The Effect of Government Expenditure on Economic Growth in Myanmar: a Fiscal Decentralization Perspective

The Effect of Government Expenditure on Economic Growth in Myanmar: a Fiscal Decentralization Perspective | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Effect of Government Expenditure on Economic Growth in Myanmar: a Fiscal Decentralization Perspective May Zin Phyu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5844647/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Jan, 2025 Read the published version in Journal of Economic Research & Reviews → Version 1 posted You are reading this latest preprint version Abstract This study's primary goal is to understand how government expenditure affects economic growth in Myanmar from the standpoint of fiscal decentralization and local governments' financial capability in the country. Also, by using 15 Myanmar regional state panel data and an econometric model for the years 2000–2021. The study commenced with the application of a fixed effects model, followed by the implementation of quantile regression to validate the robustness of the findings. Data was collected utilizing secondary sources from the government's Ministry of Planning and Finance as well as the Budget Department. The results of the analysis demonstrate that financial decentralization, government spending, centralization and decentralization, foreign direct investment, and the overall population all significantly and favorably affect economic growth in Myanmar, whereas labor and net exports have the negative effect. Government spending and fiscal decentralization, however, interact to produce economic growth in a statistically meaningful beneficial way. Recommendations, government spending, and the decentralization for economic growth significantly contribute to formulating measures for poverty alleviation. The region offers assistance to low-income or employed people without income access, and it has demonstrated efficacy as a significant strategy for poverty reduction in Myanmar regional state. Finally, the government can benefit from regional expenditures and fiscal decentralization to enhance competitiveness in development by elevating knowledge of regional supervisory capabilities beyond mere certifications, thus fostering regional autonomy and transforming national economies. Public Administration Development Economics Finance Economic Growth Expenditures Fiscal Decentralization regional state and Myanmar Figures Figure 1 1. Introduction Government expenditure is an important instrument in national development. It is pivotal in any economy's functioning at almost all growth and development stages. Nowadays most developing and developed countries use public expenditure to improve income distribution, direct the allocation of resources in desired areas, and influence the composition of national income. In economic theory, expenditure by governments is a crucial determinant for national economic progress. Economic growth is considered a measure of a country's financial performance due to its capacity to enhance living standards, public benefits, and employment levels, making it a goal for most governments. Consequently, it is crucial to understand the efficient distribution of resources and the pivotal factors that can drive economic advancement (Vtyurina, 2020). The word "government expenditure" encompasses all financial outlays made by the government, including spending, transfers, and purchases. Government expenditure is essential to reduce poverty (Farooq, et al., 2023). Keynesian theory posits that government expenditure can elevate aggregate demand, hence fostering economic growth and employment creation. Nonetheless, curtailing government expenditure may adversely impact the economy. The fiscal decentralization of governmental operations became a progressively significant element of national economic changes. Decentralization can enhance the legitimacy of governance and the state, facilitate regional economic growth, and assist in resolving some internal conflicts. Decentralization advantages both the economic and political sectors, enabling localities to utilize pertinent technology autonomously, so enhancing investment sustainability and promoting efficiency (Rodriguez-Pose et al., 2011). Decentralization has numerous potential benefits, including improved transparency in government, agility, and efficiency in service delivery, as well as creating political stability, reducing conflict, and increasing competition in politics (Paul Minoletti, 2016). Decentralization has garnered increased global interest in recent years. Decentralization, the process of reallocating authority from national to local levels, consists of three components. This encompasses decentralization in governance, administration, and fiscal matters. The administrative aspect analyzes the organizational framework of local governments, whereas the political dimension emphasizes the elected executives who rule these entities. The fiscal factor primarily focuses on the financial and expenditure arrangements between the federal government and local governments (Dick-Sagoe, 2020). Myanmar is an emerging nation with a budgetary shortfall. The efficient utilization of limited resources is essential in developing countries, especially for reducing poverty and promoting national economic development. Government expenditure is the paramount driver of this country's economic and social development. Therefore, the government must allocate its funds in a manner that promotes the development of the nation and its citizens. In 2011, Myanmar transitioned to a decentralized financial budgeting framework. Fiscal decentralization directly influences expenditure generation, thus impacting economic growth and local development. It also seeks to enhance the effectiveness and efficiency of local governments in achieving financial autonomy. This article analyzes the correlation between government expenditure and economic growth via the lens of fiscal decentralization. The primary objective of this study is to identify potential obstacles or enablers that may influence the capacity and financial sustainability of local government in Myanmar. Firstly, there is a paucity of empirical research that particularly examine Myanmar's distinct socioeconomic and political conditions. The majority of research regarding the correlation among economic development and government expenditure is either worldwide or extensive in nature. Secondly, extensive longitudinal studies that monitor changes over a prolonged duration are necessary. Third, research often neglects the impact of fiscal decentralization on infrastructure, healthcare, and education, among other sectors. A thorough sectoral analysis is essential to comprehend the effects of government investment in many areas on overall economic growth. Consequently, precisely defining fiscal decentralization, economic development, and government expenditure is difficult. Consequently, this study was undertaken to address the gap. 1.1 Objective of the Study General objective: The primary objective of this research is to understand how government expenditure effects economic growth in Myanmar from the perspective of fiscal decentralization. Specific objectives: 1. To determine the relationship between the government expenditure on economic growth in Myanmar. 2. To evaluate the impact of fiscal decentralization of government expenditure on economic growth in Myanmar. 3. To elaborate correlation between the explanatory variables and economic growth in Myanmar. 1.2 Research question To make the aim of this study more concrete, I would like to specifically ask the following questions. 1. What is relationship between government expenditure and economic growth in Myanmar? 2. What is the impact of fiscal decentralization of expenditure on economic growth in Myanmar? 3. Is there a correlation between the explanatory variables and economic growth in Myanmar? 1.3 Significance of the study Economic growth is an essential goal in a country's economy, especially for developing countries like Myanmar. The establishment of regional autonomy affects the delegation of power between the federal government and the regions in several areas. The establishment of regional autonomy will lead to decentralization, which entails managing regional funds and organizing plans delegated from the center to the regions for economic planning and regional development. The implementation of regional autonomy is a response to the aspirations of a new format regarding the relationship between the central government and the regional government in Myanmar. The implication for fiscal decentralization is that state and regional government and union government fiscal relations with significant change are critical to the future of Myanmar. Myanmar's budget system can be studied in two main periods, before 2011 and after 2011, when fiscal decentralization occurred. Before 2011, there was only one state budget, and the budget process was centralized by the government. The central government allocated all the requirement amounts that were submitted by the respective line ministries, departments, and agencies. From 2011, the system of the budget was decentralized, including the Union budget (Central) and seven states and seven regions (Local) budgets. Carrying out such decentralization requires a strategy to systematically collect the revenue sources and allocate expenditures effectively and efficiently for regional development programs and projects. Briefly, in Myanmar, the implementation of the budget system reforms, such as financial decentralization, a medium-term fiscal framework, and the effectiveness of budget allocation and sustainable national economic development. The implementation of fiscal decentralization government functions was an increasingly important aspect of economic reforms. Decentralization can promote the legitimacy of the division and state, execute regional economic development, and help address some internal conflict. Based on these factors, the study focuses on the effect of government expenditure on economic growth in Myanmar from a fiscal decentralization perspective. 1.4 The study's structure This research paper is organized into three subsections. The first section deals with a detailed review of both theoretical and empirical literature on government fiscal decentralization, expenditure, and economic growth. The second section presents and emphasis the specifics of the methodology employed in the study, such as research design and sampling techniques. Finally, the third section analyses the findings and presents conclusions. 2. Literature Review 2.1 Introduction This chapter provides a review of the literature on several Effects of Government Expenditure on Economic Growth in Myanmar: A Fiscal Decentralization Perspective by theoretical and empirical concepts. This material covers understanding of government expenditure, economic growth, and fiscal decentralization perspective. The links between government expenditure and economic growth is important because it can provide the policy makers as an empirical evidence of economic development process. Economic growth is a primary goal in a national economy, especially for developing countries. Fiscal Decentralization is an interest topic because it is include not only economic perspective, but also politic and other subject. Decentralization can create significant advantages in line with the government's policy priorities and the requirements of the people. Two important policy instruments can potentially affect and influence economic growth: government expenditure and fiscal decentralization. 2.2 Theoretical Reviews Economic Growth : An increase in GDP over time is the concept of economic growth (Weil, 2013). (Palmer, 2012) defined economic growth as the capacity of a nation to produce more commodities and services. Over a certain amount of time, it also raises productivity per person in the population (Seater & Yenokyan, 2019). According to (Weil, 2013), an increase of real GDP, GDP per capita, and national output measured in constant prices is indicative of economic growth. Economic growth is facilitated by advances in capital products and technology (Jackson, 1990). All of the definitions share the presumption that an economy is functioning well when it is producing more commodities and services. Economic growth can be evaluated by the Gross Domestic Product (GDP) and the Gross National Product (GNP). GDP is an appropriate instrument for figuring the framework, level and rate of the country’s economy during the period of time (Badan Pusat Statistick, 2020). GNP is the market value of the finished product manufactured by a country during a specific time period, excluding its geographic location (Culture, 2015) Government expenditures : According to (Cvetanovic et.al, 2015), expenditure by the government is an outflow from the federal government as well as regional and local authorities that accounts for a sizeable amount of Gross National Product (GNP). Government expenditures are defined by (Ribeiro and Lima, 2019) as the costs borne by a government to support other countries as well as maintain itself, society, and economy. Since public spending controls the amount and pace of economic growth, all economies rely on it. Fiscal decentralization, encompassing the allocation of governance, expenditure provision, and revenue generation, is an articulated goal of many national governments in developing countries and forms part of their economic growth methods (Roy W. Bahl and Johannes F. Linn, 1994). Fiscal decentralization entails the delegation of responsibilities and the distribution of authority and responsibility for decision-making within the fiscal sector, encompassing revenue as well as expense dimensions. This aims to enhance responsiveness by aligning governmental services with the needs as well as needs of the local populace. Decentralization may have improved the technical reliability and quality of the public sector by reducing administrative bottlenecks and fostering increased accountability and openness (Rotulo et al., 2020).The traditional view of fiscal federalism highlights three critical public-sector objectives: economic effectiveness, financial stability, and equitable distribution of income (Musgrave 1959; Oates 1972). The federal government ought to oversee the stability of the economy and income redistribution, whereas subnational governments, being closer to citizens and possessing greater insight into their preferences, should guarantee the efficient provision of public goods within the borders of their control (Musgrave, R, 1959). As per the conventional fiscal federation theory proposed by Oates (1969), decentralization ensures that local governments can deliver public goods more efficiently than federal governments, as they possess a better understanding of the inclinations of the local population. The primary element of fiscal decentralized governance is the allocation of financial resources and expenditure authority to subordinate levels of government. Decentralization serves as a vital tool for fostering sustainable development; yet, it encounters significant challenges in optimizing the advantages of power distribution and resource allocation to the subnational levels of authority (Rodríguez-Pose, Andrés, 2008). 2.3 Annual Budget Process and Stages of Myanmar The budget is as a fundamental policy document for the government. It is a fundamental tool of fiscal policy, and influences the operation and management of the economy. The budget illustrates the government's prioritization and allocation of resources for its annual and multiyear goals.A nation's budget can serve as a potent instrument for social transformation. It is a mechanism that enables the government to convert national resources into allocations, which, if strategically planned and effectively implemented, can result in sustainable and equitable national development. In Myanmar, the fiscal year is from 1st April to 31st March. The constitution of Myanmar from 2008, Section 103, provides the legal basis for budget preparation. The President or their designated representative must present the budget bill to Parliament and receive permission to utilize Union funds. Myanmar's Sustainable Development Plan (MSDP) for 2018-2030 acts is the primary development framework for the nation. The Ministry of Planning and Finance (MOPF) has the responsibility of creating the national plan, annual plan, and five-year medium plan. The budget planning process starts in September for the coming fiscal year. The Myanmar's government expenditure includes current, capital, and financial expenses. The Union of Budget Department (head office) estimates resource allocation for the union budget framework and calculates the amount the union provides for grants to states and regions using the MTFF method. This process is based on macroeconomic forecasts and fiscal policy objectives. Then, the Ministry of Planning and Finance issues the Budget Calendar to the line ministries at the union level and informs to the state and regional governments about the amount of the union government provides grants for regional governments. The budget cycle in Myanmar is divided into four stages as following; the budget planning and preparation stage; the budget formulation and approval stage; the budget implementation and execution stage; and the budget evaluation, reporting and auditing stage. 2.4 Empirical Literature Review Decentralizing fiscal authority is an efficient policy tool that improves performance in constrained circumstances. The decentralization of spending must be accompanied by a corresponding decentralization of income to guarantee favorable results. Without these prerequisites, fiscal decentralization may result in decreased efficiency in public service delivery. (Moussé et al., 2018). Decentralization is a mechanism that strengthens the relationship among the state and the populace, hence necessitating the involvement of a subdivision and village levels in the crucial aspects of grassroots growth and delivery of services (Tri Efriandi, 2021). Fiscal decentralization correlates positively with economic growth and may act as a mechanism to promote sustainable economic growth. It is proposed that factors such as overdependence on provincial governments, unclear functional and taxes responsibilities, and limited and inactive tax bases for the local and provincial governments may hinder the full benefits of decentralized fiscal administration (Arshad Muhammad, 2010). Decentralization serves as a mechanism to attain state objectives, specifically enhancing public services and fostering a more responsive public decision-making process. Decentralization will be accomplished by empowering subordinate levels of government to allocate spending, collect taxes, form democratically elected regional leaders and councils, and facilitate payments by the governing body (Martinez-Vazquez et al, 2017). According to the study of (Superianik,2019) asserts that fiscal decentralization enhances the potential for regional economic growth by enabling local governments to distribute their budgets more efficiently. This efficiency is achievable because local governments possess greater knowledge of their areas' public goods requirements. The principal obstacles to economic growth include price inflation, the bureaucracy economy of scale, market dispersion, and corruption. Various measures can be implemented to overcome the principal obstacles to economic growth, namely the rate of inflation, the government, economics of scale, dispersion of markets, and corruption, particularly within the context of fiscal decentralization. The functioning of regional governments can be enhanced efficiently, hence accelerating economic growth and streamlining licensing procedures. Regional guidelines and bureaucratic reforms can fulfill them. Secondly, economies of scale can be enhanced by optimizing certain regional economic sectors. Attracting regional investment is essential for economic progress. Third, fiscal and monetary regulations might be prioritized to mitigate inflation. Subsequently, productivity must enhance, and price regulation must be robust. Fourth, to combat corruption, the tracking and oversight system must be robust to deter corrupt practices, and community engagement should also drive monitoring policy and spending decisions in the regions. Ultimately, fragmentation in the market can be mitigated by implementing standardized laws and policies throughout all regions. Balaj and Lani (2017) examine the impact of governmental expenditure on the economic growth of Kosovo. The findings indicate a favorable correlation between government spending and economic development, while the two factors are not directly interdependent. The authors argue that the misallocation of public funds to non-growth-promoting initiatives has hindered the fulfillment of the original expenditure objectives in Kosovo. Okoye et al. (2019) investigate the correlation between GDP expansion and government expenditure to ascertain the influence of government spending on output growth. The results demonstrate an adverse and statistically important short-term lag in current expenditure for economic growth. The research indicates that delayed capital expenditure significantly enhances economic growth. Nonetheless, the findings failed to establish a persistent link between economic growth and government expenditure. Mishra and Mohanty (2021) assert that government expenditure positively and significantly influences economic growth. Barlas (2020) evaluates the influence of government expenditure on Afghanistan's economic growth. The findings indicate that government expenditure has a significant and detrimental correlation with Afghanistan's economic growth. Government expenditure can facilitate short-term economic growth. Increased government expenditure on profitable firms will enhance short-term GDP growth. Similarly, an increase for a long-term lucrative project can impact long-term economic growth (Kwasi. P et al., 2022). In high-income nations, financial development is the principal driver of economic growth and sustainable development, with savings serving as the key resource for the financial sector (Akmil. I and Alpon. S, 2022).Generally Spending by the government and Fiscal decentralization is still a crucial tool in the development process. It is essential to every economy's operation at practically every level of growth and development researchers reviewed many empirical and identified the problem. 3. Research Methodology 3.1 Research Type and Design According to (Cooper, 2011), research design is the methodical arrangement of parameters for gathering and analyzing data with the goal of balancing procedural efficiency with relevance. Furthermore, a research design is defined by (Orodho, 2003) as the structure, plan, or approach used to find answers to research questions. Through a unit of study and investigation, the study used a quantitative research design that makes it easier to gather all pertinent situational characteristics. After carefully collecting, presenting, and evaluating the data, the researcher came to certain conclusions and recommendations. Furthermore, as many researchers and academics have pointed out, quantitative panel data is considered more informative, more varied, less linear across variables, provides more degrees of freedom, and is therefore more efficient. 3.2 Data Collection and Procedures The study only employed secondary data from different circumstances. A secondary data collection form was utilized to compile information from financial statements about expenditures, FDI, GDP, and the population as a whole in relation to GDP, among other variables. This information was then used to calculate pertinent ratios, descriptive statistics, and regression analysis. The Central Statistical Organization, the World Bank, the IMF, the Ministry of Planning and Finance, and the Ministry of International Foreign Economic Relations were the main sources of the secondary data. 3.3 Method of Data Analysis In order to strengthen the models and lessen the cross-sectional effects of the intercepts, the study employed the fixed-effect regression technique. As per Brooks (2008), the fundamental types of fixed-effect models allow the regression model's intercept to fluctuate cross-sectional and also choose to employ fixed effects when T > N, meaning that there are twenty years more temporal dimensions than there are cross-sectional fifteen. Because the fixed-effects model takes into consideration all individual time-invariant variations, missing time-invariant features have no effect on the estimated value for the fixed-effects models. According to (Zikmund, 2010) asserts that data analysis in research is the use of logic to understand the information gathered, with the aim of spotting recurring trends and summarizing relevant aspects discovered throughout the study. The obtained data was evaluated using a Fixed-Effect Regression model, and the results were produced using the econometrics program STATA 18.0. The selection of these software programs is based on their ability to make research analysis transparent and efficient. In order to analyze entity behavior over time, the study's dataset included pooled observations from panel data collected across a number of time periods. For central/decentral and descriptive statistics, the Fixed-Effect robust Regression technique was used to analyze and interpret the panel data. 3.4 Model Specification The researcher examined government spending in Myanmar using the Fixed-Effect Regression model in addition to descriptive statistics. Many econometricians believe that the ability of the Fixed-Effect Econometric approach to identify the independent effects of a set of factors on the dependent variable is one of its main advantages. The regression technique is calculated for a thorough examination of the regional states. lnGDPit = β0 + β1lnExpit + β2CDit + β3lnPOPit + β4lnFDIit + β5lnNEit + β6lnLBit + εit Where, lnGDP is the observed natural logarithms growth domestic product of Myanmar i at year t, 𝛽0 is the constant term showing the value of GDP when all the coefficients of the independent variables are zero. lnExp it is the natural logarithms government expenditure of Myanmar i at the time t, CDit is the central /decentralization of an regional i at time t, lnPOPit is the natural logarithms population of Myanmar i at the time t, lnEit is the net export of Myanmar i at the time t, lnLBit is the natural logarithms Labor of Myanmar i at the time t, lnFDIit is the natural logarithms foreign direct investment of Myanmar i at the time t βs are the partial effect of independent variables in period t. 𝜀𝑖𝑡 is the error term of Myanmar i at time t. 4. Result and Discussion 4.1 Introduction This chapter expounds at large on the findings, data analysis, results and discussions in line with the objectives of this study. The first descriptive analysis of both dependent and independent variables, second correlation analysis, third regression result and detail interpretations. 4.2 Descriptive statistics Descriptive statistics refers to the branch of statistics that focuses on summarizing and describing the features of a dataset. It provides a simple overview of the sample and the measures derived from the data without making inferences about the population from which the sample was drawn. Descriptive statistics are typically used to present quantitative descriptions in a manageable form. Table 4.1 Descriptive Statistics (1) (2) (3) (4) (5) VARIABLES N mean sd min max lnGDP 330 10.58 4.637 0 17.27 lnLB 330 0.105 0.974 -2.591 1.329 lnPOP 330 0.865 1.032 -1.871 5.288 CD 330 0.500 0.501 0 1 lnFDI 330 2.586 3.735 -2.847 15.25 lnEXP 330 7.399 3.714 0 14.18 lnNE 330 -2.113 3.152 -11.28 2.735 Source: STATA 18.0 Results, 2025 Based on the table the descriptive statistics for fifteen region and twenty two years, 130 observations for different variables showing the number of observations (N), mean, standard deviation (sd), minimum (min), and maximum (max) values and interpretation of the statistics for each variable: LNGDP is the logarithm of GDP, with a mean of 10.58, which is relatively large compared to the standard deviation (4.637), indicating a high variation in GDP across observations. The minimum value of 0 suggests that some observations have a GDP near zero (possibly countries or regions with very low GDP), while the maximum of 17.27 indicates the presence of higher GDP countries or regions. The large standard deviation shows a high level of variation in GDP values, which is typical when dealing with economic measures across different countries or regions. LNLB is the logarithm of the labor force. The mean of 0.105 suggests that the average labor force size is moderate, though still relatively small on the log scale. The minimum value of -2.591 shows some observations with a very small labor force. The maximum value of 1.329 indicates that some regions or countries have a labor force larger than the average. The standard deviation of 0.974 indicates a moderate level of variation in labor force size. LNPOP is the natural logarithm of the population, and the mean value of 0.865 suggests a relatively low average population size on the log scale. The negative minimum value − 1.871) indicates that some regions or observations have very small populations (close to 0), while the maximum value of 5.288 suggests that other regions have significantly larger populations. The standard deviation of 1.032 shows a considerable spread around the mean, indicating a wide range of population sizes across the observations. CD ratio mean score shows 0.5 from the period. The descriptive statistics result also shows minimum and maximum value of 0 and 1 respectively. This indicates that 1 by these units applying decentralization to collect economic growth in Myanmar regional. LNFDI represents the logarithm of foreign direct investment. The mean value of 2.586 suggests a moderate average level of foreign direct investment across observations. The minimum value of -2.847 indicates that some observations have very low or negative FDI, possibly due to countries with minimal or negative foreign investments. The maximum value of 15.25 indicates some countries or regions with extremely high levels of foreign direct investment. The standard deviation of 3.735 shows a wide range of variation in FDI values. LNEXP represents the logarithm of exports. The mean value of 8.213 is relatively high, suggesting that exports are, on average, substantial for the countries or regions represented in the dataset. The minimum value of 0 indicates that some observations have no exports (or very close to zero), while the maximum of 17.31 suggests some regions or countries have extremely high export levels. The standard deviation of 4.231 indicates significant variation in export values across observations. LNNE is the natural logarithm of energy production. The mean of -2.113 suggests that, on average, energy production is low (since the log of a value less than 1 is negative). The minimum value of -11.28 indicates extreme low energy production, potentially indicating observations with very little or no natural energy production. The maximum value of 2.735 shows some observations with high levels of energy production. The standard deviation of 3.152 highlights the considerable variability in energy production levels across the dataset. Generally, the data shows significant variation across all variables, as evidenced by the high standard deviations and wide ranges (especially for GDP, FDI, NEXP). Many of the variables are in their logarithmic form, which suggests that the data might span several orders of magnitude (e.g., population, GDP, FDI), which is typical in economic or developmental datasets. The LNTP, LNGDP, LNFDI, and LNEXP variables all show wide variation, indicating different countries or regions may show drastically different characteristics. 4.3 Correlation Analysis Correlation analysis is a statistical method used to assess the strength and direction of the linear relationship between two or more variables. It helps to determine whether an increase in one variable corresponds to an increase or decrease in another, and whether this relationship is statistically significant. There are several types of correlation analysis methods, but the most common one is Pearson's correlation coefficient, which measures the strength and direction of the linear relationship between two continuous variables. To make sure that the explanatory variables are correlated, a matrix of correlations is utilized. According to Sisay (2016), Cooper & Schindler (2009) recommend that explanatory variables have a correlation coefficients above 0.8 since this indicates a multi-co linearity issue. According to Brooks (2008), the degree of linear relationship between two variables is measured by their correlation. The following Table 4.2 presents the correlation coefficient summery result as shown below. Table 4.2 Correlation Result LNGDP LNFDI LNPOP LN NE LN LB LNEXP LNGDP 1 LNFDI 0.79 *** 1 LNPOP 0.31 *** 0.46 *** 1 LN NE 0.67 *** 0.56 *** 0.18 ** 1 LN LB 0.26 *** 0.44 *** 0.99 *** 0.18 ** 1 LNEXP 0.17 ** 0.04 -0.11 0.13 * -0.11 * 1 Source: STATA 18.0 Results, 2025 A strong positive correlation with GDP tend to attract more Foreign Direct Investment (FDI), LNPOP, LNNE, LNLB, and LNEXP. This aligns with the economic theory that developed economies are more attractive to foreign investors. Positive relationship between a countries’s GDP and its government expenditures, the association. In other words, countries with higher GDP tend to have slightly higher government expenditures, but this is not a strong or definitive pattern. Foreign direct investment has positive correlation with LNPOP, LNNE, LNLB and LNEXP. Total population has positive correlation with LNNE LNLB and negative correlation with LNEXP. Net export has positive correlation with LNLB and LNEXP. Labor has positive correlation with LNEXP. 4.4 The Test of Endogeneity The amount of LNGDP may be affected by many other factors, which have not been taken into account in this study. It implies that there may be an endogeneity problem. Therefore, this study employs the methods to eliminate the estimation bias caused by endogeneity. This study introduces the first-order lag term of the LNGDP and performs the dynamic General Moment Method (GMM) estimation. The result of the Arellano Bond test shows that the p-value is 0.000, which implies that the model in this study can be estimated by the GMM method. The result of the GMM estimation is presented by Model below in Table 4.3 . The result indicates that expenditure has a significantly positive impact on economic growth in Myanmar. The estimation result indicate there is no endogeneity arises in this model. Table 4.3 the test of endogeneity (1) VARIABLES LNGDP(GMM) L.LNGDP 0.0748*** (9.863) LNEXP 0.392*** (8.079) LNFDI 0.0542*** (5.227) CD 4.022*** (10.90) LNPOP 4.687*** (8.129) LNNE 0.0383*** (4.549) LNLB -5.370*** (-29.15) Constant 1.197*** (3.869) Observations 298 Number of Regional 15 Notes: z-statistics in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1 Source: STATA 18.0 Results, 2025 4.5 Robustness Check Robust regression to adjust for issues such as heteroskedasticity or violations of normality in the error terms. Robust regression provides more reliable coefficient estimates and standard errors when the assumptions of regression are violated. A robustness check is a method used in statistical analysis, econometrics, or other quantitative research to assess the reliability and stability of the main results or conclusions of a study. It involves testing whether the findings hold under different assumptions, models, or data specifications. Robustness checks are a standard part of high-quality research, demonstrating thoroughness and resilience of the conclusions against variability or alternative conditions. To perform the robustness appropriately, this study conducted related checks in different ways. According the result on both model LNExp and CD are positively significant on Economic growth. Table 4.4 Robustness check (1) (2) (3) (4) (5) (6) VARIABLES LNGDP FE Robust LNGDP 10th LNGDP 25th LNGDP 50th LNGDP 75th LNGDP 90th LNEXP 0.393** 0.462*** 0.434*** 0.389*** 0.352*** 0.327*** (2.568) (2.990) (3.799) (5.621) (4.488) (3.109) CD 5.124*** 5.632*** 5.425*** 5.094*** 4.820*** 4.640*** (5.367) (5.625) (7.328) (11.39) (9.490) (6.784) LNPOP 4.420** 0.817 2.288 4.636*** 6.580*** 7.854*** (2.810) (0.282) (1.072) (3.460) (4.453) (3.998) LNFDI 0.0763** 0.0755** 0.0758*** 0.0763*** 0.0767*** 0.0769*** (2.410) (2.113) (2.869) (4.795) (4.231) (3.151) LNNE -0.0350*** -0.152*** -0.104*** -0.0279 0.0353 0.0767** (-4.985) (-2.855) (-2.671) (-1.071) (1.287) (2.142) LNLB -4.330*** -1.273 -2.521 -4.513*** -6.162*** -7.244*** (-3.332) (-0.557) (-1.498) (-4.248) (-5.282) (-4.678) Constant 1.215** (2.739) Observations 330 330 330 330 330 330 R-squared 0.986 Number of Regional ID 15 Robust t-statistics in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1 Source: STATA 18.0 Results, 2025 The use of robust standard errors addresses potential heteroskedasticity in the data, is crucial for obtaining valid inference, particularly when working with real-world economic data, which often violates assumptions of homoscedasticity. The robust t-statistics provided in parentheses indicate that the coefficients' significance remains strong even after correcting for heteroskedasticity. And also the use of quantile regression allows for a more detailed understanding of how the independent variables affect LNGDP across different points of the distribution (10th, 25th, 50th, 75th, and 90th quantiles).This approach is useful in capturing the heterogeneity in the data, as it accounts for varying effects at different levels of GDP. Both robust and quantile model could be enhanced by checking for multi-col-linearity and considering additional variables that might affect economic growth. Fiscal decentralization refers to the transfer of fiscal responsibilities and revenue-raising powers from central to local governments, with the aim of improving public service delivery, local accountability, and regional development. The model here includes Central/Decentralization as a key variable, which represents the governance structure’s impact on LNGDP. Decentralization (increasing local autonomy over fiscal policies) may provide room for more targeted public investments, localized tax policies, and better alignment between government spending and local needs. In a fiscal decentralization context, the model might suggest for local governments have the capacity for efficient public service delivery, decentralization could have a positive impact on economic growth. 4.6 Findings and Interpretation of the Regression Model Based on the following table, natural logarithm of expenditure, central/decentralization, natural logarithm total population and natural logarithm of foreign direct investment have statistically positive relationship with natural logarithm of economic growth in Myanmar in different regional states. On the other hand, LNNE and LNLB have statistically negative relationship with economic growth in Myanmar region. With regard to goodness of fit statistics, it is desirable to look into R-squared value, a measure of how well the regression model actually fits the data. In other words, R-squared is desirable to have an answer to the question, ‘how well does the model containing the explanatory variables that was proposed actually explain variations in the dependent variables (Brooks, 2008). The estimated result of fixed effect model robust is at a fairly satisfactory level where the R-squared value indicates that the model explains 98.6% of the variance in LNGDP, which is a very high value. Therefore, the validity of the model in the context of fiscal decentralization is strong, as it aligns with the theoretical understanding that both centralized and decentralized fiscal policies can contribute to economic growth, depending on the development level and institutional capacity of the regions being analyzed. Generally, these independent variables together are good explanatory variables of the LNGDP of regional state in Myanmar. Though this, F-statistics which was used to measure the overall test of significance of the model was presented, and null hypothesis can be clearly rejected in both of the regression models. Since the p-value is 0.000000, which is sufficiently lower, the model is well fitted at 1 percent level of significance. Table 4.5 Estimate Regression results of Fixed Effect Robust Model Interpretation of Fixed-Effects Robust Model Result Based on the results of the fixed-effects regression using robust standard errors, here’s a detailed interpretation of the coefficient estimates for each variable: LNEXP (Log of Expenditure): A 1% increase in expenditure (LNEXP) is associated with an increase of approximately 0.3929% in (LNGDP), holding all other factors constant and this coefficient is statistically significant at the 5% level (p = 0.022), indicating that expenditure has a positive and statistically significant impact on GDP. This positive relationship between expenditure and GDP can be explained through several economic mechanisms: Public Investment in Infrastructure: Higher government expenditure is often directed towards investments in infrastructure, education, healthcare, and technology, which can lead to higher productivity and, subsequently, increased GDP. CD (Centralization/Decentralization): A unit increase in centralization/decentralization (CD) is associated with an increase of 5.1242 in (LNGDP). This suggests that regions with a higher level of decentralization or centralization have significantly higher GDP, likely due to better governance or decision-making. The coefficient is highly statistically significant at the 1% level (p = 0.000), indicating a very strong and positive relationship between centralization/decentralization and GDP. Centralization/decentralization impacts multiple dimensions of governance, including fiscal policy, public service delivery, regulatory frameworks, and political stability. LNPOP (Log of Population): A 1% increase in population (LNPOP) is associated with a 4.4197% increase in GDP (LNGDP), holding all other factors constant. This suggests that larger populations may contribute to higher economic outputs, possibly due to a larger labor force or more consumer demand. LNFDI (Log of Foreign Direct Investment): A 1% increase in foreign direct investment (LNFDI) is associated with an increase of 0.0763% in GDP (LNGDP). This suggests that higher foreign investment has a positive impact on economic growth, likely through increased capital, technology transfer, or market access. The coefficient is statistically significant at the 5% level (p = 0.030), indicating that FDI contributes positively to economic growth. FDI brings in capital from foreign investors, which can be used to fund domestic projects, infrastructure, and businesses. LNNE (Log of Net Exports): A 1% increase in net exports (LNNE) is associated with a -0.03495% decrease in GDP (LNGDP), holding all other factors constant. This suggests that higher net exports may not be positively correlated with GDP in this model, potentially due to issues such as trade deficits, inefficient trade policies, or over-reliance on certain sectors. The coefficient is statistically significant at the 1% level (p = 0.000), indicating a negative but statistically reliable relationship between net exports and GDP. LNLB (Log of Labor): A 1% increase in labor (LNLB) is associated with a -4.3298% decrease in GDP (LNGDP), holding all other factors constant. This negative relationship may indicate inefficiencies in the labor market or labor force participation in sectors that are not conducive to high economic growth. The coefficient is statistically significant at the 1% level (p = 0.005), suggesting that labor (in its current form) has a negative impact on GDP, possibly due to factors such as low productivity or poor labor market conditions. Generally the results suggest that policies fostering investment in expenditure, centralization/decentralization, and foreign direct investment, while optimizing labor utilization, may lead to higher GDP growth. 5. Conclusion This study examines how government expenditures and fiscal decentralization impact on local government economic growth in Myanmar using panel data spanning the years 2000 up to 2021. The study analyzed and interpreted the impact of government expenditures and fiscal decentralization on local government economic growth in Myanmar using secondary data. In order to confirm the validity of the results regarding the fixed effects, the study utilized a robust fixed effect approach and quintile regression. The study's conclusions were drawn from the data analysis. The findings from the descriptive statistics and correlation analysis provide valuable insights into the relationships between various economic variables in the study. The descriptive statistics show significant variability in the data, with considerable variation in key economic indicators like GDP, FDI, expenditures, net export, and labor force across different fifteen regions and twenty years. These variations reflect the diverse economic conditions and development stages across the observed regions. The logarithmic transformation of variables like GDP, FDI, and Expenditures suggests that these data period several orders of magnitude, indicating disparities in economic performance and development. The results of correlation suggest that GDP and FDI has correlation together and ,also between GDP and government expenditure is pointing to the importance of correlation with other factors like political priorities, fiscal policies, and public spending needs in determining government expenditures. In this study, various model diagnostic tests were performed to ensure the robustness and reliability of the regression analysis examining the relationship between different factors (such as expenditures, FDI, population, etc.) and LNGDP (economic growth). The robustness check performed using different model specifications (fixed effect robust and quantile regression models) affirmed the stability of the findings across various model forms and quantiles. The positive impact of expenditure (LNEXP) and centralization (CD) on economic growth, remained significant across different quantiles (10th, 25th, 50th, 75th, and 90th). The robust regression and quantile regression results confirmed the validity and reliability of the model, showing that the relationships hold true across different model specifications. The inclusion of fiscal decentralization (represented by Central/Decentralization, CD) suggests that decentralizing fiscal responsibilities to local governments could have a positive impact on economic growth. The findings from the fixed-effects regression model with robust standard errors suggest that certain factors have a statistically significant positive relationship with economic growth, while others negative relationship. Government Expenditure (LNEXP) has a positive and statistically significant impact on economic growth. Increased government spending in infrastructure, education, and technology can stimulate productivity and demand, driving GDP growth. Centralization/Decentralization (CD) shows a positive relationship with GDP. Regions with more centralized or decentralized governance structures tend to experience better economic outcomes, with decentralization offering potential advantages in terms of bespoke policies that meet local needs. Population size (LNPOP) is positively associated with economic growth. A larger population provides a larger labor force and greater consumer demand, both of which contribute to higher economic outputs. Foreign Direct Investment (LNFDI) also demonstrates a positive impact on economic growth, as it brings capital, technology transfer, and market access that can boost productivity and economic expansion. Net Exports (LNNE), however, presents a negative relationship with GDP growth. This suggests that Myanmar's trade policies or structural inefficiencies in the export sector may need further review, as over-reliance on exports or trade deficits could hinder growth. Labor (LNLB) shows a negative impact on GDP. Overall, the findings provide strong evidence that well-targeted fiscal policies, decentralization of governance, and investments in human capital and infrastructure are essential for Myanmar to realize its full economic potential. Future research and policy focus should aim to refine these strategies, ensuring they are adaptable to the changing economic landscape and the diverse needs of Myanmar’s regions. The results of this study indicate that public expenditure (LNEXP) and foreign direct investment (FDI) have a significant positive effect on economic growth, as measured by LNGDP. Additionally, centralization (CD) and the appropriate application of fiscal decentralization contribute positively to economic growth by improving governance and local accountability and the study provides strong evidence for the role of targeted public investments, sound fiscal management, and decentralization in promoting sustainable economic growth in Myanmar. Declarations Declaration Statement Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement : Myanmar Planning Budget and Finance References Akmil, I., & Alpon, S. (2022). The effect of financial development on economic growth in high-income countries, Asian Economic and Financial Review. Arshad, Muhammad. (2010). Fiscal Decentralization and Economic Growth in Pakistan: An ARDL Approach, KDI School of Public Policy and Management Google Search. Balaj, & Lani. (2017). The impact of public expenditure on economic growth of Kosovo. Acta Universitatis Danubius. conomica, 13(5). https://journals.univ-danubius.ro/index.php/oeconomica/article/view/4443/4254 Barlas. (2020). The impact of government expenditure on economic growth in Afghanistan. Journal of Economics and Business, 3(2). https://doi.org/10.31014/aior.1992.03.02.234 Dick-Sagoe, C. (2020). Decentralization for improving the provision of public services in developing countries: A critical review. Cogent Economics and Finance, 8(1). https://doi.org/10.1080/23322039.2020.1804036 Farooq, et al. (2023). Public debt and environment degradation in OIC countries: The moderating role of institutional quality. Environmental Science and Pollution Research, 30(19), 55354-55371. https://doi.org/10.1007/s11356-023-26061-x Kwasi, P., et al. (2022). The influence of government expenditure on economic growth in Ghana: An ARDL approach, Cogent Economics & Finance, 10:1, 2160036, DOI: 10.1080/23322039.2022.2160036. Martinez-Vazquez, et al. (2017). The impact of fiscal decentralization: A survey. Journal of Economic Surveys. Mishra, & Mohanty. (2021). Nexus between government expenditure and economic growth: Evidence from sub-national governments in India. Journal of Developing Areas, 55(2). https://doi.org/10.1353/jda.2021.0045 Moussé, S., et al. (2018). Fiscal decentralisation and the efficiency of public service delivery, Fiscal Decentralization and Inclusive Growth © OECD, KIPF. Musgrave, R. (1959). The theory of public finance. N.Y: McGraw-Hill. Oates, W. E. (1969). The effects of property taxes and local public spending on property values: An empirical study of tax capitalization and the Tiebout hypothesis. Journal of Political Economy, 77(6), 957-971. https://doi.org/10.1086/259584 Okoye, et al. (2019). Government expenditure and economic growth: The case of Nigeria. Proceedings of SOCIOINT, 1184-1194. Paul Minoletti. (2016). Fiscal decentralisation and national reconciliation in Myanmar, Key issues and avenues for reform, International Growth Center. Rodríguez-Pose, Andrés. (2008). Decentralization and Local and Regional Development, CAF Documento de trabajo, CAF Working paper. Rodriguez-Pose, et al. (2011). Is fiscal decentralization harmful to economic growth? Evidence from the OECD countries. Journal of Economic Geography, 11(4), 619-643. https://doi.org/10.1093/jeg/lbq025 Roy W. Bahl & Johannes F. Linn. (1994). Fiscal Decentralization and Intergovernmental Transfers in Less Developed Countries. http://www.jstor.org/stable/3330701 Superianik. (2019). Analysis of The Impact of Fiscal Decentralization on Economic Growth in Indonesia. Tri Efriandi. (2021). Decentralization and the challenges of local governance in Indonesia, Four case studies on public service provision and democratization in Papua and West Papua, University of Groningen. Vtyurina. (2020). Effectiveness and Equity in Social Spending: The Case of Spain, IMF Working Paper WP/20/16. Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Published Journal Publication published 23 Jan, 2025 Read the published version in Journal of Economic Research & Reviews → 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5844647","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":403205909,"identity":"6e351793-0546-489a-9cd4-e2974c4fb511","order_by":0,"name":"May Zin Phyu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYDACZh4QeSCBjZmB8QGQxcNHihZmA5AWNsLWQLUACTYJEJOgFoPjvAcfV9TcyeNj5z1W+TXHToaNgfnhoxv4tBzmSzY8c+xZMRszX9pt2W3JQIexGRvn4NXCYybZwHY4sY2Zx+y25DZmoBYeNmkCWsx/NvyDaCmW3FZPlBYzxsY2iBbGj9sOE9YiCfSLZGMfyC88xtKM247zsDET8Avf+bMHPzZ8u5Mn33/G8OPPbdX2/OzNDx/j04ICINHKTKxyEGD8QYrqUTAKRsEoGDEAAKA/QpXOE7gFAAAAAElFTkSuQmCC","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"May","middleName":"Zin","lastName":"Phyu","suffix":""}],"badges":[],"createdAt":"2025-01-16 21:19:49","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5844647/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5844647/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.33140/JERR.05.01.02","type":"published","date":"2025-01-24T00:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":74231641,"identity":"4820c5c7-41cc-4dc0-a13b-60a273cbf674","added_by":"auto","created_at":"2025-01-20 08:09:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":41106,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 2.1: Budget Cycle\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.1.png","url":"https://assets-eu.researchsquare.com/files/rs-5844647/v1/5a9725dfb45e96e6b808ac66.png"},{"id":75959125,"identity":"18b098a5-42c8-4fc8-959a-074ea9ffa6ba","added_by":"auto","created_at":"2025-02-11 02:19:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":899064,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5844647/v1/07973601-1ed2-41c5-8938-2a141d561927.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eThe Effect of Government Expenditure on Economic Growth in Myanmar: a Fiscal Decentralization Perspective\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGovernment expenditure is an important instrument in national development. It is pivotal in any economy\u0026apos;s functioning at almost all growth and development stages. Nowadays most developing and developed countries use public expenditure to improve income distribution, direct the allocation of resources in desired areas, and influence the composition of national income. In economic theory, expenditure by governments is a crucial determinant for national economic progress. Economic growth is considered a measure of a country\u0026apos;s financial performance due to its capacity to enhance living standards, public benefits, and employment levels, making it a goal for most governments. Consequently, it is crucial to understand the efficient distribution of resources and the pivotal factors that can drive economic advancement (Vtyurina, 2020). \u0026nbsp;The word \u0026quot;government expenditure\u0026quot; encompasses all financial outlays made by the government, including spending, transfers, and purchases. Government expenditure is essential to reduce poverty (Farooq, et al., 2023). Keynesian theory posits that government expenditure can elevate aggregate demand, hence fostering economic growth and employment creation. Nonetheless, curtailing government expenditure may adversely impact the economy. The fiscal decentralization of governmental operations became a progressively significant element of national economic changes. Decentralization can enhance the legitimacy of governance and the state, facilitate regional economic growth, and assist in resolving some internal conflicts. Decentralization advantages both the economic and political sectors, enabling localities to utilize pertinent technology autonomously, so enhancing investment sustainability and promoting efficiency (Rodriguez-Pose et al., 2011). Decentralization has numerous potential benefits, including improved transparency in government, agility, and efficiency in service delivery, as well as creating political stability, reducing conflict, and increasing competition in politics (Paul Minoletti, 2016).\u0026nbsp;Decentralization has garnered increased global interest in recent years. Decentralization, the process of reallocating authority from national to local levels, consists of three components. This encompasses decentralization in governance, administration, and fiscal matters. The administrative aspect analyzes the organizational framework of local governments, whereas the political dimension emphasizes the elected executives who rule these entities. The fiscal factor primarily focuses on the financial and expenditure arrangements between the federal government and local governments (Dick-Sagoe, 2020). Myanmar is an emerging nation with a budgetary shortfall. The efficient utilization of limited resources is essential in developing countries, especially for reducing poverty and promoting national economic development. Government expenditure is the paramount driver of this country\u0026apos;s economic and social development. Therefore, the government must allocate its funds in a manner that promotes the development of the nation and its citizens. In 2011, Myanmar transitioned to a decentralized financial budgeting framework. Fiscal decentralization directly influences expenditure generation, thus impacting economic growth and local development. It also seeks to enhance the effectiveness and efficiency of local governments in achieving financial autonomy. This article analyzes the correlation between government expenditure and economic growth via the lens of fiscal decentralization. The primary objective of this study is to identify potential obstacles or enablers that may influence the capacity and financial sustainability of local government in Myanmar. Firstly, there is a paucity of empirical research that particularly examine Myanmar\u0026apos;s distinct socioeconomic and political conditions. The majority of research regarding the correlation among economic development and government expenditure is either worldwide or extensive in nature. Secondly, extensive longitudinal studies that monitor changes over a prolonged duration are necessary. Third, research often neglects the impact of fiscal decentralization on infrastructure, healthcare, and education, among other sectors. A thorough sectoral analysis is essential to comprehend the effects of government investment in many areas on overall economic growth. Consequently, precisely defining fiscal decentralization, economic development, and government expenditure is difficult. Consequently, this study was undertaken to address the gap.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.1\u003c/strong\u003e \u003cstrong\u003eObjective of the Study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGeneral objective: The primary objective of this research is to understand how government expenditure effects economic growth in Myanmar from the perspective of fiscal decentralization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSpecific objectives:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1.\u0026nbsp; \u0026nbsp;To determine the relationship between the government expenditure on economic growth in Myanmar.\u003c/p\u003e\n\u003cp\u003e2.\u0026nbsp; \u0026nbsp;To evaluate the impact of fiscal decentralization of government expenditure on economic growth in Myanmar.\u003c/p\u003e\n\u003cp\u003e3.\u0026nbsp; \u0026nbsp;To elaborate correlation between the explanatory variables and economic growth in Myanmar.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.2 Research question\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo make the aim of this study more concrete, I would like to specifically ask the following questions.\u003c/p\u003e\n\u003cp\u003e1.\u0026nbsp; \u0026nbsp;What is relationship between government expenditure and economic growth in Myanmar?\u003c/p\u003e\n\u003cp\u003e2. What is the impact of\u0026nbsp;fiscal decentralization of\u0026nbsp;expenditure on economic growth in Myanmar?\u003c/p\u003e\n\u003cp\u003e3. \u0026nbsp; Is there a correlation between the explanatory variables and economic growth in Myanmar?\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.3 Significance of the study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEconomic growth is an essential goal in a country\u0026apos;s economy, especially for developing countries like Myanmar. The establishment of regional autonomy affects the delegation of power between the federal government and the regions in several areas. The establishment of regional autonomy will lead to decentralization, which entails managing regional funds and organizing plans delegated from the center to the regions for economic planning and regional development. The implementation of regional autonomy is a response to the aspirations of a new format regarding the relationship between the central government and the regional government in Myanmar. The implication for fiscal decentralization is that state and regional government and union government fiscal relations with significant change are critical to the future of Myanmar.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMyanmar\u0026apos;s budget system can be studied in two main periods, before 2011 and after 2011, when fiscal decentralization occurred. Before 2011, there was only one state budget, and the budget process was centralized by the government. The central government allocated all the requirement amounts that were submitted by the respective line ministries, departments, and agencies. From 2011, the system of the budget was decentralized, including the Union budget (Central) and seven states and seven regions (Local) budgets. Carrying out such decentralization requires a strategy to systematically collect the revenue sources and allocate expenditures effectively and efficiently for regional development programs and projects.\u003c/p\u003e\n\u003cp\u003eBriefly, in Myanmar, the implementation of the budget system reforms, such as financial decentralization, a medium-term fiscal framework, and the effectiveness of budget allocation and sustainable national economic development. The implementation of fiscal decentralization government functions was an increasingly important aspect of economic reforms. Decentralization can promote the legitimacy of the division and state, execute regional economic development, and help address some internal conflict. Based on these factors, the study focuses on the effect of government expenditure on economic growth in Myanmar from a fiscal decentralization perspective.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.4 The study\u0026apos;s structure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research paper is organized into three subsections. The first section deals with a detailed review of both theoretical and empirical literature on government fiscal decentralization, expenditure, and economic growth. The second section presents and emphasis the specifics of the methodology employed in the study, such as research design and sampling techniques. Finally, the third section analyses the findings and presents conclusions.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003e\u003cstrong\u003e2.1 Introduction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis chapter provides a review of the literature on several Effects of Government Expenditure on Economic Growth in Myanmar: A Fiscal Decentralization Perspective by theoretical and empirical concepts. This material covers understanding of government expenditure, economic growth, and fiscal decentralization perspective. The links between government expenditure and economic growth is important because it can provide the policy makers as an empirical evidence of economic development process. Economic growth is a primary goal in a national economy, especially for developing countries. Fiscal Decentralization is an interest topic because it is include not only economic perspective, but also politic and other subject. Decentralization can create significant advantages in line with the government\u0026apos;s policy priorities and the requirements of the people. Two important policy instruments can potentially affect and influence economic growth: government expenditure and fiscal decentralization.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Theoretical Reviews\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEconomic Growth\u003c/strong\u003e: \u0026nbsp;An increase in GDP over time is the concept of economic growth (Weil, 2013). (Palmer, 2012) defined economic growth as the capacity of a nation to produce more commodities and services. Over a certain amount of time, it also raises productivity per person in the population (Seater \u0026amp; Yenokyan, 2019). According to (Weil, 2013), an increase of real GDP, GDP per capita, and national output measured in constant prices is indicative of economic growth. Economic growth is facilitated by advances in capital products and technology (Jackson, 1990). All of the definitions share the presumption that an economy is functioning well when it is producing more commodities and services. Economic growth can be evaluated by the Gross Domestic Product (GDP) and the Gross National Product (GNP). GDP is an appropriate instrument for figuring the framework, level and rate of the country\u0026rsquo;s economy during the period of time (Badan Pusat Statistick, 2020). GNP is the market value of the finished product manufactured by a country during a specific time period, excluding its geographic location (Culture, 2015)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGovernment expenditures\u003c/strong\u003e: According to (Cvetanovic et.al, 2015), expenditure by the government is an outflow from the federal government as well as regional and local authorities that accounts for a sizeable amount of Gross National Product (GNP). Government expenditures are defined by (Ribeiro and Lima, 2019) as the costs borne by a government to support other countries as well as maintain itself, society, and economy. Since public spending controls the amount and pace of economic growth, all economies rely on it.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFiscal decentralization, encompassing the allocation of governance, expenditure provision, and revenue generation, is an articulated goal of many national governments in developing countries and forms part of their economic growth methods (Roy W. Bahl and Johannes F. Linn, 1994). Fiscal decentralization entails the delegation of responsibilities and the distribution of authority and responsibility for decision-making within the fiscal sector, encompassing revenue as well as expense dimensions. This aims to enhance responsiveness by aligning governmental services with the needs as well as needs of the local populace. Decentralization may have improved the technical reliability and quality of the public sector by reducing administrative bottlenecks and fostering increased accountability and openness (Rotulo et al., 2020).The traditional view of fiscal federalism highlights three critical public-sector objectives: economic effectiveness, financial stability, and equitable distribution of income (Musgrave 1959; Oates 1972). The federal government ought to oversee the stability of the economy and income redistribution, whereas subnational governments, being closer to citizens and possessing greater insight into their preferences, should guarantee the efficient provision of public goods within the borders of their control (Musgrave, R, 1959). As per the conventional fiscal federation theory proposed by Oates (1969), decentralization ensures that local governments can deliver public goods more efficiently than federal governments, as they possess a better understanding of the inclinations of the local population. The primary element of fiscal decentralized governance is the allocation of financial resources and expenditure authority to subordinate levels of government. \u0026nbsp;Decentralization serves as a vital tool for fostering sustainable development; yet, it encounters significant challenges in optimizing the advantages of power distribution and resource allocation to the subnational levels of authority (Rodr\u0026iacute;guez-Pose, Andr\u0026eacute;s, 2008).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Annual Budget Process and Stages of Myanmar\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe budget is as a fundamental policy document for the government. It is a fundamental tool of fiscal policy, and influences the operation and management of the economy. The budget illustrates the government\u0026apos;s prioritization and allocation of resources for its annual and multiyear goals.A nation\u0026apos;s budget can serve as a potent instrument for social transformation. It is a mechanism that enables the government to convert national resources into allocations, which, if strategically planned and effectively implemented, can result in sustainable and equitable national development.\u003c/p\u003e\n\u003cp\u003eIn Myanmar, the fiscal year is from 1st April to 31st March. The constitution of Myanmar from 2008, Section 103, provides the legal basis for budget preparation. The President or their designated representative must present the budget bill to Parliament and receive permission to utilize Union funds. Myanmar\u0026apos;s Sustainable Development Plan (MSDP) for 2018-2030 acts is the primary development framework for the nation. The Ministry of Planning and Finance (MOPF) has the responsibility of creating the national plan, annual plan, and five-year medium plan. The budget planning process starts in September for the coming fiscal year. The Myanmar\u0026apos;s government expenditure includes current, capital, and financial expenses. The Union of Budget Department (head office) estimates resource allocation for the union budget framework and calculates the amount the union provides for grants to states and regions using the MTFF method. This process is based on macroeconomic forecasts and fiscal policy objectives. Then, the Ministry of Planning and Finance issues the Budget Calendar to the line ministries at the union level and informs to the state and regional governments about the amount of the union government provides grants for regional governments. The budget cycle in Myanmar is divided into four stages as following; the budget planning and preparation stage; the budget formulation and approval stage; the budget implementation and execution stage; and the budget evaluation, reporting and auditing stage. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Empirical Literature Review\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDecentralizing fiscal authority is an efficient policy tool that improves performance in constrained circumstances. The decentralization of spending must be accompanied by a corresponding decentralization of income to guarantee favorable results. Without these prerequisites, fiscal decentralization may result in decreased efficiency in public service delivery. (Mouss\u0026eacute; et al., 2018). Decentralization is a mechanism that strengthens the relationship among the state and the populace, hence necessitating the involvement of a subdivision and village levels in the crucial aspects of grassroots growth and delivery of services (Tri Efriandi, 2021). Fiscal decentralization correlates positively with economic growth and may act as a mechanism to promote sustainable economic growth. It is proposed that factors such as overdependence on provincial governments, unclear functional and taxes responsibilities, and limited and inactive tax bases for the local and provincial governments may hinder the full benefits of decentralized fiscal administration (Arshad Muhammad, 2010). Decentralization serves as a mechanism to attain state objectives, specifically enhancing public services and fostering a more responsive public decision-making process. Decentralization will be accomplished by empowering subordinate levels of government to allocate spending, collect taxes, form democratically elected regional leaders and councils, and facilitate payments by the governing body (Martinez-Vazquez et al, 2017). According to the study of (Superianik,2019) asserts that fiscal decentralization enhances the potential for regional economic growth by enabling local governments to distribute their budgets more efficiently. This efficiency is achievable because local governments possess greater knowledge of their areas\u0026apos; public goods requirements. The principal obstacles to economic growth include price inflation, the bureaucracy economy of scale, market dispersion, and corruption. Various measures can be implemented to overcome the principal obstacles to economic growth, namely the rate of inflation, the government, economics of scale, dispersion of markets, and corruption, particularly within the context of fiscal decentralization. The functioning of regional governments can be enhanced efficiently, hence accelerating economic growth and streamlining licensing procedures. Regional guidelines and bureaucratic reforms can fulfill them. Secondly, economies of scale can be enhanced by optimizing certain regional economic sectors. Attracting regional investment is essential for economic progress. Third, fiscal and monetary regulations might be prioritized to mitigate inflation. Subsequently, productivity must enhance, and price regulation must be robust. Fourth, to combat corruption, the tracking and oversight system must be robust to deter corrupt practices, and community engagement should also drive monitoring policy and spending decisions in the regions. Ultimately, fragmentation in the market can be mitigated by implementing standardized laws and policies throughout all regions. Balaj and Lani (2017) examine the impact of governmental expenditure on the economic growth of Kosovo. The findings indicate a favorable correlation between government spending and economic development, while the two factors are not directly interdependent. The authors argue that the misallocation of public funds to non-growth-promoting initiatives has hindered the fulfillment of the original expenditure objectives in Kosovo. Okoye et al. (2019) investigate the correlation between GDP expansion and government expenditure to ascertain the influence of government spending on output growth. The results demonstrate an adverse and statistically important short-term lag in current expenditure for economic growth. The research indicates that delayed capital expenditure significantly enhances economic growth. Nonetheless, the findings failed to establish a persistent link between economic growth and government expenditure. Mishra and Mohanty (2021) assert that government expenditure positively and significantly influences economic growth. Barlas (2020) evaluates the influence of government expenditure on Afghanistan\u0026apos;s economic growth. The findings indicate that government expenditure has a significant and detrimental correlation with Afghanistan\u0026apos;s economic growth. Government expenditure can facilitate short-term economic growth. Increased government expenditure on profitable firms will enhance short-term GDP growth. Similarly, an increase for a long-term lucrative project can impact long-term economic growth (Kwasi. P et al., 2022). In high-income nations, financial development is the principal driver of economic growth and sustainable development, with savings serving as the key resource for the financial sector (Akmil. I and Alpon. S, 2022).Generally Spending by the government and Fiscal decentralization is still a crucial tool in the development process. It is essential to every economy\u0026apos;s operation at practically every level of growth and development researchers reviewed many empirical and identified the problem.\u003c/p\u003e"},{"header":"3. Research Methodology","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Research Type and Design\u003c/h2\u003e \u003cp\u003eAccording to (Cooper, 2011), research design is the methodical arrangement of parameters for gathering and analyzing data with the goal of balancing procedural efficiency with relevance. Furthermore, a research design is defined by (Orodho, 2003) as the structure, plan, or approach used to find answers to research questions. Through a unit of study and investigation, the study used a quantitative research design that makes it easier to gather all pertinent situational characteristics. After carefully collecting, presenting, and evaluating the data, the researcher came to certain conclusions and recommendations. Furthermore, as many researchers and academics have pointed out, quantitative panel data is considered more informative, more varied, less linear across variables, provides more degrees of freedom, and is therefore more efficient.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Data Collection and Procedures\u003c/h2\u003e \u003cp\u003eThe study only employed secondary data from different circumstances. A secondary data collection form was utilized to compile information from financial statements about expenditures, FDI, GDP, and the population as a whole in relation to GDP, among other variables. This information was then used to calculate pertinent ratios, descriptive statistics, and regression analysis. The Central Statistical Organization, the World Bank, the IMF, the Ministry of Planning and Finance, and the Ministry of International Foreign Economic Relations were the main sources of the secondary data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Method of Data Analysis\u003c/h2\u003e \u003cp\u003eIn order to strengthen the models and lessen the cross-sectional effects of the intercepts, the study employed the fixed-effect regression technique. As per Brooks (2008), the fundamental types of fixed-effect models allow the regression model's intercept to fluctuate cross-sectional and also choose to employ fixed effects when T\u0026thinsp;\u0026gt;\u0026thinsp;N, meaning that there are twenty years more temporal dimensions than there are cross-sectional fifteen. Because the fixed-effects model takes into consideration all individual time-invariant variations, missing time-invariant features have no effect on the estimated value for the fixed-effects models. According to (Zikmund, 2010) asserts that data analysis in research is the use of logic to understand the information gathered, with the aim of spotting recurring trends and summarizing relevant aspects discovered throughout the study. The obtained data was evaluated using a Fixed-Effect Regression model, and the results were produced using the econometrics program STATA 18.0. The selection of these software programs is based on their ability to make research analysis transparent and efficient. In order to analyze entity behavior over time, the study's dataset included pooled observations from panel data collected across a number of time periods. For central/decentral and descriptive statistics, the Fixed-Effect robust Regression technique was used to analyze and interpret the panel data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Model Specification\u003c/h2\u003e \u003cp\u003eThe researcher examined government spending in Myanmar using the Fixed-Effect Regression model in addition to descriptive statistics. Many econometricians believe that the ability of the Fixed-Effect Econometric approach to identify the independent effects of a set of factors on the dependent variable is one of its main advantages. The regression technique is calculated for a thorough examination of the regional states.\u003c/p\u003e \u003cp\u003elnGDPit\u0026thinsp;=\u0026thinsp;β0\u0026thinsp;+\u0026thinsp;β1lnExpit\u0026thinsp;+\u0026thinsp;β2CDit\u0026thinsp;+\u0026thinsp;β3lnPOPit\u0026thinsp;+\u0026thinsp;β4lnFDIit\u0026thinsp;+\u0026thinsp;β5lnNEit\u0026thinsp;+\u0026thinsp;β6lnLBit\u0026thinsp;+\u0026thinsp;εit\u003c/p\u003e \u003cp\u003eWhere,\u003c/p\u003e \u003cp\u003elnGDP is the observed natural logarithms growth domestic product of Myanmar i at year t,\u003c/p\u003e \u003cp\u003e\u0026#120573;0 is the constant term showing the value of GDP when all the coefficients of the independent variables are zero.\u003c/p\u003e \u003cp\u003elnExp it is the natural logarithms government expenditure of Myanmar i at the time t,\u003c/p\u003e \u003cp\u003eCDit is the central /decentralization of an regional i at time t,\u003c/p\u003e \u003cp\u003elnPOPit is the natural logarithms population of Myanmar i at the time t,\u003c/p\u003e \u003cp\u003elnEit is the net export of Myanmar i at the time t,\u003c/p\u003e \u003cp\u003elnLBit is the natural logarithms Labor of Myanmar i at the time t,\u003c/p\u003e \u003cp\u003elnFDIit is the natural logarithms foreign direct investment of Myanmar i at the time t\u003c/p\u003e \u003cp\u003eβs are the partial effect of independent variables in period t.\u003c/p\u003e \u003cp\u003e\u0026#120576;\u0026#119894;\u0026#119905; is the error term of Myanmar i at time t.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Result and Discussion","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003e4.1 Introduction\u003c/h2\u003e\n \u003cp\u003eThis chapter expounds at large on the findings, data analysis, results and discussions in line with the objectives of this study. The first descriptive analysis of both dependent and independent variables, second correlation analysis, third regression result and detail interpretations.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003e4.2 Descriptive statistics\u003c/h2\u003e\n \u003cp\u003eDescriptive statistics refers to the branch of statistics that focuses on summarizing and describing the features of a dataset. It provides a simple overview of the sample and the measures derived from the data without making inferences about the population from which the sample was drawn. Descriptive statistics are typically used to present quantitative descriptions in a manageable form.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4.1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescriptive Statistics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(5)\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\u003eVARIABLES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emax\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elnGDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elnLB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.329\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elnPOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.288\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elnFDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elnEXP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elnNE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-11.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.735\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eSource: STATA 18.0 Results, 2025\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eBased on the table the descriptive statistics for fifteen region and twenty two years, 130 observations for different variables showing the number of observations (N), mean, standard deviation (sd), minimum (min), and maximum (max) values and interpretation of the statistics for each variable: LNGDP is the logarithm of GDP, with a mean of 10.58, which is relatively large compared to the standard deviation (4.637), indicating a high variation in GDP across observations. The minimum value of 0 suggests that some observations have a GDP near zero (possibly countries or regions with very low GDP), while the maximum of 17.27 indicates the presence of higher GDP countries or regions. The large standard deviation shows a high level of variation in GDP values, which is typical when dealing with economic measures across different countries or regions. LNLB is the logarithm of the labor force. The mean of 0.105 suggests that the average labor force size is moderate, though still relatively small on the log scale. The minimum value of -2.591 shows some observations with a very small labor force. The maximum value of 1.329 indicates that some regions or countries have a labor force larger than the average. The standard deviation of 0.974 indicates a moderate level of variation in labor force size. LNPOP is the natural logarithm of the population, and the mean value of 0.865 suggests a relatively low average population size on the log scale. The negative minimum value \u0026minus;\u0026thinsp;1.871) indicates that some regions or observations have very small populations (close to 0), while the maximum value of 5.288 suggests that other regions have significantly larger populations. The standard deviation of 1.032 shows a considerable spread around the mean, indicating a wide range of population sizes across the observations. CD ratio mean score shows 0.5 from the period. The descriptive statistics result also shows minimum and maximum value of 0 and 1 respectively. This indicates that 1 by these units applying decentralization to collect economic growth in Myanmar regional. LNFDI represents the logarithm of foreign direct investment. The mean value of 2.586 suggests a moderate average level of foreign direct investment across observations. The minimum value of -2.847 indicates that some observations have very low or negative FDI, possibly due to countries with minimal or negative foreign investments. The maximum value of 15.25 indicates some countries or regions with extremely high levels of foreign direct investment. The standard deviation of 3.735 shows a wide range of variation in FDI values. LNEXP represents the logarithm of exports. The mean value of 8.213 is relatively high, suggesting that exports are, on average, substantial for the countries or regions represented in the dataset. The minimum value of 0 indicates that some observations have no exports (or very close to zero), while the maximum of 17.31 suggests some regions or countries have extremely high export levels. The standard deviation of 4.231 indicates significant variation in export values across observations. LNNE is the natural logarithm of energy production. The mean of -2.113 suggests that, on average, energy production is low (since the log of a value less than 1 is negative). The minimum value of -11.28 indicates extreme low energy production, potentially indicating observations with very little or no natural energy production. The maximum value of 2.735 shows some observations with high levels of energy production. The standard deviation of 3.152 highlights the considerable variability in energy production levels across the dataset.\u003c/p\u003e\n \u003cp\u003eGenerally, the data shows significant variation across all variables, as evidenced by the high standard deviations and wide ranges (especially for GDP, FDI, NEXP). Many of the variables are in their logarithmic form, which suggests that the data might span several orders of magnitude (e.g., population, GDP, FDI), which is typical in economic or developmental datasets. The LNTP, LNGDP, LNFDI, and LNEXP variables all show wide variation, indicating different countries or regions may show drastically different characteristics.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003e4.3 Correlation Analysis\u003c/h2\u003e\n \u003cp\u003eCorrelation analysis is a statistical method used to assess the strength and direction of the linear relationship between two or more variables. It helps to determine whether an increase in one variable corresponds to an increase or decrease in another, and whether this relationship is statistically significant. There are several types of correlation analysis methods, but the most common one is Pearson\u0026apos;s correlation coefficient, which measures the strength and direction of the linear relationship between two continuous variables. To make sure that the explanatory variables are correlated, a matrix of correlations is utilized. According to Sisay (2016), Cooper \u0026amp; Schindler (2009) recommend that explanatory variables have a correlation coefficients above 0.8 since this indicates a multi-co linearity issue. According to Brooks (2008), the degree of linear relationship between two variables is measured by their correlation. The following Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4.2\u003c/span\u003e presents the correlation coefficient summery result as shown below.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4.2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCorrelation Result\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLNGDP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLNFDI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLNPOP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLN NE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLN LB\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLNEXP\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\u003eLNGDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNFDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNPOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.31\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.46\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLN NE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.56\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.18\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLN LB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.26\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.44\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.18\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNEXP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.11\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eSource: STATA 18.0 Results, 2025\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eA strong positive correlation with GDP tend to attract more Foreign Direct Investment (FDI), LNPOP, LNNE, LNLB, and LNEXP. This aligns with the economic theory that developed economies are more attractive to foreign investors. Positive relationship between a countries\u0026rsquo;s GDP and its government expenditures, the association. In other words, countries with higher GDP tend to have slightly higher government expenditures, but this is not a strong or definitive pattern. Foreign direct investment has positive correlation with LNPOP, LNNE, LNLB and LNEXP. Total population has positive correlation with LNNE LNLB and negative correlation with LNEXP. Net export has positive correlation with LNLB and LNEXP. Labor has positive correlation with LNEXP.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n \u003ch2\u003e4.4 The Test of Endogeneity\u003c/h2\u003e\n \u003cp\u003eThe amount of LNGDP may be affected by many other factors, which have not been taken into account in this study. It implies that there may be an endogeneity problem. Therefore, this study employs the methods to eliminate the estimation bias caused by endogeneity. This study introduces the first-order lag term of the LNGDP and performs the dynamic General Moment Method (GMM) estimation. The result of the Arellano Bond test shows that the p-value is 0.000, which implies that the model in this study can be estimated by the GMM method. The result of the GMM estimation is presented by Model below in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4.3\u003c/span\u003e. The result indicates that expenditure has a significantly positive impact on economic growth in Myanmar. The estimation result indicate there is no endogeneity arises in this model.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4.3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ethe test of endogeneity\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(1)\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\u003eVARIABLES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNGDP(GMM)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eL.LNGDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0748***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(9.863)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eLNEXP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.392***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(8.079)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eLNFDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0542***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(5.227)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.022***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(10.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eLNPOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.687***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(8.129)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eLNNE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0383***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(4.549)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eLNLB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.370***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-29.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.197***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3.869)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e298\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of Regional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003eNotes: z-statistics in parentheses *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003eSource: STATA 18.0 Results, 2025\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n \u003ch2\u003e4.5 Robustness Check\u003c/h2\u003e\n \u003cp\u003eRobust regression to adjust for issues such as heteroskedasticity or violations of normality in the error terms. Robust regression provides more reliable coefficient estimates and standard errors when the assumptions of regression are violated. A robustness check is a method used in statistical analysis, econometrics, or other quantitative research to assess the reliability and stability of the main results or conclusions of a study. It involves testing whether the findings hold under different assumptions, models, or data specifications. Robustness checks are a standard part of high-quality research, demonstrating thoroughness and resilience of the conclusions against variability or alternative conditions. To perform the robustness appropriately, this study conducted related checks in different ways. According the result on both model LNExp and CD are positively significant on Economic growth.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4.4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRobustness check\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(5)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(6)\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\u003eVARIABLES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNGDP\u003c/p\u003e\n \u003cp\u003eFE Robust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNGDP\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e10th\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNGDP\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e25th\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNGDP\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e50th\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNGDP\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e75th\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNGDP\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e90th\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNEXP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.393**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.462***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.434***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.389***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.352***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.327***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.568)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.990)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3.799)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(5.621)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(4.488)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3.109)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.124***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.632***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.425***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.094***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.820***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.640***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(5.367)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(5.625)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(7.328)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(11.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(9.490)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(6.784)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNPOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.420**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.636***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.580***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.854***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.810)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.282)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.072)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3.460)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(4.453)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3.998)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNFDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0763**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0755**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0758***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0763***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0767***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0769***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.410)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.113)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.869)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(4.795)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(4.231)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3.151)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNNE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0350***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.152***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.104***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0767**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-4.985)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-2.855)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-2.671)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-1.071)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.287)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.142)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNLB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.330***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.513***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.162***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.244***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-3.332)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.557)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-1.498)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-4.248)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-5.282)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-4.678)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.215**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.739)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e330\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR-squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of Regional ID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eRobust t-statistics in parentheses\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eSource: STATA 18.0 Results, 2025\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe use of robust standard errors addresses potential heteroskedasticity in the data, is crucial for obtaining valid inference, particularly when working with real-world economic data, which often violates assumptions of homoscedasticity. The robust t-statistics provided in parentheses indicate that the coefficients\u0026apos; significance remains strong even after correcting for heteroskedasticity. And also the use of quantile regression allows for a more detailed understanding of how the independent variables affect LNGDP across different points of the distribution (10th, 25th, 50th, 75th, and 90th quantiles).This approach is useful in capturing the heterogeneity in the data, as it accounts for varying effects at different levels of GDP. Both robust and quantile model could be enhanced by checking for multi-col-linearity and considering additional variables that might affect economic growth. Fiscal decentralization refers to the transfer of fiscal responsibilities and revenue-raising powers from central to local governments, with the aim of improving public service delivery, local accountability, and regional development. The model here includes Central/Decentralization as a key variable, which represents the governance structure\u0026rsquo;s impact on LNGDP. Decentralization (increasing local autonomy over fiscal policies) may provide room for more targeted public investments, localized tax policies, and better alignment between government spending and local needs. In a fiscal decentralization context, the model might suggest for local governments have the capacity for efficient public service delivery, decentralization could have a positive impact on economic growth.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\n \u003ch2\u003e4.6 Findings and Interpretation of the Regression Model\u003c/h2\u003e\n \u003cp\u003eBased on the following table, natural logarithm of expenditure, central/decentralization, natural logarithm total population and natural logarithm of foreign direct investment have statistically positive relationship with natural logarithm of economic growth in Myanmar in different regional states. On the other hand, LNNE and LNLB have statistically negative relationship with economic growth in Myanmar region. With regard to goodness of fit statistics, it is desirable to look into R-squared value, a measure of how well the regression model actually fits the data. In other words, R-squared is desirable to have an answer to the question, \u0026lsquo;how well does the model containing the explanatory variables that was proposed actually explain variations in the dependent variables (Brooks, 2008). The estimated result of fixed effect model robust is at a fairly satisfactory level where the R-squared value indicates that the model explains 98.6% of the variance in LNGDP, which is a very high value. Therefore, the validity of the model in the context of fiscal decentralization is strong, as it aligns with the theoretical understanding that both centralized and decentralized fiscal policies can contribute to economic growth, depending on the development level and institutional capacity of the regions being analyzed.\u003c/p\u003e\n \u003cp\u003eGenerally, these independent variables together are good explanatory variables of the LNGDP of regional state in Myanmar. Though this, F-statistics which was used to measure the overall test of significance of the model was presented, and null hypothesis can be clearly rejected in both of the regression models. Since the p-value is 0.000000, which is sufficiently lower, the model is well fitted at 1 percent level of significance.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u003cbr\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\u003c/table\u003eTable 4.5 Estimate Regression results of Fixed Effect Robust Model\n \u003c/div\u003e\n \u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003e\u003cstrong\u003eInterpretation of Fixed-Effects Robust Model Result\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003eBased on the results of the fixed-effects regression using robust standard errors, here\u0026rsquo;s a detailed interpretation of the coefficient estimates for each variable: LNEXP (Log of Expenditure): A 1% increase in expenditure (LNEXP) is associated with an increase of approximately 0.3929% in (LNGDP), holding all other factors constant and this coefficient is statistically significant at the 5% level (p\u0026thinsp;=\u0026thinsp;0.022), indicating that expenditure has a positive and statistically significant impact on GDP. This positive relationship between expenditure and GDP can be explained through several economic mechanisms: Public Investment in Infrastructure: Higher government expenditure is often directed towards investments in infrastructure, education, healthcare, and technology, which can lead to higher productivity and, subsequently, increased GDP. CD (Centralization/Decentralization): A unit increase in centralization/decentralization (CD) is associated with an increase of 5.1242 in (LNGDP). This suggests that regions with a higher level of decentralization or centralization have significantly higher GDP, likely due to better governance or decision-making. The coefficient is highly statistically significant at the 1% level (p\u0026thinsp;=\u0026thinsp;0.000), indicating a very strong and positive relationship between centralization/decentralization and GDP. Centralization/decentralization impacts multiple dimensions of governance, including fiscal policy, public service delivery, regulatory frameworks, and political stability. LNPOP (Log of Population): A 1% increase in population (LNPOP) is associated with a 4.4197% increase in GDP (LNGDP), holding all other factors constant. This suggests that larger populations may contribute to higher economic outputs, possibly due to a larger labor force or more consumer demand. LNFDI (Log of Foreign Direct Investment): A 1% increase in foreign direct investment (LNFDI) is associated with an increase of 0.0763% in GDP (LNGDP). This suggests that higher foreign investment has a positive impact on economic growth, likely through increased capital, technology transfer, or market access. The coefficient is statistically significant at the 5% level (p\u0026thinsp;=\u0026thinsp;0.030), indicating that FDI contributes positively to economic growth. FDI brings in capital from foreign investors, which can be used to fund domestic projects, infrastructure, and businesses. LNNE (Log of Net Exports): A 1% increase in net exports (LNNE) is associated with a -0.03495% decrease in GDP (LNGDP), holding all other factors constant. This suggests that higher net exports may not be positively correlated with GDP in this model, potentially due to issues such as trade deficits, inefficient trade policies, or over-reliance on certain sectors. The coefficient is statistically significant at the 1% level (p\u0026thinsp;=\u0026thinsp;0.000), indicating a negative but statistically reliable relationship between net exports and GDP. LNLB (Log of Labor): A 1% increase in labor (LNLB) is associated with a -4.3298% decrease in GDP (LNGDP), holding all other factors constant. This negative relationship may indicate inefficiencies in the labor market or labor force participation in sectors that are not conducive to high economic growth. The coefficient is statistically significant at the 1% level (p\u0026thinsp;=\u0026thinsp;0.005), suggesting that labor (in its current form) has a negative impact on GDP, possibly due to factors such as low productivity or poor labor market conditions. Generally the results suggest that policies fostering investment in expenditure, centralization/decentralization, and foreign direct investment, while optimizing labor utilization, may lead to higher GDP growth.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study examines how government expenditures and fiscal decentralization impact on local government economic growth in Myanmar using panel data spanning the years 2000 up to 2021. The study analyzed and interpreted the impact of government expenditures and fiscal decentralization on local government economic growth in Myanmar using secondary data. In order to confirm the validity of the results regarding the fixed effects, the study utilized a robust fixed effect approach and quintile regression.\u0026nbsp;The study's conclusions were drawn from the data analysis. The findings from the descriptive statistics and correlation analysis provide valuable insights into the relationships between various economic variables in the study. The descriptive statistics show significant variability in the data, with considerable variation in key economic indicators like GDP, FDI, expenditures, net export, and labor force across different fifteen regions and twenty years. These variations reflect the diverse economic conditions and development stages across the observed regions. The logarithmic transformation of variables like GDP, FDI, and Expenditures suggests that these data period several orders of magnitude, indicating disparities in economic performance and development. The results of correlation suggest that GDP and FDI has correlation together and ,also between GDP and government expenditure is pointing to the importance of correlation with other factors like political priorities, fiscal policies, and public spending needs in determining government expenditures. In this study, various model diagnostic tests were performed to ensure the robustness and reliability of the regression analysis examining the relationship between different factors (such as expenditures, FDI, population, etc.) and LNGDP (economic growth). The robustness check performed using different model specifications (fixed effect robust and quantile regression models) affirmed the stability of the findings across various model forms and quantiles. The positive impact of expenditure (LNEXP) and centralization (CD) on economic growth, remained significant across different quantiles (10th, 25th, 50th, 75th, and 90th). The robust regression and quantile regression results confirmed the validity and reliability of the model, showing that the relationships hold true across different model specifications. The inclusion of fiscal decentralization (represented by Central/Decentralization, CD) suggests that decentralizing fiscal responsibilities to local governments could have a positive impact on economic growth.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe findings from the fixed-effects regression model with robust standard errors suggest that certain factors have a statistically significant positive relationship with economic growth, while others negative relationship. Government Expenditure (LNEXP) has a positive and statistically significant impact on economic growth. Increased government spending in infrastructure, education, and technology can stimulate productivity and demand, driving GDP growth. Centralization/Decentralization (CD) shows a positive relationship with GDP. Regions with more centralized or decentralized governance structures tend to experience better economic outcomes, with decentralization offering potential advantages in terms of bespoke policies that meet local needs. Population size (LNPOP) is positively associated with economic growth. A larger population provides a larger labor force and greater consumer demand, both of which contribute to higher economic outputs. Foreign Direct Investment (LNFDI) also demonstrates a positive impact on economic growth, as it brings capital, technology transfer, and market access that can boost productivity and economic expansion. Net Exports (LNNE), however, presents a negative relationship with GDP growth. This suggests that Myanmar's trade policies or structural inefficiencies in the export sector may need further review, as over-reliance on exports or trade deficits could hinder growth. Labor (LNLB) shows a negative impact on GDP. Overall, the findings provide strong evidence that well-targeted fiscal policies, decentralization of governance, and investments in human capital and infrastructure are essential for Myanmar to realize its full economic potential. Future research and policy focus should aim to refine these strategies, ensuring they are adaptable to the changing economic landscape and the diverse needs of Myanmar’s regions. The results of this study indicate that public expenditure (LNEXP) and foreign direct investment (FDI) have a significant positive effect on economic growth, as measured by LNGDP. Additionally, centralization (CD) and the appropriate application of fiscal decentralization contribute positively to economic growth by improving governance and local accountability and the study provides strong evidence for the role of targeted public investments, sound fiscal management, and decentralization in promoting sustainable economic growth in Myanmar.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclaration Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e: Myanmar Planning Budget and Finance\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAkmil, I., \u0026amp; Alpon, S. (2022). The effect of financial development on economic growth in high-income countries, Asian Economic and Financial Review.\u003c/li\u003e\n\u003cli\u003eArshad, Muhammad. (2010). Fiscal Decentralization and Economic Growth in Pakistan: An ARDL Approach, KDI School of Public Policy and Management Google Search.\u003c/li\u003e\n\u003cli\u003eBalaj, \u0026amp; Lani. (2017). The impact of public expenditure on economic growth of Kosovo. Acta Universitatis Danubius. conomica, 13(5). https://journals.univ-danubius.ro/index.php/oeconomica/article/view/4443/4254\u003c/li\u003e\n\u003cli\u003eBarlas. (2020). The impact of government expenditure on economic growth in Afghanistan. Journal of Economics and Business, 3(2). https://doi.org/10.31014/aior.1992.03.02.234\u003c/li\u003e\n\u003cli\u003eDick-Sagoe, C. (2020). Decentralization for improving the provision of public services in developing countries: A critical review. Cogent Economics and Finance, 8(1). https://doi.org/10.1080/23322039.2020.1804036\u003c/li\u003e\n\u003cli\u003eFarooq, et al. (2023). Public debt and environment degradation in OIC countries: The moderating role of institutional quality. Environmental Science and Pollution Research, 30(19), 55354-55371. https://doi.org/10.1007/s11356-023-26061-x\u003c/li\u003e\n\u003cli\u003eKwasi, P., et al. (2022). The influence of government expenditure on economic growth in Ghana: An ARDL approach, Cogent Economics \u0026amp; Finance, 10:1, 2160036, DOI: 10.1080/23322039.2022.2160036.\u003c/li\u003e\n\u003cli\u003eMartinez-Vazquez, et al. (2017). The impact of fiscal decentralization: A survey. Journal of Economic Surveys.\u003c/li\u003e\n\u003cli\u003eMishra, \u0026amp; Mohanty. (2021). Nexus between government expenditure and economic growth: Evidence from sub-national governments in India. Journal of Developing Areas, 55(2). https://doi.org/10.1353/jda.2021.0045\u003c/li\u003e\n\u003cli\u003eMouss\u0026eacute;, S., et al. (2018). Fiscal decentralisation and the efficiency of public service delivery, Fiscal Decentralization and Inclusive Growth \u0026copy; OECD, KIPF.\u003c/li\u003e\n\u003cli\u003eMusgrave, R. (1959). The theory of public finance. N.Y: McGraw-Hill.\u003c/li\u003e\n\u003cli\u003eOates, W. E. (1969). The effects of property taxes and local public spending on property values: An empirical study of tax capitalization and the Tiebout hypothesis. Journal of Political Economy, 77(6), 957-971. https://doi.org/10.1086/259584\u003c/li\u003e\n\u003cli\u003eOkoye, et al. (2019). Government expenditure and economic growth: The case of Nigeria. Proceedings of SOCIOINT, 1184-1194.\u003c/li\u003e\n\u003cli\u003ePaul Minoletti. (2016). Fiscal decentralisation and national reconciliation in Myanmar, Key issues and avenues for reform, International Growth Center.\u003c/li\u003e\n\u003cli\u003eRodr\u0026iacute;guez-Pose, Andr\u0026eacute;s. (2008). Decentralization and Local and Regional Development, CAF Documento de trabajo, CAF Working paper.\u003c/li\u003e\n\u003cli\u003eRodriguez-Pose, et al. (2011). Is fiscal decentralization harmful to economic growth? Evidence from the OECD countries. Journal of Economic Geography, 11(4), 619-643. https://doi.org/10.1093/jeg/lbq025\u003c/li\u003e\n\u003cli\u003eRoy W. Bahl \u0026amp; Johannes F. Linn. (1994). Fiscal Decentralization and Intergovernmental Transfers in Less Developed Countries. http://www.jstor.org/stable/3330701\u003c/li\u003e\n\u003cli\u003eSuperianik. (2019). Analysis of The Impact of Fiscal Decentralization on Economic Growth in Indonesia.\u003c/li\u003e\n\u003cli\u003eTri Efriandi. (2021). Decentralization and the challenges of local governance in Indonesia, Four case studies on public service provision and democratization in Papua and West Papua, University of Groningen.\u003c/li\u003e\n\u003cli\u003eVtyurina. (2020). Effectiveness and Equity in Social Spending: The Case of Spain, IMF Working Paper WP/20/16.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"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":"Economic Growth, Expenditures, Fiscal Decentralization, regional state and Myanmar","lastPublishedDoi":"10.21203/rs.3.rs-5844647/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5844647/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study's primary goal is to understand how government expenditure affects economic growth in Myanmar from the standpoint of fiscal decentralization and local governments' financial capability in the country. Also, by using 15 Myanmar regional state panel data and an econometric model for the years 2000–2021. The study commenced with the application of a fixed effects model, followed by the implementation of quantile regression to validate the robustness of the findings. Data was collected utilizing secondary sources from the government's Ministry of Planning and Finance as well as the Budget Department. The results of the analysis demonstrate that financial decentralization, government spending, centralization and decentralization, foreign direct investment, and the overall population all significantly and favorably affect economic growth in Myanmar, whereas labor and net exports have the negative effect. Government spending and fiscal decentralization, however, interact to produce economic growth in a statistically meaningful beneficial way. Recommendations, government spending, and the decentralization for economic growth significantly contribute to formulating measures for poverty alleviation. The region offers assistance to low-income or employed people without income access, and it has demonstrated efficacy as a significant strategy for poverty reduction in Myanmar regional state. Finally, the government can benefit from regional expenditures and fiscal decentralization to enhance competitiveness in development by elevating knowledge of regional supervisory capabilities beyond mere certifications, thus fostering regional autonomy and transforming national economies.\u003c/p\u003e","manuscriptTitle":"The Effect of Government Expenditure on Economic Growth in Myanmar: a Fiscal Decentralization Perspective","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-20 08:09:43","doi":"10.21203/rs.3.rs-5844647/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":"fd6664a1-9be3-4edd-9cec-b721d4deafbe","owner":[],"postedDate":"January 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":43106950,"name":"Public Administration"},{"id":43106951,"name":"Development Economics"},{"id":43106952,"name":"Finance"}],"tags":[],"updatedAt":"2025-02-11T02:19:20+00:00","versionOfRecord":{"articleIdentity":"rs-5844647","link":"https://doi.org/10.33140/JERR.05.01.02","journal":{"identity":"journal-of-economic-research-and-reviews","isVorOnly":true,"title":"Journal of Economic Research \u0026 Reviews"},"publishedOn":"2025-01-24 00:00:00","publishedOnDateReadable":"January 24th, 2025"},"versionCreatedAt":"2025-01-20 08:09:43","video":"","vorDoi":"10.33140/JERR.05.01.02","vorDoiUrl":"https://doi.org/10.33140/JERR.05.01.02","workflowStages":[]},"version":"v1","identity":"rs-5844647","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5844647","identity":"rs-5844647","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}