Marra, G;
Radice, R;
Zimmer, DM;
(2020)
Estimating the binary endogenous effect of insurance on doctor visits by copula-based regression additive models.
Journal of the Royal Statistical Society: Series C (Applied Statistics)
10.1111/rssc.12419.
(In press).
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Abstract
The paper estimates the causal effect of having health insurance on healthcare utilization, while accounting for potential endogeneity bias. The topic has important policy implications, because health insurance reforms implemented in the USA 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 healthcare usage. The 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 numerical computations can be easily carried out by using the freely available GJRM R package. The empirical results find significant evidence of favourable selection into insurance. Ignoring such selection, insurance appears to increase doctor visit usage by 62% but, adjusting for it, the effect increases to 134%.
| Type: | Article |
|---|---|
| Title: | Estimating the binary endogenous effect of insurance on doctor visits by copula-based regression additive models |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1111/rssc.12419 |
| Publisher version: | https://doi.org/10.1111/rssc.12419 |
| Language: | English |
| Additional information: | © 2020 The Authors. Journal of the Royal Statistical Society: Series C (Applied Statistics) published by John Wiley & Sons Ltd on behalf of Royal Statistical Society. This is an open access article under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/). |
| Keywords: | Binary endogenous regressor, Copula, Count data, Moral hazard, Penalized regression spline, Simultaneous estimation |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10101338 |
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