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

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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.

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Notes

  1. https://www.insurancebusinessmag.com/us/news/breaking-news/gender-pay-gap-highest-in-us-insurance-industry-16158.aspx.

  2. We use quantile regressions because they can address both linear and nonlinear wage-offer functions.

  3. Gowri (2004) suggests that underwriters intend to rate risks even though they may not intend to deprive high risk persons of insurance.

  4. The results of the first stage RFRR machine learning models are available upon request.

  5. All the residual values are from random forest residual regressions.

  6. SAS programs for the calculation of the regression Gini indices, and the random forest two-stage residual regressions, are available upon request.

  7. Labor market impediments—as we define the term here as anything that drives a wedge between workers’ wages and the value of their marginal product—generally includes monopsony, trade unions, difficult to measure productivity, geographical immobilities, occupational immobilities, and poor information. Our framework does not discuss monopsony and trade unions because the insurance labor market is competitive and has no trade union. Our empirical models partially control for geographical immobilities.

  8. The Becker model is developed for racial discrimination. Lang and Lehmann (2012) review Becker (1957) and provide a simplified model. We modify Lang and Lehmann (2012) for gender discrimination.

  9. Female claim adjusters may also suffer from consumers’ taste-based discrimination because consumers need to interact with adjusters for claims.

  10. This argument is based on our conversation with a professor who worked for an insurance company and a former executive in an insurance company. We are not able to find any statistics to support this argument. It is anecdotal evidence.

  11. https://www.scic.com/the-power-of-female-insurance-producers/.

  12. The Young Producer Study was conducted by Regan Consulting and sponsored by The Council of Insurance Agents and Brokers. The study is to access the hiring activity and identify the firms that have successful hiring young produces in the insurance industry.

  13. If female agents and male agents have equal chance to sell insurance products, both commercial lines and personal lines, then gender inequity is not likely to persist in the long run as female referral conduits are generated, and as profit-maximizing owners seek to maximize profits by employing equally productive but lower cost alternatives to sale insurance.

  14. There are either union-enforced or social-norm-enforced restrictions on variability in pay in the school-teacher space.

  15. Wages of sale agents include commissions and bonuses paid by their employer in our empirical data.

  16. See Blau and Kahn (2017, p. 791).

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Authors and Affiliations

  1. Brigham Young University, Provo, USA

    Richard J. Butler

  2. Southwestern University of Finance and Economics, Chengdu, China

    Richard J. Butler

  3. University of North Carolina, Charlotte, USA

    Gene Lai

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  1. Richard J. Butler
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Correspondence to Richard J. Butler.

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There are no conflicts of interest. The only support for this research was academic computational support.

Appendix

Appendix

See Tables 12 , 13 , 14 and 15 .

Table 12 Residual analysis employing random forest residuals regressions for the dependent and (slope) independent variable under alternative specifications
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Table 13 Residual analysis employing random forest residuals regressions for the dependent and (slope) independent variable under alternative specifications
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Table 14 Residual analysis employing random forest residuals regressions for the dependent and (slope) independent variable under alternative specifications
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Table 15 Residual analysis employing random forest residuals regressions for the dependent and (slope) independent variable under alternative specifications
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Butler, R.J., Lai, G. Insurance wage-offer disparities by gender: random forest regression and quantile regression evidence from the 2010–2018 American Community Surveys. Geneva Risk Insur Rev 48, 192–229 (2023). https://doi.org/10.1057/s10713-022-00078-7

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