This project intends to investigate the following three questions: 1) Is there a gender wage gap in the online gig economy, or so-called on-demand economy; 2) To what degree is the gender wage gap caused by expectation bias between workers of different genders; and 3) Does information provision help reduce workers expectation bias and close the gender wage gap? To our best knowledge, there is no research examining the existence of gender wage gap in the online gig economy. While previous literature has explored reasons for the gender wage gap in traditional employment settings (e.g. females preference for flexibility), these reasons often do not apply to the online gig economy. Therefore, whether the gender wage gap exists in the gig economy remains an open question. This project addresses the question by exploring proprietary data from a major online labor market platform. Our prior study shows that ceteris paribus, males earn a higher hourly wage than females on average. Further, this gender wage gap is driven by discrimination on the demand side and differential bidding strategies on the supply side. Specifically, females tend to bid later and ask for higher wages than males while bidding, which lowers their probabilities of getting hired. After establishing the gender wage gap and its causes from both the demand and supply sides based on an empirical study, we plan to investigate the underlying mechanisms leading to differential bidding strategies of workers with different genders. In particular, the gender difference in bidding strategy may result from two expectation biases: the difference in expected wage or costs and the difference in the perceived uncertainty of expected wage or costs. On the one hand, females may tend to have higher expected wage or costs, which renders them to ask for higher wages and bid later to save their opportunity costs of waiting. On the other hand, females might perceive higher uncertainty of expected wage or costs, so they ask for higher wages owing to their higher risk premium and tend to hold off bidding until learning enough information from others bids. To identify which expectation bias is at work, we will combine a large-scale survey of labor market expectations with an information experiment, in which we manipulate the expectation bias in a number of ways.
|Effective start/end date
|7/1/18 → 6/30/20
- National Science Foundation (NSF): $15,914.00
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