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Institutional Quality and Macroeconomic Indicators on Foreign Direct Investment in Developing Asian Countries Serli Erlina Economic Development Department.
University of Lampung.
Indonesia *Email : serli.
erlina2009@gmail.
Nairobi Economic Development Department.
University of Lampung.
Indonesia Email : nairobi@feb.
Nurbetty Herlina Sitorus Economic Development Department.
University of Lampung.
Indonesia Email : nurbetty.
herlina@feb.
ARTICLE INFO :
Keywords :
Foreign Direct Investment.
Political Stability.
Corruption.
Gross Domestic Product.
Exchange Rate --------------------------Article History :
Received : 2024-05-21
Revised : 2024-07-01
Accepted : 2024-08-20
Online : 2024-09-05
ABSTRACT
This research endeavors to scrutinize the influence of institutional quality and macroeconomic factors on Foreign Direct Investment (FDI).
The institutional quality dimensions analyzed include political stability and corruption, while the macroeconomic variables encompass Gross Domestic Product (GDP) and exchange rates.
The study's sample comprises developing Asian nations over the period from 2012 to 2019.
Employing a quantitative methodology, the study leverages panel data analysis to derive its findings.
The results indicate that political stability and exchange rates do not exert a statistically significant effect on FDI.
In contrast, corruption is found to have a negative and significant association with FDI, whereas GDP shows a positive and significant Based on these outcomes, it is advised that developing countries in Asia should prioritize the enhancement of institutional quality, particularly by mitigating corruption, as it poses a substantial impediment to FDI inflows.
Future research is encouraged to refine the model by incorporating additional variables and employing dynamic panel data analysis to facilitate a more comprehensive exploration of the evolving interrelations among variables over time.
INTRODUCTION
Foreign Direct Investment (FDI) is crucial for fostering economic growth, particularly in developing countries, as it not only brings in capital but also facilitates the transfer of knowledge and technology, thereby improving local workforce skills and management practices (Awaluddin et al.
, 2.
The inflow of foreign capital plays a vital role in transferring critical resources, including advanced technology and managerial expertise, from developed to developing nations (Nairobi et al.
, 2.
In the context of intense global competition for FDI.
Asia has emerged as the foremost recipient of FDI inflows, securing $512 billion in 2018, with nearly half of this investment originating from within the region itself (UNCTAD, 2019.
Nguyen et al.
Nevertheless, there are considerable disparities in FDI levels between developed and developing countries, with the latter facing greater difficulties in attracting and sustaining investment due to various obstacles, including institutional deficiencies such as corruption (Lestari et al.
, 2.
Global FDI flows in 2016 totaled $2.
44 trillion, predominantly benefiting high-income countries (HIC.
, which received $1.
86 trillion, while lower-income countries attracted only a fraction of this amount (Aprianto 348 Page This is an open access article under the CC BY- SA license Corresponding Author : Serli Erlina DOI:10.
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& Asmara, 2.
Therefore, understanding the determinants of FDI is crucial for developing countries aiming to attract more investment.
Traditionally, economic factors like GDP growth, exchange rates, and interest rates have been emphasized as key drivers of FDI (Zuhroh & Harpiyansa, 2.
For example.
GDP, labor force size, trade openness, and tax policies significantly influence FDI (Ashurov et al.
, 2.
However, non-economic factors, particularly institutional quality, are also critical.
Research indicates that institutional aspects such as political stability and regulatory frameworks are significant determinants of FDI in developing countries (Emako et al.
, 2.
The Global Investment Competitiveness Survey (World Bank, 2.
underscores the importance of political stability, legal environments, and regulatory frameworks in shaping foreign investment decisions.
According to institutional theory, these factors influence transaction and production costs, which in turn affect economic activities (North, 1.
Investors prioritize political stability, as instability can increase internal costs and reduce profitability (Bailey, 2.
While some studies affirm a strong positive relationship between political stability and FDI (Meressa, 2022.
Marselina & Prasetyo, 2.
, others argue that political factors may not significantly disrupt FDI inflows (Nairobi & Afif, 2.
Corruption is another critical issue, particularly in developing economies, where it can distort markets, increase production costs, and deter investment (Khalid, 2024.
Bailey, 2.
The "Sand or Grease" hypothesis presents two views on corruption's impact on FDI: the "Sand" hypothesis suggests that corruption hinders FDI by increasing costs, while the "Grease" hypothesis posits that corruption can expedite business processes and attract FDI (Belloumi & Alshehry, 2021.
Shaari et al.
, 2.
Research has shown that lower corruption levels generally attract more investment (Mucha & Fetai, 2.
, though some studies argue the opposite, suggesting that corruption might, in certain contexts, facilitate FDI (Onody et al.
, 2.
Market size, often measured by GDP, is another factor influencing FDI.
While many studies find a positive relationship between GDP and FDI (Aprianto & Asmara, 2018.
Korsah et al.
, 2022.
Hakim, 2.
, others report no significant impact (Davis & Akbar, 2022.
Chandra & Handoyo, 2.
Given the mixed results in the literature, this study aims to provide additional insights, particularly on the role of institutional quality in influencing FDI in developing Asian countries.
LITERATURE RESEARCH
Investment Theory According to classical economic theory, investment levels are predominantly determined by two critical factors: income levels and interest rates (Putong, 2.
Investment is conceptualized as a function of the ratio between profit and interest rates, alongside the rate of depreciation.
The terms "user cost" or "rental cost" of capital are used to describe interest rates and depreciation.
Thus, the level of investment is contingent upon the profit-to-user cost ratio.
Higher profitability correlates with increased investment levels, whereas elevated user costs are associated with reduced investment (Blanchard & Johnson, 2.
Keynes, however, argues that investment is also shaped by expectations regarding future profitability.
Under the assumption of ceteris paribus, the volume of savings influences investment levels since savings represent funds that are not allocated for consumption.
When national income is substantial, societal savings tend to increase, thereby fostering higher levels of investment.
Consequently, investment is indirectly related to income levels.
national income results in greater savings, which, assuming other factors remain unchanged, subsequently leads to increased investment.
Eclectic Paradigm Theory The eclectic paradigm, formulated by Dunning, offers a comprehensive explanation for Foreign Direct Investment (FDI).
According to Dunning and Lundan .
, the eclectic theory, also known as the OLI paradigm, delineates three critical factors that drive capital flows from one nation to another.
These factors include: Ownership Advantage, where firms engage in investment activities due to unique ownership benefits.
Location Advantage, which pertains to the operational efficiencies a firm can achieve by situating its business in a particular, favorable location.
and Internalization Advantage, where a firm invests to mitigate potential losses or to leverage natural resources.
Furthermore, the motives for investment are categorized as marketseeking, resource-seeking, and efficiency-seeking.
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Institutional Economics Theory North's institutional theory posits that Foreign Direct Investment (FDI) inflows are shaped by a combination of macroeconomic elements and both formal and informal institutional factors.
According to North, formal institutional factors are pivotal in bolstering investor confidence in economic transactions, thereby exerting a significant influence on a country's investment landscape.
Empirical research indicates that institutional variables, including government effectiveness, political stability, and levels of corruption, significantly affect FDI inflows.
As a result, it is imperative for nations to uphold high institutional quality to successfully attract FDI (Meressa, 2.
"Sand or Grease" Corruption Theory The economic implications of corruption are a subject of ongoing debate, with discourse focused on whether corruption is beneficial or detrimental to economic activities.
Aidt .
classifies the effects of corruption on economic growth into two contrasting viewpoints: the "grease the wheels" hypothesis and the "sand the wheels" hypothesis.
Advocates of the "grease the wheels" hypothesis contend that corruption can act as a facilitator for economic processes, potentially yielding positive outcomes.
Some economists argue that, when managed effectively, corruption can "ease the wheels of commerce.
" In contrast, the "sand the wheels" hypothesis asserts that corruption adversely affects the economy.
Aidt .
characterizes corruption as functioning like "sand in the wheels," impeding economic activities by imposing additional costs on businesses, heightening uncertainty, diminishing foreign investment, and deterring potential foreign engagement in joint ventures.
Conceptual Framework Political Stability (X.
Corruption (X.
GDP (X.
Exchange Rate (X.
Foreign Direct Investment (Y) Figure 1.
Conceptual Framework Source : Research Data .
METHOD Type of Data and Research Scope This research adopts a quantitative approach, employing a comprehensive analysis of secondary data to examine the relationships under investigation.
The study utilizes annual quantitative data spanning from 2012 to 2019, focusing on the impact of key variablesAipolitical stability, corruption.
Gross Domestic Product (GDP), and exchange ratesAion Foreign Direct Investment (FDI) within developing Asian nations.
The data employed in this research are sourced from reputable international databases, including those maintained by the World Bank and Transparency International.
By leveraging these authoritative sources, the study ensures the reliability and validity of the data, providing a robust foundation for analyzing the complex interactions between these economic and institutional factors and their influence on FDI flows in the region.
Population and Sample The sample utilized in this research consists of 14 developing nations across Asia, including Cambodia.
Lao PDR.
Myanmar, the Philippines.
Vietnam.
Afghanistan.
Bangladesh.
India.
Nepal.
Pakistan.
Sri Lanka, 350 Page This is an open access article under the CC BY- SA license Corresponding Author : Serli Erlina DOI:10.
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Tajikistan.
Jordan, and Lebanon.
The selection of these countries is strategically based on their classification as Lower Middle-Income Countries (LMIC) and Lower-Income Countries (LIC), as delineated by Sabir et al.
The criteria for selecting these specific nations are multifaceted, including their economic classification, availability of reliable data, and the prevailing political conditions within these developing Asian regions.
The inclusion of these countries in the sample is intended to provide a comprehensive representation of the diverse economic and institutional contexts that characterize LMICs and LICs in Asia.
This selection ensures that the study's findings are both relevant and applicable to the broader context of developing economies within the region, thereby enhancing the generalizability of the research outcomes.
Operational Definitions of Variables Foreign Direct Investment (FDI) net inflows represent the value of inward investments made by nonresident entities within the reporting economy.
This metric captures the financial resources flowing into a country as a result of foreign investors establishing or expanding their operations.
In this study, the measurement of FDI net inflows is conducted in US dollars, specifically utilizing the FDI net inflows (BoP, current US$) as reported by the World Bank for the year 2019.
This approach ensures that the data reflects the current market conditions and provides a standardized measure for comparing investment levels across different economies.
Additionally, political stability and the absence of violence or terrorism are quantified using percentile rankings.
These rankings position a country relative to others within the aggregate indicator framework, where a score of 0 denotes the lowest rank, indicating instability or high levels of violence, and a score of 100 represents the highest rank, indicating a stable and peaceful environment.
These metrics are crucial in understanding the socio-political factors that influence FDI decisions.
The study incorporates the Corruption Perception Index (CPI) as a measure of governance quality, specifically evaluating the conduct of public officials, including politicians and civil servants, who may engage in unlawful or unethical actions for personal benefit or to favor associates.
The CPI is rated on a scale from 0 to 100, where a lower score indicates a higher level of corruption, signifying a governance environment rife with malpractice, while a higher score reflects cleaner, more transparent governance.
Additionally.
Gross Domestic Product (GDP) represents the aggregate value added by all producers within an economy.
GDP can be calculated either at current prices, which uses the prices of the given year, or at constant prices, which uses prices from a designated base year (Ambya et al.
, 2.
In this analysis.
GDP is expressed in constant 2015 dollars, with values converted from domestic currency using the official exchange rate for 2015 (World Bank, 2.
This conversion is conducted using the official exchange rate set by national authorities, calculated as an annual average based on monthly averages, expressed in terms of domestic currency relative to the U.
This thorough methodology ensures the accuracy and applicability of the economic indicators employed in the study.
Analytical Model The study utilizes panel data analysis, encompassing a range of analytical frameworks, including the Common Effect Model, the Fixed Effect Model, and the Random Effect Model.
To ascertain the most suitable model for the analysis, the research employs several diagnostic tests.
These include the Chow test, which assesses the suitability of pooling versus separate effects, the Hausman test, which evaluates whether the Fixed Effect Model or Random Effect Model is more appropriate, and the Lagrange Multiplier test, which determines the necessity of the Random Effect Model.
Additionally, classical assumption testing is conducted to ensure that the underlying assumptions of the models are met, thereby validating the robustness and reliability of the selected analytical approach.
The research adopts an investment model structured to investigate the intricacies of investment behavior within the panel data framework.
This model aims to dissect and understand the factors influencing investment decisions by leveraging the aforementioned analytical tools and tests.
By integrating these methodologies, the study strives to provide a comprehensive and nuanced examination of the investment dynamics, ensuring that the chosen model accurately reflects the data and contributes to a rigorous analysis of the research hypotheses.
The research model adopted is an investment model that is structured as follows:
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It = I Investment is dependent on the ratio of profits to user costs (Blanchard & Johnson, 2.
In this study.
GDP represents the profit function or the returns expected by investors, while political stability, corruption, and exchange rates represent the costs incurred by investors and influence investment returns.
The general regression equation for this study is as follows:
FDI
CPI
GDP
: Foreign Direct Investment (US$).
: Political Stability (Index ) : Corruption Perception Index : Gross Domestic Product (US$) : Exchange Rate (US$).
: 1,2,.
n, shows the number of cross sections : 1,2,A t, shows time series.
: Constant : Coefficient : Error Term The analysis is conducted using Stata version 14.
In this research, natural logarithms are applied to reduce significant differences between large and small observations.
Additionally, converting data into natural logarithms aims to maintain a normal distribution (Septiantoro, 2.
Studies by Ridzuan et al.
and Bekhet & Al-Smadi .
suggest that natural logarithm transformation provides more accurate and efficient results compared to simple linear models.
Furthermore.
Asongu et al.
applied natural logarithm transformation across different variable units to facilitate comparison with other variables, which helps mitigate heteroscedasticity issues.
In this study, the CPI reverse measurement is used to simplify interpretation, where 0 represents no corruption and 100 represents extreme corruption (Ananta et al.
, 2.
RESULTS AND DISCUSSION
Descriptive Statistical Test Table1.
Descriptive Statistical Test Result
Obs
Mean
Min Max
FDI
CPI
GDP
Source: Data processed by Stata 14, 2024 Std.
Dev.
The data presented in the table illustrates that, on average, the Foreign Direct Investment (FDI) across 14 developing Asian countries over the period from 2012 to 2019 amounts to approximately 5.
2 billion US This average FDI figure is accompanied by a substantial range, with the highest recorded value 6 billion US dollars and the lowest value being 23.
4 million US dollars.
In terms of political stability, the mean value observed is 23.
59, with the political stability index fluctuating between a maximum of 63.
and a minimum of 0.
The Corruption Perception Index (CPI) averages at 29.
65, with its values spanning from a maximum of 53 to a minimum of 8.
The Gross Domestic Product (GDP) figures reveal an average of 257 billion US dollars, while the GDP ranges from a minimum of 6.
81 billion US dollars to a maximum of 2.
68 trillion US dollars.
Finally, the exchange rate (ER) averages 2,666.
8, exhibiting a broad range with a maximum value of 23,050.
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and a minimum of 0.
This comprehensive dataset provides a detailed overview of the economic and institutional variables affecting FDI within these developing Asian nations.
Classic Assumption Normality Test Tabel 2.
Normality Test Result
Skewness FDI
-0,2923
0,6888
CPI
-0,1456
GDP
0,6021
-0,1025
Source: Data processed by Stata 14, 2024 Kurtosis 2,9432 2,2400 3,3146 2,6715 2,6138 The results of the normality test, conducted using the Skewness-Kurtosis method, demonstrate that the Skewness values for each variable fall within the range of -2 to 2.
Additionally, the Kurtosis values are observed to be between -7 and 7 (Hair et al.
, 2.
These findings suggest that the data adhere to the assumptions of normal distribution.
Consequently, it can be inferred that the data utilized in this study exhibit a normal distribution pattern, aligning with the established criteria for statistical normality.
Multicollinearity Test Tabel 3.
Multicollinearity Test Result
VIF
2,45
CPI
2,66
GDP
2,03
2,47
VIF Average 2,45 Source: Data processed by Stata 14, 2024 The table reveals that the Variance Inflation Factor (VIF) values for each independent variable are below the threshold of 10.
This indicates that there is no significant multicollinearity issue present in the As a result, it can be concluded that the variables included in the model are not excessively correlated with each other, ensuring the reliability and validity of the regression analysis.
Heteroscedasticity and Autocorrelation Test with Robust According to Wooldridge .
, the presence of heteroskedasticity and autocorrelation in a regression model does not affect the unbiasedness and consistency of the regression parameters when using Ordinary Least Squares (OLS).
However, it does lead to biased standard errors, resulting in either underestimation or overestimation of variance, which in turn affects the validity of the t-tests and F-tests, leading to incorrect conclusions that do not reflect the true relationships.
Econometricians have developed methods to adjust standard errors, as well as t.
F, and LM statistics, to remain valid in the presence of heteroskedasticity of unknown form.
This approach is known as the heteroskedasticity-robust procedure.
Studies by Mahyudin .
and Ananta et al.
highlight the use of robust standard errors to address serial correlation and heteroskedasticity.
These robust standard errors can be easily incorporated into panel data estimations by appending the term "robust" to the STATA command, thereby mitigating the issues of heteroskedasticity and autocorrelation (Ahmad & Nayan, 2.
Panel Data Model Selection To determine the most appropriate method for panel data regression, three tests are conducted: the Chow Test, the Hausman Test, and the Lagrange Multiplier Test.
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Tabel 4.
Panel Data Model Selection Result Prob.
Conclusion Uji Chow
0,0000
FEM
Uji Hausman 0,0773 REM Uji Lagrange Multiplier 0,0000 REM Source: Data diolah Stata 14, 2024 The results of the model selection tests reveal that the Chow Test produces a p-value of 0.
which is significantly lower than the 5% significance level.
This result suggests a preference for the Fixed Effect Model (FEM) over the alternative models.
In contrast, both the Hausman Test and the Lagrange Multiplier Test advocate for the Random Effect Model (REM) as the most suitable framework.
Given that the REM is favored by both of these tests, it indicates a stronger alignment with the data characteristics and underlying assumptions compared to the FEM.
Therefore, considering the consensus from the Hausman and Lagrange Multiplier tests, the most appropriate model for this study is the Random Effect Model (REM).
REM Selected Model Regression Results Tabel 4.
Random Effect Model Regression Result Variable Coefficient Robust Std.
Error
t-Statistic 2,4716 5,1742 0,48 -0,0027 0,0052 -0,52 CPI
-0,0230
0,0106
-2,17
GDP
0,7853
0,1782
4,41
0,1330
0,0806
1,65
R-squared 0,6687 Prob(F-statisti.
0,0000 Source: Data processed by Stata 14, 2024 Prob.
0,633
0,602
0,030
0,000
0,099
The regression analysis reveals an R-squared value of 0.
6687, signifying that approximately 66% of the variation in Foreign Direct Investment (FDI) can be attributed to the independent variables within the model.
This leaves about 34% of the variability due to other factors not accounted for by the model.
The constant term .
, with a value of 2.
4716, implies that in the absence of any influence from the independent variablesAi Political Stability (PS).
Corruption Perception Index (CPI).
Gross Domestic Product (GDP), and Exchange Rate (ER)Aithe FDI in the 14 developing Asian nations would be roughly 2.
4716 in logarithmic terms.
When converted to a standard value, this equates to an FDI of approximately 11.
84 billion U.
The t-statistic analysis indicates that among the independent variables, the CPI has a negative and statistically significant relationship with FDI, while GDP positively and significantly influences FDI.
In contrast.
Political Stability (PS) and Exchange Rate (ER) do not have a statistically significant effect on FDI.
Furthermore, the F-test results show that the F-statistic is significant, with a p-value of 0.
0000 at the 5% significance level.
This finding confirms that at least one of the independent variables has a statistically significant effect on FDI, validating the model's overall explanatory power and its importance in identifying the determinants of FDI.
Discussion The Influence of Political Stability on Foreign Direct Investment This study concludes that political stability does not have a statistically significant effect on Foreign Direct Investment (FDI) in the context of 14 developing Asian countries.
This finding is consistent with the results reported by Drajat .
, who similarly observed that political stability does not influence FDI.
The implications of this result suggest that investors must evaluate a diverse range of factors before making investment decisions, with the differing behaviors and strategies of foreign investors emerging as significant 354 Page This is an open access article under the CC BY- SA license Corresponding Author : Serli Erlina DOI:10.
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internal determinants in these decision-making processes.
The minimal impact of political stability on FDI can be attributed to the varied methodologies employed by international investors when assessing political risk within a country.
In support of these conclusions.
Nairobi and Afif .
also found that political stability does not significantly affect FDI, suggesting that political disturbances or changes in governance do not necessarily impede the inflow of foreign investments.
Similarly.
Dewi et al.
reported that political stability had no discernible impact on FDI, positing that the limited duration of their study, spanning only eight years, might not have been sufficient to capture its potential effects.
Furthermore.
Megasari .
highlights that each investor employs distinct approaches to evaluating political risk and devising strategies to attract investments, thereby underscoring the variability in investor behavior and its implications for understanding the role of political stability in influencing FDI flows.
The Influence of the Corruption Perception Index on Foreign Direct Investment The study finds that the Corruption Perception Index (CPI) has a significantly negative effect on Foreign Direct Investment (FDI) across 14 developing Asian countries.
This observation aligns with the "sand hypothesis," which asserts that elevated levels of corruption can inhibit FDI inflows.
Corruption imposes additional operational costs on businesses, amplifies uncertainty, and creates barriers to foreign investment, including hindering potential foreign involvement in joint ventures.
Companies engaged in corrupt practices experience increased costs and elevated levels of uncertainty, which, in turn, influence their investment Given that corruption is considered a form of deviant behavior, it is imperative for governments to implement robust measures to combat and address corruption effectively (Wahyudi & Palupi, 2.
Several studies corroborate this finding.
For instance.
Dang and Nguyen .
report that corruption and poor institutional quality can decelerate economic development by deterring investment.
Nations characterized by high levels of corruption become less appealing to foreign investors as corruption diminishes the return on investment and escalates uncertainty (Mucha & Fetai, 2.
Additionally.
Shaari et al.
conducted research on the effects of corruption on FDI within six major ASEAN economies using a panel ARDL Their study found that corruption has a substantial impact on FDI, with lower levels of corruption facilitating greater investment opportunities.
Generally, reduced corruption correlates with a more favorable business environment, lower business operational costs, decreased uncertainty, and more effective resource allocation, all of which collectively enhance the attractiveness of a country for foreign direct investment.
The Influence of Gross Domestic Product on Foreign Direct Investment The study reveals that Gross Domestic Product (GDP) exerts a negative and significant effect on Foreign Direct Investment (FDI) across 14 developing Asian countries.
According to DunningAos theory, which focuses on market-seeking motivations, foreign investors evaluate the size and development of the domestic market when selecting locations for their investments.
From a Keynesian perspective, investment is viewed as a means to optimize decision-making behavior.
Capital owners analyze the economic conditions of a country to maximize their anticipated returns (Zuhroh & Harpiyansa, 2.
This result is consistent with findings from Dewi et al.
, who observed that GDP has a positive influence on FDI.
A large domestic market size is essential for investors, as it provides ample opportunities for market expansion and growth.
The size of the market is indicative of the potential for substantial production factors and the consumption of goods produced by firms.
In extensive markets, resources can be utilized more efficiently, enhancing investment attractiveness.
This notion is further supported by a survey conducted by the World Bank, which highlighted that market-seeking motives are a primary driver for corporate investment Additionally.
Mahendra .
found that an increase in GDP positively affects FDI, emphasizing that a high income level in a country serves as a significant attractor for investors.
Rising income levels boost the demand for goods and services and enhance overall competitiveness.
Consequently, investors are more inclined to direct their investments toward countries with elevated income levels due to the associated economic opportunities and growth potential.
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The Influence of Exchange Rate on Foreign Direct Investment The study reveals that the exchange rate does not have a statistically significant impact on Foreign Direct Investment (FDI) across 14 developing Asian countries.
This finding is consistent with the results reported by Chandra and Handoyo .
, who similarly concluded that exchange rates do not significantly influence FDI.
Exchange rate distortions can diminish the value of assets invested by foreign investors, particularly in the context of greenfield investments or the establishment of new production facilities in foreign countries.
A stable exchange rate is generally advantageous for business operations, as it provides a predictable environment for international transactions and investments.
Conversely, fluctuations in exchange rates can complicate investment decisions for multinational corporations (MNC.
by introducing uncertainty regarding both relative and absolute profit margins.
Kurniasih .
also corroborates this conclusion, indicating that exchange rates do not have a significant effect on FDI.
The study highlights that while stable exchange rates facilitate smoother business operations and investment planning, significant exchange rate volatility can lead to unpredictable outcomes for MNCs, affecting their investment strategies.
This uncertainty may deter investment or alter the strategic decisions of foreign investors, but, as the evidence suggests, it does not necessarily constitute a decisive factor in influencing FDI decisions.
CONCLUSION
The results of the testing reveal that the Corruption Perception Index (CPI) has a detrimental impact on Foreign Direct Investment (FDI) in developing Asian countries.
This negative effect indicates that higher levels of perceived corruption are associated with reduced FDI inflows.
Conversely.
Gross Domestic Product (GDP) has a positive and significant effect on FDI, suggesting that increases in GDP are linked to greater foreign In contrast, political stability and exchange rates do not demonstrate a significant influence on FDI within this context.
The findings from the F-test further confirm that at least one of the independent variablesAinamely political stability.
CPI.
GDP, or exchange rateAiexerts a statistically significant effect on FDI.
This underscores the importance of addressing corruption as a critical barrier to enhancing FDI inflows.
To mitigate the adverse impact of corruption, developing countries in Asia should focus on improving institutional quality.
This could involve measures such as streamlining licensing procedures, bolstering oversight mechanisms, and instituting rigorous anti-corruption regulations.
For future research, it is recommended to expand the analytical framework by integrating additional variables and employing dynamic panel data analysis.
Such an approach would facilitate a more comprehensive exploration of the relationships between variables over time and capture dynamic interactions that static models may overlook.
This would provide a more nuanced understanding of the factors influencing FDI and contribute to more effective policy recommendations.
BIBLIOGRAPHY