Out of all OECD countries, Cost Rica had the highest poverty rate as of 2022, at over 20 percent. The country with the second highest poverty rate was the United States, with 18 percent. On the other end of the scale, Czechia had the lowest poverty rate at 6.4 percent, followed by Denmark.

The significance of the OECD

The OECD, or the Organisation for Economic Co-operation and Development, was founded in 1948 and is made up of 38 member countries. It seeks to improve the economic and social well-being of countries and their populations. The OECD looks at issues that impact people’s everyday lives and proposes policies that can help to improve the quality of life.

Poverty in the United States

In 2022, there were nearly 38 million people living below the poverty line in the U.S.. About one fourth of the Native American population lived in poverty in 2022, the most out of any ethnicity. In addition, the rate was higher among young women than young men. It is clear that poverty in the United States is a complex, multi-faceted issue that affects millions of people and is even more complex to solve.

Whereas less than *** percent of the population in high income countries in the world lived on less than **** U.S. dollars a day in 2018 in terms of 2017 purchasing power parities, ** percent of the population in low income countries did the same. On a more positive note, the poverty rate in low income countries fell by ** percentage points from 1994 to 2018. In 2025, the share is estimated to be around *** for high income countries.

Graph and download economic data for Estimated Percent of People of All Ages in Poverty for United States (PPAAUS00000A156NCEN) from 1989 to 2023 about child, poverty, percent, and USA.

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.

All the 20 countries with the highest poverty rates in the world are located in Africa. The Democratic Republic of Congo and Mozambique were the two countries with the highest share of people living on less than 2.15 U.S. dollars a day when adjusting for 2017 Purchasing Power Parities (PPP), both at over ** percent.

Jordan JO: Poverty Gap at National Poverty Lines: % data was reported at 3.600 % in 2010. This records an increase from the previous number of 2.600 % for 2008. Jordan JO: Poverty Gap at National Poverty Lines: % data is updated yearly, averaging 3.100 % from Dec 2008 (Median) to 2010, with 2 observations. The data reached an all-time high of 3.600 % in 2010 and a record low of 2.600 % in 2008. Jordan JO: Poverty Gap at National Poverty Lines: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Jordan – Table JO.World Bank: Poverty. 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.; ; World Bank, Global Poverty Working Group. Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines.; ; This series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. Due to differences in estimation methodologies and poverty lines, estimates should not be compared across countries.

Sweden SE: Poverty Headcount Ratio at National Poverty Lines: % of Population data was reported at 16.100 % in 2022. This records an increase from the previous number of 16.000 % for 2021. Sweden SE: Poverty Headcount Ratio at National Poverty Lines: % of Population data is updated yearly, averaging 15.900 % from Dec 2007 (Median) to 2022, with 16 observations. The data reached an all-time high of 17.100 % in 2018 and a record low of 13.500 % in 2007. Sweden SE: Poverty Headcount Ratio at National Poverty Lines: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Sweden – Table SE.World Bank.WDI: Social: Poverty and Inequality. National poverty headcount ratio is the percentage of the population living below the national poverty line(s). National estimates are based on population-weighted subgroup estimates from household surveys. For economies for which the data are from EU-SILC, the reported year is the income reference year, which is the year before the survey year.;World Bank, Poverty and Inequality Platform. Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines.;;This series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. Due to differences in estimation methodologies and poverty lines, estimates should not be compared across countries.

Qatar Poverty Headcount Ratio at Societal Poverty Lines: % of Population data was reported at 17.500 % in 2017. Qatar Poverty Headcount Ratio at Societal Poverty Lines: % of Population data is updated yearly, averaging 17.500 % from Dec 2017 (Median) to 2017, with 1 observations. The data reached an all-time high of 17.500 % in 2017 and a record low of 17.500 % in 2017. Qatar 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 Qatar – Table QA.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).

As the primary goal of the 17 Sustainable Development Goals (SDGs), poverty eradication is still one of the major challenges faced by countries around the world, and relative poverty is a comprehensive poverty pattern triggered by the superposition of economic, social, and environmental dimensions. Therefore, Therefore, this paper introduces the perspective of coupled coordination to consider the formation of relative poverty, constructs indicators in three major dimensions: economic, social, and environmental, proposes a fast and more accurate method of identifying relative poverty in a region by using machine learning, measures the degree of coupled coordination of China’s relatively poor provinces using a coupled coordination model and analyzes the relationship with the level of relative poverty, and puts forward suggestions for poverty management on this basis using typology classification. The results of the study show that: 1) the fusion of data crawlers, remote sensing space, and other multi-source data to construct the dataset and propose a fast and efficient regional relative poverty identification method based on big data with low comprehensive cost and high identification accuracy of 0.914. 2) Currently, 70.83% of the economic-social-environmental systems of the relatively poor regions are in the dysfunctional type and are in a state of disordered development and malignant constraints. The regions showing coupling disorders are mainly clustered in the three southern prefectures of Xinjiang, Qinghai, Gansu, Yunnan, and Sichuan, and their spatial distribution is relatively concentrated. 3) The types of poverty and their coupled and coordinated development in each region show large spatial variability, requiring differentiated poverty eradication countermeasures tailored to local conditions to achieve sustainable regional economic-social-environmental development.

Chad TD: Poverty Gap at National Poverty Lines: Rural: % data was reported at 22.600 % in 2011. This records a decrease from the previous number of 23.100 % for 2002. Chad TD: Poverty Gap at National Poverty Lines: Rural: % data is updated yearly, averaging 22.850 % from Dec 2002 (Median) to 2011, with 2 observations. The data reached an all-time high of 23.100 % in 2002 and a record low of 22.600 % in 2011. Chad TD: Poverty Gap at National Poverty Lines: Rural: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Chad – Table TD.World Bank.WDI: Social: Poverty and Inequality. Rural poverty gap at national poverty lines is the rural population's 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.; ; World Bank, Global Poverty Working Group. Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines.; ; This series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. Due to differences in estimation methodologies and poverty lines, estimates should not be compared across countries.

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).

The average for 2020 based on 10 countries was 30.53 percent. The highest value was in Mexico: 43.9 percent and the lowest value was in Chile: 10.8 percent. The indicator is available from 2000 to 2023. Below is a chart for all countries where data are available.

This dataset provides metadata on the comparability of poverty estimates from the Poverty and Inequality Platform (PIP) within countries over time. Strictly comparable poverty estimates within a country require a consistent production process, including the sampling frame, questionnaires, the methodological construction of the welfare aggregates and poverty lines, a consistent deflation of prices in time and space, among many other considerations.As countries frequently improve household surveys and measurement methodologies, strict comparability of poverty estimates over time is often limited. Within a country, we assume comparability of poverty estimates over time unless there is a known change to survey methodology, measurement or data structure. Thus, the assessment of comparability is country-dependent and relies on the knowledge of current and past World Bank staff, as well as a close dialogue with national data producers with knowledge of the survey design and methodology.

In 2013, the World Bank announced the goals of fighting poverty in all its forms by 2030 and promoting shared prosperity. Despite a remarkable progress made on reducing poverty in recent years, reaching the targets remains challenging. The decline in poverty has been uneven. The poverty reduction in recent years was dominated by the East Asia and Pacific (notably China and Indonesia) and South Asia (notably India) (World Bank, 2017). Despite the progress made so far, the number of extreme poor remains high, especially in Sub-Saharan Africa. In many countries, vast differences in poverty levels persist at the subnational level. In addition, global problems such as climate change, fragility, economic crises and food security pose great risks for poverty reduction, and they are often trans-border issues, which generate strong positive or negative externalities across neighboring countries. This dataset is at the administrative unit level 1, based on international poverty line(s). Administrative unit level 1 refers to the highest subnational unit level (examples include ‘state’, ‘governorate’, ‘province’).

Will all children be able to read by 2030? The ability to read with comprehension is a foundational skill that every education system around the world strives to impart by late in primary school—generally by age 10. Moreover, attaining the ambitious Sustainable Development Goals (SDGs) in education requires first achieving this basic building block, and so does improving countries’ Human Capital Index scores. Yet past evidence from many low- and middle-income countries has shown that many children are not learning to read with comprehension in primary school. To understand the global picture better, we have worked with the UNESCO Institute for Statistics (UIS) to assemble a new dataset with the most comprehensive measures of this foundational skill yet developed, by linking together data from credible cross-national and national assessments of reading. This dataset covers 115 countries, accounting for 81% of children worldwide and 79% of children in low- and middle-income countries. The new data allow us to estimate the reading proficiency of late-primary-age children, and we also provide what are among the first estimates (and the most comprehensive, for low- and middle-income countries) of the historical rate of progress in improving reading proficiency globally (for the 2000-17 period). The results show that 53% of all children in low- and middle-income countries cannot read age-appropriate material by age 10, and that at current rates of improvement, this “learning poverty” rate will have fallen only to 43% by 2030. Indeed, we find that the goal of all children reading by 2030 will be attainable only with historically unprecedented progress. The high rate of “learning poverty” and slow progress in low- and middle-income countries is an early warning that all the ambitious SDG targets in education (and likely of social progress) are at risk. Based on this evidence, we suggest a new medium-term target to guide the World Bank’s work in low- and middle- income countries: cut learning poverty by at least half by 2030. This target, together with improved measurement of learning, can be as an evidence-based tool to accelerate progress to get all children reading by age 10.

For further details, please refer to https://thedocs.worldbank.org/en/doc/e52f55322528903b27f1b7e61238e416-0200022022/original/Learning-poverty-report-2022-06-21-final-V7-0-conferenceEdition.pdf

Brazil BR: Poverty Headcount Ratio at National Poverty Lines: % of Population data was reported at 26.500 % in 2017. This records an increase from the previous number of 25.700 % for 2016. Brazil BR: Poverty Headcount Ratio at National Poverty Lines: % of Population data is updated yearly, averaging 26.100 % from Dec 2016 (Median) to 2017, with 2 observations. The data reached an all-time high of 26.500 % in 2017 and a record low of 25.700 % in 2016. Brazil BR: Poverty Headcount Ratio at National Poverty Lines: % 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: Poverty and Inequality. National poverty headcount ratio is the percentage of the population living below the national poverty line(s). National estimates are based on population-weighted subgroup estimates from household surveys. For economies for which the data are from EU-SILC, the reported year is the income reference year, which is the year before the survey year.;World Bank, Poverty and Inequality Platform. Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines.;;This series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. Due to differences in estimation methodologies and poverty lines, estimates should not be compared across countries.

In 2019, the proportion of the population below the poverty line in Taiwan was at *** percent. Comparatively, approximately ** percent of Myanmar's population were living below the poverty line in 2019.

license: apache-2.0 tags: - africa - sustainable-development-goals - world-health-organization - development

  Employed population below international poverty line (%)





  Dataset Description

This dataset provides country-level data for the indicator "1.1.1 Employed population below international poverty line (%)" across African nations, sourced from the World Health Organization's (WHO) data portal on Sustainable Development Goals (SDGs). The data is presented in a… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/employed-population-below-international-poverty-line-for-african-countries.

The indicator is calculated as the distance between the median equivalised total net income of persons below the at-risk-of-poverty threshold and the at-risk-of-poverty threshold itself, expressed as a percentage of the at-risk-of-poverty threshold. This threshold is set at 60 % of the national median equivalised disposable income of all people in a country and not for the EU as a whole. The EU aggregate is a population weighted average of individual national figures. In line with decisions of the European Council, the risk-of-poverty rate is measured relative to the situation in each country rather than applying a common threshold to all countries.

Among the OECD countries, Costa Rica had the highest share of children living in poverty, reaching **** percent in 2022. Türkiye followed with a share of ***percent of children living in poverty, while **** percent of children in Spain, Chile, and the United States did the same. On the other hand, only ***** percent of children in Finland were living in poverty.