Misaligned CARES Act: Overcrowding, Selective Verification and Unintended Racial Consequences

54 Pages Posted: 28 Sep 2020 Last revised: 16 Oct 2020

See all articles by Arka Bandyopadhyay

Arka Bandyopadhyay

City University of NY, Baruch College, Zicklin School of Business

Date Written: October 2, 2020

Abstract

I utilize a novel data on proprietary servicer comments to investigate strategic borrower responses to the mortgage forbearance program contained in the Coronavirus Aid, Relief and Economic Security Act. The unique text data allows me to corroborate the selective verification of unemployment status ((financial hardship) by the servicer. I also discern unintended distributional implications for African American and Hispanic borrowers with performing loans to reduce ex-post risk, although the servicer does not have the race information about the borrowers. The soft information obtained from servicer call transcripts helps me identify the reasons for these communications and the incentive compatibility between the borrower and the servicer. My finding sheds light on the poor-targeting of Government programs, like FHA, VA, USDA, etc., during exacerbated income shocks, such as, COVID-19.

Keywords: Strategic Behavior, Forbearance, Machine Learning, NLP, Race, COVID-19, CARES Act

JEL Classification: R38, R28, K32, J15, H12, H81, G01, E61, E63, D80, D61, D12, D18

Suggested Citation

Bandyopadhyay, Arka, Misaligned CARES Act: Overcrowding, Selective Verification and Unintended Racial Consequences (October 2, 2020). Available at SSRN: https://ssrn.com/abstract=3701130 or http://dx.doi.org/10.2139/ssrn.3701130

Arka Bandyopadhyay (Contact Author)

City University of NY, Baruch College, Zicklin School of Business ( email )

United States

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