The research is developed to explore the important drivers that will affect the Global Competitiveness Index (GCI) among ASEAN countries. Since GCI is playing an important role in facilitating ASEAN's competitive position, the main objective of this research is to identify the important factors that will affect GCI among ASEAN countries. Through this research, readers will have a better understanding of the determinants that will influence the GCI and the importance of particular determinants in ASEAN countries.
Furthermore, the World Economic Forum (WEF) has compiled a Global Competitiveness Index (GCI) as a study on the competitiveness of countries around the world, including ASEAN countries since 2004. Finally, the empirical findings indicate that the CCI, GDP, and FDI have an impact on ASEAN countries' GCI. The CCI, GDP and FDI have been proven to have a positive favorable link with GCI in ASEAN countries.
Research Overview
- Research Background
- Problem Statement
- Research Objectives
- General Objective
- Specific Objectives
- Research Questions
- Significance of Study
- Chapter Layout
- Conclusion
One of the major concerns is that corruption is often discussed; it correlates strongly with global competitiveness. We will analyze the direct effect of FDI on global competitiveness and the impact of corruption on the relationship between FDI and global competitiveness. This research therefore aimed to analyze the relationship between global competitiveness and other control variables (trade openness and gross capital formation).
In fact, the aim of this study is to determine the relationship between global competitiveness and corruption in the selected countries using a dynamic panel approach. What is the impact of corruption on the relationship between gross domestic product and global competitiveness in ASEAN countries. A). What is the impact of corruption on the relationship between foreign direct investment and global competitiveness in ASEAN countries.
Literature Review
- Review of the Literature
- Global Competitiveness Index (GCI)
- Corruption
- Gross Domestic Product (GDP)
- Foreign Direct Investment (FDI)
- Trade Openness
- Gross Capital Formation
- Review of Relevant Theoretical Models
- Adam Smith’s Theory
- Keynesian Theory
- Development Economics Theory
- Trade Theory
- Heckscher-Ohlin Theory
- Hypotheses Development
- Conclusion
Leff, 1964) However, in both studies, the Corruption Perceptions Index (CPI) is used to measure the level of corruption. In fact, we will use the Corruption Perceptions (CCI) under the World Governance Indicators (WGI) to study the relationship between the variables. Consequently, corruption is summarized that a lower level of corruption is relatively associated with a lower level of inequality undermining economic growth, claimed by the World Bank (2021).
Their findings supported their theory that trust reduces the adverse impact of corruption on economic growth. Although the relationship between GDP and corruption is still ambiguous, we expected that there is an influence on GDP regardless of any level of corruption. Bakri et al., 2018) According to Belgibayeva & Plekhanov (2019), FDI is not homogeneous and is influenced by the extent of corruption in the host country.
Methodology
- Proposed Research Framework
- Proposed Theoretical Framework
- Data
- Global Competitiveness Index (GCI)
- Control of Corruption (CCI)
- Gross Domestic Product (GDP)
- Foreign Direct Investment (FDI)
- Trade Openness
- Gross Fixed Capital Formation (GFCF)
- Data Estimation
- Pooled Ordinary Least Square Model (POLS)
- Fixed Effect Model (FEM)
- Random Effect Model (REM)
- Breusch-Pagan Lagrange Multiplier (LM) Test
- Hausman Test
- Poolability Test
- Diagnostic Testing
- Model Specification
- Conclusion
This time interval was chosen mainly due to the availability of data for the explanatory variable, GCI. CCI refers to public authority or bureaucratic regulation for private advantage, resulting in domestic corruption and potential barriers to foreign investment. In other words, it allows policymakers and central banks to determine whether. the economy is contracting or expanding and take immediate action.
It is an investment in another nation's business interests by a company or investor in a country. While inflows of FDI, on the other hand, represented the value of foreign investors' equity and net loans to companies based in the reporting economy. Trade openness refers to a country's economic orientation within the framework of international trade.
The fixed effects model (FEM) captures variations in the regression model's constant term and intercept term as they vary across cross-sectional units. This may suggest that the FEM fits the model better if the null hypothesis is rejected. According to Gujarati and Porter (2008), when the null hypothesis is rejected, the fixed effect is more appropriate or the goodness of fit increases in the FEM.
When there is a presence of heteroscedasticity, there is model misspecification, which has omitted variables in the model. In other words, the error term in the model is free from the heteroscedasticity problem if the p-value is greater than the significant level at 5 percent. Panel data can demonstrate extensive cross-sectional dependence, in which all units are associated in the same cross-section.
In this chapter, secondary data from 7 countries, namely Cambodia, Indonesia, Malaysia, the Philippines, Singapore, Thailand and Vietnam, from 2007 to 2017 were used in the study.
Data Analysis
- Descriptive Analysis
- Panel Data Analysis
- Pooled Ordinary Least Square Model (POLS)
- Fixed Effect Model (FEM)
- Random Effect Model (REM)
- Model Selection
- Breusch-Pagan Lagrange Multiplier (LM) Test
- Hausman Test
- Poolability F-Test
- Diagnostic Checking
- Heteroskedasticity
- Cross-Sectional Dependence Test
- Autocorrelation
- Robust Cluster as Remedies
- Conclusion
This means that, on average, for every dollar of increase in GDP per capita, the GCI will increase by 0.3955 index points. In addition, the GCI will increase by 0.0226 index points for each percentage increase in net FDI inflows. For every percentage point increase in trading openness, the GCI will decrease by 0.0005 index points on average.
Therefore, for every one percent increase in CCI*CCI, GCI will increase by an average of 0.0817 index points. For every one index point increase in CCI, the GCI will increase by 1.4693 index point. Meanwhile, GDP per capita has also been proven to have a positive, statistically significant effect on GCI at a significance level of 1%.
This means that every one percent increase in net FDI inflows will increase the GCI by 0.0233 index point. For every percent increase in trade openness, the GCI will fall by 0.0010 index point. It indicates that for every percent increase in GFCF, the GCI will decrease by 0.0184 index (4.2).
For every one index point increase in the CCI, the GCI will increase by 0.7112 index points on average. Net FDI inflows have a positive, statistically significant effect on GCI at the 1% level of significance. This means that, on average, the GCI will decrease by 0.0082 index points for each percentage increase in gross fixed capital formation.
It also indicates that for every one percent increase in CCI*GDP, on average, the GCI will decrease by 0.0430 index point.
Conclusion
Summary of Statistics Analyses
Discussion on Major Findings
- Control of Corruption (CCI)
- Gross Domestic Product (GDP)
- Foreign Direct Investment (FDI)
- Trade Openness
- Gross Fixed Capital Formation
It is advisable that ASEAN countries control their levels of corruption to remain globally competitive. So it is consistent with the applied Keynesian theory we mentioned in the previous chapter, where higher GDP could gain a competitive advantage over other countries in the ASEAN countries scenario. Therefore, we can conclude that the findings are consistent with our study and assume that GDP is the main driver influencing GCI in ASEAN countries.
Therefore, the impact of corruption on GDP among ASEAN countries is not too strong. In the case of FDI inflows, significant findings showed a 5% significance level, which is a strong relationship found between FDI and GCI in ASEAN countries. Although some models showed an insignificant effect on GCI, the main model, Model 5, produced a positive and significant effect on GCI in ASEAN countries.
ASEAN countries were encouraged to maximize their investment to gain comparative advantage. In both models, Model 3 and Model 5 revealed a strong negative relationship between CCI and FDI inflows to affect ASEAN countries' GCI by 5%. Unfortunately, findings from all models resulted in a highly insignificant relationship between trade openness and GCI.
In other words, trade openness is assumed to be irrelevant as it has no power to affect GCI in ASEAN countries. A study by Nguyen & Bui (2021) argued that trade could boost growth and productivity in ASEAN countries. Therefore, we conclude that trade openness has no direct relationship with GCI in ASEAN countries as it will not affect competition on a global basis.
From the result, some findings showed a positive significant effect, and some were inconsistent with GCI among the ASEAN countries.
Implications of Study
- Government Authorities
- Bank and Investors
This is because Gibescu's study (as cited in Gibescu, 2010) argued that gross fixed investment is the indicator to identify economic growth. On the other hand, GDP appears to be the robust indicator positively linking GCI in ASEAN countries. Economists usually use GDP to make decisions because it is a representation of economic activity and growth.
Therefore, policy makers in ASEAN countries should put more effort into improving the countries' GDP in order to increase the GCI. According to the study, FDI is positively related to GCI, where higher FDI inflows promote higher GCI among ASEAN countries. Therefore, it cannot be denied that FDI inflows create opportunities to increase the country's competitiveness.
It is encouraged that policy makers in ASEAN countries will do their utmost to attract foreign investment. As a result, countries can actively engage in free trade agreements to reduce or remove trade barriers to increase GCI among ASEAN. Therefore, policy recommendations for developing countries should emphasize strengthening the investment climate for FDI inflows.
Although policy makers have greater power to control corruption, investors among the ASEAN countries can also fight against corruption with their efforts. This is because corruption has a direct impact on borrowing costs, but it also has an indirect impact on the rule of law. With the effort to reduce corruption, ASEAN countries can enjoy more comparative advantage to compete against the developed countries.
As shown in many emerging economies, countries that attract FDI inflows often see higher economic growth and greater competitiveness by expanding into new markets.
Limitations of the Study
Because quantitative research could create a real-time picture of the current trend in a selected demographic group, it also revealed a limitation in assessing social change, or how people interpret their own or others' actions. Therefore, we make some suggestions in the hope that they will be useful to researchers in the future.
Recommendations for Future Research
Conclusion
Governance, competitiveness and economic performance to attract foreign direct investment inflows in SAARC and ASEAN countries. Absorptive capacity and the effects of foreign direct investment and equity foreign portfolio investment on economic growth. Role of Foreign Direct Investment on Technology Transfer and Economic Growth in Kenya: A Case of the Energy Sector.
Brunei leaps six places in WEF Global Competitiveness Index 2019. https://borneobulletin.com.bn/brunei-leapfrogs-six-place-wef-global-competitiveness-index-2019/. Macroeconomic Determinants of Economic Growth in Africa. PH rises in WEF Global Competitiveness Index 2017. https://www.rappler.com/business/183395-philippines-global- computingness-index-2017-wef/. Estimation and inference in large heterogeneous panels with a multifactor error structure. The Impact of Quality of Governance, Renewable Energy and Foreign Direct Investment on Sustainable Development in CEE Countries.
Variables, Descriptions, Theories, and Expected Relationships
Summary of Data
Descriptive Statistics for Each Variables
Result of Static Panel Regression for Model 1: POLS. FEM and REM
Result of Breusch-Pagan Lagrange Multiplier (LM) Test
Result of Hausman Test
Result of Poolability Test
Result of Wald Test
Result of Pesaran CD Test
Result of Wooldridge Test
Result of Fixed Effect Model (FEM) with Robust Cluster
Summary of Statistic Analyses Results
Summary of Panel Analysis Results
Summary of Fixed Effect Model (FEM) with Robust Cluster
Global Competitiveness Index by Value of the Selected Countries from
Dependent Variable and Independent Variables
