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Demography
Published in: Demography 3/2019

03-04-2019

Race Matters: Income Shares, Income Inequality, and Income Mobility for All U.S. Races

Authors: Randall Akee, Maggie R. Jones, Sonya R. Porter

Published in: Demography | Issue 3/2019

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Abstract

Using unique linked data, we examine income inequality and mobility across racial and ethnic groups in the United States. Our data encompass the universe of income tax filers in the United States for the period 2000–2014, matched with individual-level race and ethnicity information from multiple censuses and American Community Survey data. We document both income inequality and mobility trends over the period. We find significant stratification in terms of average incomes by racial/ethnic group and distinct differences in within-group income inequality. The groups with the highest incomes—whites and Asians—also have the highest levels of within-group inequality and the lowest levels of within-group mobility. The reverse is true for the lowest-income groups: blacks, American Indians, and Hispanics have lower within-group inequality and immobility. On the other hand, low-income groups are also highly immobile in terms of overall, rather than within-group, mobility. These same groups also have a higher probability of experiencing downward mobility compared with whites and Asians. We also find that within-group income inequality increased for all groups between 2000 and 2014, and the increase was especially large for whites. The picture that emerges from our analysis is of a rigid income structure, with mainly whites and Asians positioned at the top and blacks, American Indians, and Hispanics confined to the bottom.
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Appendix
Available only for authorised users
Footnotes
1
In the relatively few cases when differences are explained, the explanatory variables—such as employment opportunities, educational opportunities, or household characteristics—are often highly correlated with race and ethnicity. In these cases, pre-market conditions suggest disparities in access to or quality of educational institutions; these disparities are highly correlated with race and ethnicity and may simply imply an explanation for disparities, predating entry into the labor market, that are still race-/ethnicity-based.
 
2
Measures of income inequality are meaningful on their own and may determine other economic outcomes. For example, inequality may drive racial residential segregation (Reardon and Bischoff 2011) and may reduce the incentive to invest in higher education (Kearney and Levine 2016). Some evidence indicates that inequality may reduce the societal support for public assistance programs (Ashok et al. 2015). Violent crimes may also be causally related to high levels of income inequality (Fajnzylber et al. 2002; Kennedy et al. 1998). Income inequality may also affect adult health outcomes with a lag (Subramanian and Kawachi 2004). In tight housing markets, increased income inequality may disproportionately affect the poor, who have to pay more for housing (Matlack and Vigdor 2008). Kawachi et al. (1997) found that increasing income inequality reduces investment in social capital, which then increases adult mortality. Related to this, Hout (2016) found that higher levels of inequality reduce happiness.
 
3
The U.S. Census Bureau adheres to the Office of Management and Budget’s 1997 race and ethnicity standards, which specify five major race groups—white, black or African American (“black” here), American Indian or Alaska Native (“American Indian” or “AIAN” here), Asian, and Native Hawaiian or Other Pacific Islander (“Pacific Islander” or “NHPI” here)—and two ethnic groups (Hispanic and non-Hispanic). For our study, we define each racial group as that race alone and non-Hispanic, except for Other, which includes non-Hispanic multiple-race respondents as well as people who reported Some Other Race. Hispanic is defined as Hispanic of any race.
 
4
For more information on the linking process, see Wagner and Layne (2014).
 
5
In the online appendix, we examine those with W-2 (wages) data and all working-age men from the decennial censuses to assess how much bias may be induced by examining only tax filers.
 
6
The number of household members are equal to (A + 0.7K)0.7, where A = number of adults in the household, and K = the number of children (National Research Council 1995). We examine how our results change when we use a different equivalency scale as suggested by the National Research Council, and we find that the results are qualitatively very similar. Those results are available upon request.
 
7
Estimates of Americans living and working abroad range from 2.2 to 6.8 million people (Costanzo and von Koppenfels 2013). When we subtract 2.2 and 6.8 million people from the SOI estimates, our universe coverage rates of the SOI estimates increase to between 95 % and 100 %, compared with 94 % and 96 %.
 
8
Comparing 1040s and W-2s from 2010 matched to the 2010 census also helps us assess the representativeness of our universe. W-2s cover the distribution of wage earners with the exception of the self-employed. We find that 1040s cover more people ages 25–65 in the 2010 census compared with W-2s. Each racial group has a higher number of people in the 2010 census–1040 matched universe relative to the 2010 census–W-2 matched universe. Although race distributions are generally similar between both universes, the proportion of blacks in the matched 2010 census–1040 universe is lower relative to W-2s, and the proportion of Asians and Hispanics is slightly higher in the 2010 census–1040 universe. This further supports the suitability of our data for this analysis. Our universe contains a higher proportion of women relative to men, whereas the W-2 universe covers slightly more men. This higher coverage of females in our universe is likely in part because we are capturing women who are out of the labor force but married to a spouse who is earning income and filing income tax. Our W-2 analysis is available upon request. In the online appendix, we perform an analysis in which we add W-2 nonfilers to our analytic data from 2005 to 2014.
 
9
A number of studies have used administrative data to measure income inequality in the United States, especially the growth of the top percentiles. Piketty and Saez (2003) identified the increasing share of total income that has gone to the top income deciles in the last few decades. Feenberg and Poterba (2000) found an increase in the fraction of income accruing to the top 0.5 % using IRS tax data over the period 1960–1995. In the latter analysis, the authors examined adjusted gross income (AGI) measured in three ways (including and excluding capital gains and statutory gains). All recent research confirms an increase in income inequality since the 1970s.
 
10
Table A1 in the online appendix shows the population distribution and top and bottom shares for every year of our data. Patterns of change roughly hold true when looking at every year rather than the first and last year, but interesting patterns can be observed before, during, and after the Great Recession. We intend to follow up on these differences in future research.
 
11
The results indicate that the filing of tax returns differed by group because the results are all relative to non-Hispanic white. The bump disappears in the subsequent figures after the 40th percentile as the upper-income points are less sensitive to changes associated with the bottom of the income distribution.
 
12
The inequality symbol is due to the convexity of the Gini function and the fact that the function is also homogeneous of degree 0.
 
13
We restrict our analysis in Figs. A1–A7 (online appendix) to the years 2005–2014 and add individuals who had W2 earnings but did not file a 1040 form. This robustness check allows us to evaluate whether a significant difference is obtained when these individuals are included in the analysis. We find no strong qualitative (or quantitative) differences across the transition matrices with and without the additional individuals who were identified from using the W2 forms.
 
14
In Figs. A10 and A11 (online appendix), we provide additional analysis where we separate the transition matrices by race and ethnicity into two broad educational categories. As expected, education has a mitigating effect on mobility, where low-education individuals are more likely to remain entrenched or to experience downward mobility (especially for blacks and Hispanics), while high-education individuals are more likely to transition upward in the distribution. In Figs. A12 and A13 (online appendix), we separate the observations into two broad categories compared with the full sample. People at the start of their careers in our data (aged 25–34) appear to have slightly more downward mobility than those later in life, which may indicate an increasing return in the labor market as experience is gained, or it may reflect the impact of the erosion of the value of wages over time.
 
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Metadata
Title
Race Matters: Income Shares, Income Inequality, and Income Mobility for All U.S. Races
Authors
Randall Akee
Maggie R. Jones
Sonya R. Porter
Publication date
03-04-2019
Publisher
Springer US
Published in
Demography / Issue 3/2019
Print ISSN: 0070-3370
Electronic ISSN: 1533-7790
DOI
https://doi.org/10.1007/s13524-019-00773-7

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