The surprising impacts of unionization: Evidence from matched employer-employee data
Revise and resubmit at Journal of Political Economy
Using administrative data matching individual worker earnings to employers in a regression discontinuity design based on close union representation elections, this study presents new evidence on the impacts of unionization on establishment and worker outcomes. The paper first shows evidence that close union elections are subject to nonrandom selection, with large discontinuities in pre-election characteristics at the majority threshold. Estimates accounting for this selection show, perhaps surprisingly, that unionization significantly and substantially decreases establishment-level payroll, employment, average worker earnings at the establishment, and the probability of establishment survival. Estimates show the decreases in payroll and earnings are driven by union impacts on the composition of workers at unionization establishments, with older and higher-paid workers more likely to leave and younger workers more likely to join or stay. Worker-level effects on the earnings of workers who stay are small. The distinction between the large negative establishment-level effects and small worker-level effects is interpreted in a model of employer and employee selection into union jobs.
Revise and resubmit at Journal of Business & Economic Statistics
Identification in censored regression analysis and hazard models of duration outcomes relies on the condition that censoring points are conditionally independent of latent outcomes, an assumption which may be questionable in many settings. This note proposes a test for this assumption based on a Cramer-von Mises-like test statistic comparing two different nonparametric estimators for the latent outcome cdf: the Kaplan-Meier estimator, and the empirical cdf conditional on the censoring point exceeding (for right-censored data) the cdf evaluation point. The test is consistent and has power against a wide variety of alternatives. Applying the test to unemployment duration data from the NLSY, the PSID, and the SIPP suggests the assumption is frequently suspect.
Stata command: censoringtestkmcvm.ado
With Lars Lefgren
This article develops bounds on the distribution of treatment effects under plausible and testable assumptions on the joint distribution of potential outcomes, namely that potential outcomes are stochastically increasing. We show how to test the empirical restrictions implied by those assumptions. The resulting bounds substantially sharpen the classical bounds based on Frechet-Hoeffding limits. We present an application in which we identify bounds on the distribution of effects of attending a Knowledge is Power Program (KIPP) charter school on student academic achievement.
Fragmented health care occurs when care is spread out across a large number of poorly coordinated providers. We analyze care fragmentation, an important source of inefficiency in the US healthcare system, by combining an economic model of regional practice styles with an empirical study of Medicare enrollees who move across regions. Roughly sixty percent of cross-regional variation in care fragmentation is independent of patients' clinical needs or preferences for care. A one standard deviation increase in regional fragmentation is associated with a 10% increase in utilization. Our analysis also identifies conditions under which anti-fragmentation policies can improve efficiency.
We propose a "common-agency" model for explaining inefficient contacting in the U.S. healthcare system. In our setting, common agency problems arise when multiple payers seek to motivate a shared provider to invest in improved care coordination. Our approach differs from other common-agency models in that we analyze "sticking points," i.e. equilibria in which payers coordinate around Pareto dominated contracts that do not offer providers incentives to implement efficient investments. These sticking points offer a straightforward explanation for three long-observed but hard to explain features of the US healthcare system: the ubiquity of fee-for-service contracting arrangements outside of Medicare; problematic care coordination; and the historic reliance on small, single specialty practices rather than larger multi-specialty group practices to deliver care. The common-agency model also provides insights on the effects of policies (such as Accountable Care Organizations) that aim to promote more efficient forms of contracting between payers and providers.
This paper describes a randomization-based estimation and inference procedure for the distribution or quantiles of potential outcomes with a binary treatment and instrument. The method imposes no parametric model for the treatment e ect, and remains valid for small n, a weak instrument, or inference on tail quantiles, when conventional large-sample methods break down. The method is illustrated using simulations and data from a randomized trial of college student incentives and services.