The effect of varying population estimates on the calculation of enrolment rates and out-of-school rates

Enrolment rates are calculated by the UNESCO Institute for Statistics (UIS) from a combination of i) enrolment figures provided by Member States; and ii) population estimates from the UN Population Division. Using different population estimates in the calculation can result in varying enrolment rates and out-of-school rates. Moreover, the biennial revisions of UN population estimates have a direct effect on estimates of the rate and the number of out-of-school children, both past and present. If an accurate estimate of the population of a country is difficult to ascertain, determining the exact rate and number of out-of-school children within such country becomes a challenging task. Primary, lower secondary and upper secondary out-of-school rates are key thematic indicators of the UN Sustainable Development Goal 4 (SDG 4), which aims to “ensure inclusive and equitable quality education and promote lifelong learning opportunities for all.” Precise estimates for these indicators are essential so as to ensure that initiatives seeking to increase enrolment are directed at the correct target groups, and in order to guarantee that investments in the education sector are effective and efficient. The present work, therefore, entails an in-depth analysis and comparison of enrolment estimates, as well as of the rate and number of out-of-school children (OOSC) for primary and lower secondary school cohorts, followed by an explanation of observed differences and recommendations for improved assessment of school participation

Estimation of the numbers and rates of out-of-school children and adolescents using administrative and household survey data

In the last decade or so, education data from household surveys have been used to complement, supplement and sometimes even substitute for country administrative data on participation and non-participation in schooling. There has been a gradual increase in the use of these household survey data on the demand for schooling, and these data now are used widely in intra- and cross-country comparisons made by the UNESCO Institute for Statistics (UIS), UNICEF, the World Bank and many other providers and consumers of education statistics. Country-level use of household survey education data, however, has been more limited. The indicators most often produced using household survey data are the net and gross attendance ratios (NAR and GAR), which typically are treated as comparable to the net and gross enrolment ratios (NER and GER) produced using administrative data. However, there are conceptual differences between enrolment and attendance, with both providing imperfect measures of participation in schooling (UIS, 2004). As the attention to household survey data has increased, and sometimes substantial discrepancies with administrative data have emerged, awareness of the data challenges associated with the use of both administrative and household survey data has grown (see UIS, 2004; UIS, 2010; Omoeva et al., 2013; and Barakat, 2016). In some respects – given the differences in what is being measured by administrative and household survey data (enrolment versus attendance) and the multiple sources of error inherent in each approach, it would be astonishing to find that on the whole enrolment and attendance rates that are comparable. At the same time, given that the attendance and enrolment indicators measure not dissimilar things, the magnitude of difference should not be extreme. Some studies have identified substantial differences between these indicators, however, pointing to serious estimation questions (UIS, 2005; Omoeva et al., 2013). This information paper characterises and explains variations between administrative and household survey estimates of the numbers and rates of out-of-school children (OOSC) at the primary and lower secondary levels, and suggests ways that data from the two sources might be harmonised. This document also discusses issues surrounding the measurement of exclusion from education for youth of upper secondary age, given this population’s competing rights of access to education and the right to work. Administrative and household survey data efforts, as the UIS and UNICEF (2015) note, differ in purpose, in who collects the data and in what ways, and when and with what frequency. And yet because both data sources provide useful education data on similar topics, the data are examined side by side

Data to Nurture Learning : SDG 4 Data Digest 2018

According to new estimates from the UNESCO Institute for Statistics (UIS), more than 617 million children and adolescents are not be able to read or handle mathematics proficiently. About two-thirds of these children and youth are in school, some of them dropping out before reaching the last grade of the cycle (UIS, 2017g). This highlights the critical need to improve the quality of education while expanding access to ensure that no one is left behind. Not only is the learning crisis alarming from a national, social and economic perspective, but it also threatens the ability of individuals to climb out of poverty through better income-earning opportunities. Greater skills not also raise their potential income, but welleducated individuals are also more likely to make better decisions – such as vaccinating their children– and educated mothers are more likely to send their own children to school. The learning crisis is, simply, a massive waste of talent and human potential. For this reason, many of the global goals depend on the achievement of Sustainable Development Goal 4 (SDG 4), which demands an inclusive and equitable quality education and the promotion of “lifelong learning opportunities for all”. UIS data suggest that the numbers are rooted in three common problems. First, a lack of access, with children who are out of school having little or no chance of reaching a minimum level of proficiency; second, failure to keep every child on track and proceeding through the system on time and retaining them in school; and third, the issue of the quality of education and what is happening within the classroom itself

Handbook on measuring equity in education

The handbook provides a conceptual framework for measuring equality in learning; offers methodological guidance on how to calculate and interpret indicators; and investigates the extent to which measuring equity in learning has been integrated into country policies, national planning and data collection and analysis.Chapter 2 of the handbook presents a conceptual framework for equity analysis, with an emphasis on equity in learning. It begins with a summary of the philosophical literature on equity and highlights several related principles, including equality of opportunity and considerations of fairness and justice, as these relate to the distribution of education resources to compensate for unequal starting points. The chapter then proposes five categories for the classification of measures of equity: meritocracy, minimum standards, impartiality, equality of condition and redistribution. The chapter concludes with a summary of the desirable properties of equity measures.Building on the conceptual underpinning presented in Chapter 2, Chapter 3 describes different methods for measuring equity in education. It focuses on key univariate and multivariate metrics and their respective advantages and disadvantages for two of the five categories described in Chapter 2: equality of condition and impartiality. Chapter 3 begins with 14Handbook on Measuring Equity in Educationan overview of visual representations of equality of condition that can be used to gauge the degree of inequality in a dataset, among them histograms, probability density functions and the Lorenz curve. The chapter goes on to describe common metrics for measurement of inequality, organized by the kind of data to be analysed, the desired type of analysis and the type of equity measure. Chapter 3 concludes with an overview of data that can be used for analysis of equity, as well as some of the challenges that may be encountered along the way.Chapter 4 moves away from the theoretical discussions in Chapters 2 and 3 and examines the role of equity measurement in 75 national education systems, in order to offer guidance to both policymakers and other stakeholders tasked with improving equity in education. The chapter begins with an analysis of national education plans from all geographic regions to identify the presence – or absence – of equity dimensions in indicators for monitoring of progress towards increased access and learning. Based on the findings, the chapter offers a series of recommendations for expanded data collection, with an increased focus on the identification of disadvantaged groups.Chapter 5 discusses government spending as a means to increase equity in education. The chapter examines national data to assess which groups of the population benefit most from government education expenditure and describes formula funding as a way to redistribute resources to those with the greatest need. In this context, the role of household spending on education and the potential of national education accounts as a tool to identify and address inequities are also discussed.Chapter 6 concludes the handbook with a summary of the main findings and recommendations for future work on national and international education statistics

One in Five Children, Adolescents and Youth is Out of School

In 2016, 263 million children, adolescents and youth were out of school, representing nearly one-fifth of the global population of this age group. The number of children, adolescents and youth who are excluded from education fell steadily in the decade following 2000, but UIS data show that this progress essentially stopped in recent years; the total number of out-of-school children and youth has declined by little more than 1 million per year since 2012. Some 63 million, or 24% of the total, are children of primary school age (about 6 to 11 years old); 61 million, or 23% of the total, are adolescents of lower secondary school age (about 12 to 14 years old); and 139 million, or 53% of the total, are youth of upper secondary school age (about 15 to 17 years old)

Reducing global poverty through universal primary and secondary education

The eradication of poverty and the provision of equitable and inclusive quality education for all are two intricately linked Sustainable Development Goals (SDGs). As this year’s High Level Political Forum focuses on prosperity and poverty reduction, this paper, jointly released by the UNESCO Institute for Statistics (UIS) and the Global Education Monitoring (GEM) Report, shows why education is so central to the achievement of the SDGs and presents the latest estimates on out-ofschool children, adolescents and youth to demonstrate how much is at stake. The out-ofschool rate has not budged since 2008 at the primary level, since 2012 at the lower secondary level and since 2013 at the upper secondary level. The consequences are grave: if all adults completed secondary school, the global poverty rate would be more than halved

More Than One-Half of Children and Adolescents Are Not Learning Worldwide

This paper presents the first estimates for a key target of Sustainable Development Goal 4, which requires primary and secondary education that lead to relevant and effective learning outcomes. By developing a new methodology and database, the UIS has produced a global snapshot of the learning situation facing children and adolescents who are in school and out. The data show the critical need to improve the quality of education while expanding access to ensure that no one is left behind. The paper also discusses the importance of benchmarking and the concept of minimum proficiency level

Learning Divides : Using Data to Inform Educational Policy

Three premises underlie the approach taken in this report. First, the development of children’s reading skills needs to be the primary focus of educational monitoring systems. It is a pre-requisite for the development of strong academic skills at the lower and upper secondary levels and is essential to school completion and social justice (Beswick, Sloat and Willms, 2008; Snow, Burns and Griffin, 1998; Willms, 2006). Second, the literature on classroom and school effects has provided the knowledge we need to build informative educational monitoring systems. We do not require the large-scale national or international studies to continue with the quest for school effects, with numerous measures of classroom and school factors. Instead, we need these studies to focus on a small number of factors, to measure them in greater detail and to track them longitudinally. Third, the results from the large international studies, combined with national studies and small controlled experimental studies, can provide educational administrators with information for setting achievable goals, for allocating resources and for assessing the effects of policies that alter one or more of the structural features of schooling. This research is not a call for the abandonment of large-scale international studies; indeed, many of the examples used in this report are based on PISA data. The majority of low- and middle-income countries have not yet participated in an international assessment and would benefit by understanding how well their students fare compared with students in other countries. Moreover, the results of comparative studies often serve to increase a country’s political will to invest resources in education (Singer and Braun, 2018). Instead, it is intended to shift attention away from the rank-ordering of countries in their outcomes or making causal claims based on cross-sectional data

The data revolution in education

This paper recommends a data revolution in education built on the foundation of national statistical systems, supported by international organizations in a global compact for monitoring the education SDG. In this compact, international organizations that serve UN Member States provide technical assistance, enhance coordination among stakeholders, and support the production of data as a global public good, thus reducing transaction costs for countries in pursuit of the goal of quality education for all. Options include the adoption of open data practices, investment in new information technologies for data storage and presentation, and greater data integration and systematic information exchanges among different levels of government and other institutions

The Cost of not assessing learning outcomes

Recently, the international community has gone a step further in monitoring education by attempting to measure learning. Currently there is no single approach or best way to monitor learning internationally. However, the rationale to come up with a viable approach to assess learning on a universal basis is growing stronger as the Education for All (EFA) and Millennium Development Goals (MDGs) come to a close and the international community advances towards the SDGs. The focus on the quality of education has led to an emphasis on the measurement of learning outcomes at all levels. Input data, such as knowing how many children are enrolled in school or how many teachers are hired, are not enough. There is a need to know if children are learning in schools and to measure learning outcomes on a global scale to monitor progress. Five of the seven education targets in SDG 4 focus on learning outcomes (i.e. the effect of education on individual children, young people and adults). This is a shift from previous global education targets, such as those in the MDGs, which solely focused on ensuring access to, participation and completion in formal primary education and on gender parity in primary, secondary and tertiary education. The Education 2030 targets highlight that enrolment and participation (e.g. in early childhood development programmes, formal schooling or adult education programmes) are the means to attain results and learning outcomes at every stage