The poorest five percent of the population in Brazil received a monthly income of merely *** reals in 2024, with their jobs as their only source of income. By contrast, the average income of workers who fall within the 40 percent to 50 percent percentile, and from 50 percent to 60 percent are **** and **** Brazilian reals, respectively.

In 2024, the top ten percent in Brazil earned an average of 8,034 Brazilian reals per month before income taxes. This is more than 11 times the average income of the bottom half, which was 713 reals per month in that year.

The statistic shows the distribution of tax payers in Brazil based on share of the accumulated taxable gross income in 2015. Out of a total of ***** million Brazilian personal income taxpayers, the richest ** percent (**** million taxpayers) claimed to earn **** percent of the country's accumulated gross taxable income in 2015.

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Brazil. The dataset can be utilized to gain insights into gender-based income distribution within the Brazil population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Brazil, among individuals aged 15 years and older with income, there were 2,780 men and 2,931 women in the workforce. Among them, 1,568 men were engaged in full-time, year-round employment, while 1,108 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 5.17% fell within the income range of under $24,999, while 20.13% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 15.56% of men in full-time roles earned incomes exceeding $100,000, while none of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

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

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

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 median household income by race. You can refer the same here

About this dataset

Context

The dataset presents the median household income across different racial categories in Brazil. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of Brazil population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 93.58% of the total residents in Brazil. Notably, the median household income for White households is $48,592. Interestingly, White is both the largest group and the one with the highest median household income, which stands at $48,592.

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 of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Brazil.
• Median household income: Median household income, adjusting for inflation, presented in 2023-inflation-adjusted dollars

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 median household income by race. You can refer the same here

ABSTRACT Using an input-output framework, this article studies the consequences of income redistribution from rich people either to poor people or to the government upon the sectoral structure of the Brazilian economy in the 70’s. Besides the traditional use of elements of sectoral analysis, the main concept used to achieve this goal is that of “subeconomies”, focusing on industries (and associated patterns of employment and value added) which produce the components of particular expenditure bundles. Thus, a subeconomy is composed by the economic activity derived from determined expenditure groups, such as poor consumer households, government, etc. Two main results emerge from the empirical application of these concepts. First, agriculture is the most important sector in the generation of employment in response to transfers of income from rich to poor households. Second, the urbanization which accompanied growth during the 70’s favours a redistributive strategy in which the role of government becomes outstanding in terms of generation of both value added and employment.

Abstract The paper presents the functional income distribution for the Brazilian economy between 1947 and 2019. It is the first study that computes the functional income distribution for the whole period with official Brazilian national accounts. The wage share increased, the share of mixed earnings declined, while the labour share and the profit share were trendless between 1947 and 2019. The cyclical movements of the functional income distribution were driven by economic and political factors in Brazil.

Abstract Contrary to the huge development of the Brazilian economy from the post-war to the end of the seventies, the eighties signified the rupture of this cycle and the combination of a chronic inflationary process, the economic stagnation and the worsening of income inequality. These factors together have not revealed to be neutral concerning the distributive aspect. Between 1981 and 1989, the income of the 10 per cent richer increased 14,2 per cent, while the income of the 20 per cent poorer decreased 26 per cent. This work presents some international comparisons, even on the functional as well as on the personal distribution of income and concludes that this distribution becomes a fundamental aspect to the stabilization and to the economic-social development recovering.

ABSTRACT The paper analyses the Brazilian size distribution of income with the objective of identifying to what extent economic policies, macroeconomic performance and changes in the structure of the labor force are related to inequality. There is evidence of long term increases in inequality, especially between 1960 and 1970. Long term trends do not seem to be affected by economic performance, although the stagnation of the 1980s has led to absolute income losses for all individuals except those in the top percentile. Short term behavior, on the other hand, seems to have been influenced by economic performance: there is evidence that growth enhances equity, whereas high inflation has the opposite effect. A decomposition analysis highlights the importance of education in explaining inequality, but points to changes in the structure of the labor force as the major factor in accounting for changes in inequality since the mid-1970s.

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Brazil. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Brazil. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2023

In terms of income distribution across age cohorts, in Brazil, the median household income stands at $66,818 for householders within the 45 to 64 years age group, followed by $61,496 for the 25 to 44 years age group. Notably, householders within the 65 years and over age group, had the lowest median household income at $31,952.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific age group

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 median household income by age. You can refer the same here

Brazil BR: Gini Coefficient (GINI Index): World Bank Estimate data was reported at 52.000 % in 2022. This records a decrease from the previous number of 52.900 % for 2021. Brazil BR: Gini Coefficient (GINI Index): World Bank Estimate data is updated yearly, averaging 56.400 % from Dec 1981 (Median) to 2022, with 38 observations. The data reached an all-time high of 63.300 % in 1989 and a record low of 48.900 % in 2020. Brazil BR: Gini Coefficient (GINI Index): World Bank Estimate 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. Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.;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).

In Brazil, **** percent of consumers earned at least the equivalent of the highest 40 percent of global income earners as of 2022 in purchasing power parity (PPP) terms. Those who earned at least the equivalent of the top 10 percent of global income earners stood at *** percent.

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Brazil: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 149(4.48%) households where the householder is under 25 years old, 1,000(30.05%) households with a householder aged between 25 and 44 years, 1,136(34.13%) households with a householder aged between 45 and 64 years, and 1,043(31.34%) households where the householder is over 65 years old.
• The age group of 25 to 44 years exhibits the highest median household income, while the largest number of households falls within the 45 to 64 years bracket. This distribution hints at economic disparities within the city of Brazil, showcasing varying income levels among different age demographics.

Content

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

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

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 median household income by age. You can refer the same here

Using a factor decomposition of the Gini coefficient, we measure the contribution to inequality of direct monetary income flows to and from the Brazilian State. The income flows from the State include public sector workers' earnings, Social Security pensions, unemployment benefits, and Social Assistance transfers. The income flows to the State comprise direct taxes and employees' social security contributions. Data come from the Brazilian POF 2008–09. We do not measure indirect contributions to inequality of subsidies granted to and taxation of companies, nor the in-kind provision of goods and services. The results indicate that the State contributes to a large share of family per capita income inequality. Incomes associated with work in the public sector—wages and pensions—are concentrated and regressive. Components related to the private sector are also concentrated, but progressive. Contrary to what has been found in European countries, public spending associated with work and social policies is concentrated in an elite group of workers and, taken as a whole, tends to increase income inequality. Redistributive mechanisms that could reverse this inequality, such as taxes and social assistance, are very progressive but proportionally small. Consequently, their effect is completely offset by the regressive income flows from the State.

Between 2010 and 2023, Brazil's data on the degree of inequality in wealth distribution based on the Gini coefficient reached 52. That year, Brazil was deemed one of the most unequal country in Latin America. Prior to 2010, wealth distribution in Brazil had shown signs of improvement, with the Gini coefficient decreasing in the previous 3 reporting periods. The Gini coefficient measures the deviation of the distribution of income (or consumption) among individuals or households in a given country from a perfectly equal distribution. A value of 0 represents absolute equality, whereas 100 would be the highest possible degree of inequality.

ABSTRACT This article aims at providing an overview of the Brazilian income distribution controversyduring the 1960´s.To that end, we present the key arguments of theso-called “official” interpretation, based mostly on Langoni's (1973) study, as well as the main criticisms it received.The “official” interpretation states that the increase in the inequality of income distribution between 1960 and 1970 is a natural and transitory consequence of the disequilibrium between thesupply and demand of qualified labor,within the context of a high economic growth. However, from our part and in line with the main criticisms it received, we suggest that the "official" interpretation offers arguments which actually hinder the importance of crucial elements, both theoretical and empirical, linked tothe organizational hierarchy of businesses, to the economic policy adopted by the military government and to the pattern of capitalist development in Brazil.

Abstract Brazilian underdevelopment, as qualified by Celso Furtado, shows a notably distributive nature derived from the close relationship between external cultural dependence and the internal exploitation of workers. Despite the fact that this diagnosis dates back decades, nuances of the distributive structure in recent times highlight the current relevance of this interpretation, in such a way that the analysis of information mapped in a comprehensive “radiograph” of Brazilian income distribution was the objective of this work. It aimed at answering if what has happened, since Celso Furtado's last study, would have changed the most fundamental conditions described by him. In general, the information analyzed here, according to Furtado, suggested the persistence of Brazilian underdevelopment, as assessed based on the structural socio-economic asymmetries of which it is still composed.

Abstract This work aims to empirically investigate Brazilian economic growth from 1993 through 2013. It is based on Naastepad's model, which determines whether the demand-led growth is wage-led or profit-led. Thus, in order to determine the demand regime of the period, we estimated the equations for investment, exports and aggregate propensity to save. Estimating the equations allowed us to calculate the multiplier effect and investment and export elasticity effect. The demand regime in the period was wage-led, mostly due to low participation of investments and exportations in the national income. All equations were estimated by Ordinary Least Squares and showed robust standard deviation of parameters. The statistical tests, carried out after the estimation, suggest an unbiased and consistent model.

ABSTRACT This paper aims to evaluate and compare the distributive impact of the personal income tax (PIT) on individual’s income in Brazil and China by measuring the Gini Index before and after this tax incidence. The paper also proposes a methodology for transposing the PIT backets from one country to another. The results show that a more progressive scheme implemented by China, with more brackets and higher rates, does not guarantee reduction of inequality, due to the level of income exemption and to the incomes on which the marginal rates affect. Thus, it can be perceived that the PIT brackets of these two countries deserves revisions if they seek to fulfill the distributive function.

In 2023, the percentage of people who considers that income distribution is unfair in Brazil was estimated at approximately 81 percent. Between 1997 and 2023, the figure dropped by around 12 percentage points, though the decline followed an uneven course rather than a steady trajectory.