

Diversity and inclusion are not synonymous: The study proposes 21 measures to contribute towards creating an equal, diverse and inclusive research culture and collects diversity data to measure the impact of these measures. However, while diversity is simpler to monitor, increasing diversity alone does not guarantee equality or inclusion. Even with diverse research groups, individuals from underrepresented groups may still face barriers, microaggressions, or exclusion.

Balancing anonymity and rigour in grant reviews:The proposed anonymous review process proposed by Connected Everything removes personal and organisational details from the research ideas under reviewer evaluation. However, there exists a possibility that a reviewer could discern the identity of the grant applicant based on the research idea. Reviewers are expected to be subject matter experts in the field relevant to the grant proposal they are evaluating. Given the specialised nature of scientific research, it is conceivable that a well-known applicant could be identified through the specifics of the work, the methodologies employed, and even the writing style.

Expanding gender identity options: A limitation of this study emerged from the restricted gender options (male, female, other, prefer not to say) provided to respondents when answering the gender identity question. This limitation reflects the context of data collection in 2018, a time when diversity monitoring guidance was still limited. As our understanding of gender identity evolves beyond binary definitions, future data collection efforts should embrace a more expansive and inclusive approach, recognising the diverse spectrum of gender identities.

In conclusion, this study provides evidence of the effectiveness of a research network's approach to promoting equality, diversity, and inclusion (EDI) in research and funding. By collecting EDI data from individuals who engage with network activities and apply for research funding, this study has shown that the network's initiatives have had a positive impact on representation and fairness in the funding process. Specifically, the analysis reveals that the network is successful at engaging with ECRs, and those with care responsibilities and has a diverse range of ethnicities represented at Connected Everything events. Additionally, the network activities have a more equal gender balance and greater representation of researchers with disabilities when compared to the engineering and technology academic community, though there is still an underrepresentation of these groups compared to the national population.

Connected Everything introduced measures to help address inequalities in how funding is awarded. The measures introduced helped reduce the dominance of researchers who identified as male and research-intensive universities in winning research funding. Additionally, researchers with care responsibilities were more successful at winning funding than those without care responsibilities. However, inequality persisted with white researchers achieving higher award rates than those from ethnic minority backgrounds. Recommendations to make the review process fairer include: ensuring greater diversity of reviewers; reviewer anti-bias training; and automatic adjustments to correct for known biases in writing style.

Connected Everything’s approach to embedding EDI in network activities has already been shared widely with other EPSRC-funded networks and Hubs (e.g. the UKRI Circular Economy Hub and the UK Acoustics Network Plus). The network hopes that these findings will inform broader efforts to promote EDI in research and funding and that researchers, funders, and other stakeholders will be encouraged to adopt evidence-based strategies for advancing this important goal.

Availability of data and materials

The data collected was anonymously, however, it may be possible to identify an individual by combining specific records of the data request form data. Therefore, the study data has been presented in aggregate form to protect the confidential of individuals and the data utilised in this study cannot be made openly accessible due to ethical obligations to protect the privacy and confidentiality of the data providers.

Abbreviations

Early career researcher

Equality, diversity and inclusion

Engineering physical sciences research council

UK research and innovation

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Acknowledgements

The authors would like to acknowledge the support Engineering and Physical Sciences Research Council (EPSRC) [grant number EP/S036113/1], Connected Everything II: Accelerating Digital Manufacturing Research Collaboration and Innovation. The authors would also like to gratefully acknowledge the Connected Everything Executive Group for their contribution towards developing Connected Everything’s equality, diversity and inclusion strategy.

This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) [grant number EP/S036113/1].

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Oliver J. Fisher

Human Factors Research Group, Faculty of Engineering, University of Nottingham, University Park, Nottingham, UK

Debra Fearnshaw & Sarah Sharples

School of Food Science and Nutrition, University of Leeds, Leeds, UK

Nicholas J. Watson

School of Engineering, University of Liverpool, Liverpool, UK

Peter Green

Centre for Circular Economy, University of Exeter, Exeter, UK

Fiona Charnley

Institute for Manufacturing, University of Cambridge, Cambridge, UK

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OJF analysed and interpreted the data, and was the lead author in writing and revising the manuscript. DF led the data acquisition and supported the interpretation of the data. DF was also a major contributor to the design of the equality diversity and inclusion (EDI) strategy proposed in this work. NJW supported the design of the EDI strategy and was a major contributor in reviewing and revising the manuscript. PG supported the design of the EDI strategy, and was a major contributor in reviewing and revising the manuscript. FC supported the design of the EDI strategy and the interpretation of the data. DM supported the design of the EDI strategy. SS led the development EDI strategy proposed in this work, and was a major contributor in data interpretation and reviewing and revising the manuscript. All authors read and approved the final manuscript.

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Correspondence to Debra Fearnshaw .

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Fisher, O.J., Fearnshaw, D., Watson, N.J. et al. Promoting equality, diversity and inclusion in research and funding: reflections from a digital manufacturing research network. Res Integr Peer Rev 9 , 5 (2024). https://doi.org/10.1186/s41073-024-00144-w

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Tentative agreement reached after UW student employees strike for equal pay

A tentative agreement was reached after more than 6,000 University of Washington students who work in the school’s research labs went on strike to demand equal pay for equal work Tuesday.

The announcement was made Wednesday morning in an email to KOMO News from UW spokesperson Victor Balta.

"We are pleased to report that we've reached an agreement with UAW Local 4121 , the union representing academic student employees on a new contract," Balta wrote. "Union members still need to vote in favor of ratifying the contract, and our understanding is that vote will take place over the rest of this week. In accordance with the law, the union will endorse the tentative agreement for ratification and will move to ratify the agreement. The union has also agreed to suspend the strike pending the outcome of the ratification vote. Details on the full agreement will be made available soon."

The students picketing Tuesday are in a category called Academic Student Employee (ACE). 

“This tentative agreement will be life-changing for ASEs who have been struggling to stay in their research at UW," said Miro Stuke, ASE in environmental and forest sciences and recording secretary for UAW 4121. "Winning better pay that keeps up with industry standards, protecting $0 premium healthcare, and securing new rights for non-citizen ASEs dramatically improves our working conditions while ensuring UW remains competitive. We are thrilled to be part of a movement of academic workers who are continually raising the bar so we can all live and do our work with dignity."

Because they work at many UW facilities throughout the city of Seattle, they had several picket lines set up at the Fred Hutchinson Cancer Research Center, South Lake Union on Mercer Street and more. 

Candice Young told KOMO News she was at the bargaining table until 2 a.m. Tuesday. The strike started four hours later, at 6 a.m. Young spoke with KOMO on the picket line in South Lake Union while drivers honked in support, passing on Mercer Street.

She said that UW Monday night proposed taking away the zero-premium health insurance they’ve had, but she said by the end of the session, they were able to get that back on the table.

UW said it has held 17 bargaining sessions, including with a mediator, and the school values the work of academic student employees and is working to continue good-faith negotiations.

Young and other ACEs said they are not paid enough to afford rent, tuition and food. She said many ACEs suffer from food insecurity because they aren’t paid enough. The ACEs say they make less than $40,000 per year yet provide critical instructional and research services to UW.

More than 80% of ASEs who answered a 2022 survey reported being rent-burdened. UW told KOMO News Tuesday it was still at the bargaining table and wages were the only outstanding issue.

"The University increased its proposal to 12%, 8%, and 8% pay increases in each year of the contract," Balta told KOMO News Tuesday. He said the union is asking for 12% each of the three years of a new contract.

Hundreds of students also rallied in Red Square on campus Tuesday afternoon before marching into Gerberding Hall — which contains administrative offices — to hold a sit-in.

UW on May 7 filed an unfair labor practice complaint, accusing more than 100 union members of intimidation and harassing the dean and staff in the College of Arts and Sciences.

The university filed that complaint against UAW 4121 with the Washington State Public Employment Relations Commission.

The complaint accuses ACEs of occupying the dean’s office area, yelling chants and preventing staff from working to the point that UW said many left the office.

The complaint said the students intended to trap the dean in her office, repeatedly pounded on the office door, and shouted demands that she make or secure UW concessions in bargaining.

UW also claimed the ACEs followed the dean and staff to a parking area, yelling angry chants and attempting to block their progress.