Using Predictive Analytics for Public Policy: The Case for Lost Work due to the COVID-19

Kent Jason Go Cheng
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Kent Jason Go Cheng: Syracuse University

No e5z73, SocArXiv from Center for Open Science

Abstract: In this brief research article, I demonstrate how predictive analytics or machine learning can be used to predict outcomes that are of interest in public policy. I developed a predictive model that determined who were not able to work during the past four weeks because the COVID-19 pandemic led their employer to close or lose business. I used the Current Population Survey (CPS) collected from May to November 2020 (N=352,278). Predictive models considered were logistic regression and ensemble-based methods (bagging of regression trees, random forests, and boosted regression trees). Predictors included (1) individual-, (2) family-, (3) and community or societal- level factors. To validate the models, I used the random training test splits with equal allocation of samples for the training and testing data. The random forest with the full set of predictors and number of splits set to the square root of the number of predictors yielded the lowest testing error rate. Predictive analytics that seek to forecast the inability to work due to the pandemic can be used for automated means-testing to determine who gets aid like unemployment benefits or food stamps.

Date: 2021-01-03
New Economics Papers: this item is included in nep-big and nep-for
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Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:e5z73

DOI: 10.31219/osf.io/e5z73

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