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Social
Networks
j o u r n a l h o m e p a g e :w w w . e l s e v i e r . c o m / l o c a t e / s o c n e t
Referrals
and
information
flow
in
networks
increase
discrimination:
A
laboratory
experiment
Károly
Takács
a,∗,
Giangiacomo
Bravo
b,
Flaminio
Squazzoni
caMTATK“Lendület”ResearchCenterforEducationalandNetworkStudies(RECENS),CentreforSocialSciences,HungarianAcademyofSciences,Tóth
Kálmánu.4.,1097Budapest,Hungary
bDepartmentofSocialStudiesandCentreforDataIntensiveSciencesandApplications,LinnaeusUniversity,Universitetsplatsen1,35195Växjö,Sweden
cDepartmentofEconomicsandManagement,UniversityofBrescia,ViaSanFaustino74B,25122Brescia,Italy
a
r
t
i
c
l
e
i
n
f
o
Articlehistory:
Received8August2017
Receivedinrevisedform19March2018
Accepted22March2018 Keywords: Hiringdiscrimination Referrals Recommendations Informationnetworks Labormarket Laboratoryexperiment Qualitystandards
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t
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Referralsandinformationflowdistortmarketmechanismsofhiringinthelabormarket,buttheymight assistemployersunderasymmetricinformationinfindingbetteralternatives.Thispaperinvestigates whetheranimpartialinformationflowbetweenemployersinacyclicnetworkstructurecouldgenerate morediscriminationthanwhennoinformationisexchangedbetweenemployers.Wesetupanartificial labormarketinwhichtherewasnoaveragequalitydifferencebetweentwocategoriesofworkers.We askedparticipantstoplaytheroleofemployersandexaminedthepartialityoftheirhiringchoices.Results showedthatdiscriminationwasprevalentinallconditions.Higherstandardsbytheemployersforthe qualityofworkersincreaseddiscriminationasdidthepresenceofreferralsfromworkers.Unexpectedly, impartialinformationflowinacyclicnetworkofemployersdidnothelptodecreasediscrimination.We alsoshowedthatthesemechanismsinteractwithandsubdueeachotherincomplexways.
©2018TheAuthors.PublishedbyElsevierB.V.ThisisanopenaccessarticleundertheCCBY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Introduction
Hiringdiscriminationmeansdifferentialtreatmentofacertain socialcategory,basedoncategorymembershipratherthan indi-vidualmerit.Differentialtreatmentisrecurrentinhiringchoices characterizedbyasymmetryofinformationbetweenthe organiza-tionandtheapplicant(PetersenandSaporta,2004;Rooth,2010). Giventhatthetrueworker’squalitycannotbeaccuratelypredicted duringhiringdecisions,organizationsmightuserecognizabletraits (e.g.,raceandgender)‘asinexpensivescreeningdeviceswhen hir-ingforjobs,particularlyskilledjobs,inthebelief(correctornot) thatraceandsexstatusare,onaverage,relatedtoproductivity’ (Kaufman2002,p.550).Whenthereisnoorlittlestatisticalbasis todistinguishthequalityofmembersofdifferentcategories, fol-lowingrecognizabletraitscannothelptoestimatetheapplicant’s quality.Inthesecases,understandingwhydiscriminationcould persistisofparamountimportance(Bertrandetal.,2005).
∗ Correspondingauthorat:MTATK“Lendület”ResearchCenterforEducational
andNetworkStudies(RECENS),CentreforSocialSciences,HungarianAcademyof
Sciences,TóthKálmánu.4.,1097,Budapest,Hungary.
E-mailaddresses:takacs.karoly@tk.mta.hu(K.Takács),
giangiacomo.bravo@lnu.se(G.Bravo),flaminio.squazzoni@unibs.it(F.Squazzoni).
Hiringdecisionsmightreflectsignalsandinformationthat chan-nelthrough social networks. The important role of referrals in particulariswelldocumentedforgettingajob(Granovetter,1973, 1974;Linetal.,1981;Wegener,1991;Elliott,2001;Mouw,2002; FernandezandFernandez-Mateo,2006;PonzoandScoppa,2010; FountainandStovel,2014).Manystudiesarguedthatgettingajob viareferralsmightdistorttheperfectmarketlogicandreplace mer-itocraticprocessesinhiring(IoannidesandLoury,2004;Petersen etal.,2000;TassierandMenczer,2008).Forinstance,theextended useofinformaljobsearchmethodsmayhaveanegativeeffecton therateofmobilityfromlowstatustohighstatusjobs(McBrier 2003;1212).Ifoneofthegroupshasabetteraccesstoinformal jobsearch,thisisdetrimentalfortheothergroup,asinthecaseof referralsfromthe“oldboys”networkinawiderangeofcontexts (Rogers,2000;McBrier,2003;McDonald,2011;Bianetal.,2015).
Research concerning referrals highlighted how the hiring mechanismcouldenhanceinequalityofemploymentandwages (Montgomery,1991;Krauth,2004;Fontaine,2008).Giventhat con-tactsmightbehomophilouswithregardtointernal quality,the extensiveuseofreferralsleadnecessarilytogrowinginequality (Montgomery,1991;Beamanand Magruder,2012).Considering that contactsare homophilous alsowithregard to social char-acteristics that are uncorrelated with ability, research showed that initial differences in the employment rate could result in greaterwage inequalitiesovertime (Montgomery,1991;Arrow https://doi.org/10.1016/j.socnet.2018.03.005
0378-8733/©2018TheAuthors.PublishedbyElsevierB.V.ThisisanopenaccessarticleundertheCCBY-NC-NDlicense(http://creativecommons.org/licenses/by-nc-nd/4.
and Borzekowski,2004).Exogenously provided job information thatispassedonvianetworktiesalsoenlargessmallinitial dif-ferences(Calvó-ArmengolandJackson,2004,2007).
Afewstudieslookedatreferrals(Engströmetal.,2012;Beaman andMagruder,2012;Beamanetal.,2013;CariaandHassen,2013; FernandezandGreenberg,2013)andotherstructuralmechanisms thataffectdiscrimination(seeOlianetal.,1988foranearlier meta-analysis).Ourpaperaimstostudystructuralmechanismsthatcan influencedifferentialhiringpracticesexperimentally.
Themainadvantagesoftheexperimentalmethodologyarethat: (a)thehypothesizedcorrelationscanbetestedunambiguouslyin afullycontrolledenvironmentthatexcludesconfoundingeffects (e.g.,Smith,1991;Roth,1993;WebsterandSell,2007);(b) genera-tiverelationscanbeidentified;(c)replicationoffindingsispossible (e.g.,Chapin,1932;FalkandFehr,2003;WillerandWalker,2007; Fehr and Gintis,2007;Bohnet,2009; Falk and Heckman,2009; Smith,2010).Itisworthnotingthatexperimentalstudiesmight helpustoidentifymechanismsthatcanbeempiricallyexamined indifferentcontexts(Camerer,2003;Ostrom,2010;Ariely,2008; FalkandHeckman,2009).
Classicaland recent small group experiments in social psy-chologytestifiedtothehumantendencytodiscriminateunknown partnersbasedoncategorymembership(e.g.,Brewer,1979,1996; Dovidioetal.,2002;Fiske,2009).Similarly,recentlaboratoryand fieldstudiesineconomicsandsociologyconfirmedtheexistenceof discriminativepractices(e.g.,SolnickandSchweitzer,1999;Pager etal.,2009;Jackson,2009;Midtbøen,2014;Agerström,2014;Lee etal.,2015).Somelaboratorystudieswereabletoisolateimportant behavioral effectsand interactionalinfluencesin discrimination (e.g.,Keuschniggand Wolbring,2015; Takácsetal.,2015; Lane, 2016).
Veryfewstudieshavetriedtoanalyzefactorsrelatedtosocial capital in hiring experimentally (Godechot, 2016). This can be explainedasitisverydifficulttodepictthecomplex characteris-ticsofsocialcapitalinthelaboratory,causingconcernsofexternal validity. But exactlydue to thecomplex nature of social capi-talrelatedprocesses,fieldresearchcannotfullydisentanglethe informationalaspectsofsocialcapitalfromothermechanismson discrimination.Bycontrast,carefullydesignedexperimentsusing simplenetworkstructurescanprovideusatrulycausalaccountby focusingonspecificmechanismsinherentintherelational struc-ture(Kosfeld,2004;WillerandWalker,2007;Gërxhanietal.,2013; BrashearsandQuintane,2015;BrashearsandGladstone,2016).In ourcase,theexperimentaldesigncanconcentrateandrelyonsome elementaryandempiricallyrelevantmechanismsthatpotentially determinediscriminationinhiring.Oneofthesemechanisms cov-ersreferralscomingfromworkers.Anotheronesummarizesthe informationflowcomingfromotheremployerswhoareverymuch inthesamesituationandhavesimilargoals.Acquiring,passingon, andexchanginginformation betweenemployersabout employ-eesisverydifficulttotraceinfieldstudies.Tagsandsignalsthat characterizeworkersarealsomulti-dimensional,somecorrelate withinternalqualitiesandskills,whileothersdonot.Asourstudy demonstrates,thesemechanismscanbeabstractedandusedin thelab.Inordertoallowforcausalinference,ourlaboratory exper-imentsexcludeconcernsaboutstrategicchoicesandendogeneity inrecommendationsandreferralsbydesign.
Itanalyzeshiringdecisionsinacontrolledsettingandisable toreducethehighdimensionalityofrealityintoastraightforward model.
Given thecomplexity of hiring choices in the labormarket, empiricalfieldresearchisunabletotestunivocallywhether refer-ralsincreasediscriminationcomparedtoabaselinecasewithout referrals or not. Furthermore, it cannot be explored whether referralsmakeadifferencealsowithoutanyinitialbiasesor alter-natively,observedinequalitiesaretherebecauseofhistoricalpath
dependence.Besides,inexistingfieldstudies,workerreferralsand informationflowamongemployersareconsideredjointlyandtheir impactsarehardlyseparated.
In order to overcome these empirical difficulties, following Takácsetal.(2015),wehavedesignedalabormarketexperiment whereparticipants(universitystudents)wereaskedtoplay the roleofemployersandselectfictiveworkersbelongingtotwo cate-gories.Inourexperiment,byexcludingcontextualeffectsandother importantaspectsofthehiringprocess,wetestedthenetandthe jointeffectsofreferrals,informationflowfromotheremployers,and qualitystandardsondiscrimination.
Referralshavebeendefinedasrecommendationsforhiringby workersin-house(Montgomery,1991;FountainandStovel,2014). Referralsarenaturallybiasedtowardsmembersofthein-group. Thischaracteristicfeaturehasbeendepictedinourexperimental design.Informationflowhasbeenconceptualizedasanautomated process in a simple directed network of employers. This con-ceptualizationcovers multiple mechanisms accordingto which employers gettoknow thetruequalitiesof workersemployed atconnectedfirms;suchasrecommendationletters,information exchange,andobservations thattakeplaceasa resultof estab-lishedcontactbetweentheorganizations.Qualitystandardswere evaluationthresholdssetupexogenouslytodeterminewhetherit iseconomicaltokeepworkersinhouseornot.
Inourexperiment,representinganidealisticworld,therewas nodifferenceinthemeananddistributionofqualityofworkersin thetwocategories;hencetheimpactofhistoricalpathdependence andinitialbiasescouldbeexcluded.Notethatsignificantelements ofeverydayinteractionswereneglectedinourlabormarket lab-oratory.Forinstance,therewasnorecruitmentprocedureinthe experimentaswewereinterestedindiscriminationwhenhiring decisionsaremade.Recruitmentitselfcanaddanextralayerof dis-criminationbyselectivelytargetingcertaingroupsorusingbiased information channels.Moreover, in reality,workers themselves couldapplyselectively byexpectingdiscrimination.These com-plicationsarepresentinthefieldandwoulddistorttheevaluation ofimpartialityofhiringdecisionsinthelab.
Ourresearchquestionswereasfollows.
1.Doesdiscriminationoccurinanartificiallabormarketwith bal-ancedandfairconditions?
2.Dohigherqualitystandardscreatemorediscrimination?Orin otherwords,ifemployersarerewarded onlyforhighquality workers,willdiscriminationincrease?
3.Doworkerreferralsincreasediscrimination?
4.Doesflowofaccurateinformation inanetworkofemployers decreasediscrimination?
Thesequestionsconcernprimarilythebehavior ofindividual employers.Inaddition,wewerealsoabletoanalyzewhether occa-sionalindividualbiasesbalanceeachotheroutortheyaddupto inequalityofemploymentbetweengroupsinourartificiallabor marketwithbalancedandfairconditions.
Hypotheses
Aninclinationtowardsdiscrimination
AssuggestedbyTakácsandSquazzoni(2015),whobuiltasimple modelofanidealizedlabormarketinwhichtherewasnoobjective differenceinaveragequalitybetweengroupsandhiringdecisions werenotbiased,acertainlevelofdiscriminationcouldbeexpected alsoinanidealworldwithimpartialemployers.Judgmenterrorsof thiskindcouldbetheconsequenceof“rational”adaptivesampling ofavailableinformation(cf.Simon,1955;Denrell,2005;Fiedler
andJuslin,2006;LeMensandDenrell,2011;DenrellandLeMens, 2011).Thatis,peoplemakesystematic judgmenterrors asthey naïvelyextrapolate fromlimited information availabletothem. Moreover,psychologicalstudiesindicatedthatindividualsareto largeextentalsoprocessinformationinaccuratelyortheyrelyon biasedheuristics.Theyareconstrainedbyselectiveattentionand tendtoretaininformationconfirmingtheirbeliefs, while ignor-inginformationthatcontradictstheirexpectations(e.g.,Hamilton, 1981).Aninitialbiaswithsequential,path-dependenthiring deci-sionsmayresultinpersistentandself-reinforcingdiscriminative choices(e.g.,Hoefferetal.,2006).Theseexpectancyconfirmation sequenceshavebeenfoundinexperimentalresearch(Bergeretal., 1980;DarleyandFazio,1980)andareexpectedtoinfluencealso employerdecisionsinhiringprocesses.Theseconsiderationsledus toformulatethefollowinghypothesis:
Hypothesis1. Anon-zerolevelofdiscriminationisexpectedalso inthebaselineconditionofnoreferralsintheidealistic experimen-tallabormarket.
Higherstandardsanddiscrimination
Higherqualitystandardsforapplicant qualitiescanoriginate inhigherneedforthebestqualitylaborforce,inmore demand-ingtasks,inintensemarketcompetition,andinsimplegreediness. Employerswhohavehighstandardsforapplicantssortout can-didateswhowould otherwisebe abletoconductthejob.After employment,employerswithhigherstandardsarenoteasily satis-fiedandexperimentmorewithnewhires.Thatis,higherstandards areusedforselectionand alsofor keepingthelaborforce(e.g., TakácsandSquazzoni,2015).Higherstandardsaretypicalforhigh statusjobsandforjobswherespecializedknowledgeoradvanced skillsarerequired.Advancedskillscouldbelearntafter employ-mentwithinhouse,butthatrequiresexpensiveinvestmentsfrom theemployer.Theseinvestmentsareeasilylostiftheemployee quitsthejob.Turnovercoststhereforearemuchhigherinjobsthat requireadvancedskillsthaninjobsthatdonot.
Greedysearch(“over-searching”)thattriestoseekbetter alter-nativesthananemployeewiththequalitystandardoftheoptimal reservationlevelconveysaloss(e.g.,McCall,1970;Stigler,1961, 1962;Mortensen,1986).Asearchthatisextendedbeyondthe alter-nativethatisatleastasgoodasthereservationlevelresultsin anexpectedrelativelossnotjustbecauseofsearchcosts,butalso becausetheaverageexpectedqualityofnewworkersissmaller thanthatofthecurrentalternative.Extendedsearchandrepeated failuresimplythatthehigherqualitystandardsalsoresultinlower employerprofits.
Forthesakeofsimplicity,inourexperiment,weassumedthat employershavenoopportunitytotraintheirworkersandtheydo notfacedifferentialturnovercosts.Wewereprimarilyinterestedin theconsequencesofsettingahigherqualitystandardexogenously forworkerselectionforlabormarketinequality.
Ahigherqualitystandardimpliesmoreexperimentationwith new workers.Information cues therefore could have a greater importanceforemployerdecisions.Inourexperiment,theonly availableinformationcue wascategorymembership.Its impor-tance,therefore,is expectedtoincreasefor employerswho are motivatedtohirenewworkerswithhighquality.Hence,weexpect thatemployerslookingforhighqualityworkersconsidergroup markersmore closely and, dependingonavailable information, areinclinedtodevelopbiasedgroupreputations.Thefewhighly skilledworkerswhomtheyaresatisfiedwitharekeptinhouseand continuetobiastheemployer’sjudgmentaboutavailableskillsin thegroups.Becauseofthelargerperceivedroleofsupplementary informationandtheover-representationofskilledworkerskeptin houseforgroupreputationformation,higherdiscriminationrates
couldoccurforemployerswithhigherqualitystandards.Thisled ustooursecondhypothesis.
Hypothesis2. Higherqualitystandardsleadtohigher discrimi-nationrates.
Referralsanddiscrimination
Socialnetworkscanbeusedinjobhiringfortworeasons.First, duetoitsaffectivecontent,asocialtiecreatesanobligationforthe workerin-houseononeendandanopportunityforthejob-seeker attheotherendoftheconnection.Second,networktiesare chan-nelsofgathering,conveying,andsignalinginformationonhidden individualqualities.Dependingonwhichaspectismoreprevalent, referralsmighthavedifferentconsequencesfordiscrimination(cf. RubineauandFernandez,2015).
Workerreferralsbetweencurrentand prospectiveemployees buildontheaffectivecontentofrelationshipsandaremore con-cerned with the welfare of the applicant, less with employer benefits.Thismeansthatworkerreferralsdonotnecessarilyreflect informationonqualityanddonotallowtheemployertofind opti-malmatchesinhiring.Consequently,ifconsideredatall,worker referralsarenottakenintoaccountbecauseofdirectprofit-seeking motives.
Workerreferralsarebasedonsocialtiesthatarehomophilous toalargeextentintheethnicand otherimportantdimensions (McPhersonetal.,2001; RubineauandFernandez,2013).When referralnetworksare usedin whichties are typicallybased on homophily, theycause labormarket segregation(Model, 1993; Tilly,1998;Elliott1999,2001;Kugler,2003;StovelandFountain, 2009). With homophilous referrals, labor market segregation develops endogenouslyeven withnon-prejudicedagents (Barr, 2009).
Empiricalworkshowedthat membersof a particularethnic grouptendtorecommendotherswiththesameethnicbackground forajob(Elliott,2001;FernandezandFernandez-Mateo,2006).This canreinforcetheirdisadvantagedpositionandexcludethemfrom betterjobs(Wilson,1987).Therefore,thedeficitofdisadvantaged groupsdoesnotdependonthefactthattheywouldrelylessor moreonnetworksinfindingajob.Rather,itdependsonthefact thattheyextensivelyrelyon“wrongnetworks”thatcannotoffer themgoodjobs(FernandezandFernandez-Mateo,2006;Petersen etal.,2000).
Thecharacteristicfeatureofworkerreferralsthatweenteredin thelaboratoryistheirhomophilouscharacterwithregardto cat-egorymembership.Weassumethattheirpresencereinforcesthe initialrandomdelusionsofemployersaboutgroupdifferences(cf. FernandezandGreenberg,2013).Consequently,wecan hypoth-esizethatdiscriminationisstrongerwithworkerreferralsthanin caseofisolated,unconnectedemployers.Asweareinterestedinthe discriminationtendenciesofemployers,referralsbyfictiveworkers inourexperimentconsideredtobearandomwithin-groupprocess. Hypothesis3. Homophilousworkerreferralsincrease discrimi-nationrates.
Anetworkofinformationflowanddiscrimination
In contrasttoaffectivecontent ofworker referrals, theflow ofaccurateand relevant information betweenemployersabout workers coulddecrease information asymmetry in hiring deci-sions(Fernandezetal.,2000;Elliott,2001).Witha correctview onindividualqualitiesinalargerpoolofworkers,employerscould arriveatbetterinformeddecisions.Thisisinlinewiththeempirical factthatrecommendationsfrombusinesspartnersandrespected employersareconsideredseriouslyatjobinterviews.Surveysof personnelofficersfoundthatrecommendationsfromamanager
weremoreimportantforhiringthanobjectivesignals,suchashigh schoolgrades(Rosenbaumetal.,1990;Spoonley2008:27).
Whenemployersareisolatedandbasedecisionsonlyontheir ownexperience,decisionscouldbebiasedinfavorofonegroupor another.Ifadditionalinformationisreceivedaboutthequalitiesof individualworkersandgroups,thentheincreasedamountof infor-mationisexpectedtodecreasethestatisticalbiasfromindividual sampling.Inordertoprovidestandardizedconditionsforhiring decisions,informationflowisconsideredasanautomatedprocess inourexperiment.Pleasenotethatwedesignedourexperimentto guaranteethattheinformationexchangedisaccurate.Hence,we avoidedthestrategiccomplexityofrecommendationsinbusiness lifeforthesakeofsimplicity.
Employerswerearrangedinasimplecircularnetwork.Related experimentalresearchoncoordination incircular networkshas foundmixedevidenceaboutconvergencetowardsasingle con-sensus(Keseretal.,1998;Berninghausetal.,2002).
Biasesofindividualemployersfavoringonegrouporanotherare expectedtobalanceeachotheroutattheaggregatedlevelofthe fairlabormarket.Informationflowbetweenemployers,however, createsnetworkexternalitiesasemployersmightbepronetosocial influence.Inaperfectlybalancedidealisticworldasrepresentedin ourexperiment,socialinfluence(Takácsetal.,2016;Flacheetal., 2017)andthe“wisdomofthecrowd”(Galton,1907;Surowiecki, 2005;Lorenzetal.,2011)areexpectedtodrivethegrouptowards consensus.Flowoftrustedinformationbetweenemployerscould beviewedasaninsurancedevice(cf.GemkowandNeugart,2011) thatbringmarketsclosertoperfection,therebydecreasing inequal-ityinemployment.Experimentsshow,however,thatanaverage initialbiasandevenmildsocialinfluencecouldunderminethe wis-domofthecrowd(Lorenzetal.,2011),implyingthatinourcase socialinfluencecouldenlargeratherthandiminishinequalityin employment.
Thisledustoformulatethefollowinghypothesisatthelevelof individualemployersthatcould,butnotnecessarilytranslatedto morebalancedemploymentchancesattheaggregatedlevel. Hypothesis4. Accesstoreliableinformationinthenetworkof employersaboutindividualworkerqualitiesdecreases discrimi-nationrates.
Interactioneffects
Higherqualitystandardsareexpectedtoincrease discrimina-tionwhenthereisasymmetryofinformation,becauseemployers aremoredisappointedwithnewlaborforceandrelyextensively onfewhighqualityworkerskeptinhouse(TakácsandSquazzoni, 2015).Atthesametime,additionalandtrustedinformationfrom otheremployerscouldreducethisbiasandevenimprovethe situ-ation.Thisisbecauseemployersaremoremotivatedtohirequality workersandcouldovercometheirintrinsicpartialityforoneofthe groupsassoonasadequateinformationbecomesavailable.
Fortheemployers,workerreferralsenlargethepoolofworkers fromthecategorythatisalreadyoverrepresented.Ashigh stan-dardsincreasetheturnoverrateandemployerscontinuetofollow theirworkers’advices,thiscouldpotentiallyresultina positive interactioneffect(e.g.,Takácsetal.,2015).Wehaveexaminedthese potentialinteractioneffectsinourexperimentbytheinclusionof treatmentsinwhichamixtureofourmanipulationswaspresent. Method
Experimentaldesign
We designed an experiment where participants played the roleofemployersandaskedtohireworkersfortheirfirm.
Par-ticipantsplayed anonymouslyingroupsofsix,withtwogroups playingsimultaneouslyinthesamelaboratorytoavoid identifica-tionofothergroupmembers.Theyinteractedthroughacomputer networkrunningtheexperimentalsoftwarez-Tree(Fischbacher, 2007).Participantswereaskedtoimaginethattheywere employ-ers and were invited to hire 10 workers per period, which representedonecontractyear.
WorkerswerevirtualagentsandhadIDnumbersfrom1to200. Eachworkerhadafixedqualitydrawnfromauniforminteger dis-tributioninthe[0,19]intervalatthebeginningofthegame.Halfof theworkerswerelabeledas‘blues’,halfas‘greens’.Colorswerenot relatedtothequalityofworkers:thedistributionofqualitywas, onaverage,thesameforbluesandgreens.Subjectswerenotaware oftheexactdistributionofqualities.Theirinstructions,however, containedthat“ThereareplentyofBLUEandGREENworkersinthe marketwithamaximumqualityvalue”(Supp.Mat.).Inshort, sub-jectswerenotawareofanyinitialdifferencebetweentheworkers’ groupsthatcouldberesponsibleforindividualdiscriminationand suchdifferenceinfactdidnotexist.
Inallexperimentalconditions,participantscouldselecteach worker using one of thefollowing options: (i) hiring a worker randomly;(ii)hiringablueworkerrandomly;(iii)hiringagreen workerrandomly;(iv)hiringoneoftheworkerstheyhiredinthe previousperiod.Toallowparticipantstouse(iv),thelistofworkers hiredinthepreviousperiod,includingtheirqualityandcolor,was displayedontheirscreen(Supp.Mat.).Theseoptionswere supple-mentedintherespectiveexperimentalconditionswith(v)hiring workersthatwerehiredbytheconnectedemployerinthe previ-ousperiod(informationflow)and(vi)hiringworkersreferredby workersin-house.
Afterthehiringstage(i.e.,attheendofeachperiod),participants wereaskedtoprovideanestimationoftheaveragequalityofboth bluesandgreensintheentirepool,whichservedtomeasuretheir actualbeliefsandprejudice.Wesimulatedturnover(random fluc-tuation)byexcluding10percentofworkersfromthelistdisplayed tosubjectsineachround.Excludedworkerswereunavailablefor hiringwithprocedure(iv).
Eachexperimentincluded25periods.Theprofitofemployers dependedonthequalityofhiredworkers,whichwasunknown beforehiringexceptthatofworkershiredinthepreviousperiod. Lowandhighqualitystandardswereimplementedwiththe fol-lowingexogenousthresholdrule.Thethresholdwas=12inthe caseoflowqualitystandards(−S)and=17inthecaseofhigh qualitystandards(+S).Thresholdswereimperativeforindividual payoffs.Specifically,profitwascalculatedbysummingthequality ofworkerswithqi≥,anddividingtheresultbyten(thenumberof
jobsatthefirm).Therefore,qualitystandardsweresetupbydesign intheexperiment.
Attheendofeachperiod,theaveragequalityofblueandgreen workershiredbytheparticipantwascalculatedanddisplayedwith thenumberofpointsearned.Attheendoftheexperiment, partic-ipantswereaskedtocompletea shortquestionnaire.Individual profitswereaveragedacrossall25periods,witheachpointbeing exchangedwithoneEuro.Earningswerecashedimmediatelyafter theendoftheexperiment.
Wefollowedabetween-subjects2×2×2fullfactorialdesign. We manipulated:a)highorlow qualityemployerstandards,b) thepresenceofinformationflowbetweenemployers,andc)the presenceofworkerreferrals(Table1).Therefore,weincludedeight treatments,i.e.,fourtestingforeffectsofthesemanipulationsalone andfourexaminingtheirinteractions.
Incaseofinformationflowbetweenemployers(+E),each par-ticipantwaslinkedtoanotheremployerinadirectedcirclenetwork withsixnodes(Fig.1).Instructionsreferredtoemployernetwork tiesas“friends”(Supp.Mat.).Thisreferencehasbeenmadeinorder toincreasethecredibilityofinformationconveyedandtomakethe
Table1 Treatmentoverview. Treatment High standards Information flow Worker referrals Pureeffects −S−E−W
+S−E−W 䊏 −S+E−W 䊏 −S−E+W 䊏 Interaction effects +S+E−W 䊏 䊏 −S+E+W 䊏 䊏 +S−E+W 䊏 䊏 +S+E+W 䊏 䊏 䊏
experimentalsituationmorerealistic.Intheinformationflow
con-dition(+E),ownworkershiredinthepreviousperiod,andthose
hiredinthepreviousperiodbythefriendweredisplayed
automat-ically,includingcolorandqualityofworkerswithoutanyerror.
Theaveragequalityofblueandgreenworkershiredbythefriend
wasalsodisplayed.Toenhanceinformationflowfurther,subjects
receivedtheirfriends’estimatesabouttheaveragequalityofblues
andgreens.Intheinformationflowcondition(+E),subjectscould
alsoselectaworkerwhowashiredbythebusinessfriendinthe
previousperiod(selectionprocedurev).Notethatthismeantthat
thesameworkercouldbehiredbytwoormoreparticipantsinthe
sameperiodandbestworkerswerenotsubjecttocompetitionby
businessfriends.Thisexcludedthepossibilitythatdecisionspeed
woulddeterminewhichemployerhiresaworkerwithsuperb
qual-ity.Participantsdidnotknowtheentirenetworkstructure;hence
strategicconsiderationswereunimportantforwhocouldhirethe
bestworkers. In case of noinformation flow (−E),no network
existedandnoneofthesubjectsknewtheworkershiredbyother
subjectsorothers’estimationsofworkers’quality.
Intheworker referralscondition(+W),eachworker hiredin
thepreviousperiod ‘recommended’a friend of the samecolor,
randomlyselected,withoutrevealing itsquality.The suggested
workerswereshowninaspecificlistontheparticipants’screen.
Incaseof workerreferrals(+W),subjectscouldselecta worker
fromthislist(selectionprocedurevi).Incaseofnoworker
refer-rals(−W),participantsdidnothavealistofrecommendedworkers.
Participantswerefullyawarethatworkerswillgiveahomophilous
referral(seefullinstructionsinSupp.Mat.).
Subjects
Atotalof144subjects(56percentfemales)forming24groups
participatedintheexperiment,whichwasheldintheGECS
com-puterlaboftheDepartmentofEconomicsandManagementofthe
UniversityofBrescia,Italy.Subjectswereuniversitystudent
vol-unteersrecruitedacrossalluniversityfacultiesusingtheonline
platformORSEE(Greiner,2004).Theygaveinformedconsent.
Par-ticipantearningsaveragedtoD12.49(std.dev.D3.65),plusafixed show-upfee ofD5.Theexperiment, includinginstruction read-inganda finalquestionnaire,tookapproximatelyonehour.The fullinstructionsforparticipantsareincludedintheSupplementary Material.
Discriminationindexes
Wedefinedmicroleveldiscriminationastheaverageextentto whichindividualparticipantshiredworkersfromasinglegroupas ourmaindependentvariable.Themicroleveldiscriminationindex ıittakesanindividualvalueforeachparticipanti∈{1,...,144}in eachperiodt∈{1,...,25}.Itwasdefinedasoneminustheratioof thenumberofworkersofthemorediscriminatedgrouphiredbyi
Fig.1. Theinformationflownetworkintheexperiment.
Note:Nonetworkinformationwasgiventothesubjects.Subjectshavereceived
thefollowinginstructionintreatment(+E):“Duringtheexperiment,someofthe
otheremployerswillbeconsideredas‘yourfriends’.Asfriendscommunicatewith
eachother,yourfriendemployerswillshareinformationabouttheworkersthey
employ.Hence,inthesesituations,youwillgettoknowthequalityoftheworkers
employedbyyourfriendsandyoucanalsoselectaworkerfromthislist,whichwill
bedisplayedinthemiddleofyourscreen.Inthiscase,youwillbeabletoselect
workerswhomyourfriendsemployedinthepreviousyear...”
inperiodttothenumberofhiredworkersofthelessdiscriminated group.Formally: ıit={ 1−H g it Hb it if Hitg≤Hb it 1−H b it Hitg if H g it>Hitb (1)
whereHitbwasthenumberofhiredbluesandHitgwasthenumber
ofhiredgreensbyparticipantiinperiodt.Eq.(1)showsthatıitwas
0incaseofnodiscriminationand1ifallworkerswereofthesame color,withintermediatecasesofdiscriminationfallinginbetween. Toanalyzewhetheronegroupisfavoredoveranotheroneand discriminationtendenciesspreadorbalanceeachother,wealso definedmacroleveldiscriminationastheobjectiveextenttowhich groupsweredisproportionally hiredinthegivenperiod.Thisis importantbecause,althoughindividualemployersmightbe per-fectdiscriminators,thismaynotgenerateemploymentinequality ifmutualdiscriminationtendenciesacrossdifferentfirmsare bal-anced.
Themacro-leveldiscriminationindextissimilartothe
micro-levelindexbutisdefinedatthegrouplevel.Morespecifically,itwas calculatedasoneminustheratioofthenumberofworkersofthe morediscriminatedgrouphiredbyallthesixemployerstothe work-ersofthelessdiscriminatedgrouphired.Thismeansthatttook
asinglevalueineachperiodtanditsmathematicalformulation wasequivalenttoEq.(1)butHtbandHtg werecomputedasthe
sumofallbluesandgreenhiredinperiodt.Asforthemicro-level index,t was0fornoinequalityinemploymentandincreased
withincreasingdiscriminationupto1. Results
Testingforpureeffects
Wewillpresentourresultsinthreesteps.First,wewill dis-cussresultsfromtheno-interactiontreatments,inwhichonlyone manipulationwasintroducedatonce.Thiswillhelptohighlight thepureeffectof highquality standards,informationflow, and workerreferralsondiscrimination.Second,wewilldiscussresults ondiscriminationfromalltreatmentsthatincludedalso interac-tions.Third,wewillanalyzeemployerearnings.Althoughpayoffs werenotessentialtoourmainresearchquestions,theiranalysis hasimportanteconomicimplications.
Table2
Thenumberofworkershiredthroughdifferentoptions.
Treatment Newworkersrandomly(i-iii) Re-hiringworkers(iv) Hiringfromemployercontact(v) Hiringviaworkerreferrals(vi) Total(i-vi) no-interactiontreatments −S−E−W 1827 2651 4478 +S−E−W 3093 1405 4498 −S+E−W 782 2542 1155 4479 −S−E+W 1212 2493 792 4497 interactiontreatments +S+E−W 820 2708 951 4479 −S+E+W 719 2136 1280 351 4486 +S−E+W 1439 2043 985 4467 +S+E+W 745 2005 1209 510 4469 Total 10,637 17,983 4595 2638 35,853
Note:N=3600hiringdecisionswithamaximumof10workersperdecision.
Table3
Meansandstandarddeviationsofdiscriminationindexesinno-interactiontreatments.
Treatment micro–leveldiscriminationindex discriminationindexinrehiring macro-leveldiscriminationindex
N Mean SD SE N Mean SD SE N Mean SD SE
−S−E−W 450 0.283 0.275 0.013 400 0.367 0.275 0.014 75 0.173 0.060 0.034
+S−E−W 450 0.426 0.301 0.014 361 0.514 0.500 0.017 75 0.238 0.015 0.009
−S+E−W 450 0.366 0.227 0.011 402 0.466 0.293 0.015 75 0.322 0.116 0.067
−S−E+W 450 0.438 0.313 0.015 402 0.516 0.325 0.016 75 0.204 0.031 0.018
Note:Themacro-leveldiscriminationindexcouldonlybeaffectedlogicallyinthe−S+E-Wtreatment.
Table4
Mixed-effectsregressiononthemicro-leveldiscriminationindexinno-interactiontreatments(N=1800),withrandomeffectsforindividualsandgroups.Thedegreesof freedomwerecomputedusingSatterthwaite’sapproximation.
(Intercept) Estimate SE t p Estimate SE t p
0.283 0.042 6.736 0.000 0.239 0.083 2.892 0.006 treatmenteffects +S 0.143 0.060 2.406 0.043 0.150 0.063 2.392 0.045 +E 0.082 0.060 1.384 0.204 0.090 0.064 1.406 0.196 +W 0.154 0.060 2.595 0.032 0.161 0.063 2.552 0.035 subjectcharacteristics male 0.018 0.036 0.499 0.619 religious 0.006 0.038 0.166 0.868 economics 0.006 0.043 0.140 0.889 studyyear 0.011 0.013 0.859 0.394 part2 −0.016 0.012 −1.385 0.166 F 35.507 0.000 8.479 0.001
Note:Thedummyvariable“economics”hadavalueofoneiftheparticipantwasstudyingeconomicsormanagement.
Theupper four rows in Table2 show thatall available
hir-ingmethods(i–vi)wereselectedfrequentlybyparticipantsinthe no-interactiontreatments.Re-hiringofworkers(iv)wasfavored tohiring workersfromemployercontacts(v) andto hiringvia workerreferrals(vi).Re-hiringwasmorecommoninthelow qual-itystandardscondition.Higherqualitystandardsresultedinmore churnandexperimentationwithnewworkers.Table3 displays anoverviewofmeanıit valuesinno-interactiontreatments.All
factormanipulationsproducedsignificantlyhigherdiscrimination thanthebaseline(Wilcoxonranksumtestsonindividualaverages: W=83,p=0.006for+S;W=110,p=0.052for+E;W=89,p=0.010 for+W;allpvaluesareonetailed).
Notethatdiscriminationindexvaluescannotbefullyattributed toonemethodofhiringoranotherasparticipantscouldre-hire workers or select other hiring methods in every round. They typicallyused combinedstrategies and mighthave individually compensatedfortheirownbiasin onetype ofhiringby favor-inganothergroupwhenchoosinganotherhiringmethod.Still,in ordertoseetheextentofdiscriminationthatcouldpotentiallybe linkedtokeepingworkersinhouseselectively,wecalculatedıit
valuesforre-hiresonly.Table3showsthesemeanvalues(see mid-dlecolumns).Itisremarkablethatparticipantsweremorebiased
infavorofonegroupwhenre-hiringworkers.Thediscrimination indexforre-hireswashigherineveryexperimentalconditionthan themeandiscriminationvalueforotherhiringmethods.Although thesefactorsareintertwined,itisprobablethatdiscriminationby groupmembershipwasmoreinfluentialinkeepingworkersthan hiringnewones.
Asourdataincludedrepeatedobservationsofthesame indi-vidual,weestimatedamixed-effectsmodelwithrandomeffectsat theindividualandgrouplevel(hereafterMEmodel).Therecould befurtherdependenciesinourdataascurrentchoicesmighthave beeninfluencedbyearlierexperiencesindifferentways(cf.e.g., Ule,2008; Corten,2009).Asweare notinterestedin theexact nature of these dependencies,they are covered by individual-leveleffects,theexperimentalpartdummyandtheothercontrols includedinthemodel.Table4showstheresultsofthemodel.The positiveinterceptindicatesthatasignificantlevelofdiscrimination occurredinalltreatments.Resultssupportedourfirsthypothesis.
Results confirmedalsoHypothesis 2,as higher quality stan-dards resultedin higherdiscrimination rates. Asformulated in Hypothesis3,workerreferrals(+W)increaseddiscriminationrates. Meanwhile,Hypothesis4wasnotsupportedastheinformation flow(+E)coefficientwasnotsignificantand,ifanything,thisfactor
Fig.2.Socialinfluenceondiscriminationintheinformationflow(+E)treatment.
Notes:N=1800decisions.Positivevaluesindicatebiastowardsgreensandnegativevaluesindicatebiastowardsblues.Casesatthezeropointareimpartialjudgments.Fitted
smoothregressionlineisindicated.
Fig.3.Confirmationbiasintheinformationflow(+E)treatment.
Notes:N=1800decisions.Positivevaluesindicatebiastowardsgreensandnegativevaluesindicatebiastowardsblues.Casesatthezeropointareimpartialjudgments.
tendedtoincreaseratherthandecreasediscrimination.Itis impor-tanttonotethatcontrolvariablesincludedinthemodel,i.e.,gender, religion,facultystudyyearhadnosignificanteffect.Wealso con-trolledforapossibleexperimentallearning(orfatigue)effectby includingadummyandfoundnosignificantdifferencesbetween thefirstandsecondhalfoftheexperiment.
Regardingmacro-leveldiscrimination,indexvaluesshowthat individualtendenciestodiscriminatehavebeenbalancedouttoa largeextentatthemacrolevel(Table3).Thetvalueof0.173inthe
−S−E−Wbaselineconditioncorrespondstoanimbalanceofhiring atotalof32.84workersfromonegroupand27.16workersfrom theothergrouponaverage(outof60).Theinformationflow treat-ment(+E),however,ledtohigherdiscriminationthanthebaseline (Table 3).This was themanipulation under which participants couldhavebeenaffectedbytheselectionofanotheremployer.The highertvalueimpliesthatindividualdiscriminationtendencies
ruledeachotherouttoalesserextent.Thesomewhathigher macro-leveldiscriminationindexintheinformationflow(+E)condition
impliesthatsomeemployershaveadoptedthebiasesoforhave beeninfluencedbytheircontacts.Fig.2displaysthatwhilethe mostdecisionswerefreeofbias,participantsslightlyincreasedthe partialityoftheirdecisionfavoringthegroupofworkersthathad ahigheraveragequalityattheconnectedemployer.Hence, par-ticipantspartlyadaptedtheirsamplingrationallygiventhenew availableinformation(cf.LeMensandDenrell,2011).Fig.3 high-lightsmoreevidentlythatdiscriminationwascontagiousinthe informationflowtreatment(+E).Alargerbiasbytheconnected employerresultedinlargerdiscriminationinfavor ofthesame groupofworkers,andthereforediscriminationwassubjecttosocial influenceintheexperiment.
Interactioneffects
The lowerfour rows in Table 2 showthat all available hir-ingmethods(i-vi)wereselectedfrequentlybyparticipantsinthe interactiontreatments.Re-hiringofworkers(iv)wasalwaysthe
Table5
Meansandstandarddeviationsofdiscriminationindexesininteractiontreatments.
Treatment micro–leveldiscriminationindex discriminationindexinrehiring macro-leveldiscriminationindex
N Mean SD SE N Mean SD SE N Mean SD SE
+S+E−W 450 0.224 0.206 0.010 421 0.356 0.300 0.015 75 0.146 0.110 0.013
−S+E+W 450 0.413 0.284 0.013 403 0.553 0.336 0.017 75 0.264 0.202 0.023
+S−E+W 450 0.477 0.334 0.016 403 0.633 0.336 0.017 75 0.198 0.132 0.015
+S+E+W 450 0.483 0.292 0.014 370 0.592 0.328 0.017 75 0.385 0.178 0.021
Note:Themacro-leveldiscriminationindexcouldonlybeaffectedlogicallyinthe+Etreatments.
Table6
Mixed-effectsregressiononthemicro-leveldiscriminationindexincludinginteractioneffects(N=3600),withrandomeffectsforindividualsandgroups.Thedegreesof freedomwerecomputedusingSatterthwaite’sapproximation.
(Intercept) Estimate SE t p Estimate SE t p
0.283 0.040 7.064 0.000 0.229 0.062 3.662 0.001 treatmenteffects +S 0.143 0.057 2.523 0.023 0.150 0.060 2.484 0.025 +E 0.082 0.057 1.451 0.166 0.084 0.061 1.379 0.186 +W 0.154 0.057 2.721 0.015 0.163 0.061 2.699 0.016 +S*+E −0.284 0.080 −3.546 0.003 −0.292 0.086 −3.396 0.004 +S*+W −0.104 0.080 −1.292 0.215 −0.117 0.086 −1.354 0.195 +E*+W −0.107 0.080 −1.329 0.203 −0.118 0.086 −1.376 0.188 +S*+E*+W 0.314 0.113 2.769 0.014 0.335 0.123 2.732 0.015 subjectcharacteristics male 0.008 0.024 0.349 0.728 religious 0.019 0.026 0.716 0.475 economics 0.027 0.028 0.950 0.344 studyyear 0.006 0.009 0.599 0.550 part2 −0.000 0.008 −0.056 0.956 F 144.831 0.000 105.864 0.000
mostfavoredoption.Table5showsthatdiscriminationinre-hiring
washigherineveryinteractiontreatmentthan micro-level dis-crimination in general.Hiring workersfrom employercontacts (v) and new hires altogether (i-iii) weremore frequentlyused thanworkerreferrals(vi)whentheywereavailable.Treatments includinginteractionbetweenthemanipulatedfactorsgenerally ledtohighermicro-leveldiscriminationthanthebaseline,except when employers with high standards benefited from informa-tionfromotheremployersandworkerreferralswerenotpresent (Table5).Alldifferenceswiththebaseline(−S−E−W)were signif-icant:−S+E+W,+S−E+Wand+S+E+Wledtohigherdiscrimination (W=82, p=0.005,W=70,p=0.002and W=43,p<0.001 respec-tively,allpvalueswereonetailed),while+S+E−Wledtolower discrimination(W=226,p=0.022,onetailed).
We examinedinteraction effectsby estimatinga furtherME model.Notethat,unlikethepreviousmodels,alltreatmentswere includedintheanalysistocontrolforpureeffects.Severalpure andinteractioneffectsweresignificantinthenewmodel(Table6). Thesignsofallpurefactorcoefficientswerepositive,whichisin linewiththeresultspresentedintheprevioussection.Allbinary interactionsledtonegativecoefficientsbutonlytheinteractionof highqualitystandardsandinformationflow(+S*+E)was signifi-cant.ConsistentlywithHypotheses2and3,highqualitystandards (+S)and workerreferrals (+W)increaseddiscrimination.In line withHypothesis4,informationflow(+E)decreaseddiscrimination, althoughthathappenedonlyinconjunctionwithhighstandards.
Thecoefficientfortheinteractionofhighqualitystandardsand informationflow (+S*+E)wassufficientlylargetoovercomethe pureeffectsofthefactorsinvolved.Thismeansthatinthespecific caseofhighqualitystandards,informationflowactuallydecreased discrimination,despitetheoppositepureeffectsofthetwofactors. Thiscouldbeexplainedasfollows.Ontheonehand,incaseof lowqualitystandards,informationflowincreaseddiscrimination viaselectiveattentionorconfirmationbias.Ontheotherhand,in caseofhighqualitystandards,whenavailableinformationreally
mattered, information flow decreased employer partiality. This impliesthatwhentherewasashortageofworkerswith accept-ablequality,participantshaverealizedthebenefitsofadditional informationandacteduponaccordinglytoavoidprofitloss.
Theinteractionofhighqualitystandardsandworkerreferrals had a similareffect,although weaker.Given that there wasno informationbenefitinthecaseofworkerreferrals,anincreased alertness of employers withhigh quality standards couldhave reduced thepartiality of their judgment. Our results also sug-gestthateventhemutualpresenceofinformationflowbetween employersandworkerreferralscouldhavemadeemployersmore alertaboutpartiality.
Analternativeexplanationofpairwiseinteractioneffectsisthat informationflowandworkerreferralsoffseteachother.Thiscould haveoccurredwhenindividualdiscriminationdidnothavea coun-terpartintermsofmacroinequalityandneighboringemployers favoreddifferentgroups.Asthethree-wayinteractiontermshows, this balancingtendencyoccurredlesslikely foremployers with highstandards,whowerepickierintheirchoicesandtrappedmore likelybyconfirmationbias.
Exceptofthe+S+E−Wtreatmentthatshowedlower macro-leveldiscriminationthanthebaseline,informationflow(+E)ledtoa highervalueoft(Table5).Thisimpliesthatalsointheinteraction
treatments,individualbiasesdidnotruleeachotheroutcompletely inthepresenceofinformationflowbetweenemployers.This con-firmssomespread,butnotaninevitabledisseminationofbiased evaluationsinthenetworkofemployers.
Effectsonparticipants’earnings
Participants’payoffscouldbeseenasaproxyforthegeneral efficiencyintheallocationofworkerquality.Table7showsanME modelwherethedependentvariablewasthepayoffearnedineach periodofthegame.Followingtheobservationthathigher qual-itystandardsresultedinmoreexperimentationwithnewworkers
Table7
Mixed-effectsregressiononparticipant’speriodearnings(N=600),with randomeffectsforgroups.ThedegreesoffreedomwerecomputedusingSatterthwaite’s
approximation.
(Intercept) Estimate SE t p Estimate SE t p
12.388 0.639 19.375 0.000 10.415 0.941 11.074 0.000 treatmenteffects +S −5.659 0.904 −6.259 0.000 −5.609 0.915 −6.129 0.000 +E 3.497 0.904 3.868 0.001 3.671 0.926 3.964 0.001 +W −0.337 0.904 −0.373 0.714 −0.321 0.916 −0.351 0.730 +S*+E 4.603 1.279 3.600 0.002 4.361 1.303 3.346 0.004 +S*+W 2.654 1.279 2.075 0.054 2.549 1.303 1.956 0.068 +E*+W −0.447 1.279 −0.349 0.731 −0.603 1.302 −0.463 0.649 +S*+E*+W −2.820 1.808 −1.559 0.138 −2.461 1.854 −1.327 0.202 subjectcharacteristics male 0.123 0.360 0.341 0.734 religious −0.042 0.393 −0.108 0.914 economics −0.278 0.422 −0.660 0.511 studyyear 0.150 0.140 1.074 0.285 part2 3.218 0.105 30.521 0.000 F 472.233 0.000 440.719 0.000
1Itisworthoutliningthatmixedeffectsmodelswithindividualand/orgroup-levelrandomeffects,althoughtheyarenotalwaysstandardforexperimentaldataanalysis, representanefficientmethodthatcanbeprofitablyusedtoanalyzetheoutcomeofrepeatedgamespresentinginteractionsamongparticipants(e.g.,BarreraandBuskens, 2009;Boeroetal.2009a,b;Bravoetal.,2015).
(Table2),qualitystandardshadadirectimpactonpayoffs. Coher-entlywiththerulesofthegame,higherqualitystandardsledto lowerprofits.Incontrast,theinformationflow(+E)treatmentled tohigherearningsthatdemonstratesthedirectpayoffbenefitsof additionalavailableinformation.
Amongtheinteractioneffects,onlytheonebetween+Sand+E onewaspositiveandhighlysignificant,whiletheonebetween+S and+W,alsopositive,wasonlysignificantatthe10%level.None oftheindividualvariableswassignificant,whileearningsclearly increasedduringthegame.Thiswasexpectedasparticipantscould relyonalargersampleofworkerswhosequalityhasalreadybeen discoveredovertime.
Discussionandconclusions
Duetothecomplexityofmechanismsandproblemsoftheir identificationinthefield,wehavedesignedalaboratory experi-mentthatismeanttotesttheexistenceofabaselinetendencyfor impartiality,theimpactofahighernecessityofhighquality work-ers,theinfluenceofinformationflowinemployernetworks,and theroleofreferralsnetworksondiscriminationinhiringchoices. Intheexperiment,subjectsplayedtheroleofemployers. Repre-sentingasymmetricinformationintherealworld,subjectswere notawareofworkerqualitiesinadvanceandhadnoprior knowl-edgeaboutthedistributionofqualityforthetwocategories. In fact,workerqualitiesforthetwocategoriesweredrawnfromthe samedistributionandhencetherewasnoreasonforany impartial-ityinemployerdecisions.Thisneutralsituationprovidedthebest contrasttodemonstratetheinevitabilityofdiscrimination.
Wefoundthata substantiallevelof discriminationoccurred acrossalltreatments,confirmingthepresenceofabaseline incli-nationfordiscriminationalsoinanimpartialidealworld(Takács andSquazzoni,2015;Takácsetal.,2015).Thisisagainsttypical predictionsbymainstreameconomists,accordingtowhich belief-relateddiscriminationisnotviableasemployersnotsharingfalse beliefswouldgainacompetitiveadvantage(Arrow,1973;Aigner andCain, 1977).Accordingly,discriminationis costly:themost beneficialemployment strategyinourexperiment wasto com-pletelyneglectcolormarkers.Thereislittleevidence,however,that employerpracticesreflectefficientandrationalresponsesto dif-ferencesinskillsandturnovercostsinreality(BielbyandBaron, 1986;Kaufmannetal.,2015).Thisisconfirmedbythepersistence ofdiscriminationinallconditionsofourexperiment.
Wehypothesizedthatmorediscriminationcouldbeinduced byhigherstandardstowardsworkers(Hypothesis2).When stan-dardsare sethighexogenously,lower qualityworkers arealso employedbymereexperimentation,and hencepayoffsarealso lower.Ourfindingssuggestthathigherstandardsgeneratealarger biasandinequalityinhiring,confirmingprevioussimulation find-ings(TakácsandSquazzoni,2015).Subjectsactingasemployers withhighstandards reliedmoreonsupplementaryinformation ofgroupmembershipofworkersalreadyinhouseratherthanon theabundanceofinformationonthequalitiesofworkersscreened before.Inourexperiment,employerswithhighstandardskepta fewhighlyskilledworkersinhouseandthisbiasedtheirjudgment aboutthemeanqualityofsocialcategories.
Wedo notwant toconveythemessagethat thereis a gen-eralcognitivebiasofhumandecisionmakers,whichiscorrelated withhigherdesires andmore selectivepreferences.Our results rathersuggestthatanunintentionaladaptivesamplingbiasexists thatoriginatesfromtheover-generalizationofthequalityofthe smallsampleofemployeesinhouse(Denrell,2005;LeMensand Denrell,2011).Thisadaptive samplingbias wasmore probable whenemployerswithhighstandardswereinterestedinthemost qualifiedworkersonly(cf.Polman,2010;TakácsandSquazzoni, 2015).
Consideringcomplexcognitivemechanismsisnotrequiredto accountfor theemergence ofdiscrimination,althoughcomplex cognitivemechanismscouldin fact make thingsworse(Takács etal.,2015).Theeffectofhighstandardsondiscriminationcouldbe enlargedbyjudgmentbias,inparticularbyself-perceived objectiv-ity(UhlmannandCohen,2007),cherrypicking,andconfirmation bias(e.g.,Simon,1955,1956).Anotherpotentiallyrelevant mech-anismconcernsexpectancyconfirmationsequences.Inthiscase, individuals wouldbeconditionedbyselectiveattentionleading themtoconsideronlyinformationthatconfirmstheirbeliefs,while ignoringinformationthatcontradictstheirexpectations(Hamilton, 1981;Bergeretal.,1980;DarleyandFazio,1980).
Previousstudiesemphasizedtherelevanceofsocialnetworks forinequalitiesofemployment(e.g.,Montgomery,1991;Stoveland Fountain,2009; Beamanand Magruder,2012).Wages,job posi-tions, or workconditions coulddepend onsocial networks (cf. Montgomery,1991,1992;TianandLiu,2018).Weemphasizedthat socialnetworkeffectsinhiringcannotbeconsideredasasingle mechanism.Atleasttwoverydifferentmechanismsareatplay(cf. Gërxhanietal.,2013).Oneisworkerreferralthatfacilitateslabor
marketsegregationgiventheirhomophilouscharacter.Anotheris informationflowaboutworkersvianetworktiesofemployers,for instance,intheformofrecommendationsoractivesearchsuchas man-hunting.Accordingtothefirstnetworkmechanism,the affec-tivecontentof socialrelationshipscouldcreate obligationsthat makerecommendingandhiringfriendsmorelikely.Thisdisrupts thebasiclogicofa‘perfect’marketandresultsinsuboptimal allo-cationoflaborforce(e.g.,IoannidesandLoury,2004;Tassierand Menczer,2008).
Ourresultsconfirmedthatworkerreferralsincrease discrimina-tion(Hypothesis3).Wefoundincreaseddiscriminationwithworker referralsinourexperiment, althoughwe assumednoclustering ofworkerrelationsbyquality.Inamorerealisticscenario,where workersaremorelikelytogivereferralstoworkersofthesame groupandofsimilarquality,onecouldprobablyobserveaneven strongerbiasfavoringonegroupovertheother(cf.Montgomery, 1991). When contacts are homophilous, referral networks can reduceinequalityviaaninvertedadvantagemechanismonlyif ini-tialadvantageisnegativelycorrelatedwiththenewstatusvalue (DiMaggioandGarip,2012).
Contrarytoourexpectations(Hypothesis4),informationflow between employers increased discrimination. Previous studies showedthatbusinesscontactsareimportanttoreducetheeffectof informationasymmetryandhelpemployerstoevaluateproperly thelabormarketpotentialofanemployee(e.g.,Rosenbaumetal., 1990;Uzzi,1996).Ontheotherhand,incaseofdifficultevaluation ofquality,networkscanenlargefalsegeneralizationsandmight magnifytherandominitialbias.Evenunprejudicedemployersseek and rely on evaluationsof others. In case of racial discrimina-tion,thisleadstowhatisknownasracialprofiling(Groggerand Ridgeway,2006;FernandezandGreenberg,2013).
Thereisempiricalevidencethatpeoplearelargelyinfluenced bytheirsocialnetwork(e.g.,MarsdenandFriedkin,1993)and fol-lowadvicesometimesevenwhentheyhavedirectobservations (Sommerfeldetal.,2007;Gilbertetal.,2009).Weobtainedsome evidencethatintheinformationflowcondition(+E)ofour experi-ment,subjectsplayingemployerswereinfluencedtosomeextent byinformation they received. Adifferencein themean quality valuesofworkershiredbytheconnectedemployermadehiring fromthebettergroupmore likely. Participants,therefore, have learntfromtheexperiencesintheirnetwork,whichisconsistent withearlierresearchonlearninginnetworks(e.g.,Barreraandvan deBunt,2009;Hofstraetal.,2015;MasonandWatts,2012).At thesametime,discriminationwasprobablynotfullyconscious andwasnotonlyduetorationalsociallearning,asimpartiality inhiringwasoftencopiedfromtheconnectedemployer. Follow-ingthepreferencesofothersisindicativeofsocialinfluencethat resultedinsomebiascontagioninthenetwork.Furthermore, sub-jectscouldhavedevotedselectiveattentiontothegroupreputation estimatesoftheircontacts.Theypotentiallyconfirmedtheirbelief thathadfavoredoneofthegroupsifthiswasinaccordancewith theestimateoftheotheremployer.Otherwise,iftheestimateof theotheremployerwasnotinlinewiththeirs,theyprobably han-dledthisinformationwithlessattention.Thesefactorsaltogether haveresultedinsomespreadofdiscriminationinthenetwork. Indi-vidualbiasesdidnotbalanceeachotheroutcompletely,causinga certainextentofinequalityintheinformationflowcondition.
Furthermore, our resultsshowed that these factors interact witheach otherin complex ways. It is important tonote that thecombinedeffectofthesefactorswasnotastatisticalillusion duetotheparticularmethodused.Wefoundthatemployerswith highstandardshavemadeuseofinformationflowinawaythat decreaseddiscrimination.Theinteractionofhighqualitystandards andworkerreferralshadasimilardiminishingimpact.
Itseemsthatnetworkscontributedtomorepartialdecisionsof employerswithhighstandards,whohadtobemorecarefulabout
whomtheyemploy(TakácsandSquazzoni,2015).Moreover,the jointpresenceofinformationflowbetweenemployersandworker referralsdecreaseddiscrimination,probablybecausegroupquality informationoriginatingfromthesesourcesoffseteachother.We alsofoundathree-wayinteractioneffectofhighqualitystandards, informationflow,andworkerreferrals.Thisinteractioncouldhave occurredbecauseemployerswithhighstandardsweremorelikely tofallintoconfirmationbiasandneglectedthemorebalancedjoint perspectiveofinformationflowandworkerreferrals.
Obviously,ourresultsneedtobeinterpretedcautiouslygiven the high level of abstraction of the study from the real labor market.Severalimportantfeaturesofhiring processeswerenot coveredinourexperimentaldesign.Therewasnoapplication pro-cedureandhiringbydifferentemployerstookplacesimultaneously andwasrepeatedinthesamefashionseveraltimes.Participants werearrangedaccordingtoadirectedcirclenetworkandnoother informationflowstructureswereinvestigated.Workerswerenot decisionmakers,theyinfactdidnotwork,andtheircharacteristics didnotchangeovertime.Therewerenodifferencesinwagesand everyjobwasacceptabletoworkers.Theseandotherempirically relevantcharacteristicfeaturesofthehiringprocesswereexcluded onpurposeinordertoilluminatetheimpactofqualitystandards, informationflowinemployernetworks,andhomophilousworker referralsonhiringdiscrimination.
Participants themselvesplayingtherole of employersmight havediscountedtheadequatenessofthelabormarketframingfor thedecisionsituation.Theymighthaveperceivedthedecisiontask asanoptimizationproblemthatcanbeapproachedusingacertain hiringstrategy.Infact,thisisnotunrealisticatall,asmany employ-eesarestrategic intheirhumanrelations policyandlearn from theexperienceofothers.Conclusionsfromourexperiment, more-over,couldberelevanttocontextsotherthanthelabormarket. Qualitystandards,recommendations,andinformationfloware rel-evantfactorsinanyrepeatedselectiontask.Thesamemechanisms potentiallycontribute,forinstance,toconsumerdiscrimination.
Theidealisticconditionsinthelabwereaimedtohighlightsome fundamentaltendenciesofdiscriminationthatcouldbe strength-ened,altered,orevencuredinacomplexinstitutionalizedsetting. Inordertounderstandtheseaspectsbetter,furtherempiricaland fieldanalysisisneeded.Here,forinstance,empiricalstudiesthat lookatcross-culturalcomparisonscouldenrichour understand-ing of social and institutional embeddedness of discriminative outcomes(e.g.,Zschirntand Ruedin, 2016; Tianand Liu,2018). Providinga‘clean’testofcertainsimplehypotheseswhileleaving contextualeffectsinthebackgroundisanimportantstepto under-standemployerdiscriminationandexplorecountermeasuresthat couldhelpusreducethesedistortions.
Funding
Financial support for the conduct of the research has been providedbytheIntra-EuropeanFellowshipProgramofthe Euro-peanUnion(“0Discrimination”,GA-2009-236953).Thefirstauthor acknowledgessupportfromtheEuropeanResearchCouncil(ERC) undertheEuropeanUnion’sHorizon2020researchand innova-tionprogramme(GrantagreementNo648693).WethankMarco CastellaniandNiccolòCasnicifortheirpracticalhelp.
AppendixA. Supplementarydata
Supplementarydataassociatedwiththisarticlecanbefound, intheonlineversion,athttps://doi.org/10.1016/j.socnet.2018.03. 005.
References
Agerström,J.,2014.Whydoesheightmatterinhiring?J.Behav.Exp.Econ.52,
35–38,http://dx.doi.org/10.1016/j.socec.2014.06.002.
Aigner,D.J.,Cain,G.G.,1977.Statisticaltheoriesofdiscriminationinlabormarkets.
Ind.LaborRelat.Rev.30,175–187.
Ariely,D.,2008.PredictablyIrrational.TheHiddenForcesThatShapeOur
Decisions.HarperCollins,NewYork.
Arrow,K.J.andBorzekowski,R.2004LimitedNetworkconnectionsandthe
DistributionofWages.SSRNWorkingPaper.https://www.researchgate.net/
profile/KennethArrow/publication/5034990Limitednetworkconnections andthedistributionofwages/links/00463523741cbc1421000000.pdf.
Arrow,K.J.1973TheTheoryofDiscriminationinAshenfelter,OrleyandAlbert
Rees(ed.)DiscriminationinLaborMarkets,PrincetonUniversityPress.3–33.
Barr,T.,2009.Withfriendslikethese:endogenouslabormarketsegregationwith
homogeneous,nonprejudicedagents.Am.J.Econ.Sociol.68(3),703–746.
Barrera,D.,Buskens,V.,2009.Third-partyeffectsontrustinanembedded
investmentgame.In:Cook,K.,Snijders,C.,Buskens,V.,Cheshire,C.(Eds.), eTrust:FormingRelationshipsintheOnlineWorld.RussellSage,NewYork.
Barrera,D.,vandeBunt,G.G.,2009.Learningtotrust:networkseffectsthrough
time.Eur.Sociol.Rev.25(6),709–721,http://dx.doi.org/10.1093/esr/jcn078.
Beaman,L.,Magruder,J.,2012.Whogetsthejobreferral?evidencefromasocial
networksexperiment.Am.Econ.Rev.,3574–3593.
Beaman,L.,Keleher,N.,Magruder,J.,2013.DoJobNetworksDisadvantage
Women?EvidencefromaRecruitmentExperimentinMalawi.Departmentof Economics,NorthwesternUniversity(WorkingPaper).
Berger,J.,Rosenholtz,S.J.,Zelditch,M.,1980.Statusorganizingprocesses.Ann.Rev.
Sociol.6,479–508.
Berninghaus,S.,Erhart,K.M.,Keser,C.,2002.Conventionsandlocalinteraction
structures:experimentalevidence.GamesEcon.Behav.39(1),177–205.
Bertrand,M.,Chugh,D.,Mullainathan,S.,2005.Implicitdiscrimination.Am.Econ.
Rev.95(2),103–110.
Bian,Y.,Huang,X.,Zhang,L.,2015.Informationandfavoritism:thenetworkeffect
onwageincomeinChina.SocialNetworks40,129–138.
Bielby,W.T.,Baron,J.N.,1986.Menandwomenatwork:sexsegregationand
statisticaldiscrimination.Am.J.Sociol.91(4),759–799.
Boero,R.,Bravo,G.,Castellani,M.,Laganà,F.,Squazzoni,F.,2009a.Pillarsoftrust:
anexperimentalstudyonreputationanditseffects.Sociol.Res.Online14(5) (URL<http://www.socresonline.org.uk/14/5/5.html>).
Boero,R.,Bravo,G.,Castellani,M.,Squazzoni,F.,2009b.Reputationalcuesin
repeatedtrustgames.J.Socio-Econ.38,871–877.
Bohnet,I.,2009.Experiments.In:Hedström,P.,Bearman,P.(Eds.),TheOxford
HandbookofAnalyticalSociology.OxfordUniversityPress,pp.639–665.
Brashears,M.E.,Gladstone,E.,2016.Errorcorrectionmechanismsinsocial
networkscanreduceaccuracyandencourageinnovation.SocialNetworks44, 22–35.
Brashears,M.E.,Quintane,E.,2015.Themicrostructuresofnetworkrecall:how
socialnetworksareencodedandrepresentedinhumanmemory.Social Networks41,113–126.
Bravo,G.,Squazzoni,F.,Takács,K.,2015.Intermediariesintrust:indirect
reciprocity,incentives,andnorms.J.Appl.Math.2015(234528)(URL <http://www.hindawi.com/journals/jam/aa/234528/>).
Brewer,M.B.,1979.In-Groupbiasintheminimalintergroupsituation:a
cognitive-Motivationalanalysis.Psychol.Bull.86,307–324.
Brewer,M.B.,1996.In-Groupfavoritism:thesubtlesideofintergroup
discrimination.In:Messick,D.M.,Tenbrunsel,A.E.(Eds.),CodesofConduct. BehavioralResearchintoBusinessEthics.RussellSageFoundation.
Calvó-Armengol,A.,Jackson,M.O.,2004.Theeffectsofsocialnetworkson
employmentandinequality.Am.Econ.Rev.94(3),426–454.
Calvó-Armengol,A.,Jackson,M.O.,2007.Networksinlabormarkets:wageand
employmentdynamicsandinequality.J.Econ.Theory132,27–46.
Camerer,C.F.,2003.BehavioralGameTheory.PrincetonUniversityPress,Princeton.
Caria,A.S.andHassen,I.W.2013TheFormationofJobReferralNetworks.Evidence
fromaLab-in-the-FieldExperimentinUrbanEthiopia.InternationalFood
PolicyResearchInstitute(IFPRI)DiscussionPaper,No.1282.
Chapin,F.S.,1932.TheexperimentalapproachI.theadvantagesofexperimental
sociologyinthestudyoffamilygrouppatterns.Soc.Forces11,200–207.
Corten,R.,2009.Co-evolutionofSocialNetworksandBehaviorinSocialDilemmas
TheoreticalandEmpiricalPerspectives.ICSDissertationSeries,Utrecht.
Darley,J.M.,Fazio,R.H.,1980.Expectancyconfirmationsequences.Am.Psychol.35,
867–881.
Denrell,J.,LeMens,G.,2011.Seekingpositiveexperiencescanproduceillusory
correlations.Cognition119(3),313–324.
Denrell,J.,2005.Whymostpeopledisapproveofme:experiencesamplingin
impressionformation.Psychol.Rev.112,951–978.
DiMaggio,P.,Garip,F.,2012.Networkeffectsandsocialinequality.Ann.Rev.
Sociol.38,93–118.
Dovidio,J.F.,Kawakami,K.,Gaertner,S.L.,2002.Implicitandexplicitprejudiceand
interracialinteraction.J.Pers.Soc.Psychol.82,62–68.
Elliott,J.R.,1999.Socialisolationandlabormarketinsulation:networkand
neighborhoodeffectsonless-Educatedurbanworkers.Sociol.Quarterly40(2), 199–216.
Elliott,J.R.,2001.Referralhiringandethnicallyhomogeneousjobs:howprevalent
istheconnectionandforwhom?Soc.Sci.Res.30,401–425.
Engström,P.,Hesselius,P.,Holmlund,B.,2012.Vacancyreferrals,jobsearch,and
thedurationofunemployment:arandomizedexperiment.Labour26(4), 419–435.
Falk,A.,Fehr,E.,2003.‘Whylabormarketexperiments?LaborEcon.10,399–406.
Falk,A.,Heckman,J.J.,2009.Labexperimentsareamajorsourceofknowledgein
thesocialsciences.Science326,535–538.
Fehr,E.,Gintis,H.,2007.Humanmotivationandsocialcooperation:experimental
andanalyticalfoundations.Ann.Rev.Sociol.33,43–64.
Fernandez,R.M.,Fernandez-Mateo,I.,2006.Networks,race,andhiring.Am.Sociol.
Rev.71,42–71.
Fernandez,R.M.,Greenberg,J.,2013.Race,networkhiring,andstatistical
discrimination.Res.Sociol.Work24(2013),81–102.
Fernandez,R.M.,Castilla,E.J.,Moore,P.,2000.Socialcapitalatwork:networksand
employmentataphonecenter.Am.J.Sociol.105(5),1288–1356.
Fiedler,K.,Juslin,P.,2006.Takingtheinterfacebetweenmindandenvironment
seriously.In:Fiedler,K.,Juslin,P.(Eds.),InformationSamplingandAdaptive Cognition.Cambridge,UniversityPress.
Fischbacher,U.,2007.Z-Tree:zurichtoolboxforready-madeeconomic
experiments.Exp.Econ.10,171–178.
Fiske,S.T.,2009.SocialBeings:CoreMotivesinSocialPsychology.Wiley,NewYork.
Flache,A.,Mäs,M.,Feliciani,T.,Chattoe-Brown,E.,Deffuant,G.,Huet,S.,Lorenz,J.,
2017.Modelsofsocialinfluence:towardsthenextfrontiers.J.ArtificialSoc.
SocialSimulation20(4),2http://jasss.soc.surrey.ac.uk/20/4/2.html.
Fontaine,F.,2008.Whyaresimilarworkerspaiddifferently?theroleofsocial
networks.J.Econ.Dyn.Control32(12),3960–3977.
Fountain,C.,Stovel,K.,2014.Turbulentcareers:socialnetworks,employerhiring
preferences,andjobinstability.In:Manzo,G.(Ed.),AnalyticalSociology.,pp. 339–370.
Gërxhani,K.,Brandts,J.,Schram,A.,2013.Theemergenceofemployerinformation
networksinanexperimentallabormarket.SocialNetworks35,541–560.
Galton,F.,1907.Voxpopuli(Thewisdomofcrowds).Nature75(7),450–451.
Gemkow,S.,Neugart,M.,2011.Referralhiring,endogenoussocialnetworks,and
inequality:anagent-Basedanalysis.J.Evol.Econ.21,703–719.
Gilbert,D.T.,Killingsworth,M.,Eyre,R.N.,Wilson,T.D.,2009.Thesurprisingpower
ofneighborlyadvice.Science323(5921),1617–1619.
Godechot,O.,2016.Thechanceofinfluence:anaturalexperimentontheroleof
socialcapitalinfacultyrecruitment.SocialNetworks46,60–75.
Granovetter,M.,1973.Thestrengthofweakties.Am.J.Sociol.78,1360–1380.
Granovetter,M.,1974.GettingaJob:AStudyofContactsandCareers.Universityof
ChicagoPress,Chicago.
Greiner,B.,2004.Anonlinerecruitmentsystemforeconomicexperiments.In:
Kremer,K.,Macho,V.(Eds.),ForschungUndWissenschaftlichesRechnen2003. Ges.fürWiss.Datenverarbeitung,Göttingen,pp.79–93.
Grogger,J.,Ridgeway,G.,2006.Testingforracialprofilingintrafficstopsfrom
behindaveilofdarkness.J.Am.Stat.Assoc.101(475),878–887.
Hamilton,D.L.(Ed.),1981.Erlbaum,Hillsdale,N.J.
Hoeffer,S.,Ariely,D.,West,P.,2006.Pathdependentpreferences:theroleofearly
experienceandbiasedsearchinpreferencedevelopment.Organ.Behav.Hum. Decis.Process.101,215–229.
Hofstra,B.,Corten,R.,Buskens,V.,2015.Learninginsocialnetworks:selecting
profitablechoicesamongalternativesofuncertainprofitabilityinvarious networks.SocialNetworks43,100–112.
Ioannides,Y.M.,Loury,L.D.,2004.Jobinformationnetworks,neighborhoodeffects,
andinequality.J.Econ.Lit.42,1056–1093.
Jackson,M.,2009.Disadvantagedthroughdiscrimination?theroleofemployersin
socialstratification.Br.J.Sociol.60(4),669–692.
Kaufman,R.L.,2002.Assessingalternativeperspectivesonraceandsex
employmentsegregation.Am.Sociol.Rev.67(4),547–572.
Kaufmann,M.C.,Krings,F.,Sczesny,S.,2015.Lookingtooold?howanolderage
appearancereduceschancesofbeinghired.Br.J.Manage.27(4),727–739.
Keser,C.,Ehrhart,K.M.,Berninghaus,S.K.,1998.Coordinationandlocalinteraction:
experimentalevidence.Econ.Lett.58,269–275.
Keuschnigg,M.,Wolbring,T.,2015.Theuseoffieldexperimentstostudy
mechanismsofdiscrimination.Anal.Kritik38(1),179–202.
Kosfeld,M.,2004.Economicnetworksinthelaboratory:asurvey.Rev.Network
Econ.3(1).
Krauth,B.V.,2004.Adynamicmodelofjobnetworkingandsocialinfluenceson
employment.J.Econ.Dyn.Control28(6),1185–1204.
Kugler,A.D.,2003.Employeereferralsandefficiencywages.LabourEcon.10(5),
531–556.
Lane,T.,2016.Discriminationinthelaboratory:ameta-Analysisofeconomics
experiments.Eur.Econ.Rev.90,375–402.
LeMens,G.,Denrell,J.,2011.Rationallearningandinformationsampling:onthe
‘Naivety’assumptioninsamplingexplanationsofjudgmentbiases.Psychol. Rev.118(2),379–392.
Lee,S.,Pitesa,M.,Pillutla,M.,Thau,S.,2015.Whenbeautyhelpsandwhenithurts:
anorganizationalcontextmodelofattractivenessdiscriminationinselection decisions.Organ.Behav.Hum.Decis.Process.128(May),15–28.
Lin,N.,Ensel,W.M.,Vaughn,J.C.,1981.Socialresourcesandstrengthofties:
structuralfactorsinoccupationalstatusattainment.Am.Sociol.Rev.46, 393–405.
Lorenz,J.,Rauhut,H.,Schweitzer,F.,Helbing,D.,2011.Howsocialinfluencecan
underminethewisdomofcrowdeffect.Proc.Natl.Acad.Sci.108(22), 9020–9025.
Marsden,P.V.,Friedkin,N.E.,1993.Networkstudiesofsocialinfluence.Sociol.
Mason,W.,Watts,D.J.,2012.Collaborativelearninginnetworks.Proc.Natl.Acad. Sci.109(3),764–769.
McBrier,D.B.,2003.Genderandcareerdynamicswithinasegmentedprofessional
labormarket:thecaseoflawacademia.Soc.Forces81(4),120–1266.
McCall,J.J.,1970.Economicsofinformationandjobsearch.Q.J.Econ.84(1),
113–126.
McDonald,S.,2011.What’sintheoldboysnetwork?Accessingsocialcapitalin
genderedandracializednetworks.SocialNetworks33(4),317–330.
McPherson,M.,Smith-Lovin,L.,Cook,J.M.,2001.Birdsofafeather:homophilyin
socialnetworks.Ann.Rev.Sociol.27,415–444.
Midtbøen,A.H.,2014.Thecontextofemploymentdiscrimination:interpretingthe
findingsofafieldexperiment.Br.J.Sociol.,193–214,http://dx.doi.org/10.
1111/1468-4446.12098.
Model,S.,1993.Theethnicnicheandthestructureofopportunity:Immigrantsand
minoritiesinNewYorkCity.In:Katz,M.(Ed.),TheUnderclassDebate:Views fromHistory.PrincetonUniversityPress,Princeton,pp.161–193.
Montgomery,J.,1991.Socialnetworksandlabor-marketoutcomes:towardan
economicanalysis.Am.Econ.Rev.81,1408–1418.
Montgomery,J.,1992.Jobsearchandnetworkcomposition:implicationsofthe
strengthofweaktieshypothesis.Am.Sociol.Rev.57,586–596.
Mortensen,D.,1986.Jobsearchandlabormarketanalysis.In:Ashenfelter,O.,Card,
D.(Eds.),TheHandbookofLaborEconomics2.(Amsterdam:North-Holland).
Mouw,T.,2002.Racialdifferencesintheeffectsofjobcontacts:conflicting
evidencefromcross-sectionalandlongitudinaldata.Soc.Sci.Res.31(4), 511–538.
Olian,J.D.,Schwab,D.P.,Haberfeld,Y.,1988.Theimpactofapplicantgender
comparedtoqualificationsonhiringrecommendations:ameta-analysisof experimentalstudies.Organ.Behav.Hum.Decis.Process.41(2),180–195.
Ostrom,E.,2010.Revisingtheoryinlightofexperimentalfindings.J.Econ.Behav.
Organ.73(1),68–72.
Pager,D.,Bruce,W.,Bonikowski,B.,2009.Discriminationinalow-Wagelabor
market:afieldexperiment.Am.Sociol.Rev.74,777–799.
Petersen,T.,Saporta,I.,2004.Theopportunitystructurefordiscrimination.Am.J.
Sociol.109,852–901.
Petersen,T.,Saporta,I.,Seidel,M.-D.L.,2000.Offeringajob:meritocracyandsocial
networks.Am.J.Sociol.106,763–816.
Polman,E.,2010.Whyaremaximizerslesshappythansatisficers?becausethey
maximizepositiveandnegativeoutcomes.J.Behav.Decis.Making23,179–190.
Ponzo,M.,Scoppa,V.,2010.TheuseofinformalnetworksinItaly:efficiencyor
favoritism?J.Socio-Econ.39(1),89–99.
Rogers,J.K.,2000.Temps:TheManyFacesoftheChangingWorkplace.ILRPress.
Rooth,D.O.,2010.‘Automaticassociationsanddiscriminationinhiring:realworld
evidence.LabourEcon.17(3),523–534.
Rosenbaum,J.E.,Kariya,T.,Settersten,R.,Maier,T.,1990.Marketandnetwork
theoriesofthetransitionfromhighschooltowork:theirapplicationto industrializedsocieties.Ann.Rev.Sociol.16,263–299.
Roth,A.E.,1993.Theearlyhistoryofexperimentaleconomics.J.Hist.Econ.
Thought15,184–209.
Rubineau,B.,Fernandez,R.M.,2013.Missinglinks:referrerbehaviorandjob
segregation.Manage.Sci.59(11),2470–2489.
Rubineau,B.,Fernandez,R.M.,2015.Howdolabormarketnetworkswork?In:
RobertScott,A.,StephenKosslyn,M.(Eds.),EmergingTrendsintheSocialand
BehavioralSciences:AnInterdisciplinary,Searchable,andLinkableResource.,
pp.1–15,http://dx.doi.org/10.1002/9781118900772.etrds0168.
Simon,H.,1955.Abehavioralmodelofrationalchoice.Q.J.Econ.69,99–118.
Simon,H.,1956.Rationalchoiceandthestructureoftheenvironment.Psychol.
Rev.63(2),129.
Smith,V.L.,1991.PapersinExperimentalEconomics.CambridgeUniversityPress.
Smith,V.L.,2010.Theoryandexperiment:whatarethequestions?J.Econ.Behav.
Organ.73(1),3–15.
Solnick,S.J.,Schweitzer,M.E.,1999.Theinfluenceofphysicalattractivenessand
genderonultimatumgamedecisions.Organ.Behav.Hum.Decis.Process.79 (3),199–215.
Sommerfeld,R.D.,Krambeck,H.-J.,Semman,D.,Milinski,M.,2007.Gossipasan
alternativefordirectobservationingamesofindirectreciprocity.Proc.Natl. Acad.Sci.104,17435–17440.
Spoonley,P.,2008.Utilisingademand-ledapproachinalocallabormarket.Local
Econ.:J.LocalEcon.PolicyUnit23(1),19–30.
Stigler,G.J.,1961.Theeconomicsofinformation.J.PoliticalEcon.69(3),213–225.
Stigler,G.J.,1962.Informationinthelabormarket.J.PoliticalEcon.70(5),94–105.
Stovel,K.,Fountain,C.,2009.Matching.In:Hedström,P.,Bearman,P.(Eds.),The
OxfordHandbookofAnalyticalSociology.OxfordUniversityPress.
Surowiecki,J.,2005.TheWisdomofCrowds.Anchor.
Takács,K.,Squazzoni,F.,2015.Highstandardsenhanceinequalityinidealized
labormarkets.J.Artif.Soc.SocialSimul.18(4),2http://jasss.soc.surrey.ac.uk/
18/4/2.html.
Takács,K.,Squazzoni,F.,Bravo,G.,Castellani,M.,2015.Employernetworks,
priming,anddiscriminationinhiring:anexperiment.In:Manzo,G.(Ed.), AnalyticalSociology.WileyandSons,Chichester.
Takács,K.,Flache,A.,Mäs,M.,2016.Discrepancyanddislikingdonotinduce
negativeopinionshifts.PLoSOne11(6),e0157948.
Tassier,T.,Menczer,F.,2008.Socialnetworkstructure,segregation,andequalityin
alabormarketwithreferralhiring.J.Econ.Behav.Organ.66,514–528.
Tian,F.F.,Liu,H.,2018.Gendereddoubleembeddedness:findingjobsthrough
networksinthechineselabormarket.SocialNetworks52,28–36.
Tilly,C.,1998.DurableInequality.UniversityofCaliforniaPress,Berkeley.
Uhlmann,E.L.,Cohen,G.L.,2007.Ithinkit,thereforeit’strue:effectsof
self-Perceivedobjectivityonhiringdiscrimination.Organ.Behav.Hum.Decis. Process.104(2),207–223.
Ule,A.,2008.Partnerchoiceandcooperationinnetworks.theoryand
experimentalevidence.LectureNotesinEconomicsandMathematical Systems,vol.598.Springer.
Uzzi,B.,1996.Thesourcesandconsequencesofembeddednessfortheeconomic
performanceoforganizations:thenetworkeffect.Am.Sociol.Rev.61(4), 674–698.
Webster,M.,Sell,J.,2007.LaboratoryExperimentsintheSocialSciences.Academic
Press.
Wegener,B.,1991.Jobmobilityandsocialties:socialresources,priorjob,and
statusattainment.Am.Sociol.Rev.56,60–71.
Willer,D.,Walker,H.,2007.BuildingExperiments:TestingSocialTheory.Stanford
UniversityPress,Stanford.
Wilson,W.J.,1987.TheTrulyDisadvantaged.UniversityofChicagoPress,Chicago,
IL.
Zschirnt,E.,Ruedin,D.,2016.Ethnicdiscriminationinhiringdecisions:a
meta-analysisofcorrespondencetests.J.EthnicMigrationStud.42(7), 1115–1134.
