Silviase/jgov_v1.4_full_ann huggingface dataset:
whether the father lives with the mother and child. However, due to lack of information, it is

not possible to identify why a father does not live with his family; the reason may be divorce

or having a job in another location. Situations where a child is without the father’s presence

were coded as 1; those where the father lived with the family were coded as 0.

## Number of Siblings

This variable was created because the number of siblings may influence whether parents use EAs for their child. For instance, parents may not send their child to EAs because they cannot afford to pay tuition for EAs if they have a larger number of children.

Time

Finally, a time variable ranging from 0 (Wave 7) to 3 (Wave 10) was created.

#### 5.5. Method

First, the study assessed whether parents’ educational background was related to the three categories of EA participation. To clarify whether advantaged parents practiced “concerted cultivation,” means of EA variables by parental education were calculated and shown in a cross-tab. As EA variables range from 0 to 3 (for academics) or 2 (for high culture and sports), another cross-tab was created using parent education and recoded variables to indicate whether a child participated in at least one EA for each category. This helps us observe disparities in EA participation depending on parental education level.
Then, a hybrid fixed effects model was employed to answer Research Questions 2 and 3. Two models were built and run; one includes the variables of EA participation and the other excludes them. These models include both time-varying and time-invariant variables. In this model, time-varying variables used as independent variables are decomposed into two parts. One part indicates between-individual differences by looking at each individual's means of the time-varying variables to observe if these means are related to variation of the intercept, which is randomly varying between individuals. This enables us to produce better estimates in terms of time-invariant variables in the model (Allison 2009). The other part demonstrates individuals' changes (i.e., within-individual variation). Time-varying variables are group-centered at the individual level, enabling the researcher to estimate changes of the time-varying outcome variable over the observed period of time. These results of level 1 (assessing changes) from the hybrid fixed effects model and those from the conventional fixed effects model are identical (Allison 2009; Miwa & Yamamoto 2012). Thus, the hybrid fixed effects modeling enables us to assess whether changes in the three categories of EA participation relate to changes in the outcomes by controlling for time-invariant unobserved heterogeneity, while effects of levels of the three categories of EA participation on the outcomes are simultaneously estimated at between-individual level (level 2).

The number of cases at level 2 (predicting between-individual differences) is 33,799.

These cases were included as having at least one set of responses for a wave over the four
waves of the survey; individuals with partial data are retained in all the analyses to ensure unbiased estimation of parameters of models, with the assumption that missing values are missing at random. As shown in Table 1, the number of each level 1 variable, assessing within-individual changes, varies. The hybrid fixed effects models (with or without EA participation-variables) were performed with multiple imputation of level 1 variables. The analysis was performed using Mplus 7.3 (Muthén & Muthén 1998-2012) with maximum likelihood estimation with robust standard errors (MLR).

#### 5.6. Model

A random-intercept hybrid fixed effects model was specified to assess if EA participation relates to the two aspects of children’s non-cognitive development. This model is meant to provide empirical evidence of factors associated with between-individual differences and within-individual changes of BPI and POS, respectively, while controlling for the time-invariant unobserved heterogeneity for the within-individual changes. All time-varying variables except time were group-centered and then included in level 1 (predicting changes within individuals), and means of these variables were also created at the between-individual level (level 2).

For the analysis with each continuous outcome variable (BPI and POS), the following model was specified as model 2 (model 1 excludes variables regarding EA
participation). $ ^{7} $

Level 1 Model (within-individual changes):

Outcome Variable $ _{ti} = \pi_{0i} + \pi_{1i}(Time_{ti}) + \pi_{2i}(Number\ of\ siblings_{ti}) + \pi_{3i}(Father's\ absence_{ti}) + $

$$ \pi_{4i}(Housewife_{ti})+\pi_{5i}(EA\text{ Participation: Academics}_{ti})+\pi_{6i}(EA\text{ Participation: High Culture}_{ti})+\pi_{7i}(EA\text{ Participation: High Culture}_{ti}) $$

Participation: Sports $ _{ti} $ + e $ _{ti} $

Level 2 Model (between individuals):

$$ \pi_{0i}=\beta_{00}+\beta_{01}(Born in January_{i})+\beta_{02}(Female_{i})+\beta_{03}(Parental Education_{i})+\beta_{04}(Household Income_{i})+ $$

$$ \beta_{05}(Mean number of siblings_{i})+\beta_{06}(Mean of father's absence_{i})+\beta_{07}(Mean of Housewife_{i})+\beta_{08}(EA $$

Participation: Academics $ _{i} $ + $ \beta_{09} $ (EA Participation: High Culture $ _{i} $ ) + $ \beta_{010} $ (EA Participation: Sports $ _{i} $ ) +

$ r_{0i} $

$$ \pi_{1i}=\beta_{10},\pi_{2i}=\beta_{20},\pi_{3i}=\beta_{30},\pi_{4i}=\beta_{40},\pi_{5i}=\beta_{50},\pi_{6i}=\beta_{60},\pi_{7i}=\beta_{70} $$

Vectors of the three categories of EA participation, $ \pi_{5i} $ to $ \pi_{7i} $ , for the ith individual at

time t are included in each model. The intercept of level 1, $ \pi_{0i} $ , is predicted by the equation of

level 2. This part observes the differences between individuals. Vectors of the time-varying

variables (i.e., the number of siblings, father's absence, and housewife) are shown as π2i, π3i,

and $ \pi_{4i} $ , respectively. To provide a refined estimate of $ \beta_{03} $ (parental education), household

income and other variables like means of number of siblings $ (\beta_{04}-\beta_{07}) $ were included.
### 6. Results

Table 1 shows descriptive statistics of variables used in the analyses. $ ^{8} $ It demonstrates that

each category’s EA participation basically increased over the years (except EA participation

in high culture and sports from Wave 9 to 10). Table 2 shows distributions of EA participation

for each wave. Table 3 offers another perspective on EA participation using recoded EA

variables that indicate whether a child participated in at least one type of EA in each category.

According to Table 3, 27.5% of first-grade pupils did not participate in any EAs outside

school, while 39.1% were in one category of EA, 26.9% were in two categories of EAs, and

6.5% of first-graders were involved in at least one type of EA in all three categories. As Table

3 further shows, the number of children with no participation in any EAs decreased over the

observed period of time; at the fourth-grade level, only 15.4% of children had no EAs,

showing how prevalent EA participation outside the school system is in Japanese society.

#### 6.1. EA Participation in Academics, High Culture, and Sports

To answer the first research question, Tables 4 and 5 were constructed before the

main analysis. According to Table 4, the number of college-educated parents appears to relate

to the dependent variables (i.e., behavior problem index and positive orientation to school)

and EA participation. $ ^{9} $ Children with one or two parents having at least an associate degree

tend to have a lower score on the behavior problem index (i.e., fewer behavioral issues
reported by respondents) and a higher score on positive orientation to school (i.e., holding positive attitudes toward school, as observed by respondents). Disparities in these two aspects of non-cognitive development between the three groups of parental education are present in every observed wave. These advantaged children tend to participate in the three categories of EAs more than those with non-college educated parents during the observed waves. Since EA participation variables range from 0 to 3 for academics and from 0 to 2 for high culture and sports, it may be difficult to intuitively grasp a trend of disparities in EA participation. Thus, Table 5 was also constructed, showing whether children participated in at least one type of EA in each category. It displays the same trend as Table 4: children with a larger number of college-educated parents are more likely to participate in more than one type of EA in each category. Overall, children whose parents have higher levels of education participate in EAs more frequently, and many of them start at the first-grade level.

#### 6.2. Behavior Problems Index

Two sets of hybrid fixed effects modeling were employed to answer the second and third research questions. According to results of two models with BPI presented on the left side of Table 6, the second and third hypotheses are partly supported; as the results of Model 2 show, at the within-individual level, EA participation in sports is negatively associated with BPI score, and EA participation in high culture and sports relates to between-individual differences at level 2. These modest (in terms of the size of the coefficients) but significant
results suggest that changes in EA participation in sports are correlated with fewer behavioral problems, whereas the study controls for time-invariant unobserved heterogeneity. Moreover, EA participation in high culture and sports is negatively related to BPI score at level 2 (between individuals); EA participation is associated with some of the between-individual differences even when sex, parental education, annual household income, number of siblings, father's absence, and mother's occupational status are controlled. In addition, the number of siblings and father's absence appear to be positive factors increasing BPI scores at level 1; that is, having an increasing number of siblings and losing father's presence are related to increases in children's behavioral problems. At level 2, females, parents with higher educational level and income, and a larger number of siblings are related to having fewer behavioral issues, whereas those born in January, living without a father, and having a mother who is a housewife are associated with more frequent problematic behaviors.

As for comparisons between Model 1 and Model 2, coefficients of variables

indicating parental education became weaker when EA participation variables were included.

This suggests that parental education influences BPI scores through EA participation. For example, a coefficient of the ordinal variable showing the number of college-educated parents is -0.113 in Model 1, but it became -0.081; about 28% of its effect on BPI turns out to be

through EA participation: $ (0.113 - 0.081)/0.113 $ . Note that results of household income also show the same trend; the coefficient of household income became weaker, going from -0.069
to -0.047, suggesting that about 32% of household income’s effect on BPI is through EA participation: $ [(0.069 - 0.047) / 0.069] $ .

#### 6.3. Positive Orientation to School

The right side of Table 6 presents two sets of results of the hybrid fixed effects model regarding POS. According to Model 2's results, which again show significant though modest associations, all forms of EA participation appear to be related to children's positive orientation to school at both levels, supporting the second hypothesis. More concretely, while time-invariant unobserved heterogeneity is controlled for, participating in any category of EAs positively predicts increases in positive orientation to school, according to the results at the within-individual level. Increases in the number of siblings are also related to an increase in positive orientation toward school. As for between-individual differences, in addition to the three categories of EA participation, sex (female), parental education, and annual household income are positively associated with positive orientation to school, whereas being born in January and having a mother who is a housewife are negatively correlated with it.

The third hypothesis is also supported. A coefficient of parental education became

weaker once EA participation was added to the model (Model 2). More concretely, the

coefficient of parental education was 0.094 in Model 1, and it became 0.056, showing that

about 40% of its effect on POS is mediated by EA participation: $ [(0.094 - 0.056) / 0.094] $ .

Similarly, a coefficient of household income moved to 0.042 from 0.071; therefore, about
41% of the household income’s effect on POS is through children’s EA participation:

$ [(0.071 - 0.042) / 0.071] $ .

### 7. Discussion

This study provides empirical evidence of how inequality in children’s non-cognitive

development emerges during the first four years of compulsory education even in a society

where school differences are known to be relatively small because the central government

progressively funds prefectures for compulsory education with a national curriculum. The

results demonstrate that parents employ EAs differently based on their educational

background, and that the differences in using EAs are modestly but significantly associated

with children’s behavioral issues and orientation to school. In other words, what children

experience and learn outside the school system shapes their behaviors and even their

adaptability to school. Considering that elementary education in Japan is egalitarian

(Cummings 1980), with relatively low disparities between schools and between regions in

terms of the quality of teachers, school facilities, and education materials (Kariya 2009b)

2010), unequal access to EAs and the resulting effects on children’s non-cognitive

development could be considered as a mechanism of social reproduction; advantaged

children are more likely to obtain learning experiences in various adult-led, structured

activities outside the formal school system, and thus to have a positive school orientation
with fewer behavioral problems, likely influencing educators to prefer them (as illustrated in their daily interactions and in report cards). Parents and teachers may judge a child's non-cognitive aspects of development while not being aware of EA participation's relationship with behavioral issues and orientation to school. Since the progressive funding scheme creates an image of egalitarian education that "successfully provides a strong basis for a social consciousness that the 'starting line' is set equally for every child" (Kariya 2010, p. 58), teachers and parents likely interpret children's behaviors and orientation to school as their personal characteristics and preferences (e.g., whether a child likes coming to school). The study's results, based on longitudinal data capturing the first four years of compulsory education, raise the question of whether early elementary-age children should be held fully responsible for their behaviors and orientation to school.

In Japan, parents with higher education seem to practice “concerted cultivation” (Lareau 2003, 2011) more heavily, organizing their children’s time outside school hours by using EA services that, in general, the family itself must pay for. This result parallels the evidence from longitudinal studies in the U.S. (e.g., Bodovski 2010; Bodovski & Farkas 2008; Cheadle 2009; Cheadle & Amato 2011; Covay & Carbonaro 2010). As discussed noted in Note 9, parental education is the strongest predictor of between-individual differences in every category of EA-participation, even when household income and other demographic factors are controlled. Since household income also relates to EA participation positively, it is
unclear whether parental desires to develop their children’s skills are consistent across social

classes, as Chin and Phillips (2004) contended. Because parental education appears to be

correlated with children’s EA participation in all three categories, the findings seem to

support Lareau’s point that advantaged parents take a “concerted cultivation” approach,

whereas disadvantaged parents follow “the accomplishment of natural growth.” This result

also seems to be consistent with the claim by Kikkawa (2009) that Japanese society is divided

according to educational differentials; for instance, disparities with regard to attitudes toward

education (e.g., expecting one’s child to pursue higher education) are derived from whether

one is a college graduate. If parents have received post-secondary education, the likelihood of

their children’s participation in all three EA categories is much higher than that of children

whose parents have less education. This result could be understood as indicating that

college-educated parents pursue “concerted cultivation,” whereas those with high-school

education or less take the other childrearing approach, “the accomplishment of natural

growth." Future studies should further investigate this issue in detail.

The findings of this study, using national longitudinal data, also support previous

studies using regional, retrospective, or Internet-based data in Japan. More concretely,

participation rates in each EA category vary greatly according to parental education levels

within each observed wave; this appears to extend the findings of Kaneko (2004), Kataoka

(2010), Honda (2008), Nishijima et al. (2012), Sugihara (2011), and Holloway et al. (2008)
though the definitions of shadow education and methods of analysis differ among these studies conducted in Japan. Additionally, this study’s results appear to be consistent with the qualitative investigation of Japanese mothers by Yamamoto (2013); there are differences between middle-class mothers and working-class counterparts in terms of using shadow education services and extracurricular activities.

Focusing on the relationships between EA participation and children’s non-cognitive development, the study demonstrates that EA participation in sports is negatively related to within-individual changes, and that EA participation in high culture and sports is negatively associated with between-individual differences in the BPI, though these effects are not strong as might have been expected. Additionally, the results indicate that participation in all three EA categories is modestly but positively related to between- and within-individual changes in POS. Despite the fact that each previous study differently measures and assesses children’s non-cognitive development, the literature using large-scale data (Covay & Carbonaro 2010) and this study suggest an identifiable relationship between EA participation, being exposed to adult-led, structured activities outside school hours, and some aspects of non-cognitive development. It is also notable that despite a study using large-scale cross-sectional data (Howie et al. 2010) showing no difference between children with and without EA participation in relation to problem behavior, this longitudinal study identifies similar patterns to those found by previous studies assessing children’s well-being (Lagacé-Séguin & Case
2008), socio-emotional behavior (Metsäpelto & Pulkkinen 2011), and behavioral adjustment in school (Molinuevo et al. 2010). In addition, this study's finding that participating in EAs sports decreases child's BPI scores seems consistent with the literature indicating that participating in organized team sports increases self-esteem (Slutzky & Simpkins 2009) and lowers social anxiety (Schumacher Dimech & Seiler 2011); these benefits may be related to why participating in EAs sports decreases children's behavioral issues as observed by the respondents.

To fully understand the significance of the results obtained here, it is critical to consider the fact that these data come from a country seen as having minimal between-school disparities. In such a society, how a child behaves is more likely to be attributed to his or her personality, talent, or characteristics, even though the empirical evidence from this study demonstrates the unequal access to EAs due to parental education levels (and household income), the consequences of this lingering inequality on children's non-cognitive development, and the fact that EA participation partly mediates the relationship between parental education and children's non-cognitive development. As children have to behave and receive evaluations in school-like fashion at adult-led, structured EAs (Lareau, 2003, 2011), these experiences should help them to become accustomed to school, while engaging in sports decreases children's behavioral issues. In other words, children with advantaged parents tend to obtain more learning experiences through structured learning activities,
helping them to behave well and have a positive attitude toward school. As schoolteachers

likely value children with these two aspects of non-cognitive development, whether a child

possesses them could influence how teachers approach the child, positively reinforcing

children’s behaviors and attitude toward school. $ ^{10} $ In this way, EA participation outside the

education system could be linked with positive student evaluations in the formal system;

college-educated parents’ “concerted cultivation” of organizing their children’s time through

EAs may indirectly help their child to adapt to school and be positively regarded by

schoolteachers, considering that Japanese children’s behaviors, interests, and attitudes are

formally evaluated by schoolteachers and written on report cards beginning in first grade.

How classroom teachers evaluate a child could impact parents' view of the child as well as

the child’s self-image. In the meantime, children from disadvantaged families, who have had

the benefit of fewer adult-led, structured learning experiences outside school hours, may

present more behavioral issues and a less positive attitude toward school. Schoolteachers

might judge these students as not engaging in school lessons and write such an evaluation on

a report card, without considering that it could be partly explained by the relative lack of

adult-led, structured learning opportunities outside school. Teachers’ “professional” judgment

on a report card may negatively reinforce parents' view of the child, creating a negative cycle

of evaluations that pushes the child to behave more negatively. As children get older, the

behavioral and attitude gaps are likely to expand further, causing a larger number of
disadvantaged children to become disengaged in school, resulting in further negative

judgments that will continue to reinforce their behaviors and attitudes. By the time they enter junior high school, a disproportionately large percentage of children from disadvantaged families are likely to demonstrate deviant behaviors, negatively influencing their educational and occupational trajectories.

### 8. Research Implications

There are mainly four implications for future research. First, because of data limitations, the study could not test the relationship between EA participation and cognitive aspects of child development. Honda (2008) has shown that "rigorous" childrearing (which is close to Lareau's "concerted cultivation"), including shadow education at the elementary level, is related with higher academic achievement in ninth grade, which influences subsequent educational and occupational achievement. It is, thus, likely that some EA participation, especially in academics, would enhance or remedy children's cognitive skills. If so, this is one of the mechanisms by which an achievement gap emerges even in a relatively egalitarian education system. Second, due to the wording of the questionnaire, it is impossible to identify whether a respondent's child participated in a specific type of EA for the entire year or just part of it. Likewise, it would have been helpful to know how many days and hours a child participated in each EA and how much tuition was paid for it. Furthermore, the specific