Doctoral Dissertation Research in Economics: What's in a Name? The Effect of Changing Definitions of "Employer" on Worker Outcomes

经济学博士论文研究：名字有什么含义？

• 批准号: 1949415
• 负责人: W. Bentley MacLeod
• 金额: $ 2.92万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-01 至 2022-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1949415&HistoricalAwards=false
• 关键词: Doctoral; Dissertation; Research; Economics; What

This doctoral dissertation research in economics (DDRIE) project will use machine language and modern economic methods to investigate court decisions on who is an “employee” affect worker’s labor outcomes, such as employment tenure, wages, benefits, and unionization. There has been a large increase in “contingent workers” – workers who are on temporary contracts, contracted out, or are independent contractors. These workers have weaker protections in terms of compensation tenure and collective action. In spite of these developments in the labor market, there is relatively little research on the causes of the increase in the proportion of temporary work or independent contracting in the labor market. A major determinant of the increase in the growth of these “contingent workers” is the legal definition of employee as defined in court rulings. This study will use machine learning and random assignment of judges to specific cases to determine whether changing the legal definition of employee affect the growth of the size of “contingent workers” and how this impacts workers’ wages, tenure loss, unionization rates, and inequality. The results of this DDRIE project will shed light on how economist can combine machine learning tools and economic theory to investigate important labor market and other social issues. The results of the research project will also provide guidance on how to develop policies to improve the functioning of labor markets as well as how to increase the living standards of workers, especially those at the lower end of the earning spectrum.This research project will estimate the causal effects of changing legal definition of an employee. It will collect data on all cases in which a court made a decision about whether workers could be considered “contractors”, and use machine learning to analyze case text to determine the direction of decision and the set of affected workers. It exploits the random assignment of judges to specific cases to obtain exogenous variation for identification. Judges vary systematically in their decisions on employment definitions, and that variation can be predicted by judge characteristic, such as age, race, political preferences, and education. Building on work using machine learning and variable selection, we train a regularized regression model to predict case decision from characteristics of judges assigned to a case. The cross-validated model produces an exogenous instrument for use in a two-stage least squares regression. This approach is leveraged to analyze the causal effects of changing definitions of employee on worker outcomes. The project then investigates whether a legal opinion that workers can be considered contractors affects unionization rates, remuneration, and inequality. This project directly examines the impact of legal precedent on worker outcomes and contracting rates, as well as the role of legal institutions in determining contracting outcomes, and its effect on employment and wages. The results of the research project will provide guidance on policies to improve the functioning of the labor market as well as how to increase the living standards of workers at the lower end of the earning spectrum.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.

这个经济学博士论文研究(DDRIE)项目将使用机器语言和现代经济学方法来调查法院关于谁是“雇员”的裁决影响工人的劳动结果，如雇佣期限、工资、福利和工会。“临时工”--签订临时合同、外包或独立承包的工人--大幅增加。这些工人在补偿、保有权和集体行动方面受到的保护较弱。尽管劳动力市场出现了这些发展，但关于劳动力市场中临时工或独立承包比例上升的原因的研究相对较少。这些“临时工”增长的一个主要决定因素是法院裁决中对雇员的法律定义。这项研究将使用机器学习和对特定案件的随机分配法官来确定改变员工的法律定义是否会影响“临时工”的规模增长，以及这如何影响工人的工资、保有期损失、工会率和不平等。这个DDRIE项目的结果将揭示经济学家如何将机器学习工具和经济理论结合起来，研究重要的劳动力市场和其他社会问题。该研究项目的结果还将为如何制定政策以改善劳动力市场的功能以及如何提高工人的生活水平提供指导，特别是那些收入谱中较低端的工人。该研究项目将评估改变员工法律定义的因果影响。它将收集所有案件的数据，在这些案件中，法院就工人是否可以被视为“承包商”做出裁决，并使用机器学习分析案件文本，以确定裁决的方向和受影响的工人集。它利用法官对特定案件的随机分配来获得用于识别的外生变异。法官在就业定义上的决定有系统地不同，这种差异可以通过法官的特征来预测，如年龄、种族、政治偏好和教育。在机器学习和变量选择工作的基础上，我们训练了一个正则化回归模型，以根据分配到案件的法官的特征来预测案件判决。交叉验证的模型产生了一个外生工具，用于两阶段最小二乘回归。这一方法被用来分析改变员工定义对员工结果的因果影响。然后，该项目调查工人可以被视为承包商的法律意见是否会影响工会率、薪酬和不平等。该项目直接审查法律先例对工人结果和合同率的影响，以及法律机构在确定合同结果方面的作用及其对就业和工资的影响。该研究项目的结果将为改善劳动力市场功能的政策以及如何提高收入谱低端工人的生活水平提供指导。这一奖项反映了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。