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Cultural capital and college major choice in China. 1. Anning Hu and Xiaogang Wu. Abstract. Previous studies on major East Asian societies such as Japan and ...
The British Journal of Sociology 2017

Science or liberal arts? Cultural capital and college major choice in China1 Anning Hu

and Xiaogang Wu

Abstract Previous studies on major East Asian societies such as Japan and Korea generally fail to find a strong effect of cultural capital in educational inequality, partly due to the characteristic extreme focus on standardized test and curriculum. This study shifts attention to the horizontal stratification of education by investigating the association between family background, cultural capital, and college major choice in contemporary China. Based on analysis of data from the Beijing College Students Panel Survey (BCSPS), we found that, on average, cultural capital significantly mediates the relationship between family background and college major preference. Those with greater endowment of cultural capital are more likely to come from socio-economically advantaged families, and, at the same time, demonstrate a stronger propensity to major in liberal arts fields rather than science, technology, engineering and mathematics (STEM) fields. Further analyses reveal that the association between cultural capital and academic field choice comes into being by way of performance in the Chinese test in the national college entrance examination and of the non-cognitive dispositions, such as selfefficacy and self-esteem. Our findings better our understanding of formation of the horizontal stratification of higher education. Keywords: China; college major choice; cultural capital; non-cognitive attributes; liberal arts; STEM

Introduction Cultural capital – the institutionalized high-status cultural signals used for social and cultural exclusion (Lamont and Lareau 1988: 156) – has long been cited by sociologists as an essential factor in shaping educational inequality (Lamont and Lareau 1988; Lareau and Weininger 2003; Webb, Schirato and Danaher Hu (Fudan University) Wu (Hong Kong University of Science and Technology) (Corresponding author email: huanning@fudan. edu.cn) C London School of Economics and Political Science 2017 ISSN 0007-1315 print/1468-4446 online. V Published by John Wiley & Sons Ltd, 9600 Garsington Road, Oxford OX4 2DQ, UK and 101 Station Landing, Suite 300, Medford, MA 02155, USA on behalf of the LSE. DOI: 10.1111/1468-4446.12342

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2002). Coined by Bourdieu and Passeron’s seminal work (1977), cultural capital is conceptualized as the exclusionary cultural advantage that upper-class children have relative to lower-class children, which is converted into tangible academic success, resulting in class-based inequality in educational achievement. However, the role of cultural capital in educational inequality is not uniformly confirmed, and empirical results, focusing on measures of either one or multiple types of cultural capital, are mixed (Kingston 2001; Lareau and Weininger 2003). For example, while educational consequences of cultural capital have been affirmed in the United States (DiMaggio 1982; Gaddis 2013; Jæger 2011) and Brazil (Marteleto and Andrade 2014), scholars have not found such an effect in some European countries (De Graaf 1986; Sullivan 2001; Katsillis and Rubinson 1990). More noticeably, in some major societies of East Asia, such as Japan and Korea, the characteristic uniformity of curriculum and extreme focus on standardized test preparation considerably limit the room for cultural capital to play a salient role. In certain cases, cultural capital is even negatively associated with one’s academic performance (e.g., Byun, Schofer and Kim 2012; Yamamoto and Brinton 2010). In this study, we take advantage of the rich information of data collected in China,2 and add new insights to the current literature of cultural capital by shifting attention from test performance and school transition to college major choice (Roksa 2005; Roksa and Levey 2010). This is timely, given the worldwide proliferation of higher education credentials (Schofer and Meyer 2005), and the increasing scholarly attention paid to the horizontal stratification that differentiates among college graduates (Gerber and Cheung 2008; Gerber and Schaefer 2004; Hout 2012; Xie, Fang and Shauman 2015). Using a newly developed method of mediation analysis, our study, for the first time, provides affirmative evidence for the mediation role of cultural capital between family socio-economic background and college major choice. Moreover, the particular link between the endowment of cultural capital and the preference of the fields of study is further elaborated by making a distinction between individuals’ cognitive and non-cognitive advantages. In so doing, we shed light on the theoretical debate about the cognitive versus non-cognitive channels through which cultural capital works (e.g., De Graaf 1986; Kingston 2001; Lareau and Weininger 2003; Sullivan 2001), echoing previous findings on the weak consequence of cultural capital in other East Asian societies. Theoretical background The concept of cultural capital Cultural capital, a term constructed by Bourdieu and Passeron (1977, 1979), refers to the ‘widely shared high-status cultural signals (e.g. attitudes, preferences, formal knowledge, behaviours, goods, and credentials) used for social and C London School of Economics and Political Science 2017 V

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cultural exclusion’ (Lamont and Lareau 1988: 156). According to Bourdieu (1977a), there are three types of cultural capital – embodied, institutionalized and objectified. Embodied cultural capital captures one’s knowledge and consciousness to appreciate high-culture signals; institutionalized cultural capital refers to the institutionally recognized cultural endowment (e.g., academic credentials), and objectified cultural capital characterizes the physical objects related to high culture. In this study, we take advantage of the rich items in a survey conducted in China, and present the potential association of embodied and objectified cultural capital on college major choice. Following previous literature, we measure embodied cultural capital with children’s participation in different types of high-culture activities (e.g., Jaeger 2009, 2011), and objectified cultural capital with the possession of cultural goods and learning resources (e.g., Chiu 2010). Since we focus on college attendees only, institutionalized cultural capital, which is usually measured by receiving a formal credential (Kraaykamp 2010), is not examined. In our supplementary analysis, we also respond to the literature accumulated in the US, where the ‘concerted cultivation’ prevailing among the middle-class parents, such as shadow education participation, has been viewed as one type of embodied cultural capital (Buchmann 2002; Lareau 2003; Lareau and Weininger 2003). Cultural capital and college major choice Discussions about the association between cultural capital and children’s field of study can date back to the path-breaking research by Bourdieu and Passeron (1977, 1979), in which they note that students from privileged families are more likely to study fields that value cultural power. In a subsequent study in the United States, DiMaggio (1982) shows that cultural capital has a stronger effect on students’ grade in ‘nontechnical’ fields featuring relatively diffuse standards and subjective evaluations, rather than fields that are oriented toward specific skills. The link between cultural capital and college major choice has not received much attention until recent years. For instance, Hampden-Thompson, Guzman and Lippman (2008) report a positive link of participation in cultural activities with students’ literacy, but a negative link with curriculum-based achievement in mathematics and science. Jæger (2009) shows that children with more cultural capital are particularly likely to pursue upper secondary education, which is ‘cultural capital heavy’, over vocational secondary education that is ‘cultural capital light’. It is worth mentioning that some studies suggest that cultural capital could benefit the learning of science. For instance, Chiu (2010) finds that possession of objectified high-culture objects, known simply as cultural possessions at home, is positively associated with students’ mathematics scores in a cross-national study of 15-year-old students. British Journal of Sociology

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Notwithstanding these studies, to the best of our knowledge, no research has been conducted to directly look into the association between cultural capital and college majoring, not to mention one specifically on East Asian societies. In fact, previous studies in some major East Asian countries depict a picture where the effect of cultural capital, if any, is rather weak. The reason lies much in the educational system characterized by the uniformity of curriculum and extreme focus on standardized test preparation (Byun, Schofer and Kim 2012; Yamamoto and Brinton 2010). Because the determinant of academic achievements is almost solely the performance in the highly standardized tests, students in these East Asian countries attach extreme importance to the preparation of such tests, so cultural capital, as defined conventionally to be linked to the high culture, does not assume an important role. The conclusion about the lack of a strong effect of cultural capital in East Asia, as such, is drawn mainly based on the examination of the vertical type of educational stratification, such as the likelihood of making school transition. However, simply paying attention to this aspect of educational inequality fails to reveal the potential role of cultural capital in accounting for the more subtle differentiation among better-educated individuals, that is, fields of study, in an era of mass expansion of higher education (Schofer and Meyer 2005). Take China as an example – the centralized administrative system singlehandedly expanded higher education by raising the gross tertiary enrolment rate from 7 per cent in 1999 to 27 per cent in 2011 (UNESCO 2012). The unprecedented rapid expansion of tertiary education considerably inflates the population of college graduates, leading to various dimensions of horizontal stratification – for example, the STEM versus liberal arts distinction – to the fore (Hu and Vargas 2015; Hu and Hibel 2015). In China, the college major differentials can be traced back to the socialist era, when China learned after the Soviet Union to establish the college major system with a particular orientation toward promoting industrialization. In this process, STEM majors were appreciated by the state, given the civil and national-security benefits derived from their technical skills (Hao et al. 2011). As China moved into the market-oriented society in the 1970s, the horizontal stratification in terms of college majors persisted or were even intensified, because the technical specialties of STEM were profitable to business interests (Hu and Vargas 2015). As a result, it comes as no surprise that for senior high school graduates, like their counterparts in many other societies, more of them wish to pursue a STEM instead of a liberal art major. What remains understudied, however, is the formation of the socioeconomic differential in college major choice.3 Altogether, in light of the significance of major-based horizontal stratification and its potential association with people’s socio-demographic characteristics, the study is to make contributions to the literature of horizontal stratification by focusing on the mediation role of cultural capital in connecting family background and college major choice. C London School of Economics and Political Science 2017 V

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The effect of cultural capital: cognitive and non-cognitive mechanisms Another question that deserves further examining is the mechanism by which cultural capital can be associated with one’s preference for fields of study in college. Specifically, we examine two mechanisms. One mechanism linking cultural capital and college field of study is children’s cognitive skills (Bartolj and Polanec 2012; Chiu, Hong, and Hu 2015; De Graaf et al. 2000). Although it is debatable whether or not cognitive abilities are part of one’s cultural capital (De Graaf 1986; Kingston 2001; Lareau and Weininger 2003; Sullivan 2001), some studies suggest that the channel through which cultural capital comes into play should not be narrowly defined. Instead, skills and abilities can be promoted by cultural capital and bring about substantive consequences (e.g., Buchmann 2002; De Graaf et al. 2000; Farkas 1996). Following this line of thinking, we speculate that exposure to cultural capital works to improve one’s academic skills and capabilities in liberal arts fields more than in STEM fields, resulting in certain majoring preference. The other mechanism concerns non-cognitive characteristics, which are often lumped into the conception of habitus.4 Defined as ‘a system of lasting, transposable dispositions which, integrating past experiences and actions, functions at every moment as a matrix of perceptions, appreciations, and actions’ (Bourdieu 1977b: 82–3), habitus represents one’s non-cognitive peculiarities, which can be associated with the possession of cultural capital on the one hand (Dumais 2002; Gaddis 2013; Horvat and Davis 2011; McClelland 1990), and with the choice of college major on the other hand (e.g., Moakler and Kim 2014; Wildhagen 2009; Wiswall and Zafar 2015). Following this line of analysis, cultural capital could encourage one to gravitate more toward liberal arts subjects by virtue of cultivating non-cognitive dispositions, values, and propensities that are more congruent with the characteristics of these fields. In this study, cognitive performance is revealed by looking at one’s scores for the particular subjects in the national college entrance examination (NCEE). In China, admission to higher education institutions has been relying exclusively on the test scores of NCEE for more than five decades. The tests are standardized by setting their focus heavily on measurable and reproducible cognitive knowledge (e.g., Liu 2013). Given the fact that all participants of NCEE, regardless of their track in high school, should take the Chinese, mathematics and English tests, if the cognitive-skill mediator works, we would expect that those with stronger cultural capital, other things being equal (e.g., for fixed total score of NCEE), perform better in the liberal arts-related Chinese and English, but not necessarily in mathematics. Unlike the relatively well-defined cognitive skills, the meaning of noncognitive habitus is rather elusive, rendering its empirical measurement challenging. In this study, we measure non-cognitive attributes with self-efficacy and self-esteem (Gaddis 2013). Specifically, self-efficacy refers to the extent of British Journal of Sociology

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confidence to accomplish study tasks with available resources and strategies (Zimmerman 2000), and self-esteem captures one’s perception of their own value, advantages, and significance (Rosenberg, Schooler, Schoenbach and Rosenberg 1995). One merit of adopting these two measures is that psychological studies have provided well-designed scales for both of them, that is, the General Self-Efficacy Scale (GSES) (Sherer et al. 1982) and the Rosenberg Self-Esteem Scale (RSE) (Rosenberg 1965). Although the wording of these two scales are not tailored to be explicitly academically oriented, because the respondents are all college students, there are good reasons to expect that the tasks for the measure of self-efficacy and the self-value for the measure of self-esteem refer to the ones applicable to the school environment. One thing that leads support to this point of view is that the measures used in this study, despite being general in wording, have been found to be strongly related to the process of formal schooling (Xu 2017). Moreover, these measures, like habitus, are significantly linked with family background, as shown below. Hence, although self-efficacy and self-esteem are derived from psychological studies, we believe they can still reveal some peculiar dispositions related to both family socio-economic status and schooling, that is, one’s habitus. In summary, as shown in Figure I, our discussions so far depict a theoretical pathway from family background to cultural capital and then to college Figure I: Theoretical diagram

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majoring. Between cultural capital and the preference of study fields lie the mechanisms of cognitive performance and non-cognitive habitus. Data, variables and methods Data The data used for this study come mainly from the baseline wave of the Beijing College Students Panel survey (BCSPS) conducted in 2009, and some variables were retrieved from followed-up waves (wave 2 of 2010, and wave 4 of 2012).5 The population of the BCSPS is the full-time undergraduate students from all 54 public universities affiliated with the Ministry of Education (MOE), the other ministries, and the Beijing municipal government. In the sampling process, the BCSPS drew on the database of registration record cards for all students who were enrolled in 2006 and 2008 as the sampling frame (freshmen and juniors), and adopted the proportional-to-population strategy where the primary sampling units are colleges, from which the secondary sampling units are fields of study (see details in Cai and Wang 2017; Li and Feng 2017; Wu 2017). The baseline survey of the BCSPS includes 4,771 cases, with a response rate of 93.55 per cent (Wu 2017). Detailed sampling design can be found in the online supporting information. Although the BCSPS is a survey based on higher education institutions located in Beijing, it has several merits that warrant our research interest. First, the survey collects rich information on family background and prior senior high school for the sampled college students. More specifically, the BCSPS, among the large-scale surveys in China, for the first time includes the commonly used measures of objectified and embodied cultural capital, which serves our research interests and enhances comparison with other similar studies. The second merit of the survey concerns the question related to the field of study. Differing from many ad hoc survey items, the BCSPS provides us with students’ intended major when filling out the application for the attended college rather than their final actual major.6 Setting focus on the intended instead of the actual major is desirable for our research objectives because the subsequent external structural constraints, which affect the actual majoring but have no direct connection with one’s endowment of cultural capital, are not considered by the respondents to affect their preference. For example, the respondents do not know how many other senior high school graduates would choose the same major of the same university. This can be a practical structural constraint since the number of slots for a particular major of a particular institution is fixed. In the BCSPS, the strength of correlation between intended and actual majors is rather moderate (b 5 0.660). The third merit of the measures of college fields of study in the BCSPS is that the options are excellently designed by being coded according to the British Journal of Sociology

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official list of first-level disciplines as well as their sub-fields released by the Ministry of Education (MOE). These designs render the BCSPS an ideal data source for our investigation. Before moving on to the next subsection, it is necessary to mention that almost all of the universities in China are located in urban areas. Therefore, studying the institutions located in Beijing could shed light on similar institutions in other cities, such as Shanghai. However, it may not be directly enlightening for other Asian countries where the urban versus rural distinction of higher education matters. With that said, if we assume location, as well as many other attributes of higher education institutions (e.g., faculty-student ratio and academic publication), can be crystallized into the position on the Asian university rankings, our research indeed has some implications for international comparison, because the surveyed universities in the BCSPS have considerable variations in terms of their rankings among all of the universities in Asia, for example covering the top-, middle-, and lower-tier ones on the QS Asian University Rankings released in 2009.7 Variables and method The outcome variable is one’s intended college major. This variable is measured by the question ‘When you were applying for this university, which major was your first choice?’ Options to this question are used to construct four binary variables for college majors: STEM (narrow), STEM (broad), liberal arts (narrow), and liberal arts (broad). The narrow coding scheme adopts a stricter standard in defining STEM and liberal arts fields than the broad coding scheme (see the detailed coding schemes in the online supporting information). As discussed earlier, the intended major when applying for the university can better reflect one’s preference because the external structural constraints have not been taken into account by students. One additional caveat concerns the track system at senior high school. Specifically, senior high school students in China should decide on the track in the second or third year, where in general students in the science track focus on the subjects of Chinese, mathematics, English, physics and chemistry, whereas students in the liberal arts track focus on Chinese, mathematics, English, history and politics, although this arrangement is subject to regional variations. A student’s high school track determines his or her subsequent major at college, because the liberal arts track students can only choose liberal arts-related majors while the science track students can only choose STEM-related majors. This correspondence is almost perfect, because students of different tracks use different examination papers in the NCEE, which are subsequently related to college majoring. As a result, high school track choice can be reasonably viewed to be another measure of the pattern of college majoring. As such, we will not directly control for it in the following analyses (Bai, Chi, and Qian 2014). C London School of Economics and Political Science 2017 V

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The key independent variable of our study is cultural capital. For objectified cultural capital, a series of objects were asked and their scores (1 5 yes, 0 5 no) were summed up: (1) a place for learning (e.g., a study), (2) newspapers, (3) encyclopaedia, dictionary or other reference books, (4) more than 50 books (excluding textbooks and tutorial books), (5) a personal room, (6) a desk, (7) a learning machine or education software (disk), (8) accessible internet, (9) classic literature (such as a Dream in Red Mansions), (10) poetry, (11) art works (such as painting), (12) VCD or DVD, (13) a game machine (such as PSP2), (14) a MP3 or MP4, and (15) a computer. Measures about the embodied cultural capital were retrieved from the fourth wave of the BCSPS collected in 2012; we compute the total score for four cultural activities (1 5 never, 2 5 sometimes, 3 5 always): (1) watching movie in a cinema, (2) watching opera or listening to music in a theatre, (3) visiting museum and exhibition, (4) going to a pop music concert. Note that the questionnaire made it clear that these survey items refer to the time period before one attended college.8 Family background is gauged by father’s education (15 senior high school or above; 0 5 otherwise), father’s political status (1 5 CCP [Chinese Communist Party] membership; 0 5 otherwise), father’s employment status (15 full-time; 0 5 otherwise), and family annual income (log transformed). Because of the different scales of these variables, we cannot simply add them up. As an alternative, we performed the confirmatory factor analysis (CFA) to configure one latent variable of family socio-economic status for all of the observed variables. Detailed results of the CFA can be found in Table A.I of the appendix. Note that we here only use father’s socio-economic status for the sake of improving measurement reliability and CFA goodness of fit. In the supplementary analysis not shown here, we also computed a composite measure of family socio-economic background integrating mother’s educational attainment, political status and job status, which returns similar substantive conclusions. One thing that is worth mentioning is that compared with previous studies focusing on father’s educational attainment and family income, this study also takes into account father’s party membership status, as political connection and loyalty have been an influential factor in determining one’s access to various life chances even in the reform era (Bian, Shu, and Logan 2001; Dickson and Rublee 2000). Regarding the mediators between cultural capital and college major choice, performance in NCEE, as an indicator for cognitive skills, is measured by respondents’ subject-specific NCEE scores in Chinese, mathematics and English. Scores are standardized to facilitate inter-province comparison (Han and Li 2009). Because of the difference in high school track, the standardization was performed respectively for the liberal arts track and the science track. More information can be found in the online supporting information. The noncognitive attributes, as discussed earlier, are measured by respondents’ total British Journal of Sociology

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score for GSES (Schwarzer and Jerusalem 1995) and RSE (Rosenberg 1965). Both scales are standard ones, and have been tested in previous research in China (Xu 2017). The detailed items are presented in the online supporting information. One potential concern for the non-cognitive measures is that they are ad hoc in that they were collected after one attended college. Since college major choice is decided before college attendance, our analyses have to assume that one’s self-efficacy and self-esteem are consistent over time. This is partly supported by the supplementary analysis showing that the inter-year correlations of the values of self-efficacy and self-esteem are statistically significant and always range between 0.5 and 0.6. A series of control variables are available in the BCSPS. We control for the basic socio-demographic variables, including female (1 5 yes; 0 5 no), age, ethnicity (1 5 Han; 0 5 otherwise), and number of siblings. Variables capturing one’s senior high school experience are also considered, including the ranking of one’s senior high school (1 5 key point [national-level], 2 5 key point [provincial-level], 3 5 key point [regional-level], 4 5 key point [county-level], and 5 5 others), the location of senior high school (1 5 eastern provinces; 2 5 middle provinces; and 3 5 western provinces), and grade retention in senior high school (1 5 yes, 0 5 no). Finally, we fix the variables pertaining to one’s higher education characteristics and experiences, such as the year of participating in the NCEE, and the ranking of higher education institutions (1 5 three elite universities [Peking University, Tsinghua University and Renmin University of China]; 2 5 Other 211-project universities; 3 5 Non-211-project universities).9 When analysing the mediation effect of NCEE subject-specific scores, we also control for the standardized total NCEE score. Detailed descriptive information per the variables can be found in Table A.II of the appendix. In the empirical section, we adopt the method proposed by Karlson, Holm, and Breen (2012) (KHB, hereafter) to perform mediation analysis. To save space, the rationale of KHB is shown in the online supporting information. Results Cultural capital as the mediator between family background and college major choice The results of the KHB test for the mediation effect of cultural capital are shown in Table I. The upper panel describes the odds of majoring in STEM, and the lower panel, liberal arts. One unit increase in family socio-economic status is significantly related to the reduction of the odds of choosing STEM majors by 10.15 per cent (1–e20.107) and 10.33 per cent (1–e20.109), respectively for the narrow and broad coding schemes, but these odds are both significantly lowered after taking into account the effect of objectified cultural capital. The C London School of Economics and Political Science 2017 V

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Table I: The KHB result for the mediation of cultural capital between family socio-economic status and college major choice

Objectified cultural capital

Embodied cultural capital

Objectified cultural capital

Embodied cultural capital

STEM(narrow)

STEM(broad)

Reduced model

20.107 (0.024)***

20.109 (0.024)***

Full model Mediation effect Total effect mediated (%) Reduced model

20.033 (0.029) 20.074 (0.017)*** 69.09 20.122 (0.027)***

20.037 (0.029)# 20.072 (0.017)*** 65.97 20.128 (0.027)***

Full model Mediation effect Total effect mediated (%)

20.108 (0.027)*** 20.014 (0.004)*** 11.30

20.115 (0.027)*** 20.013 (0.004)** 9.87

LA(narrow)

LA(broad)

Reduced model

0.107 (0.030)***

0.119 (0.024)***

Full model Mediation effect Total effect mediated (%) Reduced Model

0.000 (0.036) 0.107 (0.021)*** 99.92 0.123 (0.033)***

0.043 (0.029) 0.075 (0.017)*** 63.51 0.136 (0.027)***

Full model Mediation effect Total effect mediated (%)

0.118 (0.033)*** 0.006 (0.005) NR

0.126 (0.027)*** 0.011 (0.004)** 7.91

Notes: Non-standardized coefficients with standard errors in parentheses. NR: not reported for insignificant mediation effects. # p < 0.1, *p < 0.05, **p < 0.01, ***p < 0.001. Data sources: BCSPS 2009, 2010 and 2012.

percentage of the total effect that is mediated by objectified cultural capital is over 65 per cent. Following the same analytical procedure, we find that embodied cultural capital also plays a significant mediation role, accounting for around 10 per cent of the total effect. Coming from a family with a higher socio-economic status is correlated with significantly higher odds of majoring in liberal arts, by 11.29 per cent (exp0.107–1) and 12.64 per cent (exp0.119–1), respectively for the narrow and broad definition, but both become insignificant after introducing objectified cultural capital into the model, rendering a significant mediation effect. The percentage of total effect that is mediated is very high (99.92 per cent and 63.51 per cent, respectively). Regarding embodied cultural capital, it cannot significantly mediate the relationship between family socio-economic status and the liberal arts majors that are narrowly coded. However, significant mediation is detected for the broadly coded liberal arts fields, which accounts for 7.91 per cent of the total effect. Altogether, the results of the KHB test, in general, support the mediation role of cultural capital between family background and college major choice, net of the other variables. That is, advantaged family socio-economic status British Journal of Sociology

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Figure II: Results of the OLS models regressing NCEE subject-specific scores on cultural capital

Notes: Control variables include female (1 5 yes; 0 5 no), age, ethnicity (1 5 Han; 0 5 otherwise), number of siblings, ranking of senior high school (1 5 key point [national-level], 2 5 key point [provincial-level], 3 5 key point [regional-level], 4 5 key point [county-level], and 5 5 others), location of senior high school (1 5 eastern provinces; 2 5 middle provinces; and 3 5 western provinces), grade retention in senior high school (1 5 yes, 0 5 no), the year of participating in the NCEE, the ranking of higher education institutions (1 5 three elite universities [Peking University, Tsinghua University, and Renmin University of China]; 2 5 Other 211-project universities; 3 5 Non-211-project universities), the standardized NCEE total score, and family socioeconomic status; r 5 OLS regression coefficient; s. e. 5 standard error; p 5 p value for the 95 per cent confidence level. Data sources: BCSPS 2009, 2010 and 2012.

bestows students with a higher endowment of cultural capital, which subsequently encourages majoring in liberal arts fields, but discouraging majoring in STEM fields. The influence of cultural capital on college major choice So far, we have presented the average mediation effect of various measures of cultural capital. More nuanced is the linkage between cultural capital and college major choice. In this section we first look into how cultural capital is related to the standardized NCEE subject-specific scores. The results of the OLS models are presented in Figure II. C London School of Economics and Political Science 2017 V

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Table II: The KHB result for the mediation of NCEE subject-specific scores between cultural capital and college major choice

Reduced model Full model Mediation effect Total effect mediated (%) Reduced model Full model Mediation effect Total effect mediated (%) Reduced model Full model Mediation effect Total effect mediated (%) Reduced model Full model Mediation effect Total effect mediated (%)

Chinese

English

STEM(narrow) 20.030 (0.008)*** 20.029 (0.008)*** 20.001 (0.000)* 3.57 STEM(broad) 20.029 (0.007)*** 20.028 (0.007)*** 20.001 (0.000)# 3.21 LA(narrow) 0.042 (0.010)*** 0.042 (0.010)*** 0.000 (0.001) NR LA(broad) 0.031 (0.007)*** 0.030 (0.008)*** 0.001 (0.000)* 3.50

STEM(narrow) 20.029 (0.008)*** 20.029 (0.008)*** 0.000 (0.000) NR STEM(broad) 20.028 (0.007)*** 20.028 (0.007)*** 0.000 (0.000) NR LA(narrow) 0.042 (0.010)*** 0.042 (0.010)*** 0.000 (0.000) NR LA(broad) 0.031 (0.007)*** 0.030 (0.008)*** 0.000 (0.000) NR

Notes: Non-standardized coefficients with standard errors in parentheses. NR: not reported for insignificant mediation effects. # p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001. Data sources: BCSPS 2009, 2010 and 2012.

Objectified cultural capital, as expected, is positively associated with students’ performance in liberal arts subjects of both Chinese and English (for English, the effect is marginal), but not necessarily mathematics. Relatively, possessing a higher endowment of embodied cultural capital seems to have a negative correlation with the performance of all three subjects. This is nevertheless inconsistent with the study conducted in South Korea, where students’ involvement in extracurricular activities might crowd out the time devoted to school learning, which further results in worse performance in standardized tests (Byun, Chafer and Kim 2012). A question follows – can the positive association between objectified cultural capital and the performance in Chinese and English tests help to explain the preference for liberal arts majors? To answer this question, we fit the KHB model, but this time the predictor is objectified cultural capital, the mediator is the performance in Chinese and English tests, and outcome is college major choice. The results are reported in Table II. It is shown that the better performance of the Chinese test, on average, significantly mediates the relationship between the endowment of objectified cultural capital and the preference for a liberal arts major to a STEM major. However, this mediation effect is slight, because the total negative effect of British Journal of Sociology

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Figure III: Results of the OLS models regressing non-cognitive attributes on cultural capital

Notes: Control variables include female (1 5 yes; 0 5 no), age, ethnicity (1 5 Han; 0 5 otherwise), number of siblings, ranking of senior high school (1 5 key point [national-level], 2 5 key point [provincial-level], 3 5 key point [regional-level], 4 5 key point [county-level], and 5 5 others), location of senior high school (1 5 eastern provinces; 2 5 middle provinces; and 3 5 western provinces), grade retention in senior high school (1 5 yes, 0 5 no), the year of participating in the NCEE, the ranking of higher education institutions (1 5 three elite universities [Peking University, Tsinghua University, and Renmin University of China]; 2 5 Other 211-project universities; 3 5 Non-211-project universities), and family socio-economic status; r 5 OLS regression coefficient; s.e. 5 standard error; p 5 p value for the 95 per cent confidence level; Data sources: BCSPS 2009, 2010 and 2012.

objectified cultural capital on STEM majoring is mediated by the advantages in the Chinese test only by 3.57 and 3.21 per cent, respectively, for the narrow and broad definition of STEM fields. For the broad definition of liberal arts majors, this percentage is 3.50 per cent. Lastly, relative to the performance in the Chinese test, the advantage in the English test that is entailed from objectified cultural capital fails to reveal a significant mediation effect. We now shift attention to the non-cognitive attributes. The results of the OLS models respectively regressing self-efficacy and self-esteem scores on cultural capital can be found in Figure III. Both objectified and embodied cultural capital can significantly promote one’s self-efficacy and self-esteem. This finding directs us to further examine whether or not non-cognitive attributes mediate C London School of Economics and Political Science 2017 V

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between cultural capital and the pattern of study field choice. The results of the KHB analyses can be found in Table III. Here, the predictors are, respectively, objectified and embodied cultural capital, the mediators are, respectively, selfefficacy and self-esteem, and the outcome is college majoring. Objectified cultural capital can significantly reduce one’s likelihood of majoring in broadly defined STEM fields by virtue of fostering both self-efficacy and self-esteem (but not for the narrowly defined STEM major). It is only selfefficacy that significantly mediates the negative relationship between embodied cultural capital and the probability of majoring in STEM. Moreover, this mediation is not very strong. For liberal arts fields, both self-efficacy and self-esteem significantly mediate between objectified cultural capital and the broad definition of liberal arts majors. In sum, the analyses presented above suggest that cultural capital could affect one’s preference in college major choice via both cognitive and non-cognitive channels. However, relative to the non-cognitive mechanism of fostering selfefficacy and self-esteem, the cognitive mechanism of improving students’ performance in the Chinese test in NCEE is rather slight.

Supplementary analysis on shadow education One insight from studies of cultural capital in the United States is that embodied cultural capital can be also gauged from the perspective of differential parental practices that contribute to children’s academic success (Lareau and Weininger 2003). One such practice in the American context is what Lareau calls the ‘concerted cultivation’ that prevails among the middle-class parents (Lareau 2003). By incorporating organized activities in their children’s lives, this parenting style fosters a type of cultural capital that enhances general motivations and skills. One practice that falls under the concerted cultivation is shadow education. As Buchmann (2002) has argued, cultural capital should be extended to shadow education such as outside-school learning. Following this line, many studies find shadow education to be positively correlated with children’s educational success (e.g., Dumais 2002; Kaufman and Gabler 2004). In light of the previous literatures on shadow education in East Asia (e.g., Bray et al. 2014; Kuan 2011; Stevenson and Baker 1992; Zhang and Xie 2016), we in our supplementary analysis examine whether or not shadow education during senior high school mediates between family background and choice of college study fields. There could be various types of shadow education and we here focus on the one that has been frequently studied in the previous literature, that is, outside-school tutoring. This variable is measured in the BCSPS by the question about whether or not one attended outside-school tutoring across the three years of senior high school, with options 1 5 yes and 0 5 no. This variable was retrieved from the second wave of the BCSPS collected in 2010. British Journal of Sociology

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Reduced model Full model Mediation effect Total effect mediated (%) Reduced model Full model Mediation effect Total effect mediated (%)

Objectified cultural capital

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(0.012)*** (0.012)*** (0.001)*

(0.007)*** (0.007)*** (0.001)*

0.047 0.047 0.000 NR 0.017 0.017 0.000 NR (0.014) (0.014) (0.001)

(0.009)*** (0.010)*** (0.001)

LA(narrow)

20.032 20.030 20.002 7.97 20.043 20.040 20.003 6.35

Notes: Non-standardized coefficients with standard errors in parentheses. NR: not reported for insignificant mediation effects. # p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001. Data sources: BCSPS 2009, 2010 and 2012.

Embodied cultural capital

Embodied cultural capital

Reduced model Full model Mediation effect Total effect mediated (%) Reduced model Full model Mediation effect Total effect mediated (%)

Objectified cultural capital

STEM(narrow)

(0.012)*** (0.012)** (0.001)**

(0.007)*** (0.007)*** (0.001)**

0.033 0.030 0.003 8.13 0.033 0.031 0.002 8.2

(0.012)** (0.012)** (0.001)**

(0.007)*** (0.007)*** (0.001)**

LA(board)

20.031 20.028 20.003 9.20 20.039 20.036 20.003 7.25

STEM(broad)

Self-efficacy

(0.012)*** (0.012)*** (0.000)

(0.007)*** (0.007)*** (0.001)

0.046 0.046 0.000 NR 0.017 0.017 0.001 NR

(0.014) (0.014) (0.001)

(0.009)*** (0.009)*** (0.001)

LA(narrow)

20.032 20.031 20.001 NR 20.042 20.042 20.001 NR

(0.012)*** (0.012)*** (0.001)

(0.007)*** (0.007)*** (0.001)

0.033 0.031 0.002 4.74 0.033 0.032 0.001 NR

(0.012)** (0.012)** (0.001)

(0.007)*** (0.007)*** (0.001)#

LA(broad)

20.031 20.030 20.001 5.040 20.038 20.038 20.001 NR

STEM(broad)

Self-esteem STEM(narrow)

Table III: The KHB result for the mediation of self-efficacy and self-esteem between cultural capital and college major choice

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Of the surveyed respondents, 28.44 per cent got involved in outside-school tutoring, which can significantly mediate between family background and the odds of majoring in STEM (the percentage of total effect that is mediated is 17.91 per cent and 16.74 per cent, respectively for the narrow and broad coding scheme) and the odds of majoring in liberal arts fields (the percentage of total effect that is mediated is 21.79 per cent and 15.46 per cent, respectively for the narrow and broad coding scheme). However, the association between shadow education and majoring does not go through either cognitive NCEE performance or non-cognitive self-efficacy and self-esteem. The independence to noncognitive attributes is understandable in light of shadow education’s orientation toward preparing standardized examination. The non-significant association with NCEE subject-specific scores could be explained by the fact that senior high school students have to spend most of their learning time at school (many of them are boarding school). Given the tight schedule and control curriculums, there is not much time left for shadow education. Moreover, there could be negative selection that only those who perform poorly may need extra private tutoring. Perhaps for these reasons, few studies on cultural capital in East Asia treat shadow education as a type of cultural capital. Despite this finding, our study shows that shadow education can significantly mediate the relationship between family socio-economic status and college major choice, although not through either raising NCEE subject-specific scores or cultivating self-efficacy and selfesteem. Therefore, there must be some other mechanisms at play to bridge shadow education and college majoring pattern, a research subject for future explorations. Conclusion and discussion Cultural capital has been widely cited to account for class inequality in academic achievements. In this study, we examine the educational relevance of cultural capital in China by paying particular attention to one important dimension of the horizontal stratification of higher education – college fields of study. This investigation responds to the trend in credential inflation in the twenty-first century, when college major, and especially the contrast between liberal arts and STEM, increasingly works as a salient mechanism of differentiating college graduates’ access to life chances (Roksa 2005; Roksa and Levey 2010; Xie, Fang, and Shauman 2015). Drawing on the data from BCSPS, we found a significant mediation effect of cultural capital on the relationship between family background and field of study. Specifically, those with greater cultural capital are more likely to come from advantaged families, and, at the same time, they demonstrate a significantly stronger propensity in majoring in liberal arts rather than in STEM. British Journal of Sociology

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Further investigation suggests that cultural capital influences one’s study field choice through improving both cognitive performance and non-cognitive habitus, although the strength of the cognitive mechanism is slight. Situated in the broader societal context of exacerbating inequality in postreform China, our findings seem to suggest that cultural capital plays a role in alleviating intergenerational economic disparity, since those from privileged families have greater endowment of cultural capital, but are more likely to choose less lucrative liberal arts majors. However, whether or not this equalizer role of cultural capital holds depends on the outcome of the preference for liberal arts. For example, in the original research conducted by Bourdieu and Passeron (1977, 1979), it is exactly those liberal arts fields ridden with cultural capital that benefit from credential privileges in society. Also, studies conducted in other societies suggest that students from advantaged family background are more likely to major in liberal arts, but meanwhile they are more likely to attend graduate school. That is, liberal arts majored students can achieve labour market advantages through greater investment in human capital at the postbaccalaureate level (Mullen, Goyette and Soares 2003). In China, we suspect that neither case would apply. To be sure, the privileges enjoyed by liberal arts majored students as described by Bourdieu’s social reproduction theory, are embedded in the peculiar socio-historical institutions of France, such as a tradition of noble culture. But these institutions are generally not equipped in China. On the contrary, due to many decades’ efforts of eliminating class differences in the socialist period, the class structure of contemporary China, despite the recent trend of ‘crystallization’, is still less rigid than some other societies where various socio-economic mechanisms have been institutionalized to favour those with certain credentials or dispositions. In terms of the likelihood of transiting to post-collegiate schooling, liberal arts majored students in China do not necessarily stand out either. Situated in the market-oriented societal context, the decision to continue to postbaccalaureate schooling has much to do with the expected labour market returns from this investment. Since most liberal arts majored students can only choose a liberal arts graduate programme that brings about more obscure labour market returns relative to the readily perceivable skill-promotion of STEM majors at graduate school, there are good reasons to suspect that the incentive of liberal arts students to pursue a higher degree is not very strong. This point of view is preliminarily buttressed by the supplementary analysis of the BCSPS, where we fitted the Probit models to regress one’s expectation after graduation on different undergraduate majors, along with the control variables listed above. It is STEM-majored rather than liberal arts-majored students who have significantly stronger intention to attend graduate school. The data limitation of this study should be acknowledged. Because the BCSPS only sampled colleges in Beijing, there exists certain student selection mechanisms. It has been shown that graduates from a Chinese university that is C London School of Economics and Political Science 2017 V

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located in a large city such as Beijing enjoy extra labour market advantages compared with those graduating from an institution located in second-tiered or third-tiered cities (Hu and Vargas 2015). It is thus not surprising that senior high school graduates have to earn a higher NCEE score to attend a university in Beijing relative to a similar ranking university in other places, resulting in a mechanism of positive selection (Liu 2007; Wang 2011). However, this type of positive selection could mainly apply to students outside Beijing, since the universities in Beijing often set a larger quota for Beijing native senior high school graduates. Hence, our research findings, if generalized, would mainly represent the situation of non-native-Beijing college attendees whose NCEE scores are located toward the high end, but native-Beijing college attendees with mediocre and advantaged NCEE scores.10 (Date accepted: November 2017) Appendix Table A.I: Results of the confirmatory factor analyses on family socioeconomic background Latent variable Observed variables

Family SES Father’s educational attainment Father’s political identity Father’s job status Log family income

Chi square Root mean squared error of approximation (RMSEA) Comparative fit index(CFI) Tucker-Lewis index(TLI) Coefficient of determination(CD)

0.956 (0.027)*** 0.546 (0.014)*** 0.186 (0.016)*** 0.518 (0.015)*** 24.826*** 0.051 0.992 0.975 0.92

Notes: Standardized coefficients with standard errors in parentheses. ***p < 0.001 Data sources: BCSPS 2009, 2010 and 2012. Table A.II: Descriptive statistics of selected variables Variable Female Age Ethnicity (Han) Number of siblings Ranking of senior high school key point (national) key point (provincial) key point (regional) key point (county-level) others Grade retention in senior high school (yes) NCEE total score (original) NCEE Mathematics score (original) NCEE Chinese score (original)

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Percentage/Mean (S.D.) 47.50% 20.541 (1.322) 88.45% 0.488 (0.790) 12.03% 47.58% 16.73% 11.28% 12.39% 16.60% 579.695 (76.253) 128.228 (76.424) 120.899 (70.177)

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Table A.II: Continued Variable

Percentage/Mean (S.D.)

NCEE English score (original) NCEE total score (standardized) NCEE Mathematics score (standardized) NCEE Chinese score (standardized) NCEE English score (standardized) Family income (log transformed) Ranking of higher education institutions elite universities Other 211-project universities Non-211-project universities CCP member (father) Educational attainment (father) Job Status (father) Objectified cultural capital Embodied cultural capital Self-efficacy Self-esteem N

128.090 (77.351) 0.924 (1.416) 1.937 (6.235) 1.087 (5.690) 1.957 (6.235) 10.618 (1.168) 29.42% 35.21% 35.37% 40.18% 77.22% 83.06% 10.047 (4.152) 5.438 (2.022) 29.225 (5.088) 38.374 (6.005) 4,771

Notes: NCEE denotes National College Entrance Examination; MOE denotes the Ministry of Education; and CCP denotes the Chinese Communist Party. Data sources: BCSPS 2009, 2010 and 2012.

Notes 1. The data analysed here are drawn from the Beijing College Student Panel Survey (BCSPS), collected by the National Survey and Data Center, Renmin University of China, with support from the HKUST Research Project Competition (RPC07/08.HS02), the General Research Fund of the Hong Kong Research Grants Council (644510) (PI, Xiaogang Wu), and the Scientific Research Fund of Renmin University of China (2009030080, 20100030415, PI, Shizheng Feng). We would like to thank Dr Wang Weidong and Dr Li Ding at Renmin University of China for their contribution to the data collection. This study was also supported by the Innovation Program of Shanghai Municipal Education Commission (15ZS001, PI, Anning Hu), Fudan First-Class Research Group Funding (IDH 3458007), and the school of social development and public policy at Fudan University. No conflict of interest is reported. 2. Mainland China was absent in many volumes of comparative studies such as Shavit, Arum, and Gamoran (2010) and C London School of Economics and Political Science 2017 V

Shavit and Blossfeld (1993). This is unfortunate since China is a major ccountry among all the East Asian societies that include Japan, Korea, Mongolia and China. 3. One exception is Hu and Hibel (2015), but in this study, no family background variables other than parental education are considered. 4. We make the cognitive and noncognitive distinction for the mechanisms by which cultural capital works. It is not our intent to argue that performance in standardized test and non-cognitive peculiarities themselves are part of the definition of cultural capital. 5. Measures of embodied cultural capital were retrieved from the fourth wave collected in 2012 and measures of shadow education were retrieved from the second wave collected in 2010. They were merged into the baseline dataset. 6. To illustrate their differences, think about a sampled person P in university U. P initially wished to major in finance, but among those who intended to major in British Journal of Sociology

Science or liberal arts? finance, P’s NCEE score is ranked 21st and the department of finance only accepts 20 students. In this case, if P wishes to attend university U, P has to accept an assigned major by the university (usually a less popular major). Otherwise, P would be rejected by university U. 7. Based on the QS Asian University Rankings, Tsinghua University and Peking University were respectively ranked 10th and 15th in Asia. In addition to these two universities, Beijing Institute of Technology was ranked 146th, Beijing University of Aeronautics and Astronautics was ranked 181st and Beijing University of Chemical Technology, Beijing Institute of Technology, and Beijing University of Posts and Telecommunications were all ranked below 200. More information about the QS Asian University Rankings can be found at www.topuniversities.com. 8. Cronbach’s alpha for the items of objectified cultural capital is 0.89, and that for the embodied cultural capital is 0.77. Supplementary analysis suggests that these items can be respectively mapped to one single latent factor. The latent construct of

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objectified cultural capital accounts for 92.57 per cent of the variance, and that of embodied cultural capital accounts for almost 100 per cent. To keep it simple, we use the summed scores. 9. Note that it is necessary to control for the ranking of higher education institutions, because in practice high school graduates might face a trade-off between institution ranking and field of study, that is, whether to choose a less prestigious institution plus a lucrative major or a more prestigious institution plus a less lucrative major. 10. To test whether or not this selectivity could alter our analytical results, we reconducted the analyses presented above, respectively leaving out those whose household registration status (hukou) before attending college is in Beijing (around one fourth of the sample cases), leaving out those whose family location is Beijing, and leaving out those who attended a local Beijing university. Due to the space limitation, we do not present the detailed results. No substantive changes are detected.

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