Recent decades have witnessed a growing focus on two distinct income patterns: persistent pay inequity, particularly a gender pay gap, and growing pay inequality. Pay transparency is widely advanced as a remedy for both. Yet we know little about the systemic influence of this policy on the evolution of pay practices within organizations. To address this void, we assemble a dataset combining detailed performance, demographic and salary data for approximately 100,000 US academics between 1997 and 2017. We then exploit staggered shocks to wage transparency to explore how this change reshapes pay practices. We find evidence that pay transparency causes significant increases in both the equity and equality of pay, and significant and sizeable reductions in the link between pay and individually measured performance.
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All data that support the findings of this study have been deposited in the Open Inter-university Consortium for Political and Social Research Repository under project number 155541 and are available at https://doi.org/10.3886/E155541V1. The names of individual academics and institutions have been blinded and are represented in the data with author-generated unique identifiers.
The statistical code generating all results reported in the manuscript has been deposited in the Open Inter-university Consortium for Political and Social Research Repository under project number 155541 and is available at https://doi.org/10.3886/E155541V1.
Shen, H. Inequality quantified: mind the gender gap. Nature 495, 22–24 (2013).
Piketty, T. & Saez, E. Inequality in the long run. Science 344, 838–843 (2014).
Eckhoff, T. Justice: Its Determinants in Social Interaction (Rotterdam Univ. Press, 1974).
Blau, F. D. & Kahn, L. M. in The New Palgrave Dictionary of Economics (eds Vernengo, M. et al.) 7118–7128 (Palgrave Macmillan, 2008).
Blau, F. D. & Kahn, L. M. The gender wage gap: extent, trends, and explanations. J. Econ. Lit. 55, 789–865 (2017).
Cobb, A. J. How firms shape income inequality: stakeholder power, executive decision making, and the structuring of employment relationships. Acad. Manage. Rev. 41, 324–348 (2015).
Alvaredo, F., Chancel, L., Piketty, T., Saez, E. & Zucman, G. World Inequality Report 2018 (World Inequality Lab, 2018).
Ramachandran, G. Pay transparency. Penn St. L. Rev. 4, 1043–1080 (2011).
Private Sector Workers Lack Pay Transparency: Pay Secrecy May Reduce Women’s Bargaining Power and Contribute to Gender Wage Gap (Institute for Women’s Policy Research, 2017); https://iwpr.org/wp-content/uploads/2020/09/Q068-Pay-Secrecy.pdf
Mas, A. Does transparency lead to pay compression? J. Polit. Econ. 125, 1683–1721 (2017).
Baker, M., Halberstam, Y., Kroft, K., Mas, A. & Messacar, D. Pay transparency and the gender gap. Preprint at NBER https://www.nber.org/papers/w25834 (2019).
Bennedsen, M., Simintzi, E., Tsoutsoura, M. & Wolfenzon, D. Do firms respond to gender pay gap transparency? Preprint at NBER https://www.nber.org/papers/w25435 (2020).
Cullen, Z. B. & Pakzad-Hurson, B. Equilibrium effects of pay transparency. Preprint at NBER https://www.nber.org/papers/w28903 (2021).
Gartenberg, C. & Wulf, J. Pay harmony? Social comparison and performance compensation in multibusiness firms. Organ. Sci. 28, 39–55 (2017).
Ma, Y., Oliveira, D. F., Woodruff, T. K. & Uzzi, B. Women who win prizes get less money and prestige. Nature 565, 287–288 (2019).
Blau, F. D. & DeVaro, J. New evidence on gender differences in promotion rates: an empirical analysis of a sample of new hires. Ind. Relat. 46, 511–550 (2007).
Babcock, L., Recalde, M. P., Vesterlund, L. & Weingart, L. Gender differences in accepting and receiving requests for tasks with low promotability. Am. Econ. Rev. 107, 714–747 (2017).
Sarsons, H. Recognition for group work: gender differences in academia. Am. Econ. Rev. Pap. Proc. 2017 107, 141–145 (2017).
2014/124/EU: Commission Recommendation of 7 March 2014 on Strengthening the Principle of Equal Pay Between Men and Women Through Transparency (Text with EEA Relevance) (European Commission, 2014); https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32014H0124&from=EN
Correia, S. Linear Models with High-Dimensional Fixed Effects: An Efficient and Feasible Estimator. Working Paper http://scorreia.com/research/hdfe.pdf (2017).
Deshpande, M. & Li, Y. Who is screened out? Application costs and the targeting of disability programs. Am. Econ. J. Econ. Policy 11, 213–248 (2019).
Freyaldenhoven, S., Hansen, C. & Shapiro, J. M. Pre-event trends in the panel event-study design. Am. Econ. Rev. 109, 3307–3338 (2019).
Cook, K. S. & Hegtvedt, K. A. Distributive justice, equity, and equality. Annu. Rev. Sociol. 9, 217–241 (1983).
Godechot, O. & Senik, C. Wage comparisons in and out of the firm: evidence from a matched employer–employee French database. J. Econ. Behav. Organ. 117, 395–410 (2015).
Card, D., Mas, A., Moretti, E. & Saez, E. Inequality at work: the effect of peer salaries on job satisfaction. Am. Econ. Rev. 102, 2981–3003 (2012).
Luttmer, E. F. P. Neighbors as negatives: relative earnings and well-being. Q. J. Econ. 120, 963–1002 (2005).
Camerer, C. F. & Fehr, E. When does “economic man” dominate social behavior? Science 311, 47–52 (2006).
Brosnan, S. F. & Waal de, F. M. B. Evolution of responses to (un)fairness. Science 346, 1251776 (2014).
Nickerson, J. A. & Zenger, T. Envy, comparison costs, and the economic theory of the firm. Strateg. Manage. J. 29, 1429–1449 (2008).
Colquitt, J. A., Conlon, D. E., Wesson, M. J., Porter, C. O. & Ng, K. Y. Justice at the millennium: a meta-analytic review of 25 years of organizational justice research. J. Appl. Psychol. 86, 425–445 (2001).
Obloj, T. & Zenger, T. Organization design, proximity, and productivity responses to upward social comparison. Organ. Sci. 28, 1–18 (2017).
Basurto, X., Blanco, E., Nenadovic, M. & Vollan, B. Integrating simultaneous prosocial and antisocial behavior into theories of collective action. Sci. Adv. 2, e1501220 (2016).
Breza, E., Kaur, S. & Shamdasani, Y. The morale effects of pay inequality. Q. J. Econ. 133, 611–663 (2018).
Auspurg, K., Hinz, T. & Sauer, C. Why should women get less? Evidence on the gender pay gap from multifactorial survey experiments. Am. Sociol. Rev. 82, 179–210 (2017).
Perez-Truglia, R. The effects of income transparency on well-being: evidence from a natural experiment. Am. Econ. Rev. 110, 1019–1054 (2020).
Malani, A. & Reif, J. Interpreting pre-trends as anticipation: impact on estimated treatment effects from tort reform. J. Public Econ. 124, 1–17 (2015).
We thank O. Shelef, J. Snyder, Z. Cullen and M. Higgins for helpful comments and suggestions, as well as seminar participants at Brigham Young University, Carnegie Mellon University, Dartmouth College, Harvard University, LMU, Ohio State University, Purdue University, the University of Indiana, the University of Maryland, the University of Michigan, the University of Minnesota and the University of Utah. We particularly thank A. Olejniczak and his team at Academic Analytics; various data providers with government agencies and universities in the states of California, Connecticut, Florida, New York, Pennsylvania, Texas, Virginia and West Virginia; and A. Eichholtzer, J. Cox and H. Yang for research assistantship. This research has been partially funded by the French National Research Agency Grant No. ANR-16-TERC-0020-01 (T.O.). Academic Analytics and this funder had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
The authors declare no competing interests.
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Extended Data Fig. 1 The unconditional and conditional gender wage gap.
Notes: The figure presents OLS regression estimates explaining (ln) salaries. Plotted coefficients of year dummies interacted with Female indicator, with 95% confidence intervals. Levels are scaled by the value on un-interacted Female indicator. Unconditional gap is based on a model with year dummies only. Conditional gap is based on models with year, academic domain, and institution fixed effects as well as controls for academic tenure (ln), number of academic articles, number of published books, number of awards, number of grants, and number of patents. Regression results used to generate this plot are reported in Supplementary Table 3.1.
Extended Data Fig. 2 Equity in organizations: The effect of pay transparency on gender wage gap.
Notes: The figure presents regression coefficients from an OLS regression model explaining (ln) wages. Reference category is 1 year prior to transparency shock. Plotted coefficients: dummy variable for Female interacted with years from (to) transparency shock with 95% CIs. Standard errors clustered on institution. Controls include academic tenure (ln), number of published academic articles, number of published books, number of awards, number of grants, and number of patents, and institution, individual, and year fixed effects. Regression results used to generate this plot are reported in Supplementary Table 3.3.
Extended Data Fig. 3 Equity in organizations: The effect of pay transparency on gender wage gap: additional specifications.
Notes: The figure presents regression coefficients from an OLS regression model explaining (ln) wages. Reference category is 1 year prior to transparency shock (1 and 2 years for 2SLS results, panel C). Plotted coefficients: dummy variable for Female interacted with years from (to) transparency shock with 95% CIs. Standard errors clustered on institution. Controls include academic tenure (ln), number of published academic articles, number of published books, number of awards, number of grants, and number of patents, and institution, individual, and year fixed effects. Panel A – population restricted to 2004-2013 inclusive; Panel B – stacked difference in differences model (see text for more details). Panel C – 2SLS results, instrumented covariate: women’s mean earnings as a % of men’s in the private sector (see below for more details), excluded instrument: lead of transparency shock. Panel D and E – population restricted to CT, FL, PA, TX, VA (omitted states: California, New York, West Virginia). Panel F – full population. Regression results used to generate these plots are reported in Supplementary Tables 3.3, 3.4, and 3.5.
Extended Data Fig. 4 Equity and equality in organizations: The effect of wage transparency on variance in market wage residuals and wage variance.
Notes: The figures present regression coefficients from an OLS regression model explaining variance in market wage residuals (Left panel) and variance in (ln) wages (Right panel). Reference category is 1 year prior to transparency shock. Both variables are calculated within Institution-Academic Field (11 categories). Plotted coefficients: years from (to) transparency shock with 95% CIs. Standard errors clustered on institution. Controls include reference group mean productivity levels and reference group productivity variances as well as year and academic field-institution fixed effects. Regression results used to generate these plots are reported in Supplementary Table 4.3.
Extended Data Fig. 5 Equality in organizations: Distribution of market wage residuals, by transparency shock.
Notes: The figure presents kernel density estimates of wage regression residuals by transparency shocks. Controls include institution, academic domain, and year fixed effects. Means and standard deviations are calculated averaging across all time periods. Residuals trimmed at 1% and 99%. Two-sample combined Kolmogorov-Smirnov tests for equality of distribution functions: 0.041, p-value<0.001.
Supplementary Sections 1–6 and Tables 1.1–6.3.
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Obloj, T., Zenger, T. The influence of pay transparency on (gender) inequity, inequality and the performance basis of pay. Nat Hum Behav 6, 646–655 (2022). https://doi.org/10.1038/s41562-022-01288-9
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