Working Paper
Madeleine Gelblum. Working Paper. “Preferences for Job Tasks and Gender Gaps in the Labor Market (Job Market Paper)”.Abstract
Women and men work in markedly different jobs, leading to persistent occupational segregation by gender. This paper provides evidence that gender differences in how individuals value activities performed at work, termed job tasks, may help explain these sorting patterns. I conduct a hypothetical choice experiment to elicit workers’ willingness to pay for a set of tasks that are more frequently performed by one gender than the other. The experimental scenarios ask participants to choose between two hypothetical jobs that differ in terms of pay and the amount of time spent on a gender-typical task, but are otherwise the same. I find significant gender differences in willingness to pay for three of the five tasks that I examine. Willingness to pay is significantly more positive among participants who report spending more time on a task in their current job, suggesting that estimates are correlated with actual sorting behavior. I show that gender differences in preferences for the tasks that I investigate can account for a substantial portion of occupational segregation in the U.S. labor market.
In Preparation
Madeleine Gelblum. In Preparation. “Automation and Skill Requirements in Cognitive Occupations: Evidence from Online Job Postings”.
Madeleine Gelblum and John Horton. In Preparation. “Wages, Productivity and Firm Hiring Decisions: Evidence from an Online Labor Market”.Abstract
How do firms interpret a worker’s wage bid when making hiring decisions? When worker skills are not perfectly observed, employers must form expectations about productivity based on the information available, including the worker’s proposed wage. If these beliefs are accurate, then firms will pay workers their marginal product and a higher-wage worker will complete a discrete project more quickly, leaving the total wage bill unchanged. We test this prediction by exploiting an institutional feature of an online labor market that creates quasi-random variation in which workers are recommended to employers. We show that the recommendation system arbitrarily induces firms to hire workers who make different wages bids but score similarly on a measure of the likelihood that they will be hired, based on historical data from the platform. Higher-wage employees work fewer hours, as expected, but cost firms more overall, suggesting that these individuals may be systematically overvalued.