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.

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.

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 2023, the around 11.1 percent of the population was living below the national poverty line in the United States. Poverty in the United StatesAs shown in the statistic above, the poverty rate among all people living in the United States has shifted within the last 15 years. The United Nations Educational, Scientific and Cultural Organization (UNESCO) defines poverty as follows: “Absolute poverty measures poverty in relation to the amount of money necessary to meet basic needs such as food, clothing, and shelter. The concept of absolute poverty is not concerned with broader quality of life issues or with the overall level of inequality in society.” The poverty rate in the United States varies widely across different ethnic groups. American Indians and Alaska Natives are the ethnic group with the most people living in poverty in 2022, with about 25 percent of the population earning an income below the poverty line. In comparison to that, only 8.6 percent of the White (non-Hispanic) population and the Asian population were living below the poverty line in 2022. Children are one of the most poverty endangered population groups in the U.S. between 1990 and 2022. Child poverty peaked in 1993 with 22.7 percent of children living in poverty in that year in the United States. Between 2000 and 2010, the child poverty rate in the United States was increasing every year; however,this rate was down to 15 percent in 2022. The number of people living in poverty in the U.S. varies from state to state. Compared to California, where about 4.44 million people were living in poverty in 2022, the state of Minnesota had about 429,000 people living in poverty.

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

In 2023, **** percent of Black people living in the United States were living below the poverty line, compared to *** percent of white people. That year, the total poverty rate in the U.S. across all races and ethnicities was **** percent. Poverty in the United States Single people in the United States making less than ****** U.S. dollars a year and families of four making less than ****** U.S. dollars a year are considered to be below the poverty line. Women and children are more likely to suffer from poverty, due to women staying home more often than men to take care of children, and women suffering from the gender wage gap. Not only are women and children more likely to be affected, racial minorities are as well due to the discrimination they face. Poverty data Despite being one of the wealthiest nations in the world, the United States had the third highest poverty rate out of all OECD countries in 2019. However, the United States' poverty rate has been fluctuating since 1990, but has been decreasing since 2014. The average median household income in the U.S. has remained somewhat consistent since 1990, but has recently increased since 2014 until a slight decrease in 2020, potentially due to the pandemic. The state that had the highest number of people living below the poverty line in 2020 was California.

Goal 1End poverty in all its forms everywhereTarget 1.1: By 2030, eradicate extreme poverty for all people everywhere, currently measured as people living on less than $1.25 a dayIndicator 1.1.1: Proportion of the population living below the international poverty line by sex, age, employment status and geographic location (urban/rural)SI_POV_DAY1: Proportion of population below international poverty line (%)SI_POV_EMP1: Employed population below international poverty line, by sex and age (%)Target 1.2: By 2030, 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 definitionsIndicator 1.2.1: Proportion of population living below the national poverty line, by sex and ageSI_POV_NAHC: Proportion of population living below the national poverty line (%)Indicator 1.2.2: Proportion of men, women and children of all ages living in poverty in all its dimensions according to national definitionsSD_MDP_MUHC: Proportion of population living in multidimensional poverty (%)SD_MDP_ANDI: Average proportion of deprivations for people multidimensionally poor (%)SD_MDP_MUHHC: Proportion of households living in multidimensional poverty (%)SD_MDP_CSMP: Proportion of children living in child-specific multidimensional poverty (%)Target 1.3: Implement nationally appropriate social protection systems and measures for all, including floors, and by 2030 achieve substantial coverage of the poor and the vulnerableIndicator 1.3.1: Proportion of population covered by social protection floors/systems, by sex, distinguishing children, unemployed persons, older persons, persons with disabilities, pregnant women, newborns, work-injury victims and the poor and the vulnerableSI_COV_MATNL: [ILO] Proportion of mothers with newborns receiving maternity cash benefit (%)SI_COV_POOR: [ILO] Proportion of poor population receiving social assistance cash benefit, by sex (%)SI_COV_SOCAST: [World Bank] Proportion of population covered by social assistance programs (%)SI_COV_SOCINS: [World Bank] Proportion of population covered by social insurance programs (%)SI_COV_CHLD: [ILO] Proportion of children/households receiving child/family cash benefit, by sex (%)SI_COV_UEMP: [ILO] Proportion of unemployed persons receiving unemployment cash benefit, by sex (%)SI_COV_VULN: [ILO] Proportion of vulnerable population receiving social assistance cash benefit, by sex (%)SI_COV_WKINJRY: [ILO] Proportion of employed population covered in the event of work injury, by sex (%)SI_COV_BENFTS: [ILO] Proportion of population covered by at least one social protection benefit, by sex (%)SI_COV_DISAB: [ILO] Proportion of population with severe disabilities receiving disability cash benefit, by sex (%)SI_COV_LMKT: [World Bank] Proportion of population covered by labour market programs (%)SI_COV_PENSN: [ILO] Proportion of population above statutory pensionable age receiving a pension, by sex (%)Target 1.4: By 2030, ensure that all men and women, in particular the poor and the vulnerable, have equal rights to economic resources, as well as access to basic services, ownership and control over land and other forms of property, inheritance, natural resources, appropriate new technology and financial services, including microfinanceIndicator 1.4.1: Proportion of population living in households with access to basic servicesSP_ACS_BSRVH2O: Proportion of population using basic drinking water services, by location (%)SP_ACS_BSRVSAN: Proportion of population using basic sanitation services, by location (%)Indicator 1.4.2: Proportion of total adult population with secure tenure rights to land, (a) with legally recognized documentation, and (b) who perceive their rights to land as secure, by sex and type of tenureSP_LGL_LNDDOC: Proportion of people with legally recognized documentation of their rights to land out of total adult population, by sex (%)SP_LGL_LNDSEC: Proportion of people who perceive their rights to land as secure out of total adult population, by sex (%)SP_LGL_LNDSTR: Proportion of people with secure tenure rights to land out of total adult population, by sex (%)Target 1.5: By 2030, build the resilience of the poor and those in vulnerable situations and reduce their exposure and vulnerability to climate-related extreme events and other economic, social and environmental shocks and disastersIndicator 1.5.1: Number of deaths, missing persons and directly affected persons attributed to disasters per 100,000 populationVC_DSR_MISS: Number of missing persons due to disaster (number)VC_DSR_AFFCT: Number of people affected by disaster (number)VC_DSR_MORT: Number of deaths due to disaster (number)VC_DSR_MTMP: Number of deaths and missing persons attributed to disasters per 100,000 population (number)VC_DSR_MMHN: Number of deaths and missing persons attributed to disasters (number)VC_DSR_DAFF: Number of directly affected persons attributed to disasters per 100,000 population (number)VC_DSR_IJILN: Number of injured or ill people attributed to disasters (number)VC_DSR_PDAN: Number of people whose damaged dwellings were attributed to disasters (number)VC_DSR_PDYN: Number of people whose destroyed dwellings were attributed to disasters (number)VC_DSR_PDLN: Number of people whose livelihoods were disrupted or destroyed, attributed to disasters (number)Indicator 1.5.2: Direct economic loss attributed to disasters in relation to global gross domestic product (GDP)VC_DSR_GDPLS: Direct economic loss attributed to disasters (current United States dollars)VC_DSR_LSGP: Direct economic loss attributed to disasters relative to GDP (%)VC_DSR_AGLH: Direct agriculture loss attributed to disasters (current United States dollars)VC_DSR_HOLH: Direct economic loss in the housing sector attributed to disasters (current United States dollars)VC_DSR_CILN: Direct economic loss resulting from damaged or destroyed critical infrastructure attributed to disasters (current United States dollars)VC_DSR_CHLN: Direct economic loss to cultural heritage damaged or destroyed attributed to disasters (millions of current United States dollars)VC_DSR_DDPA: Direct economic loss to other damaged or destroyed productive assets attributed to disasters (current United States dollars)Indicator 1.5.3: Number of countries that adopt and implement national disaster risk reduction strategies in line with the Sendai Framework for Disaster Risk Reduction 2015–2030SG_DSR_LGRGSR: Score of adoption and implementation of national DRR strategies in line with the Sendai FrameworkSG_DSR_SFDRR: Number of countries that reported having a National DRR Strategy which is aligned to the Sendai FrameworkIndicator 1.5.4: Proportion of local governments that adopt and implement local disaster risk reduction strategies in line with national disaster risk reduction strategiesSG_DSR_SILS: Proportion of local governments that adopt and implement local disaster risk reduction strategies in line with national disaster risk reduction strategies (%)SG_DSR_SILN: Number of local governments that adopt and implement local DRR strategies in line with national strategies (number)SG_GOV_LOGV: Number of local governments (number)Target 1.a: Ensure significant mobilization of resources from a variety of sources, including through enhanced development cooperation, in order to provide adequate and predictable means for developing countries, in particular least developed countries, to implement programmes and policies to end poverty in all its dimensionsIndicator 1.a.1: Total official development assistance grants from all donors that focus on poverty reduction as a share of the recipient country’s gross national incomeDC_ODA_POVLG: Official development assistance grants for poverty reduction, by recipient countries (percentage of GNI)DC_ODA_POVDLG: Official development assistance grants for poverty reduction, by donor countries (percentage of GNI)DC_ODA_POVG: Official development assistance grants for poverty reduction (percentage of GNI)Indicator 1.a.2: Proportion of total government spending on essential services (education, health and social protection)SD_XPD_ESED: Proportion of total government spending on essential services, education (%)Target 1.b: Create sound policy frameworks at the national, regional and international levels, based on pro-poor and gender-sensitive development strategies, to support accelerated investment in poverty eradication actionsIndicator 1.b.1: Pro-poor public social spending

BackgroundEducation and health are both constituents of human capital that enable people to earn higher wages and enhance people’s capabilities. Human capabilities may lead to fulfilling lives by enabling people to achieve a valuable combination of human functionings—i.e., what people are able to do or be as a result of their capabilities. A better understanding of how these different human capabilities are produced together could point to opportunities to help jointly reduce the wide disparities in health and education across populations.Methods and findingsWe use nationally and regionally representative individual-level data from Demographic and Health Surveys (DHS) for 55 low- and middle-income countries (LMICs) to examine patterns in human capabilities at the national and regional levels, between 2000 and 2017 (N = 1,657,194 children under age 5). We graphically analyze human capabilities, separately for each country, and propose a novel child-based Human Development Index (HDI) based on under-five survival, maternal educational attainment, and measures of a child’s household wealth. We normalize the range of each component using data on the minimum and maximum values across countries (for national comparisons) or first-level administrative units within countries (for subnational comparisons). The scores that can be generated by the child-based HDI range from 0 to 1.We find considerable heterogeneity in child health across countries as well as within countries. At the national level, the child-based HDI ranged from 0.140 in Niger (with mean across first-level administrative units = 0.277 and standard deviation [SD] 0.114) to 0.755 in Albania (with mean across first-level administrative units = 0.603 and SD 0.089). There are improvements over time overall between the 2000s and 2010s, although this is not the case for all countries included in our study. In Cambodia, Malawi, and Nigeria, for instance, under-five survival improved over time at most levels of maternal education and wealth. In contrast, in the Philippines, we found relatively few changes in under-five survival across the development spectrum and over time. In these countries, the persistent location of geographical areas of poor child health across both the development spectrum and time may indicate within-country poverty traps.Limitations of our study include its descriptive nature, lack of information beyond first- and second-level administrative units, and limited generalizability beyond the countries analyzed.ConclusionsThis study maps patterns and trends in human capabilities and is among the first, to our knowledge, to introduce a child-based HDI at the national and subnational level. Areas of chronic deprivation may indicate within-country poverty traps and require alternative policy approaches to improving child health in low-resource settings.

The share of the global population with access to electricity in 2022 was roughly 91 percent, up from 71.4 percent in 1990. South Sudan was the least electrified country worldwide, followed by Burundi.

Results of Multilevel logistic regression predicting multidimensional poverty among children under age five in India, 2015–2021.

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.

This data originally comes from the US Census and is illustrated by margin of error, percent, and rank of families with related children below the poverty line.

Decomposition for contributing factor to multidimensional child poverty in India, 2015–21.

DESCRIPTION Enclosed are data from CIESIN's Global subnational rates of child underweight status database. Further documentation for these data is available in the enclosed catalog and on the CIESIN Poverty Mapping web site at: http://www.ciesin.columbia.edu/povmap This is the beta release of this product. See the Poverty Mapping home page for additional information on the product. CITATION We recommend the following for citing the database: Center for International Earth Science Information Network (CIESIN), Columbia University; 2005 Global subnational rates of child underweight status [dataset]. CIESIN, Palisades, NY, USA. Available at: http://www.ciesin.columbia.edu/povmap/ds_global.html

Data on the percentage of people who are living under the poverty line in their respective country

SI: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data was reported at 4.570 % in 2021. SI: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data is updated yearly, averaging 4.570 % from Dec 2021 (Median) to 2021, with 1 observations. The data reached an all-time high of 4.570 % in 2021 and a record low of 4.570 % in 2021. SI: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Slovenia – Table SI.World Bank.WDI: Social: Poverty and Inequality. The growth rate in the welfare aggregate of the bottom 40% is computed as the annualized average growth rate in per capita real consumption or income of the bottom 40% of the population in the income distribution in a country from household surveys over a roughly 5-year period. Mean per capita real consumption or income is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries means are not reported due to grouped and/or confidential data. The annualized growth rate is computed as (Mean in final year/Mean in initial year)^(1/(Final year - Initial year)) - 1. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported. The initial year refers to the nearest survey collected 5 years before the most recent survey available, only surveys collected between 3 and 7 years before the most recent survey are considered. The coverage and quality of the 2017 PPP price data for Iraq and most other North African and Middle Eastern countries were hindered by the exceptional period of instability they faced at the time of the 2017 exercise of the International Comparison Program. See the Poverty and Inequality Platform for detailed explanations.;World Bank, Global Database of Shared Prosperity (GDSP) (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity).;;The comparability of welfare aggregates (consumption or income) for the chosen years T0 and T1 is assessed for every country. If comparability across the two surveys is a major concern for a country, the selection criteria are re-applied to select the next best survey year(s). Annualized growth rates are calculated between the survey years, using a compound growth formula. The survey years defining the period for which growth rates are calculated and the type of welfare aggregate used to calculate the growth rates are noted in the footnotes.

Percent of Children in Poverty (income below $19,806 for a family of two adults and two children in 2005) is the percentage of children under age 18 who live in families with incomes below 100 percent of the U.S. poverty threshold, as defined by the U.S. Office of Management and Budget. The federal poverty definition consists of a series of thresholds based on family size and composition and is updated every year to account for inflation. In calendar year 2005, a family of two adults and two children fell in the poverty category if their annual income fell below $19,806. Poverty status is not determined for people living in group quarters, such as military barracks, prisons, and other institutional quarters, or for unrelated individuals under age 15 (such as foster children). The data are based on income received in the 12 months prior to the survey. SOURCE: * U.S. Census Bureau, American Community Survey.

The Bangladesh Interactive Poverty Maps allow you to explore and visualize socioeconomic data at the zila (district) and upazila (sub-district) level. The tool provides users an easy way to access different types of indicators including poverty, demographics of the population, children’s health and nutrition, education, employment, and access to energy, water, and sanitation services. These maps were constructed by combining three different data sources all of which are publicly available. These include the 2010 Bangladesh Poverty Maps, the IPUMS sample from the 2011 Bangladesh Census of Population and Housing, and the 2012 Undernutrition Maps of Bangladesh.

Definition of variables and data sources

These maps were constructed by combining three different data sources all of which are publicly available. These include the 2010 Bangladesh Poverty Maps, the 2011 Census of Population and Housing sample available from the Integrated Public Use Microdata Series project (IPUMS), and the 2012 Undernutrition Maps of Bangladesh.

The 2010 Bangladesh Poverty Maps technical report describing the metholody used to construct the zila and upazila national poverty statistics can be accessed at the following link: http://www.worldbank.org/en/news/feature/2014/09/30/poverty-maps

The Population and Housing Census sample (IPUMS) dataset can be accessed at the following link: https://international.ipums.org/international-action/sample_details/country/bd

The undernutrition maps produced by the World Food Program (WFP) are available at the following link: https://www.wfp.org/content/undernutrition-maps-bangladesh-2012

Detailed information describing the construction of the variables and sources is presented below.

Basic information:

1) Total population: Total population in the zila/upazila. 2) Share of rural population: Share of the zila/upazila population who lives in rural areas. 3) Working population: Total number of working age population (15-64 years) in zila/upazila. 4) Total households: Total number of households in the zila/upazila.

Source: Indicators 1, 2, 3, and 4 were computed using the 2011 Census of Population and Housing.

Poverty (among the population):

5) Poverty headcount ratio (%): Percentage of the population that lives below the official national upper poverty line. 6) Extreme poverty headcount ratio (%): Percentage of the population that lives below the official national lower poverty line. 7) Percentage of population in bottom 40%: Percentage of the population in the zila/upazila that belongs to the bottom 40% of the national real per capita consumption distribution.

Source: Indicators 5, 6, and 7 come from 2010 Bangladesh Poverty Maps. The total number of poor, extreme poor, and population that belongs to the bottom 40% were computed using indicators 5, 6, 7 and indicator 1 (Total population in the zila/upazila).

Demographic (among population):

8) Population between 0 and 6 years old: Total population in the age range of 0-6 years old. 9) Population between 7 and 14 years old: Total population in the age range of 7-14 years old. 10) Population between 15 and 64 years old: Total population in the age range of 15-64 years old. 11) Population ages 65 and above: Total population in the age range of 65 and above.

Source: Indicators 8, 9, 10, and 11 were constructed using question 14 from the 2011 Census of Population and Housing.

Question 14. Age (Completed years)

Nutrition (among children below 5):

12) Percentage of underweight children: Percentage of children under five years of age whose standarized weight-for-age is more than two standard deviations below the median for the international reference. population (WHO standard) 13) Percentage of severely underweight children: Percentage of children under five years of age whose standarized weight-for-age is more than three standard deviations below the median for the international reference population (WHO standard). 14) Percentage of stunted children: Percentage of children under five years of age whose standarized height-for-age is more than two standard deviations below the median for the international reference population (WHO standard). 15) Percentage of severely stunted children: Percentage of children under five years of age whose standarized weight-for-age is more than three standard deviations below the median for the international reference population (WHO standard).

Source: Indicators 12, 13, 14, and 15 were produced by the World Food Program (WFP) and are constructed based on data from the Child and Mother Nutrition Survey of Bangladesh 2012 (MICS) and the Health and Morbidity Status Survey 2011 (HMSS). The total number of children under the age of 5 years was estimated using data from the 2011 Census of Population and Housing.

Primary Employment (among working population):

16) Agriculture: If employed, sector of employment is agriculture. 17) Industry: If employed, sector of employment is industry. 18) Services: If employed, sector of employment is services.

Source: Indicators 16, 17, and 18 were constructed using Question 25 from the 2011 Census of Population and Housing. Question 25 was asked for persons 7 years of age and older who reported being employed.

Question 25. If employed, field of employment (1) Agriculture (2) Industry (3) Service

Energy & Sanitation (among households):

19) With Electricity: Percentage of households with access to electricity. 20) With flush toilet: Percentage of households with access to flush toilet. 21) With non-flush, latrine: Percentage of households with access to latrine. 22) Without toilet, open defecation: Percentage of households who practice open defecation. 23) With access to tap water: Percentage of households with access to tap water. 24) With access to tube-well water: Percentage of households with access to tube-well water.

Source: Indicators 19, 20, 21, 22, 23, and 24 were constructed using questions 8, 9 and 10 from the 2011 Census of Population and Housing.

Question 8. Source of drinking water (1) Tap (2) Tube-well (3) Other

Question 9. Toilet facilities (1) Sanitary (with water seal) (2) Sanitary (no water seal) (3) Non-sanitary (4) None

Question 10. Electricity connection (1) Yes (2) No

Literacy & Educational Attainment (among adults 18 years and above)

25) Literate population: Percentage of adults who can write a letter. 26) Less than primary completed: Percentage of adults who have not completed primary education. 27) Primary completed: Percentage of adults who have completed primary education. 28) Secondary completed: Percentage of adults who have completed secondary education. 29) University completed: Percentage of adults who have completed univeristy.

Source: Indicators 25, 26, 27, 28, and 29 were constructed using Questions 21 and 23 from the 2011 Census of Population and Housing.

Question 21. Highest class passed (write class passed code)

Question 23. Can write a letter? (1) Yes (2) No

School attendance (among school-age children)

30) Overall (6-18 year olds): Percentage of children 6-18 years old who attend school. 31) Primary level (6-10 years): Percentage of children 6-10 years old who attend school. 32) Junior level (11-13 years): Percentage of children 11-13 years old who attend school. 33) Secondary level (14-15 years): Percentage of children 14-15 years old who attend school. 34) High secondary level (16-18 years): Percentage of children 16-18 years old who attend school.

Source: Indicators 30, 31, 32, 33, and 34 were constructed using Question 20 from the 2011 Census of Population and Housing.

Question 20. Student (Currently) (1) Yes (2) No

Additional Notes: * All national averages reported correspond to weighted upazila/zila level means, except for the nutrition variables and the population in national bottom 40% which correspond to unweighted upazila/zila level means.

BackgroundThe development of cognitive and socioemotional skills early in life influences later health and well-being. Existing estimates of unmet developmental potential in low- and middle-income countries (LMICs) are based on either measures of physical growth or proxy measures such as poverty. In this paper we aim to directly estimate the number of children in LMICs who would be reported by their caregivers to show low cognitive and/or socioemotional development.Methods and FindingsThe present paper uses Early Childhood Development Index (ECDI) data collected between 2005 and 2015 from 99,222 3- and 4-y-old children living in 35 LMICs as part of the Multiple Indicator Cluster Survey (MICS) and Demographic and Health Surveys (DHS) programs. First, we estimate the prevalence of low cognitive and/or socioemotional ECDI scores within our MICS/DHS sample. Next, we test a series of ordinary least squares regression models predicting low ECDI scores across our MICS/DHS sample countries based on country-level data from the Human Development Index (HDI) and the Nutrition Impact Model Study. We use cross-validation to select the model with the best predictive validity. We then apply this model to all LMICs to generate country-level estimates of the prevalence of low ECDI scores globally, as well as confidence intervals around these estimates.In the pooled MICS and DHS sample, 14.6% of children had low ECDI scores in the cognitive domain, 26.2% had low socioemotional scores, and 36.8% performed poorly in either or both domains. Country-level prevalence of low cognitive and/or socioemotional scores on the ECDI was best represented by a model using the HDI as a predictor. Applying this model to all LMICs, we estimate that 80.8 million children ages 3 and 4 y (95% CI 48.1 million, 113.6 million) in LMICs experienced low cognitive and/or socioemotional development in 2010, with the largest number of affected children in sub-Saharan Africa (29.4.1 million; 43.8% of children ages 3 and 4 y), followed by South Asia (27.7 million; 37.7%) and the East Asia and Pacific region (15.1 million; 25.9%). Positive associations were found between low development scores and stunting, poverty, male sex, rural residence, and lack of cognitive stimulation. Additional research using more detailed developmental assessments across a larger number of LMICs is needed to address the limitations of the present study.ConclusionsThe number of children globally failing to reach their developmental potential remains large. Additional research is needed to identify the specific causes of poor developmental outcomes in diverse settings, as well as potential context-specific interventions that might promote children’s early cognitive and socioemotional well-being.

In 2020, 9.4 percent of individuals in Italy were living below the poverty line. Compared to the previous year, the share of people living in absolute poverty experienced a decrease. However, the lowest rates were registered in the years from 2008 to 2012, when the real impact of the 2008 financial crisis was yet to be recorded. In Italy, around one fifth of the entire population was consistently at risk of poverty between 2011 and 2019.

Demographics of poverty

Some groups are suffering more than others from lack of money and resources. Data in fact suggest that male individuals are slightly more likely to experience absolute poverty than females. Moreover, the incidence rates of absolute poverty in households with more family nuclei and couples with three or more children are much higher than the general average.

Geography of poverty

Poverty in Italy also depends on where one lives. In the North East and in the Center of the country, in fact, around 6.5 percent of the population lived below the absolute poverty line in 2018. On the other hand, the incidence rate of absolute poverty in Sicily and Sardinia was almost double, 12 percent, with other regions in the South also recording alarming rates.