THREE ESSAYS ON THE INFLUENCE OF PEERS AND PRIMARY CARE ENGAGEMENT
In this dissertation, I study econometric issues in network and health economics. Measurement error is a ubiquitous problem in the peer effects literature that is not well understood. In Chapter 1, ``Measurement error in peer effects,'' I develop a constructive approach to empirically assess the bias caused by links missing at random. I apply my method to study the bias in peer effect estimates of recreational and physical activities among adolescents in the United States. I find that the magnitude and direction of the bias depends on the estimator. Estimators that measure the aggregate effect of peers' outcomes are more robust to measurement error and can be unbiased even when fifty percent of peer interactions are unobserved. Estimators that measure the average effect of peers'’ outcomes are more susceptible to measurement error and suffer from a persistent downward bias. My findings illustrate the importance of understanding measurement error's impact, when measurement error will likely bias results and when it can be safely ignored. In Chapter 2, ``Non-random errors in peer effects,'' I study the effects of measurement error on a generalized peer effect model that nests two of the most commonly used estimators. Measurement error in the specification of peer groups leads to biased estimates. I adapt Monte Carlo methods developed for studying measurement error when peers' interactions are missing at random to understand the effects of top-coding, non-random errors and spurious peer interactions. I find that non-random errors pose the greatest threat, often leading to overestimation and persistent biases. Top-coding can also severely bias estimates when the constraint impacts a majority of individuals but otherwise has a mild effect. While spurious links in limited quantities can often be ignored. Chapter 3, ``Nurse outreach and frequent emergency department users: A synthetic control analysis,'' studies the effects of an intervention to promote primary care engagement among frequent emergency department users. Emergency departments are one of the costliest places to receive care and are routinely overcrowded. Various policy initiatives have yielded mixed findings. I use synthetic control methods to analyze the effects of a nurse outreach program for frequent emergency department users implemented by a major U.S. insurer. The program seeks to reduce emergency department utilization by promoting primary care engagement. I leverage a unique commercial claims data set to measure the effects of the program on primary care and emergency department utilization. My findings suggest that six months after treatment nurse outreach increased primary care utilization by 15 percent; however, I find no clear effect on emergency department utilization. My findings indicate that increasing primary care engagement may not be sufficient to prevent emergency department over utilization.
Economics, Health behavior, Measurement error, Peer effects, Public health, Social networks, Synthetic controls
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