!!! Agricultural!Technologies!and!Economic!Development:!Three!Essays!on! Technology!Adoption!and!Inequality!!!!!!

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AgriculturalTechnologiesandEconomicDevelopment:ThreeEssayson TechnologyAdoptionandInequality VanessaDelRocíoCarriónYaguana DissertationsubmittedtothefacultyoftheVirginiaPolytechnicInstituteandStateUniversityin partialfulfillmentoftherequirementsforthedegreeof DoctorofPhilosophy In AgriculturalandAppliedEconomics GeorgeW.Norton,Chair JeffreyR.Alwang,CoKchair BradfordF.Mills CatherineLarochelle SusanM.Richter February19,2016 Blacksburg,Virginia Keywords:Adoption,AgriculturalTechnologies,IntergenerationalMobility,IntegratedPest Management,IntegratedCropManagement,CommutingTimes.

AgriculturalTechnologiesandEconomicDevelopment:ThreeEssayson TechnologyAdoptionandInequality VanessaDelRocíoCarriónYaguana ABSTRACT Thisdissertationiscomposedofthreeessaysexaminingadoptionofagriculturaltechnologiesin EcuadorandintergenerationalmobilityintheUnitedStates.Thefirstessayentitled DoesIPM HaveStayingPower?RevisitingaPotatoKproducingAreaYearsAfterFormalTrainingEnded examines(integratedpestmanagement)ipmspreadandadoptionseveralyearsafterformal intensiveipmoutreacheffortsceasedinapotatokproducingregioninecuador.itdescribes adoptionpatternsandsourcesofipmknowledgein2012andcomparesthemwithpatterns thatexistedwhenoutreachceasedin2003.resultsshowthatipmadoptioncontinuesinthe areabutwithalowerproportionoffarmersadoptingallpracticesandahigherproportion adoptinglowtomoderatelevelscomparedto2003.farmerktokfarmerspreadhassupplanted formaltrainingandoutreachmechanisms.ipmadoptionsignificantlylowerspesticideuseand savesproductioncostsforadopters. Thesecondessayentitled CanTextMessagesImproveAgriculturalOutreachin Ecuador? seekstounderstandhowreceiptoftextmessagescomplementstrainingfroma farmerfieldday.itmeasurestheeffectoftextmessagereceiptonadoptionof(integrated CropManagement)ICMtechnologiesandknowledgeaboutthesetechnologies.Inthefirstpart ofthepaper,wepresentatheoryofbehavioralchangeanditsapplicationtoadoptionof

agriculturaltechnologies.inthesecondpart,weuseintentiontotreat(itt)andimprovedkitt analysestomeasuretheimpactoftheintervention.theresultsofthisessaysuggestthatas providersofinformation,textmessageshavesomeknowledgebuildingeffectleadingtothe adoptionofipmpractices.asreminders,textmessageseffectivelyincreaseadoptionofipm practices,inparticularrecommendedpesticides. Thethirdessayentitled DeterminantsofAbsoluteUpwardIncomeMobility:The HiddenCostofCommuting focusesoncommutingtimesasadeterminantofupwardincome mobilityintheunitedstates.weprovideanexplanationofthechannelthroughwhichthe effectofcommutingtimesonupwardincomemobilityoperates.additionally,itevaluates empiricallytheeffectofcommutingonupwardincomemobility.theempiricalresultsconfirm thetheoreticalmodelpredictionsthatcommutingtimesaffectnegativelyupwardincome mobility.

Acknowledgments Iwouldliketoexpressmydeepestgratitudetomyadvisors,Dr.GeorgeNortonandDr. JeffreyAlwang,forthepatientguidanceandmentorshiptheyprovidedtome,allthewayfrom wheniwasfirstconsideringapplyingtothemasterprogramintheagriculturalandapplied EconomicsDepartment,throughtocompletionofmyPh.D.degree.Ithasbeenagreatprivilege andhonortobetheirstudent. Iwouldliketothankmydissertationcommitteemembers,Dr.CatherineLarochelle,Dr. BradfordMills,andDr.SusanRichter,foralloftheirguidancethroughthisprocess.Your discussion,ideas,andfeedbackhavebeenabsolutelyinvaluable.otherfacultymembershave alsocontributedenormouslytomylearningandacademicsuccess,specialthankstodr.adam Dominiak,Dr.DjavadSalehiKIsfahaniandDr.WenYou. WordscannotexpresshowgratefulIamtomylovingandcaringhusbandMichel.Thank youforlovingmeandadoringme;forstandingbymeandsupportingmethroughthesetimes inourlife.youhavegivenmethestrengthwhenifelttired,andremindedmeofallreasonsi chosethispath.youareanamazinghusbandandfather,andthisaccomplishmentwouldhave beendifficultifnotimpossiblewithoutyou.ialsothankmybeautifulandsmartdaughters DanielaandIsabella.Youinspireme. Iwouldespeciallyliketothankmyamazingfamily,inparticularmysiblingsCeciliaand Angel,forthelove,support,andconstantencouragementIhavegottenovertheyears.Special gratitudeandlovegoestomymotherkinklaw,lorgia.iundoubtedlycouldnothavedonethis withoutyourhelp. iv

LovingthankstoMinaHejazi,mydearfriend,whowasalwayswillingtohelp.Thiscrazy, wonderfuljourneywouldnothavebeenthesamewithoutyou.iwouldalsoliketothankdr. WayneDyerwhosebooksandrecordedmaterialhavebeenasourceofinspiration,guidance andencouragementinthedifficulttimes. Finallyandmostimportant,IthankGod,mygoodFather,forshowingmetheway throughallthedifficulties.ihaveexperiencedhisloveandguidancedayafterday.hehasmade theimpossiblepossible. v

TableofContents ListofTables...ix ListofFigures...xi CHAPTER1:INTRODUCTION...1 Chapter1:References...5 CHAPTER2:DOESIPMHAVESTAYINGPOWER?REVISITINGAPOTATOKPRODUCINGAREA YEARSAFTER...6 2.1.Introduction...6 2.2.StudysiteandIPMprogram...9 2.3.SourcesofIPMinformationanddepthofknowledge...14 2.4.IPMadoptionovertime...17 2.5.Econometricanalysis...20 2.6.Estimationresults...23 2.7.Conclusionsandpolicyimplications...29 AppendixA...31 AppendixB...34 Chapter2:References...36 CHAPTER3:CANTEXTMESSAGESIMPROVEAGRICULTURALOUTREACHINECUADOR?...39 3.1.Introduction...39 3.2.ATheoryofbehavioralchange...41 3.2.1.Behavioralchangetowardsadoptionofagriculturaltechnology...44 3.2.2.InformationandCommunicationTechnologies(ICTs)asameansofinducing behavioralchange...45 3.3.StudysiteandICMprogram...47 3.4.TheIntervention...50 vi

3.5.Methods...52 3.6.Results...57 3.6.1.Meancomparisonanalysis...59 3.6.1.1.Householdandfarmcharacteristics...59 3.6.1.2.ICMadoptionandknowledge...62 3.6.2.Multivariateregressionanalyses...68 3.6.2.1.AdoptionofnonKIPMpractices...68 3.6.2.2.AdoptionofIPMpractices...70 3.6.2.3.Theeffectoftheinterventiononknowledge...74 3.7.Conclusions...75 Chapter3:References...78 AppendixC...85 AppendixD...86 AppendixE...87 CHAPTER4:DETERMINANTSOFABSOLUTEUPWARDINCOMEMOBILITY:THEHIDDENCOSTOF COMMUTING...91 4.1.Introduction...91 4.2.Commutingtimes...95 4.2.1.Theproductionofhumancapital...98 4.3.Theoreticalmodel...99 4.3.1.Humancapitalaccumulationandthecostofcommuting...103 4.4.Dataandempiricalimplementation...108 4.4.1.CountyKLevelcovariates...108 4.5.Results...111 4.5.1.Descriptivestatistics...111 vii

4.5.2.Multivariateregressionanalysis...113 4.6.Conclusionsanddiscussion...117 Chapter4:References...119 AppendixF...123 CHAPTER5:CONCLUSIONS...126 Chapter5:References...128 Annex1:BlackberrySurvey...129 Annex2:Chapter3logfile...136 Annex3:Chapter4logfile...167 viii

ListofTables Table2K1:MainsourcesofIPMinformationandknowledgelevelsbysource,Carchi,Ecuador, 2012...16 Table2K2:PercentageadoptingIPMpractices,Carchi,Ecuador,2003and2012...20 Table2K3:Summarystatisticsforvariablesineconometricmodel...24 Table2K4:DeterminantsofIPMadoption...25 Table2K5:Determinantsofpesticideexpendituresforpotatoproduction...27 TableAK1:Useofprotectiveequipment,Carchi,Ecuador,(2012)...31 TableAK2:PercentageofrespondentsadoptingeachIPMpracticebymainsourceofIPM information,2012...32 TableAK3:DisadoptionofIPMpracticesandcauses(Carchi,2012)...33 TableBK1:IPMpracticesdropfromthelistofpracticesusedtomeasureadoption...34 TableBK2:IPMpracticesandweights...35 Table3K1:nonKIPMrecommendedpractices...53 Table3 2:IPMrecommendedpractices...53 Table3K3:HouseholdandFarmCharacteristicsofSampledBlackberryFarmersinEcuador, 2014...61 Table3K4:ICMadoptionandICMknowledge IntentiontoTreat(ITT),adjustedKITT a...63 Table3K5:MeancomparisoninadoptionofnonKIPMandIPMculturalindividualpracticesby treatmentgroup...66 Table3K6:MeancomparisoninknowledgeofICMindividualquestionsbytreatmentgroup..67 Table3K7:Poissonregressionresults nonkipmpractices...70 ix

Table3K8:Poissonregressionresults IPMpractices...72 Table3K9:AveragemarginaleffectsKTreatmentandEducationvariables...73 Table3K10:Poissonregressionresults knowledge...75 TableDK1:CorrespondencebetweenICMpracticesandknowledgequestions...86 Table4K1:Summarystatistics countyklevelcovariates...112 Table4K2:Correlatesofabsoluteupwardmobility:comparingalternativespecifications...115 TableFK1:ERSK2003RuralKUrbancontinuumcodes...124 TableFK2:1K5MetroKNonmetroremotenesscodes...125 x

ListofFigures Figure2K1:IPMadoptionovertime,Carchi,Ecuador,2003and2012...15 Figure3K1:Behavioralchangetechniques...43 Figure3K2:TextmessagewithblackberryICMinformation...52 CK1:Mapoftheoreticaldomainsframework(TDF)tosourcesofbehavioronCOMKBsystem.85 Figure4K1:Absoluteupwardmobility:meanchildrankforparentsat25thpercentileby commutingzone...92 Figure4K2:Workathome,workatthemarketandleisure...103 Figure4K3:Thecostofcommuting...106 xi

CHAPTER1:INTRODUCTION Thisdissertationiscomposedofthreeessaysexaminingadoptionofagriculturaltechnologiesin EcuadorandintergenerationalmobilityintheUnitedStates.Thefirstessayexaminesadoption ofintegratedpestmanagement(ipm)amongpotatofarmersinecuador.itusesadataset obtainedfromahouseholdsurveyconductedincarchiprovincein2012.thisinitiativewas undertakenaspartoftheintegratedpestmanagementinnovationlaboratory(ipmil),funded bytheunitedstatesagencyforinternationaldevelopment.thesecondessayanalyzes adoptionofintegratedcropmanagement(icm)amongblackberryproducersinecuador.it usesadatasetderivedfroma2014householdbaselineandfollowupsurveysinbolivarand Tungurahuaprovinces.ThisstudyispartoftheSustainableAgricultureandNaturalResource ManagementCollaborativeResearchSupportProgram(SANREMCRSP),fundedbytheUnited StatesAgencyforInternationalDevelopment.Thethirdessayfocusesoncommutingtimesasa determinantofupwardincomemobilityintheunitedstates.thisessayusescountykleveldata providedbytheequalityofopportunityproject,uscensusbureau,nationalcenterfor EducationStatistics(NCES),andtheUnitedStatesDepartmentofAgricultureEconomic ResearchService(USDAERS). Thefirstessayentitled DoesIPMHaveStayingPower?RevisitingaPotatoKproducing AreaYearsAfterFormalTrainingEnded contributestotheliteratureintwoways.first,it examinesipmspreadandadoptionseveralyearsafterformalintensiveipmoutreachefforts ceasedinapotatokproducingregioninecuador.wedescribeadoptionpatternsandsourcesof IPMknowledgein2012andcomparethemwithpatternsthatexistedwhenoutreachceasedin 1

2003.FewstudieshaveexaminedwhetherIPMadoptionisdurable,andwhetherIPMwill continuetospreadafterformaltraininghasended.second,weevaluatetheeffectofipm adoptiononpesticideuse.weuseatwostagesleastsquaresinstrumentalvariableapproach. InthefirststageweestimateIPMadoptionasafunctionoffarmers socioeconomic characteristicsandthemainipminformationsourceeachfarmerwasexposedto.inthesecond stageweestimatepesticidesexpendituresasafunctionofadoptionofipm,whichis instrumentedwiththeprimaryinformationsourceeachfarmerwasexposedto.theresults provideinformationonwhetherornottocontinuepublicinvestmentinipmoutreachinareas wheresuchoutreachhasnotexistedinthepast. Thesecondessayisentitled CanTextMessagesImproveAgriculturalOutreachin Ecuador? andcontributestotheliteraturebyimplementingandevaluatingarandomly assignedtextmessagekbasedinterventionthataimstoincreaseadoptionofagricultural technologies,backedbybehavioralchangetheory.theliteratureontechnologykbased agriculturalinterventionsisratherthinanddoesnotprovideabasisforunderstandinghow programsofthiskindaffectbehavioralchange.inthefirstpartoftheessay,wepresenta theoryofbehavioralchangeanditsapplicationtoadoptionofagriculturaltechnologies.inthe secondpart,weuseintentiontotreat(itt)andanimprovedkittanalysistomeasuretheimpact oftheintervention.inittanalysis,unitsrandomlyassignedtoreceivetreatmentarecompared tothoseassignedtothecontrolregardlessofwhethertheycompletedtheintervention (ArmijoKOlivo,Warren,&Magee,2009).TheimprovedKITTshowsthedifferentialeffectof treatmentwhennonkrecipientsduetotechnicalproblemsrelatedtodatasystematizationand verificationareexcluded.sincewehadarelativelylargenumberofindividualswhowere 2

assignedintothetreatmentgroup,butdidnotreceivethetextmessages,webelievethatboth analysesarerelevant.theresultscanbeexpectedtoinformgovernment,ngos,extension educatorsandpractitionersabouttheuseoftextmessagestoincreaseadoptionofagricultural technologies. Thethirdessayentitled DeterminantsofAbsoluteUpwardIncomeMobility:The HiddenCostofCommuting examinesthedeterminantsofupwardincomemobilitypaying particularattentiontocommutingtimes.ithasbeenarguedthatcommutingmayoperateby makingitmoredifficulttoreachjobsorotherresourcesthatfacilitateupwardmobility. However,thelackofaccesstonearbyjobscannotdirectlyexplainthefactthatvariationin children'soutcomesemergesbeforetheyenterthelabormarket.themechanismbehindthe effectofcommutingtimesonupwardmobilityisunclear.understandingthismechanismis importantbecauseitconstitutesthefirststeponthewaytoimplementinginitiativesaimedat reducingcommutingtimes.thisessaycontributestotheexistingliteratureonintergenerational mobilitybyprovidingapossibletheoreticalexplanationofthechannelthroughwhichtheeffect operates.resultsmayinformcompaniesandpolicykmakersonwhethertoputeffortinto reducingthecostsandhasslesofcommuting. Thethreeessaysareconnectedtothecommonthemeofdevelopment.Agricultural technologyadoptionisakeystepneededtobringthebenefitofnewsciencetoincreasing productivityamongsmallholderfarmers.todevelopacomprehensiveunderstandingandgain insightintothebestmechanismsavailabletoincreaseagriculturaltechnologyadoption iscriticaltoruraldevelopment.intergenerationalmobilityisanimportantsocialobjectivefor manyindividualsandpolicymakers,andmayaffectpublicattitudestowardothersocial 3

objectivessuchasequalityandgrowth(piketty,1995).abetterunderstandingofthe mechanismsbywhichthedeterminantsofintergenerationalmobilityoperatesisessentialto developingnewinitiativestofacilitateincomemobility. 4

Chapter1:References ArmijoKOlivo,S.,Warren,S.,&Magee,D.(2009).Intentiontotreatanalysis,compliance,dropK outsandhowtodealwithmissingdatainclinicalresearch:areview.physicaltherapy Reviews,14(1),36K49. Piketty,T.(1995).Socialmobilityandredistributivepolitics.TheQuarterlyJournalofEconomics, 551K584. 5

CHAPTER2:DOESIPMHAVESTAYINGPOWER?REVISITINGAPOTATOR PRODUCINGAREAYEARSAFTER 2.1.Introduction Pesticideuseinagriculturehasincreasedworldwideovertime.Thisincreasehashelped farmersmeetagrowingglobaldemandforfood,butseveralwellkknownproblemsare associatedwithpesticideuse.pesticidesarecostly,andtheiroverkusecanreducefarmprofits andincomes.pesticideexposureisassociatedwithnegativehumanhealtheffectsrangingfrom dermatitisandasthmatosevereproblemssuchasobstructivepulmonarydiseaseandcancer (Sanborn,Cole,Abelsohn,&Weir,2002).Pesticideuseisalsoassociatedwithenvironmental problemssuchassoilandwatercontaminationandoffkfarmdamagessuchaspoisoningof wildlife.indiscriminatepesticideuseincreasespestresistanceandreducespopulationsof beneficialinsects,whichcanpushfarmersintoacycleofincreasinguseofanddependenceon newpesticides. Tomitigatetheharmfuleffectsofpesticidesandbettermanagepestproblems, alternativemanagementmethodshavebeensought.althoughbasictacticsofintegratedpest management(ipm)havebeenusedtoreducepestkrelatedcroplossessincethelatenineteenth century,itwasnotuntiltheearly1970sthatipmbecamewidelyacceptedbythescientific community(kogan,1998).ipmisasystematicapproachtopestanddiseasecontrolusing chemical,biologicalandculturalcontrols,andstepstoincreasehostkplantresistance(koul, 6

Dhaliwal,&Cuperus,2004).IPMseekstoreducepestpopulationstoeconomicallytolerable levelswhileminimizingpesticideuse,particularlyofmoretoxicpesticides. IPMpracticeshavebeenidentifiedandtestedinvariousfarmingsystemsindeveloped anddevelopingcountries.however,ipmadoptionremainsrelativelylowinmostofthe developingworld(worldkbank,2005).inmostdevelopingkcountrycommercializedfarming systems,aggressivepesticideusestillpredominates. ThespreadofIPMinlessKdevelopedareasisconstrainedbymanyfactors.Effectiveuse ofipminvolvesunderstandingcomplexinterrelationshipsamongcropsanddiseases,and formaltraininginipmtechniquesoftenincludesdetailedinformationonthelifekcycleofpests. Thistrainingiscostlyandtimeintensiveandmaynotbeaccessibletomanyproducers.Pest pressuresevolve,sometimesrapidly,andipmpracticesoftenhavetobeadaptedtothis evolution.farmersreceivemixedandoftenconflictingmessagesfromresearchersand chemicalvendors.pesticidesareviewedbymanyfarmersastheleastriskywayofreducing croplosses,whileacultureofpesticideuseanddependenceisdifficulttoovercome. AgoalofIPMresearchprogrammesistoensurethatIPMtechniquesarewidely adopted.overtime,alternativemechanismsforipmtraininghavebeenused.forexample, farmerfieldschools(ffs)areanintensiveparticipatorytrainingprograminvolvingweekly trainingsessionsduringafullcropseason(godtland,sadoulet,janvry,murgai,&ortiz,2004). Fielddaysaredaylongeventsinwhichresearchersdemonstratespecific,oftenmultiple,IPM practicestoparticipants.observationvisitsinvolvegroupsoffarmersvisitingother communitiestogainexposuretoipmpractices.extensionagentvisitsinvolvedirectprovision 7

ofinformationtofarmers.massmediamethodsincludepamphlets,newspapers,andradio(m. Mauceri,J.Alwang,G.Norton,&V.Barrera,2007). SeveralstudieshaveexaminedtheeffectofIPMtrainingmethodsonadoptionandIPM knowledge.godtlandetal.(2004)evaluatedtheimpactofffsonipmknowledgeinpotato productioninperuusingpropensityscorematching(psm).theyfoundthatffsparticipation significantlyenhancesknowledgeinsubjectsrelevanttoipmadoptionsuchaspestdynamics, fungiciderotations,anduseofresistantvarieties.however,sinceffsparticipantsare purposivelyselectedbasedontheirdynamismandwillingnesstospreadpracticestoothers,the underlyingassumptionofpsmofselectiononlyonobservablesislikelytohavebeenviolated. Whenselectionisonunobservables,thematchedsamplemaynotrepresenttheappropriate counterfactualandthisviolationislikelytohaveintroducedbiasintotheirestimates.rickerk Gilbertetal.(2008)evaluatedthecostKeffectivenessofIPMextensionmethodsinBangladesh, usinganinstrumentalvariablesmodeltoallowforselectiononunobservables.they determinedthatffs,fielddays,andextensionagentvisitshadpositiveimpactsonfarmers adoption.ffsparticipantsweremorelikelytoadoptintermediateandcomplexipmpractices whilefielddayparticipantsweremorelikelytoadoptsimpleones.becauseofhighcostsofthe FFS,fielddaysandextensionagentvisitswerefoundtobethemostcostKeffectivesourcesof IPMinformation. MostpreviousstudiesassessIPMadoptionandknowledgeshortlyafterparticipationin IPMtraining,capturingshortKtermadoptionandknowledgeacquisitionthatmaynotlast.If IPMknowledgedegradesovertimeorbecomeslesseffectiveaspestpressuresevolve, adoptionratesmayfall.alternatively,fallingratesmightbeduetocompetingmessagesfrom 8

chemicalvendorsorotherinformationsources.thisraisesthequestionofwhetheripmhas stayingpower.dofarmerscontinuetouseipmwithoutongoingtraining?weaddressthis questionbyexaminingipmspreadandadoptionlongafterformalipmoutreacheffortsceased inapotatokproducingareaofecuador.weexamineadoptionpatternsandsourcesofipm knowledgein2012andcomparethemwithpatternsthatexistedwhenoutreachceasedinthe areain2003.factorsaffectingcurrentipmadoptionareidentifiedandtheeffectofipm adoptiononcurrentpesticideexpendituresisestimated. Thearticleisorganizedasfollows.Section1providesanintroductiontoIPMandIPM trainingmethods.section2discussesthestudysiteandthetechnologyofferedtofarmers. Section3describeshowfarmersobtaininformationonIPMandtheirknowledgelevels. Section4comparesIPMspreadandadoptionin2003and2012.InSection5,themethodsused tomeasuretheeconomicimpactofipmadoptionarepresented.resultsareshowninsection 6,andSection7concludes. 2.2.StudysiteandIPMprogram CarchiProvinceinnorthernEcuadoristhemostimportantpotatoKgrowingareainthecountry, with28%ofnationalpotatoproductionononly13%ofthetotalpotatoarea(sinagap,2012). Averageyieldissignificantlyhigher(17.9tons/hectare)thanthenationalaverage(8.3 tons/hectare).farmsincarchitendtobelarger;theaverageareaplantedtopotatoesin Ecuadoris0.57hectarewhileinCarchitheaverageis1.48hectares(SINAGAP,2012).AgroK ecologicalconditionsmakecarchianideallocationforpotato:soilsaredeep,loamyandhighin 9

organiccontent.theprovincereceivesrainfallthroughouttheyearandpotatocanbe producedyearkround. PotatofarmersinCarchifacemanypestproblems.Ofparticularconcernarelateblight (Phytophthorainfestans),theAndeanpotatoweevil(Premnotrypesvorax),andtheCentral Americantubermoth(Teciasolanivora).Underconventionalproductionpractices,thesepests aremanagedwithintensiveapplicationsoftoxicchemicals(sherwood,cole,crissman,& Paredes,2005),andstudieshaveshownthatpotatofarmersinCarchihadbecomeheavily dependentonpesticides(yanggen,cole,crissman,&sherwood,2004).bythelate1990sand early2000s,thisdependencygeneratedhighinputexpenditures,lowerprofitmargins,and evidenceofnegativehealthandenvironmentalimpacts(crissman,antle,&capalbo,1998).as aresponse,ecuador sagricultureministry,togetherwithnationalandinternationalresearch 1 anddevelopmentorganizations,designed,tested,andconductedoutreachonapackageofipm practicesforpotatoproducers. ThecollaborativeresearchandoutreachprograminCarchiwascoordinatedbythe MinistryofAgriculture slongkrunningfortipapaprogrammeandconsistedofmultiple components.between1999and2003,18ffswererun,manyfielddaysandanumberof workshopswereheld.participantsinffswereselectedbasedontheirinterestinparticipating andtheirwillingnesstosharetheirknowledgeandexperienceswithotherfarmers 2.IntheFFS, farmersandresearchersmetonceperweekduringthesixkmonthpotatogrowingseason.each sessionlastedapproximatelythreehoursandcombinedpracticekandtheorykbasedlearning. 1 TheIntegratedPestManagementCollaborativeResearchSupportProgram(IPMKCRSP),fundedbytheUnited StatesAgencyforInternationalDevelopmentwasanimportantresearchpartnerinthiseffort. 2 FarmersarepurposivelyselectedforparticipationinFFS.FFSareintendedtoprovideintensivetrainingtoafew farmers,withtheideathatthisknowledgewillspreadduetothedynamismoftheparticipants(feder,murgai,& Quizon,2004). 10

NonKFFSfarmerswereinvitedtoparticipateinfielddays.DuringthesedayKlongevents, participantsweretaughtlowkintermediatecomplexityipmpracticesusingdemonstrations, shortlectures,andposterkbasededucationalmaterials.farmersincarchiwerealsoexposedto IPMthroughmassKmediadisseminationeffortsincludingpamphlets,newspaperarticles,and radiomessages. ThepreferredIPMpackageforpotatoescombinedcultural,mechanical,andchemical pestmanagement.culturalcontrolsinclude:i)useofcertifiedandresistantseeds,ii)useof highkhillingmethodstocreateabarrierbetweeninsectpestsandthetuber,iii)improvedcrop rotations,iv)earlyharvestingtoavoiddamagebytubermoth,v)disposalofresiduestokeep fieldscleanandtopreventpropagationofpests,andvi)irrigationduringthedryseasonto manageinsectpestpopulations.mechanicalcontrols,intendedtokillapestdirectly,include:i) yellowstickymobileandfixedtrapsformonitoringandmasstrappingofleafminerinsects (Bedelliasomnulentella),andii)cardboardtrapstotargetadultAndeanweevilpopulations. Finally,IPMchemicalcontrolincludesuseoflowKtoxicitypesticideswhenotheroptionsarenot available.ipmchemicalcontrolsinclude:i)seeddisinfectionwithpesticides,ii)directedkspray pesticideapplicationtospecificpartsoftheplant,andiii)rotatinguseoffungicides(mainly againstlateblight)withdifferentactiveingredientsusinglowktoxicitypesticides. Anevaluationoftrainingmethodsconductedin2003showedthatIPMpractices commonlyadoptedbypotatogrowersincluded:i)improvedcroprotations(58.7%offarmers adopted),ii)earlyharvesting(57.8%),iii)disposalofplantresidues(50.5%),andiv)directedk spraypesticideapplication(48.6%)(m.maucerietal.,2007). 11

FormalIPMtrainingandoutreachinCarchistoppedin2003.WhiletheNational AutonomousInstituteofAgriculturalandLivestockResearch s(iniap)officeremainsopen,few outreacheventshavebeenheldandnoorganizedformalipmtraininghasoccurred.the decisiontoabandonipmtrainingintheareawasbasedonresourceconstraintsbutcouldhave importantimplicationsforcontinuedspreadanduseofipm.inthepastdecade,potatoprice variabilityhasbecomeaseriousproblemforareafarmers.thisphenomenonisattributedin parttovariablepotatoimportsfromcolombiaandperu.formanyyears,farmersactedas thoughtheywereplayingthelottery,investingincontinualproductionwhilebettingonhigh pricesatharvesttorecovertheirinvestment(sherwoodetal.,2005).recently,farmersin Carchihavedecreasedtheirlanddedicatedtopotatoproduction.In2003,8,644hectaresin theprovincewereplantedwithpotato.nineyearslaterthisamounthadalmosthalvedto4,555 hectares(sinagap,2012). WeusedatafromfarmKhouseholdsurveyscollectedin2003and2012.During SeptemberKOctober2003,anadoptionsurveywasconductedof109potatofarmersfrom Tulcan,Montufar,andEspejomunicipalities,whichaccountfor90%oftotalproductionin Carchi.Randomlyselectedrespondentsincluded30FFSparticipants,28farmerswhohadbeen exposedtoffsparticipants,and51nonkparticipantandnonkexposedfarmers(mauceri,etal., 2007).InMayKJuly2012,arandomsampleof404potatofarmersfromthesamethree municipalitieswasselectedfromalistprovidedbytheministryofagriculture,livestock, AquacultureandFisheries(MAGAP).Thissampleconsistedof74FFSparticipants,302farmers exposedtoothersourcesofipminformationand28ipmkunexposedfarmers.bothsimple 12

randomsamplesweredesignedtoberepresentativeofthepotatokproducingpopulationinthe Province. The2003and2012questionnairesweresimilar,butnotidentical.Bothincluded modulesondemographicandsocioeconomicconditions,potatoproduction,pesticideusage andhandling,andipmknowledgeandadoption.the2012surveycontainedadditional informationonpotatoproductionsuchascultivatedarea,inputcosts,andyields;mainsource ofipminformation;andhouseholdassets.althoughthesurveysweresimilar,theydonotcover anidenticalgroupoffarmersmainlybecausethe2003surveywasnotconceivedofasapanel. Thereareseveraladvantagesassociatedwithpaneldata,butthesecomeatacost.Giventhe smallsamplesizeofthe2003surveyandsubsequentattritionsincethatsurvey,noattempt wasmadetoconstructapanelin2012.ouranalysisthuscomparestwosimilarlyrepresentative crosssections 3. ToassessthespreadandadoptionofIPMlongafterformaloutreacheffortsceasedin Carchi,the2003and2012datasetsareusedtoexaminechangesinpatternsofIPMadoption. ToexaminesourcesofIPMinformationanddepthofknowledge(Section3)andfortheanalysis ofdeterminantsofadoptionandpesticideexpenditures(section5),the2012datasetaloneis usedbecausethe2003surveydidnotcontaintherequisiteinformationonpesticide expenditures. 3 Obviously,the2012survey,givenitslargersize,allowsmorepreciseestimatesofkeyparameters.The2003 surveyhaslesspowertodetectdifferences,sodiscussionofitfocusesonstatisticallysignificantdifferencesand doesnothighlightnonksignificance. 13

2.3.SourcesofIPMinformationanddepthofknowledge Knowledgeandskillsareassociatedwithagriculturaltechnologyadoption(Caviglia&Kahn, 2001).However,thevalueoftrainingdoesnotstopwithknowledgecreationamong participants.astrainingbudgetstighten,technologyspreaddependsonactivefarmerto farmerdiffusionofinformation.trainedfarmersareexpectedtoretaintheskillsand knowledgeacquiredandtransmitknowledgetoneighbors,friends,andrelatives.farmerktok farmerknowledgetransferis,infact,acornerstoneofffsapproaches;progressivefarmersare selectedfortheschoolswiththehopethattheirneighborswillfollowtheirleadandipmwill spread.akeyobjectiveofthe2012surveywastounderstandhowfarmersobtainedand retainedinformationaboutipm. RespondentswereaskedtoidentifytheirprimarysourceofIPMinformation.Potential sourcesincludeddirectparticipationinffss,fielddays,otherfarmers,observationvisits, extensionagentvisits,familymembers,localgovernmentactivities,andmassmediasources. NineyearsaftertheinterventioninCarchiended, otherfarmers constitutethemainsourceof IPMinformation.Almost36%ofrespondentsin2012reportedhavingheardaboutIPMfrom otherfarmers,whileaboutthesamepercentageoffarmersreportedreceivingformaltraining (figure2k1).animportantquestioniswhetherthequalityofipmknowledgeishighand whetherknowledgevariesbysource. 14

Figure2K1:IPMadoptionovertime,Carchi,Ecuador,2003and2012. FFSFARMERS ALLFARMERS 40% 30% 20% 10% 0% 60% 50% 40% 30% 20% 10% 0% 2003 2012 Source:Mauceri(2003)andCarrion(2012);n=109andn=404,respectively. 2003 2012 IPMknowledgemaybedefined(Federetal.,2004)as thepossessionofanalyticalskills, criticalthinking,abilitytomakebetterdecisions,familiaritywithspecificagriculturalpractices, andunderstandingofinteractionswithintheagrokecologicalsystem (p.225).tomeasure knowledgein2012,respondentswereasked20questionsaboutpestmanagementandipm. Thequestionstestedknowledgeofthethreemajorpestsinthearea,criteria(whenandwhere) forapplyingpesticides,andpesticidehandlingandstoragepractices.respondentswerealso askedaboutthemeaningofwarninglabelsonpesticidecontainers,andwhatprecautionsthey takeinapplyingandstoringpesticides.farmersweregroupedintothreecategories:low, mediumandhighipmknowledge 4. In2012,FFSparticipationwasassociatedwiththemost(lasting)highknowledgescores, followedbyfielddays;differencesinknowledgebymainsourceofinformationaresignificant atthe5%level(table2k1).farmerswhoneverparticipatedinformalipmtrainingdonothave 4 Scoresweretransformedtoa100Kpointscaleandcategorizedasfollows:noIPMknowledge=0points;low= 0 < #$ 25;medium=25$ < $#$ $50;high=$50$ < $#$ $75andfull=75$< $#$ $100.Nofarmerfellintothe zeroknowledgeorthefullknowledgecategory. 15

highknowledgelevels.knowledgeretentionvariesbyinformationsourceanditincreaseswith trainingintensity.formerparticipantsinffsandfielddaysaremoreknowledgeableaboutipm thanotherfarmers,soitisnosurprisethattheyretainmoreipmknowledgeovertime. Table2K1:MainsourcesofIPMinformationandknowledgelevelsbysource,Carchi,Ecuador, 2012 MainSourceofIPMInformation KnowledgeLevelbySource Source % Low Moderate High % % % NoformalIPMtraining 7 29 71 0 FFS 18 10 85 5 Fielddays 17 15 82 3 Otherfarmers 35 27 72 1 Othersources 23 35 63 2 Source:CarchiIPMsurvey(2012). Note:anytrainingusingFFSorfielddaysoccurredpriorto2003. a Pearsonchi 2 (12)=22.13Pr= 0.005 SometypesofIPMknowledgemaybemoreimportantthanothers.Limitedknowledge ofpesticideexposurehazardshasbeenassociatedwithunsafeapplicationpracticesinthe Carchiarea(Sherwoodetal.,2005).Itisthusimportantthatfarmersareabletoreadlabelsand understandtheirmeaning.inecuador,asinmostcountries,pesticidelabelshavecolourcoding toreflectproducttoxicity(red,yellow,blue,andgreen).themeaningofthecolourcoding schemewasnotunderstoodby42.3%ofrespondentsin2012.only1.5%wasabletoidentify correctlythemeaningofthefourstandardcolours;12.4%recognizedthree,and26.7% correctlyidentifiedtwo.fewfarmersuseprotectiveequipmentwhilespraying(seetableak1, AppendixA).Most(81.5%)areawareofrisksassociatedwithsprayingpesticideswhile performingothertasks,suchaseatingordrinkingwater,butfewtakesimplesafety precautions.thisevidenceshowsthatknowledgeaboutpotentialdamagesfrompesticidemisk 16

handlingislow,andhandlingandsafetyproceduresarenotwidelyusedintheregiondespite widekspreaduseofpesticides. 2.4.IPMadoptionovertime IPMpackagestaughtpriorto2003consistedofasetoffarmingpracticestoenhancepest control;farmersultimatelydecidewhetherornottoadoptthem.theymayadoptthecomplete packageofinnovations,adoptnothing,orpicksubsetsorbundlesofpractices.here,ipm adoptionisdefinedasthedegreeofuseofthetechnology,recognizingthatallipmpractices arenotequallyimportant.eachipmpracticewasassignedaweightbasedonadviceprovided byateamoflocalagriculturalscientists.weightswereassignedbasedonlevelofcomplexity andknowledgenecessarytosuccessfullyimplementthepractice.detailedinformationis providedinappendixb.adoptionintensitywascalculatedbysummingpracticekspecificscores foreachrespondent.basedonthesescores,respondentswereclassifiedasnonk,lowk, moderatek,highk,orfullkipmadopter 5.Thisprocedurewasconductedforbothsurveys. BecausethestudiesdifferslightlyintheIPMpracticesincluded(seeAppendixA),adoptionwas computedbasedon11ipmpracticescommontobothstudies. TheproportionoffarmersinnonKandlowKadoptioncategorieschangedsubstantially between2003and2012(figure2k1).theproportionoffarmersnotadoptinganyipmpractice fellfrom30%tolessthan10%,whiletheproportionreportinglowlevelsofipmadoptiongrew from15%tonearly50%.percentagesinthehighkandfullkadoptioncategoriesdeclinedover 5 Eachrespondentwasgivenamaximumscoreof8basedonuseofweightedIPMpractices(AppendixB,tableBK 1).Thesegradesweretransformedto100Kpointscale.Forthedescriptiveanalysis,thefollowingcategorieswere employed:nonadopters=0practices;low=0 < z$ 25;medium=25$ < $z$ $50;high=$50$ < $z$ $75;and full=75$< $#$ $100 17

time,butthemagnitudesofthesechangeswerenotaslargeasforthenonkandlowkadoption categories.theproportionofffsparticipantsinthenonk,highkandfullkadoptioncategoriesfell overtime.acorrespondingincreaseintheproportionsadoptingatlowandmoderatelevelsis observedforthosewhoreceivedtheiripminformationfromffss.incarchi,ipmusehas becomemorewidespread,butevenintensivelytrainedfarmersarenowusingrelativelyfewer IPMtechniques. ManyfarmerswithnoformaltraininginIPMhadadoptedmultiplepracticesin2012. Nevertheless,adoptionratesformostpracticeswerebelowratesforthosewithformalIPM training.formerffsparticipantshadthehighestratesofadoptionofpracticesrequiringmore knowledgeorthataremorelabourintensivesuchastrapsforandeanweevil,andfixedand mobileyellowinsecttraps(tableak2,appendixa). Manyfactorsinfluencethediffusionofanewtechnology.Forexample,whenan agriculturalinnovationisfirstintroduced,potentialadoptersmaybeuncertainaboutits benefits.overtime,ownexperiencesandinformationgatheredfromdifferentsources contributetoreducethisuncertainty,andadoptionisexpectedtogrow(pannell,2003).while someipmpracticessuchasrotatinguseoffungicideswithdifferentlowktoxicityactive ingredientsandimprovedcroprotationshavebecomewidelyused,otherpractices,suchas insecttrapsanddisposalofplantresiduestokeepfieldsclean,havebeenabandonedbyprior adopters.farmershaveexperimentedwithipmpracticesintheirowncontextandcontinueto useeffectiveandprofitablepracticesandabandonedothers. TogaininsightsintoabandonmentofIPMpractices,farmerswereaskedin2012 whethertheywereusingandhadusedaparticularipmtechniqueand(ifapplicable)whythey 18

stoppedusingthatpractice.intable2k2,2003and2012adoptionratesarecomparedforall IPMpractices.Formostpractices,earlyadoptionrates,basedonthe2003survey,were relativelyhighanddecreasedbythetimeofthe2012survey.somepracticesweretriedand discardedfordifferentreasons.primaryreasonsforabandonmentofipmpracticesare presentedintableak3ofappendixa.highabandonmentoccurredforlabourintensive practices,especially,insecttraps,withtimeconstraintscitedasthemainreasonfortheir decisiontoabandonthepractice.farmersalsoadjusttheirfarmingstrategiesinresponseto evolvingpestpressures;manyfarmersreportedabandoningipmpracticesbecausetheyareno longerneeded.infact,subsequentipmresearchhasshownthatleafminertrapsarerarely neededinanipmregimewherereducedsprayingallowsbeneficialinsectpopulationstogrow. DiscontinueduseofyellowtrapsmightbeexpectedinareaswhereIPMpracticesarerelatively widespread.limitedeffectivenessofsomepracticeswasmentionedseveraltimesby respondents,indicatingthatsomemaynotbesuitedforfarmkspecificconditions.other commonreasonsforabandonmentofipmpracticesareinclementweatherandlackofinterest. Overall,farmersusepracticestheyarecomfortablewithandareperceivedasbeingcostK effective.practicesperceivedtorequirecomplex,costly,andtimekconsumingactivities,suchas trappingandirrigation,havelowratesofuseandhighratesofabandonment. 19

Table2K2:PercentageadoptingIPMpractices,Carchi,Ecuador,2003and2012 2003 IPMpractices % 2012 % CardboardtrapsforadultAndeanweevilpopulations 11.0 11.9 DirectedKspraypesticideapplicationtospecificpartsoftheplant 48.6 31.9 Irrigationduringdryseasontomanageinsectpestpopulations 31.2 14.1 Yellowmobilestickytrapforleafminerinsects 19.3 5.7 Rotatinguseoflowtoxicityfungicideswithdifferentactive 12.8 68.1 ingredients Useofpotatoseedstorehouse 9.2 5.5 UseofhighKhillingmethodstocreatebarrierforinsectpests 31.2 25.0 Disposalofplantresiduestokeepfieldsclean 50.5 37.1 Improvedcroprotations 58.7 68.1 Yellowfixedtrapsforleafminerinsects 25.7 6.7 Earlyharvestingtoavoiddamagebytubermoth 57.8 34.4 Numberofobs. 109 404 Source:Mauceri(2003)&CarchiIPMsurvey(2012). 2.5.Econometricanalysis TheinformationpresentedabovesuggeststhatIPMhasbecomerelativelywellKentrenchedin thecarchiareaandfarmersarenowlearningfromoneanotherandfromtheirownexperience. Furtherinformationisneededon:(i)factorsdeterminingcurrentdecisionsaboutIPMadoption, and(ii)theeffectsofipmadoptiononpesticideexpenditures. IPMadoptionisassumedtoresultfromexpectedutilitymaximizationbyfarmers. Benefitsconsideredmayincludehealth,lowerrisk,andincreasedprofitability.Letadoptionof IPMbespecifiedas: -./01 = 2./01 3./0 + 567./01 8./01 + 9./01 (1) 20

wherey ;<= isacontinuousvariablerepresentingthedegreeofipmadoption 6,X ;<= isavector ofobservablecontrolcovariates(e.g.,education,experience,wealth,etc.)inf ;<= isthevector ofprimaryinformationsourceeachfarmerwasexposedtoffs,fielddays,otherfarmersand othersandε ;<=C isarandomerrorterm. Pesticideexpendituresaremodeledasfollows: DE- FG/1 = 2 FG/1 3 FG/ + HY ;<=C + 9 FG/1 (2) wherelny KL< isthenaturallogarithmofperhectarepesticideexpenditures 7.ThevectorX ;<= containsexogenousexplanatoryhouseholdklevelvariables.asdiscussedabovey ;<=C istheipm adoptionvariable,and$ε KL<C istheunobservedtherandomerrorterm. Anendogeneityproblemarisesifunobservedfactorsinε ;<=C andε KL<C makethese errortermscorrelated.inthecontextofthisstudy,unobservedvariablessuchmanagerial abilityorawarenessofdangersofpesticidescouldaffectboththedecisiontoadoptipmand pesticidesexpenditures.whenendogeneityispresentanditisignored,theparameter estimatesfromanordinaryleastsquares(ols)regressionofequation2areinconsistentand biased.toaccountfortheendogeneityofipmadoption,weuseaninstrumentalkvariabletwok stageleastsquares(ivtsls)approach.potatoipmadoptionisinstrumentedbythefour informationsourcesfarmersreportedbeingexposedto(ffss,fielddays,otherfarmers,and otherdiffusionmethods).thesevariablesarenotidealcandidatesasinstruments.theyare 6 Tofacilitatetheinterpretationofestimatedcoefficientsinourregressionmodel,wetransformour0K8practiceK weightedipmadoptionvariabletoa0k100scale. 7 Duetothehighdegreeofcommercializationofpestproductionintheareaandtheubiquitouspresenceofpests anddiseases,allfarmersusesomepesticides. 21

highlycorrelatedwithadoptionandtheireffectonpesticideexpendituresislikelytobeonly throughtheireffectonipmadoption.however,missingvariables,suchaseagernessto undertakeipmorconcernabouttheenvironmentarelikelytoaffectboth.weconduct diagnosticteststoevaluatetheirappropriatenessasinstruments,but,asiswellkknown,these testsdonotaddressthefundamentalquestionofinstrumentvalidity.weexperimentedwith alternativeinstrumentsandcomparedtheestimatesofequation2withamoreparsimonious specificationtobuildconfidenceinourfindings,butrecognizethattheidentificationofthe effectofy ;<=C iscompromised. Controlcovariates2./01 usedinestimatingequation(1)includeeducation,experience, havingbeenmadesickduetopesticideuse,landsize,wealthandhouseholdsize.these variablesareconsistentlyfoundtobekeydeterminantsofipmandagriculturaltechnology adoptionintheliterature(d Souza,Cyphers,&Phipps,1993;Teklewold,Kassie,Shiferaw, Handelshögskolan,Universityof,Departmentof,Göteborgs,Institutionenförnationalekonomi med,&schoolofbusiness,2013;yorobejr,rejesus,&hammig,2011).educationisexpected tobepositivelyrelatedtoadoptionsincemoreeducatedfarmersunderstandandrespond bettertonewtechnologies(lin,1991).theeffectofexperienceisanticipatedtobepositive becausemoreexperiencedfarmersaremorelikelytoknowhowinputsinteract.more experiencedfarmersmayalsobebetterabletoassessnewtechnologies.weexpectapositive effectofhouseholdsizebecauselargefamilieshavemorelaboravailabletoperformonkfarm activities,enablingfarmerstoadoptlaborkintensivetechnologies(gershonfeder&dinal. Umali,1993).Havingfallensickduetoexposuretopesticidesmayencouragefarmerstoadopt IPM.Hence,apositiveeffectisexpected.Wealthisanticipatedtohaveapositiveeffecton 22

adoptionbecausewealthierfarmerscanwithstandthelossesresultingfromtheadoptionof newriskyandpotentiallytechnologies(ellis,1988). Inequation(2)theequationdescribingpesticideexpenditures,vector2 FG/ contains thesameexplanatoryvariableasin2./01 exceptforhouseholdsize,sincewedonotbelieve householdsizeexplainspesticidesexpenditures.weexpectthateducationandexperiencewill negativelyaffectpesticideexpenditures.wealthmayleadtouseofmorepesticidesbecause wealthierfarmerscanaffordthecostoftheseinputsoritmayenablethemtoovercomecredit constraints. 2.6.Estimationresults Descriptivestatisticsarepresentedintable2K3.Onaverage,farmersspend$272perhectare perpotatocropcycleonpesticides.theaverageipmscoreis25outof100.twentyeight percentoffarmershadbeensickintheyearpriortothesurveyduetopesticideusage.the averageareaplantedtopotatobypotatokproducinghouseholdsis1.89hectares 8.Estimates fromequation1arereportedintable2k4.signsformostcoefficientsareconsistentwith expectationsandtheoverallfit(adjustedr 2 of.13)isacceptablegiventhenatureofthedata. HavingattendedaFFSraisesthepotatoIPMadoptionscoreby22pointscomparedtofarmers whodidnotreceiveanytraining.anincrementofthissizeisequivalenttoadoptingthreemore mediumcomplexityipmpractices(weightof0.6)ortwohighkcomplexitypractices(onewitha weightof1andonewithaweightof0.8).havinglearnedipmfromfielddaysandfromother 8 Thisnumberdiffersfromtheprovinceaverageof1.48habecausethehouseholdsurveyisrepresentativeofthe threemaincommercialpotatoproducingmunicipalities.theseaccountfor90%ofthetotalpotatoproductionin Carchiandinthesemunicipalities,potatoproductionisextensivecomparedtotherestoftheprovince. 23

farmersincreasestheadoptionscoreby12and13points,respectively,againcomparedwith untrainedfarmers. Table2K3:Summarystatisticsforvariablesineconometricmodel Variable Mean Std.Dev. Pesticidesexpendituresperhectarepercropcycle(U.S.dollars) 272.4 160.1 IPMadoption(0K100) 25.2 16.7 Farmer'sage 46.6 13.1 Farmersecondaryeducation(yes=1) 0.2 0.4 Farmingyearsofexperience 26.1 13.1 Farmerhasbeensickduetopesticideuse(yes=1) 0.3 0.5 Areaplantedtopotato 1.90 2.5 Wealthindex 0.00 1.6 Householdsize 4.2 1.7 Inormationsources(setofdummies) IPMKuntrainedfarmers 0.1 0.3 FFS 0.2 0.4 Fielddays 0.2 0.4 Otherfarmers 0.4 0.5 OthersourcesofIPMinformation 0.2 0.4 Numberofobs. 404 Source:CarchiIPMsurvey(2012) 24

Table2K4:DeterminantsofIPMadoption Independentvariable:IPMadoption(0K100) Explanatoryvariable Coefficient Farmersecondaryeducation 4.03** (2.03) Farmingyearsofexperience 0.29 (0.24) Farmingyearsofexperiencesquared K0.01 (0.00) Farmerhasbeensickduetopesticides K2.53 (1.75) Plotsize(naturallogarithm) K0.15 (0.96) Wealthindex 1.62*** (0.52) Householdsize 0.21 (0.46) FFS 22.25*** (3.48) Fielddays 12.23*** (3.53) Otherfarmers 12.56*** (3.290) OthersourcesofIPMinformation 11.20*** (3.38) _cons 8.10 (5.01) AdjustedRKsquared 0.13 Numberofobs. 404 a Significancelevels:*10%**5%***1% b Robuststandarderrorsinbrackets Theeffectofmethodoflearningonadoptionscorefollowsexpectations.FFS attendancehasthelargesteffectontheprobabilityofadoptinghigherlevelsofipm,butother methodsaresignificantandtheirmagnitudesarelarge.aoneunitincreaseinthewealthindex wasassociatedwithanincreaseof1.6pointsinipmadoption.educationandwealthare significantatthe5%and1%,respectively.farmingexperience,plotsizeandfarmerreporting beingsickduetopesticidesusearenotsignificantlyassociatedwithadoptionofipm. 25

Instrumentalvariableanalysissuffersfromcertainlimitationsthatshouldbe acknowledged.onechallengeinusingtheinformationsourcesfarmerswereexposedtoas instrumentsisthattheymaybecorrelatedwithotherunobservablefactors,suchas environmentalconcernsandmotivationthatinturnarecorrelatedwiththeunobserved confoundersthatimpactpesticidesexpenditures.toruleoutanydirecteffectofthe instrumentonthedependentvariableweestimateaparsimoniousmodelwithjustipm adoptioninvolved.resultsoftheolsandivtslsfullkmodel(equation2)andparsimoniousk modelestimatesarepresentedintable2k5.theipmadoptioncoefficientobtainedfromthe multipleregressionissimilartothatobtainedfromthesimpleregression,implyingthatomitted variablebiasmaynotbeahugeconcern.tofurthervalidatetheextenttowhichthesetof instrumentsfittheivassumptions,weemployastandardoverkidentificationstatisticaltest, whichisapartialtestoftheextenttowhichinstrumentsaretrulyexcludablefromthe pesticidesexpendituresfunction.wefailtorejectthenullhypothesis(pkvalueof0.201),which providessomeevidencethattheinstrumentsarevalid. 26

Table2K5:Determinantsofpesticideexpendituresforpotatoproduction Independentvariable:naturallogarithmpesticideexpendituresperhectare Explanatoryvariable OLS IV2SLS (1) (2) (3) (4) IPMadoption(endogenousvariable) (0K100) K0.00 (0.00) K0.00 (0.00) K0.01** (0.01) K0.02*** (0.01) Farmerhassecondaryeducation K0.01 (0.07) Farmingexperience 0.01 (0.01) Farmingexperiencesquared 0.00* (0.00) Farmerhadbeensickduetopesticides 0.07 (0.06) Plotsize(naturallogarithm) K0.16*** (0.04) Wealthindex(quartiles) 0.02 (0.02) _cons 5.42*** (0.13) 0.05 (0.08) 0.02 (0.01) K0.00** (0.00) 0.04 (0.06) K0.17*** (0.04) 0.04* (0.02) 5.73*** (0.18) RKsquared 0.00 0.07 Numberofobs. 404 404 404 404 Significancelevels:*10%**5%***1% Robuststandarderrorsinbrackets TheIPMadoptioncoefficientestimatedviaIV2SLSissubstantiallylargerthanthe correspondingolscoefficient 9.ApotentialexplanationforthelargedifferenceisthattheIPM adoptionvariablecontainsrandommeasurementerrors.hence,itsolscoefficientisbiased towardzerowiththemagnitudeafunctionofthenoisetosignalratio(behrman,pollak,& Taubman,1995).ThisproblemisusuallymitigatedusinganIVapproach(Gujarati,2003).A secondpotentialexplanationisthestrengthoftheinstrumentalvariablesusedinour2sls estimation.instrumentsaregenerallyconsideredtobeweakiftheyhaveajointfkstatisticin 9 TheOLSrobuststandarderrorofthecoefficientonIPMadoptionis0.0017,muchsmallerthantheIVrobust standarderror0.0053.thisresultistypicalinivapproaches. 27

theequationoflessthan10.inouranalysisthefkstatisticis12.17;henceitisunlikelythatthe largecoefficientdifferenceisduetoweakinstruments.finally,wetestedtheexogeneityofthe IPMadoptionvariableinequation(2)usingtheWuKHausmantest,thenullhypothesisthatIPM adoptionisexogenouswasrejectedatthe1%level.whiletheseteststonoteliminateconcern abouttheappropriatenessoftheinstruments,theydonotindicatethattheyarenotgood. AsmentionedaboveIPMadoptionisacontinuousvariablemeasuredona0K100scale. ApointincreaseintheIPMadoptionscoredecreasespesticideexpendituresby1.5%,showinga relativelyhighelasticity.thecoefficientofthelogofplotsizeisnegativeandsignificant suggestingeconomiesofscale.thewealthindexcoefficientispositiveandsignificant,meaning thatwealthierfarmers,asexpected,aremorelikelytospendmoreonpesticides.thevariables education,experience,andfarmerbeingsickduetopesticideusearenotsignificant.overall, farmersthatadoptedmorethan50%oftheipmpracticeshadaveragepesticideexpenditures of$142percropseason,whiletheircounterpartswhodidnotadoptanyipmpracticesspent $361.ArelativelymodestincreaseinIPMadoption,say10points(equivalenttoapracticewith aweightof0.8),wouldbeconsistentwithareductionofpesticideexpendituresof15%. Comparedwiththoseofapreviousstudy,ourresultsshowanoveralldecreaseinpesticides expendituresinthearea.barreraetal.(2004)found,withoutcontrollingforendogeneityof IPMadoption,thatfarmerswhoadoptedIPMhadaveragepesticideexpendituresof$353per hectare,whilethosewhoproducedusingtheconventionalapproachspent$667 10. 10 Valuesexpressedin2012U.S.dollars. 28

2.7.Conclusionsandpolicyimplications NationalgovernmentsandresearchanddevelopmentorganizationsinvestresourcesinIPM programmestoevaluateandpromoteadoptionofipmpractices.sincetheseprogrammes increasinglyfaceresourceconstraints,animportantconcerniswhetherprogramme interventionshavealongklastingimpact.fewstudieshaveexaminedwhetheripmadoptionis durable,andwhetheripmwillcontinuetospreadafterformaltraininghasended.usingfield datafromasurveyof404potatofarmersincarchi,ecuador,wefindthatnineyearsafteran intensiveinterventionended,ipmknowledgeisstillspreadingandhavinganimpactinthearea. Whileadoptioncontinues,relativelyfewfarmersfullyadoptallpractices,butmanyadopta varietyofrelativelylesskcomplexpracticescomparedto2003.farmerktokfarmerspread supplantedtheformaltrainingandoutreachmechanismsthatendedintheareain2003, stronglysuggestingthattrainingleadingfarmershasbeeneffectiveindisseminatingipm practices. ImportantbenefitsaregeneratedbyadoptionofpotatoIPMinCarchi.IPMadoptionhas significantlyloweredpesticideuseandsavedproductioncostsforadoptingfarmers.farmers experimentwithdifferentipmpractices,andcontinuetousesomewhileabandoningothers, butipmadoptionisclearlyassociatedwithlowerpesticideexpenditures.astheipm programmerecommendeduseoflowerktoxicitypesticides,thisfindingsuggestsbroadand persistentdeclinesinpesticideapplicationsintheregion. ThestudyprovidesjustificationforcontinuedpublicinvestmentsinIPMoutreachin areaswheresuchoutreachhasnotexistedinthepast.ipmmessageshavetocompeteagainst thosefromprivatepesticidedealerswhoareabletoprovidenewproductstocombatemerging 29

problems.intensivetraininginipm,althoughrelativelycostly(godtlandetal.,2004;m. Maucerietal.,2007),seemstobeeffectiveinmakingdurablechangesinhowfarmersthink aboutpestmanagement.earlierconcernsintheliteraturethatipmmaynotspreadtofarmers whoarenotintensivelytrainedappeartobealleviated,atleastintheecuadorcase. 30

AppendixA TableAK1:Useofprotectiveequipment,Carchi,Ecuador,(2012) Protectivegear Percentusing Boots 71.0 Mask 56.2 Gloves 36.6 Poncho 10.6 Glasses 0.7 Numberofobs. 404 Source:CarchiIPMsurvey(2012) 31

Table A K 2: Percentage of respondents adopting each IPM practice by main source of IPM information,2012 MAINSOURCEOFIPMINFORMATION IPMpractices Not Field Other Other FFS trained Days farmers sources CardboardtrapsforadultAndeanweevil populations 3.6% 35.1% 9.0% 6.4% 6.4% DirectedKspraypesticideapplicationto specificpartsoftheplant 10.7% 37.8% 35.8% 32.6% 29.8% Irrigationduringdryseasontomanage insectpestpopulations 7.1% 12.2% 17.9% 13.5% 16.0% Yellowmobilestickytrapforleafminer insects 0.0% 21.6% 6.0% 2.1% 0.0% Rotatinguseoflowtoxicityfungicideswith differentactiveingredients 57.1% 71.6% 59.7% 75.2% 63.8% Useofpotatoseedstorehouse 17.9% 25.7% 37.3% 19.1% 16.0% UseofhighKhillingmethodstocreatebarrier forinsectpests 7.1% 37.8% 31.3% 17.0% 27.7% Disposalofplantresiduestokeepfields clean 14.3% 45.9% 26.9% 46.1% 30.9% Improvedcroprotations 53.6% 64.9% 62.7% 74.5% 69.1% Yellowfixedtrapsforleafminerinsects 0.0% 25.7% 6.0% 1.4% 2.1% Earlyharvestingtoavoiddamagebytuber moth 0.0% 37.8% 44.8% 31.9% 38.3% Numberofobs. 28 74 67 141 94 Source:CarchiIPMsurvey,2012 32

TableAK3:DisadoptionofIPMpracticesandcauses(Carchi,2012) IPMrecommendedpractices Disadoptionrates a % ReasonforDisadoption % CardboardtrapsforadultAndeanweevil populations 21.0 Lackoftime Limitedeffectiveness Nopest Lackofinterest Other 38.8 22.4 17.6 10.6 10.6 Numberofrespondents 85 DirectedKspraypesticideapplicationto specificpartsoftheplant 0.7 Lackofmoney Lackoftime 66.7 33.3 Numberofrespondents 3 Irrigationduringdryseasontomanage insectpestpopulations 4.0 Inclementweather Lackoftime Lackofinterest Other 37.8 25.0 25.0 12.2 Numberofrespondents 16 Yellowmobilestickytrapforleafminer insects 9.9 Lackoftime Nopest Inclementweather Other 32.5 22.5 20.0 25.0 Numberofrespondents 40 Rotatinguseoflowtoxicityfungicideswith differentactiveingredients 0.7 Lackofmoney Lackoftime 66.7 33.3 Numberofrespondents 3 Useofpotatoseedstorehouse 2.2 Lackoftime Lackofmoney Other 33.3 33.3 33.4 Numberofrespondents 9 UseofhighKhillingmethodstocreate barrierforinsectpests 1.0 LimitedEffectiveness Other 75.0 25.0 Numberofrespondents 4 Disposalofplantresiduestokeepfields 0.0 clean Improvedcroprotations 0.2 Lackofmoney 100.0 Numberofrespondents 1 Yellowfixedtrapsforleafminerinsects 14.4 Lackoftime Inclementweather Nopest Limitedeffectiveness Other 25.9 19.0 19.0 15.5 20.6 Numberofrespondents 58 Earlyharvestingtoavoiddamagebytuber moth 1.5 Nopest Lackofinterest 66.7 33.3 Numberofrespondents 6 Source:CarchiIPMsurvey(2012) a Disadoptionbasedonresponsestoquestionin2012surveyaboutwhetherpracticewaseverused;rate representstheproportionofrespondentswhonolongerusetrapstototaleverusingthem. 33

AppendixB MeasuringIPMAdoption FourteenIPMpracticeswereincludedinbothsurveys.AteamofscientistsfromINIAPwho wereinvolvedintheipmresearchprogramsuggestedeliminatingthreeofthepracticesfor reasonsexplainedintable1.theremainingelevenpracticeswereassignedweightsbasedon levelofcomplexityandknowledgenecessarytosuccessfullyimplementeachofthem(table2). TableBK1:IPMpracticesdropfromthelistofpracticesusedtomeasureadoption Practice Reasonforeliminating Useofcertifiedandresistantseeds Eliminatedbecausefarmersmainlygrowpotato varietiesthatenjoymarketacceptance. Seeddisinfectionwithpesticides Eliminatedbecauseinordertocorrectlyassignaweight tothispracticeweneededinformationonpesticide concentrationandformulation(liquidorsoluble powder),whichwasnotavailable. Useofpheromonetraps Eliminatedbecausestoresintheareadonotcurrently carrypheromones. 34

TableBK2:IPMpracticesandweights IPMpractices Weight CardboardtrapsforadultAndeanweevilpopulations 1.0 DirectedKspraypesticideapplicationtospecificpartsoftheplant 1.0 Irrigationduringdryseasontomanageinsectpestpopulations 1.0 Yellowmobilestickytrapforleafminerinsects 1.0 Rotatinguseoflowtoxicityfungicideswithdifferentactiveingredients 0.8 Useofpotatoseedstorehouse 0.8 UseofhighKhillingmethodstocreatebarrierforinsectpests 0.8 Disposalofplantresiduestokeepfieldsclean 0.6 Improvedcroprotations 0.4 Yellowfixedtrapsforleafminerinsects 0.4 Earlyharvestingtoavoiddamagebytubermoth 0.2 Source:INIAPscientists,personalcommunication. 35

Chapter2:References Barrera,V.,Escudero,L.,Norton,G.,&Alwang,J.(2004).Encontrandosalidasparareducirlos costosylaexposiciónaplaguicidasenlosproductoresdepapa:experienciadela intervenciónenlaprovinciadelcarchi,ecuador.iniap,ipmicrsp,croplife,fao.quito, Ecuador. Behrman,J.R.,Pollak,R.A.,&Taubman,P.(1995).Fromparenttochild:intrahousehold allocationsandintergenerationalrelationsintheunitedstates.chicago:universityof ChicagoPress. Caviglia,J.L.,&Kahn,J.R.(2001).DiffusionofSustainableAgricultureintheBrazilianTropical RainForest:ADiscreteChoiceAnalysis.EconomicDevelopmentandCulturalChange, 49(2),311K333. Crissman,C.C.,Antle,J.M.,&Capalbo,S.M.(1998).Economic,environmental,andhealth tradeoffsinagriculture:pesticidesandthesustainabilityofandeanpotatoproduction. InternationalPotatoCenter. D Souza,G.,Cyphers,D.,&Phipps,T.(1993).Factorsaffectingtheadoptionofsustainable agriculturalpractices.agriculturalandresourceeconomicsreview,22(2),159k165. Ellis,F.(1988).Peasanteconomics:farmhouseholdsandagrariandevelopment.Cambridge [Cambridgeshire];NewYork:CambridgeUniversityPress. Feder,G.,Murgai,R.,&Quizon,J.B.(2004).Theacquisitionanddiffusionofknowledge:the caseofpestmanagementtraininginfarmerfieldschools,indonesia.journalof AgriculturalEconomics,55(2),221K243. 36

Feder,G.,&Umali,D.L.(1993).Theadoptionofagriculturalinnovations:Areview. TechnologicalForecastingandSocialChange,43(3 4),215K239. Godtland,E.M.,Sadoulet,E.,Janvry,A.d.,Murgai,R.,&Ortiz,O.(2004).Theimpactoffarmer fieldschoolsonknowledgeandproductivity:astudyofpotatofarmersintheperuvian Andes.EconomicDevelopmentandCulturalChange,53(1),63K92. Kogan,M.(1998).Integratedpestmanagement:historicalperspectivesandcontemporary developments.annualreviewofentomology,43,243k270. Koul,O.,Dhaliwal,G.S.,&Cuperus,G.W.(2004).Integratedpestmanagement:potential, constraintsandchallenges.cambridge,ma:cabipub. Lin,J.Y.(1991).Educationandinnovationadoptioninagriculture:evidencefromhybridricein China.AmericanJournalofAgriculturalEconomics,73(3),713K723. Mauceri,M.,Alwang,J.,Norton,G.,&Barrera,V.(2007).Effectivenessofintegratedpest managementdisseminationtechniques:acasestudyofpotatofarmersincarchi, Ecuador.JournalofAgriculturalandAppliedEconomics,39(3),765K780. Pannell,D.J.(2003).UncertaintyandAdoptionofSustainableFarmingSystems.InRisk managementandtheenvironment:agricultureinperspective(pp.67k81).springer Netherlands. RickerKGilbert,J.,Norton,G.W.,Alwang,J.,Miah,M.,&Feder,G.(2008).CostKeffectivenessof alternativeintegratedpestmanagementextensionmethods:anexamplefrom Bangladesh.ReviewofAgriculturalEconomics,30(2),252K269. 37

Sanborn,M.,Cole,D.,Abelsohn,A.,&Weir,E.(2002).Identifyingandmanagingadverse environmentalhealtheffects:4.pesticides.canadianmedicalassociationjournal, 166(11),1431K1436. Sherwood,S.,Cole,D.,Crissman,C.,&Paredes,M.(2005).Frompesticidestopeople: improvingecosystemhealthinthenorthernandes.thepesticidedetox:towardsamore sustainableagriculture,147k164. SINAGAP.(2012).NationalInformationSystemofAgriculture,Livestock,Aquacultureand FisheriesofEcuadorRetrievedfrom:http://sinagap.agricultura.gob.ec/ Teklewold,H.,Kassie,M.,Shiferaw,B.,Handelshögskolan(2013).Adoptionofmultiple sustainableagriculturalpracticesinruralethiopia.journalofagriculturaleconomics, 64(3),597K623. WorldKBank.(2005).TheWorldBankAnnualReport2005.Retrievedfrom: http://siteresources.worldbank.org/intannrep2k5/resources/51563_english.pdf Yanggen,D.,Cole,D.C.,Crissman,C.,&Sherwood,S.(2004).PesticideUseinCommercial PotatoProduction:ReflectionsonResearchandInterventionEffortstowardsGreater EcosystemsHealthinNorthernEcuador.EcoHealth,1(2),SU72KSU83. YorobeJr,J.M.,Rejesus,R.M.,&Hammig,M.D.(2011).InsecticideuseimpactsofIntegrated PestManagement(IPM)FarmerFieldSchools:Evidencefromonionfarmersinthe Philippines.AgriculturalSystems,104(7),580K587. 38

CHAPTER3:CANTEXTMESSAGESIMPROVEAGRICULTURALOUTREACHIN ECUADOR? 3.1.Introduction Agriculturalproductivitygrowthcanbeakeydriverofoveralleconomicgrowthand development,andofreducedpovertyandhunger(pingali,2007).productivitygrowthmustbe achievedwhileminimizingadverseimpactsontheenvironment.conventionalfarming practicescanleadtoproblemswithsoilandwaterquality.integratedcropmanagement(icm) hasemergedasaresponsetotheseproblems(kumar&shivay,2008).icmiscomprisedof practicesthatcombinethebesttraditionalandmodernfarmingmethodsformanagingsoils, water,andpests,andincludesintegratedpestmanagement(ipm)methodsasasubset.icm involvesjudicioususeoffertilizersandipmpracticestominimizepestkrelatedlosses,improve yieldsandcropquality,conservesoilandwaterresources,andpreservebiodiversity(brumfield, Rimal,&Reiners,2000).SinceICMinvolvesacomplex package ofinterkrelatedpractices, farmersmustbetrainedintheirusepriortoadoption.inarecentstudy,(parsaetal.,2014) foundthat insufficienttrainingandtechnicalsupporttofarmers wasperceivedby professionalsandfarmersasthemaincauseoflimitedipmadoptioninthedevelopingworld. Substantialefforthasgoneintodevelopingtrainingprogramstoenhanceandexpand farmers technicalknowledge.commonapproacheshavebeenfarmerfieldschools(ffss), fielddays,extensionagentvisits,observationvisits,andmassmediaactivities 11.The 11 TheFFSmethodologyfocusesonthetransferofinformationinaparticipatoryway,involvingweeklytraining sessionsduringafullcropseason(carrión,alwang,norton,&barrera,2016;feder,murgai,&quizon,2004).field daysaretrainingeventsheldonaworkingfarmdesignedtoprovideinformationaboutspecifictopicsandgive 39

effectivenessofffsshasbeenwidelydebated.literatureonffshighlightsimpactsonshortk termknowledgeimprovement,decreasedpesticideuse,andincreasedproductivity(cole, Sherwood,Crissman,Barrera,&Espinosa,2002;Godtland,Sadoulet,DeJanvry,Murgai,&Ortiz, 2004;Mauceri,Alwang,Norton,&Barrera,2007).However,thereislittleevidenceoflongK termretentionofthecomplexknowledgeimpartedbyffstraining.furthermore,adoptionof complexagriculturaltechnologiesamongsmallkscaleffsparticipantsremainslow(akudugu, Guo,&Dadzie,2012;Beaman,BenYishay,Fatch,Magruder,&Mobarak,2015;BonabanaK Wabbi,2002;Ibrahim,2013) Ifanagriculturaltechnologyisprofitable,whymightadoptionremainlow?Behavioral economicsliteratureisrifewithexamplesofbehaviorthatdoesnotconformtopredictionsof theory(dellavigna,2007).thisliteraturecouldprovidehintsaboutwhyratesofadoptionof certaintypesofagriculturaltechnologyarelow.dellavigna(2007)showsthatmoreoftenthan notindividualssimplifycomplexdecisionsbyprocessingonlyasubsetofinformation.people failtomakerationalchoicesbecausetheymightnotthinkaboutalltheirchoices,orthey systematicallymiskpredicthowtheywillfeelinthefuture.mullainathanandshafir(2013)argue thatscarcitycapturesindividuals mindsandpreventsthemfrommakingtradekoffsusinga carefulcostbenefitcalculus.afinalexplanationisthatmostagriculturalinterventionshave focusedonincreasingfarmers knowledgeastheonlymechanismtoinducebehavioralchange. farmersanoportunitytoseefarmingpracticesastheyarebeingimplemented.extensionagentvisitsinvolvedirect provisionofinformationtofarmers.observationvisitsinvolvegroupsoffarmersvisitingothercommunitiestogain exposuretoalternativepractices.massmediamethodsincludepamphlets,newspapersandradio. 40

Othermechanismscancontributetobehavioralchangeandtheirneglectmightlimit adoption 12. Iftrainingprogramsbythemselvesarenoteffectiveinproducingbehavioralchange, whatelsemightbetried?weaddressthisquestionbyimplementingalowkcost 13 intervention thataimstoinfluencedecisionmaking. Theobjectivesofthisstudyareto:(1)understandhowreceiptoftextmessages complementstrainingfromafarmerfieldday;and(2)tomeasuretheimpactonadoptionof ICMtechnologiesandknowledgeaboutthesetechnologies. Theremainderofthepaperisorganizedasfollows:Section2presentsatheoryof behavioralchangeanditsapplicationtoadoptionofagriculturaltechnologies.section3 describesthestudysiteandicmprogram.section4explainstheintervention.section5 discussesthemethods.section6examinestheresults,andsection7concludes. 3.2.ATheoryofbehavioralchange Mechanismstoinfluencebehavioralchangecanbederivedfrompsychosocialtheory.Such theorieshavecommonalities,andlittleconsensushasbeenreachedonwhicharemostuseful inexplainingbehavioralchange.michieetal.(2005)developedthetheoreticaldomain Framework(TDF)tocategorizebehaviorsandtheirdeterminants.Theyidentified12theoretical 12 Thepsychologicalliteraturedescribesadynamicprocessshapedbyinterdependentfactorsthatconvergeto facilitatetheprocessofbehavioralchange(michieetal.,2005;michie,johnston,francis,hardeman,&eccles, 2008;Stern,1999). 13 NationalgovernmentandresearchanddevelopmentorganizationsinvestresourcestopromoteadoptionofICM practices.sincetheseprogramsincreasinglyfaceresourceconstraints,lowkcostagriculturalinterventions constituteattractivealternatives.theseinterventionsprovidetheopportunityforpractitionerstoincrease farmers adoptionofagriculturaltechnologiesbyusingthesemodalitiesindependentlyorasadditionstoother extensionmethods. 41

domains 14 ofbehavioraldeterminantsthatencompasstherangeofcurrentscientific explanationsforhumanbehavior.theseare:knowledge;skills;social/professionalroleand identity;beliefsaboutcapabilities;beliefsaboutconsequences;motivationandgoals;memory, attention,anddecisionprocess;environmentalcontextandresources;socialinfluences; emotion;behavioralregulation;andnatureofthebehaviors(seeoriginalpublicationfor definitions).the12domainsofthetdfcanbecondensedintothreecorecomponents: capability,opportunityandmotivation 15 (COMKB)(Michie,vanStralen,&West,2011). Capability:Capabilityreferstothepsychologicalorphysicalabilitytoenactthe behavior.theelementsofthiscomponentare:knowledge;memory,attentionanddecision processes;behavioralregulation;andskills. Motivation:Motivationrelatestothereflectiveandautomaticmechanismsthatactivate orinhibitbehavior.itincludes:social/professionalroleandidentity;beliefsaboutcapabilities; beliefsaboutconsequences;motivationandgoals;andemotions. Opportunity:Opportunityreferstothephysicalandsocialenvironmentthatenables behaviorandincludesenvironmentalcontextandresourcesandsocialinfluences. Mostofthedeterminantsmentionedabovecannotbeexpectedtochangeoverthe courseofabrief,lowkcostintervention.hence,wewillfocusonlyonthecapabilitycomponent, whichincludesfourdeterminantsofbehavioralchange:knowledge;memory,attention,and decisionprocesses;behavioralregulations;andskills. Memory,attention,anddecisionprocesses:Inthepsychologyliterature,memory, attention,anddecisionprocessesrefertotheabilitytoobtainandretaininformation,focus 14 Atheoreticaldomainisagroupofrelatedtheoreticalconstructs. 15 AnillustrationofhowthesecomponentsinteractisprovidedinAppendixC,figureCK1. 42

selectivelyontheenvironment,andchooseamongalternatives(lipworth,taylor,& Braithwaite,2013).Manytypesofbehaviorcanbewellunderstoodintermsofpeoplemaking decisionsusingalltheavailableinformation.however,researchhasalsoshownthatmoreoften thannotindividualssimplifycomplexdecisionsbyprocessingonlyasubsetofinformation (DellaVigna,2007).Forsimplicity,fromnowonwewillrefertothisdeterminantofadoptionas limitedattention. Knowledge:Knowledgeisimportantbecauseafarmer sawarenessofthescientific rationaleandproceduresassociatedwithicmislikelytoaffectwhethershedecidesto implementit(lipworthetal.,2013). Thesetwodeterminantscanbemanipulatedwithinthecontextofashortagricultural interventionasshowninfigure3k1. Figure3K1:Behavioralchangetechniques Behavioralchangetechniques Planning, implementation INTERVENTION Behavioralchangetechnique Prompts,triggers,cues Limitedattention Knowledge Provisionofinformation SelfKmonitoring Determinants ADOPTIONof BLACKBERRYICM Source:DrawingelaboratedbytheauthorbasedonKageyamaetal.(1998) 43

Psychologistsagreethatthemosteffectivetechniquetoaddresstheknowledge determinantistheprovisionofinformationregardingthebehaviorinquestionandpossible outcomesfromchangesinsuchbehavior.totargetlimitedattention,prompts,triggers,and cuescanbeusedtoincreaseattentiveness.infact,anumberofstudieshighlightthe effectivenessofthistechnique.infinancialdecisionmaking,simplecueshavebeenshownto increasesavings(ashraf,karlan,&yin,2006;karlan,mcconnell,mullainathan,&zinman, 2010).Inhealthoutcomes,textmessagereminderscanimprovepatients adherencetochronic medication(vervloetetal.,2012)andincreasetherateofinfluenzavaccination(stockwellet al.,2012). 3.2.1.Behavioralchangetowardsadoptionofagriculturaltechnology Adoptionofafarmingpracticecanbethoughtasknowledgelearnedandusedasintended (Olsen,1998).Becausetechnologiesareimperfectlyknown,farmersseekinformationabout thembeforedecidingwhethertoadopt.farmersneedknowledgeabouthowthetechnology works,howitwillfunctioninlocalconditions,itsreturnsandrisks,andhowitcompareswith otheroptions(conley&udry,2010;feder&slade,1984).whenlearningtakesplaceand expectedreturnsarefoundtobegreaterthanthoseofexistingtechnologies,thenew informationmayaffectbehaviourandresultinoutcomessuchasincreasedprofits(foster& Rosenzweig,2010). Inanidealworld,newagriculturalpractices,oncelearned,becomeroutine.However, adoptionisaconsciousdecision.forinstance,atonepointintime,afarmermayhave 44

intendedtoadoptsomeoralloftheavailableicmpractices.however,atanotherpointintime, thoseintentionsmaynolongerbecontemplated,buriedbydailydistractions.prompts, triggers,andcuesintheformofreminderscanhelpfarmerstofocusontheirenvironment,and ignorecompetingstimuli.inotherwords,remindersdirectingattentiontoaparticularaction shouldmakeitsexecutionmorelikely(taubinsky,2014). Ourinterventionaimstochangebehaviorbyprovidinginformationandremindersto randomlyselectedfarmers.thepresumptionisthattheywillanalyzetheinformationand remindersofferedtothemthroughtextmessagesandactinwaysthatreflecttheirbest interests. 3.2.2.InformationandCommunicationTechnologies(ICTs)asameansofinducingbehavioral change WidespreadavailabilityofICTshasresultedintheirincreasedusethroughouttheword.For instance,mobilephonepenetrationindevelopingcountriesnowexceeds70%anditis projectedtocontinuetogrow(gsma,2014).mobilephoneavailabilityinruralareashas concurrentlyincreased,openingupopportunitiesfordeliveringinformationandremindersto farmers. SeveralstudieshaveevaluatedthebehavioraleffectsofICTsKbasedinterventionsand theresultsarepromising(karlanetal.,2010;stockwelletal.,2012;travis,2015;vervloetetal., 2012).Theimpactsofmobilephonesonagriculturehavebeenevaluatedfromdifferent perspectives.mobilephonetechnologycanbeevaluatedbyitscoverage,asameanof 45

informationprovider,andasameanofremindersprovider.intermsofcellkphonecoverage, AkerandFafchamps(2010)usedmicroKleveldatatoestimatetheimpactofmobilephone coverageinniger,westafrica,onproducerpricedispersion.theyfoundthatintroducing mobilephonecoveragereducesproducerpricedispersionforcowpeasby6percent.however, theseeffectsdidnotleadtohighercowpeapricesreceivedbyfarmers.theyalsofound evidenceofreducedintrakannualpricevariability.itishypothesizedthattheseeffectswere drivenbyimprovedfarmersandtraders accesstoinformation. ToevaluatecellKphonesasameansofinformationprovision,Nakasone(2014) conductedarandomizedcontroltrial(rct)inperu.farmersinrandomlyselectedvillages weregivendetailedmarketpriceinformationcorrespondingtotheirmaincrops.information wasdeliveredthroughcellphoneshortmessageservice(sms).farmerswhoreceivedthe messagesobtainedhighersalespricesfortheirproductscomparedtothoseinthecontrol group.thiseffectwaslargerforperishablecrops.themechanismbehindtheseeffectsis informationavailability.coleandfernando(2012)conductedanimpactevaluationoftheavaaj Otalo(AO)programamongcottonfarmersinGujarat,India.Thisprogramusedvoicemessages topushandpullcontent.farmersreceivedweeklydetailedagriculturalinformationandwere abletocallintoatollkfreehotlineandaskquestionsonagriculturaltopicsofinteresttothem. Thesequestionswereansweredbyagronomistswhodeliveredadvicetofarmersviarecorded voicemessages.theyfoundthathouseholdsthatbenefitedfromtheaoprogramincreased adoptionofthepesticideimidaclopridby10%. InanattempttoevaluatecellKphonesasameanofinformationandreminderproviders, FafchampsandMinten(2012)implementedanRCTinMaharashtra,Indiatoassesstheimpact 46

ofpriceandweatherinformationandcropadvicedistributedthroughsmsmessages.they foundapositivebutnonsignificantimpactonpricesreceivedbyfarmers,croplosses,and changesincropvarieties.intermsofcultivationpractices,theyfoundnoevidencethattreated farmersweremorelikelytoadoptthepracticesrecommendedthroughthesms.travis(2015) conductedarctamongpotatofarmersincarchi,ecuador.shearguedthatuncertaintyabout thebenefitsofipmandcompetingfarmingactivitieslimitipmadoption.sheattemptedto overcomethesetwolimitationstoipmadoptionbysendingpotatofarmerstextmessages.she foundthattreatedfarmersadoptedipmpracticesathigherratesthanthecontrol.treated farmersalsoexhibitedmoreknowledgeaboutipmthanthecontrolfarmers. WhilethisliteratureprovidespositiveevidenceoftheefficacyofcellKphonesandtext messagesonagriculture,thereisnobasisforunderstandingwhichproceduresareeffectivein whichcontextsbecauseitisunclearhowtheseprogramshadtheireffect(thebehaviorchange processesareunknown).inthissense,thispapercontributestotheexistingliteratureby implementingandevaluatinganinterventionthataimstoincreaseadoptionofagricultural technologies,backedbybehavioralchangetheory. 3.3.StudysiteandICMprogram Theincreaseindemandforblackberry(Rubusglaucus)intheUnitedStatesandEuropehasled toanexpansionofareadedicatedtothecropinseveralcountries(badenes&byrne,2012).in Ecuador,blackberryfarmingoccupied4,450hectaresin2005(Strik,Clark,Finn,&Bañados, 2007);blackberryhasbeentraditionallycultivatedinBolivar,CotopaxiandTungurahua Provinces,anditisnowbecomingapromisingalternativeforcropandexportdiversification. 47

However,blackberryyieldsinmajorproducingareasarelow 16,mainlyduetohighincidenceof insectsanddiseases 17,moisturestress,andpoorcropmanagement(DelgadoOrellana,2012). Farmersintheareadonotfollowanyparticularproductionprotocol.Manyfarmersdonotuse pesticidesorimplementothermethodsthatcouldsavetheirblackberrycropfrom destructivepests.farmersarenotfamiliarorinadequatelyfamiliarwithblackberryicm. Inanefforttoimproveblackberryproductionandprofitabilitythroughimproved agriculturalpractices,scientistsofthenationalautonomousinstituteofagriculturaland LivestockResearch(INIAP)andtheUSAIDKsponsoredIPMInnovationLaboratorydevelopedand testedseveralnonkipmandipmpracticestohelpblackberryfarmersmaximizeyieldsand managecommoninsectpestsanddiseases. NonKIPMpractices,asthenamesuggests,donothaveanyparticularfocusonpest management.theyaimtokeepplantshealthyandmaximizeyieldsandfruitquality.examples ofnonkipmpracticesarepruning,buildingofasimplestringtrelliswithonetwine,and harvestingattherightstageofmaturity.ontheotherhand,ipmbringstogethercultural 18 and chemicalpractices.examplesofipmpracticesrecommendedasapartofthetrainingprogram areuseofbordeauxmixture(afungicideandinsecticidemixtureofslakedlimekcalcium hydroxidekandcoppersulfate),useoffertilizersandlowktoxicitypesticides,disposalofpruned plantmaterialfromthefieldanddisinfectionoftoolsbetweenuses.thesepracticeswere 16 Intheseareas,yieldsare3kg./plant/year,farbelowthe5kg./plant/yearpotentialyield(EspínChico,2013). 17 Amongthemostdamaginginsectpestsaremites,aphids,spidermitesandkudzus.Themostcommondiseases arebotrytisfruitrot(botrytiscinerea),powderymildew(oidiumsp),downymildew(peronosporasparsa),wilt (Verticiliumsp.)andblackberryrosette(Cercosporellarubi)(Yumbo&Elvia,2014). 18 IPMculturalcontrolsarepracticesthatreducepestestablishment,reproduction,dispersal,andsurvival. 48

developed,validatedandcalibratedforlocalconditionsduringthreeyearsofappliedresearch. TheywerecombinedintoapackageandmadeavailabletofarmersinMarch2013 19. InordertoevaluatetheeffectivenessofsendingtextmessagesasblackberryKrelated providersofinformationandtaskreminders,arctwasconductedintungurahuaandbolivar provinces.rcts,whenfeasible,arethepreferredmethodforidentifyingcausaleffectsof interventionsonadoptionofagriculturaltechnologies.thekeymethodologicalcomponentsof RCTsare(1)useofacontrolconditiontowhichoutcomesfromthoseexposedtothe experimentalinterventionarecompared,and(2)randomassignmentofparticipantsinto treatmentandcontrol.incontrasttoquasikexperimentalmethodsthatrelyonasetof identifyingassumptions(notdirectlytestable)abouttheindependenceofthetreatment,rcts minimizethenumberofcomplexandquestionableassumptions.sincerandomassignment equalizestreatmentandcontrolgroupsonalltheobservableandunobservablevariables,rcts giveconfidencethatdifferencesinoutcomesbetweentreatmentandcontrolareactually causedbythetreatment(armijokolivo,warren,&magee,2009;duflo,glennerster,&kremer, 2007).WedistinguishbetweentheeffectsoftheinterventiononadoptionofnonKIPMpractices andonuseofipmpractices.thisdistinctionbetweenthetwosetsofpracticesisimportant because,whenitcomestoagriculturaltechnologiesthenatureoftheinnovationcanhavea largeimpactonitsadoption. 19 Theaveragenetreturnsfortheconventionalblackberryproductionsystemisabout$2550perhainthefirst year,$8,492inthesecondyear,and$11,242inthethirdyear,whileexpectedreturnsundertheicmregimeare $3760,$11,734,and$15,734,respectively.Blackberryplantscontinuefullyproductivemorethan15yearsunder goodmanagement(iniap,2015). 49

3.4.TheIntervention TheinterventionconsistedoftrainingfarmersinblackberryICMduringFieldDayevents followedupwithtextmessages.threefarmerfielddayswereheldduringfebruary2014,two intungurahua,andoneinbolivar.blackberryfarmerswereinvitedtoattendthrougharadio advertisingcampaignandcontactswithextensionagentsfromtheministryofagriculture, Livestock,AquacultureandFisheries(MAGAP);whileattendanceatthefielddayisnotrandom, participationinthesubsequenttextkmessageinterventionwasrandomlyassigned.thefield daysconsistedofthreestationswheretechniciansdemonstratedspecificblackberry managementpractices:(1)preparationofbordeauxmixture,(2)useofproperpruning techniques,and(3)buildingasimplestringtrelliswithjustonetwine.farmerswereorganized intosmallgroupstotourthedemonstrations,andeachparticipantreceivedacompanion brochurewithdetailedinformationonacompletesetofblackberryicmpractices. Theobjectiveofpreparingbordeauxmixtureduringthefielddaywastoprovide instructioninmethodsforpreparingdifferentchemicalscompounds.thisknowledgewouldbe usefulwhenfarmersneedtopreparecompounds,whicharepartoftheicmregime. Instructionsweredesignedtobeeasytofollowevenforthosewithlittlepriorknowledgeof preparationanduseofchemicalcompounds.thepruningstationprovidedtrainingonthe properuseofpruningtechniques.blackberryplantsneedtobeprunedatvariousstagesofcrop development.threeofthefiverecommendednonkipmpracticesrefertopruning.trellising plantsisnecessarybecausetrailingvarietiesarecommonlyplantedinecuador.agricultural scientistsrecommendusingasimplestringtrelliswithjustonetwine.thissystemisan effectiveandinexpensivewaytosupportblackberryplantsandmakethemeasytopick. 50

Aggregateattendanceatthefielddayswas422farmersfrom77villages.Aspartofa shortbaselinesurveyatthefieldday,participantswereaskedaboutmobilephoneownership, capabilitytoreceivetextmessages,sociokeconomiccharacteristics(gender,age,andpositionin thefamily)andblackberrycropdevelopmentstage 20. Tobeeligiblefortheintervention(receiptofthetextmessages),afielddayparticipant had to be literate, own a mobile phone capable of receiving text messages, and be currently growing blackberry. Two hundred ninety two farmers from 68 villages were eligible to participateinthefreetextmessageprogram.thetreatmentwasrandomizedatthevillagelevel toavoidpotentialproblemsofwithinkvillagespillovers(dufloetal.,2007).of68villageswith eligibleparticipants,32wereassignedtothetreatmentgroup.thetreatedandcontrolgroup consistedof154and138farmers,respectively. TheblackberryfruitgrowingseasoninBolivarandTungurahuabeginsinMarchwhen themoistureissufficienttoguaranteeagoodharvest,whichbeginsinmayorjune.following thefebruaryfielddays,eligiblefarmersfromtreatedvillagesreceivedasetoftextmessages informingthemabouticmpractices.messagesprovidedicminformationandreminded recipientsoftaskstooptimizeblackberryprofits.anexampleofatextmessageisprovidedin Figure3K2.Thefirstpartofthemessageprovidesthereminder: remembertodisposeof prunedplantmaterial andthesecondpartprovidesinformation: topreventspreadof diseases.thesemessagesweresequencedaccordingtotheblackberryproductioncalendar. Themessagesalsoreferredtothepageofthebrochurewheremoredetailedinformationcould befoundaboutthetaskmentionedinthemessage.threemessagesweresentperweekfor 20 ThisquestionallowedustodistinguishbetweenfarmerswithfullyKgrownblackberrycropsfromthosewhojust plantedorhadanintentiontoplant. 51

elevenweeks.messageswereconciseandeasytounderstand.noonewhowasofferedthe freetextmessageserviceduringthefielddaydeclinedtoparticipate. Figure3K2:TextmessagewithblackberryICMinformation Afollowupsurveywasadministeredtotreatmentandcontrolfarmersattheirfarms followingtheintervention(juneandjuly2014).itcontained4modulesandincludedsociok economicinformation,blackberryproductiondatasuchascultivatedareaandyields,andicm adoption.wealsoincludedanicmknowledgetest.duetoincompleteorincorrectaddresses, enumeratorswereabletolocate229farmersin62villages,ofwhich125wereintendedtobe treated. 3.5.Methods TheICMinformationdistributedthroughSMSfocusedonasetofagriculturalpracticestailored toblackberrykfarmingintheinterventionareas.practiceswerecategorizedintononkipmand 52

53 IPMpractices.Overthecourseoftheintervention,farmersreceivedadviceon5nonKIPM practicesand15ipmpractices(table3k1)thatrequiredtheuseof20differentchemicaland organicfertilizersandlowktoxicitypesticides(table3k2). Table3K1:nonKIPMrecommendedpractices Practices Helpfulfor Pruningduringtheproductionseason Keepingplantshealthyandincreasingthefruityield Pruningduringthefruitingseason Keepingplantshealthyandincreasingthefruityield Cutoffunproductivebranches Keepingplantshealthyandincreasingthefruityield Buildingofasimplestringtrelliswithonetwine Supportingagrowingblackberryplant Harvestingattherightstageofmaturity Ensuringthebesttasteandquality Table3 2:IPMrecommendedpractices CulturalPractices Helpfulfor Disposaloftheprunedplantmaterialfromthefield Preventingspreadofdiseasesthatreduceyields Makingandapplicationoforganichomemadefertilizer Keepinglowcosts(Homemadefertilizerare morecostefficientthancommercialproducts) Disinfecttoolsbetweenuses Preventingthespreadofplantdiseases Eliminationofweedplants Reducinginsectsproblems PhytosanitaryControls Problem Preparationofbordeauxmixture(1kgslakedlime+1kgcoppersulfatein200 litersofwater) Fungiandmites 1/2kgofcopper+1/2kgofsulfurin200ltofwater Powderymildew Spray1.5mlofpotassicphosphiteperliterofwater Downymildew Spray1mlofazoxystrobin(brandname:amistar)perliterofwater Downymildew Spray1mofpenconazole(brandname:topas)perliterofwater Powderymildew Spray1mlofdifenoconazole(brandname:score)+1mlofdicofol(brandname: acarin)perliterofwater Downymildew,powdery mildewandmites Foliarfertilizations Spray1mlofboronchelatesperliterofwater Sprayironchelates+zincchelates1ccperliterofwater Spray1mlofcalciumchelatesperliterofwater Soilfertilizations 100gof18K46K00fertilizer+100gofurea+2kgoforganichomemadefertilizerperplant 100gofnitrogen+150gofpotassium+2kgoforganichomemadefertilizerperplant

Ideally,theimpactanalysisshouldfocusondifferencesinprofits,becausethey correspondbesttotheoveralleconomicimpactoftheinterventions.however,wedidnot gathercostestimationinformationandcannotperformaccuratecalculationsofthisoutcome. TheanalysisinsteadfocusesontheimpactoftheinterventionsonadoptionofnonKIPMand IPMpractices. Adoptionofagriculturaltechnologiescanbedescribedasdiscrete(afarmereitheris,or isnot,anadopter)orcontinuous.weconsideronlythelattermeasuresofadoptionbecause ICMcanbecompletelyorpartiallyadopted.Hence,adoptionofnonKIPMpracticesreferstothe numberofpracticesadoptedoutoffivetotalnonkipmrecommendedpractices.tomeasure adoptionofipmpractices,weaggregateadoptionofculturalipmpracticesandtherawcount ofrecommendedpesticidesandfertilizersused. Becausethetreatmentisrandomized,theimpactoftheinterventioncanbeestimated byasimplecomparisonofmeanadoptionratesfortreatmentandcontrolgroups.this straightforwardcomparisonisonlypossiblewhenbalancebetweeninterventionandcontrol groupisachieved.balancemeanstherearenosignificantdifferencesinmeansofcovariatesby treatmentgroupandindicatesthattherandomizationprocessworked(dufloetal.,2007).in additiontothecomparisonofmeans,impactisalsoestimatedinaregressionframework.the useofregressionreducesidiosyncraticvariationandincreasesprecision(glennerster,2013). ForthecontinuousmeasureofadoptionofnonKIPMpracticesaPoissonMLEanalysisisthe naturalfirststep.poissonusestheexponentialmeanfunctionensuringthatthepredicted valuesoftheoutcomevariablearenevernegative(wooldridge,2006).formally,weestimate: 54

M NO./0 1 = exp(α U$ + α V T 1 +$α X T V 1 + X 1H Y + Z 1 )$ (1) M./0 1 = exp(α U$ + α V T 1 +$α X T V 1 + X 1H Y + \ 1 )$ (2) wherem NO./0 1 andm are the variables reflecting adoption of nonkipm and IPM practices,./01 respectively.thekeyindependentvariable] 1 isadummyindicatingtheintentiontotreat.since asignificantpercentageoftreatedfarmersdidnotreceivethetextmessagesduetotechnical problemsrelatedtodatasystematizationandverificationerrors(wediscussthisissuemorein depthinthenextsection),weincludeadummyvariable] V 1 toaccountforthefactthatfarmers in this group are less likely to adopt ICM. X is a vector of observed socioeconomic characteristicsincluding:age,education,andgenderofthefarmer,householdsize,wealth 21, blackberrykfarming experience, and area per capita planted with blackberry.e C andv C are random error terms clustered by village, and assumed to be independent across clusters but correlatedwithinclusters 22. Age,education,experienceandgenderarecharacteristicsoftenincludedas determinantsofadoptionofipmtechnologies.whentestedempiricallythroughouttheipm literature,theimpactofageonadoptionhasmixedresults.ononehand,olderfarmersover timehavegainedfarmingknowledgeandexperienceandarebetterabletoevaluate technologyinformationthanyoungerfarmers(mugisha,ogwalko,ekere,&ekiyar,2005).on theotherhand,theoriesthatmodeltheadoptiondecisionusingriskcontendthatageis 21 AwealthindexwascreatedusingPrincipalComponentAnalysisKPCAbasedonownershipofselectedassets(TV set,refrigerator,microwave,washer,computer,landline,cellphone,homedrinkingwater,toiletroominsidethe house,motorcycle,car,andcattle). 22 Acluster,inthisstudy,isdefinedasagroupofinterdependentfarmersthatoperateinageographically concentratedarea. 55

negativelycorrelatedwithadoptionasrisktolerancedecreaseswhenafarmergrowsolder (FernandezKCornejo,1998;Maucerietal.,2007).Toallowforanonlinearrelationshipbetween adoptionandage,weincludeinourmodelbothageandagesquared.educationisexpectedto bepositivelyrelatedtoadoptionbecausemoreeducatedfarmersunderstandandrespond bettertonewtechnologies(fernandezkcornejo,1998).theeffectofblackberryfarming experience 23 isanticipatedtobepositivebecausemoreexperiencedfarmersaremorelikelyto bebetterabletoassessnewtechnology(blake,sandler,coli,pober,&coggins,2007;khanna, Epouhe,&Hornbaker,1999). Thevariablegenderreferstothesexoftheparticipantintheexperiment.Thisperson wasidentifiedatthefielddayasthepersonwhoplaystheleadingroleinblackberry production.thereareahighpercentageofwomenblackberryfarmersinecuador.itwas observedduringthefieldworkthatfemalefarmersshowedspecialinterestinblackberryicm. Forthisreason,weexpectthegendervariable(1=femaleand0=male)topositivelyaffect adoptionoficm. Intermsoffarmresources,areapercapitaplantedwithblackberry(hectares), householdsize,andwealthareexpectedtohavepositiveeffectsonadoption.farmers operatingrelativelargeblackberryfarmscanaffordtodevotepartoftheirlandtotrynew technologies(cuyno,norton,&rola,2001;feder,just,&zilberman,1985).weexpecta positiveeffectofhouseholdsizebecauselargefamilieshavemorelaboravailabletoperform onkfarmactivities,enablingfarmerstoadoptsomeofthepracticesthatmayrequireextralabor (BonabanaKWabbi,2002).Thewealthindexisanticipatedtohaveapositiveeffectonadoption, 23 Inmostcontexts,ageandfarmingexperiencearehighlycorrelated.However,inourstudythisisnotthecase sinceblackberryisarelativelynewcropinthearea. 56

especiallyiftheinnovationrequirescashinputpurchases(chaves&riley,2001;teklewold, Kassie,&Shiferaw,2013). Additionally, we evaluate the impact of the intervention on knowledge. To measure knowledge,eachfarmerwasgivenamaximumscoreof5pointsbasedoncorrectresponsesto a5questionkknowledgetest.thequestionsreferredtoinformationpresentedatthefieldday andreinforcedthroughthetextmessages.thesequestionsarerelateddirectlytotheadoption ofnonkipmandculturalipmpractices.then,apoissonregressionisusedforestimation: M _ 1 = exp(γ U$ + a V T 1 +$γ X T V 1 + W 1a Y + Z 1 )$ (3) wheretheknowledgescorevariablem _ 1 isafunctionofthetreatment(t 1andT V 1 ),andavector of control variables$w$that includes age, education and experience. These factors have been widely recognized as relevant for knowledge acquisition (Feder & Slade, 1984; Luh, Jiang, & Chien,2014;Wozniak,1993).ThetermZ 1 isarandomerrorterm,assumedtobeindependent acrossclustersbutcorrelatedwithinclusters. 3.6.Results Westartouranalysisusingtheintentiontotreat(ITT)principle.InITTanalysis,unitsrandomly assignedtoreceivetreatmentarecomparedtothoseassignedtothecontrolregardlessof whethertheycompletedtheintervention(armijokolivoetal.,2009).ittanalysisiswidelyused toanalyzeexperimentsbecauseitinvolvesthepurestexperimentalcomparison.however, 57

whenunitsassignedtothetreatmentgroupdonotreceivethetreatment,ittanalysiscangive aconservativeestimateoftheeffectoftreatment(dunning&hyde,2008). AnumberofintendedKtoKtreatfarmers(29)reportednotreceivingthetextmessages. WewillrefertotheseunitsastreatmentnonKrecipients(inthestandardlanguageofRCTs,this iscallednonkcompliance).wedistinguishtwotypesoffarmerswhodidnotreceivethetext messages:(1)nonkrecipientsduetoindividualbehavior,and(2)nonkrecipientsduetotechnical problemsrelatedtodatasystematizationandverificationerrors.thefirstgroupincludes8 farmerswhosephoneswerelostordamagedorwhochangedphonenumbers.thesefarmers representatypeofnonkcompliancethatmostlikelyoccursineveryinterventionofthiskind andisdifficulttoovercome.thesecondgroupincludes21farmerswhosephonenumberswere mistyped,thosewhogaveaphonenumberbelongingtoarelativeorfriend,andfarmerswho didnotknowhowtoopenthetextmessages.thisisnotnonkcomplianceinthetraditional senseandisabarriertotreatmenteasytoovercome 24. Inordertoevaluatetheimpactoftheinterventionasitwasdesigned,wealsoestimate anadjustedversionofittwherethe21nonkrecipientsduetotechnicalproblemsrelatedto datasystematizationandverificationerrorsareexcludedfromthecalculation.theadjustedkitt analysisremovestheunintendedeffectofacontrollablenonkcompliance. Itmaybearguedthatthisestimationmightdeliverbiasedestimatesbecauserecipients vsnonkrecipientsarenolongerexpectedtobebalancedonobservedandunobserved characteristics.toruleoutthispossibility,weconductacheckofsummarystatisticsofthe differentgroups(nonkrecipientsduetoindividualbehavior,nonkrecipientsassociatedwith 24 ThiskindofnonKcompliancemaybeeasilyovercome.Onepossibilitywouldbetosendatextmessageatthe momentofenrollmenttoverifythatinformationwasrecordedcorrectlyandthatthefarmerknowshowto operatethemobilephone,butourstudydidnotdothis. 58

technicalproblemsduetodatasystematizationandverificationerrors,andcontrolgroup).we findthatnonkcomplianceislikelytoberandombecausetherearenostatisticallysignificant differencesbetweenthegroups.hence,ourresultsarenotbiasedbytheomissionofthese observations. 3.6.1.Meancomparisonanalysis 3.6.1.1.Householdandfarmcharacteristics ThesocioKeconomiccharacteristicsofthefarmersinthetreatmentandcontrolgroupsare presentedintable3k3.forcolumns(1),(2),(3),(6)and(7),themeansandstandarddeviations ofeachvariablearereported.incolumns(4)and(8)wepresentthemeandifferencesbetween controlandtreatmentgroup.clustered(byvillage)standarderrorsarereportedin parentheses 25. StatisticsobtainedusingITTanalysisarepresentedfirst(columns2K5).Sincetreatment assignmentwasrandom,weexpecttofindstatisticalbalanceacrossthecontrolandtreatment groups.thisbalancewaslargelyachieved.theonlysignificantdifferencebetweenthegroupsis thatthecontrolincluded16percentagepointfewerwomen,adifferencethatissignificantat the10%level.thispretreatmentdifferenceisrelativelyminorandisnotlikelytobeindicative ofsystematicbias.thelastfourcolumnsshowthestatisticsobtainedusingtheadjustedkitt analysis.thedifferencebetweenthepercentagesofwomeninthetwogroupsisstillpositive 25 Whendataaregroupedintoclusters,withregressionmodelerrorsindependentacrossclustersbutcorrelated withinclusters,regressionanalysisconductedusingdefaultstandarderrorscangreatlyoverstateestimator precision.thus,ifthenumberofclustersislarge(morethan30),statisticalinferenceshouldbebasedonclusterk robuststandarderrors(cameron&miller,2015).thenumberofclustersis62inthisstudy. 59

andlarge.theaveragefarmerisabout44yearsoldandhasan8thgradeeducation.shehas10 yearsofblackberryfarmingexperience.herblackberryplothas0.67hectares. 60

Table&3&(&3:&Household&and&Farm&Characteristics&of&Sampled&Blackberry&Farmers&in&Ecuador,&2014& Variable& & &ITT& Adjusted(ITT& Overall& (1)& Treatment& (2)& Control& (3)& Difference& (4)& Difference& p(value& (5)& Treatment& (6)& Control& (7)& Difference& (8)& Differenc e&p(value& (9)& N& 229& 125& 104& 229& & 104 a & 104& 208& & Age&(years)& 43.58& 44.45& 42.53& 1.92& 44.07& 42.53& 1.54& 0.35& (13.13)& (13.19)& (13.05)& (2.05)& (13.19)& (13.05)& (2.02)& 0.45& Education&(years)& 7.52& 7.55& 7.47& 0.08& 7.83& 7.47& 0.35& 0.91& (3.79)& (3.62)& (3.99)& (0.71)& (3.66)& (3.99)& (0.73)& 0.63& Secondary&education&&(1=secondary&education)& 0.35& 0.33& 0.38& (0.05& 0.36& 0.38& (0.03& 0.47& (0.48)& (0.47)& (0.49)& (0.08)& (0.48)& (0.49)& (0.08)& 0.72& Gender&(1=female)& 0.38& 0.45& 0.29& 0.16& 0.43& 0.29& 0.14& 0.05& (0.49)& (0.50)& (0.46)& (0.08)& (0.50)& (0.46)& (0.08)& 0.09& Household&size&(count)& 4.49& 4.38& 4.62& (0.23& 4.49& 4.62& (0.13& 0.35& (1.83)& (1.90)& (1.75)& (0.25)& (1.88)& (1.75)& (0.23)& 0.59& Wealth&index&& 0.00& 0.05& (0.062& 0.11& 0.06& (0.06& 0.12& 0.68& (1.65)& (1.74)& (1.55)& (0.27)& (1.79)& (1.55)& (0.30)& 0.67& Farming&experience&(years)& 25.81& 26.42& 25.08& 1.34& 25.80& 25.08& 0.72& 0.57& (14.09)& (14.54)& (13.57)& (2.35)& (14.23)& (13.57)& (2.19)& 0.74& Blackberry&farming&experience&(years)&& 10.46& 10.76& 10.09& 0.68& 10.25& 10.09& 0.16& 0.60& (8.84)& (8.90)& (8.79)& (1.27)& (8.41)& (8.79)& (1.37)& 0.90& Area&per&capita&planted&with&blackberry&(hectares)& 0.15& 0.13& 0.18& (0.05& 0.13& 0.18& (0.05& 0.11& (0.16)& (0.14)& (0.18)& (0.03)& (0.14)& (0.18)& (0.03)& 0.10& Source:&regression&results&from&study& a& Adjusted(ITT&excludes&21&non(recipients&due&to&technical&problems&related&to&data&systematization&and&verification.& In& columns& (4)& and& (8)& the& differences& were& calculated& using& the& following& regression:&y " = α%treatment + - ".& Clustered& standard& errors& are& reported& in& parentheses.& 61

3.6.1.2.&&ICM&adoption&and&knowledge& ICM$adoption$and$ICM$knowledge$scores$of$the$farmers$in$the$treatment$and$control$groups$ are$presented$in$table$3;4.$itt$estimates$are$not$significant$at$conventional$levels.$under$the$ adjusted;itt$analysis,$we$find$that$farmers$who$received$the$text$messages$have$higher$ adoption$of$ipm$practices$by$0.58$practice,$a$difference$that$is$significant$at$the$10%$level.$ipm$ includes$cultural$practices,$adoption$of$fertilizers$and$adoption$of$pesticides.$the$raw$count$of$ recommended$pesticides$used$by$farmers$in$the$treatment$group$is$on$average$0.76$compared$ to$0.47$for$farmers$in$the$control$group.$additionally,$receiving$the$text$message$reminders$ increases$the$ratio$of$recommended$used$to$total$used$pesticide$products$by$55%.$this$result$ provides$evidence$that$the$intervention$has$been$effective$in$increasing$use$of$recommended$ pesticide$products,$which$are$less$harmful$alternatives$to$toxic$chemicals$currently$used.$the$ effect$of$the$intervention$on$non;$ipm$practices$is$not$significant.$$ As$discussed$in$our$theory$of$behavioral$change,$this$intervention$targeted$two$ determinants$of$behavior:$knowledge$and$limited$attention.$it$aimed$to$increase$adoption$of$ ICM$practices$among$blackberry$farmers$by$the$provision$of$information,$alongside$reminders.$$ Thus,$the$increased$adoption$rate$of$cultural$ICM$practices$(Table$3;4)$is$expected$to$be$due$to$ the$knowledge$building,$and$reminder$effects.$to$further$validate$these$results,$we$created$a$ knowledge$score,$which$measures$the$number$of$correctly$answered$knowledge$questions.$if$ text$messages$have$a$positive$impact$on$the$knowledge$score,$this$implies$a$knowledge$building$ effect.$if$text$messages$have$no$impact$on$the$knowledge$score,$we$can$infer$that$messages$ worked$only$by$reminding$farmers$to$engage$in$icm$practices.$ 62

Table&3&(&4:&ICM&adoption&and&ICM&knowledge& &Intention&to&Treat&(ITT),&adjusted(ITT a & Variable& Treatment& (1)& Control& (2)& &ITT& Difference& (3)& Difference& p(value& (4)& Treatment& (5)& Control& (6)& Adjusted(ITT& Difference& (7)& Difference& p(value& (8)& Sample&size:&229& 125& 104& 229& & 104 a & 104& 200& & Adoption&of&non(IPM&practices&(number)&& 3.29& 3.11& 0.18& 3.33& 3.11& 0.22& 0.23& (0.97)& (1.11)& (0.15)& (0.97)& (1.11)& (0.15)& 0.15& Adoption&of&IPM&practices&(number)& 4.33& 3.93& 0.40& 4.51& 3.93& 0.58& 0.23& (1.81)& (1.79)& (0.32)& (1.85)& (1.79)& (0.33)& 0.09& &&&&&Adoption&of&IPM&cultural&practices& 2.53& (1.15)& 2.29& (0.98)& &&&&&Adoption&of&fertilizers&(raw&count&of&&& 1.14& 1.17& (0.03& 1.17& 1.17& 0.00& 0.80& &&&&&recommended&fertilizers&used)& (0.83)& (0.89)& (0.11)& (0.86)& (0.89)& (0.13)& 1.00& &&&&&Adoption&of&pesticides&(raw&count&of& 0.66& 0.47& 0.18& 0.76& 0.47& 0.29& 0.11& recommended&pesticides&used)& (0.71)& (0.61)& (0.11)& (0.72)& (0.61)& (0.12)& 0.02& Ratio&of&recommended&used&to&total&used&fertilizers& 0.55& 0.50& 0.05& 0.56& 0.50& 0.06& 0.26& (0.37)& (0.34)& (0.04)& (0.37)& (0.34)& (0.05)& 0.21& Ratio&of&recommended&used&to&total&used& 0.30& 0.22& 0.08& 0.34& 0.22& 0.12& 0.17& pesticides& (0.34)& (0.31)& (0.06)& (0.34)& (0.31)& (0.06)& 0.06& Knowledge&(number&of&correct&answers&out&of&five)& 2.98& 2.83& 0.15& 3.02& 2.83& 0.19& 0.51& (1.25)& (1.19)& (0.22)& (1.27)& (1.19)& (0.23)& 0.40& Source:&regression&results&from&study& a &Adjusted(ITT&excludes&21&non(recipients&due&to&technical&problems&related&to&data&systematization&and&verification.&& For&columns&(1),&(2),&(5)&and&(6),&the&means&and&standard&deviations&of&each&variable&in&the&treatment&and&control&groups&are&reported.&In&columns&(3)&and&(7)&the& differences&were&calculated&using&the&following&regression:&y " = α%treatment + - ".&Clustered&standard&errors&are&reported&in&parentheses.& 0.24& (0.22)& 0.27& 2.58& (1.17)& 2.29& (0.98)& 0.29& (0.23)& 0.22& 63

Mean%comparisons%show%no%difference%in%adoption%of%non2IPM%practices%and%knowledge% by%receipt%of%treatment.%to%further%understand%these%results%it%is%important%to%evaluate%the% adoption%of%individual%cultural%practices%(table%325)%and%mean%differences%in%knowledge% question%by%question%(table%326).%statistically%significant%differences%in%adoption%were%found%in%1% of%5%non2ipm%practices%and%2%of%4%ipm%cultural%practices:%(1)%cut%off%unproductive%branches,%(2)% disposal%of%pruned%plant%material%from%the%field,%and%(3)%disinfection%of%tools%between%uses.%the% intervention%increases%adoption%of%these%practices%by%13,%20,%and%13%percentage%points,% respectively.%these%differences%can%be%attributed%to%the%receipt%of%text%messages.%% For%knowledge%questions,%we%only%find%a%significant%difference%for%the%question%about% disposal%of%pruned%material,%which%is%related%to%the%ipm%cultural%practice%mentioned%above,% which%has%the%highest%adoption%rate.%this%result%indicates%that%at%least%for%the% disposal%of% pruned%plant%material%from%the%field %practice%text%messages%had%a%knowledge%building%effect.%it% is%worth%noting,%however,%that%there%is%not%a%one2to2one%correspondence%between%knowledge% questions%and%all%the%non2ipm%and%cultural%ipm%practices%(see%apendix%d).%hence,%we%cannot% verify%whether%the%intervention%had%a%knowledge%building%effect%for%each%of%the%nine%non2ipm% and%cultural%ipm%practices.% Why%are%text%messages%effective%for%some%ICM%practices%but%not%for%others?%Adoption%of% agricultural%innovations%is%a%dynamic%process%and%depends%on%a%range%of%factors.%time,% resources,%risk%aversion,%topography,%climate,%variation%in%soils,%technology%complexity,%etc.%are,% at%least%in%part,%responsible%for%limited%adoption%of%icm%technologies.%these%factors%affect%on2 farm%cost%and%benefits%of%adoption.%in%this%study,%some%practices%are%widely%adopted.%virtually% all%farmers,%whether%in%treatment%or%control,%prune%during%the%production%season%and%eliminate% 64

weed%plants%of%the%field.%this%can%be%indicative%of%the%great%success%of%this%practice%in% maintaining%blackberry%yields.%on%the%other%hand,%some%non2ipm%practices%may%be%perceived%by% farmers%as%relatively%complex%and%the%benefits%may%not%be%clearly%observable.%for%example,%the% benefits%of%building%a%simple%string%trellis%with%one%twine%do%not%show%up%immediately.%these% become%apparent%during%the%harvest%stage.%this%activity%may%appear%to%be%time%consuming%and% not%worth%the%time.%even%though%the%total%benefits%of%implementing%this%practice%outweigh%the% costs%by%a%large%margin,%if%those%gains%are%not%realized%by%the%farmer%who%bears%the%costs,%the% voluntary%adoption%of%such%practice%may%not%occur.%% One%common%feature%of%the%three%practices%mentioned%above%is%simplicity.%It%may%be%the% case%that%farmers%adopt%the%simpler%elements%of%the%technology%first%on%a%trial%basis%and%will%add% others%later%as%the%users%either%change%their%perceptions%of%risks%and%benefits%or%as%they%acquire% resources%needed%for%the%adoption%of%additional%components%of%the%package.%%% Overall,%statistically%a%significant%difference%in%the%adoption%of%only%1%of%4%cultural%IPM% practices%can%be%attributed%to%the%knowledge%building%effect%of%text%messages.%given%the%small% and%not%significant%effect%of%treatment%on%knowledge,%the%impact%of%the%intervention%should%be% attributed%mostly%to%the%reminder%effect%of%the%text%messages.%% % 65

Table&3&(&5:&Mean&comparison&in&adoption&of&non(IPM&and&IPM&cultural&individual&practices&by&treatment&group& ICM&Practices& Intended&to& Treat(group& (1)& Treated& group& (2)& Control& group& (3)& ITT& Difference& (4)& p(value& (5)& Non(IPM&practices& Adjusted(ITT& Difference& (6)& p(value& (7)& & Sample&size:&229& 125& 104 a & 104& & & & & & Pruning&during&the&production&season& 0.96& 0.96& 0.95& 0.01& 0.01& 0.78& (0.20)& (0.19)& (0.21)& (0.03)& (0.03)& 0.74& Pruning&during&the&fruiting&season& 0.56& 0.60& 0.55& 0.01& 0.05& 0.89& (0.50)& (0.49)& (0.50)& (0.09)& (0.08)& 0.56& Cut&off&unproductive&branches&& 0.85& 0.88& 0.72& 0.13& 0.15& 0.03& (0.36)& (0.33)& (0.45)& (0.06)& (0.05)& 0.01& Building&of&a&simple&string&trellis&with&one&twine& 0.51& 0.52& 0.48& 0.03& 0.04& 0.65& (0.50)& (0.50)& (0.50)& (0.07)& (0.07)& 0.57& Harvesting&at&the&right&stage&of&maturity&& 0.41& 0.38& 0.40& 0.01& (0.03& 0.96& (0.49)& (0.49)& (0.49)& (0.9)& (0.10)& 0.77& IPM&cultural&practices&& Disposal&of&the&pruned&plant&material&from&the&field& Making&and&applying&organic&homemade&fertilizer&& Disinfection&of&tool&between&uses&& Elimination&of&weeds&& 0.54& (0.50)& 0.62& (0.49)& 0.50& (0.50)& 0.87& (0.34)& 0.52& (0.50)& 0.63& (0.49)& 0.53& (0.50)& 0.90& (0.30)& 0.34& (0.47)& 0.65& (0.48)& 0.38& (0.49)& 0.92& (0.27)& 0.20& (0.09)& (0.04& (0.08)& 0.13& (0.08)& (0.05& (0.06)& 0.04& 0.63& 0.14& 0.18& (0.10)& (0.03& (0.08)& 0.15& (0.09)& (0.02& (0.05)& Source:&regression&results&from&study& a &Adjusted(ITT&excludes&21&non(recipients&due&to&technical&problems&related&to&data&systematization&and&verification.& For&columns&(1),&(2),&and&(3),&the&means&and&standard&deviations&(in&parenthesis)&of&each&cultural&practice&in&the&treatment&and&control&groups&are&reported.&In& columns&(4)&and&(6)&the&differences&were&calculated&using&the&following&regression:&y "#$_&'()*+),- = α0treatment + 8 +.&Clustered&standard&errors&are&reported&in& parentheses. 0.42& 0.07& 0.70& 0.08& 0.73& 66

Table&3&(&6:&Mean&comparison&in&knowledge&of&ICM&individual&questions&by&treatment&group& Intended&to& Treated& Control& ITT& Adjusted(ITT& ICM&knowledge&questions& Treat(group& (1)& group& (2)& group& (3)& Difference& (4)& p(value& (5)& Difference& (6)& p(value& (7)& Sample&size:&229& 125& 104 a & 104& & & & & Recognize&the&best&stage&of&maturity&of& 0.68& 0.70& 0.72& 0.04& (0.02& 0.53& blackberries&for&harvesting&& (0.47)& (0.46)& (0.45)& (0.07)& (0.06)& 0.77& Knows&the&frequency&of&harvesting&according&to& 0.56& 0.61& 0.55& 0.01& 0.05& 0.88& plot&altitude& (0.50)& (0.49)& (0.50)& (0.08)& (0.08)& 0.56& Knows&that&the&disposal&of&pruned&material&is& 0.54& 0.55& 0.34& 0.20& 0.18& 0.04& intended&to&prevent&spread&of&diseases&& (0.50)& (0.50)& (0.47)& (0.09)& (0.10)& 0.07& Knows&that&organic&homemade&fertilizer&has&to&be& 0.62& 0.64& 0.65& (0.04& (0.03& 0.61& applied&after&verifying&there&are&no&insects&present& (0.49)& (0.48)& (0.48)& (0.07)& (0.08)& 0.71& Knows&that&it&is&better&to&build&a&simple&string& 0.59& 0.58& 0.58& 0.02& 0.01& 0.80& trellis&with&just&one&twine& (0.49)& (0.50)& (0.50)& (0.06)& (0.06)& 0.88& Source:&regression&results&from&study& a &Adjusted(ITT&excludes&21&non(recipients&due&to&technical&problems&related&to&data&systematization&and&verification& For&columns&(1),&(2),&and&(3),&the&means&and&standard&deviations&(in&parenthesis)&of&each&knowledge&question&in&the&treatment&and&control&groups&are&reported.& In&columns&(4)&and&(6)&the&differences&were&calculated&using&the&following&regression:&y "#$_&'()*+),- = α0treatment + 8 +.&Clustered&standard&errors&are&reported&in& parentheses. 67

3.6.2.%%Multivariate%regression%analyses% 3.6.2.1.%%Adoption%of%non9IPM%practices Thecomparisonofmeansiscomplementedwithmultivariateregressionanalyses.In ordertoprovideamoreintuitiveunderstandingofthefactorsaffectingadoptionof ICM,theincidentrateratios(irr)ofthePoissonregressionarepresentedintable3A7. TheseareobtainedbyexponentiatingthePoissonregressioncoefficientandrepresenta oneaunitchangeinthex i withallofthevariablesinthemodelheldconstant. ThedependentvariableisadoptionofnonAIPMpractices(0A5).Equation(1) includesthecovariatestreatment,age,agesquared,gender,aformalsecondary educationdummyvariable,blackberryfarmingexperience,householdsize,wealth,and areapercapitaplantedwithblackberry.equation(2)addsadummyvariable representingtreatedfarmerswhodidnotreceivethetextmessagesduetotechnical problemsrelatedtodatasystematizationandverification.thisvariableisincludedto accountforthefactthatfarmersinthisgrouparelesslikelytoadopticm.equation(3) addstheinteractiontermseducationxtreatmenttoexplorewhethertextmessage affectsfarmerswithdifferentlevelsofeducationdifferently. Inequation(1)and(2)noneofthevariablesissignificant.Inequation(3),where theinteractiontermeducationxtreatmentisincludedthecoefficientsfortreatment becomessignificant.theinterventionincreasestheexpectednumberofnonaipm 68

practicesadoptedbyafactorof1.12,orequivalently,itincreasestheexpectednumber ofpracticesadoptedby12% 26. TheequationchiAsquaresindicatethatall3equationsexplainsignificantvariance inadoptionofnonaipmpractices.inequation(2)wearguedthattreatedfarmerswho didnotreceivethetextmessagesduetotechnicalproblemsrelatedtodata systematizationandverificationarelesslikelytoadoptipm.theresultsofequation(2) donotsupportthishypothesis.thecoefficientofnonacomplianceisnotsignificant.in Equation(3),wehypothesizedthattreatmentaffectsfarmerswithandwithout secondaryeducationdifferently.thecoefficientoftheinteractiontermtreatmentx Educationisnotsignificant,givingnosupporttosuchhypothesis.Forfurther confirmation,weconductedalikelihoodratiotest 27,comparingthelikelihoodfunctions ofequation(1)and(3).thisteststatisticshowsthatthenonacomplianceandinteraction termdonotincreasesexplanatorypowerrelativetoequation(1).theseresultssuggest thatequation(1)fitsthedatabetter.inaccordancewithmeancomparisonresults,we concludethattreatmentdoesnotleadtosignificantdifferencesinadoptionofnonaipm practices. 26 Thisresultisderivedfromthefollowingcalculation:100(1.12A1) 27 LRchi2(2)=0.96,Prob>chi2=0.62 69

Table3A7:Poissonregressionresults nonaipmpractices Variables Poisson (1) (2) (3) DependentVariable:AdoptionofnonAIPMpractices(1A5) TREATED 1.057 1.070 1.121** (0.053) (0.053) (0.065) Age 1.005 1.005 1.004 (0.011) (0.011) (0.010) Agesquared 1.000 1.000 1.000 (0.000) (0.000) (0.000) Education 0.964 0.960 1.027 (0.042) (0.042) (0.066) Gender 1.041 1.043 1.033 (0.044) (0.045) (0.045) Blackberryfarmingexperience 1.003 1.003 1.003 (0.003) (0.003) (0.003) Householdsize 1.012 1.010 1.011 (0.013) (0.014) (0.014) Wealth 1.006 1.006 1.006 (0.013) (0.013) (0.012) Areapercapitaplantedwithblackberry 1.052 1.049 1.039 (0.137) (0.136) (0.135) 0.923 0.915 DidnotreceivetheSMS (0.055) (0.054) 0.882 TreatmentXEdu (0.076) 2.726*** 2.741*** 2.681*** Constant (0.763) (0.774) (0.758) Model " 16.73* 19.12** 19.77** Samplesize 229 229 229 Source:regressionresultsfromstudy. Significancelevels:*10%**5%***1% Clusteredstandarderrorsinbrackets. 3.6.2.2.%%Adoption%of%IPM%practices% Wereporttheincidentrateratios(irr)ofthePoissonregressionintable3A8.The dependentvariable,adoptionofipmpractices,istheaggregationofthenumberof culturalipmpracticesadoptedandtherawcountofrecommendedpesticidesand 70

fertilizersusedbyeachfarmer.equation(1)includesthesamecovariatesusedinthe regressionfornonaipmpractices.equation(2)addsadummyvariablerepresenting treatedfarmerswhodidnotreceivethetextmessageduetotechnicalproblemsrelated todatasystematizationandverification.equation(3)addstheinteractionterm EducationXTreatment. InallthreeequationsEducationandWealtharesignificant.InEquations(1)and (2)weobservethatfarmerswithcompletesecondaryeducationadopt18%and16% moreipmpracticesthanfarmerswithelementaryorincompletesecondaryeducation, respectively.wealthierfarmersaremorelikelytoadoptipmpractices,ascostdoesnot detertheadoptionofthoserelativelymoreexpensivetechniques.beingpartofthe treatmentisasignificantpositivepredictorofadoptionofipmpracticesonlywhenthe effectofthecontrollablenonacomplianceisincluded(equation2),meaningthatifnon compliancewereignoredwewouldbeunderestimatingtheeffectofreceiptoftext messagesonadoptionofipmpractices.theestimatedcoefficientforthevariablethat representspeoplewhoweretreatedbutfailedtoreceivethetextmessagesdueto technicalproblemsrelatedtodatasystematizationandverificationhasanegativeeffect onadoptionofipmpracticesandissignificantatthe1%level. 71

Table3A8:Poissonregressionresults IPMpractices Variables Poisson (1) (2) (3) DependentVariable adoptionofipmpractices TREATED 1.095 1.135* 1.249** (0.076) (0.078) (0.115) Age 1.013 1.013 1.012 (0.011) (0.013) (0.013) Agesquared 1.000 1.000 1.000 (0.000) (0.000) (0.000) Education 1.176*** 1.162** 1.320*** (0.068) (0.068) (0.118) Gender 1.033 1.039 1.019 (0.057) (0.058) (0.056) Blackberryfarmingexperience 0.997 0.998 0.998 (0.004) (0.004) (0.004) Householdsize 0.995 0.991 0.992 (0.019) (0.019) (0.018) Wealth 1.037*** 1.037*** 1.038*** (0.014) (0.013) (0.013) Areapercapitaplantedwith 1.096 1.098 1.078 blackberry (0.179) (0.181) (0.173) 0.769*** 0.757*** DidnotreceivetheSMS (0.074) (0.071) 0.794** TreatmentXEdu (0.089) 2.592*** 2.633*** 2.496*** Constant (0.892) (0.886) (0.840) Model " 32.95*** 38.71*** 42.12** Samplesize 229 229 229 Source:regressionresultsfromstudy Significancelevels:*10%**5%***1% Clusteredstandarderrorsinbrackets Inequation(3),thecoefficientsfortreatmentandeducationarenotthemain effects,butareconditionaleffects 28.Amongfarmerswithsecondaryeducation,the interventionhasnoimpactonadoption.tobetterunderstandtheeffectofthe 28 Theeffectoftreatmentonadoptioniscalculatedbycomputingthederivativeoftheadoptionequation withrespecttotreatment.theeffectofeducationonadoptioniscalculatedinasimilarfashion. 72

interventionongroupswithdifferenteducationlevelswepresentmarginaleffectsof thetreatmentandeducationvariablesintable3a9.weperformwaldtestsforequality ofthemarginalvaluespresentedinthistable.thewaldtestonuntreatedfarmers (Treatment=0)revealsthatwecanrejectthehypothesisthattheseestimatesareequal. MoreeducatedfarmersinthecontrolgrouphaveahigherlevelofadoptionofIPM practices.however,theresultsconcerningtreatedfarmersindicatethatintervention hasapositiveeffectonlyamongfarmerswithoutsecondaryeducation.onaverage,less educatedtreatedfarmersadopted0.77moreipmpracticecomparedtolesseducated farmersinthecontrolgroup.amongfarmerswithcompletesecondaryeducation, adoptionofipmpracticesstaysthesame(withaveragemarginaleffectof4.07and4.03 IPMpractices)regardlessofintervention.Inotherwords,textmessageshavenoeffect onadoptionofipmpracticesforwellaeducatedfarmers. Table3A9:AveragemarginaleffectsATreatmentandEducationvariables SecondaryEducation 0 1 TREATMENT 0 1 3.08*** (0.22) 3.85*** (0.27) Source:regressionresultsfromstudy. Significancelevels:*10%**5%***1% Clusteredstandarderrorsinbrackets. 4.07*** (0.32) 4.03*** (0.18) Weagainconductedlikelihoodratiotests 29 comparingthelikelihoodfunctionsof Equations(1)and(3).Theteststatisticsshowthatadditionoftheinteractiontermand 29 LRchi2(2)=7.21,Prob>chi2=0.03 73

thenonacompliancevariableincreasesexplanatorypower.foradoptionofipm practices,equation(3)fitsthedatabetter. Insummary,treatmentleadstoincreasedadoptionofIPMpractices. Noncomplianceisamajorthreattoobtainingpowertodetecttheinterventioneffect and,inthecaseathand,shouldnotbeignored. 3.6.2.3.%%The%effect%of%the%intervention%on%knowledge% Incidencerateratios(irr)ofthePoissonregressionarepresentedintable3A10.The overallknowledgescoreisbasedonatotaloffiveknowledgequestionsandreflectsthe numberofcorrectlyansweredknowledgequestions,measuredona0to5scale. Equation(1)includesthecovariatestreatment,age,aformaleducationdummy variable,andexperience.equation(2)addsadummyvariablerepresentingtreated farmerswhodidnotreceivethetextmessagesduetotechnicalproblemsrelatedto datasystematizationandverification.equation(3)addstheinteractiontermeducation XTreatment. Receiptofthetextmessagesdoesnothaveanimpactontheknowledgescore. Inallthreeequations,theeducationcoefficientisstatisticallysignificantwithalarge impactonknowledge.havingcompletedsecondaryeducationincreasestheexpected knowledgescorebyafactorof1.21and1.20inequations(1)and(2),orequivalently,it increasestheexpectedknowledgescoreby21%and20% 30.Havingnotreceivedtext messagesduetotechnicalproblemsrelatedtodatasystematizationandverification 30 Thisresultisderivedfromthefollowingcalculation:100(1.21A1)and100(1.20A1) 74

(whenassignedtothetreatmentgroup)doesnothavesignificanteffectonknowledge. Asbeforeweperformlikelihoodratiotest 31.Sincethedifferenceisnotstatistically significant,weconcludethatequation(1)fitthedatasignificantlybetterthanequation (3). Table3A10:Poissonregressionresults knowledge Variables Poisson (1) (2) (3) DependentVariable:Knowledgescoreina0A5pointscale TREATED 1.051 1.064 1.211 (0.08) (0.08) (0.10) Age 1.005** 1.005** 1.006** (0.00) (0.00) (0.00) Education 1.208*** 1.204*** 1.442*** (0.06) (0.06) (0.10) Blackberryfarming 1.000 1.000 1.001 experience (0.00) (0.00) (0.00) 0.928 0.903 DidnotreceivetheSMS (0.09) (0.09) 0.724 EducationxTreatment (0.07) 2.098*** 2.099*** 1.885*** Constant (0.26) (0.26) (0.24) Model " 17.06*** 16.98*** 29.16*** Samplesize 229 229 229 Source:regressionresultsfromstudy. Significancelevels:*10%**5%***1% Clusteredstandarderrorsinbrackets. 3.7.%%Conclusions% InEcuador,blackberryfarmersfacemanyproductionchallenges.Limitationsto technologytransferandthelackofappropriatestrategiesforextensionmethodsare associatedwithcontinuedchallenges deliveryofinformationislimited.theacquisition 31 LRchi2(2)=4.22,Prob>chi2=0.12 75

ofknowledgeandtheabilitytousethatknowledgeinatimelymannerarecritical determinantsoffarmproductivity.despiteevidencethaticmishighlyprofitablefor blackberryproducers,icmadoptionhasnotbeenwidespread.farmersmaybe inattentivetomanagementsequencingandtothedailytasksnecessaryforoptimizing blackberryprofits.thesefactorsoftenresultinlowerproductivity. Theresultsofthispaperlendsupporttotheideathatinsightsfrompsychology andbehavioraleconomics,appropriatelyapplied,havethepotentialtoenhancethe impactofagriculturalinterventionsonfarmers behavior.targetingseveral determinantsofbehaviorincreasestheprobabilityofadoptionofatleastipmpractices. Ourinterventionaimedtoaffectknowledgeandlimitedattentionbytheprovisionof information,alongsidereminders.asprovidersofinformation,textmessageshave someknowledgebuildingeffectleadingtotheadoptiononeoftheipmcultural practices:disposalofprunedplantmaterial.asreminders,textmessageseffectively increaseadoptionofipmpractices,especiallyofthosethathaveanimmediate observableeffectsuchaspesticides.theinterventionwaseffectiveinincreasing adoptionofipmpracticesamonglesseducatedindividuals.ithasnoeffectonadoption ofnonaipmpractices.giventhelowcostofthetextmessages,theymaystillbecost effective,eventhoughtheirimpactissmall;addingatextmessagesupplementtoan alreadyexistinginapersontrainingprogramcouldbeanattractiveoptionforagricultural extensionwork.thenatureofourexperimentlimitstheextrapolationoftheresultsto thewholepopulationofblackberryfarmersintheprovincesoftungurahuaandbolívar. 76

However,webelievethatthisapproachisreplicableandcanbesuccessfully implementedindifferentsettingsandcrops. Manyintendedrecipientsoftextmessages(assignedtothetreatmentgroup)did not,foronereasonoranother,receivethemessages.nonacompliancewascaused,in part,bytechnicalglitches.toreduceratesoffailuretoreceivemessages,wesuggest thatfutureresearchundertakeupafrontverificationprocessestoensurereliabilityof mobilephoneownershipinformationanddatasystematization.onepossibilitywould betosendatextmessageatthemomentofenrollment,whenthefarmerispresent,to verifythatinformationwastakencorrectlyandtoverifythefarmer sabilitytooperate themobilephone. 77

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Appendix%C% C A 1: Map of theoretical domains framework(tdf) to sources of behavior on COMAB system OPPORTUNITY PHYSICAL'OPPORTUNITY Environment'context'and' resources SOCIAL'OPPORTUNITY Social'influences MOTIVATI REFLECTIVE'MOTIVATION AUTOMATIC'MOTIVATION Social/professional'role''''' and'identity Beliefs'about''''''''''''' capabilities Beliefs'about'''''''''' consequences Motivations'and'goals Emotion BEHAVIOR Knowledge Memory,'attention'and' decision'processes Behavioral'regulation Skills PSYCHOLOGICAL'CAPABILITY PHYSICAL'CAPABILITY CAPABILI Nature'of'the'behavior Source:DrawingelaboratedbytheauthorbasedonMichieetal.(2005);Michieetal.(2011) Thisfigureillustrateshowhumanbehaviorresultsfromtheinteractionbetweenthreecomponents:(1) capacity,(2)opportunity,and(3)motivation.thedeterminant natureofthebehavior,whichrefersto characteristicsoftheinnovation,doesnotbelongtoanyofthethreecomponents,andshouldbe analyzedindependently. 85

Appendix%D% TableDA1:CorrespondencebetweenICMpracticesandknowledgequestions ICM%practices% Harvestingattherightstageof maturity Disposaloftheprunedplantmaterial fromthefield Makingandapplicationoforganic homemadefertilizer Buildingofasimplestringtrelliswith onetwine Source:FollowAupsurvey Knowledge%Questions% Recognizethebeststageofmaturityofblackberriesfor harvesting Knowsthefrequencyofharvestingaccordingtoplot altitude Knowsthatthedisposalofprunedmaterialisintended topreventspreadofdiseases Knowsthatorganichomemadefertilizerhastobe appliedafterverifyingtherearenoinsectspresent? Knowsthatitisbettertobuildasimplestringtrelliswith justonetwine 86

Appendix%E% Blackberry%Text%Messages% Week1 1. DearFarmer:WelcometotheIPMmessagesystem.Youwillperiodicallyreceive technicaladviceformanagingyourblackberrycropbetter. 2. Rememberthatblackberryhas9phenologicalphases.Pg.3 3. Conductpruningproduction(cutoffofbranchesthathaveproducedtwobuds)to inducegrowthofotherproducingbranches.pg.6 4. Remembertodisposeofprunedplantmaterialtopreventspreadofdiseases.Pg.11 Week2 5. Conductallphytosanitarycontrolsandfertilizationswhenthesoilismoist(field capacity).pg.13 6. Conductaphytosanitarycontrolwithaneutralizedbordeauxmixture(1kgslaked lime+1kgcoppersulfatein200litersofwater).pg.7 7. Conductaphytosanitarycontrolwith1/2kgofcopper+1/2kgofsulfurin200ltof watertopreventoidium.pg.8 Week3 8. Remembertoconductasoilfertilizationwith100gof18A46A00fertilizer+100gof urea+2kgoforganichomemadefertilizerperplant.pg.8 9. Remembertoapplyhomemadefertilizerwithoutinsecteggs,especiallycutzo.Pg. 12 87

10.Conductaphytosanitarycontrolusing1.5mlofpotassicphosphiteperliterofwater topreventdownymildew.pg.9 Week4 11.Duringtheflowerbudsdevelopmentphaseconductafoliarfertilizationusing1mlof boronchelatesperliterofwater.pg.9 12.Inthefloweringstageperformaphytosanitarycontrolwith1mlofazoxystrobin (brandname:amistar)perliterofwatertopreventdownymildew.pg.9 13.Remembertodisinfecttoolsbetweenuses.Pg.14 Week5 14.Inthepollinationofthefruitstageperformaphytosanitarycontrolusing1mof penconazole(brandname:topas)perliterofwatertopreventoidium.pg.9 15.Remembertoconductasoilfertilizationusing100gofnitrogen+150gof potassium+2kgoforganichomemadefertilizerperplant.pg.9 16.Remembertoapplyhomemadefertilizerwithoutinsecteggs,especiallycutzo.Pg. 12 Week6 17.Inthevegetativegrowthphaseconductafoliarfertilizationusingironchelates+zinc chelates(1ccperliterofwater).pg.10 18.Conductallphytosanitarycontrolsandfertilizationswhenthesoilismoist(field capacity).pg.13 88

19.Duringthefruitgrowthstage,performaphytosanitarycontrolusing1mlof difenoconazole(brandname:score)+1mlofdicofol(brandname:acarin)perliterof water.pg.10 Week7 20.Duringthefruitgrowthstageconduct2foliarfertilizations(2ndafter15days) applying1mlofcalciumchelatesperliterofwater.pg.10 21.Duringtheharvestphase(inparallelwiththeswollenbudsphase),performa phytosanitarycontrolusing1mlofiprodiona(brandname:rovral)or1mlof azoxystrobin(brandname:amistar)perliterofwater.pg.10 22.Conductallphytosanitarycontrolsandfertilizationswhenthesoilismoist(field capacity).pg.13 Week8 23.Inordertoreapblackberriespermanentlythroughouttheharvestcycle,perform pruningonceamonth.pg.11 24.Remembertodisposeofprunedplantmaterialtopreventspreadofdiseases.Pg.11 25.Remembertoeliminateweedplantstoavoidinsectsandmites.Pg.11 Week9 26.Rememberetobuildiasimplestringtrelliswithonetwine. 27.Conductallphytosanitarycontrolsandfertilizationswhenthesoilismoist(field capacity).pg.13 89

28.Performallthephytosanitarycontrolsandfertilizationwhenthegroundismoist (fieldcapacity).pg.13 Week10 29.Remembertocutoffinfertileandunproductiveprimaryandsecondarybranchesto inducegrowthoffertilebranches.pg.13 30.Remembertodisposeofprunedplantmaterialtopreventspreadofdiseases.Pg.11 31.Blackberrypickingshouldbedonewhenthefruitsarereddish/blackcolor.Pg.14 Week11 32.Remembertoharvesttheblackberriesevery8daysinzonesofhighaltitudeand twiceaweekintemperatezonesinnonasunnyhours.pg.14 33.Remembertodisinfecttoolsbetweenuses.Pg.14 34.Dearfarmer,thankyouforyourparticipation.Wehopetheinformationyouhave receivedhascontributedtoimproveyourblackberrycropmanagement. 90

CHAPTER%4:%DETERMINANTS%OF%ABSOLUTE%UPWARD%INCOME%MOBILITY:% THE%HIDDEN%COST%OF%COMMUTING% 4.1.%Introduction%% EveryyearthousandsofpeopleentertheUnitedStatesinordertoestablishabetterlife forthemselvesandtheirfamilies.theyhopetoimprovetheircircumstances,tolivethe AmericanDream. TheUnitedStateshasbeenseenasthelandwhereeveryonehasa chanceofsuccess,regardlessofbackgroundorcircumstances.however,increasing incomeinequalityintheunitedstatesisanissueofconcerntopolicymakersandthe generalpublic.someinequalityisconsideredtobenaturalandindeeddesirable, becauseindividualshavedifferenttalentsandtastesandopportunitiescanneverbe fullyequalized.nevertheless,wideningincomeinequalitycanbeasignaloflackof incomemobility 32. Upwardmobility,inequality,andpovertyareinterrelated.Theyareincreasingly thesubjectsofresearch,withseveralrecentlypublishedpapersonupwardmobility (Chetty,Hendren,Kline,Saez,&NationalBureauofEconomic,2014a;Chetty,Hendren, Kline,Saez,&Turner,2014b;Hout,2015;Lee&Solon,2009).Contrarytothe widespreadbeliefthattheunitedstateshasbecomeamuchmoreclassaboundsociety (aplacewhereclimbingthesocialladderhasbecomealotharder),researchershave 32 Inaneconomicsense,incomemobilityisgenerallydefinedintermsofthepossibilitytomoveup(or down)theincomeorwagescalerelativetoone sparents. 91

foundthatsocioeconomicmobilityhasnotvariedmuchovertime 33 onaverage(chetty etal.,2014a).however,thereisconsiderablespatialvariationinmobilityacrossthe UnitedStates(4A1). Figure4A1:Absoluteupwardmobility:meanchildrankforparentsat25thpercentileby commutingzone Source:AbsoluteUpwardMobility:MeanChildRankforParentsat25thPercentilebyCZ.Reprintedfrom WhereistheLandofOpportunity?TheGeographyofIntergenerationalMobilityintheUnitedStates (2014)byChetty,R.,Hendren,N.,Kline,P.,Saez,ECambridgeMass. Chettyetal.(2014a),usingarankArank 34 specification,calculatetwomeasuresof incomemobility:(1)relativemobility,whichisthedifferenceinoutcomesbetween 33 ResearchhasfoundthatAmericanchildrentodayhaveaboutthesamechanceatupwardmobilityas childrendid50yearsago(chettyetal.,2014a) 34 Childrenarerankedbasedontheirincomesrelativetootherchildreninthesamebirthcohort.Parents 92

childrenfromtopvs.bottomincomefamilies,and(2)absoluteupwardmobility,which theydefineastheexpectedrankforchildrenfromfamiliesatthe25%bottomofthe incomedistribution.theyfocusmostoftheiranalysesonthelatter.chettyetal. (2014a)foundthatsomeareasofthecountryareassociatedwithratesofupward mobilitycomparabletothemostmobilecountriesintheworldsuchasdemark.to mentionanexample,insaltlakecityabsoluteupwardmobilityis46.2.otherareasare associatedwithlowerratesofmobilitythananydevelopedcountryintheworld(for whichdataareavailable)suchasunitedkingdom(chettyetal.,2014a).forexample,in Charlotte,N.C.,therateofabsoluteupwardmobilityis35.8. Incomemobilitydependsonahostoffactorsthatdetermineindividual economicsuccess;somerelatedtotheinheritabilityoftraits(suchasinnateabilities), othersrelatedtothefamilyandsocialenvironmentinwhichindividualsdevelop.chetty etal.(2014a)beginbynotingthatthespatialpatterningradientsofcollegeattendance andteenagebirthrateswithrespecttoparentincomeisverysimilartothespatial patterninintergenerationalincomemobility.thefactthatmuchofthespatialvariation inchildren soutcomesemergesbeforetheyenterthelabormarketsuggeststhatthe differencesinmobilityaredrivenbyfactorsthataffectchildrenwhiletheyaregrowing up. Chettyetal.(2014a)correlatemobilitystatisticswithvariousobservedfactors thathavebeendiscussedinthesociologyandeconomicsliterature,controllingforrace. Thesefactorscanbegroupedintofivecategories:(1)spatialsegregation(2)school ofthesechildrenarerankedbasedontheirincomesrelativetootherparentswithchildreninthesebirth cohorts. 93

quality,(3)incomedistribution,(4)familystructure,and(5)socialcapital.theytesta numberofproxiesforeachfactorandidentifytheproxythathasthestrongestand mostrobustunivariatecorrelationwithupwardmobilityineachcategory. Chettyetal.(2014a)identifyspatialsegregationasthefirstofthefivemajor factorsthatarestronglycorrelatedwithmobility.spatialsegregationcaneasily reinforcedisadvantageandexclusionbecauseitrestrictsthegeographicandsocial mobilityofwholeclassesandgroupsofpeople.theproxychettyusedforthisfactoris workingindividualswhocommutelessthan15minutestowork(representing commutingtimes).theyarguethatnegativeimpactsofthisvariablemayoperateby makingitmoredifficulttoreachjobsorotherresourcesthatfacilitateupwardmobility. However,theyacknowledgethatalackofaccesstonearbyjobscannotdirectlyexplain whythegradientsemergebeforechildrenenterthelabormarket.theysuggestthat thespatialmismatchcouldproducesuchpatternsifitchangeschildren sbehavior becausetheyhavefewersuccessfulrolemodelsorreducestheirperceivedreturnsto education.themechanismbehindtheeffectofcommutingtimesonupwardmobilityis unclear.understandingthismechanismisimportantbecauseitconstitutesthefirststep onthewaytoimplementinginitiativesaimedatreducingcommutingtimes. Weproposeamodelforexplainingitseffect.Wehypothesizethatcommuting timesaffectsupwardmobilitythroughhumancapitalaccumulation.wearguethat commutingtimesisakeydeterminantofhumancapitalaccumulationbecauseitaffects theamountoftimeparentsspendwithchildren.humancapital,inturn,isakey determinantofwagesandproductivity.theobjectivesofthisstudyareto:(1)model 94

theeffectofcommutingtimesonhumancapitalaccumulationandmobility;and(2) evaluateempiricallytheeffectofcommutingonupwardincomemobility. Thispaperisorganizedasfollows:Section2providesanexplanationofwhy commutingmayplayanimportantroleinmobilitypatterns;section3presentsa theoreticalmodel;section4describesthedataandtheempiricalimplementation;and Section5discussestheresultsoftheempiricalanalysis.Weconcludethepaperin section6. 4.2.%Commuting%times% Thetermcommutereferstothedailyjourneybetweenhomeandwork.Accordingto theu.s.censusbureauin2014,theaverageworkercommuted25.4minutesper journey.thismeanstheaverageworkerspendsmorethan200hoursayear(if individualswork50weeks)commuting,whichisequivalentto5standardworking weeks. Commutertripdistanceresultsfromherhomeandworklocation,andthe networkconnectingthetwo.so,howlongofacommutecanindividualsendure?there isnotshortandsimpleanswertothisquestion.itwoulddependonmanyfactors.for instance,itwoulddependonwhetherindividualsareplanningtostayatthecurrentjob longterm;whethertheyhaveorareplanningtohavekids;lifestyleandpersonality; safety;theimportancetheyplaceonproximitytofriendsandfamily,andtoleisure activities,etc. 95

Crane(1996)usingabasicurbanmodeldemonstratesthatworkerswithvery unstablejobswillconsidercommutecostsovertheirentiretimehorizonwhen evaluatingalternativeplacestolive.varady(1990),usingasurveyofrecenthomebuyers incincinnati,ohio,foundthatfamilylifeacyclepositionandlifestyle(familiarvs cosmopolitan)areimportantfactorsinexplainingthechoicebetweensuburbanand urbancincinnati.familieswithchildren,familiesseekingsuburbanattributes(largelots, goodschools)tendedtobuyinsuburbanhomes,eventhoughthisdecisionimpliesa longercommutedistance.davis(1993)conductedastudyinportland,oregon.she foundthattheaverageexurbanhomebuyerhadacommutingtrip6a7minuteslonger thanhiscounterpartinsuburbia.averageexurbanitetradeofflongertraveltimesfor morespace,aruralenvironment,lowerhousingprices,orbetterplacestoraisetheir children. Researchalsoshowsthathomebuyersplacehighimportanceonschoolquality eventattheexpenseofincreasedcommutingcostsandhouseprices.crone(1998) conductedareviewofschoolqualitymeasurescapitalizedinhousingprices.he concludedthatprospectivehomebuyersarepayingaschoolpremiumforhousesin areaswithbetterschools,manyofwhicharelocatedinsuburbanresidentialareas. Factorsaffectinghomelocationpreferencesandtheirimportancechangeover time.hence,itisextremelydifficulttogaugethedegreeofimportanceofcommuting whenchoosingaplacetolive.whatwedoknowispeople sdailycommutecanhavean importantimpactontheirdisposableincome,howmuchtimetheygettospendat homewiththeirfamilies,andqualityoflife. 96

Lengthycommuteshavebeenfoundtohavenegativeeffectsonanindividual s physicalandmentalhealthandwellbeing.kluger(1998)reportsapositiverelationship betweencommutingtimeandhighbloodpressure,selfareportedtension,reducedtask performance,andnegativemoodintheeveningafterwork.kageyamaetal.(1998) studiedtheshortatermheartratevariabilityof223malewhiteacollarworkersintokyo andfoundthosecommutingmorethan90minutesoneawayhadchronicstressand fatiguesymptoms.walslebenetal.(1999)foundrailcommutersinlongisland,new York,withlongcommuteshadsignificantlylessnocturnalsleepandincreased hypertension.costa,pickup,anddimartino(1987)studieditalianindustrialworkers andfoundthatcommutersthathaveajourneytoworkofatleast45minuteshad reducedsleepingtime.astudyintexassurveyed900womenoncommonactivities, suchasworking,intimaterelations,housework,andcommutinginboththemorning andevening(kahneman&krueger,2006).itfoundthatofthenineteensurveyed activities,themorningcommutewasratedtheleastenjoyable. Recentevidencesuggeststhatcommutingnotonlyaffectsphysicalandmental healthandwellbeing,butalsosocialwellabeing.christian(2012)foundthatamong malescommuting60minutesdaily,aonehourcommutingtimeincreaseisassociated witha21.8minutedecreaseintimespentwithaspouse,an18.6minutedecreasein timewithchildren,anda7.2minutedecreaseintimewithfriends.forfemale commuters,anhourincreaseincommutingtimeisassociatedwithan11.9minute decreaseintimespentwithfriends,withnosignificantimpactontimespentwitheither spouseorchildren.thislatterpaperprovidesabasisforunderstandingtheeffectof 97

commutingtimesonupwardmobility.longdurationcommutersspendlesstimeat homeandthustheyspendlesstimewiththeirchildren.thisdecreaseintimespend withchildrenmaybeassociatedwithlowerchildhumancapitalattainmentand,hence decreasedsocioaeconomicmobility. 4.2.1.%%The%production%of%human%capital%% UnitedStates governmentexpendituresonelementary,secondary,andpostasecondary schoolingtotaled$807billionin2011,about5.2percentofgdp(worldabank,2015). Hundredsofbillionsofdollarsinadditionarespentforhousing,feeding,clothing, transport,nonaparentalcareservices,andhealthcareservicesofchildren.oneofthe largestofallthesecostsistheopportunitycostofforgonewagelaborinorderto nurture,monitor,teachandcareforchildren.grayandchapman(2001)foundthatfor womenwhohavecompletedsecondaryeducation,havingonechilddecreasesafteratax lifetimeearningsbyaround$160,000dollars.theseinvestmentdecisionsaredictated bythedesiretomaximizehumancapitalaccumulation,whichinturnplaysakeyrolein shapingindividuallaborearningsoverthelifecycle. Certainlythetimeparentsspendengagedwiththeirchildrencontributestothe humancapitalkidsdrawuponastheydevelopinlife.severalstudieshaveinvestigated thecontributionsthatparentsmaketotheirchildren sattainment.radin(1981)found thatamong4ayearsoldboys,father snurturancewasassociatedwithboys intelligence scores.flouriandbuchanan(2004)examinedtheroleofparentalinvolvementatage7 98

inchildeducationalattainmentbyage20.theyfoundthatparentalinvolvementwasa goodpredictorofeducationalattainmentbyage20.thisassociationwasthesamefor sonsanddaughters.hooveradempseyandsander(1995)analyzedparentalinvolvement inchildeducation.theyfoundthatparentalinvolvementledtodevelopmentofskills andknowledgeandpersonalsenseofefficacyforsucceedinginschool. Otherstudiesrevealnegativeoutcomeswhenthechilddoesnotreceivethe rightamountofattentionandguidancefromhisorherparents.elliott,cunningham, Colangelo,andGelles(2011)conductedastudyamong2,004adolescents.Theyfound thatfailingto matter 35 toone sfamilyincreasestheprobabilityofviolence,whereasa strongfeelingofmatteringislikelytoprotecttheadolescentfromengaginginviolent behaviortowardafamilymember.anderson,gooze,lemeshow,andwhitaker(2011) foundthatthequalityoftheemotionalrelationshipbetweenamotherandheryoung childcouldaffectthepotentialforthatchildtobeobeseduringadolescence.infact, childrenwiththepoorestqualityearlymaternalachildrelationshipwerealmost2½ timesaslikelytobeobeseasadolescentsthanwerechildrenwhohadthebest relationshipswiththeirmothers. % 4.3.%Theoretical%model% Thefollowingisamodelthatdescribestheeffectsoflongcommutesonhouseholdtime allocationandupwardmobility.themodeldrawsonthebecker(1993)modelof 35 Matteringiscomposedofthreefactors:awareness,importance,andreliance. 99

earningsandhumancapital,andongronau(1976)leisure,homeproduction,andwork model. Oneofthemainassumptionsofthemodelisthatparentsarealtruisticandtheir utilityfunctionincludestheirownconsumptionandtheirchild swelfare.individualsare assumedtoderiveutility(u)fromtheconsumptionofthreecommodities:agood purchasedinthemarket(x M ),agoodproducedathome,whichinthecaseathandcan becalledwellacaredaforchildren(x C ),andleisuregood(l).workathomeisatimeuse thatgeneratesservicesandisassumedtohaveaclosesubstituteinthemarket,while leisureisassumedtohavepoormarketsubstitutes.inotherwords,childrenandmarket goodsareclosesubstitutesandparent sutilitydependsonalinearcombinationofthe quantitiesofallgoodsconsumed,includingleisure.sinceparentsarerational,theywill choosethecombinationofgoodsthatmaximizestheirtotalutility. U = U(X, L) X = X * + X, X, = f E f / > 0, f < 0 Subjectto: X * = WN + V L + E + N = T 100

wherewisthewagerate(assumedtobeconstant),ndenotesmarketwork,andv othersourcesofincome.inthetimeconstraint,tdenotesthetotalofdailytime endowmentwhichcanbeallocatedamongitsthreeuses:childcare(e),marketwork(n ),andleisure(l). Thenecessaryconditionsforaninterioroptimumcallforthemarginalproductof householdworktobeequaltothemarginalrateofsubstitutionbetweengoodsand leisuretime,whichinturnequalstheshadowpriceoftime,w*.ifthepersonworksin thelabormarket,thiswillalsoequaltherealwagerate,w. Z L Z X ;< ;= ;< ;> % = f / = W =? / = @ = @ TheseconditionsarerepresentedinFigure4A2.Considerahouseholdwitha singleparent.curvetao ACodescribesthehomeproductionpossibilitiesfrontier.The moretimetheindividualspendsworkingathome(asmeasuredbythehorizontal distancefrompointt),thegreatertheamountofhomegoodsproduced.intheabsence ofmarketopportunities,thecurvetao ACoistheopportunityfrontierenclosingtheset ofallfeasiblecombinationsofxandl.theexistenceofamarketwheretheparentcan selltheirworkingtimeandbuymarketgoodsexpandsthisset.thus,giventherealwage 101

ratew(describedbytheslopeofthelineado),theparentcantradetheirtimefor goodsalongthepricelineado.attheoptimumparentsmaychosegoodsaintensiveor leisureaintensivecombinationsofxandl,representedbypointaoandao,respectively, dependingontheirutilityfunctions,whichdifferinthetwocases.intheformer,the parentenjoysolaunitsofleisure,spendslanunitsonworkinthemarket,andspends NTtimeunitsonworkathome.Inthelatter,theparentdoesnotworkinthemarket andsplitshertimebetweenleisure(olb)andhomeproduction(lbt).extendingthis modeltotwoapersonhouseholdsgeneratesanalogouspredictionsformarriedcouples (Frazis&Stewart,2006). 102

Figure4A2:Workathome,workatthemarketandleisure 4.3.1.%%Human%capital%accumulation%and%the%cost%of%commuting% AsdefinedbyBildirici,Sunal,AykacAlp,andOrcan(2005), Humancapitalisthesumof abilities,knowledgeandskillsthatarespecifictoindividuals. Humancapital accumulationhasbeencloselylinkedtohouseholdandcommunityresources,andsome childhoodsurroundingcircumstances.communityresourcesrefer,forexample,to schoolqualityandstockofsocialcapital.surroundingcircumstancesrelatestolevelsof segregation,andincomeinequality. Parentscaninfluencetheeconomicwelfareoftheirchildrenintwodifferent ways.onerunsthroughinvestmentofhouseholdmaterialresourcesintoraisingthem 103

(i.e.investmentineducation).theotherrunsthroughparentaltimespendwithchildren nurturing,monitoring,teaching,andtransferringsocialnormsandethics.certainly, parentaltimehaslongbeenrecognizedasimportanttothedevelopmentofhuman capitalanditisalsobelievedtobeacentralmechanismthroughwhicheconomicstatus istransmittedfromgenerationtogeneration(guryan,hurst,&kearney,2008).inour model,thetotalamountofhumancapitalaccumulatedduringadulthood(ht)attimet isproportionaltotheamountaccumulatedduringchildhoodattimeta1. H B = ψ g BEF, E BEF, s BEF, withψ>0, g BEF ande BEF representhouseholdmaterialandtimeresourcesexpenditures, respectively,whilechildrenweregrowingup,whiles BEF characterizecommunity expendituresandsurroundingcircumstances. Becauseweareinterestedinupwardincomemobility,let sconsidermarket earningstobeafunctionofhumancapitalsothat: W B = φh B + I B whereφrepresentsthereturnstohumancapitalforchildrenandi B isarandomerror term.becausewebelievechildrenhumancapitalisrelatedtohouseholdmaterial g BEF JandtimeresourcesexpendituresE BEF,alongsidecommunityexpendituress BEF,we canwrite: 104

W B = φψ g BEF, E BEF J, s BEF + u B Ourmodelfirstpositsthatentryintothemarketiscostless.However,inpractice,work inthemarketinvolvesthecostsofcommutingintermsofmoneyandtime.inourmodel Candfrepresentthesecosts,respectively.Theintroductionofcandfimpliesa modificationofthebudgetandtimeconstraints: X * + LM = WN + V = + N + O + L? = P whereδrepresentsindividualemploymentstatus(1=employed,0=unemployed). Parentscanchoosetostayoutofthelaborforceandavoidtheccostorjointhelabor forceandassumethetimelossoff.insomesenseccanbeseenasalaborforce participationtax.asobservedinfigure4a3,ifaparentdecidesnottoparticipateinthe laborforceshecanchooseanypointontheboundarytao Co.Ifshedecidestojointhe laborforceshelosesfunitsoftime.shealsohastoassumethemoneycostof commutingrepresentedbyc.heropportunitylocusbecomest AD1.Giventhese opportunitysets,parentswithagreaterpreferenceforgoodswilljointhelaborforce (pointa1)andparentswithagreaterpreferenceforleisurewouldstayoutofthe market(pointao ).Theintroductionofcommutingtimedoesnotaffecttheallocationof timeofunemployedindividualsbutreducesthetimeofworkathomeandleisureofthe employed.workatthemarketremainsunchangedonlyiftheincomeeffectisequalto 105

0.Thelargerthecommutingtimethelargerthereductionoftimededicatedtohome production. Figure4A3:Thecostofcommuting Modelsofoptimalbehaviortypicallyapplyattheindividuallevel;ifdifferent individualsbehavedinessentiallythesameway,thengroupstatisticswouldmirrorthat commonbehavior.however,thereisevidenceofextensivedifferencesinbehavior acrossindividuals.ourindividualtheoreticalmodelaccountsforthesetastevariations byallowingparentstohaveagreaterpreferenceforgoodsoragreaterpreferencefor 106

leisure.inbothcasestheimplicationsofintroducingcommutingcostsarethesame.we assumethatmostbroadpropertiesaretransmittedbetweentheindividualleveland aggregatestatistics,atleastenoughforquantitativeanalysis. Thismodelconstitutesasimplifiedversionofaverycomplexsystemthat includesmanycomponents.asmentionedbefore,whenchoosingaplacetolive(that willobviouslyaffectsthetimepeoplespendcommutingtoandfromwork)manyfactors comeintoplay.howfamiliestradeoffcommutingtimesandotherfactorssuchas housingcostandtimewithfamilyisnotcompletelyunderstood.oneshouldbecautious aboutgeneralizationsbecausechoices,constraintsandtradeaoffsarelikelytovary significantlyacrosshouseholds.forexample,workingfamilieshavefewerandless advantageouschoicesthanupperaincomehouseholds. Commutingcostincludesmuchmorethanjustthemonetarycostofthejourney (tandf).forinstance,thecostofchildcareraiseswithlongercommutes.inthissense, parentshavetofactorinthecostofchildcare,thedailycommuteandotherworka relatedexpenseswhendecidingwhethertoparticipateinthelaborforce.we acknowledgethatcommutingtimedecisionsresultsfromcomplexhumana environmentinteractions,manyofwhichareeithertoocomplicatedtobeeasily included,orthataresimplynotunderstoodorknownaboutandwereinevitablyleftout ofourmodel.weattempttoproposeasimpletheoreticalframeworkto,atleast partially,explainhowcommutingtimesaffectshumancapitalaccumulationandhence childrenoutcomes. 107

4.4.%Data%and%empirical%implementation% IncomemobilitydataatthecountylevelwereobtainedfromtheEqualityof OpportunityProject 36 (EOP)website.ThatprojectusesmatchedSocialSecurityrecords andtaxdatatocalculateincomemobilityforthe1980a1982cohorts.theymeasure children sincomeasmeantotalfamilyincomein2011and2012,whentheyare approximately30yearsold.parents incomeismeasuredasmeanfamilyincome between1996and2000,whenthechildrenarebetweentheagesof15and20.this datasetwasmergedwithcountyleveldataforyears1997to2003providedbytheus CensusBureau,NationalCenterforEducationStatistics(NCES),andtheUnitedStates DepartmentofAgricultureEconomicResearchService(USDAERS).AsinChettyetal. (2014a),thistimeframewaschosenbecauseitreflectstheprevailingeconomicand socialconditionsofcountieswhenchildrenbelongingtothe1980a1982cohortswere growingup. % 4.4.1.%%County9Level%covariates% Theanalysisfocusedonthe2730countiesthathadatleast250childrenandforwhich completedatawereavailable.thefinaldatasetcontainsinformationonabsolute upwardmobility,andfivefactorsthatchettyetal.(2014a)identifiedashavingthe strongestcorrelationwiththevariationinupwardmobility:segregation,schoolquality, 36 TheEqualityofOpportunityprojectiscollaborationamonganumberofeconomistsfromHarvard,U.C. BerkleyandtheU.S.Treasury.TheEOPfocusonthestudyofeconomicmobility,inequalityandpovertyin theunitedstates. 108

incomeinequality,familystructure,andsocialcapital(thesevariablesaredefined below).wecontrolforraceandlaborforceparticipation.additionally,weincludea categoricalvariablethatspecifieswhetheracountybelongstooneofthefollowing categories:(1)metropolitancounties,(2)nonmetropolitancountiesadjacenttoa metropolitanarea,and(3)nonmetropolitancountiesnonadjacenttoametropolitan area. Absolute)Upward)Mobility:Absoluteupwardmobilityisthemeanrank(inthe nationalchildincomedistribution)ofchildrenwhoseparentsareatthe25thpercentile ofthenationalparentincomedistribution.countyaverageabsoluteupwardmobility wascalculatedbychildren scountyofresidenceduring1996a2000. Segregation:Spatialsegregationreferstothephysicalseparationoftwoormore groupsintodifferentgeographicareas.thereisacloseconnectionbetweenagroup s spatialpositioninsocietyanditssocioeconomicwellabeingbecauseopportunitiesand resourcesareunevenlydistributedinspace(mitchell,2001).thecountyalevelmean traveltimetoworkforthepopulation16andolderprovidesameasureofsegregation becauseitrepresentsthespatialmismatchinaccesstojobs.thisinformationwas obtainedfromunitedstatescensusbureau. Income)Distribution:Incomedistributionreferstohowevenlyorunevenly incomeisdistributedinasociety.theginicoefficientisthemostcommonly usedmeasureofincomedistributionandthecountyalevelginiisusedtoreflectthe incomedistribution.thesedatawereprovidedbytheeop. 109

Quality)of)Education:%Asameasureofoverallschoolqualityweusedropout rates9a12.dataareobtainedfromthencesforthe2000a2001schoolyear. Social)Capital:%Socialcapitalreferstothecollectivevalueofsocialnetworksand thetendenciesthatarisefromthesenetworkstodothingsforeachother.asa measureofsocialcapitalweusethe1997countyalevelsocialcapitalindex 37 from RupasinghaandGoetz(2008). Family)Structure:Familystructurereferstothecombinationofrelativesthat compriseafamily.weuseaclassificationonthisvariablethatconsiderstheshareof singlemothersineachcountyasthenumberofhouseholdswithfemaleheads(andno husbandpresent)withownchildrenpresentdividedbythetotalnumberofhouseholds withownchildrenpresent.thesedatawereprovidedfromtheeop. Race:*Raceisrepresentedbytheshareofblackpopulationwhichismeasuredby dividingthenumberofpeopleinacountywhoareblackalonetothecountypopulation. ThisdatawereobtainedfromtheUnitedStatesCensusBureau. Labor)force)participation:Laborforceparticipationrateisameasureofthe proportionofacountry sworkingagepopulationthatengagesactivelyinthelabor market,eitherbyworkingorbylookingforwork.weincludefemaleandmalelabor 37 Thisindexisconstructedbyprincipalcomponentanalysis,wherethefirstfactorisanaggregationof religiousorganizations,civicandsocialassociations,businessassociations,politicalorganizations, professionalorganizations,labororganizations,bowlingcenters,physicalfitnessfacilities,publicgolf courses,sportclubs,managers,andpromoters,membershipsportsandrecreationclubs,and membershiptoorganizationsnotelsewhereclassified.thesecond,thirdandfourthfactorsarevoter turnout,censusresponserate,andnumberofnonaprofitorganizationswithoutincludingthosewithan internationalapproach,respectively. ThisvariablerangesbetweenA4.0and7.4,alargervalueimplyalargerstockofhumancapital. 110

forceparticipation.thisinformationwasobtainedfromtheunitedstatescensus Bureau. MetroBNonmetro)Remoteness)Codes 38 :Countiesareagroupaccordingtothe followingcategories:(1)metropolitancounties,(2)nonmetropolitancountiesadjacent toametropolitanarea,and(3)nonmetropolitancountiesnonadjacenttoa metropolitanarea.thisvariablewasconstructedusinginformationprovidedbythe USDAERS. % 4.5.%%Results% 4.5.1.%%Descriptive%statistics% Table4A1showsthedescriptivestatisticsofthekeycovariates.AbsoluteUpward Mobilityisonaverage43.46.Itreachesitshighestvalue(45.64)incountiesnonadjacent toametroarea.weobserveimportantchangesinthemeanttraveltoworkacrossthe subgroupofcounties.incountiesnonadjacenttometroareas,themeantraveltowork timeisapproximate5minutesshorterthatinmetroareas.theshareofblack populationalsochangessignificantlywhenmovingfrommetrotononmetrocounties, goingfrom10.40inmetrocountiesto7.28innonmetrocountiesnonadjacenttoa metroarea.itisalsoworthnotingthestockofsocialcapitalislargerinremotecounties, reflectingthefactthatcommunitiesmayhavevaryingcapabilitytoproducesocial capital.thisvariablealsoneedstobeconsideredwithinaframeworkoflimitedtime 38 AdetailedexplanationonhowthesevariablesiscalculatedisprovidedinAppendixD. 111

availability,aspeoplespendmoreandmoretimeworkingandcommuting,there sless timeforjoiningcommunitygroupsandvoluntaryorganizations.finally,weobservethat malesparticipateinthelaborforceatahigherratethanfemales. Table4A1:Summarystatistics countyalevelcovariates Variable (1) (2) (3) (4) Samplesize 2730 1047 975 708 AbsoluteUpwardMobility Meantraveltimestowork2000 (minutes) Ginicoefficient HighschoolDropout2000(%) Socialcapital1997 Rateofhouseholdswithsinglemothers 2000(%) ShareofblackResidents(%) Rateofmalelaborforceparticipation (%) Rateoffemalelaborforceparticipation (%) 43.46 42.14 43.29 45.64 (5.44) (4.39) (5.31) (6.28) 23.83 25.44 24.33 20.74 (5.38) (5.21) (4.8) (5.1) 0.38 0.40 0.38 0.38 (0.08) (0.09) (0.07) (0.09) 3.41 3.43 3.50 3.26 (2.76) (2.8) (2.69) (2.79) A0.17 A0.48 A0.23 0.36 (1.24) (1) (1.17) (1.47) 15.42 15.97 15.48 14.54 (5.6) (5.22) (5.63) (5.97) 9.44 10.40 9.98 7.28 (14.82) (13.51) (15.79) (15.11) 67.65 71.13 65.35 65.67 (8.22) (6.76) (8.08) (8.6) 55.08 57.94 53.10 53.58 (6.56) (5.8) (6) (6.83) Metropolitancounties x Nonmetropolitancountiesadjacenttoa x metropolitanarea Nonmetropolitancountiesnonadjacent x toametropolitanarea Standarddeviationreportedinparentheses Source:EqualityofOpportunityProject,CensusBureau,EconomicResearchService,andNationalCenter foreducationstatistics. Column (1) reports summary statistics for all the counties of our sample. In Column (2) we present summary statistics for metropolitan counties only. In column (3) and (4) we restrict our sample to nonmetropolitancountiesadjacentandnonadjacenttoametroarea,respectively. 112

4.5.2.%%Multivariate%regression%analysis% Weuseamultivariateregressionapproachtounderstandeffectsofcommutingtimes onabsoluteupwardmobility,controllingforanumberofotherfactorsassociatedwith it.weestimatestandardizedbetacoefficientstocomparestrengthofpredictionacross variables.thesecoefficientsshouldnotbeinterpretedascausaldeterminantsof mobilitybecauseallofthevariablesareendogenously 39 determined.forinstance,areas withhighratesofspatialsegregationmayalsohaveothercharacteristicsthatcouldbe therootcausedrivingthedifferencesinchildren soutcomes. ResultsoftheseparateregressionsarepresentedinTable4A2.Foreachcolumn, upwardincomemobilityisthedependentvariable.column(1)presentsresultsofan OLSregression,thecovariatesincludedare:meantraveltimetowork,Ginicoefficient, highschooldropoutrates,socialcapital,rateofhouseholdswithsinglemothers,rateof blackresidents,andmaleandfemalelaborforceparticipation.column(2)shows estimationresultswhenweincludestatefixedeffects.fixedeffectallowsustocontrol fortheaveragedifferencesacrossstatesinanyunobservablepredictors.incolumn(3) and(4)weincludetwodummyvariablesrepresentingnonmetropolitancounties adjacentandnonaadjacenttoametroarea.thesevariablesallowustolookfor differencesbetweenthesetwogroupscomparedtometropolitancounties,whichisthe omittedgroup.column(4)alsoincludesstatefixedeffects. 39 Avariableissaidtobeendogenouswithinasystemifitsvalueisdeterminedorinfluencedbyoneor moreoftheothervariablesinthesystem(excludingitself). 113

TheOLSregressionresultspresentedintable4A2indicatethatabsoluteupward incomemobilityvariationsresultfromthecombinedeffectsofseveralfactors.most coefficientsremainsimilarwhenstatefixedeffectsareincluded.theonlylarge differenceisobservedinthecoefficientsforsocialcapital.thereisasubstantialdecline ionthemagnitudeofthesocialcapitaleffectwhentimeinvariantvariablesare controlled.howeveritseffectonabsoluteupwardmobilityisstilllarge.acrossallthe models,thestrongest 40 andmostrobustpredictorofupwardincomemobilityisthe fractionofsinglemotherhouseholds.incomeinequalitymeasuredbythegini coefficienthasastrongnegativecorrelationwithabsoluteupwardmobility.correlation coefficientsforthisvariabledonotdiffersignificantlyacrossspecifications. Laborforceparticipation;inparticularfemalelaborforceparticipationisastrong predictorofabsoluteupwardmobility.incolumns(3)and(4)thecorrelationcoefficient forthedichotomousvariablerepresentingnonmetropolitancountiesnonadjacenttoa metroareaarelargeandsignificant.theseresultscorroboratethepatternobservedin thedescriptivestatisticsthatshowedthatabsoluteupwardmobilityislargerinremote counties. 40 Thecloserthecoefficientsareto+1.0andA1.0,thegreateristhestrengthoftherelationshipbetween thevariables. 114

Table4A2:Correlatesofabsoluteupwardmobility:comparingalternativespecifications Variables Upwardincomemobility (1) (2) (3) (4) Meantraveltimestowork2000(minutes) A0.114** A0.057* A0.087* A0.034 (0.04) (0.03) (0.05) (0.03) Ginicoefficient A0.142*** A0.113*** A0.138*** A0.111*** (0.04) (0.02) (0.03) (0.02) HighschoolDropout2000(%) A0.035 A0.104*** A0.038 A0.104*** (0.06) (0.02) (0.06) (0.02) Socialcapital1997 0.310*** 0.155*** 0.299*** 0.152*** (0.06) (0.04) (0.06) (0.04) Rateofhouseholdswithsinglemothers2000(%) A0.503*** A0.503*** A0.488*** A0.488*** (0.08) (0.08) (0.08) (0.08) ShareofblackResidents(%) 0.031 0.124 0.021 0.115 (0.07) (0.09) (0.07) (0.09) Rateofmalelaborforceparticipation(%) 0.017 0.043** 0.025 0.052** (0.03) (0.02) (0.03) (0.02) Rateoffemalelaborforceparticipation(%) A0.099** A0.135*** A0.081 A0.111*** (0.05) (0.02) (0.05) (0.03) Nonmetropolitancountiesadjacenttoametropolitanarea 0.018 0.053** (0.04) (0.02) Nonmetropolitancountiesnonadjacenttoametropolitanarea 0.172** 0.158*** (0.07) (0.04) Constant 0.000 0.000 A0.051 A0.060*** (0.07) (0.00) (0.08) (0.02) Statefixedeffect x x R2 0.629 0.801 0.633 0.803 N 2730 2730 2730 2730 Significancelevels:*10%**5%***1% Standarderrorsreportedinparentheses Notes:EachcolumnreportscoefficientsfromanOLSregressionwithstandarderrorsclusteredatthe statelevelreportedinparentheses.thedependentvariableforallthecolumnsisabsoluteupward mobility.allthevariables,independentanddependentarenormalizedtohavemean0andstandard deviation1.column1reportsunweightedestimatesacrossallcounties.column2includesstatefixed effects.colum(3)includesdummyvariablesrepresentingnonmetropolitancountiesadjacentandnona adjacenttoametroarea.column(4)includesstatefixedeffects.isanunweightedregression Thecorrelationcoefficientsestimatedforcommutingtimearequitelargeand significantforallfourspecifications.increasedcommutingtimesarelinkedtoreduced 115

absoluteupwardmobility.theseempiricalresultssupportthehypothesisthatlarge traveltimestoworknegativelyaffectabsoluteupwardmobility.thechannelthrough whichcommutingaffectsmobilityishumancapitalaccumulation. AccordingtoanewPewResearchCenteranalysisoflongAtermdataontimeuse, in2011mothersandfathersspent,onaverage,14and7hoursperweekonchildcare activities,respectively(anaverageof2and1hoursperday).thismeansthateveryday fathersspendcommuting(50minutesonaverage)almostthesametimetheyspend withtheirchildren.inthecaseofmothers,theyspendhalfthetimecommutingthat theyspendwithchildren.hence,webelievethatasmallreductioninthetimeparents spendcommutingwouldtranslateintoanimportantincreaseinthetimetheyspend engagedwiththeirchildrenbuildinguptheirhumancapital. Inordertochangethecurrentsituation,thetraditional9A5workdayhastobe challengedbycompaniesfindingalternativewaysfortheirstafftowork.but, whywouldcompaniesbeinterestedinreducingtheiremployeescommutingtimes?to answerthisquestionitisnecessarytoplaceourresultsinalargercontext.formany peoplethecommutetakesaconsiderableamountoftimeoutoftheirdayandresultsin loweredfrustrationtolerance,fatigue,badmoodwhenarrivingatworkinthemorning, increasedlateness,andabsenteeism(koslowsky,kluger,&reich,2013).research commissionedbyglobalworkplaceproviderregusfoundstrongevidenceofthelink betweenlengthofcommuteandlikelihoodofdefection.alongcommutingtimemay causeanemployeetoleaveacompanydespiteotherwisehighjobsatisfaction. 116

Researchalsohasshownthatgreaterflexibilityinworkingschedulesmight alleviatesomeofthenegativeeffectsassociatedwithtravellingtowork.astudyof commutersinatlanta,georgiafoundevidenceforlowerdrivingstressandfeelingsof timeurgencyforflexitimecommuters(lucas&heady,2002).moreover,employees workinginanenvironmentviewedasmorefamilyasupportiveexperiencelowerlevelsof workafamilyconflicts(wfc).reducedwfcthentranslatesintogreaterjobandfamily satisfaction,followedbygreateroveralllifesatisfaction.(lapierreetal.,2008).these aremorethanenoughreasonsforcompaniestoworktowardreducingtheiremployees commutingtimes.flexibleworking,compressed 41 hoursandhomeworkingaresomeof thealternativethatshouldbeanalyzed. % 4.6.%%Conclusions%and%discussion% Theresultsofthisstudyshowthatproxiesforincomedistribution,familystructure,and femalelaborforceparticipationaffectadverselyabsoluteupwardmobility.ontheother hand,socialcapitalhasapositiveeffectonupwardincomemobility.themainvariable ofinterestiscommutingtimes.theempiricalresultsconfirmthetheoreticalmodel predictionsthatcommutingtimesaffectnegativelyupwardincomemobility.although causalanalysiscannotbeperformed,theoverallstabilityofourresultsacrosssamples confirmsthattheappliedspecificationprovidesausefultooltoanalyzeabsolute upwardmobility. 41 Inacompressedhourssystemsstaffworktheirfullworkingweekinfourdaysforexampleratherthan five. 117

Ourresults,togetherwiththefactthatcommutingtimeshaveanegativeeffecton employeeproductivity(rothbard&wilk,2011),mayencouragecompaniestoenhance businessperformancebylookingforalternativemethodstoreducecommutingtimes. Flexibleworking,compressedhoursandhomeworkingaresomeoftheoptionstobe considered. Commutinghasbeenanalyzedinthecontextoftimespentengagedwith children(buidinguptheirhumancapital).however,sincecommutingtimeshavebeen alsolinkedtoincreasedstressandirritability,thisvariablemayhavealsosomeindirect negativeeffectonthequalityoftimeparentsspendwithchildren.furtherresearch shouldbeconductedinordertoaccuratelydeterminetheindirectnegativeeffectsof commutingtimesonchildrenhumancapitalaccumulation. % 118

Chapter%4:%References% Anderson,S.E.,Gooze,R.A.,Lemeshow,S.,&Whitaker,R.C.(2011).Qualityofearly maternal childrelationshipandriskofadolescentobesity.pediatrics,peds. 2011A0972. Becker,G.S.(1993).Human*capital:*a*theoretical*and*empirical*analysis,*with*special* reference*to*education(vol.3rd).chicago:theuniversityofchicagopress. Bildirici,M.,Sunal,S.,AykacAlp,E.,&Orcan,M.(2005).DeterminantsofHumanCapital Theory,GrowthandBrainDrain:AnEconometricAnalysisfor77Countries. Applied*Econometrics*and*International*Development,*5,2. Chetty,R.,Hendren,N.,Kline,P.,Saez,E.,&NationalBureauofEconomic,R.(2014a). Where*is*the*Land*of*Opportunity?*The*Geography*of*Intergenerational*Mobility* in*the*united*states(vol.no.w19843.;no.w19843):nationalbureauof EconomicResearch. Chetty,R.,Hendren,N.,Kline,P.,Saez,E.,&Turner,N.(2014b).Is*the*United*States*still* a*land*of*opportunity?*recent*trends*in*intergenerational*mobility.retrieved from Christian,T.J.(2012).Automobilecommutingdurationandthequantityoftimespent withspouse,children,andfriends.preventive*medicine,*55(3),215a218. Costa,G.,Pickup,L.,&DiMartino,V.(1987).CommutingAAafurtherstressfactorfor workingpeople:evidencefromtheeuropeancommunity.ii.anempiricalstudy. International*archives*of*occupational*and*environmental*health,*60(5),377A385. 119

Crane,R.(1996).Theinfluenceofuncertainjoblocationonurbanformandthejourney towork.journal*of*urban*economics,*39(3),342a356. Crone,T.M.(1998).Housepricesandthequalityofpublicschools:whatarewebuying? Business*Review(Sep),3A14. Davis,J.S.(1993).Thecommutingofexurbanhomebuyers.Urban*Geography,*14(1),7A 29. Elliott,G.C.,Cunningham,S.M.,Colangelo,M.,&Gelles,R.J.(2011).Perceived matteringtothefamilyandphysicalviolencewithinthefamilybyadolescents. Journal*of*Family*Issues,*32(8),1007A1029. Flouri,E.,&Buchanan,A.(2004).Earlyfather'sandmother'sinvolvementandchild's latereducationaloutcomes.british*journal*of*educational*psychology,*74(2), 141A153. Frazis,H.,&Stewart,J.(2006).Howdoeshouseholdproductionaffectearnings inequality?evidencefromtheamericantimeusesurvey. Gray,M.,&Chapman,B.(2001).Foregoneearningsfromchildrearing.Family*Matters, 4. Gronau,R.(1976).Leisure,homeproductionandworkAAthetheoryoftheallocationof timerevisited:nationalbureauofeconomicresearchcambridge,mass.,usa. Guryan,J.,Hurst,E.,&Kearney,M.(2008).Parentaleducationandparentaltimewith children.the*journal*of*economic*perspectives,*22(3),23a46. doi:10.1257/jep.22.3.23 120

HooverADempsey,K.,&Sander,H.(1995).Parentalinvolvementinchildren'seducation: Whydoesitmakeadifference.The*Teachers*College*Record,*97(2),310A331. Hout,M.(2015).Asummaryofwhatweknowaboutsocialmobility.The*ANNALS*of*the* American*Academy*of*Political*and*Social*Science,*657(1),27A36. Kageyama,T.,Nishikido,N.,Kobayashi,T.,KUROKAWA,Y.,KANEKO,T.,&KABUTO,M. (1998).Longcommutingtime,extensiveovertime,andsympathodominantstate assessedintermsofshortatermheartratevariabilityamongmalewhiteacollar workersinthetokyomegalopolis.industrial*health,*36(3),209a217. Kahneman,D.,&Krueger,A.B.(2006).Developmentsinthemeasurementofsubjective wellabeing.the*journal*of*economic*perspectives,*20(1),3a24. Kluger,A.N.(1998).Commutevariabilityandstrain.Journal*of*Organizational*Behavior,* 19(2),147A165. Koslowsky,M.,Kluger,A.N.,&Reich,M.(2013).Commuting*stress:*Causes,*effects,*and* methods*of*coping:springerscience&businessmedia. Lapierre,L.M.,Spector,P.E.,Allen,T.D.,Poelmans,S.,Cooper,C.L.,O Driscoll,M.P.,...Kinnunen,U.(2008).FamilyAsupportiveorganizationperceptions,multiple dimensionsofwork familyconflict,andemployeesatisfaction:atestofmodel acrossfivesamples.journal*of*vocational*behavior,*73(1),92a106. Lee,C.AI.,&Solon,G.(2009).Trendsinintergenerationalincomemobility.The*Review*of* Economics*and*Statistics,*91(4),766A772. 121

Lucas,J.L.,&Heady,R.B.(2002).Flextimecommutersandtheirdriverstress,feelingsof timeurgency,andcommutesatisfaction.journal*of*business*and*psychology,* 16(4),565A571. Radin,N.(1981).Theroleofthefatherincognitive,academic,andintellectual development.the*role*of*the*father*in*child*development,*2,352a427. Rothbard,N.P.,&Wilk,S.L.(2011).Wakingupontherightorwrongsideofthebed: StartAofAworkdaymood,workevents,employeeaffect,andperformance. Academy*of*Management*Journal,*54(5),959A980. Rupasingha,A.,&Goetz,S.J.(2008).UScountyAlevelsocialcapitaldata,1990A2005.The* northeast*regional*center*for*rural*development,*penn*state*university,* University*Park,*PA. Varady,D.P.(1990).InfluencesonthecityAsuburbanchoiceAStudyofCincinnati homebuyers.journal*of*the*american*planning*association,*56(1),22a40. WorldABank.(2015).Governmentexpenditureoneducation,total(%ofGDP).Retrieved fromhttp://data.worldbank.org/indicator/se.xpd.seco.pc.zs/countries % 122

Appendix%F% TheEconomicResearchService(ERS)hasdevelopedanineAlevelurbanArural classificationscheme,whichdistinguishescountiesbythepopulationsizesofmetro areas,degreeofurbanization,andadjacencytoametroarea(tableda1).however, sinceourinterestliesinidentifyinghowfactorsinfluenceupwardincomemobility,in particular,commutingtimesacrosscountiesbythelevelofremotenessonly,weusea variationofthisclassificationschemetobetterreflectthisconcept.accordingtotheers anonmetrocountyisdefinedasadjacentifitphysicallyadjoinsoneormoremetro areas,andhasatleast2percentofitsemployedlaborforcecommutingtocentral metrocounties.nonmetrocountiesthatdonotmeetthesecriteriaareclassedas nonadjacent.forthepurposeofthisstudy,countiesnonadjacenttoametroareaare categorizedasremotecounties. 123

TableFA1:ERSA2003RuralAUrbancontinuumcodes Code Description SampleSize a Metrocounties: 1 Countiesinmetroareasof1millionpopulationormore 394 2 Countiesinmetroareasof250,000to1millionpopulation 316 3 Countiesinmetroareasoffewerthan250,000population 337 Nonmetrocounties: 4 Urbanpopulationof20,000ormore,adjacenttoametroarea 218 5 Urbanpopulationof20,000ormore,notadjacenttoametroarea 101 6 Urbanpopulationof2,500to19,999,adjacenttoametroarea 596 7 Urbanpopulationof2,500to19,999,notadjacenttoametroarea 424 8 Completelyruralorlessthan2,500urbanpopulation,adjacenttoametroarea 161 9 Completelyruralorlessthan2,500urbanpopulation,notadjacenttoametroarea 183 Source:EconomicResearchService(ERS) a Samplesizereferstothenumberofcountiesbelongingtothecode Theresultingcodeconsistsofthreelevelsthataredefinedbymetropolitan access(tableda2).level1referstometropolitanareas.level2isnonmetropolitan countiesadjacenttoametropolitanarea.level3referstononmetropolitancounties nonadjacenttoametropolitanarea. 124

TableFA2:1A5MetroANonmetroremotenesscodes Code Description SampleSize a 1 Metrocounties 1047 2 Nonmetropolitancountiesadjacenttoametroarea 975 3 Nonmetropolitancountiesnonadjacenttoametroarea 708 a Samplesizereferstothenumberofcountiesbelongingtothecode % % % % % 125

CHAPTER%5:%CONCLUSIONS% Thefirstessay,DoesIPMHaveStayingPower?RevisitingaPotatoAproducingAreaYears AfterFormalTrainingEnded,describesIPMspreadandadoptionseveralyearsafter formalintensiveipmoutreacheffortsceasedinapotatoaproducingregioninecuador. Theinstrumentalvariablesapproachwasdeterminedtobethemostappropriate methodtomeasuretheimpactofadoptiononpesticidesexpenditures.resultsshow thatipmadoptioncontinuesintheareabutwithalowerproportionoffarmersfully adoptingallpracticesandahigherproportionadoptinglowtomoderatelevelsas comparedto2003.almostallpotatofarmersintheareausesomeipmpractices, reflectingamajorincreaseinipmuse.farmeratoafarmerspreadhassupplantedformal trainingandoutreachmechanisms.ipmadoptionsignificantlylowerspesticideuseand savesproductioncostsforadopters.theseresultsprovidejustificationforcontinued publicinvestmentsinipmoutreachinareaswheresuchoutreachhasnotexistedinthe past.intensivetraininginipm,althoughrelativelycostly(godtlandetal.,2004;mauceri etal.,2007),seemstobeeffectiveinmakingdurablechangesinhowfarmersthink aboutpestmanagement. Inthesecondessay,CanTextMessagesImproveAgriculturalOutreachin Ecuador?,weimplementandevaluateaninterventionthataimedtoincreaseadoption ofagriculturaltechnologiesamongblackberryfarmersinecuador,backedbybehavioral changetheory.thisinterventiontargetedtwodeterminantsofbehavior:knowledge andlimitedattentionbytheprovisionofinformation,alongsidereminders.asproviders 126

ofinformation,textmessageshavesomeknowledgebuildingeffectleadingtothe adoptionofculturalicmpractices.asreminders,textmessageseffectivelyincrease adoptionofculturalicmpracticesandrecommendedpesticides.giventhelowcostof thetextmessages,theymaystillbecosteffective,eventhoughtheirimpactissmall, addingatextmessagesupplementtoanalreadyexistinginapersontrainingprogram couldbeanattractiveoptionforagriculturalextensionwork. Inthethirdessay,DeterminantsofAbsoluteUpwardIncomeMobility:The HiddenCostofCommuting,amodeltoexplaintheimpactofcommutingtimeson absoluteupwardmobilityisproposed.wearguethatcommutingtimesisakey determinantofhumancapitalaccumulationbecauseitaffectstheamountoftime parentsspendwithchildren.humancapital,inturn,isakeydeterminantofwagesand productivity.furthermore,usingcountyaleveldata,weevaluateempiricallytheimpact ofcommutingtimeonabsoluteupwardmobility.theempiricalresultsconfirmthe theoreticalmodelpredictionsthatcommutingtimesaffectnegativelyupwardincome mobility.althoughcausalanalysiscannotbeperformed,theoverallstabilityofour resultsacrosssamplesconfirmsthattheappliedspecificationprovidesausefultoolto analyzeabsoluteupwardmobility.ourresults,togetherwiththefactthatcommuting timeshaveanegativeeffectonemployeeproductivity(rothbard&wilk,2011),may encouragecompaniestoenhancebusinessperformancebylookingforalternative methodstoreducecommutingtimes.*furtherresearchinthisareaistodeterminethe indirecteffectsofcommutingtimesonincomemobility.* 127

Chapter%5:%References% Godtland,E.M.,Sadoulet,E.,Janvry,A.d.,Murgai,R.,&Ortiz,O.(2004).Theimpactof farmerfieldschoolsonknowledgeandproductivity:astudyofpotatofarmersin theperuvianandes.economic*development*and*cultural*change,*53(1),63a92. Mauceri,M.,Alwang,J.,Norton,G.,&Barrera,V.(2007).Effectivenessofintegrated pestmanagementdisseminationtechniques:acasestudyofpotatofarmersin Carchi,Ecuador.Journal*of*Agricultural*and*Applied*Economics,*39(3),765A780. Rothbard,N.P.,&Wilk,S.L.(2011).Wakingupontherightorwrongsideofthebed: StartAofAworkdaymood,workevents,employeeaffect,andperformance. Academy*of*Management*Journal,*54(5),959A980. 128

% % % Annex%1:%Blackberry%Survey% Adopción%de%MIP%en%el%cultivo%de%mora%en%Bolívar%y%Tungurahua% Cuestionario%de%entrevista%No.% % % MÓDULO%1.%CONDICIONES%SOCIO9ECONÓMICAS% 1. Nombrecompletodelentrevistado: 2. Nombredelentrevistador: 3. Cantón: 4. Parroquia: 5. Comunidad: 6. Fechadelaentrevista: 7. Georreferenciacióndelafinca(centrodelaparcelamásgrande): Latitud: Longitud: Altitud: 8. EstadoCivil: 1=Soltero 2=Casado 3=Separado 4=Divorciado 5=Viudo 9. Edadenaños: 10. Sexo: 1=Masculino 2=Femenino 11. Añosdeeducaciónformal: 12. NúmerodeañosqueUstedhatrabajadocomoagricultor: 13. Númerodeañosquehacultivadomora: 129

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