How Wide Is the Gap Between Black and White Americans on Key Dimensions of Well-Being?

Brouillette, Jones, and Klenow (2024) examine how large the Black–White welfare gap is in the United States when welfare is measured across multiple life dimensions. They analyze life expectancy, incarceration, consumption, leisure, and inequality using CDC mortality data, BJS incarceration statistics, CEX consumption data, and CPS labor data from 1984–2022. They find that Black welfare was about 40% of White welfare in 1984 and 59% in 2022, showing progress but leaving a large remaining gap. Life expectancy differences account for roughly 24 percentage points of the 41-point gap in 2022, while consumption explains about 12 points.

Why This Article Was Selected for The Policy Scientist

This article addresses a substantive policy topic: persistent racial gaps in economic well-being. Its importance is broad because differences in longevity, consumption, and incarceration shape lifetime opportunity, labor force capacity, and long-run human capital. The question is timely given recent stagnation in life expectancy and renewed interest in distributional metrics beyond GDP. Jones and Klenow have written extensively in this area, and this study extends that line of work by integrating multiple outcome domains using nationally representative data with long time series. The data quality is high, and the results are likely generalizable across U.S. jurisdictions. The study is descriptive rather than causal; future work could benefit from causal inference designs.

Full Citation and Link to Article

Brouillette, J.-F., Jones, C. I., & Klenow, P. J. (2025). Race and Economic Well-Being in the United States. American Economic Review: Insights, 7(4), 429–446. https://doi.org/10.1257/aeri.20240467

Central Research Question

The central research question in Brouillette, Jones, and Klenow (2025) concerns the magnitude, evolution, and composition of the Black–White welfare gap in the United States when welfare is defined as “consumption-equivalent welfare” rather than through single-dimension metrics such as earnings, life expectancy, or wealth. The authors ask how large the gap becomes when multiple life outcomes—specifically mortality, incarceration, consumption, leisure, and within-group inequality—are converted into a common utility-based metric and aggregated over the life cycle. The resulting scalar measure permits direct comparison of welfare levels across demographic groups and over time. The broader motivation is to assess the degree of convergence or divergence in racial welfare since the mid-1980s and to quantify which components drive the gap. The research question therefore bridges empirical measurement and welfare economics and reframes policy-relevant disparities in standardized units that avoid the problem of comparing heterogeneous metrics across incompatible denominators. 

Previous Literature

This study builds on two main literatures. First, it draws from the welfare measurement literature that seeks to move beyond GDP, earnings, or consumption as stand-alone proxies for well-being. The core antecedent is Jones and Klenow (2016), which introduced a cross-country methodology for converting mortality, consumption, leisure, and inequality into a single welfare measure. Second, the article engages the large empirical literature documenting racial disparities in income (Bayer & Charles), mobility (Chetty et al.), wealth (Derenoncourt et al.), and mortality (Schwandt et al.). Prior research has shown sustained earnings gaps, persistent racial wealth inequality, and differentiated mortality trajectories, but these studies typically report metrics in their native units. That approach obscures cumulative welfare effects because gaps in life expectancy, incarceration, and income do not simply average; they compound. The contribution of the present article is to unify these distinct outcome domains in a common welfare metric and to express gaps in consumption-equivalent units, enabling direct aggregation and cross-domain comparison. The authors note that welfare gaps may diverge from earnings or wealth gaps because life expectancy enters welfare through a multiplicative channel and because incarceration removes entire blocks of time from feasible consumption or labor. In that sense, the paper complements descriptive studies of racial inequality while also advancing the welfare literature by demonstrating that multi-domain aggregation yields different inferences about long-run trends than single-metric approaches. 

Data

The authors construct a panel of welfare components using U.S. microdata and administrative statistics spanning 1984–2022. Mortality is measured using CDC life tables prior to 2018 and death records from the National Vital Statistics System thereafter. Incarceration is drawn from the Bureau of Justice Statistics’ National Prisoner Statistics and the Annual Survey of Jails. Consumption comes from the Consumer Expenditure Survey, rescaled to match National Income and Product Accounts aggregates for personal consumption. Leisure is imputed using hours worked from the Current Population Survey and translated into annual leisure fractions based on a 16-hour waking day. Inequality is measured as the variance of log consumption and leisure within racial groups. The authors also integrate population weights, bridged-race classifications, and corrections for changes in racial reporting standards over time. The various data sources are nationally representative and have consistent definitions for life expectancy, incarceration, and consumption over multiple decades. The article notes potential data limitations in high-income underreporting in the CEX, omission of neighborhood quality, health status, and education as welfare inputs, and the lack of direct price-adjusted consumption measures by neighborhood or city. Those limitations qualify interpretation but do not undermine core measurement because the main drivers—mortality, consumption, and incarceration—are well measured in federal datasets with long horizons. The resulting dataset is notable for its coverage and comparability across time, which is essential for intertemporal welfare comparisons. 

Methods

The authors apply a utility-based accounting framework derived from expected lifetime utility under the veil-of-ignorance formulation. The utility function incorporates log consumption, leisure with a Frisch elasticity specification, incarceration that yields zero (or reduced) flow utility, and survival probabilities that discount utility across age cohorts. Flow utility is aggregated across ages using period life tables rather than cohort life tables to avoid extrapolation for younger birth cohorts. Consumption-equivalent welfare is then defined as the factor λ that equalizes expected utility across racial groups. Equivalent and compensating variations are computed, and the geometric mean is reported as the headline metric. Decomposition proceeds via an analytical expression separating contributions of life expectancy, incarceration, consumption levels, leisure levels, and within-group inequality in consumption and leisure. Calibration of key parameters uses standard values from the labor and mortality literature: a Frisch elasticity of 1.0, consumption-leisure parameters satisfying first-order conditions for labor supply, and an intercept u chosen to match EPA estimates of the value of statistical life (~$7.4 million in 2006 dollars). The method produces welfare estimates expressed as fractions of White welfare for each year 1984–2022. The authors do not estimate causal effects; the framework is an accounting exercise that translates descriptive racial disparities into welfare space. The approach is statistically disciplined, transparent, and analytically tractable, but by construction cannot isolate mechanisms or counterfactual policy effects. 

Findings/Size Effects

The primary finding is that consumption-equivalent welfare for Black Americans was approximately 40% of White welfare in 1984 and 59% in 2022. The gap declined rapidly between the mid-1980s and early 2010s, then plateaued. Despite convergence, the 2022 welfare gap remains large relative to consumption or earnings gaps. For example, the consumption gap was 39% in 1984 and 16% in 2022—far smaller than the 60% relative welfare level in 2022. Decomposition reveals that life expectancy contributes roughly 24 percentage points of the remaining 41-point gap in 2022, consumption explains approximately 12 points, and incarceration adds a modest portion. Leisure levels and inequality make negligible contributions. Growth decomposition shows that Black welfare grew 3.29% annually versus 2.18% for White welfare from 1984–2022, cumulating to 3.5× growth for Black Americans and 2.3× for White Americans. Most convergence arose from rising life expectancy and consumption among Black Americans, while falling incarceration rates played a secondary role. The wealth gap diverged sharply over the same period, demonstrating that wealth is not a reliable proxy for lifetime welfare under the authors’ metric. Robustness checks show that results are insensitive to alternative discount rates, household size adjustments, alternative Frisch elasticities, and incarceration utility penalties. Varying the value-of-life parameter changes levels but not rankings or trends. Extensions incorporating morbidity suggest that welfare gaps may be understated if health-adjustment is included. Geographical splits (South vs non-South) show similar trajectories, suggesting that national dynamics dominate regional variation. 

Conclusion

The authors conclude that racial welfare convergence has occurred over the past four decades, but sizable welfare disparities remain. The welfare gap is larger and more persistent than gaps in earnings, consumption, or life expectancy alone. The analysis implies that evaluations of racial inequality may be incomplete if they focus solely on income or wealth rather than comprehensive welfare metrics. The framework also suggests that policies affecting mortality can have large welfare impacts because life years carry high marginal utility. The authors emphasize that their welfare measure omits neighborhood access, price differences, and education, which may bias results in either direction depending on the omitted domain. They propose that future work could incorporate additional dimensions if reliable conversion to consumption-equivalent units becomes available. From a literature perspective, the article advances welfare measurement by applying a unified multi-domain welfare framework to a salient within-country demographic gap with long-run time coverage. It also reframes inequality debates by demonstrating that headline income or wealth gaps may not adequately summarize differences in lived welfare. Because the study is descriptive rather than causal, its implications pertain to measurement rather than policy evaluation. A natural extension would involve causal inference designs to identify mechanisms underlying mortality improvements, consumption growth, or incarceration declines, or to simulate welfare counterfactuals under specific policy interventions.