A non-parametric test for industrial specialization

Billings, Stephen B.; Johnson, Erik B.

doi:10.1016/j.jue.2011.12.001

Cited by 23 publications

(17 citation statements)

References 40 publications

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“…Our null hypothesis is no spatial similarity between industry j and industry k conditional on the spatial density of industry j. 15 Similar to Duranton and Overman (2005) and Billings and Johnson (2012), we construct our counterfactual of randomly located (pseudo) industries based on two specific criteria: (1) the sample should be drawn from the set of locations where a establishment could potentially locate, and (2) the sample size used in constructing the counterfactual must be equal to the number of establishments in the indus-14 Even though we represent Wasserstein distance as a histogram in this figure, we calculate distances based on our bivariate spatial density functions and thus have no issues with assigning establishments to spatial units and MAUP.…”

Section: Colocalization Indexsupporting

confidence: 54%

Agglomeration within an urban area

Billings

Johnson

2016

Journal of Urban Economics

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Section: Colocalization Indexsupporting

confidence: 54%

Agglomeration within an urban area

Billings

Johnson

2016

Journal of Urban Economics

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“…Note that the above null model is reminiscent of the 'counterfactuals' used in the empirical literature on agglomeration economies [27][28][29]. Also, the expression of the representation (Eq.…”

Section: Definitionsmentioning

confidence: 99%

Patterns of Residential Segregation

Louf

Barthélemy

2016

PLoS ONE

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The spatial distribution of income shapes the structure and organisation of cities and its understanding has broad societal implications. Despite an abundant literature, many issues remain unclear. In particular, all definitions of segregation are implicitely tied to a single indicator, usually rely on an ambiguous definition of income classes, without any consensus on how to define neighbourhoods and to deal with the polycentric organization of large cities. In this paper, we address all these questions within a unique conceptual framework. We avoid the challenge of providing a direct definition of segregation and instead start from a definition of what segregation is not. This naturally leads to the measure of representation that is able to identify locations where categories are over- or underrepresented. From there, we provide a new measure of exposure that discriminates between situations where categories co-locate or repel one another. We then use this feature to provide an unambiguous, parameter-free method to find meaningful breaks in the income distribution, thus defining classes. Applied to the 2014 American Community Survey, we find 3 emerging classes—low, middle and higher income—out of the original 16 income categories. The higher-income households are proportionally more present in larger cities, while lower-income households are not, invalidating the idea of an increased social polarisation. Finally, using the density—and not the distance to a center which is meaningless in polycentric cities—we find that the richer class is overrepresented in high density zones, especially for larger cities. This suggests that density is a relevant factor for understanding the income structure of cities and might explain some of the differences observed between US and European cities.

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“…The method has enjoyed widespread adoption with more than 450 citations of the original 2005 article. Subsequent recent applications and extensions include Klier and McMillen (2008) for the U.S. auto industry; Nakajima, Saito, and Uesugi (2012) for the Japanese service sector; and Billings and Johnson (2012) construct an index of industrial specialization and compare it to results from K-densities. M-function: A third approach, termed as the M-function, was proposed by Marcon and Puech (2010), generalizing Ripley's K-function to inhomogeneous space.…”

Section: Inferring Colocation From Spatial Point Patterns: Prior Apprmentioning

confidence: 99%

Localization and Industry Clustering Econometrics: An Assessment of Gibbs Models for Spatial Point Processes

Sweeney

Gómez-Antonio

2015

Journal of Regional Science

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The objective of this paper is to assess an approach to statistical modeling of point referenced establishment data that permit inclusion of “environmental” or establishment‐specific covariates and specific forms of interestablishment interaction. Gibbs models are used to decompose the conditional intensity of the spatial point process into trend and interaction components. The trend is composed of access measures (primarily different classes of roads) and three different interaction processes are tested: Geyer, area interaction, and Strauss hard core. While the models used have proved to be useful in ecology, we are unaware of any applications to establishment or firm data. In empirical application, the models yield intuitively appealing results for the trend component, and the ability to specify the interaction component gives deeper insights into interestablishment spatial dynamics than any previously published methods.

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A non-parametric test for industrial specialization

Cited by 23 publications

References 40 publications

Agglomeration within an urban area

Agglomeration within an urban area

Patterns of Residential Segregation

Localization and Industry Clustering Econometrics: An Assessment of Gibbs Models for Spatial Point Processes

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