Strengthening Citizen-Led Assessment Data

By Hannah-May Wilson, Program Manager, PAL Network Secretariat

As delegates gather for the World Bank South Asia Regional Workshop on Learning Assessment in New Delhi to share knowledge and learning on assessment practices in basic education in the South Asia region, the PAL Network of citizen-led assessment organizations spanning South Asia, Africa and Central America have just released their newly-created network-wide Data Quality Standards Framework.

In the past, citizen-led assessments have been too easily dismissed as lacking the necessary rigour or quality to be compared with other school-based assessments – an issue highlighted during a recent gathering of senior data analysts and leaders from the PAL Network. Speaking at the opening plenary, Dr. Wilima Wadhwa from the ASER Centre, India stressed the importance of PAL Network members establishing systems to ensure robust and reliable data, saying: “When you release data highlighting government failure, the first thing that will be attacked is your methodology. If someone can cast doubt on your methodology or say your data is unreliable or that the sampling hasn’t been done properly and isn’t representative, then people will find a reason not to engage with the findings.”


Catering to the realities of countries in the global South

The fact is that the citizen-led assessment model was designed carefully and deliberately to cater for the realities of countries in the global South. The design reflects a philosophy that differs from that of standard school-based assessments, as citizen-led assessments train volunteers to assess children regardless of their schooling status, using simple tests and tools, sitting one-on-one with the child, in their own homes.

What may appear to be a very simple assessment is actually backed up by highly-sophisticated processes to ensure the reliability of the data generated. These include systematic processes for sampling, partner and volunteer selection, training, monitoring and re-check. In addition, careful data cleaning and other methods are used to validate the data.

As Dr Wadhwa explained: “Documenting and strengthening these processes and sharing best practices is one of the reasons the network exists. As more countries start to conduct citizen-led assessments and the network grows, network members want to learn from each other. It was this shared focus that brought us together a year ago to draft this standards framework. Now we are back here to say – OK, we have this document. How can we support each other to implement it?”

 Making the case for citizen-led assessment data to monitor progress in education

For the past three years, the PAL Network has advocated for the inclusion of an early grade indicator in Sustainable Development Goal 4 (SDG 4). For a clear picture of progress, data used to measure this indicator must include all children, regardless of their schooling status. In the wake of the World Education Forum 2015, the PAL Network released a public statement to the Technical Advisory Group on SDG 4, demonstrating the value of citizen-led assessments to track the acquisition of foundational skills for all children. This was followed by an Open Letter to the Inter-Agency and Expert Group (IEAG) in March 2016 with an urgent appeal to retain the draft indicator for the percentage of children in Grades 2/3 who have learned the basics, and a position statement on SDG4 in July 2016.

Since May 2016, network members have been active participants in the Global Alliance to Monitor Learning (GAML), ensuring that citizen-led assessment data are included as a complementary source for reporting against SDG 4 Indicator 4.1.1. Data from citizen-led assessments are also included in the Catalogue of Learning Assessments (CLA) developed by the UNESCO Institute for Statistics (UIS): an important step towards the inclusion of learning data for children in developing countries who are less likely to be found in traditional educational settings.

Ensuring no child is left behind

To ensure that no child is left behind, active efforts are needed to identify the most disadvantaged children. And that, in turn, means  understanding where they are most likely to be found. Across the developing world, the children who are the hardest to reach are often found in the areas that are hardest to reach, and are unlikely to be in school. In places where significant numbers of children are out of school or attending sporadically, citizen-led assessments have significantly improved knowledge of the inequalities that persist in educational access and the acquisition of foundational reading and numeracy skills.

The new PAL Network Data Quality Standards Framework will help member countries improve and ensure technical rigour, with enough flexibility to accommodate the diversity of processes and adaptations to local context that are central to the citizen-led assessment model. The DQSF will be accompanied by implementation and monitoring plans, with member countries supporting each other to meet the minimum required standards, and ensuring that the PAL Network continues to make an important and robust contribution to understanding learning progress for all children.

A Sound Investment: The Benefits of Large-Scale Learning Assessments

By Silvia Montoya, Director of the UNESCO Institute for Statistics (UIS), and David Coleman, Senior Education Advisor at Australia’s Department of Foreign Affairs and Trade (DFAT) and Head of the Strategic Planning Committee of the Global Alliance to Monitor Learning (GAML)

This blog was also published by the Global Partnership for Education (GPE). 

As delegates gather in New Delhi for the South Asia Regional Conference on Using Large-Scale Assessments to Improve Teaching and Learning, a new synthesis paper from the UIS makes the case for greater investment

It’s time to make a much stronger case for investment in the data we need to reach Sustainable Development Goal 4 (SDG 4) on education. The clock is ticking towards the 2030 deadline for quality education for every child and adolescent but, as recent data show, there are still too many out of school and too many who are not learning what they need to know.

Such evidence provides a much-needed ‘carrot and stick.’ Good data is a very attractive carrot, helping decision makers to target their available resources for maximum impact. Strong evidence is also a useful stick, helping to hold governments and key stakeholders to account for their policies and investment choices.

Making the case for large-scale assessments of learning

There is, however, a problem – particularly on measuring learning outcomes. Not all countries conduct national assessments or participate in cross-national (regional or international) assessments of learning. This poses a real challenge for the basic information needed to monitor and report on progress towards SDG 4. So it’s time to set out the financial advantages of educational assessments, and most particularly the large-scale cross-national assessments, that can support the pursuit of national education priorities.

A new synthesis paper from the UNESCO Institute for Statistics (UIS) explains how many countries already use data from these large-scale assessments to enhance their educational practice and policy. It also spells out the implications for investment in education resources and the potential challenges.

The paper draws on the Review of the Use of Cross-National Assessment Data in Educational Practice and Policy and builds on the UIS Investment Case for SDG 4 Data, which compares the resources needed to produce the global and thematic indicators in relation to the costs of doing business as usual. It is also aligned with work by Australia’s Department of Foreign Affairs and Trade (DFAT) to unpack what actually works to get kids into school, keep them there and learning.

Investments in cross-national assessments have had clear benefits, as shown in Table 1. In these examples, we have seen more resources being targeted to various aspects of education. The benefits include larger budgets for education, more teachers, more materials and greater efforts to reach children who are not in school via non-formal education programmes.

Table 1. Resources invested on the basis of cross-national assessments

Area of resource investment Examples of actions taken by countries
Teachers, training, and professional development New online in-service professional development programmes for teachers and leaders
Teacher training workshops/integrating technology into classroom activities
Incentives to participate in in-service teacher training programmes, encouraging high-performing students to join the teaching profession through incentives, and increasing salaries
Improving teachers’ pedagogical skills and teaching literacy
Incentives for teachers
Education funding Increasing budget for education to provide primary and secondary education with additional financial resources to reduce class size, raise teacher salaries, and develop infrastructure
Several initiative investments to strengthen literacy development, including a generous Quality Education Fund
Funding programmes to promote reading and literacy
Donors helping to stimulate a policy response in terms of resource allocation in part through the administration of the assessment
Interventions based on the findings, which are also used to influence policy dialogue and action
Education materials and time resources An increase in classroom instruction time dedicated to mathematics leading to improved scores
Reduction in teacher shortages as a result of policy changes and efforts
Hybrid assessment data being incorporated into a national assessment system to inform curriculum and instruction
Hybrid assessment data to inform the development of materials and strategies for teaching and continuous assessment
Influencing the national educational programme, resulting in the allocation of significant funding to the building of classrooms, providing instructional materials, and addressing out-of-school children through non-formal education programmes

The variations in the extent to which countries have benefited from cross-national assessments only serve to reinforce the argument for more investment, with the countries making the greatest use of the findings reaping the greatest benefits.

Figure 1 shows the geographic distribution of large-scale learning assessments. The data gaps are apparent, with many countries – particularly in South Asia and parts of Africa – not participating in any form of cross-national assessment. Some gaps are linked to lack of coverage: for example, a regional assessment for South Asia does not yet exist.

Some gaps are linked to national capacity issues, with countries lacking the financial or technical capacity to administer national learning assessments or join cross-national learning assessments. We must also recognise that some countries are reluctant to join regional or international assessments out of concern of being compared to those with better resourced education systems. The vast majority of non-participating countries are either low-income or lower-middle-income countries.

Figure 1. Geographic distribution of large-scale learning assessments

Figure 1

Source: UIS Quick Guide #2: Making the Case for a Learning Assessment

 Figure 2 summarises the degree to which countries have used cross-national and regional assessments to shape their education systems. The darker the blue: the greater the application. For example, New Zealand is making more use of – and gaining more from – cross-national assessment data than Canada and the United States. They, in turn, are benefiting more than Brazil and the Russian Federation. A comparison between Figures 1 and 2 shows that some countries participate in large-scale assessments but do not apply the data from these assessments directly to educational policy and planning.

Figure 2. Countries using cross-national assessments in educational policy and practice

CNAs+regional assessments_v7

Source: Review of the Use of Cross-National Assessment Data in Educational Practice and Policy

Better data for better education

By showing the clear benefits, we want help to mobilise the necessary resources – whether domestic or supported by donors – to close the data gaps on cross-national assessments, and to emphasise the financial and student performance benefits of applying the lessons from assessment results to targeted policy making and to operational practice. This would be a ‘win-win’ for the effectiveness of national education systems and for the production of internationally comparable data.

We already know the immense value of education itself. Study after study has confirmed the vast benefits of education for each individual and for every society. We know that education plays a critical role in helping people to lift themselves out of poverty, improve their own quality of life and increasing their chances of contributing to the economic well-being of their communities and countries.

It is high time we positioned the collection and meaningful use of education data as a crucial part of this education narrative – as one of the most fundamental prerequisites for progress towards the world’s education goals.

The View from Madagascar: Data to Build Evidence-Based Policy

By Rolland Rabeson, Secretary-General of the National Education Ministry, Georges Solay Rakotonirainy, Secretary General of the Ministry of Employment, Technical and Vocational Education and Training, and Christian Guy Ralijaona, Secretary-General of the Ministry of Higher Education and Scientific Research, Madagascar

Reinforcing and deepening regional synergies in education will be at the forefront of the Pan-African High-Level Conference on Education (PACE 2018) in Nairobi from 25-27 April. The UNESCO Institute for Statistics (UIS), a key partner of countries across the region, will give a series of presentations on the importance of data for national education planning and for monitoring international commitments enshrined in the Sustainable Development Goal for education (SDG 4).

The UIS is working side-by-side with country partners in a UNESCO-sponsored pilot project called Capacity Development for Education (CapED). The participating countries are: Afghanistan, Cambodia, Democratic Republic of Congo, Haiti, Mali, Madagascar, Mozambique, Myanmar, Nepal and Senegal. The aim of the project is to help these countries develop and strengthen their own abilities to produce quality data.

To this end, since September 2017, our joint team of education ministries, the National Institute for Statistics and other national institutions involved in education data production in Madagascar has been working with the UIS to fulfill these objectives.


We need data to revitalize education policy and planning to build a prosperous future

As a part of the CapED initiative, we have reviewed our education sector plans to find any gaps between our national policy objectives, and our international and regional commitments.

We have also looked at our existing data sources, comparing them to our needs. Based on our particular national context, we have focused on improving several key areas such as age-related data for basic education and school readiness, and the data coverage for technical and vocational education and training (TVET) and higher education.

It has been important to find synergies between our own national data needs and our commitments to SDG 4. So, in consolidating all of the data available we have created a national indicator framework that is relevant to us while meeting our international goals.

Finally, we worked with the UIS to undertake a data quality assessment, which enabled us to look at all of our data sources and ensure that they meet international standards.

We are now entering the final phase of building our education data roadmap: a National Strategy for the Development of Education Statistics (NSDES).

To get there, our strategic objectives are based on a set of recommendations in each of the following areas:

  • Institutional framework for data collection and analysis,
  • Process and methodologies for developing relevant educational statistics and;
  • Quality of the final data product.

Financial limitations hampering data collection and development

A strong institutional framework is a key element in making sure that our team of education ministries in Madagascar collects and produces quality data with the scientific guidance of our National Institute for Statistics, along with development partners. We know that our technology needs remain high and all three ministries have ongoing requirements for qualified personnel to analyze the information. Moreover, the responsibilities along the data production cycle are not always clear, therefore we need to review our governance framework in this area.

But most crucially, this evaluation revealed that limited financial resources is hampering our ability to produce enough data in a timely fashion. Underfunding of our institutional statistical framework can make planning our educational programs more difficult. It also impacts our ability to meet our SDG 4 commitments.

Addressing this issue would involve creating a separate budget line item for data. This would ensure that funds are available for statistical needs and it would reduce our reliance on external sources of funding. Allocating funds for data in this way would enhance our national sovereignty as quality data – produced independently – should form the basis for all strategic education policy planning decisions.

Consultation with end user key to data uptake

In terms of the collection and classification of data, our partners at UIS have recommended regular, annual consultations with data users in the public and private sectors so that we can identify their needs. This would lead to data creation that is more relevant to the end user with the aim of increasing the rate of uptake.

Greater coordination would help us to put data at the heart of our policy decisions for education. This in turn would lead to more effective policies that target the education needs in-line with national priorities and SDG 4.

Availability of quality education data necessary for evidence-based policy

Finally, the quality of education data is a critical element of our data roadmap, the NSDES. Important elements for this are the accessibility and clarity of the data, as well as their coherence and timeliness. Our evaluation indicates that there are discrepancies between the different types of data produced; bridging this gap – and adhering to international standards – will be key to reinforcing the data quality and ensuring its cross-country comparability.

Asserting the role of the national statistics office would also help. It is also important to implement a national census every ten years to form a baseline reference point. The last time Madagascar conducted a census was 1993. Such a long time-lag can make data projections difficult.

We will consider all of these objectives as we document our NSDES to create a strong data environment for education planning purposes.

In the run-up to PACE 2018, we are proud to be a part of UNESCO’s innovative CapED initiative to improve education data both for national education planning and to meet the objectives of SDG 4.

As African countries meet in Nairobi to work towards our shared agenda for more prosperous, people-centered development, let’s remember that education remains at the heart of building a better future. To achieve the Africa we want, we need quality data to target education policies and to monitor progress.

Follow the Money: Tracking Education Spending to Reinforce Accountability

By Sonia Ilie, Pauline Rose and Asma Zubairi, Research for Equitable Access and Learning (REAL) Centre, University Of Cambridge

This blog was also published by the Global Partnership for Education (GPE)

It’s Global Action Week for Education, with the focus firmly on accountability. As we all know, if we want to hold our decisionmakers to account, we must have good data. Without it, we have little evidence of whether they are keeping their promises or not.

In the case of education, this certainly means knowing how many children are in school, how many are out of the classroom, and whether they are making good progress in their learning. But there is another critical area that is crucial not only for accountability on education, and that is the money. Who pays for education? How much do they pay? Where does the money go? And very importantly, who is benefiting from public spending by governments?

In the Handbook on Measuring Equity in Education, published by the UNESCO Institute for Statistics (UIS) in March, we scrutinized government spending as a way to increase equity in education. We looked at who benefits most from government education expenditure and highlighted ‘formula’ funding as a way to redistribute resources to those with the greatest need.


While tracking progress towards education goals signals political will to tackle inequality gaps, such as those based on gender or location, it is not enough, on its own, to help close those gaps. Another way to assess true commitment is to look at how government resources are allocated.

We know that reaching the most disadvantaged children is likely to cost far more, per head, than the spending needed to reach those who do not face the same challenges. It costs more to tackle the costs of inequities that are deeply embedded within societies.

Equity demands more than spending the same amount on rich and poor students

Such problems can’t be solved by spending exactly the same on both a rich and poor child. While this would be an equal spread of spending, it would not, however, be equitable. Equitable funding provides additional resources to those who face inherited disadvantages. And it is the only way to achieve the global ambition of ensuring that every child enjoys the same opportunity to achieve their potential in education.

So, how close – or far – are countries to this ambition at present? Our findings are pretty stark: in many countries, children and young people who are the hardest to reach are often the last to benefit from government investment in education.

There are still large inequalities in the distribution of government spending on education within countries. As the figure below shows, there are vast wealth gaps in government spending within 31 low- and lower-middle-income countries in sub-Saharan Africa and South Asia for which data were available, with government spending benefiting the richest groups in all 31 countries disproportionately. In Congo, Guinea, Liberia and Malawi, children from the poorest 10% of households benefit from less than $10 for every $100 spent on children living in the richest 10% of households.

In most of 31 selected low- and lower-middle-income countries, the poorest children receive just a fraction of government expenditure on education


Note: As far as possible, the data have been matched for the years available for expenditure data from the UIS with the years for the DHS school attendance data, using the most recent year available. The years of data available ranged from 2005-2014. Data on government expenditure for all levels of education were analysed.
Source: Ilie and Rose (2017) based on calculations with UIS and DHS data.

We have also looked more closely at who, exactly, pays for education. The assumption may be that it is governments, but in many low-income countries, households contribute significant amounts of money to send their children to school. In several countries, they contribute 30% or more of the combined household and government funding for primary education.

Such contributions hit the poorest households the hardest, especially in areas where there is low investment by governments. A survey of 12 African countries found that household spending on learning materials swallowed up 56% of household expenditure on education for the poorest households, compared with 27% for the richest households.

Formula funding for equitable education

To ensure more equitable spending on education, we argue for the use of ‘formula’ funding that distributes a larger proportion of resources to those in greatest need. Formula funding is already underway in some countries but is not always tackling inequities as well as it could.

For example, formulae are often based on the principle of equality of funding, with funding allocated on the basis of the numbers of children in school. But this doesn’t factor in the need to provide education to harder-to-reach children who are more likely to be out of school. By failing to differentiate between the backgrounds of students in different locations, equality of funding approaches can be highly regressive. Where they exist, formulae usually focus on access, failing to overcome wide inequalities in learning between better-resourced and the poorly-resourced schools that are more likely to educate children from the most disadvantaged backgrounds.

There are, however, successful examples. In the Netherlands, a school funding formula is weighted in favour of the number of disadvantaged children in a primary school, mostly native Dutch children whose parents have little education, and disadvantaged immigrant children. Schools with large numbers of disadvantaged children have, on average, 58% more teachers per student and more support staff.

In Pakistan, education funding to provinces was disbursed according to their share of the population until 2009. This was a regressive funding formula, as it failed to take into account the relative deprivation of different provinces and their mixed ability to raise their own resources. To address this, four criteria have been used since 2009 to determine provincial allocations: population size, ‘poverty backwardness’, provincial revenue collection and low population density. These changes are intended to favour smaller, sparsely-populated and less-developed provinces.

Six steps for equitable funding

Our review of current funding formulae reveals six pointers for their effective use:

  1. Formula funding needs to take account of both access and quality of learning.
  2. Formula funding needs to take account of regional inequalities within decentralised systems.
  3. Redistributive formulae need to include teacher salaries.
  4. Schools need autonomy over the spending of resources, with guidance for using it in ways that address education quality for disadvantaged groups.
  5. Even where quality issues are addressed at school level, schools need to ensure the resources reach the most disadvantaged groups.
  6. The effects of redistribution on narrowing learning gaps take time. Governments need to be prepared to go the distance.

National Education Accounts offer a useful way to track education finance, particularly for vulnerable and marginalised groups. For example, a USAID-funded State Education Accounts project in Nigeria found a strong bias in Kano and Zamfara states among both public and private providers to fund schools in urban areas. As a result, state planners reassigned teachers from urban to rural areas, channelled more funds to girls’ schools in Zamfara state and allocated more funds for textbooks and maintenance.

Looking ahead, there will be little progress towards SDG 4 – inclusive and equitable quality education and lifelong learning opportunities for all – unless resources are redistributed equitably, with the most disadvantaged receiving the largest share of government resources and paying the smallest share from their own pockets. While these are still early days for such approaches, there are already valuable lessons to be learned to ensure that no one is left behind in education by 2030.

Why We Need Effective Education Management Information Systems

By Silvia Montoya, Director of the UNESCO Institute for Statistics (UIS)

It may sound dry and dusty, but an education management information system (EMIS) lies at the very heart of efforts to monitor progress towards the world’s education goals, particularly Sustainable Development Goal 4 (SDG 4). It is a vital instrument that has, perhaps, had less attention than it deserves, given that an EMIS should be, in essence, in the core of the planning and policy implementation processes in a country’s education ‘machine’.

This week, I am meeting with governments and development partners at the International Conference on Education Management Information Systems in Paris. There is unanimous agreement that a truly effective EMIS goes far beyond than simply collecting administrative data on the numbers of students enrolled in schools that are gathered as a matter of course.


Modern EMIS has to be positioned in a well-known place of a national education information system, covering other areas different to the traditional ones, or at least technologically integrated to the relevant data sources allowing to know, for example, who is spending what, on whether children are actually learning what they need to know and with feasibility of data disaggregation at the school or even the student level. Only those characteristics will make an EMIS really relevant for the policy planning and the management of education systems.

No wonder the Global Partnership for Education (GPE) and the UNESCO Institute for Statistics (UIS) are working together with countries to help ensure that every EMIS is as good as it can possibly be. Through a task force of international development agencies, GPE and the UIS are working to strengthen the capacity of ministries of education on EMIS so they can produce the high-quality and timely education and finance data needed to reach their specific targets and goals.

As many of my previous blogs have reported, data collection is a key part of the challenge of monitoring progress on SDG 4. But, more important, you also need a strong EMIS to put the resulting information to good use, produce the indicators relevant for national planning and implementation of the education policies needed.

Some countries are making great strides, such as Brazil, which is a member of the Inter-Agency and Expert Group on Sustainable Development Goal Indicators (IAEG-SDGs) and the Technical Cooperation Group (TCG, which is co-chaired by the Institute). The Brazilian Institute of Geography and Statistics (IBGE) has brought together all of the country’s official data producers to help construct the country’s SDG indicators, identifying gaps and setting out ways to close them. And the National Commission for the Sustainable Development Objectives is disseminating good practices that facilitate SDG implementation and monitoring, with technical advice from IBGE. An online data platform will go live later this month with locally produced SDG global indicators. Discussions in 2018 will aim to produce and publish national indicators, making the SDG online platform a one-stop data warehouse for Brazil’s education data.

In other words, countries are rising to the measurement challenges of SDG 4. As the Brazil example shows, there is plenty that they can do to push ahead with their data. And the UIS stands ready to help, with tools and strategies to get the job done.

For example, the SDG 4 Data Digest sets out a roadmap for countries and donors to collaborate on the production of quality education data. One major aspect of this roadmap is, of course, capacity development, with the statistical capacity of many countries stretched almost to breaking point by unprecedented demand for data on new areas. This demand compels them to collect and scrutinise more data from an ever wider range of sources. So it is critical to expand national capacity, particularly in the countries with the greatest data needs.

To support countries and donors in their quest for more and better data, the Digest sets out the UIS model for statistical capacity development. This is rooted in the formulation of a national indicator framework, which is developed with the full engagement of all national stakeholders and serves to specify all the country’s data needs, follows a comprehensive mapping that identifies the data sources that are already in place, as well as the information gaps.

Meanwhile, the recent Handbook on Measuring Equity in Education provides concrete guidance for data coverage to ensure equitable education opportunities for the most disadvantaged people. It sets out what it actually means to measure equity in learning, drawing on the experiences across 75 national education systems. It represents a recognition that unless everyone moves forward on education, the world’s education goals will remain tantalisingly beyond our reach.

That is why the UIS wants to ‘re-boot’ the education sector through the kind of data innovations seen in Brazil that respond to demand, and is promoting a Global Strategy for Education Data. As part of that promotion, we are constantly making the investment case for the data needed to chart progress towards SDG 4 on education.

If we are to succeed in our attempts to reach SDG 4, all of these efforts must be underpinned by a robust EMIS in each country. In Paris this week, the UIS will share perspectives on EMIS data utilisation, good practices and lessons learned in that area, in the context of the newest statistics developments related to the Agenda SDG 4-Education 2030. My hope is that this week’s conference will help to position this key tool far higher on the world’s education agenda.


Priorities and Challenges for Education Data in Sweden

Lotta Larsson, Senior Advisor in the Department for Population and Welfare Statistics, Statistics Sweden

This blog was also published by Norrag.

As the Inter-agency and Expert Group on SDG Indicators (IAEG-SDGs) meets in Vienna from 9-12 April 2018, a perspective from Sweden illustrates the challenges even the world’s most advanced statistical systems face in producing the education data needed to monitor and achieve the global education goal.  

The Scandinavian countries are often held as a model for other countries to follow on almost any area of development you can name, from poverty reduction to health and well-being. From an international perspective, Sweden is a country with a high quality education system.

In 2013, however, the PISA results showed that the average scores had declined from previous heights to below the average for OECD countries. This started discussions on the quality of the education system at the primary and lower secondary levels in Sweden. Since 2013 the country’s PISA results have improved and it is now – once again – at or above the OECD average for mathematics, reading and science.

Among others, the discussions in Sweden have focused on three aspects of the education system:

  • The need for stronger measures for children with special needs in the earliest grades of schooling. And that, in turn, means developing the skills of teachers to work with these children;
  • Teacher shortages in the future and working to make teaching more attractive, including increasing teacher salaries and improve the working environment for teachers in schools; and
  • The question of how to tackle inequality between schools – more resources to schools with poorer results can be one way to reduce inequality.

Swedish kids

In 2017, Statistics Sweden published a detailed analysis of the country’s implementation of Agenda 2030, based on all the available data and results. The analysis found that, from an international perspective, Sweden has good access to statistics on education. But this does not mean that we face no challenges in gathering the detailed and disaggregated statistics that are needed.

We are, for example, seeing falling response rates in surveys of individuals in Sweden, such as the labour force survey, the survey on living conditions for adults and children and the adult education survey. This is a real worry, and reinforces the need to provide incentives for participation, as well as the need to try different methods, using mixed modes of data collection, non-response follow ups and dissemination of information about the surveys to the Swedish population.

Right now, disability status is not readily available from registers or administrative sources. We need comparable definitions of disability status across different sample surveys – and plans are underway to ensure this comparability so that children and adults with special needs become more visible in the surveys on the labour force and living conditions.

When we get to other variables, such as the highest levels of completed education among parents of pupils who were born abroad, we have another problem. We have register-based data on some aspects of their lives, but a large amount of information about the parents’ highest completed education in the registers is missing. This is a worrying gap, given the close links between the educational performance of children and the educational level of their parents.

 Statistics Sweden is working collaboratively with several national organizations and authorities that are engaged in monitoring progress towards Sustainable Development Goal 4 (SDG 4) on education. These include the Ministry of Education, the National Agency for Education, the Swedish Higher Education Authority and the Swedish Council for Higher Education.

The national statistics on teachers are of good quality but there is still a need for a national strategy regarding indicators at a disaggregated level for teacher salaries, teachers for children with special needs and teacher training. This involves working to align the national definitions to global definitions of teacher indicators.

The focus is firmly on equality in Sweden at the moment. Take SDG Target 4.5, for example: 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 situations. Statistics Sweden is working with national stakeholders to develop a national list of education indicators based on global and thematic education indicators in Agenda 2030.

This involves disaggregating indicators by native/foreign born, country of birth, disability status, socioeconomic background and income. It is possible to disaggregate the indicators based on administrative sources to a detailed level, which is not always the case when indicators are based on sample surveys. The number of respondents will be too small to produce the statistics at a detailed level.

Statistics Sweden is working over the long term to be able to produce the disaggregated statistics that are needed. For example, there is information on the year of immigrants’ arrival to Sweden in our administrative registers, but the quality and coverage have to be ensured. This is crucial information to ensure equitable education for recent immigrants.

We are also pursuing more data on schools where a large proportion of pupils have parents whose education has been limited, who have low incomes and who were born in another country or have only recently arrived in Sweden – vital information for the allocation of resources.

And on SDG Target 4.c – increasing the supply of qualified teachers – the focus is on more disaggregated data on the number of trained and qualified teachers, teacher salaries and pupil/teacher ratios, zooming in on both the municipal and school level.

There is still much work to do both here in Sweden and in many other countries to gather the data needed for the world’s education goals. But we’re working hard to get this done.



A Quick Win in Monitoring How Much Children Learn

By Nadir Altinok (Associate Professor of Economics, Université de Lorraine, France) and Silvia Montoya, Director of the UNESCO Institute for Statistics (UIS)

This blog was originally published by the Global Partnership for Education (GPE)

The news that more than more than half – 617 million – children and adolescents of primary and lower secondary school age worldwide are not reaching minimum proficiency levels in reading and mathematics is a wake-up call for educators and for statisticians. Without such data, we would be unaware of the learning crisis that threatens progress towards the Sustainable Development Goals (SDGs) – a crisis that demands an urgent response from education ministries, most certainly, but also from the world’s data gatherers. Continue reading