Public Choice

, Volume 150, Issue 3–4, pp 525–545 | Cite as

The importance of modeling spatial spillovers in public choice analysis

  • James P. LeSage
  • Matthew Dominguez


It is frequently assumed that regional observations on local government behavior, voters, regional taxes, etc. can be analyzed using ordinary least-squares (OLS) methods. We discuss spatial regression models in empirical studies of public choice issues using impacts arising from population migration on the provision of county-level government services as an illustration. Spatial regressions allow an examination of the direct and indirect (spatial spillover) effects which taken together determine the total impact (on the dependent variable) arising from a change in the explanatory variables. This decomposition should be quite useful in empirical public choice studies.


Spatial dependence Spatial regression models Spatial spillovers 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Becker, E. (1996). The illusion of fiscal illusion: unsticking the flypaper effect. Public Choice, 86, 82–102. CrossRefGoogle Scholar
  2. Besley, T., & Case, A. (1995). Incumbent behavior: vote-seeking, tax-setting, and yardstick competition. American Economic Review, 85(1), 25–45. Google Scholar
  3. Bureau of Business and Economic Research (2003). How well did we retain and attract highly educated workers? (Vol. 9, pp. 1–6). Morgantown: College of Business and Economics, West Virginia University. Google Scholar
  4. Cushing, B., & Rogers, C. (1996). Income and poverty in Appalachia. In Socio-economic review of Appalachia. Morgantown: Regional Research Institute, West Virginia University. Google Scholar
  5. Fischel, W. A. (2001). The homevoter hypothesis. Cambridge: Harvard University Press. Google Scholar
  6. Goetz, S. T., & Debertin, D. L. (1999). Rural education and the 1990 Kentucky educational reform act: funding, implementation and research issues. Lexington, KY: Agricultural Economics Research Report 54, University of Kentucky. Google Scholar
  7. Grossman, P. (1990). The impact of federal and state grants on local government spending: A test of the fiscal illusion hypothesis. Public Finance Quarterly, 18, 313–327. CrossRefGoogle Scholar
  8. Hall, J., & Ross, J. (2010, forthcoming). Tiebout competition, yardstick competition, and tax instrument choice: evidence from Ohio school districts. Public Finance Review. Google Scholar
  9. Holcombe, R. G., & Sobel, R. S. (1995). Empirical evidence on the publicness of state legislative activities. Public Choice, 83, 47–58. CrossRefGoogle Scholar
  10. Kelsey, T. W. (1993). Fiscal impact of population growth and decline in small communities. American Journal of Agricultural Economics, 75(5), 1169–1172. CrossRefGoogle Scholar
  11. Kirby, D. K., & LeSage, J. P. (2009). Changes in commuting to work times over the 1990 to 2000 period. Regional Science and Urban Economics, 39(4), 460–471. CrossRefGoogle Scholar
  12. Knight, B. (2002). Endogenous federal grants and crowd-out of state government spending: theory and evidence from the federal Highway Aid Program. American Economic Review, 92(1), 71–92. CrossRefGoogle Scholar
  13. Lacombe, D., & Shaughnessy, T. (2007). Accounting for spatial error correlation in the 2004 presidential popular vote. Public Finance Review, 35(4), 480–499. CrossRefGoogle Scholar
  14. Ladd, H. F. (1992). Mimicking of local tax burdens among neighboring counties. Public Finance Quarterly, 20, 450–467. CrossRefGoogle Scholar
  15. Leeson, P., & Dean, A. (2009). The democratic domino theory: An empirical investigation. American Journal of Political Science, 53(3), 533–551. CrossRefGoogle Scholar
  16. LeSage, J. P. (1997). Bayesian estimation of spatial autoregressive models. International Regional Science Review, 20(1/2), 113–129. CrossRefGoogle Scholar
  17. LeSage, J. P., & Pace, R. K. (2009). Introduction to spatial econometrics. Boca Raton: CRC Press/Taylor & Francis. CrossRefGoogle Scholar
  18. LeSage, J. P., & Pace, R. K. (2010). An introduction to spatial econometrics. In M. M. Fischer, & A. Getis (Eds.), Handbook of applied spatial analysis: software tools, methods and applications (pp. 355–376). Berlin: Springer. CrossRefGoogle Scholar
  19. LeSage, J. P., & Parent, O. (2007). Bayesian model averaging for spatial econometric models. Geographical Analysis, 39(3), 241–267. CrossRefGoogle Scholar
  20. Mazie, S. M. (1986). The form crisis of the 1980’s. In Rural development perspectives 2, 3. Washington: USDA/ERS. Google Scholar
  21. Nesbit, T. M., & Kreft, S. F. (2009). Federal grants, earmarked revenues, and budget crowd-out: State highway funding. Public Budgeting & Finance, 94–110. Google Scholar
  22. Pace, R. K., & LeSage, J. P. (2010). Omitted variables biases of OLS and spatial lag models. In A. Páez, J. Le Gallo, R. Buliung, & S. Dall’erba (Eds.), Advances in Spatial Science, Progress in spatial analysis: theory and computation, and thematic applications (pp. 17–28). Berlin: Springer. Google Scholar
  23. Parent, O., & LeSage, J. P. (2010). A spatial dynamic panel model with random effects applied to commuting times. Transportation Research Part B: Methodological, 44B(5), 633–645. CrossRefGoogle Scholar
  24. Sobel, R., & Dean, A. (2008). Has Wal-Mart buried mom and pop? The impact of Wal-Mart on self-employment and small establishments in the United States. Economic Inquiry, 46(4), 676–695. CrossRefGoogle Scholar
  25. Tiebout, C. M. (1956). A pure theory of local expenditures. The Journal of Political Economy, 64(5), 416–424. CrossRefGoogle Scholar
  26. Turnbull, G. K., & Geon, G. (2006). Local government internal structure, external constraints and the median voter. Public Choice, 129, 487–506. CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Fields Endowed Chair for Urban and Regional Economics, McCoy College of Business Administration, Department of Finance and EconomicsTexas State University - San MarcosSan MarcosUSA
  2. 2.McCoy College of Business Administration, Department of Finance and EconomicsTexas State University - San MarcosSan MarcosUSA

Personalised recommendations