Productivity in the Ecuadorian manufacturing sector: a spatial exploration with Machine Learning Una exploración espacial con Machine Learning
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Abstract
This study analyzes the spatial distribution of Ecuadorian manufacturing productivity in 2023 using unsupervised machine learning. K-means and hierarchical clustering algorithms reveal a binary macrostructure and a quaternary substructure of cantons based on their productivity, specialization, and diversity. The results show a marked territorial gap and productive heterogeneity that challenges traditional agglomeration theories. It concludes that an evidence-based industrial policy requires differentiated territorial diagnoses for targeted interventions, thereby moving beyond homogeneous policy approaches.
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