WHY GENDER DISAGGREGATED DATA? HOW ... -...

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.

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• 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.