An Application of the Quantile Regression Model to the Relationship Between Digitalization and Productivity Growth in Indonesia - Ray Sastri; Rofiq Nur Rizal (University of Mataram, 2026)
Ordinary regression models are often inappropriate for economic performance metrics due to their tendency to have non-Gaussian distributions, heavy tails, and extreme outliers. This study analyzes the link between digitalization and provincial labor productivity growth in Indonesia using a Quantile Regression Model to capture the full, varied range of regional economic dynamics. The empirical framework assesses the combined influence of digital transformation and the underlying economic structures on productivity trajectories by using a panel data set of 34 provinces from 2017 to 2023. The baseline estimates suggest that the digital-productivity relationship is highly non-linear and more pronounced at the 50th quantile in which the direct effects of digital infrastructure, household consumption and sectoral value-added are statistically significant. Importantly, the inclusion of macro-digital interaction terms significantly changes the dynamics of the model. The results show that the marginal effect of digitalization on regional industrial upgrading is not marginal. Instead, it is highly conditional on a synergistic alignment between infrastructure, intensity of use, digital skills, and strong underlying economic baselines to effectively catalyze labor productivity gains.