Predicting the Logistics Performance Index (LPI) with machine learning methods
TL;DR
These provisional reference estimates can serve as complementary benchmarks for countries where the availability and quality of big data necessary to build the KPIs do not meet the requirements of the official methodology, supporting diagnostic screening, trend assessment, and preliminary policy analysis in data-scarce contexts.
Abstract
This study explores whether modern machine learning (ML) techniques can effectively use structured open-access cross-country indicators to predict the World Bank’s Logistic Performance Index scores. The work examines different regression algorithms through a comprehensive evaluation of 139 countries employing three-based methods. Additional models were also tested as benchmarks. The analysis includes a rigorous treatment of missing data through iterative imputation, a robust model assessment using cross-validation, and interpretability via SHAP values. The results show that the Extra Tree model (test R2 = 0.889, RMSE = 0.195) shows the highest predictive precision and generalization when including geographic and income groups as features. As a final step, the index was predicted for 13 countries not included in the official 2023 LPI release. These provisional reference estimates can serve as complementary benchmarks for countries where the availability and quality of big data necessary to build the KPIs do not meet the requirements of the official methodology, supporting diagnostic screening, trend assessment, and preliminary policy analysis in data-scarce contexts.