**Data quality:** CCE quality is likely to vary considerably between states due to disparities in the levels of accountability, expertise and capacity of the agencies responsible for CCEs. A lack of accuracy in CCEs increases the basis risk experienced by farmers by increasing the non-sampling error.

CCEs are also exposed to manipulation risk, whereby the reported yield from a CCE could be intentionally lower than the true yield, triggering a higher claim payment to farmers. Although manipulation may benefit certain farmers in the short term, it would lead to high premiums and withdrawal of cover in the medium term.

**Involvement of private sector** : In its current form, NAIS is closer to a compensation scheme than an insurance program, and there is no involvement of the private sector.

**Basis risk** : Under the NAIS it is possible for a farmer to experience a large crop loss but receive no claim payment because, although the farmer’s yield is low, the average yield in the insurance unit is not low enough to trigger a claim payment. Large subsidies mean that basis risk is unlikely to limit take-up from the wealthy, who can afford to pay upfront premiums and wait for delayed claim payments. However, basis risk is likely to severely limit voluntary take-up from the most risk averse, even in the presence of large subsidies, since purchase worsens the worst that could happen: without indexed insurance the worst that could happen is that a farmer loses her entire crop, but with indexed insurance a farmer could lose her entire crop and have paid an insurance premium yet receive no claim payment due to basis risk (Clarke 2011).

**Adverse selection** : As mentioned above poor risk classification allows farmers to select against the insurer by choosing 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. In addition, it has been possible to purchase NAIS cover well into the growing season when pre-existing drought conditions were known, allowing farmers to purchase voluntary cover in advance of a predictable drought.

# **3. Options for designing and implementing an enhanced version of the NAIS**

Designing a successor to the NAIS is complex since the various components of any crop insurance program are interrelated. This section discusses the options available to designers of a modified NAIS, and provides foundation to the next section’s discussion of the mNAIS, as being piloted in the Rabi season 2010-11.

# _Public financing_

The post-disaster funding arrangement of the NAIS is one of the main causes of significant delays to claim settlement for farmers. A range of alternative funding structures is available to increase the timeliness of claim settlement.

At one extreme, government could retain all insurance risk but pre-fund NAIS claim payments. For example, government could deposit substantial reserves with the public insurer, AICI, which would then have liquidity to be able to make claim payments as they fall due. This could increase the speed of claim settlement for farmers, but is otherwise lacking in merit. Based on the 2008-9 NAIS portfolio, government would need to set aside an estimated US$1.8 billion to withstand a 1-in-100 year loss. From an economic perspective, reserving this amount of liquidity solely for agriculture in India is wasteful: liquidity is valuable and should be used to bear a diversified portfolio of risks. Moreover, under such an arrangement government would still ultimately bear all insurance risk: if large claim payments depleted AICI’s reserves, government would be required to inject additional capital. Finally, by endowing one public company with substantial assets it would be difficult to obtain beneficial competition in the market for agricultural insurance.

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