Friends and Partners: The Impact of Network Ties

dc.contributor.advisorMurrell, Peteren_US
dc.contributor.advisorKranton, Rachelen_US
dc.contributor.authorCangiano, Giulia Cristinaen_US
dc.contributor.departmentEconomicsen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2009-10-06T06:04:51Z
dc.date.available2009-10-06T06:04:51Z
dc.date.issued2009en_US
dc.description.abstractHow does a high-tech entrepreneur find the most qualified engineer for her startup? How does a scientific inventor acquire funding or recruit the best partner for his project? In chapter 1 I develop a discrete matching model with heterogeneous values and an undirected social network to address these questions. My model offers a framework to study how relative network positions affect payoffs and incentives. While an entrepreneur's expected return increases with the size of her own network, the network externalities from competing entrepreneurs are more complex. There is a tradeoff between the size of an entrepreneur's network and the competitive externality she exerts. When an entrepreneur's network increases, her closest competitors are hurt, but her less similar competitors may actually have a better chance of finding a suitable partner. In a more connected network, fewer frictions interfere with compatible matches. Results are consistent with observable patterns in high-tech and biotechnology in Silicon Valley and Massachusetts, as well as the turn of the 20th century German synthetic dye manufacturing. Initiatives to promote social networks within innovative sectors are critical and deserve future research. In Chapter 2 I consider a two-period endogenous network search model in which entrepreneurs build relationships with specialists. The model includes a period of costly network search and applies results from my companion paper. In the presence of network externalities, entrepreneurs over-invest in networking. Networks in which is it not costly to build new relationships are the least efficient. While positive externalities reduce this problem some negative inefficiencies will likely prevail. Networks in which participation is cheap - such as online career networks LinkedIn or Monster.com - have limited information about individual specialists and are the most inefficient. A network that is costly to participate in, but is more effective at targeting entrepreneur's search for qualified candidates results in a more compatible and, likely, efficient partnership. These networks might include alumni groups, trade associations or head-hunters. This chapter provides one explanation for the varied successes of government programs in fostering effective business networks. Efficient networks foster fewer, more specific relationships.en_US
dc.format.extent752530 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/1903/9548
dc.language.isoen_US
dc.subject.pqcontrolledEconomics, Theoryen_US
dc.subject.pquncontrolledApplied Microeconomic Theoryen_US
dc.subject.pquncontrolledBehavioral Economicsen_US
dc.subject.pquncontrolledEntrepreneursen_US
dc.subject.pquncontrolledMicroeconomicsen_US
dc.subject.pquncontrolledNetworksen_US
dc.titleFriends and Partners: The Impact of Network Tiesen_US
dc.typeDissertationen_US

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