MR Microinsurance_2012_03_29.indd - International Labour ...
MR Microinsurance_2012_03_29.indd - International Labour ... MR Microinsurance_2012_03_29.indd - International Labour ...
248 General insurance Conducting a similar exercise using rainfall data at village level, they fi nd that the VCI index achieves 89 per cent of the variance reduction of the village yield index. Th e rainfall measure achieves 75 per cent of the risk reduction of the village-level area yield contract. Interestingly, when the VCI and rainfall measures are combined into a hybrid index, no additional variance reduction is achieved beyond that obtainable with the VCI-based index alone. While it may be possible to improve the predictive power of rainfall data through further analysis, it is important to note that an insurance scheme is unlikely to be able to aff ord to have the village-level weather measurements that are available in the ICRISAT data. Even the most ambitious proposals for weather station construction suggest that each station would have to cover a circle with a radius of 25 kilometres. By way of comparison, some 30 separate NDVI measurements would be available within a circle of that radius, meaning that a high-density NDVI-based contract should have a further design advantage over weather-based contracts. Figure 11.3 Calculation of VCI using maximum and minimum NDVI NDVI in Kolbila BFA related to its yearly cycle 0.7 Rainy season 0.6 0.5 0.4 0.3 0.2 0.1 0 Mar 1983 Apr May Jun Jul Aug Sep Oct Nov Dec Jan 1984 Max. 1983 Min. While analysis of the ICRISAT data for West Africa suggests that NDVI can not only off er lower basis risk, at lower cost than rainfall-based indices, this fi nding should not be generalized for other agro-ecological environments. In some situations remote sensing may provide a cost-eff ective index, as is the case with livestock mortality predictions in Kenya (see Chapter 12), while in other situations it may prove to be an unreliable predictor of agricultural yields (as was discovered when satellite information was found to be a poor predictor of cotton yields in Mali). Th ese fi ndings show that designing a cost-eff ective index insurance contract that minimizes basis risk should consider a variety of index options by using micro Feb
Next-generation index insurance for smallholder farmers 249 data to ground truth and select the optimal index. At the same time, the analysis also shows that there are limits to the elimination of basis risk, even through optimal contract design. In the extreme case of the Sahel, it would appear to be difficult to use index insurance to eliminate more than half of the agricultural risk faced by farmers. Given these technical limits to the quality of index insurance, the next section explores the possibilities for further improving the development impact value and sustainability of insurance by interlinking it with credit. 11.3 Interlinking insurance and credit The analysis in section 11.1 assumed that the small-scale farm household had access to only one traditional agricultural activity. While the risks associated with such activities are important, development economics has long been preoccupied with the notion that one of the biggest costs of risk is that it induces farm households to shy away from riskier, new technologies and economic opportunities that offer improved average incomes over a period. In addition, risk stunts the development of rural financial markets, compounding the adoption problems for liquidity-constrained farm households. This section argues that explicitly connecting index insurance with these kinds of activity will not only solve the development problem that makes risk so costly, but will also resolve the problem of tepid insurance demand. 11.3.1 High-return economic activities and small-scale farm households High-return economic activities typically require significant up-front investment in purchased inputs of improved seeds and fertilizers. This factor alone increases the risk exposure of the family as a drought year means negative, not just zero, net income. In addition, the yield variance of high-return activities also tends to be higher, in part because these activities are less well-adapted to climatic stress than are traditional activities that have evolved in the farm’s specific agro-ecological system. Finally, the increased cash costs of production may simply exceed the liquidity available to the household, making access to capital through financial intermediaries or value-chain operators indispensable. To explore the performance of index insurance in combination with new, higher-return technologies, we return to the stylized household model detailed in the appendix. We now assume that with significant investment in seeds and fertilizer equal to the household’s non-farming earnings, the household can use an improved technology that increases average net agricultural income by 25 per cent over the traditional crop activity. This high-return technology offers the household the prospect of having higher income and therefore higher consumption. However, given the input
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248 General insurance<br />
Conducting a similar exercise using rainfall data at village level, they fi nd that<br />
the VCI index achieves 89 per cent of the variance reduction of the village yield<br />
index. Th e rainfall measure achieves 75 per cent of the risk reduction of the village-level<br />
area yield contract. Interestingly, when the VCI and rainfall measures<br />
are combined into a hybrid index, no additional variance reduction is achieved<br />
beyond that obtainable with the VCI-based index alone.<br />
While it may be possible to improve the predictive power of rainfall data<br />
through further analysis, it is important to note that an insurance scheme is<br />
unlikely to be able to aff ord to have the village-level weather measurements that<br />
are available in the ICRISAT data. Even the most ambitious proposals for<br />
weather station construction suggest that each station would have to cover a circle<br />
with a radius of 25 kilometres. By way of comparison, some 30 separate<br />
NDVI measurements would be available within a circle of that radius, meaning<br />
that a high-density NDVI-based contract should have a further design advantage<br />
over weather-based contracts.<br />
Figure 11.3 Calculation of VCI using maximum and minimum NDVI<br />
NDVI in Kolbila BFA related to its yearly cycle<br />
0.7<br />
Rainy season<br />
0.6<br />
0.5<br />
0.4<br />
0.3<br />
0.2<br />
0.1<br />
0 Mar<br />
1983<br />
Apr May Jun Jul Aug Sep Oct Nov Dec Jan<br />
1984<br />
Max.<br />
1983<br />
Min.<br />
While analysis of the ICRISAT data for West Africa suggests that NDVI can<br />
not only off er lower basis risk, at lower cost than rainfall-based indices, this fi nding<br />
should not be generalized for other agro-ecological environments. In some situations<br />
remote sensing may provide a cost-eff ective index, as is the case with livestock<br />
mortality predictions in Kenya (see Chapter 12), while in other situations it<br />
may prove to be an unreliable predictor of agricultural yields (as was discovered<br />
when satellite information was found to be a poor predictor of cotton yields in Mali).<br />
Th ese fi ndings show that designing a cost-eff ective index insurance contract that<br />
minimizes basis risk should consider a variety of index options by using micro<br />
Feb