Table 6 presents the simulated impact of the matched imputations of height-forage z-scores for all children ages 0–36 month who did not survive till the age of 36 months. The average z-score is higher for children with measured weight at birth than for children with mother-assessed weights at birth reflecting wealth differences.

In 1992, the average height-for-age z-score for boys who survived past age 36 months was –1.913. When the matched z-score estimates are imputed to children who died before age 36 months, the overall average height-for-age z-score becomes –1.936; the difference of –0.023 is not statistically significantly different from 0. Similar differences are observed for other years. In all years, for both boys and girls, the imputations have no significant impact on the overall anthropometric indices. In all cases, the average imputed height-for-age z-score in the simulations based on the survival model is smaller than the imputed z-score that would result in a statistically significant change in the overall height-for-age z-score as shown in first panel of table 2<sup>12</sup> . These results resonate with other studies that use longitudinal data from surviving and nonsurviving children and find a modest amount of selection via child mortality on heightfor-age z-scores i.e., Boerma et al (1992), Moradi (2010)<sup>13</sup> .

The effect of the change in mortality on gender patterns of height-for-age z-scores was also simulated using the results of hazard function estimations. This simulation closes the gender gap in mortality in the age 3–36 month group by artificially increasing the mortality among boys<sup>14</sup> . The simulation assumes that 144 of the boys in the sample with the lowest probabilities of survival did not survive and thus did not contribute to the observed height-for-age z-scores. The simulation of this increase in boys‘ mortality on the data from 1992 of NFHS demonstrates a 0.12 percent increase in the height-for-age z- score. This clearly has a negligible impact on the difference in nutritional status between boys and girls.

Finally, taking our model a step further, we simulate the health outcomes of Indian children if the mortality rates in India were as high as one of the highest rates current in Africa. In this simulation we change the surviving status of living children with lowest probabilities of survival to reach the mortality rate of 15 percent for both boys and

> **12** Table A2 in Appendix presents the simulated impact of the matched imputations of height-for-age z- scores for children ages 3–36 months who did not survive till the age of 36 months. Neonatal mortality was excluded in the hazard estimates as it could have a different set of correlates, but the results are not particularly sensitive to the exclusion of this age group.

> **13** Our empirical model fails to account for the possible selection bias due to high levels of maternal mortality in India (e.g., Ronsmans and Graham 2006). Correction of this bias would increase the gap in anthropometric outcomes between the deceased and living children. Unfortunately, the nature of IFHS sample makes it difficult to look on the relationship of maternal mortality and children health outcomes.

> **14** In principle, one could, simulate a more desirable decrease in girls‘ mortality to close this gap, but it is more direct to use the estimates from boys actually in the sample then to make projections for girls who would otherwise have been in the sample.

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