Asian Journal of Probability and Statistics

Emerging economies are increasingly recognised as key contributors to global economic growth; however, their sectoral structures often remain uneven and characterised by significant imbalances. This study examines the segmentation and classification of economic sectors in emerging economies using an integrated multivariate analytical framework. Specifically, it combines factor analysis, principal component analysis (PCA), cluster analysis and discriminant analysis to uncover underlying patterns, reduce data complexity and identify distinct sectoral groupings. Using secondary macroeconomic data on key indicators, including gross domestic product (GDP), employment, investment, infrastructure and trade performance, the analysis reveals a dominant economic dimension largely driven by investment and government expenditure. The results further identify three distinct sectoral clusters representing high-performing, moderately performing and low-performing sectors, thereby highlighting the heterogeneous nature of economic structures in emerging markets. Discriminant analysis supports the robustness of these classifications, with tourism, investment and public expenditure emerging as key variables influencing sectoral differentiation. The findings underscore the limitations of traditional broad sector classifications and demonstrate the value of multivariate techniques in providing deeper insights into economic structures. The study concludes that emerging economies exhibit uneven development, with growth concentrated in specific sectors without corresponding improvements in others. Accordingly, it advocates cluster-based policy approaches, increased investment in social and infrastructure sectors and strategic diversification to support more balanced and sustainable economic development.