Words Matter: Gender, Jobs and Applicant Behavior

We examine employer preferences for hiring men vs women using 160,000 job ads posted on an online job portal in India, linked with more than 6 million applications. We apply machine learning algorithms on text contained in job ads to predict an employer's gender preference. We find that advertised wages are lowest in jobs where employers prefer women, even when this preference is implicitly retrieved through the text analysis, and that these jobs also attract a larger share of female applicants. We then systematically uncover what lies beneath these relationships by retrieving words that are predictive of an explicit gender preference, or gendered words, and assigning them to the categories of hard and soft-skills, personality traits, and flexibility. We find that skills related female-gendered words have low returns but attract a higher share of female applicants while male-gendered words indicating decreased flexibility (e.g., frequent travel or unusual working hours) have high returns but result in a smaller share of female applicants. This contributes to a gender earnings gap. Our findings illustrate how gender preferences are partly driven by stereotypes and statistical discrimination.