Cultural capital and its effects on education outcomes

Economics of Education Review 29 (2010) 200–213

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Cultural capital and its effects on education outcomes夽 Lucia Tramonte ∗ , J. Douglas Willms University of New Brunswick, Canada

a r t i c l e

i n f o

Article history: Received 12 June 2009 Accepted 12 June 2009 Keywords: Cultural capital Human capital Educational achievement School segregation

a b s t r a c t In this study we distinguished between two forms of cultural capital, one that is static, representing the highbrow activities and practices of parents, and one that is relational, representing cultural interactions and communication between children and their parents. We used data for 28 countries from the 2000 Programme for International Student Assessment to examine whether these two types of cultural capital were associated with students’ reading literacy, sense of belonging at school, and occupational aspirations, after controlling for traditional measures of socioeconomic status. We examined whether one type of cultural capital had stronger effects than the other and whether their effects differed across outcomes and across countries. The results provide compelling evidence that dynamic cultural capital has strong effects on students’ schooling outcomes, while static cultural capital has more modest effects. © 2009 Elsevier Ltd. All rights reserved.

1. Introduction Education is a key factor for predicting social mobility in most industrialized societies (Blau & Duncan, 1967; Sewell & Hauser, 1975). Most studies of social mobility have found that academic achievement and occupational attainment are largely determined by people’s family origin and educational experiences (Bielby, 1981; Kerckhoff, 1996; Sewell, Hauser, & Featherman, 1976). These studies have focused primarily on the roles of socioeconomic status (SES), family structure, and family resources, including economic, cultural and social capital (Lareau & Weininger, 2003; Sirin, 2005). Dominant status groups hold economic, political, and symbolic power (Collins, 1971), and their success depends on the use of their social and cultural capital in strategic ways.

夽 Note: The authors are appreciative of funding from the Social Sciences and Humanities Research Council for its support of the collaborative research program, Raising and Levelling the Bar in Children’s Cognitive, Behavioural and Health Outcomes (Grant Number 512-2003-1016). ∗ Corresponding author at: Canadian Research Institute for Social Policy Suite 300 Keirstead Hall, Fredericton, New Brunswick, Canada E3B 5A3. Tel.: +1 506 458 7012. E-mail address: [email protected] (L. Tramonte). 0272-7757/$ – see front matter © 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.econedurev.2009.06.003

Economists have stressed the mediating role that schools play on the path between family background and student outcomes. The dominant approach has been to search for aspects of classroom and school inputs that affect student outcomes after controlling for family inputs, and to estimate their effects using education ‘production functions’ (Hanushek, 1989; Levin, 1974). Henry Levin contributed to this work in two important ways. First, he brought cost-benefit analysis to the production function enterprise, relating the costs of inputs to measured gains in learning (Levin, 1989). Second, he questioned the conventional wisdom that reallocating inputs based on production functions would increase the efficiency of schools; rather he argued that dramatic and sustainable change required dramatic changes in the structural and organisational features of schools that would bring disadvantaged students into the mainstream, including incentives for teachers, efficient access to information, the use of productive technology, and better assessment strategies (Levin, 1997). Sociological explanations of the association between children’s educational outcomes and family background have often referred to economic, social, and cultural capital as the main components of parental resources (De Graaf, De Graaf, & Kraaykamp, 2000). Current research is concerned

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with three main aspects of this process: differences among families in the magnitude of their investment in their children’s human capital; social reproduction via differential family access to the cultural capital required for the child to succeed in school and for parents to mediate and intercede on their child’s behalf; and social reproduction associated with families’ access to the social capital that enables children to succeed in school (Farkas, 2003). The human capital investment paradigm (Becker, 1964; Mincer, 1958, 1974; Schultz, 1960, 1981) has provided durable support that education and training are useful assets in production (Farkas, 2003). Economists have recently included a new focus on the effects of schools and intervention programs on student values, habits, and behaviours (Bowles & Gintis, 2000, 2002; Dunifon, Duncan, & Brooks-Gunn, 2001; Heckman & Lochner, 2000; Heckman & Rubinstein, 2001). Yet this paradigm, using the notion of families’ calibrated cost/benefit investments in their children’s education, leaves unexplained why low income families are generally unable to help children achieve school success (Farkas, 2003). The research on cultural capital helps address this question. It suggests that individuals possess different amounts of cultural capital which explains why some students meet school standards, are accepted at college, and finally achieve higher levels of education, and why other students do not (Swidler, 1986; Lareau, 1989). Schools promote particular linguistic structures, authority patterns, and types of curricula. Children from higher SES families are already familiar with these social arrangements when they enter school, and therefore they do not perceive school as an intimidating place. Their ongoing experiences at home help them adapt to school and maintain the pursuit of academic achievement (Lamont & Lareau, 1988). Elements of family life, especially cultural resources, are invested as capital to align students’ expectations with school norms and help solve problems concerning social acceptance (Lamont & Lareau, 1988; Lareau, 1989). The argument is that low income parents fail to support their children in succeeding in school not because they see too low a payoff to such action, but because they lack the skills, habits, and knowledge needed to effectively assist them (Swidler, 1986; Farkas, 1996). The human capital approach and the research on cultural capital share a common core: the application of resources to building skills and habits in children by parents and other adults in the extended family and neighbourhood (Farkas, 1996, 2003). Our work attempts to describe and explain variability in national patterns of the influence of family cultural resources on school achievement. Our operational definitions of cultural resources distinguish between two types of cultural capital: one that is static, which includes ‘highbrow’ activities and practices; another that is relational, which is concerned with the cultural interactions and communication between children and their parents. Conceptually, we consider the unit of analysis to differ for these two forms of capital: static cultural capital is an expression of the family’s socioeconomic advantage, while relational cultural capital embodies the resources and experiences of children that they can use in society to interact strategically and successfully in achieving their goals.

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This study uses data from the 2000 Programme for International Student Assessment (PISA), a large comparative study conducted in 28 Organisation for Economic Cooperation and Development (OECD) countries, and in a number of non-OECD countries, under the aegis of the OECD.1 We attempt to address two sets of questions relevant to the relationship between schooling outcomes and cultural capital: (1) Do the two forms of cultural capital affect reading literacy, sense of belonging at school, and occupational aspirations, after controlling for other family-related determinants of these social outcomes? If so, does the strength of the relationships, or their pattern, differ across outcomes? (2) Do static and relational cultural capital operate differently in school systems that are highly selective compared with those that are less selective? If this is the case, we expect that the two types of capital differ also in their effects on student performance within and between schools. 1.1. Social mobility in schooling systems Two theoretical frameworks have been used in this work to explain how social inequalities are embedded in schooling systems: the distinction between sponsored and contest mobility by Turner (1960), and Bourdieu’s (1977, 1984, 1986) concepts of distinction and social reproduction. Turner (1960) argued that societies emphasize social mobility to varying degrees, and that there are constant strains shaping the educational system to conform to the established norm.2 However, students’ selection into secondary school is a powerful mechanism shaping educational systems, and a key element defining contest and sponsored mobility (Turner, 1960; Kerckhoff, 1975; Kinloch, 1969). Early selection is therefore a crucial element of our analysis.

1 The Programme for International Student Assessment (PISA) is an internationally standardised assessment developed jointly by OECD member countries through the OECD’s Directorate for Education. It is administered to 15-year olds in schools. The survey was implemented in 43 countries in the 1st assessment in 2000 and 2002, in 41 countries in the 2nd assessment in 2003, in 57 countries in the 3rd assessment in 2006, and in 62 countries in the 4th assessment in 2009. Tests are typically administered to between 4500 and 10,000 students in each country. 2 Turner’s main assumption is that within a formally open class system, the organizing folk norm defining the accepted mode of upward mobility shapes the school system, and may be even more crucial than the extent of upward mobility.” (Turner, 1960, p. 856) The folk norms to which Turner refers to are contest mobility and sponsored mobility. In contest mobility systems, elite status is the prize in an open contest, which is won through students’ own efforts. The prize of successful upward mobility is not awarded by the established elite. In sponsored mobility systems, the established elite chooses ‘recruits’ it wishes to be admitted to its ranks, and carefully inducts them into the norms of elite status. The early selection of students plays a dominant role in this process, but sponsorship systems also differ from contest systems in their curricula, systems of examinations, and the processes governing access to further education. Although the English school system has often been described as a sponsored system and the US as a contest system, all systems have structural features that can be considered sponsored or contest.

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In contrast, Bourdieu’s concept of social reproduction focuses on the differential socialization of individuals coming from different social classes. This socialization provides children with a sense of what is comfortable or natural—what Bourdieu calls ‘habitus’. The selection mechanisms governing sponsored mobility are similar to those supporting social reproduction. In both cases cultural and social resources are the necessary ‘passwords’ to succeed in the selection process for elite status. The essence of cultural capital is that its effects are institutionalized (Lamont & Lareau, 1988; De Graaf et al., 2000): schools are places where codes from higher socio-economic status groups are recognized and where the possession of cultural capital is rewarded. High income parents may have highbrow skills and habits that are of little productive value but provide their children with signals of high cultural capital for their teachers (Farkas, 2003). Our main thesis is that static cultural capital, as a marker of social status, is more important in sponsored school systems, where established elites control the enrolment of their children in high-status schools. Their children’s ability and effort play a lesser role in school success. In contrast, we expect relational cultural capital to be more important in contest systems, as it is the cultural interactions and strategic communication between children and their parents that gives some children an upper hand in the different types of contests they encounter as they proceed through school. Generally, selective school systems tend to have greater variance between schools in their academic outcomes than within them, while in contest systems much of the variance in academic outcomes is within schools. If our thesis holds, static cultural capital will be more important in sponsored systems than in contest systems as it helps children achieve success in early recruitment, but thereafter, the skills that are transmitted through relational cultural capital will contribute to their success within schools. In contest systems, however, relational cultural capital will play a dominant role in both school selection and in children’s success within schools. Our analysis includes three different types of schooling outcomes, but we do not have a strong thesis on whether static versus relational cultural capital has relatively stronger effects on a particular type of outcome. Our analysis is not at odds with Bourdieu’s concept of cultural capital. Rather, we feel that a distinction between the parents’ possession of ‘highbrow’ static cultural capital and the activation of cultural capital through their relationships with their children can further our understanding of how cultural capital operates in different types of school systems. This observation can potentially direct debates about educational inequality towards discussions of equity. 1.2. Cultural capital and human capital Researchers in the fields of education and sociology have attempted to define the concept of cultural capital in ways that are consistent with some of the definitions proposed by Bourdieu. Cultural capital exists in three forms: in embodied state, in objectified state, and in an institutionalized state (Bourdieu, 1986). As a concept developed and conceived in an individualistic form, cultural capital is

very close to that of human capital in economics (Robbins, 1991; Throsby, 1999). Sometimes definitions of human capital within economics explicitly include culture as one of its components (Throsby, 1999; Costanza & Daly, 1992; Chiswick, 1983); however, Woodbury (1993) has pointed out how building culture into the human capital framework empties the theory of empirical content because no independent assessment of ‘culture’ is possible. Bourdieu’s conceptual arguments do not provide a clear direction for proceeding with empirical tests, and consequently have been only weakly linked to specific empirical analyses.3 The research evidence concerning the definition and operationalization of cultural resources is mixed. A glaring problem is that some studies assess the possession of cultural attributes and resources, particularly ‘highbrow’ preferences, tastes, and attitudes, while others do not distinguish cultural capital from the common measures of SES. Also, most studies do not distinguish between the cultural resources of the parents from those of their children, treating cultural capital as a family characteristic. To explain educational reproduction, we need to consider two important mechanisms of exchange: one is the effect of students’ cultural resources on their educational attainment; the second is the transmission of cultural resources from parents to children. The latter process is somewhat complicated: while one may easily agree that elements of cultural lifestyle are transmitted from one generation to the next, there is an important difference between the possession and the activation of cultural resources (Lareau & Horvat, 1999). It is possible to transmit cultural resources, but the beneficiaries have to activate them.4

3 Most of the fixed characteristics associated with the construct of cultural capital come from two of Bourdieu’s works, “Reproduction” and “Distinction” (Bourdieu, 1977, 1984, 1986). The interpretation of Bourdieu’s concept and the attempts to operationalize it have a long history. DiMaggio (1982) considered cultural capital as a factor capable of completely filling out models of the ‘status achievement process’. He observed an association between cultural capital and students’ grades; cultural capital acted as a resource that supplemented ability enabling students to succeed in school. The argument is that teachers communicate more easily with students who participate in elite status cultures, and perceive them as more gifted than students who lack the distinctive tastes, traits, and styles. Drawing from DiMaggio’s (1982) work, most of the subsequent Anglophone research maintained two intrinsically related conceptions of cultural capital: it comprises prestigious, highbrow activities and it is distinguished conceptually and causally from the effect of ability (Lareau & Weininger, 2003). DiMaggio’s (1982) definition of cultural capital is rather restrictive, and therefore researchers who adopted his suggestions found it difficult to explain the mechanism of educational reproduction. 4 Recent studies have considered parental and students’ cultural interests, including engagement in reading practices, as forms of cultural capital. For example, De Graaf et al. (2000) considered parents’ cultural resources, but distinguished between participation in the ‘beaux-arts’ and engagement in reading. Their research in the Netherlands found that parents’ mastery of highbrow cultural codes brought less advantage to students’ educational careers than parents’ reading behaviour. The same distinction between beaux-arts participation and parents’ verbal and reading ability was used by Crook (1997) in a study of the effects of parental cultural practices in Australia. He also found parents’ reading behaviours to have stronger effects on student outcomes than participation in the beaux-arts. Their operationalization of cultural capital is consistent with DiMaggio (1982) in that participation in elite activities is separate from ability. Sullivan (2001) proposed a much broader defi-

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1.3. Static versus relational cultural capital Our analysis examines the effects of two types of cultural capital. Static cultural capital includes both the possession of high culture goods, such as art works, musical instruments, and classical music; and highbrow activities, such as going to museums or the ballet or theatre. Parents hold and manage ‘static’ cultural resources, which they share in the household with their children. The ‘static’ attribution indicates that it is relatively constant, perhaps even more so than income or level of education. The cultural goods and activities are available to children, but they are not necessarily an expression of individual choices, tastes, and preferences.5 Relational cultural capital includes cultural resources and activities that are expressed in the relationships between parents and children. It includes discussions between children and parents on cultural, political, and social matters, on school activities, and on books that children have read. This form of cultural capital is relational because it requires an investment of parents and children in an on-going relationship that transcends the economic capital available to them. The ideas of activation, engagement, and promotion of cultural interests are embedded in this construct. We consider the distinction between static and relational cultural capital important because static cultural capital may only reflect parents’ own choices and lifestyles, while relational cultural capital reflects how that capital is transmitted and employed. The PISA 2000 study is the first large-scale international study to explicitly attempt to include indicators of cultural capital, and the student questionnaire includes questions that can be roughly categorized into static and relational cultural capital. However, the study was not designed with our schema in mind and therefore the classification of items as static and relational is far from perfect. Our analysis examines the effect of these two forms of cultural capital on three social outcomes: reading literacy, sense of belonging at school, and occupational aspirations. Each of these outcomes is influenced by practices at home, but also by the school environment and teaching practices. Willms’s (2003a) analysis of reading literacy and sense of belonging at school found that these outcomes vary considerably among countries and among schools within countries. Moreover, their relationships with a traditional measure of SES varied considerably among countries. Sense of belonging at school is considered a key component of the broader construct, student engagement, which

nition of cultural capital. It distinguished parents’ cultural capital from children’s cultural capital, and the conception of parents’ cultural capital comprised both reading behaviours and highbrow possessions and activities. Similarly, her indicators of children’s cultural capital included information about pupils’ activities, cultural knowledge, and vocabulary. She found that cultural capital had a large, direct effect on children’s educational attainment in the UK, but only partially explained social class inequalities in attainment. 5 For example, going to a ballet performance could be considered relational in that parents are making an effort to transmit their cultural capital. It is a question of whether this is more of an outward expression of parents’ possession of cultural capital versus an attempt to inculcate an appreciation of the arts in their children.

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is related to several schooling outcomes, including academic achievement (Finn & Rock, 1997) and the successful transition from middle to secondary school (Newman, Lohman, Newman, Myers, & Smith, 2000). Willms (2003a) argued that sense of belonging at school and engagement should be considered important outcomes in their own right, as they represent a disposition towards learning, working with others, and functioning in a social institution. We consider student aspirations important also, as students’ interpersonal relationships affect their educational and occupational aspirations. Early studies on status attainment focused on the importance of ‘significant others’ – peers, parents, and teachers – in mediating the effects of socioeconomic background and ability on aspirations and educational and occupational attainment (Duncan, Haller, & Portes, 1968; Haller & Butterworth, 1960; Sewell, Haller, & Portes, 1969; Sewell & Shah, 1968). Although peers and parents undoubtedly play a strong role in shaping educational and occupational aspirations, we maintain that schools also play a prominent role. 2. Materials and methods 2.1. Data and sample The data for this study are a subset of the 2000 PISA, a reading literacy skills assessment conducted with 224,058 students nested in 8364 schools from 28 OECD countries (Organisation for Economic Cooperation and Development, 2001, 2003). We limited our study to OECD countries.6 The target population for the PISA survey was all 15-year-old students in each country. The data were derived from a comprehensive test of reading literacy and a student background questionnaire covering family structure; education and occupation of the mother and father; various measures of the attitudes, habits and expectations of students; and the relationships of students with their peers, parents, and teachers. The student data set provides measures of our three outcome measures. We constructed measures of static and relational capital, family structure, and SES. Our analysis does not include school-level measures available from a questionnaire administered to school administrators, but it does take into account the hierarchical structure of the data set, with pupils nested within schools, and schools within countries. The variables used in the analysis are described below. 2.2. Measures 2.2.1. Reading literacy PISA defines reading literacy as the ability to understand, use, and reflect on written texts in order to participate effectively in everyday life. The measure of reading literacy is based on students’ performance on a wide range of literacy tasks with varying difficulty. The test items

6 The non-OECD countries tend to have lower levels of economic development and their schooling systems tend to be highly segregated because of the differential allocation of low and high SES students into rural public, urban public, and private schools (Willms, 2006).

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were scaled using techniques from item response theory, and standardized to have a mean of 500, and a standard deviation of 100 for all OECD countries.

within countries and among countries. For a discussion on the importance of considering reliability in a multilevel framework, see Rowan, Raudenbush, and Kang (1991).

2.2.2. Sense of belonging at school Sense of belonging at school is a composite index generated by a set of questions related pupils’ perceptions and feelings about their presence at school (Willms, 2003a). This measure was also standardized to have a mean of 500 and a standard deviation of 100 for all OECD countries.

2.2.8. Relational cultural capital A standardised index, with a mean of zero and a standard deviation of one for all OECD countries, was constructed from responses to questions related to cultural exchanges, communication, and elaboration of cultural experiences.8 The estimated reliability coefficients are 0.68, 0.65, and 0.99 at the individual, school and country levels respectively. These results suggest that the measure can powerfully distinguish among countries in their average scores, but is less reliable in distinguishing among schools within countries or among individuals. We conclude that this measure reliably distinguishes among countries, which is pertinent to one of the primary aims of our analyses. However, the estimates of the slopes within countries are likely to be underestimated, given the lower reliability at the individual and school levels.

2.2.3. Occupational aspirations Students were asked, “What kind of job do you expect to have when you are about 30 years old?” Responses were coded and scaled on the international occupational index developed by Ganzeboom, De Graaf, and Trieman (1992). As with reading literacy, the measure was scaled to have a mean of 500, and a standard deviation of 100 for all OECD countries. With the three outcome measures scaled in this way, one can interpret estimates of the magnitude of observed differences among countries and regression parameters as ‘effect sizes,’ as 100 points on each scale is equivalent to one standard deviation. 2.2.4. Mothers’ and fathers’ level of education Parents’ education was measured by students’ reports of the levels of education achieved by the father and mother, based on the 1997 International Standard Classification of Education (ISCED). These were recoded into years of education. 2.2.5. Parental occupation Parental occupational prestige was measured by the International Socio-Economic Index of Occupational Status (ISEI), which gauges the attributes of occupation that convert a person’s education into income. 2.2.6. Sex A dichotomous variable “female” was created with females coded one and males zero. 2.2.7. Static cultural capital A standardised index, with a mean of zero and a standard deviation of one for all OECD countries, was constructed from students’ responses to questions related to cultural consumptions and possessions.7 The reliability of this measure at the individual level, using Cronbach’s alpha, is 0.68. At the school and country levels, the reliability is 0.84 and 0.95 respectively. The latter estimates were obtained based on a three-level ‘null’ hierarchical linear model, with students nested within schools, and schools nested within countries. The results reveal that the measure adequately distinguishes among schools

7 The following items comprise the construct of Static Cultural Capital: how many books there are in your home? How often have you visited museums or art galleries? How often have you attended opera, ballet, and classic symphony? How often have you watched live theatre? How often do your parents listen to classical music with you? Do you have musical instruments at home? Do you have classic literature at home? Do you have books of poetry at home? Do you have works of art at home?

2.2.9. Variation between-schools in literacy skills For each country we estimated the proportion of variance in reading literacy that was between schools. This is the ratio of the between-school variance to the total variance, called Rho, which was estimated with a ‘null’ hierarchical linear model (Raudenbush & Bryk, 2002). We treat this as proxy for the cumulative effects of the selection of students into different types of schools. In many OECD countries students are selected as early as age 10 or 11, while in others they are selected during the transition from elementary to secondary school (Willms, Smith, Zhang, & Tramonte, 2006). In many school systems there is also selection into a private sector, which generally increases between-school variance. We include Rho in our final model as a proxy for the selectivity of the school system. Ideally one would use external indicators of this construct. The principal questionnaire of the PISA study includes some measures relevant to school selection, but these pertain mainly to the selection of students into schools at the secondary level rather than the cumulative selection of students into different types of schools during their school careers. Also, one does not usually like to include a covariate that is derived from the outcome measure. However, in this case the measure is at the country level, far removed from the student-level outcomes. Moreover, measures of Rho derived from either mathematics or science performance are highly correlated with the measure based on reading performance. 2.3. Statistical methods The complex structure of the PISA data set requires consideration of two important features. The PISA study

8 The following items comprise a measure of Relational Cultural Capital: do your parents discuss political or social issues with you? Do your parents discuss books, films, or television programs with you? Do your parents discuss how well you are doing at school? Do your parents spend time just talking with you? Do you like talking about books with other people? Do you enjoy going to a bookstore or a library?

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employed a matrix sampling design whereby each student was given only a sample of the items from the full reading test. Each student was assigned five ‘plausible values,’ estimates of what they might have achieved had they completed the full test. Also, because the sampling design was a two-stage clustered design, the data base includes a set of sampling weights derived from a technique called Balanced Repeated Replicates. Correct estimates of the standard errors of statistical estimates (e.g., means, standard deviations, regression coefficients) can be obtained by using the set of plausible values in concert with the replicate weights. The hierarchical structure of the data can also be taken into account by fitting a hierarchical linear model to the data (Raudenbush & Bryk, 2002), using each of the five plausible values (see Willms & Smith, 2005). For each of the three outcome measures, we fit a basic regression model separately for each country, using ordinary least-squares (OLS) estimation and the PV-BRR technique. The base model is: Readingi = ˇ0 + ˇ1 (Relational Capital)i + ˇ2 (Static Capital)i + ˇ3 (Parental Occupation)i + ˇ4 (Parental Education)i + ˇ5 (Female)i + εi

(1)

The model also includes two dummy variables indicating missing data for parental occupation and education. The same model was fit for sense of belonging at school and occupational aspirations. The first question is addressed by examining the strength and pattern of the coefficients, ˇ1 and ˇ2 , across countries. To test the hypotheses concerning variation among schools and countries in these relationships we fit a threelevel hierarchical linear model (HLM), with students nested within schools, and schools nested within countries. The three-level hierarchical model is described with Eq. (2) below using the notation set out by Raudenbush and Bryk (2002, see p. 231). Readingijk = 0jk + 1jk (Relational Capital)jk + 2jk (Static Capital)jk

+ 4jk (Parental Education)jk

0jk 1jk 2jk 3jk 4jk 5jk

= ˇ00k + r0jk = ˇ10k + r1jk = ˇ20k + r2jk = ˇ30k + r3jk = ˇ40k + r4jk = ˇ50k + r5jk

(2a)

(2b)

(2c)

At level 1 (Eq. (2a)) the reading score for the ith student in the jth school and the kth country is modelled as a function of student-level covariates and a random studentlevel error term. This is identical to the base model (Eq. (1)) except that the model is conceived to have different parameters for each school and country. At level 2 (Eq. (2b)) the intercepts and regression coefficients at level 1 are modelled as random effects; that is, they are allowed to vary among schools and among countries. The level 3 model (Eq. (2c)) allows the parameters for each school to vary among countries. These are described as a grand mean plus a random term that varies around the grand mean. In essence, the model fits a separate regression model for every school within every country. In our exploratory work we fit separate two-level models, one for each country, which yielded virtually identical results. However, we prefer the three-level model as HLM provides an explicit test as to whether the variation in the coefficients at levels 2 and 3 are statistically significant. For example, we can ask, “Do the effects of relational and static capital on reading (after controlling for other variables in the model) vary among schools within countries?” The analysis provides estimates of the average effects of relational and static capital (ˇ10k and ˇ10k , respectively) which we expect to be positive and greater than the effects associated with parental occupation and education. The analysis also provides estimates of the variance in r1jk and r2jk and chi-square tests are used to discern whether each variance is greater than zero. We also ask, “Do the effects of relational capital and static capital vary among countries?” The analysis yields estimates of the variance of u10k and u20k which are also tested with a chisquare test. For each of the three social outcomes, a general model was fitted and examined, and then the parameters that did not vary among countries were fixed. The third level of the model can be extended to include country-level variables. In our case, we added Rho, the proportion of the variance in reading achievement that is between schools, to the first three equations in (2c): ˇ00k = 000 + 001 (Rho)k + u00k ˇ10k = 100 + 101 (Rho)k + u10k ˇ20k = 200 + 201 (Rho)k + u20k

+ 3jk (Parental Occupation)jk

+ 5jk (Female)jk + eijk

ˇ00k = 000 + u00k ˇ10k = 100 + u10k ˇ20k = 200 + u20k ˇ30k = 300 + u30k ˇ40k = 400 + u40k ˇ50k = 500 + u50k

205

(3)

One of advantages of hierarchical modelling is that the model can be manipulated to yield separate estimates of the within- and between-school slopes associated with relational and static cultural capital. The within-school slopes can be estimated by centering the covariates at level 1 (Eq. (2a)) around their school means, rather than around the grand mean. After fitting this model, we examined graphically the bivariate relationship between the withinschool slopes and Rho, and then extended the model to include Rho at level 3, as per Eq. (3). The between-school slopes were estimated by dropping the covariates for relational and static cultural capital at

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level 1 and adding the mean levels of these two variables in the level 2 equation for the intercepts. This model asks whether the average levels of school performance, adjusted for sex, parental education and parental occupation, are related to mean levels of relational and static cultural capital within the school. These school-level relationships are allowed to vary among countries. Therefore, as with the within-school slopes, one can examine graphically their relationship with Rho, and provide an explicit test of the statistical significance of the observed relationships. This model is described with Eq. (4): Readingijk = 0jk + 3jk (Parental Occupation)jk + 4jk (Parental Education)jk + 5jk (Female)jk + eijk

(4)

0jk = ˇ00k + ˇ01k (Dynamic Capital)·k + ˇ02k (Relational Capital)·k + r0jk 3jk = ˇ30k + r3jk 4jk = ˇ40k + r4jk 5jk = ˇ50k + r5jk ˇ00k = 000 + 001 (Rho)k + u00k ˇ01k = 010 + 011 (Rho)k + u01k ˇ02k = 020 + 021 (Rho)k + u02k ˇ30k = 300 + u30k ˇ40k = 400 + u40k ˇ50k = 500 + u50k We did not include measures of family structure or immigrant status in our statistical models as we wanted estimates of the effects of cultural capital, overall, and within and between schools, unadjusted for family structure or immigrant status. Thus, our goal was not in trying to maximize explained variance, but to understand how the relationships between student outcomes and the two forms of cultural capital differ within and between schools and among countries. 3. Results Table 1 provides descriptive statistics of all variables used in the analyses. As noted earlier, the three outcome measures were scaled to have a mean of 500 and a standard deviation of 100 for all OECD countries.9 Even before

9 One of the dilemmas in scaling data from international studies is whether to determine the international mean based of data weighted at the pupil-level, which is called ‘house weighting’, or weighted at the country level, with each country contributing equally, which is called ‘senate weighting.’ For PISA, an international mean score based on house weighting would be heavily dominated by the US and Mexico, which have large populations of 15-year-old students, while countries like Iceland and Luxemburg would contribute relatively little to the weighted mean. In contrast, the senate weighted approach gives equal weight to small and large countries alike, and so it too has its shortcomings. The PISA Technical Advisory Group opted for the second approach, senate weighting, which we have also used. Also, Netherlands was not used in calculating the OECD mean as its sample did not meet PISA standards in 2000. We also excluded Netherlands in our scaling of the data. In the case of sense of belonging and

examining these results in a multilevel model, it is clear that there are large and statistically significant differences among countries in reading performance, sense of belonging at school, and occupational aspirations. The mean scores for reading performance have been reported earlier in the international report (OECD, 2001), and mean scores for sense of belonging at school were reported by Willms (2003a). (1) Do the two forms of cultural capital affect reading literacy, sense of belonging at school, and occupational aspirations, after controlling for other family-related determinants of these social outcomes? If so, does the strength of the relationships, or their pattern, differ across outcomes? Table 2 displays the effects of static and relational cultural capital on reading literacy, sense of belonging at school, and occupational aspirations, while controlling for gender, parental education, and parental occupation. Recall that the measures of relational and static cultural capital were standardized to OECD norms. Therefore the effects shown in Table 2 for these variables indicate the changes in the outcome measures associated with one standard deviation increases in either static or relational cultural capital. Considering all OECD countries together, there is a statistically significant effect of both relational and static capital for all three outcomes. The effect sizes are particularly large for relational and static cultural capitals on reading, and for relational cultural capital on sense of belonging at school. The other effects are of modest size, ranging from 1.4 to 5.6 points. These effects cannot be compared directly with those of parental education and occupation, as these effects indicate the change in the outcome measure associated with one additional year of education and a one-point increase on the ISEI occupation scale. The effect of parental education for reading is relatively small, but statistically significant. The effect is not statistically significant for sense of belonging at school and aspirations. This is noteworthy as it suggests that additional controls using traditional measures of SES would unlikely have a mediating effect on our measures of cultural capital. The effect of parental occupation, net of other covariates in the model, is very strong for reading literacy (18.7 points), but weaker for sense of belonging at school (3.7 point) and occupational aspirations (9.7 points). The effects of parents’ occupation are statistically significant for all three outcome measures. Females scored considerably higher than males on the reading test—the difference is nearly 25 points. However, sex effects were not statistically significant for sense of belonging at school, and for occupational aspirations the average score for females was about 5 points higher than that of males. If we again consider the average across all OECD countries, the results indicate that the effect on reading literacy of relational cultural capital is similar to that of static cultural capital: 14.2 points compared with 13.9 points.

aspirations, the means are not exactly 500 because of missing data. These issues have very little impact on any of the international comparisons.

L. Tramonte, J.D. Willms / Economics of Education Review 29 (2010) 200–213

207

Table 1 Descriptive analysis of the variables included in the model. Country

Australia Austria Belgium Canada Czech Rep Denmark Finland France Germany Greece Hungary Iceland Ireland Italy Japan Korea Luxemburg Mexico Netherlands New Zealand Norway Poland Portugal Spain Sweden Switzerland United Kingdom United States Total

Sample Size

Reading literacy

Sense of belonging

Occupational aspirations

Relational cultural capital

Static cultural capital

Rho

Mean

Std. Dev.

Mean

Std. Dev.

Mean

Std. Dev.

Mean

Std. Dev.

Mean

Std. Dev.

5,176 4,745 6,670 29,687 5,365 4,235 4,864 4,673 5,073 4,672 4,887 3,372 3,854 4,984 5,256 4,982 3,528 4,600 2,503 3,667 4,147 3,654 4,585 6,214 4,416 6,100 9,340 3,846

528 507 507 534 492 497 546 505 484 474 480 507 527 487 522 525 441 422 532 529 505 479 470 493 516 494 523 504

102 93 107 95 96 98 89 92 111 97 94 92 94 91 86 70 100 86 89 108 104 100 97 85 92 102 100 105

495 526 479 512 471 513 502 486 518 498 514 514 508 500 465 461 505 509 499 498 512 461 501 499 527 520 513 494

97 109 90 110 77 104 96 94 107 95 97 109 101 92 89 81 110 98 84 98 104 85 88 91 103 105 101 111

489 482 494 503 474 564 488 506 488 493 476 506 483 489 529 502 510 523 473 496 492 507 501 498 489 497 493 524

95 98 102 88 106 106 105 108 110 86 101 102 93 89 108 85 108 75 96 98 101 100 86 95 99 111 97 90

−0.198 −0.312 −0.294 −0.034 0.155 0.175 0.065 0.243 −0.297 0.330 0.421 −0.160 −0.080 0.538 0.011 −0.464 −0.332 0.018 −0.177 0.012 −0.258 0.049 0.278 0.179 −0.184 −0.179 0.067 0.247

1.037 0.999 1.011 0.975 0.876 0.937 0.847 0.965 0.999 0.883 0.802 0.988 0.954 0.871 1.148 1.127 1.019 0.927 0.955 0.991 0.964 0.980 0.882 0.919 0.945 1.028 0.915 1.083

−0.055 0.076 −0.382 −0.018 0.326 −0.026 0.039 −0.345 −0.003 0.114 0.415 0.589 −0.056 0.294 −0.333 0.013 −0.133 −0.666 −0.430 −0.129 0.127 0.135 −0.169 0.165 0.090 0.015 −0.053 −0.067

0.987 1.004 1.043 0.989 0.942 0.992 0.939 0.983 1.009 0.859 0.912 0.777 0.966 0.896 0.913 0.866 1.055 1.002 0.944 0.982 0.987 0.944 0.988 0.955 0.970 1.012 1.064 1.055

0.183 0.598 0.597 0.176 0.532 0.190 0.125 0.502 0.592 0.509 0.668 0.082 0.177 0.548 0.461 0.375 0.308 0.534 0.505 0.162 0.103 0.624 0.369 0.204 0.093 0.432 0.217 0.294

159,095

500

100

501.9

101.1

499.2

−0.007

0.99

−0.016

1.00

0.363

However, the effect of relational cultural capital is greater than that of static cultural capital for sense of belonging at school – 10.5 compared with 4.5 – and for occupational aspirations – 5.6 compared with 1.4. The results for all OECD countries presented in Table 2 indicate that the effects of relational and static cultural capital are considerably stronger for reading literacy than for sense of belonging at school and occupational aspirations. When these effects are estimated in a multilevel model, the pattern is the same; although the effects of static cultural capital on occupational aspirations are slightly larger. These results are shown in Table 3. (2) Do static and relational cultural capital operate differently in school systems that are highly selective compared with those that are less selective? If this is the case, we expect that the two types of capital differ also in their effects on student performance within and between schools. Table 3 shows the results for three separate three-level hierarchical models. The first model includes only the variables denoting sex of the student and their parents’ level of education and occupational status. The intercept for the analyses indicates the average mean score for schools after adjusting for the covariates in the model. The superscripts, S and C, indicate that the adjusted means vary among schools within countries and among countries. The average effect of parental education is 3.7, which is the increase in the outcome associated with a one-year increase in parental

98.2

education. The superscripts, S and C, indicate that this effect varies significantly among schools within countries and among countries. Similarly, the effect attributable to parental occupation, 17.04, is the increase in reading proficiency associated with a one standard deviation increase in parental occupation scores. These effects also varies at both the school and country levels. Finally, the achievement gap between males and females is 27.6 points, with females scoring higher than males. The size of this achievement gap also varies among schools and countries. The second model introduces the variables for relational and static cultural capital. There are three key findings in these results: first, the average effect of relational capital is greater than that of static cultural capital; second, the effects for both factors vary among schools within countries and among countries; third, the two measures of cultural capital exert effects on reading performance that are independent of the effects of parental education and occupation. Thus, they explain some but not all of the effects associated with the traditional measures of socioeconomic status. The third model introduces Rho, the measure of between-school segregation. It has a strong negative relationship with average school performance as well as the slopes associated with relational and static cultural capital. The results associated with sense of belonging at school and occupational aspirations follow the same pattern; however, the effects attributable to parental education and occupation and the two forms of cultural capital are not as strong.

208 Table 2 Regression coefficients estimating the effects of relational and static cultural capital on student reading performance, sense of belonging, and occupational aspirations, controlling for parents’ education, parents’ occupation, and sex. Reading

Occupational aspirations

18.7 (0.4) 19.0 (2.0) 23.2 (2.5) 16.2 (1.0) 21.8 (3.2)

24.7 (0.8) 16.6 (4.0) 25.9 (4.4) 23.8 (1.5) 25.2 (4.2)

492.0 (2.6) 4.2 (2.4) 526.2 (2.4) 6.8 (2.1) 481.7 (1.3) 9.5 (1.2) 509.9 (1.3) 6.1 (1.1) 465.0 (1.5) 13.4 (1.4)

4.3 (2.0) −0.2 (2.2) 3.1 (1.3) 6.0 (1.1) 5.1 (1.4)

1.5 (0.8) 0.5 (0.9) 0.8 (0.4) 0.7 (0.5) 0.5 (0.7)

9.9 (1.7) 9.6 (1.8) 22.5 (1.7) 19.7 (4.5) 16.6 (2.5) 26.7 (2.1) 13.1 (2.6) 13.3 (2.1) 12.1 (2.3) 15.1 (1.8) 7.8 (1.6) 27.2 (1.8) 14.7 (1.8) 11.0 (1.8) 12.9 (1.8) 19.3 (2.2) 15.0 (2.5) 15.5 (1.8) 14.6 (1.6) 16.4 (1.7) 13.6 (1.8) 16.2 (1.9)

8.9 (0.7) 3.1 (0.5) 2.9 (0.5) 5.4 (0.9) 2.8 (0.6) 11.0 (1.1) 2.6 (0.6) 2.3 (0.8) 2.9 (0.7) 1.2 (1.3) 2.9 (0.3) 3.1 (0.5) 3.9 (0.5) 2.7 (0.8) 1.9 (0.7) 1.6 (0.8) 5.6 (1.2) 0.7 (0.6) 3.1 (0.3) 1.2 (1.0) 4.6 (0.7) 5.2 (0.7)

10.1 (1.8) 11.3 (1.8) 17.6 (1.7) 26.3 (3.2) 17.6 (2.0) 16.1 (2.1) 8.5 (1.7) 21.8 (1.9) 13.5 (2.3) 2.5 (2.3) 4.0 (1.7) 21.9 (1.9) 13.0 (1.7) 17.1 (2.6) 23.7 (2.0) 15.9 (2.0) 19.6 (2.7) 23.9 (2.1) 11.3 (1.4) 17.4 (1.7) 23.3 (1.9) 24.5 (1.7)

14.8 (2.8) 39.4 (2.7) 20.0 (2.7) 27.8 (3.9) 29.4 (3.7) 22.8 (4.0) 32.0 (3.1) 18.9 (3.9) 28.7 (5.8) 15.9 (5.1) 8.3 (4.8) 22.8 (3.2) 17.2 (2.8) 18.4 (4.7) 37.9 (4.8) 34.0 (3.6) 26.7 (5.7) 14.3 (3.2) 15.3 (2.4) 30.7 (2.3) 17.1 (3.4) 18.3 (3.1)

510.5 (2.1) 500.4 (1.6) 484.7 (1.8) 518.5 (1.7) 487.4 (2.2) 499.8 (2.3) 508.2 (2.6) 507.5 (2.2) 488.8 (1.9) 467.8 (4.8) 466.3 (1.9) 511.8 (2.1) 513.1 (3.1) 496.5 (2.6) 496.5 (2.6) 506.2 (3.0) 458.9 (2.0) 499.9 (1.9) 494.3 (1.8) 526.2 (2.1) 519.4 (1.9) 511.7 (1.5)

3.3 (1.9) 8.0 (1.8) 10.5 (2.0) 5.8 (1.6) 17.1 (2.1) 14.3 (2.6) 5.8 (2.1) 7.4 (2.1) 11.2 (1.7) 8.0 (1.4) 11.8 (1.2) 4.4 (2.1) 20.6 (2.1) 7.4 (2.7) 7.4 (2.7) 7.1 (2.2) 10.1 (2.1) 17.1 (1.8) 11.4 (1.8) 7.3 (1.9) 1.3 (2.0) 7.2 (2.0)

3.3 (1.8) 2.1 (1.7) 4.5 (1.6) 6.1 (2.4) 4.2 (2.5) 12.3 (3.0) 6.5 (2.9) 1.4 (2.1) 6.6 (1.7) 3.8 (1.7) 11.7 (1.5) 9.3 (2.2) 3.9 (2.1) −3.7 (2.1) −3.7 (2.1) 4.8 (2.4) 14.2 (2.5) 10.3 (1.5) 3.7 (1.9) 1.4 (2.0) 6.0 (2.3) 2.6 (1.5)

3.5 (0.9) 0.7 (0.5) 0.2 (0.5) 0.6 (0.8) −0.8 (0.5) 1.8 (1.5) 1.3 (0.7) 0.1 (0.7) −0.3 (0.8) 1.1 (1.6) −0.4 (0.5) 1.9 (0.6) 1.1 (0.6) 0.5 (0.6) 0.5 (0.6) 2.1 (0.6) −1.1 (1.2) 0.4 (0.4) −0.3 (0.5) 1.8 (0.9) −0.4 (0.7) 0.5 (0.7)

9.6 (2.2)

23.7 (2.8)

5.3 (1.3)

18.8 (2.5)

21.8 (3.4) 489.5 (2.6) 12.5 (2.4)

3.9 (2.2)

1.6 (1.0)

501.3 (0.5) 14.2 (0.3)

13.9 (0.5)

5.2 (0.1)

18.7 (0.4)

24.7 (0.8) 493.9 (0.8) 10.5 (0.8)

4.5 (0.7)

−0.6 (0.3)

501.3 (0.5) 510.2 (2.1) 512.8 (2.7) 524.0 (1.4) 469.1 (3.2)

14.2 (0.3) 19.0 (1.7) 6.5 (1.3) 19.9 (0.9) 14.6 (1.8)

13.9 (0.5) 11.2 (2.0) 20.2 (1.8) 11.6 (1.0) 18.3 (3.7)

489.0 (1.8) 542.7 (2.5) 509.8 (2.0) 482.3 (1.9) 466.6 (4.2) 455.5 (2.8) 491.2 (2.2) 523.8 (2.6) 473.8 (3.1) 538.6 (5.2) 535.8 (2.4) 458.0 (1.6) 449.1 (3.1) 534.7 (2.6) 522.0 (2.1) 496.6 (2.7) 478.1 (3.6) 470.2 (3.5) 493.5 (2.0) 511.4 (2.3) 497.0 (3.1) 521.0 (1.8)

26.3 (1.7) 24.4 (2.4) 10.8 (1.9) 9.6 (2.2) 15.9 (1.9) 12.8 (1.9) 20.5 (1.8) 16.3 (1.9) 16.7 (1.9) 19.0 (1.7) 16.9 (1.2) 2.0 (1.9) 13.9 (1.6) 19.3 (2.0) 13.1 (1.8) 21.3 (1.9) 11.6 (2.0) 27.0 (2.0) 20.5 (1.4) 16.2 (1.7) 17.9 (1.5) 15.6 (1.6)

501.3 (4.1)

Relational cultural capital

Parents’ education

5.2 (0.1) 3.4 (0.6) 3.4 (0.6) 4.1 (0.4) 9.1 (2.3)

Static cultural capital

Parents’ education

Static cultural capital

Constant

Relational cultural capital

Parents’ Female occupation

Constant

RelationalStatic cultural cultural capital capital

0.4 (2.1) −1.5 (3.2) 489.5 (2.5) 11.3 (2.0) 0.3 (2.5) −9.4 (4.2) 484.5 (2.0) 2.8 (1.7) 0.2 (1.4) 6.9 (2.3) 499.3 (2.8) 6.5 (1.5) 1.0 (1.3) 2.1 (1.9) 503.8 (0.8) 5.0 (0.9) 1.6 (1.7) −6.1 (2.9) 472.8 (4.2) 11.5 (2.1) −2.4 (2.1) −12.1 (3.8) −2.5 (1.7) −19.9 (2.9) −2.0 (1.8) −8.0 (2.7) 1.3 (2.3) −8.3 (3.5) −1.8 (1.5) −3.4 (3.7) 0.6 (2.2) −4.7 (3.0) 3.1 (2.4) −9.0 (4.4) −1.0 (1.9) −5.2 (4.3) −5.1 (2.0) −3.1 (3.3) 0.7 (1.7) −1.8 (3.3) 3.6 (1.7) −20.4 (2.6) 6.2 (2.5) −2.7 (4.1) 3.0 (2.1) 4.1 (3.3) 4.1 (1.7) 2.8 (4.1) 4.1 (1.7) 2.8 (4.1) 2.7 (2.1) −10.7 (3.9) 4.1 (2.5) −1.3 (4.3) 3.6 (1.6) −8.3 (2.9) −1.0 (1.7) 3.1 (3.1) −3.4 (2.0)−27.2 (3.5) 3.5 (2.0) −11.8 (3.6) 4.5 (1.5) −6.5 (2.5)

5.1 (1.9) 9.0 (1.7) 6.7 (1.3) 4.3 (0.9) 6.7 (3.5)

Parents’ education 0.9 (0.8) 0.9 (0.6) 0.0 (0.4) 1.7 (0.4) 2.5 (2.9)

563.3 (1.8) 8.2 (1.8) 6.8 (1.9) 0.6 (0.9) 491.0 (2.0) 7.3 (2.0) 1.3 (2.3) 3.4 (0.7) 508.6 (2.5) 7.8 (2.3) 8.2 (2.3) −0.9 (0.8) 490.8 (1.8) 5.0 (2.2) 7.0 (3.1) 0.0 (1.1) 496.9 (2.7) 4.8 (2.1) 4.7 (2.5) 1.2 (0.6) 470.5 (3.6) 8.1 (2.1) 7.9 (2.6) 7.4 (1.4) 504.2 (2.4) 3.9 (2.0) 3.9 (2.6) 0.5 (0.7) 487.3 (1.7) 7.5 (1.8) 5.8 (2.0) 0.8 (0.7) 488.9 (2.3) 6.8 (1.6) 5.9 (1.8) 1.8 (0.5) 526.1 (5.6) −4.3 (1.7) 1.7 (1.9) 0.1 (1.7) 511.3 (1.6) −1.3 (1.3) 0.6 (1.9) 0.7 (0.5) 510.3 (1.5) 3.2 (1.8) 0.2 (1.9) −0.6 (0.6) 526.8 (1.7) 1.6 (1.4) 0.8 (1.4) −0.4 (0.4) 475.3 (2.4) 9.5 (2.5) 1.2 (2.2) 1.4 (0.8) 492.3 (1.9) 7.3 (1.5) 3.9 (2.1) 1.7 (0.8) 486.2 (2.3) 3.7 (2.2) 5.2 (2.0) 2.9 (0.9) 507.8 (2.8) 0.8 (2.0) 7.7 (1.9) 2.1 (1.0) 506.3 (2.1) 5.0 (1.9) 6.9 (2.0) 0.0 (0.5) 504.8 (2.2) 4.7 (1.6) 5.5 (2.2) 2.0 (0.4) 490.9 (2.0) 7.3 (1.9) 9.6 (1.7) 0.0 (0.9) 499.6 (1.9) 0.8 (1.9) 6.1 (1.9) 1.7 (0.6) 492.9 (2.1) 8.1 (2.0) 4.8 (1.5) 0.7 (0.7)

Parents’ Female occupation 10.9 (2.3) 16.5 (2.0) 19.5 (1.9) 4.8 (0.9) 16.9 (4.3)

−0.7 (3.0) 15.7 (5.1) 7.3 (3.0) 13.9 (1.4) −1.7 (4.5)

8.3 (2.1) 12.2 (3.1) 13.3 (1.4) 2.3 (3.0) 14.7 (1.8) 1.0 (3.6) 9.0 (3.0) −1.0 (4.0) 11.0 (1.4) 4.4 (3.3) 13.8 (2.4) 17.0 (4.0) 11.7 (1.9) −1.9 (3.2) 12.1 (2.2) 3.5 (3.6) 11.8 (1.9) 16.3 (3.9) 5.2 (2.4) 21.8 (4.7) 7.4 (1.7) −10.3 (3.0) 8.7 (2.5) −23.0 (4.7) 5.6 (1.6) −9.6 (2.4) 11.4 (2.7) −10.3 (4.6) 11.8 (1.7) 4.6 (3.4) 14.8 (2.1) 12.6 (3.2) 13.0 (2.3) 24.7 (5.3) 10.5 (1.6) −0.6 (3.0) 7.4 (1.6) 1.2 (2.9) 11.9 (1.8) 16.0 (3.1) 13.6 (2.0) 8.1 (4.1) 12.4 (1.7) −3.9 (3.0)

5.7 (4.0) 516.6 (2.5)

7.9 (1.3) −0.5 (1.8) −0.6 (1.0)

4.5 (1.8)

4.8 (2.7)

3.7 (0.9) −1.4 (1.3) 505.9 (0.7)

5.6 (0.6) 1.4 (0.7) −0.4 (0.3)

9.7 (0.7)

4.8 (1.2)

2.1 (2.4)

L. Tramonte, J.D. Willms / Economics of Education Review 29 (2010) 200–213

Australia Austria Belgium Canada Czech Republic Denmark Finland France Germany Greece Hungary Iceland Ireland Italy Japan Korea Luxemburg Mexico Netherlands New Zealand Norway Poland Portugal Spain Sweden Switzerland United Kingdom United States OECD

Sense of belonging Parents’ Female occupation

Constant

0.5SC (0.3) 0.9C (1.1) −7.1SC (3.8) 1.2SC (0.3) 3.0SC (1.2) −3.2SC (4.0)

4.0SC (0.5)

8.8SC (0.8)

2.1SC (0.4) 12.9SC (1.5) 20.3SC (2.1) 3.7SC (0.4) 17.04SC (1.8) 27.6SC (2.1)

12.3SC (0.9)

Note: Effects that vary significantly among schools within countries are denoted with a superscript S; effects that vary significantly among countries are denoted with a superscript C.

1.0SC (0.3) 9.6SC (1.0) 5.0SC (2.6) 1.5SC (0.3) 11.0SC (1.2) 7.4SC (2.6)

3.9SC (1.0)

5.3SC (0.7)

497.0SC (3.1) 4.7 (10.1) 4.3SC (0.7) −9.9 (2.5) 3.4SC (0.8) −4.6 (2.8) 1.0SC (0.3) 9.6SC (1.0) 5.0 (2.6) 496.8SC (3.0) 498.9SC (3.4)

503.8SC (5.1) −15.47 (16.0) 9.0SC (1.0) 1.9 (3.3) 4.6SC (0.6) 6.3 (3.5) 0.5SC (0.2) 0.7C (0.5) −5.9SC (1.4) 505.3SC (4.7) 505.9SC (4.1)

497.4SC (4.4) −56.7 (16.6) 13.2SC (0.9) −23.7 (2.9) 11.4SC (0.9) −10.2 (2.9) 2.1SC (0.4) 13.0SC (1.5) 20.4SC (2.1) 15.4SC (1.0)

Model 2

502.7SC (4.7) 501.9SC (4.2)

Student-level variables Adjusted mean Rho Relational cultural capital Rho Static cultural capital Rho Parent education Parent occupation Female

Model 2 Model 1 Model 2

Sense of belonging

Model 1 Model 1

Model 3 Reading literacy

Table 3 The relationship between reading performance, sense of belonging, and occupational aspiration and contextual factors.

Model 3

Occupational aspirations

Model 3

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The key findings emanating from the three-level analyses can be best understood with graphical displays. Fig. 1 shows the relationship between the adjusted mean reading literacy scores and the proportion of variation in reading scores that is between schools. Recall that the latter measure is treated as a proxy for student selection, and although direct measures of the nature and timing of student selection would be preferable, this measure serves to highlight the important relationships. For example, countries like Austria, Germany, and Czech Republic have high scores on this index. These countries allocate students early in their school career to academically oriented schools versus vocationally oriented schools (e.g., see Straková, Tomáˇsek, & Willms, 2006). Mexico also has a high score on this index, owing mainly to the ‘learning divides’ associated with rural schools serving very poor populations, and selection into private schools in urban areas (Willms, 2006). In contrast, the Scandinavian countries, which are known for their integrated school systems, have relatively low scores on this index. A strong negative effect associated with increasing segregation is readily apparent in Fig. 1. The multilevel analysis reveals that it is statistically significant, and that an increase in Rho of 0.1 is associated with a decrease in adjusted reading scores of about 6 points (−56.7 divided by 10). There are three countries that are notable exceptions: Japan and Korea have relatively high scores, despite high levels of segregation. It may be that the scores in these countries are bolstered by participation in tutoring programs. Bray (1999, 2003) estimated that between one-quarter to threequarters of students in Asian countries receive tutoring on a regular basis throughout their primary and secondary school years. The third exception is Netherlands, which we noted earlier may not have had an adequately representative sample in PISA 2000. When we examined the country-by-country results in Table 2, we observed a pattern suggesting that countries with high average levels of adjusted reading literacy scores tended to have stronger effects associated with relational cultural capital, while those with lower scores tended to have stronger effects associated with static cultural capital. The multilevel modelling of these relationships allowed us to discern how these relationships vary within and among countries, and determine whether this variation is also associated with the segregation of students within and between schools. The findings are presented in Table 3 and Fig. 2. An understanding of these relationships is not straightforward, as the slopes associated with SES, or in this case static and relational cultural capital, can differ within and among schools. For example, in a highly segregated system, such as Germany, the slope attributable to SES is typically quite large, while within schools the slope is gradual as the majority of students in each school come from similar family backgrounds (see Willms, 2010). Fig. 2 shows the amongand within-school relationships between static and relational cultural capital and the proportion of reading literacy skills that is among schools. The top two scatter-plots in Fig. 2 show the relationships among countries. The first of these plots shows the relationship between the relational cultural capital among-

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Fig. 1. The association between adjusted mean reading literacy and the variation among schools in reading performance.

school slopes and the index of segregation. The magnitude of the among-school slopes are all positive, ranging from about 5 to 60. The plot also indicates a positive but weak relationship between the among-school slopes and the index of segregation, with steeper slopes in more segregated systems. The effect is 43.1 ( 011 in Eq. (4)), which is not statistically significant (p < 0.05; standard error is 21.6). The second of the top two plots shows the relationship between the static cultural capital among-school slopes and the index of segregation. The magnitude of the amongschool slopes are all positive, ranging from about 15 to 90. The plot indicates a strong, positive relationship between the among-school slopes and the index of segregation, with much steeper slopes in more segregated systems. The effect is 68.9 ( 021 in Eq. (4)), which is statistically significant (p < 0.05; standard error is 18.6). The next two scatter-plots, at the bottom of Fig. 2, display the relationships for the within-school slopes. The first of the two plots shows the relationship between the relational cultural capital slopes and the index of segregation. The slopes range in size from about 8 to 25, and there is a strong negative relationship between the within-school slopes and the index of segregation. The effect is −22.6 ( 101 in Eq. (4)), which is statistically significant (p < 0.05; standard error is 3.4). The second of the two plots shows the relationship between the static cultural capital slopes and the index of segregation. The slopes range in size from about 3 to 20, and there is also a strong negative relationship between the within-school slopes and the index of segregation. The effect is −26.2 ( 201 in Eq. (4)), which is statistically significant (p < 0.05; standard error is 3.0). Estimates of the combined among- and within-school effects of relational and cultural capital are provided in Table 3. Relational cultural capital is an important asset in contest schooling systems, but is of relatively little impor-

tance in sponsored systems. In contrast, static cultural capital is important in both types of school systems. 4. Discussion In this study we argue that there are two types of cultural capital, one that is static, representing the highbrow activities and practices of parents, and one that is relational, representing children’s activation of communication and cultural interactions. We used data for 28 countries from the 2000 PISA to examine whether these two types of cultural capital were associated with schooling outcomes after controlling for traditional measures of socioeconomic status, and if so, whether one type of cultural capital had stronger effects than the other and whether their effects differed across outcomes and across countries. Our results provide compelling evidence that relational cultural capital has strong effects on students’ reading literacy, sense of belonging at school, and occupational aspirations. Static cultural capital has slightly smaller effects, which are also statistically significant even when controlling for socioeconomic status. The findings also suggest that the effects of relational and static cultural capital are related to the extent to which students are allocated to schools, as gauged by the proportion of variance in reading performance that is among schools. Willms (2010) distinguished between two types of segregation: ‘horizontal segregation’ which is gauged by the extent of variation socioeconomic status among schools and ‘vertical segregation,’ which refers to the extent that students with differing levels of academic performance are segregated among schools. Horizontal segregation can arise from residential segregation, but it can also be attributable to private schooling because wealthier families can better afford private school tuition. It can also be attributable to

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Fig. 2. The association between dynamic and static cultural capital slopes, among and within schools, versus the variation among schools in reading performance.

special programs within the public sector that are more attractive to families with greater economic resources. In contrast, vertical segregation mainly stems from the selection of students into particular schools based on their ability at certain points in their school career. A partitioning of the effects of the two types of capital revealed that the effects vary in their among-school and within-school relationships among countries, and this depends on the type of school system. Our interpretation of these results is that students’ static cultural capital renders them eligible for recruitment in sponsored systems, while in contest systems it does not afford them as much advantage. Relational cultural capital plays a lesser role than static cultural capital in determining what type of school a child attends. In contrast, once students are ‘in the door,’ relational cultural capital has a stronger relationship than static cultural capital, particularly in contest systems. Therefore, in contest systems we observed stronger relationships for relational cultural capital within schools than among them,

while the within-school relationships were weaker than among-school relationships in sponsored systems. Static cultural capital plays a relatively minor role within schools in both contest and sponsored systems. Our approach to cultural capital and our empirical findings are not at odds with Lareau and Weininger’s (2003) definition of cultural capital, or their call for detailed micro-analyses of the formal and informal ways students are evaluated. However, the PISA data lack information on many of these important mechanisms. For example, it would be useful to have cross-cultural data on parents’ and children’s reading habits, use of television, and engagement in other cultural and recreational activities. We also want to understand the nature of parents’ interactions with their children, and the extent to which they make an effort to transmit cultural capital to their children. This focus would be close to the work on parenting styles (e.g., Beyer, 1995; Lamborn, Dornbusch, & Steinberg, 1996; McLaughlin & Vacha, 1992; Steinberg, Lamborn,

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Dornbusch, & Darling, 1992; Taylor, Hinton, & Wilson, 1995) and somewhat related to research on parental involvement (e.g., Astone & McLanahan, 1991; Epstein, 1987; Fehrmann, Keith, & Reimers, 1987; Lareau, 1987; Stevenson & Baker, 1987), especially as it pertains to direct parental involvement at home (e.g., see Ho & Willms, 1996). Our findings suggest that part of the answer to why some schools and schooling systems perform better than others lies in a complex interplay between schools’ formal and informal evaluation criteria, the level of segregation of the educational system, parents’ skills and strategies to advance their child’s schooling, and the ways students respond and activate their cultural resources. Two important pieces of the puzzle that are missing are how parents’ static cultural capital and children’s relational capital relate to students’ acquisition of literacy skills in contest and sponsored schooling systems during the primary school years, and the role that the two forms of cultural capital plays thereafter, particularly as students with differing levels of ability encounter various selection points that channel them into particular schools or school programs. Most children make a successful transition from learning-to-read to reading-to-learn by the end of grade 3 or 4, and thereafter are able to take advantage of the learning opportunities that lie ahead. But a large percentage of children do not make this transition successfully (Beswick & Sloat, 2006); estimates for the US for example are about 40%, based on the National Assessment of Reading Proficiency (Fletcher & Lyon, 1998). A disproportionate number of these children are from low SES families (Willms, 2006). It is a reasonable hypothesis that in contest systems the crucial ‘contest’ is the acquisition of strong literacy skills during the primary years, while in sponsored systems there may be more options, including tutoring, special private schools, and alternative programs. 5. Conclusions The differences among countries in the patterns of effects associated with relational and static cultural capital, and in the effects of parental education and occupation also warrant discussion. Willms’ (2003b, 2003c, 2006) hypothesis of converging gradients holds that socioeconomic gradients in literacy skills tend to converge at higher levels of socioeconomic status. This suggests that youth from high SES backgrounds tend to do well in any jurisdiction, while those with low SES backgrounds tend to vary considerably in their proficiency among jurisdictions. Our findings are consistent with this hypothesis, but they add a new dimension. Willms attributed the convergence of socioeconomic gradients to two plausible mechanisms. One is that successful jurisdictions achieved high and gradual gradients by successful school policies and practices that bolstered the performance of low SES students. The other is that in jurisdictions with low quality schooling, high SES families provide additional educational support for their child with extra help at home, or with extra tutoring. The findings in this study suggest that there is a third plausible mechanism: jurisdictions with high and gradual gradients achieve this by cultural mechanisms that support all students, thereby increasing performance and reduc-

ing inequalities. It may be, therefore, that children’s school experience in these countries is not that much different from that of other OECD countries, but the wider culture supports the translation of static to relational cultural capital in positive ways that support the efforts of schools. We believe that all three mechanisms operate simultaneously. Levin’s work has been influential here as well. He set out a model for educational reform based on the belief that disadvantaged students could succeed if they were afforded some of the same approaches to learning as gifted students, including opportunities to construct knowledge through hands-on projects and research (Levin, 1989). He developed the concept of “accelerated schools” (Levin, 1996, 1998) which embodied a three-part philosophy: (1) unity of purpose, (2) empowerment coupled with responsibility, and (3) building on students’ strengths through the use of strategies commonly used for gifted learners. The slogan was “accelerate, don’t remediate”, which is consistent with the need for students to translate static to relational cultural capital in a contest system.

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