The World Development Indicators (WDI) is the primary World Bank collection of development indicators, compiled from officially-recognized international sources. It presents the most current and accurate global development data available, and includes national, regional and global estimates.

For further details, please refer to https://datatopics.worldbank.org/world-development-indicators/

This dataset provides a comprehensive overview of key research and development indicators across various countries from 2016 to 2022. The indicators included are:

1. Researchers per Million Inhabitants (Full-Time Equivalent - FTE): This metric offers a snapshot of the number of full-time equivalent researchers per million inhabitants in each country. It is an essential indicator for understanding the density of research professionals within a nation.

2. Gross Domestic Expenditure on R&D (GERD) as a Percentage of GDP: This metric reflects the total expenditure on research and development as a proportion of the Gross Domestic Product (GDP). It is a crucial indicator of a country's investment in R&D relative to its economic size.

The dataset encompasses a diverse set of countries, highlighting variations in research infrastructure and investment levels globally. Each entry includes the country name, the year of the data, the value of the indicator, and any relevant flags or notes on the data (e.g., national estimates).

Columns: - Indicator: The type of R&D indicator (e.g., Researchers per million inhabitants or GERD as a percentage of GDP). - Country Code: The ISO 3166-1 alpha-3 code representing the country. - Country: The name of the country. - Year: The year the data was recorded. - Value: The value of the respective R&D indicator. - Flag Codes: Any additional codes indicating special notes about the data. - Flags: Descriptions of the flag codes, such as national estimates or other relevant annotations.

This dataset is ideal for researchers, policymakers, and analysts interested in understanding and comparing the R&D landscape across different nations. It can also be used to identify trends, gaps, and opportunities in global research and development efforts.

This data file is about world development indicators from world bank database.

This dataset combines economic and development indicators from two key sources:

1. World Bank Economic Indicators (1960–2018) Covers various economic performance metrics for countries worldwide, including:

   • Electricity consumption
   • GDP per capita
   • Population statistics
   • Life expectancy
   • And many other key development indicators

2. United Nations Human Development Index (HDI) Data (1990–2021) Supplementary data tracking human development, environmental impact, and inequality through composite metrics such as:

   • HDI score (combining life expectancy, GDP per capita, education)
   • Inequality indices
   • Environmental measures

Potential Analyses

• Identify countries with the highest growth in population and GDP and analyze overlaps.
• Explore which regions have experienced the most significant HDI improvements in the 21st century.
• Analyze factors most strongly correlated with life expectancy.
• Differentiate economic and social indicators between “High Income” and “Low Income” countries.

Source

Data sourced from The World Bank and United Nations (UN). Licensed under Public Domain.

The World Development Indicators (WDI) is the primary World Bank collection of development indicators, compiled from officially-recognized international sources. It presents the most current and accurate global development data available, and includes national, regional and global estimates.

The Human Development Indicators (HDI) is a summary measure for assessing long-term progress in three basic dimensions of human development: a long and healthy life, access to knowledge and a decent standard of living. The CSV file includes statistical measures for 142 indicators in Vietnam. The pdf file presents information on the country coverage and methodology of the Statistical Update and information about key indicators of human development including the Human Development Index (HDI), the Inequality-adjusted Human Development Index (IHDI), the Gender Development Index (GDI), the Gender Inequality Index (GII) and a section with five dashboards.

The World Development Indicators is a compilation of relevant, high-quality, and internationally comparable statistics about global development and the fight against poverty. The database contains 1,400 time series indicators for 217 economies and more than 40 country groups, with data for many indicators going back more than 50 years.

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'.

Indicators and measures which provide an overview of progress toward a sustainable economy, society and environment. Published alongside the headline and supplementary indicators are assessments of change, both short and long term.

Find more Pacific data on PDH.stat.

Severely materially or socially deprived persons have living conditions severely constrained by a lack of resources, they experience at least 7 out of 13 following deprivations items: cannot afford i) to pay rent or utility bills, ii) keep home adequately warm, iii) face unexpected expenses, iv) eat meat, fish or a protein equivalent every second day, v) a week holiday away from home, vi) have access to a car/van for personal use; vii) replace worn out furniture; viii) replace worn-out clothes with some new ones; ix) have two pairs of properly fitting shoes; x) spend a small amount of money each week on him/herself (“pocket money”); xi) have regular leisure activities; xii) get together with friends/family for a drink/meal at least once a month; and xiii) have an internet connection. The indicator is based on the EU-SILC (statistics on income, social inclusion and living conditions).

Context

This dataset has been created to be of assistance to those making attempts to analyze the impact of COVID-19 on countries across the globe(Uncover COVID-19 Challenge) and answer relevant and pressing questions such as - How do World Development Indicators (WDI) affect the ability of a country to respond to the COVID-19 pandemic? - Do the population demographics of a given country have any effect on the impact of the virus in the country? - Are certain countries more vulnerable to the pandemic?

It would be great to see more questions being asked from this data and better insights taking shape with regards to how COVID-19 affects the world.

Content

The data files loaded into this dataset have all been procured from the Human Data Exchange(HDX) website. The link has 30+ data files, of which I have picked up 16 files which I felt was most relevant.

The data is organized as the following data structure.

• There is the Root Directory which is the dataset itself
• Inside this, there are 16 data files (.xls). Each file represents a given indicator.
• Each .xls file has 3 sheets - data, metadata-countries and metadata-indicators
• data : Contains the data about the indicators, country-wise and year-wise
• metadata-countries : Metadata about countries
• metadata-indicators : Information about the indicators itself

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1427648%2Ffcbdbdb52d410e35051dbb27e4e9580a%2Fdirectory-structure-covid-19-wdi-dataset.png?generation=1586055908831928&alt=media" alt="">

Acknowledgements

This dataset has been completely accessed and referenced from Human Data Exchange(HDX) website.

Credits to Image : Photo by Martin Sanchez on Unsplash

Inspiration

I hope this dataset will help fellow kagglers attempting to analyze the effect of COVID-19 on countries.

Labels: "Status" (Development Status: Developing or Developed)

Features: 1. Country: The name of the country. 2. Year: The year of data recording. 3. Life Expectancy: The average number of years a newborn, person at different age ranges, or the entire population is expected to live 4. Adult Mortality: Probability of dying between 15 and 60 years per 1000 population. 5. Infant Deaths: Number of infant deaths per 1000 live births. 6. Alcohol: Alcohol consumption per capita (in liters of pure alcohol). 7. Percentage Expenditure: Expenditure on health as a percentage of total government spending or GDP. 8. Hepatitis B: Hepatitis B immunization coverage among 1-year-olds (percentage). 9. Measles: Measles immunization coverage among 1-year-olds (percentage).

The indicators highlight issues within the priority areas of Sustainable Consumption and Production, Natural Resources, and Sustainable Communities.

Source agency: Environment, Food and Rural Affairs Designation: National Statistics Language: English Alternative title: Regional Sustainable Development Indicators

The Sustainable Development Indicators are now being published by the ONS and is available here: http://www.ons.gov.uk/ons/rel/wellbeing/sustainable-development-indicators/july-2014/index.html

Bangladesh Economic Growth and Development Indicators (1980-2019)

Dataset Overview

This dataset provides a comprehensive record of economic indicators from 1980 to 2019, covering key metrics such as GDP, GDP per capita, GDP growth, inflation rate, unemployment rate, government debt, total investment, remittance inflows, and foreign direct investment (FDI) inflows. The data can be valuable for economic analysis, forecasting, and exploring trends in economic development over the years.

Context

Understanding long-term economic trends is crucial for policymakers, economists, and researchers. This dataset allows users to analyze macroeconomic growth patterns, the impact of inflation, employment trends, and investment dynamics across four decades.

Content

The dataset contains the following columns:

• Year: The year of observation (1980–2019).
• GDP (Billion USD): The total Gross Domestic Product (GDP) in billion U.S. dollars.
• GDP per capita (USD): The GDP divided by the population, representing the average economic output per person.
• GDP growth (%): The annual percentage change in GDP.
• Inflation rate (%): The annual inflation rate based on consumer price changes.
• Unemployment rate (%): The percentage of the labor force that is unemployed.
• Government debt (% of GDP): The total government debt as a percentage of GDP.
• Total Investment (% of GDP): The share of GDP allocated to total investment.
• Remittance (Billion USD): The total amount of remittances received from abroad in billion USD.
• FDI Inflows (Billion USD): The total Foreign Direct Investment (FDI) inflows in billion USD.

Potential Uses

• Economic Forecasting: Analyze past trends to predict future economic growth.
• Macroeconomic Analysis: Study the relationship between GDP growth, inflation, and government debt.
• Investment Research: Assess how investment levels correlate with GDP growth.
• Policy Insights: Understand economic cycles and evaluate policy effectiveness.
• Machine Learning: Train models to forecast economic indicators.

Acknowledgment

This dataset is compiled for research and educational purposes. If using this dataset, please provide appropriate credit.

Feel free to explore and analyze the dataset! 🚀

Abstract

The survey provides statistically sound and internationally comparable data essential for developing evidence-based policies and programmes, and for monitoring progress toward national goals and global commitments. The Kiribati Social Development Indicator Survey (SDIS) results are critically important for the purposes of Sustainable Development Goal (SDG) monitoring, as the survey produces information on 32 global SDG indicators adopted by the National Development Indicators framework, either in their entirety or partially.

The 2018-19 Kiribati SDIS has as its primary objectives: • To provide high quality data for assessing the situation of children, adolescents, women and households in KSDIS; • To furnish data needed for monitoring progress toward national goals, as a basis for future action; • To collect disaggregated data for the identification of disparities, to inform policies aimed at social inclusion of the most vulnerable; • To validate data from other sources and the results of focused interventions; • To generate data on national and global SDG indicators; • To generate internationally comparable data for the assessment of the progress made in various areas, and to put additional efforts in those areas that require more attention; • To generate behavioural and attitudinal data not available in other data sources.

Geographic coverage

National Coverage: covering rural-urban areas and for the five district/island groups of the country (South Tarawa, Northern Gilbert, Central Gilbert, Southern Gilbert, and Line and Phoenix groups).

Analysis unit

-Household; -Household member; -Mosquito nets; -Women in reproductive age; -Birth history; -Men in reproductive age; -Mothers or primary caretakers of children under 5; -Mothers or primary caretakers of children age 5-17.

Universe

The survey covered all de jure household members (usual residents), all women aged between 15 to 49 years, all men aged between 15 to 49 years, all children under 5 and those aged 5 to 17 living in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

-SELECTION PROCESS: The sample for the Kiribati Social Development Indicator Survey (SDIS) 2018-19 was designed to provide estimates for a large number of indicators on the situation of children and women at the national, rural-urban, South Tarawa, Northern Gilbert, Central Gilbert, Southern Gilbert and Line and Phoenix group. The urban and rural areas within each district were identified as the main sampling strata and the sample of households was selected in two stages. Within each stratum, a specified number of census Enumeration Areas (EAs) were selected systematically with probability proportional to size. After a household listing was carried out within the selected enumeration areas, a systematic sample of 3280 households was drawn in each sample enumeration area. All of the selected enumeration areas were visited during the fieldwork period.

A multi-stage, stratified cluster sampling approach was used for the selection of the survey sample. The sampling frame was based on the full/national household listing (mini-census) conducted in 2018 because the last census (2015) could not be used as a sampling frame as the EA boundaries differed from the 2010 Kiribati Census. The primary sampling units (PSUs) selected at the first stage were the enumeration areas (EAs) defined for the census enumeration.

-SAMPLE SIZE AND SAMPLE ALLOCATION: Since the overall sample size for the Kiribati SDIS partly depends on the geographic domains of analysis that are defined for the survey tables, the distribution of EAs and households in Kiribati from the 2018 Household Listing /Mini Census sampling frame was first examined by region, urban and rural strata.

The overall sample size for the Kiribati SDIS was calculated as 3,280 households. For the calculation of the sample size, the key indicator used was the underweight prevalence among children age 0-4 years. Since the survey results are tabulated at the regional level, it was necessary to determine the minimum sample size for each region.

For the calculation, r (underweight prevalence) was assumed to be 15 percent based on the national estimate from the Demographic and Health SUrvey (DHS) 2009. -The value of deff (design effect) was taken as 1.0 based on the estimate from the DHS 2009, -pb (percentage of children age 0-4 years in the total population) was taken as 12 percent, -AveSize (mean household size) was taken as 6.0 based on the 2018 mini-Census, and the response rate was assumed to be 98 percent, based on experience from the DHS 2009. -It was decided that an RME of at most 20 percent was needed for the district/island group estimates; this would result in an RME of 10 percent for the national estimate. The calculations resulted in a total sample size of 3,280 households, with the sample sizes in the districts varying between 515 and 780. The sample size in South Tarawa (urban) was adjusted upwards from 780 to 1,080 households in order to improve the precision in urban/rural comparisons. The sample sizes in the other districts/island groups were reduced by 75 households each.

The number of households selected per cluster for the Kiribati SDIS was determined as 20 households, based on several considerations, including the design effect, the budget available, and the time that would be needed per team to complete one cluster.

Census enumeration areas were selected from each of the sampling strata by using systematic probability proportional to size (pps) sampling procedures, based on the number of households in each enumeration area from the 2018 Mini- Census frame. The first stage of sampling was thus completed by selecting the required number of sample EAs (specified in Table SD.2) from each of the five district/Island groups.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

-QUESTIONNAIRE DESCRIPTION: Six questionnaires were used in the survey: 1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; 2) a water quality testing questionnaire administered in 4 households in each cluster of the sample; 3) a questionnaire for individual women administered in each household to all women age 15-49 years; 4) a questionnaire for individual men administered in every second household to all men age 15-49 years; 5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and 6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household.

The questionnaires were based on the Multiple Indicator Cluster Surveys 6 (MICS6) standard questionnaires except for questionnaire for individual women/men had some add-on questions and/or modules from the Demographic and Health Surveys (DHS) programme. From the MICS6 model English version, the questionnaires were customised and translated into Kiribati language and were pre-tested in South Tarawa during September, 2018. Based on the results of the pre-test, modifications were made to the wording and translation of the questionnaires. A copy of the Kiribati Social Development Indicator Survey (SDIS) 2018-19 questionnaires is provided in the External Resources of this documentation.

-COMPOSITION OF THE QUESTIONNAIRES: The questionnaires included the following modules: -Household questionnaire: List of household members, Education, Household characteristics, Social transfers, Household energy use, Dengue, Water and sanitation, Handwashing, Salt iodisation.

-Water Quality Testing questionnaire: Water quality tests, Water quality testing results.

-Individual Women questionnaire: Background, ICT, Fertility/Birth history, Desire for last birth, Maternal and newborn health, Post-natal health checks, Contraception, Unmet need, Attitudes toward domestic violence, Victimisation, Marriage/union, Adult functioning, Sexual behaviour, HIV/AIDS, STI, Tobacco and alcohol use, Domestic violence, Life satisfaction.

-Individual Men questionnaire: Background, ICT, Fertility, Contraception, Unmet need, Attitudes toward domestic violence, Victimisation, Marriage/union, Adult functioning, Sexual behaviour, HIV/AIDS, STI, Circumcision, Tobacco and alcohol use, Life satisfaction.

-Children Under 5 questionnaire: Background, Birth registration, Early childhood development, Chil discipline, Child functioning, Breastfeeding and dietary intake, Immunisation, Care of illness, Anthropometry.

-Children Age 5-17 Years questionnaire: Background, Child labour, Child discipline, Child functioning, Parental involvment, Foundational learning skills.

Cleaning operations

Data were received at the National Statistical Office's central office via Internet File Streaming System (IFSS) integrated into the management application on the supervisors' tablets. Whenever logistically possible, synchronisation was daily. The central office communicated application updates to field teams through this system.

During data collection and following the completion of fieldwork, data were edited according to editing process described in detail in the Guidelines for Secondary Editing, a customised version of the standard MICS6 documentation.

Data editing took place at a number of stages throughout the processing (see Other processing), including: a) During data collection b) Structure checking and completeness c) Secondary editing d) Structural checking of SPSS data files

Detailed documentation of the editing of

This data set contains data from 196 countries. It has data for 4 years. Includes various features that determine the social conditions prevalent in that country.

The data set has features like Life Expectancy, Expenditure on Health and Education made by respective governments, the expenditure on health and education made by the private sector. The Share of GDP per capita and much more.

I thank my viewers on YouTube for inspiring me to do data visualization using animation with Plotly. Thank you once again.

This is for beginners and intermediate levels. I would like to learn and share more concepts and always open for further imporvement

Contains data from the World Bank's data portal covering the following topics which also exist as individual datasets on HDX: Agriculture and Rural Development, Aid Effectiveness, Economy and Growth, Education, Energy and Mining, Environment, Financial Sector, Health, Infrastructure, Social Protection and Labor, Poverty, Private Sector, Public Sector, Science and Technology, Social Development, Urban Development, Gender, Millenium development goals, Climate Change, External Debt, Trade.

Acronym: WDIType: Time SeriesTopics: Agriculture and Food Security, Climate Change, Economic Growth, Education, Energy and Extractives, Environment and Natural Resources, Financial Sector, Development,GenderHealth Nutrition and Population,Macroeconomic Vulnerability and Debt,Poverty, Private Sector Development, Public Sector Management, Social Development, Social Protection and Labor, Trade, Economy Coverage: High Income IBRD IDA Low Income Lower Middle Income Upper Middle IncomeLanguages Supported: English Arabic Chinese French SpanishNumber of Economies: 217Geographical Coverage: World East Asia & Pacific American Samoa Australia Brunei Darussalam Cambodia China FijiFrench Polynesia Guam Hong Kong SAR, China Indonesia Japan KiribatiKorea, Dem. People's Rep. Korea, Rep. Lao PDR Macao SAR, China Malaysia Marshall IslandsMongolia Myanmar Nauru New Caledonia New Zealand Northern Mariana Islands PalauPapua New Guinea Philippines Samoa Singapore Solomon Islands Thailand Timor-LesteTonga Tuvalu Vanuatu Vietnam Europe & Central Asia Albania Andorra Armenia AustriaAzerbaijan Belarus Belgium Bosnia and Herzegovina Bulgaria Croatia Cyprus Czech RepublicDenmark Estonia Faroe Islands Finland France Georgia Germany Gibraltar Greece GreenlandHungary Iceland Ireland Isle of Man Italy Kazakhstan Kyrgyz Republic Latvia LiechtensteinLithuania Luxembourg Macedonia, FYR Moldova Monaco Montenegro Netherlands NorwayPoland Portugal Romania Russian Federation San Marino Serbia Slovak Republic SloveniaSpain Sweden Switzerland Tajikistan Turkey Turkmenistan Ukraine United KingdomUzbekistan Latin America & Caribbean Antigua and Barbuda Aruba Argentina Bahamas, TheBarbados Belize Bolivia Brazil Cayman Islands Chile Costa Rica Colombia Cuba CuraçaoDominica Dominican Republic Ecuador El Salvador Grenada Guatemala Guyana HaitiHonduras Jamaica Mexico Nicaragua Panama Paraguay Peru Puerto RicoSint Maarten (Dutch part) St. Kitts and Nevis St. Martin (French part) St. LuciaSt. Vincent and the Grenadines Suriname Trinidad and Tobago Turks and Caicos IslandsUruguay Venezuela, RB Virgin Islands (U.S.) Middle East & North Africa Algeria BahrainEgypt, Arab Rep. Djibouti Iraq Iran, Islamic Rep. Israel Jordan Kuwait Lebanon Libya MaltaMorocco Oman Qatar Saudi Arabia Syrian Arab Republic United Arab Emirates TunisiaYemen, Rep. Bermuda Canada United States South Asia Afghanistan Bangladesh BhutanIndia Pakistan Nepal Maldives Sri Lanka Angola Benin Botswana Burkina Faso BurundiCabo Verde Cameroon Central African Republic Chad Comoros Congo, Dem. Rep. Congo, Rep.Côte d'Ivoire Ethiopia Eritrea Equatorial Guinea Gabon Gambia, The Ghana GuineaGuinea-Bissau Kenya Lesotho Liberia Madagascar Malawi Mali Mauritania MauritiusMozambique Namibia Niger Nigeria Rwanda São Tomé and Principe Seychelles SenegalSierra Leone Somalia South Africa South Sudan Sudan Swaziland Tanzania Togo UgandaZambia Zimbabwe

Graph and download economic data for Gross Domestic Product for Developing Countries in Europe and Central Asia (NYGDPMKTPCDECA) from 1987 to 2024 about Central Asia, Europe, and GDP.