The mechanisms of agglomeration: Evidence from the effect of inter-industry relations on the location of new firms

Jofre‐Monseny, Jordi; Marín-López, Raquel; Viladecans‐Marsal, Elisabet

doi:10.1016/j.jue.2011.05.002

Cited by 131 publications

(96 citation statements)

References 42 publications

(51 reference statements)

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“…For example, Audretsch and Feldman (1996) and Rosenthal and Strange (2001) regress levels of agglomeration on proxies for the presence of labor pooling, input sharing, and knowledge spillovers. Another recent approach is Jofre- Monseny et al (2011), who estimate count models of new firms as functions of proxies for Marshallian forces. A related body of work consists of papers that separately consider Marshall's three forces.…”

mentioning

confidence: 99%

Heterogeneous Agglomeration

Faggio

Silva

Strange

2017

Review of Economics and Statistics

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Many prior treatments of agglomeration explicitly or implicitly assume that all industries agglomerate for the same reasons, with the traditional Marshallian (1890) factors of input sharing, labor pooling, and knowledge spillovers affecting all industries similarly. An important instance of this approach is the extrapolation from one key sector to the larger economy, such as the drawing of very general lessons about agglomeration from the specific case of the Silicon Valley. Another is the pooling of data to examine common tendencies in agglomeration even across very different industries. This paper uses UK establishment-level data on coagglomeration to document substantial heterogeneity across industries in the microfoundations of agglomeration economies. The analysis shows that the Marshallian factors interact with the organizational and adaptive aspects of agglomeration discussed by Chinitz (1961), Vernon (1960), andJacobs (1969). Our findings highlight the importance of treating Marshall's microfoundations of agglomeration as complements to the analysis of Jacobs and others, rather than as alternatives.

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mentioning

confidence: 99%

Heterogeneous Agglomeration

Faggio

Silva

Strange

2017

Review of Economics and Statistics

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“…Agglomeration economies are observed in metropolitan areas, enhancing innovation and productivity, as city size and density increase (Alañón-Pardo & Arauzo-Carod, 2013;Duranton & Kerr, 2015;Hindle, 2010;Jofre-Monseny, Marín-López, & Viladecans-Marsal, 2011;Puga, 2010;Sedgley & Elmslie, 2004). Two influential works in the mid-20th century narrowed the focus to localization economies.…”

Section: Agglomeration Urbanization and Localizationmentioning

confidence: 99%

Demand pull or supply push? Metro-level analysis of start-ups in the United States

Motoyama

Malizia²

2017

Regional Studies, Regional Science

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“…Specifically, the reasons that a co-location pattern occurs in different regions mainly lie in two aspects: (1) spatial interactions are usually influenced by the geometric distance between objects, and thus the pattern existing within near regions are more possible to be due to common cause(s) than those within farther regions; and (2) in different regions, a pattern is usually formed due to different environmental factors, not just to the co-located features themselves. For example, as shown in Figure 4, urban facilities may be located together to achieve benefits from the agglomeration effects of services (the lower-left block in Figure 4) [33]. This local pattern is due to the spatial interactions between the features themselves.…”

Section: Polygon Dissimilarity Function and Clusteringmentioning

confidence: 99%

Identifying and Analyzing the Prevalent Regions of a Co-Location Pattern Using Polygons Clustering Approach

2017

IJGI

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Given a co-location pattern consisting of spatial features, the prevalent region mining process identifies local areas in which these features are co-located with a high probability. Many approaches have been proposed for co-location mining due to its key role in public safety, social-economic development and environmental management. However, traditionally, most of the solutions focus on itemsets mining and results outputting in a textual format, which fail to adequately treat all the spatial nature of the underlying entities and processes. In this paper, we propose a new co-location analysis approach to find the prevalent regions of a pattern. The approach combines kernel density estimation and polygons clustering techniques to specifically consider the correlation, heterogeneity and contextual information existing within complex spatial interactions. A kernel density estimation surface is created for each feature and subsequently the generated multiple surfaces are combined into a final surface with cell attribute representing the pattern prevalence measure value. Polygons consisting of cells are then extracted according to the predefined threshold. Through adding appended environmental data to the polygons, an outcome of similar groups is achieved using polygons clustering approach. The effectiveness of our approach is evaluated using Points-of-Interest datasets in Shenzhen, China.

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The mechanisms of agglomeration: Evidence from the effect of inter-industry relations on the location of new firms

Cited by 131 publications

References 42 publications

Heterogeneous Agglomeration

Heterogeneous Agglomeration

Demand pull or supply push? Metro-level analysis of start-ups in the United States

Identifying and Analyzing the Prevalent Regions of a Co-Location Pattern Using Polygons Clustering Approach

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