Patterns of educational attainment vary greatly across countries, and across population groups within countries. In some countries, virtually all children complete basic education whereas in others large groups fall short. The primary purpose of this database, and the associated research program, is to document and analyze these differences using a compilation of a variety of household-based data sets: Demographic and Health Surveys (DHS); Multiple Indicator Cluster Surveys (MICS); Living Standards Measurement Study Surveys (LSMS); as well as country-specific Integrated Household Surveys (IHS) such as Socio-Economic Surveys.As shown at the website associated with this database, there are dramatic differences in attainment by wealth. When households are ranked according to their wealth status (or more precisely, a proxy based on the assets owned by members of the household) there are striking differences in the attainment patterns of children from the richest 20 percent compared to the poorest 20 percent.In Mali in 2012 only 34 percent of 15 to 19 year olds in the poorest quintile have completed grade 1 whereas 80 percent of the richest quintile have done so. In many countries, for example Pakistan, Peru and Indonesia, almost all the children from the wealthiest households have completed at least one year of schooling. In some countries, like Mali and Pakistan, wealth gaps are evident from grade 1 on, in other countries, like Peru and Indonesia, wealth gaps emerge later in the school system.The EdAttain website allows a visual exploration of gaps in attainment and enrollment within and across countries, based on the international database which spans multiple years from over 120 countries and includes indicators disaggregated by wealth, gender and urban/rural location. The database underlying that site can be downloaded from here.

The number of pupils in secondary education worldwide increased almost constantly since 2000. While around 445 million children were enrolled in secondary school in 2000, the number reached about 641 million pupils in 2023. At the same time, the completion rate of lower secondary education also increased, reaching over 74 percent in 2023. Primary education Since the turn of the millennium, the number of pupils in primary education also increased significantly. About 655 million children were enrolled in primary education in 2000; a number which had grown to 771 million in 2023. However, a growing number of pupils complete primary education, reaching 88 percent in 2023. Out-of-school population Despite a growing number of students in primary and secondary schools, not all children are undertaking elementary education. 16 percent of girls were not in lower secondary schools in 2018, which was a slightly higher proportion than for boys. Furthermore, several pupils were hit by the COVID-19 pandemic which forced schools all around the world to close.

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 werer 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

The sample for the Global Education Policy Dashboard in SLE was based in part on a previous sample of 260 schools which were part of an early EGRA study. Details from the sampling for that study are quoted below. An additional booster sample of 40 schools was chosen to be representative of smaller schools of less than 30 learners.

EGRA Details:

"The sampling frame began with the 2019 Annual School Census (ASC) list of primary schools as provided by UNICEF/MBSSE where the sample of 260 schools for this study were obtained from an initial list of 7,154 primary schools. Only schools that meet a pre-defined selection criteria were eligible for sampling.

To achieve the recommended sample size of 10 learners per grade, schools that had an enrolment of at least 30 learners in Grade 2 in 2019 were considered. To achieve a high level of confidence in the findings and generate enough data for analysis, the selection criteria only considered schools that: • had an enrolment of at least 30 learners in grade 1; and • had an active grade 4 in 2019 (enrolment not zero)

The sample was taken from a population of 4,597 primary schools that met the eligibility criteria above, representing 64.3% of all the 7,154 primary schools in Sierra Leone (as per the 2019 school census). Schools with higher numbers of learners were purposefully selected to ensure the sample size could be met in each site.

As a result, a sample of 260 schools were drawn using proportional to size allocation with simple random sampling without replacement in each stratum. In the population, there were 16 districts and five school ownership categories (community, government, mission/religious, private and others). A total of 63 strata were made by forming combinations of the 16 districts and school ownership categories. In each stratum, a sample size was computed proportional to the total population and samples were drawn randomly without replacement. Drawing from other EGRA/EGMA studies conducted by Montrose in the past, a backup sample of up to 78 schools (30% of the sample population) with which enumerator teams can replace sample schools was also be drawn.

In the distribution of sampled schools by ownership, majority of the sampled schools are owned by mission/religious group (62.7%, n=163) followed by the government owned schools at 18.5% (n=48). Additionally, in school distribution by district, majority of the sampled schools (54%) were found in Bo, Kambia, Kenema, Kono, Port Loko and Kailahun districts. Refer to annex 9. for details on the population and sample distribution by district."

Because of the restriction that at least 30 learners were available in Grade 2, we chose to add an additional 40 schools to the sample from among smaller schools, with between 3 and 30 grade 2 students. The objective of this supplement was to make the sample more nationally representative, as the restriction reduced the sampling frame for the EGRA/EGMA sample by over 1,500 schools from 7,154 to 4,597.

The 40 schools were chosen in a manner consistent with the original set of EGRA/EGMA schools. The 16 districts formed the strata. In each stratum, the number of schools selected were proportional to the total population of the stratum, and within stratum schools were chosen with probability proportional to size.

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

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, 1.6 trillion U.S. dollars of GDP could be lost annually by 2040 due to the educational disruptions caused by COVID-19.

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?

Amidst school closures - part of distancing measures implemented in an attempt to limit the spread of COVID-19 - schools around the world were faced with the task of providing children with distance education material. According to a survey carried out in 30 countries in Latin America and the Caribbean between May and June 2020, around one third of countries analyzed had teachers in the primary to upper secondary levels using online platforms for distanced education. For pre-primary schools, the share of countries was 29 percent. Mobile phones were the second most used platform.

How digitally prepared were teachers?

The level of preparedness of Latin American and Caribbean teachers for providing pupils with the resources necessary for distance learning in the context of the pandemic varied depending on the school level. While the largest share of prepared teachers was for those working with secondary school students, the lowest share was observed in pre-schools. This is to a degree consistent with the developmental stages of children, as they are introduced to technological tools in further levels, rather than in earlier ones.

The case of universities

As for universities, according to a 2020 survey, only a fourth of professors felt prepared for the inclusion of digital technologies in the classroom, while around 23 percent felt little prepared or unprepared. However, more than half them admitted having access to digital training programs. Furthermore, nearly four out of ten professors were working in universities with below average internet speed or with no access at all.

Success.ai’s Education Industry Data with B2B Contact Data for Education Professionals Worldwide enables businesses to connect with educators, administrators, and decision-makers in educational institutions across the globe. With access to over 170 million verified professional profiles, this dataset includes crucial contact details for key education professionals, including school principals, department heads, and education directors.

Whether you’re targeting K-12 educators, university faculty, or educational administrators, Success.ai ensures your outreach is effective and efficient, providing the accurate data needed to build meaningful connections.

Why Choose Success.ai’s Education Professionals Data?

1. Comprehensive Contact Information
2. Access verified work emails, direct phone numbers, and LinkedIn profiles for educators, administrators, and education leaders worldwide.

3. AI-driven validation guarantees 99% accuracy, ensuring the highest level of reliability for your outreach.

4. Global Reach Across Educational Roles

5. Includes profiles of K-12 teachers, university professors, education directors, and school administrators.

6. Covers regions such as North America, Europe, Asia-Pacific, South America, and the Middle East.

7. Continuously Updated Datasets

8. Real-time updates ensure that you’re working with the most current contact information, keeping your outreach relevant and timely.

9. Ethical and Compliant

10. Success.ai’s data is fully GDPR, CCPA, and privacy regulation-compliant, ensuring ethical data usage in all your outreach efforts.

Data Highlights:

• 170M+ Verified Professional Profiles: Includes educators and administrators across various levels of education.
• 50M Work Emails: Verified and AI-validated emails for seamless communication.
• 30M Company Profiles: Rich insights into educational institutions, supporting detailed targeting.
• 700M Global Professional Profiles: Enriched datasets for comprehensive outreach across the education sector.

Key Features of the Dataset:

1. Education Decision-Maker Profiles
2. Identify and connect with decision-makers at educational institutions, including principals, department heads, and education directors.

3. Reach K-12 educators, higher education faculty, and administrative professionals with relevant needs.

4. Advanced Filters for Precision Targeting

5. Filter by educational level, subject area, location, and specific roles to tailor your outreach campaigns for precise results.

6. AI-Driven Enrichment

7. Profiles are enriched with actionable data to provide valuable insights, ensuring your outreach efforts are impactful and effective.

Strategic Use Cases:

1. Educational Product and Service Marketing
2. Promote educational tools, software, or services to decision-makers in schools, colleges, and universities.

3. Build relationships with educators to present curriculum solutions, digital learning platforms, and teaching resources.

4. Recruitment and Talent Acquisition

5. Target educational institutions and administrators with recruitment solutions or staffing services for teaching and support staff.

6. Engage with HR professionals in the education sector to promote job openings and talent acquisition services.

7. Professional Development Programs

8. Reach educators and administrators to offer professional development courses, certifications, or training programs.

9. Provide online learning solutions to enhance the skills of educators worldwide.

10. Research and Educational Partnerships

11. Connect with education leaders for research collaborations, institutional partnerships, and academic initiatives.

12. Foster relationships with decision-makers to support joint ventures in the education sector.

Why Choose Success.ai?

1. Best Price Guarantee

2. Success.ai offers high-quality, verified data at the best possible prices, making it a cost-effective solution for your outreach needs.

3. Seamless Integration

4. Integrate this verified contact data into your CRM using APIs or download it in your preferred format for streamlined use.

5. Data Accuracy with AI Validation

6. With AI-driven validation, Success.ai ensures 99% accuracy for all data, providing you with reliable and up-to-date information.

7. Customizable and Scalable Solutions

8. Tailor data to specific education sectors or roles, making it easy to target the right contacts for your campaigns.

APIs for Enhanced Functionality:

1. Data Enrichment API

2. Enhance existing records in your database with verified contact data for education professionals.

3. Lead Generation API

4. Automate lead generation campaigns for educational services and products, ensuring your marketing efforts are more efficient.

Leverage Success.ai’s B2B Contact Data for Education Professionals Worldwide to connect with educators, administrators, and decision-makers in the education sector. With veri...

Goal 4Ensure inclusive and equitable quality education and promote lifelong learning opportunities for allTarget 4.1: By 2030, ensure that all girls and boys complete free, equitable and quality primary and secondary education leading to relevant and effective learning outcomesIndicator 4.1.1: Proportion of children and young people (a) in grades 2/3; (b) at the end of primary; and (c) at the end of lower secondary achieving at least a minimum proficiency level in (i) reading and (ii) mathematics, by sexSE_TOT_PRFL: Proportion of children and young people achieving a minimum proficiency level in reading and mathematics (%)Indicator 4.1.2: Completion rate (primary education, lower secondary education, upper secondary education)SE_TOT_CPLR: Completion rate, by sex, location, wealth quintile and education level (%)Target 4.2: By 2030, ensure that all girls and boys have access to quality early childhood development, care and pre-primary education so that they are ready for primary educationIndicator 4.2.1: Proportion of children aged 24-59 months who are developmentally on track in health, learning and psychosocial well-being, by sexiSE_DEV_ONTRK: Proportion of children aged 36−59 months who are developmentally on track in at least three of the following domains: literacy-numeracy, physical development, social-emotional development, and learning (% of children aged 36-59 months)Indicator 4.2.2: Participation rate in organized learning (one year before the official primary entry age), by sexSE_PRE_PARTN: Participation rate in organized learning (one year before the official primary entry age), by sex (%)Target 4.3: By 2030, ensure equal access for all women and men to affordable and quality technical, vocational and tertiary education, including universityIndicator 4.3.1: Participation rate of youth and adults in formal and non-formal education and training in the previous 12 months, by sexSE_ADT_EDUCTRN: Participation rate in formal and non-formal education and training, by sex (%)Target 4.4: By 2030, substantially increase the number of youth and adults who have relevant skills, including technical and vocational skills, for employment, decent jobs and entrepreneurshipIndicator 4.4.1: Proportion of youth and adults with information and communications technology (ICT) skills, by type of skillSE_ADT_ACTS: Proportion of youth and adults with information and communications technology (ICT) skills, by sex and type of skill (%)Target 4.5: By 2030, eliminate gender disparities in education and ensure equal access to all levels of education and vocational training for the vulnerable, including persons with disabilities, indigenous peoples and children in vulnerable situationsIndicator 4.5.1: Parity indices (female/male, rural/urban, bottom/top wealth quintile and others such as disability status, indigenous peoples and conflict-affected, as data become available) for all education indicators on this list that can be disaggregatedSE_GPI_PTNPRE: Gender parity index for participation rate in organized learning (one year before the official primary entry age), (ratio)SE_GPI_TCAQ: Gender parity index of trained teachers, by education level (ratio)SE_GPI_PART: Gender parity index for participation rate in formal and non-formal education and training (ratio)SE_GPI_ICTS: Gender parity index for youth/adults with information and communications technology (ICT) skills, by type of skill (ratio)SE_IMP_FPOF: Immigration status parity index for achieving at least a fixed level of proficiency in functional skills, by numeracy/literacy skills (ratio)SE_NAP_ACHI: Native parity index for achievement (ratio)SE_LGP_ACHI: Language test parity index for achievement (ratio)SE_TOT_GPI: Gender parity index for achievement (ratio)SE_TOT_SESPI: Low to high socio-economic parity status index for achievement (ratio)SE_TOT_RUPI: Rural to urban parity index for achievement (ratio)SE_ALP_CPLR: Adjusted location parity index for completion rate, by sex, location, wealth quintile and education levelSE_AWP_CPRA: Adjusted wealth parity index for completion rate, by sex, location, wealth quintile and education levelSE_AGP_CPRA: Adjusted gender parity index for completion rate, by sex, location, wealth quintile and education levelTarget 4.6: By 2030, ensure that all youth and a substantial proportion of adults, both men and women, achieve literacy and numeracyIndicator 4.6.1: Proportion of population in a given age group achieving at least a fixed level of proficiency in functional (a) literacy and (b) numeracy skills, by sexSE_ADT_FUNS: Proportion of population achieving at least a fixed level of proficiency in functional skills, by sex, age and type of skill (%)Target 4.7: By 2030, ensure that all learners acquire the knowledge and skills needed to promote sustainable development, including, among others, through education for sustainable development and sustainable lifestyles, human rights, gender equality, promotion of a culture of peace and non-violence, global citizenship and appreciation of cultural diversity and of culture’s contribution to sustainable developmentIndicator 4.7.1: Extent to which (i) global citizenship education and (ii) education for sustainable development are mainstreamed in (a) national education policies; (b) curricula; (c) teacher education; and (d) student assessmentTarget 4.a: Build and upgrade education facilities that are child, disability and gender sensitive and provide safe, non-violent, inclusive and effective learning environments for allIndicator 4.a.1: Proportion of schools offering basic services, by type of serviceSE_ACS_CMPTR: Schools with access to computers for pedagogical purposes, by education level (%)SE_ACS_H2O: Schools with access to basic drinking water, by education level (%)SE_ACS_ELECT: Schools with access to electricity, by education level (%)SE_ACC_HNDWSH: Schools with basic handwashing facilities, by education level (%)SE_ACS_INTNT: Schools with access to the internet for pedagogical purposes, by education level (%)SE_ACS_SANIT: Schools with access to access to single-sex basic sanitation, by education level (%)SE_INF_DSBL: Proportion of schools with access to adapted infrastructure and materials for students with disabilities, by education level (%)Target 4.b: By 2020, substantially expand globally the number of scholarships available to developing countries, in particular least developed countries, small island developing States and African countries, for enrolment in higher education, including vocational training and information and communications technology, technical, engineering and scientific programmes, in developed countries and other developing countriesIndicator 4.b.1: Volume of official development assistance flows for scholarships by sector and type of studyDC_TOF_SCHIPSL: Total official flows for scholarships, by recipient countries (millions of constant 2018 United States dollars)Target 4.c: By 2030, substantially increase the supply of qualified teachers, including through international cooperation for teacher training in developing countries, especially least developed countries and small island developing StatesIndicator 4.c.1: Proportion of teachers with the minimum required qualifications, by education leveliSE_TRA_GRDL: Proportion of teachers who have received at least the minimum organized teacher training (e.g. pedagogical training) pre-service or in-service required for teaching at the relevant level in a given country, by sex and education level (%)

The average for 2021 based on 158 countries was 4.48 percent. The highest value was in Kiribati: 14.2 percent and the lowest value was in Nigeria: 0.38 percent. The indicator is available from 1970 to 2023. Below is a chart for all countries where data are available.

Policymakers in low- and middle-income countries who are working to improve student learning often find themselves flying blind. They see the budget that goes into education and (sometimes) the learning that students come out with, but they lack information on the crucial factors in between—the practices, policies, and politics—that drive those learning outcomes. The Global Education Policy Dashboard (GEPD) shines a light on those hidden drivers.

Many countries, despite having significantly increased access to education for their children and youth, now realize that they are facing a learning crisis (World Development Report 2018). In low- and middle-income countries, despite near universal enrollment in primary school, 53 percent of children cannot read and understand a simple story by late primary age (World Bank 2019). This statistic underlines the reality that schooling is not the same as learning—even though education policy often assumes that it is (Pritchett 2013). It shows just how far off track the world is from the aspiration embodied in Sustainable Development Goal 4, of providing at least quality secondary education to all children.

The World Development Report 2018 argued that the learning crisis has multiple causes: poor service delivery in schools and communities, unhealthy politics and low bureaucratic capacity, and policies that are not aligned toward learning for all. To tackle the crisis and improve learning for all children, countries need to know where they stand on these three key dimensions: practices (or service delivery), policies, and politics. But providing such a systemwide overview requires better measurement. Many of these drivers of learning are not captured by existing administrative systems. And although new measurement tools capture some of those aspects well, no single instrument pulls together data on all these areas. This gap leaves policymakers in the dark about what is working and what isn’t.

To fill this gap, the World Bank, with support from the Bill and Melinda Gates Foundation, the UK’s Department for International Development, and the Government of Japan, has launched a Global Education Policy Dashboard, which measures the drivers of learning outcomes in basic education around the world. In doing so, it highlights gaps between current practice and what the evidence suggests would be most effective in promoting learning, and it gives governments a way to set priorities and track progress as they work to close those gaps.

The average for 2022 based on 124 countries was 92.43 percent. The highest value was in Gibraltar: 130.58 percent and the lowest value was in Niger: 52.99 percent. The indicator is available from 1970 to 2023. Below is a chart for all countries where data are available.

The Global Report on Adult Learning and Education (GRALE) by the UNESCO Institute for Lifelong Learning (UIL) provides a clear and comprehensive picture of the state of adult learning and education around the world. Five reports have been published since 2009. GRALE 5 monitors the extent to which UNESCO Member States put their international commitments on adult education into practice and reflects data submitted by 159 countries.

The Global Report on Adult Learning and Education (GRALE) by the UNESCO Institute for Lifelong Learning (UIL) provides a clear and comprehensive picture of the state of adult learning and education around the world. Five reports have been published since 2009. GRALE 4 monitors the extent to which UNESCO Member States put their international commitments on adult education into practice and reflects data submitted by 159 countries.

This statistic shows the percentage of religious adults, by religion and education level in 2016. In 2016, 8 percent of Muslims had completed higher education while 36 percent had received no formal schooling.

The number of children, youth and adults not attending schools or universities because of COVID-19 is soaring. Governments all around the world have closed educational institutions in an attempt to contain the global pandemic.

According to UNESCO monitoring, over 100 countries have implemented nationwide closures, impacting over half of world’s student population. Several other countries have implemented localized school closures and, should these closures become nationwide, millions of additional learners will experience education disruption.

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]

Abstract

The SABER Service Delivery survey tool was developed in 2016 in the Global Engagement and Knowledge Unit of the Education Global Practice (GP) at the World Bank, as an initiative to uncover bottlenecks that inhibit student learning in low and middle income countries and to better understand the quality of education service delivery in a country as well as gaps in policy implementation. The SABER SD survey collects strategic information on school inputs and processes that influence learning outcomes. The data collected aims to uncover the extent to which policies translate into implementation and practice. As a global initiative, SABER SD provides data for the new global lead indicator on learning, which makes it easier to monitor the Sustainable Development Goal of achieving universal primary education.

SABER SD was created using knowledge and expertise from two major initiatives at the World Bank: SABER (Systems Approach for Better Education Results) and the SDI (Service Delivery Indicators) tools. The SABER program conducts research and knowledge from leading expertise in various themes of education. Using diagnostic tools and detailed policy information, the SABER program collects and analyzes comparative data and knowledge on education systems around the world and highlights the policies and institutions that matter most to promote learning for all children and youth. The SDI program is a large-scale survey of education and health facilities across Africa. The new SABER SD tool builds on and contributes to the growing SABER evidence base by capturing policy implementation measures identified as important in the frameworks of the core SABER domains of School Autonomy and Accountability, Student Assessment, Teachers, Finance, Education Management Information Systems, and Education Resilience.

The SABER SD instrument collects data at the school level and asks questions related to the roles of all levels of government (including local and regional). The tool provides comprehensive data on teacher effort and ability; principal leadership; school governance, management, and finances; community participation; and student performance in math and language and includes a classroom observation module.

Geographic coverage

The SABER SD survey in Lao PDR was nationally representative. Schools from all 18 provinces in Lao PDR were included in the sample.

Analysis unit

The unit of analysis varies for each of the modules. They are as follows: Module 1, the unit of analysis is the school. Module 2, the unit of analysis is the teacher. Module 3, the unit of analysis is the school/principal. Module 4, the unit of analysis is the classroom/school/teacher. Module 5, the unit of analysis is the student. Module 6, the unit of analysis is the teacher.

For modules where the unit of analysis is not the school (i.e., teachers and/or students), it is possible to create an average for the school based on groupings by the unique identifier – the school code.

Universe

The target was to have a nationally representative sample. All primary schools in Lao PDR were included in the original sample. The final pool of primary schools from which the sample was drawn included those with a grade 4 population of students.

Kind of data

Sample survey data [ssd]

Sampling procedure

The SABER Service Delivery survey was implemented in primary schools across Lao PDR, with detailed information being collected from Grade 4. According to official records from the Ministry of Education and Sports (MOES) education management information system for the school year 2015-2016, 8,864 primary schools exist across the country. The sample was created using probability proportional to size (PPS) according to the size of students enrolled in Grade 4. The target population of the survey was Grade 4 students, so all schools with at least one student enrolled in Grade 4 were considered in the sample.

Schools were stratified for sampling along four dimensions to ensure representation. For each of these, stratification was done on a discrete variable. The four sampling strata used for this survey with a target sample size of 200 schools across Lao PDR are the following: Urban/Rural, Public/Private, Single grade/Multi-grade, Priority/Non-Priority.

Multiple sampling scenarios were created according to the number of schools within a stratum. The final sample option was selected based on the standard errors of the sample as a whole and the errors within a subgroup. Please see the sampling appendix in the final report for more information (Appendix A).

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

After a first round of cleaning and editing carried out by IRL, the raw data was sent to the World Bank team by the survey firm. The World Bank team ran data checks on the raw data files, with comments and questions sent back to the survey firm on inconsistencies and/or missing data. The survey firm then responded to the questions, if any data are missing, the field team collects the data again or corrects the incorrect information. This happened in a few cases where the principals of the schools were contacted again to confirm and verify certain answers from the school.

Once the data was finalized, the weights were attached back to the dataset. The weighting procedure was done by the Development Economics Vice Presidency (DEC) team at the World Bank. Finally, with weights attached, the final datasets for each module (1 through 6) were produced.

For this data, many modules were also merged together to run analysis across different school components. There is one final data file which has merged modules 1, 2, 3, 5, and 6.

Response rate

100% response rate from all 200 schools in sample. No reserve schools were activated.

Success.ai’s Education Industry Data provides access to comprehensive profiles of global professionals in the education sector. Sourced from over 700 million verified LinkedIn profiles, this dataset includes actionable insights and verified contact details for teachers, school administrators, university leaders, and other decision-makers. Whether your goal is to collaborate with educational institutions, market innovative solutions, or recruit top talent, Success.ai ensures your efforts are supported by accurate, enriched, and continuously updated data.

Why Choose Success.ai’s Education Industry Data? 1. Comprehensive Professional Profiles Access verified LinkedIn profiles of teachers, school principals, university administrators, curriculum developers, and education consultants. AI-validated profiles ensure 99% accuracy, reducing bounce rates and enabling effective communication. 2. Global Coverage Across Education Sectors Includes professionals from public schools, private institutions, higher education, and educational NGOs. Covers markets across North America, Europe, APAC, South America, and Africa for a truly global reach. 3. Continuously Updated Dataset Real-time updates reflect changes in roles, organizations, and industry trends, ensuring your outreach remains relevant and effective. 4. Tailored for Educational Insights Enriched profiles include work histories, academic expertise, subject specializations, and leadership roles for a deeper understanding of the education sector.

Data Highlights: 700M+ Verified LinkedIn Profiles: Access a global network of education professionals. 100M+ Work Emails: Direct communication with teachers, administrators, and decision-makers. Enriched Professional Histories: Gain insights into career trajectories, institutional affiliations, and areas of expertise. Industry-Specific Segmentation: Target professionals in K-12 education, higher education, vocational training, and educational technology.

Key Features of the Dataset: 1. Education Sector Profiles Identify and connect with teachers, professors, academic deans, school counselors, and education technologists. Engage with individuals shaping curricula, institutional policies, and student success initiatives. 2. Detailed Institutional Insights Leverage data on school sizes, student demographics, geographic locations, and areas of focus. Tailor outreach to align with institutional goals and challenges. 3. Advanced Filters for Precision Targeting Refine searches by region, subject specialty, institution type, or leadership role. Customize campaigns to address specific needs, such as professional development or technology adoption. 4. AI-Driven Enrichment Enhanced datasets include actionable details for personalized messaging and targeted engagement. Highlight educational milestones, professional certifications, and key achievements.

Strategic Use Cases: 1. Product Marketing and Outreach Promote educational technology, learning platforms, or training resources to teachers and administrators. Engage with decision-makers driving procurement and curriculum development. 2. Collaboration and Partnerships Identify institutions for collaborations on research, workshops, or pilot programs. Build relationships with educators and administrators passionate about innovative teaching methods. 3. Talent Acquisition and Recruitment Target HR professionals and academic leaders seeking faculty, administrative staff, or educational consultants. Support hiring efforts for institutions looking to attract top talent in the education sector. 4. Market Research and Strategy Analyze trends in education systems, curriculum development, and technology integration to inform business decisions. Use insights to adapt products and services to evolving educational needs.

Why Choose Success.ai? 1. Best Price Guarantee Access industry-leading Education Industry Data at unmatched pricing for cost-effective campaigns and strategies. 2. Seamless Integration Easily integrate verified data into CRMs, recruitment platforms, or marketing systems using downloadable formats or APIs. 3. AI-Validated Accuracy Depend on 99% accurate data to reduce wasted outreach and maximize engagement rates. 4. Customizable Solutions Tailor datasets to specific educational fields, geographic regions, or institutional types to meet your objectives.

Strategic APIs for Enhanced Campaigns: 1. Data Enrichment API Enrich existing records with verified education professional profiles to enhance engagement and targeting. 2. Lead Generation API Automate lead generation for a consistent pipeline of qualified professionals in the education sector. Success.ai’s Education Industry Data enables you to connect with educators, administrators, and decision-makers transforming global...

This statistic shows the results of a 2018 survey conducted by Ipsos in 28 countries around the world on socialism. During the survey, the respondents were asked if they agree or disagree with the notion that education should be free of charge in their country. This statistic only shows those respondents who somewhat or strongly agreed with this statement. Some 98 percent of respondents in Russia agreed somewhat or strongly with this statement.

This activity uses Map Viewer. ResourcesMapTeacher guide Student worksheetGet startedOpen the map.Use the teacher guide to explore the map with your class or have students work through it on their own with the worksheet.New to GeoInquiriesTM? See Getting to Know GeoInquiries.Social Studies standardsC3: D2.Civ.1.9-12 – Distinguish the powers and responsibilities of local, state, tribal, national, and international civic and political institutions.C3:D2.Civ.3.9-12 – Analyze the effect of constitutions, laws, treaties, and international agreements on the maintenance of national and international order.Learning outcomesStudents will identify and define the various types of government systems around the world.Students will identify the most prevalent type of government in the world today.More activitiesAll Government GeoInquiriesAll GeoInquiries