About

The Below-poverty Gap measures the ratio between the rate of individual identifying as Black living below the poverty level versus the rate of individual identifying as White living below the poverty level in a county. Individual from both Hispanic and non-Hispanic ethnicities are included. According to the U.S. Census Bureau, in 2022, 17.1% of Black individuals were living below the poverty line compared to 8.6% of White individuals, highlighting a significant economic disparity between these groups ¹.

Why is the Below-poverty Gap important to the Structural Racism and Discrimination (SRD) Index?

This gap highlights persistent inequity in income between Black and White individuals resulting in multigenerational poverty for the Black families in the US. Black families experience higher rates of poverty, less upward mobility, and more downward mobility ². These disparities are not isolated outcomes but manifestations of entrenched discriminatory policies and laws that span across social and economic domains ^3,4,5. The below-poverty gap exemplifies this intersectionality, where racialized economic hardship is produced and sustained through institutional practices across domains such as education, housing, employment, and healthcare ⁶. Addressing the below-poverty gap thus necessitates recognition of racism as a foundational and enduring structural system. The below-poverty gap serves as a measurable and meaningful reflection of this system, emphasizing the need for comprehensive public health and policy responses to dismantle racial inequities ⁷.

What is the expected relation to Structural Racism and Discrimination?

A higher value of the Below-poverty Gap between the percentage of Black and White individuals living below the poverty line contributes to the higher value or score of the SRD Index.

How is the Below-poverty Gap calculated?

Data Source

We obtained data from the IPUMS National Historical Geographic Information System (NHGIS) ⁸. The data is publicly available.

Data

We used the following four variables at the county level.

Variables*	Year	Unit
Total Black or African American alone population for whom poverty status is determined	1990 | 2000 | 2010 | 2020	Number
Total White alone population for whom poverty status is determined	1990 | 2000 | 2010 | 2020	Number
Black or African American alone population with income below poverty level (in the past 12 months)	1990 | 2000 | 2010 | 2020	Number
White alone population with income below poverty level (in the past 12 months)	1990 | 2000 | 2010 | 2020	Number

* Individuals from both Hispanic and non-Hispanic ethnicities are included.

Methodology

We calculated the Below-poverty Gap using a ratio formula:
$$
RPrBlWhBePov = \frac{PrBlBePov}{PrWhBePov}
$$

Where:
RPrBlWhBePov: Ratio of proportions of Black or African American alone to White alone population with income below poverty level (in the past 12 months)
PrBlBePov = BlBePov/BlPopPov: Proportion of Black or African American alone population with income below poverty level (in the past 12 months)
PrWhBePov = WhBePov/WhPopPov: Proportion of White alone population with income below poverty level (in the past 12 months)

Missing Data

We replaced missing values by using functions that were rooted in Tobler’s First Law of Geography, i.e., “everything is related to everything else, but near things are more related than distant things” ⁹. This law also informs the concept of Spatial Autocorrelation (SA). The indicators were highly spatially autocorrelated and the missing values were imputed using the median value of the eight nearest neighbors. The eight nearest neighbors were identified using the KDTree algorithm in Python’s scipy.spatial module ¹⁰. After imputation of missing data, we have 13 counties with no data in 2020, and none for the 2010, 2000 and 1990 years.

References

1. Emily A. Shrider and John Creamer, U.S. Census Bureau, Current Population Reports, P60-280, Poverty in the United States: 2022, U.S. Government Publishing Office, Washington, DC, September 2023.

2. Trostel, S., & Reeves, R. V. (2024, April 17). Long shadows: The Black-White gap in multigenerational poverty. Brookings Institution. https://www.brookings.edu/articles/long-shadows-the-black-white-gap-in-multigenerational-poverty/

3. Chetty, R., Hendren, N., Jones, M. R., & Porter, S. R. (2020). Race and economic opportunity in the United States: An intergenerational perspective. The Quarterly Journal of Economics, 135(2), 711-783.

4. Winship, S., Reeves, R. V., & Guyot, K. (2018). The inheritance of black poverty: It’s all about the men. The Brookings Institute, The Center on Children and Families. The Brookings Economic Studies Program. Washington, DC [Google Scholar].

5. Davis, J., & Mazumder, B. (2018). Racial and ethnic differences in the geography of intergenerational mobility. Available at SSRN 3138979.

6. Tourse, R. W., Hamilton-Mason, J., Wewiorski, N. J., Tourse, R. W., Hamilton-Mason, J., & Wewiorski, N. J. (2018). Intersectionality: The Linkage of Racism with Other Forms of Discrimination. Systemic Racism in the United States: Scaffolding as Social Construction, 101-114.

7. Cobbinah, S. S., & Lewis, J. (2018). Racism & Health: A public health perspective on racial discrimination. Journal of Evaluation in Clinical Practice, 24(5), 995-998.

8. Manson, S., Schroeder, J., Van Riper, D., Knowles, K., Kugler, T., Roberts, F., & Ruggles, S. (2024). IPUMS National Historical Geographic Information System: Version 19.0 [Dataset]. IPUMS.

9. Tobler, W. R. (1970). A computer movie simulating urban growth in the Detroit region. Economic geography, 46(sup1), 234-240.

10. Virtanen, P., Gommers, R., Burovski, E., Oliphant, T. E., Weckesser, W., Cournapeau, D., … & Feng, Y. (2021). scipy/scipy: SciPy 1.6. 0. Zenodo.