About this dataset

Context

The dataset tabulates the Brazil population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Brazil across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Brazil was 8,214, a 0.37% increase year-by-year from 2022. Previously, in 2022, Brazil population was 8,184, an increase of 0.21% compared to a population of 8,167 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Brazil increased by 328. In this period, the peak population was 8,214 in the year 2023. The numbers suggest that the population has not reached its peak yet and is showing a trend of further growth. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Brazil is shown in this column.
• Year on Year Change: This column displays the change in Brazil population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. 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 Year. You can refer the same here

About this dataset

Context

The dataset tabulates the Brazil population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Brazil. The dataset can be utilized to understand the population distribution of Brazil by age. For example, using this dataset, we can identify the largest age group in Brazil.

Key observations

The largest age group in Brazil, IN was for the group of age 30 to 34 years years with a population of 683 (8.47%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Brazil, IN was the 80 to 84 years years with a population of 178 (2.21%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

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

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Brazil is shown in this column.
• % of Total Population: This column displays the population of each age group 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 Age. You can refer the same here

About this dataset

Context

The dataset tabulates the data for the Brazil, IN population pyramid, which represents the Brazil population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Brazil, IN, is 27.2.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Brazil, IN, is 30.7.
• Total dependency ratio for Brazil, IN is 57.9.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Brazil, IN is 3.3.

Content

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

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Brazil population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Brazil for the selected age group is shown in the following column.
• Population (Female): The female population in the Brazil for the selected age group is shown in the following column.
• Total Population: The total population of the Brazil for the selected age group is shown in the following column.

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 Age. You can refer the same here

The layer was derived and compiled from the U.S. Census Bureau’s 2013 – 2017 American Community Survey (ACS) 5-Year Estimates in order to assist 2020 Census planning purposes.

Source: U.S. Census Bureau, Table B04006 PEOPLE REPORTING ANCESTRY, 2013 – 2017 ACS 5-Year Estimates

Effective Date: December 2018

Last Update: December 2019

Update Cycle: ACS 5-Year Estimates update annually each December. Vintage used for 2020 Census planning purposes by Broward County.

Brazil BR: Population: Growth data was reported at 0.460 % in 2022. This records a decrease from the previous number of 0.529 % for 2021. Brazil BR: Population: Growth data is updated yearly, averaging 1.694 % from Dec 1961 (Median) to 2022, with 62 observations. The data reached an all-time high of 3.015 % in 1961 and a record low of 0.460 % in 2022. Brazil BR: Population: Growth 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. Annual population growth rate for year t is the exponential rate of growth of midyear population from year t-1 to t, expressed as a percentage . Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.;Derived from total population. Population source: (1) United Nations Population Division. World Population Prospects: 2022 Revision, (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.;Weighted average;

US Census Projection: Population: Mid Year data was reported at 204,461,198.000 Person in 2100. This records a decrease from the previous number of 205,458,306.000 Person for 2099. US Census Projection: Population: Mid Year data is updated yearly, averaging 211,450,473.000 Person from Jun 1950 (Median) to 2100, with 151 observations. The data reached an all-time high of 238,504,547.000 Person in 2052 and a record low of 53,443,075.000 Person in 1950. US Census Projection: Population: Mid Year data remains active status in CEIC and is reported by U.S. Census Bureau. The data is categorized under Global Database’s Brazil – Table BR.GAB038: Population: Projection: US Census Bureau.

This record contains the base datasets used in the research to create the maps of distribution of population.

About this dataset

Context

The dataset tabulates the Brazil population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Brazil. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 4,797 (59.49% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

Age cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the Brazil population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Brazil is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Brazil is shown in the following column.

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 Age. You can refer the same here

Brazilian? You can read a Portuguese version of this article here.

Context

Last year, while I was attending a data science course in Germany, my country was impeaching its president. My colleagues asked me to explain what was happening in Brazil and the possible political outcomes in South America. Although I was able to give a general context and tell multiple arguments in favor and against the impeachment, deep inside, my answer was "I really don't know".

Understanding what happens in Politics is something that takes a lot of effort and research. When I decided I had to use my tech skills to make myself a better citizen, I dived into government data and started Operation Serenata de Amor.

After reporting hundreds of politicians for small acts of corruption and learning how to encourage the population to engage in the democratic processes, my studies drove me to understand the legislative activity.

Brazilians elect 594 citizens to be their representatives in the National Congress. How can we be sure that they are not defending their own interests or those who paid for their campaigns? My way, as a data scientist, is to ask the data.

Content

The National Congress of Brazil is composed of a Lower (Chamber of Deputies) and an Upper House (Federal Senate). In the first version of this dataset, you are going to find data only from the Chamber of Deputies. With 513 representatives, 86% of the congresspeople, I hope you have enough data to explore for some time.

Would be impossible for me, a citizen without government ties, to collect this data without the help of public servants. I processed 9,717 fixed-width files and 73 XML's made officially available by the Chamber of Deputies and created 5 CSV's containing the same information. Multiple fields of the same file telling the same thing (e.g. body_id, body_name and body_abbreviation) were removed.

Data on session attendance, votes, and propositions since past century were collected and scripted in a reproducible manner. The data collection and pre-processing scripts are available in a GitHub repository, under an open source license.

Everything was collected from the Chamber of Deputies website at December 27, 2017, containing the whole legislative activity of the year. Presence and votes date from 1999, propositions go as far as 1946.

When in question about the legislative process and how the sessions work in real world, the Internal Regulation of the Chamber of Deputies is the best Portuguese documentation for research. It's free!

Acknowledgements

Since the data was collected from a government website and the Brazilian law states that access to this information is free to any citizen, I am placing my own work published here in Public Domain.

I'd like to thank the hundreds of people financially supporting the work of Operation Serenata de Amor and those responsible for passing the Information Access bill in 2011.

Inspiration

The legislative activity should tell the history while it's happening. How much has the Congress changed over the past decades? Do the congresspeople maintain the same political views or they vary on a weekly basis? Do people vote together with their state or party peers? How often? Can you model an algorithm to tell us the real parties inside Brazilian Congress?

IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system. The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.

• The current US Census Bureau world population estimate in June 2019 shows that the current global population is 7,577,130,400 people on Earth, which far exceeds the world population of 7.2 billion in 2015. Our estimate based on UN data shows the world's population surpassing 7.7 billion.
• China is the most populous country in the world with a population exceeding 1.4 billion. It is one of just two countries with a population of more than 1 billion, with India being the second. As of 2018, India has a population of over 1.355 billion people, and its population growth is expected to continue through at least 2050. By the year 2030, India is expected to become the most populous country in the world. This is because India’s population will grow, while China is projected to see a loss in population.
• The following 11 countries that are the most populous in the world each have populations exceeding 100 million. These include the United States, Indonesia, Brazil, Pakistan, Nigeria, Bangladesh, Russia, Mexico, Japan, Ethiopia, and the Philippines. Of these nations, all are expected to continue to grow except Russia and Japan, which will see their populations drop by 2030 before falling again significantly by 2050.
• Many other nations have populations of at least one million, while there are also countries that have just thousands. The smallest population in the world can be found in Vatican City, where only 801 people reside.
• In 2018, the world’s population growth rate was 1.12%. Every five years since the 1970s, the population growth rate has continued to fall. The world’s population is expected to continue to grow larger but at a much slower pace. By 2030, the population will exceed 8 billion. In 2040, this number will grow to more than 9 billion. In 2055, the number will rise to over 10 billion, and another billion people won’t be added until near the end of the century. The current annual population growth estimates from the United Nations are in the millions - estimating that over 80 million new lives are added yearly.
• This population growth will be significantly impacted by nine specific countries which are situated to contribute to the population growth more quickly than other nations. These nations include the Democratic Republic of the Congo, Ethiopia, India, Indonesia, Nigeria, Pakistan, Uganda, the United Republic of Tanzania, and the United States of America. Particularly of interest, India is on track to overtake China's position as the most populous country by 2030. Additionally, multiple nations within Africa are expected to double their populations before fertility rates begin to slow entirely.

Content

• In this Dataset, we have Historical Population data for every Country/Territory in the world by different parameters like Area Size of the Country/Territory, Name of the Continent, Name of the Capital, Density, Population Growth Rate, Ranking based on Population, World Population Percentage, etc. >Dataset Glossary (Column-Wise):
• Rank: Rank by Population.
• CCA3: 3 Digit Country/Territories Code.
• Country/Territories: Name of the Country/Territories.
• Capital: Name of the Capital.
• Continent: Name of the Continent.
• 2022 Population: Population of the Country/Territories in the year 2022.
• 2020 Population: Population of the Country/Territories in the year 2020.
• 2015 Population: Population of the Country/Territories in the year 2015.
• 2010 Population: Population of the Country/Territories in the year 2010.
• 2000 Population: Population of the Country/Territories in the year 2000.
• 1990 Population: Population of the Country/Territories in the year 1990.
• 1980 Population: Population of the Country/Territories in the year 1980.
• 1970 Population: Population of the Country/Territories in the year 1970.
• Area (km²): Area size of the Country/Territories in square kilometers.
• Density (per km²): Population Density per square kilometer.
• Growth Rate: Population Growth Rate by Country/Territories.
• World Population Percentage: The population percentage by each Country/Territories.

IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system. The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.

The data is taken from the 1980 Census. Socio-economic terms were obtained from the 25% sample and are stored in Stock Data from the IBGE. There are two IBGE prepared samples, 25% and 3% NOTE: ALL documentation is in Portuguese.

Abstract

The Global Consumption Database (GCD) contains information on consumption patterns at the national level, by urban/rural area, and by income level (4 categories: lowest, low, middle, higher with thresholds based on a global income distribution), for 92 low and middle-income countries, as of 2010. The data were extracted from national household surveys. The consumption is presented by category of products and services of the International Comparison Program (ICP) 2005, which mostly corresponds to COICOP. For three countries, sub-national data are also available (Brazil, India, and South Africa). Data on population estimates are also included.

       The data file can be used for the production of the following tables (by urban/rural and income class/consumption segment):
       - Sample Size by Country, Area and Consumption Segment (Number of Households)
       - Population 2010 by Country, Area and Consumption Segment
       - Population 2010 by Country, Area and Consumption Segment, as a Percentage of the National Population
       - Population 2010 by Country, Area and Consumption Segment, as a Percentage of the Area Population
       - Population 2010 by Country, Age Group, Sex and Consumption Segment
       - Household Consumption 2010 by Country, Sector, Area and Consumption Segment in Local Currency (Million)
       - Household Consumption 2010 by Country, Sector, Area and Consumption Segment in $PPP (Million)
       - Household Consumption 2010 by Country, Sector, Area and Consumption Segment in US$ (Million)
       - Household Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in Local Currency (Million)
       - Household Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in $PPP (Million)
       - Household Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in US$ (Million)
       - Household Consumption 2010 by Country, Product/Service, Area and Consumption Segment in Local Currency (Million)
       - Household Consumption 2010 by Country, Product/Service, Area and Consumption Segment in $PPP (Million)
       - Household Consumption 2010 by Country, Product/Service, Area and Consumption Segment in US$ (Million)
       - Per Capita Consumption 2010 by Country, Sector, Area and Consumption Segment in Local Currency
       - Per Capita Consumption 2010 by Country, Sector, Area and Consumption Segment in US$
       - Per Capita Consumption 2010 by Country, Sector, Area and Consumption Segment in $PPP
       - Per Capita Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in Local Currency
       - Per Capita Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in US$
       - Per Capita Consumption 2010 by Country, Category of Product/Service, Area and Consumption Segment in $PPP
       - Per Capita Consumption 2010 by Country, Product or Service, Area and Consumption Segment in Local Currency
       - Per Capita Consumption 2010 by Country, Product or Service, Area and Consumption Segment in US$
       - Per Capita Consumption 2010 by Country, Product or Service, Area and Consumption Segment in $PPP
       - Consumption Shares 2010 by Country, Sector, Area and Consumption Segment (Percent)
       - Consumption Shares 2010 by Country, Category of Products/Services, Area and Consumption Segment (Percent)
       - Consumption Shares 2010 by Country, Product/Service, Area and Consumption Segment (Percent)
       - Percentage of Households who Reported Having Consumed the Product or Service by Country, Consumption Segment and Area (as of Survey Year)

Geographic coverage notes

For all countries, estimates are provided at the national level and at the urban/rural levels. For Brazil, India, and South Africa, data are also provided at the sub-national level (admin 1): - Brazil: ACR, Alagoas, Amapa, Amazonas, Bahia, Ceara, Distrito Federal, Espirito Santo, Goias, Maranhao, Mato Grosso, Mato Grosso do Sul, Minas Gerais, Para, Paraiba, Parana, Pernambuco, Piaji, Rio de Janeiro, Rio Grande do Norte, Rio Grande do Sul, Rondonia, Roraima, Santa Catarina, Sao Paolo, Sergipe, Tocatins - India: Andaman and Nicobar Islands, Andhra Pradesh, Arinachal Pradesh, Assam, Bihar, Chandigarh, Chattisgarh, Dadra and Nagar Haveli, Daman and Diu, Delhi, Goa, Gujarat, Haryana, Himachal Pradesh, Jammu and Kashmir, Jharkhand, Karnataka, Kerala, Lakshadweep, Madya Pradesh, Maharastra, Manipur, Meghalaya, Mizoram, Nagaland, Orissa, Pondicherry, Punjab, Rajasthan, Sikkim, Tamil Nadu, Tripura, Uttar Pradesh, Uttaranchal, West Bengal - South Africa: Eastern Cape, Free State, Gauteng, Kwazulu Natal, Limpopo, Mpulamanga, Northern Cape, North West, Western Cape

Kind of data

Data derived from survey microdata

IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system. The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.

This dataset is a result of the PHFood Brazil Study, a comprehensive initiative that integrates diverse national open databases to provide insights into food production, consumption, environmental impact, and public health in Brazil from 1974 to 2022. The primary hypothesis behind the dataset is that analyzing food systems, nutrition, and agricultural practices over time will reveal patterns that can inform sustainable food policies and health interventions. Research Hypothesis: We hypothesize that by aggregating and harmonizing data from various national sources, it will be possible to identify critical relationships between food production, nutrient availability, and environmental sustainability. This integrated dataset allows us to evaluate how agricultural practices and food consumption patterns have evolved in response to changing environmental, economic, and societal conditions in Brazil. Food Production and Nutrient Data: The dataset includes detailed information on harvested areas, food production (in tons), and nutrient availability (e.g., energy, protein, fiber, vitamins, and minerals) for various food groups across different Brazilian regions and states. Water and Environmental Data: Water usage, deficit, and environmental impact data are linked to agricultural activities, allowing the assessment of water efficiency in food production. Pesticides and Residue Monitoring: Information on pesticide usage, maximum residue levels (MRL), and residue percentage in food items is provided, with additional details on authorized pesticide types, toxicity classifications, and environmental risks. Food Consumption: Data on food acquisition and consumption patterns, broken down by food groups (e.g., beans, vegetables, fruits), are presented, highlighting dietary trends across various population groups. Data Collection and Methodology: The dataset was constructed by linking several government datasets, including agricultural statistics, water use reports, and residue monitoring programs. Each dataset was meticulously harmonized to ensure compatibility and completeness, following Extract, Transform, Load (ETL) processes. The data was collected over several decades, representing longitudinal trends in food systems and health. The harmonization process ensures that all data fields are consistent and comparable across time and regions.

Interpretation and Use: This dataset can be used to explore the connections between food systems, health, and the environment. It supports studies in agrifood systems, sustainability, public health, and climate change. Researchers can investigate the impacts of food production on nutrient availability, water consumption, and pesticide use over time. It can also be used to model future food system sustainability under different climate scenarios and dietary patterns, contributing to the development of food policies that balance human and planetary health.

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 Poverty Headcount Ratio at Societal Poverty Lines: % of Population data was reported at 27.200 % in 2022. This records a decrease from the previous number of 28.900 % for 2021. Brazil Poverty Headcount Ratio at Societal Poverty Lines: % of Population data is updated yearly, averaging 34.450 % from Dec 1981 (Median) to 2022, with 38 observations. The data reached an all-time high of 42.400 % in 1983 and a record low of 23.500 % in 2020. Brazil Poverty Headcount Ratio at Societal Poverty Lines: % of Population 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: Social: Poverty and Inequality. The poverty headcount ratio at societal poverty line is the percentage of a population living in poverty according to the World Bank's Societal Poverty Line. The Societal Poverty Line is expressed in purchasing power adjusted 2017 U.S. dollars and defined as max($2.15, $1.15 + 0.5*Median). This means that when the national median is sufficiently low, the Societal Poverty line is equivalent to the extreme poverty line, $2.15. For countries with a sufficiently high national median, the Societal Poverty Line grows as countries’ median income grows.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

The Unsatisfied Basic Needs dataset consists of measures of household level wellbeing and access to basic needs (such as adequate housing conditions, water, electricity, sanitation, education, and employment) for subnational administrative units of numerous countries in Latin America: Argentina, Bolivia, Brazil, Colombia, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, and Peru. The data products include shapefiles (vector data) and tabular datasets (csv format). Additionally, a data catalog (xls format) containing detailed information and documentation is provided. This dataset is produced by the Columbia University Center for International Earth Science Information Network (CIESIN), Economic Commission for Latin America and the Caribbean (ECLAC), and Centro Internacional de Agricultura Tropical (CIAT). (Suggested Usage: To provide high spatial resolution subnational estimates of unsatisfied basic needs for use by a wide community for interdisciplinary studies of poverty, inequality and the environment.)

This paper studies the effect of additional government revenues on political corruption and on the quality of politicians, both with theory and data. The theory is based on a political agency model with career concerns and endogenous entry of candidates. The data refer to Brazil, where federal transfers to municipal governments change exogenously at given population thresholds, allowing us to implement a regression discontinuity design. The empirical evidence shows that larger transfers increase observed corruption and reduce the average education of candidates for mayor. These and other more specific empirical results are in line with the predictions of the theory.