In 2023, Indonesia had a human development index score of *****. This reflects a slight increase in comparison to the previous year. The HDI score of Indonesia has been gradually increasing for the past decade.

(1) The Human Development Index (HDI) is compiled by the United Nations Development Programme (UNDP) to measure a country's comprehensive development in the areas of health, education, and economy according to the UNDP's calculation formula.(2) Explanation: (1) The HDI value ranges from 0 to 1, with higher values being better. (2) Due to our country's non-membership in the United Nations and its special international situation, the index is calculated by our department according to the UNDP formula using our country's data. The calculation of the comprehensive index for each year is mainly based on the data of various indicators adopted by the UNDP. (3) In order to have the same baseline for international comparison, the comprehensive index and rankings are not retroactively adjusted after being published.(3) Notes: (1) The old indicators included life expectancy at birth, adult literacy rate, gross enrollment ratio, and average annual income per person calculated by purchasing power parity. (2) The indicators were updated to include life expectancy at birth, mean years of schooling, expected years of schooling, and nominal gross national income (GNI) calculated by purchasing power parity. Starting in 2011, the GNI per capita was adjusted from nominal value to real value to exclude the impact of price changes. Additionally, the HDI calculation method has changed from arithmetic mean to geometric mean. (3) The calculation method for indicators in the education domain changed from geometric mean to simple average due to retrospective adjustments in the 2014 Human Development Report for the years 2005, 2008, and 2010-2012. Since 2016, the education domain has adopted data compiled by the Ministry of Education according to definitions from the United Nations Educational, Scientific and Cultural Organization (UNESCO) and the Organization for Economic Co-operation and Development (OECD).

Human Development Index by country for 2013. This is a filtered layer based on the "Human Development Index by country, 1980-2010 time-series" layer.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 Human Development: 0.736 and higher High Human Development: 0.615 to 0.735 Medium Human Development: 0.494 to 0.614 Low Human Development: 0.493 and lower

Country shapes from Natural Earth 50M scale data. 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).

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

Countries from Natural Earth 50M scale data with a Human Development Index attribute, repeated for each of the following years: 1980, 1985, 1990, 1995, 2000, 2005, 2010, & 2013, to enable time-series display using the YEAR attribute. 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

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).

Context

Data is ranking of countries as per HDI(Human Development Index). A measure of prosperity in Country

Content

Consists of HDI(male and female),Average schooling in years(Male and Female), Average Life Expectancy(male and female) and Per Capita Income (male and female ) of nearly 190 countries

Acknowledgements

Thanks to United Nation Development Program for the Data.

Inspiration

Wanted to understand which country leads in what like some countries having huge difference in per capita income between Males and Females

Past Research

Analyzing difference between Male, Female attributes and there impact on GDI

From Wealth to Wellbeing: Translating Resource Revenue into Sustainable Human Development

In sub-Saharan Africa, a score of 0.55 was achieved on the Human Development Index (HDI) in 2021. This represented a low level of human development. Throughout the periods under study, the sub-region remained within the index scores of 0.42 and 0.56, an indication of low human development.

Human Development Index (HDI) 2014

From Wealth to Wellbeing: Translating Resource Revenue into Sustainable Human Development Papua New Guinea

The dataset includes district wise Human Developement Index for 2011. The data is extracted from Nepal Human Developement Report (2014) by UNDP (http://www.npc.gov.np/new/uploadedFiles/allFiles/NHDR_Report_2014.pdf).

The Gross Domestic Product per capita (gross domestic product divided by mid-year population converted to international dollars, using purchasing power parity rates) has been identified as an important determinant of susceptibility and vulnerability by different authors and used in the Disaster Risk Index 2004 (Peduzzi et al. 2009, Schneiderbauer 2007, UNDP 2004) and is commonly used as an indicator for a country's economic development (e.g. Human Development Index). Despite some criticisms (Brooks et al. 2005) it is still considered useful to estimate a population's susceptibility to harm, as limited monetary resources are seen as an important factor of vulnerability. However, collection of data on economic variables, especially sub-national income levels, is problematic, due to various shortcomings in the data collection process. Additionally, the informal economy is often excluded from official statistics. Night time lights satellite imagery of NOAA grid provides an alternative means for measuring economic activity. NOAA scientists developed a model for creating a world map of estimated total (formal plus informal) economic activity. Regression models were developed to calibrate the sum of lights to official measures of economic activity at the sub-national level for some target Country and at the national level for other countries of the world, and subsequently regression coefficients were derived. Multiplying the regression coefficients with the sum of lights provided estimates of total economic activity, which were spatially distributed to generate a 30 arc-second map of total economic activity (see Ghosh, T., Powell, R., Elvidge, C. D., Baugh, K. E., Sutton, P. C., & Anderson, S. (2010).Shedding light on the global distribution of economic activity. The Open Geography Journal (3), 148-161). We adjusted the GDP to the total national GDPppp amount as recorded by IMF (International Monetary Fund) for 2010 and we divided it by the population layer from Worldpop Project. Further, we ran a focal statistics analysis to determine mean values within 10 cell (5 arc-minute, about 10 Km) of each grid cell. This had a smoothing effect and represents some of the extended influence of intense economic activity for local people. Finally we apply a mask to remove the area with population below 1 people per square Km.

This dataset has been produced in the framework of the "Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)" project, Work Package 4 (WP4). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata.

Data publication: 2014-06-01

Supplemental Information:

ClimAfrica was an international project funded by European Commission under the 7th Framework Programme (FP7) for the period 2010-2014. The ClimAfrica consortium was formed by 18 institutions, 9 from Europe, 8 from Africa, and the Food and Agriculture Organization of United Nations (FAO).

ClimAfrica was conceived to respond to the urgent international need for the most appropriate and up-to-date tools and methodologies to better understand and predict climate change, assess its impact on African ecosystems and population, and develop the correct adaptation strategies. Africa is probably the most vulnerable continent to climate change and climate variability and shows diverse range of agro-ecological and geographical features. Thus the impacts of climate change can be very high and can greatly differ across the continent, and even within countries.

The project focused on the following specific objectives:

1. Develop improved climate predictions on seasonal to decadal climatic scales, especially relevant to SSA;

2. Assess climate impacts in key sectors of SSA livelihood and economy, especially water resources and agriculture;

3. Evaluate the vulnerability of ecosystems and civil population to inter-annual variations and longer trends (10 years) in climate;

4. Suggest and analyse new suited adaptation strategies, focused on local needs;

5. Develop a new concept of 10 years monitoring and forecasting warning system, useful for food security, risk management and civil protection in SSA;

6. Analyse the economic impacts of climate change on agriculture and water resources in SSA and the cost-effectiveness of potential adaptation measures.

The work of ClimAfrica project was broken down into the following work packages (WPs) closely connected. All the activities described in WP1, WP2, WP3, WP4, WP5 consider the domain of the entire South Sahara Africa region. Only WP6 has a country specific (watershed) spatial scale where models validation and detailed processes analysis are carried out.

Contact points:

Metadata Contact: FAO-Data

Resource Contact: Selvaraju Ramasamy

Resource constraints:

copyright

Online resources:

GDP per capita

Project deliverable D4.1 - Scenarios of major production systems in Africa

Climafrica Website - Climate Change Predictions In Sub-Saharan Africa: Impacts And Adaptations

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.

Brunei: Human Development Index (0 - 1): The latest value from 2023 is 0.837 points, an increase from 0.823 points in 2022. In comparison, the world average is 0.744 points, based on data from 185 countries. Historically, the average for Brunei from 1980 to 2023 is 0.816 points. The minimum value, 0.74 points, was reached in 1980 while the maximum of 0.845 points was recorded in 2014.

Korea HDI: Apt: Sejong data was reported at 101.563 Score in Jun 2018. This records an increase from the previous number of 99.219 Score for May 2018. Korea HDI: Apt: Sejong data is updated monthly, averaging 98.649 Score from Dec 2012 (Median) to Jun 2018, with 67 observations. The data reached an all-time high of 130.734 Score in Jul 2017 and a record low of 80.769 Score in Sep 2014. Korea HDI: Apt: Sejong data remains active status in CEIC and is reported by Korea Appraisal Board. The data is categorized under Global Database’s Korea – Table KR.EB053: Housing Demand Index.

Context

It seeks to identify the influence of the human development index and the fatality rate of COVID-19.

Content

This dataframe contains the countries, number of cases and deaths until April 14, 2020. And the HDI - 2014 of the countries.

tx is rate of fatality (death/cases)

Acknowledgements

https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200413-sitrep-84-covid-19.pdf?sfvrsn=44f511ab_2 https://www.br.undp.org/content/brazil/pt/home/idh0/rankings/idh-global.html

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

Korea HDI: Row House: Chungnam data was reported at 90.357 Score in Jun 2018. This records a decrease from the previous number of 92.143 Score for May 2018. Korea HDI: Row House: Chungnam data is updated monthly, averaging 98.034 Score from Jul 2012 (Median) to Jun 2018, with 72 observations. The data reached an all-time high of 107.792 Score in Feb 2014 and a record low of 86.364 Score in Jul 2012. Korea HDI: Row House: Chungnam data remains active status in CEIC and is reported by Korea Appraisal Board. The data is categorized under Global Database’s Korea – Table KR.EB053: Housing Demand Index.

In 2021, Ghana scored 0.63 on the Human Development Index (HDI), which indicated a medium level of development. The country experienced a steady increase in the index from 2000 onwards. However, it remained between the medium and low indicators of human development.

HDI: Apt: Chungbuk data was reported at 79.310 Score in Oct 2018. This records a decrease from the previous number of 81.379 Score for Sep 2018. HDI: Apt: Chungbuk data is updated monthly, averaging 100.369 Score from Jul 2012 (Median) to Oct 2018, with 76 observations. The data reached an all-time high of 103.584 Score in Jun 2014 and a record low of 79.310 Score in Oct 2018. HDI: Apt: Chungbuk data remains active status in CEIC and is reported by Korea Appraisal Board. The data is categorized under Global Database’s Korea – Table KR.EB053: Housing Demand Index.

Korea HDI: Row House: Jeonbuk data was reported at 87.755 Score in Jun 2018. This records an increase from the previous number of 85.714 Score for May 2018. Korea HDI: Row House: Jeonbuk data is updated monthly, averaging 98.214 Score from Jul 2012 (Median) to Jun 2018, with 72 observations. The data reached an all-time high of 113.393 Score in Jan 2014 and a record low of 85.714 Score in May 2018. Korea HDI: Row House: Jeonbuk data remains active status in CEIC and is reported by Korea Appraisal Board. The data is categorized under Global Database’s Korea – Table KR.EB053: Housing Demand Index.