In 2023, the state of Maranhão had the highest poverty rate in Brazil, with 51.6 percent of the population living in poverty. Santa Catarina, on the other hand, had the lowest poverty rate at 11.6 percent.

Brazil BR: Account: Income: Poorest 40%: % Aged 15+ data was reported at 58.470 % in 2014. This records an increase from the previous number of 39.405 % for 2011. Brazil BR: Account: Income: Poorest 40%: % Aged 15+ data is updated yearly, averaging 48.937 % from Dec 2011 (Median) to 2014, with 2 observations. The data reached an all-time high of 58.470 % in 2014 and a record low of 39.405 % in 2011. Brazil BR: Account: Income: Poorest 40%: % Aged 15+ 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: Banking Indicators. Denotes the percentage of respondents who report having an account (by themselves or together with someone else). For 2011, this can be an account at a bank or another type of financial institution, and for 2014 this can be a mobile account as well (see year-specific definitions for details) (income, poorest 40%, % age 15+). [ts: data are available for multiple waves].; ; Demirguc-Kunt et al., 2015, Global Financial Inclusion Database, World Bank.; Weighted average;

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.

Forecast: Coverage of Social Insurance Programs in Poorest Quintile in Brazil 2022 - 2026 Discover more data with ReportLinker!

As part of its greater portfolio in Northeast Brazil, IFAD supported the Brazilian government and State of Bahia to implement the Rural Communities Development Project in the Poorest Areas of the State of Bahia (PRODECAR), popularly referred to as Gente de Valor (GDV), between 2007 and 2013 .The purpose of GDV was to address the multitude of basic service gaps, empowerment deficit, and productive capacity needs experienced by residents of Brazil's Northeast region. Beneficiaries were drawn from the local population of sertanejos; a regional population named in reference to the dryland, sertão agro-climatic zone and among the poorest people in Brazil. As a CDD-style project, GDV's objective was to address their needs through a participatory process that would provide access to water-harvesting cisterns (primarily for household consumption), training on ecologically appropriate agricultural practices, technical assistance and technical inputs, as well as community capacitation to identify and address future development needs.

GDV was selected to be part of the IFAD10 Impact Assessment Agenda that consists of a broader set of impact assessments across the world. The aim is to generate evidence and provide lessons for better rural poverty reduction programs and to measure the impact of IFAD-supported programmes on enhancing rural people's economic mobility, increased agricultural productive capacity, improved market participation and increased resilience.

As almost six years having passed since the project closed, the analysis evaluates the sustainable impacts of GDV under the realm of access to infrastructure, agricultural productivity, poverty impacts, and empowerment of both women, youth and the community at large. Given the role that drought plays in affecting the economic opportunities of sertanejos, it is also relevant that this project evaluates outcomes following the recent multi-year drought. From the years 2010 to 2016, Bahia experienced a drought characterized as one of the worst of the century; affecting 33.4 million people and resulting in an estimated damage of approximately 30 billion USD (Marengo et al., 2017).

For more information, please, click on the following link https://www.ifad.org/en/web/knowledge/-/publication/impact-assessment-gente-de-valor-rural-communities-development-project-in-the-poorest-areas-of-the-state-of-bahia.

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).

Forecast: Benefit Incidence of Social Protection and Labor Programs to Poorest Quintile in Brazil 2022 - 2026 Discover more data with ReportLinker!

In 2022, the headcount poverty rate at 3.65 U.S. dollars per day in Brazil was 8.42 percent, meaning the share of the Brazilian population living on less than 3.65 dollars per day. The poverty rate increased steadily from 2014 to 2019 when this percentage was 10.75.

The files making up this database correspond to a household survey conducted in 2016 as part of a larger investigation into the lifeways and political subjectivities of Brazil’s “once-rising poor,” the demographic sector comprised of poor and working-class people exposed to various forms of socio-economic mobility in the early 21st century. In the corresponding methodology paper published in the Latin America Research Review (see “Publication” below for citation specifics), we reflect on the challenges of maintaining a critical perspective on class labels and relations that were the subject of intense contestation at the time. Next, we introduce the resultant survey sample (n=1,204), highlighting the variables captured. Rather than an exhaustive summary of all variables measured, we establish the demographic profile, mobility experiences, and political values, attitudes, and behaviors of our sample. As we show, the portrait that emerges for this sector is one of economic precarity, heterogeneous experiences of socioeconomic mobility (and non-mobility) over the past two decades, and significant alienation from formal politics. Here you will find: the raw BORP dataset, original survey questionnaires (in English and Portuguese), and a codebook (in English).

Sources: Author’s calculation using data from the population Census 1991 & 2000, the Institute of Applied Economic Research (IPEA) and Sousa A, et al. 2010 for neonatal mortality.Note: The models control for state fixed effects not presented in the table. Estimates were produced using robust standard errors to adjust for the presence of heteroscedasticity. We used the log of neonatal mortality as dependant variable. Statistical significance with a *p<0.05; **p<0.01; ***p<0.001. Poor refers to minimum comparable areas (MCA) with more than 50% of population below the poverty line, and non-poor otherwise. In all models, differences in the coefficients between categories of health workers are statically significant except for the densities of physicians and nurse professionals. Differences in the coefficients between poor and non-poor areas are also statistically significant. Other covariates such as the proportion of adult women (over age 15) with less than five years of education (average years) were also explored but not considered for the final analysis because of multicolinearity and for having less explanatory power than the variables finally included in the models.

This dataset comprises interviews conducted between 2016 and 2018 with health service users, health professionals and health system managers in the Municipality of São Paulo, Brazil. The interviews focused in particular on the primary health care services covering two of the poorest sub-municipal districts, Cidade Tiradentes and Sapopemba. The Unequal Voices project – Vozes Desiguais in Portuguese – aimed to strengthen the evidence base on the politics of accountability for health equity via multi-level case studies of health systems in Brazil and Mozambique. The project examined the trajectories of change in the political context and in patterns of health inequalities in Brazil and Mozambique, and carried out four case studies to compare the operation of different accountability regimes across the two countries and between different areas within each country. The case studies tracked shifts in accountability relationships among managers, providers and citizens and changes in health system performance, in order to arrive at a better understanding of what works for different poor and marginalised groups in different contexts. In each country the research team studied one urban location with competitive politics and a high level of economic inequality and one rural location where the population as a whole has been politically marginalised and under-provided with services. Health inequities - that is, inequalities in health which result from social, economic or political factors and unfairly disadvantage the poor and marginalised - are trapping millions of people in poverty. Unless they are tackled, the effort to fulfill the promise of universal health coverage as part of the fairer world envisaged in the post-2015 Sustainable Development Goals may lead to more waste and unfairness, because new health services and resources will fail to reach the people who need them most. In Mozambique, for example, the gap in infant mortality between the best-performing and worst-performing areas actually increased between 1997 and 2008, despite improvements in health indicators for the country as a whole. However, while many low- and middle-income countries are failing to translate economic growth into better health services for the poorest, some - including Brazil - stand out as having taken determined and effective action. One key factor that differentiates a strong performer like Brazil from a relatively weak performer like Mozambique is accountability politics: the formal and informal relationships of oversight and control that ensure that health system managers and service providers deliver for the poorest rather than excluding them. Since the mid-1990s, Brazil has transformed health policy to try to ensure that the poorest people and places are covered by basic services. This shift was driven by many factors: by a strong social movement calling for the right to health; by political competition as politicians realised that improving health care for the poor won them votes; by changes to health service contracting that changed the incentives for local governments and other providers to ensure that services reached the poor; and by mass participation that ensured citizen voice in decisions on health priority-setting and citizen oversight of services. However, these factors did not work equally well for all groups of citizens, and some - notably the country's indigenous peoples - continue to lag behind the population as a whole in terms of improved health outcomes. This project is designed to address the ESRC-DFID call's key cross-cutting issue of structural inequalities, and its core research question "what political and institutional conditions are associated with effective poverty reduction and development, and what can domestic and external actors do to promote these conditions?", by comparing the dimensions of accountability politics across Brazil and Mozambique and between different areas within each country. As Mozambique and Brazil seek to implement similar policies to improve service delivery, in each country the research team will examine one urban location with competitive politics and a high level of economic inequality and one rural location where the population as a whole has been politically marginalised and under-provided with services, looking at changes in power relationships among managers, providers and citizens and at changes in health system performance, in order to arrive at a better understanding of what works for different poor and marginalised groups in different contexts. As two Portuguese-speaking countries that have increasingly close economic, political and policy links, Brazil and Mozambique are also well-placed to benefit from exchanges of experience and mutual learning of the kind that Brazil is seeking to promote through its South-South Cooperation programmes. The project will support this mutual learning process by working closely with Brazilian and Mozambican organisations that are engaged in efforts to promote social accountability through the use of community scorecards and through strengthening health oversight committees, and link these efforts with wider networks working on participation and health equity across Southern Africa and beyond.

ABSTRACT: Introduction: The changes that occurred in the health/disease process, especially in the field of nutrition, corroborate the replacement of nutritional deficiencies with the pandemic emergency of overweight (overweight/obesity). Objective: To analyze the prevalence and factors associated with overweight in adults living in a poor urban area in Recife, Northeast Brazil. Methods: This is a cross-sectional study with a sample of 644 adults aged 20-59 years. Possible associations of overweight with demographic, socioeconomic, behavioral and morbidity factors were analyzed through Poisson Regression, considering as statistically significant those with p < 0.05. Results: The prevalence of overweight was 70.3%, being lower in the age range of 20-29 years, greater in the range of 30-39 years and stabilizing in the others. In the final multivariate model, it was observed that the age group, economic class, diabetes mellitus and high blood pressure were directly associated with overweight, while bean consumption showed an inverse association. The high prevalence of overweight found indicates that poor communities are already included in the nutritional transition process that is in course in country. Conclusion: The significant result of overweight found at this poor urban area imposes the need to include this problem as a public health priority in these communities.

ABSTRACT This article analyzed advances in the reduction of inequalities in Brazil during the period from 2003 to 2015, in addition to the income perspective. The data reflect that, although relevant transformations have occurred, nevertheless, Brazil persists as one of the most unequal countries in the world. However, by placing a magnifying glass on the data about access to goods and services of the poorest 5% and 20% made available by the National Household Sample Survey (PNAD), the findings contradict the commonplace that only access to income and the consumption of the poorest was promoted in the period studied, without significant changes in the framework of access to basic rights, public policies of education, health, and infrastructure.

Brazil BR: Coverage: Social Insurance Programs: Poorest Quintile: % of Population data was reported at 9.777 % in 2022. This records an increase from the previous number of 7.078 % for 2021. Brazil BR: Coverage: Social Insurance Programs: Poorest Quintile: % of Population data is updated yearly, averaging 10.063 % from Dec 2006 (Median) to 2022, with 12 observations. The data reached an all-time high of 11.554 % in 2006 and a record low of 7.078 % in 2021. Brazil BR: Coverage: Social Insurance Programs: Poorest Quintile: % 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: Social Protection and Insurance. Coverage of social insurance programs shows the percentage of population participating in programs that provide old age contributory pensions (including survivors and disability) and social security and health insurance benefits (including occupational injury benefits, paid sick leave, maternity and other social insurance). Estimates include both direct and indirect beneficiaries.;ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/);;

Brazil BR: Mobile Account: Income: Poorest 40%: % Aged 15+ data was reported at 0.804 % in 2014. Brazil BR: Mobile Account: Income: Poorest 40%: % Aged 15+ data is updated yearly, averaging 0.804 % from Dec 2014 (Median) to 2014, with 1 observations. The data reached an all-time high of 0.804 % in 2014 and a record low of 0.804 % in 2014. Brazil BR: Mobile Account: Income: Poorest 40%: % Aged 15+ 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: Banking Indicators. Mobile account denotes the percentage of respondents who report personally using a mobile phone to pay bills or to send or receive money through a GSM Association (GSMA) Mobile Money for the Unbanked (MMU) service in the past 12 months; or receiving wages, government transfers, or payments for agricultural products through a mobile phone in the past 12 months.; ; Demirguc-Kunt et al., 2015, Global Financial Inclusion Database, World Bank.; Weighted average;

Brazil BR: Benefit Incidence: Social Protection & Labour Programs (SPL) to Poorest Quintile: % of Total SPL Benefits data was reported at 6.236 % in 2022. This records an increase from the previous number of 4.670 % for 2021. Brazil BR: Benefit Incidence: Social Protection & Labour Programs (SPL) to Poorest Quintile: % of Total SPL Benefits data is updated yearly, averaging 4.076 % from Dec 2006 (Median) to 2022, with 12 observations. The data reached an all-time high of 9.145 % in 2020 and a record low of 1.503 % in 2006. Brazil BR: Benefit Incidence: Social Protection & Labour Programs (SPL) to Poorest Quintile: % of Total SPL Benefits 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: Social Protection and Insurance. Benefit incidence of social protection and labor programs (SPL) to poorest quintile shows the percentage of total social protection and labor programs benefits received by the poorest 20% of the population. Social protection and labor programs include social insurance, social safety nets, and unemployment benefits and active labor market programs. Estimates include both direct and indirect beneficiaries.;ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/);;

ObjectiveTo assess the prevalence of child development delay and to identify socioeconomic determinants.Study designWe conducted a population-based cross-sectional study of children 2 to 72 months of age residing in the state of Ceará, Brazil. In total, 3200 households were randomly selected for participation in the study and had child development assessed with the Ages and Stages Questionnaire (ASQ) version 3. Development delay was defined as a score of less than -2 standard deviations below the median of the Brazilian ASQ standard. We present population-level prevalence of delay in five development domains and assess socioeconomic determinants.ResultsA total of 3566 children completed the ASQ development assessment of which 9.2% (95% CI: 8.1–10.5) had at least one domain with development delay. The prevalence of delay increased with age in all domains and males were at higher risk for communication, gross motor and personal-social development delays as compared to females (p-values <0.05). We found robust associations of indicators of socioeconomic status with risk of development delay; increasing monthly income and higher social class were associated with reduced risk of delay across all domains (28,2% in the poorest and 21,2% in richest for any delay, p-values <0.05 for all domains). In addition, children in poor households that participated in conditional cash transfer (CCT) programs appeared to have reduced risk of delay as compared to children from households that were eligible, but did not participate, in CCT programs.ConclusionsThere is a relatively high population-level prevalence of development delay in at least one domain among children 0–6 years of age in Ceará, Brazil. Integrated child development, social support, and poverty reduction interventions may reduce the population-level prevalence of development delay in Ceará and similar settings.

Summary The aim of this article is threefold. Firstly, to present income-based poverty and extreme poverty indicators for 2015, when the macroeconomic crisis led to a generalized deterioration affecting all areas and regions. The second aim is to discuss long-term evolution, emphasizing the period since 2004, when sustained improvement of income indicators as well as convergence of regional and area results began. Considering the period from 2004 to 2014/2015, the third aim is to show that the reduction in poverty and extreme poverty was parallel to increased inequality in poverty regarding two critical aspects: the regional aspect, since inequality among the five regions became higher, thus reinforcing the dichotomy between the North/Northeast versus the Centre-South; the age aspect, because the recent improvements since 2004 have not sufficiently benefited children as to reverse their disadvantaged position, so much so that in 2015 children still had a share in poverty that was twice their share in the total population. The last section concerns policy measures that may reduce the impact of the crisis on the poor.

Brazil BR: Coverage: Social Safety Net Programs: Poorest Quintile: % of Population data was reported at 64.277 % in 2022. This records an increase from the previous number of 63.510 % for 2021. Brazil BR: Coverage: Social Safety Net Programs: Poorest Quintile: % of Population data is updated yearly, averaging 60.759 % from Dec 2006 (Median) to 2022, with 12 observations. The data reached an all-time high of 81.009 % in 2020 and a record low of 22.822 % in 2009. Brazil BR: Coverage: Social Safety Net Programs: Poorest Quintile: % 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: Social Protection and Insurance. Coverage of social safety net programs shows the percentage of population participating in cash transfers and last resort programs, noncontributory social pensions, other cash transfers programs (child, family and orphan allowances, birth and death grants, disability benefits, and other allowances), conditional cash transfers, in-kind food transfers (food stamps and vouchers, food rations, supplementary feeding, and emergency food distribution), school feeding, other social assistance programs (housing allowances, scholarships, fee waivers, health subsidies, and other social assistance) and public works programs (cash for work and food for work). Estimates include both direct and indirect beneficiaries.;ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/);;

Brazil BR: Benefit Incidence: Social Insurance Programs to Poorest Quintile: % of Total Social Insurance Benefits data was reported at 1.697 % in 2022. This records an increase from the previous number of 1.218 % for 2021. Brazil BR: Benefit Incidence: Social Insurance Programs to Poorest Quintile: % of Total Social Insurance Benefits data is updated yearly, averaging 1.655 % from Dec 2006 (Median) to 2022, with 12 observations. The data reached an all-time high of 1.797 % in 2015 and a record low of 1.218 % in 2021. Brazil BR: Benefit Incidence: Social Insurance Programs to Poorest Quintile: % of Total Social Insurance Benefits 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: Social Protection and Insurance. Benefit incidence of social insurance programs to poorest quintile shows the percentage of total social insurance benefits received by the poorest 20% of the population. Social insurance programs include old age contributory pensions (including survivors and disability) and social security and health insurance benefits (including occupational injury benefits, paid sick leave, maternity and other social insurance). Estimates include both direct and indirect beneficiaries.;ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/);;