According to the most recent national census, approximately 46 percent of the people residing in Brazil identified as Pardo Brazilians making it the largest ethnic group in the country. However, when breaking it down by regions, it can be seen that the ethnic distribution of Brazilian population varied considerably across the country. In the North, for example, 69 percent of the population identify as Pardo, while this share fell to 22 percent in the South, where 71 of inhabitants are white. The Northeast has the largest percentage of black people, with 13 percent.

About this dataset

Context

The dataset tabulates the population of Brazil by race. It includes the population of Brazil across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Brazil across relevant racial categories.

Key observations

The percent distribution of Brazil population by race (across all racial categories recognized by the U.S. Census Bureau): 93.58% are white, 0.21% are Black or African American, 0.30% are Asian, 1.64% are some other race and 4.28% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Brazil
• Population: The population of the racial category (excluding ethnicity) in the Brazil is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Brazil total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Brazil Population by Race & Ethnicity. You can refer the same here

Brazil and the United States are the two most populous countries in the Americas today. In 1500, the year that Pedro Álvares Cabral made landfall in present-day Brazil and claimed it for the Portuguese crown, it is estimated that there were roughly one million people living in the region. Some estimates for the present-day United States give a population of two million in the year 1500, although estimates vary greatly. By 1820, the population of the U.S. was still roughly double that of Brazil, but rapid growth in the 19th century would see it grow 4.5 times larger by 1890, before the difference shrunk during the 20th century. In 2024, the U.S. has a population over 340 million people, making it the third most populous country in the world, while Brazil has a population of almost 218 million and is the sixth most populous. Looking to the future, population growth is expected to be lower in Brazil than in the U.S. in the coming decades, as Brazil's fertility rates are already lower, and migration rates into the United States will be much higher. Historical development The indigenous peoples of present-day Brazil and the U.S. were highly susceptible to diseases brought from the Old World; combined with mass displacement and violence, their population growth rates were generally low, therefore migration from Europe and the import of enslaved Africans drove population growth in both regions. In absolute numbers, more Europeans migrated to North America than Brazil, whereas more slaves were transported to Brazil than the U.S., but European migration to Brazil increased significantly in the early 1900s. The U.S. also underwent its demographic transition much earlier than in Brazil, therefore its peak period of population growth was almost a century earlier than Brazil. Impact of ethnicity The demographics of these countries are often compared, not only because of their size, location, and historical development, but also due to the role played by ethnicity. In the mid-1800s, these countries had the largest slave societies in the world, but a major difference between the two was the attitude towards interracial procreation. In Brazil, relationships between people of different ethnic groups were more common and less stigmatized than in the U.S., where anti-miscegenation laws prohibited interracial relationships in many states until the 1960s. Racial classification was also more rigid in the U.S., and those of mixed ethnicity were usually classified by their non-white background. In contrast, as Brazil has a higher degree of mixing between those of ethnic African, American, and European heritage, classification is less obvious, and factors such as physical appearance or societal background were often used to determine racial standing. For most of the 20th century, Brazil's government promoted the idea that race was a non-issue and that Brazil was racially harmonious, but most now acknowledge that this actually ignored inequality and hindered progress. Racial inequality has been a prevalent problem in both countries since their founding, and today, whites generally fare better in terms of education, income, political representation, and even life expectancy. Despite this adversity, significant progress has been made in recent decades, as public awareness of inequality has increased, and authorities in both countries have made steps to tackle disparities in areas such as education, housing, and employment.

The history of modern Brazil begins in the year 1500 when Pedro Álvares Cabral arrived with a small fleet and claimed the land for the Portuguese Empire. With the Treaty of Torsedillas in 1494, Spain and Portugal agreed to split the New World peacefully, thus allowing Portugal to take control of the area with little competition from other European powers. As the Portuguese did not arrive with large numbers, and the indigenous population was overwhelmed with disease, large numbers of African slaves were transported across the Atlantic and forced to harvest or mine Brazil's wealth of natural resources. These slaves were forced to work in sugar, coffee and rubber plantations and gold and diamond mines, which helped fund Portuguese expansion across the globe. In modern history, transatlantic slavery brought more Africans to Brazil than any other country in the world. This combination of European, African and indigenous peoples set the foundation for what has become one of the most ethnically diverse countries across the globe.

Independence and Monarchy By the early eighteenth century, Portugal had established control over most of modern-day Brazil, and the population more than doubled in each half of the 1800s. The capital of the Portuguese empire was moved to Rio de Janeiro in 1808 (as Napoleon's forces moved closer towards Lisbon), making this the only time in European history where a capital was moved to another continent. The United Kingdom of Portugal, Brazil and the Algarves was established in 1815, and when the Portuguese monarchy and capital returned to Lisbon in 1821, the King's son, Dom Pedro, remained in Brazil as regent. The following year, Dom Pedro declared Brazil's independence, and within three years, most other major powers (including Portugal) recognized the Empire of Brazil as an independent monarchy and formed economic relations with it; this was a much more peaceful transition to independence than many of the ex-Spanish colonies in the Americas. Under the reign of Dom Pedro II, Brazil's political stability remained relatively intact, and the economy grew through its exportation of raw materials and economic alliances with Portugal and Britain. Despite pressure from political opponents, Pedro II abolished slavery in 1850 (as part of a trade agreement with Britain), and Brazil remained a powerful, stable and progressive nation under Pedro II's leadership, in stark contrast to its South American neighbors. The booming economy also attracted millions of migrants from Europe and Asia around the turn of the twentieth century, which has had a profound impact on Brazil's demography and culture to this day.

The New Republic

Despite his popularity, King Pedro II was overthrown in a military coup in 1889, ending his 58 year reign and initiating six decades of political instability and economic difficulties. A series of military coups, failed attempts to restore stability, and the decline of Brazil's overseas influence contributed greatly to a weakened economy in the early 1900s. The 1930s saw the emergence of Getúlio Vargas, who ruled as a fascist dictator for two decades. Despite a growing economy and Brazil's alliance with the Allied Powers in the Second World War, the end of fascism in Europe weakened Vargas' position in Brazil, and he was eventually overthrown by the military, who then re-introduced democracy to Brazil in 1945. Vargas was then elected to power in 1951, and remained popular among the general public, however political opposition to his beliefs and methods led to his suicide in 1954. Further political instability ensued and a brutal, yet prosperous, military dictatorship took control in the 1960s and 1970s, but Brazil gradually returned to a democratic nation in the 1980s. Brazil's economic and political stability fluctuated over the subsequent four decades, and a corruption scandal in the 2010s saw the impeachment of President Dilma Rousseff. Despite all of this economic instability and political turmoil, Brazil is one of the world's largest economies and is sometimes seen as a potential superpower. The World Bank classifies it as a upper-middle income country and it has the largest share of global wealth in Latin America. It is the largest Lusophone (Portuguese-speaking), and sixth most populous country in the world, with a population of more than 210 million people.

In 2023, the share of Black people killed by the police was higher than the share of the black population in almost all the Brazilian states listed. In Pernambuco, the difference between the proportion of black population and black people killed by the police was of 30 percentage points. Except for São Paulo and Piauí, more than 80 percent of civilians killed by security agents were black in all states covered in this study.

Context

Percentage of resident persons who declared themselves black in relation to the total resident population, at the reference date of the Demographic Census. Source: IBGE, Demographic Census 2010 and Municipal fabric 2010. http://www.geoservicos.ibge.gov.br/geoserver/wms?service=WFS&version=1.0.0&request=GetFeature&typeName=CGEO:vw_per_black_people& om the dataset summary Population Census and Mesh ... License not specified spatial: "type": "Polygon", "coordinates": [[- [- 74.0046, -33.7411], [- 34.7929, -33.7411], [- 34.7929,5.2727], [- 74.0046,5.2727], [- 74.0046, -33.7411 ]]] http://dados.gov.br/dataset/cgeo_vw_per_pessoas_pretas

Content

Author and Maintainer: Geosciences Directorate - IBGE and Research Directorate - IBGE Last update: June 12, 2018 package id: 4565a7e3-9509-43dc-b074-433451ef7a47 Organ - Sphere: Federal. Organ - Power: Executive.

Acknowledgements

Geosciences Directorate - IBGE and Research Directorate - IBGE http://dados.gov.br

Photo by Anomaly on Unsplash

Inspiration

Nelson Mandela: was a South African anti-apartheid revolutionary, political leader, and philanthropist who served as President of South Africa from 1994 to 1999. He was the country's first black head of state and the first elected in a in a fully representative democratic election. His government focused on dismantling the legacy of apartheid by tackling institutionalized racism and fostering racial reconciliation. https://en.wikipedia.org/wiki/Nelson_Mandela

Martin Luther King Jr. (January 15, 1929 – April 4, 1968) was an American Christian minister and activist who became the most visible spokesperson and leader in the Civil Rights Movement from 1955 until his assassination in 1968. Born in Atlanta Georgia, King is best known for advancing civil rights through nonviolence and civil disobedience, inspired by his Christian beliefs and the nonviolent activism of Mahatma Gandhi. https://en.wikipedia.org/wiki/Martin_Luther_King_Jr.

In 2023, the prevalence of extreme poverty among black men and women in Brazil was higher than that observed in other demographic groups. In particular, the rate of extreme poverty among black men reached two percent, which was the highest among all demographic groups.

The analysis of genomic data (~400,000 autosomal SNPs) enabled the reliable estimation of inbreeding levels in a sample of 541 individuals sampled from a highly admixed Brazilian population isolate (an African-derived quilombo in the State of São Paulo). To achieve this, different methods were applied to the joint information of two sets of markers (one complete and another excluding loci in patent linkage disequilibrium). This strategy allowed the detection and exclusion of markers that biased the estimation of the average population inbreeding coefficient (Wright’s fixation index FIS), which value was eventually estimated as around 1% using any of the methods we applied. Quilombo demographic inferences were made by analyzing the structure of runs of homozygosity (ROH), which were adapted to cope with a highly admixed population with a complex foundation history. Our results suggest that the amount of ROH

About this dataset

Context

The dataset tabulates the Non-Hispanic population of Brazil by race. It includes the distribution of the Non-Hispanic population of Brazil across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of Brazil across relevant racial categories.

Key observations

Of the Non-Hispanic population in Brazil, the largest racial group is White alone with a population of 7,500 (95.12% of the total Non-Hispanic population).

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the Brazil
• Population: The population of the racial category (for Non-Hispanic) in the Brazil is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Brazil total Non-Hispanic population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Brazil Population by Race & Ethnicity. You can refer the same here

In a large variety of genetic studies, probabilistic inferences are made based on information available in population databases. The accuracy of the estimates based on population samples are highly dependent on the number of chromosomes being analyzed as well as the correct representation of the reference population. For frequency calculations the size of a database is especially critical for haploid markers, and for countries with complex admixture histories it is important to assess possible substructure effects that can influence the coverage of the database. Aiming to establish a representative Brazilian population database for haplotypes based on 23 Y chromosome STRs, more than 2,500 Y chromosomes belonging to Brazilian, European and African populations were analyzed. No matter the differences in the colonization history of the five geopolitical regions that currently exist in Brazil, for the Y chromosome haplotypes of the 23 studied Y-STRs, a lack of genetic heterogeneity was found, together with a predominance of European male lineages in all regions of the country. Therefore, if we do not consider the diverse Native American or Afro-descendent isolates, which are spread through the country, a single Y chromosome haplotype frequency database will adequately represent the urban populations in Brazil. In comparison to the most commonly studied group of 17 Y-STRs, the 23 markers included in this work allowed a high discrimination capacity between haplotypes from non-related individuals within a population and also increased the capacity to discriminate between paternal relatives. Nevertheless, the expected haplotype mutation rate is still not enough to distinguish the Y chromosome profiles of paternally related individuals. Indeed, even for rapidly mutating Y-STRs, a very large number of markers will be necessary to differentiate male lineages from paternal relatives.

Abstract

The purpose of the Ageing, Wellbeing and Development Project (Brazza2) was to investigate the impact on poverty and vulnerability within beneficiary households in Brazil and South Africa of grants, social pensions and the like. The survey aimed to help researchers interrogate the extent to which social assistance was enhancing quality of life, and whether income from old-age pensions and other social grants enhanced the material and perceived well-being of social pensioners and members of households.The study also inquired into perceptions of fortune and misfortune, to provide clues to the role of social assistance in boosting poorer households' resilience and their independence from the State.

Analysis unit

Households and individuals

Universe

South Africa: the survey covered all members of African households in rural Eastern Cape and African and Coloured households in urban Western Cape.

Kind of data

Survey data

Sampling procedure

South Africa: In South Africa, a company called Development Research Africa were commissioned to conduct the data collection. To conduct the sampling for this, they requested a list of EAs from Stats SA that satisfied the following criteria:

1. Predominantly black or coloured EAs
2. Predominantly defined (by Stats SA) as urban (formal or informal) in the Western Cape
3. Predominantly defined (by Statssa) as tribal or semi urban in the Eastern Cape; and
4. Did not contain institutions or farming areas (these EAs were excluded)

These CEAs were sent to DRA in several excel spreadsheets under the following headings for each magisterial district:

1. Geographical areas by population group of head of household for person weighted (African/Black or Coloured)
2. Geographical areas by enumeration area type for person weighted (rural: tribal villages, urban: formal or urban: informal)
3. Geographical areas by age for person weighted (56 years and older)
4. Geographical areas for household weighted (which provided the total number of households per CEA).

These data files were collated and then merged into three separate spreadsheets reflecting the respondent categories. All CEAs containing less than eighty households were deleted to further ensure that institutions or farming areas (as well as urban areas in the Eastern Cape) would not become eligible and also to limit the possibility of selecting CEAs with no eligible respondent households. These three databases became the three sample frames used to select the sample.

All the remaining CEAs were sorted in ascending order. A PSS sampling method was used to select the sample. This means that CEAs with a larger number of households have a greater chance of being selected into the sample. The two CEAs directly below the selected EAs were included as possible substitutions. Once the EA numbers were selected the maps were sourced from Stats SA. Only then could one determine the location of these CEAs. Because of the PPS methodology, EAs from smaller magisterial districts fell short of being selected into the sample whilst larger magisterial districts had more than one EA selected. In the Western Cape, the EAs could relatively easily be found on Cape Town street maps.

Twenty clusters or EAs were selected per respondent category. The target per category was about 333 interviews. It follows that about 17 interviews (333/20=17) had to be done per CEA. The desired number of households that need to be approached in a cluster or EA was the segment size. The segment size was dependent on the percentage of households that contain at least one person aged 55 years and over and on the response rate assumed. The segment size for each of the CEAs in the sample was calculated individually. For example, if 33 persons aged 55 or older resided in the CEA with 120 households and assuming a 95% response rate, 59 households would have to be approached (17/(15/120)*0.95) in the CEA in order to obtain 17 successful interviews per CEA. One limitation to the study here was that this formula does not take into consideration the possibility of two or more persons in this age category residing in a household.

Once the maps were acquired from Stats SA, they were verified and updated by the fieldworker through identifying the EA boundaries and by entering any features or changes to the map. The number of households were then counted and divided into segments with approximately equal number of households. One calculates the number of segments by dividing the segment size (described in the previous paragraph) by the actual number of households found and recorded in the EA. Some EAs may have only one segment (if segment size > total number of households in EA) or may have as many as five or six segments. One segment is then randomly selected. All the households in a particular segment were approached and all target households identified and surveyed. Finally, within the households, the person most knowledgeable about how money is spent in the household was selected as the first respondent. Thereafter all individuals 55 years of age and over were interviewed. The fieldworkers had to make three visits per household where the respondents were not available to maximize the possibility that the interview would be completed with the selected respondent. The project manager monitored the number of completed interviews. In instances where it seemed that the overall target of 333 interviews per respondent category area was unlikely, the fieldworkers had to survey the whole EA.

The twenty randomly-selected EAs in the rural Eastern Cape were located in the former Transkei and Ciskei 'homelands' in the magisterial districts of Zwelitsha, Keiskammahoek, Engcobo, Idutywa, Kentani, Libode, Lusikisiki, Mqanduli, Ngquleni, Nqamakwe, Port St Johns, Qumbu, Cofimvaba, Tabankulu, Tsomo, Willowvale and Lady Frere. The twenty randomly-selected EAs in the Cape Town metropole targeting urban black households were located in the magisterial districts of Goodwood, Wynberg, Mitchell's Plain (which includes the sprawling township of Khayelitsha) and Kuils River. The twenty randomly-selected EAs targeting urban coloured households were located in the same magisterial districts in Cape Town metropole as those targeting urban black households with the addition of Bellville.

The 2002 sample design prescribed that all households selected in the last stage, in the EA segment, had to be interviewed. As a result, a larger sample size was achieved in 2002 than the originally planned sample of 1000 interviews. A total of 1111 interviews was realised in 2002: 374 in rural black households, 324 in urban black households and 413 in urban coloured households.

Approximately 79% of households included in the 2009 survey were the same ones that participated in the earlier 2002 wave. A significantly higher proportion of rural black (94%) households than urban black (72%) and urban coloured (71%) ones were traced. A household that could not be traced was replaced by another older household in the same enumerator area. An estimated 69% of the 4199 household members enumerated in 2002 were traced to 2009. In total, 1286 individuals could not be traced. In this group 18% were reportedly temporarily absent, 55% had moved away permanently, and 27% (or 346 individuals) had died. This paper is based on information supplied by a total of 1059 households in the 2009 survey: 362 rural black households, 299 urban black households, and 398 urban coloured households.

Brazil: Note that some of the information on sampling for the following section was taken from a document originally written in Portuguese and translated using Google translate. The original document is available with this dataset and is titled: "Benefícios Não-Contributivos e o Combate à Pobreza de Idosos no Brasil"

The approach taken in Brazil was similar to the one taken in South Africa, as the territorial expansiveness made it difficult to obtain a nationally representative sample of with a relatively small number of households. The alternative was to seek to expand the regional coverage as far as possible within the research budget. Two large regions were selected for field research. The first was the metropolitan area of Rio de Janeiro, in which the population of Rio de Janeiro state is most heavily concentrated. This is one of the most developed states in the country. Four counties were chosen within the metropolitan area. Three neighboring counties, Duke Caxias, Nova Iguaçu and São João de Meriti, were also selected. To represent the elderly population of the poorest regions of the country, a state in the Northeast was selected. Three possibilities were considered: Bahia, Pernambuco and Ceara. These have the the largest populations in the Northeast. The state of Bahia was chosen because of its proximity to Rio de Janeiro (making it more affordable to process the data). Of the major cities of Bahia, Ilheus was chosen as it had a more rural population, which the study aimed to capture.

The sample target was defined at around a thousand households with at least one person aged 60 or over in the household. Aiming to diversifying the population surveyed, the sample was divided into four groups, each with about one fourth of the sample. Thus, the state of Rio de January was half of the sample, and the rest distributed in the three counties in the Rio de Janeiro metropolitan area. The other half was divided in two, half being in the urban, and the other rural, in the municipality of Ilheus.

To select of households within each municipality the Brazilian 2000 Census data was used. Sectors with low income and high population of elderly, maximizing the probability of finding elderly not receiving contributory benefits, were chosen. The criteria used were:

1. At least 100

Abstract The genetic background of the Brazilian population is mainly characterized by three parental populations: European, African, and Native American. The aim of this study was to overview the genetic ancestry estimates for different Brazilian geographic regions and analyze factors involved in these estimates. In this systematic scoping review were included 51 studies, comprehending 81 populations of 19 states from five regions of Brazil. To reduce the potential of bias from studies with different sampling methods, we calculated the mean genetic ancestry weighted by the number of individuals. The weighted mean proportions of European, African, and Native American ancestries were 68.1%, 19.6%, and 11.6%, respectively. At the regional level, the highest European contribution occurred in the South, while the highest African and Native American contributions occurred in the Northeastern and Northern regions, respectively. Among states in the Northeast region, Bahia and Ceará showed significant differences, suggesting distinct demographic histories. This review contributes for a broader understanding of the Brazilian ancestry and indicates that the ancestry estimates are influenced by the type of molecular marker and the sampling method.

Brazil BR: Refugee Population: by Country or Territory of Origin data was reported at 3,798.000 Person in 2023. This records an increase from the previous number of 2,740.000 Person for 2022. Brazil BR: Refugee Population: by Country or Territory of Origin data is updated yearly, averaging 389.000 Person from Dec 1971 (Median) to 2023, with 44 observations. The data reached an all-time high of 3,798.000 Person in 2023 and a record low of 5.000 Person in 1986. Brazil BR: Refugee Population: by Country or Territory of Origin data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Brazil – Table BR.World Bank.WDI: Population and Urbanization Statistics. Refugees are people who are recognized as refugees under the 1951 Convention Relating to the Status of Refugees or its 1967 Protocol, the 1969 Organization of African Unity Convention Governing the Specific Aspects of Refugee Problems in Africa, people recognized as refugees in accordance with the UNHCR statute, people granted refugee-like humanitarian status, and people provided temporary protection. Asylum seekers--people who have applied for asylum or refugee status and who have not yet received a decision or who are registered as asylum seekers--are excluded. Palestinian refugees are people (and their descendants) whose residence was Palestine between June 1946 and May 1948 and who lost their homes and means of livelihood as a result of the 1948 Arab-Israeli conflict. Country of origin generally refers to the nationality or country of citizenship of a claimant.;United Nations High Commissioner for Refugees (UNHCR), Refugee Data Finder at https://www.unhcr.org/refugee-statistics/.;Sum;

About this dataset

Context

The dataset tabulates the Brazil Hispanic or Latino population. It includes the distribution of the Hispanic or Latino population, of Brazil, by their ancestries, as identified by the Census Bureau. The dataset can be utilized to understand the origin of the Hispanic or Latino population of Brazil.

Key observations

Among the Hispanic population in Brazil, regardless of the race, the largest group is of Mexican origin, with a population of 136 (83.44% of the total Hispanic population).

https://i.neilsberg.com/ch/brazil-in-population-by-race-and-ethnicity.jpeg" alt="Brazil Non-Hispanic population by race">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Origin for Hispanic or Latino population include:

• Mexican
• Black or African American
• Puerto Rican
• Cuban
• Other Hispanic or Latino

Variables / Data Columns

• Origin: This column displays the origin for Hispanic or Latino population for the Brazil
• Population: The population of the specific origin for Hispanic or Latino population in the Brazil is shown in this column.
• % of Total Hispanic Population: This column displays the percentage distribution of each Hispanic origin as a proportion of Brazil total Hispanic or Latino population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Brazil Population by Race & Ethnicity. You can refer the same here

Abstract

The authors combine data from 84 Demographic and Health Surveys from 46 countries to analyze trends and socioeconomic differences in adult mortality, calculating mortality based on the sibling mortality reports collected from female respondents aged 15-49.

The analysis yields four main findings. First, adult mortality is different from child mortality: while under-5 mortality shows a definite improving trend over time, adult mortality does not, especially in Sub-Saharan Africa. The second main finding is the increase in adult mortality in Sub-Saharan African countries. The increase is dramatic among those most affected by the HIV/AIDS pandemic. Mortality rates in the highest HIV-prevalence countries of southern Africa exceed those in countries that experienced episodes of civil war. Third, even in Sub-Saharan countries where HIV-prevalence is not as high, mortality rates appear to be at best stagnating, and even increasing in several cases. Finally, the main socioeconomic dimension along which mortality appears to differ in the aggregate is gender. Adult mortality rates in Sub-Saharan Africa have risen substantially higher for men than for women?especially so in the high HIV-prevalence countries. On the whole, the data do not show large gaps by urban/rural residence or by school attainment.

This paper is a product of the Human Development and Public Services Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org.

Geographic coverage

We derive estimates of adult mortality from an analysis of Demographic and Health Survey (DHS) data from 46 countries, 33 of which are from Sub-Saharan Africa and 13 of which are from countries in other regions (Annex Table). Several of the countries have been surveyed more than once and we base our estimates on the total of 84 surveys that have been carried out (59 in Sub-Saharan Africa, 25 elsewhere).

The countries covered by DHS in Sub-Saharan Africa represent almost 90 percent of the region's population. Outside of Sub-Saharan Africa the DHS surveys we use cover a far smaller share of the population-even if this is restricted to countries whose GDP per capita never exceeds $10,000: overall about 14 percent of the population is covered by these countries, although this increases to 29 percent if China and India are excluded (countries for which we cannot calculate adult mortality using the DHS). It is therefore important to keep in mind that the sample of non-Sub-Saharan African countries we have cannot be thought of as "representative" of the rest of the world, or even the rest of the developing world.

Analysis unit

Country

Kind of data

Sample survey data [ssd]

Mode of data collection

Face-to-face [f2f]

Cleaning operations

In the course of carrying out this study, the authors created two databases of adult mortality estimates based on the original DHS datasets, both of which are publicly available for analysts who wish to carry out their own analysis of the data.

The naming conventions for the adult mortality-related are as follows. Variables are named:

GGG_MC_AAAA

GGG refers to the population subgroup. The values it can take, and the corresponding definitions are in the following table:

All - All Fem - Female Mal - Male Rur - Rural Urb - Urban Rurm - Rural/Male Urbm - Urban/Male Rurf - Rural/Female Urbf - Urban/Female Noed - No education Pri - Some or completed primary only Sec - At least some secondary education Noedm - No education/Male Prim - Some or completed primary only/Male Secm - At least some secondary education/Male Noedf - No education/Female Prif - Some or completed primary only/Female Secf - At least some secondary education/Female Rch - Rural as child Uch - Urban as child Rchm - Rural as child/Male Uchm - Urban as child/Male Rchf - Rural as child/Female Uchf - Urban as child/Female Edltp - Less than primary schooling Edpom - Primary or more schooling Edltpm - Less than primary schooling/Male Edpomm - Primary or more schooling/Male Edltpf - Less than primary schooling/Female Edpomf - Primary or more schooling/Female Edltpu - Less than primary schooling/Urban Edpomu - Primary or more schooling/Urban Edltpr - Less than primary schooling/Rural Edpomr - Primary or more schooling/Rural Edltpmu - Less than primary schooling/Male/Urban Edpommu - Primary or more schooling/Male/Urban Edltpmr - Less than primary schooling/Male/Rural Edpommr - Primary or more schooling/Male/Rural Edltpfu - Less than primary schooling/Female/Urban Edpomfu - Primary or more schooling/Female/Urban Edltpfr - Less than primary schooling/Female/Rural Edpomfr - Primary or more schooling/Female/Rural

M refers to whether the variable is the number of observations used to calculate the estimate (in which case M takes on the value "n") or whether it is a mortality estimate (in which case M takes on the value "m").

C refers to whether the variable is for the unadjusted mortality rate calculation (in which case C takes on the value "u") or whether it adjusts for the number of surviving female siblings (in which case C takes on the value "a").

AAAA refers to the age group that the mortality estimate is calculated for. It takes on the values: 1554 - Ages 15-54 1524 - Ages 15-24 2534 - Ages 25-34 3544 - Ages 35-44 4554 - Ages 45-54

Other variables that are in the databases are:

period - Period for which mortality rate is calculated (takes on the values 1975-79, 1980-84 … 2000-04) svycountry - Name of country for DHS countries ccode3 - Country code u5mr - Under-5 mortality (from World Development Indicators) cname - Country name gdppc - GDP per capita (constant 2000 US$) (from World Development Indicators) gdppcppp - GDP per capita PPP (constant 2005 intl $) (from World Development Indicators) pop - Population (from World Development Indicators) hivprev2001 - HIV prevalence in 2001 (from UNAIDS 2010) region - Region

Brazil BR: Refugee Population: by Country or Territory of Asylum data was reported at 235,765.000 Person in 2023. This records an increase from the previous number of 67,522.000 Person for 2022. Brazil BR: Refugee Population: by Country or Territory of Asylum data is updated yearly, averaging 5,400.000 Person from Dec 1969 (Median) to 2023, with 55 observations. The data reached an all-time high of 235,765.000 Person in 2023 and a record low of 2,050.000 Person in 1995. Brazil BR: Refugee Population: by Country or Territory of Asylum data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Brazil – Table BR.World Bank.WDI: Population and Urbanization Statistics. Refugees are people who are recognized as refugees under the 1951 Convention Relating to the Status of Refugees or its 1967 Protocol, the 1969 Organization of African Unity Convention Governing the Specific Aspects of Refugee Problems in Africa, people recognized as refugees in accordance with the UNHCR statute, people granted refugee-like humanitarian status, and people provided temporary protection. Asylum seekers--people who have applied for asylum or refugee status and who have not yet received a decision or who are registered as asylum seekers--are excluded. Palestinian refugees are people (and their descendants) whose residence was Palestine between June 1946 and May 1948 and who lost their homes and means of livelihood as a result of the 1948 Arab-Israeli conflict. Country of asylum is the country where an asylum claim was filed and granted.;United Nations High Commissioner for Refugees (UNHCR) and UNRWA through UNHCR's Refugee Data Finder at https://www.unhcr.org/refugee-statistics/.;Sum;

COVID-19 incidence and mortality in countries with heavy social inequalities differ in population terms. In countries like Brazil with colonial histories and traditions, the social markers of differences are heavily anchored in social and racial demarcation, and the political and social dynamics and processes based on structural racism act on this demarcation. The pandemic’s actual profile in Brazil clashes with narratives according to which COVID-19 is a democratic pandemic, an argument aligned with the rhetoric of racial democracy that represents a powerful strategy aimed at maintaining the subaltern place of racialized populations such as indigenous peoples and blacks, as a product of modern coloniality. This essay focuses on the pandemic’s profile in the Brazilian black population, in dialogue with decolonial contributions and critical readings of racism. The authors discuss government responses and COVID-19 indicators according to race/color, demonstrating the maintenance of historical storylines that continue to threaten black lives. The article also discusses the importance of local resistance movements, organized in the favelas, precarious urban spaces underserved by the State and occupied by black Brazilians.

Average inbreeding coefficients (f and f’) estimates.

In the third quarter of 2024, 7.6 percent of the black population in Brazil was unemployed. The unemployment rate for Pardo Brazilians was approximately seven percent, while for whites it was five percent.

Gnotypes of Cattle Egrets (Bubulcus ibis) at 14 microsatellite loci amplified using the primers in Table S1 of the article. The protocols are described in Appendix S1 of the Supplementary Material of Miño et al. 2022.

Bubulcus ibis ibis naturally expanded its range by flying over the Atlantic Ocean from Africa or Europe (native range) to South America, being first reported in Suriname towards the end of the 19th century. However, the source populations of the birds colonising South America still remains unclear. Here, to o gain insights into the possible source and routes of colonisation, we characterize the levels of diversity at nuclear microsatellites and assessed the genetic structure of populations from central and southern Africa (n = 129, 13 sites, five countries) and from different latitudes along Brazil (n = 166, six sites). We found overall high levels of genetic diversity in the colonised range, which fit the expectations for organisms with long-distance dispersal potential, rapid growth rates and feeding plasticity. Noteworthy, the results from population-genetic analyses based on different assumptions concurrently agree in indicating that cattle egrets from Brazil harbour a genetic pool distinct from populations from Africa, suggesting restricted contemporary gene flow between these ranges. The lack of genetic differentiation among the African populations did not enable us to identify the source of Brazilian cattle egrets. Fernando de Noronha Archipelago, off the Brazilian northeastern coast, had the highest proportion of the African allelic ancestry. Approximate Bayesian computation analyses supported a scenario of population growth in Africa with subsequent expansion to Brazil and migration from Africa to Brazil at the time of colonisation. We discuss our findings in light of the anthropogenic changes that may have promoted the range expansion of this egret into Brazil.