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Estimating Incentive and Selection Effects in the Medigap Insurance Market: An Application with Dirichlet Process Mixture Model
Author(s) -
Hu Xuequn,
Munkin Murat K.,
Trivedi Pravin K.
Publication year - 2014
Publication title -
journal of applied econometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.878
H-Index - 99
eISSN - 1099-1255
pISSN - 0883-7252
DOI - 10.1002/jae.2403
Subject(s) - endogeneity , econometrics , selection (genetic algorithm) , dirichlet process , selection bias , economics , incentive , dirichlet distribution , model selection , sample (material) , statistics , computer science , mathematics , bayesian probability , microeconomics , machine learning , mathematical analysis , chemistry , chromatography , boundary value problem
Summary This paper presents an empirical study of endogenous treatment effects in the presence of heterogeneous responses. We estimate the incentive and selection effects of having prescription drug coverage on total drug expenditures using a sample from the Medicare Current Beneficiary Survey (MCBS). A Dirichlet process mixture (DPM) model is used to model the heterogeneity in treatment effects. Rather than estimating a finite mixture model with a fixed number of components, we specify a DP prior on the parameters, thus allowing the data and prior knowledge to determine the number of components. Endogeneity is modeled by the covariance of the error terms of the selection and the outcome equations in a two‐equation selection model. We find that there are strong incentive and advantageous selection effects, with the average treatment effect and the average treatment effect for the treated estimated at $1132 and $858, respectively. Substantial heterogeneity is found to exist in the selection effects, particularly for those having lower drug expenditures. Copyright © 2014 John Wiley & Sons, Ltd.