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Quality Sorting and Networking: Evidence from the Advertising Agency Industry

Mohammad Arzaghi ()

Working Papers from U.S. Census Bureau, Center for Economic Studies

Abstract: This paper provides a model of knowledge sharing and networking among single unit advertising agencies and investigates the implications of this model in the presence of heterogeneity in agencies’ quality. In a stylized screening model, we show that, under a modest set of assumptions, the separation outcome is a Pareto-undominated Nash equilibrium. That is, high quality agencies locate themselves in a high wage and rent area to sift out low quality agencies and guarantee their network quality. We identify a necessary condition for the separating equilibrium to exist and to reject the pooling equilibrium even in the presence of agglomeration economies from networking. We derive the maximum profit of an agency and show the condition has a directly testable implication in the empirical specification of the agency’s profit function. We use a sample of movers—existing agencies that relocate among urban areas—in order to extract a predetermined measure of their quality prior to relocation. We estimate the parameters of the profit function, using the Census confidential establishment-level data, and show that the necessary condition for separation is met and that there is strong separation and sorting on quality among agencies in their location decisions.

Keywords: Advertising; Agglomeration; Industrial Concentration; Business Services; Discrete Choice; Knowledge Spillovers; Learning; Location Decision; Poisson Regression; Nested Logit; Screening; Separating Equilibrium; Sorting (search for similar items in EconPapers)
JEL-codes: D82 D83 D85 L25 L84 M37 R12 R30 (search for similar items in EconPapers)
Pages: 39 pages
Date: 2005-10
New Economics Papers: this item is included in nep-dcm, nep-mic, nep-mkt and nep-ure
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3) Track citations by RSS feed

Downloads: (external link) First version, 2005 (application/pdf)

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