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Okun's law varies markedly across Indonesian districts, and growth shocks spill over to neighboring regions — calling for locally tailored, coordinated labor policies.

Minimum wage differentials across districts significantly affect worker commuting probabilities in Indonesia's Greater Jakarta Metropolitan Area.

We use new big data sources, the Cambodia Socio-Economic Survey, and machine learning methods to predict and map multidimensional poverty in Cambodia.

We study the robust determinants of regional inequality using a Bayesian average of classical estimates for panel data.

Through the lens of a multiscale geographically weighted regression (MGWR) and an updated inference framework, we assess the spatial scale at which political and socioeconomic factors affect violence.

This study constructs a novel subnational GDP dataset for Vietnam by integrating nighttime lights, agricultural land, and climate data through machine learning methods

This study explores income-luminosity dynamics in China, highlighting VIIRS's superiority over DMSP in predictive accuracy over time.

This paper introduces a user-friendly geocomputational notebook that illustrates how to process and analyze satellite NTL images.

This study explores the potential of higher-quality nighttime light (NTL) data to predict economic activity across various sectors within regions.

We analyze the space-time dynamics of Indonesia’s provincial unemployment by simultaneously accounting for their serial persistence, spatial dependence, and common factors.