thesis of relationship of fdi and gdp of cambodia, laos, and vietnam
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It's panel data analysis, fixed effect estimatorTRANSCRIPT
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Relationship between FDI and Economic Growth in
Cross-Country: Cambodia, Laos, and Vietnam
Sok Rasmey
Phnom Penh, Cambodia
Bachelor Degree in TESOL
Institute of Foreign Languages,Royal University of Phnom Penh
Year of Graduation 2011
A Dissertation
In
Finance
Presented to the Graduate Faculty
Of the Shanghai University of Finance and Economics
in Partial fulfillment of the Requirements for the
Degree of Master of Economics
2014
Supervisor of Dissertation
Ling Feng, Ph.D.
School of Finance
Shanghai University of Finance and Economics
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COPYRIGHT NOTICES
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For further use of any information contained in this research paper either in
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Copyright, Sok Rasmey 2014.
All rights Reserved.
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ACKNOWLEDGEMENTS
At the end of my thesis, I dedicate this thesis to those who have shown their support
and contribution. Without whom, this thesis might not have been written and to
whom I am greatly indebted.
To my parents, siblings, and relatives, my deepest gratitude goes to their
unconditional support physically, emotionally, and psychologically. Sense of love
from them encourages me to work harder and commit to complete this paper.
To my supervisor, Dr. Feng Ling, I would like to express my very sincere gratitude
for her continuous guidelines from the start till the end of this thesis. I cannot thank
her enough for her systematic and quick feedback through numbers of emails and
appointments. Without her presence and tireless assistance in many ways, this
research paper would not be able to finish on time. I highly respect her calm and
professional attitude as my supervisor, which motivates and guides me throughoutthis endeavor.
To my friends and country-mates, I am also indebted for their accompany and
motivation for countless times during our stay in foreign country. Without them, this
paper would not produce any better.
Lastly, to my professors and staff at SUFE, I am thankful for their sharing knowledgethrough lectures and personal stories. Special thanks should also been given to
teacher Haiping for his numerous assistances during my life journey in SUFE. I
wholeheartedly thank to Chinese government for offering me the prestigious
scholarship to pursue my higher education and let me explore this international life
experience.
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ABSTRACT
This paper aims to examine the relationship between Foreign Direct Investment (FDI)
and Gross Domestic Product (GDP) as a measurement of economic growth within
three countries including Cambodia, Laos, and Vietnam together from 1993 to 2012
by using cross-country panel data analysis. Different data of FDI will be used such as
total inward FDI, total FDI stock, and FDI flowing from China into these three
countries to examine the impacts of different types of FDI on local economic growth.
The claim from previous studies about results of the negative or positive relationships
of FDI and GDP are based on whether inward FDI or FDI stock is chosen. Therefore,
this paper aims to confirm all types of FDI data namely inward FDI and FDI stock
while amount of inward FDI from China into these 3 countries would be back-testing.
By using three different tests, the results from the Hausman Testing confirm that
Fixed-effect estimator is appropriate. Therefore, the fixed effect testing is further used
to give the result of the equation.
By using hypothesis of FDI leading to GDP as theoretical framework, the research
concludes that there is positive effect of FDI on local GDP growth, and the effect
holds for all types of FDI including inward FDI, FDI stock, and FDI from China in
these countries. It proves that FDI is a key economic growth determinant for these
three countries as a whole, which allows these countries to enjoy the economy
prosperity within the twenty years.
Key Words: FDI, GDP, inward FDI, FDI stock, Panel data, Hausman Test, and Fixed
effect
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ABSTRACT
FDI(GDP) 1993 2012
FDI GDP
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FDI GDP
Hausman
OLS
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20 FDI
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TABLE OF CONTENTS
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LIST OF TABLES
Figure Title Page
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LIST OF FIGURES
Figure Title
Page
Figure 1:Foreign Direct Investment, net inflows (% of GDP) 11
Figure 2:GDP growth (annual %) 14
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CHAPTER 1: Introduction
1.1 Motivation and Research Questions
Economic development is a main concentration of every developing nation to get rid
of poverty and to move to development in the society. Annual economic development
can be easily recognized by GDP (Gross Domestic Products), which can be defined
as a measurement of one nation's total economic activity. More precisely, GDP
represents the monetary value of all products and services produced in one country
over a period of time. The commonly known equation is GDP =
Household Consumption + Government Expenditures + Investment + (Exports
Imports).
Apart from common factors of GDP, many scholars have more concentration on other
determinants that take part into the economic growth. Foreign Direct Investment
(FDI) has been also recognized to be one of the most significantly indispensable
economic growth drivers especially in the developing countries. Therefore, the
relationship of FDI and GDP representing to the economic growth indicator is a
popular researching economic study.
By studying the FDI-GDP relationship within different individual countries or within
multiple countries, different researchers make different conclusion whether it is
positive or negative relationship, whether it is short-term or long-term relationship,
etc. Different finding results are commonly come from selecting different explanatory
variables such as policy effectiveness and current economic development of one
country, level of productivity, drawbacks of negative Balance of Payment from profit
and capital repatriation, etc. From different previously available studies, there is no
universal conclusion on the same direction of FDI on GDP. Therefore, it is another
great yield of this current research paper to study the relationship FDI-GDP within
cross-countries of Indochina countries namely Cambodia, Vietnam, and Laos. Due to
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shortage of currently available data of these three countries, the main result of
regression would be into panel data analysis.
This research paper is based on two main economic growth theories including
neoclassical theory and endogenous theory of growth. Based on these two theories,
the study aims to form hypotheses as follows.
H1: There is a positive and statistically significant relationship between net
inward FDI and GDP in the panel data.
H2: There is a contemporaneous relationship between net inward FDI and
GDP in the panel data.
H3: There is a positive and statistically significant relationship between
FDI and GDP in the panel data regardless to different types of data of FDI
H4: There is a positive and statistically significant relationship between
FDI from China and GDP to crosscheck the result with H1.
1.2 Scope and Limitation
This paper aims to examine the relationship between Foreign Direct Investment (FDI)and Gross Domestic Product (GDP) as a measurement of economic growth within
three countries including Cambodia, Laos, and Vietnam together from 1993 to 2012
by using cross-country panel data analysis. Different data of FDI will be used such as
total inward FDI, total FDI stock, and FDI flowing from China into these three
countries to examine the impacts of different types of FDI on local economic growth.
Specifically, the paper aims to address four main objectives as follows:
Investigate whether the real relationship of net inward FDI and GDP is
positive.
Investigate whether there is contemporaneous relationship between net
inward FDI and GDP in the panel data.
Investigate whether the relationship of FDI and GDP remains the same
when different types of FDI substitute into a explanatory variable.
Ensure that the relationship FDI-GDP remains although FDI from China
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replaces the independent variable FDI.
The claim from previous studies about results of the negative or positive relationships
of FDI and GDP are based on whether inward FDI or FDI stock is chosen. Therefore,
this paper aims to confirm all types of FDI data namely inward FDI and FDI stock
while amount of inward FDI from China into these 3 countries would be back-testing.
By using three different tests, the results from the Hausman Testing confirm that
Fixed-effect estimator is appropriate. Therefore, the fixed effect testing is further used
to give the result of the equation.
By using hypothesis of FDI leading to GDP as theoretical framework, the research
concludes that there is positive effect of FDI on local GDP growth, and the effect
holds for all types of FDI including inward FDI, FDI stock, and FDI from China in
these countries. It proves that FDI is a key economic growth determinant for these
three countries as a whole, which allows these countries to enjoy the economy
prosperity within the twenty years.
However, within panel data regression in Eview7 program, due to only three
countries selected into study, this paper can only have maximum of two independent
variables on the right hand side of equation. It is also worth to note that the trusted
data of FDI and GDP of Cambodia, Vietnam, and Laos is available only from 1993
to 2012. The reasons are that Cambodia suffers civil war and social instability until
early 1990s and these three countries are among least developed and developing
countries.Such a limitation on number of independent variables and period of data,the implications and analysis would cautiously and selectively apply.
1.3 Significance of the Study
Firstly, by using panel data regression, the current paper aims to extend knowledge
to existing literature by including study of the relationship between FDI and GDP of
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Indochina countries. Secondly, the findings and implications from this paper can
offer an overview of the historical data and lead to a basic guideline for policymakers
for any more effective policy. Third, the paper provides a justification of whether the
Indochina countries should attract more inflow FDI based on finding results.
1.4 Organization of the Paper
This paper will organize as following. Chapter 2 will present literature review of
related methodology and topic such as the two main theories of the neoclassical
theory, endogenous theory of growth and other empirical findings of previous papers.Afterwards, economic background in the three countries will present in Chapter 3,
data and research methodology will discuss in Chapter 4. Findings will list in Chapter
5 while Chapter 6 is conclusion.
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CHAPTER 2: LITERATURE REVIEW
2.1 Introduction
The inward FDI is welcomed by many developing countries due to economic
development contribution in various images. In the short run, great number of people
would be hired for the MNCs production. The spillover of technology, the human
resource via trainings and working standards, the financial maturity, and others would
influence on better productivity and innovation for the local to contribute positive
long-run economy development. However, there would not be always a positive side
of inflow of FDI into every country. For further understanding about this issue,
empirical studies on previous researches would be listed down.
Exogenous growth model are also known as the Solow growth model or the Neo-
classical model, created by Robert Solow, Nobel Prize Winner in 1987 for this model.
It explains that growth of FDI improves the productivity quality and quantity, the
capital flow, and the technological improvement, which ultimately accrues the
economical output in long run. Nevertheless, the exogenous model fails to explain
reasons and process of technological development through FDI. This is called
endogenous growth theory. In the endogenous growth theory, impact of FDI on the
economic growth is adduced by knowledge spillover in four ways (Blomstrom and
Kokko, 1998). The domestic firms update higher and newer technologies following
the foreign subsidiaries. Attractive career offers from the domestic firms to absorb
key human resources from the MNCs and the training courses also go to other local
staffs. This result is also confirmed by using fixed effects estimator with country-
specific time trends and mean group estimates. On average, FDI also has a significant
long-run impact on GDP in spite of any level of development.
2.2 Previous study
Lim GuechHeang and Pahlaj Moolio (2013) study relationship between GDP and FDI
in Cambodia over 19 years from 1993 to 2011. They use simple regression analysis,
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Augmented Dickey-Fuller test, Durbin-Watson test, Breusch-Godfrey Serial
Correlation LM test, Breusch-Pagan-Godfrey test, and Jarque-Bera test. Result is
shown that there is a positive relationship of FDI and GDP in Cambodia, which
mainly contributes to employment for the local people, capital accumulation, and
export. However, Cambodia has not thrived the fullest of FDI benefits and
technology spillover.
Net Seila (2011) analyzes the effect of FDI on the economic growth of three
countries, namely Cambodia, Vietnam, and Thailand from 1987 until 2008 by using
panel data and time series. Result gives that FDI contributes productivity. From his
pooled estimation, there is a long-run effect of FDI growth. Specifically, FDI has
crowding-in effect on domestic investment in Vietnam and Thailand yet only neutral
effect in Cambodia. Based on his examination on FDI data by General Statistical
Offices of Vietnam, FDI in Vietnam contributes to more industrialization
development, export, job opportunities, and government income, boost more R&D
activities, and gives massive advantages to state-owning and domestic firms.
A study of FDI and economic growth by Nguyen Phi Lan (2006) by using 61
Vietnamese provinces in panel data testing from 1996 to 2003 shows the result of a
positive and statistically significant effect of FDI on the economic growth while the
economic growth of Vietnam is an important reason for more FDI inflows into
Vietnam too.
Henrik Hansen and John Rand (2006) study causal relationship between FDI and
economic growth of 31 developing countries from three continents over the time
19702000 as their sample. Using a mean group estimator and a model specification
that is aligned with the standard neoclassical growth model, they conclude that there
is a strong causal link from FDI to GDP matching well with the expected impact in a
standard Solow growth model. Theories of exogenous growth and endogenous
growth are often used in previous research papers to explain FDI-GDP relation.
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Likewise, Nair-Reichert and Weinhold (2001) test causality for cross-country panels
by using data of 24 countries from 1971 to 1995. Based on mixed fixed and random
(MFR) coefficient approach, they find out that FDI generally has significance on
growth although it varies result in different countries.
Although all previously mentioned researches indicate the positive GDP-FDI
relationship, Carkovic and Levine (2002) study the inward FDI impact on the
economic growth by controlling for other determinants and potential biases induced
by endogeneity, country-specific effects, and the inclusion of initial income in OLS
and panel estimator testing.Result shows that exogenous FDI does not influence on
the economic growth. The coefficient of FDI is not stable in the panel data, varying
from 323 (when controlling for initial income, schooling, and inflation) to -34 (when
controlling for initial income, schooling, and financial development). The coefficient
on FDI is still unstable when their regressions are limited to have the same number of
observations. Their conclusion claims that previous macroeconomic studies that find
a positive relationship of FDI and the growth, do not fully control for endogeneity,
country-specific effects, and the inclusion of lagged dependent variables.
Another research studies relationship of FDI and economic growth in 15 East Asian
countries covering three main levels of income group from 1990-2009 based on time
series. As a result, there is no necessity of FDI to boost economic growth and the
positive impact of FDI on the economic growth only if the countries had a certain
economic conditions (Polpat Kotrajaras, 2010).
De Mello (1999) studies impact of FDI to growth in 32 countries including 17 non-
OECD countries based on time-series and panel data estimations. By using fixed
effects regressions and country-specific intercepts, he finds out that there is no
causation of FDI to growth in his non-OECD sample, and there would be a negative
short-run impact of FDI on GDP based on the mean group estimator. Foreign
enterprises not only demand standardized working output, but also have particular
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working deadline to accomplish.
These previous research papers can be concluded that many countries have the
positive causality of FDI and GDP, whereas negative impact of FDI and GDP also
can be found in some countries, mainly based on their selected variables and testing
methods
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V
CHAPTER 3: Economic Background in Cambodia, Vietnam and Laos
3.1 Benefits and Disadvantages of FDI
FDI is one of the poverty eradication mechanisms by offering mutual benefits to
MNCs (Multi-national Corporations) and host countries. FDI is defined as net inflows
of foreign investment into other countries; it includes equity capital, reinvestment of
earnings, other long-term capital and short-term capital as shown in the balance of
payments (Henrik Hansen and John Rand, 2006). With the existence of FDI, the
MNCs have benefited from cheap labor wage and friendly incentives from host
countries. Those incentives include free renting of a certain property, tax free, and so
forth. For decades, FDI has been one of the means of generating more employment,
higher wage opportunities, more internationalized working opportunities and
environment, better domestic productivity, greater human capacity development, and
technology spillover. In long term, increasing FDI growth rate can provide more
baronial benefits to the host FDI countries by accumulating domestic salary standard,
making more efficient domestic product, and narrowing foreign-domestic product
quality and foreign exchange rate gaps.
However, potential disadvantages of inward FDI are significantly noticed. The
Marxist and dependency stances regard FDI as exploitation of the foreign investors
over developing countries. The domestic firms, for instance, may likely experience
unpleasant effects from competing against the foreign MNCs for talent employment
by offering higher salary, better working environment and technology, and more
preference of employees towards international company brands and benefits.
Therefore, the domestic firms are highly likely crowded out by the MNCs with a
great financial loss and chance of bankruptcy. Completely different from expectation
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of greater employment from the higher FDI, due to efficient machines, the MNCs
probably hire few numbers of highly educated people with a great amount of salary.
Consequently, it enlarges the gap of wage between highly educated and less educated
people. FDI in some developing and poor countries, in Sub-Saharan Africa for
instance, also experiences Balance of Payment (BOP) deficits, and sadly, most of the
time, the final profits go back to original MNCs countries while FDI recipient
countries also suffer national loss of tax revenue due to some ineffective FDI-
attraction policies and corruption between authority and the MNCs.
3.2 FDI in Cambodia, Vietnam and Laos
Thus, it is not a universal agreement whether there is always a positive relationship of
FDI and GDP in every country. It is important for this current paper to study
uncertainty of FDI-economic growth by emphasizing the relationship of FDI and
GDP in three developing countries including Cambodia, Laos, and Vietnam as a
whole. Regionally, these countries are developing and neighboring countries in
Southeast Asia and used to be under the colony of France in 19th century, named as
French Indochina. They are also members of ASEAN community (The Association
of South East Asia Nation). They, however, share different economic level and
population.
From the figure 1, net inflow FDI of these three countries has similar pattern, which
FDI (in term of GDP percentage) increased for several years from 1993 until 1996
and again from 2003 until 2007. Therefore, this can conclude that net inflows of FDI
within these countries become the only one hotspot rather individual countries in the
eyes of investors. Simply it means that one country has increased or decreased FDI,
the other have also gained more or less FDI at the same time.
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Figure 1: Foreign Direct Investment, net inflows (% of GDP)
3.2.1 FDI in Cambodia
Cambodia suffered political unrest and Pol Pot Regime from 1970 to 1979, and only
until the late1980s did the country begin to stabilize again. After political and
economic stabilization, this country had 7% of annual GDP growth rate for over these
20 years despite the fact of booming FDI growth (28 times of growing amount of FDI
in 2012 and of in 1993). It shows limitation of gaining full benefits of FDI in this
country. According to a report of USAID, Cambodia and the U.S. signed a Bilateral
Textile Agreement (BTA) in 1999 for labor standard promotion. According to the
report from the U.S. embassy in Vietnam, the BTA can shortly refers to commitment
of the partner countries to produce specific goods to export into the U.S. market with
changeable tariffs assessed by satisfaction on the products into the U.S. As a result, itoffered employment for over 250,000, mostly rural women, $1.6 billion export in
2003, equal to 73 percent of Cambodian total export and 40 percent of GDP. The U.S.
and Cambodia signed a bilateral Trade and Investment Framework Agreement
(TIFA) in 2006 and discussed about a promising bilateral investment treaty (BIT) in
2012. From 1994 to 2012, the inward FDI approved by the ministry invested on
tourism (50 percent), industry (24 percent), service (19 percent), and agriculture (7
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percent). CDC (council for development of Cambodia), which has right to review and
permit projects in Cambodia, issued the Report 2009-2012 that 176 investment
projects were approved with the investment capital of USD 2,511,763,171 million
and employment to 217,265 people in 2012. Economic growth in Cambodia gained
74 percent from FDI from 1998 to 2006. According to CDRI report 2010, the largest
amount of FDI in Cambodia was originally companies from China, which mostly
focused on garment industry and consequently offered a great amount of
employment. Cambodia experienced a remarkable poverty reduction and a huge
employment for the local people. Kubny and Voss (2013) surveyed on 27 Chinese
manufacturers in Cambodia and discovered that 98% of 26,439 employees were
Cambodian. However, jobs in high positions were for Chinese while typical and low-
skilled jobs were given to Cambodian employees.
3.2.2 FDI in Vietnam
Vietnam shows a global remark as one of the fastest economic growing countries in
the Southeast Asia region with annual average GDP growth rate of 7 percent after its
economic reform in 1986 with a great success of FDI attraction (Nguyen Ngoc Anh
and Nguyen Thang, 2007). Vietnam became the third biggest inward FDI recipient in
ASEAN after Singapore and Malaysia (Mirza and Giroud, 2004). This great success
is owing to newly market-economy hotspot, stable economic and political condition
with governments commitment, cheap and largely available national mineral
resources, young and quite educated human resource, potential domestic market and
export for EU and US markets. After wars and social unrest, in 1986, the Vietnamese
government declared policy "Doi Moi" to reform their economy into "a socialist-
oriented market". The investment in Vietnam in 2005 flew into manufacturing
(50.11%), real estate (9.45%), construction (7.81%), hotels and restaurants (7.78%),
transport (7.04%). The U.S. Government put its policy to improve post-Vietnam-war
bilateral relationships with "trade normalization" (Steve Parker, 2013). With
Vietnamese commitment on trade and investment reform for the BTA of the U.S., the
tariffs on the Vietnam's products dropped from an average of 40 percent to an average
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of three percent. Ha Quang Tuyen, head of the National Account System under the
General Statistics Office (GSO) mentioned that FDI firms contribute about 20 percent
of GDP in 2013, whereas only 2 percent in 1992. Exports by FDI enterprises made up
from about 64 percent in 2012 to 66.87 percent in 2013, based on a MPI report.
Haughton and Nguyen (2002) mentions that after the effectiveness of the BTA in
2001, the BTA accumulates 30 percent more of FDI into Vietnam for the first year,
and continues doubling the inflow. It also contributes 0.6 percent of the annual GDP.
The BTA encourages Vietnam to be more open-economy and eliminate obstacles to
FDI. After the open access to the US market, this earns more confidence and
attraction of FDI in Vietnam (Varamini Hossein and Vu Anh, 2007).
3.2.3 FDI in Laos
After French colony and war in Indochina until 1986, the economy of Laos (The Lao
Peoples Democratic Republic) reformed into a free market system under great
assistances from the U.S. Nowadays, Laos still remains one of the poorest countries
and one of the five communist countries in the world, based on a statement of the
USAID. Laos is regarded as one of the most world's challenging countries for
business startup with up to one-year license permission. With the objective to assist
economy of Laos, the U.S. also offers the BTA. Based on the same report of 2007,
Laos has had Unilateral Trade agreements with 39 countries and bilateral
investment treaties with more than 25 countries(Phonethavong, 2012). The NSEDP
2006-2010 by the Committee for Planning and Investment, published in October
2006, states that within the current situation of Laos (insufficient and limited human
and capital resource), the foreign investment is a major driver of the economic
development despite of exploitation of the natural resource and environment. For
example, FDI on rubber help eliminate rural poverty (Oliver Schoenweger and Alfons
llenberg, 2009). About 80 percent of the total FDI invest in medium or long term
into sector of resource, specifically mining, hydropower, and agriculture ( Oliver
Schoenweger & Alfons llenberg , 2009).
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3.3 GDP in Cambodia, Vietnam and Laos
Figure 2 shows the annual GDP growth rate in percentage of the three countries,
which have similar growing rate of 7 percent despite the currently different economic
level. It is also worth for noting that annual rates of GDP growth of Vietnam and
Laos fluctuate less than of Cambodia, based on figure 2. Volatility of the GDP of
Cambodia is remarkably high while the data GDP of Vietnam and Laos is generally
between 6 to 8 percent.
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Previous empirical studies show various results of the relationship of FDI and GDP
mainly as positive and negative based on different regions and historically available
data. Since these three selected countries have never been selected into studying the
causality of FDI and GDP, this paper attempts to extend another yield into economic
research resource.
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CHAPTER 4: DATA AND RESEARCH METHODOLOGY
There is a common problem to collect and analyze data from developing and/or least
developed countries. Because the selected countries are developing countries in the
Southeast Asia, the data periods of which are fewer than 30 years. Consequently,
testing each countrys data individually to determine the relationship of FDI and GDP
with at least 30 observations is unfeasible. Thus, to quantify the economic analysis of
at least 30 observations, these three selected countries data is combined together to
form a panel data.
Reasons of selecting only 20 years are because of the limitation of data available
from these countries due to wars and recovery of economic circumstances.
Specifically, Cambodia has recovered herself economically from early 1990s only,
and thus, GDP records are available only from 1993 onward.
4.1 Data
The study covers data of FDI and GDP of three countries: Cambodia, Laos, and
Vietnam over 20 years (from 1993 to 2012). GDP functions as indicator of economic
development. The data of GDP, the net inward FDI, and trade was gathered from
World Bank database under Southeast Asia region classification. For a quite stable
study (reduce short-run fluctuation) and due to data availability, the annual data was
utilized in this analysis. GDP (in current USD) functions as a dependent variable
while net inflow FDI (BoP in current USD) is explanatory variable and TRADE is a
controlled variable in our panel data analysis.
Secondly, the FDI stock data from UNCTAD is also utilized for the panel data testing
in order to compare if there is different result between the FDI stock and the net
inward FDI. The same GDP data from the World Bank would be used. The FDI stock
refers to the total value of capital, reserved, and retained profits plus indebted net of
the international company in a foreign country.
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:^
Thirdly, the FDI net inflow data from China into these three countries would replace
over the variable FDI for the third panel data testing; however, the data availability
only starts from 1993 due to shortage of data of inward FDI into Cambodia again
while the other two countries already had earlier year data of Chinese FDI. The FDI
data is extracted from China Statistical Year Book 1993-2013. The main reason of
testing the relationship of Chinese FDI and GDP within these countries is to confirm
that the three different tests would modify altogether the same results for more
reliability. *DCXXNU =FD ID>BDBDBD
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:]
/"J"XC>XNU =FD C#D>CLD 85) ?M Q"D="?@ :VVK`HW:H "E XC>LD> =FCD EIC>ED @C=C0
,FD #C>"CTXDE X?LZ*5&\ CD J?>D X" =FC< =FD #C>"CTXDE *5& CD I>DMD>>D@0
1M=D> =FD M">E= DaAC="?=C
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:_
select the best method between the FE and the RE models. The RE estimator is
assumed into null hypothesis and alternative hypothesis is the FE estimator (Hausman
1978).The Hasuman test can only activate on Random effect estimation by observing
on results of P value. Thus, The Random effect testing is always the first test,
followed by Hausman Test. If P value of Hausman test result is less than 5%, null
hypothesis of the RE-estimator is rejected and FE-estimator as alternative hypothesis
is accepted into further testing, and vice versa.
In some cases of equations and available data of variables, the Random effect
estimator cannot execute the result nor any further testing, so Pooled estimationwould be the final regression calculation to provide the result.
In Eviews 7 program, number of cross sections (N) must be more than number of
coefficients (x) in Random-effect analysis. Therefore, because only three countries
(N=3) are selected into model analysis, the number of independent variables would
be no more than two for Random-effect test (x
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:V
The parameter "2 captures other controlling variables that affect growth. In this study,
2 dummy variables are entrance of WTO of the chosen countries and Asian Financial
Crisis during 1997-1998. Cambodia, Vietnam, and Laos became the member of WTO
in year 2004, 2007, and 2013 respectively. Thus, dummy value will be given 0 before
these countries become WTO member and 1 when and after they become the WTO
members. The dummy of Asian financial crisis has value of 1 for all countries in
1997 and 1998 while the other years have dummy value of 0.
Each equation would have different four main results based on value of dummy 0 and
1. For dummy WTO, it is indispensable to note that due to current available data ofFDI from 1993 to 2012 while Laos has become member of WTO from 2013 only, so
when dummy value is 0, there are 45 observations with three cross-countries. When
dummy value is 1, the whole data of Laos would be unqualified, and thus, only two
cross-countries under study with only 14 observations under study. Therefore, when
dummy value is 1, the result is excluded. For dummy Asian financial CRISIS, the
valid result when dummy value is 0, simply without year 1997 and 1998 for Asian
financial crisis time, which might affect unusual economic conditions over theselected countries for regression. Thus, from now on, only two main dummy values
are valid when dummy WTO and CRISIS =0.
4.2.1 Model with net inward FDI
Only panel data method is measured with mainly three variables (GDP, FDI, and
trade) of these three countries from 1993 to 2012. The panel data regression model is
log(GDPt)= !+ "1log(FDIt)+"2dummy + "3log(trade)+ t (2)
where log(GDP)is the dependent variable, log(FDI) is the independent variable, !,
"1, "2 and "3are coefficients, t refers to time, and t refers to error in the analysis.
The "1and "3 enhance the impact of FDI and TRADE towards the economic growth,
so the parameters "1and "3 are expected to be positive.
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HW
Using FDI as an independent variable FDIt, it is assumed there is a contemporaneous
relationship between FDI and GDP. Likewise, this equation is to study whether these
two main factors exist over the period of time. Upon completion of the
contemporaneous analysis one lagged FDI logFDI(-1) will be applied to cross-
check whether the results are consistent with prior test.
log(GDPt)= !+ "1log(FDIt-1)+"2dummy + "3log(tradet)+t (3)
By using one-lagged FDI as an independent variable instead of FDI, it is assumed that
FDI takes time to impact on GDP. Given the endogeneity of the independent variableon the right side due to the contemporaneousness, lagged independent variable is
introduced on the right side.
It is vitally indispensable to scrutiny whether the relationship between FDI and GDP
is from the contemporaneous relationship or beyond the contemporaneous
relationship (lagged FDI). Therefore, another regression is applied:
log(GDPt)= !+ "1log(FDIt)+ "2log(FDIt-1)+"3dummy +t (4)
Where log(GDP) is the dependent variable, log(FDI) and log(FDIt-1) are the
independent variables, !, "1, "2 and "3are coefficients, t refers to time, and trefers to
error in the analysis.
4.2.2 Compare Model with inward FDI and with FDI stock
From previous research studies, it is claimed that relationship of FDI-GDP findings
can be changed when different data of FDI is selected whether it is net inward FDI or
FDI stock. Therefore, it is essential to clarify which types of FDI data is into analysis.
Comparison of regression model between FDI stock and inward FDI will simply
explain by following equations:
log(GDPt)= !+ "1log(FDIt) +"2dummy +t (5-1)
log(GDPt)= !+ "1log(FDIstockt)+ "2dummy +t (5-2)
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log(GDPt)= !+ "1log(FDIt) +"2log(FDIstockt)+ "3dummy +t (6)
where log(GDP) is the dependent variable, log(FDIstock) and log(FDI) are the
independent variables of net FDI stock and net inward FDI, !, "1,and "2 are
coefficients, t refers to time, and trefers to error in the analysis.
4.2.3 Model with Chinese FDI
The independent variable FDI from equation (1) will be substituted by a new
variable of Chinese FDI flowing into these three countries so that this new equation
(4) will be a backup testing the result of equation (1) and (2). In other words,relationship of the FDI of China and GDP is tested whether it has the identical
direction to the relationship of the total FDI-GDP. The panel data regression equation
turns to
log(GDPt)= !+ "1log(FDIchinat)+ "2dummy + t (7)
log(GDPt)= !+ "1log(FDIchinat)+ "2log(Tradet) + "3dummy + t (8)
where log(GDP) is the dependent variable, log(FDIchina) is the independent variable
of net inflow of FDI from China into selected countries, !, "1,and "2are coefficients,
t refers to time, and trefers to error in the analysis.
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CHAPTER 5: EMPIRICAL FINDINGS
5.1 Panel Data Testing
First, the only way to differentiate the choice between RE-estimation and FE-
estimation, the Random Effect estimator must be first tested by letting log(GDP t) be
the dependent variable, and log(FDIt) and log(tradet) be independent and controlling
variables. Then, the qualification between Random Effect and the Fixed Effect
estimator would be determined by P-value of the Hausman Testing. If the probability
of the Hausman test result is less than 5%, null hypothesis of Random Effect
estimation is rejected, and alternative hypothesis of Fixed Effect test is accepted, and
vice versa. As mentioned earlier, due to more accurate results from the Fixed Effect
estimation, the Fixed Effect testing is more expected to be qualified for regression
calculation. Inevitably, for particular regression tests, pooled estimation is used
whenever condition of Random effect testing is invalid.
5.2 Model with Total Inward FDI
In this part, we first examine whether there is a contemporaneous impact of FDI on
GDP, for which the results are reported in table 1, and then we check whether FDI
has long-term effect on GDP, beyond the contemporaneous impact, with the results
reported in Tables 2-3.
"#$%& '( JH;2C#;29> K&H4%; 9A 17L #>D "KM7J
"#$ %&''"( IF WTO=0 )* +,)-)-./Variable Coefficient Prob. Coefficient Prob.C 5.380908 0 5.900162 0LOGFDI 0.068097 0.0145 0.069745 0.0083LOGTRADE 0.708897 0 0.684277 0R-squared 0.990121 0.992762
Data testing into result: (table 1)
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log(GDPt)= 5.380908+ 0.068097log(FDIt)+ 0.708897log(tradet)+ t for D: WTO=0
log(GDPt)= 5.900162+ 0.069745 log(FDIt)+ 0.684277 log(tradet)+ tfor D: Crisis
With 3 cross-section countries from year 1993 to 2012, there are total 45 unbalanced
observations for the case of dummy of WTO value 0. There are total 53 unbalanced
observations for the case of dummy of Asian Crisis value 0. For both dummies, the
R-squared of 99% means that 99% in dependent variable is explained by independent
variable. From the Fixed-effect testing in table 1, FDI has the probability less than
5%, indicating that FDI has a significant relationship with GDP. The coefficient of
0.068 in logFDI implies that 1% increases in logFDI will lead to a 6.8% increase inlogGDP holding other variables constant.
For both cases of dummies, the results prove that there is positive and significant
impact of net inward FDI on the economy within selective countries.
"#$%& )( JH;2C#;29> K&H4%; 9A GM6'GN617L #>D GN6"KM7J
"#$ %&''"( IF WTO=0 )* +,)-)-./ 0123452 65778Variable Coefficient Prob. Coefficient Prob. Coefficient Prob.C 4.531573 0 5.332076 0 9:; /LAG1LOGFDI 0.04915 0.0577 0.079954 0.0011 /:/>?>@A /://9BLOGTRADE 0.762703 0 0.700432 0 /:A@@?= /R-squared 0.991508 0.994544 /:==?>A?
From Table 2, the equations of regression result for one-lagged FDI are
case D: WTO log(GDPt)= 4.531573+ 0.04915log(FDIt-1)+ 0.762703log(tradet)+ t
(P value of FDIt-1=5.77% >5%)case D: Crisis log(GDPt)= 5.332076 + 0.079954log(FDIt-1)+ 0.700432log(tradet)+ t
case no dummy log(GDPt)= 9:;+ 0.063log(FDIt-1)+ 0.72239log(tradet)+ t
Based on table 3 and table 4, with 3 cross-section countries from year 1993 to 2012,
there are total 42 unbalanced observations for the case of dummy of WTO value 0.
There are total 50 unbalanced observations for the case of dummy of Asian Crisis
value 0. For both dummies, the R-square still remains more than 99%.
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For one-lagged FDI as independent variable, there are two different scenarios based
on dummies. For WTO dummy, Fixed effect result shows P value of the FDI(-1) is
more than 5%, which means that the variable is not significant. However, for case of
crisis dummy, FDI(-1) is consistent with GDP. The different result might happen
because of different number of observations compatible with the dummies. Despite
the fact that two different dummies provide different significant and insignificant P-
values, from the total 56 unbalanced observations without any dummy variables
conditions (table 2), it might be concluded that one-lagged FDI still has significant
relationship with GDP. This conclusion will be clarified by next equation of log(FDI)
and lag1logFDI. The R-squared of 99% means that 99% in dependent variable is
explained by independent variable.
From the explanation based on table 1 and 2, the result can conclude that H1
hypothesis has confirmed to be true.
The study of the relationship between FDI and GDP whether it is from the
contemporaneous relationship or beyond the contemporaneous relationship (lagged
FDI) can only in case of dummy crisis =0 while the dummy WTO =0 can be studied
by pooled estimation only. The regression result:
Table 3: Estimation Result of LOGFDI and LAG1LOGFDI
899%&D
JH;2C#;29> !"#$% $''$()
"#$ %&''"( IF WTO=0 )* +,)-)-./Variable Coefficient Prob. Coefficient Prob.
C 9.178466 0 12.83522 0LOGFDI 0.394628 0.0094 0.22482 0.0263LAG1LOGFDI 0.301467 0.0421 0.290689 0.0056R-squared 0.823352 0.934261
Pooled estimation result of dummy WTO=0
log(GDPt)= 9.178466 + 0.394628 log(FDIt)+ 0.301467log(FDIt-1) +t (table 3)
With 3 cross-section countries from year 1993 to 2012, there are total 42 unbalanced
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HR
observations for the case of dummy of WTO value 0 while the R-square is more than
82%.
With 3 cross-section countries from year 1993 to 2012, there are total 42 unbalanced
observations for the case of dummy of WTO value 0. while the R-square is more than
82%. It means that 82% of dependent variable can be explained by independent
variable. From the Fixed-effect testing in table 3, FDI has the probability less than
5%, indicating that FDI has a significant relationship with GDP.
Fixed effect estimation result of dummy crisis=0 (table 3)log(GDPt)= 12.83522+ 0.22482log(FDIt)+ 0.290689log(FDIt-1) +t
With 3 cross-section countries from year 1993 to 2012, there are total 51 balanced
observations for the case of dummy of Asian Crisis value 0. For both dummies, the
R-square still remains more than 93%, which again refers to 93% of dependent
variable can be explained by FDI and one-lagged FDI as independent variables.
Different dummies offer different types of tests for calculation yet quite similar
results. For the dummy of WTO, only pooled estimation is suitable for the analysis
while more reliable testing of fixed estimation works on crisis dummy. The positive
coefficients and P-values of FDI and one-lagged FDI (P
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5.3 Compare Model with inward FDI and FDI stock
Literature has shown different results for the effect of inward FDI and FDI stock on
local economy growth. So here we are going to check whether these two types of FDI
have different economic impacts in these three countries in our sample. Our findings
for inward FDI and FDI stocks are reported in Tables 4-6 respectively.
The Random effect estimator testing is first used to test the equation (5) and equation
(6) which have log(GDP) as dependent variable and independent variables as
log(FDI) and log(FDIstock). The Hausman test shows result of p smaller than 5
percent, which means Fixed-effect estimator is appropriate for the both equations.
Table 4: Estimation result of LOGFDI
!"#$% $''$() !"#$% $''$()
"#$ %&''"( IF WTO=0 )* +,)-)-./Variable Coefficient Prob. Coefficient Prob.C 15.65231 0 13.23919 0LOGFDI 0.357966 0 0.491279 0R-squared 0.893861 0.918883
The regression model between FDI stock and inward FDI will simply explain by
following equation results:
Fixed effect estimation result of dummy WTO=0
log(GDPt)= 15.65231+ 0.357966log(FDIt) +t (table 4) (5-1)
log(GDPt)= 19.13735+ 0.458946log(FDIstockt)+ t (table 5) (5-2)
With 3 cross-section countries from year 1993 to 2012, there are total 45 unbalanced
observations for the case of dummy of WTO value 0 while the R-square is more than89% for equation (5-1) and more than 93% for equation (5-2). It means that variable
inward FDI can explain 89% of variable GDP and 93% of variable FDI stock variable
GDP. Within the positive coefficient of independent variables, it can conclude there
is positive relationship of inward FDI and GDP and of FDI stock and GDP. The P-
value is less than 5% (P
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Table 5: Estimation result of LOGFDISTOCK
!"#$% $''$() !"#$% $''$()
"#$ %&''"( IF WTO=0 )* +,)-)-./Variable Coefficient Prob. Coefficient Prob.C 19.13735 0 ;B:9B/;< 0LOGFDISTOCK 0.458946 0 /:99/;> 0R-squared 0.936325 /:=9 !"#$% $''$()
"#$ %&''"( IF WTO=0 )* +,)-)-./Variable Coefficient Prob. Coefficient Prob.C ;>:/9A?9 0 ;9:??99> 0
LOGFDI /:;=@ /://;@ /:@@?B@B 0%&'*")-C&+D /:?A9/@> / /:=BB?
estimation result: (table 6)
dummy WTO=0
log(GDPt)= ;>:/9A?9+ /: ;=@log(FDIt)+ /: ?A9/@>Eog(FDIstockt) +t (6)
dummy crisis=0
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log(GDPt)= 15.33556+ 0.223828log(FDIt)+ 0.401644log(FDIstockt) +t (6)
From table 6, under pooled estimation regression, with 3 cross-section countries from
year 1993 to 2012, there are total 45 unbalanced observations for the case of dummy
of WTO value 0 and the R-square is more than 95%. For dummy crisis, under fixed
effect estimation, there are total 54 balanced observations and the R-square is more
than 96%. All coefficients are positive and P values of the independent variables are
less than 5%; thus, the relationship of FDI(inward), FDI(stock) and GDP is confirmed
to be positive and statistically significant.
From the whole equations of the (5) and (6) regardless of dummies, it can conclude
that the relationship of FDI and GDP for the selected countries is stably positive and
significant for both types of FDI data. This can conclude that the hypothesis H3 is
true.
5.4 Model with Chinese FDI
China is one of the most important FDI sources for the three countries in our sample.
So here we would like to examine whether the FDI from China has similar impacts on
local economic growth in these three countries. The findings are similar in that FDI
from China positively contributes to local economic growth. The findings are
reported in Tables 7-9.
The same methodology is used to calculate the equation by starting from Random
effect testing, and using Hausman Test to clarify qualified effect estimator whether
Random or Fixed effect will be further put into calculation. In some cases, when
Random Effect is not be able to test the equation, pooled estimation will be used for
the analysis.
"#$%& .( JH;2C#;29> K&H4%; 9A GN617LBOLPM
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HV
,FG647 HIIJK2 ,FG647 HIIJK2
"#$ E4LL6M )* 0C&./ )* +,)-)-./
#FN1FOEJ +4JII1K1JG2 (N4O: +4JII1K1JG2 (N4O:
+ @;:/
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KW
Random effect result when dummy WTO=0 (table 8)
log(GDPt)=
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K:
CHAPTER 6: CONCLUSION AND RECOMMENDATION
The main objective of this paper is to scrutinize what roles FDI has on the economic
growth of Indochina countries namely: Cambodia, Vietnam, and Laos. Panel data
testing was applied to assess whether FDI and Trade have a positive or negative
connection. Based on various testing, findings from the panel data regression prove
that there is a significantly positive relationship of FDI and GDP within these three
countries. The positive and significant elasticity from FDI concludes that FDI is a key
factor of GDP growth.
To confirm the credibility of conclusion drawn from the earlier research study on the
FDI-GDP relationship, different types of the FDI were incorporated into
consideration and calculation. The testing on total net inflow FDI and FDI stock into
these countries has provided the same result. Based on one-lagged net inward FDI
data analysis result, the conclusion can precisely state that FDI and GDP of these
countries are contemporaneous and it may take some period of time for FDI to boost
the whole economy. However, the inward FDI from China into these three countries
cannot be a significant impact on the total economic growth based on the result of
econometric analysis.
Prior studies about relationship of FDI and GDP for these two decades over the
selected countries, it is agreeable that FDI has contributed remarkably to the
economic growth and poverty reduction. The current paper recommends that more
FDI is highly encouraged into these countries to contribute GDP prosperity further,
yet it is also advisable to examine the negative impacts of FDI too.
This paper did not focus on long-term effects of FDI of these countries in the future
and any other possible relationships of FDI and other important variables including
productivity, and domestic firms. Therefore, new studies should focus on the long-
term economic growth in terms of human resource and productivity development,
technological spillover, and FDI-policy effectiveness.
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Appendices
Total net inward FDI of Cambodia, Vietnam, and Laos from 1993 to 2012
GDP and FDI convert into log(GDP) and log(FDI)
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Net FDI stock and Chinese FDI in Cambodia, Vietnam, and Laos
Year
Chinese FDI FDISTOCK
Cambodia Vietnam Laos Cambodia Vietnam Laos
1993 NA 11610000 NA 125000000 2020000000 57150000
1994 NA NA 50000 194000000 3960000000 116000000
1995 1310000 28300000 NA 356000000 5740000000 211000000
1996 7440000 1450000 150000 942000000 8140000000 371000000
1997 5450000 1540000 390000 956000000 10400000000 4580000001998 2900000 14140000 1120000 1200000000 12000000000 503000000
1999 2480000 130000 NA 1430000000 13400000000 554000000
2000 1940000 560000 3070000 1580000000 14700000000 588000000
2001 9300000 1480000 1040000 1730000000 16000000000 612000000
2002 13740000 2510000 5150000 1870000000 17400000000 617000000
2003 12520000 3380000000 400000 1960000000 18900000000 636000000
2004 20690000 3120000000 4250000 2090000000 20500000000 653000000
2005 2760000 2150000000 4210000 2470000000 22500000000 681000000
2006 2120000 2140000000 6870000 2950000000 24900000000 868000000
2007 6340000 730000 3000000 3820000000 31600000000 1190000000
2008 2920000 2070000 6700000 4640000000 41100000000 1420000000
2009 13370000 4420000 2430000 5180000000 48700000000 1610000000
2010 10350000 20790000 9450000 5960000000 56700000000 1890000000
2011 17370000 8880000 5880000 6850000000 64200000000 2190000000
2012 16600000 2870000 2000000 8410000000 72500000000 2480000000
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log(GDPt)= !+ "1log(FDIt)+"2dummy + "3log(trade)+ t (2)
Correlated Random Effects - Hausman TestEquation: Untitled
Test cross-section random effectsTest Summary Chi-Sq. Statistic Chi-Sq. d.f. Prob.Cross-sectionrandom 18.524683 2 0.0001
Fixed effect
Dependent Variable: LOGGDPMethod: Panel Least SquaresSample: 1993 2012 IF WTO=0Periods included: 20Cross-sections included: 3Total panel (unbalanced) observations: 45Variable Coefficient Std. Error t-Statistic Prob.C 5.380908 0.673932 7.984354 0LOGFDI 0.068097 0.026651 2.555149 0.0145LOGTRADE 0.708897 0.035909 19.74166 0
Effects Specification
Cross-section fixed (dummy variables)
R-squared 0.990121 Mean dependent var 22.53962Adjusted R-squared 0.989133 S.D. dependent var 1.217516S.E. of regression 0.126922 Akaike info criterion -1.186043Sum squared resid 0.644372 Schwarz criterion -0.985302Log likelihood 31.68596 Hannan-Quinn criter. -1.111209
F-statistic 1002.198 Durbin-Watson stat 0.484014Prob(F-statistic) 0
Dummy crisis =0
Hausman Test
Correlated Random Effects - Hausman TestEquation: UntitledTest cross-section random effectsTest Summary Chi-Sq. Chi-Sq. Prob.
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Statistic d.f.
Cross-section random 62.589427 2 0
Fixed effectDependent Variable: LOGGDPMethod: Panel Least SquaresSample: 1993 2012 IF CRISIS=0Periods included: 18Cross-sections included: 3Total panel (unbalanced) observations: 53
Variable Coefficient Std. Error t-Statistic Prob.
C 5.900162 0.470514 12.53982 0LOGFDI 0.069745 0.025322 2.754342 0.0083LOGTRADE 0.684277 0.030892 22.1505 0
Effects Specification
Cross-section fixed (dummy variables)
R-squared 0.992762 Mean dependent var 22.94192Adjusted R-squared 0.992159 S.D. dependent var 1.386225S.E. of regression 0.122751 Akaike info criterion -1.267733Sum squared resid 0.723252 Schwarz criterion -1.081856Log likelihood 38.59492 Hannan-Quinn criter. -1.196254F-statistic 1645.914 Durbin-Watson stat 0.537168Prob(F-statistic) 0
log(GDPt)= !+ "1log(FDIt-1)+"2dummy + "3log(tradet)+t (3)
Dummy WTO=0Correlated Random Effects - Hausman TestEquation: UntitledTest cross-section random effects
Test Summary Chi-Sq. StatisticChi-Sq.d.f. Prob.
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Cross-section random 74.885826 2 0
Fixed effectDependent Variable: LOGGDPMethod: Panel Least SquaresSample: 1993 2012 IF WTO=0Periods included: 19Cross-sections included: 3Total panel (unbalanced) observations: 42
Variable Coefficient Std. Error t-Statistic Prob.
C 4.531573 0.709528 6.386741 0LAG1LOGFDI 0.04915 0.025091 1.958863 0.0577LOGTRADE 0.762703 0.036465 20.91589 0
Effects Specification
Cross-section fixed (dummy variables)
R-squared 0.991508 Mean dependent var 22.57908
Adjusted R-squared 0.990589 S.D. dependent var 1.224154S.E. of regression 0.118753 Akaike info criterion -1.312203Sum squared resid 0.521782 Schwarz criterion -1.105338Log likelihood 32.55627 Hannan-Quinn criter. -1.236379F-statistic 1079.953 Durbin-Watson stat 0.486485Prob(F-statistic) 0
Dummy crisis=0
Hausman test
Correlated Random Effects - Hausman TestEquation: UntitledTest cross-section random effects
Test Summary Chi-Sq. StatisticChi-Sq.d.f. Prob.
Cross-section random 74.885826 2 0
Fixed effectDependent Variable: LOGGDP
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Method: Panel Least SquaresSample: 1993 2012 IF CRISIS=0
Periods included: 17Cross-sections included: 3Total panel (unbalanced) observations: 50
Variable Coefficient Std. Error t-Statistic Prob.
C 5.332076 0.463674 11.49962 0LAG1LOGFDI 0.079954 0.022955 3.48312 0.0011LOGTRADE 0.700432 0.029729 23.56085 0
Effects Specification
Cross-section fixed (dummy variables)
R-squared 0.994544 Mean dependent var 22.9992Adjusted R-squared 0.994059 S.D. dependent var 1.386702S.E. of regression 0.106882 Akaike info criterion -1.539541Sum squared resid 0.51407 Schwarz criterion -1.348339Log likelihood 43.48853 Hannan-Quinn criter. -1.46673F-statistic 2050.766 Durbin-Watson stat 0.548614
Prob(F-statistic) 0
log(GDPt)= !+ "1log(FDIt)+ "2log(FDIt-1)+"3dummy +t (4)
dummy WTO
pooled estimation
Dependent Variable: LOGGDPMethod: Panel Least Squares
Sample: 1993 2012 IF WTO=0Periods included: 19Cross-sections included: 3Total panel (unbalanced) observations: 42Variable Coefficient Std. Error t-Statistic Prob.C 9.178466 0.99729 9.203405 0LOGFDI 0.394628 0.14451 2.730801 0.0094LAG1LOGFDI 0.301467 0.143469 2.101264 0.0421R-squared 0.823352 Mean dependent var 22.57908
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Adjusted R-squared 0.814293 S.D. dependent var 1.224154S.E. of regression 0.527534 Akaike info criterion 1.627541
Sum squared resid 10.85338 Schwarz criterion 1.75166Log likelihood -31.17836 Hannan-Quinn criter. 1.673035F-statistic 90.88894 Durbin-Watson stat 0.251644Prob(F-statistic) 0
Dummy crisis
Correlated Random Effects - Hausman TestEquation: Untitled
Test cross-section random effects
Test Summary Chi-Sq. StatisticChi-Sq.d.f. Prob.
Cross-section random 74.885826 2 0Fixed effect
Dependent Variable: LOGGDPMethod: Panel Least SquaresSample: 1993 2012 IF CRISIS=0
Periods included: 17Cross-sections included: 3Total panel (balanced) observations: 51
Variable Coefficient Std. Error t-Statistic Prob.
C 12.83522 1.061045 12.09677 0LOGFDI 0.22482 0.097941 2.295454 0.0263LAG1LOGFDI 0.290689 0.099894 2.90998 0.0056
Effects Specification
Cross-section fixed (dummy variables)
R-squared 0.934261 Mean dependent var 23.00638Adjusted R-squared 0.928545 S.D. dependent var 1.37372S.E. of regression 0.36721 Akaike info criterion 0.927128Sum squared resid 6.202785 Schwarz criterion 1.116523Log likelihood -18.64177 Hannan-Quinn criter. 0.999502
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F-statistic 163.4354 Durbin-Watson stat 0.370418Prob(F-statistic) 0
log(GDPt)= !+ "1log(FDIt) +"2dummy +t (5)
dummy WTO
Correlated Random Effects - Hausman TestEquation: UntitledTest cross-section random effects
Test Summary
Chi-Sq.
Statistic
Chi-Sq.
d.f. Prob.Cross-section random 15.982927 1 0.0001
FIXED EFFECT
Dependent Variable: LOGGDPMethod: Panel Least Squares
Sample: 1993 2012 IF WTO=0
Periods included: 20Cross-sections included: 3Total panel (unbalanced) observations: 45
Variable CoefficientStd.Error t-Statistic Prob.
C 15.65231 1.386768 11.2869 0LOGFDI 0.357966 0.072006 4.971304 0
Effects Specification
Cross-section fixed (dummy variables)
R-squared 0.893861 Mean dependent var 22.53962Adjusted R-squared 0.886095 S.D. dependent var 1.217516
S.E. of regression 0.410909Akaike info
criterion 1.143798Sum squared 6.9227 Schwarz criterion 1.30439
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resid
Log likelihood -21.73546
Hannan-Quinn
criter. 1.203665F-statistic 115.0958 Durbin-Watson stat 0.245338Prob(F-statistic) 0
DUMMY CRISIS
Correlated Random Effects - Hausman TestEquation: UntitledTest cross-section random effects
Test Summary Chi-Sq. Statistic Chi-Sq.d.f. Prob.
Cross-section random 32.367104 2 0
Dependent Variable: LOGGDPMethod: Panel Least Squares
Sample: 1993 2012 IF CRISIS=0
Periods included: 18Cross-sections included: 3Total panel (balanced) observations: 54
Variable Coefficient Std. Error t-Statistic Prob.
C 13.23919 1.061153 12.47623 0LOGFDI 0.491279 0.053614 9.163198 0
Effects Specification
Cross-section fixed (dummy variables)
R-squared 0.918883 Mean dependent var 22.94976Adjusted R-squared 0.914016 S.D. dependent var 1.374292S.E. of regression 0.402984 Akaike info criterion 1.091345Sum squared resid 8.119789 Schwarz criterion 1.238677Log likelihood -25.46632 Hannan-Quinn criter. 1.148166F-statistic 188.7983 Durbin-Watson stat 0.457948Prob(F-statistic) 0
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log(GDPt)= !+ "1log(FDIstockt)+ "2dummy +t (6)
DUMMY WTO
Correlated Random Effects - Hausman TestEquation: UntitledTest cross-section random effects
Test Summary Chi-Sq. StatisticChi-Sq.d.f. Prob.
Cross-section random 17.116181 1 0
FIXED EFFECTDependent Variable: LOGGDPMethod: Panel Least Squares
Sample: 1993 2012 IF WTO=0Periods included: 20Cross-sections included: 3Total panel (unbalanced) observations: 45
Variable CoefficientStd.Error t-Statistic Prob.
C 19.13735 0.413695 46.25961 0LOGFDISTOCK 0.458946 0.055437 8.278729 0
Effects Specification
Cross-section fixed (dummy variables)
R-squared 0.936325 Mean dependent var 22.53962Adjusted R-squared 0.931666 S.D. dependent var 1.217516
S.E. of regression 0.318268Akaike info
criterion 0.632842Sum squaredresid 4.15308 Schwarz criterion 0.793435
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Log likelihood -10.23896Hannan-Quinn
criter. 0.69271
F-statistic 200.9653 Durbin-Watson stat 0.182328Prob(F-statistic) 0DUMMY CRISISCorrelated Random Effects - Hausman TestEquation: UntitledTest cross-section random effects
Test Summary Chi-Sq. StatisticChi-Sq.d.f. Prob.
Cross-section random 17.116181 1 0
FIXED EFFECTDependent Variable: LOGGDPMethod: Panel Least Squares
Sample: 1993 2012 IF CRISIS=0Periods included: 18Cross-sections included: 3Total panel (balanced) observations: 54
Variable Coefficient Std. Error t-Statistic Prob.
C 18.58014 0.3224 57.63079 0LOGFDISTOCK 0.55016 0.040258 13.66576 0
Effects Specification
Cross-section fixed (dummy variables)
R-squared 0.954101 Mean dependent var 22.94976Adjusted R-squared 0.951347 S.D. dependent var 1.374292S.E. of regression 0.303134 Akaike info criterion 0.521901Sum squared resid 4.594499 Schwarz criterion 0.669233Log likelihood -10.09132 Hannan-Quinn criter. 0.578721F-statistic 346.4484 Durbin-Watson stat 0.297251Prob(F-statistic) 0
Dummy WTO
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Correlated Random Effects - Hausman TestEquation: Untitled
Test cross-section random effects
Test Summary Chi-Sq. StatisticChi-Sq.d.f. Prob.
Cross-section random 16.846615 2 0.0002Fixed effect result
Dependent Variable: LOGGDPMethod: Panel Least Squares
Sample: 1993 2012 IF WTO=0Periods included: 20Cross-sections included: 3Total panel (unbalanced) observations: 45
VariableCoefficient
Std.Error t-Statistic Prob.
C 16.05735 0.953329 16.84345 0LOGFDI 0.192416 0.054981 3.499693 0.0012
LOGFDISTOCK 0.375026 0.05465 6.862294 0
Effects Specification
Cross-section fixed (dummy variables)
R-squared 0.951252 Mean dependent var 22.53962Adjusted R-squared 0.946377 S.D. dependent var 1.217516S.E. of regression 0.281936 Akaike info criterion 0.410168Sum squared resid 3.179522 Schwarz criterion 0.610908
Log likelihood -4.228772 Hannan-Quinn criter. 0.485002F-statistic 195.1352 Durbin-Watson stat 0.34484Prob(F-statistic) 0
Dummy crisis
Correlated Random Effects - Hausman TestEquation: UntitledTest cross-section random effects
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Test Summary Chi-Sq. Statistic Chi-Sq. d.f. Prob.
Cross-sectionrandom 35.993425 2 0
Fixed effect
Dependent Variable: LOGGDPMethod: Panel Least SquaresSample: 1993 2012 IF CRISIS=0Periods included: 18Cross-sections included: 3
Total panel (balanced) observations: 54
Variable CoefficientStd.Error t-Statistic Prob.
C 15.33556 0.692511 22.14487 0LOGFDI 0.223828 0.044171 5.06732 0LOGFDISTOCK 0.401644 0.044092 9.109162 0
Effects Specification
Cross-section fixed (dummy variables)R-squared 0.969883 Mean dependent var 22.94976Adjusted R-squared 0.967425 S.D. dependent var 1.374292
S.E. of regression 0.248041Akaike info
criterion 0.137576Sum squaredresid 3.014693 Schwarz criterion 0.321741
Log likelihood 1.285442Hannan-Quinn
criter. 0.208602F-statistic 394.4992 Durbin-Watson stat 0.604068
Prob(F-statistic) 0
log(GDPt)= !+ "1log(FDIchina)+ "2dummy + t (7)
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