At the Frontier Between Local and Global Interactions in Regional Sciences

  • Gary Cornwall
  • Changjoo Kim
  • Olivier ParentEmail author
Part of the Advances in Spatial Science book series (ADVSPATIAL)


Regional economists are increasingly adopting spatial analytical and spatial econometric perspectives to study questions related to local versus global spatial spillover effects. Recent studies have shown that blindly adopting traditional spatial econometric models to measure those externalities might not be relevant. Explicitly accounting on how closely related groups of economic agents such as individuals or firms interact across space can be of great benefit for researchers working at the interface of social economics and geography. Recent development in economic theory has provided new ways to incorporate a range of spillover effects into a variety of economic models (Bramoullé et al., Am Econ Rev 104:898–930, 2014). The newly redefined concept of local spillover in the socio-economic literature involves a more complex interaction structure that includes feedback effects, resulting from impact passing through neighboring observations and coming back to the original location, as typically observed for global interaction models. However, the spatial magnitude of those local externalities is more limited across space.

Facing a massive increase of socio-economic information about individual and neighborhood characteristics, recent locational theories underline how peer interaction between individuals are shaping inter- and intra-regional equilibrium. The objective of this chapter is to stimulate a new interest on how to properly model and estimate the quantitative magnitude of spillovers using recent spatial econometric methods utilizing geographical information system tools.


Spillover effects Spatial econometrics GIS Spatial interaction 


C11 C21 R4 R58 D7 


  1. Autant-Bernard C, Mairesse J, Massard N (2007) Spatial knowledge diffusion through collaborative networks. Pap Reg Sci 86:341–350CrossRefGoogle Scholar
  2. Ballester C, Calvo-Armengol A, Zenou Y (2006) Who’s who in networks. Wanted: the key player. Econometrica 74:1403–1417Google Scholar
  3. Bramoullé Y, Djebbari H, Fortin B (2009) Identification of peer effects through social networks. J Econometrics 150:41–55CrossRefGoogle Scholar
  4. Bramoullé Y, Kranton R, D’amours M (2014) Strategic interaction and networks. Am Econ Rev 104:898–930CrossRefGoogle Scholar
  5. Brock W, Durlauf S (2001) Discrete choice with social interactions. Rev Econ Stud 68:235–60CrossRefGoogle Scholar
  6. Brugere I, Gunturi V, Shekhar S (2014) Modeling and analysis of spatio-temporal social networks. In: Encyclopedia of social network analysis and mining. Springer, New York, pp 950–960Google Scholar
  7. Case AC, Rosen HS (1993) Budget spillovers and fiscal policy interdependence: evidence from the states. J Public Econ 52:285–307CrossRefGoogle Scholar
  8. Cornwall GJ, Parent O (2016) Mixture models with spatial dependence. Working paper, University of CincinnatiGoogle Scholar
  9. Goldsmith-Pinkham P, Imbens GW (2013) Social networks and the identification of peer effects. J Bus Econ Stat 31:253–264CrossRefGoogle Scholar
  10. Jackson M (2008) Social and economic networks. Princeton University Press, PrincetonGoogle Scholar
  11. Keane MP, Wasi N (2013) Comparing alternative models of heterogeneity in consumer choice behavior. J Appl Econometrics 28:1018–1045Google Scholar
  12. Kelejian H, Piras G (2014) Estimation of spatial models with endogenous weighting matrices, and an application to a demand model for cigarettes. Reg Sci Urban Econ 46:140–149CrossRefGoogle Scholar
  13. Lee LF (2007) Identification and estimation of econometric models with group interactions, contextual factors and fixed effects. J Econometrics 140:333–374CrossRefGoogle Scholar
  14. LeSage JP (2014) What regional scientists need to know about spatial econometrics. Rev Reg Stud 44:13–32Google Scholar
  15. LeSage JP, Pace, RK (2009) An introduction to spatial econometrics. Chapman Hall/CRC Press, Boca Raton, FLCrossRefGoogle Scholar
  16. Manski C (1993) Identification of endogenous social effects: the reflection problem. Rev Econ Stud 60:531–542CrossRefGoogle Scholar
  17. Marshall A (1890) Principles of economics. Macmillan, LondonGoogle Scholar
  18. Qu X, Lee LF (2015) Estimating a spatial autoregressive model with an endogenous spatial weight matrix. J Econometrics 184:209–232CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Economics, Carl H. Lindner College of BusinessUniversity of CincinnatiCincinnatiUSA
  2. 2.Department of GeographyUniversity of CincinnatiCincinnatiUSA

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