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Administrative data: How do we measure progress towards SDG 4 – Part 1

This is part of a series of blogs, aiming to inform about some of the core challenges and solutions to collecting quality data which will be discussed in depth next week at the first ever Conference on Education Data and Statistics, convened by the UNESCO Institute for Statistics (UIS).

Over half of the data to report back on our education goal is administrative data collected by governments. This is data collected by line ministries and other national authorities. They are typically collected through annual school censuses, compiled in education management information systems, and used as a key resource for day-to-day operations.

These same data also feed into our global understanding of progress towards SDG 4 through the UIS, which administers an annual formal survey to countries. The UIS assures data quality and comparability including through back-and-forth discussions with Member States. These data are then complemented with other sources and published online so that they can be used in monitoring publications, such as the GEM Report, to give a good picture of how we are progressing towards our various targets.

Four challenges in compiling and producing internationally comparable data

A country’s capacity to respond to the UIS questionnaire relies entirely on data availability at the national level. Even when national data are available, they may not be sufficient or appropriate. For instance, school response rates may be low and not representative enough to use. Countries may not agree with the values of the indicators produced by the UIS. While data are regularly produced in many countries and SDG regions for most indicators, there remain significant gaps in areas, such as teacher qualifications.

Measuring target 4.c on teachers is an example. There is a 75% coverage of indicators on teacher training, but the other indicators on teachers have lower coverage: 50% coverage for the indicator on teacher attrition, 30% on professional development and less than 20% for teacher salaries relative to other professionals. These rates reflect low reporting rates by countries to the UIS survey. In 2013–17, for instance, at least two-thirds of data fields on teachers were not filled out by countries. The UIS has since tried to fill gaps by using OECD, ILO data as sources, but important gaps remain.

Figure 1. Percentage of population in countries covered with at least one data point on teacher indicators, 20182022

The second challenge is related to quality. For data to be comparable, and to feed into contribute to policy debate at the international level, they must all fit against predefined quality standards, such as ISCED. Currently, all SDG 4 indicators are conceptually clear, have an internationally established methodology and the standards are available.

A good quality of analysis may help fix instances where national indicator definitions may not align with global standards. For instance, differences remain between how different countries understand whether a teacher is trained or qualified, a key discussion point at the Conference next week. The UIS has assembled a comprehensive database aiming to define a global minimum standard of teacher academic qualifications needed to teach at specific levels of education, and the TCG has endorsed a Global standard for teacher’s academic qualification which should help improve global comparability of data on this issue.

The UIS has also collected information on initial teacher training programmes, through the innovative ISCED-T process, which should eventually contribute to a new global minimum standard of a trained teacher.

Some data works with ratios, such as enrolment numbers which need to be mapped against the population. Biases can arise however, when different data sources are used. For example, there may not be clarity or agreement on the population data used: countries may use national population data instead of UN Population Division data, which is the default source used by the UIS. A similar situation arises with financial indicators when national data are used instead of IMF and World Bank estimates.

Developments and solutions

A few key developments, led by the UIS and partners, have been trying to address some of the above issues:

  1. Implement a new population data policy: In April 2023, the UIS introduced an important change to its population data policy, allowing countries to request that the UIS use national population data instead of UN Population Division data to increase national ownership of statistics disseminated by the UIS. The policy specifies criteria that national population data must meet to be used (such as showing a time series from 2000-2023 and having data disaggregated by sex and age). The hope is that the implementation of this policy will be gradually expand in future UIS data releases.
  2. UIS dynamic template: This tool helps reduce the burden of long questionnaires on Member States. It produces international comparable indicators immediately after entering data into the template and provides historical data and indicators for each country. The template also helps countries understand in a transparent manner how indicators are calculated following the international methodologies and has, therefore, become a capacity development tool for countries as well that could be used for their national indicators and subregional disaggregation. More than 40 countries used the template to report data to the UIS in 2023, helping reduce historical data gaps and making data collection and validation processes more efficient.
  3. Capacity development for better administrative data: The UIS works with countries to develop their capacity to collect and analyse data for policy development. It advises on the alignment of annual school census forms so that the right data are being collected for national, regional and global education frameworks, including whether they are compatible with international standards and flexible enough to accommodate future data needs. A new tool being launched at the Conference, LASER, highlights data gaps and capacity development needs per country.

These issues will feature during the Conference, including at dedicated sessions on administrative data and on teacher data. Three core areas for advancing the agenda will be tabled, notably:

  • Expand the use of the UIS dynamic template to more countries and provide support to them.
  • Develop a maturity model of an education management information system to assess and guide countries to move to advanced systems.
  • Develop standard items and formats with all variables needed to estimate SDG 4 indicators.

On teacher data specifically, discuss a revision of the indicator framework:

  • Implement ISCED-T to develop global standards for teacher training.
  • Agree global definitions of minimum trained teacher requirements at each level.
  • Consider developing policy indicators on attracting, preparing, and retaining teachers, which are not currently part of the framework



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