Introduction
Family poverty is strongly associated with children’s emotional (internalising) and behavioural (externalising) problems [
1‐
7]. The pathways linking poverty and child emotional/behavioural problems are parental ill mental health [
8], weakening of family relationships, disengaged and harsh parenting practices, and/or lack of resources to purchase services and materials that benefit child well-being [
9]. However, some children manage to escape the consequences of poverty [
10,
11], perhaps due to individual characteristics, family qualities or environmental influences working together to forge resilience through a dynamic process [
12]. An environmental factor related to children’s emotional/behavioural resilience to poverty may be school composition (or ‘mix’). This study was carried out to test this.
School-composition effects refer to the collective, rather than the individual, influence of pupil characteristics, and composition is the aggregation (at the school-level) of pupils’ characteristics, including demographic, socio-economic or academic/intellectual [
13‐
16]. In essence, school-composition effects capture the influence of pupils’ peer groups. Some research has supported the role of the socio-economic [
17] and academic [
18] composition of the school in predicting individual academic performance. There is also recent evidence for the role of such school ‘effects’ in children’s health outcomes [
19] as well as suggestions for gender differences in such effects in adolescence [
20]. There is little research, however, on the role of school composition in explaining individual pupils’ differences in psychological outcomes. This limited evidence shows that the socio-economic rather than the academic intake of the student body influences the emotional/behavioural outcomes of individual children, and that effects are small [
21‐
26].
Although school composition may have a small impact on pupils’ behaviour, it may be more important for the behaviour of pupils from socio-economically disadvantaged backgrounds. Schools can substantially halt or even reverse the effect of family poverty on children’s academic or cognitive outcomes, especially if interventions towards and investments in disadvantaged children are made early [
27]. Yet, research has not explored the role of school composition in reducing the effect of poverty on children’s emotional/behavioural problems. Theory of contextual effects on individual outcomes suggests two reasons why attending a school with a privileged socio-economic or academic intake may be particularly beneficial for the emotional/behavioural outcomes of disadvantaged children. One is because of positive peer contagion, namely the upward-levelling norms of high-achieving or well-behaving peers [
28,
29]. A second way is through institutional characteristics that may relate to favourable pupil characteristics, including higher parental involvement in schooling, higher-quality teachers, more effective management processes within schools and a more rigorous curriculum [
30]. These characteristics may compensate for a more chaotic, less organised home environment, and one where the child receives less social support and less responsive parenting, all of which are more common in poor families [
31] and strongly associated with children’s emotional/behavioural problems [
32].
Nevertheless, there is other theory and research suggesting that school socio-economic composition effects may be different for advantaged and disadvantaged children, but in the opposite direction, as poor children in such schools may experience feelings of social inferiority [
33], in turn associated negatively with achievement and mental health. Simply put, attending a school with a higher socio-economic status (SES) intake may have a detrimental rather than a positive effect for children in poverty due to relative deprivation mechanisms [
14]. Although not consistently [
34], research has certainly shown that students from relatively advantaged backgrounds tend to derive greater educational benefits from attending high-SES schools [
35,
36], suggesting that high-SES schools perpetuate social reproduction [
37]. Although we are mindful of these findings and the theory to support them, we think that any added advantage of being high-SES in a high-SES school may be age dependent. School ‘choice’ (and therefore the role of schools in perpetuating social reproduction) may become more important for families as children grow older because of the predictive role of performance later in school for future outcomes. The role of school academic composition (usually measured as school-average achievement) in individual children’s outcomes has attracted more research interest but, again, findings are mixed. Some studies find negative effects [
23], in line with predictions from the theory of relative deprivation, others positive effects, and few non-linear effects, in line with other evidence that the effect of student composition changes as it moves toward a potential tipping point [
18].
The present study
To our knowledge, this is the first study to investigate whether school composition can moderate the association between family poverty and primary school children’s behaviour. Our study used large-scale longitudinal data from the UK’s Millennium Cohort Study (MCS) and had three aims:
1.
To model the relationship between family poverty across early-to-middle childhood (ages 9 months to 7 years), in terms of both the duration of exposure and its timing, and child behaviour (measured as internalising and externalising problems and prosocial behaviour at age 7).
We hypothesised that, even after accounting for individual and family characteristics, family poverty would be associated with children’s behaviour, given prior research demonstrating this relationship.
2.
To explore the role of school composition—academic and socio-economic—in both predicting child behaviour and moderating the effects of poverty on child behaviour.
We expected to find that attending a high-achieving school or a school with a socially-privileged intake would be related to greater prosocial behaviour and fewer internalising and externalising problems, even after accounting for individual characteristics and selection into schools. We also expected that a more favourable relative to a less favourable (academic and socio-economic) school profile would be particularly beneficial for poor children.
3.
To examine gender differences in the moderated (by school composition) effect of poverty on child behaviour.
We did not anticipate any gender differences in the (expected) moderator effect of school composition on child behaviour at this age.
We controlled for child and family/parent characteristics related to both poverty and child behaviour, including maternal psychological distress [
32] and family structure. We also controlled for child cognitive ability and parental education, which, alongside family poverty, should account for families’ selective sorting into schools. Accounting for selection into schools is important if one is to ascertain whether school ‘effects’ are genuine or simply exist because individual pupil characteristics are not accounted for [
38]. In our case, selection occurs if the sorting of pupils into schools is not independent from child behaviour, our outcome. For example, child cognitive ability at the beginning of school should be related to both internalising and externalising problems and selection into schools. Similarly, poorer or less educated families are more likely to have children who both attend lower-SES or lower-achieving schools and have more internalising and externalising problems. When estimating the effect of school academic composition (i.e., school-level academic achievement), we also controlled for the corresponding individual factor (i.e., the child’s own academic achievement). We did this to avoid committing the ecological fallacy, whereby inference occurs at the group level, but is actually attributable to confounding by individual factors [
39]. When estimating the effect of school socio-economic composition (i.e., school-level free school-meal (FSM) eligibility), we did not control for the individual child’s FSM eligibility due to the strong correlation between family poverty and child FSM eligibility.
Results
Model 1
Accounting for the multiple membership of children in schools improved model fit compared to fitting either a simple two-level model or a single-level model (the DIC was lower for the multiple membership model). We then calculated the variance partition coefficient or the proportion of observed response variation that lies at each level of model hierarchy. All school random effects were significant although small. For internalising problems, for a child attending one school only, the random school effect was 6.2 % and the random child effect 93.8 %. Therefore, school contributed 6.2 % (intraclass correlation) of the variance in internalising problem scores. For a child attending two schools, the random school and child effects were 3.2 and 96.8 %. Therefore, the two schools as a whole contributed to only 3.2 % of the variance in scores, less than the single school among the non-movers, as expected. For externalising problems, if attending one school, the child effect was 95.9 % and the school effect 4.1 % (if attending two schools, the between-child and between-school variance was 97.8 and 2.2 %, respectively). For prosocial behaviour, if attending one school, the between-child variance was 97.8 % and the between-school variance was 2.2 % (if attending two schools, the numbers were 98.9 and 1.1 %, respectively). Therefore, there was evidence for a small amount of clustering within schools, particularly for internalising and externalising problems. However, we felt it was appropriate to account for even this small amount of clustering to reduce the possibility of overestimating our school effects, especially since our main study objective was to tease out a school-level effect. Below we present the results of Models 2–7 predicting both age 7 outcomes and change in outcomes between ages 5 and 7.
Models 2–7: predicting age 7 outcomes
The cumulative effect of family poverty (i.e., the number of sweeps living below the poverty line) was significantly related to all three outcomes (Model 2), and was robust to family and child controls and the MCS design variables (Model 3, Table
3). The random effect of school remained significant, although it was reduced in size, after accounting for poverty, family and child controls and the MCS design variables. Although school-level KS1 scores were associated negatively with individual children’s externalising and internalising problems (but were unrelated to prosocial behaviour) prior to accounting for individual KS1 scores, the main effects of school KS1 scores were not significant on any of our outcomes after controlling for individual KS1 scores in Model 4 (Table
3). This suggests that the effect of school academic composition on children’s internalising and externalising problems was driven by the clustering of children into schools according to their academic performance. Furthermore, school KS1 scores did not interact with the number of sweeps in poverty to affect any child outcomes (Model 5). In Model 6 (Table
3), the main effect of school-level FSM eligibility was significant (and positive) for externalising and internalising problems. As with school-level KS1 scores, school-level FSM eligibility did not interact with cumulative poverty (Model 7). All random effects remained significant.
Table 3
Fixed effects estimates (poverty measured cumulatively) and variance covariance estimates for Models 3, 4 and 6 predicting internalising problems, externalising problems and prosocial behaviour
Fixed effects |
Constant | 15.44* (1.44) | 7.65* (1.13) | 6.34* (0.64) | 8.56* (1.55) | 4.41* (1.25) | 6.63* (0.77) | 15.09* (1.47) | 7.50* (1.16) | 6.13* (0.71) |
No. of sweeps in poverty | 0.18* (0.04) | 0.20* (0.03) | −0.05* (0.02) | 0.12* (0.04) | 0.16* (0.03) | −0.05* (0.02) | 0.16* (0.04) | 0.18* (0.03) | −0.05* (0.02) |
MCS strata (Ref. = E-advantaged) |
E-disadvantaged | 0.22* (0.10) | 0.11 (0.08) | 0.03 (0.05) | 0.18 (0.11) | 0.13 (0.09) | 0.08 (0.05) | 0.09 (0.11) | 0.06 (0.09) | 0.04 (0.05) |
E-ethnic | 0.50* (0.19) | 0.25* (0.15) | −0.06 (0.09) | 0.47* (0.21) | 0.25 (0.17) | −0.01 (0.10) | 0.41* (0.20) | 0.12 (0.16) | −0.09 (0.09) |
W-advantaged | 0.11 (0.91) | −0.06 (0.72) | 0.42 (0.44) | −0.08 (1.10) | −0.48 (0.89) | 0.10 (0.56) | 0.32 (0.92) | 0.05 (0.72) | 0.37 (0.45) |
W-disadvantaged | 0.28 (0.62) | 0.15 (0.49) | 0.19 (0.32) | 0.60 (0.66) | 0.36 (0.53) | 0.29 (0.33) | 0.47 (0.67) | 0.06 (0.52) | 0.22 (0.32) |
S-advantaged | 1.15 (0.93) | 0.24 (0.73) | −0.20 (0.45) | 1.64 (1.00) | 0.14 (0.81) | −0.48 (0.50) | 1.33 (0.94) | 0.42 (0.74) | −0.09 (0.48) |
S-disadvantaged | 1.47 (1.42) | −0.91 (1.11) | −0.29 (0.68) | 0.70 (1.75) | −0.47 (1.41) | 0.15 (0.89) | 1.40 (1.43) | −0.95 (1.12) | −0.29 (0.71) |
NI-disadvantaged | 1.64 (3.16) | 5.07* (2.47) | −0.55 (1.55) | 0.53 (3.08) | 4.68 (2.48) | −0.27 (1.52) | 1.84 (3.36) | 5.23* (2.63) | −0.53 (1.57) |
Age in years at Sweep 4 | −0.87* (0.19) | −0.45* (0.15) | 0.23* (0.08) | −0.11 (0.20) | −0.07 (0.16) | 0.21* (0.10) | −0.86* (0.19) | −0.45* (0.15) | 0.27* (0.09) |
Girl | −0.97* (0.09) | −0.01 (0.07) | 0.56* (0.04) | −0.84* (0.09) | 0.07 (0.07) | 0.54* (0.05) | −0.93* (0.09) | 0.02 (0.07) | 0.56* (0.04) |
Ethnicity (Ref.: White) |
Mixed | −0.38 (0.24) | −0.17 (0.19) | 0.04 (0.12) | −0.19 (0.26) | −0.09 (0.21) | −0.00 (0.13) | −0.43 (0.25) | −0.15 (0.20) | −0.004(0.12) |
Indian | −0.68* (0.31) | 0.24 (0.20) | 0.03 (0.14) | −0.48 (0.31) | 0.48 (0.25) | −0.09 (0.16) | −0.76* (0.30) | 0.33 (0.24) | 0.04 (0.14) |
Pakistani/Bangladeshi | −0.93* (0.26) | 0.01 (0.20) | 0.08 (0.11) | −0.71* (0.28) | 0.25 (0.23) | 0.08 (0.15) | −0.95* (0.26) | 0.05 (0.21) | 0.07 (0.13) |
Black/Black British | −1.13* (0.27) | −0.16 (0.21) | 0.31* (0.13) | −1.20* (0.31) | −0.18 (0.25) | 0.44* (0.16) | −1.35* (0.29) | −0.23 (0.23) | 0.33* (0.13) |
Other | −1.07* (0.44) | −0.27 (0.34) | 0.53* (0.21) | −0.81 (0.48) | −0.20 (0.38) | 0.41 (0.23) | −1.10* (0.48) | −0.15 (0.37) | 0.51* (0.22) |
Intelligence | −0.04* (0.00) | −0.02* (0.002) | 0.00 (0.00) | −0.01* (0.004) | −0.01* (0.003) | 0.001(0.002) | −0.04* (0.003) | −0.02* (0.003) | 0.0038 (0.0017) |
University-educated (M) | −0.67* (0.11) | −0.15 (0.09) | −0.04 (0.05) | −0.39* (0.13) | 0.01 (0.11) | −0.09 (0.07) | −0.66* (0.12) | −0.11 (0.09) | −0.03 (0.06) |
Psychological distress (M) | 0.16* (0.01) | 0.16* (0.01) | −0.03* (0.01) | 0.14* (0.01) | 0.16* (0.01) | −0.03* (0.01) | 0.16* (0.01) | 0.16* (0.01) | −0.03* (0.01) |
Intact family | −0.67* (0.12) | −0.32* (0.10) | 0.07 (0.06) | −0.53* (0.14) | −0.27* (0.11) | 0.03 (0.07) | −0.70* (0.12) | −0.32* (0.10) | 0.04 (0.06) |
School KS1 | | | | 0.03 (0.02) | −0.01 (0.02) | 0.003(0.01) | | | |
Child KS1 | | | | −0.33* (0.02) | −0.13* (0.02) | 0.03* (0.01) | | | |
School FSM | | | | | | | 0.05* (0.02) | 0.036* (0.016) | 0.001 (0.01) |
Random effects |
School-level | 0.17* (0.08) | 0.15* (0.07) | 0.02* (0.004) | 0.29* (0.09) | 0.19* (0.07) | 0.04* (0.01) | 0.14* (0.04) | 0.15* (0.04) | 0.03* (0.01) |
Child-level | 10.35* (0.20) | 6.32* (0.13) | 2.37* (0.05) | 9.45* (0.21) | 6.10* (0.14) | 2.36* (0.05) | 10.35* (0.21) | 6.34* (0.13) | 2.38* (0.05) |
DIC | 29762.21 | 26958.83 | 21285.54 | 23466.77 | 21464.12 | 17047.84 | 27437.78 | 24888.85 | 19671.09 |
We then modelled the duration of poverty categorically. In Model 2, the effects of chronic poverty (being poor in every sweep) and intermittent poverty (being poor in at least one but not every sweep) relative to never being poor were significant on all three outcomes. These effects remained significant in Model 3, with one exception. The effect of intermittent poverty was no longer significantly related to prosocial behaviour. As when modelling cumulative poverty, in Models 4 and 6 the effects of school-level KS1 scores were null, and school-level FSM eligibility was positively associated with externalising and internalising problems. Again, neither school-level variable moderated the effect of either chronic or intermittent poverty across childhood.
When measuring poverty in terms of timing, poverty at any age was associated with more externalising and internalising problems and less prosocial behaviour. These effects were partially attenuated but remained significant in Model 3. There were no significant interactions between school-level KS1 scores or school-level FSM eligibility and family poverty at any age (Models 5 and 7).
In the fully-adjusted model (Model 3), when measuring either the duration (continuously or categorically) or the timing of poverty, girls had fewer externalising problems and higher prosocial behaviour scores. There were several ethnic differences in our three outcomes. Relative to white children, Indian, black, Pakistani/Bangladeshi and ‘other ethnic’ children had fewer externalising problems. Black and ‘other ethnic’ children had higher prosocial behaviour scores than white children. General intelligence was related to all three outcomes. With regard to parent/family factors, mother’s education was related to fewer externalising problems, and intact family structure was associated with fewer externalising and internalising problems. Mother’s psychological distress predicted more externalising and internalising problems as well as less prosocial behaviour.
Models 2–7: predicting change in outcomes from 5 to 7 years
As explained, we modelled change in child behaviour between ages 5 and 7 by controlling for age 5 child behaviour. In Models 2 and 3, all our poverty variables were related to changes in the outcomes. Cumulative poverty was related to an increase in internalising and externalising problems, and a decrease in prosocial behaviour. Chronic or intermittent poverty was related to an increase in externalising and internalising problems and to a decrease in prosocial behaviour. Poverty experienced at any of the three ages (9 months, 3 or 5 years) was associated with an increase in externalising and internalising problems. Additionally, poverty experienced at ages 3 or 5 was related to a decrease in prosocial behaviour. In Models 4 and 6, using any poverty measure, we found only one school main effect: higher school-level achievement predicted an increase in externalising problems, after controlling for individual achievement scores. We explored whether this newly-significant effect of school-level achievement (in the fully-adjusted model of externalising problems at age 7, the effect was also positive but not significant) occurred at parts or across the distribution of school-average academic performance. To test this, we categorised school-average academic performance into three groups:
1.
High achievement (among the top three deciles of performance: deciles 8–10).
2.
Medium achievement (among the middle four deciles of performance: deciles 4–7).
3.
Low achievement (among the bottom three deciles of performance: deciles 1–3).
Our findings suggested a non-linear effect of school-level achievement on change in externalising problems during primary school. Compared to attending a low-achieving school (i.e., when attending a low-achieving school is the reference group), attending a high or medium-achieving school was associated with an increase in externalising problems from age 5 to 7. When attending a high or medium-achieving school is the reference group, attending a low-achieving school was related to a reduction in problems from age 5 to 7. There were no protective effects of either academic or socio-economic composition on any outcomes in models adjusting for child and family/parent covariates (Models 5 and 7).
Gender differences
Finally, we examined whether school composition may moderate the effects of poverty (measured both categorically and continuously, and in terms of timing), differently for boys and girls, on age 7 child behaviour and on change in child behaviour between 5 and 7 years. As expected, there were no gender differences in the (null) moderator effects.
Discussion
There is little research on the role of school composition in young children’s behaviour. This study sought to examine whether primary school composition has promotive or protective effects for parent-reported child behaviour in a large, representative sample of families in England. Our first aim was to model the relationship between family poverty and child behaviour (internalising and externalising problems and prosocial behaviour) at age 7. As expected, and in line with previous research [
3], we found that the effects of poverty were strong and robust to adjustment for child and parent background characteristics, as well as school intake characteristics. Our second aim was to explore the role of school composition—academic and socio-economic—in both predicting child behaviour and moderating the effects of poverty on child behaviour. School composition (either socio-economic or academic) did not interact with either duration or timing of poverty to predict child outcomes. However, there was a weak main effect of school socio-economic composition on internalising and externalising problems at age 7, such that, irrespective of own poverty status, attending a school with a higher proportion of poor children was associated with more internalising and externalising problems. This effect remained significant even after controlling for individual and other family factors related to child behaviour and selection into schools, such as ethnicity, intelligence, maternal education, family structure and maternal psychological distress. As for the role of school academic composition in the child outcomes we considered, we did not find any effect at age 7 once accounting for individual academic performance, although it appeared that children in the lowest-achieving primary schools improved more in terms of externalising behaviour from ages 5 to 7. Whether this reflects a genuine school effect, different parental expectations of behaviour for those attending high versus low-performance educational institutions, or simply the fact that children in low-achieving schools start school with a higher level of externalising problems and therefore can easily improve after 2 years, is unclear. Having detailed school environment data (e.g., on school policies, school connectedness or perceptions of teacher support) would help testing the first hypothesis, and having observational or teacher-reported data on child behaviour would help testing the second. However, MCS did not collect such data (it did collect teacher-reported SDQ scores at the age 7 survey but the level of non-response was very high). Our last aim was to examine gender differences in the moderated (by school composition) effect of poverty on child behaviour. As expected, we found no gender differences. That is, the null ‘protective’ effects of academic or socio-economic school mix we identified did not differ for boys and girls.
A strength of our study is that we accounted for between-school variability in children’s outcomes. There was, however, a relatively small amount of variation between schools in child behaviour in our unadjusted models (6.2 and 4.1 % in internalising and externalising problems), reflecting previous research on the role of school in child mental health [
24]. The between-school differences remained significant in all models, and hence were not fully absorbed by child and family characteristics or school composition. The small amount of between-school variation may be partially due to the design of MCS and therefore the (limited) extent of hierarchy in our data. Roughly half of the sample did not attend school with other MCS children. Thus, our ability to estimate and therefore understand school effects was limited without information on more of the individual pupils in the schools attended. Furthermore, as mentioned, we did not have measures of the school environment. There is certainly evidence that student perceptions of teacher support and school connectedness (or ‘school belonging’) are associated with better emotional health among individual students, at least in adolescence [
51]. Therefore, future research should also explore contextual, rather than only compositional, measures of the school environment including whole-school policies, leadership and school climate (e.g., engagement), as well as aspects of the more proximal classroom environment (e.g., classroom composition, classroom management and teacher quality).
We did not find that favourable school composition characteristics were particularly beneficial for the behaviour of children from poor families. Children from poor families were a high-risk group for, particularly, internalising and externalising problems irrespective of the academic or socio-economic mix of the school they attended, and as such they should be prioritised in interventions to promote child mental health. However, our study also showed that attending a school with a privileged socio-economic intake (on the assumption, of course, that not being on free school meals is an acceptable approximation of privilege) was associated with fewer internalising and externalising symptoms (but not more prosocial behaviour). Thus, it appears that, as early as at the beginning of primary school in England, the grouping of high-SES pupils into a school creates conditions associated with even better emotional/behavioural outcomes than would be expected from individual pupils’ SES alone. As we have theorised, this may be due to positive peer influences demonstrating good behaviour. If this were true, then peer-based interventions in schools to improve children’s emotional and behavioural regulation would be a natural implication of this finding. Of course, this school ‘effect’ we found may be instead (or also) due to other school characteristics associated with social mix (e.g., greater parental involvement in learning, higher-quality teachers or superior managerial processes within schools [
33]). In that case, interventions should be developed to target the improvement of these specific characteristics to promote pupils’ mental health. Future research should therefore first explore the mechanisms of this effect to determine the best intervention approach. The nonsignificant effect of school intake on prosocial behaviour echoes previous findings that empathy and consideration for others seems to be an individual difference driven more by family than extra-familial influences [
52].