This paper studies the advertising agency industry in Manhattan to infer networking benefits among agencies in close spatial proximity. We use economic census data that allow us to distinguish locations at a fine level of geographic detail, so as to infer the strong effect on productivity of having more near advertising agency neighbours. Paying close attention to identification issues, we show, however, that there is extremely rapid spatial decay in the benefits of more near neighbours, even in the close quarters of southern Manhattan, a finding that is new to the literature. This suggests that high density of similar commercial establishments is important in enhancing local productivity for those industries found in large cities, where information sharing plays a critical role. Our results indicate that the benefits of more near neighbours are largely capitalized into rents rather than wages, challenging an existing literature, which estimates wage equations alone to infer agglomeration benefits. Copyright © 2008 The Review of Economic Studies Limited.

We thank Enrico Spolaore for early advice and Rajiv Vohra for helpful discussions on the topic. We are also grateful to James C. Davis for his help throughout the project, Christine Kearney for preparation of the federalism index, and Fumiko Tamura for her help as a research assistant. Finally we thank two anonymous referees and an editor for helpful detailed comments.

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This paper extends the utility maximization model of migration by introducing income and unemployment-related uncertainties as determinants of utility, and analyzes the effects of the informational advantages of migrants. The paper maintains that migration would expand an individual's economic choices and opportunities and allow diversification. Consequently, diversification advantages influence the location decisions of migrants, an effect captured by the correlation of incomes at the origin and potential destinations. We use the discrete choice model based on random utility maximization as the framework for our empirical investigation of migration from the United States rural to urban counties. This paper takes advantage of an equivalent relation between the conditional logit model and Poisson regression to study the migration decisions using aggregate data among a large set of spatial alternatives. The results show that the diversification concerns have significant effects on location decisions of the rural-urban migrants in the United States.