Statistical analysis of rainfall insurance payouts in southern India.

Exposure to drought among rural households in India and other countries should, at least in principle, be largely diversifiable. This is because rainfall is exogenus to the household and not likely to be strongly correlated with the systematic risk factors, such as aggregate stock returns, that are relevant for a well-diversified representative investor.

With this principle in mind, the goal of rainfall index insurance is to allow households, groups, and governments to reduce their exposure to weather risk by purchasing a contract that pays an indemnity during periods of deficient (or excessive) rainfall. Advocates argue that index insurance is transparent, inexpensive to administer, enables quick payouts, and minimizes moral hazard and adverse selection problems associated with other risk-coping mechanisms and insurance programs (see World Bank 2005; Barnett and Mahul 2007; Gine, Townsend, and Vickery 2007).

This article uses historical rainfall data to estimate the distribution of payouts on a rainfall index insurance product developed by the general insurer ICICI Lombard and offered to rural Indian households since 2003. Our empirical strategy draws on the observation that rainfall in the region we study is close to a stationary process. Correspondingly we can use historical rainfall data to calculate a putative history of insurance payouts for insurance contracts written against the 2006 monsoon.

We conduct several statistical exercises to better understand the properties of estimated insurance payouts. First, we study the probability distribution of indemnities. Does the insurance contract pay off regularly, providing income during periods of moderately deficient rainfall? Or does it operate more like disaster insurance, infrequently paying an indemnity, but providing a very high payout during the most extreme rainfall events? Our evidence suggests the truth is closer to the second case. Analyzing 14 insurance policies, each linked to a different rainfall gauge, we estimate the average probability of receiving a payout on a single phase of insurance coverage is only 11%. The maximum indemnity, paid with a probability of around 1%, provides a rate of return to the policyholder of 900%. We also find that insurance premiums are on average around three times as large as expected payouts.

Second, we study the correlation of payouts in the cross-section and through time. Spatially correlated rainfall shocks may be more difficult for households to insure against through other means, such as informal risk-sharing arrangements within local kinship groups. This in turn implies larger benefits of a formal rainfall insurance contract. On the other hand, dependence in payouts may also increase the balance sheet exposure of ICICI Lombard or their reinsurers to rainfall risk, by reducing the diversification benefits of holding a pooled portfolio of insurance contracts. Research in corporate finance argues that exposure to risk may reduce firm value when there are informational problems or other frictions in raising external finance (e.g., Froot, Scharfstein, and Stein 1993).

We find no evidence of temporal dependence in payouts. However, it is estimated that rainfall insurance payouts are significantly positively correlated across contracts at a point in time, perhaps unsurprising given that we study policies linked to rainfall within a single geographic region of India. Even so, it is estimated that there are still significant risk-reduction benefits from holding a diversified portfolio of contracts. The standard deviation of payouts on an equally weighted basket of 11 different insurance policies is only half as large as the standard deviation of an average individual contract.

Third, we find some evidence that insurance payouts are negatively correlated with growth in Indian per capita GDP. This suggests that some component of rainfall risk is aggregate to the Indian economy as a whole, perhaps reflecting the size and importance of the Indian agricultural sector for employment and economic activity.

Background and Methodology

We study a rainfall insurance product developed by the general insurer ICICI Lombard, which has been offered to rural Indian households since 2003. ICICI Lombard partners with local financial institutions to market the insurance to households. Gine, Townsend, and Vickery (2007) and Cole and Tufano (2007) provide detailed background about the insurance product. Gine, Townsend, and Vickery (2007) also study the determinants of household insurance purchase decisions, based on a 2004 household survey.

Our analysis focuses on calendar year 2006 insurance contracts linked to rainfall in the southern Indian State of Andhra Pradesh. Below, we briefly summarize the design of these contracts. Policies cover rainfall during the Kharif (monsoon season), which is the prime cropping season running from approximately June to September. The contract divides the Kharif into three phases roughly corresponding to sowing, podding/flowering, and harvest. The first two phases are 35 days in duration, while the third (harvest) phase is 40 days long. In 2006, farmers were allowed to purchase different numbers of contracts across each of the three phases.

Phase payouts are based on accumulated rainfall between the start and end dates of the phase, measured at a nearby reference weather station or rain gauge. (1) The start of the first phase is triggered by the monsoon rains. Namely, phase 1 (sowing) begins on the first date on which accumulated rain since June 1 exceeds 50 mm, or on July 1 if accumulated rain since June 1 is below 50 mm.

[FIGURE 1 OMITTED]

Insurance payouts in the first two phases are linked to low rainfall. The payout structure in these cases is illustrated in figure 1. Contract details in the figure are from the phase 1 contract linked to the Mahabubnagar weather station, which is representative of the policies studied in our empirical analysis. The policy pays zero if accumulated rainfall during the phase exceeds an upper threshold, or "strike," which in this case is 70 mm. Otherwise, the policy pays Rs 10 for each millimeter of rainfall deficiency relative to the strike, until the lower threshold, or "exit," is reached. If rainfall is below the exit value, the policy pays a fixed, higher indemnity of Rs 1,000. Phase 3 policies have the same structure, but in reverse, they pay out only when rainfall exceeds the strike, meant to correspond to unusually heavy rainfall during the harvest that causes damage to crops.

Depending on the policy, the reference weather station is one of three types: an Indian Meteorological Department (IMD) station, mandal rainfall station (a mandal is a local geographic area roughly equivalent to a U.S. county) or one of a network of automated rain gauges installed by ICICI Lombard. For this article, we focus on IMD rainfall data. These are considered to be more reliable than data from mandal stations, and include a longer and more complete history of past rainfall to construct a putative dataset of insurance payouts.

Our source data consist of policy terms for contracts indexed to 14 different IMD weather stations in Andhra Pradesh (one contract per station), as well as IMD historical rainfall data for each station. Rainfall data are measured at a daily frequency. Although the earliest rainfall data is from 1970, the starting point of the data varies by weather station, and there are also scattered individual months and years where data is missing. Across 14 stations, there are 1,089 individual contract phases for which at least some rainfall data is available. However, for 135 phases data is missing for at least one day during the contract period. We drop these from our analysis, leaving a sample of 954 phases for which we have complete daily rainfall to calculate payouts.

The amount of missing data varies significantly across weather stations. At one extreme there are 91 phases of complete rainfall data for the Anantapur weather station (equivalent to 30.3 monsoon years). At the other extreme, for the Adilabad and Nalgonda stations, only a small number of complete phases of rainfall data is available (8 and 18 phases, respectively). At least 64 phases (21.3 monsoon years) of complete daily historical data is available for 11 of the 14 stations; our empirical findings are similar if we restrict analysis to these stations only.

Applying the insurance contract terms to historical rainfall data, we calculate the hypothetical payout on the contract for each station, phase and year. Data on estimated payouts and information on contract features are presented in table 1. Strikingly, the insurance pays an indemnity in only 10.7% of phases, a point we return to below. The average estimated payout is Rs 29.7, compared to an average premium of Rs 99.9. This wedge presumably reflects, at least in part, the administrative and financing costs of designing, underwriting and selling Insurance policies, especially given the small current size of the market and lack of associated economies of scale. Although the insurance is not actuarially fair, it may still be valuable to policyholders if it pays an indemnity in times when the household's marginal utility of consumption is particularly high.

[FIGURE 2 OMITTED]

Distribution of Payouts