manufacturing and construction sector in country _j_ (the Gulf countries, Malaysia, and India). As with equation (1), I allow the errors to be correlated across time and across different villages in the same district.

The identification of this specification comes from variation in demand from different destination countries within a village for a given year as well as from the variation in demand across time for a village-destination pair. The fixed effects remove all other sources of variation that might confound the estimation.

In the next section, I present results of estimating equations (1) and (2) and discuss how the results relate to the theoretical framework outlined in Section 2.

# **5 Results and discussion**

## **5.1 A ‘push’: Rainfall shocks**

As discussed in the previous section, income shocks due to rainfall could have an ambiguous effect on migration. It increases wealth, making migration less desirable, but also relaxes liquidity constraints, which makes migration affordable. As suggested by Figure 2, the fraction of households that are induced by the rainfall shock in overcoming the liquidity constraints might be very small relative to the fraction of households that now desire migration less when the cost of migration is very high. The former proportion is likely to be higher when the cost of migration is relatively low.

Consistent with this interpretation of the framework, I find that rainfall shocks increase migration to India whereas it has no effect on migration to non-India destinations. As Table 4 shows, a one standard deviation increase in rainfall increases migration to India by 0.8 percentage points. This amounts to 31 percent increase in migration rate from its 2001 level, 19 percent of 2008 level, and 17 percent of 2010 level. On the other hand, rainfall has no impact on migration to non-India destinations. The estimated effects are small and statistically insignificant.

Since I have only three periods, arbitrary trends in normalized rainfall could be driving the results. Indeed, Table 3 and Figure 5 shows that normalized rainfall in the past year has been falling between 2001 and 2010, whereas migration rates have been increasing over this period. To check that the trend in rainfall is not driving my results, I extend the panel further back in time using district level migration rates from the census of 1981 and 1991 to proxy for the migration rates in the village in 1981 and 1991. Table 5 shows the results for this extended panel. The same specification produces almost identical results except for effects on India migration from urban areas (columns 1, 3, and 5). The effect for the urban areas is about half the size of the effect for rural areas. This could be because urban areas are less reliant on rainfall for their income<sup>17</sup> or, according to the theoretical framework, because the fraction of households affected by the liquidity constraint is smaller in urban areas. Adding a linear time trend only slightly reduces the point estimates. This indicates that time trends in rainfall are not driving the results.

In Table 6, I investigate whether rainfall shocks two years before the survey affects migration rates. I do this to capture the effect on migrants who left more than a year ago for whom it is not the last year’s rainfall but the rainfall two years ago, that matters. Baseline estimation of Equation (1) shows that normalized rainfall two years ago matters almost as much as the normalized rainfall last year for migration to India (columns 1, 3, and 5). But this relationship is

> 17Only 45 percent of urban households owns agricultural land compared to 87 percent of rural households.

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