A mediation analysis to disentangle relations between maternal education and early child development

Abstract

Maternal education is associated with early child outcomes. However, the several mechanisms that may explain this relationship remain underexplored. Using data from 1,097 children aged 12–15 months in Chile, we estimate the maternal education gap across child cognitive and language outcomes. Following a bioecological perspective, we explore potential pathways by which maternal education might influence child development, such as child characteristics, the quantity and quality of mother–child interactions, and the availability of home stimulation. We found an average maternal education gap between children with mothers with the lowest and the highest educational levels of 0.36, 0.31, and 0.25 standard deviation in child cognition, expressive language, and receptive language, respectively. The mediational analysis showed that maternal stress and depression and the quality of the home environment mediated the relation between maternal education and child language and cognitive development.

Keywords

Child development maternal education maternal stress home environment quality of interactions

Introduction

Evidence has shown childhood language and cognitive gaps by socioeconomic status (SES) at very early ages, even before children enter the educational system (Aughinbaugh & Gittleman, 2003; Rubio-Codina et al., 2015). Children from low SES backgrounds perform below their more affluent peers on standardized cognitive and language ability measures. The association between family SES—generally measured by parents’ education, income, wealth, or a combination thereof—and different dimensions of child development have been well documented in Western countries (Aughinbaugh & Gittleman, 2003; Bradley & Corwyn, 2002; Currie, 2009), developing countries (Fernald et al., 2012; Galasso et al., 2019; Lopez Boo, 2016), Latin-American countries (Paxson & Schady, 2007; Rubio-Codina et al., 2015; Schady et al., 2015), and for Chile (Abufhele et al., 2020; Coddington et al., 2014). In turn, poor development in early childhood has been identified as a determinant of adverse outcomes across life’s course, such as lower years of schooling (Currie, 2009; Walker et al., 2011), a lower probability of employment and lower earnings (Heckman et al., 2006), and worse adult health (Campbell et al., 2014).

To avoid the disadvantages found at early ages and often throughout life, we need to understand the multiple potential mechanisms that explain the relation between SES and early child cognitive and language development. However, much less is known about the pathways by which SES exerts its well-established influence, and here is where this article makes its most important contribution.

Comprehending the mechanisms through which SES influences children’s development requires a bioecological perspective of human development (Bronfenbrenner & Morris, 2006) that simultaneously analyzes several diverse proximal processes. As Bronfenbrenner and Morris (2006) demonstrated, proximal processes are the most potent forces influencing development—the engine of development—and their systematic variations are functions of peoples’ characteristics and environmental contexts. Pace et al. (2017), by combining the bioecological theory with the traditionally psycholinguistic approach (Crain & Lillo-Martin, 1999), established three pathways through which SES influences child ability: 1. child characteristics; 2. quantity and quality of parent–child interactions —proximal interactions—; 3. availability of materials for language learning in the home and informal learning opportunities outside the home —the richness of proximal and distal environments. Although these three categories continually interact and are not easily separated, they provide a framework for disentangling the complex relationship between SES and language and cognitive ability throughout early childhood.

This article builds on this perspective to contribute to the existing literature by disentangling what has been called the “black box” (Harding et al., 2015) of the association between maternal education and early child outcomes, identifying the multiple mechanisms that operate simultaneously in this association in the Chilean context. Although interrelated with household income, wealth, or employment status, evidence shows that maternal education may influence child development through particular mechanisms (Duncan & Magnuson, 2012). Maternal education is one of the most important mediators between income or wealth and children’s cognitive and language development (Abufhele et al., 2020; Lopez Boo, 2016; Rubio-Codina et al., 2016). Therefore, although the importance of maternal education for children’s cognitive and language development is widely recognized (Coddington et al., 2014; Lee & Burkham, 2002), the multiple potential mechanisms that explain this relationship are underexplored.

We conduct a mediational analysis to disentangle the association between maternal education and three developmental domains—cognition, expressive language, and receptive language, to explore the specificity of the association by developmental domain at 1 year of age. The potential mediators in the analysis were selected for theoretical reasons and because empirical evidence shows their association with children’s outcomes and maternal education (Conger et al., 2010; Harding et al., 2015).

First, we considered the child’s weight and height among the child’s characteristics. There is evidence that child measures of weight and height are significant mediators between the wealth gap and the cognitive and language development of young children in developing countries (Fernald et al., 2012; Rubio-Codina et al., 2015). Second, the proximal interaction measures included the time the mother and child spent together, the mother’s level of stress and depression, and the quality of mother–child interaction. Evidence shows that mothers with lower education experience more shocks and life events; they are disproportionately exposed to environmental hazards and violence, which could translate to augmented maternal stress and depression (Walker et al., 2011). More than half of low-income mothers with infants have some form of depression, and 11% have severe depression (Vericker et al., 2010). Mothers’ stress, in turn, negatively affects mother–child interaction and children’s development (Blair & Raver, 2016). Mothers with higher stress and depression talk less with their children (Lovejoy et al., 2000) and have children with slower vocabulary growth (Pan et al., 2005). In addition, the quality of mother–child interactions could have a pivotal role in understanding the association between maternal education and children’s cognitive and language development. Mothers with less education could be less sensitive or responsive to children’s needs, affecting their cognitive and language development (Chen et al., 2018). Third, the richness of proximal and distal environments was measured through childcare attendance and home environment quality (level of home stimulation). Evidence has shown that preschool and early primary school attendance mediates SES and child cognitive development (Berlinski et al., 2009). In addition, more educated mothers could provide a higher quality home environment and better interactions with their children (Hoff et al., 2002). Parental home stimulation activity is an essential mediator between SES and children’s cognitive and language development (Coddington et al., 2014; Fernald et al., 2012; Rubio-Codina et al., 2016).

Ultimately, using Pace et al.’s (2017) framework, we would like to deepen our understanding of the relationship between maternal education and children’s cognitive and language development. We are identifying, through the analysis of mediators, if these mediators—child characteristics, the quantity and quality of parent–child interactions, and the availability of learning materials—influence or not, in the same way, different children’s developmental domains. We think that this analysis might be helpful to guide future research on the determinants of children’s cognitive and language development in different settings and to design interventions that improve early childhood outcomes in Latin America.

Methods

Data Source and Participants

A total of 1,097 mother–child dyads were analyzed for this article. They were selected from the first round of the 2019 Chilean Longitudinal Study Mil Primeros Días (Thousand First Days) (n = 1,161) (Narea et al., 2020). Pontificia Universidad Católica de Chile’s Scientific Ethical Committee approved this research (171011003). Children were M (mean) =13.2 months, SD (standard deviation) = 1.2, range = 12–15 months old. Participants were from 33 communes of the Metropolitan Region that use the public health system (around 80% of the population).

Measurements and Procedures

Children’s cognitive and language—receptive and expressive—skills were assessed by direct observation using the Bayley Scales of Infant and Toddler Development, third edition (Bayley-III; Bayley, 2006, 2015). The Bayley III cognition scale assesses the child’s performance in several areas, such as visualization, memory, and attention. The cognitive scale measures a child’s ability to, for example, engage in pretend play, attend to objects, or look for an object that has fallen. On the contrary, the Bayley III Language scale assesses two major aspects of language, receptive and expressive communication skills. The language scale measures child ability to understand, and use spoken language to label objects or people, follow instructions, or recognize objects based on spoken description or labels (Bayley, 2006).

The Spanish version of Bayley was applied, and this version was piloted and adjusted to ensure linguistic and functional equivalence. Ten trained psychologists administered the Bayley-III in the presence of the mother at home. Cronbach’s alpha reliability (α) estimates were .83 for the cognition scale, .81 for the expressive language scale, and .69 for the receptive language scale. An Item Response Theory (IRT) score was created and normalized to a z-score (M = 0 and SD = 1) for ease of interpretation.

Maternal education was measured in numbers of years and grouped into five categories meaningful in the Chilean context: 1 = complete or incomplete primary education, 0–8 years; 2 = incomplete secondary education, 9–11 years; 3 = complete secondary education, 12 years; 4 = technical education; 13–14 years; and 5 = university and postgraduate education, 13–17 years.

Maternal stress and depression were assessed through The Parent Stress Index Scale (PSI-SF) and a Depression Scale (CES-D 20), respectively. PSI-SF (Abidin, 1995), which was validated by Aracena et al. (2016) for Chile, measures parental stress, assessing how adults feel about their role as a caregiver via a 36-item scale divided into three subscales: Parental Distress (PD, α = .86), Parent-Child Dysfunctional Interaction (P-CDI, α = .85), and Difficult Child (DC, α = .82).

CES-D20 (Radloff, 1977) is a 20-question instrument to detect depression symptoms from one’s experience of the prior week. CES-D20 is validated in the Chilean context (Gempp et al., 2004), and several studies have been carried out using it (Garrido-Rojas et al., 2021; Wolf et al., 2002). Its internal reliability is high (α = .89). We used IRT to score both instruments, in which a high score implies higher symptomatology of maternal stress and depression.

The quality of cognitive stimulation and emotional support in the home environment was measured with the Infant/Toddler version (ages 0–3) of the Home Observation for Measurement of the Environment instrument (HOME-IT; Caldwell & Bradley, 2003) (α = .80). It has six subscales: responsivity (extent of the parent’s emotional and verbal responsiveness to the child); acceptance (parental acceptance of undesirable behavior and avoidance of restriction/punishment, that is, how the parent disciplines the child); organization (how the child’s time is organized outside the family’s house and what the child’s personal space looks like); learning materials (the presence of several types of toys and activities that are available to the child); involvement (extent of parental involvement; how parents interact physically with the child); and variety (amount and range of daily stimulation, particularly how daily routine is designed to incorporate social meetings with people other than the mother) (Totsika & Sylva, 2004).

The quality of mother and child interactions was measured through the Three Bags Task (TBT) (Brady-Smith et al., 2005). Caregiver and child were video recorded for 10 min interacting with a set of play activities. A trained team of four psychologists later analyzed these videos to measure specific criteria: parental sensitivity, positive regard, stimulation of cognitive development, detachment, intrusiveness, and negative regard. All of the constructs were assessed on a 7-point scale, “1” indicating a very low incidence of the behavior and “7” indicating a very high incidence of the behavior (Brady-Smith et al., 2005). Although TBT was applied in every home, the videos that satisfied the technical requirements to be coded corresponded to 86% of the sample. Therefore, the use of TBT serves as a robustness check of the mediating role of the quality of the interactions between mother and child.

Statistical Analysis

First, we estimated the direct association between maternal education and child cognitive, receptive, and expressive language through an ordinary least squares (OLS) regression. For each cognitive and language measure, we estimated the maternal education gap by adding categories of maternal education dummies and controlling for the child’s age, gender, premature birth, foreign mother, and commune of residence. Primary education or less was set as the reference group for the estimations. The coefficient on the highest maternal education category indicated the maternal education gap, in other words, the difference in levels of the outcome (in SDs) between children with mothers with university education and mothers with primary education or less.

Second, we explored which of the potential mediators listed in Figure 1 explained the maternal education gap in child outcomes. During this process, we identified the indirect association between maternal education and child cognition via mediators (MacKinnon et al., 2002). Specifically, we used multiple regression analysis to estimate how the maternal education gap for each developmental outcome changed after the inclusion of the potential mediators, which we incorporated in five sequential steps following the approach of Hamadani et al. (2014) and Rubio-Codina et al. (2016). The null hypothesis of this mediation test is no difference between the $τ_{i}^{'}$ in Figure 1 after the inclusion of each mediator in each step. Step 1 includes the child’s characteristics. Steps 2 and 3 include the mediators related to the quality of the mother–child interaction. We include them separately in the regression to identify their own mediating effects. Finally, Steps 4 and 5 incorporate the mediators measuring the richness of proximal and distal environments (supplementary appendix Table 1 shows the definition of each variable included in the regressions). As a robustness check, alternative specifications that considered different orderings of entry of the potential mediators were conducted. All of these estimations yielded the same results. Finally, two analyses to garner an insight into the role of the quality of the home environment and interactions were conducted. First, the HOME subscales were added separately in Step 4. Second, the subscales of TBT were included separately in Step 4 because we only had a small amount of observations.

Figure 1.

Serial Mediation Model Based on MacKinnon et al. (2002), Rubio-Codina and Grantham-McGregor (2019), and Pace et al. (2017) Theoretical Framework.

Results

Descriptive Analysis

9.5% of the mothers in the study sample reported completed primary education or less (⩽8 years) and 16.9% reported university and postgraduate education. The difference in the developmental outcomes between children with mothers who belong to these two groups of education is the maternal education gap shown by the results. Descriptive statistics (see supplementary appendix Table 2) showed that children with mothers with the highest levels of education are more likely to have higher scores in the developmental domains, attend childcare, have a working mother, have a mother with less stress and depression, have a better home environment, and have higher quality interactions than children with mothers with the lowest level of education.

Mediational Analysis

We ensured that all potential mediators were significantly correlated with maternal education and at least one child’s outcomes (supplementary appendix Table 3). Birth height was discarded as potential mediator from this analysis. Figure 2 summarizes the main results from the regression analyses of mediation on the cognitive, expressive, and receptive language factors (supplementary appendix Table 4 to 7). For each outcome, the histograms show the size of the maternal education gap after controlling sequentially and incrementally for each set of potential mediators. The asterisks represent the statistical significance of the difference in the maternal education gap between steps, therefore illustrating the tests of mediation (supplementary appendix Table 8).

Figure 2.

Mediation Analysis of the Effect of Maternal Education on Cognitive, Expressive Language, and Receptive Language Development (Bayley-III IRT z-Scores). N = 1,097. For each outcome, the histograms represent the size of the estimated maternal education gap between the highest and the lowest maternal education level controlling for child’s age in months, child’s sex, premature birth, foreign mother, and communes’ dummies (Step 0). The statistical significance of the change in the maternal education gap between Steps was tested, bootstrapping the difference with 1,000 replications (Supplementary appendix Table 8). Significant changes in maternal education gap are represented by ***p < .01, **p < .05, *p < .1.

The maternal education gaps (Step 0) were statistically significant and accounted for 0.36 SD for cognition, 0.31 SD for expressive, and 0.25 for receptive language.

There were two key findings from the mediation analysis. First, the maternal stress and depression variables mediate the association between maternal education and child language development but not cognition. The inclusion of PSI and CES-D variables in Step 3 reduced the gap by 32% for expressive language and 28% for receptive language. Second, the quality of the home environment is the most critical mediator variable between maternal education and the three developmental domains under analysis. The inclusion of the HOME variable in Step 4 reduced the proportion of the remaining variance in the maternal education gap by 56% for cognition, 58% for expressive language, and 88% for receptive language. Therefore, the initial maternal education gaps over child outcomes were totally mediated by maternal stress and depression and the quality of home environment variables, decreasing 63%, 71%, and 91% for cognition, expressive, and receptive language, respectively.

Figure 3 presents the mediation analysis considering the HOME subscales separately instead of adding the complete HOME as in Step 4. The extent of the parent’s emotional and verbal responsiveness to the child (Step 4.1) and the availability of age-appropriate learning materials to the child (Step 4.4) mediate the association between maternal education and the three child outcomes. However, their importance is different for each outcome. In the case of cognitive development, the most important mediators are the availability to learning materials at home and the responsivity to the child, reducing the remaining variance in the maternal education gap by 37% and 28%, respectively. The opposite happens with expressive language development, where responsivity to the child mediate more importantly than learning materials, reducing the remaining variance in the maternal education gap by 32% and 24%, respectively. In the case of receptive language, in addition to the two previous subscales, the parents’ physical interaction with the child (Step 4.5) and the incorporation of social interaction with people other than the mother (Step 4.6) are mediator variables too. Involvement (Step 4.5) is the most important mediator, followed by responsivity, reducing the remaining variance in the maternal education gap by 48% and 45%, respectively.

Figure 3.

Mediation Analysis of the Effect of Maternal Education on Cognitive, Expressive Language, and Receptive Language Development (Bayley-III IRT z-Scores) Including HOME Subscales Separately. N = 1,097. The statistical significance of the change in the maternal education gap between Step 3 and Step 4. x was tested, bootstrapping the difference with 1,000 replications (Supplementary appendix Table 9). Significant changes in maternal education gap are represented by ***p < .01, **p < .05, *p < .1.

The analysis of TBT subscales (Figure 4) suggested that the quality of mother–child interaction mediated the association between maternal education and receptive language. Stimulation of child cognitive development and caregiver sensitivity mediated the maternal education gap, reducing it to zero. Also, intrusiveness (the degree to which the parent exerts control over the child), negative regard (expression of discontent with, anger toward, disapproval of, and rejection of the child), and detachment (lack of awareness, attention, and engagement with the child) showed a negative and significant association with receptive language, mediating its association with maternal education. However, we did not find that any TBT subscales mediated the association for expressive language or cognition.

Figure 4.

Mediation Analysis of the Effect of Maternal Education on Cognitive, Expressive Language, and Receptive Language Development (Bayley-III IRT z-Scores) Including TBT Subscales. N = 939. The statistical significance of the change in the maternal education gap between Step 3 and the inclusion of each subscale was tested, bootstrapping the difference with 1,000 replications (Supplementary appendix Table 10). Significant changes in maternal education gap are represented by ***p < .01, **p < .05, *p < .1.

Discussion

We identified statistically significant differences in early child cognitive and language outcomes attributed to maternal education. This concurs with Rubio-Codina et al. (2015, 2016) who use the same scales of the Bayley-III and Abufhele et al. (2020) who provide evidence for Chilean children. Going beyond this evidence, we explored which of a group of variables mediated the incidence of maternal education on cognitive and language development.

First, maternal stress and depression variables mediate maternal education and child language development but not cognition. Maternal stress levels and depression can influence children’s language development through a lower quantity and quality of mother–child interactions (Blair & Raver, 2016; McLaughlin & Sheridan, 2016). Evidence has shown that mothers with higher levels of stress and depression talk less with their children (Lovejoy et al., 2000) and have children with slower vocabulary growth (Pan et al., 2005). Our findings are consistent with this evidence and provide proof of the negative relation between maternal depressive symptomatology and child language development at a very early age in a Latin-American country where these mediators are not commonly available data. Considering that maternal education has been identified as the most important component of SES for predicting children’s language development (Hoff, 2013), our findings have established relations between lower levels of education, higher levels of maternal stress and depression, and lower child language development, providing a clear channel through which to improve child language development in the short term.

Second, the quality of the home environment is the most critical mediator variable between maternal education and the three developmental domains under analysis. The fact that the quality of the home environment was the most critical mediator variable between maternal education and the three developmental domains under analysis highlights the importance of the richness of proximal and distal environments for child’s development. Extending recent evidence that has similarly underscored the importance of the quality of the home environment for child development (Hamadani et al., 2014; Rubio-Codina et al., 2016), our study disentangles what constitutes these home qualities and their association by developmental domain. Receptive language appeared to be affected more by home quality scales than expressive language and cognitive development. Maternal’ physical interaction and emotional and verbal responsiveness to the child were the two most important home environment quality characteristics influencing child receptive language. Maternal emotional and verbal responsiveness to the child were also important to cognitive and expressive language which were also importantly affected by the availability of age-appropriate learning materials.

Pace et al. (2017) acknowledge that the separation of factors into three pathways through which maternal education exerts its influence on cognitive and language development is not easy because they continually interact. In our analysis, some scales of HOME, such as maternal emotional and verbal responsiveness to the child or the extent of maternal involvement, describe the quality of the home environment but are also related to the quality of mother–child interactions. The availability of the TBT instruments and its subscales allows us to identify the quality of mother–child interactions more directly. Mediation analysis showed that the quality of mother–child interactions mediated the maternal education gap in receptive language but not for expressive language and cognitive development. In particular, stimulation of child cognitive development and caregiver sensitivity mediated the maternal education gap, reducing it to zero. These findings agree with the evidence that SES may influence the ways in which parents communicate with their children, which in turn results in variations in children’s language development (Hoff, 2013). Unlike Raviv et al. (2004) who found that maternal sensitivity partially mediated the association between SES and children’s expressive and receptive language skills at age 3, we found this association for receptive language but not for expressive language, at age 1.

The evidence of the importance of maternal education in early and long-term children’s outcomes has suggested fostering education as one of the best interventions to break the intergenerational transmission of inequality (Carneiro et al., 2013). Although these long-term policies are a viable alternative, our results show that short-term alternatives can help to reduce the maternal education gap over child cognitive and language development. Parenting interventions that improve the parent’s emotional and verbal responsiveness to the child or interventions that provide higher availability of age-appropriate learning materials allowing stimulation of a child’s cognitive development at home appeared as short-term alternatives to foster child development at this very early stage. The evidence is auspicious in this regard. Parenting interventions have shown that decreasing parent stress and increasing parent internal focus of control leads to positive changes in child outcomes (Moreland et al., 2016); mindful parenting interventions seem to be effective on a broad range of child, parent, and family variables (Bögels et al., 2014); and interventions aimed at improving maternal–child interactions and stimulation in the home have shown positive impacts on children’s cognitive, language, and behavioral developments in the short term (Baker-Henningham & Lopez Boo, 2010; Engle et al., 2011; Nores & Barnett, 2010). Therefore, interventions focused on improving the cognitive stimulation and emotional support provided by parents or interventions that try to reduce maternal stress and depression levels, have the potential to positively impact early child development in the short term (Britto et al., 2017), and to reduce the maternal education gap over children’s development.

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Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Centro de Justicia Educational (grant ANID PIA CIE160007), the Center for Social Conflict and Cohesion Studies (grant ANID/FONDAP/15130009), and the Millennium Nucleus on Intergenerational Mobility: From Modelling to Policy (grant ANID/PIA/CIE160007).

ORCID iDs

Amanda Telias

Marigen Narea

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