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This study examines the dynamic relationships between economic activity and revenue from ICMS, Brazil’s state tax on goods and selected services. Annual data for the 26 states and the Federal District cover 2000–2019. A panel vector autoregression relates ICMS revenue to GDP per capita, gross value added in services and industry, industrial establishments, local units of high-growth firms, and population. The model selection criteria favor a first-order specification. The ICMS equation shows positive and statistically significant coefficients for population and industrial establishments and a negative and statistically significant coefficient for high-growth firms. Granger tests indicate bidirectional predictive relationships between ICMS revenue and services value added, industrial establishments, GDP per capita, high-growth firms, and industrial value added. These relationships describe conditional predictability within the estimated system. The findings indicate that the composition of economic activity and business dynamics are relevant to understanding state revenue patterns. They support considering sectoral and firm indicators alongside aggregate output when selecting information for revenue forecasting and fiscal monitoring.
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Angélica Pott de Medeiros (2026) studied this question.
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