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.

In 2023, 17.9 percent of Black people living in the United States were living below the poverty line, compared to 7.7 percent of white people. That year, the total poverty rate in the U.S. across all races and ethnicities was 11.1 percent. Poverty in the United States Single people in the United States making less than 12,880 U.S. dollars a year and families of four making less than 26,500 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.

Among the OECD countries, Costa Rica had the highest share of children living in poverty, reaching 28.5 percent in 2022. Türkiye followed with a share of 22 percent of children living in poverty, while 20.5 percent of children in Spain, Chile, and the United States did the same. On the other hand, only three percent of children in Finland were living in poverty.

Over the past 30 years, there has been an almost constant reduction in the poverty rate worldwide. Whereas nearly 38 percent of the world's population lived on less than 2.15 U.S. dollars in terms of 2017 Purchasing Power Parity (PPP) in 1990, this had fallen to 8.7 percent in 2022. This is despite the fact that the world's population was growing over the same period. However, there was a small increase in the poverty rate during the COVID-19 pandemic in 2020 and 2021, when thousands of people became unemployed overnight. Moreover, rising cost of living in the aftermath of the pandemic and spurred by the Russian invasion of Ukraine in 2022 meant that many people were struggling to make ends meet. Poverty is a regional problem Poverty can be measured in relative and absolute terms. Absolute poverty concerns basic human needs such as food, clothing, shelter, and clean drinking water, whereas relative poverty looks at whether people in different countries can afford a certain living standard. Most countries that have a high percentage of their population living in absolute poverty, meaning that they are poor compared to international standards, are regionally concentrated. African countries are most represented among the countries in which poverty prevails the most. In terms of numbers, Sub-Saharan Africa and South Asia have the most people living in poverty worldwide. Inequality on the rise How wealth, or the lack thereof, is distributed within the global population and even within countries is very unequal. In 2022, the richest one percent of the world owned almost half of the global wealth, while the poorest 50 percent owned less than two percent in the same year. Within regions, Latin America had the most unequal distribution of wealth but this phenomenon is present in all world regions.

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 file includes the Inequality and Poverty Key Figures (as of March 2022), constructed for all Luxembourg Income Study (LIS) Study datasets in all waves. It includes multiple national-level measures: • on inequality measures: Gini, Atkinson coefficients, and percentile ratios • on relative poverty rates for various demographic groups • median and mean of disposable household income

This project sought to renew the ESRC's invaluable financial support to LIS (formerly the Luxembourg Income Study) for a period of five more years. LIS is an independent, non-profit cross-national data archive and research institute located in Luxembourg. LIS relies on financial contributions from national science foundations, other research institutions and consortia, data-providing agencies, and supranational organisations to support data harmonisation and enable free and unlimited data access to researchers in the participating countries and to students world-wide. LIS' primary activity is to make harmonised household microdata available to researchers, thus enabling cross-national, interdisciplinary primary research into socio-economic outcomes and their determinants. Users of the Luxembourg Income Study Database and Luxembourg Wealth Study Database come from countries around the globe, including the UK. LIS has four goals: 1) to harmonise microdatasets from high- and middle-income countries that include data on income, wealth, employment, and demography; 2) to provide a secure method for researchers to query data that would otherwise be unavailable due to country-specific privacy restrictions; 3) to create and maintain a remote-execution system that sends research query results quickly back to users at off-site locations; and 4) to enable, facilitate, promote and conduct crossnational comparative research on the social and economic wellbeing of populations across countries. LIS contains the Luxembourg Income Study (LIS) Database, which includes income data, and the Luxembourg Wealth Study (LWS) Database, which focuses on wealth data. LIS currently includes microdata from 46 countries in Europe, the Americas, Africa, Asia and Australasia. LIS contains over 250 datasets, organised into eight time "waves," spanning the years 1968 to 2011. Since 2007, seventeen more countries have been added to LIS, including the BRICS countries (Brazil, Russia, India, China, South Africa), Japan, South Korea and a number of other Latin American countries. LWS contains 20 wealth datasets from 12 countries, including the UK, and covers the period 1994 to 2007. All told, LIS and LWS datasets together cover 86% of world GDP and 64% of world population. Users submit statistical queries to the microdatabases using a Java-based job submission interface or standard email. The databases are especially valuable for primary research in that they offer access to cross-national data at the micro-level - at the level of households and persons. Users are economists, sociologists, political scientists, and policy analysts, among others, and they employ a range of statistical approaches and methods. LIS also provides extensive documentation - metadata - for both LIS and LWS, concerning technical aspects of the survey data, the harmonisation process, and the social institutions of income and wealth provision in participating countries. In the next five years, for which support is sought, LIS will: - expand LIS, adding Waves IX (2013) and X (2016), and add new middle-income countries; - develop LWS, adding another wave of datasets to existing countries; acquire new wealth datasets for 14 more countries in cooperation with the European Central Bank (based on the Household Finance and Consumption Survey); - create a state-of-the-art metadata search and storage system; - maintain international standards in data security and data infrastructure systems; - provide high-quality harmonised household microdata to researchers around the world; - enable interdisciplinary cross-national social science research covering 45+ countries, including the UK; - aim to broaden its reach and impact in academic and non-academic circles through focused communications strategies and collaborations.

Abstract

Young Lives: An International Study of Childhood Poverty is a collaborative project investigating the changing nature of childhood poverty in selected developing countries. The UK’s Department for International Development (DFID) is funding the first three-year phase of the project.

Young Lives involves collaboration between Non Governmental Organisations (NGOs) and the academic sector. In the UK, the project is being run by Save the Children-UK together with an academic consortium that comprises the University of Reading, London School of Hygiene and Tropical Medicine, South Bank University, the Institute of Development Studies at Sussex University and the South African Medical Research Council.

The study is being conducted in Ethiopia, India (in Andhra Pradesh), Peru and Vietnam. These countries were selected because they reflect a range of cultural, geographical and social contexts and experience differing issues facing the developing world; high debt burden, emergence from conflict, and vulnerability to environmental conditions such as drought and flood.

Objectives of the study The Young Lives study has three broad objectives: • producing good quality panel data about the changing nature of the lives of children in poverty. • trace linkages between key policy changes and child poverty • informing and responding to the needs of policy makers, planners and other stakeholders There will also be a strong education and media element, both in the countries where the project takes place, and in the UK.

The study takes a broad approach to child poverty, exploring not only household economic indicators such as assets and wealth, but also child centred poverty measures such as the child’s physical and mental health, growth, development and education. These child centred measures are age specific so the information collected by the study will change as the children get older.

Further information about the survey, including publications, can be downloaded from the Young Lives website.

Geographic coverage

Young Lives is an international study of childhood poverty, involving 12,000 children in 4 countries. - Ethiopia (20 communities in Addis Ababa, Amhara, Oromia, and Southern National, Nationalities and People's Regions) - India (20 sites across Andhra Pradesh and Telangana) - Peru (74 communities across Peru) - Vietnam (20 communities in the communes of Lao Cai in the north-west, Hung Yen province in the Red River Delta, the city of Danang on the coast, Phu Yen province from the South Central Coast and Ben Tre province on the Mekong River Delta)

Analysis unit

Individuals; Families/households

Universe

Cross-national; Subnational

Children aged approximately 5 years old and their households, and children aged 12 years old and their households, in Ethiopia, India (Andhra Pradesh), Peru and Vietnam, in 2006-2007. These children were originally interviewed in Round 1 of the study. See documentation for details of the exact regions covered in each country.

Kind of data

Sample survey data [ssd]

Sampling procedure

Purposive selection/case studies

A key need for the study's objectives was to obtain data at different levels - the children, their households, the community in which they resided, as well as at regional and national levels. This need thus determined that children should be selected in geographic clusters rather than randomly selected across the country. There was, however, a much more important reason for recruiting children in clusters - the sites are also intended to provide suitable settings for a range of complementary thematic studies. For example, one or a few sites may be used for a qualitative study designed to achieve a deeper level of understanding of some social issues, either because they are important in that particular place, or because the sites are appropriate locales to investigate a more general concern. The quantitative panel study is seen as the foundation upon which a coherent and interesting range of linked studies can be set up.

Thus the design was decided, in each country, comprising 20 geographic clusters with 100 children sampled in each cluster.

Sampling deviation

Ethiopia: 1,912 (5-year-olds), 980 (12-year-olds); India: 1,950 (5-year-olds), 994 (12-year-olds); Peru: 1,963 (5-year-olds), 685 (12-year-olds); Vietnam: 1,970 (5-year-olds), 990 (12-year-olds)

Mode of data collection

Face-to-face interview

Research instrument

Every questionnaire used in the study consists of a 'core' element and a country-specific element, which focuses on issues important for that country.

The core element of the questionnaires consists of the following sections: Core 5 & 12 year old household questionnaire • Section 1: Parental background • Section 2: Household education • Section 3: Livelihoods and asset framework • Section 3a: Land & crops • Section 3b: Time allocation • Section 3c: Productive assets • Section 3d: Non-agricultural earnings • Section 3e: Transfers • Section 4: Consumption/Expenditure • Section 4a: Food consumption/expenditure • Section 4b: Non-food consumption/expenditure • Section 5: Social capital • Section 5a: Support networks • Section 5b: Family, group and political capital • Section 5c: Collective action and exclusion • Section 5d: Information networks • Section 6: Economic changes and recent life history • Section 7: Socio-economic status • Section 8: Child care, education & activities (blank in 12yr old household) • Section 9: Child health • Section 10: Child development (blank in 12yr old household) • Section 11: Anthropometry • Section 12: Caregiver perceptions & attitudes

Core 12 year old child questionnaire • Section 1: School and activities • Section 2: Child health • Section 3: Social networks, social skills and social support • Section 4: Feelings and attitudes • Section 5: Parents and household issues • Section 6: Perceptions of household wealth and future • Section 7: Child Development

The community questionnaire used in Ethiopia consists of the following sections: - MODULE 1 General Module • Section 1 General Community Characteristics • Section 2 Social Environment • Section 3 Access to Services • Section 4 Economy • Section 5 Local Prices - MODULE 2 Child-Specific Modules • Section 1 Educational Service (General) • Section 2 NOT INCLUDED IN ETHIOPIA CONTEXT INSTRUMENT • Section 3 Educational Services (Preschool, Primary, Secondary) • Section 4 Health Services • Section 5 Child Protection Services - MODULE 3 Country specific community level questions • Section 1 Conversion factors • Section 2 Migration • Section 3 Social protection program • Section 4 Equity and budget management in education and health

The community questionnaire used in India consists of the following sections: - MODULE 1 General Module • Section 1: General Community Characteristics • Section 2: Social Environment • Section 3: Access to Services • Section 4: Economy • Section 5; Local Prices - MODULE 2 Child-Specific Modules • Section 1: Educational Services (General) • Section 2: Child day care Services • Section 3: Educational Services (Preschool, Primary, Secondary) • Section 4: Health Services • Section 5: Child Protection Services

The community questionnaire used in Peru consists of the following sections: - MODULE 1 General Module • Section 1: General Community Characteristics • Section 2: Social Environment • Section 3: Access to Services • Section 4: Economy • Section 5: Local Prices - MODULE 2 Child-Specific Modules • Section 1: Educational Services (General) • Section 2: Child day care Services • Section 3: Educational Services (Preschool, Primary, Secondary) • Section 4: Health Services • Section 5: Child Protection Services

The community questionnaire used in Vietnam consists of the following sections: - MODULE 1 General Module • Section 1: General Community Characteristics • Section 2: Social Environment • Section 3: Access to Services • Section 4: Economy • Section 5: Local Prices • Section 6: Poverty Alleviation and Infrastructure Initiatives - MODULE 2 Child-Specific Module • Section 1: Educational Services (General and Country Specific) • Section 2: Child day care Services • Section 3: Educational Services (Preschool, Primary, Secondary) • Section 4: Health Services • Section 5: Child Protection Services

This data file includes the Gini coefficient calculated for different wealth welfare aggregates constructed for all Luxembourg Wealth Study (LWS) datasets in all waves (as of March 2022). It includes Gini coefficients calculated on: • Disposable Net Worth • Value of Principal residence • Financial AssetsThis project sought to renew the ESRC's invaluable financial support to LIS (formerly the Luxembourg Income Study) for a period of five more years. LIS is an independent, non-profit cross-national data archive and research institute located in Luxembourg. LIS relies on financial contributions from national science foundations, other research institutions and consortia, data-providing agencies, and supranational organisations to support data harmonisation and enable free and unlimited data access to researchers in the participating countries and to students world-wide. LIS' primary activity is to make harmonised household microdata available to researchers, thus enabling cross-national, interdisciplinary primary research into socio-economic outcomes and their determinants. Users of the Luxembourg Income Study Database and Luxembourg Wealth Study Database come from countries around the globe, including the UK. LIS has four goals: 1) to harmonise microdatasets from high- and middle-income countries that include data on income, wealth, employment, and demography; 2) to provide a secure method for researchers to query data that would otherwise be unavailable due to country-specific privacy restrictions; 3) to create and maintain a remote-execution system that sends research query results quickly back to users at off-site locations; and 4) to enable, facilitate, promote and conduct crossnational comparative research on the social and economic wellbeing of populations across countries. LIS contains the Luxembourg Income Study (LIS) Database, which includes income data, and the Luxembourg Wealth Study (LWS) Database, which focuses on wealth data. LIS currently includes microdata from 46 countries in Europe, the Americas, Africa, Asia and Australasia. LIS contains over 250 datasets, organised into eight time "waves," spanning the years 1968 to 2011. Since 2007, seventeen more countries have been added to LIS, including the BRICS countries (Brazil, Russia, India, China, South Africa), Japan, South Korea and a number of other Latin American countries. LWS contains 20 wealth datasets from 12 countries, including the UK, and covers the period 1994 to 2007. All told, LIS and LWS datasets together cover 86% of world GDP and 64% of world population. Users submit statistical queries to the microdatabases using a Java-based job submission interface or standard email. The databases are especially valuable for primary research in that they offer access to cross-national data at the micro-level - at the level of households and persons. Users are economists, sociologists, political scientists, and policy analysts, among others, and they employ a range of statistical approaches and methods. LIS also provides extensive documentation - metadata - for both LIS and LWS, concerning technical aspects of the survey data, the harmonisation process, and the social institutions of income and wealth provision in participating countries. In the next five years, for which support is sought, LIS will: - expand LIS, adding Waves IX (2013) and X (2016), and add new middle-income countries; - develop LWS, adding another wave of datasets to existing countries; acquire new wealth datasets for 14 more countries in cooperation with the European Central Bank (based on the Household Finance and Consumption Survey); - create a state-of-the-art metadata search and storage system; - maintain international standards in data security and data infrastructure systems; - provide high-quality harmonised household microdata to researchers around the world; - enable interdisciplinary cross-national social science research covering 45+ countries, including the UK; - aim to broaden its reach and impact in academic and non-academic circles through focused communications strategies and collaborations. All surveyed households and their members are included in our estimates of Gini and Atkinson coefficients, percentile ratios, and poverty lines. Poverty lines are calculated based on the total population. Those lines are then used to calculate poverty rates among subgroups (children and the elderly). Thus, when calculating poverty rates, the subgroups vary, but the poverty lines remain constant within any given dataset. The data file includes the Gini coefficient calculated for different wealth welfare aggregates constructed for all LWS datasets in all waves (as of March 2022).

Of the countries included, South Africa had the highest income inequality, with a Gini coefficient of 0.62. It was also the country with the highest inequality level worldwide. Of the OECD members, Costa Rica had the highest income inequality, whereas Slovakia had the lowest.

The raster dataset consists of a 500 m score grid for dairy processing industry facilities siting, produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location.

The analysis is based on proximity to cities, goat density, and poverty. The score is achieved by processing sub-model outputs that characterize production, logistical, and socioeconomic factors.

1. Supply - Goat distribution.
2. Proximity to cities - Transportation network (accessibility)
3. Poverty - Target areas with high poverty levels (Asset Wealth Index)

(Asset Wealth Index (AWI) is normalized and then inverted into the scale (100 to 0) where 100 corresponds to the highest poverty value and 0 to the lowest poverty value.

It consists of an arithmetic weighted sum of normalized grids (0 to 100): ( ”Sheep Density” * 0.4) + (“Major Cities Accessibility” * 0.4) + (”Asset Wealth” *0.2).

Data publication: 2024-02-16

Contact points:

Metadata Contact: FAO-Data

Resource Contact: Dariia Nesterenko

Data lineage:

FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets:

1. GLW Gridded Livestock of the World - Gridded Livestock of the World (GLW 3 and GLW 2). https://data.apps.fao.org/catalog/iso/47457ad7-ed00-4346-81e2-85aacd0e6d91
2. OpenStreetMap.
3. Altas AI - Asset Wealth Index 2022. https://docs.atlasai.co/economic%20well-being/asset_wealth/

Resource constraints:

Creative Commons Attribution-NonCommercial-ShareAlike 3.0 IGO (CC BY-NC- SA 3.0 IGO)

Online resources:

Zipped raster TIF file for Dairy Processing Location Score: Goat (Eswatini - ~ 500 m)

In 2024, the projected poverty rate based on the national poverty line in Morocco was at 6 percent. This was a decrease from the 2023 projection, which was 5.2 percent. Poverty levels in the country fluctuated over the years under review. This is likely related to the economic issues caused by the COVID-19 pandemic.

Comparative poverty levels

In the region of Northern Africa, Morocco had the lowest projected poverty headcount ratio among the countries in 2023. However, Morocco ranked among the leading 20 countries with the highest multidimensional poverty index score worldwide. According to a survey conducted in 2019, almost 60 percent of people in Morocco believed that education was the most effective poverty reduction strategy, followed by job creation and employment.

Growing inequality in Morocco

A 2019 survey showed that the majority of people in Morocco felt that the gap between the rich and the poor was getting worse. Morocco’s Gini coefficient, a common measure of income inequality, showed that the country had a relatively high income disparity, and this was forecasted to increase in the future. Furthermore, African countries have some of the highest Gini coefficient indexes worldwide.

Graph and download economic data for Income Gini Ratio for Households by Race of Householder, All Races (GINIALLRH) from 1967 to 2023 about gini, households, income, and USA.

Sampling Procedure Comment: Probability Sample: Stratified Sample

Of the G20 countries included, Brazil had the highest share of income being earned by the richest 20 percent of the population at 57.5 percent of total income in 2021. Mexico followed by 51.7 percent of the country's total income being earned by the wealthiest 20 percent. On the other hand, less than 40 percent of the income in France was earned by the richest 20 percent.

Percentage of people deprived in each indicator of the MPI by social groups in India.

Guyana was the South American country 20360the highest gross national income per capita, with 20,360 U.S. dollars per person in 2023. Uruguay ranked second, registering a GNI of 19,530 U.S. dollars per person, based on current prices. Gross national income (GNI) is the aggregated sum of the value added by residents in an economy, plus net taxes (minus subsidies) and net receipts of primary income from abroad. Which are the largest Latin American economies? Based on annual gross domestic product, which is the total amount of goods and services produced in a country per year, Brazil leads the regional ranking, followed by Mexico, Argentina, and Chile. Many Caribbean countries and territories hold the highest GDP per capita in this region, measurement that reflects how GDP would be divided if it was perfectly equally distributed among the population. GNI per capita is, however, a more exact calculation of wealth than GDP per capita, as it takes into consideration taxes paid and income receipts from abroad. How much inequality is there in Latin America? In many Latin American countries, more than half the total wealth created in their economies is held by the richest 20 percent of the population. When a small share of the population concentrates most of the wealth, millions of people don't have enough to make ends meet. For instance, in Brazil, about 5.32 percent of the population lives on less than 3.2 U.S. dollars per day.

In 2025, Luxembourg was the country with the highest gross domestic product per capita in the world. Of the 20 listed countries, 13 are in Europe and four are in Asia, alongside the U.S., Canada, and Australia. There are no African or Latin American countries among the top 20. Correlation with high living standards While GDP is a useful indicator for measuring the size or strength of an economy, GDP per capita is much more reflective of living standards. For example, when compared to life expectancy or indices such as the Human Development Index or the World Happiness Report, there is a strong overlap - 14 of the 20 countries on this list are also ranked among the 20 happiest countries in 2024, and all 20 have "very high" HDIs. Misleading metrics? GDP per capita figures, however, can be misleading, and to paint a fuller picture of a country's living standards then one must look at multiple metrics. GDP per capita figures can be skewed by inequalities in wealth distribution, and in countries such as those in the Middle East, a relatively large share of the population lives in poverty while a smaller number live affluent lifestyles.

From 2017 to 2021, the share of households living under the poverty line in Venezuela has been surpassing 90 percent. In addition, more than six out of every ten households (67.97 percent) lived in extreme poverty in 2021. The overall household poverty rate in Venezuela has registered a steady growth from 2014 to 2019, after having remained relatively stable, below 40 percent, since 2005. Although poverty is widespread among the population as a whole, some groups are more vulnerable than others. That is the case of younger generations and particularly children: 98.03 percent of Venezuelans aged 15 or younger lived in poverty in 2021. An economy in disarray Venezuela, the country with the largest oil reserves in the world and whose economy has been largely dependent on oil revenues for decades, was once one of the most prosperous countries in Latin America. Today, hyperinflation and an astronomic public debt are only some of the many pressing concerns that affect the domestic economy. The socio-economic consequences of the crisis As a result of the economic recession, more than half of the population in every state in Venezuela lives in extreme poverty. This issue is particularly noteworthy in the states of Amazonas, Monagas, and Falcón, where the extreme poverty rate hovers over 80 percent. Such alarming levels of poverty, together with persistent food shortages, provoked a rapid increase in undernourishment, which was estimated at 17.9 percent between 2020 and 2022. The combination of humanitarian crisis, political turmoil and economic havoc led to the Venezuelan refugee and migrant crisis. As of 2020, more than five million Venezuelans had fled their home country, with neighboring Colombia being the main country of destination.

In 2022, approximately 4.7 percent of the Mexican population were living on less than 3.20 U.S. dollars per day, a considerable decrease in comparison to the previous year. Furthermore, unemployment rate in this Latin American country during this period was at 3.2 percent. Poverty is considerably higher in the South In 2022, the three states with the highest poverty rate in the Aztec country were Chiapas, Guerrero, and Oaxaca, all in the southern region. In contrast, the top eight federal entities with the lowest were all in the North. The clear division is further accentuated by the Northern Border Free Zone, which encompasses 43 municipalities in the Mexico-U.S. border with higher minimum wages and lower taxes. Poverty in states such as Chiapas reaches over 67 percent, which means two out of three residents are under the poverty line and almost one out of three under extreme poverty conditions.
A country troubled by inequality Poverty and inequality are no news in Mexico. In the most recent data, around 80 percent of the total wealth of the country was concentrated in the top 10 percent of the population. Moreover, the bottom 50 percent had a negative share, meaning that half of the Mexican population had more debts than assets. But inequality does not only encompass wealth distribution, but Mexico also has a problem regarding gender inequality. The government has failed to achieve many of its goals to reduce the gap between genders.

In 2021, the poverty rate in France reached 14.5 percent. In recent years, poverty in France has been increasing, affecting both unemployed and working people. In fact, according to Insee, 9.2 percent of economically active persons had a living standard inferior to the poverty rate in 2021.

The increase of poverty in France

Poverty in France reached its highest rate in 2018. That year, almost 15 percent of the French population was living below the poverty line, which means that their income was less than 60 percent of the median income in the country. Despite a significant decrease between 2000 and 2004, when the rate went from 13.6 down to 12.6 percent, poverty has been rising in France in recent years. Studies have shown that the number of poor people increased in France, reaching approximately 9.3 million individuals in 2020, while 11,779 thousand people were at risk of poverty or social exclusion.

Poverty affecting youth and middle classes

Poverty seems to affect mainly younger generations. In 2016, 12.5 percent of French aged between 18 and 29 years old were considered poor. In comparison, only 2.2 percent of French aged 65 and 74 years old were in the same situation. Youth unemployment in France, one of the highest in Europe, might explain this phenomenon. However, the middle class is not spared from the rise of poverty either. In 2017, 52.4 percent of French middle-income households were having difficulties to make ends meet.