THREE ESSAYS ON THE INFLUENCE OF PEERS AND PRIMARY CARE ENGAGEMENT
type: CreativeWork
description: 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 engage…
abstract: 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 engage…
author: Kost, Edward
language: eng
keywords: Economics, Health behavior, Measurement error, Peer effects, Public health, Social networks, Synthetic controls
license: http://rightsstatements.org/vocab/InC/1.0/
date published: 2023
date modified: 2026-07-27T21:22:02Z
provider: Temple University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Temple University. Libraries

PropertyValue
property ID: Handle
value: http://hdl.handle.net/20.500.12613/8893