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A machine learning model of national competitiveness with regional statistics of public expenditure

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Abstract

Competitiveness, defined as the rate of success in attracting and maintaining industries to foster the sustained improvement in citizens’ wellbeing, has been a long-pursued goal for regions and nations. Today’s rapid advancements in technology, especially in telecommunications, open challenges for decision and policy makers to generate effective and efficient solutions in a global scenario. In this context, the latest developments in artificial intelligence, machine learning and deep learning open new paths for describing, analyzing, and representing complex phenomena in systemic environments. This paper presents a model using a neural network to predict the behavior of competitive benchmarks using public expenditure variables. The theory of control, in which the neural network approach is based, offers some advantages such as solving the problem while considering the dynamic nature of the phenomenon and allowing control blocks to be implemented in a straightforward method. The present paper establishes a neural network model that links control, administration, and systems theories in a statistically sound approach that connects both sets of variables, opening the path for extensions that allow optimal allocation of resources.

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Acknowledgements

The First and second author would like to thank the Mexican Council of Science and Technology (CONACyT) for the support given through the Scholarships Number 477685 and 740762. The authors would also like thank all the valuable comments of the reviewers that helped improving the ideas presented in this paper.

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Authors and Affiliations

  1. Facultad de Contaduría y Ciencias Administrativas, Universidad Michoacana de San Nicolás de Hidalgo, Gral. Francisco J. Múgica S/N, 58030, Morelia, México

    Artemisa Zaragoza-Ibarra, Gerardo G. Alfaro-Calderón & Víctor G. Alfaro-García

  2. Facultad de Ingeniería Eléctrica, Universidad Michoacana de San Nicolás de Hidalgo, Gral. Francisco J. Múgica S/N, 58030, Morelia, México

    Fernando Ornelas-Tellez

  3. Facultad de Economía “Vasco de Quiroga”, Universidad Michoacana de San Nicolás de Hidalgo, Gral. Francisco J. Múgica S/N, 58030, Morelia, México

    Rodrigo Gómez-Monge

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  1. Artemisa Zaragoza-Ibarra
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  2. Gerardo G. Alfaro-Calderón
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  3. Víctor G. Alfaro-García
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  5. Rodrigo Gómez-Monge
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Correspondence to Víctor G. Alfaro-García.

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Zaragoza-Ibarra, A., Alfaro-Calderón, G.G., Alfaro-García, V.G. et al. A machine learning model of national competitiveness with regional statistics of public expenditure. Comput Math Organ Theory 27, 451–468 (2021). https://doi.org/10.1007/s10588-021-09338-9

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