FW-HTF-P Understanding Gig Work and its Effects on Wellbeing over the Life Course in the United States: A Machine Learning Approach

该项目通过更好地了解零工工作的性质，特别是电子平台中介的零工工作，以及这项工作如何影响工人的福祉，促进了科学的进步。零工经济尤其令人感兴趣，因为新技术比以往任何时候都更有效地促进了这类工作，而且未来可能只会增加。此外，了解此类工作安排对于充分了解监管电子中介零工工作的政策至关重要。然而，这种工作安排特别难以衡量，反过来，学习，因为它们可能不是主要就业，可能不会被记录在税务数据或行政记录中，也可能不会在关于工作的标准调查问题中准确报告。该项目将利用经济学和信息科学方法的融合来创建一个新的数据源，以研究零工工作安排和制定未来可持续研究的计划，从而克服这些不足。该项目将使用手工编码和机器学习方法，在1996-2021年收入动态小组研究(PSID)中利用现有的、但尚未公布的关于行业和职业以及雇主姓名的叙述答复的调查数据。这样的努力将能够产生一个可追溯到25年前的纵向数据集，并使用该数据集开始检查零工工作的性质如何随着电子平台的引入而发生变化，以及这些变化如何影响个人的福祉。由此产生的数据集将在一个安全的虚拟受限数据飞地公开提供。这项努力将为评价2021年PSID中包括的新的政府间工作组工作问题提供信息，并帮助在正在进行的关于PSID及以后的数据收集中制定新的调查问题，以更好地了解不断变化的工作性质。由此产生的数据集将在一个安全的虚拟受限数据飞地公开提供。它将被用来启动和计划继续调查电子中介零工工作对福祉的影响的研究。该奖项反映了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 promotes the progress of science by providing a better understanding of the nature of gig work generally and electronic-platform-mediated gig work, in particular, and how this work affects the wellbeing of workers. The gig economy is of particular interest as new technologies facilitate such work more efficiently than ever before and are likely to only increase in the future. Moreover, understanding such work arrangements is crucial for fully informing policies regulating electronically-mediated gig work. However, such work arrangements are particularly hard to measure, and in turn, study, as they may not be primary employment, may not be captured in tax data or administrative records, and may not be accurately reported in standard survey questions on work. This project will overcome such deficiencies by employing a convergence of economics- and information-science-based approaches to create a new data source to study gig work arrangements and develop a plan for sustained future research.The project will use hand coding in conjunction with machine learning methods to leverage existing, but not published, survey data on narrative responses on industry and occupation, as well as employer names, in the 1996-2021 Panel Study of Income Dynamics (PSID). Such an effort will enable the production of a longitudinal dataset extending back over 25 years and use the dataset to begin to examine how the nature of gig work has changed with the introduction of electronic platforms and how those changes have affected individuals' wellbeing. The resulting dataset will be made available publicly and in a secure virtual restricted data enclave. The effort will inform the evaluation of the new gig work questions included in the 2021 PSID and aid in the development of new survey questions in ongoing data collection, on the PSID and beyond, to better understand the changing nature of work. The resulting dataset will be made available publicly and in a secure virtual restricted data enclave. It will be used to initiate and plan continuing research investigating the effects of electronically-mediated gig work on wellbeing.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.