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Assessment of local competitiveness: A composite indicator analysis of Costa Rican counties using the ‘Benefit of the Doubt’ model

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  • Lafuente, Esteban
  • Araya, Manuel
  • Leiva, Juan Carlos

Abstract

This study employs a Benefit-of-the-Doubt (BOD) weighting model that incorporates information generated via a participatory method—i.e., based on experts' opinion—to construct a composite indicator that evaluates the competitiveness level of Costa Rican counties during 2010–2016. The results of the empirical application based on the county competitiveness index (CCI) reveal the superior informative power of the proposed BOD composite indicator, relative to models using equal weight restrictions or weights estimated via principal component analysis. The county competitiveness index is a valuable tool to monitor counties’ competitive level, and the findings underline that an analysis based on the BOD approach may offer useful information to policy makers on what strategic actions may potentially optimize the allocation of local resources and, subsequently, enhance economic outcomes (i.e., business creation and employment).

Suggested Citation

  • Lafuente, Esteban & Araya, Manuel & Leiva, Juan Carlos, 2022. "Assessment of local competitiveness: A composite indicator analysis of Costa Rican counties using the ‘Benefit of the Doubt’ model," Socio-Economic Planning Sciences, Elsevier, vol. 81(C).
  • Handle: RePEc:eee:soceps:v:81:y:2022:i:c:s0038012119306135
    DOI: 10.1016/j.seps.2020.100864
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    1. William W. Cooper & Lawrence M. Seiford & Joe Zhu (ed.), 2011. "Handbook on Data Envelopment Analysis," International Series in Operations Research and Management Science, Springer, number 978-1-4419-6151-8, September.
    2. Hideyuki Mizobuchi, 2014. "Measuring World Better Life Frontier: A Composite Indicator for OECD Better Life Index," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 118(3), pages 987-1007, September.
    3. Lovell, C. A. Knox & Pastor, Jesus T., 1999. "Radial DEA models without inputs or without outputs," European Journal of Operational Research, Elsevier, vol. 118(1), pages 46-51, October.
    4. Alonso, Suyen & Leiva, Juan Carlos, 2019. "Business competitiveness in Costa Rica: a multidimensional approach," TEC Empresarial, School of Business, Costa Rica Institute of Technology (ITCR), vol. 13(3), pages 28-41.
    5. Zoltán J. Ács & Erkko Autio & László Szerb, 2015. "National Systems of Entrepreneurship: Measurement issues and policy implications," Chapters, in: Global Entrepreneurship, Institutions and Incentives, chapter 28, pages 523-541, Edward Elgar Publishing.
    6. Esteban Lafuente & Yancy Vaillant & Juan Carlos Leiva, 2018. "Sustainable and Traditional Product Innovation without Scale and Experience, but Only for KIBS!," Sustainability, MDPI, vol. 10(4), pages 1-18, April.
    7. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    8. Balaguer-Coll, Maria Teresa & Prior, Diego & Tortosa-Ausina, Emili, 2007. "On the determinants of local government performance: A two-stage nonparametric approach," European Economic Review, Elsevier, vol. 51(2), pages 425-451, February.
    9. Karagiannis, Roxani & Karagiannis, Giannis, 2018. "Intra- and inter-group composite indicators using the BoD model," Socio-Economic Planning Sciences, Elsevier, vol. 61(C), pages 44-51.
    10. L Cherchye & W Moesen & N Rogge & T Van Puyenbroeck & M Saisana & A Saltelli & R Liska & S Tarantola, 2008. "Creating composite indicators with DEA and robustness analysis: the case of the Technology Achievement Index," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(2), pages 239-251, February.
    11. Sahoo, Biresh K. & Singh, Ramadhar & Mishra, Bineet & Sankaran, Krithiga, 2017. "Research productivity in management schools of India during 1968-2015: A directional benefit-of-doubt model analysis," Omega, Elsevier, vol. 66(PA), pages 118-139.
    12. Araya, Manuel, 2019. "Efficiency assessment of Costa Rica’s counties: A non-parametric analysis of the county competitiveness index," TEC Empresarial, School of Business, Costa Rica Institute of Technology (ITCR), vol. 13(3), pages 78-92.
    13. Sahoo, Biresh & Singh, Ramadhar & Mishra, Bineet & Sankaran, Krithiga, 2015. "Research Productivity in Management Schools of India: A Directional Benefit-of-Doubt Model Analysis," MPRA Paper 67046, University Library of Munich, Germany.
    14. Lin, Ming-Ian & Lee, Yuan-Duen & Ho, Tsai-Neng, 2011. "Applying integrated DEA/AHP to evaluate the economic performance of local governments in China," European Journal of Operational Research, Elsevier, vol. 209(2), pages 129-140, March.
    15. Karagiannis, Giannis & Knox Lovell, C.A., 2016. "Productivity measurement in radial DEA models with a single constant input," European Journal of Operational Research, Elsevier, vol. 251(1), pages 323-328.
    16. Michael Freudenberg, 2003. "Composite Indicators of Country Performance: A Critical Assessment," OECD Science, Technology and Industry Working Papers 2003/16, OECD Publishing.
    17. Calcagnini, Giorgio & Perugini, Francesco, 2019. "Social capital and well-being in the Italian provinces," Socio-Economic Planning Sciences, Elsevier, vol. 68(C).
    18. D K Despotis, 2005. "A reassessment of the human development index via data envelopment analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(8), pages 969-980, August.
    19. Leontief, Wassily, 1971. "Theoretical Assumptions and Nonobserved Facts," American Economic Review, American Economic Association, vol. 61(1), pages 1-7, March.
    20. Friedman, Milton & Schwartz, Anna J, 1991. "Alternative Approaches to Analyzing Economic Data," American Economic Review, American Economic Association, vol. 81(1), pages 39-49, March.
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    More about this item

    Keywords

    Composite indicators; DEA; Benefit of the doubt; Counties; Costa Rica;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • R58 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Regional Government Analysis - - - Regional Development Planning and Policy

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