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

本博士论文经济学研究（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”的决定影响工人的劳动成果，如就业期限，工资，福利和工会化。 “临时工”-临时合同工、外包工或独立承包工-大量增加。这些工人在补偿、任期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，但对劳动力市场中临时工作或独立合同比例增加的原因的研究相对较少。 这些“临时工”人数增加的一个主要决定因素是法院裁决中对雇员的法律的定义。这项研究将使用机器学习和随机分配法官到特定案件，以确定改变雇员的法律的定义是否会影响“临时工”规模的增长，以及这如何影响工人的工资，任期损失，工会化率和不平等。 这个DDRIE项目的结果将揭示经济学家如何将联合收割机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。 研究成果还将为改善劳动力市场功能的政策制定以及提高劳动者特别是收入较低的劳动者的生活水平提供指导。本研究项目将对劳动者法律的定义变化的因果影响进行评估。 它将收集法院决定工人是否可以被视为“承包商”的所有案件的数据，并使用机器学习来分析案件文本，以确定决定的方向和受影响的工人。它利用随机分配的法官的具体情况下，获得识别的外生变化。 法官在他们的就业定义的决定系统地变化，变化可以通过法官的特征，如年龄，种族，政治偏好和教育预测。基于使用机器学习和变量选择的工作，我们训练了一个正则化回归模型，以根据分配给案件的法官的特征来预测案件判决。交叉验证模型产生用于两阶段最小二乘回归的外生工具。 这种方法被用来分析改变员工的定义对工人结果的因果影响。 该项目然后调查是否法律的意见，工人可以被视为承包商影响工会率，薪酬和不平等。该项目直接研究了法律的先例对工人结果和合同率的影响，以及法律的机构在确定合同结果中的作用，及其对就业和工资的影响。 该研究项目的成果将为改善劳动力市场的功能以及如何提高收入较低的劳动者的生活水平提供政策指导。该奖项反映了NSF的法定使命，通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.