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

这个项目调查了有关就业的社会规范对劳动力市场的影响。媒体叙述和现有研究表明，这些规范是工人行为的重要驱动因素，也可能是就业和工资等宏观经济结果的重要驱动因素。最近关于后Covid时代“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”--即社会对劳动适龄成年人应该被雇用的期望--可能会让工人害怕失业，从而愿意接受质量较低的工作或更差的工资，从而削弱工人的权力。为了实证检验这一假设，研究人员开发了一种基于社交媒体文本数据的新工作规范衡量标准。从政策的角度来看，这个项目揭示了促进健康的职业道德和污蔑失业者之间的微妙平衡。在一定程度上，工作规范实际上确实削弱了工人的权力，政治和媒体叙述对失业的污名存在潜在的二阶效应(例如，里根总统被广泛讨论的“福利女王”言论)。同样，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民众产生意想不到的后果。此外，该项目提供了一个方法论路线图，用于使用现在在线可用的大量非结构化文本数据来构建其他社会规范的衡量标准。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，构建了一种新的规范工作衡量标准，从而研究了规范对劳动力权力和劳动力市场结果的影响。然后，他们使用人口普查调查和经济分析局的经济数据，对这一衡量标准与各种结果之间的关系进行了实证分析，包括劳动力参与率、就业、工资和劳动力在收入中的份额。衡量工作标准的主要标准是通过关键字搜索发现的约2000万条推文样本，这些推文与失业和对工作的态度有关。最初，获得大约100,000条标签推文的数据集，其用作校准机器学习模型的训练和验证数据集。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运行的规范的地理粒度衡量标准。使用这一指标，研究人员在控制时间和地理特征的情况下，记录了城市-年份层面的工作规范和劳动力市场结果之间的关系。为了更好地理解这种关系中因果关系的方向，研究人员还进行了纳入更先进的面板数据计量经济学的分析。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。

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.