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The efficiency of voluntary risk classification in insurance markets
Author(s) -
Crocker Keith J.,
Zhu Nan
Publication year - 2021
Publication title -
journal of risk and insurance
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.055
H-Index - 63
eISSN - 1539-6975
pISSN - 0022-4367
DOI - 10.1111/jori.12326
Subject(s) - categorical variable , actuarial science , test (biology) , pareto principle , outcome (game theory) , pareto efficiency , economics , turnover , ex ante , business , microeconomics , computer science , operations management , machine learning , paleontology , management , macroeconomics , biology
It has been established that categorical discrimination based on observable characteristics such as gender, age, or ethnicity enhances efficiency. We consider a different form of risk classification when there exists a costless yet imperfectly informative test of risk type, with the test outcome unknown to the agents ex ante. We show that a voluntary risk classification in which agents are given the option to take the test always increases efficiency compared with no risk classification. Moreover, voluntary risk classification also Pareto dominates a regime of compulsory risk classification in which all agents are required to take the test.

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