greater insurance take-up. On the contrary, I find that villages that had a rainfall shock the previous year were actually less likely to purchase insurance the following year, which provides strong evidence against the argument that weather as opposed to payouts are driving the main result.

I next consider the widely hypothesized suggestion that receiving insurance payouts would cause people to gain trust in the insurance company and learn about insurance, therefore making them more likely to purchase insurance in the future. To do this I assume that in order to gain trust in the insurance company or learn how insurance works, one would not have to receive a payout themselves; witnessing a neighbor receive a payout should also have the same effect. I therefore look for evidence of spillovers within a village and do not find evidence that witnessing a payout without actually receiving it yourself has a significant effect on the propensity to purchase the following year.

I also consider the possibility that payouts cause increased take-up due to direct wealth and/or liquidity effects as opposed to psychological effects. While I do not have data to empirically separate these possible mechanisms, I argue that due to the timing and circumstances of rainfall insurance payouts, wealth and liquidity are unlikely to play an important role.

Finally, I address the concern of unobserved marketing variation. While the effect of this omitted variable is admittedly difficult to measure, I argue it is unlikely to be driving the central results.

Given that I fail to find support for all of these explanations, I argue in the next section that re-purchases are being driven by behavioral responses to receiving insurance payments.

# **4.1 Direct of Rainfall**

Since most rainfall insurance payouts come at the same time as a rainfall shock, it is possible that the rainfall shocks themselves as opposed to the insurance payouts are what is driving increased take-up the following year. There is some evidence for this happening in developed markets, as Kunreuther et al. (1985) note that purchases of flood and earthquake insurance in the US spike after a recent event, even if people were not insurance customers before.

There are a number of theories that could explain this behavior. First, recent experiences with rainfall could change subjects’ beliefs about the probability of a rainfall shock the following year (this is proposed as “recency bias” in Karlan et al. (2012)). If there is actual autocorrelation of rainfall events or if the subject has limited knowledge about the effects of rainfall shocks, people may update their beliefs about shocks and therefore have more desire for insurance the following year. Alternatively, recently experiencing a rainfall shock could make shocks more salient, increasing the chance they will buy insurance the following year. Also, rainfall shocks may affect the wealth of the farmers. If farmers become poorer due to bad rainfall, CRRA utility would suggest that they would be even more risk averse the next year as a second shock would cause greater disutility.

I start by examining whether there is actual autocorrelation in the rainfall data. To test for autocorrelation, I create a panel of various rainfall indicators from 1961-2004 for each weather station.

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