For each indicator, I run a regression of six lags of the variable on the current value, including weather station fixed effects. These results are presented in Column 1 of Table 5, with just the coefficient on the first lag shown. While a fixed effects regression with a lagged dependent variable is not generally consistent, it will converge to the true value as _T →∞_ . As T is relatively large (38), these estimates are likely to suffer from little bias. I also run a regression of the first lag using previous lags as instruments, using the methodology proposed by Arellano and Bond (1991), with results presented in Column 2. The results from both specifications are similar, and show a negative first-order autocorrelation in rainfall that appears to be driven by rains early in the season. The bottom two rows test for autocorrelation of rainfall shocks using the parameters of the 2005 insurance policy to determine shocks. “Would Have Been Payout” is a dummy variable that takes a value of 1 if the insurance policy of 2005 would have given a payout, while “Total Insurance Payout” is the size of this payout. By these measures, shocks do not appear to exhibit significant positive first-order autocorrelation.

This evidence casts doubt on the hypothesis that positive autocorrelation of weather events is driving increased insurance purchasing. It appears that total rainfall is actually negatively autocorrelated, while shocks (which are proxied by the insurance contract giving a payout) do not appear to be correlated at all.

Even if there is no positive autocorrelation of rainfall, there may be other aspects about experiencing a shock that result in people having a higher propensity to purchase insurance. In order to look at the results of weather separately from the effects of insurance, I analyze how previous weather events affected insurance purchase decisions in the first year that insurance was offered to BASIX customers, which was 2005. To accomplish this, I first aggregate the purchasing data to the village level and then test to see whether villages that experienced a rainfall shock in 2004 had more insurance purchasers in 2005 than villages who did not experience a rainfall shock. A shock is defined using each location’s insurance policies in 2005: If insurance would have paid out in 2004 based on the structure of the 2005 weather policy, this is deemed a rainfall shock. As the quality of the rainfall data is related to the amount of nearby weatherstations, I weight the observations based on the number of nearby rainfall stations.<sup>11</sup> Also, I create a hypothetical payout ratio, similar to the “Ratio of Payout to Premium” variable presented in Table 4. This is the ratio of the amount that the 2005 policy would have paid out in 2004 divided by the premium of the policy.

The results of this regression are presented in Table 6.<sup>12</sup> Column 1 presents the baseline regression,

> 11The APHRODITE weather data provides information about how many local weatherstations contributed to a certain rainfall reading. Since some of the rainfall observations are likely to be more accurate than others, I weight them according to accuracy. If there are no rainfall stations contributing to the APHRODITE data within a .75<sup>_o_</sup> x.75<sup>_o_</sup> grid around the desired BASIX weather station, the observation is given a weight of 1. If there is a least one weather station in this .75<sup>_o_</sup> x.75<sup>_o_</sup> grid, the observation is given a weight of 1.5. If there is a rainfall station within the .25<sup>_o_</sup> x.25<sup>_o_</sup> grid, the observation is given a weight of 2. The weighted results to not differ significantly from the unweighted results.

> 12Note that while it is reasonable to think that village-specific characteristics (such as village size) may have an effect on village-level insurance take-up, village-level co-variates are not included in the regression. When the regressions are run with the village characteristics from the 2005 Indian census, the coefficients of interest do not change significantly. Also, most village-level characteristics had insignificant coefficients, with the exception that a more literate population

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