Who Did the Affordable Care Act Medicaid Expansion Impact? Using Linear Discriminant Analysis to Estimate the Probability of Being a Complier

Benjamin Chu ()
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Benjamin Chu: University of Hawaii

No 202202, Working Papers from University of Hawaii at Manoa, Department of Economics

Abstract: What is the likelihood of being a complier in the ACA Medicaid expansion? Using linear discriminant analysis (LDA), I estimate how characteristics relating to socioeconomic status and race/ethnicity affect the likelihood that an individual will be a complier, defined as those induced by the expansion to obtain Medicaid coverage. Across multiple specifications, part-time and full-time workers are more likely than non-workers to be compliers. Not only is this result more prominent for Black individuals, but they are also more likely to be compliers compared to other racial/ethnic groups. This paper not only serves to identify the types of individuals who were directly impacted by the expansion, but it also introduces a new approach that combines complier analysis with techniques from machine learning.

Keywords: Medicaid; ACA; Complier; Linear discriminant analysis (search for similar items in EconPapers)
JEL-codes: I13 (search for similar items in EconPapers)
Date: 2022-08
New Economics Papers: this item is included in nep-big and nep-hea
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