Abstract
Economic inequality in the U.S. is significantly influenced by the integration trajectory of diverse immigrant and racial/ethnic minority groups. It is also increasingly clear that these processes are uniquely gendered. Few studies, however, jointly and systematically consider the complex ways in which race/ethnicity, gender, and nativity intersect to shape minority men’s and women’s economic experiences, and an intersectional understanding of these processes remains underdeveloped. To address this gap, we blend insights from assimilation, stratification, and intersectionality literatures to analyze 2015–2019 American Community Survey data. Specifically, we examine income inequality and group-level mobility among full-time working whites, Blacks, Native Americans, and Asian and Latino subgroups representative of the Southwest—the first U.S. region to reach a majority-minority demographic profile. Sociodemographic and human capital attributes generally reduce group-level income deficits, and we observe a robust pattern of economic mobility among native-born generations. But most groups remain decisively disadvantaged. Persistent income gaps signal multitiered racial/ethnic-gender hierarchies in the Southwest and suggest exclusion of minority men and women. Additionally, race/ethnicity and gender have an uneven impact on the relative position and progress observed among both U.S.- and foreign-born generations. Such findings support an intersectional approach and demonstrate the complex interplay of multiple axes of inequality that together shape contemporary U.S.- and foreign-born men’s and women’s economic experiences and returns.
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Data availability
Data used in this article are publicly available and can be accessed at https://usa.ipums.org/usa/.
Notes
We define the Southwest as: Arizona, California, Colorado, Nevada, New Mexico, Texas, and Utah.
Given the confluence of race, ethnicity, and national origin, we use the term race/ethnicity. For a discussion as this relates to Census and ACS data, see Footnote 9. When discussing racial/ethnic and/or national origin groups (U.S.- and foreign-born), we employ the term ethnics to refer to members of these groups (e.g., Asian and Latino ethnics; Chinese and Mexican ethnics). For a critique of broad racialized pan-ethnic classifications, see DiPietro & Bursik (2012); Restifo & Mykyta (2019). For a discussion of the racialization of Hispanics and Latinos, see Ortiz & Telles (2012); Estrada et al. (2020).
Estimates based on 2019 American Community Survey data.
The ACS does not directly ask respondents if they work full-time or part-time. Rather, respondents are asked to report the “usual” number of hours per week they worked the previous year and the number of weeks (Ruggles et al., 2021). Consistent with the U.S. Department of Labor (Bureau of Labor Statistics, 2020), we classify persons that reported working 35 hours or more per week as full-time. We also designate persons that reported working 48 weeks or more the previous year as full-year workers. We are unable to distinguish, however, between persons that worked 35 + hours per week, 48 + weeks per year at a single job versus those that did so at multiple part-time and/or partial-year jobs because: 1) the ACS does not provide information on whether respondents worked a single job the previous year or multiple jobs; and 2) the ACS does not provide the number of hours or weeks spent at each job for those working multiple jobs. The dataset provides only a single, primary occupation for each respondent (Ruggles et al., 2021). As such, respondents that worked multiple part-time and/or partial-year jobs that amounted to full-time, full-year work (35 + hours per week, 48 + weeks per year) are included in our analyses.
Part-time workers are in many ways unique and distinct from full-time workers. This includes access, participation, and movement in the labor market, as well as reason/purpose (economic vs. noneconomic; voluntary vs. involuntary) for working part-time (e.g., school, semi-retired, childcare, eldercare) (Bureau of Labor Statistics, 2018). By restricting our sample to persons working 35 + hours per week, 48 + weeks per year, we reduce conflating full-time and part-time workers and their experiences. Even so, we recognize that some full-time, full-year workers in our sample worked multiple part-time and/or partial-year jobs—though we cannot directly track it.
We estimate that only about 3% of full-time, full-year workers ages 25–55 in the Southwest live outside identified MSAs, with an additional 6% living in areas where metropolitan status is indeterminable.
We use detailed racial, ethnic, and national origin codes available via the ACS to construct the 10 above mentioned racial/ethnic group categories. Except for Mexicans, Guatemalans, and Salvadorans, all other racial/ethnic groups considered in our analyses consist of non-Latinos only. Native Americans include both American Indians and Alaskan Natives. In an effort to reduce potential ambiguities, multiracial respondents are not included in our analyses. Persons self-identifying as multiracial comprise a small fraction of cases (less than 2 percent) once we apply sample restrictions.
The U.S. Census Bureau, which administers the ACS, designates five broad racialized pan-ethnic categories for race: 1) white, 2) Black or African American, 3) American Indian or Alaska Native, 4) Asian, and 5) Native Hawaiian or other Pacific Islander (Jones et al., 2021). These five categories represent a general American conceptualization of race based on phenotype combined with ethnicity, national origin, and geographic region (OMB, 1997; see DiPietro & Bursik, 2012 for a critique of such classification schemes). The ACS questionnaire, when asking about race, provides survey respondents the first three racial categories (white, Black or African American, American Indian or Alaska Native) to select from, as well as several specific Asian and Pacific Islander origin categories (e.g., Asian Indian, Chinese, Native Hawaiian, and Samoan). Survey respondents can also write in other specific Asian and Pacific Islander ethnic, cultural, and national origins (e.g., Hmong, Laotian, Fijian, Tongan)—all of which are designated in the ACS questionnaire as races (Ruggles et al., 2021). Importantly, the U.S. Census Bureau conceptualizes and treats Hispanic, Latino, and Spanish origin as distinct from race, and the ACS questionnaire asks a separate question about it. As such, Hispanic origin categories are independent of and cut across race. The ACS questionnaire provides survey respondents several specific Hispanic, Latino, and Spanish origin categories to select from (Mexican/Chicano, Puerto Rican, Cuban), and respondents can write in other specific Hispanic, Latino, and Spanish ethnic, cultural, and national origins (e.g., Colombian, Dominican, Guatemalan, Salvadoran) (Ruggles et al., 2021).
Foreign-born white, in this instance, consists exclusively of non-Latino white Europeans, while foreign-born Black is comprised of non-Latino Black Africans. Given the diversity (and to a certain extent, ambiguity) of groups clustered into U.S. Census race categories, we attempt to preserve a degree of focus and precision among foreign-born whites and Blacks for comparative purposes with other racial/ethnic groups. Native American does not present a foreign-born equivalent for comparison.
U.S.-born citizen and naturalized citizen are collapsed into the single category of U.S. citizen in analyses reported in Table 3 (where each race/ethnic group is stratified by nativity) to avoid model degradation due to collinearity tied to the inclusion of more detailed citizenship and race/ethnic-nativity measures—which diagnostic tests showed posed estimate problems.
We explored the possibility of introducing measures to account for age of U.S. entry (which also taps into whether individuals received their education in the U.S. or abroad) in our analyses. Diagnostic tests, however, revealed model degradation due to collinearity between age of U.S. entry and other key predictors. This was especially problematic with models presented in Table 3 where each race/ethnic group is distinguished by nativity.
We do not include measures for both age and work experience together in our models since age is directly factored into our calculation of work experience. We explored the possibility of substituting age and age-squared in our models for work experience and work experience-squared. Both sets of estimates were consistent and all major patterns held across the board.
We draw a distinction between public and private employment sectors because prior work shows racial/ethnic minorities and women experience more equitable treatment, opportunities, and mobility prospects in the public sector—though recent evidence suggests this is changing with the privatization of the public sector (Wilson, 1997, 2009; Wilson et al, 2015). In addition, public and private employment sectors show distinct tendencies for union membership. An estimated 34.8 percent of public-sector workers are union members compared to just 6.3 percent of private-sector workers (Bureau of Labor Statistics, 2021).
Extended family members include any relative that is not the household head, his/her spouse, or his/her child.
Full results and interaction effect estimates from the pooled model are not shown but are available upon request.
All models include statistical controls for MSA (results not shown).
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Restifo, S.J., Ryabov, I. & Ruiz, B. Race, Gender, and Nativity in the Southwest Economy: An Intersectional Approach to Income Inequality. Popul Res Policy Rev 42, 48 (2023). https://doi.org/10.1007/s11113-023-09779-x
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DOI: https://doi.org/10.1007/s11113-023-09779-x