Consider a repeated interaction where it is unknown which of various stage games will be played each period. This framework separates the basic logic of intertemporal incentives from the requirement that any given strategy profile yields a well‐defined payoff vector. A natural solution concept is ex post perfect equilibrium: strategies must form a subgame‐perfect equilibrium for any realization of the sequence of stage games. When there is one long‐run player and others are short‐run, and public randomization is available, we can adapt the standard recursive approach to determine the maximum feasible gap between reward and punishment for the long‐run player. This allows us to identify which actions can be played in equilibrium and, assuming perfect monitoring, to fully characterize what outcome paths can arise. With multiple long‐run players or no public randomization, the approach fails; a diagnostic of this failure is that optimal penal codes may no longer exist.

This paper studies the labor market impacts of firm accommodation decisions after workplace disability and assesses implications for the design of firm subsidies. We leverage a workers' compensation (WC) program in Oregon that provides wage subsidies to firms for accommodating workers with workplace disabilities. Leveraging rich administrative data and a policy change to the wage subsidy, we show that accommodation rates respond to the subsidy rate and that receipt of accommodation leads to a significant increase in employment and earnings a year later. To explore welfare implications, we develop and estimate a frictional labor market model of accommodation as a form of human capital investment. Worker turnover and imperfect experience rating in WC lead to underaccommodation and inefficient labor market outcomes after workplace disability. Counterfactual simulations show that subsidizing accommodation not only improves long‐run labor market outcomes of workers experiencing work‐related disability but also yields welfare gains for most workers

Recent advances have substantially improved our ability to predict disease risk with genetic data. In response, many countries have banned insurers from using genetic data, despite concerns about adverse selection. This paper measures adverse selection in a market where insurers can underwrite only on nongenetic information while consumers also have access to genetic information. We do so by combining methods from quantitative genetics, selection measures from economic theory, and genetic and electronic health record data on nearly 500,000 individuals in the UK Biobank. We focus on the critical illness insurance market and consider scenarios in which consumers have access to current or expected future genetic prediction technology. We find noticeable levels of selection with current prediction technology, and potentially crippling selection with expected future technology

We develop a comprehensive framework to evaluate policy interventions aimed at curbing false news dissemination on social media. Using a randomized experiment on Twitter and X during the 2022 and 2024 U.S. elections, we assess priming for misinformation awareness, fact‐checking, confirmation clicks, and content consideration prompts. Priming proves most effective in reducing false‐news sharing while preserving true news dissemination. We build and structurally estimate a model of sharing, motivated by partisan persuasion, partisan signaling, and reputational concerns. We identify three channels through which policies influence sharing: (i) updating perceived veracity and partisanship of content, (ii) raising salience of reputation, and (iii) increasing engagement costs. Differences in the effects of policies are explained by the salience and cost channels. Content‐neutral priming is best at enhancing salience of reputation at minimal cost and almost as effective as fact‐checking in updating veracity. Salience channel is stronger when users encounter uncontroversially true content.

Women remain disadvantaged in promotion to managerial positions. We conduct a field experiment with 24 large garment factories in Bangladesh to test for inefficient representation of women among line supervisors. We identify the marginal female and male candidates for supervisory positions and randomly assign them to manage production lines. We document four findings: (1) In contrast to widespread negative beliefs about women's ability as supervisors at baseline, female candidates selected by the factories had similar skills to males; (2) during the trial, females performed worse than males, which we show is related to negative bias against them; (3) after the trial, however, many female candidates were retained as supervisors and, conditional on that, performed similar to males; and (4) after the end of our intervention, factories permanently increased the share of women among newly appointed supervisors. A conceptual framework of experimentation over discrimination rationalizes all these facts and cautions against the standard logic to test for discrimination: when there is uncertainty about the performance of the discriminated group, equal—or even worse—performance of the marginal candidates of that group is no longer sufficient to rule out inefficient discrimination.

This paper examines how drug diversion influences the prescribing practices of physicians and the equilibrium health impacts of prescription medications. Focusing on the case of prescription opioids, a commonly prescribed and frequently diverted medication at the heart of the worst drug crisis in U.S. history, I design and estimate a model of physician behavior in the presence of a secondary market with patient search. To access prescription opioids for medical purposes or misuse, patients search over physicians on the legal primary market or turn to an illegal secondary market. Physicians, who care both about their impact on population health and their revenue from office visits, take into account the possibility that patients might resell their prescriptions on the secondary market when prescribing. The model demonstrates that the potential for diversion will tend to make strict physicians more hesitant in their prescribing while leading lenient prescribers to loosen their prescription thresholds, thereby exacerbating prescribing differences between more and less lenient physicians. Estimates reveal that the presence of a secondary market induces most physicians to be more careful in their prescribing, which brings prescriptions closer to their optimal level, but also causes harm through the reallocation of prescriptions for abuse

We propose a framework for making Bayesian parametric models robust to local misspecification. Suppose in a baseline parametric model, a parameter of interest has an interpretation in an encompassing semiparametric model. Bayesian and maximum likelihood estimators are generally biased under local misspecification. We propose to augment the baseline likelihood by a multiplicative factor that involves scores for the baseline model, the efficient scores for the encompassing semiparametric model, and an auxiliary parameter that has the same dimension as the parameter of interest. We show that the marginal posterior for the parameter of interest in the augmented model is asymptotically normal with mean equal to the semiparametrically efficient estimator and variance equal to the semiparametric efficiency bound. The suggested augmentation robustifies the baseline parametric model to local misspecification, while preserving the appeal of Bayesian inference. We develop an MCMC algorithm for the augmented model and illustrate the approach in applications.

We study causal inference with high‐dimensional covariates, observed with various types of heteroscedastic noise. Our main assumption is that the true covariates are low rank, which we empirically evaluate with plots and interpret as approximate repeated measurements. This assumption delivers semiparametric inference on the causal parameter, as precisely as if the true covariates were available. A methodological contribution is to introduce an error‐in‐variable balancing weight for estimation and inference on cross‐sectional causal parameters with heterogeneous effects.