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A Study of the Role of Regionalization in the Generation of Aggregation Error in Regional Input -Output Models


  • Michael L. Lahr
  • Benjamin H. Stevens


Although the need for aggregation in input -output modelling has diminished with the increases in computing power, an alarming number of regional studies continue to use the procedure. The rationales for doing so typically are grounded in data problems at the regional level. As a result many regional analysts use aggregated national input -output models and trade -adjust them at this aggregated level. In this paper, we point out why this approach can be inappropriate. We do so by noting that it creates a possible source of model misapplication (i.e., a direct effect could appear for a sector where one does not exist) and also by finding that a large amount of error (on the order of 100 percent) can be induced into the impact results as a result of improper aggregation. In simulations, we find that average aggregation error tends to peak at 81 sectors after rising from 492 to 365 sectors. Perversely, error then diminishes somewhat as the model size decreases further to 11 and 6 sectors. We also find that while region - and sector -specific attributes influence aggregation error in a statistically significantly manner, their influence on the amount of error generally does not appear to be large. Copyright 2002 Blackwell Publishers Inc.

Suggested Citation

  • Michael L. Lahr & Benjamin H. Stevens, 2002. "A Study of the Role of Regionalization in the Generation of Aggregation Error in Regional Input -Output Models," Journal of Regional Science, Wiley Blackwell, vol. 42(3), pages 477-507.
  • Handle: RePEc:bla:jregsc:v:42:y:2002:i:3:p:477-507

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    Cited by:

    1. Greenberg, Michael & Mantell, Nancy & Lahr, Michael & Frisch, Michael & White, Keith & Kehler, David, 2005. "Evaluating the economic effects of a new state-funded school building program: the prevailing wage issue," Evaluation and Program Planning, Elsevier, vol. 28(1), pages 33-45.
    2. Rodolphe Buda, 2008. "Two Dimensional Aggregation Procedure: An Alternative to the Matrix Algebraic Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 31(4), pages 397-408, May.
    3. Ali Jalili, 2005. "Impacts of Aggregation on Relative Performances of Nonsurvey Updating Techniques And Intertemporal Stability of Input–Output Coefficients," Economic Change and Restructuring, Springer, vol. 38(2), pages 147-165, June.
    4. Xueting Zhao, 2014. "Disaggregating Input-Output Models," Working Papers Technical Document 2014-0, Regional Research Institute, West Virginia University.
    5. G. Lindberg & P. Midmore & Y. Surry, 2012. "Agriculture’s Inter-industry Linkages, Aggregation Bias and Rural Policy Reforms," Journal of Agricultural Economics, Wiley Blackwell, vol. 63(3), pages 552-575, September.
    6. Tony Flegg & Leonardo J. Mastronardi & Carlos A. Romero, 2015. "Evaluating the FLQ and AFLQ formulae for estimating regional input coefficients: empirical evidence for the province of C¨®rdoba, Argentina," Working Papers 20151508, Department of Accounting, Economics and Finance, Bristol Business School, University of the West of England, Bristol.
    7. repec:rri:wpaper:201003 is not listed on IDEAS
    8. Umed Temurshoev, 2015. "Uncertainty treatment in input-output analysis," Working Papers 2015-004, Universidad Loyola Andalucía, Department of Economics.
    9. Christa Court & Randall W. Jackson, 2010. "Time Dynamics and the Introduction of New Technologies within IO Analysis," Working Papers Working Paper 2010-03, Regional Research Institute, West Virginia University.
    10. Gordon Mulligan & Randall Jackson & Amanda Krugh, 2013. "Economic base multipliers: a comparison of ACDS and IMPLAN," Regional Science Policy & Practice, Wiley Blackwell, vol. 5(3), pages 289-303, August.

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