Dataset Overview 📝

The dataset includes the following key indicators, collected for over 200 countries:

• Government Expenditure on Education (% of GDP): Shows the percentage of a country’s GDP allocated to education.
• Literacy Rate (Adult Total): Represents the percentage of the population aged 15 and above who can read and write.
• Primary Completion Rate: The percentage of children who complete their primary education within the official age group.
• Pupil-Teacher Ratio (Primary and Secondary Education): Indicates the average number of students per teacher at the primary and secondary levels.
• School Enrollment Rates (Primary, Secondary, Tertiary): Reflects the percentage of the relevant age group enrolled in schools across different education levels.

Data Source 🌐

World Bank: This dataset is compiled from the World Bank's educational database, providing reliable, updated statistics on educational progress worldwide.

Potential Use Cases 🔍 This dataset is ideal for anyone interested in:

Educational Research: Understanding how education spending and policies impact literacy, enrollment, and overall educational outcomes. Predictive Modeling: Building models to predict educational success factors, such as completion rates and literacy. Global Education Analysis: Analyzing trends in global education systems and how different countries allocate resources to education. Policy Development: Helping governments and organizations make data-driven decisions regarding educational reforms and funding.

Key Questions You Can Explore 🤔

How does government expenditure on education correlate with literacy rates and school enrollment across different regions? What are the trends in pupil-teacher ratios over time, and how do they affect educational outcomes? How do education indicators differ between low-income and high-income countries? Can we predict which countries will achieve universal primary education based on current trends?

Important Notes ⚠️ - Missing Data: Some values may be missing for certain years or countries. Consider using techniques like forward filling or interpolation when working with time series models. - Data Limitations: This dataset provides global averages and may not capture regional disparities within countries.

This meticulously curated dataset offers a panoramic view of education on a global scale , delivering profound insights into the dynamic landscape of education across diverse countries and regions. Spanning a rich tapestry of educational aspects, it encapsulates crucial metrics including out-of-school rates, completion rates, proficiency levels, literacy rates, birth rates, and primary and tertiary education enrollment statistics. A treasure trove of knowledge, this dataset is an indispensable asset for discerning researchers, dedicated educators, and forward-thinking policymakers, enabling them to embark on a transformative journey of assessing, enhancing, and reshaping education systems worldwide.

Key Features: - Countries and Areas: Name of the countries and areas. - Latitude: Latitude coordinates of the geographical location. - Longitude: Longitude coordinates of the geographical location. - OOSR_Pre0Primary_Age_Male: Out-of-school rate for pre-primary age males. - OOSR_Pre0Primary_Age_Female: Out-of-school rate for pre-primary age females. - OOSR_Primary_Age_Male: Out-of-school rate for primary age males. - OOSR_Primary_Age_Female: Out-of-school rate for primary age females. - OOSR_Lower_Secondary_Age_Male: Out-of-school rate for lower secondary age males. - OOSR_Lower_Secondary_Age_Female: Out-of-school rate for lower secondary age females. - OOSR_Upper_Secondary_Age_Male: Out-of-school rate for upper secondary age males. - OOSR_Upper_Secondary_Age_Female: Out-of-school rate for upper secondary age females. - Completion_Rate_Primary_Male: Completion rate for primary education among males. - Completion_Rate_Primary_Female: Completion rate for primary education among females. - Completion_Rate_Lower_Secondary_Male: Completion rate for lower secondary education among males. - Completion_Rate_Lower_Secondary_Female: Completion rate for lower secondary education among females. - Completion_Rate_Upper_Secondary_Male: Completion rate for upper secondary education among males. - Completion_Rate_Upper_Secondary_Female: Completion rate for upper secondary education among females. - Grade_2_3_Proficiency_Reading: Proficiency in reading for grade 2-3 students. - Grade_2_3_Proficiency_Math: Proficiency in math for grade 2-3 students. - Primary_End_Proficiency_Reading: Proficiency in reading at the end of primary education. - Primary_End_Proficiency_Math: Proficiency in math at the end of primary education. - Lower_Secondary_End_Proficiency_Reading: Proficiency in reading at the end of lower secondary education. - Lower_Secondary_End_Proficiency_Math: Proficiency in math at the end of lower secondary education. - Youth_15_24_Literacy_Rate_Male: Literacy rate among male youths aged 15-24. - Youth_15_24_Literacy_Rate_Female: Literacy rate among female youths aged 15-24. - Birth_Rate: Birth rate in the respective countries/areas. - Gross_Primary_Education_Enrollment: Gross enrollment in primary education. - Gross_Tertiary_Education_Enrollment: Gross enrollment in tertiary education. - Unemployment_Rate: Unemployment rate in the respective countries/areas.

The average for 2022 based on 76 countries was 88.21 percent. The highest value was in Andorra: 100 percent and the lowest value was in San Marino: 34.16 percent. The indicator is available from 1998 to 2023. Below is a chart for all countries where data are available.

The COVID-19 pandemic had severe impacts on almost every aspect of life, from health via economy to education. School closures around the world caused disruptions in learning development of children and youth. South Asia as well as Latin America and the Caribbean had the highest number of weeks where schools were either partially or fully closed. In the former, a total of 84 weeks of education were conducted either partially or completely remote. On the other hand, Europe and Central Asia saw just above 30 weeks of some form of remote learning. Infrastructure and remote learning It may not come as a surprise, then, that South Asia and Latin America and the Caribbean were the two regions with the highest levels of learning delays caused by the COVID-19 pandemic. Moreover, different countries in different regions have different infrastructures that make remote learning possible. For instance, Sub-Saharan Africa, where many countries have a poor internet infrastructure, was the region with the highest number of academic weeks held in person as remote learning was impossible in many areas. Economic impact
The learning disruptions caused by the pandemic could also have severe economic impacts in the future if counter measures are not taken. Estimates show that globally, *** trillion U.S. dollars of GDP could be lost annually by 2040 due to the educational disruptions caused by COVID-19.

Geographic coverage

The database includes datsa from the following countries: - Albania
- Angola
- Armenia
- Azerbaijan
- Bangladesh
- Belarus
- Benin - Bhutan
- Bolivia
- Bosnia and Herzegovina
- Brazil
- Brazil (NE & SE)
- Brazil, Northeast - Bulgaria
- Burkina Faso
- Burundi
- C.A.R.
- Cambodia
- Cameroon
- Chad
- Chile - China (9 Provinces)
- Colombia
- Comoros
- Congo Rep.
- Costa Rica
- Cote d'Ivoire - DR Congo
- Dominican Rep.
- Ecuador
- Egypt - Ethiopia
- Gabon - Gambia
- Ghana - Guatemala - Guinea
- Guinea-Bissau - Guyana
- Haiti - Honduras
- India - Indonesia - Iraq
- Jamaica
- Jordan
- Kazakhstan
- Kenya - Kyrgyz Rep.
- Lao PDR
- Lesotho
- Liberia
- Macedonia - Madagascar
- Malawi
- Maldives
- Mali
- Marshall Isls.
- Mauritania
- Mexico
- Moldova
- Mongolia
- Montenegro
- Morocco
- Mozambique
- Myanmar
- Namibia
- Nepal - Nicaragua - Niger - Nigeria
- Pakistan
- Panama
- Papua New Guinea
- Paraguay
- Peru
- Philippines
- Romania
- Rwanda
- Senegal
- Serbia
- Sierra Leone
- South Africa
- Suriname
- Swaziland - Tajikistan
- Tanzania
- Thailand
- Timor Leste
- Togo
- Turkey
- Uganda
- Ukraine
- Uzbekistan
- Venezuela - Vietnam
- Zambia
- Zimbabwe

Kind of data

Aggregate data [agg]

Mode of data collection

Other [oth]

Will all children be able to read by 2030? The ability to read with comprehension is a foundational skill that every education system around the world strives to impart by late in primary school—generally by age 10. Moreover, attaining the ambitious Sustainable Development Goals (SDGs) in education requires first achieving this basic building block, and so does improving countries’ Human Capital Index scores. Yet past evidence from many low- and middle-income countries has shown that many children are not learning to read with comprehension in primary school. To understand the global picture better, we have worked with the UNESCO Institute for Statistics (UIS) to assemble a new dataset with the most comprehensive measures of this foundational skill yet developed, by linking together data from credible cross-national and national assessments of reading. This dataset covers 115 countries, accounting for 81% of children worldwide and 79% of children in low- and middle-income countries. The new data allow us to estimate the reading proficiency of late-primary-age children, and we also provide what are among the first estimates (and the most comprehensive, for low- and middle-income countries) of the historical rate of progress in improving reading proficiency globally (for the 2000-17 period). The results show that 53% of all children in low- and middle-income countries cannot read age-appropriate material by age 10, and that at current rates of improvement, this “learning poverty” rate will have fallen only to 43% by 2030. Indeed, we find that the goal of all children reading by 2030 will be attainable only with historically unprecedented progress. The high rate of “learning poverty” and slow progress in low- and middle-income countries is an early warning that all the ambitious SDG targets in education (and likely of social progress) are at risk. Based on this evidence, we suggest a new medium-term target to guide the World Bank’s work in low- and middle- income countries: cut learning poverty by at least half by 2030. This target, together with improved measurement of learning, can be as an evidence-based tool to accelerate progress to get all children reading by age 10.

For further details, please refer to https://thedocs.worldbank.org/en/doc/e52f55322528903b27f1b7e61238e416-0200022022/original/Learning-poverty-report-2022-06-21-final-V7-0-conferenceEdition.pdf

Comparing the people using education & learning services in selected countries worldwide, the highest share can be found in Nigeria with ** percent of consumers falling into this category. South Africa follows in the second place, while Japan ends up at the bottom of the ranking.Statista Consumer Insights offer you all results of our exclusive Statista surveys, based on more than ********* interviews.

the 'Worldwide Education Attainment And Enrolment' dataset, provided by the World bank group.

**Dataset description : **The dataset was derived from the original copy, which was published on the world bank website. The primary purpose of this database is to document and analyses patterns of educational attainment rates in different country around the world.

We introduce a global dataset on education policies and systems across modern history (EPSM), which includes measures on compulsory education, ideological guidance and content of education, governmental intervention and level of education centralization, and teacher training. EPSM covers 157 countries with populations exceeding 1 million people, and the time series extends from 1789 to the present. EPSM opens up for studying several questions concerning political control and the politicized nature of education systems. In addition to describing the measures, we detail how the data were collected and discuss validity and reliability issues. Thereafter, we describe historical trends in various characteristics of the education system. Finally, we illustrate how our data can be used to address key questions about education and politics, replicating and extending recent analyses on the (reciprocal) relationship between education and democratization, the impact of education on political attitudes, and how rural inequality interacts with regime type in influencing education systems.

In 2022, Duolingo was the most popular learning and education app in the world, with ** million downloads. Toca Life World ranked second, with approximately ** million downloads. Toca Life World was also the second most downloaded education app in the United States in 2022. OCR-powered Photomath ranked third, with approximately ** million downloads from global users in the examined year.

As of 2024, Angola was the country worldwide where the lowest share of the population had a higher education of a bachelor's degree or higher. A high number of the countries on the list were located in Sub-Saharan Africa. On the other hand, Montenegro was the country where the highest share of the population had completed a bachelor's degree or more.

Context

In today's interconnected world, the issue of inequality in education stands as a stark reminder of the disparities that persist across countries and communities. While strides have been made to improve access to education, a significant proportion of children still lack the opportunity to learn, particularly in low-income and conflict-affected regions. Quality of education also diverges, with well-equipped schools in affluent areas contrasting with under-resourced institutions in marginalized settings. Gender inequality further compounds the problem, as cultural norms and economic factors often impede girls' education in certain societies. Tackling inequality in education isn't just a matter of fairness; it's a critical step towards building equitable societies and empowering individuals to contribute meaningfully to their own development and that of their nations.

Content

This dataset contains historical data covering a range of indicators pertaining to educational inequality on a global scale. The dataset's prominent components include: ISO3, Country, Human Development Groups, UNDP Developing Regions, HDI Rank (2021), and Inequality in Education spanning the years 2010 to 2021.

Dataset Glossary (Column-wise)

• ISO3 - ISO3 for the Country/Territory
• Country - Name of the Country/Territory
• Human Development Groups - Human Development Groups
• UNDP Developing Regions - UNDP Developing Regions
• HDI Rank (2021) - Human Development Index Rank for 2021
• Inequality in Education (2010) - Inequality in Education for 2010
• Inequality in Education (2011) - Inequality in Education for 2011
• Inequality in Education (2012) - Inequality in Education for 2012
• Inequality in Education (2013) - Inequality in Education for 2013
• Inequality in Education (2014) - Inequality in Education for 2014
• Inequality in Education (2015) - Inequality in Education for 2015
• Inequality in Education (2016) - Inequality in Education for 2016
• Inequality in Education (2017) - Inequality in Education for 2017
• Inequality in Education (2018) - Inequality in Education for 2018
• Inequality in Education (2019) - Inequality in Education for 2019
• Inequality in Education (2020) - Inequality in Education for 2020
• Inequality in Education (2021) - Inequality in Education for 2021

Data Dictionary

• UNDP Developing Regions:
  • SSA - Sub-Saharan Africa
  • LAC - Latin America and the Caribbean
  • EAP - East Asia and the Pacific
  • AS - Arab States
  • ECA - Europe and Central Asia
  • SA - South Asia

Structure of the Dataset

https://i.imgur.com/qX5cmUX.png" alt="">

Acknowledgement

This Dataset is created from Human Development Reports. This Dataset falls under the Creative Commons Attribution 3.0 IGO License. You can check the Terms of Use of this Data. If you want to learn more, visit the Website.

Cover Photo by: Image by storyset on Freepik

Thumbnail by: Educational Vectors by Vecteezy

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.

This dataset tracks annual distribution of students across grade levels in Global Education Academy Middle School

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

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?

In 2019, global education expenditure amounted to approximately ************* U.S. dollars, with the vast majority of this spending being from governments, or from development assistance. Private households spent around *** billion dollars in 2019.

Explore Saudi Arabia's education development indicators , including net attendance rates, literacy rates, teacher salaries, and more. Discover valuable insights and trends in education data for Saudi Arabia and other countries in the region.

UIS, attendance rate, literacy rate, teacher salaries, education indicators, net enrolment rate, drop-out rate, population, schooling

Saudi Arabia, Kuwait, Oman, Qatar, Bahrain, China, India

Follow data.kapsarc.org for timely data to advance energy economics research.

Note: © 2016 The World Bank Group, All Rights Reserved.Saudi Arabia education indicator related dataset from the world bank. There are over 1300 series in the dataset, we have selected those relevant to education category. Checkout other related dataset Population, Health and Employment in demographic category of our portal.Citation: "World Development Indicators| World Databank". Databank.worldbank.org. N.p., 2016. Web. 10 Mar. 2016.

United States US: Compulsory Education: Duration data was reported at 13.000 Year in 2017. This stayed constant from the previous number of 13.000 Year for 2016. United States US: Compulsory Education: Duration data is updated yearly, averaging 13.000 Year from Dec 1998 (Median) to 2017, with 20 observations. The data reached an all-time high of 13.000 Year in 2017 and a record low of 13.000 Year in 2017. United States US: Compulsory Education: Duration data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Education Statistics. Duration of compulsory education is the number of years that children are legally obliged to attend school.; ; United Nations Educational, Scientific, and Cultural Organization (UNESCO) Institute for Statistics.; Median; Aggregate data are based on World Bank estimates.

The average for 2022 based on 113 countries was 13.94 percent. The highest value was in Sierra Leone: 29.37 percent and the lowest value was in Nigeria: 4.3 percent. The indicator is available from 1972 to 2023. Below is a chart for all countries where data are available.