Education Spending
by Max Roser and Esteban Ortiz-Ospina
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Cite this research
In most countries basic education is nowadays perceived not only as a right, but also as a duty – governments are typically expected to
ensure access to basic education, while citizens are often required by law to attain education up to a certain basic level. 1
This was not always the case: the advancement of these ideas began in the mid 19th century, when most of today’s industrialized
countries started expanding primary education, mainly through public finances and government intervention. Data from this early
period shows that government funds to finance the expansion of education came from a number of different sources, but specifically
taxes at the local level played a crucial role. The historical role of local funding for public schools is important to help us understand
changes – or persistence– in regional inequalities.
The second half of the 20th century marked the beginning of education expansion as a global phenomenon. Available data shows that
by 1990 government spending on education as a share of national income in many developing countries was already close to the
average observed in developed countries. 2
This global education expansion in the 20th century resulted in a historical reduction in education inequality across the globe: in the
period 1960-2010 education inequality went down every year, for all age groups and in all world regions. Recent estimates of
education inequality across age groups suggest that further reductions in schooling inequality are still to be expected within
developing countries. 3
Recent cross-country data from UNESCO tells us that the world is expanding government funding for education today, and these
additional public funds for education are not necessarily at the expense of other government sectors. Yet behind these broad global
trends there is substantial cross-country – and cross-regional – heterogeneity. In high-income countries, for instance, households
shoulder a larger share of education expenditures at higher education levels than at lower levels – but in low-income countries this is
not the case. Malawi is a stark example: tertiary education is almost completely subsidised by the state, yet household contribute
almost 20% of the costs of primary education.
Following the agreement of the Millennium Development Goals, the first decade of the 21st century saw an important increase in
international financial flows under the umbrella of development assistance. Recent estimates show that development assistance for
education has stopped growing since 2010, with notable aggregate reductions on flows going to primary education. These changes in
the priorization of development assistance for education across levels and regions, can have potentially large distributional effects,
particularly within low income countries that depend substantially on this source of funding for basic education. 4
When analysing correlates, determinants and consequences of education consumption, the macro data indicates that national
expenditure on education does not explain well cross-country differences in learning outcomes. This is indicative of a complex
‘education production function’ whereby for any given level of expenditure, output achieved depends crucially on the input mix.
Available evidence specifically on the importance of school inputs to produce education, suggests that learning outcomes may be more
sensitive to improvements in the quality of teachers, than to improvements in class sizes. Regarding household inputs, the recent
experimental evidence suggests that interventions that increase the benefits of attending school (e.g. conditional cash transfers) are
particularly likely to increase student time in school; and that those that incentivise academic effort (e.g. scholarships) are likely to
improve learning outcomes.
Policy experiments have also shown that pre-school investment in demand-side inputs leads to large positive impacts on education –
and other important outcomes later in life. The environment that children are exposed to early in life, plays a crucial role in shaping
their abilities, behavior and talents.
Interactive charts on Education Spending
Average OECD non-tertiary education expenditure by source of funding
Average expenditure on educational institutions given as a share of GDP, by source of funding (primary, secondary
and post-secondary non-tertiary).
Average learning outcomes by total education expenditure per capita
Private
expenditure
3.5%
600
Macao
2.5%
2%
Public
expenditure
1.5%
1%
0.5%
No data $0
$10,000
$20,000
$30,000
$40,000
$50,000
Source: Organisation for Economic Co-operation and Development (OECD)
$60,000
$70,000
$80,000
$90,000 $100,000
OurWorldInData.org/teachers-and-professors • CC BY
Annual salary of primary teachers
with 10 years of experience
0%
2000
2002
2004
2006
2008
Source: OECD: Education Statistics (2017)
2010
2012
OurWorldInData.org/financing-education • CC BY
Average OECD non-tertiary
education expenditure by source of
funding
Average harmonised learning outcome score in 2005-2015
(Angrist et al. 2019)
Russia
3%
Average reading performanc
2015
In order to maximize coverage, the most recent available data were used for both the learning outcomes (2005-2015)
and expenditure data (2012-2016). National average learning outcomes correspond to test scores across
standardised, psychometrically-robust international and regional student achievement tests. Total education
expenditure encompasses both governmental and household spending on education.
Vietnam
500
Hungary
Serbia
Ukraine
Indonesia
Palestine
Kyrgyzstan
Uganda
France
Mexico
Mongolia
400
Italy
Costa Rica
Average score on PISA reading test vs gov
price levels across countries).
Africa
Asia
Europe
North America
Oceania
South America
Oman
7M
3M
El Salvador
Cameroon
Dots sized by
Population
300
Benin
Cote d'Ivoire
200
100
0
$100
$200
$500
$1,000
$2,000
Slovakia
450
Thailand
United Arab
Mexico
Indonesia
400
Albania
Dominican Republic
350
5,000
Government exp
Source: Angrist et al. (2019); UNESCO (2019); World Bank (2019)
CC BY
Average learning outcomes by total
education expenditure per capita
Source: OECD Programme for International Stud
Average read
PISA and ave
student
2009
CHART
Germ
Italy
Czechia
Lithuania
300
Public and private per capita expenditure on education (PPP, constant 2011-intl $)
Ja
Poland
Vietnam
500
PISA: Mean performance on the reading scale
Annual salary of primary teachers with 10 years of experience, 2015
These salaries refer to the scheduled annual salary of a full-time classroom teachers with the minimum training
necessary to be fully qualified plus 10 years of experience. Income taxes are not deducted in these reported salaries,
but the employer’s contribution to social security and pension is deducted.
2015
MAP
TABLE
SOURCES
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Chart 1 of 26
Historical perspective on financing
education
When did the provision of education first become a public policy priority?
Governments around the world are nowadays widely perceived to be responsible for ensuring the provision of accessible quality
education. This is a recent social achievement. The advancement of the idea to provide education for more and more children only
began in the mid 19th century, when most of today’s industrialized countries started expanding primary education. The following
visualization, plotting public expenditure on education as a share of Gross Domestic Product (GDP) for a number of earlyindustrialized countries, shows that this expansion took place mainly through public funding. 5 Our entry on Primary Education and
Schools provides details regarding how this expansion in funding materialized in better education outcomes for these countries.
1870
CHART
1993
MAP
TABLE
SOURCES
DOWNLOAD
How did the US finance the expansion of public education?
Public schools in the US currently educate more than 90% of all children enrolled in elementary and secondary schools. 6
This is the result of a process of education expansion that relied heavily on public funding, particularly from local governments. The
visualization shows the sources of revenues for public schools in the US over the last 120 years. As it can be seen, states and localities
are – and have always been – the main sources of funding for public primary education in the US. In fact, we observe three broad
periods in this graph: there is first a period of stable revenues until 1920, then a period of sharp growth and decline during the interwar
years, and then a period of substantial growth since the second world war, slowing down in the 1970s. In all these periods, federal
funding was always very small, as can be seen when changing the visualisation from ‘absolute’ to ‘relative’. Disaggregated data from
the last couple of decades gives further insights into the specific sources of local revenues for schools in the US: the largest part comes
from property taxes (about 80% of local revenues came from property taxes in 2013), while only a very small part comes from fees
and donations (private funding for public schools, which is considered a local revenue, amounted to less than 2% of total public school
revenues in 2013). This heavily decentralised system relying on property taxes has the potential of creating large inequalities in
education, since public schools in affluent urban areas are able to raise more funding from local revenues. Indeed, a significant part of
the debate on education inequalities in the US today focuses on the importance of increasing progressive federal spending to reduce
inequalities in public school funding. 7
1890
CHART
2010
TABLE
SOURCES
DOWNLOAD
How did France finance the expansion of public education?
The case of the US above shows that funding for public schools has been historically a responsibility of local governments. In other
countries, such as France, the expansion of public education also took place initially with resources from local governments, but
relatively quickly the fiscal burden was shifted to the national level. In France this transition was associated with a sharp jump towards
universal access and a concomitant reduction in regional inequalities. The following visualization from Lindert (2004) 8 provides
evidence of the French experience. As we can see there are three distinct periods: education spending was initially low and mainly
private, then in 1833 funding began growing with local resources after the introduction of a law liberating communes to raise more
local taxes for schools, and finally in 1881 the national government took over most of the financial responsibility after the introduction
of a new law that abolished all fees and tuition charges in public elementary schools. In the source book, Lindert (2004) provides
further evidence of how this transition towards centrally funded public education reduced north-south inequalities in France.
Sources of funds for France’s public primary schools, 1820–1913 – Figure 5.5 in Lindert (2004) 9
In the US growth in education expenditure was characterized by growth specifically in the
public sector
A comparison of expenditure between public and private education institutions is helpful to contextualize the role the public sector
played in the process of education expansion in industrialized countries. The following graph does this using data from the National
Center for Education Statistics in the US. It shows that during the years 1950-1970 – a period of substantial growth in education
expenditure in the US – expenditure grew specifically in the public sector. 10
Education expenditure as share of GDP in the United States
Expenditures of educational institutions in the United States, by control of institution (public or private)
7%
6%
5%
Public education
expenditure
4%
3%
2%
1%
0%
Private education
expenditure
1950
1960
Source: NCES (2014)
1970
1980
1990
2000
2015
OurWorldInData.org/financing-education • CC BY
When did the expansion of basic education become a global phenomenon?
The second half of the 20th century marked the beginning of education expansion as a global phenomenon. The visualization, using
data from Szirmai (2005) 11, shows government expenditure on education as a share of national income for a selection of low and
middle-income countries, together with the corresponding average for high-income countries, for the period 1960-2010. As it can be
seen, by 1990 government spending on education in many developing countries was already close to the average observed in
developed countries.
It is important to point out that the remark above makes reference to convergence in expenditure relative to income. To the extent that
low-income countries remain poorer than high-income countries, gaps in levels of expenditure per pupil are persistently large. Indeed,
cross-country heterogeneity in education expenditure per pupil is currently much higher than heterogeneity in expenditure as share of
GDP. 12 One factor contributing to the slower convergence of expenditure per pupil in real terms is the fact that teachers’ salaries – the
main component of education expenditure – are much higher in high-income countries, because labour has a higher opportunity cost in
these countries. In general, the opportunity cost of labour is a key variable that governments in developing countries should factor in
when deciding whether to expand education now, rather than later.
Government expenditure on education, 1960 to 2010
Government expenditure on education as share of GDP for selected low and middle income countries (plus average
of high income countries)
Brazil
High-income countries
5%
Colombia
Ethiopia
4%
India
3%
Pakistan
2%
1%
0%
1960
1970
1980
1990
Source: Szirmai (2015)
2000
2010
OurWorldInData.org/financing-education • CC BY
Education inequality is falling around the world
An important consequence of the global education expansion is a reduction in education inequality across the globe. The following
visualization shows this through a series of graphs plotting changes in the Gini coefficient of the distribution of years of schooling
across different world regions. The Gini coefficient is a measure of inequality and higher values indicate higher inequality – you can
read about the definition and estimation of Gini coefficients in our entry on income inequality. The time-series chart shows inequality
by age group. It can be seen that as inequality is falling over time, the level of inequality is higher for older generations than it is for
younger generations. We can also see that in the reference period education inequality went down every year, for all age groups and in
all world regions.
Have gains from historical education expansion fully materialized? The breakdown by age gives us a view into the future: as the
inequality is lower among today’s younger generations, we can expect the decline of inequality to continue in the future. Thus, further
reductions in education inequality are still to be expected within developing countries; and if the expansion of global education can be
continued, we can speed up this important process of global convergence.
Education Gini coefficients by world region for selected age groups, 1960- 2010 – Figure 4 in Crespo Cuaresma et al. (2013) 13
Education inequality can decline rapidly across all levels of education – South Korea is an
example
The experience of South Korea shows that it is possible to reduce education inequality rapidly across all levels of education. The
following visualization show two graphs comparing the concentration of years of education in South Korean between the years 1970
and 2010. To be precise, each of these graphs shows an education Lorenz curve: a plot showing the cumulative percentage of the
schooling years across all levels of education on the vertical axis, and the cumulative percentage of the population on the horizontal
axis. As it can be seen, in 2010 education was much less concentrated than in 1970, not only because there was a smaller share of
individuals without schooling (shown along the bottom of the chart), but also because there was a smaller share of individuals
concentrating large proportions of school-years at higher levels of education. Indeed, in only 40 years South Korea was able to double
the mean years of schooling (from 6 to 12 years) and at the same time get remarkably close to the 45-degree line marking the
hypothetical scenario of perfect equality of schooling. In other entries we show that in this same period South Korea increased
drastically its GDP per capita while significantly improving health outcomes, such as child mortality.
Inequality of Educational Attainment in South Korea 1970 and 2010 14
Financing of education across the world
The world is expanding funding for education today
The last two decades have seen a small but general increase in the share of income that countries devote to education. The following
chart plots trends in public expenditure on education as a share of GDP. As usual, a selection of countries is shown by default, but
other countries can be added by clicking on the relevant option at the top of the chart. Although the data is highly irregular due to
missing observations for many countries – an issue we discuss in more detail in our section on Data Quality below –, we can still
observe a broad upward trend for the majority of countries. Specifically, it can be checked that of the 88 countries with available data
for 2000/2010, three-fourths increased education spending as a share of GDP within this decade. As incomes – measured by GDP per
capita – are generally increasing around the world, this means that the total amount of global resources spent on education is also
increasing in absolute terms.
You can explore the trends by education level in these interactive charts:
Government expenditure on pre-primary education as share of GDP
Government expenditure on primary education as share of GDP
Government expenditure on secondary education as share of GDP
Government expenditure on tertiary education as share of GDP
Public spending on education as a share of GDP
Total general government expenditure on education (all levels of government and all levels of education), given as a
share of GDP.
6%
United Kingdom
5%
South Korea
4%
Cameroon
3%
2%
Bangladesh
1%
0%
1970
1980
Source: UNESCO (via World Bank)
1990
2000
2010
2019
OurWorldInData.org/financing-education • CC BY
Is additional funding for education taking resources from other sectors?
The above-mentioned growth in overall government expenditure on education as a share of GDP cannot be entirely attributed to a
wide-spread change in the prioritization of education spending within domestic budgets. The following visualization shows
government expenditure on education as a share of total government expenditure. In this case the available data does not suggest a
discernible global pattern. For instance, of the 36 countries with available data for 2000 to 2010, only around half increased spending
on education relative to the other sectors. The data does suggest, however, that there is large and persistent cross-country heterogeneity
in the relative importance of education vis-a-vis other sectors, even within developing countries. For example, in 2011 education
accounted for about 8% of government spending in the Central African Republic, while it accounted for about 30% in Ghana.
Education spending as a share of total government expenditure, 2021
Total general government expenditure on education, expressed as a percentage of total general government
expenditure on all sectors.
No data 0%
Source: UNESCO (via World Bank)
2.5%
5%
10%
15%
20%
30%
OurWorldInData.org/financing-education • CC BY
European countries tend to assign a lower share of public budgets to education, relative to the
amount of their income that is devoted to education
Generally speaking, countries that spend a large share of their income on education also tend to prioritize education highly within their
budgets. The following visualization presents a snapshot of government spending on education around the world for the year 2016.
Specifically, this graph plots government expenditure on education as a share of GDP in the vertical axis, and government expenditure
on education as a share of total government expenditure in the horizontal axis. Each dot on this chart represents a different country,
with the assigned colors denoting different world regions. As we can see, there is a positive correlation, but regional differences are
stark: for almost every level of spending as a share of GDP along the horizontal axis, European countries (marked in light orange)
spend a smaller budget share on education.
Share of GDP spent on education vs. share of expenditure assigned to
education, 2018
Government expenditure on education as a share of GDP (X-axis), against government expenditure on education as a share of
total government expenditure (Y-axis)
Africa
Asia
Europe
North America
Oceania
South America
No data
Grenada
Expenditure on education as share of government expenditure
40%
Zimbabwe
30%
Ethiopia
7B
2B
Eswatini
Guatemala
20%
Madagascar
Guinea-Bissau
Togo
0%
Mexico
Russia
Azerbaijan
South Africa
Brazil
World
Mongolia
Bangladesh
Myanmar
Malaysia
Peru
Pakistan
10%
Tunisia
Indonesia
Micronesia (country)
Belize
Dots sized by
Population
Iceland
Sweden
United Kingdom
Finland
Germany
Italy
Namibia
South Sudan
2%
4%
6%
8%
10%
12%
Government expenditure on education as share of GDP
Source: UNESCO (via World Bank)
OurWorldInData.org/financing-education • CC BY
In European countries the weight of primary education within total education spending is lower
than in other countries
In comparison to countries where education started expanding later, European countries tend to assign relatively more of their
government education budgets to the secondary and tertiary levels, while at the same time devoting relatively less of their general
government budgets to education as a whole. This can be appreciated in the following visualization, where the priorization of primary
education (i.e. the share of primary education within the education budget) is plotted against the overall priorization of education (i.e.
the share of education within the entire government budget). It can be seen that European countries (marked again in light orange) are
mostly located in the bottom-left. And there is a weak positive correlation between the variables, both across all countries and across
European countries. 15
Primary education priority vs. overall education priority, 2016
Government expenditure on primary education as a share of overall government expenditure on education (X-axis)
vs. Government expenditure on education as a share of total government expenditure (Y-axis).
Observations correspond to 2016 data (or the year before/after if 2016 missing)
Africa
Asia
Europe
North America
Oceania
South America
Grenada
Expenditure on education as share of government expenditure
40%
30%
Guatemala
Chile
20%
Cote d'Ivoire
Belize
Benin Nepal
Hong Kong
Switzerland
Kazakhstan
Ukraine
Argentina
Afghanistan
Togo
Niger
Mali Jordan
Albania
Maldives
Armenia
10%
Colombia
Japan
Serbia
Mauritania
South Sudan
0%
10%
20%
30%
40%
50%
60%
Government expenditure on primary education as a share of overall government expenditure on education in
2016
Source: World Bank
OurWorldInData.org/financing-education • CC BY
In high-income countries households shoulder a larger share of education expenditures at
higher education levels than at lower levels – but in low-income countries this is not the case
The following visualization shows the percentage of total education expenditures contributed directly by households in 15 high
income countries and 15 low/middle income countries (most recent data available on 2014). The top chart in this figure, corresponding
to high income countries, shows a very clear pattern: households contribute the largest share of expenses in tertiary education, and the
smallest share in primary education. Roughly speaking, this pattern tends to be progressive, since students from wealthier households
are more likely to attend tertiary education, and those individuals who attend tertiary education are likely to perceive large private
benefits (more on this in our entry on Skill Premium). 16 In contrast, the bottom chart shows a very different picture: in several lowincome countries households contribute proportionally more to primary education than to higher levels. Malawi is a notable case in
point – tertiary education is almost completely subsidised by the state, yet household contribute with almost 20% of the costs in
primary education. Such distribution of private household contributions to education is regressive.
Percentage of total education expenditures contributed directly by households in 30 countries, grouped by country income – Figure 32 in The
Investment Case for Education and Equity (UNICEF – 2015)
Recent funding structures in OECD
countries
Primary education continues to be publicly funded in industrialized countries
We have already mentioned that those countries that pioneered the expansion of primary education in the 19th century – all of which
are current OECD member states – relied heavily on public funding to do so. Today, public resources still dominate funding for the
primary, secondary and post-secondary non-tertiary education levels in these countries. While in the last decade the share of public
funding for these levels of education has decreased slightly, the broad pattern is remarkably stable. The visualization presents OECDaverage expenditure on education institutions by source of funds. 17 By clicking on the option labeled ‘relative’ you can see the
corresponding share of each source: private funding went up from 7.9% in 2000, to 9.4% in 2012. The role of public funding for other
levels of education is however quite different. At the tertiary level public sources account for less than 70% of funding on average
(2012 figures). Below we provide evidence of the pre-school level.
Average OECD non-tertiary education expenditure by source of funding
Average expenditure on educational institutions given as a share of GDP, by source of funding (primary, secondary
and post-secondary non-tertiary).
Private
expenditure
3.5%
3%
2.5%
2%
Public
expenditure
1.5%
1%
0.5%
0%
2000
2002
Source: OECD: Education Statistics (2017)
2004
2006
2008
2010
2012
OurWorldInData.org/financing-education • CC BY
Publicly funded pre-primary education is more strongly developed in the European countries of
the OECD
High-income countries tend to have better developed pre-primary education systems than lower-income countries. However, within
high-income countries there is substantial heterogeneity in the extent to which pre-primary education is publicly financed. The
visualization presents expenditure on pre-primary educational institutions as a share of GDP across the OECD. As it can be seen,
publicly funded pre-primary education tends to be more strongly developed in the European than the non-European countries of the
OECD. In fact, the OECD reports that in Europe the concept of universal access to education for 3-6 year-olds is generally accepted:
most countries in this region provide all children with at least two years of free, publicly funded pre-primary education in schools
before they begin primary education.
Expenditure on pre-primary educational institutions (% of GDP), OECD, 2012 – Figure C2.4 in Education at a Glance (2015)
Where does funding for education go to?
The largest part of funding devoted to education in OECD countries goes to finance current expenditures, mainly compensation of
staff – specifically, teachers. The following two charts, taken from the OECD’s report Education at a Glance (2015), highlight the
labour-intensive nature of education. In the lower levels of education (i.e. primary, secondary and post-secondary non-tertiary) the
share of current expenditure is very large and exhibits little cross-country variation – between 90 and 97 percent of total expenditure
corresponds to current expenditure across all of the OECD countries. In higher levels of education (i.e. tertiary) there is more crosscountry variation, but current expenditure still dominates by a large margin across all countries.
Distribution of current and capital expenditure on educational institutions – Figure B6.2 in Education at a Glance (2015)
What drives current expenditure on education?
In the figures above we noted the importance of current expenditure in the production of education. The following table provides
further details regarding the type of expenditures that comprise current spending. Specifically, this chart shows a breakdown of
expenditure for tertiary-level institutions in the US (public and private), during the period 1980-1997. It shows that instruction
accounts for almost half of expenditure; and while there are some small differences across sectors, there is a fair amount of stability in
expenditures across time. This serves as a benchmark for lower education levels, where instruction takes an even larger share of
expenditure. 18
Percent distribution of college and university current expenditures in the US, by control over time – Table 8 in Welch and Hanushek (2006) 19
International financing flows
Education financing in developing countries has been largely affected by development assistance
Following the agreement of the Millennium Development Goals, the first decade of the 21st century saw an important increase in
international financial flows under the umbrella of development assistance (often also called development aid, or simply ‘aid’). The
following chart shows total OECD development assistance flows for education by level, in constant 2013 US dollars, for the period
2002-2013. As it can be seen, there are two distinct periods: in 2003-2010 flows for education increased substantially, more than
doubling in real terms across all levels of education; and in the years 2010-2013 funding for basic education decreased, while funding
for secondary and post-secondary education remained relatively constant. For many low income countries, where development
assistance contributes a substantial share of funding for education, this marked change in trends is important. As a reference, in 2012
development assistance accounted for more than 20 percent of all domestic spending on basic education in recipient low-income
countries. 20
Total development assistance for education by level, 2003-2013 – Figure 5 in the report Education Aid Watch 2015
The share of development assistance for education going to Sub-saharan Africa has decreased
The recent reductions in development assistance funds for primary education have been coupled with important changes in regional
priorities. Specifically, the share of development assistance for primary education going to sub-Saharan Africa has been decreasing
sharply since the agreement of the Millennium Development Goals. The following chart shows this: sub-Saharan Africa’s share in
total aid to primary education declined from 52 percent in 2002 to 30 percent in 2013, while the continent’s share in the total number
of out-of-school children rose from 46 percent to 57 percent. This pattern is something specific to the education sector within the
broader development assistance landscape: in the healthcare sector the overall slowdown of flows started a couple of years later, was
less abrupt, and affected proportionally less the sub-Saharan countries. 21 Indeed, recent studies further highlight that development
assistance for education is significantly different to assistance for healthcare in other ways: the education sector attracts less earmarked
funding through multilaterals, and includes a smaller proportion of resources that developing governments can directly control for
programming. 22
You can read more about development assistance for healthcare in our entry on Financing Healthcare.
Share of primary education disbursements from development assistance going to Sub-Saharan Africa, 2002-2013 – Figure 2.6 in Steer and
Smith (2015) 23
Development assistance priorities have a scope for increasing or reducing expenditure
inequalities
In the Historical Perspective section above, we mentioned that public spending on education has translated, in the long run, into lower
inequality in education outcomes across most of the world. But for any given country, with a determined income distribution and
demographic structure, the extent to which public spending on education contributes to reduce inequality depends crucially on the way
in which spending is focused across education levels. The recent UNICEF report The Investment Case for Education and Equity
shows that in low income countries, on average 46 percent of public resources are allocated to the 10 percent of students who are most
educated – while this figure goes down to 26 and 13 percent in lower-middle and upper-middle income countries respectively. The
following visualization shows further details on the concentration of public spending across different countries. The vertical axis
shows the percentage of public education resources going to the 10% most educated or 10% least educated students – as we can see
expenditure is heavily concentrated at the top in many low income countries.
The earlier remarks about trends in international education financing flows (namely that aid is very important in low-income
countries, and that a relatively low and shrinking share of aid is going to primary levels), suggest that inequality in public spending
will worsen in low income countries. Yet development assistance priorities have a scope for changing this. 24
Percentage of public education resources going to the 10% most educated or 10% least educated students – Figure 29 in The Investment Case
for Education and Equity (UNICEF – 2015)
What determines educational finance?
IN THIS SECTION
The big picture
School inputs
Household inputs
The big picture
Why do governments finance education?
One of the reasons to justify government intervention in the market for education, is that education generates positive externalities. 25
This essentially means that investing in education yields both private and social returns. Private returns to education include higher
wages and better employment prospects. Social return include pro-social behaviour (e.g. volunteering, political participation) and
interpersonal trust. The following chart uses OECD results from the Survey of Adult Skills to show how self-reported trust in others
correlates with educational attainment. More precisely, this chart plots the percentage-point difference in the likelihood of reporting to
trust others, by education level of respondents. Those individuals with upper secondary or post-secondary non-tertiary education are
taken as the reference group, so the percentage point difference is expressed in relation to this group. As we can see, in all countries
those individuals with tertiary education were by far the group most likely to report trusting others. And in almost every country, those
with post-secondary non-tertiary education were more likely to trust others than those with primary or lower secondary education. The
OECD’s report Education at a Glance (2015) provides similar descriptive evidence for other social outcomes. The conclusion is that
adults with higher qualifications are more likely to report desirable social outcomes, including good or excellent health, participation
in volunteer activities, interpersonal trust, and political efficacy. And these results hold after controlling for literacy, gender, age and
monthly earnings.
Likelihood of reporting to trust others, by educational attainment, OECD 2012 – Figure A8.4 in Education at a Glance (2015) 26
Do countries that spend more public resources on education tend to have better education
outcomes?
Education outcomes are typically measured via ‘quantity’ output (e.g. years of schooling) and ‘quality’ output (e.g. learning outcomes,
such as test scores from the Programme for International Student Assessment – PISA). The following visualization presents three
scatter plots using 2010 data to show the cross-country correlation between (i) education expenditure (as a share of GDP), (ii) mean
years of schooling, and (iii) mean PISA test scores. At a cross-sectional level, expenditure on education correlates positively with both
quantity and quality measures; and not surprisingly, the quality and quantity measures also correlate positively with each-other. But
obviously correlation does not imply causation: there are many factors that simultaneously affect education spending and outcomes.
Indeed, these scatterplots show that despite the broad positive correlation, there is substantial dispersion away from the trend line – in
other words, there is substantial variation in outcomes that does not seem to be captured by differences in expenditure.
Correlation between education outcomes and education expenditure (2010 data) 27
Does cross-country variation in government education expenditure explain cross-country
differences in education outcomes?
The following visualization presents the relationship between PISA reading outcomes and average education spending per student,
splitting the sample of countries by income levels. It shows that income is an important factor that affects both expenditure on
education and education outcomes: we can see that above a certain national income level, the relationship between PISA scores and
education expenditure per pupil becomes virtually inexistent. Several studies with more sophisticated econometric models corroborate
the fact that expenditure on education does not explain well cross-country differences in learning outcomes. 28 You can read more
about test scores and learning outcomes in our entry on Quality of Education.
Average reading performance in PISA and average spending per student from the age of 6 to 15 – Figure 1 in OECD (2012) 29
What inputs enter the ‘education production function’?
The fact that expenditure on education does not explain well cross-country differences in learning outcomes is indicative of the
intricate nature of the process through which such outcomes are produced. Borrowing the terms from the economics literature, the
following ‘production function’ provides a conceptual framework to think about the determinants of learning outcomes 30:
where A is skills learned (achievement), s is years of schooling, Q is a vector of school and teacher characteristics (quality), C is a
vector of child characteristics (including “innate ability”), H is a vector of household characteristics, and I is a vector of school inputs
under the control of households, such as children’s daily attendance, effort in school and in doing homework, and purchases of school
supplies.
This conceptualization highlights that, for any given level of expenditure, the output achieved will depend on the input mix. And
consequently, this implies that in order to explain education outcomes, we must rely on information about specific inputs. In the
following sections we explore evidence regarding the returns to household inputs (i.e. different elements of Q) and demand-side inputs
(i.e. elements of C,H and I).
School inputs
Each education system is different, but improving teacher quality is often more effective to
improve learning outcomes than increasing the number of teachers per pupil
A vast number of studies have tried to estimate the impact of classroom resources on learning outcomes. The following table
summarizes results from the systematic review in Hanushek (2006) 31. In this table, the left-hand side summarizes results from
econometric studies focusing on developing countries, while the right-hand side presents evidence from the US (where studies have
concentrated extensively). We can see that for all listed inputs and across all countries, the share of studies that have found a positive
effect is small – in fact, the majority of studies find either no effect, or a negative effect. This clearly does not mean that these
classroom resources are not important, but rather that it is very difficult to know with confidence when and where they are a binding
constraint to improve learning outcomes. A first conclusion, therefore, seems to be that context and input mix are fundamental to
improving outcomes – even in developing countries where the expected returns to additional resources is large across the board.
Taking the ratio of positive to negative effects detected in the literature as a proxy for what tends to work best, we can derive a second
conclusion from the table: spending more resources on better teachers (i.e. improving teacher experience and teacher education) tends
to work better to improve learning outcomes than simply increasing the number of teachers per pupil. And this seems to be true both in
developed and developing countries. This last conclusion is consistent with the main message from the OECD’s report Does money
buy strong performance in PISA?, which points out that countries that prioritised the quality of teachers over class sizes performed
better in PISA tests. 32
And it is also consistent with a recent high-quality study on the impact of teacher quality on test scores using data from the US, which
suggests that improvements in teacher quality can causally raise students’ test scores (evidence from Chetty et al. (2014) 33 – see our
entry on Quality of Education for a discussion of their results).
Percentage distribution of estimated effect of selected key resources on student performance – based on Tables 3 and 6 in Hanushek (2006) 34
Remedial teaching can yield substantial improvements in learning outcomes
Education in low-income countries is particularly difficult because there is substantial heterogeneity in the degree of preparation that
children have when they enter school – much more so than in high-income countries. Recent evidence from policy ‘experiments’ in
developing countries suggests remedial teaching, in the form of assistants teaching targeted lessons to the bottom of the class, can
yield substantial improvements in learning outcomes. The following visualization summarizes the effects of four different policy
treatments within the so-called Teacher Community Assistant Initiative (TCAI) in Ghana – this is an initiative that evaluated four
different such remedial teaching interventions. 35
The units in this figure are standard deviations of test results. The first two sets of estimates correspond to the test-score impacts of
enabling community assistants to provide remedial instruction specifically to low-performing children, either during school, or after
school. The third set of estimates corresponds to test-score impacts of providing a community assistant and reducing class size,
without targeting instruction to low-performing pupils. And the last set of results corresponds to testing the effect of training teachers
to provide small-group instruction targeted at pupils’ actual learning levels.
As we can see, while all interventions had a positive effect, the lowest impacts – across all tests – come from the non-targeted ‘normal
curriculum’ intervention that reduced class sizes, and from the intervention that provided training to teachers on how to engage in
targeted remedial teaching themselves. This suggests that the improvements in outcomes were caused by the combination of targeted
instruction and TCAs who, unlike teachers, were specifically dedicated to this purpose. These results are consistent with findings from
across Africa, suggesting that teaching at the right level causes better learning outcomes in a cost-effective way (see Glewwe and
Muralidharan 2016 36 for further details on this evidence).
Summary of treatment effects from the Teacher Community Assistant Initiative (TCAI) in Ghana (estimates by test subject in standard
deviations) – Page 2 in Innovations for Poverty Action (2014) 37
Are pay-for-performance teacher contracts an effective instrument to improve learning
outcomes?
We have already made the point that the bulk of education expenditure goes specifically towards financing teachers. And we have also
pointed out that improving teacher quality may be a particularly good instrument to improve teaching outcomes. This leads to a natural
question: are pay-for-performance teacher contracts an effective instrument to improve learning outcomes? A growing body of
literature in the economics of education has started using randomized control trials (i.e. policy ‘experiments’) to answer this question.
Glewwe and Muralidharan (2016) 38 provide the following account of the available evidence:
“Results suggest that even modest changes to compensation structures to reward teachers on the basis of objective
measures of performance (such as attendance or increases in student test scores) can generate substantial improvements
in learning outcomes at a fraction of the cost of a “business as usual” expansion in education spending. However, not all
performance pay programs are likely to be effective, so it is quite important to design the bonus formulae well and to
make sure that these designs reflect insights from economic theory.” 39
The conclusion is that well-designed pay-for-performance contracts are a cost-effective instrument to boost test scores; but this does
not mean that they are necessarily effective at achieving other – perhaps equally important – objectives of time spent in school. In
simple words, it is possible that pay-for-performance yields ‘teaching to the test’. Alternative incentive mechanisms, such as
community-based monitoring of teachers, have been proposed as an alternative. Glewwe and Muralidharan (2016) also provide a
review of the – somewhat limited – available evidence on such alternative incentive mechanisms. 40
Household inputs
School attendance and student effort are responsive to incentives
Demand-side inputs are as important as supply-side inputs to produce education. Attending school and exerting effort are perhaps the
most obvious examples: without these inputs even the best endowed schools will fail to deliver good outcomes. The table summarizes
information on different demand-side investments that have shown to successfully improve quality and quantity outcomes. More
precisely, this table gathers evidence from randomized control trials in developing countries, as per the review in Glewwe and
Muralidharan (2016). The reported figures correspond to positive/negative significant/insignificant estimates across a set of available
experimental studies (bear in mind some studies estimate more than one effect – e.g. by measuring outcomes at several points in time).
As we can see, the evidence suggests interventions that increase the benefits of attending school – such as conditional cash transfers –
are likely to increase student time in school. And those that increase the benefits of higher effort and better academic performance –
such as merit scholarships – are likely to improve learning outcomes (see Glewwe and Muralidharan 2016 for further details on the
underlying policy interventions, plus further evidence and discussion of results).
Summary of impacts for selected demand-side interventions on education outcomes in developing countries – based on Tables 4 and 5 from
Glewwe and Muralidharan (2016) 41
Targeting health problems can be a particularly cost-effective way of increasing school
attendance
In many low-income countries, health problems are an important factor preventing children from attending school. The following
visualization presents a comparison of the impact that a number of different health interventions have achieved in different countries –
together with some non-health-related interventions that serve as reference. The height of each bar in this graph reflects the additional
school years achieved per hundred dollars spent on the corresponding intervention; so these estimates can be interpreted as a measure
of how cost-effective the different interventions are. 42
We see that treating children for intestinal worms (labeled ‘deworming’ in the chart) led to an additional 13.9 years of education for
every $100 spent in Kenya; while a program targeting anaemia (labeled ‘iron fortification’) led to 2.7 additional years per $100 in
India. These interventions seem to be much more cost-effective to improve test scores than conditional cash transfers, free school
uniforms, or merit scholarships (further details on all interventions available from Dhaliwal et al. 2012 43). Of course, ranking these
interventions is not trivial since most programmes achieve multiple outcomes – indeed, we have already discussed that remedial
teaching is generally effective to increase test-scores, although here we see a particular instance where it had no impact on school
attendance. Nevertheless, health interventions seem to be particularly interesting, since they lead to substantial achievements in both
education and health outcomes (for a recent analysis of the literature on the impacts of mass deworming see Croke et al. 2016 44).
Impact of selected demand-side interventions on school participation in developing countries (Additional years of student participation per
$100) – Figure 8.1 in Dhaliwal et al. (2012) 45
How important are pre-school investments?
The environment that children are exposed to early in life, plays a crucial role in shaping their abilities, behavior and talents. To a
great extent, this is what drives large and remarkably persistent gaps in the education achievement between individuals in the same
country, but in different socioeconomic environments. Cunha et al. (2006) provide a detailed account of the theory and evidence
behind this claim, and discuss its implications for the design of education policies. In the chart we see the impacts from the Perry
Preschool Program – a flagship experimental intervention study, designed to test the impact of pre-school education on subsequent
education outcomes. 46 The chart shows disadvantaged children participating in the pre-school program (the ‘treatment group’) had
higher grades and were more likely to graduate from high school than the reference control group. Moreover, they spent substantially
less time in special education. Other programs have similarly shown evidence of very large and persistent returns to early education
interventions.
Educational effects from participating in the Perry Preschool Program – Figure 14B in Cunha et al (2006) 47
Definitions & Measurement
IN THIS SECTION
Relationship between sources
Definitions
Measurement limitations
Relationship between sources
The main source of data on international education expenditure is UNESCO’s Institute for Statistics (UIS). The same data is also then
published by the World Bank (World Bank EdStats and World Development Indicators) and Gapminder. It is also the main source of
education data for most UN reports – such as the EFA Global Monitoring Report (UNESCO), the Human Development Report
(UNDP), the State of the World’s Children report (UNICEF) and the Millennium Development Goals (UN).
The UIS database is produced mainly from yearly national reports, but it also relies on reports from international organizations. 48
Specifically, countries in the European Union, or members of the OECD, have richer data, since they collect information through the
UIS-OECD-Eurostat (UOE) survey, which is more detailed than the UIS survey (see more at FAQ-UIS)
Another, related but different source of education expenditure data, is the International Food Policy Research Institute (IFPRI), which
publishes the Statistics of Public Expenditure for Economic Development (SPEED). This source relies primarily on data from the
International Monetary Fund (IMF).
Definitions
In the UIS database, government expenditure on education includes spending by local/municipal, regional and national governments,
on public and private educational institutions, education administration, and subsidies for private entities (students/households and
other privates entities). This information is then reported by level of education, and typically as a share of national income (GDP) or as
a share of total public expenditure. In principle, expenditure on pre-primary levels as well as expenditure sourced with transfers from
international sources to government, are included. In practice, however, many countries under report these specific figures.
Measurement limitations
The UIS has been maintained since 1999 with the intent of providing comparable expenditure figures across countries and time, and
its estimates rely on reports submitted by ministries and national statistics offices. Since reporting is delegated typically to ministries
of education, in some instances data on total public expenditure on education fails to represent spending by other ministries that also
have budgets for education. Additionally, since not all countries have (or update) national education accounts, the UIS attempts to
generate estimates and impute missing data using information from national publications, official websites and other sources.
However, the UIS dataset has several missing observations, particularly for years prior to 2010.
To gauge the extent to which UIS data is reliable, the following visualization shows the proportions of regular and irregular data that
countries make available (where ‘Regular’ means data is available at least once every 3 years; and ‘Irregular’ means data is available
less frequently than every 3 years). As can be seen, the picture is not particularly encouraging: less than half of the countries reported
regularly data on total government expenditure on education over the reference period.
Apart from the above-mentioned issues regarding the availability of data, comparing education financing between different countries
can be challenging because the way that expenses are categorized is not always consistent. This can be particularly difficult when it
comes to expenses that could potentially be classified as education or non-education-related, like teaching hospitals or transportation.
While this issue is not as significant in other sectors like healthcare, it still presents some difficulties. For a further discussion of these
issues see the UIS data collection manual .
Availability of education financing data in the UIS database, 2005-2013, as a percentage of all (214) countries – Figure 1 in UNESCO–UIS
(2016) 49
Data Sources
IN THIS SECTION
Long-run estimates of education financing
Long-run country-specific statistics on education financing
Up-to-date estimates of education systems (including education finances)
Long-run estimates of education financing
As it has been mentioned, the earliest data on financing of education dates back to the late 19th century, when today’s industrialized
countries began expanding their education systems. The main sources here are academic publications.
Lindert (1994)
Data Source: Lindert, Peter H. “The rise of social spending, 1880-1930.” Explorations in Economic History 31, no. 1 (1994)
Description of available measures: Public Education Expenditure as percent of GDP
Time span: Selected years in the late 19th century
Geographical coverage: Selection of high-income countries
Flora et al. (1983)
Data Source: Flora, Peter et al. 1983. State, Economy and Society in Western Europe, 1815-1975. Frankfurt: Campus Verlag
Description of available measures: Central government expenditure by sectors, as percent of GDP and as percent of total
expenditure.
Time span: 1815-1975
Geographical coverage: Western Europe
Link: Available online from http://gpih.ucdavis.edu/Government.htm/
Tanzi and Schuknecht (2000)
Data Source: Tanzi, Vito, and Ludger Schuknecht. Public spending in the 20th century: A global perspective. Cambridge
University Press, 2000.
Description of available measures:
Public Health Expenditure as percent of GDP
Health Insurance Coverage as percent of labour force
Time span: 1910-1994
Geographical coverage: Selection of high-income countries
Szirmai (2015)
Data publisher: Adam Szirmai, (2015) The Dynamics of Socio-Economic Development, www.dynamicsofdevelopment.com
Data source: Different sources, but mainly UIS after 2000, and selected UNESCO yearbooks prior 2000
Description of available measures:
Gross enrolment ratios by educational level, country and region
Net enrolment ratios by region
Highest diploma obtained (as percentage of 25+ age bracket)
Average years of education of the population of 25 years and over
Government expenditure per pupil in selected countries, 1965-2010
Government expenditure on education as a percentage of gross national product
Cognitive performance of developing countries
Illiterates as a Percentage of the Population of 15 years and over
Time span: Selected years in the second half of 20th century (periodic updates online)
Geographical coverage: Selected low and middle income countries
Long-run country-specific statistics on education financing
Country-specific statistics are another important source of long-run data on education spending. Two references we used in this entry
are the U.S. Bureau of the Census and the U.S. National Center for Education Statistics.
U.S. Bureau of the Census
Data Source: (a) Historical Statistics of the United States Colonial Times to 1970 (1929-1970); and (b) US Census Statistical
Abstract 1990 (1970-1990). Both published by the US Bureau of the Census
Description of available measures: Total education expenditure, disaggregated by private and public spending, with further
details on specific types of expenditure (figures mainly expressed in current prices)
Time span: 1929-1990
Geographical coverage: U.S.
National Center for Education Statistics
Data Source: National Center for Education Statistics (NCES) – Digest of Education Statistics
Description of available measures: Information on a variety of subjects in the field of education statistics, including the
number of schools and colleges, teachers, enrollments, and graduates, in addition to educational attainment, finances, federal
funds for education, libraries, and international education.
Time span: Since 1970
Geographical coverage: U.S.
Up-to-date estimates of education systems (including education
finances)
The most common source of up-to-date cross-country education data is UNESCO’s Institute for Statistics (UIS). This is the source for
data published by the World Bank (World Bank EdStats and World Development Indicators) and Gapminder.
UNESCO – UIS Database
Data Source: UIS based on reports from ministries, national statistics offices and international agencies
Description of available measures:
Out-of-school children
Entry
Participation
Progression
Completion
Literacy
Educational attainment
International student mobility in tertiary education
Human resources
Financial resources
School resources and teaching conditions (Africa only)
Adult education (Latin America and the Caribbean only)
Disparities in teacher’s training, deployment, characteristics and working conditions at sub-national level (East and South
West Asia only)
Time span: 1970-2015 for some variables, but most variables available since 1998
Geographical coverage: Global by Country
Link: http://data.uis.unesco.org
Another important source is the OECD – this is arguably the most comprehensive database in terms of variables and regularity of
observations. This is the source used for the OECD’s periodic report Education at a Glance.
OECD Education Statistics
Data Source: OECD based on reports from member countries
Description of available measures:
Graduation and entry rates
Graduates and entrants by field
Profile of graduates and entrants
Student-teacher ratio and average class size
Distribution of teachers by age and gender
Enrolment rate by age
Share of enrolment by type of institution
Share of enrolment by gender, programme orientation and mode of study
Share of international students enrolled by field of education
Share of international students enrolled by country of origin
Transition from school to work
Educational attainment and labour-force status
Educational finance indicators
Time span: 1960-2015 for some variables, though substantial missing values prior 1990
Geographical coverage: OECD countries
Link: http://stats.oecd.org
Yet another relevant source of internationally comparable expenditure statistics is IFPRI’s Statistics of Public Expenditure for
Economic Development. This dataset relies mainly on IMF statistics.
IFPRI – SPEED
Data Source: IFPRI, from multiple dats sources, but mainly IMF statistics
Description of available measures:
Education expenditure in 2005 $ppp
Education expenditure in 2005 US$
percentage of education expenditure in total gdp
per capita education expenditure in 2005 $ppp
percentage of education expendtiure in total expenditure
Time span: 1980-2012
Geographical coverage: 67 countries across all continents
Link: https://www.ifpri.org/publication/statistics-public-expenditures-economic-development-speed
Endnotes
1. See the Wikipedia entry on compulsory education for a table of the ages of compulsory schooling around the world.
2. As per estimates from Adam Szirmai, (2015) The Dynamics of Socio-Economic Development, www.dynamicsofdevelopment.com
3. As per estimates of Gini coefficients for the distribution of school years in Crespo Cuaresma, J., KC, S., & Sauer, P. (2013). Age-specific education inequality,
education mobility and income growth (No. 6). WWWforEurope. Available online from www.ecineq.org
4. As per estimates reported in Steer L. and K. Smith (2015), Financing education: Opportunities for global action. Center for Universal Education. Available Online
from the Brookings Institution
5. The source for the visualization below – Tanzi & Schuktnecht (2000) – compiles estimates from many sources, including: League of Nations Statistical Yearbook
(various years), Mitchell (1962), OECD Education at a Glance (1996), UNESCO World Education Report (1993), UNDP Human Development Report (1996), UN
World Economics Survey (various years). To the extent that the authors do not specify which sources were prioritised for each year/country, it is not possible for us
to reliably extend the time series with newer data. For instance, the OECD Education at a Glance report (1998), which presents estimates for the years 1990 and
1995, suggests discrepancies with the values reported by Tanzi & Schuktnecht (2000) for 1993.
6. As per 2015 enrolment estimates from the NCES.
7. A recent article from the Huffington Post highlights this point, including interesting visualizations documenting the important role that federal funding plays in
reducing expenditure inequalities.
8. Lindert, Peter H. Growing public: Volume 1, the story: Social spending and economic growth since the eighteenth century. Vol. 1. Cambridge University Press,
2004.
9. Lindert, Peter H. Growing public: Volume 1, the story: Social spending and economic growth since the eighteenth century. Vol. 1. Cambridge University Press,
2004.
10. Bear in mind that the estimates from the National Center for Education Statistics are not broken down by source of funds. Rather, they show expenditure by type of
institution – which is not equivalent, since public institutions may spend private resources, and vice versa.
11. Szirmai, A. (2005) The Dynamics of Socio-Economic Development: An Introduction. Cambridge University Press.
12. In 2010, high income countries spent 6721 US PPP dollars per primary school pupil. Low income countries, in contrast, spent 115 US PPP dollars per pupil
(UNESCO EFA Global Monitoring Report 2014).
13. Jesus Crespo Cuaresma, Samir K.C., and Petra Sauer (2013) – Age-Specific Education Inequality, Education Mobility and Income Growth
WWWforEurope working paper; Working Paper no 6; Online at
http://www.foreurope.eu/fileadmin/documents/pdf/Workingpapers/WWWforEurope_WPS_no006_MS15.pdf
14. Data from Petra Sauer (2016) – The Role of Age and Gender in Education Expansion. Working Paper. Online at: http://epub.wu.ac.at/5186/
15. For reference, the correlation for all countries in this scatter plot is 0.24.
16. Strictly speaking, for this spending pattern to be truly progressive there must be subsidies or income-contingent loans to guarantee that low-income students can
also access tertiary education and reap the private benefits from this type of investment.
17. The OECD provides country-specific figures. However, there is relatively little variation across OECD countries in this respect. This is explained by near-universal
enrolment rates at these levels of education and the demographic structure of the population.
18. This is a stylized fact of OECD education spending. In all the OECD countries, the share of spending devoted to compensation of teachers is by far the largest
component of current expenditure. Moreover, expenditure on teachers’ compensation is larger at the combined primary, secondary and post-secondary non-tertiary
levels of education than at the tertiary level. See Table B6.2 in Education at a Glance (2015) for details on the breakdown of current expenditure across all OECD
countries by education level.
19. Welch, F., & Hanushek, E. A. (2006). Handbook of the Economics of Education, Two Volumes. North Holland.
20. Steer L. and K. Smith (2015), Financing education: Opportunities for global action. Center for Universal Education. Available Online from the Brookings
Institution
21. The share of development assistance going to sub-Saharan Africa has decreased as a whole – from 55 percent in 2002 to 40 percent in 2013 –, but as we note the
drop specifically for primary education has been steeper.
22. Steer L. and K. Smith (2015), Financing education: Opportunities for global action. Center for Universal Education. Available Online from the Brookings
Institution
23. Steer L. and K. Smith (2015), Financing education: Opportunities for global action. Center for Universal Education. Available Online from the Brookings
Institution
24. The conclusion from these figures is that, while public spending does reduce education inequality in low income countries, remaining inequalities could be further
reduced by shifting resources towards lower levels of education. This evidently does not mean that resources should be shifted – low income countries and aid
donors may have other objectives apart from reducing inequality. But the case for reducing inequality at the bottom is very strong, and some studies suggest that
returns to education at the primary level might be higher than at post-primary levels in low income countries (for a discussion of the vast literature on returns to
education, and the ongoing debate on the validity of estimates, see Heckman, J. J., Lochner, L. J., & Todd, P. E. (2006). Earnings functions, rates of return and
treatment effects: The Mincer equation and beyond. Handbook of the Economics of Education, 1, 307-458. ).
25. That positive externalities justify government intervention in the provision of education is essentially an efficiency argument. The logic is that individuals may not
spend enough on education because they fail to internalize the positive effect that their education has on other people. But there are, of course, also equity
arguments to justify government intervention in the provision of education – for instance, reducing inequality in education may be of intrinsic value, or may be
instrumental in reducing inequalities in other outcomes.
26. As per the source notes: “Percentage-point difference reflects the relative change of reporting to trust others compared to the reference category. For example, in
Norway, the percentage of individuals with tertiary education reporting to trust others increases by 20 percentage points compared to someone who has upper
secondary or post-secondary non-tertiary education. Similarly, after accounting for literacy proficiency, the percentage of individuals with tertiary education
increases by 16 percentage points compared to someone who has upper secondary or post-secondary non-tertiary education.”
27. Data on expenditure corresponds to 2010 total government education expenditure across all levels, as a share of GDP (source: World Bank Education Statistics).
Data on PISA scores corresponds to 2010 mean average test scores across categories – mathematics, reading and science (source: OECD PISA). Data on years of
schooling corresponds to 2010 mean years of schooling for the population aged 15 and over (source: Barro Lee Education dataset)
28. For a discussion of evidence supporting this claim, see Hanushek, E. A., (2006). School Resources. Handbook of the Economics of Education, 2.
29.
Does money buy strong performance in PISA? – OECD. Available online here.
30. P. Glewwe, K. Muralidharan (2016). Improving Education Outcomes in Developing Countries: Evidence, Knowledge Gaps, and Policy Implications. Handbook of
the Economics of Education, Volume 5.
31. Hanushek, E. A., (2006). School Resources. Handbook of the Economics of Education, Volume 2. Elsevier.
32. This claim is clearly only descriptive, since there are many underlying variables that simultaneously drive teacher characteristics and student outcomes in any
particular country. Indeed, most of the available evidence on whether teacher quality and quantity matters is difficult to interpret causally, as it is hard to find
instances where teacher quality / quantity varies exogenously. A recent study concludes on the topic: “teachers vary in many ways, but we found no high-quality
studies that have examined the impact of teacher characteristics on student learning or time in school” (source: page 696, Glewwe, P. and Muralidharan, K. (2016)
Improving Education Outcomes in Developing Countries: Evidence, Knowledge Gaps, and Policy Implications. Handbook of the Economics of Education, Volume
5. )
33. Chetty, Raj, John N. Friedman, and Jonah E. Rockoff. 2014. “Measuring the Impacts of Teachers I: Evaluating Bias in Teacher Value-Added Estimates.” American
Economic Review, 104(9): 2593-26
34. Hanushek, E. A., (2006). School Resources. Handbook of the Economics of Education, 2.
35. Further details in Innovations for Poverty Action, 2014. Implementation Lessons: The Teacher Community Assistant Initiative (TCAI). Available online from
www.poverty-action.org
36. Glewwe, P. and Muralidharan, K. (2016) Improving Education Outcomes in Developing Countries: Evidence, Knowledge Gaps, and Policy Implications.
Handbook of the Economics of Education, Volume 5. Elsevier. (Link to working paper)
37. Innovations for Poverty Action (2014). Implementation Lessons: The Teacher Community Assistant Initiative (TCAI). Available online from www.povertyaction.org
38. Glewwe, P. and Muralidharan, K. (2016) Improving Education Outcomes in Developing Countries: Evidence, Knowledge Gaps, and Policy Implications.
Handbook of the Economics of Education, Volume 5. Elsevier. (Link only to working paper)
39. Glewwe, P. and Muralidharan, K. (2016) Improving Education Outcomes in Developing Countries: Evidence, Knowledge Gaps, and Policy Implications.
Handbook of the Economics of Education, Volume 5. Elsevier. (Link only to working paper)
40. They conclude that “evidence on the impact of monitoring on time in school is scarce and not encouraging…[while] the evidence of the impact of monitoring on
student learning is only somewhat more encouraging”
41. Glewwe, P. and Muralidharan, K. (2016) Improving Education Outcomes in Developing Countries: Evidence, Knowledge Gaps, and Policy Implications.
Handbook of the Economics of Education, Volume 5. Elsevier. (Link only to working paper)
42. Bear in mind that the reported gains in school years are a measure of the total impact of the program across the treated population, rather than impact per treated
student. Further information on cost-effectiveness analysis is available from the source of the graph.
43. Dhaliwal, I., Duflo, E., Glennerster, R., & Tulloch, C. (2013). Comparative cost-effectiveness analysis to inform policy in developing countries: a general
framework with applications for education. Education Policy in Developing Countries, 285-338.
44. Croke, Kevin, Joan Hamory Hicks, Eric Hsu, Michel Kremer, and Edward Miguel. 2016. “Does Mass Deworming Affect Child Nutrition? Meta-analysis, Costeffectiveness, and Statistical Power.” Working Paper.
45. Dhaliwal, I., Duflo, E., Glennerster, R., & Tulloch, C. (2013). Comparative cost-effectiveness analysis to inform policy in developing countries: a general
framework with applications for education. Education Policy in Developing Countries, 285-338.
46. More specifically, the Perry pre-school ‘experiment’ consisted in enrolling 65 randomly selected black children in a pre-school program, and comparing their
outcomes later in life against those achieved by a control group of roughly the same size. The treatment consisted of a daily 2.5-hour classroom session on
weekday mornings and a weekly 90-minute home visit by the teacher on weekday afternoons to involve the mother in the child’s educational process. More
information and details on the intervention are available in Cunha et al. (2006).
47. Cunha, F., Heckman, J. J., Lochner, L., & Masterov, D. V. (2006). Interpreting the evidence on life cycle skill formation. Handbook of the Economics of Education,
1, 697-812.
48. The official documentation says: “The UIS collects education statistics in aggregate form from official administrative sources at the national level. Collected
information encompasses data on educational programmes, access, participation, progression, completion, literacy, educational attainment and human and financial
resources. These statistics cover formal education in public (or state) and private institutions (pre-primary, primary, basic and secondary schools, and colleges,
universities and other tertiary education institutions); and special needs education (both in regular and special schools)”.
49. UNESCO–UIS (2016). A roadmap to better data on education financing. INFORMATION PAPER NO. 27.
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Max Roser and Esteban Ortiz-Ospina (2016) - "Education Spending". Published online at
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@article{owidfinancingeducation,
author = {Max Roser and Esteban Ortiz-Ospina},
title = {Education Spending},
journal = {Our World in Data},
year = {2016},
note = {https://ourworldindata.org/financing-education}
}
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