


































American Interdisciplinary Journal of Business and 
Economics (AIJBE). 
ISSN: 2837-1844| Impact Factor : 7.76 

Volume. 11, Number 2; April-June, 2024; 

Published By: Scientific and Academic Development Institute (SADI) 

8933 Willis Ave Los Angeles, California 

https://sadijournals.org/index.php/AIJBE|editorial@sadijournals.org 
 

 

78 
American Interdisciplinary Journal of Business and Economics | 

https://sadijournals.org/index.php/AIJBE 
 

DEVELOPMENTAL IMPACT OF TAXATION ON THE ECONOMIC 
PERFORMANCE OF SELECTED DEVELOPED ECONOMIES AROUND 

THE WORLD. 
 

 
1Seun Adebanjo, 2Emmanuel Banchani, 3Solberg Horve Mishiwo and 4Benjamin Adejumo 

1Statistical training and consultation, Nigeria. 
2Department of Sociology, St. Francis Xavier University, Antigonish, Canada. 

3University of Cape Coast, Ghana. 
4Western Covenant University, 3333 Wilshire Blvd #700, Los Angeles, CA, USA 

                         seunadebanjo9@gmail.com/ eb1043@mun.ca/ Solberg.mishiwo@stu.ucc.edu.gh  

 benjamin@wcuniversity.edu 

DOI: https://doi.org/10.5281/zenodo.11617509 

Abstract: Taxation, the primary component of fiscal policy, definitely influences economic production because, 

in industrialized nations, the government uses the money from taxpayers to build fundamental infrastructure like 

reliable electricity, well-maintained roads, and water supplies. Therefore, the aim of this study is to investigate 

the developmental effect of taxes on the economic performance of developed economies around the world. Panel 

VAR application revealed a short-term correlation between taxation and the economic performance of 

industrialized countries. While the fitted FMOLS reveals a significant positive impact of taxation and foreign 

direct investment (FDI) on the long-term economic performance of developed nations, suggesting that higher 

levels of taxation and FDI return contribute to greater economic performance in the world’s developed economies, 

the Hausman test specifies a random-effect regression model that confirms the significant positive impact of 

taxation and GNI on economic performance. As a result, the governments of industrialized nations should keep 

putting in place a sustainable tax system that is alluring enough to raise tax payments and promote the continuation 

of FDI and GNI growth, which would improve economic performance both now and in the future. 

Keywords: Taxation, Economic performance, Panel VAR, Hausman test, FMOLS. 

 

Introduction 

Taxation implementation has had a significant impact on the economic growth of industrialized nations such as 

the United States, Canada, and Australia. Academic researchers are inclined to conduct studies that focus on the 

determinants driving economic advancement. Taxation is a highly scrutinized element due to its significant impact 

on a nation’s economic policies (Shahmoradi et al., 2019). There are typically two main classifications of taxation 

that are commonly acknowledged: direct and indirect. Direct taxation refers to the imposition of taxation on the 

income and activities of taxpayers, which are then paid directly to the government. Transferring the direct tax 

mailto:seunadebanjo9@gmail.com/
mailto:eb1043@mun.ca/
mailto:Solberg.mishiwo@stu.ucc.edu.gh
mailto:benjamin@wcuniversity.edu%20/
https://doi.org/10.5281/zenodo.11617509


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burden to another individual is not feasible. Indirect taxation refers to the imposition of tax obligations on products 

and services that can be transferred to a third- party. According to a prior study conducted by Thaçi and Gerxhaliu 

(2018), in developing countries, the revenue generated by taxes on foreign commerce surpasses that generated by 

direct taxation. Developing countries encounter greater challenges in establishing their tax systems and allocating 

domestic resources than wealthy nations. In contrast, industrialized nations generate a greater amount of money 

through direct taxation mechanisms such as income taxation, social insurance contributions and consumer 

taxation. 

The existing body of literature has presented conflicting findings regarding the impact of taxation on economic 

growth. Domestic taxation, such as tariffs on goods and services, has contributed to the increase in GDP growth 

for both affluent and developing countries (Maganya, 2020; Mdanat et al., 2018; Vintilă et al., 2021). Contrary to 

popular belief, Thaçi and Gerxhaliu (2018) assert that a negative association exists between taxation and economic 

growth in developing nations. Moreover, it has been observed that there exists a positive and statistically 

significant relationship between income tax rates and GDP in developed countries, although this relationship is 

not observed in developing countries (Shahmoradi et al., 2019). Further investigation is required to 

comprehensively comprehend the influence of taxation on economic development in developed countries, 

considering the conflicting findings. 

According to Korkmaz et al. (2019) and Nguyen (2019), empirical studies conducted in developing countries 

have provided evidence supporting the positive impact of indirect taxation on economic growth. Tariffs and 

domestic goods and services taxes are two additional tax forms that have a direct impact on a nation’s economic 

growth (Maganya, 2020; Mdanat et al., 2018). 

The empirical analysis conducted by Göndör and Îzpençe (2014) examined the fiscal policies of Romania and 

Turkey throughout the crisis period. The authors presented empirical evidence to substantiate their assertion, 

illustrating the ineffectiveness of procyclical fiscal policy in mitigating GDP shocks. Given the study’s emphasis 

on the cyclical patterns of macroeconomic aggregates, it is important to approach the conclusions with caution. 

Consequently, the outcome only presents notions on the underlying factors driving fiscal policy acts and their 

impact on macroeconomic activity. Korkmaz et al. (2019) arrived at comparable findings in their examination of 

the Turkish economy, indicating a significant and adverse correlation between taxation and economic growth. 

Several studies conducted by Abdioglu et al. (2016), Ajetunmobi et al. (2019), Mohs et al. (2018), and Shafiq et 

al. (2021) have provided evidence that foreign direct investment (FDI) in developed nations is influenced by the 

taxation policies of the host country. These findings suggest that taxation plays a significant role in shaping 

investment patterns. Ahmad and Sial (2016) conducted a study utilizing annual time series data spanning 1974-

2010 to examine the relationship between total tax receipts and economic growth. The autoregressive distributed 

lag (ARDL) bounds testing approach was employed to predict the long- and short-term relationships between the 

variables to assess co-integration. The results indicate that aggregate tax revenues have a lasting, adverse, and 

substantial influence on the expansion of the economy. Research indicates that a 1% increase taxation will lead 

to a 1.25% decline in economic growth. In their study on developing nations, Thaçi and Gerxhaliu (2018) provided 

empirical evidence supporting the existence of a negative association between taxation and economic growth. 

Shahmoradi et al. (2019) observed a significant and negative correlation between the ratio of tax collections to 

GDP and industrialized nations. Research has shown that direct taxation has a favorable impact on economic 

growth, but the consequences of indirect taxation remain uncertain (Hakim, 2020; Korkmaz et al., 2019). Research 

conducted in developing countries suggests that the implementation of indirect taxation has a favorable impact 



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on overall economic growth. According to Nguyen (2019), direct taxation has an indirect effect. Integrating both 

direct and indirect taxes into a unified study would provide further evidence to validate or disprove the previous 

research's conclusion of inconsistency. 

Two more indicators of economic development are employment and investment. The existing body of literature 

on investments has presented divergent findings on the influence of taxation on investment outcomes. Several 

studies (Abdioglu et al., 2016; Ajetunmobi et al., 2019; Mohs et al., 2018; Shafiq et al., 2021) have found that 

taxation has an impact on investment in both developed and developing countries. In contrast, alternative research 

conducted by Goodspeed et al. (2011) and Mercer-Blackman & Camingue-Romance (2020) yielded conflicting 

results when examining different country cohorts. Furthermore, various types of taxation can potentially have 

distinct impacts on investment outcomes (Appiah-Kubi et al., 2021). 

In addition, endogenous growth theory and neoclassical growth theory serve as the foundation for this 

investigation. According to Solow and Swan’s neoclassical growth theory, taxes have no impact on the steady 

state. That is to say, taxes have no long-term impact on economic growth. Romer’s endogenous growth theory, 

on the other hand, contends that taxes on economic growth can impact it over time (Romer et al., 2010). 

By examining the developmental impact of taxation on the economic performance of a few global economies, 

such as Canada, the United States, and Australia, and accounting for inflation, foreign direct investment (FDI), 

and gross national income (GNI) using the most recent annual data from 1999 to 2023, this study will make a 

unique contribution to closing the gap left by earlier research. 

Materials and Methods 

This study used secondary data, specifically a panel of recent periods from 1990 to 2023, from three developed 

countries: Canada, the United States, and Australia. These countries were selected on the basis of their economic 

performance and consistent data availability among the world’s developed economies. The econometrics 

approach, suitable for the panel, included Panel Var, the Hausman test to specify either a fixed effect or a random 

effect estimator, and FMOLS. We also performed diagnostic checks, such as the normality of the residuals and 

the endogeneity test, to assess the accuracy and validity of the econometrics model. 

Panel VAR 

The proposed panel VAR model helps to examine the short-run connection between the variables of interest and 

it is given by 

𝑌𝑖𝑡 = 𝜇𝑖 + 𝐵(𝑀)𝑌𝑖𝑡 + 𝛼𝑖 + 𝛿𝑡 + 휀𝑖𝑡                                                       (1)                                               

Where 𝑌𝑖𝑡 represents the vector of the endogenous stationary series, such as gross domestic product (GDP), 

Taxation (Tax), Inflation (Inf), Foreign direct investment (FDI), and gross national income (GNI) while 𝜇𝑖 

represents the matrix of country-specific fixed effects. The subscripts, defined as i and t, refer to country and time, 

respectively. 𝐵(𝑀) denotes the matrix polynomial in the lag operator with 𝐵(𝑀) = 𝐵1𝑀1 + 𝐵2𝑀2 + ⋯ + 𝑀𝑝𝐿𝑝, 

𝛼𝑖 indicates the vector that determines the specific effects of the country found in this regression, 𝛿𝑡 represents 

the dummy variables for the country’s specific time and 휀𝑖𝑡 denotes the residual vector. 

The matrix form of the proposed VAR in equation 1 treated as endogenous can be expressed as follows: 



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∆(𝑙𝑛𝐺𝐷𝑃𝑖𝑡) = 𝜇1𝑖 + ∑ 𝛼1𝑗

𝑝

𝑗=1
∆(𝑙𝑛𝐺𝐷𝑃𝑖𝑡−𝑗) + ∑ 𝑏1𝑗

𝑝

𝑗=1
∆(𝑇𝑎𝑥𝑖𝑡−𝑗) + ∑ 𝑐1𝑗

𝑝

𝑗=1
∆(𝐼𝑛𝑓𝑖𝑡−𝑗)

+ ∑ 𝑑1𝑗

𝑝

𝑗=1
∆(𝐹𝐷𝐼𝑖𝑡−𝑗) + ∑ 𝑒1𝑗

𝑝

𝑗=1
∆(𝐺𝑁𝐼𝑖𝑡−𝑗) + 𝛼1𝑖 + 𝛿1𝑡

+ 휀1𝑖𝑡                                                          (2) 

∆(𝑇𝑎𝑥𝑎𝑡𝑖𝑜𝑛𝑖𝑡)

= 𝜇2𝑖 + ∑ 𝛼2𝑗

𝑝

𝑗=1
∆(𝑙𝑛𝐺𝐷𝑃𝑖𝑡−𝑗) + ∑ 𝑏2𝑗

𝑝

𝑗=1
∆(𝑇𝑎𝑥𝑖𝑡−𝑗) + ∑ 𝑐2𝑗

𝑝

𝑗=1
∆(𝐼𝑛𝑓𝑖𝑡−𝑗)

+ ∑ 𝑑2𝑗

𝑝

𝑗=1
∆(𝐹𝐷𝐼𝑖𝑡−𝑗) + ∑ 𝑒2𝑗

𝑝

𝑗=1
∆(𝐺𝑁𝐼𝑖𝑡−𝑗) + 𝛼2𝑖 + 𝛿2𝑡

+ 휀2𝑖𝑡                                                          (3) 

∆(𝐼𝑛𝑓𝑖𝑡) = 𝜇3𝑖 + ∑ 𝛼3𝑗

𝑝

𝑗=1
∆(𝑙𝑛𝐺𝐷𝑃𝑖𝑡−𝑗) + ∑ 𝑏3𝑗

𝑝

𝑗=1
∆(𝑇𝑎𝑥𝑖𝑡−𝑗) + ∑ 𝑐3𝑗

𝑝

𝑗=1
∆(𝐼𝑛𝑓𝑖𝑡−𝑗)

+ ∑ 𝑑3𝑗

𝑝

𝑗=1
∆(𝐹𝐷𝐼𝑖𝑡−𝑗) + ∑ 𝑒3𝑗

𝑝

𝑗=1
∆(𝐺𝑁𝐼𝑖𝑡−𝑗) + 𝛼3𝑖 + 𝛿3𝑡

+ 휀3𝑖𝑡                                                          (4) 

∆(𝐹𝐷𝐼𝑖𝑡) = 𝜇4𝑖 + ∑ 𝛼4𝑗

𝑝

𝑗=1
∆(𝑙𝑛𝐺𝐷𝑃𝑖𝑡−𝑗) + ∑ 𝑏4𝑗

𝑝

𝑗=1
∆(𝑇𝑎𝑥𝑖𝑡−𝑗) + ∑ 𝑐4𝑗

𝑝

𝑗=1
∆(𝐼𝑛𝑓𝑖𝑡−𝑗)

+ ∑ 𝑑4𝑗

𝑝

𝑗=1
∆(𝐹𝐷𝐼𝑖𝑡−𝑗) + ∑ 𝑒4𝑗

𝑝

𝑗=1
∆(𝐺𝑁𝐼𝑖𝑡−𝑗) + 𝛼4𝑖 + 𝛿4𝑡

+ 휀4𝑖𝑡                                                          (5) 

∆(𝐺𝑁𝐼𝑖𝑡) = 𝜇5𝑖 + ∑ 𝛼5𝑗

𝑝

𝑗=1
∆(𝑙𝑛𝐺𝐷𝑃𝑖𝑡−𝑗) + ∑ 𝑏5𝑗

𝑝

𝑗=1
∆(𝑇𝑎𝑥𝑖𝑡−𝑗) + ∑ 𝑐5𝑗

𝑝

𝑗=1
∆(𝐼𝑛𝑓𝑖𝑡−𝑗)

+ ∑ 𝑑5𝑗

𝑝

𝑗=1
∆(𝐹𝐷𝐼𝑖𝑡−𝑗) + ∑ 𝑒5𝑗

𝑝

𝑗=1
∆(𝐺𝑁𝐼𝑖𝑡−𝑗) + 𝛼5𝑖 + 𝛿5𝑡

+ 휀5𝑖𝑡                                                          (6) 

Hausman Test 

The Hausman test, also known as the Hausman specification test, identifies endogenous regressors within a 

regression model. We identify these regressors as predictor factors that correlate with the error term. Other factors 

in the system to which endogenous variables belong have an impact. Ordinary least-squares estimators assume 

uncorrelated error terms and predictor variables, which restricts their usefulness in models with endogenous 

regressors. In this case, instrumental variable estimators are a useful choice. Finding the endogeneity of the 

predicting variables is essential before selecting the optimal regression strategy. Using the Hausman test, one can 

determine whether an estimator is more suitable for a given regression model by assessing the statistical 

significance of the difference between the two estimators (Zulfikar and STp, 2018). This diagnostic technique is 

often used to identify possible issues in model definition. Panel data analysis uses the Hausman test to determine 

which fixed effects (FE) and random effects (RE) models to use. As to Adebanjo and Morufu (2022), the 

alternative hypothesis holds that the preferred model includes fixed effects, whereas the null hypothesis holds that 

random effects exist. The main goal of the tests was to determine the relationship between the specific errors 



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noted and the predictors in the model. The premise that there is no association between the error term and the 

predictor variables implies that endogeneity has no effect on the model. 

The Hausman statistic can be computed as follows: 

𝐻 = (�̂�𝑅𝐸 − �̂�𝐹𝐸)
′
[𝑉𝑎𝑟(�̂�𝑅𝐸) − 𝑉𝑎𝑟(�̂�𝐹𝐸)]

−1
(�̂�𝑅𝐸 − �̂�𝐹𝐸)                                                      (7) 

The hypothesis is therefore stated as follows: 

H0: Select RE (p> 0.05) 

H1: Select FE (p <0.05) 

The generalized model in panel data analysis considers individual intercepts to evaluate the connection between 

variables. This formula clarifies the intricate relationships between factors both inside and between businesses, 

thus facilitating a more thorough comprehension of the factors affecting economic performance. There are several 

ways to express the equation: 

𝑦𝑖𝑡 = 𝛼𝑖 + 𝛽′𝑋𝑖𝑡 + 휀𝑖𝑡  ;     𝑖 = 1,2, … , 𝑁 𝑎𝑛𝑑 𝑡 = 1,2, … , 𝑇.                                                           (8) 

Where N = number of individuals or cross-section and T = the number of periods. 

According to Shahmoradi et al. (2019), the panel model can take the following form: 

  

𝑙𝑛𝐺𝐷𝑃 𝑖𝑡  =  𝛼 +  𝛽1(𝑇𝑎𝑥)𝑖𝑡  + 𝛽2(𝐼𝑛𝑓)𝑖𝑡 + 𝛽3(𝐹𝐷𝐼)𝑖𝑡 + 𝛽4(𝐺𝑁𝐼)𝑖𝑡

+ 휀𝑖𝑡                                                                                                                                  (9) 

The dependent variable in this study is the economic performance of the three selected developed economies of 

the world, which is measured by their gross domestic product (GDP). We used the natural logarithm of the 

dependent variable to improve the performance of the model. The main independent variable is taxation, which 

is measured by tax revenue, whereas the control variables include inflation, foreign direct investment, and gross 

national income, which could also influence the developmental impact of taxation on economic performance. The 

coefficient estimates of the independent variables are β1 to β4. The random error term, denoted as εit, the with unit 

represents the panel unit, which represents the selected developed economies of the world, and the time in years, 

denoted as t. 

 FMOLS Estimator 

Developed by Phillips and Hansen to provide an optimal co-integrating regression estimation, the fully modified 

least square (FMOLS) (Olofin et al., 2019). In contrast, this study used the Pedroni heterogeneous FMOLS 

estimator for panel co-integration regression. This estimator can eliminate serial correlation and endogeneity bias. 

FMOLS works well with the panel because it helps determine whether the long-term effects of the predictor 

variables are positive or negative on the dependent variable and takes into account heterogeneous cointegration 

(Rahman et al., 2021). Thus, we can express the panel FMOLS estimator (β) of the coefficient as follows: 

𝛽∗ − 𝛽 = (∑ 𝐿22𝑖
−2

𝑁

𝑖=1

∑(𝜒𝑖𝑡 − �̅�𝑖𝑡)2

𝑇

𝑖=1

) ∑ 𝐿11𝑖
−1 𝐿22𝑖

−2

𝑁

𝑖=1

(∑(𝜒𝑖𝑡 − �̅�𝑖𝑡)𝜇𝑖𝑡
∗ − 𝑇�̂�𝑖

𝑇

𝑖=1

)                         (10) 

Where; 

𝜇𝑖𝑡
∗ = 𝜇𝑖𝑡 −

�̂�21𝑖

�̂�22𝑖

Δ𝜒𝑖𝑡, 𝛾𝑖 = Γ̂21𝑖Ω̂21𝑖
0 −

�̂�21𝑖

�̂�22𝑖

(Γ̂22𝑖 + Ω̂22𝑖
0 )                             

And �̂�𝑖 was the lower triangulation of Ω̂𝑖. 

https://www.tandfonline.com/doi/full/10.1080/23322039.2022.2141423


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The asymptotic distribution of the dynamic OLS estimator was the same as that of Pedroni’s panel FMOLS 

estimate (Pradhan, 2016). As demonstrated, FMOLS estimations were carried out to verify the consistency of the 

results. The endogenous growth theory of Romer indicates that taxes may have a long-term impact on economic 

growth (Romer et al., 2010). Thus, the following can be the supporting hypothesis for the endogenous growth 

theory: 

H1: Taxation has a positive long-run effect on the economic performance of selected developed economies 

around the world. 

Table 1: Variable definitions, measurements, and sources 

Variables Definition Measurement Sources 

GDP GDP is a metric used to 

determine how much 

money is spent overall on 

all goods and services 

produced over a given 

time within a nation. GDP 

stands for the general 

economic performance of 

a nation. 

Billions of US$ World Bank, Statista, 

Countryeconomy.com 

Taxation Taxation is a crucial 

component of fiscal policy 

as it functions as a 

mechanism for 

governments to produce 

income and foster 

economic performance 

(Korkmaz et al., 2019). 

% of GDP World Bank, 

www.ceicdata.com 

Inflation Inflation is a phenomenon 

that refers to the rise in the 

general level of prices of 

commodities and services 

because of the large 

amount of money in 

circulation. When 

gasoline and oil prices 

rise, inflationary pressures 

typically follow. 

Percentage (%) World Bank 

FDI A foreign direct 

investment (FDI) is the 

acquisition of ownership 

in a foreign company or 

project by an investor, 

company, or government 

organization coming from 

a different country. By 

creating long-lasting, safe 

links between economies, 

foreign direct investment 

Billions of US$ World Bank 

https://data.worldbank.org/indicator/GC.TAX.TOTL.GD.ZS?locations=AU
https://data.worldbank.org/indicator/GC.TAX.TOTL.GD.ZS?locations=AU
https://data.worldbank.org/indicator/GC.TAX.TOTL.GD.ZS?locations=AU
https://data.worldbank.org/indicator/GC.TAX.TOTL.GD.ZS?locations=AU


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(FDI) is essential to 

promote global economic 

integration. 

GNI Gross National Income 

(GNI) refers to the 

aggregate monetary 

earnings generated by 

individuals and 

enterprises within a given 

nation. It is employed to 

quantify and monitor a 

country’s economic 

prosperity over successive 

years. The calculation of 

GNI involves adding the 

income from foreign 

sources to GDP. 

Billions of US$ World Bank 

Source: Author’s computation 

 

 

 

 

 

 

Results 

Table 2: Descriptive Statistics 

 GDP TAXATION INFLATION FDI GNI 

 Mean  5423.659  15.55996  2.535802  100.1701  5415.688 

 Median  1554.749  13.25800  2.269150  48.57084  1520.700 

 Maximum  27356.40  29.20000  8.002800  511.4340  27560.00 

 Minimum  311.4205  7.903518 -0.355500 -25.09314  295.9904 

 Std. Dev.  7007.169  5.352293  1.481598  127.2057  7090.685 

 Observations  102  102  102  102  102 

Source: Author’s computation 

Table 2 shows that the average GDP of the three selected developed economies of the world is approximately 

US$5424 billion with a variability of approximately US$7007 billion during the period under review; the average 

taxation is approximately 15.6% of GDP with a variability of approximately 5.4% of GDP; the average inflation 

rate is approximately 2.5% with a variability of approximately 1.5%; the average FDI is approximately US$100 

billion with a variability of approximately 127 billion US dollars; and the average GNI is approximately US$5416 

billion with a variability of approximately 7091 billion US dollars. 

Table 3: Panel VAR 

     
      Coefficient Std. Error t-Statistic Prob.   

     

https://data.worldbank.org/indicator/GC.TAX.TOTL.GD.ZS?locations=AU


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     lnGDP C (1) 1.007985 0.010684 94.34943 0.0000 

            C (2) 0.004157 0.002798 1.485704 0.1380 

            C (3) 0.011811 0.007066 1.671616 0.0953 

            C (6) -0.101077 0.115450 -0.875509 0.3817 

Tax      C (7) -0.050260 0.209249 -0.240191 0.8103 

            C (8) 1.029766 0.027297 37.72425 0.0000 

            C (11) 4.40E-05 3.72E-05 1.182653 0.2375 

            C (12) -0.200369 1.698740 -0.117952 0.9062 

 Inf      C (13) 0.055339 0.166248 0.332870 0.7394 

            C (15) 0.356041 0.087329 4.077020 0.0001 

            C (16) 0.000222 0.001841 0.120591 0.9041 

            C (18) 1.101120 1.166850 0.943669 0.3458 

 FDI    C (19) 3.855047 10.34953 0.372485 0.7097 

           C (22) 0.289386 0.099692 2.902806 0.0039 

           C (23) 0.011455 0.002551 4.489842 0.0000 

           C (24) -15.62439 69.77611 -0.223922 0.8229 

GNI    C (25) -34.78782 50.49331 -0.688959 0.4912 

           C (28) -1.360146 0.486376 -2.796488 0.0054 

           C (29) 1.077280 0.012448 86.54449 0.0000 

           C (30) 239.5507 340.4239 0.703684 0.4820 

     
     
Source: Author’s computation 

Table 3 shows that the first lag of lnGDP is statistically significant at the 1% level, the second lag of taxation is 

statistically significant at the 1% level, the second lag of inflation is statistically significant at the 1% level, the 

second and third lags of FDI are statistically significant at the 1% level, and the second and third lags of GNI are 

statistically significant at the 1% level, implying that at least one of the series is statistically significant. This 

suggests a short-run relationship between the series consisting of GDP (which is a proxy for economic 

performance), taxation, inflation, FDI, and GNI. 

Table 4: Correlated Random Effects: Hausman Test 

     
     

Test Summary 

Chi-Sq. 

Statistic 

 Chi-Sq  

(d. f) Prob.  

     
     Cross-section random 0. 4386 4 0.7140 

     
          

Cross-sectional random effect test comparisons: 

     

Variable Fixed (A)   Random (B)  Diff(A-B)  Prob.  

     
     TAXATION 0.01787 0.08905 -0.07118 0.6148 



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INFLATION -0.00713 -0.01217 0.00504 0.5311 

FDI                              0.00159 0.00119 0.00040 0.3359 

GNI 0.00006 0.00013 -0.00124 0.0661 

     
Source: Author’s computation 

Table 4 shows that the Hausman test result (P = 0.7140) indicates that the random-effect estimator is suitable for 

the analysis of panel data. Meanwhile, none of the independent variables are statistically significant for the 

random effect comparison to identify the ones with endogeneity issues, indicating that none of the explanatory 

variables suffer from the endogeneity problem. This suggests that the specified random-effects model by the 

Hausman test does not have the problem of endogeneity. 

Table 5: Random-effects regression 

     
     lnGDP Coefficient Std. Error t-Statistic Prob.   

     
     C 7.76129 0.222045 34.95 0.000 

TAXATION 0.08905 0.012011 4.90 0.000 

INFLATION -0.01217 0.036164 -0.34 0.736 

FDI 0.00119 0.000929 1.29 0.198 

GNI 0.00013 .0.000018 7.31 0.000 

     
 

 

    

R-squared        

 

 

 

0.898 

 
   

Adj. R-Squared  0.891    

F-statistic    138.84    

Prob (F-statistic) 

00000.00 

0.000    

     
     

Source: Author’s computation 

 

Table 5 indicates the overall P = 0.000 for the random-effects model, indicating a significant relationship between 

taxation and economic performance while controlling for inflation, FDI, and GNI. According to the model results, 

the coefficient estimates of taxation and GNI are statistically significant at the 1% level and have a significant 

positive effect on economic performance. This means that a high level of taxation and GNI will lead to better 

economic performance in the world’s developed economies. This supports the work of Korkmaz et al. (2019) and 

Nguyen (2019), who found that taxation has a positive effect on economic growth. The R-squared of 0.898 

suggests that taxation, inflation, FDI, and GNI account for 89.8% of the variation in economic performance. 

Table 6: Panel Fully Modified Least Squares (FMOLS) 

      
      lnGDP Coefficient Std. Error t-Statistic Prob.   VIF 

      
      TAXATION 0.042948 0.060307 0.712160 0.0482  1.170193 

INFLATION -0.000601 0.062403 -0.009631 0.9923  1.109935 



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FDI 0.002731 0.001451 1.882798 0.0329  2.228070 

GNI 3.17E-05 3.52E-05 0.901841 0.3695  2.142718 

      
      R-squared 0.896082    

Adjusted R-squared 0.889305    

S.E. of regression 0.454215    

Long-run variance 0.585706     

      
      
Source: Author’s computation 

Table 6 shows that the coefficient estimate of taxation and FDI has a long-term significant positive effect on 

developed nations’ economic performance at the 5% level. This means that, in the long run, a high level of taxation 

and FDI will help developed nations do better economically. This supports the research hypothesis and 

endogenous growth theory, but it contradicts the work of Shahmoradi et al. (2019), which states that taxation has 

a significant negative effect on the economic performance of developed nations in the long run. 

Figure 1 shows the graph of GDP for the three selected developed economies of the world, and we can see that 

the United States demonstrated the highest GDP level among the developed nations under review. The figure 2 

shows the graph of taxation for the three selected developed nations under review. Australia demonstrated the 

highest taxation proxy by tax revenue among the three developed economies of the world and figure 3 shows the 

normality test for the residual of the fitted FMOLS using the Jarque-Bera test. The result shows that P > 0.05, 

suggesting that the model’s residual is normally distributed. 

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Figure 1: GDP of selected developed economies during the period under review 

 



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Series: Residuals

Sample 1991 2023

Observations 99

Mean      -0.017451

Median   0.049400

Maximum  1.626933

Minimum -0.956131

Std. Dev.   0.439741

Skewness   0.180208

Kurtosis   3.652413

Jarque-Bera  2.291612

Probability  0.317968


 
Figure 3: Normality test for the residuals of FMOLS 

 

Conclusion 

Taxation, a fundamental component of fiscal policy, undeniably exerts an influence on economic production due 

to the allocation of taxpayer funds, particularly in industrialized nations, toward the provision of essential 

infrastructure such as reliable electricity, well-maintained roads and sufficient water supply, among other 

necessities. Hence, the objective of this research is to examine the extent to which taxes influence the economic 

performance of developed economies globally. The analysis reveals a correlation between taxation and the 

economic success of developed nations in the near term. The analysis’s findings suggest that taxation and foreign 



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direct investment (FDI) have a statistically significant positive relationship with developed nations’ long-term 

economic performance. This implies that higher levels of taxation and increased returns on foreign direct 

investment contribute to enhanced economic performance in developed economies worldwide. Therefore, 

governments in developed economies should persist in implementing a sustainable tax policy that is sufficiently 

appealing to augment tax revenue and foster the sustained expansion of foreign direct investment (FDI) and gross 

national income (GNI). This, in turn, would improve economic performance in both the short and long term. 

 

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