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Application of the Kernel Density Function for the Analysis of Regional Growth and Convergence in the Service Sector through Productivity

Author

Listed:
  • Ronny Correa-Quezada

    (Department of Economics, Universidad Técnica Particular de Loja (UTPL), Loja 11-01-608, Ecuador)

  • Lucía Cueva-Rodríguez

    (Researcher and Consultant in Economics and Business, Loja 11-01-608, Ecuador)

  • José Álvarez-García

    (Financial Economy and Accounting Department, Faculty of Business, Finance and Tourism, University of Extremadura, 10071 Cáceres, Spain)

  • María de la Cruz del Río-Rama

    (Business Management and Marketing Department, Faculty of Business Sciences and Tourism, University of Vigo, 32004 Ourense, Spain)

Abstract

The aim of this research work is to analyze growth and convergence processes in the service sector and its large groups, market, and non-market services, at the regional level in Ecuador by taking the labor productivity variable as a reference. The methodology used is an analysis of distributive dynamics of the data, applying the non-parametric method of Kernel density functions from a mathematical economics approach. The results obtained show that the service sector has non-alarming levels of inequality, its trend over time is increasing. When disaggregating the data, it was observed that non-market services show a rapid growth in inequality. In contrast, market services show greater stability during the period analyzed. Regarding intra-distribution dynamics for the service sector and its subsectors, in the long term, poor regions improve, while rich regions deteriorate. However, deterioration of advanced regions is less intense in non-market services.

Suggested Citation

  • Ronny Correa-Quezada & Lucía Cueva-Rodríguez & José Álvarez-García & María de la Cruz del Río-Rama, 2020. "Application of the Kernel Density Function for the Analysis of Regional Growth and Convergence in the Service Sector through Productivity," Mathematics, MDPI, vol. 8(8), pages 1-20, July.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:8:p:1234-:d:390378
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