GROUP ASSIGNMENT COVER SHEET
STUDENT DETAILS
Student name:
Le Tran My Duyen
Student ID number:
31211022066
Student name:
Dao Tran Phuong My
Student ID number:
31211021971
Student name:
Duong Ngoc Hoang Thi
Student ID number:
31211025504
Student name:
Nguyen Minh Thi
Student ID number:
31201029022
Student name:
Ho Le Ha Thuong
Student ID number:
31211022880
UNIT AND TUTORIAL DETAILS
Unit name:
Applied Econometrics
Tutorial/Lecture:
Lecturer or Tutor name:
Unit number: ECO202
Class day and time: Mon 8:00AM
Dr. Le Anh Tuan
ASSIGNMENT DETAILS
Title:
FINAL GROUP PROJECT
Length:
6,500
Due date: 08/04/2023 11:59PM
Date submitted:
08/04/2023
DECLARATION
I hold a copy of this assignment if the original is lost or damaged.
I hereby certify that no part of this assignment or product has been copied from any other student’s work
or from any other source except where due acknowledgement is made in the assignment.
I hereby certify that no part of this assignment or product has been submitted by me in another
(previous or current) assessment, except where appropriately referenced, and with prior permission
from the Lecturer / Tutor / Unit Coordinator for this unit.
No part of the assignment/product has been written/ produced for me by any other person except
where collaboration has been authorised by the Lecturer / Tutor /Unit Coordinator concerned.
I am aware that this work may be reproduced and submitted to plagiarism detection software programs
for the purpose of detecting possible plagiarism (which may retain a copy on its database for future
plagiarism checking).
My Duyen
Student’s signature:
Student’s signature:
Phuong My
Student’s signature:
Hoang Thi
Student’s signature:
Minh Thi
Student’s signature:
Ha Thuong
Note: An examiner or lecturer / tutor has the right to not mark this assignment if the above declaration has
not been signed.
THE IMPACT OF FOREIGN DIRECT INVESTMENT (FDI) ON GROSS
DOMESTIC PRODUCTS (GDP) IN ASIA REGION
Le Tran My Duyen
Student ID: 31211022066
Dao Tran Phuong My
Student ID: 31211021971
Duong Ngoc Hoang Thi
Student ID: 31211025504
Nguyen Minh Thi
Student ID: 31201029022
Ho Le Ha Thuong
Student ID: 31211022880
April 08th, 2023
Applied Econometrics
Lecturer: Dr. Le Anh Tuan
Class: AE-DH47ISB-14
1
Table of Contents
Abstract........................................................................................................................................ 4
1. Introduction..............................................................................................................................5
a. Overview the problem........................................................................................................5
b. Research gaps.................................................................................................................. 7
c. Research questions...........................................................................................................7
d. Motivations........................................................................................................................ 7
e. What are the contributions of the paper/ proposal?......................................................... 8
2. Literature review & Hypothesis development..........................................................................9
a. Literature review................................................................................................................ 9
b. Hypothesis development................................................................................................. 11
3. Methodology.......................................................................................................................... 13
a. Data selection...................................................................................................................13
b. Measure of variables........................................................................................................ 13
c. Baseline model................................................................................................................. 17
d. Interaction-terms model.................................................................................................. 17
4. Empirical results..................................................................................................................... 18
4.1 Descriptive Statistics.......................................................................................................18
4.2 Correlation......................................................................................................................19
4.3 Diagnostics (Heteroskedasticity)................................................................................... 21
4.4 Baseline Results..............................................................................................................22
4.5 The interaction between FDI and GDP........................................................................... 25
5. Conclusions............................................................................................................................ 27
References..................................................................................................................................27
Appendix.................................................................................................................................... 28
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Abstract
The study examines how foreign direct investment (FDI) may have an impact on Asia
countries' gross domestic products (GDP) from 2008 to 2020. In addition, the study wants to
know if a nation's ICT Goods Exports affect how FDI and GDP are related positively. We use VIF
to quantify the strength of the correlation between the independent variables in regression
analysis. While running the models, the robust standard error is used to produce accurate
findings and eliminate the negative impacts of heteroskedasticity. The F-test is used to assess
the significance of a group of controls when testing individual results in insignificance.
Additionally, the empirical findings show that when compared to nations with low levels of
ICT, nations with high levels of ICT have a stronger impact from FDI on GDP indicators.
Highlighted keywords: Gross Domestic Product (GDP), Foreign Direct Investment (FDI), ICT
Goods Exports, Asia.
Highlights:
(1) Foreign direct investment (FDI) positively impacts on Gross domestic products (GDP) of
host country.
(2) For countries with high exports of ICT goods, FDI has a greater positive effect on GDP.
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1. Introduction
a. Overview the problem
In 2005, the book "The World is Flat" by Thomas Friedman brought up the situation of the
world diving into Globalization 3.0. He argued that this new kind of globalization had made
profound differences compared to the previous Globalization 1.0 and 2.0. One key distinct of
Globalization 3.0 is the playing field being flatter and driven by not only European and
American giant corporations and individuals, but also by smaller ones of other continents
(Friedman, 2005). Asia is now a destination where lots of international trades have been
arriving and departing. The region has continued to experience economic globalization in the
21st century, growing increasingly integrated into the global economy, illustrated by a range of
trend indicators, such as trade and financial flows, foreign direct investments (FDI),
communications (information flows), and personal and business travel (Lo & Marcotullio,
2000).
According to research, Asia has prioritized trade and investment integration over other
aspects such as institutional and social integration, giving them less weight (Huh & Park,
2018). This could potentially describe a dynamic and complex context of Foreign Direct
Investment (FDI) in the region. Rapid FDI expansion over the several decades was also fueled
by the creation of global value chains (GVCs) and regional production networks, which were
inspired by outsourcing cost reductions and improvements in information and communication
technology (Asian Development Bank, 2020). FDI inflows have grown in the SAARC, ASEAN,
and Central Asian areas on account of the numerous changes implemented including
economic partnership agreements and financial sector liberalization for many years (Asamoah
et al. 2016). As reported by ESCAP (2020), Asia had record levels of both inward and outward
FDI in 2018, becoming the most alluring location for FDI inflows and the greatest source of FDI
outflows worldwide. The region's proportion of worldwide FDI inflows decreased from 45% in
2018 to 35% in 2019, and its share of global FDI outflows slowed from 52% to 41% in 2019 due
to political and economic concerns. Then, the COVID-19 pandemic hindered the flow of
investment, hastening the region's 2020 greenfield FDI inflows and outflows' declining
trajectories (ESCAP, 2020). However, it is impossible not to recognize many opportunities being
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offered by FDI. According to Asian Development Bank (2016), the benefits are not always
straightforward, rather, they rely on the type of FDI and the recipient economy's ability for
absorption. As a result, policymakers may want to draw in FDI that best complements their
overall development strategy. Countries falling in these regions can gain many benefits from
FDI inflows such as improvement in technology and managerial skills that have further
contributed to their economic growth. Moreover, FDI inflows helped through higher domestic
market rivalry, price mechanisms, productivity, and improved resource utilization (Ullah &
Khan, 2017).
Gross domestic product (GDP) can be defined by OECD (2022b) as a measure of the total
worth of goods and services generated within a country over a specific period. According to
Callen (2022), GDP is an essential indicator of the size and performance of an economy. It is
commonly used to measure the economy's health, with a rise in GDP typically indicating
economic well-being. Tonby and his colleague published a paper in 2019, discussing that major
macroeconomic statistics, such as GDP and consumption, reflected that Asia was becoming
crucially influential at the global level. Asia made up 32% of the world's GDP in terms of
purchasing power parity in 2000. In 2017, this percentage climbed to 42%, and by 2040, it is
projected to reach roughly 52%. In terms of the link between FDI and GDP, it can fluctuate
over time and between different countries, depending on their foreign investment policies as
well. Still, the Asia area has generally been a key beneficiary of FDI inflows, and FDI has
significantly contributed to the region's economic development and prosperity. Asian
Development Bank (2020) reported that also in 2017, as a proportion of GDP, FDI was highest
in the more open smaller economies, particularly Hong Kong, China (30.6%) and Singapore
(19.1%). Many other Asian economies, including those in Cambodia, Georgia, the Lao People's
Democratic Republic, the Maldives, Mongolia, and Palau, rely substantially on FDI, with FDI
inflows making up about 10% of GDP in these countries. Since FDI has contributed a
considerable proportion to the total GDP of a nation, we believe that measuring the
regression relationship between FDI and GDP can provide insights into the economic
interdependence between countries.
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b. Research gaps
A previous study by Hsiao and Hsiao (2006) discussed the Granger causality relationships
between GDP, exports, and FDI among the eight quickly developing East and Southeast Asian
countries using time series and panel data from 1986 to 2004, and found that FDI did have an
effect on GDP directly and indirectly. Behname’s paper in 2012 examines the impact of FDI on
economic development in Southern Asia from 1977 to 2009 and the results show that FDI has
a positive impact on economic growth in the Southern Asia region. Though, limited research
has solely focused on some representative nations of Asia as well as the data used has been
taken a long time ago. This study will update the data by distributing an analysis of the
relationship between FDI inflow and GDP in 20 countries of Asia during the 2008-2020 period.
Also, we attempt to test whether the effect of FDI inflow on GDP differs for countries having
different levels of ICT Goods Exports.
c. Research questions
The study aims to examine the relationship between FDI and GDP in the Asian area over 13
years (2008-2020). The data collected from the licensed websites investigated the relationship
between FDI and other parameters such as gross fixed capital formation, currency rate,
interest rate, control of corruption, and Balance of Trade.
To reach the goal of a greater understanding of how FDI might influence GDP attraction in
Asian countries. The research addresses the following issues through empirical and theoretical
study:
● How is the relationship between FDI and GDP?
● Does FDI and GDP differ significantly between countries having low ICT and ones
having high ICT?
d. Motivations
By knowing the importance of FDI to economic growth, this study will take a depth
understanding of how FDI affects GDP and it differs between countries with different ICT Good
Exports. Therefore, our research can provide crucial statistics for comprehending a country's
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economic growth and advancement. As a result, policymakers can use them as references in
order to create FDI policies that are appropriate for the economy's development stage,
comparative advantage, and industrial policy viewpoint (Asian Development Bank, 2016, pp.
123–168). Also, policy should be developed to support a rebound in FDI and maximize its input
into sustainable development (ESCAP, 2020), and strike a balance between attracting FDI and
promoting domestic investment to ensure long-term economic growth and stability.
e. What are the contributions of the paper/ proposal?
Although researchers determined that FDI has a positive effect on GDP, the limited paper
found that there hasn't been a study for the recent time period on this issue as well as a study
about whether the level of ICT Goods Exports of a country modifies the positive impact of FDI
on GDP. Therefore, this research is needed to analyze the current relationship between FDI
and GDP and to explore the potential moderating effect of ICT Goods Exports on this
relationship. We do this by expanding the analysis to include more countries in the Asia
region, investigating the impact of FDI on GDP in these countries, and identifying any potential
changes in this relationship over time. This would provide a more comprehensive
understanding of the impact of FDI on GDP today for policymakers, with valuable insights into
the effectiveness of FDI as a tool for promoting economic growth.
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2. Literature review & Hypothesis development
a. Literature review
The Gross Domestic Product (GDP) estimates the monetary value of the final goods and
services—those consumed by consumers—produced in a country over a given time frame (a
quarter or a year). It totals all the output produced entirely within a country's borders. GDP is
made up of goods and services produced for market consumption and non-market production,
such as defense and educational services supplied by the government (Callen, 2022). Personal
consumption expenditures, gross domestic investment, net exports of goods and services,
government consumption expenditures, and gross investment are all included in the
calculation of GDP (Jason Fernando, n.d.). Because GDP serves as a measurement instrument,
it may also be used to calculate the macroeconomic activity in a nation (Jain, Ohri & Majhi,
2009).
Foreign direct investment (FDI) is a cross-border investment in which an investor from one
economy progressively obtains a substantial stake in and control over a firm in another
country. There are some common kinds of FDI based on the nature and objectives of
investments such as horizontal FDI, vertical FDI, or conglomerate FDI, greenfield FDI,
brownfield FDI, inward FDI, outward FDI. FDI is distinguished from all other investment forms,
including portfolio capital and aid, by its ability to transmit production know-how and
management skills. Due to the strong, durable linkages it creates between countries'
economies, FDI, in general, is vital to global economic integration. Access to foreign markets
and technology transfer between countries are two important functions of FDI, which
promotes international trade. Also, it can be a very effective strategy for fostering economic
growth (OECD, 2019). The foreign players encourage the local ones to improve their
operations and concentrate on homegrown innovations to combat competition, which
encourages the foreign players to introduce the most recent technology and know-how to
contend with the local competition. This is how FDI causes productivity spillover effects.
(Blomstrom & Kokko, 1998). Additionally, FDI speeds up the host country's globalization
process and assists it in entering international markets (Dondeti & Mohanty, 2007).
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Related findings give out many different results about the extent of how FDI influences
GDP. In Choe’s study (2003), there is a two-way causal relationship between economic growth
and FDI, with economic growth appearing to have a greater impact on FDI. The study of
Hansen and Rand in 2005 indicates that although the impact of FDI on GDP is not significantly
larger than the expected impact of domestic investment in a Solow model, there is a
significant composition effect, meaning that a higher ratio of FDI in gross capital formation has
favorable effects on the level of GDP and, consequently, on growth. Hansen and Rand went on
arguing that FDI generally has a considerable long-term influence on GDP, through using a
mean group estimator and a model specification compatible with the traditional neoclassical
growth model. Additionally, many aspects of the economic environment benefit from FDI. FDI
may accelerate growth by creating jobs in the host country, covering the savings gap and
meeting the enormous investment demand, and facilitating the transfer of knowledge and
managerial skills through backward and forward linkages in the host nation (Frenkel et al.,
2004). FDI can also have positive impacts on economic growth by promoting export market
access and human capital development in host countries, but the extent of its effects depends
on the absorptive capacity of the country, which is influenced by factors such as the quality of
human capital, financial sector development, infrastructure, and technological development
(Borensztein et al., 1998; De Mello, 1999; UNCTAD, 2001; Hermes and Lensink, 2003).
On the other hand, Alfaro (2003) discovered a vague relationship between FDI and GDP, he
also argues that the effect on the host country varies depending on the types of trade and FDI
regulations that the host country adopts. A study by Chowdhury and Mavrotas (2005) found
that FDI can have negative effects on GDP growth, particularly in countries with weak
institutions and a high degree of corruption. Similarly, a study by Carkovic and Levine (2005)
reported mixed results, finding that FDI has a positive effect on GDP growth in some countries
but not in others as well as the authors noted that the relationship between FDI and GDP is
complex and depends on a variety of factors.
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b. Hypothesis development
By using regression analysis, Farkas (2012) examined the relationship between FDI and GDP
and came to the conclusion that there is a positive relationship between the two variables.
However, the extent to which FDI affects GDP relies on the host nation's capacity for
absorption, its level of human capital, and the development of its financial markets
(Redirecting, 2023). Chadee and Schlichting (1997) explore various features of Foreign Direct
Investment in the Asia-Pacific Region and found that FDI has contributed positively to the
economies of the entire region. According to Lee (2005), Foreign Direct Investment and trade
liberalization are the key to economic development. By studying data from 11 countries in East
Asia and Latin America using econometric approaches such as unit root and cointegration
tests, Zhang (2001) presents evidence that FDI improves economic growth in nations with a
liberalized trade policy and a more educated labor population. After thoroughly reading
papers about the relationship between FDI and GDP, we propose the first hypothesis to
conduct our research:
H1: FDI is positively associated with GDP.
Foreign Direct Investment (FDI) has been recognized as a crucial driver of economic growth,
particularly in developing countries. However, the positive impact of FDI on Gross Domestic
Product (GDP) is likely to vary across countries, depending on their level of economic
development and the sectoral composition of their economy. According to a study by the
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International Monetary Fund (IMF), the impact of FDI inflows on economic growth is lower for
countries with lower levels of technological capability, including ICT (IMF, 2019). This is
because FDI inflows into non-ICT sectors may not generate the same level of productivity gains
and technology spillovers as in the ICT sector. Furthermore, the absorption capacity of these
countries to take advantage of FDI inflows may be limited by institutional and human capital
constraints. Another study by the United Nations Conference on Trade and Development
(UNCTAD) found that countries with high ICT exports and FDI inflows in the ICT sector
experienced higher economic growth rates and significantly impacted employment and
technological advancements (UNCTAD, 2020). Therefore, for countries with low ICT Goods
Exports, it may be more challenging to realize the benefits of FDI, and they may need to focus
on improving their technological capabilities to attract and benefit from FDI inflows. We next
generate the second hypothesis for this research:
H2a: The positive impact of the FDI on GDP is less pronounced for countries that have low
ICT Goods Exports
FDI has been recognized as a crucial driver of economic growth, particularly in developing
countries. However, the positive impact of FDI on GDP is likely to vary across countries,
depending on their level of economic development and the sectoral composition of their
economy. Recent research has shown that the positive impact of FDI on GDP is more
pronounced for countries with high Information and Communication Technology (ICT) Goods
Exports. This is because FDI inflows into the ICT sector can boost productivity, innovation, and
technology transfer, leading to higher economic growth. A study by UNCTAD found that
countries with high ICT exports and FDI inflows in the ICT sector experienced higher economic
growth rates and significantly impacted employment and technological advancements
(UNCTAD, 2020). Another study by the World Bank also concluded that FDI inflows into the ICT
sector positively affect GDP growth (World Bank, 2019). Therefore, countries with a high level
of ICT Goods Exports should encourage FDI inflows into their ICT sector to enhance economic
growth and development. Finally, we come up with the third hypothesis:
H2b: The positive impact of the FDI on GDP is more pronounced for countries that have
high ICT Goods Exports.
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3. Methodology
a. Data selection
This research paper is focused on the regression relationship between GDP and FDI among
48 Asian countries from 2010 to 2020. We analyze the data mainly collected from the World
Bank, since it contains high-quality statistical information of our essential indicators.
Initially, we aim to collect observations in 45 Asia countries, and two special administrative
regions of China. However, when we get started to analyze the data, there may be some
countries with missing data. Those observations are going to be excluded to reduce error. And
finally we decide to structure an unbalanced panel data, comprising 20 countries within 229
observations.
b. Measure of variables
Measurement for Gross Domestic Product (GDP)
Gross Domestic Product (GDP) is one of the most often used indicators of economic
performance. In general, GDP determines the value of all the products and services generated
inside a nation’s boundaries in a given year (Khan Academy, 2018). There are three main
techniques for calculating GDP: expenditure approach, income approach, and production
approach (Statistics Canada, 2019). World Bank, from which we collect the data, has used a
production approach. About the measurement, the World Bank Metadata Glossary wrote:
“Gross domestic product (GDP) represents the sum of value added by all its producers. Value
added is the value of the gross output of producers less the value of intermediate goods and
services consumed in production, before accounting for consumption of fixed capital in
production.” (World Bank, n.d.). However, the unit of this is quite large, making up to trillions
of dollars, hence, we use logarithmic transformation to address the high skewness and
heteroskedasticity, which is a violation of one of the assumptions of the ordinary least squares
(OLS) regression method.
There are some limitations as well as concerns about using GDP. Although the GDP formula
is common to access, it does have some limitations. The capacity of GDP to assess human
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well-being is constrained since it solely considers the monetary value of economic output and
ignores other essential elements like income distribution, environmental harm, and the status
of health and social welfare (Kapoor and Debroy, 2019). According to Piketty et al. (2017, as
cited in Dynan & Sheiner, 2018), the income distribution, which has a significant impact on
how well people are doing inside an economy, is not captured by GDP as an economy-wide
notion. Before employing GDP as a measurement, it is critical to have a firm grasp on the study
topic and objectives.
Measurement for Foreign Direct Investment (FDI)
Foreign Direct Investment, which the International Monetary Fund (IMF, 1993) defined as
nonresidents acquiring at least 10% of the ordinary shares or voting power in either public or
private businesses, is a key indicator of economic growth (Zhang & Song, 2001). Businesses
can participate in management, joint ventures, technology transfers, and know-how
exchanges by bringing in FDI (Agrawal & Khan, 2021). The World Bank has retrieved a database
of FDI inflows, which represent the amount of investment made by foreign corporations in a
particular nation, and is used as the main independent variable in this study. Following by
balance of payments, FDI is calculated by:
FDI = Equity Capital + Reinvestment of earnings + long - term capital + short - term capital
Herzer's research from 2008 further supports the idea that FDI should be measured as a
percentage of GDP, even if there are years when FDI value is negative and logarithms cannot
be taken. Therefore, in this research, we decide to regard its unit of measurement as
percentage (%) of GDP, meaning that FDI has its own identity as a component of GDP through
national income accounting.
Measurement for Control variables
There are 7 control variables to support the positive relationship of FDI on GDP. First of all,
real interest rates using percent (%) as a unit measurement, indicate value that is gained in
the effort of a value that has been saved or invested (Rinaldi et al., 2019). A strong correlation
between real interest rate and GDP was proved by Udoka and Anyingang in 2012, specifically
a negative correlation at 5% level of significance.
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Secondly, GDP and exchange rates usually have a strong positive link. According to Lubis in
2017 , a nation's GDP is impacted by its currency's exchange rate. The findings regarding the
inner weight between exchange rates and GDP produce a path coefficient of 1.1419 at 1%
significance level. However, negative relationships happen when very high over-valuations are
associated with slower economic growth (Razin & Collins, 1997). Balassa-Samuelson Theorem
is the source of the idea that real exchange rate undervaluation might threaten growth and
exacerbate the economy's fragility (Harris, 2001). To reduce heteroskedasticity because of
outlier data, we take log for this variable and the essence unit is LCU per US$, period average.
Thirdly, since one of the key factors in the formula used to compute a country's gross
domestic product is the trade balance, trade has a significant impact on GDP (GDP). When
domestic consumers purchase more goods from abroad than domestic producers sell to
international customers, trade imbalances cause the GDP to fall (Ross, 2019). A paper written
by Abbas and Raza in 2013 in Pakistan indicates a negative result of the trade deficit on GDP.
The reason is because the government doesn't offer the investor facilities or support. Because
Pakistan's government underutilized its natural resources, the country's GDP is extremely low.
When we gather the statistics, the unit is a percentage of GDP.
In addition, according to Mbulawa findings, gross fixed capital formation which uses
percent of GDP as unit measurement had a negligible impact on GDP growth in 2015. Gross
fixed capital creation has a coefficient value of 2.79, indicating a positive correlation between
this measure and GDP. The outcome of the Ordinary Least Squares (OLS) analysis suggests that
GFCF in the study were tested at the 5% level of significance. They indicate that only gross
fixed capital formation is important for development and positively impacts GDP in each of the
ASEAN-4 nations (Hussin and Saidin, 2012). Besides, ICT Goods Exports (% of total goods
exports) benefit GDP expansion. The world has significantly altered into an information society
as a result of the fast expansion of information and communication technology (ICT) during
the past several decades. We currently have considerably better access to information,
knowledge, and wisdom than we did in the past in terms of volume, scope, and speed because
to ICT infrastructure like mobile phones, the Internet, and broadband (Bahrini and Qaffas,
2019). ICT diffusion has improved resource allocation efficiency, reduced production costs, and
raised investment across all economic sectors(Grimes, Ren and Stevens, 2012; Pradhan, Arvin
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and Norman, 2015; Vu, 2011). Moreover, a positive relationship between control of corruption
is proven by Ozturk in 2019, In the long run, boosting energy efficiency in income group
economies is statistically significant for increasing corruption perception.
Finally, the financial development index is made up of 3 elements: broad money, domestic
credit to the private sector and domestic credit to the private sector by banks. According to
the research, financial development and economic growth are integrated and extending
financial development appears to be an effective way to support economic growth (Nguyen,
2021). Following Jammeh, 2022 we can determine that sustainable economic growth has been
greatly aided by the expansion of the financial sector. This is tied to a nation's economic
development as well since a growing economy increases the resources available to the private
sector for financing small- to medium-sized businesses. Financial depth is assessed from two
perspectives: from the perspective of broad money, which includes demand and broad money,
and from the perspective of financial system deposits, which includes savings and time
deposits, both of which are expressed as a percentage of GDP. These perspectives were
inspired by the Financial Development and Structure Dataset (FDSD) and finance literature
(Asongu, 2011). In the research by Beck et al. (1999), they argue that the complexity and
effectiveness of the financial sector are mirrored in these metrics, making them crucial gauges
of financial growth. Therefore, we combine 3 minor variables: Broad money, Domestic Credit
to Private Sectors, Domestic Credit to Private Sectors by Bank (financial system deposits) to
create the Financial Development index. In Stata, we typed “cpa BM DCPS DCPSB” command
to estimate the parameters of principal-component models based on these three variables
and “predict FD” command Financial Development index.
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c. Baseline model
Based on the multiple linear regression model, the following equation is constructed to
evaluate the connection between the variables:
l_GDP = β0+ β1 FDI + β2 RIR +β3 l_ER + β4 Trade+ β5 GFCF + β6 ICT + β7 CoCorr + β8 FD + ui,t
l_GDPi,t = β0 + β1FDIi,t + α’ Controli,t + Country fixed effects + ui,t
The model above shows specifically the impact of FDI on GDP (FDI is the foreign direct
investment inflow of the country i in year t, GDP demonstrates the gross domestic product
growth rate of the country i in the t time and we take log for this variable since the unit
measurement is large). In addition, u is the error term and β0 is the intercept while β1 to β8 is
the slope coefficients of explanatory variables. Other control variables behind FDI give other
effects on GDP which are included: real interest rate, log of exchange rate, trade, gross fixed
capital formation, ICT exports - Information & Communications Technologies, Control of
Corruption and Financial Development Index. The relevant coefficients of those control
variables are also denoted by a single sign "α". In order to account for any time-invariant
country-level factors that we think may be jointly related to the degree of GDP and FDI, the
model also includes country dummies. In the appendix 1, the study provides detailed
descriptions of each variable's source, unit of measurement, main independent variable and
control variables.
d. Interaction-terms model
In order to determine whether different groups represented by the dummy variable have
different effects of the primary independent variable (FDI) on the primary dependent variable
(GDP), we create the interaction-terms model:
GDP = β0+ β1 FDI + β2FDI*low_ICT + β3RIR + β4l_ER + β5 Trade + β6GFCF +β7ICT + β8CoCorr +
β9FD + ui,t
Here low_ICT stands for countries that have a low amount of ICT Goods Exports (lower than
the Mean of its sample), and equals to 1. The base group, equalling to 0, falls into countries
that have a high amount of ICT Goods Exports. The coefficient β2 describe the difference in
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these two group on the main relationship between FDI and GDP, being used for testing our
hypothesis H2a and H2b.
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4. Empirical results
4.1 Descriptive Statistics
Table 1. Descriptive Statistics
Variable
Obs
Mean
Std. Dev.
Min
Median
Max
l_GDP
FDI
RIR
l_ER
Trade
GFCF
ICT
CoCorr
FD
229
229
229
229
229
229
229
229
229
26.168
5.673
3.984
3.617
112.834
25.807
14.509
.279
0
2.197
9.716
5.191
3.029
100.894
7.339
15.336
1.009
1.672
20.507
-37.173
-20.497
-.035
11.855
12.324
.001
-1.673
-2.421
26.452
2.82
3.755
2.088
88.495
25.57
7.909
-.094
.052
30.318
58.519
35.415
10.052
442.62
44.519
57.953
2.279
5.972
Table 1 reports descriptive statistics for variables in the baseline model regression. The
average value of log gross domestic product (l_GDP) is 26.168 in a range of 20.5 to 30.3 (log
of current US). The average of foreign direct investment inflow is 5.67% of GDP, with the
standard deviation of 9.716 and the range from -37.173 to 58.519. The set of control variables
has mean values: 3.9843.76, 3.617,112.834, 25.57, 14.509, 0.279 and 0 corresponding to real
interest rate (RIR), log of exchange rate (l_ER), trade, gross fixed capital formation (GFCF), ICT
export, control of corruption (CoCorr) and financial development index (FD). Trade varies
significantly when standard deviation is 100.894 and the set of variables are highly clustered
around mean is:, CoCorr, FD and l_ER since the standard deviation is low (, 1.009, 1.67, 3.02
respectively). The others have different distributions with standard deviation value is: 5.191,
7.339, 15.336 corresponding to RIR, GFCF, ICT Goods Exports.
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4.2 Correlation
Table 2. Pairwise correlations
Variables
(1)
(2)
(1) l_GDP 1.000
(2) FDI
-0.077
1.000
(3) RIR
-0.194*** -0.083
(4) l_ER
0.183*** -0.233***
(5) Trade
-0.140** 0.785***
(6) GFCF
0.503*** -0.100
(7) ICT
0.322*** 0.475***
(8) CoCorr 0.115*
0.438***
(9) FD
0.423*** 0.430***
*** p<0.01, ** p<0.05, * p<0.1
(3)
(4)
(5)
(6)
(7)
(8)
(9)
1.000
0.103
-0.054
0.056
-0.146**
-0.108*
-0.213***
1.000
-0.263***
0.235***
-0.081
-0.534***
-0.248***
1.000
-0.144**
0.640***
0.521***
0.533***
1.000
0.101
-0.095
0.142**
1.000
0.242***
0.652***
1.000
0.614*** 1.000
Table 3. Variance inflation factor
Variable
VIF
1/VIF
Trade
4.176
.239
FD
3.087
.324
ICT
2.842
.352
CoCorr
2.771
.361
FDI
2.64
.379
l ER
1.483
.674
GFCF
1.2
.833
RIR
1.08
.926
Mean VIF
2.41
.
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We use the pairwise correlation to test whether the variables of regression model have
perfect colinearity or multicollinearity with each other. Our Table 2 gives out quite complex
case so we try to interpret step by step. First and foremost, it is obvious that in column (1), the
correlation between GDP and FDI is not significant. However, we can not conclude right away
that there is no statistically significant linear relationship between these two primary
variables. Rather, we have to run other advanced Stata commands for seeking significance
levels, standard errors of the estimated coefficients and review the other previous papers,
from then we can make a conclusion. The other below tables, especially Table 5 & Table 7, will
try to reveal this main relationship in our research.
With regard to the correlation of those 8 independent variables, we focus on the columns
from (2) to (9), seeing that most of them are not highly correlated, except for FDI having a
connection above 0.7 with Trade. We may have to deliberately consider whether to drop the
variable “Trade” from the regression by testing the VIF since multicollinearity is determined by
not only correlation but also VIF. The mean VIF from Table 3 is 2.41, which is lower than 10,
inferring that despite the strong correlation between the FDI and Trade, the multicollinearity
in the overall model is not a serious problem. Eventually, we accept these above outcomes
and decide to keep all variables.
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4.3 Diagnostics (Heteroskedasticity)
White's test for Ho: homoskedasticity
against Ha: unrestricted heteroskedasticity
chi2(44)
= 159.74
Prob > chi2 = 0.0000
Cameron & Trivedi's decomposition of IM-test
Table 4.
Source
chi2
df
Heteroskedasticity
159.740
44
0.000
Skewness
28.340
8
0.000
Kurtosis
7.610
1
0.006
195.680
53
0.000
Total
p
Given that the p-value obtained from the White test is 0.0000, the null hypothesis H0 of
homoskedasticity is rejected, and heteroskedasticity problem arises in our model. To avoid the
negative effects of heteroskedasticity, such as incorrect standard error estimation leading to
unreliable confidence intervals and hypothesis tests, the robust standard error is utilized when
running the models in order to obtain relable results.
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4.4 Baseline Results
Table 5. Baseline Results
VARIABLES
FDI
RIR
l_ER
Trade
GFCF
ICT
CoCorr
FD
Constant
Country fixed effects
Year fixed effects
Observations
R-squared
Robust standard errors in parentheses
(1)
l_GDP
0.0078**
(0.00294)
-0.00302
(0.00241)
-0.00506
(0.0258)
-0.00219*
(0.00108)
-0.00844
(0.00674)
0.0180**
(0.00811)
0.593***
(0.143)
0.0652
(0.0824)
26.19***
(0.267)
Yes
No
229
0.398
*** p<0.01, ** p<0.05, * p<0.1
Table 5 shows the baseline result of the influence of Foreign Direct Investment on Gross
Domestic Product. Parentheses indicate robust standard errors, which the model has
incorporated to lessen the heteroskedasticity issue. We perform baseline regression with all
control variables and with country-fixed effects. As can be seen, the FDI coefficient is
statistically significant at 5% and is positive. To be more precise, it is 0.0078 and statistically
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significant at 5% (standard error equals 0.0029), showing that, when all other factors are held
constant, an increase in the FDI results in a 0.78% increase in the GDP index. Our results are
consistent with the findings reported by Hansen and Rand (2005) and Johnson (2006), who
claimed that FDI has a relationship and also has a positive impact on GDP of the host nation.
Therefore, our findings support the existing literature that FDI plays a crucial role in enhancing
economic growth in host countries. The estimation findings clearly support hypothesis H1a,
which states that FDI is positively related to GDP.
The baseline results table also presents how these control variables influence the
dependent variable. The coefficients on the value of Trade, ICT, and Control Corruption
(CoCorr) are statistically significant, implying that these control variables impact GDP. The
results are consistent with some previous research. For instance, the significant and positive
coefficient on ICT, which is 0.0180, demonstrates that given other factors fixed, the increase in
the percentage of ICT clearly leads to an increase by 1.8% in GDP. The study by Lee and Kim
(2010) provides evidence to support the statement that an increase in ICT leads to an increase
in GDP. Specifically, the study found that there is a positive relationship between ICT and
productivity in developing countries. Since productivity is a key driver of economic growth,
this finding suggests that the use of ICT can lead to increased economic growth and thus an
increase in GDP. In the Baseline Result, we have the negative value of Trade, but it is significant
at 10%, so it is categorized as weakly significant or marginally significant. By this reason, the
Trade value does not prove the strong evidence that it has negative impact on GDP.
Apart from the significant coefficients on the control variables, there are also insignificant
ones on other control variables, namely the Real Interest Rate (RIR), log of Exchange Rate
(l_ER), Gross Fixed Capital Formation (GFCF), and Financial Development Index (FD).
Consequently, the study applied the F-test to determine whether these control variables are
jointly significant.
The result of the table 6 show that p-value is 0.0047, which is lower than 5%. We can
conclude that, while not significant when tested individually, these four control variables are
significant when tested together. In other words, they are accepted to keep as control
variables in the regression model continue to have an impact on GDP as a group.
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Table 6. F-test
(1)
RIR = 0
(2)
l_ER = 0
(3)
GFCF = 0
(4)
FD = 0
F( 4, 220) = 5.34
Prob > F = 0.0047
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4.5 The interaction between FDI and GDP
Table 7. Interaction term result
(1)
(2)
(3)
VARIABLES
l_GDP
l_GDP
l_GDP
FDI
0.0188*
0.105***
0.113***
(0.0094)
(0.029)
(0.0273)
-0.170
-1.107***
-1.093***
(0.219)
(0.345)
(0.352)
-0.0198*
-0.194***
-0.212***
(0.0110)
(0.0510)
(0.0519)
-0.0028
-0.0599***
-0.0449**
(0.0030)
(0.0193)
(0.0198)
-0.0102
0.154***
0.162***
(0.0289)
(0.0283)
(0.0290)
-0.0021**
-0.0187***
-0.0199***
(0.0008)
(0.0029)
(0.00280)
-0.0059
0.0971***
0.0957***
(0.0064)
(0.0160)
(0.0157)
0.607***
0.602***
0.640***
(0.139)
(0.1383)
(0.141)
0.122*
0.346***
0.364***
(0.0655)
(0.0895)
(0.0920)
26.46***
25.67***
26.38***
(0.1660)
(0.5624)
(0.565)
fixed Yes
No
No
Year fixed effects No
No
Yes
Observations
229
229
low_ICT
c.FDI#c.low_ICT
RIR
l_ER
Trade
GFCF
CoCorr
FD
Constant
Country
effects
229
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R-squared
0.384
0.670
0.688
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
The robust standard errors, which are employed to overcome heteroskedasticity, appear in
parenthesis and are similar to the outcomes of the basic model. As can be seen in the table 7
above, all three columns include control variables and interaction terms, but differ from their
fixed effects. While the fixed effects are different for the other three columns, all control
variables are covered. As reported in column (1), with country-fixed effect, the coefficient of
FDI*low_ICT is -0.0198 and highly significant at 10%. Regarding column 2, with no fixed effect
here, we receive a highly significant and negative coefficient on the interaction term
FDI*low_ICT, which equals -0.194 and is significant at 1%. In column 3, with year-fixed effect
the coefficient on FDI*low_ICT is -0.212 and significant at 1%. All three columns, we receive
the same sign of the coefficient of this interaction term which can indicate that compared to
countries having high ICT, the impact of FDI on GDP is less pronounced than countries with low
ICT and vice versa. According to a UNCTAD research, countries with strong exports of
information and communication technology (ICT) goods see FDI's benefits more visibly on GDP
(UNCTAD, 2020). The result is consistent with findings in the literature and our hypothesis H2a
and H2b.
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5. Conclusions
This study contributes to the banking literature research on the effects of foreign direct
investment on GDP by comparing the Asia region. We used multiple regression models to
explore the impact of FDI and control factors on GDP. The final result is suggested after this
research is that FDI inflow has a positive impact on GDP. On top of that, the low ICT Goods
export index negatively affects GDP in all 3 scenarios with the outturn of country fixed effects
and year fixed effects.
After this research, we suggest that Asian countries should put an emphasis on the efficient
transformation of growth advantages into the development process, as well as on the
productive usage of both local and foreign capital. We recommend advancing technology,
growing agribusiness, and fortifying ties to global value chains. Industrialization is typically a
phase that cannot be skipped on the way to becoming a high-income economy.
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Appendix
Appendix 1: (Source Data: taken from World Bank Data)
Variables
Unit Measure
Description
Gross Domestic Product
log[GDP(current
Estimates the monetary value of the final goods and
$US)]
services—those consumed by consumers—produced in a
country over a given time frame
Foreign Direct
FDI, net inflows (% net investments made to purchase a long-term
Investment
of GDP)
management stake ( >=10 % of voting stock) in a company
that operates in a country other than the investor's own.
Real Interest Rate
RIR (%)
Loan rate that has been deflated by the GDP to account
for inflation.
Exchange rate
Trade
log [ER(LCU per
Decided by national authorities or the market rate for
US$, period
currency that is permitted by law. An annual average is
average)]
computed using monthly averages.
Trade (% of GDP)
Total of goods and service exports minus imports
expressed as a percentage of gross domestic product.
Gross fixed capital
GFCF (% of GDP)
formation
Official national accounts use a macroeconomic concept.
Governments and "pure" households (excluding their
unincorporated firms) have less disposals of fixed assets
than the commercial sector, according to statistics used
to calculate GDP expenditure.
ICT Goods Exports
ICT (% of total
Among the ICT goods exported are computers and
goods exports)
peripherals,
communication
electronics,
electronic
equipment,
components,
consumer
and
other
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Group 3
Project Report
commodities related to information and technology
(miscellaneous).
Control of Corruption
Extent to which public power is utilized for personal
benefit, including perceptions of both small- and
large-scale corruption as well as the "capture" of the
state by elites and private interests. Estimation offers the
nation's overall indicator score in standard normal
distribution units.
Financial Development
BM = Broad
The total amount of money kept outside of banks,
Index
Money (% of GDP)
demand deposits other than those made by the federal
government, time, savings, and foreign currency deposits
made by non-federal residents, bank and traveler's
checks, as well as other securities like CDs and CPs.
DCPS = Domestic
Financial resources provided to the private sector by
Credit to Private
financial institutions, including loans, purchases of
Sectors
non-equity securities, trade credits, and other receivables
that give rise to a demand for repayment. These claims
include praise for state-run businesses in several
countries.
DCPSB = Domestic
Resources offered by other depository corporations to
Credit to Private
the private sector (deposit taking corporations except
Sectors by Banks
central banks)
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