Designing Index-Based Livestock Insurance for Managing Asset Risk in Northern Kenya
Summary (3 min read)
1. Introduction
- The remainder of the paper is organized as follows.
- Section 2 describes the northern Kenya context.
- Section 3 explains the livestock mortality and remote sensing vegetation data available.
- Section 4 details the IBLI contract design, the construction of key variables and the estimation methods employed.
- Section 6 discusses contract pricing and risk exposure.
2. The Northern Kenya Context
- Meanwhile, most herd losses occur in droughts as covariate shocks affecting many households at once, sparking a humanitarian emergency.
- The resulting large-scale catastrophe induces emergency response by the government, donors and international agencies, commonly in the form of food aid.
- As the cost and frequency of emergency response in the region has grown, however, mounting dissatisfaction with food aid-based risk transfer has prompted exploration for more comprehensive and effective means of livestock mortality and drought risk management, including the development of viable financial risk transfer products.
- The most recent parliamentary campaign in Kenya included widespread, highly publicized promises by prominent politicians to develop livestock insurance for the northern Kenyan ASAL.
3. Data description
- Kenya does not have longstanding seasonal or annual livestock surveys of the sort used for computing area average mortality, the index used in the developing world's other IBLI contract, in Mongolia.
- The ALRMP data the authors use in contract design are collected for the Government of Kenya, which might have a material interest in IBLI contract payouts, thereby rendering those data unsuitable as the basis for the index itself.
- Rainfall estimates based on satellite-based remote sensing remain controversial within climate science.
- 5 NDVI is a satellite-derived indicator of the amount and vigor of vegetation, based on the observed level of photosynthetic activity (Tucker 2005) .
- And thus, eastbound of the rectangular = max (the available GPS Y-coordinate) +0.02, westbound = min (the available GPS Y-coordinate) -0.02, northbound of the rectangular = max (the available GPS X-coordinate) +0.02 and southbound = min (the available GPS still, but their need to do so should be reflected in pasture conditions within their normal grazing range.
4. Designing Vegetation Index Based Livestock Insurance for Northern Kenya
- In order to confirm the appropriateness of their approach to IBLI contract design, from May-August 2008 the authors undertook extensive community discussions in five locations in Marsabit District, surveyed and performed field experiments with 210 households in those same locations.
- Chantarat et al. (2009c) and Lybbert et al. (2009) describe those studies, which confirmed (i) pastoralists' keen interest in an IBLI product, (ii) their comprehension of the basic features of the IBLI product the authors explain below, and (iii) significant willingness to pay for the product at commercially viable premium rates.
- Pastoralists in these communities worry about livestock loss, clearly associated this with pasture conditions, and readily accept the idea that greenness measures gathered from satellites ("the stars that move at night" in local dialectics) can reliably signal drought and significant livestock mortality.
- With demand for an IBLI product established, the authors proceed now with the specifics of contract design.
4.1 Contract design
- The authors favor the seasonal contract payout -in contrast to a yearly payout -because pastoralists' financial illiquidity typically means that catastrophic herd losses threaten human nutrition and health in the absence of prompt response.
- The rapid response capacity of seasonal insurance contracts is one of the great appeals of this approach to drought risk management as compared to reliance on food aid shipments, which typically involve lags of five months or more after the emergence of a disaster (Chantarat et al. 2007) .
4.2 Variable construction and estimation of the predictive models
- These cumulative vegetation indices effectively capture the myriad, complex interactions between climate and stocking rates, reflected in rangeland conditions, and livestock mortality rates.
- The authors estimate simple linear regressions within each of the two regimes using the most parsimonious specification that fits the data well.
- With only eight years' data available for each location, limited degrees of freedom preclude estimating location-specific predictive models.
- Insurance companies would be unlikely to implement contracts at such high spatial resolution anyway, so this is not a serious problem.
- The authors also pool data for both LRLD and SRSD seasons but include a seasonal dummy to control for the potential differences across the two seasons.
5. Estimation results and out-of-sample performance evaluation
- As another diagnostic over a longer period, the authors compare well-known severe drought events reported by communities with the predicted area average mortality constructed using their available dekadal NDVI data from 1982-2008.
- The authors find the predicted mortality index time series quite accurately capture the regional drought events of 1984, 1991-92, 1994, 1996, 2000 and 2005-06 , predicting average herd mortality rates of 20-40% during those seasons and never generating predictions beyond 10% in seasons when communities indicate no severe drought occurred.
- This is a more statistically casual approach to forecast evaluation, but encompasses a longer time period and the authors find it effective for communicating to local stakeholders the potential to use statistical models to accurately capture average livestock mortality experience for the purposes of writing IBLI contracts.
6.1 Unconditional pricing
- Where T covers the available 27 years of data.
- The fair premium rates (%), standard deviations and US dollar equivalent premia per TLU are reported in the top panel of Table 8 .
- Intuitively, the annual premium is roughly twice as much as the seasonal premium.
- By having pastoralists retain the layer of small risks, index insurance appears affordable even in the face of recurring severe droughts.
- Depending on the pastoralist's location and chosen strike rate, a herder needs to sell one goat or sheep to pay for annual insurance on 1-10 camels or cattle, an expense they appear willing to incur (Chantarat et al. 2009b and 2009c) .
6.2 Conditional pricing
- As Table 8 shows, the two conditional premia vary markedly.
- When the ex ante rangeland state is favorable, premia are only 2-5% for contracts with a 10% strike.
- Given marketing and political considerations, it is unclear whether insurers will be willing to vary IBLI premia in response to changing ex ante range conditions, leaving open a real possibility of intertemporal adverse selection issues.
6.3 Risk exposure of the underwriter
- The authors now consider a simple reinsurance strategy where the loss beyond 100% of the pure premium is transferred to a reinsurer.
- For contracts with un premia, actuarially fair stoploss reinsurance rates quoted as percentage of IBLI premium would range from 49% (32%) for a 10% strike contract to 68% (49%) for a 30% strike contract (Table 10 ).
- These high estimated pure reinsurance rates only take into consideration the local drought risk profile, however, and should fall markedly as international reinsurers are better able to diversify these risks in international financial markets.
- Indeed, this diversification opportunity through international risk transfer is one of the key benefits of developing IBLI products.
7. Conclusions and some implementation challenges
- Fourth, as already mentioned, IBLI underwriters and their commercial partners must make difficult choices in balancing the administrative simplicity and marketing appeal of offering IBLI contracts priced uniformly over space and time (which the authors termed "unconditional" pricing in the preceding analysis) versus more complex ("conditional") pricing to guard against the possibility of spatial or intertemporal adverse selection.
- Harmonized pricing is a common practice of Kenyan insurance companies that have ventured into the agricultural sector, using the less risky areas to subsidize premiums for the more risky areas.
- As indicated in their analysis, the potential intertemporal or spatial adverse selection issues could be greater with index-based products and thus merit attention as this market develops.
- By addressing serious problems of covariate risk, asymmetric information and high transactions costs that have precluded the emergence of commercial insurance in these areas to date, IBLI offers a novel opportunity to use financial risk transfer mechanisms to address a key driver of persistent poverty.
- The design detailed in this paper overcomes the significant challenges of a lack of reliable ground climate data (e.g., from location rainfall station) or seasonal or annual livestock census data, as well as the need to control for the path dependence of the effects of rangeland vegetation on livestock mortality.
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Frequently Asked Questions (17)
Q2. What are the future works in "Designing index based livestock insurance for managing asset risk in northern kenya" ?
A range of implementation challenges nonetheless remain and are the subject of future research. In the northern Kenya IBLI case, their commercial partners are tapping into a network of local agents equipped with electronic, rechargeable point-of-sale ( POS ) devices being extended throughout northern Kenya by a commercial bank working with the central government and donors on a new cash transfer program. Fourth, as already mentioned, IBLI underwriters and their commercial partners must make difficult choices in balancing the administrative simplicity and marketing appeal of offering IBLI contracts priced uniformly over space and time ( which the authors termed “ unconditional ” pricing in the preceding analysis ) versus more complex ( “ conditional ” ) pricing to guard against the possibility of spatial or intertemporal adverse selection. First, the existence of household-level data permit direct exploration of basis risk, looking in particular for any systematic patterns so that prospective insurance purchasers can be fully informed as to how well suited ( or not ) the index-based contract might be for their individual case.
Q3. What are the main challenges to commercial insurance provision in low-income countries?
familiar asymmetric information problems – adverse selection and moral hazard – pose a serious challenge to commercial insurance provision.
Q4. What is the effect of the zndvi?
Catastrophic drought seasons routinely exhibit a continuous downward trend in cumulative zndvi , leading to a large value for CNzndvi, which should have a significantly positive impact on mortality.
Q5. What is the main cause of failure in low-income countries?
Covariate risk is a major cause of insurance market failures in low-income countries as spatially-correlated catastrophic losses can easily exceed the reserves of an insurer, leaving policyholders unprotected (Besley 1995).
Q6. What is the effect of the cumulative vegetation indices?
These cumulative vegetation indices effectively capture the myriad, complex interactions between climate and stocking rates, reflected in rangeland conditions, and livestock mortality rates.
Q7. Why is the ALRMP data used in contract design unsuitable?
The ALRMP data the authors use in contract design are collected for the Government of Kenya, which might have a material interest in IBLI contract payouts, thereby rendering those data unsuitable as the basis for the index itself.
Q8. What is the way to address the uninsured risk problem faced by pastoralists?
IBLI to provide covariate asset risk insurance can effectively address the uninsured risk problem faced by pastoralists only if underwriters can manage the covariate risk effectively, perhaps through reinsurance markets or securitization of risk exposure (e.g., in catastrophe bonds).
Q9. What is the importance of livestock mortality risk management for pastoralists?
The importance of livestock mortality risk management for pastoralists is amplified by the apparent presence of poverty traps in east African pastoral systems, characterized by multiple herd size equilibria such that losses beyond a critical threshold – typically 8- 16 tropical livestock units (TLUs)1 – tend to tip a household into collapse into destitution (Barrett et al., 2006; Lybbert et al., 2004; McPeak and Barrett, 2001).
Q10. What is the reason why private insurance fails to emerge in areas like northern Kenya?
As the authors discussed in the introduction to this paper, covariate risk exposure is a major reason why private insurance fails to emerge in areas like northern Kenya, where climatic shocks like droughts lead to widespread catastrophic losses.
Q11. What is the main cause of the existence of poverty traps among east african pastoralists?
uninsured risk appears a primary cause of the existence of poverty traps among east African pastoralists (Santos and Barrett 2008).
Q12. What is the common method of restocking a herd?
Some households can draw on cash savings and/or informal credit from family or friends to purchase animals to restock a herd after losses.
Q13. What is the probability of herd mortality exceeding 20%?
the probability of herd mortality exceeding 20% (30%) is approximately 15% (9%) for Chalbi in contrast to 19% (14%) for Laisamis, while the proportion of extreme herd mortality exceeding 50% is approximately 6% for Chalbi in contrast to only 2% for Laisamis.
Q14. How many potential trigger payments per year can be designed?
It is also possible to design a one-year contract covering two consecutive seasonal contracts, consisting of two potential trigger payments per year (at the end of each dry season),X-coordinate) - 0.02.
Q15. How did the authors define the location boundary for the three locations with available GPS?
To define location boundary for the three locations with available GPS for water points, the authors first identified GPS bound on each side of the rectangular among all the available GPS points and extended 0.02 degree (around 10 km.) to each side of the GPS bound.
Q16. What are the main reasons for the lack of consistent rainfall data?
The Kenya Meteorological Department station rainfall data for northern Kenya exhibit considerable discontinuities and inconsistent and unverifiable observations.
Q17. Why does the insurer have to set the price before prospective IBLI purchasers make their insurance?
Because the insurer must set the price before prospective IBLI purchasers make their insurance decisions, the latter may have superior information, leading to some level of intertemporal adverse selection.