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NOTESAGRICULTURE & RURAL DEVELOPMENT
Filling the Data Gap on Gender in Rural Kenya
WHY GENDER DISAGGREGATED DATA?AgricultureisoneofthemostimportantsectorsinKenyaanditsperformancegreatlyaffectsthepoor.Inadditiontoitsimportanceasasourceoffoodandincome,thesectordirectlyaccountsfor24percentofKenya’sGDP,andforanother25percentindirectlythroughlinkageswithothereconomicsectors.Itprovidesabout70percentofruralemployment.Kenyanagricultureisdominatedbysmallholderfarmers,pastoralists,andfisher-folkwhotogethercomprisearound4millionhouseholds.Farmsaresmall,averagingonehectare.Thesectorfacesmanychallengesincludinglowproductivity,poormarketaccess,lowlevelsofcommercialization,inadequateinfrastructure,andincreasingweathervariability.
TheGovernmentofKenya(GoK)withfinancialsupportfromtheWorldBankisimplementingtheKenya Agricultural Productivity and Agribusiness Program(KAPAP)whichaimstoincreaseagriculturalproductivityandsmallholderincomebyimprovingagriculturaltechnologysystems,empoweringmenandwomenfarmers,andpromotingagribusinesses.Womenfarmersinparticularoperatewellbelowtheirpotential.Improvingtheircapacitytoaccumulateresourcesandtoretainincomeareimportantobjec-tivesoftheKAPAP.Theprojectalsoseekstoprovidewomenwithavoiceindecision-makingbodies.
However,amajorchallengequicklypresenteditself –alackofexistinginformationongendergaps.Muchoftheinformationwhichisavailableisout-of-date,andmostofitisbasedoncasestudies.ThismadeitnecessaryforKAPAPtocollectauniquesetofgender-disaggregatedbaselinedatatoprovideguidanceoncriticalgendergaps.Thisinformationwillcontributetoanevidence-basedgenderpolicydialogueinKenya’sagriculturesector.Althoughappealsforgender-disaggregateddataarefrequentlyheard,theprocessiscomplicatedandcostly,andentailstheneedtoovercomeanumberofmethodologicalhurdles.ThisARDNotedescribestheprocessofgender-disaggregateddatacollectionthathasbeenemployedbyKAPAP,andpresentsthekeylessonslearnedfromthepreliminaryresultsofthedataanalyses. Balancing many responsibilities. Photo: Asa Torkelsson.
HOW GENDER DISAGGREGATED DATA?Becausedatacollectionmethodologiestypicallyfocusonheadsofhouseholds,andbecausemosthouseholdheadsaremen,women’sviewsonagriculturehavebeenlargelyunderreported.Thisisaseriousdrawbackbecausewomenareoftentheprimaryfarmersintheirhouseholds.Failingtocaptureinformationfromthemleadstoadistortedunderstandingoffarmingoperations.
ThegeneralconstraintswomenfaceinagriculturehavehoweverbeencomprehensivelydocumentedbytheWorldBank,FAOandIFADintheGender and Agriculture Sourcebook,the2012WorldDevelopmentReport Gender Equality and Development, andthe2010-2011StateofFoodandAgriculture:Closing the Gender Gap for Development. Thesepublicationshaveaffirmedthatwhilewomenperformaverysubstantialproportionofagriculturalwork,theygenerallyhavelessaccessthanmentoavarietyofresources.
QUESTIONNAIRE DEVELOPMENT AND SURVEY DESIGN Thequestionnairesandsurveyweredesignedtoaligndatacollectionwiththeneedsoftheagriculturesector,andtocontributetothedevelopmentofasector-wideapproachtogender-disaggregatedruraldiagnostics.Theywerealsointendedtoinformthe
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agriculturesectorgenderpolicywhichisbeingdevelopedbyKenya’sthirteen-ministryAgriculturalSectorCoordinationUnit(ASCU).
Threequestionnairesweredesigned:ahousehold, individual,and communityquestionnaire.Tworespondentswereinter-viewedineachhousehold.Thehouseholdquestionnairewasgearedtowardsthe‘primaryfarmer,’asself-reportedbythehousehold,andwasusedtocollectinformationaboutactivitiesthatallhouseholdmembersengagein.Theotherkeycontribu-tortofarming,usuallythespouse,wouldthenrespondtotheindividualquestionnaire.Thehouseholdandindividualquestion-nairesweresimilarincontentandpartlyoverlapped.
PRE-TESTINGThequestionnaireswerefinalizedusingahighlyiterativeprocesstoensurerelevanceacrossthevariedfarmingcondi-tionsthatarefoundinKenya.Thequestionsthemselvesweredesignedtobeeasytorespondto,effectiveincapturingtheintendedinformation,andeasyforcodingandinputtingtheinformation.Anumberofduplicationsandmisunderstandingswereidentifiedandweededout.
IncollaborationwithacapacitybuildinginitiativeofGenderFocalPointsinthewatersector,theywerepre-testedinperi-urbanandruralsettingsintheCoastProvince.AteamfromKAPAPandtheWorldBankpilotedthequestionnairesintheNorthEasternandEasternProvinces.Importantchallengeswereencounteredrelatedtospecificationofmeasurements,particularlyoftimeandquantities.Pre-testingalsohelpedtorefinecodesandidentifyomissions(forinstancecreatingcodesforbothsweetandfoodbananainthecropinventoryandaddinglivestock‘lostorstolen,’inadditiontothosewhichhaddied).Italsotaughttheteamtobeasspecificaspossible.Forexample,‘registeredgroups,’referredtothoseregisteredbytheMinistryofGender,ChildrenandSocialDevelopment.
SAMPLING STRATEGYTogenerateasamplewiththenecessarystatisticalpowertorepresentarobustevidencebase,samplingwasbasedonarandomselectionofhouseholdsrepresentingaproportionofregionalhouseholdsinProjectareas.Mostofthesefellintoasetofpanelhouseholdsthathadbeengeneratedearlier.However,duetothenewdistrictset-up,severalhouseholdspreviouslyinterviewedascontrolsnowfellwithinprojectareasandthereforethenewlyaddedhouseholdsweresampledascontrols.Multi-stagesamplingmethodswereusedinthenon-Projectlocations,usingrandomselectionfromalistofallvillagehouseholdsidentifiedtogetherwitheachvillageelderorareaassistantchief.Beforedatacollectionbegan,appoint-mentsoninterviewdatesforeachofthesampledhouseholdswasmadethroughareaassistantchiefsandvillageelderstomaximizetheresponserate.
ENUMERATOR RECRUITMENT AND TRAININGCallforapplicantswasplacedinthedailynewspaper,and54enumeratorswithbachelor’sdegreeinagriculture-relateddisci-plineswereselectedforatwo-weektrainingwhichinvolvedgo-ingthroughthequestionnairesquestionbyquestion,clarifyingthemeaningofeachquestionandtheinformationsought,andusingpracticalexercisesonhowtoaskquestions,probeforandrecordtheresponses.Ateamof45enumerators—32menand13women—wasfinallyselectedforthedatacollection.Commitmentsweremadetohaveabalancedrepresentationbutitwasdifficulttofullymeetsuchatarget,sinceespeciallyyoungmotherswouldfinditdifficulttobeoutofhomeforsuchalongstretchoftime.Asymmetryinthegenderofenumera-torsandthatofrespondentscouldbeaprobleminsomeareasofKenya,butdidnotimpactdatacollectionareasaccordingtothelongstandingexperienceofthefirm.
DATA COLLECTION Datacollectionwascarriedoutbetween1May2011-25June2011bynineteams,eachcomprisingfiveenumerators,onesupervisorandadriver.Supervisorsmanagedactivitiesandmadespotcheckstoensuredataquality.Inthefirstweekofdatacollection,researchersvisitedtheteamstoprovidetechni-calbackstopping.
STEPS IN GENDER-DISAGGREGATED DATA COLLECTION AND ANALYSIS
Number of interviews: 4,100Approximate cost: US$500,000Time frame: April2010-May2012
1. Development of survey instruments(April2010)andfirstroundofpre-testing(June-August2010)
2. Recruitment of Firmtoundertakedatacollection(January2011)
3. Data collection:
– Samplingofhouseholds(oneweek)– Preparationoflistoftargetandcontrolhouseholds
tobeinterviewed(oneweek)– Appointmentsforinterviews(oneweek)– Enumeratorrecruitment,shortlistingandinterview-
ing(oneweek)– Enumeratortraining(tendays)– Questionnairetrainingandre-cap(fourdays)– Fielddatacollection(eightweeks,May-June2011)
4. Dataentry,cleaninganddatabasecreation(August-October2011)
5. Descriptiveanalysis(October-November)
6. Analysisofmaterial,includingdevelopmentofGenderPolicyNote(January-May2012)
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Overall,4,141interviewswereconducted,comprising2,529households(1,799targetand730control),1,523individuals,and89communityinterviews.Amajority(54%)ofallrespon-dentsinthehouseholdsurveywerewomensuggestingtheyareprimarilyresponsibleforfarming.Around31%ofwomenrespondentsheadedtheirhouseholdswhilenearly93%ofthemalerespondentsheadedtheirhouseholds.
Theindividualquestionnairewascollectedfrom566menand957women.Inspiteofgreatefforttolocatethetwopartnersfortheindividualinterviews,manyhouseholdsweremanagedbyoneprimaryfarmeralone,andinmostcasesthiswasawoman.Thereasonsforfailingtointerviewanadditionalhouseholdmemberevenafterrepeatedvisitswasprimarilyduetothefactthattheadditionalhouseholdmembercouldnotbefound(45.3%ofthecases),followedbytherenotbeinganotherqualifiedmembertorespond(36.8%percentofthecases)andthehouseholdbeingrunbyasingleperson(13.4%ofthecases).
DATA ENTRY, CLEANING AND ANALYSISDataentry,cleaningandanalysiswasdoneintheStatisticalPackagefortheSocialSciences(SPSS).Preparationofdataentrytemplatesbeganimmediatelyuponstartoffieldworkanddataentrybeganinthesecondweekofdatacollection,andcontinueduntilmidAugust,2011,engagingateamoffourclerks.Datacleaningtookaperiodoftwomonths,andinvolvedateamoftenresearchassistantsandthreeenumeratorswhocheckedforandcorrectederrorsand/oromissionsintheentereddata.
SURVEY CHALLENGES AND LESSONS LEARNED
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Severalchallengeswerefacedduringthedesignanddatacollection.First,thesurveyinstrumentswererevisedseveraltimestoensuretheycapturedrelevantinformationandfortheinterviewstonotbelongerthat1.5hours.Thiscalledforpa-tience,technicalinputsandexperiencetogettherightbalanceandthesolidexperienceoftheconsultingfirmcameinhandy.Thedeterminationofthesamplesizetogeneratestatisticalrobustnesswithinatightbudgetwasanothermajorchallenge.
Secondly,thetimingoffieldworkcoincidedinsomeareaswiththelongrainswhichcontributedtologisticalchallengessuchasvehiclebreakdownandfuelshortages,andinothersitcoincid-edwiththepeakseasoninfarming,andsomeinterviewshadtobeginlaterthanscheduledtoallowrespondentstofinishtheirfarmwork.
Interviewingtwomembersinahouseholdwasausefulin-
novation,butitwaschallengingtoidentifytworespondentsforsimultaneousinterviewsinahousehold,despitetheappoint-mentsmadepriortothevisit.Thisresultedinfrequentcall-backsandmoreintensivefieldwork.
Insomeareas,notablyintheNorth-EasternProvince,itwasachallengetoidentifytranslatorsfortheinterviewsandtrans-lationalsoprolongedinterviewswhenrespondentsdidnotunderstandSwahiliorEnglish.
PRELIMINARY RESULTSAmajorityofthecommunitiesreportedadecliningtrendinmanyoftheaspectsofcommunitywelfareduringthelastfiveyears,exceptforinputavailabilityandrevenuesgener-atedfromraisinglivestock.Drought,increasesinfarminputpricesandfoodpriceswerecitedtohaveadverselyaffectedthelivelihoodsofsmallholderfarmers.Theaverageageoftherespondentsof48years—withmenbeingfouryearsolderthanwomenonaverage—suggestsaratheragingfarmingpopula-tion.Ahigherproportionofwomen(30%)thanmen(12%)hadnoformaleducationandcouldnotreadnorwriteandgendergapswereaccentuatedatthehighereducationallevels.Thefol-lowingkeyfindingsaresingledoutfromthesurveyreport:
• Ahigherpercentageofmen(81%)comparedtowomen(19%)ownedlandindividually.Areasoflandownedbymenwereaboutfourtimeslargerthanthoseownedbywomen,andmenalsofarmedlargerparcels.
• Themajorityofwomenconcentratedmainlyintheproduc-tionoffoodcropsandfarmedsmallerlandholdingsthanmenwhogrewthesamecrops.Womenhadhigheryieldsforselectedcrops(Irishpotatoes,bananasandtea)butmenregisteredhigheryieldforallothercrops.Ahigherpercent-ageofmenthanwomenownedalltypesoflivestockexceptchicken.
• Ahigherpercentageofmenthanwomensoldcrops.Mendecidedontheuseofrevenuefromthesaleofmostcrops.Regardinglivestock,womenmadedecisionsregardingchickenonly.
• Fewmen(27%)andwomen(13%)actuallysoughtexten-sionadvice.Halfofthemenand36percentofthewomenwhosoughtextensionactuallyreceived.Themainreasongivenwasthatitwastimeconsumingorthatextensionagentswerenotavailable.Mostrespondentsweresatisfiedwiththeextensionadvicetheyhadreceived,andmosthadappliedtheadvice.Forthosewhodidnot,themainreasongivenwasthatputtingtheadviceintoplacewascostly.
• Althoughtheproportionsofwomenandmenwhoweremembersingroupsweresimilar,largerproportionsofmenthanwomenheldleadershipfunctionsingroups.
• Theruralgenderresourcegapwasvalidated,asshowninthegraphbelow.
1818 H Street. NW Washington, DC 20433 www.worldbank.org/ard
• Meanincomeformenwasthreetimeshigherthanforwomen.Ahigherpercentageofmenwasengagedinoff-farmactivitiescomparedtowomenandtheyearnedtwiceasmuchincomeaswomenearnedfromtheseactivities.Overhalfofthemenhadasavingsaccount,whereasasmallerproportionofwomenhadanaccount.Aboutathirdofmenandafourthofwomenhadappliedforcredit,withahighsuccessrateforboth.Men’screditvolumeswerehoweverlarger.
CONCLUSIONThisnotehassummarizedthelessonslearnedfromagender-disaggregatedsurveyinKenya.Thedistinctionbetweena‘primary farmer’ anda ‘head of household’ provedtoberelevantandusefulbecausewomeninmostcasesweretheprimaryfarmersintheirhouseholds,butseldomheadedtheirhouseholds.
Oftenwhenimplementingsurveyswearecautioustoavoidrespondentfatigue.Thistime,theteamactuallylearnedthatparticipatingasarespondentinasurveycanactuallyhaveanempowermentfunctionaswell.Feedbacksuggestedforexamplethatrespondents,throughtheinterview,saidthey
hadactuallylearntalotregardingthecostsandbenefitsoftheirfarmingenterprise.
Wealsolearnedthatpartnershipandcollaborationtocollectgender-disaggregateddataisagreatwaytoovercometheincreasedcostsinvolvedinaquantitativesurveyapproach.
References“In Kenya, Survey of Female Farmers Uncovers Challenges“.WorldBank.http://go.worldbank.org/ETKDJPYK70
WorldBank,FoodandAgricultureOrganization(FAO),InternationalFundforAgriculturalDevelopment(IFAD).2008.Gender in Agriculture Sourcebook.WorldBank/IFAD/FAO.
WorldBank.2012.World Development Report: Gender Equality and Development.WorldBank.
Preparedbyauthors:AndrewKaranja(AFTAR)andAsaTorkelsson(PRMGE).Reviewedby:VictoriaStanley,PirkkoPoutiainen,andEijaPehufromtheGenderinRuralDevelopmentThematicGroup.Editedby:GunnarLarson(ARD).
Men
Women
60 70 80 90
100
0 10 20 30 40 50
Having savings account
Receiving extension service
Use of improved maize seed
Mean income
Land ownership
Cattle ownership
Transportation equipment
Owning farm machinery
Having communication
equipment
Chickenownership
ACCESS TO RESOURCES OF RURAL WOMEN AND MEN IN KENYA
Source:PreliminaryanalysisofIndividualSurveyData.Note: Percentages of wo/men respondents with access to the specified assets. Women’s income as a percentage of men’s.