Countries from Natural Earth 50M scale data with a Human Development Index attribute for each of the following years: 1980, 1985, 1990, 1995, 2000, 2005, 2010, 2013, 2015, & 2017. The Human Development Index measures achievement in 3 areas of human development: long life, good education and income. Specifically, the index is computed using life expectancy at birth, Mean years of schooling, expected years of schooling, and gross national income (GNI) per capita (PPP $). The United Nations categorizes the HDI values into 4 groups. In 2013 these groups were defined by the following HDI values: Very High: 0.736 and higher High: 0.615 to 0.735 Medium: 0.494 to 0.614 Low: 0.493 and lower

In 2015 & 2017 these groups were defined by the following HDI values: Very High: 0.800 and higher High: 0.700 to 0.799 Medium: 0.550 to 0.699 Low: 0.549 and lower

Human Development Index attributes are from The World Bank: HDRO calculations based on data from UNDESA (2013a), Barro and Lee (2013), UNESCO Institute for Statistics (2013), UN Statistics Division(2014), World Bank (2014) and IMF (2014). 2015 & 2017 values source: HDRO calculations based on data from UNDESA (2017a), UNESCO Institute for Statistics (2018), United Nations Statistics Division (2018b), World Bank (2018b), Barro and Lee (2016) and IMF (2018).

Population data are from (1) United Nations Population Division. World Population Prospects, (2) United Nations Statistical Division. Population and Vital Statistics Report (various years), (3) Census reports and other statistical publications from national statistical offices, (4) Eurostat: Demographic Statistics, (5) Secretariat of the Pacific Community: Statistics and Demography Programme, and (6) U.S. Census Bureau: International Database.

India's performance on UNDP's Human Development Index (HDI) - score, rank, and comparison with global peers.

The human development index (HDI) score of Russia slightly increased in 2023, having reached *****. That was the highest observation since 1990. The HDI is a statistic that combines life-expectancy, education levels and GDP per capita. Countries with scores over 0.800 are considered to have very high levels of development, compared with countries that score lower. The HDI score of Russia declined between 1990 and 1995 before recovering from 2000 onwards.

Context

There's a story behind every dataset and here's your opportunity to share yours.

Content

There are two tables :- 1) World_Richest.csv - It includes the Forbes Top 950 richest people by Name, Age, Asset and Country. For 2015. 2) Human Development Index (HDI).csv - It the Global Human Development Index (1990-2015). From (http://hdr.undp.org/en/data).

Acknowledgements

Forbes - https://www.forbes.com/billionaires/list/ United Nations Development Programme - http://hdr.undp.org/en/data

Inspiration

I organised these datasets because I believe leaders come from the people. So I am looking at the success of individuals versus the success of a group (Country).

Lower Mekong countries HDI scores from the United Nations Human Development reports, 2015. Collated for an interactive visualization on the OD Mekong Social development page (https://opendevelopmentmekong.net/topics/social-development/).

In sub-Saharan Africa, a score of around 0.57 was achieved on the Human Development Index (HDI) in 2023. This represented a low level of human development. In 2018, the sub-region moved from being categorized as low human development to medium human development.

The Gender Inequality Index (GII), released by the UN Development Programme (UNDP), is an inequality index for 159 countries from 1995 to 2015.

Mean annual change in forest growing stock (Δ GS) in countries, 1990–2015, in relation to income level.

Context

In today's interconnected world, the issue of inequality in education stands as a stark reminder of the disparities that persist across countries and communities. While strides have been made to improve access to education, a significant proportion of children still lack the opportunity to learn, particularly in low-income and conflict-affected regions. Quality of education also diverges, with well-equipped schools in affluent areas contrasting with under-resourced institutions in marginalized settings. Gender inequality further compounds the problem, as cultural norms and economic factors often impede girls' education in certain societies. Tackling inequality in education isn't just a matter of fairness; it's a critical step towards building equitable societies and empowering individuals to contribute meaningfully to their own development and that of their nations.

Content

This dataset contains historical data covering a range of indicators pertaining to educational inequality on a global scale. The dataset's prominent components include: ISO3, Country, Human Development Groups, UNDP Developing Regions, HDI Rank (2021), and Inequality in Education spanning the years 2010 to 2021.

Dataset Glossary (Column-wise)

• ISO3 - ISO3 for the Country/Territory
• Country - Name of the Country/Territory
• Human Development Groups - Human Development Groups
• UNDP Developing Regions - UNDP Developing Regions
• HDI Rank (2021) - Human Development Index Rank for 2021
• Inequality in Education (2010) - Inequality in Education for 2010
• Inequality in Education (2011) - Inequality in Education for 2011
• Inequality in Education (2012) - Inequality in Education for 2012
• Inequality in Education (2013) - Inequality in Education for 2013
• Inequality in Education (2014) - Inequality in Education for 2014
• Inequality in Education (2015) - Inequality in Education for 2015
• Inequality in Education (2016) - Inequality in Education for 2016
• Inequality in Education (2017) - Inequality in Education for 2017
• Inequality in Education (2018) - Inequality in Education for 2018
• Inequality in Education (2019) - Inequality in Education for 2019
• Inequality in Education (2020) - Inequality in Education for 2020
• Inequality in Education (2021) - Inequality in Education for 2021

Data Dictionary

• UNDP Developing Regions:
  • SSA - Sub-Saharan Africa
  • LAC - Latin America and the Caribbean
  • EAP - East Asia and the Pacific
  • AS - Arab States
  • ECA - Europe and Central Asia
  • SA - South Asia

Structure of the Dataset

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Acknowledgement

This Dataset is created from Human Development Reports. This Dataset falls under the Creative Commons Attribution 3.0 IGO License. You can check the Terms of Use of this Data. If you want to learn more, visit the Website.

Cover Photo by: Image by storyset on Freepik

Thumbnail by: Educational Vectors by Vecteezy

Armenia Multidimensional Poverty Headcount Ratio: UNDP: % of total population data was reported at 0.200 % in 2015. Armenia Multidimensional Poverty Headcount Ratio: UNDP: % of total population data is updated yearly, averaging 0.200 % from Dec 2015 (Median) to 2015, with 1 observations. The data reached an all-time high of 0.200 % in 2015 and a record low of 0.200 % in 2015. Armenia 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 Armenia – Table AM.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/);;

Kazakhstan Multidimensional Poverty Headcount Ratio: UNDP: % of total population data was reported at 0.500 % in 2015. Kazakhstan Multidimensional Poverty Headcount Ratio: UNDP: % of total population data is updated yearly, averaging 0.500 % from Dec 2015 (Median) to 2015, with 1 observations. The data reached an all-time high of 0.500 % in 2015 and a record low of 0.500 % in 2015. Kazakhstan 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 Kazakhstan – Table KZ.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/);;

The Sustainable Development Goals (SDGs) adopted by the United Nations in 2015 represent the current perceptions of humans regarding understanding and monitoring development. Achieving all 17 goals simultaneously is unrealistic. Considering the interconnected nature of SDGs, identifying their critical dimensions, goals, indicators, and mutual relationships is necessary. In addition, with increasing reservations about the sustainability of SDGs, it is crucial to explore consistency across different dimensions to ensure policy coherence in maximizing synergies and minimizing trade-offs. Our study employed multiple factor analysis (MFA) and hierarchical clustering on principal components (HCPC) to investigate these issues and analyze the results based on the public value (PV) theory. The results indicated that the Human Development Index (HDI) and gross domestic product per capita (GDPP) constitute the first principal component (PC) and are determinants in differentiating country clusters. However, they contradict environmental indicators such as CO2 emissions per capita and ecological footprint gha per person (EFP) and have low synergy with the Happy Planet Index (HPI). Additionally, the relationships between income level, inequality, and environmental quality correspond to a combined Kuznets curve and an environmental Kuznets curve (EKC). Moreover, governance capacity has become increasingly crucial in sustainable development, particularly in the capability to prioritize different PVs in a timely and strategic manner. Finally, despite the novelty of EFP and HPI, they cannot reveal the entire development story. SDGs require embracing more such indicators to enrich the value bases of development and achieve a sustainable future.

The description of the variables included in the data set are explained at below: 1. The dataset covers 106 countries and the period between 2009 and 2020. 2. Economic growth: The four-year average growth rate of real GDP per capita (constant 2015 $) Source: The World Development Indicators 3. The Environmental Performance Index : The four-year average EPI index Source: Socioeconomic Data and Application Center (SEDAC) 4. Gross fixed capital formation : The four-year average gross fixed capital formation (% GDP) Source: The World Development Indicators 5. Tourism development: The four-year average of number of international tourist arrivals per active population (15+) Source: The World Development Indicators 6. Initial real GDP per capita: Natural Logarithmic form of the real GDP per capita at the beginning of each period (constant 2015 $) Source: The World Development Indicators 7. Fertility: Logarithmic form of total births per woman at the beginning of each period Source: The World Development Indicators 8. Life Expectancy: Initial logarithmic form of life expectancy at birth. Source: Human Development Reports (UNDP) 9. Government Expenditures : The average proportion of general government final consumption expenditure (% GDP )Source: The World Development Indicators 10. Trade Openness: Average sum of exports and imports (% GDP) Source: The World Development Indicators 11. Inflation : The average annual percentage change in the consumer price index during each period Source: The World Development Indicators 12. Mean Years of Schooling: The four-year average number of years of education received by people ages 25 and older Source: Human Development Reports (UNDP)

Afghanistan Multidimensional Poverty Headcount Ratio: UNDP: % of total population data was reported at 55.900 % in 2015. Afghanistan Multidimensional Poverty Headcount Ratio: UNDP: % of total population data is updated yearly, averaging 55.900 % from Dec 2015 (Median) to 2015, with 1 observations. The data reached an all-time high of 55.900 % in 2015 and a record low of 55.900 % in 2015. Afghanistan 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 Afghanistan – Table AF.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/);;

Multidimensional Poverty Headcount Ratio: UNDP: % of total population data was reported at 3.800 % in 2015. Multidimensional Poverty Headcount Ratio: UNDP: % of total population data is updated yearly, averaging 3.800 % from Dec 2015 (Median) to 2015, with 1 observations. The data reached an all-time high of 3.800 % in 2015 and a record low of 3.800 % in 2015. 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 Brazil – Table BR.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/);;

Over recent years, online government services have become increasingly common. In 2024, Denmark was ranked first with a near-perfect E-Government Development Index (EGDI) rating of ******. The EGDI assesses e-government development at a national level based on three components: the online service index, the telecommunication infrastructure index, and the human capital index. E-government development and the persisting digital divide According to the UN, e-government is a pivotal factor in advancing the implementation of the Sustainable Development Goals. Public services should be accessible to all, and e-government has to harness existing and new technologies to ensure that. There is a risk of a new digital divide, as low-income countries with insufficient infrastructure are lagging, leaving already vulnerable people even more at risk of not being able to gain any advantage from new technologies. Despite some investments and developmental gains, many countries are still unable to benefit from ICTs because of poor connectivity, high cost of access and lack of necessary skills. These factors have a detrimental effect on the further development of e-government in low EGDI-ranked regions such as Africa as the pace of technological progress intensifies. E-government services Transactional services are among the most common features offered by e-government websites worldwide. In 2018, it was found that *** countries enabled their citizens to submit income taxes via national websites. The majority of countries allow citizens to access downloadable forms, receive updates or access archived information about a wide range of sectors such as education, employment, environment, health, and social protection.

In 2022, the E-Government Development Index (EGDI) composite score of India was ****. In the same year, India ranked *** out of 193 countries. India slipped down from 100th place in the year 2020. The United Nations Department of Economic and Social Affairs has been publishing this survey report since 2001 biennially which includes all member states of the United Nations.

What is EGDI?

The widespread outreach of new communication technologies and the internet is compelling governments all over the world to build digital infrastructure and provide online access to public services. The EGDI is a composite indicator that consists of three indices namely the online service index (OSI), telecommunication infrastructure index (TII), and human capital index (HCI). The assessment is a relative measure of the e-governance performance of countries, rather than an absolute measure. Higher-income countries usually have a higher EGDI value as compared to lower-income countries.

India and e-governance 

According to the United Nations,despite being in the lower-income group, India is one of the countries with a fairly high level of human capital development (HCI) and online services provision (OSI). However, it is held back in terms of lower levels of infrastructure development (TII). The Indian government’s Digital campaign and its consequent products such as the UMANG e-governance platform, Accessible India campaign, AgriMarket app, MyGov platform, and many more are aiming to bridge the digital divide amongst the Indian population.

Between 2010 and 2023, Haiti's data on the degree of inequality in income distribution based on the Gini coefficient reached 41.1, same as the previous period. Although having one of the lowest human development indexes in Latin America, Haiti's Gini coefficient was deemed as one of the most equal countries in Latin America. The Gini coefficient measures the deviation of the distribution of income (or consumption) among individuals or households in a given country from a perfectly equal distribution. A value of 0 represents absolute equality, whereas 100 would be the highest possible degree of inequality.

Link to this report's codebookAt the halfway point, and aware of the remaining challenges to achieve the Sustainable Development Goals (SDGs), the Government of Benin asked the UN Sustainable Development Solutions Network (SDSN) to support it in the monitoring and evaluation of the 2030 Agenda. This initial report presents an evaluation of Benin’s current performance and trends on the SDGs, as well as an analysis of its policies to achieve them through SDSN’s “Six Transformations” framework (Sachs et al, 2019). This report serves as a baseline following the first issue of the SDG Eurobond by the Government of Benin in July 2021, which demonstrates their strong commitment to accelerate the implementation of the 2030 Agenda.The key findings of this initial baseline report are the following:Benin is halfway to achieving the SDGs with a score of 50.7 out of 100 across all 17 SDGs.Benin stands out from the rest of the Economic Community of West African States (ECOWAS) with progress on SDGs 2 (Zero hunger), 8 (Decent work and economic growth), 9 (Industry, Innovation and Infrastructure) and 14 (Life below Water) since 2015, for which the majority of countries in the sub-region are stagnating or even regressing.Compared to higher income countries, Benin performs relatively well on SDGs 12 (Responsible Consumption and Production) and 13 (Climate Action).There are persistent challenges to achieving the majority of the SDGs in the region. In particular, the trends for SDGs 4 (Quality Education), 10 (Reduced Inequalities) and 11 (Sustainable Cities and Communities) must be monitored and reversed.At the subnational level, the “leave no one behind” index covers the following four dimensions: inequalities in access to public services, extreme poverty and material deprivation, gender inequalities, and inequalities of income and wealth. The index reveals disparities between the regions of Benin.The data used in these analyses come from international sources to facilitate comparisons with other ECOWAS countries, as well as from national sources for the subnational index. However, as in other developing countries, missing data and delays in statistical production do not allow timely and accurate measurement of the progress and efforts made by Benin. Therefore, our analysis of the government’s efforts, in terms of public policies and investment, provides additional information to assess Benin’s performance.The 2022 Sustainable Development Report ranks Benin among the countries with “strong commitment” to the SDGs according to our global survey of government efforts. The analysis of the Government’s Action Program (PAG 2021-2026) through the framework of the “Six Transformations” shows that the PAG coherently targets Benin’s challenges in achieving the SDGs. The intensification of the government’s efforts, such as the insurance program for the reinforcement of human capital (ARCH) and its program to ensure universal access to drinking water in rural areas, will make it possible to accelerate the achievement of several SDGs and to “leave no one behind”, including in the most disadvantaged regions of the country.Similarly, the analysis of the institutional framework for the implementation of the 2030 Agenda in Benin has revealed the strong institutional capacities for the achievement of the SDGs. Since 2016, Benin has appropriated the SDGs to domesticate the goals and adopt a coherent development strategy. The country has a cross-cutting institutional apparatus and strong political will that could enable it to achieve significant results in the years to come.Furthermore, achieving the SDGs requires large-scale public and private investments. Benin must be supported in its resource mobilization to achieve the 2030 Agenda. The issuance of the SDG Eurobond by the Government of Benin in July 2021 constitutes an important turning point in the commitment and means mobilized for the implementation of the 2030 Agenda.