BASIX insurance policies are sold in April and May, which are the months that precede the monsoon in India. Insurance policies cover only one season, so customers must purchase insurance again if they want coverage for the following year.

Table 2 presents summary statistics for the insurance policies studied.

# **2.2 Data**

The data set consists of the entire set of BASIX’s purchasers of rainfall index insurance from 2005-2007, which covers six states.<sup>4</sup> Though it ran small pilots in 2003 and 2004, BASIX began to mass-market rainfall insurance starting in 2005. The data contains limited personal information about each customer including their location, how many policies they purchased, and what payouts they received during that season. The BASIX data covers 42 weather stations, and includes a total of 19,882 customers from 2005-2007.<sup>5</sup> After numerous rainfall shocks in 2006, BASIX realized that many customers who had purchased only a small amount of insurance were disappointed that they received small payouts. In response, BASIX instituted a rule in 2007 that required all customers to purchase insurance coverage with a maximum payout of at least Rs 3000. This was meant to encourage people to buy a level of coverage that would actually provide meaningful payouts in the event of a shock, but resulted in a sharp decrease in the number of customers in 2007. A summary of characteristics of BASIX customers is given in Table 3.

For rainfall data, I use a historical daily grid of rainfall, which is interpolated based on readings from thousands of rainfall stations throughout India. The data is provided by the Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of water resources.<sup>6</sup> This data set has daily readings of rainfall from 1961-2004, at a precision of .25<sup>_◦_7</sup> . For each .25<sup>_◦_</sup> x .25<sup>_◦_</sup> block, the data contains the amount of rainfall in millimeters and the number of stations within the grid that contributed to the data. This data is used to evaluate how the insurance policies would have paid out historically, which can be used as a proxy for past rainfall shocks.

The three individual years of BASIX customer data were converted into a panel by manually matching individual customers using available identifying data. Errors in matching customers from year to year create the possibility of introducing non-classical measurement error into the analysis. In

> 4The states are, in descending order of number of buyers: Andhra Pradesh, Maharashtra, Jharkand, Karnataka, Madhya Pradesh, Orissa.

> 5Note that BASIX also sold many policies in the district of Deogarh in Jarkhand, and those buyers are omitted from this analysis. The reason for this is that the policy for Deogarh is heavily subsidized, resulting in a policy that is completely different from all the others. For instance, the Deogarh policy for 2005 has an expected payout of Rs 1140 compared to an average of Rs 149, although the policy does not cost more than average. Because of its incredibly generous terms, the Deogarh policy has huge payouts for all years of the study, and therefore does not seem to be ’normal’ enough to warrant inclusion in the main dataset. All the analysis below is performed excluding all buyers in Deogarh, though most results do not change substantially when it is included.

> 6APHRODITE’s water resources project; http://www.chikyu.ac.jp/precip.

> 7.25 _◦_ Latitude equals about 27.5km, while .25 _◦_ longitude varies by latitude. Over the range of latitudes in this survey it equals roughly 26km.

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