Targeting with In-Kind Transfers: Evidence from Medicaid Home Care
Author(s) -
Ethan M.J. Lieber,
Lee Lockwood
Publication year - 2019
Publication title -
american economic review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 16.936
H-Index - 297
eISSN - 1944-7981
pISSN - 0002-8282
DOI - 10.1257/aer.20180325
Subject(s) - medicaid , value (mathematics) , economics , distortion (music) , transfer (computing) , public economics , population , actuarial science , microeconomics , business , medicine , health care , economic growth , computer science , environmental health , telecommunications , amplifier , bandwidth (computing) , parallel computing , machine learning
Making a transfer in kind reduces its value to recipients but can improve targeting. We develop an approach to quantifying this tradeoff and apply it to home care. Using randomized experiments by Medicaid, we find that in-kind provision significantly reduces the value of the transfer to recipients while targeting a small fraction of the eligible population that is sicker and has fewer informal caregivers than the average eligible. Under a wide range of assumptions within a standard model, the targeting benefit exceeds the distortion cost. This highlights an important cost of recent reforms toward more flexible benefits.
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