In 2022, Ethiopia scored almost 0.5 in the Human Development Index (HDI), which indicated a low level of development. The country experienced no change in the HDI score since the 2019. However, an improvement was recorded from 2000 onwards. That year, Ethiopia's score was 0.29, meaning that the country had a lower human development. The country's categorization was low throughout the period under review.

The aim of the Human Development Report is to stimulate global, regional and national policy-relevant discussions on issues pertinent to human development. Accordingly, the data in the Report require the highest standards of data quality, consistency, international comparability and transparency. The Human Development Report Office (HDRO) fully subscribes to the Principles governing international statistical activities.

The HDI was created to emphasize that people and their capabilities should be the ultimate criteria for assessing the development of a country, not economic growth alone. The HDI can also be used to question national policy choices, asking how two countries with the same level of GNI per capita can end up with different human development outcomes. These contrasts can stimulate debate about government policy priorities. The Human Development Index (HDI) is a summary measure of average achievement in key dimensions of human development: a long and healthy life, being knowledgeable and have a decent standard of living. The HDI is the geometric mean of normalized indices for each of the three dimensions.

The 2019 Global Multidimensional Poverty Index (MPI) data shed light on the number of people experiencing poverty at regional, national and subnational levels, and reveal inequalities across countries and among the poor themselves.Jointly developed by the United Nations Development Programme (UNDP) and the Oxford Poverty and Human Development Initiative (OPHI) at the University of Oxford, the 2019 global MPI offers data for 101 countries, covering 76 percent of the global population. The MPI provides a comprehensive and in-depth picture of global poverty – in all its dimensions – and monitors progress towards Sustainable Development Goal (SDG) 1 – to end poverty in all its forms. It also provides policymakers with the data to respond to the call of Target 1.2, which is to ‘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 definition'.

Compared to other African countries, Seychelles scored the highest in the Human Development Index (HDI) in 2022. The country also ranked 67th globally, as one of the countries with a very high human development. This was followed by Mauritius, Libya, Egypt, and Tunisia, with scores ranging from 0.80 to 0.73 points. On the other hand, Central African Republic, South Sudan, and Somalia were among the countries in the region with the lowest index scores, indicating a low level of human development.

Explore The Human Capital Report dataset for insights into Human Capital Index, Development, and World Rankings. Find data on Probability of Survival to Age 5, Expected Years of School, Harmonized Test Scores, and more.

Low income, Upper middle income, Lower middle income, High income, Human Capital Index (Lower Bound), Human Capital Index, Human Capital Index (Upper Bound), Probability of Survival to Age 5, Expected Years of School, Harmonized Test Scores, Learning-Adjusted Years of School, Fraction of Children Under 5 Not Stunted, Adult Survival Rate, Development, Human Capital, World Rankings

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Last year edition of the World Economic Forum Human Capital Report explored the factors contributing to the development of an educated, productive and healthy workforce. This year edition deepens the analysis by focusing on a number of key issues that can support better design of education policy and future workforce planning.

In 2022, Ethiopia had an overall gender gap index score of 0.71, placing it 74th out of 156 countries globally. During the period under review, gender disparity diminished slightly from 0.66 in 2016 to 0.71 in 2020, before slightly widening again in 2021. The index measures the discrepancy between genders in four different areas, namely economic participation and opportunity, educational attainment, health and survival, and political empowerment.

The index provides the only comprehensive measure available for non-income poverty, which has become a critical underpinning of the SDGs. Critically the MPI comprises variables that are already reported under the Demographic Health Surveys (DHS) and Multi-Indicator Cluster Surveys (MICS) The resources subnational multidimensional poverty data from the data tables published by the Oxford Poverty and Human Development Initiative (OPHI), University of Oxford. The global Multidimensional Poverty Index (MPI) measures multidimensional poverty in over 100 developing countries, using internationally comparable datasets and is updated annually. The measure captures the severe deprivations that each person faces at the same time using information from 10 indicators, which are grouped into three equally weighted dimensions: health, education, and living standards. The global MPI methodology is detailed in Alkire, Kanagaratnam & Suppa (2023)

As of 2020, the median age stood at 19.5 years in Ethiopia, which is an increase of nearly a year compared to the previous four years. Despite the increment, the median age remained low, indicating a high fertility rate among Ethiopian population. Looking at the population from a different perspective, the human development index scored countries based on health and living standards, in which Ethiopia registered a steady but low level of development in 2019. The country experienced a slight increase in the HDI score since the previous year, which was 0.48. Other measures such as the gender gap index and the economic freedom give further insights into Ethiopia’s population.

Gender gap index score

The overall gender gap index score of Ethiopia amounted to 0.69, ranking 97th out of 156 listed countries globally in 2021. It indicates the discrepancy between genders in four different areas: economic participation and opportunity, educational attainment, health and survival, and political empowerment. Categorizing the index score by industry, Ethiopia scored low (0.38) in political empowerment in 2021, indicating a low share of women in politics. Health and survival, on the other hand, had a more equal score of 0.97.

Economic freedom in Ethiopia

 Concerning the economic freedom of Ethiopia, which is an index based on 12 categories ranging from property rights to financial possibilities, the country scored 53.6 in 2021. This was slightly lower compared to Africa’s average (55.1). However, Ethiopia registered a steady increase from 2013 onwards, indicating slight improvements in the economic freedom of the people living there.

Abstract

The authors combine data from 84 Demographic and Health Surveys from 46 countries to analyze trends and socioeconomic differences in adult mortality, calculating mortality based on the sibling mortality reports collected from female respondents aged 15-49.

The analysis yields four main findings. First, adult mortality is different from child mortality: while under-5 mortality shows a definite improving trend over time, adult mortality does not, especially in Sub-Saharan Africa. The second main finding is the increase in adult mortality in Sub-Saharan African countries. The increase is dramatic among those most affected by the HIV/AIDS pandemic. Mortality rates in the highest HIV-prevalence countries of southern Africa exceed those in countries that experienced episodes of civil war. Third, even in Sub-Saharan countries where HIV-prevalence is not as high, mortality rates appear to be at best stagnating, and even increasing in several cases. Finally, the main socioeconomic dimension along which mortality appears to differ in the aggregate is gender. Adult mortality rates in Sub-Saharan Africa have risen substantially higher for men than for women?especially so in the high HIV-prevalence countries. On the whole, the data do not show large gaps by urban/rural residence or by school attainment.

This paper is a product of the Human Development and Public Services Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org.

Geographic coverage

We derive estimates of adult mortality from an analysis of Demographic and Health Survey (DHS) data from 46 countries, 33 of which are from Sub-Saharan Africa and 13 of which are from countries in other regions (Annex Table). Several of the countries have been surveyed more than once and we base our estimates on the total of 84 surveys that have been carried out (59 in Sub-Saharan Africa, 25 elsewhere).

The countries covered by DHS in Sub-Saharan Africa represent almost 90 percent of the region's population. Outside of Sub-Saharan Africa the DHS surveys we use cover a far smaller share of the population-even if this is restricted to countries whose GDP per capita never exceeds $10,000: overall about 14 percent of the population is covered by these countries, although this increases to 29 percent if China and India are excluded (countries for which we cannot calculate adult mortality using the DHS). It is therefore important to keep in mind that the sample of non-Sub-Saharan African countries we have cannot be thought of as "representative" of the rest of the world, or even the rest of the developing world.

Analysis unit

Country

Kind of data

Sample survey data [ssd]

Mode of data collection

Face-to-face [f2f]

Cleaning operations

In the course of carrying out this study, the authors created two databases of adult mortality estimates based on the original DHS datasets, both of which are publicly available for analysts who wish to carry out their own analysis of the data.

The naming conventions for the adult mortality-related are as follows. Variables are named:

GGG_MC_AAAA

GGG refers to the population subgroup. The values it can take, and the corresponding definitions are in the following table:

All - All Fem - Female Mal - Male Rur - Rural Urb - Urban Rurm - Rural/Male Urbm - Urban/Male Rurf - Rural/Female Urbf - Urban/Female Noed - No education Pri - Some or completed primary only Sec - At least some secondary education Noedm - No education/Male Prim - Some or completed primary only/Male Secm - At least some secondary education/Male Noedf - No education/Female Prif - Some or completed primary only/Female Secf - At least some secondary education/Female Rch - Rural as child Uch - Urban as child Rchm - Rural as child/Male Uchm - Urban as child/Male Rchf - Rural as child/Female Uchf - Urban as child/Female Edltp - Less than primary schooling Edpom - Primary or more schooling Edltpm - Less than primary schooling/Male Edpomm - Primary or more schooling/Male Edltpf - Less than primary schooling/Female Edpomf - Primary or more schooling/Female Edltpu - Less than primary schooling/Urban Edpomu - Primary or more schooling/Urban Edltpr - Less than primary schooling/Rural Edpomr - Primary or more schooling/Rural Edltpmu - Less than primary schooling/Male/Urban Edpommu - Primary or more schooling/Male/Urban Edltpmr - Less than primary schooling/Male/Rural Edpommr - Primary or more schooling/Male/Rural Edltpfu - Less than primary schooling/Female/Urban Edpomfu - Primary or more schooling/Female/Urban Edltpfr - Less than primary schooling/Female/Rural Edpomfr - Primary or more schooling/Female/Rural

M refers to whether the variable is the number of observations used to calculate the estimate (in which case M takes on the value "n") or whether it is a mortality estimate (in which case M takes on the value "m").

C refers to whether the variable is for the unadjusted mortality rate calculation (in which case C takes on the value "u") or whether it adjusts for the number of surviving female siblings (in which case C takes on the value "a").

AAAA refers to the age group that the mortality estimate is calculated for. It takes on the values: 1554 - Ages 15-54 1524 - Ages 15-24 2534 - Ages 25-34 3544 - Ages 35-44 4554 - Ages 45-54

Other variables that are in the databases are:

period - Period for which mortality rate is calculated (takes on the values 1975-79, 1980-84 … 2000-04) svycountry - Name of country for DHS countries ccode3 - Country code u5mr - Under-5 mortality (from World Development Indicators) cname - Country name gdppc - GDP per capita (constant 2000 US$) (from World Development Indicators) gdppcppp - GDP per capita PPP (constant 2005 intl $) (from World Development Indicators) pop - Population (from World Development Indicators) hivprev2001 - HIV prevalence in 2001 (from UNAIDS 2010) region - Region

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.