Skip to main content

Interpreting Spatial Econometric Models


Past applications of spatial regression models have frequently interpreted the parameter estimates of models that include spatial lags of the dependent variable incorrectly. A discussion of issues surrounding proper interpretation of the estimates from a variety of spatial regression models is undertaken. We rely on scalar summary measures proposed by LeSage and Pace (Introduction to spatial econometrics. Taylor Francis/CRC Press, Boca Raton, 2009) who motivate that these reflect a proper interpretation of the marginal effects for the nonlinear models involving spatial lags of the dependent variable. These nonlinear spatial models are contrasted with linear spatial models, where interpretation is more straightforward. One of the major advantages of spatial regression models is their ability to quantify spatial spillovers. These can be defined as situations where nonzero cross-partial derivatives exist that reflect impacts on outcomes in region i arising from changes in characteristics of region j. Of course, these cross-partial derivatives can be interpreted as impacts of changes in an own region characteristic on other regions or changes in another regions’ characteristic on the own region. The ability to produce empirical estimates along with measures of dispersion that can be used for inference regarding the statistical significance, magnitude, and spatial extent of spillovers provides a major motivation for using spatial regression models.


This is a preview of subscription content, log in via an institution.


  • Allers M, Elhorst JP (2005) Tax mimicking and yardstick competition among local governments in the Netherlands. Int Tax Public Financ 12(4):493–513

    Article  Google Scholar 

  • Anselin L (1988) Spatial econometrics: methods and models. Kluwer, Dordrecht

    Book  Google Scholar 

  • Anselin L (2003) Spatial externalities, spatial multipliers and spatial econometrics. Int Reg Sci Rev 26(2):153–166

    Article  Google Scholar 

  • Bell KP, Bockstael NE (2000) Applying the generalized-moments estimation approach to spatial problems involving microlevel data. Rev Econ Stat 87(1):72–82

    Article  Google Scholar 

  • Bivand R, Albrecht G (2000) Implementing functions for spatial statistical analysis using the R language. J Geogr Syst 2(3):307–317

    Article  Google Scholar 

  • Crane R, Chatman D (2004) Traffic and sprawl: evidence from U.S. commuting, 1985–1997. Plan Mark 6(3):14–22

    Google Scholar 

  • Deskins J, Hill B (2010) Have state tax interdependencies changed over time? Public Financ Rev 38(2):244–270

    Article  Google Scholar 

  • Drukker DM, Prucha I, Raciborski R (2001) A command for estimating spatial-autoregressive models with spatial-autoregressive disturbances and additional endogenous variables. Stata J 1(3):1–13

    Google Scholar 

  • Elhorst JP (2010) Applied spatial econometrics: raising the bar. Spat Econ Anal 5(1):9–28

    Article  Google Scholar 

  • Fingleton B (2001) Theoretical economic geography and spatial econometrics: dynamic perspectives. J Econ Geogr 1(2):201–225

    Article  Google Scholar 

  • Gordon P, Lee B, Richardson HW (2009) Commuting trends in U.S. cities in the 1990s. J Plan Educ Res 29(1):78–89

    Article  Google Scholar 

  • Haining R (1990) Spatial data analysis in the social and environmental sciences. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Kelejian HH, Tavlas GS, Hondronyiannis G (2006) A spatial modeling approach to contagion among emerging economies. Open Econ Rev 17(4/5):423–442

    Article  Google Scholar 

  • Kim CW, Phipps TT, Anselin L (2003) Measuring the benefits of air quality improvement: a spatial hedonic approach. J Environ Econ Manag 45(1):24–39

    Article  Google Scholar 

  • Kirby DK, LeSage JP (2009) Changes in commuting to work times over the 1990 to 2000 period. Reg Sci Urban Econ 39(4):460–471

    Article  Google Scholar 

  • LeSage JP (1999) The theory and practice of spatial econometrics, a manual to accompany the spatial econometrics toolbox. Freely available at:

  • LeSage JP, Ha C (2012) The impact of migration on social capital – do migrants take their bowling balls with them? Growth Chang 43(1):1–26

    Article  Google Scholar 

  • LeSage JP, Pace RK (2009) Introduction to spatial econometrics. Taylor Francis/CRC Press, Boca Raton

    Book  Google Scholar 

  • Ord JK (1975) Estimation methods for models of spatial interaction. J Am Stat Assoc 70(3):120–126

    Article  Google Scholar 

  • Pace RK, LeSage JP (2008) A spatial Hausman test. Econ Lett 101(3):282–284

    Article  Google Scholar 

  • Pace RK, Zhu S (2012) Separable spatial modeling of spillovers and disturbances. J Geogr Syst 14(1):75–90

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to James P. LeSage .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer-Verlag GmbH Germany, part of Springer Nature

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

LeSage, J.P., Pace, R.K. (2019). Interpreting Spatial Econometric Models. In: Fischer, M., Nijkamp, P. (eds) Handbook of Regional Science. Springer, Berlin, Heidelberg.

Download citation

  • DOI:

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-36203-3

  • Online ISBN: 978-3-642-36203-3

  • eBook Packages: Springer Reference Economics and FinanceReference Module Humanities and Social SciencesReference Module Business, Economics and Social Sciences

Publish with us

Policies and ethics