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Policies and Variables affecting FDI: A Panel Data Analysis of North African Countries

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. This study investigates the main variables and policies that affect FDI inflows and evaluates the effectiveness of these policies on attracting FDI inflows in five North African countries, namely Algeria, Egypt, Libya, Morocco, and Tunisia. To achieve that aim, a panel data of North Africa countries is used within the timeframe of 1996 to 2013, the study has adopted three types of FDI related variables that may affect host country attractiveness: economic variables, institutional variables, and political variables. Also, we have investigated the influence of two kinds of investment policies on FDI: domestic FDI policies, and international FDI policies. The results indicate that the trade liberalization policies and integration into global business have a positive and significant correlation with FDI inflows growth. Additionally, the study also found that increasing domestic investment in host countries attracts more FDI. and adopting more efficient investment policies (investment freedom policies) are statistically significant and have a positive impact on FDI inflows growth in the North Africa region.
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İktisat Politikası Araştırmaları Dergisi - Journal of Economic Policy Researches Cilt/Volume: 7, Sayı/Issue: 1, 2020
İktsat Poltkası Aratırmaları Dergs -
Journal of Economc Polcy Researches
Clt/Volume: 7, Sayı/Issue: 1, 2020
E-ISSN: 2148-3876
RESEARCH ARTICLE / ARATIRMA MAKALESİ
Policies and Variables affecting FDI: A Panel Data
Analysis of North African Countries
Ahmed MUSABEH1 , Mehdi ZOUAOUI2
DOI: 10.26650/JEPR635016
1Dr., Kadr Has Unversty, Istanbul, Turkey
2Istanbul Unversty, Department of
Internatonal Relatons, Istanbul, Turkey
ORCID: A.M. 0000-0003-2923-6204;
M.Z. 0000-0001-9700-2691
Corresponding author/Sorumlu yazar:
Ahmed MUSABEH,
Kadr Has Unversty, Istanbul, Turkey
E-mail/E-posta: ahmedmusabeh88@gmal.com
Submitted/Başvuru: 20.10.2019
Revision Requested/Revizyon Talebi:
02.12.2019
Last Revision Received/Son Revizyon:
27.12.2019
Accepted/Kabul: 28.12.2019
Citation/Atıf: Musabeh, A., Zouaou, M. (2020).
Polces and varables affectng FDI: A panel
data analyss of North Afrcan Countres. İktisat
Politikası Araştırmaları Dergisi - Journal of
Economic Policy Researches, 7(1), 1-20.
https://do.org/10.26650/JEPR635016
ABSTRACT
North Africa region is one of the wealthiest areas due to its natural
resources and strategic location. But, it is still fragile according to
economic indicators, especially investment environment and foreign
direct investment, “FDI”, which represents a considerable challenge for
governments and policymakers in these countries. This study investigates
the main variables and policies that affect FDI inflows and evaluates the
effectiveness of these policies on attracting FDI inflows in five North
African countries, namely Algeria, Egypt, Libya, Morocco, and Tunisia. To
achieve that aim, a panel data of North Africa countries is used within
the timeframe of 1996 to 2013, the study has adopted three types of FDI
related variables that may affect host country attractiveness: economic
variables, institutional variables, and political variables. Also, we have
investigated the influence of two kinds of investment policies on FDI:
domestic FDI policies, and international FDI policies. The results indicate
that the trade liberalization policies and integration into global business
have a positive and significant correlation with FDI inflows growth.
Additionally, the study also found that increasing domestic investment in
host countries attracts more FDI. and adopting more efficient investment
policies (investment freedom policies) are statistically significant and
have a positive impact on FDI inflows growth in the North Africa region.
Keywords: Inward FDI, North Africa, investment policies, trade openness,
panel data analysis
Jel Code: F14, F21, F3
This work is licensed under Creave Commons Aribuon-NonCommercial 4.0 Internaonal License
2İktsat Poltkası Aratırmaları Dergs - Journal of Economc Polcy Researches Clt/Volume: 7, Sayı/Issue: 1, 2020
Policies and Variables aecting FDI: A Panel Data Analysis of North African Countries
1. Introduction
In the last three centuries, several changes had occurred in the structure of the global
economy, especially with the appearance of globalization and financial liberalization. These
changes have made the flows of foreign investments between countries a vital element in
economic development through supporting productivity, disseminating technology between
countries, creating job opportunities, improving trade and accelerating growth and
development (Asiedu,2006; Pradhan et al.2017). In this regard, FDI flows are considered
one of the primary sources of capital flows that have played a crucial role in increasing
development and economic growth through its role to enhance the resource transfer effects,
which include capital transfer, technology transfer, and management transfer. Thus, and as a
result of spillovers of FDI, governments in developing countries motivated to look for best-
practice policies towards FDI, and they strived to be more liberalized to gain the confidence
of investors (Te Velde, 2001). According to Dunning (2002) developing countries need to
attract FDI from more developed industrialized nations which seek complementary
knowledge, intensive resource, and capabilities. As a result of this, the developing countries
need to build supportive transparent commercial and legal communication infrastructure in
addition to favorable government policies to streamline globalization and innovation.
Consequently, governments in developing countries started to implement a wide range of
policies that can achieve a stable environment for investors to support them in carrying out
their businesses without incurring avoidable risks. But even though the importance of FDI
and its role in economic growth, it remains a controversial point among economists
especially with regard to its impact on host country. Within that, an extensive number of
empirical studies in the last two decades investigated the relationship between FDI and
economic growth. For example, many studies including Koojaroenprasit (2012), Pradhan et
al. (2017) were concerned with the examination of the relationship between FDI and
economic growth. The findings showed that there is strong and positive relationship between
FDI and economic growth. Conversely, the number of studies including Mah (2010), Marc
(2011) have found that FDI does not necessarily lead to higher economic growth. And,
regarding FDI studies in MENA countries and the Arab world, there are limited studies that
touched on policies and variables affecting FDI in this region, and the empirical evidence
about their impact on FDI has not been fully fathomed yet. For instance, Onyeiwu (2004),
Laabas & Abdalmoulah (2009) studied the FDI determinants on MENA and they found that
a weak infrastructure hurts FDI. Furthermore, some studies including Mina (2007) found
that institutional quality and infrastructure development have a significant influence on FDI
inflows but, contrary to expectations, stable macroeconomic policies are not sufficient
conditions to attract FDI in MENA countries. Other studies done by Mohamed &
Sidiropoulos (2010) examined the determinants of FDI inflows in the MENA region,
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Ahmed MUSABEH, Mehdi ZOUAOUI
İktisat Politikası Araştırmaları Dergisi - Journal of Economic Policy Researches Cilt/Volume: 7, Sayı/Issue: 1, 2020
revealed that the existence of a strong financial system and remove trade barriers tare
important elements for attracting FDI.
However, few studies dealt with the evaluation of governmental investment policies and
its role to attract FDI as well as determinants of FDI inflows in North Africa countries
separately and deeply. Thus, this paper intends to have a closer look and stand on the
mechanism of attracting foreign investment and examines the determinants of FDI inflows
to this region.
In this context, the present research is designed to investigate the main policies and
variables that have an effect on FDI inflows in the North Africa region using panel data
regression covering the period 1996-2013. The second section of this paper presents an
overview of the North African economy and FDI trend during the last 20 years, and the third
section presents a brief review of literature of policies and variables related to attracting
FDI. The third section sheds lights on the main literature on FDI policies and variables. The
fourth section investigates the main policies and variables affecting FDI using panel data
regression, and finally the fifth section provides results and a conclusion of the study.
2. FDI Inflows Trend in North Africa in the last twenty years
The trend of FDI inflows in this region shows a significant fluctuation in the last twenty
years where the amount of FDI flows into North Africa countries have raised from an annual
average of US $ 2.2 billion during the 1990s to US$ 12.5 billion during the 2000s and
reached its peak in 2007 at US $ 23.1 billion. As shown in figure 1, FDI flows into North
Africa reached its peak in 2007 with 4.5 % of the region’s GDP. However, the level of FDI
inflows notably decreased in 2011 by 1.5 % of GDP due to political disturbances (the Arab
Spring) to reach an annual average of 2% from 2011 to 2015.
Figure 1. Trend of FDI inflow as % of GDP in North Africa region 1996-2017
0.01 .02 .03 .04
FDI % GDP
1995 2000 2005 2010 2015
Year
Source: World Investment Report, UNCTAD, (2018)
4İktsat Poltkası Aratırmaları Dergs - Journal of Economc Polcy Researches Clt/Volume: 7, Sayı/Issue: 1, 2020
Policies and Variables aecting FDI: A Panel Data Analysis of North African Countries
Despite the previous indicators, an increasing rate is still emerging compared to what
North Africa countries have had from natural resource and geographic location. Interestingly,
it is still meager in respect to FDI inward stock as a percentage of GDP. For example, the
average of FDI inflow stock over GDP (1996-2013) in the North Africa region was 25.7 %
compared 47.3 % in the Southern Africa region, and 49.7 % in South-East Asia.
3. Literature Review for Determinants of FDI Inflows
In order to attract FDI, it became imperative for policymakers in host countries to identify
the policies and variables that influence the FDI. Consequently, a lot of studies have been
conducted in this regard which have the potential of helping policymakers understand the
scale and direction of FDI flows. According to Dunning (2002), developing countries need to
attract FDI from developed industrialized nations which seek complementary knowledge,
intensive resource, and capabilities. As a result, developing countries need to build supportive
transparent commercial and legal communication infrastructures along with favorable
government policies to streamline globalization and innovation. This brings us to the three
main types of variables that can affect FDI flows into host country (economic variables,
institutional variables, and political variables) with two kinds of investment policies that may
have effects directly on FDI (Domestic FDI policies, and International FDI policies).
3.1. Economic variables
In terms of economic variables, the governments in the host countries must effectively
manage the policies related to economic variables to increase locational advantage by
improving the economic fundamentals (Young et al,2017). According to Wang et al (2012),
the location theory provided explanations for the reasons behind the choice of the host
country for overseas investment and explained why globally successful industries emerge in
specific countries. These explanations depended on the variances among nations concerning
access to local markets, availability of comparatively cheap factors of production such as
natural resource, and labor force. According to Buettner and Ruf (2007), the location theory
of FDI is also concerned with the behavior of the government in the host country towards
improving the investment environment through offering investment incentives and
strengthening the legal framework.
An extensive range of studies including Mottaleb and Kalirajan (2010), and Abbott et al.
(2012) mentioned that the host country’s government must pay attention to the overall
economic policies. This include specific measurements like market size, natural resources,
quality of human capital, infrastructure quality, exchange rate stability, and inflation rate. In
terms of variables selection, the study employs the following variables and policies which
are classified as a main factor effect on FDI inflows.
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Ahmed MUSABEH, Mehdi ZOUAOUI
İktisat Politikası Araştırmaları Dergisi - Journal of Economic Policy Researches Cilt/Volume: 7, Sayı/Issue: 1, 2020
Market size: is seen as one of the vital factors that affect the flows of FDI, where a
large current market or increasing expected market size creates more investment
opportunities and profits. An extensive number of studies including Asiedu (2006),
Boateng et al. (2015) concluded that foreign firms move to countries with broader markets
and with higher purchasing powers. This study will use the natural logarithm of real GDP
as proxy for market size. The expected sign of the estimated coefficient of market size is
positive.
Trade liberalization: The relationship between the host country’s openness to trade and
FDI inflows is heavily influenced by the goals of these firms from a trade perspective. For
example, if the investment aims mainly to be an export-oriented policy, this encourages
businesses (vertical FDI flows) to expand in countries with high degree of openness. On the
other hand, according to the tariff-jumping hypothesis, foreign firms (horizontal FDI) that
aim to serve the local market prefer less openness to enhance their marketplace and to be
protected from imports of competitors. While, the resource-seeking FDI, which is the main
aim of expansion in the host country, is to reduce production costs.
Therefore, this type of FDI is more concerned about trade cost, and consequently;
countries that pursue an open trade policy are more attracted to this kind of investment
(Dunning,1993).
Based on empirical studies, many studies such as Bilel & Mouldi (2011), and Guris &
Gozgor (2015) concluded that the countries with more trade liberalization could attract more
FDI inflows. This study uses the ratio of export plus import over GDP as a proxy for trade
openness. The expected result is a positive or negative sign of coefficient concerning FDI.
Natural resources: is considered as an essential locational advantage, many studies
including Mina (2007), Poelhekke & Van der (2013) pointed out that the countries with
fewer resources might be more successful in attracting FDI than those nations with a wealth
of resources. The idea behind this adverse effect is “resource curse” where the abundant
natural resources may create opportunities for rent-seeking behavior and reduce the
transparency in resource sales and revenue spending. Regarding the literature review
concerned with the effect of the natural resource on FDI, several studies including Asiedu
(2004), Yimer (2017) and Yang et al,.( 2017) concluded that attracting FDI to the host
countries is improved by the availability of natural resources. In contrast, the study of
Poelhekke and Ploeg (2013) indicated that the availability of natural resources discourages
foreign investment to expand theirs. Our study employs a dummy variable for the countries
that have natural resource rent more than 10 % of GDP. The expected effect of the natural
resource on FDI is to be negative/ positive.
6İktsat Poltkası Aratırmaları Dergs - Journal of Economc Polcy Researches Clt/Volume: 7, Sayı/Issue: 1, 2020
Policies and Variables aecting FDI: A Panel Data Analysis of North African Countries
Infrastructure development: which is seen as a fundamental element in encouraging FDI
as it can contribute to reduce the entering cost (such as transportation costs, and electricity
costs) and increase the rate of return on private investment and attract more FDI (Bellak et
al.,2009). A series of studies, asghar et al (2011),Choi and Shoham (2016), Kaur et al.,
(2016) indicated that FDI inflows is positively associated with infrastructure development.
This study will use electric power transmission and distribution losses (% of output) to
measure the infrastructure quality. And the result is expected to be a negative sign of
coefficient concerning FDI (Asiedu, 2004; Banerjee et al. ,2006).
Stability of Macroeconomic indicators: also plays an essential role in FDI attractiveness,
especially when a foreign firm decides to invest abroad. These indicators involve exchange
rate stability index and inflation ratio where the stability of these indicators reflects a high
degree of certainty. A high rate of inflation is taken to be a sign of internal economic
instability in the host country, whereas price instability indicates that the government has
shortcomings to conduct appropriate monetary policy. Many studies including Asiedu
(2006), Hailu (2010) and Boateng et al. (2015) showed that the inflation negatively affects
FDI and a low volume of inflation is likely to attract more inward FDI in developing
countries. This study employs the annual percentage change in Consumer Price Index (CPI)
as the proxy for the inflation rate. The expected sign of the estimated coefficient of inflation
is negative. Many studies including Abbott et al. (2012) mentioned the desire of foreign
firms to invest abroad increase when the exchange rate in the host country is stable. Thus, to
attract FDI inflows the government in the host country should reduce the fluctuations in
exchange rates. This paper also employs the exchange rate stability index as a proxy of
exchange rate stability, with the expectation of positive sign of coefficient concerning FDI.
Gross fixed capital formation: several studies including Adhikary (2010), Dash and
Sahoo (2010), Feeny et al (2014) confirmed that enhancing the domestic investment plays a
vital role in achieving economic growth. Gross fixed capital formation GFCF (% of GDP) is
employed to measure the development of domestic investment (and some studies used it as
infrastructure development proxy). The expected effect of the domestic investment on FDI is
to be positive.
3.2. Institutional Quality Variables
As mentioned above, economic reforms and FDI policies are essential in terms of
encouraging FDI, but these policies and reforms will not be enough without the existence of
a healthy institutional environment to facilitate the exchange and increase confidence
between economic players and reduce transactional cost. The presence of good institutional
quality depends on the quality of its rules and providing a clear legal framework to govern
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Ahmed MUSABEH, Mehdi ZOUAOUI
İktisat Politikası Araştırmaları Dergisi - Journal of Economic Policy Researches Cilt/Volume: 7, Sayı/Issue: 1, 2020
the activities of direct investment, which is an important factor for the success of the foreign
investment (Bevan & Estrin 2004). In this context, corruption control is seen as one of the
prominent institutional factors that reflect the quality of the country’s institutional
environment. Several studies including Wei (2000), Kwok and Tadesse (2006), Sayan (2009)
concluded that there is a negative relationship between corruption level in the host country
and FDI inflows. Also, foreign investors, generally try to avoid investing in corrupt countries.
However, some empirical studies including Egger and Winner (2005), Biesenbender and
Tosun (2014), to cite a few, argue that corruption is a stimulus for FDI, and corruption can
have a positive impact on investment by facilitating transactions in countries with excessive
regulation. This study employs the Corruption Perception Index (CPI) to measure the
institutional quality.
3.3. Political Instability Variables
Political instability is considered as one of the bugbears that hinders the attraction of FDI
in developing countries. Certainly, increases in political risk would reduce the certainty of
the investment environment in the host country and make the investment climate and
economic outcome very unpredictable. Studies by Dupasquier and Osakwe (2006), and Kim
(2010) concluded that political instability is a prominent reason that has been responsible for
the low inward FDI. However, some studies including Asiedu (2002) Kandiero and Chitiga
(2006) concluded that political instability and absence of political rights in a country are not
significant in influencing FDI. This study uses the Political Constraints Index (POLCON)
which measures the extent of change in political actors and its influence on government
policies and reforms. The expected sign of political instability’s effect on FDI inflows is
negative.
3.4. FDI Policies
These types of policies can directly affect a foreign firm’s decision, where these policies
aim to reduce the transaction cost of foreign companies entering the economy, regulate the
flow of FDI. In addition to the creation of incentives and restrictions on operations work at
the domestic and international level.
International FDI Policies: this type of policy targets the enhancement FDI through
signing agreements and treaties with other regions or countries, and these agreements
include improving the main terms and condition that control the investment activities
between countries. The first type of these agreements is Bilateral Investment Treaties (BIT)
that target the regulation of investment operations by means of laying down specific
standards of investment protection and transfer of funds. The second type of these agreements
is Regional Investment Agreements (RIA). According to OECD (2010), regional investment
8İktsat Poltkası Aratırmaları Dergs - Journal of Economc Polcy Researches Clt/Volume: 7, Sayı/Issue: 1, 2020
Policies and Variables aecting FDI: A Panel Data Analysis of North African Countries
agreements (RIAs) help attract more foreign investment through participation in ensuring a
stable, predictable and transparent regulatory framework for international investment.
Furthermore, they enhance the deployment framework for FDI, strengthen and facilitate
cooperation between the host country and international investors in the investment fields,
and reduce the gaps between national and international investment policies. Finally, Double
Taxation Agreements (DTAs), which are defined as an agreement between two countries that
reduce the tax bill for a foreign investor. These agreements seek to prevent the taxpayer from
paying tax to both countries. Several studies including Buss et al. (2010), Berger et al (2013),
Buthe and Milner (2014) found that these kinds of agreements can be considered as one of
the elements of institutional reforms that foster the FDI inflow. This study uses the
accumulated number of the countries that have in-force international investment agreements
including (Bilateral Investment Treaties, Treaties with Investment Provisions, and Double
Taxation Agreement) with the host country to measure the international FDI – Policies. The
expected sign of the estimated coefficient of investment international agreements with FDI
inflows is positive.
Domestic Investment Policies: these policies mainly aim to eliminate admission and
establishment restrictions such as closing specific sectors or activities to foreign firms and
minimize the ownership and control restrictions and remove any obstacles that hinder
investments after entry such as constraints on employment of foreign labor and skilled
manpower (Duarte et al,2017).
Many studies including Banga (2003) Zhao (2013) concluded that these policies had
caused a rapid and steep increase in FDI and therefore, wage increase, and job opportunities
decrease. The study will use the Business Freedom Index which measures the host county’s
investment openness, and this index refers to ease of starting, operating, and closing a
business.
4. Empirical Strategy
North Africa region is considered one of the wealthiest areas regarding natural resources
and geographic location, but the performance of FDI attractiveness is still weak and needs
more effort. Thus, this paper employs a panel data estimation on a sample of Five North
Africa countries (Algeria, Egypt, Libya, Morocco, and Tunisia) over the period 1996-2013.
To examine the determinants of FDI inflows and impact of FDI-policies that are adopted by
the host countries (North African countries) to encourage the inward foreign direct
investment.
The choice of these years is attributed to data availability due to a shortage of this latter
especially the one related to Algeria and Libya. In terms of selection of variables, it based on
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Ahmed MUSABEH, Mehdi ZOUAOUI
İktisat Politikası Araştırmaları Dergisi - Journal of Economic Policy Researches Cilt/Volume: 7, Sayı/Issue: 1, 2020
the empirical work of most researchers, which is also appropriate for this study. The variables
have been categorized into different classifications according to their effect on FDI inflows
as follows: economic variables, institutional variables, and political variables, with two
kinds of investment policies that may have direct effects on FDI (Domestic FDI policies,
and International FDI policies).The specification of the regression model used in this study
can be outlined as follows:
LnFDIstock = α + β1 Investment agreement + β2investment freedom + β3LnMarket size
+ β4Trade oppeness + β5Naturl + β6GFCF + β7Infrastracture + β8Inflation + β9FX
+ β10Corruption + β11Regulation + β12Politcal + γti + εit ,
4.1. Data definition and Sources.
Empirically, there are several methods used to measure the FDI inflows, and there is no
consensus on a particular way. For example, many studies such as Adhikary (2010),Bhavan
and Zhong (2011) Boubakri et al (2013) used net FDI inflows as a percentage of GDP. While
Balakrishnan et al (2013) mentioned that using net FDI inflows as a percentage of GDP is
not desirable in transition economies because of its high sensitivity to changes in a location’s
characteristics. Furthermore, they concluded that using the FDI relative GDP creates a
problem with dependency and accuracy, where small states dominate the top ten FDI
recipients and it is hard to distinguish the effect of explanatory variables on FDI.
On the other hand, many studies including Busse et al. (2010), Goodspeed et al. (2011),
Barassi and Zhou (2012), Estrin and Uvalic (2014) used the total FDI stock as a measurement
of FDI within a country. This measurement refers to the value of the share of affiliates’
capital and reserves (including retained profits) attributable to the parent enterprise, plus the
net indebtedness of subsidiaries to the parent enterprises. According to Estrin and Uvalic
(2014) using the FDI stock is desirable because it is always positive, and hence natural log
transformation does not usher into a loss of information in this variable. Moreover, it is
mentioned that using the FDI stock is more appropriate for the transition and unstable
economics. Thus, this study utilizes the natural logarithm value of total inward FDI stock.
10 İktsat Poltkası Aratırmaları Dergs - Journal of Economc Polcy Researches Clt/Volume: 7, Sayı/Issue: 1, 2020
Policies and Variables aecting FDI: A Panel Data Analysis of North African Countries
Table1: Data definition and Sources.
Variable Description Source
LnFDIstock The natural logarithm of total inward FDI stocks. UNCTAD
Ln Market size Real Gross domestic product in US$ (Natural Log) UNCTAD
Trade openness The ratio of export plus import over GDP UNCTAD
Natural resources =1 if the natural resource rents are more than 10% of GDP.
“Total natural resources rents are the sum of oil rents, natural gas rents,
coal rents (hard and soft), mineral rents, and forest rents.”
World Bank
Investment
agreements
Accumulated number of the countries that have in force international
investment agreements including (Bilateral Investment Treaties, Treaties
with Investment Provisions, and Double Taxation Agreement) with host
country.
UNCTAD
Investment
freedom
Average index of business freedom, finance freedom, tax freedom, Heritage
Foundation
Infrastructure Electric power transmission and distribution losses (% of output) “its
include losses in transmission between sources of supply and points of
distribution and in the distribution to consumers, including pilferage”.
World Bank
GFCF Gross fixed capital formation GFCF (% of GDP), this measurement
reflects the government investments in terms of infrastructure
improvements such as constructing roads and railways, building hospitals
and schools as well as houses and industrial buildings.
IMF
FX Exchange Rate Stability Index1.The Trilemma
Indexes
Inflation The annual percentage change in consumer price index (CPI) IMF
Regulation Regulatory quality index, reflects the ability of the government to
formulate and implement sound policies and regulations that permit and
promote private sector development.
World
Governance
indicators
Corruption Corruption Perception Index Transparency
International
Dataset
Political Political Constraints Index (POLCON) Henisz, Witold J.
2002
Based on Polity
I V,
4.2. Pre- Estimation Tests Results
As a first step in any econometric analysis, we examined the stationarity of the variables
that are used in the model. This test aims to ensure that the variables are integrated, where
non-stationary series could generate spurious regression results. In that context, there are
numerous unit root tests for panel data and this study used the Levin-Lin-Chu test (LLC),
Breitung test, Hadri Lm test,and Pearsan test that assumes homogeneity in the dynamics of
the autoregressive coefficients for all cross-section data (series) (Aziz,2016). Table 2 shows
that the series are stationary at first differences.
1 It indicates an annual standard deviation of the monthly exchange rate between the home country and the base
country. The study used this proxy instead of real exchange rates due to the data limitation in these countries.
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Ahmed MUSABEH, Mehdi ZOUAOUI
İktisat Politikası Araştırmaları Dergisi - Journal of Economic Policy Researches Cilt/Volume: 7, Sayı/Issue: 1, 2020
Table 2 : Panel Unit Root Tests (1st differences).
Variable LLC Test
Ho: Panels
contain unit
roots
Breitung Test
Ho: Panels
contain unit
roots
Hadri LM test
Ho: All panels are
stationary
Pearsan test (xtcips)
Ho: non-stationary
(Tcips > T critical)
T cips T critical 1%
LnFDIstock (0.0319) (0.0468) (0.1047) -4.248 -3.46
Investment agreements (0.0000) (0.0000) (0.4328) -4.346 -3.20
Investment free (0.0000) (0.0000) (0.8649) -5.046 -3.46
LnMarket (0.5065) (0.0284) (0.9541) -4.663 -3.20
Trade openness (0.0030) (0.0434) (0.5799) -4.305 -3.46
GFCF (0.0000) (0.0001) (0.4110) -4.398 -3.46
infrastructure (0.0154) (0.0000) (0.9109) -5.506 -3.20
Inflation (0.0000) (0.0027) (0.9764) -4.893 -3.20
Fx (0.0000) (0.0000) (0.9348) -4.362 -3.20
Corruption (0.0000) (0.0000) (0.7282) -4.725 -3.20
Regulation (0.0000) (0.0000) (0.4644) -4.640 -3.20
Poltical (0.0001) (0.0551) (0.7306) -3.027 -3.20
Notes : -
- all tests in constant with the time trend
- In LLC test ( demean is used ) to control the effect of cross-sectional means.
- For Breitung and Hadri LM test (controlled the effect of cross-sectional means and allowed cross-sectional dependence).
- For Parson (xtcips) test ( controlled the effect of cross-sectional dependence)
Before employing estimations, we also conducted specific pre-estimation tests: we made
sure that there was no multicollinearity among the variables included in the models, where
the mean Variance Inflation Factor (VIF) of 1.24.and based of correlation matrix between
the variables, the correlation among variables was less than 0.5. (See tables 3.and 4)
The Breusch-Pagan test displays that the p-value is 0.240 which indicates that there is no
heteroscedasticity, and Hausman tests revealed that ‘Random effects’ specification was the
appropriate model for estimations. Based on the result of the Wooldridge test (Wooldridge,
2002; Drukker, 2003) for autocorrelation, which indicated that the Prob > F = 0.1165. Hence,
our model is not affected by the first-order autocorrelation.
Moreover, according to the Freidman test of cross-sectional independence, the p-value
was 0.0020, and this amount is smaller than 0.05. Therefore, we reject a null hypothesis,
which means that there is cross-sectional dependence
Hence, ignoring cross-sectional correlation would lead to severely biased results
(Hoechle, 2007). Thus, we employ Driscoll and Kraay (1998)’s standard error correction
method (labeled as ‘scc’, as in spatial correlation consistent) in our estimations, in order to
simultaneously deal with cross-sectional dependence (which also deal with serial correlation
and heteroskedasticity).
12 İktsat Poltkası Aratırmaları Dergs - Journal of Economc Polcy Researches Clt/Volume: 7, Sayı/Issue: 1, 2020
Policies and Variables aecting FDI: A Panel Data Analysis of North African Countries
Table 3: Partial correlation VIF test.
Variable VIF 1/VIF
∆ Trade openness 1.50 0.668615
Inflation(∆CPI) 1.44 0.692557
∆ LnMarket 1.41 0.706782
Natural resourse.D 1.31 0.760468
∆ Regulation 1.29 0.774447
∆ Infrastructure 1.21 0.829762
∆Political 1.19 0.842960
∆ Corruption 1.16 0.861560
∆ Investment agreements 1.15 0.722000
∆ GCF 1.12 0.889518
∆ investment freedom 1.08 0.922233
∆ Fx 1.07 0.935123
Mean VIF 1.24
5. Results and Conclusion
Several previous studies dealt with three main driving factors of FDI in host countries
which are market factors, resource factors and efficiency seeking factors (Dunning,1988).
This study attempted to investigate the impact of macroeconomic and role of domestic and
international FDI policies in attracting FDI. Thus, the findings of this paper are instrumental
for policymakers in North African countries in a way that helps governments make a well
justified and more informed decision about how they can encourage and attract foreign
direct investment and determine which investment policies are suitable according to current
and future predictions (see tables 5 and 6).
This study found a positive and significant relationship between investment freedom
variable and groth FDI in host countries at the 1 % level, which implies that foreign firms
prefer to expand their activities within less restrictive business environments and,
enhancement of investment conditions may attract more foreign investors to the North
African region. The coefficient of trade openness is positive and significant at the 5 % level
with change of FDI inflows.
Therefore, promoting integration into global trade, and country liberalization toward
international trade leads to more polarization of FDI to that region, and foreign investors prefer
investing in countries with sizeable trade volume. With regard to the natural resources effect, the
results showed that the natural resources dummy has a negative and insignificant relationship
with change of FDI inflows. This could be the result of state strong hold over of the oil sector
especially in Algeria and Libya. Moreover, the insignificant relationship of natural resources
might be a result of considerable variation in North African countries concerning natural
resources reserves. However, this should not be necessarily interpreted as evidence of the
absence of a relationship between this and other measures and economic outcomes.
13
Ahmed MUSABEH, Mehdi ZOUAOUI
İktisat Politikası Araştırmaları Dergisi - Journal of Economic Policy Researches Cilt/Volume: 7, Sayı/Issue: 1, 2020
Table 4: Correlation matrix between variables.
Variable 1234567891011 12 13
1∆ LnFDIstock 1
2∆ Investment agreements 0.0258 1
3∆ investment freedom 0.2424 -0.1594 1
4∆ LnMarket 0.1553 0.0462 00216 1
5∆ Trade openness 0.2959 0.0569 0.0082 0.5002 1
6∆ GFCF 0.4648 -0.0128 0.115 0.0230 0.1311 1
7∆ Infrastructure 0.0555 -0.0640 -0.0508 0.0013 -0.1175 0.0068 1
8Inflation(∆CPI) 0.2571 -0.0777 0.1341 0.1147 0.2122 -0.0954 -0.271 1
9∆ Fx 0.0234 0.0389 -0.0629 -0.0053 -0.0158 0.0013 0.0361 -0.0622 1
10 ∆ Corruption 0.0011 0.0513 0.0249 0.1203 0.1978 0.0569 0.0453 0.0574 0.0285 1
11 ∆ Regulation 0.1798 0.2440 0.1181 0.2341 0.2120 0.1348 -0.0853 0.0955 -0.2085 0.2009 1
12 ∆ Political 0.0776 -0.0108 0.0066 -0.0043 0.1135 0.0831 0.2605 -0.0523 -0.0052 0.2574 -0.0038 1
13 Natural.D 0.2192 -0.1510 0.0980 -0.0807 0.0216 0.1369 -0.0932 0.4003 0.0095 -0.0883 -0.0009 -0.0898 1
14 İktsat Poltkası Aratırmaları Dergs - Journal of Economc Polcy Researches Clt/Volume: 7, Sayı/Issue: 1, 2020
Policies and Variables aecting FDI: A Panel Data Analysis of North African Countries
Table 5: Random Effects Estimate with Driscoll and Kraay Standard Errors (time trend)
VARIABLES (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)
∆ Investment agreements 0.00316
(0.00206)
0.00329*
(0.00166)
0.00341*
(0.00167)
0.00335*
(0.00175)
0.00342*
(0.00157)
0.00336
(0.00164)
0.00391
(0.00193)
0.00388
(0.00201)
0.00339
(0.00197)
0.00394
(0.00197)
0.00394
(0.00211)
∆ investment free 0.0120**
(0.00420)
0.00958***
(0.00325
0.00987***
(0.00322)
0.00994***
(0.00332)
0.00984**
(0.00296)
0.00992**
(0.00304)
0.00949**
(0.00279)
0.00962**
(0.00274)
0.00902**
(0.00290)
0.00947**
(0.00277)
0.00960**
(0.00272)
∆ LnMarket 0.00581
(0.0888
0.0326
(0.0879)
0.0246
(0.0917)
0.0244
(0.0932)
0.0239
(0.0845)
0.0236
(0.0851)
0.0405
(0.0939)
0.0412
(0.0947)
0.0267
(0.0895)
0.0428
(0.0945)
0.0455
(0.0957)
∆ Trade openness 0.00513**
(0.00188)
0.00404**
(0.00148)
0.00425**
(0.00168)
0.00426**
(0.00171)
0.00421*
(0.00159)
0.00421*
(0.00161)
0.00401*
(0.00153)
0.00420*
(0.00168)
0.00393*
(0.00152)
0.00395*
(0.00160)
0.00412*
(0.00175)
∆ GFCF 0.0323**
(0.0112
0.0321**
(0.0114)
0.0321**
(0.0113)
0.0323**
(0.0103)
0.0323**
(0.0102)
0.0308**
(0.0101)
0.0310**
(0.0103)
0.0302**
(0.0100)
0.0307**
(0.0102)
0.0308**
(0.0103)
∆ Infrastructure 0.00529
(0.00384)
0.00524
(0.00393)
0.00550
(0.00385)
0.00549
(0.00392)
0.00656
(0.00359)
0.00686
(0.00412)
0.00668
(0.00373)
0.00622
(0.00336)
0.00627
(0.00371)
∆ Fx 0.0233
(0.0503)
0.0248
(0.0473)
0.0233
(0.0506)
0.0256
(0.0517)
0.0362
(0.0475)
0.0233
(0.0503)
0.0258
(0.0516)
Inflation(∆CPI) 0.000232
(0.000778)
0.000282
(0.000793)
0.000367
(0.000748)
0.000378
(0.000787)
0.000348
(0.000679)
0.000360
(0.000737)
0.000367
(0.000769)
Natural.D 0.0545
(0.0277)
0.0528
(0.0290)
0.0534
(0.0282)
0.0549
(0.0279)
0.0535
(0.0288)
∆ Corruption -0.0263
(0.0250)
-0.0292
(0.0272)
∆ Requlation 0.138
(0.0844)
∆ Political 0.0286
(0.0690)
0.0526
(0.0724)
trend 0.00427
(0.00547)
0.00464
(0.00429)
0.00439
(0.00427)
0.00432
(0.00437)
0.00439
(0.00397)
0.00431
(0.00404)
0.00364
(0.00422)
0.00333
(0.00393)
0.00410
(0.00430)
0.00373
(0.00420)
0.00346
(0.00398)
Constant -8.449
(10.96)
-9.200
(8.610)
-8.706
(8.578)
-8.560
(8.767)
-8.699
(7.976)
-8.543
(8.104)
-7.218
(8.463)
-6.603
(7.870)
-8.148
(8.625)
-7.400
(8.431)
-6.869
(7.976)
R-squared 0.160 0.326 0.333 0.334 0.335 0.339 0.356 0.360 0.361 0.356 0.362
Observations 85 85 85 85 85 85 85 85 85 85 85
Number of groups 5 5 5 5 5 5 5 5 5 5 5
15
Ahmed MUSABEH, Mehdi ZOUAOUI
İktisat Politikası Araştırmaları Dergisi - Journal of Economic Policy Researches Cilt/Volume: 7, Sayı/Issue: 1, 2020
Table 6: Panel model with lagged variables (Robustness test)
VARIABLES (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (10)
∆ Investment agreements 0.00325
(0.00197)
0.00329*
(0.00151)
0.00345*
(0.00151)
0.00342*
(0.00157)
0.00348*
(0.00155)
0.00344
(0.00162)
0.00405
(0.00192)
0.00403
(0.00196)
0.00343
(0.00204)
0.00404
(0.00193)
0.00404
(0.00199)
∆ investment free 0.0117**
(0.00378)
0.00921**
(0.00298)
0.00957**
(0.00291)
0.00964**
(0.00298)
0.00955**
(0.00288)
0.00963**
(0.00295)
0.00957**
(0.00279)
0.00971**
(0.00273)
0.00892**
(0.00288)
0.00959**
(0.00276)
0.00967**
(0.00269)
∆ LnMarket 0.0544
(0.100)
0.0688
(0.0944)
0.0639
(0.0963)
0.0637
(0.0967)
0.0642
(0.0956)
0.0639
(0.0961)
0.0721
(0.101)
0.0727
(0.100)
0.0550
(0.0975)
0.0716
(0.102)
0.0736
(0.101)
∆ trade openness 0.00722**
(0.00205)
0.00536**
(0.00180)
0.00579**
(0.00208)
0.00579*
(0.00211)
0.00579**
(0.00204)
0.00578**
(0.00207)
0.00547*
(0.00212)
0.00564*
(0.00236)
0.00531*
(0.00210)
0.00550*
(0.00226)
0.00559*
(0.00247)
∆ GFCF 0.0327**
(0.0107)
0.0321**
(0.0110)
0.0321**
(0.0109)
0.0325**
(0.0107)
0.0325**
(0.0106)
0.0316**
(0.0104)
0.0317**
(0.0105)
0.0309**
(0.0102)
0.0316**
(0.0106)
0.0316**
(0.0105)
∆ Infrastructure 0.00557
(0.00353)
0.00553
(0.00360)
0.00607
(0.00386)
0.00609
(0.00393)
0.00722
(0.00351)
0.00752
(0.00388)
0.00726
(0.00362)
0.00736
(0.00353)
0.00728
(0.00364)
∆ Fx 0.0184
(0.0469)
0.0216
(0.0480)
0.0225
(0.0479)
0.0248
(0.0474)
0.0373
(0.0449)
0.0228
(0.0464)
0.0242
(0.0462
Inflation(∆CPI) 0.000481
(0.000799)
0.000532
(0.000817)
0.000436
(0.000834)
0.000461
(0.000867)
0.000379
(0.000750)
0.000449
(0.000865)
0.000437
(0.000895)
NaturalD 0.0535
(0.0318)
0.0521
(0.0330)
0.0523
(0.0325)
0.0534
(0.0319)
0.0521
(0.0328)
∆ Corruption -0.0233
(0.0236)
-0.0242
(0.0266)
∆ Requlation 0.165
(0.0934)
∆ Political -0.00895
(0.0904)
0.0174
(0.0997)
trend 0.00519
(0.00532)
0.00525
(0.00398)
0.00495
(0.00391)
0.00487
(0.00398)
0.00488
(0.00393)
0.00478
(0.00401)
0.00404
(0.00421)
0.00372
(0.00382)
0.00478
(0.00426)
0.00398
(0.00414)
0.00382
(0.00388)
Constant -10.30
(10.67)
-10.44
(7.978)
-9.836
(7.852)
-9.677
(7.995)
-9.698
(7.890)
-9.496
(8.054)
-8.043
(8.441)
-7.389
(7.658)
-9.509
(8.540)
-7.925
(8.310)
-7.593
(7.786)
R-squared 0.174 0.335 0.343 0.345 0.344 0.346 0.356 0.361 0.365 0.365 0.369
Observations 80 80 80 80 80 80 80 80 80 80 80
Number of groups 5 5 5 5 5 5 5 5 5 5 5
16 İktsat Poltkası Aratırmaları Dergs - Journal of Economc Polcy Researches Clt/Volume: 7, Sayı/Issue: 1, 2020
Policies and Variables aecting FDI: A Panel Data Analysis of North African Countries
The domestic investment variables relation is positive and significant, which indicates
that enhancing the volume of local investment including constructing roads and railways,
building hospitals and schools as well as houses and industrial buildings reflected putatively
on the FDI in North African countries. Regarding institutional quality, the investment profile
variable is positive and significant, and this finding proves that the investment conditions
attract more foreign firms in this region. However, market size was found to have an
insignificant relationship with the growth of FDI. This is likely due to the region’s fragility
in terms of market size, or it might be the result of insufficient variation in the data to detect
a statistical relationship. In terms of institutional quality, the findings illustrate that corruption
in North African countries has a negative but insignificant coefficient.
In sum FDI inwards to the North Africa region has notably increased in the last twenty
years but it still looks weak compared to other developing countries. The current paper
examined the role of investment policies and determinant of FDI flows to North African
countries. The results of our model showed that the ease of doing investment and business
and establishment procedures have a positive impact on FDI attractiveness. Therefore, the
enhancement of investment conditions may attract more foreign investors to the North
Africa region. Furthermore, the country liberalization toward international trade leads to
more polarization of FDI in that region. As for examining the effect of corruption on the
growth of FDI inflow, the findings also illustrate that corruption in North Africa countries
have a negative but insignificant coefficient. This might be attributed to insufficient variation
in data used to detect a statistical relationship. Regarding the effect of market size, the
findings show that the growth of real GDP doesn’t have a strong statistical relationship with
the growth of FDI. The results also revealed that the enhancement of the domestic
investments could make the investment conditions in North Africa countries more attractive
to foreign firms, and therefore, policymakers in this region should focus on infrastructure
investments and allocate more resources for projects that may promote it.
Peer-review: Externally peer-reviewed.
Conflict of Interest: The authors have no conflict of interest to declare.
Grant Support: The authors declared that this study has received no financial support.
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... Pertumbuhan ekonomi yang pesat akan berakibat pada meningkatnya kebutuhan prasarana dan sarana sosial ekonomi. Permintaan terhadap pelayanan infrastruktur akan meningkat pesat seiring dengan pertumbuhan ekonomi suatu negara (Musabeh & Zouaoui, 2020 Studi ini disusun dengan empat bagian. Bagian pertama yaitu pendahuluan, bagian kedua yaitu metode penelitian, dan bagian ketiga yaitu hasil dan pembahasan, serta bagian keempat yaitu penutup. ...
... A study of Musabeh and Zouaoui (2020) investigated the determinants of FDI inflows and impact of FDI-policies adopted by the host countries in North Africa, namely Algeria, Egypt, Libya, Morocco, and Tunisia over the period 1996-2013. The independent variables have been categorized into different classifications as economic variables, institutional variables, and political variables, with two kinds of investment policies. ...
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