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COST‐BENEFIT ANALYSIS OF THEAFRICANRISKCAPACITYFACILITY1

JUNE5 , 2012

1ThispaperwascommissionedbytheWFPincooperationwithandonbehalfoftheAfricanUnionCommissiontocontributetotheevidencebasefortheAfricanRiskCapacity(ARC)facility.Withoutimplicatingthemintheshortcomingsofthework,particularthankstotheARCteamfordata,support,guidanceandfeedback,andtoStefanDerconandMaximoToreroforusefulcomments.Viewsexpressedinthispaperaretheauthors’andshouldnotbeattributedtoIFPRIorUniversityofOxford.Email:[email protected],[email protected].

DANIEL J . CLARKE

DEPARTMENT OF STATISTICS AND CENTRE FOR THE STUDY OF AFRICAN ECONOMIES , UNIVERSITY OF OXFORD

RUTHVARGASHILL

INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

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TABLEOFCONTENTS

LIST OF TABLES .................................................................................................................................................... 2 LIST OF FIGURES .................................................................................................................................................. 3

EXECUTIVE SUMMARY ................................................................................................................................. 4

Introduction ................................................................................................................................................ 4 Direct benefits from ARC through improved Sovereign risk management ................................................... 4 Benefits from early response ...................................................................................................................... 6 Summary .................................................................................................................................................... 7

1. INTRODUCTION ................................................................................................................................... 8

2. AFRICA RISK CAPACITY: A SPECIFICATION ........................................................................................... 10

3. PRINCIPLES OF ANALYSIS ................................................................................................................... 11

4. A STYLIZED FINANCIAL ANALYSIS OF ARC ........................................................................................... 13

4.1. SUITABILITY OF AFRICA RISKVIEW AS AN INSURANCE INDEX ............................................................................ 13 4.2. PREMIUM MULTIPLE AND CLAIM PAYMENT FREQUENCY ................................................................................. 18 4.3. FINANCIAL ANALYSIS OF A HYPOTHETICAL ARC PORTFOLIO ............................................................................. 24 4.4. RISK FINANCING ................................................................................................................................... 28

5. INDIRECT BENEFITS OF EARLY ASSISTANCE ......................................................................................... 32

5.1. TIMELINE OF A SLOW‐ONSET EMERGENCY SUCH AS A DROUGHT ....................................................................... 32 5.2. THE BENEFITS OF ACTING EARLY ................................................................................................................ 39

The cost of reduced consumption ............................................................................................................. 41 The cost of asset losses ............................................................................................................................. 42

5.3. SUMMARY: INDICATIVE ESTIMATES OF THE BENEFITS OF ACTING EARLY .............................................................. 43

6. BENEFITS OF ACTING EARLY UNDER FOUR CONTINGENCY PLANNING SCENARIOS............................... 46

6.1. A STYLIZED BASELINE AND FOUR CONTINGENCY PLANNING SCENARIOS ............................................................... 46 Stylized baseline ....................................................................................................................................... 46 Scenarios 1 and 2: Improved functioning of the food aid system .............................................................. 47 Scenario 3: Scaling up AN existing safety net ............................................................................................ 48 Scenario 4: Insuring government budgets for a state‐contingent scheme ................................................. 50

6.2. COMPARING BENEFITS AND LIMITATIONS ACROSS SCENARIOS .......................................................................... 52 6.3. LIKELIHOOD OF THESE SCENARIOS ............................................................................................................. 54

7. CONCLUSION AND SUMMARY OF RECOMMENDATIONS..................................................................... 58

BIBLIOGRAPHY ........................................................................................................................................... 60

LISTOFTABLES

TABLE 1. ARV RESULTS AND WFP INTERVENTIONS 1996‐2009 (KORPI ET AL. 2011) .......................................................... 15 TABLE 2. CORRELATION BETWEEN AFRICA RISKVIEW MODELED BENEFICIARIES AND WFP DACOTA DROUGHT‐ATTRIBUTED

BENEFICIARIES, 2001‐2009 ............................................................................................................................ 16 TABLE 3. AVERAGE MODELED RESPONSE COSTS ............................................................................................................ 25 TABLE 4. DECOMPOSITION OF MODELED RESPONSE COST RISK INTO THAT WHICH CAN BE DIVERSIFIED WITH COUNTRIES, THAT WHICH

CAN BE DIVERSIFIED BETWEEN COUNTRIES AND THAT WHICH MUST BE RETAINED OR TRANSFERRED................................... 27 TABLE 5. MAXIMUM HISTORICAL MODELED RESPONSE COST BY COUNTRY AND AGGREGATED ACROSS COUNTRIES ........................ 28 TABLE 6. ASSUMED ANNUAL MODELED RESPONSE COST ATTACHMENT AND EXHAUSTION POINTS AND CEDING PERCENTAGES ......... 29 TABLE 7: EVIDENCE OF COPING STRATEGIES USED BY HOUSEHOLDS IN THE FACE OF DROUGHT (SUB‐SAHARAN AFRICA) ................. 35

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TABLE 8: A STYLIZED TIMELINE OF DROUGHT CAUSED BY END‐SEASON FAILURE OF RAINS ........................................................ 36 TABLE 9: A REVIEW OF VULNERABILITY ASSESSMENTS ..................................................................................................... 39 TABLE 10: ECONOMIC COST OF DELAYED RESPONSE PER HOUSEHOLD ................................................................................. 45 TABLE 11: SUMMARY OF SCENARIOS .......................................................................................................................... 53 TABLE 12: INDICATIVE BENEFITS FROM IMPROVED SPEED AND TARGETING .......................................................................... 54 TABLE 13: AVAILABILITY OF GOVERNMENT GRAIN RESERVES, SAFETY NETS AND STATE‐CONTINGENT SCHEMES ............................. 55

LISTOFFIGURES

FIGURE 1. ANALYSIS OF CORRELATION BETWEEN AFRICA RISKVIEW MODELED BENEFICIARIES AND WFP DACOTA DROUGHT‐

ATTRIBUTED BENEFICIARIES, 2001‐2009 ........................................................................................................... 16 FIGURE 2. SENSITIVITY OF WELFARE BENEFIT OF ARC TO PREMIUM MULTIPLE ...................................................................... 22 FIGURE 3. SENSITIVITY OF WELFARE BENEFIT OF ARC TO CLAIM PAYMENT FREQUENCY .......................................................... 23 FIGURE 5. HISTORICAL MODELED RESPONSE COSTS, 1983‐2011 ..................................................................................... 25 FIGURE 6. DECOMPOSITION OF RISK ........................................................................................................................... 28 FIGURE 7: HOUSEHOLD GRAIN STOCKS IN MALAWI (CONSTRUCTED FROM DATA PRESENTED IN DEVEREUX 2007) ....................... 33 FIGURE 8: HOUSEHOLD GRAIN STOCKS IN ETHIOPIA (REPRODUCED FROM MINOT 2008) ....................................................... 33

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EXECUTIVESUMMARY

INTRODUCTION

1. AcrossSub‐SaharanAfricathecurrentsystemforrespondingtodroughtsisnotastimelyorequitableasitcouldbe.Fundingistypicallysecuredonanadhocbasisafterdisasterstrikesandmeantime,livesandlivelihoodsarelost,andgainsindevelopmentexperiencesignificantsetback.

2. AfricaRiskCapacity(ARC)isaproposedpan‐Africadroughtriskfacility,towhichdonorsand,toatleastanotionalextent,membercountrieswouldpayannualpremiums.Inreturnthefacilitywouldmaketimelyclaimpaymentstoinsuredgovernmentsifsatelliteweatherindicesindicatethataresponsetoaseveredroughtisneeded.TobeeligibleforARCeachgovernmentwillhavetodevelopacontingencyplanforhowtheywilluseanyclaimpayments.ARCisstillinthedesignphaseandmanyofthedetailsmaychange,butforthepurposesofthisreportweanalyzeaspecificationprovidedbytheARCteamasrepresentativeofwhatiscurrentlybeingconsidered.

DIRECTBENEFITSFROMARCTHROUGHIMPROVEDSOVEREIGNRISKMANAGEMENT

3. Usingsubnationaldataonhistoricalmodeledfoodsecurityneedsweestimatethat,comparedtoasysteminwhicheachsubnationalunitisresponsiblefortheirownfoodsecurityneeds,theaveragepercapitavarianceinfoodsecurityneedsacrosssixpotentialARCmembercountries: Canbereducedby66percentthroughpoolingwithincountries,between

subnationalunits. Canbereducedbyafurther25percentthroughpoolingbetweenallsixcountries. Canbereducedbyafurther6percentthroughthepoolbudgetingoverathreeyear

timehorizon. Intotal,only3percentoftheaveragevariancecannotbemanagedthroughpooling

withinandbetweenthesesixcountries,andsmoothingoverathreeyearperiod.ThissuggeststhatthebiggestpotentialwelfaregainsfromARCarefrombetterallocationofresourceswithincountries,poolingbetweencountriesandsmoothingovertime,withonlysmallpotentialgainsfromtransferringriskawayfromARC.WhilstreinsurancemaybeimportantforthefinancialmanagementofARC,itisnotcentraltothewelfareproposition.

4. Givenlimitedhistoricaldataitisnotpossibletodetermine,eithernoworafterfurthernational‐levelcalibration,anaccurateestimateofthecorrelationbetweentheweatherindexthatdeterminesclaimpaymentsfromARC,andnationalneed.ThewelfaregainsfromARCarehighlysensitivetothecorrelationoftheindexandneed,andassuchincorporatinganymechanismsintothedesignofARCthatimprovethedegreetowhichcountriescanrelyonARCinextremeyears,willincreasethewelfarepropositionofARC.TheindexusedbyARCpredictsemergencyfoodneedbasedonseasonalrainfallshortages,butfoodsecurityisonlypartlydeterminedbyfoodavailability.Theabilityofvulnerablepopulationstoaccessfoodisalsoveryimportant(Sen1981).Consideringhowtomakegreateruseofotherindicatorscurrentlycollectedinearlywarningsystems,suchasFEWSNET,tocomplementorverifytheindex(forexample,havinga

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doubletriggersystem),orincorporatingsomedegreeofground‐truthingareworthfurtherinvestigation.

5. InanalyzingthedirectwelfarebenefitfromtheARCintermsofimprovedmacroriskmanagementforcountries,wecompareARCtothecounterfactualwherebydonorspaywhattheywouldhavecontributedtoARCasannualbudgetsupport.ThiswelfarebenefitcriticallydependsonthecombinationofthecorrelationbetweenresponsecostneedandclaimpaymentsfromARC,thecostofcoverasmeasuredbythepremiummultipleandthefrequencyofclaimpayments.EvenifthecorrelationbetweenresponsecostneedandclaimpaymentsfromARCturnsouttobelow,thefacilitycoulddirectlybenefitmembercountriesrelativetothiscounterfactualifthecostsaresufficientlylowand/orcoverisofferedonlyforlowprobability,highseverityevents.

6. ARChascommittedtoacaponoperationalcosts.Inaddition,notingthelowpotentialforwelfaregainsintransferringriskawayfromARC,toensurevalueformoneyfordonorsandmembercountriesARCshouldnotspendtoomuchonreinsuranceorassociatedfeessuchasbrokeragefees.Giventhelevelofdiversificationavailable,ARCwillhavehighreturnstoretainingriskanditwillnotmakefinancialsensetoexposeonlyaquarterofitsreservesinthelowestlayerinagivenyear.ARCmaywanttocommittoonlypurchasereinsurancefor1‐in‐10yearannualportfolio‐widelossesandabove,ortoacaponexpenditureonreinsurance(includingbrokeragefees)expressedasapercentageofpremiumvolume.

7. ThereisnostrongactuarialrationaleforARCtobeinitiallycapitalizedinperpetuityasopposedto,forexample,havingathreeyearcapitalization.AthreeyearcapitalizationwouldallowARCtobenefitfromdiversificationovertimeinretainedrisk.BasedonahypotheticalportfolioandeveninthetotalabsenceofreinsuranceARCcouldhavesurvivedanythreeyearperiodinthelast29withinitialreservesoflessthanUS$60m,threetimestheannualaveragetotalclaimpayment.Withminimalreinsurance,thisreducesto$50m,twoandahalftimestheannualaveragetotalclaimpayment.MorecapitalmayberequiredinthemediumtermifARCistoexpandtomorecountriesoroffersubstantiallymorecoverpercountry.

8. ARCwillmaximizeitsimpactonwelfareifitfocusesonmakinglargeclaimpaymentsinyearsinwhichtheindexsuggeststhattherehavebeenextremelosses,ratherthanmakingmorefrequentsmallerclaimpayments. Insuranceisnottherightfinancialmechanismformanagingrecurrentlossessuchas

thosethatareexpectedtooccuronceeveryfiveyearsorless,onaverage.Forsucheventsaregularbudgetallocationismoreappropriate.

Ifcoveristobeofferedseparatelyforeachseason,thetriggersshouldbesuchthatnocountrywillreceiveaclaimpaymentoverallelementsofcovermorefrequentlythanonceeveryfive(ormore)years.Togiveanexampleifcoveristobeofferedseparatelyforeachseason,witheachelementofcoverexpectedtopayclaimsonceeveryfiveyearsonaverage,thenclaimswouldbepaidtoacountryeverytwoorthreeyearsonaverage.SuchahighexpectedclaimpaymentfrequencywillsignificantlydecreasethewelfarebenefitsfromARC.

9. IfARCisaninsurancefacility,focusedonmakinglargeclaimpaymentsinyearsthatare

extremelybadatthenationallevel,countriesanddonorswillneedmechanismsforfinancingthesmaller,morefrequenteventswhichtogetheradduptoaroundthree

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quartersofaveragelongtermfoodsecurityresponsecostneeds.Thisreflectsthedualroleofemergencyfoodaidaspartinsuranceandpartfrequentresourcetransferforaninitialportfolio.

BENEFITSFROMEARLYRESPONSE

10. Thelargestindirectbenefitsfromearlypaymentstofamiliescomefrompreventinglossoflife,malnutritionofyoungchildrenandassetlosses.Themortalityrate18monthsintothefamineinSomaliain2011wasbetween2.2and6.1deathsper10,000peopleperdayandtheunder‐fivemortalityratewas4.1to20.3deathsper10,000perday,dependingontheregion.Malnutritionofchildrenundertwocarrieslong‐runcostsofanestimated14%oflifetimeearnings.Thecombinationofreducedconsumptionandassetlossesreduceshouseholdincomegrowthbyanestimated16%overadecadepost‐drought.

11. WhilsttherearepotentialspeedbenefitsfromanearlypayoutfromARC,theactualmagnitudeoftheincreaseinspeedofdeliveryofassistancetotargetbeneficiariesiscruciallydependentonthetypeofcontingencyplanninginplaceatthenationallevel.TimelypayoutsfromARCwillnotautomaticallytranslateintotimelyreceiptofaidtobeneficiaries.Comparedtoanemergencyassistancebaselineinwhichcashorfoodisprovided7‐9monthsafterharvest,anearlyARCpayoutalonewillonlyprovideamarginalspeedbenefitof2months.However,whencombinedwithimprovedcontingencyplanningtherearesubstantialspeed,costandtargetinggains.Speedbenefitscouldbeaslargeas9monthimprovements.

12. Thespeed,costandtargetinggainsfromimprovementsinthecurrentfoodaidsystemseemtobemuchlowerthanthegainsfromscalingupexistingsafetynetsorawell‐functioningstate‐contingentscheme.Attheextreme,withonlymarginalimprovementsinthecurrentwithin‐countryfoodaiddistributionsystem,thebenefitscouldbelowerthanthecostsofrunningtheARC.Giventhatfewpotentialpilotcountrieshaveinplacenationalsafetynetschemes(betheystatecontingentornot),furtherinvestmentinnationalsafetynetschemesseemstobeanimportantpartofensuringstrongpositivebenefitsfromARC.

13. Propertargetingofassistancewithinthecountryreliesonlivelihoodindicatorscollectedaspartofcroporvulnerabilityassessments,butwithoutsubstantialimprovementsinthespeedwithwhichtheseindicatorsbecomeavailable,thereisalimittohowquickaresponsecanbethatreliesontheseindicatorstotargetaidbeneficiaries.

14. Aschemethatisautomaticallytriggeredtoprovideincreasedassistanceinthetimeofneeddoesnotneedtorelyontheselivelihoodindicators,andassuchcanprovideawaytomeetemergencyneedsquickly.Evidencesuggeststhattheseschemesarealsowell‐targetedcomparedtofoodaid.Examplesofsuchschemesareemploymentguaranteeschemes,targetedindexinsuranceprogramsandself‐targetingsubsidiesthatincreaseinvaluewhentimesarehard.

15. Wenotelimitstothescopeoftheanalysispresentedinthisreport.First,giventhecontingencyplanningisatanearlystage,thisreportcouldnotmakefullcalculationsof

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directcostsavingsthatmayresultfromcontingentplans.Weprovideindicativeevidenceonthepotentialgainsfromlowerlogisticalandcommoditycosts,andquantifythebenefitsfromtheimprovedtargetingthatislikelytoresult,butoncecontingentplansareinplaceitwouldbeusefulforaproperassessmentofdirectcostsavingstobeundertaken.Secondly,wedidnotdiscusspoliticaleconomybenefitsfromasustainablecooperativemechanismownedbyAfricangovernments.

SUMMARY

16. ARCisaninnovationthatbringselementsofinsuranceintoemergencyfinancinginordertoensuretimely,predictablepayoutsduringtimesofneed.AssuchthemagnitudeofARC’sbenefitsdependscruciallyontheprinciplesofinsurance.Benefitswillbehigherwhentheinsuranceisforextremeratherthanfrequentevents;whenthecostofinsuranceisnottoohigh;whenpayoutsaretriggeredbyindexesthataccuratelycapturetheimpactofextremeevents;andwhenpayoutsprovideinsuranceforwell‐functioningsub‐nationalaidprovision.

17. TheanalysisinthisreportsuggeststhatthebenefitsofARCarelargestwhen: Thereisalargescale,welltargetedsafetynetorstate‐contingentschemethatcanbe

scaled‐upquicklyintimesofhardship; Furtherprogressismadeinusingadditionalindicatorstocomplementorverify

weather‐basedindicessothatthedegreetowhichcountriescanrelyonARCinextremeyearsisincreased;

ARCactsascatastropheinsuranceforthegovernment’scontingentliability,andotherinstrumentsareusedforregular,smallerlosses;and

Thefacilitypaysoutlessfrequentlyandretainsmoreriskthanthespecificationconsideredinthisreport.

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1. INTRODUCTION

AcrossSub‐SaharanAfricathecurrentsystemforrespondingtofoodcrisesisnotastimelyorequitableasitcouldbe.Fundingistypicallysecuredonanadhocbasisafterdisasterstrikesandonlythencanreliefbemobilizedtowardsthepeoplewhoneeditmost.Inthemeantime,livesandlivelihoodsarelost,assetsaredepletedanddevelopmentgainsexperiencesignificantsetbacks.Droughtisparticularlyharmful;overtheperiod2001‐2009,droughtwasdirectlyresponsibleforoveronethirdofallWorldFoodProgramme(WFP)assistance,withanotherthirdattributedtoconflictorwar.

Emergencyfoodaidsupportincreasesinyearsinwhichdisasterstrikes,butitisalsoafrequentformofaidassistanceformanycountries.Thetwentysub‐SaharanAfricancountriesthatreceivedthemostemergencyfoodaidfromWFP,receivefoodaidonceeverytwotothreeyearsonaverage.2Thisreflectsthedualroleemergencyfoodaidplaysasbothinsuranceandamoreregularbudgetsupporttopoorcountries.

TheAfricanUnionCommission,withtechnicalandmanagerialsupportfromWFP,isworkingtowardstheestablishmentofapan‐Africadroughtriskfacility,AfricanRiskCapacity(ARC),whichcouldoffercountriesaccesstotimelyfundsbasedonobjectivetriggers,reducingdependenceonadhocandunreliableinternationalappealsforemergencyfoodaidassistance.Thisfacilitybringsinelementsofinsuranceintothefinancingofemergencyfoodaid,reflectingtheinsurancerolethatemergencyfoodaidoftenplays.Donorsand,perhapstosomedegree,membercountrieswouldpayannualpremiumstoARC,whichwouldinreturnmaketimelyclaimpaymentstoinsuredgovernmentsifsatelliteweatherindicesindicateaseverefoodsecurityresponsecostneed.TobeeligibleforARCeachgovernmentwillhavetodevelopacontingencyplanforhowtheywilluseanyclaimpayments.

ARChasthepotentialtogeneratesubstantialwelfaregains,butmanydetailswillbecritical.ThispaperoffersacostbenefitanalysisoftheproposedARC,withindepthdiscussionofsomeoftheareasthatARCwillneedtogetrightifitistobecomeacosteffectivemechanismfordonorsandmembercountries.

TheARCconceptdrawsonarecenttrendtowardsusingobjectiveindicesinsovereign‐leveldisasterriskfinancingandinsurance.Suchindicescanoftenbecalculatedquicklyintheaftermath,orduringtheonset,ofadisasterandcanbedesignedtobedifficultforanybodytomanipulate,leadingtothepotentialforquickclaimpaymentsandgoodpricesfrominsurersandreinsurers.Forexample,satellite‐basedrainfallindicescanbecalculatedduringaseasonoratharvest‐time,andareplausiblyrobusttomanipulation.However,suchindexinsuranceproductsdosufferfromtheproblemoftheindexnotbeingperfectlycorrelatedwiththeasset,incomestreamorcontingentliabilitytobeinsured,whichmeansthattheinsurancemightnotalwayspayoutintimesofneed.Theextenttowhichthisisaproblemdependsonthedegreetowhichtheindexedinsuranceproductcanbereliedontocapturetheworstyears.Inextremecases,wherethereisfairlyhighbasisrisk,thatislowcorrelationbetweentheclaimpayment

2http://www.wfp.org/fais/reports/quantities‐delivered‐two‐dimensional‐report/run/year/2010;2009;2008;2007;2006;2005;2004;2003;2002;2001;2000;1999;1998;1997;1996;1995;1994;1993;1992;1991;1990;1989;1988/recipient/SUB‐SAHARAN+AFRICA+%28aggregate%29/cat/Emergency/donor/WFP/code/All/mode/All/basis/0/order/0/

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andloss,anindexedinsuranceproductcanbedetrimentaltowelfare,actingmorelikeanexpensivelotteryticketthanacheapwayofpurchasingprotection.

ARCalsodrawsonideasfromotherfacilities.OnesuchfacilityistheCaribbeanCatastropheRiskInsuranceFacility(CCRIF),establishedin2007asaresponsetoHurricaneIvan,whichcausedbillionsofdollarsoflossesacrosstheCaribbeanin2004(CumminsandMahul2008).TheCCRIFhashad16membercountriessinceinceptionand,liketheproposedARC,paysclaimstogovernmentbasedonaparametricmodel.However,underCCRIFpremiumcostsarepaidforbymembercountries(withtheexceptionofHaiti)notdonors,therearenorestrictionsonhowcountriescanspendclaimpayments,andCCRIFinsurancetypicallyonlycovers1‐in‐15yeareventsorlargerunlikeARCwhichplanstocovermuchmorefrequentevents.

AsecondfacilityistheCentralEmergencyResponseFund(CERF),aquickdisbursementfundwhichprovidesgrantsorloanstoUNagenciesforrapidresponsehumanitarianemergenciesorunder‐fundedor‘forgotten’emergencies(CERF2011).LikeARC,CERFprimarilyactsasacommitmentdevicefordonorfunding,butunlikeARCdisbursementisnotbasedonsatelliterainfalldatabutratherrequiresUNagenciestosubmitanapplicationforaresponseincountrywhichisthenreviewedbasedonasetofobjectivecriteria.

TwoothernotableantecedentsaretheGovernmentofEthiopia’sandGovernmentofMalawi’sweatherderivatives.WhilsttheEthiopianmacroweatherindexedinsuranceproductwaspaidforbyUSAIDandtransactedbyWFPin2006butnotrenewedin2007,theMalawianNationalDroughtInsurancehasbeeninforcesince2008,andinrecentyearshasbeenpartlypaidforbytheGovernmentofMalawi(SyrokaandNucifora2010).

Thepresentcostbenefitanalysisdrawsonthelatesttheoryandevidencefromadiverserangeofareas,includingfoodaid,householdcopingresponses,nutrition,targeting,agriculturalinsurance,publicfinance,sovereigndisasterriskfinancingandinsurance,andactuarialtheory.Totheauthors’knowledgeitisthefirstreviewthatattemptstocombineinsightsfromallthesedisciplinestoassessaproposedmulti‐countryriskpool.

WeshowthatthemagnitudeofARC’sbenefitsdependscruciallyonwhetherpayoutsareforextremeratherthanfrequentevents;thequalityoftheindicesthattriggerpayouts;thecostsofrunningthescheme;andwhetherthesepayoutsprovideinsurancetogovernmentagainstitscontingentliabilityfromawell‐functioningsafetynetschemethatautomaticallyscalesinbadyears.Assuchwerecommendthatcomparedtothespecificationconsideredinthisreport,thefacilitypaysoutlessfrequently,additionalresourcesareinvestedintheindexdevelopmentanddatacollectionneededtocalibrateit,andsupportisincreasedtothedevelopmentofnationalsafetynetschemesthatcanscalequicklyintimesofhardship.

Thestructureofthispaperisasfollows.FirstweoutlinethespecificationofARCconsideredinthisreport,andtheapproacheswewilltakeinanalyzingARC.TheanalysisbeginswithanevaluationofthedirectwelfaregainsfromARCthroughimprovedsovereignriskmanagementandananalysisofthecapitalneedsofARC,beforeanoverviewoftheevidenceofthebenefitsfromearlyresponseandanevaluationofthepotentialwelfaregainsfromARCunderfourearlyresponsescenarios.WeconcludewithaseriesofsuggestionsifARCistobeimplemented.

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2. AFRICARISKCAPACITY:ASPECIFICATION

Forthepurposesofthisevaluationitishelpfultobespecificaboutthepreciseschemeweanalyze.ThefollowingliststhekeyfeaturesofARCwewillbeassumingforthisreport.AllassumptionshavebeenagreedwiththeARCteamasrepresentativeofwhatiscurrentlybeingconsidered,butreadersshouldnotethecaveatthatARCisstillinadevelopmentphaseandmanyofthedetailshavenotyetbeenfullyworkedoutorfixed.

1. ARCaimstogivecountriesaccesstoimmediatefunds,basedonobjectivetriggers,foruseinextremedroughtevents,therebyreducingdependenceoninternationalappealsforemergencyfoodaidassistance.

2. ClaimpaymentsfromARCwillbebasedsolelyonresponsecostsasmodeledbyAfricaRiskView.AfricaRiskViewgeneratesmodeledresponsecostsbasedonlyonsatelliteweatherdataandthemodel’sinternalparameters.

3. TheinitialcapitalizationofARCisexpectedtobepaidforbydonors.ARCislikelytoseekcapitalizationoftheorderofUS$150million.

4. Themajorityofpremiumsareexpectedtobepaidforbydonors,atleastinthemediumterm.

5. ARCintendstoexposeapproximatelyaquarterofitsreservesinthebottomlayerofriskinanyoneyear,andpurchasereinsuranceforportfoliolossesgreaterthanthis.Thiswouldimplyexposingapproximately150%oftheaverageannuallossinthebottomlayerandreinsuringtheremainder.

6. Eachcountrywillpurchasecoverforannualaggregateresponsecostsbetweenthe1‐in‐5yearand1‐in‐50yearannualresponsecosts.

7. ThecedingpercentageofeachmembercountryforaninsuredseasonwillbesetsothatthemaximumclaimpaymenttothatcountryequalsUS$30m.

8. ARCwillcapoperationalcostsat5%ofpremiumvolume.Thiscapwillapplytoallcostsofrunningthefacilityexceptforreinsurancepremiumsandclaimpayments.Additionalcostssuchasinitialcapacitybuilding,monitoringandanyadditionalresearchanddevelopmentwillnotbefinancedthroughpremiumincome.

9. ForthepurposesoffinancialmodelingwewillassumethattheARCconsistsofthefollowingsixlikelypilotcountries,Ethiopia,Kenya,Malawi,Mozambique,NigerandSenegal.

10. Eachgovernmentwillhavetodevelopacontingencyplanforhowtheywilluseanyclaimpayments.Therewillberestrictionsonhowgovernmentscandistributethemoney,butthesearestillindevelopment.

11. Itwouldbepossibleforacountryfacingadroughttoputinanappealforassistancethroughtheexistingsystem,regardlessofwhetheranARCpayouthadbeentriggered.

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3. PRINCIPLESOFANALYSIS

AnalyzingthewelfarepropositionofARCrequiresdrawingontheoryandevidencefromadiverserangeoffields.Looselyspeaking,wesplittheanalysisintwo,separatingthedirectbenefitsofARCintermsofimprovedsovereigndisasterriskfinancing(Section4)andthepotentialindirectanddirectbenefitsintermsofearlyassistance(Sections5and6).Theformerdrawsoninsurance,financialeconomics,publicfinanceandactuarialscience,andthelatterdrawsonevidencefromfoodaid,householdcopingresponses,nutrition,andtargeting.

TheoverallbenefitofARCisthesumofthebenefitsfromimprovedriskfinancingandthebenefitsofearlypayoutstofundpre‐agreedcontingencyplans.ToevaluatethebenefitsofARCresultingfromimprovedsovereigndisasterriskfinancingwecompareARCtoacounterfactualinwhichcountriesreceiveanequalamountofdonorsupport,butitisnottimedtocoincidewithemergencyneeds.ToevaluatethebenefitsofARCresultingfromearlypayoutstofundpre‐agreedcontingencyplanswecompareARCtoastylizedversionofcurrentemergencyfoodaiddistributioninwhichresourcesarriveintheformofemergencyaidonaverage9monthsafterharvestshavefailed.

Inthissectionweprovideanoutlineofhowweassessthebenefitsineachofthesecases.Beforecontinuingwenotethatthereareothernon‐economicbenefitstoARCthatarenotdiscussedinthisreport.Specifically,wedonotdiscusshowamultiplecountryfacilitylikeARCmighthastenthebuildingoftrustrelativetoasetofstandalonepoliciesforeachcountry(asdiscussedintheMalawicountrycasestudy,Clarke2012),anypoliticaleconomybenefitsfromtheestablishmentofasustainablecooperativemechanismownedbyAfricangovernments;oranybenefitsthatmayresultiftherearechangesintheincentivesformembercountriestoinvestindisasterpreparedness.

ForanalyzingthedirectwelfaregainoftheARCfromimprovedmacroriskmanagementforcountries,wecompareARCtothecounterfactualwherebydonorspaywhattheywouldhavecontributedtoARCtomembercountriesasannuallumpsumbudgetsupport,increasinggovernment’scapacitytofinancefoodsecurityresponsecosts.ForouranalysisbothofARCandofthecounterfactual,weadopttheassumptionthatallfoodsecurityneedsfromnon‐droughtperils,suchaswidespreadfloodsoroutbreaksofpestilenceorcropdisease,arealreadyfullyinsuredthroughothermechanismsandsobothARCandourcounterfactualbudgetsupportwillonlyeverbeusedtofinancefoodsecurityneedsfromdrought.3

Ourwelfareanalysiswillcaptureanimportanttrade‐off,betweenthebettertargetingofsupportthroughARC(moresupportonaverageinthebadyears,lessinthegoodyears)withthepotentiallowercostsofregulardirectbudgetsupportfordrought.Overall,weconsiderthistobeasomewhatARC‐favorablecounterfactualthatislikelytohighlightgainsfromimprovedmacroriskmanagementrelativetocurrentemergencyaid.Thisisbecause,evenwiththecurrentlevelsofuncertaintyinemergencyaid,wemaystillexpectittoincreaseinbadyearsandfallingoodyears,onaverage.Moreover,itmaybeanunrealisticcounterfactualif,forexample,donorsareonlyabletoofferbudgetsupportformonitorablehumanitarianinterventions,ratherthangeneralbudgetsupport.However,itisanintuitivecounterfactualand

3ThisassumptionisfavorabletoARCifinpracticeotherfoodsecurityneedsarenotfullyinsuredandbudgetsupportcouldbeusedto,forexample,financelossesfromfloodsaswellaslossesfromdroughts,orifdonorsareabletotargetsupporttosomedegree,forexamplethroughfacilitiesliketheCERF.

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onethateliminatestheneedtomakeassumptionsonthelevelofmacroriskmanagementthatmayormaynotexistinthecurrentsystemofemergencyrelief.InSection4.2wediscusstheextenttowhichourfindingsarerobustacrossarangeofpotentialcounterfactuals.

Tocomplementthiswelfareanalysis,weprovideevidenceonthreeadditionalfinancialaspectsofARC.FirstwediscusstheextenttowhichthereisevidencethatAfricaRiskViewwillaccuratelycapturethemostextremedroughts.Aspartofthisexerciseweconsiderthehistoricalcorrelationbetweenthenumberofdrought‐attributedbeneficiariesrecordedbyWFPandthemodeledlossesthatwouldhavebeengeneratedbyAfricaRiskView(usingcurrentparameterization).Second,usinghistoricalmodeledresponsecostsweestimatethedegreetowhichthefoodsecurityneedsriskcanbediversifiedwithincountries,betweencountries,andoverathreeyearperiod,andthedegreeoftheresidual,aggregateriskthatcouldbereinsured.Finally,weusehistoricaldatafrom1983to2011toassessthecapitalneedsofahypotheticalARCportfoliobothintermsofinitialcapitalizationandreinsuranceneeds.

Toassessthebenefitsofearlyassistanceonthewelfareofvulnerablehouseholds,somethingreferredtointhetermsofreferenceandthereforeinthisreport,asindirectbenefits,weconductareviewoftheeconomicandnutritionliteratureonhouseholds’responsetodrought.Thisliteratureprovidesanunderstandingofthelikelytimingofhouseholdresponsemechanismsinthepresenceofaseveredrought;andthelikelylong‐runcostimplicationsofengaginginthesemechanisms.Thisallowsustoprovidesomeestimatesofthepotentialwelfarebenefitsofactingearly,howevertheactualbenefitsdependonhowdevelopedthesafetynetmechanismsare,andthelossrateinthetransferoffunds.

Wedevelopfourcontingencyplanningscenarioswithincreasinglysophisticatedsafetynetmechanismstohelpunderstandthewelfarebenefitsthatcanberealizedbyinterveningearly.Wecomparethespeed,targeting,efficiencyandlikelyrunningcostsoftheseschemestothestylizedversionofthecurrentemergencyresponse.Toassessthebenefitsthatmaycomefromthereducedcostsandlossratesassociatedwithimplementingthesecontingencyplans,wereviewasmall,generalliteratureoncostofearlyresponse;andamoreextensiveliteratureontargetingefficiencyofdifferentaiddeliverysystems.Theanalysiswouldbenefitfromfurtherinformationonthelikelydirectcostsavingsthatcomefromcontingencyplanning,butwithoutknowledgeofthespecificmechanismsthatwouldbeinplaceineachcountry;thiswasnotsomethingthatthisreportcouldquantify.

Armedwithestimatesofbenefitsfromearlyresponse,andestimatesofimprovedtargetinglikelytoresultfrombettercontingentplanning,wediscussandassessthebenefitsoffourcontingencyplanningscenarios.Thisallowsustodrawsomelessonsonprinciplesforcontingencyplanning,andthecostofrunningthefacility.

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4. ASTYLIZEDFINANCIALANALYSISOFARC

InthissectionweevaluateARCthroughtheprismoffinance.FirstwediscussAfricaRiskView,thesatellite‐basedrainfallindexedmodelwhichisproposedasthebasisforARCinsurancecoverage.Second,wediscussthecostandclaimpaymentfrequencyofARC.Finally,wediscussthedegreeofdiversificationpossiblewithinandbetweenpotentialARCmembercountries,andovertime,andtheriskfinancingneedsofARC.

4.1. SUITABILITYOFAFRICARISKVIEWASANINSURANCEINDEX

AconvincingfinancialanalysisofARCwouldrequireevidencetobepresentedonwhetherAfricaRiskViewislikelytotriggerclaimpaymentsintheworstyears.IfthecorrelationisveryhighARCcouldbeaninexpensivewayofprovidingreliableprotectiontocountries,butifthecorrelationislowitwouldbelessvaluabletocountriesanddonors.

AfricaRiskViewiscurrentlyaprototypeindex,containingmanypartsthatwillundergosubstantialverificationforeachcountrythroughanincountry‐consultationprocessbeforeacountryusestheindextotransferrisk.TheARCtechnicalteamhasshownthattheperformanceoftheindexishighlysensitivetomodelparametersthatwillbefinalizedduringthein‐countryconsultationprocess.Conductingarobustanalysisonthisprototypeindexisthusoflimiteduse.Wethereforeofferanoverviewoftheexistingknowledgebase,reportontheresultsofahistoricalcorrelationanalysisfortheindexascurrentlydefined,andmakesuggestionsonhowtomoveforward.

Thereareclearconceptualandstatisticallinksbetweenrainfallanddrought.However,itisstillasubstantialchallengetodesignarainfallindexthatwillaccuratelypredictfoodsecurityneedsfromdrought.

First,wenotethatconceptuallyitisadifficulttaskasitrequiresbothestimatingyieldlossesfromrainfallandpredictingtheimpactoftheseyieldlossesonnationalfoodinsecurity.

Designingaweatherindexthataccuratelycapturesyieldlossesisdifficult,inpart,becauseitisdifficulttodefineanindexwhichaccuratelycapturesfarmerbehavior(DickandStoppa2011).Togiveasimpleexample,itisdifficulttopredict,usingweatherdataalone,whenfarmerswillplant(orreplant)crops.Sincemostcropsareparticularlysensitivetorainfallduringspecificperiodsinthegrowthcycle,anindexwhichinaccuratelypredictsplantingtimeswillnotnecessarilybeappropriatelysensitivetorainfallduringthekeyperiods(Collieretal.2010,Osgoodetal.2007).Whilst,givenenoughdata,anagronomist/statisticianteammaybeabletoovercomesuchchallenges,inpracticetheredoesnotseemtobeenoughdatatoaccuratelyspecifyaprecisefunctionalform,particularlyforpredictingyieldlossesattheextreme.Moreover,unlessagriculturalproductionissufficientlyhomogenous,whichisnotthecasethroughoutmostofsub‐SaharanAfrica,theamountofdatathatwouldbeneededforsuchanexerciseisunlikelytoeverexist.Althoughrainfallindicesatthenationallevelmaybemoreresilienttounexpectedchangesinfarmerbehaviorthandistrictorsubdistrict‐levelrainfallindices,thiscanstillbeaconcern,particularlyasfarmershavebetteraccesstoimprovedforecastswhichmakeuseofdatanotavailableatthetimeofindexdesign.

Althoughimportant,Sen(1981)showedinhisseminalworkonfamines,thatlackoffoodavailabilityisoftenacontributingfactortowardsfamine,butisnottheonlycause.Entitlementfailurescanresultinlackofaccesstofoodevenwhenfoodisavailable(asepitomizedintheBangladeshifaminethathecase‐studied).Assuchthefoodaidliteratureoftenhighlightsthat

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foodinsecurityisnotonlyaboutfoodavailability,butalsoaccesstoanduseoffood(BarrettandMaxwell2005).Establishedmarketflowsoffoodproductionanddemandcancausefooddeficitsinsomeregionstohaveamuchlargerimpactonnationalfoodsecuritythanfooddeficitsinotherregions.Additionally,thecharacteristicsofassetmarketsonwhichvulnerablehouseholdsrely(forexamplelabororlivestockmarkets)canalsodeterminewhetherornotafoodproductiondeficitwillresultinwidespreadfoodinsecurity.Afocusonfoodproductionalonewillthusnotguaranteethatwesatisfactorilypredictdrought‐relatedfamineatthenationallevel.AfricaRiskViewcurrentlyfocusesonlyoffoodproductiondeficitsandnomarketdynamicanalysisofflowsofsupplyanddemandorintegrationofotherassetmarketsiscurrentlyincludedorplanned.

AfricaRiskViewtakesasitsstartingpointtherealitythattherewillbe,formanycountries,arelationshipbetweenfoodsecurityneedsandrainfall.Foragivenrainfallexperienceitestimatesbothproductionlossesandthenumberofbeneficiaries.Itsvalueasarisktransfertoolwillbedeterminedbythedegreetowhichitcapturessomeaspectoffoodsecurityneeds:itdoesnotneedtoperfectlypredictfoodsecurityneedstobeuseful,butatthesametimethedegreetowhichitwillhelpmanageriskdoesdependonhowmuchofacountry’sfoodsecurityneedsitcanpredict.Thisisanempiricalquestion.

OneexercisethatcouldbeperformedistousehistoricalweatherdatatocalculatewhatAfricaRiskViewpredictsresponsecostwouldhavebeeninpreviousyearsandcorrelatethesemodeledresponsecostswithactualdataonresponsecosts.Unfortunately,itisnotpossibletoperformthisanalysiswithahighdegreeofconfidenceduetoalackofdata.AfricaRiskViewusesRFE2weatherdatawhichisavailablefrom2000,althoughothersatellitedataproductscanbeusedtobuildalongerhistoryofpredictedresponsecosts.However,thereisverylittlelong‐rundataoncountryneedagainstwhichtocorrelatethesepredictions.ThismakesitdifficulttocometopreciseconclusionsaboutthejointdistributionofclaimpaymentsfromARCandneed.Onesourceoflong‐rundataonneedisWFP’sDACOTAdatabasewhichrunsfrom2001tothepresentandprovidesdataonthenumberofdrought‐attributedbeneficiariesreachedbyWFP.However,thisdatasetcontainsquiteabitofmeasurementerror,andithasbeenlightlyusedtosomedegreetocalibrateAfricaRiskView(specificallyithasbeenusedtohelpdefinethevulnerabilitysettings,andtohighlightsomemeasurementerrorsintheDACOTAdatathatneedaddressing).4Thus,althoughitundoubtedlyitisindependenttosomedegree,itisnotafullyindependentcheckontheoutputofthemodel.

Korpietal.(2011)performsuchanexerciseonacountrybycountrybasis,comparinganextractfromtheWFP’sDACOTAdatabasefortheperiod2001‐2009combinedwithaWFPHumanitarianTrendsDatabase(HTD)databasefrom1996‐2000withhistoricalmodeleddrought‐attributedbeneficiariesusinghistoricalWRSIdataandAfricaRiskView.Somewhat

4PartofthemeasurementerrorarisesfromhowbeneficiariesarecodedintheDACOTAdatabase.Forexample,theDACOTAdatabaselistsprecisely7millionassistedWFPbeneficiariesinNigerin2009asdrought‐affectedeventhoughthemajorityofthesewerepartofblanket‐feedingprogramsforallchildren6‐23months(basedonheight)andtheirfamilies.InpracticethenumberofseverelydroughtaffectedinNigerin2009wasmostlikelymateriallylowerthanthefigureintheDACOTAdatabase.Thisdatapoint(Niger,2009)isoneofthecausesoflowcorrelationforNigerbetweentheDACOTAdatabaseandAfricaRiskView.However,forthepurposesofestimatingthecorrelationbetweenthetwodatasetsitisnotstatisticallyvalidtomanuallyadjustthisDACOTAdatapointwithoutasystematicassessmentoftheDACOTAdatabasewhichincludesafullreassessmentofyearsinwhichtheDACOTAdatasetandAfricaRiskViewcloselyagreeinestimatingtheresponsecostneed.

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discouragingly,thisanalysisfoundlowcorrelationbetweentheAfricaRiskViewmodeledbeneficiariesandWFPbeneficiaries.Forexample,oneinterventionoutofthreewasnotdetectedbyAfricaRiskViewandmorethanoneinterventionoutoftwothatwasdetectedbyAfricaRiskViewwasnotactuallyadrought(Table1).

TABLE1.ARVRESULTSANDWFPINTERVENTIONS1996‐2009(KORPIETAL.2011)

Period:1996‐2009DidWFPintervene?

Nointervention Intervention TotalAfricaRiskViewcategorizes

populationas:

Notaffected 209 36 245Affected 116 87 203Total 325 123 408

However,theseresultsshouldbeinterpretedwithcaution.First,theWFPdatasetdoesnotperfectlycapturefoodsecurityneedsfromdrought,andsopartofthelowcorrelationmayarisefrominaccuraciesintheWFPdataset.Indeed,Chantaratetal.(2007)usesdatafromKenyaandfindscorrelationbetweenthecostofWFPfood‐relatedprogramsandtotalseasonalrainfallofonly‐26%.However,insteadofinterpretingthisasevidencethattotalrainfallisnotagoodproxyforneed,theyarguethatthisprovidesevidencethattheWFPdoesnotdisburseintheworstyears.Second,populationfigureshavechangedbetween1996and2009whereashistoricalmodeledbeneficiarynumbersarecalculatedusingcurrentvulnerabilityprofiles,andsopartofthelowcorrelationcouldbefromthismismatch.Third,thisanalysisincludesAfricancountriesthatarenotassusceptibletodroughtasthesixconsideredinthisreport.Finally,thereareparticularquestionsabouttheaccuracyoftheWFPHumanitarianTrendsDatabase,usedbyKorpietal.(2011)before2001.

Forthesereasonswemayrestrictanalysistothesixcountriesconsideredinthisreportandtheperiod2001‐2010.TheWFPARCteamprovidedcorrelationestimatesforthesecountriesandyearsusingtheirpredictionsfromAfricaRiskViewandtheirextractionofthedrought‐affectedbeneficiariesfromtheDACOTAdatabasewithsomecorrectionsfromcase‐studyreportswheretheydeemedsuchcorrectionsappropriate.Thepointestimatesofthesecorrelationcoefficientsrangefrom39%forNigerto82%forSenegal(Table2).

Howeverrelyingontheseestimatestodeterminethattheindexisgoodorbadwouldbemisleadinggiventhattheyarecalculatedusingonly9yearsofdata.Wethereforealsocalculate95%confidenceintervalsforthecorrelationcoefficientforeachcountry,basedoneachcountry’snineyearhistory.

Wecalculatetheseconfidenceintervalsusingthefollowingnon‐parametricbootstrap.DenotingthevectorofDACOTAdrought‐attributedbeneficiariesandAfricaRiskViewmodeledbeneficiariesforagivencountryinyear by , wetake20,000samplesof9pairsfromthehistoricalsetofpairs,withreplacement,andcalculatethePearsonproduct‐momentcorrelationcoefficientforeachsample.The95%confidenceintervalistakentobetheintervalspanningfromthe2.5thtothe97.5thpercentileoftheresampledcorrelationcoefficients.5

5TheFishertransformationcombinedwithanassumptionofbivariatenormalitycanalsobeusedasalternativemethodforcalculating95%confidenceintervals.ForourdatasetwefindsimilarconfidenceintervalsexceptforEthiopia,wheretheconfidenceintervallowerboundsubstantiallydecreasesfrom

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Wefindthattheconfidenceintervalsforthecorrelationcoefficientarequitelarge.Forexample,foranycountryitisnotpossibletorejectanullhypothesisthatthecorrelationcoefficientislessthanorequalto50%atsignificancelevelof2.5%.Ethiopiaistheonlycountryforwhichitispossibletorejectanullhypothesisthatthecorrelationcoefficientislessthanorequalto25%atsignificancelevelof2.5%.Moreover,EthiopiaandKenyaaretheonlycountriesforwhichitispossibletorejectanullhypothesisthatthecorrelationcoefficientisgreaterthanorequalto98%atasignificancelevelof2.5%.Correlationsof98%seemimplausiblyhighbutthedataisnotsufficienttobeabletorejectsuchhighcorrelations.Similarly,whilstcorrelationsof25%mightseemimplausiblylow,thereareprecedentsforweatherindicesdesignedbasedonaplausiblestorybutwhichturnedouttohavemuchlowercorrelationthanexpected.Forexample,Clarkeetal.(2012)findacorrelationbetweenindexedclaimpaymentsandyieldlossesofmerely13%foraportfolioofweatherindexedmicroinsuranceproductssoldacrossoneIndianstate.

FIGURE1.ANALYSISOFCORRELATIONBETWEENAFRICARISKVIEWMODELEDBENEFICIARIESANDWFPDACOTADROUGHT‐ATTRIBUTEDBENEFICIARIES,2001‐2009

TABLE2.CORRELATIONBETWEENUNCUSTOMIZEDAFRICARISKVIEWMODELEDBENEFICIARIESANDWFPDACOTADROUGHT‐ATTRIBUTEDBENEFICIARIES,2001‐2009

Ethiopia Kenya Malawi Mozambique Niger Senegal AverageUpperboundfor95%confidenceintervalfor

correlation92% 94% 98% 100% 99% 100% 97%

Pointestimateforcorrelation

75% 69% 75% 63% 39% 82% 67%

Lowerboundfor95%confidenceintervalfor

correlation50% 23% ‐68% 10% ‐22% 0% ‐1%

50%to23%,andMalawi,wheretheconfidenceintervallowerboundsubstantiallyincreasesfrom‐68%to23%.

‐100%

‐80%

‐60%

‐40%

‐20%

0%

20%

40%

60%

80%

100%

Point estim

ate and 95% confidence

interval for correlation coefficient

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ThisanalysisshowsthatnineyearsofdataisnotenoughtobeabletomakemeaningfulstatementsaboutthecorrelationbetweenthebeneficiarynumbersmodeledbyAfricaRiskViewandtheactualnumberofbeneficiaries.Moreover,wearemostinterestedinhowwellAfricaRiskViewcapturesthemostextremeyearsandcorrelationanalysisusingnineyearsofdataisevenlessinformativeforthis.Whilstadditionalsourcesofdataarelikelytobeavailableatthenationallevelwhichwillincreasetheprecisionofcorrelationestimates,thenumberofyearsisnotlikelytoincreasemuch.TherainfalldatabaseusedforAfricaRiskViewonlygoesbackto2000.Othersatelliteproductsofferlongertimespans,butnotenoughtoresultinaprecisepredictionofcorrelationestimates.Theincountrycustomizationprocessshouldimprovetheperformanceoftheindex,butitwillnotbepossibletoproceedwithusingthisindexforrisktransferwithaclearunderstandingofhowwellthisindexperformsinpredictingdroughtyears.Inadditionitiscurrentlyenvisagedthatallavailabledataandrankingofdroughtswillbeusedincustomizingtheindex.Whilstthisisaperfectlysensibleapproach,itwillleavenodataasanindependentcheckonhowwelltheindexwillperform.

Insummary,althoughthereisevidencethatAfricaRiskViewispositivelycorrelatedwithfoodsecurityneedsarisingfromdrought,thestatisticalevidencedoesnotallowustosaymuchbeyondthis.ThewelfareandfinancialanalysispresentedinthenextsubsectionsthereforeprovideestimatesforarangeofplausiblecorrelationsfromTable2,rangingfrom25%to100%correlation.Theresultingrangeofbenefitsshouldbeconsidered,asfocusingonlyonthepointestimateswillnotprovideanaccuratepictureoflikelybenefits.

Astheanalysisinthenextsubsectionswillshow,thebenefitsofARCarehighlydependentonthequalityoftheindex,particularlywhenthereinsurancepremiumislarge.WethereforeconcludebydiscussingoptionsforimprovingAfricaRiskViewasaninsuranceindexinthecomingyears.

Themostimportantthingtonoteisthataninsuranceindexshouldbeabletobereliedontopayoutincatastrophicyears.Ifoneislookingtocovercostsarisingfromfoodinsecuritythentheindexshouldbehighlycorrelatedwiththesecosts,particularlyintheworstyears.6Thatanindexisbasedonaplausiblestoryorisgoodenoughforforecastingisnotenoughtoguaranteethatitisgoodenoughforinsurancepurposes(DickandStoppa2011).Fromtheperspectiveofinsurancetheory,asmallimprovementinthereliabilityofprotectionincatastrophicyearscansignificantlyincreasethevalueoftheprotectiontopolicyholders(Clarke2011).

IncountriessuchastheUnitedStates,thereisevidencethatareayieldindicesbasedonastatisticalsampleofcropcuttingexperimentscanofferreliableprotection(Dengetal.2007).Moreover,inMexicoandIndia,advancesinutilizationoftechnologyareleadingtomuchmoreefficientprocessesforrobust,manipulation‐resistantcropcutting.Forexample,theGovernmentofIndiaisexperimentingwithoutsourcingofcropcuttingexperimentsforinsurancepurposes,wheretheentireexperimentisconductedbyaprivatefirmandvideographedusingaGPS‐enabledcellphone,andtheresults/documentation/images/video

6Usingterminologyfrominsuranceeconomicsthereisacriticaldistinctionbetweenbackgroundrisk,thatisothersourcesofriskthatarestatisticallyindependenttotheoutcomeofinterest,andbasisrisk,whichariseswhenaninsuranceindexisnotperfectlycorrelatedwiththeoutcomeofinterest.

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footagearesenttotheinsurerelectronicallyonthedayoftheexperimentforscrutinyand,ifnecessary,verificationinadvanceofharvest(Mahuletal.2012).

AnotherrelevantapproachisthatoftheFamineEarlyWarningSystemsNetwork(FEWSNET),anationalearlywarningandvulnerabilityinformationsystemalreadyinplaceforthesixcountriesweconsider.Insteadofjustusingweatherdata,FEWSNETusesacombinationofweatherandvegetationsatellitedata,aswellaslocalpriceinformationandextensiveground‐truthing.

Whethersuchdata,technologiesandprocessescouldbeimplementedandutilizedforinsurancepurposesinanAfricancontextisanopenquestion,butgiventhepotentialwelfaregainsfromincreasingthereliabilityofindexandthelimitationsofpureweather‐basedapproachesindevelopingcountries,thereseemtobesignificantpotentialbenefitsfrominvestinginotherreliabledatasourcesthatcouldhelpARCtoverifiablycaptureextremeeventsatthenationallevel.Theultimateobjectiveofanysuchdatasourcewouldnotbetocapturelargelocalizedlossesorsmallnationallosses,butratherlargedroughtswhichhaveanationalimpact.Thesedatasourcescouldbecombinedwithweatherdatatogeneratetheindex,ortheycouldactasasecondgapinsurancetrigger,designedtocaptureextremeeventsnotcapturedbytheweathertrigger(DohertyandRichter2002).Anysecondtriggerwouldneedtobeobjectiveanddemonstrablyrobusttomanipulation,althoughifARCcouldretaintheriskitselfitwouldnotneedtobeofareinsurablequality,norwouldtherenecessarilyneedtobealonghistoryofdataforaccuratereinsurancepricing.

4.2. PREMIUMMULTIPLEANDCLAIMPAYMENTFREQUENCY

AsalreadymentionedapreciseestimateofthedirectbenefitofARCtomembercountriesfromimprovedfinancialriskmanagementrequiresapreciseestimateofhowaccurateAfricaRiskViewislikelytobeinprovidingclaimpaymentswhenneeded.Nonetheless,evenintheabsenceofsuchinformation,itispossibletosetoutgeneralprinciplesforthedirectvaluetocountriesoftheARC,inparticularrelatingtothecostsofthefacilityandtheclaimpaymentfrequency.

Throughoutthissectionwewilluseasimplemodel,basedonClarke(2011),toillustratetheprinciplesofhowthevalueofARCtoanotionalmembercountryisaffectedbythelevelofbasisrisk,thecostofthefacilityandtheclaimpaymentfrequency.Thismodelhasbeendeliberatelyoversimplifiedsoasnottomisleadthereaderintothinkingthatweareabletoconductafullwelfareanalysis;asdescribedintheprevioussection,wearenotabletoaccuratelyassessthejointdistributionofindexedclaimpaymentsandresponsecostsandsoareunabletooffermorethanjustprinciples.Therefore,althoughourkeyresultsabouthowthewelfarebenefitsofARCwouldchangeasthepremiummultiple,claimpaymentfrequency,andlevelofbasisriskchangedwouldfollowthroughtomorerealisticmodelsand,indeed,toothercounterfactuals,theabsolutelevelofthewelfarebenefitfromARCshouldbeinterpretedwithsomedegreeofcaution.

Inmotivatingourcounterfactualwemaystartbyconsideringthestatusquoofex‐postbudgetreallocationfromgovernmentandlargelyunreliableex‐postdonorassistance.Inasenseanyunreliabilityortargetingerrorsofdonorassistancemaybemodeledinasimilarfashiontothebasisriskinanindexinsuranceschemeinthatthereisapossibilitythatdonorassistancewillnotarrivewhenmostneeded,justasthereisapossibilitythatanindexinsuranceproductmaynotpayclaimswhenmostneeded.ComparingARCwithsuchacounterfactualwouldthereforedependcriticallyupontherelativecorrelationbetweenneedanddonorassistance/ARCclaim

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payments,andtherelativecostsofex‐postdonorassistanceascomparedtoARC.However,neithercorrelationiswellunderstood.Barrett(2001)andDiven(2001)suggestthatfoodaidflowsfromtheUSmightbenegativelycorrelatedwithfoodaidneed.Kuhlgatz(2010)suggestsfoodaidfromtheUS,AustraliaandJapanisuncorrelatedwithfoodaidneedandthatfoodaiddoesnotrespondtoslow‐onsetnaturaldisasterssuchasdrought.Kuhlgatz(2010)alsofindthatfoodaidfromtheEUandCanadaispositivelycorrelatedwithfoodaidneed,andadditionallyBarrettandHeisey(2002)suggeststhatmultilateralfoodaiddistributionbytheWFPispositivelycorrelatedwithfoodaidneedatthenationallevelandsignificantlypositivelycorrelatedattheregionallevel.

Thisstatusquocounterfactualwouldbedifficulttoanalyzeduetothelackofgoodinformationaboutthecorrelationbetweenex‐postdonorassistanceandneed.Rather,asoutlinedinSection3,weassessthedirectwelfaregainoftheARCfromimprovedmacroriskmanagementforcountriesbycomparingARCtothecounterfactualwherebydonorspaywhattheywouldhavecontributedtoARCtomembercountriesasregularannuallumpsumbudgetsupport,increasinggovernment’scapacitytofinancefoodsecurityresponsecosts.Relativetodonorassistancethatisatleastslightlypositivelycorrelatedwithneedatthenationallevel,thisisaslightlyfavorablecounterfactualforARCinthatweassumethatthecorrelationispreciselyzerounderourcounterfactualasdonorassistanceisintheformofconstant,regularbudgetsupport,anddoesnotrespondatalltoneed,

Ourchosencounterfactualallowsustocaptureanimportanttrade‐off,betweenthebettertargetingofsupportthroughARCwiththepotentiallowercostsofregulardirectbudgetsupportfordrought.ItthusallowsustodetermineawelfarebenefittoARCinatransparentmannerwithouttakingapositiononwhether(andtowhatextent)currentemergencyaidflowsarepositivelyornegativelycorrelatedwithneed.Wenotethatthelevelofcorrelationfoundofmostrelevanceforthisreport(thepositivecorrelationfoundinBarrettandHeisey2002)isverylowwhichsuggeststhatanassumptionofzerocorrelationforthepurposesofexpositionisquiteuseful.Forthosewhobelievecurrentemergencyaidmaybepositivelycorrelatedwithneed,theestimatespresentedwillbeanupperboundofthewelfaregainsinthattheywillshowthemaximumpossiblegainfromARC.

Inaddition,thereareothercounterfactualsthatwecouldhaveconsidered.Forexample,wecouldconsiderthecounterfactualthatdonorsprovidereliablefinance,butlate.ThisseemsunlikelygivenavailableevidenceandwouldbesignificantlyunfavorabletoARC,sincetheonlybenefitofARCwouldbeanincreaseinspeedofresponse,withareduction,notanincrease,inthedegreetowhichemergencyaidrespondstoneed.Second,wecouldconsideracounterfactualofnoactionbydonors,therebyimplyingthatARCencouragesdonorstospendnewmoneyonaid.Inthiscasethewelfarebenefitswouldbesignificantlypositive,butagainthisseemsunlikely.

Underalternativecounterfactualsthelevelofthewelfarebenefitwouldchangefromthatpresentedherebuthowthewelfarebenefitswouldchangeasthepremiummultiple,claimpaymentfrequencyandlevelofbasisriskchangedwouldfollowthrough.

Returningtoourmaincounterfactualofregularannualbudgetsupport,theassumptionsunderlyingthemodelareasfollows.

ARCclaimpayments:ARCmakesanall‐or‐nothingclaimpayment,payingthefullsuminsuredonceeveryfiveyearsonaverage(i.e.withprobability20%)andzerootherwise(i.e.withprobability80%).

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Responsecostneeds:Ourcountryexperiencesaseveredroughtonaverageonceeveryfiveyears(i.e.withprobability20%).Inyearswithaseveredroughtthereisalargeresponsecostneed,butinallotheryearsthereisazeroresponsecostneed.Allfoodsecurityneedsfromnon‐droughtperilsarealreadyperfectlyinsuredthroughothermechanisms.

Basisrisk:Thecorrelationbetweenclaimpaymentsandneedis25%,50%,75%or100%,where0%correspondstostatisticalindependence,and100%correspondstoperfectcorrelation.7

Multiple:Thepremiummultipleforcountriesis1.5.Withreferencetoourcounterfactual,weareassumingthatthecostofprovidinganexpectedclaimpaymentof$1throughARCcostsoneandahalftimesthecostofproviding$1ofbudgetsupport,wheretheextra50%coversoperational,reinsuranceandothercosts.

Premium:ThetotalannualpremiumpaidtoARCis3%ofthelossinaseverefoodcrisisyear,whichmayberestatedas15%oftheannualaveragelossofthecountry.

Welfarefunction:Ourcountryhaspreferencesoverfinancialresourcesavailableminusresponsecostneed, ,withex‐postwelfaregivenby , 1/ .Moreover,aseverefoodcrisisisassumedtoreduceproductionby40%.LettingdenotethefinancialresourcesavailableintheabsenceofARCorthecounterfactual

budgetsupport,inourmodelweassumethatthe1‐in‐5yearresponsecostneedis40%of .8.

ModelingbothresponsecostneedsandARCclaimpaymentsasall‐or‐nothingisparticularlyunrealistic.Thesearedeliberateoversimplifications,madebecausethereisnotgoodenoughdatatobeabletomodelthejointdistributionwithanydegreeofaccuracy.TheassumptionsforthefrequencyofARCclaimpaymentsandthetotalannualpremiumhavebeencalculatedtobeconsistentwiththespecificationofARCgiveninSection2,andtheassumptionthataseverefoodcrisisreducesproductionby40%isconsistentwiththeevidenceinDevereux(2007).ItisalsoconsistentwiththedefinitionsofdroughtcurrentlyusedinAfricaRiskView:amediumdroughtcausesa30%decreaseinagriculturalandlivestockincomeandaseveredroughtcausesa45%decreaseinagriculturalandlivestockincome.

Ourchoiceofwelfarefunctionissomewhatmoresubtle. isanon‐satiated,riskaversewelfarefunction,whichensuresthatourcountrycaresaboutboththelevelandriskofseverefoodcrises.Thedegreeofriskaversionissuchthatthecountrywouldbeindifferentbetweenayearwithfoodproductionequaltothehistoricalaverageandafaircointossbetween150%and

7Weconsidera2 2statemodelwithtwopossibleresponsecostneedsandtwopossibleclaimpayments,andthereforeonlyfourpossiblestates.Wemayfullycharacterisethesestateswiththreevariables,theprobabilityofasevereresponsecostneed,theprobabilityofanARCclaimpayment,andthejointprobabilityofasevereresponsecostneedbutnoARCclaimpayment,whichwedenoteby , and ,respectively.GiventhisnotationthePearsonproduct‐momentcorrelationcoefficientbetweenlossandindexisgivenby .Notethatperfectcorrelationisonlypossiblewhen and 0,sowe

donotconsiderthecaseofperfectcorrelationinFigure2.

8Usingterminologyfrominsurancetheory,thisismathematicallyequivalenttoassumingthatourcountryisexposedtoalossof40%ofinitialwealth .

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75%ofthehistoricalaverage.9Attheendofthissubsectionwelookathowourresultschangeascountriescaremoreorlessabouttheimpactofseverefoodcrisesontheircitizens.

First,weabstractfromthedetailsofhowacountrybehavesorthechoicesitmakes.Weassumethat,allowingforacountry’sstrategy,whateverthatmightbe,ex‐postwelfareisincreasingintheamountoffinancialresourcesavailableinagivenyear( )anddecreasingintheresponsecostneed( ).Wearenotexplicitabouthowexactlywelfareislowerinayearinwhichfinancialresourcesareinsufficient(ormorethansufficient)tocoverresponsecostneeds,butratherjustenumerateindirectwelfareasafunctionof .Moreover,ourindirectwelfarefunctionisconcavein ,sothatthemarginalbenefitfromadditionalfinancialresourcesishigherthemoreseverethesituation(thelowerthe ).Thisassumptionthatwelfareisconcavewillgenerateademandforinsuranceinthatwelfarecanbeincreasedthroughpayinganinsurancepremiumingoodyearstoreceiveaclaimpaymentinbadyears,evenifthepremiumisgreaterthantheaverageclaimpayment(Pratt1964,Arrow1965).

Ourspecificfunctionalformforwelfare , 1/ issomewhatarbitrary,albeitconsistentwithtypicalassumptionsandavailableevidence.Thewelfarefunctionisoftheconstantrelativeriskaversionform,withrelativeriskaversionof2,andissuchthatthecountrywouldbeindifferentbetweenayearwithsome andafaircointossbetween150%and75% .AsnotedbyWilson(1968)whenagovernmentbehavesasa

representativeagent,maximizingexpectedwelfareofcitizens,andallcitizenshavethesamedegreeofriskaversionandareexposedtothesameshock,itwouldactwiththesamelevelofriskaversionasitscitizens.10Studiesofthelevelofrelativeriskaversionattheleveloftheindividualtypicallyfindcoefficientsbetween0.5and2(HalekandEisenhauer2001),andrecentevidencefromEthiopiaandUgandasuggestsrelativeriskaversionof0.88and1.02,respectively(Harrisonetal.2010).However,thereislittleevidenceonthelevelofriskaversionthatcountriesuseorshoulduseinevaluatingwelfare.Attheendofthissubsectionwelookathowourresultschangeascountriescaremoreorlessabouttheimpactofseverefoodcrisesontheircitizens.

Wewillnowvarythreeofthemajorassumptionsinturntoshowtherelationshipbetweenthewelfaregainandtheseassumptions.SinceARCislikelytobepaidmostlybydonorsintheshortterm,wefirstquantifythewelfaregainarisingfromourmodelifcountriesarepaidtheARCpremiumdirectlyeveryyearasbudgetsupport.WethenexpressthewelfaregainfromARCrelativetothis.Soforexample,arelativewelfaregainfromARCof10%meansthattheinsuranceofferedbyARCincreasesthevalueofthesupport,relativetothewelfaregainifthesupportwasgivenasbudgetsupport,by10%.Similarlyarelativewelfaregainof‐10%meansthattheinsuranceofferedbyARCis10%lessvaluablethanbudgetsupport.11

9Ourwelfarefunctionisoftheconstantrelativeriskaversionform,withrelativeriskaversionof2.

10ArrowandLind(1970)proposedthatagovernmentshouldhaverelativeriskaversionofzerowhenevaluatingpublicinvestments,astatementnowknownastheTheArrow‐LindPublicInvestmentTheorem.However,asarguedbyFoldesandRees(1977),anddiscussedindetailforthecaseofdevelopingcountrydisasterriskfinancinginGhesquiereandMahul(2007),thisresultdoesnotholdifthepublicinvestmentiscorrelatedwithnationalincome.

11Ifcountriespayallorpartofthepremium,thenumericalanalysisperformedhereisunchanged,buttheinterpretationoftherelativewelfarebenefitisslightlydifferent;therelativewelfarebenefitisthewelfareonpurchaseofARCcoverminusthewelfareiftheARCinsurancepremiumisinsteaddestroyed,all

22

First,ascanbeexpected,therelativewelfarebenefitofARCisdecreasingastheoverheadsofARC,asmeasuredbythepremiummultiple,increase(Figure2).ForsufficientlyhighenoughpremiummultipletherelativewelfarebenefitfromARCisnegative.Forexample,eveniftheindexperfectlycapturestheneed,ifthepremiummultipleisgreaterthan2thenthewelfaregainfromgivingcountriesthemoneydirectlyisbiggerthanthewelfaregainofgivingmoneythroughARC.Thisisbecause,evenifARCoffersaperfecttargetingofmoney,halfofthepremiumisbeingspentonoverheadssuchasadministration,researchanddevelopment,reinsuranceoverheadsandbrokeragefees,andonlyhalfofthepremiumactuallygoestowardsclaimpayments.

Second,keepingboththecorrelationbetweenARCclaimpaymentsandresponsecostsneeds,thepricingmultiple,andthetotalpremiumpaidconstant,ARCisofmorebenefitthelowertheclaimpaymentfrequency(Figure3).12So,forexample,theARCismorevaluabletocountriesifitonlypaysoutonceevery10yearsonaveragethanifitpaysoutonceeverytwoyearsonaverage.ThisinlinewithKennethArrow’swellknownresultontheoptimalityofdeductibles,whichsaysthatifyouhaveafixedinsurancebudgetandtheinsurancemultipleisconstantthenitisalwaysbettertospendyourinsurancepremiumonfullcoverforthemostextremeyears,ratherthanspendinganyofyourpremiumoncoverforthelessextremeyears(Arrow1965).

FIGURE2.SENSITIVITYOFWELFAREBENEFITOFARCTOPREMIUMMULTIPLE

Thismeansthat,whilsttheARCfacilitymaywanttomakeclaimpaymentsfrequentlysothatcountriescanseethatARCpaysclaims,fromawelfarepointofviewitisbetterforARCtomakelargeclaimpaymentsintheworstyearsratherreducingclaimpaymentsintheworstyearsto

dividedbythewelfareiftheARCpremiumisaddedtotheannualbudgetminusthewelfareiftheARCinsurancepremiumisinsteaddestroyed.

12Inpractice,theaveragepricingmultipleislikelyforhigher,moreextreme,layersofrisk.InsuchacasethelinesinFigure3wouldbeflatter,inalllikelihoodstillupwardsslopingduetothehighattachmentpointandlowcedingpercentage(seeTable6).

‐80%

‐60%

‐40%

‐20%

0%

20%

40%

60%

80%

100%

1.0 1.5 2.0 2.5 3.0

Welfare benefit of ARC relative to paying

ARC premium directly to country

Premium multiple of ARC products (premium paid to facility / annual expected claim payment)

Correlation 25% Correlation 50% Correlation 75% Correlation 100%

23

increaseclaimpaymentsinthemoderatelybadyears.Countrieswillmostlikelywanttodeliverassistancetotargetbeneficiariesmorefrequentlythanonceeveryfiveyears;acrossthesixcountriesweconsiderassistanceisprovidedalmosteveryotheryear.However,thisdoesnotmeanthatinsuranceistherightmechanismtofundtheserecurrentliabilities;annualormulti‐yearbudgetallocations,oralineofcredithavethepotentialtobemuchmorecosteffectiveinthemediumterm.Thesepointshavebeenextensivelydocumentedbothingeneral(e.g.Gollier2003)andspecificallyforsovereigndisasterriskmanagementschemes(CumminsandMahul2008,GhesquiereandMahul2007),butareworthreiterating.

WenotethattheARCteamisconsideringofferingcoverseparatelyforeachseason.IfthisisthecasethenthereturnperiodinFigure3shouldbeinterpretedasthereturnperiodoverallcoverforoneyear.Forexample,ifeachelementdisburseseveryfiveyearsonaveragethenacountrywithtwoorthreeseasonswouldexpecttoreceiveaclaimonceeverythreeortwoyearsonaverage,respectively.SuchahighexpectedclaimpaymentfrequencywouldsignificantlydecreasethewelfarebenefitsfromARC.

IfARCspecifiesaminimumattachmentpoint,forexamplebystatingthatcountriescannotoptforinsurancepoliciesthattriggermorethanonceeveryfiveyears,onaverage,theexperienceoftheCCRIFsuggeststhatitislikelythatthisminimumattachmentpointwillbeselectedbyallmembercountriesforpoliticaleconomyreasons.

FIGURE3.SENSITIVITYOFWELFAREBENEFITOFARCTOCLAIMPAYMENTFREQUENCY

Third,wevarythedegreetowhichourwelfarefunctionpenalizesriskfacedbythecountry.Inourbenchmarkmodelweassumelogarithmicwelfare,whichisequivalenttoconstantrelativeriskaversionof2.InFigure4weplothowtherelativewelfaregainswouldchangeifriskwaspenalizedtoagreaterdegree(higherrelativeriskaversion)oralesserdegree(lowerrelativeriskaversion).AsmightbeexpectedwefindthatgenerallyspeakingthegainfromtheARCishigherthemoreriskaversethewelfarefunction.ThismeansthatcountriesthataremoreriskaversewillgenerallyderivegreaterwelfaregainsfromtheARC.However,ifthecorrelationbetweenARCclaimpaymentsandresponsecostneedsisonly25%thentheARCdoesnotreallyaddvalue.

‐30%

‐20%

‐10%

0%

10%

20%

30%

1 2 3 4 5 6 7 8 9 10

Welfare benefit of ARC relative to paying


Return Period of ARC products (average frequency of claim payments in years)

Correlation 25% Correlation 50% Correlation 75%

24

FIGURE4.SENSITIVITYOFWELFAREBENEFITOFARCTORISKAVERSIONOFCOUNTRY

Tosummarize,oursimplemodelprovidesusintuitionconsistentwitheconomictheory,namelythattheARCismorevaluableifthecorrelationbetweenclaimpaymentsandresponsecostneedsishigher,thepremiummultipleislower,thefrequencywithwhichARCpaysclaimpaymentsislowerandthewelfarefunctionismoreaversetorisk.GiventhatARCisunlikelytobeabletoaffecthowriskaversecountriesareandthatintheshorttermifARCisdependentonrainfallindicessotheremaybelittlethatcanbedonetoincreasethecorrelationbetweenresponsecostneedandclaimpayments,itiscriticalthatthepremiummultipleandclaimpaymentfrequencyarekeptlow.

Inkeepingtheinsurancemultiplelowalthoughthecommitmenttospendamaximumof5%ofpremiumvolumeonoperationalcostsisimportant,whatismostcriticaltodonorsandcountriesisthatthepremiummultipleislow.Thismeansthatitisnotonlyoperationalcoststhatmatter,butalsothecostofriskfinancing,includingreinsurancecostsandbrokeragefees.

4.3. FINANCIALANALYSISOFAHYPOTHETICALARCPORTFOLIO

Havingmotivatedtheneedtokeepthepremiummultiplelow,wenowturntoissueofriskfinancingwhichforthecurrentpurposesinvolvesunderstandinghowmuchreinsuranceARCshouldpurchase,andhowlargereservesshouldbe.Unlikeintheprevioustwosectionswheredatawasnotavailableforacredibleanalysis,thereissufficienthistoricalweatherdatatoperformacredibleriskfinancinganalysisofARC.OurlackofunderstandingofthecorrelationbetweenARCclaimpaymentsandresponsecostneeddoesnotmatterforthissection;weonlyneedtobeabletounderstandhowtofinanceARC’sproposedindexinsurancepoliciesandthisisunrelatedtothecorrelation.

InouranalysisweapplytheAfricaRiskViewmodeltohistoricalsatelliterainfallestimatedatafrom1983to2011togenerateasetofhistoricalmodeledresponsecostsforeachseason/area/year.WeusetheAfricanRainfallClimatologyv2satelliterainfallestimatedatafromNOAACPC,whichcoverstheAfricancontinentwith10x10kmresolutiononadailyand10‐daybasisfrom1983.Theproductionofthisdatasetwasco‐fundedbytheARCProjectand

‐50%

0%

50%

100%

150%

200%

0 1 2 3 4 5 6

Welfare benefit of ARC relative

to paying


Relative Risk Aversion of country

Correlation 25% Correlation 50% Correlation 75% Correlation 100%

25

weunderstandthatthedatasetwillbeusedasoneofCPC’sprimarymonitoringproductmovingforward.

Figure5presentsthetotalannualmodeledresponsecostsforsixpotentialARCmembercountriesbetween1983and2011,expressedintermsoftheempiricalfrequencyoftheresponsecostaccordingtoAfricaRiskView.Notethatallhistoricalmodeledresponsecostshavebeencalculatedbyapplyingcurrentpopulationandvulnerabilitydatatohistoricalweatherdata.Thesefiguresthereforeprovideestimatesforwhattheresponsecostwouldinthecomingyearifthosemeteorologicaleventsoccurredthisyear,nottheresponsecostthatwouldhavebeenneededtakingtoaccountthehistoricalpopulationandvulnerability,andcanthereforebeusedasthebasisforariskprofileforARCoverthecomingyear.So,forexample,usingcurrentpopulationandvulnerabilityprofilesthemodeledresponsecostforEthiopiawouldhavebeengreaterthanUS$800mfourtimesinthe29yearperiod1983‐2011,whichisapproximatelyonceevery7years.

Overthesesixcountriestheaverageannualmodeledresponsecostovertheperiod1983to2011isapproximately$700m,whichcorrespondstoanannualaveragepercapitaresponsecostofUS$3.7,rangingfrom$1.9inKenyato$5.6inMalawi(seeTable3).

FIGURE5.HISTORICALMODELEDRESPONSECOSTS,1983‐2011

TABLE3.AVERAGEMODELEDRESPONSECOSTS

Country Populationin20101

Averagemodeledresponsecost1983‐2011(US$

millions)

Averagepercapitamodeledresponsecost1983‐2011(US$)

Ethiopia 82,949,541 319 3.8Kenya 40,512,682 78 1.9Malawi 15,511,953 84 5.6

Mozambique 12,433,728 128 5.5Niger 14,900,841 72 4.7Senegal 23,390,765 26 2.1

Notes:1.WorldBank(2011)

‐

100

200

300

400

500

600

700

800

900

1,000

0 5 10 15 20 25 30

Historical m

odelled response cost (USD

millions)

Frequency with which a modeled response cost at least this large occurred between 1983‐2011 (years)

Ethiopia Kenya Malawi Mozambique Niger Senegal

26

NowwemayaskhowmuchdiversificationispossiblewithinapotentialARCportfolio.SincetheAfricanRainfallClimatologyv2producedmodeledresponsecostsatthesubnationallevel,itispossibletoassessthedegreetowhichresponsecostscanbediversifiedwithincountries,betweencountriesandovertime.

Let and denotethepopulationandtotalmodeledresponsecostrespectivelyforcountry,year ,andarea .Notethatweassumethesamepopulationineachyear,sinceweareinterestedinmodelingwhattheresponsecostwouldbeinthecomingyeariftheweathereventsofthatyearweretooccurinthecomingyear.

Ourstartingpointisconsideringthepopulation‐weightedaveragesamplevarianceofpercapitamodeledresponsecost,whichforcountry wecalculateas

/ /

1, (1)

where, : ∑ denotesthetotalpopulationforcountry inyear , ∑ denotes

theaveragehistoricalmodeledresponsecostforarea incountry ,and 29denotesthenumberofyearsofdata.Thisgivesusanestimateofthevarianceofresponsecostswithinareas,weightedbypopulation,beforeanydiversification.Indeed,giventhatresponsecostneedisnotevenlyspreadwithinanygivenarea,thisestimatewillbeanunderestimateoftheaverageper‐capitaresponsecostneed.

Nextwemayconsiderthesamplevarianceofmodeledcountryresponsecosts,percapita,whichforcountry isgivenby

/ /

1, (2)

where, ∑ denotesthetotalmodeledresponsecostforcountry inyear and

∑ denotestheaveragehistoricalmodeledresponsecostforcountry .Thisgivesus

anestimateofthevarianceofresponsecostswithincountries,afterwithin‐countrydiversification.

Nextwemayconsiderthesamplevarianceofmodeledcountryresponsecostsafterpoolingbothwithinandbetweencountries.Todothisweassumethateachcountrybearsthepooledresponsecostriskinproportiontotheannualaveragehistoricalmodeledresponsecostforthatcountry.Thesamplevarianceofmodeledcountryresponsecostsafterpoolingforcountry istherefore

/ /

1, (3)

where, ∑ denotesthetotalmodeledresponsecostinyear overallsixcountriesand

∑ denotestheaveragetotalhistoricalmodeledresponsecostoverallsixcountries.

Thisgivesusanestimateofthevarianceofresponsecostsafterbothwithin‐countryandbetween‐countrydiversification.

Table4calculatesthesethreeitemsfortheportfolioofsixcountriesusingtheAfricanRainfallClimatologyv2datasetandtheAfricaRiskViewmappingbetweenrainfallandmodeledresponsecost.Diversificationwithincountriesreducesthepercapitavarianceofresponsecost

27

from75to25,areductionoftwothirdsanddiversificationbetweencountriesreducesfrom25to6.8,afurtherreductionofmorethantwothirds.

Inadditiontopoolingwithinandbetweencountries,itispossibleeitherforcountriesorthepooltousemulti‐yearreservestospreadshocksovertime.Ifcountriesorthepooljointlypoolriskoverathreeyearperiodinadditiontopoolingwithinandbetweencountriesthen,underanassumptionthataverageresponsecostsoveranydistinctthreeyearperiodsarestatisticallyindependentofeachother,theannualaveragesamplevarianceofmodeledcountryresponsecostsreducesbyafurthertwothirds(Figure6).13

TABLE4.DECOMPOSITIONOFMODELEDRESPONSECOSTRISKINTOTHATWHICHCANBEDIVERSIFIEDWITHCOUNTRIES,THATWHICHCANBEDIVERSIFIEDBETWEENCOUNTRIESANDTHAT

WHICHMUSTBERETAINEDORTRANSFERRED

Country

Population‐weightedaveragesamplevarianceofpercapitamodeledresponsecosts(US$)

Samplevarianceofmodeledcountryresponsecosts,per

capita(US$)

Samplevarianceofmodeledcountry

responsecostsafterpooling,percapita

(US$)Ethiopia 57.0 13.8 6.5Kenya 14.9 1.3 1.6Malawi 219.4 105.6 13.9

Mozambique 118.7 42.7 13.2Niger 142.1 59.0 9.5Senegal 45.8 10.9 2.0

Population‐weightedaverage

74.61 25.40 6.80

Overallwefindthat,97%ofresponsecostvariancecanbeeliminatedthroughdiversificationwithinandbetweencountries,andthroughriskretentioneitherbythepoolorthecountryoverathreeyearperiod.

Anotherwayofcomingtothesameconclusionistolookatthemaximumhistoricalmodeledresponsecostbycountryandaggregatedoverallcountries,eitheronanannualbasisoronathreeyearmovingaveragebasis(Table5).Whilstthesumofeachcountry’smaximumlossoverthe29yearperiod1983‐2011isUS$2,895millionthemaximumtotallossinanyoneyearisonlyUS$1,925million,andthemaximumthreeyearmovingaverageisonlyUS$1,292million,only182%oftheannualaveragetotalloss.

13WhilsttheAfricanRainfallClimatologyv2datasetsuggestsa60%,not67%reductioninvariancefromdiversificationoverathreeyearperiod,itcontainsonlyninedistinctthreeyearperiodsandsotheestimatearisingfromthisdatasetmaynotbeparticularlyaccurate.Althoughthereissomeevidencethatthemodeledresponsecostsforagivencountryinyear ispositivelycorrelatedwiththatcountry’sresponsecostinyear 1thereisnosuchevidenceforthecorrelationbetweenaverageresponsecostsinthethreeyearsstartingwithyear andthethreeyearsprecedingyear .

28

FIGURE6.DECOMPOSITIONOFRISK

TABLE5.MAXIMUMHISTORICALMODELEDRESPONSECOSTBYCOUNTRYANDAGGREGATEDACROSSCOUNTRIES

Country

Maximumhistoricalmodeledresponsecost,1983‐2011(US$millionsandpercentageof

average)

Maximumthreeyearmovingaveragehistoricalmodeledresponsecost,1983‐2011(US$millionsand

percentageofaverage)Ethiopia 994(312%) 752(235%)Kenya 161(208%) 116(154%)Malawi 554(660%) 348(388%)

Mozambique 538(420%) 339(250%)Niger 507(702%) 218(323%)Senegal 141(535%) 70(290%)Allsix

countries1,925(272%) 1,292(182%)

4.4. RISKFINANCING

Theaboveanalysishasanumberofimplications.First,supportingcountriesinretainingriskthatcanbepooledatthenationallevelhassignificantbenefits;thegainsareovertwicethatoftheriskpoolingandtransferbenefitsavailablefromapan‐Africariskpool.Foracountrytobeabletoefficientlyretainshocksthatarenotlargefromanationalperspective,itwillneedbothabudgetlinefortheseshocksandtheabilitytodistributethemoneytoaffectedpopulation.Second,evenwithoutanyreinsurancepurchase,theveryactofpoolingmodeledresponsecostriskbetweencountriesandspreadingresponsecostsoverathreeyearhorizonreducesmodeledresponsecostvarianceby8/9ths.TomanagesuchriskcheaplyARCwillneedtobeabletoretainriskandspreadthecostofshocksovertime,forexamplethroughmulti‐yearreserves.

100%

34%

9.1%3.0%

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

Samplevariance of per

capitamodelled

response costs

Afterdiversification

withincountries

Afterdiversification

betweencountries

Afterdiversificationover a threeyear period

Sample variance of net per captial

modelled response costs as percentage

of sample variance without an

y diversification benefits

29

Finally,whilstpurchasingreinsurancefortheARCportfoliocanprotectagainstlargeaggregatelosses,thevastmajorityofthepotentialwelfaregainoftheARCseemtoarisefrompoolingbetweenandwithinAfricancountries,andovertime.Reinsurancepurchase,althoughimportantforARC’sriskmanagement,isnotcriticaltothevaluepropositionoftheARC.WewouldthereforeexpectARCnottohavetospendmuchofitspremiumincomeonreinsurance.

Tocomplementtheaboveanalysis,wemayimposeaspecificstructureonARCproductsandanalyzethecapitalneedsoftheARCportfolio.ForthepurposesofillustrationletussupposethatARCprovidescovertoeachoftheabovesixcountriesbasedonthetotalannualmodeledresponsecost,withannualattachmentpointtakentobetheestimateofthe1‐in‐5yearmodeledresponsecostusingdatafrom1983‐2011,theannualexhaustionpointtakentobethemaximummodeledresponsecostbetween1983‐2011,andthecedingpercentagechosensothatthemaximumclaimpaymenttoeachcountryisUS$30m(Table6).ThissomewhatoverstatesthelevelofcoverpercountryascomparedtothespecificationinSection2,underwhichtheannualexhaustionpointwouldbesetattheestimated1‐in‐50year,not1‐in‐29yearloss,buthasthebenefitofbeingsimple,anddoesnotrequireassumptionstobemadeaboutthedistributionofresponsecosts.

TABLE6.ASSUMEDANNUALMODELEDRESPONSECOSTATTACHMENTANDEXHAUSTIONPOINTSANDCEDINGPERCENTAGES

Country Annualattachmentpoint(US$millions)

Annualexhaustionpoint(US$millions)

CedingPercentage

Ethiopia 572 994 7%Kenya 118 161 69%Malawi 130 554 7%

Mozambique 241 538 10%Niger 135 507 8%Senegal 40 141 29%

Analyzingtheportfolioofaboveproductsusingthe29yearsofdatafrom1983‐2011yieldsthefollowingresults.TheaveragemodeledresponsecostovertheperiodwasUS$707mandtheaverageresponsecostintheinsurancelayerwasUS$175m.Thismeansthatwerecountriestoreceivefullcoveragefortheinsurancelayer,thiswouldcomprise25%ofthetotalaverageannualresponsecostneed.TheaverageclaimpaymentfromARCovertheperiodwouldhavebeenUS$19.8mandthemaximumannualclaimpaymentwouldhavebeenUS$63.6m,payablein2004.MoreoverthemaximumtotalclaimpaymentpayableoverathreeyearperiodwouldhavebeenUS$142m,inrespectoftheperiod1989‐1991.WereARCtochargepremiumincomewithamultipleof1.5andincurannualoperationalcostsof5%ofpremiumvolumeitcouldhaveretainedtheentirecostwithouthavingtopurchaseanyreinsuranceifitstartedthethreeyearperiodwithreservesofUS$57.4m,evenbeforeaccountingforinterestearnedonreserves.14

TheabovediscussionalsohasimplicationsfortheinitialcapitalizationofARC.RelativetoacatastrophicfacilityliketheCCRIF,ARCisexpectedtocompriseamuchmorewell‐diversifiedportfolio,withsubstantiallylowercapitalrequirements.Basedonthehypotheticalportfolioanalyzedinthissection,evenintheabsenceofreinsuranceARCcouldhavesurvivedanythreeyearperiodinthelast29withinitialreservesoflessthanUS$60m,approximatelythreetimes

1419.8 1.5 1 5% 3 57.4 142.

30

theaverageannualclaimpayment.Withreinsuranceforlossesabove250%oftheannualaveragelossforthehypotheticalportfolio,ARCcouldhavesurvivedanyofthesethreeyearperiodswithlessthan$50m,approximatelytwoandahalftimestheaverageannualclaimpayment.ThiscompareswiththeinitialcapitalizationoftheCCRIF,acatastropheriskinsurancefacility,whichwasmuchlargerasamultipleoftheaverageannualclaim.

Fromafinancialperspective,itwouldbesomethingofawasteifARCwascapitalizedwithUS$150mofdonorfundsbutonlyexposedaquarterofitsreserveseachyear.WhilstthismayresultinARCsurvivinginperpetuitywithanextremelyhighprobabilityitwouldnotnecessarilyoffergoodvaluetodonors,sinceinanyyearthreequartersofreserveswouldnotbebeingusedtobearrisk.Basedontheportfolioassumptionsinthissectionandassumingminimalreinsurance,evenoverathreeyearperiodonlyaroundUS$50mofinitialcapitalwouldtypicallybeexposed.

Ofcourse,ifARCinsteadofferedcatastrophiccovertocountries,whereverylargeclaimpaymentswouldbepaidintheworstyears,butagivencountrywouldreceiveaclaimpaymentonlyonceeverytenorfifteenyearsonaverage,thecapitalneedsofARCwouldbemuchgreater,andtherewouldbeamuchlargerroleforreservesandreinsurance.Also,ifadditionalcountriesjoinedorthelevelofcoverforexistingcountriesincreasedthecapitalneedsofARCwouldbegreater.However,ifexperienceinthefirstfewyearsofARCoperationsisgood,thatisifclaimpaymentsarelow,thenARCmaynotneedfurthercapitalinjectionsevenasitsportfolioincreases.

RecapitalizationmightbenecessaryintheaftermathofaseriesofcatastrophicyearsinwhichARCmadelargeclaimpaymentstoanumberofcountries,butinsuchacasedonorswouldbewellplacedtojudgehoweffectivelyithaddisbursed,bothintermsofwhichcountriesreceivedclaimpaymentsandhowthemoneywasspentwithinthecountry,andthereforetojudgewhetherARCshouldnotonlyberecapitalizedbutalsoscaledupintermsofthelevelofcoverofferedtoeachcountry.

TheabovediscussionalsohasimplicationsforthepremiummultiplesARCmightbeabletooffertodonorsandcountries.Asproposedinthespecificationconsideredinthisreport,ARCwouldpurchasereinsuranceonanannualbasisforallaggregatelossesabove150%oftheannualaverageloss.Assumingtheaboveportfolio,thiswouldcorrespondtoreinsuringallaggregatelossesabovethe1‐in‐3yearaggregateloss,comprisingapproximately30%ofthetotalannualaverageloss.Assumingthatreinsurancewaspricedwithamultiple(includingbrokeragefees)of2.4andoperationalcostswere5%ofthegrosspremium,ARCwouldneedtopriceproductswithamultipleof1.5.15However,wereARCtoincreaseitslevelofretentioninthefirstlayerto250%oftheannualaverageloss,approximatelyequaltothe1‐in‐5yearaggregateloss,whilstholdingalltheotherassumptionsfixed,itwouldbepossibleforARCtopriceproductswithamultipleof1.2.16AsisclearfromSection4.2,wereARCtobeabletoofferapremiummultipleof1.2itcouldhaveapositiveeffectonwelfareevenifthecorrelationwithlosseswasonly25%anditpaidclaimstocountriesasfrequentlyasonceeveryfiveyears.

Givenachoicebetweeninvestinginbettermechanismstodistributeresponsecoststhroughoutacountry,investingininfrastructuretoallowARCtoofferproductswithlowerbasisrisk,and

151 5% 1.5 2.4 1 30% 1.5.

161 5% 1.2 2.4 1 10% 1.2.

31

investingincapitalizingARCtoenableittobeself‐sufficientformorethanthreeyears,theburdenofevidencewouldsuggestthattheformertwowouldofferahighersocialreturn.FollowingthenarrativeofFigure6,itseemsprudenttofocusresourcesontheareasthatcangenerate97%ofthepotentialwelfarebenefits(accuratelypoolingwithandbetweencountries,anddiversifyingoverathreeyearhorizon),ratherthancapitalizationofARCinperpetuity,whichispartoftheresidual3%.

32

5. INDIRECTBENEFITSOFEARLYASSISTANCE

AmajoradvantageoftheARCistheprovisionoffinancingforthegovernmentandemergencyservicestodisburseaidearlytothoselivingindevastatedareas.Itisthisearlydisbursementofassistancethatislikelytoaffordthelargestwelfarebenefits.Tohelpassesstheselikelybenefits,inthissectionofthereportwepresentevidencearoundthetimingofactionsduringadroughtandthelikelycostoftheseactions.

Itisfirstusefultogroundourdiscussionoftheadvantagesofearlydisbursementwithadescriptionofthechronologyofatypicaldrought.Suchadescriptionisnecessarilystylizedandthusafterpresentingthestylizeddescription,wewilldiscussforwhichemergenciesthisisanaccuratedescription;andforwhichemergenciesthechronologyofdroughthasbeensomewhatdifferent.Thisprovidesuswithsomecontextforunderstandingthetypicalbenefitsthatwearelikelytosee.Finallywereviewthenutritionandeconomicliteratureonthecostsassociatedwiththetypesofstrategiesthathouseholdsusewhennotreceivingearlyassistance.

5.1. TIMELINEOFASLOW‐ONSETEMERGENCYSUCHASADROUGHT

Lifeinruralareasinsub‐SaharanAfricaisinherentlyseasonal.Withoneortwoharvestsayearfarmersexperienceseasonsofplentyandscarcityeveryyear.Atharvest,seasonsofplentyallowfarmerstopayoffdebts,investindurableconsumptionpurchasesandsavefoodandmoneyforhardertimeslaterintheyear,orevenforfutureyears.Formanyhouseholdshowever,harvestsarenotsubstantialenoughtoprovideforanentireyearoffood.InMalawi,Devereuxestimatedthatin2000/2001whichwasagoodproductionyear,themedianfarmerwouldharvestenoughmaizetoprovideforhouseholdconsumptionforbetween6and9months(Figure7).Somewhatsimilarly,inEthiopia,Minot(2008)estimatedthatthemedianhouseholdwouldhaveenoughgraininstoretoprovideforconsumptionfor7monthsafterthe2007Meherharvest(Figure8),aharvestslightly,butnotsubstantially,belowaverage.Oncegrainstocksareexhaustedhouseholdsliquidatesavings,anddurableassetstofinancepurchasesofgrainandotherfoodsfortheremainderoftheyear.Consumptionduringthisperiodalsotendstobelowerthaninthemonthsimmediatelyfollowingharvest(seeforexampleSahn1989andthepaperstherein).

Aswithcultivatorhouseholds,pastoralisthouseholdsfaceanaturalseasonality,inwhichwetseasonsarecharacterizedbycattlegrazingnearbyfarmsteadsandabundanceofmilkandgoodlivestockweight.Dryseasonsseecattle(orsomeportionoflivestock)takenfurtherfromthehomestead,reducedmilksupply,andreducedlivestockweight.

33

FIGU

F

Themajrainthrleansea

Wecanfailureofarmersandtheycanofte(suchaslaterin

URE7:HOUSE

FIGURE8:HO

jorityofagrroughthecroasonsarelen

looselycharofrainsdurisastheyloseyarenowfaentakesthesshort‐matutheseason,

1

1

2

2

3

3

4

Percentage of households

EHOLDGRAI

OUSEHOLDG

riculturalproppingcyclengthenedan

racterizeraingflowerinetheinvestmacedwiththformofrepuringmaizecroplosste

0

5

10

15

20

25

30

35

40

12+

"Good

NSTOCKSINDE

GRAINSTOCK

oductioninethushavendsavingsa

infallfailurengandgrainmentsmadeheneedtorelantingshor,orsorghumendstonotb

9‐12

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d" year (2000/

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nsatplantincauselossescropthatisesult.Replanarietiesorcrrainsarepre

3

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34

Failureofrainsduringfloweringandgrainfillingareparticularlycostly.Atthispointintheseasonthereisnoopportunityforfarmerstoreplant,orswitchtoshortermaturingcrops.Croplossescanbesevere.Thisistruebothatthefarmandatthenationallevel.Whenaskedtoestimatetheproportionofyieldslostduringtheworstrainfallyearinthelastfive,farmersintheOromiaregionofEthiopiarespondedthattheylost,onaverage,61%oftheiryields,withtheinterquartilerangerangingbetween50%and83%ofyieldslost.ComparingyieldsfromCSAcrop‐cuttingexperimentsinthesamestudyareasyieldlosseswerefoundtobealittlelower,butstillsubstantial:lossesintheworstyearwerefoundtobe16%to55%oftheaverageyieldofyearsexcludingthisyear.InMalawi,in2001/2,therainsfailedresultinginareductionofthenationalharvestby32%andatthehouseholdlevel,householdslookingforalternativesourcesoffoodfor3‐4monthslongerthanusual(Devereux2007).Figure7depictsthischange.

Forhouseholdsinpastoralistareas,rainfailurethatreducestheavailabilityofpastureisparticularlyproblematic.Whendryseasonsareprolongedasaresultofdrought,milkproductionandlivestockweightisfurtheraffected.Thisputsagreaterstrainonlivestockhealthandproductivity.Whenrainsfail,milkproductionfalls,livestockweightdeterioratesandtheincidenceoflivestockill‐healthincreases.The2008‐2011droughtsinKenyaresultedindiseaseaffectingmorethan40%oflivestockherds(GovernmentofKenya2012).Therearealsoreductionsinconceptionratesoflivestock.Mortalityoflivestockasaresultofdroughtwillcomelater,andistheextrememanifestationofriskspastoralistsface(Lybbertetal2004).Lybbertetal(2004)showthatpoorrainfallyearsresultinincreasesoflivestockmortalityof25%andMcPeak(2004)notesthatanumberofstudiesreportlossesofuptohalfahousehold’sherdoveraperiodofmonthsinEastAfrica(Coppock,1994;McCabe,1987;Sobania,1979).TheKenyangovernmentestimatedlivestockmortalitytoreach9%ofexistinglivestockherds(GovernmentofKenya2012).Whenlossesaresolargeastoresultinsubstantialreductionsinherd‐sizehouseholdswillbeunabletomaintainapastorallifestylewhichresultsinextremepovertygiventheabsenceofalternativelivelihoodoptionsintheseenvironments(McPeak2004andthereferencestherein).

Numerouseconomicanalyseshavedocumentedhowhouseholdscopewithshockstoharveststhatarerealizeduponsuchafailureofrains.

Table7summarizesanumberofthesestudiesforsub‐SaharanAfrica.Weseethathouseholdsincreasetheamountoflaborsuppliedtooff‐farmactivities,rundownsavings,takeconsumptionloans,increaserelianceonremittancesandgiftsfromfamilymembersoutsideoftheirimmediategeographiclocale,sellassets,reduceconsumption,takechildrenoutofschool,reduceinvestmentsinhealthcarecostsandmigrate.Veryfewstudies,howeverhaverigorouslydocumentedthetimingoftheseactivities.Suchanunderstandingiscrucialinbuildingupadescriptionofthechronologyofatypicaldrought,andwhatbenefitsresultfrominterveningearly.Morestudiesonthiswouldbebeneficial,intheremainderofthissubsectionwepiecetogetherwhatwecurrentlyknow.

Inthefollowingparagraphswedetailwhatweknowaboutthetimingofdifferentstrategiesusedbyhouseholdsinthefaceofdrought.ThediscussionissummarizedinTable8.Togroundthediscussionwetakeourexampleasthefailureofrainsduringfloweringandgrainfilling.Wealsopresentevidenceonthetimingofcopingstrategiesinpastoralistareasthroughoutthediscussion.

Rainsfailsome1‐2monthspriortoharvest;andfarmersknowatthispointthattheirharvestswillbepoor.Atthispointintimetheymaystarttolookforothersourcesofincome,knowingthattheircropincomeisgoingtobelowerthanhouseholdneeds.Thiscouldbeagriculturalornon‐agricultural.However,theopportunitiesforsuchlaborengagementmaybelimitedor

35

short‐lived.AsSen(1981)described,droughtreducesthedemandforgoodsandservicesinaffectedcommunitiesimpactingthosewhoseincomeisnotderivedprimarilyfromagriculture,suchastradersandruralbarbers.Whenharvesttimecomes,farmersharvestwhatisthere.

TABLE7:EVIDENCEOFCOPINGSTRATEGIESUSEDBYHOUSEHOLDSINTHEFACEOFDROUGHT(SUB‐SAHARANAFRICA)

Study Country and year

Household behavior post-drought

Dercon, Hoddinott and Woldehanna (2005)

Ethiopia, 1994-2004

Drought is associated with a loss of productive assets by 41 percent of households. A loss of income and consumption by 77 percent of households.

Alderman, Hoddinott, and Kinsey (2006)

Zimbabwe, 1982-84

Reduced consumption: permanent loss of stature of 2.3 cm Reduced education: a delay in starting school of 3.7 months, and 0.4 grades less of

completed schooling. Yamano, Alderman and Christiaensen (2005)

Ethiopia, 2002 Crop losses result in reduced consumption, affecting the growth of children particularly in the 6-24 month group. Estimates suggest a 50% crop loss results in a reduction of 9mm over six months.

Jensen (2000) Cote d’Ivoire, 1986

Enrollment rates declined by about 20 percentage points (more than one-third of the original rate) in regions that experienced adverse weather shocks, compared to regions that did not.

The percentage of sick children taken for consultation fell from about 50% to around one-third in regions that experienced the adverse weather shock.

Malnutrition among children increased by 3-4 percent in regions receiving the rainfall shock.

Fafchamps, Udry and Czukas (1998)

Burkina Faso, 1984

Little relation between cattle transactions and rainfall shocks, a stronger negative correlation between small stock net purchases and rainfall, but combined livestock sales offsetting 15-30%, of the income losses resulting from drought during this period.

Kazianga and Udry (2006) Burkina Faso, 1984

A quarter of rainfall induced crop losses were smoothed through depleting grain stocks. Over half of the rainfall induced crop losses during this period were passed onto reduced

consumption. Median calorie consumption per adult was less than 2000, 30 percent below WHO recommendations.

Households supplied more labor to combat crop losses. Almost no within village risk sharing. Livestock were not sold to manage crop losses.

Lybbert et al, (2004) Southern Ethiopia (Borana), 1980-1997

Rainfall patterns and mortality of livestock herds do not trigger sales of livestock. Herd changes are primarily due to natural reproduction and mortality.

McPeak and Barrett (2001) Northern Kenya, 2001

Pastoralists reduce food intake and activity levels Households do not sell livestock to protect consumption.

Reardon et al (1988) Burkina Faso, 1984

Households deplete grain stocks.

Udry (1995) Nigeria, 1988 Grain stocks are saved and depleted to smooth consumption. Livestock savings do not respond to income shocks.

Devereux et al (2006) Malawi, 2005 Reduced consumption.

36

TABLE8:ASTYLIZEDTIMELINEOFDROUGHTCAUSEDBYEND‐SEASONFAILUREOFRAINS

Number of months post-harvest

Harvest cycle Farmers actions (average farmer) Response

-2 Rainfall fails

Look for non-farm work Eat less preferred food

-1

0

Harvesting Harvest what is there

Use savings, sell non-productive assets Borrow money from those not affected

Cut-back on durable purchases 1

2

Two-season: planting for next season

Cut-back on input investments (if two cropping seasons)

3

Reduce food intake

Respond to save livelihoods

4 5 One-season: planting

for next season

Sell productive assets

6 7

8

9

Increased mortality

Respond to save lives

10

11

Harvesttimeisoftenatimewhenfarmersinvestindurableassetsrangingfromgoatstomobilephones.Thisyear,thesepurchasesarenotmade.Bythispointintimetheyhavealsostartedtomakeotherchangestotheirconsumptionpatterns.Theymayconservethefoodtheyhaveandeatlesspreferredfoods.Murphy(2009)describeshowintheabsenceofsufficientquantitiesofapreferredcereal,otherfoods,suchascassava,arelikelytomakeupsomeoftheshortfallinhouseholdconsumption.Overthenext2‐3monthstheyconsumethegrainstockstheyhavefromthisyear’sharvest,asthisstartstorunouttheywillusecashholdings,andliquidatenon‐productiveassetssuchassmallruminants(suchasgoats),gold,andjewelrytopurchasefoodforconsumption.

Theuseofgrainstockspriortotheliquidationofproductiveassetsiswell‐documentedinanumberofsettingsandusingavarietyofmethodologies.Fafchamps,UdryandCzukas(1998)notethat“droughtsinAfricatypicallyleadtocropfailureandtoadepletionoffoodstockswellbeforetheybeginaffectinglivestocksurvival”andthisissupportedbytheevidencepresentedinSandford(1983)andSwift(1986).AfteranumberofyearsofdroughtinBurkinaFasointhemid‐1980s,90%ofhouseholdsstillhadlivestock(Fafchamps,UdryandCzukas1998),butgrainstockswereexhaustedby1985(Reardonetal1988).Givendroughtaffectsallhouseholdsinthesameareas,thereislittlerelianceonloansandgiftsfrombetterofcommunitymemberstoworseoffcommunitymembersasameanstomanagelosses.Althoughsuchtransfersarequiteeffectiveathelpinghouseholdsinsureagainstidiosyncraticshockstheydonothelphouseholds

37

insureagainstcovariateeventssuchasdrought.Ifhouseholdshaveaccesstourbanremittancestheymayrelyon.

Ifthehouseholdlivesinanagro‐ecologicalzoneinwhichtheycultivatetwoseasonsintheyear,bytwo‐threemonthsaftertheharvestwewillstarttoseethefirsteffectsofthedroughtonproductiveinvestments.Farmerswillbelessabletoinvestinimprovedseedsandfertilizerstosecurehighyieldsinthefollowingseasons.Ifthehouseholdlivesinanagro‐ecologicalzoneinwhichonlyoneseasoniscultivatedperyear,wewillseereductionsintheseproductiveinvestmentsaboutsixmonthsafterharvest.TheKenyandroughtin2008resultedincroplossesthatyear,butalsomanyyearsafterasfarmerswerelessabletoinvestinseedandfertilizerforproductiononaccountofincreasedindebtedness,reducedsavings,andconsumptionofseedstocksthatwouldhavebeenkeptforplanting(GovernmentofKenya2012).TheKenyangovernmentestimatesthatthecostofrehabilitatingcropproductionisaboutUSD60million.

Threetofivemonthsafterharvestandsomefivetoeightmonthsaftertheinitialfailureofrains,wemayseeotheractionstakenwhichhavelong‐runwelfareimplications.Ashouseholdsexhaustthelimitedfoodstocksthattheyhaveavailable,theywillreducefoodintake,reducingthenumberofcaloriesavailabletohouseholdmembers,andoftenreducingthecaloricintakeofwomenfirst(Hoddinott2006).Forhouseholdsinpastoralistareas,rainfailurethatreducestheavailabilityofpasturereducestheavailabilityofmilk,meatandbloodbothforhouseholdconsumptionandsale,resultsinreducedhouseholdconsumption.Whilefailureofoneseasonrainscanshowupinincreasedlevelsofmalnutritionsome3‐4monthslater(Chantaratetal2011),humanitariancrisesarecharacterizedbyrainsfailinginconsecutiveseasons(Chantaratetal2007).

Householdswillalsostarttosellproductiveassets.Salesofproductiveassetsareusuallyintheformoflivestocksales.Landsalesarerarethroughoutsub‐SaharanAfricasometimesonaccountlegalrestrictions,limitedcertificationofpropertyrights,andinsomecases,asaresultoftherelativeabundanceofland(Platteau1992).

Thereisconsiderablediscussionofthedegreetowhichlivestockaresoldintimesoffamine,andwhetherhouseholdswillchoosetocutbackonconsumptionoronproductiveassets.Itisthusworthspendingsometimediscussingtheevidenceonthispoint.Abodyofcarefuleconometricevidencefromanumberoffaminesindifferentcountriesinsub‐SaharanAfricashowsthatalthoughlivestockaresoldduringtimesoffamine,thedegreetowhichtheyaresoldismuchlessthansimplenarrativeswouldsuggest.InBurkinaFasoin1984,combinedlivestocksalesoffsetonly15‐30%,oftheincomelossesresultingfromdroughtduringthisperioddespitehouseholdsowninglivestockofsufficientvaluetomorethancompensateforincomelost(Fafchamps,UdryandCzukas1998).Overhalfoftherainfallinducedcroplossesduringthisperiodwerepassedontoreducedconsumption(KaziangaandUdry2006).InSouthernEthiopia,droughtdidnottriggersalesoflivestock(Lybbertetal2004)andinnorthernKenyainsteadofliquidatingassetstofinanceconsumptionindrought,householdschosetoprotecttheassetstheyhadbyreducingfoodintakeandenergylevels(McPeakandBarrett2001).Theauthorsoftheseworksconcludethatalthoughthereisconsiderableanthropologicalandanecdotalworkwhichshowshowsalesofproductiveassetsareusedtosmoothconsumption,thereislittleeconometricevidenceinsupportoftheideathatthisisthemainwaythathouseholdscopewithdroughts.Rahmato(1991)foundthatduringthe1984‐5famineinEthiopia,householdscuttheirconsumptiontodangerouslylowlevelsratherthansellingoffassets,oncetheassetstermsoftradehadcollapsed.Thisisnottosaythatproductiveassetsarenotsold.Rathertheyappeartobesoldbyhouseholdswithmoreassetstobeginwith.ThishasbeenshownforZimbabwe(Hoddinott2006)andEthiopia(Littleetal2006).

38

Thisevidencesupportsanearlierliteratureonthenarrativeoffamines.AsHoddinottremarks:“anolderliteraturethathasfocusedonhouseholdbehaviorunderfamineconditionsmadethispointexplicitly—whilecurrentcircumstancesmayhavebeendire,tosellofthemeagerassetsahouseholdposesevenwhenfoodconsumptionhadfallendramaticallywastoinvitefuturedestitution”(Hoddinott2006).Thisisparticularlythecasewhenliquidityconstraintsarepresentandtheassetsinquestionprovideanincomestreamforpoorhouseholds.Poorintegrationoflivestockmarketsandindivisibilityoflivestockassetsareadditionalreasonsastowhytheremaybefewsalesofproductiveassets.Considerableevidencesuggestslivestockmarketsareindeedpoorlyintegratedgiventhecostoftruckinganimalsoverlongdistances(FafchampsandGavian(1996)inNiger1995,GovernmentofKenya2012)andwhenlivestockmarketsarepoorlyintegrated,droughtdampensdemandforlivestocksuppressinglivestockprices.Assuchhouseholdsdecidetokeeplivestockdespitereducedincomeandconsumption,depressedanimalproductivity,andincreasedchanceofmortalityiftheanimalsremainlocally(Lybbertetal2004).Additionally,becauselivestockareindivisibleassetshouseholdsmaychoosenottoselllivestocktosmoothmoderatedropsinconsumption(Dercon1998).KaziangaandUdryfindthistobethecasefor30%ofhousehold‐yearsintheirsample.

Dercon(2004)providesadescriptionofthecopingstrategiesusedbyhouseholdsduringthefamineinthemid‐1980s.Hefindsthat85%ofhouseholdsreducedfoodconsumption,39%soldvaluables(onaverage29%oflivestockholdingswereliquidated),7%ofhouseholdsmigratedindistressand11%ofhouseholdshadatleastonemembergotoafeedingcamp.Thisorderingoftheprevalenceofcopingstrategies(reducedconsumption,liquidationofassetsanddistressmigrationofsomeform)wasconstantineveryvillage,eventhoughtheseverityofharvestfailurevariedacrossvillages.Althoughthisrankingofcopingstrategiesdoesnotindicatethetimingsoftheseevents,totheextentthatthereisvariationacrosshouseholdsinthelengthoftimethatexistinggrainstocksandsavingstooktobedepleted,itisquitelikelythatonaveragethisrankingreflectstheorderinwhichtheseactionsareundertakenbyhouseholds.

Whilstthisreviewhasfocusedonthequantitativemicroeconomicsliteratureoncopingmechanisms,wenotethattheconclusionsareechoesinthevulnerabilityanalysesandassessmentsbasedonhouseholdeconomyapproachesusedbyWFPandSavetheChildrenandcarriedoutbytheFoodEconomyGroup.WesummarizethefindingsfromareviewofthesestudiesinTable9.Thistableshowsthat,similarlytoDercon(2004),droughtsnearlyalwaysresultinreducedconsumption,butlessoftenresultinsalesofproductiveassetssuchaslivestock.

Forthepurposesofouranalysisweassumethathouseholdswillreduceconsumptionfor2‐3monthspriortosellingproductiveassets.Assuchweassumethatsalesofproductiveassetswilloccur,onaverage,some5‐8monthsafterharvest.Assetlossesmayberealizedearlieronaccountoflivestockmortality.

Devereux(2007)characterizesthistimelinewehavesketchedhereasoccurringinfoursequencesofentitlementfailure:firstproductionfailsasaresultoftherainsfailing,thenlabormarketsfailashouseholdsarelessandlessabletofindworkopportunitiesonothersfarmsorinoff‐farmactivities,thencommoditymarketsfailasgrainpricesincreaseandpricesofliquidableassetsdecrease(Sen1981,Shapiro1979).Finallytransfersfailashouseholdscannotrelyonthesupportofothersintheirnetworkalsofacingthesameconstraintsinmeetingeverydaybasicneeds.Devereuxnotesthattheseentitlementfailuresareiterativeandinteractingwithdifferenthouseholdsreachingtheirexhaustionofoptionsatdifferentpoints.However,thisdescriptionispresentedtoillustratethefollowingpoints:“first,weathershocks(droughtsandfloods)triggernotonlyharvestfailuresbutasequenceofknock‐onshocksto

39

localeconomiesandsocieties,andsecond,thereareseveralpointsinthissequencewhereeffectiveinterventioncouldmitigatetheshockandpreventaproductionshockfromevolvingintoafull‐blownfamine”(Devereux2007).Byinterveningearliersomeoftheseentitlementfailurescanbepreventedfromprogressing.Inthenextsubsectionwepresentevidencefromtheliteraturetodateonwhatthebenefitsofstemmingeachoftheseentitlementfailuresarelikelytobe.

TABLE9:AREVIEWOFVULNERABILITYASSESSMENTS

Country Year Source Sold non‐productive

assets, used savings

or took loans, looked

for work

Reduced

consumption

Sold productive

assets

Kenya 2006 Save the Children

2007

Yes Yes Yes

Ethiopia 1999 Christian Aid‐

Ethiopia; Laura

Hammond et al.;

Save the Children

1999

Yes Yes Yes

Eritrea 2000 Food Economy

Group 2001

Yes Yes

Tanzania 1997 Food Economy

Group 1999

Yes Yes Yes

Niger 2004 Save the Children

2006 and 2009

Yes Yes

Tanzania 2005 WFP 2006 Yes

Rwanda 2008 WFP 2009 Yes Yes

Tanzania 2009 WFP 2010 Yes Yes

Niger 2009 WFP 2010 Yes Yes

Chad 2009 WFP 2010, OXFAM

2011

Yes Yes

Burkina Faso 2007 WFP 2008, Yes Yes Yes

5.2. THEBENEFITSOFACTINGEARLY

Droughtshaveimmediatewelfareandhumanlifecosts.Itisestimatedthatthe1984famineinEthiopiacausedhalfamilliondeaths.Itisestimatedthatthe2011HornofAfricadroughtresultedin50‐100,000deaths(SavetheChildrenandOxfam2012).InAugust2011,18monthsintothefamineinSomalia,theCDCcalculatedthemortalityratetobebetween2.2and6.1deathsper10,000peopleperday,dependingontheregion.Childrenweremostatriskwiththeunder‐fivemortalityraterangingfrom4.1to20.3deathsper10,000childrenperday.Whenfoodaidisprovided,mortalityratesdecline.AssuchWFPhasobservedthatthehumanlifelostduetolackoffoodduringdroughtsfrom1993to2003fellby40%(BarrettandMaxwell2005,p.124).Earlierprovisionofreliefislikelytoensureevenmoreliveswillbesaved.A

40

famineisdeclaredwhenmortalityratesexceed2per10,000perday.Interveningtopreventadroughtfrombecomingafaminewillseethusdailybenefitsoflivessaved.

Droughtsalsohavesubstantiallong‐runeconomiccosts.Experiencingadroughtatleastonceinthepreviousfiveyearslowerspercapitaconsumptionby20%inEthiopia(Dercon,HoddinottandWoldehanna2005),evenforawell‐manageddrought.Droughtshocksexperiencedinthe1980sinEthiopiawerecausallyassociatedwithslowergrowthinthe1990s(Dercon2004).Assuch,receivingfoodaidwithinayearoftheinitialfailureofrainshasbeenshowntohavelong‐runbenefits.GilliganandHoddinott(2007)showthathouseholdsthatparticipatedinanemergencyrelieffoodforworkprograminEthiopiawithin12monthsoffoodshortagesin2002,sawa4.4%higherannualgrowthrateinthefiveyearsfollowingparticipationthansimilarhouseholdsthatdidnotreceivefoodaid.Themagnitudeoftheincreaseinthegrowthinfoodconsumptionwasevenhigherat6%highergrowthrate.Effectsofasimilarorderofmagnitudewereobservedforthosewhoreceivedemergencyfoodaidrationsduringthisperiod.

Earlierinterventionislikelytoseefurtherlivessaved,andadditionallong‐runbenefits.Theseestimatesprovidesomeindicationofthebenefitsofinterveninginthecaseofdrought.However,tounderstandthebenefitsofactingearly,weneedtounderstandtheimmediateandlong‐runbenefitsthatarelikelytoemergefromprotectinghouseholdsbeforetherearelossestoconsumptionandassetsinthemonthsfollowingthedisaster.Despiteawidelyheldandoftenstatedbeliefthattherearelargebenefitsfromactingearly,therearenocarefulimpactstudiesthathavedocumentedthis.AstheUnitedNationsOfficefortheCoordinationofHumanitarianAffairs(OCHA)notes:“Althoughmostanalystsbelievethatearlyresponseisnotonlybetterforthelivesofpeopleinneedbutalsomorecost‐effectivefordonors,betterevidencetosupportthisbeliefisneeded.OCHAshouldsupportmonitoringandevaluationofearlyresponseinitiativessothereissomeclearproofthattheywork.”17

Bearingthisinmind,webringtogethertheavailableevidenceonwhathappenswhenhouseholdsundertakespecificrisk‐copingstrategiesdescribedintheprevioussection.Thisprovidesuswithanestimateofthebenefitsofactingbeforethesestrategiesareusedbytoomanyhouseholds.

Weassumethattherearenonegativeorlong‐runeffectsfromrunningdowngrainstocksorliquidatingnon‐productiveassetstomaintainconsumptionlevels.Thisisbecausetheseactivitiesarepartoftheregularseasonalbehaviorofhouseholds.Indroughtyearsgrainstockswillbeexhaustedmorequicklyandnon‐productiveassetswillbeliquidatedearlierthanusual.Indroughtyearsmorenon‐productiveassetsthanusualmayalsobeliquidated,butanemergencyfoodaidresponse,evenifreceivedlaterintheyear,provideshouseholdswithresourcestocompensateforthis.Wherenon‐productiveassetsarenotfullyliquidable,ormarketsfornon‐productiveassetsarenotfullyintegratedtheremaybesmalllossesasaresultofsellingandbuying,orsellingataninopportunetime,butwecounttheselossesasnegligible.Wealsoassumethattherearenodetrimentaleffectsofswitchingtolesspreferredfoodcommodities,howeverwherethenutritionalcontentofless‐preferredfoodcommoditiesisinferior(forexamplecassava)wenotethatthismaynotbeanadequateassumption.

17http://ochanet.unocha.org/p/Documents/OCHA_OPB_SlowOnsetEmergencies190411.pdf

41

Themaincoststoimmediateandlong‐runwelfareareassumedtocomefromreductionsinconsumption,lossesofproductiveassets(asaresultofdirectlossesordistresssales),andinvestmentopportunitiesforegone.

THECOSTOFREDUCEDCONSUMPTION

Attheextreme,reducedconsumptionamongadultsresultsinincreasedmortality.Inaddition,reducedconsumptioncanexacerbatetheimpactofotherchronicoracutehealthconditionspresentinthepopulation.MalnutritionhasbeencorrelatedwithincreasedCD4countsandhighermortalityamongthosereceivingARVtreatmentforHIV(Patonetal2006).However,randomizedcontroltrialsoffoodsupplementationamongthoseonARVshavenotshownthatimprovednutritionresultsinlowerCD4countsormortality(Cantrelletal2008,Ndekhaetal2009).DerconandHoddinott(2005)assesstheevidenceforwhetherthereispersistenceoflowadultBMIafterasubstantialshock.TheevidenceisinconclusivewithevidencefromZimbabwe(HoddinottandKinsey2001)suggestingthereisnopersistenceandevidencefromEthiopiasuggestingtheremaybesomelaginadjustmenttooptimallevels(DerconandKrishnan2000).EvidencesuggeststhatadultBMIispositivelycorrelatedwithagriculturalproductivityandwages(Dasgupta,1993;DerconandKrishnan,2000;StraussandThomas,1998;Pitt,RosenzweigandHassan,1990),andassuchfluctuationsinBMI,howevertemporary,willresultinlowerlifetimeearnings.Theimpactofreducedconsumptiononchildrenismoreextreme.Inadequatenutritionisaprimarycauseofdeathofchildrenunder5yearsofageinsub‐SaharanAfricaandhasalargeeffectonhealth.Itisestimatedtocause33%ofchildhooddeathsandtocontributetoone‐fifthofalldisability‐adjustedlife‐yearslostindevelopingcountries(WHOandUNICEF2010,Blacketal2008).Thereisarapid,exponentialincreaseintheprobabilityofmortalityonceachildreachesextremelevelsofmalnutrition.Forexample,O’Neilletal(2012)showthatonceachild’sweight‐for‐lengthorBMI‐for‐ageZ‐scorefallsbelow‐2thereisanexponentialincreaseintheprobabilitythatthechildwilldiewithinthreemonths,fromlessthanonepercenttoabove10%ataz‐scoreoflessthan‐3(O’Neilletal2012).Thefindingthatmortalityincreasesexponentiallyatanthropometricz‐scoreslowerthan‐2isfoundinotherstudiesalso.Specificnutritionaldeficienciesalsohavebeenfoundsignificantincausingincreasedinfantmortalityanddisease.Christian(2009)providesanoverviewofthisliteratureandreportsthatVitaminAdeficiencyincreasestheriskofchildmortalityby23‐30%(SommerandWest1996);andzincdeficiencyincreasestheriskofmortalityby18%,andisassociatedwithanincreasedriskofsevereandpersistentdiarrhea,pneumonia,andstunting(Tielschetal2009).Intheabsenceofafastoradequateaidresponse,infantmortalityratesincreasesubstantially.1.25millionchildrendiedasaresultofthedroughtsacrossAfricain1984(Christian2009).KellyandBuchanan‐Smith(1994)reportincreasedinfantandchildmortalityratesinSudanin1991aspartofthecrisis.Iffoodaiddoesnotproperlybalancenutritionalneeds,longrunconsequencesonchildhealthandwelfarewillberealized.XerophthalmiaandresultantblindnesshasbeenshowntoresultfromseverevitaminAdeficiencyamongfaminepopulations(Nieburgetal1998).The1982‐4droughtinZimbabweresultedinareductioningrowthvelocityof15‐20%(HoddinottandKinsey2001)andapermanentlossofstatureof2.3cm(Alderman,HoddinottandKinsey2006)amongchildrenaged12‐23monthsduringthedrought.Childrenolderthan2

42

didnotexperiencethesamelong‐runlossofstature(Hoddinott,2006).DerconandPorter(2010)showthatchildrenaffectedbythe1984famineinEthiopiagrewtobe3cmshorterthanchildrenofthesameagethatwerenotaffected.Lowerlevelsofheightgrowthhavebeenassociatedwithlowerschoolperformance,lowercognitivefunction,andpoorerpsychomotordevelopmentandfinemotorskills,andahigherincidenceofproblemsinchild‐birth.Recentstudieshaveprovidedabetterunderstandingofthelong‐runcausalimpactofgoodnutritioninthefirsttwoyearsoflife.Hoddinottetal(2008)estimatedtheimpactofimprovednutritiononeducationalattainmentandwageearningsbycomparingchildreninanutritionalsupplementstudywiththosenotinanutritionalsupplementstudy.Themagnitudeoftheseeffectsisunlikelytoprovidemuchindicationofthelikelyeffectofreceivingtimelyfoodaid,butitdoesshowthelong‐runimportanceofnutrition.Thestudyfoundthatwhenreceivingadequatenutritioninthefirsttwoyearsoflife,thesechildrengrewtobeadultswithhighercognitivefunctioning(scoringhigheronknowledge,numeracy,readingandvocabularytests,Maluccioetal2009)andwageratesthatwere46%higherthanthosewhohadnotreceivedthesupplementation(Hoddinottetal2008).Alderman,Hoddinott,andKinsey(2006)findthatthe1982‐84Zimbabwedroughtresultedinadelayinstartingschoolof3.7months,and0.4gradeslessofcompletedschooling.Thecombinedeffectofthesefactorswasestimatedtoreducelifetimeearningsby14%.DerconandPorter(2010)findthatchildrenyoungerthan36monthsattheheightofthefaminewerelesslikelytohavecompletedprimaryschoolandmorelikelytohavesufferedrecentillness.Indicativecalculationssuggestthisledtoanincomelossof3%peryear.

THECOSTOFASSETLOSSES

Whenassetsarelostduringdroughts,eitherasaresultofdistresssalesorasaresultofdirectlosses(suchasincreasedmortalityamonglivestock),itcantakemanyyearsfortheselossestoberecovered.Dercon(2004)findsthattenyearsafterthemid‐1980sfamineinEthiopia,cattleholdingswereonlytwo‐thirdsofwhattheywerejustbeforethefamine.Thislossinassetshasanimpactonthelivelihoodstrategiesthatahouseholdcanengagein.Thosewithlowlevelsofassetsarelesslikelytoinvestinproductivityenhancinginvestmentsinthenextagriculturalharvest(Haile2005,GovernmentofKenya2012).Levelsofassetsarealsolikelytoaffectactivitychoice.Thosewithfewerlevelsofassetsaremorelikelytoenterlowreturnactivities.DerconandKrishnan(1996)findthatthoseenteringintolowreturnactivitieswerehouseholdswithverylowassetandlivestocklevelsin1989,partlyasaresultofassetlossesduringthefamineperiod.Somewhatsimilarly,Lybbertetal(2004)findthatsouthernEthiopianpastoraliststhatexperiencedlargelossesinassets,suchthattheywereleftwithfewerthan15headofcattle,didnotrecover,insteadreducingtheirassetholdingsfurtherandenteringasedentarylifestyle(alifestyleassociatedwithabjectpovertyinsouthernEthiopia).Onlyathirdofhouseholdswithlessthan15cattle,householdsthathadlostmorethan25%theircattle,wereabletorecoverto95%oftheirassetholdingsinthreeyears.AmongpastoralistsinnorthernKenya,Barrettetal(2006)findthattheminimumlevelofcattlerequiredforahouseholdof6toavoidfallingintosuchpovertyis30cattle.Whenhouseholdshavemorethan30headofcattle,theirassetsgrow,whenhouseholdshavelessthan30headofcattletheytendtoexperiencefurtherlossesincattleuntiltheyfalltojustonecattleperhousehold.PermanenceinassetlevelsmoregenerallywasalsoobservedbyBarrettetal(2006)amongcrop‐cultivatinghouseholdsinKenya.Householdsthatstayedpooracrossthestudyperiod,hadsmallerassetbases(lessthanoneacreoflandandnocattle)thanhouseholdswhoremainedoutofpoverty.

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Giventhisdynamic,Elbersetal(2002)findthatforamodelcalibratedusingZimbabweandata,shockstoassetsreduceaggregategrowthbyafifthovera20yearperiod.Abouthalfofthisreduction(i.e.onetenth)isestimatedtocomedirectlyfromlossesinassets.

Lossesinassetsarecostlyoverthelongrunforhouseholds.Thereareanumberofinterventionsthatcanreducethelossesinlivestockresultingfromdrought.Supplementaryfeedingprogramsforlivestockhaveproveneffectiveatkeepinglivestockaliveduringdroughtyears.Andtheevidencesuggeststhatthiscostislowerthanthecostofreplacinganimalslosttodrought.Estimatessuggestthatthecostofsupplementaryfeedingprogramsforlivestockisbetween3and14timeslessexpensivethanthecostofrestockingtoreplacelivestockthathavedied(SavetheChildrenandOxfam2012).

Distresssalesarelikelytocomeafterreducedconsumption.Itisthususefultohaveacombinedestimateoftheimpactofreducedadultconsumptionandreducedlivestocksalesonhouseholdincome.Dercon(2004)providessuchanestimateforthelongrungrowtheffectsofthemid1980sfamineinEthiopia.Hefindsthathouseholdsthatreducedconsumptionandsoldtheirmostvaluablepossessionssawa16percentagelowergrowthrateinthe1990scomparedtothoseonlymoderatelyaffected.

5.3. SUMMARY:INDICATIVEESTIMATESOFTHEBENEFITSOFACTINGEARLY

Thissectionhasreviewedanumberofcarefuleconometricstudiesthathavetriedtoidentify,asmuchaspossible,theimpactofdroughtonhouseholdcopingstrategiesandwelfare.Wemadeanumberofassumptions,basedonevidenceasmuchaspossible,toproposeastylizedtimelinefortheaveragehouseholdintheyearafterabaddrought(Table8).Tofurthergofromthisreviewtoanestimateofthelikelycostsofadelayedresponse(andthusthelikelybenefitsofanearlierresponse)requiresafurthersetofassumptions.Giventhevariationinthewayfaminesunfold,thiswillnecessarilybestylized.Figure7andFigure8alsohighlightthathouseholdsinanygivencontextvarysubstantiallyinhowlongtheywilltaketostartengaginginlivelihood‐endangeringactions.

Whendroughtconditionsaresevere,oraffecthouseholdsthatarealreadyverypoorandhavealreadyexhaustedmanyoftheircopingmechanisms,increasedmortalitywillresultintheabsenceofintervention.Thisisanunacceptablecostofadelayedresponse(SavetheChildrenandOxfam2012).

Inaddition,weusethestylizedtimelinethatwehavediscussedtoprovideanestimateofthelikelyeconomiccostofadelayedresponseforanaveragehouseholdinacountrysuchasMalawiorEthiopia,whichstartslivelihood‐endangeringrisk‐copingstrategiesthreemonthsafterharvest.

Weuseestimatesonthelong‐runcostsofreducedconsumptionperchildfromAldermanetal(2006)andestimatesonthelong‐runcostsofreducedhouseholdconsumptionandassetsalesonhouseholdgrowthratesfromDercon(2004).Thecostofadelayedresponseisnotthesameasthecostofnoresponse.Wethereforechooseourexamplescarefully.InboththeZimbabweandEthiopiaexamplestherewasaresponsetothedroughtbutitwasdelayed.Weassumethatthelong‐runlosseshouseholdsexperiencedareasaresultofthedelayedresponse;ratherthanlossesabsentaresponseentirely.

Aldermanetalestimatethateachchildunder2yearsofagethatreceivedreducednutritionlost14percentoflifetimeearnings.Tocalculatethepresentvalueoflifetimeearningsofthese

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under2‐yearolds,weusetheUSD1.25percapitaadaypovertylinetocalculatethefuturedailyearningsrateforthechildrenbeingtargeted.Weassumethatoncethesechildrenare18yearsoldtheywillprovidehalfoftheearningsforahouseholdoffouradultequivalentsonUSD1.25percapitaperdayin2012realterms,andthattheywilldothisuntiltheyare58yearsold.ThishouseholdearnsUSD5perdayandUSD1,825ayear.Thechildrenbeingtargetedwillcontributehalfofthis(i.e.USD912.50ayear).Ifwediscountusingarealinterestrateof10%perannumthepresentvalueoflifetimeearningsisUSD1,765and14percentofthisisUSD247.18Ifweassumethat20%ofhouseholdshaveachildlessthan2yearsofagethisamountstoUSD49perhouseholdonaverage.

However,itshouldbenotedthattheabovefigureishighlysensitivetothechoiceofinterestrate,asonewouldexpectforanearningsprofilesofarinthefuture.So,forexample,ifoneusedaninterestrateof5%insteadof10%,thenetpresentvalueoftheaveragecostperhouseholdwouldincreasetoUSD196,andifoneusedaninterestrateof15%,thefigurewoulddecreasetoUSD17.NotethatwehavealsoassumedthatthechildwillearnUSD1.25percapitaperdayin2012realterms;ifthechildwillearnmorethiswouldacttoincreasethepresentvalueoflifetimeearnings.Forexample,ifthechildexperiencedrealincomegrowthof5%perannumoveritslifetime,thiswouldchangethepresentvaluebyasimilaramounttoiftherealinterestratetousefordiscountingwasreducedby5%.

Derconestimatesthatasaresultofreducedconsumptionandincreaseddistresssaleshouseholdsexperienced16%lowertotalgrowthover9years,thatistosayincomeattheendofthe9yearswas16%lowerthancounterpartswhohadnotsufferedtothesamedegree.Again,weassumethatthehouseholdswearetargetinghaveanadultequivalentpercapitaconsumptionofUSD1.25in2012realterms,andthatthereare4adultequivalentsperhousehold.AssuchthestartingyearlyincomeofthishouseholdisUSD1825andwillearnthisamount,in2012realterms,fortwentyyears.Thepresentvalueofhouseholdincomeoverthecoming20yearsbeforeanyreductioningrowth,discountingusinganinterestrateof10%,isUSD16,301,andtheequivalentfigureassuminganaccumulatedreductionof16%overthefirst9yearsisUSD14,675.19ThepresentvalueofgrowthlostisthusUSD1,082perhousehold.Thisfigureislesssensitivetothechoiceofinterestratethanthepreviousfigureduetothelowerdiscountedmeanterm,increasingtoUSD2,619ifaninterestrateof5%isused,anddecreasingtoUSD1,082ifaninterestrateof15%isused.

Wealsoaddanestimateofthedirectlossesresultingfromlivestockdeaths.Theseareestimatedat25%oflivestockherdsbyLybbertetal(2004).Takingourprototypicalhouseholdasanagrarianhouseholdwith2cattlethistranslatestohalfacattlelostonaverage.OnecattleisvaluedatanaverageofUSD325inKenyaandEthiopia(CabotVentonetal2012).Wemakethe

18Denotingtheinterestratefordiscountingas ,thenetpresentvalueoffutureearningsofUSD912.50perannum,payablecontinuouslybetweenage18and58,foralifecurrentlyaged1isgivenby

912.5 1 .

19Denotingtheinterestratefordiscountingas ,and suchthat1 1 0.84 / ,thenetpresentvalueoffutureearningsofUSD1,825perannum,payablecontinuouslyfor20yearsstarting

immediatelyisgivenby1,825 andthenetpresentvalueoffutureearningsofUSD1,825per

annum,decreasingcontinuouslyoverthefirstnineyearsandthenremainingconstantis1,825

1,825 1 .

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somewhatoveroptimisticassumptionthatanearlyresponsecouldstemallofthelivestockdeathsresultingfromdrought.Webasethisassumptionontheevidencethatsupplementalfeedingandwatercouldsubstantiallyreducethenumberoflivestockdeaths.HoweverwenotethatintheCabotVentonetal(2012)analysisthenumberoflivestockdeathsisassumedtofallbyhalfasaresultofanearlyresponse.

ResultsarepresentedinTable10.WeusetheestimatesinTable10incalculatingthepotentialeconomicbenefitstoactingearly,interveningtopreventreducedconsumption,livestockdeathanddistresssales.

TABLE10:ECONOMICCOSTOFDELAYEDRESPONSEPERHOUSEHOLD

Cost of delaying response until … months after harvest -1 0 1 2 3 4 5 6 7 8 9

negligible USD 49 USD 1294

IncountrieswherehouseholdsarelessresilientthaninMalawiandEthiopiaandwherelivelihoodendangeringrisk‐copingstrategiesarelikelytobeengagedinpriortothreemonthsafterharvestperhapsasaresultofconflictorprioremergenciesthatdidnotreceiveanadequateaidresponseandthereforereducedhousehold’sownershipofnon‐productiveassets,thistimetableandassociatedcostswillbemovedclosertoharvesttime.PerhapsagoodexampleofthisisSomalia.Insuchacaseearlyresponsewillneedtobeveryearly.

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6. BENEFITSOFACTINGEARLYUNDERFOURCONTINGENCYPLANNINGSCENARIOS

Theprevioussectionhashighlightedthattherearepotentiallylargebenefitstobegainedbyinterveningearlyafterrainfailureandprovidingaidtohouseholdsbeforetheyreduceconsumptionorsellassets.Guaranteeinganearlypaymenttogovernmentscanhelpensurethatbenefitsreachhouseholdsintime,butwithouttheappropriatedistributionsystemwithincountry,anearlypaymenttoagovernmentwillnot,onitsown,ensurethatthesebenefitsaremet.

Itisimperativethatcountrieshaveinplaceawell‐functioningstrategytoensurethatassistanceisgivenquicklytotherighthouseholds.Inthissectionwepresentfourcontingencyplanningscenariosanddiscusstheextenttowhichtheywillbeabletodeliverbenefitsquickly,andtotherightpeople.Wediscusstheassumptionsabouthoweachcontingencyplanwouldbeimplemented,andtheconditionsthatneedtobeinplaceforeachplantofunctionwellandthecoststhatarelikelytobeassociatedwitheachplan.

Finally,weendbyexaminingthepoliciesthatthesixlikelypilotcountriescurrentlyhaveinplace,toassessthelikelihoodthatcountrieswillhavethecapacitytoimplementtheseschemes.

6.1. ASTYLIZEDBASELINEANDFOURCONTINGENCYPLANNINGSCENARIOS

STYLIZEDBASELINE

Inthestylizedbaselinescenariowecharacterizethecurrentemergencyresponsetoaslowonsetemergencysuchasdrought.Impendingdroughtsaremonitoredthroughseasonalforecasts,rainfallandcropassessmentsduringthecourseoftheseason.Whilstearlywarningsignsareavailable,formalevaluationsofharvestlossesandneedsassessmentsarerequiredinordertolaunchanyemergencyappealthatmayberequired.Acropandfoodsupplyassessmentmission(CFSAM)assessesthestatusoffoodproduction.AfoodneedsassessmentmaybeconductedinparalleloraftertheCFSAM.Thisassessmentprovidestheinformationrequiredforthehumanitariancommunitytofacilitateapossibleexternalintervention.Theseassessmentsbecomeavailable3‐4monthsafterharvest(Haile2005,Chanteratetal2008).Whenthesemeasuresindicatethatalarge‐scaleemergencyisdeveloping,anemergencyappealislaunchedbythegovernmenttotheUNConsolidatedAppealsProcess(CAP)askingdonorsforaid.Donorsrespondtothisappealduringthecourseofthefollowingmonths,choosingthedegreeofresources(cashorfood)toprovidetothecountry.

Resourcesareusedtopurchaseanddistributefoodaid(orcashvouchers)aspercommonpracticetodevastatedareas.BothHaile(2005)andChanteratetal(2008)suggestthathumanitariandeliverystarts4monthsafteranappeal(Haile2005).Atthispointassistanceisarriving7to8monthsaftertheharvestfailure.However,thenatureofassistanceprovided(food,cashorvouchers)andthemannerinwhichfoodisprocureddeterminetheamountoftimeittakesfromfilingaformalrequesttodistribution.Whenemergencyreliefisprovidedintheformoffoodaidshipments,themedianresponsetimerangesfrom3to5.5monthsfromfilingaformalrequesttothefinaldistributioncenter,dependingonwhetherlocalandregionalprocurementisused(medianof3months)orwhethertransoceanicshipmentsaremade(medianof5.5months).ThesenumberscomefromacarefulstudyoffoodaiddeliveriesconductedbyLentzetal(2012)(thesenumbersalsocorrespondtothosepresentedin

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HaggbladeandTshirley2007).Wepresentthemedianresultshere,buttherearemanyinstancesoflongerperiodsofdelay(BarrettandMaxwell2005).Lentzetalalsoshowthatcashdistributiontakesplacein2monthsafterfilingaformalrequestandvoucherdistributionin4months(withsomevoucherdistributionoccurringmuchmorequickly).Formalrequestsareonlyfiledsomemonthsaftertheinitialappeal.

Forouranalysisweassumethat:(i)appealsaremade3‐4monthsafterharvest,(ii)donorsrespondtoappeals2monthsaftertheyaremade,(iii)foodaidtakes3monthsfromappealtodistribution(i.e.localorregionalprocurementisused),and(iv)cashtakes2monthsfromappealtodistribution.Foodisthusdistributed8‐9monthsafterharvestandcashisdistributed7‐8monthsafterharvest.Theseestimatestallywellwiththetimelineofresponsetothe2011HornofAfricadroughtandthe2005‐6droughtinKenyapresentedinSavetheChildrenandOxfam(2012)

Dependingonthecontext,improvementsincostmaycomewhenfoodaidisprocuredregionally(Lentzetal2012),orwhenemergencyassistanceisdistributedincash(Hessetal2006).Itmayalsobethecasethatslowdonorresponsesincreasetheamountoftimeittakesforfoodaidtoarrive,andthatbythetimeitarrivesitismorecostlytoprovideasmoreexpensivetransportlogistics(suchasairlift)areused,andasmoreprocessedcommoditiesareneededfortherapeuticfeedingpackaged.InNovember2004thegovernmentofNigerissuedarequestforemergencyfoodaid.InitialdeliveriesbyWFPtookplacefourmonthslaterinFebruary2005andcost$7perbeneficiary.Theresponseatthistimewasinadequatecomparedtoneeds,andwhenfurtherdeliveriesweremadetenmonthsaftertheoriginalrequest(inAugust2005)thecostsofdeliverywere$23perbeneficiaryonaccountoftheneedforprocessedcommoditiesandmoreexpensivetransportationlogistics(Chanteratetal2007).

Distributioniseasiestandcheapestinareaswherefoodaidhasbeenpreviouslydistributed,leadingtoabiasinlocationsselectedforfoodaiddistribution(seeJayneetal2002foreconometricevidenceonthis).Withinselectedlocations,communityleadersareaskedtoprioritizewhoshouldreceivefoodaidwithwomenandchildrenreceivingrationsfirst.Targetingerrorsintheselectionofindividualsatthelocallevelhavebeenestimatedtoresultinerrorsofinclusionof42%anderrorsofexclusionat40%(Jayneetal2001).UsingdatapresentedinFigure1ofJayneetal(2002)weestimatethatthepoorest40%receive43%ofthefood‐aiddistributed.Targetingisprogressive,butnotbymuch.20

SCENARIOS1AND2:IMPROVEDFUNCTIONINGOFTHEFOODAIDSYSTEM

Inthisscenario,twomajordifferencesareintroducedintohowemergencyappealsfunction,bothasaresultofacountry’smembershipinARC.

First,countrieshavedevelopedaplanandbudgetastowhereandhowemergencyfundswillbedistributed.Theseplansconditiondisbursementsbasedonspecificlivelihoodindicatorscollectedatthecountrylevel.Itisexpectedthattheseplanswillresultinimprovedtargetingofbeneficiaries.Inparticular,itisexpectedthatreductionsintargetingerrorsresultingfromtheselectionofincorrectlocationsforaiddelivery(asaresultinpermanenceinfoodaiddistributionsystems,orpoliticalpreferences)wouldbereduced.

20Givenimprovementsinfoodaiddeliveryinrecentyears,itmaybethecasethatthisunderestimatesthequalityofthetargetingoffoodaid,butintheabsenceofotherestimatesweusethismeasure.

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Itisalsoexpectedthathavingacontingencyplaninplacereducesthecostsofdistributingfoodaidwhenagreed.Choularton(2007,p.5.)statesthat“fromapracticalandoperationalperspective,oneofthemostimportantbenefitsofcontingencyplanningisidentifyingconstraints–informationgaps,forinstance,oralackofportcapacity–priortotheonsetofacrisis.Identifyingtheseconstraintsallowsactiontobetakentoaddressthem.”Thisallowsforcostreductionstoberealizedasandwhencontingencyplansareputintopractice.Itmaybethecasethatcontingencyplanningidentifiesdisasterriskreductionstrategiesthatcanbeimplemented(suchasinvestinginirrigationorwateringsites).Investmentinsuchactivitieswillreducetheneedforemergencyassistance,andhavesubstantialdirectandindirectbenefitsasdiscussedinCabotVentonetal(2012).Estimationofsuchbenefitsfromcontingencyplanningisbeyondthescopeofthisanalysis,sowedonotdiscussthishere.Second,countrieswillreceiveanearlypayoutoffundsneeded,uptoamaximumvalueof$30million,basedontheAfricaRiskViewindex.Thispayoutwillbemadeatharvest.Countrieswillstillundertaketheneedsassessmentdescribedabove,andemergenciesrequiringfundsinexcessoftheamountdisbursedwillstillgothroughtheCAPprocessinordertoraisemoneyfortheexcessofamountsreceived.

PayoutsfromARCwillbeusedtodistributefoodorcashasperthecountry’spracticetodevastatedareasaccordingtothepre‐agreedplan.Thisplanrequireslivelihoodindicatorsthatwillonlybeobservedsometimeafterharvest.Earlypayoutswillbeusedinoneofthefollowingtwowaysuntiltheseindicatorsareobserved:

Scenario1:ARCpayoutsareimmediatelyusedtopurchasegrainforthenationalgrainreservefordisbursementassoonaslivelihoodindicatorsareobserved.Scenario2:ARCpayoutsarekeptinaholdingaccountuntilthelivelihoodindicatorsareobserved.

IfAfricaRiskViewtriggeredapayoutinacircumstancewhennopayoutwasneeded(i.e.observedlivelihoodindicatorsaregood),thenthegrainormoneywouldbeheldinthereserveoraccountuntilafutureseasonwhenitisneeded.

Asaresultoftheearlypayout,aiddisbursementisabletobeginfasterthanitwouldunderthetraditionalscenario.Thespeedofdisbursementdependsonwhetherfoodorcashisbeingdistributedandwhetherpayoutsareusedtopurchasefoodorarekeptinaholdingaccount.IfdisbursementsaremadeinfoodthenScenario1resultsintimefromharvesttodistributionof4‐5monthsgivensomeoverlapinthetimeofprocuringfoodandtimewaitingforharvestassessments.Theoverlapisnotcomplete,assometimeisstillneededfordeliveryaftertheareasfordeliveryhavebeenidentified.Weassumethatthisdifferenceis1month.Scenario2resultsinatimefromharvesttodistributionof6‐7months,becauseprocurementoffoodonlystartswhenlivelihoodindicatorsareavailable.Ifdisbursementsaremadeincash,Scenarios1and2resultintimefromharvesttodistributionof4‐5months,givenoverlapinreceivingfinancingandtimewaitingforharvestassessments.Again,theoverlapisnotcompletegivensometimeisstillneededfordeliveryoncethelivelihoodindicatorsareavailable(andagainweassumeadifferenceofonemonth).

SCENARIO3:SCALINGUPANEXISTINGSAFETYNET

Thisscenariodiffersquitesubstantiallyfromthebaselinescenario.Inthisscenario,thecountryhasagovernment‐financed,nationalsafetynetschemewhichtargetslowincomehouseholds.EthiopiahassuchassafetynetinplacewiththeProductiveSafetyNetProgramforlargeparts

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ofthepopulationandMalawiispilotingatransferfortheultra‐poorandlabor‐constrainedin7districts.

Inadditiontohavingapre‐establishedsafetynet,inthisscenarioweassumethatcountrieshavedevelopedaplanandbudgetastohowtoscaleupthissafetynetineachareaofthecountryinanemergency,basedonspecificlivelihoodindicators.Emergencysupportmaybedifferentforthosecurrentlytargetedinthesafetynetfromthesupportprovidedtootherhouseholdslivinginaffectedareas.Forexampleassistancemaybemadeavailableonlytocurrentsafetynetbeneficiaries,orassistancemaybeprovidedtoeveryonebutmaybelargerforcurrentsafetynetbeneficiaries.Itcouldalsobethecasethatassistanceisprovidedtoeveryoneinanareaequallyregardlessofwhetherornottheyareinthesafetynet.Inthiscase,thebenefitsoftyingassistancetoanexistingsafetynetcomefromusingthedeliverysystemsalreadyinplaceasaresultofthesafetynetprogram.

Again,countrieswillreceiveanearlypayoutoffundsatharvesttime,uptoamaximumvalueof$30million,basedontheAfricaRiskViewindex.Countrieswillstillundertaketheneedsassessmentdescribedinthebaselinescenario,andemergenciesrequiringfundsinexcessoftheamountdisbursedwillstillgothroughtheCAPinordertoraisemoneyfortheexcessofamountsreceived.However,allemergencyassistancewillnowbedeliveredbyscalingupanexistingsafetynet,ratherthanbyrelyingonfoodaiddistributionsystems.

PayoutsfromARCareusedtoscaleupthesafetynetaspertheplan.Thisplanrequiresindicatorsthatwillonlybeobservedsometimeafterharvest.However,earlypayoutswillbeusedtoprovidetheresourcesatthenationallevelforscalingupthesafetynetwhenneeded(i.e.resourceswillbeheldinfoodorcashdependingonhowthesafetynetpayoutsaremade).IfAfricaRiskviewtriggeredapayoutinacircumstancewhennosafetynetscale‐upwasneeded(i.e.observedlivelihoodindicatorsaregood),thentheresourceswillbeheldbythegovernmentuntilafutureseasonwhenneeded.

Giventherelianceonanexistingsafetynetschemeitisassumedthatwewillobservethefollowingadditionaldifferencesbetweenthisapproachandthebaselinescenario:improvedtargetingandfasterandcheaperdeliveryofassistance.TheearlyARCpayout,andtheuseoftheexistingsafetynetdistributionsystemallowsassistancetobeprovidedtobeneficiaries3‐4monthsafterharvest,assoonaslivelihoodindicatorsareobserved.Givenpayoutscanusetheexistingstructurefordisbursements,thespeedofaiddeliverywillincreaseandthecostofaiddeliverywillbelower.Thisisinadditiontothecostbenefitsthatarisefromhavingacontingencyplaninplace(discussedinscenario1and2).

Asinscenario1and2,theimprovedmonitoringresultsinimprovedtargetingofcommunitiesrequiringassistance.Howeverwealsoassumethattargetingwillimprovewithincommunitiesasaresultofprioridentificationofsafetynetbeneficiaries.Forexample,Gilliganetal(2010)showthattargetingoftheProductiveSafetyNetProgram(PSNP)inEthiopiaisquiteprogressive,eventhougherrorsdoremain.Wenotethattargetingwillremaindifficultgiventhedifficultiesofidentifyingthenewlypoororthosevulnerabletobeingpoorwithoutassistance(AldermanandHaque2006).Ideally,targetingwouldbeconductedonthebasisoftransitoryneedratherthanchroniccorrelatesofpoverty,andtherearefewcaseswherethishasbeensuccessfullydone.Intheabsenceofsuchtargeting,geographictargetingcanoftenworkwelltopick‐upcovariateshockssuchasdrought,andwhenthisiscombinedwithcarefultargetingtochronicallypoorhouseholdsinaffectedareas,itislikelytoimprovetargetingovercurrentfood‐aiddistribution.AldermanandHaque(2006)providetheexampleofMexicoinwhicha

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specializedagriculturalfundtransfersfinancetoweatheraffectedmunicipalitiesbasedonarainfallindexandtransfersaredistributedtoindividualswithinthemunicipalitybasedonfarm‐sizewhichisastaticindicator.ForthisscenarioanalysisweassumethattargetingwithincommunitiesimprovestothelevelofPSNPtargeting.UsingdatafromGilliganetal(2010),theimprovedtargetingissuchthatthebottom40%receive56%ofthebenefitsofpayouts.OneofthereasonsthatPSNPtargetinghasperformedwellisthatithasemployedaself‐targetingstrategyinwhichtheprovisionofassistanceinreturnforlaborexertedonpublicworksprojects.WhilstpotentialPSNPbeneficiariesaretargeted(i.e.noteveryonecanparticipateinpublicworks),toreceivethebenefitsofferedtothemtheyhavetoengageinmanuallaboronpre‐identifiedpublicworks(elderlyanddisabledbeneficiariesareexemptfromthisrequirement,receivingdirect,unconditionalsupportinstead).Ahousehold’sdesiretoparticipateinpublicworksislikelytoincreaseinhardtimes,allowingthisformofself‐targetingtoreflectchangesintransitoryneedovertime.21

SCENARIO4:INSURINGGOVERNMENTBUDGETSFORASTATE‐CONTINGENTSCHEME

Thisfinalscenariorepresentsthelargestdeparturefromthebaselinescenario.Inthisscenario,governmentshaveastate‐contingentwelfareschemeinplacethatprovidesadditionalsupporttopoorfarmersintimesofneed.Examplesofsuchschemesincludewelfareprogramsbasedonself‐targeting(e.g.theEmploymentGuaranteeSchemeinIndia),conditionaldebtforgivenessprograms(suchastheFondsdeguaranteeinSenegal),andgovernmentsubsidizedagriculturalinsuranceschemes(e.g.theNationalAgriculturalInsuranceSchemeinIndiaortheMongolianlivestockinsurancescheme).Alloftheseprogramsautomaticallyprovideincreasedgovernmentsupporttohouseholdswhendroughtorotherclimaterisksstrike.

Thetimingoftheprovisionofsupportdependsonthenatureoftheprogram.Inthecaseofemploymentguaranteeschemestheassistanceisimmediate,ashouseholdscanengageinemploymentopportunitiesassoonastheadverseweathershockisobserved.Inthecaseofinsuranceprogramsitcanalsobeimmediate(iftheindexisalsobasedonweather)oritcanbeinthemonthsafterharvestifitisbasedonarea‐yieldindicators.Wenotethattheseindexesneedtobeavailablequicklyifsuchschemesaretoprovidetimelinessadvantages.

Alloftheseprogramsexposethegovernmentbudgettoweatherriskasaresultofthecontingentnatureoftheseschemes.ARCpayoutsgodirectlytofundthegovernmentbudget,essentiallyprovidingahedgetothegovernmentfortheclimaterisktheyareexposedtoasaresultofrunningthisscheme.

Countrieswillstillundertaketheneedsassessmentdescribedinthebaselinescenario,andemergenciesrequiringfundsinexcessoftheamountdisbursedbyARCwillstillgothroughtheCAPprocessinordertoraisemoneyfortheexcessofamountsreceived.However,allfinancingreceived(ARCpayoutsandotheremergencyassistance)willbeusedtoprovidebudgetsupportagainsttheriskthegovernmentholdsbyimplementingthisscheme.IfAfricaRiskViewtriggeredapayoutinacircumstancewhenthestate‐contingentprogramdidnotmake

21Otherformsofself‐targetingcanbeusedwithinbothfoodaidandsafetynetdistributionsystemstoimprovetargeting.Askingrecipientstostandinline,ordistributingless‐preferredfooditems(suchasyellowmaize,DrezeandSen1989)isonewaytoimprovetargetingofindividualswithinacommunity.

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increasedpayoutsthengovernmentswillreceivebudgetsupportinaninstancewhentheydidnotneedit.

TheexperienceoftheEmploymentGuaranteeSchemeinIndiashowsthatpublicworkscanbeself‐targetingandallowforincreasedprovisionofassistanceintimesofemergencies.Theschemewasabletoexpendby64%inresponsetoadroughtin1982(Echeverri‐Gent,1988).22Thishadastrainonadministrativecapacity,butthegeneralimpressionofanumberofstudiesisthattherewasaflexiblemanagementstructure,andtargetingtolowincomebeneficiaries(AldermanandHaque2006).Ifincreasedbudgetscannotbesecuredwhenadditionalassistanceistobeprovided,ornewpublicworksprogramsarenoton‐theshelfandavailabletobeimplemented,thenrationingofexistingsupportisrequired,anditislikelythatlocaleliteswillbebetterabletosecureassistanceinthesecases(Ravallion,Datt,andChaudhuri1993).

TherearenoAfricanexperiencesknowntotheauthorsofself‐targetingprogramsthatareopentoallwhowouldliketowork.ToestimatetheimprovementsintargetingthatmayresultfromthistypeofschemeweusethereviewconductedbyCoady,GroshandHoddinott(2004).Thisreviewsuggeststhattargetingintheseschemeshasthehighesttargetingperformanceofallsafetynets.Themedianestimatessuggestthatthebottom40%ofthedistributionsee76%ofthebenefitsoftheseprograms,muchhigherthanthatestimatedforfoodaidorsafetynetschemes(thatmayhaveacomponentofpublicworks).However,itisworthnotingthatthisreviewincludedhigherincomecountriesthanthosethatweareconsidering,andtargetingwasingeneralfoundtobebetterinthesecountries.AssuchweassumeasmallerimprovementintargetingfromsuchschemesinAfrica,assumingthatthebottom40%ofthedistributionwouldsee66%ofthebenefitsoftheseprograms.

Itisalsoworthemphasizingthattherearesomevulnerablepoorthatareunabletobenefitfromtheseschemesandwouldbeleftoutofreceivingassistancewasthistheonlyformofassistancetobeprovided.Thosewhoareelderlyordisabledarenotabletoworkandthereforenotabletoparticipateinsuchschemes.Asafetynetthatprovidesfortheseindividualsingoodandbadyearsisneeded.Inadditiontheseschemesassumethatallable‐bodiedpoorhouseholdsaretime‐rich.Whilstthelevelofsuccessfultargetingsuggeststhisisoftenthecase,itmaynotbe,aspointedoutinBarrett,HoldenandClay(2004).Forexample,awidowedmotherofyoungchildrenmaynotbetime‐richenoughtobenefitfromthisscheme.

Thereareotherself‐targetingschemesthatcouldbeusedtoprovidestate‐contingentbenefits.AldermanandHaque(2006)describehowasubsidytolivestocktransportinpastoralregionsofKenyaiscounter‐cyclical.Thissubsidyreducesthecostoftruckinganimals,somethingthatisrelieduponmorebypastoralistsduringtimesofdrought.Intheeventofadrought,pastoralistsareabletoselllivestockinmoredistantmarketstherebygettingahigherprice.

Asinscenario3,becausepayoutsuseexistingstructuresfordisbursements,thespeedofaiddeliverywillincreaseandthecostofaiddeliverywillbelower.Becausescale‐upisautomatic,

22Itisworthnotingthatthenetimpactonincomesofsuchemploymentmaynotbeequaltotheassistanceprovidedthroughsuchschemes.Becauseemploymentinpublicworksreplacesotheractivities,othersourcesofearningsmaybelost.Ravallionetal.(2005)findthattheseotherearningsmayaccountfor25%ofpublicworksearningsinIndiaand50%ofpublicworksearningsinArgentina.Thisislesslikelytobeaconcerninthecontextofalocaleinsub‐SaharanAfricainadroughtyearwhenalternativeemploymentopportunitiesareextremelylimited.

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theassistanceisavailabletofarmerswithouttheneedforlivelihoodassessmentsresultinginbothspeedandcostsavings.

6.2. COMPARINGBENEFITSANDLIMITATIONSACROSSSCENARIOS

ThedescriptionofthescenariosandthelikelyspeedandcostbenefitstheyprovidearesummarizedinTable11.Thecolorcodingindicateshoweachscenariocomparesinspeedorcostvis‐à‐visthestylizedbaseline.Redrepresentsnoimprovementorworseningrelativetothebaseline,orangerepresentssomeimprovement,andgreenrepresentsthelargestmagnitudeofimprovements.

Whilsttherearepotentialspeedbenefitsfromanyearlypayout,theactualmagnitudeoftheincreaseinspeedofdeliveryofassistancetotargetbeneficiarieswillbecruciallydependentonthetypeofcontingencyplanningthatisencouragedaspartoftheARC.AnearlypayoutalonewillonlyprovideamarginalspeedbenefitaslistedinScenario2.Whencombinedwithimprovedcontingencyplanning,therearesubstantialspeed,costandtargetinggainsacrossallscenarios.However,weseethatthemagnitudeofbenefitsismuchgreaterwhenthecontingentplansinvolvescalingupexistingprograms(Scenarios3and4).ThisprovidessomequantitativebackingtothestatementfromSavetheChildrenandOxfamthat“long‐termprogrammesareinthebestpositiontorespondtoforecastsofacrisis”(2012,p18).Scenario4offersthelargestgainsasaresultofbothimprovedtargetingandimprovedspeed.Wenotethatthisisthecase,evenwhenwearenotconsideringthepotentialforearlyinterventiontosavelives.

Therelianceonlivelihoodindicatorsisanecessarypartofensuringpropertargetingofassistancewithinthecountryinscenarios1to3,butwithoutsubstantialimprovementsinthespeedwithwhichtheseindicatorsbecomeavailable,thereisalimitonhowquickaresponsecanbe.Assuch,eventhoughweassumedinallscenariosthattherewouldbeanARCpayoutatharvest,thequickspeedofthispayout,didnotresultindeliveryofaidthatquickly.ThechoiceofARC’sownindexperhapsdoesnotbedrivensolelybythespeedatwhichitbecomesavailable.

Theexceptionstothisaresomeoftheself‐targetingschemesdescribedinscenario4.Aschemethatisautomaticallytriggeredtoprovideincreasedassistanceinthetimeofneeddoesnotrequirecollectionoflivelihoodindicatorsforoperation.

WecalculatetheeconomicbenefitsfromARCforeveryUSD1millionspentinTable12.Eventhoughthesedonotincludethebenefitsofsavinglivesnordirectcostsavings(otherthanimprovedtargeting)resultingfrommoreefficientaiddisbursement,itisinstructivetocomparetheseacrossscenarios.

First,abitmoreonthecalculationsinTable12:wecountthecostsofrunningARCasbeingcomprisedoftheoperationalcostsandthecostsofreinsurance.WhenARCisruncost‐effectivelywithamultipleof1.2(i.e.ARCrunningcostsarecappedat5%andlowlevelsofreinsurancearepurchased,assuggestedinSection4),forevery$1spentonARCinscenarios1through4,$0.83isavailableforaiddisbursement.ThecurrentresponsecostperbeneficiaryusedinAfricaRiskViewisUSD100andweusethisassumptionherealsotocalculatethenumberofhouseholdsreached.AfricaRiskViewusesthissettingbecauseitisthecostofastandardWFP6‐9monthfoodaidresponseincountrieswhereWFPiscalledupontolaunchlargescalehumanitarianoperationsinAfrica.Reachingonehouseholdswith4adultequivalentsthuscostsUSD400inaiddisbursement.

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TABLE11:SUMMARYOFSCENARIOS

Baseline:Stylizedemergencyassistance

Scenario1:Improvedfoodaidviadeposittonationalgrainreserve

Scenario2:Improvedfoodaidviadeposittoholdingaccount

Scenario3:Scalingupexistingsafetynet

Scenario4:Insuringgovernmentbudgetsforastate‐contingentscheme

Description Improvedmonitoringresultinginbetterdirectingofresourceswithincountry

Contingencyplanwhichresultsincostsavings

FastpayoutfromARC

Payoutusedimmediatelytobuygrain

Improvedmonitoringresultinginbetterdirectingofresourceswithincountry

Contingencyplanwhichresultsincostsavings

FastpayoutfromARC

Payoutheldinaholdingaccount

Improvedmonitoringresultinginbetterdirectingofresourceswithincountry

Improvedtargetingwithincommunities

Disbursementusesexistingdistributionstructure

FastpayoutfromARCusedimmediatelytoprepareresourcesneededforpayout

Usesself‐targetingratherthanmonitoring

Improvestargeting Disbursementusesexistingdistributionstructure

PayoutfromARCgoestooffsetincreasedgovernmentbudgetexpendituresonprogram

Speed(fromharvesttodelivery)

Cash:7‐8monthsFood:8‐9months




Self‐targetingthroughwork:immediateInsurance:dependsonthetrigger

Targetingaccuracy

Inaccuratecommunitytargeting

Inaccurateindividualtargeting

Poorest40%receive43%ofprogrambenefits

Improvedcommunitytargeting







Improvedindividualtargeting


Self‐targetingthroughwork:Poorest40%receive66%ofprogrambenefits

Costoflogisticsanddisbursement

High Medium Medium Low Self‐targetingthroughwork:low

Costofassessment

Medium High High High Self‐targetingthroughwork:noneInsurance:High

Thenumberofpoorhouseholdsactuallyreachedwilldependontheeffectivenessoftargetingineachscenario.Thespeedbenefitswilldependonhowfastassistancecanbeprovidedrelativetothebaseline.Inthebaselineaidtakesbetween7and9monthstoarrive,whichmeansthathouseholdsarealreadysubjecttoeconomiclossesofUSD1,294.Speedingupthedisbursementofaidreducestheeconomiclossesthehouseholdwillface.Wecountthisreductionineconomiclossesasthespeedbenefit.GettingaidtohouseholdsinthethreemonthsafterharvestwillresultineconomicgainsofUSD1,294.Gettingaidtohouseholdsfivemonthsafterharvestwillresultinlowerspeedgainsasthereisalreadyaneconomiccosttostrategiespursuedat5months.

Thetotalbenefitstopoorhouseholdsiscomprisedofthenumberofhouseholdsreached,theaidflowofUSD400perhousehold,andtheeconomiclossesavoidedperhouseholdasaresultofimprovedspeed.InthefinalrowofTable12wepresenttheadditionalbenefitreceivedbypoor

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householdsfromadollarofaidgiventoARCcomparedtoadollarofaiddistributedthroughthecurrentemergencysystem.

WeseethattherearepositivegainstoARCunderallscenarios,exceptscenario2inwhichfinancingisprovidedbutaiddisbursementtakesplaceintheformoffoodoncelivelihoodindicatorsareobserved.Thegainsarenegativeinthiscasebecausethereisnoeconomicgainfromimprovedspeed,andthecostofrunningARC(themultiple)doesnotoutweightheminimaltargetinggains.Theservestoemphasizeapointalreadydiscussed,thatthecontingencyplanningscenarioputinplacehastoallowassistancetoreachvulnerablepopulationsinanefficientandtimelymannerforbenefitstoberealized.Afastpayoutatthenationallevelwithoutthisinplace,willnotguaranteewelfaregains.

Gainsinscenario1andinscenario2withcashdisbursementaresubstantialundertheassumptionswehavemade,butthegainsaremuchlargerforscenario3and4onaccountofbothimprovedtargetingandgainsinspeed.WereARCtohaveahighermultiplegainstoallthesescenarioswouldbelower.Forexamplewerethemultipletobe1.5(asassumedinSection3),thepositivegainswouldrangefromUSD0.94toUSD1.26perdollarspent.

Beforediscussingthelikelihoodofthesescenarioswealsoemphasizethattheseresultswillchangeindifferentcontexts.Inacountrywherelivelihoodendangeringrisk‐copingstrategiesarelikelytobeengagedinpriortothreemonthsafterharvesttheneedforspeedisevengreater.Assuchscenarios1and2willofferfewerbenefitstothesehouseholdsasbythetimeaidreachesthemtheywillalreadybeengagingincostlyriskcopingstrategies.Finally,wenotethatwehavemadetheimportantassumptionthatdisbursementsarebeingmadeduringyearsinwhichthereisneed—i.e.wehaveassumedthereisnobasisrisk.Asdiscussedearlierinthereport,basisriskwilllimitthedegreetowhichtheamountdisbursedcanbeavailablewhenneeded,andwilljeopardizethelargepotentialgainsindicatedinTable12.

TABLE12:INDICATIVEBENEFITSFROMIMPROVEDSPEEDANDTARGETING,ASSUMINGAMULTIPLEOF1.2

Baseline Scenario1 Scenario2 Scenario3 Scenario4Donorfinancing(USD) 1,000,000 1,000,000 1,000,000 1,000,000 1,000,000Amountdisbursed(USD) 1,000,000 833,333 833,333 833,333 833,333Targeting:numberofhouseholdsinbottom40%receivingassistance

1,075 1,042 1,042 1,167 1,375

Speedbenefit:costsavoidedasaresultofearlierassistance(USD)

0 1,245 Cash:1,245Food:0

1,294 1,294

Totalbenefitsreceivedbypoorhouseholds(USD)

430,000 1,710,000

Cash:1,710,000Food:420,000

1,980,000 2,330,000

Additionalbenefitstopoorhouseholdsperdollarspent(comparedtobaseline,USD)

1.28 Cash:1.28Food:‐0.01

1.55 1.90

6.3. LIKELIHOODOFTHESESCENARIOS

InTable13wesummarizethepresenceoftheseschemesinthesixcountriesinwhichARCismostlikelytostart.Nationalgrainreservesarethemostcommonoftheinstrumentsavailablethatwehavediscussed,beingpresentin4ofthe6countriesconsidered.Safetynetschemesarealsoquitecommon,presentin3ofthesixcountries,butnocountrieshavetheseavailableatanationalscale.Malawi’sispresentin7districts.InNigerandEthiopiatheyarepresentinthemostfoodinsecureregions,butthelargerofthetwoschemes,Ethiopia’sProductiveSafetyNet

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Program,isnotyetfunctionalinpastoralistareaswhichlimitswhereinthecountryitcanbeusedtoscaleupassistance(asdiscussedintheEthiopiacountrycasestudy,Hill2012).

Fewcountrieshavestate‐contingentschemesthatareavailabletoall.EthiopiaandNigerhavesafetynetswithelementsoffoodforworkwhichresemblesemploymentguaranteeschemes.Howeveronlypre‐identifiedhouseholdscanparticipateintheseschemesandthereisanannuallimitofthenumberofdaystheschemecanbeaccessed.Oneorbothofthesewouldneedtobeover‐riddeninanemergency.Thereisnoexperienceoflarge‐scaleagriculturalinsuranceinthesixcountries,butSenegaldoeshaveadisasterfundthatcanbeusedtowriteofffarmerdebtsduringdroughts.Therulesoftheschemearenotagreedtopriortodroughts.

TABLE13:AVAILABILITYOFGOVERNMENTGRAINRESERVES, SAFETYNETSANDSTATE‐CONTINGENTSCHEMES

Nationalgrainreserve

Safetynetscheme Employmentguaranteescheme Large‐scaleagriculturalinsuranceorstate‐contingentcreditforgiveness

Ethiopia • • • •

Kenya • • • •Malawi • • • •Mozambique • • • •Niger • • • •Senegal • • • •

Key:Greencirclesindicatethatthecountryhassomeaspectsoftheseschemesinplace(seetextforclarification).Redmeansnoschemeisinplace.

Thetablesuggeststhatwhilstthereareimportantstepstohavingsafetynetorstate‐contingentaidschemesinplaceinanumberofthesixcountrieswhereARCislikelytostart,additionalinvestmentsintheseschemeswouldbeneededbeforetheycouldbewhollyreliedonforthedisbursementofARCpayouts.Thismeansthatinearlyyears,formanycountriesscenarios1and2aremorelikelythanscenarios3or4.AsweseefromTable12thismeanslowergainswouldberealized.ToseethelargestpotentialbenefitsfromimplementingARC,furtherinvestmentinsafetynets(state‐contingentorotherwise)isneeded.

Throughouttheassessmentofbenefits,bothintheprevioussectionandinbuildingthescenarios,wehavebeenexplicitabouttheassumptionsbeingmade.Theseassumptionsimpacttherankingofthesealternativescenarios.Theseassumptionscanbetestedandchangedovertime,asbetterdatabecomesavailable.

Inadditionwehavemadesomeassumptionsaboutthewell‐functioningofcontingentfinancingschemesthatmaynotbeaccurateinallcountrysettings.Wediscussthesefurtherhere:

1. Foodgrainreservescanbewell‐managed:AsTable13indicates,anumberofthepilotcountriesbeingconsideredforARCalreadyhavenationalfoodgrainreserves.However,notallofthesegrainreservesfunctionequallywell,andingeneral,throughoutsub‐SaharanAfricaandthedevelopedworldtherehasbeenamixedexperienceregardingthemanagementofnationalfoodgrainreserves,andtheirperformanceinmeeting

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humanitarianneedsduringtimesoffoodshortage.TherecentPREPAREcost‐benefitanalysispreparedfortheG20meeting,documentsthiswellinconsideringthepotentialmeritsofaregionalgrainreserveforearlyresponsetoemergenciesinWestAfrica.ThisreporthighlightsthatpastexperienceoftheutilizationoffoodsecuritystocksinWestAfricaisthatlittleisactuallywithdrawnfromnationalreservesduringfoodemergencies.Forexample,theynotethatthemaximumquantitydrawndowninBurkinaFasopriorto2004wasin2003andamountedtoonly12,050MT.23AreviewbyRashidandLemma(2011)alsoprovidesausefulsummaryofrecentexperiences.Insum,theynotethatthereservesthathaveperformedwellarethosethatarethoseinwhichnationalauthoritieshaveplayedanactiveroleinthegovernance,managementandfinancingofthereserve.Ensuringthatearlypayoutsusedtobuildupgrainreservesareproperlymanagedwillrequireproperinvestmentintheinstitutionalstructuresurroundinganationalgrainreserve.

2. Holdingaccountscanbewell‐managed:Somewhatsimilarly,wehaveassumedthatholdingaccountswillbewell‐managedbythegovernmentsofparticipatingcountries.Thisisanassumptionthatmayalsodeservescrutinydependingonthegivencountrycontext.

3. Improvedmonitoringresultsinimprovedcommunitytargeting:ThepoorlevelsofcurrenttargetingoffoodaidpresentedinJayneetal(2001,2002)andClayetal(1999),suggestthatfoodaiddoesnotalwaysflowtoareasofgreatestneedwithinacountry.Thesestudieshaveprovidedanumberofreasonswhythismaybethecase,citingthelikelyinertiapresentinthefoodaiddeliverysystemandalsopoliticalmotivationsfortargetingfoodtoparticularareasofthecountry.Indeed,studiesatthenationallevelhaveindicatedthatfoodaiddisbursementsareofteninfluencedbypoliticalratherthanpurelyhumanitarianfactors,anditislikelythatthisdynamicispresentatasub‐regionallevelalso.Wehaveassumedthatthissituationcanbeimprovedbybetterdatacollectiononlivelihoodindicatorsandbettercontingencyplanning.However,thisisanassumptionthatmaynotholdinallcontexts,andshouldbeground‐truthedbeforeassumingthattargetingimprovementswillarisepurelyasaresultofbettercontingencyplanning.

4. Safetynetscanbescaledupquicklyandatlowcost:Oneofthereasonsscalingupsafetynetsscoredmorehighlythanimprovedcontingencyplanningwithinatraditionalfoodaidresponseisbecauseitwasassumedthattheexistingstructuresinplacewouldallowalmostimmediatedeliveryofassistanceandalsolowercostsinthedeliveryofassistance.WhilstthisseemsplausibleforthesizeoffinancingthatARCwillprovidetocountries,wearenotawareofanystudiesthathavetestedthis,andcomparedthespeedandcostofdeliverythroughanexistingscheme,versusthroughfoodaiddelivery.Itisimportanttocollectdatatotestthisassumption.

Finally,itisworthnotinganadditionalbenefitthatbecomesavailableaswemovetowardsscenarios3and4.Iftherulesofemergencyassistancearecleartofarmersinadvanceoftheseason,andtheprovisionofemergencyassistanceprovestobereliable,farmerswillstarttomakeproductionandinvestmentdecisionsasiftheyareinsured.Thiscouldresultinfarmersengaginginmoreprofitableactivitiesasaresult.Thebenefitsthatresultfromthiswilldepend

23EmergencyHumanitarianFoodReserves:FeasibilityStudy,Cost‐BenefitAnalysisandProposalforPilotProgrammehttp://www.foodsecurityportal.org/sites/default/files/PREPARE_feasibility_study_and_pilot_proposal.pdf

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ontheavailabilityofprofitableactivitiestoinvestin,butstudiessuggestthatthereturnscouldbeashighas20%increasescropincomeperyear(Karlanetal2011).

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7. CONCLUSIONANDSUMMARYOFRECOMMENDATIONS

ARCisaninnovationthatbringselementsofindexinsuranceintoemergencyfinancinginordertoensuretimely,predictablepayoutsduringtimesofneed.AssuchthemagnitudeofARC’sbenefitsdependscruciallyontheprinciplesofindexinsurance(namelythatbenefitswillbehigherwhenthemultipleislower,wheninsuranceisforextremeratherthanfrequentevents,andwhenpayoutsaretriggeredbyindexesthatcloselymatchtheseevents)andwhenpayoutsarecost‐effectivelydeliveredtobeneficiariesthroughwell‐functioningsub‐nationalreliefprovision.

Inlinewiththis,theanalysisinthisreporthasshownthefollowing:

1. ARCoffersthehighestadvantagesinbothspeedandimprovedtargetingwhenmembercountrieshavealargescale,welltargetedsafetynetorstate‐contingentscheme,suchasanemploymentguaranteescheme(Table12).UnderthesecontingencyplanningscenariosthebenefitstoARCarelarge,butitislikelytorequiretimeandresourcestofurtherdeveloptheseschemes(Table13).

2. WelfaregainsarehigherwhenARCfocusesitscoverageonlessfrequentevents(Figure3).ThismeansthatARCshouldconsidernotpayingclaimpaymentstoanycountrymorefrequentlythanonceeveryfiveyears,onaverage.IfARCisofferscoveronaseasonalbasis,thistranslatestoeachelementofcoverpayingoutapproximatelyonceeverytenorfifteenyearsforacountrywithtwoorthreeseasons,respectively.Reducingtheclaimpaymentfrequencytoonceeveryeightortenyearsonaverage,andincreasingthelevelofcoveragefortheseextremeyears,wouldbebetterstillfromawelfareperspective.

3. Giventhepotentialformembercountriestopoolrisk,ARCcanchooseafinancialstrategythatenablesittoretainasubstantialproportionofriskoverathreeyeartimehorizon,whilststillenablingittopayallclaimsastheyfallduewithanestimatedprobabilityof99.5%(Figure6).ThismaymeanthatARCwouldexposeclosertoahalfofitsreservesinthebottomlayerofriskinanyoneyearratherthanaquarter.Intheeventofaseriesofextraordinarilybadyears,ARCmightneedrecapitalizationfromdonorsormembercountries,butdonorsandmembercountrieswouldreceivesubstantiallybettervalue.WhilstreinsuranceislikelytobeimportantforthefinancialmanagementofARC,itisnotcentraltothewelfareproposition.ARCcouldthereforecommittoonlypurchasereinsurancefor1‐in‐10yearannualportfolio‐widelosses,ortoacaponexpenditureonreinsurance(includingbrokeragefees)expressedasapercentageofpremiumvolume.

4. Inadditiontoretainingasmuchriskaspossible,furtherstepstoreducetheinsurancemultiplewillensurethelargestbenefitsfromthefacility(Figure2).

5. Thereislittleadvantageinmakingclaimpaymentsbeforeharvesttimeunlessthereisevidencethatthiswillincreasetheultimatespeedofdeliveryofassistancetotargetbeneficiaries(Table11).Paymentsareneededbybeneficiariesthreemonthsafterharvestbeforetheyengageincostlyriskcopingmechanisms(Table8andTable10).

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Thispotentiallybroadensthesetoftriggersuponwhichearlypayoutsaremadetoincludetriggersthatarecollectedatharvesttime.

6. ItisnotpossibletoassesshowwellAfricaRiskViewwouldperformasthebasisforaninsuranceindex,anditwillnotbepossibletohaveanaccurateestimateofthecorrelationbetweenneedandtheindexbeforeinsurancecontractsarewritten.TherearepotentialwelfaregainsfromtakingamuchbroaderapproachthancurrentlyplannedtoensureARCmakespayoutsinyearswithextremelyhighnationalfoodsecurityresponsecostneeds.Thisincludesgroundtruthingbothtocalibratetheparametricindicesandperhapstoactassecondtriggers,orgapinsuranceforextremecasesofbasisrisk.

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