Poverty affects billions of people around the globe. On a daily basis, they face low wages and substandard health, education, and living standards. Because of this, poverty must be understood and approached as a multidimensional issue. Through the use of storytelling, videos, and interactive maps, this Map Journal takes a look at one method of measuring global poverty. This measure, called the Multidimensional Poverty Index (MPI), addresses poverty through an integrative approach.For more information about the Multidimensional Poverty Index, click here.

The FGGD poverty map is a global vector datalayer at scale 1:5 000 000. The map depicts the differences among countries with respect to the national population estimated to be living in poverty as of the latest year for which data was available in 2005. Data have been compiled by FAO from data reported in World Bank, WDI Online, as of April 2005.

In 2025, nearly 11.7 percent of the world population in extreme poverty, with the poverty threshold at 2.15 U.S. dollars a day, lived in Nigeria. Moreover, the Democratic Republic of the Congo accounted for around 11.7 percent of the global population in extreme poverty. Other African nations with a large poor population were Tanzania, Mozambique, and Madagascar. Poverty levels remain high despite the forecast decline Poverty is a widespread issue across Africa. Around 429 million people on the continent were living below the extreme poverty line of 2.15 U.S. dollars a day in 2024. Since the continent had approximately 1.4 billion inhabitants, roughly a third of Africa’s population was in extreme poverty that year. Mozambique, Malawi, Central African Republic, and Niger had Africa’s highest extreme poverty rates based on the 2.15 U.S. dollars per day extreme poverty indicator (updated from 1.90 U.S. dollars in September 2022). Although the levels of poverty on the continent are forecast to decrease in the coming years, Africa will remain the poorest region compared to the rest of the world. Prevalence of poverty and malnutrition across Africa Multiple factors are linked to increased poverty. Regions with critical situations of employment, education, health, nutrition, war, and conflict usually have larger poor populations. Consequently, poverty tends to be more prevalent in least-developed and developing countries worldwide. For similar reasons, rural households also face higher poverty levels. In 2024, the extreme poverty rate in Africa stood at around 45 percent among the rural population, compared to seven percent in urban areas. Together with poverty, malnutrition is also widespread in Africa. Limited access to food leads to low health conditions, increasing the poverty risk. At the same time, poverty can determine inadequate nutrition. Almost 38.3 percent of the global undernourished population lived in Africa in 2022.

Series Name: Employed population below international poverty line by sex and age (percent)Series Code: SI_POV_EMP1Release Version: 2020.Q2.G.03This dataset is the part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 1.1.1: Proportion of the population living below the international poverty line by sex, age, employment status and geographic location (urban/rural)Target 1.1: By 2030, eradicate extreme poverty for all people everywhere, currently measured as people living on less than $1.25 a dayGoal 1: End poverty in all its forms everywhereFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

Series Name: Proportion of population below international poverty line (percent)Series Code: SI_POV_DAY1Release Version: 2021.Q2.G.03 This dataset is the part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 1.1.1: Proportion of the population living below the international poverty line by sex, age, employment status and geographic location (urban/rural)Target 1.1: By 2030, eradicate extreme poverty for all people everywhere, currently measured as people living on less than $1.25 a dayGoal 1: End poverty in all its forms everywhereFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

This dataset is provided by the World Bank Group.

Data have been aggregated to district level.

From what was historically known as 'Arabia Felix', a land of prosperity and happiness, Yemen has become the most impoverished among the Arab countries. The government of the united Yemen, formed in 1990, has launched so far three five-year economic reform plans with the goal of restoring Yemen's prosperity. Have these efforts succeeded What policies are needed to further reduce poverty The poverty assessment report aims to answer these questions. This report measures poverty in Yemen in 2005-06, and evaluates the change in poverty compared to 1998, the two years for which comparable household budget surveys are available. The period between the two survey years (1998 and 2005-06), more or less overlaps the first two five-year economic plans and captures the effect of the economic reform programs launched since 1995. In addition to measuring poverty, this report has three objectives: evaluating the role of growth and past reforms on poverty, identifying better ways to target the vulnerable poor through public action, and an assessment of the poverty monitoring system. By examining the effect of the key policies on poverty, such as the petroleum price reform and the government's social protection mechanisms between 1998 and 2005-06, the study aims to equip policy makers and development partners with the knowledge needed to improve the effectiveness of their efforts to reduce poverty in Yemen.

Global poverty looks radically different in the 21st century as climate-related events, political-religious conflicts and economic growth-inequality nexuses add to persistent forms of social exclusion based on gender, race, and class. In this uncertain and unpredictable context, we require new approaches to understand complex pathways into and out of poverty, directing attention to poor people's collective capacity to bring about transformative change i.e., their agency, as constituted by social networks and relations with nature, and mediated by science and technology. The collection deposited includes the transcripts of life history interviews with anonymised participants in south India and in Kenya. Focus of the interviews was on understanding participants' relations with modern sciences and technologies as well as with social structures. The eventual aim was to understand how these socio-material relations constitute participants' agency. All names are pseudonyms.

Global poverty looks radically different in the 21st century as climate-related events, political-religious conflicts and economic growth-inequality nexuses add to persistent forms of social exclusion based on gender, race, and class. In this uncertain and unpredictable context, we require new approaches to understand complex pathways into and out of poverty, directing attention to poor people's collective capacity to bring about transformative change i.e., their agency, as constituted by social networks and relations with nature, and mediated by science and technology. Our aim is to develop the concepts and methods of an innovative 'relational agency pathways approach', drawing on theories from Science, Technology and Society studies and the 'pathways approach' to poverty reduction and social justice, which emphasise interactions between social, technological and environmental change.

We will develop this new approach to understand diverse pathways out of poverty for smallholders and the landless in agriculture, in two arenas. First, we will study how small farmers and farmworkers adapt new technologies on the farm, as their cultivation practices are transformed due to technological and environmental change. Second, we will study how farmers turn a harvested crop into a commodity for the market, negotiating their relationships with credit providers and traders. Both these arenas played out dramatically under the 'Green Revolution', from the 1960s onwards, when technology, markets and government support were used to intensify agricultural production.

The first geographical focus of our work will be on the North Arcot region of Tamil Nadu, India, a classic exemplar of the Green Revolution in Asia, where extensive historical data since the early 1970s are available. Collaborating with our co-investigators at Madras Institute of Development Studies, and collecting new life history data in the field, we will map long-term agency pathways into and out of poverty constituted by changing technologies, natural resources and social worlds, as lived by people of different genders, classes and castes. We will test the approach in Machakos County in Kenya (in collaboration with our co-investigator at African Centre for Technology Studies), where several attempts have been made to get a Green Revolution off the ground, but none have been sustainable. In addition to relying on archival data and collecting life histories using ethnographic engagement with the study's participants, we will use a workshop format to collect data on how people evaluate diverse pathways out of (and into) poverty along a range of criteria derived from conventional indicators of welfare and well-being as well as those designed by the participants themselves.

To communicate our approach in other low-income contexts, we will develop a training programme for junior researchers. There will be broad-based participation from researchers, policymakers and farmers throughout the project, and we will organise a final workshop in Kenya, which will bring these participants together in a safe space for collective learning, where our findings and approach can be confronted with their different knowledges and experiences.

We will present our work in academic and policy forums, produce policy briefs and web blogs and a short documentary film (to engage with audiences beyond academia and policy). We see our research to be of interest to at least five groups: a) government institutions attempting to intensify smallholder agriculture through better use of natural resources and new technologies; b) rural development organisations (including non-governmental ones), active in organising initiatives for poverty alleviation; c) academic researchers working on agricultural sustainability and poverty issues in the global south; d) environmental NGOs at international and grassroots levels; e) farmers' associations such as the East African Farmers' Federation.

The 2020 Global Multidimensional Poverty Index (MPI) data and publication "Charting pathways out of multidimensional poverty: Achieving the SDGs" released on 16 July 2020 by the Oxford Poverty and Human Development Initiative (OPHI) at the University of Oxford and the Human Development Report Office of the United Nations Development Programme (UNDP). The global Multidimensional Poverty Index (MPI) measures the complexities of poor people’s lives, individually and collectively, each year. This report focuses on how multidimensional poverty has declined. It provides a comprehensive picture of global trends in multidimensional poverty, covering 5 billion people. It probes patterns between and within countries and by indicator, showcasing different ways of making progress. Together with data on the $1.90 a day poverty rate, the trends monitor global poverty in different forms. The COVID-19 pandemic unfolded in the midst of this analysis. While data are not yet available to measure the rise of global poverty after the pandemic, simulations based on different scenarios suggest that, if unaddressed, progress across 70 developing countries could be set back 3–10 years. It is 10 years before 2030, the due date of the Sustainable Development Goals (SDGs), whose first goal is to end poverty in all its forms everywhere. The MPI provides a comprehensive and in-depth picture of global poverty – in all its dimensions – and monitors progress towards Sustainable Development Goal (SDG) 1 – to end poverty in all its forms. It also provides policymakers with the data to respond to the call of Target 1.2, which is to ‘reduce at least by half the proportion of men, women, and children of all ages living in poverty in all its dimensions according to national definition'. By detailing the connections between the MPI and other poverty-related SDGs, the report highlights how the lives of multidimensionally poor people are precarious in ways that extend beyond the MPI’s 10 component indicators.

Poverty gap at national poverty lines is the mean shortfall from the poverty lines (counting the nonpoor as having zero shortfall) as a percentage of the poverty lines. This measure reflects the depth of poverty as well as its incidence.

The dataset portrays poverty rates at the country level. The data for the poverty dataset comes from the Tajikistan Living Standard Measurement Survey 2009 (TLSS) collected by the State Statistical Agency of Tajikistan in collaboration with the World Bank, and the 2010 Census of Tajikistan. The TLSS provides information on food and non-food expenditure, labor activities, migration, agriculture, education, dwelling, utilities, and durable goods. The Census of Tajikistan covers approximately 1.6 million households and 8 million individuals.

Poverty rates vary from 12.7 to 76.2 percent, a higher percentage representing areas of higher poverty incidence.

This dataset has been produced based on the data provided in the "Poverty Mapping in Tajikistan: Method and Key Findings" report. This report is the joint product of the World Bank Group (WBG) and the Agency of Statistics under the President of Tajikistan (TajStat).

Data publication: 2021-11-03

Contact points:

Metadata Contact: Dariia Nesterenko

Resource Contact: World Bank Group

Resource constraints:

Creative Commons Attribution 3.0 IGO http://creativecommons.org/licenses/by/3.0/igo/ for the poverty data Creative Commons Attribution-ShareAlike 2.0 https://creativecommons.org/licenses/by-sa/2.0/ for the administrative boundaries

Online resources:

The World Bank

Online resource

Data Series: Employed population below international poverty line, by sex and age Indicator: I.14 - Proportion of the population living below the international poverty line by sex, age, employment status and geographic location (urban/rural) Source year: 2023 This dataset is part of the Minimum Gender Dataset compiled by the United Nations Statistics Division. Domain: Economic structures, participation in productive activities and access to resources

This marginality hotspot map of Ethiopia uses the lowest quartile as thresholds for the dimensions of marginality. Again, this map shows how many dimension of marginality - as defined by Gatzweiler et al. (2011) - overlap. Quality/Lineage: Indicator - Input - Cut-off point total expenditure at household level - HICE survey data - Total expenditure is defined as all household consumption expenditures as well as non-consumption expenditures; regional level lowest quartile (1671.92 Birr) Prevalence of stunting among children under five, by lowest available subnational administrative unit, varying years (FGGD) - Global raster data layer with 5 arc-minutes resolution. Data compilation by FAO including the prevalence of stunting, LandScan global population database and the percentage of children under five - Percentage of children below 3 standard deviations of WHO growth standards (18.85%) Travel time to major cities: A global map of Accessibility (by Andrew Nelson) - Infrastructural data (based on data of: populated places, cities, road network, travel speeds, railway network, navigable rivers, major waterbodies, shipping lanes, borders, urban areas, elevation and slope); 30 arc-seconds resolution - More than 12 hours travelling to the next agglomeration with ≥50,000 people. percentage of households having health problem in last 2 months and not going to health institution or traditional healer - WMS survey data; regional level - Lowest quartile (49.11%) Global land area with soil constraints Depth, soil chemical status and natural, fertility, drainage, texture, miscellaneous land; - 5 arc-minutes resolution - Soils that have „very frequent severe“ soil constraints as well as soils “unsuitable for agriculture” according to FAO 2007 (FGGD) definition percent of households getting drinking water from unprotected well or spring - DHS survey data; regional level - Lowest quartile (15.83%) percentage of women saying wife beating is ok if she neglects children - DHS survey data - Lowest quartile (70.75%)

Much recent attention has been paid to the interaction between poverty and conflict in developing countries. However, it is surprising that neither the academic nor the international development community has as of yet, systematically examined the influence of international inequalities upon poverty and conflict. The project proposes that the prevalence of poverty and conflict is strongly conditioned by countries' position within the international economic system. The nature of a country's economic ties with the rest of the world - often deeply unequal - can create significant dependencies and / or incentives to challenge the status quo, resulting in poverty-provoked violence. The project uses network analysis and matching methods. The network analysis is used to map out key international economic networks (aid, trade, and FDI) and generate measures of countries' direct and indirect relations with other states plus their position within the overall structure. These network measures are then used in a statistical method of matching countries to infer whether dependent countries are more likely to succumb to poverty-provoked conflict. The findings from the project will identify the extent to which international inequality traps lead to poverty and conflict traps in developing countries, and help to draw out the policy implications of this.

These data were developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau across all standard and custom geographies at statewide summary level where applicable. .
For a deep dive into the data model including every specific metric, see the ACS 2018-2022 Data Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics. Find naming convention prefixes/suffixes, geography definitions and user notes below.Prefixes:NoneCountpPercentrRatemMedianaMean (average)tAggregate (total)chChange in absolute terms (value in t2 - value in t1)pchPercent change ((value in t2 - value in t1) / value in t1)chpChange in percent (percent in t2 - percent in t1)sSignificance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computedSuffixes:_e22Estimate from 2018-22 ACS_m22Margin of Error from 2018-22 ACS_e102006-10 ACS, re-estimated to 2020 geography_m10Margin of Error from 2006-10 ACS, re-estimated to 2020 geography_e10_22Change, 2010-22 (holding constant at 2020 geography)GeographiesAAA = Area Agency on Aging (12 geographic units formed from counties providing statewide coverage)ARC21 = Atlanta Regional Commission modeling area (21 counties merged to a single geographic unit)ARWDB7 = Atlanta Regional Workforce Development Board (7 counties merged to a single geographic unit)BeltLineStatistical (buffer)BeltLineStatisticalSub (subareas)Census Tract (statewide)CFGA23 = Community Foundation for Greater Atlanta (23 counties merged to a single geographic unit)City (statewide)City of Atlanta Council Districts (City of Atlanta)City of Atlanta Neighborhood Planning Unit (City of Atlanta)City of Atlanta Neighborhood Statistical Areas (City of Atlanta)County (statewide)Georgia House (statewide)Georgia Senate (statewide)HSSA = High School Statistical Area (11 county region)MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)Regional Commissions (statewide)State of Georgia (single geographic unit)Superdistrict (ARC region)US Congress (statewide)UWGA13 = United Way of Greater Atlanta (13 counties merged to a single geographic unit)ZIP Code Tabulation Areas (statewide)The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2018-2022). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2018-2022Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the data manifest: https://opendata.atlantaregional.com/documents/3b86ee614e614199ba66a3ff1ebfe3b5/about

The vector dataset portrays poverty rates at the country level. The data for the poverty dataset comes from: the Tajikistan Living Standard Measurement Survey 2009 (TLSS) collected by the State Statistical Agency of Tajikistan in collaboration with the World Bank, and the 2010 Census of Tajikistan. The TLSS provides information on food and non-food expenditure, labor activities, migration, agriculture, education, dwelling, utilities, and durable goods. The Census of Tajikistan covers approximately 1.6 million households and 8 million individuals. This dataset has been produced based on the data provided in the "Poverty Mapping in Tajikistan: Method and Key Findings" report. This report is the joint product of the World Bank Group (WBG) and the Agency of Statistics under the President of Tajikistan (TajStat).

These data were developed by the Research & Analytics Department at the Atlanta Regional Commission using data from the U.S. Census Bureau across all standard and custom geographies at statewide summary level where applicable.For a deep dive into the data model including every specific metric, see the ACS 2019-2023. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics. Find naming convention prefixes/suffixes, geography definitions and user notes below.Prefixes:NoneCountpPercentrRatemMedianaMean (average)tAggregate (total)chChange in absolute terms (value in t2 - value in t1)pchPercent change ((value in t2 - value in t1) / value in t1)chpChange in percent (percent in t2 - percent in t1)sSignificance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computedSuffixes:_e23Estimate from 2019-23 ACS_m23Margin of Error from 2019-23 ACS_e102006-10 ACS, re-estimated to 2020 geography_m10Margin of Error from 2006-10 ACS, re-estimated to 2020 geography_e10_23Change, 2010-23 (holding constant at 2020 geography)GeographiesAAA = Area Agency on Aging (12 geographic units formed from counties providing statewide coverage)ARC21 = Atlanta Regional Commission modeling area (21 counties merged to a single geographic unit)ARWDB7 = Atlanta Regional Workforce Development Board (7 counties merged to a single geographic unit)BeltLineStatistical (buffer)BeltLineStatisticalSub (subareas)Census Tract (statewide)CFGA23 = Community Foundation for Greater Atlanta (23 counties merged to a single geographic unit)City (statewide)City of Atlanta Council Districts (City of Atlanta)City of Atlanta Neighborhood Planning Unit (City of Atlanta)City of Atlanta Neighborhood Statistical Areas (City of Atlanta)County (statewide)CCDIST = County Commission Districts (statewide where applicable)CCSUPERDIST = County Commission Superdistricts (DeKalb)Georgia House (statewide)Georgia Senate (statewide)HSSA = High School Statistical Area (11 county region)MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)Regional Commissions (statewide)State of Georgia (single geographic unit)Superdistrict (ARC region)US Congress (statewide)UWGA13 = United Way of Greater Atlanta (13 counties merged to a single geographic unit)ZIP Code Tabulation Areas (statewide)The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2019-2023). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2019-2023Open Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the data manifest: https://opendata.atlantaregional.com/documents/182e6fcf8201449086b95adf39471831/about

The Global 15x15 Minute Grids of the Downscaled GDP Based on the Special Report on Emissions Scenarios (SRES) B2 Scenario, 1990 and 2025, are geospatial distributions of Gross Domestic Product (GDP) per Unit area (GDP densities). These global grids were generated using the Country-level GDP and Downscaled Projections Based on the SRES B2 Scenario, 1990-2100 data set, and CIESIN's Gridded Population of World, Version 2 (GPWv2) data set as the base map. First, the GDP per capita was developed at a country-level for 1990 and 2025. Then the gridded GDP was developed within each country by applying the GDP per capita to each grid cell of the GPW, under the assumption that the GDP per capita was uniform within a country. This data set is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).

Overlaying the number of marginality dimensions with percentage of people living below 1.25$/day. This map is included in a global study on mapping marginality focusing on Sub-Saharan Africa and South Asia. The Dimensions of Marginality are based on different data sources representing different spheres of life. The poverty dataset used in this study is based on calculations by Harvest Choice. The underlying Marginality map is based on the approach on Marginality Mapping (http://www.zef.de/fileadmin/webfiles/downloads/zef_wp/wp88.pdf). The respective map can be found here: https://daten.zef.de/#/metadata/ae4ae68c-cea3-44e7-8199-1c2ae04abb88 Quality/Lineage: Poverty Data was provided and generated by Harvest Choice GIS lab. Marginality hotspots are based on the approach by Graw, V. using five dimensions of marginality. In ArcGIS thresholds were defined based on percentages and overlapping dimensions. Using raster data this data was reclassified and overlayed to build a new classification with regard to the here presented purpose. This approach is similar to the overlap over marginality and poverty mass except this map shows percentage of poverty instead of number of poor people. Purpose: This map was created in the MARGIP project to identify the marginalized and poor by highlighting those areas where the "spheres of life" have a low performance. Those areas where multiple "low performance indicators" did overlap got the highest attention for further research.

Proportion of the population living below the international poverty line by sex, age, employment status and geographic location (urban/rural)