Person:

McGovern, Mark

This paper examines the relationship between stature and later life health in 6 emerging economies, each of which are expected to experience significant increases in the mean age of their populations over the coming decades. Using data from the WHO Study on Global Ageing and Adult Health (SAGE) and pilot data from the Longitudinal Ageing Study in India (LASI), I show that various measures of health are associated with height, a commonly used proxy for childhood environment. In the pooled sample, a 10 cm increase in height is associated with between a 2 and 3 percentage point increase in the probability of being in very good or good self-reported health, a 3 percentage point increase in the probability of reporting no difficulties with activities of daily living or instrumental activities of daily living, and between a fifth and a quarter of a standard deviation increase in grip strength and lung function. Adopting a methodology previously used in the research on inequality, I also summarise the height-grip strength gradient for each country using the concentration index, and provide a decomposition analysis.

The timing and sequencing of fertility transitions and early-life mortality declines in historical Western societies indicate that reductions in sibship (number of siblings) may have contributed to improvements in infant health. Surprisingly, however, this demographic relationship has received little attention in empirical research. We outline the difficulties associated with establishing the effect of sibship on infant mortality and discuss the inherent bias associated with conventional empirical approaches. We offer a solution that permits an empirical test of this relationship while accounting for reverse causality and potential omitted variable bias. Our approach is illustrated by evaluating the causal impact of family size on infant mortality using genealogical data from 13 German parishes spanning the sixteenth, seventeenth, eighteenth, and nineteenth centuries. Overall, our findings do not support the hypothesis that declining fertility led to increased infant survival probabilities in historical populations.

Few research papers in economics have examined the extent, causes or consequences of physical stature decline in aging populations. Using repeated observations on objectively measured data from the English Longitudinal Study of Aging (ELSA), we document that reduction in height is an important phenomenon among respondents aged 50 and over. On average, physical stature decline occurs at an annual rate of between 0.08% and 0.10% for males, and 0.12% and 0.14% for females—which approximately translates into a 2–4 cm reduction in height over the life course. Since height is commonly used as a measure of long-run health, our results demonstrate that failing to take age-related height loss into account substantially overstates the health advantage of older birth cohorts relative to their younger counterparts. We also show that there is an absence of consistent predictors of physical stature decline at the individual level. However, we demonstrate how deteriorating health and reductions in height occur simultaneously. We document that declines in muscle mass and bone density are likely to be the mechanism through which these effects are operating. If this physical stature decline is determined by deteriorating health in adulthood, the coefficient on measured height when used as an input in a typical empirical health production function will be affected by reverse causality. While our analysis details the inherent difficulties associated with measuring height in older populations, we do not find that significant bias arises in typical empirical health production functions from the use of height which has not been adjusted for physical stature decline. Therefore, our results validate the use of height among the population aged over 50.

The prevalence of low birth weight is an important aspect of public health which has been linked to increased risk of infant death, increased cost of care, and a range of later life outcomes. Using data from a new Irish cohort study, I document the relationship between birth weight and socio-economic status. The association of maternal education with birth weight does not appear to be due to the timing of birth or complications during pregnancy, even controlling for a wide range of background characteristics. However, results do suggest intergenerational persistence in the transmission of poor early life conditions. Birth weight predicts a number of outcomes at age 9, including test scores, hospital stays and health. An advantage of the data is that I am able to control for a number of typically unmeasured variables. I determine whether parental investments (as measured by the quality of interaction with the child, parenting style, or school quality) mediate the association between birth weight and later indicators. For test scores, there is evidence of non-linearity, and boys are more adversely affected than girls. I also consider whether there are heterogeneous effects by ability using quantile regression. These results are consistent with a literature which finds that there is a causal relationship between early life conditions and later outcomes.

Background Selection bias in HIV prevalence estimates occurs if non-participation in testing is correlated with HIV status. Longitudinal data suggests that individuals who know or suspect they are HIV positive are less likely to participate in testing in HIV surveys, in which case methods to correct for missing data which are based on imputation and observed characteristics will produce biased results.

Methods The identity of the HIV survey interviewer is typically associated with HIV testing participation, but is unlikely to be correlated with HIV status. Interviewer identity can thus be used as a selection variable allowing estimation of Heckman-type selection models. These models produce asymptotically unbiased HIV prevalence estimates, even when non-participation is correlated with unobserved characteristics, such as knowledge of HIV status. We introduce a new random effects method to these selection models which overcomes non-convergence caused by collinearity, small sample bias, and incorrect inference in existing approaches. Our method is easy to implement in standard statistical software, and allows the construction of bootstrapped standard errors which adjust for the fact that the relationship between testing and HIV status is uncertain and needs to be estimated.

Results Using nationally representative data from the Demographic and Health Surveys, we illustrate our approach with new point estimates and confidence intervals (CI) for HIV prevalence among men in Ghana (2003) and Zambia (2007). In Ghana, we find little evidence of selection bias as our selection model gives an HIV prevalence estimate of 1.4% (95% CI 1.2% – 1.6%), compared to 1.6% among those with a valid HIV test. In Zambia, our selection model gives an HIV prevalence estimate of 16.3% (95% CI 11.0% - 18.4%), compared to 12.1% among those with a valid HIV test. Therefore, those who decline to test in Zambia are found to be more likely to be HIV positive.

Conclusions Our approach corrects for selection bias in HIV prevalence estimates, is possible to implement even when HIV prevalence or non-participation is very high or very low, and provides a practical solution to account for both sampling and parameter uncertainty in the estimation of confidence intervals. The wide confidence intervals estimated in an example with high HIV prevalence indicate that it is difficult to correct statistically for the bias that may occur when a large proportion of people refuse to test.