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A new approach to modelling the input–output structure of regional economies using non-survey methods

Author

Listed:
  • Anthony T. Flegg

    (University of the West of England)

  • Guiseppe R. Lamonica

    (Università Politecnica delle Marche)

  • Francesco M. Chelli

    (Università Politecnica delle Marche)

  • Maria C. Recchioni

    (Università Politecnica delle Marche)

  • Timo Tohmo

    (University of Jyväskylä)

Abstract

This paper proposes a new approach to the regionalization of national input–output tables where suitable regional data are scarce and analysts are considering using location quotients (LQs). We focus on the FLQ formula, which frequently yields the best results of the pure LQ-based methods, and develop an enhanced way of implementing this approach. We use a modified cross-entropy (MCE) method, along with a regression model, to estimate values of the unknown parameter δ in the FLQ formula, specific to both region and country. An analysis of survey-based data for 16 South Korean regions reveals that the proposed FLQ+ approach yields more accurate estimates of both input coefficients and sectoral output multipliers than those from simpler LQ-based methods or the MCE approach alone. Sectoral outputs (or employment) are the only regional data required. The MCE method also clearly outperforms GRAS.

Suggested Citation

  • Anthony T. Flegg & Guiseppe R. Lamonica & Francesco M. Chelli & Maria C. Recchioni & Timo Tohmo, 2021. "A new approach to modelling the input–output structure of regional economies using non-survey methods," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 10(1), pages 1-31, December.
    • Handle: RePEc:spr:jecstr:v:10:y:2021:i:1:d:10.1186_s40008-021-00242-8
    DOI: 10.1186/s40008-021-00242-8
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    References listed on IDEAS

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    Cited by:

    1. Cristian Mardones & Darling Silva, 2023. "Evaluation of Non-survey Methods for the Construction of Regional Input–Output Matrices When There is Partial Historical Information," Computational Economics, Springer;Society for Computational Economics, vol. 61(3), pages 1173-1205, March.
    2. Shogo Fukui, 2023. "Estimating Input Coefficients for Regional Input-Output Tables Using Deep Learning with Mixup," Papers 2305.01201, arXiv.org, revised May 2023.
    3. Matthew S. Lyons, 2023. "The economic impact of COVID-19 on the creative industries: a sub-regional input–output approach," Letters in Spatial and Resource Sciences, Springer, vol. 16(1), pages 1-12, December.

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