1
A Statistical Analysis on the Relationship between GDP Growth and FDI, Trade
Liberalisation and Gross Capital Formation in Turkey
2
Executive Summary
The choice to perform this study was after successful submission and approval of a proposal
on assessment topic dubbed: “the impact of foreign direct investment inflow on economic
growth in Turkey”. Intuitively, it can be stated that foreign direct investment inflow is a
direct way to improve the domestic economy because of the value coming from the investors.
In this dissertation the researcher benchmarked the methodology on what other scholars
attempted but with determination to have been as objective as possible. Data considered in
this dissertation consisted of annual figures ranging in the period 1990-2015 (25 years) where
the variables of the study included real gross domestic product (GDP) and FDI (net) inflows.
The source of data was World Bank Development Indicators (World Bank, 2016). The
moderating variables were balance of trade (trade liberalisation), exchange rates, and interest
rates. The correlation matrix depicts that FDI_GDP correlated with Y at .377; this was
indication of a weak but positive correlation. It meant the high trends for Y did relate to the
high trends of FDI_GDP but then in a weak manner. The regression analysis has the Anova
results standing at .8360; it means there is no significant relationship between Y (GDP) and
FDI_GDP and so a relationship between them was not tenable. The null hypothesis that Y
does not Granger FDI_GDP (.000) was rejected. Then FDI_GDP does not Granger Cause Y
(.005) was rejected. The rejection of the two null hypothesis asserted that there existed a
bidirectional causality between FDI_GDP and Y.
Keywords: FDI, Turkey, GDP, GCF, Trade Liberalisation, Granger Causality
3
Table of Contents
Executive Summary ................................................................................................................... 2
Declaration ................................................................................................................................. 4
Acknowledgments...................................................................................................................... 5
List of Tables ............................................................................................................................. 6
List of Figures ............................................................................................................................ 7
INTRODUCTION ..................................................................................................................... 8
Problem Statement ............................................................................................................... 11
Scope of the study ................................................................................................................ 12
Aims and Objectives ................................................................................................................ 12
Significance of the study ...................................................................................................... 12
Dissertation structure ............................................................................................................... 12
LITERATURE REVIEW ........................................................................................................ 15
Analysis of Empirical Studies .............................................................................................. 15
Analysis of empirical studies recently completed about Turkey’s FDI and Economic
Growth .................................................................................................................................. 18
Summary of chapter ................................................................................................................. 22
METHODOLOGY & DATA .................................................................................................. 23
Model specification .............................................................................................................. 24
Data Analysis ....................................................................................................................... 25
COUNTRY PROFILE OF TURKEY ...................................................................................... 26
MODEL APPLICATION AND FINDINGS ........................................................................... 30
Data Screening ..................................................................................................................... 30
Descriptive Statistics ................................................................................................................ 35
Relationship analysis of FDI inflows and economic growth ................................................... 38
Forecast analysis of FDI inflows and economic growth in Turkey ......................................... 41
Granger causality tests results .................................................................................................. 43
Summary of Chapter ................................................................................................................ 44
CONCLUSION, IMPLICATIONS AND FUTURE RESEARCH ......................................... 45
Re-evaluation of key primary and secondary findings......................................................... 45
Economic Policy Implications to Turkey ............................................................................. 47
Recommendations for future research ..................................................................................... 48
Career implications of the research .......................................................................................... 48
References ................................................................................................................................ 49
Appendix .............................................................................................................................. 51
Appendix A: Dixon test results for GCF ............................................................................. 51
Appendix B: Dixon test results for NX ................................................................................ 52
Appendix C: Screenshot from EViews for FDI_GDP ......................................................... 53
Appendix D: Screenshot from EViews for GCF_GDP ........................................................ 54
Appendix E: Screenshot from EViews for GCF_GDP ........................................................ 55
Appendix F: Screenshot on Granger tests from EViews ..................................................... 56
4
Declaration
I hereby affirm that the completed work is my own effort to prepare an original work!
However, all external sources have been duly acknowledged
5
Acknowledgments
As I cross over, the completed dissertation would not have seen the light of day were it not
for the support and ruthless guidance from my supervisor. The supervision was critical to
everything going into this dissertation. I also thank the support of the university in general for
the good library resources I got access to when preparing this dissertation. More thanks shall
go to my family members who have been very supportive to me as I prepared my
dissertation.
6
List of Tables
Table 1: Regional FDI inflows, M&As and Greenfield projects
Table 2: Summary of empirical studies
Table 3: Dixon test results for the FDI/GDP (Independent Variable)
Table 4: Normal distribution test results for Y variable (GDP)
Table 5: Unit Root test results for Y (GDP variable)
Table 6: KPSS test results for Y variable
Table 7: Unit root test results for FDI_GDP
Table 8: Unit root test results for FDI_GDP at 1st difference
Table 9: Unit root test results for GCF_GDP at 1st difference
Table 10: Unit root test results for NX_GDP at 1st difference
Table 11: Descriptive statistics for main variables
Table 12: Correlation analysis test results
Table 13: Regression analysis test results I
Table 14: Regression analysis test results II
Table 15: Trend analysis for forecast data
Table 16: Granger causality test results
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List of Figures
Figure 1: Cultural dimensions for Turkey
Figure 2: Turkey bond yield curve
Figure 3: Inflation trend in Turkey
Figure 4: Exchange rates in Turkey 2016
Figure 5: Graphical analysis on GDP 1990-2015 in Turkey
Figure 6: Graphical analysis on FDI 1990-2015 in Turkey
Figure 7: Graphical analysis on GCF 1990-2015 in Turkey
Figure 8: Graphical analysis on NX 1990-2015 in Turkey
Figure 9: Graphical analysis on model regression I
Figure 10: Graphical analysis on model regression II
8
INTRODUCTION
The choice to perform this study was after successful submission and approval of a
proposal on assessment topic dubbed: “the impact of foreign direct investment inflow on
economic growth in Turkey”. Intuitively, it can be stated that foreign direct investment
inflow is a direct way to improve the domestic economy because of the value coming from
the investors. Looking around, there would be ongoing projects that in turn create
opportunities for employment and nationals being the beneficiaries. However, in order to
distinguish between perception from facts and myths from truths the proposed study will base
its insights on deeper data analysis so as to illustrate fully on the supported relationship
between foreign direct investment inflow and economic growth in the case of Turkey. Indeed,
FDI is a major development indicator for any country hence being sure that it is impacting
positively on economic growth is not only essential but a pathway to shape foreign policy.
Foreign direct investment (FDI) serves as an investment which is undertaken by
another company or individual in a different country; normally, this is motivated by business
interests such as establishment of business operations or acquisition of business assets in a
different country. Foreign direct investments are not portfolio investments where the latter
involves purchasing equities of foreign-based firms. The main component of foreign direct
investment is that it serves as an investment which leads to effective control or substantial
influence in decision making of a foreign company.
Noteworthy, is that foreign direct investment takes place more in open economies as
compared to tight regulated economies since the former offer a skilled workforce as well as
higher potential for growth prospects for the investors. Indeed, foreign direct investment is
not comparable to capital investment since it may incorporate management and technology
provisions too. Turning to the methods of foreign direct investment is that it can be
implemented in a number of ways such as subsidiary or mergers or joint ventures or even
acquisitions in a foreign country. According to the Organization of Economic Cooperation
and Development (OECD), the limit for a foreign direct investment controlling interest can
only be 10 per cent in terms of ownership.
Foreign direct investments can be classified as either horizontal, vertical and
conglomerate. A horizontal direct investment is whereby the investor establishes a similar
kind of business operation as that which it operates in the home country. A good case would
be where a computer hardware company opens workshops on related maintenance services
for hardware in China or Singapore or South Africa. On the other hand, vertical direct
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investment involves the case where a foreign company invests a different but related venture
in a different country. A good example would be where a manufacturing company say for
building materials such as Cement ventures in another country and runs a logistics firm for
supplies of general construction products to developers. In the case of conglomerate foreign
direct investment is where a foreign company ventures in a totally different venture in a home
country. For instance, Apple Inc would be indulging in a conglomerate direct investment in
country B if it takes a path of manufacturing fashion clothes.
In a report prepared by the UNCTAD (2016) it was held that global FDI had grown
by 36% to record an estimate of US$ 1.7 trillion being the highest level after the occurrence
of the global financial crisis of 2008-2009. The report further depicts that there was witnessed
strong growth in the United States and the European Union where FDI grew by four times.
The developed countries are believed to account for about 55% in relation to FDI inflows in
the period 2015. The growth was attributed to cross-border mergers and acquisitions and a
limited distribution coming from greenfield investments. On the other hand, for the
developing economies it was alleged that FDI had attained a new high value estimate at
US$741 billion which was a 5% growth compared to 2014.
Developing Asia recorded FDI flows exceeding half a trillion US dollars and
emerging as the largest recipient of FDI globally. The region is said to account for about one
third of FDI flows at the global level. However, in the case of Africa and Latin America
including the Caribbean FDI did not show sharp growth trends a problem attributed to the
high fluctuation on commodity prices. Other findings depicted that cross-border merges and
acquisitions had grown by 61% in the year 2015 and greenfield investment projects having
negligible growth from previous years. Table 1 below illustrates the trends in FDI inflows,
announced greenfield projects, and cross-border M&As by region 2014-2015.
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Table 1: Regional FDI inflows, M&As and Greenfield projects
Region/Economy FDI Inflows
Cross-Border
M&As
Announced greenfield project
estimates
2014 2015 Change (%) 2014 2015 Change
(%)
2014 2015 Change (%)
World 1,24
5
1,699 36.47% 398.9 643.7 61.37% 714.3 720.7 0.90%
Developed Economies 493 936 89.86% 274.5 566.8 106.48% 229.6 247.4 7.75%
European Union 254 426 67.72% 160.6 269.2 67.62% 122.4 139.8 14.22%
North Africa 146 429 193.84% 44.1 242.3 449.43% 77.7 76.4 -1.67%
Developing Economies 703 741 5.41% 120.1 67.6 -43.71% 459.1 439.4 -4.29%
Africa 55 38 -30.91% 5.1 20.4 300.00% 88 71.1 -19.20%
Latin America and the
Caribbean
170 151 -11.18% 25.5 10.1 -60.39% 89.3 68.6 -23.18%
Developing Asia 475 548 15.37% 89.3 35.3 -60.47% 280.6 299.3 6.66%
Transition Economies 49 22 -55.10% 4.2 9.3 121.43% 25.7 33.8 31.52%
Source: (UNCTAD)
The schedule data above is a depiction of the average FDI inflows accumulated globally and regionally. For instance, Turkey could be
categorised under “Developing Asia”. Later, the researcher did rank Turkey in terms of FDI vis a vis the performance in the world at large. More
of this featured in chapter four and five of the dissertation.
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Problem Statement
In a report by the UNCTAD (2013a) foreign direct investment inflows grew from
US$ 13.346 billion to US$ 2.002 trillion in the world. In the same respect, studies referring to
the effect of FDI inflows towards national economies emerged the more. Therefore, there is
wide literature and research on the effects of FDI inflows to the host region; where the same
increases financial development, productivity, technological progress and in the overall
economic growth. It is held that FDI inflows are highly expected to impact positively to the
economic growth by influencing national economies i.e. technology transfer, expertise,
enhancement of labour force, improving enterprise development, bettering global trade, and
integration, and enabling to create a much more competitive business environment (OECD,
2002).
According to De Mello (1997 cited in Bayar, 2014) FDI inflows contribute to the
economic growth but in the short-run; this is held by the traditional neo-classical growth
models. The reason is because economies would return or flow back to their “steady state
with diminishing returns to capital inputs in the long run” (Cited Bayar, 2014 p.69). The
endogenous growth models opine that governments may affect economic growth in the long
run through economic policies. In relation to the same is that FDI affects economic growth in
both short and long run via transfer of technology, productivity spillovers, developing and
enhancing skilled labour, and in creating a more competitive business environment.
Research indicates that Turkey did not attract FDI inflows as a result of much
occurrence of the financial crises not to mention the political instability in the period 1980s
and 1990s. However, in the year 2001 Turkey began gaining momentum in its FDI inflows
which increased following the increasing rate of privatisation; in 2001 it was the time Turkey
gained political stability and began to recover from its financial crisis (Bayar, 2014).
Noteworthy, is that before 1990 Turkey has created many obstacles that hindered
foreign direct investment and trade. Later, the Turkish government ushered a number of
economic reforms where FDI received major boost and encouragement; the same saw the
liberalization of foreign exchange market including removal of trade restrictions which led
Turkey to be more attractive of foreign resource as well as international trade (Ilgun et al,
2010). The new legislation on foreign investment enacted in 2003 was well positioned in
encouraging FDI inflow to Turkey; as indicated earlier in the study, the new law does not
require foreign investors to receive official permission to invest in their preferred sectors
while in Turkey (Aktar and Ozturk, 2009). As per the study by Kiliç & Ateû (2009) foreign
12
investors in Turkey investors target more on sectors such as mining, agriculture, services and
manufacturing.
Scope of the study
The focus of the proposal is to analyse the impact of foreign direct investment on
economic growth in Turkey for the period 1990-2015.
Aims and Objectives
The aim of the study was to establish whether there has been any supported causal
relationship between FDI inflows and economic growth in Turkey.
The specific objectives read as follows:
1) To illustrate the trend of FDI inflows and economic growth in Turkey for
the period 1990-2015
2) To forecast the trend of FDI inflows and economic growth in Turkey
3) To critically analyse whether FDI inflows Granger-Causes economic
growth in Turkey
Significance of the study
It is going to be valuable to empirically determine whether FDI inflows in Turkey
have had supported relationship with economic growth. Also, the study investigated the
cause-effect relationship between FDI inflows and economic growth in Turkey. Overall, this
is an important subject for the reason it will be looking at two major development indicators
that the government can turn to and formulate better and citizen friendly fiscal policies.
Indeed, the successful completion of this project shall add to the existing literature and
researches done in Turkey on related topics.
Dissertation structure
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The structure of the dissertation was as follows:
As can be seen from the structure of the dissertation there shall be six chapters which
provided an optimum path to making a god research. The focus of the researcher was to
ensure there was systematic analysis to theories and empirical findings. Basically, the
introduction was meant to give a background overview of the problem and provide general
global trend on foreign direct investment inflow. In the introductory chapter, the research
objectives were highlighted as well as the value of the study.
The second chapter (literature review) was meant to evaluate on all existing
researches on the effect of FDI inflows to economic growth. The establishments of past
scholars were presented and keen interest going to works featuring Turkey. In the process the
researcher used this chapter to conceive the testable hypotheses. The conceptual framework
was equally developed with the view to show clear link among identified variables.
The third chapter featured the methodology proposed to analyse data once it was
extracted from credible sources such as the World Bank Development Indicators. The
country profile chapter, on the other hand, performed a mild PEST analysis of Turkey so as to
furnish the reader with the overall composition of the country in terms of its people, culture,
social fabric and political climate among other issues. Basically, the researcher focused to
Introduction
Literature
Review
Methodology
Country
Profile
Data
Analysis
Conclusion
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position Turkey in the global map in terms of political stability, corruption index, government
effectiveness, social cohesion among other issues which in themselves provide a conducive
environment for foreign direct investment.
The fifth chapter was central to this dissertation because it is where the researcher
presented the findings; that way affirming whether there was any significant relationship
between FDI inflows and economic growth in Turkey. Lastly, the sixth chapter presented the
main summary of findings and implications; therefore, provided comprehensive summary of
the main ideas and policy implications to Turkey.
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LITERATURE REVIEW
In this section the focus was to develop a literature review that sought re-look on the
works of other scholars in regard to the impact of foreign direct investment (FDI) on
economic growth. Indeed, the topic proposed has been widely researched and applied to
many countries and regions; in this making it much possible to analyze theory and proceed to
make critical hypothesis. The anticipation is that the works done by other scholars on the
topic of FDI and economic growth may have differed in meaning and also in methodology
implementation. This is why in the current dissertation there will be efforts to establish what
might have been the gaps with past research regarding the effects of FDI on economic
growth. However, as indicated in the introduction section, the present study looked at the
period 1990-2015 which is much recent hence the expectations are that the outcomes of the
dissertation may be used to inform existing policy as pertains to FDI in Turkey. In summary,
in performing the literature review, the researcher shall be keen to establish the following:
1) The methodologies used by past scholars
2) The findings from other scholars
3) The theories invoked by other scholars
All the above revolving around the debate over the impact of FDI on economic
growth. It is equally worth stressing that the researcher is not so much interested in whether
the findings of past scholars were true or valid or correct; no, the intention is actually to
establish that Turkey benefits from its policies on FDI and that it has been managed properly
to promote the wider wellbeing of the country in terms of economic growth. This is solely the
importance of this study.
Analysis of Empirical Studies
As noted, majority of the studies have been done in the past regarding the nexus
between FDI inflows and economic growth. Bayar (2014) in his publication entitled “effects
of Foreign Direct Investment Inflows and Domestic Investment on Economic Growth” basing
on Turkey established that scholars alluded to mixed reactions in their findings. For instance,
Mun et al (2008) and Soumia and Abderrezak (2013) found that FDI inflows did have a
positive impact over economic growth; on the contrary, studies by Saqib et al, (2013)
depicted that FDI inflows negatively impacted on economic growth. See more summaries on
findings of different scholars in table 1. Moving on, is that scholars such as Choe (2003)
relied on a panel VAR model where they established that there existed a bidirectional
causality in the case of FDI inflows and economic growth.
16
Xu and Wang (2007) established that FDI inflows impacted positively on China’s
GDP growth and other development indicators such as domestic investment, exports and
imports in the period 1990-1999. Bilgili et al (2007) used VAR analysis and proceeded to
assert that FDI inflows showed a bidirectional causality between FDI and economic growth
in Turkey for the period 1992-2004. Another study refers to the one conducted by Tang et al
(2008) which using a multivariate VAR system and cointegration found a unidirectional
causality from FDI inflows of GDP. This based on the case of China in the period 1988-2000.
Another study was applied to ASEAN nations i.e. Association of South East Nations
for the period 1968-2002 where the authors using the ARDL cointegration established that
there existed a positive significant effect on FDI inflows on economic growth (See Almasaied
et al, 2008). Another study evaluated the case of Tunisia, Egypt and Morocco for the period
1970-2006 and Elboiashi et al (2009) relied on cointegration and causality test. In their
findings, they established that FDI inflows did have both short-run negative effect as well as
long run positive effect on economic growth. Similarly, the same authors established that
there existed a unidirectional causality in the case of FDI inflows and economic growth for
Morocco and Egypt while a bidirectional causality existed between FDI inflows and
economic growth in the case of Tunisia.
Worth noting is the study by Adams (2009) applied to Sub-Saharan Africa for the
period 1990-2003; the author used panel OLS estimation where it was established that DI
positively and significantly correlated to economic growth; however, FDI showed positive
and significant effect to economic growth but limited to the OLS estimation. In Taiwan it was
established that for the period 1981-2008 FDI inflows did positively affect economic growth;
this was based on the use of error-correction model. In Pakistan it was illustrated that there
existed a unidirectional causality in the case of FDI inflows and economic growth but in the
long term.
Another study had been performed by Lean and Tan (2011) where they established
that FDI inflows positively impacted on economic growth in the case of Malaysia for the
period 1970-2009.The authors relied on Johansen-Juselius cointegration test including
Granger causality test. Moving on, the same authors established a unidirectional causality
from economic growth to FDI inflows but on short-term basis. Similar findings were evident
in the works by Chakraborty and Mukherjee (2012) in where their cointegration and causality
tests established a unidirectional causality from economic growth towards FDI inflows in
India.
17
In Malaysia, scholars such as Mohamed et al (2013) noted that there existed no
causality in the case of FDI inflows and economic growth within the period 1970-2008
basing on the long-run. The researchers relied on vector error correction model, variance
decomposition analysis and impulse response function. Sooreea and Sooreea-Bheemul in
their analysis based on 28 developing and emerging economies for the period 1989-1998
established a bidirectional causality between FDI inflows and economic growth whilst using
panel Granger causality tests. Then Chowdhary and Kushwaha (2013) established that in the
case of India for the period 1992-2012 there existed on causality in the case of FDI inflows
and economic growth.
In the case of Borensztein et al. (1998) the scholars examined the impact of FDI and
economic growth rate in the case involving 69 developing nations. The study was for the
period 1970-1989. In the results, there was established to be a positive relationship between
FDI and economic growth; but only where the host country did have absorptive competence
as well as high level in terms of the education establishments for progressive technologies.
On the same study, it was established that domestic investment has less impact than FDI
when it came to promotion of economic growth. On the other hand, for countries that had low
degree of education sectors as well as low level in terms of human capital the economic
growth rate showed negative relationship to FDI.
Another study was performed by Zhang (2001) back in China which sought to
examine the relationship between FDI and economic growth rate in the period 1984-1998.
The methodology used was cross-sectional data, growth model and panel data. In the
outcome, it was seen that foreign direct investments indicated to support economic growth in
China. Further, Ozturk and Kalyoncu (2007) used Granger Causality and Engle-Granger
cointegration tests; in the analysis it was confirmed that there existed a positive causality
relationship in the case of FDI and economic growth in Turkey but in the case of Pakistan
only economic growth was seen to cause foreign direct investment.
Table 2: Summary of empirical studies
Author(s) Methodology Findings
Mun et al (2008), Chang (2010), Not Revealed Positive Impact of FDI on Economic Growth
Heteş et al. (2009), Anwar and Nguyen
(2010),
" "
Tiwari and Mutascu (2011), Soumia and
Abderrezzak (2013)
" "
Lean and Tan (2011), " "
Saqib et al, (2013), Mencinger (2003) " Negative Impact of FDI on Economic
Growth
18
Lyroudi et al (2004), " No significant relationship between FDI
inflows and economic growth
Chowdhary and Kushwaha (2013),
Mohamed et al, (2013)
" "
Choe (2003) Pane VAR
Model
Bidirectional causality exists between FDI
inflows and economic growth
Bilgili et al (2007) VAR Analysis Bidirectional causality on DI, FDI and
economic growth
Tang et al, (2008) Multivariate
VAR system,
Cointegration
Unidirectional causality between FDI inflows
towards GDP
Almasaied et al, (2008) ARDL
cointegration
Positive significant effect on economic
growth
Elboiashi et al, (2009) Cointegration;
Causality Test
Short run negative effect between FDI
inflows on economic growth
Adams (2009) Panel OLS
analysis
Positive and Significant relationship between
FDI inflows and economic growth
Chang (2010) Error-correction FDI positive effect on economic growth
Ghazali (2010) Cointegration;
Causality Test
Unidirectional causality between FDI inflows
and economic growth
Lean and Tan (2011) Granger
Causality;
Johansen-
Juselius
cointegration
test
FDI inflows positively impacted on economic
growth
Unidirectional causality from economic
growth to FDI inflows
Chakraborty and Mukherjee (2012) Cointegration;
Causality Tests
Unidirectional causality from economic
growth to FDI inflows
Mohamed et al (2013) Vector error
correction
model’
Variance
decomposition
analysis;
impulse
response
function
No causality between FDI inflows and
economic growth
Sooreea-Bheemul and Sooreea (2013) Panel Granger
causality tests
Bidirectional causality between FDI inflows
and economic growth
Chowdhary and Kushwaha (2013) Granger
causality
No causality from FDI inflows to economic
growth
Source: (Prepared by Author from assorted readings)
Analysis of empirical studies recently completed about Turkey’s FDI and Economic
Growth
A related study to the quest for this dissertation was the one done by Yilmaz Bayar
(2014) entitled “Effects of Foreign Direct Investment Inflows and Domestic Investment on
Economic Growth: Evidence from Turkey”. As can be seen, in this study there were two
independent variables namely FDI and DI and the dependent variable being the economic
growth. Looking at the methodology used by Yilmaz it was seen that yearly data representing
growth in the real gross domestic product (RGDP), net FDI inflows (% of GDP) and DI (% of
GDP) in the period 1990-2012 had been considered.
19
In terms of sources is that this author acquired data from the United Nations
Conference on Trade and Development (UNCTAD) for RGDP and for the independent
variables sourced it from the World Bank i.e. World Development Indicators. Moving on, the
methods adopted by Yilmaz indicated the use of stationarity tests based on Augmented
Dicker-Fuller and Phillips-Perron models. Further, a co-integration test was conducted to
establish the short and long run relationship of the established variables. On evaluating
Yilmaz’s publication, the researcher was keen to capture the empirical findings. For instance,
the GDPGR depicted to have no unit root and the PP affirmed a stationary trend.
However, FDI and DINV depicted to have no unit root and were stationary on 1st
difference derivation. In terms of the Co-Integration Test (ARDL bound test), it was
established that there existed a long run relationship on GDPGR, FDI and DINV. Other
results depicted that the long-run coefficients of variables were statistically significant. But in
the study by Yilmaz it was established that FDI inflows negatively impacted on economic
growth and gross domestic investments showing positive impact on the latter. In addition, 1%
of FDI showed to decrease economic growth by 0.33%; on the contrary, 1% increase in gross
domestic investments depicted to increase economic growth by 0.30%. Similarly, when
Vector Error Correction Model was applied to the analysis it was established that there
existed a negative effect of FDI inflows towards economic growth but in the short run.
Another single publication worth evaluating was the one completed by Josh Durnel in
the year 2012 as a Master Thesis. The researcher chooses this study because it has been
published in a credible database under Oxford University meaning it can be used as a
resource benchmark to comment on the impact of FDI on Economic Growth. Of interest to
the researcher, were the findings in the work and the methodology used to achieve the same.
Coskun (2012) examined the “Effects of Foreign Direct Investment on Turkish Economy”
based on a Sectoral Level Empirical Analysis. It is seen that the author focused on analysing
the long-run effect of FDI on GDP based on a steady state context.
Noteworthy, the same author used FDI stock unlike in other studies where FDI inflow
has been widely used. The FDI stock was basically a way to capture and bring into the model
the foreign-owned capital stock in Turkey. Based on the benchmark regression model,
Coskun (2012) aimed to examine whether log of FDI stock had any effect on log of GDP
using panel data techniques. Thus, a panel of ten sectors in the period 2000-2009 was
considered and included an aggregate of 100 observations. The sources of the data in
Coskun’s study included TURKSTAT where historical data on gross domestic product
20
(GDP), export (XPR) were collected; on the other hand, The Central Bank of the Republic of
Turkey (CBRT) was used to acquire data for Foreign Direct Investment (FDI) and Labour
Productivity (LPR) sourced from the OECD (Organization for Economic Co-operation and
Development). As mentioned earlier, the methodology used in Coskun’s thesis was panel
data analyses. The first step was to perform a panel unit root as well as panel cointegration
Upon confirmation of the long-run relationship, panel Granger-Causality test was
administered. The panel Conitegration test results depicted that there was long-run
relationship between Log FDI and Log GDP.
An important note from the above work was that all panel data must have been
approved to have stationary trend for the Granger-Causality test to have been implemented.
In anticipation, the researcher saw this as a rule of thumb for the present study given that
there was the use of Granger causality as part of the methodology. Moving back to the results
obtained in the work by Coskun (2012) it was established that LogFDI Granger causes
LogGDP in light of the first lag. On the other hand, the degree of coefficients depicted that
1% increase in LogFDI led to 0.011% increase towards LogGDP. On the same note, the study
failed to positively approve that GDP Granger-Causes FDI meaning there was only a
unidirectional causality i.e. FDI Granger-Causes GDP.
Another study refers to works by Abdulrahman and Khder (2014) entitled “The
Impact of Foreign Direct Investment on Economic Growth: Turkey Case Study 1990-2012. In
the methodology, the author considered evaluating the cointegration relationship in the case
of FDI and GDP in the long run. In terms of variables, the dependent protocol was gross
domestic product (GDP) while the independent protocol was foreign direct investment (FDI),
domestic investment (DI) and lastly Trade Liberalisation (TL) where the latter independent
variable included both export and import. The source of the data in the study by
Abdulrahman and Khder (2014) derived from “World Bank Development Indicators”. The
focus of the study was to establish the relationship in the case of economic growth and FDI.
The equation coined was Y = f (FDI, DIN, TL).
Further, OLS and Vector Autoregression model (VAR) were adopted with the scope
to approximate the casual linkage in the case of foreign direct investment and economic
growth. Here, the authors coined an econometric equation as shown here i.e. Log GDP = α +
β1 FDI + β2 DIN + β3 TL + ε. The key hypothesis was to say whether FDI inflows did cause
economic growth in Turkey. From the Ordinary Least Squares regression model results, the
derivations were as shown next: FDI (β = 0.435257, Sig = 0.0001), DIN (β = -0.197327, Sig
21
= 0.6026) and TL (β = 0.444244, Sig = 0.2706). It means FDI if increased by 1% would in
turn increase GDP by .43%; on the same point, it has related positively to GDP in the short-
term. In the same work, the authors went ahead to perform the OLS regression based on the
first difference mode. Here, the researcher was keen on the output for FDI (β = 0.0274, Sig =
0.4662); it meant if FDI was increased by 1% it would raise GDP by ~0.03% whereas the
significance was below the 0.05 margin of error to confirm the hypothesis that FDI was
positively related to GDP in the short-term.
Basically, the p value was indication that FDI did positively relate to GDP and on this
output the reader should recall that it was an OLS regression in the First different mode.
Other notable issues in the study were the use of unit root tests which was a pre-condition
evaluation for Granger-Causality. Similarly, there was the use of Co-Integration Test which
was used to establish the long-run relationship among the variables. In the outcomes, it was
established that there lacked long run equilibrium relationship in the case of GDP, FDI, DIN
and TL. On the same point, given the co-movement across the variables, it was asserted that
there lacked a long run relationship for the period 1990-2012 in Turkey. Finally, the Granger-
Causality test depicted a rejection of the bidirectional causality between GDP and FDI. It
meant, GDP did not Granger-Cause FDI neither did FDI Granger-Cause GDP. Moreover,
there existed no bidirectional Granger-Causality across all other variables thereby failing to
reject any of the null hypothesis.
The fourth study performed in Turkey refers to Salih Katircioglu (2009) entitled
“Foreign Direct Investment and Economic Growth in Turkey”. It was an empirical
investigation involving Co-integration and Causality Tests. In the work, the author focused
on evaluating the degree of relationship as well as the direction of causality in the case of net
foreign direct investment (FDI) inflows and economic growth. In the methodology, the author
used annual figures for the period 1970-2005 including that the main variables for the study
were real gross domestic product (GDP) and net FDI inflows. The source of the data was the
World Bank Development Indicators and variables extracted from the 2000 constant US $
prices. There was also the use of the Augmented Dickey-Fuller (ADF) and Phillips-Perron
(PP) where Unit Root Tests and Stationarity of data were examined. Co-integration was
equally used to check for the long-run relationship among the variables i.e. GDP and FDI
inflows. It was confirmed that FDI was stationary at level while real GDP was stationary at
1st difference in the case of Turkey.
22
Upon such confirmation, moved to testing Salih Katircioglu (2009) the long-run
equilibrium relationship using ARDL co-integration. The results depicted that there existed
no long-run relationship in the case of real GDP vis a vis net FDI inflows; however, this
outcome was on the circumstances that FDI served as the dependent variable. However, there
emerged a long-run relationship in the case involving real GDP and net FDI inflows but now
the former been the dependent variable. In running the Granger-Causality, it was established
that there existed a long-run unidirectional causation in the case of real GDP growth towards
net FDI growth; then a short-run causation in the case of FDI to real GDP growth had not
been confirmed.
Summary of chapter
In this part of the study the main findings have been identified given the review of
past studies. It is from this lessons the researcher proceeded to formulate variables that would
be used to build models for the rest of the research analysis. The wide ranging remarks from
past scholars is the supported relationship between FDI inflow and economic growth of
countries.
23
METHODOLOGY & DATA
In this chapter the methodology used for the study was profiled. Basically, the model
specification and the parameters in it were captured. The informative criteria for the selection
of the methodology to be used based on the works done in the past and the researcher sought
to at least add more value to related studies by doing more on methodology and even trying
to evaluate their validity and reliability. All the same, the methodological framework for the
dissertation was as follows.
In the previous studies it has been seen that different scholars used either similar or
varying methodologies in their quest to illustrate the relationship between FDI inflow and
economic growth. In chapter one it was established that the FDI inflow in the world and
continents have differed with some parts showing more estimates than others. The other issue
evident in past studies was that data ranged within period 1970-2013; thus the period 2014-
2015 was not come across in the course of the study. However, the important point is that the
authors of these works used methodologies such as regression analysis (OLS), Co-Integration
tests for long-run relationships, and Granger Causality. Other tests that were seen to feature
mostly include the tests for unit root and stationarity tests based on Augmented Dicker Fuller
Test and Perron and Peter Phillips. Moreover, other scholars relied on panel data analysis and
so on and so forth.
In this dissertation the researcher benchmarked the methodology on what other
scholars attempted but with determination to have been as objective as possible. Data
considered in this dissertation consisted of annual figures ranging in the period 1990-2015
(25 years) where the variables of the study included real gross domestic product (GDP) and
FDI (net) inflows. The source of data was World Bank Development Indicators (World Bank,
2016). The moderating variables were balance of trade (trade liberalisation), exchange rates,
and interest rates.
The justification for the variables was that FDI inflows is affected by other
environmental factors given the fiscal and monetary policies in a country. Foreign investors
would be interested by the trends for exchange rates because in most cases they would be
required to denominate their cash flows in foreign currencies and not the local currency.
Thus, a less favourable spread in exchange rates would not attract the investors to a country
where it would be expensive to invest due to costly logistics, production and salaries and so
on. In the case of interest rates this is also an important business environment metrics that
would make it possible for FDI to positively impact on the economy. The reason is because
24
having high interests charged especially to foreign investors would create more pessimism
and in the long-run cause exit of the foreign investors. Lastly, tax payable from gains, profits
and income is a major consideration for foreign investors anywhere. It means a favourable
tax regime would attract more FDI inflows and in the process impact positively to the
economy.
The econometric analysis used in the dissertation included the following:
▪ Unit root and stationarity tests
▪ OLS Regression (Short-term relationship)
▪ Granger causality test
It is worth noting that the diagnostic tests picked are not for the sake of it because
they were used to determine other tests in the model. For instance, the use of regression and
Granger-Causality needed that the data be stationary hence no unit root. The Augmented
Dickey-Fuller (ADF) and Phillips-Perron (PP) are Unit Root Tests applied to test for the
integration level and possibility for cointegration among the variables. On one hand, the PP
tests estimate the “residual variance that is robust to auto-correlation”.
Basically, the researcher attempted to assess the relationship in the case of Gross
Domestic Product (GDP), Foreign Direct Investment inflow (FDI), Net Exports, and Gross
Capital Formation within a time series model-analysis. The first step was to run stationarity
tests using both Augmented Dickey-Fuller and Phillips-Perron tests. Afterwards, tested for
long and short run relationships among the variables using co-integration test within ARDL
bound test approach. Granger Causality Test as Studenmund (2006) noted depicts he situation
whereby one series changes in a consistent manner prior to another series variable; it also
tests for causality in both directions.
Model specification
The proposed model sought to specify that economic growth in Turkey i.e. Gross
Domestic Product (GDP) is significantly related to Foreign Direct Investment inflow (FDI),
Net Exports, and Gross Capital Formation. The methodology adopted to develop the
empirical analysis was regression. Economic growth estimated through GDP growth rate
served as the dependent variable; FDI inflow, on the other hand, was the independent
variable. GDP growth is equally determined by other development indicators which in this
study are the explanatory variables: this include GCF (alternately gross domestic investment)
and net export. The gross capital formation is the aggregate investment in the economy
within a given period. This includes domestic investment and other investments deriving
25
from the activities of multinational forms present in the host country. It is also encompassing
the tangible stock of capital. Therefore, incorporating the GCF in the regression equation will
illustrate the effects of the general investment trend to economic growth and actually in this
dissertation such shall be compared to the contribution of FDI alone. Net exports is going to
be featured in the study as the balance after subtracting total export from total import.
Alternately, it may be referred to as the balance of trade. It evaluates the effects of foreign
trade towards the economy.
The regression model equation is as shown below:
GDP = f (FDI, GCF, NX)
Yt = α + β1fdit-1 + β2gcft-1 + β3nxt-1 + εt
β = Intercept
β1 – β3 = Regression Coefficients derived from the independent variables. There is use of
one-year lag across the independent variables
Y = (GDP – GDP t-1) / GDPt-1
fdi = FDI/GDP
gcf = GCF/GDP
nx = NX/GDP
Data Analysis
In terms of data analysis EViews was used to run most of the tests and data
summarised for the purposes of the study. Any estimations were done in four decimal places
where applicable. In the quest to perform data the researcher relied on log values of identified
variables meaning they were converted from their actual trend. Additionally, descriptive
statistics were featured just as an attempt to explain data in terms of central tendency and
dispersion rate i.e. mean, median, mode, variance, percentiles, quartiles, and standard
deviations. The same included charts and graphs so as to visually indicate the trends
especially for FDI inflows and economic growth in Turkey.
26
COUNTRY PROFILE OF TURKEY
In this section the profile of Turkey was developed so as to have an environmental
understanding of the country and its people. Basically, the use of PEST model is adopted so
as to articulate in the political, economic, social, and technological factors in Turkey.
According to the World Bank, Turkey is one of the grown middle-income partners including
being the 18th largest economy across the world. Also, in less than a decade, per capita
income in Turkey was reported to have increased by three times to exceed $10,000 (World
Bank, 2016).
Geert Hofstede in his cultural dimensions ranked countries in respect to their national
culture. See figure 1 below.
Figure 1: Cultural dimensions for Turkey
Source: (itim International, 2016)
It can be seen that the dimension for uncertainty avoidance is ranking at 85 points
being the highest of all. According to Hofstede Uncertainty Avoidance refers to the manner
in which a society handles matters of the future; basically, referring to the notion and belief
that the future cannot be predicted. It means Turkey has a disposition for skepticism about the
future. In most cases, where there is uncertainty of this kind people turn to rituals or
foreigners may turn to religion and reverence for a higher being (itim International, 2016).
The lowest ranking dimension is individualism at 37. Individualism points to the level of
interdependence which a society keeps and practices among its very members. In
individualist societies individuals are required to take of themselves and their direct family
27
but in a collective society people exist in groups and look after them for loyalty (itim
International, 2016).
Away from National Culture the next analysis shall be to check up the ranking of
Turkey by the Transparency International in terms of corruption perception index. For
instance, Turkey has been ranked at position 66 globally in the year 2016. In other words,
Turkey ranks as the 66th lowest country in terms of corruption out of 175 other countries.
However, looking keenly at the figures it can be said that Turkey has lost the war on
corruption given it did deteriorate from 42nd position in 2015 score (Transparency
International, 2016). Moving on, in terms of ease of doing business Turkey has been ranked
at position 69 out of 190 while starting a business ranking lower at 79 (The World Bank
Group, 2016).
The yield curve is among the development indicators that could be used to predict the
economy of a country (Diebold and Redubusch, 2013). It is a curve that plots the fixed-
interest securities against the period of time they have to stretch to maturity (Douglas, 2008).
According to analysts it is held that Turkish Bond Market was returning to Normal albeit
increasing inflation (Bloomberg, 2016). Figure 2 below is an illustration of Turkey’s yield
curve in the most recent times i.e. November 16, 2016.
Figure 2: Turkey bond yield curve
Source: (Author’s Design using Excel Program)
It can be seen that the trend from 1Y to 2Y depicts a normal performance which
means the investors in this time expect the returns to be higher. This is also an indication of a
positive economic turnaround in Turkey. However, from 2Y to 3Y it can be seen that there
28
are signs for decline which denotes an inverted curve; normally this may be used to indicate a
trend for inflation and bad economic times. It means the investors are foreseeing likelihood of
yields falling and would shy away from making investments in the future. Overly, the period
3Y to 10Y depicts signs of a normal curve trend; as earlier noted, a normal curve is a sign if
high expectations from investors that there are good times ahead in terms of returns to their
investment. It means, the economy is doing well and with minimal chances for recession as it
is the case for an inverted yield curve (See Choudhry, 2011). The conclusion is that the yield
curve in the long-term indicates that Turkey is poised for better economic times ahead; also
that there is no much danger it will plunge into a recession.
The analysis on yield curve prompts the researcher to further make analysis on
Turkey’s trend for inflation, interest rates and exchange rates. This is because these are
important conditions for foreign direct investment inflow in any country. From a report by
the Central Bank of Turkey, exchange rates have been stable as shown in figure 3 below.
From the Turkish Statistical Institute bulletins, consume price index as at October 2016 has
been rated at 7.16%; the unemployment rate in August 2016rated at 11.3% and GDP growth
rate for the 2 Quarter 2016 approximated at 3.1%. In addition, Industrial Production Index as
at September 2016 had been rated at -3.8% and total production for year 2015 being at
78,741,053 (Turkish Statistical Institute, 2016).
Figure 3: Inflation trend in Turkey
Source: (Author’s Model using Excel Program)
29
As can be seen the trend for inflation in Turkey indicates that from 1990-2015 it has
been declining and actually in the recent years 2010 to 2015 has been at the lowest rates.
Also, the forecast depicts that the same trend would appear and expected that Turkey would
be having lowest inflation throughout the years. It is important to assess the inflation risk
because this can discourage investors if not well managed. Foreign direct investment thrives
where there is stable inflation.
The other development shall be exchange rates in Turkey and data analysed is the one
provided by the Central Bank of Turkey. See figure 4 below.
Figure 4: Exchange rates in Turkey 2016
The trend depicts that Real Effective Exchange Rate has been up and down for the
year 2016; however, the recent performance shows a low performance meaning the local
currency for Turkey has been gaining strength especially against the U.S. Dollar. The
exchange rate is an important performance for foreign investors because it tells them how
much cost they are going to incur due to differences in currencies. It would be expected that
majority of the foreign investments in Turkey involve a lot of importation or exportation and
the trending for the currency may weaken gains especially when buying from a markets with
stronger currencies.
30
MODEL APPLICATION AND FINDINGS
In this part of the dissertation the focus shall be to implement the model specified in
the methodology section. To recall is that FDI inflow was the independent variable while
GDP being the dependent variable. However, Gross Capital Formation and Net Exports were
used as the main moderating variables. The Excel Program was used to execute some of the
analysis and EViews used to run the Granger Causality tests. The objective has been to
ground using theory and empirical the extent to which FDI inflow has impacted positively on
economic growth in Turkey. Already, it has been shown that quite a number of studies were
performed in the past showing different variations between FDI and economic growth in
Turkey. The uniqueness with the current dissertation is that it examined the period 1990-
2015; there exists no studies that talk about this period.
In order to develop the dissertation analysis section in a systematic and valuable way
the focus was to relate each finding to the objectives set forth in chapter 1 earlier. They stated
as follows:
The aim of the study was to establish whether there has been any supported causal
relationship between FDI inflows and economic growth in Turkey.
The specific objectives read as follows:
1. To illustrate the trend of FDI inflows and economic growth in Turkey for the period
1990-2015
2. To forecast the trend for FDI inflows and economic growth in Turkey
3. To critically analyse whether FDI inflows Granger-Causes economic growth in
Turkey
The analysis section was divided into four chapters as shown below:
▪ Data Screening
▪ Descriptive statistics
▪ Relationship analysis of FDI Inflows and economic growth
▪ Forecast analysis of FDI inflows and economic growth in Turkey
▪ Summary of chapter
Data Screening
A few tests were performed using XLSTAT which was a function supported by excel
program which was used to execute tests such as Dicker Fuller, KPSS, Dixon test, and
Normality test. These tests were all meant to ensure the data used to implement the model
was inherently consistent.
31
To begin with is the Dixon test which captured the presence of possible outliers in the
data for the main independent variables. The results are as shown below. The test
interpretation for the outlier test was guided by the arguments below:
H0: There is no outlier in the data
Ha: The minimum or maximum value is an outlier
Table 3: Dixon test results for the FDI/GDP (Independent Variable)
Dixon test for outliers / Two-tailed test (FDI/GDP):
R10 (Observed value) 0.137
R10 (Critical value) 0.313
p-value (Two-tailed) 0.603
alpha 0.05
The p-value has been computed using 1000000 Monte Carlo simulations.
99% confidence interval on the p-value:
( 0.602, 0.605 )
The test results have a p-value at .603. It means the rejection of H0 could not be
upheld meaning there was no outlier in the main independent variable (FDI/GDP). Moving to
GCF (0.547) and NX (0.684) the Dixon test results are as shown in appendix A and B where
all the p-values are greater than 0.05. It means these moderating variables did not have
outliers.
The second data screening was based on Shapiro-Wilk test for normal distribution.
Basically, aimed to establish whether the dataset used to implement the model consisted of a
normal distribution. The test results are as shown tables 3 and 4 below. The main argument
checklist used for the interpretation was follows:
H0: The variable from which the sample was extracted follows a Normal Distribution
Ha: The variable from which the sample was extracted does not follow a Normal Distribution
Table 4: Normal distribution test results for Y variable (GDP)
Summary:
Variable\Test Shapiro-Wilk
(Y) < 0.0001
(FDI/GDP) < 0.0001
(GCF/GDP) 0.001
(NX/GDP) 0.001
32
From table 3 it can be seen that the p-value for Y (<0.0001), FDI/GDP (<0.0001),
GCF/GDP (0.001), and NX/GDP (.001). It means the null hypotheses H0 must be rejected
and alternate affirmed. Overall, stating that the dataset used for the model did not follow a
Normal Distribution.
The last diagnostic test was for the unit root which was an important sine qua non to
running Granger tests. The reason is because Granger tests only go to data with no unit root
or are stationary. The results will be presented next guided by the argument below:
H0: There is a unit root for the series
Ha: There is no unit root for the series. The series is stationary
Table 5: Unit Root test results for Y (GDP variable)
Dickey-Fuller test (ADF(stationary) / k: 2 / Y):
Tau (Observed value) -3.649
Tau (Critical value) -0.538
p-value (one-tailed) 0.039
The p-value is at 0.039 which means Y variable which is the timeseries for GDP did
not have a unit root. It meant the dataset was stationary. In light of the same the KPSS test
result is as shown below. The checklist for the interpretation is as shown below.
H0: The data series is stationary
Ha: The data series is not stationary
Table 6: KPSS test results for Y variable
KPSS test (Level / Lag: Short / Y):
Eta (Observed value) 0.178
Eta (Critical value) 0.437
p-value (one-tailed) 0.351
alpha 0.05
Looking at the p-value at .351 it shows that that the series used to build Y variable
was stationary. Overall, this finding affirms the possibility to run Granger test on GD data
series. That been the case the tests for the independent variables are shown next. For the
independent variables XLSTAT program was used to check for the unit root but all the
results indicated presence of unit root. Unfortunately, XLSTAT program does not give
further analysis on the same. Due to this the researcher turned to using EViews to check for
33
the unit root and stationarity based on either 1st difference or 2nd difference. The results
outputs are as shown below.
Table 6: Unit root test results for FDI_GDP
Null Hypothesis: FDI_GDP has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic - based on SIC, maxlag=0) t-Statistic Prob.* Augmented Dickey-Fuller test statistic -1.080742 0.7070
Test critical values: 1% level -3.724070
5% level -2.986225
10% level -2.632604
*MacKinnon (1996) one-sided p-values.
The ADF test statistic at -1.080742 shows to be less than the critical values at 1%, 5%
and 10%; actually the probability value is at (.7070). The decision rule is the ADF statistic
should be less than the critical values at all levels for the null hypothesis for unit root to be
rejected. The test was redone in first difference with intercept.
Table 8: Unit root test results for FDI_GDP at 1st difference
Null Hypothesis: D(FDI_GDP) has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic - based on SIC, maxlag=0) t-Statistic Prob.* Augmented Dickey-Fuller test statistic -8.120897 0.0000
Test critical values: 1% level -3.737853
5% level -2.991878
10% level -2.635542
*MacKinnon (1996) one-sided p-values.
The ADF test statistic is at -8.1209 which less than the critical values at 1%, 5% and
10%; also the probability value is at .0000. It means the null hypothesis for unit root must be
rejected; this also leads to the assertion that the dataset for the FDI_GDP is stationary.
Appendix C is a screenshot of the similar results in table 7 as generated from the EViews
program.
So far it is confirmed that the series for the main dependent and independent variables
i.e. GDP (Y) and FDI (FDI_GDP) are free from unit root; it means they are stationary making
it valid to run a Granger Causality test on them. The same needed to be confirmed for the
case of the moderating variables namely: NX and GCF. The researcher decided to check for
34
their unit root in 1st difference at level intercept or at trend and intercept. See the results in
table 8 and 9. Worth noting also is that all the unit roots were done at maximum lags of 0.
Table 9: Unit root test results for GCF_GDP at 1st difference
Null Hypothesis: D(GCF_GDP) has a unit root
Exogenous: Constant, Linear Trend
Lag Length: 0 (Automatic - based on SIC, maxlag=0) t-Statistic Prob.* Augmented Dickey-Fuller test statistic -3.619233 0.0493
Test critical values: 1% level -4.394309
5% level -3.612199
10% level -3.243079 *MacKinnon (1996) one-sided p-values.
Foremost is that the test was in 1st difference at trend and intercept; the critical values
at 1%, 5% and 10% indicated to exceed slightly the ADF statistic and the probability value
showing a value of .0493. In that case, the unit root null hypothesis is rejected. It meant the
variable for GCF_GDP did not have unit root. See appendix D which shows the screenshot
output from EViews.
Table 10: Unit root test results for NX_GDP at 1st difference
Null Hypothesis: D(NX_GDP) has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic - based on SIC, maxlag=0) t-Statistic Prob.* Augmented Dickey-Fuller test statistic -5.646695 0.0001
Test critical values: 1% level -3.737853
5% level -2.991878
10% level -2.635542 *MacKinnon (1996) one-sided p-values.
The test result above indicates the ADF statistic (-5.6467) is less compared to the
critical values at 1%, 5% and 10%. It means the unit root null hypothesis must be rejected
and proceed to infer that the data series is stationary. See screenshot E of the same from
EViews mode.
Overall, the tests for unit root have affirmed that all data series used to implement the
model are free from unit root. As mentioned, this was an important pre-condition ahead of the
Granger Causality tests that ultimately justified a causal relationship between FDI and
economic growth in Turkey. All in all, having done the data screening the next step is to
present the main descriptive statistics as explored next.
35
Descriptive Statistics
The descriptive statistics for the main variables are as shown below.
Table 11: Descriptive statistics for main variables
Statistic Y FDI/GDP GCF/GDP NX/GDP
No. of observations 26 26 26 26
Minimum -0.083 1.171 0.000 0.855
Maximum 1.378 27.427 3.857 1.977
1st Quartile 0.925 1.749 0.646 1.000
Median 0.946 4.509 2.792 1.066
3rd Quartile 1.024 8.486 3.329 1.422
Mean 0.958 6.493 2.108 1.221
Variance (n-1) 0.060 42.288 2.195 0.092
Standard deviation (n-1) 0.246 6.503 1.482 0.304
The coloured parts designate the key descriptive statistics. For instance, the minimum
output shows the lowest derivations from each of the variables. Y being the GDP growth rate
indicates a minimum value of -0.083 meaning such was the lowest growth rate for GDP
between the period 1990-2015 in Turkey’s economic growth. It is equivalent to say in such
period -8.3% was the lowest score GDP growth rate ever attained. In terms of FDI/GDP the
minimum score is 1.171 meaning it was the lowest value in the indicator. In the case of mean
values, the one for Y stood at 0.958. It means such was the average performance of the
economic growth rate in Turkey for the period 1990-2015.
In the case of FDI/GDP the mean score is 6.493, GCF/GDP at 2.108 and NX/GDP
being at 1.221. On the other hand, the standard deviations depict the dispersion rate from the
mean performance of each of the economic indicators in Turkey for the period 1990-2015.
For instance, Y (Economic Growth) shows a standard deviation of .246 which is not
deviating so much from the mean; it is indication that there was a change in GDP that did not
detract so much from the mean performance occurring in the period 1990-2015. Other
standard deviations for FDI_GDP (6.503), GCF_GDP (1.482) and NX_GDP (0.304) are
below the mean values indicating a less or negligible dispersion rate from the mean
performance.
The next attempt will be to graphically depict the trends for each of the variables in
their raw data form. By this it means the trend for GDP, FDI, GCF and Net Exports (NX) for
the period 1990-2015. The graphs have been generated using Excel program as shown next.
36
Figure 5: Graphical analysis on GDP 1990-2015 in Turkey
The trend model indicates that from 1990 towards 2014 the GDP for Turkey has been
having a stiff upward growth with the year 2015 as most recent recording the highest points
since 1990. It is arguably so that 2015 is when Turkey has had the greatest GDP.
Figure 6: Graphical analysis on FDI 1990-2015 in Turkey
The trend for FDI 1990-2015 depicts that at the most recent years especially 2014-
2015 Turkey did record the highest rates of FDI than ever before. This could be due to the
country’s more openness to trade with other countries across the world.
37
Figure 7: Graphical analysis on GCF 1990-2015 in Turkey
In the trend above it can be seen that GCF trend in Turkey has been growing since the
period 1990 although recently it dropped towards 2015. Basically, the period 2012-2015
shows an up and down decline and could be due to changes in other economic conditions.
Figure 8: Graphical analysis on NX 1990-2015 in Turkey
The trend for NX shows to have been weakening overtime; thus Turkey recorded
lowest balance of trade in the recent years like it is the case between 2010-2015.
The next step shall be to proceed to create relationship analysis for the model
specified in chapter 3; it is worth noting that sign of relationship are not in any way affirming
a cause-effect relationship among FDI, GDP, GCF and NX. This and more shall be analysed
next.
38
Relationship analysis of FDI inflows and economic growth
The relationship analysis was carried out using correlation and regression. In fact, the
execution of the regression test was central in this work because ultimately it was the way to
implement the model GDP = f (FDI, GCF, NX) or Yt = α + β1fdit-1 + β2gcft-1 + β3nxt-1 + εt
Table 12: Correlation analysis test results
Variables Y FDI/GDP GCF/GDP NX/GDP
Y 1.000 0.377 0.591 -0.015
FDI/GDP 0.377 1.000 0.478 -0.215
GCF/GDP 0.591 0.478 1.000 -0.207
NX/GDP -0.015 -0.215 -0.207 1.000
The correlation matrix depicts that FDI_GDP correlated with Y at .377; this was
indication of a weak but positive correlation. It meant the high trends for Y did relate to the
high trends of FDI_GDP but then in a weak manner. The correlation between Y and
GCF_GDP had a coefficient of .591 meaning a positive and strong linear relationship.
Therefore, the high trends of Y related directly to the high trends for GCF_GDP. Lastly, the
correlation analysis between Y and NX_GDP indicates a parameter of -0.015 which means
the linearity has been negative but weak within the se variables. This confirms a negative
linear relationship depicting that where Y increased such was a decrease in the NX_GDP. On
basis of the results above, the following inferences may be deduced:
➢ GDP (Economic Growth) has had a weak but positive linear relationship with
FDI_GDP in Turkey 1990-2015
➢ GDP (Economic Growth) has had a strong but positive linear relationship with
GCF_GDP in Turkey 1990-2015
➢ GDP (Economic Growth) has had a weak but negative linear relationship with
NX_GDP in Turkey 1990-2015
39
Table 13: Regression analysis test results I
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.0427
R Square 0.0018 Adjusted R
Square -0.0398
Standard Error 0.2508
Observations 26.0000
ANOVA
df SS MS F
Significance
F
Regression 1.0000 0.0028 0.0028 0.0438 0.8360
Residual 24.0000 1.5092 0.0629
Total 25.0000 1.5119
Coefficients
Standard
Error t Stat P-value Lower 95%
Upper
95%
Lower
95.0%
Upper
95.0%
Intercept 0.97 0.07 13.79 0.00 0.82 1.11 0.82 1.11
FDI/GDP 0.00 0.01 -0.21 0.84 -0.02 0.01 -0.02 0.01
Foremost the researcher ran the first regression model using only Y (GDP) and
FDI_GDP; this was meant to see how the outputs behaved without the moderating variables
in this case GCF_GDP and NX_GDP. In the model summary, the R Square is at .0018 which
was not a strong parameter to affirm a goodness of fit between Y and FDI_GDP. It also
meant that only 0.18% of the cases for GDP (Y) were explained by the variations in the
FDI_GDP. It means 99.82% of economic growth in Turkey is explained by other factors not
considered in this study; in regression II next was shown by how much NX_GDP and
GCF_GDP improved the goodness of fit. In other words, could it be the two were the 99.82%
other factors that did explain economic growth in Turkey?
Moving on, the regression analysis has the Anova results standing at .8360; it means
there is no significant relationship between Y (GDP) and FDI_GDP and so a relationship
between them was not tenable. The same meant any relationship between the two if any has
been by chance. Then, the regression summary indicated the p value to be 0.84; this way
implying that FDI_GDP was not a significant predictor of economic growth in Turkey. Even
the coefficient or Beta value is at .00 meaning the rate at which FDI_GDP increased
economic growth in Turkey was merely at 0%. The main inference would be:
40
➢ A weak goodness of fit exists between Turkey’s economic growth and FD_GDP
➢ A non-significant relationship exists between Turkey’s economic growth and
FD_GDP
➢ A lack of predictive relationship exists between Turkey’s economic growth and
FD_GDP
Table 14: Regression analysis test results II
Goodness of fit statistics:
Observations 25.000
Sum of weights 25.000
DF 21.000
R² 0.083
Adjusted R² -0.048
MSE 0.064
RMSE 0.253
DW 2.447
Analysis of variance:
Source DF Sum of
squares
Mean
squares
F Pr > F
Model 3 0.122 0.041 0.635 0.601
Error 21 1.346 0.064
Corrected Total 24 1.469
Computed against model Y=Mean(Y)
Model parameters:
Source Value Standard
error
t Pr > |t| Lower
bound
(95%)
Upper
bound
(95%)
Y 1.200 0.230 5.213 < 0.0001 0.721 1.679
FDI/GDP 0.007 0.010 0.727 0.475 -0.013 0.028
GCF/GDP -0.030 0.041 -0.722 0.478 -0.115 0.056
NX/GDP -0.190 0.222 -0.857 0.401 -0.652 0.272
Equation of the model:
1.16180606682567 = 1.19994+0.00718*1.17134138843814-0.02958*4.08598440789977E-02-
0.19027*1.00020141476207
Turning to the second regression model which now incorporated the moderating
variables for NX_GDP and GCF_GDP it can be seen that R2 was at 0.083. It meant 8.30% of
41
NX_GDP and GCF_GDP together with FDI_GDP explained economic growth (Y) in
Turkey. If the reader may recall regression (I) showed only 0.18% of FDI so to speak
explained GDP; it means if GCF and NX are considered there is more combined goodness of
fit towards GDP growth. In the Anova results it can be seen the F test significance is at .601
meaning still there is no combined effect of NX_GDP and GCF_GDP together with
FDI_GDP in how they relate to GDP; this way says any relationship must be by chance.
Lastly, turning to the model parameters it can be seen that FDI_GDP (.475),
GCF_GDP (.478) and NX_GDP (.401) all have the significance intervals exceeding 0.05
threshold. For that reason, the three do not significantly predict economic growth (GDP, Y)
in Turkey. It can be concluded as follows:
➢ There is no significant combined effect among NX_GDP, FDI_GDP, and GCF_GDP
➢ All NX_GDP, FDI_GDP, and GCF_GDP do not show to significantly predict
economic growth in Turkey
Forecast analysis of FDI inflows and economic growth in Turkey
In this part of the analysis the focus was to analyse regression (I) and (11) on both the
intercept and the beta values for the relationship between GDP (Y) and FDI_GDP.
In regression 1 beta (β) was -0.0016 and intercept or alpha value (α) at 0.9700
In regression 2 beta (β) was -0.030 and alpha value (α) at 1.2000
The period under review was 1990-2015 which amounted to 25 years down the line;
so the researcher would like to show how the trend would be using the parameters for
regression for both variables say for the next five years i.e. it means the 26th year down to the
3oth year being 2020. The model equation for achieving the forecast was:
Y = Ab + X where A served as the period while b is the beta and X the intercept.
Table 15: Trend analysis for forecast data
Forecast Period Count
on
Period
Forecast Results
A
Forecast Results B
2016 26 0.9284 0.42
2017 27 0.9268 0.39
2018 28 0.9252 0.36
2019 29 0.9236 0.33
2020 30 0.922 0.3
In order to understand the data in table 14 above see graphical models in figures 9 and
10 below.
42
Figure 9: Graphical analysis on model regression I
The above model reflects the trend for forecast results A whereby the indication is
that FDI_GDP would deteriorate in its contribution to economic growth in Turkey come the
end of five years i.e. towards 2020; however, this is based on the trend for 1990-2015
considered.
Figure 10: Graphical analysis on model regression II
The mode is based on forecast results B which still indicate that FDI_GDP within an
environment of other development indicators such as NX_GDP and GCF_GDP would still
fail to significantly contribute to the growth in Turkey’s economy for the next five years i.e.
2020. Having said all the above the last part of the analysis endeavoured to establish possible
cause-effect relationship between FDI and economic growth in Turkey.
43
Granger causality tests results
EViews program was used to run the Granger test. The results were exported verbatim
as shown in table 16below. Appendix F depicts the same results from EViews using a
screenshot.
Table 16: Granger causality test results
Pairwise Granger Causality Tests
Date: 12/08/22 Time: 02:34
Sample: 1990 2015
Lags: 2 Null Hypothesis: Obs F-Statistic Prob. GCF_GDP does not Granger Cause FDI_GDP 24 0.69168 0.5129
FDI_GDP does not Granger Cause GCF_GDP 5.85365 0.0105 NX_GDP does not Granger Cause FDI_GDP 24 0.45595 0.6406
FDI_GDP does not Granger Cause NX_GDP 1.62834 0.2225 Y does not Granger Cause FDI_GDP 24 21.8119 1.E-05
FDI_GDP does not Granger Cause Y 7.01439 0.0052 NX_GDP does not Granger Cause GCF_GDP 24 3.08132 0.0693
GCF_GDP does not Granger Cause NX_GDP 4.18995 0.0311 Y does not Granger Cause GCF_GDP 24 0.19680 0.8230
GCF_GDP does not Granger Cause Y 0.67126 0.5228 Y does not Granger Cause NX_GDP 24 2.74693 0.0896
NX_GDP does not Granger Cause Y 4.68660 0.0222
The model output for Granger Causality indicated the unidirectional and bidirectional
stature of the manner in which the selected variables related to one another other in terms of
causation. Now, it could be seen that the null hypothesis that Y does not Granger FDI_GDP
(.000) has been rejected. It means it does Granger cause the latter. Then FDI_GDP does not
Granger Cause Y (.005) must be rejected. The rejection of the two null hypothesis asserted
that there exists a bidirectional causality between FDI_GDP and Y.
The null hypothesis Y does not Granger Cause GCF_GDP (.8230) cannot be rejected;
GCF_GDP does not Granger Cause Y (.5228) also cannot be rejected. It means there exists
not unidirectional or bidirectional causality between GCF and economic growth in Turkey.
Lastly, the null hypothesis Y does not Granger Cause NX_GDP (0.0896) cannot be
rejected; however, NX_GDP does not Granger Cause Y (.0222) must be rejected. This
affirms a unidirectional causality between net exports in Turkey towards economic growth.
44
Summary of Chapter
The results in this part of the dissertation have depicted that Turkey has been having
better performance within its economy with both FDI and GDP recording highest turnovers
in the most recent years. However, the findings have indicated weak significant relationships
and combined effects between Turkey’s economic growth and FDI, Net Exports and Gross
Capital Formation and. However, in a few occasions it has been shown that FDI and GDP
have had a bidirectional causality; also, net exports and GDP have had a unidirectional
causality. The other emerging result was that in the future the relationship between Turkey’s
economic growth and FDI would keep on deteriorating come the next five years from 2015.
45
CONCLUSION, IMPLICATIONS AND FUTURE RESEARCH
The focus of the present study was to examine the impact of foreign direct investment
inflow on economic growth in Turkey. The researcher succeeded in developing secondary
research where past studies were evaluated in regard to effects of FDI inflow on economic
growth. Equally, the studies performed in Turkey region were presented and served as a
benchmark for the present dissertation. In the problem analysis it was seen that Turkey has
undergone a number of political and economic struggles hailing from the global financial
crisis to political impasse, and terror attacks. Indeed, this kind of crisis can plunge a country
not just Turkey into slowed pace in economic expansion; on the other hand, investors would
find it difficult to get into the country and explore Green Fields investments. However, the
dissertation shows what has been the effect of FDI inflow to economic growth in Turkey by
looking at the period 1990-2015. The objectives of the research read as follows:
▪ To illustrate the trend of FDI inflows and economic growth in Turkey for
the period 1990-2015
▪ To forecast the trend of FDI inflows and economic growth in Turkey
▪ To critically analyse whether FDI inflows Granger-Causes economic
growth in Turkey
Re-evaluation of key primary and secondary findings
In the development of the literature review it was seen that majority of the studies
either confirmed a unidirectional or bi-directional causality in the case of FDI inflow and
economic growth. For instance, recalling the works by Yilmaz Bayar it was seen that the
main variables used included GDP, net FDI inflows and DI covering the period 1990-2012.
The study was similar to the current dissertation but differed in terms of methodology and
choice of variables. However, FDI and GDP variables were the same as what the researcher
used to examine the effects of FDI inflow on economic growth in the current dissertation.
The test results for the above cited study indicated that FDI inflows had negative effect to
economic growth.
Another study worth re-asserting was the one performed by Coskun (2012) where the
findings affirmed that in the case of Turkey FDI caused GDP. Similarly, that 1% increase in
FDI led to .011% increase to GDP. The one for Yilmaz depicted that 1% increase in FDI
deceased GDP by 33% meaning an inverse relationship. Moving on the researcher would also
like to recall the works by Abdulrahman and Khder (2014) performed in Turkey in the period
46
1990-2012. In the results it was seen that 1% raise on FDI led to 0.03% increase in GDP.
Equally, the OLS regression did show FDI did positively relate to GDP.
In terms of Co-Integration, it was established that there lacked a long-run equilibrium
relationship between GDP and other indicators such as FDI, DI and Trade Liberalisation. In
the same research, the bidirectional Granger-Causality between FDI and GDP (Economic
Growth) was not confirmed. In similar analysis, was the study by Salih Katircioglu (2009)
which did not establish the long-run equilibrium relationship between Turkey’s real GDP
using ARDL co-integration. In terms of Granger-Causality the study did not confirm a short-
run causation in the case of FDI to real GDP growth.
A review of the secondary findings above which were also illustrated in the literature
review of the present dissertation continued to show weak arguments as to the relationship
between FDI and economic growth in Turkey. On this background, the researcher would like
to assess how these establishments corroborated with the dissertation’s findings; the gaps
involved and the opportunities for future research. Foremost, is that the variables used were
FDI inflows, GDP and Net Exports with the data sourced from the IMF terminals. The main
findings indicated a number if things. For instance, a weak but positive linearity between
GDP and FDI; even when moderated with GCF a weak but positive relationship still
emerged. However, net exports did show negative linearity to GDP.
In the regression model I in chapter four a few things were confirmed such as a weak
linear relationship in the short-run between FDI and GDP. Even in regression II a combined
effect between GDP against FDI, GCF and NX was not confirmed. Not even a significant
predictive relationship was confirmed in regression II between GDP and the three
independent variables. However, in the Granger Causality a bi-directional causality was
established between FDI and GDP; but bi-directional causality between GDP and GCF was
not confirmed; however, a uni-directional causality was affirmed between net exports to
GDP. In other words, the latter finding meant Net Exports in Turkey Granger Caused GDP
(economic growth).
Overall, the study did confirm similar results with the study by Yilmaz where
negative relationship between FDI and economic growth persisted in the case of Turkey. It
means even over the present economic environment, Turkey has not been benefited from its
FDI inflows to shape and grow the economy. By limitation the findings of the present
dissertation did not re-affirm past works that used co-integration to confirm long-run
47
equilibrium relationship between FDI and economic growth; this was because co-integration
was not part of the methodology sought in this work.
Economic Policy Implications to Turkey
The general analysis in the past studies and on the present dissertation it can be said
that depending on methodology a positive relationship between Turkey’s economic growth
and FDI inflow is both supported or rejected. Indeed, the outcomes of the research could be
supported by a number of circumstances right from the methodology of execution down to
analysis and variables. It is equally worthy to note that it is not a weakness for FDI inflow to
not relate positively to economic growth; actually, the independence of the two should be
treated as good news for Turkey’s economy structure. Why is this so? It is because it means
should Turkey have less FDI inflow coming into the country, the economy in terms of GDP
would not go into a deficit. It would still remain vibrant and moving due to low pressures
caused by dwindling FDI inflow.
Therefore, economic experts in Turkey need to closely evaluate whether FDI inflows
in the real sense has any added value to government revenue and to what extent including the
contribution coming from FDI proceeds to finance the national budget. Otherwise, if the
government of Turkey is getting funds to cover deficits in the national budget then the lack of
relationship both in the long-run and short-run should be considered a threat to economic
growth; and lack of a better word that there could be corruption going into the management
of FDI inflow proceeds. For instance, the proceeds could be channeled to other personal
wealth advancement instead of benefiting the national economic growth.
On the other hand, the weak relationship between FDI and economic growth in
Turkey should be analysed more in-depth. As mentioned, the weak relationship may be
treated as a positive thing for Turkey; but the question arising is how would it be if the
government began to channel in a good way the proceeds of FDI inflow to GDP growth?
Absolutely, it would raise more revenues for the government and in the end stimulate GDP
wealth. So, economic experts in Turkey especially the Central Bank need to begin monitoring
FDI performance and through better regulation ensure the proceeds are used for the greater
good of the people of Turkey. For instance, the government can use revenues from FDI to
enhance infrastructure development, support education by extending more facilities to
schools, and improve health. Ideally, increase spending for the well-being of Turkish people.
In conclusion, the government of Turkey through the Ministry of Finance and
Treasury should have clear laws about FDI and create a transparent office that will account to
48
the public how the accounts for net FDI have been utilised to support national budget for
economic expansion and development.
Recommendations for future research
On basis of the findings, the following recommendations will be proposed:
1) In terms of future research, the researcher proposes a multi-methods research on the
debate or investigation of the relationship between FDI Inflow and economic growth.
This is to have one-force study that will bring together all possible methodologies of
assessing the relationship between FDI and economic growth. Then come to a
stronger conclusion as to the real effects of FDI and economic growth in Turkey.
2) A critique of methodologies and choice of variables in all studies investigating the
relationship between FDI and economic growth in Turkey would be resourceful. This
is to set pace for future enquiries and bring more light as the gaps emerging from past
studies and what future scholars need to look at the more; probably, turn the research
into a qualitative analysis and there be more engagement with key stakeholders and
agencies in Turkey that directly govern FDI processes.
Career implications of the research
Well, this dissertation paves way for the researcher to take on an economic policy-
making job in Turkey especially on issues related to FDI management. The researcher will
publish this dissertation in future to ensure it is in the public debate and accessibility. Also,
use the presently completed dissertation to generate more publications in macro-economic
issues. In the end, venture into doing a PhD in Public Policy and Development because of the
passion to ensure the people of Turkey benefit more from all opportunities both hidden and
unhidden.
49
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Appendix
Appendix A: Dixon test results for GCF
Dixon test for outliers / Two-tailed test (GCF/GDP):
R10 (Observed value) 0.042
R10 (Critical value) 0.313
p-value (Two-tailed) 0.547
alpha 0.05
The p-value has been computed using 1000000 Monte Carlo simulations.
99% confidence interval on the p-value:
( 0.545, 0.548 )
52
Appendix B: Dixon test results for NX Dixon test for outliers / Two-tailed test (NX/GDP):
R10 (Observed value) 0.125
R10 (Critical value) 0.313
p-value (Two-tailed) 0.684
alpha 0.05
The p-value has been computed using 1000000 Monte Carlo simulations.
99% confidence interval on the p-value:
( 0.683, 0.685 )
53
Appendix C: Screenshot from EViews for FDI_GDP
54
Appendix D: Screenshot from EViews for GCF_GDP
55
Appendix E: Screenshot from EViews for GCF_GDP
56
Appendix F: Screenshot on Granger tests from EViews
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