the set of explanatory variables. As discussed in the theoretical model, human and<sup>financial capital</sup> variables are important links in the intergenerational transmissions of<sup>socioeconomicstatus.To</sup> isolate the effects of other 'intangible' factors that include the genetic<sup>transmissionsthrough</sup> endowment and preference and the role model effects, we introduce<sup>humanandfinancialcapital</sup> variables to control for the 'tangible' source of intergenerational<sup>correlations.Inadditiontothe</sup> level of education eX, we include age of an individual as a<sup>humancapitalvariablerepresenting</sup> the work experience.<sup>25</sup> Total asset owned by the household<sup>isalsointroducedasaregressorto</sup> control for access to capital. Transfer income (remittances)<sup>receivedbythe householdandtravel</sup> time to nearest commercial bank are also included as additional controls<sup>foraccesstocapital.</sup> We include individual's marital status to account for taste and/or<sup>genderrelateddifferences.</sup> In Table 2, columns 3a -3b and 6a-6b present the results for the daughters'<sup>andsons'samples</sup> respectively ignoring the existence of potential endogeneity of human<sup>capitalandassets.26While</sup> individual, household level and network variables have statistically significant<sup>effectson both son's</sup> and daughter's probability of non-farm participation, the results<sup>on intergenerationalcorrelations</sup> in occupation improve slightly for sons and remains nearly unchanged for daughters.<sup>For instance,</sup> having either parent in the non-farm sector raises son's probability of non-farm<sup>participationby</sup> 0.09 which is slightly higher than the marginal effect [0.07] reported in<sup>column5a of Table2.</sup> The inclusion of a large number of variables as additional controls<sup>does</sup> not<sup>affectthestrength</sup> of parental influence on son's and daughter's non-farm participation<sup>significantly.Thissuggests</sup> that parent's occupation choice variables did not act as proxies for individual's<sup>humancapitalor</sup> access to financial capital or household and network variables.

# [See Table 3]

> 25In addition, age and its squared term will capture any cohort effect also.

> 2"If parental occupation dummies were excluded from these regressions, 3a-3b and 6a-6b (Table 2) would correspond directly to the standard specification used in the available literature (Ferrira<sup>andLanjouw,2001;Lanjouw,</sup> 2001, Lanjouw and Shariff, 2000). Consistent with the findings of these studies,<sup>we findthat an individual'sproba-</sup> bility of non-farm participation varies significantly and positively<sup>withthelevelof education(marginaleffectequal</sup> to 0.07 and p-value=0.0 for daughters and to 0.03 with p-value=0.03 for sons).<sup>Apartfromthe ethnicitydummies,</sup> having a migrant in the household also significantly improves son's but not<sup>daughter'sprobabilityof participation</sup> in the non-farm sector suggesting strong network effects in securing a non-farm<sup>jobformen.On theotherhand,</sup> participation in non-farm sector varies negatively with household size<sup>in</sup> the<sup>caseof daughtersbut has no significant</sup> relationship with household size for sons. Participation in non-farm sector<sup>variesnegativelywithownershipof as-</sup> sets (mainly agricultural land) implying some occupational following<sup>inagriculture.Ethnicitydummiesare jointly</sup> significant for both sons and daughters. Most of the other variables<sup>haveexpectedsignsandimpactsonnon-farm</sup> participation.

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