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 contains country level economic and social measures for 183 countries. Part 1, World Tables (1980 File), contains, where available, measures of (1)population, (2)national accounts and price data for 1950, 1955, 1960 through 1977, (3)data on external trade for 1962, 1965, 1970, and 1977, (4)data on balance of payments, debt, central government finance and trade indices for 1970-1977, and (5)social data for 1960, 1970, and (estimated) 1977. More specifically, the groupings include population, GDP by industrial origin and expenditures in constant local prices and current local prices, exchange rates and indices, balance of payments and external debt ($US), central government finance in local currency, social indicators, and external trade. Part 2, World Tables (1982 File), contains data on national accounts, prices, exchange rates and population for 1960-1981. The groupings include GDP by industrial origin as well as expenditure in current local prices and constant local prices, area, population, exchange rates, and indices and savings.

The World Bank EdStats All Indicator Query holds over 4,000 internationally comparable indicators that describe education access, progression, completion, literacy, teachers, population, and expenditures. The indicators cover the education cycle from pre-primary to vocational and tertiary education. The query also holds learning outcome data from international and regional learning assessments (e.g. PISA, TIMSS, PIRLS), equity data from household surveys, and projection/attainment data to 2050. For further information, please visit the EdStats website.

For further details, please refer to https://datatopics.worldbank.org/education/wRsc/about

Graph and download economic data for Boone Indicator in Banking Market for World (DISCONTINUED) (DDOI051WA156NWDB) from 1999 to 2015 about boone indicator, banks, and depository institutions.

National statistical systems are facing significant challenges. These challenges arise from increasing demands for high quality and trustworthy data to guide decision making, coupled with the rapidly changing landscape of the data revolution. To help create a mechanism for learning amongst national statistical systems, the World Bank has developed improved Statistical Performance Indicators (SPI) to monitor the statistical performance of countries. The SPI focuses on five key dimensions of a country’s statistical performance: (i) data use, (ii) data services, (iii) data products, (iv) data sources, and (v) data infrastructure. This will replace the Statistical Capacity Index (SCI) that the World Bank has regularly published since 2004. The SPI focus on five key pillars of a country’s statistical performance: (i) data use, (ii) data services, (iii) data products, (iv) data sources, and (v) data infrastructure. The SPI are composed of more than 50 indicators and contain data for 174 countries. This set of countries covers 99.2 percent of the world population. The data extend from 2016-2019, with some indicators going back to 2004.

This selection includes data related to SPC member countries and territories for some of the indicators available in the original database published by the World Bank.

Find more Pacific data on PDH.stat.

The Atlas of Sustainable Development Goals 2018 is a visual guide to the trends, challenges and measurement issues related to each of the 17 Sustainable Development Goals. The Atlas features maps and data visualizations, primarily drawn from World Development Indicators (WDI) - the World Bank’s compilation of internationally comparable statistics about global development and the quality of people’s lives. Given the breadth and scope of the SDGs, the editors have been selective, emphasizing issues considered important by experts in the World Bank’s Global Practices and Cross Cutting Solution Areas. Nevertheless, The Atlas aims to reflect the breadth of the Goals themselves and presents national and regional trends and snapshots of progress towards the UN’s seventeen Sustainable Development Goals related to: poverty, hunger, health, education, gender, water, energy, jobs, infrastructure, inequalities, cities, consumption, climate, oceans, the environment, peace, institutions, and partnerships.

Citation
“World Bank. 2018. Atlas of Sustainable Development Goals 2018 : From World Development Indicators. World Bank Atlas;. Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/29788 License: CC BY 3.0 IGO.”

URI
http://hdl.handle.net/10986/29788

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.

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.

The Worldwide Bureaucracy Indicators (WWBI) database is a unique cross-national dataset on public sector employment and wages that aims to fill an information gap, thereby helping researchers, development practitioners, and policymakers gain a better understanding of the personnel dimensions of state capability, the footprint of the public sector within the overall labor market, and the fiscal implications of the public sector wage bill. The dataset is derived from administrative data and household surveys, thereby complementing existing, expert perception-based approaches. The WWBI indicators are estimated from microdata drawn from the labor force and household surveys and augmented with administrative data for 202 economies in five categories: the demographics of the private and public sector workforces; public sector wage premiums; relative wages and pay compression ratios, gender pay gaps; and the public sector wage bill. The micro and administrative data utilized in the construction of the WWBI are drawn from data catalogs housing surveys conducted by national statistical organizations (NSO) or multilateral organization data teams. Together, these provide an important, albeit narrow, picture of the skills and incentives of bureaucrats. Indicators on public employment track key demographic characteristics including the size of the public sector workforce (in absolute and relative numbers), their age, and distributions across genders, industries, occupations, income quintiles, and academic qualifications. Variables on compensation capture both the competitiveness of public sector wages (compared to the private sector) as well as wage differentials across industry or occupation of employment, genders, education, and income quintiles within the public and private sectors as well as pay compression ratios in public and private sectors. The indicators on the size of the wage bill offer a glimpse into the structure and affordability of the public sector within the larger economy.

For further details, please refer to https://datacatalog.worldbank.org/search/dataset/0038132

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, Climate Change, External Debt, Trade.

Lebanon LB: Proportion of People Living Below 50 Percent Of Median Income: % data was reported at 10.700 % in 2011. Lebanon LB: Proportion of People Living Below 50 Percent Of Median Income: % data is updated yearly, averaging 10.700 % from Dec 2011 (Median) to 2011, with 1 observations. The data reached an all-time high of 10.700 % in 2011 and a record low of 10.700 % in 2011. Lebanon LB: Proportion of People Living Below 50 Percent Of Median Income: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Lebanon – Table LB.World Bank.WDI: Social: Poverty and Inequality. The percentage of people in the population who live in households whose per capita income or consumption is below half of the median income or consumption per capita. The median is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries, medians are not reported due to grouped and/or confidential data. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

National statistical systems are facing significant challenges. These challenges arise from increasing demands for high quality and trustworthy data to guide decision making, coupled with the rapidly changing landscape of the data revolution. To help create a mechanism for learning amongst national statistical systems, the World Bank has developed improved Statistical Performance Indicators (SPI) to monitor the statistical performance of countries. The SPI focuses on five key dimensions of a country’s statistical performance: (i) data use, (ii) data services, (iii) data products, (iv) data sources, and (v) data infrastructure. This will replace the Statistical Capacity Index (SCI) that the World Bank has regularly published since 2004.The SPI focus on five key pillars of a country’s statistical performance: (i) data use, (ii) data services, (iii) data products, (iv) data sources, and (v) data infrastructure. The SPI are composed of more than 50 indicators and contain data for 186 countries. This set of countries covers 99 percent of the world population. The data extend from 2016-2023, with some indicators going back to 2004.For more information, consult the academic article published in the journal Scientific Data. https://www.nature.com/articles/s41597-023-01971-0. For further details, please refer to https://documents.worldbank.org/en/publication/documents-reports/documentdetail/815721616086786412/measuring-the-statistical-performance-of-countries-an-overview-of-updates-to-the-world-bank-statistical-capacity-index

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, Climate Change, External Debt, Trade.

United States US: Regulatory Quality: Estimate data was reported at 1.628 NA in 2017. This records an increase from the previous number of 1.497 NA for 2016. United States US: Regulatory Quality: Estimate data is updated yearly, averaging 1.532 NA from Dec 1996 (Median) to 2017, with 19 observations. The data reached an all-time high of 1.761 NA in 2000 and a record low of 1.256 NA in 2015. United States US: Regulatory Quality: Estimate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WGI: Country Governance Indicators. Regulatory Quality captures perceptions of the ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development. Estimate gives the country's score on the aggregate indicator, in units of a standard normal distribution, i.e. ranging from approximately -2.5 to 2.5.

Contains data from World Bank's data portal covering various economic and social indicators (one per resource).

The average for 2024 based on 48 countries was 6.62 points. The highest value was in Finland: 7.74 points and the lowest value was in Hong Kong: 5.32 points. The indicator is available from 2013 to 2024. Below is a chart for all countries where data are available.

Abstract copyright UK Data Service and data collection copyright owner. The World Bank 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.These data were first provided by the UK Data Service in February 2004. Main Topics: The following topics are covered:people: population and demographics; labour and employment; poverty and income distribution; education; healthenvironment: land use and agricultural production; energy production and use; urbanization; emissions; adjusted savingseconomy: national accounts (local currency); national accounts (US dollars); derived national accounts; purchasing power parity; trade; government finance; monetary; balance of payments; external debtstates and markets: investment climate; business environment; financial depth; tax and trade policies; defense expenditures and arms trade; military expenditures and arms trade; transport, power, and communications; information and technologyglobal links: investment and trade; financial flows; development assistance and aid; migration; travel and tourism

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, Climate Change, External Debt, Trade.

Analysis of ‘World Bank WDI 2.12 - Health Systems’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/danevans/world-bank-wdi-212-health-systems on 21 November 2021.

--- Dataset description provided by original source is as follows ---

World Bank - World Development Indicators: Health Systems

This is a digest of the information described at http://wdi.worldbank.org/table/2.12# It describes various health spending per capita by Country, as well as doctors, nurses and midwives, and specialist surgical staff per capita

Content

Notes, explanations, etc. 1. There are countries/regions in the World Bank data not in the Covid-19 data, and countries/regions in the Covid-19 data with no World Bank data. This is unavoidable. 2. There were political decisions made in both datasets that may cause problems. I chose to go forward with the data as presented, and did not attempt to modify the decisions made by the dataset creators (e.g., the names of countries, what is and is not a country, etc.).

Columns are as follows: 1. Country_Region: the region as used in Kaggle Covid-19 spread data challenges. 2. Province_State: the region as used in Kaggle Covid-19 spread data challenges. 3. World_Bank_Name: the name of the country used by the World Bank 4. Health_exp_pct_GDP_2016: Level of current health expenditure expressed as a percentage of GDP. Estimates of current health expenditures include healthcare goods and services consumed during each year. This indicator does not include capital health expenditures such as buildings, machinery, IT and stocks of vaccines for emergency or outbreaks.

1. Health_exp_public_pct_2016: Share of current health expenditures funded from domestic public sources for health. Domestic public sources include domestic revenue as internal transfers and grants, transfers, subsidies to voluntary health insurance beneficiaries, non-profit institutions serving households (NPISH) or enterprise financing schemes as well as compulsory prepayment and social health insurance contributions. They do not include external resources spent by governments on health.

2. Health_exp_out_of_pocket_pct_2016: Share of out-of-pocket payments of total current health expenditures. Out-of-pocket payments are spending on health directly out-of-pocket by households.

3. Health_exp_per_capita_USD_2016: Current expenditures on health per capita in current US dollars. Estimates of current health expenditures include healthcare goods and services consumed during each year.

4. per_capita_exp_PPP_2016: Current expenditures on health per capita expressed in international dollars at purchasing power parity (PPP).

5. External_health_exp_pct_2016: Share of current health expenditures funded from external sources. External sources compose of direct foreign transfers and foreign transfers distributed by government encompassing all financial inflows into the national health system from outside the country. External sources either flow through the government scheme or are channeled through non-governmental organizations or other schemes.

6. Physicians_per_1000_2009-18: Physicians include generalist and specialist medical practitioners.

7. Nurse_midwife_per_1000_2009-18: Nurses and midwives include professional nurses, professional midwives, auxiliary nurses, auxiliary midwives, enrolled nurses, enrolled midwives and other associated personnel, such as dental nurses and primary care nurses.

8. Specialist_surgical_per_1000_2008-18: Specialist surgical workforce is the number of specialist surgical, anaesthetic, and obstetric (SAO) providers who are working in each country per 100,000 population.

9. Completeness_of_birth_reg_2009-18: Completeness of birth registration is the percentage of children under age 5 whose births were registered at the time of the survey. The numerator of completeness of birth registration includes children whose birth certificate was seen by the interviewer or whose mother or caretaker says the birth has been registered.

10. Completeness_of_death_reg_2008-16: Completeness of death registration is the estimated percentage of deaths that are registered with their cause of death information in the vital registration system of a country.

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Inspiration

Does health spending levels (public or private), or hospital staff have any effect on the rate at which Covid-19 spreads in a country? Can we use this data to predict the rate at which Cases or Fatalities will grow?

--- Original source retains full ownership of the source dataset ---