Iceland had the highest level of the Human Development Index (HDI) worldwide in 2023 after adjusting for inequality, with a value of ****. Its Nordic neighbors Norway and Denmark followed behind. Meanwhile, Iceland also topped the HDI not adjusted for inequality.

Europe and Central Asia were the regions with the highest Human Development Index (HDI) when adjusting for inequality. The lowest inequality-adjusted HDI was found in Sub-Saharan Africa, underlining the high prevalence of poverty in the region. Meanwhile, Iceland topped the HDI not adjusted for inequality.

South Sudan had the lowest level of the Human Development Index (HDI) worldwide in 2022 after adjusting for inequality, with a value of 0.22. Its neighbors Chad and the Central African Republic followed behind. Meanwhile, Switzerland topped the HDI not adjusted for inequality.

Dataset

This dataset was created by Sreedevi Sasidharan

Released under Database: Open Database, Contents: Database Contents

Contents

0,687 (Index, 1=the most developed) in 2010.

Note. Bootstrap results are based on 1,000 bootstrap samples. All regression models are based on logarithmic transformations of the MMR, IMR, ENMR, LNMR, and PNMR variables.Simple OLS Regression Models for MMR, IMR, ENMR, LNMR, and PNMR on HDI and IHDI.

Iceland had the highest inequality-adjusted education index score worldwide, amounting to **** out of one on the index. Germany followed with an index score of ****. The inequality-adjusted education index is the education index in the Human Development Index adjusted for inequality.

Note. Bootstrap results were based on 1,000 bootstrap samples. Bias corrected 95% confidence intervals are displayed in parentheses. All correlations were based on natural logarithmic transformations of the MMR, IMR, ENMR, LNMR, and PNMR variables.Bivariate Pearson Correlation Coefficients (N = 145).

Europe and Central Asia was the region with the highest Human Development Index (HDI) worldwide at ***. Meanwhile, the lowest HDI was found in Sub-Saharan Africa, underlining the high prevalence of poverty in the region. The difference between the regions was even stronger after adjusting for inequality.

(1) Gender Inequality Index (GII) is compiled by the United Nations Development Programme (UNDP) to measure gender inequality in the areas of reproductive health, empowerment, and labor market. Our country calculates the index based on the UNDP formula.(2) Explanation: (1) GII is used to measure the difference in development achievements between the two genders, with a value between 0 and 1, where a smaller value is better. (2) Due to our country's non-membership in the United Nations and unique international situation, the index is calculated by our department according to the UNDP formula, incorporating our country's data. The calculation of the composite index for each year mainly uses the data year of various indicators adopted by UNDP. (3) In order to have the same standard for international comparison, the composite index and rankings, once published, will not be retrospectively adjusted.(3) Notes: (1) In 2011, UNDP adjusted the formula for the maternal mortality ratio in the Human Development Report, resulting in a significant decrease in GII values for each country, and the data for retrospective adjustments will not be re-ranked. (2) The original indicator "Labor force participation rate for ages 15-64" has been changed to "Labor force participation rate for ages 15 and above"; UNDP has not released the global GII ranking for 2016.

Data derived from three sources: a) provincial-level data from the United Nations (UN) Human Achievement Index (HAI), b) a landmark study of the geospatial distribution of ethnic groups headed by Mahidol University (Office of the National Culture Commission, 2004) c) district-level intra-provincial income data, we present the country’s first dataset on ethnic inequalities on health, education, employment, income, housing and living environment, transport and communication, family and community life, and political and civil participation. To compute the ethnic HAI, we combine data on ethnic group population sizes within each province with the provincial-level UNDP HAI. A group’s final score on any given HAI component is averaged across each of the provinces in which an ethnic group lives, taking population size into consideration. Thus, a province contributes more or less to the group’s final score depending on its proportion of the group’s overall population. Before this cross-provincial weighting, a group’s provincial-level HAI score is first adjusted based on its location of residence. We know from a vast qualitative literature (and on-the-ground knowledge) that most minority groups (Tai-Kadai and non-Tai-Kadai) tend to live in remote, mountainous regions, hence the government category of ‘Hill Tribe’. We use information on the average income levels in these most remote districts compared to the provincial average income level to adjust the score. This adjustment further depends on the degree income correlates with a given HAI component. Thus, a group’s score may be adjusted up or down at the provincial level before the cross-provincial weighting depending on whether the correlation is positive or negative.

Purpose: To study the impact of sociodemographic and socioeconomic factors on the cataract burden in Caribbean small island developing states (SIDS) using disability-adjusted life-years (DALYs). Methods: National and regional age and sex specific cataract DALY numbers and rates from 1990 to 2016 for Caribbean SIDS, were extracted from the Global Burden of Disease Study 2016. The human development index (HDI), healthcare access and quality (HAQ) index, and the World Bank’s classification of economies were used as socioeconomic status indicators. The Gini coefficient, Atkinson, Theil and concentration indices were used to measure health inequality. Paired Wilcoxon signed rank test, Pearson correlation, and linear regression analyses were performed to evaluate the sociodemographic and socioeconomic factors associated with differences in cataract burden. Results: Men had higher age-standardized DALY rates than women (P