The impact of ICT ON FDI and Poverty Reducation

PARIS EST CRETEIL UNIVERSITY
FACULTY OF ECONOMICS AND MANAGEMENT
MASTER 1 IN INTERNATIONAL ECONOMIC STUDIES
THE IMPACT OF INFORMATION
COMMUNICATION TECHNOLOGY ON FOREIGN
DIRECT INVESTMENT
Author:
Anastasia Romanschii
August, 2016

TABLE OF CONTENTS
ABSTRACT………………………………………………………………………………..…….1
ACKNOWLEDGEMENT………………………………………………………………..……..1
1.INTRODUCTION…………………………………………………………………………......2
2.RELTED LITERATURY…………………………………………………………………......3
2.1 The impact of ICT on FDI……………………………………………………………3
2.2 The impact of ICT on poverty reduction………….………………………………5
3.DATA DESCRIPTION……………………………………………………………………......6
4.EMPERICAL MODEL………………………………………………………………..............8
5.RESULTS……………………………………………………………………………….……..13
6.CONCLUSIONS AND POLICY IMPLICATIONS…………………………….…………..16
REFERENCES………………………………………………………………………….…...…..18
APPENDIX……………………………………………………………………….……….……..21

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ABSTRACT
The objective of this paper is to examine whether Information Communication Technology
(ICT) affects the flows of Foreign Direct Investment (FDI), and to see the relationship between
Information Communication Technology and poverty reduction. With this aim, World Bank
(WB) data sources are used, and panel econometric models are estimated for a sample of 33
countries over a 14 year period (2000-2013). In addition, this paper uses a dynamic model as an
extension of the analysis to establish whether such an effect exists and what its indicators and
significance may be, and interaction terms, to see whether the relationship between certain
variables affects differently the dependent variable. The results show that ICT is significant and
has a positive impact on FDI, moreover, ICT is significant and has a positive influence on
poverty reduction.
Keywords: Information Communication Technology, Foreign Direct Investment, Poverty
Reduction
ACKNOWLEDGEMENT:
I would like to thank Ph.D. Ronald Davies from University College Dublin for providing
guidance, valuable comments and criticism during the research process.

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1. INTRODUCTION
My thesis analyses the relationship between investments in information and communication
technology (ICT) and flows of foreign direct investment (FDI), with reference to its
implications on poverty reduction.
This is an important question because FDI is a key point in the international economic
integration. It generates stable and long-lasting links between economies. FDI is a significant
channel for the transfer of technology between countries, promotes international trade through
access to foreign markets, and can be an important vehicle for poverty reduction (Tulus
Tambunan 2003, Xiaolun Sun 2002). Global FDI flows jumped 36% in 2015 to an estimated
US$1.7 trillion, from just US$1.3 trillion in 2000 and US$200 billion in 1993, which is the
highest level since the global economic and financial crisis of 2008-2009. In 1980, FDI stock
represented the equivalent of only 5% of world GDP, by the end of 1990 it reached 14% and in
2013 it tripled to 34.3%. (UNCTAD, January 2016)
The extent and character of foreign direct investment flows have long been influenced by
consecutive waves in the invention and adoption of new technologies. The internet is the latest
wave in the revolution of information and communication technology which has been reshaping
the global system. The Internet can boost the productivity. First, the Internet can reduce the
prices by reducing search costs. The Internet is especially efficient in reducing the cost of
international communication and searching. The Internet makes it easier the entry into several
markets by reducing entry costs. Thus, lower search costs and lower entry barriers lead to a
greater market competition, and intensiﬁed competition can lead to a better productivity.
(Georgios Zekos 2005). Second, Internet use can decrease the cost of holding inventories by
letting big suppliers to avoid retailers and contact customers directly (DePrince and Ford, F.
William 1999). This leads to the enhancement of productivity (McGukin, Stiroh, Kevin J 1998).
Lastly, Internet usage can improve the transparency of the host countries and make it
comfortable to do business. For example, the effects of corruption in a country can be reduced
by the extensive use of the Internet (Vinod, 1999). All these factors would increase GDP and
thus reduce the poverty. Therefore, it is very natural that international direct investors may
prefer to invest in a country with a well-developed internet infrastructure.

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I contribute to the debate by analysing the relationship between the primary key variables of
interest, FDI and ICT, with reference on poverty reduction. The hypothesis to be tested is
whether a developed ICT infrastructure of the host country leads to more FDI and poverty
reduction.
Various statistical and econometric techniques will be applied to achieve this aim. As both time
series and cross-section information are available for various countries, panel data analysis
forms the basis of this work. One of the advantages of the panel data modeling approach is that
it gives more informative data, more variability, less co-linearity among the variables, more
degrees of freedom and more efficiency.
According to my results ICT has a positive sign and is statistically significant at five percent.
Thus, an increase by 1% in ICT, will lead to 0.106% in FDI. Moreover ICT has a positive
impact on poverty reduction since an increase in ICT by one percent will lead to an increase by
0.166% in GDP per capita.
The following section gives a review of the literature, followed by a description of the database,
presentation of the empirical model, the results of the study. The paper finishes by summarizing
its main conclusions.
2. RELATED LITERATURE
2.1 The impact of Information Communication Technology on Foreign Direct Investment
During the last twenty years, there have been many studies on the effect of ICT on FDI
particularly in developing countries. This study reflects the effect of ICT on FDI as an
instrument to attract more investments. Internet based innovations have turned into the main
source of information in the world. ICT (internet) offer information services to the investors
which help them to choose the best opportunities and locations. The multilateral investment
guarantee agency organization (MIGA) is a well-known online service that has some expertise
in promoting FDI by offering technical help for investors (opportunity of investment and
measure of promotional activity, risks and offering insurance etc.). MIGA is a less costly

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organization in offering data for investors. Generally, its main mission is to promote foreign
direct investment into developing countries to support economic growth, reduce poverty, and
improve people's lives. Recently, the most significant innovative tools utilized by investment
promotion intermediaries (IPIs) which are more than 500 around the world are specialized in
offering advertisement consulting, aftercare program, disseminating information, research on
the internet, contacting important agencies and assessing investment promotion campaigns.
Presently ICT has the most important role in investment promotion globally. (OECD, 2008).
The advancements in ICT has modified the patterns of global trade, which consequently, has
changed the patterns and the trends of FDI in the global economy. Starting with 1990s, the
phenomenon of international fragmentation of production has developed because of information
communication technologies which permits the division of the product into two or more steps in
various locations and has prompted the decrease in costs of transportation in trade of parts and
components. (Jones and Kierzkowski, 2001). Multinational enterprises are the most important
players in the trade in parts and components within vertical foreign direct investment (FDI).
Since the middle 1990s, around two-third of the world trade involved MNEs through vertical
integration which can be accomplished through intra-firm trade (Broadman, 2005). The vast
majority of the MNEs depend on the distribution of products between its diverse branches in
various countries according to a comparative advantage in these countries, where they get the
most reduced costs of goods production, taking advantage of the global production network.
These procedures and transformations fundamentally depend on the advancement in ICT which
has contributed to the reduction of the cost of services.
Gholami, Lee and Heshmati, (2005) investigated the simultaneous causal relationship between
investments in information and communication technology (ICT) and flows of foreign direct
investment (FDI). Their results suggest that there is a causal relationship from ICT to FDI in
developed countries, which means that a higher level of ICT investment leads to an increase
inflow of FDI.
Choi, 2003 studied the effect of the Internet on the volume of inward foreign direct investment
and his results suggest that when the number of the Internet hosts or users in a host country
increased by 10%, FDI inﬂows increased by more than 2%.

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According to Addison and Heshmati (2003) investment in the ICT infrastructure and skills
helps to diversify economies, which leads to less dependence on the economy’s natural-resource
endowments and diminish some of the locational disadvantages of landlocked and
geographically remote countries. This can attract more FDI, especially investment in non-
traditional sectors. But as the availability of ICT infrastructure and skills becomes more and
more important in the decisions of foreign investors, underdeveloped countries could fall further
behind if they are unable to build this capacity.
Fakher (2016), studied the Impact of Investment in ICT Sector on Foreign Direct Investment in
Egypt. According to the results, there is an insignificant positive relationship between ICT
investments and FDI in Egypt during the estimation period. It means that the effect of ICT on
FDI is weak and insignificant in Egypt. It may be due to the weakness ICT infrastructures in
Egypt particularly during the estimation period and this result is not different from the results
for some other studies on developing countries, where these countries do not have enough ICT
infrastructures to attract more foreign direct investment compared with developed countries.
2.1 The impact of Information Communication Technology on poverty reduction
Starting with the 1990s, there were numerous studies that concentrated on the impact of
technology on productivity (Hitt and Brynjolfsson, 1996; Chun and Nadiri, 2008) and on
development and growth (Mansell and Wehn, 1998; Papaiounnou and Dimelis,2007), the
results of these studies are different of developed and middle or low income countries. The
outcomes demonstrate that there is positive and significant impact of ICT on profitability,
however less significant for low and middle income nations (Pilat and Frank, 2001). All in all,
the ICT investment can impact economic development through various channels: it permits
information flow, market extension, more effectiveness, build profitability and after that
expansion in new capital and foreign direct investment.
Vu (2004), provided a cross-country view on this issue by assessing the impact of ICT on
economic growth for 50 major ICT spending countries, which together account for over 90% of
the global ICT market. He found out that ICT investment has a significant impact on economic

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growth not only as traditional investment, but also as a boost to efficiency in growth: a higher
level of ICT capital stock per capita allows an economy to achieve a higher growth rate for
given levels of growth in labor and capital inputs.
Moreover, Gholami, Lee and Heshmati (2005), in the same study “The Causal relationship
between investments in information and communication technology (ICT) and flows of foreign
direct investment (FDI)”, assessed the implication of primary key variables ICT and FDI on
economic growth. So The causality from ICT to FDI in developed countries implies that ICT
may contribute to economic growth indirectly by attracting more FDI. Increases in information
and knowledge may result in efficient collaboration and coordination. Up-to-date and accurate
information on consumers, suppliers and competitors is essential for successful businesses.
Telecommunications and information technology increases information availability and
accuracy and provides better conditions for businesses. ICT is considered as a production factor
with great impact on skill and productivity of labour. Therefore, ICT can attract more FDI to
developed countries.
3. DATA DESCRIPTION
The data used in this study consist of a sample of 33 countries from Central Eastern and
Southern Europe and Central Asia (Albania, Armenia, Austria, Azerbaijan, Belarus, Bosnia &
Herzegovina, Bulgaria, Croatia, Cyprus, Czech Republic, Estonia, Georgia, Greece, Hungary,
Kazakhstan, Kygystan, Latvia, Lithuania, Macedonia, Malta, Moldova, Montenegro, Poland,
Romania, Russia, Serbia, Slovakia, Slovenia, Tajikistan, Turkey, Turkmenistan, Ukraine,
Uzbekistan) observed over the period 2000-2013. The indicator ICT was not found for each
country in each year so in countries like Albania, Bosnia & Herzegovina, Macedonia, Malta,
Montenegro, Serbia, Tajikistan and Uzbekistan, ICT is over the period 2006-2013 or 2001-
2013. Given this I have an unbalanced data.
The variables used are classified as dependent, independent, and country characteristic
variables. The independent variables include those perceived to be determinants of FDI are:
ICT, education, openness, tax, GDP, population and determinants of poverty are: FDI, ICT,

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unemployment, education, innovation. Country characteristics variables include: rule of law,
corruption, political stability.
FDI is defined as net flows of foreign direct investment expressed as a percentage of GDP
(World Bank Data).
ICT is defined as information communication technology and Internet users (per 100 people) is
a proxy for this variable (World Bank Data).
Tax is defined as Total tax rate % of commercial profits (World bank Data).
Openness of the economy is defined as the trade (import plus export) share of GDP (World
Bank Data).
Dummy variable EU takes the value 1 if the country is EU member and the value 0 if it is not
an EU member.
Education is defined Gross enrolment ratio, tertiary, both sexes (%) (World Bank Data).
Rule of law reflects perceptions of the extent to which agents have confidence in and abide by
the rules of society, and in particular the quality of contract enforcement, property rights, the
police, and the courts, as well as the likelihood of crime and violence (Worldwide Governance
Indicators Data).
Control of corruption reflects perceptions of the extent to which public power is exercised for
private gain, including both petty and grand forms of corruption, as well as "capture" of the
state by elites and private interests. (Worldwide Governance Indicators Data).
Population is number of population expressed in billions (World Bank Data).
GDP is current gross domestic product in US $ (World Bank Data).
GDP per Capita is gross domestic product per capita and it is used as a proxy for poverty
(world bank data).
RND expenditure (% of GDP) is used as a proxy for innovation (World Bank Data).
Unemployment is defined as % unemployed of total labor force (World Bank Data).

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Political stability measures perceptions of the likelihood of political instability and/or
politically-motivated violence, including terrorism (Worldwide Governance Indicators Data).
4. EMPERICAL MODEL
To examine the relationship between information communication technologies and foreign
direct investment with its implication on poverty reduction, this study applies panel data model
using an unbalanced time-series of observations. Panel data models are usually estimated using
either fixed or random effect techniques. Fixed effects and random effects models work to
remove omitted variable bias by measuring change within a group. The most fundamental
difference between fixed and random effect is of inference. A fixed-effects analysis can only
support inference about the group of measurements you actually have. A random-effects
analysis, by contrast, allows you to infer something about the population from which you drew
the sample. In other words if you use fixed effects on a random sample, you cannot make
inferences outside your data set. Random effects assume a normal distribution, so you can make
inferences to a larger population.
As the number of available data was different for various countries during the period of study,
unbalanced panel would be used. Since the period of data is limited to 14 years and data are
unbalanced, stationary or non-stationary situation of variables should be tested. So, unit root test
is offered for variables. For this purpose Fisher test has been employed which works well with
an unbalanced panel. The results of the test are presented in the table 1 and table 2.
The null hypothesis of this test is that all panels contain a unit root and the alternative
hypothesis is that at least one panel is stationary. Given my results we reject the null hypothesis
since P-values are larger than 0.01, so we can reject the null hypothesis at the 1% level of
statistical significance. This means there are no unit roots in my panels under the given test
conditions (panel means and time trend included). Therefore my data is stationary.
First, I want to examine the relationship between information communication technologies and
foreign direct investment.

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To identify the right estimation model Hausman test is applied:
A significant p-value indicates that the models yield different results so if the results diverge,
odds are that the random effects model is biased, so I use fixed model.
Gravity model has become a standard analytical tool in explaining bilateral flows of capital,
especially in explaining determinants of foreign direct investments (FDI). Blonigen and Piger
(2016) used Bayesian statistical techniques which allows one to select from a large set of
candidates those variables most likely to be determinants of FDI activity. The variables with
consistently high inclusion probabilities are traditional gravity variables, cultural distance
factors, parent-country per capita GDP, relative labor endowments, and regional trade
agreements. Blonigen and Davies (2004) in measuring the effect of bilateral U.S tax treaties on
aggregate inbound and outbound U.S FDI activity also used gravity model as a traditional
empirical framework.
However, the main obstacle in construction of FDI gravity models is that the values of bilateral
FDI flows are available only for selected countries, mostly for developed countries (OECD,
UE) or countries from one region. Consequently, construction of FDI gravity models for all
countries of the world seems to be not possible and since I have unilateral data, I could not
include in my regression variables such as distance.
Therefore, given the decision to include fixed effects, the regression equation can be written as
follow:
Table.3 Hausman Test
Chi-Sq statistic 39.45
Prob>chi2 0.000
Fixed or Random Model? Fixed

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Model 1
ln(𝐹𝐷𝐼)𝑖𝑡 = 𝛼𝑖 + 𝛽1ln(ICT)𝑖𝑡 + 𝛽2ln(education)𝑖𝑡 + 𝛽3ln(𝑡𝑎𝑥)𝑖𝑡 + 𝛽3ln(𝑜𝑝𝑒𝑛𝑛𝑒𝑠𝑠)𝑖𝑡
+ 𝑖. 𝑒𝑢 + 𝛽4 𝑟𝑢𝑙𝑒𝑜𝑓𝑙𝑎𝑤𝑖𝑡 + 𝛽5ln(𝑐𝑜𝑟𝑟𝑢𝑝𝑡𝑖𝑜𝑛)𝑖𝑡 + 𝛽6ln(𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛)𝑖𝑡
+ 𝛽7ln(𝑔𝑑𝑝)𝑖𝑡 + 𝜀𝑖𝑡
When using a fixed effects model, cross-section specific effects are captured by the intercept. In
my case, this implies each country has its own intercept α.
I expect the control variable education to be positive because human capital is a part of the
investment climate of the economy and it is regarded as one of the main driving forces of
innovation and development. The variable tax is expected to be negative, since the higher is the
tax, the less attractive is the country for investors. Openness of the host country is expected to
be positively associated with FDI because economies in which trade is important have relatively
higher FDI. As for the dummy variable “eu” I expect that if the country is EU member, it will
attract more FDI because EU member countries are developed, are less corrupt and have better
institutions in general. Rule of Law is expected to be positive because investors seek assurances
from governments that their investments will be secured for the long term. Corruption is
expected to be negative because corruption affects firm performance. For example, about 74%
of the firms that participated in the World Business Environment Survey (WBES) conducted by
the World Bank reported that corruption was an obstacle to the operation and growth of their
business. Very large populations tend to attract high levels of FDI. For example, in 2005 India
was forecasted as the greatest consumer market opportunity, receiving the highest FDI
confidence index. There is a positive relationship between GDP and FDI, explaining the fact
that horizontal FDI (FDI looking for the domestic market) is attracted to economies in which
real income, and therefore domestic purchasing power, is higher.
Over time, investment may attract more investment in the future. Agglomeration economies are,
therefore, taken into account, with the dependent variable being lagged one year on the right
side of the equation, as follow:
Model 2

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ln(𝐹𝐷𝐼)𝑖𝑡 = 𝛼𝑖 + ln(𝐹𝐷𝐼)𝑖𝑡−1+𝛽1ln(ICT)𝑖𝑡 + 𝛽2ln(education)𝑖𝑡 + 𝛽3ln(𝑡𝑎𝑥)𝑖𝑡
+ 𝛽3ln(𝑜𝑝𝑒𝑛𝑒𝑠𝑠)𝑖𝑡 + i. eu + 𝛽4 𝑟𝑢𝑙𝑒𝑜𝑓𝑙𝑎𝑤𝑖𝑡 + 𝛽5ln(𝑐𝑜𝑟𝑟𝑢𝑝𝑡𝑖𝑜𝑛)𝑖𝑡
+ 𝛽6ln(𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛)𝑖𝑡 + 𝛽7ln(𝑔𝑑𝑝)𝑖𝑡 + 𝜀𝑖𝑡
As we can see in the above equation, which is a dynamic model, it is necessary to be careful
when estimations are carried out because the lagged dependent variable and the correlated
errors lead to inconsistent estimates of parameters. Therefore, the above equation is estimated
by means of the best known method, that was used by Arellano and Bond (1991).
In order to expand the understanding of the relationships among the variables in the model, I
add some interaction terms to my model. The aim of these interaction terms is to see if the
effect of corruption and GDP is different for an EU member country than a non EU member
country.
Model 3
ln(𝐹𝐷𝐼)𝑖𝑡 = 𝛼𝑖 + 𝛽1ln(ICT)𝑖𝑡 + 𝛽2ln(education)𝑖𝑡 + 𝛽3ln(𝑡𝑎𝑥)𝑖𝑡 + 𝛽3ln(𝑜𝑝𝑒𝑛𝑒𝑠𝑠)𝑖𝑡 + 𝑖. 𝑒𝑢
+ 𝛽4ln(𝑟𝑢𝑙𝑒𝑜𝑓𝑙𝑎𝑤)𝑖𝑡 + 𝛽5ln(𝑐𝑜𝑟𝑟𝑢𝑝𝑡𝑖𝑜𝑛)𝑖𝑡 + 𝛽6ln(𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛)𝑖𝑡
+ 𝛽7 ln( 𝑔𝑑𝑝)𝑖𝑡 𝛽8(ln(𝑐𝑜𝑟𝑟𝑢𝑝𝑡𝑖𝑜𝑛) ∗ 𝑒𝑢) + 𝛽9(ln(𝑔𝑑𝑝) ∗ 𝑒𝑢) + 𝜀𝑖𝑡
Next, I want to examine the relationship between information communication technologies and
poverty reduction. According to the Hausman test I should use fixed model:
Table.4 Hausman Test
Chi-Sq statistic 98.83
Prob>chi2 0.000
Fixed or Random Model? Fixed

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Model 4
(ln(𝑔𝑑𝑝𝑝𝑐)𝑖𝑡 = 𝛼𝑖 + 𝛽1 ln(ICT)𝑖𝑡 + 𝛽2 ln( 𝐹𝐷𝐼)𝑖𝑡 + 𝛽3 ln(unemployment)𝑖𝑡
+ 𝛽4 ln( 𝑒𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛)𝑖𝑡 + 𝛽5 ln( 𝑝𝑜𝑙𝑖𝑡𝑖𝑐𝑠𝑡𝑎𝑏𝑖𝑙𝑖𝑡𝑦)𝑖𝑡 + 𝛽5 𝑟𝑢𝑙𝑒𝑜𝑓𝑙𝑎𝑤𝑖𝑡 + i. eu
+ 𝛽6 ln( 𝑐𝑜𝑟𝑟𝑢𝑝𝑡𝑖𝑜𝑛)𝑖𝑡 + 𝛽7 ln( 𝑟𝑛𝑑)𝑖𝑡 + 𝜀𝑖𝑡
Where α is the intercept and in a fixed model each country has its own intercept α.
In order to fit a dynamic model of information communication technologies and poverty
reduction to an unbalanced panel, I will use the one-step Arellano–Bond estimator and request
their robust VCE:
Model 5
ln(𝑔𝑑𝑝𝑝𝑐)𝑖𝑡 = 𝛼𝑖 + 𝛽1ln(ICT)𝑖𝑡−1 + 𝛽2ln(𝐹𝐷𝐼)𝑖𝑡 + 𝛽3ln(unemployment)𝑖𝑡
+ 𝛽4ln(𝑒𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛)𝑖𝑡 + 𝛽5ln(𝑝𝑜𝑙𝑖𝑡𝑖𝑐𝑠𝑡𝑎𝑏𝑖𝑙𝑖𝑡𝑦)𝑖𝑡 + 𝛽5 𝑟𝑢𝑙𝑒𝑜𝑓𝑙𝑎𝑤𝑖𝑡 + i. eu
+ 𝛽6ln(𝑐𝑜𝑟𝑟𝑢𝑝𝑡𝑖𝑜𝑛)𝑖𝑡 + 𝛽7ln(𝑟𝑛𝑑)𝑖𝑡 + 𝜀𝑖𝑡
This method assumes that there is no autocorrelation in the idiosyncratic errors and requires the
initial condition that the panel-level effects be uncorrelated with the first difference of the first
observation of the dependent variable.
As in the first regression which shows the impact of ICT on FDI, I add an interaction terms in
order to expand this model and to see whether corruption has a different effect if the country is
an EU member than a non EU member, and the equation becomes:
Model 6
ln 𝑔𝑑𝑝𝑝𝑐)𝑖𝑡 = 𝛼𝑖 + 𝛽1ln(ICT)𝑖𝑡 + 𝛽2ln(𝐹𝐷𝐼)𝑖𝑡 + 𝛽3ln(unemployment)𝑖𝑡
+ 𝛽4ln(𝑒𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛)𝑖𝑡 + 𝛽5ln(𝑝𝑜𝑙𝑖𝑡𝑖𝑐𝑠𝑡𝑎𝑏𝑖𝑙𝑖𝑡𝑦)𝑖𝑡 + 𝛽5 𝑟𝑢𝑙𝑒𝑜𝑓𝑙𝑎𝑤𝑖𝑡 + i. eu
+ 𝛽6ln(𝑐𝑜𝑟𝑟𝑢𝑝𝑡𝑖𝑜𝑛)𝑖𝑡 + 𝛽7ln(𝑟𝑛𝑑)𝑖𝑡
+ 𝛽8(ln( 𝑟𝑢𝑙𝑒𝑜𝑓𝑙𝑎𝑤) ∗ 𝑒𝑢) + 𝛽9( 𝑙𝑛𝑐𝑜𝑟𝑟𝑢𝑝𝑡𝑖𝑜𝑛) ∗ 𝑒𝑢) + 𝜀𝑖𝑡

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5. RESULTS
Before moving on to the results of the panel data analysis, this paper will consider some
descriptive evidence. The descriptive statistics for the variables considered in this study can be
seen in Tables 5,6,7 and 8. As shown in Table 5 and 6, the different control variables used in the
study, as well as the variable of interest, have different degrees of association among them.
The Variance Inflation Factor (VIF) was used to test multicollinearity among the different
independent variables and was not found among the variables. The test values of the VIF are
below 4.63, which is below the accepted limit.
In the Model 1 (Table 9), we can see the results of the econometric analysis carried out on the
database. In order to give a better picture of the effect of the information communication
technology on foreign direct investment, we start with Model 1, for which, according to the
Hausman test, the appropriate method is fixed effects. The coefficient associated with the
variable of interest (ICT) has a positive sign and is statistically significant at five percent. Thus,
an increase by 1% in ICT, will lead to 0.106% increase in FDI which is very close to the result
found in the paper of Choi, 2003 where he stated that when the number of the Internet hosts or
users in a host country increased by 10%, FDI inflows increased by more than 2%. This
suggests that in the countries selected for this study ICT infrastructure is an important factor in
attracting foreign investors. This can be explained by the fact that ICT influences FDI inflow,
mainly in two ways: first, it reduces time and expenses needed for exchanging information
through all possible channels and second it partly defines the volume of communication costs,
because it determines how much the company should pay in order to be connected to the global
network.
The coefficient for corporate tax rate is negative and significant. When the corporate tax rate in
a host country decreases, FDI proved to increase. This helps to explain the recent OECD paper
over tax competition for FDI with one of the aims to relax a number of restrictive assumptions
adopted in a previous OECD publication, Taxing Profits in a Global Economy (OECD, 1991).
Education is also proved to attract more FDI, since the result suggests that an increase in level
of tertiary education by 1% will lead to an increase by 0.705% in FDI which is explained by the

14
fact that foreign investors tend to invest in countries with skilled workforce. The dummy
variable EU takes the value 1 if the country is European Union member and 0 if it is not. Even
though the result is insignificant, if the country is member of the European Union, FDI
increases by 0.195%. Quality of institutions such as rule of law and corruption also have a
significant impact on FDI. The regression results suggest that an increase in the quality of rule
of law by 1% will lead to an increase by 0.214% in FDI which means that extent to which
agents have confidence in and abide by the rules of society, and in particular the quality of
contract enforcement, property rights, the police, and the courts, as well as the likelihood of
crime are important for foreign investors when making a decision to invest. There is a negative
relationship between corruption and FDI, thus, an increase by 1% in the level of corruption will
lead to a decrease in FDI by -0.143%. Population and GDP are negative and insignificant
according to the results.
Since over time, investment may attract more investment in the future, Model 2 (table 10)
reflects the effect of agglomeration economies on the analysis. As previously mentioned, this
variable is considered important because investments made today may have an effect on the
attraction of investment in the future. For this reason, the dependent variable is lagged by one
period. The variable added has a positive effect however its coefficient is statistically
insignificant at the level of five percent. The variable of interest retains its positive effect on
investment but the result is insignificant. Education and tax kept their positive and negative
effect but the result is also insignificant. Openness became negative and insignificant.
Corruption is positive and insignificant. Population and GDP remained insignificant as they
were in the first model.
In the model 3 (table 11), I added some interaction terms in order to expand my model and see
how corruption, and GDP are influenced by the dummy variable EU. According to the results
corruption is positive and insignificant. Thus, the impact of corruption is no different for an EU
country than a non-EU one. GDP on the other hand was negative and became positive and
significant if the country is EU member which is expected because EU countries have a higher
GDP than the non EU countries included in this study and this yields that EU countries have a
higher purchasing power thus EU members attract more FDI.

15
In the 4th
model (table 12), I estimate the impact of ICT on poverty reduction. The coefficient
associated with the variable of interest (ICT) has a positive sign and is statistically significant at
five percent. Thus, an increase in ICT by one percent will lead to an increase by 0.166% in GDP
per capita. This can be explained by the technological innovation and use of ICTs throughout
the value chain which contributes to multi-factor productivity. Moreover, ICTs offer the
potential to share information across traditional barriers, to give a voice to traditionally unheard
peoples, to provide valuable information that enhances economic, health and educational
activities. (OECD 2003b). However, according to the results FDI is negative and insignificant
which does not correspond to the empirical evidence that says that FDI has a positive influence
on GDP growth (Levine and Carkovic 2002, Alfaro 2003). Hence, I run a regression without
including the variable internet (table 13), to see if the FDI becomes positive and significant.
According to the results, FDI becomes positive and insignificant after excluding the variable
internet and this would suggest that the positive effect of FDI is driven by omitted variable bias,
calling into question the pro-FDI studies that do not include internet. This shows that
information communication technologies, foreign direct investment and poverty reduction are
all tied together. Education and Innovation are both positive and significant as expected since
both of them are the engine of economic growth and poverty reduction. Both corruption and
political stability are insignificant.
In the 5th
model (table13) lagged model is introduced in order to explore the dynamic behavior
of GDP per capita. The lagged variable added has a positive effect and its coefficient is
statistically significant at the level of five percent. The variable of interest retains its positive
effect on investment however it becomes insignificant.
In the 6th
(table 14) model I added an interaction term, to see whether being a member of EU
impacts on corruption. The result became positive but still insignificant, which means the
impact of corruption is no different for an EU country than a non-EU one.

16
6. CONCLUSION AND POLICY IMPLICATIONS
The main goal of this paper was to examine the impact of information communication
technologies on foreign direct investment. The ascendancy of information and communication
technologies, especially the Internet, has been an important development reshaping the global
system. On the other hand, FDI is a key point in the international economic integration which
generates stable and long-lasting links between economies.
I contribute to the debate by analyzing the relationship between the primary key variables of
interest, FDI and ICT, with reference on poverty reduction. The hypothesis tested was whether a
developed ICT infrastructure of the host country leads to more FDI and poverty reduction.
The analysis carried out in this study covered a total of 33 countries over the period between
2000 and 2013. In this study, I examined this issue using panel data fixed effect model with an
unbalanced time-series of observations. First, I examined the impact of ICT on FDI, then I
examined the impact of ICT on poverty reduction.
The study’s principal findings can be summarized as follows:
According to the results from ICT and FDI relationship regression, ICT has a positive sign and
is statistically significant at five percent and an increase by 1% in ICT, will lead to 0.106% in
FDI. This suggests that in the countries selected for this study ICT infrastructure is an important
factor in attracting foreign investors. After running a dynamic model in order take into account
the effect of agglomeration economies, the lagged variable FDI added had a positive effect and
its coefficient is statistically significant at the level of five percent. The variable of interest
(ICT) retains its positive effect on investment and it is still statistically significant. In order see
how corruption, and GDP are influenced by the dummy variable EU, I added interaction terms.
According to the results corruption is positive and insignificant. Thus, the impact of corruption
is no different for an EU country than a non-EU one. GDP on the other hand was negative and
insignificant and became positive and significant if the country is EU member.
The results from ICT and poverty reduction regression suggest that ICT has a positive impact
on poverty reduction given ICT has a positive sign and is statistically significant at five percent.
Thus, an increase in ICT by one percent will lead to an increase by 0.166% in GDP per capita.

17
FDI is also positive which proves that information communication technologies, foreign direct
investment and poverty reduction are all tied together. After introducing lagged model in order
to explore the dynamic behavior of GDP per capita, the lagged variable added has a positive
effect and its coefficient is statistically significant at the level of five percent. The variable of
interest retains its positive effect on investment however it becomes insignificant. As in the ICT
and FDI regression the interaction term corruption and EU showed positive and insignificant
result, which means the impact of corruption is no different for an EU country than a non-EU
one.
Policy implications can be drawn as follows. First, a country that intends to attract FDI, have to
develop ICT infrastructure since the progress of the ICT will contribute to the worldwide
increase in cross-border FDI. In addition to regional and global institutions such as free trade
areas, WTO, etc., the ICT will be one of main driving forces in the integration of the world
economy and thus poverty reduction.

18
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21
APPENDIX
Table 1: Unit root test for ICT and FDI
Statistic P-value
lnFDI Inverse chi-squared(66) P
Inverse normal Z
Inverse logit t(169) L*
Modified inv. chi-squared Pm
186.0153
-8.0562
-8.4068
10.4460
0.0456
0.0129
0.8761
0.0339
lninternet Inverse chi-squared(66) P
Inverse normal Z
40.3362
-15.2037
-19.2659
29.3613
0.0783
0.0298
0.7732
0.0029
Lneduc
(education)
Inverse chi-squared(66) P
Inverse normal Z
36.2255
3.8438
3.6920
-2.5915
0.9989
0.9999
0.9998
0.9952
lntax Inverse chi-squared(66) P
Inverse normal Z
95.5743
-2.6071
-2.6183
2.7908
0.9992
0.9996
0.9995
0.9959
lnopeness Inverse chi-squared(66) P
Inverse normal Z
57.2202
0.4830
0.5120
-0.7642
0.7710
0.6854
0.6953
0.7776

22
lnruleoflaw Inverse chi-squared(66) P
Inverse normal Z
252.7453
-10.2637
-11.7115
16.2541
0.6589
0.0514
0.0238
0.0113
Lncorruption Inverse chi-squared(66) P
Inverse normal Z
82.0219
-0.0643
-0.7338
1.3945
0.0881
0.4744
0.2320
0.0816
lnpopulation Inverse chi-squared(66) P
Inverse normal Z
510.3178
-0.4735
-12.9358
42.9290
0.0957
0.3179
0.0433
0.0923
lnGDP Inverse chi-squared(66) P
Inverse normal Z
29.0948
- 8.3485
-8.4006
-3.2122
1.0000
1.0000
1.0000
0. 9993

23
Table 2: Unit root test for ICT and poverty reduction
Statistic P-value
lnGDPpercapita Inverse chi-squared(66) P
Inverse normal Z
68.5107
2.1224
1.5628
0.2185
0.3922
0.9831
0.9400
0.4135
lninternet Inverse chi-squared(66) P
Inverse normal Z
279.3373
-3.3029
-9.1095
18.5686
0.0459
0.8776
0.3803
0.5246
Lnunempl
(unemployment)
Inverse normal Z
107.6447
-0.7553
-1.2942
3.6247
0.0009
0.2250
0.0987
0.0001
Lnpoliticstab
(political
stability)
Inverse normal Z
171.3229
-5.3271
-6.4767
9.1672
0.0117
0.3640
0.6105
0.0064
lncorruption Inverse chi-squared(66) P
Inverse normal Z
82.0219
-0.0643
-0.7338
1.3945
0.0881
0.4744
0.2320
0.0816
lnrnd Inverse chi-squared(66) P
Inverse normal Z
103.6645
0.0983
-0.4889
0.0021
0.5392
0.3128

24
Table 5: Descriptive statistics for ICT and FDI regression
Table 6: VIF for ICT and FDI regression
Modified inv. chi-squared Pm 3.2783 0.0005

25
Table 7: Descriptive statistics for ICT and poverty reduction regression
Table 8: VIF for ICT and poverty reduction regression

The impact of ICT ON FDI and Poverty Reducation

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