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: 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/);;

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

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;

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

Abstract

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.

Geographic coverage

Regional coverage.

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The qualitative portion of the evaluation was conducted prior to the quantitative survey in order to collect information on project targeting and implementation in the targeted areas. Two primary methodologies were employed: Focus Group Discussions (FGD) and Key Informant Interviews (KII). Qualitative interviews took place across seven sub-territories and 17 communities. Communities chosen for the qualitative survey were identified based on the following economic activities: cassava, goats, and backyard gardens in combination with high intensity of water-based activities.

The quantitative data collection covered 2,019 households, and 3,615 individuals (counting 1,615 partners interviewed for the WEAI), in 228 communities. Given that the nature of the intervention expected both household and community impacts, the construction of a counterfactual was a multi-stage process stratified at the community, and then household level.

More details on the sampling procedure can be found in the IA plan and reports, attached in the documentations tab.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The data were collected using a mixed-method approach in order to capture both expected and unexpected impacts of GDV. The data collection took place six years after the closing of GDV, offering time to identify longer-term outcomes that can lead to more realistic interpretations of impact rather than if the project had been assessed immediately after closure. The event of the multi-year drought, in tandem with continuing erratic rainfall and the loss of support from farmer-oriented public programs, further allows for assessment of the ability of the project to make beneficiaries resilient to drought and economic shocks.

The quantitative portion of the evaluation was primarily used for measurement of impact and consisted of two main instruments: a household-level questionnaire and a community-level questionnaire. These instruments covered a range of modules in order to estimate the multi-faceted aspects of welfare. In particular, the household questionnaire focused on agricultural production, agricultural sales, other income sources such as employment or government assistance, and consumption. Additionally, it included modules on assets, shocks, and migration in order to assess any wealth accumulation, exposure to shocks, and coping strategies. Given that the project placed emphasis on increasing women's leadership and decision-making, an abridged version of the Women's Empowerment in Agriculture Index (WEAI), known as the Project WEAI (Pro-WEAI) was fielded to collect data on indicators that comparatively assess agency and empowerment of male and female decision-makers in a household.

The community questionnaire focused on services that are available to the community and relevant institutions such local infrastructure, economic activities, and access to services. The community questionnaire identified levels of community agency and resilience by asking about recent shocks, coping strategies, and collective action to promote local development. Because the project baseline was incomplete, project baseline data was not used, and respondents were asked to recall levels of assets owned at a reference period pre-GDV in both the community and household questionnaires.

Note: some variables have missing labels. Please, refer to the questionnaire for more details.

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. 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. Interviewee sampling was purposive and made use of snowballing. The interviews focused in particular on the primary health care services covering two of the poorest suprefeituras (sub-municipal districts), Cidade Tiradentes and Sapopemba. The dataset includes a mix of transcripts and summary notes from individual and group interviews. All material is in Portuguese.

Abstract

The objective of the research is to provide adequate information for planning, monitoring and analysis of economic policies and social programs in relation to their impacts on the conditions of home life, especially those of the poorest populations. Substantially, the survey provides an overview of the well-being of household residents and allows the study of its determinants. Starting from the premise that quantifying and situating a problem is not enough, the research seeks explanations that allow indicating solutions. For example, knowing how many poor people there are, how and where they live and what they do is only part of the investigation. In order to produce information that can support more effective solutions, a detailed survey of the causes and consequences of poverty is necessary. The same principle applies to other areas of social welfare. In this way, the survey questionnaire is designed to provide a set of integrated information with the aim of:

· Measure the distribution of well-being and the level of poverty, mainly in areas where subsistence agriculture, the informal economy and seasonal employment predominate · Describe the patterns of access and use of public services - education, health, basic sanitation, etc. · Understand how households react to economic conditions and the impacts of government measures · Allow complex analyses of the relationships between the various aspects of social well-being, such as the impact of health on employment, the pattern of spending on nutritional levels of residents, etc.

The research, however, does not address the various topics investigated with the same depth as the information collected in topical research. At the same time, due to having a small sample, the accuracy of the results is less than that of topical research. However, due to its thematic scope, the research allows a good multidimensional summary of well-being and the study of the interactions between the various factors.

Geographic coverage

National

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

(a) THE SAMPLE PLANNING

The sample design of the PPV - Research on Living Standards - was discussed with World Bank technicians and the sample size was determined according to the budget available for the research. As a pilot research, it was chosen to be carried out only in the Northeast and Southeast regions of the country, considering 10 geographic strata, namely: Metropolitan Region of Fortaleza, Metropolitan Region of Recife, Metropolitan Region of Salvador, remainder of the urban area of the Northeast, remainder of rural area of the Northeast, Metropolitan Region of Belo Horizonte, Metropolitan Region of Rio de Janeiro, Metropolitan Region of São Paulo, remainder of the urban area of the Southeast and remainder of the rural area of the Southeast. As in other household surveys conducted by IBGE, a design with two stages of selection was chosen, with stratification of the primary units and selection proportional to a size measure and random selection of the second stage units. The primary unit is the sector of the geographical base of the 1991 Demographic Census and the second stage unit is the household.

(b) SAMPLE SIZE The sample size for each geographic stratum was fixed at 480 households. In each geographic stratum, the number of sectors to be selected was set at 60 and 8 households in each sector, with the exception of strata that correspond to the rest of the rural area of each Region, where the number of sectors was fixed at 30 and 16 the number of households to be selected by sector, due to the difficulty of access to these sectors, which would imply increase costs. The size of the fixed sample was defended by World Bank technicians due to the experience in other countries where the research was or is being conducted, the need to produce information as quickly as possible and because the research is not intended to produce tabulation with crossings of variables, as occurs with the information from the National Household Sample Survey - PNAD, but to provide trend or variation indicators at very aggregate levels.

(c) THE DEFINITION OF STATISTICAL STRATA

The final sample size of households was fixed according to the cost, more specifically the financial resources available. As a result, the sample size of sectors and the number of households to be selected by sector were also fixed, namely: - 60 sectors and 8 households per sector, in urban geographic strata and metropolitan regions - 30 sectors and 16 households per sector, in rural geographic strata

Before the allocation in the income strata, the total sample in the 10 geographic strata had 540 sectors and 4,800 households. Proportional allocation was used, based on the number of occupied permanent private households, obtained by Census 91.

Mode of data collection

Face-to-face [f2f]

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.

This dataset comprises interviews conducted between 2016 and 2018 with health service users, health professionals and health system managers in Maputo, Mozambique. 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 conducted examined the trajectories of change in the political context and in patterns of health inequalities in Brazil and Mozambique, and carried out four cases 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...

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/);;

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).

Brazil’s Legal Amazon, here called Amazônia, comprises nine states, most of which rank among the poorest in Brazil. Amazônia is one of the world’s last frontier regions. But economic expansion has moved into those ancient forests, destroying them at a rapid rate especially in Amazônia’s southeast, within what is known as the “Arc of Deforestation” and threatening the ways of life of many traditional communities. There is an urgent need for an alternative development path for Amazônia that promotes inclusion and sustainable natural-resource use. This memorandum presents a multipronged approach, a balancing act that seeks to simultaneously provide a pathway to higher incomes for Amazonians while also protecting natural forests and traditional ways of life by focusing on four strategic actions.

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;

The electoral success of the Right in poor nations is typically attributed to non-policy appeals such as clientelism. Candidate profiles are usually ignored, because if voters value class-based descriptive representation, it should be the Left that uses it. In this article we develop and test a novel theory of policy choice and candidate selection that defies this conventional wisdom: it is the Right that capitalizes on descriptive representation in high poverty areas. The Right is only competitive in poor regions when it matches the Left’s pro-poor policies. To credibly shift its position, it nominates candidates that are descriptively closer to the poor. Using a regression discontinuity design in Brazilian municipal elections, we show that Right-wing mayors spend less on the poor than Left-wing mayors only in low-poverty municipalities. In high-poverty municipalities, not only does the Right match the Left’s policies, it also does so while nominating less-educated candidates.

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: Benefit Incidence: Social Safety Net Programs to Poorest Quintile: % of Total Safety Net Benefits data was reported at 30.163 % in 2022. This records an increase from the previous number of 28.470 % for 2021. Brazil BR: Benefit Incidence: Social Safety Net Programs to Poorest Quintile: % of Total Safety Net Benefits data is updated yearly, averaging 30.163 % from Dec 2009 (Median) to 2022, with 11 observations. The data reached an all-time high of 33.794 % in 2017 and a record low of 15.329 % in 2011. Brazil BR: Benefit Incidence: Social Safety Net Programs to Poorest Quintile: % of Total Safety Net 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 safety net programs to poorest quintile shows the percentage of total social safety net benefits received by the poorest 20% of the population. Social safety net programs include 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/);;

Guyana was the South American country 20360the highest gross national income per capita, with 20,360 U.S. dollars per person in 2023. Uruguay ranked second, registering a GNI of 19,530 U.S. dollars per person, based on current prices. Gross national income (GNI) is the aggregated sum of the value added by residents in an economy, plus net taxes (minus subsidies) and net receipts of primary income from abroad. Which are the largest Latin American economies? Based on annual gross domestic product, which is the total amount of goods and services produced in a country per year, Brazil leads the regional ranking, followed by Mexico, Argentina, and Chile. Many Caribbean countries and territories hold the highest GDP per capita in this region, measurement that reflects how GDP would be divided if it was perfectly equally distributed among the population. GNI per capita is, however, a more exact calculation of wealth than GDP per capita, as it takes into consideration taxes paid and income receipts from abroad. How much inequality is there in Latin America? In many Latin American countries, more than half the total wealth created in their economies is held by the richest 20 percent of the population. When a small share of the population concentrates most of the wealth, millions of people don't have enough to make ends meet. For instance, in Brazil, about 5.32 percent of the population lives on less than 3.2 U.S. dollars per day.