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Del, L., (2023). Measuring regional competitiveness [Pontificia Universidad Católica del Perú]. http://hdl.handle.net/20.500.12404/25298
Del, L., Measuring regional competitiveness []. PE: Pontificia Universidad Católica del Perú; 2023. http://hdl.handle.net/20.500.12404/25298
@phdthesis{renati/527168,
title = "Measuring regional competitiveness",
author = "Del Carpio Castro, Luis Alfonso",
publisher = "Pontificia Universidad Católica del Perú",
year = "2023"
}
Title: Measuring regional competitiveness
Authors(s): Del Carpio Castro, Luis Alfonso
Advisor(s): Marquina Feldman, Percy Samoel
Keywords: Competitividad--Administración; Competitividad--Perú; Innovaciones tecnológicas; Desempeño económico
OCDE field: https://purl.org/pe-repo/ocde/ford#5.02.04
Issue Date: 4-Jul-2023
Institution: Pontificia Universidad Católica del Perú
Abstract: El objetivo de la investigación fue proponer un nuevo enfoque metodológico para calcular
la competitividad de espacios territoriales subnacionales (Regiones). Para tal fin, como
primer paso, se realizó la revisión de la literatura respectiva que fundamenta la relación
entre el desempeño económico de las regiones y los determinantes de la competitividad
regional. En este contexto se define la competitividad de las regiones como un efecto
inobservable y para su determinación se propone un modelo que capture la heterogeneidad
no observable, la cual se postula como una simplificación de la competitividad de las
regiones de un país, es decir el elemento que explicaría las diferencias del desempeño
económico entre las regiones. El segundo paso consistió en formular un modelo
econométrico de panel de datos de efectos fijos con la técnica de variables dummy. La
variable dependiente representa a una región modelo y se construye promediando el PBI
en términos reales para cada región, como aproximación a una región modelo. Para las
variables independientes se proponen cinco dimensiones que explican la competitividad
regional. La data utilizada está centrada en 91 variables para cada una de las 25 regiones
del Perú entre el periodo 2012 a 2018. El tercer paso es detallar los resultados; en tal
sentido el primer hallazgo es que el modelo propuesto es adecuado para explicar el
desempeño económico de la región modelo y las estimaciones para cada región son
relevantes para medir las diferencias entre ellas; y el segundo hallazgo es que se determina
que sí existe relación entre el desempeño económico y competitividad regional y que está
representada por el modelo propuesto, las variables que la operacionalizan y los efectos
fijos del modelo. La diferencia en el crecimiento de las regiones está reflejada en la
heterogeneidad no observable capturada por los efectos fijos del modelo.
The objective of the research was to propose a new methodological approach to calculate the competitiveness of subnational territorial spaces (Regions). For this purpose, as a first step, a review of the respective literature was carried out supporting the relationship between the economic performance of the regions and the determinants of regional competitiveness. In this context, the competitiveness of the regions is defined as an unobservable effect and it is proposed a model for its determination. This model captures the unobservable heterogeneity, which is postulated as a simplification of the competitiveness of the regions of a country, that is, the element that would explain the differences in economic performance between regions. The second step consisted in formulating a fixed effects panel data econometric model using the dummy variables technique. The dependent variable represents a region model and is constructed by averaging GDP in real terms for each region. For the independent variables, five dimensions are proposed that explain regional competitiveness. The data used is centered on 91 variables for each of the 25 regions of Peru between the years of 2012 to 2018. The third step is to detail the results; in this sense, the first finding is that the proposed model is adequate to explain the economic performance of the region model and the estimates for each region are relevant to measure the differences between them. The second finding is that there is a relationship between economic performance and regional competitiveness and that it is represented by the proposed model, the variables that operationalize it and the fixed effects of the model. The difference in the growth of the regions is reflected in the unobservable heterogeneity captured by the fixed effects of the model.
The objective of the research was to propose a new methodological approach to calculate the competitiveness of subnational territorial spaces (Regions). For this purpose, as a first step, a review of the respective literature was carried out supporting the relationship between the economic performance of the regions and the determinants of regional competitiveness. In this context, the competitiveness of the regions is defined as an unobservable effect and it is proposed a model for its determination. This model captures the unobservable heterogeneity, which is postulated as a simplification of the competitiveness of the regions of a country, that is, the element that would explain the differences in economic performance between regions. The second step consisted in formulating a fixed effects panel data econometric model using the dummy variables technique. The dependent variable represents a region model and is constructed by averaging GDP in real terms for each region. For the independent variables, five dimensions are proposed that explain regional competitiveness. The data used is centered on 91 variables for each of the 25 regions of Peru between the years of 2012 to 2018. The third step is to detail the results; in this sense, the first finding is that the proposed model is adequate to explain the economic performance of the region model and the estimates for each region are relevant to measure the differences between them. The second finding is that there is a relationship between economic performance and regional competitiveness and that it is represented by the proposed model, the variables that operationalize it and the fixed effects of the model. The difference in the growth of the regions is reflected in the unobservable heterogeneity captured by the fixed effects of the model.
Link to repository: http://hdl.handle.net/20.500.12404/25298
Discipline: Administración Estratégica de Empresas
Grade or title grantor: Pontificia Universidad Católica del Perú. CENTRUM
Grade or title: Doctor en Administración Estratégica de Empresas
Juror: Percy Samoel Marquina Feldman; Rubén Guevara Moncada; Kurt Johnny Burneo Farfán; Pablo José Arana Barbier
Register date: 4-Jul-2023
This item is licensed under a Creative Commons License