Impacto Distributivo Potencial de Reformas na Tributacao Indireta no Brasil: Simulacoes Baseadas na PEC 45/2019
This paper analyzes the redistributive impacts of indirect taxation reforms in Brazil inspired by PEC 45/2019, particularly in the version that led to EC 132/2023. Comparisons are made between the current system and the simulated reforms, considering the distribution of the tax burden among families in different income classes, as well as the impact on poverty and inequality indicators. The simulations are conducted based on the combination of effective tax rates that apply to goods and services consumed by households, using nationally representative microdata from family budgets.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Impacto Redistributivo da Reforma da Tributacao do Consumo no Brasil: Simulacoes Baseadas no PLP68/2024
The aim of this study is to estimate the implications for the reference rate and for the distribution of the tax burden among households from the consumption tax reform as outlined in the version of the Projeto de Lei Co…
Virtual impactor-based label-free bio-aerosol detection using holography and deep learning
Exposure to bio-aerosols such as mold spores and pollen can lead to adverse health effects. There is a need for a portable and cost-effective device for long-term monitoring and quantification of various bio-aerosols. To…
Nota Sobre Algumas Interpretacoes da Teoria de Tributacao Otima
This note discusses some aspects of interpretations of the theory of optimal taxation presented in recent works on the Brazilian tax system.
Estudo explorat\'orio de categorias gramaticais com potencial de indicadores para a An\'alise de Sentimentos (An Exploratory study of grammatical categories as potential indicators for Sentiment Analysis)[In Portuguese]
Physics-Informed Framework for Impact Identification in Aerospace Composites
This paper introduces a novel physics-informed impact identification (Phy-ID) framework. The proposed method integrates observational, inductive, and learning biases to combine physical knowledge with data-driven inferen…