What's My Employee Worth? Information Frictions, Pay, and Pay Equity

确定合适的工资对公司来说至关重要，因为工资通常代表着最大的支出。一方面，支付过高的工资可能是浪费。另一方面，支付过低的工资会使企业难以吸引、激励和留住员工。雇主如何找到最佳位置？本项目旨在加深我们对公司如何确定员工薪酬的理解。该项目分为两部分。第一部分研究了使用汇总市场数据来设定工资，这种做法被称为工资基准。尽管薪资基准工具无处不在，但它们很少进入公众视野，也没有被经济学家研究过。研究人员就薪酬基准对薪酬的影响提供了理论和实证证据。该项目的第二部分研究性别薪酬差距。企业对缩小员工内部的性别薪酬差距表现出越来越大的兴趣。这种兴趣创造了一个围绕薪酬公平咨询的完整行业。研究人员研究了薪酬公平工具。这是一种数据分析工具，它使用雇主的工资单数据来识别劳动力中的性别薪酬差异。例如，雇主可能会发现，在银行柜员中，女性柜员的平均收入是男性柜员的90%。研究人员探讨了使用这些薪酬平等工具是否真的有助于缩小性别薪酬差距。该项目旨在为劳动经济学领域做出重大贡献。此外，调查结果可能对与工资基准、薪酬透明度和公平薪酬有关的政策产生深远影响。研究人员采用理论模型来理解企业如何使用薪酬基准和薪酬公平工具。这些模型预测了这些工具对员工薪酬和离职率等结果的影响。然后，研究人员用多种数据来源和识别策略来测试这些假设。为了记录薪酬做法和政策，调查人员与人力资源主管一起设计并进行了量身定制的调查。最重要的是，研究人员利用了与美国最大的工资处理公司的合作关系。这家公司提供最先进的薪资基准和薪酬公平工具。研究人员利用了丰富的管理数据来源。例如，他们使用跨越数百万员工和数千家公司的工资单记录。此外，他们还利用雇主在日常运营中如何使用基准和薪酬公平工具的数据。为了确定这些工具的因果关系，研究人员利用了多种识别策略。研究人员利用准实验来源的变化。例如，他们使用差异中的差异设计，利用不同公司之间交错采用的工具。简而言之，该策略比较了公司采用该工具之前和之后的结果演变。除了非实验数据外，研究人员还设计并进行现场实验，以在工具的使用中产生外生变化。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。

Determining the right salaries is of utmost importance for companies, as salaries often represent the largest expenditure. On the one hand, paying salaries that are too high can be wasteful. On the other hand, paying salaries that are too low can make it difficult to attract, motivate and retain employees. How do employers find the sweet spot? This project seeks to enhance our understanding of how companies determine their employees' compensation. The project has two parts. The first part studies the use of aggregated market data to set salaries, a practice that is known as salary benchmarking. Despite their pervasiveness, salary benchmarking tools rarely make their way into public view and have not been studied by economists. The researchers provide theoretical and empirical evidence on the effects of salary benchmarking on compensation. The second part of the project studies the gender pay gap. Companies are showing growing interest in narrowing the gender pay gaps within their workforce. This interest has created a whole industry around pay-equity consulting. The researchers study pay equity tools. These are data analytics tools that use an employer’s payroll data to identify gender pay disparities in the workforce. For instance, an employer might discover that among its bank tellers, female tellers on average earn 90% of what their male counterparts make. The researchers explore whether the use of these pay equity tools indeed helps in reducing the gender pay gap. The project seeks to make significant contributions to the field of labor economics. Moreover, the findings can have far-reaching consequences for policies related to salary benchmarking, pay transparency and fair pay.The researchers employ theoretical models to understand how firms use salary benchmarking and pay equity tools. These models make predictions about the impact of these tools on outcomes such as employee pay and turnover. The researchers then test those hypotheses with multiple sources of data and identification strategies. To document pay practices and policies, the investigators design and conduct tailored surveys with Human Resources executives. Most importantly, the researchers capitalize on a partnership with the largest payroll processing company in the United States. This company offers state-of-the-art salary benchmarking and pay equity tools. The researchers leverage rich sources of administrative data. For instance, they use payroll records spanning millions of employees and thousands of firms. Additionally, they leverage data on how employers utilize benchmarking and pay equity tools in their daily operations. To identify the causal effects of these tools, the researchers exploit multiple identification strategies. The investigators leverage quasi-experimental sources of variation. For instance, they use a differences-in-differences design that leverages the staggered adoption of the tools across different companies. In a nutshell, this strategy compares the evolution of the outcomes right before and right after the company adopts the tool. In addition to the non-experimental data, the investigators design and conduct field experiments to create exogenous variation in the usage of the tools.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.