Women in the United States disproportionately work at lower-pay-premium firms. Sorkin (2017), estimating an Abowd–Kramarz–Margolis decomposition and a random search model separately by gender on US administrative employer–employee data (LEHD), found that sorting across firms accounts for roughly 0.09 log points of the 0.335 log point gender gap in annual earnings, and that women draw job offers from a distribution centered about 0.06 log points lower in firm pay premia, while their nonpay advantages nearly offset the pay disadvantage. The estimated offer distribution is a reduced-form object: it does not reveal whether the gender gap in offers is generated by employer behavior, by workers' constrained search decisions, or by frictions that differ by gender. The open problem is to identify and quantify these mechanisms — to apportion the offer-distribution gap among employer behavior, workers' constrained search decisions, and frictions that differ by gender, and to establish whether it manifests discrimination or constrained, preference-driven search. Subsequent work has documented each candidate mechanism in other countries and a 2026 equilibrium search model decomposes the gap for Brazil, but the structural origins of the US gender-differentiated offer distribution remain unidentified.

Background

A large labor-economics literature asks how much of the gap between men's and women's earnings is attributable to the firms where people work rather than to the workers themselves. The workhorse tool is the Abowd–Kramarz–Margolis (AKM) two-way fixed effects decomposition: a regression of log earnings on a fixed effect for each worker and a fixed effect for each firm, which separates a worker's own persistent earnings level from a firm-specific pay premium shared by all of a firm's employees. Card, Cardoso, and Kline (2016) extended this decomposition to let firm pay premia differ for men and women and found, in Portuguese administrative data, that firm-specific pay premia explain just over one-fifth of the gender gap, with sorting of women into lower-premium firms playing a central role. Sorkin (2017) carried this agenda to the United States using the Longitudinal Employer Household Dynamics (LEHD) dataset, a quarterly administrative register built from unemployment-insurance records that links workers to their employers. In LEHD, gender differences in sorting across firms account for roughly 0.09 log points of the 0.335 log point mean gap in annual earnings, with more than half of the sorting arising across broad sectors.

To ask why women sort into lower-pay-premium firms, Sorkin (2017) embedded the estimated firm premia in a Burdett–Mortensen-style random search model — a canonical frictional labor market model in which employed and nonemployed workers randomly receive job offers drawn from an offer distribution over firms and accept offers that raise their value. Estimating the model separately for men and for women, he found that women draw from an offer distribution centered about 0.06 log points lower in firm pay premia, while the total values workers place on firms — combining pay with nonpay characteristics such as amenities, flexibility, and working conditions — are highly correlated across genders (about 0.9), and women's nonpay advantages at their employers nearly offset their pay disadvantage. His conclusion: the dominant explanation for gendered sorting is differences in opportunities (which offers arrive), not differences in preferences over firms. The methodology builds on Sorkin (2018), which recovers firm values and a 'Rosen motive' of compensating differentials — pay differences that offset differences in nonpay job attributes — from the structure of employer-to-employer transitions (workers moving directly from one employer to another without an intervening nonemployment spell), while showing that a normalization of the scale of idiosyncratic utility shocks leaves the 'Mortensen motive' (amenity variation correlated with firm value) unidentified in that partial-equilibrium setting.

The open question follows from what this estimation cannot deliver. The gender-specific offer distribution is a reduced-form object: it summarizes which firm offers arrive, but not the process that generates it. The gap could reflect employer behavior — discriminatory recruiting or referral hiring, task-based assignment, gender-specific hiring or pay standards — or workers' own search decisions under constraints such as childcare and commuting, or frictions that differ by gender, such as layoff and outside-offer arrival rates; combinations of these are possible. Because the model is estimated separately by gender, men and women are effectively treated as searching in separate markets, which precludes interaction effects between the genders' markets. And because LEHD measures earnings but not hours of work, the role of hours differences per job in generating the observed sorting and earnings differences cannot be assessed. The specification and estimation of richer structural models of the offer process and of dynamic career mobility — gender differences in climbing the job ladder (progression to successively better employers), job-to-job flows, involuntary separations, and firm-side heterogeneity in hiring standards — therefore remains future work; such models could change whether the observed offer-distribution differences are interpreted as manifestations of discrimination or of constrained, preference-driven search.

Problem Statement

Identify the structural origins of the gender differences in firm offer distributions documented in US data. The decomposition of the reduced-form fact that women draw job offers from firm pay-premium distributions centered lower than men's (about 0.06 log points in Sorkin's LEHD estimates, with sorting into firms accounting for about 0.09 log points of the 0.335 log point gender earnings gap) into its generating mechanisms turns on whether the offer gap arises from employer-side behavior (for example discriminatory recruiting, referral-based hiring, task-based assignment, or gender-specific hiring and pay standards), from worker-side search decisions under constraints (for example childcare or commuting), from labor-market frictions that differ by gender (for example layoff and outside-offer arrival rates), or from combinations of these. A resolution must separate these three channel families — employer-side behavior, worker-side search under constraints, and gender-asymmetric frictions — and apportion the roughly 0.06 log point offer-location gap among them, or establish that a subset of them accounts for the gap.

References

  1. Sorkin, Isaac. 2017. "The Role of Firms in Gender Earnings Inequality: Evidence from the United States." American Economic Review 107(5): 384–387. DOI: 10.1257/aer.p20171015. https://doi.org/10.1257/aer.p20171015
  2. Card, David, Ana Rute Cardoso, and Patrick Kline. 2016. "Bargaining, Sorting, and the Gender Wage Gap: Quantifying the Impact of Firms on the Relative Pay of Women." Quarterly Journal of Economics 131(2): 633–686. DOI: 10.1093/qje/qjv038. https://doi.org/10.1093/qje/qjv038
  3. Sorkin, Isaac. 2018. "Ranking Firms Using Revealed Preference." Quarterly Journal of Economics 133(3): 1331–1393. DOI: 10.1093/qje/qjy001. https://doi.org/10.1093/qje/qjy001 (also NBER Working Paper 23938)
  4. Barth, Erling, Sari Pekkala Kerr, and Claudia Olivetti. 2021. "The dynamics of gender earnings differentials: Evidence from establishment data." European Economic Review 134: 103713. DOI: 10.1016/j.euroecorev.2021.103713. https://doi.org/10.1016/j.euroecorev.2021.103713 (also NBER Working Paper 23381)
  5. Le Barbanchon, Thomas, Roland Rathelot, and Alexandra Roulet. 2020. "Gender Differences in Job Search: Trading off Commute against Wage." Quarterly Journal of Economics 136(1): 381–426. DOI: 10.1093/qje/qjaa033. https://doi.org/10.1093/qje/qjaa033
  6. Cortés, Patricia, Jessica Pan, Laura Pilossoph, Ernesto Reuben, and Basit Zafar. 2023. "Gender Differences in Job Search and the Earnings Gap: Evidence from the Field and Lab." Quarterly Journal of Economics 138(4): 2069–2126. DOI: 10.1093/qje/qjad017. https://doi.org/10.1093/qje/qjad017
  7. Pham, Tho, Daniel Schaefer, and Carl Singleton. 2026. "Unequal hiring wages and their impact on the gender pay gap." Economic Inquiry (first published online August 25, 2026). DOI: 10.1111/ecin.70085. https://doi.org/10.1111/ecin.70085
  8. Li, Jiang, Benoit Dostie, and Gaëlle Simard-Duplain. 2023. "Firm Pay Policies and the Gender Earnings Gap: The Mediating Role of Marital and Family Status." ILR Review 76(1): 160–188 (first published online April 2022). DOI: 10.1177/00197939221093562. https://doi.org/10.1177/00197939221093562
  9. Masso, Jaan, Jaanika Meriküll, and Priit Vahter. 2022. "The role of firms in the gender wage gap." Journal of Comparative Economics 50(2): 454–473. DOI: 10.1016/j.jce.2021.10.001. https://doi.org/10.1016/j.jce.2021.10.001
  10. Morchio, Iacopo, and Christian Moser. 2026. "The Gender Pay Gap: Micro Sources and Macro Consequences." American Economic Review 116(5): 1765–1810. DOI: 10.1257/aer.20231107. https://doi.org/10.1257/aer.20231107 (also NBER Working Paper 32408)
  11. Jewell, Sarah Louise, Giovanni Razzu, and Carl Singleton. 2020. "Who Works for Whom and the UK Gender Pay Gap." British Journal of Industrial Relations 58(1): 50–81 (first published online 2019). DOI: 10.1111/bjir.12497. https://doi.org/10.1111/bjir.12497
  12. Cruz, Gabriel, and Tomás Rau. 2022. "The effects of equal pay laws on firm pay premiums: Evidence from Chile." Labour Economics 75: 102135. DOI: 10.1016/j.labeco.2022.102135. https://doi.org/10.1016/j.labeco.2022.102135
  13. Palladino, Marco G., Alexandra Roulet, and Mark Stabile. 2025. "Narrowing industry wage premiums and the decline in the gender wage gap." Labour Economics 94: 102693. DOI: 10.1016/j.labeco.2025.102693. https://doi.org/10.1016/j.labeco.2025.102693
  14. Buzaglo-Baris, Shira. 2025. "Firms, industries and the gender wage gap." Labour Economics 97: 102817. DOI: 10.1016/j.labeco.2025.102817. https://doi.org/10.1016/j.labeco.2025.102817
  15. Vattuone, Giulia. 2026. "Worker Sorting and the Gender Wage Gap." SSRN working paper. DOI: 10.2139/ssrn.6513778. https://doi.org/10.2139/ssrn.6513778