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Assessing regional competitiveness in Peru: An approach using nonlinear machine learning models.

Yvan J Garcia-Lopez1,2, Luis A Del Carpio Castro1,2

  • 1CENTRUM Católica Graduate Business School (CCGBS), Lima, Peru.

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Machine learning models effectively measure regional competitiveness in Peru, outperforming traditional methods by handling complex data and providing actionable insights for sustainable development.

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Area of Science:

  • Economics
  • Data Science
  • Regional Development

Background:

  • Traditional methods struggle with the complex, non-linear determinants of regional competitiveness.
  • Machine Learning (ML) offers advanced predictive modeling capabilities for such data.

Purpose of the Study:

  • To develop and evaluate non-linear ML models for measuring sub-national regional competitiveness in Peru.
  • To assess the impact of ML on the Peruvian Regional Competitiveness Index (IRCI).

Main Methods:

  • Utilized the ODD protocol for methodological transparency.
  • Applied six non-linear ML models (Gradient Boosting, Random Forest, XGBoost, AdaBoost, Neural Networks, Decision Trees) to data from 25 Peruvian regions (2016-2023).
  • Developed a suitability index (IoI) and performed exploratory data analysis (EDA).

Main Results:

  • Gradient Boosting and Random Forest demonstrated the highest predictive accuracy.
  • Achieved low Mean Squared Error (MSE) and Root Mean Squared Error (RMSE), with high R2 values (e.g., R2 of 0.9768 for Gradient Boosting).
  • ML effectively analyzed complex data, identified key variables, and reduced score distortions.

Conclusions:

  • Non-linear ML models are effective tools for assessing regional competitiveness.
  • Findings offer a data-driven framework for policymakers to enhance regional competitiveness and promote sustainable development.