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: 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/

Poverty Headcount Ratio at Societal Poverty Lines: % of Population data was reported at 19.000 % in 2021. This records a decrease from the previous number of 20.900 % for 2020. Poverty Headcount Ratio at Societal Poverty Lines: % of Population data is updated yearly, averaging 31.700 % from Dec 1990 (Median) to 2021, with 19 observations. The data reached an all-time high of 72.000 % in 1990 and a record low of 19.000 % in 2021. Poverty Headcount Ratio at Societal Poverty Lines: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s China – Table CN.World Bank.WDI: Social: Poverty and Inequality. The poverty headcount ratio at societal poverty line is the percentage of a population living in poverty according to the World Bank's Societal Poverty Line. The Societal Poverty Line is expressed in purchasing power adjusted 2017 U.S. dollars and defined as max($2.15, $1.15 + 0.5*Median). This means that when the national median is sufficiently low, the Societal Poverty line is equivalent to the extreme poverty line, $2.15. For countries with a sufficiently high national median, the Societal Poverty Line grows as countries’ median income grows.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

Peru Population in Extreme Poverty: Urban data was reported at 3.211 % in 2023. This records an increase from the previous number of 2.617 % for 2022. Peru Population in Extreme Poverty: Urban data is updated yearly, averaging 1.173 % from Jun 2011 (Median) to 2023, with 13 observations. The data reached an all-time high of 3.211 % in 2023 and a record low of 0.796 % in 2018. Peru Population in Extreme Poverty: Urban data remains active status in CEIC and is reported by National Institute of Statistics and Informatics. The data is categorized under Global Database’s Peru – Table PE.G016: Population in Poverty.

Context

Two centuries ago the majority of the world population was extremely poor. Back then it was widely believed that widespread poverty was inevitable. But this turned out to be wrong. Economic growth is possible and poverty can decline. The world has made immense progress against extreme poverty.

But even after two centuries of progress, extreme poverty is still the reality for every tenth person in the world. This is what the ‘international poverty line’ highlights – this metric plays an important (and successful) role in focusing the world’s attention on these very poorest people in the world.

The poorest people today live in countries which have achieved no growth. This stagnation of the world’s poorest economies is one of the largest problems of our time. Unless this changes millions of people will continue to live in extreme poverty.

Content

Data comes from https://ourworldindata.org/extreme-poverty-in-brief Thanks to them to aggregate this kind of informations!

Acknowledgements

https://media.globalcitizen.org/thumbnails/90/19/90190c20-1182-47d6-a86e-3a2dcc912e73/extreme-poverty-un-explainer-social-share.jpg_1500x670_q85_ALIAS-hero_image_crop_subsampling-2.jpg" alt="Extreme Poverty">

Inspiration

Compare country, by year the % of persons in extreme poverty

Peru Population in Extreme Poverty: Rural data was reported at 16.153 % in 2023. This records an increase from the previous number of 14.605 % for 2022. Peru Population in Extreme Poverty: Rural data is updated yearly, averaging 13.934 % from Jun 2011 (Median) to 2023, with 13 observations. The data reached an all-time high of 20.518 % in 2011 and a record low of 9.827 % in 2019. Peru Population in Extreme Poverty: Rural data remains active status in CEIC and is reported by National Institute of Statistics and Informatics. The data is categorized under Global Database’s Peru – Table PE.G016: Population in Poverty.

Series Name: Employed population below international poverty line by sex and age (percent)Series Code: SI_POV_EMP1Release Version: 2021.Q2.G.03 This dataset is 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/

The underlying data are used to provide inputs for both the Vision indicator on the global population living in poverty as well as the Client Context indicator on the percentage of the population in FCV countries living in poverty. The Vision indicator measures the percentage of the population living on less than $2.15 a day in 2017 purchasing power parity (PPP) adjusted prices. Measures are based on internationally comparable poverty lines hold the real value of the poverty line constant across countries when making national and temporal comparisons. The current extreme poverty line ($2.15 a day, 2017 PPP) represents the median of the poverty lines found in low-income countries.

Peru Population in Extreme Poverty data was reported at 5.747 % in 2023. This records an increase from the previous number of 5.009 % for 2022. Peru Population in Extreme Poverty data is updated yearly, averaging 4.283 % from Jun 2011 (Median) to 2023, with 13 observations. The data reached an all-time high of 6.342 % in 2011 and a record low of 2.806 % in 2018. Peru Population in Extreme Poverty data remains active status in CEIC and is reported by National Institute of Statistics and Informatics. The data is categorized under Global Database’s Peru – Table PE.G016: Population in Poverty.

The "Forest Proximate People" (FPP) dataset is one of the data layers contributing to the development of indicator #13, “number of forest-dependent people in extreme poverty,” of the Collaborative Partnership on Forests (CPF) Global Core Set of forest-related indicators (GCS). The FPP dataset provides an estimate of the number of people living in or within 1 kilometer of forests (forest-proximate people) for the year 2019 with a spatial resolution of 100 meters at a global level.

For more detail, such as the theory behind this indicator and the definition of parameters, and to cite this data, see: Newton, P., Castle, S.E., Kinzer, A.T., Miller, D.C., Oldekop, J.A., Linhares-Juvenal, T., Pina, L., Madrid, M., & de Lamo, J. 2022. The number of forest- and tree-proximate people: a new methodology and global estimates. Background Paper to The State of the World’s Forests 2022 report. Rome, FAO.

Contact points:

Maintainer: Leticia Pina

Distributor: Sarah E., Castle

Data lineage:

The FPP data are generated using Google Earth Engine. Forests are defined by the Copernicus Global Land Cover (CGLC) (Buchhorn et al. 2020) classification system’s definition of forests: tree cover ranging from 15-100%, with or without understory of shrubs and grassland, and including both open and closed forests. Any area classified as forest sized ≥ 1 ha in 2019 was included in this definition. Population density was defined by the WorldPop global population data for 2019 (WorldPop 2018). High density urban populations were excluded from the analysis. High density urban areas were defined as any contiguous area with a total population (using 2019 WorldPop data for population) of at least 50,000 people and comprised of pixels all of which met at least one of two criteria: either the pixel a) had at least 1,500 people per square km, or b) was classified as “built-up” land use by the CGLC dataset (where “built-up” was defined as land covered by buildings and other manmade structures) (Dijkstra et al. 2020). Using these datasets, any rural people living in or within 1 kilometer of forests in 2019 were classified as forest proximate people. Euclidean distance was used as the measure to create a 1-kilometer buffer zone around each forest cover pixel. The scripts for generating the forest-proximate people and the rural-urban datasets using different parameters or for different years are published and available to users. For more detail, such as the theory behind this indicator and the definition of parameters, and to cite this data, see: Newton, P., Castle, S.E., Kinzer, A.T., Miller, D.C., Oldekop, J.A., Linhares-Juvenal, T., Pina, L., Madrid, M., & de Lamo, J. 2022. The number of forest- and tree-proximate people: a new methodology and global estimates. Background Paper to The State of the World’s Forests 2022. Rome, FAO.

References:

Buchhorn, M., Smets, B., Bertels, L., De Roo, B., Lesiv, M., Tsendbazar, N.E., Herold, M., Fritz, S., 2020. Copernicus Global Land Service: Land Cover 100m: collection 3 epoch 2019. Globe.

Dijkstra, L., Florczyk, A.J., Freire, S., Kemper, T., Melchiorri, M., Pesaresi, M. and Schiavina, M., 2020. Applying the degree of urbanisation to the globe: A new harmonised definition reveals a different picture of global urbanisation. Journal of Urban Economics, p.103312.

WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University, 2018. Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00645

Online resources:

GEE asset for "Forest proximate people – 1km cutoff distance (100-m resolution)"

Peru Population in Extreme Poverty: Mountain Region data was reported at 10.355 % in 2023. This records an increase from the previous number of 8.876 % for 2022. Peru Population in Extreme Poverty: Mountain Region data is updated yearly, averaging 8.876 % from Jun 2011 (Median) to 2023, with 13 observations. The data reached an all-time high of 13.766 % in 2011 and a record low of 6.265 % in 2018. Peru Population in Extreme Poverty: Mountain Region data remains active status in CEIC and is reported by National Institute of Statistics and Informatics. The data is categorized under Global Database’s Peru – Table PE.G016: Population in Poverty.

United States Poverty Headcount Ratio at Societal Poverty Lines: % of Population data was reported at 19.200 % in 2022. This records an increase from the previous number of 16.700 % for 2021. United States Poverty Headcount Ratio at Societal Poverty Lines: % of Population data is updated yearly, averaging 19.200 % from Dec 1963 (Median) to 2022, with 60 observations. The data reached an all-time high of 20.500 % in 1993 and a record low of 16.700 % in 2021. United States Poverty Headcount Ratio at Societal Poverty Lines: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Social: Poverty and Inequality. The poverty headcount ratio at societal poverty line is the percentage of a population living in poverty according to the World Bank's Societal Poverty Line. The Societal Poverty Line is expressed in purchasing power adjusted 2017 U.S. dollars and defined as max($2.15, $1.15 + 0.5*Median). This means that when the national median is sufficiently low, the Societal Poverty line is equivalent to the extreme poverty line, $2.15. For countries with a sufficiently high national median, the Societal Poverty Line grows as countries’ median income grows.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

This layer shows childhood poverty figures at a country scale. Population figures were obtained in 2023.This layer uses bivariate choropleth mapping to symboloise the relationship between children living in poverty (as defined globally) and children engaged in economic activity (i.e. work).Global patterns indicate that children are most impacted by poverty. Across the globe, a staggering 333 million children live in conditions of extreme poverty. This layer has been designed to help school children in New Zealand and the South Pacific explore these claims.

Poverty Headcount Ratio at Societal Poverty Lines: % of Population data was reported at 7.800 % in 2021. This records an increase from the previous number of 7.600 % for 2020. Poverty Headcount Ratio at Societal Poverty Lines: % of Population data is updated yearly, averaging 7.800 % from Dec 1987 (Median) to 2021, with 23 observations. The data reached an all-time high of 8.400 % in 2010 and a record low of 4.200 % in 1995. Poverty Headcount Ratio at Societal Poverty Lines: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Finland – Table FI.World Bank.WDI: Social: Poverty and Inequality. The poverty headcount ratio at societal poverty line is the percentage of a population living in poverty according to the World Bank's Societal Poverty Line. The Societal Poverty Line is expressed in purchasing power adjusted 2017 U.S. dollars and defined as max($2.15, $1.15 + 0.5*Median). This means that when the national median is sufficiently low, the Societal Poverty line is equivalent to the extreme poverty line, $2.15. For countries with a sufficiently high national median, the Societal Poverty Line grows as countries’ median income grows.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

The "Tree Proximate People" (TPP) dataset is one of the data layers contributing to the development of indicator #13, “number of forest-dependent people in extreme poverty,” of the Collaborative Partnership on Forests (CPF) Global Core Set of forest-related indicators (GCS). The TPP dataset provides an estimate of the number of people living in or within 1 kilometers of trees outside forests (forest-proximate people) for the year 2019 with a spatial resolution of 100 meters at a global level. Trees outside forests are defined as areas classified as agricultural lands with at least 10% tree cover. Code available to update annually using Google Earth Engine. For more detail, such as the theory behind this indicator and the definition of parameters, and to cite this data, see: Newton, P., Castle, S.E., Kinzer, A.T., Miller, D.C., Oldekop, J.A., Linhares-Juvenal, T., Madrid, M., & Pina, L. 2022. The number of forest- and tree-proximate people: a new methodology and global estimates. Rome, FAO.

Dataset Structure & Description

https://imgur.com/AYzsmYU.jpg" alt="Dataset Structure">

Context and Inspiration

I read an article yesterday which got my mind storming, A article by Worldbank on August 15th, 2022 better explains it, It has been quoted below,
I already have a project i'm working on since Feb 2021, trying to solving this problem, listed in my datasets

This dataset showcases the statistics over the past 6-7 decades which covers the production of 150+ unique crops, 50+ livestock elements, Land distribution by usage and population, As aspiring data scientists one can try to extract insights incentivizing the optimal use of natural resources and distribution of resources

August 15, 2022 - Worldbank

Record high food prices have triggered a global crisis that will drive millions more into extreme poverty, magnifying hunger and malnutrition, while threatening to erase hard-won gains in development. The war in Ukraine, supply chain disruptions, and the continued economic fallout of the COVID-19 pandemic are reversing years of development gains and pushing food prices to all-time highs. Rising food prices have a greater impact on people in low- and middle-income countries, since they spend a larger share of their income on food than people in high-income countries. This brief looks at rising food insecurity and World Bank responses to date.

Please leave a upvote if you found this helpful ☮️

Hello 👋, If you are enjoying so far, Please checkout my other Datasets, I would love to hear your support & feedback on it, Thank you !

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Checkout my other Datasets & Notebooks

France Poverty Headcount Ratio at Societal Poverty Lines: % of Population data was reported at 13.300 % in 2021. This records an increase from the previous number of 12.700 % for 2020. France Poverty Headcount Ratio at Societal Poverty Lines: % of Population data is updated yearly, averaging 12.200 % from Dec 1970 (Median) to 2021, with 31 observations. The data reached an all-time high of 15.200 % in 1970 and a record low of 10.600 % in 2005. France Poverty Headcount Ratio at Societal Poverty Lines: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s France – Table FR.World Bank.WDI: Social: Poverty and Inequality. The poverty headcount ratio at societal poverty line is the percentage of a population living in poverty according to the World Bank's Societal Poverty Line. The Societal Poverty Line is expressed in purchasing power adjusted 2017 U.S. dollars and defined as max($2.15, $1.15 + 0.5*Median). This means that when the national median is sufficiently low, the Societal Poverty line is equivalent to the extreme poverty line, $2.15. For countries with a sufficiently high national median, the Societal Poverty Line grows as countries’ median income grows.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

This global dataset of regions at the frontlines of cooling poverty risk includes five different files:
(1) Global cooling demand in People-CDDs at ~1 km resolution (GeoTIFF), quantifying population-weighted cooling degree days by combining climate observations with gridded population data;
(2) Global ability to access cooling in $/CDD per capita (PPP-adjusted) at ~10 km resolution (geoTIFF), estimating the economic capacity of populations to afford cooling by integrating GDP per capita with heat exposure;
Population at the frontlines of cooling poverty risk at ~10 km resolution (geoTIFF), with two separate maps that identify populations (3) at high risk and (4) at extreme risk of cooling poverty by combining cooling demand, affordability constraints, and electricity costs;
and (5) Summary of results by country in Excel, providing aggregated national-level indicators including total People-CDDs, economic capacity to afford cooling, and the population at high and extreme risk.
Together, these datasets provide a global, spatially detailed estimate of cooling poverty risk under present-day climate conditions.
Further information regarding the data sources and methods used to produce these datasets is available in the corresponding scientific article.

BR: Survey Mean Consumption or Income per Capita: Total Population: 2011 PPP per day data was reported at 20.390 Intl $/Day in 2019. This records an increase from the previous number of 20.250 Intl $/Day for 2014. BR: Survey Mean Consumption or Income per Capita: Total Population: 2011 PPP per day data is updated yearly, averaging 20.320 Intl $/Day from Dec 2014 (Median) to 2019, with 2 observations. The data reached an all-time high of 20.390 Intl $/Day in 2019 and a record low of 20.250 Intl $/Day in 2014. BR: Survey Mean Consumption or Income per Capita: Total Population: 2011 PPP per day 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. Mean consumption or income per capita (2011 PPP $ per day) used in calculating the growth rate in the welfare aggregate of total population.; ; World Bank, Global Database of Shared Prosperity (GDSP) (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity).; ; The choice of consumption or income for a country is made according to which welfare aggregate is used to estimate extreme poverty in the Poverty and Inequality Platform (PIP). The practice adopted by the World Bank for estimating global and regional poverty is, in principle, to use per capita consumption expenditure as the welfare measure wherever available; and to use income as the welfare measure for countries for which consumption is unavailable. However, in some cases data on consumption may be available but are outdated or not shared with the World Bank for recent survey years. In these cases, if data on income are available, income is used. Whether data are for consumption or income per capita is noted in the footnotes. Because household surveys are infrequent in most countries and are not aligned across countries, comparisons across countries or over time should be made with a high degree of caution.

Poverty Headcount Ratio at Societal Poverty Lines: % of Population data was reported at 8.000 % in 2021. This records an increase from the previous number of 7.500 % for 2020. Poverty Headcount Ratio at Societal Poverty Lines: % of Population data is updated yearly, averaging 7.100 % from Dec 1987 (Median) to 2021, with 23 observations. The data reached an all-time high of 8.200 % in 2010 and a record low of 5.000 % in 1995. Poverty Headcount Ratio at Societal Poverty Lines: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Denmark – Table DK.World Bank.WDI: Social: Poverty and Inequality. The poverty headcount ratio at societal poverty line is the percentage of a population living in poverty according to the World Bank's Societal Poverty Line. The Societal Poverty Line is expressed in purchasing power adjusted 2017 U.S. dollars and defined as max($2.15, $1.15 + 0.5*Median). This means that when the national median is sufficiently low, the Societal Poverty line is equivalent to the extreme poverty line, $2.15. For countries with a sufficiently high national median, the Societal Poverty Line grows as countries’ median income grows.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).