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Article

Government Revenue Structure and Fiscal Performance in the G7: Evidence from a Panel Data Analysis

Department of Business Administration, Dunărea de Jos University, 800008 Galați, Romania
World 2025, 6(3), 97; https://doi.org/10.3390/world6030097
Submission received: 25 April 2025 / Revised: 31 May 2025 / Accepted: 7 July 2025 / Published: 9 July 2025

Abstract

In a global context characterized by budgetary pressures, aging populations, and accelerated economic transitions, the capacity of countries to mobilize stable and sustainable tax revenues represents a crucial pillar for maintaining macroeconomic stability and social cohesion. This research investigated the determinants of total tax revenues in the developed economies of the G7 group (Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States) during the period 2000–2022, employing both static and dynamic panel econometric approaches. The estimated model considered total tax revenues as the dependent variable, while the explanatory variables encompassed the main categories of government revenues: direct taxes (personal and corporate income), indirect taxes (consumption, trade, and other taxes), social contributions, grants, other non-tax revenues, and institutional quality indicators (regulatory quality and control of corruption). The empirical findings revealed that all tax components analyzed exert a positive and significant influence on total tax revenues, with particularly strong effects observed for consumption taxes, social contributions, and personal income taxes. Based on these results, the study provides policy recommendations aimed at diversifying revenue sources, balancing direct and indirect taxation, and broadening the tax base equitably. The study advances the literature on international taxation by offering an integrated and comparative analysis of the revenue structures in advanced economies, while also identifying relevant pathways for sustainable tax reforms in a dynamic global environment.

1. Introduction

In a dynamic and rapidly evolving global context, where countries are simultaneously facing structural fiscal challenges, economic instability, and profound demographic and climate transitions, the efficiency of domestic resource mobilization has become a strategic priority in the international fiscal governance architecture. Against this background, the International Monetary Fund (IMF), through its Fiscal Affairs Department (FAD), has developed a number of diagnostic and intervention tools aimed at supporting governments’ efforts to strengthen institutional capacity and formulate sustainable fiscal policies.
Figure 1 presents the FAD interventions that are organized under five main thematic pillars: macro-fiscal policies and institutional frameworks, revenue administration, public financial management, tax policy, and expenditure policy. They reflect a coherent vision of the role of fiscal policy in achieving sustainable development goals and strengthening global economic resilience.
Figure 1 illustrates the five strategic areas of intervention in fiscal capacity development as defined by the IMF’s Fiscal Affairs Department (FAD). These areas—macro-fiscal policies and institutional frameworks, revenue administration, public financial management, tax policy, and expenditure policy—reflect an integrated approach to strengthening government revenue mobilization and supporting sustainable development. Each pillar plays a crucial role in enhancing fiscal resilience and ensuring the efficient allocation of resources, with a particular emphasis on creating an enabling environment for economic growth and social welfare. This comprehensive framework underscores the multifaceted nature of fiscal capacity development, highlighting the interconnectedness of policy areas that collectively contribute to building robust and sustainable public finance systems.
In parallel, international tax strategies promoted by multilateral bodies such as the IMF, the World Bank, and the OECD emphasize the importance of modern, fair, and transparent tax governance. Initiatives such as the 2030 Agenda for Sustainable Development, the OECD Base Erosion and Profit Shifting (BEPS) package, as well as the G20 initiatives on global tax justice, require countries to fundamentally reassess their tax architecture. In this context, it becomes imperative to understand the structure of public revenue sources and the relative share of each type of tax or contribution in total tax revenues.
This is the purpose of the present study, which sought to investigate the structure of tax revenues in developed economies through an analysis applied to the G7 group-Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States of America. The choice of this group was motivated both by its systemic influence on international tax governance and its advanced degree of institutional development, which allows for a comparative analysis of the different tax components. The G7 also plays an active role in promoting global standards of transparency, tax digitization, and anti-avoidance, and is thus a relevant laboratory for assessing the coherence and effectiveness of tax policies.
The period under analysis, 2000–2022, was deliberately selected to capture the dynamics of government revenues at different phases of the business cycle and under the impact of major external shocks such as the global financial crisis, the euro area sovereign debt crisis and the COVID-19 pandemic. This time window made it possible to observe structural changes in tax regimes and the adaptation of revenue sources to new economic and social conditions.
To guide the empirical analysis and to align with the study’s objectives, the following research question was explicitly formulated: Which categories of government revenues and institutional quality factors most influenced total tax revenues in G7 countries between 2000 and 2022? This question aimed to clarify the study’s focus on both structural fiscal components and the impact of governance quality, thereby providing a comprehensive understanding of the determinants of fiscal performance in advanced economies.
The main objective of this research was to estimate, using both static and dynamic panel data models, the influence of different categories of government revenues on the total level of tax revenues (TaxRev) in G7 countries over the period 2000–2022. The model includes, as explanatory variables, both direct taxes (personal and corporate income) and indirect taxes (property, sales, international trade), as well as social contributions, grants, other government revenues, and institutional quality indicators (regulatory quality and control of corruption). This approach highlights the relative importance of each revenue component in shaping fiscal capacity and provides valuable insights into the resilience and long-term sustainability of public finances in advanced economies with mature institutional frameworks.
The novelty of this research lies in the integration of multiple revenue sources and institutional factors into a unified explanatory framework, along with the use of dynamic modeling techniques (Arellano–Bond estimator) to capture both short- and long-term effects. Furthermore, the paper contextualizes the empirical results within the broader framework of global fiscal reforms and macroeconomic events (such as the 2008 financial crisis and the COVID-19 pandemic), offering practical recommendations for fiscal-budgetary policies that align with contemporary international standards.

2. Literature Review

In a global fiscal context marked by profound structural transformations—from the digital transition and the emergence of new economic models to the overlapping crises generated by the COVID-19 pandemic, the conflict in Ukraine and the acceleration of climate change—the literature reflects a growing concern to understand the determinants of tax revenues and the optimal architecture of collection regimes. In developed economies, such as the G7, strengthening the tax base and balancing the mix between direct taxes, indirect taxes and social contributions are no longer just goals of economic efficiency, but essential components of long-term sustainability [2,3,4,5].
Within this framework, expert studies have analyzed multiple facets of tax policy: the institutional capacity to mobilize domestic resources, the fairness of the distribution of the tax burden, the resilience of collection regimes to external shocks and, last but not least, the consistency between national policies and international standards promoted by bodies such as the IMF, the OECD, and the European Commission [6,7,8,9]. Empirical research increasingly adopts multidimensional approaches, integrating advanced econometric methods with institutional, structural, and geopolitical perspectives. Within this analytical endeavor, the study of the G7 countries provides a privileged laboratory, as these economies account for a significant share of global GDP, have well-established administrative systems, and play an active role in shaping international fiscal standards.

2.1. Contemporary Fundamentals of Fiscal Policy in Developed Economies

The literature, in diverse stages of theoretical and applied development, emphasizes more and more clearly that tax revenues are not only the result of a technical collection capacity, but also express in an essential way the nature of the relationship between the state and its citizens, the level of democratic legitimacy of institutions and the coherence of the public policy architecture. In this approach, taxation becomes not only an economic instrument but also an expression of social and institutional capital. A number of contributions in the literature emphasize that modern taxation should be understood not only as a mechanism for collecting resources, but as a fundamental instrument of democratic governance, strengthening the legitimacy of public institutions and ensuring the sustainability of the functioning of the state [10,11,12,13,14,15]. In this context, the efficient mobilization of domestic resources is often considered an essential condition for the development of fair and efficient tax systems. In developed economies, the structure of revenues is often analyzed through a lens of fiscal decentralization and tax autonomy, as highlighted by Musgrave’s theory of fiscal federalism [16].
Different studies [17,18,19,20,21,22] have introduced the concept of ‘state capacity’ into tax analysis, arguing that developed institutions generate more coherent, equitable, and efficient tax regimes. This view is also supported by studies [23,24,25] that have called for a reconfiguration of tax systems around the principles of progressivity and transparency, especially in the context of rising inequality.
In addition to the traditional approaches to tax policy, current specialized studies [26,27,28,29] on the concept of sustainability-enhancing taxation propose an integrated vision of the role of taxation, in which economic efficiency objectives are explicitly linked to sustainability imperatives. This perspective argues that tax systems should be designed not only to maximize budgetary efficiency, but also to actively contribute to achieving environmental goals, promoting social inclusion and facilitating the digital transition. The reconfiguration of the tax function in a way that is compatible with sustainability objectives is gaining increasing visibility in the international debate and is supported by multiple initiatives and tax policy guidelines at the global level. These include efforts to create a more equitable framework for the taxation of multinational companies, rethinking the principles for taxing the digital economy and developing coordinated measures to combat tax base erosion and profit shifting [30,31,32]. These approaches reflect a paradigm shift in which tax systems are designed not only to secure public revenues, but also to support global equity, economic sustainability, and adaptation to the new challenges of the international economy.
These initiatives reflect a paradigm shift in which taxation is becoming an essential tool for correcting cross-border inequities, ensuring fairness between jurisdictions and financing the transition to a sustainable economy. These developments reflect a global trend of aligning tax policies to new economic and social realities in a concerted effort to promote a more equitable and sustainable development model.

2.2. Tax Structure: Direct Taxes, Indirect Taxes and Social Contributions

A key dimension in tax policy analysis concerns the conceptual and functional distinction between direct and indirect taxes, both of which have significant implications for distributional equity, economic efficiency, and the sustainability of public revenues. Direct taxes, in particular those levied on individual income (personal income tax—PIT) and corporate profits (corporate income tax—CIT), are generally perceived as fairer instruments with the potential to correct inequalities through progressivity. However, studies emphasize that direct taxes, both personal income and corporate taxes, are particularly vulnerable to factor mobility, aggressive tax planning, and tax avoidance or evasion behavior [33,34,35]. The pressure of international tax competition is also contributing to a decrease in the efficiency of these taxes, especially in economies integrated into global markets [36,37,38]. This study builds upon both recent and classic literature addressing the relationship between macroeconomic policies and fiscal structures. For example, Tanzi [39] highlights the significant influence of macroeconomic conditions on the level of taxation and fiscal balance, particularly in developing countries—a perspective that remains relevant when examining the stability of tax systems in advanced economies.
Indirect taxes such as value added tax (VAT) and excise duties are recognized in the literature for their ability to generate consistent and predictable revenues due to their broad base of application and increased administrative efficiency [40,41,42]. They are thus frequently used as fiscal consolidation instruments, especially in times of budgetary adjustment or economic volatility. However, some studies [43,44,45,46,47] highlight the regressive nature of these taxes, showing that the tax burden tends to be relatively higher for low-income households, which creates risks of social inequality. Consequently, a significant part of the literature [3,48,49,50] advocates the need to reassess the balance between direct and indirect taxation, through an integrated approach that seeks not only tax efficiency but also to correct regressive effects. Proposed solutions include the introduction of complementary redistributive mechanisms—such as income-contingent transfers or negative tax credits—to mitigate the negative impact on vulnerable groups and ensure greater fairness in the tax system.
Social contributions are a distinct pillar in the public revenue architecture and are associated with the financing of social protection and social insurance systems. Recent studies show that these contributions generate stable and predictable revenues, but can negatively influence the cost of labor and employment incentives, especially in low-productivity sectors [51,52,53]. Various studies [54,55,56] also raise questions about intergenerational equity and the sustainability of social systems in an aging population. Some expert studies suggest the need to reconfigure these contributions by broadening the funding base, diversifying sources, and integrating automatic stabilizing components.
A series of comparative meta-analyses [57,58,59,60] highlights that the optimal structure of taxation cannot be universalized, but depends on administrative capacity, the level of economic development, the degree of informality, and social cohesion. Modern tax systems must therefore simultaneously respond to the need for efficiency, stability, equity, and resilience. Various specialized studies [3,47,61,62,63] converge on the idea that a balance between direct progressive taxes, efficiently administered consumption taxes, and calibrated social contributions can contribute to strengthening the tax base and achieving sustainable development goals. Moreover, the integration of tax justice, digitization, and green transition principles into tax design is increasingly present in recent analyses, marking a transition towards more complex and interconnected tax paradigms [64,65,66,67].
The structure of taxation continues to be a central topic in the contemporary tax literature, where the focus is shifting from mechanistic analysis of income types to an integrated approach that takes into account the economic, social, and institutional implications of the tax policy mix. This reconceptualization of tax design reflects the increasing need to adapt to a dynamic global environment characterized by economic volatility, environmental challenges, and accelerated technological change.

2.3. Tax Innovations and Emerging Sources of Revenue in G7 Economies

In addition to the traditional pillars of taxation, the contemporary literature is paying increasing attention to alternative sources of budget revenue, especially in the context of recurrent economic crises, fiscal volatility, and accelerated structural transitions. In the developed G7 economies, these sources—such as grants, royalties, revenues from public assets, and administrative fees—are gaining in relevance in times of fiscal pressure. Empirical research shows that revenues from extra-fiscal sources can provide temporary budgetary stability, but they cannot replace structural tax revenues, particularly given the need for sound public finance management and transparency requirements [68]. Recent studies [69,70,71,72] suggests that strategically integrating these sources into the budgetary architecture can strengthen fiscal resilience, as long as they are linked to structural reforms and sustainable public investment.
A burgeoning area of research is the digitization of tax administration, a phenomenon accentuated in the G7 economies by technological advances, database interoperability, and the development of digital tax infrastructures [67,73,74,75]. Empirical studies show that technologies such as e-invoicing, online registers, e-auditing, and artificial intelligence-based systems for risk assessment contribute significantly to improving tax compliance and combating fraud [76,77,78,79]. In the G7 economies, these innovations are supported by mature legislative frameworks and advanced IT infrastructures, which amplify the impact on tax collection and the efficiency of tax administrations [80,81,82]. At the same time, the literature warns of emerging risks related to cybersecurity, fairness of access to digital services, and the fragmentation of international tax data regulation [83,84,85].
In response, international bodies and G7 governments have backed global reforms on taxing the digital economy, including the OECD packages “Pillar One” [32,86] and “Pillar Two” [87,88]. Recent tax reforms show considerable potential to mitigate the effects of harmful tax competition and contribute to rebalancing the international tax architecture, but they are accompanied by significant challenges related to multilateral coordination and the complexity of technical implementation.
Increasingly, the literature supports the integration of green taxes and green financial instruments into the public revenue mix of G7 countries [2,89,90]. Emissions taxation, negative externality taxes, and carbon trading schemes are becoming essential levers both to mobilize resources and to accelerate the transition to a low-emission economy [91,92,93]. The OECD [94] reports a steady increase in the share of environmental taxes in the GDP of the G7 countries, especially in the context of carbon taxes and the expansion of corporate sustainability regulations. At the same time, it is becoming increasingly clear that these instruments need to be carefully calibrated to avoid regressive distributional consequences and to support a just transition towards sustainability.
In the case of the G7 economies, modernizing the fiscal revenue architecture involves not only optimizing traditional taxes, but also harnessing tax innovation, digitalization, green, and extra-budgetary instruments. Coherent integration of these emerging sources into national tax policies is essential for ensuring stable and resilient public finances, while also preserving social legitimacy in the evolving global economic context.

2.4. Comparative Empirical Models and the G7-OECD-BRICS Perspective

Comparative analysis of the determinants of tax revenues has benefited in recent years from a substantial methodological advance, supported by the proliferation of panel databases, improvements in the quality of tax data and the strengthening of econometric methods. Recent studies mainly use panel models with fixed or random effects [34,95,96], estimators of the generalized method of moments (GMM) [97,98,99], distributed autoregressive models (ARDL) [100,101], or spatial regression models (SDM, SAR) [102,103,104], aimed at capturing both time dynamics and cross-national interdependencies between tax systems.
A significant line of research examines the impact of tax structures on macroeconomic performance and the long-term stability of public finances. Studies with large samples of developed countries, including in the G7, show that a tax composition oriented towards consumption and property taxation has more favorable effects on long-term economic growth than taxes on labor or profit [105,106,107]. However, this relationship depends on the level of institutional development, the degree of progressivity of the tax system and the level of initial inequality.
In the G7, comparative studies point to a higher capacity to mobilize fiscal resources, reflected in higher levels of revenue as a percentage of GDP and diversification of tax sources. The econometric models applied (panel VAR, fixed effects, dynamic GMM) confirm that, in advanced economies, the tax mix and the quality of institutions are key determinants of revenue stability and adaptability to external shocks [108,109,110].
In contrast, for the BRICS countries, the literature points to a high dependence on natural resource revenues and indirect taxes, lower direct tax collection, and persistent tax compliance challenges [111,112,113]. Regression models applied to these economies indicate a negative correlation between the level of tax evasion and government revenue performance, highlighting the weak role of tax institutions and the fragmentation of administrations [80,114,115].
An important methodological contribution is the inclusion of institutional and demographic variables in the empirical models, an aspect highlighted in recent literature by integrating indicators such as efficiency of governance, degree of budgetary transparency, administrative independence, and dynamics of the working population or age structure of the population [116,117,118]. These approaches allow a more refined assessment of the differences between the G7, the extended OECD, and the BRICS, not only in terms of the volume of tax revenues, but also in terms of their sustainability, efficiency, and legitimacy.
Following the most recent theoretical and methodological developments, the literature emphasizes that empirical models of the structure and performance of tax systems need to be firmly anchored in the specific institutional and socioeconomic context of each economy. In comparison, the G7 offers a model of advanced fiscal mobilization based on diversification, progressivity, and effective governance, while the BRICS countries face persistent structural constraints. The integration of the institutional dimension and the use of robust dynamic methods are key directions for future research on the convergence and divergence of global fiscal regimes.
The literature review clearly emphasizes the complexity and multidimensionality of tax revenue issues in developed economies. In the case of the G7, the structure of public revenues is the result of a subtle combination of policy choices, administrative traditions, and economic constraints. Direct taxes, social contributions, and consumption taxes coexist in a variable equilibrium, reflecting tensions between efficiency, equity, and sustainability.
Amid pressures from globalization, digitization, and demographic change, the literature proposes a strategic rethinking of tax regimes, with a focus on diversifying revenue sources, adapting the tax base to new economic realities, and strengthening administrative capacity. In addition, the G7-OECD-BRICS comparison shows that while the level of revenues may be similar in some cases, their composition and sustainability vary considerably depending on the institutional context.
In conclusion, the literature provides a solid framework for understanding the determinants of tax revenues, but also numerous avenues for further research: integrating institutional factors, modeling dynamic relationships, and exploring fiscal convergence and divergence across different regions of the world. The present study is in this direction, making an applied contribution to understanding the composition of government revenues in the G7 economies and providing an empirical basis for sustainable and equitable tax policy recommendations.

3. Methodology

To investigate the structure of tax revenues and their main determinants in advanced economies, this research adopted a panel econometric approach, applied to the group of seven G7 member countries (Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States), covering the period 2000–2022. The choice of this group of countries reflects the interest in analyzing the convergence of fiscal policies in developed economies characterized by high GDP levels, mature institutional systems, and a significant influence on the international fiscal framework. The econometric model used was a multiple linear regression model in which the dependent variable was total tax revenue as a percentage of GDP (TaxRev), while the set of explanatory variables was composed of the main determinants of government revenue, also measured as a percentage of GDP. These included personal income and profit taxes (TaxIncI), corporate income and profit taxes (TaxIncC), property taxes (TaxPro), sales and production taxes (TaxSal), customs duties on international trade (TaxTra), unclassified taxes (TaxOth), compulsory social contributions (SocialCon), grant revenues (Grants), and other government revenues (RevOth). In addition, the model incorporated two institutional indicators from the Worldwide Governance Indicators (WGI) database: regulatory quality (RegQual), which reflects the ability of governments to formulate and implement policies and regulations, and control of corruption (ConCorr), which captures the perception of the extent to which public power is exercised in the private interest. The inclusion of these institutional variables allowed a more comprehensive analysis of the structural and qualitative factors that influence fiscal performance in advanced economies. All fiscal indicators were collected from the International Monetary Fund (IMF) fiscal policy database, as detailed in Table 1, while the institutional indicators were obtained from the World Bank’s WGI dataset.
The selection of the indicators used in the econometric model was based on the recommendations of the literature on the classification of government revenues [105,121,122], as well as the availability of standardized and internationally comparable time series provided by the International Monetary Fund [119]. As the main objective of the research was to identify the factors influencing the level of total tax revenue (TaxRev) in developed economies, indicators were selected that reflect the main sources of budget revenue classified according to international tax reporting standards. Thus, the indicators included covered all major dimensions of the tax system: individual (TaxIncI) and corporate (TaxIncC) income taxation capture the direct contribution of economic activities to government revenue formation, being closely linked to the performance of the labor market and the private sector. Property taxes (TaxPro) reflect the redistributive and progressive nature of the tax system and are of greater importance in developed economies where real estate capital is significant. Indirect taxes, represented by sales and production taxes (TaxSal), include VAT and excise duties and are considered as stable sources of revenue, sensitive to domestic consumption dynamics.
As a complement, taxes related to international trade (TaxTra) have been included to capture the interdependencies between trade openness and the mobilization of budgetary resources, especially in the case of economies dependent on foreign trade. The category “Taxes not elsewhere classified” (TaxOth) captures possible residual tax revenues or revenues from less standardized sources, contributing to the completeness of the model. Outside the strict tax system, compulsory social contributions (SocialCon) are a relevant source of financing for social security budgets and reflect the social dimension of tax systems.
Two non-tax revenue components have also been included: grants (Grants), which can come from external sources or intergovernmental transfers, and which influence the tax structure, especially in times of crisis or transition, and other government revenues (RevOth), which include revenues from public assets, royalties, administrative fees or other non-tax sources. By incorporating institutional, demographic, and structural variables in the empirical models, the literature provides an integrated perspective on the architecture of government revenues in the G7 economies, facilitating the analysis of the relationship between the tax component per se and the other complementary sources of government financing.
The regulatory quality (RegQual) indicator reflects perceptions regarding the ability of governments to formulate and implement sound policies and regulations that enable and promote the development of the private sector. A high level of regulatory quality contributes to the stability of the economic environment and enhances the efficiency of public revenue collection.
In contrast, the control of corruption (ConCorr) indicator captures perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as the “state capture” by elites and private interests. Lower levels of corruption are frequently associated with improved fiscal revenue mobilization, greater institutional transparency, and enhanced budgetary efficiency.
The integration of these institutional variables into the econometric model addresses the need to control for qualitative dimensions of governance, which can significantly influence a state’s capacity to generate sustainable fiscal revenues. Moreover, their inclusion helps to mitigate the risk of omitted variable bias, thereby improving the model specification and the validity of the resulting estimates.
The choice of these indicators was guided by the need to construct an explanatory model capable of capturing the direct relationships between the various sources of public revenue and the total fiscal aggregate and the possible substitutions or complementarities between them depending on the specific economic, political, and institutional context of each G7 country.
The econometric model adopted in this research aims to investigate the influence of different categories of government revenue on the level of total tax revenue as a percentage of GDP in the G7 member states for the period 2000–2022. The dependent variable of the model is TaxRev, while the set of explanatory variables includes nine indicators relevant to the structure of government revenue according to standardized international classifications.
The extended functional form of the model is expressed by the following equation:
T a x R e v i t = β 0 + β 1 T a x I n c I i t + β 2 T a x I n c C i t + β 3 T a x P r o i t   + β 4 T a x S a l i t + β 5 T a x T r a i t + β 6 T a x O t h i t   + β 7 S o c i a l C o n i t + β 8 G r a n t s i t + β 9 R e v O t h i t   + β 10 R e g Q u a l i t + β 11 C o n C o r r i t + ε i t
For a synthetic and generalized formulation, the same relation can also be expressed in the following form:
T a x R e v i t = β 0 + k = 1 11 β k X k i t + ε i t
where Xkit stands for each of the nine explanatory variables included in the model, i denotes the unit of analysis (country), and t is the year of observation. The term εit reflects errors specific to each spatiotemporal unit and captures omitted or random influences that are not explained by the specified model.
To complement the static panel regression and enhance the robustness of the empirical strategy, the study incorporated a dynamic panel model using the Arellano–Bond estimator. This approach is particularly appropriate in the context of fiscal analysis, where revenue performance may exhibit inertia and be influenced by past policy decisions. The Arellano–Bond estimator accounts for unobserved heterogeneity and potential endogeneity by applying first-differencing and using internal instruments derived from lagged levels of the regressors.
In the dynamic specification, one-step GMM is applied with robust standard errors and a collapsed instrument matrix to avoid overfitting. The model includes the first and second lags of the dependent variable (TaxRev) to capture short-term fiscal persistence, along with lagged values of key explanatory variables representing tax structure and institutional quality. The general structure of the estimated model is given by the following equation:
T a x R e v i t = α 1 T a x R e v i , t 1 + α 1 T a x R e v i , t 2 k = 1 11 β k X k , i , t 1 + μ i t + ε i t
where TaxRevit denotes the total tax revenue (% of GDP) for country i in year t; TaxRevi,t−1 and TaxRevi,t−2 are the first and second lagged values of the dependent variable; Xk,i,t−1 represents the set of lagged structural and institutional explanatory variables; μi captures the unobserved country-specific fixed effects; and εit is the idiosyncratic error term, assumed to be free from second-order serial correlation.
Given the objective of the paper to analyze the determinants of total tax revenues in developed economies and based on the conceptual framework provided by the international tax literature, the research formulated the following hypotheses:
H1: 
Direct taxes, particularly personal income (TaxIncI) and corporate income (TaxIncC) taxes, have a positive and statistically significant impact on total tax revenues, highlighting their essential role as stable and predictable sources of budgetary income in developed economies.
H2: 
Property taxes (TaxPro) contribute significantly and positively to overall tax revenues, consistent with their perceived equity and their relatively constant presence in the tax systems of advanced countries.
H3: 
Among indirect taxes, sales and production taxes (TaxSal) and taxes on international trade (TaxTra) exert a significant and positive influence on total tax revenue, underscoring their fiscal importance and functional efficiency in consumption-based tax regimes.
H4: 
Compulsory social contributions (SocialCon) are positively and significantly associated with total tax revenues, reflecting both the size of the active labor force and the scale of social security systems in advanced economies.
H5: 
Other government revenues (RevOth), beyond the traditional tax structure, contribute positively and significantly to total fiscal revenues, indicating the complementary role of alternative income sources in supporting public budgets.
H6: 
Institutional factors, as measured by regulatory quality (RegQual) and control of corruption (ConCorr), exhibit a statistically significant negative relationship with tax revenues, suggesting that weaker institutional environments may hinder effective revenue collection and reduce overall fiscal performance.
Thus, the proposed approach provides an empirical perspective on the composition of tax revenues in a representative group of developed economies, allowing the identification of the structural factors underpinning tax performance in the international context and providing a basis for comparative assessments and tax policy recommendations.

4. Results and Discussions

4.1. Descriptive Statistics

In a global context marked by rising fiscal pressures, persistent economic inequality, and accelerated structural transitions, from digitization to the green transition, the capacity of countries to generate sustainable tax revenues is becoming a key pillar of economic resilience and social cohesion. Fiscal policies are no longer just instruments to finance public spending but have increasingly become strategic means through which governments shape the distribution of revenues, stimulate economic growth and intervene in the management of overlapping crises such as the COVID-19 pandemic, the war in Ukraine or the challenges of climate change. Understanding the tax revenue architecture becomes essential for formulating effective, equitable and adaptable public policies. The Group of Seven Developed Economies (G7) made up of Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States plays a central role in the international tax system, not only because of its economic dimension, but also through its influence on global regulations, tax standards, and multilateral initiatives. The descriptive analysis presented in Table 2 provides an overview of the average level, variability, and extremes of each variable over the period 2000–2022 for the seven G7 member countries.
As can be seen from Table 2, total tax revenue (TaxRev) averaged 24.47% of GDP, with a relatively moderate variation (standard deviation of 4.44 percentage points) and a range from a low of 15.11% to a high of 30.91%. This range reflects the significant differences in tax intensity across the G7 Member States. Among the direct components of tax revenue, personal income taxes (TaxIncI) averaged 9.26% of GDP, considerably higher than the average corporate income taxes (TaxIncC), which stood at 2.85%. This ratio is in line with the tax structure in developed countries, where taxation of labor prevails over taxation of capital. Property taxes (TaxPro) also averaged 2.55%, indicating a relatively constant presence in the tax revenue structure, peaking at almost 4.5% in some cases. In terms of indirect taxes, sales and production taxes (TaxSal) stood out with an average contribution of about 8.99% of GDP, confirming the importance of VAT and excise taxes in the tax architecture of the countries analyzed. In contrast, taxes from international trade (TaxTra) showed a very low average value (0.097%), reflecting the open and liberalized nature of the G7 economies, where the share of customs revenues is marginal. The component of unclassified taxes (TaxOth) had a low average of 0.43%, but with a relatively large standard deviation (1.35) and a notable maximum (16.09%), suggesting sporadic variations or the emergence of extraordinary revenue categories in certain national contexts or fiscal years. Social contributions (SocialCon) averaged around 10.99% of GDP, with a peak over 19%, highlighting their essential role in financing social protection systems, but also significant variations between countries with different social regimes. In terms of non-tax revenues, grants had a low average value (0.074%) and limited variability, while other government revenues (RevOth) averaged 4.61%, which may reflect the diversity of extra-budgetary resources mobilized by the countries analyzed. The regulatory quality (RegQual) variable, measured as a percentile rank between 0 and 1, had a mean of 0.161 and ranged from a low of 0.335 to a high of 0.49. These values suggest that, on average, the regulatory environment in the sample countries is moderately developed, though still displaying considerable heterogeneity, particularly in the capacity of governments to formulate and implement sound policies and regulations. Similarly, the control of corruption (ConCorr) variable presented a mean value of 0.161 and ranged from 0.517 to a minimum of 0.01, indicating that perceived corruption levels varied significantly across G7 countries. The relatively low average values in both indicators underlined the relevance of institutional factors in understanding cross-national differences in tax performance, particularly in relation to fiscal compliance, administrative efficiency, and policy implementation.
Figure 2 reveals significant variations across G7 countries in the average level of tax revenues and social contributions. France, Italy, and Canada had the highest average total tax revenues, at around 28–29% of GDP. In contrast, the United States and Japan stood out with significantly lower average tax levels, below 20%, reflecting more conservative taxation and a preference for low taxation.
In terms of social security contributions, France and Germany stood out with high average values, above 17% of GDP, confirming the orientation of these countries towards extensive social protection models, financed mainly by wage contributions. At the opposite pole, Canada, the United Kingdom and the United States had the lowest levels of social contributions, between 4% and 7%, indicating a tax structure based more on direct and indirect taxes than on traditional social financing. The observed cross-country discrepancies suggest the existence of distinct fiscal models within the G7: a continental one, with a focus on social contributions and high tax revenues (e.g., France, Germany), and an Anglo-Saxon one, oriented towards moderate taxation and relatively low public involvement in the financing of social protection (e.g., USA, Canada, UK). These differences further justify the structured analysis of the determinants of tax revenues, given the distinct role of each component in the fiscal architecture of developed economies.
Figure 3 shows the dynamics of the average percentage values of the main categories of government revenues in the G7 Member States for the period 2000–2022, expressed as a percentage of GDP.
Figure 3 highlights a relative stability in the average structure of government revenues of the G7 countries over the more than two decades analyzed. Total tax revenues (TaxRev) remained at a high level throughout the period, ranging between 22% and 25% of GDP, with no major variations, reflecting a consistent degree of fiscal mobilization in developed economies. In terms of social contributions (SocialCon), they were consistently in second place, at around 17% of GDP, confirming their importance as a key source of financing social protection systems in most G7 countries. Taxes on sales and production (sales and output taxes) also remained at a stable level (around 9%), demonstrating their predictable and widely applicable role in the tax structure of developed countries.
Taxes on personal income (TaxIncI) and on corporate profits (TaxIncC) varied slightly over time, but without major upward or downward trends, remaining around 8% and 3% respectively. This stability suggests continuity in direct tax regimes and in the structure of the tax base. Indicators such as property taxes (TaxPro), non-tax revenues (RevOth), and unclassified taxes (TaxOth) showed moderate and constant values, while grants (Grants) and taxes on international trade (TaxTra) remained at very low levels (below 1%), confirming their marginal character in the G7 tax architecture.

4.2. Matrix of Correlations and Variance Inflation Factor

Table 3 presents the matrix of Pearson correlation coefficients calculated between the variables included in the econometric model to examine possible linear relationships between them and to identify possible collinearity problems.
The low value of correlations between most of the explanatory variables suggests that there are no multicollinearity problems, which supports the robustness of the proposed econometric model. However, the relationships identified indicate significant differences across income categories, which justifies the inclusion of all these variables in the model to fully capture the tax revenue structure.
Table 4 presents the variance inflation factor (VIF) values for the explanatory variables included in the econometric model. VIF analysis is a standard tool used to detect multicollinearity; i.e., strong linear relationships between the independent variables that can affect the precision of the estimates and the significance of the coefficients.
The results of the VIF analysis validated the model specification from a multicollinearity perspective and allowed further econometric analysis without further adjustments to the structure of the explanatory variables.
To assess the stationarity properties of the panel data series, we employed the Hadri LM test, which tests the null hypothesis that all panels are stationary against the alternative that some contain unit roots.
The results presented in Table 5 indicate that, for all variables included in the model, the null hypothesis is strongly rejected at the 1% significance level. This finding suggests that most of the series are non-stationary in levels, thereby justifying the use of a dynamic panel data approach to properly account for the time-series characteristics of the data. Notably, both institutional indicators—regulatory quality and control of corruption—also exhibited non-stationarity, reinforcing the need to control for their dynamic behavior when evaluating their influence on tax revenue performance.

4.3. Analysis Linear Regression

Table 6 presents the results of the multiple linear regression model estimated with total tax revenue (TaxRev), expressed as a percentage of GDP, as the dependent variable. The explanatory variables include the key structural components of government revenue, alongside institutional quality indicators, across G7 member countries over the period 2000–2022. The model displayed a high degree of statistical robustness, explaining 99.7% of the variance in the dependent variable (R2 = 0.997), with the overall model being highly significant (F = 5391.372, p < 0.001).
The results clearly validated hypothesis H1, as direct taxes—on personal income (TaxIncI) and on corporate income (TaxIncC)—exhibited strong positive and statistically significant effects on total tax revenue. TaxIncI registered a coefficient of 1.044 and TaxIncC of 1.119 (both p < 0.01), confirming the essential role of direct taxation in supporting fiscal capacity in advanced economies. These findings are consistent with prior literature emphasizing the fiscal reliability of direct taxes [123,124,125].
Hypothesis H2 is also supported, as property taxes (TaxPro) displayed a positive and statistically significant coefficient (1.058; p < 0.01). This result reinforces the notion that property taxation remains a structurally important and relatively stable source of revenue in mature tax systems [126,127,128,129,130].
Regarding H3, the model confirmed the substantial contribution of indirect taxes. Sales and production taxes (TaxSal) had a significant and positive coefficient (1.095; p < 0.01), while international trade taxes (TaxTra) also showed a strong and significant effect (1.857; p < 0.01). By contrast, unclassified taxes (TaxOth) did not exhibit a statistically significant influence (p = 0.988), suggesting that their impact on total revenue was negligible within this sample. Nevertheless, the overall evidence supports the fiscal importance of indirect taxation in broad-based consumption tax regimes. These results confirm the role of indirect taxes in providing a broad and efficient tax base, as argued by various studies [49,131,132] in analyzing VAT efficiency at the global level.
H4 was validated by the positive and significant relationship between compulsory social contributions (SocialCon) and total tax revenue (coef. = 0.037; p < 0.01). This reflects the direct connection between labor market formality, social security coverage, and fiscal resource mobilization, in line with the theories of social protection financing. This result is consistent with the theories of welfare state taxation supported by the literature [133,134,135].
For hypothesis H5, only RevOth (other government revenues) demonstrated a positive and statistically significant effect on tax revenue (coef. = 0.167; p < 0.01), while grants did not reach statistical significance (p = 0.483). This suggests that, although other government income streams can enhance fiscal outcomes, grants may have a less stable or systematic influence within the G7 context, as observed in different studies [29,136,137].
H6 was strongly supported. Both institutional indicators, regulatory quality (RegQual) and control of corruption (ConCorr) were negatively associated with tax revenue and statistically significant (RegQual: coef. = −0.504; p < 0.01; ConCorr: coef. = −0.313; p < 0.01). These results highlight that weaker regulatory environments and higher perceived corruption correlate with lower fiscal performance, underscoring the critical role of institutional quality in supporting effective revenue systems.
The regression findings offered robust empirical confirmation of all six hypotheses, underscoring the fact that both the structural composition of government revenues (tax and non-tax) and the quality of institutional governance play a critical role in determining the level of total tax revenues in advanced economies. These results carry significant implications for the formulation of sustainable and equitable tax policies, particularly in a global context increasingly defined by fiscal pressures, institutional reforms, and the pursuit of efficiency and resilience in public finance.

4.4. Dynamic Model Results

While the static panel regression provided valuable insights into the structural and institutional determinants of tax revenue, it did not fully address potential endogeneity, reverse causality, or the temporal persistence of fiscal outcomes. In advanced economies, fiscal variables are often influenced by past decisions and exhibit inertia over time. To capture these dynamic effects and improve the robustness of the empirical analysis, we estimated a dynamic panel-data model using the Arellano–Bond generalized method of moments (GMM) estimator.
This methodological choice allowed for the inclusion of lagged dependent and explanatory variables, thus controlling for unobserved heterogeneity and potential simultaneity between fiscal revenues and their determinants. The Arellano–Bond approach is particularly suitable to panels with a small number of cross-sectional units and a moderate time dimension, as in the case of the G7 countries over the 2000–2022 period.
The dynamic specification aimed to assess whether tax revenues were influenced not only by contemporaneous structural factors but also by past fiscal performance and delayed policy effects. Moreover, this approach reinforces the validity of causal inference by mitigating bias arising from endogenous regressors. The results of the dynamic estimation are summarized in Table 7.
The estimation results in Table 7 provide several important insights into the temporal dynamics of tax revenue generation in advanced economies. Although the first lag of the dependent variable (L1.TaxRev) was not statistically significant (coef. = 0.017; p = 0.213), its positive sign suggests that, while fiscal performance may exhibit some inertia, this effect is relatively weak in the case of the G7 countries. The second lag (L2.TaxRev) was also statistically insignificant, reinforcing the idea that recent tax policy and structural factors exert a stronger influence than older fiscal trends.
In terms of structural determinants, the results remained consistent with those observed in the static panel model. Direct taxes—both personal (TaxIncI) and corporate (TaxIncC)—maintained strong, positive, and statistically significant effects on total tax revenue (coef. = 1.038 and 1.119, respectively, both p < 0.01). These findings emphasize the ongoing centrality of income-based taxation in the revenue structures of developed economies, even when accounting for dynamic adjustments over time.
Property taxes (TaxPro) and sales and production taxes (TaxSal) also remained positively and significantly associated with TaxRev, with coefficients of 1.058 and 1.095 respectively (p < 0.01), confirming their stable contribution across both static and dynamic frameworks. Notably, taxes on international trade (TaxTra) showed a robust and highly significant positive effect (coef. = 1.857; p < 0.01), suggesting that despite trade liberalization, customs duties still play a non-negligible role in some G7 fiscal contexts.
Other government revenues (RevOth) retained their significance (coef. = 0.167; p < 0.01), indicating their complementary role in strengthening revenue bases beyond traditional tax categories. Interestingly, compulsory social contributions (SocialCon) displayed a negative and statistically significant coefficient (−0.047; p < 0.01), which may reflect structural pressures on labor markets, contribution ceilings, or substitution effects between payroll-based financing and general taxation.
Institutional quality indicators—regulatory quality and control of corruption—were not statistically significant in the dynamic model, in contrast to their negative and significant effect in the static framework. This suggests that their influence may be more contemporaneous and less persistent over time, or that their impact is mediated through other variables not captured in the current specification.
The results of the Arellano–Bond autocorrelation tests are summarized in Table 8.
From a diagnostic perspective, the model demonstrated strong internal consistency. The Arellano–Bond test for AR(1) was significant at the 5% level (z = −0.93; p = 0.035), as expected, indicating first-order autocorrelation in the differenced residuals. Importantly, the AR(2) test was not significant (z = −0.60; p = 0.551), confirming the absence of second-order serial correlation and supporting the validity of the moment conditions. These results reinforce the reliability of the instrument set and the dynamic specification.
Overall, the dynamic panel model provided strong empirical support for the relevance of tax structure components in determining fiscal outcomes, while also accounting for intertemporal effects and feedback mechanisms. The findings confirm the robustness of direct and indirect tax in sustaining public revenues and highlight the importance of analyzing fiscal policy not only in static terms but also through a dynamic lens that captures delayed effects and policy inertia.
In conclusion, the dynamic panel estimation confirmed the robustness of the structural relationships identified in the static model, while offering additional insights into the temporal dimension of fiscal performance and reinforcing the importance of tax policy continuity in advanced economies.
This study contributes to the existing literature by integrating multiple sources of government revenue (direct taxes, indirect taxes, social contributions, non-tax revenues, and institutional quality indicators) into a unified explanatory framework. Unlike previous research focusing on single revenue categories, the analysis encompasses the complex fiscal structure of advanced economies characterized by high institutional maturity. Moreover, the results are contextualized considering major global macroeconomic events, such as the 2008 financial crisis and the COVID-19 pandemic, which have tested the resilience of tax systems. These findings offer important implications for sustainable fiscal policies, underscoring the need for diversified revenue sources and robust governance structures to navigate fiscal challenges in a volatile global environment.

5. Conclusions and Implications for Fiscal Policies in the G7

The empirical analysis presented in this study confirms the pivotal role played by the structure of government revenues in determining the capacity of developed countries to generate sustainable fiscal resources. The econometric evidence, drawn from both static and dynamic panel models, highlights that core tax categories of direct taxes (personal and corporate income), indirect taxes (consumption and trade), and social contributions exerted a positive and significant influence on total tax revenues. Notably, consumption taxes, personal income taxes, and social contributions emerged as the most robust determinants, underscoring their essential contribution to revenue stability and policy resilience. The results further emphasize the complementary role of other government revenues (such as royalties, fees, and other non-tax sources) in bolstering public finances, particularly during periods of economic volatility or crisis, such as the COVID-19 pandemic. However, grants did not exhibit a statistically significant influence on total tax revenues, indicating their less systematic impact in the fiscal architecture of the G7 countries. Institutional factors, captured through the regulatory quality and control of corruption indicators, revealed a significant negative relationship with tax revenues in the static model, indicating that better regulatory frameworks and lower corruption levels are conducive to more effective revenue mobilization. However, in the dynamic model, these effects appeared less persistent over time, suggesting that institutional improvements may have more immediate rather than enduring fiscal impacts. Considering these findings, the study recommends a strategic approach to fiscal policy design that balances direct and indirect taxation, leverages the stability of social contributions, and integrates complementary non-tax revenue streams in a transparent and sustainable manner. Additionally, efforts to enhance institutional quality—through regulatory reforms and anti-corruption measures—can further strengthen the fiscal performance of developed economies. The study contributes to the existing literature by integrating multiple revenue sources and governance factors into a unified empirical framework, providing a comprehensive understanding of fiscal structures in advanced economies. The inclusion of dynamic modeling techniques enhanced the robustness of the analysis by accounting for temporal effects and feedback mechanisms. Finally, the study’s results have important implications for tax policy reform, emphasizing the need for a diversified and resilient revenue base to support sustainable development in an evolving global economic context, particularly considering pandemic-related fiscal challenges.
G7 countries, as leaders in global economic governance, should promote international tax coordination. The adoption of common standards on tax transparency, automatic exchange of information, and the fight against base erosion (BEPS) can help to increase tax collection efficiency and ensure fair tax competition between countries. Although the results obtained in this research provide an empirical basis for understanding the determinants of tax revenue in the developed G7 economies, the paper is not devoid of certain methodological and conceptual limitations that deserve to be explicitly mentioned, both for a proper contextualization of the findings and to inform future directions for further scientific investigation. First, the use of a static linear regression model, while effective for an aggregate cross-sectional analysis, limited the ability to capture the complex time dynamics between the variables included the lag effects that can occur between changes in the tax structure and their impact on the level of total tax revenues. A methodological extension would entail the estimation of dynamic models, such as the VECM (VECM) or models of the generalized method of moments (GMM) class, which would allow the investigation of short- and long-run causality relationships as well as control for endogeneity. Another important limitation concerns the absence of explanatory variables of an institutional, structural or demographic nature, such as indicators on the quality of governance, the degree of fiscal digitization, the age structure of the population or the level of income inequality. Integrating such factors into future econometric models could contribute to a more nuanced understanding of the inter-state variation in fiscal performance and allow a more accurate assessment of the role of the economic and social context in shaping public revenue policies. Future research directions should aim at developing more complex econometric frameworks with dynamic modeling capability and control for structural heterogeneity, expanding the database to include a larger sample of states, and exploring the interaction between fiscal and non-fiscal factors in the generation of government revenue. At the same time, an analysis focusing on the efficiency and equity of collection beyond the absolute level of revenue would contribute to a comprehensive approach to fiscal sustainability in the current global context.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data that support the findings of this study is available from the corresponding author upon request.

Acknowledgments

The author acknowledges the use of artificial intelligence tools, particularly for improving the precision of argumentation and refining the wording of some sections of the manuscript that was originally conceived and written by the author and subsequently revised.

Conflicts of Interest

No potential conflict of interest was reported by the authors. The author have no relevant financial or non-financial interests to disclose.

References

  1. International Monetary Fund IMF Capacity Development Strategy and Policies. Available online: https://www.imf.org/en/Capacity-Development/strategy-policies (accessed on 5 March 2025).
  2. Saqib, N.; Usman, M.; Radulescu, M.; Șerbu, R.S.; Kamal, M.; Belascu, L.A. Synergizing Green Energy, Natural Resources, Global Integration, and Environmental Taxation: Pioneering a Sustainable Development Goal Framework for Carbon Neutrality Targets. Energy Environ. 2023. [Google Scholar] [CrossRef]
  3. Dahmani, M. Environmental Quality and Sustainability: Exploring the Role of Environmental Taxes, Environment-Related Technologies, and R&D Expenditure. Environ. Econ. Policy Stud. 2024, 26, 449–477. [Google Scholar] [CrossRef]
  4. Nadiri, A.; Gündüz, V.; Adebayo, T.S. The Role of Financial and Trade Globalization in Enhancing Environmental Sustainability: Evaluating the Effectiveness of Carbon Taxation and Renewable Energy in EU Member Countries. Borsa Istanb. Rev. 2024, 24, 235–247. [Google Scholar] [CrossRef]
  5. Jiang, W.; Chen, S.; Tang, P.; Hu, Y.; Liu, M.; Qiu, S.; Iqbal, M. Role of Natural Resources, Renewable Energy Sources, Eco-Innovation and Carbon Taxes in Carbon Neutrality: Evidence from G7 Economies. Heliyon 2024, 10, e33526. [Google Scholar] [CrossRef]
  6. OECD. Tax Policy Reforms 2023. Available online: https://www.oecd.org/en/publications/tax-policy-reforms-2023_d8bc45d9-en.html (accessed on 15 February 2025).
  7. European Commission. Annual Report on Taxation 2024 Review of Taxation Policies in EU Member States. Available online: https://www.google.com.hk/url?sa=t&source=web&rct=j&opi=89978449&url=https://www.europarl.europa.eu/RegData/docs_autres_institutions/commission_europeenne/swd/2024/0172/COM_SWD(2024)0172_EN.pdf&ved=2ahUKEwj61oznoK-OAxWbklYBHZ8lBDkQFnoECBcQAQ&usg=AOvVaw0s8lpcXhSVZmucAFZZj6dV (accessed on 5 March 2025).
  8. Verdier, G.; Rayner, B.; Muthoora, P.S.; Vellutini, C.; Zhu, L.; Koukpaizan, V.d.P.; Marahel, A.; Harb, M.; Benmohamed, I.; Hebous, S.; et al. Revenue Mobilization for a Resilient and Inclusive Recovery in the Middle East and Central Asia. Dep. Pap. 2022, 2022, A001. [Google Scholar] [CrossRef]
  9. Benitez, J.C.; Mansour, M.; Pecho, M.; Vellutini, C. Building Tax Capacity in Developing Countries. Staff Discuss. Notes 2023, 2023, A001. [Google Scholar] [CrossRef]
  10. Rashid, H.; Warsame, H.; Khan, S. Tax Collections and Democracy in Developing and Developed Countries. In Global Encyclopedia of Public Administration, Public Policy, and Governance; Farazmand, A., Ed.; Springer International Publishing: Cham, Switzerland, 2022; pp. 12593–12597. ISBN 978-3-030-66252-3. [Google Scholar]
  11. Blanton, R.E.; Fargher, L.F.; Feinman, G.M.; Kowalewski, S.A. The Fiscal Economy of Good Government: Past and Present. Curr. Anthropol. 2021, 62, 77–100. [Google Scholar] [CrossRef]
  12. Krampe, F.; Hegazi, F.; VanDeveer, S.D. Sustaining Peace through Better Resource Governance: Three Potential Mechanisms for Environmental Peacebuilding. World Dev. 2021, 144, 105508. [Google Scholar] [CrossRef]
  13. Svetlozarova Nikolova, B. Strengthening the Integrity of the Tax Administration and Increasing Tax Morale. In Tax Audit and Taxation in the Paradigm of Sustainable Development: The Impact on Economic, Social and Environmental Development; Svetlozarova Nikolova, B., Ed.; Springer Nature: Cham, Switzerland, 2023; pp. 157–180. ISBN 978-3-031-32126-9. [Google Scholar]
  14. Wirba, A.V. Corporate Social Responsibility (CSR): The Role of Government in Promoting CSR. J. Knowl. Econ. 2024, 15, 7428–7454. [Google Scholar] [CrossRef]
  15. O’Reilly, C.; Murphy, R.H. An Index Measuring State Capacity, 1789–2018. Economica 2022, 89, 713–745. [Google Scholar] [CrossRef]
  16. Musgrave, R.A. Theories of Fiscal Federalism. Public Financ. 1969, 24, 521–536. [Google Scholar]
  17. Savoia, A.; Sen, K. The Origins of Fiscal States in Developing Economies: History, Politics and Institutions. J. Institutional Econ. 2023, 19, 303–313. [Google Scholar] [CrossRef]
  18. Bradlow, B.H. Urban Social Movements and Local State Capacity. World Dev. 2024, 173, 106415. [Google Scholar] [CrossRef]
  19. Singh, M.K. What Is State Capacity and How Does It Matter for Energy Transition? Energy Policy 2023, 183, 113799. [Google Scholar] [CrossRef]
  20. Okunogbe, O.; Santoro, F. The Promise and Limitations of Information Technology for Tax Mobilization. World Bank Res. Obs. 2023, 38, 295–324. [Google Scholar] [CrossRef]
  21. Hearson, M.; Rasmus Corlin, C.; Randriamanalina, T. Developing Influence: The Power of ‘the Rest’ in Global Tax Governance. Rev. Int. Polit. Econ. 2023, 30, 841–864. [Google Scholar] [CrossRef]
  22. Root, H.L. The Religious Origins of State Capacity in Europe and China. J. Econ. Behav. Organ. 2024, 218, 456–469. [Google Scholar] [CrossRef]
  23. Dianov, S.; Koroleva, L.; Pokrovskaia, N.; Victorova, N.; Zaytsev, A. The Influence of Taxation on Income Inequality: Analysis of the Practice in the EU Countries. Sustainability 2022, 14, 9066. [Google Scholar] [CrossRef]
  24. Sevilla-Bernabéu, B.; and Del-Valle-Calzada, E. Tax Policies with a Human Rights Perspective: Towards Greater Tax Justice. S. Afr. J. Account. Res. 2024, 38, 264–277. [Google Scholar] [CrossRef]
  25. Roland, A.; Römgens, I. Policy Change in Times of Politicization: The Case of Corporate Taxation in the European Union. JCMS J. Common Mark. Stud. 2022, 60, 355–373. [Google Scholar] [CrossRef]
  26. Cima, E.; Esty, D.C. Making International Trade Work for Sustainable Development: Toward a New WTO Framework for Subsidies. J. Int. Econ. Law 2024, 27, 1–17. [Google Scholar] [CrossRef]
  27. Shaikh, S.S.; Amin, N.; Shabbir, M.S.; Song, H. Going Green in ASEAN: Assessing the Role of Eco-Innovation, Green Energy, Industrialization, and Environmental Taxes in Achieving Carbon Neutrality. Sustain. Dev. 2024, 33, 3596–3614. [Google Scholar] [CrossRef]
  28. Sun, Y.; Rahman, M.M.; Xinyan, X.; Siddik, A.B.; Islam, M.E. Unlocking Environmental, Social, and Governance (ESG) Performance through Energy Efficiency and Green Tax: SEM-ANN Approach. Energy Strategy Rev. 2024, 53, 101408. [Google Scholar] [CrossRef]
  29. Ajeigbe, K.B.; Ganda, F.; Enowkenwa, R.O. Impact of Sustainable Tax Revenue and Expenditure on the Achievement of Sustainable Development Goals in Some Selected African Countries. Environ. Dev. Sustain. 2024, 26, 26287–26311. [Google Scholar] [CrossRef]
  30. OECD. Tax Challenges Arising from the Digitalisation of the Economy—Subject to Tax Rule (Pillar Two): Inclusive Framework on BEPS. Available online: https://www.oecd.org/en/publications/tax-challenges-arising-from-the-digitalisation-of-the-economy-subject-to-tax-rule-pillar-two_9afd6856-en.html (accessed on 7 March 2025).
  31. Tax Foundation Europe. Digital Taxation Around the World. Available online: https://taxfoundation.org/research/all/global/digital-taxation/ (accessed on 25 February 2025).
  32. OECD. Base Erosion and Profit Shifting (BEPS). Available online: https://www.oecd.org/en/topics/base-erosion-and-profit-shifting-beps.html (accessed on 9 March 2025).
  33. Alstadsæter, A.; Johannesen, N.; Le Guern Herry, S.; Zucman, G. Tax Evasion and Tax Avoidance. J. Public Econ. 2022, 206, 104587. [Google Scholar] [CrossRef]
  34. Saptono, P.B.; Mahmud, G.; Salleh, F.; Pratiwi, I.; Purwanto, D.; Khozen, I. Tax Complexity and Firm Tax Evasion: A Cross-Country Investigation. Economies 2024, 12, 97. [Google Scholar] [CrossRef]
  35. Hossain, M.; Lobo, G.J.; Mitra, S. Firm-Level Political Risk and Corporate Tax Avoidance. Rev. Quant. Financ. Account. 2023, 60, 295–327. [Google Scholar] [CrossRef]
  36. Devereux, M.P. International Tax Competition and Coordination with A Global Minimum Tax. Natl. Tax J. 2023, 76, 145–166. [Google Scholar] [CrossRef]
  37. Lin, X.; Baskaran, A. Regional Economic Growth, Digital Economy and Tax Competition in China: Mechanism and Spatial Assessment. J. Asia Pac. Econ. 2024, 1–27. [Google Scholar] [CrossRef]
  38. Perry, V.J. Pillar 2: Tax Competition in Low-Income Countries and Substance-Based Income Exclusion. Fisc. Stud. 2023, 44, 23–36. [Google Scholar] [CrossRef]
  39. Tanzi, V. The Impact of Macroeconomic Policies on the Level of Taxation and the Fiscal Balance in Developing Countries. IMF Staff Pap. 1989, 1989, A005. [Google Scholar]
  40. Almarri, A.S. How the Law Can Ensure the Success of Value-Added Tax. Int. J. Law Manag. 2024, ahead-of-print. [Google Scholar] [CrossRef]
  41. de la Cuesta, B.; Martin, L.; Milner, H.V.; Nielson, D.L. Do Indirect Taxes Bite? How Hiding Taxes Erases Accountability Demands from Citizens. J. Polit. 2023, 85, 1305–1320. [Google Scholar] [CrossRef]
  42. Badiu (Cazacu), C.E.; Bărbuță-Mișu, N.; Chirita, M.; Soare, I.; Zlati, M.L.; Fortea, C.; Antohi, V.M. Modelling the Impact of VAT Fiscality on Branch-Level Performance in the Construction Industry-Evidence from Romania. Economies 2024, 12, 30. [Google Scholar]
  43. Bachas, P.; Jensen, A.; Gadenne, L. Tax Equity in Low- and Middle-Income Countries. J. Econ. Perspect. 2024, 38, 55–80. [Google Scholar] [CrossRef]
  44. Mgammal, M.H.; Al-Matari, E.M.; Alruwaili, T.F. Value-Added-Tax Rate Increases: A Comparative Study Using Difference-in-Difference with an ARIMA Modeling Approach. Humanit. Soc. Sci. Commun. 2023, 10, 121. [Google Scholar] [CrossRef]
  45. Valodia, I.; Francis, D. Ethics, Politics, Inequality. In New Directions; Bohler-Muller, N., Soudien, C., Reddy, V., Eds.; Lynne Rienner Publishers: Boulder, CO, USA, 2022; pp. 173–192. ISBN 9780796926142. [Google Scholar]
  46. Schechtl, M. Taking from the Disadvantaged? Consumption Tax Induced Poverty across Household Types in Eleven OECD Countries. Soc. Policy Soc. 2024, 23, 377–391. [Google Scholar] [CrossRef]
  47. Coelho, M.; Davis, A.; Klemm, A.; Osorio-Buitron, C. Gendered Taxes: The Interaction of Tax Policy with Gender Equality. Int. Tax Public Financ. 2024, 31, 1413–1460. [Google Scholar] [CrossRef]
  48. ALshubiri, F. Do Foreign Direct Investment Inflows Affect Tax Revenue in Developed and Developing Countries? Asian Rev. Account. 2024, 32, 781–810. [Google Scholar] [CrossRef]
  49. Abd Hakim, T.; Karia, A.A.; David, J.; Ginsad, R.; Lokman, N.; Zolkafli, S. Impact of Direct and Indirect Taxes on Economic Development: A Comparison between Developed and Developing Countries. Cogent Econ. Financ. 2022, 10, 2141423. [Google Scholar] [CrossRef]
  50. Taufik, A. Direct Versus Indirect Taxes: Impact on Economic Growth and Total Tax Revenue. Int. J. Financ. Res. 2020, 11, 16112. [Google Scholar] [CrossRef]
  51. Cui, W.; Hicks, J.; Norton, M. How Well-Targeted Are Payroll Tax Cuts as a Response to COVID-19? Evidence from China. Int. Tax Public Financ. 2022, 29, 1321–1347. [Google Scholar] [CrossRef] [PubMed]
  52. Giupponi, G.; Landais, C. Subsidizing Labour Hoarding in Recessions: The Employment and Welfare Effects of Short-Time Work. Rev. Econ. Stud. 2023, 90, 1963–2005. [Google Scholar] [CrossRef]
  53. Ochi, A.; Labidi, M.A.; Saidi, Y. The Nexus Between Pro-Poor Growth, Inequality, Institutions and Poverty: Evidence from Low and Middle Income Developing Countries. Soc. Indic. Res. 2024, 172, 703–739. [Google Scholar] [CrossRef]
  54. International Labour Organization. The Contribution of the Social and Solidarity Economy and Social Finance to the Future of Work. Available online: https://www.ilo.org/sites/default/files/wcmsp5/groups/public/@ed_emp/documents/publication/wcms_739377.pdf (accessed on 6 March 2025).
  55. Fritz, B.; de Paula, L.F.; Prates, D.M. Developmentalism at the Periphery: Addressing Global Financial Asymmetries. Third World Q. 2022, 43, 721–741. [Google Scholar] [CrossRef]
  56. Miró, J.; Kyriazi, A.; Natili, M.; Ronchi, S. Buffering National Welfare States in Hard Times: The Politics of EU Capacity-Building in the Social Policy Domain. Soc. Policy Adm. 2024, 58, 215–227. [Google Scholar] [CrossRef]
  57. McKay, A.; Jukka, P.; Schimanski, C. The Tax Elasticity of Formal Work in Sub-Saharan African Countries. J. Dev. Stud. 2024, 60, 217–244. [Google Scholar] [CrossRef]
  58. Torm, N.; Oehme, M. Social Protection and Formalization in Low- and Middle-Income Countries: A Scoping Review of the Literature. World Dev. 2024, 181, 106662. [Google Scholar] [CrossRef]
  59. Banerjee, A.; Hanna, R.; Olken, B.A.; Sverdlin Lisker, D. Social Protection in the Developing World. J. Econ. Lit. 2024, 62, 1349–1421. [Google Scholar] [CrossRef]
  60. Lv, J.; Li, S.; Zhu, M.; Huang, W. Can the Digital Economy Development Limit the Size of the Informal Economy? A Nonlinear Analysis Based on China’s Provincial Panel Data. Econ. Anal. Policy 2024, 83, 896–921. [Google Scholar] [CrossRef]
  61. Anjarwi, A.W. Tax Burden and Poverty in Lower-Middle-Income Countries: The Moderating Role of Fiscal Freedom. Dev. Stud. Res. 2025, 12, 2466511. [Google Scholar] [CrossRef]
  62. Go, E.; Hill, S.; Jaber, M.H.; Jinjarak, Y.; Park, D.; Ragos, A. Developing Asia’s Fiscal Landscape and Challenges. Asia Pac. Econ. Lit. 2024, 38, 225–258. [Google Scholar] [CrossRef]
  63. Suleiman, F.M.S. Social Justice and Economic Policy: Analyzing the Interplay Between Welfare and Market Forces. Open Eur. J. Soc. Sci. Educ. 2025, 1, 34–45. [Google Scholar]
  64. Abdul Rashid, S.F.; Sanusi, S.; Abu Hassan, N.S. Digital Transformation: Confronting Governance, Sustainability, and Taxation Challenges in an Evolving Digital Landscape. In Corporate Governance and Sustainability: Navigating Malaysia’s Business Landscape; Alias, N., Yaacob, M.H., Eds.; Springer Nature: Singapore, 2024; pp. 125–144. ISBN 978-981-97-7808-9. [Google Scholar]
  65. Ben Youssef, A.; Dahmani, M. Assessing the Impact of Digitalization, Tax Revenues, and Energy Resource Capacity on Environmental Quality: Fresh Evidence from CS-ARDL in the EKC Framework. Sustainability 2024, 16, 474. [Google Scholar] [CrossRef]
  66. Sánchez-García, E.; Martínez-Falcó, J.; Marco-Lajara, B.; Manresa-Marhuenda, E. Revolutionizing the Circular Economy through New Technologies: A New Era of Sustainable Progress. Environ. Technol. Innov. 2024, 33, 103509. [Google Scholar] [CrossRef]
  67. OECD. Tax Administration 3.0: The Digital Transformation of Tax Administration 2020. Available online: https://www.oecd.org/en/publications/tax-administration-3-0-the-digital-transformation-of-tax-administration_ca274cc5-en.html (accessed on 10 March 2025).
  68. OECD. Global Outlook on Financing for Sustainable Development 2025. Available online: https://www.oecd.org/content/dam/oecd/en/publications/reports/2025/02/global-outlook-on-financing-for-sustainable-development-2025_6748f647/753d5368-en.pdf (accessed on 10 March 2025).
  69. OECD. OECD Economic Outlook. Available online: https://www.oecd.org/content/dam/oecd/en/publications/reports/2024/05/oecd-economic-outlook-volume-2024-issue-1_1046e564/69a0c310-en.pdf (accessed on 10 March 2025).
  70. Matthijs, M. Hegemonic Leadership Is What States Make of It: Reading Kindleberger in Washington and Berlin. Rev. Int. Polit. Econ. 2022, 29, 371–398. [Google Scholar] [CrossRef]
  71. United Nations. Financing for Sustainable Development Report 2024; Department of Economic and Social Affairs: New York, NY, USA, 2024. [Google Scholar]
  72. Heimberger, P. The New EU Fiscal Framework: Implications for Public Spending on the Green and Digital Transition; 2025. Available online: https://wiiw.ac.at/the-new-eu-fiscal-framework-implications-for-public-spending-on-the-green-and-digital-transition-dlp-7281.pdf (accessed on 11 March 2025).
  73. Datta, P.M. Digital Transformation in a Globalized World. In Global Technology Management 4.0: Concepts and Cases for Managing in the 4th Industrial Revolution; Datta, P.M., Ed.; Springer International Publishing: Cham, Switzerland, 2022; pp. 227–260. ISBN 978-3-030-96929-5. [Google Scholar]
  74. Dimitropoulos, G. Digital Plurilateralism in International Economic Law: Towards Unilateral Multilateralism? J. World Invest. Trade 2025, 26, 116–155. [Google Scholar] [CrossRef]
  75. World Bank. Digital Transformation of Tax and Customs Administrations. Available online: https://documents1.worldbank.org/curated/en/099448206302236597/pdf/IDU0e1ffd10c0c208047a30926c08259ec3064e4.pdf (accessed on 4 March 2025).
  76. Puaschunder, J.M. Global Perspectives. In The Future of Resilient Finance: Finance Politics in the Age of Sustainable Development; Puaschunder, J.M., Ed.; Springer International Publishing: Cham, Switzerland, 2023; pp. 103–151. ISBN 978-3-031-30138-4. [Google Scholar]
  77. Baghdasaryan, V.; Davtyan, H.; Sarikyan, A.; Navasardyan, Z. Improving Tax Audit Efficiency Using Machine Learning: The Role of Taxpayer’s Network Data in Fraud Detection. Appl. Artif. Intell. 2022, 36, 2012002. [Google Scholar] [CrossRef]
  78. Zheng, Q.; Xu, Y.; Liu, H.; Shi, B.; Wang, J.; Dong, B. A Survey of Tax Risk Detection Using Data Mining Techniques. Engineering 2024, 34, 43–59. [Google Scholar] [CrossRef]
  79. Belahouaoui, R.; Attak, E.H. Digital Taxation, Artificial Intelligence and Tax Administration 3.0: Improving Tax Compliance Behavior—A Systematic Literature Review Using Textometry (2016–2023). Account. Res. J. 2024, 37, 172–191. [Google Scholar] [CrossRef]
  80. Miao, N.; Sharif, A.; Ozturk, I.; Razzaq, A. How Do the Exploitation of Natural Resources and Fiscal Policy Affect Green Growth? Moderating Role of Ecological Governance in G7 Countries. Resour. Policy 2023, 85, 103911. [Google Scholar] [CrossRef]
  81. Angelakis, A.; Manioudis, M.; Koskina, A. Τhe Political Economy of Green Transition: The Need for a Two-Pronged Approach to Address Climate Change and the Necessity of “Science Citizens”. Economies 2025, 13, 23. [Google Scholar] [CrossRef]
  82. OECD. G7 Toolkit for AI in the Public Sector. Available online: https://www.oecd.org/content/dam/oecd/en/publications/reports/2024/10/g7-toolkit-for-artificial-intelligence-in-the-public-sector_f93fb9fb/421c1244-en.pdf (accessed on 9 March 2025).
  83. Shandilya, S.K.; Datta, A.; Kartik, Y.; Nagar, A. Navigating the Regulatory Landscape. In Digital Resilience: Navigating Disruption and Safeguarding Data Privacy; Shandilya, S.K., Datta, A., Kartik, Y., Nagar, A., Eds.; Springer Nature: Cham, Switzerland, 2024; pp. 127–240. ISBN 978-3-031-53290-0. [Google Scholar]
  84. European Union. The Future of European Competitiveness. 2024. Available online: https://commission.europa.eu/document/download/97e481fd-2dc3-412d-be4c-f152a8232961_en?filename=The%20future%20of%20European%20competitiveness%20_%20A%20competitiveness%20strategy%20for%20Europe.pdf (accessed on 6 March 2025).
  85. World Bank. Global Trends in AI Governance. Available online: https://documents1.worldbank.org/curated/en/099120224205026271/pdf/P1786161ad76ca0ae1ba3b1558ca4ff88ba.pdf (accessed on 8 March 2025).
  86. OECD. OECD Transfer Pricing Guidelines for Multinational Enterprises and Tax Administrations. Available online: https://www.oecd.org/en/publications/2022/01/oecd-transfer-pricing-guidelines-for-multinational-enterprises-and-tax-administrations-2022_57104b3a.html (accessed on 7 March 2025).
  87. OECD. Tax Challenges Arising from Digitalisation of the Economy—Global Anti-Base Erosion Model Rules (Pillar Two): Inclusive Framework on BEPS, OECD/G20 Base Erosion and Profit Shifting Project. Available online: https://www.oecd.org/en/publications/tax-challenges-arising-from-digitalisation-of-the-economy-global-anti-base-erosion-model-rules-pillar-two_782bac33-en.html (accessed on 7 March 2025).
  88. OECD. Tax Challenges Arising from the Digitalisation of the Economy—Administrative Guidance on the Global AntiBase Erosion Model Rules (Pillar Two). December 2023. Available online: https://www.oecd.org/content/dam/oecd/en/topics/policy-sub-issues/global-minimum-tax/administrative-guidance-global-anti-base-erosion-rules-pillar-two-december-2023.pdf (accessed on 7 March 2025).
  89. Xiao, C.; Tabish, R. Green Finance Dynamics in G7 Economies: Investigating the Contributions of Natural Resources, Trade, Education, and Economic Growth. Sustainability 2025, 17, 1757. [Google Scholar] [CrossRef]
  90. Doğan, B.; Chu, L.K.; Ghosh, S.; Diep Truong, H.H.; Balsalobre-Lorente, D. How Environmental Taxes and Carbon Emissions Are Related in the G7 Economies? Renew. Energy 2022, 187, 645–656. [Google Scholar] [CrossRef]
  91. Binyet, E.; Hsu, H.-W. Decarbonization Strategies and Achieving Net-Zero by 2050 in Taiwan: A Study of Independent Power Grid Region. Technol. Forecast. Soc. Change 2024, 204, 123439. [Google Scholar] [CrossRef]
  92. Baştuğ, S.; Akgül, E.F.; Haralambides, H.; Notteboom, T. A Decision-Making Framework for the Funding of Shipping Decarbonization Initiatives in Non-EU Countries: Insights from Türkiye. J. Shipp. Trade 2024, 9, 12. [Google Scholar] [CrossRef]
  93. Beckmann, M.; Zöttl, G.; Grimm, V.; Becker, T.; Schober, M.; Zipse, O. Setting the Course for Net Zero. In Road to Net Zero: Strategic Pathways for Sustainability-Driven Business Transformation; Zipse, O., Hornegger, J., Becker, T., Beckmann, M., Bengsch, M., Feige, I., Schober, M., Eds.; Springer International Publishing: Cham, Switzerland, 2023; pp. 17–59. ISBN 978-3-031-42224-9. [Google Scholar]
  94. OECD. Effective Carbon Rates 2023: Pricing Greenhouse Gas Emissions Through Taxes and Emissions Trading; OECD: Paris, France, 2023. [Google Scholar]
  95. Bartak, J.; Jabłoński, Ł.; Tomkiewicz, J. Does Income Inequality Explain Public Debt Change in OECD Countries? Int. Rev. Econ. Financ. 2022, 80, 211–224. [Google Scholar] [CrossRef]
  96. Alexeev, M.; Zakharov, N. Who Profits from Windfalls in Oil Tax Revenue? Inequality, Protests, and the Role of Corruption. J. Econ. Behav. Organ. 2022, 197, 472–492. [Google Scholar] [CrossRef]
  97. Khan, S. Investigating the Effect of Income Inequality on Corruption: New Evidence from 23 Emerging Countries. J. Knowl. Econ. 2022, 13, 2100–2126. [Google Scholar] [CrossRef]
  98. Halili, B.L.; Rodriguez Gonzalez, C. The Contingent Effects of Economic Growth and Institutions on Income Inequality: An Empirical Study. J. Int. Trade Econ. Dev. 2025, 1–32. [Google Scholar] [CrossRef]
  99. Farzana, A.; Samsudin, S.; Hasan, J. Drivers of Economic Growth: A Dynamic Short Panel Data Analysis Using System GMM. Discov. Sustain. 2024, 5, 393. [Google Scholar] [CrossRef]
  100. Altin, H. An Analysis of Global Stock Markets With the Autoregressive Distributed Lag Method. Int. J. Risk Conting. Manag. 2022, 11, 1–21. [Google Scholar] [CrossRef]
  101. Rauf, R.I.; Alrasheedi, M.A.; Sadiq, R.; Aldawsari, A.M.A. Evaluating Predictive Accuracy of Regression Models with First-Order Autoregressive Disturbances: A Comparative Approach Using Artificial Neural Networks and Classical Estimators. Mathematics 2024, 12, 3966. [Google Scholar] [CrossRef]
  102. Duran, H.E. Validity of Okun’s Law in a Spatially Dependent and Cyclical Asymmetric Context. Panoeconomicus 2022, 69, 447–480. [Google Scholar] [CrossRef]
  103. Zhou, L.; Zhang, Z. Ecological Well-Being Performance and Influencing Factors in China: From the Perspective of Income Inequality. Kybernetes 2023, 52, 1269–1293. [Google Scholar] [CrossRef]
  104. Wang, Q.; Sun, T.; Li, R. Does Artificial Intelligence Promote Green Innovation? An Assessment Based on Direct, Indirect, Spillover, and Heterogeneity Effects. Energy Environ. 2023, 36, 1005–1037. [Google Scholar] [CrossRef]
  105. Ho, T.T.; Xuan Hang, T.; Nguyen, Q.K. Tax Revenue-Economic Growth Relationship and the Role of Trade Openness in Developing Countries. Cogent Bus. Manag. 2023, 10, 2213959. [Google Scholar] [CrossRef]
  106. Ahmad, M.; Satrovic, E. How Do Fiscal Policy, Technological Innovation, and Economic Openness Expedite Environmental Sustainability? Gondwana Res. 2023, 124, 143–164. [Google Scholar] [CrossRef]
  107. Fatima, N.; Yanting, Z.; Guohua, N. Role of Environmentally Related Technologies and Revenue Taxes in Environmental Degradation in OECD Countries. Environ. Sci. Pollut. Res. 2023, 30, 73283–73298. [Google Scholar] [CrossRef]
  108. Nasim, A.; Nasir, M.A.; Downing, G. Determinants of Bank Efficiency in Developed (G7) and Developing (E7) Countries: Role of Regulatory and Economic Environment. Rev. Quant. Financ. Account. 2024, 65, 257–294. [Google Scholar] [CrossRef]
  109. Saeed, U.F.; Rabiatu, K.; Wiredu, I. The Roles of ICT and Governance Quality in the Finance-Growth Nexus of Developing Countries: A Dynamic GMM Approach. Cogent Econ. Financ. 2025, 13, 2448228. [Google Scholar] [CrossRef]
  110. Zhang, C.; Waris, U.; Qian, L.; Irfan, M.; Rehman, M.A. Unleashing the Dynamic Linkages among Natural Resources, Economic Complexity, and Sustainable Economic Growth: Evidence from G-20 Countries. Sustain. Dev. 2024, 32, 3736–3752. [Google Scholar] [CrossRef]
  111. Pipatnarapong, J.; Beelitz, A.; Jaafar, A. Corporate Social Responsibility and Tax Avoidance: Evidence from BRICS Countries. Corp. Gov. Int. J. Bus. Soc. 2025, ahead-of-print. [Google Scholar] [CrossRef]
  112. Sun, J.; Li, P.; Wang, Y. Policy Tools for Sustainability: Evaluating the Effectiveness of Fiscal Measures in Natural Resource Efficiency. Resour. Policy 2024, 89, 104575. [Google Scholar] [CrossRef]
  113. Abbas, S.; Xu, D.; Yuna, G.; Hussain, J.; Abbas, H.; Rafique, K. The Contribution of Resource-Based Taxation, Green Innovation, and Minerals Trade toward Ecological Sustainability in Resource-Rich Economies. Resour. Policy 2024, 93, 105092. [Google Scholar] [CrossRef]
  114. Wang, Y.; Wang, X.; Zhang, Z.; Cui, Z.; Zhang, Y. Role of Fiscal and Monetary Policies for Economic Recovery in China. Econ. Anal. Policy 2023, 77, 51–63. [Google Scholar] [CrossRef]
  115. Ouni, M.; Mraihi, R.; Mrad, S.; El Montasser, G. Exploring the Dynamic Linkages Between Poverty, Transportation Infrastructure, Inclusive Growth and Technology: A Continent-Wise Comparison in Lower-Middle-Income Countries. J. Knowl. Econ. 2025. [Google Scholar] [CrossRef]
  116. Liu, M.; Lu, J.; Liu, Q.; Wang, H.; Yang, Y.; Fang, S. The Impact of Executive Cognitive Characteristics on a Firm’s ESG Performance: An Institutional Theory Perspective. J. Manag. Gov. 2025, 29, 145–173. [Google Scholar] [CrossRef]
  117. Tran, Y.T.; Nguyen Phong, N.; Le, T.B.N.; Thi Thu Hao, N. Enhancing Public Organizational Performance in Vietnam: The Role of Top Management Support, Performance Measurement Systems, and Financial Autonomy. Public Perform. Manag. Rev. 2024, 47, 1192–1227. [Google Scholar] [CrossRef]
  118. Negri, C.; Dincă, G. Public Sector’s Efficiency as a Reflection of Governance Quality, an European Union Study. PLoS ONE 2023, 18, e0291048. [Google Scholar]
  119. International Monetary Fund Fiscal Policies: World Revenue Longitudinal Database. 2024. Available online: https://www.imf.org/en/Topics/fiscal-policies/world-revenue-longitudinal-database (accessed on 9 March 2025).
  120. World Bank Group. Interactive Data Access. Available online: https://www.worldbank.org/en/publication/worldwide-governance-indicators/interactive-data-access (accessed on 9 March 2025).
  121. Hall, S.; Marisol, L.; Stuart, M.; O’Hare, B. Government Revenue, Quality of Governance and Child and Maternal Survival. Appl. Econ. Lett. 2022, 29, 1541–1546. [Google Scholar] [CrossRef]
  122. Okunogbe, O.; Tourek, G. How Can Lower-Income Countries Collect More Taxes? The Role of Technology, Tax Agents, and Politics. J. Econ. Perspect. 2024, 38, 81–106. [Google Scholar] [CrossRef]
  123. Balasoiu, N.; Chifu, I.; Oancea, M. Impact of Direct Taxation on Economic Growth: Empirical Evidence Based on Panel Data Regression Analysis at the Level of Eu Countries. Sustainability 2023, 15, 7146. [Google Scholar] [CrossRef]
  124. Rumasukun, M.; Noch, M. Comparative Analysis of Tax System Effectiveness in Developed and Developing Countries. Golden Ratio Tax. Stud. 2023, 3, 100–112. [Google Scholar] [CrossRef]
  125. Choudhary, R.; Ruch, F.U.; Skrok, E. Taxing for Growth: Revisiting the 15 Percent Threshold. Available online: https://documents.worldbank.org/en/publication/documents-reports/documentdetail/099062724151523023/p1778861e0c40b081186a61ced16cac6cde (accessed on 10 March 2025).
  126. Gindelsky, M.; Moulton, J.; Wentland, K.; Wentland, S. When Do Property Taxes Matter? Tax Salience and Heterogeneous Policy Effects. J. Hous. Econ. 2023, 61, 101951. [Google Scholar] [CrossRef]
  127. Bielecki, M.; Stähler, N. Labor tax reductions in Europe: The role of property taxation. Macroecon. Dyn. 2022, 26, 419–451. [Google Scholar] [CrossRef]
  128. Mishra, S.; Mishra, A.K.; Panda, P. What Ails Property Tax in India? Issues and Directions for Reforms. J. Public Aff. 2022, 22, e2299. [Google Scholar] [CrossRef]
  129. Koster, H.R.A.; Pinchbeck, E.W. How Do Households Value the Future? Evidence from Property Taxes. Am. Econ. J. Econ. Policy 2022, 14, 207–239. [Google Scholar] [CrossRef]
  130. Decker, J.W. An (in) Effective Tax and Expenditure Limit (TEL): Why County Governments Do Not Utilize Their Maximum Allotted Property Tax Rate. Public Adm. 2023, 101, 376–390. [Google Scholar] [CrossRef]
  131. Shaqiri, V.; Elshani, A.; Ahmeti, S. The Effect of Direct and Indirect Taxes on Economic Growth in Developed Countries. Ekonomika 2024, 103, 123–139. [Google Scholar] [CrossRef]
  132. Mascagni, G.; Dom, R.; Santoro, F.; Mukama, D. The VAT in Practice: Equity, Enforcement, and Complexity. Int. Tax Public Financ. 2023, 30, 525–563. [Google Scholar] [CrossRef]
  133. Ferrera, M.; Corti, F.; Keune, M. Social Citizenship as a Marble Cake: The Changing Pattern of Right Production and the Role of the EU. J. Eur. Soc. Policy 2023, 33, 493–509. [Google Scholar] [CrossRef]
  134. Lompo, A.A.B. How Does Financial Sector Development Improve Tax Revenue Mobilization for Developing Countries? Comp. Econ. Stud. 2024, 66, 91–125. [Google Scholar] [CrossRef]
  135. Corti, F.; Vesan, P. From Austerity-Conditionality towards a New Investment-Led Growth Strategy: Social Europe after the Recovery and Resilience Facility. Soc. Policy Adm. 2023, 57, 513–548. [Google Scholar] [CrossRef]
  136. Balasundharam, V.; Basdevant, O.; Benicio, D.; Ceber, A.; Kim, Y.; Mazzone, L.; Selim, H.; Yang, Y. Fiscal Consolidations: Taking Stock of the Success Factors, Impact, and Design. IMF Work. Pap. 2023, 2023, A001. [Google Scholar] [CrossRef]
  137. Camara, A. The Effect of Foreign Direct Investment on Tax Revenue. Comp. Econ. Stud. 2023, 65, 168–190. [Google Scholar] [CrossRef]
Figure 1. Strategic areas of intervention in fiscal capacity development. Source: Prepared by authors according to International Monetary Fund [1].
Figure 1. Strategic areas of intervention in fiscal capacity development. Source: Prepared by authors according to International Monetary Fund [1].
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Figure 2. Average values of total tax revenues (TaxRev) and compulsory social contributions (SocialCon) in G7 countries (2000–2022). Source: Elaborated by the authors using Stata 18 program.
Figure 2. Average values of total tax revenues (TaxRev) and compulsory social contributions (SocialCon) in G7 countries (2000–2022). Source: Elaborated by the authors using Stata 18 program.
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Figure 3. Dynamic evolution of average values of the main government revenue categories in the G7 Member States, 2000–2022. Source: Elaborated by the authors using Stata 18 program.
Figure 3. Dynamic evolution of average values of the main government revenue categories in the G7 Member States, 2000–2022. Source: Elaborated by the authors using Stata 18 program.
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Table 1. Indicators analyzed.
Table 1. Indicators analyzed.
SymbolIndicatorsU.MSource
TaxRevTax revenuePercentage of GDPInternational Monetary Fund [119]
TaxIncITaxes on income and profit of individuals Percentage of GDPInternational Monetary Fund [119]
TaxIncCTaxes on income and profits of corporationsPercentage of GDPInternational Monetary Fund [119]
TaxProTaxes on propertyPercentage of GDPInternational Monetary Fund [119]
TaxSalTaxes on sales and productionPercentage of GDPInternational Monetary Fund [119]
TaxTraTaxes on international tradePercentage of GDPInternational Monetary Fund [119]
TaxOthTaxes not elsewhere classifiedPercentage of GDPInternational Monetary Fund [119]
SocialConSocial contributionsPercentage of GDPInternational Monetary Fund [119]
GrantsGrants revenuePercentage of GDPInternational Monetary Fund [119]
RevOthOther revenuePercentage of GDPInternational Monetary Fund [119]
RegQualRegulatory qualityPercentile rankWorld Bank Group
[120]
ConCorrControl of corruptionPercentile rankWorld Bank Group
[120]
Source: Elaborated by authors.
Table 2. Descriptive statistics.
Table 2. Descriptive statistics.
VariableMeanStd. Dev.MinMax
TaxRev24.4734.43915.1130.914
TaxIncI9.2582.2504.02613.179
TaxIncC2.8510.8631.3095.813
TaxPro2.551.1620.574.484
TaxSal8.9872.928412.596
TaxTra0.0970.10900.398
TaxOth0.4251.3490.00116.091
SocialCon10.9934.9994.46619.016
Grants0.0740.12101.019
RevOth4.6061.5851.629.204
RegQual1611.3660.3350.49
ConCorr1611.4110.5170.01
Source: Elaborated by the authors using Stata 18 program.
Table 3. Matrix of correlations.
Table 3. Matrix of correlations.
Variables(1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)
(1) TaxRev1.000
(2) TaxIncI0.6821.000
(3) TaxIncC−0.010−0.2981.000
(4) TaxPro0.2340.0960.1921.000
(5) TaxSal0.7640.228−0.151−0.1811.000
(6) TaxTra−0.3870.0510.2970.400−0.8331.000
(7) TaxOth0.1860.218−0.165−0.3130.234−0.2391.000
(8) SocialCon0.049−0.349−0.161−0.4250.488−0.6120.1421.000
(9) Grants0.2740.328−0.233−0.5990.395−0.3990.3880.2961.000
(10) RevOth0.4750.635−0.1110.3070.0450.194−0.076−0.1100.0641.000
(11) RegQual−0.0440.1280.1100.254−0.2010.206−0.429−0.418−0.3710.2711.000
(12) ConCorr−0.177−0.1340.2500.293−0.2590.252−0.437−0.248−0.4040.2430.8161.000
Source: Elaborated by the authors using Stata 18 program.
Table 4. Variance inflation factor (VIF).
Table 4. Variance inflation factor (VIF).
VariableVIF1/VIF
TaxTra7.1680.14
TaxSal6.7080.149
TaxIncI6.0720.165
RegQual5.1980.192
ConCorr4.7050.213
SocialCon3.7310.268
RevOth3.3110.302
Grants2.6380.379
TaxPro2.6330.38
TaxIncC1.6420.609
TaxOth1.5180.659
Mean VIF4.12
Source: Elaborated by the authors using Stata 18 program.
Table 5. Hadri LM.
Table 5. Hadri LM.
H0: All panels are stationaryNumber of panels = 7
Ha: Some panels contain unit rootsNumber of periods = 23
Time trend: Not includedAsymptotics: T, N -> Infinity sequentially
Heteroskedasticity: Not robust             LR variance: (not used)
Statisticp-value
TaxRev18.07610.0000
TaxIncI14.64660.0000
TaxIncC10.34860.0000
TaxPro3.48330.0002
TaxSal19.33090.0000
TaxTra12.64030.0000
TaxOth1.24770.0005
SocialCon26.26670.0000
Grants3.17580.0007
RevOth3.94600.0000
RegQual14.65860.0000
ConCorr14.39600.0000
Source: Elaborated by the authors using Stata 18 program.
Table 6. Linear regression model.
Table 6. Linear regression model.
TaxRevCoef.St.Err.t-Valuep-Value[95% Conf. Interval]Sig
TaxIncI1.0440.02052.3401.0041.083***
TaxIncC1.1190.02741.4101.0661.172***
TaxPro1.0580.02541.6301.0081.109***
TaxSal1.0950.01668.0101.0631.127***
TaxTra1.8570.4494.1400.9712.744***
TaxOth00.017−0.020.988−0.0330.033
SocialCon0.0370.0075.2700.0230.051***
Grants0.1720.2450.700.483−0.3120.656
RevOth0.1670.0218.0100.1260.209***
RegQual−0.5040.124−4.070−0.748−0.259***
ConCorr−0.3130.076−4.100−0.464−0.162***
Constant−1.160.236−4.910−1.627−0.693***
Mean dependent var
24.473
SD dependent var
4.439
R-square
0.997
Number of obs
161
F-test
5391.372
Prob > F
0.000
Akaike crit. (AIC)
−4.413
Bayesian crit. (BIC)
32.564
Source: Elaborated by the authors using Stata 18 program. *** p < 0.01.
Table 7. Arellano–Bond dynamic estimation of tax revenue determinants in G7 countries.
Table 7. Arellano–Bond dynamic estimation of tax revenue determinants in G7 countries.
TaxRevCoef.St.Err.t-Valuep-Value[95% Conf. Interval]Sig
L0.0170.0141.250.213−0.0100.044
L200.010−0.010.994−0.0210.020
TaxIncI1.0740.01764.8601.0421.107***
TaxIncC0.9470.01754.7700.9130.981***
TaxPro0.9850.02933.5700.9271.042***
TaxSal1.1020.01862.7401.0681.137***
TaxTra0.9780.1964.9800.5941.363***
TaxOth−0.0050.006−0.940.349−0.0160.006
SocialCon−0.0470.012−3.800−0.071−0.023***
Grants−0.1060.09−1.180.236−0.2820.070
RevOth0.0450.0123.8200.0220.068***
RegQual0.0090.0630.140.892−0.1150.132
ConCorr0.0570.0620.920.356−0.0640.179
Constant−0.8510.223−3.810−1.288−0.413***
Mean dependent var24.448SD dependent var4.441
Number of obs140Chi-square37998.012
Source: Elaborated by the authors using Stata 18 program. *** p < 0.01.
Table 8. Arellano–Bond autocorrelation tests for the dynamic panel model.
Table 8. Arellano–Bond autocorrelation tests for the dynamic panel model.
Arellano-Bond test for AR(1) in first differencesz = −0.93Pr > z = 0.035
Arellano-Bond test for AR(2) in first differencesz = −0.60Pr > z = 0.551
Source: Elaborated by the authors using Stata 18 program.
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