village that had experienced payouts would have also experienced uninsured rainfall shocks during the same season. Therefore it may be possible that there are effects of trust and learning, but they are outweighed by opposite effects of the weather. As we saw in the previous section, rainfall shocks tend to have a negative effect on insurance demand, so the (lack of) evidence of spillovers may be a result of a more complex interaction between trust/learning and direct effects of weather.

Overall, these results do not support the hypothesis that trust, learning, or any other effects of simply witnessing insurance payouts are driving increased purchasing. While it is possible that our measurements of spillovers are too crude and miss more subtle effects, the data simply does not provide evidence that here are strong spillover effects. Since we do not see these spillover effects, this casts doubt on the theory that repurchases are being driven by increased trust in the insurance company or learning about insurance payouts.

# **4.3 Direct Effects of Payouts on Wealth and Liquidity**

The previous two sections discount the possibility that trust, learning, or weather effects are driving the result that receiving an insurance payout is correlated with purchasing insurance the following year. This points to the actual reception of money from the insurance company as being the driving force behind greater takeup. The most natural explanation for this phenomenon would be that recving an insurance payout could directly affect choices the next year due to its effects on wealth and liquidity. For instance, if insurance is a normal good then increased wealth would result in greater insurance demand.<sup>16</sup> While the BASIX data set does not offer the opportunity to test the direct effects of a cash payment separately from an insurance payout, there are a number of reasons why it is unlikely that wealth or liquidity effects are driving the results. Most importantly, insurance payouts are given in the context of a rainfall shock, which would most likely result in a loss of income. It may help to recall that the empirical results are being driven by variation in rainfall across locations, not by levels of insurance within a village. Therefore, for wealth effects to be driving the results, one would need to think that experiencing an insurance payout in the context of a rainfall shock resulted in people becoming wealthier than those people who didn’t experience a shock at all. Given the fact that most buyers bought a relatively low amount of insurance coverage relative to their incomes, experiencing a rainfall shock, even when insured, would likely decrease future wealth. Therefore, wealth effects seem like a poor explanation as to why receiving payouts spur future insurance sales.

If people who received insurance payouts had a decrease in wealth it is also unlikely that receiving the insurance payout would increase their liquidity the next season. Insurance payments were generally made in January, while people had the opportunity to purchase insurance for the next season only in May. It is doubtful that these payments would have a lasting enough liquidity effect to influence

> 16This is consistent with the empirical findings of Cole et al. (2013).

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