of CCEs could be conducted in areas where potential crop yield losses are large, and thus insurance payouts are anticipated to be large, whilst a lower number of CCEs could be conducted in other areas. The partial outsourcing of CCEs could also be considered, supplemented by a strong audit mechanism. For example, partial outsourcing to private sector service providers, banking correspondents or NGOs could be considered.

Regardless of the improvements in the targeting and conduct of CCEs, there is a tradeoff between reducing basis risk and increasing the number of CCEs to be conducted. Currently it is challenging to asses this tradeoff for each crop in each state since the cost of conducting CCEs for insurance purposes are not factored into the insurance premium. Were this to change, the tradeoff may be clearer, leading to improved decision making.

The move to an ex-ante premium subsidy system increases the exposure of the mNAIS portfolio to fraudulent crop cutting experiment reports by local state employees. Under NAIS, states contributed towards any claim payments and therefore had an incentive to monitor crop cutting experiments. However, under mNAIS, states contribute towards claim payments through upfront premium subsidies and underreporting of yields would lead to large claim payments to local farmers at no additional short term cost to the state. Any perceived moral hazard will increase reinsurance prices, leading to high commercial premiums to be paid by farmers, states and central government. Were systematic underreporting to occur in practice in any state it would be damaging to the long term prospects of mNAIS in all states.

Another approach would be to note that states are in a better position than the insurer to monitor the quality of crop cutting experiments, and for the insurer to use a statistical sample of manipulation-free crop cutting experiments as an independent check on the full set of statewide crop cutting experiments. For example, the insurer could conduct or co-observe a sample of crop cutting experiments across the state and use this data to estimate the manipulation-free aggregate claim payment to be paid to farmers in the state. If the aggregate state-wide claim payment rate arising from the full set of crop cutting experiments is sufficiently higher than this manipulation-free estimate based on the manipulation-free sample, then the insurer has reasonable grounds for believing that there has been systematic manipulation of crop cutting experiments across the state. Conducting random audits of, say, 5% of all CCEs would be much less expensive than conducting audits of all CCEs. However, for random auditing to be effective, the maximum punishment in the event of detected manipulation of CCEs must be large enough to deter manipulation. The threat of future exclusion from the mNAIS may be sufficient to ensure that states conduct sufficient monitoring of their employees. Insurers could also incorporate independent data, such as remote sensing or weather station data, in their validation of CCEs.

**Learning how to increase outreach** : Currently, most of the insured farming households are covered on a compulsory basis, with 95 million farming households not insured. With a subsidized premium, improved product design and faster claim settlement, the mNAIS product should be attractive to most farmer households across India: most farmers will pay around a third of the commercial premium and for every 1 rupee paid in premium will expect to receive approximately 2 rupees in claim payments.  (Whilst this was true on an aggregate basis under the NAIS, expected claim payments were not evenly distributed between insurance units and so NAIS was poor value for some farmers.). However, designing a good insurance product is not enough to guarantee voluntary purchase; insurance is never bought, always sold. If the mNAIS is to offer protection to the poorest, most vulnerable farmers, insurers must actively market the product to these farmers.

Communication is particularly important since farmers who, under NAIS, were paying a maximum premium rate of 3.5% may be paying up to 6% under mNAIS, albeit for a more valuable product. Communicating the changes between NAIS and mNAIS is therefore important both to encourage existing policyholders to renew, and for increasing voluntary purchase. In a recent survey of farmers 17