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.

Using a poverty metric of 2.15 U.S. dollars per day, 37 percent of the women in Sub-Saharan Africa were living in extreme poverty in 2023. This is expected to fall to one third by 2023. On the other hand, less than one percent of the population in Europe and North America as well as Australia and New Zealand were living in extreme poverty. Nevertheless, there are also many people in these regions struggling to make ends meet.

Chile Poverty Statistics: Extreme Poverty: Average Gap data was reported at 0.700 % in 2017. This records a decrease from the previous number of 0.900 % for 2015. Chile Poverty Statistics: Extreme Poverty: Average Gap data is updated yearly, averaging 1.650 % from Dec 2006 (Median) to 2017, with 6 observations. The data reached an all-time high of 3.600 % in 2006 and a record low of 0.700 % in 2017. Chile Poverty Statistics: Extreme Poverty: Average Gap data remains active status in CEIC and is reported by Ministry of Social Development. The data is categorized under Global Database’s Chile – Table CL.H020: National Socio-Economic Characterization Survey: Poverty Situation.

This dashboard is part of SDGs Today. Please see sdgstoday.orgExtreme poverty poses a major challenge to the livelihood of current and future generations everywhere and threatens Agenda 2030’s promise of leaving no one behind. The World Poverty Clock developed by the World Data Lab provides real-time poverty estimates through 2030 for nearly all countries. The World Poverty Clock uses publicly available data on income distributions, production factors, and household consumption provided by various international organizations, including the World Bank and the International Monetary Fund (IMF). These organizations compile data provided to them by the local governments, and when this information is not available, the World Poverty Clock uses specific models to estimate poverty in these countries. The models include how individual incomes might change over time using IMF growth forecasts for the medium-term complemented by long-term “shared socio-economic pathways” developed by the International Institute for Applied Systems Analysis (IIASA) and similar analysis developed by the OECD. The World Poverty Clock dataset was updated in February 2021, taking into consideration the COVID-19 pandemic effects on the economy.

In 2025, around ***** million people in Africa were living in extreme poverty, with the poverty threshold at **** U.S. dollars a day. The number of poor people on the continent dropped slightly compared to the previous year. Poverty in Africa is expected to decline slightly in the coming years, even in the face of a growing population. The number of inhabitants living below the extreme poverty line would decrease to around *** million by 2030.

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.

Data for Indicator 1.1.1 comes from the Census Bureau's American Community Survey (ACS) poverty estimates. The U.S. poverty threshold varies based on year and family size. For example, in 2020, a household with two adults and two children would be considered under the poverty line if the household had an annual income less than $26,246. We define people living in extreme poverty line as people from households which earn less than 50% of the U.S. national poverty level for the specific year.

This dataset contains data from the World Development Indicators on Poverty and Shared Prosperity presenting indicators that measure progress toward the World Bank Group’s twin goals of ending extreme poverty by 2030 and promoting shared prosperity in every country in a sustainable manner.

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.

This statistic shows the additional number of people below the extreme poverty threshold caused by agriculture worldwide in 2030. In the positive (high impact) scenario, agriculture is forecasted to put **** million additional people worldwide below the extreme poverty line.

Chile Poverty Statistics: Extreme Poverty: Severity data was reported at 0.300 % in 2017. This records a decrease from the previous number of 0.400 % for 2015. Chile Poverty Statistics: Extreme Poverty: Severity data is updated yearly, averaging 0.700 % from Dec 2006 (Median) to 2017, with 6 observations. The data reached an all-time high of 1.600 % in 2006 and a record low of 0.300 % in 2017. Chile Poverty Statistics: Extreme Poverty: Severity data remains active status in CEIC and is reported by Ministry of Social Development. The data is categorized under Global Database’s Chile – Table CL.H020: National Socio-Economic Characterization Survey: Poverty Situation.

Overall, both the number of people living in poverty and the number of people living in extreme poverty in Latin America increased between 2015 and 2022, reaching 202 million and 81 million people, respectively. Since then, the number of people living in poverty has declined. In 2024, an estimated 170 million people were projected to be living in poverty in the region. . Moreover, indigenous peoples in Latin America continue to experience extremely high poverty rates.

A new basis for an international poverty measurement is proposed based on linear programming for specifying the least cost diet and explicit budgeting for nonfood spending. This approach is superior to the World Bank's $1-a-day line because it is (i) clearly related to survival and well being; (ii) comparable across time and space since the same nutritional requirements are used everywhere while nonfood spending is tailored to climate; (iii) adjusts consumption patterns to local prices; (iv) presents no index number problems since solutions are always in local prices; and (v) requires only readily available information. The new approach implies much more poverty than the World Bank's, especially in Asia.

This dataset contains estimates of the poor and extreme poor rural population within each region (administrative level 1) of the CRPs countries. The poverty lines are defined using the thresholds of 3.10$/day and 1.90$/day respectively, expressed in 2011 PPP $. With the exception of India, all the other estimates are based on authors’ calculations using data from nationally representative household surveys.

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.

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.

While many data gaps remain, we know that women are vulnerable to extreme poverty because they face greater burdens of unpaid work, have fewer assets and productive resources than men, are exposed to gender-based violence (GBV), and are more likely to be forced into early marriage —all factors that reduce their ability to participate fully in the economy and to reap the benefits of growth. This paper begins with a discussion of these factors and how they predispose women to extreme poverty. It then presents opportunities for reducing women’s extreme poverty through gender-sensitive programming in three key sectors: agriculture, education, and reproductive health. It outlines the challenges inherent in this type of work, including a need to better connect how sector-specific outcomes—which reflect improvements in women’s lives—also contribute to poverty reduction. Recommendations for moving forward include considering the unique links between gender and extreme poverty early in the project design process, taking into consideration underlying cultural practices and gender norms, and collecting rigorous, sex-disaggregated data to evaluate the effects of interventions on women.

The package provides data on the risk of poverty among those aged 18 and over in terms of activity status groups and education. The aim of the calculation of poverty indicators is to develop national indicators of absolute poverty and comparable indicators of poverty risk and social exclusion with other countries of the European Union, to monitor their changes, and to identify population groups most likely to fall into absolute poverty, poverty risk and social exclusion. Statistics on income and living conditions shall be used to develop poverty indicators. The classifications used do not exist. Statistical observation unit: Private household and a private household member aged 16 and over. Statistical survey population: all private households of Lithuania and permanent residents of Lithuania. Individuals living in institutional households (nursing homes, families, prison facilities, monasteries) are not investigated. Geographical coverage: Statistical information is produced at national and regional level, the indicator “Share of people living at risk of poverty or social exclusion” also at county level. Time coverage: Absolute poverty rates since 2016, share of people living at risk of poverty or social exclusion under the current definition, high levels of material and social deprivation, level of education since 2021, other indicators since 2005 Periodicity annual.

The West Africa Coastal Vulnerability Mapping: Gridded Subset of Sub-national Poverty and Extreme Poverty Prevalence represents the HarvestChoice Subnational Poverty and Extreme Poverty Prevalence data set as a one kilometer raster, and includes values within 200 kilometers of the coast. Poverty levels affect the "defenselessness" of populations in the low elevation coastal zone. These data were developed by the Harvest Choice project funded by the Bill and Melinda Gates Foundation. Harvest Choice measured 2005 poverty levels using 2005 purchasing power parity data for two thresholds: $1.25/day and $2/day international poverty lines. The $2/day threshold was selected for this mapping exercise.

Background: Value of a Statistical Life Year (VSLY) provides an important economic measure of an individual’s trade‑off between health risks and other consumption, and is a widely used policy parameter. Measuring VSLY is complex though, especially in low‑income and low‑literacy communities. Methods: Using a large randomized experiment (N = 3027), we study methodological aspects of stated‑preference elicitation with payment cards (price lists) in an extreme poverty context. In a 2 × 2 design, we systematically vary whether buying or selling prices are measured, crossed with the range of the payment card. Results: We find substantial effects of both the pricing method and the list range on elicited VSLY. Estimates of the gross domestic product per capita multiplier for VSLY range from 3.5 to 33.5 depending on the study design. Importantly, all estimates are economically and statistically significantly larger than the current World Health Organization threshold of 3.0 for cost‑effectiveness analyses. Conclusions: Our results inform design choice in VSLY measurements, and provide insight into the potential variability of these measurements and possibly robustness checks.