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Effects of Political and Socio-Economic Indicators on Foreign Direct Investments: Stochastic Frontier Analysis

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Abstract

In this framework, we model economic factors affecting foreign direct investments together with social and political factors. The model includes economic freedom index, openness rate, real effective exchange rate, labor force population, gross domestic product, the commodity price, which reflects global commodity costs, internet users in population that pictures infrastructure level of economy, Gross Domestic Product deflator for inflation rate besides democracy index to measure the influence of social and political indicators. The purpose of working with these models is to determine the influence of economic, social and political indicators on foreign direct investment of selected countries. Stochastic frontier analysis was used in the implementation to achieve the objectives of the study. Proposals for policy implication have been made in the framework of foreign direct investments in order to enable the countries to take advantage of their potential investments and to increase this potential.
İNSAN VE TOPLUM BİLİMLERİ
ARAŞTIRMALARI DERGİSİ
Cilt / Vol: 7, Sayı/Issue: 2, 2018
Sayfa: 1078-1096
Received/Geliş: Accepted/Kabul:
[21-03-2018] [29-04-2018]
Effects of Political and Socio-Economic Indicators on Foreign
Direct Investments: Stochastic Frontier Analysis1
Memduh Alper DEMİR
Research Assistant, Dokuz Eylül University, Faculty of Business and Administrative Sciences,
Department of Economics, İzmir- Turkey,
0000-0002-9926-2611
alper.demir@deu.edu.tr
Mustafa BİLİK
Research Assistant, Dokuz Ey
1
lül University, Faculty of Business and Administrative Sciences,
Department of Economics, İzmir- Turkey,
0000-0003-4425-9316
mustafa.bilik@deu.edu.tr
Üzeyir AYDIN (Corresponding Author)
Assistant Professor, Dokuz Eylül University, Faculty of Business and Administrative Sciences,
Department of Economics, İzmir- Turkey,
0000-0003-2777-6450
uzeyir.aydin@deu.edu.tr
Abstract
In this framework, we model economic factors affecting foreign direct investments together with
social and political factors. The model includes economic freedom index, openness rate, real effective
exchange rate, labor force population, gross domestic product, the commodity price, which reflects
global commodity costs, internet users in population that pictures infrastructure level of economy,
Gross Domestic Product deflator for inflation rate besides democracy index to measure the influence
of social and political indicators. The purpose of working with these models is to determine the
influence of economic, social and political indicators on foreign direct investment of selected
countries. Stochastic frontier analysis was used in the implementation to achieve the objectives of the
study. Proposals for policy implication have been made in the framework of foreign direct
investments in order to enable the countries to take advantage of their potential investments and to
increase this potential.
Keywords: Stochastic Frontier Analysis, Efficiency, Foreign Direct Investments, Political, Socio -
Economic Indices. JEL Classification: F21, C23, D91, G11
Siyasi ve Sosyo-Ekonomik Göstergelerin Doğrudan Yabancı
Yatırımlara Etkisi: Stokastik Sınır Analizi
Öz
Bu çalışmada doğrudan yabancı yatırımları etkileyen ekonomik faktörleri sosyal ve siyasi faktörlerle
birlikte modellemekteyiz. Modele; ekonomik özgürlük endeksi, dışa açıklık oranı, reel efektif döviz
kuru endeksi, işgücünün dinamik yapısını görmek için çalışma çağındaki nüfus, gayri safi yurtiçi
hasıla, küresel emtia maliyetlerini gözlemlemek için emtia fiyat endeksi, ekonominin altyapı
seviyesini ölçmek için nüfus içinde internet kullanımı sayısı ve enflasyon oranını yansıtan gayrisafi
yurtiçi hasıla deflatörü olmak üzere ekonomik göstergeler dahil edilirken sosyal ve siyasal
göstergelerin etkisini ölçmek için de demokrasi endeksi dahil edilmektedir. Bu modellemeler ile
çalışmanın amacı, seçilmiş ülkelerin doğrudan yabancı yatırımlarına ekonomik, sosyal ve siyasal
göstergelerin etkisini belirlemektir. Amaca ulaşmak için çalışmanın uygulamasında stokastik sınır
analizi yöntemi kullanılmıştır. Elde edilen sonuçlardan hareketle ülkelerin potansiyel yatırımlarından
yararlanabilmesi ve bu potansiyelini daha da arttırılabilmesi için doğrudan yabancı yatırımlar
çerçevesinde politik önermelerde bulunulmuştur.
Anahtar Kelimeler: Stokastik Sınır Analizi, Etkinlik, Doğrudan Yabancı Yatırımlar, Siyasal,
Sosyo - Ekonomik Endeksler JEL Sınıflandırması: F21, C23, D91, G11
1
"This article is an extended and revised version of the paper presented in
full text at the “3rd SCF International Conference on Economics and Social
Impacts of Globalization, 5-7 October 2017, Antalya-TURKEY”congress."
Effects of Political and Socio-Economic Indicators on Foreign Direct Investments: Stochastic
Frontier Analysis
“İnsan ve Toplum Bilimleri Araştırmaları Dergisi”
“Journal of the Human and Social Sciences Researches”
[itobiad]
ISSN: 2147-1185
[1079]
Introduction
Foreign direct investments (FDI) is the flow of capital arising from the
behavior of multinational corporations. In general, FDI is an international
category of investment, which aims to provide a lasting interest to an
enterprise located in another country by a resident unit in an economy.
(OECD, 1996: 7-8). Countries noticed the importance of FDI since the 1990s,
it has turned into a reciprocal competition between countries. According to
Dunning (1994), the reasons why FDI gained this momentum are addressed
separately in terms of country and firm perspectives. Such as, the liberal
market mechanism, economic globalization, increasing mobility of
prosperity assets, increasing number of countries taking the take-off stage,
convergence of economic structures of developed and some new
industrialized countries, better evaluation of the benefits and costs of FDI by
countries. On the other hand, the increase in the need for access to the global
market, competitive pressures to provide input from the cheapest sources
available, regional integration to accelerate investments in search for more
efficiency, reduced transportation costs and increased cross-border
communication, increased oligopolistic competition among leading firms,
the emergence of new spatial opportunities, the need for better balancing the
advantages of globalization and localization are among the reasons for the
FDI to draw attention and became important in terms of the firm's
perspective.
FDI provides benefits, both a home country, where the capital goes and a
host country where the capital comes from. As is well known from the
theories of growth and trade (Kesgingöz, 2015:85-93), (Kesgingöz ve Oğuz,
2016). FDI is a more important factor in the long-term growth and
development programs of countries compared to other forms of capital
(Kesgingöz, 2013:1-10), (Kesgingöz and Karataş, 2016:597-610). It plays a role
in restructuring global production and in shaping international income
distribution among developed and developing countries. Moreover, there is
a general unanimity in the literature that foreign technology and
management skills are easier for FDI-invested countries (Walsh and Yu,
2010: 3; Dhar and Joseph, 2012: 5-6). In addition, FDI is also provides to risk
sharing process between countries that owns capital and imports capital. In
short, FDI provides some benefits to countries such as market discipline, job
creation, economic growth, transfer of technology and managerial expertise,
risk sharing (Özcan and Arı, 2010: 66).
Companies are evaluating a number of economic, social and political factors
of the host country when they choose the countries they will invest in.
Among the economic determinants that have found the highest occupation
in the literature are technology, labor and commodity costs, trade deficit, ,
trade barriers, openness, exchange rate, taxes, inflation, growth rate,
infrastructure investments and market size. In addition to economic factors;
corruption, political instability, democracy / freedom and weak institutional
Memduh Alper DEMİR / Mustafa BİLİK / Üzeyir AYDIN
“İnsan ve Toplum Bilimleri Araştırmaları Dergisi”
“Journal of the Human and Social Sciences Researches”
[itobiad / 2147-1185]
Cilt: 7,
Sayı: 2
Volume: 7,
Issue: 2
2018
[1080]
qualities are also influencing foreign direct investment (Primorac and
Smoljic, 2011: 178, Gedik, 2013:121-126; Oransay and Mike, 2016: 98-100).
In this framework, the economic factors determining foreign direct
investment in this study are modeled by comparing countries with social
and political factors. The purpose of the study in this framework, is
analyzing the effect of political and socio-economic factors on FDI and its
efficiency. In the following section, model and econometric approach are
presented, in the last part, analysis results and policy recommendations are
given.
1. Theoretical framework and empirical literature on FDI determinants
Due to the above mentioned gains, countries have implemented various
policies in order to withdraw FDI. Because FDI inflows depends on the
provision of certain conditions (Torrisi, 1985: 33-36, Coşkun, 2001, Blonigen,
2005: 385-391, Karaege, 2006: 35-36, Holland and Pain, 1998: 4-8 Lim, 2001:
12-13, Özcan and Arı, 2010). Dunning (1993) suggests that three conditions
must be met in order for FDI to enter the country, and this is called the OLI
paradigm. These are; the advantage of ownership of the firm, spatial
advantage of foreign market (Location), and internalization advantages.
Ownership advantage, derives from product, technology, patent, brand,
etc… factors, which are specific to the company. The advantage of
internalization is ensured by individual production in the country, rather
than marketing the product or process through international licensing or
franchising. The spatial advantage includes factor prices, government trade
regulations, exchange rates, institutional and political stability (Bevan and
Saul Estrin, 2004: 777-778; Dunning, 1993). In addition to Dunning (1993),
many authors in the literature have grouped the elements that define FDI
inflows into several perspectives. Tuselman (1999) and Torrisi (1985) have
classified FDI determinants from both supply and demand side factors,
Nunnenkamp (2002) traditional and non-traditional, Kar and Tatlısöz (2008)
and Lipsey (2000) are driving and attractive, and Gumro and Hakro (2007)
have classified FDI determinants as cost-related and macroeconomic factors.
The effect of the determinants and efficiency of FDI, set out in this
theoretical framework, can be briefly described as follows:
The first example of the economic model was carried out by Dunning (1981).
The main determinants of FDI in this study are market size, unit labor cost,
service sector productivity and inflation rate. Root and Ahmed (1979) stated
that the social status of the country is also effective in determining FDI. The
development of human capital, the quality of life, the adequacy of the health
system and the rate of urbanization are some of the variables that constitutes
the social status of the country. Similarly, Schneider and Frey (1985) pointed
out that human capital can motivate FDI because it informs about the size of
labor quality of the country.
Effects of Political and Socio-Economic Indicators on Foreign Direct Investments: Stochastic
Frontier Analysis
“İnsan ve Toplum Bilimleri Araştırmaları Dergisi”
“Journal of the Human and Social Sciences Researches”
[itobiad]
ISSN: 2147-1185
[1081]
Bevan and Estrin (2000, 2004) identified a positive relationship between FDI
and market size, and a negative relationship whit unit labor cost and the
distance between countries. On the other hand, the host country risk is
estimated to be insignificant. Similarly, Janicki and Wunnava (2004) found
that unit labor costs, market size, and trade openness are key determinants
of FDI.
Nunnenkamp (2002) investigated whether there is a change in the factors
that determine FDI with globalization. While market size maintained the
incentive feature for FDI, it is concluded that the importance of the cost of
production factors and trade openness did not increase with globalization as
expected. In Onyeiwu and Shrestha (2004); economic growth, inflation,
international reserves, economic openness and access to natural resources
have been identified as the main reasons behind FDI. Infrastructure level
and political rights in the country had no effect on FDI.
Ang (2008) observed that financial development, commercial openness and
infrastructure has encouraged investments, while GDP growth has been
found to be extremely insignificant. Özcan and Arı (2010) found that FDI
affects growth rate, infrastructure level and inflation positively, while
openness and current account balance are, as opposed to theoretical
expectation. Drabek and Payne (2002) find that non-transparent policies are
a very important factors, affecting foreign investors' decisions. Büthe and
Milner (2008) have concluded that countries that are members of
international trade agreements are more successful than other countries in
terms of attracting foreign direct investment. Azam and Khattak (2009) tried
to explain the effect of socio-political factors on FDI over human capital and
political stability. In the study, positive correlation between human capital
and foreign direct investment, and negative correlation with political
stability is estimated. Martinez and Allard (2009) found that equality and
social protection policies positively contribute to countries' attractiveness of
foreign direct investment. Adams (2010) suggests that strengthening of
intellectual property rights (IPRs) has a positive effect on foreign direct
investments.
Kim (2010) studied the relationship between political stability and foreign
direct investment in his work. İt is concluded in the study that countries
with high political rights have higher capital outflows, while countries with
higher corruption and lower democracy have higher capital inflows.
However, the findings also show that the performance of foreign direct
investment is positively correlated with corruption levels of governments,
and negatively associated with political rights.
Julio, Alves and Tavares (2011) addressed geographical, economic and
institutional factors in terms of foreign direct investment interaction. In the
socio-political sense, the financial system's independence, level of
corruption, flexibility of the labor market, power and independence of the
legal system, rule of law and labor legislation have played a very important
Memduh Alper DEMİR / Mustafa BİLİK / Üzeyir AYDIN
“İnsan ve Toplum Bilimleri Araştırmaları Dergisi”
“Journal of the Human and Social Sciences Researches”
[itobiad / 2147-1185]
Cilt: 7,
Sayı: 2
Volume: 7,
Issue: 2
2018
[1082]
role in attracting foreign direct investments. Anyanwu (2012), on the other
hand, concluded that there is a positive relationship between the rule of law
and foreign direct investment. Alexander (2014) also concluded that
significant results with the rule of law and foreign direct investment in his
work, insignificant whit judicial independence and labor rights. Kimono et
al. (2007), stated that political conditions and risk factors in the investing
country affect foreign direct investment decisions significantly. In countries
where a positive investment environment and where political risk is lowest,
capital inflows are more easily achieved. However, according to Schneider
and Fray (1985), countries with political turmoil are considered more risk
and are more successful in attracting FDI relative to other countries with a
property right guarantee and political stability.
According to Klerman (2007), the independence of the legal system in
general promotes FDI into the country by undertaking important preventive
measures in the sense of fulfilling contracts and protecting property rights.
According to Drabek and Payne (1999), there is a positive relationship
between FDI and transparent economic policies. According to Kennedy
(2001), the application of a transparent and efficient competition law or
policy can play an important role in enhancing the attractiveness of
investing country economies.
Oransay and Mike (2016) modeled socio-political factors which influencing
foreign direct investment as ownership rights, independence of the legal
system, fairness and suitability of competition conditions, transparency of
applied policies and political stability. According to the estimation results;
There is a positive relationship between socio-political factors and direct
foreign investments. Ay et al. (2016), decrease in the level of corruption and
the increase in the level of democracy for developing countries affect foreign
direct investments positively. On the other hand, Şanlısoy (2016)
investigated the effects of the information economy on the international
income distribution by establishing statistical relations between foreign
direct investment and information and communication technologies. It is
emphasized in the that a partial improvement in the distribution of
international income has been achieved due to the fact that foreign direct
investment, creates international information convergence.
As well as the above literature, several case studies on Turkey have been
conducted. Erdal and Tatoglu (2002) questioned the importance of spatial
factors for investments preferring Turkey, and found that Turkey's market
size, infrastructure and openness were perceived as positive values for
foreigners, while exchange rate and economic stability negatively affected
FDI It has. In addition, from the researches on Turkey, Berkoz and Türk
(2007) evaluated the factors motivating foreign investments by sectors and
regions. According to the results, the growth of GDP and population, the
Effects of Political and Socio-Economic Indicators on Foreign Direct Investments: Stochastic
Frontier Analysis
“İnsan ve Toplum Bilimleri Araştırmaları Dergisi”
“Journal of the Human and Social Sciences Researches”
[itobiad]
ISSN: 2147-1185
[1083]
improvement in infrastructure and the increase of bank credits increase the
amount of FDI. Coastal areas, on the other hand, seem to be the reason for
preference. Berkoz and Türk (2009) determined that the availability of
infrastructure, input quality and cost, close proximity to the market,
communication and transportation quality, as well as accessibility to the
infrastructure, are very significant in determining regional FDI, as expected
in theory. Armutçuoğlu and Şanlısoy (2016) investigated the co-integration
relationship between patent registrations in Turkey and FDI using Gregory-
Hansen co-integration method. In the study, it is concluded that, there is a
negative relationship between patents and foreign direct investments before
1984, and a positive relationship after 1984 due to the increasing openness.
Considering the above literature on FDI, it seems that there are two types of
researchers. A group focuses on the impact of FDI on macroeconomic
variables such as technology, growth and labor productivity. Another group
of researchers aim to determine the factors behind FDI. The result obtained
from the studies in the first group is that foreign investments in general lead
to technological diffusion and positively affect growth and labor
productivity. In the second group of studies, economic, social and political
factors have been extensively studied both country and region levels. These
empirical studies often focus on economic variables. The reason why the
political and social factors are less preferred is that it is not suitable for
implementation because of data incompleteness.
2. Data Set and Methodology
Data set of this study includes foreign direct investments (positive net
inflow), democracy index that averages of Electoral process and pluralism,
Functioning of government, Political participation, Political culture and Civil
liberties indices. Another combination index that we used in the model is
economic freedom index that averages of property rights, government
integrity, tax burden, government spending, business freedom, labor
freedom, monetary freedom, trade freedom, investment freedom and
financial freedom indices. The other variables in this study are respectively,
openness rate of economy, exchange rate, labor force population, gross
domestic product, the commodity price indices (energy price index and non-
energy price index ) which reflects global commodity costs, internet users in
population that pictures infstructure level of economy, gross domestic
product deflator for inflation rate Also we include year dummy for fixed
effects. Democracy index is obtained from The Economist Intelligence Unit,
Economic freedom index is from Heritage foundation. Other variables
obtained from IMF and World Bank. Time span is 2010 to 2016. Data set
includes 57 countries. The selected countries are those with GDP above $ 50
billion compared to the year 2016. We select 57 counties that have positive
net in word flow of FDI and economics that bigger than $ 50 billion (GDP in
2016 data). We select time span between 2010 and 2016 because of these
constraints;
Memduh Alper DEMİR / Mustafa BİLİK / Üzeyir AYDIN
“İnsan ve Toplum Bilimleri Araştırmaları Dergisi”
“Journal of the Human and Social Sciences Researches”
[itobiad / 2147-1185]
Cilt: 7,
Sayı: 2
Volume: 7,
Issue: 2
2018
[1084]
Negative FDI flows (we use logarithmic form so it gives null. If we
use negative FDI flows)
Because of global financial crisis we don’t want to include 2008 and
2009 datas.
All variables are in logarithmic form. Table 1 gives descriptive statistics of
variables.
Table 1. The Descriptive Statistics of Variables
Variable
Observation
Mean
Standart
Deviation
Mininum
Maximum
GDP
399
26.5256
1.4024
24.0877
30.5555
Population
399
17.1111
1.2975
14.7638
20.8495
Exchange Rate
399
4.7587
2.8340
- 0.4980
10.3389
Energy Price
Index (2010=100)
399
4.5874
0.3281
4.0073
4.8573
Non- Enery Price
Index (2010=100)
4.5830
0.1331
4.3862
4.7855
Foreign Direct
Investment
399
22.8555
1.6598
17.9960
26.9501
Internet Users
399
16.5401
1.2625
13.3966
20.4132
GDP Deflator
(2010=100)
399
4.7587
0.2136
4.4038
6.1332
Economic
Freedom Index
399
4.1331
0.1625
3.6963
4.5009
Democracy Index
399
1.7411
0.4128
0.5364
2.2213
Trade Openness
399
4.2328
0.5964
2.8225
6.0927
Year
399
2013
2.0025
2010
2016
Basically, efficiency is the rate of observed value to potential value (Kalirajan
and Shand, 1999). In this context, measurement of efficiency requires an
estimate of the magnitude of potential values , which can not be observed.
Several approaches have been developed in order to carry out this
estimation process and to measure the technical efficiency. In the literature,
nonparametric Data Envelopment Analysis (DEA) and parametric Stochastic
Frontier Analysis (SFA) are the most dominant of these approaches (Zhang
et al., 2013: 654-655).
Stochastic frontier technique approach were first recommended by Aigner et
al. (1977) and Meeusen and van den Broeck (1977) was originally conceived
for an analysis of cross-sectional data, but different models to account for
panel data have also been presented by Pitt and Lee (1981); Kumbhakar
(1990); Cornwell et al. (1990); Kumbhakar and Wang (2005); Kumbhakar et
al. (1991); Battese and Coelli (1988); Battese and Coelli (1992); Battese and
Effects of Political and Socio-Economic Indicators on Foreign Direct Investments: Stochastic
Frontier Analysis
“İnsan ve Toplum Bilimleri Araştırmaları Dergisi”
“Journal of the Human and Social Sciences Researches”
[itobiad]
ISSN: 2147-1185
[1085]
Coelli (1995); LeeSchmidt (1993); and Kumbhakar et al. (2012); (Onder et al.
2003:100). In our study we run Battese and Coelli (1995) model.
Battese and Coelli (1995) model consists of a single step and predicts
efficiency values and environmental factors affecting these efficiency values
are modeled. Thus, the model provides a significant advantage over the two-
stage methods. Therefore, Battese and Coelli (1995) model also takes into
account the influence of environmental factors when the SFA parameters are
estimated simultaneously with the inefficiency model. (Ekinci and Kök,
2017: 180)
Stochastic frontier function using the panel data can be expressed as follows;
(1)
where; X represents inputs, y represents output. In the stochastic frontier
function, the error term is divided into two parts. The first ( ) is the
random error term, which makes the frontier function to be stochastic, and
the second ( ) expresses the inefficiency effects.
In estimating country-specific efficiency scores, Jondrow et al. (1982)
proposed the following formula:
(2)
The and parameters in the equation are defined as follows;
ve ,
Using the equation (2) the technical efficiency is calculated as follows;
(3)
3. Empirical Findings
Prior to the stochastic frontier regression we run OLS (ordinary least
squares) regression. We control the error term skewness from the OLS
regression, so that the errors are skewed to the left thus model has
inefficiency, the model is appropriate for stochastic frontier model. In
addition, Wald test results in stochastic frontier model indicated that model
is significant.
Memduh Alper DEMİR / Mustafa BİLİK / Üzeyir AYDIN
“İnsan ve Toplum Bilimleri Araştırmaları Dergisi”
“Journal of the Human and Social Sciences Researches”
[itobiad / 2147-1185]
Cilt: 7,
Sayı: 2
Volume: 7,
Issue: 2
2018
[1086]
The empirical phase of this study takes shape of two parts. Firstly,
maximum likelihood based regression estimates of stochastic frontier are
introduced. Secondly, country specific FDI efficiency scores are supplied
using Jondrow et.al (1982) formula.
Table 2. Estimation Results of the Stochastic Frontier Model
Variables
Stochastic Frontier Model
Constant
142.500
(0.90)**
GDP
0.882
(12.67)*
Population
-0.176
(-2.38)*
Energy Price Index
-0.250
(-0.91)
Non- Energy Price Index
0.373
(0.45)
Internet Users
0.243
(2.52)*
GDP Deflator (Inflation)
0.928
(3.72)*
Economic Freedom Index
2.251
(5.69)*
Democracy Index
0.153
(1.35)
Trade Openness
0.703
(9.07)*
Exchange Rate
0.012
(0.90)
Year
-0.079
(-2.23)*
2
(u)
1.884
(4.29)*
2
(v)
0.529
(12.66)*
3.556
(8.07)*
LOG-LIKELIHOOD
-457.4670
Notes: 1- () values in parentheses are z scores. 2-* significance at 5% and **
significance at % 10 3 - γ=
2
(u)/
2
(v) 4-
2
(v)The variance of the random
error term 5-
2
(u)the variance of the efficiency
Effects of Political and Socio-Economic Indicators on Foreign Direct Investments: Stochastic
Frontier Analysis
“İnsan ve Toplum Bilimleri Araştırmaları Dergisi”
“Journal of the Human and Social Sciences Researches”
[itobiad]
ISSN: 2147-1185
[1087]
Depends on the further studies and theoretical expectations; GDP variable is
positive and have significant effect on FDI. A %1 increase in GDP increases
FDI % 0.88. Population variable in the study is negative and significant. A
%1 increase in population decreases FDI 0.17 percent. Internet users variable
is positive and significant. A %1 increase in Internet users increases FDI 0.24
percent. GDP deflator variable is positive and significant so a %1 increase in
inflation increases FDI 0.92 percent. Economic freedom index is positive and
significant. A %1 increase in economic freedom index increases FDI 2.25
percent. Trade openness variable has impact on FDI flows. It is positive and
significant. A %1 increase in trade openness variable increases FDI 0.70
percent. Finally, year variable for observing fixed effect is negative and
significant. That means model has a fixed effect but it is so weak in this
model. Policy implications and suggestions about variables is shown in
conclusion part of study.
FDI efficiency scores were supplied using the results of our model.
Estimated efficiency scores of countries for the years 2010-2016 are
submitted in Appendix 1. Jondrow et. al. (1982) formula is used in the
estimation of Country-specific efficiency scores. Efficiency is estimated to be
57.7 percent on average, maximum 87.8 percent and minimum 4.5 percent.
Countries scored above the average (% 57.7) in every year during 2010-2016
are; Brazil, Chile, China, Colombia, Costa Rica, Dominican Republic,
Ethiopia, Guatemala, Hong Kong, India, Indonesia, Ireland, Kazakhstan,
Lebanon, Netherlands, Peru, Portugal, Singapore, Vietnam. Except for the
Portugal, Ireland, Netherlands and Ethiopia this result shows the success of
Asian and Latin American countries for pulling the FDIs. These countries
are developing countries. In these development process the role of FDIs are
important. It seems that they apply proper social and economic policies for
pulling FDIs.
4. Conclusion
This paper analyzes effects of political and socio-economic indicators on FDI
by using stochastic frontier regression. The countries in this paper was
chosen from countries that have positive net inflow in FDIs and have GDP
above $ 50 billion compared to the year 2016. Following the introduction, we
discuss theoretical framework and empirical literature on FDI determinants.
Data set and methodology of stochastic frontier regression are explained.
Model results are then presented and finally efficiency scores for each
country are estimated for the 2010-2016 period. Overall efficiency is
estimated to be be 57.7 percent on average, minimum 4.5 percent, and
maximum 87.8 percent.
According to the results, gross domestic product, working age population,
openness ratio, internet usage, Economic Freedom Index and GDP Deflator
were found to have significant effects on foreign direct investments.
However, democracy index, energy and non-energy goods prices and
exchange rate parameters are statistically insignificant. According to this, it
Memduh Alper DEMİR / Mustafa BİLİK / Üzeyir AYDIN
“İnsan ve Toplum Bilimleri Araştırmaları Dergisi”
“Journal of the Human and Social Sciences Researches”
[itobiad / 2147-1185]
Cilt: 7,
Sayı: 2
Volume: 7,
Issue: 2
2018
[1088]
can be argued that the changes in the democracy index, energy and non-
energy goods prices and exchange rates are not very effective in the
development of foreign direct investments. Especially the prices of energy /
non-energy goods and the effect of exchange rate on FDI are related to
changes in the volatility of prices and exchange rates. Short-term movements
in price and exchange rates increases FDI inflows, but they can create long-
term risk increases and adversely affect FDI inflows.
A significant and positive coefficient on the GDP variable means that
production should be able to reach a new frontier with high-tech growth
policies. This can be achieved by the policies proposed by the endogenous
growth model.
The increase of the working age population (productive population) can be
seen as a demographic opportunity. Therefore, it is expected that the
countries with growing population and growing local market and increasing
labor power will have significant potential to attract FDI. However, in our
study, the relationship between working age population and FDI was found
to be significant and negative. This situation is in fact compatible with the
literature findings (Hisarcıklı, Gültekin-Karakaş and Aşı2009, Vergil and
Ayash 2009, Brady and Wallace 2000, Williams 2003). This is because the
FDI’s are often made to service sub-sectors (finance, communication and
transport) with limited employment capacity. Similarly, it has been revealed
that FDI does not create positive effects on employment, but rather
negatively affects the efficiency and productivity of the working age
population. In this context, the results point to two different policy
implications. First, shifting Turkey's FDI potential to different areas, such as
manufacturing, tourism or mining, may be a more appropriate option.
Secondly, the introduction of FDI into the forefront of such fields as
technology transfer, exports and prices may become an alternative policy
tool.
The free entry and exit of capital to the country and the elimination of trade
restrictions are the factors that encourage FDI (Chakrabarti 2001: 91-2).
According to Deichmann (2001), trade openness and FDI are complementary
to each other. The result of the study was estimated to be positive for
foreign direct investment (Culem (1988)), as expected in the theory. Trade
liberalization in a country has a positive impact on FDIs.
A significant and positive coefficient on the economic freedom index
variable means that an increase in economic freedoms increases FDI. This
situation is examined in the context of the other sub-indices constituting the
content in the index. Thus, in the host country, labour and business markets,
monetary institutions, trade, investment and financial sector have to be more
independent. In addition, depending to sub-indices of economic freedom
index some applications should be done for increasing economic freedoms.
Effects of Political and Socio-Economic Indicators on Foreign Direct Investments: Stochastic
Frontier Analysis
“İnsan ve Toplum Bilimleri Araştırmaları Dergisi”
“Journal of the Human and Social Sciences Researches”
[itobiad]
ISSN: 2147-1185
[1089]
These are; government expenditures have to be more transparent,
government intervention on economy should be reduced, consumer rights
should be increased and tax burden should be eliminated.
In this study, internet usage is taken into the model in terms of
infrastructure level. It is expected to be positively associated with FDI, as an
advanced infrastructure network will provide externalities and economies of
scale. The result is positive and statistically significant as expected.
Estimation results confirm that foreign direct investments prefer countries
that are easily accessible to infrastructure services (telephone, internet,
electricity, water, etc.). In addition, countries with an advanced
infrastructure will have lower production costs. For this reason, as expected
in theory, there is a positive relationship between infrastructure and FDI.
The estimated coefficient of inflation rate is positive and statisticly
significant. Thus, it can be argued that FDI towards countries are mostly for
profit purposes. On the other hand, volatility of the inflation is often a more
decisive factor than the magnitude of the inflation rate for investments. This
situation arises, primarily because market size and growing economies are
seen as attractive for investing, as inflation rates in growing economies
increases.
In summary, according to the study, foreign direct investments are
significantly affected by GDP, the working age population, trade openness
ratio, internet usage, economic freedom index and inflation. For FDI’s that
are focused on the market and profitability, countries with high economic
growth are preferred primarily because they promise high returns both in
the short and long run. On the other hand, countries with developed
infrastructures have also been found to be in an advantageous position for
FDI inflows. The widespread communication and transportation network
will produce positive externalities by reducing both production and
transportation costs. The inflation rate, on the other hand, has a positive
relationship with foreign direct investments. The inflation rate can be seen as
an indicator of macroeconomic stability, reflecting the accordance and
success of monetary and fiscal policies in the country on the one hand, and
high rates of return for investors on the other. Openness ratio, as expected in
theory, is estimated to be positively related to foreign direct investment.
Therefore, foreign direct investment inflows are influenced by the global
market. The increase in trade volume and the success of previous
investments will encourage foreign investment in the following years.
Economic freedoms have positive influence on FDIs. Governments should
be make reforms that increases economic freedoms. Determination of the
factors affecting foreign direct investment inflows and presentation of
appropriate policies are important in terms of benefiting from the positive
effects as a policy tool.
Memduh Alper DEMİR / Mustafa BİLİK / Üzeyir AYDIN
“İnsan ve Toplum Bilimleri Araştırmaları Dergisi”
“Journal of the Human and Social Sciences Researches”
[itobiad / 2147-1185]
Cilt: 7,
Sayı: 2
Volume: 7,
Issue: 2
2018
[1090]
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APPENDIX 1 : ESTIMATED EFFICIENCY SCORES OF COUNTRIES
Country/Year
2010
2011
2012
2013
2014
2015
2016
Argentina
77.06
70.11
77.94
70.33
51.16
69.18
25.63
Australia
55.72
66.95
63.03
64.56
63.62
60.89
68.03
Bangladesh
65.71
56.53
61.62
71.02
63.76
63.28
49.81
Brazil
83.80
82.94
81.36
79.12
82.95
81.76
82.55
Bulgaria
56.18
51.78
49.88
54.27
55.98
64.20
40.68
Canada
33.49
38.27
49.38
60.92
60.68
59.97
47.05
Chile
69.01
73.84
77.89
73.10
76.30
74.99
66.31
China
82.13
80.74
79.48
81.05
79.87
78.68
75.29
Colombia
61.98
72.62
72.35
73.83
74.30
70.64
75.37
Costa Rica
67.24
71.40
69.82
74.35
73.95
70.79
69.45
Croatia
51.36
48.16
53.86
40.83
77.29
9.31
66.39
Czech Republic
53.92
25.94
53.55
47.57
49.71
14.14
44.98
Dominican Rep.
67.42
68.70
76.16
62.64
70.30
68.14
69.98
Ecuador
13.89
43.32
37.00
46.59
46.36
65.23
54.52
Ethiopia
70.76
76.67
40.10
78.27
78.66
78.16
81.42
France
45.64
47.43
43.92
43.44
10.33
59.43
54.52
Germany
46.79
48.13
40.76
41.25
12.04
35.90
35.77
Greece
7.53
15.98
32.95
53.62
52.39
37.34
66.09
Guatemala
57.69
60.41
64.01
66.35
60.51
58.98
56.59
Hong Kong
70.46
72.09
67.02
68.42
77.21
80.71
75.94
India
68.02
68.39
57.43
61.47
64.86
71.18
69.06
Memduh Alper DEMİR / Mustafa BİLİK / Üzeyir AYDIN
“İnsan ve Toplum Bilimleri Araştırmaları Dergisi”
“Journal of the Human and Social Sciences Researches”
[itobiad / 2147-1185]
Cilt: 7,
Sayı: 2
Volume: 7,
Issue: 2
2018
[1096]
Indonesia
69.17
70.33
70.87
73.46
74.03
70.33
26.36
Iran
63.29
60.93
56.91
40.36
35.78
36.53
43.95
Iraq
46.64
41.84
52.91
61.09
58.71
61.51
5.26
Ireland
70.14
61.35
75.58
79.34
84.03
87.81
80.82
Israel
54.40
57.22
61.03
69.23
51.11
66.79
67.48
Kazakhstan
74.90
76.94
75.96
70.13
64.00
66.60
82.56
Kenya
21.84
72.10
68.20
64.33
50.99
41.23
25.14
Korea
15.12
13.93
14.65
20.65
15.68
7.81
20.11
Lebanon
79.69
74.38
73.95
72.35
75.27
71.90
74.67
Malaysia
47.68
52.61
38.16
50.12
45.00
44.87
54.70
Mexico
49.73
50.07
43.27
69.71
59.21
62.81
62.91
Morocco
35.07
55.06
59.64
65.94
68.76
66.81
56.29
Netherlands
73.56
84.87
83.68
86.21
77.29
81.26
81.79
Nigeria
67.12
68.65
64.29
63.64
57.97
53.77
67.75
Pakistan
62.38
41.70
29.44
41.02
51.11
43.37
55.70
Panama
68.52
74.45
68.78
73.51
77.70
77.04
79.56
Peru
74.76
68.11
76.80
75.68
62.30
75.46
72.17
Philippines
20.07
33.37
45.49
48.68
58.58
53.97
61.65
Poland
61.90
59.03
34.87
4.51
62.54
56.08
58.72
Portugal
63.80
67.45
82.23
73.69
76.85
34.95
71.81
Romania
46.25
32.65
43.30
48.52
47.67
51.71
57.89
Russia
77.45
75.60
72.92
77.67
54.52
27.43
73.30
Saudi Arabia
75.46
53.34
47.86
42.51
40.36
51.58
54.50
Singapore
64.16
58.32
63.65
67.71
71.19
71.37
70.94
South Africa
34.90
32.29
36.97
56.98
47.29
15.81
24.47
Spain
59.54
50.58
49.49
70.98
63.50
64.83
62.95
Sri Lanka
43.78
54.13
51.99
47.91
45.54
36.44
42.65
Sudan
79.76
76.57
79.84
72.21
60.59
60.63
44.52
Thailand
61.67
13.99
54.26
62.24
31.26
50.02
19.91
Turkey
42.44
54.18
50.66
48.52
46.26
57.71
48.02
Ukraine
78.85
76.68
78.51
68.06
22.99
60.86
60.90
United Arab Emir.
43.56
28.12
31.23
33.82
38.02
36.72
39.84
United Kingdom
49.64
24.46
42.09
47.95
50.42
50.86
82.59
United States
54.34
53.36
55.18
61.67
57.48
73.77
73.95
Uzbekistan
81.20
79.02
54.50
54.05
49.42
6.60
6.85
Vietnam
75.55
66.73
67.68
67.40
66.81
69.16
67.45
Note: The logic of reading FDI efficiency scores: for example; Turkey’s net
positive inward FDI in 2016 has an efficiency score of 48.02%. Observed FDI
12.307.000.000 is dollars. Potential FDI is (100*(Observed FDI))/48.02. So it is
25.628.904.623 dollars. Source: Authors own.
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