Can Gender-Blind Algorithmic Pricing Eliminate the Gender Gap?

Abstract

Insurance companies frequently use consumer attributes, such as gender or age, to set prices for their services. Young male drivers, for example, are often charged more than young females for car insurance, as they are considered as 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 in pricing 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 highlight the limitations of anti-discrimination policies that impose group-blind pricing and provide insights for designing fairer regulations for algorithms.

Keywords: Algorithmic pricing, gender discrimination, machine learning JEL Codes: D81, G22, J16

JEL Classification: D81, G22, J16