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Why Is Education Considered an Economic Good? – Education tends to raise productivity and creativity, as well as stimulate entrepreneurship and technological breakthroughs. All of these factors lead to greater output and economic growth.
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Who Analyse the relationship between education and economy?

Musai et al. (2011) studied the relationship between education and economic growth of 79 countries. They revealed that the elasticity of the production of human capital, physical capital and labor force are 0.28, 0.696 and 0.044 respectively.
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What is the relationship between economic prosperity and education?

Business Esther Ejim Last Modified Date: November 10, 2022 Esther Ejim Last Modified Date: November 10, 2022 The link between economic development and eductation lies in the fact that education is a facilitator for economic development. Education is a human capital investment, which is expected to yield results that will translate to the improvement and growth of the economy of a nation.

This effect can be seen in areas with a high percentage of well-educated people. Such people are able to channel their knowledge into concerete actions that lead to the development of the economy in comparison to those areas where there are few well-educated people. An example of the link between economic development and education is the value derived from improvement of knowledge.

Education involves a formal or informal process of instructing people and depositing a capiatal wealth of knowledge inside of them. For instance, when someone goes to school to train as an engineer, the process will involve series of trainings, tests and other forms of practical and theoretical teachings aimed at improving that person’s knowledge. Education is a human capital investment, which is expected to yield results that will translate to the improvement and growth of the economy. Another example of the link between economic development and education is the ability of the members in an educated society to use their knowledge to discover new opportunities for wealth creation.

For instance, most people in third world countries may not be able to develop models that will help them effectively utilize their resources in an beneficial manner due to a marked low ratio of educated people in comaprison to the percentage of educated people in developed countries. An example of this scenario can be seen in a situation where there is a lack of proper infrastructure for sustaining the efficient distribution of energy, leading to constant power outages and lack of power in certain parts of the community.

Such a situation affects the ability of a country to produce successfully and often results in low Gross Domestic Product ( GDP ) for these regions. Economic development and education are related by the manner in which education leads to the ability of individuals to create opportunities for economic development.
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What is the difference between economic and education?

The difference between economics of education and ordinary economics is the dominance of education as a variable in every argument and application of economic principles, laws, and concepts to education issues.
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How does education drive the economy?

Education builds human capital; it increases individuals’ capabilities, enhancing economic productivity and facilitating the development and adoption of frontier technologies. In such a context, highly educated individuals enjoy a large premium in employability and earnings.
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Is education part of economy?

Education and economic growth In 1900, Spain and Finland were very similar: they were underdeveloped, largely agricultural countries with a low level of literacy (scarcely 40% of the population) and a similar income per capita.50 years on, Finland’s income per capita doubled Spain’s, all Finns were literate and secondary education had started to spread to all social classes in the country.

Meanwhile, in Spain, illiteracy was still widespread and secondary education a rarity. Almost 70 years later, and in spite of Spain’s huge economic development and improvements in terms of education, Finland’s income per capita is still higher than Spain’s. And so is its level of education. Therefore, were Finland’s educational improvements the key to its success? This must certainly be partly the case.

Education directly affects economic growth insofar as it is essential to improve human capital. Let’s take this step by step. An economy’s production capacity depends on different factors. These include physical capital, technology and the number of workers, as well as their quality.

1. This quality is largely determined by what is called human capital (the stock of knowledge, skills and habits).
2. An increase in workers’ educational level improves their human capital, increasing the productivity of these workers and the economy’s output.
3. Numerous studies in the field of labour economics have attempted to measure this relationship between a worker’s education and its productivity, called the private return to education.

And the findings have been incredibly positive. The precursor to all such studies is the equation developed by Jacob Mincer in 1974, known as the Mincer Equation. This relates workers’ earnings (seen as a way of measuring their productivity) with their years of schooling and work experience,1 It goes without saying that equating a worker’s education with their years of schooling is highly flawed since it assumes that, for instance, one additional year of primary education has the same effect on a worker’s productivity as an additional year of university education.

• Neither does it take into account possible differences in the quality of the education received, particularly relevant for analyses carried out with data from different countries.
• Some studies therefore distinguish between primary, secondary and tertiary education and add quality controls such as the results from tests carried out internationally.

Another problem, more substantial and therefore more difficult to resolve, is whether such studies actually measure the effect of education on productivity or rather the result of talent. For instance, if more talented people are the ones who receive more education, then the estimated effect of education on productivity would largely reflect this greater talent and not the higher level of education.

In order to avoid this problem (in technical terms, an omitted-variable bias), some articles have attempted to use natural experiments. One of the most curious used identical twins with different lengths of schooling. Such twins are genetically identical and tend to have the same family environment, so their skills and habits should be very similar.

Such studies have found that one additional year of schooling results in an increase in earnings, and therefore productivity, of between 6% and 10%,2 In addition to education’s direct effect on a worker’s productivity, numerous economists also point to important education externalities for growth, larger than private returns.

• Paul Romer, for instance, suggests that societies with a large number of highly skilled workers generate more ideas and consequently grow more.
• In a recent work, Aghion et al present a theoretical model and some empirical evidence that shows more advanced economies benefit from workers with a university education since this promotes technological innovation, augmenting the productivity of both physical capital and the workforce as a whole.

On the other hand, developing economies benefit from workers with a primary and secondary education as this helps them imitate the technologies developed in richer countries, thereby also increasing the productivity of their physical capital and workforce,3 Given their huge importance, the existence of such externalities, or social returns, and their quantification are undoubtedly important when designing educational policies in order to avoid underinvestment in education.

1. Individuals tend to decide the level of educational training they wish to attain based on the private returns they expect to receive and do not take social returns into account.
2. A significant social return would therefore justify policies to encourage greater investment in education.
3. But studies focusing on quantifying the effects of education on economic growth and which therefore attempt to reflect both private returns and externalities also face several complications.

Like studies focusing on private returns, they need to accurately measure the education variable, distinguishing between different educational levels and controlling via quality. They must also deal with a problem of inverse causality: is it the case that countries which invest the most in education grow the most and achieve the highest levels of income? Or, alternatively, do countries with higher levels of income tend to invest more in education? Both relationships are bound to exist but, in this case, we need to know the extent of the former since it will determine what kind of educational policies need to be implemented.

In order to identify this relationship, some studies make use of what are called instrumental variables. In other words, they look for countries or regions whose educational level has changed for some reason, independently of their growth rates. A mission which, in many cases, is almost impossible. Changes in mandatory education policies or appointments of politicians on legislative committees responsible for educational investment in US states are some of the events that have been considered.

However, in such cases the findings of the different empirical studies are not conclusive: some show clearly greater social returns than private while others find that both types of return are similar,4 Lastly, other kinds of externalities also result from education.

But beyond the relevance of education in economic growth and in fostering democracy, in the words of the United Nations: «education is a fundamental human right and essential for the exercise of all other human rights».Clàudia CanalsMacroeconomics Unit, Strategic Planning and Research Department, CaixaBank

1. See Mincer, Jacob (1974), «Schooling, Experience, and Earnings», NBER Book. On the other hand, although wage income largely reflects a worker’s productivity, there are other elements that can affect it, such as legislation, the role of trade unions, etc.2.

See Card, D. (1999), «The causal effect of education on earnings», Handbook of Labor Economics 3: 1801-1863, for a summary of the empirical literature. In this summary, David Card also comments on the use of the geographical proximity variable for individuals to university as a good proxy of the talent-free educational level of individuals.3.

See Romer, P.M. (1990), «Human Capital and Growth: Theory and Evidence», Carnegie-Rochester Conference Series on Public Policy, Vol.32. And Aghion, P. et al. (2009), «The Causal Impact of Education on Economic Growth: Evidence from U.S.», Brookings Paper.4.

Acemoglu, D. and Joshua, A. (2000), «How Large Are Human-Capital Externalities? Evidence from Compulsory-Schooling Laws», NBER macroeconomics annual 15: 9-59, show a small social return. And Moretti, E. (2004), «Estimating the social return to higher education: evidence from longitudinal and repeated cross-sectional data», Journal of Econometrics 121, 1: 175-212, a clearly higher social return.5.

See Glaeser, E.L., Ponzetto, G. and Shleifer, A. (2007), «Why Does Democracy Need Education?», Journal of Economic Growth 12.2: 77-99. : Education and economic growth
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How education is related to economic development?

Education is considered to play a key role in the economic development of any country because, it is the mechanism through which knowledge, skills and experience regarding different fields can be acquired and ultimately creating the comparative advantage for the country.
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Is economy and school interlinked?

Education and Economy – Sociology of Education The relation between education and economy is interdependent and reciprocal. Education is a form of human capital, an intangible form of accumulated capital stock, which includes level and dispersion of education as well as those of applied and basic research.

1. It has many measurable forms, including years of aggregate schooling, rates of enrollment, public education expenditures, and levels, types, and use of on the job training programs.
2. Economic activity is understood as economic growth, usually measured as changes in the size or rate of gross or per capita gross domestic output, and determinant of how much improvement will occur in a society’s standard of living.

Unlike business cycles, which reflect short term ( The major dimensions of education and economy include the causal directions and the levels of analysis for effects. The effects of education on economic growth are to be distinguished from effects of the economy on educational expansion.

1. Microscopic research analyzes the effects of education on individual characteristics such as wages and occupational status.
2. Macroscopic research focuses on the effects of education on aggregate output and productivity for national economies.
3. Five theories guide related research: class reproduction, human capital, functional, institutional, and stratification.

In addition, contemporary growth models are more likely to rely on total factor production, addressing the efficiency with which factors of production are used and reflecting a broad range of economic and socio cultural influences, rather than growth accounting, which is limited to a narrower range of economic factors of production.

• Early reliance on human capital theory in economics and functional theory in sociology posited that education increased the productivity of national economies through increasing the productivity of individuals.
• Human capital and aggregate productivity studies assumed that more highly educated workers were more productive on the job, arguing that wages were the measure of worker productivity.

It was questionable, however, whether wages should be used as a measure of marginal productivity, since this assumed a perfectly competitive labor market in equilibrium. In regard to effects of the economy on education, earlier empirical studies challenged the functional theory view that as economies industrialize and jobs require greater literacy and technical skills, education expands in response.

1. Secular mass schooling often preceded demand for high level industrial jobs in industrial and undeveloped countries.
2. Early industrialization was also found to retard educational development.
3. Early pressures to develop formal schooling were typically from political, religious, or cultural elites and focused on training state bureaucrats, military leaders, and religious cohorts, not on developing economic skills.

Class reproduction, human capital, functional, institutional, and stratification theories on the whole present clear though different images of education and the economy. The empirical evidence through the mid-1990s blurred lines separating them, many variables used were proxies for difficult to measure attributes, and the quality of data varied across studies.

Bleaney and Nishiyama (2002) examined three competing models of economic growth. All three study models had 26 explanatory measures in common, including the log of initial per capita GDP. No one model dominated the others, implying that an encompassing model with explanatory variables from all three fit the data better than any of the original models or any pair of them.

In the final encompassing model passing a battery of tests for adequacy, human capital (that is, male schooling), institutions, specialization in primary products, and terms of trade changes were all determinants of growth between 1965 and 1990. Although inconsistencies across studies and complexities about relationships remain, contemporary research benefits from cross country, cross sectional panel data with a focus on the question, “Under what conditions does education contribute to economic growth and vice versa?” Barro (2001) has shown that economic growth is positively related to the starting level of average years of school attainment of adult males at the secondary and higher levels and has no relationship to primary education.

• Judson (1998) has shown that allocation mat ters: higher investment in universal primary education plays a positive role in economic growth, especially in poorer countries.
• Alaitzidakis et al.
• 2001) show a nonlinear relationship between education, measured as mean years of schooling, and economic growth, measured as per capita GDP growth between 1960 and 1990.

They also report no relationship between education and economic growth for high income/capital countries, due in part to contrasting effects of male (positive) and female (negative) education. Krueger and Kumar (2004) contend that higher rates of publicly subsidized investments in vocational education was one possible factor contributing to increased economic growth in Europe vis-a-vis that of the US in the 1960s and 1970s.

As the rate of technological progress increased throughout the 1980s and 1990s, such subsidies contributed to the slower rate of economic growth than that of the US. Bils and Klenow (2000) show that schooling accounted for less than one third of per capita GDP growth and that schooling responded to the anticipated rate of growth from income accompanying increases in GDP.

They also note the importance of institutional factors such as better enforcement of property rights and greater openness in inducing faster GDP growth and higher school enrollments. Galor and Tsiddson (2002) show that the evolutionary pattern of human capital distribution, income distribution, and economic growth were determined simultaneously by the inter play between a local home environment externality and a global technological externality.

When the home environment externality was the dominating factor, the distribution of human capital and the wage differential between skilled and unskilled labor became polarized. Inequality enabled members of more highly educated segments of society to overcome forces of a low, stable, steady state equilibrium and to increase investment in human capital.

As such investment increases and “trickles down” to the less educated segments of society via technological progress in production, the return to skill improves, and investment in human capital becomes more beneficial to members of all segments of society.

Finally, correcting for the conceptual unsuitability of many indicators of institutional quality, both political and social, Glaeser et al. (2004) show that human capital investment between 1960 and 2000 was a robust predictor of economic growth independently of institutional development and that institutional improvement follows economic growth.

Equally important, findings of this cross national study indicated that the key human capital externality was not technological, but political: courts and legislators replaced guns. These institutional improvements in turn brought about greater security of property and economic growth.
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What is the meaning of education and economics?

Liberal approaches – The dominant model of the demand for education is based on human capital theory. The central idea is that undertaking education is investment in the acquisition of skills and knowledge which will increase earnings, or provide long-term benefits such as an appreciation of literature (sometimes referred to as cultural capital ).

1. An increase in human capital can follow technological progress as knowledgeable employees are in demand due to the need for their skills, whether it be in understanding the production process or in operating machines.
2. Studies from 1958 attempted to calculate the returns from additional schooling (the percent increase in income acquired through an additional year of schooling).

Later results attempted to allow for different returns across persons or by level of education. Statistics have shown that countries with high enrollment/graduation rates have grown faster than countries without. The United States has been the world leader in educational advances, beginning with the high school movement (1910–1950).

There also seems to be a correlation between gender differences in education with the level of growth; more development is observed in countries that have an equal distribution of the percentage of women versus men who graduated from high school. When looking at correlations in the data, education seems to generate economic growth; however, it could be that we have this causality relationship backwards.

For example, if education is seen as a luxury good, it may be that richer households are seeking out educational attainment as a symbol of status, rather than the relationship of education leading to wealth. Educational advance is not the only variable for economic growth, though, as it only explains about 14% of the average annual increase in labor productivity over the period 1915-2005.

1. From lack of a more significant correlation between formal educational achievement and productivity growth, some economists see reason to believe that in today’s world many skills and capabilities come by way of learning outside of traditional education, or outside of schooling altogether.
2. An alternative model of the demand for education, commonly referred to as screening, is based on the economic theory of signalling,

The central idea is that the successful completion of education is a signal of ability.
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Why is education so important for economic growth and development?

The role of education in economic development To understand the, we first have to understand what economic development is. It refers to the process where a country’s poor living conditions improve for the better, which ultimately results in improved economic and social conditions for the population.

Education is one of the primary resources of change; its role is to help people acquire knowledge and skills, which can, in turn, be used to acquire jobs. Households with educated people stand a better chance of lifting themselves out of poor living conditions than households without educated people in them.

Consistent income offered to the working class ensures financial security for the working class, their families and their communities. Because of its contribution to economic development, education is viewed as human capital. Any type of investment made in education builds opportunities for national economic development.
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How does economy affect education?

Negative effect 1: The reduction in adult income makes it harder for the parents to bear the direct costs of education such as tuition, fees, books, supplies, uniforms, and private tutoring. Educational outcomes are consequently harmed because the child is either withdrawn from school or inadequately prepared for it.
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How does economic status affect education?

Home This chapter shows how strongly socio-economic status is associated with performance in the countries and economies that participated in PISA 2018. It first examines the large heterogeneity in socio-economic status observed both between and within countries.

• It also discusses how student performance varies, even amongst students of similar socio-economic status, depending on the country/economy in which the students are enrolled in school.
• The chapter also illustrates how some school systems achieve excellence and weaken the association between students’ socio-economic status and performance in PISA.

Many modern societies suffer from rising inequality and low social mobility ( OECD, 2018 ). Income inequality in OECD countries today is at its highest level since the 1980s ( OECD, 2015 ), and the economic recovery observed since 2010 has not reversed this trend.

Rising inequality and low social mobility not only threaten long-term growth ( Cingano, 2014 ) but more fundamentally endanger democratic societies. Young people may lack confidence in political institutions if they feel that they have to limit their expectations for their future because of their family’s or their own financial situation.

Long-standing research finds that the most reliable predictor of a child’s future success at school – and, in many cases, of access to well-paid and high-status occupations – is his or her family. Children from low-income and low-educated families usually face many barriers to learning.

Less household wealth often translates into fewer educational resources, such as books, games and interactive learning materials in the home. From the beginning, parents of higher socio-economic status are more likely to provide their children with the financial support and home resources for individual learning.

As they are likely to have higher levels of education, they are also more likely to provide a more stimulating home environment to promote cognitive development ( Sirin, 2005 ; Thomson, 2018 ). These parents may be more at ease teaching their child the specific behaviours and cultural references that are the most valued at school.

• Advantaged parents may also provide greater psychological support for their child in environments that encourage the development of the skills necessary for success at school ( Evans et al., 2010 ).
• However, results from previous rounds of PISA suggest that school systems may be able to help mitigate the impact of families’ socio-economic status on their child’s life outcomes.

Schools can serve to channel resources towards disadvantaged children and thus help create a more equitable distribution of learning opportunities and outcomes ( Downey and Condron, 2016 ). copy the link link copied!

• Socio-economically advantaged students usually perform better in PISA than disadvantaged students, but the gap in reading performance related to socio-economic status varies considerably across countries. In PISA 2018, advantaged students outperformed disadvantaged students in reading by 89 score points. Nine years earlier, in PISA 2009, this gap related to socio-economic status, was 87 score points.
• On average across OECD countries, 12% of reading performance was accounted for by the PISA index of economic, social and cultural status.
• In 11 countries and economies, including the OECD countries Australia, Canada, Denmark, Estonia, Finland, Japan, Korea, Norway and the United Kingdom, average performance was higher than the OECD average while the relationship between socio-economic status and reading performance was weaker than the OECD average.
• On average across OECD countries, 17.4% of advantaged students, but only 2.9% of disadvantaged students were top performers in reading, meaning that they attained Level 5 or 6 in the PISA reading test. Amongst the 23 countries and economies where the proportions of top performers were larger than the OECD average, the socio-economic disparities in top performance were smallest in Macao (China) and largest in France.

In PISA, a student’s socio-economic status is estimated by the PISA index of economic, social and cultural status, a composite measure that combines into a single score the financial, social, cultural and human capital resources available to students (see ).

The socio-economic status of students varies between countries/economies (); but in the vast majority of cases, differences in socio-economic status, which may be seen as a proxy of the socio-economic inequalities in the countries, are larger within than between countries/economies. In only 7 countries, namely Belarus, Denmark, Finland, Japan, the Russian Federation (hereafter “Russia”), Slovenia and Ukraine, the within-country gap between the most- and least-advantaged students (i.e.

the difference between the 95th and 5th percentiles of the distribution of socio-economic status) is narrower than the gap between the highest and lowest mean socio-economic status measured at the country/economy level. Particularly wide within-country gaps in socio-economic status were observed in Morocco, Panama, Colombia, Mexico, Costa Rica, Brazil and Viet Nam (in descending order). Note: All differences between the 95th and the 5th percentiles are statistically significant (see Annex A3). Countries and economies are ranked in ascending order of the difference between the mean PISA index of economic, social and cultural status of students in the 95th percentile and the 5th percentile.

Source: OECD, PISA 2018 Database, Table II.B1.2.1. Variations in socio-economic status within and between countries/economies should be taken into account when comparing students’ performance. This can be achieved by measuring students on the same scale, which allows for a comparison of the performance of groups of students of similar socio-economic status across countries and economies.

shows performance differences by international deciles of the PISA index of economic, social and cultural status. Countries and economies differ substantially in their national wealth and socio-economic heterogeneity; thus the proportion of 15-year-old students at each decile on the international scale varies considerably (see Table II.B1.2.2 available on line).

For example, in Denmark, Iceland and Norway, more than 20% of 15-year-old students were in the top decile of the international distribution of socio-economic status, while in 16 countries (Albania, Argentina, Brazil, Colombia, Costa Rica, the Dominican Republic, Indonesia, Mexico, Morocco, Panama, Peru, the Philippines, Thailand, Turkey, Saudi Arabia and Uruguay) more than 20% of students were in the bottom decile of this distribution.

In all of these countries where there were large proportions of disadvantaged students, except Argentina, Indonesia and Saudi Arabia, less than 80% of 15-year-olds were eligible to sit the PISA test (see on the coverage of the PISA sample). copy the link link copied! Box II.2.1.

Definition of socio-economic status in PISA Socio-economic status is a broad concept that aims to reflect the financial, social, cultural and human-capital resources available to students ( Cowan et al., 2012 ). Socio-economic status may also be referred to as “the relative position for the family or individual on a hierarchical social structure, based on their access to, or control over, wealth, prestige and power” (see Willms and Tramonte, 2015 quoting ( Mueller and Parcel, 1981 ).

Socio-economic status is thus a measure of students’ access to family resources (financial capital, social capital, cultural capital and human capital) and the social position of the student’s family/household. In PISA, a student’s socio-economic status is estimated by the PISA index of economic, social and cultural status (ESCS), a composite measure that combines into a single score the financial, social, cultural and human-capital resources available to students (see PISA 2018 Technical Report ( OECD, forthcoming )).

In practice, it is derived from several variables related to students’ family background that are then grouped into three components: parents’ education, parents’ occupations, and an index summarising a number of home possessions that can be taken as proxies for material wealth or cultural capital, such as possession of a car, the existence of a quiet room to work, access to the Internet, the number of books and other educational resources available in the home.

The comparability of these indicators across countries and over time raises several challenges ( Rutkowski and Rutkowski, 2013 ; Rutkowski and Rutkowski, 2017 ; Pokropek, Borgonovi and McCormick, 2017 ). The more serious concerns are related to the items proxied by home possessions, as the meaning and the national examples included in the items may vary significantly across countries, undermining cross-country comparability.

In addition, the prevalence of access to technological goods and services, such mobile phones, has increased over time, thus these items convey distinct information at different times. For example, use of a mobile phone shortly after the technology was introduced could be a proxy for high social status; later on, mobile phones may be regarded as a basic resource, accessible to nearly everyone.

For this reason, the index summarising home possessions is computed in a different way for all new cycles, and some items may be included in a way specific to each country, in order to take into account distinctive use by countries. In PISA 2018, the three components (parents’ education, parents’ occupation and the index of home possessions) are weighted equally.

As in 2015, all countries and economies contributed equally to the estimation of ESCS values. Analyses were systematically conducted in order to identify those items that may have been interpreted differently across countries. For these items, country-specific parameters were assigned ( OECD, 2017 ). For the purpose of reporting, the ESCS scale was transformed with 0 as the value of an average OECD student and 1 the standard deviation across equally weighted OECD countries.

illustrates how the performance of students of similar socio-economic status varied, depending on the country/economy in which they live. The figure also shows, for individual countries/economies, the proportions of students in the top and bottom international deciles of socio-economic status and the PISA coverage indices, which should be taken into account when interpreting the figure.

• Notes: Percentage of students who are in the top/bottom international decile of the PISA index of economic, social and cultural status are shown next to the country/economy name.
• Bottom, second, ninth and top deciles correspond to the average performance of students who are in the corresponding deciles of the distribution of the PISA index of economic, social and cultural status across all countries and economies; the middle decile corresponds to students whose socio-economic status ranges from the 45th to the 55th percentile of this distribution.
• Coverage Index 3 is shown next to the country/economy name.
• Only results of countries and economies with at least 3% of students in each international decile are shown.
• Countries and economies are ranked in ascending order of the mean reading performance of students in the international middle decile of socio-economic status.

Source: OECD, PISA 2018 Database, Table II.B1.2.2. For instance, while Thailand and Turkey show similar proportions of students in the bottom decile of socio-economic status (38% in Thailand and 34% in Turkey) and the two countries have similar shares of 15-year-olds who were enrolled in school in 2018 (around three in four), the average reading score of the students in the bottom international decile was higher in Turkey (440 points) than in Thailand (370 points).

In Denmark, Iceland and Norway, three high-income countries where more than 20% of students are in the top international decile of socio-economic status and more than 87% of 15-year-olds were eligible to sit the PISA test, the average score amongst students in the top international decile of socio-economic status was 510 points in Iceland, 531 points in Norway and 542 points in Denmark.

Amongst those students whose socio-economic status was close to the median decile of the international distribution, average reading scores were 438 points in Iceland, 476 points in Norway and 473 points in Denmark. copy the link link copied! Box II.2.2.

Inclusive education: Attaining minimum proficiency, regardless of students’ socio-economic status Ensuring that all children, whatever their personal circumstances, have access to education is the main requirement for achieving equity in education. Chapter 3 of PISA 2018 Results (Volume I): What Students Know and Can Do ( OECD, 2019 ) analyses in detail how enrolment in secondary education has evolved over the different cycles of PISA, notably through the proportion of the population of 15-year-olds who were not enrolled in grade 7 or higher (the “target population” of the sample in PISA).

As discussed in that chapter, the proportion of 15-year-olds in each country/economy who were covered by the PISA 2018 sample, known as Coverage Index 3, exceeded 80% in most OECD countries. However, Colombia (62%), Mexico (66%) and Turkey (73%) did not reach this threshold.

• In addition, while the coverage index was over 99% in Germany, over 98% in Hong Kong (China), and over 97% in Brunei Darussalam, Malta and Slovenia, in 18 countries it was below 75%.
• In Brazil, Jordan and Panama, Coverage Index 3 was below 65% and in Albania and Baku (Azerbaijan) it was below 50% (see Table II.B1.2.1).

For these countries, results showing the link between socio-economic status and performance need to be interpreted with caution. For instance, if only teenagers from low-income families drop out of school early because of poor school performance, only those disadvantaged students with the highest performance would be sampled for the PISA assessment.

In this hypothetical case, the relationship between socio-economic status and performance as estimated in PISA may be weaker than would be observed if measured across the entire population of 15-year-olds. Chapter 10 of Volume I ( OECD, 2019 ) also discusses how the proportion of students who scored at or above the minimum level of proficiency on the PISA scales – Level 2 – has evolved over time.

This level of proficiency may be equated with the “minimum proficiency level” defined in the first target of the United Nations Sustainable Development Goal 4, which was adopted by the 70th General Assembly of the United Nations in 2015. On average across OECD countries in 2018, 22.6% of 15-year-olds scored below Level 2 in reading.

• However, this proportion was strongly associated with students’ socio-economic status.
• Some 35.6% of students in the bottom quarter of the PISA index of economic, social and cultural status (see for details) scored at that level, while only 10.7% of students in the top quarter of the index did (Table II.B1.2.6 available on line).

Disadvantaged students were 2.7 times more likely than advantaged students not to attain the minimum level of proficiency in reading. While there were significant variations in the magnitude of this difference, the association between socio-economic disadvantage and low performance was statistically significant in all PISA-participating countries and economies, except Macao (China).

1. In 25 of the 79 PISA-participating countries and economies, disadvantaged students were at least three times as likely as advantaged students to be low achievers in reading (Table II.B1.2.6 available on line).
2. The sections above show that in all countries and economies, student performance in PISA is related to socio-economic status; but they also emphasise that this relationship is far from deterministic.

While countries and economies differ widely in terms of economic development and socio-economic structure, an analysis of the socio-economic disparities in academic performance at the national level provides an indication of whether a school system helps promote social mobility.

• While socio-economic status in PISA can be seen as a proxy of the “rank” of students’ access to family resources within their country/economy, a strong relationship between socio-economic status and performance in PISA may indicate low social mobility within the country/economy.
• In PISA, the socio-economic gradient is traditionally used to examine the relationship between students’ socio-economic status and their performance ( OECD, 2016 ).

More specifically, the slope of the gradient summarises the differences in performance observed across socio-economic groups, while the strength of the gradient refers to how well socio-economic status predicts performance. For a detailed discussion, see ( OECD, 2016 ; OECD, 2018 ; OECD, 2013 ).

The slope of the socio-economic gradient indicates the degree of the disparity in average performance between two students whose socio-economic status differs by one unit in the PISA index of economic, social and cultural status. A positive value for the slope of the socio-economic gradient signals that advantaged students generally performed better than disadvantaged students in PISA 2018.

On average across OECD countries in 2018, a one-unit increase in the PISA index of economic, social and cultural status was associated with an increase of 37 score points in the reading assessment. The performance gap related to students’ socio-economic status was widest in Belarus, where a one-unit increase in the index was associated with a difference of as much as 51 score points in reading.

In Belgium, the Czech Republic, France, Hungary, Israel, the Slovak Republic and Ukraine, the increase in the index was associated with a difference of between 45 and 50 score points. By contrast, in 15 countries and economies, the associated change in performance amounted to less than 25 score points (Table II.B1.2.3 available on line).

However, the slope of the socio-economic gradient does not describe the magnitude of the gap in performance related to socio-economic status that may be observed between the most and the least advantaged students within a country/economy. On average across OECD countries, the difference in the average index of socio-economic status between disadvantaged students (defined as those in the bottom quarter of the distribution in the PISA index of economic, social and cultural status within their countries/economies; see ) and advantaged students (those in the top quarter of the distribution) corresponded to 2.36 standard deviations in the index.

But in 9 countries, namely Belarus, Croatia, Denmark, Finland, Iceland Japan, Korea, Russia and Ukraine, this difference is less than 2 standard deviations in the index, while in 11 countries/economies, namely Argentina, Brazil, Colombia, Costa Rica, Mexico, Morocco, Panama, Peru, Portugal, Saudi Arabia and Turkey, it is greater than 3 standard deviations in the index ( Table II.B1.2.1).

copy the link link copied! Box II.2.3. Definition of disadvantaged and advantaged students in PISA The PISA index of economic, social and cultural status (ESCS) makes it possible to draw comparisons between students and schools with different socio-economic profiles.

In this report, students are considered socio-economically advantaged if they are amongst the 25% of students with the highest values in the ESCS index in their country or economy; students are classified as socio-economically disadvantaged if their values in the index are amongst the bottom 25% within their country or economy.

Students whose values in the ESCS index are in the middle 50% within their country or economy are classified as having average socio-economic status. Following the same logic, schools are classified as socio-economically advantaged, disadvantaged or average within each country or economy, based on their students’ mean values in the ESCS index.

One may compare how these categories are characterised in relation to the variables that are used to estimate the three components of the ESCS index: parents’ educational attainment, the status of their occupation and home possessions. On average across OECD countries, parents of socio-economically advantaged students are highly educated: a large majority attained tertiary education (98%) and works in a skilled, white-collar occupation (72%).

By contrast, the parents of socio-economically disadvantaged students have much lower educational attainment. Across OECD countries, 53% of parents of disadvantaged students attained some post-secondary non-tertiary education as their highest level of formal schooling, 33% attained lower secondary education or less, and only 14% attained tertiary education.

• Few disadvantaged students have a parent working in a skilled occupation (5%).
• Many parents of these students work in semi-skilled, white-collar occupations (11%); the majority (84%) work in elementary occupations or semi-skilled, blue-collar occupations.
• One of the home possessions that most clearly distinguishes students of different socio-economic status is the number of books at home.

While 46% of advantaged students reported having more than 200 books at home, on average, this is the case for only 6% of their disadvantaged peers. Advantaged students also reported a greater availability of other educational resources, such as educational software.

In addition, more than 90% of advantaged students but only 69% of disadvantaged students, on average across OECD countries, reported having a quiet place to study at home and a computer that they can use for schoolwork.1. Defined by the first three major groups of the ISCO 08 (managers, professionals, technicians and associated professionals).

Semi-skilled, white-collar occupations are defined by the major groups 4 and 5 (clerical support workers, and service and sales workers) and elementary occupations or semi-skilled, blue-collar occupations by the major groups 6 to 9 (skilled agricultural, forestry and fishery workers, craft and related trades workers, plant and machine operators, and assemblers, elementary occupations).

In order to have an idea of the magnitude of the performance gap related to socio-economic status within countries/economies, after taking into account variations in socio-economic status, one may compare the average performance of the least-advantaged students with that of the most-advantaged students.

On average across OECD countries in 2018, advantaged students scored 89 points higher in reading than disadvantaged students. The gap between the two groups of students was larger than 100 score points in 19 countries, including the OECD countries Belgium, the Czech Republic, France, Germany, Hungary, Israel, Luxembourg, the Slovak Republic and Switzerland (Table II.B1.2.3 available on line).

Some countries were able to combine higher average performance in reading with smaller socio-economic gaps in performance. In 13 countries and economies, including the OECD countries Canada, Denmark, Estonia, Finland, Ireland, Japan, Korea, Norway, Slovenia and the United Kingdom, average performance was higher than the OECD average while the performance difference between advantaged and disadvantaged was smaller than the OECD average (Table II.B1.2.3 available on line).

The strength of the gradient is measured by the proportion of the variation in performance that is accounted for by differences in socio-economic status. When the relationship between socio-economic status and performance is strong, socio-economic status is a good predictor of performance.

• On average across OECD countries in 2018, students’ socio-economic status accounted for a significant share of the variation in their performance in the core PISA subjects (reading, mathematics and science).
• In reading, 12% of the variation in student performance within each country was associated with socio-economic status.

In 20 of the 79 countries and economies that participated in PISA 2018 students’ socio-economic status predicted 15% or more of the variation in performance. By contrast, in 31 countries the strength of the gradient predicted less than 10% of this variation (Table II.B1.2.3 available on line).

Socio-economic status is even more related to mathematics and science performance. On average across OECD countries, students’ socio-economic status predicted 13.8% of their performance in mathematics, and 12.8% of their performance in science. In Argentina, Belarus, Belgium, France, Hungary, Peru and the Slovak Republic, more than 20% of mathematics performance was related to students’ socio-economic status (Table II.B1.2.4 available on line).

A weak gradient means that the relationship between socio-economic status and performance is not accurately described by a linear relationship; it may be multidimensional and cannot be fully captured by socio-economic indicators. This may also happen when the relative disadvantage of being at the bottom of the national distribution of socio-economic status is greater than the relative advantage of being at the top of this distribution – or the opposite.

Both patterns are illustrated in, which shows the average performance of students by their socio-economic status. In all countries, average performance improved with each successive quarter of socio-economic status. However, in some countries, differences in performance were more marked at the bottom of the distribution of socio-economic status, as disadvantaged students scored much lower in reading than students in the three higher quarters of socio-economic status – amongst whom differences in performance were comparatively small.

This was the case in Bosnia and Herzegovina, the Czech Republic, Hong Kong (China), Italy, Japan, Macao (China), Malta, Norway, the Slovak Republic and Sweden, where the gap in average reading performance between students in bottom quarter of socio-economic status and those in the next-highest quarter accounted for 40% to 50% of the performance difference between the most-advantaged and least-advantaged students in these countries.

By contrast, in some countries, such as Croatia, the Dominican Republic, Kosovo, Morocco, Thailand and Turkey, socio-economic disparities in performance were observed at the top of the distribution of the socio-economic index, as most of the link between socio-economic status and performance was related to the fact that advantaged students outperformed students in the three lower quarters of socio-economic status by a wide margin.

Identifying these complex patterns may be useful for designing policies that aim to tackle both underperformance and inequity in education (Table II.B1.2.3 available on line). One may compare differences in performance related to socio-economic status in PISA 2018 with those that were observed in 2009.

Comparing the most disadvantaged students with the most advantaged in their country/economy, as defined in 2009 and 2018, no significant changes were observed in the vast majority of countries (see ). In only six countries and economies, namely Bulgaria, Georgia, Kazakhstan, Malta and Montenegro, the socio-economic gap shrank.

Only in Georgia and Montenegro was this due to a significant improvement in the performance of disadvantaged students, while the performance of advantaged students remained unchanged. However, in Kazakhstan, the narrowing of the performance gap was due to both a significant decline in the performance of advantaged students and significant improvements in the performance of disadvantaged students; in Bulgaria only the performance of advantaged students declined.

In the Czech Republic, Finland, Malaysia, the Republic of Moldova (hereafter “Moldova”), Qatar and the Slovak Republic, disparities in performance related to socio-economic status increased over the period. In Moldova and Qatar, the performance of advantaged students improved at a faster rate than that of disadvantaged students; in Finland and the Slovak Republic, the performance of disadvantaged students declined while the performance of advantaged students did not change significantly over the period.

copy the link link copied! Countries and economies are ranked in ascending order of mean reading performance for students in the second quarter of ESCS. Source: OECD, PISA 2018 Database, Table II.B1.2.3. copy the link link copied! Differences in achievement related to socio-economic status are even more pronounced when one compares not only average performance, but the attainment of the highest levels of proficiency (as described in PISA 2018 Results : What Students Know and Can Do ( OECD, 2019 )).

1. On average across OECD countries, 8.6% of students were top performers in reading in PISA 2018, meaning that they attained Level 5 or 6 in the PISA reading test.
2. At these levels, students can comprehend lengthy texts, deal with concepts that are abstract or counterintuitive, and establish distinctions between fact and opinion, based on implicit cues pertaining to the content or source of the information.

Only 2.9% of disadvantaged students, compared with 17.4% of advantaged students, attained these levels of performance, on average across OECD countries. In 51 countries and economies, less than 2% of disadvantaged students were top performers; in only 10 countries and economies, namely Australia, Beijing, Shanghai, Jiangsu and Zhejiang (China) (hereafter “B-S-J-Z ), Canada, Estonia, Finland, Hong Kong (China), Ireland, Korea, Macao (China) and Singapore, were more than 5% of disadvantaged students top performers.

In all countries, the proportion of top performers amongst advantaged students largely exceeded that amongst disadvantaged students (Table II.B1.2.6 available on line). The countries with the largest proportions of top performers were also those that achieved high levels of performance amongst all of their students.

However, within countries, there were large differences, related to socio-economic status, in the probability of achieving the highest levels of performance. For instance, while around 10% of disadvantaged students in B-S-J-Z (China) and Singapore were top performers in reading (the largest proportions observed amongst all participating countries and economies), four times as many advantaged students attained that level of performance.

This suggests that even in high-performing school systems social inequities may be perpetuated. The index of inequality in the probability of attaining the highest levels of reading performance provides an indication of the link between top performance and socio-economic status. This indicator measures how top performers are concentrated along the national distribution of socio-economic status, by “ranking” all students by their level of socio-economic status ( Erreygers, Clarke and Van Ourti, 2012 ; Wagstaff, 2011 ; Kjellsson and Gerdtham, 2013 ).

It considers only the relationship between the probability of being a top performer and where the student is located in the distribution of socio-economic status within his or her country/economy; it does not consider the variability of socio-economic status or the degree of socio-economic inequality within the country/economy (see Annex A3 for details).

The index ranges from -1 to 1. The more the index shifts from 0, the more performance is strongly related to socio-economic status. A negative value means that those students at the bottom of the socio-economic distribution are over-represented amongst top performers in reading; a positive value means that students at the top of the socio-economic distribution in their countries/economies are over-represented amongst top performers.

shows this index alongside the proportion of top performers in the country/economy, in school systems where at least 3% of 15-year-old students were top performers in reading. In all countries, the index is positive, meaning that the top performers were more often amongst those at the top of the socio-economic distribution in their country/economy.

• The extent of socio-economic disparities in the probability of being a top performer was also negatively related to the proportion of top performers in the school system (the R2 is 0.25).
• On average across OECD countries, the value of the index was 0.42.
• The highest level of the index, 0.56, was observed in Turkey, where only 3% of students were top performers in reading.

However, the socio-economic disparities in top performance were far from perfectly predicted by the proportions of top performers amongst the population of 15-year-old students: amongst the 23 countries and economies where the proportions of top performers were larger than the OECD average, the index of socio-economic disparities ranged from 0.18 in Macao (China) to 0.47 in France. Notes: Only countries and economies with at least 3% of top performers in reading (students performing at Level 5 or above) are shown. Socio-economic status is measured by the PISA index of economic, social and cultural status. The differences related to socio-economic status in the probability of a student attaining Level 5 in reading in corresponds to the relative concentration of high performers by socio-economic status (ESCS).

• The higher the indice, the more prevalent are most advantaged students amongst high performers (see Annex A3).
• Source: OECD, PISA 2018 Database, Table II.B1.2.6.
• No one should be satisfied with a school system where everyone performs equally, but poorly.
• PISA consistently finds that strong performance and a weak relationship between socio-economic status and education outcomes are not mutually exclusive: some education systems manage to attain both a high level of average performance and equity in education ( OECD, 2016 ).

In 11 of the 25 countries and economies that scored above the OECD average in reading in PISA 2018, the strength of the relationship between student performance and socio-economic status was significantly below the OECD average. School systems in Australia, Canada, Denmark, Estonia, Finland, Hong Kong (China), Japan, Korea, Macao (China), Norway and the United Kingdom achieved high performance in reading while socio-economic status was less predictive of performance than average (). Note: Socio-economic status is measured by the PISA index of economic, social and cultural status. Source: OECD, PISA 2018 Database, Table II.B1.2.3. Cingano, F. (2014), “Trends in Income Inequality and its Impact on Economic Growth”, OECD Social, Employment and Migration Working Papers, No.163, OECD Publishing, Paris,,

Cowan, C. et al. (2012), Improving the Measurement of Socioeconomic Status for the National Assessment of Educational Progress: A Theoretical Foundation, Downey, D. and D. Condron (2016), “Fifty Years since the Coleman Report”, Sociology of Education, Vol.89/3, pp.207-220,, Erreygers, G., P. Clarke and T.

Van Ourti (2012), “”Mirror, mirror, on the wall, who in this land is fairest of all?”—Distributional sensitivity in the measurement of socioeconomic inequality of health”, Journal of Health Economics, Vol.31/1, pp.257-270,, Evans, M. et al. (2010), “Family scholarly culture and educational success: Books and schooling in 27 nations”, Research in Social Stratification and Mobility, Vol.28/2, pp.171-197,,

1. Hanushek, E. et al.
2. 2019), The Unwavering SES Achievement Gap: Trends in U.S.
3. Student Performance,
4. Jellsson, G. and U.
5. Gerdtham (2013), “On correcting the concentration index for binary variables”, Journal of Health Economics, Vol.32/3, pp.659-670,,
6. Mueller, C. and T.
7. Parcel (1981), “Measures of Socioeconomic Status: Alternatives and Recommendations”, Child Development, Vol.52/1, p.13,,

OECD (2019), PISA 2018 Results (Volume I): What Students Know and Can Do, PISA, OECD Publishing, Paris,, OECD (2018), A Broken Social Elevator? How to Promote Social Mobility, OECD Publishing, Paris,,

1. OECD (2018), Equity in Education: Breaking Down Barriers to Social Mobility, PISA, OECD Publishing, Paris,,
2. OECD (2017), PISA 2015 Technical Report,
3. OECD (2016), PISA 2015 Results (Volume I): Excellence and Equity in Education, PISA, OECD Publishing, Paris,,
4. OECD (2015), In It Together: Why Less Inequality Benefits All, OECD Publishing, Paris,,
5. OECD (2013), PISA 2012 Results: Excellence through Equity (Volume II): Giving Every Student the Chance to Succeed, PISA, OECD Publishing, Paris,,
6. OECD (forthcoming), PISA 2018 Technical Report, OECD publishing, Paris.

Pokropek, A., F. Borgonovi and C. McCormick (2017), “On the Cross-Country Comparability of Indicators of Socioeconomic Resources in PISA”, Applied Measurement in Education, Vol.30/4, pp.243-258,, Rutkowski, D. and L. Rutkowski (2013), “Measuring Socioeconomic Background in PISA: One Size Might not Fit all”, Research in Comparative and International Education, Vol.8/3, pp.259-278,,

Rutkowski, L. and D. Rutkowski (2017), “Improving the Comparability and Local Usefulness of International Assessments: A Look Back and A Way Forward”, Scandinavian Journal of Educational Research, Vol.62/3, pp.354-367,, Sirin, S. (2005), “Socioeconomic Status and Academic Achievement: A Meta-Analytic Review of Research”, Review of Educational Research, Vol.75/3, pp.417-453,,

Thomson, S. (2018), “Achievement at school and socioeconomic background—an educational perspective”, npj Science of Learning, Vol.3/1,, Wagstaff, A. (2011), “The concentration index of a binary outcome revisited”, Health Economics, Vol.20/10, pp.1155-1160,,

• Willms, J. and L.
• Tramonte (2015), “Towards the development of contextual questionnaires for the PISA for development study”, OECD Education Working Papers, No.118, OECD Publishing, Paris,,
• A correlation of 0.79 is observed with this indicator and an index of inequalities in incomes (World Bank GINI index) measured in 2015 across the 50 PISA-participating countries with available data.

See Table II.B1.2.3 available on line; for instance, the average score of students in the bottom quarter of the distribution of ESCS in Italy was 474 points, the average score of students in the second quarter was 474 points and the average score of those in the top quarter was 511 points, so: (474-436)/(511-436)=0.51.

• In order to measure changes in fairness in education over time, this report compares how students who are ranked similarly in the distribution of socio-economic status in the same country/economy, but at different time periods, perform in PISA.
• This approach relies on an indicator that measures the performance difference between the most-advantaged 25% of students and the least-advantaged 25% in the country, as defined at the time of the assessment.

This means that a change in this indicator from one to another PISA assessment may be due to a change in the way students’ socio-economic status is related to performance in PISA; and/or a change in the variation of students’ socio-economic status in the country.

• As emphasised by ( Hanushek et al., 2019 ), an advantage of this approach is that it makes it possible to compare the relative position of students in the distribution of socio-economic status at the time of the assessment.
• This approach does not assume that an index of home possessions, which is measured by the same set of items, is invariant across time; nor does it assume that individual items have the same meaning when they are used to measure students’ socio-economic status over time.

Even within the same country, some items, such as “access to the Internet”, may not mean the same today as they did ten years ago. This indicator is similar to the “concentration index” commonly used to measure inequality in health outcomes. References OECD (2018), A Broken Social Elevator? How to Promote Social Mobility, OECD Publishing, Paris,,

1. References OECD (2015), In It Together: Why Less Inequality Benefits All, OECD Publishing, Paris,,
2. References Cingano, F.
3. 2014), “Trends in Income Inequality and its Impact on Economic Growth”, OECD Social, Employment and Migration Working Papers, No.163, OECD Publishing, Paris,,
4. References Sirin, S.

(2005), “Socioeconomic Status and Academic Achievement: A Meta-Analytic Review of Research”, Review of Educational Research, Vol.75/3, pp.417-453,, References Thomson, S. (2018), “Achievement at school and socioeconomic background—an educational perspective”, npj Science of Learning, Vol.3/1,,

References Evans, M. et al. (2010), “Family scholarly culture and educational success: Books and schooling in 27 nations”, Research in Social Stratification and Mobility, Vol.28/2, pp.171-197,, References Erreygers, G., P. Clarke and T. Van Ourti (2012), “”Mirror, mirror, on the wall, who in this land is fairest of all?”—Distributional sensitivity in the measurement of socioeconomic inequality of health”, Journal of Health Economics, Vol.31/1, pp.257-270,,

References Cowan, C. et al. (2012), Improving the Measurement of Socioeconomic Status for the National Assessment of Educational Progress: A Theoretical Foundation, References Willms, J. and L. Tramonte (2015), “Towards the development of contextual questionnaires for the PISA for development study”, OECD Education Working Papers, No.118, OECD Publishing, Paris,,

1. References Erreygers, G., P.
2. Clarke and T.
3. Van Ourti (2012), “”Mirror, mirror, on the wall, who in this land is fairest of all?”—Distributional sensitivity in the measurement of socioeconomic inequality of health”, Journal of Health Economics, Vol.31/1, pp.257-270,,
4. References Erreygers, G., P.
5. Clarke and T.

Van Ourti (2012), “”Mirror, mirror, on the wall, who in this land is fairest of all?”—Distributional sensitivity in the measurement of socioeconomic inequality of health”, Journal of Health Economics, Vol.31/1, pp.257-270,, References Rutkowski, D. and L.

Rutkowski (2013), “Measuring Socioeconomic Background in PISA: One Size Might not Fit all”, Research in Comparative and International Education, Vol.8/3, pp.259-278,, References Rutkowski, L. and D. Rutkowski (2017), “Improving the Comparability and Local Usefulness of International Assessments: A Look Back and A Way Forward”, Scandinavian Journal of Educational Research, Vol.62/3, pp.354-367,,

References Pokropek, A., F. Borgonovi and C. McCormick (2017), “On the Cross-Country Comparability of Indicators of Socioeconomic Resources in PISA”, Applied Measurement in Education, Vol.30/4, pp.243-258,, References OECD (2017), PISA 2015 Technical Report,

References OECD (2019), PISA 2018 Results (Volume I): What Students Know and Can Do, PISA, OECD Publishing, Paris,, References OECD (2019), PISA 2018 Results (Volume I): What Students Know and Can Do, PISA, OECD Publishing, Paris,, References OECD (2016), PISA 2015 Results (Volume I): Excellence and Equity in Education, PISA, OECD Publishing, Paris,,

References OECD (2016), PISA 2015 Results (Volume I): Excellence and Equity in Education, PISA, OECD Publishing, Paris,, References OECD (2018), Equity in Education: Breaking Down Barriers to Social Mobility, PISA, OECD Publishing, Paris,, References OECD (2013), PISA 2012 Results: Excellence through Equity (Volume II): Giving Every Student the Chance to Succeed, PISA, OECD Publishing, Paris,,

1. References OECD (2019), PISA 2018 Results (Volume I): What Students Know and Can Do, PISA, OECD Publishing, Paris,,
2. References Erreygers, G., P.
3. Clarke and T.
4. Van Ourti (2012), “”Mirror, mirror, on the wall, who in this land is fairest of all?”—Distributional sensitivity in the measurement of socioeconomic inequality of health”, Journal of Health Economics, Vol.31/1, pp.257-270,,

References Wagstaff, A. (2011), “The concentration index of a binary outcome revisited”, Health Economics, Vol.20/10, pp.1155-1160,, References Kjellsson, G. and U. Gerdtham (2013), “On correcting the concentration index for binary variables”, Journal of Health Economics, Vol.32/3, pp.659-670,,

• References OECD (2016), PISA 2015 Results (Volume I): Excellence and Equity in Education, PISA, OECD Publishing, Paris,,
• References Hanushek, E. et al.
• 2019), The Unwavering SES Achievement Gap: Trends in U.S.
• Student Performance,
• This document, as well as any data and map included herein, are without prejudice to the status of or sovereignty over any territory, to the delimitation of international frontiers and boundaries and to the name of any territory, city or area.

Extracts from publications may be subject to additional disclaimers, which are set out in the complete version of the publication, available at the link provided. Chapter 2. Students’ socio-economic status and performance : Home
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Who is the founder of economics of education?

Short history of the economics of education – Economists are normally associated with ensuring that profit-making companies and the overall economy functions well, but they have slowly expanded their interests to new spheres of society. The origin of the economics of education as a significant field within economics dates back to the theoretical and empirical developments made by American economists such as Gary Becker and Jacob Mincer in the 1960s.

Their work introduced the idea of education as human capital and they attempted to calculate the economic returns to acquiring education. Over the past decade there has been an enormous growth of interest by economists in education policy, both in the UK and across the world. This has been accompanied by a growing political interest in market-based reforms across the public sector.

These types of reforms include devolvement of financial planning to front-line institutions such as hospitals and schools and giving consumers of public services choice about which provider to use. Economists from other fields such as labour economics have been attracted by the growing availability of large-scale datasets that facilitate complex statistical analysis to analyse the impact of particular policy initiatives.
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Who is the father of economics of education?

Key Takeaways –

Adam Smith was an 18th-century Scottish philosopher.He is considered the father of modern economics.Smith is most famous for his 1776 book, “The Wealth of Nations.”Smith’s writings were studied by 20th-century philosophers, writers, and economists.Smith’s ideas–the importance of free markets, assembly-line production methods, and gross domestic product (GDP)–formed the basis for theories of classical economics.During his time in France and abroad, his contemporaries included Voltaire, Jean-Jacques Rousseau, Benjamin Franklin, Anne-Robert-Jacques Turgot, and François Quesnay.

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