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.

这项在经济学（DDRIE）项目中的博士论文研究将使用机器语言和现代经济方法来调查法院关于谁是“员工”的决定，以影响工人的劳动成果，例如就业权限，工资，福利和工会。 “偶然工人”（签订临时合同，签约或是独立承包商）的“有特遣队工人”的增长很大。这些工人在薪酬任期和集体行动方面的保护措施较弱。尽管在劳动力市场上有这些发展，但关于临时工作比例或劳动力市场独立签约比例增加的原因的研究相对较少。对这些“偶然工人”增长的增长的主要决定因素是法院裁决中定义的员工的法律定义。这项研究将使用机器学习和将法官随机分配到特定案件中，以确定改变员工的法律定义是否会影响“有或有工人”的规模的增长，以及这如何影响工人的工资，任期损失，工会化率和不平等。该DDRIE项目的结果将阐明经济学习工具和经济理论如何调查重要的劳动力市场和其他社会问题。研究项目的结果还将提供有关如何制定政策以改善劳动力市场功能以及如何提高工人的生活水平的指导，尤其是那些在收入谱系较低端的工人的生活水平。这项研究项目将估计改变员工法律定义的灾难性影响。它将收集有关法院是否可以视为“承包商”的所有案件的数据，并使用机器学习来分析案例文本以确定决策方向和受影响的工人的集合。它探讨了法官在特定情况下的随机分配，以获得外源性变化以进行识别。法官在就业定义的决定中有系统地变化，并且可以通过法官特征（例如年龄，种族，政治偏好和教育）来预测差异。在使用机器学习和可变选择的工作基础上，我们训练一个正规的回归模型，从分配给案件的法官的特征中预测案件决策。交叉验证的模型产生了一种用于在两阶段最小二乘回归中的外源仪器。利用这种方法来分析改变员工定义对工人成果的因果影响。然后，该项目调查是否可以将工人视为承包商的法律意见会影响工会化率，薪酬和不平等。该项目直接研究了法律先例对工人成果和承包率的影响，以及法律机构在确定签约成果及其对就业和工资的影响中的作用。该研究项目的结果将为改善劳动力市场运作的政策提供指导，以及如何在收入谱系的下端提高工人的生活水平。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子和更广泛的影响审查标准来通过评估来通过评估来获得的支持。