A Dynamic Model for determining Inward Foreign ... - Business School
A Dynamic Model for determining Inward Foreign ... - Business School
A Dynamic Model for determining Inward Foreign ... - Business School
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A <strong>Dynamic</strong> <strong>Model</strong> <strong>for</strong> <strong>determining</strong> <strong>Inward</strong> <strong>Foreign</strong> Direct Investment in Jordan<br />
Ghaith N. Al-Eitan a,*<br />
<strong>Foreign</strong> direct investment has become an increasingly important channel <strong>for</strong> developing<br />
countries to enhance their economic and financial systems. A significant part of economic<br />
and financial research is the view that a host country's risks affect investment inflows. The<br />
purpose of this paper is to test the argument that Jordan’s country risk, stock market price and<br />
macroeconomic variables determine inward FDI in Jordan. <strong>Model</strong>s are <strong>for</strong>malised based on<br />
the theory of <strong>for</strong>eign direct investment and the missing gaps in the literature. This study<br />
covered the period from 1996 to 2010. Monthly data of collective country risk,<br />
macroeconomic variables and the price of stock market sectors were obtained from risk<br />
rating agencies, Central Bank of Jordan and the Jordan Securities Commission respectively.<br />
Moreover, the following methods are applied: Ordinary Least Squares (OLS) regression<br />
analysis (unlagged monthly data), Co-integration and exogeneity analysis based on<br />
multivariate models (lagged monthly data) such as vector autoregressive (VAR) and Granger<br />
Causality. ). Based on the analysis, the results showed that Jordan economic risk, the price of<br />
stock market sectors and two of the macroeconomic variables (inflation and GDP)<br />
significantly caused inward FDI in Jordan. Also, the variables appear to have a long run<br />
relationship. Some strategic implications have been drawn in conclusion <strong>for</strong> FDI attraction<br />
policy in Jordan.<br />
Keywords: <strong>for</strong>eign direct investment, stock market price, financial risk, economic risk,<br />
political risk, inflation, GDP, VAR<br />
a <strong>School</strong> of Economics and Finance, Curtin University, Perth, Western Australia, 6001, Australia. E-mail:<br />
g.aleitan@postgrad.curtin.edu.au<br />
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PREFACE<br />
Thesis title: <strong>Model</strong>ling <strong>Inward</strong> <strong>Foreign</strong> Direct and Indirect Investment <strong>for</strong> Jordan and<br />
Australia (Policy Implications)<br />
Supervisor: Associated Professor John Simpson<br />
Previous studies have determined the effects of country risk, macroeconomic factors and<br />
globalisation on inward <strong>for</strong>eign direct and indirect investment (FDI and FII) in host<br />
economies. Some researchers study one or two countries risks like political and economic<br />
risk as well as including macroeconomic factors such as inflation and GDP to investigate<br />
their effects on (FDI and FII) in <strong>for</strong>eign countries. However, a few studies have analysed<br />
the relationship between FII, a country’s risk, macroeconomic factors and globalisation to<br />
determine the extent to which a country’s business environment influences FII in host<br />
country. For example, the assumption common to these studies is that a country’s risks<br />
affect FII negatively, some macroeconomic and globalisation factors positively and<br />
negatively affect FII. This means that conclusions have been mixed. There<strong>for</strong>e, this study<br />
investigates the main determinants of inward FDI and FII in Jordan and Australia as<br />
developed economies in order to obtain some policy implications by using advanced<br />
econometric techniques such as vector autoregressive model, impulse response functions,<br />
variance decomposition, Granger causality, co-integration and error correction model.<br />
The thesis examines a number of key issues over the period 1996-2010 within the above<br />
context: firstly, investigating the true state of inward <strong>for</strong>eign direct and indirect investment<br />
in Jordan and Australia. Secondly, identifying the major determinants affect inward <strong>for</strong>eign<br />
direct and indirect investment in Jordan and Australia. .Thirdly, identifying the country's<br />
risk factors that influence inward <strong>for</strong>eign direct and indirect investment. Finally, analysing<br />
macroeconomic factors affect inward <strong>for</strong>eign direct and indirect investment.<br />
The thesis has been taken the following Structure:<br />
I. Chapter One: Introduction<br />
II. Chapter Two: Study Background<br />
III. Chapter Three: Theoretical Base and Literature Review<br />
IV. Chapter Four: Empirical <strong>Model</strong>s and Hypothesises<br />
V. Chapter Five: Data and Methodology<br />
VI. Chapter Six: Main Empirical<br />
VII. Chapter Seven: Discussion<br />
VIII. Chapter Eight: Conclusion and Final Remark<br />
This paper based on advance econometric techniques (lagged models) such VAR, VECM,<br />
Co-integration, Granger causality<br />
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1. Introduction<br />
<strong>Foreign</strong> direct investment (FDI) is the most significant factor of economic growth, which<br />
encourages productivity of the national economy, augment’s the use of technology,<br />
reducing unemployment by creating new jobs and other encouraging output that<br />
differentiate this <strong>for</strong>m of investment from other funding sources.<br />
Consequently, most developing countries realised the need <strong>for</strong> FDI to increase rates of<br />
economic growth. Despite the race among Arab economies to improve its business<br />
environment to pull <strong>for</strong>eign investment via contemporary legislations to attract this <strong>for</strong>m of<br />
investment and increase the competitiveness of their national economy, most of these<br />
countries are still suffering from a low volume of inward FDI compared to other<br />
economies. This is due to the absence of efficient legislation that should support the process<br />
of attracting inward FDI. For example, impose higher taxes, the absence of political and<br />
economic stability in some countries and the existence of administrative and financial<br />
corruption.<br />
Like the rest of the world, <strong>for</strong>eign investment plays a vital economic role in Jordan and<br />
remains one of the main sources to enhance economic development. There<strong>for</strong>e, the main<br />
aim of this paper is to investigate the dynamic movements of inward FDI in Jordan, country<br />
risk (financial, economic and political risk), macroeconomic factors (inflation, interest rate<br />
and GDP) and stock market price by implementing VAR, ECM, co-integration test and<br />
Granger causality test.<br />
Politically, Jordan’s <strong>for</strong>eign policies play a major role in making Jordan highly attractive<br />
<strong>for</strong> (FDI). For instance, in 1996, Jordan signed a peace agreement with Israel accompanied<br />
by Bill Clinton. As a result the Qualifying Industrial Zones (QIZ) was created by the U.S.<br />
Congress to support the peace process. In 1998, the United States Trade Representative<br />
(USTR) designated Jordan's Al-Hassan Industrial Estate in the northern city of Irbid as the<br />
world's first QIZ (CRS Report <strong>for</strong> Congress 2001, 2).<br />
Economically, Jordan is an open economy and has undertaken a program of economic<br />
re<strong>for</strong>m covering most features of Public Finance Management. The three core objectives of<br />
the program of re<strong>for</strong>m are to ensure fiscal sustainability, efficient resource allocation and<br />
operational efficiency. The economy has experienced sustained economic growth in recent<br />
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years due to a combination of strong economic policies nationally and spillover from<br />
regional growth, mainly by the rich Gulf economies. Annual real GDP growth doubled<br />
during 2000-05 from the earlier five years (Jordan Per<strong>for</strong>mance Report 2007, 8).<br />
The growth of Jordan’s Gross Domestic Product (GDP) was recorded at 7.6% percent<br />
and 2.9% percent respectively <strong>for</strong> the years 2008 and 2009, with a total contribution from<br />
Transport, storage and communication, manufacturing and the produce of government<br />
services sectors amounting to almost 16% percent, 17% percent and 18% percent <strong>for</strong> 2009 (<br />
Treasury Jordan 2009, 36). The global financial crisis has affected Jordan’s GDP by 4.7%.<br />
However, Jordan economy is recovering from the effects of this. For example, Jordan’s<br />
GDP was estimated at 3.4% in 2010 and the International Monetary Fund (IMF) <strong>for</strong>ecasted<br />
by 4.2% by 2011(Central Bank of Jordan 2010).<br />
The Jordan government has eliminated most fuel and agricultural subsidies, passed<br />
legislation targeting corruption, and begun tax re<strong>for</strong>m. It has also worked to liberalize trade,<br />
joining the World Trade Organization (WTO) in 2000; signed an Association Agreement<br />
with the European Union (EU) in 2001; and also signed the first bilateral free trade<br />
agreement (FTA) between the U.S. and an Arab country, which entered into <strong>for</strong>ce in 2001.<br />
Jordan has established some economic regions and promotional institutions such as the<br />
Aqaba Special Economic Zone (ASEZ) and the Jordan Investment Board (JIB) in order to<br />
attract the <strong>for</strong>eign investors’ attention. First of all, ASEZ was inaugurated in 2001 as a bold<br />
economic initiative by the government of Jordan. A liberalized, low tax duty-free and<br />
multi–sector development zone, the ASEZ offers multiple investment opportunities in a<br />
strategic location on the Red Sea covering an area of 375 km² and encompassing the total<br />
Jordanian coastline (27 km), the sea-ports of Jordan and an international airport. Secondly,<br />
JIB was established in 1995. The Jordan Investment Board is a government institution<br />
committed to working with the private sector to promote Jordan <strong>for</strong> its unique, friendly<br />
business environment, diverse investment opportunities and attract <strong>for</strong>eign investments.<br />
Financially, the most important reason to attract <strong>for</strong>eign investors to the Amman<br />
financial market is there is a need to invest in portfolio diversifications on the basis of the<br />
principle of diversifications investment, which is part of the basic principles of investment<br />
policy. A <strong>for</strong>eign investor seeking markets does not have a connection with the advance<br />
markets, which are not affected by increasing and decreasing them. In addition, <strong>for</strong>eign<br />
investors search <strong>for</strong> companies or sectors that have prospects of a future boom. Moreover,<br />
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investors look <strong>for</strong> the markets which provide accurate and quick in<strong>for</strong>mation in an<br />
appropriate time in order to control their investments (Amman financial market, 2009).<br />
Public shareholding companies were set up and their shares were traded in long be<strong>for</strong>e the<br />
setting up of the Jordanian securities market. In the early thirties the Jordanian public<br />
already subscribed to and traded in the shares. For example, Arab Bank was the first public<br />
shareholding company to be established in Jordan in 1930, followed by Jordan Tobacco and<br />
Cigarettes in 1931, Jordan Electric Power in 1938 and Jordan Cement Factories in 1951. In<br />
the 1975 and 1976, the Jordan Central Bank cooperated with the World Bank’s<br />
International Finance Corporation to establish the Amman financial market (Amman<br />
financial market, 2009).<br />
Jordan has done well in catching the attention of <strong>for</strong>eign investors as a consequence of<br />
numerous factors, such as internal and external political stability, supported investment<br />
legislations, privatisation plans, advanced private sectors, joining and merging with a<br />
variety of unilateral, bilateral and multilateral trade agreements. Moreover, <strong>for</strong>eign capital<br />
has been attracted by some factors such as the legal system, developed infrastructure, cheap<br />
and skilled labour, and feasible projects to be undertaken (Investment Encouragement<br />
Corporation, Amman, Jordan).<br />
The rest of the paper is organised as follows: section 2 describes the literature review and<br />
theoretical framework and hypotheses. Section 3 presents the research methodology<br />
including research models, the mechanism used to measure the extent of risk, data analysis<br />
method and methods of gathering data. Section 4 shows the results and discussion and<br />
section 5 conclusion.<br />
2. Theoretical Framework and Review of Literature on FDI and Country Risk.<br />
Numerous studies have analysed the relationship between FDI, country risk,<br />
macroeconomic factors and stock market price to determine the extent, if any, to which<br />
country risk influences FDI in host economies. The assumption common to these studies is<br />
that country risk affects inward FDI. Overall, conclusions have been mixed, but most<br />
research find that country risk prevent FDI to flow into hot countries. The differences in the<br />
findings could arise from a number of methodological and conceptual factors such as lack<br />
of comprehensive, different definitions of FDI and different econometric specifications.<br />
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2.1Theoretical Framework<br />
The theory of FDI noted by Dunning 1977 that the structure market failure hypotheses of<br />
Hymer and Caves or the internalization approach of Buckley and Casson was very much<br />
couched. In that case, Dunning brought the computing theories together to build a new<br />
single theory. Dunning's new theory shaped via combining Hymer's ownership advantages<br />
with the internalization school meanwhile added a location dimension to the new theory.<br />
Also, he considers the impact of Country's and industry's characteristics on the ownership<br />
location and internalization advance of FDI (Dunning and Lundan 2008, 86).<br />
This paper has been focused on a host country’s characteristics. This is an interesting<br />
topic, as while virtually all countries now compete energetically <strong>for</strong> FDI inflows, the<br />
distribution of those inflows is far from uni<strong>for</strong>m. While some countries pull in massive<br />
amounts of FDI inflows, but others such as those in developing countries lag far behind.<br />
Thus, it is vital <strong>for</strong> FDI-seeking policymakers to have a good grasp of the underlying<br />
drivers of the MNEs’ location decisions in order to attract inward FDI. According to<br />
Dunning's eclectic paradigm of FDI shows that a firm will directly invest in a <strong>for</strong>eign<br />
country if it ensures three conditions (Jones and Wren 2006, 36): An ownership-specific<br />
asset must be possessed by the firm, which gives it advantages over other firms. Secondly,<br />
these assets must be internalized within the firm. Finally, there must be a benefit in settingup<br />
production in a <strong>for</strong>eign country rather than relying on exports.<br />
In particular, this paper has been focused on a country’s risk which is closely related with<br />
the level of business risk. It seems intuitively plausible to believe that a sound institutional<br />
environment (efficient bureaucracy, low corruption, secure property rights, etc.) should<br />
attract more FDI. Similarly, higher business risk due to high country risk would discourage<br />
<strong>for</strong>eign investment by multinationals. Hence, there are several ways of characterizing a<br />
country's L specific advantages; one of them is ESP paradigm. According to economic<br />
environment, economic system and government policies, countries are classified in the ESP<br />
paradigm of Koopmans and Montias (1971). Here environment encompasses the resources<br />
and capabilities, including a wide range of intangible assets to a particular country as well<br />
as the ability of its enterprises to use these to service domestic or <strong>for</strong>eign markets. System<br />
means the macro-organizational mechanism within which the allocation of these resources<br />
and capabilities is decided. Policy means the strategic objective of government and the<br />
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macro or micro measures taken by them, to implement and advance these objectives within<br />
the system and environment of which they are part (Dunning and Lundan 2008, 223).<br />
2.2 Literature Review and Hypotheses<br />
2.2.1 Political risk<br />
Political risk is a type of risk faced by <strong>for</strong>eign investors, MNfs and governments. It is a risk<br />
that can be understood and managed via reasoned <strong>for</strong>esight and investment. Many studies<br />
have examined the determinants of FDI in a host country. Using different econometric<br />
techniques and periods, Harms and Ursrung (2000), Jensen (2003) and Busse (2004) point<br />
out that MNfs are more likely to be attracted to a democracy. Nevertheless, Egger and<br />
Winner explore the relationship between corruption and inward FDI by using general<br />
equilibrium models and data of 73 developed and less developed countries. They highlight<br />
a clear positive relationship between corruption and FDI which means corruption is a<br />
stimulus <strong>for</strong> FDI (2005, 935-949). The analysis comprises the primary data from 145<br />
affiliates of western MNEs in Turkey via a survey by Demirbag et al, who find that political<br />
risk, financial incentives and culture distance do not have any significant impact on the<br />
perceived per<strong>for</strong>mance of affiliates (2007, 330). On the other hand, according to Busse and<br />
Hefeker political risks have a significant impact on FDI inflows (2007, 401). Institutional<br />
quality and democracy appear more important <strong>for</strong> FDI in services than general investment<br />
risk or political stability (Kolstad and Villanger 2008, 530). According to Cuervo-Cazurra,<br />
corruption, arbitrary corruption and pervasive corruption have a negative influence on FDI.<br />
However, transition economies show high levels of corruption and also high levels of FDI<br />
(2008, 25). Asiedu et al state that the optimal levels of FDI decrease as the risk of<br />
expropriation rises (2009, 269). Consequently, based on this overview of the related<br />
literature, the corollary hypothesis is as follows:<br />
H 1a : There is a negative relationship between political risks and inward <strong>for</strong>eign direct<br />
investment.<br />
2.2.2 Economic risk<br />
Economic risk could be manifested in assessing a country's economic strengths and<br />
weaknesses, which include real gross domestic product (GDP), growth, the annual inflation<br />
rate, and gross national product per head. The previous studies consider that economic risk<br />
as an important variable <strong>for</strong> <strong>for</strong>eign investors to make a decision about investment in a host<br />
5
country. Alfaro et al study the various links among FDI, financial market and economic<br />
growth. The authors find that countries with well-developed financial markets gain<br />
significantly from FDI (2004, 108). Jinjarak studies FDI and macroeconomic risk <strong>for</strong> each<br />
US multinational industry via measuring vertical FDI share as a ratio of exports to a parent<br />
country relative to local sales by <strong>for</strong>eign affiliates. He finds that FDI activities of US<br />
multinationals in industries with a higher share of vertical FDI respond more<br />
disproportionately to negative effects of macro level demand, supply and sovereign risks<br />
(2007, 509-511). However, establishing an investment promotion agency is an effective<br />
way to attract FDI flows. This is achieved by collecting the IPA data via questionnaires<br />
from 68 countries where the Korean KTIPA maintains an overseas office and macro data<br />
from published sources and conducting a series of path analysis with maximum-likelihood<br />
estimation (Lim 2008, 44-50). Moreover, Speed and Kenisarin establish quantitative<br />
relationships between levels of FDI per capital to the year 2004, and three sorts of<br />
indicators relating, respectively to governance, economic freedom and corruption<br />
perception. Based on this, they highlight that the level of FDI in the Former Soviet Union<br />
states has been determined significantly via a planned economy moving towards a market<br />
economy(2008, 306). Azemar and Delios test the influence of corporate taxes on FDI in<br />
developing countries. They find a strong negative correlation between FDI and corporate<br />
tax rates (2008, 92). On the other hand, if the MNFs’ probability of taking part in the<br />
production process is reported as high then the MNfs pay a high level of tax (Karabay 2010,<br />
222). Hence, after discussing the associated literature and theories, this leads to the<br />
following hypothesis:<br />
H 2a : There is a negative relationship between economic risks and inward <strong>for</strong>eign direct<br />
investment.<br />
2.2.3 Financial risk<br />
Financial risk is an umbrella term <strong>for</strong> any risk associated with any <strong>for</strong>m of financing.<br />
Typically, in finance, risk is synonymous with downside risk and is intimately related to the<br />
shortfall or the difference between the actual return and the expected return. Estrin and<br />
Bevan employ data to determine FDI inflows from western countries, mainly in the<br />
European Union and in central Eastern Europe. They find that the host country risk proves<br />
not to be a significant determinant (2004, 785). On the other hand, Xing uses panel data<br />
covering Japanese direct investment in China's nine major manufacturing sectors from 1981<br />
to 2002. This is in order to examine how FDI inflows from Japan were affected by the real<br />
6
exchange rate between the Japanese Yen and Chinese Yuan. He suggests that the real<br />
exchange rate is one of the significant factors affecting Japanese FDI in China (2006, 207).<br />
What is more, Demmirbag et al use primary data from 145 affiliates of western MNfs in<br />
Turkey via a survey <strong>for</strong> the purpose of exploring the institutional incorporation of the host<br />
country and firm variable as determinants of the factors influencing perceptions of <strong>for</strong>eign<br />
affiliate per<strong>for</strong>mance. They find that financial incentives do not have any significant impact<br />
on the perceived per<strong>for</strong>mance of the affiliate (2007, 330). However, Tomlin uses the<br />
implications of the model of investment under uncertainty to examine the relationship<br />
between exchange rates and FDI in 207 U.S industries. He states that dollar appreciations<br />
are positively correlated with service FDI flows into the U.S (2008, 537). Additionally,<br />
Alfaro et al <strong>for</strong>malize a mechanism that emphasizes the role of the local financial market in<br />
enabling FDI to promote growth through linkages. They conclude that there is an increase<br />
in the share of high level growth in financially developed economies by using realistic<br />
parameter value (2010, 248). Nevertheless, Arratibel et al highlight that a negative effect of<br />
exchange rate volatility on FDI stock and negative relation between exchange rate volatility<br />
and FDI is even more negative <strong>for</strong> more open economies (2010, 11). Thus, after reviewing<br />
the related literature, the hypothesis is as follows:<br />
H 3a : There is a negative relationship between financial risks and inward <strong>for</strong>eign direct<br />
investment.<br />
2.2.4 Stock Market Price<br />
De Santis et al. (2004) and Klein et al. (2002) test stock market valuations as a determinant<br />
of aggregate and firm-level FDI, respectively, but use these valuations as proxy <strong>for</strong><br />
traditional FDI determinants – in particular intangible assets – or do not control <strong>for</strong><br />
traditional FDI determinants, and there<strong>for</strong>e do not test <strong>for</strong> a strict finance-FDI effect in the<br />
sense of Baker et al (2009). Baker et al note that relative wealth shocks of the type that<br />
results from exchange rate changes in Froot and Stein (1991) may also originate in stock<br />
market price misalignments. They discuss the possibility of an effect on FDI through a<br />
‘cheap finance’ channel (source-country overvaluation) or a ‘cheap assets’ channel (targetcountry<br />
undervaluation5), and find strong evidence in favour of a ‘cheap finance’ effect on<br />
annual aggregate US FDI flows over the 1974–2001 period.<br />
H 5a : There is a positive relationship between stock market price and inward <strong>for</strong>eign direct<br />
investment.<br />
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2.2.4 Gross Domestic Product<br />
GDP is the market value of all final goods and services produced within an economy in a<br />
given period of time. Several studies have shown that the importance of GDP in attracting<br />
FDI. For example, Bitzenis et al highlights that the economic variables such as GDP is<br />
considered as first order in <strong>determining</strong> FDI (2007, 693; Caves, 2007; Dunning and<br />
Lundan, 2008). In addition, Asiedu points out that the size of a country’s market as<br />
measured by GDP is a key determinant of FDI inflows (2006, 73). Blonigen et al conduct a<br />
general examination of spatial interaction in empirical FDI models using data on US<br />
outbound FDI activity. As a result, they state that the traditional determinant of FDI such a<br />
host country’s GDP has a strong positive and significant coefficient FDI (2007, 1314).<br />
Consequently, based on this overview of the related literature, the hypothesis has been<br />
<strong>for</strong>mulated as follows:<br />
H 5a : There is a positive relationship between Gross Domestic Product and inward <strong>for</strong>eign<br />
direct investment.<br />
2.2.5 Inflation<br />
The inflation rate means that the general level of price <strong>for</strong> goods and services is rising and<br />
subsequently purchasing power is falling. The Inflation rate is frequently used as an<br />
indicator of macroeconomic instability reflecting the presence of internal economic tension<br />
of the inability or unwillingness of government. There<strong>for</strong>e, Central banks attempt to stop<br />
severe inflation, along with severe deflation, in an attempt to keep the excessive growth of<br />
prices to a minimum (Mankiw 2007, 76-85). Rammal and Zurbruegg examine the<br />
determinants of FDI <strong>for</strong> five Asian economies namely: Indonesia, Malaysia, Philippines,<br />
Singapore and Thailand, by using a panel data set containing in<strong>for</strong>mation on FDI flows<br />
from home to host countries. As a result, the negative relationship shows that an increase in<br />
the inflation rate lessens FDI in that country (2006, 409). However, Trevino et al<br />
investigate the process of institutionalization and legitimization in countries in Latin<br />
America and its impact on organizational decision-making regarding inward <strong>for</strong>eign direct<br />
investment (FDI).They highlight that control variable inflation insignificant support that<br />
lower inflation leads to greater levels of FDI.<br />
H 6a: The level of inflation in the host country is negatively associated with its level of<br />
inward FDI.<br />
8
Research Methodology and Empirical Analysis<br />
3.1 Variables<br />
The main variables used to explain the drives of <strong>for</strong>eign direct investment inflows to Jordan<br />
are country risk ( finance, economic and political risk), macroeconomic factors (inflation,<br />
gross domestic products GDP and interest rate) and stock market price of following sectors<br />
(banks, services, industries and general sectors).<br />
3.1.1 International Country Risk Guide<br />
ICRG gathers monthly data on a variety of financial, economic and political risk variables<br />
to calculate risk indexes in each of these categories. For instance, five financial, six<br />
economic and 13 political factors are used. Each factor is assigned a numerical rating<br />
within a specified range. The specified allowable range <strong>for</strong> each factor reflects the weight<br />
attributed to that factor. As high score indicates low risk. The Financial Risk on 50 points,<br />
Economic Risk on 50 points and Political Risk index is based on 100 points.<br />
First of all, the financial risk’s aim is to provide a means of assessing a country’s ability<br />
to pay its way. In essence, this needs a system of quantifying a country’s ability to finance<br />
its official, commercial, and trade debt obligations. Secondly, the economic risk is aimed at<br />
assessing a country’s current economic strengths and weaknesses. In general, if its strengths<br />
outweigh its weaknesses, it will present a low economic risk and if its weaknesses outweigh<br />
its strengths, it will present a high economic risk. Finally, the political risk is purposed to<br />
deliver a means of assessing the political stability of the countries covered by ICRG on a<br />
comparable basis. This is done by assigning risk points to a pre-set group of factors, termed<br />
political risk components.<br />
3.1.2 Stock Market Price<br />
Stock prices shed light on the connection with FDI. Host country stock market valuations<br />
contain relatively more in<strong>for</strong>mation about the marginal productivity of FDI, while source<br />
country valuations are likely to be more relevant to a <strong>for</strong>eign investor’s cost of capital.<br />
Thus, price of different stock market sectors are used including stock mark price of<br />
Jordanian bank, services, industries and general sectors.<br />
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3.1.3 Macroeconomic Variables<br />
To improve the empirical analysis, three macroeconomic variables are considered: GDP,<br />
inflation and interest rate. There several studies have used GDP as control variable in<br />
<strong>determining</strong> inward FDI in a host economy such as (2007, 693; Caves, 2007; Dunning and<br />
Lundan, 2008) interest rate and as well as inflation <strong>for</strong> example, (Rammal and Zurbruegg<br />
2006; Mankiw 2007). Table 2 lists these variables and identifies the sources of data <strong>for</strong><br />
each.<br />
3.2 Methods of Gathering Data<br />
Monthly data <strong>for</strong> collective financial, economic and political risk are obtained from the<br />
International Country Risk Guide (ICRG) 2 . As <strong>for</strong> <strong>for</strong>eign direct investment inflows to<br />
Jordan, GDP, (GDP data are converted to monthly) and inflation data are collected from the<br />
Central Bank of Jordan (CBJ). The sample covered period of time from 1996 to 2010.<br />
According to the definition of the International <strong>Foreign</strong> Direct Investment Bank (IFDIB),<br />
the percentage is the net flow of investments directed to obtain constant returns of (10%) or<br />
more of shares with voting power at organizations functioning in a <strong>for</strong>eign economy to the<br />
investor. This variable is share capital, reinvested returns; long and short term capital<br />
shown in the payables balance, and this series, according to (IFDIB) indicates the net flow<br />
of investments in the country.<br />
Third: Data Analysis Methods<br />
The current paper investigates the behaviour of inward <strong>for</strong>eign direct investment in Jordan<br />
by illustrating the effects of country risk (financial, economic and political risk), the price<br />
of different stock market sectors (banks, services, industries and general sectors) and<br />
macroeconomic factors such as inflation, interest rate and GDP. As the data are gathered, it<br />
was entered into Eviews-7 program in order to analyse and apply different statistical<br />
methods. In the first stage, OLS, diagnostic tests (serial correlation, heteroskedasticity<br />
white test, unit root test ADF) are implemented to analyse the unlagged model. In the<br />
second stage, vector autoregressive model (VAR), co-integration test, Granger causality<br />
2 Country risk rating consists of three variables: firstly, political risk provides a mean of assessing the political stability of the<br />
countries including government stability, corruption, and democratic accountability …exe. Secondly, economic risk provides<br />
a means of assessing a country’s current economic strengths and weaknesses including GDP per head, real GDP and annual<br />
inflation rate …exe. Thirdly, financial risk provides a means of assessing a country’s ability to pay its way which means the<br />
country’s ability to finance its official, commercial and trade debt obligations …exe.<br />
10
and error correction model (ECM) are applied to explain the dynamic movement of the<br />
variables in the lagged system.<br />
The quarterly GDP time series data are converted into monthly using Chow and Lin<br />
(1971 372-375) procedure. The idea is that the GDP is observable at the quarterly<br />
frequency, but the indicators used the indicators employed to disaggregate it are observable<br />
at a highest frequency, the data are available on a monthly basis, and are potentially<br />
in<strong>for</strong>mative variables.<br />
In order to illustrate the method, is monthly values of one of the GDP components<br />
and n are set of variables available monthly and contain in<strong>for</strong>mation about .<br />
( )<br />
Where ( ). The monthly error term is ( )with unknown serial correlation<br />
coefficient and V is the error covariance matrix <strong>for</strong>mulated as follows:<br />
[ ]<br />
( )<br />
Taking quarterly averages of equation number (4.50) to obtain the following equation:<br />
( ) or y.=X.β+μ.<br />
Where is the matrix that converts monthly observations to quarterly averages,<br />
where a dot subscript is a quarterly average. ( ) ( ) is the quarterly<br />
error covariance matrix. Finally, the estimated monthly values <strong>for</strong> the GDP component ̂<br />
are computed by Chow-Lin’s <strong>for</strong>mula as follows:<br />
̂ ̂ ̂ ( ) ̂<br />
11
Table 1: Description and Sources of Data<br />
Variable Sub-variables Description Source of Data<br />
FDI NP* <strong>Inward</strong> <strong>Foreign</strong> Direct Investment Central Bank of Jordan<br />
(FR) Jordan Financial Risk International Country Risk Guide<br />
Country Risk<br />
(ER) Jordan Economic Risk International Country Risk Guide<br />
(PR) Jordan Political Risk International Country Risk Guide<br />
(BS) banks Stock Market Valuation Amman Stock Exchange<br />
Stock Market Price<br />
(SS) Services Stock Market Valuation Amman Stock Exchange<br />
(IS) Industries Stock Market Valuation Amman Stock Exchange<br />
(GS) General Stock Market Valuation Amman Stock Exchange<br />
Macroeconomic<br />
Factors<br />
(GDP) Jordan Gross Domestic Products Central Bank of Jordan<br />
(INF) Jordan Inflation Rate Central Bank of Jordan<br />
(INT) Jordan Interest Rate Central Bank of Jordan<br />
*: Not applicable<br />
12
3 <strong>Model</strong> Specifications<br />
3.3.1 Unlagged <strong>Model</strong> Specification<br />
The empirical literature on the determinants of <strong>for</strong>eign direct investment inflows to<br />
developing countries has generally focused on identifying the location specific factors and<br />
relevant government policies that influence FDI and use models that do not have strong<br />
macro-foundations and all country risk variables and all country risk variables (financial,<br />
economic and political risk) such as (Alfaro et al 2004; Lim 2008; Asiedu et al 2009; Alfaro<br />
et al 2010; and Karabay 2010; are examples of such studies). The specification of the<br />
equation and choice of variables are inspired by the extensive empirical literature and<br />
theories on FDI. In order to study the impact of country risk on <strong>for</strong>eign direct investment<br />
inflows to Jordan there are two stages. The first stage involves a general <strong>for</strong>m (unlagged<br />
mode), the model is specified as follows:<br />
( ) ( ) ( ) ( ) ( ) ( ) ( )<br />
( ) ( ) ( ) ( ) ( )<br />
Where represents the inward <strong>for</strong>eign direct investment in Jordan, the country risks<br />
variables is as follows: stands <strong>for</strong> Jordan financial risk; means Jordan economic risk<br />
and represents Jordan political risk. The stock market price variables are namely: where<br />
refers to banks stock market price, represents services stock market price, stand<br />
<strong>for</strong> industries stock market price and means stock market price of general sectors.<br />
Finally, the macroeconomic factors as follows: where is Jordan gross domestic<br />
products, is measured the inflation rate in Jordan and is the interest rate in Jordan<br />
market<br />
All variables in the above model have been selected on the basis of how frequently they<br />
were cited in previous applied studies and how important (significant) were in <strong>determining</strong><br />
inward <strong>for</strong>eign direct investment.<br />
Country risks such as financial, economic and political risk are included in the model<br />
as several studies have found connection between inward <strong>for</strong>eign direct investments in host<br />
economies. For example, Xing (2006, 2007) highlights that the real exchange rate risks as<br />
one of the significant factors affecting Japanese FDI in China. , Busse and Hefeker (2007,<br />
401) found that the political risks have significant impact on <strong>for</strong>eign direct investment<br />
13
inflows. Kaisaris and Speed (2008, 309) point out that the level of FDI in the FSU states is<br />
determined significant via a planned economy towards and a market economy. Awokuse<br />
and Yin (2010, 222) indicate that the strengthening of intellectual property right (IPR)<br />
protection in China has a positive and significant effect on FDI. Thus, it could be argued<br />
that the country’s risk variables have a negative influence on inward FDI.<br />
The model also includes the price of stock market sectors such as banks, services,<br />
industries and general sectors. There are many researchers who have used stock market<br />
valuations as determinants of inward FDI in host countries. For instance, Froot and Stein<br />
1991; De Santis et al 2004and Baker et al 2009)<br />
Some of macroeconomic factors are introduced to the model such as inflation, interest<br />
rate and GDP, in order to improve the empirical analyses. Inflation and interest rate are<br />
frequently used as indicator of macroeconomic instability. A high rate of inflation and<br />
interest ( ) is a sign of internal economic tension and of the inability of the<br />
government and central bank to balance the budget and to restrict money supply. As a rule,<br />
the higher the rate of inflation and interest, the less <strong>for</strong>eign direct investment inflow to host<br />
countries.<br />
Gross Domestic Products (GDP) is included in the model as control variable in<br />
<strong>determining</strong> inward FDI in Jordan, (2007, 693; Caves, 2007; Dunning and Lundan, 2008).<br />
Also, inward FDI is expected to be positively related to this control variable, (Asiedu 2006;<br />
Blonigen et al 2007).<br />
An important consideration to be made in relation to estimating the unlagged model is<br />
to do with the existence of spurious regression. Results of Augment Dickey-Fuller test (see<br />
table 3) indicate that the variables should be estimated using the log first differences. The<br />
final version of the unlagged model has the following <strong>for</strong>m:<br />
( ) ( ) ( ) ( ) ( ) ( )<br />
( ) ( ) ( ) ( )<br />
( ) ( )<br />
Where: denotes the first differences of natural logarithm. The possible existence<br />
of heteroskedasticity is a major concern in the application of ordinary least squares (OLS)<br />
analysis, including the analysis of variance, because the presence of heteroskedasticity can<br />
invalidate statistical tests of significance that assume that the modelling errors are<br />
14
uncorrelated and normally distributed and that their variances do not vary with the effects<br />
being modelled. There<strong>for</strong>e, the White test was applied to detect whether the errors are<br />
heteroskedasticity or homoskedasticity<br />
( ) (<br />
Where<br />
The ARCH (1) model indicates that when a big shock happens in period , it is<br />
more likely that the value of will be bigger as well. This is, when is large or small,<br />
the variance of the next error term is also large or small. The estimated coefficient of<br />
has to be positive <strong>for</strong> positive variance. The ARCH model implanted in E-view in the mean<br />
and variance equations where stated above respectively. The results of ARCH show that the<br />
model is stable.<br />
3.3.2 Lagged <strong>Model</strong> Specification<br />
In the second stage the lagged model is introduced to explore the dynamic behaviour of<br />
Jordanian country risk (financial, economic and political risk), stock market price (banking,<br />
industry, services and general sectors) and macroeconomic factors (inflation, interest rate<br />
and GDP). Testing <strong>for</strong> long and short run relationships the following dynamic methods are<br />
implemented: firstly, vector autoregressive (VAR) model, Granger causality, Johansen’s<br />
Co-integration test and Error Correction <strong>Model</strong>.<br />
3.3.2.1 Vector Autoregressive model<br />
VAR model has the advantage of treating each variable under the study as an endogenous<br />
variable when economic theory cannot offer a priori in<strong>for</strong>mation regarding the variables<br />
used in the VAR. This makes VAR estimation simple and OLS estimation method can be<br />
used provided all variables included in the VAR are integrated of the same order Gujarati<br />
(1995 749). In this case, the time series is affected by current and past values of ,<br />
simultaneously, as well as the time series is a series affected by current and past values<br />
of the series. There<strong>for</strong>e, the following simple bivariate VAR model is considered<br />
(Brooks 2008 290, 291)<br />
( )<br />
( )<br />
15
Where and are stationary and and are uncorrelated white-noise error<br />
terms. These equations constitute a first order of VAR model as the longest lag length is<br />
unity. Hence, equations (7) and (8) are not reduced <strong>for</strong>m since the gives a<br />
contemporaneous effect on and gives a contemporaneous effect on .<br />
3.3.2.2 Lag Order Selection Criteria <strong>for</strong> Vector Autoregressive <strong>Model</strong><br />
The selection criteria <strong>for</strong> the appropriate lag length are used to avoid over parameterising<br />
the model and produce a parsimonious model. The Bayesian Schwartz (BSC), The Hannan-<br />
Quinn Criterion (HQ) and the Akaike In<strong>for</strong>mation Criterion (AIC) are often used as<br />
alternative criterion. They rely on in<strong>for</strong>mation similar to the Chi-Squared test and are<br />
derived as follows (see table 5):<br />
( ) ( ̂)<br />
( ) ( ̂) ( )<br />
( ) ( ̂) ( ( ))<br />
3.3.2.3 Error Correction <strong>Model</strong> and Co-integration<br />
Clearly, a good time series modelling should define both short-run dynamic movements and<br />
the long-run equilibrium simultaneously. For this purpose, the error correction model is<br />
introduced in this study. The error correction model is a dynamical system with the<br />
characteristics that the deviation of the current state from its long-run relationship will be<br />
fed into its short-run dynamics. The error correction model can be used to conduct the short<br />
effect of the endogenous variables on the exogenous variables and as wall as the speed<br />
adjustment at which the exogenous variable return to equilibrium after a deviation has<br />
occurred.<br />
There are two different ways to conduct the co-integration test. Engle and Granger<br />
(1987) based on single equation and Johansen (1998) based on systems of equation. Engle<br />
and Granger test the stationary of residual based on single-equation static regression of one<br />
variable. There<strong>for</strong>e, Johansen estimation technique is better in the sense that it uses<br />
maximum likelihood of a full system that provides test of Max-Eigen and Trace statistics<br />
(shown in equations 7 and 8 respectively) to determine the number of co-integrating<br />
vectors. There<strong>for</strong>e, in this paper the Johansen and Jesulius estimation technique has been<br />
applied in order to determine the co-integration and the number of co-integrating vectors.<br />
16
( ) ( ) ∑ ( ̂ )<br />
( ) ( ) ( ̂ )<br />
Where is the sample size, is the number of co-integrating vectors under the null<br />
hypothesis and ̂ is the estimated value <strong>for</strong> the row of matrix ordered eigenvalue from the<br />
matrix. Thus, a significantly non-zero eigenvalue indicates a significant co-integrating<br />
vector.<br />
4. Empirical Analyses<br />
The aims of this paper are that: to identify the major determinants of <strong>for</strong>eign direct and<br />
indirect investment, to analyse macroeconomic factors influencing inward <strong>for</strong>eign direct<br />
investment and to identify the country's risks factors that have an effect on <strong>for</strong>eign direct<br />
and indirect investment flows.<br />
4.1 Descriptive Statistics<br />
The descriptive statistics shown in table (2, a) reveal that the average inward <strong>for</strong>eign direct<br />
investment (FDI) in Jordan is about 41with a sample range of almost 0.67 and 330.167<br />
maximum. This implies that Jordan receives a good amount of inward FDI. According to<br />
World Investment Report in 2010 Jordan has been ranked 14th regarding inward FDI<br />
per<strong>for</strong>mance among Middle East countries. For instance, Saudi Arabia, Qatar and Lebanon<br />
have been levelled 17th, 13th and 6th respectively. The country risks play a major role in<br />
attracting <strong>for</strong>eign direct investment to inflow a host country. The three major variables of<br />
Jordan country risk explain also the reasonable amount of inward FDI in Jordan. The<br />
median of Jordan financial, economic and political risk are (38, 36, and 71.5) respectively,<br />
this shows that Jordan has sensible business environment to attract <strong>for</strong>eign investors. Table<br />
(3, b) presents the stock market price sectors and macroeconomic variables descriptive<br />
statistics. The standard deviations of stock market price sectors (banks, services, industries<br />
and general sectors) are more than the mean. This indicates a good variance. Table 2, b<br />
indicates the range price of the stock market price in Amman Stock Exchange<br />
17
Table 2, a: Descriptive Statistics of <strong>Foreign</strong> Direct Investment and Country Risk<br />
Descriptive Statistics FDI Financial Risk Economic Risk Political Risk<br />
Mean 73.71902 38.06111 35.62556 71.14722<br />
Median 41 38 36 71.5<br />
Maximum 330.167 42 40 74<br />
Minimum 0.66667 36.5 24.5 66.5<br />
Std. Dev. 77.58222 1.426657 4.181605 1.746263<br />
Skewness 1.226474 1.06577 -1.74138 -0.46582<br />
Kurtosis 3.793871 3.822879 4.920728 2.179005<br />
Table 2, b: Descriptive Statistics of Stock Market Price and Macroeconomic Variables<br />
Descriptive Statistics Banking Services Industries General GDP Inflation Interest Rate<br />
Mean 5229.854 1125.929 1829.334 2914.147 771.7389 94.60444 10.04372<br />
Median 316.5756 118.5533 135.9339 209.512 599.783 88.65 9.385<br />
Maximum 18963.12 3801.511 11032.49 10490.8 1702.82 128.7 13.04<br />
Minimum 170.5687 98.3537 71.29547 132.6499 372.5 75.2 7.42<br />
Std. Dev. 5818.578 1184.276 2251.261 3183.196 375.2218 14.99955 1.649419<br />
Skewness 0.56062 0.588648 1.398183 0.542872 1.055012 0.874888 0.237631<br />
Kurtosis 1.749135 1.851636 5.108951 1.769236 2.867063 2.400902 1.679963<br />
18
4.2 Unit Root Test<br />
The Augmented Dickey –Fuller test (ADF) is employed to test the data in the levels and log first differences wether the variables are stationary<br />
or no-stationary.<br />
Table 3: Summery Statistics of Unit Root Test<br />
Variables t- Statistics in Levels t-statistics in Log First Differences<br />
FDI -2.434251 -10.82526***<br />
Financial Risk -1.710746 -13.77530***<br />
Economic Risk -1.836278 -13.30841***<br />
Political Risk -2.080084 -14.40819***<br />
Banks Stock Market Price -0.950598 -11.70533***<br />
Services Stock Market Price -1.034862 -11.63246***<br />
Industries Stock Market Price -1.922609 -6.887888***<br />
Stock Market Price of General Sectors -1.050766 -10.61605***<br />
GDP 1.992962 -3.214103**<br />
Inflation 1.375250 -10.86555***<br />
Interest Rate -0.572853 -18.46510***<br />
***,** indicate statistical significant at 1%, 10% level respectively<br />
Table 3 illustrates that the result of ADF test in levels and first differences. The results of t-statistics in levels series indicate that the data are<br />
non-stationary. In order to avoid the problem of spurious regression analysis and significant regression result from unrelated data it has been<br />
taken the first difference <strong>for</strong> the data to become the data stationary, t-statistics in first differences show stationary data. There<strong>for</strong>e, the problems<br />
of non-stationary data are solved, then and can be concluded that the data are I(1) processes the model can be estimated <strong>for</strong> co-integration.<br />
19
4.3 Unlagged <strong>Model</strong><br />
The results of OLS regression in levels show that Jordan economic risk has significant<br />
impact on inward FDI in Jordan. Also, the stock market sectors price (banks, services,<br />
industries and general sectors) have significant influence on inward FDI in Jordan.<br />
Moreover, interest rate is significantly <strong>determining</strong> inward FDI. However, the D.W test<br />
indicates a serial correlation problem and white test indicates a heteroskedasticity problem.<br />
In order to solve these problems, the unlagged model is specified in log first differences to<br />
remove the serial correlation and non-stationary in order to obtain robust results of OLS regression.<br />
The Durbin Watson test (2.060448) shows that the model does not have a serial correlation<br />
problem, but there is no significant impact of the country risk on inward FDI. The null<br />
hypothesis of the diagnostic white test is rejected which indicates that the model suffers<br />
from Heteroskedasticity. There<strong>for</strong>e, ML-ARCH model is applied to solve the problem of<br />
heteroskedasticity. According to GARCH result in table (4) the model is stable.<br />
The results in table (4) provide the first evidence that stock market price play an important<br />
role in FDI patterns in Jordan. The coefficient of banking, services and industry stock<br />
market price sectors are negatively significant. This indicates that a cheap assets channel <strong>for</strong><br />
<strong>for</strong>eign investors. However, the coefficient of general stock market price sector is<br />
significantly positive which consider as a cheap capital channel. There<strong>for</strong>e, this adds a new<br />
dimension to the FDI literature<br />
The inflation is negatively significant affect inward FDI and this is consistent with Rammal<br />
and zurbruegg (2006, 409) show a negative relationship between annual inflation rate and<br />
FDI. This means that an increase in the inflation rate lessens FDI in the host country. This<br />
is consistent with Hasen and Gianlulgi (2007, 23) findings. They study the determinants of<br />
FDI inflows to Arab Maghreb Union (AMU) countries. They highlight that the annual<br />
inflation rate has a negative effects and significant which explain why Maghreb countries<br />
attract FDI less than other countries at a similar stage of development. Tevino et al (2008,<br />
131) find insignificant direction that a lower level of inflation rate in host Latin American<br />
economies leads to greater level of FDI. Asiedu and Lien (2011, 104) indicate that a less<br />
inflation attract more <strong>for</strong>eign investors and promote FDI.<br />
.<br />
20
Table 4: Summery Statistics of the Unlagged <strong>Model</strong> Results<br />
Independent<br />
Variable<br />
Coefficient t-Statistic z-Statistic (Ml-ARCH) Diagnostics Test<br />
D(FR) 0.42671 0.085711 3.558430*** Durbin-Watson Stat (2.060448)<br />
D(ER) 0.809387 0.401469 1.397760<br />
D(PR) -0.4667 -0.12343 2.233652* F-Statistic (2.647082)***<br />
D(BS) -0.18892** -2.42733** -21.38985***<br />
D(SS) -0.33693*** -2.96961*** -19.91513*** Serial Correlation<br />
D(IS) -0.21832** -2.53558** -17.27124*** LM test *R-squared (0.220649)<br />
D(GS) 0.620441** 2.600391** 21.49498***<br />
D(INF) -6.487274** -2.438774** 0.630178<br />
D(INT) -13.2095 -1.09285 0.097132 Variance Equation ( ML-ARCH)<br />
D(GDP) 0.066 1.102694 2.750441** GARCH(-1) (0.725844)***<br />
***,** indicate statistical significant at 1%, 10% level respectively<br />
21
4.4 Lagged <strong>Model</strong><br />
In this study dynamic models are introduced such as vector autoregressive model (VAR), Granger causality, error correction model (ECM) and<br />
co-integration test. In order to shed the light on the interaction and dynamic movements of inward <strong>for</strong>eign direct investment in Jordan, country<br />
risk (financial, economic and political risk), stock market price( banking, services, industry and general sectors) and macroeconomic factors<br />
(inflation, interest rate and GDP).<br />
Table 5: Vector Autoregressive Lag Length Order Selection Criteria<br />
Lag LogL LR FPE AIC SC HQ<br />
0 -8836.62 NA 4.88E+30 104.7173 104.9396 104.8075<br />
1 -6928.6 3522.496 4.20E+21 83.84138 86.73052* 85.01385<br />
2 -6800.11 218.96 5.17E+21 84.02497 89.581 86.27971<br />
3 -6640.5 249.327 4.56E+21 83.84027 92.0632 87.17729<br />
4 -6482.6 224.236 4.36E+21 83.67578 94.5656 88.09507<br />
5 -6268.96 273.0611 2.34E+21 82.85158 96.4083 88.35315<br />
6 -6084.36 209.7221 1.99E+21 82.37111 98.59473 88.95496<br />
7 -5869.77 213.3195 1.40E+21 81.53574 100.4262 89.20186<br />
8 -5664.7 174.737 1.36E+21 80.81298 102.3704 89.56138<br />
9 -5357.01 218.4786 5.42E+20 78.87581 103.1001 88.70649<br />
10 -4927.33 244.0774 8.10E+19 75.495 102.3862 86.40796<br />
11 -4515.16 175.5975* 3.17e+19* 72.32144* 101.8795 84.31667*<br />
*Indicates lag order selected by the criterion. LR: sequential modified LR test statistic (each test at 5% level), FPE: Final prediction error, AIC:<br />
Akaike in<strong>for</strong>mation criterion, SC: Schwarz in<strong>for</strong>mation criterion, HQ: Hannan-Quinn in<strong>for</strong>mation criterion<br />
22
Table (5) presents results of VAR lag order selection criteria <strong>for</strong> three tests. The<br />
maximum possible lag length considered was eleven (months). The first column provides<br />
the lag length <strong>for</strong> each test and the last three columns of the table illustrate the test statistics.<br />
In this case the choice is ambiguous, because the reveal only one lag is needed by the SC,<br />
eleven lags with the AIC and HQ. Further examination found serial correlation at one lag.<br />
There<strong>for</strong>e, the eleven lags length of VAR have been selected by AIC and HQ in<strong>for</strong>mation<br />
criterion, since they are not serially correlated.<br />
Having confirmed the existence of unit roots <strong>for</strong> all the data series (see table 3). The next<br />
step is to check the existence of long-run relationship among the variables. The estimated<br />
results of Johansen co-integration test are reported in table (6). Since calculated λmax<br />
(405.585) and Trace (1694.175) are above the critical values (70.53513) and (285.1425)<br />
respectively at 1 percent, it can be clearly rejected the null hypothesis stating there is no cointegration.<br />
Moreover, the second null hypothesis stating two versus three co-integrating<br />
vectors, it also can be rejected the null hypothesis since calculated λmax (336.8631) and<br />
Trace (1288.59) are above the critical values (64.50472) and (239.2354) respectively.<br />
Thus, it could be seen from the table 6 that Johansen Co-integration analyses based on<br />
unrestricted VAR results indicate 11 co-integrating equations in the system. That means the<br />
results confirmed that <strong>for</strong>eign direct investment and its determinants, share a long run<br />
equilibrium relationship in Jordan. This indicates that there is possibility of causality<br />
between inward <strong>for</strong>eign direct investment in Jordan, country risk, macroeconomic factors<br />
and stock market price. There<strong>for</strong>e, Error Correction <strong>Model</strong> (ECM) is implemented to<br />
investigate the direct of causality between inward FDI and its determinants.<br />
23
Table 6: Johansen Co-integration Analysis, Unrestricted Co-integration Rank of Trace and Max-Eigen Test (VAR Lag=11)<br />
Hypothesized No of CE(s) Eigenvalue Trace Statistic 0.05 Critical Value<br />
Max-Eigen<br />
0.05 Critical<br />
Statistics<br />
Value<br />
None * 0.96623 2263.388 334.9837 569.2128*** 76.57843<br />
At most 1 * 0.910561 1694.175*** 285.1425 405.585*** 70.53513<br />
At most 2 * 0.865358 1288.59*** 239.2354 336.8631*** 64.50472<br />
At most 3 * 0.810217 951.7268*** 197.3709 279.1944*** 58.43354<br />
At most 4 * 0.605425 672.5324*** 159.5297 156.2311*** 52.36261<br />
At most 5 * 0.564063 516.3013*** 125.6154 139.4833*** 46.23142<br />
At most 6 * 0.524498 376.8179*** 95.75366 124.8885*** 40.07757<br />
At most 7 * 0.491871 251.9294*** 69.81889 113.7393*** 33.87687<br />
At most 8 * 0.331241 138.1902*** 47.85613 67.59171*** 27.58434<br />
At most 9 * 0.191416 70.59847*** 29.79707 35.69512*** 21.13162<br />
At most 10 * 0.109291 34.90335*** 15.49471 19.44397*** 14.2646<br />
At most 11 * 0.087913 15.45938*** 3.841466 15.45938*** 3.841466<br />
Trace test indicates 11 co-integrating equations at the 0.05 level<br />
*denotes rejection of the hypothesis at 0.05 level<br />
**Mackinnon-Haug-Michelis (1999) p-value, (*** indicates significant at 1%)<br />
24
Table (7) presents the Granger causality tests <strong>for</strong> this 11 variables model. Each of the 11<br />
variables appears to have explanatory power <strong>for</strong> one or more of the other variable in the<br />
system. The effects are direct, but often complex and indirect. In the first equation, inward<br />
FDI in Jordan appears to be significantly influenced by economic risk, the price of stock<br />
market sectors (banks, services, industries and general sectors), inflation rate and GDP.<br />
However, economic risk, stock market price of services and industry sectors, inflation rate,<br />
GDP appear to have strong revers Granger causality on inward FDI in Jordan.<br />
The results in table (7) are consistent with the following studies. Tekin (2012, 873)<br />
investigates potential Granger causality among GDP and <strong>for</strong>eign direct investment least<br />
developed countries <strong>for</strong> the period between 1970 and 2009 using panel data. Tekin reports<br />
that GDP Granger causing FDI in Burkina Faso, Gambia, Madagascar and Malawi. Hansem<br />
and Rand (2006 ) test <strong>for</strong> Granger causality between FDI and GDP in a sample of 31<br />
developing countries finding that FDI has a positive impact on GDP in long run. Gurn-<br />
Gharana and Adhikari (2011, 42) state that Granger GDP causality towards FDI get very<br />
strong support at 1% significant level. Also, Feridun and Sissoko (2011, 13) examine the<br />
relationship between GDP and FDI <strong>for</strong> Singapore. They find that causality running from<br />
FDI to GDP.<br />
The third equation inward FDI and financial risk have effects on economic risk at 10%, but<br />
the stock market price appears to have a significant impact on economic risk at 1%. The<br />
services stock market price sector is explained by inward FDI, banks, industries and general<br />
sector at 10% and 5% respectively. <strong>Inward</strong> FDI, economic risk, banks, services, general<br />
and GDP affect the behaviour of the industries stock market price at 5%. The stock market<br />
price sectors affect inflation at 10%, but the inward FDI, economic and financial risk at 5%<br />
and 1% respectively. <strong>Inward</strong> FDI, economic, industries stock market price, inflation,<br />
interest rate, GDP appears to be explained by the movement of all variables at 1%.<br />
The error correction model is applied to capture the short run dynamic of this model. Table<br />
8 (part a and part b) illustrate the results of error correction model (ECM). Only one error<br />
correction term is included even there are more than one co-integrating vectors as suggested<br />
by Johansen’s multivariate tests because this study investigate the inward <strong>for</strong>eign direct<br />
investment in Jordan rather than of other equations from the variables (i.e country risk,<br />
25
inflation, interest rate, GDP and so on). More precisely, financial risk has negative effect on<br />
inward FDI and political risk significantly influences inward FDI with negative sign, but<br />
the economic risk has a significant positive sign in <strong>determining</strong> inward FDI. This seems to<br />
indicate that <strong>for</strong>eign investors would continue investing in Jordan although economic risk<br />
perceptions have worsened in the short-run. Nevertheless, this empirical observation does<br />
not encourage economic risk, but to highlight the short-run responses of inward FDI to<br />
Jordan’s economic risk. In the long run, the economic risk has negative impacts on the<br />
inward FDI in Jordan. It can be explained by Uctum and Uctum (2011, 475) study that<br />
economic risk reduces the inflow of FDI.<br />
Services, industry and general stock market price sectors affect inward FDI negatively, but<br />
the banking stock market positively influences <strong>Inward</strong> FDI and statistically insignificant. In<br />
the short-run high interest rate attracts more inward FDI. This shows that <strong>for</strong>eign investors<br />
would remain investing in Jordan. It can be explained by Farrell et al (200, 17); Pan (2003,<br />
832); Tolentino (2010, 114) and Uctum and Uctum (2011, 466) who report that interest rate<br />
increase the inflow of FDI to host country. However, high interest rate would discourage<br />
the inflow of FDI in the long-run as it considers high borrowing cost. This conclusion<br />
supports Cuyvers et al (2011, 255) study the effects of interest rate on inward FDI in a<br />
developing economy such as Cambodia. Cuyvers et al point out that a negative nexus<br />
between interest rate and the level of inward FDI in a host country. Also, Wei and Liu<br />
findings (2001); they indicate economic linkages between FDI and cost of borrowing. This<br />
specifies that a lower cost of borrowing in the home country than in host country gives the<br />
home country firms a cost advantage over their rivals in the host economy.<br />
The significance of the error correction terms of these ECMs further reveals the following<br />
interpretations. Firstly, it confirms the presence of a long –run equilibrium relationship<br />
among the inward FDI and its determinants. Secondly, Jordan country risks,<br />
macroeconomic factors and stock market price do jointly Granger causes the level of<br />
inward FDI in Jordan. Finally, the estimated coefficient of error correction term indicate the<br />
speed of adjustment among the variables towards long run equilibrium take 2 years to 2.5<br />
years (58 months) approximately to return to equilibrium.<br />
26
Table7: Vector Autoregressive (VAR) Granger Causality/ Block Exogeneity Wald Test<br />
Equations FDI FR ER PR BS SS IS GS INF INT GDP<br />
FDI 21.35447* 22.92724* 25.2104** 29.84367** 39.17632***<br />
FR 20.75454* 24.30078* 19.93121*<br />
ER 29.21642** 27.27689** 26.3925** 19.97094*<br />
PR<br />
BS 34.61184*** 43.15024*** 20.22188** 28.55694** 23.27832* 25.60021**<br />
SS 34.52445*** 30.65587** 28.45206** 23.4917* 26.55821**<br />
IS 35.52189*** 57.01017*** 18.648** 23.83057* 23.53805*<br />
GS 34.40376*** 42.53835*** 19.38172** 28.65669** 23.55297* 24.77008**<br />
INF 18.46742* 19.13671* 23.60268* 19.72975* 27.92124**<br />
INT 20.73333* 26.89246**<br />
GDP 17.58099* 25.57754** 52.80833***<br />
Joint 405.9396*** 141.0835 681.7122*** 90.45212 127.8285 125.1251 301.3650*** 127.4321 280.6545*** 179.0602*** 456.8541***<br />
The Chi-square tests are reported in each cell with their associated p-value. Significant at 10% (*), 5% (**) and 1% (***)<br />
27
Table 8 (part: a): Estimates of the ECM and VAR <strong>for</strong> <strong>Inward</strong> <strong>Foreign</strong> Direct Investment in Short and Long Run<br />
Variables FR Equation ER Equation PR Equation BS Coefficient SS Equation<br />
Short-run Long-run Short-run Long-run Short-run Long-run Short-run Long-run Short-run Long-run<br />
FDI<br />
FR<br />
ER<br />
PR<br />
BS<br />
SS<br />
IS<br />
GS<br />
INF<br />
INT<br />
-0.58428 -4.17685 499.1209*** -10.29361 -344.719*** -31.851** 39.89018 3.632974* -2.34232 6.082111*<br />
0.002759* 0.133002 -0.84103*** 0.044799 0.351183 0.06692 0.471769*** -0.00463 0.002785 -0.00455<br />
-0.00444*** 0.176497 0.142864 -0.15495 -0.93659*** -0.12032 -0.23753 -0.00869* -0.01682* -0.0096<br />
-0.00075 -0.20117 0.157733 -0.10803 -0.1715 -0.06593 -0.58922*** 0.004554 -0.01059 0.007012<br />
0.633813 -68.4023 -254.991* -26.1562 0.335842 -31.1058 -76.9462 -3.72718 -7.42221* -3.2945<br />
0.404434*** -32.6659 -57.3767 -6.88342 20.29474 -9.81836 -32.5763 0.415197** -3.42464*** 0.651393<br />
0.189297 -84.6241 -100.129 27.68631 115.2764** 75.38897 -83.4084** 2.458763 -4.64637** 2.992217<br />
0.49025 -71.8689 -147.252* 0.681154 49.0084 12.88406 -69.6166* -0.15387 -5.4687** 0.215538<br />
9.66E-05 -0.1713 -0.48658 0.077932 0.37458 0.165987 0.071116 0.007232* 0.003084 0.015732*<br />
-0.00038 -0.04043 0.10411 -0.0003 0.021566 0.015338* 0.078301 0.000831 0.005628* -0.00053<br />
GDP -3.51313*** 66.02533 202.7077 158.595* -272.503 107.2114* -17.2428 -2.89997 -6.61346 -4.87181<br />
Significant at 10% (*), 5% (**) and 1% (***)<br />
28
Table 8 (part: b): Estimates of the VECM and VAR <strong>for</strong> <strong>Inward</strong> <strong>Foreign</strong> Direct Investment in Short and Long Run<br />
Variables IS Equation GS Equation INF Equation INT Equation GDP Equation<br />
Short-run Long-run Short-run Long-run Short-run Long-run Short-run Long-run Short-run Long-run<br />
FDI<br />
FR<br />
ER<br />
PR<br />
BS<br />
SS<br />
IS<br />
GS<br />
INF<br />
INT<br />
-2.41573 4.170897 -2.30647 -11.6267 6.290668 -65.7163* 220.6307* -377.194* 526.706*** 0.083573**<br />
0.014498 -0.00477 0.005592 0.013543 -0.01465 0.018601 0.052862 -0.00541 0.191129 0.000269<br />
-0.01844 -0.00942 -0.01909* 0.025153 0.049816* -0.00207 0.061526 -0.32065 0.176253 3.07E-05<br />
-0.01486 0.004191 -0.01153 -0.01404 0.032584 0.149825 0.166238 0.205616 0.438684 -6.82E-06<br />
-4.33729 -4.07269 -7.33197 10.96304 19.62065 8.623752 -155.459* 397.48 338.6473** 0.132226<br />
-5.36268*** 0.241972 -3.82368*** -0.97456 10.64303*** 18.59765 -35.372 79.62014 82.69469** 0.083573**<br />
-10.299*** 2.455609* -6.08664*** -7.29357 16.22712** -12.8238 -184.649*** 74.15281 231.4844*** -0.12228<br />
-7.09607* -0.37447 -6.09448*** 0.586294 16.42828** 6.899991 -133.784*** 191.0597 233.6194*** 0.034507<br />
-0.00212 0.008562 0.000812 -0.02432 -0.00542 0.151846 -0.51827** 0.035628 0.368117 0.000405<br />
0.008108 0.000901 0.006749* -0.00164 -0.01735 0.043507 0.086024 -0.11008 -0.19278* 0.000101<br />
GDP -13.6764 -3.68304 -8.41064 9.162577 22.52664 51.29052 306.119* -331.765 -217.217 -0.00559<br />
Significant at 10% (*), 5% (**) and 1% (***)<br />
29
5. Conclusion<br />
The present work investigates the determinants of inward <strong>for</strong>eign direct investment in Jordan over<br />
the period 1996-2010. Using vector autoregressive model (VAR), Johansen co-integration test,<br />
Granger causality and error correction model (lagged model). They suggest the following findings:<br />
Johansen co-integration test confirmed the existence of a long run equilibrium relationship<br />
among inward FDI and endogenous variables (.Jordanian country risk (financial, economic,<br />
and political risk), macroeconomic factors (inflation, interest, GDP) and stock market price.<br />
The Granger causality finds the presence of bidirectional causality between inward FDI,<br />
Jordanian economic risk, the price of stock market sectors (banking, services, industry and<br />
general sectors), inflation rate and GDP, but <strong>for</strong> other determinants, there is presence of<br />
unidirectional causality only. Error correction models captures the short run relationship<br />
among the exogenous and endogenous variables.in the short-run inward FDI in Jordan is<br />
significantly influenced by financial risk, political risk, interest rate, GDP. Vector<br />
autoregressive model capture the long run relationship in the system. In the long-run<br />
political risk, banking and services stock market price, interest rate, inflation rate and GDP<br />
appear to influence the flow of FDI.<br />
The results suggest inward <strong>for</strong>eign investment in Jordan are largely influenced by financial,<br />
economic and political risks, inflation, interest rate, GDP, stock market price. There<strong>for</strong>e, in order to<br />
attract more FDI inflows to Jordan the following actions should be taken into account: insisting on<br />
opening policy, taking investment incentives (fiscal incentives and financial incentives),<br />
sustaining stable economic growth, limiting inflation, improving competitiveness,<br />
strengthening the protection of intellectual property rights, overcoming bureaucratic<br />
corruption phenomenon, making further deepening of economic re<strong>for</strong>ms, en<strong>for</strong>cing hard<br />
budget constraints, etc. Of these factors, protecting intellectual property right and striking<br />
bureaucratic corruption are especially important. This is because a weak protection of<br />
intellectual property right will increase the probability of imitation and thus make a host<br />
country a less attractive location <strong>for</strong> <strong>for</strong>eign investors.<br />
The policy makers should concentrate on creating the conditions, such as improving<br />
shareholder rights and the quality of local legal system, that allow corporations to issue and<br />
trade shares abroad efficiently. In addition, they should encourage that its local trading<br />
system is linked tightly or merged with global markets. Furthermore, the government<br />
should encourage <strong>for</strong>eign trading system and clearing and settlement operators to provide<br />
30
services locally, remove any impediments against <strong>for</strong>eign participation. Finally, illiquid and<br />
non-transparent local market, portfolio restrictions that require investment in local<br />
instruments more than 10% should be lifted to attract more FDI.<br />
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