In the past five decades, the global literacy rate among adults has grown from 67 percent in 1976 to 87.36 percent in 2023. In 1976, males had a literacy rate of 76 percent, compared to a rate of 58 percent among females. This difference of over 17 percent in 1976 has fallen to just seven percent in 2020. Although gaps in literacy rates have fallen across all regions in recent decades, significant disparities remain across much of South Asia and Africa, while the difference is below one percent in Europe and the Americas. Reasons for these differences are rooted in economic and cultural differences across the globe. In poorer societies, families with limited means are often more likely to invest in their sons' education, while their daughters take up a more domestic role. Varieties do exist on national levels, however, and female literacy levels can sometimes exceed the male rate even in impoverished nations, such as Lesotho (where the difference was over 17 percent in 2014); nonetheless, these are exceptions to the norm.

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 (%)

BackgroundInternet addiction has emerged as a major global concern as a potential adverse impact of internet exposure on adolescents. Internet addiction is associated with many demographic variables; however, there is a lack of consensus on its relationship with health literacy. Therefore, the aim of the present study was to assess the rates of internet addiction and health literacy level among middle school students (grades 7 to 12) in Chongqing, China, as well as to investigate the association between them.MethodsA cross-sectional, questionnaire-based study was conducted among 8971 students who were randomly selected by using stratified cluster sampling between November and December 2019. The Internet Addiction Diagnostic Questionnaire, Adolescent Health Literacy Scale and a self-designed basic information questionnaire were used to collect data. Chi-square tests were performed to compare the differences in the distribution of internet addiction across health literacy levels as well as some sociodemographic characteristics. Multivariate logistic regression analyses were conducted to identify the association between health literacy and internet addiction.ResultsThe prevalence of internet addiction among middle school students in Chongqing was 6.1%. The percentage of the students who spent more than 4 hours online every day in the past week was 14.3%. In addition, 26.7%, 26.0%, 28.3% and 26.3% of the participants reported low functional, interactive, critical and total health literacy, respectively. After adjusting for the confounding effects of demographics, multivariate regression analysis showed that critical health literacy was a protective variable for internet addiction, while functional, and interactive health literacy were the risk variable (P

Armenia State Budget: Expenditure: Education data was reported at 231,004.200 AMD mn in 2024. This records an increase from the previous number of 183,571.000 AMD mn for 2023. Armenia State Budget: Expenditure: Education data is updated yearly, averaging 101,415.450 AMD mn from Dec 1997 (Median) to 2024, with 28 observations. The data reached an all-time high of 231,004.200 AMD mn in 2024 and a record low of 15,465.500 AMD mn in 1997. Armenia State Budget: Expenditure: Education data remains active status in CEIC and is reported by Statistical Committee of the Republic of Armenia. The data is categorized under Global Database’s Armenia – Table AM.F007: State Expenditure: by Functional Classification: Annual.

This Dataverse contains Stata 15.1 .do files and Excel spreadhseets with code to replicate the analyses. This paper used Multiple Indicator Cluster Survey (MICS) program data. MICS study data are the property of the UNICEF MICS program and the data use agreement is that the data “may not be redistributed or passed on to others in any form” so the databases themselves are not included in this Dataverse. However, the MICS study databases are available upon request to the MICS program for legitimate research purposes at the following website: https://mics.unicef.org/surveys. Abstract Background: Multiple studies have highlighted the inequities minority and Indigenous children face when accessing health care. Health and wellbeing is positively impacted when Indigenous children are educated and receive care in their maternal language. However, less is known about the association between minority or Indigenous language use and child development risks and outcomes. In this study, we provide global estimates of development risks and assess the associations between minority or Indigenous language status and early child development using the 10-item Early Child Development Index (ECDI), a tool widely used for global population assessments in children aged 3–4 years. Methods: We did a secondary analysis of cross-sectional data from 65 UNICEF Multiple Indicator Cluster Surveys (MICS) containing the ECDI from 2009–19 (waves 4–6). We included individual-level data for children aged 2–4 years (23–60 months) from datasets with ECDI modules, for surveys that captured the language of the respondent, interview, or head of household. The Expanded Graded Intergenerational Disruption Scale (EGIDS) was used to classify household languages as dominant versus minority or Indigenous at the country level. Our primary outcome was on-track overall development, defined per UNICEF’s guidelines as development being on track for at least three of the four ECDI domains (literacy–numeracy, learning, physical, and socioemotional). We performed logistic regression of pooled, weighted ECDI scores, aggregated by language status and adjusting for the covariables of child sex, child nutritional status (stunting), household wealth, maternal education, developmental support by an adult caregiver, and country-level early child education proportion. Regression analyses were done for all children aged 3–4 years with ECDI results, and separately for children with functional disabilities and ECDI results. Findings: 65 MICS datasets were included. 186 393 children aged 3–4 years had ECDI and language data, corresponding to an estimated represented population of 34 714 992 individuals. Estimated prevalence of on-track overall development as measured by ECDI scores was 65·7% (95% CI 64·2–67·2) for children from a minority or Indigenous language-speaking household, and 76·6% (75·7–77·4) for those from a dominant language-speaking household. After adjustment, dominant language status was associated with increased odds of on-track overall development (adjusted OR 1·54, 95% CI 1·40–1·71), which appeared to be largely driven by significantly increased odds of on-track development in the literacy–numeracy and socioemotional domains. For the represented population aged 2–4 years (n=11 465 601), the estimated prevalence of family-reported functional disability was 3·6% (95% CI 3·0–4·4). For the represented population aged 3–4 years (n=292,691), language status was not associated with on-track overall development among children with functional disability (adjusted OR 1·02, 95% CI 0·43–2·45). Interpretation: In a global dataset, children speaking a minority or Indigenous language were less likely to have on-track ECDI scores than those speaking a dominant language. Given the strong positive benefits of speaking an Indigenous language on the health and development of Indigenous children, this disparity is likely to reflect the sociolinguistic marginalisation faced by speakers of minority or Indigenous languages as well as differences in the performance of ECDI in these languages. Global efforts should consider performance of measures and monitor developmental data disaggregated by language status to stimulate efforts to address this disparity.

This biannual operation gives information on the economic activity of all the private centres of regulated education, following an economic-functional classification of registered expenditure and the finance of this expenditure according to its origin. Another objective is to obtain the macro-figures for the branch of private education for its incorporation into the Economic accounts and Input-Output Tables of the Basque Country.

IntroductionThe COVID-19 pandemic caused massive disruption to medical education in Nigeria, necessitating the call for online medical education in the country. This study assessed the readiness, barriers, and attitude of medical students of Ebonyi State University Abakaliki, Nigeria, to online medical education.MethodsA cross-sectional study design was employed. All matriculated medical students of the university participated in the study. Information was obtained using a pre-tested, semi-structured questionnaire which was self-administered. Good attitude towards information and communication technology (ICT) based medical education was determined by the proportion of respondents correctly answering 60% of nine variables. Readiness for online classes was determined by the proportion of students who preferred either a combination of physical and online lectures or only online medical education amidst the COVID-19 pandemic. Chi-square test and multivariate analysis using binary logistic regression analysis were used in the study. A p-value of

The literacy rate among females living in rural areas in Karnataka was around 60 percent in 2011. The literacy rate among males was higher than females in the state. Literacy rate disparity
Low literacy rates among rural areas of the country are mainly due to a lack of adequate facilities for education. The presence of a school in the vicinity does not guarantee the availability of resources in terms of functional toilets and access to drinking water. Even though there is free and compulsory education for children below 14 years, the necessity of money pars educational needs in many rural areas. Leading district with colleges
The urban district of the capital city of Bengaluru, Karnataka, had about 880 colleges as of 2019. Bengaluru is known for its wide range of national, international, and professional levels of education. The city, as a hub for various companies and industries, is promising for graduates and a popular destination for students.

Abstract

SDES in Kabul was launched in June 2013, jointly by the Central Statistics Organization (CSO) and the United Nations Population Fund (UNFPA) where the latter provided the technical assistance to the entire survey operations. SDES data serve as the benchmark for demographic information at the district level and to some extent, group of villages/enumeration areas. It is the only survey that addresses the need of local development planners for information at the lower level of disaggregation. There are other surveys that CSO has conducted but these are available only at the national and provincial levels.

To achieve a responsive and appropriate policymaking, statistics plays a vital role. In Afghanistan, there has been a longstanding lack of reliable information at the provincial and district levels which hinders the policy making bodies and development planners to come up with comprehensive plans on how to improve the lives of Afghans. With SDES data, though it is not complete yet for the whole country, most of the important indicators in monitoring the progress towards the achievement of Afghanistan's Millennium Development Goals (MDGs) are being collected.

The main objectives of the survey were: · Gathering data for evidence based decision making, policy, planning and management · Providing data for business and industries · Providing policy and planning for residence housing · Providing data about vulnerable populations · Providing data for the basis of humanitarian assistance · Availability of data for research and analysis

Geographic coverage

Kabul Province Kabul Districts Kabul Villages

Analysis unit

Individuals, households

Universe

The survey covered all de jure household members (usual residents)

Kind of data

Sample survey data [ssd]

Sampling procedure

The survey consisted of two related activities: a) the extensive listing and mapping of houses, establishments and institutions (conducted before the household survey) and b) the household survey.

The listing and mapping covered all houses, businesses and institutions in every village and urban area in Kabul Province and included the preparation of sketch maps on which the physical location of each building structure was marked during the canvassing. The locations of important public services, establishments and institutions such as schools, hospitals, banks, etc., were pinpointed using global positioning system (GPS) devices at a later date.

The surveyors used the mapping outputs to guide them in conducting the survey and ensure complete coverage. In total, 16 nahias, and around 843 villages in 14 districts in Kabul Province were canvassed, divided into 3,068 enumeration areas.

The survey first involved a listing of every household in each village. Half of these listed households (i.e. every other household) were taken as samples and asked questions on education, literacy, employment, migration, functional difficulty, fertility, mortality, parents’ living status, birth registration and household and housing characteristics.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used to collect the survey data. - Listing sheet for village/enumeration area - Household questionnaire - Summary sheets for village/enumeration area

Cleaning operations

Central Statistics Organization (CSO) and UNFPA technical staff were responsible for editing the questionnaires, spot-checking, re-interviewing and recording observations during household interviews in all 16 nahias and 14 districts. This helped to ensure errors were corrected at an early stage of enumeration.

Data encoding and cleaning were also done in Karte-char where 178 encoders were hired and four CSO supervisors were detailed to oversee the whole data processing stage.

Thailand GDP: 2002p: CVM: GGCE: by Function: Education data was reported at 398,829.000 THB mn in 2016. This records an increase from the previous number of 389,284.000 THB mn for 2015. Thailand GDP: 2002p: CVM: GGCE: by Function: Education data is updated yearly, averaging 231,609.000 THB mn from Dec 1990 (Median) to 2016, with 27 observations. The data reached an all-time high of 398,829.000 THB mn in 2016 and a record low of 134,181.000 THB mn in 1990. Thailand GDP: 2002p: CVM: GGCE: by Function: Education data remains active status in CEIC and is reported by National Economic and Social Development Board. The data is categorized under Global Database’s Thailand – Table TH.A024: SNA1993: GDP: Government Consumption Expenditure: CVM: 2002 Reference Year (Annual).