Peer-to-Peer Markets

32 Pages·2015·0.25 MB·English

EC08CH22-Levin ARI 20September2016 13:52
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Peer-to-Peer Markets
Liran Einav,1,2 Chiara Farronato,3
and Jonathan Levin1,2
1DepartmentofEconomics,StanfordUniversity,Stanford,California94305-6072;
email:[email protected],[email protected]
2NationalBureauofEconomicResearch,Cambridge,Massachusetts02138
3HarvardBusinessSchool,Boston,Massachusetts02163;email:[email protected]
Annu.Rev.Econ.2016.8:615–35 Keywords
TheAnnualReviewofEconomicsisonlineat marketdesign,Internetmarkets,sharingeconomy,e-commerce,two-sided
economics.annualreviews.org
platforms
Thisarticle’sdoi:
10.1146/annurev-economics-080315-015334 Abstract
Copyright(cid:2)c 2016byAnnualReviews. Peer-to-peermarketssuchaseBay,Uber,andAirbnballowsmallsuppliers
Allrightsreserved
to compete with traditional providers of goods or services. We view the
JELcodes:D47,L11,L81,L86 primaryfunctionofthesemarketsasmakingiteasyforbuyerstofindsellers
and engage in convenient, trustworthy transactions. We discuss elements
of market design that make this possible, including search and matching
algorithms,pricing,andreputationsystems.Wethendevelopasimplemodel
ofhowthesemarketsenableentrybysmallorflexiblesuppliers,andhow
theyimpactexistingfirms.Finally,weconsidertheregulationofpeer-to-peer
marketsandtheeconomicargumentsfordifferentapproachestolicensing
andcertification,data,andemploymentregulation.
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1.INTRODUCTION
In1995,PierreOmidyarstartedtheconsumerauctionwebsitethatbecameeBay.Anoften-cited
storyisthatherecognizeditspotentialwhenheputabrokenlaserpointerupforsale,andsolditfor
$14.83toabuyerwhoturnedouttobeacollectorofbrokenlaserpointers.Asthestorysuggests,
theInternetisapowerfultooltohelpbuyersandsellersfindeachother.Ithasenabledthecreation
ofmarketplacesforlocalgoodsandservices(e.g.,Craigslist),computerprogramming(Upwork,
formerlyoDesk,andFreelancer),consumerloans(Prosper,LendingClub),crafts(Etsy),start-up
financing(Kickstarter),accommodations(Airbnb),babysitting(Care.com),andcurrencyexchange
(TransferWise,CurrencyFair).Thesedays,hundredsofcompaniesaretryingtocreatemarkets
for“ondemand”servicessuchasrides(Uber,Lyft,BlaBlaCar),deliveries(Instacart,Postmates),
andhouseholdtasks(TaskRabbit,Handy).1
Althoughthesebusinesseseachhavespecificfeatures,theysharecommonelements.Theylower
entrycostsforsellers,allowingindividualsandsmallbusinessestocompetewithtraditionalfirms.
Theyrelyonspottransactions,ofteneschewinglong-termcontractsoremploymentrelationships.
Theytakeadvantageoftechnologytoimprovethematchingofbuyersandsellersortoimplement
flexibleorauction-basedpricing.Theyfrequentlydolittleup-frontscreeningorcertificationand
insteadtrytomaintainqualitybyusingreputationandfeedbackmechanisms.And,atleastinsome
cases,theyhavemadeinroadsbyskirtingregulatorybarriers.
Foreconomists,theriseofpeer-to-peermarketshasprovidedafascinatingsetofexamplesof
innovativemarketdesign.CompaniessuchaseBay,Etsy,andAirbnballowthousandsofsellers
toexperimentwithprices,sellingmechanisms,andadvertisingstrategies.Financeplatformssuch
as Prosper and Kickstarter use a variety of public good mechanisms to enable individuals to
collectively fund loan or project investments. Labor markets such as Upwork and TaskRabbit
allow buyers to run small-scale procurement auctions for specialized tasks. Businesses such as
InstacartandUberusecentralizedmechanismstoassignworkerstojobs,butthesemechanisms
alsorelyonmarketforces.Whenaridersubmitsadesiredroute,Uberadvertisesthejobtonearby
drivers.Theallocationofthejobisinvisibletoriders,butUbertriestobalancedemandandsupply
andlimitwaittimesbyadjustingpricestocurrentmarketconditions.
In this review, we take stock of the academic research on peer-to-peer markets and discuss
someofthebroadereconomicissuesaroundthesenewplatforms.Wedividethearticleintothree
parts,lookingfirstatthedesignofInternetmarkets,thenattheeconomicsofpeerproduction,
andfinallyatsomeemergingregulatoryissues.
We identify and discuss some key issues in Internet market design in Section 2. Businesses
thathopetocreatesuccessfulmarketplacesformatchingbuyersandsellershavetosolveseveral
problems.Theyneedtohelpbuyersandsellersfindeachother,eitherbydevelopingacentralized
assignmentmechanismorbyenablingeffectivesearch.Theyneedtosetpricesthatbalancedemand
andsupply,or,alternatively,ensurethatpricesaresetcompetitivelyinadecentralizedfashion.
Andimportantly,theyhavetomaintainanadequateleveloftrustbydevelopingmechanismsto
guardagainstlowquality,misbehavior,andoutrightfraud.
Insolvingtheseproblems,peer-to-peerbusinessesusuallyhavetotradeoffbetweentwoim-
portantobjectives:designingmarketmechanismsthatefficientlyelicitandincorporatedispersed
1AsofJune2015,therewere583peer-to-peerstart-upslistedonAngelList,awebsitethattracksearly-stageinvestment
opportunities.Tryingtofigureoutwhichoftheseareviablewasanenjoyablepartofour“research.”ConsiderDrizly,
anUber-esqueservicethatdeliversbeerandotheralcoholicbeveragesatthepressofabutton(itfacesadozenormore
competitors),orDufl,whichwillmoveyourtravelbagfromcitytocityandwashyourclothesbetweentrips(apotentially
attractiveserviceforfrequenttravelers,althoughthepricetagof$99plusamonthlysubscriptionfeeledonecausticonline
commentatortoremarkthat“itseemslikeanexpensivesolutiontoaproblemthatdoesn’treallyexist”).
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information,andminimizingvariousformsoftransactioncoststokeeptheuserexperienceconve-
nient.Toseeanexample,considerthecasesofAirbnbandUber.Inaccommodations,heterogene-
ityisacentralproblem.GivenachoiceofParisapartments,noteveryonewillagreeonacommon
ranking,andsellersmayhavewidelyvaryingcosts.Thedispersednatureofinformationcallsfora
marketdesignthatprioritizeschoicebyinitiallypresentingfairlybroadsetsofoptionsandrelying
onbuyerstonarrowthemdown.Inridesharing,matchesneedtobemadeinrealtime.Mostpeo-
pleheadinghomefromabaronaFridaynightwantanimmediateandsaferide,andcarelessabout
choosingbetweenanicercarandamoreexperienceddriver.ItthereforemakessenseforAirbnb
torunadecentralizedmarketplace,whereasUberusesacentralizedassignmentmechanism.We
argueinSection2thatmanyaspectsofpeer-to-peermarketdesigncanbeanalyzedsimilarly.
The way that peer-to-peer markets deal with quality assurance and trust is perhaps more
surprising.ItdoesnottakeaPhDineconomicstoseethepotentialincentiveproblemsinsemi-
anonymousonlinetransactions.Yetweknowplentyofprofessionalgametheoristswhoarewilling
tospendthenightinarentalapartmenttheyfoundonAirbnb.Whatisespeciallystrikingaboutthis
degreeoftrustisthatpeer-to-peerbusinessesoftendramaticallylowerthebarrierstobecominga
seller.Londondrivershistoricallyspentyearsstudyingtoobtainablackcablicense,andbecoming
licensedtorunabedandbreakfastcouldtakemonths.TheapplicationprocesstobecomeanUber
driverorAirbnbhosttakesdaysorhours.Ofcoursethesebusinessesdosomeup-frontscreening,
but they also rely heavily on user feedback to provide ongoing monitoring. We discuss recent
workontrustmechanismsinSection2.3,includingsubtletiessuchasone-sidedversustwo-sided
reviews,whatinformationtocollect,whethertolimitthesetofreviewers,andhowtoprovide
incentivestoleavemeaningfulandtruthfulfeedback.
In Section 3, we turn from market design to the broader effects of peer-to-peer businesses
ontraditionalindustries.Todothis,wesetoutasimplemodelinwhichgoodsandservicescan
beproducedbydedicatedprofessionalsorflexiblepeers.Certainfeaturesofmarketsmakethem
moreamenabletopeerproduction.Theseincludevariabilityordiversityindemand,theabsence
ofscaleeconomiesinproduction,andofcoursetheexistenceofwell-functioningspotmarketsto
matchbuyersandsellerseffectively.Wethinkoftheentryofpeer-to-peermarketsaspotentially
performingtwotasks:improvingtheefficiencyofspottransactionsandloweringthecostrequired
forsellerstoadvertisethemselvestobuyers.Theseinnovationsespeciallybenefitflexiblesellers,
whomightnototherwisedoenoughbusinesstojustifylargeinvestmentsinadvertising,reputation,
orcustomerrelationships.Wealsodiscusstheextenttowhichpeer-to-peermarketscangenerate
revenuefromusers,andthelonger-termprospectsforpeerproduction.
Thefinalsectionofthearticleconsiderstheregulationofpeer-to-peermarkets.Onehighly
publicizedissueishowpeer-to-peerbusinessesenteringintolocaltaxiandhotelmarketsshouldbe
incorporatedintotheexistingregulatorystructure.Anotherissuethatwillgrowinimportanceif
flexibleworkbecomesmoreprevalentisatwhatpointcontractworkersonpeer-to-peermarkets
should be viewed as employees of the market maker. A further set of questions surrounds the
collectionofdataonworkersandcustomers,andthewaysinwhichpeople’shistoriescanbeused
or shared. Each of these areas is relatively novel, so after laying them out, we focus on posing
questionsforfutureresearch.
2.MARKETDESIGN:SEARCH,PRICING,ANDTRUST
Thegoalofpeer-to-peermarketsistocreatetradebetweenlargenumbersoffragmentedbuyers
andsellers.Doingthis efficientlyrequires solvingseveralcoremarketdesignproblems.Oneis
tomatch buyers andsellerseffectively while keeping searchfrictionslow.A relatedproblemis
toestablishprices,ortoorganizethemarketsothatpriceswillbesetcompetitively.Finally,a
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potentially difficult problem is to ensure that transactions are safe and reliable for buyers and
sellers.Recentresearchshedslightoneachoftheseproblems,whichweconsiderinturn.
2.1.MatchingBuyersandSellers
Peer-to-peermarketsoftenarecharacterizedbyahighdegreeofheterogeneity.Buyersmaybe
interestedinveryspecificproductsorservices,andsellersmaybequitedifferentiated.Thiscreates
aproblemofmatchingbuyersandsellersandaproblemoffiguringoutappropriate,andperhaps
personalized,prices.Bothproblemshaveanimportantinformationalcomponent.Informationis
dispersedaboutwhoshouldbematchedandatwhatprices,soaneffectivemarketmustaggregate
informationsuccessfully.Marketsalsoneedtominimizetransactioncosts,suchasthetimeittakes
tosortthroughoptionsorcommunicateinformation.ManyaspectsofInternetmarketdesigncan
be viewed as trading off between these two priorities: keeping transaction costs low and using
informationefficiently.
Onesolutiontomatchingbuyersandsellersistocentralizetheprocess.Thisisthestrategy
followedbyon-demandservicessuchasUber.WhenUbercustomerslookforaride,theyspecify
thetypeofservicetheywant(e.g.,aBlackCaroranSUV)butnotthespecificdriver.Drivers
seearequestandcanchoosewhethertorespond,buttheyarenotshownwheretheriderwants
to go.2 Although some riders might value the option to pick their driver, most are apparently
happy to delegate the choice provided they get to their destination quickly and safely. It then
becomesUber’sproblemtoensureasufficientsupplyofdriversatanygiventime,andtoweed
outproblematicorunsafeones.Providedthatthishappens,centralizationkeepstransactioncosts
forridersanddriverslow.3
Incontrast,decentralizedmarketsfacilitateindividualproductchoice.Inmarketswheresell-
ers are diverse and offer a wide array of products and services, a main challenge is to create a
streamlinedandinformativesearchprocess.Oftentheprocessbeginswithbuyersspecifyingwhat
they want and being presented with search results. This can be straightforward in some cases.
AnybuyerwhosearchesforausedtextbookonAmazonprobablycaresaboutgettingitquickly,
reliably,cheaply,andingoodcondition.Notsurprisingly,Amazondisplaysallofthisinformation
prominently,placingthecheapestoptionatthetopofitssearchresultsandhighlightingproducts
whosedeliveryishandledthroughtheAmazonPrimeprogram.Incontrast,buyerslookingfora
weekendapartmentinBarcelonamaypreferdifferentneighborhoodsanddifferenttypesofapart-
ments.Consequently,Airbnbpresentsbuyerswithaninitialsetofoptionsbutprominentlyoffers
avarietyofwaysforbuyerstorefinetheirsearch,forinstance,bynarrowingdownthelocationor
typeofapartmentorpricerange.
Aconsistentempiricalfindingisthatthepresentationofsearchresultsmattersagreatdeal,
even in settings where it seems as if buyers should be able to browse easily through multiple
listings.InInternetsearchadvertising,buyersareabouttwiceaslikelytoclickalistinginthetop
positionastheywouldbeifitweremovedonepositiondown(Goldman&Rao2014).Takingthis
asmotivation,arecenttheoreticalliteraturehasaskedwhetherintermediarieshaveanincentive
2Sometimesthismechanismdoesnotworkwell,asoneofusdiscoveredwhenheorderedanUbercarinLondonafter
midnight,andthedriverrefusedtodrivehimhomeonthegroundsthatitwastoofar.
3Althoughtheparallelmaynotbeimmediatelyobvious,theadvertisingmarketsrunbyGoogleandFacebookarecentralized
inasimilarway.Whenthereisanopportunitytoshowanadvertisement,thesecompaniesrunspotauctionstoallocatethe
opportunity.Usersgetsomesayaboutwhattheywanttosee(someonewhosearchesforautorepaironGooglewillnotseeads
forshoestores),asdoadvertisers,whocansubmitbidstargetedtocertainkeywordsordemographicsorbrowsinghistories.
However,GoogleandFacebookthenadjustthebidstoreflecttheirbestguessesofwhattheuserwantstosee,andinthat
waytrytoleveragetheirsuperiordata.
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topresentsearchresultsinwaysthatcreatemaximalbenefitsforusers.Thesepaperspointout
thattheincentivesfacedbyintermediariesmaynotalignfullywithconsumerinterests,especially
ifintermediariesobtainhigherrevenueifabuyerchoosesaspecificseller(Armstrong&Zhou
2011,Eliaz&Spiegler2011,Hagiu&Jullien2011),whichcanhappenifcertainsellerspayhigher
feesorifthereisverticalintegration(deCorniere&Taylor2014).
Afewrecentstudieshaveuseddatafromonlinepeer-to-peermarketstotrytoquantifysearch
frictions.Dinersteinetal.(2014)compareshoppingbehaviorandpricecompetitiononeBayunder
alternativesearchdesigns:rankingsbasedona“relevancescore”andamultistagesearchprocess
inwhichthebuyerfirstchoosestheexactproductandthenseessellersrankedbyprice.Theyshow
thatguidingbuyerstowardapricerankingcanleadtohighersurplus,butonlywhentherelevant
productisclearlydefinedwithfewvariants.Fradkin(2015)looksatsearchfrictionsinAirbnb’s
more complex apartment rental market. He shows that on Airbnb, even after buyers identify
apartments of interest, many transactions fall through. Transactions can fail because the seller
rejectsthebuyerorbecausemultiplebuyerscontacttheselleratthesametime.Horton(2014)
showsthatthelattercongestionproblemisalsocommonintheUpworkonlinelabormarket.He
arguesthatitresultsinpartfromshowingbuyerssimilarsellerrankingsinasettingwheresellers
havelimitedcapacity(seealsoArnostietal.2015foratheoreticalanalysis).4
2.2.PricingMechanisms
TheInternetenablespeer-to-peermarketstouseawidearrayofdifferentpricingmechanisms.In
theearlydaysofelectroniccommerce,oneofeBay’sinnovationswastointroducetheuseofproxy
bidding,whichenableddynamicauctionstorunoveraperiodofdayswithoutbuyersbeingatten-
tiveateveryminute.Prosper,anearlyentrantinpeer-to-peerlending,introducedanauctionmodel
inwhichborrowerspostedamaximuminterestrateandlenderswereabletomakeoffersatlower
rates.Onlinelabormarketsallowbuyerstopostjobsandinvitebidsfrompotentialsuppliers.The
InternetadvertisingmarketsrunbyGoogle,Facebook,andotherfirmsalsorelyonspotauctions.
Auctionsareappealingbecausetheyallowpricestorespondtomarketconditions.However,
contingentpricingdoesnotnecessarilyrequireanauction.MarketplacessuchasAirbnb,Etsy,and
Amazonmakeiteasyandinexpensiveforsellerstoadjustpricesinrealtime,andsomesellersdothis
usingautomatedalgorithms.LendingClubusesaproprietaryalgorithmtoassesstheriskinessof
eachpotentialborrowerandsetsinterestratesbasedonthisscore,adjustingformarketconditions
andrequiredriskpremia.Ubersimilarlyusesitssurge-pricingalgorithmtovarytheper-mileprice
ofarideassupplyanddemandconditionschange.Inthelattercases,informationcollectedand
processedbytheplatformsubstitutesfortheprice-discoverybenefitofanauctionmechanism.
Wearguedabovethatpeer-to-peermarketsoftenhavetotradeoffbetweenkeepingtransac-
tioncostslowandelicitingandusingdispersedinformation.Itisusefultothinkofpricingmecha-
nismsfromthisperspective.Thecanonicalpricingprobleminvolvesasellerwithseveralpotential
buyers,eachofwhomprivatelyknowshisorherwillingnesstopay.Auctionstendtobetheoptimal
mechanismtoensureanefficientallocationormaximizerevenue,whilemaintainingproperincen-
tivesforbuyers.Butinpractice,itcanbecumbersometoidentifypotentialbuyersandsellersand
toelicitinformationfromthem.Asaresult,simplerpricingmechanismsmaybepreferredifinfor-
mationisavailablefromothersourcesorifthereisnottoomuchuncertaintyabouttherightprice.
4Inthesestudies,buyersknowwhattheypreferoncetheyseedifferentoptions.Inpractice,onewayInternetmarketsareable
tocreatevalueisbycreatingrecommendersystemsthatsteerbuyerstowardpotentiallydesirableproducts.Thesesystems
generallyrelyonhistoricalpurchasepatterns(inthecaseofAmazon’s“peoplewhoboughtXalsoboughtY”)orotheruser
feedbacksuchasreviews(inthecaseofNetflix’srecommendationsystem).
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Wehavestudiedthetrade-offsbetweenauctionsandpostedpricesusingdataoneBaysellers
(Einavetal.2015).Asonewouldexpect,sellerstendtouseauctionsforusedgoodsorwhenthey
havelesssellingexperience.Moresurprisingisthefactthatauctionshavebeeninsteadydecline
formorethanadecade.Onemighthypothesizethatthisisduetocompositionalchanges—that
is, trade shifting toward more commoditized products or more professional sellers. As it turns
out,thisisnotthecase.Instead,weshowthatforagivenseller,offeringagivenitem,thereturns
tousinganauctionwererelativelyhigh15yearsagoandaremuchlowertoday,partlybecause
competition has become more intense and lowered seller margins and partly because buyers’
interest in participating and bidding in auctions has declined over time, even though they can
expectonaveragetoobtainabetterprice.
The decline of auctions in e-commerce extends to other peer-to-peer markets (Einav et al.
2015). In 2010, for instance, Prosper replaced its auction mechanism for funding loans with a
system whereby interest rates are set centrally based on the borrower’s credit score. Lemmen
Meyer (2015) studied this evolution and found that the centralized algorithm to price risk was
just about as effective as the auction format, while simplifying the funding process. Another
example comes from a study of TaskRabbit’s peer-to-peer labor market (Cullen & Farronato
2015).TaskRabbitinitiallyallowedbuyerseithertopostapricefortheirjobortorequestbids
andthenpicktheirpreferredoffer.Becausethenumbersofactivebuyersandsellersvarywidely,
onemightexpectanauctionmechanismtobeparticularlyuseful.ButCullen&Farronatofound
that auction prices do not adjust much with market conditions (mainly because the supply of
laborontheTaskRabbitplatformisquiteelastic),suggestingthatasimplermechanismmightbe
preferable.Indeed,TaskRabbithassubsequentlymovedtowardamechanisminwhichworkers
postanhourlywageandschedule,givingbuyersasimpleandconvenientwaytohire.
TheTaskRabbitexampleillustratesanotherinterestingpoint,thatthepricingunitcanmatter
a great deal. On TaskRabbit, most jobs are relatively standard—involving, for instance, house
cleaningordelivery—buttheymighttakeverydifferentamountsoftime.Sosettingafixedprice
forcleaningjobswouldbeproblematic,butsettinganhourlyratewouldworkwellinthiscontext.
A similar insight was crucial in search advertising. Advertisers initially paid for impressions or
pageviews.Butthevalueofanimpressioncanvarydependingonwheretheadisplacedonaweb
page and who is looking at the page. By instead pricing clicks or conversions, advertisers have
abettersenseofwhattheyarepayingfor,whichmakesiteasierforthemtobidforadvertising
opportunities(Milgrom2010,Varian2010).
2.3.TrustandReputation
Markettransactionsrequiretrust,andthisisespeciallytrueinmarketsthatseektofacilitatespot
tradesbetweenlargenumbersofdispersedbuyersandsellers.Infact,onemightbesurprisedthat
someInternetmarketsworkatall.WheneBaystarted,itwasnotobviousthatpeoplewouldsend
moneytonearlyanonymoussellersorthatthesesellerswouldreciprocatebysendingthepromised
items.Similarly,onemightdoubtthatpeoplewouldrepaypeer-to-peerloans,hirebabysitterson
thestrengthofafewonlinereviews,orrentroomsintheirhousetolightlyvettedstrangers.Yetall
ofthesetransactionsseemtobeworkable.5Apartfromgeneralgoodwill,whatarethemechanisms
thatmakethispossible?
5Thisisnottosaythatthereneverareanyproblems,asarecentNewYorkTimesstoryaboutatrulyhorrendousAirbnbstay
illustrated(Leiber2015).Indeed,wehavehadourownlesstraumaticbutstillunhappyexperiences,suchaswhenoneofus
rentedouthercaronGetaroundtoanewdriverwhopromptlycrashedit.
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Trustcanderivefromup-frontinspection,fromreputation,andfromexternalenforcement.
Internet markets rely on all three, but often in different degrees from traditional markets. In-
spection is more difficult when buyers and sellers meet online. This creates opportunities for
misrepresentation,asJin&Kato(2007)exploredinanearlystudy.Theycomparedthequalityof
gradedbaseballcardspurchasedonlineandoffline.Theyfoundthatonlinesellersweremorelikely
tooverstatethequalityoftheircards.RelatedevidencecomesfromLewis(2011),whostudied
autosalesoneBayandfoundthatbuyerstendtobeskepticalwhensellerspostfewpicturesofthe
cartheyareselling.
Analternativewaytogeneratetrustisforplatformstoimposeexternalregulations:limiting
entry,certifyingquality,orinsuringagainstbadtransactions.Amazon’ssellersandUber’sdrivers
mustadheretominimumqualitystandards.Airbnboffersapartmentownerstheopportunityto
havecertifiedphotostakenoftheirapartments,providingasignaltobuyersthattheapartmentis
accuratelyrepresented.Marketmakersmayalsooffertocompensateusersforbadexperiences.In
2010,eBayintroducedabuyerguaranteeandnowcompensatesbuyersiftheypurchaseaproduct
and the seller does not deliver as advertised (Hui et al. 2014). Roberts (2011) studies a similar
warrantyprogram.Theseinterventionsarecostlybutdirectwaystoensurequality.
Akeycomponentofmanypeer-to-peermarketsistheuseofreputationorfeedbackmech-
anisms. These mechanisms are easy to set up online and appear to have significant bite even
thoughresearchershavepointedoutmanyoftheirflawsandshortcomings.Forinstance,research
oneBay’sfeedbackmechanismhasshownthatdisappointedbuyersoftendonotleavefeedback
(Nosko & Tadelis 2015), that buyers can be deterred from truthful reporting by the threat of
retaliatoryfeedback(Boltonetal.2013),andthatbecause98%ofpositive/negativefeedbackis
positive,averagefeedbackscoresdonotvarymuch(seealsoHorton&Golden2015forsimilar
observationsaboutUpwork).Allthesame,eBay’sreputationsystemseemstohaveworkedwell
enoughtoscreenoutmostofthereallybadactorsandhighlyfraudulentbehavior(Resnicketal.
2002,Dellarocas2003,Cabral&Hortacsu2010).
Inmarketswherethestakestoindividualtransactionsarehigher,orwherepersonalsafetyis
aconcern,reputationmechanismshavebecomeincreasinglysophisticated.Prosper,forinstance,
collectsandpostscreditbureauinformationaboutpotentialborrowers,andAirbnbverifiesthe
true identity of both buyers and sellers. Two-sided reviews also play an important role. For
instance, Uber uses customer reviews to screen out problematic drivers, and it shows drivers
theratingsofpotentialriders,sothatriderswhobehavebadlymayhaveahardertimefinding
a ride in the future. Of course, heavy reliance on feedback scores raises the concern that users
willseektomanipulatethesescores.Mayzlinetal.(2014)arguethatonreviewwebsitessuchas
TripAdvisororYelp,whereanyonecanpostareview,manipulationispervasive.Onemightexpect
manipulationtobemorelimitedwhenreviewscanonlybewrittenafteraconfirmedtransaction,6
butbecausereviewsareinsomesenseapublicgood,peoplemaystillunderreportinformation
that would be helpful to future customers. Fradkin et al. (2015) report on an experiment they
conductedatAirbnb,showinghowadditionalincentivesforreviewingcanimproveinformation
aggregation.
Oneinterestingquestionistheextenttowhichawell-functioningfeedbacksystemremoves
theneedforup-frontscreeningorqualitycertification.Iflow-qualitysellersorserviceproviders
can be quickly identified, the need to screen them out is arguably less important. Of course, if
6Dowd(2015),however,amusinglydescribesherdiscoverythatshecouldraiseherlowUberriderratingbypromisingher
driver“fiveforfive”(aperfectratinginexchangeforaperfectrating).
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buyers insist on trading only with sellers who have strong feedback, the reputation system can
createanentrybarriertonewsellers,whomightinfactbenefitfromacertificationprocess(Pallais
2014).Althoughthesepossibilitiesexistintraditionalmarkets,itisinterestingtoconsiderhow
technology has affected the trade-offs by making continuous monitoring so much easier. We
returntothisthemebelowindiscussingapproachestoregulation.
3.PEERPRODUCTIONANDTRADITIONALINDUSTRIES
Wenowturntothemoregeneraleconomicsofpeerproduction.Herewedevelopasimple,stylized
model.Weconsideramarketwhereflexiblepeerproducerscanofferservicesincompetitionwith
professionalswhomakeup-frontinvestmentsindedicatedcapacity.Forinstance,Hiltonbuilding
ahotelandanapartmentownerrentingaspareroomarealternativewaystoprovideshort-term
accommodations.Weusethemodeltoidentifyconditionsthatarefavorableforpeerproduction
andtohighlighthowpeer-to-peermarketplacesallowsmallflexiblesupplierstoreachconsumers,
loweringthebarrierforthemtoenterandcompete.Themodelhighlightshowpeerentrycan
leadtochangesinmarketstructure,allowfortradeinnewservices,andgeneratelowerconsumer
prices.Italsoprovidessomecontextforourearlierdiscussionofmarketdesignandsubsequent
discussionofregulation.
3.1.PeersversusProfessionalSellers
We consider a market for a product or service that can be produced by two types of sellers. A
dedicatedorprofessionalsellerincursanup-frontcostk(q)tocreatequnitsofcapacity—thinkof
theconstructionofahotel,thepurchaseofinventory,orthehiringoffull-timeemployees—and
subsequentlyhasmarginalcostc foreachunit.Incontrast,aflexibleorpeersellerhasunitcapacity,
0
paysnoup-frontcost,andhasamarginalcostc +c.Thecostc isdrawnfromadistributionG,
0
with support [0,∞). Sellers offer their services to a pool of buyers whose demand is variable.
Wewritedemandas D(p),wheres isthedemandstatedrawnfromadistribution H,and p is
s
themarketprice.Weassumethathighvaluesofs areassociatedwithhighdemand,so D(p)is
s
increasingins anddecreasingin p.
Bothprofessionalandpeersellersmustadvertisetheirservicestobuyersinordertoberec-
ognizedandmakesales.Weassumethisadvertisingrequirementtakestheformofafixedcost
f thateachsellermustincurtobecomevisibletobuyers.Thelargerthecostofvisibility f,the
largertheadvertisingorreputationalbarriertoentry,andthefewersellerswhowillbeactivein
marketequilibrium.Ofcourse,professionalsellershaveanadvantagewhenitcomestoadvertising
becausetheycanspreadthefixedadvertisingcostoveralargernumberofsales.Later,wewill
thinkofpeer-to-peermarketsasprovidingacheaperalternativetospending f,makingiteasier
forpeersellerstobecomevisible.
Tostreamlinethemodel,weabstractfromsomerealisticfeaturesof mostpeer-to-peermarkets
suchassearchfrictions,productdifferentiation,andsellermarketpower.Instead,weassumethat
sellingiscompetitiveandthatineachstatethemarketpriceadjuststoequatedemandandsupply.
Inthisspirit,weassumethatdespitethefixedentrycost f,scaleeconomiesfordedicatedsellers
aresufficientlylimitedtojustifyacompetitiveanalysis.Inparticular,(f +k(q))/q hasaunique
minimumq∗,whichimplicitlyissmallrelativetomarketdemand.
The timing of the model is as follows. First, potential sellers decide whether to enter the
market.Wewilllet Q denotetheamountofdedicatedcapacityand Q theamountofflexible
k c
capacity.Second,thedemandstates isrealizedandpeersellersdrawtheirmarginalcosts.Third,
themarketclearsataprice p thatequatesdemandandsupply.
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EC08CH22-Levin ARI 20September2016 13:52
Supply with only Supply with
Price
dedicated sellers dedicated and
flexible sellers
D (p)
High
c
0
D (p)
Low
Q Q + Q Quantity
k k c
Figure1
Spotmarketwithdedicatedandflexiblesellers.
Tosolvethemodel,westartbyidentifyingthemarketclearingpricethatresultsineachdemand
state.Becauseallsellershavemarginalcostofatleastc ,itisconvenienttowritethemarketprice
0
as p =c +π,whereπ ≥0isthe“pricepremium.”Withthisnotation,supplyis Q +G(π)Q,
0 k c
anddemandinstates is D(c +π).Let
s 0
π∗(s)={π|D (c +π)= Q +G(π)Q }
s 0 k c
denotethepricepremiumthatclearsthemarketwhen D(c )≥ Q.
s 0 k
Themarket-clearingpremiumπ(s)isgivenby
(cid:2)
π =0 if D(c )< Q
π(s)= s 0 k . (1)
π∗(s) if D(c )≥ Q
s 0 k
Figure 1 illustrates how the market clears when there are only dedicated sellers, and when
there is a mix of dedicated and flexible sellers. If demand is low, π = 0 and dedicated sellers
are paid their marginal cost. If demand is high, there is a positive price premium. With only
dedicatedcapacity,capacityisfixedat Q andtheremaybeagreatdealofpricevariability,asin
k
Prescott’s(1975)well-known“hotel”model.Withflexiblesellers,short-runsupplywillbemore
elastic.Inthiscase,demandvariabilityispartiallyaccommodatedbyasupplyincreasesoprices
fluctuateless.Forinstance,Cullen&Farronato(2015)studiedthelaborsupplyofpeerworkerson
TaskRabbitandfoundittobeveryelastic.Theyestimatedthata10%increaseinthewagerateleads
workerstoapplyforapproximately30%morejobs(seealsoSheldon2015forananalysisofUber
drivers).
Wenextderivesellerprofits.Adedicatedsellerexpectstomakeaprofitπ(s)foreachunitof
capacityinstates,soherexpectedprofit,perunitofcapacity,is
(cid:3)
f +k(q)
U = π(s)dH(s)− . (2)
k q
s
It is apparent from this expression that each dedicated seller will maximize per-unit profits by
choosingthecapacitylevelq∗thatminimizes(f +k(q))/q,andinequilibriumthisisexactlywhat
willhappen.
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EC08CH22-Levin ARI 20September2016 13:52
A flexible seller expects to sell only when π(s) exceeds her marginal cost c, so her expected
profitis
(cid:4) (cid:5)
(cid:3) (cid:3)
π(s)
U = (π(s)−c)dG(c) dH(s)− f. (3)
c
s 0
Anyincreaseinsellercapacityreducesbothspotmarketpricesandsellerprofits.Increasesin
dedicated and flexible capacity are not identical, however. In the Appendix (Result 1), we note
thatanextraunitofdedicatedcapacityhasabiggereffectonpricesthanaunitofflexiblecapacity
because of the lower associated marginal costs. Similarly, dedicated sellers in the market are
affectedmorebyanyincreaseincapacity—becausetheyhopetosellineverystate,theirexpected
profitsfallmoresharplyasspotmarketpricesarereduced.
3.2.WhenisPeerProductionEfficient?
Underourassumptionthattheindustryiscompetitive,capacitywilladjusttodrivesellerprofits
to 0. An equilibrium is a pair of capacity levels (Q∗,Q∗) such that there is no opportunity for
c k
furtherentryandnoactivesellerwantstoexit.Thatis,equilibriumrequiresthatU (Q∗,Q∗)≤0
c c k
andU (Q∗,Q∗) ≤ 0, meaning there is no incentive for profitable entry, and if a given type of
k c k
sellerτ ∈ {c,k}isactive,Uτ(Q∗c,Qk∗) = 0.WeshowintheAppendix(Results2and3)thatan
equilibriumalwaysexists,isunique,andisefficientinthesensethatitmaximizesconsumerand
totalsurplus.
Figure2illustratesthetwozero-profitlinesdefinedbyU =0andU =0.Thenumberof
k c
flexiblesellersQ isonthex-axisanddedicatedcapacityQ isonthey-axis.Eachzero-profitline
c k
isdownwardslopinganddividesthespaceintoregionswhereentrybytherespectivetypeofseller
isprofitableorunprofitable.Theequilibrium,whichinvolvesamixedmarketstructure,involves
eachtypeofsellermakingzeroprofit.Othermarketstructuresarealsopossible.Forinstance,if
thezero-profitlinefordedicatedsellerswereeverywhereabovethelineforflexiblesellers,the
equilibriumwouldinvolveonlydedicatedsellers,andiffixedcostsaresufficientlyhigh,nosellers
willenteratall.
Dedicated
capacity, Q
k
Uc(Qc,Qk) = 0 Both seller types
unprofitable
(Qc*,Qk*)
Both seller types
profitable Uk(Qc,Qk) = 0
Flexible
capacity, Q
c
Figure2
Equilibriummarketstructure.
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624 Einav Farronato Levin