Can Gender-Blind Algorithmic Pricing Eliminate the Gender Gap?

63 Pages Posted: 15 Apr 2024

Date Written: April 1, 2024

Abstract

Insurance companies frequently use consumer attributes, such as gender or age, when setting a price for their services. Young male drivers, for example, are often charged more than young females for car insurance, as they are expected to be riskier. In 2019, California banned auto insurance companies from using information on gender in their pricing algorithms. I study how this ban affects the gender gap in prices, using a difference-in-differences strategy with older individuals and other states as control groups. I find that the ban reduced the gender gap in the insurance premiums paid by young drivers by around 55 percent, but it failed to eliminate it completely. My analysis of the pricing algorithm of a large insurance company in California indicates that algorithms are adjusted in a way that characteristics that are correlated with the riskiest gender group receive larger weights after the policy. For instance, drivers using specific car models associated with young males were charged up to 22 percent more after the ban. My findings illustrate the limitations of anti-discrimination policies that impose group-blind pricing, with implications for the design of fairer regulations for algorithms.

Keywords: Algorithmic pricing; gender discrimination; machine learning; statistical discrimination

JEL Classification: D81, G22, J16

Suggested Citation

Demirci, Ozge, Can Gender-Blind Algorithmic Pricing Eliminate the Gender Gap? (April 1, 2024). Available at SSRN: https://ssrn.com/abstract=4780217 or http://dx.doi.org/10.2139/ssrn.4780217

Ozge Demirci (Contact Author)

Harvard Business School

Harvard Business School
Cotting Hall 321
Boston, MA Boston 02135
United States

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