before. I then regress the number of each type of buyer on payout statistics and the total number of buyers in the previous year. When there is an insurance payout in the previous year, most of the repeat buyers the following year received money from the insurance company, while new buyers did not receive anything.<sup>14</sup> These results are presented in Table 7.

In order to compare results with the main specification in Table 4, I again provide a dummy for whether there was a payout in the village along with a quadratic effect of the ratio of payouts to the premium. When aggregating the village data I used the mean of the payout ratios in the village to create a payout ratio for the village.<sup>15</sup> The overall results of the table tell a consistent story: payouts drive repeat buyers but not new purchasers, showing few spillover effects. Column 3 shows how payouts affect the number of repeat buyers the next year, and the results are very consistent with the baseline results from Table 4. A dummy for whether there was any payout is negative and significant, but the payout size has a positive effect. This suggests that low payouts have a marginally negative effect on the number of repeat purchasers, but this effect flips to positive as the size of the payout ratio increases above 1. Column 2 shows the effect of payouts on new buyers in a village. Here all the payout coefficients are insignificant, but due to large standard errors I cannot reject that they are the same as the effects on repeat buyers.

In Panel B I restrict the analysis to villages that had at least one buyer the year after insurance outcomes, creating a sample analogous to the ‘Marketing Restricted Sample’ in Table 4. The logic behind this is that if a village had zero buyers it is likely that insurance was not marketed in the village in that year, and therefore customers did not have an opportunity to purchase insurance. Restricting the data set in this way gives a much clearer pattern. Column 6 now shows much stronger effects of payouts on repeat buying, though the pattern is the same as in Column 3. Small payouts have a negative effect, while increasing the payout ratio increases repeat buying. The squared term on the payout ratio is now negative and significant, indicating that high payout ratios have diminishing These coefficients are now all significantly different from the coefficients for new buyers found in Column 5. In fact, the coefficients in Column 5 flip signs, suggesting that payouts have the opposite effect on people who did not receive payouts. These results suggest that low payouts actually induce more new buyers, but that these effects decrease and then turn negative as the payout in the village increases. The average mean village payout in this sample (for villages that received any payout) is 3.25, and the coefficients suggest that this level of payout will have roughly no effect on new purchasers compared to a village that did not experience a payout. The opposite effects of spillovers versus direct effects are somewhat perflexing, but are not consistent with the hypothesis that receiving payout increases trust and learning in the village where it occurs.

One important clarification of these results is that most of the potential “new buyers” living in a

> 14Some buyers may not have received money if they bought one phase of the insurance policy but one of the other phases paid out. This happened in 427 cases, and removing these individuals does not change there results.

> 15The results are not sensitive to using the mean, and are very similar using the median, maximum, and mode.

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