Economists and policymakers are interested in how government spending affects economic performance. The purpose of this study is to examine the effect of government capital expenditures by component on the GDP of Iran's provinces between 2011 and 2019 using a Bayesian approach. To do so, after estimating over 500,000 regressions and averaging the Bayesian model's coefficients, the five sub-categories of industry, judiciary, energy, health and information and communications technology were identified as the most effective sub-categories of government capital expenditures on provincial GDP. Then, a hierarchical Bayesian panel model was specified and estimated to determine the extent to which and how each of these subsections affects the provinces' GDP. Monte Carlo simulations using Markov chains indicate that while judicial and health expenditures reduced production during the study period, expenditures on industry, energy, information and communications technology increased production in Iran's provinces.
