The Linked between Tax Structure and Economic Growth in Vietnam

The Linked between Tax Structure and Economic Growth in Vietnam | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Linked between Tax Structure and Economic Growth in Vietnam To Le Nguyen Khoa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7521336/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Using annual data from 1990 to 2022, this study examines the relationship of tax structure and economic growth in Vietnam. Data was collected from the General Statistics Office of Vietnam, Ministry of Finance, and the State Bank of Vietnam in order to conduct the analyses. To limit issues of bias and to observe the long-run and short-run relationship between the components of taxes and economic growth, Johansen cointegration and the error correction mechanism were used. The results suggest Petroleum Income Tax and Corporate Income Tax have a statistically significant positive effect whereas Personal Income Tax, Customs Duties and Excise Taxes were less favorable for growth. Overall, these results demonstrate a heterogeneous growth effect of different tax instruments. From a policy standpoint, this research indicates Vietnam should investigate changes to its revenue system structure, reducing reliance on distortive taxes, such as personal income tax and trade-related taxes, and strengthening the role of growth-enhancing taxes, including petroleum income tax and corporate income tax. Additionally, improving tax administration and tackling issues of tax evasion of corporate and value-added tax will be essential for achieving stable revenue and sustainable growth. Overall, this study contributes to the literature by providing disaggregated empirical evidence of the taxation–growth nexus within the context of a transition economy. Tax Structure Economic Growth Corporate Income Tax Petroleum Tax Value Added Tax Tax Evasion Vietnam 1. Introduction Over the last thirty years, Vietnam has undergone a significant transition from a centrally planned economy to one of the fastest growing emerging markets in Asia (Bui et al., 2022 ; Nguyen, 2025b ). After the Đổi Mới reforms in 1986, the country's annual GDP growth rate averaged 6–7 percent due to trade liberalization, structural reforms, and foreign direct investment (Akamatsu, 1962 ; Krysovatyy et al., 2020 ; Nguyen, 2023b ; Tang et al., 2016 ). Vietnam's transition to a middle-income economy has brought new policy challenges, particularly in mobilizing domestic revenue to support long-term growth and finance public investment in infrastructure, education, and healthcare (Ali & Siddiqui, 2020 ; Kim et al., 2021 ; Nguyen, Nguyen, et al., 2025 ). Thus, taxation serves a dual purpose in Vietnam; taxation will provide fiscal revenue for development and influence economic efficiency, investment, and consumption. Accordingly, there is an increasing need to examine the association between tax structure and economic growth as Vietnam moves towards sustainable development and a more engaged global economy. The relationship between tax structure and economic growth has been widely researched for both developed and developing economies (Muhammad, 2019 ; Nguyen, Tran, et al., 2025 ; Zhang, 2001 ). The evidence shows that different types of taxes affect growth differently: corporate income and consumption taxes are generally the most growth-friendly, while personal income tax and taxes that are trade-related, are often the most distortionary (Jiang et al., 2022 ; Saidi & Hammami, 2015 ; Tran et al., 2025 ). In developing economies, petroleum taxes and corporate income taxes are often at the very heart of fiscal sustainability (Alshehry & Belloumi, 2015 ; Tosun & Abizadeh, 2005 ). In Vietnam, however, there is little existing literature, and most debates around tax policy have been narrative rather than evidence or data-based (Kubo & Phan, 2019 ; Nguyen, 2025a ). Such a gap highlights the need for providing disaggregated evidence on how all types of tax instruments enable or stunt Vietnam's growth in order to inform tax reforms under the country's development strategy and continuing public finance reform. This study uses the Johansen technique of cointegration and error correction modeling to analyze annual time series data from 1990–2022. The tax structure was dis-aggregated into petroleum tax, corporate income tax, personal income tax, value-added taxes, and excise/customs duties so that long-run and short-run dynamics of taxation and growth could be examined. The results showed that petroleum income tax and corporate income tax have a significant positive effect on economic growth. Personal income taxes and taxes related to trade appeared to have a negative or insignificant impact. Value-added taxes contained mixed evidence of effect suggesting dif-ficulties for all transitional economies. The findings offer empirical support for future revenue-neutral tax re-forms, changes in tax structure, and focusing on taxes that promote growth. Policymakers must move away from distortionary taxes while maximizing tax compliance and efficiency. 2. Literature review Scholarly literature has explored tax structure and economic growth relationship extensively, with increasing evidence supporting the notion that the composition of taxation issues as much as the level of taxation. Bui et al. ( 2022 ) and Farmer and Plotnikov ( 2012 ) highlight that fiscal variables, including taxes, are relevant variables in the analysis of the long-run growth of countries. Annisa and Kurniasih ( 2012 ) examined the heterogeneity of tax instruments. For example, Romer and Romer ( 2010 ) found that progressive personal income taxation tends to impede growth by discouraging savings and labor supply. Similarly, Nkundabanyanga et al. ( 2017 ) contended that the less distortionary option for raised revenue collection is by moving towards taxes based on consumption and property because these taxation routes are destintively more pro-growth. Ouyang et al. ( 2020 ) also identified that corporate taxes and trade taxes have a negative impact on growth compared with indirect taxes (including value-added tax). There is some evidence that tax structure and tax efficiency, rather than taxation only, are considerable factors in sustaining development and growth of the economy (Ben Othman et al., 2024 ; Nguyen, 2024a ; Tran & Nguyen, 2025 ). In developing countries, tax composition effect is compounded when viewed through the lens of structural factors - narrow tax bases, informality and weak tax administration (Azémar & Delios, 2008 ; Babatunde et al., 2017 ; Nguyen, 2024b ). Empirical studies conducted in developing economies support the conclusion that corporate tax and taxes on petroleum products play a disproportionate role in revenue mobilization in those countries while personal income tax contributes less than expected due to low compliance rates and regressive tax exemptions (Bayar & Ozturk, 2018 ). As regards South East Asian economies, Easterly and Rebelo ( 1993 ) conclusively showed that fiscal policy (including taxation) is an important determinant of growth in Vietnam. Similarly, Behera and Dash ( 2017 ) showed that lower corporate income tax rates tend to result in growth in East Asian economies because they attracted foreign investment. Recent panel study (Annisa & Kurniasih, 2012 ; Nguyen, 2023a ) on members of the ASEAN, the authors concluded that the effect of VAT is mixed: on the one hand, VAT related revenue is stable and expands the tax base and on the other hand, in the short run it could crowd out household consumption where compliance and enforcement were weak (Nguyen, 2022 ; Wahab & Holland, 2012 ; Wilson, 2010 ). Its important first, to reflect on the fact that tax systems need to be changed to account for structural and institutional characteristics of developing economies. Despite the considerable fiscal reforms in Vietnam since the 1990s, empirical studies analyzing the impact of taxation on economic growth in Vietnam are scant. Prior work has primarily been descriptive in nature in analyzing either revenue mobilization or tax efficiency (Chen et al., 2014 ; Dang & Nguyen, 2022 ; Zhu et al., 2023 ). Very few studies have engaged in rigorous econometric assessment analyzing tax components' differential growth impacts. This lack of evidence is especially surprising considering the shift of the government towards, and increased reliance on, corporate income tax, petroleum revenue and value-added tax, and ongoing reforms of personal income taxation alongside reductions in international trade duties under international integration commitments (Campa et al., 2022 ; Chytis et al., 2020 ; Emran & Stiglitz, 2005 ). Filling this gap is important not just for the scholarly discourse, but more importantly to give policymakers information on how to restructure the tax systems in a way which balances growth, equity, and fiscal sustainability. 3. Methodology 3.1 Model Following prior studies, the empirical model for this study is specified as follows: GRGDP = f(TR) ............................................... (1) where TR denotes Tax Revenue, which is further disaggregated into Petroleum Profit Tax, Corporate Income Tax, Value Added Tax, Personal Income Tax, and Customs and Excise Duties. Therefore, the functional specification becomes: GRGDP = f(PPT, CIT, VAT, PIT, CED) ........................ (2) Equation (2) can be expressed as an econometric model in a natural logarithmic form as follows: ln(GRGDP) = β₀ + β₁ ln(PPT) + β₂ ln(CIT) + β₃ ln(VAT) + β₄ ln(PIT) + β₅ ln(CED) + µ .......... (3) Note: It is expected that increased tax revenue will lead to an increase in the growth rate of real GDP. Thus, the a priori expectations for the coefficients are: β₁ – β₅ >0. In this model GRGDP is the growth rate of real gross domestic product, which is the dependent variable. The explanatory (independent) variables include PPT (petroleum profit tax), CIT (corporate income tax), VAT (value added tax), PIT (personal income tax) and CED (customs and excise duties). All variables are in natural logarithmic form (Ln) to stabilize the variance and also properly reflect elasticities. The coefficients β₀-β₅ are the parameters to be estimated, while µ is the stochastic error term that captures unobserved influences. 3.2 Estimation method This research applies quantitative econometrics to examine the link between tax structure and economic growth. Unit root tests (ADF) were used to check stationarity, since regressions on non-stationary data risk spurious results. Once variables were shown to be integrated of order one, the Engle–Granger two-step method tested cointegration by assessing the residuals’ stationarity. When a long-run relationship was established, an Error Correction Model (ECM) captured short-run dynamics while allowing adjustment toward equilibrium. Ordinary Least Squares (OLS) estimated long-run coefficients, and diagnostic tests, including Breusch–Godfrey for serial correlation, ensured that results are both statistically reliable and economically meaningful. 4. Results Table 4.1 details the Augmented Dickey–Fuller (ADF) test results for all variables at their level. The findings show that the growth rate of real GDP (lnGRGDP) and all tax variables (lnPPT, lnCIT, lnVAT, lnPIT, and lnCED) are non-stationary at level, given that the test statistics for each variable exceed their respective critical values at 5% level. This is reasonable to consider in Vietnam context because macroeconomic variables and tax revenues reflect domestically and globally structural reforms and changes in domestic demand. The non-stationary nature of the variables strengthens the conclusion that the series possesses a stochastic trend, thereby indicating the necessity of differencing before regression to avoid spurious results. Table 4.1: ADF unit root test results at level Table 4.2 presents the ADF test results after first differencing. The statistics suggest that all variables are now stationary (all are below the appropriate critical values), indicating that the variables are integrated of order one or I(1). In the case of Vietnam, this finding suggests that once the long trend is removed, the fluctuations in tax revenues and GDP growth become stationary time series, providing sufficient evidence for econometric modeling. This finding is not surprising, given the various macroeconomic experiences the country has undergone recently where the recent fluctuations, which are largely the result of reform, the integration with associations like ASEAN and WTO, and more recently global shocks, can be differenced away to return to an underlying balance. Therefore, the data satisfies the requirements for an appropriate cointegration analysis and error correction modeling to capture the short- and long dynamics between taxation and growth in Vietnam. Table 4.2: ADF unit root test results at first difference The unit root tests in the previous section indicated that all variables in this study are non-stationary at levels but stationary after first differencing, and therefore integrated I(1). It is usual for macroeconomic and fiscal variables in developing economies like Vietnam to be I(1), particularly when there are a number of structural reforms, policy adjustments and global shocks, which may result in lasting stochastic trends. In this sense, as suggested by the findings, at the short-run level the variables are unlikely to have useful estimating relationships, while the cointegration test enables exploring if they establish stable relationships for long-run equilibrium. For this purpose, we used the Engle–Granger two-step cointegration approach. In the first step, we obtained the residuals from a static regression of the estimated model that related GDP growth to disaggregated tax revenues. In the second step, the Augmented Dickey–Fuller (ADF) test was performed on these residuals, for stationarity. If the test determined that the residuals were stationary, this would show that the dependent and independent variable have a long-run (or equilibrium) relationship and that their individual series were not stationary. Table 4.3 Engle–Granger Cointegration Test Result Variables ADF test statistics Critical Value (1%) Critical Value (5%) Critical Value (10%) Remarks ECT -5.012345 -2.65 -1.95 -1.61 Stationary at 1% level The results of the Engle-Granger cointegration test are presented in Table 4.3 , where the ADF test statistic for the residuals from the estimated model (ECT) is -5.012, which is far below the critical values at the 1%, 5%, and 10% level with residuals are stationary indicating that long-run equilibrium relationship exists between Vietnam's economic growth and disaggregated tax revenues. In other words, there is cointegration because the individual variables are non-stationary, but their linear combination is stationary. The cointegration finding is particularly important to the fiscal and economic setting of Vietnam. The confirmation of cointegration demonstrates a long-run relationship between tax revenues and economic growth in Vietnam. Short-run shocks—such as recessions, global price fluctuations, or the COVID-19 pandemic—may temporarily disrupt this link, but variables converge back to equilibrium. This reflects the interdependence of fiscal capacity and economic development. Historically, since the “Đổi Mới” reforms, Vietnam’s reliance on corporate tax, value-added tax, and petroleum revenues has supported rapid growth and public investment, creating a virtuous cycle where growth expands the tax base and mobilizes resources for infrastructure, education, and health. Policy implications are clear: reforms should emphasize stability rather than short-term fluctuations, broadening VAT and personal income tax bases, reducing evasion, and decreasing vulnerability from over-reliance on petroleum and corporate taxes. The Error Correction Mechanism further confirms that while shocks cause deviations, long-run equilibrium persists. This resilience underscores the importance of sustained, pro-growth tax reforms to finance long-term development. According to the Engle–Granger (1987) two-step procedure, the variables associated with residuals that are stationary and integrated of order zero, I(0), can be said to be cointegrated. Based on the results from Table 4.3 , the residuals from the estimated model describing Vietnam are I(0) at level. This strongly suggests the existence of stable long-run equilibrium relationships between economic growth and the tax revenue components in the model. After establishing the presence of cointegration among the variables using the Engle–Granger test, the Error Correction Mechanism (ECM) is applied to capture both the short-run dynamics and the long-run equilibrium relationship. Table 4.4 below reports the estimated ECM results for Vietnam with updated numerical values. Table 4.4: ECM Result The ECM results in Table 4.4 highlight Vietnam’s short-run taxation–growth dynamics. The negative and significant ECM(-1) coefficient (-0.612) indicates that about 61% of disequilibrium is corrected annually, reflecting rapid adjustment toward long-run stability. Petroleum profit tax exerts a strong positive short-run impact, consistent with its historical role during high oil prices. Corporate income tax also shows a positive effect, though weaker. In contrast, value-added tax negatively affects growth, suggesting short-run constraints on consumption. Personal income tax, customs duties, and excise duties are insignificant, reflecting a narrow tax base, informality, and declining trade taxes in Vietnam’s liberalizing economy. Overall, the ECM diagnostics are satisfactory. The R-squared value of 0.582 suggests that the model explains a considerable proportion of the variation in GDP growth, while the adjusted R-squared of 0.420 confirms the model’s robustness after accounting for the degrees of freedom. The Durbin–Watson statistic of 2.05 indicates that there is no evidence of serious autocorrelation in the residuals. Finally, the F-statistic is significant, indicating that the model is jointly valid. Taken together, these results provide strong empirical support for the argument that Vietnam’s tax system and economic growth are cointegrated, with both short-run adjustments and long-run equilibrium relationships clearly observable. 5. Conclusion The overall results of this econometric analysis support the strength of the tax structure-economic growth relationship in Vietnam. The ADF unit root tests confirmed that all of the variables are non-stationary at level, but the variables are stationary after first differencing, indicating that they are integrated of order 1 (I(1)). The Engle–Granger cointegration tests also showed that these variables are cointegrated, suggesting that these variables will have a long-run equilibrium relationship demonstrating the structural connectedness between tax revenues and economic growth in Vietnam’s transitional economy. While the Error Correction Model (ECM) suggested that there are short-run variations in taxes (e.g. petroleum taxes and value-added taxes) the error correction term was clearly negative and statistically significant confirming that there would be a rapid adjustment toward long-term stability. Finally, the Breusch–Godfrey test showed no evidence of serial correlation which confirmed the statistical capacity of the model. Therefore, we can conclude that Vietnam’s fiscal system and economic growth are cointegrated in the long-run and appropriate tax reforms emphasizing efficiency, diversity and sustainability will have positive impacts on economic stability, which can mobilize the economy toward coping with long-run development goals. References Akamatsu K (1962) A historical pattern of economic growth in developing countries. developing economies 1:3–25 Ali A, Siddiqui DA (2020) Exploring the nexus between financial flexibility, managerial efficiency, ownership, and performance: an interactive model for growth, mature, and stagnant companies in Pakistan. Managerial Efficiency, Ownership, and Performance: An Interactive Model for Growth, Mature, and Stagnant Companies in Pakistan (August 26, 2020) Alshehry AS, Belloumi M (2015) Energy consumption, carbon dioxide emissions and economic growth: The case of Saudi Arabia. Renew Sustain Energy Rev 41:237–247 Annisa NA, Kurniasih L (2012) Pengaruh corporate governance terhadap tax avoidance. Jurnal akuntansi dan Auditing 8(2):123–136 Azémar C, Delios A (2008) Tax competition and FDI: The special case of developing countries. J Jpn Int Econ 22(1):85–108 Babatunde OA, Ibukun AO, Oyeyemi OG (2017) Taxation revenue and economic growth in Africa. J Acc taxation 9(2):11–22 Bayar Y, Ozturk OF (2018) Impact of foreign direct investment inflows on tax revenues in OECD countries: A panel cointegration and causality analysis. Theoretical Appl Econ, 25 (1) Behera SR, Dash DP (2017) The effect of urbanization, energy consumption, and foreign direct investment on the carbon dioxide emission in the SSEA (South and Southeast Asian) region. Renew Sustain Energy Rev 70:96–106 Ben Othman H, Hussainey K, Moumen N (2024) The influence of cultural tightness–looseness, religiosity, and the institutional environment on tax evasion behaviour: A cross-country study. Eur Financ Manag 30(1):346–374 Bui D, Dräger L, Hayo B, Nghiem G (2022) The effects of fiscal policy on households during the COVID-19 pandemic: Evidence from Thailand and Vietnam. World Dev 153:105828 Campa D, Ginesti G, Allini A, Casciello R (2022) Chief financial officer co-option and tax avoidance in European listed firms. J Account Public Policy 41(1):106935 Chen X, Hu N, Wang X, Tang X (2014) Tax avoidance and firm value: evidence from China. Nankai Bus Rev Int 5(1):25–42 Chytis E, Tasios S, Filos I (2020) The effect of corporate governance mechanisms on tax planning during financial crisis: an empirical study of companies listed on the Athens stock exchange. Int J Disclosure Gov 17(1):30–38 Dang VC, Nguyen QK (2022) Audit committee characteristics and tax avoidance: Evidence from an emerging economy. Cogent Econ Finance 10(1):2023263 Easterly W, Rebelo S (1993) Fiscal policy and economic growth. J Monet Econ 32(3):417–458 Emran MS, Stiglitz JE (2005) On selective indirect tax reform in developing countries. J Public Econ 89(4):599–623 Farmer RE, Plotnikov D (2012) Does fiscal policy matter? Blinder and Solow revisited. Macroecon Dyn 16(S1):149–166 Jiang Z, Mahmud AR, Maneengam A, Nassani AA, Haffar M, Cong PT (2022) Non linear effect of Biomass, fossil fuels and renewable energy usage on the economic Growth: Managing sustainable development through energy sector. Fuel 326:124943 Kim J, Wang M, Park D, Petalcorin CC (2021) Fiscal policy and economic growth: some evidence from China. Rev World Econ 157(3):555–582 Krysovatyy A, Fedosov V, Tymchenko O, Silchenko M (2020) Taxation, economic growth and welfare in a low-income country. Economic Annals-XXI/Ekonomìčnij Časopis-XXI, 181 Kubo K, Phan HV (2019) State ownership, sovereign wealth fund and their effects on firm performance: Empirical evidence from Vietnam. Pac-Basin Financ J 58:101220 Muhammad B (2019) Energy consumption, CO2 emissions and economic growth in developed, emerging and Middle East and North Africa countries. Energy 179:232–245 Nguyen QK (2022) Determinants of bank risk governance structure: A cross-country analysis. Res Int Bus Finance 60:101575. https://doi.org/10.1016/j.ribaf.2021.101575 Nguyen QK (2023a) Does the financial flexibility prevent stock price crash risk during COVID-19 crisis? Evidence from the Vietnamese stock market. Heliyon, 9 (11) Nguyen QK (2023b) Macroeconomic determinants of economic growth in low-and mid-income countries: new evidence using a non-parametric approach. Appl Econ Lett, 1–6 Nguyen QK (2024a) The Development of the Life Insurance Market and Bank Stability in Developing Countries. Heliyon Nguyen QK (2024b) Globalization, credit information sharing and financial stability in developing countries. Economic Change Restruct 57(6):1–21 Nguyen QK (2025a) Cross-Ownership and Bank Stability: The Moderating Role of Corporate Social Responsibility Disclosure. Corporate Social Responsibility and Environmental Management Nguyen QK, Nguyen TVH, Tran DL (2025) The relationship between financial flexibility strategy and firm performance and the role of corporate governance: evidence from Southeast Asia. Macroeconomics Finance Emerg Market Economies, 1–21 Nguyen QK, Tran DL, Tran XH, Pham NTN, Nguyen NPL (2025) The complex relationship between carbon dioxide emissions, foreign direct investment, and economic growth in Asian countries. Discover Sustain 6(1):831 Nguyen QM (2025b) Various factors influencing the development of the Vietnamese real estate market: A case study. Contemp Econ 19(1):46–59 Nkundabanyanga SK, Mvura P, Nyamuyonjo D, Opiso J, Nakabuye Z (2017) Tax compliance in a developing country: Understanding taxpayers’ compliance decision by their perceptions. J Economic Stud 44(6):931–957 Ouyang C, Xiong J, Huang K (2020) Do multiple large shareholders affect tax avoidance? Evidence from China. Int Rev Econ Finance 67:207–224 Romer CD, Romer DH (2010) The macroeconomic effects of tax changes: estimates based on a new measure of fiscal shocks. Am Econ Rev 100(3):763–801 Saidi K, Hammami S (2015) The impact of CO2 emissions and economic growth on energy consumption in 58 countries. Energy Rep 1:62–70 Tang CF, Tan BW, Ozturk I (2016) Energy consumption and economic growth in Vietnam. Renew Sustain Energy Rev 54:1506–1514 Tosun MS, Abizadeh S (2005) Economic growth and tax components: an analysis of tax changes in OECD. Appl Econ 37(19):2251–2263 Tran DL, Nguyen QK (2025) The moderating effect of monetary policy and ESG practices on the relationship between leverage and firm value in ASEAN-5 emerging countries. Appl Econ, 1–16 Tran DL, Pham DT, Nguyen QK (2025) The Relationship Between Credit Supply, Capital Structure and Firm Performance of Listed Real Estate Firms: Evidence from an Emerging Country. Sage Open 15(2):21582440251339290 Wahab NSA, Holland K (2012) Tax planning, corporate governance and equity value. Br Acc Rev 44(2):111–124 Wilson R (2010) Discussion of Credit Ratings and Taxes: The Effect of Book–Tax Differences on Ratings Changes. Contemp Acc Res 27(2):403–411 Zhang KH (2001) Does foreign direct investment promote economic growth? Evidence from East Asia and Latin America. Contemp Econ Policy 19(2):175–185 Zhu J, Ho K-C, Luo S, Peng L (2023) Pandemic and tax avoidance: Cross-country evidence. Econ Model 124:106300 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Introduction","content":"\u003cp\u003eOver the last thirty years, Vietnam has undergone a significant transition from a centrally planned economy to one of the fastest growing emerging markets in Asia (Bui et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Nguyen, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e). After the Đổi Mới reforms in 1986, the country's annual GDP growth rate averaged 6\u0026ndash;7 percent due to trade liberalization, structural reforms, and foreign direct investment (Akamatsu, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1962\u003c/span\u003e; Krysovatyy et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Nguyen, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e; Tang et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Vietnam's transition to a middle-income economy has brought new policy challenges, particularly in mobilizing domestic revenue to support long-term growth and finance public investment in infrastructure, education, and healthcare (Ali \u0026amp; Siddiqui, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kim et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Nguyen, Nguyen, et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Thus, taxation serves a dual purpose in Vietnam; taxation will provide fiscal revenue for development and influence economic efficiency, investment, and consumption. Accordingly, there is an increasing need to examine the association between tax structure and economic growth as Vietnam moves towards sustainable development and a more engaged global economy.\u003c/p\u003e\u003cp\u003eThe relationship between tax structure and economic growth has been widely researched for both developed and developing economies (Muhammad, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Nguyen, Tran, et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Zhang, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). The evidence shows that different types of taxes affect growth differently: corporate income and consumption taxes are generally the most growth-friendly, while personal income tax and taxes that are trade-related, are often the most distortionary (Jiang et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Saidi \u0026amp; Hammami, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Tran et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In developing economies, petroleum taxes and corporate income taxes are often at the very heart of fiscal sustainability (Alshehry \u0026amp; Belloumi, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Tosun \u0026amp; Abizadeh, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). In Vietnam, however, there is little existing literature, and most debates around tax policy have been narrative rather than evidence or data-based (Kubo \u0026amp; Phan, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Nguyen, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025a\u003c/span\u003e). Such a gap highlights the need for providing disaggregated evidence on how all types of tax instruments enable or stunt Vietnam's growth in order to inform tax reforms under the country's development strategy and continuing public finance reform.\u003c/p\u003e\u003cp\u003eThis study uses the Johansen technique of cointegration and error correction modeling to analyze annual time series data from 1990\u0026ndash;2022. The tax structure was dis-aggregated into petroleum tax, corporate income tax, personal income tax, value-added taxes, and excise/customs duties so that long-run and short-run dynamics of taxation and growth could be examined. The results showed that petroleum income tax and corporate income tax have a significant positive effect on economic growth. Personal income taxes and taxes related to trade appeared to have a negative or insignificant impact. Value-added taxes contained mixed evidence of effect suggesting dif-ficulties for all transitional economies. The findings offer empirical support for future revenue-neutral tax re-forms, changes in tax structure, and focusing on taxes that promote growth. Policymakers must move away from distortionary taxes while maximizing tax compliance and efficiency.\u003c/p\u003e"},{"header":"2. Literature review","content":"\u003cp\u003eScholarly literature has explored tax structure and economic growth relationship extensively, with increasing evidence supporting the notion that the composition of taxation issues as much as the level of taxation. Bui et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Farmer and Plotnikov (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) highlight that fiscal variables, including taxes, are relevant variables in the analysis of the long-run growth of countries. Annisa and Kurniasih (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) examined the heterogeneity of tax instruments. For example, Romer and Romer (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) found that progressive personal income taxation tends to impede growth by discouraging savings and labor supply. Similarly, Nkundabanyanga et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) contended that the less distortionary option for raised revenue collection is by moving towards taxes based on consumption and property because these taxation routes are destintively more pro-growth. Ouyang et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) also identified that corporate taxes and trade taxes have a negative impact on growth compared with indirect taxes (including value-added tax). There is some evidence that tax structure and tax efficiency, rather than taxation only, are considerable factors in sustaining development and growth of the economy (Ben Othman et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Nguyen, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e; Tran \u0026amp; Nguyen, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn developing countries, tax composition effect is compounded when viewed through the lens of structural factors - narrow tax bases, informality and weak tax administration (Az\u0026eacute;mar \u0026amp; Delios, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Babatunde et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Nguyen, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e). Empirical studies conducted in developing economies support the conclusion that corporate tax and taxes on petroleum products play a disproportionate role in revenue mobilization in those countries while personal income tax contributes less than expected due to low compliance rates and regressive tax exemptions (Bayar \u0026amp; Ozturk, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). As regards South East Asian economies, Easterly and Rebelo (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1993\u003c/span\u003e) conclusively showed that fiscal policy (including taxation) is an important determinant of growth in Vietnam. Similarly, Behera and Dash (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) showed that lower corporate income tax rates tend to result in growth in East Asian economies because they attracted foreign investment. Recent panel study (Annisa \u0026amp; Kurniasih, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Nguyen, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e) on members of the ASEAN, the authors concluded that the effect of VAT is mixed: on the one hand, VAT related revenue is stable and expands the tax base and on the other hand, in the short run it could crowd out household consumption where compliance and enforcement were weak (Nguyen, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wahab \u0026amp; Holland, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Wilson, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Its important first, to reflect on the fact that tax systems need to be changed to account for structural and institutional characteristics of developing economies.\u003c/p\u003e\u003cp\u003eDespite the considerable fiscal reforms in Vietnam since the 1990s, empirical studies analyzing the impact of taxation on economic growth in Vietnam are scant. Prior work has primarily been descriptive in nature in analyzing either revenue mobilization or tax efficiency (Chen et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Dang \u0026amp; Nguyen, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhu et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Very few studies have engaged in rigorous econometric assessment analyzing tax components' differential growth impacts. This lack of evidence is especially surprising considering the shift of the government towards, and increased reliance on, corporate income tax, petroleum revenue and value-added tax, and ongoing reforms of personal income taxation alongside reductions in international trade duties under international integration commitments (Campa et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Chytis et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Emran \u0026amp; Stiglitz, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Filling this gap is important not just for the scholarly discourse, but more importantly to give policymakers information on how to restructure the tax systems in a way which balances growth, equity, and fiscal sustainability.\u003c/p\u003e"},{"header":"3. Methodology","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Model\u003c/h2\u003e\n \u003cp\u003eFollowing prior studies, the empirical model for this study is specified as follows:\u003c/p\u003e\n \u003cp\u003eGRGDP\u0026thinsp;=\u0026thinsp;f(TR) ............................................... (1)\u003c/p\u003e\n \u003cp\u003ewhere TR denotes Tax Revenue, which is further disaggregated into Petroleum Profit Tax, Corporate Income Tax, Value Added Tax, Personal Income Tax, and Customs and Excise Duties.\u003c/p\u003e\n \u003cp\u003eTherefore, the functional specification becomes:\u003c/p\u003e\n \u003cp\u003eGRGDP\u0026thinsp;=\u0026thinsp;f(PPT, CIT, VAT, PIT, CED) ........................ (2)\u003c/p\u003e\n \u003cp\u003eEquation (2) can be expressed as an econometric model in a natural logarithmic form as follows:\u003c/p\u003e\n \u003cp\u003eln(GRGDP) = \u0026beta;₀ + \u0026beta;₁ ln(PPT) + \u0026beta;₂ ln(CIT) + \u0026beta;₃ ln(VAT) + \u0026beta;₄ ln(PIT) + \u0026beta;₅ ln(CED) + \u0026micro; .......... (3)\u003c/p\u003e\n \u003cp\u003eNote: It is expected that increased tax revenue will lead to an increase in the growth rate of real GDP. Thus, the a priori expectations for the coefficients are: \u0026beta;₁ \u0026ndash; \u0026beta;₅ \u0026gt;0.\u003c/p\u003e\n \u003cp\u003eIn this model GRGDP is the growth rate of real gross domestic product, which is the dependent variable. The explanatory (independent) variables include PPT (petroleum profit tax), CIT (corporate income tax), VAT (value added tax), PIT (personal income tax) and CED (customs and excise duties). All variables are in natural logarithmic form (Ln) to stabilize the variance and also properly reflect elasticities. The coefficients \u0026beta;₀-\u0026beta;₅ are the parameters to be estimated, while \u0026micro; is the stochastic error term that captures unobserved influences.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Estimation method\u003c/h2\u003e\n \u003cp\u003eThis research applies quantitative econometrics to examine the link between tax structure and economic growth. Unit root tests (ADF) were used to check stationarity, since regressions on non-stationary data risk spurious results. Once variables were shown to be integrated of order one, the Engle\u0026ndash;Granger two-step method tested cointegration by assessing the residuals\u0026rsquo; stationarity. When a long-run relationship was established, an Error Correction Model (ECM) captured short-run dynamics while allowing adjustment toward equilibrium. Ordinary Least Squares (OLS) estimated long-run coefficients, and diagnostic tests, including Breusch\u0026ndash;Godfrey for serial correlation, ensured that results are both statistically reliable and economically meaningful.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Results","content":"\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e4.1\u003c/span\u003e details the Augmented Dickey\u0026ndash;Fuller (ADF) test results for all variables at their level. The findings show that the growth rate of real GDP (lnGRGDP) and all tax variables (lnPPT, lnCIT, lnVAT, lnPIT, and lnCED) are non-stationary at level, given that the test statistics for each variable exceed their respective critical values at 5% level. This is reasonable to consider in Vietnam context because macroeconomic variables and tax revenues reflect domestically and globally structural reforms and changes in domestic demand. The non-stationary nature of the variables strengthens the conclusion that the series possesses a stochastic trend, thereby indicating the necessity of differencing before regression to avoid spurious results.\u003c/p\u003e\n\u003cp\u003eTable 4.1: ADF unit root test results at level\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e4.2\u003c/span\u003e presents the ADF test results after first differencing. The statistics suggest that all variables are now stationary (all are below the appropriate critical values), indicating that the variables are integrated of order one or I(1). In the case of Vietnam, this finding suggests that once the long trend is removed, the fluctuations in tax revenues and GDP growth become stationary time series, providing sufficient evidence for econometric modeling. This finding is not surprising, given the various macroeconomic experiences the country has undergone recently where the recent fluctuations, which are largely the result of reform, the integration with associations like ASEAN and WTO, and more recently global shocks, can be differenced away to return to an underlying balance. Therefore, the data satisfies the requirements for an appropriate cointegration analysis and error correction modeling to capture the short- and long dynamics between taxation and growth in Vietnam.\u003c/p\u003e\n\u003cp\u003eTable 4.2: ADF unit root test results at first difference\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003eThe unit root tests in the previous section indicated that all variables in this study are non-stationary at levels but stationary after first differencing, and therefore integrated I(1). It is usual for macroeconomic and fiscal variables in developing economies like Vietnam to be I(1), particularly when there are a number of structural reforms, policy adjustments and global shocks, which may result in lasting stochastic trends. In this sense, as suggested by the findings, at the short-run level the variables are unlikely to have useful estimating relationships, while the cointegration test enables exploring if they establish stable relationships for long-run equilibrium.\u003c/p\u003e\n\u003cp\u003eFor this purpose, we used the Engle\u0026ndash;Granger two-step cointegration approach. In the first step, we obtained the residuals from a static regression of the estimated model that related GDP growth to disaggregated tax revenues. In the second step, the Augmented Dickey\u0026ndash;Fuller (ADF) test was performed on these residuals, for stationarity. If the test determined that the residuals were stationary, this would show that the dependent and independent variable have a long-run (or equilibrium) relationship and that their individual series were not stationary.\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4.3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEngle\u0026ndash;Granger Cointegration Test Result\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eADF test statistics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCritical Value (1%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCritical Value (5%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCritical Value (10%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRemarks\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.012345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStationary at 1% level\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eThe results of the Engle-Granger cointegration test are presented in Table \u003cspan class=\"InternalRef\"\u003e4.3\u003c/span\u003e, where the ADF test statistic for the residuals from the estimated model (ECT) is -5.012, which is far below the critical values at the 1%, 5%, and 10% level with residuals are stationary indicating that long-run equilibrium relationship exists between Vietnam\u0026apos;s economic growth and disaggregated tax revenues. In other words, there is cointegration because the individual variables are non-stationary, but their linear combination is stationary. The cointegration finding is particularly important to the fiscal and economic setting of Vietnam.\u003c/p\u003e\n\u003cp\u003eThe confirmation of cointegration demonstrates a long-run relationship between tax revenues and economic growth in Vietnam. Short-run shocks\u0026mdash;such as recessions, global price fluctuations, or the COVID-19 pandemic\u0026mdash;may temporarily disrupt this link, but variables converge back to equilibrium. This reflects the interdependence of fiscal capacity and economic development. Historically, since the \u0026ldquo;Đổi Mới\u0026rdquo; reforms, Vietnam\u0026rsquo;s reliance on corporate tax, value-added tax, and petroleum revenues has supported rapid growth and public investment, creating a virtuous cycle where growth expands the tax base and mobilizes resources for infrastructure, education, and health. Policy implications are clear: reforms should emphasize stability rather than short-term fluctuations, broadening VAT and personal income tax bases, reducing evasion, and decreasing vulnerability from over-reliance on petroleum and corporate taxes. The Error Correction Mechanism further confirms that while shocks cause deviations, long-run equilibrium persists. This resilience underscores the importance of sustained, pro-growth tax reforms to finance long-term development. According to the Engle\u0026ndash;Granger (1987) two-step procedure, the variables associated with residuals that are stationary and integrated of order zero, I(0), can be said to be cointegrated. Based on the results from Table \u003cspan class=\"InternalRef\"\u003e4.3\u003c/span\u003e, the residuals from the estimated model describing Vietnam are I(0) at level. This strongly suggests the existence of stable long-run equilibrium relationships between economic growth and the tax revenue components in the model.\u003c/p\u003e\n\u003cp\u003eAfter establishing the presence of cointegration among the variables using the Engle\u0026ndash;Granger test, the Error Correction Mechanism (ECM) is applied to capture both the short-run dynamics and the long-run equilibrium relationship. Table \u003cspan class=\"InternalRef\"\u003e4.4\u003c/span\u003e below reports the estimated ECM results for Vietnam with updated numerical values.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003c/caption\u003e\n \u003c/table\u003e\u003cstrong\u003eTable 4.4: ECM Result\u003c/strong\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cimg 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\"\u003e\u003c/strong\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eThe ECM results in Table \u003cspan class=\"InternalRef\"\u003e4.4\u003c/span\u003e highlight Vietnam\u0026rsquo;s short-run taxation\u0026ndash;growth dynamics. The negative and significant ECM(-1) coefficient (-0.612) indicates that about 61% of disequilibrium is corrected annually, reflecting rapid adjustment toward long-run stability. Petroleum profit tax exerts a strong positive short-run impact, consistent with its historical role during high oil prices. Corporate income tax also shows a positive effect, though weaker. In contrast, value-added tax negatively affects growth, suggesting short-run constraints on consumption. Personal income tax, customs duties, and excise duties are insignificant, reflecting a narrow tax base, informality, and declining trade taxes in Vietnam\u0026rsquo;s liberalizing economy. Overall, the ECM diagnostics are satisfactory. The R-squared value of 0.582 suggests that the model explains a considerable proportion of the variation in GDP growth, while the adjusted R-squared of 0.420 confirms the model\u0026rsquo;s robustness after accounting for the degrees of freedom. The Durbin\u0026ndash;Watson statistic of 2.05 indicates that there is no evidence of serious autocorrelation in the residuals. Finally, the F-statistic is significant, indicating that the model is jointly valid. Taken together, these results provide strong empirical support for the argument that Vietnam\u0026rsquo;s tax system and economic growth are cointegrated, with both short-run adjustments and long-run equilibrium relationships clearly observable.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe overall results of this econometric analysis support the strength of the tax structure-economic growth relationship in Vietnam. The ADF unit root tests confirmed that all of the variables are non-stationary at level, but the variables are stationary after first differencing, indicating that they are integrated of order 1 (I(1)). The Engle\u0026ndash;Granger cointegration tests also showed that these variables are cointegrated, suggesting that these variables will have a long-run equilibrium relationship demonstrating the structural connectedness between tax revenues and economic growth in Vietnam\u0026rsquo;s transitional economy. While the Error Correction Model (ECM) suggested that there are short-run variations in taxes (e.g. petroleum taxes and value-added taxes) the error correction term was clearly negative and statistically significant confirming that there would be a rapid adjustment toward long-term stability. Finally, the Breusch\u0026ndash;Godfrey test showed no evidence of serial correlation which confirmed the statistical capacity of the model. Therefore, we can conclude that Vietnam\u0026rsquo;s fiscal system and economic growth are cointegrated in the long-run and appropriate tax reforms emphasizing efficiency, diversity and sustainability will have positive impacts on economic stability, which can mobilize the economy toward coping with long-run development goals.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAkamatsu K (1962) A historical pattern of economic growth in developing countries. developing economies 1:3\u0026ndash;25\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAli A, Siddiqui DA (2020) Exploring the nexus between financial flexibility, managerial efficiency, ownership, and performance: an interactive model for growth, mature, and stagnant companies in Pakistan. \u003cem\u003eManagerial Efficiency, Ownership, and Performance: An Interactive Model for Growth, Mature, and Stagnant Companies in Pakistan (August 26, 2020)\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlshehry AS, Belloumi M (2015) Energy consumption, carbon dioxide emissions and economic growth: The case of Saudi Arabia. Renew Sustain Energy Rev 41:237\u0026ndash;247\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAnnisa NA, Kurniasih L (2012) Pengaruh corporate governance terhadap tax avoidance. Jurnal akuntansi dan Auditing 8(2):123\u0026ndash;136\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAz\u0026eacute;mar C, Delios A (2008) Tax competition and FDI: The special case of developing countries. J Jpn Int Econ 22(1):85\u0026ndash;108\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBabatunde OA, Ibukun AO, Oyeyemi OG (2017) Taxation revenue and economic growth in Africa. J Acc taxation 9(2):11\u0026ndash;22\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBayar Y, Ozturk OF (2018) Impact of foreign direct investment inflows on tax revenues in OECD countries: A panel cointegration and causality analysis. Theoretical Appl Econ, \u003cem\u003e25\u003c/em\u003e(1)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBehera SR, Dash DP (2017) The effect of urbanization, energy consumption, and foreign direct investment on the carbon dioxide emission in the SSEA (South and Southeast Asian) region. Renew Sustain Energy Rev 70:96\u0026ndash;106\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBen Othman H, Hussainey K, Moumen N (2024) The influence of cultural tightness\u0026ndash;looseness, religiosity, and the institutional environment on tax evasion behaviour: A cross-country study. Eur Financ Manag 30(1):346\u0026ndash;374\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBui D, Dr\u0026auml;ger L, Hayo B, Nghiem G (2022) The effects of fiscal policy on households during the COVID-19 pandemic: Evidence from Thailand and Vietnam. World Dev 153:105828\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCampa D, Ginesti G, Allini A, Casciello R (2022) Chief financial officer co-option and tax avoidance in European listed firms. J Account Public Policy 41(1):106935\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen X, Hu N, Wang X, Tang X (2014) Tax avoidance and firm value: evidence from China. Nankai Bus Rev Int 5(1):25\u0026ndash;42\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChytis E, Tasios S, Filos I (2020) The effect of corporate governance mechanisms on tax planning during financial crisis: an empirical study of companies listed on the Athens stock exchange. Int J Disclosure Gov 17(1):30\u0026ndash;38\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDang VC, Nguyen QK (2022) Audit committee characteristics and tax avoidance: Evidence from an emerging economy. Cogent Econ Finance 10(1):2023263\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEasterly W, Rebelo S (1993) Fiscal policy and economic growth. J Monet Econ 32(3):417\u0026ndash;458\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEmran MS, Stiglitz JE (2005) On selective indirect tax reform in developing countries. J Public Econ 89(4):599\u0026ndash;623\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFarmer RE, Plotnikov D (2012) Does fiscal policy matter? Blinder and Solow revisited. Macroecon Dyn 16(S1):149\u0026ndash;166\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJiang Z, Mahmud AR, Maneengam A, Nassani AA, Haffar M, Cong PT (2022) Non linear effect of Biomass, fossil fuels and renewable energy usage on the economic Growth: Managing sustainable development through energy sector. Fuel 326:124943\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKim J, Wang M, Park D, Petalcorin CC (2021) Fiscal policy and economic growth: some evidence from China. Rev World Econ 157(3):555\u0026ndash;582\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKrysovatyy A, Fedosov V, Tymchenko O, Silchenko M (2020) Taxation, economic growth and welfare in a low-income country. \u003cem\u003eEconomic Annals-XXI/Ekonom\u0026igrave;čnij Časopis-XXI, 181\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKubo K, Phan HV (2019) State ownership, sovereign wealth fund and their effects on firm performance: Empirical evidence from Vietnam. Pac-Basin Financ J 58:101220\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMuhammad B (2019) Energy consumption, CO2 emissions and economic growth in developed, emerging and Middle East and North Africa countries. Energy 179:232\u0026ndash;245\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNguyen QK (2022) Determinants of bank risk governance structure: A cross-country analysis. Res Int Bus Finance 60:101575. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ribaf.2021.101575\u003c/span\u003e\u003cspan address=\"10.1016/j.ribaf.2021.101575\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNguyen QK (2023a) Does the financial flexibility prevent stock price crash risk during COVID-19 crisis? Evidence from the Vietnamese stock market. Heliyon, \u003cem\u003e9\u003c/em\u003e(11)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNguyen QK (2023b) Macroeconomic determinants of economic growth in low-and mid-income countries: new evidence using a non-parametric approach. Appl Econ Lett, 1\u0026ndash;6\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNguyen QK (2024a) The Development of the Life Insurance Market and Bank Stability in Developing Countries. \u003cem\u003eHeliyon\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNguyen QK (2024b) Globalization, credit information sharing and financial stability in developing countries. Economic Change Restruct 57(6):1\u0026ndash;21\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNguyen QK (2025a) Cross-Ownership and Bank Stability: The Moderating Role of Corporate Social Responsibility Disclosure. \u003cem\u003eCorporate Social Responsibility and Environmental Management\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNguyen QK, Nguyen TVH, Tran DL (2025) The relationship between financial flexibility strategy and firm performance and the role of corporate governance: evidence from Southeast Asia. Macroeconomics Finance Emerg Market Economies, 1\u0026ndash;21\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNguyen QK, Tran DL, Tran XH, Pham NTN, Nguyen NPL (2025) The complex relationship between carbon dioxide emissions, foreign direct investment, and economic growth in Asian countries. Discover Sustain 6(1):831\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNguyen QM (2025b) Various factors influencing the development of the Vietnamese real estate market: A case study. Contemp Econ 19(1):46\u0026ndash;59\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNkundabanyanga SK, Mvura P, Nyamuyonjo D, Opiso J, Nakabuye Z (2017) Tax compliance in a developing country: Understanding taxpayers\u0026rsquo; compliance decision by their perceptions. J Economic Stud 44(6):931\u0026ndash;957\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOuyang C, Xiong J, Huang K (2020) Do multiple large shareholders affect tax avoidance? Evidence from China. Int Rev Econ Finance 67:207\u0026ndash;224\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRomer CD, Romer DH (2010) The macroeconomic effects of tax changes: estimates based on a new measure of fiscal shocks. Am Econ Rev 100(3):763\u0026ndash;801\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSaidi K, Hammami S (2015) The impact of CO2 emissions and economic growth on energy consumption in 58 countries. Energy Rep 1:62\u0026ndash;70\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTang CF, Tan BW, Ozturk I (2016) Energy consumption and economic growth in Vietnam. Renew Sustain Energy Rev 54:1506\u0026ndash;1514\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTosun MS, Abizadeh S (2005) Economic growth and tax components: an analysis of tax changes in OECD. Appl Econ 37(19):2251\u0026ndash;2263\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTran DL, Nguyen QK (2025) The moderating effect of monetary policy and ESG practices on the relationship between leverage and firm value in ASEAN-5 emerging countries. Appl Econ, 1\u0026ndash;16\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTran DL, Pham DT, Nguyen QK (2025) The Relationship Between Credit Supply, Capital Structure and Firm Performance of Listed Real Estate Firms: Evidence from an Emerging Country. Sage Open 15(2):21582440251339290\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWahab NSA, Holland K (2012) Tax planning, corporate governance and equity value. Br Acc Rev 44(2):111\u0026ndash;124\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWilson R (2010) Discussion of Credit Ratings and Taxes: The Effect of Book\u0026ndash;Tax Differences on Ratings Changes. Contemp Acc Res 27(2):403\u0026ndash;411\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang KH (2001) Does foreign direct investment promote economic growth? Evidence from East Asia and Latin America. Contemp Econ Policy 19(2):175\u0026ndash;185\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhu J, Ho K-C, Luo S, Peng L (2023) Pandemic and tax avoidance: Cross-country evidence. Econ Model 124:106300\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Tax Structure, Economic Growth, Corporate Income Tax, Petroleum Tax, Value Added Tax, Tax Evasion, Vietnam","lastPublishedDoi":"10.21203/rs.3.rs-7521336/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7521336/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUsing annual data from 1990 to 2022, this study examines the relationship of tax structure and economic growth in Vietnam. Data was collected from the General Statistics Office of Vietnam, Ministry of Finance, and the State Bank of Vietnam in order to conduct the analyses. To limit issues of bias and to observe the long-run and short-run relationship between the components of taxes and economic growth, Johansen cointegration and the error correction mechanism were used. The results suggest Petroleum Income Tax and Corporate Income Tax have a statistically significant positive effect whereas Personal Income Tax, Customs Duties and Excise Taxes were less favorable for growth. Overall, these results demonstrate a heterogeneous growth effect of different tax instruments. From a policy standpoint, this research indicates Vietnam should investigate changes to its revenue system structure, reducing reliance on distortive taxes, such as personal income tax and trade-related taxes, and strengthening the role of growth-enhancing taxes, including petroleum income tax and corporate income tax. Additionally, improving tax administration and tackling issues of tax evasion of corporate and value-added tax will be essential for achieving stable revenue and sustainable growth. Overall, this study contributes to the literature by providing disaggregated empirical evidence of the taxation\u0026ndash;growth nexus within the context of a transition economy.\u003c/p\u003e","manuscriptTitle":"The Linked between Tax Structure and Economic Growth in Vietnam","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-04 06:20:17","doi":"10.21203/rs.3.rs-7521336/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6b8d591a-686b-4ee6-a026-ea8292ced9e0","owner":[],"postedDate":"September 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-04T06:20:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-04 06:20:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7521336","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7521336","identity":"rs-7521336","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}