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In this paper we study gender differences in preferences over jobs. Our main contribution is that we consider a real stakes situation: applying to real job ads using a rich proprietary data from an online job board, which is representative to the entire labor market. This contrasts to studies using experimental settings with no comparable consequences for subjects and to surveys applied to narrow segments of the job market (usually, undergraduate students). By defining consideration sets of job ads for applicants using a network formulation, we document dimensions of jobs which attract females more than males, such as female-wanted, remote work, inclusive policies, and minorities recruiting. In contrast, job complexity (measured as the content of adjectives in the ad text) deters female applications more than males’. Background similarity between workers’ last/current occupation and job ad title also spur applicants, but do less for females. We also relate these preferences for inclusivity (and other dimensions) to gender gaps in what job seekers expect to earn in narrowly defined labor markets and show that, while all individuals with higher wage expectations prefer inclusive policies, this is stronger for women. Finally, a social-interaction effects model also allows us to estimate that applicants prefer jobs with fewer expected applications, an effect that is notably larger for females.
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