An Econometric Analysis of Tax Revenue and Economic Growth in the Philippines
Abstrak
Economic growth is a vital driver of national development, fostering employment, reducing poverty, and raising living standards. In the Philippines, taxation remains the government’s primary tool for resource mobilization, yet the distinct contributions of various tax instruments to economic performance have not been thoroughly examined. This study analyzed the effects of Corporate Income Tax (CIT), Value-Added Tax (VAT), and Excise Tax revenues on the country’s Gross Domestic Product (GDP) using a quantitative econometric approach. Time-series data covering 2014 to 2024 were obtained from the Philippine Statistics Authority, World Bank, and Bureau of Internal Revenue. The Ordinary Least Squares (OLS) regression method was employed, supported by descriptive statistics and correlation analysis to validate the results. Findings show that tax revenues collectively explain 95 percent of the variation in GDP (R² = 0.95, F = 44.21, p < 0.01), confirming their substantial role in driving economic growth. Among the three instruments, VAT had the strongest and most significant positive effect (B = 0.48, β = 0.79, p < 0.01), followed by Excise Tax (B = 0.22, β = 0.49, p = 0.03), while CIT displayed a negative but statistically insignificant impact (B = -0.19, p = 0.23). The Durbin–Watson statistic (2.42) indicated no autocorrelation, and multicollinearity was within acceptable levels. These results suggest that indirect taxes, particularly VAT and Excise Tax, are more effective drivers of economic expansion than corporate taxation. The study concludes that strengthening the administration of VAT and Excise Tax, improving compliance mechanisms, and broadening the tax base are essential for sustaining growth, while future research should incorporate other macroeconomic variables to provide a fuller picture of taxation’s role in development.
Penulis (3)
Charisse C. Laganson
Jorish Miah S. Suganob
Karl P. Campos
Akses Cepat
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- 2025
- Bahasa
- en
- Sumber Database
- Semantic Scholar
- DOI
- 10.9734/ajess/2025/v51i102533
- Akses
- Open Access ✓