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Private information in healthcare utilization: specification of a copula‐based hurdle model
Author(s) -
Shi Peng,
Zhang Wei
Publication year - 2015
Publication title -
journal of the royal statistical society: series a (statistics in society)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.103
H-Index - 84
eISSN - 1467-985X
pISSN - 0964-1998
DOI - 10.1111/rssa.12065
Subject(s) - health care , actuarial science , information asymmetry , econometrics , adverse selection , copula (linguistics) , covariate , private information retrieval , medical expenditure panel survey , payment , population , business , economics , statistics , medicine , environmental health , microeconomics , health insurance , mathematics , finance , economic growth
Summary We study whether individuals' private information on health risk affects their medical care utilization. The presence of such information asymmetry is critical to the optimal payment design in healthcare systems. To do so, we examine the relationship between self‐perceived health status and healthcare expenditures. Because of simultaneity, we employ a copula regression to model jointly the mixed outcomes, with the association parameter capturing the residual dependence conditional on covariates. The semicontinuous nature of healthcare expenditures leads to a two‐part interpretation of private health information: the hurdle component assesses its effect on the likelihood of using medical care services, and the conditional component quantifies its effect on the expenditures given consumption of care. The methodology proposed is applied to a survey data set of a sample of the US civilian non‐institutionalized population to test and quantify the effects of private health information. We find evidence of adverse selection in the utilization of various types of medical care services.