Forecasts and insights into Japan’s fiscal future: Machine learning-based projections of city-level taxpayer numbers and total income from 2020 to 2100 - Chao Li; Alexander Ryota Keeley; Shunsuke Managi (Machine Learning with Applications, 2025)
Japan’s economic landscape is undergoing profound transformations due to shifting demographic trends, including population decline, aging, and urban-rural disparities. This study applies advanced machine learning techniques and stepwise updating methodologies to predict city-level taxpayer numbers and total income across 1896 Japanese cities from 2020 to 2100. The models achieve high accuracy, with validation R2 exceeding 98 %, ensuring robust long-term predictions. The findings reveal a 14.52 % decline in total taxpayers by 2100, closely following population trends, while total income remains relatively stable, even with an increase of 5.21 %. On the other hand, average income is projected to increase by 23.07 % by 2100. Despite an overall economic contraction, increasing labor participation helps sustain the tax base. However, spatial disparities persist, with rural areas experiencing severe declines in taxpayers and income, while metropolitan centers maintain higher resilience but still face income stagnation. These results underscore the need for regionally tailored policy interventions to mitigate the fiscal impacts of demographic shifts. The study contributes to predictive economic modeling by integrating high-resolution spatial and demographic data with explainable machine learning and offers valuable insights for policymakers navigating Japan’s long-term economic evolution.