Equal pay laws increasingly require that workers doing “similar” work are paid equal wages within firm. We study such “equal pay for similar work” (EPSW) policies theoretically and test our model’s predictions empirically using evidence from a 2009 Chilean EPSW. When EPSW only binds across protected class (e.g., no woman can be paid less than any similar man, and vice versa), firms segregate their workforce by gender. When there are more men than women in a labor market, EPSW increases the gender wage gap. By contrast, EPSW that is not based on protected class can decrease the gender wage gap. 

Mechanisms used to derive optimal allocations are typically designed assuming agents fully know their preferences. It is often impossible to duplicate optimal allocations when agents imperfectly observe object characteristics. I present a crowdsourcing mechanism to approximate optimal allocations under imperfect observations. To ensure truth-telling, agents are punished when their reports differ from the “wisdom-of-the-crowd.” Under mild conditions, this crowdsourcing-with-punishment mechanism replicates the full-information optimal allocation with probability exponentially converging to one in the size of the market, with small waste. No alternative mechanism can meaningfully do better. The proposed mechanism can be applied in many settings, including two-sided matching markets.

We consider sequential search by an agent who cannot observe the quality of goods but can acquire information from a profit-maximizing principal with limited commitment power. The principal can charge higher prices for more informative signals, but high future prices discourage continued search, thereby reducing the principal's profits. A unique stationary equilibrium outcome exists: the principal (i) sells the agent only partial information, (ii) induces the socially efficient stopping rule, and (iii) extracts the full surplus. However, introducing an additional, free source of information can lead to inefficiency in equilibrium.

We estimate the value employees place on remote work using revealed preferences in a high-stakes, real-world context, focusing on U.S. tech workers. On average, employees are willing to accept a 25% pay cut for partly or fully remote roles. Our estimates are three to five times that of previous studies. We attribute this discrepancy partly to methodological differences, suggesting that existing methods may understate preferences for remote work. Because of the strong preference for remote work, we expected to find a compensating wage differential, with remote positions offering lower compensation than otherwise identical in-person positions. However, using novel data on salaries for tech jobs, we reject that hypothesis. We propose potential explanations for this puzzle, including optimization frictions and worker sorting.

Consider indivisible-object allocation with contracts, such as college admissions where contracts specify majors. Can a designer guarantee a stable and (student) efficient matching? I show that contracts put stability and efficiency at odds; a necessary condition to ensure these properties is student-lexicographic priorities—schools must rank contracts from "second-tier" students consecutively. I present the weakest restriction guaranteeing stability and efficiency, and characterize necessary and sufficient conditions for any mechanism within a general class to deliver a stable and efficient matching in an incentive compatible manner. I apply this result to two well-known mechanisms: deferred acceptance and top trading cycles.

The public discourse around pay transparency has focused on the direct effect: how workers seek to rectify pay inequities through renegotiation. The question of how wage-setting and employment practices of the firm respond has received less attention. To study these equilibrium outcomes, we test our model of bargaining under incomplete information with an analysis of pay transparency mandates in the context of the U.S. private sector. Our model predicts that transparency reduces the individual bargaining power of workers, leading to lower average wages. A key insight is that employers credibly refuse to pay high wages to any one worker to avoid costly renegotiations with others under transparency. In situations where workers do not have individual bargaining power, such as under a collective bargaining agreement or in markets with posted wages, greater transparency has a muted impact on average wages. We test these predictions by evaluating the adoption of U.S. state legislation protecting the right of workers to inquire about the salaries of their coworkers. Consistent with our prediction, the laws lead wages to decline by approximately 2% overall, but effects are muted when workers have low individual bargaining power. Our model provides a unified framework to analyze a wide range of transparency policies, and reconciles effects of transparency mandates documented in a variety of countries and contexts. 

Decisions agents make before and after matching can be strategically linked through the match. We demonstrate this linkage in a game where universities either force students to commit to majors before matriculating or allow students to pick majors during their studies. The interaction between "matching forces" (competition for higher quality students) and "principal-agent forces" (moral hazard and adverse selection) leads to two equilibria that mirror the admissions systems in the US and England. With monetary transfers, our model provides insights into athletic scholarships. Payment caps that restricts transfers to potential athletes who decide not to play sports can maximize welfare. 

School-choice clearinghouses instruct applicants to "rank preferences truthfully" without allowing them to express preferences over peers. Empirically, we show college applicants value relative peer ability using data from Australia. Theoretically, we prove stable matchings exist under mild conditions when peer preferences are included, yet standard assignment mechanisms typically fail to find them. The status-quo procedure frequently employed by clearinghouses, which reveals prior cohort peer information, inadvertently induces a tâtonnement process that shapes peer expectations and fuels instability---an outcome we confirm empirically. To resolve this, we introduce a modified assignment mechanism that consistently ensures stability and incentivizes truth-telling.

Salary negotiations are a widespread phenomenon that can shape important labor market outcomes such as worker welfare, inequality, and the gender pay gap. Using survey and experimental data from job seekers in the U.S. tech sector, we investigate the role of information frictions in salary negotiations. We find that many workers refrain from negotiating due to uncertainty about whether employers are open to bargaining. A light-touch information treatment significantly increases negotiation attempts and compensation gains, particularly among those who underestimate how common negotiations are. A second treatment arm shows that negotiation expertise is less important to understand why some people do not attempt to negotiate. We develop a theoretical model incorporating risk and information frictions, which rationalizes our empirical findings and provides welfare and policy implications. Our results suggest that policies that promote negotiation could improve labor market efficiency and pay equity.

An agent engages in sequential search. He does not directly observe the quality of the goods he samples, but he can purchase signals designed by profit maximizing principal(s). We formulate the principal-agent relationship as a repeated contracting problem within a stopping game, and characterize the set of equilibrium payoffs. We show that when the agent's search cost falls below a given threshold, competition does not impact how much surplus is generated in equilibrium nor how the surplus is divided. In contrast, competition benefits the agent at the expense of total surplus when the search cost exceeds that threshold. Our results challenge the view that monopoly decreases market efficiency, and moreover, suggest that it leads to more information provision than does competition.

Many social movements encourage reporting of wrongdoing, but critics argue this increases false accusations. We study how #MeToo---a social movement that encouraged reporting of sexual harassment---changed the truthfulness of sexual harassment accusations and adjudicators' willingness to believe them. We estimate that #MeToo increased the probability of winning a sexual harassment complaint by 10.1 pp. This increase can reflect stronger complaints filed (selection) or more favorable adjudicators (direct treatment). We develop a framework to separate these effects and find evidence for both channels. Exploiting complaints filed before but resolved after #MeToo to estimate the direct treatment effect, we show that adjudicators became more likely to rule in favor of complainants, particularly male complainants. Newly induced complaints became more likely to be substantiated, with women’s complaints positively selected (more credible) and men’s negatively selected (less credible).

We study a variation of the price competition model a la Bertrand, in which firms must offer menus of contracts that obey monotonicity constraints, e.g., wages that rise with worker productivity to comport with equal pay legislation. While such constraints limit firms' ability to undercut their competitors, we show that Bertrand's classic result still holds: competition drives firm profits to zero and leads to efficient allocations without rationing. Our findings suggest that Bertrand's logic extends to a broader variety of markets, including labor and product markets that are subject to real-world constraints on pricing across workers and products.

Guided by matching theory, school choice markets are designed to generate stable matchings. The entry and exit of educational programs poses a barrier to stability if a long horizon is required for students to learn their preferences. In this paper, we study how entry and exit affect learning about a payoff relevant feature of educational programs: student quality. Theoretically, we show how entry and exit can inhibit stability. Empirically, using data from the college admissions market in New South Wales, Australia, we find gradual within-program convergence to stability and show how the persistent churn of programs in this marketplace inhibits overall-market convergence, leading to an unstable matching. This instability is primarily experienced by lower-ability students and those from marginalized groups, thus potentially increasing inequality.