A plausible example of this situation is when social norms result in imitation behavior. Women are indeed more likely to participate in the labor force when many of their relatives, friends and neighbors participate. In this case, participation rates increase, other things equal, when employment levels are high. And this should be true across all localities, independently of the tightness of their labor markets. If so, the effect of job opportunities will arguably be overestimated. The purely economic incentive that local employment levels provide for women to participate in the labor force will be amplified by imitation behavior. It is not clear that this in itself makes the estimates irrelevant, as policy makers may be interested in knowing what impact job creation has on female labor force participation regardless of whether that impact takes place through economic or social channels. But for those interested in the economic channels only, one alternative is to redefine Lj so that it captures the structure of local employment by type of job, rather than the number of jobs relative to the working-age population.

This alternative only provides a lower bound for the estimate of the effect of job opportunities on labor force participation. Indeed, Model D implicitly combined the effect of India’s jobs deficit with that of its rapid decline in farming jobs, while this alternative only captures the latter effect. But an estimate based on the redefined Lj has the merit of not suffering from endogeneity. If this lower-bound estimate is statistically significant, it is safe to conclude that job opportunities matter for female labor force participation.

The results obtained when using this alternative approach confirm that the five hypotheses advanced by this paper hold. The regressions are run using the instrumental variables approach outlined above, when discussing spurious correlation, to address the omitted variable bias problem.<sup>9</sup> The results are very similar to the ones presented in the previous estimation using the instrumental variable (table 5, fourth column, Model D3). The effect of the employment variables is an exception.

Even when using this lower-bound approach, it appears that the composition of employment at local levels does affect female labor force participation. The marginal effects of all types of jobs remain statistically significant, a point that was confirmed by replacing the type of job chosen as a default (non-farm regular wage employment is set as the default category). The availability of farming jobs and casual wage jobs matter more than non-farm regular jobs. One plausible explanation for this could be the lack of flexibility that regular, full time jobs offer to women. The literature on female labor force participation in advanced countries often makes the argument that women prefer “service sector” or “white collar” jobs. But in the context of a patriarchal society like India, where women bear the bulk of the child care and domestic work burden, the flexibility offered by working at home or on the farm, or farm casual jobs, may outweigh the benefits offered by a regular, full-time job. This hypothesis is supported by the NSS data on the

> 9 The employment shares in the Census are constructed relative to the total employment in the district, as opposed to the share employment in working age.

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