Weather Index Drought Insurance: An Ex Ante Evaluation for Millet Growers in Niger

Abstract

In the Sudano-Sahelian region, which includes South Niger, the inter-annual variability of the rainy season is high and irrigation is limited. As a consequence, bad rainy seasons have a massive impact on crop yield and regularly result in food crises. Traditional insurance policies based on crop damage assessment are not available because of asymmetric information and high transaction costs compared to the value of production. We assess the risk mitigation capacity of an alternative form of insurance which has been implemented at a large scale in India since 2003: insurance based on a weather index. We compare the efficiency of various weather indices to increase the expected utility of a representative risk-averse farmer. We show the importance of using plot-level yield data rather than village averages, which bias results due to the presence of idiosyncratic shocks. We also illustrate the need for out-of-sample estimations in order to avoid overfitting. Even with the appropriate index and assuming substantial risk aversion, we find a limited gain of implementing insurance, which roughly corresponds to, or slightly exceeds, the cost observed in India for implementing such insurance policies. However, when we separately treat the plots with and without fertilisers separately, we see that the benefit of insurance is slightly higher in the former case. This suggests that insurance policies may slightly increase the use of risk-increasing inputs such as fertilisers and improved cultivars, and hence improve average yields, which remain very low in the region.

Appendix

1.1 In-sample Calibrations

Figure 3 shows the indemnification of the $CR_{siva}$-based insurance across the area and over the period considered. In spite of a relatively low basis risk: most of the low yield situations are indeed insured, the certain equivalent income gain is rather low (1.27 %).

1.2 Robustness Checks

1.2.1 Prices

We now take the millet cultivation income (plot income summary statistics are displayed in Table 1) for one hectare and compute the CEI gain associated to the distribution of income for the 2004–2010 period. The only difference between Tables 3 and  12 is that in the latter, we multiplied the yield by the post-harvest millet price, which varies across years. This does not alter any of the results (ranking of index performance, superiority of indices with bounded daily rainfall and superiority of simulated crop cycles) as shown by the comparison of Tables 12 with 3. The only difference between Tables 3 and 12 is that we multiplied the yield by the annual post-harvest millet price for the Table 12, the sample and parameters are all the same in each case.

Table 12 Average plot income CEI gain of index insurance

1.2.2 Initial Wealth

Table 13 shows how modifying the initial level hypothesis alters the results of Table 3, displayed in its first part. If risk premium increases when choosing very low levels of $W_0$ and large values for $\rho$, we can say that these results are quite robust regarding this hypothesis since with slight modifications (from 1/5 to 1.5 times average yield) the results are of the same order.

1.2.3 Influence of the Period used for Calibration

As explained above, our results so far are based on only seven years of data (2004–2010), since yield data are not available for a longer period.

However, weather data are available for a much longer period: 1990–2010. Because of this absence of yield data, we cannot optimize an insurance contract over this longer period, but we can apply over this longer period the contracts optimized over 2004–2010, in order to check whether our optimization period is representative or too specific. With this aim, Fig. 10 displays the evolution of the $CR_{siva}$ index during the 1990–2010 period in each of the ten villages. Fortunately, the 2004–2010 period does not show significantly lower or higher values of the index than the longer, 1990–2010 period.

Fig. 10

Evolution of the $CR_{siva}$ index during the period 1990–2010: the greyscale represents the latitude; the northern villages are represented in darker grey) and the horizontal line shows the strike level, calibrated on the 2004-2010 period

Table 13 Average income gain of index insurance

Table 14 Correlation beween non rainfall-related damages (occurrence in percent of plots in a village) and indices

One could also argue that the occurrence of droughts is correlated to locust invasions or other non weather-related events¹⁰. Such correlation would be a strong issue because it would artificially increase the insurance gain. Fortunately, these damages are reported in the survey we use. We display the correlation matrix between the indices and the non rainfall-related damages in Table 14. Damages are classified in three categories, from the least severe (degree 1) to the most severe (degree 3). Whatever the index, the correlation is lower than 10 %, so we are confident that our results are not due to a spurious correlation between drought and locust invasions.

1.3 Incentive to use Costly Inputs

(Figures 11, 12, 13, 14, 15)

Fig. 11

CEI (in FCFA) of encouraged and regular plots without (plain lines) and with $BCR_{obs}$ based insurance (dotted lines), depending on the risk aversion parameter, $\rho$ and an initial wealth ($W_0$) of 1/3 of average income

Fig. 12

CEI (in FCFA) of encouraged and regular plots without (plain lines) and with $CR_{siva}$ based insurance (dotted lines), depending on the risk aversion parameter, $\rho$ and an initial wealth ($W_0$) of 1/3 of average income

Fig. 13

CEI (in FCFA) of encouraged and regular plots without (plain lines) and with $BCR_{siva}$ based insurance (dotted lines), depending on the risk aversion parameter, $\rho$ and an initial wealth ($W_0$) of 1/3 of average income

Fig. 14

CEI (in FCFA) of encouraged and regular plots without (plain lines) and with $WACR_{siva}$ based insurance (dotted lines), depending on the risk aversion parameter, $\rho$ and an initial wealth ($W_0$) of 1/3 of average income

Fig. 15

CEI (in FCFA) of encouraged and regular plots without (plain lines) and with $WABCR_{siva}$ based insurance (dotted lines), depending on the risk aversion parameter, $\rho$ and an initial wealth ($W_0$) of 1/3 of average income