Insurance wage-offer disparities by gender: random forest regression and quantile regression evidence from the 2010–2018 American Community Surveys

Richard J. Butler () and Gene Lai ()
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Richard J. Butler: Brigham Young University
Gene Lai: University of North Carolina

The Geneva Risk and Insurance Review, 2023, vol. 48, issue 2, No 2, 192-229

Abstract: Abstract This paper examines differences in the wage-offer functions between males and females in the insurance industry. The results of random forest regression (RFR) residual analysis and quantile regressions (QRs) by gender indicate considerable inequities for underwriters, sales agents, and claims adjusters. We find relatively modest wage inequities among actuaries. Underwriters’ and adjusters’ gender wage inequality lies between the actuaries and sales agents. Across the specifications (RFR, QR, and the OLS benchmark), males benefit more from experience than females except for actuaries. In addition, males generally have a greater return to education than females (except for actuaries). Sales agents’ jobs exhibit the greatest inequality, with extremely high values for the regression Gini index of inequality at the upper quantiles. Actuaries exhibit the least amount of gender inequality across the board, with demographic responses suggesting competitive pressures across states yielding the least wage-offer inequality across gender. In summary, taste-based discrimination, social employment networks, difficulties in assessing productivity in heterogeneous work situations, competitiveness in the labor market, and the flexibility of work hours help explain our findings for different occupations in the insurance industry.

Keywords: Insurance wage-offer inequities by gender; Regression Gini index; Random forest regression residual analysis; Quantile regression; Underwriters; Actuaries; Insurance sales agents; Claims adjusters (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1057/s10713-022-00078-7

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