Data and replication materials for the LESG index: logistics, governance, sustainability and development readiness

This dataset supports an empirical investigation into national systemic readiness for sustainable development beyond conventional outcome-based indicators such as Gross Domestic Product (GDP). The underlying research hypothesis is that development readiness is not adequately captured by isolated measures of income, logistics performance, or sustainability outcomes, but instead emerges from the structural coherence among logistics capability, governance quality, and environmental and social sustainability conditions. The dataset operationalizes this concept through the construction of the LESG (Logistics–Environmental, Social and Governance) index using country-level data for a balanced cross-sectional sample of 123 countries. Four widely used international indicators are included: the World Bank’s Logistics Performance Index (LPI), the Worldwide Governance Indicators (WGI), the Environmental Performance Index (EPI), and the Sustainable Development Goals (SDG) Index. All variables are derived from publicly available sources and represent relatively stable structural characteristics rather than short-term economic fluctuations. Prior to analysis, indicators were harmonized to a common scale to ensure cross-country comparability, and an alternative equal-weight index was constructed to assess sensitivity to aggregation choices.

The analytical framework underlying the dataset is diagnostic rather than causal. Principal Component Analysis (PCA) is employed to identify the latent structure underlying the four dimensions and to derive variance-based weights for the LESG index. The results consistently indicate a single dominant component explaining more than 80% of total variance, with all dimensions loading strongly and positively, suggesting that logistics performance, governance quality, and sustainability outcomes form a coherent readiness construct. External validation is conducted through regression analysis against GDP per capita, interpreted as an assessment of coherence rather than causality. To explore structural heterogeneity, hierarchical and k-means clustering techniques are applied to classify countries into distinct systemic readiness regimes. The dataset enables full replication of these procedures and supports comparative analysis of development readiness across countries. It is intended to be used as a transparent diagnostic tool for research and policy analysis, allowing users to examine how different structural configurations shape development capacity while remaining explicit about methodological choices and limitations.