Blog & News
State Health Compare Users Can Now Explore Unemployment by Race/EthnicityNovember 18th, 2020:
SHADAC has updated our State Health Compare online data tool to provide estimates of unemployment according to race and ethnicity. Previously, State Health Compare users could analyze unemployment nationwide and at the state level for years 2000 to 2019, but subgroup analyses were not possible. Now, users can explore State Health Compare’s unemployment estimates, which come from the federal Bureau of Labor Statistics, by four racial/ethnic breakdowns: Hispanic/Latino, African-American/Black, Asian and White.
Why This Change Matters
Racial/ethnic breakdowns provide an important lens for analyzing unemployment as a social determinant of health, with unemployment often varying widely across racial/ethnic categories in a way that can be masked by a consideration of population-wide unemployment numbers. We see this scenario at the national level, where the overall unemployment rate in 2019 was 3.7%, but an examination of unemployment for racial/ethnic subgroups reveals 2.7% unemployment among Asian Americans, 3.3% unemployment among Whites, 4.3% unemployment among Hispanics/Latinos, and 6.1% unemployment among African-Americans/Blacks.
Minnesota provides a state case that parallels the national picture on this point. If we look at statewide unemployment in 2019, we find that Minnesota’s unemployment rate was 3.2%, which is 0.5 percentage points below the national rate of 3.7%. However, if we look at the numbers sorted by race/ethnicity, a different picture emerges: The rate for Asians in Minnesota is 2.4%, Whites are at 3.0%, Hispanics/Latinos are at 5.0%, and African-Americans/Blacks are at 5.5%. Not only do Hispanics/Latinos and African-Americans/Blacks have higher unemployment rates than Whites and Hispanics/Latinos, but their rates are also above the broader Minnesota state average, as well as the national average. Thus, what looks at first glance like a state doing well on unemployment turns out to be a state with major disparities in unemployment for certain racial/ethnic subgroups.
An examination of trends in unemployment over time confirms that the 2019 data for Minnesota are not an anomaly. Though unemployment numbers for Asians in Minnesota were less stable from year to year, White Minnesotans consistently had unemployment rates below those of Hispanic/Latino Minnesotans each year during the 10-year period from 2009 to 2019, and African-American and Black Minnesotans consistently had the highest unemployment rates of any analyzed racial/ethnic group during this time (Figure 1.)
The Importance of Considering Individual States
Additionally, it is worth exploring both overall and racial/ethnic subgroup numbers within each individual state, as there is considerable variation both across states and between states and the nation. Washington state, for example, saw an overall unemployment rate of 4.3%, or 0.6 percentage points above the national rate. And among racial/ethnic subgroups, Washington’s numbers followed a different pattern than that seen nationally, with African-Americans/Blacks having the second lowest unemployment rate (4.2%), Whites having the third lowest rate (4.3%), and Hispanics/Latinos having the highest rate (5.7%).
Nevada is another state that had 2019 unemployment numbers that differed from the national story when looking at racial/ethnic breakdowns. Like Washington, Nevada’s state’s overall unemployment rate of 3.9% was above the national rate. However, unemployment in Nevada was second lowest among Hispanics/Latinos (3.8%), third lowest among Whites (3.9%), and highest among African-Americans/Blacks (5.9%).
Taking these state variations into consideration, it’s clear that efforts to analyze and/or address disparities in unemployment by race/ethnicity would potentially look different in Washington and Nevada, or even going to back our example in Minnesota, based on their respective divergences in overall and subgroup numbers.
Discussion: Unemployment as a Social Determinant of Health
Unemployment has important implications for health and health care, as employment affects access to stable housing, food, and health insurance coverage and care. At the same time, unemployment can also have a number of direct negative health consequences, including depression, anxiety, and stress-related illness such as high blood pressure, stroke, heart attack, heart disease, and arthritis.1-7
An examination of unemployment that takes into consideration important nuances in unemployment numbers among subgroups reveals the need for critical, targeted attention to unemployment, even in states where unemployment appears to be trending well overall. Consideration of interstate variation of unemployment by race/ethnicity reveals that policy levers to address unemployment that take race/ethnicity into consideration will require state-specific modifications as well.
Explore the Data
Visit State Health Compare to learn more about unemployment by race/ethnicity within and across the states.
About the Estimates
State Health Compare’s estimates of unemployment are produced using data from the federal Bureau of Labor Statistics and represent the percent of the civilian labor force (age 16 and older) that was unemployed. State refers to place of residence.
1 Avendano, M., Berkman, L. F. (2014). Labor markets, employment policies, and health. In L. F. Berkman, I. Kawachi, & M. Glymour (Eds.), Social Epidemiology (2nd ed., pp. 182-233). Open University Press.
2 Murray, L. R. (2003). Sick and tired of being sick and tired: scientific evidence, methods, and research implications for racial and ethnic disparities in occupational health. Am J Public Health, 93(2), 221-226.
3 Kasl, S. V., Cobb, S. (1970). Blood pressure changes in men undergoing job loss: a preliminary report. Psychosom Med, 32(1), 19-38.
4 Frumkin, H. E., Walker, D., Friedman-Jiménez, G. (1999). Minority workers and communities. Occup Med, 14(3), 495-517.
5 James, S. A., LaCroix, A. Z., Kleinbau, D. G., Strogatz, D. S. (1984). John Henryism and blood pressure differences among black men. II. The role of occupational stressors. J Behav Med, 7(3), 259-275.
6 Robert Wood Johnson Foundation. (2013). How does employment—or unemployment—affect health? Health policy snapshot. Available from http://www.rwjf.org/content/dam/farm/reports/issue_briefs/2013/rwjf403360
7 U.S. Department of Labor, Bureau of Labor Statistics. (2012). A profile of the working poor, 2010. News release. Available from https://www.bls.gov/opub/reports/working-poor/archive/workingpoor_2010.pdf