Avance en el cumplimiento de las Metas

Although many papers have shown family socio-economic status to have a large effect on children’s early years cognitive outcomes, SES in total is still limited in explanatory power. Melhuish (2008) quotes a meta- analysis of studies by White (1982) which estimated that SES can explain about 5% of the difference in academic achievement, and explains that the limited explanatory power of family income provides their motivation for looking further abroad for other important factors. Their paper focuses on parenting behaviours and the home environment, as well as pre-school, as factors which may be able to help explain the achievement gap. I now proceed to introduce a range of papers which have looked specifically at the possible contributing factors to the gap in children’s early years test scores. Due to the vast literature on children’s development, this section necessarily discusses various papers more or less briefly, and is unable to provide a comprehensive compilation. My aim is to provide an indication of

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the findings in the current literature as to the impact of each factor on children’s cognitive development. This not only helps identify relevant variables but also shapes a priori expectations as to their signs and significance levels.

Looking firstly at the effects of maternal labour force engagement, the results in the literature are very mixed, as some papers show negative effects, some show insignificant effects, and some show positive effects. For example, Baum (2003) finds negative effects of maternal employment, especially early maternal employment i.e. in the first year of the child’s life. However, he also finds that the negative effect is offset to some extent by the increase in family income. James-Burdumy (2005) uses blended child/family fixed effects and instrumental variable fixed effects methods and finds that there is some evidence of a negative effect of the mother working in the child’s first year of life, no effect in the second year, and a positive effect in the third year. Ruhm (2008) looks at how the effect of mother’s employment affects outcomes at ages 10 and 11 for different subgroups of the population, and finds substantial negative effects for youths from advantaged households compared to neutral or positive effects for disadvantaged youths. Furthermore, Waldfodel et al (2002), using data from the National Longitudinal Survey of Youth, finds some persistent adverse effects (lasting to age 8) of maternal employment in the first year of the child’s life and some positive effects of second- and third-year maternal employment on cognitive outcomes for non-Hispanic white children, but not for African American or Hispanic children. In summary, it appears there could be a negative effect early on which is less notable in the later years as the child grows up, and furthermore that children from well-off families where the mother possibly has high ability and more social capital suffer more from her absence, whereas children from less well-off families actually benefit more from the extra income her employment brings in. While most studies focus on maternal labour market engagement, some also consider the role of the father, e.g. Brown et al (2007) who find the father working long hours has a negative impact on the child’s time spent in language learning activities, especially in poor families. Gregg and Washbrook (2003) find that fathers are more involved in childrearing in

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households where mothers return to work early and that this involvement of the father has a positive impact on the child’s later outcomes.

Turning to family structure, it is clear that in the US, the UK and other developed countries, ever fewer children are growing up in households with two married, biological parents. The effects of changing family structure have been documented in various papers, for example, Gennetian (2005) uses US data and methodology that allows her to control for individual specific unobserved heterogeneity and finds that the role of family structure is modest compared to the well-documented influence of family income. In general, there is a strong relationship between family income and family structure and this affects the estimates of the impact of family structure related variables on child’s outcomes. Aughinbaugh et al (2005) for example, found that children from families with both biological parents scored significantly better on the BPI and the PIAT-math and PIAT-reading assessments than did children from non-intact families but that much of the difference disappeared when they controlled for background variables. In the same vein, Joshi et al (1999) found income to be among the factors which reduced the size and significance of family structure as a predictor of behavioural and cognitive outcomes.

One aspect of family structure that does appear to have a clear impact is the number of siblings. Hanushek (1992) examines the trade-off between child quantity and child quality, where child quality is defined in regards to cognitive achievement. Families are seen as making fertility related decisions to maximise their utility subject to the production function for child quality, a budget constraint and a time constraint. The empirical results show a systematic negative effect on achievement of increasing family size. This is due to the fact that the parents’ finite time allocation must be spread more thinly where there is a greater number of children. More recent studies of factors effecting children’s cognitive development also frequently show a negative impact of increased family size.

In terms of the characteristics of the child themselves, studies on gender are more profuse in relation to later achievement, at secondary school and following. The child’s month of birth does appear to have a significant impact, as for example in Melhuish et al (2008) who found that

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‘summer born’ tended to perform more poorly on National assessments at age 11, when compared to older, autumn born, children and were more likely to be identified as having a special educational need (SEN). This phenomenon is also known as the “August birth penalty” (Crawford et al, 2007) and occurs because the September cut-off for starting school in a given year makes August born children the youngest in their cohort. September born children must wait an extra year before they start and will be the oldest in their year.

Ethnicity is a strong predictor of children’s outcomes. There is an extensive literature on the black-white test score gap, especially from the US, for example, Fryer and Levitt (2005) uses a recent US longitudinal database, the Early Childhood Longitudinal Study, and reconfirms previous findings about the growth of the test score gap during the school years. They explore several hypotheses as to the cause of the gap but find that none are supported by the empirical evidence. Hanushek and Rivkin (2005) however, demonstrate some strong links to school quality. The different ethnic mix in the US and the UK makes comparison of the performance of other ethnic groups more difficult, for example, “Asian” in the US generally means Chinese, who tend to outperform Whites in educational attainment, whereas in the UK, “Asian” refers more often to people of Bangladeshi, Indian and Pakistani origin.

Another important group of factors relate to the parents investment in physical goods or particular activities which are beneficial for their child’s development. This could include books and toys in the home, and better quality pre-school or tutoring. It is also related to what parents are able to buy more generally, for example, if the family owns a car or a home computer, since this can also impact on the child’s development. A possible theoretical framework for parents investing in their child is based on the Becker-Tomes model where parents invest in their children’s education because they care about their children’s future well-being, investing up until the point that marginal benefit equals marginal cost (Becker and Tomes, 1986). If there were no credit constraints, parental income should not influence child outcomes, however, as this seems unlikely, (since not all families will be able to self-finance the investment or borrow against future

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earnings), poorer families may well not be able to invest optimal amounts. Datcher-Loury (1989) included measures of the child’s ownership of books and toys and also considered paints, records, musical instruments, a children's dictionary or encyclopaedia, and puzzles and found some evidence of a positive impact (although parental behaviours appear to be more important).

Several factors surrounding the birth of the child have been found to have a persistent impact, which is precisely measurable several years later. Breastfeeding, for example, is a current topic and several papers have even employed an instrumental variables technique to try to identify the causal impact of this factor. These include Doyle and Denny (2010) who use emergency caesarean section as their instrument and test the impact of breastfeeding on cognitive skills at young ages; and Fitzsimons and Vera- Hernández (2012) who use being born on the weekend as their instrument (arguing that hospitals cut-down on non-essential services such as breastfeeding support on weekends and this significantly reduces the likelihood that a mother will start to breastfeed), and find that breastfeeding has large positive effects on cognitive development, especially for children of less educated mothers. There is also a growing literature on the long run impacts of higher birthweight, for example, Behrman and Rosenzweig (2004) use data on monozygotic twins to demonstrate the impact on schooling level (and adult height).

Another important area of research is the effect of parenting behaviours. Melhuish et al (2008) explores this in some detail. Their decision to focus on parenting practices (as well as the influence of pre- school) is based on research that shows that parenting practices such as reading to children, using complex language, responsiveness, and warmth in interactions are all associated with better developmental outcomes (Bradley, 2002), are more frequently practiced by higher SES parents (Hess et al, 1982), and that between 20–50% of the variance in child outcomes can be accounted for by differences in parenting (Conger et al, 1992). In their own work, they use parents’ responses on the survey questions relating to the frequency of performing certain activities with the child to construct a Home Learning Environment (HLE) index. They find that

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the HLE coefficient is statistically significant for both numeracy and literacy achievement at age 5 and there is some evidence of the effects persisting until age 7. The results clearly support the importance of the HLE, as the influence of the HLE was over and above that of standard proxy measures of parental education and SES. Sylva et al (2008) also find a positive effect of the home learning environment and write “what is surprising is the continuing strong influence of the early years HLE” and “What parents do is therefore vitally important and can counteract other disadvantaging influences”. Several studies have found that differences in the home environment, as measured by the HOME scale (which includes items on household resources, such as reading materials and toys, and 4 parental practices, such as discipline methods), account for a substantial portion of the effect of income on the cognitive development of preschool children and on the achievement scores of elementary school children (e.g. Duncan, et

al, 1994).

The influences on a child’s development can be seen as starting with the child’s own characteristics, their family and home environment, as well as influences from a broader sphere such as their neighbourhood and the society as a whole. Studies which have examined the influence of the neighbourhood include Sonbonmatsu et al (2006), on the effects of the US “moving to opportunity” lottery, who found the change of neighbourhood did not produce any significant effects on the reading or maths test scores of the children of families assigned housing vouchers by the lottery; Ginther et

al (2000), who find that the effects of neighbourhood on children’s cognitive

test scores are heavily dependent on how well unobservables are controlled for; Gagne and Ferrer (2006), using data for Canada, who find that poor neighbourhood quality has negative effects especially for girls (and that home ownership has a positive effect); and Mohanty and Raut (2009), using the PSID Child Development Supplement and the corresponding PSID main data sets, who find positive significant effects of home environment, neighbourhood quality, and residential stability on the reading and math performance of children between the ages of three and twelve (but no significant effect of home ownership).

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These are just a few of the many papers that have been written on each of these topics, and there are also many other possible influences to be explored. The aim here has been to give an indication of the key factors identified in the literature and if current work on these tends to show a consensus on whether there is a positive effect, a negative effect, or no effect. Some papers on the same issues differ in their findings, this could be due to the fact that they explore data from a different context, use different methodology and control for a different array of covariates. Nonetheless, there is a relatively clear consensus on many of the factors discussed above. My own results in the results section will be discussed in the light of these findings and build on them further to contribute the understanding of the factors that have an important influence on children’s early years cognitive development. The next section describes the data set used to explore the effects of these various factors.

4.3 Data