In most countries basic education is nowadays perceived not only as a right, but also as a duty – governments are typically expected to ensure access to basic education, while citizens are often required by law to attain education up to a certain basic level.1

This was not always the case: the advancement of these ideas began in the mid-19th century, when most of today’s industrialized countries started expanding primary education, mainly through public finances and government intervention. Data from this early period shows that government funds to finance the expansion of education came from a number of different sources, but taxes at the local level played a crucial role. The historical role of local funding for public schools is important to help us understand changes – or persistence – in regional inequalities.

The second half of the 20th century marked the beginning of education expansion as a global phenomenon. Available data shows that by 1990 government spending on education as a share of national income in many developing countries was already close to the average observed in developed countries.2

This global education expansion in the 20th century resulted in a historical reduction in education inequality across the globe: in the period 1960-2010 education inequality went down every year, for all age groups and in all world regions. Recent estimates of education inequality across age groups suggest that further reductions in schooling inequality are still to be expected within developing countries.3

Recent cross-country data from UNESCO tells us that the world is expanding government funding for education today, and these additional public funds for education are not necessarily at the expense of other government sectors. Yet behind these broad global trends, there is substantial cross-country – and cross-regional – heterogeneity. In high-income countries, for instance, households shoulder a larger share of education expenditures at higher education levels than at lower levels – but in low-income countries, this is not the case.

Following the agreement of the Millennium Development Goals, the first decade of the 21st century saw an important increase in international financial flows under the umbrella of development assistance. Recent estimates show that development assistance for education has stopped growing since 2010, with notable aggregate reductions in flows going to primary education. These changes in the prioritization of development assistance for education across levels and regions can have potentially large distributional effects, particularly within low-income countries that depend substantially on this source of funding for basic education.4

When analyzing correlates, determinants and consequences of education consumption, the macro data indicates that national expenditure on education does not explain well cross-country differences in learning outcomes. This suggests that for any given level of expenditure, the output achieved depends crucially on the mix of many inputs.

Global Primary Education Expenditure by Country, 2023 Discover more data with ReportLinker!

Context

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

Content

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.

Acknowledgements

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.

Inspiration

Of total government spending, what percentage is spent on education?

This dataset offers a detailed comparison of key global players like USA, Russia, China, India, Canada, Australia, and others across various economic, social, and environmental metrics. By comparing countries on indicators such as GDP, population, healthcare access, education levels, internet penetration, military spending, and much more, this dataset provides valuable insights for researchers, policymakers, and analysts.

🔍 Key Comparisons:

Economic Indicators: GDP, inflation rates, unemployment rates, etc. Social Indicators: Literacy rates, healthcare quality, life expectancy, etc. Environmental Indicators: CO2 emissions, renewable energy usage, protected areas, etc. Technological Advancements: Internet users, mobile subscriptions, tech exports, etc. Military Spending: Defense budgets, military personnel numbers, etc. This dataset is perfect for those who want to compare countries in terms of development, growth, and global standing. It can be used for data analysis, policy planning, research, and even education.

✨ Key Features:

Comprehensive Coverage: Includes multiple countries with key metrics. Multiple Domains: Economic, social, environmental, technological, and military data. Up-to-date Information: Covers data from the last decade to provide recent insights. Research Ready: Suitable for academic research, visualizations, and analysis.

Time series data for the statistic Government expenditure on tertiary education as % of GDP (%) and country United States. Indicator Definition:Total general (local, regional and central) government expenditure on tertiary education (current, capital, and transfers), expressed as a percentage of GDP. It includes expenditure funded by transfers from international sources to government. Divide total government expenditure for a given level of education (ex. primary, secondary, or all levels combined) by the GDP, and multiply by 100. A higher percentage of GDP spent on education shows a higher government priority for education, but also a higher capacity of the government to raise revenues for public spending, in relation to the size of the country's economy. When interpreting this indicator however, one should keep in mind in some countries, the private sector and/or households may fund a higher proportion of total funding for education, thus making government expenditure appear lower than in other countries. Limitations: In some instances data on total public expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/

Global Public Spending on Tertiary Education by Country, 2023 Discover more data with ReportLinker!

Governments of developing countries typically spend between 20 and 30 percent of gross domestic product. Hence, small changes in the efficiency of public spending could have a major impact on aggregate productivity growth and gross domestic product levels. Therefore, measuring efficiency and comparing input-output combinations of different decision-making units becomes a central challenge. This paper gauges efficiency as the distance between observed input-output combinations and an efficiency frontier estimated by means of the Free Disposal Hull and Data Envelopment Analysis techniques. Input-inefficiency (excess input consumption to achieve a level of output) and output-inefficiency (output shortfall for a given level of inputs) are scored in a sample of 175 countries using data from 2006–16 on education, health, and infrastructure. The paper verifies empirical regularities of the cross-country variation in efficiency, showing a negative association between efficiency and spending levels and the ratio of public-to-private financing of the service provision. Other variables, such as inequality, urbanization, and aid dependency, show mixed results. The efficiency of capital spending is correlated with the quality of governance indicators, especially regulatory quality (positively) and perception of corruption (negatively). Although no causality may be inferred from this exercise, it points at different factors to understand why some countries might need more resources than others to achieve similar education, health, and infrastructure outcomes.

Time series data for the statistic Government expenditure on tertiary education as % of GDP (%) and country Switzerland. Indicator Definition:Total general (local, regional and central) government expenditure on tertiary education (current, capital, and transfers), expressed as a percentage of GDP. It includes expenditure funded by transfers from international sources to government. Divide total government expenditure for a given level of education (ex. primary, secondary, or all levels combined) by the GDP, and multiply by 100. A higher percentage of GDP spent on education shows a higher government priority for education, but also a higher capacity of the government to raise revenues for public spending, in relation to the size of the country's economy. When interpreting this indicator however, one should keep in mind in some countries, the private sector and/or households may fund a higher proportion of total funding for education, thus making government expenditure appear lower than in other countries. Limitations: In some instances data on total public expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/The indicator "Government expenditure on tertiary education as % of GDP (%)" stands at 1.36 as of 12/31/2017, the highest value since 12/31/2005. Regarding the One-Year-Change of the series, the current value constitutes an increase of 2.33 percent compared to the value the year prior.The 1 year change in percent is 2.33.The 3 year change in percent is 1.95.The 5 year change in percent is 2.58.The 10 year change in percent is 13.86.The Serie's long term average value is 1.04. It's latest available value, on 12/31/2017, is 31.10 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/1970, to it's latest available value, on 12/31/2017, is +121.32%.The Serie's change in percent from it's maximum value, on 12/31/2004, to it's latest available value, on 12/31/2017, is -10.73%.

Time series data for the statistic Government expenditure on tertiary education as % of GDP (%) and country Lithuania. Indicator Definition:Total general (local, regional and central) government expenditure on tertiary education (current, capital, and transfers), expressed as a percentage of GDP. It includes expenditure funded by transfers from international sources to government. Divide total government expenditure for a given level of education (ex. primary, secondary, or all levels combined) by the GDP, and multiply by 100. A higher percentage of GDP spent on education shows a higher government priority for education, but also a higher capacity of the government to raise revenues for public spending, in relation to the size of the country's economy. When interpreting this indicator however, one should keep in mind in some countries, the private sector and/or households may fund a higher proportion of total funding for education, thus making government expenditure appear lower than in other countries. Limitations: In some instances data on total public expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/The indicator "Government expenditure on tertiary education as % of GDP (%)" stands at 0.7528 as of 12/31/2017, the lowest value at least since 12/31/1996, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes a decrease of -8.37 percent compared to the value the year prior.The 1 year change in percent is -8.37.The 3 year change in percent is -43.39.The 5 year change in percent is -45.53.The 10 year change in percent is -24.57.The Serie's long term average value is 1.11. It's latest available value, on 12/31/2017, is 32.42 percent lower, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/2017, to it's latest available value, on 12/31/2017, is +0.0%.The Serie's change in percent from it's maximum value, on 12/31/2011, to it's latest available value, on 12/31/2017, is -48.40%.

Time series data for the statistic Government expenditure on tertiary education as % of GDP (%) and country Italy. Indicator Definition:Total general (local, regional and central) government expenditure on tertiary education (current, capital, and transfers), expressed as a percentage of GDP. It includes expenditure funded by transfers from international sources to government. Divide total government expenditure for a given level of education (ex. primary, secondary, or all levels combined) by the GDP, and multiply by 100. A higher percentage of GDP spent on education shows a higher government priority for education, but also a higher capacity of the government to raise revenues for public spending, in relation to the size of the country's economy. When interpreting this indicator however, one should keep in mind in some countries, the private sector and/or households may fund a higher proportion of total funding for education, thus making government expenditure appear lower than in other countries. Limitations: In some instances data on total public expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/The indicator "Government expenditure on tertiary education as % of GDP (%)" stands at 0.7516 as of 12/31/2017. Regarding the One-Year-Change of the series, the current value constitutes an increase of 3.04 percent compared to the value the year prior.The 1 year change in percent is 3.04.The 3 year change in percent is -5.51.The 5 year change in percent is -3.80.The 10 year change in percent is 3.57.The Serie's long term average value is 0.681. It's latest available value, on 12/31/2017, is 10.38 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/1970, to it's latest available value, on 12/31/2017, is +122.27%.The Serie's change in percent from it's maximum value, on 12/31/1999, to it's latest available value, on 12/31/2017, is -9.93%.

Series Name: Proportion of total government spending on essential services education (percent)Series Code: SD_XPD_ESEDRelease Version: 2020.Q2.G.03 This dataset is the part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 1.a.2: Proportion of total government spending on essential services (education, health and social protection)Target 1.a: Ensure significant mobilization of resources from a variety of sources, including through enhanced development cooperation, in order to provide adequate and predictable means for developing countries, in particular least developed countries, to implement programmes and policies to end poverty in all its dimensionsGoal 1: End poverty in all its forms everywhereFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

Description:

A good education offers individuals the opportunity to lead richer, more interesting lives. At a societal level, it creates opportunities for humanity to solve its pressing problems.

The world has gone through a dramatic transition over the last few centuries, from one where very few had any basic education to one where most people do. This is not only reflected in the inputs to education – enrollment and attendance – but also in outcomes, where literacy rates have greatly improved.

Getting children into school is also not enough. What they learn matters. There are large differences in educational outcomes: in low-income countries, most children cannot read by the end of primary school. These inequalities in education exacerbate poverty and existing inequalities in global incomes.

About Dataset: There are 4 dataset in this page: 1- share-of-the-world-population-with-at-least-basic-education:

Access to education is now seen as a fundamental right – in many cases, it’s the government’s duty to provide it.

But formal education is a very recent phenomenon. In the chart, we see the share of the adult population – those older than 15 – that has received some basic education and those who haven’t.

In the early 1800s, fewer than 1 in 5 adults had some basic education. Education was a luxury, in all places, it was only available to a small elite.

But you can see that this share has grown dramatically, such that this ratio is now reversed. Less than 1 in 5 adults has not received any formal education.

This is reflected in literacy data, too: 200 years ago, very few could read and write. Now most adults have basic literacy skills.

2- learning-adjusted-years-of-school-lays:

There are still significant inequalities in the amount of education children get across the world.

This can be measured as the total number of years that children spend in school. However, researchers can also adjust for the quality of education to estimate how many years of quality learning they receive. This is done using an indicator called “learning-adjusted years of schooling”.

On the map, you see vast differences across the world.

In many of the world’s poorest countries, children receive less than three years of learning-adjusted schooling. In most rich countries, this is more than 10 years.

Across most countries in South Asia and Sub-Saharan Africa – where the largest share of children live – the average years of quality schooling are less than 7.

3- number-of-out-of-school-children:

While most children worldwide get the opportunity to go to school, hundreds of millions still don’t.

In the chart, we see the number of children who aren’t in school across primary and secondary education.

This number was around 260 million in 2019.

Many children who attend primary school drop out and do not attend secondary school. That means many more children or adolescents are missing from secondary school than primary education.

4- gender-gap-education-levels:

Globally, until recently, boys were more likely to attend school than girls. The world has focused on closing this gap to ensure every child gets the opportunity to go to school.

Today, these gender gaps have largely disappeared. In the chart, we see the difference in the global enrollment rates for primary, secondary, and tertiary (post-secondary) education. The share of children who complete primary school is also shown.

We see these lines converging over time, and recently they met: rates between boys and girls are the same.

For tertiary education, young women are now more likely than young men to be enrolled.

Have a great analysis !

By Hannah Ritchie, Veronika Samborska, Natasha Ahuja, Esteban Ortiz-Ospina and Max Roser

This dataset provides a detailed view of South Asian countries' socio-economic, environmental, and governance metrics from 2000 to 2023. It compiles key indicators like GDP, unemployment, literacy rates, energy use, governance measures, and more to facilitate a comprehensive analysis of each country’s growth, stability, and development trends over the years. The data covers Bangladesh, Bhutan, India, Pakistan, Nepal, Sri Lanka, Afghanistan, and Maldives.

Key Indicators Economic Metrics: Includes GDP (both total and per capita in USD), annual GDP growth rates, inflation, and foreign direct investment. These metrics offer insight into economic health, growth rate, and international investment trends across the region. Employment and Trade: Tracks unemployment rates as a percentage of the labor force and trade (as a percentage of GDP), helping assess workforce stability and international commerce engagement. Income and Poverty: Features the Gini index (for income inequality) and poverty headcount ratio at $2.15/day, showing income distribution and poverty levels. These indicators reveal disparities and poverty within each country. Population Statistics: Includes total population, annual population growth, and urban population percentage, capturing demographic trends and urbanization rates. Social Indicators: Covers literacy rates, school enrollment in primary education, life expectancy at birth, infant mortality rates, and access to electricity, basic water, and sanitation services. These data points help measure the population’s health, education levels, and access to essential services. Environmental and Energy Metrics: Tracks CO2 emissions, PM2.5 air pollution, renewable energy consumption, and forest area. This environmental data is crucial for analyzing air quality, sustainable energy use, and forest coverage trends. Governance Indicators: Includes metrics such as control of corruption, political stability, regulatory quality, rule of law, and voice and accountability. These indicators reflect each country’s governance quality and institutional stability. Digital and Technological Growth: Measures internet usage rates, research and development spending, and high-technology exports. These statistics indicate digital access, innovation, and technological progress. This dataset, sourced from the World Bank DataBank, provides a robust foundation for studying South Asia's socio-economic, environmental, and governance progress. By analyzing these diverse indicators, researchers and policymakers can gain a deeper understanding of the region’s development path and identify areas that need improvement.

Global Economic, Environmental, Health, and Social indicators Ready for Analysis

📝 Description

This comprehensive dataset merges global economic, environmental, technological, and human development indicators from 2000 to 2020. Sourced and transformed from multiple public datasets via Google BigQuery, it is designed for advanced exploratory data analysis, machine learning, policy modeling, and sustainability research.

Curated by combining and transforming data from the Google BigQuery Public Data program, this dataset offers a harmonized view of global development across more than 40 key indicators spanning over two decades (2000–2020). It supports research across multiple domains such as:

• Economic Growth
• Climate Sustainability
• Digital Transformation
• Public Health
• Human Development
• Resilience and Governance

for formulas and more details check: https://github.com/Michael-Matta1/datasets-collection/tree/main/Global%20Development

📅 Temporal Coverage

• Years: 2000–2020

• Includes calculated features:

  • years_since_2000
  • years_since_century
  • is_pandemic_period (binary indicator for pandemic periods)

🌍 Geographic Scope

• Countries: Global (identified by ISO country codes)
• Regions and Income Groups included for aggregated analysis

📊 Key Feature Groups

• Economic Indicators:

  • GDP (USD), GDP per capita
  • FDI, inflation, unemployment, economic growth index

• Environmental Indicators:

  • CO₂ emissions, renewable energy use
  • Forest area, green transition score, CO₂ intensity

• Technology & Connectivity:

  • Internet usage, mobile subscriptions
  • Digital readiness score, digital connectivity index

• Health & Education:

  • Life expectancy, child mortality
  • School enrollment, healthcare capacity, health development ratio

• Governance & Resilience:

  • Governance quality, global resilience
  • Human development composite, ecological preservation

🔍 Use Cases

• Trend analysis over time
• Country-level comparisons
• Modeling development outcomes
• Predictive analytics on sustainability or human development
• Correlation and clustering across multiple indicators

⚠️ Note on Missing Region and Income Group Data

Approximately 18% of the entries in the region and income_group columns are null. This is primarily due to the inclusion of aggregate regions (e.g., Arab World, East Asia & Pacific, Africa Eastern and Southern) and non-country classifications (e.g., Early-demographic dividend, Central Europe and the Baltics). These entries represent groups of countries with diverse income levels and geographic characteristics, making it inappropriate or misleading to assign a single region or income classification. In some cases, the data source may have intentionally left these fields blank to avoid oversimplification or due to a lack of standardized classification.

📋 Column Descriptions

• year: Year of the recorded data, representing a time series for each country.
• country_code: Unique code assigned to each country (ISO-3166 standard).
• country_name: Name of the country corresponding to the data.
• region: Geographical region of the country (e.g., Africa, Asia, Europe).
• income_group: Income classification based on Gross National Income (GNI) per capita (low, lower-middle, upper-middle, high income).
• currency_unit: Currency used in the country (e.g., USD, EUR).
• gdp_usd: Gross Domestic Product (GDP) in USD (millions or billions).
• population: Total population of the country for the given year.
• gdp_per_capita: GDP divided by population (economic output per person).
• inflation_rate: Annual rate of inflation (price level rise).
• unemployment_rate: Percentage of the labor force unemployed but seeking employment.
• fdi_pct_gdp: Foreign Direct Investment (FDI) as a percentage of GDP.
• co2_emissions_kt: Total CO₂ emissions in kilotons (kt).
• energy_use_per_capita: Energy consumption per person (kWh).
• renewable_energy_pct: Percentage of energy consumption from renewable sources.
• forest_area_pct: Percentage of total land area covered by forests.
• electricity_access_pct: Percentage of the population with access to electricity.
• life_expectancy: Average life expectancy at birth.
• child_mortality: Deaths of children under 5 per 1,000 live births.
• school_enrollment_secondary: Percentage of population enrolled in secondary education.
• health_expenditure_pct_gdp: Percentage of GDP spent on healthcare.
• hospital_beds_per_1000...

Einstellung von Studierenden zum Hochschulwesen. Ziele der Hochschulbildung. Kriterien der Studienortwahl. Auslandsstudium. Zusammenarbeit der Hochschule mit Unternehmen. Bachelor und Master. Themen: Einstellung zu einem Recht aller Abiturienten auf ein Studium oder nur der Allerbesten; Universitäten sollten ein Selektionsrecht haben; Akzeptanz von Studiengebühren; Zustimmung zu folgenden Aussagen (Skala): Hochschulen sollten mehr Programme für Teilzeitstudenten anbieten, Hochschulen sollten Studenten mit vielfältigem sozialen und kulturellen Hintergrund aufnehmen, Studienpläne sollten sich auf spezifisches Fachwissen oder auf die Vermittlung allgemeiner Kompetenzen konzentrieren; wichtigste Ziele der Hochschulbildung (Skala): Ausbildung für den Arbeitsmarkt, persönliche Entwicklung, Ausbildung zum aktiven Bürger; wichtigste Aspekte der Studienortwahl: erfolgt nach Ruf der Hochschule, nach Lage, in Hinblick auf Freunde und Kosten, unabhängige Berichte über die Qualität sowie Rankings über die Leistung von Universitäten dienen als Entscheidungshilfe, Mitarbeit von Studierenden bei der Erstellung von Qualitätsberichten und Rankings; beabsichtigtes Auslandsstudium; Hindernisse für ein Auslandsstudium (fehlende Informationen, Geldmangel, fehlende Möglichkeit der Leistungsanerkennung der bisherigen Studienzeit im Ausland, unterschiedliche Qualität der Bildung, Sprachbarrieren, keine Förderung durch Dozenten); Einstellung zum Auslandsstudium (Skala): Anerkennung kurzer Studienaufenthalte im Ausland durch die Heimatuniversität, Auslandstudium sollte Bestandteil eines jeden Studienplans sein, ECTS Credit Points für Kurse an eigener Hochschule und für Auslandsaufenthalte, Wunsch nach Praktika in Privatunternehmen als Teil des Studienplans, Wichtigkeit der universitären Förderung von Innovation und unternehmerischem Denken bei Studenten und Angestellten, Wunsch nach einem Angebot maßgeschneiderter Studienpläne für Unternehmen zur Förderung der Weiterleitung von Arbeitskräften; Unternehmen sollten stärker an Hochschulorganisation beteiligt sein; Zukunftspläne nach dem Abschluss des Studiums. Demographie: Einrichtung, an der der Befragte studiert; Geschlecht; Alter; Land, in dem Hochschulreife erlangt wurde; Studiendauer; Studienrichtung; Vollzeitstudent; Studienstatus; an der Universität vergebene Abschlüsse. Zusätzlich verkodet wurde: Befragten-ID; Land; Interviewer-ID; Interviewsprache; Interviewdatum; Interviewdauer (Interviewbeginn und Interviewende); Interviewmodus (Mobiltelefon oder Festnetz); Region. Attitudes of students towards higher education. Topics: preference of selected statements: right of all qualified students to study vs. right only for very best students, admittance of all students to universities vs. right of universities to select, higher education free of charge vs. acceptability of student fees in combination with grants and loans; attitude towards the following statements on higher education institutions (HEIs): should provide more programmes for part time students, should promote activities to increase variety of social and cultural backgrounds of students, study programmes should focus on teaching specialized knowledge, study programmes should include generic competences; importance of each of the following purposes of higher education: provide students with skills to be successful on the labour market, enhance personal development, education for active citizenship; attitude towards selected statements regarding the choice of the institution where to study: choice on the basis of reputation of the institution and study programmes, choice on the basis of other factors (e.g. location, friends, cost, …), sufficient availability of information materials, need for quality reports on universities, need for performance rankings, involvement of students in quality reports and rankings; considerations to study abroad; importance of each of the following obstacles with regard to studying abroad: lack of information on study opportunities, lack of funds, difficulty to obtain recognition for periods spent abroad, different quality of education abroad, language barriers, lack of encouragement by home university; attitude towards the following statements: short study periods abroad are mostly recognised by home university, all study programmes should include short study periods abroad, most non-mobile students obtain ECTS credit points for studies completed at their institutions, most mobile students obtain ECTS credit points for studies abroad, possibility of work placements in private enterprises as part of study programme, importance for HEIs to foster innovation and entrepreneurial mindset among students and staff, provision of tailor-made study programmes for enterprises by HEIs, more involvement of enterprises in higher education; future plans after graduation. Demography: study institute; sex; age; country where upper secondary diploma was obtained; number of years in higher education; field of study; full time student; study status; obtainable degrees at institution. Additionally coded was: respondent ID; country; interviewer ID; language of the interview; date of interview; time of the beginning of the interview; duration of the interview; type of phone line; region.

Time series data for the statistic Government expenditure on tertiary education as % of GDP (%) and country Panama. Indicator Definition:Total general (local, regional and central) government expenditure on tertiary education (current, capital, and transfers), expressed as a percentage of GDP. It includes expenditure funded by transfers from international sources to government. Divide total government expenditure for a given level of education (ex. primary, secondary, or all levels combined) by the GDP, and multiply by 100. A higher percentage of GDP spent on education shows a higher government priority for education, but also a higher capacity of the government to raise revenues for public spending, in relation to the size of the country's economy. When interpreting this indicator however, one should keep in mind in some countries, the private sector and/or households may fund a higher proportion of total funding for education, thus making government expenditure appear lower than in other countries. Limitations: In some instances data on total public expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/The indicator "Government expenditure on tertiary education as % of GDP (%)" stands at 0.6968 as of 12/31/2012, the lowest value since 12/31/1982. Regarding the One-Year-Change of the series, the current value constitutes a decrease of -0.8269 percent compared to the value the year prior.The 1 year change in percent is -0.8269.The 10 year change in percent is -40.91.The Serie's long term average value is 0.754. It's latest available value, on 12/31/2012, is 7.59 percent lower, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/1971, to it's latest available value, on 12/31/2012, is +77.65%.The Serie's change in percent from it's maximum value, on 12/31/2000, to it's latest available value, on 12/31/2012, is -42.25%.

This dataset provides a country–year panel for OECD countries covering the period 2010–2024. It combines annual data on public, private and total social expenditure as a share of GDP with the World Happiness Index (WHI) and the Human Development Index (HDI).The data are constructed to analyze the relationships between social spending, subjective well-being and human development in OECD countries. The panel structure (one observation per country per year) makes the dataset suitable for descriptive analysis as well as regression-based empirical research.ContentsThe main Excel file contains a single data sheet:Sheet: data_setEach row corresponds to a specific country–year observation for an OECD member state.Variables:Country: Country name (OECD member; e.g., “Australia”, “Türkiye”, “United States”).iso3: ISO 3166-1 alpha-3 country code (e.g., “AUS”, “TUR”, “USA”).year: Calendar year (2010–2024).pub_socexp_gdp: Public social expenditure as a percentage of GDP (%).priv_socexp_gdp: Private (mandatory and voluntary) social expenditure as a percentage of GDP (%).tot_socexp_gdp: Total social expenditure (public + private) as a percentage of GDP (%).WHI: World Happiness Index; average national happiness score on a 0–10 scale based on the Cantril ladder question.HDI: Human Development Index; composite index of three basic dimensions of human development (health, education, and standard of living).income_group: Binary country income group indicator used in the analysis. High‑income OECD countries are coded as 1 (“High”), and all other OECD members (upper‑middle, lower‑middle and low income) are coded as 0 (“NonHigh”). Income groups were constructed using data from the OECD Data Explorer (2024) and the World Bank country income classification for 2024, based on PPP (purchasing power parity) income thresholds.Empty cells indicate that data for the corresponding country–year observation are not available in the original sources or were not included in the analytical sample due to missingness.Data sourcesSocial expenditure (pub_socexp_gdp, priv_socexp_gdp, tot_socexp_gdp)Data are taken from the OECD Social Expenditure Database (SOCX). SOCX provides reliable and internationally comparable statistics on public and mandatory and voluntary private social expenditure at the program level for 38 OECD countries (and some accession countries), with coverage from 1980 and estimates for more recent years.Reference: OECD Social Expenditure Database (SOCX), https://www.oecd.org/en/data/datasets/social-expenditure-database-socx.html.World Happiness Index (WHI)Happiness data are drawn from the World Happiness Report, accessed via HumanProgress.org (World Happiness Report section). The index is based on average national values for answers to the Cantril ladder question, which asks respondents to evaluate their current life on a 0–10 scale, with the worst possible life as 0 and the best possible life as 10.Reference: World Happiness Report; HumanProgress.org, https://humanprogress.org.Human Development Index (HDI)HDI data are drawn from the Human Development Index series compiled by the United Nations Development Programme (UNDP), accessed via HumanProgress.org (Human Development Index section). The HDI measures three basic dimensions of human development: life expectancy at birth; an education component (adult literacy rate and school enrollment); and GDP per capita (purchasing power parity, PPP, in U.S. dollars), combined into a composite index.Reference: United Nations Development Programme (UNDP), Human Development Reports; HumanProgress.org, https://humanprogress.org.Data construction and coverageThe dataset is restricted to OECD member countries and the years 2010–2024.WHI and HDI series are matched to OECD social expenditure data using ISO3 country codes and calendar years.In addition, a binary income group variable (income_group) was created to distinguish high‑income OECD countries from other OECD members, using the World Bank’s 2024 income thresholds (PPP‑based) and country information from the OECD Data Explorer (2024).Some country–year combinations, particularly in later years (e.g., 2022–2024), contain missing values where the original sources do not provide data or only provide partial estimates. These are retained as empty cells.The empirical analyses in the associated study are conducted on subsets of the data restricted to complete cases for the relevant variables.Researchers can use this dataset to replicate the results of the associated study or to conduct additional analyses on the links between social expenditure, happiness and human development within the OECD context.If you use this dataset, please cite both this data file and the original data providers (OECD, World Happiness Report, UNDP, and HumanProgress.org).

Abstract

The dashboard project collects new data in each country using three new instruments: a School Survey, a Policy Survey, and a Survey of Public Officials. Data collection involves school visits, classroom observations, legislative reviews, teacher and student assessments, and interviews with teachers, principals, and public officials. In addition, the project draws on some existing data sources to complement the new data it collects. A major objective of the GEPD project was to develop focused, cost-effective instruments and data-collection procedures, so that the dashboard can be inexpensive enough to be applied (and re-applied) in many countries. The team achieved this by streamlining and simplifying existing instruments, and thereby reducing the time required for data collection and training of enumerators.

Geographic coverage

National

Analysis unit

Schools, teachers, students, public officials

Kind of data

Sample survey data [ssd]

Sampling procedure

The aim of the Global Education Policy Dashboard school survey is to produce nationally representative estimates, which will be able to detect changes in the indicators over time at a minimum power of 80% and with a 0.05 significance level. We also wish to detect differences by urban/rural location. For our school survey, we will employ a two-stage random sample design, where in the first stage a sample of typically around 200 schools, based on local conditions, is drawn, chosen in advance by the Bank staff. In the second stage, a sample of teachers and students will be drawn to answer questions from our survey modules, chosen in the field. A total of 10 teachers will be sampled for absenteeism. Five teachers will be interviewed and given a content knowledge exam. Three 1st grade students will be assessed at random, and a classroom of 4th grade students will be assessed at random. Stratification will be based on the school’s urban/rural classification and based on region. When stratifying by region, we will work with our partners within the country to make sure we include all relevant geographical divisions. For our Survey of Public Officials, we will sample a total of 200 public officials. Roughly 60 officials are typically surveyed at the federal level, while 140 officials will be surveyed at the regional/district level. For selection of officials at the regional and district level, we will employ a cluster sampling strategy, where roughly 10 regional offices (or whatever the secondary administrative unit is called) are chosen at random from among the regions in which schools were sampled. Then among these 10 regions, we also typically select around 10 districts (tertiary administrative level units) from among the districts in which schools were sampled. The result of this sampling approach is that for 10 clusters we will have links from the school to the district office to the regional office to the central office. Within the regions/districts, five or six officials will be sampled, including the head of organization, HR director, two division directors from finance and planning, and one or two randomly selected professional employees among the finance, planning, and one other service related department chosen at random. At the federal level, we will interview the HR director, finance director, planning director, and three randomly selected service focused departments. In addition to the directors of each of these departments, a sample of 9 professional employees will be chosen in each department at random on the day of the interview.

Sampling deviation

In order to visit two schools per day, we clustered at the sector level choosing two schools per cluster. With a sample of 200 schools, this means that we had to allocate 100 PSUs. We combined this clustering with stratification by district and by the urban rural status of the schools. The number of PSUs allocated to each stratum is proportionate to the number of schools in each stratum (i.e. the district X urban/rural status combination).

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The dashboard project collects new data in each country using three new instruments: a School Survey, a Policy Survey, and a Survey of Public Officials. Data collection involves school visits, classroom observations, legislative reviews, teacher and student assessments, and interviews with teachers, principals, and public officials. In addition, the project draws on some existing data sources to complement the new data it collects. A major objective of the GEPD project was to develop focused, cost-effective instruments and data-collection procedures, so that the dashboard can be inexpensive enough to be applied (and re-applied) in many countries. The team achieved this by streamlining and simplifying existing instruments, and thereby reducing the time required for data collection and training of enumerators.

More information pertaining to each of the three instruments can be found below: - School Survey: The School Survey collects data primarily on practices (the quality of service delivery in schools), but also on some de facto policy indicators. It consists of streamlined versions of existing instruments—including Service Delivery Surveys on teachers and inputs/infrastructure, Teach on pedagogical practice, Global Early Child Development Database (GECDD) on school readiness of young children, and the Development World Management Survey (DWMS) on management quality—together with new questions to fill gaps in those instruments. Though the number of modules is similar to the full version of the Service Delivery Indicators (SDI) Survey, the number of items and the complexity of the questions within each module is significantly lower. The School Survey includes 8 short modules: School Information, Teacher Presence, Teacher Survey, Classroom Observation, Teacher Assessment, Early Learner Direct Assessment, School Management Survey, and 4th-grade Student Assessment. For a team of two enumerators, it takes on average about 4 hours to collect all information in a given school. For more information, refer to the Frequently Asked Questions.

• Policy Survey: The Policy Survey collects information to feed into the policy de jure indicators. This survey is filled out by key informants in each country, drawing on their knowledge to identify key elements of the policy framework (as in the SABER approach to policy-data collection that the Bank has used over the past 7 years). The survey includes questions on policies related to teachers, school management, inputs and infrastructure, and learners. In total, there are 52 questions in the survey as of June 2020. The key informant is expected to spend 2-3 days gathering and analyzing the relavant information to answer the survey questions.

• Survey of Public Officials: The Survey of Public Officials collects information about the capacity and orientation of the bureaucracy, as well as political factors affecting education outcomes. This survey is a streamlined and education-focused version of the civil-servant surveys that the Bureaucracy Lab (a joint initiative of the Governance Global Practice and the Development Impact Evaluation unit of the World Bank) has implemented in several countries. The survey includes questions about technical and leadership skills, work environment, stakeholder engagement, impartial decision-making, and attitudes and behaviors. The survey takes 30-45 minutes per public official and is used to interview Ministry of Education officials working at the central, regional, and district levels in each country.

Cleaning operations

Data quality control was performed in R and Stata Code to calculate all indicators can be found on github here: https://github.com/worldbank/GEPD/blob/master/Countries/Rwanda/2019/School/01_data/03_school_data_cleaner.R

Sampling error estimates

The aim of the Global Education Policy Dashboard school survey is to produce nationally representative estimates, which will be able to detect changes in the indicators over time at a minimum power of 80% and with a 0.05 significance level.

In many developing countries, weak institutions and fiscal mismanagement often lead to poor access and weak delivery of public services, such as health, education, and basic infrastructure. Improving the efficiency of public spending is an important economic and political tool, along with good governance and a strong revenue stream, for improved public sector management. An efficient public sector serves an important role in a country’s economic development by promoting adequate and appropriate allocation of resources. Even small steps toward prudent fiscal management can benefit the poor and other disadvantaged groups by allowing the provision of effective and targeted public services. A better understanding of the linkages between public expenditure and development can provide insights for poverty reduction strategies and key development goals. With this objective, IFPRI has compiled the Statistics of Public Expenditure for Economic Development (SPEED) database, providing the most comprehensive and publicly available public expenditure information for 67 countries and six sectors: agriculture, education, health, defense, social protection, and transportation and communication, for the time period 1980-2007. The SPEED database is available for use by researchers, policymakers, donors, and others in the development community for a variety of economic and policy applications.