School meal crowd out in the 1980s

This paper explores whether state provision of school meals in the 1980s crowded out private provision by examining two UK policy reforms that dramatically reduced school meal take-up. The paper examines whether this affected children’s BMI, using a large, unique, longitudinal dataset of primary school children from 1972 – 1994. This period is characterized by –for some– relative scarcity of foods. The reforms placed further constraints on some families’ already tight food budgets, leading to nutritionists expecting children to become malnourished. The findings however, show no evidence of any such effects. In addition, I find no support for the hypothesis of intra-household food reallocation. As some of those affected are relatively poor, and as sample sizes are often large with fairly precise estimates, the analysis should have been able to detect any effects. With no such evidence, this suggests that the state provision of school meals was crowding out private provision of similarly nutritious packed and home lunches.Crowd Out; School Meal Provision; Body Mass Index; Difference-in-Difference

Maternal Employment and Overweight Children: Does Timing Matter?

Recent literature has shown consistent evidence of a positive relationship between maternal employment and children’s excess body weight. These studies have largely focused on the effect of average weekly work hours over the child’s life on its overweight status. The aim of this paper is to explore the importance of the timing of employment. Timing of maternal absences has been shown to matter for child cognitive and behavioral outcomes. This paper explores whether this timing effect also exists with respect to children’s excess body weight. Data on a nationally representative British birth cohort are used to examine this, permitting a detailed exploration of the potential endogeneity of mother’s employment. The results show a significant positive correlation between full-time maternal employment during mid-childhood and the probability of being overweight at age 16. There is no evidence that part-time or full-time employment at earlier or later ages leads to a higher probability of being overweight at age 16. Subgroup analysis suggests this effect is driven by lower socio-economic groups. Various econometric techniques are used to explore whether employed mothers are systematically different from non-employed mothers, but there is no evidence that this unobserved heterogeneity biases the estimates.Childhood obesity; Maternal Employment; Timing of Employment; Overweight

Smarter Task Assignment or Greater Effort: the impact of incentives on team performance

We use an experiment to study the impact of team-based incentives, exploiting rich data from personnel records and management information systems. Using a triple difference design, we show that the incentive scheme had an impact on team performance, even with quite large teams. We examine whether this effect was due to increased effort from workers or strategic task reallocation. We find that the provision of financial incentives did raise individual performance but that managers also disproportionately reallocated efficient workers to the incentivised tasks. We show that this reallocation was the more important contributor to the overall outcome.Incentives, Public Sector, Teams, Performance

Genetic Markers as Instrumental Variables

The use of genetic markers as instrumental variables (IV) is receiving increasing attention from epidemiologists, economists, statisticians and social scientists. This paper examines the conditions that need to be met for genetic variants to be used as instruments. Although these have been discussed in the epidemiological, medical and statistical literature, they have not been well-defined in the economics and social science literature. The increasing availability of biomedical data however, makes understanding of these conditions crucial to the successful use of genotypes as instruments for modifiable risk factors. We combine the econometric IV literature with that from genetic epidemiology using a potential outcomes framework and review the IV conditions in the context of a social science application, examining the effect of child fat mass on academic performance.ALSPAC; Fat mass; Genetic Variants; Instrumental Variables; Mendelian Randomization; Potential Outcomes

Genetic Markers as Instrumental Variables:An Application to Child Fat Mass and Academic Achievement

The use of genetic markers as instrumental variables (IV) is receiving increasing attention from economists. This paper examines the conditions that need to be met for genetic variants to be used as instruments. We combine the IV literature with that from genetic epidemiology, with an application to child adiposity (fat mass, determined by a dual-energy X-ray absorptiometry (DXA) scan) and academic performance. OLS results indicate that leaner children perform slightly better in school tests compared to their more adipose counterparts, but the IV findings show no evidence that fat mass affects academic outcomes.Instrumental variables; Mendelian randomization; Genetic variant; Potential outcomes; Academic performance; Educational attainment; Adiposity; Fat mass; Body Mass Index; ALSPAC

Child height, health and human capital: evidence using genetic markers

Height has long been recognised as associated with better outcomes: the question is whether this association is causal. We use children’s genetic variants as instrumental variables (IV) to deal with possible unobserved confounders and examine the effect of child and adolescent height on a wide range of outcomes: academic performance, IQ, self-esteem, symptoms related to depression and behavioural problems, including hyperactivity, emotional, conduct and peer problems. OLS findings show that taller children have higher IQ scores, perform better in school tests, and are less likely to have emotional or peer problems. The IV results differ. They show that taller children have better cognitive performance but, in contrast to the OLS, indicate that taller children are more likely to have behavioural problems. The magnitude of these IV estimates is large. For example, the effect of one standard deviation increase in height on IQ is comparable to the IQ difference for children born approximately 6 months apart within the same school year, while the increase in hyperactivity is comparable to the raw difference in hyperactivity between boys and girls.Child and adolescent height; human capital; mental health; behavioural outcomes; instrumental variables; Mendelian randomization; genetic variants; ALSPAC

Genetic markers as instrumental variables: an application to child fat mass and academic achievement

The use of genetic markers as instrumental variables (IV) is receiving increasing attention from economists. This paper examines the conditions that need to be met for genetic variants to be used as instruments. We combine the IV literature with that from genetic epidemiology, with an application to child adiposity (fat mass, determined by a dual-energy X-ray absorptiometry (DXA) scan) and academic performance. OLS results indicate that leaner children perform slightly better in school tests compared to their more adipose counterparts, but the IV findings show no evidence that fat mass affects academic outcomes.

The use of instrumental variables in peer effects models

Instrumental variables are often used to identify peer effects. This paper shows that instrumenting the \peer average outcome" with \peer average characteristics" requires the researcher to include the instrument at the individual level as an explanatory variable. We highlight the bias that occurs when failing to do this

Genetic markers as instrumental variables

AbstractThe use of genetic markers as instrumental variables (IV) is receiving increasing attention from economists, statisticians, epidemiologists and social scientists. Although IV is commonly used in economics, the appropriate conditions for the use of genetic variants as instruments have not been well defined. The increasing availability of biomedical data, however, makes understanding of these conditions crucial to the successful use of genotypes as instruments. We combine the econometric IV literature with that from genetic epidemiology, and discuss the biological conditions and IV assumptions within the statistical potential outcomes framework. We review this in the context of two illustrative applications

The many weak instruments problem and Mendelian randomization.

Instrumental variable estimates of causal effects can be biased when using many instruments that are only weakly associated with the exposure. We describe several techniques to reduce this bias and estimate corrected standard errors. We present our findings using a simulation study and an empirical application. For the latter, we estimate the effect of height on lung function, using genetic variants as instruments for height. Our simulation study demonstrates that, using many weak individual variants, two-stage least squares (2SLS) is biased, whereas the limited information maximum likelihood (LIML) and the continuously updating estimator (CUE) are unbiased and have accurate rejection frequencies when standard errors are corrected for the presence of many weak instruments. Our illustrative empirical example uses data on 3631 children from England. We used 180 genetic variants as instruments and compared conventional ordinary least squares estimates with results for the 2SLS, LIML, and CUE instrumental variable estimators using the individual height variants. We further compare these with instrumental variable estimates using an unweighted or weighted allele score as single instruments. In conclusion, the allele scores and CUE gave consistent estimates of the causal effect. In our empirical example, estimates using the allele score were more efficient. CUE with corrected standard errors, however, provides a useful additional statistical tool in applications with many weak instruments. The CUE may be preferred over an allele score if the population weights for the allele score are unknown or when the causal effects of multiple risk factors are estimated jointly.This is the final version. It was first published by Wiley at onlinelibrary.wiley.com/doi/10.1002/sim.6358/abstrac