A Dynamic Bayesian Regional Input–Output Model for Forecasting Provincial Economic Transformation, Resource Constraints, and Poverty Reduction - Yi-Ming Mei; Zhu Tian; Jianwu Li; Xiaobo He (Journal of Economics Business Industry, 2024)
China's structural economic transformation presents a critical analytical challenge: how to forecast macroeconomic phenomena at the provincial level while simultaneously incorporating resource constraints and poverty reduction objectives within a single modelling framework. This study constructs a dynamic, stochastic, Bayesian provincial input–output model for the Chinese economy denoted hereafter the SD R-IO China model capable of producing regional gross value added (GVA) forecasts and provincial multiplier estimates. The model couples system dynamics modelling with regional input–output analysis. Sector growth follows the limits-to-growth hypothesis. Application is made to China's resource sectors (agriculture, coal, oil and gas, electricity and water) and to provincial poverty indices. The model is constructed in Vensim®. Bayesian inference calibrates Type I and Type II multipliers (output, income, employment, GVA) within empirically defensible ranges. Model accuracy is assessed using the mean absolute percentage error (MAPE) statistic. The model replicates national GVA with a MAPE of 4.1% over the test period 2018–2022. Provincial forecasts achieve "highly accurate" classification for 11 of 31 provincial units and at least "reasonable" for all others. Sector multipliers conform to a priori expectations. A negative correlation between resource sector multipliers and provincial poverty headcount is observed, most prominently in the agriculture and electricity subsectors. The SD R-IO China model offers a scientifically rigorous, freely accessible tool for provincial macroeconomic analysis, with direct implications for green economy policy and poverty alleviation programming.