Abstract
In this chapter I develop an empirical framework to estimate the role of agglomeration externalities, especially those stemming from input–output linkages, in the location process of US manufacturing plants. Furthermore, drawing on the model of Holmes and Stevens (J Econ Geogr 4: 227–250, 2004b), I propose a way to reconcile some previous puzzling results about proximity to consumers’ demand and the scope of agglomeration forces. Results suggest that flows of intermediate goods have a positive impact, especially for big plants, on local specialization. By contrast, consumers’ demand has a negative effect and this result is consistent with theory. However, the majority of both effects comes from very local interactions with spatial spill-overs being quite weak but with a very large geographical scope. This result suggests some kind of strong non-linearity in the underlying spatial process. Very close interactions are extremely important but, when considering what is beyond the limit of local markets, then distance does not matter so much.
JEL Classification: L60, R12, R15, R31, R34.
I’m very grateful to Vernon Henderson and Eric Strobl for fruitful discussions and comments. The usual disclaimer applies.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
See Rosenthal and Strange (2004) for a review of the literature.
- 2.
Gaigné et al. (2003) considered this result as misleading and did not try to interpret it.
- 3.
Consider, for example, a service good called “grocery wholesaling”. One component of the demand for this good is for the wholesaling of milk, an important service that must be provided locally because of milk’s perishability. A second component, the wholesaling of crackers, could be satisfied by imports because perishability is not an issue.
- 4.
This is the closest year for which input–output tables are available with the NAICS classification.
- 5.
- 6.
With respect to the BEA classification, I have aggregate sectors 2,301, 2,302 and 2,303 into a unique sector 2,300 (Construction). The same applies to sectors 332A and 332B that has been pooled into one industry 3325 (Ordnance and other fabricated metal products). The reason of these changes is to make the commodity classification of the BEA look as close as possible to the NAICS 4 digits. In such a way comparability with other studies should be substantially improved.
- 7.
A similar approach has been used by Redding and Venables (2004) in their analysis of trade flows.
- 8.
Whenever there is no wage for a couple sector-county, it is simply considered as missing. Contrary to other regressors, there is in fact no obvious value for a missing wage value.
- 9.
- 10.
The variables constructed with distance bands are in general highly collinear among themselves. This lead to reject the significance of more distant observations because of their poor (biased) t-ratios.
- 11.
For the parameters \( {{\lambda}_1} \), \( {{\lambda}_2} \), and \( {{\lambda}_3} \) to be interpreted as elasticities, spatial variables should be constructed as averages of the corresponding local variables (that are already in log). Therefore, I normalize weights \( {{w}_{{i,j}}} \) dividing them by their average row sum. In such a way, \( \sum\nolimits_j {{w}_{{i,j}}} \) will (on average) be equal to one.
- 12.
As one can easily check, the number of observation in estimations is slightly less than the product of the number of industries times the number of counties. This is due to the fact that, in roughly 9 % of the cases, the information on state-level wages is missing because there is no county with positive employment from which an information on wages can be retrieved.
- 13.
Henderson (2003) find a little scope for spatial spill-overs. However, he considers only 9 three-digit SIC industries and within a subset of 742 of the 3,111 US continental counties.
- 14.
As mentioned earlier, Rosenthal and Strange (2003) measure proximity effect by introducing many spatial variables; each corresponding to a given distance band. The problem with such an approach is that these variables usually turn out to be very correlated among themselves so biasing the inference.
- 15.
Note that the sampling does not apply to the construction of any variables. In other words, all sectors are still used in order to build both specialization and input–output variables, but only a fraction of the observations (those referring to the 10 industries chosen) are used in the estimation.
References
Amiti M, Cameron L (2007) Economic geography and wages. Rev Econ Stat 89(1):15–29
Combes P-P (2000) Economic structure and local growth: France, 1984–1993. J Urban Econ 47:329–355
Combes P-P, Lafourcade M (2001) Transport costs, geography and regional inequalities: evidence from France. CEPR discussion paper, 2894
D’Costa S, Gibbons S, Overman H, Pelkonen P (2013) Agglomeration and labour markets: the impact of transport investments on labour market outcomes. In: Crescenzi R, Percoco M (eds) Geography, institutions and regional economic performance. Springer, Berlin (forthcoming)
Davis DR, Weinstein DE (1999) Economic geography and regional production structure: an empirical investigation. Eur Econ Rev 43:379–407
Ellison G, Glaeser E (1999) The geographic concentration of industry: does natural advantage explain agglomeration? Am Econ Rev 89:311–316
Fujita M, Krugman P, Venables AJ (1999) The spatial economy: cities, regions and international trade. MIT Press, Cambridge, MA
Gaigné C, Huiban JP, Schmitt B (2003) Labor market and industrial location: evidence from French data. INRA-ENESAD CESAER working paper, 2
Hanson G (2005) Market potential, increasing returns, and geographic concentration. J Int Econ 67(1):1–24
Head K, Mayer T (2004) Market potential and the location of Japanese investment in the European Union. Review of Economics and Statistics
Henderson JV (2003) Marshall’s scale economies. J Urban Econ 53:1–28
Hillberry R, Hummels D (2002) Explaining home bias in consumption: the role of intermediate input trade. NBER working paper, 9020
Holmes TJ, Stevens JJ (2004a) Spatial distribution of economic activities in North America. In: Henderson V, Thisse JF (eds) Handbook of regional and urban economics, vol 4. Elsevier-North Holland, Amsterdam
Holmes TJ, Stevens JJ (2004b) Geographic concentration and establishment size: analysis in an alternative economic geography model. J Econ Geogr 4(3):227–250
Krugman P (1991) Increasing returns and economic geography. J Polit Econ 99:483–499
Krugman P, Venables AJ (1995) Globalization and the inequalities of nations. Q J Econ 110:857–880
Lafourcade M, Mion G (2007) Concentration, agglomeration and the size of plants. Reg Sci Urban Econ 37(1):46–68
Mills ES, Hamilton BW (1994) Urban economics, 5th edn. Harper- Collins, New York
Mion G (2004) Spatial externalities and empirical analysis: the case of Italy. J Urban Econ 56(1):97–118
Ottaviano GIP, Tabuchi T, Thisse JF (2002) Agglomeration and trade revisited. Int Econ Rev 43:409–436
Puga D (1999) The rise and fall of regional inequalities. Eur Econ Rev 43:303–334
Redding S, Venables A (2004) Economic geography and international inequality. J Int Econ 62(1):53–82
Rodríguez-Pose A, Comptour F (2013) Do clusters generate greater innovation and growth? An analysis of European regions. In: Crescenzi R, Percoco M (eds) Geography, institutions and regional economic performance. Springer, Berlin (forthcoming)
Rosenthal S, Strange W (2001) The determinants of agglomeration. J Urban Econ 50:191–229
Rosenthal S, Strange W (2003) Geography, industrial organization, and agglomeration. Rev Econ Stat 85:377–393
Rosenthal S, Strange W (2004) Evidence on the nature and sources of agglomeration economies. In: Henderson V, Thisse JF (eds) Handbook of regional and urban economics, vol 4. Elsevier-North Holland, Amsterdam
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Mion, G. (2013). Input–Output Linkages, Proximity to Final Demand and the Location of Manufacturing Industries. In: Crescenzi, R., Percoco, M. (eds) Geography, Institutions and Regional Economic Performance. Advances in Spatial Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33395-8_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-33395-8_12
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33394-1
Online ISBN: 978-3-642-33395-8
eBook Packages: Business and EconomicsEconomics and Finance (R0)