Identifying the impact of health insurance on subgroups with changing rates of diagnosis

Daniel Kaliski

Health Economics, 2023, vol. 32, issue 9, 2098-2112

Abstract: Expanded access to health care often leads to new diagnoses for previously undetected conditions. New diagnoses make it difficult to identify the causal effect of expanding health insurance on individuals with particular diagnoses: the newly diagnosed in the treatment group are likely to differ in unobserved ways from the control group. This paper provides two methods for dealing with this problem depending on the data available to the researcher and diagnosis‐specific knowledge. If there is no panel dimension to the data, then the causal effect for the subgroup of interest can be bounded from either above or below depending on the condition in question. If panel data are available, then the newly diagnosed can be identified, and their treated outcomes subtracted from the overall effect of interest. I apply these methods to find that the difference‐in‐discontinuities estimator underestimates the effect of Medicare prescription drug coverage on the uptake of insulin by first‐time users by 20%.

Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1002/hec.4718

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:hlthec:v:32:y:2023:i:9:p:2098-2112

Access Statistics for this article

Health Economics is currently edited by Alan Maynard, John Hutton and Andrew Jones

More articles in Health Economics from John Wiley & Sons, Ltd.
Bibliographic data for series maintained by Wiley Content Delivery ().