network analysis of farmer groups...network analysis of farmer groups, nercrd rdp-57 3 2. collecting...

NetworkAnalysisofFarmerGroups,NERCRDRDP-57

NetworkAnalysisofFarmerGroups

ATrainingManualforExtensionEducators*

TennesseeStateUniversityDelawareStateUniversity

UniversityofMaryland–EasternShorePennStateUniversity

*FundingunderUSDANIFACapacityBuildingGrantNo.2011-38821-30966isgratefullyacknowledged.NERCRDRuralDevelopmentPaperRDP-57.

NetworkAnalysisofFarmerGroups,NERCRDRDP-57

NetworkAnalysisofFarmerGroupsATrainingManualforExtensionEducators

byContributingAuthorsListedAlphabetically:

StephanJ.Goetz1,YicheolHan1,EricaHildabridle1,LanLi2,FissehaTegegne2,StephanTubene3,andAndyWetherill4

NERCRDRDP-57.©2017TheAuthors.Allrightsreserved.

TableofContents PageIntroduction

1.AnOverviewofNetworkAnalysis 12.CollectingDataforaNetworkAnalysis(operationalizing) 33.AnalyzingtheNetworkSurveyData 74.InterpretingtheResults 105.Summary 19Coverimagecourtesy:http://vidi.cs.ucdavis.edu/images/publications/441.pngTheauthorsgratefullyacknowledgethecollaborationsofHirenBhavsar2,AlyssaBrown4,MichelleMcCulley3,RoseOgutu4,ChandraOwens4,Dez-AnnSutherland4,andDanielSweeney3,withoutwhomthisworkwouldnothavebeenpossible.Authoraffiliations:1.TheNortheastRegionalCenterforRuralDevelopmentandPennStateUniversity;2.TennesseeStateUniversity;3.UniversityofMarylandEasternShore;4.DelawareStateUniversity.

http://vidi.cs.ucdavis.edu/images/publications/441.png

NetworkAnalysisofFarmerGroups,NERCRDRDP-57 1

NetworkAnalysisofFarmerGroupsIntroductionThisdocumentpresentsthecontentofacurriculumpreparedforextensioneducatorsandotherswhoareinterestedinconductinganetworkanalysisoffarmergroups.Whiletheemphasishereisonminorityfarmergroups,theprinciplesdiscussedareuniversal.Afteroutlininghowtoconductanetworkanalysisanddescribingbasicnetworkconceptsweuseprimarydatacollectedbytheauthorsunderathree-stateCapacityBuildingGrant(CBG)ledbyTennesseeStateUniversity(TSU)toillustratehowthistypeofanalysiscanbeusedinareal-worldsetting.1.AnoverviewofNetworkAnalysisTheideathatindividualsareconnectedwithoneanother–i.e.,thattheyeachhavenet-works–introducespowerfulnewwaysofanalyzingandunderstandingtheirincentives,situationsandbehaviors.Forexample,inthepastresearchersmayhavelookedatthede-terminantsoffarmprofitsbyrelatingnetfarmincometofactorssuchasfarmerexperience,skills,assetsandlocation.Networkanalysis,ormorespecificallyananalysisofthenetworkwithinwhicheachindividualisembedded,introducesanimportantnewelementinthatitalsoconsidersfarmers’multidimensionalrelationshipswithotherfarmers.1Knowledgeofafarmer’spositioninhisorherlocalnetworkmayyieldimportantadditionalinsightsintofarmers’economicwellbeingorvariablessuchassalesorprofits.Understandingthesebasicunderlyingnetworkrelationshipsamongtheirfarmerstake-holderscanalsobecriticalforExtensioneducatorswhoseektodisseminatenewscience-basededucationalmaterials.Anetworkanalysiscanidentifyopinionleadersandother-wisecentrally-locatedindividualswithinanetworkwhocanmoreeffectivelytransmitthenewknowledgetoothernetworkmembersthanlesscentralactors.Atitsessence,anetworkconsistsoftheindividualparticipatingmembers(thenodes)andhowtheyareconnected(thelinks).Inourcaseweareprimarilyinterestedingroupsoffarmerswhohavesomethingincommonsothattheboundariesoftheirnetworksarewellestablished.Forexample,thefarmersmayliveinthesameregion,ortheymaybemem-bersofacooperative,acertainethnicgroup,orfollowjointproductionpracticessuchasorganicagriculture.Inthecaseoffarmers,theanalysiscanbemademoreinterestingandtheinsightsgeneratedmoreusefulifwealsoconsiderthatfarmershavetobeconnectedtoinputsuppliers,aswellastomarketingagentsiftheyaretogettheirproductstoconsum-ers.Thisextendsthenetworkanalysisbeyondfarmerstomarketagents.ArecentpaperbyBorgattietal.(2009)succinctlysummarizeswhyandhownetworksmatterinthesocialsciences,andalsoexplainshowanetwork’sstructureororganizationmatters.Afirstusefuldescriptionofanetworkistheextenttowhichitiscentralizedor1Insteadofviewingeachobservationonanactorasarow,theactorisplacedintoamatrixofinterrelation-ships.


decentralized(Figure1).Forexample,inthecaseofawheel-shapednetwork,theremaybeoneveryimportantandcentral(inthenetworksense)farmerwhoisconnectedtoalloftheotherfarmers.Andtheseotherfarmersareconnectedtooneanother,astheywouldbeinthecaseofacircle,whichrepresentsadecentralizednetwork.Forexample,thewheelstructureisfamiliarfromthehub-and-spokesystemthatairlinesuse.Inthiscasethecentralhubisquitepowerfulinthatitcancontrolinformationflows,anddecidewhogetstoknowwhat.Itisonlybyconnectingthroughthishubthatthedif-ferentmembersarelinkedorconnected.Ontheotherhand,inthecircleeachfarmerisconnectedtotwootherfarmerssothattheyallhavemoreevenstandingwithoneanother.Onecanalsoimagineasituationwherethewheelandthecirclearecombined.Forexam-ple,thismaybeacooperativeinwhichfarmer-membersmeetregularlyandexchangein-formation.Whilethereisstillacentralhubfarmerorleaderofthecooperativeinthissce-nario,thatindividualdoesnothaveasmuchimportanceasinthewheelstructurebecausetheotherfarmersarealsoconnectedtooneanother.Figure1alsoshowsthatbetweentheextremesofawheelandacirclestructurethereareatleasttwootherinterestingconfigurations.ThefirstisaY-shapedstructure.Thiscouldrepresentafarmer(atthebottom)whosellstoawholesaler,whointurnsellstoadistrib-uter(theredcircle)whosellstotwodifferentstoresorfarmersmarkets.Theselattertworetailersmaycompetewithoneanotherforthefoodsoldbythedistributor.Inthecaseofachainwehaveasimilararrangementexceptforthefactthatthereisonlyoneenduser,storeorconsumer,sothatcompetitionmaybereduced.Thiswouldbeanexampleofaverticallyintegratedsupplychain,withoutanyhorizontalconnections.Real-worldnet-workanalysisissorichandpowerfulbecauseoneusuallyencountersanynumberofcom-binationsoftheconfigurationsshowninFigure1.Forexample,combiningall4basicformsshownyieldsaso-calledkitenetworkstructurewhichisnotshownhere.

Figure'1:'Classifying'Basic'Network'Structures'

More'centralized' More'decentralized'

Wheel'(Hub)' Y'(Supply)'Chain'

Circle'

Source:'Goetz'(2016),'adapted'from'BorgaJ'et'al.'(2009)'


2.CollectingDataforaNetworkAnalysis(operationalizing)InanearlierNIFA-fundedprojectweworkedwithagroup(network)ofHmongfarmers,aChesapeakeBayfarmers’alliance,oforganicfarmersorganizedintoacooperative,andfe-malefarmers(seeGoetzetal.2006;Brasieretal.2007).Asnoted,toconductameaningfulnetworkanalysis,thegroupthatisbeingstudiedintermsofitsnetworkneedstobede-finedasclearlyaspossible.Graham(2015)reviewsrecentworkonidentifyingandmodel-ingnetworks.InthecaseoftheHmongfarmersitwasstraightforwardtoconductacensusanddeterminegroupmembership.Theseindividualshadacommonlanguageandcultureaswellasagriculturalproductionpracticesintheirbackgroundsandgenerallyhigherlev-elsoftrustamongthemselvesthanwithoutsiders.Theycouldeasilybeidentifiedbythespecialtycropstheygrew,aswellasthecommonmarketstheyserved,sometimesincom-petitionwithoneanother.ThefarmersintheChesapeakeBayareaforthemostpartgrewcommoditycrops,alsoincompetitionwithoneanother,buttheywereallunitedbythecommongoalofpreservinglandandagricultureinthebayregionbyensuringfarmprofitability.Thisgroupwasfor-mallyorganizedintoacooperativeandevenfundeditsownresearcharm.Theorganicfarmersgroupcooperatedtogetherinsynchronizingproductionschedulestodeliverfreshproducetohigh-endrestaurantsinDC.Thelastexample,anetworkoffemalefarmopera-torsinPennsylvaniahadaconnectionwithoneanotherthroughtheirgender.Theseindi-vidualsfelttheycouldfindmoresupportandadvicefromwithinthegroupthanfromotherentities.Otherstatessimilarlyhaveofficialorganizationsofwomenfarmers.Ineachoftheseexamples,delineatingthenetworkanditsboundarieswasverystraightforward.Tooperationalizethisfirststepofdatacollectionalistofpotentialnetworkmembersneedstobegenerated.OftenExtensioneducatorsalreadyhavesuchlistsiftheyworkwithindividualsorfarmerstakeholderswhocometomeetingsorreceiveeducationalmaterials,etc.Ifthegroupisnotwelldefined,however,itispossibletousesnowballsamplingtocompletethelist.Inthiscase,amemberisaskedaboutotherswhoshouldbeaddedtolistofmembers.2.1.IdentifyingNetworkMembersAfirststepincarryingoutanetworkanalysisisidentifyingagroupoffarmersandotherswhoshouldbeincluded.Thisisacriticalstepbecauseitdetermineswhoisincludedintheanalysisandwhatresourcesareneededtosurveynetworkmembers.InthecaseoftheTSU-ledCapacityBuildingProjectweidentifiedthreedistinctgroupswhowerelocatedinthesameareasorhadthesameethnicbackgrounds.FortheremainderofthismanualwerefertoourfarmergroupsasresidinginStates1,2and3withoutidentifyingtheactualstatesoastopreservesurveyrespondents’anonymity.State1.Producerssurveyedinthisnetworkwereselectedviaacombinationofcriteria.TheStateUniversity’scooperationextensionprogramhasamasterlistofproducersinitssmallfarmsprogramwhoareservedonaregularbasis.Thesmallfarmsprogramprovides


technicalassistancetoitsclientele,viafarmvisits,telephonecalls,emails,workshopscon-ferencesandmentorship.Thus,mostoftheindividualsselectedforthesurveywerepro-ducerswhoparticipatedinamyriadofextensionseventsandoutreachprogramsthatwerehostedbytheStateUniversity.Theproducersselectedwereprimarilylandownersorleaseholderswhowereengagedincommercialsmallscaleagriculturalproduction.Select-edparticipantsgrowandmarketplantandanimalproductsforhumanconsumption.Blacks,Whites,AsianIndiansandHispanicsareallservedbytheStateUniversityandareallincludedinthenetworksurvey.Anygroupthatwasnotrepresentedinthesurveywasnotomittedintentionally.Large-scaleproducerswereforthemostpartexcludedfromthissurvey.EventhoughtheStateUniversityisopentoprovidingtechnicalassistancetolargeproducers,considerabletimeisspentexpandingopportunitiesforlimited,smallandmi-norityproducersinthestate.Producersfromallcountiesinthestatewererepresentedinthenetworksurvey.Theyoperatetheirbusinessinbothurbanandruralcommunities.Oneadditionalselectioncriterionwastoidentifyproducerswhohadthepotentialtomarketfreshagriculturalproduceandvalue-addedproductsinlargeneighboringmetropolitanar-easwithdiversepopulations.State2.Farmersincludedinthisstate’snetworkwereselectedbasedontheagro-ecologicalzoneofthestate.Infact,theareaschosenareknownforgrowinghigh-valuecropsandareinconstantquestofdiversifyingagriculturalenterprisesandexpandingproducesalesintotheNortheastmetromarketcorridor.Thesefarmerswerereachedbyphoneusingadata-baseofcommercialsmallfarmersandranchersmaintainedbytheUniversity.Inaddition,somefarmerswerecontactedatthesmallfarmconferencesandmeetingsorganizedbytheUniversity.Hence,farmersselectedwereamixoflandownersandleaseholdersgrowingavarietyofproductsrangingfromhigh-valuevegetables(i.e.,hotpeppers,eggplants,okra,amaranth,gardeneggs,etc.)tocutflowers,mushrooms,lamb,andgoat.FarmersinthenetworkwereamixofAfricanAmericans,Caucasians,Asians,males,females,singles,andmarried.Anyothergroupsnotrepresentedinthesurveywereomittedunintentionally.Giventhenatureofthefarmerdatabaseused,itisobviousthatonlycommercialsmall-scalesociallydisadvantagedfarmerswereincludedinthenetworkanalysis.InthisStatetherearetwosubgroupswithintheoverallnetwork. State 3. The network coveredfivecountiesinthestatethreeofwhichareadjacenttometroareas.Eachcountyprovidedalistofpre-identifiedproducers.Thesefarmerswerere-trievedfromlistsmaintainedbycountyExtensioneducatorsandtheyareethnicallydi-verse,consistingprimarilyofLaotians,WhitesandBlacks.Thepre-identifiedlistincludedsmallvegetablefarmersandexcludedlargecommodityproducers.Italsoexcludesthosethatresideinthesamehouseholdandworkonthesamefarm.Additionally,thosewiththesamelastnamewereexcluded.Thefarmersweregivenspaceonthesurveyinstrumenttoaddotherfarmersnotinthelist.Anyduplicatenamesweredropped.Thissurveyofpro-ducersgeneratedinformationincludingcharacteristicsoftheoperatorssuchaseducation,ageandgenderaswellastheiroperationssuchassales,yearsfarming,experienceoperat-inginnetworkandfutureplantocontinueordiscontinuefarming.Allcountiesinthisstatehadadequatenumberoffarmersthatprovidedusefulnetworkinformation.Therearefivesub-groupsinthisstate,clusteredwithincounties.


2.2.DevelopingtheSurveyInstrumentOncethislistoffarmer-respondentsiscompiled,thenextstepistodevelopandadministerasurveythatwillallowonetoconductthenetworkanalysis.Ifthepopulationtobesur-veyedisnottoolarge,thesimplestwaytodothisistoshowsurveyrespondentsatableormatrixthatlistsfarmersacrossthetopaswellasdowntherows.Ifthelistisverylong,farmerscouldbeaskedtojustwritedown(acrosscolumns,withoneperfarmer)withwhomtheyhaveanetworkrelationship.Farmersarethenaskedabouttheirrelationships,ifany,withtheotherfarmersinthenetwork.Itisalsoagoodideatogivethemspacethatwouldallowthemtoaddotherindividualswhoarenotlisted.SampleSurveyInstrumentforCollectingDataonFarmers;oneforeachStateBasicQuestion:Onthefollowinglist,pleaseplaceacheckmarknexttothenameofanyfarmeryouwouldseekoutformarketing(orproduction)advice,orshareequipment,etc. Production

AdviceMarketingAdvice

SharingofResources

FarmerA FarmerB FarmerC FarmerD Etc. Noweachrespondent(thatis,FarmersA,B,C,etc.)canbeaskedtoplaceacheckmarkoranXintotheboxcorrespondingtootherfarmerswithwhomheorshehasdifferentkindsofrelationships.Respondentsdonotneedtodoanythingwiththeirownboxes(downalongthediagonalofthetable).Asnoted,adifferentwayofdoingthisistoprovidealistofnamesoffarmerstotherespondents,withenoughspacenexttothenameforrespondentstoenterthenumbercorrespondingtotheotherfarmerwithwhomarelationshipexists.Thespecificquestionrelatedtothenatureoftherelationshipiskey.Averysimpleques-tionwouldbe:Doyouknowthisfarmer:yesorno.Amoreelaboratequestionmaybe:Howoftendoyousee,orinteractwith,thisfarmer:daily,weekly,monthly?Themorefre-quenttheinteraction,orinterdependency,themoreintensetherelationship.Adifferentandmorerefinedwayofposingthequestionistoask:Amongthesefarmers,whichonewouldyougoto,togetinformationaboutaproductionproblem?Whodoyougotoforamarketingproblem?Whodoyouaskforadviceonhowtoapplyforcredit,orfiletaxes?Thepurposeofthestudyandwhatoneistryingtolearnaboutthenetworkiskeytodecidingwhatkindofquestiontoask.Thisinformation,properlycollectedandana-lyzed,willrevealwhothemostcentralandimportantindividualsarewithinthenetwork(e.g.,theinfluencersorinformationbrokers).Withthesurveytheeducatororresearchermayalsobeinterestedinotherfarmercharac-teristics,suchasincome,sales,productionpractices,yearsofexperience,assets,andopin-


ionsorattitudes.Oftenthisinformationcanberelatedinmeaningfulandimportantwaystotheresultsofthenetworkanalysis.Forexample,onehypothesismaybethatfarmerswhooccupymorecentralpositionswithinanetworkalsohavehigherincomes,salesorac-cesstoresources,etc.Tosummarize,resultsofanetworkanalysiscanservetwodistinctpurposes.Thefirstissimplytodescribethenetworkusingvariousnetworkstatistics,whichwediscussbelow.Thesecondistorelateeachindividual’snetworkmeasuretohisorhersocioeconomiccharacteristicstoassessiftheyarecorrelated.Lastly,itisalsointerestingtocomparetheoveralloraggregatenetworkstatisticsofdifferentgroupswithoneanother.Thisisillus-tratedbyanexamplebelow.2.3.AdministeringtheSurveyVeryhighparticipationratesinthesurveyareessential(>90%ifpossible),ifthenetworkistobeproperlydescribed.Thehighertheparticipationratethegreaterwillbethequalityofthedataandthereforethereliabilityoftheresultsofsubsequentanalysis.Inordertosurveyfarmersitisoftenbesttoadministerthequestionnaireatameeting,etc.Describeprocedureandwhatworksbestfordifferentgroups(Wetherill,2013).Hedis-cussesculturalfactorsthatcanleadtodifferencesinrelationshipswithminorityfarmersandemphasizestheimportanceofattendingsocialeventsandthenutilizingwordofmouthtospreadwordofExtensionprogramsfromoneminorityfarmertotherestoftheircom-munity.Thefirsttaskwastofamiliarizeparticipantswiththeguidelinesforparticipatinginthesurveyandthegoaloftheproject.Inonestate,thiswasachievedbyreadingwrittenstate-mentsgivenatthetopofthesurveyinstrument.Theentiresurveyquestionswerealsoreadtotheparticipantsonebyonepriortobeginningthesurvey.Thisprovidedopportuni-tyforclarificationofthesurveyquestions.Respondentswereaskedtocompletethesurveypriortoturningitin.Theywerealsoscreenedtoavoidduplication.Forexample,brotherslivinginthesamehouseholdandworkingonthesamefarmwereeliminated.Wealsoveri-fiedthatparticipantswereindeedfromthecountybeingsurveyed,andnotanothercounty.Wemadesuretheparticipantsweresmallfruitandvegetableproducers.AtranslatorwasusedinonecountywheretherespondentshadverypoorornoknowledgeofEnglish.Thesurveywasadministeredatcountyextensionfacilitieswithlocalextensionagentsinat-tendance.Inthefirststate,farmerswereselectedfromadatabasethatismanagedbytheSmallFarmsProgramwithinCooperativeExtensionatthelandgrantuniversity.Thesurveywasmailedtofarmersusingthisdatabase.Farmerswereaddedtothesurveylistbyextensioneducatorsviafarmsvisitsandatextensioneventssuchasfielddays,workshopandannualsmallfarmsconferences.Thiswasfollowedupbyavisittothefarmerforthepurposeofconductingandcompletingthesurvey.Inafewcases,thesurveywasadministeredviaoneononeposteventmeetingatanextensionforumsuchasaworkshoportheannualsmallfarmconferences.Atotalofthreepersonnelassistedinadministeringthesurveyrelatedto


theproject.AllthreepersonnelwerepaidorweredirectlyaffiliatedwiththeCBGfarmer’snetworkproject.Oncethesurveywascompleted,theresponsesweresecuredintheofficeofthecampusprojectdirector,andelectroniccopiesofthesedocumentsweresenttotheleadinstitution.Inthesecondstate,alistoffarmerswasobtainedfromthecooperativeextensionprogramdatabaseatthelandgrantuniversity.Thesurveywasmailedtofarmersusingthedatabase.Afollowupwasmadewithfarmerswhodidnotreturnthecompletedsurvey.Phonecalls,farmvisitsand/orfarmers’interactionsduringworkshopsandsmallfarmsannualconferenceswereused.Inthethirdstate,alistoffarmerswasobtainedfromtheCooperativeExtensionOfficeofthelandgrantuniversity.Thesurveywasconductedface-to-facewithsmallfruitandvege-tableproducersinfivecounties.Theselectionofthespecificproducerswasinlinewiththegoaloftheproject.Thevenueforthesurveyinallcaseswasthecountyextensionoffice.Threeofthefivecountiessurveyedwerelocatedadjacenttometroareaswhereretailers,wholesellersandfarmersmarketsarelocated.Countyextensioneducatorswereinstru-mentalinorganizingthemeetingsandassistinginconductingthesurvey.2.4.EnteringtheDataOncethedatacollectionwascompleted,datawereenteredintosoftwareforsubsequentanalysis.Anexceptiontothisisifthedatahavebeencollectedonline,viasoftwaresuchasQualtrixorSurveyMonkey.Itdoesnotmatterwhetherthedataareavailableinmatrixformatorasalist(usingthesparsemethod).Nomatterwhatthedatacollectionformat,gettingthedataintoaforminwhichtheycanbeanalyzedisrelativelystraightforward.3.AnalyzingtheNetworkSurveyDataListedbelowaresomeofthebestsourcesthatwehavefoundforanalyzingthenetworksurveydata.EachhasstrengthsandweaknessesbutwillgenerallyworkforyourownNetworkAnalysis.UCINET:Ifyouareinterestedinconductingnetworkanalysisofyourowndata,afree90-daysoftwareprogramcalledUCINETisavailable.Aftertheinitialtrialperiodthesoftwarecanbepurchasedfromthewebsite.ThissoftwarewasdevelopedwiththeNetDrawnet-workvisualizationtool.Itaidsresearchersinshowingtheconnectionsbetweennodesandcanbeusedtocreatetablesfordatademonstration.UCINEThastoolsthatcanhelpuserswiththeirworkandanalysis.Inaddition,therearenumeroushelplinesandindividualsavailableforcontactfromUCINET:https://sites.google.com/site/ucinetsoftware/homeOthersourcesthatareusefulforconductingaSocialNetworkAnalysisarelistedbelowwithbriefdescriptions.

https://sites.google.com/site/ucinetsoftware/home


ForafreesourcewithagoodoverviewofSocialNetworkAnalysis,visit:http://www.faculty.ucr.edu/~hanneman/nettext/ThissourceshowsdifferentSocialNetworkAnalysisSoftwarePrograms:http://www.gmw.rug.nl/~huisman/sna/software.htmlThisfinallinkhasacomprehensivereviewofSocialNetworkAnalysisanditsrelatedsoft-ware.http://en.wikipedia.org/wiki/Social_network_analysis_softwareWhileanumberofstatisticscanbecalculatedforanetwork,afewarekey.Inonewayoranother,theycapturehowcentral(orimportant)anindividualiswithinanetwork.Theseareindividual-levelstatistics.Inaddition,wecanconsiderhowwelltheoverallnetworkisconnected,forexample,incomparisontoanothernetwork.Somenetworktermstobefamiliarwithtounderstandthegraphsarelistedbelow.Thesetermscanthenbeusedtoexplaintheresultstotheparticipants,oncetheparticipantshavebeentaughtwhattheyeachmean.

§ Degrees-In:Thenumberofconnectionsdirectedtothenode;thisisameasureofhowmanypeopleconnecttotheperson,orhowpopulartheindividualis.Thehigh-erthenumber,themorepopularorprestigious.

§ Degrees-Out:Thenumberofconnectionsthatthenodehastoothers;thismeasureshowsociableorout-goinganindividualis.Afarmermaylistmanyotherstowhomheorshefeelsconnectedwithouttheothersnecessarilyreciprocating.Afarmerwithhighoutdegrees,thatis,whoisfollowedbymanyotherfarmers(e.g.,onTwit-terorFacebook)wouldbeagoodpersontoasktodisseminatenewknowledgeorimportantupdates.

§ Closeness-In:Inverseoftotalshortestpathlengthdirectedtothenode,propagationtimeofinformationfromotherstothenode.Thismeasureshowquicklyanindivid-ualreceivesamessagefromanotherpersonwithinthenetwork;herespeedreferstothenumberofothernodesthroughwhichtheinformationmusttravel,notneces-sarilythetimerequiredtotravel.Someonewithlowin-closenesshastorelyonmanyotherindividualstopassinformationontohimorher.

§ Closeness-Out:Inverseoftotalshortestpathlengththatthenodedirectstoothers,propagationtimeofinformationfromthenodetoothers.Anindividualwithhighoutclosenesscanrapidly(involvingfewothernodesorintermediaries)spreadoutnewinformation,withoutithavingtopassthroughotherindividuals,thatis,relyingonotherstopassontheinformation.

§ Betweenness:Thisisthefrequency(ornumberoflinks)ofthenodethatsitsontheshortestpathbetweentwoothernodes;itisalsoameasureofabilitytocontrolin-formationflowsinthenetwork.Thisisameasureofthenumberofothernodesbe-tweenwhichtheindividualisinserted.Inotherwords,betweenhowmanyotherrelationshipsisthisindividualpositioned?Thelargerthenumber,themorethenumberofflowsbetweenwhichtheindividualisplaced.

http://www.faculty.ucr.edu/~hanneman/nettext/

http://www.gmw.rug.nl/~huisman/sna/software.html

http://en.wikipedia.org/wiki/Social_network_analysis_software


Eachofthesemeasuresisnuancedanddiffersinsubtlewaysintermsofhowitreflectsorrepresentsanindividual’spositionwithinthenetwork.Thefollowinggraphichighlightsthedifferencebetweenthedegreeandclosenesscentralitymeasures.Thisinturnallowsustodevelopinsightsintotheoverallstructureofthenetwork.

Degree vs. Closeness

A and B have same degree But closeness of A is larger than B

A and B have same degree and closeness

-> We can get insights into overall structure


4.InterpretingtheResults PART1:UsingnetworkdataonlyThetablebelowshowsthedatasamplefromourstudy.Specifically,therewere23farmersinthenetworkofthefirstState,followedby46and127respectivelyintheothertwoStateswherethesurveywasconducted.Inthefirststate,19ofthe23nodeshadatleastoneconnection(entire),whetheritwasintermsofproductionormarketingadvice,orsharingresources.Actually,sharingresourceswasmorecommonandoccurredatahigherrateinthisState,at82.6%or19outof23possibleconnections,thanintheothertwo,whereitwas43.5%and44.9%,respectively.Ontheotherhand,sharingmarketingadvicewascomparableacrossthesethreenetworks,at56.5%(or13/23),52.2%and48.8%,re-spectively.

State Connectiontype Totalnodes

Connect-ednodes Degree Close-

ness-inClose-ness-out Betweenness

1

Entire

23

19 2.65 0.1999 0.2014 5.83Prod.advice 18 1.57 0.1307 0.1319 5.57Market.advice 13 1.13 0.0647 0.0640 2.52Sharingres. 19 2.52 0.1880 0.1868 5.39

2

Entire

46


3

Entire

127


ThetablealsorevealsthatthenetworkoffarmersinState1isthemostdenselyconnected,asindicatedbytheaverage2.65degreesthatlinktheentirenetwork,followedbysharingresources(2.52)andproductionandmarketingadvice,respectively.Notethatthe“entire”networkmeasurejustcombinestheotherthree,andthatislargerthananyoneofthethreeindividualmeasures.ThedifferenceisespeciallypronouncedinState3(1.02comparedwith0.70asthenext-highest).NotealsothatwhilesharingofresourceshasthehighestdegreeoftheindividualmeasuresinState1,ithasbyfarthelowestinState2(0.30com-paredto0.76).InState2thetwotypesofadvicealsoshowthesameaveragedegrees,of0.67,suggestingthatexactlytwo-thirdsofallfarmersaresomehowlinkedinsharingpro-ductionormarketingadvice.Ingeneraltheclosenessmeasures(inandout)trackthepatternsinthedegreesaswellastheBetweennessmeasures,intermsoftheirrelativesizes,withineachofthethreenet-works(States).Thisconfirmsthatthesemeasuresarealltosomeextentinterrelated.Atthesametimeitisnotablethatinandoutclosenessmeasuresforthe“entire”measurearebiggerinState2comparedtoState3,eventhoughthedegreesarehigherinState3thanin


2.Thus,theaveragedensityofconnections(degrees)ishigherinState3than2,buttheclosenessscoresarelower.Again,thisshowsthesubtledifferencesintheworkingsofthe-senetworks,andhoweffectivelytheymayoperateormoveinformation,forexample.Thebetweennessscoreshowsthesamepatternasthedegrees,withthehighestvaluerec-orded(fortheentirenetwork)inState1followedbyStates3and2.However,inthecaseofState1itisnoteworthythatthebetweennessscoreforobtainingproductionadvice(5.57)isgreaterthatthanforsharingresources(5.39)eventhoughtheaveragedegreesforthelatter(2.52)isconsiderablyhigherthanthatforproductionadvice(1.57).Thissug-geststhatindividualsinState1aremorelikelytobeinserted(orlie)betweentheconnec-tionsofothernodesintermsofproductionadvice,eventhoughtheaveragedensityofcon-nectionsislowerthanisthecaseforsharingresources.ThesamecanbeobservedinState3,forthesetwoparticularnetworkmeasures.Graphs(examiningonlythenetworkstatistics)Comparingtheentirenetworkswithoneanothergraphicallyconfirmsvisuallywhatisal-readyevidentfromthetableabove.TheAppendixshowsgraphsforeachoftheninenet-workscontainedinthedifferentstates(1inState1,2inState2and6inState3).Alsoincludedaredetailedsummarystatisticsforeachgroup.Followingimmediatelybe-lowaretheoverallgraphsforjustoneState,extractedhereasanexampleformorede-taileddiscussion.State1InthisStateonlyfourfarmersarenotconnectedwithanyoneelse(thisisalsocalculatedasthedifferencebetweenthetotalnumberofnodesinthenetwork(23)andthosewhoareconnected(19).Overallthisnetworkstillhasthehighestaveragedegrees,whichindicatethateachfarmerisonaverageconnectedwith2.65otherfarmers.This(entire)networkalsohasthehighestcloseness(inorout)andbetweennessscores,allofwhichareevidentfromtherelativelytightconnectionpatternsinthegraphic.Particularlynoteworthyhereisthatmanyoftheconnectionsarereciprocal,i.e.,thearrowsaredrawninbothdirectionsbetweenthenodes.


State2Noteinthiscasethatarelativelylargeshareoffarmersarenotconnected(46-29=17,or37.0%ofnodes)sothatthisnetworkhasthelowestaveragedegreesandbetweennessscores.Interestingly,however,itdoesnothavethelowestin-orout-closenessscoresdespitethisfact;thatdistinctionisheldbythenetworkinState3.AlsoofnoteistheStar-shapedsub-networkinthisfigure,inwhichfiveofthearrowspointawayfromthecentralnode(hub)andonlyasingleonepointstowardsit.

State3Inthisnetworkthereareclearlyalsomanynodes(farmers)thatarenotconnectedwithothers(32.2%),butevensotherearesufficientconnectionsamongtheotherfarmerstostillgivethisStatethesecondhighestaveragedegrees.Asnoted,however,italsohasthelowestclosenessscores,perhapslargelybecausemostnodesareonlyconnectedinonedi-rectionratherthaninboth,asisthecaseinState1.

ComparingtheresultsacrossthedifferenttypesofnetworkswithinaState(inthiscaseState1)alsoconfirmswhatisalreadyevidentinthetableabove.Thedegreesarehighestforsharingresources,followedbyproductionadviceandmarketingadvice.Inotherwords,theknowledgenetworksurroundingproductionismoredensethanthatformar-ketinginformation,andtheremaybeimportantopportunitiesforextensioneducatorstointensifyordeepenthelattertypeofnetwork.


ProductionAdvice

MarketingAdvice:notethesimilaritywithanddifferencefromtheproductionadvicenet-work.

SharingResources:thisisthemostdenselyconnectedofthenetworks,suggestingthatmorefarmersshareresourcesamongoneanotherinthisparticularstatethanaskeachotherforproductionormarketingadvice.


PARTIIoftheanalysis:Combiningnetworkwithsocioeconomicdata.HowFarmers’PositionsintheNetworkRelatetoTheirDemographics,andviceversaAspartofthisprojectwealsocollecteddataonfarmerssocioeconomicstatus,includingfactorssuchasjobtitle,age,gender,whentheystartedtofarm,computeruse,amountofsales,etc.Inthissectionwediscusssomeofthesalientfindingsthatrelatethesevariablestofarmers’positionsinthenetwork.Firstofallweplotthein-degreesforeachjobtitle,whichisowner(N=37),manager(N=13),farmer(N=16)orother(N=46,notspecified)inthebargraphbelow.Wenotethatthereareonly13managersinthissample,or11%ofthetotalnumberofrespondents.Es-peciallynoteworthyisthattheseindividualsalsohavethehighestin-andout-degreesamongallthejobtitlesprovided,andtheyalsohavethehighestclosenessscores.Thisfactinturnindicates(evenwithoutmappingthenetwork)thatthenetworkfollowsatree-ish(orstar-shaped)structure,wherebythemanagersarelocatedinthecentralareaofinfor-mationflows,whichiswhatwouldbeexpected.Thismeansnotonlythatthesemanagershavesocialinfluencewithinthenetworks,butalsothattheyhavethegreatestabilitytodisseminateinformationtoothernetworkmembers(Lubelletal.2014).Thehighestdegreesareobservedforresourcesharing,whichalsooccurswithorfrommanagers.Altogether,thisindicatesthatmanagershavetheabilitytoexertacertainde-greeofcontrolovertheinformationflowswithinthesenetworks.Alternatively,theywouldbethego-tosourcesfordisseminatingnewinformation,ifthegoalwastogettheinformationoutasquicklyandefficientlyaspossible.Themanagersarecentralbothintermsofgivingandreceivingadviceand,assuch,canbeseenasinformationbrokers.Acrossthetypesofinformationshared,productiondominates(hashighercentrality)thanmarketing,althoughthedifferencesinmostcasesaresmall.Thedifferencesacrossthebarsareevenhigherinthecaseofclosenessscores,andagainmanagershavethehighestscores.Asnotedearlier,anindividualwithahighclosenesscorehasapositioninthenetworkthatallowshimorhertoveryquicklyreceiveinfor-mation(forin-closeness)ordisseminateinformationdirectlytootherswithoutgoingthroughintermediaries(out-closeness).Totheextentthatmanagersarepaid(unlikefarmersorowners)toknowaboutproductionandmarketingprocesses,thisresultistobeexpected.Forequipmentsharing,ontheotherhand,theexplanationisperhapslessobvi-ousorintuitive.


Anothernotabledemographicvariableisage.Herethosewhoareinthe55-64yearoldagecohorthavethemostinandoutdegrees,wherethelatterareevenhigherthantheformer.Thissuggestsbothoftheseindividualsreceivethemostinquiriesintermsofothersseek-ingadvice,andtheyalsoaremorelikelytoseekadviceofothers.Atleastfortheformer(indegrees),thissuggeststhatotherfarmersinthenetworkcometotheseindividualsforad-vice,thatis,otherslikelyseekthemoutbecauseoftheirgreaterexperiencefarming.


Itisnotedthatthesedegreesarenormalized,inthesensethattheyareaveragedforeachoftheagecohorts.Inotherwords,theaverage55-64yearoldhasanaveragein-degreeof1.5forproductionadviceandanout-degreeofabout2.25.Itisalsonotedthatsomeindividu-alswhowerenotpartofthenetworkwereaskedforadvice.Thisexplainswhytheout-degreesaregreaterthanthein-degrees.Again,amongthein-degrees,thelargestnumbersarefoundforsharingofequipment,andingeneralmoreproductionthanmarketingadviceistradedamongthesefarmers.Itisplausiblethatfarmersaregettingtheirmarketingad-viceelsewhere,suchasontheinternetorfromExtensioneducators,althoughthediffer-encesarenotthatpronounced.Theeducationalattainmentmeasureisalsointerestinginthatfarmerswithlessthanahighschooleducationhavemuchsmallerin-degrees,whiletheirout-degreesaremorecompa-rabletothoseofthemorehighlyeducatednetworkmembers.Inotherwords,theyaremuchlesslikelytobeaskedforanyofthetypesofadvice,oreventoshareresources.Atthesametime,thosewithoutahighschooldiplomaareaboutaslikelytoseekadvice(butnotshareresources)fromothersasthosewithgraduatedegrees.Againthisresultseemsplausibleandgivestheanalystsomedegreeofconfidenceinthesurveyresults.

Intermsofgenderdifferences,indegreesforallfourmeasuresarecomparablebetweenmalesandfemales,andthesameistrueforoutdegreeswithoneexception:forfemalesthedegrees(0.65)aresmallerthanformales(1.25)inthecaseofmarketingadvice,whereastheyaremorecomparableforproductionadvice(around1.35foreithergroup).Thissug-gestsfemalesarelesslikelytogiveorbeaskedformarketingadvicethanproductionad-vice.Butatthesametime,malesareslightlylesslikelythanfemalestoshareequipment(about1.5comparedto1.2).Onthesharing-inside,malesandfemaleshaveaboutthesamedegrees,ontheotherhand(about0.8each).Forgrossfarmsales,theout-degreesaregreaterthanthein-degrees,withthepossibleex-ceptionofthoseselling$100,000andmore.Especiallynoteworthyarethehighoutde-grees(informationgiven)forthosesellingbetween$3,000and$99,999worthofproducts.


Furtheranalysisshowsthatfarmerswhoarelocatedincentralareasofthenetworkhaveseveralcharacteristicsincommon.Theytendtobemanagersofmedium-sizefarms(grossfarmsales$3,000–$10,000),andtheybegantooperatefarmsonlyin2001-2005whentheywere40-50yearsold.Nowtheyare55-64yearsold.Thisfactcouldsuggestthatop-eratingofthefarmmightbeasecondjob.TheusageofcomputersandtheInternetdoesnotsignificantlyrelatetothelocalnetworkbutitdoestotheglobalnetworkasshownintheDegreevs.Closenessfigure.ThedegreecentralitydoesnotshowsignificantdifferencesbetweenfarmerswhodoanddonothavecomputerandInternetaccess.Thismeansthatthefarmershaveasimilarnumberofconnectionstootherfarmers,andsimilarlocalnetworkstructures.However,thefarmerswhohavecomputerandInternetaccessalsohaveahigherclosenesscentrality.Thishigherclosenesscentralityindicatesthefarmersarelocatedinmorecentralareasofthenetworkandthattheinformationfrom/tothisgroupspreadsoutquicklytootherfarmersinthenetworkeventhoughthenumberofdirectlyconnectedfarmersissimilarintermsofdegreecentrality.Intermsofhowcomputersareused,Skypebyfarmdominatesinin-degreesacrossallfournetworktypes,i.e.,any,production,marketingandsharing.ThissuggeststhatSkypeisused,evenamongthesefarmerswhoarelocatedrelativelyclosetogetherinspace,tose-cureinformationorsharedequipment.Ontheout-degreessidethepatternsaremuchmoreeven,withsearchingforinformation,emailandsocialnetworkmediaslightlydomi-natingSkypeusageforproductionadvice,andalluses(includingsellingandbuyinginputs)dominatingSkypeusageinthecaseofmarketingadvice.Anotherinterestingstratificationisintermsofhowmanyyearstherespondenthasbeeninthenetworksurveyed(with63beinglessthan5years,6between6and10years,and17beingover10years).Thosewhohavebeeninthenetworkthelongestalso(byfar)havethehighestin-degreesand(toalesserextent)thehighestout-degrees.Thisconfirmsthatasindividualshavetheopportunitytogettoknowoneanothermoreclosely,withfamiliar-ityarisingastimepasses,theyalsopassaroundmoreinformationamongthemselves.Thesameisnotobservedforsharingequipmentwithothers;heretheaveragedegreesaresimilar(around1.5)regardlessofyearsinthenetwork.Forsharing-in,ontheotherhand,theaveragedegreesareabouttwiceashighforthoseinthenetworktenyearsorlonger.HowtheNetworkPositionAffectsFarmers’SalesWenextanalyzehowinvolvementinthenetworkaffectsindividualfarmers’sales.Fivecentralitymeasuresrepresentingproximity/distanceoftheactorsintermsofinformationaccess,controlofinformationflowaswellasthenumberofconnectionsareused.Thecen-tralitymeasuresusedare:betweennesscentrality,close-incentrality,close-outcentrality,degree-inncentrality,anddegree-outcentrality.Inthiscaseallfarmersareconsideredto-gether,asonegroup,anddonotdistinguishbetweenfarmersfromthethreedifferentStates.Theoverallresultsconfirmasignificantlypositiverelationshipofsalesvolumewithinvolvementinnetwork.


Asignificantconcernhereisinferringcauseandeffectbetweennetworkpositionandtheoutcomevariable.Forexample,salescouldbehighbecauseofafarmer’shighcentralitywithinthenetworkbuttheoppositeisequallyplausible.Whileitisbeyondthescopeofthismanualtodiscussthisissue,researchershavestartedtodealwiththestatisticalas-pectsofthisproblem(e.g.,Boehmkeetal.(2016)).Interestedreadersshouldconsultthispaper.BetweennesscentralityTheresultsshowasignificantlypositiverelationshipbetweenbetweennesscentralityandfarmsales.Betweennesscentralityisanindicatorofabilitytocontrolinformationflowinthenetwork.Apositiveregressioncoefficientintherelationshipbetweenbetweennesscentralityandsalessuggeststhatsalesvolumeincreasesasthefarmer’spowertocontrolinformationflowincreases.Theresultsrevealthataone-pointincreaseinbetweennesscentralityisassociatedwitha3.3%increaseinsalesvolume.Degree-inanddegree-outcentralityAsignificantlypositivecoefficientofdegree-inanddegree-outcentralityonfarmsaleswasfound.Apositive0.188coefficientofdegree-incentralitysuggeststhattheexpectedsalesincreasewiththenumberoffarmerswhoknowthefarmerinquestion—a19%increaseinexpectedsalesisassociatedwithaoneunitincreaseindegree-incentrality.Similarly,apositive0.246coefficientofdegree-outcentralitysuggestsasalesincreaseasafarmerknowsmoreotherfarmers—a25%increaseinexpectedsalesisassociatedwithaoneunitincreaseindegree-outcentrality.4.2.PresentingtheresultstotheparticipantsOncetheanalysishasbeencompletedthequestionariseswhetherornottosharethere-sultswiththepopulationsurveyed,andhow.Tomakethemostuseoftheresults(andtheeffortthatwentintocollecting,entering,andanalyzingthedata),theyshouldideallybesharedwiththerespondents.Thereasonforthisisthatthereisevidencethatgroupsworkingtowardsacommongoal(andassumingthatisthecasehereaswell)workmoreeffectivelyaftertheyhavebeenpresentedwiththeresults.Oftenindividualsgetnewideasaboutwhomtoworkwithorcontactforinformationoncetheyhaveseenthenetworkgraphic.Thus,inadditiontobenefitingtheextensioneducatorwhocangaindeeperinsightsintothenatureoftherelationshipsamonghisorherfarmerstakeholders,theindividualfarmersmayalsobenefitfromseeingthemselves,andothers,withinthelargernetwork.Ofcourse,careanddiscretionareneededwhensharingtheresults,toavoidembarrassinganyoneofthesurveysubjects.Thus,educatorsneedtodecideonacasebycasebasisontheleveloftrustwithinthegroupandwhatitisthatisappropriatetoshare.


5.SummarySocialNetworkAnalysisisapowerfultooltousewhenworkingincommunitiestryingtoexpandanddisseminateknowledge.Itmaybeaparticularlyusefultoolworkingwithmi-norityfarmerswhohavedifferentculturesandnormsthandootherfarmerswhomayhavealongerhistoryofworkingwithExtensionAgents.ByusingSocialNetworkAnalysis,itispossibletoreachmanyofthefarmersfromcommunitieswhomightotherwisenotworkwithExtensionondifferentprojects.ThisManualhasshownhowtocollectdata,organizeandinputdata,howtointerprettheresults,andsharingthefindings.Fromthis,andtheresourceslisted,acomprehensivein-troductionintoSocialNetworkAnalysishasbeenprovided,whentouseit,howtouseit,andthegoodthatcancomefromitsuse.

WorksCited

Boehmke,F.J.,O.ChyzhandC.G.Thies(2016)“AddressingEndogeneityinActor-SpecificNetworkMeasures,”PoliticalScienceResearchandMethods,4(1):123-149.Borgatti,S.P.,A.Mehra,D.J.Brass,andG.Labianca(2009),“NetworkAnalysisintheSocialSciences”Science13:Vol.323no.5916pp.892-895DOI:10.1126/science.1165821Brasier,K.J.,S.J.Goetz,L.A.Smith,M.Ames,J.Green,T.Kelsey,A.RangarajanandW.Whitmer(2007),“HowClustersofAgriculturalFirmsAffectLocalCommunitySustainabil-ity,”JournaloftheCommunityDevelopmentSociety.38(3):8-22.Genius,M.P.Koundouri,C.Nauges,andV.Tzouvelekas(2013)“InformationTransmissioninIrrigationTechnologyAdoptionandDiffusion:SocialLearning,ExtensionServices,andSpatialEffects,”AmericanJournalofAgriculturalEconomics,96(1):328-344.Goetz,S.J.(2016)“TheRoleofAgriculturalEconomistsinFoodSystemsResearch,”ReviewofAgriculturalandEnvironmentalEconomics.[inpress]Goetz,S.J.etal.(2006)“TheSmallFarmsIndustryClusters(SFIC)Project,”D.Ebodaghe,Editor,ProceedingsoftheFourthNationalSmallFarmsConference,USDA,Washington,D.C.,pp.167-70.Graham,B.S.(2015)“MethodsofIdentificationinSocialNetworks,”AnnualReviewofEco-nomics,7:465-485.Lubell,M.,M.NilesandM.Hoffman(2014)“Extension3.0:ManagingAgriculturalNetworkKnowledgeSystemsintheNetworkAge,”SocietyandNaturalResources,27:1089-1103.Wetherill,A.(2013)]“ApracticalapproachtoattractingimmigrantsandotherminoritygroupstosustainableagriculturalprogramontheDelmarvaPeninsula,”posterpres.atthe2013NationalRiskManagementEducationConference,Denver,Colorado.


TechnicalAppendixNetworkcentralityCentralityofnodei degree closeness

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out- ∑N

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Where,N=totalnumberofnodesinanetworkwij=1whennodeiandjareconnected,else0Ji=numberofreachablenodesfrom(orto)nodeidij=shortestpathlength(numberofedges)fromnodeitoj


Appendix

network analysis of farmer groups...network analysis of farmer groups, nercrd rdp-57 3 2. collecting...

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