Okun's law and spatial regimes in Indonesia: A machine learning approach

We examine how output growth translates into unemployment changes across Indonesian districts from 2011 to 2020. Using a data-driven approach instead of predetermined geographic groupings, we identify districts sharing similar growth-unemployment dynamics. We find that the growth–unemployment relationship (Okun’s law) varies markedly across districts, with some experiencing substantial unemployment reductions while others show negligible or reversed effects. Spatial models decompose these effects into local responses and neighboring district spillovers. The findings underscore the limitations of aggregate Okun estimates and the need for policies that are locally tailored and coordinated across neighboring regions.

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