FOREIGN DIRECT INVESTMENT (FDI) AND ECONOMIC DEVELOPMENT: COMPARATIVE ANALYSIS OF SOUTH ASIAN ECONOMIES

Copyright © ISRG Publishers. All rights Reserved. DOI: 10.5281/zenodo.17569250 1 ISRG PUBLISHERS Abbreviated Key Title: Isrg J Econ Bus Manag ISSN: 2584-0916 (Online) Journal homepage: https://isrgpublishers.com/isrgjebm/ Volume – III Issue - VI (November-December) 2025 Frequency: Bimonthly FOREIGN DIRECT INVESTMENT (FDI) AND ECONOMIC DEVELOPMENT: COMPARATIVE ANALYSIS OF SOUTH ASIAN ECONOMIES Dr, Priti Kumari Department of Economics Patna Women's College Patna University, Patna | Received: 01.11.2025 | Accepted: 04.11.2025 | Published: 10.11.2025 *Corresponding author: Dr, Priti Kumari Department of Economics Patna Women's College Patna University, Patna Abstract This research examines the effect of foreign direct investment (FDI) on economic growth by following the South Asian countries from 2006 to 2022 and using a large panel dataset and strong econometric methods. The inquiry begins with the use of a method called Feasible Generalized Least Squares (FGLS). To ensure its reliability, it employs the Fix Effects with Driscoll-Kraay standard errors, Two-Stage Least Squares (2SLS), and the Common Correlated Effects Mean Group (CCEMG) estimator. There is little to no correlation between economic growth and the human capital index (HCI), trade openness (TO), or inflation (I). Conversely, there is a strong positive correlation between economic progress and FDI and GCF respectively. Given the inverse relationship between growth and government spending (GS), it would seem that taxpayer dollars are going unused. According to the findings of the study, increasing absorption capacity may be accomplished by performing activities such as investing in education, enhancing the quality of institutions, and facilitating commerce. Several policy recommendations have been made, including the following: to encourage public-private partnerships in technical and vocational education; to improve the investment climate through the use of digital platforms and infrastructure upgrades; to promote regional integration through the implementation of standardized investment regulations and cross-border special economic zones; and to promote regional integration. This information may be used by policymakers in South Asian countries in order to provide direction for their attempts to entice foreign direct investment (FDI) for the purpose of fostering long-term economic development. Keywords: Foreign Direct Investment (FDI), Economic Development, South Asian Economies, Gross Capital Formation (GCF), Human Capital Index (HCI) Copyright © ISRG Publishers. All rights Reserved. DOI: 10.5281/zenodo.17569250 2 INTRODUCTION This introduction will discuss the significance of foreign direct investment (FDI) in South Asia, the elements that influence its effectiveness, the difficulties caused by FDI flows, and the policy implications stemming from these issues. Due to the fact that South Asia is characterized by its varied economies and differing degrees of development, foreign direct investment (FDI) plays a complex role in the area, having an influence on growth, employment, and technical innovations [1]. Significance of FDI in South Asia The foreign direct investment (FDI) is a vital external capital source that may be used to increase the amount of savings and investments made locally. In addition to being essential for the economic growth of South Asian countries, it enables the transfer of managerial skills, technological advancements, and access to international markets. More and more money is flowing into nations like India, Bangladesh, and Sri Lanka as a result of foreign direct investment (FDI). This is mainly because these nations provide better business infrastructure, cheap labor, and huge consumer markets. Factors Influencing FDI Effectiveness Foreign direct investment (FDI) affects regional economic growth in several ways. Its efficacy is dependent on many variables:  Institutional Quality: By minimizing corruption and guaranteeing effective distribution of resources, strong institutions boost the beneficial benefits of FDI [2].  Human Capital: In order to absorb and make good use of foreign investments, a competent staff is necessary.  Infrastructure: Foreign firms rely on adequate infrastructure to fulfill their operational demands and promote economic integration.  Policy Environment: Attracting and maintaining foreign direct investment (FDI) is heavily influenced by government policies, such as trade openness and investment incentives. Challenges in Harnessing FDI Foreign direct investment (FDI) has the potential to assist South Asia, yet there are obstacles to its full utilization:  Regulatory Hurdles: Inconsistent and complicated laws could discourage international investment.  Political Instability: The risks connected with investing abroad might be amplified by political concerns.  Infrastructure Deficiencies: Operating expenses and the region's appeal may both be negatively impacted by inadequate infrastructure [3].  Human Capital Gaps: The potential benefits of foreign direct investment (FDI) might be diminished if the workforce's skill set is inadequate to meet the demands of foreign investors. Policy Implications and Recommendations South Asian nations should think about these policy options to make the most of foreign direct investment:  Enhancing Institutional Frameworks: For more openness and less corruption, we need stronger regulatory and legal frameworks.  Investing in Education and Training: Meeting the needs of international investors requires investing in human capital via educational and occupational training programs.  Improving Infrastructure: Supporting economic activity via investing in energy, transportation, and communication infrastructure [4].  Streamlining Regulatory Processes: Making the regulatory landscape more investor-friendly via streamlining and standardizing policies.  Promoting Regional Cooperation: Developing a coherent and attractive investment area via cooperation with neighboring nations. There is a significant possibility that South Asia's economy might be boosted by foreign direct investment (FDI). However, in order to fully fulfil its potential, it would be necessary to make significant adjustments to existing laws, make investments in human resources, and establish robust institutions. It is possible for South Asian governments to achieve sustainable economic growth and increased investor interest if they take action to tackle existing difficulties and put wise policies into effect. OBJECTIVES 1. To learn more about the effects of FDI on economic development in South Asian nations. 2. To examine how different South Asian countries' FDI contributions to growth compare. RESEARCH METHODOLOGY Our analysis of the impact of FDI on GDP growth is based on econometric regression. Multiple independent variables are included in this panel data analysis of the South and Southeast Asian economies. Factors like TO and GS are part of this set of variables that also includes FDI, GCF, HCI, TO, GS, and I. Government spending and gross domestic product are two important factors to think about. Collectively, these elements are called "dependent variables." In this analysis, GDP per capita serves as the dependent variable. The researchers in this study drew both theoretical and practical inspiration for the control variables they used from an earlier study on the growth of FDI. Global capital flows (GCF) may support foreign direct investment (FDI) in infrastructure development and capital accumulation, according to the Solow growth model. The end effect is that it influences domestic investment decisions. However, HCI is a great indicator of an economy's potential to entice FDI and achieve the productivity gains that come with it [5, six]. This is true since HCI is a metric for evaluating the caliber of human capital. Human capital intelligence (HCI) is a crucial statistic for endogenous growth models to evaluate the qualitative characteristics of human capital. It is possible that a country's level of global market integration affects the quantity and effectiveness of foreign direct investment (FDI). Because of this, you really can't afford to ignore this detail. How much money the government puts into GS depends on how much money the government spends and the fiscal policies it Copyright © ISRG Publishers. All rights Reserved. DOI: 10.5281/zenodo.17569250 3 passes on to private enterprises and international investors. The inflation rate is a strategy for maintaining a stable macroeconomic environment; it affects investment confidence and the likelihood of future growth. To illustrate the point, inflation is used as a control case. Between 2006 and 2022, researchers looked at data from a variety of sources to determine how FDI affected economic development. These included the World Bank's World Development Indicators, the UNCTAD's human capital index (2025), and Our World in Data's government expenditure index [7,8]. The World Bank's meta-data was used to create indices that predict that in 2025, the net change in inventory levels and spending on adding to the economy's fixed assets will make up gross capital formation. The ratio of a country's total imports and exports to its GDP provides a measure of its trade openness. To get the total amount of capital that is brought into an economy from outside the nation, we divide it by the gross domestic product (GDP). This is called foreign direct investment (FDI). In economic parlance, "government spending" is outlays that are expressed as a proportion of GDP. The term "government spending" is used to describe the overall amount that the government sets aside to buy goods and services for the public. Included are costs and fees related to interest that have already been covered by the government. Many factors are taken into account by the United Nations Conference on Trade and Development (UNCTAD) when working on the human capital index for 2025. Factors such as national health, education levels, skill sets, research funding, and personnel are included in this category. To make sure the index is reflecting human potential correctly, this step is taken. In order to show how much money the government spends on education, a percentage of GDP is employed. There are three main parts to a budget: current, capital, and transfers. Spending in this category includes all government expenditures that are made possible by foreign financial aid. When referring to many tiers of government, such as the federal, provincial, and state ones, the term "general governments" is often used. The gross domestic product per capita is used as the dependent variable in this study, while the independent factors that are thought to affect GDP growth are chosen with care. As part of this selection process, we look at both existing data and literature that is relevant to the time period under consideration. Data used in this inquiry are quantitative in nature and are often called secondary data in certain jurisdictions. The focus of the study is on eleven countries in South and Southeast Asia. This group includes the following nations: Vietnam, the Philippines, India, Indonesia, Malaysia, Pakistan, Cambodia, Bhutan, and Bangladesh. In addition, Bangladesh and Bhutan are there. When choosing which countries to include in the sample, we mostly looked at how easily we could get our hands on the data and how consistent it was across. It was not possible to extend the data beyond 2022 due to the data being inadequate for the critical components.. Model Specification 𝐺𝐷𝑃𝐶𝑖𝑡=𝛼+𝛽1𝐹𝐷𝐼𝑖𝑡+𝛽2𝐻𝐶𝐼𝑖𝑡+ 𝛽3𝐺𝐶𝐹𝑖𝑡+𝛽4𝐼𝑖𝑡+𝛽5𝑇𝑂𝑖𝑡+𝛽6𝐺𝑆𝑖𝑡 +𝜀𝑖𝑡, 𝑖=1,…,7; 𝑡=2006,…,2022 where GDPCit Growth index for GDP per capita (annual%) for nation i at period t. The specification model's right side contains the following definitions of all the independent variables: FDIit cash flows into nation i as a proportion of GDP at time t. HCIit Index of human capital for nation i at period t. GCFit is the yearly gross capital formation index as a percentage of GDP for nation I at time t. The consumer price inflation index for nation I at time t is expressed as a percentage. Trade openness as a percentage of GDP for nation I at time t is denoted by TOit. GSit is the country's government spending index, represented as a percentage of GDP at a certain moment. εit Idiosyncratic term. Estimation Strategy The purpose of this research was to test the hypothesis that FDI contributes to economic development using the Feasible Generalized Least Squares (FGLS) estimator. This estimator finds widespread use in macroeconomic research, particularly in studies concerning economic growth and FDI, due to its capacity to manage heteroskedasticity and autocorrelation in panel data [10]. And because of the FGLS model's temporal fixed effects, all countries may feel the consequences of unobserved time-specific shocks at the same time [11]. The inclusion of temporal fixed effects in the model makes this possible. A change in policy or a trend in the economy of a certain area can be one of these surprises. One may say that these two things have taken place. By providing more precise and efficient estimations, FGLS offers a more trustworthy alternative [12]. Conventional models, such fixed-effects or random-effects, may not be the best fit for studies that examine the increase in foreign direct investment (FDI). For this reason, this remains true. The FGLS model is wellsuited to studying the dynamic interactions that take place over time in a diverse area like South and Southeast Asia because it excels at handling changes in both cross-sectional and time-series data. The rationale for this is because incorporating both kinds of modifications into the model is theoretically possible. Because of this, it is a fantastic tool for studies of this nature. In time, macroeconomic panels that include a large number of countries often exhibit heteroskedastic and connected disturbances; this approach is consistent with their evolution. You may find these disruptions in all sorts of places. According to diagnostic tests described further on, economic shocks or policy interventions in one nation in the area might affect other countries in the region at the same time. There is statistical evidence of cross-sectional dependence (CSD) based on these tests. Traditional panel estimators are very reliant on the assumption of cross-sectional independence, which is compromised by this reliance. This dependency is a significant factor in determining the validity of these estimators. In spite of the fact that FGLS with time-fixed effects could be able to mitigate the influence of unobserved heterogeneity to a certain degree, it will not be sufficient to address the issue of considerable cross-sectional dependence independently. since of this, the validity of statistical inference is called into question since it is feasible for skewed Copyright © ISRG Publishers. All rights Reserved. DOI: 10.5281/zenodo.17569250 4 standard errors and inefficient coefficient estimates to occur in this environment. In order to circumvent this limitation, a fixed-effects model that makes use of Driscoll-Kraay (FE-DK) standard errors is used in combination with the FGLS estimate [13]. Because it is resistant to serial correlation, cross-sectional dependence, and heteroskedasticity, the FE-DK estimator performs very well when applied to macro-panel datasets that include shared shocks and regional spillovers. Additionally, according to the data provided by simulations [14], FE-DK standard errors function reliably in samples ranging from very small to fairly big, as well as in panels that are unbalanced. The empirical findings are enhanced, and concerns about model misspecification due to cross-sectional dependence are lessened, as a consequence of the fact that the results are consistent across both estimating approaches. For the purpose of addressing slope heterogeneity in situations where CSD is present, the common correlated effects mean group (CCEMG) model is used as an extra comprehensive exercise [15]. The strict condition of slope homogeneity is loosened up significantly by the CCEMG estimator, which allows for different slope coefficients to be used to different cross-sectional units. Despite this, it is still able to provide consistent average effect estimates [16]. It is particularly vital to have this methodological flexibility in cross-country growth regressions since different nations may have varied responses to explanatory variables such as foreign direct investment (FDI) owing to the structural, institutional, and economic heterogeneity that exists across such countries. The CCEMG paradigm allows for such variability, which results in an improvement in the empirical resilience of growth models in a variety of economic scenarios [17]. Two-Stage Least Squares (2SLS) is a model that is used in order to take into consideration the potential of endogeneity, which might occur as a consequence of missing variables or reverse causation. This means that when economic growth is positive, foreign direct investment (FDI) is positive. The 2SLS estimator is a well-known instrumental variable (IV) technique. It is characterized by the use of endogenous regressors that are associated with the structural error term in order to accomplish the task of providing consistent parameter estimates. The Gauss-Markov assumptions are the foundation upon which the consistency of ordinary least squares (OLS) estimators is determined. On the other hand, endogeneity may be brought about by a variety of factors, including measurement error, simultaneity, or bias when variables are removed from the analysis. Due to the fact that OLS generates biased and inconsistent findings when confronted with such econometric challenges, it is necessary to use IV techniques such as 2SLS. In the next part, we will examine the results that were obtained from the research. RESULT After doing a descriptive analysis of the data in this section, the next step is to proceed with the discussion and the findings of the regression analysis. The publication of relevant descriptive and preliminary analyses necessitates the use of a methodical approach in order to avoid producing an inaccurate portrayal of the findings of the study. We may be able to classify and compress the data by using these summary statistics, which will make it simpler to recognize patterns in the data that was initially collected. The use of descriptive statistics alone is not going to get you very far, but they are an absolute need for any data analyst who wants to see and comprehend their data. Table 1. Descriptive data. Variable Observation Mean Std. Dev. Min Max GDPC 170 3.854688 3.249526 −10.82261 14.76314 FDI 170 2.865923 2.650023 −0.8579895 11.15249 GCF 170 29.64181 10.49799 14.53469 69.44873 HCI 170 37.25706 8.271151 17.7 58.7 TO 170 86.16138 46.43332 24.70158 202.5771 I 170 5.419074 4.103842 −1.241718 24.09685 GS 170 22.18486 6.621323 9.626749 45.38908 There is a large range of variation in GDP growth rates between nations; but, on average, they are 3.85%. A maximum value of 14.76 indicates rapid growth in economies that are operating well, while a negative minimum value of 10.82 indicates significant economic contractions in countries that have experienced financial crises or the COVID-19 epidemic. An indication of a moderate dependency on foreign direct investment (FDI) is the fact that the average amount of FDI inflow is 2.87 percent of GDP from foreign sources. Investment in industrial hubs has a positive maximum and a negative minimum, indicating that capital flows in certain years (e.g., during political unrest) but not others (11.15%). There is no difference between the maximum and FDI earnings for industrial areas. FGLS, Driscoll-Kraay FE, 2SLS, and CCEMG results The results of the tests used to determine autocorrelation, crosssectional dependency, and heteroscedasticity will be given in the section that follows. After a thorough evaluation of this data, the FGLS model was determined to be the best option. We need to know the outcomes of the baseline survey to back up these claims. You can see that the amount of foreign direct investment (FDI) this country receives is positively correlated with its GDP from the data presented in Table 2 below. Many pieces of evidence point to this conclusion. The average annual growth rate of a country's gross domestic product (GDP) is 0.22 percentage points higher for every 1 percentage point rise in FDI relative to GDP. When foreign direct investment (FDI) increases annually, this correlation remains. The statistical significance of the link between GDPC and GCF demonstrates the presence of a positive relationship between the two variables. It has been shown that this partnership is advantageous. Gross domestic product will grow by 0.12% in tandem with a 1% growth in GCF. The data points to a positive relationship between GDPC and GS, although a statistically significant negative correlation is found between the two variables. Copyright © ISRG Publishers. All rights Reserved. DOI: 10.5281/zenodo.17569250 5 Because of its usefulness, this link's presence is beneficial. Decline in GDPC of 0.11 percentage points is associated with each 1% increase in GS. We (HCI and I) came to the conclusion that the findings were completely meaningless. We discovered effects that depended on the passage of time while conducting the experiment. Using FGLS, one can account for unexpected temporal shocks, such changes in policy or global financial crises, that could distort the outcomes. What makes this possible is the inclusion of timefixed components in the model. Utilizing supplementary regressions, the results of the basic FGLS model may be bolstered. We use the FE-DK estimator, the CCEMG, and the 2SLS methods to provide robustness. Using these regressions to verify the findings before moving on is crucial. Regression after regression led us to the same conclusion: FDI has a positive and statistically significant impact on the expansion of the regional economy. In contrast to the CCEMG model, which has a negligible effect on economic growth, the FE-DK model and the 2SLS approach both significantly affect economic development. Table 2. Results from ccemg, 2sls, driscoll-kraay fe, and fgls. FGLS FE-DK CCEMG 2SLS FDI 0.2193 *** (0.0787) 0.6478 *** (0.1452) 1.2479 * (0.6434) 1.413 ** (0.614) GCF 0.1180 *** (0.0208) 0.1535 (0.1535) 0.3342 *** (0.1060) 0.417 *** (0.158) HCI −0.0374 (0.0325) −0.0369 (0.0702) 0.3798 (0.3811) 0.205 (0.369) TO 0.0007 (0.0063) 0.0409 * (0.0211) 0.0868 (0.1097) 0.0803 (0.0642) I −0.005 (0.0491) −0.0612 (0.0755) −0.0132 (0.1522) −0.109 ** (0.0475) GS −0.1086 *** (0.0317) −0.3203 * (0.1554) −0.2888 (0.2176) −0.617 ** (0.240) Constant 4.2832 *** (1.131) 2.7350 (3.9862) −19.8629 (22.228) 0.0165 (0.280) Tests for diagnosis Table 3 summarizes the findings of the tests for heteroscedasticity, autocorrelation, and cross-sectional dependence; it will be highlighted in the following paragraphs. Because the regression model's error components significantly deviate from the assumption of constant variance, this is shown using the BreuschPagan/Cook-Weisberg heteroskedasticity test. This may be shown if the statistically significant outlier is located. The data do not support the null hypothesis of constant variance since the p-value is substantially lower than the customary significance threshold of 0.05. We may infer that the regression model is probably heteroskedastic from the test results. The fact that this is true proves that the error components differ significantly across different data sets. This means that estimates and standard errors produced by ordinary least squares (OLS) regression might be skewed. Table 3. Tests for residual diagnosis Test Null Hypothesis p-Value Heteroscedasticity No heteroscedasticity 0.0000 *** Autocorrelation No autocorrelation 0.2622 Cross-sectional dependence Cross sectional independence 0.0000 *** The model does not show indications of first-order autocorrelation, according to the findings of the Wooldridge test for panel data autocorrelation. The incompatibility of the null hypothesis with the data prevents its rejection even when the p-value exceeds the commonly accepted threshold of 0.05. It is because the p-value is greater than the previously set threshold that this result is obtained. Panel data regression model was found to be devoid of statistically significant first-order autocorrelation after all tests were run. The lack of autocorrelation is given more weight than it would otherwise be due to the fact that the error terms do not have any linkages together throughout time. This is due to the fact that there are no connections between the incorrect phrases. Whether you have error terms that appear in many cross-sectional units, such countries, you may use Pesaran's (2004) cross-sectional independence test in a data model panel [18] to find out whether they're independent. Unobserved shared shocks, regional factors, or global economic issues might be affecting all units at the same time, leading to cross-sectional dependency. Some potential causes of cross-sectional dependency are as follows. The results that follow from Pesaran's test confirm that the panel data's crosssectional units are highly dependent on each other. This conclusion was reached based on the examination's results. Without proper consideration of cross-sectional dependence, the econometric model runs the risk of producing skewed standard errors and inaccurate approximations. The results of the diagnostic tests provide credence to the previously offered FGLS model. Copyright © ISRG Publishers. All rights Reserved. DOI: 10.5281/zenodo.17569250 6 DISCUSSION South Asian countries have been significantly impacted by foreign direct investment (FDI), which highlights the need of implementing policies that have the potential to attract and keep FDI in order to stimulate economic growth. Both theoretical and empirical evidence lend credence to the notion that foreign direct investment (FDI) is beneficial to development since it involves the transfer of financial resources, technological advancements, and management expertise [19]. Increasing productivity via foreign direct investment (FDI) is consistent with endogenous growth theories, which emphasize the significance of human capital accumulation and knowledge spillovers [20]. One encouraging trend is the manner in which FDI is increasing productivity, which lines up with these opinions. Foreign direct investment (FDI) opens doors to global markets, managerial expertise, and technological advancement, all of which are vital to economic growth (Agrawal, 2015). The facts back up his assertions. Research by Iamsiraroj (2016) and Emako et al. (2022) [22,23] provides support for the idea that FDI contributes positively to economic development. A positive correlation between the two variables demonstrates this. The results of the Gross Capital Formation (GCF) study demonstrate the significance of domestic investment in financial systems that are located in South Asian countries. This is due to the fact that, in addition to foreign direct investment (FDI), increased capital formation contributes to the creation of greater infrastructure and increases the capacity for production. This is consistent with the findings of earlier studies conducted by Solow (1956) and others, such as Fatmawati et al. (2018), which emphasize the significance of both domestic and foreign investment in promoting economic growth, the proliferation of technological advancements, and the establishment of new employment opportunities [24,25]. The geographical relevance of GCF demonstrates that domestic savings and foreign investment are interdependent for the continuation of economic growth by demonstrating that they are interconnected. The government spending (GS) has a negative coefficient, which indicates that in certain South Asian countries, public monies are either not being spent effectively or are being channeled into activities that do not contribute to the nation's overall productivity. In line with the findings of Bhaskara-Rao and Hassan (2011) [26], it would seem that a greater GS is associated with a slower rate of economic progress. Infrastructure expenditure, education spending, and healthcare spending are three areas in which public spending has the potential to drive development. However, the effectiveness of this spending is determined by the content of this spending as well as its efficiency. In many South Asian nations, a significant amount of money is wasted due to government subsidies, recurrent costs, and inefficient state-owned enterprises. This may restrict the amount of money that is available for productive purposes and discourage private investment. These findings demonstrate that public expenditures need to be wisely targeted and of high institutional quality in order to foster growth over the long run. One of the possible causes for the Human Capital Index's (HCI) insignificant regional impact is that it is difficult to measure the index's effects. Another possible explanation is that there is a gap between educational accomplishments and work requirements. Endogenous development theory relies on human capital, but how that capital helps South Asian countries grow depends on how well educational systems perform. It is possible that the observed effects may not meet the criteria for statistical significance due to issues such as low absorptive capacity and skills mismatches. These constraints restrict the productivity improvements that can be achieved by investments in human capital. Additionally, trade openness (TO) has a little direct impact on economic development in South Asian countries. Increasing efficiency and attracting foreign direct investment (FDI) are two potential outcomes that should result from liberalizing trade. In actuality, however, this could not be the case because of the diverse economic systems of different countries and the inadequate development of infrastructure. It's possible that the absence of a link between inflation (I) and regional economic growth is due to the fact that different countries have different monetary policies and different macroeconomic situations. The robustness tests provide much more insight on the dynamics that are present in the region. Given that the Driscoll-Kraay fixed effects (FE-DK) model highlights a greater impact from foreign direct investment (FDI) and no influence from global capital flows (GCF), it seems that the benefits of domestic investment may be disguised by geographical correlations. While the CCEMG estimate confirms that foreign direct investment (FDI) is beneficial on average, it highlights the large cross-country heterogeneity that exists in South Asia. This variety is a reflection of differences in infrastructure, policy frameworks, and institutional quality. As the 2SLS technique demonstrates a negative association with government effectiveness (GE) and underlines the good benefits of foreign direct investment (FDI), it is possible that institutional inefficiencies may hinder the ability of public governance to stimulate growth. The importance of human capital is still not taken into account in any of the models, the function of trade openness is not very significant, and the effects of government spending are contingent on the specific conditions. These results provide insight on the ways in which the effectiveness of foreign direct investment (FDI) is impacted by characteristics such as the quality of governance, infrastructure, institutional capacity, and absorptive ability in South Asia, as well as the ways in which FDI benefits are contingent on these aspects. In order for regional policymakers to make the most of foreign direct investment (FDI) for development, they should strive to build institutional frameworks, distribute public monies in a prudent manner, and invest in human capital that is in line with market expectations. LIMITATIONS In spite of the robustness of the multi-method approach, which comprises FGLS, FE-DK, CCEMG, and 2SLS, there are still a few drawbacks that need to be addressed. Despite the fact that FE-DK is helpful, FGLS is not yet capable of completely resolving the issue of contemporaneous correlation. Estimates of slope heterogeneity could be affected when T equals 17, and the CCEMG estimator requires bigger time series in order to achieve their desired level of precision. In the event that the validity of the lag FDI instrument is not totally satisfied, it is probable that the conclusions of the 2SLS will not be wholly correct. Data restrictions also include the potential of measurement errors in institutional quality measures and the obfuscation of subnational variations owing to aggregation bias in data gathered at the national level. Also included in this category is the possibility of missing data. In light of this, it would be prudent to approach with caution when attempting to determine the amount to which foreign Copyright © ISRG Publishers. All rights Reserved. DOI: 10.5281/zenodo.17569250 7 direct investment (FDI) affects the economies of South Asian countries, even if the trend is obvious. CONCLUSION It has been shown via research that two of the most important variables that contribute to the expansion of the economy in South and Southeast Asia are Gross Capital Formation (GCF) and incoming Foreign Direct Investment (FDI). There is a possibility that government spending may hinder economic development owing to inefficient resource allocation. On the other hand, human capital, trade openness, and inflation all have the potential to have limited or context-dependent effects. According to the findings, it is of the utmost importance to work towards improving absorptive capacity via improved governance, targeted human capital development, and simpler trade and investment procedures. To attract more foreign direct investment (FDI), governments can adopt policies like performance-based budgeting, digital investment platforms, public-private partnerships in technical education, and regional integration initiatives like the ASEAN Comprehensive Investment Agreement (ACIA). Investment from outside, often known as foreign direct investment (FDI), continues to be an essential component in the process of fostering equitable and sustainable economic growth in South and Southeast Asia. For foreign direct investment (FDI) to be effective, however, there must be institutions of a high quality, public investments that are intelligent, and regulatory conditions that are enabling. REFERENCES 1. Chizema, D. (2025). The Impact of Foreign Direct Investment on Economic Development in South Asia and Southeastern Asia. Economies, 13(6), 157. https://doi.org/10.3390/economies13060157 2. Sapkota, S., & Gautam, D. R. (2023). Foreign Direct Investment and Economic Growth of South Asia. Lumbini Journal of Business and Economics, 11(2). https://www.nepjol.info/index.php/ljbe/article/download/ 64721/49083/189861 3. Rao, D. T., Sethi, N., Dash, P., & Bhujabal, P. (2019). Foreign Aid, FDI and Economic Growth in South-East Asia and South Asia. 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