The Global Data Regulation Diagnostic provides a comprehensive assessment of the quality of the data governance environment. Diagnostic results show that countries have put in greater effort in adopting enabler regulatory practices than in safeguard regulatory practices. However, for public intent data, enablers for private intent data, safeguards for personal and nonpersonal data, cybersecurity and cybercrime, as well as cross-border data flows. Across all these dimensions, no income group demonstrates advanced regulatory frameworks across all dimensions, indicating significant room for the regulatory development of both enablers and safeguards remains at an intermediate stage: 47 percent of enabler good practices and 41 percent of good safeguard practices are adopted across countries. Under the enabler and safeguard pillars, the diagnostic covers dimensions of e-commerce/e-transactions, enablers further improvement on data governance environment.
The Global Data Regulation Diagnostic is the first comprehensive assessment of laws and regulations on data governance. It covers enabler and safeguard regulatory practices in 80 countries providing indicators to assess and compare their performance. This Global Data Regulation Diagnostic develops objective and standardized indicators to measure the regulatory environment for the data economy across countries. The indicators aim to serve as a diagnostic tool so countries can assess and compare their performance vis-á-vis other countries. Understanding the gap with global regulatory good practices is a necessary first step for governments when identifying and prioritizing reforms.
80 countries
Country
Observation data/ratings [obs]
The diagnostic is based on a detailed assessment of domestic laws, regulations, and administrative requirements in 80 countries selected to ensure a balanced coverage across income groups, regions, and different levels of digital technology development. Data are further verified through a detailed desk research of legal texts, reflecting the regulatory status of each country as of June 1, 2020.
Mail Questionnaire [mail]
The questionnaire comprises 37 questions designed to determine if a country has adopted good regulatory practice on data governance. The responses are then scored and assigned a normative interpretation. Related questions fall into seven clusters so that when the scores are averaged, each cluster provides an overall sense of how it performs in its corresponding regulatory and legal dimensions. These seven dimensions are: (1) E-commerce/e-transaction; (2) Enablers for public intent data; (3) Enablers for private intent data; (4) Safeguards for personal data; (5) Safeguards for nonpersonal data; (6) Cybersecurity and cybercrime; (7) Cross-border data transfers.
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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/
https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
The World Bank is an international financial institution that provides loans to countries of the world for capital projects. The World Bank's stated goal is the reduction of poverty. Source: https://en.wikipedia.org/wiki/World_Bank
This dataset combines key education statistics from a variety of sources to provide a look at global literacy, spending, and access.
For more information, see the World Bank website.
Fork this kernel to get started with this dataset.
https://bigquery.cloud.google.com/dataset/bigquery-public-data:world_bank_health_population
http://data.worldbank.org/data-catalog/ed-stats
https://cloud.google.com/bigquery/public-data/world-bank-education
Citation: The World Bank: Education Statistics
Dataset Source: World Bank. This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.
Banner Photo by @till_indeman from Unplash.
Of total government spending, what percentage is spent on education?
Databank (databank.worldbank.org) is an online web resource that provides simple and quick access to collections of time series data. It has advanced functions for selecting and displaying data, performing customized queries, downloading data, and creating charts and maps. Users can create dynamic custom reports based on their selection of countries, indicators and years. They offer a growing range of free, easy-to-access tools, research and knowledge to help people address the world's development challenges. For example, the Open Data website offers free access to comprehensive, downloadable indicators about development in countries around the globe.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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.
World Bank activity file for TOGO
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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
Population of countries (1960 to 2023) dataset from World Bank.
This dataset includes procurement tender notices for World Bank Group financed projects. Learn more about the World Bank Group's operations in procurement at http://go.worldbank.org/9KQZWXNOI0.
World Bank Trust Funds activity file for tf072444
https://www.worldbank.org/en/about/legal/terms-of-use-for-datasetshttps://www.worldbank.org/en/about/legal/terms-of-use-for-datasets
This dataset was uploaded as supplemental data for the 2019 Kaggle ML & DS Survey. It allows classification of countries into income groups - low, lower-middle, upper-middle and high - by gross national income (GNI) per capita, in U.S. dollars,.
For details of this calculation see here and here.
The csv file consists of 218 countries listed by name and country code and their corresponding income group and lending category.
Thanks to the World Bank for providing the data at "https://datahelpdesk.worldbank.org/knowledgebase/articles/906519">https://datahelpdesk.worldbank.org/knowledgebase/articles/906519
This dataset allows any other data containing country names or codes to be supplemented with income group data.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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).
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.
An economy's financial markets are critical to its overall development. Banking systems and stock markets enhance growth, the main factor in poverty reduction. Strong financial systems provide reliable and accessible information that lowers transaction costs, which in turn bolsters resource allocation and economic growth. Indicators here include the size and liquidity of stock markets; the accessibility, stability, and efficiency of financial systems; and international migration and workers\ remittances, which affect growth and social welfare in both sending and receiving countries.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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.
World Bank Trust Funds activity file for tf072496
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
The Gender Statistics database is a comprehensive source for the latest sex-disaggregated data and gender statistics covering demography, education, health, access to economic opportunities, public life and decision-making, and agency.
For further details, please refer to https://datacatalog.worldbank.org/search/dataset/0037654/gender-statistics
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Latvia Multidimensional Poverty Headcount Ratio: World Bank: % of total population data was reported at 0.500 % in 2021. This records a decrease from the previous number of 0.800 % for 2020. Latvia Multidimensional Poverty Headcount Ratio: World Bank: % of total population data is updated yearly, averaging 1.300 % from Dec 2008 (Median) to 2021, with 14 observations. The data reached an all-time high of 2.300 % in 2010 and a record low of 0.500 % in 2021. Latvia Multidimensional Poverty Headcount Ratio: World Bank: % of total population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Latvia – Table LV.World Bank.WDI: Social: Poverty and Inequality. The multidimensional poverty headcount ratio (World Bank) is the percentage of a population living in poverty according to the World Bank's Multidimensional Poverty Measure. The Multidimensional Poverty Measure includes three dimensions – monetary poverty, education, and basic infrastructure services – to capture a more complete picture of poverty.;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).
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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 Global Data Regulation Diagnostic provides a comprehensive assessment of the quality of the data governance environment. Diagnostic results show that countries have put in greater effort in adopting enabler regulatory practices than in safeguard regulatory practices. However, for public intent data, enablers for private intent data, safeguards for personal and nonpersonal data, cybersecurity and cybercrime, as well as cross-border data flows. Across all these dimensions, no income group demonstrates advanced regulatory frameworks across all dimensions, indicating significant room for the regulatory development of both enablers and safeguards remains at an intermediate stage: 47 percent of enabler good practices and 41 percent of good safeguard practices are adopted across countries. Under the enabler and safeguard pillars, the diagnostic covers dimensions of e-commerce/e-transactions, enablers further improvement on data governance environment.
The Global Data Regulation Diagnostic is the first comprehensive assessment of laws and regulations on data governance. It covers enabler and safeguard regulatory practices in 80 countries providing indicators to assess and compare their performance. This Global Data Regulation Diagnostic develops objective and standardized indicators to measure the regulatory environment for the data economy across countries. The indicators aim to serve as a diagnostic tool so countries can assess and compare their performance vis-á-vis other countries. Understanding the gap with global regulatory good practices is a necessary first step for governments when identifying and prioritizing reforms.
80 countries
Country
Observation data/ratings [obs]
The diagnostic is based on a detailed assessment of domestic laws, regulations, and administrative requirements in 80 countries selected to ensure a balanced coverage across income groups, regions, and different levels of digital technology development. Data are further verified through a detailed desk research of legal texts, reflecting the regulatory status of each country as of June 1, 2020.
Mail Questionnaire [mail]
The questionnaire comprises 37 questions designed to determine if a country has adopted good regulatory practice on data governance. The responses are then scored and assigned a normative interpretation. Related questions fall into seven clusters so that when the scores are averaged, each cluster provides an overall sense of how it performs in its corresponding regulatory and legal dimensions. These seven dimensions are: (1) E-commerce/e-transaction; (2) Enablers for public intent data; (3) Enablers for private intent data; (4) Safeguards for personal data; (5) Safeguards for nonpersonal data; (6) Cybersecurity and cybercrime; (7) Cross-border data transfers.
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