cost‐benefit analysis of the african risk … · 1 cost‐benefit analysis of the african risk...
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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
ARC premium directly to country
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
ARC premium directly to country
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
Numbe
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NMALAWI(CEVEREUX200
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er of months m
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ONSTRUCTE07)
IA(REPRODU
anAfricaisrimpactsonmincomeis
ategories:faureofrainsrtilizerandlashorterseangandlessprelosses,buDinkuetal2
3‐6
maize stocks l
Bad" year (20
DFROMDAT
UCEDFROM
rainfall‐reliacropyields.morequick
ailureofrainatplantinglaboroftheasonsasarepreferredvatprovidedr009).
6 0‐3
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TAPRESENTE
MINOT2008
ant.Shortage.Whenrainfklyexhauste
nsatplantincauselossescropthatisesult.Replanarietiesorcrrainsarepre
3
EDIN
)
esoffallfailsd.
ng,andstoslost,ntingropsesent
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
Cash:4‐5monthsFood:4‐5months
Cash:4‐5monthsFood:6‐7months
Cash:3‐4monthsFood:3‐4months
Self‐targetingthroughwork:immediateInsurance:dependsonthetrigger
Targetingaccuracy
Inaccuratecommunitytargeting
Inaccurateindividualtargeting
Poorest40%receive43%ofprogrambenefits
Improvedcommunitytargeting
Inaccurateindividualtargeting
Poorest40%receive50%ofprogrambenefits
Improvedcommunitytargeting
Inaccurateindividualtargeting
Poorest40%receive50%ofprogrambenefits
Improvedcommunitytargeting
Improvedindividualtargeting
Poorest40%receive56%ofprogrambenefits
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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