**Public financing** : The current NAIS is mainly funded by ex-post public contributions, whereby at the end of the crop season aggregate claims exceeding premium income are funded 50-50 by the state and central governments. While subsidy for agriculture insurance programs are used around the world and can be justified as a development measure (Mahul and Stutley, 2010), this post-disaster funding arrangement leads to an open ended fiscal exposure for governments and volatile annual contributions.  This post-disaster funding arrangement was in turn partly necessitated on account of a lack of an actuarially sound premium rating methodology without which predicting likely payouts was not feasible.

**Delays in claims settlements** : Another critical problem has been the systematic delay of NAIS claims settlement by 9-12 months or more. This has been partly caused by the time taken for the CCE data to be collated, but perhaps more importantly by state and central governments providing funding on an ex-post basis without adequate ex-ante budgeting. Delays in claims settlements not only cause cash flow problems for farmers already under the stress of a poor harvest, but also mean that they are not eligible for the next round of formal credit from banks for the next crop cycle, which follows immediately from the previous cycle. This can expose them to a debt trap and continued financial stress at the household level. This delay in claim settlement has contributed to the relatively low take up of crop insurance, despite significant increase in outreach in recent years. In a recent survey of farmers in Andhra Pradesh, over half of farmers cited delays in claim settlement as the key issue facing NAIS (Raju and Chand 2008), consistent with the findings of Joint Group (2004).

**Risk classification** : The NAIS rules for designing and pricing products, mean that the value of NAIS coverage for any given crop varies considerably across insurance units in the same state, and changes significantly year to year, even though farmers’ premium rates are uniform for each crop. As an illustration, consider two insurance units which are exposed to the same level of agronomic risk but where one insurance unit has suffered a serious crop loss in the last five years but the other has not. The threshold yield for the former insurance unit would be much lower than the threshold yield for the latter insurance unit, since the five year moving average yield would be much lower. However, this difference in threshold yields is not from a fundamental difference in the risk in each insurance unit, just from one insurance unit having been unlucky in the previous five years and the other having been lucky (Joint Group Report 2004).

From a statistical point of view, a three or five year average yield is not an efficient estimate of the true mean yield; it may not be representative of the true long term average yield because of unusually good or bad years having occurred in the last five years. Further, the fixing of premium rates and indemnity levels across the state implicitly relies on the probability distribution of yields in different insurance units across the state varying only through scaling by the average yield. This may be unrealistic if, for example, yield risk is much higher in hilly insurance units than in plains.

Poor risk classification has three negative side effects. First, the NAIS portfolio is exposed to significant adverse selection from farmers voluntarily purchasing cover in high risk insurance units or when the three or five year moving average yield is above the true mean, and therefore the NAIS product was unusually valuable. There is strong evidence for adverse selection in the NAIS portfolio (see James and Nair, 2009 and Figure 3).

Second, poor risk classification leads to an inequitable distribution of the public subsidy among farmers. The actuarial value of the NAIS public subsidy per hectare of land varies substantially, even for one crop within one state. Moreover, much of this variation is arbitrary, being caused by large fluctuations in the three or five year average moving average yields.

Third, poor risk classification can lead to poor agriculture policy signaling. When premium rates do not reflect the inherent actuarial cost farmers could be incentivized to make economically inefficient decisions to grow crops with lower farmer insurance premiums, despite them having higher risk or lower expected yield.

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