which shows that villages that experienced a rainfall shock in 2004 actually had an average of 3.8 _fewer_ purchasers in 2005. One worry with this regression may be that since the insurance policies and rainfall patterns of each location are different, the definition of a shock may vary from one place to another. Therefore, the estimates may be improved with the inclusion of location and policy-specific covariates, which I title “Weather Station Constants”. In Column 2 I add controls for the historical average rainfall, historical rainfall standard deviation, the policy premium in 2005, historical average payout of the policy, and the percentage of historical years there would have been a payout. Note that all the “historical” data is calculated from 1961-2000. With the addition of these controls, the coefficients on having a rainfall shock in 2004 remains negative, and even decreases slightly.

Following previous results that suggest that the size of the insurance payout is important, in Columns 3 and 4 I include variables for the severity of the shock in 2004 using the ratio of the hypothetical payout to the premium (the payout ratio) and the payout ratio squared. In both specifications these variables are insignificant, suggesting that most of the variation in purchasing in 2005 is explained by the binary shock variable.

The main conclusion to be drawn from these regressions is that the data does not support the hypothesis that bad weather induces people to purchase insurance in the following season. If anything, bad weather seems to decrease insurance purchases. I can only speculate on the reasons for this; it may be due to the fact that people recognize the actual negative autocorrelation of rainfall, or it may be that the rainfall shocks decrease the available liquidity to purchase insurance the following year. Regardless, this data provides relatively convincing evidence that the direct effect of weather is not causing people who receive insurance payments to purchase again the following year.

# **4.2 Trust, Learning, and Spillover Effects**

It is also possible that the propensity to purchase insurance after receiving a payout results from learning about insurance and trusting the insurance company, as opposed to being a direct result of the payout. In order to separate the effects of trust and learning from that of receiving the payout, I make the assumption that if trust and learning are playing an important role in causing people to purchase insurance after they have received a payout, then we should be able to see a positive spillover effect of payouts within the village.<sup>13</sup> This is because one shouldn’t need to actually receive a payout to gain the effects of trust and learning, as someone who witnesses a payout gains all the same information as someone who receives a payout.

To perform this analysis I aggregate all buyers to the village level, but divide them into two types: repeat buyers and new buyers, where repeat buyers are people who purchased insurance the year was correlated with higher takeup. Since village-level coefficients were only available for around 50% of the villages, these variables are not included in the main specifications.

> 13If people can only gain trust and learning by actually receiving a payout themselves, then then data gives us no way to separate trust and learning from other possible mechanisms of receiving a payout.

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