Classification of Latin American and Caribbean Countries Based on Multidimensional Development Indicators: A Multivariate Empirical Analysis
This study develops a multidimensional classification of Latin American and Caribbean countries based on a multidimensional set of economic, social, technological, and environmental indicators. This study develops a multidimensional assessment of the performance of Latin American and Caribbean count...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Economies |
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| Online Access: | https://www.mdpi.com/2227-7099/13/6/178 |
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| author | Adel Mendoza-Mendoza Delimiro Visbal-Cadavid Enrique De La Hoz-Domínguez |
| author_facet | Adel Mendoza-Mendoza Delimiro Visbal-Cadavid Enrique De La Hoz-Domínguez |
| author_sort | Adel Mendoza-Mendoza |
| collection | DOAJ |
| description | This study develops a multidimensional classification of Latin American and Caribbean countries based on a multidimensional set of economic, social, technological, and environmental indicators. This study develops a multidimensional assessment of the performance of Latin American and Caribbean countries, taking into account the following indicators for the period 2017–2022: education expenditure (% of GDP), health expenditure (% of GDP), GDP per capita (constant USD), CO<sub>2</sub> emissions per capita (metric tons), energy consumption per capita (kWh), internet users (% of population), mobile phone subscriptions (per 100 inhabitants), and the Global Innovation Index (GII). Initially, through the application of principal component analysis (PCA), the objective was to reduce the complexity of the data set and reveal the main structural dimensions. Subsequently, cluster analysis was used to classify countries according to shared development patterns. To achieve this, the average of the indicators for the 2017–2022 period was used as a basis, which enabled the reduction in short-term distortions and the capture of structural trends. The results reveal the existence of distinct groups, with countries with higher levels of digital connectivity, investment in human capital, and economic dynamism experiencing more favorable development conditions. |
| format | Article |
| id | doaj-art-5c4be69bbe7742d4bd8ef1b8434a5238 |
| institution | Kabale University |
| issn | 2227-7099 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Economies |
| spelling | doaj-art-5c4be69bbe7742d4bd8ef1b8434a52382025-08-20T03:24:32ZengMDPI AGEconomies2227-70992025-06-0113617810.3390/economies13060178Classification of Latin American and Caribbean Countries Based on Multidimensional Development Indicators: A Multivariate Empirical AnalysisAdel Mendoza-Mendoza0Delimiro Visbal-Cadavid1Enrique De La Hoz-Domínguez2Industrial Engineering Program, Faculty of Engineering, Universidad del Atlántico, Barranquilla 080001, ColombiaIndustrial Engineering Program, Faculty of Engineering, Universidad del Magdalena, Santa Marta 470004, ColombiaStatistical and Quantitative Methods Research Group (GEMC), Universidad del Magdalena, Santa Marta 470004, ColombiaThis study develops a multidimensional classification of Latin American and Caribbean countries based on a multidimensional set of economic, social, technological, and environmental indicators. This study develops a multidimensional assessment of the performance of Latin American and Caribbean countries, taking into account the following indicators for the period 2017–2022: education expenditure (% of GDP), health expenditure (% of GDP), GDP per capita (constant USD), CO<sub>2</sub> emissions per capita (metric tons), energy consumption per capita (kWh), internet users (% of population), mobile phone subscriptions (per 100 inhabitants), and the Global Innovation Index (GII). Initially, through the application of principal component analysis (PCA), the objective was to reduce the complexity of the data set and reveal the main structural dimensions. Subsequently, cluster analysis was used to classify countries according to shared development patterns. To achieve this, the average of the indicators for the 2017–2022 period was used as a basis, which enabled the reduction in short-term distortions and the capture of structural trends. The results reveal the existence of distinct groups, with countries with higher levels of digital connectivity, investment in human capital, and economic dynamism experiencing more favorable development conditions.https://www.mdpi.com/2227-7099/13/6/178principal component analysis (PCA)cluster analysismultidimensional assessmentsocioeconomic indicators |
| spellingShingle | Adel Mendoza-Mendoza Delimiro Visbal-Cadavid Enrique De La Hoz-Domínguez Classification of Latin American and Caribbean Countries Based on Multidimensional Development Indicators: A Multivariate Empirical Analysis Economies principal component analysis (PCA) cluster analysis multidimensional assessment socioeconomic indicators |
| title | Classification of Latin American and Caribbean Countries Based on Multidimensional Development Indicators: A Multivariate Empirical Analysis |
| title_full | Classification of Latin American and Caribbean Countries Based on Multidimensional Development Indicators: A Multivariate Empirical Analysis |
| title_fullStr | Classification of Latin American and Caribbean Countries Based on Multidimensional Development Indicators: A Multivariate Empirical Analysis |
| title_full_unstemmed | Classification of Latin American and Caribbean Countries Based on Multidimensional Development Indicators: A Multivariate Empirical Analysis |
| title_short | Classification of Latin American and Caribbean Countries Based on Multidimensional Development Indicators: A Multivariate Empirical Analysis |
| title_sort | classification of latin american and caribbean countries based on multidimensional development indicators a multivariate empirical analysis |
| topic | principal component analysis (PCA) cluster analysis multidimensional assessment socioeconomic indicators |
| url | https://www.mdpi.com/2227-7099/13/6/178 |
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