intergenerational correlations remains undiminished in the daughter's sample even after assuming that education and assets are endogenous to non-farm participation decision (columns 2a & 2b). As before, having either (ne) or both _(nb)_ parents employed in the non-farm sector increases daughtet's probability of non-farm participation significantly. However, having only mother _(n_<sup>_m_</sup> _)_ in the non-farm sector has the strongest effect while only father _(nf)_ has no appreciable influence on daughter's probability of non-farm participation. The estimated marginal effects are only slightly smaller compared with the estimates in columns la and lb. Even if we focus on the most conservative estimates (columns 2a & 2b), the intergenerational correlations in the case of daughter remains quite substantial. For instance, switching the indicator variable representing either parents employed in non-farm sector from zero to one raises daughter's probability of participation in non-farm sector from 0.15 to 0.24, providing an estimated marginal effect of 0.09. This represents nearly 63 percent increase in daughter's probability of being employed in non-farm sector if either parent was employed in the same sector. The effect of mother's nonfarm participation (n<sup>m</sup> ) on daughter's probability is stronger still: it raises daughter's probability of being in non-farm employment by 203 percent. If both parents were employed in non-farm sector _(nb),_ it increases daughter's probability by 169 percent. These estimates suggest that intergenerational correlations in employment choice is rather dramatic for daughters. Indeed, only the level of education has an impact on non-farm participation stronger than those observed for either both parents in non-farm sector (nb) or only mother in non-farm sector (nm).<sup>3</sup> 0 In contrast with the results for daughters, the moderate positive correlations observed between parents and sons (columns 3a and 3b) turn out to be non-robust once the endogeneity of education and assets are recognized. When endogeneity of education and assets is properly accounted for, the TSCMLE results show that the estimated coefficients have expected positive signs (4a and 4b), but their magnitudes decline considerably and they are not statistically significant, separately or jointly.<sup>3</sup> 1 The results from exogeneity tests indicate that expected occupation plays an important

> 30The level of education has a marginal effect of 0.29 on daughter's probability of non-farm participation. This result might seem counterintuitive given that education for girls is not geared to labor markets. However, there is no contradiction here. The result only shows that once a daughter gets education the probability that she will choose employment and actually be employed in the non-farm sector is much higher, whatever the original purpose of education might be.

> 31Endogeneity correction greatly enhances the impact of education on non-farm participation; the marginal effect of education on non-farm participation probability increases from 0.03 (in 3a & 3b ignoring endogeneity problem) to 0.20 (in 4a & 4b with proper correction for endogeneity).

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