Specifically, for each district _d_ and year _t_ , we define the binary variable _PosShockdt_ equal to 1 if the annual rainfall in year _t_ falls in the top quintile of the district’s long-term rainfall distribution.<sup>5</sup> _NegShockdt_ is defined analogously, where the dummy equals 1 if the annual rainfall falls in the bottom quintile of the district’s long-term rainfall distribution. Annual rainfall incidences falling in the 2nd, 3rd and 4th quintiles of the district’s long-term rainfall distributions are classified as _ZeroShock_ . Based on this formulation of _PosShock_ , _NegShock_ , and _ZeroShock_ , we use the following empirical specification to identify the causal impact of quasi-exogenous weather shocks on farm productivity.

In (2), our outcome of interest is logged aggregate district farm productivity where _FarmProd_ is calculated as specified in (1). _α_ and _δ_ denote district and year fixed effects, controling for time-invariant unobservable factors affecting a district’s productivity (such as underlying soil quality), as well as time-varying shocks common to all districts (such as fluctuations in international crop prices). **X** is a vector of district-specific time-varying covariates such as fertilizer usage, share of cultivable land, population density, share of crop area that is irrigated, number of banks and a Simpson’s index in crop diversification which serves as a proxy for agricultural diversification.<sup>6</sup> – Our coefficient of interest is _β_ 1 the impact of a positive rainfall shock on district farm productivity. Since the outcome variable is measured in logs, _β_ 1 reflects a percentage change in district farm productivity in response to a positive rainfall shock. As we directly control for the impact of the zero-shock situation on farm productivity ( _ZeroShock_ ), _β_ 1 is benchmarked against years in which the district receives a negative rainfall shock.

# **3.2 Rainfall Shocks and Non-Farm Enterprise Performance**

The following empirical specification is used to identify the causal impact of quasi-exogenous weather shocks on enterprise performance: – _FE_ in specification (3) denotes our battery of fixed effects namely district, quarter, survey-year, and 4-digit industry fixed effects. These control for time-invariant district and industry specific characteristics, and common time-specific shocks affecting enterprise performance. **X** includes district and enterprise-specific covariates. Enterprise covariates include a quadratic in enterprise age and other enterprise characteristics. District covariates include district literacy, financial infrastructure, monthly per capita consumption, labor force participation rate, employment in public and private enterprises and number of census towns in the district. With the exception of financial infrastructure – measured as bank branches per

> 5 We construct _PosShockdt_ and _NegShockdt_ using rainfall data from a 40-year period between 1975 and 2015.

> 6 The Simpson’s index is computed by summing the square of the share of area allocated to each crop, and subtracting the sum of squares from 1. Higher values indicate higher levels of crop diversification.

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