Gender wage gap in online gig economy and gender differences in job preferences
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Citation (published version)Chen Liang, Yili Hong, Bin Gu, Jing Peng. "Gender Wage Gap in Online Gig Economy and Gender Differences in Job Preferences." SSRN Electronic Journal, https://doi.org/10.2139/ssrn.3266249
We explore whether there is a gender wage gap in the gig economy and examine to what degree gender differences in job application strategy could account for the gap. With a large-scale dataset from a leading online labor market, we show that females only earn around 81.4% of the hourly wage of their male counterparts. We further investigate three main aspects of job application strategy, namely bid timing, job selection, and avoidance of monitoring. After matching males with females using the propensity score matching method, we find that females tend to bid later and prefer jobs with a lower budget. In particular, the observed gender difference in bid timing can explain 7.6% of the difference in hourly wage, which could account for 41% of the gender wage gap (i.e. 18.6%) observed by us. Moreover, taking advantage of a natural experiment wherein the platform rolled out the monitoring system, we find that females are less willing to bid for monitored jobs than males. To further quantify the economic value of the gender difference in avoidance of monitoring, we run a field experiment on Amazon Mechanical Turk (AMT), which suggests that females tend to have a higher willingness to pay (WTP) for the avoidance of monitoring. The gender difference in WTP for the avoidance of monitoring can explain 8.1% of the difference in hourly wage, namely, 44% of the observed gender wage gap. Overall, our study reveals the important role of job application strategies in the persistent gender wage gap.