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Estimating suppressed data in regional economic databases: A goal-programming approach

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  • Zhang, Sumei
  • Guldmann, Jean-Michel

Abstract

To avoid disclosure of individual establishment information, data records may have to be suppressed in regional economic databases, with values represented by flags. This paper investigates this suppression process and presents a goal-programming optimization approach to estimate these flagged data, using the 2000 County Business Patterns (CBP) database as a case study. The approach minimizes the sum of weighted deviations between the estimates and target values, subject to constraints related to county and sector total employment, as well as to flag and establishment size intervals. The model is tested using Ohio and Arizona data, for both sources of inconsistencies and parameter selection. A decision-theoretic analysis of the test results points to specific strategies that yield the best estimates of the suppressed data.

Suggested Citation

  • Zhang, Sumei & Guldmann, Jean-Michel, 2009. "Estimating suppressed data in regional economic databases: A goal-programming approach," European Journal of Operational Research, Elsevier, vol. 192(2), pages 521-537, January.
    • Handle: RePEc:eee:ejores:v:192:y:2009:i:2:p:521-537
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    Cited by:

    1. Zekai He & Michael Romanos, 2016. "Spatial agglomeration and location determinants: Evidence from the US communications equipment manufacturing industry," Urban Studies, Urban Studies Journal Limited, vol. 53(10), pages 2154-2174, August.
    2. Craig Wesley Carpenter & Anders Van Sandt & Scott Loveridge, 2022. "Measurement error in US regional economic data," Journal of Regional Science, Wiley Blackwell, vol. 62(1), pages 57-80, January.
    3. Sumei Zhang & Jean-Michel Guldmann, 2015. "A Regression-constrained Optimization Approach to Estimating Suppressed Information using Time-series Data," International Regional Science Review, , vol. 38(2), pages 119-150, April.

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