# **1 Introduction**

Roughly 60% of India’s population is employed in agriculture, and over 50% of agricultural land is dependent on rainfall to nurture the crops.<sup>1</sup> But the Indian monsoon is notoriously unpredictable, prone to droughts and floods that can have devastating effects on the livelihood of rural Indians. While Townsend (1994) argues that Indian villages do an effective job of providing informal consumption insurance against idiosyncratic shocks, a poor monsoon will hit whole villages and districts at once, likely rendering intra-village transfers ineffective. Beginning in the early 2000s, rainfall index insurance was introduced in India as a potentially important tool to help poor farmers deal with rainfall risk (Hess, 2004), but has struggled to reach a critical mass of customers, especially when unsubsidized.<sup>2</sup> Unlike physical goods (or even credit), it is difficult for customers to evaluate the benefits of insurance since its main benfits only occur when a payment is received. If customers are unfamiliar with how insurance works, they may be influenced by their recent experiences with insurance and also by the experiences of their friends and neighbors. Evidence in the developed world (Kunreuther et al., 1985; Gallagher, Forthcoming) shows that purchases of flood and earthquake insurance in the US are greatly influenced by recent experiences with disasters and insurance payouts, that peoples’ insurance decisions are influenced by their friends and neighbors’ experiences with insurance. Reacting to low rates of rainfall insurance uptake in Andhra Pradesh, India, Giné et al. (2008) suggest that “over time, lessons learned by insurance ‘early adopters’ will filter through to other households, generating higher penetration rates among poor households.” This paper seeks to understand how previous insurance payouts can affect future insurance purchasing decisions, and what mechanisms can explain this behavior. Using data on three years of insurance purchasers from the Indian micro-finance institution BASIX, I find that customers who received an insurance payout are 9-22% more likely to repurchase in the following year as compared to customers who did not receive any insurance payments. I argue that this effect is caused by the behavioral effects of receiving an insurance payout, and cannot be accounted for by other explanations such as wealth effects, changing expectations about weather, or trust in the insurance company.

I test two main alternative hypotheses as to why receiving payouts could increase insurance demand the following year. First, it is possible that weather shocks themselves could have an effect on insurance demand, such as Kunreuther et al. (1985) observe in the US. This could happen because weather shocks change customers’ beliefs about future shocks, change their wealth, or simply increase the salience of shocks. I look for direct effects of weather by testing how rainfall in the year before insurance was introduced affected insurance purchases, and find evidence that previous rainfall shocks tend to _decrease_ insurance purchasing. This provides evidence against the argument that it is weather shocks

> 1CIA World Factbook: India; Indiastat.com

> 2There have been some areas with a large number of insured, but this is generally due to heavy government subsidies or requiring insurance to obtain government loans. Exposing official uncertainty about the prospects of rainfall index insurance, the government of India decided to delay expansion of its rainfall index insurance pilot (WBCIS) to all of India, and in 2010 piloted a modified version of its area-yield index product (NAIS).

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