Racial differences in the relationship between the receipt of informal financial support and social insurance

Abstract

In this paper, we examine the relationship between the reliance on informal financial support and social insurance programs such as unemployment insurance to meet financial hardships imposed during the economic downturn associated with COVID-19. We use the U.S. Census Bureau’s Household Pulse Survey to compare the likelihood of receiving informal financial support from family and friends for households that did or did not receive social insurance controlling for observable household characteristics. We pay special attention to differences by race/ethnicity and by homeownership – a proxy for wealth. Our results suggest that (1) some types of social insurance receipt are a weak substitute for informal financial support, (2) the substitution between informal financial support and social insurance receipt is stronger among White households than households of color, and (3) wealth is a more consistent buffer against financial hardship than social insurance receipt.

KEYWORDS:

• Race and ethnicity
• social insurance
• informal financial support
• wealth gap

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data that support the findings of this study are openly available at the U.S. Census Bureau Household Pulse Survey Data: https://www.census.gov/programs-surveys/household-pulse-survey/datasets.html. Replication STATA do files are available on Harvard Dataverse at https://doi.org/10.7910/DVN/CIDZ6T

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Notes

1 The characteristics of respondents with missing observations typically do not vary from the overall sample.

2 We estimated the models based on the sample without imputed observations as well. Our regression estimates are not materially different from the one based on the sample with imputed observations.

3 The conclusions hold if we use other financial insecurity measures such as food insufficiency or being behind on rent or mortgage payments.

4 The HPS is a household survey, but all statistics are reported using personal weight as is the case for public summary data tables based on U.S. Census Bureau (2022). We use the term people and households interchangeably in this paper. The conclusions do not change if we use household weights instead of personal weights.

5 Authors’ calculations based on U.S. Census Bureau (2022).

6 All calculations in this paragraph based on Fed (2022). We combine data for 2020 and 2021 to have sufficiently large sample sizes by race and ethnicity. The combination of years of data does not impact our conclusions.

7 We do not report the average share of people receiving EIP payments since that number is meaningless in repeated cross sections. Each household only received a one-time payment in the spring of 2021, unlike ongoing CTC or UI payments. A large share of households received EIP checks early in the spring, while some taxpayers waited well into the spring to get theirs. Taking an average across all recipients recorded in the HPS would indicate an artificially low share of people with such checks.

8 Figure A1 in the appendix summarizes EIP receipt by HPS week, race and ethnicity. The share of Black and Latino recipients is generally larger than the share of White recipients across all weeks.

9 Calculations based on the HPS show that 27.1 percent of homeowners saved their EIP checks, while 19.3 percent of renters did. Also, 33.7 percent of homeowners saved their CTC checks, while 18.2 percent of renters did. Authors calculations based on Census (2022).

10 Calculations based on Fed (2022). Correlations with other measures of access to liquid savings show similarly large gaps between renters and homeowners.

11 In all instances, the parameter estimates for the other correlates are either statistically insignificant or have the expected sign.

12 Combined effects are the exponentiated odds ratios of the sum of the two parameters or the product of the two odds ratios.

13 This difference holds for all months in the first half of 2021, when data for EIP receipt are available.

14 Authors’ calculations based on Census (2022).

15 Authors’ calculations based on Census (2022)

16 Authors’ calculations based on Census (2022).

Additional information

Notes on contributors

Dania V. Francis

Dania V. Francis is an associate professor of economics at University of Massachusetts Boston. Her research interests include examining racial and socioeconomic disparities in education, wealth accumulation, and labor markets.

Christian E. Weller

Christian E. Weller is a professor of public policy at the University of Massachusetts Boston and a senior fellow at the Center for American Progress. He specializes in retirement income security, wealth inequality and economic policy.

Emek Karakilic

Emek Karakılıç is a Ph.D. student in public policy at the University of Massachusetts Boston. His research focuses on precarious work, wage bargaining, and wealth inequality in the United States.

Maryam Salihu

Maryam Salihu is a doctoral student at American University (Washington, DC). Her research lies at the intersection of development and labor economics.