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The Two-Margin Problem in Insurance Markets
Author(s) -
Michael Geruso,
Timothy Layton,
Grace McCormack,
Mark Shepard
Publication year - 2021
Publication title -
the review of economics and statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 8.999
H-Index - 165
eISSN - 1530-9142
pISSN - 0034-6535
DOI - 10.1162/rest_a_01070
Subject(s) - margin (machine learning) , sorting , welfare , computer science , feature (linguistics) , feature selection , selection (genetic algorithm) , econometrics , economics , business , artificial intelligence , machine learning , linguistics , philosophy , market economy , programming language
Insurance markets often feature consumer sorting along both an extensive margin (whether to buy) and an intensive margin (which plan to buy). We present a new graphical theoretical framework that extends a workhorse model to incorporate both selection margins simultaneously. A key insight from our framework is that policies aimed at addressing one margin of selection often involve an economically meaningful trade-off on the other margin in terms of prices, enrollment, and welfare. Using data fromMassachusetts, we illustrate these trade-offs in an empirical sufficient statistics approach that is tightly linked to the graphical framework we develop.

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