Revisiting Fiscal Policy and Income Inequality in the Context of Economic Complexity

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Employing both the SGMM and Bayesian regression, the analysis provides robust evidence on their intertwined effects. The results reveal that economic complexity and government expenditure tend to exacerbate inequality, while tax revenue serves as a strong mitigating factor. Interestingly, when fiscal expenditure interacts with economic complexity, the combined effect significantly reduces inequality with a posterior probability of 100%, highlighting the corrective role of fiscal policy in complex economies. Beyond these core findings, control variables such as GDP per capita, unemployment, inflation, and trade openness are shown to worsen inequality, whereas foreign direct investment and institutional quality help narrow it. By integrating frequentist and Bayesian approaches, this research contributes to a more nuanced understanding of the mechanisms through which fiscal tools and structural economic characteristics jointly shape inequality. The findings underscore the importance of aligning fiscal strategies with economic complexity to promote equitable growth. Policymakers should prioritize redistributive spending, strengthen tax systems, and tailor interventions to the structural dynamics of their economies to effectively address income disparities in an era of globalization and rising complexity. Business and commerce/Economics Social science/Economics Physical sciences/Mathematics and computing fiscal policy income inequality economic complexity 1. Introduction Global income inequality has emerged as one of the most critical challenges facing contemporary societies. Despite remarkable economic progress in recent decades, the benefits of globalization have been unevenly distributed, often amplifying the gap between high- and low-income groups. Such disparities not only hinder inclusive growth but also threaten social cohesion, political stability, and sustainable development on a global scale. Recognizing this, the United Nations has explicitly identified the reduction of income inequality as one of its Millennium Development Goals, and later reinforced it as a central component of the Sustainable Development Goals (Dinh, 2025). The urgency of this issue is particularly evident in developing economies, where inequality tends to manifest more severely. In these contexts, governments often prioritize rapid economic expansion as a proxy for social welfare improvements; however, growth-driven strategies do not always translate into equitable outcomes (Rachely et al., 2023 ). Consequently, while developing countries may achieve impressive GDP growth rates, the unequal distribution of wealth and opportunities can undermine long-term development, exacerbate poverty traps, and reinforce structural disadvantages for vulnerable populations. Addressing income inequality, therefore, requires not only sustaining growth but also implementing effective redistributive policies that ensure the gains of globalization and economic progress are shared more broadly across society. In the era of globalization, the growing disparity in income between the affluent and the underprivileged worsens for a couple of reasons. Firstly, the advantages in knowledge, wealth, and experience held by the wealthy enable them to reap greater benefits from globalization compared to the less privileged (United Nations, 2020 ). Secondly, the conventional use of GDP as an economic gauge falls short in capturing the intricate nature of production and competition (Chu and Hoang, 2020 ). Understanding the factors driving income inequality (II) is complex, as it intertwines with various economic, social, and institutional dynamics, including resources, institutions, social capital, historical trajectories, technological advancements, and returns on capital (Acemoglu and Robinson, 2012; Collier, 2007 ; Piketty, 2014 ). These factors often manifest in a nation's production capacity (Hausmann et al., 2014 ), with Hidalgo and Hausmann ( 2009 ) introducing the concept of "economic complexity" to gauge this capacity, which is reflected in the diversity and ubiquity of a country's production capabilities. The Economic Complexity Index (ECI) is a notable index that encompasses factors related to knowledge, scientific and technological content in the economy. A high ECI is a factor ensuring long-term economic growth that in fact, some countries like Saudi Arabia have a high GDP, but their ECI is not high due to their economy mainly relying on oil sales. For these countries, the risk of growth is significant. This prompts consideration of investigating the correlation between economic complexity and income inequality. Additionally, fiscal policy emerges as a pivotal instrument in economic management, aiding governments in navigating through economic fluctuations (Bon, 2023 ). During periods of economic downturn characterized by high unemployment rates, governments proactively increase spending or decrease taxes, or both (known as expansionary policy). Conversely, in times of rapid economic growth accompanied by high inflation rates, governments trim spending or hike taxes, or both (contractionary policy). Notably, governments may channel more resources towards facilitating access to healthcare and education for low-income individuals through social subsidies, aiming to mitigate income disparities between high and low earners and thereby narrow societal income inequality. Given the irreversible nature of economic complexity, and the persistent importance of fiscal spending in effective wealth redistribution, this study proposes to delve deeper into exploring the impact of the interplay between public expenditure and economic complexity on income inequality. To the best of our knowledge, research linking Economic Complexity (EC) and income inequality (II) remains fragmented. Prior studies report mixed evidence: Le Caous and Huarng ( 2020 ) and Hartmann et al. ( 2017 ) found that higher EC reduces inequality, whereas Lee and Vu ( 2020 ) showed that EC exacerbates disparities, and Chu and Hoang ( 2020 ) highlighted a non-linear effect. However, these contributions largely neglect the role of fiscal policy as a potential conditioning factor in this nexus. Since EC reflects a long-term, path-dependent process that requires substantial resources and knowledge accumulation (Hidalgo & Hausmann, 2009 ), fiscal policy may act as a decisive instrument that moderates or redirects its distributional consequences. This underscores the importance of examining whether the EC–II relationship is contingent on fiscal interventions or subject to structural turning points. In terms of the academic aspect, it is noteworthy that the literature review in Section 2 highlights three factors that differentiate this study from related research. Firstly, the interaction between fiscal policy and EC is addressed. Secondly, this study provides experimental evidence demonstrating the different roles of fiscal policies including tax revenue and public expenditure, and EC in income inequality. Thirdly, unlike previous studies, this research adopts a Bayesian approach in probability theory, offering advantages in handling small samples, autocorrelation phenomena, and endogeneity issues in the model. Furthermore, the SGMM regression is also conducted for comparison with the Bayesian regression. 2. Literature review 2.1. Fiscal policy and income inequality The theoretical standpoint regarding the influence of fiscal policy on wealth and II originates from the growing significance of government intervention in the economy, a viewpoint championed by Keynes and his followers. They stress the importance of governments utilizing fiscal policy to manage economic fluctuations and address market failures. Through mechanisms such as expenditure and taxation, governments can redistribute national income and mitigate income disparities within society. Notably, Alesina and Ardagna ( 1998 ) assert that public spending plays a crucial role in reducing inequality, particularly through social welfare initiatives like healthcare, education, social safety nets, and employment schemes. In contrast, the conventional economic-political perspective suggests that governments resort to distortionary taxation to redistribute national income in response to heightened levels of inequality (Alesina and Rodrik, 1994 ; Persson and Tabellini, 1991 ). However, it's argued that direct taxation negatively impacts economic growth by hampering the accumulation of human and physical capital. Conversely, the emerging economic-political viewpoint refutes the notion of distortionary taxes having an adverse effect on economic growth. Instead, it advocates for the positive repercussions of expenditure redistribution on growth. In this study, we posit that government fiscal spending is invariably initiated in response to rising II within the economy. Thus, the following hypothesis is established: H1: Fiscal expenditure reduces II. 2.2. Economic complexity and income inequality The wealth of an economy is fundamentally linked to knowledge accumulation and labor specialization (Hausmann et al., 2014 ). National prosperity, therefore, depends on the capacity of individuals and organizations to generate, combine, and apply knowledge. Building on this view, Hidalgo et al. (2014) introduced the concept of Economic Complexity (EC), which captures both the quality of production factors and their transformation into output. Differences in development across countries can thus be explained by differences in the types of goods they produce. High-complexity and competitive products require not only raw materials but, more importantly, advanced inputs such as skilled labor, experience, and intellectual property. Unlike raw materials, which are easily traded, knowledge—particularly its latent component—cannot be transferred quickly across borders, as it develops slowly within individuals through costly and uncertain processes (Hausmann et al., 2014 ). To quantify this, Hidalgo and Hausmann ( 2009 ) developed the EC, which measures two dimensions: diversity, reflecting the range of products a country can competitively produce, and ubiquity, indicating how many other countries are able to produce those same goods. Together, these dimensions provide a comprehensive measure of a nation’s productive knowledge and potential for sustained growth. The necessity of specific product production hinges on a country's knowledge of productivity and the diversity in its exports and products, illustrating the breadth and depth of its productivity insights. According to Hidalgo and Hausmann ( 2009 ), a country boasting diverse and distinctive production knowledge is more prone to achieving heightened specialization levels, a phenomenon linked to two principal processes. Firstly, they can innovate new products by amalgamating existing knowledge, and secondly, they can amass new capabilities and merge them with existing ones to foster product expansion. Research has indicated that EC can forecast adverse trends in II (Le Caous and Huarng, 2020 ) and can mitigate II by generating employment opportunities across various workforce strata (Albassam, 2015 ; Egger and Etzel, 2012 ; Hartmann, 2014 ). Conversely, in economies with low complexity, production structures and employment predominantly rely on low-skilled labor, culminating in elevated II. The concept of a complex economy aids in diminishing II and ensuring business resilience in a dynamically evolving global landscape (Barnes and Coogan, 2015; Joya, 2015 ). Consequently, heightened specialization levels can enhance productivity and bolster profit margins, thus elevating lifelong incomes for workers (Constantine, 2017 ). Enhanced wages facilitate upward mobility for the impoverished and contribute to II reduction (Hartmann et al., 2017 ; Hidalgo, 2015). In fact, Le et al. ( 2020 ) proposed a reverse U-shaped relationship between economic diversification and II, contending that economic diversification initially augments II until reaching a threshold, beyond which a complex economic framework aids in diminishing II. However, in this study, we posit that EC might perpetually exacerbate income disparities for the following rationale: In a knowledge-based economy, while heightened specialization engenders more job opportunities, each production endeavor necessitates skilled labor endowed with expertise and knowledge to attain optimal productivity (Constantine, 2017 ). The demand for skilled labor escalates (Constantine, 2017 ) concomitant with the economic structural diversification process. Consequently, there exists an uneven distribution of job opportunities between skilled and unskilled labor. Notably, skilled workers assimilate new knowledge expeditiously owing to their pre-existing competencies and superior adaptability to evolving labor market requisites, thus reaping greater benefits from the economy's complexity. In essence, heightened EC may continually exacerbate income disparities. Drawing from these arguments, we formulate the second hypothesis as follows: H2: EC exacerbates II. Constructing a complex economy is a long-term process (Hidalgo & Hausmann, 2009 ), during which the drivers of II may evolve. The skill-biased technological change theory alone cannot fully explain the persistent positive link between EC and inequality (Card & DiNardo, 2002 ; Weiss, 2008 ), suggesting that moderating factors may reshape this relationship. Prior studies highlight several mitigating channels: enhancing wages and employment opportunities (Lee & Sissons, 2016 ), improving education to raise lifetime earnings (Castro Campos et al., 2016 ; Norris et al., 2015), and trade liberalization that increases demand for lower-skilled labor (Asteriou et al., 2014 ). In particular, fiscal policy—through redistributive spending and taxation—has been widely recognized as a direct mechanism to reduce disparities (Anderson et al., 2015 ). Building on this perspective, we argue that fiscal instruments, when implemented in economies with higher complexity, can counterbalance inequality pressures. Accordingly, we propose the following hypothesis: H3: The interaction of fiscal policy and EC reduces II. 2.3 Related literature review 2.3.1. Impact of fiscal policy on income inequality A growing body of research has investigated the distributive effects of government spending on income inequality (II), though the evidence remains mixed. Wong ( 2016 ) shows that public expenditure reduces inequality in 16 Asia–Pacific economies, while Wong ( 2017 ) finds contrasting results, with spending lowering inequality in Asia but increasing it in Latin America. Using IV–GMM techniques, Cevik and Correa-Caro ( 2020 ) report that government spending contributes to reducing II in China and 33 developing economies over the long term. Similar evidence is provided by Kollmeyer ( 2015 ), who, based on Western economies, highlights the equalizing role of fiscal expenditure. By contrast, tax-related effects appear more nuanced: Apergis ( 2021 ) demonstrates that tax revenue increases II in developed economies, whereas budget deficits help to narrow it, while Taghizadeh-Hesary et al. ( 2020 ) find that tax revenue reduces inequality in Japan using a VECM approach. Clifton et al. ( 2020 ) and Gunasinghe et al. ( 2021 ) provide evidence that fiscal policies—through government spending and taxation—can narrow income inequality (II). Clifton et al. ( 2020 ), using fixed effects and LSDVC estimations for 17 Latin American countries (1990–2014), show that redistributive fiscal policies reduce disparities. Similarly, Gunasinghe et al. ( 2021 ), applying a simultaneous equation model to 19 developed economies, find that governments employ redistributive spending financed by direct taxes as an effective mechanism to curb inequality. However, results are not uniform across contexts. Malla and Pathranarakul ( 2022 ), analyzing 2000–2019 data with a system GMM approach, report that progressive income taxes reduce inequality only in developing countries, while government size and higher spending on education and health alleviate inequality in developed nations. By contrast, taxes on goods and services appear to have no significant global effect, and institutional quality exerts only a limited influence. In Sub-Saharan Africa, Oseni et al. ( 2023 ) highlight further complexity: while income tax reduces inequality, it simultaneously worsens health outcomes, whereas health-oriented assistance improves life expectancy. Overall, the literature suggests that fiscal policy can shape inequality, but its effectiveness depends on country-specific structures, institutional capacity, and spending priorities. Yet, existing studies often assess fiscal instruments in isolation, without considering how their distributive impact may interact with structural economic factors such as economic complexity. This leaves open an important question: under what conditions does fiscal policy effectively offset the inequality pressures generated by complex, knowledge-driven economies? 2.3.2. The impact of EC on II Hartmann et al. ( 2017 ), using multivariate regression on 150 countries (1963–2008), found that EC contributes to reducing income inequality (II). Similarly, Le Caous and Huarng ( 2020 ) confirmed the positive role of EC in fostering human development through inequality reduction in 87 developing countries, employing a hierarchical linear model with robustness checks. Other works further highlight this equalizing effect: Albassam ( 2015 ) links economic diversification to job creation, Egger and Etzel ( 2012 ) emphasize its role in curbing corruption, and Hartmann ( 2014 ) stresses the institutional improvements stemming from more complex economies. More recently, Dinh ( 2025a ), applying Bayesian and GMM methods to 14 countries (2005–2021), also reported that EC narrows income inequality. Collectively, these studies reinforce the view that EC enhances development by promoting diversification, institutional quality, and social inclusion. Chu and Hoang ( 2020 ) investigated the relationship between EC and II using panel data from 88 countries spanning from 2002 to 2017. Their findings indicated a significant association between EC and higher levels of II. These findings hold relevance for policymakers, suggesting the need for adjustments in policies to address inequality while transitioning towards knowledge-based economies. Rachely et al. ( 2023 ) explored the impact of healthcare spending, education spending, social welfare spending, and trade on income distribution disparities in Indonesia. Utilizing secondary data from various sources, they employed panel data regression analysis using the Random Effects Model (REM). The study covered all 34 provinces in Indonesia from 2010 to 2022. The results unveiled that both aggregate and individual spending on healthcare, education, social welfare, and trade significantly influenced income distribution disparities in Indonesia. Particularly, investments in healthcare and education exhibited an inverse correlation with II, whereas social welfare and trade expenditure showed a positive correlation. Moreover, the impact of EC on widening income gaps was further substantiated by research conducted by Berman et al. (1998), Card and DiNardo ( 2002 ), and Lee and Vu ( 2020 ). 2.4. Research Gap Through our literature review, we identified the following limitations: Firstly, previous studies have been conducted in isolation, focusing on individual aspects such as the impact of fiscal policies on II or the effect of EC on II. However, there has been a notable absence of research delving into the interaction between EC and fiscal expenditure on II. Secondly, prior research has largely depended on conventional frequency-based models, which impose a set of restrictive assumptions that may not adequately capture real-world complexities, thereby risking biased inference and prediction errors. These methods generally regard parameters as fixed but unknown, even though their values can vary as sample sizes change. In contrast, the Bayesian framework treats parameters as random variables with probability distributions, allowing uncertainty to be explicitly incorporated into the estimation process. A key advantage of this approach is its reduced reliance on large samples, while also providing flexibility in dealing with econometric issues such as autocorrelation, heteroskedasticity, and endogeneity. Consequently, Bayesian methods offer a more reliable and comprehensive tool for hypothesis testing and inference in the study of income inequality. In this study, the authors employ Bayesian methods to examine the impact of fiscal policies, EC, and their interaction on II. This approach allows for the derivation of appropriate policy implications. 3. Description of ResearchVariables and Data 3.1. Model and Methodology Based on the studies by Bon ( 2023 ), Lee and Vu ( 2020 ), and Chu and Hoang ( 2020 ), the authors establish the research model as follows: $$\:{II}_{i,t}={\beta\:}_{o}+{\beta\:}_{2}{II}_{i,t-1}+\:{\beta\:}_{2}{ECI}_{i,t}+\:{\beta\:}_{3}{FS}_{i,t}\:+\:{\beta\:}_{4}ECI*{FS}_{i,t}+\:\:{\beta\:}_{x}{X}_{i,t}\:+\:{\:\epsilon\:}_{i,t}$$ 1 Where: II represents II; ECI represents EC; FP represents fiscal policy including government spending (GS) and tax revenue (TR); X is a vector of control variables including GDP per capita (GDP), unemployment rate (UNE), public governance/institutional quality (RQ), inflation rate (INF), and foreign direct investment (FDI). The specific research variables are described in Table 1 . Several macroeconomic and institutional factors are widely recognized as key determinants of income inequality. Economic growth plays a dual role: while it can reduce inequality by creating more job opportunities and increasing overall income levels, it may also exacerbate inequality if the benefits of growth are disproportionately captured by higher-income groups (Barro, 2000 ). Unemployment rate is another critical factor, as higher unemployment typically leads to income loss and social exclusion, particularly among low-skilled workers, thereby widening income disparities. FDI can contribute to income equalization by transferring technology and generating employment; however, in many cases, FDI benefits are concentrated in capital-intensive sectors, limiting its redistributive effect (Herzer & Nunnenkamp, 2013 ). Similarly, inflation tends to hurt low-income households more severely due to their limited ability to hedge against rising prices, leading to a regressive impact on income distribution (Albanesi, 2007 ). The effect of trade openness on inequality is context-dependent: it may reduce inequality through comparative advantage and efficiency gains, but can also increase it by displacing uncompetitive domestic sectors (Dinh, 2025). Finally, institutional quality plays a pivotal role, as strong institutions enhance transparency, improve policy effectiveness, and ensure equitable access to opportunities and services, thus helping to reduce inequality (Acemoglu & Robinson, 2012). Model (1) encounters the following issues: Fiscal policy variables including government spending, tax revenue, economic growth, and unemployment rate may be correlated with unobserved components leading to endogeneity. The bidirectional relationship between variables, such as between government spending and economic growth, can lead to endogeneity issues as both variables influence each other, making it difficult to clearly determine cause and effect. Furthermore, unobserved factors, such as institutional quality or consumer expectations, may simultaneously affect these variables, and if not accounted for in the model, they could cause bias in the results. Additionally, the simultaneity effect between variables like unemployment rate and tax revenue also introduces endogeneity, as an increase in unemployment may reduce tax revenue, and vice versa. Finally, selection bias may arise when countries determine their levels of spending or taxation based on specific economic and political characteristics, leading to distortion in the model. To address these endogeneity concerns, methods such as instrumental variables, the Generalized Method of Moments (GMM), or random effects models can help control for unobserved factors, providing more accurate estimates and isolating the effects of the variables under study. The presence of unobserved factors—such as culture, geography, customs, and demographic characteristics—can pose a serious econometric issue when they are correlated with independent variables like fiscal policy indicators. These time-invariant characteristics, commonly referred to as fixed effects, vary across countries but remain constant over time. If not properly accounted for, they can cause omitted variable bias, leading to inconsistent estimates of the effects of fiscal policies on income inequality. For instance, cultural attitudes toward taxation or redistribution may affect both the structure of fiscal policy and the level of income inequality, but if such variables are excluded from the model, their influence may be wrongly attributed to the observed independent variables. To control for this, fixed effects models are often employed to absorb such unobserved heterogeneity (Van Bon, 2022 ; Dinh 2025a ). High correlation among independent variables—such as between GDP and fiscal policy indicators like government spending or tax revenue—can lead to multicollinearity, a situation that undermines the reliability of regression estimates. When multicollinearity is present, it becomes difficult to isolate the individual impact of each variable on the dependent variable, as they provide overlapping information. For example, a booming economy (reflected by higher GDP) often leads to increased tax revenues and government spending, making it challenging to distinguish the separate effects of each. Multicollinearity inflates standard errors and may render statistically significant relationships appear insignificant. Addressing this issue may involve dropping or transforming variables, applying dimensionality reduction techniques (e.g., Principal Component Analysis), or using instrumental variables.. The presence of lagged II ( \(\:{II}_{i,t-1}\) ) may lead to autocorrelation issues. Specifically, in a model where the current level of inequality depends on its past values, residuals may also exhibit time dependence. If this issue is not addressed, it could distort the interpretation of fiscal policy impacts over time. Common remedies include using dynamic panel data models such as the Arellano-Bond GMM estimator, which is designed to handle both autocorrelation and endogeneity due to lagged dependent variables. The aforementioned econometric concerns can lead to biased estimates in Ordinary Least Squares (OLS) regression. Traditional alternatives such as the Random Effects Model (REM) and Fixed Effects Model (FEM) are also inadequate, as they do not properly address issues of error serial correlation and endogeneity. To overcome these limitations, this study follows Judson and Owen ( 1999 ) and employs the System Generalized Method of Moments (SGMM). Diagnostic tools including the Arellano–Bond AR(2) test, together with the Sargan and Hansen tests, are applied to validate the model by detecting serial correlation and potential endogeneity. Beyond the GMM framework, this research also incorporates Bayesian regression, which offers distinct advantages in correcting for model weaknesses such as autocorrelation, heteroskedasticity, and endogeneity (Thach, 2020 ). By combining frequentist and Bayesian perspectives, the analysis enhances robustness. While SGMM, rooted in the frequentist tradition, relies on p-values and treats parameters as fixed but unknown, the Bayesian approach uses prior distributions and observed data to derive posterior probabilities. This probabilistic framework enables researchers to explicitly incorporate prior knowledge, update beliefs as new evidence emerges, and provide a richer interpretation of parameter estimates and model certainty (Kim & Quoc, 2024 ; Van & Le Quoc, 2024 ; Nguyen The et al., 2024; Quoc et al., 2025 a; Quoc et al., 2025 b; Le Quoc et al., 2025 ; Dinh, 2025b ). Table 1 Variable description Variable Sign Descriptions Studies Source Dependent variable Income inequality II After-tax income inequality index Chu và Hoang (2020); Behera & Karthiayani ( 2022 ) World Income Inequality Database (WIID) Independent variables Economic complexity ECI Economic complexity Index Dinh (2025) WIID Fiscal policy FP WB + Government spending GS Government spending/GDP Bon ( 2023 ); Huy et al. ( 2025 ) WB + Tax revenue TR Tax revenue/GDP Bon ( 2023 ); Huy et al. ( 2025 ) WB Control variables Economic growth GDP Economic growth per capita Chu and Hoang ( 2020 ), Oanh et al. ( 2023 ), Le Quoc ( 2024 ) WB Unemployment rate UNE This index is measured by the number of unemployed individuals as a percentage of the labor force Bon ( 2023 ) WB Foreign Direct Investment FDI FDI/GDP (%) WB Inflation rate INF Annual inflation growth (%) Dinh el al., (2024) WB Trade openness OPEN Percentage of total import and export of goods and services/GDP (%) Chu và Hoang (2020) WB Institutional quality IQ WB + Voice and accountability VOA WGI's set of 6 indexes receive values from − 2.5 to 2.5 Bon ( 2023 ); Van et al ( 2025 ) WGI + Political stability POL WGI + Effective state management GOV WGI + Regulated quality REG WGI + The rule of law RUL WGI + Control corruption COR WGI Source: Compiled by the author. 3.2. Data The measure of income inequality (II) is obtained from the World Income Inequality Database (WIID), which provides post-tax inequality indicators. EC, representing a country’s ability to produce a diverse and sophisticated set of goods, is sourced from the MIT Media Lab, constructed from international trade data that maps countries to their export products. Fiscal policy is proxied by government spending (GS) and tax revenue (TR), while additional controls such as GDP per capita and unemployment rate are taken from the World Bank. Institutional quality is derived from the Worldwide Governance Indicators (WGI), which include six governance dimensions—voice and accountability, political stability, government effectiveness, regulatory quality, rule of law, and control of corruption—scored from − 2.5 to 2.5. Although widely used in prior research (e.g., Dinh, 2025), these indicators are highly correlated and may introduce multicollinearity or over-parameterization if entered separately into the model. To address this issue, principal component analysis (PCA) is employed to combine the six dimensions into a single composite index of institutional quality, following Ullah and Khan (2017). Based on data availability, this study constructs an unbalanced panel covering 79 countries and territories, comprising 32 high-income and 46 middle-income economies according to the World Bank classification. The sample period spans 2002–2022, as continuous information from the Worldwide Governance Indicators is only available from 2002 onward. Descriptive statistics for the key variables are reported in Table 1 . To normalize their distribution, both II and EC are expressed in logarithmic form. The results in Table 2 indicate that the average tax revenue of the 79 countries is 49.1646, which is lower than the government expenditure of 53.3165, suggesting a fiscal deficit in these countries. Table 2 Descriptive statistics 79 countries worldwide Mean Std. Dev. Minimum Maximum II 3.4312 0.2813 3.1315 4.1203 GS 53.3165 8.1346 15.1346 65.0364 TR 49.1646 9.1364 19.1646 58.1346 ECI 0.4312 0.8936 -2.4364 2.2513 GDP 8.7913 1.3313 3.1344 11.6331 UNE 7.5613 4.0165 2.0013 27.466 FDI 22.8579 1.6743 15.8559 27.3215 INF 4.9686 5.5369 -2.0970 49.7211 OPEN 71.0953 27.0628 22.1060 146.1061 IQ 1.1133 0.2987 -0.6766 2.2346 Source: Author's processing 4. Regression Results and Discussion The regression outcomes are reported in Table 3 . Across all estimation approaches—most notably in the two-step System GMM—the fiscal policy variables, government spending (GS) and tax revenue (TR), exhibit endogeneity. To address this issue, both GS and TR were treated as endogenous instruments within the GMM specification, while the other explanatory variables were considered exogenous and used as instruments in the IV framework. The regression results derived from both the GMM and Bayesian approaches reveal consistent directional effects, thereby reinforcing the robustness and credibility of our findings. In particular, EC is shown to intensify II, aligning with the results of previous studies such as Chu and Hoang ( 2020 ) and Rachely et al. ( 2023 ), and lending strong support to our second hypothesis (H2). This outcome can be attributed to the structural dynamics of EC, which drives economies toward innovation-intensive and knowledge-based activities that disproportionately benefit highly skilled labor. As Constantine ( 2017 ) emphasizes, such transformations tend to deepen labor market segmentation—skilled workers, due to their adaptability and ability to absorb complex knowledge, are better positioned to capitalize on new economic opportunities. Meanwhile, unskilled workers often struggle to keep pace, resulting in a growing disparity in income distribution. Thus, EC acts as a catalyst for inequality by amplifying the returns to skill in an increasingly complex economic landscape. Moreover, tax revenue is found to have a mitigating effect on income inequality, echoing the findings of Bon ( 2023 ) and providing strong support for our first hypothesis (H1). This relationship highlights the critical role of effective tax collection in expanding the government’s fiscal capacity to implement redistributive policies and invest in social welfare programs. By channeling resources toward education, healthcare, and poverty alleviation initiatives, enhanced tax revenues can significantly reduce disparities across income groups. Thus, tax revenue serves not only as a key instrument for financing public goods but also as a powerful mechanism for promoting greater social and economic equity. However, contrary to expectations and previous findings in the literature (Bon, 2023 ; Chu and Hoang, 2020 ; Rachely et al., 2023 ), our results indicate that government expenditure is associated with an increase in income inequality, which runs counter to our first hypothesis (H1). This counterintuitive outcome may be attributed to inefficiencies in fiscal allocation or misalignment between spending priorities and equity objectives. In many cases, public funds may be disproportionately channeled toward growth-oriented investments—such as infrastructure, defense, or capital-intensive industries—that tend to benefit wealthier individuals or corporate interests, while neglecting pro-poor sectors like education, healthcare, and targeted social protection. When government spending favors sectors with high barriers to entry or limited employment generation for the low-skilled population, it can inadvertently exacerbate existing income disparities. Interestingly, when exploring the interaction between fiscal expenditure and economic complexity (EC), our analysis reveals a mitigating effect on income inequality. Specifically, in more economically diverse and complex environments—where inequality tends to be more pronounced—fiscal expenditure appears to serve as an effective corrective mechanism. This interaction term suggests that when government spending is strategically aligned with the challenges posed by EC, it can help offset the widening gaps by redistributing resources more equitably. As highlighted by Anderson et al. ( 2015 ), fiscal policies that prioritize social transfers, public goods, and inclusive development have the potential to directly narrow the income divide created by structural economic shifts. This nuanced finding adds an important dimension to the current discourse, emphasizing that the effectiveness of government spending in reducing inequality depends not only on its volume but also on its strategic alignment with broader economic dynamics. Table 3 The results of Bayesian and GMM regression models The dependent variable II GMM regression Bayes regression Coef P-value Coef P-value Mean MCSE Mean MCSE Lag.II 0.8316*** 0.0000 0.8431*** 0.0000 0.5366 0.0037 0.5367 0.0046 GS 0.1093*** 0.0000 0.1083*** 0.0000 0.1566 0.0020 0.1677 0.0026 TR -0.1335** 0.0135 0.0933*** 0.0094 -0.1933 0.0022 -0.2103 0.0003 ECI 0.0813** 0.0351 0.0903** 0.0236 0.0931 0.0013 0.0891 0.0016 GDP 0.0049*** 0.0010 0.0045*** 0.0012 0.0328 0.0003 0.0533 0.0006 UNE 0.0331** 0.0151 0.0332** 0.0134 0.0248 0.0001 0.0009 0.0001 FDI -0.0264* 0.0861 -0.0213** 0.0464 -0.0283 0.0001 -0.0316 0.0002 INF 0.8225*** 0.0000 0.7635*** 0.0000 0.2425 0.0003 0.1364 0.0008 OPEN 0.0354** 0.0494 0.0295** 0.0330 0.0434 0.0001 0.0115 0.0003 IQ -2.136* 0.0764 -1.3648* 0.0646 -3.4324 0.0163 -3.2344 0.0188 ECI_GS -0.0533*** 0.0000 0.04931*** 0.0001 -0.0756 0.0003 0.0813 0.0001 ECI_TR -0.0135*** 0.0009 0.0238*** 0.0005 -0.0234 0.0006 0.0211 0.0005 Sargan Test 0.190 0.198 AR(2) Test 0.187 0.265 Avg acceptance rate 0.8213 0.8223 Avg efficiency: min 0.6571 0.6631 Rc 1.0000 1.0000 Source: Stata 17 software output The results for GMM regression show that both the Sargan test and the AR(2) test yield p-values greater than 5%, indicating that the GMM model is not misspecified. Additionally, all variables in the GMM regression model are statistically significant at the 1% level. In contrast to the GMM approach, the Bayesian approach using the Metropolis–Hastings (MH) algorithm involves simulating the regression model 10,000 times, resulting in a coefficient for each iteration. Therefore, the regression results table will display the mean values. From Table − 4, it can be observed that the average acceptance rates are 0.8213 and 0.8223, respectively, exceeding the required threshold of 0.1. The minimum average efficiencies in both models are 0.6571 and 0.6631, respectively, higher than the acceptable threshold of 0.01, indicating that both models meet the requirements. The Monte-Carlo Standard Errors (MCSE) of all parameters are very small, following the criteria suggested by Flegal et al. (2008). They state that an MCSE approaching 0 indicates a stable MCMC chain, and an MCSE smaller than 6.5% of the standard deviation is acceptable, with values less than 5% being optimal. Therefore, the analysis results in Table 3 demonstrate that all R-hat values for the coefficients are 1, suggesting convergence of the MCMC chain. Hence, it can be concluded that the Bayesian simulation results are robust. Table 4 Probability of the independent variable's impact on the dependent variable Probability With the interaction variable ECI_GS With the interaction variable ECI_TR Mean Std. Dev. MCSE Mean Std. Dev. MCSE {II: Lag.II} >0 1.0000 0.0000 0.0000 0.9818 0.0801 0.0005 {II: GS} >0 0.9860 0.1179 0.0006 0.9730 0.1313 0.0007 {II: TR} 0 1.0000 0.0000 0.0000 1.0000 0.0000 0.0000 {II: ECI} >0 0.9538 0.2099 0.0001 0.8958 0.3056 0.0017 {II: UNE} >0 0.9203 0.3842 0.0038 0.9999 0.0216 0.0001 {II: FDI} 0 1.0000 0.0000 0.0000 1.0000 0.0000 0.0000 {II: OPE} >0 0.9999 0.0082 0.0000 1.0000 0.0000 0.0000 {II: IQ} < 0 0.9954 0.0674 0.0004 1.0000 0.0000 0.0000 {II: ECI_GS} < 0 0.8931 0.3243 0.0009 0.8905 0.3123 0.0018 {II: ECI_TR} < 0 1.0000 0.0000 0.0000 1.0000 0.0000 0.0000 Source: Analysis results conducted using Stata 17.0 software Moreover, the Bayesian method also provides additional probabilities of the impact of the independent variable on the dependent variable. The results in Table 4 show that the probability of EC increasing II is up to 95.38% and 89.58%. Meanwhile, under the regulation of fiscal policy, EC reduces II with an absolute probability of 100%. Regarding the control variables, we find that the coefficients of GDP per capita, unemployment rate, inflation, and trade openness promote II. Meanwhile, FDI and institutional quality narrow II. Therefore, countries can improve their inequality situation by enhancing FDI and improving institutional quality. 5. Conclusion and implication 5.1. Conclusion II remains one of the most pressing global challenges, especially in the context of accelerating globalization and EC. As globalization deepens, the disparity between rich and poor has intensified, primarily because the benefits of global integration tend to favor individuals and groups with greater access to capital, knowledge, and networks—further marginalizing low-income populations. This growing divide threatens social cohesion and long-term economic stability across both developed and developing economies. Fiscal policy, therefore, emerges as a crucial instrument for governments to counterbalance the adverse effects of globalization and EC. By channeling public resources toward inclusive programs—such as universal healthcare, education, and targeted social assistance—governments can empower disadvantaged groups, improve social mobility, and reduce disparities in income distribution. In this context, fiscal spending plays a vital redistributive role that cannot be overlooked, especially as economic complexity continues to rise and reshape global production structures. Recognizing the irreversible nature of EC, this study takes a further step by investigating not only the direct impacts of fiscal variables on II but also how these effects may be moderated by EC itself. Using panel data from 79 countries and territories spanning the period 2002–2022, the analysis applies both the System GMM and Bayesian regression approaches to ensure robustness and methodological rigor. The results consistently reveal that EC and government expenditure are associated with higher levels of income inequality, whereas tax revenue contributes to narrowing the gap. Importantly, the interaction between EC and government spending is found to significantly reduce II—with a posterior probability of 100%—indicating that fiscal spending becomes more effective in mitigating inequality in more complex economies. Beyond these core findings, the study also identifies other key determinants of II. Specifically, GDP per capita, inflation, unemployment, and trade openness are found to exacerbate inequality, while foreign direct investment (FDI) and institutional quality appear to have an equalizing effect. These insights offer strong empirical foundations for policy intervention, suggesting that governments should not only strengthen their redistributive mechanisms but also tailor fiscal strategies to the unique structural characteristics of their economies in the era of globalization and complexity. 5.2. Implication Based on the study’s findings, several key policy implications emerge. First, governments should reorient public spending toward inclusive sectors, especially in contexts of rising economic complexity. Increased investment in education, healthcare, and social protection can reduce structural inequalities between skilled and unskilled workers, thereby enhancing social equity and building a more resilient labor force. Second, tax revenue should be leveraged as a powerful redistributive tool. Efforts to broaden the tax base and improve tax collection efficiency—especially through progressive taxation—can provide the fiscal space needed to support welfare programs that benefit low-income groups. Third, fiscal policies must be designed in alignment with economic complexity. Public spending becomes more effective in reducing inequality when it is tailored to the structural characteristics of a complex economy, such as by promoting sectors that generate widespread employment opportunities. Additionally, to counter the skill-biased effects of economic complexity, governments should address labor market inequalities through targeted skill development programs, particularly for disadvantaged populations. Enhancing vocational education and upskilling initiatives can foster more equal access to emerging opportunities. Furthermore, since institutional quality plays a key role in narrowing income gaps, strengthening governance, legal frameworks, and anti-corruption mechanisms is essential to ensure that redistribution efforts are transparent and effective. Policymakers must also mitigate the negative effects of globalization and trade openness, which may exacerbate inequality in the absence of strong social safety nets. Complementary policies such as re-skilling programs and income support are crucial in ensuring that the benefits of global integration are shared equitably. Finally, promoting responsible foreign direct investment (FDI)—particularly in inclusive, labor-intensive sectors—can contribute to reducing inequality, especially when combined with incentives for local employment and technology transfer. Collectively, these strategies can help governments more effectively address income inequality in an era defined by growing globalization and economic complexity. While this study provides important insights into the relationship between economic complexity, fiscal policy, and income inequality, several limitations should be acknowledged. First, although the dataset includes 79 countries and territories over a 20-year period (2002–2022), data availability and consistency across countries may vary, particularly for income inequality indicators, which could affect the comparability and generalizability of the results. Second, the study primarily focuses on macro-level indicators, which may not fully capture within-country heterogeneity or the impact of subnational fiscal policies. Lastly, although the interaction between economic complexity and fiscal policy is explored, other moderating factors—such as demographic structure, political stability, or technological change—are not incorporated and could be considered in future research. Declarations -Funding Declaration No funding was received for conducting this study. - Ethical Approval : This study does not contain any studies with human participants or animals performed by any of the authors. - Informed Consent : This study does not contain any studies with human participants or animals performed by any of the authors. Author Contribution Huy Nguyen Quoc conceived the research idea, designed the study framework, and supervised the overall project. Dinh Le Quoc collected and processed the data, performed the econometric analysis, and drafted the initial manuscript. Both authors contributed equally to the interpretation of results and the revision of the manuscript. All authors have read and approved the final version of the paper. Acknowledgement The authors acknowledge being supported by Lac Hong University, Viet Nam. Data Availability The data used in this study are available upon request. References Albanesi, S. (2007). Inflation and inequality. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7611309","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":539177934,"identity":"ec2bed57-4c7c-479b-be85-5c75c0227d3a","order_by":0,"name":"Huy Nguyen Quoc","email":"","orcid":"","institution":"Lac Hong University","correspondingAuthor":false,"prefix":"","firstName":"Huy","middleName":"Nguyen","lastName":"Quoc","suffix":""},{"id":539177935,"identity":"499428b7-593e-4029-bcaf-6bd70866237f","order_by":1,"name":"Dinh Le Quoc","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvUlEQVRIiWNgGAWjYDCCwwxsDA8YbGQMQBweorUkMKTxkKDlAFjLYRK08B3nPfYgoew8j7lEAuODt20MidsJaZE8zJdukHDuNo/ljARmw7lALTsbCGgxOMxjJpHYdpvH4EYCmzRvG4OxwQHitJwDaWH/TYqWA2BbmIFa5AhqkQRpSTiXzGNw5mGz5JxzEoS18J0/YybxocxOzuB48sEPb8pseAhqgQA2EMHYACQkiFIP0zIKRsEoGAWjAAcAAOTiO8stb7L+AAAAAElFTkSuQmCC","orcid":"","institution":"Lac Hong University","correspondingAuthor":true,"prefix":"","firstName":"Dinh","middleName":"Le","lastName":"Quoc","suffix":""}],"badges":[],"createdAt":"2025-09-14 08:23:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7611309/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7611309/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":95381225,"identity":"3f8046a7-531d-4e85-bd1d-03347b4cea95","added_by":"auto","created_at":"2025-11-07 12:02:53","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":73234,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7611309/v1/6950838aea2a5d00f671b1ca.docx"},{"id":95381224,"identity":"e7921e59-ff63-40f1-add9-95ffe185ace1","added_by":"auto","created_at":"2025-11-07 12:02:53","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4450,"visible":true,"origin":"","legend":"","description":"","filename":"601d0ddafda34c02a1ee0a9bd23dee3b.json","url":"https://assets-eu.researchsquare.com/files/rs-7611309/v1/23cef86bc14ec61e875d8af7.json"},{"id":95381226,"identity":"34d7cd41-cec4-4a59-9099-64a17cbb434d","added_by":"auto","created_at":"2025-11-07 12:02:53","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":154262,"visible":true,"origin":"","legend":"","description":"","filename":"601d0ddafda34c02a1ee0a9bd23dee3b1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7611309/v1/e24abb303a5d94e053383610.xml"},{"id":95525783,"identity":"cdabe338-db91-4ced-bce2-7c4a8f703957","added_by":"auto","created_at":"2025-11-10 10:05:41","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":151894,"visible":true,"origin":"","legend":"","description":"","filename":"601d0ddafda34c02a1ee0a9bd23dee3b1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7611309/v1/90d19cca1dfef9afd9ddeac5.xml"},{"id":95381228,"identity":"5abe96be-6d52-40d8-ae22-027f12817af3","added_by":"auto","created_at":"2025-11-07 12:02:53","extension":"html","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":158473,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7611309/v1/1a0ca1e4a7c3d5b6fc3c8dff.html"},{"id":104405285,"identity":"5f8d41f4-a155-444a-a5f3-6e3449816b6b","added_by":"auto","created_at":"2026-03-11 12:22:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1079621,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7611309/v1/ec7fea2b-4fff-422d-a68e-9b8ff770b250.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Revisiting Fiscal Policy and Income Inequality in the Context of Economic Complexity","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGlobal income inequality has emerged as one of the most critical challenges facing contemporary societies. Despite remarkable economic progress in recent decades, the benefits of globalization have been unevenly distributed, often amplifying the gap between high- and low-income groups. Such disparities not only hinder inclusive growth but also threaten social cohesion, political stability, and sustainable development on a global scale. Recognizing this, the United Nations has explicitly identified the reduction of income inequality as one of its Millennium Development Goals, and later reinforced it as a central component of the Sustainable Development Goals (Dinh, 2025). The urgency of this issue is particularly evident in developing economies, where inequality tends to manifest more severely. In these contexts, governments often prioritize rapid economic expansion as a proxy for social welfare improvements; however, growth-driven strategies do not always translate into equitable outcomes (Rachely et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Consequently, while developing countries may achieve impressive GDP growth rates, the unequal distribution of wealth and opportunities can undermine long-term development, exacerbate poverty traps, and reinforce structural disadvantages for vulnerable populations. Addressing income inequality, therefore, requires not only sustaining growth but also implementing effective redistributive policies that ensure the gains of globalization and economic progress are shared more broadly across society.\u003c/p\u003e\u003cp\u003eIn the era of globalization, the growing disparity in income between the affluent and the underprivileged worsens for a couple of reasons. Firstly, the advantages in knowledge, wealth, and experience held by the wealthy enable them to reap greater benefits from globalization compared to the less privileged (United Nations, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Secondly, the conventional use of GDP as an economic gauge falls short in capturing the intricate nature of production and competition (Chu and Hoang, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Understanding the factors driving income inequality (II) is complex, as it intertwines with various economic, social, and institutional dynamics, including resources, institutions, social capital, historical trajectories, technological advancements, and returns on capital (Acemoglu and Robinson, 2012; Collier, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Piketty, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). These factors often manifest in a nation's production capacity (Hausmann et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), with Hidalgo and Hausmann (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) introducing the concept of \"economic complexity\" to gauge this capacity, which is reflected in the diversity and ubiquity of a country's production capabilities. The Economic Complexity Index (ECI) is a notable index that encompasses factors related to knowledge, scientific and technological content in the economy. A high ECI is a factor ensuring long-term economic growth that in fact, some countries like Saudi Arabia have a high GDP, but their ECI is not high due to their economy mainly relying on oil sales. For these countries, the risk of growth is significant. This prompts consideration of investigating the correlation between economic complexity and income inequality.\u003c/p\u003e\u003cp\u003eAdditionally, fiscal policy emerges as a pivotal instrument in economic management, aiding governments in navigating through economic fluctuations (Bon, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). During periods of economic downturn characterized by high unemployment rates, governments proactively increase spending or decrease taxes, or both (known as expansionary policy). Conversely, in times of rapid economic growth accompanied by high inflation rates, governments trim spending or hike taxes, or both (contractionary policy). Notably, governments may channel more resources towards facilitating access to healthcare and education for low-income individuals through social subsidies, aiming to mitigate income disparities between high and low earners and thereby narrow societal income inequality. Given the irreversible nature of economic complexity, and the persistent importance of fiscal spending in effective wealth redistribution, this study proposes to delve deeper into exploring the impact of the interplay between public expenditure and economic complexity on income inequality.\u003c/p\u003e\u003cp\u003eTo the best of our knowledge, research linking Economic Complexity (EC) and income inequality (II) remains fragmented. Prior studies report mixed evidence: Le Caous and Huarng (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Hartmann et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) found that higher EC reduces inequality, whereas Lee and Vu (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) showed that EC exacerbates disparities, and Chu and Hoang (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) highlighted a non-linear effect. However, these contributions largely neglect the role of fiscal policy as a potential conditioning factor in this nexus. Since EC reflects a long-term, path-dependent process that requires substantial resources and knowledge accumulation (Hidalgo \u0026amp; Hausmann, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), fiscal policy may act as a decisive instrument that moderates or redirects its distributional consequences. This underscores the importance of examining whether the EC\u0026ndash;II relationship is contingent on fiscal interventions or subject to structural turning points.\u003c/p\u003e\u003cp\u003eIn terms of the academic aspect, it is noteworthy that the literature review in Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e highlights three factors that differentiate this study from related research. Firstly, the interaction between fiscal policy and EC is addressed. Secondly, this study provides experimental evidence demonstrating the different roles of fiscal policies including tax revenue and public expenditure, and EC in income inequality. Thirdly, unlike previous studies, this research adopts a Bayesian approach in probability theory, offering advantages in handling small samples, autocorrelation phenomena, and endogeneity issues in the model. Furthermore, the SGMM regression is also conducted for comparison with the Bayesian regression.\u003c/p\u003e"},{"header":"2. Literature review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Fiscal policy and income inequality\u003c/h2\u003e\u003cp\u003eThe theoretical standpoint regarding the influence of fiscal policy on wealth and II originates from the growing significance of government intervention in the economy, a viewpoint championed by Keynes and his followers. They stress the importance of governments utilizing fiscal policy to manage economic fluctuations and address market failures. Through mechanisms such as expenditure and taxation, governments can redistribute national income and mitigate income disparities within society. Notably, Alesina and Ardagna (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) assert that public spending plays a crucial role in reducing inequality, particularly through social welfare initiatives like healthcare, education, social safety nets, and employment schemes. In contrast, the conventional economic-political perspective suggests that governments resort to distortionary taxation to redistribute national income in response to heightened levels of inequality (Alesina and Rodrik, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Persson and Tabellini, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). However, it's argued that direct taxation negatively impacts economic growth by hampering the accumulation of human and physical capital. Conversely, the emerging economic-political viewpoint refutes the notion of distortionary taxes having an adverse effect on economic growth. Instead, it advocates for the positive repercussions of expenditure redistribution on growth. In this study, we posit that government fiscal spending is invariably initiated in response to rising II within the economy. Thus, the following hypothesis is established:\u003c/p\u003e\u003cp\u003e\u003cb\u003eH1: Fiscal expenditure reduces II.\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Economic complexity and income inequality\u003c/h2\u003e\u003cp\u003eThe wealth of an economy is fundamentally linked to knowledge accumulation and labor specialization (Hausmann et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). National prosperity, therefore, depends on the capacity of individuals and organizations to generate, combine, and apply knowledge. Building on this view, Hidalgo et al. (2014) introduced the concept of Economic Complexity (EC), which captures both the quality of production factors and their transformation into output. Differences in development across countries can thus be explained by differences in the types of goods they produce. High-complexity and competitive products require not only raw materials but, more importantly, advanced inputs such as skilled labor, experience, and intellectual property. Unlike raw materials, which are easily traded, knowledge\u0026mdash;particularly its latent component\u0026mdash;cannot be transferred quickly across borders, as it develops slowly within individuals through costly and uncertain processes (Hausmann et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). To quantify this, Hidalgo and Hausmann (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) developed the EC, which measures two dimensions: diversity, reflecting the range of products a country can competitively produce, and ubiquity, indicating how many other countries are able to produce those same goods. Together, these dimensions provide a comprehensive measure of a nation\u0026rsquo;s productive knowledge and potential for sustained growth.\u003c/p\u003e\u003cp\u003eThe necessity of specific product production hinges on a country's knowledge of productivity and the diversity in its exports and products, illustrating the breadth and depth of its productivity insights. According to Hidalgo and Hausmann (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), a country boasting diverse and distinctive production knowledge is more prone to achieving heightened specialization levels, a phenomenon linked to two principal processes. Firstly, they can innovate new products by amalgamating existing knowledge, and secondly, they can amass new capabilities and merge them with existing ones to foster product expansion. Research has indicated that EC can forecast adverse trends in II (Le Caous and Huarng, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and can mitigate II by generating employment opportunities across various workforce strata (Albassam, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Egger and Etzel, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Hartmann, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Conversely, in economies with low complexity, production structures and employment predominantly rely on low-skilled labor, culminating in elevated II. The concept of a complex economy aids in diminishing II and ensuring business resilience in a dynamically evolving global landscape (Barnes and Coogan, 2015; Joya, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Consequently, heightened specialization levels can enhance productivity and bolster profit margins, thus elevating lifelong incomes for workers (Constantine, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Enhanced wages facilitate upward mobility for the impoverished and contribute to II reduction (Hartmann et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Hidalgo, 2015). In fact, Le et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) proposed a reverse U-shaped relationship between economic diversification and II, contending that economic diversification initially augments II until reaching a threshold, beyond which a complex economic framework aids in diminishing II. However, in this study, we posit that EC might perpetually exacerbate income disparities for the following rationale: In a knowledge-based economy, while heightened specialization engenders more job opportunities, each production endeavor necessitates skilled labor endowed with expertise and knowledge to attain optimal productivity (Constantine, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The demand for skilled labor escalates (Constantine, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) concomitant with the economic structural diversification process. Consequently, there exists an uneven distribution of job opportunities between skilled and unskilled labor. Notably, skilled workers assimilate new knowledge expeditiously owing to their pre-existing competencies and superior adaptability to evolving labor market requisites, thus reaping greater benefits from the economy's complexity. In essence, heightened EC may continually exacerbate income disparities. Drawing from these arguments, we formulate the second hypothesis as follows:\u003c/p\u003e\u003cp\u003e\u003cb\u003eH2: EC exacerbates II.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eConstructing a complex economy is a long-term process (Hidalgo \u0026amp; Hausmann, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), during which the drivers of II may evolve. The skill-biased technological change theory alone cannot fully explain the persistent positive link between EC and inequality (Card \u0026amp; DiNardo, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Weiss, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), suggesting that moderating factors may reshape this relationship. Prior studies highlight several mitigating channels: enhancing wages and employment opportunities (Lee \u0026amp; Sissons, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), improving education to raise lifetime earnings (Castro Campos et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Norris et al., 2015), and trade liberalization that increases demand for lower-skilled labor (Asteriou et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In particular, fiscal policy\u0026mdash;through redistributive spending and taxation\u0026mdash;has been widely recognized as a direct mechanism to reduce disparities (Anderson et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Building on this perspective, we argue that fiscal instruments, when implemented in economies with higher complexity, can counterbalance inequality pressures. Accordingly, we propose the following hypothesis:\u003c/p\u003e\u003cp\u003e\u003cb\u003eH3: The interaction of fiscal policy and EC reduces II.\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Related literature review\u003c/h2\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.3.1. Impact of fiscal policy on income inequality\u003c/h2\u003e\u003cp\u003eA growing body of research has investigated the distributive effects of government spending on income inequality (II), though the evidence remains mixed. Wong (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) shows that public expenditure reduces inequality in 16 Asia\u0026ndash;Pacific economies, while Wong (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) finds contrasting results, with spending lowering inequality in Asia but increasing it in Latin America. Using IV\u0026ndash;GMM techniques, Cevik and Correa-Caro (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) report that government spending contributes to reducing II in China and 33 developing economies over the long term. Similar evidence is provided by Kollmeyer (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), who, based on Western economies, highlights the equalizing role of fiscal expenditure. By contrast, tax-related effects appear more nuanced: Apergis (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) demonstrates that tax revenue increases II in developed economies, whereas budget deficits help to narrow it, while Taghizadeh-Hesary et al. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) find that tax revenue reduces inequality in Japan using a VECM approach.\u003c/p\u003e\u003cp\u003eClifton et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Gunasinghe et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) provide evidence that fiscal policies\u0026mdash;through government spending and taxation\u0026mdash;can narrow income inequality (II). Clifton et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), using fixed effects and LSDVC estimations for 17 Latin American countries (1990\u0026ndash;2014), show that redistributive fiscal policies reduce disparities. Similarly, Gunasinghe et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), applying a simultaneous equation model to 19 developed economies, find that governments employ redistributive spending financed by direct taxes as an effective mechanism to curb inequality. However, results are not uniform across contexts. Malla and Pathranarakul (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), analyzing 2000\u0026ndash;2019 data with a system GMM approach, report that progressive income taxes reduce inequality only in developing countries, while government size and higher spending on education and health alleviate inequality in developed nations. By contrast, taxes on goods and services appear to have no significant global effect, and institutional quality exerts only a limited influence. In Sub-Saharan Africa, Oseni et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) highlight further complexity: while income tax reduces inequality, it simultaneously worsens health outcomes, whereas health-oriented assistance improves life expectancy.\u003c/p\u003e\u003cp\u003eOverall, the literature suggests that fiscal policy can shape inequality, but its effectiveness depends on country-specific structures, institutional capacity, and spending priorities. Yet, existing studies often assess fiscal instruments in isolation, without considering how their distributive impact may interact with structural economic factors such as economic complexity. This leaves open an important question: under what conditions does fiscal policy effectively offset the inequality pressures generated by complex, knowledge-driven economies?\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.3.2. The impact of EC on II\u003c/h2\u003e\u003cp\u003eHartmann et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), using multivariate regression on 150 countries (1963\u0026ndash;2008), found that EC contributes to reducing income inequality (II). Similarly, Le Caous and Huarng (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) confirmed the positive role of EC in fostering human development through inequality reduction in 87 developing countries, employing a hierarchical linear model with robustness checks. Other works further highlight this equalizing effect: Albassam (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) links economic diversification to job creation, Egger and Etzel (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) emphasize its role in curbing corruption, and Hartmann (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) stresses the institutional improvements stemming from more complex economies. More recently, Dinh (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025a\u003c/span\u003e), applying Bayesian and GMM methods to 14 countries (2005\u0026ndash;2021), also reported that EC narrows income inequality. Collectively, these studies reinforce the view that EC enhances development by promoting diversification, institutional quality, and social inclusion.\u003c/p\u003e\u003cp\u003eChu and Hoang (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) investigated the relationship between EC and II using panel data from 88 countries spanning from 2002 to 2017. Their findings indicated a significant association between EC and higher levels of II. These findings hold relevance for policymakers, suggesting the need for adjustments in policies to address inequality while transitioning towards knowledge-based economies. Rachely et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) explored the impact of healthcare spending, education spending, social welfare spending, and trade on income distribution disparities in Indonesia. Utilizing secondary data from various sources, they employed panel data regression analysis using the Random Effects Model (REM). The study covered all 34 provinces in Indonesia from 2010 to 2022. The results unveiled that both aggregate and individual spending on healthcare, education, social welfare, and trade significantly influenced income distribution disparities in Indonesia. Particularly, investments in healthcare and education exhibited an inverse correlation with II, whereas social welfare and trade expenditure showed a positive correlation. Moreover, the impact of EC on widening income gaps was further substantiated by research conducted by Berman et al. (1998), Card and DiNardo (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), and Lee and Vu (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Research Gap\u003c/h2\u003e\u003cp\u003eThrough our literature review, we identified the following limitations:\u003c/p\u003e\u003cp\u003eFirstly, previous studies have been conducted in isolation, focusing on individual aspects such as the impact of fiscal policies on II or the effect of EC on II. However, there has been a notable absence of research delving into the interaction between EC and fiscal expenditure on II.\u003c/p\u003e\u003cp\u003eSecondly, prior research has largely depended on conventional frequency-based models, which impose a set of restrictive assumptions that may not adequately capture real-world complexities, thereby risking biased inference and prediction errors. These methods generally regard parameters as fixed but unknown, even though their values can vary as sample sizes change. In contrast, the Bayesian framework treats parameters as random variables with probability distributions, allowing uncertainty to be explicitly incorporated into the estimation process. A key advantage of this approach is its reduced reliance on large samples, while also providing flexibility in dealing with econometric issues such as autocorrelation, heteroskedasticity, and endogeneity. Consequently, Bayesian methods offer a more reliable and comprehensive tool for hypothesis testing and inference in the study of income inequality.\u003c/p\u003e\u003cp\u003eIn this study, the authors employ Bayesian methods to examine the impact of fiscal policies, EC, and their interaction on II. This approach allows for the derivation of appropriate policy implications.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Description of ResearchVariables and Data","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Model and Methodology\u003c/h2\u003e\u003cp\u003eBased on the studies by Bon (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), Lee and Vu (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and Chu and Hoang (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), the authors establish the research model as follows:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{II}_{i,t}={\\beta\\:}_{o}+{\\beta\\:}_{2}{II}_{i,t-1}+\\:{\\beta\\:}_{2}{ECI}_{i,t}+\\:{\\beta\\:}_{3}{FS}_{i,t}\\:+\\:{\\beta\\:}_{4}ECI*{FS}_{i,t}+\\:\\:{\\beta\\:}_{x}{X}_{i,t}\\:+\\:{\\:\\epsilon\\:}_{i,t}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere: II represents II; ECI represents EC; FP represents fiscal policy including government spending (GS) and tax revenue (TR); X is a vector of control variables including GDP per capita (GDP), unemployment rate (UNE), public governance/institutional quality (RQ), inflation rate (INF), and foreign direct investment (FDI). The specific research variables are described in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eSeveral macroeconomic and institutional factors are widely recognized as key determinants of income inequality. Economic growth plays a dual role: while it can reduce inequality by creating more job opportunities and increasing overall income levels, it may also exacerbate inequality if the benefits of growth are disproportionately captured by higher-income groups (Barro, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Unemployment rate is another critical factor, as higher unemployment typically leads to income loss and social exclusion, particularly among low-skilled workers, thereby widening income disparities. FDI can contribute to income equalization by transferring technology and generating employment; however, in many cases, FDI benefits are concentrated in capital-intensive sectors, limiting its redistributive effect (Herzer \u0026amp; Nunnenkamp, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Similarly, inflation tends to hurt low-income households more severely due to their limited ability to hedge against rising prices, leading to a regressive impact on income distribution (Albanesi, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The effect of trade openness on inequality is context-dependent: it may reduce inequality through comparative advantage and efficiency gains, but can also increase it by displacing uncompetitive domestic sectors (Dinh, 2025). Finally, institutional quality plays a pivotal role, as strong institutions enhance transparency, improve policy effectiveness, and ensure equitable access to opportunities and services, thus helping to reduce inequality (Acemoglu \u0026amp; Robinson, 2012).\u003c/p\u003e\u003cp\u003eModel (1) encounters the following issues:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eFiscal policy variables including government spending, tax revenue, economic growth, and unemployment rate may be correlated with unobserved components leading to endogeneity. The bidirectional relationship between variables, such as between government spending and economic growth, can lead to endogeneity issues as both variables influence each other, making it difficult to clearly determine cause and effect. Furthermore, unobserved factors, such as institutional quality or consumer expectations, may simultaneously affect these variables, and if not accounted for in the model, they could cause bias in the results. Additionally, the simultaneity effect between variables like unemployment rate and tax revenue also introduces endogeneity, as an increase in unemployment may reduce tax revenue, and vice versa. Finally, selection bias may arise when countries determine their levels of spending or taxation based on specific economic and political characteristics, leading to distortion in the model. To address these endogeneity concerns, methods such as instrumental variables, the Generalized Method of Moments (GMM), or random effects models can help control for unobserved factors, providing more accurate estimates and isolating the effects of the variables under study.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eThe presence of unobserved factors\u0026mdash;such as culture, geography, customs, and demographic characteristics\u0026mdash;can pose a serious econometric issue when they are correlated with independent variables like fiscal policy indicators. These time-invariant characteristics, commonly referred to as fixed effects, vary across countries but remain constant over time. If not properly accounted for, they can cause omitted variable bias, leading to inconsistent estimates of the effects of fiscal policies on income inequality. For instance, cultural attitudes toward taxation or redistribution may affect both the structure of fiscal policy and the level of income inequality, but if such variables are excluded from the model, their influence may be wrongly attributed to the observed independent variables. To control for this, fixed effects models are often employed to absorb such unobserved heterogeneity (Van Bon, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Dinh \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025a\u003c/span\u003e).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eHigh correlation among independent variables\u0026mdash;such as between GDP and fiscal policy indicators like government spending or tax revenue\u0026mdash;can lead to multicollinearity, a situation that undermines the reliability of regression estimates. When multicollinearity is present, it becomes difficult to isolate the individual impact of each variable on the dependent variable, as they provide overlapping information. For example, a booming economy (reflected by higher GDP) often leads to increased tax revenues and government spending, making it challenging to distinguish the separate effects of each. Multicollinearity inflates standard errors and may render statistically significant relationships appear insignificant. Addressing this issue may involve dropping or transforming variables, applying dimensionality reduction techniques (e.g., Principal Component Analysis), or using instrumental variables..\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eThe presence of lagged II (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{II}_{i,t-1}\\)\u003c/span\u003e\u003c/span\u003e) may lead to autocorrelation issues. Specifically, in a model where the current level of inequality depends on its past values, residuals may also exhibit time dependence. If this issue is not addressed, it could distort the interpretation of fiscal policy impacts over time. Common remedies include using dynamic panel data models such as the Arellano-Bond GMM estimator, which is designed to handle both autocorrelation and endogeneity due to lagged dependent variables.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eThe aforementioned econometric concerns can lead to biased estimates in Ordinary Least Squares (OLS) regression. Traditional alternatives such as the Random Effects Model (REM) and Fixed Effects Model (FEM) are also inadequate, as they do not properly address issues of error serial correlation and endogeneity. To overcome these limitations, this study follows Judson and Owen (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) and employs the System Generalized Method of Moments (SGMM). Diagnostic tools including the Arellano\u0026ndash;Bond AR(2) test, together with the Sargan and Hansen tests, are applied to validate the model by detecting serial correlation and potential endogeneity. Beyond the GMM framework, this research also incorporates Bayesian regression, which offers distinct advantages in correcting for model weaknesses such as autocorrelation, heteroskedasticity, and endogeneity (Thach, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). By combining frequentist and Bayesian perspectives, the analysis enhances robustness. While SGMM, rooted in the frequentist tradition, relies on p-values and treats parameters as fixed but unknown, the Bayesian approach uses prior distributions and observed data to derive posterior probabilities. This probabilistic framework enables researchers to explicitly incorporate prior knowledge, update beliefs as new evidence emerges, and provide a richer interpretation of parameter estimates and model certainty (Kim \u0026amp; Quoc, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Van \u0026amp; Le Quoc, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Nguyen The et al., 2024; Quoc et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2025\u003c/span\u003ea; Quoc et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2025\u003c/span\u003eb; Le Quoc et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Dinh, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eVariable description\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSign\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDescriptions\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStudies\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSource\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eDependent variable\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIncome inequality\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAfter-tax income inequality index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChu v\u0026agrave; Hoang (2020); Behera \u0026amp; Karthiayani (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWorld Income Inequality Database (WIID)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIndependent variables\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEconomic complexity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eECI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEconomic complexity Index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDinh (2025)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWIID\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFiscal policy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWB\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e+ Government spending\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGovernment spending/GDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBon (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e); Huy et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWB\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e+ Tax revenue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTax revenue/GDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBon (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e); Huy et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWB\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eControl variables\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEconomic growth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEconomic growth per capita\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChu and Hoang (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), Oanh et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), Le Quoc (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWB\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnemployment rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUNE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThis index is measured by the number of unemployed individuals as a percentage of the labor force\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBon (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWB\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eForeign Direct Investment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFDI/GDP (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWB\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInflation rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eINF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAnnual inflation growth (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDinh el al., (2024)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWB\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrade openness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOPEN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePercentage of total import and export of goods and services/GDP (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChu v\u0026agrave; Hoang (2020)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWB\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInstitutional quality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWB\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e+ Voice and accountability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVOA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eWGI's set of 6 indexes receive values from \u0026minus;\u0026thinsp;2.5 to 2.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eBon (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e); Van et al (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWGI\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e+ Political stability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePOL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWGI\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e+ Effective state management\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGOV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWGI\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e+ Regulated quality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eREG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWGI\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e+ The rule of law\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRUL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWGI\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e+ Control corruption\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWGI\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSource: Compiled by the author.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Data\u003c/h2\u003e\u003cp\u003eThe measure of income inequality (II) is obtained from the World Income Inequality Database (WIID), which provides post-tax inequality indicators. EC, representing a country\u0026rsquo;s ability to produce a diverse and sophisticated set of goods, is sourced from the MIT Media Lab, constructed from international trade data that maps countries to their export products. Fiscal policy is proxied by government spending (GS) and tax revenue (TR), while additional controls such as GDP per capita and unemployment rate are taken from the World Bank. Institutional quality is derived from the Worldwide Governance Indicators (WGI), which include six governance dimensions\u0026mdash;voice and accountability, political stability, government effectiveness, regulatory quality, rule of law, and control of corruption\u0026mdash;scored from \u0026minus;\u0026thinsp;2.5 to 2.5. Although widely used in prior research (e.g., Dinh, 2025), these indicators are highly correlated and may introduce multicollinearity or over-parameterization if entered separately into the model. To address this issue, principal component analysis (PCA) is employed to combine the six dimensions into a single composite index of institutional quality, following Ullah and Khan (2017).\u003c/p\u003e\u003cp\u003eBased on data availability, this study constructs an unbalanced panel covering 79 countries and territories, comprising 32 high-income and 46 middle-income economies according to the World Bank classification. The sample period spans 2002\u0026ndash;2022, as continuous information from the Worldwide Governance Indicators is only available from 2002 onward. Descriptive statistics for the key variables are reported in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. To normalize their distribution, both II and EC are expressed in logarithmic form.\u003c/p\u003e\u003cp\u003eThe results in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e indicate that the average tax revenue of the 79 countries is 49.1646, which is lower than the government expenditure of 53.3165, suggesting a fiscal deficit in these countries.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive statistics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003e79 countries worldwide\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStd. Dev.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMinimum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMaximum\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.4312\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2813\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.1315\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.1203\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53.3165\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.1346\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.1346\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e65.0364\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49.1646\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.1364\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.1646\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e58.1346\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eECI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.4312\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.8936\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.4364\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.2513\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.7913\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.3313\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.1344\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.6331\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUNE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.5613\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.0165\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.0013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27.466\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22.8579\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.6743\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.8559\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27.3215\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.9686\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.5369\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.0970\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e49.7211\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOPEN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e71.0953\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.0628\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22.1060\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e146.1061\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.1133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2987\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.6766\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.2346\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eSource: Author's processing\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Regression Results and Discussion","content":"\u003cp\u003eThe regression outcomes are reported in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Across all estimation approaches\u0026mdash;most notably in the two-step System GMM\u0026mdash;the fiscal policy variables, government spending (GS) and tax revenue (TR), exhibit endogeneity. To address this issue, both GS and TR were treated as endogenous instruments within the GMM specification, while the other explanatory variables were considered exogenous and used as instruments in the IV framework.\u003c/p\u003e\u003cp\u003eThe regression results derived from both the GMM and Bayesian approaches reveal consistent directional effects, thereby reinforcing the robustness and credibility of our findings. In particular, EC is shown to intensify II, aligning with the results of previous studies such as Chu and Hoang (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Rachely et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and lending strong support to our second hypothesis (H2). This outcome can be attributed to the structural dynamics of EC, which drives economies toward innovation-intensive and knowledge-based activities that disproportionately benefit highly skilled labor. As Constantine (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) emphasizes, such transformations tend to deepen labor market segmentation\u0026mdash;skilled workers, due to their adaptability and ability to absorb complex knowledge, are better positioned to capitalize on new economic opportunities. Meanwhile, unskilled workers often struggle to keep pace, resulting in a growing disparity in income distribution. Thus, EC acts as a catalyst for inequality by amplifying the returns to skill in an increasingly complex economic landscape. Moreover, tax revenue is found to have a mitigating effect on income inequality, echoing the findings of Bon (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and providing strong support for our first hypothesis (H1). This relationship highlights the critical role of effective tax collection in expanding the government\u0026rsquo;s fiscal capacity to implement redistributive policies and invest in social welfare programs. By channeling resources toward education, healthcare, and poverty alleviation initiatives, enhanced tax revenues can significantly reduce disparities across income groups. Thus, tax revenue serves not only as a key instrument for financing public goods but also as a powerful mechanism for promoting greater social and economic equity.\u003c/p\u003e\u003cp\u003eHowever, contrary to expectations and previous findings in the literature (Bon, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Chu and Hoang, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Rachely et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), our results indicate that government expenditure is associated with an increase in income inequality, which runs counter to our first hypothesis (H1). This counterintuitive outcome may be attributed to inefficiencies in fiscal allocation or misalignment between spending priorities and equity objectives. In many cases, public funds may be disproportionately channeled toward growth-oriented investments\u0026mdash;such as infrastructure, defense, or capital-intensive industries\u0026mdash;that tend to benefit wealthier individuals or corporate interests, while neglecting pro-poor sectors like education, healthcare, and targeted social protection. When government spending favors sectors with high barriers to entry or limited employment generation for the low-skilled population, it can inadvertently exacerbate existing income disparities.\u003c/p\u003e\u003cp\u003eInterestingly, when exploring the interaction between fiscal expenditure and economic complexity (EC), our analysis reveals a mitigating effect on income inequality. Specifically, in more economically diverse and complex environments\u0026mdash;where inequality tends to be more pronounced\u0026mdash;fiscal expenditure appears to serve as an effective corrective mechanism. This interaction term suggests that when government spending is strategically aligned with the challenges posed by EC, it can help offset the widening gaps by redistributing resources more equitably. As highlighted by Anderson et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), fiscal policies that prioritize social transfers, public goods, and inclusive development have the potential to directly narrow the income divide created by structural economic shifts. This nuanced finding adds an important dimension to the current discourse, emphasizing that the effectiveness of government spending in reducing inequality depends not only on its volume but also on its strategic alignment with broader economic dynamics.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe results of Bayesian and GMM regression models\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThe dependent variable II\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eGMM regression\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e\u003cp\u003eBayes regression\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCoef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMCSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eMCSE\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLag.II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.8316***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.8431***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.5366\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.5367\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.0046\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1093***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1083***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.1566\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.1677\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.0026\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.1335**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0933***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0094\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.1933\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.2103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.0003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eECI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0813**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0351\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0903**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0236\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0931\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.0891\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.0016\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0049***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0045***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0328\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.0533\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.0006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUNE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0331**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0151\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0332**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0248\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.0009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0264*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0861\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0213**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0464\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.0283\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.0316\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.0002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.8225***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.7635***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.2425\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.1364\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.0008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOPEN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0354**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0494\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0295**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0330\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0434\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.0115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.0003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-2.136*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0764\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.3648*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0646\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-3.4324\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-3.2344\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.0188\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eECI_GS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0533***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.04931***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.0756\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.0813\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eECI_TR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0135***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0238***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.0234\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.0211\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.0005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSargan Test\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.190\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAR(2) Test\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.187\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.265\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAvg acceptance rate\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.8213\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e0.8223\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAvg efficiency: min\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.6571\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e0.6631\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRc\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e1.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e1.0000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eSource: Stata 17 software output\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe results for GMM regression show that both the Sargan test and the AR(2) test yield p-values greater than 5%, indicating that the GMM model is not misspecified. Additionally, all variables in the GMM regression model are statistically significant at the 1% level. In contrast to the GMM approach, the Bayesian approach using the Metropolis\u0026ndash;Hastings (MH) algorithm involves simulating the regression model 10,000 times, resulting in a coefficient for each iteration. Therefore, the regression results table will display the mean values. From Table \u0026minus;\u0026thinsp;4, it can be observed that the average acceptance rates are 0.8213 and 0.8223, respectively, exceeding the required threshold of 0.1. The minimum average efficiencies in both models are 0.6571 and 0.6631, respectively, higher than the acceptable threshold of 0.01, indicating that both models meet the requirements. The Monte-Carlo Standard Errors (MCSE) of all parameters are very small, following the criteria suggested by Flegal et al. (2008). They state that an MCSE approaching 0 indicates a stable MCMC chain, and an MCSE smaller than 6.5% of the standard deviation is acceptable, with values less than 5% being optimal. Therefore, the analysis results in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e demonstrate that all R-hat values for the coefficients are 1, suggesting convergence of the MCMC chain. Hence, it can be concluded that the Bayesian simulation results are robust.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eProbability of the independent variable's impact on the dependent variable\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eProbability\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eWith the interaction variable ECI_GS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eWith the interaction variable ECI_TR\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStd. Dev.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMCSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eStd. Dev.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMCSE\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e{II: Lag.II} \u0026gt;0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.9818\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0801\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e{II: GS} \u0026gt;0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.9860\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1179\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.9730\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.1313\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e{II: TR} \u0026lt; 0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.9860\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1179\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e{II: GDP} \u0026gt;0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e{II: ECI} \u0026gt;0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.9538\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2099\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.8958\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.3056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e{II: UNE} \u0026gt;0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.9203\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3842\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.9999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0216\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e{II: FDI} \u0026lt; 0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.7742\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.4181\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0242\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.7121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.4528\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0026\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e{II: INF} \u0026gt;0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e{II: OPE} \u0026gt;0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.9999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0082\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e{II: IQ} \u0026lt; 0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.9954\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0674\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e{II: ECI_GS} \u0026lt; 0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.8931\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3243\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.8905\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.3123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0018\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e{II: ECI_TR} \u0026lt; 0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eSource: Analysis results conducted using Stata 17.0 software\u003c/em\u003e\u003c/p\u003e\u003cp\u003eMoreover, the Bayesian method also provides additional probabilities of the impact of the independent variable on the dependent variable. The results in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e show that the probability of EC increasing II is up to 95.38% and 89.58%. Meanwhile, under the regulation of fiscal policy, EC reduces II with an absolute probability of 100%.\u003c/p\u003e\u003cp\u003eRegarding the control variables, we find that the coefficients of GDP per capita, unemployment rate, inflation, and trade openness promote II. Meanwhile, FDI and institutional quality narrow II. Therefore, countries can improve their inequality situation by enhancing FDI and improving institutional quality.\u003c/p\u003e"},{"header":"5. Conclusion and implication","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e5.1. Conclusion\u003c/h2\u003e\u003cp\u003eII remains one of the most pressing global challenges, especially in the context of accelerating globalization and EC. As globalization deepens, the disparity between rich and poor has intensified, primarily because the benefits of global integration tend to favor individuals and groups with greater access to capital, knowledge, and networks\u0026mdash;further marginalizing low-income populations. This growing divide threatens social cohesion and long-term economic stability across both developed and developing economies. Fiscal policy, therefore, emerges as a crucial instrument for governments to counterbalance the adverse effects of globalization and EC. By channeling public resources toward inclusive programs\u0026mdash;such as universal healthcare, education, and targeted social assistance\u0026mdash;governments can empower disadvantaged groups, improve social mobility, and reduce disparities in income distribution. In this context, fiscal spending plays a vital redistributive role that cannot be overlooked, especially as economic complexity continues to rise and reshape global production structures. Recognizing the irreversible nature of EC, this study takes a further step by investigating not only the direct impacts of fiscal variables on II but also how these effects may be moderated by EC itself. Using panel data from 79 countries and territories spanning the period 2002\u0026ndash;2022, the analysis applies both the System GMM and Bayesian regression approaches to ensure robustness and methodological rigor. The results consistently reveal that EC and government expenditure are associated with higher levels of income inequality, whereas tax revenue contributes to narrowing the gap. Importantly, the interaction between EC and government spending is found to significantly reduce II\u0026mdash;with a posterior probability of 100%\u0026mdash;indicating that fiscal spending becomes more effective in mitigating inequality in more complex economies. Beyond these core findings, the study also identifies other key determinants of II. Specifically, GDP per capita, inflation, unemployment, and trade openness are found to exacerbate inequality, while foreign direct investment (FDI) and institutional quality appear to have an equalizing effect. These insights offer strong empirical foundations for policy intervention, suggesting that governments should not only strengthen their redistributive mechanisms but also tailor fiscal strategies to the unique structural characteristics of their economies in the era of globalization and complexity.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e5.2. Implication\u003c/h2\u003e\u003cp\u003eBased on the study\u0026rsquo;s findings, several key policy implications emerge. First, governments should reorient public spending toward inclusive sectors, especially in contexts of rising economic complexity. Increased investment in education, healthcare, and social protection can reduce structural inequalities between skilled and unskilled workers, thereby enhancing social equity and building a more resilient labor force. Second, tax revenue should be leveraged as a powerful redistributive tool. Efforts to broaden the tax base and improve tax collection efficiency\u0026mdash;especially through progressive taxation\u0026mdash;can provide the fiscal space needed to support welfare programs that benefit low-income groups. Third, fiscal policies must be designed in alignment with economic complexity. Public spending becomes more effective in reducing inequality when it is tailored to the structural characteristics of a complex economy, such as by promoting sectors that generate widespread employment opportunities. Additionally, to counter the skill-biased effects of economic complexity, governments should address labor market inequalities through targeted skill development programs, particularly for disadvantaged populations. Enhancing vocational education and upskilling initiatives can foster more equal access to emerging opportunities. Furthermore, since institutional quality plays a key role in narrowing income gaps, strengthening governance, legal frameworks, and anti-corruption mechanisms is essential to ensure that redistribution efforts are transparent and effective. Policymakers must also mitigate the negative effects of globalization and trade openness, which may exacerbate inequality in the absence of strong social safety nets. Complementary policies such as re-skilling programs and income support are crucial in ensuring that the benefits of global integration are shared equitably. Finally, promoting responsible foreign direct investment (FDI)\u0026mdash;particularly in inclusive, labor-intensive sectors\u0026mdash;can contribute to reducing inequality, especially when combined with incentives for local employment and technology transfer. Collectively, these strategies can help governments more effectively address income inequality in an era defined by growing globalization and economic complexity.\u003c/p\u003e\u003cp\u003eWhile this study provides important insights into the relationship between economic complexity, fiscal policy, and income inequality, several limitations should be acknowledged. First, although the dataset includes 79 countries and territories over a 20-year period (2002\u0026ndash;2022), data availability and consistency across countries may vary, particularly for income inequality indicators, which could affect the comparability and generalizability of the results. Second, the study primarily focuses on macro-level indicators, which may not fully capture within-country heterogeneity or the impact of subnational fiscal policies. Lastly, although the interaction between economic complexity and fiscal policy is explored, other moderating factors\u0026mdash;such as demographic structure, political stability, or technological change\u0026mdash;are not incorporated and could be considered in future research.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cb\u003e-Funding Declaration\u003c/b\u003e\u003c/p\u003e\u003cp\u003eNo funding was received for conducting this study.\u003c/p\u003e\u003cp\u003e\u003cb\u003e- Ethical Approval\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eThis study does not contain any studies with human participants or animals performed by any of the authors.\u003c/p\u003e\u003cp\u003e\u003cb\u003e- Informed Consent\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eThis study does not contain any studies with human participants or animals performed by any of the authors.\u003c/p\u003e\u003c/div\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eHuy Nguyen Quoc conceived the research idea, designed the study framework, and supervised the overall project. Dinh Le Quoc collected and processed the data, performed the econometric analysis, and drafted the initial manuscript. Both authors contributed equally to the interpretation of results and the revision of the manuscript. All authors have read and approved the final version of the paper.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors acknowledge being supported by Lac Hong University, Viet Nam.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data used in this study are available upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlbanesi, S. (2007). Inflation and inequality. Journal of monetary Economics, 54(4), 1088-1114.\u003c/li\u003e\n\u003cli\u003eAnderson, E., Jalles d\u0026rsquo;Orey, M. 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H. 2016. \u0026ldquo;Globalization, spending and income inequality in Asia Pacific\u0026rdquo;. \u003cem\u003eJournal of Comparative Asian Development\u003c/em\u003e, 15(1):1-18. https://doi.org/10.1080/15339114.2015.1115746 \u003c/li\u003e\n\u003cli\u003eWong, M.Y., 2017. \u0026ldquo;Public spending, corruption, and income inequality: A comparative analysis of Asia and Latin America\u0026rdquo;. \u003cem\u003eInternational Political Science Review\u003c/em\u003e, 38(3), pp.298-315. https://doi.org/10.1177/0192512116642617 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"fiscal policy, income inequality, economic complexity","lastPublishedDoi":"10.21203/rs.3.rs-7611309/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7611309/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the impact of fiscal policy and economic complexity on income inequality using panel data from 79 countries over the period 2002\u0026ndash;2022. Employing both the SGMM and Bayesian regression, the analysis provides robust evidence on their intertwined effects. The results reveal that economic complexity and government expenditure tend to exacerbate inequality, while tax revenue serves as a strong mitigating factor. Interestingly, when fiscal expenditure interacts with economic complexity, the combined effect significantly reduces inequality with a posterior probability of 100%, highlighting the corrective role of fiscal policy in complex economies. Beyond these core findings, control variables such as GDP per capita, unemployment, inflation, and trade openness are shown to worsen inequality, whereas foreign direct investment and institutional quality help narrow it. By integrating frequentist and Bayesian approaches, this research contributes to a more nuanced understanding of the mechanisms through which fiscal tools and structural economic characteristics jointly shape inequality. The findings underscore the importance of aligning fiscal strategies with economic complexity to promote equitable growth. Policymakers should prioritize redistributive spending, strengthen tax systems, and tailor interventions to the structural dynamics of their economies to effectively address income disparities in an era of globalization and rising complexity.\u003c/p\u003e","manuscriptTitle":"Revisiting Fiscal Policy and Income Inequality in the Context of Economic Complexity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-07 12:02:48","doi":"10.21203/rs.3.rs-7611309/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"04b2ed35-11e3-4d0a-8618-8e915acf0dda","owner":[],"postedDate":"November 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":57340549,"name":"Business and commerce/Economics"},{"id":57340550,"name":"Social science/Economics"},{"id":57340551,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-03-10T14:41:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-07 12:02:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7611309","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7611309","identity":"rs-7611309","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}