Knowledge that Transforms

Abstract Pharmacy benefit managers dominate the U.S. pharmaceutical market but are controversial and poorly understood. We analyze PBMs as market intermediaries that operate formulary contests in which on-patent brand-drug makers compete for favorable placement by offering rebates off list price. These formulary contests deliver efficiency gains compared to drug makers selling directly to consumers; PBMs capture some of these gains. Our approach answers key questions regarding the determinants of efficiency, rebates, list prices, and PBM market power in the pharmaceutical market. Our analysis also explains how common contracting practices, federal regulations, and incentives within formulary contests can undermine market efficiency.

Abstract We study the aftermath of the 1968 Washington, D.C. civil disturbance to illuminate the mechanisms that drive urban redevelopment in the presence of low demand and racial tension. Using a within-block identification strategy, we show that destruction caused lots to remain vacant for the next thirty years and only recently converge in terms of structure value. The city acted to preclude for-profit land owners from leaving land vacant until demand conditions improved by purchasing nearly half of all properties in damaged neighborhoods. Despite this and other steps, the city had limited success in speeding up redevelopment.

Abstract We propose a method to detect shifts in housing price expectations by observing excess capacity. Anticipated future price hikes lead to increased current supply, resulting in temporary vacancies. Using a structural vector autoregression with sign restrictions, we analyze the impact of these expectations on the U.S. housing market. Our findings indicate that price expectation shocks primarily drove the 1996–2006 boom, especially in the Sand States. At the boom’s peak, these shocks stemmed from unrealistic growth expectations, which reversed during the bust.

Abstract We investigate the causal effects of financial incentives supporting geographical mobility among unemployed workers on their job search behavior and labor market outcomes. Exploiting regional variation in the promotion of mobility programs along administrative borders of German employment agency districts, we show that promoting mobility—as intended—causes job seekers to increase their search radius, apply for, and accept distant jobs. At the same time, local job search is reduced with adverse consequences for reemployment and earnings. A detailed analysis of the underlying mechanisms suggests spatial search frictions as the driver of the unintended adverse labor market effects.

Abstract I partially identify the marginal treatment effect (MTE) when the treatment is misclassified. I explore two restrictions, allowing for dependence between the instrument and the misclassification decision. If the signs of the propensity scores’ derivatives are equal, I identify the MTE sign. If those derivatives are similar, I bound the MTE. To illustrate, I analyze the impact of alternative sentences (fines and community service versus no punishment) on recidivism in Brazil, where court appeals processes generate misclassification. The estimated misclassification bias may be as large as 10% of the largest possible MTE, and the bounds contain the correctly estimated MTE.

Abstract In markets with asymmetric information between sellers and buyers, feedback mechanisms are important to increase market efficiency and reduce the informational disadvantage of buyers. Feedback mechanisms might work because of self-selection of more trustworthy sellers into markets with such mechanisms or because of reputational concerns of sellers. We show in a field experiment how to disentangle self-selection from reputation effects. Based on 476 taxi rides with four different types of taxis, we find strong evidence for reputation effects but little support for self-selection effects. We discuss policy implications of our findings.

Abstract We propose a Cramér–von Mises–type test for testing whether the mean potential outcome given a specific treatment level has a weakly monotonic relationship with the continuous treatment under unconfoundedness. To flexibly control for a possibly high-dimensional set of covariates, our test is based on a double debiased machine learning method. We show that our test controls asymptotic size and is consistent against any fixed alternative. We apply our test to evaluate the Job Corps program and reject a weakly negative relationship between the treatment (hours in academic and vocational training) and labor market performance among relatively low treatment values.

Abstract We study the influence of workplace peers on a high-stakes, irreversible retirement plan choice. Midcareer U.S. military personnel choose between higher future pension payouts or an immediate bonus plus lower future payouts. With peers defined as those who have locked in their choices and personnel assignment rules ensuring that peer groups are exogenously formed, we capture the causal impact of peers. Greater peer take-up of the bonus, which is difficult to compare to the alternative plan but often extremely costly over one’s lifetime, discourages choosers from selecting the bonus. Peers have special impact within professional, race, and gender groups.

Abstract We provide the first estimates of intergenerational income mobility using tax data for a large developing country, namely Brazil. We measure formal income from tax and payroll data, and we train machine learning models on census and survey data to predict informal income. We quantify the estimation bias resulting from income imputation and other sources of measurement error, and show that such bias remains negligible in our context. A 10 percentile increase in parental income rank is associated on average with a 5.5 percentile increase in child income rank, with considerable variation across sociodemographic groups and geographical areas.

Abstract Predictive algorithms inform consequential decisions in settings with selective labels: outcomes are observed only for units selected by past decision makers. This creates an identification problem under unobserved confounding — when selected and unselected units differ in unobserved ways that affect outcomes. We propose a framework for robust design and evaluation of predictive algorithms that bounds how much outcomes may differ between selected and unselected units with the same observed characteristics. These bounds formalize common empirical strategies including proxy outcomes and instrumental variables. Our estimators work across bounding strategies and performance measures such as conditional likelihoods, mean squared error, and true/false positive rates. Using administrative data from a large Australian financial institution, we show that varying confounding assumptions substantially affects credit risk predictions and fairness evaluations across income groups.