Ga Young Ko

I study gender discrimination in an online auction-based platform for freelance jobs. To this end, I build an equilibrium model of demand and supply for freelance jobs, in which workers bid prices for each job they are interested in and the employer (who posted the job ad) makes a discrete choice from the offers tendered. 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 enables 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, which is not observed by the employer before 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 jobs. 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.

The gender gap in hiring and performance evaluations is persistent and prevalent across many sectors in the economy. In this study, we investigate whether (a) gender gap exists in performance ratings and customer retention in the gig economy and (b) uncover the underlying mechanisms behind the gap. Using data from QANDA, a leading math learning app in South Korea, we focus on evaluations from students where they are randomly assigned to tutors. In contrast with current literature, our results show that female tutors receive higher ratings than male tutors from both male and female students. We also find that students matched with female tutors are more likely to remain on the platform. We investigate possible mechanisms through which tutor gender affects student satisfaction and retention. The novel data enables us to directly quantify teaching skills and style, where we extract various features from solution images and dialogue narratives between tutor and student utilizing computer vision techniques and text mining. We find that female tutors are more likely to use smile symbols and write solutions with a variety of colors compared to male tutors, which explains about 5.7% and 3.3% of the rating gap between male and female tutors, respectively.