The Shades of Inequality: Race/Ethnicity, Skin Tone, and Socioeconomic Status

The Shades of Inequality: Race/Ethnicity, Skin Tone, and Socioeconomic Status | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Shades of Inequality: Race/Ethnicity, Skin Tone, and Socioeconomic Status Matthew Painter, Malcolm Holmes, Jennifer Tabler, Mary Campbell This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8604916/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background . The social costs of skin tone inequality in the United States are substantial and can, at times, be as great as – or greater – than that of the Black-White divide. One area where the effects of both racial/ethnic and skin tone stratification can be clearly seen is in the study of socioeconomic inequalities. Method . Using the 2015 Texas Diversity Survey, we carry out not only within-group analysis for racial/ethnic subsamples but also break out these groups by skin tone. Results . Our results allow us to offer insight into how colorism may operate to differentially shape socioeconomic outcomes within racial/ethnic groups. Implications . We conclude by discussing how focusing on a specific location can provide insight into how place matters, whereas assessing colorism at a national level potentially erases place-based diversity of economic outcomes for racial/ethnic groups. race/ethnicity skin tone income employment education Figures Figure 1 Figure 2 Figure 3 INTRODUCTION The social costs of skin tone inequality in the United States are substantial and can be as great as – or greater – than that of the Black-White divide so often studied. For example, Monk ( 2015 :402) documents that the average education gap between Black and White individuals is smaller (10.2 months) than that of between the lightest- and darkest-skinned Black individuals (15.4 months). Such dual influences – of both race- and skin tone-based inequality – are apparent for other educational, health, and criminal justice outcomes as well (e.g., Monk, 2021 ). Hunter ( 2007 :238) describes these dual influences as two “systems of discrimination” that both distinctly and jointly affect life chances. These systems of discrimination are rooted in historical inequalities, contributing to lighter skin – independent of a White racial status – facilitating the transmission of education, health, and socioeconomic advantage. Such a “preference for whiteness” (Goldsmith et al., 2007) continues today with White and lighter-skinned individuals receiving advantages due to their elevated positions within race- and skin tone-based hierarchies. One area where the effects of both racial/ethnic and skin tone stratification can be clearly seen is in the study of socioeconomic inequalities (e.g., Goldsmith et al., 2007; AUTHOR; Reece, 2021 ). The present study joins with this sizeable and growing body of research that documents the relationships between race/ethnicity and skin tone with socioeconomic outcomes, including educational attainment (e.g., Gullickson, 2005 ; Hersch, 2006 ; Hughes & Hertel, 1990 ; Monk, 2014 ), employment (e.g., Monk, 2014 ), and income (e.g., Hersch, 2006 ; Hughes & Hertel, 1990 ; Hunter, 2002 ; Keith and Herring, 1991 ; Kiang & Takeuchi, 2009 ; Monk, 2014 ). This research consistently finds that darker-skin toned individuals are generally associated with relative disadvantage in these – and other – dimensions of socioeconomic status, all else being equal. Some studies examine skin tone effects within racial/ethnic groups (e.g., Hersch, 2006 ; Keith & Herring, 1991 ; Monk, 2014 ; Reece, 2021 ), though often the average effect of skin tone is presented alongside the average effects of multiple racial/ethnic groups (e.g., Hersch, 2008 ; AUTHOR). More recent research creates combinations of race/ethnicity and skin tone so that skin tone effects can be simultaneously examined within and between groups (e.g., Adames, 2023 ). Our study makes three contributions to the existing literature on skin tone and socioeconomic inequality. First, we use the 2015 Texas Diversity Survey (TDS) (Keith & Campbell, 2015 ). This is a unique dataset that possesses several strengths that complement existing research in this area. One strength is that due to its focus on sampling Black and Hispanic individuals, there are sufficient sample sizes that allow for us to carry out within-group analyses. Therefore, we are able to present findings for the full sample as well Black Hispanic, and White subsamples. Our second contribution is to adopt the approach of Adames ( 2023 ) and break out the racial/ethnic categories by skin tone. This allows us to assess the effects of race and ethnicity and then skin tone within those categories. Finally, our study draws attention to the importance of states and regions. Studies using nationally representative data provide valuable points of comparison with their estimates averaged across all sampled geographic units. However, there is meaningful variation within the United States and the TDS data allow us to speak to that. In particular, Texas is distinct from other U.S. states in a variety of ways and has a dual location as part of the historic Southwest and the South U.S. Census region. Historically, it has the unique distinction of being both a Spanish colony and slave state. It may be that skin tone inequalities, like racial/ethnic inequalities more broadly, are place-based; therefore, it is important to look beyond national samples to document important, more locally driven, racial/ethnic processes. CONCEPTUAL FRAMEWORK Colorism and Racial/Ethnic Inequality Skin tone- and race/ethnicity-based inequality constitute two distinct and simultaneously entangled “systems of discrimination” (Hunter, 2007:238). Both systems concurrently privilege lighter-skinned individuals and/or Whites over darker-skinned and non-White individuals via “subtle cues of disfavor” as well as overt discrimination (Hunter, 2007:241). Evidence of these systems of discrimination abounds across U.S. institutions, including education, criminal justice, housing, and financial, among others (e.g., Brown, 2016; Herring and Henderson 2016; Lanuza, Peterson and Omori 2023; Lee et al. 2015; Massey 2007; Monk 2019; see also Pager and Shepherd 2008). In this way, darker-skinned and non-White individuals have been persistently disadvantaged (Brown, 2016), which has resulted in disparate life chances for these individuals (Bonilla-Silva 1997, 2013). The social significance of skin tone has ancestral roots in the European colonization of the indigenous peoples of Africa and Latin America (e.g., Abascal & Garcia, 2022; strmic-pawl, Gonlin & Garner, 2021). The supremacy of lighter-skinned Europeans over darker-skinned indigenous populations engendered a skin tone hierarchy and social structures that benefited colonizers politically, economically, and socially. Skin tone-based structural inequality was supported by an ideology of colorism, a preference or favoritism for lighter skin, which justified greater oppression of darker-skinned people (Hunter, 2005). For Black individuals in the United States, this preference can be traced back to slavery (e.g., Reece, 2018, 2019). Skin tone served as a means for sorting the enslaved population into domestic and agricultural jobs (e.g., Frazier 1957; Johnson 1996). Less physically demanding domestic positions (e.g., house servants, butlers, maids, cooks) were commonly occupied by lighter-skinned enslaved individuals, who generally had access to better food, shelter and opportunities to learn to read and write (Bodenhorn & Ruebeck, 2007; Frazier, 1957; Horton & Horton, 1997) or acquire a skilled trade (Frazier, 1957; Horton & Horton, 1997; Margo, 1992). In contrast, darker-skinned enslaved individuals were often forced to perform labor-intensive agricultural jobs. Colorism and its associated skin tone stratification persisted after slavery ended. In the late 19th century, “Mulatto” individuals continued to experience numerous socioeconomic advantages compared with those of “pure” Black heritage (Bodenhorn & Ruebeck, 2007; Gullickson, 2010; Reece, 2018). Persistent skin tone stratification reflected White individuals’ belief that “Mulatto” individuals’ white ancestry made them more intelligent, harder working, and less deviant than Black people (e.g., Bodenhorn, 2006; Frazier, 1957; see Reece, 2018 for further discussion). Moreover, social distinctions among Black individuals existed. Lighter-skinned persons sought to preserve their distinctiveness and advantaged position relative to darker-skinned persons (Bodenhorn, 2003; Meier & Lewis, 1959). The practice of social distancing from darker-skinned Black individuals may have provided Mulatto individuals connections to powerful White individuals, which could provide greater economic opportunity and strengthen social boundaries between them and Black individuals (Reece, 2018). For Hispanic individuals, the system of colorism – and the skin tone stratification that accompanies it – originated during the Spanish colonization of the Americas (Murguia & Telles, 1996). The colonizers perceived themselves as innately superior, which was the ideological foundation of a social and economic hierarchy that placed White individuals at the top, mixed-race people in the middle, and enslaved indigenous peoples and Africans at the bottom (Hunter, 2005; Keith & Monroe, 2016). Variations in skin tone and other phenotypical characteristics were conflated into a multi-category racial caste system that determined access to power and privilege, a system that favored “pure-blood” Spaniards and, to a lesser degree, their mixed-heritage offspring (Chavez-Dueña et al., 2014). These hierarchies existed throughout the Spanish colonies for three centuries. After Latin American countries achieved independence, an ideology of “ mestizaje ” – the idea that all Latin Americans are of mixed heritage – emerged, which obfuscated Spanish privilege but did little to actually change the system of racial privilege. In fact, it bolstered the notion that dark skin signified barbarism and inferiority (Hunter, 2005). This ideology was incorporated into the American Southwest following the US conquest of Mexico in 1848 (Hunter, 2005). White settlers who moved into this sparsely populated region saw themselves as superior to mestizo, indigenous and Black people, consistent with prevailing color ideologies in the US and Mexico. The Mexican citizens incorporated into the United States were subordinated into low-paying jobs in agriculture, ranching, mining, and railroad building (Acuña, 1988; Barrera, 1979). They were stereotyped as biologically, culturally, and socially inferior (Murgia & Telles, 1996). By the early 1920s, escalating industrialization and urbanization increasingly concentrated the Mexican-origin population into economically disadvantaged southwestern barrios (Acuña, 1988; Escobar, 1999), a pattern that continues today (Alba & Nee, 2003). Colorism may play a role in segregation and disadvantage among this population, with some research indicating lighter-skinned people are less likely than darker-skinned people to live in segregated, less affluent areas of some cities (Grebler et al., 1970; Relethford et al., 1983). Colorism and the Preference for Whiteness Hypothesis Scholars have sought to explain the development and persistence of skin tone disadvantages in two ways, one involving structural approaches, the other sociocognitive processes. As described above, the structural perspective emphasizes longstanding, large-scale patterns of social relationships that created persistent patterns of skin-tone disadvantage. In this view, socioeconomic disadvantages associated with darker skin tones reflect not only present-day skin tone discrimination, but also cumulative effects of skin tone-based discrimination generated over previous decades (Abascal & Garcia, 2022; Branigan et al., 2019). Ancestors with lighter or darker skin tones also likely experienced advantage or disadvantage, respectively, which allowed them to acquire and then transmit different resources over generations. This process has been called “ancestrally accumulated disadvantage” (Hill, 2002) or “inherited (dis)advantage” (Abascal & Garcia, 2022), whereby one’s ancestors’ location within racial/ethnic and skin tone hierarchies substantially affects one’s present-day socioeconomic status, independent of a person’s own racial/ethnic status and location on a skin tone continuum. Whereas the structural approach focuses on the origin and persistence of skin tone stratification systems, the complementary social-psychological perspective builds on it by focusing on how these embedded patterns of discrimination manifest as implicit and explicit biases in day-to-day interactions. The “preference for whiteness” hypothesis has been advanced to explain how colorism produces inequalities and subsequently extended to incorporate the important role of stereotypes (Goldsmith et al., 2007; Painter et al., 2016). Initially, this hypothesis relied on social identity theory to explain how sociocognitive dynamics elicit discriminatory responses based on skin tone (Goldsmith et al., 2007), an approach also used to explain discrimination based on racial categorization (e.g., Bonilla-Silva et al., 2006). It maintains that social assignment to in-groups and out-groups generates differential treatment and allocation of social rewards based on in-group favoritism. Individuals are said to mentally categorize the social world in terms of group membership and respond to others on the basis of those categorizations (e.g., Tajfel et al., 1971). Members of in-groups are treated with favorable attitudes and behaviors while out-groups receive unfavorable responses, even when group membership is defined in the most minimal way (e.g., in arbitrarily assigned groups lacking normative structure and interpersonal interaction) (see Brewer, Brown, & Gilbert, 1998). In situations involving intergroup relations, shared perceptions of in-group similarity increase group cohesiveness and promote ethnocentrism, resulting in favoritism toward the in-group (Turner et al., 1987). Concerning colorism, these processes manifest both inter- and intra-racially (strmic-pawl et al., 2021). When a low-status (darker-skin) group accepts the perceived superiority of a high-status (lighter-skin) group, the members of the low-status in-group may show favoritism for the high-status out-group (Tajfel & Turner, 1979, 1986). Goldsmith and colleagues (2007) argue that the preference for whiteness occurs when this "status effect" outweighs the categorization effect of race/ethnicity, which explains why both whites and dark-skinned blacks respond more favorably to light-skinned blacks. The bias predicted by social identity theory primarily involves relatively mild forms of in-group favoritism. Although it may be essential to the maintenance of in-group cohesion and solidarity, this in-group bias alone may not be sufficient to produce more serious forms of out-group discrimination (Brewer, 2007). Painter and colleagues (2016) build on the original preference for whiteness hypothesis insights about group categorization processes by incorporating the related sociocognitive conceptualization of stereotyping. Their approach develops the cognitive dynamics of derogation and discrimination toward out-groups. Stereotypes both assign an out-group’s position (Fiske, 1998) and justify (Jost & Banaji, 1994) the existing social status hierarchy. Members of various racial/ethnic groups (including Blacks, Hispanics, and Whites) tend to judge darker-skinned members of both their in-group and out-groups more stereotypically than lighter-skinned persons (Blair et al., 2002; Hannon, 2015; Maddox & Gray, 2002; Uhlmann et al., 2002). Stereotypes allow people to use socially salient phenotypical characteristics to quickly categorize and rapidly respond to others in routine interactions without conscious deliberation, particularly during interactions involving situational factors (e. g., time constraints, complex stimuli) that overload cognitive resources (Fiske, 1998; Macrae & Bodenhausen, 2000). Stereotypes may trigger subtle but meaningful discriminatory responses to out-group members (e.g., in employment decisions) without either conscious deliberation or emotionally based prejudice being involved (e.g., Fiske, 1998; Pager et al., 2009). Stereotype-based responses to out-group members are, however, conditioned by social context. Skin tone and racial/ethnic related stereotypes may be activated only when relevant to the situation at hand (e.g., Maddox & Chase, 2004). Those involving characteristics such as intelligence and education (see Maddox & Gray, 2002) may be especially relevant in situations involving individual economic outcomes (e.g., Monk, 2014). Although overt patterns of intra- and interracial discrimination have increasingly faded away in the United States (e.g., Bonilla-Silva, 2013), the historical legacy of colorism continues to be seen in these more covert forms of preferential treatment that favors whites and lighter-skin minority persons in the allocation of social rewards (e.g., Bonilla-Silva et al., 2006; Hannon, 2015; Wade et al., 2004). Current Study In this study, we build upon prior work by examining skin tone and racial/ethnic stratification for several key dimensions of socioeconomic status. Following previous research (e.g., Goldsmith et al., 2007; Painter et al., 2016; Reece, 2021), we hypothesize that darker-skinned individuals, all else being equal, will be disadvantaged relative to lighter-skinned individuals. Most studies of colorism and skin tone stratification use datasets that have an interviewer-supplied measure of skin tone. Other surveys, including the TDS, collect skin tone information via respondent self-assessment, also referred to as “reflected appraisal” or “self-rated” skin tone within the literature. Other datasets that include a reflected appraisal of skin tone are the National Survey of American Life (NSAL), the Detroit Area Study (DAS), and the National Social Life, Health, and Aging Project (NSHAP). The advantage of a relative measure is that it captures how respondents see themselves and is likely grounded in their understanding of where their skin tone falls along a continuum within their community (Monk, 2015). A respondent’s appraisal of their skin tone reflects a “sense of place and stature among . . . peers” (Monk, 2015:431-32), which could include neighborhoods and peer groups, as well as experiences with gatekeepers of educational and business organizations. Reflected appraisal measures also eliminate concerns about potential interviewer effects since interviewers are not involved (see Hannon & Defina, 2014; Hill, 2002; Monk, 2015). 1 Our study joins with previous work that has relied upon valuable data collection efforts that sampled from specific areas, like Detroit (DAS), San Francisco and Honolulu (Filipino American Community Epidemiological Study), or Atlanta, Boston, Detroit, and Los Angeles (Multi-City Study of Urban Inequality). Our focus on Texas provides an important contribution because place-specific investigation provides valuable points of comparison so that (potential) variation by skin tone and/or race/ethnicity can be better understood. We anticipate interesting contrasts with studies using geographically concentrated datasets and/or the nationally representative data of the National Survey of Black Americans, NSAL, and the General Social Survey. Our study contributes to this line of research in a unique way – we use data that is representative of an entire state, the second largest in the United States, to provide more insight into this body of city- and region-specific research. It will also allow us to assess how Texas aligns (or not) with larger national-level patterns by comparing our findings to nationally representative studies. It is critical to understand how the context of particular places, such as Texas, moderate the influences of colorism. Texas is notable for a number of reasons. For one, its population (~30.5M) trails only that of California (~39M). In terms of racial/ethnic diversity, it is a majority-minority state with Hispanics constituting the largest racial/ethnic group (U.S. Census, 2023a). Since we are analyzing socioeconomic outcomes in this study, state-level values are informative: Texas ranks 27 th for the percent of the population holding a bachelor’s degree or higher, 34 th for the unemployment rate, and 19 th for average household income (U.S. Census Bureau, 2023b, 2023c; U.S. Bureau of Labor Statistics, 2024). Texas is officially part of the South Census region; however, researchers who examine the Hispanic population commonly include it in a Southwest sub-region, along with Arizona, California, Colorado, and New Mexico (e.g., Barrera, 1979; Holmes & Painter, 2023; McWilliams et al, 2016). Historically, a substantial majority of the Hispanic population, primarily those of Mexican-origin, has been concentrated in the Southwest and the Southwestern states, including Texas, remain the cultural, economic, and political center of the Mexican-origin community in the US (Saenz & Morales, 2015). DATA AND METHODS Data In this study, we use the 2015 Texas Diversity Survey (Keith & Campbell, 2015). This was a representative telephone survey (98% via cell phone) that collected information from 1,306 Black, Hispanic, and White adult Texans. 2 Data were collected by the Public Policy Research Institute at Texas A&M University and respondents could complete the survey in either English or Spanish. For our purposes, the TDS is valuable data because its stratified sampling strategy ensured adequate sample sizes for each of the three racial/ethnic groups. Further, the survey’s detailed questions on race/ethnicity, skin tone, and socioeconomic status allow us to test our hypotheses within a relatively understudied, yet rapidly growing, part of the United States. Previous work has used the TDS to explore racial/ethnic identities and statuses (Gonlin et al., 2020), skin tone and discrimination (Gonlin, 2020), everyday discrimination (Harnois et al., 2019), media consumption and racial residential preferences (Korver-Glenn et al., 2020), and discrimination and social media use (Miller et al., 2021). We used the detailed racial/ethnic identity information within the TDS to remove 61 non-Hispanic multiracial respondents. This allowed us to retain 276 Black, 343 Hispanic, and 626 White individuals for our subsample analysis. Our full analytic sample was 1,245. Variables Outcome variables . We include three variables from the TDS that measure socioeconomic status. First, the TDS asked respondents about their highest completed grade in school. Response options, among others, included “some high school,” “high school diploma or GED,” “some college,” and “graduate degree (master’s or doctorate).” We recoded this variable to create a continuous education variable using, for example, 10, 12, 14, and 18 years of education for the examples just listed. Second, respondents in the TDS selected the best option from a list that described their current employment status. Here, we followed Monk (2014) and created a dichotomous variable for full-time employment (1=yes). Last, TDS respondents selected a range (e.g., “$45,001 - $60,000”) that reflected their before-tax household income. We created a continuous income variable by using the midpoint of each range. Explanatory variables . The TDS collected skin tone information, which proxies for colorism, by asking respondents to describe their skin color/complexion compared to most people in their racial/ethnic group. Following previous research that used the TDS to examine skin tone (Gonlin 2020), we combined the “very light” and “light” categories, retained the original “medium” category, and then combined the “dark” and “very dark” categories. We then combined the skin tone and racial/ethnic variables to create three skin tone variables (light, medium, dark) for the three racial/ethnic groups. Light-skinned White individuals were the reference category. Control variables . For controls, we included a continuous variable for age and dichotomous variables for first-generation immigrant (1=born outside the United States), sex (1=female), and relationship status (1=married). 3 Analytic Approach We used OLS and logistic regression in our analysis. All results were weighted with the weights created by the TDS investigators using the American Community Survey. Age and education (when used as a control variable) were centered, using the analytic sample and subsample means where appropriate. Table 1 contains descriptive information, including t-tests for differences between both Black and White respondents and then Hispanic and White respondents. We use the Satterthwaite method that assumes unequal variances. Tables 2 and 4 contain our results for the full analytic sample and then subsample analyses. In both tables, we present two models. Model 1 contains the skin tone-race/ethnicity combinations and Model 2 adds the control variables. To address missing data, we used the multiple imputation, then deletion (MID) procedure (von Hippel, 2007). 4 Practically, we used SAS Proc MI to create ten data sets for each model using the variables in the regression analyses. We then removed observations with missing values on the relevant outcome variable after the imputation process. Analyses were conducted using SAS Proc Surveyreg or Surveylogistic, which provided robust standard errors. Results were returned with SAS Proc MIAnalyze. Table 3 contains the standardized total, direct, and indirect effects for the race/ethnicity-skin tone combinations from a path analysis (using Proc Calis) of the full analytic sample. Missing data for the path analysis was addressed using Proc Calis’s full information maximum likelihood approach. RESULTS Descriptive Statistics Table 1 contains descriptive statistics for the variables in our analysis. In the full sample, TDS respondents average about 14 years of education, which differs between Hispanic (about 13 years) and White (15 years) respondents. 52 percent of the sample is engaged in full-time work and this does not differ among the racial/ethnic subsamples. In contrast, White respondents have the highest income (~$110,000), which is greater than both Black (~$77,000) and Hispanic (~$60,000) respondents. These statistically significant differences remain for the log of income as well. For skin tone, about half of the full sample assess their skin tone as medium (47%) relative to most people in their racial/ethnic group. Only 10% rates their skin tone as darker. By racial/ethnic group, there are notable differences. Fewer Black and Hispanic respondents assess their skin tone as lighter than the group average when compared to White respondents and more racial/ethnic minorities chose medium when rating their skin tone. *** Table 1 about here *** To better highlight how skin tone varies across respondents in the TDS, Figure 1 contains the percentage distribution (unweighted) of the three racial/ethnic groups and then of the skin tone-racial/ethnic combinations. Relatively few Black respondents rate their skin tone as lighter and more self-assess as either medium or darker. In contrast, more Hispanic respondents think their skin tone is either light or medium compared to most people within their ethnic group; few assess their skin tone as darker. Among White respondents, “light” is the modal category. *** Figure 1 about here *** Regression Results – Full Sample Table 2 contains regression results for our three outcome variables. In Model 1 for education, “Black – light” is significantly different than the reference category of “White – light”. This was anticipated from our descriptive analysis in Table 1. For Hispanic individuals, all three skin tone categories are statistically significant. This indicates that Hispanic respondents – particularly those with a self-assessed medium skin tone – are associated with less education than light-skinned White respondents. In Model 2 when controls are introduced, only “Hispanic – light” and “Hispanic – medium” are still associated with significantly less education (about two-thirds and 1.67 years, respectively) than light-skinned White respondents. For Hispanic individuals with dark skin tones, the loss of statistical significance for these categories when including controls, particularly first-generation immigrant status and being married, suggests the importance of these two statuses for educational attainment. Notably, there are only 27 Hispanic individuals with a self-assessed darker complexion. This small sample size could explain why the coefficient is in the expected direction though non-significant. Last, there are no differences in educational attainment among White respondents by skin tone. For full-time employment, there is no statistically significant variation by race/ethnicity or skin tone in either Models 1 and 2. For the log of income, the statistically significant differences by skin tone and race/ethnicity that are evident in Model 1 persist when controls are included in Model 2. Here, Black respondents with medium and dark relative skin tone are associated with lower logged income than light-skinned White respondents. There is no relationship between light skin tone for Black respondents and income in either model. There are 40 individuals in this group (while there are 136 medium- and 95 darker-skinned Black respondents); therefore, the small group size for light skin toned Black respondents could explain the lack of a statistically significant relationship with income. An equality of coefficients test indicated that the medium and dark categories were statistically equivalent for Black respondents. This suggests a simultaneous race- and skin tone hierarchy between these two groups: White respondents (regardless of skin tone) and lighter-skinned Black respondents are associated with higher income while darker-skinned Black individuals are associated with lower income. Among Hispanic individuals, there is clear stratification by skin tone: Hispanic respondents who assess their skin tone as lighter than their co-ethnic peers are associated with the smallest income difference relative to the White sample (regardless of skin tone) while those with a darker complexion are associated with the largest difference. Equality of coefficients tests indicate that there is a statistically significant difference between light and dark Hispanic participants. Taken together, there are three tiers between Hispanic and White respondents: White participants report the highest income, followed by lighter-skinned Hispanic, and then darker-skinned Hispanic participants. *** Table 2 about here *** *** Figure 2 about here *** To illustrate our findings for logged income, Figure 2 presents predicted values by skin tone-race/ethnic combinations, holding variables at their means. White respondents are associated with the most income, regardless of skin tone, with about $70,000. Black respondents with a medium and dark self-assessed skin tone are associated with about $50,000. This amount is similar to the predicted value for light-skinned Hispanic respondents (~$52,000). Among all of the skin tone-race/ethnic combinations, medium- (~$46,000) and dark-skinned (~$36,000) Hispanic respondents are associated with the lowest predicted values for income. Regression Results – Path Analysis Our next analytic approach is a path analysis, illustrated in Figure 3. This allows us to better understand how race/ethnicity and skin tone are related to our outcomes. For example, path analysis allows us to demonstrate how the combinations of skin tone and race/ethnicity are directly connected with income and also indirectly connected through education and/or full-time employment. This approach improves upon Monk (2014), who can only speculate about how skin tone indirectly operates for his various outcomes while we can explicitly test the relationships. *** Figure 3 about here *** To conserve space, Table 3 contains select standardized results from the path analysis for the skin tone-race/ethnic combinations. For both “Black – light” and “White – medium” we observe negative relationships with equivalent coefficient sizes between these groups and education. For Hispanic respondents, however, the coefficient sizes are much larger and still negatively associated with education. For full-time employment, there are no direct effects with skin tone. We do see evidence of an indirect relationship between skin tone and full-time employment through education for light-skinned Black and medium skin-toned White respondents. Again, the coefficient sizes are equivalent. There is a similar indirect relationship through education for light- and medium-hued Hispanic respondents, also with larger coefficients sizes. Last, for income, there is a direct effect of medium and dark skin tones for Black respondents on income; however, there are no indirect effects through either education or employment. For Hispanic respondents, there are both direct effects of light and medium skin tones on income and indirect effects flowing through both education and full-time employment. We do not observe any relationships, either direct or indirect, between skin tone and income for White respondents. *** Table 3 about here *** Regression Results – Subsample Analysis The final piece of our analytic approach is presented in Table 4, which contains select results from our Model 2 (Table 2) specification. This approach allows us to assess darker skin tones versus a light skin tone for group-specific outcomes while accounting for group-specific averages with the control variables. Because of the smaller sample sizes for these Black (N=276) and Hispanic (N=343) respondents, we broaden our assessment of statistical significance. First, among Black respondents in Panel 1, both medium and dark skin tone are associated with more education when compared to Black respondents who assess their skin tone as lighter than their same-race peers. This is counter to previous research that examines Black Americans within the NSAL (Monk, 2014) and NSBA (Hersch, 2006; Hunter, 2002). These studies’ findings align with the larger body of literature on skin tone inequality that consistently documents that as skin tone lightens, education increases. Notably, Monk (2014) uses the interviewer-assessment of skin tone in the NSAL instead of the relative skin tone measure, which was used in Keith et al. (2010) who analyzed the same data. Second, there is no relationship between skin tone and either full-time employment or income among Black respondents. The former finding is reflected in Monk’s (2014) study, but the latter is not as Monk finds that lighter skin tone is associated with higher income. Importantly, as we note above, there are 40 Black respondents who report a light skin tone relative to most people in their racial group, which is the smallest of the three skin tone categories among Black respondents. Among Hispanic respondents (Panel 2), those who rate their skin tone as medium relative to other Hispanic individuals are associated with less education than light-toned Hispanic respondents. The coefficient indicates this is almost 1 year of education. As with Black respondents, there is no relationship between skin tone and full-time employment among Hispanic respondents. However, darker-toned Hispanic respondents are associated with less income than lighter-toned Hispanic respondents. As we note above, there are only 27 dark-toned Hispanic respondents; therefore, this finding must be treated with caution. Last, we note that there is a negative relationship between medium skin tone and education among White respondents. This indicates that this group is associated with less education than light-skinned White participants. *** Table 4 about here *** DISCUSSION In this study, we used the TDS to examine the relationship between race/ethnicity, skin tone, and socioeconomic status. Briefly, our results from our full sample indicated that that race and skin tone did not affect two critical dimensions of socioeconomic status – educational attainment and full-time employment – for Black individuals. We did see evidence of a skin tone hierarchy for income, with lighter-toned Black individuals having similar incomes as White individuals (regardless of skin tone) and darker-skinned Black individuals having a lower income. For Hispanic respondents, we found that those with medium and dark skin tone had fewer years of educational attainment and medium-skin toned Hispanics were less likely to be employed full time. For income, however, we saw a clear White-Hispanic racial-ethnic divide and then further stratification by skin tone. Our path analysis illuminated how skin tone was both directly related to socioeconomic status and indirectly affected a given outcome. For example, relative to light-skinned White respondents, Hispanic respondents with a medium and dark skin tone were associated with a direct effect of skin tone on income and a simultaneous indirect effect via lower educational attainment. This pattern contrasts with that for Black respondents, where there was only a direct effect associated with income, and White respondents, where there was only limited evidence of direct effects and no relationships with indirect effects for our given outcomes. Our approach here demonstrates the indirect connections – at least for Hispanic respondents – between skin tone and various outcome measures that previous research pointed to, but did not test (Monk, 2014 ). For our analysis of skin tone stratification among the racial/ethnic subsamples, we documented few differences where skin tone affected socioeconomic attainment. Among Black individuals, we found a positive relationship between skin tone and educational attainment, which is counter to the general finding throughout the skin tone literature that darker skin tones are associated with worse socioeconomic outcomes (e.g., Hersch, 2006 ; Hunter, 2002 ; Monk, 2014 ). We attribute this unique counter-finding to two potential reasons. First, the TDS used a relative skin tone measure. As we note above, scholars have called attention to the possibility that different approaches to measuring skin tone could produce inconsistent findings regarding skin tone and various outcomes, including measures of socioeconomic status (e.g., Keith et al., 2010 ; Monk, 2015 ). With a relative measure of skin tone, respondents are making self-evaluations relative to their same-race/co-ethnic peers, which could reflect their social position when compared to their reference group (Monk, 2015 ). Further, a relative measure of skin tone also removes any concerns of interviewer effects (e.g., Hannon & Defina, 2014 ; Hill, 2002; Monk, 2015 ). Second, the TDS sampled Black Texans while the NSAL (Monk, 2014 ) and NSBA (Hersch, 2006 ; Hunter, 2002 ) are nationally representative. Earlier in this study we noted Texas’ unique position as both part of the historic Southwest and the South regions and we noted where Texas ranks, relative to other states, on various socioeconomic measures. Texas’s Black population may also differ in important ways from those in other states. For example, Texas has more Black residents than any other state (~ 3.6 million, followed by Georgia at ~ 3.4 million; authors’ calculations from 2023 American Community Survey 5-year estimates). However, as a percent of its population, Texas’s black population (12.2%) is slightly less than the national average (12.4%), far behind some Southern states (e.g., Mississippi (37.0%), Georgia (31.3%), and higher than all Southwest states (e.g., Nevada (9.4%), California (5.5%)). State-specific data, like that of the TDS, provide a valuable reference point for scholars to compare states and communities to understand variation in socioeconomic – and other – outcomes within Black populations and relative to other racial/ethnic groups. Future research should continue to examine skin tone stratification or colorism within the Black community across the United States. Such information would allow us to better understand whether our findings for skin tone among Black Texans in this study were due to the wording of the TDS question and/or its sampling frame. Together, these results highlight the contributions of this study. One contribution was the relatively large subsamples of Black and Hispanic respondents, which allowed for analysis of these groups. Our second contribution was our use of racial-ethnic-skin tone combinations (Adames, 2023 ). This allowed us to assess racial/ethnic differences and skin tone stratification. Because of the subsample sizes within the TDS, we were able to make valuable comparisons to light-skinned White individuals and then lighter-skinned Black and Hispanic respondents. Last, the TDS allowed us to analyze racial/ethnic and skin tone inequality within the second largest U.S. state, one that has a rapidly growing and diversifying population. Indeed, the Hispanic population (~ 12.1M) in Texas is now larger than the White-only population (~ 11.8M) (U.S. Census 2023a ). Texas’ Hispanic population grew by approximately 7 percent between 2000 and 2020, placing it third among all U.S. states for the percent Hispanic (39.3%), trailing only California (39.4%) and New Mexico (47.7%) (authors’ calculations from 2020 U.S. Census data). This contribution underscores the importance of scholars moving beyond national averages to explore underlying variation that is unique to regions, states, and other geo-political boundaries. Scholars should continue to take up research questions that explore race/ethnicity, skin tone, and a host of other outcomes within these areas to better understand how the life chances of racial/ethnic minorities and those with darker skin tones fare compared to White persons and those with lighter skin tones. Future research could target, for example, other dimensions of socioeconomic status like asset ownership, debts, and wealth. Expanding beyond SES to examine mental and physical health could also be fruitful. Alongside the strengths of this study, we should note that our conclusions are based on a 2015 sample of Texans. The year of the TDS is notable because our study captures a time period before the first election of Donald Trump and the subsequent turmoil of that administration, before the George Floyd murder and the rise of the Black Lives Matter movement, and before Covid-19 swept across the world. A future round of data collection for the TDS would be informative because it would provide an opportunity to assess how racial/ethnic and skin tone dynamics have changed – if at all – and how those dynamics matter for socioeconomic inequality. The place of the TDS is also notable because though it is representative of Texas, it is not representative of the United States. Texas, however, in and of itself is worthy of scholarly attention. Texas is the second largest state with slightly more than 30 million residents, a population that is about 8 million residents behind California and ahead of Florida. During the 2010s, Texas’s population grew, in percent change, by 15.9%, which was the third largest among all states. About 4 million residents in the 2010s were foreign-born and Texas had about 10 million Hispanics during the decade. The state has been majority-minority for about two decades, one of only seven U.S. states, and the Hispanic population is now slightly larger than that of the non-Hispanic white population. This is all to say that Texas is a racially/ethnically diverse place with growing populations of color. In short, it is an important place to study racial/ethnic and skin tone stratification and inequality. Conclusion Our study makes several major contributions to the existing literature on skin tone stratification, colorism, and socioeconomic inequality. By utilizing a novel measure of skin tone that requires research subjects to evaluate their own skin tone relative to ethnic peers, we are able to better assess how colorism may operate to shape socioeconomic outcomes within racial/ethnic groups. And by focusing on a specific geographic location of Texas, we are able to isolate whether well established relationships between colorism and socioeconomic inequality are generalizable to specific regions or places; assessing colorism at a national level potentially erases place-based diversity of economic outcomes for racial/ethnic groups. Declarations Table 4. Regression Results for Skin Tone and Socioeconomic Status for Racial/Ethnic Subsamples Note The light gray bars indicate predicted values value that are not statistically significant from “White – light” respondents (see Model 2 for Income in Table 2). Author Contribution MP, MH, and JT conceptualized the study and wrote the main manuscript text.MP prepared the tables and the figures.MC provided the data, provided valuable feedback, and revised the manuscript.All authors reviewed the manuscript. Data Availability The data are available, via request, from Dr. Mary Campbell. References Abascal, M. & Garcia, D. (2022). 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Regression with missing Y’s: An improved strategy for analyzing multiply imputed data. Sociological Methodology, 37, 1, 83–117. U.S. Census Bureau. 2023a. “Hispanic or Latino Origin by Race – Table B03002, 2023: ACS 1-Year Estimates Detailed Tables.” American Community Survey . Retrieved from [https://data.census.gov/table/ACSDT1Y2023.B03002?t=Hispanic%20or%20Latino&g=040XX00US48&y=2023]. U.S. Census Bureau. 2023b. “Educational Attainment – Table S1501, 2023: ACS 1-Year Estimates Subject Tables.” American Community Survey . Retrieved from [https://data.census.gov/table/ACSST1Y2023.S1501?q=s1501&g=010XX00US$0400000]. U.S. Census Bureau. 2023c. “Income in the Past 12 Months (in 2023 Inflation-Adjusted Dollars) – Table S1901, 2023: ACS 1-Year Estimates Subject Tables.” American Community Survey . Retrieved from [https://data.census.gov/table/ACSST1Y2023.S1901?g=010XX00US$1600000]. U.S. Bureau of Labor Statistics. 2024. “Unemployment Rates for States . ” Local Area Unemployment Statistics. Retrieved from [https://www.bls.gov/web/laus/laumstrk.htm]. Wade, T.J., Romano, M.J., & Blue, L. (2004). The effect of African American skin color on hiring preferences. Journal of Applied Social Psychology , 34, 2550-2558. Footnotes The relative validity of the interviewer- and respondent-supplied skin tone assessments has been a source of some debate, but studies employing both find report that they are highly correlated ( r = .80) (Keith et al, 2010 :53; Monk, 2015 :418), and one suggests that respondent self-appraisals may be a better predictor in some contexts (Monk, 2015 ). The TDS screened respondents by asking them about their racial or ethnic background early in the survey. Respondents must have identified Black or African American, Hispanic or Latino/a, or White or European American or Anglo as one of their racial/ethnic backgrounds to continue with the survey. The TDS does not contain information on parental socioeconomic status. The absence of this information may not affect our results as some previous research on socioeconomic attainment demonstrates that parental factors, like education and income, do not explain away skin tone effects (e.g., Keith & Herring, 1991 ; AUTHOR); however, Abascal & Garcia (2023) demonstrate the importance of considering how skin tone shapes familial resources, which in turn, can affect labor market outcomes. Missing data are quite low in the TDS. For race/ethnicity, there was no missing data and only one respondent did not provide education information. Among our variables, missingness was highest for income, though only 174 respondents (14%) did not report a valid response. Tables Tables 1 to 4 are available in the Supplementary Files section. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8604916","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":591102178,"identity":"605646c3-dc9d-4b76-9cc0-c6ce91283905","order_by":0,"name":"Matthew 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2","display":"","copyAsset":false,"role":"figure","size":88930,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePredicted Values of Income, by Race/Ethnicity and Skin Tone\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: The light gray bars indicate predicted values value that are not statistically significant from “White – light” respondents (see Model 2 for Income in Table 2).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8604916/v1/c3c545333a6aa08337e880ab.png"},{"id":103343821,"identity":"99699bf7-099d-45b5-b54e-955606cda2ff","added_by":"auto","created_at":"2026-02-24 15:56:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":80852,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConceptual Diagram of Path Analysis for Race/Ethnicity, Skin Tone, and Socioeconomic Status\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8604916/v1/cc7ce1052272456c05e36466.png"},{"id":103506039,"identity":"6040bd1d-ebd1-4b54-a46d-389f7c4e043c","added_by":"auto","created_at":"2026-02-26 13:33:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":932967,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8604916/v1/a6aad375-a208-4806-babc-be2c37adbf6a.pdf"},{"id":103343820,"identity":"168d8503-7fe8-414a-9e45-50f3ff18c9d9","added_by":"auto","created_at":"2026-02-24 15:56:49","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":32780,"visible":true,"origin":"","legend":"","description":"","filename":"Table1234.docx","url":"https://assets-eu.researchsquare.com/files/rs-8604916/v1/31e988b9e44739de9ea2a79c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Shades of Inequality: Race/Ethnicity, Skin Tone, and Socioeconomic Status","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThe social costs of skin tone inequality in the United States are substantial and can be as great as \u0026ndash; or greater \u0026ndash; than that of the Black-White divide so often studied. For example, Monk (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2015\u003c/span\u003e:402) documents that the average education gap between Black and White individuals is smaller (10.2 months) than that of between the lightest- and darkest-skinned Black individuals (15.4 months). Such dual influences \u0026ndash; of both race- and skin tone-based inequality \u0026ndash; are apparent for other educational, health, and criminal justice outcomes as well (e.g., Monk, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Hunter (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2007\u003c/span\u003e:238) describes these dual influences as two \u0026ldquo;systems of discrimination\u0026rdquo; that both distinctly and jointly affect life chances. These systems of discrimination are rooted in historical inequalities, contributing to lighter skin \u0026ndash; independent of a White racial status \u0026ndash; facilitating the transmission of education, health, and socioeconomic advantage. Such a \u0026ldquo;preference for whiteness\u0026rdquo; (Goldsmith et al., 2007) continues today with White and lighter-skinned individuals receiving advantages due to their elevated positions within race- and skin tone-based hierarchies.\u003c/p\u003e \u003cp\u003eOne area where the effects of both racial/ethnic and skin tone stratification can be clearly seen is in the study of socioeconomic inequalities (e.g., Goldsmith et al., 2007; AUTHOR; Reece, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The present study joins with this sizeable and growing body of research that documents the relationships between race/ethnicity and skin tone with socioeconomic outcomes, including educational attainment (e.g., Gullickson, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Hersch, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Hughes \u0026amp; Hertel, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Monk, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), employment (e.g., Monk, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), and income (e.g., Hersch, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Hughes \u0026amp; Hertel, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Hunter, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Keith and Herring, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Kiang \u0026amp; Takeuchi, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Monk, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This research consistently finds that darker-skin toned individuals are generally associated with relative disadvantage in these \u0026ndash; and other \u0026ndash; dimensions of socioeconomic status, all else being equal. Some studies examine skin tone effects within racial/ethnic groups (e.g., Hersch, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Keith \u0026amp; Herring, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Monk, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Reece, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), though often the average effect of skin tone is presented alongside the average effects of multiple racial/ethnic groups (e.g., Hersch, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; AUTHOR). More recent research creates combinations of race/ethnicity and skin tone so that skin tone effects can be simultaneously examined within and between groups (e.g., Adames, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur study makes three contributions to the existing literature on skin tone and socioeconomic inequality. First, we use the 2015 Texas Diversity Survey (TDS) (Keith \u0026amp; Campbell, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This is a unique dataset that possesses several strengths that complement existing research in this area. One strength is that due to its focus on sampling Black and Hispanic individuals, there are sufficient sample sizes that allow for us to carry out within-group analyses. Therefore, we are able to present findings for the full sample as well Black Hispanic, and White subsamples. Our second contribution is to adopt the approach of Adames (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and break out the racial/ethnic categories by skin tone. This allows us to assess the effects of race and ethnicity and then skin tone within those categories. Finally, our study draws attention to the importance of states and regions. Studies using nationally representative data provide valuable points of comparison with their estimates averaged across all sampled geographic units. However, there is meaningful variation within the United States and the TDS data allow us to speak to that. In particular, Texas is distinct from other U.S. states in a variety of ways and has a dual location as part of the historic Southwest and the South U.S. Census region. Historically, it has the unique distinction of being both a Spanish colony and slave state. It may be that skin tone inequalities, like racial/ethnic inequalities more broadly, are place-based; therefore, it is important to look beyond national samples to document important, more locally driven, racial/ethnic processes.\u003c/p\u003e"},{"header":"CONCEPTUAL FRAMEWORK","content":"\u003cp\u003e\u003cstrong\u003eColorism and Racial/Ethnic Inequality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSkin tone- and race/ethnicity-based inequality constitute two distinct and simultaneously entangled \u0026ldquo;systems of discrimination\u0026rdquo; (Hunter, 2007:238). Both systems concurrently privilege lighter-skinned individuals and/or Whites over darker-skinned and non-White individuals via \u0026ldquo;subtle cues of disfavor\u0026rdquo; as well as overt discrimination (Hunter, 2007:241). Evidence of these systems of discrimination abounds across U.S. institutions, including education, criminal justice, housing, and financial, among others (e.g., Brown, 2016; Herring and Henderson 2016; Lanuza, Peterson and Omori 2023; Lee et al. 2015; Massey 2007; Monk 2019; see also Pager and Shepherd 2008). In this way, darker-skinned and non-White individuals have been persistently disadvantaged (Brown, 2016), which has resulted in disparate life chances for these individuals (Bonilla-Silva 1997, 2013).\u003c/p\u003e\n\u003cp\u003eThe social significance of skin tone has ancestral roots in the European colonization of the indigenous peoples of Africa and Latin America (e.g., Abascal \u0026amp; Garcia, 2022; strmic-pawl, Gonlin \u0026amp; Garner, 2021). The supremacy of lighter-skinned Europeans over darker-skinned indigenous populations engendered a skin tone hierarchy and social structures that benefited colonizers politically, economically, and socially. Skin tone-based structural inequality was supported by an ideology of colorism, a preference or favoritism for lighter skin, which justified greater oppression of darker-skinned people (Hunter, 2005). For Black individuals in the United States, this preference can be traced back to slavery (e.g., Reece, 2018, 2019). Skin tone served as a means for sorting the enslaved population into domestic and agricultural jobs (e.g., Frazier 1957; Johnson 1996). Less physically demanding domestic positions (e.g., house servants, butlers, maids, cooks) were commonly occupied by lighter-skinned enslaved individuals, who generally had access to better food, shelter and opportunities to learn to read and write (Bodenhorn \u0026amp; Ruebeck, 2007; Frazier, 1957; Horton \u0026amp; Horton, 1997) or acquire a skilled trade (Frazier, 1957; Horton \u0026amp; Horton, 1997; Margo, 1992). In contrast, darker-skinned enslaved individuals were often forced to perform labor-intensive agricultural jobs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eColorism and its associated skin tone stratification persisted after slavery ended. In the late 19th century, \u0026ldquo;Mulatto\u0026rdquo; individuals continued to experience numerous socioeconomic advantages compared with those of \u0026ldquo;pure\u0026rdquo; Black heritage (Bodenhorn \u0026amp; Ruebeck, 2007; Gullickson, 2010; Reece, 2018). Persistent skin tone stratification reflected White individuals\u0026rsquo; belief that \u0026ldquo;Mulatto\u0026rdquo; individuals\u0026rsquo; white ancestry made them more intelligent, harder working, and less deviant than Black people (e.g., Bodenhorn, 2006; Frazier, 1957; see Reece, 2018 for further discussion). Moreover, social distinctions among Black individuals existed. Lighter-skinned persons sought to preserve their distinctiveness and advantaged position relative to darker-skinned persons (Bodenhorn, 2003; Meier \u0026amp; Lewis, 1959). The practice of social distancing from darker-skinned Black individuals may have provided Mulatto individuals connections to powerful White individuals, which could provide greater economic opportunity and strengthen social boundaries between them and Black individuals (Reece, 2018).\u003c/p\u003e\n\u003cp\u003eFor Hispanic individuals, the system of colorism \u0026ndash; and the skin tone stratification that accompanies it \u0026ndash; originated during the Spanish colonization of the Americas (Murguia \u0026amp; Telles, 1996). The colonizers perceived themselves as innately superior, which was the ideological foundation of a social and economic hierarchy that placed White individuals at the top, mixed-race people in the middle, and enslaved indigenous peoples and Africans at the bottom (Hunter, 2005; Keith \u0026amp; Monroe, 2016). Variations in skin tone and other phenotypical characteristics were conflated into a multi-category racial caste system that determined access to power and privilege, a system that favored \u0026ldquo;pure-blood\u0026rdquo; Spaniards and, to a lesser degree, their mixed-heritage offspring (Chavez-Due\u0026ntilde;a et al., 2014). These hierarchies existed throughout the Spanish colonies for three centuries. After Latin American countries achieved independence, an ideology of \u0026ldquo;\u003cem\u003emestizaje\u003c/em\u003e\u0026rdquo; \u0026ndash; the idea that all Latin Americans are of mixed heritage \u0026ndash; emerged, which obfuscated Spanish privilege but did little to actually change the system of racial privilege. In fact, it bolstered the notion that dark skin signified barbarism and inferiority (Hunter, 2005).\u003c/p\u003e\n\u003cp\u003eThis ideology\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ewas incorporated into the American Southwest following the US conquest of Mexico in 1848 (Hunter, 2005). White settlers who moved into this sparsely populated region saw themselves as superior to mestizo, indigenous and Black people, consistent with prevailing color ideologies in the US and Mexico. The Mexican citizens incorporated into the United States were subordinated into low-paying jobs in agriculture, ranching, mining, and railroad building (Acu\u0026ntilde;a, 1988; Barrera, 1979). They were stereotyped as biologically, culturally, and socially inferior (Murgia \u0026amp; Telles, 1996). By the early 1920s, escalating industrialization and urbanization increasingly concentrated the Mexican-origin population into economically disadvantaged southwestern barrios (Acu\u0026ntilde;a, 1988; Escobar, 1999), a pattern that continues today (Alba \u0026amp; Nee, 2003). Colorism may play a role in segregation and disadvantage among this population, with some research indicating lighter-skinned people are less likely than darker-skinned people to live in segregated, less affluent areas of some cities (Grebler et al., 1970; Relethford et al., 1983).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eColorism and the Preference for Whiteness Hypothesis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eScholars have sought to explain the development and persistence of skin tone disadvantages in two ways, one involving structural approaches, the other sociocognitive processes. As described above, the structural perspective emphasizes longstanding, large-scale patterns of social relationships that created persistent patterns of skin-tone disadvantage. In this view, socioeconomic disadvantages associated with darker skin tones reflect not only present-day skin tone discrimination, but also cumulative effects of skin tone-based discrimination generated over previous decades (Abascal \u0026amp; Garcia, 2022; Branigan et al., 2019). Ancestors with lighter or darker skin tones also likely experienced advantage or disadvantage, respectively, which allowed them to acquire and then transmit different resources over generations. This process has been called \u0026ldquo;ancestrally accumulated disadvantage\u0026rdquo; (Hill, 2002) or \u0026ldquo;inherited (dis)advantage\u0026rdquo; (Abascal \u0026amp; Garcia, 2022), whereby one\u0026rsquo;s ancestors\u0026rsquo; location within racial/ethnic and skin tone hierarchies substantially affects one\u0026rsquo;s present-day socioeconomic status, independent of a person\u0026rsquo;s own racial/ethnic status and location on a skin tone continuum.\u003c/p\u003e\n\u003cp\u003eWhereas the structural approach focuses on the origin and persistence of skin tone stratification systems, the complementary social-psychological perspective builds on it by focusing on how these embedded patterns of discrimination manifest as implicit and explicit biases in day-to-day interactions. The \u0026ldquo;preference for whiteness\u0026rdquo; hypothesis has been advanced to explain how colorism produces inequalities and subsequently extended to incorporate the important role of stereotypes (Goldsmith et al., 2007; Painter et al., 2016). Initially, this hypothesis relied on social identity theory to explain how sociocognitive dynamics elicit discriminatory responses based on skin tone (Goldsmith et al., 2007), an approach also used to explain discrimination based on racial categorization (e.g., Bonilla-Silva et al., 2006).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eIt maintains that social assignment to in-groups and out-groups generates differential treatment and allocation of social rewards based on \u003cem\u003ein-group\u0026nbsp;\u003c/em\u003efavoritism.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIndividuals are said to mentally categorize the social world in terms of group membership and respond to others on the basis of those categorizations (e.g., Tajfel et al., 1971). Members of in-groups are treated with favorable attitudes and behaviors while out-groups receive unfavorable responses, even when group membership is defined in the most minimal way (e.g., in arbitrarily assigned groups lacking normative structure and interpersonal interaction) (see Brewer, Brown, \u0026amp; Gilbert, 1998). In situations involving intergroup relations, shared perceptions of in-group similarity increase group cohesiveness and promote ethnocentrism, resulting in favoritism toward the in-group (Turner et al., 1987). Concerning colorism, these processes manifest both inter- and intra-racially (strmic-pawl et al., 2021). When a low-status (darker-skin) group accepts the perceived superiority of a high-status (lighter-skin) group, the members of the low-status in-group may show favoritism for the high-status out-group (Tajfel \u0026amp; Turner, 1979, 1986). Goldsmith and colleagues (2007) argue that the preference for whiteness occurs when this \u0026quot;status effect\u0026quot; outweighs the categorization effect of race/ethnicity, which explains why both whites and dark-skinned blacks respond more favorably to light-skinned blacks.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe bias predicted by social identity theory primarily involves relatively mild forms of in-group favoritism. Although it may be essential to the maintenance of in-group cohesion and solidarity, this in-group bias alone may not be sufficient to produce more serious forms of out-group discrimination (Brewer, 2007). Painter and colleagues (2016) build on the original preference for whiteness hypothesis insights about group categorization processes by incorporating the related sociocognitive conceptualization of stereotyping. Their approach develops the cognitive dynamics of derogation and discrimination toward out-groups.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStereotypes both assign an out-group\u0026rsquo;s position (Fiske, 1998) and justify (Jost \u0026amp; Banaji, 1994) the existing social status hierarchy. Members of various racial/ethnic groups (including Blacks, Hispanics, and Whites) tend to judge darker-skinned members of both their in-group and out-groups more stereotypically than lighter-skinned persons (Blair et al., 2002; Hannon, 2015; Maddox \u0026amp; Gray, 2002; Uhlmann et al., 2002). Stereotypes allow people to use socially salient phenotypical characteristics to quickly categorize and rapidly respond to others in routine interactions without conscious deliberation, particularly during interactions involving situational factors (e. g., time constraints, complex stimuli) that overload cognitive resources (Fiske, 1998; Macrae \u0026amp; Bodenhausen, 2000). Stereotypes may trigger subtle but meaningful discriminatory responses to out-group members (e.g., in employment decisions) without either conscious deliberation or emotionally based prejudice being involved (e.g., Fiske, 1998; Pager et al., 2009).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStereotype-based responses to out-group members are, however, conditioned by social context. Skin tone and racial/ethnic related stereotypes may be activated only when relevant to the situation at hand (e.g., Maddox \u0026amp; Chase, 2004). Those involving characteristics such as intelligence and education (see Maddox \u0026amp; Gray, 2002) may be especially relevant in situations involving individual economic outcomes (e.g., Monk, 2014). Although overt patterns of intra- and interracial discrimination have increasingly faded away in the United States (e.g., Bonilla-Silva, 2013), the historical legacy of colorism continues to be seen in these more covert forms of preferential treatment that favors whites and lighter-skin minority persons in the allocation of social rewards (e.g., Bonilla-Silva et al., 2006; Hannon, 2015; Wade et al., 2004).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCurrent Study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, we build upon prior work by examining skin tone and racial/ethnic stratification for several key dimensions of socioeconomic status. Following previous research (e.g., Goldsmith et al., 2007; Painter et al., 2016; Reece, 2021), we hypothesize that darker-skinned individuals, all else being equal, will be disadvantaged relative to lighter-skinned individuals.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMost studies of colorism and skin tone stratification use datasets that have an interviewer-supplied measure of skin tone. Other surveys, including the TDS, collect skin tone information via respondent self-assessment, also referred to as \u0026ldquo;reflected appraisal\u0026rdquo; or \u0026ldquo;self-rated\u0026rdquo; skin tone within the literature. Other datasets that include a reflected appraisal of skin tone are the National Survey of American Life (NSAL), the Detroit Area Study (DAS), and the National Social Life, Health, and Aging Project (NSHAP). The advantage of a relative measure is that it captures how respondents see themselves and is likely grounded in their understanding of where their skin tone falls along a continuum within their community (Monk, 2015). A respondent\u0026rsquo;s appraisal of their skin tone reflects a \u0026ldquo;sense of place and stature among . . . peers\u0026rdquo; (Monk, 2015:431-32), which could include neighborhoods and peer groups, as well as experiences with gatekeepers of educational and business organizations. Reflected appraisal measures also eliminate concerns about potential interviewer effects since interviewers are not involved (see Hannon \u0026amp; Defina, 2014; Hill, 2002; Monk, 2015).\u003ca href=\"#_ftn1\" name=\"_ftnref1\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eOur study joins with previous work that has relied upon valuable data collection efforts that sampled from specific areas, like Detroit (DAS), San Francisco and Honolulu (Filipino American Community Epidemiological Study), or Atlanta, Boston, Detroit, and Los Angeles (Multi-City Study of Urban Inequality). Our focus on Texas provides an important contribution because place-specific investigation provides valuable points of comparison so that (potential) variation by skin tone and/or race/ethnicity can be better understood. We anticipate interesting contrasts with studies using geographically concentrated datasets and/or the nationally representative data of the National Survey of Black Americans, NSAL, and the General Social Survey.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur study contributes to this line of research in a unique way \u0026ndash; we use data that is representative of an entire state, the second largest in the United States, to provide more insight into this body of city- and region-specific research. It will also allow us to assess how Texas aligns (or not) with larger national-level patterns by comparing our findings to nationally representative studies. It is critical to understand how the context of particular places, such as Texas, moderate the influences of colorism. Texas is notable for a number of reasons. For one, its population (~30.5M) trails only that of California (~39M). In terms of racial/ethnic diversity, it is a majority-minority state with Hispanics constituting the largest racial/ethnic group (U.S. Census, 2023a). Since we are analyzing socioeconomic outcomes in this study, state-level values are informative: Texas ranks 27\u003csup\u003eth\u003c/sup\u003e for the percent of the population holding a bachelor\u0026rsquo;s degree or higher, 34\u003csup\u003eth\u003c/sup\u003e for the unemployment rate, and 19\u003csup\u003eth\u003c/sup\u003e for average household income (U.S. Census Bureau, 2023b, 2023c; U.S. Bureau of Labor Statistics, 2024). Texas is officially part of the South Census region; however, researchers who examine the Hispanic population commonly include it in a Southwest sub-region, along with Arizona, California, Colorado, and New Mexico (e.g., Barrera, 1979; Holmes \u0026amp; Painter, 2023; McWilliams et al, 2016). Historically, a substantial majority of the Hispanic population, primarily those of Mexican-origin, has been concentrated in the Southwest and the Southwestern states, including Texas, remain the cultural, economic, and political center of the Mexican-origin community in the US (Saenz \u0026amp; Morales, 2015). \u0026nbsp;\u003c/p\u003e"},{"header":"DATA AND METHODS","content":"\u003cp\u003e\u003cstrong\u003eData\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, we use the 2015 Texas Diversity Survey (Keith \u0026amp; Campbell, 2015). This was a representative telephone survey (98% via cell phone) that collected information from 1,306 Black, Hispanic, and White adult Texans.\u003ca href=\"#_ftn1\" name=\"_ftnref1\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e2\u003c/sup\u003e Data were collected by the Public Policy Research Institute at Texas A\u0026amp;M University and respondents could complete the survey in either English or Spanish. For our purposes, the TDS is valuable data because its stratified sampling strategy ensured adequate sample sizes for each of the three racial/ethnic groups. Further, the survey\u0026rsquo;s detailed questions on race/ethnicity, skin tone, and socioeconomic status allow us to test our hypotheses within a relatively understudied, yet rapidly growing, part of the United States. Previous work has used the TDS to explore racial/ethnic identities and statuses (Gonlin et al., 2020), skin tone and discrimination (Gonlin, 2020), everyday discrimination (Harnois et al., 2019), media consumption and racial residential preferences (Korver-Glenn et al., 2020), and discrimination and social media use (Miller et al., 2021).\u003c/p\u003e\n\u003cp\u003eWe used the detailed racial/ethnic identity information within the TDS to remove 61 non-Hispanic multiracial respondents. This allowed us to retain 276 Black, 343 Hispanic, and 626 White individuals for our subsample analysis. Our full analytic sample was 1,245.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eOutcome variables\u003c/u\u003e. We include three variables from the TDS that measure socioeconomic status. First, the TDS asked respondents about their highest completed grade in school. Response options, among others, included \u0026ldquo;some high school,\u0026rdquo; \u0026ldquo;high school diploma or GED,\u0026rdquo; \u0026ldquo;some college,\u0026rdquo; and \u0026ldquo;graduate degree (master\u0026rsquo;s or doctorate).\u0026rdquo; We recoded this variable to create a continuous education variable using, for example, 10, 12, 14, and 18 years of education for the examples just listed. Second, respondents in the TDS selected the best option from a list that described their current employment status. Here, we followed Monk (2014) and created a dichotomous variable for full-time employment (1=yes). Last, TDS respondents selected a range (e.g., \u0026ldquo;$45,001 - $60,000\u0026rdquo;) that reflected their before-tax household income. We created a continuous income variable by using the midpoint of each range.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eExplanatory variables\u003c/u\u003e. The TDS collected skin tone information, which proxies for colorism, by asking respondents to describe their skin color/complexion \u003cem\u003ecompared to most people\u003c/em\u003e in their racial/ethnic group. Following previous research that used the TDS to examine skin tone (Gonlin 2020), we combined the \u0026ldquo;very light\u0026rdquo; and \u0026ldquo;light\u0026rdquo; categories, retained the original \u0026ldquo;medium\u0026rdquo; category, and then combined the \u0026ldquo;dark\u0026rdquo; and \u0026ldquo;very dark\u0026rdquo; categories.\u003c/p\u003e\n\u003cp\u003eWe then combined the skin tone and racial/ethnic variables to create three skin tone variables (light, medium, dark) for the three racial/ethnic groups. Light-skinned White individuals were the reference category.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eControl variables\u003c/u\u003e. For controls, we included a continuous variable for age and dichotomous variables for first-generation immigrant (1=born outside the United States), sex (1=female), and relationship status (1=married).\u003ca href=\"#_ftn2\" name=\"_ftnref2\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalytic Approach\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used OLS and logistic regression in our analysis. All results were weighted with the weights created by the TDS investigators using the American Community Survey. Age and education (when used as a control variable) were centered, using the analytic sample and subsample means where appropriate.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1 contains descriptive information, including t-tests for differences between both Black and White respondents and then Hispanic and White respondents. We use the Satterthwaite method that assumes unequal variances. Tables 2 and 4 contain our results for the full analytic sample and then subsample analyses. In both tables, we present two models. Model 1 contains the skin tone-race/ethnicity combinations and Model 2 adds the control variables. To address missing data, we used the multiple imputation, then deletion (MID) procedure (von Hippel, 2007).\u003ca href=\"#_ftn3\" name=\"_ftnref3\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e4\u003c/sup\u003e Practically, we used SAS Proc MI to create ten data sets for each model using the variables in the regression analyses. We then removed observations with missing values on the relevant outcome variable after the imputation process. Analyses were conducted using SAS Proc Surveyreg or Surveylogistic, which provided robust standard errors. Results were returned with SAS Proc MIAnalyze.\u003c/p\u003e\n\u003cp\u003eTable 3 contains the standardized total, direct, and indirect effects for the race/ethnicity-skin tone combinations from a path analysis (using Proc Calis) of the full analytic sample. Missing data for the path analysis was addressed using Proc Calis\u0026rsquo;s full information maximum likelihood approach.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003eDescriptive Statistics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 1 contains descriptive statistics for the variables in our analysis. In the full sample, TDS respondents average about 14 years of education, which differs between Hispanic (about 13 years) and White (15 years) respondents. 52 percent of the sample is engaged in full-time work and this does not differ among the racial/ethnic subsamples. In contrast, White respondents have the highest income (~$110,000), which is greater than both Black (~$77,000) and Hispanic (~$60,000) respondents. These statistically significant differences remain for the log of income as well.\u003c/p\u003e\n\u003cp\u003eFor skin tone, about half of the full sample assess their skin tone as medium (47%) relative to most people in their racial/ethnic group. Only 10% rates their skin tone as darker. By racial/ethnic group, there are notable differences. Fewer Black and Hispanic respondents assess their skin tone as lighter than the group average when compared to White respondents and more racial/ethnic minorities chose medium when rating their skin tone.\u003c/p\u003e\n\u003cp\u003e*** Table 1 about here ***\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;To better highlight how skin tone varies across respondents in the TDS, Figure 1 contains the percentage distribution (unweighted) of the three racial/ethnic groups and then of the skin tone-racial/ethnic combinations. Relatively few Black respondents rate their skin tone as lighter and more self-assess as either medium or darker. In contrast, more Hispanic respondents think their skin tone is either light or medium compared to most people within their ethnic group; few assess their skin tone as darker. Among White respondents, \u0026ldquo;light\u0026rdquo; is the modal category.\u003c/p\u003e\n\u003cp\u003e*** Figure 1 about here ***\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRegression Results \u0026ndash; Full Sample\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 contains regression results for our three outcome variables. In Model 1 for education, \u0026ldquo;Black \u0026ndash; light\u0026rdquo; is significantly different than the reference category of \u0026ldquo;White \u0026ndash; light\u0026rdquo;. This was anticipated from our descriptive analysis in Table 1. For Hispanic individuals, all three skin tone categories are statistically significant. This indicates that Hispanic respondents \u0026ndash; particularly those with a self-assessed medium skin tone \u0026ndash; are associated with less education than light-skinned White respondents. In Model 2 when controls are introduced, only \u0026ldquo;Hispanic \u0026ndash; light\u0026rdquo; and \u0026ldquo;Hispanic \u0026ndash; medium\u0026rdquo; are still associated with significantly less education (about two-thirds and 1.67 years, respectively) than light-skinned White respondents. For Hispanic individuals with dark skin tones, the loss of statistical significance for these categories when including controls, particularly first-generation immigrant status and being married, suggests the importance of these two statuses for educational attainment. Notably, there are only 27 Hispanic individuals with a self-assessed darker complexion. This small sample size could explain why the coefficient is in the expected direction though non-significant. Last, there are no differences in educational attainment among White respondents by skin tone.\u003c/p\u003e\n\u003cp\u003eFor full-time employment, there is no statistically significant variation by race/ethnicity or skin tone in either Models 1 and 2.\u003c/p\u003e\n\u003cp\u003eFor the log of income, the statistically significant differences by skin tone and race/ethnicity that are evident in Model 1 persist when controls are included in Model 2. Here, Black respondents with medium and dark relative skin tone are associated with lower logged income than light-skinned White respondents. There is no relationship between light skin tone for Black respondents and income in either model. There are 40 individuals in this group (while there are 136 medium- and 95 darker-skinned Black respondents); therefore, the small group size for light skin toned Black respondents could explain the lack of a statistically significant relationship with income. An equality of coefficients test indicated that the medium and dark categories were statistically equivalent for Black respondents. This suggests a simultaneous race- and skin tone hierarchy between these two groups: White respondents (regardless of skin tone) and lighter-skinned Black respondents are associated with higher income while darker-skinned Black individuals are associated with lower income.\u003c/p\u003e\n\u003cp\u003eAmong Hispanic individuals, there is clear stratification by skin tone: Hispanic respondents who assess their skin tone as lighter than their co-ethnic peers are associated with the smallest income difference relative to the White sample (regardless of skin tone) while those with a darker complexion are associated with the largest difference. Equality of coefficients tests indicate that there is a statistically significant difference between light and dark Hispanic participants. Taken together, there are three tiers between Hispanic and White respondents: White participants report the highest income, followed by lighter-skinned Hispanic, and then darker-skinned Hispanic participants.\u003c/p\u003e\n\u003cp\u003e*** Table 2 about here ***\u003c/p\u003e\n\u003cp\u003e*** Figure 2 about here ***\u003c/p\u003e\n\u003cp\u003eTo illustrate our findings for logged income, Figure 2 presents predicted values by skin tone-race/ethnic combinations, holding variables at their means. White respondents are associated with the most income, regardless of skin tone, with about $70,000. Black respondents with a medium and dark self-assessed skin tone are associated with about $50,000. This amount is similar to the predicted value for light-skinned Hispanic respondents (~$52,000). Among all of the skin tone-race/ethnic combinations, medium- (~$46,000) and dark-skinned (~$36,000) Hispanic respondents are associated with the lowest predicted values for income.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRegression Results \u0026ndash; Path Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur next analytic approach is a path analysis, illustrated in Figure 3. This allows us to better understand how race/ethnicity and skin tone are related to our outcomes. For example, path analysis allows us to demonstrate how the combinations of skin tone and race/ethnicity are directly connected with income and also indirectly connected through education and/or full-time employment. This approach improves upon Monk (2014), who can only speculate about how skin tone indirectly operates for his various outcomes while we can explicitly test the relationships.\u003c/p\u003e\n\u003cp\u003e*** Figure 3 about here ***\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;To conserve space, Table 3 contains select standardized results from the path analysis for the skin tone-race/ethnic combinations. For both \u0026ldquo;Black \u0026ndash; light\u0026rdquo; and \u0026ldquo;White \u0026ndash; medium\u0026rdquo; we observe negative relationships with equivalent coefficient sizes between these groups and education. For Hispanic respondents, however, the coefficient sizes are much larger and still negatively associated with education. For full-time employment, there are no direct effects with skin tone. We do see evidence of an indirect relationship between skin tone and full-time employment through education for light-skinned Black and medium skin-toned White respondents. Again, the coefficient sizes are equivalent. There is a similar indirect relationship through education for light- and medium-hued Hispanic respondents, also with larger coefficients sizes. Last, for income, there is a direct effect of medium and dark skin tones for Black respondents on income; however, there are no indirect effects through either education or employment. For Hispanic respondents, there are both direct effects of light and medium skin tones on income and indirect effects flowing through both education and full-time employment. We do not observe any relationships, either direct or indirect, between skin tone and income for White respondents.\u003c/p\u003e\n\u003cp\u003e*** Table 3 about here ***\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRegression Results \u0026ndash; Subsample Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe final piece of our analytic approach is presented in Table 4, which contains select results from our Model 2 (Table 2) specification. This approach allows us to assess darker skin tones versus a light skin tone for group-specific outcomes while accounting for group-specific averages with the control variables. Because of the smaller sample sizes for these Black (N=276) and Hispanic (N=343) respondents, we broaden our assessment of statistical significance. First, among Black respondents in Panel 1, both medium and dark skin tone are associated with more education when compared to Black respondents who assess their skin tone as lighter than their same-race peers. This is counter to previous research that examines Black Americans within the NSAL (Monk, 2014) and NSBA (Hersch, 2006; Hunter, 2002). These studies\u0026rsquo; findings align with the larger body of literature on skin tone inequality that consistently documents that as skin tone lightens, education increases. Notably, Monk (2014) uses the interviewer-assessment of skin tone in the NSAL instead of the relative skin tone measure, which was used in Keith et al. (2010) who analyzed the same data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSecond, there is no relationship between skin tone and either full-time employment or income among Black respondents. The former finding is reflected in Monk\u0026rsquo;s (2014) study, but the latter is not as Monk finds that lighter skin tone is associated with higher income. Importantly, as we note above, there are 40 Black respondents who report a light skin tone relative to most people in their racial group, which is the smallest of the three skin tone categories among Black respondents.\u003c/p\u003e\n\u003cp\u003eAmong Hispanic respondents (Panel 2), those who rate their skin tone as medium relative to other Hispanic individuals are associated with less education than light-toned Hispanic respondents. The coefficient indicates this is almost 1 year of education. As with Black respondents, there is no relationship between skin tone and full-time employment among Hispanic respondents. However, darker-toned Hispanic respondents are associated with less income than lighter-toned Hispanic respondents. As we note above, there are only 27 dark-toned Hispanic respondents; therefore, this finding must be treated with caution.\u003c/p\u003e\n\u003cp\u003eLast, we note that there is a negative relationship between medium skin tone and education among White respondents. This indicates that this group is associated with less education than light-skinned White participants.\u003c/p\u003e\n\u003cp\u003e*** Table 4 about here ***\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn this study, we used the TDS to examine the relationship between race/ethnicity, skin tone, and socioeconomic status. Briefly, our results from our full sample indicated that that race and skin tone did not affect two critical dimensions of socioeconomic status \u0026ndash; educational attainment and full-time employment \u0026ndash; for Black individuals. We did see evidence of a skin tone hierarchy for income, with lighter-toned Black individuals having similar incomes as White individuals (regardless of skin tone) and darker-skinned Black individuals having a lower income. For Hispanic respondents, we found that those with medium and dark skin tone had fewer years of educational attainment and medium-skin toned Hispanics were less likely to be employed full time. For income, however, we saw a clear White-Hispanic racial-ethnic divide and then further stratification by skin tone.\u003c/p\u003e \u003cp\u003eOur path analysis illuminated how skin tone was both directly related to socioeconomic status and indirectly affected a given outcome. For example, relative to light-skinned White respondents, Hispanic respondents with a medium and dark skin tone were associated with a direct effect of skin tone on income and a simultaneous indirect effect via lower educational attainment. This pattern contrasts with that for Black respondents, where there was only a direct effect associated with income, and White respondents, where there was only limited evidence of direct effects and no relationships with indirect effects for our given outcomes. Our approach here demonstrates the indirect connections \u0026ndash; at least for Hispanic respondents \u0026ndash; between skin tone and various outcome measures that previous research pointed to, but did not test (Monk, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor our analysis of skin tone stratification among the racial/ethnic subsamples, we documented few differences where skin tone affected socioeconomic attainment. Among Black individuals, we found a positive relationship between skin tone and educational attainment, which is counter to the general finding throughout the skin tone literature that darker skin tones are associated with worse socioeconomic outcomes (e.g., Hersch, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Hunter, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Monk, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). We attribute this unique counter-finding to two potential reasons. First, the TDS used a relative skin tone measure. As we note above, scholars have called attention to the possibility that different approaches to measuring skin tone could produce inconsistent findings regarding skin tone and various outcomes, including measures of socioeconomic status (e.g., Keith et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Monk, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). With a relative measure of skin tone, respondents are making self-evaluations relative to their same-race/co-ethnic peers, which could reflect their social position when compared to their reference group (Monk, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Further, a relative measure of skin tone also removes any concerns of interviewer effects (e.g., Hannon \u0026amp; Defina, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Hill, 2002; Monk, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSecond, the TDS sampled Black Texans while the NSAL (Monk, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and NSBA (Hersch, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Hunter, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) are nationally representative. Earlier in this study we noted Texas\u0026rsquo; unique position as both part of the historic Southwest and the South regions and we noted where Texas ranks, relative to other states, on various socioeconomic measures. Texas\u0026rsquo;s Black population may also differ in important ways from those in other states. For example, Texas has more Black residents than any other state (~\u0026thinsp;3.6\u0026nbsp;million, followed by Georgia at ~\u0026thinsp;3.4\u0026nbsp;million; authors\u0026rsquo; calculations from 2023 American Community Survey 5-year estimates). However, as a percent of its population, Texas\u0026rsquo;s black population (12.2%) is slightly less than the national average (12.4%), far behind some Southern states (e.g., Mississippi (37.0%), Georgia (31.3%), and higher than all Southwest states (e.g., Nevada (9.4%), California (5.5%)). State-specific data, like that of the TDS, provide a valuable reference point for scholars to compare states and communities to understand variation in socioeconomic \u0026ndash; and other \u0026ndash; outcomes within Black populations and relative to other racial/ethnic groups. Future research should continue to examine skin tone stratification or colorism within the Black community across the United States. Such information would allow us to better understand whether our findings for skin tone among Black Texans in this study were due to the wording of the TDS question and/or its sampling frame.\u003c/p\u003e \u003cp\u003eTogether, these results highlight the contributions of this study. One contribution was the relatively large subsamples of Black and Hispanic respondents, which allowed for analysis of these groups. Our second contribution was our use of racial-ethnic-skin tone combinations (Adames, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This allowed us to assess racial/ethnic differences and skin tone stratification. Because of the subsample sizes within the TDS, we were able to make valuable comparisons to light-skinned White individuals and then lighter-skinned Black and Hispanic respondents. Last, the TDS allowed us to analyze racial/ethnic and skin tone inequality within the second largest U.S. state, one that has a rapidly growing and diversifying population. Indeed, the Hispanic population (~\u0026thinsp;12.1M) in Texas is now larger than the White-only population (~\u0026thinsp;11.8M) (U.S. Census \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). Texas\u0026rsquo; Hispanic population grew by approximately 7 percent between 2000 and 2020, placing it third among all U.S. states for the percent Hispanic (39.3%), trailing only California (39.4%) and New Mexico (47.7%) (authors\u0026rsquo; calculations from 2020 U.S. Census data). This contribution underscores the importance of scholars moving beyond national averages to explore underlying variation that is unique to regions, states, and other geo-political boundaries. Scholars should continue to take up research questions that explore race/ethnicity, skin tone, and a host of other outcomes within these areas to better understand how the life chances of racial/ethnic minorities and those with darker skin tones fare compared to White persons and those with lighter skin tones. Future research could target, for example, other dimensions of socioeconomic status like asset ownership, debts, and wealth. Expanding beyond SES to examine mental and physical health could also be fruitful.\u003c/p\u003e \u003cp\u003eAlongside the strengths of this study, we should note that our conclusions are based on a 2015 sample of Texans. The year of the TDS is notable because our study captures a time period before the first election of Donald Trump and the subsequent turmoil of that administration, before the George Floyd murder and the rise of the Black Lives Matter movement, and before Covid-19 swept across the world. A future round of data collection for the TDS would be informative because it would provide an opportunity to assess how racial/ethnic and skin tone dynamics have changed \u0026ndash; if at all \u0026ndash; and how those dynamics matter for socioeconomic inequality. The place of the TDS is also notable because though it is representative of Texas, it is not representative of the United States. Texas, however, in and of itself is worthy of scholarly attention. Texas is the second largest state with slightly more than 30\u0026nbsp;million residents, a population that is about 8\u0026nbsp;million residents behind California and ahead of Florida. During the 2010s, Texas\u0026rsquo;s population grew, in percent change, by 15.9%, which was the third largest among all states. About 4\u0026nbsp;million residents in the 2010s were foreign-born and Texas had about 10\u0026nbsp;million Hispanics during the decade. The state has been majority-minority for about two decades, one of only seven U.S. states, and the Hispanic population is now slightly larger than that of the non-Hispanic white population. This is all to say that Texas is a racially/ethnically diverse place with growing populations of color. In short, it is an important place to study racial/ethnic and skin tone stratification and inequality.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study makes several major contributions to the existing literature on skin tone stratification, colorism, and socioeconomic inequality. By utilizing a novel measure of skin tone that requires research subjects to evaluate their own skin tone relative to ethnic peers, we are able to better assess how colorism may operate to shape socioeconomic outcomes within racial/ethnic groups. And by focusing on a specific geographic location of Texas, we are able to isolate whether well established relationships between colorism and socioeconomic inequality are generalizable to specific regions or places; assessing colorism at a national level potentially erases place-based diversity of economic outcomes for racial/ethnic groups.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003e \u003cb\u003eTable\u0026nbsp;4. Regression Results for Skin Tone and Socioeconomic Status for Racial/Ethnic Subsamples\u003c/b\u003e \u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003eThe light gray bars indicate predicted values value that are not statistically significant from \u0026ldquo;White \u0026ndash; light\u0026rdquo; respondents (see Model 2 for Income in Table\u0026nbsp;2).\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMP, MH, and JT conceptualized the study and wrote the main manuscript text.MP prepared the tables and the figures.MC provided the data, provided valuable feedback, and revised the manuscript.All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data are available, via request, from Dr. Mary Campbell.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbascal, M. \u0026amp; Garcia, D. (2022). 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(1987). \u003cem\u003eRediscovering the social group: A self-categorization theory\u003c/em\u003e. Oxford, UK: Basil Blackwell.\u003c/li\u003e\n \u003cli\u003eUhlmann, E., Dasgupta, N., Elgueta, A., Greenwald, A.B., \u0026amp; Swanson, J. (2002). Subgroup prejudice based on skin color among Hispanics in the United States and Latin America.\u0026rdquo; \u003cem\u003eSocial Cognition\u003c/em\u003e, 20, 198-225.\u003c/li\u003e\n \u003cli\u003evon Hippel, P.T. (2007). Regression with missing Y\u0026rsquo;s: An improved strategy for analyzing multiply imputed data. \u003cem\u003eSociological Methodology,\u003c/em\u003e 37, 1, 83\u0026ndash;117.\u003c/li\u003e\n \u003cli\u003eU.S. Census Bureau. 2023a. \u0026ldquo;Hispanic or Latino Origin by Race \u0026ndash; Table B03002, 2023: ACS 1-Year Estimates Detailed Tables.\u0026rdquo; \u003cem\u003eAmerican Community Survey\u003c/em\u003e. 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Retrieved from [https://data.census.gov/table/ACSST1Y2023.S1901?g=010XX00US$1600000].\u003c/li\u003e\n \u003cli\u003eU.S. Bureau of Labor Statistics. 2024. \u0026ldquo;Unemployment Rates for States\u003cem\u003e.\u003c/em\u003e\u0026rdquo;\u003cem\u003e\u0026nbsp;Local Area Unemployment Statistics.\u0026nbsp;\u003c/em\u003eRetrieved from [https://www.bls.gov/web/laus/laumstrk.htm].\u003c/li\u003e\n \u003cli\u003eWade, T.J., Romano, M.J., \u0026amp; Blue, L. (2004). The effect of African American skin color on hiring preferences. \u003cem\u003eJournal of Applied Social Psychology\u003c/em\u003e, 34, 2550-2558.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e The relative validity of the interviewer- and respondent-supplied skin tone assessments has been a source of some debate, but studies employing both find report that they are highly correlated (\u003cem\u003er\u003c/em\u003e = .80) (Keith et al, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2010\u003c/span\u003e:53; Monk, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2015\u003c/span\u003e:418), and one suggests that respondent self-appraisals may be a better predictor in some contexts (Monk, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e The TDS screened respondents by asking them about their racial or ethnic background early in the survey. Respondents must have identified Black or African American, Hispanic or Latino/a, or White or European American or Anglo as one of their racial/ethnic backgrounds to continue with the survey.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e The TDS does not contain information on parental socioeconomic status. The absence of this information may not affect our results as some previous research on socioeconomic attainment demonstrates that parental factors, like education and income, do not explain away skin tone effects (e.g., Keith \u0026amp; Herring, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; AUTHOR); however, Abascal \u0026amp; Garcia (2023) demonstrate the importance of considering how skin tone shapes familial resources, which in turn, can affect labor market outcomes.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Missing data are quite low in the TDS. For race/ethnicity, there was no missing data and only one respondent did not provide education information. Among our variables, missingness was highest for income, though only 174 respondents (14%) did not report a valid response.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 4 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"race/ethnicity, skin tone, income, employment, education","lastPublishedDoi":"10.21203/rs.3.rs-8604916/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8604916/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e. The social costs of skin tone inequality in the United States are substantial and can, at times, be as great as – or greater – than that of the Black-White divide. One area where the effects of both racial/ethnic and skin tone stratification can be clearly seen is in the study of socioeconomic inequalities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod\u003c/strong\u003e. Using the 2015 Texas Diversity Survey, we carry out not only within-group analysis for racial/ethnic subsamples but also break out these groups by skin tone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e. Our results allow us to offer insight into how colorism may operate to differentially shape socioeconomic outcomes within racial/ethnic groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImplications\u003c/strong\u003e. We conclude by discussing how focusing on a specific location can provide insight into how place matters, whereas assessing colorism at a national level potentially erases place-based diversity of economic outcomes for racial/ethnic groups.\u003c/p\u003e","manuscriptTitle":"The Shades of Inequality: Race/Ethnicity, Skin Tone, and Socioeconomic Status","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-24 15:56:44","doi":"10.21203/rs.3.rs-8604916/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4c84bba6-c3be-496c-a740-8b646ecb86c7","owner":[],"postedDate":"February 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-24T15:56:44+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-24 15:56:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8604916","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8604916","identity":"rs-8604916","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}