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The Role of Institutions and Good Governance for Attracting Foreign Direct Investments: Evidence from Southeast Europe

Valbona Zeneli*

Professor of national security studies, George C. Marshall European Center for Security Studies, Garmisch, Germany

*Corresponding Author:
Valbona Zeneli
Professor of national security studies
George C. Marshall European Center for Security Studies, Garmisch, Germany
Tel: 0049 88217502633
E-mail: [email protected]

Received date: February 13, 2014; Accepted date: March 24, 2014; Published date: March 30, 2014

Citation: Valbona Zeneli (2014) The Role of Institutions and Good Governance for Attracting Foreign Direct Investments: Evidence from Southeast Europe. Bus Eco J 5:92. doi: 10.4172/2151-6219.100092

Copyright: © 2014 Zeneli V. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Abstract

Institutional infrastructure and governance play a critical role in attracting foreign investments. This is especially true in transition countries. This paper examines the role of Foreign Direct Investment (FDI) in South Eastern European Countries during the 1992-2010 timeframe. Using GMM estimations applied to dynamic panel data, visibility into regional foreign investments highlight the role of institutions as well as the distinctions between total FDI and Non-Privatization related FDI. The empirical data point to the importance of the quality of the institutions and the role of the privatization process in these countries.

Introduction

Foreign Direct Investment (FDI) remains one the most important features when considering the economics of globalization. The general negative attitude towards FDI prevalent until the 1980s has changed to the view where it is typical that economies sought and would encourage FDI inflow. However, this debate would restart again during the recent crises as the world economy suffered the deepest financial and economic crisis since World War II, witnessing major declines in output, trade, investments and employment. To some, FDI were seen much more volatile when compared to domestic investments. The nature of FDI and its role during the crisis has been one of the main topics of debate in the recent years.

Hence, determining what drives FDI towards certain countries and not others is a topic worthy of study. In both developing and transition economies FDI is viewed by most scholars and policymakers as a major catalyst to assist in development, and an important indicator of economic health and stability. Over the past two decades developing and transition economies have used FDI to supplement investment resources, transfer technology and managerial skills, upgrade quality and productivity, and gain access to world markets. FDI inflows have stimulated the development of market economies and their supporting regulatory infrastructures, which are so essential to their efficiency [1].

However, the empirical evidence has been mixed especially since FDI’s macro and micro economic performance remains unclear. Existing studies show the effects of FDI largely depend on the conditions of the host country, précising that their benefits do not accrue automatically and evenly across countries, sectors, and local communities. Instead, national policies and the international investment architecture are very important for attracting FDI and reaping their full benefits for development [2]. The local conditions or “absorptive capacities” are: human capital [3], trade openness [4] and domestic financial institutions [5].

According to the data available and various empirical studies, the majority of global FDI flows move within developed countries and there is a lack of mobility towards developing countries. The reasons may be the external push-factors and asymmetric information, or on pull-factors such as the quality of institutions, imperfections of the financial markets (Alfaro, 2005) and technological differences.

The worldwide competition for FDI attraction has resulted in a number of responses such as policy changes [6], provision of incentives and inducements for seeking investments and selective targeting for “quality FDI” [7]. Various scholars generally interpret this latter idea of “desirability "of FDI related to the magnitude of likely economic impacts. Hence, the idea is usually built on simple dichotomies, such as the size of firm (larger firms more desirable than smaller, industry (higher value-added industry is preferred to lower value-added), the functional focus of an affiliate (higher order functions such as research and development (R&D) or regional headquarters are preferred to assembly operations), the form of entry (Greenfield investment is superior to mergers and acquisitions or Brownfield investment), or the orientation (motive) of a firm [8].

In recent years FDI has gone through unprecedented growth, showing also new features such as increased inflows towards developing countries; a shift from primary and manufacturing sectors to services such as finance, communication and business consulting; and, a trend shifting from privatization related to Greenfield investments and cross-border mergers and acquisitions. Hence, to understand and explain the FDI flows it is necessary to understand why companies invest abroad and how they choose the locations where to invest. In the empirical literature, FDI determinants have been examined both at micro and macro levels, but in the case of Southeast Europe, it has been absent from the literature.

The FDI investment picture concerning Southeast Europe (SEE) raises some compelling questions that need to be addressed. What is the distribution of FDI inflows in the SEE? Which economies have been most successful in attracting FDI? What are the factors that determine the volume of FDI inflow that these countries receive? What type of FDI is noticed in SEE? Are there specific conditions in transition countries that play particularly important roles for the attraction of FDI? Is there any connection between the transition process, privatization, and FDI?

This paper will focus on the importance of the quality of institutions in creating a friendly environment for the attraction of FDI. The economies taken in consideration in this study are the South East European countries (The countries taken in consideration are according the EBRD definition of South East Europe: Albania, Bulgaria, Romania, Croatia, FR of Macedonia, Bosnia & Herzegovina, Serbia, Montenegro.). Before the transition process from centrally planned to market economies, FDI inflows in the region were at minimal levels. At the beginning of the 1990s, the total inflow of FDI in Central and Eastern Europe was less than 1 percent of the world total. By 2008 it had increased significantly to 6.9 percent, suggesting that the rising share of FDI in the countries’ GDP was a factor helping them to integrate into the global economy [9]. The availability of natural resources played an important role for the attraction of FDI in the region at the beginning of the transition, followed by the liberalization of the trade regimes, incentives from governments and privatization process.

This research is based on the New Paradigm of Development that introduced the concept of institutional assets into the eclectic Paradigm or OLI Model [10]. As a result of new developments in the world economy, the content and the quality of the institutions are becoming more significant components of the competitive advantages for the firms and the attractiveness of a particular country’s location. For this very reason, a careful attention is being given to the quality, content and origin of institutions, their instruments and mechanisms of implementation.

This paper adds to the literature on FDI in four main aspects.

First, I attempt to explain the importance of the quality of the institutions as the main determinant for attracting FDI in SEE.

Second, I distinguish between privatization and non-privatization related FDI, since FDI is usually time bound, and in most of these countries the privatization process is in the process of being completed. The focus of the governments should be on the ability of these economies to attract non-privatization related FDI.

Third, traditional variables are used in the analysis such as market size, trade openness, exchange rate, cost of labor, infrastructure variables, but also determinants that remained unnoticed in the existing literature such as remittances, domestic investment, quality of institutions, presence of foreign banks, and privatization processes.

Finally, the study fills in the existing gap in literature by using the GMM econometric technique for the time frame 1992-2010 for SEE, since other studies may suffer from the endogeneity problems and short term series issues leading to biased results.

The paper is organized as follows: In section 2, I make a brief literature review on international trade and FDI, and various characteristics of FDI in SEE are discussed, in section 3 the econometrical model and empirical approach are introduced, followed by section 4 the empirical results of the study are reported, and section 5 concludes.

Literature Review

In this section the theoretical base for FDI will be traced. The new international trade reality poses questions on the explanation of trade flows between countries, the nature and the extent of gains or losses for an economy, and the effects of trade policies. Initially, the pure economic theory, the international trade and the theory of the firms were adopted as theoretical base for empirical studies on FDI flows. The Theory of Absolute Advantage, with the origins in 1876 with Adam Smith, was the first attempt to explain why countries engage freely in international trade. It was followed by the Theory of Comparative Advantage of David Ricardo which emphasized the concept of “specialization” by countries, promoting the efficiency in the production processes, based on the labor theory of values. This latter would be redefined in terms of opportunity costs and comparative advantages for countries, where many of the principles of the World Trade Organization are based on the belief of the validity of the law of comparative advantage [11].

It was later followed by Factor Proportion Theory or the Theory of Heckscher Ohlin (H-O) which extended the concept of economic advantage by considering the endowments and costs of the factors of production. Usually multinational companies use the imperfections in the market in terms of factors of production and capitalize on the resources of a foreign country creating in this way an opportunities for foreign direct investments.

With the significant technological progress witnessed in the 1960s and the rise of the multinational enterprises, the Product Life Cycle Theory of international trade [12] and the Technology Gap Theories [13] were found to be useful for explaining and predicting international trade patterns. The essence was that technological innovation and market expansion were critical issues being technology a key factor in creating and developing new products, while market size and structure influential in determining the patterns of the international trade.

While theories of perfect competition dictate that firms produce homogenous products enjoying the same level of accessing the factors of production, The Market Imperfections Theory states that firms constantly seek market opportunities and their decision to invest overseas is explained as a strategy to capitalize on certain capabilities not shared by competitors in foreign countries [14].

John Dunning developed the International Production Theory, addressing the reasons why foreign production is considered the most desirable means of harnessing the firms’ advantage, explaining that not only resource differentials and the advantages of the firm play a part in determining overseas investment activities, but foreign government actions may significantly influence the piecemeal attractiveness and entry conditions for international firms.

To explain the behavior of MNCs, Dunning [15] developed the Eclectic Paradigm of OLI Theory, which largely dominated the thinking in the 1970s and early 1980s. According to this latter theory, a foreign firm must own three advantages over local firms in order to decide to take an FDI, each represented by one of the letters: O (Ownership); L (Location); and I (Internalization).

However, adapting to fast globalization patterns, new development thinking started for the world economy with The New Paradigm of Development (NPD), after the post-1980 liberalization of markets and technological, information and knowledge advances in cross border transportation and communication. As a result of new developments in the world economy, the content and the quality of the institutions have become very important components of both competitive advantages of firms and the location attractions of countries. The NPD [16] explains that shifts in economic ideology, recent advances in technology, and new insights into the determinants of growth have shown that however necessary the three determinants Resources(R), Capabilities(C) and Markets (M) may be for the competitiveness of firms and for the growth of host countries, they may not be a sufficient condition anymore. For this reason a very careful attention needs to be given to the quality, content and origin of institutions, their instruments and mechanisms of implementation. This is why Dunning incorporates institutions in NPD, as a variable that both influences the extent, content and quality of (R), (C) and (M) and is influenced by them. My research was mainly based in this recent theory.

The general proposition would be that the more the institutions favor a particular location, the more the MNCs will choose to create or add value to their global ownership specific advantages to that location. From this perspective host countries’ governments have a huge responsibility to ensure that institutions and societal entities are best able to create, organize and utilize the resources, capacities and markets available to them and at the same time supplement the foreign investors in order to create a structure of value adding activities [17]. According to this new theory, institutions are seen as ‘sets of common habits, routines, established practices, rules and laws that regulate the interaction between individuals and groups’. They create the milieu within which innovation is undertaken; establish the ground rules for interaction between economic actors and represent the economic culture of the country.

Many scholars believe that governments are essential to promoting inter-linkages between the elements of absorptive capacity and to creating the opportunities for economic actors to absorb and internalize spillovers, arguing even that efficient institutions contribute more to economic growth than location or trade [18]. Among others there is a strong contribution of the three Nobel Laureates- Amartya Sen [19], Joseph Stiglitz [20] and Douglas North (1990, 1994, 1999, and 2005) on the importance of the institutions as one the critical determinants for the international trade and economic development.

Theoretical determinants for the attraction of foreign direct investments

Theories suggest a set of host-specific location determinants attributing to the uneven distribution of FDI flows across countries. However, FDI inflows depend usually on four main types of motives for the activities of multinationals and their interest in various markets: resource seeking, market seeking, efficiency seeking and strategic assets seeking [21,22]. On the basis of location of production sectors, FDI could also be categorizes as vertical or horizontal. For horizontal FDI, the size and the purchasing power of the local market are very important. A horizontal model of MNCs is the one with multiple production facilities producing homogeneous goods, functioning on the key assumption of the presence of economies of scale, a source of competitive advantage over the domestic firms [23]. On the other side, investors interested in vertical FDI would pay more attention to the costs of the factors associated to their investment since they would always have the objective to minimize their cost factors. A third type of FDI is known as the Knowledge-Capital Model, as an integration of horizontal and vertical models in the sense that both economies of scale and factor price differences play an important role [24-26].

Location-specific attractiveness in the form of political and economic stability, property and profit tax system, market size, labor-force composition, geographic proximity, competition, freedom of entry and exit from markets, domestic financial markets, are all factors influencing the volume and the type of FDI. In addition, energy and water resources, transportation and telecommunication infrastructure are some other critical elements having a huge influence in the attraction of FDI. Country specific characteristics determining the attractiveness for foreign investors also include the openness of an economy and the extent to which it is multicultural and tolerant of different belief systems and ready to assimilate the positive institutional practices of other countries. The stage of economic and social development is very important because it affects the quality of its supportive institutional infrastructure. The institutional demands of a particular industrial entity such as the size, the culture towards wealth creation and entrepreneurship, the extent and seriousness of its social unrest or social dysfunction, and the extent of democracy and freedom of action on business practices, are all key factors governing the decision making concerning FDI. Other specific assets might be imposed by the home or host country governments or supranational entities, such as patents protection, banking regulations, transparency regarding bribery and corruption, safety procedures, environment protections and so on.

The human capital stock has also a very significant value. Labor has to be sufficiently educated and trained to absorb and use new technology and serve as an infrastructure to meet FDI needs in the host countries. Human capital stock in developing countries becomes a prerequisite to take advantage of and absorb such advanced technologies to achieve economic growth [3].

Empirical literature review

In the empirical literature only few studies focus on FDI inflows Eastern Europe. In particular the region of SEE has attracted little attention, because of the low level of FDI. Of over 60 empirical studies reviewed only a few of them cover this region. Studies include Culem [27], Bevan and Estrin [28], De Mello [29] who presents a summary of case studies, among others.

Sample: SEE countries: Albania, Bulgaria, Romania, Croatia, Bosnia & Herzegovina, FR of Macedonia, Serbia and Montenegro. Years of study (1992-2010)

Wang and Swain [30] explored the factors that explain FDI inflow into Hungary and China during 1978-1992, using the OLS (Ordinary Least squares) estimation method. The finding of their study suggested that the size of the market and the cost of capital significantly affect FDI inflows. Little support was founded in their study about the tariff barriers and import variables.

Lankes and Venables [31] is an important paper that analyzes the determinant of FDI inflows, based on a survey of 117 managers of Western firms that were investors or potential investors in the Region of East Europe. The results of the work indicate that the progress of transition, the political stability, new market opportunities and risk levels were important management decisions about investment in this region.

Holland and Pain [32] studied the determinants of FDI to eleven Central European economies during the period 1992 to 1996 using a panel data (Table 1). The paper finds that the method of privatization, labor costs, trade linkages and proximity to the European Union are important for FDI inflows.

Resmini [33] used a unique panel data (Table 1) set on the sector level to study determinants of FDI in eleven CEE economies during 1990-1995. The study concentrates on the manufacturing sector and the results suggest that FDI inflows are determined primarily by market variables such as population and GDO per capita.

Bevan and Estrin [34] studied the FDI inflows to transition countries using panel data (Table 1). Their worked aimed to identify FDI inflows from 18 individual source countries to ten CEE economies and Ukraine for the period 1994-1998. The paper finds that FDI inflows are significantly affected by market size, distance, risk and labor costs.

Botric and Skuflic [35] analyze FDI determinants in South European countries during the period 1996-2002, using GLS regression analysis on a pooled sample. Their study showed that openness and infrastructure exerts a positive influence on FDI, but the study did not find any significant effect on the market seeking determinants such as GDP per capita, GDP growth or population.

Cartesen and Toubal [36] studied the FDI inflows to eight economies during the period 1993-1999, using a dynamic panel data (Table 1), exactly the Generalized Method of Moments (GMM) estimation technique. The results indicate that market size, the method of privatization and country risk affect significantly the volume of FDI inflows.

Variables Obs Mean Std.dev Min Max
PCFDI 126 962.651 1729.298 0 10175
PCFDI flow 126 310.555 1226.323 0 13388
PCFDI2flow 126 270.210 1184.299 0 12934.68
PRIV 126 1.054 1.435 0 8
FDI flow 126 1.43e+0.9 2.58e+09 0 1.39e+10
FDI stock 126 7.24e+09 2.58e+0.9 0 1.39e+10
PCGDP 126 3157.016 2777.483 217 15637
GDPG 126 3.536 10.431 -20 89
INFLAT 126 70.909 203.739 0 1467
OPEN 126 86.428 25.535 30 149
HC 126 74.722 10.697 50 92
HDI 121 0.815 0.843 0 9.78
TARIF 126 5.636 3.055 0 12.6
BANK 125 0.447 0.282 0 0.923
PCREDIT 126 32.008 19.342 3 76
TEL LINE 126 22.446 11.507 1.2 45
INFRAS 126 2.057 0.693 1 3.33
RESOURC 126 15.252 7.551 4 54.4
INVEST 126 21.217 5.462 8.4 37.8
REM 126 8.437 9.977 0 49.7
INSTU 126 2.642 0.682 1.11 4.9
CORRUP 126 2.372 1.446 0 4.4
ERI 126 2.666 0.672 1.22 3.55
MTR 126 3.056 0.693 0.55 3.88
FSR 126 2.118 0.692 1 3.5
LSP 126 2.582 0.890 1 4
SSP 126 3.428 0.745 1 4.33
ER 126 2.023 0.587 1 3
PL 126 3.840 0.707 1 4.33
TFS 126 3.644 0.968 1 4.33
CP 126 1.743 0.639 1 3
BRIRL 126 2.458 0.839 1 4
SMNBFI 126 1.783 0.611 1 3

Table 1: Summary statistics PANEL DATA.

Campos and Kinoshota [37] examined the FDI determinants analyzing 25 transitional countries and using a panel data (Table 1), based on GMM estimation Technique. This paper stressed the importance of institutions and natural resource abundance in foreign investor’s location decisions.

Demekas et al. [38] tried to explain FDI inflows in to SEE countries by using the gravity equation. They find that FDI allocation across counties is explained in terms of macroeconomic and initial condition variables. Their second paper focuses on non-privatization FDI and find evidence of non-linearity's, with the impact of policies changing above a certain level of income.

Foreign direct investments in south east Europe

FDI has been vital for the smooth transition process in Eastern Europe, helping the economic development of the region and creating jobs and employment, followed by higher wages and better living conditions. FDI has played a significant role in replacing most of the outdated capital stock and shifting production toward goods and services both for the domestic consumption and exports. It has been an important tool for resource transfer, not only capital, but also technological and managerial knowledge, translating into higher GDP per capita for these countries.

Some compelling questions are raised regarding FDI in our region of research interest. What is the distribution of FDI inflows in the South European countries? Which economies have been most successful in attracting FDI? What are the factors that determine the volume of FDI inflow that these countries receive? What type of FDI do we notice in SEE countries? Are they market seeking investments looking for big markets and satisfying the local demand for goods and services? Are they resource-seeking FDI with the objective of exploiting natural resources? Are there specific conditions of transition countries that play an important role for the attraction of FDI? Is there any connection between the privatization process and FDI?

Inflows of FDI began to run into Southeastern Europe at the beginning of the 1990s, as a result of the transition of these countries to the free market economy, which created new opportunities for foreign investors. At the beginning, the availability of natural resources has played a very important role on the attraction of FDI in the region. Later, the liberalization of the trade regimes and price systems, as well as the incentives offered by the host countries governments stimulated more attraction of FDI, alongside with a large privatization process.

The Euro-Atlantic integration process of most SEE countries increased the importance of these economies in the international market and increased the confidence of serious investors in the stability of the business environment. During this process, countries were introduced step by step into the free trade zone representing an intermediary phase towards complete accession to EU. The economic integration positively influenced the inflows of FDI in the region, through a perceived decreased risk on the investments, improvement of the business climate, reformation and harmonization of the countries’ legislative regulations with the EU regulations. Importantly, agreements for EU accession allow free market access for foreign investors to new and bigger European markets.

There is empirical evidence from the region, that FDI geographic distribution is strongly influenced by the host country's political and institutional quality, because it reflects the foreign investors’ confidence on the local investment environment. Political stability, favorable regulations, rule of law are among the main considerations of foreign investors.

SEE offers unique opportunities for foreign investors in terms of market size, strategic position, trade openness, natural resources, flexible low cost labor force, investment incentives and tax regimes. However, FDI inflows remain low in the global context, with the lowest level in Albania with 1462 US dollars per capita and Montenegro the highest with 9178 US dollars per capita in 2011 (See UNCTAD, 2012).

Among the main concerns that serious potential investors have expressed are: unfinished transition process for most of the countries, political and institutional instability in the region, underdevelopment of the market economy, non-sufficient infrastructure, superficial institutional reforms, rampant corruption, lack of transparency in the privatization process, and overwhelming regulations and administrative procedures. Foreign investors and international institutions also raise concerns about the quality of education and professional training in SEE, not only because of inadequate public expenditures but also because of institutional weaknesses in the formulation and implementation of good policies.

Model and Methodology

To understand the relationship between the preferences of foreign investors for the host country characteristics and their contribution to the distribution of FDI in SEE a panel data (Table 1) has been constructed covering eight countries (Albania, Bosnia & Herzegovina, Bulgaria, FYR of Macedonia, Croatia, Romania, Serbia and Montenegro) within the timeframe 1992-2010. The number of the panel observations is 126 (18×7) (For the purpose of data availability until 2006 we consider the two different countries, Serbia and Montenegro as one.)

Equation (1.1)

Equation (1.2)

Yit is the FDI per capita in country (i) in the year (t). Xit is a vector of macroeconomic and general explanatory variables listed in the next section and Zi, t the vector of institutional reforms explanatory variables. The error term μit contains two components, the unobservable country effect vi and the white noise εit. In addition to the standard variables our model on the right side will further propose, an agglomeration effect of FDI which is captured by introducing a one-year lagged stock of FDI per capita, and also the one-year lagged indexes of reforms we took in consideration, of the three variables that show the quality of the institutions. This model was used for two different dependent variables, making a research distinction between the total inflows of FDI and Non-privatization related FDI.

The new model will be:

Equation (1.3)

In this equation, a positive ß1 would be taken as indication of the agglomeration effect. In order to have consistent estimations, GMM Generalized Method of Moments has been used, a model proposed by Arellano and Bond [39] in the context of the endogeneity problem. This is a well-known concern in the empirical literature, since some of the regressors may be potentially endogenous or predetermined in determining FDI flows. For example FDI may be attracted to a country that has a more liberalized financial system, but at the same time the financial system may be enhanced by the presence of FDI [40].

The variables in Xit are assumed to be endogenous. Because causality may run in both directions, from capital inflows to investment and vice versa, these regressors may be correlated with the error term. This model has proved in the past that valid instruments can be obtained in a dynamic panel model if one utilizes the orthogonality conditions that exist between lagged values of Yit, Xit, and the disturbance εit. Under this approach, the new equation will be first-differenced to eliminate the country-specific effect.

Equation

Since Δ Yi,t-1 is correlated with Δ εit, OLS estimates are biased. According to Arellano and Bond (1991), the valid instruments for Δ Yi,t-1 are all the lagged levels of Yi,t, Yi,t-s, where s ≥ 2.

The model can be estimated in one stage or in two stages of GMM. In this research just the one stage estimates will be used, since Arellano and Bond (1991) warns caution against interpretation on coefficients within the two stages, due to an underestimation of the standard error of the coefficients.

To check for the robustness of the model two specification tests have been conducted, to check the validity of the lagged instruments as well as the appropriateness of the model. These tests are namely: the SOC test, the Arellano and Bond test of second order correlation in the first-differenced error terms and the Sargan Test of over identification which tests for correlation between instruments that are excluded from the second-stage model and the residuals.

The main objective of this research paper is to explain the rationale for FDI attraction in SEE. Variables used, denomination and the sources of date are presented in Table 2.

Variable Definition Source
PCFDI flow Flow ofper capita total FDI UNCTAD World Development Indicator online database
PCFDI2 flow Flow of per capita non privatization FDI Self-calculation based on IMF CountryArticle 4 data and UNCTAD online database
FDI Stock of Per capita FDI UNCTAD World Development Indicator online database
PRIV Ratio of privatization to the total GDP Self-calculation based on IMF CountryArticle 4 data
PCGDP Per capita GDP World Development Indicator online database
GDPG Growth rate of per capita annual Indicator World Development Indicator online database
INFLAT Average inflation rate (%) IMF International Financial Statistics Database
OPEN Trade share (export+imports) in GDP (%) World Development Indicator online database
HC Net enrolment rate at secondary/tertiary level (%) World Development Indicator online database: Barro and Lee (2000)
HDI Human Development Index UNDP United Nations Development Index
TEL LINE Fixed telephone lines per 100 people World Development Indicator online database
PCREDIT Credit to the private sector World Development Indicator online database
TARIF Tariff revenues as a percentage of Imports IMF International Financial Statistics Database
INVEST Domestic Investments as a% of GDP IMF International Financial Statistics Database
BANKS foreign Ratio of Foreign Banks to total Banks Self-calculation based on IMF Country yearly data tables
REM Remittances as% of GDP UNCTAD World Development Indicator online database
RESOURCE Ratio ofFuel and gas exports+ ores and metals exports to the total exports World Development Indicator online database
INSTU Quality of the Institutions Index (Average of 8 different indexes) Self calculations with EBRD data

Table 2: Variables, Definitions and Sources.

The special focus of this paper will be concentrated in understanding the role of good institutions and structural reforms in attracting FDI. The role of the privatization process is also research, since it is believed to be a very important determinant for the allocation and distribution of FDI in the region. In the empirical work different categories of determinants will be tested, starting with traditional factors, looking at the quality of institutional reforms, and ultimately dividing FDI in privatization related investment and Greenfield FDI.

Two different dependent variables will be used for two individual estimations: the total FDI per capita in the host country and the non-privatization related FDI per capita or Greenfield investment (detracting privatization related capital from total FDI) (The calculations on FDI per capita, non-privatization related has been made by the author). The reason is twofold: first to measure the robustness of my hypothesis on the importance of the privatization process for the FDI attraction in the region, and second to understand the differences in terms of host country determinants for the FDI attraction of privatization related and on-privatization related FDI.

The independent variables taken into consideration are: GDP per capita, growth of GDP, inflation, natural resources, human capital, trade openness, credit facilities, infrastructure, domestic investments, remittances, tariff and trade barriers, agglomeration effects, privatization. The quality of institutions will be measured using the EBRD reform indicators, assessing the transition progress in seven areas of economic governance.

The variables taken in consideration are the EBRD reform indicators. EBRD tries to assess transition progress by measuring are seven areas of which the following are assessed: large scale privatization, small scale privatization, enterprise restructuring, price liberalization, trade and foreign exchange system, competition policy, banking reform and interest rate liberalization, security markets and non-bank financial institutions. The measurement scale is 1 to 4+ where 1 represents little or no change from a centrally planned economy and 4+ represents the standards of an industrialized economy. Since it was not very practical to include eight different indicators in the regression, they were divided in three categories: the enterprise reform indicator (large scale privatization index; small scale privatization index; enterprise reform indicator); market and trade reform (price liberalization, index of foreign exchange and trade liberalization, trade and forces system) and financial sector reform (banking sector reform, non-financial sector reform).

Main Empirical Results and Interpretation

This section provides the empirical results on the determinants for the attraction of FDI based on the panel data (Table 1) for the South European countries during the period 1992-2010 using GMM estimation. The results are presented at the tables. The results are presented at the tables at the end of the section. The Sargan test and the Second order correlation test (SOC) are reported in the last rows of each table we are presenting.

Dependent Variable: Flows of FDI per capita (Total flows)

Estimations in Table 3 show the results of the research, with the first columns designed to include the effects of the traditional determinants for FDI inflows based in the Gravity Theory, and in the other columns, the other variables are added one by one to the estimations in order to capture the effect of each of those.

  1 2 3 4 5 6 7 8 9 10
PCGDP 0.293
(0.383)
0.546
(0.414)
0.614
(0.411)
0.614
(0.414)
1.064
(0.435)**
1.282
(0.436)***
1.269
(0.444)***
0.931
(0.489)**
0.869
(0.485)*
0.783
(0.496)*
GDPG 0.159
(0.117)
0.104
(0.121)
0.087
(0.120)
0.086
(0.123)
0.137
(0.121)
0.104
(0.119)
0.104
(0.120)
0.065
(0.121)
0.061
(0.120)
0.118
(0.136)
INFLAT -0.012
(0.072)
-0.018
(0.072)
-0.015
(0.071)
-0.016
(0.102)
0.046
(0.076)
0.088
(0.080)
0.087
(0.081)
0.072
(0.080)
0.076
(0.080)
0.032
(0.087)
OPEN   0.730
(0.448)*
1.086
(0.470)**
1.084
(0.475)**
1.726
(0.516)***
1.762
(0.582)***
1.765
(0.587)***
1.451
(0.611)**
1.237
(0.627)**
1.245
(0.637)**
HDI     -7.795
(3.364)**
-7.821
(3.427)**
-3.413
(3.689)
-1.251
(3.678)
-1.187
(3.719)
-0.195
(3.743)
0.067
(3.704)
2.146
(3.915)
TEL LINES       -0.0172
(0.341)
-0.243
(0.342)
-0.320
(0.344)
-0.295
(0.376
-0.418
(0.322)*
-0.419
(0.377)
-0.509
(0.395)
PCREDIT         -0.612
(0.214)***
-0.629
(0.214)***
-0.614
(0.228)***
-0.464
(0.244)*
-0.562
(0.254)*
-0.632
(0.263)**
BANK foreign           0.398
(0.188)**
0.383
(0.192)**
0.349
(0.190)*
0.274
(0.196)
0.325
(0.224)
RESOURC             -0.036
(0.183)
-0.099
(0.185)
-0.135
(0.184)
-0.170
(0.190)
TARIF               -0.123
(0.141)
-0.149
(0.141)
-0.200
(0.166)
DI                 0.923
(0.735)
1.155
(0.752)
REM                   0.009
(0.108)
INSTU t-1 0.500
(0.175)**
0.515
(0.174)***
0.493
(0.173)***
0.495
(0.178)***
0.516
(0.74)***
0.507
(0.170)***
0.499
(0.177)***
0.497
(0.174)***
0.366
(0.201)**
0.371
(0.204)**
FDI t-1 0.250
(0.152)*
0.253
(0.151)*
0.283
(0.150)
0.279
(0.169)*
0.215
(0.167)
0.220
(0.169)
0.221
(0.170)
0.172
(0.172)
0.204
(0.171)
0.031
(0.212)
Constant 0.214
(0.069)***
0.161
(0.075)**
0.203
(0.077)***
0.205
(0.085)**
0.206
(0.083)**
0.152
(0.084)*
0.155
(0.086)*
0.172
(0.086)**
0.152
(0.086)*
0.138
(0.088)
Obs 78 78 78 78 78 74 74 73 73 69
Sargan Test 0.997 0.997 0.999 0.999 0.999 1 1 1 1 1
SOC Test 0.396 0.419 0.501 0.503 0.339 0.187 0.186 0.154 0.165 0.144

Table 3: Determinants of FDI- Institutions and Agglomeration effect. Notes: 1. All regression is estimated by GMM Arrellano Bond (one-step) estimator. 2. All the variables are expressed in natural log (ln). Standard errors are reported in parentheses. (***), (**), and (*) indicate coefficient significant at 1, 5 and 10 % respectively. 3. Sargan Test (p-value, Prob >chi2): null hypothesis is no misspecification with the instrument sets, so that the instruments are not correlated with the residuals. 4. SOC test (p-value): Arrellano and Bond Test with null hypothesis of no secondorder correlation in differenced term errors, so that the errors in the first difference regression exhibit no second order serial correlation.

In this research the most important variable to be explored is the quality of the institutions. First the effect of the institutional reforms as a whole (INSTU) were be measured, represented by one variable; later in this paper the variable was decomposed to see the specific importance and effects. The lagged value of the institutional variables was used, since the effects of reforms are not shown immediately.

Across all specifications from Column (1) through column (10), the quality of institutional reforms is notably significant and positive. It ranges between 0.37 and 0.516 with a majority staying around 0.5 indicating a well-founded effect of the quality of the institutions for attraction of FDI inflows. The positive and strongly significant coefficients confirm our main hypothesis and both the Sargan and SOC tests show that instruments are valid throughout the regressions.

The other variables taken in consideration are explained as follows. To be consistent with the Theoretical Gravity Model, which consistently explains about 60% of aggregate FDI stocks or flows, regardless the region, GDP per capita representing the existing demand in the economy and Growth of GDP, representing the potential for future demand in the market, being an indicator of profitability for companies, are used.

The positive and significant coefficient of per capita GDP indicates that a host country with a higher degree of market demand is more attracted to foreign investors, providing empirical ground for the theory of market seeking (horizontal) FDI.

The growth of GDP has a positive coefficient in all specifications, but not significant, and this empirical result is also known in the literature. Growth is important, because higher rates of economic growth are usually associated with an increase of the profitability of the corporations, but also is a signal of economic stability and favorable investment climate.

Inflation is another determinant for consideration, which exerts negative influence on the profitability of FDI, since it increases the user cost of capital but also increases macroeconomic instability in the host country. In this research, the relationship is not stable (positive or negative) and not significant, the reason could lay on the fact that the panel taken in examination is made of transition countries from centrally planned to market economy and inflation oscillations were expected in the first years.

The variable trade openness, measures trade as a percentage of GDP and as expected this variable has a positive effect on FDI inflows, resulting significant and positive in all the estimations. This result is theoretically sound, identifying the magnitude of trade liberalization and especially important for efficiency and market seeking FDI.

Human capital, theoretically a very important factor for the attraction of FDI, especially the low cost of labor, is considered to be a strong comparative advantage for developing countries. However, in several studies, labor costs are not always found to be significant, due in part to the difficulties in accurately measuring productivity differentials, and also because low wages do not necessarily reflect low production costs since labor productivity may be low too . Instead, the skilled labor is becoming among the investor’s top considerations in the decisions of location. For this reason, the variable of Human Development Index (HDI) (HDI combines indicators of three dimensions of human development as: a long and healthy life measured by life expectancy at birth, knowledge measured by the adult literacy rate and combined gross enrollment ratios for primary secondary and tertiary schooling), and a decent standard of living measured by GDP per capita.

HDI is measured by UNDP as (1/3 life expectancy index +1/3 education index +1/3 GDP index). Education index itself is measured as (2/3 adult literacy rate +1/3 combined gross enrollment index)) is introduced in this research. Throughout the estimations, human capital does not appear to be stable and significant, evidentially being not so important determinant for foreign investors who are interested in SEE.

The variable pertaining to natural resources, an important characteristic especially for resource-driven FDI, taking as a proxy the percentage of fuels and natural gas and the percentage of ores and metal in total exports has a negative and significant coefficient. This shows that the abundance of natural resources is not a significant driver for FDI in the SEE. Statistical data reveal that in the recent years in SEE, the exports of natural resources have decreased, when at the same time the level of FDI inflows has continued to increase.

Tariffs, have a negative sign as expected but not significant. In this case it could be argued that lower taxes tend to attract more FDI, particularly vertical FDI. The literature suggests that trade barriers attract horizontal FDI and deter vertical FDI. Lower tariff rates help to reduce trade barriers, which mean a less restrictive trade environment. The insignificance of this variable could be explained with the captured of the effect from the variable trade openness.

The infrastructure, represented by the (fixed telephone lines per 100 people) has a negative but insignificant sign, opposing to what the theory suggests according to which a sound infrastructure is an advantage for the foreign investors. The reason for these results could lay on the variable used, since the expansion of high tech and more advanced communication technologies in all these countries such as mobile phones or internet.

The variable domestic investment, measured by domestic private investments as a percentage of GDP has a positive and significant sign, showing that the relationship between FDI and domestic investment is complementary.

Credit Facilities are represented in these estimations by two variables such as private credit measured by the credit of the domestic banking sector to the private business and foreign banks measured by the ratio of foreign banks to the total of the banking system in the host country. Private credit has a negative and significant sign as expected. Recalling the theory, this means that FDI are a major source of capital accumulation in the countries taken in examination. The variable foreign banks are positive and significant, as expected, positively influencing the FDI attraction, as a signal of stability and market security.

The variable, remittances, measured as a percentage of GDP shows a negative sign, but not significant. Usually in the literature, the variable emerges positively since remittances are expected to capture the market-seeking motivation of FDI as major source of income for the region’s population. At the same time this could be a source for domestic investments, making in this case the relationship not clear.

The agglomeration effect is investigated in these estimations and it is noticed that the lagged value of the stock of FDI is positive and significant in the first four columns, later it loses significance but not the sign, probably because of the correlation with the other variables taken in consideration. Since FDI is considered a long term capital that is irreversible in the short term, foreign investors are really very cautious about their investment location choice.

FDI and privatization process

The variable Privatization is a country-specific variable, measured by the ratio of privatization to the GDP of the country, and has not been used in previous studies.

Dependent Variable: Flows of FDI per capita (total flows)

Table 4 presents the results of the estimations when the variable that measures the importance of privatization in the attraction of FDI. Throughout all the estimations from column 1 through 10, the variable is positive and strongly significant, confirming the initial hypothesis.

  1 2 3 4 5 6 7 8 9 10
PCGDP 0.851
(0.553)
1.353
(0.656)**
1.733
(0.691)***
1.848
(0.704)***
2.098
(0.724)***
2.440
(0.749)***
2.380
(0.771)***
2.364
(0.793)***
2.292
(0.803)***
2.507
(0.778)***
GDPG -0.148
(0.169)
-0.239
(0.179)
-0.295
(0.180)
-0.416
(0.184)**
-0.360
(0.198)*
-0.406
(0.192)**
-0.409
(0.195)**
-0.408
(0.198)**
-0.386
(0.201)**
-0.379
(0.195)**
INFLAT 0.102
(0.097)
0.075
(0.097)
0.076
(0.096)
0.022
(0.102)
0.033
(0.103)
-0.020
(0.112)
-0.024
(0.114)
-0.025
(0.116)
-0.007
(0.119)
-0.144
(0.139)
OPEN   0.826
(0.607)*
1.151
(0.634)*
1.231
(0.645)**
1.760
(0.726)**
1.345
(0.821)*
1.333
(0.832)*
1.319
(0.851)
1.174
(0.885)
1.234
(0.855)
HDI     -6.677
(4.080)*
-6.915
(4.144)*
-5.113
(4.313)
-2.388
(4.207)
-2.037
(4.333)
-2.011
(4.391)
-1.887
(4.400)
-0.644
(4.265)
TEL LINES       -0.715
(0.385)*
-0.821
(0.385)**
-0.803
(0.387)**
-0.740
(0.417
-0.755
(0.444)*
-0.691
(0.457)
-1.021
(0.466)**
PCREDIT         -0.485
(0.301)*
-0.363
(0.302)
-0.308
(0.330)
-0.301
(0.340)
-0.319
(0.342)
-0.478
(0.342)
BANK foreign           0.476
(0.214)**
0.453
(0.223)**
0.450
(0.227)**
0.409
(0.237)*
0.395
(0.227)*
RESOURC             -0.080
(0.182)
-0.084
(0.188)
-0.108
(0.192)
-0.097
(0.186)
TARIF               -0.015
(0.139)
-0.030
(0.142)
0.061
(0.175)
DI                 0.472
(0.786)
0.429
(0.753)
REM                   -0.133
(0.121)
PRIV 0.113
(0.057)**
0.108
(0.057)***
0.105
(0.056)**
0.119
(0.057)**
0.126
(0.058)**
0.130
(0.057)**
0.136
(0.056)**
0.104
(0.056)**
0.131
(0.058)**
0.147
(0.057)***
INSTU t-1 1.120
(0.497)**
1.149
(0.488)***
1.188
(0.485)***
1.534
(0.526)***
1.670
(0.536)***
1.663
(0.526)***
1.581
(0.564)***
1.584
(0.572)***
1.359
(0.684)**
1.523
(0.657)**
PCFDIt-1 0.093
(0.168)
0.132
(0.167)
0.157
(0.166)
0.032
(0.182)
-0.053
(0.190)
0.062
(0.188)
0.070
(0.191)
0.066
(0.196)
0.100
(0.204)
0.031
(0.212)
Constant 0.159
(0.085)**
0.062
(0.109)
0.058
(0.108)
0.089
(0.111)
0.128
(0.114)
0.019
(0.115)
0.021
(0.116)
0.023
(0.119)
0.007
(0.121)**
0.042
(0.753)
Sargan Test 1 1 1 1 1 1 1 1 1 1
SOC Test 0.515 0.588 0.521 0.501 0.540 0.9953 0.986 0.998 0.979 0.949

Table 4: Determinants of FDI- Privatization. 1. All regression is estimated by GMM Arrellano Bond (one-step) estimator. 2. All the variables are expressed in natural log (ln). Standard errors are reported in parentheses. (***), (**), and (*) indicate coefficient significant at 1, 5 and 10 % respectively. 3. Sargan Test (p-value, Prob >chi2): null hypothesis is no misspecification with the instrument sets, so that the instruments are not correlated with the residuals. 4. SOC test (p-value): Arrellano and Bond Test with null hypothesis of no second-order correlation in differenced term errors, so that the errors in the first difference regression exhibit no second order serial correlation.

What happens with the other variables? The quality of institutions is positive and strongly significant throughout all the estimations. The effect ranges between 1.120 and 1.670 with a majority staying around 1.5 indicating the strong effect of the variable. The other variables remain the same. Both the Sargan and SOC tests show that the instruments are valid throughout the regressions.

Decomposition of the “quality of institutions”

Table 5 shows the variable institution decomposed in three different variables to understand the effect of each of them. First, enterprise restructuring reforms (ERI index measured as the average other three indexes: the quality of large scale privatization (LSP), small scale privatization (SSP) and enterprise restructuring (ER). The second variable is market and trade reforms (MTR measured as the average of three variables: price liberalization (PL), trade and forces system (TFR) and competition policy (CP). The third variable is financial system reform (FSR which is an average of two variables: banking reforms and interest rate liberalization (BRIRL) and securities market & non-bank financial institutions (SMNBFI). In this regression the lagged values of these indicators will be considered to understand the effects of reforms over time.

Variable Definition Source
LSP Large Scale Privatization Index EBRD Transitional Indicators data
SSP SmallScale Privatization Index EBRD Transitional Indicators data
ER Enterprise restructuring Index EBRD Transitional Indicators data
PL Price Liberalization Index EBRD Transitional Indicators data
TFS Trade and Forces System Index EBRD Transitional Indicators data
CP Competition Policy Index EBRD Transitional Indicators data
BRIRL Banking Reforms and Interest rate liberalization Index EBRD Transitional Indicators data
SMNBFI Securities markets and non-bank financial institutions Index EBRD Transitional Indicators data
ERI Enterprise Restructuring Reforms Self-calculations with EBRD data
MTR Market and Trade Reforms Self-calculations with EBRD data
FSR Financial System Reforms Self-calculations with EBRD data

Table 5: Decomposition of Institutions.

All the variables taken in consideration are positively correlated with the inflow of FDI, but only one of them, the variable that shows the reforms in the market and trade (MTR) is strongly significant (column 3 in Table 6. Trade liberalization and competition policy remain very important for foreign investors. In Table 6, column 1, enterprise restructuring is positive but not significantly correlated to the FDI inflow. Further, the findings show that the index related with the privatization of large scale enterprises is positively and significantly correlated with FDI inflows, reconfirming the initial hypothesis on the importance of the privatization process for FDI attraction.

  1 2 3 4 5 6
PCGDP 2.058
(0.341)***
2.024
(0.352)***
1.867
(0.342)***
2.028
(0.365)***
1.997
(0.382)***
2.042
(0.882)**
GDPG -0.300
(0.240)
-0.320
(0.247)
-0.286
(0.243)
-0.305
(0.202)
-0.326
(0.254)
-0.328
(0.205)
INFLAT -0.100
(0.063)
-0.097
(0.065)
-0.109
(0.080)
-0.078
(0.060)
-0.090
(0.054)*
-0.008
(0.188)
OPEN 0.701
(0.284)**
0.568
(0.309)*
0.519
(0.435)
0.680
(0.328)**
0.826
(0.376)**
1.389
(0.688)*
HDI -0.218
(4.267)
0.323
(3.935)
0.904
(3.920)
-0.273
(4.579)
-0.491
(4.660)
5.370
(5.461)
TEL LINES -0.561
(0.261)**
-0.843
(0.239)**
-0.706
(0.291)**
-0.651
(0.255)**
-0.700
(0.311)**
-0.810
(0.473)*
PCREDIT -0.312
(0.247)
-0.284
(0.187)
-0.263
(0.224)
-0.334
(0.221)
-0.334
(0.234)
-0.603
(0.385)
BANK foreign 0.231
(0.067)***
0.272
(0.091)***
0.135
(0.106)
0.302
(0.101)***
0.270
(0.049)***
0.882
(0.427)**
RESOURC -0.243
(0.102)**
-0.199
(0.109)*
-0.233
(0.114)
-0.205
(0.100)***
-0.171
(0.135)
0.057
(0.221)
TARIF 0.011
(0.196)
-0.022
(0.168)
-0.051
(0.167)
0.003
(0.197)
0.011
(0.181)
0.038
(0.185)
DI 1.384
(0.613)**
1.448
(0.539)***
1.465
(0.630)**
1.351
(0.611)**
1.279
(0.556)**
0.103
(0.940)
REM -0.113
(0.062)*
-0.120
(0.061)**
-0.080
(0.063)
-0.104
(0.077)
-0.100
(0.074)
-0.079
(0.137)
PRIV 0.138
(0.040)***
0.132
(0.036)***
0.118
(0.043)
0.140
(0.040)***
0.139
(0.036)***
0.145
(0.061)**
ERIT-1 0.341
(0.928)
         
LSPt-1   0.701
(0.370)**
       
MTRT-1     3.638
(0.994)***
     
FSRT-1       0.891
(0.822)
   
SMNBFIt-1         0.844
(0.461)*
 
INSTU T-1           0.525
(0.243)**
FDI t-1 0.156
(0.208)
0.059
(0.164)
0.146
(0.160)
0.090
(0.214)
0.081
(0.193)
0.020
(0.216)
Constant -0.005
(0.070)
0.009
(0.065)
0.122
(0.077)
0.007
(0.062)
0.025
(0.076)
0.052
(0.121)
Sargan Test 1 1 1 1 1 1
SOC Test 0.506 0.246 0.601 0.340 0.446 0.982

Table 6: Determinants of FDI: Decomposition of Institutions. 1. All regression is estimated by GMM Arrellano Bond (one-step) estimator. 2. All the variables are expressed in natural log (ln). Robust Standard errors are reported in parentheses. (***), (**), and (*) indicate coefficient significant at 1, 5 and 10 % respectively. 3. Sargan Test (p-value, Prob >chi2): null hypothesis is no misspecification with the instrument sets, so that the instruments are not correlated with the residuals. 4. SOC test (p-value): Arrellano and Bond Test with null hypothesis of no secondorder correlation in differenced term errors, so that the errors in the first difference regression exhibit no second order serial correlation.

The financial system reforms are also important, maybe the variable is not significant in this estimation because the effect might be captured form the other variables such as private credit and foreign banks. It is argued that foreign capital inflows can boost growth only when the recipients countries financial markets are developed enough to channel foreign capital efficiently to finance productive investment. To understand the effect of the variable used for the financial system reforms, I have decomposed it in other two variables, and I note that the variable (SMNBFI) security markets and non-financial institutions is positively and significantly correlated to the inflow of FDI, represented in Column (5) Table 6.

Determinants of FDI- Non Privatization related investments

To measure the robustness of my hypothesis on the importance of privatization process in SEE and also more importantly to understand the determinants for the attraction of Greenfield FDI (non-privatization related), inflows of non-privatization FDI per capita (detracting privatization related capital from total FDI) (The calculations on FDI per capita, non-privatization related has been made by the author), is used as the dependent variable in a new econometric analysis, where all the other explanatory variables remain the same (Table 7).

  1 2 3 4 5 6 7 8 9 10
PCGDP 0.989
(0.547)
1.299
(0.589)
1.441
(0.599)**
1.419
(0.608)
1.711
(0.689)**
1.957
(0.649)***
1.854
(0.649)***
1.709
(0.850)**
1.668
(0.871)**
1.512
(0.582)***
GDPG 0.196
(0.187)
0.139
(0.189)
0.136
(0.189)
0.120
(0.196)
0.134
(0.198)
0.074
(0.180)
0.067
(0.1061)
0.072
(0.263)
0.076
(0.254)
0.890
(0.272)
INFLAT 0.050
(0.090)
0.043
(0.098)
0.040
(0.098)
0.032
(0.102)
0.057
(0.107)
0.079
(0.105)
0.067
(0.181)
0.054
(0.064)
0.055
(0.068)
-0.008
(0.080)
OPEN   0.802
(0.600)*
1.083
(0.650)*
1.067
(0.657)*
1.408
(0.767)*
0.990
(0.802)
0.980
(0.808)
0.815
(0.609)
0.704
(0.463)
0.726
(0.307)***
HDI     -7.918
(6.750)
-8.081
(6.826)**
-6.386
(7.158)
0.499
(6.687)
0.518
(5.206)
0.623
(6.460)
0.839
(5.708)
2.136
(6.805)
TEL LINES       -0.153
(0.454)
-0.249
(0.471)
-0.434
(0.448)
-0.322
(0.470)
-0.460
(0.365)
-0.455
(0.361)
-0.506
(0.266)*
PCREDIT         -0.293
(0.332)
-0.200
(0.311)
-0.101
(0.333)*
-0.326
(0.207)*
-0.068
(0.215)
-0.292
(0.189)
BANK foreign           0.715
(0.252)**
0.674
(0.256)***
0.665
(0.195)***
0.635
(0.191)***
0.572
(0.146)***
RESOURC             -0.196
(0.243)
-0.246
(0.099)
-0.264
(0.112)**
-0.262
(0.076)***
TARIF               -0.194
(0.058)***
-0.208
(0.069)***
-0.414
(0.038)***
DI                 0.422
(1.290)
0.886
(0.886)
REM                   0.159
(0.074)
INSTU t-1 0.485
(0.239)**
0.499
(0.237)**
0.484
(0.236***
0.503
(0.245)***
0.517
(0.248)**
0.496
(0.227)**
0.451
(0.234)***
0.464
(0.074)***
0.403
(0.238)*
0.368
(0.220)*
FDI t-1 0.251
(0.200)*
0.245
(0.198)*
0.211
(0.197)
0.211
(0.207)
0.180
(0.214)
0.248
(0.202)
0.247
(0.203)
0.230
(0.204)
0.250
(0.253)
0.260
(0.222)*
Constant 0.143
(0.093)
0.082
(0.102)
0.106
(0.109)
0.124
(0.118)
0.122
(0.119)
0.017
(0.112)
0.036
(0.114)
0.055
(0.110)
0.047
(0.101)*
0.024
(0.074)
Sargan Test 0.999 0.999 0.999 0.999 1 1 1 1 1 1
SOC Test 0.162 0.172 0.135 0.131 0.159 0.187 0.186 0.154 0.162 0.20

Table 7: Determinants of FDI- Non Privatization Related. Notes: 1. All regression is estimated by GMM Arrellano Bond (one-step) estimator. 2. All the variables are expressed in natural log (ln). Robust Standard errors are reported in parentheses. (***), (**), and (*) indicate coefficient significant at 1, 5 and 10 % respectively. 3. Sargan Test (p-value, Prob >chi2): null hypothesis is no misspecification with the instrument sets, so that the instruments are not correlated with the residuals. 4. SOC test (p-value): Arrellano and Bond Test with null hypothesis of no secondorder correlation in differenced term errors, so that the errors in the first difference regression exhibit no second order serial correlation.

Dependent Variable: Flows of FDI per capita (FDI2- Non privatization related)

GDP per capita continues to have a very strong and significant positive correlation with the FDI inflow, ranging the coefficient from 0.9939 to 2.085. The relationship is stronger than for the total FDI considered in the first part of this research.

GDP growth is positive through all the estimations, but not significant, inflation is not stable (positive or negative) and not significant. Trade openness is strongly positively and sometimes significantly correlated with the attraction of FDI. Human capital, represented from HDI has a not stable sign, but more variables we add in the model, it tends to be positively but not significant. The coefficient for infrastructure is negative through all the estimations, but not significant.

Foreign banks is very strongly and significantly correlated throughout all regressions indicating that FDI that are not attracted from privatization, depend strongly on their home countries banks operating in the host country, as a signal for security, stability and interest.

The abundance of the resources is negatively related and in column (9) is significant, because the foreign investors are not resource-seeking in this case. Trade barriers, represented by the tariff revenues as a percentage of imports are negatively correlated with FDI in the column 10, even significantly confirming the theory and showing that FDI in these countries are both horizontal and vertical. Low tariffs are important for the attraction of Greenfield foreign investments. Domestic investment has a positive but not significant sign, the same for the remittances, which are a source for future domestic investment.

The lagged FDI coefficient, which shows the agglomeration effect, calculated by the stock of the three last years of non-privatization related FDI, is positive and significant through all estimations. We notice that the agglomeration effect is much stronger and significant in the case of inflows of FDI which are not related with the privatization process in these countries, than in the case where we consider the total inflows of FDI.

Finally, the coefficient of the variable of the quality of institutions is strongly positive and significantly correlated with the FDI. So, once again my hypothesis is verified, that FDI strongly depend on the quality of the institutions in the host country.

In Table 8, the complete estimations of total FDI and non-privatization related FDI, the difference in the importance of the determinants can be easily understood. As noticed, more factors are important on the attraction of FDI that are not related with the privatization of the state owned companies. The quality of institutions and the GDP per capita is positive and significant in both cases. Trade openness is more important for the non- privatization related FDI (FDI2), natural resources are significant and negatively correlated, this variable was not significant in the case of total FDI inflows. Foreign banks are strongly significantly and positively correlated with FDI attraction. Tariffs that in the case of total FDI inflows are not significant, in the case on non- privatization related FDI are significantly negatively correlated, confirming the theory. The agglomeration effect is stronger and significant in the case of Non-privatization FDI.

  FDI
Total FDI inflow
FDI
Total FDI inflow
FDI2
FDI non-privatization
PCGDP 0.783
(0.496)*
2.507
(0.778)***
1.512
(0.582)***
GDPG 0.118
(0.136)
-0.379
(0.195)**
0.890
(0.272)
INFLAT 0.032
(0.087)
-0.144
(0.139)
-0.008
(0.080)
OPEN 1.245
(0.637)**
1.234
(0.855)
0.726
(0.307)***
HDI 2.146
(3.915)
-0.644
(4.265)
2.136
(6.805)
TEL LINES -0.509
(0.395)
-1.021
(0.466)**
-0.506
(0.266)*
PCREDIT -0.632
(0.263)**
-0.478
(0.342)
-0.292
(0.189)
BANK foreign 0.325
(0.224)
0.395
(0.227)*
0.572
(0.146)***
RESOURC -0.170
(0.190)
-0.097
(0.186)
-0.262
(0.076)***
TARIF -0.200
(0.166)
-0.061
(0.175)
-0.414
(0.038)***
DI 1.155
(0.752)
0.429
(0.753)
0.886
(0.886)
REM 0.009
(0.108)
-0.133
(0.121)
0.159
(0.074)
PRIV   0.147
(0.057)***
 
INSTU t-1 0.371
(0.204)**
1.523
(0.657)**
0.368
(0.220)*
FDI t-1 0.031
(0.212)
0.031
(0.212)
0.260
(0.222)*
Constant 0.138
(0.088)
0.042
(0.753)
0.024
(0.074)
Sargan Test 1 1 1
SOC Test 0.144 0.949 0.20

Table 8: Determinants of Total FDI (FDI) and Non Privatization Related FDI (FDI2). 1. All regression is estimated by GMM Arrellano Bond (one-step) estimator. 2. All the variables are expressed in natural log (ln). Robust Standard errors are reported in parentheses. (***), (**), and (*) indicate coefficient significant at 1, 5 and 10 % respectively. 3. Sargan Test (p-value, Prob >chi2): null hypothesis is no misspecification with the instrument sets, so that the instruments are not correlated with the residuals.4. SOC test (p-value): Arrellano and Bond Test with null hypothesis of no second-order correlation in differenced term errors, so that the errors in the first difference regression exhibit no second order serial correlation.

Summary of the main results

Per Capita GDP is one of the most significant variables and determinants found to have a favorable effect on FDI attraction throughout all the estimations, with a stronger and more significant effect for non-privatization related FDI. These findings emphasize the necessity of a large market for the efficient utilization of resources and the exploitation of economies of scale. Market size helps attract FDI, and a larger market offers higher demand and absorptive capacity in an economy, providing an empirical confirmation for the theory of horizontal (market-seeking) FDI, conform to the Gravitational Theory of FDI.

GDP growth is positive in the majority of estimations, but only few times significant. High growth rates usually indicate credible and stable macroeconomic policies.

The Agglomeration effect, measured by the variable of the lagged value of stock of FDI (stock of previous years), is significant and positively correlated with the attraction of FDI, as a signal of security for foreign investors about the favorable business climate at the foreign location. This shows to be more important for non-privatization related FDI. It can also be associated with a number of positive externalities such as technology improvement, advanced labor skills and efficient production and distribution network. This also explains why the relatively advanced economies in the SEE which absorbed FDI earlier than the others, continue to attracts the larger share of FDI in the region.

Inflation throughout the estimations is dominantly negative but not significant. The reason could lay on the fact that the countries taken in examination are transition countries, which have really had very large oscillations of inflation in the first years of transition, but this has not stopped foreign investments.

Trade Openness is positive and highly significant in almost all the estimations, but the effects seem to be stronger on the attraction of FDI when the inflow of investments is non-privatization related. This identifies the magnitude of trade liberalization, being more important when the FDI are efficiency seeking than market seeking, so multinationals are attracted by countries with location advantages, aiming at the exports of their final products to other larger markets, but also for the imports of raw materials and other instruments. Our positive correlation between trade openness and FDI is theoretically sound.

Human capital, represented by the HDI is another important variable, which represents not only the level of education of the population (showing the quality of the workforce), but also indirectly the cost of labor. The sign of this variable is not stable and not significant for both dependent variables, meaning that the quality of the labor force and the education are not so significant for the foreign investors which consider the countries of SEE. Theoretically, the impact of qualified education should have been positive, but based in the statistical data, education in transition countries does not pay back the same ways it does in developed countries. Obviously, skilled labor force is crucial to the implementation of innovative production technologies, but in SEE the low level of new industrial technologies is confirmed.

Infrastructure and Communication Facilities, measured in terms of numbers of fixed telephone lines per 100 people, has a negative and significant in both cases when we consider the total flows of FDI and those not-related with privatization. These findings are not sound to the theory because a good infrastructure is an advantage for the foreign investors and it is believed to attract them. The reason may lie on the variable used.

Private Credit is the variable used to measure the level of development of the financial system on the host country, and the level of crediting of banking system to the private sector. The sign is negative as expected, but significant only in few cases. This means that FDI is a strong source of capital accumulation for the countries of SEE and this is sound to the theory.

Foreign banks, is a new explanatory variable, which represents the share of foreign banks in the banking system of the host country, and can act as a strong signal of stability for foreign investors. This variable is positive and highly significant in all the estimations, both in the case of total FDI and non-privatization related.

Natural Resources, is represented in the equation from the ratio of exports of oil and natural gas and ores and metals to the total exports. In all the estimations, the sign for natural resources is negative and in the case of non-privatization related FDI also significant. This means that FDI in those countries are not resource-seeking, but market or efficiency seeking. This result is dubious, since some of the countries of SEE are rich in natural resources.

Tariffs and Trade barriers, measured by the tariff revenues as a percentage of imports. When the total flow of FDI is considered, the variable is not stable and significant. On the contrary, for non-privatization FDI the sign of the coefficient is negative and significant, as expected and sound with the theory. Because FDI inflows rise with decreasing taxes and tariffs, the complementary relationship between trade and FDI may be due to vertical multinational activities. However, the attraction of FDI because of the tax incentives can be affected from the other factors such as corruption, non- transparency of tax policies, the immersion of new non-formal taxes such as “time tax" or "bribe tax", and could turn to be ignored.

Domestic Investment represents the host country investments and has a positive and sometimes significant sign, meaning that the relationship between FDI and domestic investment is complementary. This could mean that foreign investors feel more secure in investing in countries where they can find network of industry for their business linkages.

Remittances, could be used capture two effects in this study; the market seeking motivation of multinationals and the variable has a positive but not significant sign in the case of non-privatization FDI, because they are an easy source of income for recipients. On the other side, remittances are a source of capital accumulation, which can be used for domestic investments in the host countries.

Privatization is the explanatory variable we have used for understanding the importance of the privatization process. The results confirm the initial hypothesis, with the sign of the variable used for privatization (ratio of privatizations to the GDP), is always positive and highly significant throughout all the estimations.

Quality of Institutions is strongly positively correlated with the attraction of FDI throughout all the estimations of this study, both for the total flows of FDI and non-privatization FDI. The importance of new policies, reforms and implementation of those seems to be considered even more significant in the decision making process of foreign investors where to locate their investments in the South- European countries that the traditional well-known variables used in various works. When the variable is decomposed for understanding the reforms considered more important for foreign investors, the variable market trade reforms is positive and significant for FDI. In the enterprise reforms, the importance of the process of the large scale privatization is confirmed. Financial sector reforms are also positively correlated with FDI, and security markets and non-financial institutions reforms is also strongly significant for the attraction of FDI. The results of this research are sound with the Theory of Dunning (2006) that explains the importance of institution on the location decisions of foreign companies.

The GMM estimation procedure was used to correct the potential endogeneity problem. However, to ensure the appropriateness of GMM model, I need to verify that the instrument sets used in the estimations are properly specified and that there is no second-order serial correlation of first-differenced error terms. For this reason, two tests are performed in order to ensure the appropriateness of this model, the Sargan Test and the Arrellano & Bond second order correlation (SOC) Test. the p-value for both Sargan tests (null of no instruments misspecification) and SOC Test (null of no-correlation) indicate that neither of the null hypothesis can be rejected. The results of both tests confirm the properness of my interpretation (Table 9).

Variables FDI per capita Non- Privatization related FDI
GDP per capita Positive, significant Significant, positive
GDP growth Positive, not always significant Positive, not always significant
Agglomeration effect Positive, significant Positive, highly significant
Inflation Negative, non-significant Negative, non-significant
Trade openness Positive, significant Positive, highly significant
Human capital (enrollment rate%) Sign not stable, not significant Sign not stable, not significant
Human Development Index Sign not stable, not significant Sign not stable, not significant
Infrastructure (telephone lines) Negative, significant Negative, significant
Bank credit to the private sector Negative, not always significant Negative, not always significant
Tariff revenues as% of imports Not stable, not significant Negative, significant
Domestic investment as%GDP Positive, not always significant Positive, not always significant
Foreign Banks (ratio) Positive, significant Positive, highly significant
Remittances as% GDP Positive, not significant Positive, not significant
Natural resources Negative, not significant Negative, significant
Quality of Institutions Positive, significant Positive, highly significant

Table 8: representing the main results of the research.

Robustness check

To further check the robustness of this analysis and model, I rerun the regressions using different dependent variables. I use as a dependent variable the flow of FDI in million $.

The results in Table 10 confirm the findings and the initial hypotheses about the important role of the quality of the institutions and the process of privatization. Furthermore, two other dependent variables are included, the stock of FDI per capita in column (3) and the aggregate stock FDI stock in column (4). The same patterns are noted also in the new model we have used.

  1
PCFDI flow
2
FDI flow
2
PCFDI stock
4
FDI stock
PCGDP 2.507
(0.778)***
1.639
(0.522)***
1.512
(0.411)***
1.332
(0.390)***
GDPG -0.379
(0.195)**
-0.075
(0.099)
-0.009
(0.105)
0.113
(0.086)
INFLAT -0.144
(0.139)
-0.238
(0.075)***
-0.091
(0.078)
0.023
(0.104)
OPEN 1.234
(0.855)
0.759
(0.457)*
0.818
(0.461)*
0.569
(0.418)
HDI -0.644
(4.265)
2.591
(3.125)
-3.540
(2.392)
-2.446
(2.082)
TEL LINES -1.021
(0.466)**
-0.396
(0.304)
-0.783
(0.237)***
-0.666
(0.401)
PCREDIT -0.478
(0.342)
-0.165
(0.144)
-0.331
(0.193)*
-0.409
(0.401)
BANK foreign 0.395
(0.227)*
0.305
(0.105)***
0.220
(0.121)*
0.300
(0.126)**
RESOURC -0.097
(0.186)
-0.036
(0.128)
0.123
(0.104)
0.254
(0.210)
TARIF 0.061
(0.175)
0.147
(0.078)*
0.027
(0.100)
-0.131
(0.121)
DI 0.429
(0.753)
-0.067
(0.252)
-0.053
(0.454)
0.375
(0.569)
REM -0.133
(0.121)
-0.088
(0.062)
-0.104
(0.068)
0.102
(0.116)
PRIV 0.147
(0.057)***
0.224
(0.027)***
0.058
(0.032)*
0.043
(0.018)**
INSTU t-1 1.523
(0.657)**
0.138
(0.046)***
0.570
(0.124)***
0.167
(0.060)***
FDI t-1 0.031
(0.212)
6.30e-12
(9.23e-12)
0.163
(0.093)*
0.074
(0.136)
Constant 0.042
(0.753)
0.019
(0.048)
0.107
(0.061)*
0.122
(0.051)
Sargan Test 1 1 1 1
SOC Test 0.949 0.423 0.208 0.782

Table 9: Robustness Check of the Dependent Variable- Total FDI. 1. All regression is estimated by GMM Arrellano Bond (one-step) estimator. 2. All the variables are expressed in natural log (ln). Robust Standard errors are reported in parentheses. (***), (**), and (*) indicate coefficient significant at 1, 5 and 10 % respectively. 3. Sargan Test (p-value, Prob >chi2): null hypothesis is no misspecification with the instrument sets, so that the instruments are not correlated with the residuals. 4. SOC test (p-value): Arrellano and Bond Test with null hypothesis of no secondorder correlation in differenced term errors, so that the errors in the first difference regression exhibit no second order serial correlation.

In sum, the main findings on importance of the structural reforms and quality of institutions withstand the robustness tests. Differences are also noted in the determinants for the attraction of FDI when they are privatization or non-privatization related.

Conclusions

The inflows of foreign investment have been vital for economic growth and development in SEE. Still today a great demand exists for FDI, to be used in the restructuring of enterprises in order to create competitive market economies. The data indicates that FDI are distributed unevenly through the countries of this region and thus it is interesting to know whether certain country-specific characteristics can help explain the FDI attraction and distribution in this region.

The objective of this paper was to provide an empirical analysis for the explanation of the geographic distribution of FDI inflows across seven countries of SEE, region which was left out of the research recently, using a panel data (Table 1) between 1990-2010 and recent innovative econometric methodologies, such as Generalized Method of Moments (GMM).

  1
PCFDI2 flow
Non-privatization
2
FDI2 flow
Non-privatization
PCGDP 1.512
(0.582)***
7.457
(0.148)***
GDPG 0.890
(0.272)
0.008
(0.0045)*
INFLAT -0.008
(0.080)
-0.003
(0.034)
OPEN 0.726
(0.307)***
0.080
(0.017)***
HDI 2.136
(6.805)
0.142
(0.119)
TEL LINES -0.506
(0.266)*
-0.059
(0.012)***
PCREDIT -0.292
(0.189)
0.002
(0.008)
BANK foreign 0.572
(0.146)***
0.009
(0.066)
RESOURC -0.262
(0.076)***
-0.023
(0.005)***
TARIF -0.414
(0.038)***
-0.316
(0.005)***
DI 0.886
(0.886)
0.045
(0.021)**
REM 0.159
(0.074)
0.003
(0.035)
INSTU t-1 0.368
(0.220)*
0.336
(0.160)**
FDI t-1 0.260
(0.222)*
0.150
(0.494)***
Constant 0.024
(0.074)
0.030
(0.029)
Sargan Test 1 1
SOC Test 0.20 0.23

Table 10: Robustness Check of the Dependent Variable- Non privatization related FDI. Notes: 1. All regression is estimated by GMM Arrellano Bond (one-step) estimator. 2. All the variables are expressed in natural log (ln). Robust Standard errors are reported in parentheses. (***), (**), and (*) indicate coefficient significant at 1, 5 and 10 % respectively. 3. Sargan Test (p-value, Prob >chi2): null hypothesis is no misspecification with the instrument sets, so that the instruments are not correlated with the residuals. 4. SOC test (p-value): Arrellano and Bond Test with null hypothesis of no second-order correlation in differenced term errors, so that the errors in the first difference regression exhibit no second order serial correlation.The results confirm the importance of the quality of the institutional reforms and the role of privatizations in the attraction of FDI in SEE countries. The same robustness check is considered for the second dependent variable, the non-privatization related FDI (Table 11), using the aggregate flow of non-privatization FDI as dependent variable.

In our hypothesis the potential explanatory determinants include both traditional gravity factors, such as market size, GDP growth, human capital, natural resources, tariffs, but also indicators showing the quality of reforms and institution building in these countries. In this model FDI was differentiated into the total inflow of FDI and non-privatization related FDI. Privatization attractiveness and the agglomeration effect were also included in the model.

The findings confirm a strong and significant relationship between the quality of the reforms, the performance of the institutions in the host countries and the attraction and distribution of FDI in this region. This relationship is true for both total flows of FDI and non-privatization related FDI, with stronger effects for the latter. The importance of new policies, reforms and implementation of those, seems to be considered even more significant that the traditional well-known variables used in various works in the decision making process of foreign investors where to locate their investments in SEE. The most important reforms are market trade reforms, the large scale privatization process and security markets and non-financial institutions reforms, confirming the Theory of Dunning (2006) on the importance of institutions on the location decisions of multinational companies. Consistent political system, political stability, strong enforcement laws and healthy monetary and fiscal policy and strong anticorruption policies contribute to the growth of FDI in these countries. The empirical results also confirm our hypothesis of the important role of privatizations of state owned companies, especially in the first years of transition, meaning that FDI in the countries taken in consideration are strongly attracted from the privatization process.

The existence of a strong agglomeration effect, especially for non-privatization related FDI, helps explaining the herding of many investors in SEE during the last two decades. This model also predicts the importance of trade openness, which is stronger for non-privatization related FDI. The quality of the labor force and the education are not so significant for the foreign investors which consider the countries of SEE, showing that education in transition countries does not pay back the same ways it does in developed countries. This shows also the low innovative level production and management technologies established in this region. Foreign companies are not only motivated by relatively cheap and not skilled labor but also discriminate between the countries of SEE with less or more skilled labor. This model provides a strong support to the view that FDI inflow to SEE economies are predominantly horizontal, market or efficiency seeking but not resource-seeking and they are a strong source of capital accumulation for these countries. Banking sector and particularly the presence of foreign banks can act as a very strong signal of stability for foreign investors, playing also the role of communicative effect, acting as promotion and marketing through the reputation, both in the case of total FDI and non-privatization related. Tariffs and taxes are particularly significant for non-privatization FDI.

The analysis suggests that the larger inflows of FDI to some of the countries of SEE rather than the others can be explained by better opportunities for market seeking investments due to stronger host country demand and a faster transition process. Countries that have implemented transition policies successfully have had relatively speedy membership into European Union, which has further accelerated FDI that has generated more growth and development. SEE countries should continue to focus on policies and reforms that promote institutional development and develop a friendly environment for the attraction and targeting of “qualitative” foreign investments, in order to start a new cycle of development. Consistent political stability, efficient law enforcement, healthy fiscal and monetary policies and strong anti-corruption reforms could contribute not only for the attraction of “qualitative” foreign investments but also for boosting longer and sustainable positive effects for growth and development. New and effective legislation should be able to direct the inflows of FDI to the sectors that augment domestic investment and lead to sustainable economic growth. To the governments of all SEE countries, these findings may serve for the policy stipulation aimed at improving the overall investment environment, and these changes may work as a strong signal of a more favorable host destination for foreign investors. Hover ever, more research effort need to be devoted to the topic, as counties of Southeast Europe are getting more integrated in the global economy.

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