The Educational Achievement of Brazilian Adolescents

Abstract

Most studies find a positive correlation between family cultural capital and educational achievement. As compelling as the evidence on the advantages of family cultural capital for educational achievement is, most studies have focused on countries characterized by having a large middle class and high levels of income, not addressing societies with high levels of social inequality. Importantly, few studies have examined whether schools interact with families in determining the relationship between cultural capital and educational achievement. The goal of this article is to examine how cultural capital is associated with achievement in Brazil, one of the most unequal countries in the world. Using data from the 2006 Program for International Student Assessment (PISA), results from multilevel models show that the science and reading achievement gaps associated with cultural capital are magnified in Brazilian schools, an important finding given that the country is an already highly stratified society.

Keywords

achievement inequality Brazil Latin America cultural capital

Introduction

Researchers have long been interested in the influence of cultural capital—defined as high-status signals such as participation in highbrow cultural activities and possession of cultural goods (Bourdieu 1977)—on children’s education. Simply put, Bourdieu’s (1977) cultural capital theory suggests that children from the upper class are exposed to high-status culture and are therefore more likely than those from the lower class to succeed in school. The empirical evidence has, for the most part, documented the significance of cultural capital for educational attainment and educational achievement in societies with a large middle class such as the United States and Western Europe (de Graaf, de Graaf, and Kraaykamp 2000; DiMaggio 1982; Dumais 2002; Farkas 1996; Hampden-Thompson, Guzman, and Lippman 2008; Jaeger 2009; Lareau 1987). Additional research has focused on specific groups within those societies, such as immigrant children in the United States (Goyette and Conchas 2002; Kao 2002). More recently, there has also been evidence of the significant role of cultural capital for the educational achievement of Japanese (Yamamoto and Brinton 2010) and South Korean (Byun, Schofer, and Kim 2012) students. Although this literature has generally demonstrated that family cultural capital brings significant educational advantages to children beyond traditional measures of family socioeconomic status, for the most part, research has neglected to explore highly unequal societies with significant levels of poverty. A concrete case is Brazil, one of the world’s most unequal countries, where the top 10 percent of the socioeconomic distribution holds more than half of the country’s total income. Brazil’s sharp inequalities contrast with the countries where most research on cultural capital has focused, making it an important case study to test the overall significance of cultural capital for children’s education. The first goal of this article, therefore, is to examine whether cultural capital is associated with higher levels of adolescent educational achievement in Brazil, a highly stratified society along class lines.

An important aspect of Bourdieu’s framework is that schools and families interact, reinforcing or alleviating the different levels of cultural capital students bring to school from their home environments. Students’ familiarity with institutionalized high-status cultural signs must be sanctioned at the school level to be translated into academic achievement (Bourdieu and Passeron 1977). Thus, adolescents in families that possess the cultural resources shared by the dominant social class who are also enrolled in schools that sanction these cultural traits may obtain a double advantage. Despite the fundamental importance of schools in shaping the context within which cultural capital translates or does not translate into educational achievement, this last dimension of Bourdieu’s cultural capital framework has received less attention. The second goal of this article is therefore to examine whether the achievement gap due to variations in family cultural capital increases depending on school quality in Brazil.

The significance of this work is threefold. First, it expands the comparative research examining whether cultural capital represents an important factor in adolescents’ educational achievement to incorporate a highly unequal society with generally low levels of income, such as Brazilian society. Second, by considering the part of Bourdieu’s theory that cultural capital has to be translated within the realm of the school, this research goes beyond previous conceptualizations of cultural capital in studies of its implications for adolescents. Thus, a key contribution of this study is the incorporation of theoretically informed variation into how cultural capital is associated with adolescent achievement in Brazil—variation that depends on schools. Third, this study provides useful evidence on the relative importance of schools and families for student achievement. This is a relevant question to the long-standing debate on the importance of family versus school factors for students’ educational achievement in less developed countries (Baker, Goesling, and LeTendre 2002; Baker and LeTendre 2005; Buchmann and Hannum 2001; Heyneman and Loxley 1983).

Families, Schools, and Cultural Capital

Cultural Capital

The idea that cultural capital constitutes a nonmaterial resource that is just as relevant for educational success as economic resources comes from Bourdieu’s theory of cultural reproduction (Bourdieu 1977; Bourdieu and Passeron 1977).¹ Bourdieu proposes three relevant forms of cultural capital—embodied, institutionalized, and objectified cultural capital—that are relevant for children’s education (Bourdieu 1977). Embodied cultural capital refers to the knowledge and skills necessary to appreciate cultural signals; institutionalized cultural capital refers to the educational credentials that are sanctioned by the upper class; objectified cultural capital refers to possession of high-status cultural resources that are sanctioned by groups positioned to promote relative social advantage (Bourdieu 1977; Bourdieu and Passeron 1977). Empirical research has focused on each one of these three different aspects of cultural capital.

Previous empirical studies have generally confirmed that cultural capital has a positive effect on children’s education (e.g., DiMaggio 1982; Dumais 2002; Jaeger 2011). The quantitative literature has measured cultural capital in several different ways. We identified three main approaches that follow Bourdieu’s main conceptualizations of cultural capital. Institutionalized cultural capital has often been measured through complete years of education (e.g., Kraaykamp and Radboud University 2010; Roose and Stichele 2010). Embodied cultural capital has been measured as children’s attendance at classical concerts, museums, and theater (e.g., Dumais 2002; Jaeger 2009, 2011; Roksa and Potter 2011). Additional research has also measured embodied cultural capital as parents’ reading habits (Buchmann 2002; De Graaf et al. 2000). Finally, objectified cultural capital has been measured through the possession of cultural goods such as art objects and books of classical literature and poetry (e.g., Byun et al 2012; Kraaykamp and van Eijck 2010; Yamamoto and Brinton 2010). Another aspect of cultural capital examined in the literature on children’s schooling is the availability of educational resources in the home, such as desks and computers (Teachman, Paasch, and Carver 1996). Importantly, some studies have considered more than one measure of cultural capital simultaneously (e.g., Yamamoto and Brinton [2010] focused on both embodied and objectified measures while Jaeger [2009] has considered all three concepts).

Given its theoretical ambiguity, there is little consensus in the literature about which quantitative measure is most consistent with Bourdieu’s concept. Of particular interest for this study is Bourdieu’s focus on objectified cultural capital, that is, possession of highbrow cultural objects such as art objects, classical music, and books of classic literature and poetry. The availability of such resources in the home enhances children’s familiarity with specific cultural tastes recognized in schools (DeGraff et al. 2000; Dumais 2002; Jaeger 2009; Lamont and Lareau 1988). By instilling cultural values and norms that the school system rewards, parents have invested in the kind of cultural capital that benefits their children’s education (Bourdieu and Passeron 1977).

We focus on objectified cultural capital through the measure of home possession of artwork and books of classical poetry and literature for three main reasons. First, despite discrepancies in how to operationalize cultural capital, the core of the cultural capital concept denotes familiarity with high-culture possessions and activities. Children become familiar with highbrow cultural objects if these are available in the home. Importantly, most of the major publications on cultural capital have operationalized cultural capital through knowledge of or participation in high-culture activities (Byun et al. 2012). Second, measures of objectified cultural capital have been widely used by past studies and are readily available in the Program for International Student Assessment (PISA) data—a nationally representative data set available for several countries. Consequently, there is an established body of research that has examined objectified cultural capital in a similar way, therefore ensuring some consistency in the literature. It is noteworthy that virtually all studies considering the role of objectified cultural capital for student learning have used the exact same index of cultural capital we use, as we will discuss in detail in the following; that is, home possession of art objects and books of poetry and classical literature (e.g., Byun et al 2012; Chiu 2010; Jaeger 2009).

At the same time, because we recognize that cultural capital is often an elusive concept and also because we expect that the cultural codes that are deemed valuable vary from society to society (De Graaf et al. 2000), we also consider number of books in the home in our analysis. Importantly, we consider number of books as a valuable form of cultural capital because of the limited availability and high cost of books in Brazil. Consistent with the idea that the important codes of cultural capital vary across societies and reflect what is valued in the educational experiences of each place (Buchmann 2002; Hannum and Fuller 2006), we argue that a high number of books in the home is a particularly important measure of cultural capital in a context of low education levels. In 2006, 22.2 percent of the Brazilian adult population was functionally illiterate (Ministério da Educação 2008).² The availability of books in the home promotes a cultural climate that transmits a positive attitude toward education that may further children’s educational success (Buchmann 2002; DeGraaf et al. 2000). Indeed, possession of books in the home, particularly in developing societies, sets a “way of life in homes where books are numerous, esteemed, read, and enjoyed” (Evans et al. 2010:1). A conceptualization of cultural capital that includes the availability of books in the home is especially relevant among lower status populations and in developing countries.

Cultural Capital and Schools

Bourdieu’s concept suggests that parents must possess and transfer cultural capital to their children, while children must absorb this cultural capital and translate it into educational success in the realm of the school (Bourdieu and Passeron 1977). Middle- and upper-middle-class children are more likely to be exposed to such cultural beliefs and high-culture activities that are consistent with the materials taught in school. The transformation of cultural capital into educational achievement, therefore, depends not only on the possession of cultural resources but on the translation of such family cultural capital by the school (Bourdieu [1979] 1984).

Academic capital is in fact the guaranteed product of the combined effects of cultural transmission by the family and cultural transmission by the school (the efficiency of which depends on the amount of cultural capital directly inherited from the family). (Bourdieu [1979] 1984:23)

Cultural capital must therefore be activated³ in the school context to have a tangible impact on students’ educational achievement (Jaeger 2009; Lareau and Horvat 1999). At the core of Bourdieu’s concept is the idea that cultural capital is mainly an advantage possessed by families in socioeconomically advantaged environments. Consequently, the educational advantages associated with cultural capital are highest for students from advantaged families, whose children therefore attend high-quality and, particularly important for the case of Brazil, private schools. Importantly, under the cultural reproduction model, not only do students in advantaged families possess more cultural capital than students from disadvantaged families, they also use this cultural capital in environments (i.e., high-quality and private schools) that are particularly receptive to cultural capital.

This idea is in contrast with the cultural mobility thesis that proposes that cultural capital can contribute to the educational success of all children (DiMaggio 1982). Importantly, the cultural mobility argument suggests that cultural resources are not a mechanism for preserving privilege; rather, cultural resources contribute to the upward mobility of the least advantaged (DiMaggio 1982). In this scenario, children from disadvantaged backgrounds, or those attending low-quality schools, would benefit the most from cultural capital. We will return to these theoretical frameworks in light of the specificities of the Brazilian context.

Cultural Capital in a Highly Unequal Society

Most research examining the significance of cultural capital for educational attainment and educational achievement has focused on developed societies. To the best of our knowledge no study has considered how schools mediate the association between family cultural capital and educational achievement in Latin America. This gap is unfortunate because Latin America in general and Brazil in particular are among the most unequal societies in the world.⁴ In these contexts, the disparity between rich and poor is more pronounced, making it more difficult to transpose social and cultural barriers for those who lack these resources at home.

In highly unequal countries such as Brazil, there is also a more unequal distribution of resources across schools, notably between private and public schools, as we will show in the following, which results in more variation in student achievement (Gamoran and Long 2007). Equally important, countries with low levels of GPD per capita such as Brazil might not have the resources to reduce the advantage of family cultural capital, while this is a less challenging endeavor in more equalitarian and richer countries. While most schools in richer countries have basic educational resources (i.e., certified teachers, material resources), these resources vary widely in low-income countries, which in turn yield greater differences in student achievement (Gamoran and Long 2007). In addition to providing basic school resources, richer countries tend to have more public resources (museums, library books) that can substitute for the lack of family cultural capital. In these contexts, public resources might dilute the importance of the cultural capital opportunities provided by families, thus weakening their impact on achievement (Blossfeld and Shavit 1993).

Such extreme unequal distribution of resources at the country level often impair student learning (Chiu 2010), generating overall low levels of attainment. Indeed, the Brazilian educational system has traditionally exhibited low attainment, low educational coverage, and a high incidence of grade repetition (Birdsall and Sabot 1996). Smaller cohorts of school-age children (Lam and Marteleto 2008) and educational policies implemented since the mid-1990s have contributed to recent improvements (Veloso 2009). Our analysis of the nationally representative National Household Survey (PNAD) data confirms these recent large improvements in school enrollment. For example, while only 69.6 percent of children ages 7 to 14 were enrolled in school in 1977, by 2007, school enrollment was nearly universal for this age group, reaching 96.0 percent.

Although the quantity of education received has risen significantly, the overall quality of Brazilian schools remains low (Carnoy, Gove, and Marshall 2007). Differences in quality between school sectors (public and private) are especially large (Somers, McEwan, and Willms 2004), mirroring the persistently high levels of socioeconomic inequality in the country. Our analysis of the 2006 Program for International Student Assessment (PISA 2006) data shows, for example, that while students enrolled in private schools scored 479.77 in science, those in public schools scored 367.55. To place these results in perspective, the mean science score of Brazilian students enrolled in private schools is not statistically different from the scores of children in Organization for Economic Cooperation and Development (OECD) countries—491.00. The differences in math and reading are of similar magnitudes. The private school advantage in test scores was 119.13 points in math and 114.41 points in reading, for example.

The low quality and overall lack of resources and materials in public secondary schools have been documented (Carnoy et al. 2007; Castro 2009; Lee, Franco, and Albernaz 2009). In contrast, private schools generally provide an environment in which institutionalized high-status cultural capital is more likely to be recognized and promoted. The mainstream educational resources in private and high-resource schools—field trips to museums, availability of books/libraries, reading classical literature and poetry, high-quality teachers—are familiar to students with high-status cultural resources coming from home. These schools likely provide an environment that can prove difficult for students who do not possess cultural resources. Bourdieu’s theory of cultural reproduction would be consistent with this hypothesis, suggesting that high-quality private schools act to further inequalities found at the societal level (Bourdieu and Passeron 1977). Importantly, recent research has shown a strengthening of the inequality in private school access in Brazil (Marteleto et al. 2012).

Alternatively, it is also possible that students with low levels of cultural capital enrolled in Brazilian high-quality private schools benefit from a school environment where cultural capital is promoted. This scenario is possible if schools actively work to provide these students with the cultural capital lacking from home. Under this scenario, Brazilian private schools would work to compensate for cultural capital gaps in a way that low-quality schools cannot. This is in line with the cultural mobility thesis that proposes that cultural capital can contribute to the educational success and upward mobility of children from the lower class (DiMaggio 1982).

In sum, several features of Brazilian society suggest that cultural capital can be a valued resource for children’s education. Notably, the dramatic quality divide in Brazil’s public and private schools can enhance or ameliorate the educational gap coming from cultural capital inequalities coming from home. The analysis that follows seeks to understand how families and schools utilize cultural capital to help children get ahead in Brazil. The central research questions of this study are:

Research Question 1: What is the association between students’ family cultural capital and educational achievement—in science, reading, and math—in Brazil? Does the relationship vary across subject? We hypothesize that cultural capital is a significant predictor of achievement beyond traditional measures of social origin despite the pronounced role of family socioeconomic status on achievement in Brazil.

Research Question 2: Does school quality— measured by school sector, school’s level of resources, and teacher qualification—interact with family cultural capital to further enhance the inequity in educational achievement generated by family cultural resources? Finding that high-quality private schools magnify the advantages of students with high levels of cultural capital from home lends support for cultural reproduction theory. Alternatively, finding that high-quality schools do not amplify the association between family cultural capital and educational achievement would lend support for a cultural mobility argument. Because of the country’s notoriously high levels of social and economic inequality and traditionally low levels of education, we hypothesize that the cultural reproduction hypothesis will better fit the Brazilian case. The intense quality divide between public and private schools discussed previously also lends support for the cultural reproduction hypothesis.

It is important to note that while we examine test scores in three subjects of academic achievement—reading, math, and science—we anticipate that it is unlikely that a homogenous pattern of association across all subjects will emerge. We expect that cultural capital exercises greatest effects on students’ grades in nontechnical subjects such as reading (DiMaggio 1982). There are two main reasons for that. First, the concept of objectified cultural capital is composed of home possessions of books of classical poetry and books of classical literature, assets that are central for succeeding in reading. Second, standards are more diffuse and evaluation in reading is more subjective (DiMaggio 1982). The empirical evidence confirms that cultural capital is more important for reading achievement than for achievement in other subjects (Chiu 2010; DiMaggio 1982; Hampden-Thompson et al. 2008; please see Lareau and Weininger [2003] for an excellent review of this point).

Data and Methods

Data and Analytical Sample

We use data from PISA 2006. PISA is a triennial test of 15-year-old students that focuses on the assessment of reading, writing, mathematics, and science. PISA has been carried out by the Organization for Economic Cooperation and Development since 2001, but non-OECD countries are allowed to join the study, and Brazil has done so.⁵

Our sample includes 9,295 students nested in 625 schools. PISA uses the schools as part of a two-stage stratified design for sampling optimization (OECD 2009a).⁶ The PISA sample is representative of 15-year-old students attending educational institutions in grades seven and higher (OECD 2009b).⁷ One limitation of the PISA study is that the sampling frame excludes adolescents who have dropped out of school.

Measures

The outcomes of interest in our analyses are the PISA-produced plausible scores⁸ for science, math, and reading literacy. The independent variables at the student level are sex, age, grade at assessment, family wealth, highest level of parental education, and instructional time in each subject. Sex was coded as a dummy variable (female takes the value of 1). We recoded age into a dummy variable; students age 16 and over are coded as 1. We also control for the grade the student is enrolled. Combined, age and grade control for age for grade disparity (grade delay). This is important because Brazil’s relatively high levels of grade repetition and school dropout yield variation in the grade distribution of 15-year-olds. Also, since PISA does not provide a measure of past scores, age and grade is our best indicator for past achievement also used in past studies (i.e., Chiu 2010; Lee, Zuze, and Ross 2005).

Family wealth is an index of home possessions constructed by PISA.⁹ Given that respondents are 15 years old, a measure of family wealth based on home possessions is more reliable than a measure of income. We standardized the PISA family wealth index to reflect wealth standards within Brazil—the mean is 0 and the standard deviation is 1; greater values of the index indicate wealthier families. Parental education is expressed as a four-level categorical variable; tertiary (some or complete) education is the reference, and the other categories are no formal education, primary education, and secondary education. Instructional time in each subject is a three-level categorical variable—no instruction, less than two hours per week, and more than two hours per week of instruction; more than two hours per week is the reference category. Instructional time is a control variable that refers to the time spent in each domain of the PISA test (science, mathematics, and reading) (OECD, 2009b). Instructional time therefore varies by subject. Students were asked to report how much time they spend per subject on a weekly basis; we used as instructional time the student’s report related to regular lessons within the school.

The main independent variable of interest is a latent factor measuring family cultural capital via students’ possession of high-status cultural resources. We use a composite index of cultural capital in the home that includes the possession of works of art, books of classical literature, books of poetry, and a large number of books (excluding magazines, newspapers, and school books). The latent factor measure of cultural capital in the home was constructed using confirmatory factor analyses (CFA) with categorical indicators (Muthén and Muthén 1998-2010). With the exception of number of books,¹⁰ all items are ordinal—measures of whether or not a family possesses¹¹ works of art (e.g., paintings), classic literature, and books of poetry.¹² The questions on classical poetry and literature are context specific—the questionnaire uses Machado de Assis, one of the greatest Brazilian novelists of the nineteenth century, as an example of writers of books of classical literature and classical poetry. It is important to note that several studies have used this exact same index—composed of these items measuring home possession of art objects and books of poetry and classical literature—to measure cultural capital (e.g., Byun et al. 2012; Chiu 2010; Jaeger 2009).

As explained previously, we added number of books to our index of cultural capital because it is a sensible proxy for high-status symbols in a setting with relatively low levels of formal education and high functional illiteracy. In addition, the confirmatory factor model shows that a significant proportion of the variance in books is explained by the latent factor that measures high-status cultural symbols. The results of the CFA suggest an excellent overall fit—the root mean square error approximation (RMSEA) is below .05 and the confidence interval ranges from .029 to .038. Based on the standardized factor loading, this latent factor is driven by classic literature and books of poetry. In addition, by using only one factor of cultural capital we save degrees of freedom and present a more parsimonious model.

Table 1 shows the distribution of the sample for student-level variables. Forty-six percent of the sample is female and 81 percent are in ninth grade or lower. The average family wealth was one-and-a-half standard deviations below the average for OECD countries. Seventeen percent of the students had parents whose highest level of education was primary, while 39 percent had parents with secondary education, and 36 percent had parents with tertiary education. The parents of the remaining 8 percent of the students had no formal education. The estimated average instructional time in science was almost 2.5 hours per week, while the estimated average time in both math and reading was longer, about 2 hours and 45 minutes per week. Although these averages seem low, it is worth noting that schools are offered in shifts in Brazil. Moreover, PISA reports that there are a number of countries, such as Chile and the Netherlands, where the majority of students reported 2 hours or less of each subject per week (OECD 2007).

Table 1.

Sample means and standard deviations: student- and school-level covariates: Brazil 2006

	Proportion	Mean	Standard deviation
Student level
Gender		0.54	0.49
Male	0.46
Female	0.54
Age		15.79	0.29
Under 1	0.68
16 or older	0.32
Grade		8.74	0.96
Lower than tenth grade	0.81
Tenth grade or higher	0.19
Instructional time in science		2.46	0.70
None	0.08
Less than 2 hours per week	0.41
More than 2 hours per week	0.51
Instructional time in math		2.71	0.65
None	0.04
Less than 2 hours per week	0.26
More than 2 hours per week	0.68
Instructional time in reading		2.70	0.67
None	0.04
Less than 2 hours per week	0.29
More than 2 hours per week	0.67
Family wealth		–1.52
First quintile	0.21
Second quintile	0.20
Third quintile	0.18
Fourth quintile	0.21
Fifth quintile	0.20
Parental education		2.93	1.70
No formal education	0.08
Primary education	0.17
Secondary education	0.39
Tertiary and higher (some or complete)	0.36
Cultural capital in family		–0.04	0.92
Low	33.56
Medium	33.27
High	33.18
School level
School sector		0.15	0.36
Public	0.84
Private	0.15
Index of school educational resources		0.07	1.02
First tercile	0.34
Second tercile	0.33
Third tercile	0.33
School location		0.06	0.22
City or town	0.94
Village	0.06
Shortage of qualified teachers		0.52	0.49
Low (below 0)	0.52
High (above 0)	0.48
N	9,295

In addition to a control for school location—a dummy indicator where 1 indicates that the school is located in a village—the three factors we use to measure school and teacher quality are school sector, index of school resources, and teacher quality. School sector has two categories—public and private—and public is the reference category. We restandardized the index of school educational resources within Brazil (the mean is 0 and the standard deviation is 1) because the original metric was based on OECD countries. Higher values of the index indicate higher levels of school educational resources. Shortage of qualified teachers is a PISA measure that we restandardized within Brazil to create a dummy variable using 0 as the cutoff point. This cutoff point follows the natural distribution of the index, which was quite skewed, indicating the need to categorize this measure. The comparison category is above 0; schools in this category face a significant shortage of qualified teachers.

We present proportions and means for school characteristics in Table 1. Eighty percent of Brazilian students attended public schools. Not surprisingly, Brazilian students attend schools with lower levels of educational resources (almost 1 standard deviation lower) than students in OECD countries. About 6 percent of the students attended schools in villages, while 48 percent of students were enrolled in schools with a significant shortage of qualified teachers. For most variables, less than 5 percent of the sample had missing information; in these cases, we imputed missing data using single imputation.¹³

Analytical Strategy

We use hierarchical linear modeling (HLM) (Bryk and Raudenbush 1992), where the first level is the student and the second level is the school and weight the analyses by the student final weight. We develop six nested models for each subject available in PISA (science, reading, and math). The first model is the unconditional model. The second model contains the index of cultural capital, while in the third we add student demographic characteristics, instructional time, parental education, family wealth, and grade level. The inclusion of grade level is particularly important because we do not have a measure of past achievement and therefore are unable to make inferences about value-added models.¹⁴ The strategy we follow is to examine what happens to the association between the latent index of cultural capital and scores in science, math, and reading when we add student-level controls. We construct Models 4 through 6 by adding school-level measures and interactions between the index of cultural capital and each of the measures of school and teacher quality.

We run each of the six models for each of the subjects and then combine the scores according to the formulas recommended by PISA.¹⁵ We used HLM version 6.06, developed by Raudenbush and colleagues, to estimate all models (Raudenbush et al. 2004).¹⁶ All variables at level 1 were group centered, while continuous variables at the school level were grand centered. We kept dummy variables uncentered to facilitate the interpretation of interlevel interactions with the latent factor of cultural capital at the first level. The equation for the HLM first-level Model 3 can be described as:

\begin{matrix} Science literacy = β_{0 j} + β_{1 j} (cultural capital at home) \\ + β_{2 j} (less than tenth grade) + β_{3 j} (age) \\ + β_{4 j} (female) + β_{5 j} (instructional time dummy 1) \\ + β_{6 j} (instructional time dummy 2) \\ + β_{7 j} (family wealth) \\ + β_{8 j} (parental education dummy 1) \\ + β_{9 j} (parental education dummy 2) \\ + β_{10 j} (parental education dummy 3) + r_{ij} . \end{matrix}

For the second level, the full equations for Models 4 through 6 follow Equation 2, in which the interlevel interaction terms, B_1j, vary for school sector, educational resources, and shortage of qualified teachers:

\begin{matrix} B_{0 j} = γ_{00} + γ_{01} (private) \\ + γ_{02} (index of school educational resources) \\ + γ_{03} (school located in village) \\ + γ_{04} (teacher social capital) \\ + γ_{05} (shortage of qualified teachers) \\ + u_{o j} . \\ B_{1 j} = γ_{10} + γ_{11} (private) + u_{1 j} . \end{matrix}

Results

Table 2 shows that the overall mean score for science is 385.25 (the standard deviation is 85.97). Students enrolled in tenth grade or higher, those with highly educated parents, and those in the top of the income distribution scored higher than their counterparts in all three subjects. Students who live in families with high levels of cultural capital scored 41 points higher in science than those in families with low levels of cultural capital. Similar gaps occur for reading and math (39 and 38 points, respectively).

Table 2.

Sample test scores by subject and student- and school-level covariates: Brazil, 2006

	Science		Math		Reading
	Mean	Standard Deviation	Mean	Standard Deviation	Mean	Standard Deviation	N
Overall science test score	385.30	85.97	365.57	87.27	389.18	94.26	9,295
Student-level covariates
Gender
Male	390.79	88.46	377.41	88.79	373.34	96.51	4,258
Female	380.57	83.51	355.56	84.7	402.58	90.18	5,037
Age
Under 16	384.15	84.73	364.87	85.69	387.94	93.78	6,333
16 or older	387.62	88.51	367.06	90.57	391.85	95.24	2,962
Grade
Lower than tenth grade	371.00	80.31	351.4	81.42	374.09	90.29	7,571
Tenth grade or higher	447.82	81.94	427.8	84.84	455.48	81.95	1,724
Family wealth
First quintile	347.14	68.36	324.43	67.42	352.21	79.39	1,931
Second quintile	361.97	72.64	338.33	72.078	367.38	83.08	1,897
Third quintile	370.41	73.76	352.19	72.54	376.45	84.38	1,754
Fourth quintile	401.52	82.71	381.83	83.27	403.57	92.98	1,873
Fifth quintile	446.87	92.15	433.04	93.57	447.95	99.19	1,840
Instructional time
None	343.28	66.52	297.51	67.22	330.61	83.71	761
Less than 2 hours per week	361.96	72.67	323.43	66.72	354.53	84.14	3,838
More than 2 hours per week	411.10	86.71	386.95	87.31	408.04	93.22	4,696
Parental education
No formal education	337.86	69.08	309.74	65.79	343.97	81.36	710
Primary education	355.57	68.77	333.69	69.39	359.17	79.83	1,464
Secondary education	367.17	73.00	348.58	70.73	373.73	82.98	1,926
Tertiary and higher (some or complete)	406.80	90.24	388.48	92.10	409.56	98.04	5,195
Latent index of cultural capital
Low	369.00	76.70	350.91	77.25	368.07	86.42	3,119
Medium	378.02	81.86	357.34	83.25	383.40	90.50	3,092
High	408.95	93.43	388.65	95.70	416.33	99.00	3,084
School-level covariates
School sector
Public	367.55	75.14	346.84	74.73	371.74	85.90	7,829
Private	479.77	78.11	465.61	81.16	482.34	81.45	1,466
Index of school resources
First tercile	353.02	70.18	332.56	70.81	355.33	82.48	3,150
Second tercile	375.48	75.00	354.32	72.34	379.00	88.66	3,063
Third tercile	427.92	93.23	410.48	96.91	433.91	95.45	3,082
School location
City or town	387.77	85.77	368.08	87.04	392.31	93.11	8,820
Village	338.61	75.72	318.94	77.96	331.02	96.63	475
Shortage of qualified teachers
Low (below 0)	405.03	88.33	385.93	88.80	410.23	93.90	4,481
High (above 0)	364.00	77.94	343.69	80.04	366.57	89.29	4,814
N	9,295

Not surprisingly, students in private schools scored significantly higher than students in public schools (479.77 vs. 367.55 for science, 465.61 vs. 346.84 for math, and 482.34 vs. 371.74 for reading). What is remarkable is the large magnitude of the achievement gap between students enrolled in private versus public schools—112.22 points for science, 118.77 points for math, and 110.6 points for reading.

Importantly, our results also show that students in schools with high levels of educational resources scored much higher than students in schools with few resources, yielding differences of 75 points in science, 78 in math, and 79 in reading. Students in schools with a shortage of qualified teachers scored 364.00 in science, while students in schools that do not face this issue scored 405.03, suggesting that some schools have a difficult time in attracting and retaining qualified teachers.

Tables 3, 4, and 5 show the HLM models for science, reading, and math, respectively. The average science score when holding all covariates at zero is 380.21 points. The intraclass correlation suggests that nearly 48 percent of the variation in science scores occurs between schools. The intraclass correlation is 51 percent for reading and 55 percent for math.

Table 3.

Results from multilevel models predicting science achievement by student and school characteristics: Brazil, 2006

	Model 1		Model 2		Model 3		Model 4		Model 5		Model 6
	Coefficient	SE	Coefficient	SE	Coefficient	SE	Coefficient	SE	Coefficient	SE	Coefficient	SE
Intercept	380.2***	4.01	380.17***	4.01	380.03***	4.03	371.91***	3.67	372***	3.77	371.64***	3.67
Student level
Female					–10.82***	2.21	–10.81***	2.20	–10.80***	2.21	–41.70***	2.71
Age (16 years or older)					–11.22***	2.23	–11.23***	2.23	–11.22***	2.23	–11.33***	2.22
Less than tenth grade					–41.55***	2.72	–41.5***	2.71	–41.50***	2.71	–10.7***	2.20
Instructional time in science
No instruction					–37.0***	4.15	–37.1***	4.16	–37.0***	4.17	–37.0***	4.16
Less than 2 hours per week					–18.5***	2.3	–18.5***	2.29	–18.6***	2.30	–18.5***	2.30
Family wealth					2.9*	1.58	2.87*	1.57	2.88*	1.58	2.92*	1.57
Parental education
No formal education					–9.95**	4.31	–9.9**	4.293	–9.9**	4.322	–9.8**	4.309
Primary education					–5.49*	3.19	–5.64*	3.192	–5.53*	3.189	–5.44*	3.204
Secondary education					–5.5	3.29	–5.49	3.301	–5.47	3.303	–5.4 3	3.303
Cultural capital			3.95**	1.48	1.93	1.46	1.46	1.54	2.10	1.45	–0.5	1.72
School level
Private							62.69***	11.35	61.79***	11.26	61.77***	11.26
School educational resources							20.38***	3.18	20.68***	3.15	20.39***	3.18
School located in village							–49.5***	8.78	–49.4***	8.77	–49.5***	8.77
No/low shortage of qualified teachers							14.5***	4.97	14.5***	4.97	15.3***	4.96
School-level interactions
Private × cultural capital							6.43*	3.30
School educational resources × cultural capital									2.14*	1.09
No/low shortage of qualified teacher × cultural capital											5.26**	2.56
Variance component	Intraclass correlation = 0.48
Between schools	3,810***		3,819.7***		3,863.4***		1,769.54***		1,769.79***		1,769.64***
Cultural capital			65.55**		50.29*		45.92*		46.44*		43.93
Within schools	4,208.5		4,146.7		3,763.8		3,760.67		3,760.43		3,760.35
N	9,295

Note: *significant at the 0.10 level. **significant at the 0.05 level. ***significant at the 0.001 level.

Table 4.

Results from multilevel models predicting reading achievement by student and school characteristics: Brazil, 2006

	Model 1		Model 2		Model 3		Model 4		Model 5		Model 6
	Coefficient	SE	Coefficient	SE	Coefficient	SE	Coefficient	SE	Coefficient	SE	Coefficient	SE
Intercept	380.40***	6.59	380.36***	6.60	380.27***	6.64	348.32***	4.47	348.34***	4.47	348.28***	4.49
Student level
Female					26.45***	2.51	–19.56***	2.03	–19.56***	2.02	–19.50***	2.02
Age (16 years or older)					–11.22***	2.41	–10.87**	2.64	–10.87**	2.64	–10.93**	2.66
Less than 10th grade					–42.78***	3.69	–37.88***	2.92	–37.88***	2.91	–37.98***	2.94
Instructional time in reading
No instruction					–31.11***	6.15	–34.28***	5.77	–34.28***	5.76	–34.18***	5.80
Less than 2 hours per week					–27.72***	2.75	–32.07***	2.40	–32.08***	2.38	–32.03***	2.36
Family wealth					–1.31	1.44	2.60*	1.38	2.60*	1.38	2.63*	1.37
Parental education
No formal education					–16.54**	5.67	–19.55***	4.85	–19.58***	4.84	–19.52***	4.84
Primary education					–6.96*	4.01	–9.55**	3.38	–9.50**	3.37	–9.45**	3.37
Secondary education					–7.12*	3.68	–4.67	2.85	–4.66	2.84	–4.64	2.84
Cultural capital			7.43***	1.64	4.14**	1.78	0.36	1.65	0.64	1.50	–0.80	1.86
School level
Private							64.24***	13.36	64.04***	13.38	64.03***	13.38
School educational resources							21.15***	4.02	21.22***	4.03	21.15***	4.03
School located in village							–60.19***	13.70	–60.16***	13.69	–60.21***	13.70
No/low shortage of qualified teachers							19.70**	6.11	19.70**	6.11	19.84**	6.16
School-level interactions
Private × cultural capital							5.84*	3.07
School educational resources*cultural capital										1.43
No/low shortage of qualified teachers × cultural capital											6.88**	2.28
Variance component	Intraclass correlation = 0.51
Between schools	5,717.22***		5,735.22***		5,515.56		2,314.43***		2,314.59***		2,314.60***
Cultural capital			54.02*		64.81		32.51		33.04		33.00
Within schools	5,431.20		5,348.26		4,784.91		3,348.30		3,347.75		3,347.19
N	9,295

Note: *significant at the 0.10 level. **significant at the 0.05 level. ***significant at the 0.001 level.

Table 5.

Results from multilevel models predicting math achievement by student and school characteristics: Brazil, 2006

	Model 1		Model 2		Model 3		Model 4		Model 5		Model 6
	Coefficient	SE	Coefficient	SE	Coefficient	SE	Coefficient	SE	Coefficient	SE	Coefficient	SE
Intercept	357.97***	4.99	357.95***	4.99	357.77***	5.02	348.32***	4.47	348.38***	3.77	348.27***	4.49
Student level
Female					–19.98***	2.03	–19.55***	2.03	–19.99***	2.03	–19.51***	2.02
Age (16 years or older)					–10.79**	2.63	–10.87**	2.64	–10.80**	2.63	–10.93**	2.66
Less than tenth grade					–36.28***	2.93	–37.88***	2.92	–36.24***	2.90	–37.98***	2.94
Instructional time in math
No instruction					–48.69***	5.63	–48.64***	5.65	–48.66***	5.63	–48.80***	5.64
Less than 2 hours per week					–32.46***	2.22	–32.44***	2.24	–32.48***	2.30	–32.43***	2.23
Family wealth					2.57*	1.37	2.60	1.37	2.54*	1.37	2.63	1.37
Parental education
No formal education					–18.77***	4.80	–18.77***	4.82	–18.82***	4.82	–19.52***	4.83
Primary education					–10.09**	3.40	–10.20**	3.45	–10.16**	3.43	–9.45**	3.36
Secondary education					4.25	2.88	–4.26	2.89	–4.26	2.88	–4.64	2.84
Cultural capital			3.74*	1.52	0.19	1.56	0.37	1.65	0.35	1.55	–0.80	0.66
School level
Private							64.24***	13.36	64.05***	13.38	64.03***	13.38
School educational resources							21.15***	4.03	21.27***	4.03	21.15***	4.03
School located in village							–60.19***	13.70	–59.93***	13.63	–60.00***	13.63
No/low shortage of qualified teachers							19.70**	6.11	19.58**	6.09	19.84**	6.16
School-level interactions
Private × cultural capital							3.84	3.47
School educational resources × cultural capital									0.98	1.43
No/low shortage of qualified teachers × cultural capital											2.88	2.68
Variance component	Intraclass correlation = 0.55
Between schools	4,769.03***		5,735.22***		4,841.05***		2,317.23***		2,317.42***		2,317.46***
Cultural capital			54.02*		35.68*		32.39*		32.64*		32.08*
Within schools	3,901.75		5,348.26		3,318.55		3,318.90		3,318.16		3,317.12
N	9,295

Note: *significant at the 0.10 level. **significant at the 0.05 level. ***significant at the 0.001 level.

In Model 2 we include the latent factor of family cultural capital. Results from Table 3 show that an increase of one standard deviation in cultural capital possessions in the home is related to an increase of 3.95 points in students’ science scores. The corresponding increase is 7.43 for reading (Table 4) and 3.74 for math (Table 5), suggesting that cultural capital plays a stronger role in reading than in science and math achievement.

Results for Model 3—adding controls for family background characteristics—show that a one standard deviation increase in cultural capital is related to an increase of 1.93 points in science (Table 3). The corresponding coefficient for reading (Table 4) is statistically significant and twice as large as the coefficient for science. Students with high levels of cultural capital possessions at home score 3.74 higher in math than their peers with limited availability of cultural possessions (Table 5).

The next models include school-level variables to explore whether family cultural resources are translated into educational achievement in the school context. In Models 4 through 6 (in Tables 3, 4, and 5) we include a school-level control variable—location—and variables reflecting school quality—school sector, index of school resources, and proportion of qualified teachers in the school. In each model we include interactions between the latent factor of cultural capital and each of the three measures reflecting overall school quality. We added each interaction separately to isolate the associations, facilitate the interpretation of results, and have parsimonious models that avoid unnecessary complexity.

In the results for science in Model 4 (Table 3), the interaction term representing cultural capital and school sector is 6.43 and statistically significant. Our results show that holding all other covariates constant, students who are enrolled in private school and have families with higher levels of cultural capital scored 7.89 points higher in science than students who are enrolled in public schools and have average levels of cultural capital. The corresponding coefficient in Model 4 is 5.84 for reading (Table 4) and 3.84 for math (Table 5). Results from Model 5 reveal similar findings in that students in schools with higher levels of resources and living in families with higher levels of cultural capital scored 4.24 points higher in science than their counterparts with average cultural capital enrolled in schools with average levels of educational resources. The interaction is statistically significant for the science models (Table 3), showing that students in schools with higher resource levels have an advantage of 2.14 points. The corresponding interactions for reading (Table 4) and math (Table 5) are statistically insignificant.

Next, we explore whether the role of another aspect of school quality (whether the school faces an issue of shortage of qualified teachers) is equally important in activating cultural capital. Results for Model 6 (Tables 3, 4, and 5) indicate that students attending schools with qualified teachers score 15.31 points higher in science, 19.84 points higher in reading, and 19.84 points higher in math than students enrolled in schools facing a shortage of qualified teachers. The large gap in scores between students in schools with a wealth versus a shortage of qualified teachers in all three subjects confirms the large inequalities in Brazil’s educational system discussed previously.

Results from Model 6 also show that students who are enrolled in schools with a qualified faculty and whose families have high levels of cultural capital score 4.76 more points in science (Table 3). The coefficient representing this interaction is 5.26 and statistically significant at the .05 level. The corresponding interaction coefficient for reading (Table 4) suggests an equally strong advantage for students with high levels of cultural capital enrolled in schools with a qualified teacher body (coefficient = 6.88; statistically significant at the .05 level). Importantly, students who are enrolled in schools with a qualified faculty and whose families have high levels of cultural capital score 6.08 more points in reading. Results from Model 6 in Table 5 suggest that the cultural capital advantage incurred for science and reading scores are not equally important for math scores.

Discussion and Conclusions

The goal of this study was to examine whether and how family cultural capital gets translated into adolescent educational achievement in one of the most unequal societies in the world. We are particularly interested in whether and how schools channel the educational advantages associated with high levels of cultural capital possessions in the home. Research has for the most part not addressed the significance of cultural capital in societies with high levels of poverty and socioeconomic inequalities. Brazil has historically been one of the most unequal countries in the world, where family socioeconomic background plays a strong role on the educational opportunities available for children.

The first contribution of this study was therefore to help fill a gap in the literature on the importance of cultural capital and the school context for adolescent educational achievement outside developed nations. The contextualization of this study within the Brazilian setting brings an important perspective on how a highly unequal educational system might reproduce and perhaps increment the social and economic differences experienced by most Brazilians in their everyday interactions. Despite the fact that the educational system in Brazil has been greatly improving in the past decades—with universal school enrollment, for example—there remain great challenges to equalize educational opportunities and outcomes among its population.

The second contribution of this work was to uncover the ways in which cultural capital translates into educational achievement through the school. Despite the central role of the interactions between family cultural capital and schools in Bourdieu’s conceptualization of how cultural capital matters for educational outcomes, with few exceptions, most studies focusing on the associations between cultural capital and educational outcomes have failed to account for the school context. Here we examined whether Brazilian families and schools interacted in ways that would fit a cultural reproduction or a cultural mobility framework.

Our findings suggest that cultural resources from home are an important mechanism associated with gaps in adolescent achievement in science, reading, and math in Brazil. However, not surprisingly in the Brazilian context, once family socioeconomic status is controlled, cultural capital is only associated with achievement in reading. This important result suggests that the association between cultural capital and achievement depends not only on the society’s characteristics, but also on the subject examined. This is in line with DiMaggio’s (1982) findings that cultural capital exercises greatest effects on students’ grades in nontechnical subjects such as reading. Standards are more diffuse and evaluation in reading is more subjective than in math. Our findings are also in line with previous empirical findings in Japan (Yamamoto and Brinton 2010), Europe, and the United States (DiMaggio 1982; Hampden-Thompson et al. 2008). Importantly, however, when we consider that cultural capital must be activated in the school context to be translated into academic achievement, and that not all schools equally recognize and sanction high-status cultural capital, our results offer a different story.

Our findings show that students who possess cultural capital and are enrolled in high-quality and private schools score significantly higher in science and reading. High-quality schools capitalize on students’ possession of the right cultural knowledge and therefore magnify the educational advantages of adolescents who possess a high level of cultural resources. In this sense, our findings indicate an exacerbating of social inequalities in an already highly stratified society with generally low levels of school and teacher quality, lending support to a cultural reproduction framework. This is an important finding, particularly for the Brazilian context.

Universal education is often mentioned as the most promising way to lessen the persistently high levels of social inequality in Brazil. However, results from this research show that Brazilian schools reinforce inequalities coming from home, suggesting that access to school alone is not likely to equalize educational outcomes. Unfortunately, the school role in activating students’ cultural capital does not work to alleviate the social inequalities that increase educational gaps between more privileged students and students who lack resources and opportunities to compete successfully in the Brazilian context. Our findings are in line with Bourdieu’s cultural reproduction theory. Our findings are also in line with recent research showing a strengthening of the inequality in private school access in Brazil at the same time that there is a weakening of the inequality in the quantity of education (Marteleto et al. 2012).

Importantly, while our analyses do not directly test the hypothesis of effective maintained inequality (Lucas 2001)—data limitations prevent us from doing so—we borrow the idea that once a specific level of education becomes universal, such as primary schooling in Brazil, qualitative dimensions of education—such as achievement in lower grades and the school measures that we focus on in this study—have increasingly important implications for subsequent levels. As a whole, findings from this work also call attention for the important difference between the possession and the activation of cultural capital (Jaeger 2009). It is not enough to accumulate cultural capital; schools must work to activate these resources and translate them into academic success.

Our findings suggest that the strongest association between cultural capital and achievement is through reading (both direct and interaction effects are significant), followed by science (interaction effects are significant). That we found no significant association between cultural capital and math achievement is not surprising. Our results are in line with several studies that find that cultural capital exercises greatest effects on students’ grades in nontechnical subjects such as reading (DiMaggio 1982; Hampden-Thompson et al. 2008; Lareau and Weininger 2003).

This study also contributes to a growing literature examining the role of schools on educational achievement in developing countries. In those settings, the role of families has been overemphasized in explanations of children’s and adolescents’ educational successes vis-à-vis the role of schools (Buchman and Hannum 2001). Scholars have neglected the role of schools because of a lack of appropriate data for examining school-level associations and also because conducting school-based analyses entails missing children and adolescents who have dropped out of school. The latter restriction has changed in Brazil and several other less developed countries as a consequence of recent universal rates of primary school enrollment. In that sense, the current research contributes to a growing body of evidence shifting the paradigm of sociological studies of education in developing countries from an emphasis on families versus schools following the Coleman Report (Baker and LeTendre 2005; Heynman and Loxley 1983) to a focus on how families and schools interact in shaping children’s and adolescents’ educational outcomes (Park 2008). While this study is relevant to the long-standing debate on the importance of family versus school factors for students’ educational achievement in less developed countries (Baker et al. 2002; Baker and LeTendre 2005; Buchmann and Hannum 2001; Heyneman and Loxley 1983), the results go a step further by showing that families and schools do not work in isolation, but rather school factors and what students bring from home interactively shape Brazilian adolescents’ science and reading achievement.

Despite its important findings, this study has at least three limitations. First, without controlling for prior achievement it is not possible to make causal inferences regarding the way in which family cultural capital and teacher and school quality interactively lead to student achievement (Raudenbush and Kim 2002). This is the same limitation faced by most studies focusing on educational achievement internationally (e.g., Byun et al. 2012; Chiu 2010; Lee et al. 2005; Yamamoto and Brinton 2010). Lack of panel data has prevented studies from accounting for prior test scores. Nonetheless, we have included the next best controls—age-grade disparity—for past attainment that have been used in the literature (e.g., Chiu 2010; Lee et al. 2005). Second, our measure of cultural capital is constrained by the variables available in the PISA data set. Further research should extend this study by examining the two other forms of cultural capital conceptualized in Bourdieu’s work—institutional and embodied cultural capital. Another limitation of this study is potential selection bias due to the way students are selected into schools. Because high-quality schools tend to have a greater concentration of high socioeconomic status students than other schools, the associations we found could partly reflect the more privileged status of students in these schools. However, whether or not the achievement advantage related to cultural capital found in this study is amplified by school and teacher quality or other school factors such as peer groups, the key point here is that our findings confirm the overall significance of schools in translating cultural capital into achievement in Brazil. Our findings provide strong evidence supporting the idea that schools can differentially translate cultural capital into achievement, thus increasing overall social and economic inequalities in a highly stratified society along class lines.

This study’s overall findings indicate that a useful working hypothesis for future research is that school factors beyond family background may explain a portion of the educational inequities due to cultural capital in a highly unequal country. Failure to recognize that disparities in cultural capital mediated by schools are a potential source of educational inequality could lead to misguided assumptions regarding the educational gap in Brazil and in other highly unequal societies. Despite numerous studies investigating how family cultural capital influences the educational achievement of adolescents in developed countries, there remains an important gap in understanding how the interaction between family cultural capital and school contexts shapes educational processes and success. This study takes a step toward filling this gap in the case of Brazil.

Footnotes

Acknowledgements

We would like to thank Valerie Lee, Chandra Muller, David Bills, and SoE anonymous reviewers for their helpful comments and suggestions.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by grant, 5 R24 HD042849, Population Research Center, awarded to the Population Research Center at The University of Texas at Austin by the Eunice Kennedy Shriver National Institute of Child Health and Human Development.

Notes

Author Biographies

Leticia J. Marteleto is currently an assistant professor in the Department of Sociology and a faculty research associate of the Population Research Center at The University of Texas at Austin. Her current research explores the influences of social and demographic contexts on educational opportunity and inequality in different national contexts, particularly Brazil and South Africa.

Fernando H. Andrade is currently a postdoctoral fellow at the University of Michigan Center for Substance Abuse Research Center (UMSARC). His research is focused on school effects on academic outcomes and substance use during adolescence.

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