data from 1997 to maintain consistency in the number of districts used in the analysis.<sup>1</sup> We use this data to construct an aggregate district-level measure for annual farm productivity. Specifically, we define _FarmProd_ for district _d_ and year _t_ as: In (1), _c_ refers to the crop while _Prod_ denotes the output, measured in thousands of tons for each crop. _Area_ denotes the area alloted to the crop in the district for that year, measured in thousand of hectares. As we are aggregating across 14 crops, we use the federally administered minimum support price (MSP) as the crop-specific weight to convert the cropspecific yields into an uniform monetary value (scaled by area). The MSP provides the minimum price for 22 major crops in India, including both food and non-food crops. The prices are set by the federal government each year prior to the cropping season, allowing producers to know in advance the minimum price guaranteed by the government for their output. As the MSP is binding across all states and districts in India, it provides a national measure of prices which we use to convert the crop-specific yield measure into an uniform monetary value. As the MSP prices are measured as rupees per quintal, our productivity measure is scaled accordingly and measured as rupees per quintal.

Monthly data on district rainfall is also provided by ICRISAT. We aggregate the monthly data into annual data by summing monthly rainfall across all months. We use the two-decade long rainfall data for each district to obtain district-specific rainfall distributions, which we subsequently use to define positive and negative shocks for each district-year combination in Section 3.

Table 1A shows the summary statistics from the ICRISAT data. We see that 54% of district area is used for agricultural activities while 20% of the area in the average district is unsuited for agricultural production. On average, almost 50% of a district’s area under cultivation is irrigated. Over this period, a district’s probability of receiving a positive rainfall shock was 0.15, and a negative rainfall shock, 0.19.

# **2.2 Non-Farm Enterprise Data**

Our data on non-farm enterprises comes from two rounds of a nationally representative survey on unincorporated enterprises. These surveys are conducted by the National Sample Survey Organisation (NSS) in 2010-11, and 2010-15, and includes data from every state and district in India. The surveys are in the form of repeated cross-sections and the enterprises covered are enterprises not registered under the Companies Act of 1956. Loosely, the survey can be considered to cover informal enterprises in India.

Each survey covers in excess of 290,000 enterprises and we restrict our sample to a total of 13,000 enterprises located in the state of Rajasthan. The surveys provide enterprise weights which can be applied to make the figures representative at the national level. Based on the enterprise weights, the 2010-11 survey covered a total of 50 million enterprises and 100 million workers, reflecting approximately a fifth of the national workforce.

> 1 Three new districts were formed in the 1990s in Rajasthan, limiting us to using data since 1996.

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