Document Type

Thesis - Open Access

Award Date

2019

Degree Name

Master of Science (MS)

Department / School

Economics

First Advisor

Myoung-Jin Keay

Keywords

Copula Regression, Emergency Department, Endogeneity, Instrumental Variable, Medicaid, Monte Carlo simulation

Abstract

This thesis paper strives to identify the relationship between Medicaid expansion and Emergency Department use. I use a Monte Carlo simulation for demonstrating the endogeneity problem and a copula model using the Oregon Health Program (OHP) data to show the previous literature has exaggerated the causal relation between Medicaid expansion and Emergency Department use. This paper can be divided into two parts. First, it tries to focus on the under-identification of multiple endogenous variables problem in typical econometrics papers, where researchers correct for a single endogenous variable but intentionally or unintentionally ignore the endogeneity of one or more other independent regressors. So, the motivation for first part of this thesis comes from the fact that the previous literature does correct the multiple endogeneity issue. Second, I endeavored to solve this under-identification problem of multiple endogeneity by incorporating a copula regression, along with OLS and 2SLS. The new approach to solve the under-identification problem is a copula method where we have flexibility of using different distribution methods to choose the best one. Using a copula method, we have found that Medicaid does indeed increase the emergency department use, however, not at the rate as the previous literature showed. This is the major contribution of this thesis

Library of Congress Subject Headings

Emergency medical services -- Utilization -- Oregon -- Econometric models.
Medicaid -- Utilization -- Oregon -- Econometric models.
Managed care plans (Medical care) -- Oregon.

Format

application/pdf

Number of Pages

51

Publisher

South Dakota State University

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Rights Statement

In Copyright