Gender Wage Gap in Online Gig Economy and Gender Differences in Job Preferences

Chen Liang (), Yili Hong, Bin Gu () and Jing Peng ()
Additional contact information
Chen Liang: Department of Information Systems, W.P. Carey School of Business, Arizona State University, USA
Bin Gu: Department of Information Systems, W.P. Carey School of Business, Arizona State University, USA
Jing Peng: Department of Operations and Information Management (OPIM), School of Business, University of Connecticut, USA

No 18-03, Working Papers from NET Institute

Abstract: 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.

Keywords: gender wage gap; job application strategy; gig economy; quasi-natural experiment (search for similar items in EconPapers)
JEL-codes: D31 J16 J24 J31 (search for similar items in EconPapers)
Pages: 33 pages
Date: 2018-09
New Economics Papers: this item is included in nep-exp, nep-gen, nep-hrm and nep-lma
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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