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Abstract

Brazil is at the forefront of research and policies aimed at redressing racial inequality in Latin America. However, there is a paucity of scholarship on Brazilian racial inequality in wealth. The lack of research is largely a by-product of data limitations. In this study, we analyze the best publicly available data on various indicators of wealth, controlling for multiple factors. We find a tiered system with White households having the most wealth, followed by mixed-race households, and then Black/Brown households. We find that White Brazilian families have approximately 1.5 to 2 times the wealth of Black/Brown families, which is similar to the U.S. Black-White gap for comparable measures, with the notable exception of homeownership. Our findings provide novel insights into racial wealth inequality in Brazil that may inform the study of wealth in countries where data on wealth are limited and/or where traditional measures (e.g., homeownership) are not suitable indicators of wealth.

Keywords

racial inequality wealth Brazil Black individuals interracial couples

Brazil has served as a foundational case for early comparative work on race (Degler 1971; Marx 1998; Tannenbaum [1946] 1992; Van den Berghe 1967) and provided an initial basis for understandings of the “Latin American” system of race (Bonilla-Silva and Glover 2004). Some of the earliest research on racial inequality in Latin America was based on Brazil (Degler 1971; Fernandes 1969; Hasenbalg 1985; Silva 1985), where census data on Blacks, in particular, has been much more available (Loveman 2014:Table 6.3). More recent research has continued to document the existence of Brazilian racial inequality with regards to income and other indicators of socioeconomic status (Bailey, Saperstein, and Penner 2014; Paixão et al. 2011; Telles 2004). Largely based on these data, Brazil has pursued an aggressive anti-discrimination agenda, including the introduction of affirmative action policies (Francis and Tannuri-Pianto 2013; Telles 2004), which now serve as a model for race-based public policy in the region (Paschel 2016).

Although Brazil has been at the forefront of research and policies aimed at redressing racial inequality in Latin America, there is a noticeable paucity of scholarship and public policy discussion on the topic of racial inequality in wealth. The absence is particularly noteworthy given the scholarly and political emphasis on the significant racial wealth gap in the United States, the most frequent comparative counterpart for Brazil in the literature on race (Degler 1971; Tannenbaum [1946] 1992; Telles 2004). The lack of research on the topic is presumably a by-product of data limitations. Public, nationally representative Brazilian data sets do not include standard wealth indicators that are used to calculate financial net worth or total assets, such as home value and financial or durable assets, minus any debts.

The lack of data and subsequent research on wealth inequality hinders comprehensive understanding of race in countries such as Brazil. In the United States, where robust wealth data are available, racial gaps in wealth inequality have been shown to far exceed racial gaps in other key indicators, such as income (Conley 2007; Oliver and Shapiro 2006). Moreover, estimates of income inequality are increasingly seen as distinct from estimates of wealth because income provides only a snapshot of a financial flow, whereas wealth represents a stock of assets, reflecting the cumulative effect of societal structures and how they can disproportionately deny some groups the ability to access and accumulate wealth (Killewald, Pfeffer, and Schachner 2017; Oliver and Shapiro 2006; Pfeffer and Killewald 2018). Research on wealth inequality can facilitate a broader understanding of the mechanisms that create and reproduce racial inequality than is permitted with a sole focus on income inequality. In Latin America, wealth may be particularly influential for shaping economic paths compared to income (Torche and Spilerman 2009).

The absence of knowledge on race and wealth in Brazil risks missing an important piece of the Brazilian racial inequality puzzle and potentially, how racial inequality manifests in other Latin American contexts. Due to the aforementioned data limitations, there are only a few studies that exist on race and wealth in Brazil. Telles (2004) examined one of the only wealth indicators available—home value—and found significant Black-White differences. More recently, Wainer (2018) found a gap between Black and White Brazilian families in household goods. However, Telles did not examine additional assets, and Wainer did not control for a number of potentially relevant variables. Moreover, neither study examined mixed-race households.

Expanding on these studies, we ask the following: What are the observed differences in wealth across various racial household combinations in Brazil? What are the wealth implications of crossing the White-Black/Brown divide in intimate relationships? How are factors such as income and education associated with racial wealth gaps? What factors should be taken into consideration when attempting to measure wealth in Brazil? In the absence of data on the most common indicators of wealth (e.g., home value, investments, savings), we examined a compilation of available proxy measures, drawing on two major Brazilian data sets—the 2015 Brazilian National Household Survey (PNAD) and the 2017 Consumer Expenditure Survey (POF).

For homeownership, the most direct measure of wealth available and a clear source of Black-White wealth inequality in the United States (Kuebler and Rugh 2013), we found a very small gap between White households and Black/Brown¹ households, which initially suggested the lack of a substantial racial wealth gap in Brazil. However, our examination of proxies for home value and additional proxies for wealth showed evidence of a more substantial gap. Consistent with this, our model that controls for age, education, household income, spouse, number of people in the household, and region also revealed a statistically significant gap between White households and Black/Brown households on all of our wealth indicators. Finally, our examination of multiple household racial compositions revealed a tiered system of wealth with White households having the most wealth, followed by White-Black/Brown households and then Black/Brown households. Additional analysis suggested that single White individuals have equal or even more wealth compared to White individuals in marital/cohabiting relationships with Black/Brown individuals, whereas single Black/Brown individuals are worse off than their counterparts who are relationships with White individuals.

In contrast to prior research on the United States that has found that the racial wealth gap far exceeds the income gap (Oliver and Shapiro 2006), we found the Black/Brown-White gap on most wealth proxy measures to be similar to or even smaller than the income gap in Brazil. This suggested that the racial wealth gap in Brazil is much smaller than the racial wealth gap in the United States. For a more direct comparison, we examined the Black-White wealth gap in the United States using comparable wealth measures to those available in Brazil. Interestingly, we found similarly sized Black-White gaps across the Brazilian and U.S. measures, leaving open the possibility that the Brazilian racial wealth gap may be similar in magnitude to the United States and larger than available proxies are capturing.

Our findings provide initial insights into racial wealth inequality in Brazil and potentially inform the study of wealth in other countries in which data on wealth are limited and/or traditional measures of wealth (e.g., homeownership) are not ideal indicators of wealth. Due to the indicators on which we base our analysis, we consider our estimates of the racial gap to represent the lower bound of racial inequality in wealth. At the same time, dynamics of “money whitening” may be obfuscating the portrait of racial inequality in Brazil, a topic we revisit in the conclusion. From a comparative perspective, our findings point to a larger Black-White gap in homeownership in the United States compared to Brazil, which is likely driven by the entrenched historical and contemporary racialized housing policies and segregation in the United States (Massey and Denton 1993) and greater accessibility to homes overall in Brazil (Telles 2004). However, this explanation does not appear to extend to home value because the gap in home value proxy measures is similar to or larger in Brazil compared to the United States despite divergent histories and institutional housing policies. Taken together, this begs the question of what unique mechanisms may be driving Black-White inequality in home value but not homeownership in Brazil. Our findings clearly point to the need for more expansive data on wealth in Brazil to answer questions such as this, among many others.

Literature

The Importance of Wealth

Wealth is an important source of social, cultural, and political capital (Shapiro 2017). Wealth has been shown to affect not only individuals’ financial security but also their health and well-being (Brown 2016; Shapiro 2017). Wealth often begets more wealth through investments, the securement of loans, private schooling, and engagement in new and risky economic enterprises. Wealth allows families to start businesses, weather economic storms, and plan for retirement (Shapiro 2017). Later in life, wealth can provide economic security and contribute positively to emotional well-being (Brown 2016). For families, wealth can help to secure the future of the next generation through investments in education, support for first time homeownership, and direct inheritance. Studies have found that parental wealth is associated with higher educational and cognitive outcomes for children (for an overview, see Killewald et al. 2017). The effects of parental wealth have been mostly studied in the context of the United States, but similar findings have been documented in countries such as Mexico (Torche and Spilerman 2009). Overall, ample research has found that inequalities in wealth have significant implications for various forms of inequality.

Across the globe, wealth data are much more limited than data on income (Alvaredo et al. 2017), although data on wealth have become increasingly available in the past few decades (Killewald et al. 2017). Analyses of these data have shown that wealth inequality is not only extreme, but it is increasing over time in many countries (Alvaredo et al. 2017; Killewald et al. 2017). This is the case in the United States, for example, where wealth inequality has sharply increased since the 1980s (Alvaredo et al. 2017; Oliver and Shapiro 2006). In contrast, Brazilian wealth inequality has declined since 2000, but the level remains fairly high (Grimm et al. 2019). The importance of wealth in Latin America may be even higher than in the United States given lower levels of income and less secure employment, which means that families need to rely more heavily on assets (Torche and Spilerman 2009).

Racial Gaps in Wealth

Wealth inequality is distributed in highly unequal ways and can include unequal distribution along racial lines. In countries such as the United States, where comprehensive wealth data are available, studies have shown the average Black family has a dime for every dollar of wealth that a White family has (Shapiro 2017; Weller and Hanks 2018) and that the Black-White wealth gap exists at every income level (Conley 2007; Killewald et al. 2017). There is also a significant Black-White gap in homeownership (Conley 2007; Kuebler 2013; Weller and Hanks 2018), a major source of wealth for most Americans (Killewald et al. 2017; Kuebler 2013; Oliver and Shapiro 2006) and one of the most important channels for intergenerational wealth transmission (Pfeffer and Killewald 2018). Furthermore, it has been shown that the average value of African American homes is substantially lower than similar White homes (Oliver and Shapiro 2006) and that Black-owned homes appreciate at much slower rates than White-owned homes (Kuebler 2013). However, Sedo and Kossoudji (2004) find that the racial gap in homeownership is much larger than the gap in home value, indicating that, at least in the U.S. context, homeownership may be the primary source of racial inequality in wealth.

Although data on wealth in Latin America are scarce, Painter, Noy, and Holmes (2020) relied on data from the Latin American Public Opinion Project for 18 Latin American countries to assess the relationship between race, skin tone, and wealth. More specifically, they examine ownership of homes, cars, and motorcycles, finding that individuals with darker skin are less likely to own vehicles but not necessarily homes. This reflects the fact that compared to the United States, homeownership is much more accessible in Latin America (Painter et al. 2020; Telles 2004; Torche and Spilerman 2009). Although this study fills an important gap in the literature, as the authors readily admit, their broad regional focus obscures important country-level variation. Moreover, their sole focus on homeownership (as opposed to home value) does not capture the full picture of racial inequality in housing, especially given high levels of homeownership in Latin America. Whereas owning a home is an important indicator of wealth in the United States, in Latin American contexts, home value may be a more important measure of wealth among homeowners. Supporting this, Torche and Spilerman (2009) found that in Mexico, parental wealth significantly affected the home value of adult children but was only weakly related to their levels of homeownership. Most notably for the purposes of our study, the aforementioned studies did not include Brazil.

Brazil is known for being one of the most unequal societies in the world and home to extremely high levels of income inequality (Alvaredo et al. 2017; Bucciferro 2017; Organização Internacional do Trabalho 2007). Historically, this inequality has not been viewed through the lens of race even though racial inequality has deep historical roots, with Brazil being the last country in the Americas to abolish slavery. However, an important distinction between the U.S. and Brazilian race systems is that many Black people in Brazil were not enslaved (Tannenbaum [1946] 1992) and Brazil did not impose legal race-based segregation as seen in the United States (Degler 1971). Therefore, being Black in Brazil has historically represented a more heterogeneous experience compared to being Black in the United States. That said, after abolition, many Black Brazilians worked as wage laborers on plantations and in cities, with Black laborers receiving significantly lower wages than White laborers (Bucciferro 2017). The situation improved starting in the 1930s, although in the later part of the twentieth century, wage and occupational discrimination increased (Bucciferro 2017). Research on contemporary income inequality in Brazil has revealed continuing racial inequality (Bailey et al. 2014; IPEA 2011; Ribeiro 2006; Telles 2004), especially at the upper end of the income distribution (Bailey, Loveman, and Muniz 2013; Telles 2004).

There are only two studies of which we are aware that address racial wealth inequality in Brazil, and both examined home value. In the first study, Telles (2004) analyzed data from the 1996–1997 Brazilian Survey of Life Patterns, which collected data in northeast and southeast Brazil. Comparing home values for White, Brown, and Black individuals within six occupational categories, he found a racial gap in home value that exceeds the racial income gap. More specifically, he found that among those in lower-level occupations (where home value represents homeowners’ major financial asset), the average home value for White individuals was twice as much as for Brown individuals and 3 times the value of Black individuals’ homes, yet the income gap was less than twice as much between White individuals and Brown/Black individuals. He argues that the wealth gap would likely be higher if data on additional assets were included.

In a second study, Wainer (2018) used data from the 2015 PNAD and found that after controlling for income, White families in Brazil have a higher probability of owning household goods such as mobile phones, computers, and freezers compared to Black families. He also found a gap between White and Black families for car ownership and homeownership. As a proxy for home value, Wainer examined the number of rooms in a home and found that White individuals’ homes had more rooms on average than Black individuals’ homes, again controlling for income. However, Wainer only discusses the statistical significance of Black-White differences and not the magnitude of the gap. Moreover, neither of these studies controlled for additional factors such as age, education, family size, and region, which are standard in many studies of U.S. wealth inequality (Oliver and Shapiro 2006). These studies also used a limited number of proxy variables for wealth and did not include mixed-race families in their analyses.

Explaining Racial Wealth Gaps

Most theorizing on the racial wealth gap is based on the context of the United States. Institutional racism has been identified as a major force blocking African Americans’ ability to accumulate wealth (Brown 2021; Conley 2007; Killewald et al. 2017; Kuebler 2013; Oliver and Shapiro 2006). Black individuals have been denied access to land and homes through racist exclusions (e.g., the 1862 Homestead Act), lending policies (e.g., redlining and FHA policies), realtor practices (e.g., steering), restrictive covenants, and other institutional mechanisms (Conley 2007; Oliver and Shapiro 2006). Even when Black individuals have been able to purchase homes, their home values are lower because of the effects of racial segregation and racism in the housing market. Moreover, there is consistent evidence that African Americans continue to be discriminated against in home lending markets (Conley 2007) and receive fewer tax benefits from homeownership (Brown 2021; Oliver and Shapiro 2006). Because of this, Shapiro (2017:159) deems institutional racism in housing a “fundamental contemporary pillar of the racial wealth gap in the United States.”

Whereas one of the main drivers of housing inequality in the United States is residential segregation, Brazil has comparatively low levels of residential segregation by race (Telles 2004; Valente and Berry 2020). Telles (2004) notes that the kinds of institutional racism and institutional actors that fueled segregation in the United States—real estate agents, lenders, insurers—are generally not involved in the Brazilian housing market because of the more informal status of housing. For example, Rolnick (1999, cited in Telles 2004:208) found that 3 times as many urban Brazilians reside in self-constructed and unregulated housing compared to those living in regulated housing. However, Telles argues that interpersonal discrimination, wealth-based discrimination, network-based migration, and self-segregation by Black and Brown individuals may still be operating because residential segregation by race in Brazil cannot be explained by socioeconomic status alone. Valente and Berry (2020) posit that the low to moderate levels of racial segregation in Brazil are due to a combination of factors, including internal migration of predominantly non-White migrants, which has led to rapid urbanization, and a lack of choices in low-income areas. In this study, we draw on these insights to contextualize our findings on race and wealth in Brazil.

Measuring Racial Inequality in Brazil: Racial Categories and Mixed-Race Households

The measurement of racial inequality in Brazil needs to contend with important aspects of the Brazilian racial system. Whereas the rule of hypodescent in the United States resulted in a largely binary Black-White system, Brazil has been characterized as a multiracial system (Skidmore 1993). Degler (1971), in his classic comparative analysis of race in the United States and Brazil, pointed to a “mulatto escape hatch” as the key to understanding race in Brazil and a fundamental difference between the two countries. Since that time, researchers have empirically interrogated the idea of a “mulatto escape hatch” by examining whether mulattoes (pardos, or Browns) are better off than Blacks in Brazil. Some scholars have found the Black-Brown gap is very small for primary components of socioeconomic status, such as income (Telles 2004), and used this as a justification to combine the Brown and Black categories in analyses of racial inequality (Hasenbalg 1985; Silva 1985). Nevertheless, whether Browns occupy a nontrivially better position than Blacks in Brazil is a subject of ongoing debate (for a discussion, see Bailey et al. 2013). Because there is very little understanding of wealth inequality by race in Brazil, we originally incorporated Black, Brown, and White households into our analyses. However, we found that the major wealth cleavage was between White and Black/Brown households. We thus combined the two categories (and present descriptive analyses of the uncombined categories in Appendix A in the supplemental material) to allow for a focus on a less discussed aspect of Brazilian measures of inequality: White-Black/Brown couples.

Scholars have noted Brazil’s much higher rates of Black-White interracial marriage as an important source of distinction between the U.S. and Brazilian racial systems (Telles 2004; Telles and Esteve 2019). However, Black-White households are not generally examined in studies of inequality because most studies focus on the individual as opposed to the couple unit. When the household unit is incorporated into studies of inequality, scholars generally focus on White, Brown, and Black families (Telles 2004). This means that mixed-race households are often not considered in analyses of racial inequality in Brazil. Osuji (2013) conducted a qualitative study on Black-White couples in Rio de Janeiro and found that these couples had experienced some but not great deals of hostility toward their relationships, which may suggest intermediate levels of discrimination. In this study, we focus on the implications of crossing the White-Black/Brown divide in intimate relationships for the accumulation of wealth.

Data and Methods

For the purposes of this study, we follow the work and guidance of Killewald et al. (2017) for modeling wealth. Our primary goal is to provide an estimation of wealth for various racial household compositions relative to measures that contribute to and can be understood as components of wealth using publicly available Brazilian data.

Data

We draw on two nationally representative data sets. The PNAD (Pesquisa Nacional por Amostra de Domicílios; National Household Sample Survey) is an annual, complex multistage sampling survey of households. The PNAD surveys the general characteristics of the population, including education level, occupation, income, and housing status. Its basic aim is to serve as an instrument for the formulation, validation, and evaluation of policies aimed at the socioeconomic development of the population and the improvement of living conditions in the country. In this analysis we draw on the 2015 PNAD data, which included 117,939 households, although we excluded 1.2 percent of families based on the restrictions for race of household (discussed later). The originally formatted PNAD was last taken in 2015 and was subsequently replaced with the PNAD Contínua.

We also utilize the POF (Pesquisa de Orçamentos Familiares; Consumer Expenditure Survey), a complex multistage sampling survey of households designed to evaluate the consumption, expenditure, and income sources of families. The POF data profiled the living conditions of the Brazilian population through questions on household budgets and quality of life measures. The survey design makes it possible to study the composition of household expenditures by income brackets, regional disparities, urban and rural situations, the diffusion and volume of transfers among the various brackets, and the size of the consumer market for groups of products and services. The survey was conducted in 2002–2003, 2008–2009, and 2017–2018. For this study, we draw on the 2017–2018 data, which consisted of 56,933 households. We excluded 3.4 percent of the households based on our race of household restrictions (discussed later).

Both surveys make use of a self-declared “head of the family,” who may or may not have a spouse. If the “head of the family” has a spouse, we treat both the head and their spouse as the “reference adults.” Households can include adult relatives and/or children of the reference adults, which we include as household characteristics in our analysis. Households can also include nonrelatives, such as domestic employees, but we exclude these individuals from our analysis.

Our regression model is inspired by Oliver and Shapiro (2006:135, Table 6.2) but we used fewer independent variables to investigate wealth either due to lack of available data or because some of the measures are less clearly operationalized in the context of Brazil (e.g., middle-class occupations). From Oliver and Shapiro’s model, we included age and age-squared, education, region, total household income, and number of children and workers (which are included in the total number of persons in the household).

We operationalized these concepts, among others, in the following way.

Race of the household

For the purposes of this study, we consider the race of the reference adult/s only. More precisely, we coded families into three categories: (1) BlackBrown households (reference adult/s is/are identified as Black, Brown, or Black and Brown, (2) WhiteMixed households (one reference adult is identified as White and the other as Brown or Black), and(3) White households (reference adult/s is/are identified as White). We excluded families with reference adults who identified as Asian or Native (1,418 for the PNAD and 1,920 for the POF) because they are not a focal point of our analysis.

Age of the household

The age of the household is either that of a single reference adult or the average age of the reference adults (couples).

Household income

The total income derived from employment/retirement/social benefits of the reference adult/s and any of their relatives and children living in the household.

Number of people in the household

The number of people in the household includes all relative adults and children in the home.

Spouse

This measures whether the household has one or two reference adult/s. Accounting for the presence of a spouse is particularly important vis-à-vis homogeneous racial households because they include single-headed households.

Education

Education level is either that of the single reference adult or the average education level of the reference adults.

Region

Region references the geographic location of the household: (1) north, (2) northeast, (3) southeast, (4) south, and (5) central west. Racial inequality in Brazil has been tied to region. The northeast of Brazil was the destination of most enslaved Africans and today is the region with a higher proportion of Black families. White settlement occurred in the south and southeast regions of Brazil, with only small numbers of non-Whites migrating from the northeast before the mid-twentieth century (Telles 2004).

We use the following proxy measures of wealth.

Homeownership

Homeownership refers to whether the home is owned by the reference adult/s.²

Vehicle ownership

Vehicle ownership indicates whether the household owns at least one vehicle. A car is usually a household’s first major investment and is often essential for work (Williams 2017). Similarly, motorcycles often serve as a primary form of transportation and are less expensive than cars. However, unlike a home, the value of vehicles typically depreciates. Nevertheless, vehicles can be considered important assets in Latin America (Painter et al. 2020).

Number of rooms

This measurement reports the total number of rooms (including bedrooms, living rooms, and bathrooms) in the owned home.

Wealth index

The wealth index (Rutstein 2015) is a composite index that indirectly measures the wealth of a family by assessing various measures of a household’s condition, such as the materials used in the home’s construction, water and sanitation access, and possession of large electric appliances, such as a TV, refrigerator, clothes washer, and computer, as well as internet access. We compute our index using available data by taking the first principal component of binary variables indicating different aspects of household possessions and living conditions.³ However, we excluded variables for vehicle ownership, homeownership, and number of rooms in the home to avoid confounding the results because we analyze them separately.

Property tax

Propery tax includes the annual property tax paid (in Brazilian reais) by the reference adult/s. Only 15,494 of the records in the POF data had information regarding the property tax paid, and thus, only those records were used in the regression analysis.⁴

Estimated rent

Estimated rent measured the monthly rental value (in Brazilian reais) of the owned home as estimated by the head of household.

Model

We used the following regression model:

\begin{array}{l} D V = r a c e + a g e + a g e^{2} + e d u c a t i o n + i n c o m e + \\ n u m p e r s o n s + s p o u s e + r e g i o n + e r r o r, \end{array}

where DV (the dependent variable) represents the aforementioned proxy measures of wealth. Household income, property tax, and estimated rent are log transformed, and all nondichotomous independent variables are centered around their mean before entering into the regression.

For the continuous-level proxy wealth measures (wealth index, property tax, and estimated rent), we used a standard ordinary least square regression. For the dichotomous (0/1) proxy wealth measures of homeownership and vehicle ownership, we used a logistic regression. Finally, for the categorical proxy wealth measure, number of rooms (1/28) in the owned home, we used a Poisson regression. All regressions are conducted using Stata v18 and specified with robust standard errors.

Findings

In this section, we address the main questions that guide our research: What are the observed differences in wealth across various racial household combinations in Brazil? What are the implications of crossing the White-Black/Brown divide in intimate relationships for wealth? And how are factors such as income and education associated with racial wealth gaps? We also discuss methodological insights pertaining to the measurement of wealth in Brazil.

Observed Differences in Wealth across Various Racial Household Combinations in Brazil

In Table 1, we present the descriptive statistics of our independent variables for each of the three household race indicators (BlackBrown, WhiteMixed, and White) and for the entire sample (“All Households”).⁵ As shown, on average, both BlackBrown and WhiteMixed households have lower household age, education, and income relative to White households.⁶ White households also have fewer persons than both BlackBrown and WhiteMixed households. Moreover, White households are slightly more likely to report having a spouse present than BlackBrown households. In regard to geographical distribution, we find that White households are more prevalent in the southeast and south compared to BlackBrown households, whereas BlackBrown households are more common in the north and northeast, consistent with historical settlement patterns (Telles 2004).

Table 1.

Descriptive Statistics of Predictor Variables by Brazilian Household Racial Composition (Weighted), Pesquisa Nacional por Amostra de Domicílios (Brazilian National Household Survey) 2015.

	BlackBrown Household	WhiteMixed Household	White Household	All Households
	n = 23,992,700 (44.1%)	n = 9,821,150 (18.1%)	n = 20,564,850 (37.8%)	N = 54,378,700 (100.0%)	p Value
Household
Age	48.1 ± 15.7	44.9 ± 13.8	51.5 ± 16.1	48.8 ± 15.7	<.001
Education	7.6 ± 4.3	8.9 ± 4.0	9.8 ± 4.6	8.7 ± 4.5	<.001
Income (reais)	2,207.8 ± 2467.2	3,258.7 ± 3,731.8	4,238.2 ± 5,788.3	3,158.8 ± 43,15.0	<.001
Number of persons	3.1 ± 1.6	3.5 ± 1.3	2.7 ± 1.3	3.0 ± 1.5	<.001
Presence of spouse	54.3%	100.0%	55.3%	62.9%	<.001
Geographic region
North	10.8%	6.8%	2.1%	6.8%	<.001
Northeast	37.6%	26.6%	10.9%	25.5%
Southeast	37.7%	45.0%	52.4%	44.6%
South	5.5%	12.4%	29.0%	15.6%
Central west	8.3%	9.2%	5.6%	7.4%

Note: Mean ± standard deviation.

Regarding our proxy measures of wealth in Table 2, we find that BlackBrown households consistently have lower values compared to White households and that WhiteMixed households hold an intermediate position between White households and BlackBrown households on all wealth indicators. In other words, White households have the highest wealth index, the most rooms in the houses they own, the highest rates of homeownership, and the highest property taxes and estimated rent, which likely indicates a higher home value compared to other households. The only exception to this pattern is vehicle ownership, in which a higher percentage of WhiteMixed households own at least one vehicle compared to White households. However, when broken down by type of vehicle (car vs. motorcycle, not shown), White households are the most likely to own a car (49.7 percent), followed by WhiteMixed households (42.6 percent) and BlackBrown households (23.3 percent). Therefore, the exception to the aforementioned wealth pattern is driven by possession of motorcycles, which are a cheaper transportation alternative to a car. Car ownership is consistent with the other wealth proxies.

Table 2.

Descriptive Statistics of Outcome Variables by Brazilian Household Racial Composition (Weighted), Pesquisa Nacional por Amostra de Domicílios (PNAD; Brazilian National Household Survey) 2015 and Pesquisa de Orçamentos Familiares (POF; Consumer Expenditure Survey) 2017–2018.

	BlackBrown Households	WhiteMixed Households	White Households	All Households	p Value
PNAD	n = 23,992,700 (44.1%)	n = 9,821,150 (18.1%)	n = 20,564,850 (37.8%)	N = 54,378,700 (100.0%)
Household wealth index	−0.2 ± 1.0	0.1 ± 0.9	0.4 ± 0.9	0.1 ± 1.0	<.001
Household number of rooms	5.4 ± 1.7	5.9 ± 1.9	6.2 ± 2.2	5.8 ± 2.0	<.001
Own home	73.0%	73.8%	77.2%	74.8%	<.001
Own vehicle	45.5%	70.3%	66.5%	58.0%	<.001
POF	n = 30,558,509 (45.1%)	n = 12,513,905 (18.5%)	n = 24,664,906 (36.4%)	N = 67,737,321 (100.0%)
Property tax (reais)	218.1 ± 387.6	266.3 ± 420.6	433.7 ± 797.2	338.6 ± 645.1	<.001
Estimated rent (reais)	515.0 ± 464.2	679.2 ± 615.8	1,063.3 ± 1191.6	746.0 ± 864.9	<.001

Note: Mean ± standard deviation.

In sum, for five out of our six proxy measures of wealth, BlackBrown households appear to have less wealth than homogenous White households, followed by WhiteMixed households. In the next section, we examine more closely the implications of crossing the White-Brown/Black boundary for wealth outcomes.

Wealth Implications for Crossing the White-Black/Brown Divide in Intimate Relationships

In Figure 1, we depict the unadjusted means (for table of adjusted means, see Appendix D in the supplemental material) of each wealth proxy by the following types of households: (1) BlackBrown single (single individual households where the reference adult is identified as Black or Brown), (2) BlackBrown couple (couple households where the reference adults are identified as Black, Brown, or Black and Brown), (3) White-Mixed couple (couple households where one reference adult is identified as White and the other as Black or Brown, (4) White single (single individual households where the reference adult is identified as White), and (5) White couple (couple households where the reference adults are identified as White).

Figure 1.

Unadjusted Means of Wealth Proxies by Racial Composition and Relationship Status of Household (HH), Pesquisa Nacional por Amostra de Domicílios (Brazilian National Household Survey) 2015 and Pesquisa de Orçamentos Familiares (Consumer Expenditure Survey) 2017–2018.

The results depict the raw differences between the various household compositions, highlighting the combined or intersectional effects of race and relationship status. As shown in Table 2, we find clear racial disparities across all measures. Notably, although there are marked disparities in homeownership, they are less pronounced compared with other measures, with several household formations showing similar probabilities. In racial household categories, couple households generally have higher means than single households, suggesting a general wealth advantage to being in a couple, with one important exception, which we discuss below. White couples consistently represent the most advantaged position, whereas Black/Brown single households are consistently the most disadvantaged.

What do these findings suggest about the wealth implications of crossing the White-Black/Brown racial divide? As shown in Figure 1, White couples are associated with higher wealth than single White individuals. However, WhiteBlack/Brown couples (i.e., WhiteMixed, or interracial couples) have similar or even less wealth than single White individuals (with the exception of vehicle ownership), possibly indicating a decrease in wealth for White individuals upon intermarriage, at least based on the wealth proxies we examine. Thus, from a wealth perspective, there does not appear to be an incentive for White individuals to partner outside of their race. In contrast, single Black/Brown households are generally disadvantaged in terms of wealth relative to homogeneous Black/Brown couples and White-Black/Brown couples, suggesting that becoming a couple household is advantageous for Black/Brown individuals in terms of wealth, especially if they partner with a White individual. In other words, the findings presented in Figure 1 suggest that the wealth implications for crossing the racial divide in intimate partnerships is neutral to negative for single White individuals and beneficial for single Black/Brown individuals. Viewed another way, our findings suggest that the wealth disadvantage associated with Black/Brown individuals is more readily transferrable to White individuals than the White wealth advantage is transferrable to Black/Brown individuals through intimate partnerships.

Overall, these unadjusted results show raw disparities between categories before accounting for any socioeconomic factors. They reflect the combined effects of various underlying factors, including potential differences in income, education, age, and other socioeconomic variables, and possible effects of discrimination or structural inequalities. In the next section, we examine various factors that may be contributing to these racial gaps.

Explaining the Racial Gap for Wealth Proxy Measures

Figure 2 displays the regression results associated with our three primary household race categories—White, WhiteMixed, and BlackBrown—on each of the wealth proxy variables while controlling for age, age squared, education, income, number of persons, presence of spouse, and region (for full table of results, see Appendix E in the supplemental material). Generally, we find a consistent set of results as previewed in the aforementioned descriptive results. Relative to White households, BlackBrown households have a lower wealth index, are less likely to own a home, have fewer rooms in the homes they own, pay less property tax, live in homes with lower estimated rent, and are less likely to own a vehicle net of the aforementioned controls. WhiteMixed households have rates of wealth that are consistently in between BlackBrown households and White households, with two exceptions: Their wealth index is not significantly different from White households, and they are less likely to own a home compared to BlackBrown households, although that difference is not statistically significant. Overall, these results indicate that even after controlling for key explanatory variables, BlackBrown families, followed by WhiteMixed families, are less likely to possess wealth on all of our wealth proxy indicators compared to White families.

Figure 2.

Predicted Coefficients/Probabilities of Wealth Poxies by Household (HH) Racial Composition, Pesquisa Nacional por Amostra de Domicílios (Brazilian National Household Survey) 2015 and Pesquisa de Orçamentos Familiares (Consumer Expenditure Survey) 2017–2018.

A Comparison of Wealth Proxies and Income: Brazil versus the United States

The findings presented thus far establish the existence of a racial household gap in Brazil for available wealth indicators but do not speak to the size of the gap. The last column in Table 3 shows the estimated size of the White-Black⁷ gap in Brazil (top panel) via a ratio and according to various wealth proxies. We find a racial gap across all wealth proxies. For example, as indicators of home value, median property taxes in Brazil are nearly 40 percent,⁸ or 1.61 times, higher for White families’ homes than for Black families’ homes, and the estimated median rent value is about 45 percent, or 1.82 times, higher on average. Although the sizes of these gaps are certainly nontrivial, they are nothing near the 10-fold gap that has been identified in the United States regarding Black-White wealth inequality (Shapiro 2017; Weller and Hanks 2018). Furthermore, White Brazilian households report median incomes that are about 39 percent, or 1.65 times, higher than those of Black families, which also contrasts to the U.S. situation, where the magnitude of the Black-White wealth gap has been shown to far exceed the income gap (McIntosh et al. 2020). This all suggests that race-based wealth inequality may be much less severe in Brazil compared to the United States.

Table 3.

White-Black Household Ratios of Median Income and Wealth Proxies in Brazil and the United States, Pesquisa Nacional por Amostra de Domicílios (PNAD; Brazilian National Household Survey) 2015, Pesquisa de Orçamentos Familiares (POF; Consumer Expenditure Survey) 2017–2018, and American Community Survey (ACS) 2016.

	White	Black	White/Black Ratio
Brazil: PNAD 2015 and POF 2017–2018
Median total monthly income (reais)	2,600	1,576	1.65
Own home	77.2%	73.0%	1.06
Median number of rooms	6.0	5.0	1.20
Median property tax (reais)	184	114	1.61
Median estimated rent (reais)	730	402	1.82
Own car (1+, excluding motorcycles)	60.3%	29.8%	2.02
United States: ACS 2016
Median total annual income (dollars)	61,349	38,555	1.59
Own home	68.7%	40.7%	1.69
Median home value (dollars)	205,500	143,900	1.43
Own vehicle (1+, excluding motorcycles)	93.4%	81.4%	1.15

However, as also shown in Table 3, when we compare the size of the Black-White gap in Brazil to comparable proxies in the United States (bottom panel), we see that the gaps are of similar magnitude, ranging from 1.06 to 2.02 in Brazil and 1.15 to 1.69 in the United States. The greatest difference between the two countries lies with vehicle ownership followed by homeownership. The latter is typically a much more significant indicator of wealth, although, as we have argued, it is not an ideal measure of wealth in Brazil. Moreover, when we compare the U.S. wealth proxy ratios to that of the U.S. income gap (1.59), the income gap is at the higher end of the wealth proxy ratio range, indicating that there is not a substantial difference between the size of the gap on wealth proxy measures in the United States and income. Given the much more extensive research and general finding that the racial wealth gap far exceeds the income gap in the United States, it is quite possible that data on savings, inheritance, and investments, which are major sources of wealth and future wealth accumulation, may reveal a much larger Black-White wealth gap in Brazil. Overall, the fact that our wealth indicators show a racial gap at all—given that they likely underestimate the size of the wealth gap in Brazil and that the size of the gap is generally on par with comparable U.S. measures—is nontrivial.

We do find a notable difference between Brazil and the United States regarding homeownership. On average, nearly 3 out of 4 households report owning their home in Brazil, with White households being only about 5 percent more likely to own homes than Black households. In contrast, U.S. White households are about 40 percent (or 1.69 times) more likely to own their home than Black families. Nearly 7 out of 10 U.S. White families own homes, whereas only 4 out of 10 Black families do. However, this U.S.-Brazil distinction does not extend to home value. Our proxies for home value in Brazil (number of rooms, property tax, and estimated rental value) range from a ratio of 1.2 to 1.82, and the Black-White home value ratio for the United States is 1.43, which falls squarely within this range. This suggests that the racial gap with regards to home value is fairly similar in the United States and Brazil. Taken together, these findings indicate that a major racial cleavage exists around homeownership in the United States, but this is not the case in Brazil, where the prominent racial gap pertains to home value.

Conclusion

The 2001 World Conference on Racism, held in Durban, South Africa, was a defining moment in Latin America’s approach to race and racism. Brazil sent a robust delegation to the conference and became a key player, especially after the withdrawal of the U.S. and Israeli delegations. Black activists in Brazil placed the topic of racism in the country front and center on the world stage. Shortly after the conference, the Brazilian government began to implement affirmative action to reduce racial inequality in the country, becoming a model in the region for anti-racist policy. However, more than two decades have passed since this watershed moment, and despite Brazil’s position as a regional leader in data collection on race and attention to racial inequality, the topic of wealth distribution along racial lines has been conspicuously absent, largely due to data limitations.

In this study, we drew on publicly available data to answer preliminary questions about racial wealth inequality in Brazil. Our study builds on prior work by Telles (2004) and Wainer (2018) that estimated components of the racial wealth gap. We extended these analyses by examining additional proxies for wealth, controlling for multiple factors that could be contributing to racial wealth differences, and relying on additional and more recent data sets. We also examined the wealth of mixed-race (White-Black/Brown) households compared to (both single and couple) Black/Brown and White households, which is rare in the literature on racial inequality. Moreover, the crossing of racial boundaries is of conceptual interest in that it speaks to how the advantages of Whiteness and/or disadvantages of Blackness/Brownness potentially shape mixed-race household wealth.

We found that after controlling for income, age, education, spouse, region, and number of people in the household, compared to White families, Black/Brown and mixed-race households were similarly less likely to own a home, that the average value of their home is lower, and that they are less likely to own a car. Couple households where one spouse is White and the other is Brown or Black are generally positioned in between White families and Brown/Black families, with the exception of homeownership (where Black/Brown families are more likely to own homes but not at a statistically significant level) and the wealth index (where mixed-race families have a similar score as White families). A comparison of single White, couple White, single Black/Brown, couple Black/Brown, and mixed-race households revealed that single White households are often better off in terms of wealth compared to White-Black/Brown couples, but that single Black/Brown households are worse off compared to White-Black/Brown couples. This suggests that wealth accumulation is higher among households that are headed by at least one White individual compared to households headed by at least one Black/Brown individual. These findings add empirical and conceptual insights related to wealth and mixed-race partnerships.

Adopting a comparative approach, we found that White Brazilian families have up to 1.5 to 2 times the amount of wealth, based on available proxies, compared to Black/Brown Brazilian families. This is a much smaller racial wealth gap than has been identified in the United States, where the average White family has 10 times the amount of wealth as the average Black family (Oliver and Shapiro 2006). Moreover, we found the size of the Black-White income gap in Brazil to be largely on par with the racial gap in our wealth measures, which is also distinct from the U.S. case, where prior research has found race-based wealth inequality to be much more pronounced than income inequality (Conley 2007; Oliver and Shapiro 2006). Finally, we found an absence of a substantial racial gap in homeownership after controlling for various factors, which is also distinct from the U.S. context (Kuebler and Rugh 2013). These findings initially suggested that the Black-White wealth gap in Brazil may be substantially smaller than in the United States.

However, when we compared the size of the Black-White gap in the United States using similar wealth measures as those available in Brazil, we found that the sizes of the gaps were comparable or even slightly greater in Brazil than in the United States. This allows for the possibility that Brazil has a much larger racial wealth gap than our measures are capturing and that the wealth gap may be on par or with or even exceed the racial wealth gap in the United States. Only with comprehensive wealth data will we be able to fully investigate the extent of Brazilian wealth inequality by race.

More extensive data on wealth would not only enable more accurate estimates of the size of the racial wealth gap in Brazil, but it would also allow scholars to better understand the institutional mechanisms contributing to wealth inequality and thus inequality more broadly. Our comparative findings on home value and homeownership in Brazil and the United States provide some clues as to the mechanisms that may be driving different outcomes in these two contexts. Whereas we found the Black-White gap in homeownership to be larger in the United States compared to Brazil, we found the gap in home value (based on approximations) in Brazil to be very similar to (and even slightly larger than) that in the United States. Regarding homeownership, like Telles (2004), we interpret the relatively smaller Black-White gap in Brazil as a likely result of the absence of many of the institutional racist policies embedded in the U.S. housing market (e.g., via lenders, real estate agents, insurers, and U.S. government policy more broadly). Because Brazil lacks an intricate and institutionalized housing system and given the high degree of informal and unregulated housing, Black and Brown Brazilians have had more access to homeownership compared to African Americans. As Telles (2004) notes, this does not preclude the existence of interpersonal discrimination in housing in Brazil, but informal discrimination is less likely to drive the kind of widespread and systematic exclusion of Black and Brown individuals from homeownership as seen in the United States. The dynamic of greater access to homeownership across the racial spectrum appears to extend to other Latin American countries (Painter et al. 2020; Torche and Spilerman 2009). Therefore, future work on wealth inequality in contexts with more accessible or affordable housing should be cautious about using homeownership as a primary or sole determinant of wealth inequality. In these contexts, home value may be a better indicator of wealth inequality (for additional explanation of how homeownership can be a problematic measure of wealth in countries such as Brazil, see text in Appendix E in the supplemental material).

Our findings that the Black-White gap in home value among homeowners is comparable or even potentially larger in Brazil than in the U.S. could be seen as surprising given the stark differences in the history of race and housing in the two countries. There are reasons to expect a much larger Brazil-U.S. difference than we found given the countries’ unique sociohistorical contexts, including that the correlation between a Black racial classification and slave status was much higher in the United States than in Brazil, that the United States has much higher rates of Black-White residential segregation, and the extensive institutionalized racism embedded in the U.S. housing market, all of which affect home values. Given the general absence of U.S-type sustained institutional racism and extreme segregation in housing in Brazil, our findings raise questions about the mechanisms involved in producing racially unequal housing values in Brazil. However, a limitation of our data on home value is that we needed to rely on proxy measures (number of rooms, property tax, and estimated rent) because data on home value is not publicly available. In addition to the need for direct measures of home values, data on savings, investments, real estate, luxury goods, and wealth transfers are also essential to answering fundamental questions on the state of wealth inequality in Brazil.

Given that the available indicators of wealth are far from complete measures of total assets or net worth and that these same measures in the United States produced a much smaller Black-White gap than what has been uncovered with more comprehensive measures, we believe that our findings, which show a Black/Brown-White racial wealth gap that is less than twofold, likely underestimate (and perhaps significantly so) the size of the racial wealth gap in Brazil. That said, research on race in Brazil and other Latin American countries has revealed an endogenous relationship between racial classification and socioeconomic status (Roth, Solís, and Sue 2022; Villarreal and Bailey 2020). Given the identified dynamic of “money whitens” in Brazil (Degler 1971; Schwartzman 2007; Telles and Lim 1998; Telles and Paschel 2014), there is also reason to believe that our findings may overestimate the size of the racial wealth gap (see Bailey et al. 2013).⁹

On the other hand, Telles and Paschel (2014) found that status did not affect the large majority of people’s racial identifications, and some scholars (Muniz 2016; Muniz and Bastos 2017; Ribeiro 2017) argue that whitening or other forms of racial fluidity are unlikely to affect estimates of inequality. For our study, given that our unit of analysis is the household and that our general findings held when we restricted the analyses to couple-based households (see Appendix C in the supplemental material), both individuals would need to be “whitened” due to wealth for a strong effect to occur, which may be less likely than a single individual being “whitened.” Ultimately, it is unclear if and how an endogenous relationship between racial classification and wealth may be affecting our results. Future research should consider options such as using an instrumental variables model (Villarreal and Bailey 2020) or incorporating multiple measures of race (e.g., self-classification and external classification) when investigating race-based wealth inequality (Bailey et al. 2013). We hope our findings will further this research conversation, addressing the important but little understood topic of racial wealth inequality in Brazil.

Supplemental Material

sj-docx-1-srd-10.1177_23780231241286561 – Supplemental material for Estimating the Racial Wealth Gap in Brazil

Supplemental material, sj-docx-1-srd-10.1177_23780231241286561 for Estimating the Racial Wealth Gap in Brazil by Christina A. Sue, Salvador Rivas and Jacques Wainer in Socius

Footnotes

ORCID iD

Christina A. Sue

Supplemental Material

Supplemental material for this article is available online.

1

In contrast to the historic Black-White racial system of the United States, Brazil has a three-tier racial system (White, Brown, Black). Although we analyzed a range of racial household compositions recognized in this system, we opted to combine the Brown and Black households in our final analyses because the difference between these households on key independent variables was generally small (for full results, see Appendix A in the supplemental material) and because it allowed us to focus on the most significant differences across households. This justification and practice is consistent with that of some prior scholarship on racial inequality in Brazil (e.g., Hasenbalg 1985; Silva 1985) and recent research showing that the Brown-Black gap is decreasing further over time ().

2

The U.S. Census Bureau considers a home to be “owned” even if there is a mortgage on it (). For comparative purposes, we defined homeownership in the same way.

3

The variables for our wealth index include type of home, wall material, piped water source, type of bathroom, type of sewage, destination of garbage, type of lighting, energy source for stove, and whether the household possesses a cell phone, land line, filter for drinking water, radio, TV, DVD, refrigerator, freezer, washing machine, computer, tablet, and internet access, and employs a domestic servant.

4

Sensitivity analyses (not shown) reveal a positive association between household income and the likelihood of reporting property taxes, controlling for household racial composition and its interaction with household income. This indicates that higher income families are more likely to report their property tax, which is also the case for White families. However, the nonsignificant interaction term suggests a leveling out at the higher income levels relative to Black families. Therefore, based on the available data, the true average property tax is likely lower, along with the apparent gap.

5

For a comparison of PNAD and POF values, which are generally comparable between the two samples, see Appendix B in the supplemental material.

6

WhiteMixed households are, by definition, composed of at least two reference adults. Thus, for comparability purposes, we provide descriptive statistics of couples-only households in Appendix C in the supplemental material. Results are comparable to those presented in .

7

For comparative purposes, we include only White households and Black households (defined in the Brazilian data as Black or Brown classification).

8

Percent change is calculated by taking the difference between White and Black, and dividing that difference by White x 100.

9

More recent research has documented a status “darkening” effect (Marteleto 2012; McNamee 2020; Telles and Flores 2013; ), but these findings are mainly capturing levels of education.

Author Biographies

Christina A. Sue received her PhD from UCLA and is a Professor of Sociology & Demography at the University of Texas-San Antonio. Her expertise is in the areas of comparative race and ethnicity, with a regional focus on the Americas. She is author of Land of the Cosmic Race: Race Mixture, Racism, and Blackness in Mexico, which examines how national ideologies in Mexico influence Mexicans’ understandings of racism, race mixture, and blackness. In addition to being a contributing author to Pigmentocracies: Ethnicity, Race, and Color in Latin America, she coauthored Durable Ethnicity: Mexican Americans and the Ethnic Core, which examines various dimensions of the Mexican American experience.

Salvador Rivas is a research scientist at the University of Luxembourg, specializing in social and educational inequality. His research focuses on identifying noncognitive factors that predict college success and teaching potential, particularly among diverse student populations. Dr. Rivas leads the development of admissions exams and procedures for Luxembourg’s teacher training programs, ensuring fair access for all applicants. He also investigates issues related to race/ethnicity and immigrant generational status in educational contexts. He completed his postdoctoral fellowship at the University of Wisconsin-Madison’s Center for Demography and Ecology, Dr. Rivas holds a PhD in sociology from the University of Michigan-Ann Arbor and a bachelor’s degree in sociology from UCLA.

Jacques Wainer received a PhD in computer science from the Pennsylvania State University, State College in 1991. He is a professor with the Computing Institute, State University of Campinas, Campinas, Brazil. He has worked in areas such as collaborative computing, medical informatics, bibliometrics, social impacts of computers, and others. His current main focus of research is machine learning.

References

Alvaredo

Facundo

Chancel

Lucas

Piketty

Thomas

Saez

Emmanuel

Zucman

Gabriel

. 2017. “World Inequality Report 2018.” World Inequality Lab. https://wir2018.wid.world/.

Bailey

Stanley

Loveman

Mara

Muniz

Jeronimo

. 2013. “Measures of ‘Race’ and the Analysis of Racial Inequality in Brazil.” Social Science Research 42(1):106–19. doi:10.1016/j.ssresearch.2012.06.006.

Bailey

Stanley

Saperstein

Aliya

Penner

Andrew

. 2014. “Race, Color, and Income Inequality across the Americas.” Demographic Research 31(24):735–56.

Bonilla-Silva

Eduardo

Glover

Karen S.

2004. “‘We Are All Americans’: The Latin Americanization of Race Relations in the United States.” Pp. 149–83 in The Changing Terrain of Race and Ethnicity, edited by Krysan

Lewis

A. E.

New York, NY: Russell Sage Foundation.

Brown

Dorothy A.

2021. The Whiteness of Wealth: How the Tax System Impoverishes Black Americans and How We Can Fix It. New York, NY: Crown.

Brown

Tyson H.

2016. “Diverging Fortunes: Racial/Ethnic Inequality in Wealth Trajectories in Middle and Late Life.” Race and Social Problems 8:29–41.

Bucciferro

Justin R.

2017. “Racial Inequality in Brazil from Independence to the Present.” Pp. 171–94 in Has Latin American Inequality Changed Direction? Looking over the Long Run, edited by Bértola

Williamson

Cham, Switzerland: Springer Open.

Conley

Dalton

. 2007. “Decomposing the Black-White Wealth Gap: The Role of Parental Resources, Inheritance, and Investment Dynamics.” Sociological Inquiry 71:39–66. doi:10.1111/j.1475-682X.2001.tb00927.x.

Degler

Carl N.

1971. Neither Black nor White: Slavery and Race Relations in Brazil and the United States. New York, NY: MacMillan Publishing Co. Inc.

10.

Fernandes

Florestan

. 1969. The Negro in Brazilian Society, translated by Skiles

J. D.

Brunel

Rothwell

New York, NY: Columbia University.

11.

Francis

Andrew M.

Tannuri-Pianto

Maria

. 2013. “Endogenous Race in Brazil: Affirmative Action and the Construction of Racial Identity among Young Adults.” Economic Development and Cultural Change 61(4):731–53.

12.

Grimm

Michaela

Heise

Michael

Holzhausen

Arne

Romero

Patricia Pelayo

. 2019. Allianz Global Wealth Report 2019. Munich, Germany: Allianz SE.

13.

Hasenbalg

Carlos

. 1985. “Race and Socioeconomic Inequalities in Brazil.” Pp. 160–78 in Race and Class Power in Brazil, edited by Fontaine

P.-M.

Los Angeles, CA: Center for Afro-American Studies.

14.

IPEA. 2011. Retrato das desigualdades de gênero e raça. Brasília: Instituto de Pesquisa Econômica Aplicada.

15.

Killewald

Alexandra

Pfeffer

Fabian T.

Schachner

Jared N.

2017. “Wealth Inequality and Accumulation.” Annual Review of Sociology 43(1):379–404. doi:10.1146/annurev-soc-060116-053331.

16.

Kuebler

Meghan

. 2013. “Closing the Wealth Gap: A Review of Racial and Ethnic Inequalities in Homeownership.” Sociology Compass 7/8:670–85. doi:10.1111/soc4.12056.

17.

Kuebler

Meghan

Rugh

Jacob

. 2013. “New Evidence on Racial and Ethnic Disparities in Homeownership in the United States from 2001 to 2010.” Social Science Research 42:1357–74. doi:10.1016/j.ssresearch.2013.06.004.

18.

Loveman

Mara

. 2014. National Colors: Racial Classification and the State in Latin America. New York, NY: Oxford University Press.

19.

Marteleto

Leticia J

. 2012. “Educational Inequality by Race in Brazil, 1982–2007: Structural Changes and Shifts in Racial Classification.” Demography 44(1):337–358. doi: 10.1007/s13524-011-0084-6.

20.

Marx

Anthony

. 1998. Making Race and Nation: A Comparison of the United States, South Africa, and Brazil. Cambridge, UK: Cambridge University Press.

21.

Massey

Douglas

Denton

Nancy

. 1993. American Apartheid: Segregation and the Making of the Underclass. Cambridge, MA: Harvard University Press.

22.

McIntosh

Kriston

Moss

Emily

Nunn

Ryan

Shambaugh

Jay

. 2020. “Examining the Black-White Wealth Gap.” Brookings. https://www.brookings.edu/articles/examining-the-black-white-wealth-gap/.

23.

McNamee

Lachlan

. 2020. “Colonial Legacies and Comparative Racial Identification in the Americas.” American Journal of Sociology 126(2): 318–353. doi: 10.1086/711063.

24.

Muniz

Jerônimo Oliveira

. 2016. “Inconsistências e consequências da variável raça para a mensuração de desigualdades.” Civitas - Revista de Ciências Sociais 16(2):62–86. doi:10.15448/1984-7289.2016.2.23097.

25.

Muniz

Jerônimo Oliveira

Bastos

João Luiz

. 2017. “Classificatory Volatility and (In)Consistency of Racial Inequality.” Cadernos de Saúde Pública 33(1):1–12. doi:10.1590/0102-311X00082816.

26.

Oliver

Melvin L.

Shapiro

Thomas M.

2006. Black Wealth/White Wealth: A New Perspective on Racial Inequality. New York, NY: Routledge.

27.

Organização Internacional do Trabalho. 2007. Igualdade no trabalho: Enfrentar os desafi os. Geneva, Switzerland: Organização Internacional do Trabalho.

28.

Osuji

Chinyere

. 2013. “Confronting Whitening in an Era of Black Consciousness: Racial Ideology and Black-White Interracial Marriages in Rio de Janeiro.” Ethnic and Racial Studies 36(10):1490–506.

29.

Painter

Matthew

Noy

Shiri

Holmes

Malcolm D.

2020. “Skin Tone and Asset Inequality in Latin America.” Journal of Ethnic and Migration Studies 46(18):3892–919. doi:10.1080/1369183X.2019.1592881.

30.

Paixão

Marcelo

Rossetto

Irene

Montobanale

Fabiana

Carvano

Luiz M.

, eds. 2011. Relatório anual das desigualdades raciais no Brasil, 2009-2010. Rio de Janeiro: Garamond.

31.

Paschel

Tianna S.

2016. Becoming Black Political Subjects: Movements and Ethno-racial Rights in Colombia and Brazil. Princeton, NJ: Princeton University Press.

32.

Pfeffer

Fabian T.

Killewald

Alexandra

. 2018. “Generations of Advantage. Multigenerational Correlations in Family Wealth.” Social Forces 96(4):1411–42. doi:10.1093/sf/sox086.

33.

Ribeiro

Carlos Antonio Costa

. 2006. “Classe, raça e mobilidade social no Brasil.” Dados 49:833–73.

34.

Ribeiro

Carlos Antonio Costa

. 2017. “Contínuo Racial, Mobilidade Social E ‘Embranquecimento.’” Revista Brasileira de Ciências Sociais 32(95):1–25. doi:10.17666/329503/2017.

35.

Roth

Wendy

Solís

Patricio

Sue

Christina A.

2022. “Beyond Money Whitening: Racialized Hierarchies and Socioeconomic Escalators in Mexico.” American Sociological Review 87(5):827–59.

36.

Rutstein

Shea O.

2015. Steps to Constructing the New DHS Wealth Index. Rockville, MD: ICF International.

37.

Schwartzman

Luisa

. 2007. “Does Money Whiten? Intergenerational Changes in Racial Classifications in Brazil.” American Sociological Review 72(6):940–63.

38.

Sedo

Stanley

Kossoudji

Sherrie

. 2004. “Rooms of One’s Own: Gender, Race and Home Ownership as Wealth Accumulation in the United States.” SSRN Electronic Journal. doi:10.2139/ssrn.621061.

39.

Shapiro

Thomas M.

2017. Toxic Inequality: How America’s Wealth Gap Destroys Mobility, Deepens the Racial Divide, and Threatens Our Future. New York, NY: Basic Books.

40.

Silva

Nelson do Valle

. 1985. “Updating the Cost of Not Being White in Brazil.” Pp. 42–55 in Race, Class, and Power in Brazil, edited by Fontaine

P.-M.

Los Angeles, CA: UCLA Center for Afro-American Studies.

41.

Skidmore

Thomas

. 1993. “Bi-racial U.S.A. vs. Multi-racial Brazil: Is the Contrast Still Valid?” Journal of Latin American Studies 25(2):373–86.

42.

Tannenbaum

Frank

. [1946] 1992. Slave and Citizen. 3rd ed. Boston, MA: Beacon Press.

43.

Telles

Edward E.

2004. Race in Another America: The Significance of Skin Color in Brazil. Princeton, NJ: Princeton University Press.

44.

Telles

Edward

Esteve

Albert

. 2019. “Racial Intermarriage in the Americas.” Sociological Science 6:293–320.

45.

Telles

Edward

Flores

René

. 2013. “Not Just Color: Whiteness, Nation, and Status in Latin America.” Hispanic American Historical Review 93(3):411–449. doi: 10.1215/00182168-2210858.

46.

Telles

Edward E.

Lim

Nelson

. 1998. “Does It Matter Who Answers the Race Question? Racial Classification and Income Inequality in Brazil.” Demography 35(4):465–74.

47.

Telles

Edward

Paschel

Tianna

. 2014. “Who Is Black, White, or Mixed Race? How Skin Color, Status, and Nation Shape Racial Classification in Latin America.” American Journal of Sociology 120(3):864–907.

48.

Torche

Florencia

Spilerman

Seymour

. 2009. “Intergenerational Influences of Wealth in Mexico.” Latin American Research Review 44(3):75–101. doi:10.1353/lar.0.0089.

49.

Valente

Rubia da Rocha

Berry

Brian J. L.

2020. “Residential Segregation by Skin Color: Brazil Revisited.” Latin American Research Review 55(2):207–26.

50.

Van den Berghe

Pierre L.

1967. Race and Racism: A Comparative Perspective. New York, NY: John Wiley & Sons, Inc.

51.

Villarreal

Andrés

Bailey

Stan

. 2020. “The Endogeneity of Race: Black Racial Identification and Men’s Earnings in Mexico.” Social Forces 98(4):1744–72.

52.

Wainer

Jacques

. 2018. “Evidências de uma divisa racial de riquezas no Brasil urbano.” Revista Brasileira de Sociologia - RBS 6(13):178–94. doi:10.20336/rbs.264.

53.

Weller

Christian E.

Hanks

Angela

. 2018. “The Widening Racial Wealth Gap in the United States after the Great Recession.” Forum for Social Economics 47(2):237–52. doi:10.1080/07360932.2018.1451769.

54.

Williams

Robert

. 2017. “Wealth Privilege and the Racial Wealth Gap: A Case Study in Economic Stratification.” The Review of Black Political Economy 44:1–23. doi:10.1007/s12114-017-9259-8.

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