قد کودک، سلامت و سرمایه انسانی : شواهد با استفاده از نشانگرهای ژنتیکی

Height has long been recognized as being associated with better outcomes: the question is whether this association is causal. We use children's genetic variants as instrumental variables to deal with possible unobserved confounders and examine the effect of child/adolescent height on a wide range of outcomes: academic performance, IQ, self-esteem, depression symptoms and behavioral problems. OLS findings show that taller children have higher IQ, perform better in school, and are less likely to have behavioral problems. The IV results differ: taller girls (but not boys) have better cognitive performance and, in contrast to the OLS, greater height appears to increase behavioral problems.

The association between height and wealth has been noted in the academic literature for many decades. As early as the 17th Century, Guarinoni – one of the founders of preventive medicine – pointed to the difference in growth rates between the rich in towns and the poor in the countryside (Tanner, 1982). More recent studies find height to be positively related to education (Magnusson et al., 2006) and income (Persico et al., 2004). The advantages associated with greater height have also been reported for children. For example, Case and Paxson (2008) find that taller children perform better in school tests compared to shorter children and suggest that the relationship between childhood height and income and education in adulthood is due to height being associated with greater intelligence. One problem in estimating the relationship between height and outcomes is that the relationship may not be causal. Height is influenced by a wide range of environmental factors experienced in childhood which may be the determinants of the outcomes, rather than height per se, for example, unobserved family wealth or differences in children's nutrition. To the extent that some of these unobserved differences are family specific, one approach is to identify the causal impact from twin or sibling differences in height and outcomes. Case and Paxson (2010) use this approach, exploiting differences between siblings. They conclude that taller children perform better in school, progress faster through school and consider themselves more scholastically competent than their shorter siblings. However, accounting for fixed unobserved family effects using twin (or sibling) differences does not necessarily eliminate the inconsistency of the conventional cross-sectional estimator and can even aggravate it (Griliches, 1979 and Bound and Solon, 1999). The intuition is that taking twin or sibling differences filters out some, but not all, endogenous variation but also filters out exogenous variation. If the endogenous variation comprises as large a proportion of the remaining within-sibling variation as it does of the between-sibling variation, the parameters using within-sibling estimation are as vulnerable to endogeneity bias as that found in between-sibling estimation. For example, some potentially endogenous variation that may remain in a within-sibling estimation are unobserved differences between siblings in nutrition and physical activity, both of which affect growth and final attained height. To argue that the within-sibling estimator is more consistent than the between-sibling estimator, this endogenous variation as a share of the total variation should be less in the within than the between-sibling estimation. There is no reason to be confident that this is the case, as the within-sibling analysis also removes exogenous variation, which – together with the endogenous variation – determines the inconsistency (Bound and Solon, 1999). This paper therefore takes a different approach to estimate the causal effect of child height on children's cognitive and non-cognitive outcomes. Our approach is also called Mendelian randomization, which refers to the use of genetic variants as instrumental variables (IV) to examine the causal effect of an exposure (here height) on outcomes. It exploits the random assignment of an individual's genotype at conception (Davey Smith and Ebrahim, 2003) to enable genetic variants to instrument for a particular phenotype (the trait that the genetic variants is related to, e.g. height).1 At conception, genes are randomly allocated from parents to offspring. Whilst this random allocation is at a family trio level, at a population level it has been demonstrated that genetic variants are largely unrelated to the many socioeconomic and behavioral characteristics that are closely linked with each other and that confound conventional observational studies (Bhatti et al., 2005, Davey Smith et al., 2008, Kivimäki et al., 2008 and Lawlor et al., 2008). Furthermore, since genetic variation is determined at conception, it cannot be affected by later outcomes. Hence, in addition to dealing with fixed characteristics that affect both height and the outcome, Mendelian randomization can also deal with time-varying characteristics that affect height and outcomes. Therefore, under certain assumptions that we discuss below, genetic variants will allow us to isolate the causal effect of child height on the outcome of interest. This paper is the first to exploit genetic variants for height in an attempt to estimate the causal effect of height on cognitive and non-cognitive outcomes for children. We begin therefore by outlining the conditions needed to use genetic variants as instruments. To examine and indirectly test the validity of the IV approach in our context, we show first that the genetic variants are uncorrelated with a large set of family background variables which may confound the relationship between height and outcomes. We then discuss biological pathways of our genetic variants, and run two ‘falsification checks’. First, we examine the effect of height on an outcome for which we have clear theoretical reasoning that there should not be an effect (maternal education). And second, we investigate the effect of height on an outcome for which we have strong beliefs that there should be an effect (body weight). Finding no evidence against the validity of the instruments, we then use the genetic variants as instruments to examine the relationship between height and an extensive set of cognitive, mental health and behavioral outcomes. In so doing, we add to the range of outcomes examined in the previous literature. In addition to children's academic attainment, scholastic competence and self-worth studied by Case and Paxson (2010), we investigate the effects of height on IQ, symptoms of depression and behavioral problems, including hyperactivity, emotional, conduct and peer problems. Note here, that our IQ measure is an index of general intellectual functioning, which is shaped by both inherited and acquired attributes, including any family and environmental influences. In other words, it does not simply measure ‘innate’ ability. We use data from a cohort of UK children currently in their late teens (the ALSPAC survey, described below). The OLS results show that taller children perform better in school tests, have higher IQ, and are less likely to have emotional and peer problems, though these relationships differ slightly by gender. Tall girls have higher depression scores, but we find no evidence of differences in self-esteem for children of different heights. The IV results suggest there is a causal relationship between height and cognitive functioning, though only robustly for girls. In contrast to Case and Paxson (2010), we find no evidence that height explains variation in scholastic self-esteem, global self-worth or depression. Further, we find evidence that height confers disadvantage rather than advantage as it increases hyperactive behavior (girls), emotional and peer problems (boys). These findings are robust to a set of instrument specification and robustness checks. We discuss the results, relating back to the assumptions made in Mendelian randomization, and speculate about possible reasons for these findings. The next section begins by examining the possible mechanisms through which height may be related to the outcomes of interest. In Section 3, we set out our methodology and Section 4 describes the data. The results are presented in Section 5 and Section 6 concludes.

This paper is the first to exploit genetic variation in height to examine the causal effects of height on human capital accumulation. OLS results show that taller children perform better in terms of cognitive performance and are less likely to have emotional and peer problems (girls), though tall girls are more likely to show symptoms of depression. Using genetic variation in height in an IV specification, we attempt to deal with the problems of endogeneity. The IV findings for girls are similar to the OLS for cognitive performance, showing a positive effect of height on KS3 and IQ. However, we do not find this for boys, where the results are indistinguishable from zero. We also find no effects of height on self-esteem and depression symptoms. In addition, we find a negative relationship of height with behavior. This suggests that the OLS results are downwardly biased and that height increases rather than decreases these behavioral problems. Taller children are more hyperactive and are more likely to have emotional problems. In addition, taller boys are more likely to have peer problems, though there is a negative relationship for girls. This suggests that height is endogenous to cognitive performance and behavior, though perhaps less so to self-esteem and depression symptoms, for which the OLS and IV estimates do not differ substantively. We are unsure why height would be endogenous to some, but not other outcomes. This may simply be due to the large standard errors, precluding us from making more precise inferences. Alternatively, it may be that unobserved factors such as pre- and postnatal nutrition affect cognitive functioning and behavior, but not self-esteem or depression. We cannot distinguish between such potential explanations. In many of our results, the IV estimates suggest that OLS is biased downwards. One possible explanation for the difference between IV and OLS could be a genetic one. For example, (one of) our SNPs could be pleiotropic or in LD with another variant that directly affects IQ or cognition. Although our tests of associations with known confounders, our falsification checks, the ‘multiple IV test’, and the scientific literature do not give any reason to expect this to be the case, we cannot rule this out. For instance, it may be that our sample is too small to detect any association between the SNPs and the covariates, and it may be that any pleiotropic effects have simply not yet been identified, or that we do not observe the relevant confounders. From the evidence discussed in Section 3.2 and from the fact that we use only nine SNPs out of possibly hundreds or thousands SNPs coding for height, we assume that our assumptions hold. However, as in any other IV study, we cannot directly test this, and it remains an assumption. A possible explanation for our IV findings that indicate that being taller increases rather than decreases behavioral problems could be the differential treatment of children of different stature. A ‘size-appropriate’ rather than ‘age-appropriate’ treatment of tall children may trigger behavioral problems. Expectations and reactions to ‘tall-for-age’ children's (what may seem childish) behavior can in turn affect children's development. As factors such as socio-economic position are positively related to height and negatively related to behavioral problems, the OLS estimates will be downward biased if these factors are insufficiently controlled for. Though possible, these are speculations as we currently have no further evidence to confirm these. However, the finding of increased behavioral problems is consistent with the psychological literature that has shown a positive relationship between height and children's behavioral problems, though this literature has mainly examined outcomes such as aggression and violence ( Raine et al., 1998 and Farrington, 1989) rather than those we examine here. Finally, the IV effects for behavior and IQ are large: a one standard deviation increase in height raises these scores by about 0.2–0.7 standard deviations. Comparing these effects with those of other child characteristics shows they are substantial. For example, a 0.4 standard deviation difference in girls’ IQ (Table 9) is comparable to the difference in this score for girls born approximately 6 months apart within the same school year. Likewise, the difference between girls’ and boys’ raw hyperactivity scores is approximately 0.37 standard deviations which is similar to the estimated effect of one standard deviation increase in height on hyperactivity for girls. In conclusion, our findings suggest that height is an important factor in children's human capital accumulation in both childhood and adolescence, most likely as a result of the social reactions that are triggered by variations in height. We show that being tall may not only confer advantage but also disadvantage. Our examination of behavioral problems contrasts with the more positive view of height that emerges from the existing empirical literature on height and children's cognitive performance.