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The Enduring Significance of Ethno-Racial Inequalities in Poverty in the U.S., 1993–2017

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Abstract

Ethno-racial inequality in poverty is an enduring but misunderstood problem. Most prior research relies on the flawed official poverty measure, data with underreported income, and models omitting essential predictors. Using the Current Population Survey, we adjust for benefit underreporting and estimate levels and trends in both relative and Supplemental Poverty Measure poverty rates for ethno-racial groups relative to White individuals in the U.S. from 1993 to 2017. We then focus on the five most recent years (2013–2017) and decompose Black–White, Latino–White, and Asian–White poverty gaps. We expand prior decomposition analyses by better incorporating employment and geographic context and better measuring immigration. Our findings show ethno-racial inequalities in poverty declined from 1993 to 2017 but remained large. Our estimates of relative poverty reveal that millions more Black and Latino individuals are poor than with the official measure—even after adjusting for benefit underreporting. By 2013–2017, Black and Latino individuals remain about twice as likely to be poor as White individuals. By contrast, the evidence is mixed on Asian–White differences. Decomposition results show employment explains the largest share of the Black–White gap, whereas immigration matters most for the Latino–White and Asian–White gaps. Geographical context also explains a significant portion of each racial gap and is particularly central to Asian–White gaps. Compared to prior decompositions, which would explain roughly half of the Black–White gap in poverty, our models explain more than three quarters. Beyond the novel empirical description, this study encourages structural, political, and critical race theories of poverty over behavioral explanations.

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Notes

  1. To this point, a recent controversial commentary in Society, “Poverty and Culture” by Lawrence Mead (2020) led national poverty center directors to write a public statement calling for a correction of the lack of understanding of racial inequality in poverty. See https://www.irp.wisc.edu/a-statement-from-former-directors-of-the-institute-for-research-on-poverty-about-research-on-poverty-and-race/.

  2. Specifically, we use the Urban Institute’s Transfer Income Model, version 3 (TRIM3) imputations to account for underreporting of means-tested income transfers such as Supplemental Nutrition Assistance Program (SNAP) and Social Security Insurance (SSI). We provide more details in the Methods section below. A description of the TRIM3 model is available online at http://trim3.urban.org/.

  3. The CPS ASEC refers to individuals from Latin American countries as “Hispanic.” We use the term “Latino” to signify that these individuals are of Latin American origins rather than from any Spanish-speaking country (“Hispanic”).

  4. For example, the OPM counts Old Age Survivor’s Insurance and Temporary Assistance for Needy Families (TANF) as income, but omits near cash benefits like the Supplemental Nutritional Assistance Plan (SNAP), housing subsidies, childcare vouchers, and tax credits like the Earned Income Tax Credit (EITC). By 2017, SNAP and EITC were each more than four times as large as TANF. Unfortunately, even when using more defensible relative measures, scholars tend to continue to use the OPM’s flawed definition of income (Iceland, 2019; Takei & Sakamoto, 2011; Thiede et al., 2017).

  5. We follow Golash-Boza’s (2016) definition of racism as both an ideology (i.e., the idea humans can be separated into distinct biology groups and can be hierarchically ordered) and structure (i.e., the micro- and macro-level practices that subordinate those groups believed to be inferior).

  6. While Asians have also experienced employment discrimination, this has lessened in the Post-Civil Rights era (Hilger, 2016), particularly relative to Blacks and Latinos.

  7. TRIM3 matches administrative records on TANF/SNAP caseloads across states to impute benefits back into the survey data. While the uncorrected CPS survey data misses about half of TANF/SNAP cash transfers (Meyer & Mittag, 2019), Parolin (2019a) demonstrates that the unadjusted CPS in 2015 fails to account for 47.4% of SNAP benefits, 54.6% of TANF benefits, and 11.5% of SSI benefits. In contrast, the TRIM3-adjusted CPS captures 88.7% of SNAP benefits, 85.2% of TANF benefits, and 98.1% of SSI benefits in 2015. The TRIM3 is, thus, a substantial improvement over the uncorrected CPS.

  8. Iceland (2019) includes most of these variables. However, we advantageously identify the lead earner rather than the arbitrarily identified household head. Smaller differences are that he controls for sex, only includes age and age squared, distinguishes between high school degrees and some college, and controls for household size but omits the count of over 65 members. None of these change our conclusions. Larger differences—and clear advantages of our measures—are his omission of employment and measuring geographical context only with dummies for four regions and a binary measure of residing in a metro area. Despite arguing family structure is pivotal for the Black–White gap, he only measures whether a household is a female-headed family or “other family” (reference = married couple). Despite arguing immigration is pivotal for the Latino–White gap, he omits citizenship status, and only measures nativity.

  9. In analyses not shown, we experimented with estimating the decompositions on three-year rather than five-year moving averages across the entire period. We found the results were sensitive to business cycles (e.g., unsurprisingly, employment became less important when only including good economic years), hence we used a broader time window.

  10. For instance, Appendix Table 7 in 2017 shows with the OPM, Black individuals are 249% more likely to be poor than Whites individuals, Latino individuals are 215% more likely, and Asian individuals are 118% more likely.

  11. Similarly, using the OPM, Iceland (2019) finds the White-Asian difference in poverty to be too small to meaningfully decompose in his models of 1979 and 2015.

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Acknowledgements

We thank Courtney Boen, Camille Z. Charles, Chenoa Flippen, John Iceland, Wendy Roth, and Arthur Sakamoto for comments and suggestions.

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Authors and Affiliations

  1. University of Pennsylvania, 3718 Locust Walk, 218 McNeil Building, Philadelphia, PA, 19104, USA

    Regina S. Baker

  2. University of California, Riverside, Riverside, USA

    David Brady

  3. WZB Berlin Social Science Center, Berlin, Germany

    David Brady

  4. Bocconi University, Milan, Italy

    Zachary Parolin

  5. Columbia University, New York, USA

    Zachary Parolin

  6. University of Tennessee, Knoxville, USA

    Deadric T. Williams

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  1. Regina S. Baker
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Appendix

Appendix

See Figs. 3, 4

Fig. 3
figure 3

Poverty rates by race/ethnicity relative to White individuals by year (50% median). These poverty estimates include the Transfer Income Model, version 3 (TRIM3)

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Fig. 4
figure 4

Poverty rates by race/ethnicity relative to White individuals by year (SPM w/TRIM3). These poverty estimates include the Transfer Income Model, version 3 (TRIM3)

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and Tables 4, 5, 6, 7, 8, 9, 10, 11, 12, 13.

Table 4 Sample means by race/ethnicity (2013–2017)
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Table 5 Descriptive characteristics for full population (2013–2017)
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Table 6 Poverty rates by race/ethnicity and year
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Table 7 Official poverty (OPM) rates by race/ethnicity and year
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Table 8 Estimates of racial differences in black and white poverty when accounting for incarcerated population (relative poverty measure)
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Table 9 Fairlie logistic regression decomposition of differences in poverty by race (Native American vs. White), 2013–2017
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Table 10 Fairlie logistic regression decomposition of differences in poverty by race (Black vs White), 2013–2017
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Table 11 Fairlie logistic regression decomposition of differences in poverty by race (Latino vs White), 2013–2017
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Table 12 Fairlie logistic regression decomposition of differences in poverty by race (Asian vs White), 2013–2017
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Table 13 Fairlie logistic regression decomposition of differences in poverty by race (Native American vs White), 2013–2017
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Baker, R.S., Brady, D., Parolin, Z. et al. The Enduring Significance of Ethno-Racial Inequalities in Poverty in the U.S., 1993–2017. Popul Res Policy Rev 41, 1049–1083 (2022). https://doi.org/10.1007/s11113-021-09679-y

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  • DOI: https://doi.org/10.1007/s11113-021-09679-y

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