Gender Discrimination in the Gig Economy: Evidence from Online Auctions for Freelancing

I study gender discrimination in an auction-based online platform for freelance jobs. To this end, I build an equilibrium model of demand and supply for freelance jobs, in which workers submit bids for each job they are interested in and the employer (who posted the job ad) makes a discrete choice from the tendered offers. The demand for workers in my model nests both taste-based and statistical discrimination against a gender within a random utility framework. I use rich and novel data from an online platform for different kinds of freelancing jobs (e.g., cleaning, moving, and gardening), which enable me to quantify variation in discrimination across job categories. To distinguish the two sources of gender discrimination, I combine past, present, and future performance measures of a worker to estimate workers' true quality -- a measure that is not observed by the employer at the time of hiring. I show that observing this measure is sufficient to separate the effect of taste-based discrimination from statistical discrimination in the hiring process. My estimates suggest that taste-based discrimination is the primary form of discrimination in most job types. If the platform imposes a gender-blind hiring policy, I find that the welfare of the disfavored group increases by 2% to 18%, depending on the job category.