Doctoral Dissertation Research in Economics: The Impact of Product Market Competition on the Labor Market

该奖项将资助一篇博士论文，该论文使用独特的数据来衡量市场高度集中对工人的影响，包括更好地了解公司销售产品所面临的竞争与其寻求雇用的工人所面临的竞争之间的联系。经济学理论预测，当有许多雇主提供工作时，工人将获得更高的工资。然而，在一些地方，只有少数雇主。在这种集中的劳动力市场中，雇主有优势，因为工人缺乏可行的替代方案。此外，如果一个公司在其销售的产品的市场上具有支配地位，它也可能在劳动力市场上具有支配地位。这样的公司将在工人和消费者身上发挥巨大的作用。该项目将系统地调查集中度如何影响工资、就业持续时间和利润传递。这一项目的结果可帮助决策者了解旨在减少市场集中的政策的成本和效益。该项目基于2008年至2020年的三个行政数据集。第一个是逐年就业咒语的准宇宙;研究小组将使用它来系统地构建就业和工资账单集中指数。出于保密原因，第一个数据集缺乏随时间跟踪工人的能力。第二个数据集是一个雇主-雇员面板，包含有关工资、拼写持续时间、行业、职业和一些人口统计信息的信息。这个数据集允许我们多年来跟踪工人和公司。最后，第三个数据集包含公司层面的资产负债表信息（销售额、利润、库存等）。使这些数据集独一无二的是使用唯一的公司标识符合并它们的可能性。 该项目通过劳动力市场建立集中度指数，并将该指标与我们的面板数据和每家公司的资产负债表合并。结果是一个新的面板，我们可以看到工资如何根据产品和劳动力市场的集中度而变化。这个分析是项目的核心。第一步是正确定义劳动力市场。我们将依赖于传统的概念，如通勤区的职业，除了基于工人的跨公司过渡的聚类方法（应用随机块建模中的新开发技术）。然后，我们将运行劳动力回归，以记录工资（水平和增长）、集中度、利润和销售之间的模式。我们希望记录下，在更集中的市场中，工人的境况是更好还是更差。如上所述，在更集中的市场中，公司可能有更大的讨价还价能力，但它们也应该是生产力更高的公司，因此支付更高的工资。不过，这些回归并不能告诉我们市场集中于工资的因果关系。为了缓解这一点，该项目的下一步将是提出一个搜索和-匹配模型，在该模型中，离散数量的公司竞争工人，并使用我们的实证结果来校准它。然后，我们将能够对集中度对工人福利的影响进行反事实分析。该奖项反映了NSF的法定使命，并通过使用基金会的知识产权进行评估，被认为值得支持。优点和更广泛的影响审查标准。

This award will fund a doctoral dissertation that uses unique data to measure the effects of high market concentration on workers, including work to better understand the links between the competition a firm faces for the products it sells and the competition it faces for workers it seeks to employ. Economic theory predicts that workers will earn higher wages when there are many employers offering jobs. However, in some local areas there are only a few employers. In this kind of concentrated labor market, employers have an advantage because workers lack viable alternative offers. Furthermore, if a firm has a dominant position in the market for the product/s it sells, it may also be dominant in the labor market. This kind of firm would then have an outsized role on both its workers and its consumers. The project will systematically investigate how concentration affects wages, employment spell duration, and profit pass-through. The results of this project may help policymakers understand the costs and benefits of policies designed to reduce market concentration. This project is based on three administrative datasets spanning the years 2008 to 2020. The first is the quasi-universe of employment spells year-by-year; the research team will use this to systematically construct the employment and wage bill concentration indexes. This first dataset lacks the ability to track workers over time for confidentiality reasons. The second dataset is an employer-employee panel with information on wages, duration of spell, industry, occupation and some demographic information. This dataset allows us to follow workers and firms over the years. Finally, the third dataset contains balance sheet information at the firm’s level (sales, profits, inventories, etc.). What makes these datasets unique is the possibility to merge them using a unique firm identifier. The project builds the concentration index by labor market and merges that measure with our panel data and each firm’s balance sheet. The result is a new panel in which we can look at how wages vary depending on concentrations in the product and labor market. This analysis represents the core of the project. The first step will be to properly define labor markets. We will rely on traditional notions like occupation by commuting zone, in addition to a clustering approach based workers’ transitions across firms (applying newly developed techniques in stochastic block modeling). We will then run labor regressions to document patterns between wages (level and growth), concentration and profits and sales. We want to document whether workers in more concentrated markets are better or worse off. As pointed out above, in more concentrated markets, firms are likely to have more bargaining power, yet they should also be more productive firms, and therefore be paying higher wages. These regressions will not inform us on the causality of market concentration on wages though. To alleviate that point, the next step of the project will be to propose a search-and-matching model in which a discrete number of firms compete for workers and use our empirical results to calibrate it. We will then be able to run counterfactual analyses on the impact of concentration on workers’ welfare.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.