Race and Ethnicity by the Numbers
Why smart policies require better data. (video interview available)
Despite growing recognition that racial and ethnic groups across the Americas are disproportionately poor, government interventions that address their needs have been inadequate. Yet if the region’s current economic growth is going to be sustained, it will require policy tools that address the exclusion of these groups from the larger society.
The lack of solid data has been the biggest obstacle to developing programs that target these marginalized groups. There has never been a more critical moment to get such programs under way.
Eleven of the 20 most unequal countries in the world are in Latin America, and four nations in the region have become more unequal—measured by the Gini coefficient—since 2000.1 Most tragically, the systemic lack of access to socioeconomic opportunities for Indigenous peoples and Afro-descendants not only cripples future development, but poses a current threat to stability. For example, six of the fourteen most violent countries in the world are in Latin America,and three of them have the highest levels of global inequality.2
Quality data, particularly via household surveys, can assist in developing programs that can systematically address exclusion.
Learning How to Count
Household survey data are one of the most important sources of information for determining levels of access and gaps in national coverage. Reliable data help decision-makers analyze successes and failures of a wide range of programs, such as water and sanitation. More broadly, the data serve as a cornerstone for measuring development progress, designing targeted programs, and monitoring the success and efficiency of development programs.
But when household surveys inadequately incorporate racial or ethnic populations in sampling, they provide limited socioeconomic information on the most marginalized groups in society—the very groups that often lack access to basic services. Another problem is that marginalized racial and ethnic groups are often not incorporated in the data collection exercises that serve as the basis for national policy and investment decisions.
In countries such as Panama, Paraguay, Nicaragua, Guatemala, Honduras, and Bolivia, over 60 percent of the Indigenous and Afro-descendant populations are poor. Nevertheless, in too many cases the extent of these populations’ exclusion from society has not been accurately measured because of household surveys that do not include ethnicity—even in countries where poverty is as high as 80 percent in Indigenous or Afro-descendant communities.
In those countries where data are available, the lack of access to services in Indigenous and Afro-descendant regions is so high in certain sectors that their exclusion distorts national indicators. For example, Latin America and the Caribbean may have difficulties reaching Millennium Development Goal targets in areas such as maternal health in part because of persistent racial and ethnic gaps.
In Guatemala, Indigenous women are three times more likely to die in childbirth than their non-Indigenous counterparts. This gap is significant enough to affect the overall statistics for the country as a whole.3 In Panama, the maternal mortality rate among the rural Indigenous is 10 times higher than the national average of 70 per 100,000 live births.4
Further, we have discovered that race and ethnicity are a more important determinant of people’s access to quality health care than income. For example, Indigenous citizens in Mexico receive overall lower quality health care regardless of their level of personal income. In a 2005 report based on an assessment of health protocols in rural areas of the country by Barber, Bertozzi and Gertler, Indigenous peoples in higher income brackets were only able to access quality health care at a level equivalent to the poorest quintile of non-Indigenous peoples.5
Brazil Shows the Way and Why It’s Important
Despite challenges, several countries in the region have made advances in data collection. Brazil, with over two decades of solid experience collecting statistics on race and ethnicity, is leading Latin America in data collection and analysis of race and ethnicity statistics.
With the largest Afro-descendant population in Latin America, and the second largest in the world, race and ethnicity data are taken seriously in Brazil. They are well-collected, analyzed and effectively disseminated. The 2010 census shows that 97 million Brazilians, or 50.7 percent of the population, define themselves as black or mixed race.
Civil society has taken on racial identification in the census as an important element for policy change in the country and has emphasized the importance of classification and self-identification since the 1990s. During the 1991 census in Brazil, the slogan “Não deixe sua cor passar em branco: Responda com bom C/senso” (“Don’t let your color pass as white: respond with good sense”) was promoted by civil society groups to urge Brazilians to accurately report their race in the census by checking the color that most reflected their skin tone and racial identity.6
This emphasis on quality self-identification has led to the creation of policy tools that have been successfully tested in recent national policy initiatives. The conditional cash transfer program Bolsa Família (Family Allowance) has done an excellent job of targeting Afro-descendants and the Indigenous poor by using quality data found in household surveys, census data, and the Ministry of Social Development’s cadastro único survey of low-income families. But this is only possible if these populations are counted. Recent estimates by the Laboratório de Análises Econômicas, Sociais e Estatísticas das Relações Raciais (LAESER), led by Marcelo Paixão, used government statistics to demonstrate that Afro-Brazilians are more likely than whites to receive Bolsa Família benefits—an important tool to alleviate poverty.7
In Brazil, 18 percent of all families receive Bolsa Família; 9.8 percent of white families receive transfers, while 24 percent of Afro-Brazilian families get them. In regions of the country with the highest levels of beneficiaries, such as the north and northeast, Afro-Brazilians make up a significant percentage of cash transfer recipients. This distribution is aligned with poverty statistics that demonstrate income disparities by race. In the northeast, 34.6 percent of all families receive Bolsa Família; 36.1 percent of black families participate, compared to 21.8 percent of white families. In the north, where 25.3 percent of all families receive Bolsa Família, the ratio is 27.4 percent of black families to 11.9 percent of white families.
Launched in August 2011, Brasil sem Miséria is an extension of Bolsa Família and aims to reach an even larger segment of the poorest of the poor, specifically individuals who have the most limited access to government services. This new program defines extreme poverty as households that earn less than $50 (70 reais) per person and has the goal of reaching 16.2 million Brazilians living in extreme poverty.
The launch of Brasil sem Miséria was accompanied by an important data analysis exercise conducted by Instituto Brasileiro de Geografia e Estatística (IBGE), the government statistics agency, which is a major component of the government media campaign and outreach strategy.
The government has established program goals based on IBGE definitions of poverty, setting a target that 2 percent of recipients of the program will be Indigenous and 70.8 percent will be Afro-Brazilian.
Detailed information on race and ethnicity through household survey data and the census is also used to construct and validate market research studies, such as the Fundo Baobá-Data Popular survey, launched in November 2011, which demonstrates that 44.9 percent of Afro-Brazilians are now middle class (classe C)—an 11.2 percent increase since 2004—compared to an increase of only 6.4 percent for non-Afro-Brazilians during the same period. This study also shows that the Afro-Brazilian consumer market represented $369 billion (673 billion reais) in 2011, while in 2009 this amount was $320 billion (584 billion reais) and $203 billion (370 billion reais) in 2004.8 Improved understanding of the needs of these emerging Afro-Brazilian middle-class consumers will be vital for determining the success of core business sectors in the country...
1. IDB, 2011. While there have been decreases in inequality as measured by the Gini coefficient in ten countries of the region, inequality has actually increased in four of these countries from 2000 to 2006 (Lustig, 2009). CIA- The World Factbook 2011 (www.cia.gov). The top seven countries are Namibia, Seychelles, South Africa, Lesotho, Botswana, Sierra Leone, and the Central African Republic. The Latin American countries by order and rank are Haiti (8), Colombia (9), Bolivia (10), Honduras (11), Guatemala (12), Brazil (13), Paraguay (16), Nicaragua (17), Chile (18), Mexico (19), and Panama (20). Of the 20 most unequal countries in the world 11 are in Latin America or the Caribbean.
2. Six of the 14 most violent countries in the world are in Latin America revealed the second edition of the report “Armed Violence and Development” published 2011 Geneva Declaration on Violence and Development. El Salvador, Honduras, Colombia, Venezuela, Guatemala and Belize.
3. Maternal mortality rates (deaths per 100,000 live births) in Guatemala. 211 Indigenous compared with 70 non-Indigenous. (IDB, 2011). Indigenous peoples make up 40.6% of the total population based on household survey data weights, yet the census places the population size at closer to 66%.
4. IDB, 2010
5. Sarah L. Barber,Stefano M. Bertozzi and Paul J. Gertler, “Variations in Prenatal Care Quality for the Rural Poor in Mexico,” Health Affairs, 2007.
6. Nobles 2000, 1744
7. Ministerio do Desenvolvimento Social (MDS), CADUNICO microdata (Feb/2009), IBGE, PNAD microdata, 2008
8. As cited in Cronista.Com– Argentina– 09/11/11 (http://www.cronista.com/contenidos/2011/11/09/noticia_0038.html)
9. Ministerio de Desarrollo Social, Insituto Nacional de las Mujeres, http://www.inmujeres.gub.uy/mides/text.jsp?contentid=9922&site=1&channel...