composition, wealth index, religion, and caste; and the availability of community services and infrastructure. The descriptive statistics for these variables are presented in table 3.

Tables 4 and 5 show the coefficients of the Weibull proportional hazard estimation for children younger than age 36 months using three rounds of the NFHS.<sup>10</sup> Table 4 includes the child‘s weight at birth estimated by the mother; Table 5 uses weight measured at birth. The weight at birth is one of the important factors affecting neo-natal child mortality that combines the unobserved information about pre-natal conditions and shocks experienced by the mother and her child (i.e., Claeson, et al. 2000). The results of the two estimations are not directly comparable as one is a categorical variable (mother‘s assessment of weight at birth) and the other is a continuous variable (measured weight), but the implications are similar<sup>11</sup> . The remainder of the discussion focuses on the estimations based on the specification using mother-assessed weight at birth because the sample sizes are much larger.

The estimated hazard odds ratios on the control variables in the estimations of proportional hazard models reveal the expected relationship between child mortality and characteristics of the child, mother, and household. A child‘s gender has no effect on mortality hazard: mortality rates for boys are higher than for girls in the six months after birth and lower after that. A higher birth order has a negative impact on survival probabilities (Miller et al. 1992). Weight at birth, whether assessed by the mother or measured at birth, is a strong predictor of mortality. Children whose mother‘s assessed their weight as small (with an odds ratio greater than 1 on the dummy variable reported in table 4) and children with low measured weight (with odds ratios less than one on the continuous measured weight variables in table 6) are significantly less likely to survive than are children who weigh more at birth.

Children living in the wealthiest households and with better educated mothers have better prospects for survival than do children from poor households and with less educated mothers. In an inverted U-shaped relationship, children‘s survival improves with the mother‘s age till about age 40 and declines thereafter.

> **10** The specification with Weibull distribution is selected based on the comparison of Akaike (1974) information criterion values for specifications with exponential, Weibull, Gompertz, log-normal, loglogistic, and general gamma distributions.

> **11** Note that the interpretation of the numbers presented in tables 5 and 6 are different from the standard interpretation of the regression coefficients. The odds ratios are always positive; odds ratios that are less than one indicate that an increase in a particular factor reduces the probability of an event, while odds ratios greater than 1 mean that a particular factor increases the probability of an event. Correspondingly, the t- tests of the odds ratio test the null hypothesis of odds ratios being equal to 1. For comparability, we reestimated the hazard model shown in Table 5 with the continuous variable on weight at birth categorized as a binary indicator equal to 1 for children with weight at birth < 2,500 grams. The odds ratio for this dummy is less than 1 and significant, but the effect of being born with low weight is smaller compared to coefficient in Table 5. One explanation for this could be that children whose weight was measured at birth come from the wealthier families with better access to post-natal health care.

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