Learning and the Life Cycle: Inequality of Opportunities from Preschool Education to Adulthood.

39

39

The quality of a country’s education system is critical for its future and

the progress of its citizens. Therefore, research to provide

comprehensive analysis of the education system and to suggest ways

to improve it is crucial.

This study examines the influence of students’ socioeconomic origin

on their academic performance and the extent to which individual

schools can be a factor to compensate for families’ lack of resources.

It analyses and compares data from all education levels, from

preschool through adult education.

The conclusions indicate that investment in the initial levels of

education can be particularly effective in fostering academic success.

Social Studies CollectionNo. 39

Learning and the Life Cycle Inequality of Opportunities from Preschool Education to Adulthood

Héctor Cebolla-BoadoJonas RadlLeire Salazar

Learnin

g and th

e life cycle

community Projects. the sPirit of ”la caixa”.

social studies collection No. 39

learning and the life cycle inequality of opportunities from Preschool education to adulthood


The opinions expressed in the documents in this collection are

the sole responsibility of the authors and do not necessarily

reflect those of the ”la Caixa” Foundation.

Published by

”la Caixa” Foundation

Publication:

Learning and the Life Cycle. Inequality of Opportunities

from Preschool Education to Adulthood

Design and production

”la Caixa” Foundation

PublicationAuthors

Héctor Cebolla-Boado

Jonas Radl

Leire Salazar

Design and layout

CEGE

Coordination of publication:

© the authors

© ”la Caixa” Foundation, 2014

Av. Diagonal, 621 – 08028 Barcelona

B O A R D O F T R U S T E E S O F ” l a C a i x a ” B A N K I N G F O U N D AT I O N

Chairman Isidro Fainé Casas

Deputy Chairman Alejandro García-Bragado Dalmau

Trustees Antonio Aguilera Rodríguez, Salvador Alemany Mas, César Alierta Izuel,

Maria Teresa Bassons Boncompte, Josefina Castellví Piulachs, Eugenio Gay Montalvo,

Javier Godó Muntañola, Francesc Homs Ferret, Jaime Lanaspa Gatnau,

Juan-José López Burniol, Carlos Slim Helú, Javier Solana Madariaga, Xavier Ventura

Secretary (non trustee) Óscar Calderón de Oya

Deputy Secretary Alejandro García-Bragado Dalmau

CEO of ”la Caixa” Banking Foundation Jaume Giró Ribas

Héctor cebolla boado has a degree in political science and sociology from the complutense University of Madrid, and a Master of advanced Studies in arab and Islamic studies (autonomous University of Madrid). He obtained a Master in Social Sciences at the Juan March Institute center for advanced Studies in the Social Sciences and has a doctorate in Sociology from the University of oxford. In his pre-doctoral and post-doctoral research he has analysed educational differences by social origin and immigrant status and school effects. He has published papers in interna-tional journals such as the European Sociological Review, Socioeconomic Review and the British Journal of Sociology of Education. He is currently associate professor in the department of Sociology II at Spain’s National University of distance education (UNed).

JoNaS radl obtained his degree in sociology from berlin’s Freie Universität, fol-lowed by a masters in research and a doctorate from the european University In-stitute in Florence. He has been a visiting researcher at the Juan March Institute, Spain’s Higher council for Scientific research and Yale University. He is currently a post-doctoral researcher in the department of Sociology II at Spain’s UNed and in the social sciences department at the carlos III University. He has published arti-cles in diverse scientific journals, such as Social Forces, European Sociological Review and Social Science Research. He received the Gerhard-Fürst-Förderpreis 2006 for his undergraduate thesis, awarded by Germany’s Federal Statistics Institute, and the FNa-Forschungspreis 2011, granted by Germany’s social security administration, for his doctoral thesis.

leIre Salazar has a degree in sociology from Spain’s deusto University, a Mas-ter in Social Sciences from the Juan March Institute center for advanced Studies, a doctorate in sociology from the University of oxford and is doctor member of the Juan March Institute. She is currently associate professor in the department of Sociology II at the UNed. Her research has focused on diverse processes of social stratification and has been published in journals such as the Journal of Family His-tory, European Sociological Review and American Journal of Sociology, as well as in books and book chapters. between august 2014 and January 2015 she will be a Visit-ing Scholar at the centre for Family research of the University of cambridge.

table of contents

Presentation 9

introduction: education throughout the life course 11 I.1. Motivation 11 I.2. design of the study, methodological approach and

structure of this book 13 I.3. Statistical analysis: quick guide 18

i. early childhood education and its effects on learning outcomes in spain and the developed world 21

1.1. Introduction 21 1.2. the state of research on early childhood education 25 1.3. differential effects of preschool education on reading

ability 27 1.4. Substitution effects between preschool education and

family context 33 1.5. conclusions: Preschool as equalizing institution 40

ii. school effects in the reproduction of educational inequalities in compulsory education in spain 41

2.1. Introduction 41 2.2. What do we know about school effects? 43 2.3. School effects in Spain and their impact on educational

inequalities 47 2.4. conclusions: the “inactive” schools 59

iii. expectations of continuing in the education system beyond compulsory schooling 62

3.1. Introduction 62 3.2. economic context and educational decisions 65 3.3. recession and the reproduction of inequalities 75 3.4. conclusions: crisis and pessimism 81

iV. university competencies and the education of teachers in spain. how are the different is the quality of universities? 83

4.1. Introduction: 83 4.2. determinants of school effects on university

education in Spain 85

4.3. Is there really a difference in the results produced by distinct university faculties? 90

4.4. conclusions: a system with low diversification 99

V. educational expansion in spain and adult skills 101 5.1. Introduction 101 5.2. Mathematics and reading skills of adults in Spain 104 5.3. age and cohort effects 107 5.4. lifelong learning 111 5.5. the impact of socioeconomic origin on skills 113 5.6. the difference between education and skills 115 5.7. conclusions: the long shadow of social origin 117

conclusions 119

appendix 125

appendix a. Data and descriptive evidence 125 a.1. PIrlS (Progress in International Reading Literacy Study) 125 a.2. General diagnostic assessment for primary (2009) and

secondary (2010) 132 a.3. tIMSS (Trends in Mathematics and Science Study) 136 a.4. tedS-M 2009 (Teacher Education Study in Mathematics) 140 a.5. PIaac (Programme for the International Assessment

of Adult Competencies) 142

appendix B. methodology 144 b.1. Fixed effects model 144 b.2. Multi-level regression model with random intercept 145 b.3. the random slopes model 146 b.4. on the interpretation of the effects of school/country/

cohort in this study 147

appendix c. appendix to chapter 149 c1. appendix to chapter 1 149 c2. appendix to chapter 2 150 c3. appendix to chapter 3 152 c4. appendix to chapter 4 152 c5. appendix to chapter 5 155

Bibliography 157

index of tables and graphs 167

9

Presentation

education is one of the pillars of advanced society. the quality of an edu-cation system determines to a great extent the future possibilities of a soci-ety, as countries with effective education systems are better able to address the challenges of an increasingly globalised and more competitive world.

In addition to the implications for the economy and the future, educa-tion decisively impacts new generations, providing them with the capaci-ties needed to function successfully in increasingly complex societies, as it is a tool to provide individuals with opportunities for promotion and improvement. thus, quality education for everyone is a key element when evaluating the degree of inclusion, social mobility and equality of oppor-tunity that a country offers its citizens.

this study focuses on this social dimension of education. the authors examine the extent to which social inequalities are a factor explaining dif-ferences in academic performance; in addition, there is a particular focus on the effects of family origin and parental stimulation in achieving edu-cational success and an assessment of the extent to which schools in Spain are able to compensate for the deficits of students from socially disadvan-taged backgrounds.

to achieve these objectives, the authors analyse some of the most reli-able secondary sources, using both national and international data on educational attainment. In addition, they take into account different lev-els of education, from preschool to university and beyond - including adult education, now referred to as lifelong learning. this comprehensive perspective, in addition to being new in research on education in Spain, has made it possible to draw firm conclusions regarding the influence of

10

social background on educational attainment at different times in the life cycle.

the main conclusion of this analysis is that the initial stages of education, particularly preschool education, are key in the development of subse-quent educational performance. It is at these early stages of education that family socioeconomic origin has the greatest influence on students’ academic performance. as a result, it is also at these stages that the com-pensatory character of the school as an instrument to reduce social disad-vantages can be decisive

With this addition to the Social Studies collection, ‘la caixa’ Welfare Pro-jects hopes to contribute to reflection on the issue of education, which is decisive for the future of a country and its citizens. the empirical data and findings presented here will without a doubt benefit decision making in this area. our aim is to improve the quality of education to foster a more competitive and at the same time, more inclusive and just society.

Enric Bandadirector area of Science and environment“la caixa” Foundation

barcelona, december 2014

IntroductIon: educatIon throughout the lIfe course 11

introduction: education throughout the life course

i.1. motivation

the public interest generated by the PISa (Programme for International Student assessment) reports is unprecedented in the history of research on education in the social sciences. each new report produces new controversy in the media, generating so much political pressure that it is no exaggeration to say that the PISa programme sets the tone for much public debate on education. Few studies from academia have had such a social impact. this phenomenon is not limited to Spain, but can also be found in discursive spheres in each of the countries participating in the programme. It is no surprise that the most discussed issues tend to be related to the position of each country in the international ranking - above all in those countries in which average performance is below the atavistic symbolic threshold of the oecd average. However, of all the conclusions drawn from the PISa study, those that have had the greatest impact have often been reached hastily and rarely touch on the inequalities education systems generate related to students’ family origins.

as a result, it is surprising that other studies of a similar nature - such as the tIMSS (Trends in International Mathematics and Science Study), PIrlS (Progress in International Reading Literacy Study) and PIaac (Programme for the International Assessment of Adult Competencies) – have not had anywhere near the same impact in the media as PISa.(1) In addition, the scientific community has used these other studies much less often than PISa. In the case of the PIaac, this may be due to the very recent publication of its

(1) to illustrate this, a search on Google with the words “education” and “PISa” leads to more than 4 million results, while the word “education” with “tIMSS”, “PIrlS” or “PIacc” leads to no more than 60,000 results.

12 learnIng and the lIfe cycle

initial results in october 2013. but this argument does not apply to the tIMSS or PIrlS studies, which have been carried out periodically since 1995 (before PISa) and 2001, respectively. a possible explanation for the low public profile of these studies is that, although the total number of participating countries is greater in both, not all wealthy countries participate, so that the resulting international rankings and the positions the countries occupy therein are less informative.

Whatever the reasons for the disparity in perceptions about these different studies on students’ cognitive abilities, the limited visibility of the findings of the other studies is surprising, particularly in Spain, which suffers from an alarming lack of statistics on education. PISa, for example, only provides a sample of 8th grade students, whereas PIrlS and tIMSS also cover primary education. these sources of data are, as a result, essential instruments for studying education dynamics in Spain, above all now that scientific research is increasingly focused on early childhood education, the stimuli children receive during the first years of life and the extent to which these affect future school success (Heckman, 2006; esping-andersen, 2008; Marí-Klose et al., 2010). these data sets are also important because they address other indicators over the life course that are not necessarily directly related to education. despite this, public and media interest in Spain continues to be focused on secondary education. However, the most alarming gap in “public sociology” (burawoy, 2005) regarding education is independent research that covers the complete cycle of education and makes use of more than one source of data at a time. this is clearly the case in Spain, a country that is regularly shaken by debates over the need to carry out a substantial reform of its education system (every time the governing party changes, it seems). the widespread opinion that the functioning of the Spanish education system is inadequate is more than justified, as the country heads the list of developed countries with the highest rates of school failure. However, the debate over educational reforms in Spain has traditionally not been very open to empirical scientific research and, instead, has systematically been influenced by political opinions and normative perspectives that are not necessarily supported by expert knowledge. the current reform of the organic law on education through the organic law for the Improvement of the Quality of education [ley orgánica de educación en la ley


orgánica para la Mejora de la calidad educativa (loMce)] is a recent example. Few studies have attempted to systematically evaluate the impact of earlier education reforms (but cf. Martínez García, 2013; Fernández enguita et al., 2010), in part because of the lack of a commitment from competent public administrations on matters of education to produce and make data available that would permit such studies in optimal conditions.

In this study we propose to carry out a rigorous analysis of available data to capture some of the dynamics determining educational attainment in Spain, with particular emphasis on the changing importance that family origin has on students throughout their life course, as a determinant of their competencies and educational results. to do this, we use data that has been little used up until now in Spanish sociology but that allows us to concentrate on different stages in individuals’ educational paths. In this way, we can address the alarming gap in studies of this type, despite the lack of reliable data in Spain to carry out a longitudinal analysis of the relationship between the life course and the most important indicators of educational performance (grades, educational expectations and cognitive abilities). our study, in short, is a small advance in knowledge regarding some of the factors that are decisive for understanding the situation of Spanish students from the earliest stages of their lives until adulthood. In the process, we hope to bring to the attention of the broader public some of the findings that the sociology of education and social stratification have generated in the international sphere, with the hope that they may in some way be incorporated into debates on education in Spain and, ideally, influence current and future education reforms.

i.2. Design of the study, methodological approach and structure of this book

the main object of research in this study is the educational inequalities found in Spain and, more broadly, in developed countries. More specifically, we aim to increase our understanding of patterns of social stratification in educational results, based on social origin. educational achievement is of great interest as an object of study, as it is the primary vehicle for individual advancement in post-industrial societies and, as is


well known, correlates with almost every indicator of socioeconomic status as measured in later stages of adulthood. In this sense, understanding the causes of educational inequality also increases our knowledge about the intergenerational reproduction of social inequalities. this is an old concern of classic studies on social mobility; however, it is far from an antiquated issue, as can be seen in the attention given to children’s education by the media and in academia, which views education as a research priority if we are to create more inclusive societies, and it is recognized as such in the new eU Framework Programme, “Horizons 2020”.(2)

our approach and methodology fit within the tradition of analytical sociology (elster, 1989; Hedström and Swedberg, 1998; Hedström and Ylikoski, 2010). committed to the goal of demonstrating the operative causal mechanisms that are behind social phenomena, we have used some of the most advanced quantitative research techniques. despite the obvious attraction from an academic perspective, our work also has strong normative implications, as it seeks to contribute to our understanding of the processes that foster social inequities through inequality in the educational opportunities available to students based on family socioeconomic characteristics. We believe that the humanist values that are the moral basis of modern societies establish a minimum level of equal opportunity, which is violated whenever children due to the socioeconomic status of their families begin their educational careers and life trajectories from very different starting points.

a key aspect of this study is the life course perspective adopted throughout this work. according to the life course paradigm, “[c]hanges in human lives....are considered over a long stretch of time” (Mayer, 2009: 414). In other words, our focus goes beyond the static observation of phenomena to include a dynamic component. applying this principle to education, in the chapters that make up the backbone of this work we cover the complete educational cycle.(3) We begin our analysis looking at preschool and early education (chapter 1). We then examine processes of inequality that appear in primary education (chapter 2). this is followed by an exploration

(2) See http://ec.europa.eu/programmes/horizon2020/en/h2020-section/europe-changing-world-inclusive-inno-vative-and-reflective-societies.(3) an omission that we must mention in this context is vocational training, a theme addressed extensively in the study by Homs (2008).


of secondary education (chapters 2 and 3), and an examination of the competences of individuals in university (chapter 4). lastly, we look at adults that have already left the education system, analyzing the sphere of continuing education or lifelong learning (chapter 5). this holistic design provides coherency to the study, connecting the different chapters through a clear common thread. In addition, our approach also differentiates this study from previous contributions in the field that have tended to focus their attention on one or, at most, two stages of the educational cycle, without considering it in its entirety. In short, this is the first study in the Spanish context that applies a life course approach to the analysis of educational inequalities.

the diagram below summarizes the conceptual framework that inspires the theoretical approach of our study. the lower part of the diagram illustrates the five stages that constitute educational attainment in a broad sense. as already mentioned, the importance of education in the context of our study is primarily a result of the strong impact that it has been found to have on individuals’ social destination (arrow b in the diagram). the explanandum of this study is found in arrow a in the diagram, which indicates the influence of social origin (socioeconomic status of family of origin) on attainment in formal education. In addition to attempting to quantify the direct effect of origin, we also look at three different types of factors that inevitably play a role when we examine the relationship between parents’ socioeconomic status and the educational achievement of their children: i) cognitive abilities of children, to the extent that these abilities determine the level from which they initiate their learning and the ease with which they will gain new knowledge and skills (chapters 3 and 5); ii) parental practices, because they are the greatest source of the stimulation that children receive outside of school (chapter 1); and iii) school itself (or more in general, the educational institution, as it is the institution that puts formal education into practice and provides children with educational content (chapters 2 and 4). thus, in the following chapters we address the most important mediating factors in the well-known association between social origin and educational performance.

In addition, as indicated by the three arrows at the top of the diagram, our theoretical perspective is quite complex, as these three factors tend to have


unequal effects on educational attainment, depending on students’ social class origin. cognitive abilities are in part genetically transmitted, generating an advantage at the start in children of more cognitively favoured families. What is more, parents with higher socioeconomic status tend to employ parenting techniques that foster the education of their children. added to the advantages resulting from these socialisation practices is the fact that these parents often choose the best schools for their children. In other words, social reproduction and educational attainment take place within a determined socioeconomic context. this suggests that the decision to continue formal education after completing compulsory education will be different in a context of economic growth than in a context of economic crisis (chapter 3). Moreover, attending university does not have the same implications when half of each birth cohort does so, as it did when getting a university degree was a privilege reserved for the children of the elite (chapter 5).

dIagram I.1

conceptual framework

socIoeconomIc context

Parental PractIces

schoolcognItIve abIlItIes

socIal orIgIn

educatIonal attaInment

stages of the educatIonal cycle

Preschool PrImary secondary unIversIty contInuIng

a bsocIal

destInatIon

source: calculations by the authors.


the analytical potential of any empirical study is determined by the quality of the data used. In other words, while a solid database alone is not enough to carry out a good empirical study, it is a necessary condition, and without it, a significant contribution to any chosen field of study cannot be made. In this study we have gathered and systematized a series of high quality databases available to the scientific community regarding education. We are referring to international studies on educational performance and cognitive competencies, some of which we have already mentioned: PIrlS, tIMSS, tedS-M (Teacher Education and Development Study in Mathematics) and PIaac. the first three studies are carried out by the International association for the evaluation of educational achievement (Iea), a consortium founded in 1959 as an independent organization and that includes among its members a large number of national agencies and institutions related to education. the PIaac, in turn, is an oecd study, although the Iea is also a participant. throughout this study we use each one of these four international databases, along with other national databases. among the latter is the General diagnostic of the education System database for primary and secondary education produced by the National Institute for the evaluation of education (INee, under Spain’s Ministry of education, Science and Sport). the fact that all of these are official databases and have emerged based on meticulous methodological processes guarantees their high quality and represents one of the strengths of our research. However, we want to point out that to optimally carry out research on the life course and to study the impact of early transitions on later stages in the educational process requires proper longitudinal panel data, which exists in the majority of Western european countries, but, unfortunately, not in Spain. despite this limitation, in carrying out this study we make use of the knowledge generated in the initial chapters (regarding the first stages of the educational cycle) to interpret the results in the final chapters (regarding the more advanced educational stages). to an extent, this disadvantage is compensated for by having access to the comparative-international dimension. thus, we offer a new approach for the analysis of data referring to all the stages of the educational cycle in a single study and with a comparative dimension, which is introduced strategically in certain chapters. this both places the situation found in


Spain within a broader context and allows us to analyse the role of the socioeconomic context in the educational process.

i.3. statistical analysis: quick guide

Statistical analysis is the basic tool of quantitative social research. Its great virtue is that it permits us to accurately understand social phenomena based on a representative sample of the population of interest (in the present study, the student population of a determined age or that studied during a specific academic course). among statistical techniques, we can distinguish two major families: 1) descriptive statistics, and 2) analytical or inferential statistics.

Descriptive analysis consists of univariate or bivariate explorations of data that tend to take the form of tables or graphs. an example of univariate analysis would be calculating the average score of students on a standardised test, or other properties of the distribution, such as the percentage of students that score above 600 points. In contrast, we would turn to bivariate statistics if we want to compare, for example, the performance of girls and boys on a test. In this case the two variables would be score and gender. a typical format is a frequency table with several rows and columns or a simple graph with various bars to compare the performance of different groups.

Inferential statistics go beyond the mere description of the basic characteristics of a sample. When respondents have to respond to a complex question about a multifaceted phenomenon, social scientist often use multivariate analysis. depending on the properties of the dependent variable (the phenomenon that must be explained) and its relationship with the independent variables (the explanatory factors), there is a wide variety of multivariate models available. In this study we have primarily used an advanced technique called hierarchical or multilevel regression, which is explained in more detail in the technical appendix.

However, it is not necessary to understand the functioning of this technique in depth to understand the essential results presented in this study, once one understands the basic functioning of regression models.


the great advantage of any type of multivariate model is that it permits us to evaluate the importance of each of our variables while maintaining the values of the other variables constant. this approach - in which we can isolate the effect of a single source of variance, maintaining the influence of other factors at the margin - is known as the ceteris paribus condition (all other things being equal). In this way, the analyst can control for other factors, in other words, isolate the effect of a great number of factors that affect the phenomenon being explained, and focus exclusively on the variable that is the focus of the empirical study.

how does one read a regression table?

the results of multivariate models tend to be presented in the form of a table. these tables, in general, contain certain basic information about the sample, such as the number of cases (subjects) included in the analysis (N) or regarding the precision of the fit of the model (in other words, the extent to which the variables considered by the analyst effectively account for the phenomenon). but the central ingredient in a regression table is the coefficients of the statistical model. If the dependent variable is of a continuous nature (as is the case in all the chapters of this study), the coefficients tell us what effect each of the independent variables has on the dependent variable when we maintain isolated (control for) the influence of the other variables. For example, if the independent variable whose effect we want to interpret is gender, the dependent variable is mathematics score, and the coefficient is 3.0, we should interpret this to mean that the conditional difference between the sexes in their mathematics scores is three points.

It must be taken into account that each model is necessarily a simplification of reality. In addition, the data that we work with tends to contain different types of errors that can introduce “noise” into the results. therefore, another key function of statistical models is that they permit us to know the level of accuracy with which our results fit reality. this information is gathered in standard errors that accompany each coefficient in the model. In this way we obtain an estimate of the reliability of the findings. there are many ways to process this information, but the key issue is if the coefficient is statistically significant. We refer to significance


when a coefficient is reliable because the correlations in the data show a clear association between the independent and dependent variables. Fortunately, the conventional way of presenting this information is very intuitive. For each coefficient, a series of asterisks are added that indicate its level of significance. the basic rule is the following: the greater the number of asterisks, the more significant is the effect in question, and the more reliable is the result obtained. For this reason, it is often said ironically that the most efficient way to read a regression table is to “look at the stars”. being sceptical researchers, we only want to focus on significant coefficients, and these can be recognised easily because they are accompanied by at least one star.

early chIldhood educatIon and Its effects on learnIng outcomes In sPaIn and the develoPed world 21

1.1. introduction

In this chapter we examine early childhood or preschool education, the first stage of the educational cycle and the stage prior to commencing compulsory education. Primary education begins at different ages in different countries and in Spain at approximately six years of age. It has become an object of study of great interest, with growing public and academic concern regarding preschool education as we have come to understand that the first years of children’s lives are a crucial stage that can have significant impact on their future. In short, the basic idea underlying this argument – promoted enthusiastically by Noble prizewinner James J. Heckman – is very intuitive: when very young, individuals are more permeable than when they are older, so that the stimulation they receive at early ages has a great impact on their development. However, the further-going argument made by various experts, that the majority of the abilities that we end up acquiring are determined before we reach six years of age, is still controversial. If this is the case, it is much more effective to invest in early childhood education than in primary, secondary or tertiary education. although these types of calculations are not based on exact science (currie, 2001), Heckman (2011) estimates that the resulting benefits for society are so high that every dollar spent on high quality early childhood education generates an annual rate of return of 7 to 10 percent (Heckman, 2011).(1) despite arguments made in favour of education continuing throughout the life cycle, in the form of

(1) there is a website dedicated to promoting this objective with a range of content, including videos: http://www.heckmanequation.org/.

i. early childhood education and its effects on learning outcomes in spain and the developed world


lifelong learning, investment in early childhood education is a much less costly way to increase a society’s human capital than paying for the continuing education of working adults or providing vocational training for the unemployed. this new and still fragile consensus about the importance of early socialisation contrasts with the perspective prevalent in the past, when the issue of schooling prior to compulsory stages was not on the research or policy agenda. today, in contrast, early childhood education polices are considered to have great potential to increase equality in educational opportunities and to reduce social inequalities.

as most of the stimulation received in early childhood takes place within the family (in general through parents), it is logical that the quality of this stimulation depends on the family’s resources, abilities and knowledge. It is difficult to intervene in the relationship between parents and children through public policy, and it is questionable whether it is legitimate or normatively desirable to impose a specific style of child-raising “from above”, beyond the prevention of negligence. a useful vehicle within the reach of policy consists of educating parents through the provision of basic information, with media campaigns or through intermediaries such as schools and paediatricians, but these are “soft” instruments of limited efficacy.

therefore, the parameter that most easily lends itself to public intervention is the quantity of time that children spend in the education system, or, in other words, outside of the direct influence of their families. If, as some experts argue, early education is the most effective means of public intervention (Heckman, 2006; esping-andersen, 2008), the years prior to the age of compulsory education will be decisive in avoiding the emergence of social inequalities. as compulsory schooling in the majority of countries does not begin before children are 5 or 6 years of age, the percentage of children that are not in preschool is quite high, particularly among children under three years of age (though, as we will see, the percentage varies by national context). as a result, proponents of preschool education argue that increasing the proportion of children from disadvantaged families in preschool education is the best way to improve their life opportunities in the long-term. It is well-documented that there are great differences in educational practices among parents related to their socioeconomic status.


In short, parents with little education make greater use of the television and unsupervised play, consume fewer books and do fewer formal educational activities with their children than parents with higher levels of education. thus, a common idea among experts is that every hour that children from lower classes spend in preschool – and not with their families – is beneficial, as the stimulation they receive there is richer and more varied than at home and similar to that which children in affluent families receive within their families.

another reason for the great public interest in early childhood education is that the care of young children is of great normative and ideological interest. there are many preconceived ideas about the proper way to raise small children. every mother and father has heard a range of opinions (typically expressed with great passion) about different aspects of child-rearing. If questions about nutrition, dress and sleep generate debate, it is no surprise that preschool education is controversial (both within families and within the broader society). Some parents prefer their children to remain at home for as long as possible, because they believe that the individual attention that a parent or other family member, or even a babysitter, can offer is better than that provided in preschool; in addition, they see the demand for early schooling of children as paternalistic. others clearly prefer that their children attend a preschool, which provides a routine, teaches discipline that will be needed later in compulsory education and socialises children to be with others of the same age, as they understand that a diverse environment is the best “school of life”. a recent example that reveals the delicacy of this issue can be seen in the major debate that has taken place in Germany over providing monetary compensation to parents who do not send their children to daycare. recently, the Merkel government introduced this compensation (referred to as betreuungsgeld), which was promoted primarily by the bavarian conservative party, the cSU, despite strong criticisms that viewed it as a step backward in terms of gender equality, given that it would likely lead to a decline in the employment rate among women.

In this chapter, we explore to what extent early childhood education reduces or increases inequalities in children’s educational outcomes that originate in their social environment. our approach consists of analysing


who benefits the most from attending preschool: Is preschool more beneficial for children whose parents invest a lot of time in their education, or for those whose parents are less actively involved? Is it more useful for the offspring of parents with a high or low level of education? We consider preschool attendance as well as the cultural capital of parents and their investment in their children’s education to be different forms of stimulation, and we ask if these stimuli are complementary or substitute for one another. In other words, we are interested in determining if attending a preschool has an independent effect, or if the impact of early childhood education on children’s abilities depends on the resources in the home. In addition, we adopt a comparative perspective, which allows us to respond to questions such as the following: What international differences exist regarding the benefits of early childhood education? does preschool attendance have a different effect depending on social origin, parents’ degree of involvement in child rearing and national context?

For our analysis, we have used data from the Progress in International Reading Literacy Study (PIrlS).(2) this database from 2011 includes a standardised measure of the reading ability of students in the fourth grade in primary school (between the ages of 9 and 11) for a broad group of countries. to generate a sample of countries comparable to Spain in terms of their socio-political context, we have excluded countries with lower economic levels (like azerbaijan and botswana), as well as oil producing countries (such as Qatar and the arab emirates). as in subsequent chapters, we apply multilevel regression techniques (with constant and random slopes) to estimate the effect of preschool attendance on children’s learning (for more details see appendix b). there are few comparative studies on early childhood education, especially ones that focus on stratification of educational processes by family social origin. by breaking down the observed variation into its respective components at the national and individual level, we enter a new terrain of studies on formal education prior to compulsory education. at the same time, given that our main interest is Spain, we will emphasise

(2) See appendix a for more detail on the variables used, and appendix b for the estimation techniques used for the analysis presented in this chapter.


its position in each aspect of the analysis regarding overall trends in all the 30 countries included in the sample.

1.2. the state of research on early childhood education

there are many empirical studies about the effect of family context on the cognitive and non-cognitive abilities of children of different ages. ermisch (2008) reveals a positive association between parents’ income and cognitive ability in three year olds in the United Kingdom. Similarly, he finds a negative relationship between household income and behavioural problems. the findings of West et al (2000), based on a sample of children in kindergarten in the United States, clearly indicate that the children of mothers with a high level of education have higher reading levels. Feinstein (2003) finds the socioeconomic status of parents to have a positive effect on the development of british children between 2 and 10 years of age. In addition, the scores obtained on tests at an early age were, according to his results, a strong predictor of the educational level of these same individuals when they reached 26 years of age.

these studies suggest a series of factors to explain the association between social origin and the abilities of children when they are still very young. these include genetic factors, basic necessities such as nutrition, housing and health coverage, and parental behaviour, such as providing emotional support, child-rearing practices and parenting styles. Parents with more resources seem to offer their children a more stimulating environment: they use more diverse and complex language (Hart and risley, 1995); they provide more toys and books; they spend more time reading stories to their children and they are more receptive when their children talk to them (bradley et al., 2001). they also participate in more group games and take them to the library more often (becker, 2013). In general, they tend to provide their children with more overall support (Mistry et al., 2008).

there is a tentative consensus on the short and medium term benefits of preschool, in particular, in terms of cognitive abilities, such as language acquisition and academic performance. bassok (2010), for example, has found that in the United States, children of four years of age that attend


preschool obtain significantly better results on literacy tests than those that only receive parental care. there are also clear positive effects on health (currie, 2001). available evidence is less conclusive regarding the long-term benefits of preschool education, to the extent that the effects seem to be less than when they are first measured (barnett, 1995; see barnett, 2008 for a review of the literature). In any case, positive effects have generally been confirmed using different research designs, whether experimental studies or evaluations, such as the analysis by Schweinhart et al. (1993) and by Heckman et al. (2013), based on the well-known Perry Preschool Project, or longitudinal studies of birth cohorts, such as the analysis of bassok (2010). although the study by belsky et al. (2007) found positive effects from preschool attendance in several dimensions, it also found an apparent adverse effect on certain behavioural problems up until 6th grade in primary school. However, in subsequent analyses of the same sample of children when they reached 15 years of age (Vandell et al., 2010), this negative effect could only be verified in the case of problems in adolescence such as risky or impulsive behaviour, while the effect on other dimensions was positive at this age for those children that had spent more hours in preschool. a very fruitful line of research has been to estimate the effect of the intensity of mothers’ work outside of the home on the cognitive development of young children in different types of preschools (see, for example, brooks-Gunn et al., 2002).

although a consensus exists around the overall benefits of early childhood education in school success (see Gutiérrez, domènech and adserà, 2012 for the Spanish case), a question of greater interest in the context of the present study is whether there are differences in the advantages that preschool education offers related to social origin there are studies that indicate that such differences do not exist, finding that the beneficial effect of preschool is distributed equally among children proceeding from different social strata (Peisner-Feinberg et al., 2001; Vandell et al., 2010). However, other studies, in the United States (Mccartney et al., 2007 and bassok, 2010), the United Kingdom (becker, 2011) and Germany (Felfe and lalive, 2012), suggest that the beneficial effect of preschool is systematically greater for children from disadvantaged families. this


finding does not seem to depend on the indicator used as a measure of social origin, as some of the studies cited take into account family income and others the education of parents. We do not know of studies that indicate the opposite, in other words, that preschool would be more beneficial for children from affluent families. In short, although the empirical literature is not in complete agreement on this issue, there are indications that preschool attendance compensates to a certain degree for the disadvantages of coming from a family context that lacks stimuli for cognitive and personal development.

1.3. Differential effects of preschool education on reading ability

there are now few persons who question the idea that preschool education has a favourable impact on the social and intellectual development of children. It is also unquestionable that the active involvement of parents in their children’s education has a positive effect on their cognitive development. What is less clear, however, is whether these two sources of stimulation are interdependent, and, if there is an interaction between preschool attendance and parenting, what form it takes. We also do not know if this relationship depends on national context. do the benefits of early childhood education vary among countries and according to the distinct characteristics of the education system? In what follows we develop a series of hypotheses that will be tested empirically through an analysis of our data from PIrlS.

the most important function of the education system is to provide learning opportunities to everyone. although participation in preschool education is generally optional, children who receive less attention at home can benefit greatly because participating in preschool leads to interactions with other children who are at more advanced stages of cognitive development. In addition, given that most activities carried out in preschool are not educational in a formal sense, but are instead primarily a form of play, the children who receive more and better stimulation in their family environment may not acquire that much knowledge in school. therefore, beyond the component of social interaction, it is possible that what can be learned in preschool contributes


little educationally to children with very actively involved parents. In contrast, children with little stimulation at home will, upon entering preschool, be exposed to the acquisition of certain types of knowledge for the first time, which will translate into a greater marginal benefit for their intellectual development. this argument is supported by the notion of a learning curve, illustrated in graph 1.1.

graPh 1.1

illustration of the learning curve

amount learned

tIme of learnIng


this graph represents the stock of knowledge that is acquired over time. the idea is that a person who is just beginning to learn something new will commit many errors but will also learn more rapidly during the initial learning period. the learning rate will decrease as time passes and with the quantity of knowledge already adopted until reaching an almost flat rate. applying this understanding to early childhood, this curve indicates that preschool can make a greater contribution to children who grow up in less stimulating households and have less knowledge than to children being raised by more actively involved parents. the latter also learn something in preschool education, but the marginal benefits are lower. as a result, according to our initial hypothesis, participation in


preschool is more beneficial for children whose parents are not actively involved in the education of their children than for children whose parents practice more active parenting. expressed in terminology used in microeconomics, we postulate that the relationship between the intensity of parental involvement and preschool education is substitutive in regard to the effect on learning.(3) In such a case – and this is our hypothesis H1a – , the benefits of learning derived from preschool attendance are lower for the children of parents that are highly involved in educational tasks than for those who receive little intellectual stimulation at home.

It is also possible that the “production function” for cognitive ability during early childhood takes a very different form. Perhaps parental involvement and preschool attendance are complementary elements in terms of their effects on learning, rather than substitutes for each other. there are two reasons why this may be: first, it is likely that parents who are involved in educating their children would also be involved in choosing their children’s preschool, and that thanks to their intensive and careful search, they would manage to place their children in the best preschools. although other determining factors also exist (facilities, location, etc.), we assume that an important characteristic that these parents take into account is the quality of education the school offers. It is likely that parents who are less involved do not invest as much time or effort in choosing their child’s preschool, or that they simply do not have access to the information necessary to make the best choice. a pairing process of this type leads to a concentration of children of highly involved parents in the same preschools. the interactions within the groups created in this manner generate positive peer effects. as a result, due to segregation and school effects, the benefits in terms of learning will then be greater for children that enter preschool with an advantage in terms of the stimulation received from parents. Secondly, it would be logical that active parents would also be more attentive to the development and integration of their children in preschool, as they are more likely to actively

(3) In microeconomics a distinction is made between substitutive and complementary goods. two goods are perfect substitutes for each other if each one can be consumed in place of the other. classic examples of substitutive goods are butter and margarine. two goods are perfect complements for each other when both have to be consumed together to have utility. the typical example of this is the right and left shoe, which have no utility separately.


communicate with preschool staff and with their own children. In addition, they are more likely to supplement what their children learn in preschool with complementary activities based on their needs and interests. activities at home help to maximise the benefits of preschool. thus, hypothesis H1b states that the involvement of parents in preschool generates accumulative advantages. the educational benefits resulting from attending preschool are greater for children whose parents are very involved in their education than for children who receive less intellectual stimulation from their parents.

another factor that can affect the benefits of early childhood education is social origin. Similar to the argument in support of the first hypothesis regarding the benefit of attending preschool for children from disadvantaged backgrounds, starting from a disadvantaged social position may paradoxically be beneficial. Whether a consequence of genetic inheritance or the disadvantages that manifest at early ages, children from disadvantaged social environments have, on average, lower intellectual abilities. thanks to their position on the learning curve, it is relatively easy for them to increase their knowledge and abilities. In this sense, early childhood education can be more beneficial for children of lower socioeconomic origin than for those from affluent backgrounds. children of parents with high levels of education normally already have a solid level of intellectual ability when they begin preschool, not only because of their genetic inheritance, but also because they are more likely to have enjoyed a more varied and stimulating social environment. consequently, as the marginal benefits of learning will decrease more rapidly for children of parents with high levels of education, participation in preschool should have a greater impact on the cognitive development of children from more disadvantaged family backgrounds, whose social environments outside school are less stimulating. our hypothesis H2a, therefore, is that the educational benefits of attending preschool are higher for children from disadvantaged social origins.

From a theoretical perspective, if we focus on the relationship between social origin and early childhood education, we must also take into account the other side of the coin. Similarly to what was argued for hypothesis H1b, we can say that the effects of choosing a particular


preschool makes this stage of education more beneficial for children from more privileged backgrounds than for children from disadvantaged ones. according to this perspective, the effort made by parents with high levels of education to choose a good preschool adds to the inequality resulting from residential segregation. both factors contribute to an obvious high level of variation in the quality and socioeconomic composition of preschools. In fact, there is evidence that children from disadvantaged environments have lower rates of participation in preschool education (Schober and Spiess, 2013). Segregation in schools tends to reinforce pre-existing inequalities related to genetic predisposition and social environment. although this is not an effect caused by preschool education itself, in practice these factors generate a “Matthew effect”, in other words, those who start with more resources benefit more. In short, the hypothesis of complementarity argues that preschool education is more beneficial to children of privileged social origins than to those who come from less affluent households. based on hypothesis H2b, the educational benefits from attending preschool are greater for children from affluent social origins.

the four hypotheses presented here are depicted in graph 1.2. they are grouped in pairs, and to simplify their illustration, they refer to types of stimuli and resources in the home (regarding the former, time and effort dedicated by parents in educating their children, and regarding the latter, parents’ socioeconomic position, as measured by their education level). H1a and H2a are based on the substitutive character of different inputs, while H1b and H2b describe this relationship as complementary.

lastly, we look at the possible moderating effects that the institutional level can have on the relationship described. Naturally, we would expect that in general, better quality education systems would contribute more to improving the reading ability of children than those that are of poor quality. but a more intriguing less obvious question is the following: if we compare different education systems, in what contexts would we expect a higher or lower social gradient in the impact of early childhood education on children’s learning?


graPh 1.2

hypotheses of substitution and complementarity

Hypotheses of substitution (H1A and H2a)

Hypotheses of complementarity (H1B and H2B)

readIng abIlIty

famIly stImulatIon

more tIme In Preschool

less tIme In Preschool

readIng abIlIty

famIly stImulatIon

more tIme In Preschool

less tIme In Preschool


regarding curriculum standardisation, it can be argued that in standardised education systems the positive effects on learning would be relatively higher for children from less advantaged social origins or with parents who are less involved in their education. the reason for this is that by minimizing the variance in the quality of preschools, standardisation reduces the possible influences of both choice of school and residential segregation, which clearly provide better positions for the children of parents with more resources. If the state guarantees certain universal


standards of quality, this minimizes the advantages linked to residing in a privileged neighbourhood and having parents that are highly involved in choosing a preschool, producing a more egalitarian distribution of the benefits of early childhood education. and vice versa: in contexts in which there are no regulations establishing minimum standards and controls, it is likely that differences in quality among preschools will increase. the lack of standardisation, magnifying the effects of segregation and school choice, harms children from more modest backgrounds or with less involved parents. therefore, our third hypothesis states that in diversified education systems without standardised curriculums, children from disadvantaged family environments obtain relatively fewer benefits from attending preschool, while in systems with standardised curriculums the positive impact of preschool on learning outcomes for children from disadvantaged backgrounds or with parents that have little involvement in their education is greater.

1.4. substitution effects between preschool education and family contex

table 1.1 (in the statistical appendix for this chapter) shows the results of a series of multilevel regression models which analyse the factors on which reading ability for children in 4th grade in primary school depend. It should be noted that the models control for gender, with the expected result that girls are better readers than boys.

In our first step we are simply interested in knowing the effect of attending preschool. Given that in some education systems it is compulsory that children spend a certain amount of time in preschool education, we control for the age of compulsory schooling; in this way we can correctly interpret the coefficient corresponding to preschool education. the results indicate that the more time children have spent in preschool, the better results they will achieve on subsequent reading tests. the effect, which is strongly significant, leaves little margin for doubt that the association between both variables is positive. the expected positive effects – strong and statistically significant – of both the involvement of parents and their education levels are also demonstrated.


as we use a multilevel model, each coefficient represents the average effect on the overall sample, taking into account the idiosyncratic effects of each country. the following graph shows how the effect of preschool education differs from this average effect for each one of the country clusters. It is clear that with few exceptions – Israel, Hungary, romania and Singapore, with stronger effects, and Malta, Hong Kong and Finland, with weaker effects – the great majority of countries are concentrated around the 0 line, which corresponds to the average effect for the overall sample. this includes Spain, which falls just below this line. In other words, processes of early childhood education in Spain are represented in a vary faithful manner by our heterogeneous sample. the advantage of preschool attendance in Spain is very similar to that observed in the majority of developed countries.

graPh 1.3

Deviations from the average effect of preschool on reading ability by country

10

5

0

–5

source: PIrls 2011. own elbaoration.


Progressing toward verification of the central hypotheses of this chapter, the intention of the second model in table 1.1 of the appendix is to empirically test the first hypothesis regarding the mediating effect of the involvement of parents. Is early childhood education more beneficial for children that receive little stimulation at home? the empirical evidence supports hypothesis H1a: children who receive less stimulation from their parents obtain greater benefit from attending preschool than children whose parents provide more stimulation. In other words, the two effects (family versus school) seem to be substitutive and not complementary, as stated in hypothesis H1b. as we argued above, the cause may be found in the declining slope of the learning curve. Given that children with parents who are not very involved in stimulating their learning have more to learn when they first go to preschool, they learn more quickly than those who arrive better prepared.

graPh1.4

marginal effect of attending preschool on reading ability by intensity of parental stimulation

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bluP r e by country time preschool source: PIrls 2011. calculations by the authors.


It is not easy to quantify or understand the effects of interaction by only looking at the coefficients from a regression table. the following graph 1.4,, based on the results of model 2, shows the relationship between these three variables with greater detail and clarity. It shows that the effect of attending preschool is always positive (all the values on the Y axis are positive) and statistically significant (the low threshold of the confidence interval never crosses the zero line within the range of the mediating variable), but its magnitude declines with higher levels of parental involvement. In the background of the graph, we present the univariable distribution of the latter.(4)

returning to table 1.1 in the appendix, we can confirm, based on model 3, that the interaction between amount of time of preschool attendance and parents’ education is negative. the higher the socioeconomic origin of children’s families, the lower the benefits obtained from preschool education. In other words, the empirical evidence again supports the idea of the interchangeability of the stimulation children receive. the message transmitted by the data is again that preschool education has a greater impact when children start from a lower level, as tends to be the case with children from less affluent backgrounds, not only because of questions of genetics but also because of their social environment and living conditions. again, the use of a graph provides a more intuitive understanding of this complex three-dimensional nexus. as in the previous graph, graph 1.5 illustrates the marginal effect of preschool education estimated in model 3 by the education level of the parents. the resulting pattern is very similar: the slope is negative, with increasingly smaller effects as we advance along the horizontal axis (toward higher levels of education for the parents), but always in the space indicating a positive marginal effect. In this context, it should be mentioned that there is little international dispersion (random effects) regarding the effect of the interaction between preschool education and the education level of parents. In addition, the specific estimations place Spain near the overall average for the sample; thus, in the Spanish context the egalitarian effect of early childhood education is demonstrated as well.

(4) this distribution includes a characteristic peak in the maximum values, which reflects a high percentage of parents that stated they frequently carried out the activities mentioned in the questionnaire.


graPh 1.5

marginal effect of attending preschool on reading ability by parents’ education level

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Parents level of education

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avg. lev. educ. parents

0 6 8

the dashed lines give a confidence interval of 95% source: PIrls 2011. calculations by the authors.

returning again to the results from table 1.1 in the appendix, beyond the issue of preschool, the last model (M4) serves only to disprove an elitist assumption regarding the time invested in child rearing by different types of parents, namely, that the involvement of parents with a high level of education will have a greater effect on their children’s abilities than the involvement of parents with less education. It seems, however, that the opposite is the case: the intensity of early child rearing has a greater positive effect on children of more modest social origins than on those from more privileged backgrounds. If we understand the resources of the home and the active involvement of parents in their children’s education as two types of stimuli that children receive, it seems that one can compensate for the absence of the other. the fact that this third interactive effect is also negative once more confirms the substitutive character of the different influences regarding the learning benefits generated.

once these factors at the micro level of educational achievement have been analysed, we focus on how certain factors at the macro level may


affect the reading comprehension of children in 4th grade in primary school. concretely, we are interested in determining if the standardisation at the national level of the lessons and activities carried out in preschool has any importance in explaining (a) the differences between countries and (b) the patterns of social stratification that we observe within countries. Standardisation of curriculums in preschool education is measured through a dichotomous variable that is based on surveys with directors of preschools who participated in the PIrlS study. the majority of countries, including Spain, have a standardised system (86.57%).

We present our results in table 1.2 in the appendix to this chapter. the first model presented in the table (M0) examines the differences between countries. although the coefficient is negative, the effect does not exceed conventional significance levels. this means that a standardised curriculum is not necessarily more common in countries with better educational results.

In the second model (M1) we introduce an interaction effect between attending preschool and the standardisation of curriculum. as can be seen, the interaction is clearly positive. as a consequence, we must reject the (null) hypothesis that the standardisation of preschool education has no impact on the reading ability of children when they move on to primary school. apparently, standardisation increases the quality of preschool education. In countries that have a standardised system, children seem to learn more in preschool, and as a consequence, they benefit more from attendance, which is reflected in their reading ability when they reach 10 or 11 years of age.

to test hypothesis 3 empirically, the model goes beyond analysing the average score in each country and examines if standardisation affects the social gradient in advantages derived from preschool education. In technical terms, this test is implemented through an interaction effect between three variables. the estimated effect of the interaction is negative, which implies an equalising impact from standardisation that can be seen in graph 1.6, based on the same model 3.


graPh 1.6

marginal effect of attending preschool on reading ability by parents’ education level and the standardisation of the preschool system

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* * * * * * * * * * * * * * * * * * * * * * * * * * * * *

* * * * * * * * * * * * * * * * * * * * * * * *

the continuing line corresponds to standardised systems source: PIrls 2011. calculations by the authors.

In this graph, the continuing line corresponds to the effect of attending preschool in standardised systems, and the dashed line corresponds to the effect of attending preschool in non-standardised systems. In the graph we can see that in both systems a social gradient exists based on the education level of parents, but in opposite directions. In standardised systems, the children of parents with high levels of education obtain less benefit from their participation in preschool than the children of parents with low levels of education; this is the dominant pattern we found in the first part of the analysis (table 1.1 of the appendix). In contrast, if the preschool system is not standardised, the social gradient is in the other direction: those that most benefit from preschool education are children from affluent families. this supports the validity of hypothesis 3. our interpretation of this finding refers to the increase in the variation in the quality of preschool education. If the state does not guarantee a minimum level of quality, the effects of residential segregation and parental choice of preschool have a greater impact.


1.5. conclusions: Preschool as equalizing institution

our main objective in this chapter has been to establish whether preschool attendance generates greater or lesser benefit for children from disadvantaged families in comparison to those from more affluent backgrounds. In short, the answer has been very consistent and confirms that the benefits of attending preschool are greater for children from disadvantaged families. disadvantage has been measured in two ways: socioeconomic status and parents’ investment of time in their children’s education. our results show that early childhood education should be considered an equalising institution.

another finding in this chapter is that this equalising potential seems to be due to (or is strengthened by) curricular standardisation in preschools. Standardisation produces a certain uniformity of conditions and limits differences in quality between schools. In countries where there is no standardisation we find that preschool education has a regressive effect. these findings clearly suggest that if we consider a division between first and second-class preschools reducing the impact of social inequalities on educational achievement to be an important political objective, we must guarantee the quality of all preschool education and that structural residential segregation does not lead to the existence of preschools. these findings clearly suggest that if we consider reducing the impact of social inequalities on educational achievement to be an important political objective, we must guarantee the quality of all preschool education and that structural residential segregation does not lead to a division between first and second-class preschools.

Investing in preschool education, therefore, not only increases a society’s human capital, but also helps to cushion the effects of social inequalities in the early years of the life cycle. this finding, revealed through standardised tests on the reading ability of children from a wide range of developed countries, clearly also applies to Spain, with results near to the dominant trends in all of the aspects analysed. as a result consequence, the argument for early intervention in the education of children – which has increasing support in public discourse – should gain more strength in the Spanish context.

school effects In the reProductIon of educatIonal InequalItIes In comPulsory educatIon In sPaIn 41

2.1. introduction

Spain has one of the highest drop-out rates of any of the advanced economies. While the percentage of the adult population that has not completed compulsory education in oecd countries is 19%, in Spain that figure reaches 36%. there has been much public debate about the reasons for this high percentage, with blame usually falling on the schools. In this chapter we explore the extent to which attributing responsibility to the schools is supported by the facts and to what extent schools are able to transform pre-existing inequalities. In other words, we propose to demonstrate how much schools are able to increase or decrease the effect of students’ social origin, a recognised determinant of their performance.

It is a recognised fact that in core subjects, such as mathematics, reading comprehension and natural sciences, the average scores of students in Spain are below those of the majority of developed countries. this has been systematically confirmed since the PISa studies (2000, 2003, 2006, 2009 y 2012) began analysing the impact of schools on learning (oecd, 2012b).(1) and has spurred enormous debate about educational reforms and the degree to which they affect the organisation of schools. to start, we might assume that schools adopt two basic positions. When they receive students of different origins (and therefore with differences in performance determined by the characteristics of their household or their genetic inheritance), schools can actively intervene to foster equality and neutralise some of the (dis)advantages of origin, or they can remain

(1) the variance between schools in Spain is the smallest in the oecd, with the exception of Finland. While the percentage of variance for the oecd overall is 41.7%, in Spain it is only 20% (oecd, 2012a).

ii. school effects in the reproduction of educational inequalities in compulsory education in spain


passive, letting these factors continue to have an influence on school performance (which, from the perspective of the schools, is exogenous, because it is determined outside of the schools). In the first case, there will be notable differences between schools in the way that origin determines performance, based on the attention given to students with lower initial abilities. In other words, schools can adopt active or passive roles in addressing the original characteristics of their students. In the latter case, the impact of the social origin of the students will be almost identical among different schools.

In order to assess how schools really act, this chapter uses data from Spain’s National Institute for educational assessment (INee in its Spanish acronym, an agency under the Ministry of education) for primary school (2009 data) and secondary school (2010 data). the data come from school inspections and include the results of standardised tests on different subjects of a representative sample of schools and students across Spain. the data, the quality of which is among the best available for the study of school effects in Spain, enable us to analyse the impact of a wide range of household characteristics on student performance. of all the data, and thanks to the findings of both national and international specialised literature, two factors stand out in their importance in the context of contemporary Spain: family socioeconomic level and immigrant status. the link between social class of origin and the academic performance of students is a widely confirmed empirical regularity in all of the developed countries (breen, 2004). Moreover, in a country such as Spain, where migration flows have matured, it is easy to find compulsory school age populations of immigrant family origin. the parents or the students themselves having been born in the country where they live is also a well-known determinant of academic success (Heath et al., 2008).

In the following section we will present and summarise studies published on school effects and their impact on individual student performance. afterwards, in the empirical sections we quantify the contribution schools make to average performance and educational inequality in Spain.


2.2. What do we know about school effects?

over the past two decades numerous studies on the impact of schools on academic performance have come out. according to the dominant perspective in these studies, school effects are nothing more than the result of the composition of the student body once exposed to different levels of resources, models of organization and the recruitment of teachers. these studies are highly sophisticated in both substantive and theoretical terms as well as technically and methodologically.

It seems paradoxical that the development of research on school effects has been carried out apart from research on the social stratification of academic performance. that said, there are notable exceptions to this. Reproductionist theories view schools as active agents in the reproduction of social inequalities in education. one of the most well-known and recognized reproductionist theorists was the French sociologist Pierre bourdieu. For bourdieu (1974), social class differences in education (that is, the propensity of students from different social classes to obtain different grade averages) are primarily due to the existence of different types of habitus. the habitus is a mindset that generates practices in accordance with structural principles of social stratification and, therefore, mediates in the process of the accumulation of capital (economic and cultural) and in the risk of social exclusion and disadvantage. the habitus is acquired in the process of socialization, both primary (determined primarily by the family) and secondary (in which social environments beyond the home intervene), and produces ideas about the groups of reference individuals compare themselves to and, more generally, preconceptions about what is suitable, productive, aesthetic or not. In terms of the habitus, bourdieu (1974, but also 1977, and bourdieu and Passeron, 1977) describes schools as conservative forces. this is because of the strong preference that schools (and teachers) have for the habitus of the middle and upper classes in comparison to the working and lower classes. thus, schools offer a certain advantage from the start to middle and upper class children in comparison to working and lower class children, which is in addition to the advantage they already have in terms of family resources and to a certain extent, cognitive abilities. children from the middle and upper classes begin school with a level of cultural capital and a series of practices related to culture


that schools reward. In the case of working and lower class children, the challenge is not only formal learning, but also modifying their habitus to adapt to the what schools value the most. In contrast, the children of the middle and upper classes only face the first of these challenges. this is why, according to this perspective, schools are active agents (and preservers of the status quo) in the treatment of the disadvantages associated with certain social classes. Schools and teachers – bourdieu considers them to be members of the middle class – seek to preserve the privileges of the most favoured (or do so unintentionally). the analytical advantage of bourdieu’s theory is that it does not require assuming that the preference for education itself and the value of education for children be different between families of the lower or upper classes. as all families want their children to be well-educated, the differences in the average performance of children from different social classes could be a function of the schools and teachers’ preferences.

In contrast to the position represented by bourdieu, other theorists examining the impact of schools on the social stratification of educational results have argued that schools function as institutions that diminish differences based on class. In general, this is a more recently adopted perspective and is thanks to the combination of technical advances in statistical analysis and the production of better data and empirical evidence that combines observation at the individual level and at the level of schools. one of the more interesting lines of research in this direction has been the evaluation of the impact of summer breaks on students’ learning processes. If, as is suggested by reproductionist theories, schools were catalysts of social inequality in learning, the differences between students of different social classes would be reduced during the summer holidays. In contrast, if schools reduced the impact of the social class of origin, the average grades of students of upper and lower classes would immediately increase after vacations. of course, the type of empirical evidence needed to do these studies is very demanding in terms of the type of data required. Studies of this type do not exist in Spain or practically any country in europe. In the United States, in contrast, they have found that the summer break tends to increase differences between upper and lower class students’ performance on cognitive tests and,


therefore, that continuous attendance in school reduces the weight of social origin on learning (downey et al., 2004).(2)

beyond these two very different perspectives, studies on school effects have primarily focused on their accurate measurement and on detecting the factors that increase or decrease the importance of attending one school or another, without too much regard for the impact that this may have on children from different social classes. In the United States, where such research has advanced further, findings confirm that the impact of schools is increasing, in other words, choosing a good school is more important today than it was in the past (Konstantopoulos, 2005). this has spurred an ambitious international research agenda to identify the specific reasons why the school a student attends determines performance. to a certain extent, some conclusions can already be drawn based on the most reliable studies. the majority of school effects are spurious (that is, not causal) because they are due to the way that students are distributed on the school map. In other words, given that the decision to attend one school or another is not independent of family resources, a large part of what appear to be school effects is in reality an effect of school composition. In short, the best students and those of the upper classes are concentrated in specific schools (Morgan and Sorensen, 2000). However, almost all of the research confirms that there are additional school effects that are not linked to the distribution of students across schools (chiu, 2010).

there are two dominant perspectives on what produces net school effects (in other words, discounting the effects of the social composition of the school) (raudenbush and Willms 1995). one focuses on contextual effects, such as the importance of the school environment and so-called peer effects or micro-interactions, which are produced when individuals enter into contact and interact at school. this is the primary mechanism referred to in studies that examine the effect of the concentration of immigrants and ethnic minorities in schools in ethnically heterogeneous countries (cebolla-boado, 2007). one

(2) the first wave of research on school effects concluded that these only had marginal importance (cole-man-campbell report, 1966; Mosteller and Moyihan, 1972). In the 1980s, there were significant technical advances that made it possible to improve the measurement of school effects (Hanushek, 1986). as a result, in the 1990s, the research became much more sophisticated (Hanushek, 1996), although the validity of cer-tain independent contextual variables is still being debated (raudenbush y Willms, 1995; Hanushek et al., 2003).


classic line of research in this direction associates the social capital of some schools with student academic performance (consider, for example, the effect of catholic schools in the United States (coleman and Hoffer 1987)).

In contrast, the other perspective understands school effects to be the result of differences in pedagogical practices among schools, and, even more importantly, of differences in available resources. resources may be material (for example, facilities and teachers’ salaries) or human (quantity and quality of teachers and other staff). this is the perspective most often taken in oecd studies on school effects, although the empirical support for this explanation may be weak. In a detailed review of 147 studies, Hanushek (1986) concluded that after taking family characteristics into account, the variables strictly referring to the school – spending per student, classroom size, student-teacher ratio, teacher training, their experience and even their salaries – have little importance. However, some dissenting voices among other researchers – using quasi-experimental techniques (estimating the effect of school resources as endogenous variables) – argue that they do have some importance (Steele et al., 2007). In a widely cited study, chubb and Moe (1990) suggested that it is not so much the resources as it is the way schools are organized. these authors identified important differences in school performance among schools based on the organisation of staff, the definition of objectives, management leadership and teaching practice.

More recent research has been oriented toward exploring the extent to which school effects are in fact teacher effects (angrist et al., 2012). Some studies affirm that the way teachers operate in the classroom may explain up to one-third of gross school effects (angrist et al., 2012). However, and unfortunately, the numerous studies on teacher effects have not been able to identify the specific factors which explain why teachers can have such an impact on learning. Few observable characteristics of teachers (for example, their academic credentials) seem to have significant importance (Podgursky and Springer, 2007). even so, teachers have a considerable importance. In other words, being a good teacher is the result of little understood processes. the same applies to what happens in choosing a good school. choosing a good school and having good teachers seem to be essential for guaranteeing academic success, but we have identified few of the factors determining the processes hidden behind the best choices.


2.3. school effects in spain and their impact on educational inequalities

2.3.1. the importance of spanish schools in academic performance

It is often said that school effects in Spain are not as important as would be expected based on the country’s level of development. However, there has not been much research on this subject in Spain. PISa data (which records the cognitive abilities of students of 15 years of age at the end of compulsory secondary education) for all of the participating countries has shown that 44% of the total variance in student performance is determined by individual characteristics, while one-fourth of total variance is determined by the schools students attend (chiu, 2010). Using a similar breakdown, the data from the National Institute for educational assessment (graph 2.1) shows that in Spain the percentage of variance explained by the schools is significantly lower.(3)

graPh 2.1

Percentage of results in different subjects that depend on the school and on the characteristics of the students in primary and secondary education

School Individual

–0,046

20% 100%80%60%0% 40%

MATHEMATICS

ESPAÑOL

NATURAL SCIENCES

SOCIAL SCIENCES

MATHEMATICS

ESPAÑOL

NATURAL SCIENCES

SOCIAL SCIENCES

SECO

NDAR

YPR

IMAR

Y

21.4%

21.3%

23.9%

17.3%

18.9%

22.8%

18.0%

17.7%

source: estimates based on our calculations using micro data from Inee assessment.

(3) For this we have used multilevel regression models with a random constant. See appendix b for details on this technique.


In the best of cases, less than one-quarter of the variance in performance is the result of processes that take place in school. In general, graph 2.1 reveals that the effects of the school are greater in secondary education and somewhat less in primary. Furthermore, school effects seem to have a greater impact on performance in natural sciences and, curiously, less of an impact in mathematics. In social sciences and Spanish they are relatively low in primary education but increase in secondary education.

although the breakdown of the variance reveals the intensity of the processes differentiating performance among schools, it does not reveal the degree to which school effects are contributing to social class inequality and to the impact of immigrant status. In what follows we attempt to confirm whether or not the characteristics of student origin have a similar effect on performance among them. We focus on the results obtained in mathematics. this is a very widespread practice in studies on school effects (see chiu, 2010 and Konstantopoulos, 2005), since maths is a subject that is less sensitive to cultural differences in families, given that it is an objective language and less dependent on command of the language used in school. Moreover, maths skills are important in the labour market.

a simple regression analysis (see table 2.1 in the statistical appendix of this chapter) allows us to break down the variance, as demonstrated in graph 2.1. the two panels presented in graph 2.2 show the differences among schools regarding their distance from the overall average grade. both in graph 2.2a and in 2.2b, the red line marks the average point at which a school will not deviate from the average behaviour defined by the constant of the corresponding models (502 for primary and 504 for secondary). regarding these average scores, we see that some schools add or subtract up to 100 points in both cases; that is, the range in Spanish schools is between 400 points in the worst case and 600 points in the best schools. Given that the range in scores falls between zero and 800, these 200 points of deviation between the best and worst schools represents a maximum deviation of 25% in scores.

In other words, attending the best or worst school can foster a jump or a fall in grades of 25%. this is the maximum magnitude of school effects in Spain. obviously, these effects are considerable.


graPh 2.2

Deviation in average performance in mathematics in spanish schools (broken red line)

graph 2.2 a Primary education

dev

iatio

ns o

f sch

ools

from

ave

rage

per

form

ance

w

ith 9

5% c

I

200

100

0

–100

–200

graph 2.2 b secondary education

dev

iatio

ns o

f sch

ools

from

ave

rage

per

form

ance

w

ith 9

5% c

I

200

100

0

–100

–200

source: estimates based on models 1 and 2 in table 2.1 of the statistical appendix for this chapter.


2.3.2. the impact of sources of differentiation in performance among schools

In the previous section we described the magnitude of school effects in Spain. In this chapter we want to go beyond mere quantification and examine to what extent the ascriptive family characteristics of students influence differences in performance in different schools. there are two alternative techniques to do this. one possible, although somewhat parsimonious option, consists of estimating independent regression models for each school. We can introduce the socioeconomic and immigrant status of the families into these models as single predictors and compare the slopes of their different lines. the second option, also technically correct but more synthetic, is to estimate a single regression model at two levels, such as those used in the last section to describe school effects, but adding corrections for the average effect of both socioeconomic status and immigrant status.

First of all, using the first alternative technique, we see in graphs 2.3 a and 2.3 b the differences in the effect of family origin. as can be seen, the slope representing socioeconomic origin is normally positive (although with notable exceptions). In the case of immigrant status, the trend is not as clear: while in some schools, immigrants obtain better scores than authochthonous students, the differences are minimal; in very few schools do immigrants obtain worse results than the children of natives.

What has been said in reference to socioeconomic status applies to both primary and secondary education and confirms well-known processes of educational inequality. to a lesser extent, the same is true for immigrant status.

the second technique for examining the differences between schools in the effects of origin on performance is to estimate the effects at two levels: one for the complete sample and one that reflects the specificity of each of the schools. this allows the slope that summarizes the effect of each variable to change between schools. to distinguish which of the two mentioned possibilities are applied by Spanish schools at primary and secondary levels (an active or passive strategy in addressing the social inequalities students commence with), it is enough to look at the slope of the line that links origin (social class or immigrant status) to individual performance in different schools (b1):


graPh 2.3

Graph 2.3. Primary and secondary education: Differences among schools in the effect of the socioeconomic origin and immigrant status of students’ parents. estimates based on independent regressions for each school

Index of socioeconomic status

700

600

500

400

300–4 20–2

Immigrant status

700

600

500

400

300natives Immigrants

2.3.a. Primary education

source: calculations by the authors based on the Primary education survey of the Inee (2009)


700

600

500

400

300–4 20–2

Immigrant status

700

600

500

400


2.3.b. secondary education

each line represents the effect of the variable recorded on the horizontal axis for each school. the results are from linear regression estimated by olssource: calculations by the authors based on the secondary education survey of the Inee (2010).


• If the schools simply reflect the inequality imposed by students’ origin, there will be few differences in the slopes of this line between schools [Var(b1)=0];

• In contrast, if based on some characteristic of each school, some schools are able to neutralise the effect of origin on academic performance, the slopes of the lines will be different in different schools [Var(b1) = 0].

the second model of table 2.2 in the statistical appendix for this chapter, gathers the results of these estimations, introducing the socioeconomic status of the parents. this is a composite index created by the INee based on the resources of families and the education, employment situation and occupation of the parents. as can be seen, both in primary and secondary education, the socioeconomic status of parents has a positive effect on results in maths (+24 points for a 1 point increase on the socioeconomic status index). However, we see little variation between schools in the way this indicator of origin influences maths grades (the corrections estimated for each school regarding the slope are distributed with a dispersion (variance) of only 7.7 points for primary and 21.5 points for secondary school). In other words, schools do not seem to create many differences in the way that the variables of origin affect student performance.

the third model included in the same table shows the average effect of the condition derived from being the child of two immigrants (or of one, in the case of single parents or widows/widowers) in comparison to the child of two authochthonous parents. both cases confirm a disadvantage associated with the condition of being an immigrant, which in primary school reaches 25 negative points and in secondary, 29 points. Here we find that there are few differences between schools in primary education. again, in terms of the average effect of being an immigrant (25 points), we see a distribution of random perturbations with a variance of exactly 0 points. In other words, the disadvantage of having immigrant parents seems to be treated in exactly the same way in all primary schools.

the results are very different in the case of secondary education and among immigrant children. the average effect on maths grades of being


the child of an immigrant reveals a loss of 25 points. However, it should be noted that the variance among schools regarding this effect is 60 points. this means that there really are significant differences among schools in the way being a child of an immigrant affects grades.

the most reliable and technically most accurate estimate can be obtained from a new series of models (the results of which are presented in table 2.3 in the appendix), which make it possible to represent the differences between schools graphically, as can be seen in the two panels of graph 2.3. In this case, the estimate is produced for all school effects jointly and simultaneously; in addition, it is more conservative, as it corrects for deviations, pushing schools toward the general average when the sample from a specific school has a low number of students or when the distribution of scores in the schools (in this case of maths scores) is more dispersed. this effect, which is often not taken into account, is referred to as shrinkage (Hox, 2010; cebolla boado, 2013; see the note on shrinkage in the statistical appendix of this chapter). the two panels of graph 2.4 show the differences between schools for two of the variables selected to capture the effect of student family origin on maths scores, once corrected by a reliability index. this means that the image obtained from these comparisons between schools is more reliable (although more conservative) than that shown in graph 2.3.

as can be seen, the conclusion we obtain from this empirical exercise is clear. there are few differences in the way the primary sources of educational disadvantage are treated in the schools in the sample. there is a strong intergenerational transmission of disadvantage in both primary and secondary education; this is confirmed by the positive slope of the lines referring to socioeconomic status and the negative slope that is associated with being the child of immigrants. However, between the schools, the slopes are practically identical. although the lines marking the trend for each school are actually not completely parallel, the differences are hardly noticeable to the eye, as seen in graph 2.4.


graPh 2.4

Differences among schools in the impact of socioeconomic origin and immigrant status on maths scores

2.4.a Primary education


700

600

500

400

300–4 20–2

Immigrant status

600

550

500

450


source: calculations by the authors based on models 2 and 3 of table 2.2

2.4.b. secondary education


650

600

550

500

450

400

350

300–3 2–2 0–1 1

Immigrant status

650

600

550

500

450

400

350


source: calculations by the authors based on models 2 and 3 of table 2.3


these findings confirm the hypothesis that Spanish schools are largely passive in addressing the sources of educational disadvantage; in other words, they receive students with differing family characteristics but are largely incapable of differentiating how they work with them.

In order to offer more reliable results, models 4 and 5 of table 2.3 of the appendix demonstrate the stability of the results controlling for type of school (public vs private/publicly-funded private) and the average socioeconomic status of the parents of the students in each school. the inclusion of these two variables does not change the results already presented. It may be surprising that in the models presented there are no statistically significant differences between public and private schools. this implies that the intense debate in Spain in recent years regarding the impact of the growing private and publicly-funded private sector in compulsory education in Spain has been irrelevant, at least in terms of educational performance. Given the importance of this conclusion for discussions regarding public policy, in graph 2.5 we present a visual image of the residuals associated with the constant of the last models specified for each educational segment in this analysis, differentiating between public and private schools. as can be seen, the behaviour of schools does not seem to be explained by whether they are public or private in either primary or secondary education. In short, no pattern exists clearly distinguishing public from private schools.

It is interesting, however, to find that the average composition of the student body based on resources in the home is an important predictor of performance. this shows that what marks the difference between a good school and one which is not as good, is not if the school is public or private, but the composition of the student body. this is an important finding not only for parents who try to choose the best school for their children, but also for defining public policy. this suggests that it is the concentration of disadvantage or deprivation in the student body which largely determines school effects, and not so much the management model of different schools. Statistically, this can be seen in the fact that controlling for the variable for school composition, the dispersion of random effects associated with the


constant decreases from 885 to 685 in primary education and from 733 to 552 in secondary.(4)

graPh 2.5

Differences between public and private schools in primary and secondary education measured by average performance of each sample (broken line)

2.5.a Primary education

• Public schools n Private schools

100

50

0

–50

–100

sch

ool e

ffect

s

source: calculations by the authors based on model 5 of table 2.2 in the statistical appendix.

2.5.b secondary education

• Public schools n Private schools

100

50

0

–50

–100

sch

ool e

ffect

s

source: calculations by the authors based on model 5 of table 2.3 in the statistical appendix.

(4) although the results are not presented in tables 2.2 and 2.3, other indicators of school environment and resources are also included, without any of them being found to be statistically significant, except for the already mentioned average for the socioeconomic resources of families of students attending the school.


Lastly, we turn our attention to an aspect not addressed so far: possible differences in school effects based on autonomous regions. With the exception of the PISA 2009 and 2012 studies, there is little data available for carrying out a study of all of Spain’s autonomous regions; in addition, not all autonomous regions have chosen to participate in PISA. However, using the logic of the previous measurement, graph 2.6 shows the results of a breakdown of the variance to see the importance of school effects in the autonomous regions that participated in PISA.

Graph 2.6

Percentage of maths performance based on student body composition: importance of school effects in the autonomous regions with their own sample in PISA 2009

Variación entre escuelas Variación individual

CEUTA AND MELILLA

BASQUE COUNTRY

CANARY ISLANDS

MADRID

ANDALUSIA

CATALONIA

CASTILLA AND LEON

GALICIA

BALEARIC ISLANDS

CANTABRIA

MURCIA

ARAGON

NAVARRE

ASTURIAS

LA RIOJA

–0,046

20%10% 30% 50% 70% 100%80%60%0% 40% 90%

36.5%

26.2%

21.5%

18.6%

17.8%

17.2%

14.4%

14.4%

14.2%

14.2%

14.1%

13.5%

13.3%

13.3%

12.7%

Note: The Community of Valencia did not have its own sample in pISa 2009. Source: Calculations by the authors based on multilevel regressions with random intercept for each autonomous region.

Analysis of the graph suggests that there is no significant differentiated pattern between regions. Most of them have low school effects, ranging


between 12.7% and 14.4%. the regions with stronger school effects are (in order) ceuta and Melilla, the basque country, the canary Islands, the community of Madrid, andalusia and catalonia. It is not easy to find a pattern common among these regions to explain their positions. the latter three regions are the most populated in the country, which could create a greater demand for diversification in the market for schools; this may be the only explanation for the pattern we see. the specificity of ceuta and Melilla is surely the result of the many socio-political particularities of these autonomous cities; among these would be the presence of an ethnically heterogeneous population that has pushed the middle classes into private or publicly-funded private schools with greater intensity than in the rest of Spain, leading to greater levels of school segregation.

Finally, graph 2.7 helps us to better understand the inter-territorial variation between schools. In this graph we see the reduction in gross school effects, incorporated from the previous graph, after controlling for the socioeconomic composition of each school’s student body. First of all, it could be argued that our suspicion that there is greater segregation in schools in ceuta and Melilla based on the socioeconomic origin of the students is confirmed, as the heterogeneity within schools is reduced by 80%. In these cities there are few schools, but with important differences between them regarding the social profile of students. Something similar is found in la rioja, although on a more moderate level, since in the previous graph it is the region where schools have the least effect. on the opposite end of the scale we find the curious case of andalusia, which as we have seen, is characterized by strong school effects, which in graph 2.7 we find have little relation to socioeconomic segregation. the situation is similar in the basque country and the canary Islands. castilla and león, a region frequently discussed in analyses of comparative education for heading performance classifications in Spain, reveals little difference between schools and with little connection to the socioeconomic composition of the student body. Madrid, in the middle-high part of the ranking, has a high level of segregation between schools. catalonia, in this regard, seems to be slightly more egalitarian.


graPh 2.7

reduction in school effects when controlling for the average socioeconomic composition of the student body in each school by autonomous region

0

10

20

30

40

80

60

70

50

90

0102030405060708090

CEUTA Y MELILLALA RIOJANAVARRAMADRIDBALEARESARAGÓNCATALUÑAASTURIASCANTABRIAGALICIAMURCIACANARIASPAÍS VASCOCASTILLA Y LEÓNANDALUCÍA

ANDA

LUSI

A

CAST

ILLA

AND

LEO

N

BASQ

UE C

OUN

TRY

CANA

RY IS

LAND

S

MUR

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ABRI

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A

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ON

BALE

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ISLA

NDS

MAD

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NAVA

RRE

LA R

IOJA

CEUT

A AN

D M

ELIL

LA

25.8 28.0

40.9 41.5 42.245.6

53.2 55.8 56.8 57.5

65.3 66.4 67.672.2

79.2

note: percentage of reduction in variance between schools obtained with an empty model compared to another which only controlled for the average class of the parents of students in the schools (according to the IseI classification of PIsa, based on the parent with the highest socioeconomic class).

2.4. conclusions: the “inactive” schools”

In this chapter we have quantified school effects in Spain and their impact on the educational inequalities that originate in the socioeconomic and immigrant status of the parents of students in primary school and compulsory secondary school. It should first be noted that the magnitude of school effects in Spain are below the oecd average. this is not in itself necessarily negative, in fact, in concrete terms, it means that Spanish schools generate fewer differences in student performance than do schools in other advanced economies. as a result, it is less likely that a child’s future education will be hampered by a poor choice of schools. However, this is not necessarily good news either. Given that Spain scores below the international average on academic performance in the available studies,


we can conclude that Spanish schools may be more equal in comparison to other advanced economies, but they are of less quality (or with lower average performance).

the differences among Spanish schools account for approximately 20% of a student’s academic performance. However, this generalisation masks differences of up to 200 points between the worst and the best schools on INee tests for primary and secondary education (with scores ranging from 0 to 800 points). thus, although the effects of schools are less important in Spain than in other developed countries, the differences between the best and the worst Spanish schools are significant.

this chapter has also contributed information on the way in which these differences between schools could affect the two selected sources of family disadvantage (household resources and parents’ immigrant status). Schools can impact on these two factors in two ways. on the one hand, schools can play a passive role. their inaction in the face of differences based on students’ origin is not an approach that generates equality, as it leaves the determination of the level of student performance in the hands of the family. Whether because of a lack of family resources to invest in education, differences in the habitus or cultural capital of the parents, or because of differences between their place of birth and place of residence, the children of less fortunate families may face greater problems in the learning process than the children of other families. Inaction is an organisational characteristic to take into account, given that the impact of a student’s family environment on academic performance is now well recognised. on the other hand, schools can act decisively to counter inequalities. In this chapter, we have been able to confirm that in general Spanish schools treat all students similarly, regardless of family characteristics, which in itself generates significant differences in performance. We can conclude, therefore, that schools in Spain do not intervene decisively in the (re)production of educational inequality. In short, what we are saying is that although authochthonous parents and those who have greater socioeconomic resources are able to provide a certain advantage to their children, the schools that Spanish students attend do not seem to modify these processes of differentiation in results based on family origin.


lastly, we have found that the differences in school effects are not determined by type of school (whether public or private). We want to emphasise that despite a public debate that very frequently insists that the public or private status of the schools is an important differentiating factor, in terms of performance, public schools do not differ from private or publicly-funded private schools. However, there is no doubt that the concentration of disadvantage, that is, the average socioeconomic origin of the parents of students in a school, is a crucial factor. We can, therefore, conclude that the bottom line is not so much which school a student attends, but who the other students are in the school.


3.1. introduction

the effects of the current global economic crisis were initially felt in 2007; since then, the crisis has continued with differing levels of intensity and at different rates in different parts of the world. recently, we have seen the publication of the first sociological contributions on the effects of the crisis on distinct social indicators (Grusky et al., 2011; danziger, 2013). the majority of these contributions have focused on the US, where the symptoms of what is being called “the great recession” were first evident. the most visible effects of the crisis have been the loss of jobs, the decline in household income and a resulting growth in poverty. but it is still too early to know if the crisis will have lasting effects once the economic cycle changes, or, on the contrary, if the effects will be temporary and even fleeting, with individuals and households recovering their prior levels of well-being. However, even if there is a sustained economic recovery in the future, certain consequences of the crisis could last and manifest over the life cycle of the individuals that have experienced them. In addition, if the disadvantages are inherited by the following generation, children’s life opportunities will not only be affected during the crisis as a direct result of the worsening of their families’ economic situations, but in the medium and long-term as well.

there is one dimension in which these lasting effects may already be visible: the educational expectations of children who are currently finishing compulsory education. to the extent that education is crucial in predicting some of the most important indicators marking individuals’ life cycles – such as occupational achievement, family income and family

iii. expectations of continuing in the education system beyond post-compulsory schooling

exPectatIons of contInuIng In the educatIon system beyond Post-comPulsory schoolIng 63

formation -, a decline in students’ expectations, especially among students from socially disadvantaged backgrounds, could jeopardise long-term collective achievements in intergenerational social mobility (in other words, between parents and children) in many developed countries (breen, 2004).

therefore, in this chapter we analyse the impact of the great recession on inequality of educational opportunities (in other words, how individuals’ socioeconomic origin affects their likelihood of reaching certain education levels) in a broad sample of countries with differing levels of economic wealth and where the crisis has had different effects and at different rates. the global economic crisis can be understood analytically as an external shock that has affected all countries to some extent.

In academic studies on social stratification, the consequence of contextual changes at the macro-social level on students’ decision-making process regarding their future education and, more concretely, on inequality of educational opportunities, has hardly been examined. efforts have been primarily focused on two issues. on the one hand, there are very comprehensive studies aimed at measuring the impact of the expansion of education in developed societies to explain changes in the level of education that individuals of different socioeconomic origins achieve (breen and Jonsson, 2005). on the other hand, their are studies that have examined the influence that the differing institutional arrangements of education systems have on inequality (specifically, their degree of stratification and standardisations(1)); while the early separation into distinct vocational and academic tracks seems to increase inequality of opportunities based on social origin, the standardisation of educational systems reduces inequalities (Van de Werfhorst and Mijs, 2010). Nevertheless, we know very little about how educational inequality is affected by trends at the macro level, such as, for example, changes in the economic cycle or in unemployment rates. there are three notable exceptions to this. the first, a study by reardon (2011) on the United

(1) an education system is vertically stratified if the proportion of students that reach the different levels of post-compulsory education is low. a system is horizontally stratified if there exist different types of schools that lead to degrees or certificates of different quality or if it differentiates between academic and applied branches. the most well-know example of a highly stratified education system (both vertically and horizon-tally) is Germany.


States, explores changes in income inequality and the extent to which students’ grades depend on the socioeconomic situation of their families. another study, somewhat more tangential (barr and turner, 2013), addresses the tension that the crisis has created in the United States as education systems are having to deal with more students in non-compulsory education with smaller budgets. these two studies only look at the US, so we cannot know if their conclusions are applicable to other contexts. Finally, a third study (torche, 2010) incorporates a comparative element in analysing how cohorts in four countries with different exposure to the crisis in latin america in the 1980s have made successive educational transitions. It is clear, therefore, that the effects of the crisis should be evaluated looking at a broad range of dimensions of educational inequality and incorporating the widest sample possible of developed countries. this will allow us to draw conclusions that can be used in developing appropriate public polices in each context. If until now, academic contributions have been largely lacking, international organisations (see, for example, Ilo, 2009; oecd, 2010; UNeSco, 2010) and foundations (the russell Sage Foundation has, in recent years, financed various projects on the impact of the economic crisis in the United States) seem to have picked up the slack. although we must admit that children’s educational expectations do not completely predict real achievements, they do clearly constitute a significant determinant in the decision-making process to continue or leave school. thus, in this chapter we analyse the relationship between family socioeconomic origin, educational results – measured in this case as students’ expectations of the highest level of education they will attain – and macroeconomic conditions. concretely, the first research question this chapter addresses is: Has the economic crisis increased the impact of social origin on students’ educational expectations? although it is to be expected that any lasting economic downturn will modify students’ educational expectations to some degree, independently of the socioeconomic resources of their families, it is important to determine if the impact differs based on social origin. throughout this chapter we try to systematize both types of effects – the larger macro context/effect and the micro level effect of family origin. the second question in our research refers to the permeability of students’ expectations with different levels of prior


achievement: Who is more sensitive to changes in the macro-social context, children with high levels of academic performance, or those with average or low levels?

3.2. economic context and educational decisions

the specialised literature that analyses inequality in educational opportunities has been increasingly focused on rational choice models at the micro level. In an essential work in the field, Mare (1981) proposed analysing the effects of family origin on educational achievements as a series of transitions that, from one level to the next, mark the educational path of students. based on this analytical framework, diverse theoretical mechanisms have been constructed to explain how family social origin affects children’s’ educational paths. among the most consolidated are the Relative Risk Aversion model (rra) of breen and Goldthorpe (1997), the theories of Maximally Maintained Inequality (MMI) of raftery and Hout (1993), and Effectively Maintained Inequality (eMI) of lucas (2009). In reality, all these models are based on the seminal theory of the French sociologist, raymond boudon (1974), called “Inequality of educational opportunity-Inequality of Social origin”, which explains why, contrary to what would be expected, the expansion of education (incorporating students from less favoured socioeconomic origins) has not been accompanied by a reduction in differences in participation by social class. In other words, the theory provides an explanation for why higher rates of participation in education can be compatible with persistent relative differences in participation in post-compulsory levels of education that favour students from more affluent classes.

the theory argues that there is a correlation between social origin and the individual aptitudes that affect success in school, and that what determines the costs and benefits that motivate individual educational decisions is the combined effect of social position and the characteristics of the education system. thus, class differences in education emerge from two fundamental sources: the cognitive abilities or skills demonstrated in school (referred to as primary effects) and the specific structure of costs and benefits in each moment of transition within the education system


(secondary effects). While class differences in school performance that persist from one generation to another can be related to biological and/or sociocultural factors, class differences related to the decision whether to continue in the education system are fundamentally related to costs and benefits and the likelihood of success associated with each decision (erikson and Jonsson, 1996). Since then, one recurring effort in the literature has been to break down the impact of primary and secondary effects in inequalities in educational transitions (in other words, the fact that the children of certain social classes are more likely to make the transition to certain levels of post-compulsory education) (Jackson et al., 2007; Stocké, 2007; Kloosterman et al., 2009; Jackson, 2013).

aside from these studies, there are other contributions that have considered other ways in which family origin affects different indicators of educational performance. We have evidence, for example, that shows a positive association between family wealth and attaining a post-secondary education level (conley, 2001), and between household income and university access (acemoglu and Pischke, 2001). the effect of parents’ unemployment on a range of different indicators of educational achievement, including cognitive results (levine, 2011), aspirations (see reed, 2012, for a review of this literature), effort in school (andersen, 2013), attaining post-secondary education (Wightman, 2012) and repeating a year of school (Stevens and Schaller, 2011), has also been analysed.

despite important advances in determining the extent to which and why educational inequalities depend on the specific institutions of each country (Pfeffer, 2008, Van de Werfthorst and Mijs, 2010), we still know little about the effects of other contextual factors, such as the economic climate. the studies published suggest that students’ educational plans are a product of their (estimated) cognitive abilities, the availability of the necessary resources to pay for education and the willingness to do so, as well as the incentives that the labour market offers upon attaining each education level (Morgan, 1998). the latter factor is crucial to our argument and suggests that changes in the economy that alter the incentives to join the labour market perceived by students (or their families) would affect educational expectations and, as a consequence,


educational careers. the studies that analyse only a single country, such as reardon’s (2011) for the United States, provide important information but are not specifically designed to evaluate the influence of contextual factors, to the extent that there is no variability in the context evaluated in these studies. torche (2010) explicitly incorporates a comparative dimension, but her results refer to a regional crisis that occurred several decades ago. to rigorously quantify the impact of the economic cycle it is necessary to turn to comparative studies that include data from a broad sample of countries (to provide variation in the macro factors among these countries) and in different time periods (to offer variability in the contextual conditions within each country). to do this we use the international tIMSS database (Trends in International Mathematics and Science Study).(2)

there has been an interesting conceptual debate regarding the definition of motivations, aspirations and expectations. In studies on social stratification, aspirations regarding educational transitions tend to be conceived of as the educational level desired by the individual, while expectations incorporate a subjective calculation of the likelihood an individual has of reaching the desired level (Hanson, 1994). In short, expectations are more realistic aspirations. therefore, in this chapter we understand expectations as probabilistic statements about the future education level that each individual is most likely to attain (Morgan, 2005).

expectations have been an object of debate in the social sciences, from both a theoretical and an empirical perspective, since the Second World War. their study has a long tradition, above all in the fields of social psychology and sociology, and particularly among researchers into the intergenerational transmission (from parents to children) of inequalities. In social psychology, ajzen and Fishbein (1980) defined intentions as determined by social norms and as determinants of behaviour, showing that expectations are correlated across generations. In sociology, although the tradition of studying expectations goes back to the 1950s (Kahl, 1953), this issue was never a significant part of the research agenda until

(2) For details over the data sources used in this chapter, see appendix a.


the Wisconsin School systematised the influence of parental expectations on children.

Studies on educational expectations have developed along two main lines. First, there are those that have attempted to explain how expectations are formed. the original contribution of the Wisconsin model proposed that parents shape the expectations of their children in processes of early socialisation (Sewell and Hauser, 1993), and that this explains the stability of aspirations over time. In the Wisconsin model of status attainment (Sewell, Haller and Portes, 1969; Haller and Portes, 1973), educational aspirations are a key variable that exercise a mediating power that transforms factors related to socioeconomic origin into the behaviour of individuals. Students internalise their educational expectations under the influence of persons of reference (parents, teachers, peers) and taking into account their educational performance. It is also well-known that educational plans help to turn ambition and motivation into effort, significantly improving educational performance (Spenner and Featherman, 1978). More recently a certain consensus has been established around the idea that expectations are far more than mere affective fantasies or value orientations based on status: they are, instead, the result of rational calculations constantly subjected to updating as information becomes available on the context and students’ own estimated potential. In fact, “the student-specific expectations that significant others hold can be regarded as rational constructions because they are based on the recognition of student and family characteristics, and on reasonable appraisals of how these characteristics will affect a student’s future success” (Morgan, 1998: 136). recent studies offer more detailed and sophisticated models to explain the process of how expectations are formed (Morgan, 2004; andrew and Hauser, 2011).

Secondly, researchers have tried to analyse the effect of expectations on actual educational performance (see Jacob and linkow, 2011). In the United States, much of the debate has revolved around the differences between groups in their ability to realise their expectations. Hanson (1994) suggested that the fact that the african-american population in the United States translated their expectations into results to a lesser degree than the white population is related to a certain lack of realism.


beattie (2002) more recently demonstrated that white people are more sensitive to the returns on education than african-americans. other studies have analysed the relationship between expectations and performance among asian-americans (Goyette and Xie, 1999), and in european contexts (see, for example, teney, devleeshouwer and Hanquinet, 2013). However, beyond these associations, the causal relationship between expectations and performance is very difficult to establish and is still an object of intense debate. there is a certain scepticism around the idea of studying intentions to predict behaviour, and some authors have come to completely reject the importance of expectations (Manski, 1990). even so, the analysis of educational expectations is still an important subject in sociological research (alexander, bozick and entwisle, 2008; andrew and Hauser, 2011).

Some recent studies have shown that students’ beliefs about their educational and occupational future are in fact vague and imprecise, but they also reveal that there is a dynamic relationship between expectations and performance (Morgan et al., 2013a and 2013b), such that expressed educational plans are much more than unrealistic desires. It is clear that students make decisions about their education with a significant lack of information, but their expectations are also subject to influences such as their families’ beliefs in their abilities and about the amount of effort required to successfully pass each education cycle (breen, 1999).

hypotheses

the relationship between the economic context and the effects of social origin on educational transitions is very complex. a large number of mechanisms that may impact on this relationship in different directions can be identified. In what follows we formulate several hypotheses on the possible impact of the economic crisis.

on the individual level, the decision whether to continue studying past a certain education level (or not) is determined by the equation e = p * b – c. Students (and their families) evaluate if the expected benefits (b) that they would obtain by attaining this level of education conditioned by the likelihood of successfully passing it (p) are greater than the direct or indirect costs (c) associated with completing it. the expectations (e) the


individual has of completing the education level are higher the more that the benefits exceed the costs.

our first group of hypotheses refers to the effect of the economic crisis (or, in more general terms, any change in the country’s annual growth rate) on the average expectations of students. a popularly held belief in Spain is that students stay in school longer in times of recession, as high unemployment, lower wages and uncertainty about the immediate potential returns of education in the labour market are assumed to reduce the real or perceived benefits of leaving formal education to enter the labour market. the opposite of such behaviour was observed in Spain during the real estate bubble, which generated relatively attractive jobs in construction and real estate sales, and the argument is that when the economy gets worse, the education system retains those students who in boom times would be most attracted by the possibility of entering the labour market. If this argument is valid, we would expect students to remain in school longer during recessions and, in contrast, to leave school earlier during periods of economic expansion, when there are more attractive entry level jobs available. according to the equation presented above, either the employment benefits associated with remaining in school decline, or the opportunity cost associated with education does. In these conditions, a recession should, ceteris paribus, increase educational expectations through a substitution effect.

but the effect could be completely the opposite: the crisis could negatively affect expectations (and as a result, reduce the length of the average education trajectory of students) in two ways. on the one hand, given that an economic recession leads to a decline in households’ material well-being, the direct costs of education may no longer be affordable for some families. on the other hand, the response of some governments to the fall in income during the crisis has included cuts in education budgets or the adoption of policies that affect household income. a decline in public investment in education may involve a worsening of the quality of programmes and/or an increase in enrolment costs, which families must assume. either of these cases reduces the attractiveness of non-compulsory education levels. In the above equation, the crisis would increase the costs associated with education, c, at the same time that the expected benefits of continuing in school, b, would be reduced. In this


scenario, a recession should, ceteris paribus, lead to decreased educational expectations, e, through an income effect.

these first two hypotheses refer to general level of expectations, and consequently, all students, regardless of their characteristics, would be affected by them. However, the effect of a change in the economic cycle on expectations could in fact be more complicated. Now we turn to a second group of hypotheses and present two additional scenarios that the economic crisis could lead to.

as we pointed out above, the relative risk aversion model (rra) establishes that the relative utility students assign to the completion of each education level differs by social origin because the main objective – avoiding downward social mobility, that is, ending up in a lower social position than that of one’s parents – is accomplished less often by students from lower social classes. there is also evidence that students from more affluent socioeconomic origins are more likely to obtain greater benefits (occupational and other types) from education, even when controlling for school performance (brunello, lucifora and Winter-ebmer, 2004). this finding leads us to ask if these differences in the returns associated with each educational transition contribute to explaining educational differences based on students’ social origin. children from more affluent families not only have, on average, higher expectations regarding the education level they will attain, but the association between their expectations and later performance is also stronger than that found for students from more disadvantaged backgrounds (alexander, entwisle and bedinger, 1994; Hanson, 1994). While university students seem to have quite realistic expectations about the return on their university degrees (botelho and Pinto 2004), secondary school students’ perceptions are not as accurate. concretely, it has been shown that the wages they expect to earn are lower than what they in fact will earn (Jensen, 2010). In addition, returns on education depend on institutional characteristics (brunello, lucifora and Winter-ebmer, 2004). Given that education works as insurance against the risk of unemployment, the returns on employment will be sensitive to changes in the economic climate (see blöndal, Field and Girouard, 2002). Inequality in educational outcomes – in our case, the expectations regarding continuing in the education system – may be affected by changes in the


economic cycle if the level of knowledge about job opportunities and salaries is not equally distributed across different socioeconomic classes. Specifically, differences should increase if students of more affluent origins (and their families) have expectations that are not very sensitive – or, in fact, inflexible – to the extent that they are able to perceive the need to accumulate greater endowments of human capital in a context of growing competition for scarce jobs, or if they are more able to accurately estimate the changes in the economic cycle. this mechanism, which we refer to as privileged information, affects term b in our equation. With the economic crisis, perceptions of the benefits from remaining in the education system are more accurate (and practically inelastic) among students of higher social class origin.

the crisis can also have immediate consequences on the micro level if there is a negative effect from parents’ unemployment or the loss of buying power associated, for example, with a reduction in pay, on the educational attainment of children . decisions about continuing school are partially conditioned by the resources available in the home. Students from more disadvantaged families could be forced to leave school and enter the labour market to a greater extent than children from more affluent families. although this income factor should operate in all circumstances, its effect would be greater during recessions due to a composition effect. this mechanism, which we call the compensation effect for the loss of household income, may increase differences in expectations based on social position. In our equation, c, the costs of remaining in school, increases disproportionately among poorer students.

We could also, however, find the opposite effect on social inequality. rising unemployment is often linked to the contraction of low-skill employment sectors, while skilled jobs are more resistant to crisis.(3) In such a case, a recession might not reduce the incentives to leave school to the same degree for all students, as we argued in the first group of hypotheses. If the loss of jobs is concentrated in low-skill sectors and students of lower socioeconomic origin assign less utility to school diplomas than do more affluent students,

(3) For example, in the period from 2008 through 2011, the unemployment rate among adults with a low education level increased by 5 percentage points in the eU. In contrast, the corresponding rate for adults with a high education level increased by only 1.5 percentage points.


as suggested by the relative risk aversion model, early school leaving could be concentrated among the former. However, the crisis, by encouraging remaining in school, could paradoxically help these students to avoid short-sighted decisions, given that there are not enough attractive jobs that represent a real alternative to education for students from more disadvantaged families. In terms of the equation that explains our hypotheses, this effect, which we will call the effect of reduced alternatives to education, is captured in term c. In a context of crisis and reduced opportunities in the labour market for children from more humble family origins, the opportunity cost of remaining in school decreases, and as a result, educational expectations increase.

Students form and revise their perceptions about their educational future taking their grades into account (breen, 1999). this reinforces social inequality in academic results, as students from different social origins assign a different value to innate ability and effort. thus, students from higher social classes tend to be more conscious of the importance of effort, while those from more disadvantaged origins assign greater importance to luck and innate ability. the grades obtained determine how accurately students and their families estimate the likelihood of success at each education level. In the case of students with the best academic results, the likelihood of success is practically 100 percent. Students with poorer results in school, in an analogous manner, are quite certain that their likelihood of successfully passing the following education level is very low, practically zero. In contrast, it is more difficult for students that have an average level of academic performance to make precise calculations. In other words, they might not know exactly what is required to make the following education transition and, therefore, they may be more sensitive to external signals (bernardi and cebolla-boado, 2014). It is precisely among students with an average level of performance that we expect an economic recession to have more of an impact. While in periods of expansion a positive climate is created that goes beyond the economic dimension, moments of crisis bring greater uncertainty (european commission, 2010). We expect, therefore, that students with average performance levels will be more affected by the crisis as the effect of seeing things through rose-coloured glasses during periods of expansion disappears.


In the following series of graphs we present a summary of the empirical implications of our hypotheses.

graPh 3.1

hypotheses and empirical implications

h1: hypothesis on the impact of economic growth on conditional expectations empirical implications. the constant function that

links resources/education of parents shifts as a result of growth

h3

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h1b

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education of parents/resources

h2a

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h2: hypothesis on the impact of economic growth on inequality in economic expectations

empirical implications. regardless of the movement of the constant, the original slope of social origin as predictor changes with economic growth

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h3: hypothesis on the impact of economic growth on cognitive inequality

empirical implications: marks in/at the centre of the distribution do not provide as clear information to families about potential future success; it is among this group of students that growth will have stronger effects.

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economic expansion economic recession In economic recession controlling for household resources



3.3. recession and the reproduction of inequalities

table 3.1 summarises the properties of the variables used in this chapter. We see that the majority of students have ambitious expectations regarding their educational future. the average score on the dependent variable – educational expectations – is 80. the maximum on the scale (a doctorate) is 100 points, and a university degree corresponds to a score of 90. the high level of expectations is also apparent in comparing it with the highest level of education attained between their parents (educational resources), which is an average of approximately 60 points. another result that stands out is that the countries included in the sample had an annual GdP growth rate of 3%, although there was significant variation in this rate, both between countries and within countries.

table 3.1

Description of the timss variables

A) VAriAble At indiViduAl leVelA average standard devIatIon [95% cI]

Continuous variables

expectations 80.26 22.56 80.17 80.35

educational resources 60.44 31.02 60.30 60.58

material resources 0.23 0.94 0.23 0.24

scores in mathematics 523.37 85.71 523.03 523.72

Dichotomous variables

women 49.6% 0.50 49.4% 49.8%

autochthonous 91.3% 0.28 91.2% 91.4%

b) VAriAbles At the country leVelb average standard devIatIon mIn. max.

gdP per capita (in thousands) 31.81 8.89 15.25 53.59

gdP annual growth rate (%) 3.03 2.04 –0.70 9.84a n= 235.022 observations by imputation; m= 5 imputation.b 65 countries/regions-year of 24 countries.

the results of the multivariate analysis are presented in table 3.2 of the statistical appendix. In model 1 we estimate the effect of economic growth on the regression constant (level of expectations), controlling for the effect of other relevant variables. Prior school performance, measured based on grades in mathematics, is broken down into five dichotomous variables. each one represents one-fifth of the distribution of grades. In the analysis, the first quintile (the 20% of students with the lowest scores) is the reference category.


We have included two controls: sex (the reference category is male) and the immigrant status of the student. Family resources are incorporated into the analysis through two indicators: the education level of the parent with the highest level (educational resources) and the material well-being of the household (economic resources). the specific economic context of each country is represented by two variables: on the one hand, the wealth of the country, measured by per capita GdP in thousands and, on the other hand, the changes between pairs of years/surveys on wealth, measured based on the annual change in GdP (positive or negative) expressed in percentages.

our coefficients for model 1 show that having higher grades is related to higher values on the variable measuring educational expectations. the effects of the control variables are consistent with what other studies have found: girls have lower expectations, while boys and immigrants are more optimistic or more ambitious than the children of native parents, which is what is generally found in european and american studies (Kao and tienda, 1995). the two indicators of family resources also have the expected effect, with more of both types of resources associated with higher expectations. In addition, a higher rate of GdP growth is associated with higher educational expectations: if in one year, GdP grows five percentage points, the educational expectations of students increase about six points. even after controlling for the level of wealth of countries, a growing economy leads to higher expectations. We can therefore say that economic crises are associated with lower aspirations, probably due to the loss of purchasing power or education becoming more costly

Having confirmed that the economic context affects the average expectations of all students, our interest now shifts from the constant of the regression equation to the slope of the regression line for the relationship between social origin and expectations. to analyse to what degree economic growth affects the way that variables on social origin ‘produce’ inequality, in model 2 we introduce an interactive effect between the two types of resources, educational and economic, respectively, and changes in GdP. the results suggest first of all that both types of resources have a similar, negative effect. In other words, an expanding economy weakens the main effect of family educational resources (0.228) by –0.014 and economic resources (1.913) by -0.154. therefore, when the economy grows, family resources lose weight in explaining educational


expectations. In contrast, when the economy contracts, social origin becomes a more decisive factor in students’ aspirations, reinforcing inequalities. although in general, family resources – both educational and material – have a positive effect on expectations, this relationship is weakened in times of economic expansion. and the opposite, when growth rates fall, material and cultural deprivation negatively affect aspirations to a larger degree.

We now focus our attention on the impact changes in the economic cycle have on what can be conceived of as an additional type of inequality, the origin of which is found in students’ prior school performance. We are referring to inequality based on cognitive abilities. our last hypothesis is that while it is less likely that the expectations of the worst and best students are influenced by the changes in the economic cycle, students with an average performance level, whose likelihood of success is more subject to uncertainty, are more sensitive to the changes in cycle. empirically, we would expect a curvilinear relationship (an inverted U shape) in the interactive effect between economic growth and grades. the results of our estimation (based on a series of interactions between growth and grades in mathematics and that are shown in graph 3.1) support this theoretical proposal: the impact of growth in GdP is highest in the middle sections of the grades distribution, specifically in the second (0.378) and third quintiles (0.252). In the graph we illustrate the marginal effect of an annual decline of 5% in GdP on the educational expectations of the five groups of students created according to school performance (quintiles). In the graph, we see that an economy in recession has, as a result, lower expectations (note the sign is negative for all of the bars), regardless of the grades. However, the decline in expectations is marginally greater among students who are in the middle part of the distribution, especially in the second quintile. We can conclude, based on these results, that inequality based on cognitive differences increases during recessions and decreases during periods of economic expansion.

our results confirm the inverted U shape in the interaction between economic growth and grades. Growth pushes the expectations of students with average performance levels upward, while the expectations of students with the worst and best grades remain relatively unaffected. Seeing things through rose-coloured glasses seems, therefore, to be restricted to students with less certainty regarding their likelihood of success in their educational


transitions. Moreover, given that the interaction is significantly stronger in the second quintile than in the fourth, growth appears to have an equalising effect on educational expectations.

graPh 3.2

effect of a decline in GDP by 5 points on the educational expectations of students with different levels of performance

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–12quintile 1 quintile 2 quintile 3 quintile 4 quintile 5

note: estimated based on model 3 in table 3.1

Finally, we have tried to understand the impact of macroeconomic changes on three types of inequality (based on cognitive ability, on the educational resources of the family and on economic resources). to do this, we added an interaction at three levels between the grades obtained, both of the indicators of social origin, and annual GdP growth. Given that the interpretation of these types of interactions is not clear based on the coefficients, we only present the marginal effects of economic change that are more central to test our argument; that is, for students in the different quintiles of the distribution of grades whose parents have high and low levels of education (graph 3.2) and of material resources (graph 3.3).(4)

Having established the equalising effect of economic growth with respect to economic and educational resources (model 2) and cognitive abilities

(4) the results of this last regression are not shown in detail, but they are available for interested readers.


(model 3), our objective is to see if the inequality associated with an economic recession has a greater impact among students from families with fewer resources, especially if they fall within the middle of the distribution of grades. to illustrate this, graph 3.2 shows the marginal effect of a 5 percentage point fall in annual growth in GdP. a decrease of this magnitude was observed, for example, in Singapore between 2007 and 2011 (see graph a.5 in appendix a).

For each quintile in the distribution of school grades (with Q1 representing the 20% of students with the lowest grades and Q5, the 20% with the best grades), we find a difference between the children of families with more and fewer economic resources ( we refer to these families as ‘poor’ and ‘not poor’). on the one hand, comparing the size of the ‘poor’ and ‘not poor’ bars within each quintile, we obtain information on the impact of the recession depending on household economic resources. on the other hand, when we compare the bars among the five quintiles, we find the effect of this macroeconomic change in groups of students with differences in academic success.

graPh 3.3

effect of a 5 point decline in GDP on educational expectations, by the academic performance of students and household material resources

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Poor not poor



the results of graph 3.2 confirm that in the face of a hypothetical fall in GdP, as we have illustrated, the most successful students (those who are found in quintiles four and five) behave in the same way, regardless of family material resources. In other words, there is practically no difference in the impact of the crisis on students with higher grades regardless of whether they come from affluent families or not. In contrast, among students with the lowest grades, the effect of the crisis is much greater among those from poor families. as we expected, the crisis also has a stronger impact on the expectations of students in the middle section of the distribution.

Finally, we repeated the same analysis using families’ educational resources as the indicator of family socioeconomic origin. Graph 3.3 illustrates the marginal effect that a 5% decrease in GdP has on students’ expectations based on family educational resources for the five quintiles of grade distribution.

graPh 3.4

effect of 5 point decline in GDP on educational expectations, according to the academic performance of students and household educational resources

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low high



In this case, the results are different from those we have just summarised on graph 3.2. the effect of the crisis is considerably more damaging for the expectations of the children of parents with little education in all five quintiles; that is, regardless of how academically successful the students are (note the distance between the two bars in each quintile). Moreover, if we focus on the the group of children with the least educated parents, the negative effect of the crisis is greatest among the students found in the middle range of the grade distribution (Q2 to Q4). although economic recession also affects the expectations of children from families with higher education levels, the grades of these children appear to be less important. It is interesting to note that while economic resources are nearly irrelevant in explaining the negative effect of the crisis on the expectations of the best students, educational resources seem to protect students at all levels of academic performance.

3.4. conclusions: crisis and pessimism

comparative studies regarding the effects of factors on the macro level of educational paths have primarily focused on the analysis of institutional design as the main explanatory factor. the analysis presented in this chapter adds to what has been dealt with in other studies by focusing on the role of the broader economic context in the formulation of students’ expectations regarding their continuing to study after completing compulsory education.

our analysis confirms that economic growth has a dual effect on student expectations. In periods of growth, students tend to be more ambitious or more optimistic about their educational futures. the income effect is then greater than the substitution effect, such that average expectations are reduced during a macroeconomic contraction. In addition to this effect on average expectations, in this chapter we have shown that crises also have an effect on the inequality of expectations based on family social origin. crises strengthen the effect of origin on expectations. this important finding is consistent with the so-called privileged information effect, explained in detail in the formulation of our hypotheses. children from affluent families understand the increased importance of education in times of crisis and


therefore prolong their studies, while children from families with fewer resources do not seem to fully realize that a labour market during times of crisis offers few opportunities to students who leave the education system early. thus, recessions not only lead to greater social inequality in the short-term by reinforcing the weight of socioeconomic origin in the educational attainment of more disadvantaged students, but they also may have an effect on individuals’ life opportunities in the medium and long-term. our findings totally discard the possibility of the crisis having an equalising effect. regarding inequalities based on cognitive abilities, the analysis confirms that students with an average performance level, because of the greater uncertainty regarding their possibilities of success in subsequent education levels, are more affected by recessions. In this chapter we have shown that while the material resources of families are irrelevant in explaining changes in expectations among students with higher performance levels, the educational resources of parents seem to act as insurance for all students, regardless of their performance level.

unIversIty comPetencIes and the educatIon of teachers In sPaIn 83

iV. university competencies and the education of teachers in spain. how different is the quality of universities?

4.1. introduction

the sociology of education is committed to revealing the causal mechanisms that underline the transmission of educational disadvantages between generations, concretely, between parents and children. In the previous chapters we looked at the influence of early childhood education, school effects in primary and secondary education, and the effects of the economic context on educational outcomes, such as competencies, curricular knowledge and the formulation of students’ expectations regarding their academic trajectories. our findings contribute to refuting the principles of modernisation theory, according to which the relationship between social origin and educational performance, on the one hand, and between educational performance and social destiny, on the other, will be enormously weakened or even disappear in the advanced economies as meritocracy becomes the organising principle of their educational systems.

as is well-known, this has not been the case, at least not to the extent that was initially expected in the 1960s. as a consequence, sociologists have rethought class theory and its impact on educational attainment. Five major perspectives have emerged providing explanations for the failure of modernisation theory: 1) one based on cultural deprivation or the habitus of the middle and upper classes (bourdieu and Passeron, 1977); 2) one that focuses on the systematic importance of material advantage (raftery and Hout, 1993; lucas, 2001); 3) one that uses preferences for education as exogenous explanatory variables determinant of performance (Gambetta, 1987; Murphy, 1990); 4) one that differentiates between the effect of limitations on learning capacities and the calculation of costs and benefits


that each individual makes when facing educational transitions (boudon, 1974) and 5) one that analyses the impact of the institutional structure of educational systems and, in particular, the selection of students schools make, and its effect on learning. In the second chapter we spoke in detail about the effects of the school on compulsory education (primary and secondary levels). In this chapter we will look at the importance of these effects in tertiary education, a level of education that has rarely been studied from this perspective. the chapter has a dual aim. on the one hand, we seek to quantify the contribution that university faculties and schools make to the knowledge that individuals acquire during their university education. on the other, focusing specifically on degree programmes in education, we reflect on the crucial issue of the selection of students who, upon finishing their degrees, will become teachers in compulsory education.

research on university students has tended to focus on their subjective evaluation of their educational experiences. However, some studies that have assessed the validity of their recollections have concluded that university students are not capable of accurately evaluating the impact of their experiences during university on their learning (Porter, 2011; browman, 2011). Something similar happens with their perceptions of the utility of their degrees in the labour market for which they lack a reference point. there is little empirical research on achievement measured through the individual knowledge of students in tertiary education. although the need to evaluate education in all stages is recognised, most data sources available for studying differences in outcomes and the stratification of schools have focused on the compulsory stages of education, and have been aimed at identifying the mechanisms that explain the high levels of school failure among disadvantaged students. the university has been largely ignored. Given the compulsory nature of most of secondary education in advanced economies, the studies developed by the oecd (such as PISa) and the Iea (tIMSS and PIrlS) have constructed measures of competencies acquired in this stage of education that are comparable across countries. to a certain extent it is reasonable to have done this. However, given that attending university is one of the most important determinants of an individual’s subsequent life trajectory in the advanced economies, the study of differences in access, the likelihood of obtaining a degree and the


competencies that university students acquire should be a much more frequent objects of analysis in empirical studies. In this chapter, we will analyse the competencies of university students who aspire to become teachers in Spain.

4.2. Determinants of school effects on university education in spain

anyone familiar with the system of tertiary education in Spain would readily recognise that it is not a very stratified system. What this means is that for a student about to enrol in a Spanish university, it is not easy to know which school would be the best choice among available options. In general, comprehensive compulsory education systems have highly diversified university systems. In other words, when students have not been tracked in compulsory school, the university system helps those that are most capable (or most advantaged given their social origin) to differentiate themselves from the rest by accessing the most prestigious schools. despite having a comprehensive system in Spain (although vocational training exists, it has always been a minority choice, Homs, 2008), the higher education system is only minimally horizontally stratified. the aim of this chapter is to verify the validity of this statement using the competencies of university students as the dependent variable. For various reasons, we assume that school effects in university have little effect on learning in Spain. In this section we enumerate certain factors that, in our opinion, may be determinants in understanding this working hypothesis.

First, there is a significant positive selection of students once compulsory secondary education is finished (bernardi, 2012). Spain occupies one of the worst positions in regard to school failure among oecd countries. this means that most selection takes place during compulsory education and not when it is finished. In other words, we expect to find that the school effects are much more decisive during secondary education – as shown in the second chapter – than in university education. this hypothesis emerges because the differentiation of educational institutions is one of the key factors identified by some sociologists of education as a determinant in the reproduction of social advantages. the well-known hypothesis of effectively


maintained inequality (lucas, 2001 and 2009) explains why the ability to choose a more prestigious institution is decisive in transmitting inequality between generations in the stages in which the educational system has been democratised and access is a universal right. Given that the most selective phase in the Spanish education system comes at the end of secondary education, it would be reasonable to expect that school effect in tertiary education would be less than in prior stages of education.

beyond the validity of this theoretical position, given the general characteristics of the Spanish university system there are two essential reasons why we would expect school effects to have little importance. First of all, the size of school effects in university is determined by the very limited capacity universities have to choose students based on their prior educational merits. In addition, they often have limited control over the number of places available for students in different degree programmes (in our case, there are no limits or quotas on the number of students that can enrol in education degree programmes). Secondly, Spain, like other countries that participated in the bologna process fostered by the european Union to promote the harmonisation of the different national university systems in the eU, has a national agency responsible for the evaluation and accreditation of institutions of higher education (National agency for Quality assessment and accreditation, aNeca). However, although the existence of aNeca has provided structure to most of the processes currently followed in Spanish universities, it has had little impact on the evaluation of degree programmes for future teachers. aNeca is limited to accrediting proposed educational programmes, but it does not provide any information enabling us to rank them based on quality or to evaluate them based on competitiveness. this problem not only affects the different faculties and schools offering teacher education, but university studies in Spain overall. In other words, it is difficult to make a distinction between universities based on the quality of their teaching staff, their curriculum and the employment prospects of students, which could have as an indirect consequence (through eliminating the effects of self-selection) less differentiation of the effects of the specific school in which students enrol (tatto et al., 2012: 46-7).


the only characteristic of the Spanish education system that would suggest a high degree of stratification is the high participation rate in tertiary education. In contrast to other european countries, where access to university is much more limited (the most extreme example is Germany), in Spain almost half of each age cohort attends university. In theory, when a high percentage of students reaches this education level, it is more likely that differentiation occurs within the university than in a highly selective process in a prior stage. through the analysis of our data we will ascertain which of these different mechanisms prevails in practice.

a minority in the sociology of education, closely involved in research on public policy and science policy, has closely studied the conditions necessary for the diversification of university systems (a review of the most important arguments can be found in Noelke et al., 2012). the expansion of education that has democratised access to higher education has meant an increasing differentiation between schools of greater or lesser prestige. this process has taken place most clearly in anglo-Saxon countries (think of, for example, the selective Ivy league schools in the United States), while the majority of policies adopted in continental europe have been essentially aimed at increasing access. In the United Kingdom, for example, an important distinction between universities created during the 1990s and those with a longer tradition has been institutionalised, leading to a significant gap in the prestige provided by studying in one university or another. at the same time, this distinction overlaps with the difference between universities that have established intensive research programmes and those that have not. as has been demonstrated, attending a more or less prestigious university has a significant effect on occupational achievement in the medium and long-term (bratti et al., 2004; Power and Whitty, 2008). In addition, access to the most prestigious universities is stratified to a great extent based on socioeconomic origin and ethnicity (zimdars et al., 2009; boliver, 2011). In the United States similar processes of stratification within the university system have occurred. In Spain, the most solid evidence available suggests that inequality in access to the most selective universities based on ascriptive family characteristics has recently increased. Moreover, access has become more complicated for students from the lowest socioeconomic classes (astin and oseguera, 2004; Sigal,


2009), as well as for those of non-white races and minority ethnic groups (Hu and St. John, 2001). However, in contrast to what takes place in the UK, evidence exists that attending more prestigious universities in Spain has less direct impact on students’ subsequent employment (Noelke, et al., 2012; brand y Xie, 2010).

at this point it is necessary to consider the factors that increase the diversification of university systems (see a systematisation of the most accepted arguments in Van Vught, 2007). among the most convincing explanations is the organisational uniformity of universities, which includes access to financing and other resources and the influence of academic norms and values associated with scientific research.

a lack of means exposes organisations in general to more intense competition to attain an acceptable level of status and, in some cases, to even survive. this is the case in the United Kingdom and the United States, where universities are accustomed to carrying out campaigns to attract financing from sources of diverse origin and nature (Sigal, 2009). However, in Spain, the number of public universities, which account for 89.2% of university students (data from Spain’s National Institute of Statistics for the academic year 2008-2009), has not stopped growing since the 1970s in order to satisfy the demand for tertiary education at the local level, as well as, in many cases, the needs of regional politics. one figure may be revealing: there are 74 public universities in Spain’s 17 autonomous communities. of these communities, only aragon, Navarre and the basque country have only one public university. the number of public universities is not the only factor that has shaped schools effects in Spain’s university system. the speed of this expansion may also be a factor, as in general it was concentrated between 1982-2004 when the number of universities rose from 33 to 70. at the end of this period the number of private universities began to expand. this whole process has been more closely linked to the territorial decentralisation of the state than to a true reflection of teaching and scientific needs, which means that access to resources for the university system has been more or less guaranteed for all the institutions. as a result, there is almost no competition among Spain’s public universities.

the expansion of the university system over a period of more than 20 years was also carried out without sufficient thought in terms of the organisation


of new universities. While the european university system began during that period to converge toward standards similar to the most prestigious american universities, in Spain the process was very different. the speed with which european universities have approached american one’s has varied from country to country. It was, for example, the smallest countries with minority languages that were the first to adopt english as their research language. this process is still not complete in the larger european countries, such as Germany, France, Italy and Spain. It is here where the process of the internationalisation of the university is taking place more slowly. this can be seen in certain indicators, such as the percentage of faculty that participate in visiting professorships in the United States (borghans and cörvers, 2009). the choice of this country as a destination is not arbitrary. International rankings of the best universities in the world place seven american universities among the top ten. Spain, as is well known, does not have any university among the top 150.(1)

beyond these general characteristics of the Spanish system of higher education, there are certain characteristics specific to degree programmes in education that, in contrast to what occurs in other fields, could be contributing to reducing the importance of the effects of the specific school. the majority of students that are enrolled in degree programmes to become teachers want to find a job in the public sector that they can keep until retirement. teachers that manage to enter the public education system develop their professional careers within a well-organised environment in which promotion depends to a great extent, although not exclusively, on seniority within the system. this is part of a dual system in combination with private schools. For this other segment of the labour market, primary and secondary school teachers in private schools, the prospects for job stability and promotion are more uncertain and take place within a market context. as a result, students studying to become teachers in Spain clearly prefer public employment. For this reason there is greater expected equality of conditions in future employment among these Spanish university students than for students in countries where teaching careers are more uncertain (tatto et al., 2012: 39). to a certain

(1) World University Rankings 2011-2012 de Thomson Reuters (The Times Higher Education World Univer-sity Rankings).


extent, this homogeneity of aspirations could also contribute to the lack of differentiation between universities educating future teachers in Spain.

4.3. is there really a difference in the results produced by distinct university faculties?

the tedS-M data for 2009 (teacher education Study in Mathematics) gathered by the Iea (International association for the evaluation of educational achievement) for 17 countries, among them Spain, allows us to evaluate the knowledge of Spanish university students studying to become teachers in two subject areas: mathematics and mathematics pedagogy.(2) the dependent variable in this chapter is the score that students obtain in these subjects. the first subject is not part of the curriculum for future primary school teachers, while the second is part of the core subject matter and, therefore, ideal for studying the impact of attendance at a specific university on learning. Graphs 4.1 and 4.2 describe the differences among students in each of the schools (44 faculties or universities) that form part of the Spanish sample (represented by the vertical points) and among the schools themselves (described by the thick line). In neither of these cases did we find great differences in the average score attained by the students, regardless of the university they attended. the average for the different schools is the result of a certain degree of internal dispersion. as a result, most of the cases fall within a range of 400 to 600 points (from a total range of 0 to 800). a greater dispersion in the distribution of scores was found in pedagogy than in mathematical knowledge.

these results confirm, at least initially, the assumption that a system of higher education like the Spanish one is not very diversified (little horizontal stratification), and therefore, it is not important what university students study in to predict their knowledge in either of these two subject areas.

(2) See appendix a for more information regarding this data and the variables used, and appendix b for a description of the estimation techniques used for the analysis presented in this chapter.


graPh 4.1

scores in mathematics, description of the distribution among schools and within them

average for the schools • Individual scores

800

600

400

200

0

source: calculations by the authors based on the teds-m 2009 sample, spain

graPh 4.2

scores in the pedagogy of mathematics, description of the distribution among schools and within them

800

600

400

200

0

average for the schools • Individual scores

source: calculations by the authors based on the teds-m 2009 sample, spain


the tedS-M data provides variables on the prior trajectory of students and their family origin, which allows us to partially explain their results. of these, we have chosen two explanatory factors. one is grades obtained in secondary school, which provide a relatively effective way of controlling for residual heterogeneity that may still exist in higher education, despite the positive selection that took place during secondary school. our indicator is only an approximation, as it is based on the recall of the students themselves regarding their grades, using as a generic reference the grades obtained by their classmates at the end of secondary school (the range in values for their response is between 1 – well above the average – and 5 – well below the average). the second, related to family origin, is parental education, recoded in 7 levels based on the standard international ISced classification, which takes as the value for the household, the highest education level, whether that of the mother or father. Graph 4.3 provides two graphs for each of these indicators, the first for grades in mathematics and the second in mathematics pedagogy.

the four individual graphs reveal an absence of large variations in the impact of grades in secondary school and the education of parents as determinants of knowledge in the subject areas being tested for in the different universities. although the differences between schools are not very significant, the greatest differences occur among students that occupy the most extreme positions in the range of independent variables, in other words, among the children of parents with higher and lower levels of education and among students that had grades that were further from the average in their secondary school class.

to produce a reliable estimate of the differences between schools, it is necessary, as done in chapter 2, to jointly estimate school effects using models with two levels (one for individual effects and the other for the universities). For each subject, a series of models that calculate the effects of the school on different specifications are presented in tables 4.1 and 4.2 in the statistical appendix for this chapter. the minimal importance of the difference represented by attending one school or another can be seen in graph 4.4, which, in addition, includes the


confidence intervals for each school. With the exception of the schools at the extremes (in other words, those that deviate most from the average performance), confidence intervals overlap; thus, we can conclude that not only are school effects small, but that the differences are not statistically significant for the majority of the possible contrasts.

graPh 4.3

effect of (a) grades in secondary and (b) the education of parents on grades in mathematics and the pedagogy of mathematics among schools. independent estimate for each school

1 5

grades in secondary school

gra

des

in m

athe

mat

ics

650

600

550

500

450

400

gra

des

in m

athe

mat

ics

peda

gogy

600

550

500

450

4002 3 4 1 72 3 4 5 6

Parental education

1 52 3 4 1 72 3 4 5 6

source: calculations by the authors based on the teds-m 2009 sample, spain. each line describes the reality of each of the schools that have been included in the spanish sample. the estimate was done independently using linear regressions by ols.


graPh 4.4

comparison of the effects of the university on grades in mathematics and the pedagogy of mathematics

dev

iatio

ns fr

om a

vera

ge p

erfo

rman

ce. m

athe

mat

ics

20

15

10

5

0

–5

–10

–15

–20

–25

dev

iatio

ns fr

om a

vera

ge p

erfo

rman

ce. m

athe

mat

ics

peda

gogy

20

15

10

5

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–5

–10

–15

–20

–25

source: estimation based on the results of models 0 in tables 4.1 and 4.2. the points represent average differences. the lines through each point are confidence intervals. when the intervals corresponding to two schools do not overlap, the differences between them are statistically significant.

beyond the description of the differences between schools, the models that are presented in the tables in the appendix of this chapter introduce the two variables used to model the impact of student origin. In the first model the gross effect of secondary school grades is estimated, and in the second, the effect of parents’ education, controlling for the previous variable. as expected, secondary school grades have a positive and highly significant effect on learning. However, surprisingly, parental education has a very small effect, and although positive, it is not statistically significant as a predictor of grades in mathematics and is only marginally so in the tests on mathematics pedagogy. this is clear evidence of the strong selection by social origin that takes place in Spain at the end of compulsory education or at least, before higher


education. by introducing fixed effects in the third models in tables 4.1 and 4.2 in the appendix, this guarantees an estimation of the effects of the independent variables on the individual level, controlling for all those factors that have some effect on students’ scores at the level of the school. In short, the model of fixed effects is equivalent to an estimation of the effects of the independent variables at the individual level once the average corresponding to the school is subtracted. In this type of estimation, which is more reliable than that presented in model 2, we do not find any statistical significance associated with parents’ education.

returning to the previous models for a moment, in this specification a random effect has been included, associated with secondary school grades and parents’ education. thus, as we did in chapter 2 with primary and secondary schools, we can examine if the schools exert an influence on the effect of social origin in an unequal manner; for example, increasing, or, the reverse, mitigating inequality resulting from students’ performance in early stages of their schooling or from their family origin. It is noteworthy that in this aspect as well, the schools the surveyed students attended are irrelevant for the way their secondary school grades are related to their scores on the two tests, or practically irrelevant in the case of parents’ education (we can check this extreme by comparing the variance associated with the slopes of these independent variables with the residual variance, which corresponds to the individual level). In short, graph 4.5 represents the results of models 1 and 2 for each subject. despite the inclusion of a random effect associated with previous grades, the lines between schools are practically parallel. In the case of parental education, there are some differences, which are accentuated among students whose parents have higher levels of education. this means that although school effects are small, they seem to have a greater impact in this group. regarding what can be seen in the corresponding graphs, the differences never involve a negative slope for parents’ education, but are limited to flat slopes for some schools, while others are slightly positive.


graPh 4.5

the impact of grades in secondary and parental education on grades in mathematics and the pedagogy of mathematics. these models allow us to quantify the differences between schools and the effect of each of the variables on the horizontal axis

1 5

grades in secondary school

gra

des

in m

athe

mat

ics

520

500

480

460

440

gra

des

in m

athe

mat

ics

peda

gogy

540

520

500

480

460

4402 3 4 1 72 3 4 5 6

Parental education

1 52 3 4 1 72 3 4 5 6

source: estimation based on models 1 and 2 of tables 4.1 and 4.2. linear regression models with constant and random slope nota: estos modelos permiten cuantificar las diferencias entre centros y en el efecto de cada una de las variables graficadas en el eje horizontal.

despite these results, the reader should remember that the most important conclusion from the previous analysis is that in a model of fixed effects, there is no association between parental education and scores on mathematics for the overall sample. Without this being contradictory, the above graph shows, however, that some schools are associated with an increase in the importance of parental education with respect to the average. are the differences in the impact of parent’s education significant


among schools? this question can be answered based on graph 4.6, which again gathers the deviations of each of the schools with regard to average performance. as in the previous graphs, comparing confidence intervals we can conclude that these differences are not statistically significant in the majority of the cases (in the case of the tests on mathematics pedagogy, they are not found even among the schools at the extremes).

graPh 4.6

comparison of school effects on the slope for parental education by grades in mathematics and pedagogy of mathematics

diff

eren

ces

in th

e sl

ope

for t

he v

aria

ble

pare

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catio

n m

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4

3

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source: estimation based on the results of models 2 in tables 4.1 and 4.2

the last model (model 4) from the tables in the appendix has two aims: First of all, to confirm that the effects considered here are produced with more demanding specifications that include the common controls in academic studies, such age and sex, and if they studied mathematics in secondary school (as students who did not study mathematics in secondary school can do so in university), and secondly, to explore the


importance of certain independent variables corresponding to the university or individual level. at the level of the university, two variables have been chosen. the first is an estimate of the level of choice the administration of the school has regarding the admission of students. there are five possible answers to this question (from 1, which corresponds to the highest level of selectivity to 5, the lowest). as can be seen, this variable has no statistically significant effect for either mathematics or mathematics pedagogy.(3) this not disprove the importance of the selection of students in creating the school effect or in driving up the average performance of the student body. It is very likely due to the fact that the margin for manoeuvring among universities in Spain in selecting students is very small, and although some variation among schools exists, it is not very important. the other variable on the level of the school, with which we complete the specification of model 4 in each table, is the average education level of students’ parents. as we have seen in chapter 2, this is one of the indicators that best explains school effects in compulsory education. although the causal mechanism that underlies the effect of this variable is unclear and could be compatible with the existence of micro-interactions (the known peer effects) and correlate with other types of resources that may be more abundant in schools attended by students whose parents have more education, it is striking that the effect of this variable is statistically significant in the case of scores in mathematics but not in the case of tests on mathematics pedagogy, which as explained in the introduction to this chapter, is the only one of the two subjects that explicitly forms part of the teacher training curriculum.

the last block of explanations introduces two more variables, which, while not mutually exclusive, incorporate into the model the effect of students’ reasons for choosing this major on their grades. In the first of these two variables, students who chose this major in order to find good jobs (as explained, primarily in the public sector), are assigned the highest values. In the second, vocation as a reason for selecting this major is

(3) also used, with the aim of testing the robustness of the results, is an alternative variable that gathers the degree of selectivity of the university in a more objective form. concretely, the ratio between the number of students that finish their studies in the observation year and the number of students that began their studies in the same cohort is used. this variable also does not reveal a statistically significant effect for mathematics or mathematics pedagogy.


predominant. these questions offered four possible answers in the questionnaire. It is clear from both tables (4.1 and 4.2. statistical appendix to this chapter) that the students who appear to be guided more by vocation than by professional prospects perform better on both mathematics and pedagogy of mathematics. this has important implications for the organisation of the education system: it seems clear that if the intention is to recruit the most qualified teachers, teacher training programmes should select students who explicitly see teaching as a vocation (only 40% of the education students in the sample said that vocation was their main reason for choosing this major).

4.4. conclusions: a system with low diversification

although the data used in this chapter does not permit us to evaluate the entire Spanish university system, as it is focused solely on students majoring in education, it does permit us to draw some important conclusions, regarding both higher education in general and the impact its present organisation will have on the educational system of the future.

First of all, we have confirmed that the Spanish higher education system is not very diversified. the arguments outlined in the introduction to this chapter pointed to different explanations for why this might be. among these, we have highlighted the limited effectiveness of the accreditation system in the training of future teachers, and the conditions determining access to financing that universities in Spain enjoy, particularly public universities. our findings seem to indicate that the specific school attended is not a strong determinant of the level of substantive knowledge in the subjects analysed here: mathematics and mathematics pedagogy. according to tedS-M data, the school or university faculty attended determines only 2% of the knowledge acquired in these subjects.

Secondly, in the theoretical part of the chapter and in previous chapters, we have speculated on the selection process that takes place at the end of compulsory education. In this chapter we have shown that this selection is in fact of such magnitude that parental education is not a significant predictor of the knowledge demonstrated at the end of university


education. although there are indicators that in some of the faculties included in the sample family origin may have a greater effect on learning, we have demonstrated empirically that these differences are not statistically significant.

Finally, the supply of universities has generated a Spanish university system that leaves little room for student selection. However, our analyses have shown that the selection of the best students as well as students who manifest a clear vocation for their chosen profession could dramatically improve future teacher quality and thus contribute to the improvement of the compulsory education system in the medium and long term.

educatIonal exPansIon In sPaIn and adult sKIlls 101

V. educational expansion in spain and adult skills

5.1. introduction

one of the most solid empirical findings in the sociology of education and in studies on social stratification is the association between individuals’ social origin and the level of education they attain. Knowing the socioeconomic position of an individual’s family of origin (whether measured through their parents’ education level, material resources or occupational level) helps us to predict the education level he or she will attain. there are generally two broad theoretical perspectives to account for this empirical regularity. there are culturalist theories, which are based on the idea that families with more cultural capital are favoured because their children gain access to levels of education that those from more disadvantaged families do not (bourdieu and Passeron, 1970). and there are explanations based on rational choice theory (boudon, 1983) that suggest that differences in educational attainment based on social origin are due to families’ calculations of expected costs and benefits, which differ based on their socioeconomic positions.

Since the 1990s, when the influential work edited by Shavit and blossfeld appeared (1993), in which thirteen countries with different levels of economic development and very different education systems were compared, there have been many studies that reveal the existence of inequality of educational opportunities (See breen and Jonsson, 2005). there is no doubt that unequal access to different levels of education based on social origin exists in all the different contexts researchers have analysed. although the results by country are not identical and are often difficult to compare (because of the use of different methods, variables


and time periods), differences between social classes (or other indicators of family origin) in children’s access to educational credentials have been found to a greater or lesser degree in all cases. In the case of Spain, this pattern is clear (carabaña, 2004; Martínez García, 2007). according to the data of bernardi and requena (2007), corresponding to the Spanish population born between 1920 and 1966, there is a close relationship between social class of origin and education level attained. they found that the children of employers, executives and professionals were approximately twenty times more likely to obtain a university degree than the children of farmworkers. While most of the children of urban workers obtained only a basic (60%) or primary school (19%) education, these percentages were dramatically lower among the children of white-collar workers and especially among the children of employers, executives and professionals.

there is less agreement about the trends over time in the association between origin and education. a fundamental issue in the specialised literature has been to determine whether the expansion of education associated with the public provision of a minimum level of universal education has weakened the association between social origin and education level attained. With expansion certain levels of education become compulsory, but in addition, non-compulsory levels also expand (by becoming free or significantly reducing their cost), such that the social mix becomes significantly more heterogeneous. this incorporation of additional social groups into the education system is clearly compatible with a reduction (in fact demonstrated by empirical research), although not a disappearance, of inequality of educational opportunities (breen et al., 2010). again, the Spanish case fits the pattern to perfection. the educational level of successive birth cohorts in Spain increased throughout the 20th century: While among the generations born in the first decades of the past century, half the population did not obtain any educational diploma or certificate, the cohorts born at the end of the 1970s had, in their majority, attained a level of formal education that prepared them for entering the labour market (Garrido, 2004; requena and bernardi, 2005). this gradual increase in human capital coincided in time with an increase in public spending on education and the adoption of successive education laws,


which not only extended compulsory education but also fostered more equal access to education. In line with the findings for other countries, (bernardi and requena, 2007), inequality of educational opportunities clearly declined in Spain throughout this period.

traditionally, the sociology of education and in particular the study of the intergenerational transmission of disadvantage, have focused on three types of dependent variables: First, the highest education level attained by individuals or, alternatively, their educational transitions (transitions to each successive level); secondly, students’ expectations of completing each level of education (analysed in chapter 3 in this study), and thirdly, the scores students obtain on certain cognitive tests (e.g. PISa, tIMSS or PIrlS, used in chapter 1), or simply the grades received on exams to measure progress during compulsory education (as in the analysis presented in chapter 2 in this study) or tertiary education (for example, the tedS-M study in chapter 4). While these types of studies have been carried out for a long time in the United States, in europe there were few such studies until after the publication of the first PISa data in 2000.

the Survey of adult Skills (PIaac) study produced by the oecd, and which Spain participated in with its own sample, has, for the first time, permitted the combined use of both the maximum level of education attained and the level of cognitive skills in the adult or working age population (between 16 and 65 years of age).(1) though it is clear that all individuals who have attained the same level of education do not necessarily have the same level of cognitive skills, little empirical research has been carried out on this. differences in cognitive skills among adults with the same educational level may be due to multiple factors. one of the most obvious is that a specific degree or diploma may require a minimum of cognitive skills but this does not imply a maximum, and, as we have seen in the third chapter, there are differences among students in the way they plan their educational paths, even among those having the same academic abilities. other factors have already been analysed in this study (for example, the importance of school effects at each stage of the educational cycle). Finally, we cannot discard the possibility that some

(1) See appendix a for greater detail on these data and the variables used and appendix b regarding t the estimation techniques used for the analysis presented in this chapter.


individuals continue to invest in educating themselves once their formal education has ended. this process, called Lifelong Learning, implies that adults can continue to acquire skills throughout their lives and that to the extent that they do so, they will be more able to profit from their prior investment in formal education.

In this chapter we are going to analyse these last two ideas in greater depth. our purpose is to determine the heterogeneity of adult skills at each level of education, in addition to examining the impact of parents’ education – as a measure of social origin – on these skills and the difference that may result from how much adults use these skills in their daily lives. Given that educational expansion took place in Spain later than in most of its neighbouring countries, our study has taken into account the birth cohort of those adults who participated in the PIaac study. Unfortunately, as we explain in more detail later on, the design of the PIaac does not permit us to distinguish age beyond the birth cohort. PIaac is a transversal survey that has not yet been repeated. More information on this interesting, new database labelled the “PISa for adults” – and whose analytical potential is still untapped – can be found in the methodological appendix.

5.2. mathematics and reading skills of adults in spai

First, we determine the heterogeneity of the skills of Spanish adults on two of the three tests in the oecd Survey of adult Skills: literacy and numeracy. We leave aside the the results of the third test, problem solving in technology-rich environments. Graph 5.1 shows the average scores on each of the two tests for respondents for each of the three major education levels the data allow us to distinguish: compulsory education or less, upper secondary and university. both in numeracy and literacy, a higher education level correlates with a higher score. Individuals with more formal education, therefore, obtain higher scores. However, within each of these levels, there is noticeable heterogeneity. While it is not surprising that the intellectual abilities of Spanish adults differ based on the maximum level of education obtained, the magnitude of the differences found is surprising, reaching more than one standard deviation in both reading and mathematics.


graPh 5.1

average scores in mathematics and reading comprehension by education level for the spanish between the ages of 16 and 65

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source: PIaac. calculations by the authors.

this phenomenon also occurs, although with a different level of intensity depending on the case, in the other countries included in the PIaac sample. However, the association between education and cognitive skills is far from perfect. In fact, within the same country, a substantial proportion of individuals who only completed upper secondary education obtained higher scores than those with university educations. When we compare individuals from different countries, the evidence supporting the existence of a gap between nominal credentials and real skills is even more striking. thus, for example, dutch and Japanese adults between 25 and 34 who obtained a post-compulsory secondary school diploma have greater skills on average than Spanish and Italian university graduates in the same age group (oecd, 2013)

there are at least three reasons for this gap. First of all, there is an expected negative effect of age on skills demonstrated on objective tests. Using age, however, leads to a problem that is well-known to social scientists: the difficulties in distinguishing the effects of age from the effects of the time


period and the cohort (Yang Yang and land, 2013), especially when sufficient longitudinal data is not available, which is the case for the PIaac, used in this chapter. In the context that concerns us, age has a dual effect: a strictly biological one, as cognitive decline may affect the numeracy and literacy skills demonstrated, and an effect related to human capital, as age can also reflect the depreciation of human capital acquired in formal education that begins to occur once education is completed. this depreciation may lead to a loss in the applicability of skills in the labour market or in every day life activities or may simply be due to no longer using some of the skills acquired. It is therefore to be expected that the longer the time that has passed since leaving school (calculated by approximation based on age), the weaker the relationship will be between formal education and skills. the effect suggests that the association between education level and skills would be stronger among the younger respondents than among older ones. cohort effects would be behind the enormous social change that has taken place in Spain in the last four decades and whose effects on access to education we have described in the introduction to this chapter. We are referring to generations of Spanish who have been exposed to different socioeconomic and cultural conditions (crucial among these, is the increased propensity to acquire education) relevant for the types of findings we are analysing here. Finally, the effects of the particular time period affect all individuals, regardless of age, or the cohort (generation) to which they belong. to distinguish the effects of age from those of cohort we would need data on skills for different age groups born in different periods of time. to test the validity of this first hypothesis, the depreciation of human capital, while acknowledging the already mentioned problems of identification, we will examine if the relationship shown in graph 5 is altered or not when we control for birth cohort or age group.

to compensate for the depreciation in human capital, some individuals choose to continue acquiring skills in other educational spheres (academies, online courses, etc.), in the workplace (through specific courses related to the tasks carried out at work or more general courses) or at home. We expect, then, as a second hypothesis that the different propensities of individuals to participate in educational activities, whether formal or informal, once they have finished formal education, would be important in explaining variations in skills beyond the formal level of


education obtained. as an indicator of the differing propensity toward Lifelong Learning or continuous education throughout the life cycle, in this chapter we examine the use individuals make in their daily lives of certain important skills, in this case, mathematics and reading.

the imperfect translation of formal education into real skills may also be related to intergenerational transmission – from parents to children – of certain cognitive and non-cognitive abilities that are not directly related to educational credentials. In the PIaac data the only indicator of respondents’ social origins is the education level of the parents, grouped into three categories. We assume that once we control for education level, there will be a certain inequality in the distribution of skills that can be explained by individuals’ socioeconomic origins, in other words, by the relative position of their family of origin.

In the following three sections, we explore these three explanations in greater detail: depreciation of human capital throughout the life cycle, activation of skills through Lifelong Learning and factors related to intergenerational transmission.

5.3. age and cohort effects

to verify the extent of the differences in the average skills of Spanish adults by age, graph 5.2 presents the average skills in mathematics and reading comprehension broken down by age group (five-year cohorts that reflect the age of the respondents at the time the data was collected). It is striking to see that in the two subjects studied significant improvement has taken place in the skills of Spanish adults, something that received broad coverage in the media when the PIaac study was initially released. this implies that the poor position Spain occupies in international comparisons improves greatly if we focus on younger cohorts. as the graph shows, there is a sharp drop in skills when we go from younger to older cohorts starting with those from 41 to 45 years of age at the time of the survey; that is, among those born roughly during the transition. From that point on, the decrease continues in an almost linear pattern in which each older cohort demonstrates lower intellectual abilities. the trend is more noticeable in maths skills than in reading, above all in the last age group included here.


graPh 5.2

the average skills of spanish adults by five-year cohortsm

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source: PIaac, calculations by the authors the green and red lines portray the trends in the average skills of each birth cohort. the blue points reflect the score obtained by each of the respondents for each test.

In any case, these results must be interpreted cautiously. on the one hand, it is undeniable that the social change that has taken place in the past half century can be seen in the data and this cohort effect is an important explanation for the improvement in the level of cognitive skills in Spain. on the other hand, we can certainly assume that there is also an age effect, understood both in the strictly biological sense and as a result of the life cycle – related to the time elapsed since the completion of formal schooling. With each year that individuals spend out of school, they lose some of the knowledge and skills they have acquired for the simple reason that they forget what they have learned, although it is likely that this effect is stronger regarding maths skills than reading. Moreover, the oldest individuals considered here are probably already experiencing the inevitable consequences of ageing, that is, the natural loss of intellectual abilities in old age. although the greatest deterioration in health due to age usually comes later, we cannot discard the fact that for some of the participants,


especially those from 61 to 65, this decline has already begun. For example, it may be that for the oldest respondents it is more difficult to complete the tasks in the time allowed. It is important to distinguish these two possible explanations for the trend observed in graph 5.2, because they have very different implications: if the low level of abilities found among older adults is due to a cohort effect, and concretely to the fact that they did not receive much education when they were young, it is likely that this is a disappearing phenomenon, without much importance for the long-term future of the country. If, on the other hand, the low level of skills of older adults is due to an age effect, it is likely that such an effect will also occur among younger cohorts (when they reach an older age), so that it is a finding with important implications for the future.

the most plausible, in fact, is that both effects, the cohort effect and age effect, are behind this pattern. If we look at the average scores by five-year groups in other countries or for the oecd average (not shown here), the pattern is quite similar: consistently, the older groups obtain lower scores than the younger groups. this pattern is found both in countries with very low overall scores – for example, Spain, Italy and France – and in countries with the highest scores – the Netherlands, Finland and Japan (oecd, 2013). the difference lies in the fact that in all the countries the average scores in all of the older age groups are significantly higher than in Spain. the only exception is Italy, with results by age that are almost identical to those of Spain. the fact that this pattern is so consistent and that it is found both in countries which more recently undertook educational expansion and in those that began such expansion earlier, supports the persistence of an age effect even when we take into account the differences in the composition of the different cohorts based on educational qualifications.

breaking down cognitive performance by age and by education level for the Spanish sample (table 5.1), we can refine our analysis even more. If older individuals with university educations do not differ much in their performance from younger persons with university educations, this would be an indication of the existence of a cohort effect. In contrast, if the decrease with age occurs equally at all levels of education, the evidence would support the hypothesis of the age effect.


table 5.1

average scores by age and the education level of parents

table 5.1a average scores in mathematics by age and education

age

educatIon level

comPulsory or less uPPer secondary unIversIty total

16-20 years old 239.6 273.6 272.8 249.5

21-25 years old 230.6 270.0 282.0 256.7

26-30 years old 220.1 254.3 276.9 249.5

31-35 years old 228.0 257.1 281.0 256.9

36-40 years old 223.3 251.8 283.3 253.0

41-45 years old 225.4 258.3 277.9 249.5

46-50 years old 214.9 250.7 277.8 240.0

51-55 years old 208.5 252.1 268.9 232.8

56-60 years old 204.5 246.8 266.2 223.1

61-65 years old 191.8 237.8 257.8 207.4

total 218.6 257.5 277.3 243.3

maximum difference 47.8 35.8 25.5

source: PIaac. calculations by the authors

table 5.1 b: average scores in reading by age and education

age

educatIon level

comPulsory or less uPPer secondary unIversIty total

16-20 years old 250.1 282.6 272.6 259.4

21-25 years old 234.8 278.8 288.6 263.0

26-30 years old 230.4 259.7 285.5 258.0

31-35 years old 229.9 259.8 283.9 259.4

36-40 years old 231.6 257.2 289.4 259.8

41-45 years old 232.3 263.1 279.9 254.3

46-50 years old 225.6 256.3 282.4 247.9

51-55 years old 216.4 251.9 271.5 237.8

56-60 years old 210.3 244.7 262.7 225.9

61-65 years old 204.6 244.4 262.6 218.3

total 226.8 262.5 281.4 249.7

maximum difference 45.5 38.2 26.7

source: PIaac. calculations by the authors


looking at numeracy and literacy, the evidence is not clear. on the one hand, there is less loss of knowledge the higher the level of education. For example, the maximum difference observed between age groups is 47.8 points for the lowest education level, compared to 25.5 points for the highest level in maths skills, and 45.5 points for the lowest education level and 26.7 points for the highest level in reading skills. on the other hand, this same data reveals that there is a clear decrease even within the category of university education. In short, although we do not have the data required to settle this issue, everything points toward the existence of an age effect related to the loss of human capital and a cohort effect related to social change in general and educational expansion in particular.

5.4. lifelong learning

one way for individuals to avoid losing intellectual capacities as they get older is for them to use the skills they acquired in formal education in their daily life. activating skills acquired in the past and incorporating new skills can occur in different spheres of daily life (in the family, on the job, in educational institutions) and can happen formally or informally. It is obvious that certain types of professions require the continuous use of one or more of the three types of skills measured by the PIaac: reading, mathematics and problem solving. We will now look at two graphs to introduce Lifelong Learning. For our purpose, we use two factors (both multidimensional variables composed of different measures included in the study). For mathematics we have used responses to questions regarding how often mathematics or statistics are used in every day life: the frequency of use of simple algebra and formulas, preparing graphs and tables, use of a calculator, and calculating expenses and budgets. the responses are scored from 1 (never) to 5 (every day). In the area of reading, the questions refer to the frequency of reading accounts statements, manuals or reference materials, newspapers and magazines, letters, reports, emails, instructions, books, professional journals and the like, and the use of diagrams, maps and plans. We have grouped these two sets of questions into one single factor through Varimax rotation.

Graphs 5.3 and 5.4 show with scatter plots (each individual is represented by a point) the relationship of numeracy and literacy skills, as measured by the PIaac, with their frequency of use in daily life. In the first graph, which


refers to mathematics, the points representing older adults are concentrated in the area of the graph representing scarce use of mathematics skills. It also reveals that it is young people who use these types of skills most frequently.

graPh 5.3

numeracy skills of adults by age group and by use in every day life

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graPh 5.4

reading comprehension skills of adults by age group and by use in every day life

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Graph 5.4 shows that reading is more homogeneously distributed than numeracy skills in every day life, although again there is a certain concentration of older respondents in the lower part of the distribution. the high density of lower values in the distribution in the three age groups in both graphs is striking; this points to a significant percentage of the sample not using either of these skills in their daily life.

even so, in neither of the above cases do we find a clear relationship between use and skills.

5.5. the impact of socioeconomic origin on skills

Finally, as a third possible cause of internal dispersion (within each education level), we examine the impact of parents’ education as an indicator of social origin. In the two following tables (tables 5.2 and 5.3) we compare the education of the respondents with that of their mothers or fathers (whichever parent reached the highest education level) to calculate the averages for each of the skills tests – mathematics and reading comprehension. the two tables also show a measure of dispersion for each of the profiles of individuals and, in parentheses, the size of the sample used for this calculation.

table 5.2

numeracy skills by education of respondent and parents

neIther Parent has comPleted

uPPer secondary

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uPPer secondary


tertIary

total

compulsory or less 217.88 241.06 248.17 221.33

45.55 41.05 41.43 45.82

(2,450) (290) (155) (2,895)

upper secondary 254.57 256.68 271.97 257.69

36.98 38.47 38.22 37.96

(749) (270) (208) (1,227)

university 272.67 280.50 288.91 278.24

35.79 34.36 37.31 36.53

(922) (323) (414) (1,659)

source: PIaac, calculations by the authors, maths average (weighted), standard deviation (weighted) and observations.


as we pointed out before, a higher level of education is associated with a higher level of numeracy skills. this association is maintained no matter the education level of the parent. However, those who suggest that the effect of social origin has no significant impact once we control for the respondent’s educational attainment will be surprised by the results in table 5.2. In mathematics skills, among respondents with a low education level there is a 30 point difference between those having parents with only secondary education or less and those having at least one parent with a university education. It is interesting that this difference is much less (17 points) when the respondent has an upper secondary diploma and approximately 16 points among those with university degrees.

table 5.3

reading skills by education of respondent and parents

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total

compulsory or less 225.52 246.29 253.53 228.64

42.26 40.84 40.73 42.80

(2,450) (290) (155) (2,895)

upper secondary 257.22 265.15 276.88 261.9

36.79 36.24 38.19 37.54

(749) (270) (208) (1,227)

university 276.83 285.93 292.42 282.49

35.92 34.36 38.08 36.77

(922) (323) (414) (1,659)

source: PIaac, calculations by the authors, reading average (weighted), standard deviation (weighted) and observations

regarding reading skills, the pattern is almost identical. It is clear that social origin determines intellectual abilities, even beyond the well-known effect of inheritance on children’s education. an alternative (more optimistic) way of interpreting tables 5.2 and 5.3 is that not all of the effect of parents’ education on the intellectual abilities of their children (whether because of genetics or social environment) translate into educational qualifications.


In general, the PIaac study reveals that the effects of social origin on adult skill levels is very clear in countries such as the United Kingdom, Germany, Italy, Poland and the United States, even when we take into account the effect of other intervening factors. It does not appear, however, that there is a conflict between equality and quality. In some contexts general skills above the average seem to coexist with a high level of equality; that is, with a very weak effect from social origin. this is the case in Japan, australia, the Netherlands, Norway and Sweden. We have also found the opposite situation: France, Germany, Poland and the United States have a low average performance level and differences based on social origin are greater (oecd, 2013).

5.6. the difference between education and skills

Having established the bivariate relationship between literacy and numeracy skills and the three factors already found to be possible moderators of the (low) average level found in Spain (age, parents’ education and frequency of use of relevant knowledge in every day life), we can carry out a multivariate analysis.

table 5.4 in the statistical appendix of this chapter contains regressions with the two dependant variables: mathematics and reading comprehension. the groups that define the clusters on the macro level in this case are the five-year cohorts. In this way we can analyse the effect of the respondents’ education on their demonstrated skills, and thanks to the multilevel design we can see the evolution of cognitive inequality between generations and age groups. as we are primarily interested in understanding the impact of social origin and given that this has varied significantly in Spain in the generations included in the PIaac data, our analysis has elements in common with classic studies on social mobility, with the notable difference that our explanandum does not refer to occupation or acquired education but to intellectual abilities. based on these results we can see that most of what determines the knowledge of adults in Spain is found on the individual level and only a small part in the birth cohorts or age groups. In other words, when we allow the weight of the level of formal education on skills to vary between cohorts/age, the results remain practically the same.


to conclude, in table 5.5, also in the appendix, we have incorporated the education level of the respondents, as we did in the previous table, in addition to the relevant controls by sex and immigrant status of the student. We can see that the inclusion of these controls does not change the results in education. being an immigrant is related to a lower level of both types of skills. Women have lower skills than men, especially in mathematics. the models we show in the table include, in addition to the three variables already discussed, another series of indicators: age in five-year groups, which constitute the macro level of the hierarchical analysis; the education of the parents as indicator of social origin, and the daily practice of numeracy and literacy activities, as indicators of active participation in continuing education (Lifelong Learning). the three factors pointed out in the hypotheses all clearly demonstrate a statistically significant positive effect on the level of skills demonstrated. In addition, the inclusion of these variables reduces, to a certain extent, the coefficient associated with education in such a way that we can say that in reality a part of the (limited) effect of level of formal education operates through these other factors. crucial for our work is the finding of the persistence of intergenerational transmission of advantages, which in this case is manifested in skills.

among the most important conclusions of this empirical analysis, we can consider that our explanations are better for explaining the differences between cohorts than the differences that occur on the individual level, and this applies to both mathematics and reading comprehension (the summary of the statistical studies on which this conclusion is based is shown in graphs 5.5 and 5.6 in the statistical appendix of this chapter). In a certain way, this is not strange, since the level of cohort analysis will not be affected by innate abilities as would be expected on the individual level. With the simple incorporation of the education variable to the model we are able to understand up to 34% of the differences in the mathematics knowledge of adults (and 35% in reading) and 17% on the individual level (31% in reading). although this level of success in the explanation may appear relevant, we have to take into account that it implies that most of the variance in the two levels is still dependent on other factors (not observed), even controlling for the level of education. Ultimately,


education does not explain more than a third of the skills of the adults in different cohorts.

two factors highlighted here may be helpful in contextualising the relative failure of the education of the respondents in predicting their skills: First is, and with all the methodological caveats already mentioned, the lasting importance of parents’ education. In Spain the impact of the parents’ education as a determinant of their children’s skills in adult life remains stable among all the different birth cohorts. the variance explained corresponding to the differences between cohorts increases more than ten percentage points both in reading skills and in mathematics when controlling for family origin. the second factor, the daily use of both types of skills, has a greater effect, along with level of education, in the explanation of differences between cohorts. It seems clear that beyond formal educational qualifications, numeracy and reading comprehension depend to a large degree on using these skills, and this explanation surely has more importance among older adults than among younger adults, who were in school more recently.

5.7. conclusions: the long shadow of social origin

the PIaac study represents a fresh perspective on educational inequality. as it is a representative sample of the Spanish adult population, it covers up to three generations who acquired their formal educations under radically diverse historic and socioeconomic circumstances. the social change experienced in the last half century is, therefore, very relevant as part of the explanatory context for the differences described in this chapter among adults born in different periods. In this sense, it is no coincidence that the findings presented here confirm in various ways the conclusions of classic studies on intergenerational mobility, especially regarding the important role of educational expansion, which has brought with it significant increases in the average levels of numeracy and reading skills, although as we have seen, there is a negative age effect. another empirical regularity that our research has confirmed and that has already been noted by these studies is the persistent difference by gender in adult skills. However, returning to our main interest in this study, the most


significant finding refers to the long shadow of social origin over the distribution of socially valued resources, such as education and intellectual abilities.

our analysis has demonstrated that the influence of parents’ education is still a significant predictor of the numeracy and reading skills of Spanish adults, even among older generations. the impact of social origin remains once we control for the effect of individuals’ own education levels, a factor of obvious importance that explains approximately one-third of the variance observed in adult skills. Is this a little or a lot? the answer to this question depends on the perspective.

It is a lot in the sense that, not surprisingly, there is no other variable with such a large explanatory potential. but at the same time it may seem to be little if we consider the preconceptions that we usually have regarding education. as we know the close relationship it has with other relevant findings, surely academics in general, and sociologists of education, in particular, sometimes exaggerate the importance of educational qualifications by considering them almost synonymous with intelligence or other elements not related to formal education. at least the PIaac findings serve to put this notion in perspective. For example, it is clear that continuing education and the daily use of knowledge are also important. We can therefore conclude that even for Spanish adults who have not been fortunate enough to reach higher education levels the option still remains to stimulate their intellectual abilities – and with this their economic opportunities – by remaining mentally active.

conclusIons 119

conclusions

education is an issue of frequent debate and discussion among the public and in the media, as well as among academics, public administrators and politicians. In all of these spheres, the discussion has two dimensions. one is focused on identifying factors that can improve the quality of a education as a whole and, therefore, make education systems more productive, which, in turn, will have a direct impact on countries’ economic competitiveness. the other dimension, which is discussed less frequently, perhaps because of how complicated it is to clearly communicate the issue to the broader population, has to do with how education outcomes are distributed in a population. In other words, this dimension of the discussion is focused on equality of opportunities and educational outcomes. against this backdrop, we believe our study introduces new elements in the analysis of the Spanish context. First, we provide an in depth and methodologically rigorous analysis, using the best databases in Spain and other developed countries. these data have not been used or interpreted together in this way until now. Secondly, we offer a unique analysis of the way in which educational opportunities are distributed over the course of the life cycle.

thanks to the use of very robust and diverse sources of data, we have shown that socioeconomic origin, measured by different indicators, has a persistent effect on individuals’ competencies, educational achievements, expectations and opportunities from early childhood until retirement. In the first chapter we analysed the different ways in which families begin to accumulate important resources for the education of their children. In particular, we looked at the positive effects of preschool education in Spain and other developed countries and the importance of the stimulation parents can provide their children before beginning primary


school. the chapter confirms that preschool education is a net positive resource for learning and that it also reduces the disadvantages associated with being socialised in a home with fewer resources. the stimulation that parents offer their children is an effective way to improve their educational opportunities. all of this is now well recognised, but going beyond established wisdom our study shows that the way in which preschool education and active parental involvement interact is substitutive. In other words, although both factors increase children’s reading abilities in primary school, there is a ceiling beyond which their marginal impact is ever decreasing. early childhood education and early stimulation produce effects that are not cumulative. to the extent that family stimulation is difficult to modify through public policies, preschool education is the best tool for reducing the early disadvantages of students from families in which there is an increased risk of school failure.

In the second chapter we have analysed how schools contribute to the reproduction of educational inequalities during compulsory education. although Spain is one of the oecd countries with lower “school effects” (that is, with fewer differences between schools), public debate over education in Spain has been overwhelmingly focused on organisational problems within the schools and on differences in resources as two of the main problems in the education system. In this study we have shown that in contrast to what is generally believed, schools do not alter the effect of family origin or immigrant status on student competencies in mathematics, language or other core subjects in the curriculum. In addition, the second chapter refutes other myths about school effects, such as the relevance of whether a school is public or private. It also confirms that segregation by socioeconomic origin is at the heart of the differences found between schools in Spain, as in other countries.

the third chapter represents something radically new. International comparative studies on education have almost completely ignored the impact of the broader economic context on the formation of students’ educational expectations before finishing compulsory education. In our study we have found that an economic recession – such as the one currently occurring in many developed countries and especially affecting Spain – can have serious medium and long-term effects by reducing the

conclusIones 121

capacity of lower status families to pay for postcompulsory education and the enthusiasm students need to successfully make educational transitions. concretely, an economic recession leads to a general decline in expectations, and this effect appears to be greatest among children from disadvantaged families.

the fourth chapter offers generally unflattering conclusions about Spanish universities. In contrast to other developed countries, Spanish universities are not highly stratified. this means that there are few really excellent universities, but there are also few very sub-standard ones. In other words, all of them are within a similar range in terms of quality. We base this assertion on our findings that only 3% of the knowledge acquired by university students in education programmes in the subjects of mathematics and mathematics pedagogy depends on the specific school or university they attend. although our study only analyses the results of students preparing to become teachers, this conclusion may be extrapolated to other areas of university study. the relative equality in quality that we find in the Spanish university system could be considered something positive, but our final conclusion is that this situation also has a negative side, as the average level of Spanish universities is low in international terms.

In the fifth and final chapter we analyse inequalities in numeracy and reading skills among adults in Spain. although the sociology of education has traditionally focused on studying the conditions under which individuals acquire their educational qualifications, we show that even taking into account formal education, skill levels among adults differ based on their parents’ education level; in other words, according to their socioeconomic origin. despite the important differences between birth cohorts, the empirical evidence presented here suggests the importance of an active intellectual life to reduce differences in competencies, which continue to be quite evident among Spanish citizens from 16 to 65 years of age.

our findings can be synthesised in the idea that educational inequalities are maintained, although not symetrically, throughout the life cycle. to summarise the conclusions of this study, we have used four of our five data sources to produce a synthetic image of the effects of social origin on education. Graph c.1 contains the standardised coefficients of a series of simple bivariate models, estimated using ordinary least squares (olS).


graPh c.1

Gross effect of parents’ education on the competencies of their children in different stages of the life cycle. standardised coefficients from an ols regression

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.0prIMary

(pIrls reaDING)

prIMary (eGD

MaTheMaTIcs)

prIMary (ecD

laNGUaGe)

secoNDary (ecD

MaTheMaTIcs

secoNDary (ecD

laNGUaGe)

UNIVersITy (TeDs

MaTheMaTIcs)

UNIVersITy (TeDs

peDaGoGy)

aDUlTs (pIaac

MaTheMaTIcs)

aDUlTs (pIaac

reaDING)

gross effect of parental education

note: estimation based on PIrls, egd primary and secondary, teds-m and PIaac. estimations are presented in empty models in which parents’ education is the only predictor. the dependent variable is always the score obtained on competency tests in different subjects. the dependent variables have been standardised to compare the effect of social origin between models.

comparing the impact of social origin over different stages of the education cycle, this analysis provides several interesting findings. First, we can see a curvilinear pattern: the hereditary effect reaches its maximum level in primary school and declines slightly in secondary school, but then falls dramatically in tertiary education and rebounds again in adulthood. throughout this study we have stressed that the process of selecting the best students in the successive stages of their school trajectories explains the declining weight of family origin over the life cycle. to the extent that the university system leaves out a significant proportion of young people, it is understandable that social origin is not as important at this stage.(1) the fact that the effect increases again afterwards is consistent with our

(1) It should be pointed out that the tedS sample is not representative of the total population of university students. It could be argued that a certain negative selection exists among students getting degrees in educa-tion (to the extent that this programme has low admission standards), and as a result it is possible that we are underestimating heridary effects in the university stage.

conclusIones 123

expectations, as we again have a sample that is representative of the whole population, eliminating the possibility of a self-selection bias.(2)

We conclude our study with some reflections regarding policy. although it may be risky to recommend master recipes for education reform, we believe that the evidence provided by this study supports the effectiveness of the following proposals.

the best way to reduce educational inequality over the life cycle is by investing in early childhood and primary education. although public opinion and political debate view the problem of school failure as something that occurs in secondary school, when it is manifested most dramatically, the remedy is to be found in in the initial stages of children’s educational trajectories, as the disadvantages experienced by children from families lacking resources accumulate over the years. From a cost/benefit perspective and when resources are limited, focusing on the first stages of education (whether through stimulation of children at home or promoting preschool education and an effective standardisation of the competencies acquired in primary education) could be much more beneficial and effective than increasing spending on secondary education (of course, this should not be neglected either). In our opinion, the debate over education reform in Spain has the wrong focus. Without denying the importance of regulating the way schools function, or their curriculum, or the provision of necessary resources in secondary school, it is surprising that the focus is not on those stages of education in which inequality based on social origin, as we have shown in graph c.1, is greatest and in which the foundations for learning of a whole generation of children are laid.

(2) although the fall in the effect from primary education to tertiary education is coherent, the slight decline in the reproduction effect that takes place between primary and secondary education is to a certain extent surprising. In general, it is argued that social differences increase during the first years of schooling. Here, in contrast, it seems that Spanish schools mangage to equalise to some degree the initial differences that stu-dents begin their educations with. one explanation for this difference would be school failue itself. We know that adolescents that do not complete compulsory secondary education before dropping out of school have high levels of absenteism or other signs of a lack of involvement in their educations before leaving school. as a result, it is likely that a significant percentage of students of humble origins are not well-represented in the samples we have used, which would introduce a bias in our results. Secondly, it is striking that in the majority of cases there are barely any differences in the heriditary effects on the different competencies stu-dies (mathematics, reading, language and pedagogy). However, the notable differences between the findings with data from the PIrlS and eGd make it clear that the effect of scial origin depends to some extent on the test used to measure it..


lastly, it is essential that all public administrations with competency over matters of education commit to the production of longitudinal data or cohort studies that will allow us to more accurately understand the processes which determine success or failure in school. educational statistics, which are generally lacking, of poor quality and managed with the typical lack of transparency and distrust of so many issues in the public sphere, should be considered priority infrastructure, just as education must be considered a matter of state.

aPPendIx a. data 125

appendix

appendix a. Data and descriptive evidence

a.1. Pirls (Progress in International Reading Literacy Study)

In chapter 1 (“early childhood education and its effects on educational outcomes in Spain and the developed world: the role of parents and educational policies in the benefits of preschool education”), we use the most recent version (2011) of the PIrlS study (Progress in International Reading Literacy Study).(1) these data evaluate the reading ability of students in primary school in a broad sample of countries. the 2011 edition of PIrlS is the third wave of the study, following its first two editions in 2001 and 2006. PIrlS is based on a stratified sample that selects a representative sample of schools within each country, and then, a representative number of students within each school. Its objective is to measure – in a manner that is comparable internationally – the performance of students in the 4th grade of primary school (students between 9 and 10 years of age) in reading comprehension. Fifty countries including Spain participated in the study. the data have been chosen mainly because of the quality of the retrospective information provided on the pre-compulsory stage of education, early childhood or preschool education. another important advantage is that these data contain an objective measure of school performance, in this case, reading ability, measured in the 4th grade. reading ability is a measure of essential importance for school success in subsequent stages of formal education, for basic skills that must be used in daily life in adulthood and in more analytical terms, as a dependent variable that responds very well to the

(1) additional data, questionnaires and documentation regarding PIrlS are availabe in SaS and SPSS format free at http://timss.bc.edu/pirls2011/international-database.html.


research interests of the chapter. to gather these data, the schools that participated in PIrlS carried out a test of reading comprehension. In addition, the study employed survey questionnaires to obtain additional information about the personal, family and school context of each student. the teachers, as well as the principals of the schools, also filled out a questionnaire that formed part of the sample in order to understand the national educational context and the reading curriculum.

table a.1 offers information about the sample sizes of the databases of each country. It is clearly a large sample, with a total of 168,497 children, in addition to national sub-samples that range from a minimum of 3,586 (in Northern Ireland) to a maximum of 32,206 (in canada). In Spain, 8,580 students from 312 schools participated, as well as 403 teachers.

as we pointed out in chapter 1, our dependent variable is reading ability. this is defined as “the ability to understand and use ... written language forms”. It was measured using two partial tests of 40 minutes each, with a break of 20 minutes between the two. In each evaluation block, students were presented a text of around 750 words and had to answer twelve questions. there were multiple choice questions, semi-structured questions and open response or constructed response questions. Normally, one of the two texts was literary (a short story) and the other informational (for example, an article written for children on a topic or a tour brochure).


table a.1

sample sizes in Pirls by country

country country code n (samPle) Percentage of the total samPle

australia aus 6,126 3.64

bulgaria bgr 5,261 3.12

canada can 23,206 13.77

taiwan twn 4,293 2.55

croatia hrv 4,587 2.72

czech rep. cZe 4,556 2.70

denmark dnK 4,594 2.73

finland fIn 4,640 2.75

france fra 4,438 2.63

germany deu 4,000 2.37

hong Kong hKg 3,875 2.30

hungary hun 5,204 3.09

Ireland Irl 4,524 2.68

Israel Isr 4,186 2.48

Italy Ita 4,189 2.49

lithuania ltu 4,661 2.77

malta mlt 3,598 2.14

netherlands nld 3,995 2.37

new Zealand nZl 5,644 3.35

Poland Pol 5,005 2.97

Portugal Prt 4,085 2.42

romania rom 4,665 2.77

singapore sgP 6,367 3.78

slovakia svK 5,630 3.34

spain esP 8,580 5.09

sweden swe 4,622 2.74

united states usa 12,726 7.55

england eng 3,927 2.33

northern Ireland nIr 3,586 2.13

belgium (wallonia) bfr 3,727 2.21

total 168,497 100.00

source: calculations by the authors from PIrls 2011.


Graph a.1 shows the distribution of the scores received on reading comprehension. although in theory the range is 1 to 1,000 points, most of the students score between 300 and 700. the average score of 500 points (for all of the countries) in the first edition of PIrlS in 2001 (with a standard deviation of 100 points) serves as the reference point. the average of the sample used here is higher (540.92) because less developed countries, which have averages below 500, have been excluded from our analysis. the auxiliary curve illustrates that the shape of the distribution is close to the normal distribution, with the typical concentration of students in the middle areas. the average score (unweighted) for Spain in 2011 was 518.38 points.

graPh a.1

univariate distribution of reading ability

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6

4

2

0200 400 600 800

source: calculations by the authors based on PIrls 2011

our most important independent variable is preschool attendance. this is measured in six categories. In addition to distinguishing between the


children who have attended preschool and those who have not, the big advantage of the data is that they also provide information on the amount of time children spent in preschool. table a.2 summarises the distribution found in the sample.

table a.2

univariate distribution on the amount of time spent in preschool

n Percentage

did not attend Isced=0 7,372 5.62

one year or less 13,104 10.00

between one and two years 8,450 6.45

two years 17,390 13.27

between two and three years 19,891 15.18

three or more years 64,864 49.49

total 131,071 100.00

nota. Isced=International standard classification of education. source: calculations by the authors based on PIrls 2011

only a small minority of children (less than 6%) did not attend preschool at any time prior to the age of compulsory education. that is, most children do attend preschool at some time. as would be expected, however, there are differences in the amount of time spent in preschool: one out of ten children attended one year or less, while one out of two attended three years or more. of course there are significant differences between countries in this regard.

table a.3 shows the average amount of time spent in preschool by country, after transforming this variable into a linear variable. Spain’s position in this classification is quite positive; with an average attendance of 4.4, it is in the group of countries with higher levels of preschool attendance.


table a.3

Distribution of preschool education by country

country average years standard devIatIon

northern Ireland 1.96 1.18

lithuania 2.30 1.41

slovak republic 2.32 1.46

Ireland 2.47 1.56

australia 2.84 2.08

canada 2.96 2.16

Poland 3.12 0.97

croatia 3.25 0.87

netherlands 3.61 1.96

malta 3.64 1.68

Portugal 3.67 1.61

Finland 3.71 1.61

total sample 3.76 1.36

new Zealand 3.79 1.21

taiwan 3.85 1.75

bulgaria 4.13 1.46

romania 4.14 1.40

Israel 4.30 1.06

sweden 4.36 1.32

singapore 4.37 1.01

spain 4.39 1.13

czech republic 4.40 1.10

germany 4.57 0.92

Italy 4.58 0.92

hong Kong 4.60 0.69

burkina faso 4.66 0.79

france 4.69 0.66

denmark 4.69 0.79

hungary 4.79 0.64


our research design in chapter 1 includes two mediating independent variables: the educational practices of parents and their education level.


We are primarily interested in the the extent to which these two variables moderate the relationship between preschool attendance and later academic performance (in primary school). the first variable (parental involvement in educational activities) is included in the PIrlS data in a retrospective manner and refers to how often parents did the following educational activities with their children: reading books, telling stories, singing songs, playing alphabet games, talking about what they have done, discussing what they read, word games, writing letters, reading signs out loud, singing counting songs, counting objects, playing with shapes and building blocks, playing board games or cards. these frequencies were converted through factor analysis into one single factor of continuous nature. Graph a.2 shows the distribution of parental involvement in activities related to reading for the complete sample of countries, although there are obviously considerable differences among the different contexts.

graPh a.2

univariate distribution of parental involvement in activities related to reading

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5

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3

2

1

0–4 –2 0 2



the other mediating variable is the education level of the parents, which we use here as an indicator of student social origin. this variable has been included in the data by using the standard schema in comparative research, the International Standard classification of education (ISced). We apply dominance criteria to determine the highest level of both parents, and we convert that variable into a linear variable, rescaling the values of each category so that the jump from one to another does not imply an identical change, but rather proportional to the impact they have as a predictor of our dependent variable.

the structure of the PIrlS sample data (students nested within countries) offers an ideal structure for multi-level or hierarchical analysis.

a.2. General diagnostic assessment for primary (2009) and secondary (2010)

chapter 2 (“School effects in the reproduction of educational inequalities in compulsory education in Spain”) uses data produced and systematised by the National Institute for educational assessment, a part of Spain’s Ministry of education, Science and Sport, and concretely, two evaluative studies the Ministry has issued in recent years, known as general diagnostic assessments for 2009 (for primary education) and 2010 (for secondary education). the availability of these data has been a major advance in transparency, always essential for the production of quality scientific research, and contrasts with the norm generally followed by the Ministry itself in the past and which continues to be followed by the autonomous communities, as they have only rarely made their evaluation and diagnostic studies available (which, moreover, they all carry out periodically). the dependent variables these studies provide serve to assess outcomes in the Spanish education system. thus, in this case we are not referring to cognitive skills but to the evaluation of knowledge included in the curriculum of the final year of primary or secondary school.

the general diagnostic assessment for primary education (eGdP-2009) is based on a sample of 28,708 students in over 900 schools. Unfortunately the ministry has not distributed the regional level variables and, therefore,


it is not possible to distinguish in this study, or in that referring to secondary school, the autonomous community where each school is located. In table a.4 we present a description of the valid sample size for each variable used in the analysis of primary education, as well as its distribution.

table a.4

Descriptive statistics of the variables used in the eGDP-2009

varIable n average standard devIatIon mIn. max.

mathematics 27,888 502.27 91.55 163.06 838.28

language 27,769 502.64 93.51 162.70 822.92

natural sciences 27,800 502.35 92.97 139.05 815.20

social sciences 27,898 502.48 93.17 164.93 763.48

socioeconomic status 28,708 0.02 0.99 –3.35 1.60

sex 27,466 0.49 0.50 0 1

Immigrant 26,994 0.10 0.30 0 1

no. of books 27,177 3.24 1.47 1 6

Public school 28,708 0.65 0.48 0 1

average ses in the school 28,708 0.02 0.54 –1.80 1.15

source: calculations by the authors based on egdP-2009.

Graph a.3 summarises in greater detail the distribution of the dependent variables of the edGP-2009, although most of the analysis is focused on scores in mathematics. as can be seen, all of the distributions are more or less normal and are adjusted for an average of around 500 points.

the general diagnostic assessment for secondary education includes interviews and tests of 29,154 students distributed among 933 schools. table a.5 provides information on the available samples for each variable used in chapter 2 and its distribution.


graPh a.3

Description of the dependent variables from the assessment in primary school

mathematics

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0200 400 600 800

Per

cent

age

of c

ases

language

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6

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0200 400 600 800

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cent

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de lo

s ca

sos

natural sciences

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Per

cent

age

of c

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social sciences

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Por

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aje

de lo

s ca

sos

source: calculations by the authors based on egdP-2009

table a.5

Descriptive statistics of the variables used in the eGDs-2010

vvarIable n average standard devIatIon mIn. max.

mathematics 27,744 503.67 88.18 141.53 840.98

language 27,696 504.48 93.96 151.29 812.51

natural sciences 27,814 504.08 92.01 160.31 806.47

social sciences 27,909 503.29 92.85 193.39 775.21

socioeconomic status 29,153 0.00 1.00 –3.22 1.82

sex 27,784 0.50 0.50 0 1

Immigrant 27,347 0.11 0.32 0 1

no. of books 27,455 2.25 1.13 1 4

Public school 29,153 0.67 0.47 0 1

average ses of the school 29,153 0.00 0.56 –1.82 1.46

source: calculations by the authors based on egds-2010


Finally, graph a.4 summarises in detail the distribution of the diagnostic assessment tests on the subjects that were evaluated. as in the case of primary education, the unweighted average is approximately 500 points and has, for the four cases, a normal distribution.

graPh a.4

Description of the dependent variables from the assessment in secondary school

mathematics

8

6

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0200 400 600 800

Per

cent

age

of c

ases

language

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natural sciences

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social sciences

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source: calculations by the authors based on egds-2010


a.3. timss (Trends in Mathematics and Science Study)

In chapter 3 (“expectations of continuing in the education system before reaching post-compulsory education: the weight of cognitive inequalities by social origin”), we use as our primary source of data, tIMSS (Trends in Mathematics and Science Study).(2) tIMSS is an international database which measures educational performance in different subjects, as well as students’ expectations regarding continuing their education. the tIMSS study has been developed by the Iea (International association for the evaluation of educational achievement) and is a comparable assessment of knowledge in mathematics and sciences of students in the fourth and eighth year in school. tIMSS was administered for the first time in 1995 and since that time, every four years (there have therefore been to date five points in time to examine). the study includes data provided directly by the students, teachers and representatives of the schools in each participating country. this hierarchical structure allows us to use the multi-level techniques that were explained in the previous section.

In the analysis we provide in chapter 3 we have only used data from the eighth grade in 2003, 2007 and 2011, since the sample of countries prior to 2003 was very small. We have included all of the countries with a GdP of over $15,000 per capita and we have excluded the oil producing countries because of their special characteristics. our final sample includes 24 countries, although not all of them participated in all three waves. these countries, as well as the samples of students interviewed by country are presented in table a.6. Note that the sample for Spain is not national but corresponds only to the basque country.

the period covered by the data collected allows us to observe students before, during and for some countries, after the economic crisis, which continues in Spain. Since countries initially experienced the economic recession at different times and to differing degrees, the selection of this period is almost ideal, as it provides the greatest possible variation in macroeconomic conditions and individual responses to these conditions in different countries and over time.

(2) additional data, questionnaires and documentation regarding tImss are available in sas and sPss format free at http://timss.bc.edu/timss2011/international-database.html.


table a.6

sample sizes in the timss, rich countries excluding the oil producing countries

2003 2007 2011 samPle

australia x x x 16,416

belgium x 4,970

canada x x x 35,992

chile x 5,835

cyprus x x 8,401

czech rep. x 4,845

finland x 4,266

hong Kong x x x 12,457

hungary x x x 12,591

Israel x x x 12,311

Italy x x x 12,665

Japan x x x 13,582

Korea. x x x 14,715

lithuania x x 8,738

malta x 4,670

holland x 9,137

norway x x x 12,622

singapore x x x 16,544

slovenia x x x 12,036

spain x x 4,810

sweden x x x 15,044

united Kingdom x x x 18,283

united states x x x 32,628

source: calculations by the authors based on tImss

For comparing the effect of changes in the economic context in different countries on students’ expectations regarding their future educational trajectories, the tIMSS data is not as rich as that offered by national studies specifically designed to investigate this issue. tIMSS contains limited information about future educational paths, but it does include a large number of countries that can be analysed; this, in turn, allows us to


examine the variability in educational outcomes over time (specifically, between 2003 and 2011).

We have limited our analysis to students in eighth grade at the time of data collection. these students are usually between 13 and 14 years old and are approaching the end of the compulsory stage of education. the students are asked how long they think they will continue in the education system (what level of education they think they will attain). their responses are based on ISced (International Standard Classification of Education) levels. regarding the analysis, responses were recoded to place them in a range between 0 and 100. the ISced categories were recoded as follows: 0 = less than compulsory secondary (<ISced 2); 10 = compulsory secondary (ISced 2); 40 = upper secondary (baccalaureate) (ISced 3); 50 = post secondary (for example, advanced vocational training) (ISced 4); 80 = short-cycle tertiary education (ISced 5b); 90 = tertiary education, bachelors, masters or equivalent (ISced 5a) and 100 = Phd (ISced 6). by including different distances between levels, this scheme attempts to give more weight to tertiary education and reflect the disadvantage that less qualified individuals face. this decision is necessarily arbitrary, so tests on the robustness of the results were carried out using alternative schemes. No major differences were detected.

as students’ decisions about continuing their education are strongly influenced by past performance, we adjust the expectations expressed by the students using the results on standardised mathematics tests (note that the results are identical after adjusting for science scores). Since the effect of school performance on expectations is non-linear, we use dichotomous variables that divide the sample of students into five quintiles according to their test scores. If these scores are sorted in ascending order, the first quintile is the 20% of the distribution with the lowest scores, while the fifth quintile is the 20% of students with the highest scores. the dependent variable in chapter 3 is defined, therefore, as expectations, controlling for performance or, more synthetically, conditional expectations. this measure allows us to capture the influence of social origin on student expectations, once deducting the effect of abilities, before making a decision on whether to continue beyond compulsory education and, if so, which path (academic or professional) to choose.


contextual data to capture changes in the economic cycle were obtained from the World bank database World Development Indicators (WdI),(3) this agency has, since 1960, provided historical series of data on a large number of indicators, currently for over 200 countries worldwide. For chapter 3 we used both data on GdP per capita (in PPP constant 2005 and in thousands for easier reading of the coefficients) and GdP growth (expressed in annual, positive percentages in case of expansion, and negative percentages in case of recession). Graph a.5 presents a description of these variables for all the countries in the sample and all the years they participated. the combined use of these two measures reveals not only the annual change in the economic climate, but also the general level of economic development in each country, which is crucial in a heterogeneous sample of countries such as ours.

graPh a.5

Description of per capita GDP and GDP growth in the sample of countries-year

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010 20 30 40 50 60

Per capita gdP (in thousands)

ann

ual g

row

th o

f gd

P (%

)

2003 2007 2011

source: calculations by the authors based on tImss.

(3) the WdI data can be downloaded in excel format on the web page: http://data.worldbank.org/data-catalog/world-development-indicators.


the final data of chapter 3 present a hierarchical structure of students nested in countries. this design allows us to empirically evaluate, at the level of countries, the effect of macro explanations on processes that occur at the individual level.

a.4. teDs-m 2009 (Teacher Education Study in Mathematics)

the teacher education Study in Mathematics (tedS-M) was developed by the International association for the evaluation of educational achievement (Iea) in 2009 with the aim of understanding how different countries have prepared their teachers to teach mathematics in primary school and in the initial stage of secondary school. this forms the database of chapter 4 (“University competencies and the education of teachers and professors in Spain. How important are the different universities?”). this data set allows us to specifically analyse (a) the types of institutional and professional opportunities provided for future teachers, (b) differences in curricula and regulations that structure the programmes, (c) the content of the programmes and the organization of teaching, and (d) previous experience of those responsible for the implementation of these programmes. the data contains samples of university students from botswana, canada, chile, taipei, Georgia, Germany, Malaysia, Norway, oman, the Philippines, Poland, russia, Singapore, Spain, Switzerland (German-speaking cantons), thailand and the United States. In this chapter we focus exclusively on the analysis of the Spanish case.

While the policy motivations behind the creation of this database came from the need to understand teaching dynamics in primary and secondary schools, tedS-M provides a standardised measure of education students’ knowledge of mathematics before graduating from university. In other words, this dataset represents a unique tool for the study of school effects in tertiary education. tedS-M developed a sampling design in two stages. First, a sample of institutions offering teacher education to the population or universe under study in each country was selected. For each institution chosen, information was collected on all programmes related to the mathematical preparation of future primary and secondary


school teachers. Subsequently, a final sample of educators and future teachers was selected within the institutions and programmes included in the study.

the teacher preparation programmes for primary education in Spain provide prospective teachers with the same accreditation regardless of the subject they wish to specialise in and the relevant differences in their pedagogical training. these components are included in the first phase of post-secondary education and supported by a single credential. the sample included 1,093 future teachers in Spain and 44 institutions of higher education. table a.7 provides information on the size of the sample for the variables used in chapter four and their distribution.

table a.7

Description of the teDs-m variables

n average standard devIatIon

scores in mathematics 1,093 479.30 56.5

Pedagogy of mathematics 1,093 490.95 63.2

selection 1,068 4.00 0.8

age 1,093 23.20 4.4

women 1,093 0.80 0.4

average (parental education) 1,093 3.40 0.7

Parental education 1,054 3.40 2.1

studied maths before 1,048 2.30 1.1

grades in secondary 1,080 3.20 1.1

chose major because of job opportunities 1,059 2.10 0.9

chose major because of vocation for teaching 1,064 1.80 0.9

source: calculations by the authors based on teds-m 2009


graPh a.6

Distribution of the dependent variables in the teDs-m 2009 study for spain

20

15

10

5

0200 400 600 800

Per

cent

age

of c

ases

mathematics

20

15

10

5

0200 400 600

Per

cent

age

of c

ases

Pedagogy of mathematics

0 800

source: calculations by the authors based on teds-m 2009

a.5. Piaac (Programme for the International Assessment of Adult Competencies)

the data used in chapter 5 (“educational expansion in Spain and adult skills by social origin”) are from the PIaac study conducted for the first time in 2009 in certain oecd member countries, including Spain. although 33 countries participated in the study, our analysis is restricted to Spain. the PIaac measures competencies or skills that adults may need for participation in society and which are also necessary to guarantee the positive development of society as a whole. the sampling frame for the PIaac is the entire population aged 16 to 65 in the participating countries. all respondents did three tests of skills, including a test on numeracy problems and a reading test, which are the dependent variables used in our study.(4)

(4) additional information can be obtained on the data and on access to it at the following web page: http://www.oecd.org/site/piaac/surveyofadultskills.htm.


table a.8

Description of variables used based on the Piaac data

n average standard devIatIon mIn. max.

reading 5,971 249.72 48.34 78.76 375.54

mathematics 5,971 243.33 50.73 82.32 380.86

education 5,972 1.77 0.86 1 3

Parents’ education 5,781 1.42 0.72 1 3

Immigrant 5,969 0.13 0.34 0 1

woman 6,055 0.51 0.50 0 1

uses reading 5,960 0 0.78 –1.07 2.48

uses maths 5,965 0 0.84 –0.60 3.51

source: calculations by the authors based on PIaac-spain

Finally, graph a.7 shows the distribution of the dependent variables that have been used in the chapter

graPh a.7

Distribution of the dependent variables in the Piaac study for spain

8

6

4

2

0100 200 300 400

Per

cent

age

of c

ases

numeracy problems

8

6

4

2

0200 300 400

Per

cent

age

of c

ases

reading comprehension

100

source: calculations by the authors based on PIaac-spain


appendix B. methodology

Multilevel regression is a research technique frequently used in the field of education. Its uniqueness is that it makes it possible to expand the basic principles of multivariate regression to an environment in which the explanatory variables operate in two or more levels of aggregation. the classic example, and which applies in one way or another to all the chapters in this book, is that of students nested in schools, observed in different countries, or individuals belonging to different cohorts. think of a conventional linear regression model that considers only one level of explanation estimated by ordinary least squares (olS)

yij=b0+b1x1+…+bnxn+ei

If part of the systematic unexplained heterogeneity in our dependent variable is due to characteristics of the individuals and the group in which they are nested, the above equation involves a significant error: the variance of level j will be reflected in the error (ei) and we will have missed some relevant information. If this is the case, there will be something similar among the residuals within each group, and they will follow a pattern of distribution of greater or lesser strength, depending on the importance of nesting in the dependent variable.

the way to avoid this problem is to estimate models of more than one level: hierarchical or multilevel models. there are three basic types (both used in the analyses presented in the preceding chapters): the fixed effects model, the random constant model, and the random slopes model.

B.1. fixed effects model

one alternative is the estimation of a two-level model with fixed effects. these models are not intended to explain the variance that differentiates the groups from one another. rather, they serve to “freeze” it in order to obtain unbiased conditional estimates of the effects operating at the individual level. In this way, they allow us to make reliable inferences about what is happening at the lower level of aggregation, knowing that at the higher level there are processes about which we express no view, although technically they are considered in our equation. thus, by opting

aPPendIx b. methodology 145

for this type of model it is assumed that there is a process that happens within groups (and that therefore applies equally to all members) and that it is relevant for understanding how the variance of our dependent variable is ordered. However, not knowing this process, its operationalisation being impossible, or simply because it is just a control for our theory, it is enough for us to consider it as a control in our modelling of reality. obviously, fixed effects models cannot be estimated in contexts in which all or most of the variance is intergroup, and none or very little of it is in the intra-group level. the specification for a fixed effects model is:

yij −yj =(xij −xj)b+eij −ej

Here yij is the value of the dependent variable for the individual and in the group j. apply this same logic to the other terms in the equation.

B.2. multi-level regression model with random intercept

Multilevel random intercept regressions are also known as regressions “of resulting intercept”, since the constant can in turn be expressed as a separate model or as a regression within a regression. In its empty version, the specification does not include explanatory variables or controls. the performance of the individual i in school j (yij) is a function of the average of their school (b0j) and the deviation this student represents from the school average (eij).

yij=b0j+eij

as with all intercepts, the constant can be thought of as the average value of the dependent variable in our analytical sample. this model contains two levels of random variation, one for level i and another for level j. the one for level j is included within the intercept in its conventional notation (b0j).

b0j=g00+u0j

the intercept results from calculating a mean for all the groups or aggregated units that we use in the analysis (g00, referred to as the Grand Mean and that corresponds to the overall mean of the sample), and a


deviation reflecting the distance of each group j with respect to the overall mean (u0j). this ‘residual’ of the aggregate level can be conceptualised as a latent variable that captures the effect of nesting the cases in aggregated units; in other words, the idiosyncrasy of each group.

yij=g00+u0j +eij

both u0j and eij are independent and distributed with a mean of 0 and a variance, s2(u0) and s2(e). the correlation between two units i and i’ taken at random within the same aggregated unit is:

r(Yij, Yi’j)=[ (s2u0/(s2u0+s2e)]

thus, we can see that what we estimate is a proportion – its values necessarily range between 0 and 1 – of the variance explained by the aggregate level [var(u0)] over the total variance [var(yij)].

We can add any independent variable (or vector of variables), measured at the level of schools, to the specification of the vacuum model. thus, the constant will not only be a function of the overall mean (g00) and a random disturbance (u0j), but also the effect of being a public or private school (or any other aggregate level variable, zj) estimated by a new parameter (g01)

b0j=g00+g01zj+u0j

by placing this equation in the full model, we have the new specification, of the random intercept model:

yij=g00+g01zj+u0j+eij

B.3. the random slopes model

to the above specification we can add an effect similar to what we have agreed on to model the constant, in the parameter that we estimate for the variable, xij. that is, we convert the slope into result, introducing a new sub-equation into the main equation, so that it could be specified in a latent way as:

b1j=g10+u1j


the substantive interpretation of the slope of our explanatory effect xij on the dependent variable is comparable to an additive parameter and a type of interaction between the independent variable at the individual level and each of the units of aggregation. Specifically, (g10) would be the equivalent to the main parameter of an interaction, specifically the average effect of our independent variable on the dependent; u1j u1j would in turn be the specific correction of each group on the effect of xij over y, such that it would be substantially equivalent to the interactive parameter between our explanation of the individual level and a certain fictitious variable that will model the specificity of each group in an equation of a single level. In summary, the full resulting specification of the random slopes model is:

yij=g00+g10xij+u0j+u1j+eij

thus our model has two parts, one fixed (g00+g10xij) and one with random components (u0j+u1j+eij). While the fixed part describes the mean effects of the independent variables, the random components are margins of error that impose these mean effects on the groups.

B.4. on the interpretation of the effects of school/country/cohort in this study

In the different chapters of this study we have presented discussions of the group residuals as a complement to the traditional discussion of the effects of the independent variables. these residuals are the great analytical advantage of multilevel regression. For ordinary regression (Y=b0+b1xi+…+bnzi+ei) the residual is unique (ei) and is calculated by subtracting the observed value, predicted by our equation.

ei=Y–(b0+b1xi+…+bnzi)

If we apply the same logic of observation minus prediction to generate the residuals in a multilevel regression we would only obtain the so-called crude residuals:

rij=yij-y^ij


but this residual, conceptually equivalent to the single-level regression, is too simple to be useful in the evaluation of multilevel regression. If the specification of the model evaluated is:

yij=g00+g01zj+u0j+b1x1ij+…+bnxnij+eij

the residue of the first level or the individual level (eij) results from subtracting the specific random component of each group of the second level or aggregate level (u0j) to the crude residual.

eij=yij-y^ij-u0j=rij-u^0j

In turn, u0j is obtained by multiplying the average residual generated from the average distance of all the observations at the individual level belonging to each group (rj) by the amount of adjustment (Shrinkage) kj, a parameter that adjusts the gap that separates the constant (or slope) from each straight line of the group from the overall line.(1) thus,

u0j=k*rj

these residuals can be used to identify the distance separating a specific group from our interest in the general behaviour of all the aggregate units. the same can be said for the observations on the individual level.

(1) kj=s2u0/(s2

u0+s2ee/nj)


appendix c. appendix to chapter

c1. appendix to chapter 1

table 1.1

multilevel models with random constant (Gls) on reading ability

m1 m2 m3 m4

girl 11.68*** 11.70*** 11.68*** 11.67***

Involvement of parents 10.89*** 15.49*** 10.82*** 12.93***

time in preschool 4.63*** 4.48*** 7.07*** 4.59***

compulsory age of schooling –2.34 –2.12 –2.20 –2.37

Parents' education 12.76*** 12.75*** 14.05*** 12.75***

Preschool * Involvement –1.90***

Preschool * Parents' education -0.48***

Parents' education * involvement -0.40***

constant 474.89*** 474.01 467.63*** 475.31

n 119,008 119,008 119,008 119,008

n countries 28 28 28 28

chi2 24,414.15 24,565.36 24,445.55 24,433.01

* p<.05; ** p<.01; *** p<.001 source: PIrls 2011. calculations by the authors.

table 1.2

multilevel models with random constant (Gls) on reading ability

m0 m1

time in preschool 0.49 1.09

Involvement of parents 10.89*** 10.79***

Parents' education 12.75*** 10.23***

girl 11.67*** 11.67***

compulsory age of schooling –3.77 –2.34

standardised curriculum –15.30 –41.07***

Parents' education* standardisation 4.48***

Preschool* standardisation 7.16***

Parents' education* Preschool 0.79**

Parents' education*Preschool* standardisation –1.50***

constant 536.08*** 488.50***

25.70 8.37

n 119,008 119,008

n countries 28 28

chi2 24,427.80 24,491.16

* p<.05; ** p<.01; *** p<.001 source: PIrls 2011. calculations by the authors.



table 2.1 gathers the results of a regression analysis of mathematics grades in primary (model 1) and secondary (model 2) education.table 2.1

multilevel regressions with random constant. the dependent variable is mathematics grades

model 1 model 2

constant 502.16* 503.60*(1.39) (1.28)

n 25,429 25,605n. schools 900 933variance (u0) 1,431.9 1,278.8variance (e) 6,860.4 6,524.5

legend: * p<.05.. estimates and standard errors.

these models are presented in table 2.2 (for the analysis of primary education) and table 2.3 (for secondary education).

table 2.2

Primary education. multilevel regressions with random constant and slopes (m2, m3) and random constant (m4, m5). the dependent variable is mathematics grades

model 2 model 3 model 4 model 5

Individual levelsocioeconomic status of parents 24.43* 21.89* 19.91*

0.59 0.71 0.73Immigrant status of parents –24.99* –13.40* –12.29*

1.88 1.85 1.84school level

Public school 3.862.60

average (socioeconomic status) 29.34*2.33

constant 502.58* 504.89* 503.03* 501.18*1.15 1.38 1.94 2.52

statisticsn 25,429 25,429 25,429 25,429n. schools 900 900 900 900chi2 1,697.48 175.98 1,991.33 2,274.24variance (u0) 897.2 1.372.1 885.1 684.9variance (u1) 7.7 0variance (e) 6,501.3 6,820.7 6,450.8 6,449.6

note: * p<.05. estimated coefficients and standard error. In models 4 and 5 cultural capital of the families is controlled for (measured through number of books), as is student sex. In model 2, the socioeconomic status of the parents is centred using the average education of the parents for the whole sample.

aPPendIx c. aPPendIx to chaPter 151

table 2.3

secondary education. mulitlevel regressions with random constant (m1, m4, m5) and random slope (m2, m3). the dependent variable is mathematics grades

model 2 model 3 model 4 model 5

Individual levelsocioeconomic status of parents 24.67* 17.21* 14.61*

(0.58) (0.74) (0.76)Immigrant status of parents –29.00* –13.58* –13.40*

(1.76) (1.71) (1.70)school level

Public school –4.19(2.28)

average (socioeconomic status) 24.87*(1.99)

constant 503.68* 506.88* 491.12* 494.09*(1.02) (1.27) (1.83) (2.31)

statisticsn 25,605 25,605 25,605 25,605n. schools 933 933 933 933chi2 1,827.08 270.76 2,345.99 2,712.97variance (u0) 730.3 1.224.5 732.7 552.4variance (u1) 21.5 60.1variance (e) 6,148.9 6,454.7 6,079.8 6,072.7

note: * p<.05. estimates and standard errors. In models 4 and 5 cultural capital of the families is controlled for (measured through number of books), as is student sex. In model 2, the socioeconomic status of the parents is centred using the average education of the parents for the whole sample.

note on shrinkage

Shrinkage is also known as the reliability index k for group differences with respect to the overall average

s2uk = —————

s2u + s2

e / nj

where s2u is the dispersion of the specific effects from each one of the

schools (in other words, in the random terms referring to the variables of socioeconomic status and immigrant status) and s2

e is a measure of the dispersion of grades within each school. lastly, nj is the number of students in the sample for each school.



table 3.2

linear model with random constants. the dependent variable is expressed educational expectations. level 1 is the student; level 2 is the country

model 1 model 2

b se b se

Prior results (ref. 1st quintile)2nd quintile (q2) 7.135*** (0.127) 7.110*** (0.127)3rd quintile (q3) 11.836*** (0.127) 11.809*** (0.127)4th quintile (q4) 15.764*** (0.127) 15.743*** (0.127)5th quintile (q5) 19.608*** (0.130) 19.612*** (0.130)

controls

girl (ref. boy) –4.484*** (0.078) –4.500*** (0.078)native (ref. immigrant parents) –2.299*** (0.147) –2.326*** (0.147)

family resourceseducational resources 0.185*** (0.002) 0.228*** (0.003)economic resources 1.385*** (0.069) 1.913*** (0.124)

economic contextgdP per capita. (x 1,000) 0.225* (0.085) 0.208* (0.085)gdP growth (annual % ) 1.134** (0.372) 1.963*** (0.374)

Interactionseduc. res. x gdP grwth. –0.014*** (0.001)econ. res. x gdP grwth. –0.154*** (0.027)

constant 2.926*** (0.002) 2.924*** (0.002)n students 235,020 235,020 n countries 24 24

note: * p<.05; ** p<.01; *** p<.001; standard errors in parentheses.


In all cases. model 0 (unconditional or empty model) is the reference. based on the successive models. we conclude. as anticipated. that there is little effect from attendance at a specific school or university. as can be seen. the variance between schools. which in an empty model correspond with the constant. is negligible in comparison with the variance that resides at the level of the student (or at the individual level). this is the case for both mathematics and mathematics pedagogy. although in the latter case there is a slight increase in the importance of the nesting of the students.


table 4.1

multilevel linear regression models (random constant and slope). Grades in mathematics

m0 m1 m2 m3 m4

controls

age 0.48

0.47

woman –30.01***

4.22

mathematics course before –9.61***

1.63

origin

secondary grades –13.15*** –12.68*** –12.77*** –9.32***

1.46 1.50 1.53 1.51

Parents’ education 1.38 0.78 –0.18

0.84 0.83 0.81

school

selection of students 3.20

2.59

avg. (educ. parents) 7.18*

3.22

1.63

motive for choice

choice: work –3.65*

1.76

choice: like mathematics 10.41***

2.06

constant 479.30*** 521.83*** 516.07*** 518.41*** 498.20***

2.09 5.16 5.95 6.09 21.05

statistics

n 1,093 1,080 1,043 1,043 958

chi2 80.69 76.33 250.84

f 35.70

var (slope) 0.00 2.85

var (constant) 65.46 67.14 20.07 13.88 64.07

var (residual) 3,122 2,909 2,918 5,424 2,522

source: teds-m 2009. calculations by the authors.


tabla 4.2

multilevel linear regression models (random constant and slope). Dependent variable: Grades in mathematics pedagogy

m0 m1 m2 m3 m4

controls

age –0.48

0.55

woman –13.36**

4.98

mathematics course before –4.92*

1.93

origin

secondary grades –9.65*** –9.29*** –9.24*** –6.95***

1.67 1.68 1.72 1.78

Parents' education 1.90* 1.26 0.66

0.91 0.93 0.96

school

selection of students 2.52

2.83

avg. (educ. parents) 5.83

3.54

motive for choice

choice: work –5.20*

2.08

choice: like mathematics 8.71***

2.44

constant 490.96*** 522.32*** 514.87*** 516.83*** 510.59***

2.37 5.90 6.70 6.82 23.92

statistics

n 1,093 1,080 1,043 1,043 958

chi2 33.37 36.30 81.26

f 15.89

var (slope) 0.00 1.73

var (constant) 90.35 92.40 49.69 15.98 52.11

var (residual) 3,895.18 3,786.21 3,683.48 60.80 3,542.80

source: teds-m 2009. calculations by the authors.



table 5.4

linear regression models with random constantmathematIcs readIng

m0 l0

education 29.43*** 27.56***(0.66) (0.63)

constant 190.29*** 200.11***(4.21) (4.28)

n 5,970 5,970chi2 1,974.79 1,897.35var (constant) 168.5 160.3var (residual) 1,630.6 1,787.3


table 5.5

linear regression models with random constant and random slopemathematIcs readIng

m1 m2 l1 l2

education 28.71*** 22.09*** 26.54*** 20.59***(0.64) (0.70) (0.62) (0.68)

Immigrant status –27.14*** –25.96*** –28.44*** –27.51***(1.58) (1.55) (1.52) (1.50)

woman –13.60*** –11.56*** –5.83*** –3.75***(1.06) (1.04) (1.02) (1.00)

education of parents 6.79*** 6.65***(0.80) (0.77)

use reading skills 9.17*** 8.45***(0.79) (0.76)

use maths skills 6.25*** 5.48***(0.76) (0.74)

constant 201.93*** 203.40*** 208.46*** 208.74***4.51 3.38 4.63 3.44

n 5,766 5,766 5,766 5,766n cohorts 10 10 10 10chi2 2,606.25*** 3,102.72*** 2,409.16*** 2,854.98***var (constant) 185.13 88.89 197.33 95.00var (residual) 1,655.43 1,516.05 1,530.70 1,416.54



graPh 5.5

evolution of variance explained between cohorts and at the individual level. mathematics skills

educatIon

0,70

0,60

0,50

0,40

0,30

0,20

0,10

0,00Parents’

educatIoncontrols lIfelong

learnIng

r2 between cohorts r2 individual level

0,34

0,46

0,36

0,62

0,17

0,26

0,310,34


graPh 5.6

evolution of variance explained between cohorts and at the individual level. reading skills

educatIon

0,70

0,60

0,50

0,40

0,30

0,20

0,10

0,00Parents’

educatIoncontrols lIfelong

learnIng

r2 between cohorts r2 individual level

0,35

0,52

0,44

0,66

0,310,27

0,330,36



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index of tables and graphs

tables

1.1 Multilevel models with random constant (GlS) on reading ability 149

1.2 Multilevel models with random constant (GlS) on reading ability 149

2.1 Multilevel regressions with random constant. the dependent variable is mathematics grades. 150

2.2 Primary education. Multilevel regressions with random constant and slopes (M2, M3) and random constant (M4, M5). the dependent variable is mathematics grades. 150

2.3 Secondary education. Mulitlevel regressions with random constant (M1, M4, M5) and random slope (M2, M3). the dependent variable is mathematics grades 151

3.1 description of the tIMSS variables 75

3.2 linear model with random constants. the dependent variable is expressed educational expectations. level 1 is the student; level 2 is the country. 152

4.1 Multilevel linear regression models (random constant and slope). Grades in mathematics. 153

4.2 Multilevel linear regression models (random constant and slope). dependent variable: Grades in mathematics pedagogy 154

5.1 average scores by age and the education level of parents 110

5.2 Numeracy skills by education of respondent and parents 113

5.3 reading skills by education of respondent and parents 114

5.4 linear regression models with random constant 155


5.5 linear regression models with random constant and random slope 155

a.1 Sample sizes in PIrlS by country 127

a.2 Univariate distribution on the amount of time spent in preschool 129

a.3 distribution of preschool education by country 130

a.4 descriptive statistics of the variables used in the eGdP-2009 133

a.5 descriptive statistics of the variables used in the eGdS-2010 134

a.6 Sample sizes in the tIMSS, rich countries excluding the oil producing countries 137

a.7 description of the tedS-M variables 141

a.8 description of variables used based on the PIaac data 143

c.1 Gross effect of parents’ education on the competencies of their children in different stages of the life cycle. Standardised coefficients from an olS regression. 122

Graphs

I.1 conceptual framework 16

1.1. Illustration of the learning curve 28

1.2. Hypotheses of substitution and complementarity 32

1.3. deviations from the average effect of preschool on reading ability by country 34

1.4. Marginal effect of attending preschool on reading ability by intensity of parental stimulation. 35

1.5. Marginal effect of attending preschool on reading ability by parents’ education level. 37

1.6. Marginal effect of attending preschool on reading ability by parents’ education level and the standardisation of the preschool system. 39

2.1 Percentage of results in different subjects that depend on the school and on the characteristics of the students in primary and secondary education 47

2.2 deviation in average performance in mathematics in Spanish schools (broken red line) 49

Index of tables and graPhs 169

2.3 Primary and secondary education: differences among schools in the effect of the socioeconomic origin and immigrant status of students’ parents. estimates based on independent regressions for each school. 51

2.4 differences among schools in the impact of socioeconomic origin and immigrant status on maths scores. 54

2.5 differences between public and private schools in primary and secondary education measured by average performance of each sample (broken line). 56

2.6 Percentage of maths performance based on student body composition: importance of school effects in the autonomous regions with their own sample in PISa 2009 57

2.7 reduction in school effects when controlling for the average socioeconomic composition of the student body in each school by autonomous region 59

3.1 Hypotheses and empirical implications 74

3.2 effect of a decline in GdP by 5 points on the educational expectations of students with different levels of performance 78

3.3 effect of a 5 point decline in GdP on educational expectations, by the academic performance of students and household material resources 79

3.4 effect of 5 point decline in GdP on educational expectations, according to the academic performance of students and household educational resources 80

4.1 Scores in mathematics, description of the distribution among schools and within them 91

4.2 Scores in the pedagogy of mathematics, description of the distribution among schools and within them 91

4.3 effect of (a) grades in secondary and (b) the education of parents on grades in mathematics and the pedagogy of mathematics among schools. Independent estimate for each school 93

4.4 comparison of the effects of the university on grades in mathematics and the pedagogy of mathematics 94

4.5 the impact of grades in secondary and parental education on grades in mathematics and the pedagogy of mathematics. these models allow us to quantify the differences between schools and the effect of each of the variables on the horizontal axis 96


4.6 comparison of school effects on the slope for parental education by grades in mathematics and pedagogy of mathematics 97

5.1 average scores in mathematics and reading comprehension by education level for the Spanish between the ages of 16 and 65 105

5.2 the average skills of Spanish adults by five-year cohorts 108

5.3 Numeracy skills of adults by age group and by use in every day life. 112

5.4 reading comprehension skills of adults by age group and by use in every day life. 112

5.5 evolution of variance explained between cohorts and at the individual level. Mathematics skills 156

5.6 evolution of variance explained between cohorts and at the individual level. reading skills 156

a.1 Univariate distribution of reading ability 128

a.2 Univariate distribution of parental involvement in activities related to reading 131

a.3 description of the dependent variables from the assessment in primary school 134

a.4 description of the dependent variables from the assessment in secondary school 135

a.5 description of per capita GdP and GdP growth in the sample of countries-year 139

a.6 distribution of the dependent variables in the tedS-M 2009 study for Spain 142

a.7 distribution of the dependent variables in the PIaac study for Spain 143

Index of tables and graPhs 171

social studies collectionavailable on the internet: www.laCaixa.es/ObraSocial

Published titles

1. ForeIGN IMMIGratIoN IN SPaIN (Out of stock) eliseo aja, Francesc carbonell, colectivo Ioé (c. Pereda, W. actis and M. a. de Prada), Jaume Funes and Ignasi Vila

2. ValUeS IN SPaNISH SocIetY aNd tHeIr relatIoN to drUG USe (Out of stock) eusebio Megías (director)

3. FaMIlY PolIcIeS FroM a coMParatIVe PerSPectIVe (Out of stock) lluís Flaquer

4. YoUNG WoMeN IN SPaIN (Out of stock) Inés alberdi, Pilar escario and Natalia Matas

5. tHe SPaNISH FaMIlY aNd attItUdeS toWard edUcatIoN (Out of stock) Víctor Pérez-díaz, Juan carlos rodríguez and leonardo Sánchez Ferrer

6. old aGe, dePeNdeNce aNd loNG-terM care (Out of stock) david casado Marín and Guillem lópez and casasnovas

7. YoUNG PeoPle aNd tHe eUroPeaN cHalleNGe Joaquim Prats cuevas (director)

8. SPaIN aNd IMMIGratIoN Víctor Pérez-díaz, berta Álvarez-Miranda and carmen González-enríquez

9. HoUSING PolIcY FroM a coMParatIVe eUroPeaN PerSPectIVe carme trilla

10. doMeStIc VIoleNce (Out of stock) Inés alberdi and Natalia Matas

11. IMMIGratIoN, ScHoolING aNd tHe laboUr MarKet colectivo Ioé (Walter actis, carlos Pereda and Miguel a. de Prada)

12. acoUStIc coNtaMINatIoN IN oUr cItIeS benjamín García Sanz and Francisco Javier Garrido

13. FoSter FaMIlIeS Pere amorós, Jesús Palacios, Núria Fuentes, esperanza león and alicia Mesas

14. PeoPle WItH dISabIlItIeS aNd tHe laboUr MarKet colectivo Ioé (carlos Pereda, Miguel a. de Prada and Walter actis)

15. MoSleM IMMIGratIoN IN eUroPe Víctor Pérez-díaz, berta Álvarez-Miranda and elisa chuliá

16. PoVertY aNd SocIal eXclUSIoN Joan Subirats (director)

17. tHe reGUlatIoN oF IMMIGratIoN IN eUroPe eliseo aja, laura díez (coordinators)

18. eUroPeaN edUcatIoNal SYSteMS: crISIS or traNSForMatIoN? Joaquim Prats and Francesc raventós (directors), edgar Gasòliba (coordinator)

19. PareNtS aNd cHIldreN IN todaY’S SPaIN Gerardo Meil landwerlin

20. SINGle PareNtING aNd cHIldHood lluís Flaquer, elisabet almeda and lara Navarro

21. tHe IMMIGraNt bUSINeSS coMMUNItY IN SPaIN carlota Solé, Sònia Parella and leonardo cavalcanti

22. adoleSceNtS aNd alcoHol. tHe PareNtal VIeW eusebio Megías Valenzuela (director)

23. INterGeNeratIoNal ProGraMMeS. toWardS a SocIetY For all aGeS Mariano Sánchez (director)

24. Food, coNSUMPtIoN aNd HealtHcecilia díaz Méndez y cristóbal Gómez benito (coordinators)

25. VocatIoNal traINING IN SPaIN. toWard tHe KNoWledGe SocIetY

oriol Homs

26. SPort, HealtH aNd QUalItY oF lIFe david Moscoso Sánchez and eduardo Moyano estrada (coordinators)

27. tHe rUral PoPUlatIoN IN SPaIN. FroM dISeQUIlIbrIUM to SocIal SUStaINabIlItY luis camarero (coordinator)

28. carING For otHerS a cHalleNGe For tHe 21St ceNtUrY constanza tobío, M.ª Silveria agulló tomás, M.ª Victoria Gómez andM.ª teresa Martín Palomo

29. ScHool FaIlUre aNd droPoUtS IN SPaIN Mariano Fernández enguitaluis Mena Martínez and Jaime riviere Gómez

30. cHIldHood aNd tHe FUtUre: NeW realItIeS, NeW cHalleNGeS Pau Marí-Klose, Marga Marí-Klose, elizabeth Vaquera and Solveig argeseanu cunningham

31. IMMIGratIoN aNd tHe WelFare State IN SPaIN Francisco Javier Moreno Fuentes María bruquetas callejo

32. INdIVIdUalIzatIoN aNd FaMIlY SolIdarItY Gerardo Meil

33. dISabIlItY aNd SocIal INclUSIoN colectivo Ioé (carlos Pereda, Miguel Ángel de Prada, Walter actis)

34. tHe traNSItIoN to adUltHood IN SPaIN: ecoNoMIc crISIS aNd late eMaNcIPatIoN almudena Moreno Mínguez (coordinator)

35. crISIS aNd SocIal FractUre IN eUroPe. caUSeS aNd eFFectS IN SPaIN

Miguel laparra and begoña Pérez eransus (coords.)

available in english from No. 23

36. tHe FertIlItY GaP IN eUroPe: SINGUlarItIeS oF tHe SPaNISH caSe Gøsta esping-andersen (editor), bruno arpino, Pau baizán, daniela bellani, teresa castro-Martín, Mathew J. creighton, Maike van damme, carlos eric delclòs, Marta domínguez, María José González, Francesca luppi, teresa Martín-García, léa Pessin, roberta rutigliano

37. NeIGHboUrHood crIMe PercePtIoNS aNd reactIoNS alfonso echazarra

38. GroWING UP IN SPaIN: tHe INteGratIoN oF tHe cHIldreN oF IMMIGraNtS rosa aparicio and alejandro Portes

39. learNING aNd tHe lIFe cYcle. INeQUalItY oF oPPortUNItIeS FroM PreScHool edUcatIoN to adUltHood Héctor cebolla-boado, Jonas radl and leire Salazar

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The quality of a country’s education system is critical for its future and

the progress of its citizens. Therefore, research to provide

comprehensive analysis of the education system and to suggest ways

to improve it is crucial.

This study examines the influence of students’ socioeconomic origin

on their academic performance and the extent to which individual

schools can be a factor to compensate for families’ lack of resources.

It analyses and compares data from all education levels, from

preschool through adult education.

The conclusions indicate that investment in the initial levels of

education can be particularly effective in fostering academic success.

Social Studies CollectionNo. 39

Learning and the Life Cycle Inequality of Opportunities from Preschool Education to Adulthood


Learnin

g and th

e life cycle

Learning and the Life Cycle: Inequality of Opportunities from Preschool Education to Adulthood.

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Transcript of Learning and the Life Cycle: Inequality of Opportunities from Preschool Education to Adulthood.