Doctoral Dissertation Research in Economics: Investigating the Impact of the 'Norm to Work' on Worker Power and Labor Market Outcomes

本项目研究有关就业的社会规范对劳动力市场的影响。媒体的叙述和现有的研究表明，这些规范是工人行为的重要驱动力，也可能是就业和工资等宏观经济结果的重要驱动力。最近关于新冠肺炎疫情后“大辞职”的报道呼应了这一观点，声称对工作重要性的集体重新评估已经将权力转移到了工人身上。特别是，这个项目探讨了这样一种观点，即“工作规范”-工作年龄的成年人应该被雇用的社会期望-可能会使工人害怕失业，从而愿意接受低质量的工作或更低的工资，从而削弱工人的权力。为了实证检验这一假设，研究人员基于社交媒体文本数据开发了一种新的工作规范测量方法。从政策的角度来看，该项目揭示了促进健康的职业道德和侮辱失业者之间的微妙平衡。在某种程度上，工作规范确实削弱了工人的权力，政治和媒体叙事对失业的污名化有潜在的二阶效应（例如，里根总统被广泛讨论的“福利女王”言论）。类似地，默许工作规范的政策，如“工作福利”计划或“工作权”法律，可能会对更广泛的就业人群产生意想不到的后果。此外，该项目还提供了一个方法路线图，用于使用目前在线提供的大量非结构化文本数据构建其他社会规范的测量方法。研究人员首先使用应用于非结构化文本数据的机器学习方法构建一个新的工作规范测量方法，研究工作规范对工人力量和劳动力市场结果的影响。然后，他们使用来自人口普查调查和经济分析局的经济数据，实证分析了这一措施与各种结果之间的关系，包括劳动力参与率，就业率，工资和劳动收入份额。工作规范的主要衡量标准是基于大约2000万条推文的样本，这些推文是通过与失业和工作态度相关的关键字搜索发现的。最初，获得大约100，000条标记推文的数据集，其用作训练和验证数据集以校准机器学习模型。这个经过训练的模型随后被用来标记样本中剩余的推文，这些推文被聚合成过去十年在美国工作的规范的地理粒度度量。使用这个指标，研究人员记录了城市年度水平的工作规范和劳动力市场结果之间的关系，控制了时间和地理特征。为了更好地理解这种关系中的因果关系方向，研究人员还使用更先进的面板数据计量经济学进行分析。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估而被认为值得支持。

This project investigates the labor market implications of social norms regarding employment. Media narratives and existing research suggest that such norms are important drivers of worker behavior and also, potentially, macroeconomic outcomes like employment and wages. Recent coverage of the “great resignation” post-Covid has echoed this idea, claiming that a collective re-evaluation of the importance of work has shifted power toward workers. In particular, this project explores the idea that the “norm to work”—the social expectation that working-age adults should be employed—may erode worker power by making workers fearful of unemployment, and thus willing to accept lower-quality jobs or worse wages. To empirically test this hypothesis, the researchers develop a novel measure of work norms based on social media text data. From a policy perspective, this project sheds light on the delicate balance between promoting a healthy work ethic and stigmatizing the unemployed. To the extent that work norms do in fact diminish worker power, there are potential second-order effects of political and media narratives that stigmatize unemployment (e.g., President Reagan’s widely discussed “welfare queen” rhetoric). Similarly, policies that tacitly enshrine work norms, such as “workfare” programs or “right to work” laws, may have unintended consequences for the broader employed populace. Moreover, this project provides a methodological roadmap for constructing measures of other social norms using the vast trove of unstructured text data now available online.The researchers study the effect of the norm to work on worker power and labor market outcomes by first constructing a novel measure of the norm to work using machine learning methods applied to unstructured text data. They then empirically analyze the relationship between this measure and various outcomes, including labor force participation, employment, wages, and labor’s share of income, using economic data from Census surveys and the Bureau of Economic Analysis. The primary measure of the norm to work is based on a sample of about 20 million tweets found through keyword searches related to unemployment and attitudes toward work. Initially, a dataset of roughly 100,000 labeled tweets is obtained, which serves as a training and validation dataset to calibrate a machine learning model. This trained model is subsequently used to label the remaining tweets in the sample, which are aggregated into a geographically granular measure of the norm to work in the U.S. over the past decade. Using this metric, the researchers document the relationship between the norm to work and labor market outcomes at the city-year level, controlling for temporal and geographic characteristics. In order to better understand the direction of causality in this relationship, the researchers also conduct analyses incorporating more advanced panel data econometrics.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.