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

7

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

9

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.

10

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.

11

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

13

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

14

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.

15

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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51

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