Tax Incidence of the Implementation of the Philippine Tax Reform for Acceleration and Inclusion (TRAIN) Act: A Computable General Equilibirium-Microsimulation Approach

• Main Author: Dizon, Ricardo
• Format: text
• Published: Archīum Ateneo 2019
• Subjects: n/a
• Online Access: https://archium.ateneo.edu/theses-dissertations/438
• Institution: Ateneo De Manila University

The effects on occupational choice, labor income, and distributional impact in the advent of the implementation of first package of Republic Act No. 10963, the Comprehensive Tax Reform Program of the Philippines also known as Tax Reform Acceleration and Inclusion (TRAIN) is a topic of enduring interest for researchers, economists and policy makers. Economists use the concept of tax incidence in evaluating the effects of the changes in tax policies of the government on economic welfare. The primary objective of the tax reform is to design an effective system of tax collection that is capable of financing government expenditures that will translate to a better position of the economy. The purpose of this paper is to determine the anticipated effects of the changes in the tax system through the implementation of the TRAIN Law focusing on distributional and labor effects on the household sector. This dissertation applied the CGE- Microsimulation framework to obtain the macro and micro levels of the impact of TRAIN that is currently implemented by the Philippine Government. In particular, the Top-Down Behavioral Microsimulation approach was employed in this paper. The results of the simulation revealed that it was anticipated that the implementation of the TRAIN Law results not only in an increase in the household income but in the disposable income as well. However, despite the increase in household income and disposable income, Region IV remained to have the lowest estimated household income among regions in the Philippines. v In terms of the poverty effects, the measurement of the poverty indices revealed that there were significant reductions in the number of poor as well as in the magnitude of poor due to the implementation of the TRAIN Law. However, based on the simulated FGT poverty gap, there was no significant difference in the poverty gap among the poor before and after the implementation of TRAIN Law.