Does Digital Financial Development Promote Tax to GDP Ratio in the South Asian Region? The Moderating Role of Governance

Does Digital Financial Development Promote Tax to GDP Ratio in the South Asian Region? 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The Moderating Role of Governance Mohammed Kamrul Hasan, Md Mufizur Rahman, khairul alom, Ashik Imran Khan, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3393979/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This paper sheds light on digital financial development and its impact on the Tax-to-GDP ratio for the five South Asian countries. The panel pooled mean group, mean group, augmented mean group, and Dumitrescu Hurlin causality models applied to investigate long-run and short run relationship among the variables from 1990 to 2021. The results reveal that digital financial development significantly and robustly impacts the Tax-to-GDP ratio of the South Asian Countries in the long run. However, traditional financial development has failed to impact the region's Tax-to-GDP ratio significantly. The causality test results confirmed that digital financial development has a bi-directional causal link with the tax-to-GDP ratio in the short run. The governance indicator, rule of law also played a decisive moderating role in digital financial development to improve the Tax-to-GDP ratio for South Asian countries. The findings of GMM and DOLS are also constituent that digital financial development has a substantial and positive impact on Tax-to-GDP ratio. Thus, policymakers should be concerned about digitalizing financial sector activities to improve the tax-to-GDP ratio in the region through monitoring and tracking transactions. Digital Financial development Growth Rule of Law Tax-to-GDP South Asia Figures Figure 1 1. Introduction Modern economies heavily depend on in-house collections of tax revenue to promote their economic growth, and in this dimension, the South Asian region is not the exception. The South Asian region is one of the fastest growing and promising to grow faster than other parallel countries worldwide. This region's tax-to-GDP ratio is far from developed OECD and Non-OECD countries. Most of the countries in the South Asian Region are positioned in the SGD United Nations. Therefore, to become a successful SDG graduate government needs to focus more on internal tax collection and maintenance. This paper emphasizes five countries in the South Asian Region such as Bangladesh, India, Nepal, Pakistan, and Srilanka. Among these five countries, the tax-to-GDP ratio in Bangladesh disappointed researchers, analysts, and foreign donors to lend funds for development. The tax revenue as a percentage of GDP in Bangladesh still stands at 7%, which is quite frustrating, and the trend from 1990 to 2021 is declining. This scenario envisages the main obstacle to sustainable growth in the country's social safety nets, education, health, and infrastructure development through mobilizing internal funds. The tax revenue percentage of India and Pakistan are not quite impressive yet. Both countries are holding positions of double digits in the tax-to-GDP ratio though it's at a threshold level. The scenario is much better in this indicator for Nepal and Srilanka if we ignore the recent economic crisis in Srilanka. Nepal is showing benchmark performance in this indicator in the region, which is close to 20 percent in some cases. Developed countries heavily rely on the revenue of income tax, while the opposite is true for the context of developing countries. Zee, H. (1996) postulates that trade, goods and services, and levis are the primary sources of tax revenue for developing countries, whereas income and consumption taxes are the critical triggering point for growth success in developed countries. The findings also reported that the income and consumption tax rate is usually 7% higher in developed countries while the tax rate is 23% lower than in developing countries in trade statistics. Financial development has a significant direct effect on improving the tax-to-GDP ratio in numerous ways. First, financial development expands economic activities, increasing direct tax revenue through the financial system (Alom, K. 2018). A developed financial system indicates the involvement of people in financial activities. This effect would be direct and positive to improve the tax-to-GDP ratio. Second, finance leads economic growth push the demand for goods and services, boosting new investments and sectoral development (Alom, K. et al., 2023). The outcomes contribute to collecting tax revenue from expanded business activities. Third, both financial development and economic growth might spur the growth of the formal economy through legal channels. Hence, financial development is a prerequisite to increase tax revenues as it expedites the tracking and collection of taxes ( Bose et al., 2012, and Capasso and Jappeli, 2013). The endogenous growth model by Levine (1991) pointed how sustainable financial and economic development promotes taxation has been a sparkle in earlier research. The findings suggest that to accelerate tax revenue, financial development, and economic growth have a vital role. However, Barro and Sala-I-Martin (1992) and Futagami, Morita, and Shibata (1993) have argued that taxation positively impacts financial development and economic growth using endogenous growth models and vice versa. From a general perspective, to overview the nexus between finance-growth-taxation, it is required to understand the relationship dynamics among; (i) the finance-taxation nexus; (ii) the growth-taxation nexus; and (iii) the finance-growth nexus. The tax-to-GDP ratio is one of the crucial indicators to promote sustainable economic growth in the South Asian region (i.e., SDG 8) through digital financial development. Figure 1 encompasses the flow of relationship from financial development to Tax to GDP ratio via moderating relationship of rule of law. In the framework financial development categorized in two parts that is traditional and digital which is the focal point of this paper. Globalization and digital financial development work as a catalyst to boost fiscal revenue and tax collections as trade barriers and other no-tariff restrictions dismantle due to integration among nations. This mechanism leads to an upward trend in government tax revenue collection in the short run. As a result, the tax-to-GDP ratio will increase due to the country's trade efficiency. However, developing countries are suffering due to administrative and process development inefficiencies. Digital financial development makes countries efficient in collecting excessive tax revenue from different economic activities quickly, as the system will make the collection process smooth in the long run. The South Asian region is positioned in one of the most densely populated regions in the world. Thus the volume of economic events will be huge that will have a direct impact on the collection of tax. Moreover, South Asian countries are treated as emerging economies in the world. Thus government spending trend is significantly upward for infrastructure and fiscal spending development. The institutional quality and bribery in the tax department are the dominant reasons for the low tax-to-GDP ratio in developing countries (Tanzi, V., 1987). Tax is an economic resource that collects from the private sector and moves to the public sector to finance government expenditures. When the government fails to collect appropriate tax revenue, it seems to be its weakness and inefficiency. They need to print money or borrow from external sources such as donor agencies to operate economic functions. In developing countries, institutional weakness, poor application rule of law, and corruption are the main catalysts for a lower tax-to-GDP ratio. Digital financial development is the key tipping point in this paper to promote the tax-to-GDP ratio. Digital financial development indicates providing financial services using technology (Alom K, 2023). This service can be termed fin-tech as well.In the Fig. 1 , Mobile based digitalize financial development is the principal weapon for acclerating Tax-to-GDP ratio that will contribute in the growth process. Mobile connectivity has created a milestone in digital financial development through mobile financial services. The MFS has increased financial coverage up to the unbanked root level of the region. For example, through mobile banking services, Bangladesh and India have reached a milestone of success in the financial sector. As all the MFS transactions are recorded in the system, the process becomes much easier for taxing authorities to collect tax revenue that will boost economic growth. Moreover, mobile and internet-based financial development mesmerizes economic development nowadays (Alom, K, et al., 2022). Online banking and internet banking facilities make our life much more accessible from users' and regulators' points of view. Thus, tax revenue collection from a regulator's perspective has become more convenient due to information and data availability. Due to digitalization, financial system participants must pay taxes to excel in government tax revenue. The increased tax revenue can escalate the employment level of the economy by spending more on industrialization and the well-being of the citizens. The rule of law and Government effectiveness (World Bank, 2020a; 2020b) are significant governance indicators indicating a solid relationship with a country's tax-to-GDP ratio. Government effectiveness reflects perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government's commitment to such policies (World Bank). According to Australian Taxation Office (1997), the heart of the tax reform strategies was building a professional, responsive, fair, open, and accountable tax official. These strategies contribute to helping to increase public trust, and respect, and support all government programs. Well-functioning government always promotes efficient tax collection as they have institutional development and empowerment to ensure the quality of services, trust, and utilization of public goods for the nation's well-being. Hence, government effectiveness ensures a higher tax-to-GDP ratio as instructions substantially provide good services. The rule of law specifies perceptions of agents to the extent to which they have confidence and abide by the rules of society, and in particular, the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence (world bank). The rule of law will provide positive energy for the climate of business; it will encourage investors to continue investing (Dickinson, 2010). This process will flexibly lead to tax collection of the economy without any avoidance by the community members. Government effectiveness and the rule of law may affect economies as it impacts different economic activities in general. We believe government effectiveness and the rule of law will play a decisive moderating role in financial development to promote the tax-to-GDP ratio, which is the underlying proposition of this paper. Therefore, it is essential to analyze the effect of government effectiveness and the rule of law on tax to GDP ratio and economic growth in five South Asian countries. The objective of this research is to emphasis on the digital financial development of the South Asian region, which means financial development through mobile subscriptions and internet connections. Traditional financial development has significant limitations in identifying people who avoid taxes. But in digital financial development paying taxes becomes evident in the system. South Asian Region is one of the promising regions in the world with a strong population base, but the cultural perception is to avoid tax. Thus, the central proposition of this paper is to answer the question: Does Digital Financial Development promote tax to GDP ratio in the South Asian Region?: The moderating role of governance upon investigation. The rule of law is one of the majors governance indicators, and their impact on the tax-to-GDP ratio is the value addition in this paper, along with digital financial development. In the past literature, we found strong gap on digital financial development and its impact on Tax-to-GDP ratio in presence of governance. We strongly believe, this value addition of this paper will strongly contribute to the sustainable development of South Asian countries through internal revenue mobilization. The policy makers will also find the direction on internal tax collections polices by using the digital applications. The paper is organized as follows. Section 2 provides a short selective review of the relevant literature on the underlying proposition of this paper. Section 3 discusses the methodological underpinning of the paper—the results of different econometric analyses presented in Section 4 with dynamic interpretations. Section 5 describes concluding remarks and how this paper's findings will benefit the policymakers. 2. Literature Review According to Schumpeter (1911), financial development enriches economic growth through its ability to mobilize savings, efficient allocation of resources, and manage risk. In Contrast, Keynes (1936) argued that stock market returns instability affects economic growth negatively as it encourages foreign capital outflow and allocation of resources inefficiently. Taxation is a crucial element of economic growth, confirmed by Levine(1999). Similarly, a high level of economic growth is also an effective indicator to prompt tax revenue collection. Though the impact will depend on government fiscal policy, level of effectiveness and the rule of law, and the utilization of the financial sector, thus situations might differ from country to county. In sustainable growth, fiscal policy design is the rudimentary catalyst (Barro & Sala-I-Martin, 1992; Lee & Gordon, 2005). On the one hand, taxation can speed up economic growth through income distribution for economic development; conversely, this process can also obstruct growth by distorting investment activities (Kesner-Škreb, 2000). Rovčanin and Grzinić (2008) and Park et.al.(2014) showed that an exogenous fiscal policy could lead to positive short- and long-run economic growth in competitive equilibrium. This paper also headed toward short-run and long-run equilibrium, where government expenditure explains fiscal policy. The main proposition is justified in the context of South Asian countries as the promising and emerging regions in the world Alom K(2022a, 2022b). The relationship between economic growth and tax revenue is direct and significantly positive, confirmed by Hossain and Tsigaris (2010), analyzing data from 28 OECD countries for 1960–2005, except for a few countries. If borrowed funds finance higher government expenditure, the challenge will be on future tax revenue collections in the long run. This developmental process has significantly endangered sustainable economic growth. Levine (1991) found that stock market development promotes economic growth by ensuring trading facilities in different portfolios which ultimately transfer to the real sector of the economy. Tax policy is a prime indicator of a country's economic growth, both directly and indirectly. The tax incentives act as a direct influencer in the growth process; however, tax policy indirectly affects the financial markets by realizing the benefits of stock market investment incentives. Stoilova and Patonov (2012) postulated that the applications of taxation act as a stimulus of financial resources allocation in the economy, while Levine (1997) argued that financial development can efficiently allocate financial resources in the economy. The positive relationship between financial development and tax revenue is a justified proposition by past researchers. This paper endeavors to attempt that not only traditional financial development but also the role of digital financial development on tax to GDP ratio in the context of emerging tigers in the South Asian region. Moreover, Kate and Milionis (2019) supported empirical evidence favoring efficient tax revenue collection, allocation, and distribution to accelerate economic growth because a developed financial system can remove all the bottlenecks regarding tax collections. In the relationship between tax revenue and financial development, the literature is dominated by three schools of thought, first, the tax revenue-led financial development view (Alom, 2018, 2015); second, the financial development-led tax revenue perspective and third, the feedback hypothesis concerning both tax revenue and financial development. Among these three views, the most popular and prominent view is that tax revenue lead to financial sector development, and the ultimate goal is sustainable economic growth (Volckaert, 2016; Ismail et al., 2017). Okon (2018) induced the concept of the financial development-led tax revenue hypothesis focusing on financial development increases tax revenues by tracking and collecting taxes from participants in the financial system. Hence, the importance of digital financial development comes in radiance in light of the discussion in this paper. We strongly believe digital financial development will promote the tax-to-GDP ratio faster than traditional financial development. Okon's (2018) study found that financial development boosted the tax revenue collection capacity, increased the depth, access, and stability of the financial sector, augmented the collection of tax revenue in Nigeria (Okon, 2018:93), also supported by other researchers based on their study on different countries (Akçay, Sagbas and Demirtas, 2016). The feedback hypothesis reveals that tax revenue and financial development promote each other. From a causal point of view, it's a bidirectional relationship between these two components Akram (2016). Generally, the financial system plays a vital role in collecting tax revenue and reforming the system by monitoring investors' financial activities (Loganathan et al. , 2020). Empirical evidence supports the direct and indirect association between financial development and tax revenue (Nnyanzi, Bbale, and Sendi, 2018). Generally, the financial system can monitor FDI, remittances, and international trade activities performed by individuals in the country. Digitalization in financial services is inevitable to promote tax payment, revenue collection in financial transactions, and monitor the customers (Alom, K. 2022a, 2022b). Banks, other financial institutions, and financial companies often perform digital transactions with businesses and customers through various payment systems for liquidity. If a nation's financial institutions are well-established, transparent, and operate efficiently, both businesses and taxpayers will prefer to carry out their financial transactions through these institutions Alom K. (2018, 2022a). As a result, the taxing authorities can acquire crucial data regarding the income and assets of taxpayers through these institutions (Okon, 2018). On the flip side, the significant discrepancy in the magnitude of the banking industry across different stages of economic development brings attention to the challenge of monitoring and taxing economic activities in less developed nations (Ilievski, 2012). When financial institutions are weak, the underground economy tends to expand, leading to difficulty in obtaining accurate tax data. Consequently, the level of development in the financial sector plays a crucial role in predicting tax revenue (Akram, 2016). The role of stock markets in generating tax revenue is also substantial. Enhancements in the stock market increase the capital accessible to companies for investment endeavors and boost the overall liquidity in the market (Alom, K. 2018). Consequently, if the total value of traded stocks to the Gross Domestic Product (GDP) increases, the government is expected to collect higher tax revenue as a percentage of GDP (Ilievski, 2015). It is widely acknowledged that developed economies generally have a higher proportion of government tax revenue as a share of their GDP than developing economies. The observed pattern suggests a positive correlation between financial development and the tax-to-output ratio, as wealthier countries with more developed financial markets tend to exhibit higher levels of tax revenue relative to their economic output (Guo and Hung, 2020). Therefore, this paper attempts to quantify the impact of digital financial development on Tax-to GDP ratio if two critical governance indicators rule of law and government effectiveness play as moderating role in the context of South Asian Countries. The relationship has been derive in the Fig. 1 , how digital financial development contribute to Tax-to-GDP ratio in the process of escalating tax collections propelling to economic growth and feedback to sustaianable development. So, the hypothesis postulated in the is, does digital financial development promote Tax-to-GDP ration in the South Asian region? The moderating role of governance. 3. Methodology This study investigates the empirical relationship among the variables of GDP per capita, financial development, digital financial development using mobile connections, digital financial development using internet connections, government expenditure, trade openness, remittance inflows, foreign direct investment, and the rule of law, government effectiveness and Tax-to-GDP ratio in the context five countries of the South Asian region. The relevant variables in the study are discussed in detail with source and unit of measurement. 3.1. Variables and Data Variables Variable Category Definition Source Y: Tax-to-GDP Ratio(TAXGDP) Dependent Tax revenue (% of GDP) WDI X1: GDPPC Independent GDP per capita (constant 2015 US$) WDI X2:Financial Development (FINDEV) Independent Domestic credit to the private sector (% of GDP) WDI X3: Digital Financial Development (DFINDEV) Independent Domestic credit to the private sector (% of GDP) interaction with Mobile cellular subscriptions (Per 100 people). Authors Calculation X4: Government Expenditure (GovExp) Independent General government final consumption expenditure (% of GDP) WDI X5: Rule of Law (RLaw) Independent Rule of Law: Percentile Rank WDI X6: Foreign Direct Investment (FDI) Control Foreign direct investment, net inflows (% of GDP) WDI X7: Remittance Inflows (REMITTANCE) Control Personal remittances received (current US$) WDI X8: Rule of Law (DFINDEV*RL) Moderating Financial development interaction with rule of law Authors Calculation The required data are collected from the World Bank Development Indicators (WDI) and International Financial Statistics (IFS). Four variables have been prepared based on the authors calculation in this study. The annual data are considered that cover the period of 1990 to 2021 for five South Asian countries such as Bangladesh, India, Nepal, Pakistan, and Srilanka. To empirically investigate the proposed relationship in this study, all variables are transformed into natural logarithms forms. 3.2. Econometric Approach. The Peseran, Shin, and Smith (1999) pooled mean group tests have been applied to examine the long-run and short-run relationship between Tax to GDP ratio and other explanatory, control, and moderating variables. The Dumitrescu and Hurlin (2011) casualty test has been used to establish short-run relationship dynamics among the explanatory, control, and moderating variables. The cross-sectional dependency test was examined to check the existence of dependency among the variables followed by widely used second-generation unit root tests such as; CIPS, Bai, and Ng tests employed to check the data stationarity. The Arellano, M. and S. Bond (1991) system GMM and Stock and Watson (1993) DOLS tests employed to check robustness in this study. A robustness check will confirm the empirical validity of this research on the underlying proposition of digital financial development and its impact on tax to GDP ratio: the moderating role of governance. The heterogeneity issues have been investigated through Pesaran and Smith's (1995) MG estimator, Pesaran's (2006) CCEMG, and AMG estimator developed by Eberhardt and Teal (2010) tests. This study's underlying proposition is that digital financial development promotes the tax-to-GDP ratio in the Soth Asian Region: the moderating role of governance. To investigate the proposed relationship, three models are employed to justify the underlying proposition, and the expressions of relationship among variables are labeled in the following equations. 4. Results and Discussions Table 1 Results of Descriptive Statistics TAX-GDP LGovExp LGDPPC LFINDEV LDFINDEV LRL LREMITTANCE LFDI Mean 10.95186 2.196707 6.985807 3.378955 8.064439 3.569400 22.21843 0.866969 Median 10.84219 2.280231 6.915363 3.359824 8.015352 3.431869 22.42893 0.688893 Maximum 19.80906 2.868530 8.410879 4.482325 21.82489 6.165007 25.21611 3.668323 Minimum 6.483344 1.399519 6.042022 2.177158 0.000602 2.669161 17.60333 -0.635657 Std. Dev. 3.376159 0.332100 0.576279 0.453661 7.379154 0.539253 1.691998 0.732246 Skewness 0.803694 -0.544366 0.661758 -0.101386 0.191666 1.966291 -0.472251 1.305553 Kurtosis 2.885608 2.748584 2.951725 2.818665 1.417691 10.73863 3.321547 5.347176 Jarque-Bera 17.31187 8.323639 11.69350 0.493325 17.67096 502.3443 6.636517 82.18076 Probability 0.000174 0.015579 0.002889 0.781404 0.000145 0.000000 0.036216 0.000000 Sum 1752.297 351.4731 1117.729 540.6328 1290.310 571.1039 3554.948 138.7150 Sum Sq. Dev. 1812.354 17.53615 52.80356 32.72353 8657.854 46.23619 455.1944 85.25322 Observations 160 160 160 160 160 160 160 160 The variables in this study are found to be normally distributed in Table 1 , except for the variable of scientific publications. The mean-to-median ratio of each variable falls in the close line. The standard deviation is also low for all the variables in this study. Their range of variation between maximum and minimum is also reasonable. The Jarque-Bera test statistics also accept the null hypothesis of the normal distribution of each variable except scientific publication. This striking finding will be rechecked by using a unit-root test of the variable. Thus, the normal distribution of the data is ensured in the study. Table 2 Cross-sectional dependence (CD) and unit root test Variables CD CIPS Bai and Ng LTAXGDP -1.691*** -2.57*** -18.053*** LFINDEV 4.329*** -1.895*** 4.928*** LGDPPC 17.669*** -1.609*** 2.32*** LDFINDEV 5.533 *** -1.738*** -1.450** LGovExp -1.542* -2.052*** -2.018** LFDI 2.516*** -3.27*** -12.367*** LREMITTANCE 13.274*** -1.393** 4.692*** LRL 11.337*** -1.683** 14.606*** Note: ***, **, and * denote 1%, 5%, and 10% significance levels, respectively. This paper has detected cross-sectional dependency in the five South Asian countries due to regional, cultural, and political homogeneity. The Peseran CD test was applied in this study, and all the variables were found to be cross-sectionally dependent in the results. The second generation unit root tests have been conducted in the study to address this issue. The CIPS and Bai and Ng tests are employed in this study to check whether stationary exists among the data. The results reveal that all the variables are stationary at a 5% level, including the control and moderating variables. Table 3 Results of Pooled Mean Group (PMG) test Model1 Model2 Variables Coefficient t-Statistic (p) Coefficient t-Statistic (p) Long Run Equation Long Run Equation LGDPPC 5.745157 2.348872(0.0208) 17.82378 7.413352(0.0000) LFINDEV 6.375399 2.444508(0.0173) -------- ------------- LGovExp 3.122662 1.658588(0.1003) 2.878112 3.381174 (0.0011) LRL 3.296323 2.481438(0.0147) -1.682123 -0.313759(0.7544) LDFINDEV ……………… ----------- -0.602312 -7.774542 (0.0000) LFDI -1.581492 -6.695968 (0.0000) -0.570535 -3.253578 (0.0016) LREMITTANCE -1.039684 -2.563252 (0.0118) 0.124518 0.376705(0.7073) LDFINDEV*RL ----------------- ------------------ 0.765161 1.431030 (0.0975) Short Run Equation Short Run Equation COINTEQ01 -0.256439 -1.385755 (0.1688) -0.207593 -2.678415(0.0077) LGDPPC 5.767069 1.519831 (0.1316) 8.561734 2.403708 (0.0468) LFINDEV 1.084081 1.538758 (0.1270) ---------- ------------ LGovexp -1.516515 -2.711229 (0.0079) -1.980533 -2.316318 (0.0228) LRL -1.078192 -2.4060 (0.0179) -3.710854 -3.440051(0.0110) LDFINDEV -------------- -------------- -0.053281 -1.859428 (0.0764) LFDI 0.508766 1.928321 (0.0566) 0.487183 1.650649 (0.0944) LREMITTANCE 0.832931 2.527322 (0.0191) 0.074243 1.675858 (0.0409) LDFINDEV*RL ----------------- ------------------- 0.770058 3.311693(0.0360) Note: ***, **, and * denote 1%, 5%, and 10% significance levels, respectively. Table 3 estimates pooled mean group model (PMG). PMG is the best model for a single test to investigate long- and short-run results of a panel data pooling data. We estimate three independent models to investigate the relationship among the independent, control, dependent, and moderating variables. This study's underlying proposition is whether digital financial development promotes tax to GDP ratio in the South Asian Region: the moderating role of governance. To investigate the empirical relationship, two models are employed to justify the underlying proposition; the first model is traditional financial development, the second model is mobile based digital financial development. The results show interesting findings that traditional financial development holds an insignificant relationship with tax to GDP ratio in the short run, but in the long run, the relationship is significant. Other independent variables, GDP per capita, the rule of law show a 5% significant relationship with tax to GDP ratio. Two control variables, FDI and Remittances, also confirm a significant relationship with tax to GDP ratio in the long run. Government expenditure, the rule of law are significant in the short run with the tax-to-GDP ratio. Among the control variables, remittance and FDI also maintained a significant relationship with tax to GDP ratio among five South Asian Countries. The second model is about digitalizing financial development through mobile connections. PMG test results reveal that in the long run, mobile-based digital financial development has a 1% level significant relation with tax to GDP ratio, Romer(1984). Moreover, economic growth, government expenditure, and government effectiveness significantly influence the tax-to-GDP ratio in South Asian countries. The rules of law is an important cases to observe in the context of five South Asian countries are of interest in this study. This phenomenon is evident in the daylight that South Asian countries are still significantly lacking in applications of the rule of law and government effectiveness and extensively suffering from corruption, ruling party misuse of law, and effective allocation of budget and management. The findings of this study confirm that, in the long run, digitalization ensures better government performance. However, control variables also maintain a significant relationship with tax to GDP ratio in the presence of digital financial development supplemented by the fundamental proposition of this paper rationally. This study also investigates the moderating relationship between digital financial development and the rule of law. The findings are supportive and significant that digital financial development in the presence of strong applications of law and effective management promotes tax to GDP ratio. In the short run, digital financial development holds a significant relationship with tax to GDP ratio which is found missing in traditional financial development, Romer (1990). Government expenditure, the rule of law significantly impact the tax-to-GDP ratio if the country can endorse financial development through digitalization. The control variables FDI and remittance, also maintain a strong association with tax to GDP ratio in the framework of digital financial development. Moreover, in the short run, the moderating relationship of governance found significant with digital financial development. Hence, these findings shed light on the importance of the rule of law and its applications to collect tax revenue, and the government must be effective in identifying and collecting tax revenue for sustainable growth and financial development in the South Asian region. Table 4 Estimation results from MG, AMG, and CCEMG models. Variables MG AMG CCEMG Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 Independent LGDPPC 2.53 ***(0.011) 2.13**(0.033) 1.56(0.087) 3.48*(0.003) 1.96(0.056) 3.31(.000) LFINDEV 0.78(0.463) ----------- 1.30 ----- -0.74 ---------- LGovExp 1.88**(0.041) 2.79*(.065) -1.79(0.053) -3.23***(0.001) -1.43(0.82) 2.98*(.028) LRL 1.13(0.268) 1.97*(0.05) 1.31(0.52) 1.68(0.062) 2.20(0.058) 2.45*(0.035) LDFINDEV ------------- 2.91**(.049) ----- 2.40*(0.358) ----- 3.48(0.000) Control LFDI 1.16(0.263) 1.92*(0.050) 2.65*(0.010) 2.21*(0.027) 0.60(.65) 0.59(0.501) LREMITTANCE 1.36*(0.097) 2.13*(0.026) -1.52*(0.18) -1.23(0.28) -1.69*(0.10) 2.59*(.031) Moderating LFINDEV*RL --------- 1.81**(0.058) ------------- 1.60**(.050) ---------- 1.94*(.035) Model(Wald chi2) 52.73(0.000) 26.17(0.000) 6.85(0.00) 10.55(0.00) 6.14(0.1890) 15.88(0.0032) CDP(common dynamic process 3.34(0.001) 4.73(0.000) Note: ***, **, and * denote 1%, 5%, and 10% significance levels, respectively. Table 4 results interpret the heterogeneity of the panel data. As in the unit root test, we found cross-sectional dependency, i.e., heterogeneity among the five South Asian countries. Among the five countries, India, Bangladesh, and Pakistan are larger economies than Nepal and Srilanka. Thus to address the heterogeneity, we have investigated through Pesaran and Smith's (1995) MG estimator, and Pesaran (2006) CCEMG and Eberhardt and Teal (2010) AMG estimator. The mean group test shows that economic growth, government expenditure, and government effectiveness significantly affect the tax-to-GDP ratio. However, this study found the traditional financial development and the rule of law insignificant to tax to GDP ratio. Control variables, FDI and remittances also impact the relationship with tax to GDP ratio in the digital financial development model. In the second model of mean group regression is digital financial development where we found that GDP per capita, government expenditure, digital financial development, and the rule of law have a significant relationship with the tax-to-GDP ratio of every country in the South Asian region. Remittance and FDI significantly and positively impact the tax-to-GDP ratio. The moderating relationship of governance with digital financial development has a strong relationship with digital financial development confirmed in the study. The augmented mean group regression results reveal that traditional financial development has no significant relationship with the tax-to-GDP ratio. Other explanatory variables, government expenditure and GDP per capita have a 10% significant influence on the tax-to-GDP ratio. However, the rules of law shows an insignificant impact on the tax-to-GDP ratio. The outcomes of digital financial development show interesting directions for policymakers. Digital financial development has a significant and positive effect on the tax-to-GDP ratio. Other explanatory variables also show similar findings for cases such as government expenditure, GDP per capita, the rule of law, and government effectiveness have a 5% significant relationship with the tax-to-GDP ratio. The control variables in this study, remittance and FDI influence the tax-to-GDP ratio at a 5% level. The moderating associations of the rule of law affecting the tax-to-GDP ratio. This indicator is a trigering issue in the context of the South Asian region, where most of the countries are staying behind the benchmarking developed countries. Due to a lack of applications of rule of law and government control, this region suffers from corruption, institutional development, and practical policy applications. The commonly correlated effects of the mean group show insignificant relation of financial development with tax-to-GDPratio in the model of traditional financial development. This relationship shows consistent findings across different econometric tests. However, in the model of traditional financial development, GDP per capital and the rule of law significantly impact the tax-to-GDP ratio among South Asian countries. Among the control variables, remittance shows a significant relationship with the tax-to-GDP ratio in this study. Thus, control variables manifested plausible associations with the tax-to-GDP ratio confirmed by different econometric estimations. The underlying proposition of this study is that digital financial development promotes tax-to-GDPratio and the moderating role of governance. Hence, the CCEMG test results for digital financial development show rationale and provoke directions for policymakers in the South Asian region. The mobile and internet-based digital financial development has a significant impact on tax-to-GDPratio in the South Asian region, confirmed by the results. Moreover, this study confirms that other explanatory variables, such as economic growth, government expenditure, the rule of law have a significant relationship with the tax-to-GDP ratio. Both of the control variables, FDI and remittances also have a significant relationship with the tax-to-GDP ratio in digitalized financial development models. This relationship dynamics is quite logical and paradoxical in the context of a pragmatic point of view. Moreover, the moderating relationship is also of interest to the authors in this study, which is treated as a value proposition in the existing literature. We found a strong moderating relationship between mobile-based digital financial development with the rule of law that strongly affect tax-to-GDP ratio in the South Asian region. This proposition is also supported in rationale judgment based on existing facts and figures, the significant lackings for the South Asian region. The most widely used test of causality in panel data is the DH test, developed by Dumitrescu and Hurlin (2011). The test considers the not homogeneously cause null hypothesis, that means no causal relationships are assumed to exist of observed instruments for any panel member in this study. The DH test is based on an aggregated Wald statistic of individual coefficients causality tests. The Dumitrescu and Hurlin test has an advantage over the Granger-causality test, which focuses on individual coefficients. The results of Table 5 show the outcomes of estimations of two models: traditional and digital financial development. The causal relationship indicates a short-run relationship among the variables, and their directions are also equally important. In traditional financial development shown in the Table 5 , model1, we found no bi-directional causal link among the explanatory variables in this study. However, a unidirectional causal relationship has been traced among the independent variables. The unidirectional causal link has been documented from GDP per capita to tax-to-GDP ratio, financial development to tax-to-GDP ratio, government expenditure to GDP per capita, financial development to GDP per capita, and finally, the rule of law to Tax-to-GDP ratio. Thus, the surfacing note is that traditional financial development has causal connections with tax-to-GDP ratio in the short run. However, the relationship deviates in different directions in the long run due to sustainability issues. Therefore, we have proposed digital financial development and its relation with tax-to-GDPratio and other explanatory variables in the short run. On the other hand, the results of mobile based digital financial development promote the tax-to-GDP ratio in the short run found in Table 5 , model2. The relationship is bi-directional, which indicates that digital financial development promotes the tax-to-GDP ratio of a country, and the tax-to-GDP ratio also promotes digital financial development in the short run. Digital financial development has a bi-directional causal link with economic growth and vice versa. Moreover, economic growth to tax-to-GDP ratio, digital financial development to rule of law, and government expenditure to economic growth have bi-directional causal relationship, documented in this study. However, a unidirectional causal link has been found among the variables of government expenditure to digital financial development. Thus, this study can strongly claim the plausibility of shaded areas and their impact on the policy-making level for the highlighted countries. Both the short-run and long-run findings are consistent and follow the same direction. For sustainable development and to maintain internal growth mobilizing internal funds is imperative now a day. Global turmoil and other exogenous issues will uncontrollably exist in the global arena for various geopolitical and power, and interest conflicts. Hence, South Asian economies need to reap the benefits of the demographic dividend to mobilize internal funds through digitalizing financial activities to ensure sustainable development. Table 5 Dumitrescu Hurlin Panel Causality Test Model1. Null Hypothesis: W-Stat. Zbar-Stat. Prob. LGDPPC does not homogeneously cause TAXGDP 4.08130 4.15876 0.0005 TAXGDP does not homogeneously cause LGDPPC 0.90927 -0.23207 0.8165 LFINDEV does not homogeneously cause TAXGDP 3.43792 3.26816 0.0011 TAXGDP does not homogeneously cause LFINDEV 1.61226 0.74103 0.4587 LGOVEX does not homogeneously cause TAXGDP 0.05026 -1.42115 0.1553 TAXGDP does not homogeneously cause LGOVEX 0.73706 -0.47045 0.6380 LRL does not homogeneously cause TAXGDP 2.12216 2.56261 0.0501 TAXGDP does not homogeneously cause LRL 1.04992 -0.03738 0.9702 LFINDEV does not homogeneously cause LGDPPC 2.37110 1.79144 0.0732 LGDPPC does not homogeneously cause LFINDEV 1.78385 0.97856 0.3278 LGOVEX does not homogeneously cause LGDPPC 2.35809 1.77343 0.0762 LGDPPC does not homogeneously cause LGOVEX 1.40095 0.44853 0.6538 LRL does not homogeneously cause LGDPPC 2.15172 1.48777 0.1368 LGDPPC does not homogeneously cause LRL 1.48248 0.56139 0.5745 LGOVEX does not homogeneously cause LFINDEV 0.60386 -0.65483 0.5126 LFINDEV does not homogeneously cause LGOVEX 0.43961 -0.88218 0.3777 LRL does not homogeneously cause LFINDEV 2.14524 1.47879 0.1392 LFINDEV does not homogeneously cause LRL 1.83940 1.05544 0.2912 LRL does not homogeneously cause LGOVEX 1.17767 0.13945 0.8891 LGOVEX does not homogeneously cause LRL 1.09337 0.02277 0.9818 Model2. Null Hypothesis: W-Stat. Zbar-Stat. Prob. LGDPPC does not homogeneously cause TAXGDP 4.08130 4.15876 0.0005 TAXGDP does not homogeneously cause LGDPPC 2.90927 2.23207 0.0540 LGOVEX does not homogeneously cause TAXGDP 0.05026 1.42115 0.1553 TAXGDP does not homogeneously cause LGOVEX 0.73706 0.47045 0.6380 LRL does not homogeneously cause TAXGDP 1.12216 0.06261 0.9501 TAXGDP does not homogeneously cause LRL 1.04992 0.03738 0.9702 LDFINDEV does not homogeneously cause LTAXGDP 4.93637 5.34238 0.0001 LTAXGDP does not homogeneously cause LDFINDEV 3.30681 3.31822 0.0003 LGOVEX does not homogeneously cause LGDPPC 2.35809 1.77343 0.0562 LGDPPC does not homogeneously cause LGOVEX 2.40095 2.14853 0.0503 LRL does not homogeneously cause LGDPPC 2.15172 1.48777 0.1368 LGDPPC does not homogeneously cause LRL 1.48248 0.56139 0.5745 LDFINDEV does not homogeneously cause LGDPPC 2.96566 2.61445 0.0089 LGDPPC does not homogeneously cause LDFINDEV 3.16542 3.26173 0.0070 LRL does not homogeneously cause LGOVEX 1.17767 0.13945 0.8891 LGOVEX does not homogeneously cause LRL 1.09337 0.02277 0.9818 LDFINDEV does not homogeneously cause LGOVEX 1.05221 -0.03422 0.9727 LGOVEX does not homogeneously cause LDFINDEV 2.51344 1.98847 0.0468 LDFINDEV does not homogeneously cause LRL 3.43130 3.49054 0.0007 LRL does not homogeneously cause LDFINDEV 3.62638 3.76057 0.0009 To check this study's robustness, generalized momentum, and dynamic ordinary least square methods have been employed. Both models are superior to other relevant econometric models in panel data analysis. The results of the first differenced GMM are shown in Table 6 . The results reveal in the model 1 that traditional financial development has no significant relationship with the tax-to-GDP ratio. The other independent variables, such as government expenditure, the rule of law were also found as significant with tax-to-GDP ratio in this study. Remittance was also found to be significant among the control variables in this study. However, the othe control variable, such as FDI was found insignificant to the tax-to-GDP ratio in traditional financial development.The overall model was also found significant, and no Arellano-Bond Serial Correlation has been noticed in this study. Model two is designed for digital financial development show meaningful findings in this study. In the mobile based financial development model, all the explanatory variables significantly influence tax-to-GDPratio at a 5% significance level. The control variables also justified its impact on tax-to-GDP ratio for remittance and FDI. The moderating relationship of rule of law with tax-to-GDP ratio was found to be significant at a 1% level, leading to some guiding issues for South Asian countries. As this region shown strong lacking in good governance, thus to improve the tax-to-GDP ratio is imminent to improve the rule of law for government effectiveness along with digitalization. However, digitalization will propel the tax-to-GDP ratio faster in the South Asian region. Thus, the robustness check results were consistent with the baseline econometric estimations in this study. Table 6 Generalized Methods of Moments (GMM) Model1 Model2 Variables Coefficient t-Statistic (p) Coefficient t-Statistic (p) LGDPPC -0.541 -0.410(0.681) 3.325 5.909(0.000) LFINDEV 1.458 1.351(0.178) ------------ -------------- LGovexp -5.448 -3.463(0.000) 3.473 4.485 (0.000) LRL 3.583 3.091 (0.002) -3.820 -6.883(0.000) LDFINDEV ……….. ----------- -0.270 -4.771(0.000) LFDI -1.012 -1.529 (0.128) 1.291 2.399(0.017) LREMITTANCE 0.232 1.962(0.037) 0.180 1.847(0.039) LFINDEV*RL ------------- ------------ 10.099 7.409 (0.000) J-statistic 93.776(0.000) 28.254(0.000) Arellano-Bond Serial Correlation Test AR(1) 8.153 (0.273) 22.838(0.818) AR(2) 33.437(0.632) 45.899(0.707) Table 7 visualize Stock and Watson's dynamic ordinary least square method (1993). By contrast, the Stock Watson method is a robust single equation approach that corrects for regressor endogeneity by including leads and lags of first differences of the regressors and for serially correlated errors by a GLS procedure. The results of dynamic ordinary least squares confirm that economic growth significantly relates to the tax-to-GDP ratio in the traditional financial development model. Government expenditure, and rule of law are significantly related to the tax-to-GDP ratio. However, the traditional financial development is showing insignificant impact on tax-to-GDP ratio relationship dynamics in the South Asian region. The FDI also show a significant relationship with the tax-to-GDP ratio. Table 7 Dynamic Ordinary Least Square (DOLS) Model1 Model2 Variables Coefficient t-Statistic (p) Coefficient t-Statistic (p) LGDPPC 12.479 2.787(0.016) 0.3570 3.233(0.001) LFINDEV -3.919 1.477 (0.165) ------------ -------------- LGovexp -8.872 4.168 (0.001) -0.1849 4.1352 (0.000) LRL 10.297 2.815 (0.015) -2.245 -9.253 (0.000) LDFINDEV ……………… ----------- -0.2702 -4.906(0.000) LFDI -0.5071 -0.3480 (0.733) -0.412 -2.126(0.0427) LREMITTANCE -1.7297 -1.183(0.199) -0.028 -0.4265 (0.598) LDFINDEV*RL ------------- ------------ 7.974 5.653 (0.000) R-squared 0.8862 0.7888 Adjusted R-squared 0.8350 0.7677 In the digital financial development model, mobile based financial development has a significant relationship with the tax-to-GDP ratio. Moreover, economic growths, government expenditure, and rule of law have significantly impact on the tax-to-GDP ratio. The control variables have a significant impact on the tax-to-GDP ratio. We have considered these two variables as control variables in this study in the South Asian region. The moderating relationship is also found as significant in the models of digital financial development in the South Asian region. It implies that in the financial development process rule of law and government effectiveness is one of the vital issues in the South Asian region. 6. Conclusions and Policy Implications This paper starts with an underpinning question on the issue of whether digital financial development promotes the tax-to-GDP ratio in the context of five South Asian countries and the moderating role of governance. South Asian region is one of the fastest growing and promising regions to contribute to the world economic community. Moreover, the role of governance is one of the key concerns in this paper, which investigated whether relationship dynamics get affected or not if governance plays a moderating role. In this paper, rigorous econometric models have been applied to investigate the underlying proposition of impact of digital financial development on the tax-to-GDP ratio. The relationship dynamics between the tax-to-GDP ratio and digital financial development investigated focusing on five South Asian countries from 1990 to 2021. The cross-sectional dependency test results reveal that CSD exists among the variables; hence, we have decided to forward this paper with the second generation unit roots test, namely CIPS and Bai and Ng, and the results confirm that all the variables are stationary. The pooled mean group (PMG) results confirm that traditional financial development has a significant relationship with the tax-to-GDP ratio in the long run. However, in the short run, this relationship is not significant. Moreover, other explanatory variables demonstrate a significant relationship with the tax-to-GDP ratio. Economic growths, government expenditure, and the rule of law significantly impact the tax-to-GDP ratio in the long run. However, the short-term relationship between traditional financial development and the tax-to-GDP ratio is insignificant. Government expenditure and rule of law are also significant with the tax-to-GDP ratio in the short run. But economic growth has no significant relationship with the tax-to-GDP ratio in the short run. The control variables also show a steady relationship in the long and short run with the tax-to-GDP ratio, which is significant. The results of the PMG test for digital financial development lead to meaningful findings consistent with this paper's underlying proposition. Digital financial development has an enormously positive effect on the tax-to-GDP ratio. The moderating relationships of digital financial development with the rule of law significantly positively affect the short and long-run tax-to-GDP ratio. The results of Mean Group (MG), Augmented Mean Group (AMG), and Common Correlated Effects of Mean Group (CCEMG) were also found consistent with earlier econometric tests results in this study. In the mean group analysis, economic growth, government expenditure, and rule of law significantly affect the tax-to-GDP ratio. However, this study found the traditional financial development is insignificant to tax-to-GDP ratio. The control variables, FDI and remittances have also positive impact to tax-to-GDP ratio. On the other hand, in the mobile-based digital financial development model, we found that GDP per capita, government expenditure, digital financial development, and the rule of law have a significant relationship with the tax-to-GDP ratio of every South Asian region. Remittance, and FDI significantly and positively impact tax-to-GDP ratio. The moderating relationship of governance with digital financial development has a strong effect on Tax-to-GDP ratio, confirmed in the study. The AMG and CCEMG tests results also reveal that digital financial development significantly influences the tax-to-GDP ratio for five South Asian countries. Furthermore, GDP per capita, government expenditure, and rule of law have a5% level statistical impact on Tax-to-GDPratio in the region. The moderating relationship of rule of law also impacting tax-to-GDP ratio strongly in the presence of digital financial development. The control variables, trade and FDI, significantly influence the tax-to-GDP ratio and sometimes remittances, which were also significant in the econometric analysis. The Dumitrescu Hurlin causality test results show that traditional financial development has unidirectional causality with the tax-to-GDP ratio. Whereas the relationship among tax-to-GDP ratio to digital financial development, digital financial development to rule of law, digital financial development to economic growth, government expenditure to growth and economic growth to Tax-to GDP ratio are bidirectional, which mean relationship dynamics work in both ways. GMM and DOLS tests have been analyzed to confirm the robustness of this study. In both cases, it has been found that in digital financial development, all the explanatory variables influence the tax-to-GDP ratio at a 5% significance level. The control variables also justified its impact on the tax-to-GDP ratio for Remittanceand FDI in the study. The moderating connection between the rule of law with the tax-to-GDP ratio is showing impact at 1% level significant. As this region is lacking behind in good governance, thus improving the tax-to-GDP ratio, this is imminent to digitalization economic activities to improve the rule of law that will empower government effectiveness. Hence, policymakers of this region country-specific should pay more attention to digitalizing their financial activities to improve tax revenue collection and escalate the tax-to-GDP ratio, as this region has tremendous potential with the allure of growth prospects. But due to a lack of governance applications, citizens tend to avoid tax. This is happening due to corruption, reluctance of regulatory authority, and lack of policy guidelines and modernization of the tax collections procedure and the breaucracy. Despite the economic size, all five countries in this region have similar economic outlooks. The only way to mitigate this issue is to digitalize financial services by creating a link with the Board of Revenue that will work as a catalyst to accelerate the tax-to-GDP ratio in the South Asian region. Declarations Future research prospect This study can further be extended to the country specific or sector specific analysis. The impact of digitalization in the financial sector can be extended to differnent real sectors of the economy to find more robust output. Competing interests: This research work has no financial or non-financial interests that are directly or indirectly related to the work submitted for publication. Funding: This paper receives no grant or funding from anybody or organizations. Authors' contributions: All authors equally contributed to the study conception and design, material preparation, data collection and analysis. Acknowledgements: We gratefully acknowledge the insightful comments from Prof. Moritz Ritter, Prof. ANM Wahid, Prof. Jeff Gow, Prof. Salahuddin, Prof. Ahkam in the earlier version of the paper. Availability of data and materials: All the data will be available upon request. References Alom K, Ahkam SN, Adnan AM (2022b) The ICT-led Financial Inclusion and Economic Growth Nexus in Bangladesh. J Knowl Globalization 14(1):35–59 Alom K (2015) Exchange Rate and Economic Growth Nexus: Evidence from Bangladesh. J Knowl Globalization 8(1):109–130 Alom k et al (2022a) Financial Development, Exchange Rate fluctuation, and Remittances inflows in Bangladesh: A Cointegration study, AIUB Journal of Business and Economics, Volume19, Issue1, Pages:121–138, 2022 Alom k (2018) Financial development and economic growth dynamics in South Asian region, The Journal of Developing Areas, Volume 52 Issue 4, Pages 47–66, 2018 Bai J, Ng S (2004) ìA PANIC Attack on Unit Roots and Cointegrationî. Econometrica 72(4):1127–1178 Arellano M, Bond S (1991) Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations. Rev Econ Stud 58:277–298 Blundell RW, Bond SR, Windmeijer F (2000) Estimation in Dynamic Panel Data Models: Improving on the Performance of the Standard GMM Estimator', in B. Baltagi (ed.), 8.Nonstationary Panels, Panel Cointegration, and Dynamic Panels, Advances in Econometrics 15, JAI Press, Elsevier Science Dumitrescu E-I, Hurlin C (2012) Testing for Granger Non-Causality in Heterogeneous Panels. Econ Model 29:1450–1460. https://doi.org/10.1016/j.econmod.2012.02.014 Pesaran MH, Shin Y, Smith RP (1999) Pooled Mean Group Estimation and Dynamic Heterogeneous Panels. J Am Stat Assoc 94(446):621–634 Pesaran MH (2006) Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure. Econometrica 74:967–1012. https://doi.org/10.1111/j.1468-0262.2006.00692.x Francis T, Markus, Eberhardt (2010) "Productivity Analysis in Global Manufacturing Production," Economics Series Working Papers 515, University of Oxford, Department of Economics Pesaran MH, Smith RP (1995) Estimating long-run relationships from dynamic heterogeneous panels. J Econ 68:79–113 Pesaran HM (2003) A Simple Panel Unit Root Test in the Presence of Cross Section Dependenceî. Mimeo, University of Southern California Pesaran HM, Smith LV, Yamagata T (2013) Panel unit root tests in the presence of a multifactor error structure. J Econ 175:94–115 Romer P (1990) "Human Capital and Growth: Theory and Evidence." Carnegie-Rochester Conference Series on Public Policy, 32: 251–286 Zee H, Howell (1996) Taxation and Unemployment, International Monetary Fund, Volume/Issue: Volume 1996: Issue 045, ISSN: 1018–5941, Pages:56, https://doi.org/10.5089/9781451974348.001 Sheikh W, Alom K (2021) Corporate governance, board practices and performance of shipping firms in Bangladesh, Asian J. Shipping Logist., 37 (3) (2021), pp. 259–267 Alom K, Hasan MK, Khan SA, Khan AI, Ahmed W (2023) The Scientific Research Publications and Sustainable Sectoral Value Addition in Bangladesh. International Journal of Empirical Economics, 2(01), p.2350002 Barro RJ, Sala-i-Martin X (1992) Convergence J Political Econ 100:223–251. https://doi.org/10.1086/261816 Bose N, Capasso S (2012) The impact of banking development on the size of the shadow economy. J Economic Stud 39(6):620–638. https://doi.org/10.1108/01443581211274584 Capasso S, Jappeli T (2013) Financial development and the underground economy. J Dev Econ 101(3):167–178 Levine R (1991) Stock Markets, Growth, and Tax Policy. J Finance 46:1445–1465. https://doi.org/10.1111/j.1540-6261.1991.tb04625.x Tanzi V (1987) Quantitative Characteristics of the Tax Systems of Developing Countries. In: Newberry D, Stern NH (eds) The Theory of Taxation for Developing Countries. Oxford University Press for the World Bank, New York, pp 205–241 Guo J-T, Hung F-S (2020) Tax evasion and financial development under asymmetric information in credit markets. J Dev Econ Volume 145 102463:0304–3878. https://doi.org/10.1016/j.jdeveco.2020.102463 Bojan Ilievski (2015) Stock Markets and Tax Revenue. J Appl Finance Bank SCIENPRESS Ltd 5(3):1–1 Sieni Toussaint OULAI (2022) "Heterogeneous Effects of Financial Development on Tax Revenues: Accounting for Institutional Quality in Sub-Saharan Africa," Applied Economics and Finance, Redfame publishing, vol. 9(1), pages 54–67, December èna Kimm Gnangnon (2022) Financial development and tax revenue in developing countries: investigating the international trade channel, vol 2. SN Business & Economics, Springer,, January, pp 1–26. 1 Gnangnon Sèna, Kimm (2019) Financial Development and Tax Revenue in Developing Countries: Investigating the International Trade and Economic Growth Channels, EconStor Preprints 206628. ZBW - Leibniz Information Centre for Economics Akram 2016N, Akram (2016) Do financial sector activities affect tax revenue in Pakistan? Lahore J Econ 21(2):153–169. 10.35536/lje.2016.v21.i2.a Ilievski B (2012) Tax Revenue and financial development: Theory and Evidence. PhD Dissertation, Southern Illinois University Carbondale, USA Arellano M, Bover O (1995) Another Look at the Instrumental Variable Estimation of Error-components Models. J Econ 68:29–52. http://dx.doi.org/10.1016/0304-4076(94)01642-D Tanzi V, Zee H (2000) Tax policy for emerging markets: Developing Countries. National Tax Journal, (2000), 299–322. https://doi.org/10.17310/ntj.2000.2.07 Gupta AS (2007) Determinants of Tax Revenue Efforts in Developing Countries. IMF Working Paper, WP/07/184, Washington: International Monetary Fund Okon, Okon B (2018) (2018). Financial sector development and tax revenue in Nigeria, International Journal of Economics, Commerce and Management, 6 (6), pp. 93–109, http://ijecm.co.uk/wp-content/uploads/2018/06/667.pdf Ashraf M, Sarwar S (2016) Institutional determinants of tax buoyancy in developing nations. J Emerg Economies Islamic Res 4(1):62–73 Bergman M (2010) Tax evasion and the rule of law in Latin America: the political culture of cheating and compliance in Argentina and Chile. Penn State Press Nnyanzi JB, Babyenda P, Bale JM (2016) Regional economic integration and tax revenue: East African community. J Econ Integr 31(4):932–967 Kate Ft, Milionis P (2019) Is capital taxation always harmful for economic growth? Int Tax Public Finance 26:758–805. https://doi.org/10.1007/s10797-019-09530-3 Volckaert K (2016) The role of the financial sector in tax planning. TAXE2 Special Committee of the European Parliament Akcay S, Sagbas I, Demirtas G (2016) Financial development and tax revenue nexus in Turkey. J Econ Dev Stud 4(1):103–108 Loganathan N, Ismail S, Streimikiene D, Hassan AG, Zavadskas EK, Mardani A (2017) Tax reform, inflation, financial development and economic growth in Malaysia. Romanian J Economic Forecast 20(4):152–165 Schumpeter JA (1911) The Theory of Economic Development. Harvard University Press, Cambridge Keynes JM (1936) The General Theory of Employment, Interest, and Money. John Maynard Keynes (London: Macmillan. 1936. Pp. 403. 5s.) Levine R (1999) Law Finance and Economic Growth. J Financ Intermediation 8:8–35. https://doi.org/10.1006/jfin.1998.0255 Young Lee RH, Gordon (2005) Tax structure and economic growth, Journal of Public Economics, Volume 89, Issues 5–6, Pages 1027–1043, ISSN 0047-2727, https://doi.org/10.1016/j.jpubeco.2004.07.002 Kesner-Škreb Marina. (2002). TAX POLICY AND ECONOMIC GROWTH. Croatian Economic Survey; No.4 Abdon A, Estrada GB, Lee M, Park D (2014) “Fiscal Policy and Growth in Developing Asia”, ADB Economics Working Paper No. 412 (Asian Development Bank) Rovčanin A, Gržinić J (2008) Economic development challenges in transitional countries - Financial aspects. Economic Res (dbenazic@efpu hr) ; Vol.21 No.1. 21. Stoilova D, Patonov N (2013) An Empirical Evidence for the Impact of Taxation on Economy Growth in the European Union. Tourism & Management Studies. Vol. 3. 1031-39 Fabian ten Kate & Petros Milionis (2019) "Is capital taxation always harmful for economic growth?" International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 26(4), pages 758–805, August Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-3393979","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":268160122,"identity":"023c5882-8507-4965-9594-2e517c60216f","order_by":0,"name":"Mohammed Kamrul Hasan","email":"","orcid":"","institution":"American International University Bangladesh","correspondingAuthor":false,"prefix":"","firstName":"Mohammed","middleName":"Kamrul","lastName":"Hasan","suffix":""},{"id":268160123,"identity":"ade7731c-af5e-4b5d-83c2-ee4d49151688","order_by":1,"name":"Md Mufizur Rahman","email":"","orcid":"","institution":"Southeast University - Sipailou Campus: Southeast University","correspondingAuthor":false,"prefix":"","firstName":"Md","middleName":"Mufizur","lastName":"Rahman","suffix":""},{"id":268160124,"identity":"ee0b5d9e-443b-40b1-8ec9-6894b969302c","order_by":2,"name":"khairul alom","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYBACCQkGhgMMDBYMfAwMjA/AbCK1SDCwMTAwGxCthQGqhU2CKC2Ss5sfHq5sk5BnY+9Oq+apuSPHz8D88NENPFqkZY4ZHDzbJmHYxnN2222eY8+MJRvYjI1z8GiRk0gwONjYJsHYJpEL1MJ2OHHDAR42afxa0j+AtNiDtBTz/CNCi7REDtiWRJAWZt42IrRIzsgpONhwTiIZ6JfNknP7DhtLNhPwi8SN9M0fG8psbPvZezd+ePPtsBw/e/PDx/i0oAAmHhDJTKxyEGD8QYrqUTAKRsEoGDEAADdOTFx1VMvsAAAAAElFTkSuQmCC","orcid":"","institution":"Northern University Bangladesh","correspondingAuthor":true,"prefix":"","firstName":"khairul","middleName":"","lastName":"alom","suffix":""},{"id":268160125,"identity":"7b1f99bf-80cc-4da7-9eb9-454576cc64b5","order_by":3,"name":"Ashik Imran Khan","email":"","orcid":"","institution":"North South University","correspondingAuthor":false,"prefix":"","firstName":"Ashik","middleName":"Imran","lastName":"Khan","suffix":""},{"id":268160126,"identity":"77bfff0c-7989-4992-a413-094e688325e7","order_by":4,"name":"Wayes Ahmed","email":"","orcid":"","institution":"Dhaka University: University of Dhaka","correspondingAuthor":false,"prefix":"","firstName":"Wayes","middleName":"","lastName":"Ahmed","suffix":""}],"badges":[],"createdAt":"2023-09-28 03:42:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3393979/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3393979/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50017410,"identity":"dfbf19ba-7617-4c71-8535-b21932af5771","added_by":"auto","created_at":"2024-01-23 07:09:49","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":358831,"visible":true,"origin":"","legend":"\u003cp\u003eThe digital financial development to Tax-to-GDP ratio nexus.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3393979/v1/005308aa99481aa1b70edc17.jpeg"},{"id":60958560,"identity":"a514c0bb-c7d2-4f8f-828e-8df5f5d55bc7","added_by":"auto","created_at":"2024-07-24 03:59:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1252010,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3393979/v1/8f8aaa75-880d-46bd-955b-4ffea8756c76.pdf"}],"financialInterests":"","formattedTitle":"Does Digital Financial Development Promote Tax to GDP Ratio in the South Asian Region? The Moderating Role of Governance","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eModern economies heavily depend on in-house collections of tax revenue to promote their economic growth, and in this dimension, the South Asian region is not the exception. The South Asian region is one of the fastest growing and promising to grow faster than other parallel countries worldwide. This region's tax-to-GDP ratio is far from developed OECD and Non-OECD countries. Most of the countries in the South Asian Region are positioned in the SGD United Nations. Therefore, to become a successful SDG graduate government needs to focus more on internal tax collection and maintenance. This paper emphasizes five countries in the South Asian Region such as Bangladesh, India, Nepal, Pakistan, and Srilanka. Among these five countries, the tax-to-GDP ratio in Bangladesh disappointed researchers, analysts, and foreign donors to lend funds for development.\u003c/p\u003e \u003cp\u003eThe tax revenue as a percentage of GDP in Bangladesh still stands at 7%, which is quite frustrating, and the trend from 1990 to 2021 is declining. This scenario envisages the main obstacle to sustainable growth in the country's social safety nets, education, health, and infrastructure development through mobilizing internal funds. The tax revenue percentage of India and Pakistan are not quite impressive yet. Both countries are holding positions of double digits in the tax-to-GDP ratio though it's at a threshold level. The scenario is much better in this indicator for Nepal and Srilanka if we ignore the recent economic crisis in Srilanka. Nepal is showing benchmark performance in this indicator in the region, which is close to 20 percent in some cases.\u003c/p\u003e \u003cp\u003eDeveloped countries heavily rely on the revenue of income tax, while the opposite is true for the context of developing countries. Zee, H. (1996) postulates that trade, goods and services, and levis are the primary sources of tax revenue for developing countries, whereas income and consumption taxes are the critical triggering point for growth success in developed countries. The findings also reported that the income and consumption tax rate is usually 7% higher in developed countries while the tax rate is 23% lower than in developing countries in trade statistics. Financial development has a significant direct effect on improving the tax-to-GDP ratio in numerous ways. First, financial development expands economic activities, increasing direct tax revenue through the financial system (Alom, K. 2018). A developed financial system indicates the involvement of people in financial activities. This effect would be direct and positive to improve the tax-to-GDP ratio. Second, finance leads economic growth push the demand for goods and services, boosting new investments and sectoral development (Alom, K. et al., 2023). The outcomes contribute to collecting tax revenue from expanded business activities. Third, both financial development and economic growth might spur the growth of the formal economy through legal channels. Hence, financial development is a prerequisite to increase tax revenues as it expedites the tracking and collection of taxes ( Bose et al., 2012, and Capasso and Jappeli, 2013).\u003c/p\u003e \u003cp\u003eThe endogenous growth model by Levine (1991) pointed how sustainable financial and economic development promotes taxation has been a sparkle in earlier research. The findings suggest that to accelerate tax revenue, financial development, and economic growth have a vital role. However, Barro and Sala-I-Martin (1992) and Futagami, Morita, and Shibata (1993) have argued that taxation positively impacts financial development and economic growth using endogenous growth models and vice versa. From a general perspective, to overview the nexus between finance-growth-taxation, it is required to understand the relationship dynamics among; (i) the finance-taxation nexus; (ii) the growth-taxation nexus; and (iii) the finance-growth nexus. The tax-to-GDP ratio is one of the crucial indicators to promote sustainable economic growth in the South Asian region (i.e., SDG 8) through digital financial development.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e encompasses the flow of relationship from financial development to Tax to GDP ratio via moderating relationship of rule of law. In the framework financial development categorized in two parts that is traditional and digital which is the focal point of this paper. Globalization and digital financial development work as a catalyst to boost fiscal revenue and tax collections as trade barriers and other no-tariff restrictions dismantle due to integration among nations. This mechanism leads to an upward trend in government tax revenue collection in the short run. As a result, the tax-to-GDP ratio will increase due to the country's trade efficiency. However, developing countries are suffering due to administrative and process development inefficiencies. Digital financial development makes countries efficient in collecting excessive tax revenue from different economic activities quickly, as the system will make the collection process smooth in the long run. The South Asian region is positioned in one of the most densely populated regions in the world. Thus the volume of economic events will be huge that will have a direct impact on the collection of tax.\u003c/p\u003e \u003cp\u003eMoreover, South Asian countries are treated as emerging economies in the world. Thus government spending trend is significantly upward for infrastructure and fiscal spending development. The institutional quality and bribery in the tax department are the dominant reasons for the low tax-to-GDP ratio in developing countries (Tanzi, V., 1987). Tax is an economic resource that collects from the private sector and moves to the public sector to finance government expenditures. When the government fails to collect appropriate tax revenue, it seems to be its weakness and inefficiency. They need to print money or borrow from external sources such as donor agencies to operate economic functions. In developing countries, institutional weakness, poor application rule of law, and corruption are the main catalysts for a lower tax-to-GDP ratio.\u003c/p\u003e \u003cp\u003eDigital financial development is the key tipping point in this paper to promote the tax-to-GDP ratio. Digital financial development indicates providing financial services using technology (Alom K, 2023). This service can be termed fin-tech as well.In the Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Mobile based digitalize financial development is the principal weapon for acclerating Tax-to-GDP ratio that will contribute in the growth process. Mobile connectivity has created a milestone in digital financial development through mobile financial services. The MFS has increased financial coverage up to the unbanked root level of the region. For example, through mobile banking services, Bangladesh and India have reached a milestone of success in the financial sector. As all the MFS transactions are recorded in the system, the process becomes much easier for taxing authorities to collect tax revenue that will boost economic growth.\u003c/p\u003e \u003cp\u003eMoreover, mobile and internet-based financial development mesmerizes economic development nowadays (Alom, K, et al., 2022). Online banking and internet banking facilities make our life much more accessible from users' and regulators' points of view. Thus, tax revenue collection from a regulator's perspective has become more convenient due to information and data availability. Due to digitalization, financial system participants must pay taxes to excel in government tax revenue. The increased tax revenue can escalate the employment level of the economy by spending more on industrialization and the well-being of the citizens.\u003c/p\u003e \u003cp\u003eThe rule of law and Government effectiveness (World Bank, 2020a; 2020b) are significant governance indicators indicating a solid relationship with a country's tax-to-GDP ratio. Government effectiveness reflects perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government's commitment to such policies (World Bank). According to Australian Taxation Office (1997), the heart of the tax reform strategies was building a professional, responsive, fair, open, and accountable tax official. These strategies contribute to helping to increase public trust, and respect, and support all government programs. Well-functioning government always promotes efficient tax collection as they have institutional development and empowerment to ensure the quality of services, trust, and utilization of public goods for the nation's well-being. Hence, government effectiveness ensures a higher tax-to-GDP ratio as instructions substantially provide good services.\u003c/p\u003e \u003cp\u003eThe rule of law specifies perceptions of agents to the extent to which they have confidence and abide by the rules of society, and in particular, the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence (world bank). The rule of law will provide positive energy for the climate of business; it will encourage investors to continue investing (Dickinson, 2010). This process will flexibly lead to tax collection of the economy without any avoidance by the community members. Government effectiveness and the rule of law may affect economies as it impacts different economic activities in general. We believe government effectiveness and the rule of law will play a decisive moderating role in financial development to promote the tax-to-GDP ratio, which is the underlying proposition of this paper. Therefore, it is essential to analyze the effect of government effectiveness and the rule of law on tax to GDP ratio and economic growth in five South Asian countries.\u003c/p\u003e \u003cp\u003eThe objective of this research is to emphasis on the digital financial development of the South Asian region, which means financial development through mobile subscriptions and internet connections. Traditional financial development has significant limitations in identifying people who avoid taxes. But in digital financial development paying taxes becomes evident in the system. South Asian Region is one of the promising regions in the world with a strong population base, but the cultural perception is to avoid tax. Thus, the central proposition of this paper is to answer the question: Does Digital Financial Development promote tax to GDP ratio in the South Asian Region?: The moderating role of governance upon investigation. The rule of law is one of the majors governance indicators, and their impact on the tax-to-GDP ratio is the value addition in this paper, along with digital financial development. In the past literature, we found strong gap on digital financial development and its impact on Tax-to-GDP ratio in presence of governance. We strongly believe, this value addition of this paper will strongly contribute to the sustainable development of South Asian countries through internal revenue mobilization. The policy makers will also find the direction on internal tax collections polices by using the digital applications.\u003c/p\u003e \u003cp\u003eThe paper is organized as follows. Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides a short selective review of the relevant literature on the underlying proposition of this paper. Section \u003cspan refid=\"Sec3\" class=\"InternalRef\"\u003e3\u003c/span\u003e discusses the methodological underpinning of the paper\u0026mdash;the results of different econometric analyses presented in Section 4 with dynamic interpretations. Section 5 describes concluding remarks and how this paper's findings will benefit the policymakers.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eAccording to Schumpeter (1911), financial development enriches economic growth through its ability to mobilize savings, efficient allocation of resources, and manage risk. In Contrast, Keynes (1936) argued that stock market returns instability affects economic growth negatively as it encourages foreign capital outflow and allocation of resources inefficiently. Taxation is a crucial element of economic growth, confirmed by Levine(1999). Similarly, a high level of economic growth is also an effective indicator to prompt tax revenue collection. Though the impact will depend on government fiscal policy, level of effectiveness and the rule of law, and the utilization of the financial sector, thus situations might differ from country to county. In sustainable growth, fiscal policy design is the rudimentary catalyst (Barro \u0026amp; Sala-I-Martin, 1992; Lee \u0026amp; Gordon, 2005). On the one hand, taxation can speed up economic growth through income distribution for economic development; conversely, this process can also obstruct growth by distorting investment activities (Kesner-Škreb, 2000).\u003c/p\u003e \u003cp\u003eRovčanin and Grzinić (2008) and Park et.al.(2014) showed that an exogenous fiscal policy could lead to positive short- and long-run economic growth in competitive equilibrium. This paper also headed toward short-run and long-run equilibrium, where government expenditure explains fiscal policy. The main proposition is justified in the context of South Asian countries as the promising and emerging regions in the world Alom K(2022a, 2022b). The relationship between economic growth and tax revenue is direct and significantly positive, confirmed by Hossain and Tsigaris (2010), analyzing data from 28 OECD countries for 1960\u0026ndash;2005, except for a few countries. If borrowed funds finance higher government expenditure, the challenge will be on future tax revenue collections in the long run. This developmental process has significantly endangered sustainable economic growth. Levine (1991) found that stock market development promotes economic growth by ensuring trading facilities in different portfolios which ultimately transfer to the real sector of the economy. Tax policy is a prime indicator of a country's economic growth, both directly and indirectly. The tax incentives act as a direct influencer in the growth process; however, tax policy indirectly affects the financial markets by realizing the benefits of stock market investment incentives.\u003c/p\u003e \u003cp\u003eStoilova and Patonov (2012) postulated that the applications of taxation act as a stimulus of financial resources allocation in the economy, while Levine (1997) argued that financial development can efficiently allocate financial resources in the economy. The positive relationship between financial development and tax revenue is a justified proposition by past researchers. This paper endeavors to attempt that not only traditional financial development but also the role of digital financial development on tax to GDP ratio in the context of emerging tigers in the South Asian region. Moreover, Kate and Milionis (2019) supported empirical evidence favoring efficient tax revenue collection, allocation, and distribution to accelerate economic growth because a developed financial system can remove all the bottlenecks regarding tax collections.\u003c/p\u003e \u003cp\u003eIn the relationship between tax revenue and financial development, the literature is dominated by three schools of thought, first, the tax revenue-led financial development view (Alom, 2018, 2015); second, the financial development-led tax revenue perspective and third, the feedback hypothesis concerning both tax revenue and financial development. Among these three views, the most popular and prominent view is that tax revenue lead to financial sector development, and the ultimate goal is sustainable economic growth (Volckaert, 2016; Ismail et al., 2017). Okon (2018) induced the concept of the financial development-led tax revenue hypothesis focusing on financial development increases tax revenues by tracking and collecting taxes from participants in the financial system. Hence, the importance of digital financial development comes in radiance in light of the discussion in this paper. We strongly believe digital financial development will promote the tax-to-GDP ratio faster than traditional financial development. Okon's (2018) study found that financial development boosted the tax revenue collection capacity, increased the depth, access, and stability of the financial sector, augmented the collection of tax revenue in Nigeria (Okon, 2018:93), also supported by other researchers based on their study on different countries (Ak\u0026ccedil;ay, Sagbas and Demirtas, 2016). The feedback hypothesis reveals that tax revenue and financial development promote each other. From a causal point of view, it's a bidirectional relationship between these two components Akram (2016). Generally, the financial system plays a vital role in collecting tax revenue and reforming the system by monitoring investors' financial activities (Loganathan \u003cem\u003eet al.\u003c/em\u003e, 2020). Empirical evidence supports the direct and indirect association between financial development and tax revenue (Nnyanzi, Bbale, and Sendi, 2018).\u003c/p\u003e \u003cp\u003eGenerally, the financial system can monitor FDI, remittances, and international trade activities performed by individuals in the country. Digitalization in financial services is inevitable to promote tax payment, revenue collection in financial transactions, and monitor the customers (Alom, K. 2022a, 2022b). Banks, other financial institutions, and financial companies often perform digital transactions with businesses and customers through various payment systems for liquidity. If a nation's financial institutions are well-established, transparent, and operate efficiently, both businesses and taxpayers will prefer to carry out their financial transactions through these institutions Alom K. (2018, 2022a). As a result, the taxing authorities can acquire crucial data regarding the income and assets of taxpayers through these institutions (Okon, 2018). On the flip side, the significant discrepancy in the magnitude of the banking industry across different stages of economic development brings attention to the challenge of monitoring and taxing economic activities in less developed nations (Ilievski, 2012). When financial institutions are weak, the underground economy tends to expand, leading to difficulty in obtaining accurate tax data. Consequently, the level of development in the financial sector plays a crucial role in predicting tax revenue (Akram, 2016).\u003c/p\u003e \u003cp\u003eThe role of stock markets in generating tax revenue is also substantial. Enhancements in the stock market increase the capital accessible to companies for investment endeavors and boost the overall liquidity in the market (Alom, K. 2018). Consequently, if the total value of traded stocks to the Gross Domestic Product (GDP) increases, the government is expected to collect higher tax revenue as a percentage of GDP (Ilievski, 2015). It is widely acknowledged that developed economies generally have a higher proportion of government tax revenue as a share of their GDP than developing economies. The observed pattern suggests a positive correlation between financial development and the tax-to-output ratio, as wealthier countries with more developed financial markets tend to exhibit higher levels of tax revenue relative to their economic output (Guo and Hung, 2020).\u003c/p\u003e \u003cp\u003eTherefore, this paper attempts to quantify the impact of digital financial development on Tax-to GDP ratio if two critical governance indicators rule of law and government effectiveness play as moderating role in the context of South Asian Countries. The relationship has been derive in the Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, how digital financial development contribute to Tax-to-GDP ratio in the process of escalating tax collections propelling to economic growth and feedback to sustaianable development. So, the hypothesis postulated in the is, does digital financial development promote Tax-to-GDP ration in the South Asian region? The moderating role of governance.\u003c/p\u003e"},{"header":"3. Methodology","content":"\u003cp\u003eThis study investigates the empirical relationship among the variables of GDP per capita, financial development, digital financial development using mobile connections, digital financial development using internet connections, government expenditure, trade openness, remittance inflows, foreign direct investment, and the rule of law, government effectiveness and Tax-to-GDP ratio in the context five countries of the South Asian region. The relevant variables in the study are discussed in detail with source and unit of measurement.\u003c/p\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003e3.1. Variables and Data\u003c/h2\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Taba\" border=\"1\"\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariables\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariable Category\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDefinition\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSource\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eY: Tax-to-GDP Ratio(TAXGDP)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDependent\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTax revenue (% of GDP)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eWDI\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eX1: GDPPC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIndependent\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGDP per capita (constant 2015 US$)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eWDI\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eX2:Financial Development (FINDEV)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIndependent\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDomestic credit to the private sector (% of GDP)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eWDI\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eX3: Digital Financial Development (DFINDEV)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIndependent\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDomestic credit to the private sector (% of GDP) interaction with Mobile cellular subscriptions (Per 100 people).\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors Calculation\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eX4: Government Expenditure (GovExp)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIndependent\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGeneral government final consumption expenditure (% of GDP)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eWDI\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eX5: Rule of Law (RLaw)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIndependent\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRule of Law: Percentile Rank\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eWDI\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eX6: Foreign Direct Investment (FDI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eControl\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eForeign direct investment, net inflows (% of GDP)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eWDI\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eX7: Remittance Inflows (REMITTANCE)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eControl\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePersonal remittances received (current US$)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eWDI\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eX8: Rule of Law (DFINDEV*RL)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModerating\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFinancial development interaction with rule of law\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors Calculation\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe required data are collected from the World Bank Development Indicators (WDI) and International Financial Statistics (IFS). Four variables have been prepared based on the authors calculation in this study. The annual data are considered that cover the period of 1990 to 2021 for five South Asian countries such as Bangladesh, India, Nepal, Pakistan, and Srilanka. To empirically investigate the proposed relationship in this study, all variables are transformed into natural logarithms forms.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003e3.2. Econometric Approach.\u003c/h2\u003e\n\u003cp\u003eThe Peseran, Shin, and Smith (1999) pooled mean group tests have been applied to examine the long-run and short-run relationship between Tax to GDP ratio and other explanatory, control, and moderating variables. The Dumitrescu and Hurlin (2011) casualty test has been used to establish short-run relationship dynamics among the explanatory, control, and moderating variables. The cross-sectional dependency test was examined to check the existence of dependency among the variables followed by widely used second-generation unit root tests such as; CIPS, Bai, and Ng tests employed to check the data stationarity. The Arellano, M. and S. Bond (1991) system GMM and Stock and Watson (1993) DOLS tests employed to check robustness in this study. A robustness check will confirm the empirical validity of this research on the underlying proposition of digital financial development and its impact on tax to GDP ratio: the moderating role of governance. The heterogeneity issues have been investigated through Pesaran and Smith's (1995) MG estimator, Pesaran's (2006) CCEMG, and AMG estimator developed by Eberhardt and Teal (2010) tests.\u003c/p\u003e\n\u003cp\u003eThis study's underlying proposition is that digital financial development promotes the tax-to-GDP ratio in the Soth Asian Region: the moderating role of governance. To investigate the proposed relationship, three models are employed to justify the underlying proposition, and the expressions of relationship among variables are labeled in the following equations.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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MDWUBhiVKenCuVlA8c81LiBXwfAMDSGm6tTJGY0GSP3AsEHugCmPpdNa5M8oje4maHrujz/+GNwSVOFVyYJAFyF/s4RkPFdec4vYadCiA2h0Eue1a9eG/zVFehjoUAzdUQxXpt8wxYl1cU31hlDc9tprr+BWMSy8msb3wgsvBBe9ULd5xCLJCt9vJB7UncQZwxdZyRm+TfWGyqnWtFSRm26vtWsYvdQ/xKvJ5hHEiXzMQfjJf8JKfNH9fJexC5ay7OSo67DN1UFCadwEZBbDg3q4i/wozYu27RszORbGgGo6ukFBY+0NDcXcfOJf/OIXg195UER8OBYlLsVNQaQxF/dgzQpqEFc1/pR+VQtvNYJV1auinjcUUU5xaI58VcM8hz6i17RBxf0osDZH03elcWX9BSMYVSA3yAfpRAOM56+55ppwLvcRv0mjD0dXVVyEE7RAnzA//fTTQX7YZaoOrY+QLMRod6km64vUs992XV9TWP8FWiPWJayNaVI2xo1686vCJH2RGhnoQ5UhjBn0ITI/Se67777g0khvgzYlaTqbIYWyjTFHWtFIQi9Ujc43YZ5kRXWn1p3k1hw31Rsqp1r3m6LGuYylGBm97BjIiCH12rCGcCk0zpEH2hvEl/SiDdHViHsJsyY7VWjzrlx7TnKQ6zijs2bYKGAOZotgqJMfTdpu5F36sexS2rZvzORYCAMq17AbBsJLDxBb4OaoM6AoNPS202BMK0QpexWOWUE7j+UafygMpZ+2oI0hvowsoLDSyoS0p+eZ5+kNrOp903tLKiMpJZQZ0EvDwXtmBfWKxsqWcH/11Ve1Da/cVvCa9qKh/2lCwwFyve+SaSpg5SONARpC/D9s8b0aLCykHhV1gJAPbT490AY1rugp5/0x/M80IvUm4kpu66aSYFwwekf5obdbz0za6EjR7nG58krcCC9pH++MxnSvnIEwqiHSFBok6GaNijSBDhDqBZ4v0VWgvE9lAi666KKgO+kV12hJimXlZz2a67hpqjcYMeJ9pEuunDISmIZH0KlFAxwjmvtKN49IYUZB3CFGOqA3VKcBnWiAjLRlqctOFTK6c+05yUE6tZL05rk2n2kh39nUA7R7bgnIPs9CaV4shfaNGbBlAWCxPlHl2FCwUFUbR3D0K8vB2e3pK7dt98SLAXm2r3wr36WwcE9usee0ICyEiSP+iC1x6xtGIcwsUs2hOPUrlsGZrQuitcCWa6RXFdyrNOs3vgdnh8P9vHsW6TeGtos3sqD0IY5cI81J3zjddC2G+ziHn6MgmR2FWBYk34RPfnO+Sq5L3q90w5U/vEfvTcsU74v95BnJHTIVl00hec0taE/hee6N86gO3s39hEu6A5fn0/DL7zROKVznvlxc2iD/hr23DuVTqi/4LT2SbjKSovjHzwvJPPk4DOVn6o/8yOkerknnpOmgcMXPERfFmbyUbAq9K6e/9FwOZINrHJKXHIsqK6Qr9yEHdbpe78BV3hDmKr2Bn5ynnOrdKqfDZFfPDpPNVDcJPa8w8S7emZMd1QfEK0Z+14VTTEt2AP9y5W8U4jJDfqncKP84n6aXyjtxVDyrwlWnTyRPcRpVlX3Ck+ZraV4A93XZvsG/rvNi0RmtNbUEkGKNjzrhpdAg9LqXAqMCKhDC2D/u5x4KR/xsWqDS54Yp4ElDHEgvxQEXhUS8UNi5Bmcap/gg/iiANP1Scs8NYxzKvktQqsSDMJLPseKSEiV94wof9ExKabrUobxqC/nI88gCfqkS51Ac47iklL6f+1RR6X2US9y4waDKMj3q5A4Zju9NK8kY3hXfm1bKVZC/ykcdClOcPgpLXZqB0m3YfaWo7JRU4lUof0i/OK6S6WFlXo3GqjSVDuKo05N1+Rn7kctnznEtToc0z3UQBuStSl7id1UdVeA3sl3HIsqK3s2BH7n6J6ZUbwj8j3XIsPAI6cG6xm2VbooP3p/KWyxfkk8d+AlpmHNxi5mW7AD+4W+XKF3IK45cnqeQZsovyVUuXHX6BFR3c8i/YWUfmReleaEwdtm+wb+u82LRmXsDyswn41D2s4AaGCmcq6uwS1CatUWVR9twjPr+eYNKfVjDGZCJkvtKUeWM2wbKHM+3DRMNUBodahCa4SyqrAB+SHeMqgMXkWnJDpBnXTfakQH8LTUuuA8jS20FyTThwqDpOnx1lOaF5L3L9s048mLRWagP6Zr5gV3/+opo2/zieSG38xNzoqH0A55VaPeftmidmr78bkaD+fglG1ywJqHLjTC0iUrbxclapN1mcTRrTPqNiLBe8oEHHhicNcNYRFkR6Hh0F/o+XiNkypiW7ACyU7XuuS1a/1S6DThrojZt2hQ+ys9aIn2Chk/U8HmaSVKaF+No34wjLxYdG1BmSYKyjyvl3ALtpYh2iIoXGOubSiunvDvShx9+GNxJL/qfR7TxStWuloKFxxAbrdOWdW1mcvzxxwe3CdqOn00C5q3zY1wsoqywyD7d7czy0pylLDtVsNEFHTClm7ik3zjbMPgEzZrBN5EmZVSU5gXMa/tm3rABZZYs2g6cnbHG9WHMSaMdovTRPhoR7CaH8VT6/aNxgVInbKUVVwzx0LfCZmE79mnzr3/9K7hsr8xOV/FuV/SSqkEj+FYO8sBOTG0/xN0VyscVK1YEtxS+i0dPMDuYqTFMnNj9y1SziLLC6MDq1au3NRzRGegfzplylrLs5FB4GZ0ZlS+//HLwazLU5QUdS+nOfPPYvpk7+ha4MUsOzYNmLcW8zetlUTDz0RU/Fsp2ORe6DZo33mbdCs/ljkWej806IObl5+SXtNGaE/JdssD9XS4qbgthIdxN0ML7qsNUs2iyAqxNUVwUn0XWF21ZyrKTgzgQRuSjDaQHdZjkapIyVZcXpH18bp7bN/PETvzpZ5QxxpgpQ087355j7YinLJk6LCumLZad2YERP9Znbdy4ceqzTEwzPIXPGGNmhCeeeKL3/PPPu1FjhmJZMW2x7MwO9957b2/9+vU2npYgHoEyxhhjjDHGmEI8AmWMMcYYY4wxhdiAMsYYY4wxxphCbEAZY4wxxhhjTCE2oIwxxhhjjDGmEBtQxhhjjDHGGFOIDShjjDHGGGOMKcQGlDHGGGOMMcYUYgPKGGOMMcYYYwqxAWWMMcYYY4wxhdiAMsYYY4wxxphCpmpAffPNN70bb7yxt++++/Z22mmncJxzzjm9jz/+eHCHMfMJMn7ZZZf1li1bFuQel/8pE8YYMw3QP3fddVfviy++GJwp5+GHH+69+OKLg/+MMWa+mZoBRQPyV7/6Ve+VV17pPf74470tW7b0Nm7cGP4/4YQT3JA0c8szzzzTO/jgg3vffvtt74MPPgiy/+CDD/Yeeuih3vnnnz+4y0wDOnDUmVN3vPnmm4MnjJkPqJPRPxdeeGFvn332GZz9GYwqOjwpIzkuvvji3v/93/+FjiBjjJl3pmZAXXTRRcF99tlne8cee2z4fdBBB/Wuv/763nfffdd77rnnwjlj5gkaITQwDjvssGBIqaFCo2TlypW9l19+2SOwU+SGG24Y/Or11q9fH4xbHXTw7LrrroOrxswP6Jz/+q//6j311FO93XbbbXB2K+gsRqXQWbfffnvv+++/H1zZEYwosBFljJl3pmJA0XCk5x1jKdfTBT/88MPglzHTg5GGLkcc7r777tBBcN999w3O7MhPP/00+GWqoEFHvnQNnThr1qwJv3/zm99sNxLOtTvuuGPwnzHzATKO8XTbbbftYDwBdfS1117be/LJJwdn6rn11lvDTBKm9BljzLwyFQPqnnvuCe5ZZ50V3Jgvv/wyuHvttVdwS9FakvRgfRXTDtIpgSj33P3xQSNNxP7X9a5xLXdfPDUIvzTKoHPDDoUldy13xGGPIR24fvjhhw/O7Egc19RwINy6HscjhrTFf/nBffHaNlxdqzpOOeWUcO88QdozTW/58uXbRl1jPv/88+DusccewS2hJC0lC1UyTxkhf3JGYur/IvQs01iktx1D9/LLLx+c3cpJJ500+GXMfHDvvfeG6cRVU/PEfvvtN/hVD0bY7373uzCa22YtlTHGLAm2TJhNmzZt4bX9BsrgzM9wbddddw3H5s2bB2fL4H78Xbly5eDMVv8uvfTSyvetXr06XNuwYcPgzFb4nzCsWbNmcOZn/zmqwqe4Vb2Pc1zjPqF75R/v5JzClIal6f0pa9euDfdzxOGIiePab+zvENf169eHa7gx3EfYeP+6desGZ39+Zxwm+ZGGkzDhR5yP04T0JJxK31EgTfALuUtRerSJd1Va5mQhlXnyjOd5L+cpLynyP3dtWhAnwjQuNm7cGPznIP7GzCOqs0rLNveW6CiVn1nSGcYY0yUTH4F67733gnvyyScHV9A7v2rVqtDry4L63FSCOqqmHuBXv0EepgymPey77LLL4Nf2MDrQV/yD/7Yi/zlPGF999dXwfwzrtvqVS/idC4/OpdMWmc5VFd9cWJreH/OnP/1p2/VcHEB+E5d+Bdt77LHHwv9i55133s4V9GSSzqR53JvJvHili0ifFaTNLbfcMvhvvnjjjTeCe8wxxwRXMMrDdLG+sbNt+lgTqtIyJwupzJPXp556au+ll14KecQIWTp6Kf/33nvv4C4C8VS+K664YocRbGPmAa01Pv7444PbFZQf9BnT9Y0xZh6ZuAGVNiJpmLD16ZFHHhkMk/Xr1w+dStCUKmOjjj/+8Y9hKk/K6aefHioGTUOMufPOO0Njqwl9IzY7nSsmDkvT+2NkQGLgEIdHH300/F8FcWG62XXXXbdt+l0V5CMLjLk/l3800HNhyqEG/bzBugBA1oHpLUyrY9fJI444ovf666+HhkeXVMlCDuQCkGPz81Q+OhHoHDBm3vif//mf4O65557B7RJ0GnW6tzY3xswjEzeg3n///eBqPvXuu+/eO+2008LoE2tAaDx3Df5iMAwzPEqgNx4DgZGWeH43PW3Lli0bS/i74umnnw7pDIwApnFIIa5sMQ8YUSK3Rufdd98Nrvw324OBSUMcAxODHmOW36wTwHDBYOzaeGoKo38YDDR6cuuhFgmt46OMgIxfY+YJyfeKFSuC2yW//OUvg/vJJ58E1xhj5omJGlA0IlHYNBw1jW3z5s29tWvXhtGLrheoYxxg7LBAlt79Emg4DdvAQFu1xlPbGJG65pprBv/NHqQ907P4xgecccYZwa2axicwOjGK2F6btIF0CiKokqyaFlmCNiyYR2Rgauoq6YpBhXyee+65nU91aZuWbUZr5wnKCYbTJZdcEvQUW5cz6quOH2PmhbiTZBzl/pBDDgnua6+9FlxjjJknJmpAffrpp8Gll1uguDFIWG+QW3+Ro24HORr62jGMBhB89tlntb37xx133LZnaDgNA7/wm/ACFRGN4dyugrMChhLpLuNHu4kNm8YHGLeM4DFaQgOzSxjZUtrzcdk62E2R+7oOA2A0KxzxgWxALCPxUbpboAzMWA7JC0afWHuEETXqqE+TtOySceaL4pMeGhHNXeMo0SM5tI6PPMGobTIqiF6q003GGGOMmQ8makC9/fbbwVXPVMzRRx8d3LS3Ktc40rSDHDR86DFmZIuREz7UO2yUZcOGDeEZDkbDSmC0ialONLKYGsdIwiz33jNCdtVVVw3+22q4kj6k5bCGLw19vn9DfNNtnUcFw1lpT29/HR9++GEwXMeRzkyhUzjiA9mAWEbio3StluR6//33D27MiSeeGFyVjxQMK+SLKaIaBczRJC27ZJz5Eqd1fGiDh9w1jtJ1XynqFDnzzDOD2wTKkg0oY4wxZv6ZqAGlRqSMpZgff/xx8GtHcg2kYdCYe+CBB8LICVMDS79HwWhYSaNYo00YJjS6rr766vD/LMJ0Lhp3jHLEhijGJQwzMIF0YQSLZ3IjJZq6N8oHkOntr8tb8kXfSqoDo5b4zRKMjEJuHV5VmmHYIrsXXHBB77e//W2Yiqrpo8MYlpZVyJhu8i2q0nwhLrP+fS86CSBn6A6D9NZGHHXw3a22I2TGGGOMmT4TNaC0hXmuEfnWW28FN7e+pi0YUddff31oFN19992Ds92A32wRjWHCSE6X4e4aRi0Io4xPHUw7hBdeeCG4w2D7dOCL9SlHHXVUcGdhsT1Gbbpt+jTRDoZVYVKapR+Pvummm8I11k9NYnMSOhmQZ0aTupZn/Maw1WjbrPP1118Pfm0PBiYjgW2nW7IjGeUu14lkzCSJ6+HSDsYmqGNoqZR5Y4xpwsQMKBqRGDJalxRDo0RTZ84777zgdgWbJjAKhf9NKgl6iFnbUYfC+vvf/z64swhpS8NVm0bE0EgmPxhV0shDHVS4q1ev3jaaEqN1YTTAc41LRh5Ke915Pp4KxXMaNatruBJPevcJQ7wWjvPT5J133gluzighPoQXGdW6NKChjcyyC+I4psblIG/h/vvvD+4wSvOFcqTd/eJ1WuNotI0KHQ2g3SdT+G6ORqlEvH6uDu5jx1HQmjrkNYWySNpWbf/s675edb3qWtV5rUf+97//Hdw6mpZXpvbCAQccEFxjjJkrtkyIdevWhS+Tc6xZs2bL5s2bw/kNGzZs6SvxcJ57Uji/cuXKbc/ym6+cp+Af1/uN+MGZn1m7du22Z/Ve6DcYw3nCEIP//QZtCKeQ//G5HJsGX3YnTimKJ/dUcemll4Z71q9fPzhTz7D7CW/ddYWpNK5cI224XpVuHLrG/QpD7B/hyb2D+wkTeRWjvBoG7+W+0vQbhvxL49oE5RHpEss4v5VeqUz3G/JBljlP3EkP3JzsKC15zzByMs/zKmO5PK/zvzRfJAPkbxfIv66R/Cm+cXorv7iWykNOZnNwD/fWobhVxc/Xfb3qetW1qvOqG3GHoXs56uowgf6ivHRV5o0xZpbIa+gxoEYkipzGoRoiHPxf1UClASkFjNKm8ZFTyrF/OSNKjSI1SuPKoOogrCL2P2fogQwI3Rc3OImjznNP2mAGpZGOqveIYffH13lnel1pokOV6LC4co5ruTwjXmn+8h7Cojjj6lrVkTZG8XNYwxPUUOiq0iaO+FclnyUgc/hBuslQ4SCNSJdcYyS+znMckv34/jQtub+KKplX/lTlZ3xv6n9pvpQYDk1QPo8DZIe0ivOKg3zkHO9O84x8wZgcBv4Mu498wD/SNoev+3rV9aprVeeRY2SyTm/wbFwOdKQ6OqbEX2OMWcpMzICi8YRCHbVhq95wNfbNYkDlX9JA7bqhrsYDbhuQd55vGiaeSRsoCkts2E+b0nwh3CX3lTJOA6opMjKHjXoq/4bdZ8wkoVxSjruE+hk/c51DxhgzD0xkDRTzr7U4fdT1HDvvvHNwR9ntzSwttH7umGOOGZyphrVP+lhtF7Duq19OshuflKAP6HaxvbXC8NFHHwV32pTmi9ZHleRfKWxTTr7MAlrjduSRRwa3Cm1TP+w+YybJlVdeOfQTCU3505/+FD59McubKxljzChMxIDSB3SbNmxZ9MrWxzHa7twLUxeH0gaqGuoHHnhgcAHjfZroA7rHH398cEuhsyENuxZx576jNg2aGg777bdfcKedJ13z+uuvh8X4wzqH+IxD3Ik0b+lglibI4/PPPx+Mni42dsEQo64v/eSCMcYsRSZiQKkB1eSr/oKdyNQwRrnffPPNobEyiW2dzWzw1VdfBZeKPv2mFzuZpTvAMTpJ45R72TVtmujbZytWrAhuKXyomVFb9QoTn9WrV4fd+vQNsmlTlS/kR25Hup9++incc/755++QZ0uZTZs2hTQgj/jgsaADKPfdK+4j/lyzEWVmAepmjCh0zCgyyY6njEyXfA/NGGOWMhMxoNSI1LeCSuFbKStXruz953/+Z2iQYTgxFarkQ7dmfmDqF4YDWz6ffvrpldNCMFKQl0suuSSMinDvtHtBmVJI2Jt2HhDuNWvWhF5hZH/33XcP5xntmJVpMaX5Qp4w8nLwwQcHQ4t4tZ0SOYucffbZIZ8xDG+44YbB2R3hg8h8DJm8fPrpp4Me02iUMdMGHcXH5x977LFWI1EYT0yxt/FkjFkEdmIh1OC3MUsKvmlyxRVXhHVGbojODnz3iVGZaX9/a9rQ2XPRRRd5KpMxxhgzZ0zsQ7rGdM0TTzwRpp3YeJod6LnGeKIne5HBeGRkysaTMcYYM394BMoYY4wxxhhjCvEIlDHGGGOMMcYUYgPKGGOMMcYYYwqxAWWMMcYYY4wxhdiAMsYYY4wxxphCbEAZY4wxxhhjTCE2oIwxxhhjjDGmEBtQxhhjjDHGGFOIDShjjDHGGGOMKcQGlDHGGGOMMcYUYgPKGGOMMcYYYwqxAWWMMcYYY4wxhdiAMsYYY4wxxphCbEAZY4wxxhhjTCE2oIwxxhhjjDGmEBtQxhhjjDHGGFOIDShjjDHGGGOMKcQGlDHGGGOMMcYUYgPKGGOMMcYYYwqxAWWMMcYYY4wxhdiAMsYYY4wxxphCbEAZY4wxxhhjTCE2oIwxxhhjjDGmEBtQxhhjjDHGGFOIDShjjDHGGGOMKcQGlDHGGGOMMcYUYgPKGGOMMcYYYwqxAWWMMcYYY4wxhdiAMsYYY4wxxphCbEAZY4wxxhhjTCE2oIwxxhhjjDGmEBtQxhhjjDHGGFOIDShjjDHGGGOMKcQGlDHGGGOMMcYUYgPKGGOMMcYYYwqxAWWMMcYYY4wxhdiAMsYYY4wxxphCbEAZY4wxxhhjTCE2oIwxxhhjjDGmEBtQxhhjjDHGGFOIDShjjDHGGGOMKcQGlDFmJF588cXeOeec09tpp53Cse+++/buuuuuwVWzKHz88ce9yy67rLds2bIgB7j8/8033wzuMKZbHn744d7hhx++TffwG31kTBtcl5kmzLUBRYWuglB6lFT23EPB4v4333xzcHZHYsXOccoppwyu9HrPPPPMdtc4Zg3ihjJRg4iDeHMuV0ml8dXB88SdONdBuuae11Hih5ksNJBPO+203iGHHNLbvHlzb8uWLb2TTz65d91117niWSAolwcffHDv22+/7X3wwQdBDh588MHeQw891Dv//PMHdzXnxhtvzOoCDvQBMmYDbfEgz6lvbrjhht5VV10V5A39A+ijunrZmByuy0xj+kIyt6xbt24LUbz00ku39AtEOLd+/fpwbuXKleF/2LhxYzh32GGHDc7Us2bNmnA/x4YNGwZnd4R37rrrrtvepzCIVatWhWvcQxhmBcKpsBHXTZs2hfPEVfHhfArPLV++fIfrpDnx5zxpnKZDjPKCI05bfvNs1bvN5FFZWr169eDMz3AeWTHzD/qBvM7pT5X7UfSbdFGqs9HrkrNZ0p9m/KBzyHt0UAz1BOeRGWNKcV1m2jDXBhSFgQZ93GCX4k0b4ZyjQh5G3FjgmToDCvS+XMEkbLNW+ZNWihsGaMratWtr460GU+56riGUg3s4UqTkrMzagczn0rUtkt+cQVyVh2b6qJE5THeVIkMm51+dPihFcpvrOJE+qpJDMxsgB8P0finUweR5zj/JdlfvMtOnS9mpwnVZGZPIi6XEXE/he+WVV3q33XZbb7fddhuc2XoODjjggOCKvlD0jj/++MF/1fQNod4RRxzRO/vss8P///jHP4Jbxa9//evg6r2CqSn9iqD3+uuv9w466KDB2elz7733hik4xJOpein7779/cI899tjgprz33nvBXbFiRXBjbr/99uC+/PLLYXplDp3vG3HBjTnyyCOD+9133wXXTA+mcCK/yEhcvuCLL74Ibr9SCm5T0rU0mq5FmYmna8Vz1eODqT08r3DEsGYi9wxHOjW1bgow78CvHISRsGqaLwe/53E9EPFhmh55ndMJn3/+eXD32GOP4Lbho48+Cm6qs+Hiiy8O70YnPPfcc4OzW6mSj/gQsayRVyKWl3gKtiBPdZ3fIvYvPuqmHZaEl6lpyGUa3lifptPD43DNA4899lhwL7jgguDGfP3118HdZ599gtuUXNmVPonTWHA/+Znen041T/OEe2JiOeLg/jpdpUNTy2LZQTbi98dh4z5oogfnna7rsjb1Rpp/OVkzM8jAkFoYiDJHm95K9W4xYlTXK5qiER16zkBTC3MjPNNE8Wvbm6ueQXpzquAa99BznEM9yrkRO4WPwzSnyxEojTqkU2hAo64lZSNFZYPRSpUXXHq9OJ+OZKhsCe7FD8lZrowpfLFfxAO553wcJ416xnGh/Ou9qZxyDX+4Lv8pS3WjNJNG5aiLsCi/cuVVaTdqj6XyUvKQorTNTdtK5QPi/BCc4/+c7pK8cOT0Fu/giJF/cdxVbyAfHLmZB7nwQjryz7MKU05X5+R2mpAOo8qBkDzk4q30ayPbVWW3Sp9xjXt5Jr5fsoUbozxMZUXk9FLuHPA/743DVPVe7s2dz/ldpQenSZeyk0PplotvVd4PI1f+kC/JJ/6m6FpOrmeFcefFUqOb1tQSQYokV0mWgIBLCVEwSguWFCf3Skk3LZCTQFPscoW7BDWmUkUdQ+GrSzeFIdfwHVYBzSooU8WLgzSQkpQctU3zJuhdXVDViJEMkEdNKwIayGrApOBX3FARkqcU3c+19BmlQ3peYY8b4tIZqbwSVs7zjhhVgmljvyr800Bx6iIsVY0P6TkOfrdF6Vyns5WfuYq9Tj7S81V+4D/5qvikcs0zueeq/FP6V/mVCy8QhjjPqmQN0JU5/6dFVRo1RfKQ0xGSxbr6pw7lcS49CXuqA/S+XDlSPsb1mGSuSpbRO2kaVekqoM6Iw4T/ktE4DoQzl/ZVfuf04DQh7Lnwd8U46jKV8VRmJAMcKZKZWWbcebHUWKhtzN9+++3gsrNKUxh27Sul3tVXXz04U85JJ50U3EceeaR3wgknhOHaa6+9NpybFRiqfvbZZ8NvTTtsyhtvvBHcUaYkMn0QmCYZw1QJdlyCW265JbhtYRpMPKze5Gi6uxNTM9jZh2H5vvLctlPU5ZdfHtL8zjvvDNMVr7zyynB+KUC4KQuEW1MemHLA1Jdzzz2316+wey+99NIO0yGGwdQcpmKxq1YKfrHDW9XU0RTuZxc4+POf/xzcYey5557B/f7774Nbh6YIxdNJmQqC/BL/dApR0/AvFTQ1WdNrkQ10JXqOMjzqFGVNCUbWuoT86Nd/g/+Gw/133HFHyG/K7iggA8hIbtphHe+///528nPRRRcF99VXXw1uzKOPPpqdkrTUkTzEdTg6WTu09hus28p9E1R2SbPc9D/0WVxnI+dMXUUuc2X6iiuuCO7jjz8eXCAv+kZJ0J3pFC3qN+rf3LTEKv74xz9uFyb8f/LJJ4Nc9Y2rcI60IV2apEkTPbjUGVddVsW8lcdFZ6EMKM2lL1nrFKPG+/XXX7+DcpWfdfCMFCeNijYKftyoYlq+fHnrBg8VPBx11FHBzUFaViFlRhiUztxPBUADjYph3bp1vVNPPTVcawsVHo2nNkeTBjDz06lkUcLkOcqTg0qSyvLuu+8O9/F7KSnWf/7zn8HVPH7iyRbWxGPjxo3b4toUNcbV4TAq8kcdA8PQ+olDDz00uHUgqxA37N96663gnn766cGddyibKq/kN401fqMrkQEaHqOu7/z73/8eXAyyKn744YfBr/HCequVK1cGeYo7UnIN7mFIRv76178Gtw2Sb4zUGGQTY4DwzhuShwMPPDC4GE7HHXdc+P3ZZ5+17phsWnZzhlyM6ijW+8acccYZwX3nnXeCKzCCd91112DAjQLvpa0hGf3v//7vYPg3kdEmenCpM666rArVG+SRWfoslAGlBlpug4M6brrppuBeeOGFwQUtaC7ppaGhoXf/8pe/DO6sodGjNqNzQBw1elTXaNI9Rx99dHBjVCnRKNOIz+677977wx/+EMK1YcOGkSuYSYGiZHSJSvHWW28dnN2KevgwrugxbNMAmyZqbKgjgkYLlQ1xpaHbdgGsZKOrCqvUHxnpjBYSh7ic56C3WhVgPBr64YcfBnfnnXcO7rzz7rvvBlc6g84Fyi5llN7beCF7W6Q3tXlNDqX7iSeeGNxhED4tvm8KIxxAJwhyA3vvvXdwm9BERqrCi97AgKexp7AAI7mcH9V4nUUkD5qhgJG+fv36UHdgTMXp0ISmZferr74K7i677BLcOuIwyehlhDDmnnvuCR1tJTDCm9vQRLBZE3oMwxIdWGpIN9WD88C46rIcGLTUG5TNBx54YHDWLGm2LAj9ij3ML+0XjMGZMpi/z3PMKY/RHNdh80GZ89ovMOE+3s0zTefTCp5tc6TzcHMQvqp7+4p9Bz9TStJDCyur8oA53VwvCe+so/VapF2K0mpc657wu81Rmu6SFcpGDHJN3nK0kXGFowkKSxU5P4mnzutgHjx5hZ6IUV7FB/HrV4ThWozCkp6vQ2UiXUPUBQpP06OuDMcoHVPdCG3SIofCVAVypntSeYSqNMjJOudzcefe+Hyqp9Lroso/kFyl1/mf8+mRCy9wnuux/CDL8dqbGKXXOHRPrqyUHqVywr25+qMqPUtRupeGQ+lelS/A9Zyfepd0pNomqe4BvSc9hsWzpC7N+V2lBwVtGY6umYTs5FBedF2XVcWnLj8UllLmLS+WIgszAqXRjXRtzTD4CjVccskl262F0bSBYTBfnqlnTz311LbRkybz3mP6+dXqKJnWoPTJjQwxjK0e975CCb3MKVpfVtcL/Le//S24VaNI6l3MhWGpoak5uSkhSquqHj56AkkjyRq/m/Ss5mSAo6+8a6+XTn/RtJS0h5veTsoX8v7pp58Ozv4MvZusBWtKvMUvz3fVK9hX9tviznbbyHnVaCBpp3tZy0RcmkznrOKTTz4J7n777RfcLqF3XmGOD+INcfzjg+dKeO2114KbGx2SHpCsA6Oy6RbR5G2VbGuaXL9hEdwcWv/D1MG6EZc4fqNOn2G9Iu+jbuiyhzqmNLxKG+lW0gz5rJoGq3KpKXBdUjc1mnBy5K5xlJQlyUOuDtfz8ZQ55EqjNZI3pmo12aKbcq5nOdqMqqZxO/PMM4Mr2aU9QNrUzUSIy+ratWsHZ/MQb31aoERGm+hBZgmkW7B3wbhlp4q2dRlpTF1UNxIIqjdoM5EfzErpSmfMW14sRRbGgNLc6dJpHoCypIAxRSAVFDVC0jnOMTQWMAqef/75UCDPO++8cD4dvp8FUBR1UPiBaQY55arGVO5bLUDjCaUOuY04UEiawlVaCPGTSk0VayncH1eKTY7Sd0kucg1jNhOBqkoKpczU0M2bNwfFS7oMU9STQvFHiebINYZ5hkYz07rq5AzjHFI/qNCRO2DKY+nUJPkj2R03mp77448/BrcEjFb0SV3jCdSQmyUk47nymluXhLGEDmBhPXGmIcj/miKdIuOrbi0G03uB7/2VQlqWdhbkQJfff//94bc62JoiGRmW71AXXsoC8q2G/dNPPx06XAhjDjWOqjqxRFvdOk7qOulyeoeGLx2fpDFxVscfm/qkjdiqsks6se4WVkffRtTUvS+//DK4VUinxci4feGFF4JLfdBk8wim5NV1ctBpi76k3QHabKQLSEf08TDQ922nyU6KNnWZUMd4Kcggeo9nusqPecqLpcrCGFBNRzcoPFTOFK7cpgW/+MUvBr/yUKExFxkBV4OPyovKjgYxFdQsISVS1fhT+h1zzDHBTdEIVlVPunpR6ZHJNRq0nqJKmeXQBxWb9nqoEdHmaPquNK70iNJDXAVyg3yQTjSCeP6aa64J55r0nI4LfTi6qhEjI1gfTiXMNOqQHxogdWgtjWQhRutMmqwdUQ9v23V9TdFGB5pX3yWskWhSNsaNGqBVYZK+2GuvvYIr0IcqQ/oIrhr/Kdqgp2q0hA4qGsWEYZhB0DXUCbwXI1KdR03QiFHTDY1yoFtpmNEgJC272DyirW4dJ5KHXB0unZF2lvC/GpnoUn0A/6effgquqCu7WrMar3fSRkmS8xQ1znNySTgOO+yw8Cz3UR90Jb/aTZBRUtod1CP8T70zKQgD5XLWZ5I0rcsEZUx6pwmUJZ7B3yqdVwWjXXUGXRVLJS+WKgthQNUVhiqoQBA8bUeaUmdA0bigtz3usRJqzKmCmhXUy5urQDD2lH7arjiG+FKBU1mlBgNpT88zz9MrVtWTqvemz+fgfQxdY6CCRod4z6ygnsfYUCbcLD6um0aa2wpeU6Q03WuaaMevtGEMkmkqCeUjDU0aMPw/bMG1dqhS7/4oIHd0gJAPbT490Ab1LDOqklZ2/E8lqIYVruS2rneQipYeRMoPjXU907QC7hrtIpYrr8SN8JL28VQyes1zDcWq8qDGae46DULKPw1RpkdPA+SaONbNQsiBHkBGCHuThjOj0LlOFH12glEMdHDVCK1kp240e5Z1q+QhV4dLZ/zud78LLtBgZUpaDCNG5Fm6kVRd2c1BGqPnaCOoTMfo0wmadZLCKAR1Jnmm0fWmoDfoRBCE+ze/+U1IC41AMk0cmWBnzFE6bZEZyUId3McIH7DMgfvRX7NI07oMSEPKArOIUtkqQSPmcktQ+0p5Oo95sWTZsgBoQSXHhoJFbto4gqOvIAdnt2dNtAAzXsDLs30FXfkuhYV72ixOHBfxosl4ATJx6yvgEOaqBYuK06row3ukG/7oWdKrCu5VmvUV1uDscLifd88i/Upxu3gjC0of4sg10pz0jdNN12K4j3P4OQqS2VGIZUHyTfjkN+er5Lrk/Uo3XPnDe/TetEzxvthPnpHcIVNx2RSS19zmByk8z71xHtXBu7mfcEl34PJ8Gn75ncYphevcl4tLG+TfsPfWoXxK9QW/pUdymzrEKP7x80LX8EeQt5xXGSFNq2RNSD6q9LjAH+5DblKQF8KRe5dkOtVb8i8+Txi4H78IVy59UnkWkquqPFP5yKWlUJgIwzC4r0vdSjqkadQE0o4wcZDvubJVpx9Ja66TTlVyqTROy67kLU031fUcKpuksWSiLi/i+OhdOaSr0nzXuxUm3lslOwoP12MZbqIHgedL8pB7uLcrRpWdKlRmCKvSN86/NL2AcCjNuacqXNJfOZmUvKbli/dxPob38470/LzlxVJlx9I2Z6iij4+6hgNKD8WkeylkqYJTAdPB/dxDoYmfTQUtfS5XWU8T4kB6KQ64FHbiRSHMKdo0TvFB/FESdRUE5J4bBnnIvaq4Zo1Y8ZHPUrog5Ur6kt6xktYzKaXpUofyqi3kI88jC/glhc+hOKYVTkzp+7lPlZveR7nEjRs/qojSo07ukOH43rpGDu+K781VhjnIX+WjDoUpTh+FpS7NQOk27L5SVHbq9OAwlD+kXxxXyfSwMq8GYC5NUz0aH8gAz5SEPZYP/ItlJyV+X6yXY3mJz8dwPi2bufBzjvvwM5eXVfIcH1XxJk24XicjkudhOnMcupV4p2nUBOSMMBFPjpx+qCKWT56tkwPinNNrPJer/9I6k4N8LJFP/OZdVaS6KncQNojfT3iEypmu8RtSv+vST/Bs2ujPgX8l95UyquzkaFOXkWZxfnFvLlwqZzri/ADerTyRfyVlP2ae8mIpM/cGlJlPum5Uzgoop1RZQheKUGnWFhoXo4Rj1PfPG1SadQ0ogUyU3FeKGsgljbwclDmebxsmNSCIv5kcajQP05nj0K2jNrzQOYRpFKMOeUdmkb1hBr7ZnqVsfOdoWpdJZ8XGN88TLs51Hb465i0vljIL9SFdMz+wcLtfGVbuNrVUyX1omTnQUPLRxjq041tbtE5tHFsgLyKs6SjZ4II1Nl1uhMHaEOSg7QYBWrDfZgtd1mn0DaewLsMfk5wsrPko0Znj0K2sfyvdHj+H1j+Nst0/8s6Hr1lP0vZTIouK1jzm1kDHaKfEYfc1YVTZydG0LkNekJuDDz54u/VH6GbOTZJ5y4uljA0osyRBccUNuJKFv0sB7QYVLxbXdyhWNtz1p2v0tf66TTBMGVoYXLWrpdAC9biin7asazOTNjvIaQMDKuF56/yYdZoY7LOmW9mUBKO7ZJMhQdlJN1tpsoun+Zkmxjf5pPtmtV5uWpep8zE+gDqZ35M0KuYtL5YyNqDMkkXbgbMjF99lmAfOOuussEvUzTffHBQeu/6wYw+Kump3rUlBw4qwNWnECOKh7Z5zO4ktGv/617+CS4OORl7c0KN3M93Zi+8qIQ/sADXt3nPlY7qT2TC07fizzz67XaXunaHGD+mMwc4oNsZ7vHsbu7nJsBWzpFtVFmg0NoEeeHbD1fPoIHbHQ4ehZ005lFvKLHIU7xyZkx3gPtKda/yeNUapy2KI26TjN295saTpW8/GLDk0J555yczZnyc0p1rxYxHqtNd6aT51m3UrPJc75i3fmsCcehYr5+SXtNH6JPJdssD9Xc5nbwthIdxNIL7K99xhxo/kKNUnyB/XxKzpVsJAeHKbONSBHiWulBvJGforXsdiylAeICdx+qWyw2YUyA33zkK9lWOUukyojChNJsk85cVSZyf+9BPYGGPMlGF0jm/Psc5o2BQNY7qA6Xp8l6iLD/CaxcKyMzs4LyaPDShjjJkRmJLBRy+nPV3TLAZMHeXj3lUfODemCsvO7OC8mA42oIwxxhhjjDGmEG8iYYwxxhhjjDGF2IAyxhhjjDHGmEJsQBljjDHGGGNMITagjDHGGGOMMaYQG1DGGGOMMcYYU4gNKGOMMcYYY4wpxAaUMcYYY4wxxhRiA8oYY4wxxhhjCrEBZYwxxhhjjDGF2IAyxhhjjDHGmEKmakB98803vRtvvLG377779nbaaadwnHPOOb2PP/54cIcx8wkyftlll/WWLVsW5B6X/ykTZnq8+OKLQQdJH6Gb7rrrrsFVY4wxZjI8/PDDvTfffHPwXzk843pr/EzNgKIB+atf/ar3yiuv9B5//PHeli1behs3bgz/n3DCCW5ImrnlmWee6R188MG9b7/9tvfBBx8E2X/wwQd7Dz30UO/8888f3GUmDQbsaaed1jvkkEN6mzdvDvly8skn96677jpXRsaMCI1BdUzUHS5rZtGh/XvKKaf0jjrqqN6xxx47OLv1fEnHK88cffTRwQ+3pcdIv5EwFQ477LAtu+6665ZNmzYNzmxlzZo1WwjW2rVrB2eMmR+Qd+Qe+U9ZuXJlkP2NGzcOzphJsX79+pD2q1evHpz5Gc6TZ8aY9mzevHnL8uXLs+WMa+hErtEGMGZRUVnYsGHD4MxWOE89xEFbQeWFg3LF9ZR169aF+3LXzOhMZQSKHnh63q+//vrePvvsMzi7PT/88MPglzHTg6FwenraDKPnuPvuu3vfffdd77777huc2ZGffvpp8MtUQS81+dIVV1xxRa9fMfWuvPLKwZntIc+MmVXoaeaYFswoYTp+HbvttluYbQK33377djqVa48++ujgP2MWl8svv7x3+OGHbzfyBDfddFNoM3/22We9l156qff+++/3+kZWqLc2bdoUrqcwHb1vXAU/TfdMxYC65557gnvWWWcFN+bLL78M7l577RXcUjSkmR6sYUCxp8OYJdMJ4qkEsf8MmVbBtdx98boK/NI6L50bdigsuWu5o2oaBOnAdQpoFXFcU8OBcOt6HI8Y0hb/5Qf3xWvbcHWt6phmY2BckPZM00OhpcoRPv/88+DusccewS2hJC0lC1UyTxkhf3JGYup/newvVVj3RAVEGtCQi/niiy+CS541oSpfKAvINp1IKaPkZexfm/LbJrwQv6vqAJ6Pz6X6J9abHFXvG4W69CU8pGlKld4220Onz4cffjj4rxr03urVq8PvCy64YLt6+aCDDmpczoyZJ6iLnn322d7FF188OLM911577XZ1FOWJ6f9A2yLH73//++AnfpuOGYxETQymMPHa3BQmTW/iaDrkyP34y9CmwL9LL7208n1MI+BaOlTK/4Qhnkog/zmqwqe4Vb1PQ67cJ3Sv/NMURoUpDUvT+1OYGsn9HHE4YuK45oaGNd0JN4b7CBvvZ+hY6J1xmORHGk7ChB9xPk4T0pNwKn1HgTTBr9w0MaVHm3hXpWVOFlKZJ894XtMHKS8p8j93bVoQJ8LUBdIRxDNF6ZWmbQm5fCHdY51UVbaa5GVc1qBt+W0TXr0rlVvO6zkhPYA/Oar0cZfk4siUWcLE+VzZ1LU07rME6T9NnUmelb6fdEQuq9LbmEWFcsHRFMoSRxXUHW38NfVMfATqvffeCy6Ls2PoiVq1alWYKoNFnfYEDyN3P9MD8atfAYYpg2lv7C677DL4tT1Y9f3Kf/DfVuQ/5wnjq6++Gv6Pee6553r9SiT8zoVH59Jpi0znqopvLixN74/505/+tO16Lg4gv4lL36DpPfbYY+F/sfPOO2/ninvvvTekM2lOz62gN0XpItJnBWlzyy23DP6bL954443gHnPMMcEV9Gr/5je/CUPx/Ybd4Gw5VWmZk4VU5snrU089NUwJII/oxUpHL+X/3nvvHdx5g41r4MgjjwyuYBSEqUbojwsvvHBwtpxcvqjHkHymrKSbhrTJyz333DO4om35bRPeKj3E+VtvvXXw31Y046BqOiRhJcy8c1zk4sjIB/IP5HeK4lgVV9MM0jGeyudRPWO2LhdAB6Zt41Kop6rAT/x2WeuWiRtQaSMSw4mhRRovVKzr16/frvHdBW0qvj/+8Y9huDTl9NNPDw1dTUOMufPOO8Naiib0jdihDYY4LE3vj5EBSYOIOAybc05cli9fHnYhG1bwyEcqQ+7P5R8NlFyYcqhBP2+kDXWmhzFtiF0njzjiiN7rr78eGnNdUiULOZALQI4XBfKAioXKR3oCWWfa77nnnhuMFmSx68YzeUJZefnll4srtSZ5CU3K7zDahJc0Q18J/qeTLFeRoz+YZsK0rmnQdf6aeqjDVg+m8l100UXBNWaR+d///d/gNu2o1DTzs88+O7g52FkW/vKXvwTXdMPEDSgWvsF+++0X3N133z1sHUzFyhoQGs9dg78YDF30bNKDiYFAb6wEF+itXrZs2VjC3xVPP/10SGegRyKNQwpxVU8hjTCRW6Pz7rvvBlf+m+2hgUjDkUYojTWMWX7fcMMNwXChkd618dQURv8wJOjISEdr55V//vOfwdWaHEbf2GKexjyfVSBvxtW4VlnBKBkHTcpvCV2E94wzzgjuO++8E1zBaDg6uknnGTLK2qQukB60/ho/+taaRvvQi3X1kDGLgNYQHnDAAcEthZlPtCXqZklotkLJOkVTzkQNKBqRNNrJbE1j27x5c2/t2rVBmXa9QB2ljKLmezv07pfAiMCwDQy0wC+eGsOI1DXXXDP4b/Yg7ZmepUKmhkzVND6B0UmjgkaTFlmnUxDhk08+CW7VtMgS6JXuqkE0a8jA1PA86UrDAflkpKPrRfNt03LReuLfeuut4B5//PHBZaQFw4nGPCODo47c1FFaVkYpF6Xlt4TS8KJr2JgkZ4SfdNJJwU1Hv9GfdVOPY7SpBYYgU/54H3VH1cY5wyCcpBGdBw888MDgrKkCWUyP4447LshY7lqcL+QTHaboPmabMEJJ/dxWJo2ZF9QxlZtmXAW6jxkjdJTV1d37779/cEfp/DI7MlED6tNPPw1uPFeTTMcgYY59bv1Fjrod5GIljqEGbPtY17uP8tczl1xyyeBsNdotSLueUAFTIeR2FZwVMJRId1VUVQ2ZHBi3NCgZLaHAdgk940p7ev7rYFoV93UdBqBRpnDEB7IBsYzExzBjW8jAjOWQvGCEg0YgRtSooz5N0rJLxpkvik96aEQld42jtDGtHrkVK1YEF8gjTSGd1ke9u8zLcZZfEetdZhWgD3Og75F3OtIUFjq6+L90ndlTTz0VXKaAsaaWMogB3GR6Y5y+lG2mvzA7oosOBOqnujqqLRpxyx2kf5wH6TGqbonB6EmPDRs2hHzNXVO+MPJEnYksUr6azNYYp44xZqnC9uRsbd7F7CrTnIkaUG+//XZwNR8zhq8mw2uvvRZckVYEHFS2VUiJM7JFryJTcYaNsqD8pewZDSuB0SamOtETytQ4RhJmufeeHt6rrrpq8N/P6xHihkwVNPTvuOOOEN+uvyeA4ay0p+e/Dhq7GK7jSGcqdIUjPpANiGUkPtTQHobkWj1BMSeeeGJwVT5SaPwgX0wRzW21LJqkZZeMM1/itI4P4lp3vbQxrR65tIOFuLAuDZlXx08KZV+Gd9cjVV3m5TjLr4gbz+hedV7lOPPMM4MrvazNd0pHIfhGIGtpv//++/A/72rSawtKXww9nqcXt6s8RKeOw4CikaQ0Tg/Sr8qA4ZiFBtYTTzwRXEbhm+qKceoYY5YitAVoEzTpODLdMlEDSo1IGUsxP/744+DXjuQqhGGgaJmOQW8X0wZK51gzGlbSKNZoE4YJvWpXX311+H8WoWFApc4oR2yIYlzCMAMTSBdGsHgm15up6T2jfACZRmxd3pIv+lZSHTRsid8soYZ6riFTlWYYtsguC+t/+9vfhqkumj46jGFpWYWM6SbrZErzhbiUjthNAskxDc8cVR0L6BIayEybUEM8NcBKUL4P++Zd27yMGVZ+SygNL7oXeahqtGv0+4UXXgjuI4880mjzCDp+GHFi9BYjlxG2v/3tb62m8GG0kY8Yl11tZkBeaUOWOpjm2Hba4VJEBm+uA3UYS1XHGDMOaOPQrivRM2Z8TNSA0hbmuYpVaxFKeyFLoCJneJPK8e677x6c7Qb8Zs4+hgkVepfh7hp6KggjFXt8aJqNGjLDYPt0uO2224Ibc9RRRwVXO81NE4zaqkbxNFDPdlWYlGZpw5Qvi3ON9VOT2JxEU6no6e1anvEbpa/RtlngH//4R3BzYcJ4Ii0gNiY5T1kijWjUtTGchDowMAImQV35LaGr8CJbGHPINsYcHQOMsJbCVDvu//e//x06JvCPhkTbnljqI8om+Y2MNoEe4CpDuw6ms6F/c52J844+lp8D46etUTmLOsaYUtQ++Prrr4NbBTJOm62J8aQBillqF80DEzOgaERiyNDwSKEC0nqi8847L7hdwbx6RqHwv3QUClDizLuuQ2HlS8+zCmlLgdOmETE0PMgPGkYljQAaGqw70GhKDA1J/KIRkuvhblIx8nw8BYbnNGpW13tOPOnVJQzxeoCmjaKu0Y5jOaOE+BBeZFQ986D1AsMWh3aJthW+//77gzuM0nyhHNFgpvzHa0+alMdxoI1lciMq2iCGCifON751RsN31M0G6NTAH9I8JxfjoK78DqNNeNEpVQYGoz3IAyNPpZtHpBAfOoK64A9/+MN2bgmq01Q+NZ2Tow7uYyMF0NpK9Na8o6mb6OOcTJCeqWxOUscg4xxV+LqvV12vulb3TMqhhx4a3K+++iq4OSg7dBDn6h/KFCOwObQGW+8wHdGvgCYCX8vndRxr1qzZ9lV3vmDeV3zhfPpFfeB8vxGz7Vl+b9y4cXD1Z/CP6/1G/ODMz+gL+Dyr90K/MRDOE4YY/O83aLf7Wr38j8/l6Dcywn3EKUXx5J4q+o2JcA9fzC9h2P2Et+66wlQaV66RNlyvSjcOXeN+hSH2j/Dk3sH9hIm8ilFeDYP3cl9p+g1D/qVxbYLyiHSJZZzfSq9UpletWhVkmfPEnfTAzcmO0pL3DCMn8zyvMpbL8zr/S/NFMkD+doH8GwXSFz+Qt5y8cj4NL3lFOpAmuKQb+iUXL6VbnKbkp+SBPE6fa5OXqd7Ez/S9gmvEgeupTLcJr96VllfQczmkJznq9GHX1KUv8eMa6RojHRlDvFVmYnK6Kwf3cG9X4F/Je8eF9NQwJBPEPdZ5yKLSOZXbSegY3s+zHGm5AF/39arrVdfqnslBeeBe9FAOtaGlY9KDa9RFOVTuSsJhyhmtBdIAZSBKDgFRJc7B/1UZi1BJIVLRIjw8myrJ2L+cESXlrEapjKq6I1bksf85Qw/wN74vrqRVOXNwT1x5CKWRjqr3iGH3x9d5Z3pdaaJDhW9YXDnHtVyeEa80f3kPYVGccXWt6kAhxOAn/gxjlEo0h5TgKIpHDXXSTYqOgzQiXXINyPg6z3FI9uP707Tk/iqqZF75U5Wf8b2p/6X5QrxL7itF+dwWNeAJE37hKo7kF+dSGZIscJ1GHY1x0pQ8SeNWJeM8S5rlytUoeRn7F5e93Hs4x7U4v9uEF+J3VR1V4HeXMjGMYemLTCg+Chdxj5/JHTE8X2JI8FzJfaWoEbUUQJZI11h2+E34SZNUD01Cx1DWkUeOXN3h675edb3qWt0zVej+FHX8DDuq3lPlrxmNiRlQKLa6DC5FgqTGvlkMShsmXTfU1WjOGRclIO883zRMPJM2iBSW2LCfNtNqMI5qQEmPNAlTVforLG1lxMwHMtCQrTokR8PuawK6ItUX88K0dIwxk0b1ErqkK0r1kmnORNZAMTeTdR59C3jk9RzarnaU3d7M0kJrDY455pjBmWqYQ6+P1XaB1lngtkEf0O1iW2OF4aOPPgrutCnNF61dKMm/UtgwgHxpizatOfDAA4M7CtoIQJtSmMVEax2PPPLI4FahzxUMu68JbGjCMW9MU8cYM2nYLGrVqlXhsxNdwRos/JzERlSLxkQMKH1HpWnDlgWk6aI47SZywAEHBNfMP6UNE1WicaM4t1h5kmjxJtsuN4HOhjTsWhDdZhvgcdC0wbjffvsFd9p5AvqAbpMd5bQbX9p5o12Tct/4MosDm5IcdthhQzsJ+ZxH3Jk4C+VhVlnKOsaYNrBBxKZNm8JGUqNCBwS7lo666ZHJMxEDSsqtzZa/7ESmhjENyJtvvjlUUramFwftSkODA4M63lkptzsTDVwqUO7lI53TRN8+W7FiRXBL4UPNjNpqBx/is3r16rBbn75BNm2q8oX8IF9Sfvrpp3DP+eefv0OeTRpGKknLJjvgcS89ealOYuc2dFLbUUozH9DooSxQVuNt2ekIzH2biPuQI665wZ9nKesYY9qArDOazIenMYDawrPsSIlf+GnGQJjIN2aYm82rms7rZK44zzIHmudxWfg76joqs7Rg7i55zyLIdB4vcqG1J8iFZC137zSQ3LaBtTXEAz84WEzd5dzoUanKF63xEIRZ8SB/ph0HhY/0bAoyhg6yTjIpWguXyjjnOSfYREHyY9mpZ6nqGGO6gLX+at80gWe8T8D42Yk/faVjzJKDIe4rrrgirDNyD8vswDdZ6I2f9ve3jJkFWP/Id68uvvjiwRkzKtYxxphpM7EP6RrTNQxxP//88zaeZgimz9Cw8ZxrY7Z++PLss8+28dQh1jHGmFnAI1DGGGOMMcYYU4hHoIwxxhhjjDGmEBtQxhhjjDHGGFOIDShjjDHGGGOMKcQGlDHGGGOMMcYUYgPKGGOMMcYYYwqxAWWMMcYYY4wxhdiAMsYYY4wxxphCbEAZY4wxxhhjTCE2oIwxxhhjjDGmEBtQxhhjjDHGGFOIDShjjDHGGGOMKcQGlDHGGGOMMcYUYgPKGGOMMcYYYwqxAWWMMcYYY4wxhdiAMsYYY4wxxphCbEAZY4wxxhhjTCE2oIwxxhhjjDGmEBtQxhhjjDHGGFOIDShjjDHGGGOMKcQGlDHGGGOMMcYUYgPKGGOMMcYYYwqxAWWMMcYYY4wxhdiAMsYYY4wxxphCbEAZY4wxxhhjTCE2oIwxxhhjjDGmEBtQxhhjjDHGGFOIDShjjDHGGGOMKcQGlDHGGGOMMcYUYgPKGGOMMcYYY4ro9f4/H2kKW96KD50AAAAASUVORK5CYII=\" alt=\"\" /\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Results and Discussions","content":"\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\u003eResults of Descriptive Statistics\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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTAX-GDP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLGovExp\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLGDPPC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLFINDEV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLDFINDEV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLRL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLREMITTANCE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eLFDI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.95186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.196707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.985807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.378955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.064439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.569400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e22.21843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.866969\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.84219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.280231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.915363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.359824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.015352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.431869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e22.42893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.688893\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.80906\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.868530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.410879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.482325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.82489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.165007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e25.21611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.668323\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.483344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.399519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.042022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.177158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.669161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e17.60333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.635657\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStd. Dev.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.376159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.332100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.576279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.453661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.379154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.539253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.691998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.732246\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkewness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.803694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.544366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.661758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.101386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.191666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.966291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.472251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.305553\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKurtosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.885608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.748584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.951725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.818665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.417691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.73863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.321547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.347176\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJarque-Bera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.31187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.323639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.69350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.493325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.67096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e502.3443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.636517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e82.18076\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProbability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.015579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.781404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.036216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1752.297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e351.4731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1117.729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e540.6328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1290.310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e571.1039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3554.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e138.7150\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSum Sq. Dev.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1812.354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.53615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52.80356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.72353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8657.854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e46.23619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e455.1944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e85.25322\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e160\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\u003eThe variables in this study are found to be normally distributed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, except for the variable of scientific publications. The mean-to-median ratio of each variable falls in the close line. The standard deviation is also low for all the variables in this study. Their range of variation between maximum and minimum is also reasonable. The Jarque-Bera test statistics also accept the null hypothesis of the normal distribution of each variable except scientific publication. This striking finding will be rechecked by using a unit-root test of the variable. Thus, the normal distribution of the data is ensured in the study.\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\u003eCross-sectional dependence (CD) and unit root test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCIPS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBai and Ng\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLTAXGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.691***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.57***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-18.053***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLFINDEV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.329***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.895***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.928***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLGDPPC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.669***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.609***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.32***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDFINDEV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.533 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.738***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.450**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLGovExp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.542*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.052***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.018**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLFDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.516***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3.27***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-12.367***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLREMITTANCE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.274***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.393**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.692***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLRL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.337***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.683**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.606***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: ***, **, and * denote 1%, 5%, and 10% significance levels, respectively.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThis paper has detected cross-sectional dependency in the five South Asian countries due to regional, cultural, and political homogeneity. The Peseran CD test was applied in this study, and all the variables were found to be cross-sectionally dependent in the results. The second generation unit root tests have been conducted in the study to address this issue. The CIPS and Bai and Ng tests are employed in this study to check whether stationary exists among the data. The results reveal that all the variables are stationary at a 5% level, including the control and moderating variables.\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\u003eResults of Pooled Mean Group (PMG) test\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\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eModel1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eModel2\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\u003eVariables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et-Statistic (p)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003et-Statistic (p)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eLong Run Equation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003eLong Run Equation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLGDPPC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.745157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e2.348872(0.0208)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.82378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e7.413352(0.0000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLFINDEV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.375399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e2.444508(0.0173)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--------\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-------------\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLGovExp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.122662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.658588(0.1003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.878112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e3.381174 (0.0011)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLRL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.296323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e2.481438(0.0147)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.682123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-0.313759(0.7544)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDFINDEV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e-----------\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.602312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-7.774542 (0.0000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLFDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.581492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e-6.695968 (0.0000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.570535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-3.253578 (0.0016)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLREMITTANCE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.039684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e-2.563252 (0.0118)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.124518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.376705(0.7073)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDFINDEV*RL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-----------------\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e------------------\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.765161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.431030 (0.0975)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eShort Run Equation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003eShort Run Equation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOINTEQ01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.256439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e-1.385755 (0.1688)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.207593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-2.678415(0.0077)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLGDPPC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.767069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.519831 (0.1316)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.561734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.403708 (0.0468)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLFINDEV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.084081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.538758 (0.1270)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e----------\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e------------\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLGovexp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.516515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e-2.711229 (0.0079)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.980533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-2.316318 (0.0228)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLRL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.078192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e-2.4060 (0.0179)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3.710854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-3.440051(0.0110)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDFINDEV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e--------------\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e--------------\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.053281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-1.859428 (0.0764)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLFDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.508766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.928321 (0.0566)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.487183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.650649 (0.0944)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLREMITTANCE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.832931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e2.527322 (0.0191)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.074243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.675858 (0.0409)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDFINDEV*RL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-----------------\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e-------------------\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.770058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e3.311693(0.0360)\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\u003eNote: ***, **, and * denote 1%, 5%, and 10% significance levels, respectively.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e estimates pooled mean group model (PMG). PMG is the best model for a single test to investigate long- and short-run results of a panel data pooling data. We estimate three independent models to investigate the relationship among the independent, control, dependent, and moderating variables. This study's underlying proposition is whether digital financial development promotes tax to GDP ratio in the South Asian Region: the moderating role of governance. To investigate the empirical relationship, two models are employed to justify the underlying proposition; the first model is traditional financial development, the second model is mobile based digital financial development. The results show interesting findings that traditional financial development holds an insignificant relationship with tax to GDP ratio in the short run, but in the long run, the relationship is significant. Other independent variables, GDP per capita, the rule of law show a 5% significant relationship with tax to GDP ratio. Two control variables, FDI and Remittances, also confirm a significant relationship with tax to GDP ratio in the long run. Government expenditure, the rule of law are significant in the short run with the tax-to-GDP ratio. Among the control variables, remittance and FDI also maintained a significant relationship with tax to GDP ratio among five South Asian Countries.\u003c/p\u003e \u003cp\u003eThe second model is about digitalizing financial development through mobile connections. PMG test results reveal that in the long run, mobile-based digital financial development has a 1% level significant relation with tax to GDP ratio, Romer(1984). Moreover, economic growth, government expenditure, and government effectiveness significantly influence the tax-to-GDP ratio in South Asian countries. The rules of law is an important cases to observe in the context of five South Asian countries are of interest in this study. This phenomenon is evident in the daylight that South Asian countries are still significantly lacking in applications of the rule of law and government effectiveness and extensively suffering from corruption, ruling party misuse of law, and effective allocation of budget and management. The findings of this study confirm that, in the long run, digitalization ensures better government performance. However, control variables also maintain a significant relationship with tax to GDP ratio in the presence of digital financial development supplemented by the fundamental proposition of this paper rationally. This study also investigates the moderating relationship between digital financial development and the rule of law. The findings are supportive and significant that digital financial development in the presence of strong applications of law and effective management promotes tax to GDP ratio.\u003c/p\u003e \u003cp\u003eIn the short run, digital financial development holds a significant relationship with tax to GDP ratio which is found missing in traditional financial development, Romer (1990). Government expenditure, the rule of law significantly impact the tax-to-GDP ratio if the country can endorse financial development through digitalization. The control variables FDI and remittance, also maintain a strong association with tax to GDP ratio in the framework of digital financial development. Moreover, in the short run, the moderating relationship of governance found significant with digital financial development. Hence, these findings shed light on the importance of the rule of law and its applications to collect tax revenue, and the government must be effective in identifying and collecting tax revenue for sustainable growth and financial development in the South Asian region.\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\u003e\u003cb\u003eEstimation results from MG, AMG, and CCEMG models.\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eAMG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eCCEMG\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndependent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLGDPPC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.53 ***(0.011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.13**(0.033)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.56(0.087)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.48*(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.96(0.056)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.31(.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLFINDEV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.78(0.463)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-----------\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-----\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e----------\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLGovExp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.88**(0.041)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.79*(.065)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.79(0.053)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3.23***(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-1.43(0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.98*(.028)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLRL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.13(0.268)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.97*(0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.31(0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.68(0.062)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.20(0.058)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.45*(0.035)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDFINDEV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-------------\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.91**(.049)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-----\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.40*(0.358)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-----\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.48(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eControl\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLFDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.16(0.263)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.92*(0.050)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.65*(0.010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.21*(0.027)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.60(.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.59(0.501)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLREMITTANCE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.36*(0.097)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.13*(0.026)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.52*(0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.23(0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-1.69*(0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.59*(.031)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModerating\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLFINDEV*RL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e---------\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.81**(0.058)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-------------\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.60**(.050)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e----------\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.94*(.035)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel(Wald chi2)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.73(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.17(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.85(0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.55(0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e6.14(0.1890)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15.88(0.0032)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCDP(common dynamic process\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.34(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.73(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNote: ***, **, and * denote 1%, 5%, and 10% significance levels, respectively.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e results interpret the heterogeneity of the panel data. As in the unit root test, we found cross-sectional dependency, i.e., heterogeneity among the five South Asian countries. Among the five countries, India, Bangladesh, and Pakistan are larger economies than Nepal and Srilanka. Thus to address the heterogeneity, we have investigated through Pesaran and Smith's (1995) MG estimator, and Pesaran (2006) CCEMG and Eberhardt and Teal (2010) AMG estimator.\u003c/p\u003e \u003cp\u003eThe mean group test shows that economic growth, government expenditure, and government effectiveness significantly affect the tax-to-GDP ratio. However, this study found the traditional financial development and the rule of law insignificant to tax to GDP ratio. Control variables, FDI and remittances also impact the relationship with tax to GDP ratio in the digital financial development model. In the second model of mean group regression is digital financial development where we found that GDP per capita, government expenditure, digital financial development, and the rule of law have a significant relationship with the tax-to-GDP ratio of every country in the South Asian region. Remittance and FDI significantly and positively impact the tax-to-GDP ratio. The moderating relationship of governance with digital financial development has a strong relationship with digital financial development confirmed in the study.\u003c/p\u003e \u003cp\u003eThe augmented mean group regression results reveal that traditional financial development has no significant relationship with the tax-to-GDP ratio. Other explanatory variables, government expenditure and GDP per capita have a 10% significant influence on the tax-to-GDP ratio. However, the rules of law shows an insignificant impact on the tax-to-GDP ratio. The outcomes of digital financial development show interesting directions for policymakers. Digital financial development has a significant and positive effect on the tax-to-GDP ratio. Other explanatory variables also show similar findings for cases such as government expenditure, GDP per capita, the rule of law, and government effectiveness have a 5% significant relationship with the tax-to-GDP ratio. The control variables in this study, remittance and FDI influence the tax-to-GDP ratio at a 5% level. The moderating associations of the rule of law affecting the tax-to-GDP ratio. This indicator is a trigering issue in the context of the South Asian region, where most of the countries are staying behind the benchmarking developed countries. Due to a lack of applications of rule of law and government control, this region suffers from corruption, institutional development, and practical policy applications.\u003c/p\u003e \u003cp\u003eThe commonly correlated effects of the mean group show insignificant relation of financial development with tax-to-GDPratio in the model of traditional financial development. This relationship shows consistent findings across different econometric tests. However, in the model of traditional financial development, GDP per capital and the rule of law significantly impact the tax-to-GDP ratio among South Asian countries. Among the control variables, remittance shows a significant relationship with the tax-to-GDP ratio in this study. Thus, control variables manifested plausible associations with the tax-to-GDP ratio confirmed by different econometric estimations. The underlying proposition of this study is that digital financial development promotes tax-to-GDPratio and the moderating role of governance. Hence, the CCEMG test results for digital financial development show rationale and provoke directions for policymakers in the South Asian region. The mobile and internet-based digital financial development has a significant impact on tax-to-GDPratio in the South Asian region, confirmed by the results.\u003c/p\u003e \u003cp\u003eMoreover, this study confirms that other explanatory variables, such as economic growth, government expenditure, the rule of law have a significant relationship with the tax-to-GDP ratio. Both of the control variables, FDI and remittances also have a significant relationship with the tax-to-GDP ratio in digitalized financial development models. This relationship dynamics is quite logical and paradoxical in the context of a pragmatic point of view. Moreover, the moderating relationship is also of interest to the authors in this study, which is treated as a value proposition in the existing literature. We found a strong moderating relationship between mobile-based digital financial development with the rule of law that strongly affect tax-to-GDP ratio in the South Asian region. This proposition is also supported in rationale judgment based on existing facts and figures, the significant lackings for the South Asian region.\u003c/p\u003e \u003cp\u003eThe most widely used test of causality in panel data is the DH test, developed by Dumitrescu and Hurlin (2011). The test considers the not homogeneously cause null hypothesis, that means no causal relationships are assumed to exist of observed instruments for any panel member in this study. The DH test is based on an aggregated Wald statistic of individual coefficients causality tests. The Dumitrescu and Hurlin test has an advantage over the Granger-causality test, which focuses on individual coefficients. The results of Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e show the outcomes of estimations of two models: traditional and digital financial development. The causal relationship indicates a short-run relationship among the variables, and their directions are also equally important.\u003c/p\u003e \u003cp\u003eIn traditional financial development shown in the Table\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, model1, we found no bi-directional causal link among the explanatory variables in this study. However, a unidirectional causal relationship has been traced among the independent variables. The unidirectional causal link has been documented from GDP per capita to tax-to-GDP ratio, financial development to tax-to-GDP ratio, government expenditure to GDP per capita, financial development to GDP per capita, and finally, the rule of law to Tax-to-GDP ratio. Thus, the surfacing note is that traditional financial development has causal connections with tax-to-GDP ratio in the short run. However, the relationship deviates in different directions in the long run due to sustainability issues. Therefore, we have proposed digital financial development and its relation with tax-to-GDPratio and other explanatory variables in the short run.\u003c/p\u003e \u003cp\u003eOn the other hand, the results of mobile based digital financial development promote the tax-to-GDP ratio in the short run found in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, model2. The relationship is bi-directional, which indicates that digital financial development promotes the tax-to-GDP ratio of a country, and the tax-to-GDP ratio also promotes digital financial development in the short run. Digital financial development has a bi-directional causal link with economic growth and vice versa. Moreover, economic growth to tax-to-GDP ratio, digital financial development to rule of law, and government expenditure to economic growth have bi-directional causal relationship, documented in this study. However, a unidirectional causal link has been found among the variables of government expenditure to digital financial development. Thus, this study can strongly claim the plausibility of shaded areas and their impact on the policy-making level for the highlighted countries. Both the short-run and long-run findings are consistent and follow the same direction. For sustainable development and to maintain internal growth mobilizing internal funds is imperative now a day. Global turmoil and other exogenous issues will uncontrollably exist in the global arena for various geopolitical and power, and interest conflicts. Hence, South Asian economies need to reap the benefits of the demographic dividend to mobilize internal funds through digitalizing financial activities to ensure sustainable development.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDumitrescu Hurlin Panel Causality Test Model1.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNull Hypothesis:\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eW-Stat.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eZbar-Stat.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProb.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLGDPPC does not homogeneously cause TAXGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.08130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.15876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTAXGDP does not homogeneously cause LGDPPC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.90927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.23207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8165\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLFINDEV does not homogeneously cause TAXGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.43792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.26816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTAXGDP does not homogeneously cause LFINDEV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.61226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.74103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4587\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLGOVEX does not homogeneously cause TAXGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.42115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1553\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTAXGDP does not homogeneously cause LGOVEX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.73706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.47045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6380\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLRL does not homogeneously cause TAXGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.12216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.56261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0501\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTAXGDP does not homogeneously cause LRL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.04992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.03738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9702\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLFINDEV does not homogeneously cause LGDPPC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.37110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.79144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0732\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLGDPPC does not homogeneously cause LFINDEV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.78385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.97856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3278\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLGOVEX does not homogeneously cause LGDPPC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.35809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.77343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0762\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLGDPPC does not homogeneously cause LGOVEX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.40095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.44853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6538\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLRL does not homogeneously cause LGDPPC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.15172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.48777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1368\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLGDPPC does not homogeneously cause LRL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.48248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.56139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5745\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLGOVEX does not homogeneously cause LFINDEV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.60386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.65483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5126\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLFINDEV does not homogeneously cause LGOVEX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.43961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.88218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3777\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLRL does not homogeneously cause LFINDEV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.14524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.47879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1392\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLFINDEV does not homogeneously cause LRL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.83940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.05544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2912\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLRL does not homogeneously cause LGOVEX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.17767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.13945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8891\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLGOVEX does not homogeneously cause LRL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.09337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9818\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 \u003cb\u003eModel2.\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNull Hypothesis:\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eW-Stat.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eZbar-Stat.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProb.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLGDPPC does not homogeneously cause TAXGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.08130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.15876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTAXGDP does not homogeneously cause LGDPPC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.90927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.23207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0540\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLGOVEX does not homogeneously cause TAXGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.42115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1553\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTAXGDP does not homogeneously cause LGOVEX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.73706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.47045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6380\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLRL does not homogeneously cause TAXGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.12216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9501\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTAXGDP does not homogeneously cause LRL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.04992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9702\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDFINDEV does not homogeneously cause LTAXGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.93637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.34238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLTAXGDP does not homogeneously cause LDFINDEV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.30681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.31822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLGOVEX does not homogeneously cause LGDPPC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.35809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.77343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0562\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLGDPPC does not homogeneously cause LGOVEX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.40095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.14853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0503\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLRL does not homogeneously cause LGDPPC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.15172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.48777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1368\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLGDPPC does not homogeneously cause LRL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.48248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.56139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5745\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDFINDEV does not homogeneously cause LGDPPC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.96566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.61445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0089\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLGDPPC does not homogeneously cause LDFINDEV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.16542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.26173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0070\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLRL does not homogeneously cause LGOVEX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.17767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.13945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8891\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLGOVEX does not homogeneously cause LRL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.09337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9818\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDFINDEV does not homogeneously cause LGOVEX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.05221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.03422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9727\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLGOVEX does not homogeneously cause LDFINDEV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.51344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.98847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0468\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDFINDEV does not homogeneously cause LRL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.43130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.49054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLRL does not homogeneously cause LDFINDEV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.62638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.76057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0009\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\u003eTo check this study's robustness, generalized momentum, and dynamic ordinary least square methods have been employed. Both models are superior to other relevant econometric models in panel data analysis. The results of the first differenced GMM are shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. The results reveal in the model 1 that traditional financial development has no significant relationship with the tax-to-GDP ratio. The other independent variables, such as government expenditure, the rule of law were also found as significant with tax-to-GDP ratio in this study. Remittance was also found to be significant among the control variables in this study. However, the othe control variable, such as FDI was found insignificant to the tax-to-GDP ratio in traditional financial development.The overall model was also found significant, and no Arellano-Bond Serial Correlation has been noticed in this study.\u003c/p\u003e \u003cp\u003eModel two is designed for digital financial development show meaningful findings in this study. In the mobile based financial development model, all the explanatory variables significantly influence tax-to-GDPratio at a 5% significance level. The control variables also justified its impact on tax-to-GDP ratio for remittance and FDI. The moderating relationship of rule of law with tax-to-GDP ratio was found to be significant at a 1% level, leading to some guiding issues for South Asian countries. As this region shown strong lacking in good governance, thus to improve the tax-to-GDP ratio is imminent to improve the rule of law for government effectiveness along with digitalization. However, digitalization will propel the tax-to-GDP ratio faster in the South Asian region. Thus, the robustness check results were consistent with the baseline econometric estimations in this study.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneralized Methods of Moments (GMM)\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=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel2\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\u003eVariables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003et-Statistic (p)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et-Statistic (p)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLGDPPC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.410(0.681)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.909(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLFINDEV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.351(0.178)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e------------\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--------------\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLGovexp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.463(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.485 (0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLRL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.091 (0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-6.883(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDFINDEV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026hellip;\u0026hellip;\u0026hellip;..\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-----------\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4.771(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLFDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.529 (0.128)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.399(0.017)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLREMITTANCE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.962(0.037)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.847(0.039)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLFINDEV*RL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-------------\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e------------\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.409 (0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJ-statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93.776(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.254(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArellano-Bond Serial Correlation Test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAR(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.153 (0.273)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.838(0.818)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAR(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.437(0.632)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45.899(0.707)\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\u003eTable\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e visualize Stock and Watson's dynamic ordinary least square method (1993). By contrast, the Stock Watson method is a robust single equation approach that corrects for regressor endogeneity by including leads and lags of first differences of the regressors and for serially correlated errors by a GLS procedure. The results of dynamic ordinary least squares confirm that economic growth significantly relates to the tax-to-GDP ratio in the traditional financial development model. Government expenditure, and rule of law are significantly related to the tax-to-GDP ratio. However, the traditional financial development is showing insignificant impact on tax-to-GDP ratio relationship dynamics in the South Asian region. The FDI also show a significant relationship with the tax-to-GDP ratio.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDynamic Ordinary Least Square (DOLS)\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=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel2\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\u003eVariables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003et-Statistic (p)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et-Statistic (p)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLGDPPC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.787(0.016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.233(0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLFINDEV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.477 (0.165)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e------------\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--------------\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLGovexp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-8.872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.168 (0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.1849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.1352 (0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLRL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.815 (0.015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-9.253 (0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDFINDEV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-----------\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.2702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4.906(0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLFDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.5071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.3480 (0.733)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.126(0.0427)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLREMITTANCE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.7297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.183(0.199)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.4265 (0.598)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDFINDEV*RL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-------------\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e------------\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.653 (0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.8862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.7888\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted R-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.8350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.7677\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\u003eIn the digital financial development model, mobile based financial development has a significant relationship with the tax-to-GDP ratio. Moreover, economic growths, government expenditure, and rule of law have significantly impact on the tax-to-GDP ratio. The control variables have a significant impact on the tax-to-GDP ratio. We have considered these two variables as control variables in this study in the South Asian region. The moderating relationship is also found as significant in the models of digital financial development in the South Asian region. It implies that in the financial development process rule of law and government effectiveness is one of the vital issues in the South Asian region.\u003c/p\u003e"},{"header":"6. Conclusions and Policy Implications","content":"\u003cp\u003eThis paper starts with an underpinning question on the issue of whether digital financial development promotes the tax-to-GDP ratio in the context of five South Asian countries and the moderating role of governance. South Asian region is one of the fastest growing and promising regions to contribute to the world economic community. Moreover, the role of governance is one of the key concerns in this paper, which investigated whether relationship dynamics get affected or not if governance plays a moderating role. In this paper, rigorous econometric models have been applied to investigate the underlying proposition of impact of digital financial development on the tax-to-GDP ratio. The relationship dynamics between the tax-to-GDP ratio and digital financial development investigated focusing on five South Asian countries from 1990 to 2021. The cross-sectional dependency test results reveal that CSD exists among the variables; hence, we have decided to forward this paper with the second generation unit roots test, namely CIPS and Bai and Ng, and the results confirm that all the variables are stationary.\u003c/p\u003e \u003cp\u003eThe pooled mean group (PMG) results confirm that traditional financial development has a significant relationship with the tax-to-GDP ratio in the long run. However, in the short run, this relationship is not significant. Moreover, other explanatory variables demonstrate a significant relationship with the tax-to-GDP ratio. Economic growths, government expenditure, and the rule of law significantly impact the tax-to-GDP ratio in the long run. However, the short-term relationship between traditional financial development and the tax-to-GDP ratio is insignificant. Government expenditure and rule of law are also significant with the tax-to-GDP ratio in the short run. But economic growth has no significant relationship with the tax-to-GDP ratio in the short run. The control variables also show a steady relationship in the long and short run with the tax-to-GDP ratio, which is significant. The results of the PMG test for digital financial development lead to meaningful findings consistent with this paper's underlying proposition. Digital financial development has an enormously positive effect on the tax-to-GDP ratio. The moderating relationships of digital financial development with the rule of law significantly positively affect the short and long-run tax-to-GDP ratio.\u003c/p\u003e \u003cp\u003eThe results of Mean Group (MG), Augmented Mean Group (AMG), and Common Correlated Effects of Mean Group (CCEMG) were also found consistent with earlier econometric tests results in this study. In the mean group analysis, economic growth, government expenditure, and rule of law significantly affect the tax-to-GDP ratio. However, this study found the traditional financial development is insignificant to tax-to-GDP ratio. The control variables, FDI and remittances have also positive impact to tax-to-GDP ratio. On the other hand, in the mobile-based digital financial development model, we found that GDP per capita, government expenditure, digital financial development, and the rule of law have a significant relationship with the tax-to-GDP ratio of every South Asian region. Remittance, and FDI significantly and positively impact tax-to-GDP ratio. The moderating relationship of governance with digital financial development has a strong effect on Tax-to-GDP ratio, confirmed in the study.\u003c/p\u003e \u003cp\u003eThe AMG and CCEMG tests results also reveal that digital financial development significantly influences the tax-to-GDP ratio for five South Asian countries. Furthermore, GDP per capita, government expenditure, and rule of law have a5% level statistical impact on Tax-to-GDPratio in the region. The moderating relationship of rule of law also impacting tax-to-GDP ratio strongly in the presence of digital financial development. The control variables, trade and FDI, significantly influence the tax-to-GDP ratio and sometimes remittances, which were also significant in the econometric analysis. The Dumitrescu Hurlin causality test results show that traditional financial development has unidirectional causality with the tax-to-GDP ratio. Whereas the relationship among tax-to-GDP ratio to digital financial development, digital financial development to rule of law, digital financial development to economic growth, government expenditure to growth and economic growth to Tax-to GDP ratio are bidirectional, which mean relationship dynamics work in both ways.\u003c/p\u003e \u003cp\u003eGMM and DOLS tests have been analyzed to confirm the robustness of this study. In both cases, it has been found that in digital financial development, all the explanatory variables influence the tax-to-GDP ratio at a 5% significance level. The control variables also justified its impact on the tax-to-GDP ratio for Remittanceand FDI in the study. The moderating connection between the rule of law with the tax-to-GDP ratio is showing impact at 1% level significant. As this region is lacking behind in good governance, thus improving the tax-to-GDP ratio, this is imminent to digitalization economic activities to improve the rule of law that will empower government effectiveness.\u003c/p\u003e \u003cp\u003eHence, policymakers of this region country-specific should pay more attention to digitalizing their financial activities to improve tax revenue collection and escalate the tax-to-GDP ratio, as this region has tremendous potential with the allure of growth prospects. But due to a lack of governance applications, citizens tend to avoid tax. This is happening due to corruption, reluctance of regulatory authority, and lack of policy guidelines and modernization of the tax collections procedure and the breaucracy. Despite the economic size, all five countries in this region have similar economic outlooks. The only way to mitigate this issue is to digitalize financial services by creating a link with the Board of Revenue that will work as a catalyst to accelerate the tax-to-GDP ratio in the South Asian region.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e \u003cb\u003eFuture research prospect\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis study can further be extended to the country specific or sector specific analysis. The impact of digitalization in the financial sector can be extended to differnent real sectors of the economy to find more robust output.\u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests:\u003c/h2\u003e \u003cp\u003eThis research work has no financial or non-financial interests that are directly or indirectly related to the work submitted for publication.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis paper receives no grant or funding from anybody or organizations.\u003c/p\u003e\u003ch2\u003eAuthors' contributions:\u003c/h2\u003e \u003cp\u003eAll authors equally contributed to the study conception and design, material preparation, data collection and analysis.\u003c/p\u003e\u003ch2\u003eAcknowledgements:\u003c/h2\u003e \u003cp\u003eWe gratefully acknowledge the insightful comments from Prof. Moritz Ritter, Prof. ANM Wahid, Prof. Jeff Gow, Prof. Salahuddin, Prof. Ahkam in the earlier version of the paper.\u003c/p\u003e\u003ch2\u003eAvailability of data and materials:\u003c/h2\u003e \u003cp\u003eAll the data will be available upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlom K, Ahkam SN, Adnan AM (2022b) The ICT-led Financial Inclusion and Economic Growth Nexus in Bangladesh. J Knowl Globalization 14(1):35\u0026ndash;59\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlom K (2015) Exchange Rate and Economic Growth Nexus: Evidence from Bangladesh. J Knowl Globalization 8(1):109\u0026ndash;130\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlom k et al (2022a) Financial Development, Exchange Rate fluctuation, and Remittances inflows in Bangladesh: A Cointegration study, AIUB Journal of Business and Economics, Volume19, Issue1, Pages:121\u0026ndash;138, 2022\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlom k (2018) Financial development and economic growth dynamics in South Asian region, The Journal of Developing Areas, Volume\u0026nbsp;52 Issue 4, Pages 47\u0026ndash;66, 2018\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBai J, Ng S (2004) \u0026igrave;A PANIC Attack on Unit Roots and Cointegration\u0026icirc;. Econometrica 72(4):1127\u0026ndash;1178\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArellano M, Bond S (1991) Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations. Rev Econ Stud 58:277\u0026ndash;298\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlundell RW, Bond SR, Windmeijer F (2000) Estimation in Dynamic Panel Data Models: Improving on the Performance of the Standard GMM Estimator', in B. Baltagi (ed.), 8.Nonstationary Panels, Panel Cointegration, and Dynamic Panels, Advances in Econometrics 15, JAI Press, Elsevier Science\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDumitrescu E-I, Hurlin C (2012) Testing for Granger Non-Causality in Heterogeneous Panels. Econ Model 29:1450\u0026ndash;1460. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.econmod.2012.02.014\u003c/span\u003e\u003cspan address=\"10.1016/j.econmod.2012.02.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePesaran MH, Shin Y, Smith RP (1999) Pooled Mean Group Estimation and Dynamic Heterogeneous Panels. J Am Stat Assoc 94(446):621\u0026ndash;634\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePesaran MH (2006) Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure. Econometrica 74:967\u0026ndash;1012. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1468-0262.2006.00692.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1468-0262.2006.00692.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFrancis T, Markus, Eberhardt (2010) \"Productivity Analysis in Global Manufacturing Production,\" Economics Series Working Papers 515, University of Oxford, Department of Economics\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePesaran MH, Smith RP (1995) Estimating long-run relationships from dynamic heterogeneous panels. J Econ 68:79\u0026ndash;113\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePesaran HM (2003) A Simple Panel Unit Root Test in the Presence of Cross Section Dependence\u0026icirc;. Mimeo, University of Southern California\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePesaran HM, Smith LV, Yamagata T (2013) Panel unit root tests in the presence of a multifactor error structure. J Econ 175:94\u0026ndash;115\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRomer P (1990) \"Human Capital and Growth: Theory and Evidence.\" Carnegie-Rochester Conference Series on Public Policy, 32: 251\u0026ndash;286\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZee H, Howell (1996) Taxation and Unemployment, International Monetary Fund, Volume/Issue: Volume 1996: Issue 045, ISSN: 1018\u0026ndash;5941, Pages:56, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5089/9781451974348.001\u003c/span\u003e\u003cspan address=\"10.5089/9781451974348.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSheikh W, Alom K (2021) Corporate governance, board practices and performance of shipping firms in Bangladesh, Asian J. Shipping Logist., 37 (3) (2021), pp.\u0026nbsp;259\u0026ndash;267\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlom K, Hasan MK, Khan SA, Khan AI, Ahmed W (2023) The Scientific Research Publications and Sustainable Sectoral Value Addition in Bangladesh. International Journal of Empirical Economics, 2(01), p.2350002\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarro RJ, Sala-i-Martin X (1992) Convergence J Political Econ 100:223\u0026ndash;251. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1086/261816\u003c/span\u003e\u003cspan address=\"10.1086/261816\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBose N, Capasso S (2012) The impact of banking development on the size of the shadow economy. J Economic Stud 39(6):620\u0026ndash;638. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1108/01443581211274584\u003c/span\u003e\u003cspan address=\"10.1108/01443581211274584\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCapasso S, Jappeli T (2013) Financial development and the underground economy. J Dev Econ 101(3):167\u0026ndash;178\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLevine R (1991) Stock Markets, Growth, and Tax Policy. J Finance 46:1445\u0026ndash;1465. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1540-6261.1991.tb04625.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1540-6261.1991.tb04625.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTanzi V (1987) Quantitative Characteristics of the Tax Systems of Developing Countries. In: Newberry D, Stern NH (eds) The Theory of Taxation for Developing Countries. Oxford University Press for the World Bank, New York, pp 205\u0026ndash;241\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo J-T, Hung F-S (2020) Tax evasion and financial development under asymmetric information in credit markets. J Dev Econ Volume 145 102463:0304\u0026ndash;3878. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jdeveco.2020.102463\u003c/span\u003e\u003cspan address=\"10.1016/j.jdeveco.2020.102463\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBojan Ilievski (2015) Stock Markets and Tax Revenue. J Appl Finance Bank SCIENPRESS Ltd 5(3):1\u0026ndash;1\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSieni Toussaint OULAI (2022) \"Heterogeneous Effects of Financial Development on Tax Revenues: Accounting for Institutional Quality in Sub-Saharan Africa,\" Applied Economics and Finance, Redfame publishing, vol. 9(1), pages 54\u0026ndash;67, December\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u0026egrave;na Kimm Gnangnon (2022) Financial development and tax revenue in developing countries: investigating the international trade channel, vol 2. SN Business \u0026amp; Economics, Springer,, January, pp 1\u0026ndash;26. 1\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGnangnon S\u0026egrave;na, Kimm (2019) Financial Development and Tax Revenue in Developing Countries: Investigating the International Trade and Economic Growth Channels, EconStor Preprints 206628. ZBW - Leibniz Information Centre for Economics\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkram 2016N, Akram (2016) Do financial sector activities affect tax revenue in Pakistan? Lahore J Econ 21(2):153\u0026ndash;169. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.35536/lje.2016.v21.i2.a\u003c/span\u003e\u003cspan address=\"10.35536/lje.2016.v21.i2.a\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIlievski B (2012) Tax Revenue and financial development: Theory and Evidence. PhD Dissertation, Southern Illinois University Carbondale, USA\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArellano M, Bover O (1995) Another Look at the Instrumental Variable Estimation of Error-components Models. J Econ 68:29\u0026ndash;52. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1016/0304-4076(94)01642-D\u003c/span\u003e\u003cspan address=\"10.1016/0304-4076(94)01642-D\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTanzi V, Zee H (2000) Tax policy for emerging markets: Developing Countries. National Tax Journal, (2000), 299\u0026ndash;322. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.17310/ntj.2000.2.07\u003c/span\u003e\u003cspan address=\"10.17310/ntj.2000.2.07\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGupta AS (2007) Determinants of Tax Revenue Efforts in Developing Countries. IMF Working Paper, WP/07/184, Washington: International Monetary Fund\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOkon, Okon B (2018) (2018). Financial sector development and tax revenue in Nigeria, International Journal of Economics, Commerce and Management, 6 (6), pp.\u0026nbsp;93\u0026ndash;109, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ijecm.co.uk/wp-content/uploads/2018/06/667.pdf\u003c/span\u003e\u003cspan address=\"http://ijecm.co.uk/wp-content/uploads/2018/06/667.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAshraf M, Sarwar S (2016) Institutional determinants of tax buoyancy in developing nations. J Emerg Economies Islamic Res 4(1):62\u0026ndash;73\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBergman M (2010) Tax evasion and the rule of law in Latin America: the political culture of cheating and compliance in Argentina and Chile. Penn State Press\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNnyanzi JB, Babyenda P, Bale JM (2016) Regional economic integration and tax revenue: East African community. J Econ Integr 31(4):932\u0026ndash;967\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKate Ft, Milionis P (2019) Is capital taxation always harmful for economic growth? Int Tax Public Finance 26:758\u0026ndash;805. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10797-019-09530-3\u003c/span\u003e\u003cspan address=\"10.1007/s10797-019-09530-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVolckaert K (2016) The role of the financial sector in tax planning. TAXE2 Special Committee of the European Parliament\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkcay S, Sagbas I, Demirtas G (2016) Financial development and tax revenue nexus in Turkey. J Econ Dev Stud 4(1):103\u0026ndash;108\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLoganathan N, Ismail S, Streimikiene D, Hassan AG, Zavadskas EK, Mardani A (2017) Tax reform, inflation, financial development and economic growth in Malaysia. Romanian J Economic Forecast 20(4):152\u0026ndash;165\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchumpeter JA (1911) The Theory of Economic Development. Harvard University Press, Cambridge\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKeynes JM (1936) The General Theory of Employment, Interest, and Money. John Maynard Keynes (London: Macmillan. 1936. Pp. 403. 5s.)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLevine R (1999) Law Finance and Economic Growth. J Financ Intermediation 8:8\u0026ndash;35. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1006/jfin.1998.0255\u003c/span\u003e\u003cspan address=\"10.1006/jfin.1998.0255\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoung Lee RH, Gordon (2005) Tax structure and economic growth, Journal of Public Economics, Volume\u0026nbsp;89, Issues 5\u0026ndash;6, Pages 1027\u0026ndash;1043, ISSN 0047-2727, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jpubeco.2004.07.002\u003c/span\u003e\u003cspan address=\"10.1016/j.jpubeco.2004.07.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKesner-Škreb Marina. (2002). TAX POLICY AND ECONOMIC GROWTH. Croatian Economic Survey; No.4\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbdon A, Estrada GB, Lee M, Park D (2014) \u0026ldquo;Fiscal Policy and Growth in Developing Asia\u0026rdquo;, ADB Economics Working Paper No. 412 (Asian Development Bank)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRovčanin A, Gržinić J (2008) Economic development challenges in transitional countries - Financial aspects. Economic Res (dbenazic@efpu hr) ; Vol.21 No.1. 21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStoilova D, Patonov N (2013) An Empirical Evidence for the Impact of Taxation on Economy Growth in the European Union. Tourism \u0026amp; Management Studies. Vol.\u0026nbsp;3. 1031-39\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFabian ten Kate \u0026amp; Petros Milionis (2019) \"Is capital taxation always harmful for economic growth?\" International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 26(4), pages 758\u0026ndash;805, August\u003c/span\u003e\u003c/li\u003e\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":"Digital Financial development, Growth, Rule of Law, Tax-to-GDP, South Asia","lastPublishedDoi":"10.21203/rs.3.rs-3393979/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3393979/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper sheds light on digital financial development and its impact on the Tax-to-GDP ratio for the five South Asian countries. The panel pooled mean group, mean group, augmented mean group, and Dumitrescu Hurlin causality models applied to investigate long-run and short run relationship among the variables from 1990 to 2021. The results reveal that digital financial development significantly and robustly impacts the Tax-to-GDP ratio of the South Asian Countries in the long run. However, traditional financial development has failed to impact the region's Tax-to-GDP ratio significantly. The causality test results confirmed that digital financial development has a bi-directional causal link with the tax-to-GDP ratio in the short run. The governance indicator, rule of law also played a decisive moderating role in digital financial development to improve the Tax-to-GDP ratio for South Asian countries. The findings of GMM and DOLS are also constituent that digital financial development has a substantial and positive impact on Tax-to-GDP ratio. Thus, policymakers should be concerned about digitalizing financial sector activities to improve the tax-to-GDP ratio in the region through monitoring and tracking transactions.\u003c/p\u003e","manuscriptTitle":"Does Digital Financial Development Promote Tax to GDP Ratio in the South Asian Region? The Moderating Role of Governance","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-23 07:09:43","doi":"10.21203/rs.3.rs-3393979/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":"76010b65-3c06-4447-9269-4ad6f7e97c34","owner":[],"postedDate":"January 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-24T03:51:22+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-23 07:09:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3393979","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3393979","identity":"rs-3393979","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}