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2015 | OriginalPaper | Chapter

6. Delineating Metropolitan Areas: Measuring Spatial Labour Market Networks Through Commuting Patterns

Author : Gilles Duranton

Published in: The Economics of Interfirm Networks

Publisher: Springer Japan

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Abstract

This chapter first discusses the necessity of defining metropolitan areas and current practice in several countries. It argues for the use of a simple algorithm that exploits cross-municipality commuting patterns. Municipalities are aggregated iteratively provided they send a share of their commuters above a given threshold to the rest of a metropolitan area. This algorithm is implemented on Colombian data and its robustness is assessed. Finally, the properties of the resulting spatial labour market networks are explored.

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previous chapter Economic Geography and Interfirm Transaction Networks

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The French government defines ‘statistical’ metropolitan areas though its statistical institute (INSEE). At the same time, there are many ‘urban communities’ which are voluntary unions of neighbouring municipalities, i.e{.}, political metropolitan areas. The two differ, sometimes considerably, but coexist to serve extremely different purposes.

See for instance the well known ‘Modifiable Areal Unit Problem’ (MAUP). See Cressie (1993) for a presentation and a discussion.

For instance, it is obvious that policies that allocate money to ‘places’ need discrete spatial units.

This phenomenon is not unique to the coffee region. The same is observed in the region of the Caribbean coast where three of the main cities: Barranquilla, Cartagena and Santa Marta do not merge even for a low commuting threshold of 1 %.

This region is technically contiguous with the Valledupar-La Guajira region to its north-east. However, this contiguity is minimal and the Sierra Nevada mountain separates these two regions which are probably best treated as separate. Going from Santa Marta to ‘neighbouring’ Valledupar is a 5 h drive. Should these two regions be treated as one, they would form a region with 5.3 million inhabitants over 50 municipalities.

We also start seeing satellite municipalities which are not geographically adjacent to the rest of their metropolitan areas. There are two such cases. The first is the Satanderian municipality of Sucre which get attached to Bucaramanga which is more than 200 km far. Given that this municipality is not negligibly small and sends about 7 % of its commuters to Bucaramanga, this corresponds to real flows, perhaps mostly students which are counted together with workers. The other case is Guacamayas, a tiny municipality at the North of the Boyacá region which gets attached to Bogotá which is nearly 400 km away. Given that this case is driven by only 17 ‘commuters’, this may be a statistical glitch.

While in general municipalities that get aggregated to a core for a given threshold are also aggregated to this core or to a larger one for a lower threshold, this need not always be the case. Although exceptional, the municipality of Sutatausa provides an interesting illustration which shows the potential pitfalls of iterative aggregation. This small municipality located to the north of Bogotá sends 6 % of its workforce to San Diego de Ubaté to its north, 5 % to Tausa, 4 % to Nemocón, and 1 % to Bogotá to the south. At a 10 % threshold, Sutatausa gets aggregated to Bogotá after Tausa and Nemocón get aggregated to Bogotá. However, with a 5 % threshold, Sutatausa gets immediately aggregated to San Diego de Ubaté. Since the latter is much larger and barely sends any worker to its south, it remains an independent core with Sutatausa as satellite. This municipality of 5000 inhabitants is the only case of a satellite of Bogotá at a 10 % threshold which disappears with a 5 % threshold.

First, because the dependent variable is computed directly from the explanatory variable, measurement error on the ‘true’ population also affects the rank and thus leads to a downward bias for the standard errors with OLS. Gabaix and Ibragimov (2011) show that the standard error on ξ is asymptotically \(\sqrt{2/n}\;\xi\) where n is the number of observations. With our data, this implies a standard error of 0.14. The values of the standard errors for the other estimates of ξ reported here are of the same magnitude.

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Title: Delineating Metropolitan Areas: Measuring Spatial Labour Market Networks Through Commuting Patterns
Author: Gilles Duranton
Publisher: Springer Japan
Book: The Economics of Interfirm Networks
Print ISBN: 978-4-431-55389-2

Electronic ISBN: 978-4-431-55390-8

Copyright Year: 2015
DOI: https://doi.org/10.1007/978-4-431-55390-8_6

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