Quality standards for agricultural index insurance: an agenda for action
Agricultural index insurance can accelerate progress towards the Sustainable Development Goals (SDGs) and the ending of poverty worldwide, say Michael R. Carter and Tara Chiu. By courtesy of the Micro Insurance Network.
A minimum quality standard for agricultural index insurance
When index insurance contracts fail, farmers may be left worse off than they would have been without insurance. They will have lost both crop and insurance premiums, as well as defaulting on any loans taken out with the confidence the insurance gave them. Contracts that fail too often and at the wrong times can make farmers worse off, which is why the Feed the Future Innovation Lab for Assets and Market Access (AMA Innovation Lab) has developed a Minimum Quality Standard (MQS) measure and a spreadsheet tool to implement it. Insurance can make a farmer better off by transferring money from good years to bad, but when an index contract fails to pay in a bad year, its value to the farmer diminishes. Two things should reduce the measure of index insurance quality: frequency of failure and the value of money when the contract fails. The MQS measure does precisely this.
Figure 1 illustrates the MQS standard for the hypothetical case of a farmer facing a severe drought once every five years, which reduces farm income from US$1,000 to US$250. Using the standard economic tool of expected utility theory, we can calculate the risk-discounted expected level of well-being the farmer could anticipate. Under these assumptions, the risk-discounted expected well-being of the farmer is US$725, resulting in little difference between running the farming operation (which has an average income of US$850) and receiving a guaranteed income of US$725, irrespective of the weather. Thus the farmer would be better off with insurance if the risk-discounted expected wellbeing is higher than US$725. The downward sloping line in Figure 1 shows this measure of well-being as the risk of contract failure. If the contract never fails, the farmer’s level of wellbeing would be almost US$800, well above the level without insurance. This ‘no failure’ insurance contract easily passes the MQS as the farmer is better off with insurance than without. This holds true even though our calculations assume a 50% mark-up on the insurance premium. On the other hand, as the failure probability increases, the farmer’s risk-discounted wellbeing with insurance declines. If a contract has a failure rate higher than 50%, the farmer would be better off without the insurance. At these higher failure rates, a contract does not pass the MQS. Given that simple rainfall contracts often have failure rates of 50% or more, this is a very real possibility. Price also matters. The downward sloping orange line in Figure 1 examines this hypothetical situation for a contract with a higher mark-up. At that higher mark-up rate, the contract does not pass the MQS once the contract failure rate exceeds 15%. The same economics apply even if the contract is subsidised. A contract that fails the MQS means the farmer is better off keeping the premium rather than having a worthless insurance contract. Even if the insurance is fully subsidised, the farmer would gain economically by receiving the premium as a simple cash transfer rather than having an unreliable contract. Ultimately, measurement of quality is not dependent on who pays for the insurance.
Implementing the MQS in practice
The use of MQS in practice can be illustrated by using an index insurance contract design analysis undertaken in a rice-growing region of north-eastern Tanzania. With a budget of approximately US$10,000, we collected retrospective rice yield data from 600 randomly sampled farmers in the region. After estimating the underlying probability distribution drivers of rice yield, we were able to calculate the expected level of risk-discounted well-being for a typical risk averse farmer with a one-hectare rice plot. The solid horizontal line in Figure 2 shows the economic well-being value for this farmer is just over US$1,225. Expected income for the farmer (with no risk discounting) is about US$1,400. We can then calculate the same well-being measure for a series of possible contracts: an area yield contract; a satellite-based predicted area yield contract; and that same satellite-based contract backed up with a ‘fail-safe’ audit. Each of the curves reflects the actual failure that would occur if these contracts were actually implemented. The downward sloping, blue dashed line shows the risk-discounted well-being for the farmer as the price of the contract increases. The actuarially fair price (or pure premium) of this area yield contract is US$67 per hectare. At this price, the contract easily passes the MQS, but does not account for the cost of the annual yield surveys or other administration costs. Including the cost of yield surveys alone pushes the cost of the insurance to about US$87 per hectare. While the area yield still passes the MQS at this premium level, there is little room for further mark-ups before the contract fails to pass the MQS.
Designing contracts to meet the MQS
The problem with the area yield contract is the cost of implementing it in an environment where there is no existing yield data provided by the public or private sector. Figure 2 illustrates two alternative contracts that were analysed in order to try and strengthen the MQS for Tanzanian rice farmers. The first is a pure satellite-based index that estimates yields using a measure of gross primary production. 1 While less accurate than an area yield contract (hence the dark brown dashed line lying below the line for the area yield index), the satellite-only contract has a base price of only A hybrid contract which backs up the satellite yield predictor with an ‘on-demand’ fail-safe audit mechanism performs even better. The fail-safe audit provision (which has been tested with maize contracts in Tanzania and Mozambique) allows farmers to call for a yield survey or crop cut in those years when the satellite-based yield index fails to accurately record average losses.
The orange dashed line in Figure 2 shows the riskdiscounted expected level of well-being under this hybrid contract. It is almost as high as the area yield but it can be implemented at only a fraction of the cost, as yield surveys would be needed only 4% of the time (as opposed to 100% of the time for the area yield contract). The dashed green vertical line in Figure 2 shows the pure premium for this hybrid contract adjusted upwards for the cost of these occasional audits. There is ample space for further contract mark-ups before failing the MQS. Further analysis suggests this contract will be in greater demand than either the area yield or satellite-only contracts. This is just one practical example of how contracts designed using MQS can help farmers. Remote sensing technology is advancing rapidly, and with it, so is our ability to predict yields accurately using higher resolution imagery from drones and even smartphones, for example.
Agenda for action
MQS is only the first step towards consistently highquality, sustainable agricultural index insurance. The AMA Innovation Lab proposes a regional certification board of technical specialists, drawn from public and private sectors. They would then conduct objective tests for levels of quality that meet government or donor regulatory requirements, and issue a Quality Index Insurance Certification (QUIIC) to those products that pass the test. This approach has the potential to revolutionise product safety assessment and communication. For farmers and rural households at risk of catastrophic losses, MQS certified index insurance would ensure a degree of transparency for complicated products. Structured certification could also help prevent substandard products from entering the market and driving out highquality insurance. This would help insurance fulfil its potential to end poverty for smallholder farmers across the world.