Within-family variation in obesity

Price, Joseph; Swigert, Jeffrey

doi:10.1016/j.ehb.2012.04.013

Cited by 13 publications

(12 citation statements)

References 28 publications

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“…6 The distributions in Figure 1 show that there is a large di¤erence in BMI overall regardless of the sibling types. This is consistent with the results in Price and Swigert (2012) that analyze a sibling sample from the National Longitudinal Survey of Youth 1979 Survey. The family …xed e¤ect estimation, however, has limited ability to remove the omitted variable bias.…”

Section: Empirical Modelsupporting

confidence: 80%

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The long-run effect of education on obesity in the US

Kim

2016

Economics & Human Biology

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The proportion of obese population has been gradually increasing in the US over the past few decades. In this study I investigate how education is associated with Body Mass Index (BMI) in later stages of life. BMI, weight(kg)=height(m) 2 ; is the principle measure used for classifying people as obese. Using sibling data and methods that take account of unobserved endowments and environment shared by siblings, I …nd that there is large variation in BMI between siblings and that education is negatively associated with BMI. One more year of schooling is associated with an estimated reduction of 0.15 in BMI. When considering di¤erent education levels, completing college education is associated with 0.7 reduction in BMI relative to high school graduation only. The signi…cant e¤ect of education on obesity that remains in the long-run has policy implications.

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Section: Empirical Modelsupporting

confidence: 80%

“…This implies there is variation in these key variables. Following Price and Swigert (2012), I next examine the distribution of BMI di¤erence between siblings. The Figure 1 plots distributions of the BMI di¤erence by sibling types using a kernel density.…”

Section: Empirical Modelmentioning

confidence: 99%

The long-run effect of education on obesity in the US

Kim

2016

Economics & Human Biology

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“…First, individual unobserved characteristics can be controlled for using data that include information about identical twins (Webbink et al, 2010;Price and Swigert, 2012). Since identical twins share the same genetic inheritance from their parents and growing environments, any differences in their obesity status must be accounted for by other factors.…”

Section: Methodsmentioning

confidence: 99%

Childhood obesity and the income gradient: evidence from Australia

Nghiem

Khanam

2016

Applied Economics

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This paper examines the dynamic nature of human capital formation in the context of childhood obesity and the association of household income and childhood obesity in Australia using the first five waves of the Longitudinal Study of Australian Children (LSAC). Our results show a strong evidence of dynamic nature of child obesity: the lag obesity is a significant and robust predictor of obesity in the current period. We also found that the main channel for childhood obesity is inter-generational trait: the probability of obesity in children born to an obese mother or father is 15% higher than that of other children. Other important determinants are lifestyle factors, including the consumption of drinks with a high sugar content and the amount of time watching TV. Income becomes an insignificant determinant of childhood obesity once we control for unobserved individual heterogeneity.

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“…Thus, for many if not most psychological constructs of interest, the variation observed in the population arises from the variation within families. This pattern of substantial within-family differentiation is seen in many attributes pertaining to a wide variety of psychological and health outcomes—and testing environmental effects using only between -family variation results in biased estimates (Almond, Chay, & Lee, 2005; Conley & Glauber, 2008; Gottfredson, 2004; Price & Swigert, 2012). …”

Section: Introductionmentioning

confidence: 99%

Is self-regulation “All in the family”? Testing environmental effects using within-family quasi-experiments

Deater‐Deckard

2016

International Journal of Behavioral Development

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Most of the individual difference variance in the population is found within families, yet studying the processes causing this variation is difficult due to confounds between genetic and nongenetic influences. Quasi-experiments can be used to test hypotheses regarding environment exposure (e.g., timing, duration) while controlling for genetic confounds. To illustrate, two studies of cognitive self-regulation in childhood (i.e., working memory [WM], effortful control [EC], attention span/persistence [A/P]) are presented. Study 1 utilized an identical twin differences design (N = 85 to 98 pairs) to control for genetic differences while using relative twin birth weight difference to predict relative twin difference in WM and EC. Larger relative twin difference in WM and EF was predicted by the combination of shorter gestation and larger relative birth weight difference. Study 2 utilized an adoptive sibling relative difference design (N = 123 same-sex pairs) to control for genetic similarity while using relative sibling difference in the age at time of adoption to predict relative sibling difference in A/P. Larger relative sibling difference in A/P was predicted by the combination of larger relative difference in time in the adoptive home and age at adoption. Within-family quasi-experimental designs allow stronger inferences about hypothesized environmental influences than between-family designs permit.

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Within-family variation in obesity

Cited by 13 publications

References 28 publications

The long-run effect of education on obesity in the US

The long-run effect of education on obesity in the US

Childhood obesity and the income gradient: evidence from Australia

Is self-regulation “All in the family”? Testing environmental effects using within-family quasi-experiments

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