Do grandparents matter? A multigenerational perspective on educational attainment in Taiwan

Social Science Research 51 (2015) 163–173

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Do grandparents matter? A multigenerational perspective on educational attainment in Taiwan Yi-Lin Chiang ⇑, Hyunjoon Park 1 Department of Sociology, University of Pennsylvania, 3718 Locust Walk, Philadelphia, PA 19104-6299, USA

a r t i c l e

i n f o

Article history: Received 6 August 2013 Revised 19 September 2014 Accepted 29 September 2014 Available online 20 October 2014 Keywords: Grandparents Educational attainment Educational expansion Taiwan

a b s t r a c t In response to the growing interest in multigenerational effects, we investigate whether grandparents’ education affects grandchildren’s transitions to academic high school and university in Taiwan. Drawing on social capital literature, we consider potential heterogeneity of the grandparent effect by parents’ characteristics and propose that grandparents’ education yields differential effects depending on parents’ education. Our results show tenuous effects of grandmother’s and grandfather’s years of schooling, net of parents’ education. However, the positive interaction effects between grandparents’ and parents’ years of schooling indicate that grandparents’ additional years of schooling are more beneficial to students with more educated parents than for students with less educated parents. The diverging gap in the likelihood of attending academic high school or university between students with parents in higher and lower ends of the educational hierarchy, along with increased levels of grandparents’ education, supports our hypothesis that grandparents’ education augments educational inequality by parents’ education. Ó 2014 Elsevier Inc. All rights reserved.

1. Introduction Sociologists have long been interested in the relationship between social origin, represented by parents’ status, and social destination, represented by individuals’ own standing in educational, occupational, or economic hierarchies. Building on the status attainment model developed by Blau and Duncan (1967), numerous studies on educational attainment in a variety of societies have shown that parents’ education significantly affects child’s educational outcomes (Shavit and Blossfeld, 1993; Buchmann and Hannum, 2001). Recently, a growing number of studies point to the limitations of the two-generation framework that constrains investigation to the parent–child relationship, and propose an alternative framework that considers the effects of grandparents and even further ancestors beyond the effects of parents in educational and social stratification processes (Mare, 2011, 2014; Pfeffer, 2014). Compared to the current two-generation paradigm, a multigenerational view of inequality allows a more holistic understanding of educational attainment. While parents are adults who directly influence children’s educational success, extended kin, especially grandparents, may independently affect children’s education as well. Grandparents may provide help with childcare, supervision, and other emotional, social, and economic resources, all of which can be beneficial for grandchildren’s educational outcomes. However, despite the expected positive effects of grandparents on grandchildren’s educational and occupational outcomes, empirical evidence of a direct effect of grandparents, net of parents, is mixed (Chan and Boliver, 2013; Erola and ⇑ Corresponding author. Fax: +1 (215) 573 2081. 1

E-mail addresses: [email protected] (Y.-L. Chiang), [email protected] (H. Park). Fax: +1 (215) 573 2081.

http://dx.doi.org/10.1016/j.ssresearch.2014.09.013 0049-089X/Ó 2014 Elsevier Inc. All rights reserved.

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Moisio, 2007; Warren and Hauser, 1997; Zeng and Xie, 2014). The ambiguity of the results suggests that it is important to extend research on this issue to other settings, especially beyond Western societies. Anticipating considerable variation in the effect of grandparents across contexts, for instance, Mare (2011) and Pfeffer (2014) called for more research on multigenerational effects across a range of societies that vary in institutional arrangements, which would help identify societal contexts in which the effects of grandparents should be strong, weak, or negligible. In this paper, we examine multigenerational educational attainment in a non-Western setting—Taiwan. The Taiwanese case provides an opportunity to assess multigenerational effects in an interesting context of family arrangements and educational expansion (described in detail later). In particular, our study is motivated by the hypothesis that the null effect of grandparents on grandchildren’s educational outcomes, found in some previous studies, might result from offsetting effects for different subgroups of the grandchildren generation. Specifically, we explore heterogeneity in the grandparent effect by investigating whether the effect of grandparents’ education on grandchildren’s educational attainment differs by parents’ levels of education. Our focus on the interaction between parents’ and grandparents’ levels of education in affecting grandchildren’s schooling is inspired by research that suggests possible variation of the grandparent effect under various family contexts (Jæger, 2012; Solon, 2013; Zeng and Xie, 2014). In other words, ours is an attempt to move beyond previous studies that have assumed a uniform effect of grandparents without considering parental characteristics. Drawing on literature of social capital, we propose that grandparents’ education ‘‘augments’’ parents’ education in affecting grandchildren’s educational outcomes. Our hypothesis points to the possibility that grandparents’ additional years of schooling may be particularly beneficial to grandchildren whose parents have relatively high levels of education. In contrast, when parents have relatively low levels of education, grandparents’ education may not have a substantial effect or may even result in an adverse effect on grandchildren’s education. In other words, we anticipate that the interaction effect between grandparents’ and parents’ education on grandchildren’s education should be positive, implying that grandparents’ education augments educational gaps between children of parents with more education and children of parents with less education. These differential effects of grandparents’ education on grandchildren by parents’ education may offset each other so that the overall effect of grandparents as a whole can appear to be tenuous. As explained in subsequent sections, the rapid expansion of education during the past few decades in Taiwan makes the Taiwanese case particularly useful to test our augmentation hypothesis. In the sections below, we first review existing literature on multigenerational educational attainment and highlight the lack of attention to the potentially heterogeneous effects of grandparents by parents’ socioeconomic status. We then introduce literature on social capital, from which we derive our hypothesis of the augmentation effect. Next, we provide a brief introduction to the Taiwanese context, particularly focusing on the degree of educational expansion and its implications for the multigenerational effect of education. In the data and methods section, we describe our data and modelling strategies to test our hypothesis. Using a Taiwanese longitudinal data set of middle school students who were followed up to five years after middle school graduation, we assess the effects of grandparents’ education on grandchildren’s high school and university attendance. We compare models with and without interaction effects between grandparents’ and parents’ education to test whether the grandparent effect is conditioned by parents’ education. Finally, we summarize our findings and point to some implications and limitations of our study.

2. Literature review While grandparents’ socioeconomic status may affect grandchildren’s socioeconomic attainment in various ways, an important question is whether grandparents independently affect grandchildren after controlling for parents’ socioeconomic status. Studies in the U.S. and Finland showed that grandparents’ socioeconomic positions were not significantly associated with grandchildren’s socioeconomic positions, after parents’ socioeconomic positions were taken into account (Warren and Hauser, 1997; Erola and Moisio, 2007). These findings suggest that the effects of grandparents are likely present only through their impacts on parents, which in turn affect children’s education. In contrast, studying class mobility across three generations in Britain, Chan and Boliver (2013) found that social class position of grandparents had a significant effect on the class position of grandchildren even after taking into account parental characteristics. Research from Sweden also showed a significant grandparent (and great-grandparent) effect on grandchildren’s education and occupation, net of parents’ effect (Lindahl et al., 2012; Hällsten, 2014). However, regardless of whether they found independent effects of grandparents, both sides of researchers often assumed homogeneity in the effect of grandparents’ education on grandchildren’s education and failed to consider the possibility that grandparent effects may depend on parental characteristics or other factors. Investigating the effect of grandparents on grandchildren’s schooling in rural China, Zeng and Xie (2014) showed that the effects of grandparents were contingent upon multigenerational coresidence. Specifically, the authors found that only coresident grandparents significantly increased grandchildren’s chances of staying in school, while non-coresident and deceased grandparents did not. This finding suggests that grandparents may not uniformly affect grandchildren’s education, but may differently influence grandchildren depending on family contexts. We argue that parents’ characteristics, particularly parents’ levels of education, condition the effect of grandparents’ education on grandchildren’s educational attainment. Parents directly influence their children’s education and connect grandparents with grandchildren. Therefore, depending on how parents utilize support and resources from grandparents, such

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support and resources from grandparents may or may not be useful in fostering children’s education. Our augmentation hypothesis is derived from literature on social capital, which suggests that support and resources embedded in social network and relationships do not automatically translate into educational benefits for children, but depend on both the nature of family relationship and parents’ capacity to activate such social capital (Bourdieu 1986; McNeal 1999; Portes 1998; Wong, 2002). The activation of social capital requires the possession of economic or cultural capital (Bourdieu, 1986). More educated parents with professional skills and knowledge may better use resources from grandparents to maximize their impacts on children’s education than less educated parents, whose limited knowledge and information on the educational system may hinder them from effectively mobilizing grandparental resources to produce substantial effects on children’s educational success. Studies of parental involvement in children’s education, often considered as a kind of social capital, illustrate that more educated parents not only have a higher level of parental involvement but also make their involvement more effective in boosting their children’s educational outcomes than less educated parents (Lareau and Horvat, 1999; Lareau, 1987; McNeal, 1999; Perna and Titus, 2005; Ren and Hu, 2013). On the other hand, parents who obtained lower levels of education, particularly those who experienced downward educational mobility compared to their own parents (i.e., grandparents of children), may exhibit feelings of embarrassment or guilt associated with their downward mobility that can lead to strained relationships with their parents (i.e., the grandparents), thus preventing effective use of grandparents’ resources for their children’s education (Newman, 1988). Further, tensions in family relations are found to have adverse effect on children’s academic outcome (Amato, 2001; Amato and Booth, 1997). Considering grandparents as resources embedded in the family’s network, we expect that, compared to parents with lower levels of education, parents with higher levels of education should be able to reap more benefits for their children’s education from grandparents’ additional years of schooling. While we propose the augmentation hypothesis, we acknowledge that the opposite pattern of the interaction between grandparents’ and parents’ education may be possible as well. In other words, grandparents’ education may compensate for parents’ low levels of education to enhance grandchildren’s education. For instance, using the data from Wisconsin Longitudinal Study, Jæger (2012) showed that the effect of grandparents’ education on grandchildren’s education was stronger for grandchildren whose parents had relatively lower levels of education (and lower family income). However, as the author acknowledged, the negative interaction between grandparents’ and parents’ education is difficult to be distinguished from the pattern of regression to the mean. Moreover, it is uncertain how the negative interaction found in Wisconsin can be generalized to other contexts. We expect that the greater benefits of grandparents’ education for children with more educated parents than children with less educated parents can be particularly evident in a context where the educational system has rapidly expanded, such as Taiwan. Studies showed that educational expansion led to a shift of educational competition to higher levels (Boudon, 1974; Raftery and Hout, 1993). and educational expansion coexisted with sustained class advantage (Breen and Goldthorpe, 1997). One possibility is that more educated parents may recognize the necessity for children to achieve even higher levels of education in order to maintain high status, and thus heavily draw on grandparents to assist the process. In the context of rapid educational expansion where grandparents have seen significant returns of their investment in their own children who successfully attained high levels of education, grandparents may willingly support grandchildren’s education. In particular, when both grandparents and parents have relatively high levels of education and are consistent in their approaches to educational success, support and resources from grandparents can be particularly effective in fostering grandchildren’s education. In contrast, when the parent generation experienced a rapid expansion of education and thus the majority of the parent generation improved their education levels over the generation of grandparents, grandparents whose children failed in educational competition may have particularly unfavorable attitudes towards investing in grandchildren’s education. The feeling of relative failure in educational competition could be even more substantial for grandparents who had higher levels of education relative to other peers in their generation, but whose children made considerable downward mobility by ending up with relatively lower levels of education compared with peers in the respective generation. In this situation, grandparents may be particularly pessimistic about values of education for their grandchildren and instead encourage grandchildren to enter labor market sooner than later. In other words, when parents have lower levels of education, having grandparents who were more successful in education relative to their peers may adversely affect grandchildren’ educational continuation, if having any effect.

3. The Taiwanese context This paper scrutinizes the prominence of grandparents’ education on grandchildren’s education in Taiwan. Taiwan provides an interesting context to examine whether and how grandparents’ education affects grandchildren’s educational attainment under the rapid expansion of education. Although educational expansion occurred in many parts of the world during the second half of the 20th century (Schofer and Meyer, 2005), the degree of expansion was particularly considerable in Taiwan. Taiwanese mandatory education increased from six to nine years in 1968. Since then, secondary and tertiary educational systems have expanded substantially, such that the proportion of population (ages 15 and above) with a college degree more than quintupled over three decades: from 7% in 1976 to 33% in 2006 (Ministry of Education, 2013).

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With regard to educational transitions, government statistics (Ministry of Education, 2013) show that high school education, grades 10–12, has become near-universal in Taiwan—the proportion of middle school graduates enrolling in high schools increased from 51% to 96% between 1950 and 2006. This transition rate is impressive, given that high school is not compulsory education and enrollment is determined by student performance in the standardized entrance examination, administered to students upon middle school graduation in 9th grade. Although the overall high school attendance has become near-universal, it is important to note that middle school graduates are separated into two different types of high school: academic and vocational high schools. Students’ admissions to academic and vocational high schools are determined by students’ test scores in the entrance examination. Typically, academic high schools are perceived as more prestigious and require higher admission cutoff scores than vocational high schools. Students in the academic track are also more likely to come from higher socioeconomic backgrounds than their peers in the vocational track (Lin, 1999). After high school, students can move to tertiary education that consists of four-year universities and two-year junior colleges. The transition rates from high school to tertiary institutions have increased substantially to the extent that 91% of academic high school graduates and 70% of vocational high school graduate made transition to tertiary education in 2006 (Ministry of Education, 2013). In other words, in Taiwan, even tertiary education has become mass education in which the majority of high school graduates enroll in tertiary education after high school. This trend indicates that the track difference between four-year universities and two-year junior colleges has become more meaningful in educational stratification than the mere attendance in tertiary education. Another context of Taiwan to be highlighted in light of multigenerational effects is the comparably high level of coresidence with grandparents. Households in which grandchildren coresided with grandparents accounted for as high as onethird of all households in Taiwan (Taiwan Social Change Survey, 2005). The comparable figure in the U.S. was approximately 16% in 2008 (Pew Research Center, 2010). The high level of coresidence with grandparents suggests that the effect of grandparental characteristics on grandchildren’s educational outcomes can be particularly evident in Taiwan. As Zeng and Xie (2014) suggested, living in the same household implies frequent contact and allows grandparents to establish close relationship, which may facilitate direct influences of grandparents on grandchildren’s educational outcomes. Yet, it is important to note that Zeng and Xi focused on the possibility of school dropout in rural China. Grandparents’ coresidence may increase grandchildren’s chances of staying in school by reducing grandchildren’s time for household chores and other possible family labor demands. However, grandparents with fairly low levels of education such as those in rural China or even in Taiwan (reflecting the recent expansion of education) may not know much about school curriculum and schooling processes, and therefore may not be able to directly benefit grandchildren’s academic achievement or transitions to the next levels of education. In other words, it remains an empirical question whether grandparents coresiding with grandchildren can positively affect grandchildren’s educational outcomes other than staying longer in school.

4. Data and methods 4.1. Data and variables Our data come from the Taiwan Youth Project (TYP), a longitudinal survey of students and parents in northern Taiwan. The data sampled 5711 students in 7th (1st year in middle school) and 9th grades (3rd and last year in middle school) through multi-stage stratified cluster sampling. The students were surveyed annually since the launch of the project in 2000. Parents of the students answered parental questionnaires to provide information on their own parents (i.e., grandparents) and other family environments. In other words, with the TYP dataset, we can obtain information on educational attainment over three generations. Moreover, the longitudinal TYP survey contains information on students’ timing of transition between educational institution, which allows us to retain the students who do not go through standard transitions to high school and college in the sample.2 We combine student and parent data from Wave 1 to Wave 8 (2000–2007) for the 7th grade cohort and Wave 1 to Wave 6 (2000–2005) for the 9th grade cohort. By doing so, the combined data contains student information up to two years after high school graduation for both cohorts. As many longitudinal surveys, TYP suffers from attrition. For the purpose of this study, we dropped the students whose high school enrollment status was not identified due to either attrition or nonresponse (N = 522). We further excluded students for whom we could not identify whether a mother or a father answered the parent questionnaire where we can obtain information on grandparents (N = 218). With reasons explained later, we carry out separate analyses for students whose mothers answered the parent questionnaire from students whose fathers answered the parent questionnaire. The final sample used to analyze transition from middle school to high school consists of 4971 students. We distinguish students who moved from middle school to academic high school (N = 2271) from their counterparts who transited to vocational high school (N = 2438). Among our 4971 respondents, only 262 did not advance to high school. Considering its small number, we combine those who did not attend high school with those who transitioned to vocational high school.3 2 Examples of non-standard transitions include taking fewer or additional years to prepare for the high school and college examination, boys entering military service before college, or students switching between vocational and academic track. 3 Note that we will call them vocational high school students for convenience even though 262 cases did not attend high school.

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The second outcome examined in the current study is whether students attended university by up to two years after high school graduation. Given the substantial proportion of Taiwanese high school graduates making transition to tertiary education as described earlier, it seems more meaningful to examine university attendance vs. no university attendance rather than to examine tertiary education vs. no tertiary education. In our analysis, the category of no university attendance includes those who attended junior college and those who did not attend tertiary education at all. After excluding respondents who did not attend any high school and those whose college enrollment status was not identified, the sample was reduced to 4047. The final sample for the analysis of transition from high school to university consists of 3907 students for whom we could identify whether a mother or a father filled out the parent questionnaire. In the final sample, 1632 attended university by two years after high school and the remaining 2275 enrolled in junior college (N = 1568) or did not attend any tertiary education (N = 707). Our key independent variables are grandparents’ highest levels of education. These variables come from parents’ answers to the question ‘‘What are your father and mother’s levels of education?’’ in 2000 (Wave 1). The parent interviewees in Wave 1 consist of two-thirds of the mothers and one-third of the fathers. Therefore, depending on who answered the parent questionnaire, we have information on either maternal (when a mother answered the parent questionnaire) or paternal (when a father answered the parent questionnaire) grandparents, but not both sides of grandparents. Considering the possibility that students whose mothers answered the parent questionnaire may differ in their characteristics from students whose fathers did, we conduct separate analysis for the two types of students. In order to investigate how grandmothers and grandfathers may have different effects, moreover, we include both grandmother’s and grandfather’s years of schooling in the same model (see Pfeffer, 2014). Given that grandparents’ levels of education were reported retrospectively by parents, missing information on grandparents’ education could be non-random. Fortunately, the percentage of students who have missing information on either grandmother’s or grandfather’s education is relatively small (6% in each group of students in maternal and paternal lineages). We retain those students missing on grandparents’ education using multiple imputation. In estimating the effects of grandparents’ years of schooling on grandchildren’s educational outcomes, we control for several individual and family background characteristics. Parents’ years of schooling are based on parents’ reports of their own and spouse’s levels of education. Our measure of log family income utilizes answers from multiple waves of survey. We first use parents’ reported family income (per thousand TWD) when the students were in 9th grade. The missing values are replaced with answers in the other waves of the parental survey, starting from the nearest. We then use students’ report on family income in 9th grade if the parents did not respond to family income questions in any survey. Three-generation coresidence is a dummy variable of whether students co-resided with any grandparent at 9th grade (0 = no, 1 = yes). Stratification research in Taiwan has demonstrated the relevance of ethnicity in educational attainment, showing that Mainlanders are advantaged over other ethnic groups (Chen, 2005; Jao and McLeever, 2006). Thus, we control for ethnicity using father’s ethnicity as a categorical variable (1 = Minnan, 2 = Mainlander, 3 = Hakka or Aboriginal). Other control variables include student’s gender (0 = male, 1 = female), residential area (1 = urban, 2 = suburban, 3 = rural), and sampled cohort (0 = 7th grade cohort, 1 = 9th grade cohort). For all control variables, except for residential area and cohort variables with

Table 1 Descriptive statistics for student samples.

Academic high school attendance University attendancec Grandfather’s years of schooling Grandmother’s years of schooling Mother’s years of schooling Father’s years of schooling Log family income Female Coresidence with grandparents at 9th grade Father’s ethnicity Minnan Mainlander Hakka or aboriginal Location Urban Suburban Rural Cohort: 9th grade (vs. 7th grade)

Students: maternal grandparents (n = 3343)a

Students: paternal grandparents (n = 1628)b

percentage or mean

SD

percentage or mean

SD

48.8 44.7 6.7 4.5 10.6 11.0 3.9 53.0 25.0

– – 4.3 4.2 3.0 3.3 0.8 – –

39.4 35.3 6.8 4.8 9.5 10.7 3.9 43.2 29.1

– – 4.4 4.3 3.0 3.1 0.8 –

77.5 14.0 8.6

– – –

80.5 11.4 8.1

– – –

38.7 39.8 21.5 52.1

– – –

33.7 41.2 25.2 53.4

– – – –

a This group of students refer to those whose mothers answered the parent questionnaire so that only maternal grandparents’ years of schooling are known. b This group of students refer to those whose fathers answered the parent questionnaire so that only paternal grandparents’ years of schooling are known. c The same size for university attendance is 2692 for students of maternal lineage and 1215 for students of paternal lineage, respectively.

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no missing cases, we use imputed values from multiple imputation with five imputed datasets.4 Table 1 presents the descriptive statistics for all the variables used in our analyses. 4.2. Methods Using five imputed datasets, we run logistic regression to predict academic high school attendance and university attendance for students of maternal lineage and students of paternal lineage separately. Three separate models are estimated for each outcome. Model 1 includes grandmother’s and grandfather’s years of schooling as well as all other independent variables, except for mother’s and father’s years of schooling. This model shows whether grandmother’s and grandfather’s years of schooling are related to grandchildren’s education before controlling for parents’ education. Model 2 additionally controls for mother’s and father’s years of schooling to test whether grandmother’s and grandfather’s years of schooling remain associated with grandchildren’s education net of parents’ years of schooling. In Model 3, we add two interaction terms to examine whether the effects of grandparents’ education vary by parents’ education. Specifically, the two interaction terms in the analysis of students of maternal lineage are: (1) maternal grandmother’s years of schooling  mother’s years of schooling; and (2) maternal grandfather’s years of schooling  mother’s years of schooling. The corresponding two interaction terms in the analysis of students of paternal lineage are: (1) paternal grandmother’s years of schooling  father’s years of schooling; and (2) paternal grandfathers’ years of schooling  father’s years of schooling. Positive coefficients of the interaction terms indicate that the beneficial effects of grandparents’ education are stronger for parents with more years of schooling, while the negative coefficients indicate an opposite relation. For easier interpretation of the results in the interaction terms, we provide a graphic presentation to demonstrate changes in log odds for grandchildren to attend academic high school or university across different years of grandparents’ and parents’ schooling.5 5. Results 5.1. Academic high school attendance We first discuss the results for students of the maternal lineage. Model 1 in Table 2 shows that, for students who have information on maternal grandparents, maternal grandfather’s education is positively associated with the odds for grandchildren to attend academic high school when controlling for all independent variables except for mother’s and father’s education. In contrast to maternal grandfather’s education, maternal grandmother’s education is not associated with increased odds of attending academic high school. Moreover, once mother’s and father’s years of schooling are included in Model 2, the positive effect of grandfather’s education disappears. However, as pointed out earlier, this null effect of grandparents’ education in Model 2 may disguise varying effects of grandparents’ education across different levels of parents’ education. To investigate the hypothesis of differential effects of grandparents’ education by parents’ education, we include interaction terms between maternal grandparents’ education and mother’s education in Model 3. The results show that the interaction term between maternal grandfather’s education and mother’s education is significantly positive (0.008), which indicates that children whose mothers have higher levels of education reap larger benefits from additional years of maternal grandfather’s schooling than children whose mothers have lower levels of education. In other words, the pattern of positive interaction is consistent with our augmentation hypothesis. The interaction term between maternal grandmother’s education and mother’s education is also positive, although it is not significant. To facilitate interpretation of interaction coefficients in Model 3, we illustrate how the log odds for grandchildren to attend academic high school vary across different years of maternal grandfather’s schooling (0–18) for three different years of mother’s schooling (9, 12, and 16 years of schooling) in Fig. 1.6 For students whose mothers received university education (i.e. 16 years of schooling), maternal grandfather’s additional years of schooling increase the log odds of grandchildren attending academic high school. For students whose mothers received a high school diploma (i.e. 12 years of schooling), maternal grandfather’s years of schooling hardly make any change in the log odds of attending academic high school. Similar to students whose mothers have 12 years of schooling, maternal grandfathers’ additional years of schooling do not increase log odd for students with middle school-educated mothers (i.e. 9 years of schooling). Although the log odds seem to decrease along with additional years of grandfather’s schooling for the group of students with middle school-educated mothers, we caution the interpretation of this finding. In our data, 90% of the maternal grandfathers of students with middle-school educated mothers have 9 or less years of schooling. Therefore, while the figure shows 4 We do not use imputed values for the dependent variables, which are academic high school attendance and university attendance. Those who were missing on dependent variables were also included for multiple imputation for independent variables but were excluded for the analysis of predicting the educational outcomes of students. This strategy was recommended by von Hippel (2007). 5 As an anonymous review pointed out, comparisons of logit coefficients across different groups can be affected by differences in residual variation across groups (Allison, 1999; Mood, 2010). To test whether our results were robust, we estimated linear probability models as well as logit models. The interaction terms in linear probability models showed similar patterns with those in logit models. 6 For Fig. 1, we estimated Model 3 again but without the interaction term between grandmother’s years of schooling and mother’s years of schooling, which was insignificant in Model 3 in Table 2. We fixed grandmother’s years of schooling as 6, father’s years of schooling as 12, log family income as the mean value, coresidence with grandparents as not coresiding, father’s ethnicity as Minnan, gender as male, location as urban, and cohort as 7th grade cohort.

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Y.-L. Chiang, H. Park / Social Science Research 51 (2015) 163–173 Table 2 Logit models of grandchildren’s academic high school attendance by grandparents’ years of schooling. Independent variables

Maternal grandparents

Grandfather’s years of schooling Grandmother’s years of schooling

Paternal grandparents

M1

M2

M3

M1

M2

M3

0.028* (0.012) 0.019 (0.011)

0.014 (0.012) 0.022 (0.012) 0.115*** (0.017) 0.126*** (0.016)

0.103* (0.046) 0.077 (0.052) 0.044 (0.025) 0.124*** (0.016) 0.008* (0.004) 0.005 (0.005)

0.005 (0.017) 0.000 (0.017)

0.035 (0.018) 0.001 (0.018) 0.063** (0.024) 0.173*** (0.025)

0.174* (0.068) 0.006 (0.071) 0.058* (0.024) 0.100** (0.036)

Mother’s years of schooling Father’s years of schooling Grandfather’s years of schooling  mother’s years of schooling Grandmother’s years of schooling  mother’s years of schooling

0.507*** (0.057) 0.070 (0.072) 0.052 (0.084)

0.212*** (0.057) 0.092 (0.074) 0.085 (0.087)

0.208*** (0.057) 0.084 (0.075) 0.074 (0.087)

0.472*** (0.082) 0.116 (0.106) 0.317** (0.121)

0.161 (0.082) 0.076 (0.106) 0.383** (0.125)

0.012* (0.006) 0.001 (0.006) 0.158 (0.083) 0.071 (0.110) 0.386** (0.125)

0.308** (0.110) 0.236 (0.131)

0.040 (0.117) 0.324* (0.135)

0.004 (0.119) 0.331* (0.136)

0.127 (0.168) 0.105 (0.192)

0.118 (0.176) 0.098 (0.198)

0.168 (0.179) 0.093 (0.200)

0.393*** (0.082) 0.561*** (0.102) 0.134 (0.072) 1.829*** (0.247) 3343

0.105 (0.087) 0.305** (0.107) 0.094 (0.074) 3.138*** (0.261) 3343

0.092 (0.087) 0.293** (0.107) 0.101 (0.075) 2.382*** (0.319) 3343

0.251* (0.121) 0.693*** (0.148) 0.208* (0.105) 1.902*** (0.359) 1628

0.021* (0.128) 0.533** (0.154) 0.175 (0.109) 2.967*** (0.368) 1628

0.016 (0.128) 0.505** (0.155) 0.170 (0.109) 2.131*** (0.469) 1628

Grandfather’s years of schooling  father’s years of schooling Grandmother’s years of schooling  father’s years of schooling Log family income Female Coresidence with grandparents at 9th grade Father’s ethnicity (ref: Minnan) Mainlander Hakka or aboriginal Location (ref: urban) Suburban Rural 9th Grade cohort Constant Observations

Note: All the missing values on independent variables were imputed by multiple imputation (5 different datasets). Standard errors are in parentheses. * p < 0.05. ** p < 0.01. *** p < 0.001.

1.2 1 0.8 0.6

log odds

0.4 0.2 0 -0.2 -0.4 -0.6

Mother's years of schooling = 9

-0.8

Mother's years of schooling = 12

-1

Mother's years of schooling = 16

-1.2

0

2

4

6

8

10

12

14

16

18

Grandfather's years of schooling Fig. 1. Log odds of attending academic high school by materna grandfather’s education and mother’’s education.

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the expected log odds up to 18 years of maternal grandfather’s schooling, the line for students with middle school-educated mothers would mostly end before 10 years of grandfather’s schooling. In other words, the seemingly declining pattern may be artificial. On the other hand, in our earlier discussion on the augmentation hypothesis, we pointed out the possibly adverse effect of having educationally successful grandparents on grandchildren’s educational continuation when parents experienced downward mobility in education despite the rapid expansion of educational system. Our data do not allow us to systematically test this potentially negative effect of grandparents’ education, and therefore we are cautious in making a strong claim that the log odds for grandchildren to attend academic high school indeed decrease with additional years of grandfather’s schooling. With regard to mothers who were university graduates, however, more than 30% of these mothers have fathers with six or less years of schooling, which reflects the rapid expansion of education in Taiwan. Therefore, the solid line in Fig. 1 is a fair presentation for students whose mothers have university education (16 years of schooling). An important consequence of the differential effects of maternal grandfather’s education by mother’s education in Fig. 1 is the increased gap in the likelihood of attending academic high school between students with more educated mothers and students with less educated mothers, as grandfather’s education increases. We find that the gap between students with more educated mothers and their counterparts with less educated mothers is much larger among students whose grandfathers have relatively more years of schooling than students whose grandfathers have relatively fewer years of schooling. This diverging gap in relation to increased years of grandfather’s schooling is consistent with the pattern expected by our augmentation hypothesis. Turning to the results for the paternal lineage, we do not find any positive relationship between grandparents’ and grandchildren’s education in either Model 1 or 2. However, similar to the results for the maternal lineage, the interaction term between paternal grandfather’s education and father’s education is significantly positive in Model 3. In other words, there is evidence that the effect of paternal grandfather’s education depends on father’s education. Students of fathers with more years of schooling particularly benefit from grandfathers’ additional years of schooling. We do not repeatedly present another figure to show changes in log odds of academic high school attendance for the paternal lineage. However, similar to Fig. 1, we find that the gap in the likelihood of attending academic high school increases between students with fathers having higher and lower levels of education as paternal grandfathers’ years of schooling increase. Other control variables associated with academic high school attendance in Table 2 are log family income, ethnicity, and residential area. However, we also find that the ways in which those control variables are related to academic high school attendance somewhat differ by lineages. For instance, father’s ethnicity (Hakka or aboriginal) is significantly related to academic high school attendance for students whose mothers answered the questionnaire (and thus only maternal grandparents’ years of schooling are known) but not for students whose fathers did (and thus only paternal grandparents’ years of schooling are known). On the other hand, coresidence with grandparent(s) is not significantly associated with academic high school attendance for students whose mothers answered the questionnaire, but is negatively associated for students whose fathers were the responding parent. These differences between the two types of students confirm our decision to conduct separate analysis by lineages. It is worth discussing the effect of coresidence with grandparent(s) in more detail. As described above, Zeng and Xie (2014) found that grandparents’ educational levels were significant for grandchildren’s staying in school only when grandparents coresided with grandchildren in rural China. To address the proposed pattern by Zeng and Xie, we included interaction terms between coresidence and grandparents’ education in supplementary analysis but found them to be insignificant. However, our coresidence variable is not ideal in that it does not necessarily indicate that students lived with the grandparent(s) whose years of schooling were available in the dataset. For instance, students could live with paternal grandparent(s) but only have information on maternal grandparents’ years of schooling because mothers answered the questionnaire. To check for the robustness of our result, we conducted an additional analysis in which we selected only students for whom coresident grandparents were those whose years of schooling were known. For these students, we consistently found no significant interaction between coresidence and grandparents’ education. Given that grandchildren in Taiwan are more likely to live with paternal grandparents than maternal grandparents, most students of paternal lineages have information on coresiding grandparents (who are mostly paternal grandparents). As seen in Table 2, in this subgroup of students, we find a negative relationship between coresidence and academic attendance. In a supplementary analysis that included interaction terms (not reported), we found no significant interaction between coresidence and grandparent’s education. In short, our result is not consistent with Zeng and Xie’s finding of positive interaction between coresidence and grandparent’s education. 5.2. University attendance We turn to the results for university attendance in Table 3. Model 1 shows that, for students whose maternal grandparents’ years of schooling are available, grandfather’s education is significantly associated with increased odds of attending university. Similar to the result of Model 1 in Table 2 for high school attendance, grandmother’s education does not increase the odds of attending university. Both grandfather’s and grandmother’s years of schooling are not related to university attendance once mother’s and father’s years of schooling are included in Model 2. Furthermore, Model 3 shows that the null effect of grandmother’s education in Model 2 is the result of differential effects of grandmother’s education by mother’s education. The positive interaction coefficient (0.01) is significant at the .1 level (p = 0.057), indicating that students of mothers who

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Y.-L. Chiang, H. Park / Social Science Research 51 (2015) 163–173 Table 3 Logit models of grandchildren’s university attendance by grandparents’ years of schooling. Independent variables

Grandfather’s years of schooling Grandmother’s years of schooling

Maternal grandparents

Paternal grandparents

M1

M2

M3

M1

M2

M3

0.028* (0.013) 0.016 (0.013)

0.016 (0.014) 0.019 (0.013) 0.133*** (0.020) 0.120*** (0.018)

0.042 (0.054) 0.135* (0.062) 0.079** (0.029) 0.118* (0.019) 0.003 (0.005) 0.010 (0.005)

0.002 (0.020) 0.002 (0.019)

0.048* (0.022) 0.006 (0.021) 0.061* (0.029) 0.218*** (0.031)

0.210* (0.085) 0.068 (0.087) 0.056 (0.029) 0.162*** (0.043)

Mother’s years of schooling Father’s years of schooling Grandfather’s years of schooling  mother’s years of schooling Grandmother’s years of schooling  mother’s years of schooling

0.515*** (0.066) 0.296*** (0.081) 0.085 (0.093)

0.186** (0.066) 0.350*** (0.084) 0.114 (0.096)

0.181** (0.067) 0.344*** (0.085) 0.103 (0.097)

0.692*** (0.112) 0.107 (0.126) 0.092 (0.140)

0.259* (0.114) 0.066 (0.131) 0.146 (0.146)

0.014* (0.007) 0.005 (0.007) 0.253* (0.115) 0.064 (0.132) 0.147 (0.146)

0.332** (0.122) 0.172 (0.154)

0.076 (0.129) 0.246 (0.158)

0.053 (0.130) 0.253 (0.159)

0.107 (0.197) 0.078 (0.234)

0.188 (0.209) 0.181 (0.247)

0.222 (0.211) 0.185 (0.248)

0.616** (0.093) 0.479*** (0.112) 0.056 (0.081) 2.159*** (0.289) 2692

0.320** (0.098) 0.217 (0.118) 0.023 (0.084) 3.509*** (0.303) 2692

0.311** (0.099) 0.205^ (0.118) 0.037 (0.084) 2.902*** (0.370) 2692

0.531*** (0.145) 0.692*** (0.170) 0.035 (0.125) 2.991*** (0.481) 1215

0.263 (0.154) 0.511** (0.180) 0.001 (0.131) 4.063*** (0.488) 1215

0.254 (0.155) 0.477** (0.180) 0.000 (0.131) 3.387*** (0.608) 1215

Grandfather’s years of schooling  father’s years of schooling Grandmother’s years of schooling  father’s years of schooling Log family income Female Coresidence with grandparents at 9th grade Father’s ethnicity (ref: Minnan) Mainlander Hakka or aboriginal Location (ref: urban) Suburban Rural 9th Grade cohort Constant Observations

Note: All the missing values on independent variables were imputed by multiple imputation (5 different datasets). Standard errors are in parentheses. * p < 0.05. ** p < 0.01. *** p < 0.001.

have more years of schooling benefit more from maternal grandmothers’ additional years of schooling than do students of mothers who have less years of schooling. Compared to the results for academic high school attendance in Table 2, in Table 3 the interaction between maternal grandmother’s education and mother’s education is significant, but not the interaction between maternal grandfather’s education and mother’s education. Nevertheless, the significantly positive coefficients are consistent with our augmentation hypothesis. The result for students who have information on their paternal grandparents in Table 3 is also similar to the result for academic high school attendance in Table 2. Both paternal grandfather’s and grandmother’s years of schooling are not significantly related to university attendance even prior to controlling for mother’s and father’s years of schooling; parents’ levels of education are significantly associated with increased odds for their children to attend university. However, Model 3 lends support to the augmentation hypothesis by showing a significantly positive interaction between paternal grandfather’s education and father’s education in affecting students’ university attendance. For students who have information on paternal grandparents’ education, we find a significantly positive interaction between grandfather’s education and father’s education for both academic high school attendance and university attendance. We do not find any significant interaction between father’s education and paternal grandmother’s education. 6. Conclusion In response to the growing interest in multigenerational effects in educational stratification processes, we have examined how grandmother’s and grandfather’s years of schooling are related to grandchildren’s educational outcomes (academic high school and university attendance), net of parents’ education in Taiwan. In particular, we have attempted to expand existing

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literature on multigenerational effects, which tends to ignore potential heterogeneity of the grandparent effect, by investigating how the effects of grandparents’ education differ by parents’ education. Drawing on literature on social capital, we have proposed an augmentation hypothesis, which suggests that grandparents’ education should augment parents’ education in affecting grandchildren’s educational outcomes. In other words, we expect that students with more educated parents are more likely to reap benefits from grandparents’ education than students with less educated parents. We find evidence supporting our augmentation hypothesis for both educational outcomes examined in this study. Additional years of grandparents’ schooling increase the odds of attending academic high school and university only for students whose parents have high levels of education. In contrast, additional years of grandparents’ education do not increase the log odds of attending academic high school or university for students whose parents have middle or low levels of education. Due to the differential effect of grandparent’s education by parents’ education, the gap in the likelihood of attending academic high school (and university) between students with parents in higher and lower ends of educational hierarchy diverges along with the increased years of grandparents’ education. In other words, grandparents’ education augments inequality in educational outcomes posed by parents’ education. Our finding contradicts Jæger’s (2012) alternative hypothesis on the function of grandparents’ education, which was found to compensate for the lack of parents’ education. According to the compensation hypothesis, grandparents’ education may ameliorate educational inequality associated with parents’ education. However, we do not find evidence supporting the compensation hypothesis in Taiwan. Rather, in our study, grandparents’ education is found to increase educational inequality by parents’ education because students with more educated parents benefit more from grandparents’ education than students with less educated parents. Our review of literature on social capital suggests that grandparents’ education as a social capital embedded in family networks should be more likely to augment, rather than to compensate, parents’ education. Given the important implications multigenerational studies hold for educational inequality, more research that examines whether grandparents compensate or augment parents in different contexts is needed. We acknowledge that our conclusion from Taiwan cannot be easily generalized to other Western contexts that vastly differ in education and family systems. However, we also note that several countries, especially neighboring countries like Japan and Korea, share with Taiwan some key aspects of social institutions, particularly the degrees of educational expansion, features of the educational system (e.g., Ishida 2007; Park 2007), and family structures, all of which are relevant for the investigation of multigenerational effects. Therefore, our understanding of the specific context in which grandparents’ education augments educational inequality by parental education would be significantly expanded if future comparative research in Asian countries provides empirical evidence that supports the augmentation hypothesis. In addition to heterogeneity of the grandparent effect by parents’ education, we have separately examined grandmother’s and grandfather’s years of schooling for each group of students, for whom either maternal grandparents’ years of schooling or paternal grandparents’ years of schooling are known. Compared to many previous studies, our study addressed potential heterogeneity of the grandparent effect in various aspects, including the difference between grandmothers and grandfathers as well as the difference between lineages. Without controlling for parents’ levels of education, grandfather’s education but not grandmother’s education is significantly associated with both academic high school attendance and university attendance for students whose maternal parents’ years of schooling are known. Moreover, grandfather’s education interacts with either mother’s or father’s education to the extent to which educational inequality by parents’ education further diverges. An exceptional case is university attendance for students with information on maternal grandparents, for whom it is not grandfather’s education but grandmother’s education that interacts with mother’s education. Our analysis does not provide any apparent conclusion on differences between grandmothers and grandfathers effects or by lineage. However, as Pfeffer (2014: 2) suggested, more research on multigenerational effects should explore ‘‘heterogeneity across groups and populations,’’ as the mechanisms and processes through which multigenerational effects are generated may differ. A limitation of our data is that we only have information on either maternal grandparents or paternal grandparents depending on whether the mother or the father responded to the parent questionnaire. Due to this lack of information on the other side of family, we could not directly compare the effects of maternal grandparents and paternal grandparents for the same group of students. Students whose mothers answered the parent questionnaire and their counterparts whose fathers answered likely differ in their individual and family characteristics (a glimpse of which shown is in Table 1). Since we do not have additional information that gauges the correlation between maternal and paternal grandparents’ characteristics, we are not comfortable using multiple imputation to impute the years of schooling of maternal or paternal grandparents that are completely missing. How maternal and paternal grandparents may have different impacts on grandchildren’s education awaits data with information on both sides of grandparents. Acknowledgments Hyunjoon Park acknowledges support from the Academy of Korean Studies Grant funded by the Korean Government (MEST) (AKS-2010-DZZ-2101). References Allison, P.D., 1999. Comparing logit and probit coefficients across groups. Soc. Method Res. 28, 186–208. Amato, P.R., 2001. Children of divorce in the 1990s: an update of the Amato and Keith (1991) meta-analysis. J. Fam. Psychol. 15 (3), 355–370.

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