# _Market structure_

Some authors have noted that there has been less innovation in product design following the introduction of WBCIS, despite the large amount of data available. This may be caused by an increase in competition having reduced the private benefits from designing improved indices. States could limit this by tendering for WBCIS contracts in two phases: first a firm could be hired on a multiple-year contract to design weather products across the state; and second, insurers could compete on price or other specified dimensions to offer the stated products. Alternatively, states could tender for multi-year contracts with an insurance provider to design and offer products.

At present there is no uniform procedure adopted by state governments on the selection of insurance providers. It would be prudent to consider establishing and following a sound and transparent policy in insurance provider selection. Decisions about weather indexed insurance products are complex and there is a strong rationale for creating a technical support unit to support central and state governments in making informed decisions about insurance product design, and in choosing between insurance providers. Incidentally, formation of such a unit was one of the key suggestions of a recent evaluation study conducted on WBCIS on behalf of the Government of India (Government of India 2011).

# **4. Ratemaking for Weather Based Crop Insurance**

As for any insurance product, the price of a weather indexed insurance product should reflect the expected cost to the insurer. Moreover, while it is important for estimates of the expected cost to be statistically unbiased, neither systematically too high or too low, it is also important the estimates are statistically efficient, so that the price for any one product is not significantly ‘wrong’. It is commonly assumed that indexed weather insurance products are not subject to adverse selection against the insurer, as the insurer typically has access to long time series data that is at least as good as any information possessed by potential purchasers. However, this relies on the insurer making the best use of available data. If, as seems common across India, ratemaking procedures are statistically inefficient, insurers may be exposed to adverse selection.

# _Standalone approaches to ratemaking_

In India, weather index insurance policies are typically rated on a standalone basis, with each calculation based on up to 30 years of historical weather data for the respective contractual weather station. First, weather data would be cleaned and analyzed for any trends which might lead to systematic increases or decreases in average claim payments over time. This cleaned, and possibly de-trended, data would then be applied to the specific contract to be priced, and the claim payments that would have been paid had that contract been in force, the _burn cost_ , would be calculated for each year for which historical data exists. The _Historical Burn Rate_ for the contract is then estimated as the arithmetic average historical burn cost divided by the sum insured. This is known as a Historical Burn Analysis (HBA), the simplest method of weather contract pricing.

The premium rate is then set to be the historical burn rate, adjusted by various loading factors to account for the cost of capital and administrative expenses, for example. Syroka (2007) suggests the following formula for WBCIS products: (4) where the Historical Burn Rate ( ) denotes the arithmetic average burn cost for the contract in question, the Data Uncertainty Factor ( ) is a load to account for any missing data, the Catastrophe Load ( ) accounts for the cost of acquiring risk capital, and the Administrative Load 12