Gender gap in the desired wages: Evidence from large administrative data

Taiyo Fukai, Keisuke Kawata, Mizuki Komura and Takahiro Toriyabe

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Abstract: This study analyzes the gender gap in desired wages using large administrative data of public job referrals, which allows us to look at the desired salaries of individuals from a wider wage distribution. We conduct a decomposition analysis using available information on age, desired work region, and desired occupation. We find that of the three factors, desired occupation is the most important in generating differences in desired wages; however, the residuals are the largest outside of the three factors. To further probe the unexplained residuals, we also conduct heterogeneity and sensitivity analyses using the available data.

Date: 2025-11
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