Doctoral Dissertation Research in DRMS: Expectation Bias and the Gender Wage Gap in the Online Gig Economy

传统劳动力市场的性别工资差距(即妇女从事同等工作的收入低于男子)部分归因于母亲的惩罚和妇女偏爱工作灵活性。有初步证据表明，零工经济中也存在性别工资差距。如果是这样的话，这就成了一个难题，因为无论是母亲的惩罚，还是灵活性偏好的解释，都不适用于在线零工经济。这项博士论文改进拨款研究试图证实在线零工经济中性别工资差距的存在，并探索一种潜在的供给侧解释：这种差距的产生是由于男性和女性之间的求职策略因期望的差异而产生的差异。如果预期差异是造成这一差距的原因，一个可能的解决方案是向竞标者提供准确的信息。该项目将利用一个主要的在线劳动力市场平台的专有数据来研究求职策略上的性别差异。将探讨两种信念的差异：一种是由于对预期工资或成本的信念的差异，另一种是由于预期工资或成本的感知不确定性的差异。为了找出哪些信念上的差异在起作用，这项研究将结合大规模劳动力市场预期调查的结果，并进行一项信息实验，在实验中，预期将受到系统性的操纵。总体而言，该项目可以为信息政策如何减少或消除预期偏差，从而缩小性别工资差距提供见解。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The gender wage gap (i.e., women receiving less than men for equivalent work) in traditional labor markets has been attributed in part to a motherhood penalty and to women having a preference of job flexibility. There is preliminary evidence suggesting there is also a gender wage gap in the gig economy. If so, it poses a puzzle to the extent that neither the motherhood penalty nor flexibility preference explanations are readily applicable to the online gig economy. This Doctoral Dissertation Improvement Grant research seeks to confirm the existence of the gender wage gap in the online gig economy and explore a potential supply side explanation: that the gap arises due to differences in job application strategies between men and women resulting from differences in expectations. If differences in expectations are responsible for the gap, a possible solution is to provide bidders with accurate information. This project will ascertain whether this is the case.Gender differences in job application strategies will be studied using proprietary data from a major online labor market platform. Two differences in beliefs will be explored: one arising from a difference in beliefs regarding expected wages or costs and the other arising from a difference in the perceived uncertainty of expected wages or costs. To identify which difference in beliefs is at work, the research will combine results from a large-scale survey of labor market expectations with an information experiment in which expectations will be systematically manipulated. Overall, this project can provide insights on how information policy may reduce or eliminate expectation biases and therefore narrow the gender wage gap.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.