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

传统劳动力市场上的性别工资差距（即同等工作的妇女收入低于男子）部分归因于母性的惩罚和妇女对工作灵活性的偏好。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，它就构成了一个谜，因为母性惩罚和灵活性偏好的解释都不容易适用于在线零工经济。本博士论文改进资助研究旨在确认在线零工经济中性别工资差距的存在，并探索潜在的供给侧解释：差距的产生是由于男性和女性之间的期望差异导致的求职策略的差异。如果期望的差异是造成差距的原因，一个可能的解决方案是向投标人提供准确的信息。这个项目将确定情况是否如此。工作申请策略中的性别差异将使用来自一个主要在线劳动力市场平台的专有数据进行研究。我们将探讨两种信念的差异：一种源于对预期工资或成本的信念差异，另一种源于对预期工资或成本的感知不确定性的差异。为了确定是哪种信念的差异在起作用，该研究将结合对劳动力市场预期的大规模调查结果，以及对预期进行系统操纵的信息实验。总体而言，该项目可以提供信息政策如何减少或消除预期偏见，从而缩小性别工资差距的见解。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.