City Research Online

Abstract

This paper seeks to estimate the causal effect of having health insurance on health care utilization, while accounting for potential endogeneity bias. The topic has impor- tant policy implications, because health insurance reforms implemented in U.S. in recent decades have focused on extending coverage to the previously uninsured. Consequently, understanding the effects of those reforms requires an accurate estimate of the causal effect of insurance on utilization. However, obtaining such an estimate is complicated by the discreteness inherent in common measures of health care usage. This paper presents a flexible estimation approach, based on copula functions, that consistently estimates the coefficient of a binary endogenous regressor in count data settings. The relevant numeri- cal computations can be easily carried out using the freely available GJRM R package. The empirical results find significant evidence of favorable selection into insurance. Ignoring such selection, insurance appears to increase doctor visit usage by 62%, but adjusting for it, the effect increases to 134%.

Publication Type:	Article
Additional Information:	© 2020 The Authors Journal of the Royal Statistical Society: Series C (Applied Statistics) Published by John Wiley & Sons Ltd on behalf of the Royal Statistical Society This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Publisher Keywords:	binary endogenous regressor; copula; count data; moral hazard; penalized regression spline; simultaneous estimation
Subjects:	H Social Sciences > HA Statistics H Social Sciences > HF Commerce > HF5601 Accounting R Medicine > RA Public aspects of medicine
Departments:	Bayes Business School > Actuarial Science & Insurance
SWORD Depositor:	Symplectic Administrator

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