– the coefficient is significant at the 1% level. The coefficient is also economically significant as household per capita consumption when households do not receive a positive precipitation shock is INR 2,211 (USD 33.5), the coefficient implies that a positive rainfall shock increases per capita monthly household consumption by INR 133. As the average household size is 5, this implies an increase of INR 8,000 (USD 121) per household on an annual basis.

Columns (2)-(6) use the occupation classifications in the CP data to identify which households drive the consumption response to positive rainfall shocks. We interact a dummy for each occupational classification of interest with the indicator of positive rainfall shock to test for differential responses to positive rainfall shocks. We see that except for households where at least one adult is self-employed or engaged in small business, the remaining households do not exhibit any differential consumption response to positive rainfall shocks. As the sum of the coefficients is not statistically significant in columns (4) and (5), it suggests consumption in households with individuals engaged as qualified self-employed professionals, or business owners, do not respond to precipitation shocks. The sum of the coefficients in the remaining columns are all statistically significant at the 5% level, confirming an aggregate increase in per capita monthly household expenditures for these households.

Table 7 shows the corresponding impact for urban households. We note that while the coefficient on positive rainfall shock is positive, it does not achieve statistical significance in any of the specifications. The only category of urban households for which a positive rainfall shock disproportionately increases household spending is households with at least 1 member employed in the informal sector. This signifies that positive rainfall shocks in our sample affect household consumption in rural areas, but have a muted impact for urban households.

Tables 8, 9 and 10 focus solely on rural households and examine the source of increase in average monthly per capita expenditures identified in Table 6. We note that consistent with the evidence shown in the binned scatter plots, rainfall shocks have little impact on household food and essential food expenditures (Tables 8 and 9). Only households with any member employed as a wage laborer or employed in informal activities register a significant increase in either of these expenditure categories in response to a positive rainfall shock. On the contrary, consistent with the descriptive evidence in Figure 3, Table 10 records a positive and statistically significant impact of a positive rainfall shock on luxury expenditures. As seen in columns (2)-(6) of Table 10, this increase is observed amongst all categories of households, suggesting that the increase in monthly per capita consumption for rural households is driven by luxury expenditures. As luxury expenditures include snacks, beverages, packaged foods, sweets and dry fruits, the findings in this section are consistent with those in Section 4.2, where we saw that rainfall shocks had a positive impact on the revenues of rural non-farm enterprises engaged in retail trade.

# **5 Policy Implications**

Our empirical results allow us to draw four clear policy implications. The first is that rural non-farm enterprises comprise a major source of employment in the rural economy. Applying the survey weights to the 2015 survey round, the sample indicates the presence of 2.6 million – enterprises, providing employment to 4 million workers approximately 16% of the rural workforce as per Census 2011. Despite being such a large component of the economy, these 13