Knowledge that Transforms

We investigate whether participants in the patent system are sensitive to information quality by examining how they treat inaccurate information. We use a novel approach to identify patents with inaccurate information: patent-paper pairs where the paper has been retracted and the corresponding patent contains the retracted material. Despite containing inaccurate information, we find that these patents are prosecuted and maintained by many applicants, are not rejected by examiners, and continue to be cited by some downstream readers after retraction. Insensitivity to inaccurate information may lead to erroneous decisions during examination and has implications for patent quality, disclosure, and knowledge flows.

Using Chicago police data, we train a machine learning model to predict the risk of being shot in the next 18 months. Out-of-sample accuracy is strikingly high. A central concern with using police data is “baking in” bias, or overestimating risk for groups likelier to interact with police conditional on behavior. Our predictions, however, accurately recover risk across demographic groups. Legal, ethical, and practical barriers should prevent using victimization predictions to target law enforcement. But using them to target social services could increase both the potential for interventions to reduce shootings and the available statistical power to detect those reductions.

This paper explores whether and why a maternal child penalty to earnings would emerge even without changes in employment and hours worked. Using a matched event study design, we trace monthly changes in determinants of wages (job performance, human capital accumulation, and promotions). Data come from a usefully unusual setting with required multiyear employment and detailed personnel data: the US Marine Corps. Mothers’ job performance initially declines, and gaps in promotion grow through 24 months postbirth. Fathers’ physical fitness performance drops somewhat but recovers. These patterns lead mothers to earn relatively lower wages, even absent changes in employment postbirth.

This paper investigates the returns to health care provision during the mortality transition. We construct a new panel data set covering German municipalities from 1928 to 1936. The endogeneity of health care supply is addressed by using the expulsion of Jewish physicians from statutory health insurance as exogenous variation in regional physician supply. Increases in the supply of physicians reduce infant mortality and mortality from common childhood diseases. Using a semiparametric control function approach, we find diminishing marginal returns to health care provision. The results are consistent with historical trends in infant mortality over the twentieth century.

This paper estimates the returns to human capital accumulation during the first era of mega-firms in the United States by linking employees at General Electric—a canonical enterprise associated with the “visible hand” of managerial hierarchies—to the 1940 census. I find large returns to higher education through seniority in the hierarchy, span of control, earnings, and selection into management training, using the proximity of land-grant colleges and historical universities to birth states for identification. The findings highlight the human capital determinants of the managerial revolution at a prominent firm, driven by earlier public investments in the U.S. education system.

Consignment auctions are two-stage mechanisms to (re)allocate emission permits. Firms are first endowed with permits and then allowed to trade them. We determine theoretically endowments that enable efficient allocation, subject to incentive compatibility, individual rationality, and no deficit. All firms prefer efficient consignment auctions to efficient standard auctions, making them politically palatable. Firms’ investment incentives align with the first-best in efficient consignment auctions. Grandfathering based on efficient long-run allocations induces efficiency-permitting endowments. A simple calibration to data from Southern California’s RECLAIM program validates our no-deficit assumption and shows that grandfathering provides the best theoretical match for the empirically observed endowments.

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.

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.

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.

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.