Inequality in socio-emotional skills: A cross-cohort comparison

Abstract

We examine changes in inequality in socio-emotional skills very early in life in two British cohorts born 30 years apart. We construct comparable scales using two validated instruments for the measurement of child behaviour and identify two dimensions of socio-emotional skills: 'internalising' and 'externalising'. Using recent methodological advances in factor analysis, we establish comparability in the inequality of these early skills across cohorts, but not in their average level. We document for the first time that inequality in socio-emotional skills has increased across cohorts, especially for boys and at the bottom of the distribution. We also formally decompose the sources of the increase in inequality and find that compositional changes explain half of the rise in inequality in externalising skills. On the other hand, the increase in inequality in internalising skills seems entirely driven by changes in returns to background characteristics. Lastly, we document that socio-emotional skills measured at an earlier age than in most of the existing literature are significant predictors of health and health behaviours. Our results show the importance of formally testing comparability of measurements to study skills differences across groups, and in general point to the role of inequalities in the early years for the accumulation of health and human capital across the life course.

Keywords: Cohort studies; Inequality; Measurement invariance; Socio-emotional skills.

Fig. 1

Distribution of factor scores. Notes: The figure shows the distribution of the externalising and internalising socio-emotional skills scores at age five obtained from the factor model, by gender and cohort. The scores are estimated from the parameter estimates in Table A13, using an Empirical Bayes Modal approach. Higher scores correspond to better skills. The distribution is estimated nonparametrically, using an Epanechnikov kernel. The figure also reports the p-value from Kolmogorov-Smirnov tests of equality between the distribution in BCS and MCS.

Fig. 2

Skill inequality by mother's education. Notes: The figure shows unconditional mean values of socio-emotional skills scores by gender, cohort, and mother's education at age five. Mother's education is a dummy for whether the mother continued schooling past the minimum leaving age, based on her date of birth. The four panels on top present mean and 95% confidence intervals. Given that we cannot compare means of skills, all scores are normalised to take value zero for the ‘Compulsory’ category, so that the gradient is emphasised. The bottom two panels present the unconditional distribution of mother's education.

Fig. 3

Skill inequality by mother's pregnancy smoking. Notes: The figure shows unconditional mean values of socio-emotional skills scores by gender, cohort, and mother's pregnancy smoking. Maternal smoking is a dummy for whether the mother reported smoking during pregnancy. The four panels on top present mean and 95% confidence intervals. Given that we cannot compare means of skills, all scores are normalised to take value zero for the ‘Non-smoker’ category, so that the gradient is emphasised. The bottom two panels present the unconditional distribution of mother smoking status in pregnancy.

Fig. 4

Skill inequality by father's occupation. Notes: The figure shows unconditional mean values of socio-emotional skills scores by gender, cohort, and father's occupation at age five. Father's occupation is based on the Registrar General's social class, with classes I to III Non Manual being ‘White collar’ and classes III Manual to V (plus ‘other’) being ‘Blue collar’. ‘No father figure’ is defined as absence of a male figure living in the household. The four panels on top present mean and 95% confidence intervals. Given that we cannot compare means of skills, all scores are normalised to take value zero for the ‘Blue collar’ category, so that the gradient is emphasised. The bottom two panels present the unconditional distribution of father's occupation. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 5

RIF decomposition of changes in measures of inequality in socio-emotional skills – Males. Notes: The figures show the total changes in five measures of inequality in socio-emotional skills between the BCS and the MCS, and decomposes them in composition and coefficient effects, following the RIF decomposition (with reweighting) of Firpo et al. (2018). The top figure presents the decompositions for the externalising skills score, and the bottom figure for the internalising skills score. The five inequality measures considered are the quantile differences 90–10, 75–25, 50–10, 90–50, and the variance. Full results are in Table A14 and Table A15. Bootstrapped standard errors over the entire procedure (500 replications) were used to compute the p-values. ^⁎⁎⁎p ≤ 0.01, ^⁎⁎ p ≤ 0.05, ^⁎ p ≤ 0.1.

Fig. A1

Distribution of sum scores. Notes: The figure shows the distribution of the externalising and internalising sum scores at age five, by gender and cohort. The scores are obtained by assigning 0, 1, or 2 points for each item in the scale in Table 2. Zero points are assigned for ‘Certainly Applies /True’ responses, one point for ‘Sometimes applies/somewhat true’, and two points for ‘Doesn't apply’. Only 0 or 1 points are assigned for items that are coded as having two categories (5 and 11). Higher scores correspond to better skills.

Fig. A2

Factor scores excluding items 5 and 11. Notes: The figure shows the relationship between factor scores from the full 11-item model (horizontal axis) against factors scores from a model excluding items 5 and 11 (vertical axis). Pearson correlations are reported in the plot area.

Fig. A3

Item-level inequality by mother's education. Notes: The graph displays the ratio between the prevalence of each item in our scale in children of educated vs uneducated mothers, by cohort and gender. All items that have three categories in the scale have been dichotomised. For example, if the prevalence of the ‘Restless’ behaviours among children of mothers with compulsory schooling in the BCS cohort is 7.5%, and 5% among mothers with post-compulsory schooling, the ratio will be 1.5. The error bars represent 95% confidence intervals.

Fig. A4

Item-level inequality by mother's pregnancy smoking. Notes: The graph displays the ratio between the prevalence of each item in our scale in children of mothers who smoked in pregnancy vs non-smokers, by cohort and gender. All items that have three categories in the scale have been dichotomised. For example, if the prevalence of the ‘Restless’ behaviours among children of smoker mothers in the BCS cohort is 7.5%, and 5% among non-smoker mothers, the ratio will be 1.5. The error bars represent 95% confidence intervals.

Fig. A5

Item-level inequality by father's occupation. Notes: The graph displays the ratio between the prevalence of each item in our scale in children of white collar vs blue collar fathers, by cohort and gender. All items that have three categories in the scale have been dichotomised. For example, if the prevalence of the ‘Restless’ behaviours among children of blue collar fathers in the BCS cohort is 7.5%, and 5% among white collar fathers, the ratio will be 1.5. The error bars represent 95% confidence intervals. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)