Economic disparities in the U.S. among racial demographic groups are large. In 2019, median net worth was $188,000 for White families, $24,100 for Black families, and $36,100 for Hispanic families.1 Those of Asian descent had somewhat lower median wealth than White families but significantly higher median wealth than Black and Hispanic families. Such economic disparities directly affect the well-being of individuals in the affected racial groups as well as society at large.

When economic statistics are disaggregated by racial group, social scientists can learn more about these disparities. In her paper, “Economic Activity by Race,” Fatima Mboup of the Philadelphia Fed describes how she developed an index of economic activity across four U.S. demographic groups. This comprehensive index, which she calls Economic Activity by Race, or EAR, can help policymakers support economic well-being across racial groups.

“We lack a timely, more encompassing measure of economic activity, such as national GDP [gross domestic product], for demographic groups,” she notes, but EAR fills that gap by providing a current pulse on economic activity in the U.S. by race. EAR is especially useful during recessions, she points out, because Black and Hispanic workers are disproportionately hard hit during an economic downturn, and they benefit less from the subsequent recovery.2

Although some surveys and data measure components of economic well-being by race, they are not “well-rounded timely measure[s] of economic activity for any given demographic group,” Mboup explains. Fortunately, the Bureau of Labor Statistics, the Federal Reserve Board of Governors, and the U.S. Census Bureau produce several economic indicators that help us measure national economic activity by race.

To create EAR, Mboup used nine of these macroeconomic indicators across the Hispanic, Asian, Black, and White populations in the U.S. for the 1980–2022 period. These indicators are average hours worked weekly, number of people employed, the unemployment rate, median weekly wages, assets, percent of the population in poverty, median annual income, net worth, and consumer expenditures.3 Because many of these macroeconomic indicators move together over the business cycle (that is, the data are “noisy”), she developed the index using an econometric technique known as the Kalman filter, which allowed her to extract the relevant component within all the data series. Although she uses a mix of monthly, quarterly, annual, and triennial data, she has been able to update her index monthly.4

When compared to real GDP, EAR in the aggregate (across all races) “is a good proxy for U.S. economic health,” Mboup writes, and thus she expects EAR for each individual race to be a good proxy for the economic conditions of that racial category. She finds that economic activity for each race fluctuated similarly across the business cycle over her sample period, but the index itself shows differences by race. Indeed, she finds sizable disparities in economic activity among racial groups, including both during and after recessions. Notably, she finds large differences in EAR for the White and Black populations, which is consistent with the findings from the literature on racial stratification.5 For example, according to the existing literature, the Black unemployment rate has generally been about twice the White unemployment rate, even when the labor market has been strong. Such disparities in income and wealth, she stresses, in turn limit access to consumption and investment by disadvantaged demographic groups.

Mboup also finds that an economic shock has a longer-lasting effect on White economic activity than it does on Black, Asian, or Hispanic economic activity. She attributes this to the larger role that assets play in White income and economic activity. These assets are more sensitive to shocks, explaining why a negative shock impacts White households’ net worth for longer, but those same assets provide a cushion during these negative shocks. What’s more, assets gain value during a business cycle upswing, outpacing even income growth, further benefiting White households more than others.

By measuring EAR, Mboup argues, “we can get a timelier sense of what kind of policies need to be put in place to eliminate racial economic disparities.” If the EAR for all races decreases, for example, that could signal the need for a more aggressive monetary policy. However, if the EAR is doing relatively well for some races while less so for others, that could signal the need for a fiscal policy designed to shrink the economic disparities between races.

  1. The views expressed here are solely those of the author and do not necessarily reflect the views of the Federal Reserve Bank of Philadelphia or the Federal Reserve System.
  2. Neil Bhutta, Jesse Bricker, Andrew C. Chang, et al., “Changes in U.S. Family Finances from 2016 to 2019: Evidence from the Survey of Consumer Finances,” Federal Reserve Bulletin, 106:5 (2020).
  3. Fenaba R. Addo and William A. Darity, Jr., “Disparate Recoveries: Wealth, Race, and the Working Class After the Great Recession,” ANNALS of the American Academy of Political and Social Science, 695:1 (2021), pp. 173–192; and Hilary Hoynes, Douglas L. Miller, and Jessamyn Schaller, “Who Suffers During Recessions,” Journal of Economic Perspectives, 26:3 (2012), pp. 27–48.
  4. Data sources for EAR are the Bureau of Labor Statistics, the U.S. Census Bureau, and the Federal Reserve Board of Governors.
  5. Similarly, the Philadelphia Fed’s Aruoba–Diebold–Scotti business conditions index (ADS) uses the Kalman filter to extract a signal from variables of different frequencies to measure daily business conditions. For more details on the ADS, see S. Borğan Aruoba, Francis X. Diebold, and Chiara Scotti, “Real-Time Measurement of Business Conditions,” Journal of Business & Economic Statistics, 27:4 (2009), pp. 417–427.
  6. See, for example, Joseph Ritter and Lowell Taylor, “Racial Disparity in Unemployment,” Review of Economics and Statistics, 93:1 (2011), pp. 30–42; and Raphaël Charron-Chénier, Joshua J. Fink, and Lisa A. Keister, “Race and Consumption: Black and White Disparities in Household Spending,” Sociology of Race and Ethnicity, 3:1 (2017), pp. 50–67.