matching of children to parents allows us to estimate both sibling and intergenerational correlations for the same sample of children. The second advantage of NFHS is that education data are more detailed. Instead of reporting education level in discrete intervals as is done in the case of NSS, NFHS collected information on years of schooling for both parents and children, which facilitates more precise estimates of sibling and intergenerational correlations.

To define the estimation sample, we follow the literature and restrict our sample to closely spaced young adult siblings between the age of 16 and 27 years. The argument for estimating sibling correlations from closely spaced siblings rests on the fact that there may be important changes in the family structure as well as shocks to family life over a longer time horizon diluting the already conservative estimate of family background on children‘s outcome. To check the sensitivity of our results, we report the estimates of sibling and intergenerational correlations for other age groups also.

While the sample for estimation of the sibling correlations should ideally focus on coresident children, it may bias the estimation of intergenerational link in education between parents and children. If, for example, among older children, the best educated ones tend to leave household earlier than less educated children, it may bias intergenerational regression coefficient β downward, but may not necessarily bias the estimate of intergenerational correlation. Because such exit of better educated children from the household would also reduce the variance in children‘s education, thus offsetting the decline in the intergenerational regression coefficients.

The problem of not observing all of the children as co-residents in the household is more prevalent in the case of older age cohorts, particularly for women who usually leave their natal household upon marriage. In the case of women, if educated women delay marriage and we have better probability of observing them as co-resident children, then estimate of intergenerational correlations from our sample may be biased upward. On the other hand, if marriage timing follows birth order and there is a substantial birth order effect as reported in Black, Deverux and Salvanes (2005) and Booth and Kee (2009), then estimates from our sample will be more on the conservative side. We address the issue of non-coresident children in two ways. We keep all singleton households in the sample. This is likely to reduce the bias in the estimate of intergenerational correlations by allowing the two opposing factors discussed above to offset each other. This also improves the precision of the estimate of individual component in the case of sibling correlation. Second, we check robustness of our results by estimating both sibling and intergenerational correlations from a sample of younger age cohort (16-20 year) where possibility of having non-coresident children is lower. Note also that for older age cohorts, the co-residency pattern changes, as it is the parents who coreside with children at old age. If parents tend to co-reside with better educated and well off children 12