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The influence of pay transparency on (gender) inequity, inequality and the performance basis of pay

Nature Human Behaviour volume 6, pages 646–655 (2022)Cite this article

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Abstract

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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Fig. 1: Equity in organizations: distribution of market wage residuals by gender and transparency shocks.
Fig. 2: The effect of wage transparency on salary adjustments associated with promotions.

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Data availability

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.

Code availability

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.

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Acknowledgements

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.

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Authors and Affiliations

  1. HEC Paris, Jouy-en-Josas, France

    Tomasz Obloj

  2. University of Utah, Salt Lake City, UT, USA

    Todd Zenger

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  1. Tomasz Obloj
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  2. Todd Zenger
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Contributions

T.O. and T.Z. jointly conceived the project and supervised the data collection. T.O. conducted the data analyses with input from T.Z. T.O. and T.Z. jointly drafted the manuscript.

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Correspondence to Tomasz Obloj.

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The authors declare no competing interests.

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Nature Human Behaviour thanks the anonymous reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

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.

Extended Data Table 1 Conditional gender wage gap by state and main academic disciplines
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Supplementary information

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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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