what affects the relationship between foreign direct investments and gdp growth evidence from eec

8/7/2019 What affects the relationship between Foreign Direct Investments and GDP growth Evidence from EEC

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Endrit Avdullari What affects the relationship between Foreign Direct Investments and GDP growth: Evidence from Eastern Europe

What affects the relationship between Foreign DirectInvestments and GDP growth: Evidence from Eastern Europe

1. Introduction, organisational context and research objectives

The relationship between the economic growth and the level of FDI has always been a

matter of discussion between economists. The theoretical framework explaining the

relationship between FDI and GDP growth derives from two major models. The neoclassical

growth model states that FDI cause an increase in investments and their efficiency leadingto increases in growth. In the long-run, according to the endogenous growth model, FDI

promote growth, which is considered a function of technological progress, originating from

diffusion and spillover effects (Nair-Reichert and Weinhold 2001; Jones 2002). Nevertheless,

a number of studies have proved that FDI are not always correlated with GDP growth and

recent research has concentrated more in identifying the reasons behind these results

stressing the importance of economic and political economy of the countries under

consideration.

This study determines which variables are most often correlated to FDI in our sample of 11

EEC such establishing which ones are most influential and as a result deserve particular

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Macroeconomic data series were collected from highly reputable international bodies. the

research is mostly based on the WDI dataset from the WB but data from UNESCO, The

World Factbook from CIA, The Wall Street Journal and The Heritage Foundation publication

on freedom, The UN, The EC for Economic and Financial Affairs as well as other national

statistical institutes are also used for an eleven-year time frame from 1998 to 2008.

After testing the relationship between GDP growth and FDI for each country individually, the

results were confronted to other possible factors that have been suggested by the literatureto be influencing FDI. A regression model collecting all influential determinants was then

created to prioritize the impact of each determinant on FDI and check whether there was any

general pattern that explained the difference between the countries where the correlation

between FDI and GDP growth was significant and the countries where it was not.

This study tested the importance and prioritized previously researched settings affecting the

efficiency of FDI in regard to economic growth. Prior research has often concentrated on

testing or finding one or few individual influencing variables and aimed to either build a

model for the whole region or alternatively for a single country. This research does not

concentrate on one or few but includes a wide range of determinants. The countries are

di id d i Th fi i l d h i h FDI i l d i h


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studies that covered all the allegedly influential factors together, especially not in so many

EEC countries altogether. In addition, to the author`s best knowledge, there is no other study

offering both a national and regional insight in this context.

The second part of this paper will cover the literature available up to date which includes

findings of important studies and the evidence that they presented to support their theses.

Starting by offering a general background on how the theory has evolved, this paper

continues by presenting some of the most influential suggestions, results and methodologyfrom previously published works. Different views and the arguments on where they are

based are included in this section. In part three, details on how this research was carried out

and a closer view to the variables and the sample taken into investigations are presented.

Reasons on why this particular approach, data set, data sources and research design were

used are also explained in this section together with a closer look to the tests used. Main

findings are included in part four together with a selection of illustrating graphs and tables to

support the analysis. In part five main conclusions and recommendations are drawn adding

a note regarding this study`s limitations and other implications for future research. Other

relevant information such as the bibliography, full statistical results, the data and the

variables is attached at the end of this paper.


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2. The Literature Review

Multinational enterprises expanding their influence in foreign countries in order to havecontrol on local assets and production activities has been a growing trend of the world

economy (Mallampally and Sauvant 1999). Starting from the late 1980`s a rapid increase of

FDI was experienced almost anywhere in the world. This new striking economic feature

revitalised the debate on the net resulting effect of the costs and benefits that FDI inflows

produce on the economy of host country and/or vice-versa (Hansen and Rand 2006).

2.1 The relationship between FDI and GDP growth

Developing countries, rightly, do not appear to show as much interest in foreign bank lending

and portfolio investment as they do in FDI. The reason behind this preference is related to

the fact that countries are not very motivated by short-term profits (e.g. portfolio investments)

which are volatile and do not guarantee a steady income for the economy (Mallampally andSauvant 1999). Hence, since the 1990`s, FDI has become the leading source of external

finance. Long-term prospects for making profits in production activities which are not as

flexible and hence reflect stability motivates this choice. Many countries are inclined to

liberalise their policies toward MNC in order to benefit from the positive influence that the

investments are thought to have on a countrys trade and industry. Policy makers have been


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Casson (1990) sees the theory of FDI as a mixture of other existing theories. He argues that

it intersects with the theory of international capital markets which illustrates the risk-sharing

and financing agreements, the theory of the firm which specifies the location, management

and utilization of the output and the trade theory which indicates the location of production

and the destination of sales.

MNC are believed to bring new advanced technologies in developing countries such playing

an important role in their growth (Borensztein, Gregorio and Lee 1998). Since in developingcountries there exists a low efficiency of the capital, FDI inflows can help these economies to

increase their capital-worker ratio by introducing more productive technologies (Bosworth,

Collins and Reinhart 1999). The inflow of FDI was seen as crucial in resolving the issues of

EEC related to their scarcity of capital and low productivity (Sergi 2004). Investors,

especially risk seeking ones, are interested to expand in developing countries since

investments in capital-scarce countries are expected to yield a higher return (Asiedu 2002).

There is a general belief that FDI can give valuable help in transforming former communist

countries by adding significantly to the low amount of domestic savings directed to

investments. In contradiction to the Solow growth model 1 where technology is assumed

exogenous, the growth literature supports the hypothesis that economic growth depends on

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The first group argues that FDI assist in the transfer of technological and business skills and

understanding to the host country. In contrast, the second group believes that FDI will hurt

resource allocation and slow growth (Carkovic and Levine 2005). The effects of FDI on the

level, composition and growth of the output of the host country also depend to a large extent

on the macroeconomic policy in operation in that country. In general, it seems that FDI can

exert an impact on the output of the host country if it is possible to absorb surplus resources

and/or improve efficiency through alternative allocations (Moosa 2002).

In order to benefit from technological transfer, Xu (2000) has found that a minimum threshold

level of human capital needs to be reached. As Mansfield and Romeo (1980), have found in

their rather dated study, multinational companies tend to protect their technologies as long

as they can from domestic firms to strengthen their position in the local market. Neither they

nor Germidis (1977) found any significant evidence of technology transfer to local

competitors. However, recent evidence suggests that the technological environment has

changed completely in the last three decades. In the long term, technology diffusion might

occur from labour turnover by learning-by-observing or learning-by-doing as domestic

employees move from foreign to domestic firms (Alfaro et al. 2004). Alternatively, domestic

firms might benefit just from observing the products of these foreign firms (Blomstrm and

K kk 1997) B l b S li d S f d (1996) h hi ill


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income but instead be just a substitution for imports such explaining the different findings

from researchers in this subject.

On one hand, one could argue that given appropriate policies and a basic level of

development, FDI can play a key role in the process of creating a better economic

environment. On the other hand potential drawbacks do exist, including a deterioration of the

balance of payments, as profits are repatriated having negative impacts on competition in

national markets. As in many other fields of development economics, there is not universalagreement about the positive association between FDI inflows and economic growth

(Hansen and Rand 2006). Different developing countries have been able to take advantage

of the presence of FDI in their economies but on average they have not been as successful

as middle or high-income countries which have shown a noticeably higher rate of success in

this process (Singh and Jun 1995). From the macro point of view FDI may lead to a rise in

output and income for the economy. This is particularly true for developing countries when

there is usually a high rate of unemployment and the capital is scarce. Being considered as

foreign borrowing but which does not have to be paid b ack, FDI will have a positive impact

on the balance of payments. The net effect on trade however, will depend on whether FDI

substitutes the current imports or whether its impact falls on exports. From the micro point of

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through intense competition, concentrating only in the local market and not aiming to export,

as well as adapting and promoting inappropriate social and cultural norms which damage the

host countrys traditions and inheritance (Ram and Zhang 2002).

2.2 The Determinants

Blomstrm, Lipsey and Zejan (1994) state that when a country becomes relatively rich, FDI

have a positive effect on economic growth. They measure the wealth in terms of GDP per

capita . Lower income countries find it difficult to attract FDI mainly because they lackinfrastructure facilities especially in crucial areas such as transport, communications and

information technologies (Obwona 2001). The view is also supported by Binici, Hutchison

and Schindler (2010) who suggest that income levels affect capital flows and cross-border

asset holdings. Agreed on principle, the matter is seen from a different perspective by Lane

and Milesi-Ferretti (2003). They associate a country with higher income per capita with lower

risk and conclude that since international investments are more risky than domestic ones, a

higher level of GDP per capita leads to an increase in international assets trade. In addition

to the importance of GDP per capita, a research of UNCTAD (1999) has found that the GDP

growth-FDI relationship depends also on the levels of other variables including political and

financial environment, education and trade of the country under consideration. Schneider

d F (1985) h i li i l i h i f d i


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similar results adding to the discussion the importance of market liberalisation. Their findings

contradict with those of Blomstrm, Lipsey and Zejan (1994) who did not find evidence that

human capital is critical and with the findings of Bruno, Crin and Falzoni (2004) who stated

that FDI tend to have a more positive effect on countries that have abundant unskilled

labour. Balasubramanayam, Salisu and Sapsford (1996) point out that developing countries

have relatively less trained human capital and as a result technological progress does not

account for a big part of growth. Because of a noticeable gap in levels of human capital

between developed and developing countries, it becomes very challenging for developingcountries to undertake investments in R&D since they would rarely be able to generate new

significantly useful knowledge having a less trained human capital.

Beck, Levine and Loayza, (2000) support the idea that a developed financial system

stimulates growth. Martin and Rey (2003) have found that financial integration decreases the

cost of capital. Borensztein, Gregorio and Lee (1998) go further stating that it is a two way

relationship: capital generates FDI and FDI generate per se more capital. A very important

point regarding the financial environment and the adoption of best technological practices is

made by Alfaro et al. (2004) who state that if access to credit is restricted, FDI cannot be

fully efficient since local entrepreneurs would not be able to acquire these new technologies

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general agreement seems to be that tax rate affects the volume of FDI only when market

and political factors are seldom equal. Still, the tax rate effect seems to vary according to the

firm and the local characteristics of the country (Morisset and Pirnia 2000). For example,

export-oriented companies (Gusinger 1986) and companies financed by retained earnings

are more sensitive to tax advantages (Feldstein 1994). Start-up companies highly appreciate

incentives that help them avoid or postpone initial expenses (Rolfe et al. 1993). Small

countries tend to have a small corporate tax rate in order to attract big companies (Morisset

and Pirnia 2000) etc. Nevertheless, policy makers still feel that significant differences amongneighbours or other countries with which they believe to be competing with in attracting FDI

are to be avoided whenever possible. The EU is an example of how countries can increase

pressure on different members to increase rates of certain taxes which are relatively lower

as for example income tax and taxes on the self-employed in the case of Greece (IMF 2009)

or the corporate tax in the case of Ireland (Barber 2010). Taxation policy is decided

unilaterally by national governments under EU law but the union pressured these two

countries in return of a bail-out package when they were experiencing financial difficulties.

Other cases include pressure on the UK crown dependencies of Isle of Man, Guernsey and

Jersey which have been asked to increase their corporate tax rate since some members of

the EU have expressed concerns that the policy does not coincide with the spirit of the

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given the right incentives are provided, can compete to attract FDI (Blomstrm and Kokko

2003).

Ancharaz (2002) offers a helpful review of the most important studies that have tested the

importance of government spending . He has found that foreign investors pay particular

attention to the amount that the government spends in the host country and believe that the

size of the government reflects the attitude of the country towards foreign investments. The

general consensus is that a large amount of spending by the government is perceived asharmful to markets and free competition when it goes beyond a certain optimal level. This

conclusion is often related to the fact that an investor might have to go through a long line of

bureaucracy leading to extra costs and potential inefficiencies including corruption. On this

line of logic, large government spending is considered as negative to FDI. The alternative

view is that a large amount of expenditure by the government may also be considered as a

sign of development and investment to productive sectors such as infrastructure or transport

to provide businesses a positive environment to invest efficiently.

Levy-Yeyati, Panizza and Stein (2007) found an inverse relationship between interest rate

cycles and FDI inflows suggesting that FDI inflows are expected to increase during

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Barros and Cabral (2000) have found that a country which has a high unemployment rate is

expected to be more attractive to investors. Billington (1999) found the same results. He

suggests that unemployment encourages FDI since the situation implies to the investor that

the required labour will be available in the host country. However, Jones and Wren (2010)

stress that while unemployment attracts FDI, a very high rate will make the region

unattractive suggesting to entrepreneurs that it is depressed.

The study will take all of these factors into consideration and will investigate their impact inorder to establish whether there is any relationship between these determinants and FDI for

the countries of the selected sample. The study will also prioritize these factors according to

their impact and will check for patterns between the countries where FDI is positively

associated to GDP growth and the presence or not of significant correlations between

certain variables with FDI in these countries.


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3. Research Design and Methods

In their efforts to explain why FDI are associated with economic growth in some countriesand not in others, previous studies have based their research on one or few explanatory

variables and have tested their statistical significance (Levine and Renelt 1992). This paper

takes an alternative approach offering a wide view of the available knowledge on the subject

so far and then tests all the variables collected in each country separately. Macroeconomic

data series were collected from reputable international bodies such as the WB, The WSJ

and The Heritage Foundation, CIA The World Factbook, The UN, EC for Economic and

Financial Affairs and other national statistical institutes for an eleven-year time frame from

1999 to 2008.

The study tested the influence of the following variables on FDI: GDP growth, human capital,

market size, economic freedom, political freedom, openness to trade, tax rate, GDP percapita, R&D, government spending, real interest rate, accessibility to credit, infrastructure,

inflation and unemployment. Details on the variables can be found in Appendix I.

To test the FDI-GDP relationship the net FDI inflows and the GDP growth rate variables

have been used respectively as suggested by Borensztein, Gregorio and Lee (1998). All the

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taking into consideration the quality difference between educational institutions. Barro and

Lee (1994) found this correlation to be positive. Based on the same study, to measure the

government spending , the ratio of government consumption (expenditure) to the GDP has

been used. Based on their findings we will be expecting FDI to be positively related to

human capital and to have a negative relationship with the government spending. However,

other studies (see for example Ancharaz 2002) have expressed alternative explanations on

how the correlation between FDI inflows and government size can also be positive when

investors see it as an effort to provide a more positive economic environment so we canexpect both results.

The WGI data on political freedom was collected by different institutes on behalf of and

published by the WB. It captures perceptions from enterprises, citizens and expert survey

respondents as reported by a number of survey institutes, think tanks, non-governmental

organizations and international organizations on the possibility that the government is

overthrown by any means other than those included in the constitution of the country. These

include terrorism and politically-motivated violence. The data range within the (-2.5: 2.5)

interval where higher values represent a more stable environment (Kaufmann, Kraay and

Mastruzzi 2010; The World Bank Group 2010). If significant, the correlation with FDI is


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We will be expecting similar results for all the countries in our sample where the relationship

is significant.

Although the data sets of both political and economic freedom are widely used, there is still

much discussion about their validity. Besides the concerns listed in the paragraph above in

regards to measuring political freedom, based on the work of Thomas (2010), the data set of

economic freedom might in addition suffer also from high correlation between the used

indicators for calculation. As Thomas (2010) states, social scientists have continuouslydeveloped quantitative methods to measure abstract concepts and they have concerned

with the issue of validity. He concludes that because the WGI are an important step ahead

in attempting to quantify the data, there relies the danger of prematurely accepting them as

valid. The data sets on political and on economic freedom will be used in this study being, to

the author`s knowledge, the most accurate yet, but since there is still discussion about their

validity, particular caution will be taken before reaching any conclusions in this subject.

Market size is measured simply by GDP level and a large number of studies consider this

variable as crucial in fostering FDI (see for example Asiedu 2002 or Bengoa and Sanchez-

Robles 2003). The relationship between the two variables is expected to be positive.

However, in our sample, all countries are part of either CEFTA or the EU so according to


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might highly influence the relationship. Inflation represents a general increase in the

consumer prices index. Most studies have found that the relationship between inflation and

FDI is negative but according to Campos and Kinoshita (2003) it can also be positive when

associated with growth during a transition period so we might expect to find a positive

relationship for countries that have had a long transition. Unemployment is the percentage of

the labour force that is available and looking for work to the total labour force. Its relationship

with FDI is also ambiguous. On most cases, unemployment has a positive relationship with

FDI because it signals investors that labour is available (Barros and Cabral 2000) but if acertain high level of unemployment is reached, it can reflect a poor economic environment

and as a result discourage investors to enter the country Jones and Wren (2010).

3.1 The Regression Model

The multiple regression model will have the following linear form:(1) = b o +b 1x 1+ b 2 x 2 +b 3 x 3 +b 4 x 4 +b 5 x 5 -b 6 x 6 +b 7 x 7 +b 8 x 8 b 9 x 9 -b 10 x 10 +b 11 x 11 +b 12 x 12 b 13 x 13 +b 14 x 14 b 15 x 15

Where: = FDI, b 0 = constant, b 1-b 15 the coefficients of the variables.

x1= Market size; x2= Human capital; x3=Economic freedom; x4=Political freedom

x5=Openness to trade; x6= Tax rate; x7= GDP per capita; x8=R&D; x9=Government


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frame are still very limited especially for Albania, B&H and Macedonia. Studies that have

covered this period had to exclude at least these three countries. Nevertheless, the

adopted11-year period with a sample of 11 countries still allows reaching significant

conclusions. Other studies 1 have managed to reach robust results working with even shorter

periods and smaller samples.

The reason why EEC offer a useful study context to test FDI determinants relies on the fact

that although these countries have a common starting point, i.e. the time when the current

economic systems was adopted, they have significant differences in economic and

institutional development as well as in size (World Bank 2002). Coming from a communist

background, all the countries under investigation have transited to market economy in the

early 90`s facing similar challenges most of which unknown before. Yet, some were more

successful than others such offering researches the opportunity to compare their individual

features that lead to these differences and take important lessons from them for the future.

3.3 Exclusions and Missing Data

The study attempted to include all the countries of the region in the sample. Unfortunately,

this was not possible. Montenegro and Kosovo declared their independence respectively in

2006 and 2008 from Serbia. Having all been part of State Union of Serbia and Montenegro


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3.4 Multicollinearity

Multicollinearity amongst two predicting variables is to be expected especially when usingsmall samples and time periods. For example, economic freedom is by definition correlated

with openness to trade, political freedom and government spending. Market size is naturally

correlated with government spending, R&D, GDP per capita and openness to trade since

they all are calculated as a share of GDP. Accessibility to capital is highly affected by the

real interest rate which is in turn affected by inflation etc. We will check whether

multicollinearity has caused any problems in our correlat ion tests by spotting any odd

results in our coefficients in section 4.2.

3.5 Tests

The data will be processed with PASW (former SPSS) Statistics 17 TM . The association of

the determinants with FDI will be tested statistically in order to provide a framework that can

explain which factors are more strongly related to FDI in each country. Initial hypotheses

regarding the expected sign of the coefficients of each significant factor have been

constructed based on the literature review. The results will be particularly important for

variables that can be both positively and negatively related to FDI (e.g government

spending) according to their economic and political settings such providing additional


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according to the correlation results between the GDP growth and the level of FDI aiming to

observe potential differences between the models of the countries where the relationship

was important and those where it was not. This enabled us to determine a pattern of

features where countries that have a strong FDI - GDP growth relationship differentiate from

the other countries under investigation. Countries where the FDI- GDP growth relationship

was not important were further divided into subgroups according to the reasons identified by

this study that eventually lead to this relationship being insignificant. As in most cross-

country studies on growth according to Kormendi and Meguire (1985), the explanatoryvariables were entered independently and linearly.

Among other objectives, this study also aimed to calculate the correlation of each

determinant with FDI and build for each country a regression model that considered FDI the

variable which was dependent from one or more determinants collected from the literature

with at least a 90% confidence interval. We did not choose a lower significance level due to

the limited number of variables and the short time period. However, significance has been

noted throughout the results if practitioners are interested in a higher level (i.e. 0.05 or 0.01).

The default method for this analysis is Enter but this would mean that we would f orce all

correlated variables into the equation and most possibly decrease the variance explained by


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4. Findings, analysis and evaluation

The existing literature offers a number of variables that are believed to affect FDI. In ourstudy we selected 15 variables which were most often encountered in previous and most

influential studies. The variables taken into consideration for all the countries were GDP

growth, human capital, market size, economic freedom, political freedom, openness to trade,

tax rate, GDP per capita, R&D, government spending, real interest rate, accessibility to

credit, infrastructure, inflation and unemployment.

4.1 Correlation and Regression results

Albania

The correlation was significant between FDI and the following variables : market size,

economic freedom, openness to trade, GDP per capita, accessibility to credit, infrastructure

and unemployment.

The model for Albania

FDI = 7249333.24 + 2.131E7 * accessibility to credit

R2 =0.903 1


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The model for Bulgaria

FDI= -5.538 + 0.192 * openness to trade

R2 = 0.797

Croatia

The correlation was significant between FDI and the following variables : human capital,

market size, GDP per capita, real interest rate, accessibility to credit, unemployment political

freedom # and economic freedom #.

The model for Croatia

FDI = - 7.047E9 + 0.365 * market size

R2= 0.682

Czech Republic

The correlation was significant only between FDI and R&D #.

The model for Czech Republic

FDI= -6.145E9 + 1.011E10 * R&D2


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Poland

The correlation was significant between FDI and the following variables: GDP growth,

market size, openness to trade, GDP per capita, accessibility to credit and unemployment #.

The model for Poland

FDI= 8.275E8 + 1.839E9 * GDP growth

R2= 0.592

Romania

The correlation was strong between FDI and the following variables : GDP growth, human

capital, market size, economic freedom, GDP per capita, government spending, accessibility

to credit, infrastructure and inflation.

The model for RomaniaFDI= -1.887E10 + 0.527 * market size

R2=0.923

Slovak Republic

The correlation was significant between FDI and the following variables : openness to trade,


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were included in the analysis. As mentioned previously (section 3.4) this may have resulted

from the fact that many determinants are correlated to each other and they have been

excluded from the model due to multicollinearity. Another potential reason is related to our

relatively small sample. Adding extra variables to the model would have increased the

probability that the relationship is signifiicant due to chance. For example, in the case of

B&H, adding two additional variables would have increased the coefficient of determination,

but two of the variables had to be excluded since the model would have become insignificant

had we not done so (See Appendix II). A summary of the correlation results that show whichvariables are correlated to FDI for each country can be seen in Table 1a and Table 1b.

ALB B&H BUL CRO CZE HUN MAC POL ROM SVK SLO+/-

GDP growth .649* .77** .636* +Human capital .822** .723 * -.764* .949 ** +-Market size .898 ** .816 ** .762** .826 ** .653 * .721* .961 ** +Economicfreedom

.902 ** .712 * .803** .59 # -.697* .847 ** .623 # +-

PoliticalFreedom

.815 ** .637 # .787 * .687*+

Openness totrade

.902 ** .798** .893** .610 * .670* .640 * +

Tax rate -.916 # -GDP per capita .893 ** .823 ** .769** .817 ** .655 * .714* .955 ** +R&D .966 * .533 # -.576 # -.697 * +-Governmentspending

.946 ** -.579 # +-

Real interest -.652 # -.705 * -.603 #


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Graph 2. Political stability and absence of violence index 1998-2008. Source: The World Bank Group.

4.2.1 Wrong signs

Since according to the theory, the relationship between political freedom and FDI is positive,

we were expecting the coefficient of political freedom to be positive for all countries (if

significant) except for Albania, B&H and Macedonia for which the data is negative . Hence, a


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adds evidence to the concerns presented by different authors (See Thomas 2010) on the

validity of the used methodology in calculating this indicator.

From the statistical point of view, the problem might be noticed when dealing with a relatively

small sample (in our case 11 years or even less when there are missing data) if the

independent variable has a large variance and the determinants have a minimal variation

especially when also highly correlated with each other (Kennedy 2005; Campos and

Kinoshita 2003). Table 2 shows that in Poland, human capital, economic freedom, R&D and

GDP growth are highly correlated with each other (as well as with other various

determinants). A closer look at the descriptive statistics of the variables correlated with FDI

in the case of Poland, Albania, Macedonia and Slovak Rep. offers further explanations. We

can see that FDI being a relatively high figure in all four countries has a relatively high

variance while the variance of the determinants under discussion is very small leading to the

wrong coefficient sign, which as Kennedy (2005) notes, is indeed an indicator ofmulticollinearity.

Economic freedom R&D GDP growth

Human capital Pearson Correlation .844 ** .735 * -.722**


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Table 3. Standard deviation and variance of FDI and variables significantly correlated to FDI in Poland

N Std. deviation Variance

FDI 10 1.3393166E9 1.794E18

N Std. deviation Variance

FDI 11 4.154E9 1.725E19

GDP growth 11 1.7388580333E0 3.024

Human capital 9 .223763281 .050

Market size 11 2.5930241E10 6.724E20

Economic freedom 11 1.9305 3.727

Political freedom 9 .30494502706 .093

Openness to trade 11 6.427150259 41.308

GDP per capita 11 697.8691521 487021.353

R&D 11 .05419925758 .003

Accessibility to credit 11 7.713724738 59.502

Unemployment 11 4.44524 19.760


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Political freedom in Albania and Macedonia has also a very small variance (Table 6).

Missing data for 1999 and 2001 has increased the chances of multicollinearity and the

results in both countries have been affected. This had led to a wrong sign for this indicator.

For the above reasons, since these correlations are not genuine but occur due to their

association with other dependent variables, they will be not considered as significantly

correlated with FDI in our analysis. Our final correlation matrix will be as shown in Table 7.

Table 6. Standard deviation and variance of FDI and variables significantly correlated to FDI.

a.Albania b. Macedonia

N Std.deviation

Variance N Std.deviation

Variance

FDI 11 249812059.9 6.241E16 FDI 11 175523719.8 3.08086E16

Market size 11 800911154.6 6.41459E17 Market size 11 368859150.9 1.36057E17

Economicfreedom

11 3.079433242 9.482909091 Politicalfreedom

9 0.304945027 0.09299147

Politicalfreedom

9 0.298306567 0.088986808 Opennessto trade

11 5.407232676 29.23816521

Openness totrade

11 5.440651211 29.6006856 GDP percapita

11 170.3464452 29017.91138


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30

Table 7. Correlation coefficients and significance (corrected).

ALB B&H BUL CRO CZE HUN MAC POL ROM SVK SLO Sign Total

GDP growth .649* .77** .636* + 3

Human capital .822** .723* * .949 ** + 3

Market size .898 ** .816 ** .762** .826**** .653 * .721* .961 ** + 7

Economic freedom .902 ** .712 * .803** .590 ~ .847 ** .623 # + 6

Political freedom .637 # .687* + 2

Openness to trade .902 ** .798** .893** .610 * .670* .640 * + 6

Tax rate -.916 # - 1

GDP per capita .893 ** .823 ** .769** .817**** .655 * .714* .955 ** + 7

R&D .966 * .534 # + 2

Government spending .946 ** -.579 # +- 2

Real interest rate -.652 # -.705* * -.603* - 3

Accessibility to credit .950 ** .690 * .705* .811** .671 * .635* .948 ** + 7

Infrastructure .578 # .739 ** + 2

Inflation .734 * -.736 ** -.589 # +- 3

Unemployment -.817**

-.772*

-.569#

-.651* -.577 - 5

Total 6 9 8 8 1 0 6 7 9 4 1


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4.3 Bulgaria, Romania and Poland

A correlation between FDI and GDP growth was calculated for each country. The test foundthat the relationship between the two variables was significant (Sig < 0.1) only in Poland,

Bulgaria and Romania. In all three countries, the relationship was positive and strong

(Pearson correlation > 0.5).

Bulgaria Romania PolandPearson correlation 0.649 0.636 0.77Sig. 0.031* 0.035* 0.006**

Table 8. Correlation results between FDI and GDP growth in Bulgaria Romania and Poland.

Bulgaria, Poland and Romania have in common the following variables: market size, GDP

per capita and accessibility to credit correlated to FDI. Those are also the variables most

often correlated to FDI for the 11 countries in our sample (Table 7).

Poland, Romania Hungary and Czech Rep. used to attract most of the FDI entering theregion in 1998 but as shown clearly in Graph 3. Hungary has fallen behind since 2006 while

Bulgaria has joined the top leading countries.


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Graph 4. GDP growth (annual %) 1998-2008. Source WDI.

Romania leads the way (in 2008) with Bulgaria and Poland being in the same range with

Albania, B&H, Macedonia and Slovak Rep. On one hand we have three countries in

transition with problems in infrastructure (Albania), human capital (Albania and Macedonia),


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might also be a maximum level of GDP per capita for a country to be able to take

advantage of FDI. This level is at the (6221.68USD; 6796.341USD) interval (i.e higher than

GDP per capita for Poland but lower than the same figure for Croatia) according to our

findings. These results seem to suggest that not only there is a minimum level of

development for countries in order to take advantage of FDI as some studies suggest (e.g.

Obwona 2001; Singh and Jun 1995; Blomstrm, Lipsey and Zejan 1994; Bengoa and

Sanchez-Robles 2003 etc.) but there seems to exist also a saturation point meaning that

after reaching a certain level of income per capita, countries cannot keep relying on FDI buthave to start developing their own resources. After all, GDP per capita is one of the variables

most often correlated with FDI (Table 9). The finding coincides in principle with what

Blonigen and Wang (2005) have suggested, i.e. FDI increases growth in developing nations

but not in industrialised ones.


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growth relationship was important, have an unemployment rate in the [5.5-7.5] range for

2008. For B&H and Macedonia, the high unemployment discourages the entering of FDI and

not surprisingly, the low unemployment rate seems to be causing the same effect for Czech

Rep. and Slovenia.


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only 2008 was an unsuccessful year from this point of view (2.46% of GDP growth compared

to 4.71%, the average for the whole region for 2008). Another important factor seems to be

the tax rate policy. Hungary had the highest tax rate in the region for the last two years of the

period studied. It taxed on average 57.5% of the yearly profits in its peak in 2008, more than

30% higher than the rate in Poland, the country which attracts more FDI in EEC. Next

highest in 2008 is Czech Rep. which taxes 48.6% of the profit of business, still more than

10% compared to Poland for the same year. Since Poland, Hungary and Czech Rep. have

similar levels in most of the other indicators, we can assume that most investors havediverted their attention to Poland moving away from Hungary and Czech Rep. These results

confirm the findings of Feldstein (1994) and Morisset and Pirnia (2000) which suggested that

when other variables are not significantly different, a lower tax rate becomes an important

incentive for investors. Czech Rep. and Slovenia have the lowest rate of unemployment in

the region. As mentioned before, based on the evidence and on what Jones and Wren

(2010) have found, this fact is not attractive to investors since they prefer a higher (but not

too much) rate of unemployment in order to avoid competition on human capital as much as

possible. Slovenia`s indicators are all very positive. It is the country with the higher income

per capita in our sample but is the least attractive when it comes to foreign investments.

From 2005 to 2008 the country has lost more investments than it has attracted (FDI is


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economic freedom variables are significantly correlated with FDI and hence have an

important impact on the results. Together with Czech Rep and Hungary, Croatia and Slovak

Rep. have been hit by the global recession in 2008 with a noticeable decrease in their GDP

growth trend. Nevertheless, the real problem for Slovak Rep. is its poor infrastructure,

unusual for a country of this income level, but even more importantly the fluctuations in

inflation which is inversely correlated to FDI.

4.5 Albania, Bosnia and Herzegovina, Macedonia

These three countries have the worst figures in most of the variables under consideration

(See Appendix I) but all three of them are showing signs of significant improvement. Albania

has a better-than-average economic freedom, a stable economic growth (6.7% a year on

average from 1998 to 2008) and an increasingly better index of political freedom. However it

still has to deal with a low level of human capital and infrastructure, a high real interest rate,

low accessibility to credit and a very limited openness to trade. These last two variables

deserve particular attention since they are strongly correlated to the level of FDI. As Alfaro et

al. (2004) have found when access to credit is restricted, even if there are useful spillovers

from foreign investors to the domestic economy, local investors find it extremely challenging

to take advantage of them unless access to funds is granted. Moore (2010) has stressed


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5. Conclusions, implications for practice and policy and recommendations

5.1 Conclusions

The study identified the countries where the relationship was important, the variables that

were most often correlated with FDI and those that best explained the variance of FDI. A

robust linear regression model for FDI was successfully built for 8 countries. In addition

patterns of different country settings that explained the results were also identified.

The research found that among the countries of our sample there is a strong, significant and

positive association between FDI and GDP growth in Bulgaria, Romania and Poland. Market

size, GDP per capita, and accessibility to credit were the three variables which were

correlated more often with FDI (in 7 out of the 11 countries tested). Market size, openness

to trade and R&D were each included twice in the regression models, being the

determinants which best explained the variance of FDI. The main objective, identifying

patterns between countries where the relationship between GDP growth and FDI was strong

and significant and countries where it was not, was fully achieved as the study succeeded in

offering an explanation which was supported by both the data and previous economic

theories.


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on the other hand seem to not be able to harness significant benefits from FDI once they

have reached a certain level of development. Middle income countries seem to be more

advantaged in this matter. In our sample this category is estimated to start at the

(2177.73USD: 2569.99USD) GDP per capita interval and to end within the (6221.68USD;

6796.34USD) GDP per capita interval. In our Eastern Europe-based study, Albania, B&H

and Macedonia did not have a GDP growth correlated to FDI possibly due to their low

income figures and economic as well as political still ongoing challenges. Bulgaria, Romania

and Poland had over one third of the variables tested correlated with FDI and a higherincome per capita (over 2500USD in 2008). The higher income countries did not have many

determinants associated with FDI inflows except for maybe Croatia (7 out of 15) and

Slovakia (4 out of 15) which seemed to be too rich to be benefiting significantly from FDI.

Last but not least, investors seem to pay particular attention to the existing level of FDI in the

host countries. Bulgaria, Romania and Poland were also amongst the countries that had the

highest level of FDI.

In terms of projection, we would expect Croatia, if it continued to receive an increasing

amount of FDI inflows at this rate, to start developing a stronger relationship between FDI

and GDP growth and perhaps expand the interval set for the GDP per capita range. The


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important contribution to the significance and the accuracy of all the regression models. The

study confirmed some of the concerns related to the methodology used to calculate the data

on human capital and economic freedom. However, even more serious problems were

experienced with the WGI dataset used to calculate political freedom. The study

recommends that an alternative data set is used for this variable or, if not possible, a critical

approach is adopted.

The study achieved its objective to calculate the correlation coefficients between FDI andeach of the other 15 variables tested. Among the 11 countries only on four occasions (R&D

for Albania; human capital for B&H and tax rate for Croatia and B&H) could the test not be

carried out due to data unavailability. In order to meet the requirements implied by the

objectives that this study had to meet (i.e. time and length), the study concentrated on 15

variables which were considered as more important by the literature in determining the FDI.

However it failed in finding a variable significantly correlated with FDI in Hungary and the

regression models for Czech Rep. and Slovak Rep. were not very robust. In the case of

Hungary none of the variables had any important correlation with FDI so no significant model

could be built. In addition the model for Czech Rep. and Slovak Rep. was significant but

could describe the trend of FDI only in a minor scale (less than 50%). If additional resources


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Czech Rep. Hungary and Slovenia are noticeably amongst the most successful economies

in Eastern Europe. Our study has found that once certain economic and political conditions

are fulfilled, investors prefer to expand in the country with the least income per capita, lowest

tax rate, greatest market size and level of FDI. Czech Rep. and Hungary have a relative big

market size but they are perhaps penalised by th eir high tax rate compared to Poland or

Bulgaria. Slovenia on the other hand seems to be less attractive due to its smaller market

size and noticeably higher level of GDP per capita. It is fair to assume that these three

countries have enjoyed a much shorter transition period and having already benefited fromFDI in their early stages, their economies are now less dependent from FDI.

Slovak Rep. and Croatia have had a relatively successful transition period but they are a

step behind the above-mention countries. In order to compete with the highest ranked

countries, Croatia needs to address its low index of economic freedom and has to adjust its

education-R&D ratio by either investing more in human capital or alternatively concentrate

spending in R&D only in its most productive fields where there are possibilities of spillovers.

Slovenia on the other hand, needs to invest more in infrastructure and engage more actively

in policies to put inflation under control.


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market and economies of scale. The tax rate and the behaviour of other investors are also

essential in this process.

Assuming that policy makers continuously aim to maximise the national income, they should

also in help investors access credit as easily and cheaply as possible, ease international

trade in order to maximise exports and increase the efficiency of R&D investments.


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50

Appendices

Appendix I: Data Set by Determinant and Definitions of Variables

FDI

Foreign direct investment, net (BoP, current US$) 1998-2008

Foreign direct investment is net inflows of investment to acquire a lasting management interest (10 percent or more of voting stock) in an

enterprise operating in an economy other than that of the investor. It is the sum of equity capital, reinvestment of earnings, other long-term

capital, and short-term capital as shown in the balance of payments. This series shows total net, that is, net FDI in the reporting economy from

foreign sources less net FDI by the reporting economy to the rest of the world. Data are in current U.S. dollars.

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Albania 45010000 41200000 143000000 207300000 135000000 178036400.7 327681210.2 258417488.7 314638316.8 647438679.9 843676732.3

Bosnia and Herzegovina 66736468.36 176780594.8 146075610.9 118495219.8 267769569 381784637.4 708285175.3 607373915.4 718392985.2 2087522392 1041976684

Bulgaria 537244256.2 801671162.9 998219916.7 803281565.3 876321850.2 2070285898 2879178250 4004786052 7582756076 11432517580 8472194673

Croatia 842125015.2 1392387341 1104792348 1397845452 552483187.4 1927270597 732256714.9 1551018911 3193722244 4745580940 4604006989

Czech Rep. 3574817996 6222615311 4944400745 5476031435 8285212459 1813296004 3940296137 11628782840 4042698664 8963737281 8966891345

Hungary 3065242420 3060155003 2181836566 3579630098 2730682166 516181669.3 3405012378 5396156974 1120336541 4658704613 1667089846

Macedonia 150471594.6 88107550 215675542 446263168 105470966 117464189 321867202 94236004.92 423969779.6 320744089.9 612032086.2

Poland 6049000000 7239000000 9327000000 5804000000 3901000000 4284000000 11761000000 6951000000 10727000000 17987000000 11747000000

Romania 2040000000 1025000000 1048000000 1174000000 1128000000 1805000000 6447000000 6512280000 10971010000 9647000000 13606000000

Slovak Republic 417252734.17 730400699.62 2030972175.38 4100939036.20 535685661.04 3050538869.05 2266463455.83 3798756919.35 2960033821.42 2979922569.56

Slovenia 221200000 58900000 70500000 370700000 1508300000 -174300000 281500000 -88500000 -255971421.5 -268631005.7 512619024.3

Source: WDI.


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51

GDP Growth

GDP growth (annual %) 1998-2008

GDP growth measured as the annual percentage growth rate of GDP at market prices based on constant local currency. Aggregates are based

on constant 2000 U.S. dollars. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and

minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or

for depletion and degradation of natural resources.

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008Albania 12.7 10.1 7.3 7 2.9 5.7 5.9 5.5 5 6 6

Bosnia and Herzegovina 15.6 9.6 5.5 4.4 5.3 4 6.1 5 6.2 6.838 5.42

Bulgaria 4 2.3 5.4 4.1 4.5 5.00717 6.641666 6.245586 6.746421 6.167265 6.013859

Croatia 2.125999 -1.50269 3.032454 3.834939 5.441967 4.957599 4.249673 4.209077 4.739421 5.471415 2.359253

Czech Republic -0.759 1.339505 3.647897 2.456366 1.896838 3.602294 4.484635 6.316355 6.807848 6.131017 2.463661

Hungary 4.857864 4.171342 5.039515 4.1 4.4 4.3 4.7 3.9 4 1.2 0.6

Macedonia 3.378764 4.339212 4.549038 -4.52534 0.85349 2.816485 4.085092 4.102403 3.954069 5.861086 4.987142

Poland 4.981651 4.524249 4.253011 1.205288 1.443448 3.867059 5.344715 3.617051 6.227495 6.785224 4.889497

Romania -4.78906 -1.2 2.100002 5.700002 5.1 5.199998 8.399992 4.172297 7.9 6 9.425802

Slovak Republic 4.357399 0.028742 1.370192 3.484678 4.589554 4.778911 5.030544 6.665366 8.503333 10.57938 6.170468

Slovenia 3.566907 5.36587 4.388756 2.849162 3.973687 2.835231 4.286534 4.34857 5.902689 6.764554 3.540631

Source: WDI


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52

Human Capital

Primary to secondary school-life expectancy for male (years) 1999*-2008.

School life expectancy is defined as the total number of years of schooling which a child of a certain age can expect to receive in the future,

assuming that the probability of his or her being enrolled in school at any particular age is equal to the current enrolment ratio for that age. This

indicator shows the overall level of development of an educational system in terms of the number of years of education that a child can expect

to achieve.

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008Albania 10.45785 10.2583 10.31073 10.35 10.38345 10.4305694

Bosnia and Herzegovina 11.56749 11.49434

Bulgaria 10.73771 10.79639 10.90846 10.9666 11.29392 11.4827304 11.39343 11.3281 11.36767 11.23096

Croatia 10.4967 10.59509 10.6968 10.80081 10.92785 11.27172 11.33505 11.37082

Czech Republic 11.52353 12.08726 12.53221 12.57404 12.54881 12.435091 12.48873 12.44755 12.40826 12.46961

Hungary 11.50085 11.66414 11.82597 11.96661 12.16773 11.7809312 11.79757 11.76778 11.74193 11.87032

Macedonia 10.77872 10.86656 10.84878 10.74066 10.75354 10.6854312 10.67572 10.57842

Poland 11.9755 12.05759 12.16375 12.26061 12.3356 11.6959265 11.84406 11.82307 11.79639

Romania 10.49481 10.47555 10.51404 10.70233 10.82817 11.1313256 11.19606 11.30803 11.42955 11.50209

Slovak Republic 11.64585 11.74339 11.76317 11.86214 12.01939 12.263628 12.3126 12.29474 12.17885 12.13826

Slovenia 11.83259 11.74131 12.36141 12.58805 12.94616 12.5032586 12.55712 12.56479 12.59797 12.62817

Source UNESCO: Institute for Statistics.

*No data were available for this variable in 1998.


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53

Market Size

GDP (constant 2000 US$) 1998-2008

GDP at purchaser's prices is the sum of gross value added by all resident producers in the economy plus any product taxes and minus anysubsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for

depletion and degradation of natural resources. Data are in constant 2000 U.S. dollars. Dollar figures for GDP are converted from domestic

currencies using 2000 official exchange rates. For a few countries where the official exchange rate does not reflect the rate effectively applied

to actual foreign exchange transactions, an alternative conversion factor is used.

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008Albania 3120648082 3435833539 3686649387 3944714844 4059111575 4290480934 4543619309 4793518372 5033194290 5335185947 5655297104

Bosnia and Herzegovina 4616097181 5059242511 5337500849 5572350886 5867685483 6102392902 6474638869 6798370813 7219869803 7713564500 8131639696

Bulgaria 11685123847 11953881696 12599391307 13115966351 13706184836 14392476788 15348377074 16306973117 17407110257 18480652845 19592053197

Croatia 21035325723 20719230111 21347531223 22166196073 23372473132 24531186576 25573681727 26650097658 27913157941 29440402527 30134976130

Czech Rep. 54001191849 54724540426 56720835331 58114106567 59216437149 61349587387 64100892591 68149732482 72789262997 77251985335 79155212395

Hungary 43762553933 45588039514 47885455754 49848759439 52042104855 54279915364 56831071386 59047483170 61409382496 62146295086 62519172857

Macedonia 3288135722 3430814903 3586883989 3424565368 3453793697 3551069283 3696133717 3847764010 3999907260 4234345269 4445518087

Poland 157177779331 164288893911 171276118424 173340489224 175842569475 182642504560 192404226787 199363586523 211778943588 226148618304 237206147127

Romania 36731311750 36290534542 37052636395 39164637562 41162034208 43302459253 46939862239 48898332853 52761301149 55926979218 61198545647

Slovak Republic 28304680302 28312815552 28700755482 29700884467 31064022480 32548544315 34185913027 36464529206 39565229409 43750985812 46450626497

Slovenia 18081623890 19051860359 19887999964 20454641387 21267444768 21870425982 22807909254 23799727188 25204551149 26909526641 27862293677Source: WDI


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54

Economic Freedom

Economic freedom index 1998-2008

Ten components of economic freedom are measured assigning a grade in each using a scale from 0 to 100, where 0 means no freedom and

100 represents the maximum freedom. The ten components of economic freedom are: business freedom, trade freedom, fiscal freedom,

government spending, monetary freedom, investment freedom, financial freedom, property rights, freedom from corruption, labour freedom.

The ten component scores are then averaged to give an overall economic freedom score for each country.

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008Albania 53.9 53.4 53.6 56.6 56.8 56.8 58.5 57.8 60.3 61.4 62.4

Bosnia and Herzegovina 29.4 29.4 45.1 36.6 37.4 40.6 44.7 48.8 55.6 54.4 53.9

Bulgaria 45.7 46.2 47.3 51.9 57.1 57 59.2 62.3 64.1 62.7 63.7

Croatia 51.7 53.1 53.6 50.7 51.1 53.3 53.1 51.9 53.6 53.4 54.1

Czech Republic 68.4 69.7 68.6 70.2 66.5 67.5 67 64.6 66.4 67.4 68.2

Hungary 56.9 59.6 64.4 65.6 64.5 63 62.7 63.5 65 64.8 67.6

Macedonia 58 60.1 56.8 56.1 59.2 60.6 61.1

Poland 59.2 59.6 60 61.8 65 61.8 58.7 59.6 59.3 58.1 60.3

Romania 54.4 50.1 52.1 50 48.7 50.6 50 52.1 58.2 61.2 61.7

Slovak Republic 57.5 54.2 53.8 58.5 59.8 59 64.6 66.8 69.8 69.6 70

Slovenia 60.7 61.3 58.3 61.8 57.8 57.7 59.2 59.6 61.9 59.6 60.2

Source: The WSJ and The Heritage Foundation.


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Political Freedom

Political stability and absence of violence index 1998-2008.

The WGI index measures the perceptions of the likelihood that the government will be destabilized or overthrown by unconstitutional or violent

means, including domestic violence and terrorism. The indicator combines the views of a large number of enterprise, citizen and expert survey

respondents in industrial and developing countries. The data range within the (-2.5: 2.5) interval where higher values represent a more stable

environment. The individual data sources underlying the aggregate indicators are drawn from a diverse variety of survey institutes, think tanks,

non-governmental organizations, and international organizations. A working paper describing the WGI methodology is available at this address:http://econ.worldbank.org/external/default/main?pagePK=64165259&theSitePK=469372&piPK=64165421&menuPK=64166093&entityID=000158349_20100924120727

[Accessed 08 January 2010]

1998 2000 2002 2003 2004 2005 2006 2007 2008

Albania -0.84194 -0.95855 -0.60928 -0.42873 -0.82319 -0.60123 -0.40784 -0.2223 -0.0635

Bosnia and Herzegovina -0.6155 -0.64398 -0.67468 -0.44565 -0.60055 -0.47588 -0.58163 -0.49995

Bulgaria 0.565803 0.485279 0.498947 0.30848 0.165726 0.240477 0.463179 0.429432 0.430525

Croatia 0.115977 0.321501 0.482494 0.430274 0.507515 0.413307 0.53242 0.604709 0.574009

Czech Republic 0.827281 0.643711 1.002806 0.920736 0.750508 0.869937 1.002787 0.973637 1.030266

Hungary 1.157167 0.830496 1.123998 1.098947 0.867278 0.932611 0.953007 0.748341 0.747624

Macedonia -0.91382 -0.84129 -0.96639 -0.98625 -1.08974 -0.99314 -0.61579 -0.35229 -0.23799

Poland 0.695265 0.484553 0.739216 0.706039 0.281233 0.430492 0.433109 0.674372 0.881676

Romania 0.192782 0.022679 0.446989 0.40207 0.170874 0.223641 0.246364 0.290636 0.262891

Slovak Republic 1.090586 0.550379 0.869611 0.943695 0.59542 0.828752 0.746968 0.949854 1.030896

Slovenia 1.065722 0.92253 1.16299 1.117266 0.98685 0.989378 1.036418 1.047632 1.083631Source: The World Bank Group.
http://econ.worldbank.org/external/default/main?pagePK=64165259&theSitePK=469372&piPK=64165421&menuPK=64166093&entityID=000158349_20100924120727http://econ.worldbank.org/external/default/main?pagePK=64165259&theSitePK=469372&piPK=64165421&menuPK=64166093&entityID=000158349_20100924120727http://econ.worldbank.org/external/default/main?pagePK=64165259&theSitePK=469372&piPK=64165421&menuPK=64166093&entityID=000158349_20100924120727


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Openness to Trade

Exports of goods and services (% of GDP) 1998-2008

Exports of goods and services represent the value of all goods and other market services provided to the rest of the world. They include the

value of merchandise, freight, insurance, transport, travel, royalties, license fees, and other services, such as communication, construction,

financial, information, business, personal, and government services. They exclude compensation of employees and investment income

(formerly called factor services) and transfer payments.

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008Albania 10.7614 17.28705 19.13554 20.51982 20.41931 20.6194 21.54212 22.27307 25.09053 28.41784 31.15115


Bulgaria 47.0654 44.55781 55.70251 55.57065 51.54036 53.26954 56.9849 60.20424 64.54783 63.39498 60.47108

Croatia 35.11377 36.5444 42.03559 43.77752 41.04418 42.92961 43.26327 42.62009 43.3993 42.78849 41.9091

Czech Republic 54.2244 55.45558 63.37428 65.35154 60.22081 61.78114 70.14895 72.20994 76.41713 80.05479 77.09346

Hungary 61.92891 64.32642 72.15652 71.24169 63.09876 60.78051 63.20433 65.97984 77.09578 80.36387 81.44972

Macedonia 41.2065 42.17177 48.63065 42.69268 38.02971 37.87646 41.1227 45.4757 48.13341 53.42853 52.56695

Poland 25.9813 24.16901 27.12436 27.05604 28.63482 33.31383 37.49234 37.08499 40.35506 40.7573 39.76983

Romania 22.62165 28.018 32.69133 33.3941 35.40244 34.69488 35.92703 32.92276 29.55487 30.72413 29.90738

Slovak Republic 59.20509 61.21043 70.50569 72.76835 71.16953 75.90018 74.59914 76.30487 84.42221 86.71862 83.03161

Slovenia 51.43708 47.55332 53.94595 55.47758 55.23588 53.97143 58.00382 62.22053 66.6298 70.1649

Source: WDI


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Tax Rate

Total tax rate (% of profit) 2005*-2008

Total tax rate is the total amount of taxes payable by businesses (except for labour taxes) after accounting for deductions and exemptions as a

percentage of profit.

2005 2006 2007 2008Albania 58.2 57.3 46.8 50.5

Bosnia and Herzegovina 44.1 44.1 44.1 44.1

Bulgaria 46 42.5 36.7 34.9Croatia 32.5 32.5 32.5 32.5

Czech Republic 49.6 49.1 48.6 48.6

Hungary 56.6 55.7 56.2 57.5

Macedonia, FYR 50.4 21.6 21.6 18.4

Poland 40.9 40.9 41 44.1

Romania 57.2 49.8 46.9 48

Slovak Republic 49.7 48.4 48.4 47.4

Slovenia 40 40 39.2 36.7Source: WDI.

*Data for this variable were available only since 2005.


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GDP per Capita

GDP per capita (current US$) 1998-2008

GDP per capita is gross domestic product divided by midyear population. GDP is the sum of gross value added by all resident producers in the

economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions

for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in current U.S. dollars.

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008Albania 1014.638 1119.217 1201.82 1285.182 1319.486 1389.973 1466.309 1541.037 1612.298 1703.195 1799.164


Bulgaria 1415.178 1456.37 1563.2 1658.15 1741.795 1839.764 1972.546 2106.844 2260.951 2412.692 2569.991

Croatia 4673.478 4549.677 4823.211 4992.387 5264.071 5525.042 5761.136 5999.572 6286.747 6636.7 6796.341

Czech Republic 5245.431 5321.846 5521.189 5684.087 5802.772 6010.327 6274.549 6657.96 7088.16 7475.401 7593.31

Hungary 4262.627 4453.031 4689.608 4893.093 5122.956 5358.57 5622.86 5853.791 6097.421 6180.157 6228.133

Macedonia, FYR 1648.773 1712.354 1783.088 1696.718 1706.493 1750.631 1818.795 1890.503 1962.795 2075.824 2177.743

Poland 4064.997 4249.803 4454.08 4532.005 4599.553 4780.645 5039.105 5223.667 5552.488 5932.458 6221.677

Romania 1632.285 1615.929 1650.966 1769.593 1887.896 1991.648 2164.635 2260.217 2444.048 2595.596 2844.642

Slovak Republic 5250.692 5247.861 5326.06 5521.739 5774.948 6050.309 6351.367 6768.986 7338.569 8106.06 8591.426

Slovenia 9120.157 9595.498 9998.994 10268.39 10665.72 10958.78 11421.09 11896.89 12559.15 13333.94 13784.23

Source: WDI.


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Research and Development

Research and development expenditure (% of GDP) 1998-2008

Expenditures for research and development are current and capital expenditures (both public and private) on creative work undertaken

systematically to increase knowledge, including knowledge of humanity, culture, and society, and the use of knowledge for new applications.

R&D covers basic research, applied research, and experimental development.

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008Albania*Bosnia and Herzegovina 0.01854 0.016112 0.028119 0.020652

Bulgaria 0.519427 0.508193 0.569097 0.565143 0.52058 0.46632 0.488649 0.499631 0.499708 0.486343 0.48

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