Effects of racial discrimination on culture and poverty
Analyze some of the effects of racial discrimination on culture and poverty?
1. Introduction
In the Americas, the United States has long been considered unique in terms of race relations – primarily for its norm of hypodescent, which erased mixed-race classifications and assigned black status to anyone with any apparent African ancestry (Davis 1991). At the other end of the stylized regional spectrum are Latin American countries characterized by large mestizo populations and national efforts to promote “whitening” (Schwartzman 2007; Telles and Garcia 2013). Scholars have contrasted these racial schemes as defined, respectively, by ancestry versus phenotype (Nogueira 1985; Davis 1991) and as associated with contrasting systems of racial stratification – racial versus color hierarchies (Skidmore 1993; Bonilla-Silva 2004).
It is also possible that, instead of being the bases for different race paradigms, categorical race and skin color are best viewed as two distinct dimensions of the same race construct. Recent research suggests their utility as analytic concepts may vary across contexts (Ñopo, Saavedra, and Torero 2007; Villarreal 2010; Roth 2010; Telles and Steele 2012; Loveman, Muniz, and Bailey 2012); hence, the appropriateness of using one measure or the other, or both, is an empirical question. However, until recently data limitations have prohibited an explicit comparison of these two approaches in the U.S. versus Latin America. Now, for the first time, nationally representative data including both self-identified race and perceived skin color is available in the U.S. and in similar recent surveys across Latin America. We use these data to provide fresh insight into cross-national patterns of racial inequality by comparing the degree to which per capita household income varies along these two dimensions of race in the United States and 18 countries in Latin America.
2. Data and methods
Our data are from the 2012 General Social Survey (GSS) in the United States (Smith et al. 2013) and the 2012 AmericasBarometer (AB) survey in Latin America. In both surveys, interviewers rated respondent skin color after concluding their interview using similar 10-point (GSS) or 11-point (AB) scales with visual color referents. Respondents provided their racial identification using national categorization schemes. Household income is self-reported in national currencies using a list of 25 (GSS) and 16 (AB) intervals.
We first graph mean per capita household income values for each point on a country‟s skin color scale and for each category of its national racial categorization scheme that includes 30 cases or more. Hence, some countries register fewer color points; in others, small racial populations are not included. Skin color category five
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serves as our benchmark in each country; we present all other average incomes in relation to the value for that mid-range color point.
To examine whether racial self-identification or skin color best accounts for income inequality in each country, we predict logged per capita household income using the same measures presented in our descriptive analysis. These models use ordinary least squares regression with interviewer fixed effects. Supplementary analysis using household-size adjusted household income (dividing by the square-root of household size) yielded similar results.
In parallel fashion to Figure 1, our regression results are intended to highlight the overall observed level of income inequality. Hence, we do not control for factors through which racial inequality might be mediated or reproduced in a given setting. Those considerations, such as education and region, are important for the purposes of identifying intervening factors, but our aim in this study is not to isolate the country- specific mechanisms through which racial inequalities arise. Rather, we lay the groundwork for such analyses by determining the extent to which skin tone and/or self- identified race best characterize economic inequality across the Americas. To this end we compare three models: one with skin color alone, one with racial categories alone, and one that contains both perceived skin color and self-identification.
In order to discern the preferred of our three models, we focus on the Bayesian Information Criterion (BIC) and the Akaike Information Criterion (AIC), where lower values indicate better fit. AIC generally favors complexity over parsimony, whereas BIC penalizes additional model parameters more heavily. We follow Raftery (1995) in determining the strength of evidence in favor of the most parsimonious model, ranging from “weak” (BIC difference of 2 or less) to “very strong” (BIC difference of 10 or more). When the BIC difference is less than two we conclude that the two models fit equally well for our purposes. (See Appendix for full details on data and measures.)
3. Results
3.1 Skin color inequality
In most countries there is a relatively linear relationship between perceived skin color and per capita household income: lighter colors are associated with higher incomes, and darker colors with lower incomes (see Figure 1 and Table A1). For example, Paraguayans with the lightest color have incomes 47% greater, on average, than those in color category five, while Paraguayans with the darkest skin color have incomes 36% less. Overall, results are consistent with a tendency toward color hierarchy across the region, but the degree to which specific skin colors are associated with advantage or
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Bailey, Saperstein & Penner: Race, color, and income inequality across the Americas
disadvantage varies considerably across countries. The largest gaps between the lightest color category and the mid-range category (suggesting extreme light-skinned elitism) are in the Dominican Republic and Guatemala. At the other end of the spectrum, results from El Salvador and Colombia reveal stark disadvantages for those with the darkest skin tones. In some countries such as the United States and Bolivia we find substantial clustering at different points in the color distributions, suggesting that each step along the color scale is not always equally consequential.
Several countries – including Panama and Honduras – appear to complicate the traditional notion of color hierarchy. Panamanians with the darkest skin color have the highest mean income, while those with the lightest skin color that registers in our sample have an average income 25 percentage points lower than the medium color category. The unusually high ranking of dark skin in Panama and Honduras underscores the importance of understanding country-specific histories of how color interacts with social status. The relative advantage of darker skin in these contexts follows in part from selective West-Indian (Afro-Antillean) migration for jobs involving large-scale, transnational enterprises, including the Panama Canal Company and the United Fruit Company (Andrews 1997; Guerrón-Montero 2006). These contrasting cases aside, the overarching pattern is that color hierarchy is a significant aspect of inequality across the Americas, and the United States is no exception.
Bailey, Saperstein & Penner: Race, color, and income inequality across the Americas
Samples included sizeable black populations in ten countries, yet self-identified blacks are at the bottom of the hierarchy in just four: Brazil, Ecuador, El Salvador, and Nicaragua. In Ecuador, black racial disadvantage appears especially deep, with average household incomes 36 percentage points lower than Ecuadorians of medium skin color, and well below the indigenous and mulatto categories. In five other countries, the United States, Venezuela, Uruguay, Colombia, and the Dominican Republic, blacks are in positions of disadvantage compared to whites, but have higher incomes than some other racial populations. In Panama and Honduras, blacks rank at the top, as was also suggested by the color measure. In most cases, though, the experience of blackness is one of distinct disadvantage in comparison to whites.
The indigenous category is typically found at the bottom of the racial hierarchy, occupying the lowest position in 9 of the 12 countries with large enough indigenous populations to analyze. This includes the United States, where the extreme disadvantage of self-identified, monoracial American Indians often goes unacknowledged in large national studies of inequality, due to their small numbers and segregation from much of the population (Snipp and Saraff 2011).
Finally, 17 out of the 19 countries include at least one explicit mestizo or “mixed race” category. In most cases, mestizos are disadvantaged compared to whites but have higher mean incomes than any other racial category. The exception is Venezuela where mestizos earn slightly more on average than whites. The relative advantage of mixed- race populations may reflect whitening strategies in Latin America, in which higher status individuals try to distance themselves from blackness and indigeneity (Schwartzman 2007). Notably, in the U.S. racial hierarchy the position of the multiracial category – people who gave two or more responses to the GSS race question – is similar to the general pattern of mestizos throughout Latin America.
3.3 Comparing color and self-identification
Table 1 lists goodness of fit statistics for our models regressing household income on skin color, on categories of racial identification, and on both measures simultaneously. Looking first at BIC (privileging parsimony), in 11 of the 19 countries the variation in household income is better explained by differences in skin color than by self-identified race or a combination of self-identified race and skin color. In Colombia and Uruguay the models with both race categories and skin color fit as well as the models with color alone. In three countries – the United States, Ecuador, and Guatemala – models that include both racial identification and skin color provide the best account for observed variation in income, even when privileging parsimony.
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