Evaluating the geographic concentration of industries using distance-based methods

By Eric Marcon, Florence Puech. 2003

270 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Plant Science

View Paper: https://www.semanticscholar.org/paper/0ece7e2a5ace82c07835998dc1a328b6d59b552a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Eckhardt Bode
2. Frank Bickenbach
3. Tony E. Smith
4. Giuseppe Arbia
5. Zheng Tian
6. Giuseppe Espa
7. Tomoya Mori
8. Koji Nishikimi

In Plant Science

1. Eckhardt Bode
2. Giuseppe Arbia
3. Zheng Tian
4. Giuseppe Espa
5. D. Quah
6. Frank Bickenbach

Citations in the corpus

• Disproportionality Measures of Concentration, Specialization, and Localization (2008, methodology): “For georeferenced microdata that provide information on the distances between any pair of establishments, Duranton and Overman (2005, 2008) and Marcon and Puech (2003, 2007) propose describing concentration using functions based on the Ripley’s K function.”
• Accounting for Neighboring Effects in Measures of Spatial Concentration (2011, unknown): “Borrowing from techniques used in the spatial statistics for the analysis of spatial point patterns, Marcon and Puech (2003) and Duranton and Overman (2005, 2008) pioneered the use of distance-based indicators of spatial concentration.”
• A class of spatial econometric methods in the empirical analysis of clusters of firms in the space (2008, methodology): “Univariate K–functions (proposed by Ripley, 1976; 1977) have been already used in economic geography to characterize the geographical concentration of industries (see e. g. Arbia and Espa, 1996; Marcon and Puech, 2003a; Quah and Simpson, 2003).”
• Measuring Agglomeration Using the Standardized Location Quotient with a Bootstrap Method (2013, methodology): “Besides the SLQ approach of O’Donoghue and Gleave (2004), three studies attempt to address this problem by building some statistical tests to determine the cut-off value. Moineddin et al. (2003) derive an expression of the standard deviation of the LQ and construct the confidence interval by assumin”
• A Divergence Statistic for Industrial Localization (2005, background): “2 A notable exception is the K-density approach of Duranton and Overman (2005) and Marcon and Puech (2003) [based on Ripley (1977)].”