Steckel (2009) suggests that this impact of mortality on the height of survivors can be tested through simulations. The current paper is in keeping with this strategy. Although it does not simulate the effect of sample truncation on nutritional status across countries, it does employ simulations at the individual level that can indicate whether selective mortality might mask improvements in nutritional status over time in a country where mortality rates have been declining rapidly or whether truncation might bias comparisons across genders. Three rounds of nationally representative health survey data from India are used to simulate the nutritional status of the child population under the polar counterfactual that all children who died in the first three years of life are alive at the time of measurement.

The objective of this study is similar to that of studies by Boerma et al (1992), Pitt (1997) and Dancer, Rammohan, and Smith (2008), although the approach differs. Boerma et al. use data from longitudinal studies and retrospective data from crosssectional studies in 17 countries to analyze the effect of selective survival on children‘s anthropometric measures. Their study concludes that selective survival has only a marginal effect on the comparisons of anthropometric outcomes across geographic areas, subpopulation and time. Pitt recognizes that children who fail to survive through childhood are not a random draw from a population and furthermore that fertility itself is a choice. He addresses this by simultaneously estimating the probability of these two events along with nutritional status and finds that although fertility and mortality are statistically significant determinates of nutrition there is no behaviorally significant bias in the parameters if this selection is ignored. Dancer, Rammohan, and Smith use a selection correction to estimate models of nutritional status and find that survival is positively associated with nutritional status. That is, they find that scarring, to use Deaton‘s terminology, is more prevalent in the sample than is selection.

Similarly, a paper by Gorgens et al. (2007) finds evidence that extremely high mortality rates during the 1959-196 famine in China impacts trends in adult height in rural areas, but the affect of the estimated selection is relatively small. At the same time, no significant mortality bias was found for urban population in China. Bozzoli, Deaton and Quintana-Domeque (2009) also demonstrate that the selection could have a significant positive impact on adult height at very high mortality rates<sup>2</sup> . A recent paper by Moradi (2010) estimates the size of the selection affect of survival in Gambia and finds it to be too small to account for the tall adult heights observed in Sub-Saharan Africa.

> **2** The paper presents several specifications of the regression of adult height on pre-adult mortality rates and mortality rates squared. The coefficients on the linear mortality rates are negative and those on quadratic mortality rates are positive and significant. The estimated inflection rate after which higher pre-adult mortality rates have a positive effect on adult height varies by specification, but for all specifications this rate is outside of the data range (i.e. at or above pre-adult 250 deaths per 1000).

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