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Journal ArticleDOI

Designing Index-Based Livestock Insurance for Managing Asset Risk in Northern Kenya

TL;DR: In this paper, the authors describe a novel index-based livestock insurance (IBLI) product piloted among pastoralists in Northern Kenya, where insurance markets are effectively absent and uninsured risk exposure is a main cause of poverty.
Abstract: This article describes a novel index-based livestock insurance (IBLI) product piloted among pastoralists in Northern Kenya, where insurance markets are effectively absent and uninsured risk exposure is a main cause of poverty. We describe the methodology used to design the contract and its underlying index of predicted area-average livestock mortality, established statistically using longitudinal observations of household-level herd mortality fit to remotely sensed vegetation data. Household-level performance analysis based on simulations finds that IBLI removes 25-40 percent of total livestock mortality risk. We describe the contract pricing and the risk exposures of the underwriter to establish IBLI's reinsurability on international markets. INTRODUCTION Formal insurance contracts are rarely available for the small-scale agricultural and pastoral households who populate the often highly risky environments found in rural areas of low-income countries. Although a rich literature analyzes the wide array of informal social arrangements and diversification strategies that these households employ to manage risk, in nearly all cases these mechanisms are highly imperfect and in many cases carry very high implicit insurance premia. The net result is that risk contributes significantly to the level and persistence of rural poverty. In response to this challenge, a small, but growing number of projects are trying to fill this insurance void by developing index insurance contracts that offer payoffs based on the realization of an aggregate performance indicator, or index, rather than on individual-specific outcomes. (1) Because it relies on an objectively and cost-effectively measured aggregate indicator--not manipulable by insured parties--index insurance is potentially viable in low-income agriculture, where transactions costs, moral hazard, and adverse selection typically cripple contracts based on individual-specific outcomes. A key challenge in developing effective index insurance revolves around identifying an index that minimizes the associated basis risk representing discrepancies between the contract's index-triggered indemnity payments and the insured's actual loss experience. Although index insurance principles thus seem to offer a way to reduce the costs of uninsured risk, most projects to date have insured stochastic income streams (e.g., crop yield insurance), despite the fact that globally most insurance sold is actually asset insurance. This article designs and implements a methodology for using satellite-based information to create asset insurance contracts for some of the poorest and most vulnerable people on the planet, namely, the pastoralist populations of the arid and semi-arid regions of East Africa. Our focus on asset insurance is not accidental. Effective demand appears sluggish for the various agriculture index insurance contracts presently on offer to protect rural income streams. Although there are a variety of reasons for this sluggishness, (2) one likely reason is that static income insurance offers the farmer a zero sum proposition: does the farmer want to spend a fraction of a given income level on insurance, implying a reduction in spending on other goods and services? Arguably, demand for insurance will be stronger and more sustainable when it offers the farmer a nonzero sum choice. Income insurance can become a nonzero sum proposition if it simultaneously underwrites an increase in expected income even as it reduces risk exposure. This positive sum game can happen if income insurance crowds in the adoption of new, higher-returning technologies, either by improving the supply of credit to purchase these technologies or by increasing farmers' willingness to bear the risk to borrow and otherwise adopt these technologies. By preserving productive assets for future periods, asset insurance similarly offers not just an effective buffer against current risk exposure but also higher expected incomes over time and thereby makes insurance a positive sum game. …

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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DESIGNING INDEX BASED LIVESTOCK INSURANCE
FOR MANAGING ASSET RISK IN NORTHERN KENYA
Sommarat Chantarat, Andrew G. Mude,
Christopher B. Barrett and Michael R. Carter
July 2009
The authors are Ph.D. candidate, Cornell University, Research Scientist, International Livestock Research
Institute, Nairobi, Kenya, S.B. & J.G. Ashley Professor of Applied Economics and Management, Cornell
University, and Professor, Department of Agricultural and Applied Economics, University of Wisconsin-
Madison, respectively. This research was funded through a USAID Norman E. Borlaug Leadership
Enhancement in Agriculture Program Doctoral Dissertation Improvement Grant, the World Bank
Commodity Risk Management Program, the Global Livestock Collaborative Research Support Program,
funded by the Office of Agriculture and Food Security, Global Bureau, USAID, under grant number DAN-
1328-G-00-0046-00, the Assets and Market Access Collaborative Research Support Program and the
Graduate School of Cornell University. We thank Munenobu Ikegarmi, John McPeak, Calum Turvey and
seminar participants at Cornell University and the International Livestock Research Institute, Nairobi,
Kenya for their helpful comments. The opinions expressed do not necessarily reflect the views of the U.S.
Agency for International Development. Any remaining errors are the authors’ sole responsibility.

1
DESIGNING INDEX BASED LIVESTOCK INSURANCE FOR MANAGING ASSET
RISK IN NORTHERN KENYA
Abstract
This paper describes a novel effort at developing index-based insurance for location-
averaged livestock mortality as a means to fill an important void in the risk management
instruments available to protect the main asset of pastoralists in the arid and semi-arid
lands of Kenya, where insurance markets are effectively absent and uninsured risk
exposure is a main cause of the existence of poverty traps. We describe the detailed
methodology in designing such insurance contract with the underlying index uniquely
constructed off explicit statistical predictions established using longitudinal observations
of household-level herd mortality, fit to high quality, objectively verifiable remotely
sensed vegetation data not manipulable by either party to the contract and available at
low cost and in near-real time. The resulting index performs very well out of sample,
both when tested against other complementing household-level herd mortality data from
the same region and period and when compared qualitatively with community level
drought experiences over the past 27 years. We describe contract pricing and potential
risk exposures of the underwriter using a rich time series of satellite-based vegetation
data available from 1982-present. And finally, implementation opportunities and
challenges are discussed to spur the product’s pilot potential.
Keywords: Drought risk management, index insurance, Kenya, livestock insurance,
livestock mortality, pastoralists, vegetation index, weather derivatives
1. Introduction
Uninsured risk has long been recognized as a serious obstacle to poverty reduction in
poor agrarian nations. In order to limit risk exposure, risk averse poor households often
select low-risk, low-return asset and activity portfolios that trade off growth potential and
expected current income for a lower likelihood of catastrophic outcomes (Eswaran and
Kotwal 1989, 1990; Rosenzweig and Binswanger 1993; Morduch 1995; Zimmerman and
Carter 2003; Dercon 2005; Carter and Barrett 2006; Elbers et al. 2007). Furthermore,
because risk exposure leaves lenders vulnerable to default by borrowers, uninsured risk
commonly limits access to credit, especially for the poor who lack collateral to guarantee
loan repayment. And if an asset used to secure the loan is itself at risk, lack of insurance
can even compromise the opportunities afforded through collateral. The combination of
conservative portfolio choice induced by risk aversion and credit market exclusion due to
uninsured default and asset risk helps to perpetuate poverty.

2
Rural populations in low-income countries commonly face much uninsured risk
because covariate risk, asymmetric information, and high transaction costs preclude the
emergence of formal insurance markets. 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).
Such covariate risk exposure explains why crop insurance policies are generally available
only where governments take on much of the catastrophic risk exposure faced by insurers
(Binswanger and Rosenzweig 1986; Miranda and Glauber 1997). Meanwhile, familiar
asymmetric information problems – adverse selection and moral hazard – pose a serious
challenge to commercial insurance provision. Finally, the transaction costs of contracting
and claims verification are much higher in rural areas than in cities due to limited
transportation, communications and legal infrastructure. While informal insurance
through social networks can address many of the asymmetric information and
transactions costs problems, these too are typically overwhelmed by covariate risk. The
end result is widespread insurance market failure.
Index insurance based on cumulative rainfall, cumulative temperature, area
average yield, area livestock mortality, and related indices have recently been developed
to try to address otherwise-uninsured losses caused by various natural perils in low-
income countries (Recently reviewed by Skees and Collier 2008; Barrett et al. 2008;
Alderman and Haque 2007). Unlike traditional insurance, which makes indemnity
payments to compensate for individual losses, index insurance makes payments based on
realizations of an underlying – transparent and objectively measured – index (e.g. amount
of rainfall or cumulative temperature over a season, or area-average livestock mortality)
that is strongly associated with insurable loss.
An index insurance contract has three main components. First, it requires a well-
defined index and an associated strike level that triggers an insurance payout. The index
must be highly correlated with the aggregate loss being insured, and based on data
sources not easily manipulated by either the insured or the insurer, and with adequate,
reliable historical data to estimate the probability distribution of the index for proper
pricing and risk exposure analysis. Second, it requires well-defined spatiotemporal
coverage with premium pricing specific to that place and period. Third, the contract
requires a clear payout timing and structure to all covered clients conditional on the index
reaching the contractually specified strike level.
The benefits to such a contract design are several and especially appropriate to
rural areas of developing countries where covariate risk, asymmetric information and
high transactions costs render conventional insurance commercially unviable. By
construction, the index captures covariate risk since it reflects the average (e.g., yield,
mortality) or shared (e.g., rainfall, temperature) experience of the insurable population. If
this covariate risk can be reinsured or securitized, locally-covariate risk can be transferred
into a broader (international) risk pool where it is weakly or uncorrelated with the returns
to other financial assets (Hommel and Ritter 2005; Froot 1999). Furthermore, index
insurance contracts avoid the twin asymmetric information problems of adverse selection
(hidden information) and moral hazard (hidden behavior) because the indices are not
individual-specific; they explicitly target – and transfer to insurers – covariate risk within
the contract place and period. Finally, insurance companies and insured clients need only

3
monitor the index to know when a claim is due and indemnity payments must be made.
They do not need to verify claims of individual losses, which can substantially reduce the
transactions costs of monitoring and verification of the insurance contracts.
These gains come at the cost of basis risk, which refers to the imperfect
correlation between an insured’s potential loss experience and the behavior of the
underlying index on which the index insurance payout is based. A contract holder may
experience the type of losses insured against but fail to receive a payout if the overall
index is not triggered. Conversely, while the aggregate experience may result in a
triggered contract, some insured individuals may not have experienced losses yet still
receive payouts. The tradeoff between basis risk and reductions in incentive problems
and costs is thus a critical determinant of the effectiveness of index insurance products.
Although the overwhelming majority of insurance worldwide covers asset risk, to
date almost all retail-level IBRTPs in developing countries have been designed to insure
stochastic income streams, primarily crop income plagued by weather risk. This paper
demonstrates the potential of index-based insurance contracts to manage livestock asset
risk among pastoral communities in northern Kenya, what we call Index-Based Livestock
Insurance (IBLI). Mongolia has the only current example of an IBLI product. Offered
commercially to individual herders by private insurance companies, the Mongolian IBLI
product is based on area average mortality collected by a national census; the insurers are
then reinsured through a contingent debt facility with the national government and the
World Bank Group (Alderman and Haque 2007; Mahul and Skees 2005, 2006). Concerns
exist, however, because of both the cost and timeliness of collecting accurate annual
census data, and the capacity of government – an interested party to the contracts – to
manipulate the livestock mortality data.
Mongolian-type IBLI is infeasible in our setting, as government does not
routinely and reliably collect livestock mortality data. But advances in remote sensing
make it possible to design index insurance based on increasingly precise, inexpensive,
objectively verifiable, real-time estimates of key observable geographic variables.
Because grazing systems ultimately revolve around forage availability, we use the
increasingly popular remotely sensed Normalized Differential Vegetation Index (NDVI),
an indicator of vegetative cover widely used in drought monitoring programs and early
warning systems in Africa (Sung and Weng, 2008), to predict livestock mortality. NDVI-
based index insurance contracts have recently emerged. The United States Department of
Agriculture’s Risk Management Agency now issues pasture insurance based on both
rainfall and NDVI indices. The Millennium Villages Project (Earth Institute at Columbia
University and UNDP) in partnership with Swiss Re has just developed a drought index
insurance program in a number of rural African villages. Preliminary results show that
NDVI reliably signals most major drought years in regions with high seasonal NDVI
variance, such as the semi-arid Sahel region of Africa (Ward et al. 2008).
We make three important innovations in this paper. First, we explain the design of
the first index insurance contract for developing countries designed based on household-
level panel data measuring asset loss experiences. Second, we demonstrate how one can
build index insurance contracts off explicit statistical predictions of the variable of
intrinsic insurable interest – in our case, livestock mortality – rather than relying only on
implicit relationships between that variable and measurable proxies (e.g., NDVI, rainfall,

4
temperature). Third, our data permit unprecedented out-of-sample performance testing of
these contracts. The resulting contract has attracted significant financial sector interest in
the region and will launch commercially in early 2010.
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 5 reports and evaluates the
performance of the estimated livestock mortality models that underpin the IBLI contract.
Section 6 discusses contract pricing and risk exposure. Section 7 concludes with a
discussion of implementation challenges for this and similar index insurance products.
2. The Northern Kenya Context
The more than three million people who occupy northern Kenya’s arid and semi arid
lands (ASALs) depend overwhelmingly on livestock, which represent the vast majority of
household wealth and account for more than two-thirds of average income. Livestock
mortality is therefore perhaps the most serious economic risk these pastoralist households
face. 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). Indeed, uninsured
risk appears a primary cause of the existence of poverty traps among east African
pastoralists (Santos and Barrett 2008).
Most livestock mortality is associated with severe drought. In the past 100 years,
northern Kenya recorded 28 major droughts, 4 of which occurred in the last 10 years
(Adow 2008). The climate is generally characterized by bimodal rainfall with short rains
falling in October – December, followed by a short dry period from January-February.
The long rain – long dry spell runs March-May and June-September, respectively.
Pastoralists commonly pair rainy and dry seasons, for example observing that failure of
the long rains results in large herd losses at the end of the following dry season.
Pastoralist households commonly manage livestock mortality risk ex ante,
primarily through animal husbandry practices, in particular nomadic or transhumant
migration in response to spatiotemporal variability in forage and water availability.
When pastoralists suffer herd losses, there exist social insurance arrangements that
provide informal interhousehold transfers of a breeding cow; but these schemes cover
less than ten percent of household losses, on average, do not include everyone and are
generally perceived as in decline (Lybbert et al. 2004, Santos and Barrett 2008,
Huysentruyt et al. 2009). Some households can draw on cash savings and/or informal
credit from family or friends to purchase animals to restock a herd after losses. But the
vast majority of intertemporal variability in herd sizes is biologically regulated, due to
1
TLU is a standard measure that permits aggregation across species based on similar average metabolic
weight. 1 TLU = 1 cattle = 0.7 camels= 10 goats or sheep.

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Journal ArticleDOI
TL;DR: In this article, the authors use panel data on investments in rural India to examine how the composition of productive and nonproductive asset holdings varies across farmers with different levels of total wealth and across farmers facing different degrees of weather risk.
Abstract: Despite the growing evidence that farmers in low-income environments are risk-averse, there has been little empirical evidence on the importance of risk in shaping the actual allocation of production resources among farmers differentiated by wealth. The authors use panel data on investments in rural India to examine how the composition of productive and nonproductive asset holdings varies across farmers with different levels of total wealth and across farmers facing different degrees of weather risk. Income variability is a prominent feature of the experience of rural agents in low-income countries. The authors report evidence, based on measures of rainfall variability, that the agricultural investment portfolio behavior of farmers in such settings reflects risk aversion, due evidently to limitations on consumption-soothing mechanisms such as crop insurance or credit markets. The authors' results suggest that uninsured weather risk is a significant cause of lower efficiency and lower average incomes: a one-standard-deviation decrease in weather risk (measured by the standard deviation of the timing of the rainy season) would raise average profits by up to 35 percent among farmers in the lowest wealth quartile. Moreover, rainfall variability induces a more unequal distribution of average incomes for a given distribution of wealth. Wealthier farmers are willing to absorb significant risk without giving up profits to reduce production risk. Smaller farmers have to invest their limited wealth in ways that reduce their exposure to risk at the cost of lower profit rates. The authors found that at high levels of rainfall variability, differences in rates of profit per unit of agricultural assets were similar across classes of wealth. But over the sample range of rainfall variability, these rates of profit were always higher for the poorer farmers than for the wealthier ones, suggesting that the disadvantages of small farmers in risk diffusion are more than offset by their labor cost advantage.

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Frequently Asked Questions (17)
Q1. What are the contributions mentioned in the paper "Designing index based livestock insurance for managing asset risk in northern kenya" ?

This paper describes a novel effort at developing index-based insurance for locationaveraged livestock mortality as a means to fill an important void in the risk management instruments available to protect the main asset of pastoralists in the arid and semi-arid lands of Kenya, where insurance markets are effectively absent and uninsured risk exposure is a main cause of the existence of poverty traps. The authors describe the detailed methodology in designing such insurance contract with the underlying index uniquely constructed off explicit statistical predictions established using longitudinal observations of household-level herd mortality, fit to high quality, objectively verifiable remotely sensed vegetation data not manipulable by either party to the contract and available at low cost and in near-real time. The authors describe contract pricing and potential risk exposures of the underwriter using a rich time series of satellite-based vegetation data available from 1982-present. And finally, implementation opportunities and challenges are discussed to spur the product ’ s pilot potential. 

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. 

familiar asymmetric information problems – adverse selection and moral hazard – pose a serious challenge to commercial insurance provision. 

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. 

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). 

These cumulative vegetation indices effectively capture the myriad, complex interactions between climate and stocking rates, reflected in rangeland conditions, and livestock mortality rates. 

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. 

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). 

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). 

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. 

uninsured risk appears a primary cause of the existence of poverty traps among east African pastoralists (Santos and Barrett 2008). 

Some households can draw on cash savings and/or informal credit from family or friends to purchase animals to restock a herd after losses. 

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. 

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. 

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. 

The Kenya Meteorological Department station rainfall data for northern Kenya exhibit considerable discontinuities and inconsistent and unverifiable observations. 

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.