educational attainment research paper

An IERI – International Educational Research Institute Journal

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• Published: 07 February 2017

Degrees of competency: the relationship between educational qualifications and adult skills across countries

• Natascha Massing   ORCID: orcid.org/0000-0002-9926-1504 1 &
• Silke L. Schneider 1  

Large-scale Assessments in Education volume  5 , Article number:  6 ( 2017 ) Cite this article

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Educational qualifications and literacy skills are highly related. This is not surprising as it is one aim of educational systems to equip individuals with competencies necessary to take part in society. Because of this relationship educational qualifications are often used as a proxy for “human capital”. However, from a theoretical perspective, there are many reasons why this relationship is not perfect, and to some degree this is due to third variables. Thus, we want to explore the net relationship between educational attainment (harmonized according to the International Standard Classification of Education, ISCED) and literacy skills, and how much skills vary within education levels across countries.

We use data from 21 countries from the Programme for the International Assessment of Adult Competencies 2012. This paper compares the literacy skills of adults who achieved different levels of educational attainment across countries. Given the high degree of educational differentiation in most countries, we do this using a more differentiated educational attainment variable than what is commonly used.  In our analyses we firstly adjust for factors that are likely to affect access to education and the acquisition of educational qualifications and literacy skills, such as parental education and language and migration background. In a second step, we also take into account factors affecting skill development after initial formal education, such as occupation and skill use at home.

We firstly find a high degree of heterogeneity of skills across countries for equivalent education categories. Secondly, we find skill similarities for equivalent education categories classified at different broad education levels, sometimes even breaking the hierarchical order of ‘higher education entails higher competencies’.

We conclude that ISCED levels cannot be taken as a cross-nationally comparable proxy for human capital in terms of literacy skills, and that education has to be harmonized in a substantively more meaningful way in future adult literacy surveys.

Educational attainment and how it relates to social and migration background as well as labor market outcomes has been studied extensively using comparative data and methods (see for example Breen and Jonsson 2005 ; Heath and Brinbaum 2014 ; Shavit and Blossfeld 1993 ; Shavit and Müller 1998 ). Due to limited data on nationally representative adult samples, there is much less cross-national research on these relationships taking basic competencies or skills into account. Research using large-scale assessment data of the adult population across countries has shown that education is a key determinant of adult basic competencies (Maehler et al. 2013 ; OECD 2013a ; OECD and Statistics Canada 2000b , 2005 ). This is not surprising as one aim of formal education is to develop basic competencies in order to prepare students for life and specifically the job market.

However, OECD ( 2013a ) and other authors (Desjardins 2003 ; Park and Kyei 2011 ; Reder 2009 ) also show that the relationship between educational qualifications and skills is imperfect, meaning that formal education does not entirely explain skill differences amongst adults, and other factors must also play an important role. Thus we agree that “better understanding the link between formal qualifications and actual skills is important because qualifications are more readily observable than skills and therefore often serve as an important proxy for the latter” (Heisig and Solga 2015 , p. 203). The use of education as a proxy for skills is especially common when arguing from the point of view of human capital theory (Becker 1964 ; Steedman and Murray 2001 ) and other functionalist approaches to education. Footnote 1 However, there is also longstanding opposition to the functionalist view of education, claiming that education also or even mostly reflects the distribution of power in society (e.g., Collins 1971 ). In fact, large-scale assessments are largely motivated by the desire to more directly measure adult competencies than using educational attainment as a proxy.

In addition, policy makers may want to know whether individuals who completed a given level of education show the same level of competencies in their country as in other countries, or whether they ‘lag behind’. They are also keen to find out whether migrants from other countries possess similar competency levels as natives at comparable levels of educational attainment. Comparable or equivalent education levels are typically defined by the International Standard Classification of Education [ISCED; (UNESCO Institute for Statistics 2006 )] used in many cross-national surveys. We however do not yet know very much about how and why competencies differ across countries for the same, supposedly comparable, education levels (but see Heisig and Solga 2015 ; Park and Kyei 2011 ).

Prior research looking at the relationship between formal education and competencies (for example, Desjardins 2003 ; OECD 2013a ; OECD and Statistics Canada 2000a , 2005 ) found that educational attainment shows the strongest relationship with competencies of all background variables Footnote 2 examined, when adjusting for other socio-demographic factors. At the same time, there are substantial differences across countries in average proficiency levels at the same broad level of educational attainment (Maehler et al. 2013 ; OECD 2013a ; OECD and Statistics Canada 2005 ). Part of the OECD ( 2013a , chapter 5) report also looks more closely into different factors that may affect adult literacy skills beyond demographics, which is an important analysis step. For example, if the higher educated show comparatively low literacy skills in one country, this may be due to many of them working in occupations not nurturing competencies, i.e. the structure of the labor market, rather than ineffective formal education or low selectivity of educational transitions by skills. This kind of confounding is also referred to as compositional effects in the literature (Raudenbush and Kim 2002 ). Therefore, when trying to compare the relationship between formal education and competencies across countries, it is important to adjust for these factors: otherwise, cross-country differences could come about through mere differences in the composition of countries according to e.g. parental education or respondents’ occupation since some countries have more expanded educational systems than others, and they differ in their industrial and thus occupational structures.

Furthermore, the OECD, as most reports in official statistics, mostly uses three very broad education levels derived from the International Standard Classification of Education (ISCED) 1997, by only distinguishing between (1) less than upper secondary, (2) upper secondary and post-secondary non-tertiary and (3) tertiary levels of education. Academic studies (for example Heisig and Solga 2015 ; Park and Kyei 2011 ) often follow this approach. Sometimes the investigation is even limited to the contrast between (1) less than upper secondary and (3) tertiary education. However, we know from previous research that such highly aggregated education measures may not be valid measures of attainment within countries (Müller and Klein 2008 ), and may, as a consequence, be cross-nationally not actually comparable (Schneider 2010 ). For example, if upper secondary graduates in one country are largely graduates of vocationally oriented programs, and in another country of academically oriented programs, lumping them together in one education category for analysis does not help our understanding of differences and similarities in outcomes of basic skill acquisition and educational selection across these two countries. Therefore, before jumping to conclusions about differences in the quality and academic selectivity of education across countries, it is important to describe ‘net’ skill differences by disaggregated, cross-nationally more comparable educational attainment categories than has so far been done.

With our analyses we want to broaden the scope of the analyses presented by the OECD in 2013 based on data from the Programme of International Assessment of Adult Competencies (PIAAC) (OECD 2013a , chapter 3). In doing so, we build on related work by Desjardins ( 2003 ), Park and Kyei ( 2011 ), Maehler et al. ( 2013 ) and Heisig and Solga ( 2015 ). This paper has two aims: First of all, we describe how detailed educational attainment relates to literacy skills across different countries. Secondly, we explore how far cross-country differences in skills by detailed educational attainment remain or change when adjusting for a wide range of micro-level variables likely to influence educational attainment and/or adult competencies. We will thereby be able to approximate ‘net’ cross-country differences and similarities in competencies by educational attainment.

With the results we hope to be able to answer the following research questions: Firstly, how closely are adult basic competencies related to educational attainment across countries? Are the competencies of individuals who have achieved ‘comparable’ levels of education also comparable, adjusting for factors related to the acquisition of formal education and basic competencies? Do we find the same differences in skills across countries already identified in the OECD reports when looking at detailed rather than highly aggregated education levels and controlling for a wide range of individual level variables? If we find substantial differences, these potentially point to (a) differences in the quality (effectiveness of skill acquisition) and skill selectivity of education between countries, (b) substantive lack of comparability of harmonized education categories regarding competencies as one outcome of formal education or (c) omission of important further confounding factors influencing educational attainment and competencies.

We first describe the theoretical relationship between detailed educational attainment categories and competencies, reviewing the literature and evidence in the field. Then, we turn to the data, measures and methods we use. In our results we show how adult competency scores of groups with ‘comparable’ educational qualifications vary between countries. We include several variables in order to disentangle which other factors could influence this relationship. We summarize our findings and discuss them in relation to potential improvements when measuring educational qualification in surveys, as well as opportunities for further research.

The relationship between educational qualifications and competencies

Competencies “…refer to the ability or capacity of an agent to act appropriately in a given situation” (OECD 2013b ), especially to someone’s proficiency in performing certain tasks. Competencies “… represent skills essential for accessing, understanding, analyzing and using text-based information and, in the case of some mathematical information, information in the form of representations (e.g. pictures, graphs)” (OECD 2013b ). We use the terms ‘competency’ and ‘skill’ interchangeably in this study.

Although the specific competencies measured in large-scale assessments can be expected to be closely related to general cognitive ability or fluid intelligence, and some authors treat them as almost exchangeable (Kerckhoff et al. 2001 ; Marks 2014 ) these two concepts are theoretically and empirically distinct. Most importantly, competencies are conceptualized as domain specific skills, focusing e.g. on literacy or numeracy, whereas fluid intelligence refers to generalized cognitive functioning (Baumert et al. 2009 ).

While competencies are understood as a continuum and typically unobserved latent characteristics, educational qualifications, going along with receiving a formal diploma, certificate or an academic title, reflect manifest thresholds or steps in the educational career. Having achieved an educational qualification usually confers some explicit opportunity or entitlement to the holder of the qualification, e.g. the opportunity to enroll in a university or (further qualify to) practice a specific occupation. Educational qualifications correspond to ‘institutionalized’ cultural capital (Bourdieu 1986 ). They allow the individual to objectify their embodied cultural capital, which includes competencies, and “makes the difference between the capital of the autodidact, which may be called into question at any time […] and the cultural capital academically sanctioned by legally guaranteed qualifications, formally independent of the person of their bearer” (Bourdieu 1986 ). Educational qualifications facilitate the conversion of cultural capital to economic capital. Qualifications also serve as signaling devices (Arrow 1973 ; Spence 1973 ) that employers, university admissions or other selecting agents can actually see when an applicant sends in copies of the diplomas and degrees she holds, whereas her actual competencies remain unobserved.

Basic competencies are “[…] the results of cumulative processes of knowledge acquisition that are moderated to some extent by reasoning ability” (Baumert et al. 2009 , p. 174). Many of these processes are facilitated by formal education. Therefore, the more opportunities for knowledge acquisition are provided to and used by an individual, the higher the level of formal education and basic competencies achieved. This point of view thus leads to the expectation that educational attainment and basic competencies are closely related. Indeed, using data from the International Adult Literacy Study (IALS), the Adult Literacy and Life Skills Survey (ALL) and PIAAC, OECD and Statistics Canada ( 2000b , 2005 ), Boudard ( 2001 ), Desjardins ( 2003 ) and OECD ( 2013a ), among others, show that across countries, education has the strongest relationship with competencies of all background variables examined, confirming results from the US National Adult Literacy Survey (Kerckhoff et al. 2001 ; Kirsch et al. 1993 ).

However, as the imperfect correlation between educational attainment and competencies suggests, knowledge acquisition and competency formation are not limited to formal education (Desjardins 2003 ): competency development is an experience that is both “lifewide” (occurring in the home, at school, work and in the community) and “lifelong” (starting during fetal development and continuing into old age). Practice engagement theory (Reder 1994 ) posits that literacy is generally learned through engagement in literacy practices, which extend far beyond formal education. Also, some of the correlation may be spurious, i.e. due to common causes. In order to assess the net relationship between educational attainment and skills, we first need to theoretically reflect on factors affecting both success in education and skills (in the sense of common causes), as well as factors potentially depending on education further impacting skills (in the sense of mediators). Only after adjusting for these factors and thus controlling compositional differences across countries, we can try to better understand competency differences across countries within supposedly comparable education levels.

To a large extent, especially in the early years of life, competency formation, especially relating to language, takes place through informal learning or primary socialization in the family context. The family is also important in nourishing curiosity and motivation to learn in children. These early skills and attitudes to learning facilitate further competency gains and transitions in formal education. Therefore, the gross or total relationship between educational attainment and competencies will partly be due to opportunities for informal learning as well as attitudes to learning bred in the home, which differ across families. When estimating the net relationship between formal education and competencies, it is thus important to adjust for characteristics of the family of origin that likely influence their offspring’s education and skills.

The literature discusses a diverse range of family characteristics when dealing with educational outcomes, which can mostly be attributed to three dimensions, namely genetic, cultural and economic resources. Firstly, cognitive abilities or general intelligence have been shown to correlate strongly between parents and their children (by 0.4–0.7, see the review by Marks 2014 , chapter 4), and, using twin and adoption studies, to have a substantial degree of heritability, with monozygotic twins reared apart showing a correlation of cognitive abilities of around 0.7 (Marks 2014 ). This may be due to either genetic or pre-natal/pre-separation environmental commonalities though. At the same time, cognitive abilities positively influence competency formation and success in formal education (Marks 2014 , chapter 5). However, only few studies have both measures of general cognitive abilities and later specific competencies, none of them cross-national. Therefore, we need to be aware that some of the effects of family characteristics that are described in the following will have some (maybe substantial) genetic component, and that some genetic effects remain unobserved.

Secondly, in terms of culture, parents’ own educational attainment is regarded as the most important asset boosting offspring’s educational opportunities (Erikson and Jonsson 1996 ; Shavit and Blossfeld 1993 ): more educated parents provide a more stimulating home environment to their children than less educated parents, for example by reading more to their children and using more complex language. They also have their own experience navigating the educational system and can thus better support their children in educational decision making, leading to higher levels of attainment. The influence of parents’ education on adult literacy net of formal education was already found for the US by Kirsch et al. ( 1993 ), cross-nationally by OECD and Statistics Canada ( 2000a , 2005 ) and Desjardins ( 2003 ), and confirmed with PIAAC data by OECD ( 2013a ). Bynner and Parsons ( 2009 ) find in their research using British cohort study data that family background has an effect on proficiency, which is mediated through earlier skill acquisition.

Closely related with parental education is the availability of books in the home when growing up, commonly used as an indicator of family’s cultural capital in large-scale assessments. This factor mediates some of the effect of parental education, but also has an effect on top of that: families with low levels of formal education that nevertheless possess more books provide a more cognitively stimulating environment, especially more opportunities for engagement in literacy practice, for their children than families with no or fewer books (Evans et al. 2010 ). Children’s reading practice has been shown to strongly support their reading competencies (Anderson et al. 1988 ). OECD ( 2013a ) however does not control for the number of books in the home.

Another cultural family characteristic is migration background, which has often been shown to affect educational outcomes (Heath and Brinbaum 2014 ; Heath et al. 2008 ; Marks 2005 ; OECD 2012 ). First of all, respondents who were educated abroad may have had very different educational experiences, including different quality of basic education, in their country of origin. Secondly, first generation migrants and their children will lack knowledge of and first-hand experience with a country’s educational system and thus may not navigate it in an optimal way. Thirdly, it can be assumed that respondents who were born in the survey country and are familiar with the survey language can more fully benefit from the learning opportunities provided to them in formal education than those born abroad or having a different mother tongue, positively contributing to both educational attainment and skills. Finally, the assessment is also language based so that respondents completing it in their native (and thus likely most proficient) language are expected to show higher competencies in literacy measured in this language. This has also been shown empirically before (Boudard 2001 ; Desjardins 2004 ; Elley 1992 ; Kirsch et al. 1993 ; OECD 2012 ; OECD and Statistics Canada 2000a , 2005 ). Using multivariate models and PIAAC data, Heisig and Solga ( 2015 ) as well as OECD ( 2013a ) find substantial effects of migration background on numeracy and literacy skills, after adjusting for parental education, educational attainment and occupation.

Thirdly, in terms of economic circumstances, the most often-discussed family characteristic is social class or status (Breen et al. 2010 ; Erikson and Jonsson 1996 ; Shavit and Blossfeld 1993 ). It reflects the occupational position, economic security and material circumstances of the family, for example nutrition, housing and access to healthcare. Economically secured parents have more capacity to support their children’s learning than those struggling to make ends meet. Some educational resources, such as a quiet place to study, books or out-of-school tutoring also have direct costs. Further family characteristics reflecting economic circumstances during childhood are parental income and wealth. Since these cannot be reliably measured in a survey interviewing the children’s generation only, they are rarely used in empirical studies. Bynner and Parsons ( 2009 ) provide a vivid insight into social and material conditions of literacy skill development for Britain. Cross-national large-scale assessments have however shown considerably less interest in material than cultural conditions of competency development, using parental education as a proxy measure for ‘socio-economic status’ (following NALS, see Kirsch et al. 1993 ) instead of differentiating cultural and economic aspects, leading to a gap in comparative research on this issue.

Moving on to secondary socialization in the formal education system, higher early literacy skills facilitate learning and thus performance in education, the successful completion of an educational level, and making the transition to the next higher level of education. Earlier literacy skills are thus predictive of later literacy skills (Bynner and Parsons 2009 ). Therefore, the relationship between education and literacy is reciprocal: more literate individuals stay in education for longer and achieve a higher level of attainment, and staying in education for longer and reaching higher levels of attainment produce higher literacy (Kirsch et al. 1993 ). This reciprocity however cannot be disentangled with cross-sectional data lacking information on skills at earlier points in life (OECD and Statistics Canada 2005 ) so that to date, no cross-national evidence is available on this. What however can be disentangled with available data are differences in skills (whether coming about by differential skill selectivity or opportunities to learn and practice literacy) within broad education levels, namely those between attainment of vocational and non-vocational educational qualifications. Given that vocational programs focus on learning of vocational rather than basic skills such as literacy, and students with low literacy more likely select (or are selected into) vocational over generally or academically oriented courses, we expect average literacy skills related to vocational qualifications to be lower than those related to general qualifications (Heisig and Solga 2015 ). This was shown to be the case for most PIAAC countries allowing this analysis by Maehler et al. ( 2013 ).

Finally, competency acquisition does not end with the end of formal education but continues through the life course especially through work (and life) experience, opportunities for skill use, as well as deliberate efforts of life-long learning. Previous research has shown that literacy levels of individuals indeed change after the completion of educational qualifications (Reder 2009 ), and more so for respondents with non-manual jobs because they have been able to further develop their skills throughout their working lives (Steedman and McIntosh 2001 ). Opportunities for literacy skill use as well as adult training strongly differ across occupations or types of jobs, even after controlling for educational attainment (Desjardins et al. 2006 ; OECD and Statistics Canada 2005 ). Individuals in different occupational groups, even if measured inconsistently across studies, therefore show diverging literacy skills, on top of education, parental education, and language (Desjardins 2003 ; OECD 2013a ; OECD and Statistics Canada 2000a ). Also, not only the work context offers opportunities for skill use, and reading for leisure or other forms of literacy practice outside of work have also been shown to contribute to skill maintenance and enhancement after formal education both for the US (Smith 1996 ) as well as cross-nationally (Desjardins 2003 ; OECD 2013a , chapter 5; OECD and Statistics Canada 2000a ). With respect to adult training, using IALS data, Park and Kyei ( 2011 ) find that training participation is related to literacy gaps. However, they only measured training participation at the country level. Desjardins ( 2003 ), OECD ( 2013a ) and OECD and Statistics Canada ( 2000a , 2005 ) find that adult training has an—albeit weak—effect, on top of formal education and other variables, since training participation is strongly related to formal education.

Turning to cross-country differences in the relationship between educational attainment and competencies as well as skill gaps between education levels, OECD and Statistics Canada ( 2000a , 2005 ) and OECD ( 2013a ) find that firstly, higher educational attainment goes along with higher competencies, but secondly, that there are marked differences in average competency scores across countries for equivalent levels of education, as well as in the competency gaps between education levels. Differences in skills across countries are more pronounced for the low than the highly educated. Using multivariate models, Park and Kyei ( 2011 ) find that in all countries, individuals with higher qualifications have higher literacy skills, as measured in IALS. They also find differences in average literacy across countries for comparable education levels. Again, the differences are more pronounced at the lower than the higher education level. The OECD report (2013a) provides more detailed multivariate results in chapter 5, which does not substantially change the result: formal education is still considerably related to adult competencies, and literacy skills still vary substantially across countries for equivalent levels of formal education (even if less so than in the unadjusted model). In line with this, Heisig and Solga ( 2015 ), using PIAAC data, find that respondents with completed upper secondary education generally acquire higher numeracy scores than respondents with lower educational qualifications, and that some of the variation between countries is related to compositional effects. However, the latter only adjust for age, sex, and migration/language status.

Available studies to date only look at skill gaps between the high and low educated (OECD 2013a , chapter 3), between the medium and the low educated (Heisig and Solga 2015 ) or between low, medium and high educated (Park and Kyei 2011 ), ignoring the heterogeneity of educational programs and qualifications within these broad levels. When unpacking broad education levels into more detailed educational attainment categories, as suggested by Schneider ( 2010 ), important cross-country differences but also previously hidden similarities may emerge. Different distributions of education within broad education categories, such as vocationally educated individuals dominating the medium educated in one country and generally educated individuals dominating the medium educated in another country, may partly explain why the same broad education levels show different average literacy skills across countries, or why some countries show surprisingly small skill gaps between educational groups (look e.g. at Switzerland and Germany in Park and Kyei 2011 ) or low overall associations between attainment and competencies (for example, see the weak associations for Sweden, the Czech Republic and Germany in OECD and Statistics Canada 2000a , all countries with substantial differentiation of education within broad levels). In some analyses, the OECD ( 2013a , pp. 200–205) report actually looks at some of those more detailed differences by looking at differences between respondents with vocational vs. general upper secondary education, and type of education at the tertiary level, but only for age groups that are potentially still in education. Maehler et al. ( 2013 ) report competency by detailed education categories for Germany, finding that on average, individuals with vocational tertiary education achieve lower competency levels than individuals with general upper secondary education, contradicting the general finding of higher competencies at higher education levels found using broad education categories. In this paper we argue that such detailed analyses should be the rule rather than the exception because detailed educational attainment categories are more substantively comparable across countries and easier to interpret than broad education levels.

Summing up, we expect that parents’ education as well as migration background has an influence on the way competencies are developed. It is likely that part of this effect is mediated through educational qualifications. Furthermore, we expect individuals working in occupations requiring higher literacy skills, individuals who regularly read at home, and those who participate in adult training to better sustain or even further develop their competencies than individuals who do not work, work in manual occupations, do not read at home, and do not participate in adult training. Because all these factors are likely to be influenced by educational attainment, we furthermore expect the skills gaps by education to be further reduced when taking post-educational experiences into account. Regarding the cross-country comparison of literacy skills by detailed educational attainment, we expect substantial differences in competencies for equivalent education categories even when adjusting for the above micro-level factors: equivalent educational programs (as defined by ISCED) in different countries differ in both skill selectivity upon entrance, as well as effectiveness of skill development.

Data and methods

For this paper, we use data from the Programme for the International Assessment of Adult Competencies (PIAAC) 2012 (OECD 2013a , 2013c ). PIAAC is an international study which assessed central basic skills which are considered essential for successful participation in today’s society: literacy, numeracy and problem-solving in technology-rich environments. In this paper, we concentrate on literacy, defined as “[…] understanding, evaluating, using and engaging with written texts to participate in society, to achieve one’s goals, and to develop one’s knowledge and potential” (PIAAC Literacy Expert Group 2009 ). In the first round of PIAAC, 24 countries took part. The focus of the study was on the working-age population between the ages of 16 and 65. In each country, respondents for PIAAC were chosen using probability based methods, thus aiming at a representative sample of the population. In addition to this, PIAAC data benefitted from a high degree of input harmonization and other high quality control standards.

We restrict the sample to respondents aged 25 and older because in many countries, respondents are still in their initial phase of education and not yet highly involved with the labor market when they are younger than 25 years (see also Desjardins 2003 ). In contrast to Park and Kyei ( 2011 ), we do not restrict our sample to young adults in an attempt to eliminate the effects of post-educational learning. Instead, we take advantage of the measures of continuing training, occupational group and private reading habits available in PIAAC (see below) and analyze the whole PIAAC sample. Finally, in comparison Park and Kyei ( 2011 ) we do not restrict the sample to respondents born in the country. Instead we exclude respondents who have completed their highest education abroad as the aim is to measure the relationship between education and measured competencies within various educational systems. Footnote 3 For our analyses we include 21 countries. We excluded data from the Russian Federation as the data is not representative for the whole resident population of the Russian Federation (OECD 2013a ). We also exclude Australia, since this data is not publicly available, and Cyprus, because results showed unusual patterns, shedding substantial doubt on comparability with the other countries, as well as the high degree of literacy related non-response (LRNR) in Cyprus, meaning the non-participation because of language difficulties, or learning or mental disabilities (see Heisig and Solga 2015 ).

Measures used

Competencies (or skills) are measured using psychometric tests based on IRT scaling procedures (OECD 2013c ). As mentioned above, in PIAAC, three different competency domains were measured: literacy, numeracy and problem-solving in technology-rich environments. The results of the scaling produced one scale for each of the domains assessed. Each of these scales ranges from 0 to 500. Tasks at the lower end of the scale are easier than those at the higher end. In order to make interpretation of the scales easier, each scale was divided into proficiency levels with intervals of 50 points (Levels 1–5 for literacy and numeracy and levels 1–3 for problem-solving in technology-rich environment, OECD 2013b ). For the purpose of this study, we have opted for literacy (rather than numeracy) because it is the more generally needed competency. As such, the acquisition of further skills such as numeracy to some degree presupposes literacy, and most of the numeracy test items in PIAAC were text based, too. Both scales are thus highly correlated.

Educational qualifications are measured by directly asking respondents for the highest qualification they have obtained or level of education completed, using a country-specific show card representing the relevant responses in any given country. The resulting categories are harmonized into a common scheme, which is based on the International Standard Classification of Education (ISCED) 1997 (UNESCO Institute for Statistics 2006 ). ISCED 1997 main levels are known for their heterogeneity and thus risk of lacking validity for cross-national comparisons (Schneider 2009 , 2010 ). Therefore in this study, we do not employ ISCED main levels but code the detailed ISCED variable available in the PIAAC data in such a way as to render the resulting categories less heterogeneous.

Not all countries provided the ISCED information in the same level of detail. With the PIAAC data it is not possible to distinguish program destinations “A”, “B” or “C” at ISCED levels 3 and 4. Instead, we distinguish whether the qualification is vocationally oriented or not (including all qualifications that are considered ‘general’ or ’unspecified’ orientation). Footnote 4 Due to limited cell sizes in ISCED levels 3 and 4 in a large number of countries, especially when distinguishing vocational and non-vocational education, we had to aggregate both levels into one. This is as close as possible to the previously tested, ISCED-based “European Survey Version of ISCED” (ES-ISCED) coding scheme (Schneider 2010 ). For the final coding and the distribution of education categories across countries, see Table  1 . We do not report results for cells including fewer than 30 individuals in this table.

The social background of respondents in both cultural and economic Footnote 5 terms is mostly indicated by their parents’ education. Parental education is measured with broad ISCED levels only. Three categories can be distinguished: (1) ISCED 2 or below, (2) ISCED 3 and 4 and, (3) ISCED 5 and 6. We have integrated the information from both parents and distinguish whether (1) both parents have ISCED 2 or below (reference category), (2) at least one parent has achieved ISCED 3 or 4, (3) both parents have ISCED 3 or 4, (4) at least one parent has achieved ISCED 5 or 6 or (5) both parents have completed ISCED 5 or 6.

As another variable concerning family cultural background, we include a measure of books in the home when the respondent was 16 years old. Respondents were asked about the number of books in their home based on five different categories ranging from ‘10 books or less’ to ‘more than 500 books’. We standardized the measure to a mean of 0 and a standard deviation of 1.

Respondents’ migration background is measured by a combination of two indicators: (1) whether a respondent is born abroad and (2) whether his or her native language (mother tongue) is different from the assessment language. The resulting indicator distinguishes whether a respondent has the following status: (1) native-born and native language (reference category), (2) native-born and foreign language, (3) foreign-born and native language and (4) foreign-born and foreign language.

The PIAAC background questionnaire provides three measures of post-educational experiences likely to affect skills and probably partly determined by educational attainment: Firstly, we include a scale indicating whether people read at home (‘reading practice’). Footnote 6 The scale was calculated by OECD based on several items on different types of reading activities outside work (e.g. reading of instructions, letters, books, professional journals etc.). It is divided into quintiles, where the lowest category reflects that respondents read never or rarely (e.g. less than once a month) outside work and the highest category that respondents read different types of texts daily or weekly (OECD 2013a , p. 217).

Secondly, we also include participation in formal or non-formal education in the last 12 months. In the following, we refer to this as training activities. Respondents were asked about various different training activities, such as courses and on-the-job training. A variable was created that separated between respondents who had taken part in any activities during the last 12 months or not (reference category). Beyond a direct effect on competencies, these respondents can be expected to have been participating in continuing training in the past.

Thirdly, we include dummy variables indicating occupational group, combining information on employment and an aggregation of major groups (according to skill levels) of the International Standard Classification of Occupations (ISCO) 2008 (International Labour Organisation 2007 ) and manual/non-manual work. They distinguish whether a respondent is (1) a skilled worker (ISCO major groups 1 to 3, i.e. managers, senior officials, legislators, professionals, technicians and associate professionals), (2) a semi-skilled white collar worker (ISCO major groups 4 and 5, i.e. clerks; service workers and shop and market sales workers), (3) a semi-skilled blue collar worker (ISCO major groups 6 to 8, i.e. skilled agricultural and fishery workers; craft and related trades workers; plant and machine operators and assemblers) or (4) an elementary worker (ISCO major group 9) and finally (5) currently not employed (reference category). We thus do not only distinguish whether a respondent is working or not like Park and Kyei ( 2011 ), but also take variation in skill use across occupational groups into account (like OECD 2013a ; Steedman and McIntosh 2001 ).

We finally use age and gender as control variables. Ageing may relate negatively with competencies due to decreasing cognitive capacities (Barrett and Riddell 2016 ; Desjardins and Warnke 2012 ) and older cohorts have received less formal education (OECD and Statistics Canada 2000a ; Shavit and Blossfeld 1993 ). Also, the specific educational experiences in (in terms of ISCED) equivalent educational programs have changed across cohorts. OECD ( 2013a ) also finds age effects in multivariate models of literacy. As this information is not available as continuous information for all countries, it is categorized into 5-year age bands. Because the effect of age does not seem to be linear (Kirsch et al. 1993 ), we included dummies for each 5-year band, the dummy for age 25–30 being the reference category. Previous research has shown that there are net gender effects on the different competency domains in some but not many industrialized countries (Desjardins 2003 ; Maehler et al. 2013 ; OECD 2011 , 2013a , 2015 ; OECD and Statistics Canada 2000a ) so that we also include a dummy variable for gender (male = 0, female = 1).

Analysis method and strategy

The aim of this paper is to estimate net differences in literacy skill by educational attainment across countries. In order to do so, we run country-wise multiple linear regression models in Stata. Our dependent variable is literacy competency, captured by ten plausible values. The main independent variable is educational attainment, measured as highest educational qualification obtained, coded in detailed ISCED. For the analyses, we use the ado PIAACtools , accounting for the complex sampling structure in PIAAC. Footnote 7

In our first or baseline model, we include respondents’ detailed educational attainment as independent variable only. This model thus estimates the gross (unadjusted) relationship between formal education and competencies. The results of this model will likely overestimate the effect of education on adult competencies because educational attainment and competencies are both to some degree caused by two common third variables, family conditions and cognitive ability (confounding bias, see for example Elwert and Winship 2013 ). In the second model, we thus introduce variables measuring family conditions, namely parental education, migration background and books in the home at age 16. This allows us to estimate the relationship between educational attainment and competencies net of family background. We also control for age and gender in this model.

Even then, this model may still overestimate the direct impact of formal education on skill because so far omitted variables that are related to educational attainment may affect skills rather than educational attainment itself. In our third model, we thus finally include variables affecting skills after completion of formal educational, such as occupational group, reading at home and participation in training activities. Theoretically, these variables are considered to lie on the causal path between educational attainment and adult competencies so they may explain to some degree why educational attainment positively relates to literacy skills, or why the relationship between education and literacy differs across countries. The resulting residual education-skill relationship is thus the remaining direct relationship, not total relationship, between education and skills, which cannot be explained by either family background or post-school skill development. Footnote 8

We start out by describing the unadjusted results regarding the gross relationship between educational qualifications and skills (Model 1, see Table 3 in appendix for more details), and then turn to the adjusted regression models, first describing differences compared to the unadjusted model when adjusting for antecedents of education and skills (Model 2, see Table 4 in appendix for more details), and second describing further differences when also adjusting for post-education factors of skill development (Model 3, see Table 5 in appendix for more details).

Figure  1 shows average literacy scores by detailed education categories for all PIAAC countries (basically, conditional means) resulting from Model 1. According to this model, the different ISCED categories follow the same order in most countries, meaning that higher level educational qualifications are associated with higher literacy skills, and within ISCED levels, non-vocational education is usually associated with higher competencies than vocational education. For example, in all countries, respondents with less than lower secondary education have lower average literacy scores than respondents with completed lower secondary education, and those in turn have lower average scores than respondents with completed vocational and non-vocational upper secondary education. Within tertiary education, we find considerable differences between qualifications from short programs that are vocationally oriented (ISCED 5B, usually 2–3 years duration full-time) and academic degrees at Bachelor level (ISCED 5A medium) for all countries in which qualifications classified as ISCED 5B exist.

Literacy skills by detailed educational attainment and country, unadjusted. Source: Programme for the International Assessment of Adult Competencies (PIAAC), 2012; own calculations. Notes ISCED = International Standard Classification of Education. Sample is restricted to adults aged 25–65 years in 2011 and to respondents who completed their highest educational qualification in the country they participated in for PIAAC. a  ISCED 5A BA for England/Northern Ireland includes both BA and MA, as well as ISCED 6

However, there are some remarkable exceptions to the hierarchical ordering of average literacy skills by education category, mostly affecting upper secondary and vocational tertiary education. The first one are respondents with ISCED 3 or 4 non-vocational in comparison to respondents with vocational tertiary education (ISCED 5B): the former group achieves significantly higher or at least comparable (average) competency scores than the latter group in several countries (especially Austria, Finland, the Netherlands and Germany). However vocational tertiary qualifications consistently go along with higher competencies than vocational upper secondary ones. Another exception is that in Germany, non-vocational upper secondary graduates even slightly (and significantly) outperform tertiary graduates with degrees from medium-duration 5A (i.e. academic) programs—a group that is comparatively small though and potentially negatively selected due to Bachelor’s degrees having been introduced in Germany only recently. Footnote 9 Finally, in Estonia, graduates from long (Master’s level) academic tertiary programs show 9 points lower literacy scores than those from medium (Bachelor’s level) duration programs. Here again, the Bachelor’s level category is rather small though.

In some cases, while the order is not broken, there are only very small competency differences between categories located at different main ISCED levels: respondents in Flanders and Spain with ISCED 3 or 4 vocational in comparison to ISCED 2 or 3C short only achieve average literacy levels which are 7 points higher. The difference of mean competency scores between respondents with ISCED 3 or 4 non-vocational and qualifications from vocational tertiary education (ISCED 5B) is small in the Czech Republic, England/Northern Ireland, Norway and Estonia, and the difference between the former and those with medium-duration, i.e. Bachelor’s level, university programs (ISCED 5A) is small in Austria, Flanders, Finland and the Netherlands.

Looking more closely at vocational and non-vocational qualifications at ISCED levels 3 and 4, we find substantial literacy skill differences in two-thirds of the countries. In total, in ten out of 21 countries, respondents with non-vocational education show a statistically significantly higher average literacy score than respondents with vocational education. The highest differences can be found in Germany and Finland, where non-vocational upper secondary education is associated with average literacy scores that are 42 or respectively 39 points higher than vocational education. While respondents with vocational upper secondary education on average score slightly higher than respondents with non-vocational education at this level in the United States and Canada (9 and 8 points respectively), these differences are not statistically significant. The within-upper secondary competency differences in Estonia, Ireland, Japan and Korea seems to be almost negligible and are statistically not significant in Estonia and Ireland.

Turning to differences between countries within detailed education categories, in all educational groups, we see a range of about 40–60 competency scores, i.e. about one competency level, between the countries with the highest average competency scores and the lowest average scores in all education groups. The range is especially high in the lowest educational group, i.e. for respondents who have no educational qualification or a qualification below ISCED level 2 (ranging from average competency scores of 179 in Denmark to 236 in Finland). In most countries, the group of low educated adults is very small, however, accounting for only 5.6% of all respondents across countries (see Table  1 ). With respect to completed lower secondary education, we find somewhat smaller competency differences of around 45 points between countries. Finland, Japan and England/Northern Ireland Footnote 10 score highest in this group with around 260 points and the United States the lowest with 217 points. Looking at the completion of vocational upper and post-secondary education (ISCED 3 and 4) we find similar cross-national differences (a difference of 44 points between Japan with 289 and Spain with 245 points). The cross-country range is, with 53 points, rather large again for non-vocational upper and post-secondary education (United States: 257, Finland: 310). For vocational tertiary, Bachelor and Master level education the ranges are 37, 46 and 46 points respectively.

This also means that respondents with more education score lower on the literacy scale in some countries than respondents with less education in other countries. As an example, respondents with ISCED 3 or 4 non-vocational in Finland score about one competency level above respondents with ISCED 5B in Spain. Therefore, in terms of literacy skills, ISCED categories are neither substantively comparable nor consistently ordered across countries, at least when looking at the unadjusted means.

In terms of overall strength of the association as measured by explained variance in this bivariate model, we can see that it also varies considerably between countries: The adjusted R 2 for the Estonia is 0.15, for the Czech Republic 0.18 and for Austria 0.19, while the value is 0.32 for Flanders, and 0.34 for France and the Netherlands (see Table  2 ). Education and literacy competency thus seem to be more closely related in the latter countries than in the former, but in all countries the association is far from perfect.

When looking at the results of model 2 adjusting for age, gender, parental education, migration background and books in the home at age 16 (Fig. 2 ), the hierarchy of skills between ISCED levels remains mostly the same as in model 1. The negative literacy gap between respondents with ISCED 3 or 4 non-vocational and respondents with ISCED 5B has substantially decreased in all countries with the exception of Germany and the Netherlands, however. When comparing vocational and non-vocational qualifications at ISCED level 3 and 4 while adjusting for various background variables, we still find lower competencies for vocational qualifications in ten countries. However, the gap between these qualifications has diminished in all countries but the Netherlands, especially in Germany and Finland, and to a lesser extent in Austria. The difference is still significant, and in Germany and Finland they remain substantial. Japan, Korea, Ireland and Estonia join Canada and the United States in vocational upper secondary education leading to the same or even slightly higher literacy skills as non-vocational upper secondary education. Turning to the differences between vocational and general/academic education at tertiary level, we find that also in model 2 respondents with a general education score higher in literacy than respondents with a vocational qualification in all countries. Similar as for ISCED level 3 and 4, the gap between qualifications classified as ISCED 5A Bachelor level and ISCED 5B is lower now than in model 1. Especially in Estonia, Finland and Austria, this gap diminished after the adjustment but especially in the former two countries it remains quite substantial and significant. In summary, while some of the relationship between educational attainment and literacy skills is due to social background and migration/language status, formal education still makes a large difference for the achievement of adult literacy skills, whether because of differential skill selectivity or differential skill acquisition in different programs.

Literacy skills by detailed educational attainment and country, adjusting for age, gender, parental education, migration background and books in the home at age 16. Source: Programme for the International Assessment of Adult Competencies (PIAAC), 2012; own calculations. Notes ISCED = International Standard Classification of Education. Sample is restricted to adults aged 25–65 years in 2011 and to respondents who completed their highest educational qualification in the country they participated in for PIAAC. Reference categories: 25–30 years, male, both parents ISCED 2 or below, no migration background, standardized measure of books. a  ISCED 5A BA for England/Northern Ireland includes both BA and MA, as well as ISCED 6

In Model 2, the competency gap between respondents with high and low educational qualifications is smaller than in the unadjusted model. However, as in the unadjusted model, there are still large competency differences between respondents with ‘equivalent’ educational qualifications across countries. Only for respondents below ISCED level 2, the differences across countries diminish between model 1 and model 2. For lower secondary or non-vocational upper secondary education and, to a lesser extent, for the different qualifications at the tertiary level, the differences between the country with the highest and the lowest average proficiency even increase: in the Netherlands, respondents at level 3 or 4 vocational achieve 318 points, while in the United States respondents achieve 254 points—a competency gap of more than half a competency level and 10 points more than in model 1. So while adjusting for important antecedents of both educational attainment and skills reveals that the low levels of literacy of the low educated are to a large extent explained by social and migration background, differences across countries in composition by social and migration background do not make cross-country differences in literacy skills for comparable education categories disappear—on the contrary. Also the countries are ordered more similarly across education categories in terms of average literacy skills than in model 1, with Finland, the Netherlands and Japan always amongst the top and Italy, the Slovak Republic and the United States always amongst the bottom performers.

Between models 1 and 2, the adjusted R 2 has increased by almost 10% (from 26 to 35%) on average across countries. The strongest increase can be seen for Sweden, where the adjusted R 2 for model 2 is 47%, up from 24% in the unadjusted Model 1 (see Table  2 ). This may be due to the Swedish educational system not being very selective, an education policy measure to counter social inequalities in education, but skill development still strongly depending on family background. Therefore, formal education is not as strongly a mediator of social background effects on skills in Sweden as it is in other countries.

In model 3, we have introduced further adjustments, namely variables which are likely to affect literacy skills after initial education (Fig. 3 ). These were occupational group, reading at home, and participation in training activities in the last 12 months. After introducing these variables, the general patterns we already saw in models 1 and 2 remain the same. We will only highlight the most important differences. In model 3 the skill differences between different educational groups become even less distinct within each country, this time specifically in the top education categories: the highly educated have substantially better opportunities for further developing their literacy skill in their working lives than the lower educated, and they also read more in their leisure time. However, we can still find considerable differences in literacy skills between respondents in the same ISCED category across countries.

Literacy skills by detailed educational attainment and country, additionally adjusting for occupational group, reading at home, and participation in training activities in the last 12 months. Source: Programme for the International Assessment of Adult Competencies (PIAAC), 2012; own calculations. Notes ISCED = International Standard Classification of Education. Sample is restricted to adults aged 25–65 years in 2011 and to respondents who completed their highest educational qualification in the country they participated in for PIAAC. Reference categories: 25–30 years, male, both parents ISCED 2 or below, no migration background, standardized measure of books, not currently working, low score on reading at home scale, participated in formal training during the last 12 months. a  ISCED 5A BA for England/Northern Ireland includes both BA and MA, as well as ISCED 6

In comparison to models 1 and 2, we see that the hierarchy of educational levels is less obvious. In particular, this concerns the differences between non-vocational ISCED 3 and 4 qualifications and qualifications at ISCED level 5B. As in model 1 and 2, in some countries, respondents with ISCED 3 or 4 non-vocational score higher than respondents with ISCED 5B. What has changed, however, is the gap between these two which became smaller in all countries. It can now be observed in several countries that respondents with lower secondary education (ISCED level 2 or 3C short) score higher than respondents with ISCED level 3/4 vocational (Flanders, England/Northern Ireland, Finland and Norway). This hints at the literacy skill advantage of those with vocational upper secondary education compared to those with lower secondary education in model 2 being due to their more favorable labor market placement and reading habits rather than their vocational upper secondary education itself (however, their labor market placement to some degree depends on it obviously). Furthermore, we also find in model 3 that literacy scores of respondents with ISCED level 3 or 4 non-vocational do not differ much from scores of respondents with Bachelor level education (with the exception of Canada, Estonia, Ireland, Japan, Korea, Sweden and the United States).

Comparing fully adjusted average literacy by ISCED levels across countries, we see that the gap between countries scoring the highest and scoring the lowest has diminished for the lowest educational group but has increased for ISCED levels 3 and above. For respondents below ISCED 2, the score is 239 for Sweden and 210 for Denmark, a competency gap of a bit more than half a competency level. It was 56 points in Model 1. At ISCED 5B, the competency gap between countries increased from 37 points (between Japan with 304 and 266 in Spain) to 49 points (between Japan with 298 and Spain with 249). Altogether, even after adjusting for a wide range of factors, there are still substantial differences in average literacy skills between countries for supposedly comparable education categories.

The adjusted R 2 suggests that with model 3 not more variance in skills can be explained in all countries, in contrast to model 2. This suggests that the background variables in model 2 seem to be more important in explaining the variation in literacy skills. Introducing the additional mediating variables in model 3 does not add explanatory power to the model. However, since the effects of education on skills somewhat decrease between models 2 and 3 in most countries, those additional variables mediate some of the effects of educational attainment on skills so that model 3 can be interpreted as showing the relationship between educational attainment and literacy skills ‘net’ of labor market experiences and cross-country differences therein.

Summary and discussion

We find considerable differences across countries in the average literacy skills associated with supposedly equivalent education levels, as well as in the strength of association of educational qualifications and skills. Our results suggest that some of these differences are due to differences across populations in characteristics that influence education and skill acquisition before achieving educational qualifications, such as family background, as well as experiences that occur after the completion of educational qualifications, such as daily reading practices and the job situation. However, even after adjusting for a wide range of correlates of education and literacy skills, substantial cross-country differences in average skills within education categories remain—and in some cases even become stronger. In contrast to Park and Kyei ( 2011 ), we do not find that the differences between countries are smaller at higher education levels than at lower education levels, which may be due to our more comprehensive set of controls as well as a broader set of countries covered in PIAAC than in IALS.

Furthermore, confirming results by Maehler et al. ( 2013 ) for Germany on an international scale, we find substantial heterogeneity in literacy within broad education levels across countries. This shows that it is in fact worth looking at detailed education categories rather than just broad heterogeneous levels. The cross-country differences in skills by detailed education categories seem to be related to characteristics of the respective educational systems: In those countries where there are no substantial skill differences between vocational and non-vocational qualifications at the upper secondary level (Canada, Estonia, Ireland, Japan, Korea and the United States), vocational education is not very vocationally specific, which may mean that in such ‘pseudo-vocational’ programs literacy competencies are improved as much as in general programs. Another potential explanation, however, is that the results reflect sorting and educational choices by competency: In the above countries, skill selectivity may not differ between (pseudo-)vocational and non-vocational programs. In contrast, in countries with a strong vocational upper secondary system, such as Germany and Finland, people who initially have a higher literacy competency follow more general tracks while people with lower competencies engage in vocationally oriented programs.

This puts the validity of broad education levels as proxies for general skills into considerable doubt: In many countries, specifically those with distinct vocational training systems, graduates of vocational education and training have substantially lower literacy skills than graduates of non-vocational education at both the upper secondary and tertiary levels. Literacy skills are usually analyzed and reported in only three broad education levels (low, medium and high) and our results suggest that average literacy scores by broad education level for any given country seem to dependent to a large degree on the prevalence of the vocationally educated groups within those levels.

Available comparative research on differences across countries in adult competencies for comparable education groups concentrate on differences in the organization of or resource inequality within educational systems (Heisig and Solga 2015 ; Park and Kyei 2011 ). Another explanation concerns differences between countries in the selectivity of specific educational categories. We cannot tell whether the skill differences that we find within broad education levels can be explained by selection effects or skill acquisition effects. This is due to the fact that variables such as prior learning experiences, cognitive ability and—relevant for differences at tertiary level—literacy skills at completion of secondary education cannot be accounted for with PIAAC.

There is, however, also an interpretation for these results that concerns the methodology of PIAAC, and specifically the measurement of educational attainment using ISCED. The ISCED classification criteria (UNESCO Institute for Statistics 2006 ), which are admittedly proxy-criteria due to lacking direct indicators, may be ill-suited to capture the actual complexity of content of educational programs, the concept ISCED intends to measure. The complexity of content of an educational program should theoretically be quite strongly related to the average literacy skills that completers of the program show, because literacy skills highly correlate with other types of general skills. The most important classification criteria defined by ISCED are typical age of entry into an educational program and theoretical program duration, together forming the cumulative duration of education at the end of the program. Sometimes additionally a minimum entry requirement in terms of a level and/or type of program previously completed, or the level and/or type of program the program to be classified is designed to prepare for, are also defined. Obviously, these criteria exclusively refer to the structure of educational systems, not to the complexity of content and related demand placed on learners or even skill outcomes. In fact, we are not aware of any study evaluating the extent to which the ISCED criteria do capture complexity of content. Our results make us skeptical in this regard: Even though ISCED offers the tools to distinguish between general and vocational education, for the same duration of education, equal complexity of content is assumed for vocational and general programs, and thus they are assigned to the same main ISCED level. Our research however suggests that in terms of literacy, the complexity of content of vocational programs may be substantially lower, so they more strongly draw in participants from the lower end of the skill distribution at the completion of the previous level, especially in countries with highly occupationally specific vocational training. The current ISCED criteria seem, on their own, incomplete to well differentiate educational programs by their degree of complexity of content.

Limitations of the study are similar to those of previous studies using IALS, ALL or NALS data, because by and large, these surveys share some design weaknesses (see also Desjardins 2003 ; Kerckhoff et al. 2001 ; Park and Kyei 2011 ): incomplete measures of family conditions and post-school experiences as well as the absence of a measure of generalized cognitive ability or literacy skills at earlier time points lead to residual confounding, so that data better describing learning contexts during childhood, youth and adulthood would improve the interpretability of results. Because these variables are not measured in PIAAC, the estimation of the net effect of level of education on literacy skills is problematic, as the influences of theses variables cannot fully be accounted for. Therefore the education effect is likely still overestimated in models 2 and 3. in the absence of panel (let alone experimental) data, it is impossible to correctly model causal relationships between formal education, adult competencies, and their mediators such as employment, occupation, adult training or reading practice and thus better understand the skill formation process and make public policy recommendations (Raudenbush and Kim 2002 ). Basically, research based on cross-sectional surveys such as PIAAC cannot differentiate between the theoretically equally plausible causal mechanisms of literacy selection (i.e. students with higher literacy progressing further or to different types of programs in formal education) and literacy development (i.e. formal education producing higher literacy) distinguished by Reder ( 1998 ). This is especially relevant for the differences between vocational and non-vocational upper secondary education, as well as results at the tertiary level.

Another issue that needs to be considered when interpreting our results are differences in ‘literacy related nonresponse’ (LRNR) across countries. The number of literacy related non-respondents ranges between 0% (Finland, Poland and Sweden) and 5.2% (Flanders) in our sample (OECD 2013a ). Van de Kerckhove et al. ( 2013 ) show that a LRNR share of 2% has little impact on the overall score but that significant bias can be introduced with a share of 8% LRNR. This needs to be considered when interpreting the results of our analyses as it can be assumed that literacy related non-response is related to lower literacy skills in the interview language (Van de Kerckhove et al. 2013 ). This means that countries with a higher share of LRNR are likely to have lower literacy skills than reported. Furthermore, it is likely that LRNR occurs more often in lower educational groups in most countries.

Conclusions

We would like to offer two kinds of conclusions: one for researchers trying to proxy competencies with information on educational attainment, and one for future PIAAC studies. With respect to the first issue, looking at detailed ISCED categories reveals skill similarities across and differences within ISCED main levels, which means that for analyzing skills, ISCED levels show a low degree of validity. Therefore, analysts trying to use educational attainment data to proxy differences between individuals in literacy (or other general basic) skills should not use ISCED main (or even broad) levels, but rather code detailed education categories according to their competency outcomes. This means that individuals with qualifications from vocational tertiary education should be aggregated with individuals with non-vocational upper secondary education (ideally ISCED level 4 only) rather than with individuals with academic tertiary qualifications, as is usually done, or, better still, be kept separate. Furthermore, given the strong differences between vocational and non-vocational upper secondary education in a large number of countries covered in PIAAC, these two categories should also be coded separately whenever possible, at least for those countries where skill differences are large. In many countries, the average competencies of the vocationally educated are closer to those of individuals with lower secondary education (ISCED level 2) than to those of individuals with non-vocational upper secondary education. Basically, when proxying competencies, in countries with strong vocational training systems the vocationally educated should, be downgraded to the next lower ISCED level. Footnote 11

Regarding recommendations for PIAAC, there are several points to make. Firstly, despite the fact that formal education is undisputedly the most important context of skill formation, educational attainment is treated in adult literacy surveys such as PIAAC as a mere ‘background variable’. As a consequence, it is not as well measured as one might wish: For example, qualifications resulting from vocational and general programs, or between those preparing primarily for university and those preparing primarily for the labor market, are not easily distinguished even though these differences can be expected to be important for literacy skill formation. The variable on orientation was not specified ex-ante, apparently leading to ex-post coding problems for many countries. As another example, the differentiation between the Bachelor’s and Master’s level cannot be drawn in all countries due to limitations of the measurement instruments. In the UK, it cannot be drawn at all, and in Germany, there is a large element of misclassification in these categories of the variable. Therefore, we would strongly recommend (1) to give the relationship between educational attainment, basic skills and labor market outcomes more theoretical thought and thus specify more relevant and valid harmonized target variables, and (2) to put more quality control into place regarding the ex-ante output-harmonization of educational attainment in any future PIAAC cycle (regarding the harmonization of education in comparative surveys, see e.g. Schneider 2010 ; Schneider et al. 2016 ; Wolf et al. 2016 ).

Secondly, we do not know anything about the pathway an individual has taken through the educational system, i.e. how the highest qualification that is measured was achieved. Different pathways, especially in countries where multiple options are available at every transition point, are likely to provide different access barriers and learning environments, and thus result in different literacy skills. Fortunately, OECD is already investigating these issues for the upcoming PIAAC cycle. Thirdly, we would strongly suggest enriching the set of background variables to be more able to tease out different causal mechanisms concerning adult skill development and avoid conflating many different effects in the measure of parental education. Without going full-scale longitudinal, causal modeling more strictly speaking will of course remain impossible.

Finally, we would like to offer some ideas for further research: Firstly, it would be worthwhile to extend this study by also including the nine PIAAC round 2 countries for which data were collected in 2014. Given these countries are less developed than round 1 countries, we would expect to find even more variation in literacy skills by educational attainment. Secondly, we have ignored potential interaction effects in this study in order not to overcomplicate the models. Most importantly, it is quite plausible that the relationship between education and literacy skills changes across cohorts, mostly because younger generations have benefited from educational expansion (Shavit and Blossfeld 1993 ) and formal education can be expected to be more relevant to the skills of younger individuals just because they have left education more recently. Thirdly, one could try to systematically scale educational attainment by directly assessed skills across countries to develop more comparable measures of skills, based on information on educational attainment coded in ISCED only, which could then also be applied to other data than PIAAC. One could also use PIAAC data for benchmarking specific ISCED categories for specific age groups across countries, following the approach taken by. Finally, the obvious next step in substantive analysis would be to investigate contextual effects on cross-country differences in competencies at given education levels or gaps between specific education levels, building on prior research by Park and Kyei ( 2011 ) and Heisig and Solga ( 2015 ). In our view it is important to learn more about the individual determinants of adult skills and how these differ across countries, since this could provide us with important lessons for the future: it is very clear that adults to a large extent transmit their competencies to their children in most if not all countries. Because of data constraints, prior sociological research has largely focused on inequality of educational opportunity in terms of educational attainment (for a review, see Breen and Jonsson 2005 ). With PIAAC data, as limited as they may be in terms of background measures, it is possible to add to this the study of social inequality in competencies across countries.

A notable exception is some literature on educational and skill mismatches in the labor market, which critically examines the relationship between educational certificates and actual skills (Allen and Van der Velden 2001 ; Green and McIntosh 2007 ).

It is interesting to note that the OECD treats educational attainment as a mere ‘socio-demographic’ variable (just as socio-economic background, another important factor in skill development).

In order to compute this information, the age when completing the highest degree as well as the age of immigration was needed. However, Germany, Canada, Estonia, and the United States did not provide this information as continuous variable. For the three latter countries, the information had to be derived from the categorized age and can therefore only be treated as proxy. For Germany, the continuous variable from the German Scientific Use File (Rammstedt et al. 2015 ) was used.

The orientation of the highest qualification is documented in a separate variable in the PIAAC data, which was derived from the national educational attainment questions ex-post. Not all countries seem to have succeeded in this task, resulting in ‘unspecified’ (Canada and Japan, Denmark, Germany, Sweden, England/Northern Ireland, United States) or missing (Italy and Flanders) orientation, despite the fact that the ISCED mappings indicate an orientation for every educational program. We treated ‘unspecified’ and missing orientation as non-vocational, i.e. merged it with the general category, based on the theoretical argument that vocational programs will put less emphasis on the development of basic competencies. For the German data, we used the German Scientific Use File (Rammstedt et al. 2015 ) in order to derive this information from the country-specific education variable on vocational and higher education.

Parental education is used as a proxy for both cultural and economic resources because there is no measure of parental occupation or wealth available in PIAAC allowing us to differentiate between cultural and economic social background effects.

OECD ( 2013a ) shows that reading outside work has an even stronger relationship with literacy skills than reading at work. Therefore, we have included this scale.

To cross-check our results we also estimated our models using syntax-based programs based on syntax provided by Jan Paul Heisig. We did not find any differences.

We distinguish models 2 and 3 for two reasons: firstly, because we want to estimate the total net relationship between education and skills, which is achieved by model 2, where entering variables on the causal path from education to skills (like in model 3) would introduce overcontrol bias (Elwert and Winship 2013 ). Secondly, model 3 is, like many approaches trying to disentangle direct and indirect ‘effects’, at risk of endogenous selection bias (Elwert and Winship 2013 ): rather than conceptualizing occupation, reading habits and adult learning as a mediator between formal education and skills, they could also be regarded as common outcomes (descendants) of education and skills. In this alternative theoretical model, they would be collider variables and controlling for them would introduce a spurious association between education and skills. Still we consider the estimation of model 3 worthwhile in order to control for compositional effects regarding post-educational experiences between countries.

The common pre-Bologna qualification from polytechnic higher education (‘ Diplom Fachhochschule ’) should have been classified here, too, but individuals with this qualification are included in the ISCED 5A long category because the measurement instrument used in PIAAC does not differentiate them from university graduates.

This result is different from what is usually found for the UK, because we reclassified all respondents with GCSEs, the main general school leaving qualification at age 16 which is required to proceed to A-Levels, which in turn give access to university studies, to ISCED level 2. In OECD statistics, only those with ‘weak’ GCSEs (less than 5, or grades lower then C) are classified at ISCED level 2. Our reason for doing so is that other countries classify such programs at ISCED level 2, and this is in better accordance with ISCED criteria. While ISCED category 3C was never meant to be used for general educational programs, the UK classifies their GCSEs at ISCED 3C if the result is ‘strong’ (5 or more at grades A to C). Using the official ISCED mapping for the UK, the competency levels at lower and upper secondary education in England/Northern Ireland would be much lower. Unfortunately, the international organizations have only very limited influence on how countries assign educational programs and qualifications to ISCED, which opens the door to politically motivated classification decisions.

This does not imply that vocational education is generally less valuable than general education—only that, in terms of literacy skill outcomes, it is not comparable to general education at the same level of education.

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NM and SLS participated in the rationale of the paper. NM carried out the coding and statistical analyses and prepared the tables and graphs. SLS carried out the literature review and theoretical work. NM and SLS both drafted the manuscript. Both authors read and approved the final manuscript.

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A Review of the Literature on Socioeconomic Status and Educational Achievement

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The foundations of socioeconomic inequities and the educational outcomes of efforts to reduce gaps in socioeconomic status are of great interest to researchers around the world, and narrowing the achievement gap is a common goal for most education systems. This review of the literature focuses on socioeconomic status (SES) and its related constructs, the association between SES and educational achievement, and differences among educational systems, together with changes over time. Commonly-used proxy variables for SES in education research are identified and evaluated, as are the relevant components collected in IEA’s Trends in International Mathematics and Science Study (TIMSS). Although the literature always presents a positive association between family SES and student achievement, the magnitude of this relationship is contingent on varying social contexts and education systems. TIMSS data can be used to assess the magnitude of such relationships across countries and explore them over time. Finally, the literature review focuses on two systematic and fundamental macro-level features: the extent of homogeneity between schools, and the degree of centralization of education standards and norms in a society.

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• Centralization versus decentralization
• Educational inequality
• Forms of capital
• Homogeneity versus heterogeneity
• International large-scale assessment
• Student achievement
• Socioeconomic status
• Trends in International Mathematics and Science Study (TIMSS)

Educational inequality occurs in multiple forms. Van de Wefhorst and Mijs ( 2010 ) discussed its existence through the inequality of educational opportunity in terms of the influence of social background on students’ test scores, as well as in learning, as expressed by the performance distribution in test scores. According to the authors, these two characteristics of inequality are conceptually different in that an educational system may have equality in terms of dispersion (or variance) in educational achievement but inequality in terms of opportunities; yet, in general, societies that are equal in terms of dispersion are also more equal in terms of opportunities.

Different education systems take part in each cycle of TIMSS, but 25 education systems took part in the grade eight mathematics student assessment in both 1995 and 2015. For these 25 participating systems, the average mathematics achievement score increased by only five score points between 1995 and 2015 (Mullis et al. 2016 ). Focusing only on more recent trends, for the 32 education systems that participated in the grade eight mathematics student assessment in both 2011 and 2015, there was a gain of nine scale score points between 2011 and 2015, suggesting that many of the education systems with the largest gains are those starting from a low base. As there is limited information on family and home background and its relationship with TIMSS international achievement, this spread in achievement is not sufficient to explain why education systems perform differently. Therefore, our study focuses on the other aspect of educational inequality, namely how SES background is related to educational achievement. In the next two sections of this chapter, we review the concept and measurement of socioeconomic status, and the literature regarding the relationship between family SES and student academic achievement. The rest of this chapter focuses on differences between the various education systems and changes in educational inequality over time.

2.1 Socioeconomic Status and Related Constructs and Measures

The American Psychological Association (APA) defines socioeconomic status as “the social standing or class of an individual or group” (APA 2018 ). SES has been commonly used as a latent construct for measuring family background (Bofah and Hannula 2017 ). However, among empirical studies, there is no consensus on how to best operationalize the concept. In many studies, the measurement of SES does not receive much attention, with very limited discussion over why certain indicators were used rather than others (Bornstein and Bradley 2014 ). Liberatos et al. ( 1988 ) argued that there was no one best measure, because the choice of the SES measure depended on the conceptual relevance, the possible role of social class in the study, the applicability of the measure to the specific populations being studied, the relevance of a measure at the time of study, the reliability and validity of the measure, the number of indicators included, the level of measurement, the simplicity of the measure, and comparability with measures used in other studies.

Historically, SES has been conceptualized and measured in various ways. Taussig ( 1920 ) conceptualized SES as the occupational status of the father. Later, Cuff ( 1934 ) adopted a score card proposed by Sims ( 1927 ) as a measure of SES; this included questions about items possessed by the home, parents’ education, father’s occupation, and other relevant information. Moving on from these early studies, development of instruments for measuring SES has become more complicated, including more advanced methods such as factor analysis or model-based approaches (NCES [National Center for Educational Statistics] 2012 ). By the 1980s, one general agreement had emerged: SES should be a composite variable, typically measuring education, income, and occupation, since these three indicators reflect different aspects of family background (Brese and Mirazchiyski 2013 ).

However, collecting this information is known to be challenging. Besides privacy concerns, there are also concerns about information accuracy (Keeves and Saha 1992 ). For example, the National Assessment of Educational Progress (NAEP) in the United States does not collect family income or parental occupation directly from students, as many of them are unable to accurately report such data (Musu-Gillette 2016 ). Similarly, TIMSS decided not to include questions about parental occupation and income because of doubts about the reliability and utility of similar information collected by previous IEA surveys (Buchmann 2002 ). Therefore, the grade eight student questionnaires for TIMSS include only three proxy components for SES: parental education, books at home, and home possessions (such as ownership of a calculator, computer, study desk, or dictionary), with some evolution in the home possession items over time owing to rapid advancements in technology over the 20 years of TIMSS (more recent items include the internet, or computer tablet, for example).

The abstract nature of the concept of SES leaves some room for researchers to decide what proxy variables to use as SES measures. Yang ( 2003 ), for example, found that the possession of a set of household items may be used as SES indicators. Despite variability and limitations in the measurement of SES, its association with student performance has been demonstrated in numerous studies (Sirin 2005 ).

2.2 Family SES and Student Achievement

Theoretical and empirical work has emphasized that family SES has an impact on children’s educational outcomes, examined mechanisms through which family SES is related to children’s achievement, and identified potential pathways behind this relationship, one of which uses three forms of capital: economic, cultural, and social capital (Bourdieu 1986 ; Coleman 1988 , 1990 ). In other words, differences in the availability of these forms of capital Footnote 1 across households eventually lead to disparities in children’s academic achievement (Buchmann 2002 ).

Bourdieu ( 1986 ) posited that capital can present itself in three fundamental forms and that economic capital is the source of all other forms of capital. The other types of capital are treated as transformed and disguised forms of economic capital. Economic capital can be used in pursuit of other forms of capital; for example, family income can be used to pay for organized after-school activities, to access elite educational opportunities, or to build up valuable social networks (Lareau 2011 ). Children from disadvantaged backgrounds are constrained by the financial resources they and their family possess (Crosnoe and Cooper 2010 ). As such, economic capital determines the extent to which parents can offer financial support to children’s academic pursuits.

In addition to economic capital, cultural capital, namely knowledge of cultural symbols and ability to decode cultural messages, helps parents transmit their advantages to children and to reproduce social class (Bourdieu 1986 ). According to Bourdieu ( 1986 ), an individual’s cultural capital can exist in an embodied state as well as in an objectified state. In the embodied state, cultural capital focuses on “physical capital,” where the body itself is a marker of social class, as particular embodied properties exist as a consequence of specific class practices (Tittenbrun 2016 ). Through this state, inequality in socioeconomic class can find expression in embodied ways, such as physical appearance, body language, diet, pronunciation, and handwriting. In the objectified state, inequality is expressed in forms of cultural goods, such as accessibility to pictures, books, dictionaries, and machines. Therefore, in this view, Bourdieu sees the body and cultural goods as forms of currency that result in the unequal accumulation of material resources and, by extension, represent an important contributor to class inequality (Perks 2012 ).

Children from higher social classes also have advantages in gaining educational credentials due to their families. Cultural capital is considered an important factor for school success. Yang ( 2003 ) suggested possession of cultural resources had the most significant impact on students’ mathematics and science achievement in most countries. If cultural resources are differentiated according to family background, and if some cultural resources have more value than others in the education system, it is reasonable to assume that differential achievement is related to an individual’s social class (Barone 2006 ). For example, a student’s social ability and language style, as well as attitudes toward the school curriculum and teachers, may differ according to social class origins (Barone 2006 ). As such, parental school choice in some countries favors children from those families that already possess dominant cultural advantages (i.e., children attending private schools in the United States), thus confirming the cultural inequalities between classes and status groups of families to produce educational inequalities among their children (Shavit and Blossfeld 1993 ). Lareau ( 1987 , 2011 ) further posited that middle-class parents have a different parenting style, which she termed concerted cultivation, fostering their child’s talent through organized activities, while working-class parents tend to have a natural growth parenting style, letting their children create their own activities with more unstructured time. Consequently, middle-class families prepare their children better for school since their parenting style is more valued and rewarded by the school system.

Finally, the possession of social capital reflects the resources contained in social relations, which can be invested with expected benefits (Bourdieu 1986 ). Differences in educational success can be attributed to different levels of existing social capital, which is produced in networks and connections of families that the school serves (Rogošić and Baranović 2016 ). Coleman ( 1988 ) developed a conceptual framework of social capital in which social structure can create social capital, through family, school, and community. The relationships between the family and the community may be used to explain the higher educational achievements of students based on expected achievements with respect to their socioeconomic status (Mikiewicz et al. 2011 ).

In summary, while the overall association between family SES and students’ academic achievement is well documented in theoretical and empirical work, the magnitude of the relationship between family SES and achievement differs across countries. This may be related to differences in education systems and jurisdictions, and societal changes over time.

2.3 Differences in Education Systems and Changes Over Time

In any society, there are two systematic and fundamental macro-level features that highlight the differences in education systems and how they have changed over time. First, is the extent of homogeneity among education systems. Second, is the degree of centralization of education standards and norms in a society. The association between family background and children’s achievement depends on the education system and the social context (i.e., the level of homogeneity and centralization). Where educational inequality is prominent, students from different backgrounds may demonstrate larger achievement gaps.

2.3.1 Homogeneous Versus Heterogeneous

Previous research has shown that students at lower levels of SES perform better in education systems with lower levels of inequality than their counterparts in countries with more significant SES differences (Ornstein 2010 ). That is, some education systems are more homogeneous than others, with schools being more similar to each other in terms of funding. As an example, Finnish households have a narrow distribution of economic and social status at the population level and their schools show little variation in terms of funding (Mostafa 2011 ).

Furthermore, Mostafa ( 2011 ) found that school homogeneity on a large scale is a source of equality since it diminishes the impact of school characteristics on performance scores. Finland is often seen as an example of a homogeneous education system with high levels of similarity between schools, which in turn reduces the impact of school variables on performance scores (Kell and Kell 2010 ; Mostafa 2011 ). More specifically, Montt ( 2011 ) examined more than 50 school systems, including Finland, in the 2006 cycle of PISA and found that greater homogeneity in teacher quality decreased variability in opportunities to learn within school systems, potentially mitigating educational inequality in achievement.

By contrast, Hong Kong has a relatively high-income disparity compared to other societies (Hong Kong Economy 2010 ). However, the relationship between socioeconomic status and mathematics achievement was found to be the lowest among the education systems participating in the 2012 cycle of PISA (Ho 2010 ; Kalaycıoğlu 2015 ). This suggests that, despite diversity in their SES background, most students from Hong Kong access and benefit from the education system equally. Hong Kong’s high performance in reading, mathematics, and science also suggests the average basic education is of high quality (Ho 2010 ).

However, in many other countries with heterogeneous education systems, educational inequality has manifested itself primarily through the stratification of schools on the basis of socioeconomic composition, resource allocation, or locale. For example, unlike schooling in many other countries, public schooling policies in the United States are highly localized. Local property taxes partially finance public schools, school assignments for students depend on their local residence, and neighborhoods are often divided by racial and socioeconomic background (Echenique et al. 2006 ; Iceland and Wilkes 2006 ). Cheema and Galluzzo ( 2013 ) confirmed the persistence of gender, racial, and socioeconomic gaps in mathematics achievement in the United States using PISA data from its 2003 cycle. Inequalities in children’s academic outcomes in the United States are substantial, as children begin school on unequal terms and differences accumulate as they get older (Lareau 2011 ; Lee and Burkam 2002 ).

In Lithuania, there has also been a growing awareness that an ineffectively organized or poorly functioning system of formal youth education increases the social and economic divide and the social exclusion of certain groups (Gudynas 2003 ). To ensure the accessibility and quality of educational services in Lithuania, special attention has traditionally been paid to a student’s residential location. Gudynas ( 2003 ) suggested that the achievement of pupils in rural schools in Lithuania was lower than that of pupils in urban schools, with the difference being largely explained by the level of parental education in rural areas, which was on average lower than that of urban parents. Similarly, in New Zealand, residential location is considered to be a barrier to educational equality. Kennedy ( 2015 ) observed that students residing in rural residential areas on average tend to have lower SES than those in urban areas, and receive a considerably shorter education than their counterparts living in urban centers, thereby promoting SES disparities in access to education.

In the Russian Federation, Kliucharev and Kofanova ( 2005 ) noted that the inequality between well-off and low-income individuals regarding access to education has been increasing since the turn of the century. According to Kosaretsky et al. ( 2016 ), the greatest inequality in educational access in the Russian Federation was observed in the 1990s, where the rising number of educational inequalities was largely determined by the accelerating socioeconomic stratification of the population, as well as significant budget cuts to education. Although the state articulated policies aiming for universal equality of educational opportunities, they argued that the policies were not implemented with the required financial and organizational support. As a result, in the immediate post-Soviet era, the Russian Federation has observed increasing educational inequality and some loss of achievement compared to the Soviet period.

A final example is Hungary. Horn et al. ( 2006 ) noted that OECD’s PISA studies in the early 2000s highlighted the need for the Hungarian school system to improve both in effectiveness and equality. They contended that achievement gaps among schools make the Hungarian education system one of the most unequal among the participating countries in the PISA 2000 and 2003 cycles. The variation in performance between schools in Hungary is alarmingly large, about twice the OECD average between-school variance (OECD 2004 ). By contrast, the within-school variance is less pronounced, suggesting that students tend to be grouped in schools with others sharing similar characteristics. In other words, students’ achievement gaps seemingly mirror the differences in socioeconomic backgrounds of students across different schools (OECD 2001 , 2004 ). In recent years, persistent education performance gaps with regard to socioeconomic background of students have been observed in Hungary, with 23% of the variation in students’ mathematics performance being explained by differences in their SES background, well above the average of 15% for OECD countries (OECD 2015 ).

2.3.2 Centralized Versus Decentralized

In addition to differences in homogeneity, education systems can be classified as centralized or decentralized. A centralized education system is one that would have centralized education funding (e.g., at the national level) across the education system with little local autonomy, while in decentralized education systems, municipalities oversee school funding for both public and private schools (Böhlmark and Lindahl 2008 ; Oppedisano and Turati 2015 ). Centralization generally leads to the standardization of curriculum, instruction, and central examinations in an education system, and can be helpful in reducing inequalities since it mitigates the influence of a student’s family background (Van de Wefhorst and Mijs 2010 ). By contrast, high levels of decentralization can create greater disparities between schools, especially when the level of funding is determined by the local context (Mostafa 2011 ).

Sweden is an example of a decentralized education system that was centralized until the implementation of wide-reaching reforms in the early 1990s (Hansen et al. 2011 ). The previously centralized Swedish school system has been thoroughly transformed into a highly decentralized and deregulated one, with a growing number of independent schools and parental autonomy in school choice (Björklund et al. 2005 ). Concurrently, examining multi-level effects of SES on reading achievement using data from IEA’s Reading Literacy Study from 1991 and PIRLS data from 1991 to 2001, the SES effect appears to have increased in Sweden over time, with between-school differences being greater in 2001 than in 1991, suggesting school SES has a strong effect (Hansen et al. 2011 ).

Similarly, there has also been growing debate about educational inequality in the Republic of Korea in recent years. By analyzing grade eight TIMSS data from the 1999, 2003, and 2007 cycles of the assessment, Byun and Kim ( 2010 ) found the contribution of SES background on student achievement had increased over time. They suspected the higher educational inequality might be related to various factors, including a widening income gap and recent educational reforms geared toward school choice, as well as increased streaming by academic ability and curriculum differentiation created by a decentralized education system.

Researchers have found evidence to support the view that decentralized education systems in developed countries perform better than centralized systems in terms of reducing students’ achievement inequality (see, e.g., Rodríguez-Pose and Ezcurra 2010 ). Conversely, Causa and Chapuis ( 2009 ) used PISA data for the OECD countries to confirm that decentralized school systems were positively associated with equity in educational achievement. Furthermore, according to PISA 2000 and 2006, in European countries inequality in educational outcomes has apparently declined in decentralized school systems, while it has concomitantly increased in centralized systems (Oppedisano and Turati 2015 ).

Mullis et al. ( 2016 ) argued that efficiency and equality can work together. They found that many countries have improved their TIMSS national averages while also reducing the achievement gap between low- and high-performing students. Similarly, an analysis using TIMSS scores from 1999 and 2007 discovered a prominent inverse relation between the within-country dispersion of scores and the average TIMSS performance by country (Freeman et al. 2010 ; Mullis et al. 2016 ). The pursuit of educational equality does not have to be attained at the expense of equity and efficiency.

In conclusion, the positive association between family background and children’s achievement is universal. However, the magnitude of such associations depend on the social context and education system. In other words, the achievement gap between students from different backgrounds is more pronounced in education systems where overall inequality (e.g., income inequality) is strong. Narrowing the achievement gap is a common goal for most education systems. But it is well understood that stagnant scores for low-SES students and declines in the scores of high-SES students should not be seen as an avenue for enhancing equality. Rather, education systems should strive for equality by improving the performance of all students while focusing on improving the achievement of low-SES students at a faster rate to reduce gaps in achievement (Mullis et al. 2016 ). In recognition of this, our study not only focuses on how inequalities in educational outcomes relate to socioeconomic status over time for select participating education systems in TIMSS but also tracks the performance of low-SES* Footnote 2 students separately. In order to make a comparable trend analysis, we first constructed a consistent measure of family SES* based on a modified version of the TIMSS HER. Chapter 3 describes the data and methods used in the study and Chap. 4 presents the trends in SES* achievement gaps of the 13 education systems that participated in three cycles of TIMSS, including the 1995 and 2015 cycles.

Note that family socioeconomic status is clearly related to Bourdieu’s theory of capital in the empirical world. Conceptually, however, they do not equate with each other.

The SES measure used in this study is a modified version of the TIMSS home educational resources (HER) index and does not represent the full SES construct, as usually defined by parental education, family income, and parental occupation. In this report, we therefore term our measure SES* to denote the conceptual difference (Please refer to Chap. 1 for more details).

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Broer, M., Bai, Y., Fonseca, F. (2019). A Review of the Literature on Socioeconomic Status and Educational Achievement. In: Socioeconomic Inequality and Educational Outcomes. IEA Research for Education, vol 5. Springer, Cham. https://doi.org/10.1007/978-3-030-11991-1_2

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The Impact of Grading Standards on Student Achievement, Educational Attainment, and Entry-Level Earnings

Despite recent theoretical work and proposals from educational reformers, there is little empirical work on the effects of higher grading standards. In this paper we use data from the High School and Beyond survey to estimate the effects of grading standards on student achievement, educational attainment, and entry level earnings. We consider not only how grading standards affect average outcomes but also how they affect the distribution of educational gains by skill level and race/ethnicity. We find that higher standards raise test scores throughout the distribution of achievement, but that the increase is greatest toward the top of the test score distribution. Higher standards have no positive effect on educational attainment, however, and indeed have negative effects on high school graduation among blacks and Hispanics. We suggest a relative performance hypothesis to explain how higher standards may reduce educational attainment even as they increase educational achievement.

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The influence of education on health: an empirical assessment of OECD countries for the period 1995–2015

Viju raghupathi.

1 Koppelman School of Business, Brooklyn College of the City University of New York, 2900 Bedford Ave, Brooklyn, NY 11210 USA

Wullianallur Raghupathi

2 Gabelli School of Business, Fordham University, 140 W. 62nd Street, New York, NY 10023 USA

Associated Data

The dataset analyzed during the current study is available from the corresponding author on reasonable request.

A clear understanding of the macro-level contexts in which education impacts health is integral to improving national health administration and policy. In this research, we use a visual analytic approach to explore the association between education and health over a 20-year period for countries around the world.

Using empirical data from the OECD and the World Bank for 26 OECD countries for the years 1995–2015, we identify patterns/associations between education and health indicators. By incorporating pre- and post-educational attainment indicators, we highlight the dual role of education as both a driver of opportunity as well as of inequality.

Adults with higher educational attainment have better health and lifespans compared to their less-educated peers. We highlight that tertiary education, particularly, is critical in influencing infant mortality, life expectancy, child vaccination, and enrollment rates. In addition, an economy needs to consider potential years of life lost (premature mortality) as a measure of health quality.

Conclusions

We bring to light the health disparities across countries and suggest implications for governments to target educational interventions that can reduce inequalities and improve health. Our country-level findings on NEET (Not in Employment, Education or Training) rates offer implications for economies to address a broad array of vulnerabilities ranging from unemployment, school life expectancy, and labor market discouragement. The health effects of education are at the grass roots-creating better overall self-awareness on personal health and making healthcare more accessible.

Introduction

Is education generally associated with good health? There is a growing body of research that has been exploring the influence of education on health. Even in highly developed countries like the United States, it has been observed that adults with lower educational attainment suffer from poor health when compared to other populations [ 36 ]. This pattern is attributed to the large health inequalities brought about by education. A clear understanding of the health benefits of education can therefore serve as the key to reducing health disparities and improving the well-being of future populations. Despite the growing attention, research in the education–health area does not offer definitive answers to some critical questions. Part of the reason is the fact that the two phenomena are interlinked through life spans within and across generations of populations [ 36 ], thereby involving a larger social context within which the association is embedded. To some extent, research has also not considered the variances in the education–health relationship through the course of life or across birth cohorts [ 20 ], or if there is causality in the same. There is therefore a growing need for new directions in education–health research.

The avenues through which education affects health are complex and interwoven. For one, at the very outset, the distribution and content of education changes over time [ 20 ]. Second, the relationship between the mediators and health may change over time, as healthcare becomes more expensive and/or industries become either more, or less hazardous. Third, some research has documented that even relative changes in socioeconomic status (SES) can affect health, and thus changes in the distribution of education implies potential changes in the relationship between education and health. The relative index of inequality summarizes the magnitude of SES as a source of inequalities in health [ 11 , 21 , 27 , 29 ]. Fourth, changes in the distribution of health and mortality imply that the paths to poor health may have changed, thereby affecting the association with education.

Research has proposed that the relationship between education and health is attributable to three general classes of mediators: economic; social, psychological, and interpersonal; and behavioral health [ 31 ]. Economic variables such as income and occupation mediate the relationship between education and health by controlling and determining access to acute and preventive medical care [ 1 , 2 , 19 ]. Social, psychological, and interpersonal resources allow people with different levels of education to access coping resources and strategies [ 10 , 34 ], social support [ 5 , 22 ], and problem-solving and cognitive abilities to handle ill-health consequences such as stress [ 16 ]. Healthy behaviors enable educated individuals to recognize symptoms of ill health in a timely manner and seek appropriate medical help [ 14 , 35 ].

While the positive association between education and health has been established, the explanations for this association are not [ 31 ]. People who are well educated experience better health as reflected in the high levels of self-reported health and low levels of morbidity, mortality, and disability. By extension, low educational attainment is associated with self-reported poor health, shorter life expectancy, and shorter survival when sick. Prior research has suggested that the association between education and health is a complicated one, with a range of potential indicators that include (but are not limited to) interrelationships between demographic and family background indicators [ 8 ] - effects of poor health in childhood, greater resources associated with higher levels of education, appreciation of good health behaviors, and access to social networks. Some evidence suggests that education is strongly linked to health determinants such as preventative care [ 9 ]. Education helps promote and sustain healthy lifestyles and positive choices, nurture relationships, and enhance personal, family, and community well-being. However, there are some adverse effects of education too [ 9 ]. Education may result in increased attention to preventive care, which, though beneficial in the long term, raises healthcare costs in the short term. Some studies have found a positive association between education and some forms of illicit drug and alcohol use. Finally, although education is said to be effective for depression, it has been found to have much less substantial impact in general happiness or well-being [ 9 ].

On a universal scale, it has been accepted that several social factors outside the realm of healthcare influence the health outcomes [ 37 ]. The differences in morbidity, mortality and risk factors in research, conducted within and between countries, are impacted by the characteristics of the physical and social environment, and the structural policies that shape them [ 37 ]. Among the developed countries, the United States reflects huge disparities in educational status over the last few decades [ 15 , 24 ]. Life expectancy, while increasing for all others, has decreased among white Americans without a high school diploma - particularly women [ 25 , 26 , 32 ]. The sources of inequality in educational opportunities for American youth include the neighborhood they live in, the color of their skin, the schools they attend, and the financial resources of their families. In addition, the adverse trends in mortality and morbidity brought on by opioids resulting in suicides and overdoses (referred to as deaths of despair) exacerbated the disparities [ 21 ]. Collectively, these trends have brought about large economic and social inequalities in society such that the people with more education are likely to have more health literacy, live longer, experience better health outcomes, practice health promoting behaviors, and obtain timely health checkups [ 21 , 17 ].

Education enables people to develop a broad range of skills and traits (including cognitive and problem-solving abilities, learned effectiveness, and personal control) that predispose them towards improved health outcomes [ 23 ], ultimately contributing to human capital. Over the years, education has paved the way for a country’s financial security, stable employment, and social success [ 3 ]. Countries that adopt policies for the improvement of education also reap the benefits of healthy behavior such as reducing the population rates of smoking and obesity. Reducing health disparities and improving citizen health can be accomplished only through a thorough understanding of the health benefits conferred by education.

There is an iterative relationship between education and health. While poor education is associated with poor health due to income, resources, healthy behaviors, healthy neighborhood, and other socioeconomic factors, poor health, in turn, is associated with educational setbacks and interference with schooling through difficulties with learning disabilities, absenteeism, or cognitive disorders [ 30 ]. Education is therefore considered an important social determinant of health. The influence of national education on health works through a variety of mechanisms. Generally, education shows a relationship with self-rated health, and thus those with the highest education may have the best health [ 30 ]. Also, health-risk behaviors seem to be reduced by higher expenditure into the publicly funded education system [ 18 ], and those with good education are likely to have better knowledge of diseases [ 33 ]. In general, the education–health gradients for individuals have been growing over time [ 38 ].

To inform future education and health policies effectively, one needs to observe and analyze the opportunities that education generates during the early life span of individuals. This necessitates the adoption of some fundamental premises in research. Research must go beyond pure educational attainment and consider the associated effects preceding and succeeding such attainment. Research should consider the variations brought about by the education–health association across place and time, including the drivers that influence such variations [ 36 ].

In the current research, we analyze the association between education and health indicators for various countries using empirical data from reliable sources such as the Organization for Economic Cooperation and Development (OECD) and World Bank. While many studies explore the relationship between education and health at a conceptual level, we deploy an empirical approach in investigating the patterns and relationships between the two sets of indicators. In addition, for the educational indicators, we not only incorporate the level of educational attainment, but also look at the potential socioeconomic benefits, such as enrollment rates (in each sector of educational level) and school life expectancy (at each educational level). We investigate the influences of educational indicators on national health indicators of infant mortality, child vaccinations, life expectancy at birth, premature mortality arising from lack of educational attainment, employment and training, and the level of national health expenditure. Our research question is:

What are some key influencers/drivers in the education-health relationship at a country level?

The current study is important because policy makers have an increasing concern on national health issues and on policies that support it. The effect of education is at the root level—creating better overall self-awareness on personal health and making healthcare more accessible. The paper is organized as follows: Section 2 discusses the background for the research. Section 3 discusses the research method; Section 4 offers the analysis and results; Section 5 provides a synthesis of the results and offers an integrated discussion; Section 6 contains the scope and limitations of the research; Section 7 offers conclusions with implications and directions for future research.

Research has traditionally drawn from three broad theoretical perspectives in conceptualizing the relationship between education and health. The majority of research over the past two decades has been grounded in the Fundamental Cause Theory (FCT) [ 28 ], which posits that factors such as education are fundamental social causes of health inequalities because they determine access to resources (such as income, safe neighborhoods, or healthier lifestyles) that can assist in protecting or enhancing health [ 36 ]. Some of the key social resources that contribute to socioeconomic status include education (knowledge), money, power, prestige, and social connections. As some of these undergo change, they will be associated with differentials in the health status of the population [ 12 ].

Education has also been conceptualized using the Human Capital Theory (HCT) that views it as a return on investment in the form of increased productivity [ 4 ]. Education improves knowledge, skills, reasoning, effectiveness, and a broad range of other abilities that can be applied to improving health. The third approach - the signaling or credentialing perspective [ 6 ] - is adopted to address the large discontinuities in health at 12 and 16 years of schooling, which are typically associated with the receipt of a high school diploma and a college degree, respectively. This perspective considers the earned credentials of a person as a potential source that warrants social and economic returns. All these theoretical perspectives postulate a strong association between education and health and identify mechanisms through which education influences health. While the HCT proposes the mechanisms as embodied skills and abilities, FCT emphasizes the dynamism and flexibility of mechanisms, and the credentialing perspective proposes educational attainment through social responses. It needs to be stated, however, that all these approaches focus on education solely in terms of attainment, without emphasizing other institutional factors such as quality or type of education that may independently influence health. Additionally, while these approaches highlight the individual factors (individual attainment, attainment effects, and mechanisms), they do not give much emphasis to the social context in which education and health processes are embedded.

In the current research while we acknowledge the tenets of these theoretical perspectives, we incorporate the social mechanisms in education such as level of education, skills and abilities brought about by enrollment, school life expectancy, and the potential loss brought about by premature mortality. In this manner, we highlight the relevance of the social context in which the education and health domains are situated. We also study the dynamism of the mechanisms over countries and over time and incorporate the influences that precede and succeed educational attainment.

We analyze country level education and health data from the OECD and World Bank for a period of 21 years (1995–2015). Our variables include the education indicators of adult education level; enrollment rates at various educational levels; NEET (Not in Employment, Education or Training) rates; school life expectancy; and the health indicators of infant mortality, child vaccination rates, deaths from cancer, life expectancy at birth, potential years of life lost and smoking rates (Table ​ (Table1). 1 ). The data was processed using the tools of Tableau for visualization, and SAS for correlation and descriptive statistics. Approaches for analysis include ranking, association, and data visualization of the health and education data.

Variables in the Research

Analyses and results

In this section we identify and analyze patterns and associations between education and health indicators and discuss the results. Since countries vary in population sizes and other criteria, we use the estimated averages in all our analyses.

Comparison of health outcomes for countries by GDP per capita

We first analyzed to see if our data reflected the expectation that countries with higher GDP per capita have better health status (Fig. ​ (Fig.1). 1 ). We compared the average life expectancy at birth, average infant mortality, average deaths from cancer and average potential year of life lost, for different levels of GDP per capita (Fig. ​ (Fig.1 1 ).

Associations between Average Life Expectancy (years) and Average Infant Mortality rate (per 1000), and between Deaths from Cancer (rates per 100,000) and Average Potential Years of Life Lost (years), by GDP per capita (for all countries for years 1995–2015)

Figure ​ Figure1 1 depicts two charts with the estimated averages of variables for all countries in the sample. The X-axis of the first chart depicts average infant mortality rate (per 1000), while that of the second chart depicts average potential years of life lost (years). The Y-axis for both charts depicts the GDP per capita shown in intervals of 10 K ranging from 0 K–110 K (US Dollars). The analysis is shown as an average for all the countries in the sample and for all the years (1995–2015). As seen in Fig. ​ Fig.1, 1 , countries with lower GDP per capita have higher infant mortality rate and increased potential year of life lost (which represents the average years a person would have lived if he or she had not died prematurely - a measure of premature mortality). Life expectancy and deaths from cancer are not affected by GDP level. When studying infant mortality and potential year lost, in order to avoid the influence of a control variable, it was necessary to group the samples by their GDP per capita level.

Association of Infant Mortality Rates with enrollment rates and education levels

We explored the association of infant mortality rates with the enrollment rates and adult educational levels for all countries (Fig. ​ (Fig.2). 2 ). The expectation is that with higher education and employment the infant mortality rate decreases.

Association of Adult Education Levels (ratio) and Enrollment Rates (ratio) with Infant Mortality Rate (per 1000)

Figure ​ Figure2 2 depicts the analysis for all countries in the sample. The figure shows the years from 1995 to 2015 on the X axis. It shows two Y-axes with one axis denoting average infant mortality rate (per 1000 live births), and the other showing the rates from 0 to 120 to depict enrollment rates (primary/secondary/tertiary) and education levels (below secondary/upper secondary/tertiary). Regarding the Y axis showing rates over 100, it is worth noting that the enrollment rates denote a ratio of the total enrollment (regardless of age) at a level of education to the official population of the age group in that education level. Therefore, it is possible for the number of children enrolled at a level to exceed the official population of students in the age group for that level (due to repetition or late entry). This can lead to ratios over 100%. The figure shows that in general, all education indicators tend to rise over time, except for adult education level below secondary, which decreases over time. Infant mortality shows a steep decreasing trend over time, which is favorable. In general, countries have increasing health status and education over time, along with decreasing infant mortality rates. This suggests a negative association of education and enrollment rates with mortality rates.

Association of Education Outcomes with life expectancy at birth

We explored if the education outcomes of adult education level (tertiary), school life expectancy (tertiary), and NEET (not in employment, education, or training) rates, affected life expectancy at birth (Fig. ​ (Fig.3). 3 ). Our expectation is that adult education and school life expectancy, particularly tertiary, have a positive influence, while NEET has an adverse influence, on life expectancy at birth.

Association of Adult Education Level (Tertiary), NEET rate, School Life Expectancy (Tertiary), with Life Expectancy at Birth

Figure ​ Figure3 3 show the relationships between various education indicators (adult education level-tertiary, NEET rate, school life expectancy-tertiary) and life expectancy at birth for all countries in the sample. The figure suggests that life expectancy at birth rises as adult education level (tertiary) and tertiary school life expectancy go up. Life expectancy at birth drops as the NEET rate goes up. In order to extend people’s life expectancy, governments should try to improve tertiary education, and control the number of youths dropping out of school and ending up unemployed (the NEET rate).

Association of Tertiary Enrollment and Education with potential years of life lost

We wanted to explore if the potential years of life lost rates are affected by tertiary enrollment rates and tertiary adult education levels (Fig. ​ (Fig.4 4 ).

Association of Enrollment rate-tertiary (top) and Adult Education Level-Tertiary (bottom) with Potential Years of Life Lost (Y axis)

The two sets of box plots in Fig. ​ Fig.4 4 compare the enrollment rates with potential years of life lost (above set) and the education level with potential years of life lost (below set). The analysis is for all countries in the sample. As mentioned earlier, the enrollment rates are expressed as ratios and can exceed 100% if the number of children enrolled at a level (regardless of age) exceed the official population of students in the age group for that level. Potential years of life lost represents the average years a person would have lived, had he/she not died prematurely. The results show that with the rise of tertiary adult education level and tertiary enrollment rate, there is a decrease in both value and variation of the potential years of life lost. We can conclude that lower levels in tertiary education adversely affect a country’s health situation in terms of premature mortality.

Association of Tertiary Enrollment and Education with child vaccination rates

We compared the performance of tertiary education level and enrollment rates with the child vaccination rates (Fig. ​ (Fig.5) 5 ) to assess if there was a positive impact of education on preventive healthcare.

Association of Adult Education Level-Tertiary and Enrollment Rate-Tertiary with Child Vaccination Rates

In this analysis (Fig. ​ (Fig.5), 5 ), we looked for associations of child vaccination rates with tertiary enrollment and tertiary education. The analysis is for all countries in the sample. The color of the bubble represents the tertiary enrollment rate such that the darker the color, the higher the enrollment rate, and the size of the bubble represents the level of tertiary education. The labels inside the bubbles denote the child vaccination rates. The figure shows a general positive association of high child vaccination rate with tertiary enrollment and tertiary education levels. This indicates that countries that have high child vaccination rates tend to be better at tertiary enrollment and have more adults educated in tertiary institutions. Therefore, countries that focus more on tertiary education and enrollment may confer more health awareness in the population, which can be reflected in improved child vaccination rates.

Association of NEET rates (15–19; 20–24) with infant mortality rates and deaths from Cancer

In the realm of child health, we also looked at the infant mortality rates. We explored if infant mortality rates are associated with the NEET rates in different age groups (Fig. ​ (Fig.6 6 ).

Association of Infant Mortality rates with NEET Rates (15–19) and NEET Rates (20–24)

Figure ​ Figure6 6 is a scatterplot that explores the correlation between infant mortality and NEET rates in the age groups 15–19 and 20–24. The data is for all countries in the sample. Most data points are clustered in the lower infant mortality and lower NEET rate range. Infant mortality and NEET rates move in the same direction—as infant mortality increases/decrease, the NEET rate goes up/down. The NEET rate for the age group 20–24 has a slightly higher infant mortality rate than the NEET rate for the age group 15–19. This implies that when people in the age group 20–24 are uneducated or unemployed, the implications on infant mortality are higher than in other age groups. This is a reasonable association, since there is the potential to have more people with children in this age group than in the teenage group. To reduce the risk of infant mortality, governments should decrease NEET rates through promotional programs that disseminate the benefits of being educated, employed, and trained [ 7 ]. Additionally, they can offer financial aid to public schools and companies to offer more resources to raise general health awareness in people.

We looked to see if the distribution of population without employment, education, or training (NEET) in various categories of high, medium, and low impacted the rate of deaths from cancer (Fig. ​ (Fig.7). 7 ). Our expectation is that high rates of NEET will positively influence deaths from cancer.

Association of Deaths from Cancer and different NEET Rates

The three pie charts in Fig. ​ Fig.7 7 show the distribution of deaths from cancer in groups of countries with different NEET rates (high, medium, and low). The analysis includes all countries in the sample. The expectation was that high rates of NEET would be associated with high rates of cancer deaths. Our results, however, show that countries with medium NEET rates tend to have the highest deaths from cancer. Countries with high NEET rates have the lowest deaths from cancer among the three groups. Contrary to expectations, countries with low NEET rates do not show the lowest death rates from cancer. A possible explanation for this can be attributed to the fact that in this group, the people in the labor force may be suffering from work-related hazards including stress, that endanger their health.

Association between adult education levels and health expenditure

It is interesting to note the relationship between health expenditure and adult education levels (Fig. ​ (Fig.8). 8 ). We expect them to be positively associated.

Association of Health Expenditure and Adult Education Level-Tertiary & Upper Secondary

Figure ​ Figure8 8 shows a heat map with the number of countries in different combinations of groups between tertiary and upper-secondary adult education level. We emphasize the higher levels of adult education. The color of the square shows the average of health expenditure. The plot shows that most of the countries are divided into two clusters. One cluster has a high tertiary education level as well as a high upper-secondary education level and it has high average health expenditure. The other cluster has relatively low tertiary and upper secondary education level with low average health expenditure. Overall, the figure shows a positive correlation between adult education level and compulsory health expenditure. Governments of countries with low levels of education should allocate more health expenditure, which will have an influence on the educational levels. Alternatively, to improve public health, governments can frame educational policies to improve the overall national education level, which then produces more health awareness, contributing to national healthcare.

Association of Compulsory Health Expenditure with NEET rates by country and region

Having explored the relationship between health expenditure and adult education, we then explored the relationship between health expenditure and NEET rates of different countries (Fig. ​ (Fig.9). 9 ). We expect compulsory health expenditure to be negatively associated with NEET rates.

Association between Compulsory Health Expenditure and NEET Rate by Country and Region

In Fig. ​ Fig.9, 9 , each box represents a country or region; the size of the box indicates the extent of compulsory health expenditure such that a larger box implies that the country has greater compulsory health expenditure. The intensity of the color of the box represents the NEET rate such that the darker color implies a higher NEET rate. Turkey has the highest NEET rate with low health expenditure. Most European countries such as France, Belgium, Sweden, and Norway have low NEET rates and high health expenditure. The chart shows a general association between low compulsory health expenditure and high NEET rates. The relationship, however, is not consistent, as there are countries with high NEET and high health expenditures. Our suggestion is for most countries to improve the social education for the youth through free training programs and other means to effectively improve the public health while they attempt to raise the compulsory expenditure.

Distribution of life expectancy at birth and tertiary enrollment rate

The distribution of enrollment rate (tertiary) and life expectancy of all the countries in the sample can give an idea of the current status of both education and health (Fig. ​ (Fig.10). 10 ). We expect these to be positively associated.

Distribution of Life Expectancy at Birth (years) and Tertiary Enrollment Rate

Figure ​ Figure10 10 shows two histograms with the lines representing the distribution of life expectancy at birth and the tertiary enrollment rate of all the countries. The distribution of life expectancy at birth is skewed right, which means most of the countries have quite a high life expectancy and there are few countries with a very low life expectancy. The tertiary enrollment rate has a good distribution, which is closer to a normal distribution. Governments of countries with an extremely low life expectancy should try to identify the cause of this problem and take actions in time to improve the overall national health.

Comparison of adult education levels and deaths from Cancer at various levels of GDP per capita

We wanted to see if various levels of GDP per capita influence the levels of adult education and deaths from cancer in countries (Fig. ​ (Fig.11 11 ).

Comparison of Adult Education Levels and Deaths from Cancer at various levels of GDP per capita

Figure ​ Figure11 11 shows the distribution of various adult education levels for countries by groups of GDP per capita. The plot shows that as GDP grows, the level of below-secondary adult education becomes lower, and the level of tertiary education gets higher. The upper-secondary education level is constant among all the groups. The implication is that tertiary education is the most important factor among all the education levels for a country to improve its economic power and health level. Countries should therefore focus on tertiary education as a driver of economic development. As for deaths from cancer, countries with lower GDP have higher death rates, indicating the negative association between economic development and deaths from cancer.

Distribution of infant mortality rates by continent

Infant mortality is an important indicator of a country’s health status. Figure ​ Figure12 12 shows the distribution of infant mortality for the continents of Asia, Europe, Oceania, North and South America. We grouped the countries in each continent into high, medium, and low, based on infant mortality rates.

Distribution of Infant Mortality rates by Continent

In Fig. ​ Fig.12, 12 , each bar represents a continent. All countries fall into three groups (high, medium, and low) based on infant mortality rates. South America has the highest infant mortality, followed by Asia, Europe, and Oceania. North America falls in the medium range of infant mortality. South American countries, in general, should strive to improve infant mortality. While Europe, in general, has the lowest infant mortality rates, there are some countries that have high rates as depicted.

Association between child vaccination rates and NEET rates

We looked at the association between child vaccination rates and NEET rates in various countries (Fig. ​ (Fig.13). 13 ). We expect countries that have high NEET rates to have low child vaccination rates.

Association between Child Vaccination Rates and NEET rates

Figure ​ Figure13 13 displays the child vaccination rates in the first map and the NEET rates in the second map, for all countries. The darker green color shows countries with higher rates of vaccination and the darker red represents those with higher NEET rates. It can be seen that in general, the countries with lower NEET also have better vaccination rates. Examples are USA, UK, Iceland, France, and North European countries. Countries should therefore strive to reduce NEET rates by enrolling a good proportion of the youth into initiatives or programs that will help them be more productive in the future, and be able to afford preventive healthcare for the families, particularly, the children.

Average smoking rate in different continents over time

We compared the trend of average smoking rate for the years 1995–201 for the continents in the sample (Fig. ​ (Fig.14 14 ).

Trend of average smoking rate in different continents from 1995 to 2015

Figure ​ Figure14 14 depicts the line charts of average smoking rates for the continents of Asia, Europe, Oceania, North and South America. All the lines show an overall downward trend, which indicates that the average smoking rate decreases with time. The trend illustrates that people have become more health conscious and realize the harmful effects of smoking over time. However, the smoking rate in Europe (EU) is consistently higher than that in other continents, while the smoking rate in North America (NA) is consistently lower over the years. Governments in Europe should pay attention to the usage of tobacco and increase health consciousness among the public.

Association between adult education levels and deaths from Cancer

We explored if adult education levels (below-secondary, upper-secondary, and tertiary) are associated with deaths from cancer (Fig. ​ (Fig.15) 15 ) such that higher levels of education will mitigate the rates of deaths from cancer, due to increased awareness and proactive health behavior.

Association of deaths from cancer with adult education levels

Figure ​ Figure15 15 shows the correlations of deaths from cancer among the three adult education levels, for all countries in the sample. It is obvious that below-secondary and tertiary adult education levels have a negative correlation with deaths from cancer, while the upper-secondary adult education level shows a positive correlation. Barring upper-secondary results, we can surmise that in general, as education level goes higher, the deaths from cancer will decrease. The rationale for this could be that education fosters more health awareness and encourages people to adopt healthy behavioral practices. Governments should therefore pay attention to frame policies that promote education. However, the counterintuitive result of the positive correlation between upper-secondary levels of adult education with the deaths from cancer warrants more investigation.

We drilled down further into the correlation between the upper-secondary education level and deaths from cancer. Figure ​ Figure16 16 shows this correlation, along with a breakdown of the total number of records for each continent, to see if there is an explanation for the unique result.

Association between deaths from cancer and adult education level-upper secondary

Figure ​ Figure16 16 shows a dashboard containing two graphs - a scatterplot of the correlation between deaths from cancer and education level, and a bar graph showing the breakdown of the total sample by continent. We included a breakdown by continent in order to explore variances that may clarify or explain the positive association for deaths from cancer with the upper-secondary education level. The scatterplot shows that for the European Union (EU) the points are much more scattered than for the other continents. Also, the correlation between deaths and education level for the EU is positive. The bottom bar graph depicts how the sample contains a disproportionately high number of records for the EU than for other continents. It is possible that this may have influenced the results of the correlation. The governments in the EU should investigate the reasons behind this phenomenon. Also, we defer to future research to explore this in greater detail by incorporating other socioeconomic parameters that may have to be factored into the relationship.

Association between average tertiary school life expectancy and health expenditure

We moved our focus to the trends of tertiary school life expectancy and health expenditure from 1995 to 2015 (Fig. ​ (Fig.17) 17 ) to check for positive associations.

Association between Average Tertiary School Life Expectancy and Health Expenditure

Figure ​ Figure17 17 is a combination chart explaining the trends of tertiary school life expectancy and health expenditure, for all countries in the sample. The rationale is that if there is a positive association between the two, it would be worthwhile for the government to allocate more resources towards health expenditure. Both tertiary school life expectancy and health expenditure show an increase over the years from 1995 to 2015. Our additional analysis shows that they continue to increase even after 2015. Hence, governments are encouraged to increase the health expenditure in order to see gains in tertiary school life expectancy, which will have positive implications for national health. Given that the measured effects of education are large, investments in education might prove to be a cost-effective means of achieving better health.

Our results reveal how interlinked education and health can be. We show how a country can improve its health scenario by focusing on appropriate indicators of education. Countries with higher education levels are more likely to have better national health conditions. Among the adult education levels, tertiary education is the most critical indicator influencing healthcare in terms of infant mortality, life expectancy, child vaccination rates, and enrollment rates. Our results emphasize the role that education plays in the potential years of life lost, which is a measure that represents the average years a person would have lived had he/she not died prematurely. In addition to mortality rate, an economy needs to consider this indicator as a measure of health quality.

Other educational indicators that are major drivers of health include school life expectancy, particularly at the tertiary level. In order to improve the school life expectancy of the population, governments should control the number of youths ending up unemployed, dropping out of school, and without skills or training (the NEET rate). Education allows people to gain skills/abilities and knowledge on general health, enhancing their awareness of healthy behaviors and preventive care. By targeting promotions and campaigns that emphasize the importance of skills and employment, governments can reduce the NEET rates. And, by reducing the NEET rates, governments have the potential to address a broad array of vulnerabilities among youth, ranging from unemployment, early school dropouts, and labor market discouragement, which are all social issues that warrant attention in a growing economy.

We also bring to light the health disparities across countries and suggest implications for governments to target educational interventions that can reduce inequalities and improve health, at a macro level. The health effects of education are at the grass roots level - creating better overall self-awareness on personal health and making healthcare more accessible.

Scope and limitations

Our research suffers from a few limitations. For one, the number of countries is limited, and being that the data are primarily drawn from OECD, they pertain to the continent of Europe. We also considered a limited set of variables. A more extensive study can encompass a larger range of variables drawn from heterogeneous sources. With the objective of acquiring a macro perspective on the education–health association, we incorporated some dependent variables that may not traditionally be viewed as pure health parameters. For example, the variable potential years of life lost is affected by premature deaths that may be caused by non-health related factors too. Also there may be some intervening variables in the education–health relationship that need to be considered. Lastly, while our study explores associations and relationships between variables, it does not investigate causality.

Conclusions and future research

Both education and health are at the center of individual and population health and well-being. Conceptualizations of both phenomena should go beyond the individual focus to incorporate and consider the social context and structure within which the education–health relationship is embedded. Such an approach calls for a combination of interdisciplinary research, novel conceptual models, and rich data sources. As health differences are widening across the world, there is need for new directions in research and policy on health returns on education and vice versa. In developing interventions and policies, governments would do well to keep in mind the dual role played by education—as a driver of opportunity as well as a reproducer of inequality [ 36 ]. Reducing these macro-level inequalities requires interventions directed at a macro level. Researchers and policy makers have mutual responsibilities in this endeavor, with researchers investigating and communicating the insights and recommendations to policy makers, and policy makers conveying the challenges and needs of health and educational practices to researchers. Researchers can leverage national differences in the political system to study the impact of various welfare systems on the education–health association. In terms of investment in education, we make a call for governments to focus on education in the early stages of life course so as to prevent the reproduction of social inequalities and change upcoming educational trajectories; we also urge governments to make efforts to mitigate the rising dropout rate in postsecondary enrollment that often leads to detrimental health (e.g., due to stress or rising student debt). There is a need to look into the circumstances that can modify the postsecondary experience of youth so as to improve their health.

Our study offers several prospects for future research. Future research can incorporate geographic and environmental variables—such as the quality of air level or latitude—for additional analysis. Also, we can incorporate data from other sources to include more countries and more variables, especially non-European ones, so as to increase the breadth of analysis. In terms of methodology, future studies can deploy meta-regression analysis to compare the relationships between health and some macro-level socioeconomic indicators [ 13 ]. Future research should also expand beyond the individual to the social context in which education and health are situated. Such an approach will help generate findings that will inform effective educational and health policies and interventions to reduce disparities.

Acknowledgements

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ORIGINAL RESEARCH article

This article is part of the research topic.

Activating Academia for an Era of Colliding Crises

Seven Years of Embracing the Sustainable Development Goals: Perspectives from University of South Africa's Academic Staff Provisionally Accepted

• 1 University of South Africa, South Africa

The final, formatted version of the article will be published soon.

As this paper was being finalised, the world was left with less than seven of the 15 years of Sustainable Development Goals (SDGs) implementation to 2030. There were still huge gaps in the attainment of the SDGs in institutions of higher learning globally, especially that COVID-19 brought a barrier leading to a known pushback. However, the pandemic did not imply there was no work done prior, during and after COVID-19. This paper investigates the extent to which the University of South Africa's academic staff activated and mainstreamed the SDGs in their core mandates between 2016 and 2022. Data was generated through a survey (n=121), participatory action research, and document analysis. It emerged there is a greater degree of awareness of the SDGs, with 78% of academic respondents confirming this. However, the percentages drop across the four core mandate areas when it comes to SDGs implementation. About 52.6% of academics indicated they were promoting SDGs in their teaching, research (63.3%), community engagement (55.5%) and academic citizenship (54.5%). Findings further reveal key enabling institutional policies like the SDGS Localisation Declaration, and the Africa-Nuanced SDGs Research Support Programme. Large gaps remain on the publication front, where over 60% of the responding academics had not published an article explicitly on SDGs. There is also bias in publications towards certain SDGs. The work recommends that UNISA management continue raising awareness on the SDGs and systematically address barriers identified in the main paper to enhance the mainstreaming of the SDGs across all core mandate areas.

Keywords: Quality education, SDGs Stakeholders, sustainability, higher education, Academic Staff

Received: 13 Dec 2023; Accepted: 11 Apr 2024.

Copyright: © 2024 Nhamo and Chapungu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Prof. Godwell Nhamo, University of South Africa, Pretoria, South Africa

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