Policy Research Working Paper 7744

How to Measure Whether Index Insurance Provides Reliable Protection

Karlijn MorsinkDaniel Jonathan Clarke

Shadreck Mapfumo

Finance and Markets Global Practice GroupJuly 2016

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Abstract

The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

Policy Research Working Paper 7744

This paper is a product of the Disaster Risk Financing and Insurance Program (DRFIP), a partnership of the World Bank’s Finance and Markets Global Practice Group and the Global Facility for Disaster Reduction and Recovery, with funding from the UK Department for International Development. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at dclarke2@worldbank.org.

Agricultural index insurance offers the promise of an affordable and sustainable insurance product for farm-ers that can help reduce their vulnerability to aggregate agricultural shocks such as large-scale drought or flooding. However, index insurance provides claim payments based on a trigger that is only imperfectly correlated with losses. This implies that it carries basis risk: it may provide claim payments in years when there are no losses, and no claim payments in years when there are losses. The impact of index insurance on poverty outcomes is highly sensitive to the degree to which the product offers reliable protec-tion. Offering unreliable index insurance may lead to high reputation risk for donors, governments, and the private sector. This study proposes to measure the reliability of

index insurance in terms of two policy objectives that stake-holders may have when offering index insurance: the extent to which the insurance captures losses caused by the peril covered by the contract (insured peril basis risk) and the extent to which the insurance covers losses from agricul-tural production (production smoothing basis risk). For both types of basis risk two indicators are proposed: the probability of catastrophic basis risk and the catastrophic performance ratio. Donors, governments, and insurers can use the proposed monitoring indicators without much prior technical knowledge. Although the indicators specifically focus on agricultural index insurance for low-income farm-ers, they can be applied to any context where payments are provided based on indices that are correlated with losses.

HowtoMeasureWhetherIndexInsuranceProvidesReliableProtection*

KarlijnMorsink1,2,DanielJonathanClarke3,ShadreckMapfumo3

"Ifyoucannotmeasureit,youcannotimproveit."–LordKelvin

JELclassifications:G22,I38,O13

Keywords:Agriculture,IndexInsurance,Reliability,BasisRisk,MonitoringIndicators

*ThispaperbuildsonworkbytheDisasterRiskFinancingandInsuranceProgram,apartnershipoftheWorldBankGroup'sFinance&MarketsGlobalPracticeandtheGlobalFacilityforDisasterReductionandRecovery(GFDRR),andthe Global Index Insurance Facility, a multi‐donor trust fund managed by the Finance & Markets GlobalPractice.FundingfromtheUKDepartmentforInternationalDevelopment,theUnitedStatesAgencyforInternationalDevelopment, the Netherlands Ministry of Foreign Affairs, the European Union, Japan, and GFDRR is gratefullyappreciated. We would also like to thankMartin Buehler, Michael Carter, Chloe Dugger, Gilles Galludec, HuybertGroenendaal,LenaHeron,VijayKalavakonda,FelixLung,BarryMaher,MichaelMbaka,OlivierMahul,AndrewMude,Michel Noel, Fredrick Odhiambo, Sharon Onyango, Gary Ruesche, Rachel Sberro, James Sinah, Quentin Stoeffler,Andrea Stoppa, Charles Stutley, PanosVarangis,RuthVargasHill, AnitaUntario, JoseAngel Villalobos, andAndriyZaripov.1Correspondingauthor:karlijn.morsink@economics.ox.ac.uk2CentrefortheStudyofAfricanEconomies(CSAE),EconomicsDepartment,UniversityofOxford3Finance&MarketsGlobalPractice,WorldBankGroup

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1.INTRODUCTION

Despiteusingavarietyofrisk‐managementstrategies,low‐incomefarmers4indevelopingcountriesremainvulnerabletoshocks.Farmersindevelopingcountriesuseavarietyofstrategiestomitigate,transferandcopewithrisksuchascropdiversification,vaccinationofcattle,engaginginprecautionarysavings,takingemergencyloansorrelyingontransfersfromotherswithwhomtheyinformallysharerisk.Despitetheuseofthisvarietyofstrategies,evidencesuggeststhattheextenttowhichfarmersareabletomanageriskandinsurethemselvesinformallyisinsufficienttofullyprotectthem(Townsend,1994;Udry,1994;Dercon&Krishnan,2000;Duflo&Udry,2003).

Animportantexplanationoftheinabilityoffarmerstoprotectthemselvesistheexistenceofaggregateagriculturalshockssuchaslarge‐scaledroughtorflooding.Thecorrelatednatureoftheseshocksimpliesthatfarmersareallaffectedinthesamewayatthesametime.Thislimitstheirabilitytohelpeachotherintimeswhenitismostneeded.Empiricalevidenceshowsthatthethreatoftheseaggregateshockscauseshouseholdstoengageincostlyriskmanagementactivitiesandinvestinlowrisk,lowreturnproductionpractices(Rosenzweig&Binswanger,1993;Morduch,1995;Carteretal.,2007).Somestudiesestimatethataveragefarmincomescouldbesignificantlyhigherintheabsenceofdownsiderisk(Gautam,HazellandAlderman1994;SakuraiandReardon1997).Theinvestmentinlowreturnproductionactivitiesimpliesthathouseholds,whenconfrontedwithrepeatedassetlossesandincomeshocks,remainatalow‐levelequilibrium,potentially‘trapping’theminpoverty(Barnett,BarrettandSkees2008).

Agriculturalriskmayalsonegativelyaffectlow‐incomefarmers’developmentoutofpovertybyaffectingsupplyandtake‐upofcredit.Forcreditprovidersthecorrelatednatureofagriculturalrisksimpliesthatexpecteddefaultratesforcreditprovidedtolow‐incomefarmersarehigh.Incombinationwithasymmetricinformation,imperfectenforcementofcreditcontractsandhightransactioncosts,thismakeslow‐incomefarmersanunattractivetargetgroup(RosenzweigandBinswanger,1986).Furthermore,evenifcreditissupplied,agriculturalriskoftenpreventslow‐incomefarmersfromtakingoutloansbecauseoftheirpreferenceforlow‐risk,low‐returnproductionpractices(DerconandChristiaensen,2008;GinéandYang,2009).

Agriculturalinsuranceoffersthedualpromiseofprotectinglow‐incomefarmersagainstagriculturalshockswhileunlockingcreditandproductiveinvestments.Thetheoreticalpropositionofinsuranceisthatitprovidesclaimpaymentstocoverlossesinbadyearsinexchangeforregularpremiumpaymentsingoodyears(Barréetal.,2015).Assuchitmaysmoothconsumptionlevelsandpreventtheuseofharmfulriskcopingstrategies,suchassellingproductionassets,whichhaveseriousnegativeconsequencesforfuturewelfare.Indexinsurance,throughprovidingprotectionagainstaggregateshocks,mayactuallycomplementprotectionprovidedagainstidiosyncraticshocksthroughinformalrisk‐sharingarrangementsandtherebysubstantiallyimprovefarmers’abilitytosmoothconsumptionafteraggregateshocks(MobarakandRosenzweig,2012;Derconetal.,2014).Second,formalinsurancemay

4Wedefine farmersas individualsengaged in activities inagriculture, e.g. forestry,hunting, and fishing, aswell ascultivation of crops and livestock production following the divisions 1‐5 of the International Standard IndustrialClassification(ISIC)revision3.

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changeinvestmentbehaviororencouragecredituptakebeforeshocksoccurthroughprovidingprotectionandthereforereducingtheneedforsmoothingincomethroughactivitiesthatdepressrisk.Finally,formalinsuranceagainstaggregateshocksmayprotectlendersbyreducingdefaultratesandtherebyunlocktheprovisionofcredittolow‐incomefarmers(Karlanetal,2012).Despitethepotentialofindexinsurancetocontributetopovertyreduction,indexinsurancecarriesbasisrisk,whichmaychallengethereliabilityofindexinsuranceprotection,especiallyforlow‐incomefarmers.Basisriskarisesbecauseindexinsuranceclaimpaymentsarebasedonatriggerthatiscorrelatedwithlosses,butimperfectlyso.Thisimpliesthatindexinsurancemayprovideclaimpaymentsinyearswhentherearenolosses;andnoclaimpaymentsinyearswhentherearelosses.

Fromapovertyperspectivethereliabilityofindexinsuranceisimportantbecausetheimpactofindexinsuranceonpovertyoutcomesishighlysensitivetothedegreetowhichtheproductoffersreliableprotection,especiallyinthecaseswherefarmersexperienceseverelosses(Clarke2016).

Thereliabilityofindexinsuranceisalsoimportantfordonors,governments,theprivatesector,andotherstakeholdersinvolvedinofferingindexinsuranceasbasisriskmayleadtohighreputationriskwithpotentialsevereconsequencesfortrustinmarketplayersandmoregenerallyforinsurancedemand.

Despitetheimportanceofreliableprotection,especiallyinacontextwhereindexinsuranceisofferedtoalreadylow‐incomeindividuals,monitoringofreliabilityisrarelyconducted.Monitoringbasisriskrequiresaclearoperationalandmeasurabledefinition,andneedstobebasedontheuseofappropriatestatisticaltechniques.AsexplainedinarecentCornellUniversity/ILRIworkingpaper:

‘Todate,noneofthestudiesassociatedwithindexinsuranceproductsindevelopingcountriesofferhousehold—levelestimatesofbasisrisk.Infact,fewstudiesexplicitlyincludeanymeasureofbasisriskatall.Thelackofempiricalattentiontobasisriskis especially disturbing because without it, there is no guarantee that indexinsuranceisriskreducing.Incaseswhereanindividual’sidiosyncraticriskishighorif the index is inaccurate, index products can represent a risk increasing gambleratherthantheriskreducinginsurancetheyareadvertisedtooffer.Discerningthemagnitude and distribution of basis risk should be of utmost importance fororganizations promoting index insurance products, lest they inadvertently peddlelotteryticketsunderaninsurancelabel.’(Jensen,BarrettandMude,2014:p.2)

Inthispaperwediscussthereliabilityofindexinsuranceandproposeasetofindicators,tomeasurethisreliability.Donorsandgovernmentscanapplytheproposedindicatorswithoutmuchpriortechnicalknowledge.Theindicatorscanbeusedtocompareagriculturalinsuranceproductsagainstabenchmark,compareoneproduct’svalueovertimebasedonchangesinindicatorsorcomparedifferentproductswitheachother.Theycouldbeincorporatedbygovernmentsorregulatorsasindustrystandards.Furthermore,donors,governments,andinsuranceproviderscanalsoincorporatetheindicatorsinstrategicplanning

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processestoimprovethequalityofproducts,protectconsumers,andreducereputationalrisk(Jensenetal,2014).

Theprinciplesandindicatorstomeasureindexinsurancereliabilitycanbeappliedtoanycontextwhereclaimpaymentsareprovidedbasedonindicesthatarecorrelatedwithlosses,eventhoughthediscussioninthispaperisspecificallyfocusedonagriculturalindexinsuranceforlow‐incomefarmers.Examplesofotherindiceswheretheseprinciplescanbeappliedareindicesthatprotectmicrofinanceorganizationsorcountriesagainstnaturaldisasters.

Therestofthispaperisorganizedasfollows.InSection2theconceptofthereliabilityofindexinsuranceisdefined.Section3discussesthemeasurementofthereliabilityofindexinsurance.InSection4theindexinsurancereliabilityindicatorsarepresented.Section5and6explainhowtheindexinsurancereliabilityindicatorscanbecalculatedandused,respectively.Thelastsectionconcludes.

2. DEFININGTHERELIABILITYOFINDEXINSURANCE

Thereliabilityofindexinsuranceisoftenlooselydefinedbytheterm‘basisrisk’,whichistheriskthatanindexinsuranceproductdoesnotpaywhenitshould.AsanexampleTable1providesanoverviewoftheclassificationofyieldsandclaimpaymentsto2,430farmersoveraperiodof9yearsfor270actualindexproductssoldacrossoneIndianstateundertheWeatherBasedCropInsuranceScheme(WBCIS).5TheWBCISwasapubliclysubsidizedprograminIndiathatinsured9millionIndianfarmersthrougharainfallindex.Thisis,todate,oneoftheonlyproductsforwhichdataisavailable.Aswecansee75farmersexperiencedabadyieldwhilereceivingnooraninsignificantclaimpayment.Thisiscalleddownsidebasisrisk.801farmersreceivedasignificantclaimpaymentwhilehaveagoodyield,whichiscalledupsidebasisrisk.Thetermbasisriskhasitsfoundationinthederivativesmarketwhereitisdefinedastheriskthatthereisadifferencebetweenthepriceoftheassettobehedged(suchasabarrelofoil)andthepriceofthehedge(suchasaforwardcontractonabarrelofoil).

TABLE1:CLASSIFICATIONOF‘GOOD’AND‘BAD’YEARSFOR270PRODUCTS(1999‐2009)

Goodyear

>30%ofaverageyield

Badyear

≤30%ofaverageyield

Total

Noorinsignificantclaimpayment

1,481 75 1,556

Significantclaimpayment

801 73 874

2,282 148 2,430

5AnalyzedinClarkeetal.(2012).

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Eventhoughthisdefinitionmayseemstraightforward,differentstakeholdersmayhavedifferentviewsastowhenindexinsuranceproducts‘should’pay.Theseviewsarebetterunderstoodintermsoftheobjectivesofthestakeholderswhoaresupportingtheinsuranceproducts.Whentheobjectiveofindexinsuranceistocontributetothereductionofpovertyorvulnerability,suchasisoftenthecasefordonors,reliabilityisoftenconsideredastheextenttowhichtheindexinsuranceprotectslow‐incomeindividualsagainstlossesfromagriculturalproduction(Barréetal.,2015).Fromtheperspectiveofaninsuranceprovider,whichhaslegalobligationsandmayhaveprofitorefficiencyobjectives,anindexinsurancecontractisreliablewhenitpaysoutincaselossesfromproductionarecausedbytheperilsthatareclearlyspecifiedintheinsurancecontract.

Whenpeoplerefertotheconceptofbasisrisk,whattheyoftenrefertoiswhatwewouldliketomorepreciselydefineas‘insuredperilbasisrisk.’6Insuredperilbasisriskcomparesclaimpaymentswithlossesfromperilsexplicitlynamedintheinsurancecontract.Itaskswhetheranindexinsurancecontractispayinginyearsitmightreasonablybeexpectedtobyapolicyholderwhounderstandsthebasicconceptbutnotthesmallprintofthepolicy.Assuch,insuredperilbasisriskissimilartotheconceptofnon‐performance(DohertyandSchlesinger,1991)orimperfectperformance(MahulandWright,2007)andhasseriousconsequencesforrationaldemandforinsurancefromtheperspectiveoftheconsumer(Clarke,2016).Thisclearlyraisesquestionsaboutconsumerliteracyandconsumerexpectations,andtheresponsibilityofindexinsuranceproviderstomanagethese,especiallywhenindexinsuranceisofferedtoalow‐incometargetpopulationwithlowlevelsoffinancialliteracy.Insuredperilbasisriskprovidesagoodreflectionoftheextenttowhichtheindexisactuallycapturingtheshareofthelossesinproductionthatarecausedbytheinsuredperilmentionedinthecontract.

Indexinsuranceisespeciallyusefulforprotectionagainstprogressiveperils,7suchasdroughtandflooding,wheretheimpactonlossesgraduallybuildsupovertimeandisdifficulttoisolatefromtheeffectofotherperilsandmanagementrelatedfactors.Insuringanindependentlyverifiableindexbasedonrainfalloraveragelossesprovidesawaytoinsureagainstashareofthelosses.Insuredperilbasisriskreflectstheextenttowhichtheindexinsuranceproductactuallycaptureslossescausedbythisparticularperil.Forexample,imaginearainfallindexinsurancecoveringrainfalldeficitandconsecutivedrydaysbasedonrainfallrecordedatalocalweatherstationthatcoversanareawitharadiusof5kilometersaroundtheweatherstation.Insuredperilbasisriskreferstotheextenttowhichrainfalldeficitandconsecutivedrydaysactuallycapturetheeffectofrainfalloncropproduction,andtheextenttowhichrainfallexperiencedbyfarmerswithinthe5kilometersradiusisadequatelyreflectedbytherainfallmeasuresrecordedattheweatherstation(whichisoftenreferredtoasspatialbasisrisk).

Iftheobjectiveoftheindexinsuranceistocontributetopovertyreductionreliabilityreferstotheextenttowhichtheinsuredperilinthecontractisreflectiveoflossescausedbyagriculturalproduction(Elabedetal.,2013,8FlatnesandCarter,20169).Iflossesfrom

6WithintheWorldBankGroupbasisriskrefersto‘insuredperilbasisrisk.’7Index insurancealsohas thepotential toovercomeasymmetric informationproblems, suchasmoralhazardandadverseselection,inherentinmulti‐perilcropinsurance(MPCI).8 Elabedetal.,(2013)compareasingle‐scaleandmulti‐scaleindexinsuranceproductandcompareclaimpaymentstoyieldfromagriculturalproduction,notfromlossescausedbytheperilinthecontracts. 9FlatnesandCarter(2016)compareanindexinsurancewhichcombinesasatellitebasedindexwith

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agriculturalproductionaremostlycausedbyflooding,itwouldbeirresponsiblefortheinsurertodesignandselladroughtcontractjustbecausethatistheeasiesttodesignandcheapertosell.Despitebeinginsuredwithahighqualitydroughtcover,itwouldnotprotectfarmersfromexperiencingsevereshockstotheirconsumption.

Anadditionalmeasureisneededtoreflecttheextenttowhichtheindexreliablyprotectsagainstlossesinproduction.Forthisweproposetousetheterm‘productionsmoothingbasisrisk’.Productionsmoothingbasisriskcomparesclaimpaymentswithlossesfromagriculturalproduction,suchascroplossesforfarmersandherdlossesforpastoralists.Apovertyreductionperspectiveofindexinsurancemakesitespeciallyimportanttoconsiderproductionsmoothingbasisriskinadditiontoinsuredperilbasisrisk.ThisishighlightedinBox1below.

BOX1:ILLUSTRATIONOF‘PRODUCTIONSMOOTHINGBASISRISK’VERSUS‘INSUREDPERILBASISRISK’

AnexamplethatillustratesthedifferencebetweenproductionsmoothingbasisriskandinsuredperilbasisriskisthecaseofAngela,afarmerinGhana’sNorthernRegionwhopurchasedrainfall‐deficitindexinsuranceforherrainfedmaizecrop.ThemaincauseofAngela’scroplossesinthepast10yearshasbeendeficitrainfallsoAngeladecidedtopurchasetheinsuranceandtotakeoutcreditandpurchaseahigh‐yieldingvarietyofmaize,incombinationwithfertilizersandpesticides.Angelamaynothavemadethisinvestmentwithouttheinsurance.Theinsuranceproductwasproperlyadvertisedasarainfall‐deficitindexinsuranceproduct,andmeasuresrainfallataraingaugestation2kilometersawayfromAngela’sfield.Itpaysoutbasedonacountofthenumberofdrydaysandtheamountofrainfalldeficitatthefloweringstage.Angelaboughttheinsurancetoinsureasumof1,000Ghanacedi(GHS)foronehectareofmaizeandpaidapremiumof100GHS(10%premiumrate).Angelawasunluckythisseasonbecauseaseverelocustdamagedthemaizecropearlyintheseasonandexcessrainandfloodingpriortoharvestledtofullcroplossforallfarmersinthevillage.SincetheindexwasnottriggeredAngelaandherneighborsdidnotreceiveclaimpayments.TheyarenowfacingproblemswithrepayingtheirmaizeloansandAngelaisworseoffthanshewouldhavebeenwithouttheinsurancebecauseshehaspaidapremiumof100GHS,takenaloantoinvestinseeds,fertilizerandpesticidesandexperiencedfullcroplossbutdidnotreceiveaclaimpayment:theworstcasescenario.SinceAngelaincurredacatastrophicproductionlossbutdidnotreceiveaclaimpaymentthiswouldcountasa‘downsideproductionsmoothingbasisrisk’event.However,sincetheproductwasadvertisedasarainfalldeficitcoverageAngelacouldnotreasonablyexpectthattheproductwouldpayaclaimandsoitwouldnotcountasa‘downsideinsuredperilbasisrisk’event.TheindexinsuranceproductperformedasintendedandAngelawillstillrenewherpolicynextyearbecauseshestillhasenoughsavingstopayforthepremiumandshebelievesthatdeficitrainfallisstillthemaincauseofhercroplosses.However,AngelapitiesherneighborAstridwhodoesnothaveanysavingsandisfacedwithrepayingtheloanwithouthavinganyrevenueoraclaimpayment.AngelathinksitisunlikelythatAstridwillbeabletopayforthepremiumnextyear.

3.MEASURINGTHERELIABILITYOFINDEXINSURANCE

the potential for a second‐stage audit to panel data from a retrospective yield survey recording yield fromagriculturalproduction,notfromlossescausedbytheperilinthecontracts. 

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Tomeasurethereliabilityofindexinsuranceonewouldneeddataonclaimpaymentsanddataonlossesfortheinsurancecoverageperiod.Claimpaymentsdatacanbeobtainedfrominsuranceproviders.Dataonlossesfromagriculturalproductionisdifficulttoobtainandrequiresanassessmentofcurrentagriculturalproductionrelativetoahistoricalaverageofproduction,inprincipleinthefirststage,attheleveloftheindividualconsumer.Inaddition,tocalculateinsuredperilbasisrisk,onewouldneedtoobjectivelyassesstheshareoflossesthatarecausedbytheperilthatisinsuredinthecontract(seeAppendix1fordatarequirements).

Thechallengeofacquiringdataonagriculturalproductionlossesleadsustoproposetwomethodsformeasuringagriculturalproductionlosses.Thefirstmethodistheclassificationmethod.Thismethodisbasedonfarmer‐levelsurveyswithrecallquestionsaboutaclassificationofhistoricalagriculturalproductionaseither`good’or`bad’.Thesecondmethodisthestatisticalmethodthatisbasedonpaneldatacollectedthroughfarmer‐levelsurveysthathaverecordedagriculturalproductiondataovertime.Asrecalldataisoftencharacterizedbymeasurementerrorwearereluctanttoaskfarmersabouttheirrecallofactualproductioninquantityorincome.However,weareconfidentthatfarmerscanrecallaclassificationofagriculturalproductionintermsof‘good’or‘bad’years(DeNicolaandGine,2014).Weconsiderthestatisticalmethodtobemorerigorous.Wheneverproductionlossdataareavailable,thestatisticalmethodisthepreferredmethod.Westronglyencouragebetterdatacollectiononagriculturalproductionsothatwecanincreasetheapplicationofthestatisticalmethodtocalculatethereliabilityofindexinsurance.

Tomeasurethereliabilityofindexinsuranceitisimportanttoanalyzethecorrelationbetweenclaimpaymentsandlossesofaportfolioofindexinsuranceproductsandnotofasingleindexedagriculturalinsuranceproduct.Forexample,forasingleannualinsuranceproductwhichpaysoutonceevery10yearsitwilltakedecadestohaveseenenoughclaimpaymentstobeabletounderstandwhetherclaimsarelikelytobepaidwhentheyshouldbe.Inagriculture,whereproductionpracticesandhazardschangeovertime,bythetimeyouhavelearnedaboutthecorrelationbetweenclaimpaymentsandlosses,theriskprofileofagriculturalproductionwillmostlikelybetotallydifferent,andsoyouwillneverreallylearn.However,aswewilldemonstrateinthispaperitissometimespossibletolearnoverashorttimespanfromacollectionofsimilarproducts,bymakinguseofbothtemporalandspatialvariation.

Forbothinsuredperilbasisriskandproductionsmoothingbasisriskachallengeistodecideiflossesshouldbedefinedaslossesinassets,lossesinoutput,orlossesinexpenditure.Assetmeasuresofbasisriskarelikelytobemostusefulforlivestockreplacementindexinsurance;outputmeasuresofbasisriskarelikelytobemostusefulforcropindexinsurance;andexpendituremeasuresofbasisriskarelikelytobemostusefulforindexinsuranceproductswhichpayearlytofinanceearlyriskreductionactions.Justaspovertyeconomistschoosebetweenmeasuresofassetpovertyandconsumptionpoverty,dependingonthequestiontheyareasking,thechoicebetweenassetandproductionmeasuresofbasisriskshouldbemadebasedonthenatureofwhatisbeingprotected.Forexample,Jensenetal.(2014)andChantaratetal.(2013)compareclaimpaymentsfromlivestockindexinsurancewithlivestockmortalityrates(relatedtoassets)whereasClarkeetal.(2012)compareclaimpaymentsfromcropindexinsurancewithcroplosses(relatedtoproduction).Therewill,ofcourse,beexceptionstothisrule.Forexample,livestockprotectionindexinsurancethatpaysearlyindroughtyearstohelpkeepanimalsalivemaybemoreusefullycomparedtoexpenditures.

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Forbothinsuredperilandproductionsmoothingbasisriskoneneedstodecideifclaimpaymentsshouldbecomparedtothevalueoflosses(e.g.indollars)orjusttheamountoflosses(e.g.bagsofwheatornumberofcows).BothClarkeetal.(2012)andJensenetal.(2014)ignorepricesandcompareclaimpaymentstotheamountoflosses.Thisismotivatedbythefactthatpricesofagriculturalcommoditiestypicallyhavehighspatialcorrelationandarestickyovertime,sothatpricesintwoadjacentyearsarelikelytobecloserthanpricesintwodistantyears,andlargepriceshockscanhavealongtermeffect.Multiplyingtheamountoflosseswiththepricetogetthevalueoflossesmaythereforeaddtemporallyautocorrelated,spatiallycorrelatednoisetoadatasetrelativetoadatasetbasedontheamountoflosses.Addingevenalittleautocorrelatednoisetothetemporaldimensioncanmakeitmuchmoredifficulttolearnaboutbasisriskstatistically.Soingeneral,comparingclaimpaymentstotheamountoflossesismostuseful.Table2presentsanoverviewofthedifferenttypesofbasisriskanddefinitionsoflosses.

TABLE2:DIFFERENTOPERATIONALDEFINITIONSOFBASISRISK

Whichcorrelationdoyouconsider?

Howdoyoudefinelosses?

Insuredperilbasisrisk

Correlationbetweenclaimpaymentsandlossesduetoinsuredperil

Productionsmoothingbasisrisk

Correlationbetweenclaimpaymentsandlossesduetoagriculturalproduction

Asset Quantity √ √

Value √ √

Output Quantity √ √

Revenue Value √ √

Expenditure Quantity √ √

Value √ √

Measuringlossesischallengingbecausedataonaggregateproduction,forexampleatcommunityordistrictlevel,mayhidelossesexperiencedattheindividualfarmers’level.Thiswouldnotbeproblematiciffarmerswouldfullyshareidiosyncraticlossesthroughinformalrisk‐sharingarrangements.However,eventhoughinformalrisk‐sharingissubstantial(Townsend,1994;Udry,1990),thestrengthofandcommitmenttoinformalrisk‐sharingarrangementsisshowntovarysignificantlyacrosscontexts(CoateandRavallion,2003;Attanasioetal.,2012).Inthiscaseaggregatemeasuresoflossesmasktheactualextentofbasisriskduetoaveragingoutofextremeswhileitistheextremecasesthatshouldreceivemostconcern,frompovertyandreputationalperspectives.Ifcorrelationsbasedonaggregatedatademonstratehighlevelsofbasisriskthenthiswillcertainlybethecaseforfarmer‐levelbasisrisk.However,lowcorrelationsonaggregatedatamaystillimplyhighlevelsofbasisriskatthefarmerleveliftheextentofinformalrisk‐sharingislow.

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Bothinsuredperilandproductionsmoothingbasisriskmaycapturemoralhazard.Forproductionsmoothingbasisriskthisproblemismoresubstantialbecauseitcomparesclaimpaymentstolossesfromagriculturalproduction.However,eventhoughinsuredperilbasisriskonlycomparesclaimpaymentstotheshareoflossescausedbythespecificperil,becauseindexinsurancecoversprogressiveperilsitisstilldifficulttodistinguishiftheshareoflossesispurelycausedby,forexample,lossofrainfall,oralsobyafarmerchoosingtoexpandmoreeffortonhisnon‐farmactivities,andlesseffortonhisfarm,afterobservinglowlevelsofrainfallbecausehebelievesthereisgoingtobeadroughtandpayout.Forproductionsmoothingbasisriskitismoreobviousthatitmaycapturemoralhazard.Forexample,ifafarmerdecidestotakeupadaily‐wagedlaborjobandthereforedoesnotexpendasmucheffortonherpaddycrop,theremightbelossofagriculturalproductionwhile,basedontheindextrigger,thereisnoclaimpayment.Theindicatorrecordsthisasindistinguishablefromasituationinwhichthefarmerworkshardandloseshercropthroughnofaultofherown,anddoesnotreceiveaclaimpayment.Thisdiscussiononmoralhazardshouldnotbeconfusedwiththeincentivizationofmoralhazard,throughforexamplemulti‐perilcropinsurance.Theauthorspositthatmoralhazardislikelytobelowbecausethedesignofindexinsurance,withclaimpaymentsbasedonindependentlyverifiableindices,incentivizesfarmerstoexpendeffortontheirfarms.

Forthepurposeofmonitoringthereliabilityofindexinsuranceitisimportantthatproposedindicatorsaresimpletocalculateandeasytounderstand,withoutmuchpriortechnicalknowledge,bydonors,governmentsandtheinsurancesector.Moresophisticatedmeasures,relyingonacarefulcomparisonoftheextenttowhichtheindexinsurancecontractdeviatesfromperfectproductionsmoothing(whichwouldbecreatedbyperfectinsurance)atallpotentialrealizationsofthefullyielddistributionhaverecentlybeendeveloped(Barréetal.,2015)butwouldrequireadvancedpriortechnicalknowledge.Downsidebasisriskismuchmoreofaconcernfromapovertyperspectivethanupsidebasisrisk,suggestingthatindicatorstomonitorthereliabilityofindexinsuranceshoulddefinitelycapturedownsidebasisrisk.Downsidebasisriskimpliesthatfarmersmaybeworse‐off,intermsofpoverty,thantheywouldhavebeenwithouttheinsurancebecausetheymayenduppayingapremium,experiencingaloss,butgettingnoclaimpaymentinreturn.Upsidebasisriskisalsoproblematicfromthefarmers’perspectivebecauseclaimpaymentstofarmerswithgoodproductionwillincreasetheoverallcostoftheproductandtherebyincreasethepremiumpaidwithoutofferingadditionalprotectionforcatastrophiclossevents.However,fromapovertyperspectivedownsidebasisriskismuchmoreofaconcernthanupsidebasisrisk(Clarke,2016).Traditionalbasisriskmeasures,suchasthePearson’sproductmomentcorrelationcoefficient,arethereforenotparticularlyusefulfromanindexinsurancereliabilityperspectivesincetheyweighbothdownsideandupsidebasisriskequally(Martyniak,2007;Staggenborgetal.,2008).

Tocapturethereliabilityofindexinsuranceitisthereforeimportanttomonitorthevalueoftheindexinsuranceespeciallyinthosesituationswherefarmersexperiencecatastrophiclosses.Inthiswaythevalueoftheindexinsuranceisassessedforyearswhentherearelargedownwarddeviationsfromaverageproductionandnotwhenthesedeviationsaresmall.Thisisimportantbecausereliableinsuranceshouldfocusonnon‐frequentandsevereshocks.Theexactdefinitionofwhatconstitutescatastrophiclossesisapolicydecisionsthat

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shouldbeinformedbythespecificpolicyobjectivesandthecharacteristicsofthelossesandpolicyholders.Thiscanforexamplebedonebydefiningcatastrophiclossesaslossesthatleadfarmerstouseinformalcopingstrategiesthathavenegativeconsequencesforfuturewelfare(takingchildrenoutofschoolorsellingassets).Asanexamplewedefinecatastrophicproductionlossesasthoselossesofmorethan70%ofaveragehistoricalyield.Inthecalculationoftheindicatorsbasedonthestatisticalmethodthefulldistributionofdeviationsfromlossesrelativetoclaimpaymentsisusedsoonecaneasilychangethispercentage.Fortheclassificationmethodonecandevelopcarefulinterpretationsofwhatconstitutesgoodandbadyears.

Weproposetwoindicatorstoassessthereliabilityoftheinsuranceintheyearswithcatastrophiclosses:theprobabilityofcatastrophicbasisriskandthecatastrophicperformanceratio.Theformerestablishestheprobabilitythatafarmerexperiencesmorethan70%lossofagriculturalproduction,suchaslosingmorethan70%ofheraveragecroporaveragelivestock,butbecausetheindexisnottriggered,receivesnoclaimpayment.Thelatterreflectswhat,onaverage,afarmergetsbackper$1ofcommercialpremiumpaidinthecasethatsheexperiencescatastrophiccroploss.ThecalculationoftheindicatorswillbeexplainedinSection5.

TABLE3:INDICATORSTOMONITORINDEXINSURANCERELIABILITY

Indicator Interpretation Datacollectionmethod

Probabilityofcatastrophicbasisrisk

Probabilityofnotreceivingaclaimpaymentwhenafarmerhascatastrophiclosses

ClassificationmethodandStatisticalmethod

Catastrophicperformanceratio

Onaverage,ifthefarmerexperiencescatastrophiclosses,whatdoeshereceivebackrelativetothepremiumpaid

Statisticalmethod

4.CALCULATIONOFINDEXINSURANCERELIABILITYINDICATORS

Tocalculatetheindexinsurancereliabilityindicatorstwodifferentmethodsareproposedtogetmeasuresoflosses.Thefirstmethodistheclassificationmethod.Thismethodisbasedonfarmer‐levelsurveyswithrecallquestionsaboutaclassificationofhistoricalagriculturalproductionaseither`good’or`bad’.Thesecondmethodisthestatisticalmethodthatisbasedonpaneldatacollectedthroughfarmer‐levelsurveysthathaverecordedagriculturalproductiondataovertime.

ThecalculationofindicatorsisillustratedbytakingalookatthedatathatwerepresentedinTable1.TheGovernmentofIndiahascommittedtocollectthesedatabetween1999and2007(9years)on270actualindexproductssoldacrossoneIndianstateundertheWeatherBasedCropInsuranceScheme(WBCIS).10TheWBCISisapubliclysubsidizedprograminsuring9millionIndianfarmersthrougharainfallindex.Thisis,todate,oneoftheonlyproductsforwhichdataisavailabletoconductthisanalysis.TheGovernmentof10AnalyzedinClarkeetal.(2012).These270productsfallunderthesameWBCISschemesoareessentiallythesameproductbutarestandardizedbasedonthehistoricalaverageyieldandcropsfor270regionswithintheIndianstate.

11

India(GoI)hasbeenapioneerinpushingtheboundariesonmonitoringthevalueofagricultureinsuranceproductsforfarmerwelfare.TheGoIhasturnedthedesignofindexinsurancefromanactivitybasedonanecdotalevidencetoonewherestatisticsprinciplesareusedtomonitorandimprovecropinsuranceschemes.Thisapproachisclearlyrepresentedintheevidence‐basedpolicyadvicecomingoutofthereviewoftheimplementationofcropinsuranceschemesinIndia(Mishra,2014).Fortheclassificationmethodfarmersarefirstaskedforthepast10years,whichyearstheyconsideredas`good’yearsand`bad’yearsintermsofagriculturalproduction.Forthesesameyears,basedonhistoricaldataitiscalculatedwhenconsumerswouldhavereceivedclaimpayments.Forproductionsmoothingbasisriskaclassificationofgoodandbadyearswouldbesufficient.Forinsuredperilbasisrisktheprocedurewouldbetofirstaskfarmerstorecallgoodandbadyearsandthenfollowthisquestionupwithaskingwhichofthebadyearswerecausedbytheperilnamedintheinsurancecontract.FortheWBCISproductdatahaveonlybeencollectedonlossesfromagriculturalproductionnotontheshareoflossescausedbyrainfall.Thereforeitisonlypossibletocalculatetheindicatorsforproductionsmoothingbasisriskbuttheprocedurewouldbeexactlythesameforinsuredperilbasisriskifwehaddataonwhichbadyearswerecausedbylackofrainfall.AswasalreadydemonstratedinTable1,theWBCISyielddataareclassifiedintoyearswithyieldmorethan30%ofaverageagriculturalproductionbasedon9years(goodyear)andyearswithyieldlowerthan30%ofaverageagriculturalproduction(badyears).TheWBCISclaimpaymentdataareclassifiedintoclaimpaymentsthatarehigherandlowerthanthecommercialpremium,implyingthat,attheminimum,thepayoutcoverstheinsurancepremium.Basedonthisclassificationitispossibletodrawupthe2X2gridthatwaspresentedinTable1.

Intheapplicationofthismethoditiscriticaltocarefullydefinecatastrophiclossesandsignificantclaimpayments.Farmers,inclassifyingbadyears,mayincorporatemilddroughtsthatdidnotleadtocatastrophiclosses,intheirclassification.ThereforeitiscrucialtocarefullyclassifygoodandbadyearsandpotentiallytriangulatefarmerrecalldatawithdatafromFEWSNETandMinistriesofAgriculture.Indeterminingthelevelofcatastrophiclossesitisimportanttorefertothespecificinsurancecontract.Forexample,incaseswhereindexinsuranceisbundledwithcredit,farmersmayconsidersignificantclaimpaymentsasthoseclaimpaymentsthatcoveratleasttheinsurancepremiumandtheinterestrateontheloan.

Tocalculatetheprobabilityofcatastrophicbasisriskofthisproductwedividethenumberofbadyearswithnoorinsignificantclaimpaymentsbythenumberofbadyearswheretheyieldwaslessthan≤30%ofaverageyield.Theprobabilityofcatastrophicbasisriskofthisproductis75/148=51%.Thistellsusthatthereisa51%probabilitythattheinsurancewillnotpayoutinyearswherefarmersexperiencecatastrophiclosses.

ToillustratethecalculationofindicatorsbasedonthestatisticalmethodwealsousetheWeatherBasedCropInsuranceScheme(WBCIS)datafromIndia.ToillustratehowtheprobabilityofcatastrophicbasisriskiscalculatedthegraphpresentedinFigure1isused.Appendix2describeshowthisfigurecanbeproduced.Thex‐axispresentstheyieldsoffarmersasapercentageofaverageyields.100%onthex‐axisimpliesthatthefarmerhasanaverageyieldwhile50%impliesthatthefarmerhaslost50%ofheryieldincomparisontotheyieldaverageoveranumberofyears.Apercentageabove100%impliesthatthefarmerhasagoodyieldwhile0%impliestotallossofagriculturalproduction.Averticallinegoingthroughthe

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30%pointonthex‐axisisthethresholdbelowwhichyieldsaredefinedascatastrophiclosses(>70%loss).They‐axispresentstheprobabilitythattheinsuredreceivesaclaimpayment(Asopposedtothecalculationsfortheclassificationmethod,claimpaymentrefersheretoanyclaimpayment,notaclaimpaymenthigherthanthecommercialpremium).Thebluelineinthefigurerepresentsaninsurancecontractwithzerobasisriskandatriggerlevelof80%ofthehistoricalaverageareayield.Itwillprovideaclaimwithaprobabilityof100%ifthefarmers’yieldisbelowthetrigger‐levelof80%oftheaverageyieldanditwillprovideaclaimwithaprobabilityof0%ifthefarmers’yieldisabovethetrigger‐levelof80%oftheaverageyield.Theredlinepresentstherelationship,inferredbasedoncollecteddataonfarmeryieldsandclaimpayments,betweentheaverageyieldsandtheprobabilityofaWBCISclaimpayment.Thegreenlinespresenttheupperandlowerboundofthe95%confidenceintervals.Thegraphshowsthatforcatastrophiclosses(30%ofaverageyield)theprobabilityofcatastrophicbasisriskis33%.Drawingaverticallinethroughthex‐axisat30%ofaverageyieldshowsthattheprobabilityofreceivingaclaimpaymentincaseofcatastrophiclossesis67%.Thismeansthattheprobabilityofnotreceivingaclaimpaymentincaseofcatastrophiclossesis100‐67%=33%.

FIGURE1:PROBABILITYOFCATASTROPHICBASISRISKOFWBCISININDIA

Source:AdaptedfromClarkeetal.(2012)

Thecatastrophicperformanceratiocanonlybederivedfromthestatisticalmethodandiscalculatedbymultiplyingtheprobabilityofreceivingaclaimincasethefarmerhascatastrophiccroplosswiththeaverageamountofclaimshereceivesinthesecasesanddividingthisbythecommercialpremium.11Toillustratehowthisindicatorcanbemeasured

11 The catastrophic performance ratio can be interpreted as the average payout in case of catastrophic losses per $1,‐ of premium paid. An alternative  interpretation  is to view the catastrophic performance ratio as the performance of the contract, by considering the average payout  in case of catastrophic  losses relative to the average  payout  in  all  years.  The  ratio  can  then  be  rewritten  as:  (Expected  claim  payment  in  case  of catastrophic  losses/  Expected  average  claim  payment)*(Expected  average  claim  payment/commercial premium).  If  the contract  is designed  to  fully cover catastrophic  losses  the  ratio would  tend  to 1 while  if  it performs poorly the ratio would tend to 0.  

0%10%20%30%40%50%60%70%80%90%100%

0% 50% 100% 150% 200%

Probability that claim

 paymen

t is 

positive

Subdistrict average yield, as percentage of average historical yield 1999‐2007

Best estimate weather index insurance Upper bound weather index insuranceLower bound weather index insurance Area yield index insurance

13

thegraphpresentedinFigure2isused.Appendix2describeshowFigure2canbeproduced.Statisticalanalysiscanhelptoestimate‘onaverage’whattheexpectedclaimpaymentisincaseafarmerexperiencescatastrophiccroploss.Thex‐axisagainrepresentstheyieldsoffarmersasapercentageofaverageyield.They‐axispresentstheCatastrophicPerformanceRatio,whichrepresentshowmuch,onaverage,afarmergetsbackforeach$1commercialpremiumpaid.Thebluelineinthefigurepresentsaperfectinsurancecontract.Underthiscontractthefarmerdoesnotreceiveanyclaimpaymentifheryieldsareabove80%ofaveragelossesbuttheinsurancestartstograduallypayclaimsassoonasyieldsfallbelowthe80%triggerlevel.Ifthefarmerhascatastrophiclossesandyieldsareatlessthan30%ofaverageyieldsaperfectareayieldindexinsuranceproductwouldgiveaninsuredfarmer$1backforevery$1commercialpremiumpaid.AlsotoillustratethecalculationoftheCatastrophicPerformanceRatiothedataonthe270productssoldacrossoneIndianstateundertheWBCISareused.TheredlineinFigure2presentstheinferredcorrelationbetweenaverageyieldandaverageclaimpaymentsper$1commercialpremiumpaidbythefarmerforthe270products.Thegreenlinespresenttheupperandlowerboundofthe95%confidenceintervals.Asisseentheregressionlineisalmostflat.Forcatastrophiccroploss(30%averageyield)theCatastrophicPerformanceRatioforproductionsmoothingbasisriskis1.01,whichmeansthefarmergets$1.01backforeach$1commercialpremiumpaid.Clarke(2016)demonstratesthatafarmerwhocaresaboutcatastrophiclossesinagriculturalproductionshouldnotpurchasetheindexinsuranceiftheCatastrophicBasisRiskRatioisbelow1forallpotentiallevelsoflosses.

FIGURE2:CATASTROPHICPERFORMANCERATIOOFTHEWBCISININDIA

Source:AdaptedfromClarkeetal.(2012)

0

1

2

3

4

5

0% 50% 100% 150% 200%

Claim

 paymen

t divided

 by 

commercial premium

Subdistrict average yield, as percentage of average historical yield 1999‐2007

Best estimate weather index insuranceUpper bound weather index insuranceLower bound weather index insurance

14

Empiricaldataontheextenttowhichfarmersshareagriculturallossesareoftenunavailablebutwillliesomewherebetweenfullsharingoflossesandnosharingoflosses.Thereforethebasisriskindicatorswillbecomparedtoaggregate‐levelaverageyieldstoreflectanupper‐boundoffulllocalrisk‐sharingandfarm‐levelyieldstoreflectalower‐boundofnorisk‐sharing.Iffarmersfullysharelossesataggregate‐level(suchascaste‐level,12village‐level,orfarmer‐association‐level13)thisimpliesthattheaggregate‐levelaverageyieldsareanadequatereflectionofthefarmers’yields.Acomparisonofclaimpaymentstotheaggregate‐levelaverageyieldstocalculatethebasisriskindicatorswouldthenbethebestwaytorepresentthevalueoftheinsuranceforfarmerwelfare.However,iffarmersdon’tsharelosses,acomparisontoaggregate‐levelaverageyieldswouldoverestimateyieldsforsomefarmersandunderestimateyieldsforotherfarmers.Especiallyanoverestimationofyieldsisproblematicforalreadyvulnerablelow‐incomefarmers.Inthislattercaseacomparisonofclaimpaymentstofarmer‐levelyieldstocalculatethebasisriskindicatorswouldthenbejustified.Intheexampleofthe270WBCISproducts,claimpaymentsarecomparedtosub‐districtaverageyieldsandthus,assumingrisk‐sharingispartial,productionsmoothingbasisriskcanbeassumedtobehigher.Theratioof1.01thusprovidesanupperboundofthecatastrophicbasisriskratio.

Thecalculationofbasisriskindicatorsrequiresdataonfarmer‐levelyieldsandagriculturalindexinsuranceclaimpaymentstobecompiledfromaportfolioofproductsandnotfromasingleproduct.Thelowprobabilityofagriculturalshocksandthenatureofbasisriskimplythatreliableestimatesforthebasisriskindicatorsbasedonasufficientnumberofobservationscanonlybecollectedwithinareasonabletimeframeifdatafromavarietyofproducts(preferablyfromwithinalargerprogram)arecombined.Forexample,theIndianweatherindex‐insurancedatausedtodemonstratethecalculationofthebasisriskindicatorsused9yearsofdatafor270productssoldacrossoneIndianstate.Thestatisticalanalysiswaspossibleespeciallybecauseofthespatialandtimevariationcreatedbyjointlyanalyzingalargenumberofproductsunderonescheme.

5.HOWTOUSETHEINDEXINSURANCERELIABILITYINDICATORS?

Theindicatorscanbeusedtocompareagriculturalinsuranceproductsagainstabenchmark,compareoneproduct’svalueovertimebasedonchangesinindicatorsorcomparedifferentproductswitheachother.Indicatorscollectedbasedonstatisticalmethodsaresuitabletobeincorporatedbygovernmentsasindustrystandards,suchastheInsuranceCommissionoftheGovernmentofthePhilippineshasdonewith‘ThePerformanceStandardsforMicroinsurance’14or‘TheInterAfricanConferencefortheInsuranceMarket’hasdonewithkeyperformanceindicators.15However,donors,governments,andinsuranceproviderscanalsoincorporatetheindicatorsinstrategicplanningprocessestoimprovethequalityofproducts,protectconsumers,andreducereputationalrisk(Jensenetal,2014).To

12SeeforexampleMobarakandRosenzweigforcaste‐levelsharingofagriculturalshocksinIndia.13See for example Dercon et al. (2014) for farmer‐association level risk‐sharing (iddir) of agricultural shocks inEthiopia.14 http://www.insurance.gov.ph/htm/..%5C_@dmin%5Cupload%5Creports%5CCL%2005%20‐%202011.pdf on 4July2014.15 http://www.microfact.org/news/new‐west‐african‐insurance‐legislation‐recognises‐microinsuance‐key‐performance‐indicators‐/on4July2014.

15

developabenchmark,stakeholderscanestablishthevalueoftheindicatorsforarangeofindexinsuranceproductsinahigh‐qualitydataenvironmentsothatthesevaluescanserveasabenchmark.

Itisimportantthatstakeholderstakeintoaccountthattheindicatorsarebasedondatathatareimperfectlymeasuredandthateffortsarerequiredtostandardizedataquality.Forexample,noisyrecalldatamayleadtoanoverstatementofbasisriskbutthisshouldnotleadtosystematicbiaswhencomparingproductsagainsteachotherorabenchmark,whenstandardizedmethodsareusedtocollecttherecalldata.Furthermore,whencomparingindicatorsovertime,improvementsindataqualitymayleadtomoreprecisemeasurementofindicators.

Theindexinsurancereliabilityindicatorsarebestusedjointlyandpreferablyincombinationwithotherinsurancemonitoringandperformanceindicatorsthatfocusonaspectsoftheperformanceofinsuranceproductssuchasincurredexpenseratio,renewalratio,complaintsratio,insuranceliteracyrates,accessibilityindicators(WipfandGarand,2010;SandmarkandSimanowitz,2010;Matul,Tatin‐JaleranandKelly,2011).Box2providesanillustrationofthevaluegainedfromtheuseofmultipleindicators.

BOX2:ANILLUSTRATIONOFHOWTOUSEMULTIPLEINDICATORSTOUNDERSTANDAPORTFOLIOOFPRODUCTS 

TheinsuranceindustryisaccustomedtocalculatingtheIncurredClaimsRatio.Anincurredclaimsratioof80%forindexinsuranceimpliesthatonaverage0.80$per$1commercialpremiumisgivenbacktothefarmer.Eventhoughthisindicatorprovidesanaverage,itdoesnotprovideinformationabouthowmuchconsumersgetback,onaverage,forthesituationswhentheyexperiencelossofagriculturalproduction.Thisiswhatisespeciallyimportantwheninsuringalreadyvulnerablefarmers.Thisinformationisprovidedbythecatastrophicperformanceratio.Acatastrophicperformanceratioof110%tellsusthatfarmersonlyget1.10$backper$1commercialpremiumpaidincasesofcatastrophiclossofagriculturalproduction.Thiswouldbelowvalueforafarmerwhocaresmostabouttheworst‐casescenario.Acatastrophicperformanceratioof600%wouldimplythattheyget6timesthecommercialpremiumincaseoftheworst‐casescenario.Thiswouldbeareliablecoverforanaffordableproductfromtheperspectiveofthefarmer.Anincurredclaimsratioof80%andacatastrophicbasisriskratioof600%mayindicateavaluableproductintermsofthebenefitsforevery1$commercialpremiumpaid.Intheinsuranceindustrytherearestandardsdevelopingforthespeedwithwhichclaimsarepaid.Evenwithanincurredclaimsratioof80%andacatastrophicbasisriskratioof600%,anindexinsurancemaystillbeoflowvalue,especiallyforlow‐income,vulnerablefarmers,ifclaimpaymentsaremadelongafterlossesareincurred.Thedropinconsumptionmayleadfarmerstoselloffassets,reducetheirfoodintake,ortakechildrenoutofschooltowork,whichcanstillhaveseriousconsequencesforfuturewelfare.

6.CONCLUSION

Agriculturalindexinsuranceoffersthepromiseofanaffordableandsustainableinsuranceproductforfarmersthatcancontributetoreducingpoverty.However,indexinsuranceprovidesclaimpaymentsbasedonatriggerthatiscorrelatedwithlosses,butimperfectlyso.Thisimplies

16

thatitcarriesbasisrisk:itmayprovideclaimpaymentsinyearswheretherearenolosses;andnoclaimpaymentsinyearswhentherearelosses.Therefore,theimpactofindexinsuranceonpovertyoutcomesishighlysensitivetothedegreetowhichtheproductoffersreliableprotection,especiallyinthecaseswherefarmersexperienceseverelosses.Offeringunreliableindexinsurancemayleadtohighreputationriskfordonors,governments,andtheprivatesector.Despiteitsimportance,monitoringofindexinsurancereliabilityisrarelyconductedduetothelackofanoperationalandmeasurabledefinitionofbasisrisk,andunderutilizationofappropriatestatisticaltechniques.Inthispaperweproposetwonewindicatorstomeasureindexinsurancereliability:theprobabilityofcatastrophicbasisriskandthecatastrophicperformanceratio.Theproposedindicatorscanbeappliedwithoutmuchpriortechnicalknowledgeandmayallowustolearnaboutthereliabilityofindexinsuranceprotection.

17

APPENDIX1:DATAREQUIREMENTS

TABLE4:ADDITIONALDATACOMPUTEDFROMINSURANCEPROVIDERS’ADMINISTRATIVEDATA

Indicator Additionaldatatobecalculated

1.Probabilityofcatastrophicbasisriskevent

Areainsuredatfarmer‐levelpercommodity

Pay‐outreceivedatfarmer‐levelpercommodity

2.Thecatastrophicperformanceratio

Areainsuredatfarmer‐levelpercommodity

Premiumatfarmer‐levelpercommodity

Suminsuredatfarmer‐levelpercommodity

Pay‐outreceivedatfarmer‐levelpercommodity

TABLE5:ADDITIONALDATACOLLECTIONEFFORTS

Indicator Data Proposedcollectionmethods

1.Probabilityofcatastrophicbasisriskevent;

Productionatfarmer‐levelperagriculturalcommoditycollectedonaseasonalbasis.

Surveyafterharvestingseason

2.Thecatastrophicperformanceratio

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APPENDIX2:CALCULATIONOFINDICATORS

For the indicators in Table 6 the time period n will typically be defined as one productionseason

TABLE6:CALCULATIONOFINDICATORS

Indicator Calculation Specificmeasurement

1.Probabilityofcatastrophicbasisrisk

Tocalculatethisindicatorakernel‐regressionneedstoberunwithastatisticalpackagesuchasSTATA,SPSSorR.Beforethiscanbedonetheindependentvariable(farmeryieldaspercentageofaveragefarmeryield)andthedependentvariable(probabilitythattheclaimpaymentispositive)needtobecomputedfromtherawdata.

Computingindependentvariable‘Farmeryieldaspercentageoffarmeraverageyield’Sumofyearlyfarmeryielddividedbynumberofyearsgivesthefarmeraverageyield.Eachyear’syieldcanbeexpressedasapercentageofaverageyieldsbydividingtheyieldinaspecificyearbytheaverageyield.

Computingdependentvariable‘Probabilitythattheclaimpaymentispositive’Thisvariableisabinaryvariableindicating1ifthefarmerhasreceivedaclaimpayment(irrespectiveoftheheightoftheclaimpayment)and0ifthefarmerhasnotreceivedaclaimpayment.

Eachfarmerineachyearisnowanobservationthatcanbeusedtorunthekernel‐regressionwiththepreferredstatisticalpackage.

2.Catastrophicperformanceratio

Tocalculatethisindicatorakernel‐regressionneedstoberunwithastatisticalpackagesuchasSTATA,SPSSorR.Beforethiscanbedonetheindependentvariable(farmeryieldaspercentageofaveragefarmeryield)andthedependentvariable(claimpaymentaspercentageofsuminsured)probabilitythattheclaimpaymentispositiveneedtobecomputedfromtherawdata.

Computingindependentvariable‘Farmeryieldaspercentageoffarmeraverageyield’Sumofyearlyfarmeryielddividedbynumberofyearsgivesthefarmeraverageyield.Eachyear’syieldcanbeexpressedasapercentageofaverageyieldsbydividingtheyieldinaspecificyearbytheaverageyield.

Computingdependentvariable‘Claimpaymentdividedbycommercialpremium’Dividetheclaimpaymentperfarmerperyearforthetotalsuminsuredbythecommercialpremiumpaidperfarmerforthetotalsuminsured.

Eachfarmerineachyearisnowanobservationthatcanbeusedtorunthekernel‐regressionwiththepreferredstatisticalpackage.

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