In 2022, Ethiopia scored almost *** 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 ****, meaning that the country had a lower human development. The country's categorization was low throughout the period under review.

Ethiopia: Human Development Index (0 - 1): The latest value from 2023 is 0.695 points, an increase from 0.61 points in 2022. In comparison, the world average is 0.744 points, based on data from 185 countries. Historically, the average for Ethiopia from 2000 to 2023 is 0.427 points. The minimum value, 0.283 points, was reached in 2000 while the maximum of 0.695 points was recorded in 2023.

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.

Ethiopia Multidimensional Poverty Headcount Ratio: UNDP: % of total population data was reported at 68.700 % in 2019. Ethiopia Multidimensional Poverty Headcount Ratio: UNDP: % of total population data is updated yearly, averaging 68.700 % from Dec 2019 (Median) to 2019, with 1 observations. The data reached an all-time high of 68.700 % in 2019 and a record low of 68.700 % in 2019. Ethiopia Multidimensional Poverty Headcount Ratio: UNDP: % of total population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ethiopia – Table ET.World Bank.WDI: Social: Poverty and Inequality. The multidimensional poverty headcount ratio (UNDP) is the percentage of a population living in poverty according to UNDPs multidimensional poverty index. The index includes three dimensions -- health, education, and living standards.;Alkire, S., Kanagaratnam, U., and Suppa, N. (2023). ‘The global Multidimensional Poverty Index (MPI) 2023 country results and methodological note’, OPHI MPI Methodological Note 55, Oxford Poverty and Human Development Initiative (OPHI), University of Oxford. (https://ophi.org.uk/mpi-methodological-note-55-2/);;

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.

In 2022, Ethiopia had an overall gender gap index score of ****, placing it 74th out of 156 countries globally. During the period under review, gender disparity diminished slightly from **** in 2016 to **** 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.

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