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

点击翻译按钮获取中文摘要

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.