About

The Unemployment Gap measures the ratio between the unemployment rate of individuals identifying as Black versus the unemployment rate of individuals identifying as White in a county. Individuals from both Hispanic and non-Hispanic ethnicities are included. According to the U.S. Bureau of Labor Statistics (BLS), in the third quarter of 2024, the unemployment rate was 3.8% for White individuals and 6.2% for Black or African American individuals, highlighting a significant employment disparity between these groups ¹.

Why is the Unemployment Gap important to the Structural Racism and Discrimination (SRD) Index?

This gap highlights inequity in job opportunities and the labor market between Black vs White communities rooted in a complex interplay of historical and ongoing factors. These factors are systemic racism, discrimination, and disparities in access to resources like education and social networks ^2,3,4. These disparities result in lower earnings, higher unemployment rates, and reduced opportunities for Black workers. The legacy of slavery, Jim Crow laws, segregation, and ongoing unequal access to education, job training, and professional networks has led to persistent disparities in employment status ^5,6. This measure is crucial for understanding the persistent income and wealth disparities that negatively shape the quality of life and access to resources. Addressing the unemployment gap is essential for promoting income equity and mitigating racial discrimination in the labor market.

What is the expected relation to Structural Racism and Discrimination?

A higher value of the Unemployment Gap between the unemployment rates of Black and White individuals contributes to the higher value or score of the SRD Index.

How is the Unemployment Gap calculated?

Data Source

We obtained data from the IPUMS National Historical Geographic Information System (NHGIS) ⁷. 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 civilian population 16 years and over in labor force	1990 | 2000 | 2010 | 2020	Number
Total White alone civilian population 16 years and over in labor force	1990 | 2000 | 2010 | 2020	Number
Black or African American alone unemployed civilian population 16 years and over in labor force	1990 | 2000 | 2010 | 2020	Number
White alone unemployed civilian population 16 years and over in labor force	1990 | 2000 | 2010 | 2020	Number

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

Methodology

We calculated the Unemployment Gap using a ratio formula:
$$
RPrBlWhUnemp = \frac{PrBlUnemp}{PrWhUnemp}
$$

Where:
RPrBlWhUnemp: Ratio of proportions of (Black or African American alone to White alone) unemployed civilian population 16 years and over in labor force
PrBlUnemp = BlUnemp/BlCLF: Proportion of unemployed Black individuals among Black civilian labor force
PrWhUnemp = WhUnemp/WhCLF: Proportion of unemployed White individuals among White civilian labor force

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” ⁸. 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 ⁹. 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. U.S. Bureau of Labor Statistics. (n.d.). Labor Force Statistics from the Current Population Survey. Retrieved December 2, 2024.

2. Bertrand, M., & Mullainathan, S. (2014). Are Emily and Greg More Employable Than Lakisha and Jamal? A Field Experiment on Labor Market Discrimination. American Economic Review, 104(4), 991-1013.

3. Pager, D., & Shepherd, H. (2008). The Sociology of Discrimination: Racial Discrimination in Employment, Housing, Credit, and Consumer Markets. Annual Review of Sociology, 34, 181-209.

4. Yang, Jenny R., and Jane Liu. 2021. Strengthening Accountability for Discrimination: Confronting Fundamental Power Imbalances in the Employment Relationship. Economic Policy Institute.

5. Rydgren, J. (2004). Mechanisms of exclusion: ethnic discrimination in the Swedish labour market. Journal of Ethnic and Migration studies, 30(4), 697-716.

6. Hamilton, D., & Darity, W. A. (2017). The political economy of education, financial literacy, and the racial wealth gap.

7. 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.

8. Tobler, W. R. (1970). A Computer Movie Simulating Urban Growth in the Detroit Region. Economic Geography, 46(sup1), 234–240.

9. SciPy Developers. (n.d.). scipy.spatial.KDTree. SciPy v1.10.1 Manual. Retrieved November 19, 2024, from https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.KDTree.html