In 2022, South Africa scored **** points in the Human Development Index (HDI), which indicated a high level of development. Moreover, this was the highest score achieved in the Southern African region. Botswana followed closely behind, with an HDI of **** points. Conversely, Mozambique recorded the lowest in the region with **** points, which signifies low human development.

In 2021, South Africa scored **** in the Human Development Index (HDI), which indicated a high level of development. The country experienced a drop in the HDI score compared to the previous year, which was ****. However, an improvement was recorded from 2005 onwards. At that year, South Africa's score was ****, meaning that the country had a medium human development. The categorization changed from medium to high in 2013.

The average for 2023 based on 184 countries was 0.744 points. The highest value was in Iceland: 0.972 points and the lowest value was in South Africa: 0.388 points. The indicator is available from 1980 to 2023. Below is a chart for all countries where data are available.

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.

The average for 2023 based on 46 countries was 0.569 points. The highest value was in the Seychelles: 0.848 points and the lowest value was in South Africa: 0.388 points. The indicator is available from 1980 to 2023. Below is a chart for all countries where data are available.

0.72 (score) in 2020. A composite index measuring average achievement in three basic dimensions of human development—a long and healthy life, knowledge and a decent standard of living

In 2022, Mauritius and the Seychelles scored just over *** points on the Human Development Index (HDI), which indicated a very high level of development. Moreover, this was the highest score achieved in the East African region. Kenya followed, with an HDI of *** points. Conversely, Somalia and South Sudan recorded the lowest in the region with **** points, which signifies low human development.

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'.

Context

The Gender Development Index (GDI) is a composite measure designed to assess gender disparities and inequalities in a society by considering factors related to human development. It is an extension of the Human Development Index (HDI) and focuses on three key dimensions: health, education, and income. In the GDI, these dimensions are assessed separately for males and females, allowing for a comparison of gender-based development gaps. Health indicators typically include life expectancy at birth for both genders. Education indicators encompass literacy rates and enrollment in primary, secondary, and tertiary education for both males and females. The income component typically examines income levels and workforce participation for both genders.

Content

This dataset provides comprehensive historical data on gender development indicators at a global level. It includes essential columns such as ISO3 (the ISO3 code for each country/territory), Country (the name of the country or territory), Continent (the continent where the country is located), Hemisphere (the hemisphere in which the country is situated), Human Development Groups, UNDP Developing Regions, HDI Rank (2021) representing the Human Development Index Rank for the year 2021, and Gender Development Index spanning from 1990 to 2021.

Dataset Glossary (Column-wise)

• ISO3 - ISO3 for the Country/Territory
• Country - Name of the Country/Territory
• Continent - Name of the Continent
• Hemisphere - Name of the Hemisphere
• Human Development Groups - Human Development Groups
• UNDP Developing Regions - UNDP Developing Regions
• HDI Rank (2021) - Human Development Index Rank for 2021
• Gender Development Index from 1990 to 2021 - Gender Development Index from 1990 to 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

https://i.imgur.com/NI4UY57.png" alt="">

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: Freepik

Thumbnail by: Freepik

******* had the highest level of the Human Development Index (HDI) worldwide in 2023 with a value of *****. With a score of ****, ****** followed closely behind *********** and had the second-highest level of human development in that year. The rise of the Asian tigers In the decades after the Cold War, the four so-called Asian tigers, South Korea, Singapore, Taiwan, and Hong Kong (now a Special Administrative Region of China) experienced rapid economic growth and increasing human development. At number eight and number 13 of the HDI, respectively, *********************** are the only Asian locations within the top-15 highest HDI scores. Both locations have experienced tremendous economic growth since the 1980’s and 1990’s. In 1980, the per capita GDP of Hong Kong was ***** U.S. dollars, increasing throughout the decades until reaching ****** in 2023, which is expected to continue to increase in the future. Meanwhile, in 1989, Singapore had a GDP of nearly ** billion U.S. dollars, which has risen to nearly *** billion U.S. dollars today and is also expected to keep increasing. Growth of the UAE The United Arab Emirates (UAE) is the only Middle Eastern country besides Israel within the highest ranking HDI scores globally. Within the Middle East and North Africa (MENA) region, the UAE has the third-largest GDP behind Saudi Arabia and Israel, reaching nearly *** billion U.S. dollars by 2022. Per capita, the UAE GDP was around ****** U.S. dollars in 1989, and has nearly doubled to ****** U.S. dollars by 2021. Moreover, this is expected to reach over ****** U.S. dollars by 2029. On top of being a major oil producer, the UAE has become a hub for finance and business and attracts millions of tourists annually.

The global Multidimensional Poverty Index provides the only comprehensive measure available for non-income poverty, which has become a critical underpinning of the SDGs. 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 acute 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. Critically, the MPI comprises variables that are already reported under the Demographic Health Surveys (DHS), the Multi-Indicator Cluster Surveys (MICS) and in some cases, national surveys.

The subnational multidimensional poverty data from the data tables are published by the Oxford Poverty and Human Development Initiative (OPHI), University of Oxford. For the details of the global MPI methodology, please see the latest Methodological Notes found here.

South Sudan and Somalia had the ****** levels of human development based on the Human Development Index (HDI). Many of the countries at the bottom of the list are located in Sub-Saharan Africa, underlining the prevalence of poverty and low levels of education in the region. Meanwhile, Switzerland had the ******* HDI worldwide.

Spearman rank correlation between alternatives for indicator standardization and aggregation at district level.

South Sudan had the lowest level of the Human Development Index (HDI) worldwide in 2023 after adjusting for inequality, with a value of ****. Its nearby countries, Somalia and the Central African Republic, followed behind. Meanwhile, Iceland topped the HDI not adjusted for inequality.

The continuum of care for maternal health index, sub- indices and indicators for North West Province, South Africa, in the period 2013–2017.

Afrique du Sud: Human Development Index (0 - 1): Pour cet indicateur, The United Nations fournit des données pour la Afrique du Sud de 1980 à 2023. La valeur moyenne pour Afrique du Sud pendant cette période était de 0.638 points avec un minimum de 0.381 points en 2022 et un maximum de 0.713 points en 2021.

Abstract

This project commissioned by the KwaZulu-Natal Provincial Government was designed to obtain baseline data on subjective and objective development indicators. The project comprised a household survey conducted during November and December 1996. The complete survery covered at least 6 500 households acress the province of KwaZulu-Natal. It followed a pilot study of perceptions of development conducted among 678 adults in October 1995. As one of the most comprehensive contributions on development indicators in the history of South Africa, it is the first large survey covering the usual "hard" indicators - such as service delivery levels - and peoples' comments and perceptions of these services and of their governments' development programmes and priorities. The study/project was motivated by the need to establish an information database for the preparation and monitoring of the province's RDP business and development plans, to synthesise subjectively articulated (bottom-up) and objectively defined (top-down) approaches to the determination of needs, to modify and improve on the usefulness of the Human Development Index (HDI),to provide an opportunity for research capacity building among civil servants and thereby providing a means to effect good governance practices and, to provide a basis for the development of objective matrices, objectives-by-time-scales and, a semi-rational budgeting and planning tool.

Geographic coverage

KwaZulu-Natal, South Africa.

Analysis unit

Two persons/respondents per household.

Universe

All households within the KwaZulu-Natal province

Sampling procedure

The survey covered the 66 magisterial districts of KwaZulu-Natal but the two Hlabisa districts and the two Ubombo districts were each combined into single districts for the purpose of the study, thereby creating 64 “study domains”. Stratified sampling by study domain and socio-economic category was conducted. The Enumerator Area (EA), as the smallest countable area available in the 1991 census dataset, was used as the smallest spatial unit for the selection of the households. Households were clustered at the Enumerator Area (EA) level, i.e. instead of each household being sampled separately, the group of households was sampled for each visited EA. Where two or more households were found at the same visiting point, the interviewer applied simple random selection procedures to select the household at which the interview would take place.

Mode of data collection

Face-to-face [f2f]

Exploratory factor analysis of the items of the continuum of care service delivery framework.

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

https://i.imgur.com/qX5cmUX.png" alt="">

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

BackgroundDespite rising incidence and mortality rates in Africa, cancer has been given low priority in the research field and in healthcare services. Indeed, 57% of all new cancer cases around the world occur in low income countries exacerbated by lack of awareness, lack of preventive strategies, and increased life expectancies. Despite recent efforts devoted to cancer epidemiology, statistics on cancer rates in Africa are often dispersed across different registries. In this study our goal included identifying the most promising prevention and treatment approaches available in Africa. To do this, we collated and analyzed the incidence and fatality rates for the 10 most common and fatal cancers in 56 African countries grouped into 5 different regions (North, West, East, Central and South) over 16-years (2002–2018). We examined temporal and regional trends by investigating the most important risk factors associated to each cancer type. Data were analyzed by cancer type, African region, gender, measures of socioeconomic status and the availability of medical devices.ResultsWe observed that Northern and Southern Africa were most similar in their cancer incidences and fatality rates compared to other African regions. The most prevalent cancers are breast, bladder and liver cancers in Northern Africa; prostate, lung and colorectal cancers in Southern Africa; and esophageal and cervical cancer in East Africa. In Southern Africa, fatality rates from prostate cancer and cervical cancer have increased. In addition, these three cancers are less fatal in Northern and Southern Africa compared to other regions, which correlates with the Human Development Index and the availability of medical devices. With the exception of thyroid cancer, all other cancers have higher incidences in males than females.ConclusionOur results show that the African continent suffers from a shortage of medical equipment, research resources and epidemiological expertise. While recognizing that risk factors are interconnected, we focused on risk factors more or less specific to each cancer type. This helps identify specific preventive and therapeutic options in Africa. We see a need for implementing more accurate preventive strategies to tackle this disease as many cases are likely preventable. Opportunities exist for vaccination programs for cervical and liver cancer, genetic testing and use of new targeted therapies for breast and prostate cancer, and positive changes in lifestyle for lung, colorectal and bladder cancers. Such recommendations should be tailored for the different African regions depending on their disease profiles and specific needs.