The Annals of Regional Science

, Volume 60, Issue 1, pp 143–170 | Cite as

Grid and shake: spatial aggregation and the robustness of regionally estimated elasticities

  • Gábor BékésEmail author
  • Péter Harasztosi
Original Paper


This paper proposes a simple and transparent method for measuring spatial robustness of regionally estimated coefficients and considers the role of the administrative districts and of the size of regions. The procedure offers a new solution for a practical empirical issue: comparing the variables of interest across spatially aggregated units. It improves upon existing methods, especially when spatial units are heterogeneous. To illustrate the method, we use Hungarian data and compare estimates of agglomeration externalities at various levels of aggregation. Using the procedure, we find that the method of spatial aggregation seems to be of equal importance to the specification of the econometric model.

JEL Classification

R12 R30 C15 


  1. Ács ZJ, Armington C (2004) The impact of geographic differences in human capital on service firm formation rates. J Urban Econ 56(2):244–278CrossRefGoogle Scholar
  2. Amrhein CG (1995) Searching for the elusive aggregation effect: evidence from statistical simulations. Environ Plan A 27(1):105–119CrossRefGoogle Scholar
  3. Amrhein CG, Flowerdew R (1992) The effect of data aggregation on a Poisson regression model of Canadian migration. Environ Plan A 24(10):1381–1391CrossRefGoogle Scholar
  4. Andersson M, Klaesson J, Larsson JP (2016) How local are spatial density externalities? Neighbourhood effects in agglomeration economies. Reg Stud 50(6):1082–1095CrossRefGoogle Scholar
  5. Anselin L (2010) Thirty years of spatial econometrics. Pap Reg Sci 89(1):3–25CrossRefGoogle Scholar
  6. Anselin L, Bera AK (1998) Spatial dependence in linear regression models with an introduction to spatial econometrics. Stat Textb Monogr 155:237–290Google Scholar
  7. Armington C, Ács ZJ (2002) The determinants of regional variation in new firm formation. Reg Stud 36(1):33–45CrossRefGoogle Scholar
  8. Bacher H, Brülhart M (2013) Progressive taxes and firm births. Int Tax Public Finance 20(1):129–168CrossRefGoogle Scholar
  9. Békés G, Harasztosi P (2013) Agglomeration premium and trading activity of firms. Reg Sci Urban Econ 43(1):51–64CrossRefGoogle Scholar
  10. Briant A, Combes PP, Lafourcade M (2010) Dots to boxes: do the size and shape of spatial units jeopardize economic geography estimations? J Urban Econ 67(3):287–302CrossRefGoogle Scholar
  11. Burger MJ, van Oort FG, van der Knaap B (2010) A treatise on the geographical scale of agglomeration externalities and the MAUP. Sci Reg 9(1):19–39Google Scholar
  12. Cainelli G, Montresor S, Vittucci Marzetti G (2014) Spatial agglomeration and firm exit: a spatial dynamic analysis for italian provinces. Small Bus Econ 43(1):213–228CrossRefGoogle Scholar
  13. Ciccone A, Hall RE (1996) Productivity and the density of economic activity. Am Econ Rev 86(1):54–70Google Scholar
  14. Devereux MP, Griffith R, Simpson H (2007) Firm location decisions, regional grants and agglomeration externalities. J Public Econ 91(3–4):413–435CrossRefGoogle Scholar
  15. Dewhurst J, McCann P (2007) Specialisation and regional size. Edward Elgar, Cheltenham, pp 204–220Google Scholar
  16. Dubé J, Brunelle C (2014) Dots to dots: a general methodology to build local indicators using spatial micro-data. Ann Reg Sci 53(1):245–272CrossRefGoogle Scholar
  17. Duranton G, Overman HG (2008) Exploring the detailed location patterns of U.K. manufacturing industries using microgeographic data. J Reg Sci 48(1):213–243CrossRefGoogle Scholar
  18. Fotheringham AS, Wong DS (1991) The modifiable areal unit problem in multivariate statistical analysis. Environ Plan 23(7):1025–1044CrossRefGoogle Scholar
  19. Gobillon L, Milcent C (2013) Spatial disparities in hospital performance. J Econ Geogr 13(6):1013–1040CrossRefGoogle Scholar
  20. Holl A (2004) Transport infrastructure, agglomeration economies, and firm birth: empirical evidence from Portugal. J Reg Sci 44(4):693–712CrossRefGoogle Scholar
  21. Hortaçsu A, Syverson C (2007) Cementing relationships: vertical integration, foreclosure, productivity, and prices. J Polit Econ 115:250–301CrossRefGoogle Scholar
  22. Iyer G, Seetharaman P (2008) Too close to be similar: product and price competition in retail gasoline markets. Quant Mark Econ 6(3):205–234CrossRefGoogle Scholar
  23. Larsson J (2014) The neighborhood or the region? Reassessing the density-wage relationship using geocoded data. Ann Reg Sci 52(2):367–384CrossRefGoogle Scholar
  24. Lychagin S, Pinkse J, Slade ME, Reenen JV (2010) Spillovers in space: does geography matter? NBER working papers 16188, National Bureau of Economic Research, IncGoogle Scholar
  25. Marshall A (1920) Principles of economics. Prometheus Books, New YorkGoogle Scholar
  26. Martin-Barroso D, Nunez Serrano JA, Velazquez FJ (2010) A different look at agglomeration effects in Spain. MPRA Paper 33601. University Library of Munich, GermanyGoogle Scholar
  27. Melo PC, Graham DJ, Noland RB (2009) A meta-analysis of estimates of urban agglomeration economies. Reg Sci Urban Econ 39(3):332–342CrossRefGoogle Scholar
  28. Menon C (2012) The bright side of MAUP: defining new measures of industrial agglomeration. Pap Reg Sci 91(1):3–28CrossRefGoogle Scholar
  29. Rosenthal SS, Strange WC (2008) The attenuation of human capital spillovers. J Urban Econ 64(2):373–389CrossRefGoogle Scholar
  30. Sutaria V, Hicks DA (2004) New firm formation: dynamics and determinants. Ann Reg Sci 38(2):241–262CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Institute of Economics of CERS-HAS and CEPRCentral European UniversityBudapestHungary
  2. 2.Joint Research CentreIspraItaly

Personalised recommendations