DETERMINANT FACTORS OF FOREIGN DIRECT INVESTMENT FLOWS IN CENTRAL AND EASTERN EUROPEAN COUNTRIES

DETERMINANT FACTORS OF FOREIGN DIRECT INVESTMENT FLOWS IN CENTRAL AND EASTERN EUROPEAN COUNTRIES Nicoleta Ciurila Academy of Economic Studies Bucharest Faculty of Finance and Banking, Money and Banking Department Mihai Eminescu Street, no. 13 15, room 1103 010511 Romania e-mail: nciurila@yahoo.com telephone: +40745063731 Abstract This paper investigates the factors which determine the dynamics of foreign direct investment (FDI) in six Central and Eastern European countries, including Romania. We search for a long term relationship between the level of FDI and a number of fundamental variables: productivity, unemployment, the level of the real exchange rate and the average taxation rate as a proxy for the taxation environment. The cointegration technique is used to establish for each country the variables which influence the level of the FDI; the results show that for each country another set of variables explains the level of the FDI. Another aim of the paper is to solve the dilemma regarding the causality between the real exchange rate and the FDI using the Vector Error Correction Model. Keywords: FDI, VEC, Central and Eastern European countries, fundamental variables JEL classification: F21, F23, F31 1. Introduction The analysis of the determinants of the FDI inflows is especially important in the view of the benefits that FDI inflows have for a host country: knowledge and technology transfer, innovation diffusion, increase in productivity and competition. Most important, FDI s are non-debt-creating sources of financing the current account deficit. 207

The empirical studies regarding FDI have shown the importance of two main types of determinant factors: the gravity factors and the policy related factors. The gravity factors refer to issues such as market size and the proximity of the host country to the source country and have been fond to explain a big part of FDI flows. Policy related factors regard overall macroeconomic stability, trade policies (trade costs, openness degree), fiscal policies (average taxation rate or the fiscal burden, tax incentives), labour policies (labour costs and skills), the degree of regional integration, infrastructure and institutions. An OECD study on FDI, published in 2003, shows that the role of tax incentives is limited and can sometimes even prove counterproductive, especially if policymakers tend to change their conditions quite frequently. The study advises against multiple tax incentives, special economic zones and situation adapted tax incentives (also known as non-automatic criteria). Empirical studies have investigated a number of variables which can be considered determinant factors of FDI flows. The main interest is in estimating the significance of a certain factor and the direction in which it influences the FDI flows. Cross sectional or panel regressions have analyzed the role of the population or the GDP per capita (as a proxy for market size), the distance between source and host countries, the ration of tariff revenues to the value of imports, the index of foreign exchange and trade liberalization, the index of infrastructure reform, unit labour cost, the real exchange rate and so on. A number of empirical papers have focused on the causality effect between FDI and the exchange rate. Most studies find that gravity factors are more important in explaining FDI flows, a fact which signifies that policies can be limitedly used by decision making bodies in order to attract FDI. The determinant factors of FDI flows is a particular important subject in case of Central and Eastern European countries and, consequently in case of Romania. Although, FDI flows have significantly increased in the last years, Romania is still the country with the lowest foreign investment per capita stock from the new EU member states. Figure 1 208

shows the developments of the FDI flows (including privatization related inflows) in Romania since 1998. Figure 1. Foreign Direct Investment in Romania for the period of 1998-2006 (millions of Euro) 2000 1800 1600 1400 1200 1000 800 600 400 200 0 1998q01 1998q04 1999q03 2000q02 2001q01 2001q04 2002q03 2003q02 2004q01 2006q02 2005q03 2004q04 Source: Eurostat Database As Figure 1 clearly shows, the FDI flows have substantially increased since 2004. This moment seems to have represented a structural break in the data as the FDI flows became three or four times greater than previous values. This has lead to a catching up process in terms of FDI accumulation. However, as Figure 2 shows, Romania has the lowest FDI stock per capita of all EU new member states. 209

Figure 2. Foreign Direct Investment per capita stock in Euro, as of December 2006 7000 6000 5000 4000 3000 2000 1000 0 Romania Serbia Croatia Lithuania Latvia Bulgaria Slovenia Slovakia Poland Hungary Czech Republic Source: Vienna Intitute for International Economic Studies This paper is organised as follows: the relevant papers regarding the fundamental factors which influence the FDI flows are reviewed in the next section. Section 3 describes the data employed and the methodology used in the empirical analysis. Section 4 presents the results of the econometric estimation, while section 4 concludes. 2. Literature review The issue regarding the empirical connection between foreign direct investments and macroeconomic fundamental variables has been one of the most controversial problems in international economics. A large body of literature examining determinants of FDI begins with a partial equilibrium firm-level framework based in industrial organization and finance to motivate empirical analysis. These studies then typically examine how exogenous macroeconomic factors affect the firm s FDI decision, with the primary focus on exchange rate movements, taxes, unemployment or productivity. The effect of exchange rates on FDI has been examined both with respect to changes in the bilateral level of the exchange rate between countries and in the volatility of exchange rates. Froot and Stein (1991) debated the common perception that expected changes in the level of the exchange rate would not alter the decision by a firm to invest in a foreign country. This common theory states that, while an appreciation of a firm s 210

home country s currency would lower the cost of assets abroad, the expected nominal return goes down as well in the home currency, leaving the rate of return the same. Froot and Stein (1991) present an imperfect capital markets model in which a currency appreciation may increase foreign investment by a firm. Imperfect capital markets imply that the internal cost of capital is lower than borrowing from external sources. Thus, an appreciation of the currency leads to increased firm wealth and provides the firm with greater low-cost funds to invest relative to the counterpart firms in the foreign country that experience the depreciation of their currency. Froot and Stein (1991) provide empirical evidence of increased inward FDI with currency depreciation through simple regressions using a small number of annual US aggregate FDI observations. Stevens (1998) finds that this empirical evidence is rather fragile to specification. However, Klein and Rosengren (1994), confirm that exchange rate depreciation increases US foreign direct investments using various samples of US foreign direct investments disaggregated by country source and type of FDI. Blonigen (1997) provides another way in which changes in the exchange rate level may affect inward FDI for a country. If FDI by a firm is motivated by acquirement of assets that are transferable within a firm across many markets without a currency transaction, as in the case of firm specific assets, such as technology or managerial skills, then an exchange rate appreciation of the foreign currency will lower the price of the asset in that foreign currency, but will not necessarily lower the nominal returns. In other words, a depreciation of a country s currency may allow a fire sale of such transferable assets to foreign firms operating in global markets versus domestic firms that may not have such access. Blonigen uses industry-level data on Japanese mergers and acquisition FDI into the US in order to test this hypothesis and finds strong support of increased inward US acquisition FDI by Japanese firms in response to real dollar depreciations relative to the yen. Blonigen finds that these exchange rate effects on acquisition FDI are typically for high technology industries where these specific assets are expected to be of substantial importance. There is an increasing body of empirical literature analyzing the effects of shortrun movements in exchange rates. Swenson (1994) and Kogut and Chang (1996) found consistent evidence that short-run movements in exchange rates lead to increased inward FDI. There was found with limited evidence that the effect is larger for merger and 211

acquisition FDI (Klein and Rosengren, 1994). Thus, the evidence has largely been consistent with the Froot and Stein (1991) and Blonigen (1997) hypotheses. One serious issue in the literature is that these exchange rate effects have been tested almost exclusively with US data. Some studies have focused on US outbound FDI, while others have used US inbound FDI. Also, as Bojesteanu and Bobeica (2006) show, in small open economies, an important role is played by the way expectations are formed. Interest in the effects of taxes on FDI has been considerable from both international and public economists. The effects of taxes on FDI can vary substantially by type of taxes, measurement of FDI activity, and tax treatment in the host and home countries. Gropp and Kostial (2000) and Benassy-Quere, Fontagne and Lahreche-Révil (2000) suggest that FDI is sensitive to tax rate differences. Empirical approaches and data samples have been substantially different, so that there are still significant questions about how much tax affects FDI. Assaf Razin and Efraim Sadka (2007) focus on bilateral FDI flows among OECD countries. They study the effects of two sets of driving forces that affect FDI: productivity and taxation. Specifically, they attempt to shed some light on some key mechanisms though which these sets affect FDI flows. They develop a framework in which the host country productivity has a positive effect on the size of FDI flows, which they called the intensive margin, but an ambiguous effect on the likelihood of FDI flows to occur (the extensive margin). The source-country productivity has a negative effect on the extensive margin. One of the most fundamental questions about foreign direct investment activity is why a firm would choose to service a foreign market through affiliate production, rather than other options such as exporting or licensing arrangements. Answers to these questions refers to the presence of intangible assets specific to the firm, such as technologies, managerial skills as in Rugman (1980) and Dunning s (2001) work on transactions costs and the development of the ownershiplocation-internalization (OLI) paradigm. 212

3. Data and methodology In our analysis of the determinant factors of FDI flows, we used quarterly data spanning the period of 1997Q1 2007Q1 for a number of six countries: Bulgaria, Czech Republic, Hungary, Poland, Slovakia and Romania. We conducted our analysis using five data series for each country: the net flow of FDI, the real exchange rate against the Euro, the unemployment rate, labour productivity and the average wealth tax rate. As FDI can take negative values due to disinvestments, we didn t express this variable in logarithms. Thus, the parameters must be interpreted as semielasticities. The real exchange rate was computed according to formula (1) and we expressed this variable in logarithms. CPI EURO CPI Q = E CPI DOMESTIC (1) where E is the nominal exchange rate; CPI Q is the real exchange rate computed using the Consumer Price Index (CPI); EURO CPI is the CPI in the Euro Area computed so that year 2005 equals 100; DOMESTIC CPI is the CPI in each of the six countries considered, computed so that year 2005 equals 100. Labour productivity was computed by dividing the real GDP expressed in Euro by the number of employees. As there was no available data for the number employees in case of Bulgaria and Poland, we didn t include in our analysis the labour productivity series for these countries. The rest of the data series were collected from the Eurostat and the Statistic Data Warehouse of the European Central Bank. In order to establish the existence of a long run relationship between FDI flows and the fundamental variables we conducted individual Johansen cointegration tests. This was necessary as all variables were found to be I(1). In case one or more cointegrating relationships existed, we estimated the Vector Error Correction model (VEC). The analysis focused on the sign and statistical significance of the coefficients of the cointegration equation and the speed of adjustment parameters. In conclusion, we estimated for each country the following VEC(p): 213

X t = α β X + Γ X + K + Γ X + ε t 1 1 t 1 p t p t (2) Where X is a vector containing all the variables under analysis; α is a vector containing the speed of adjustment coefficients; β is a vector containing the coefficients of the cointegration relationship. The sign of the cointegration equation coefficients ( β ) will provide evidence of the direction of the long term relationship between the variables. Also, the statistical significance of the coefficients comprised in α show the speed of adjustment and will help us decide on the weak exogenity of the variables. Finally, we will check for Granger causality between the variables by determining the statistical significance of the coefficients contained by Γ i. We estimated three different versions of model (2) for each country. The first model comprised the net flow of FDI, the real exchange rate against the Euro, the unemployment rate, labour productivity and the average wealth tax rate. We tested the existence of a long term relationship between the variables and we analysed the signs of the cointegration equation coefficients. The second model eliminated the average wealth tax rate from the previous variables. Finally, the third model focused solely on the relationship between the FDI flows and the real exchange rate. In addition to checking the magnitude and direction (positive/negative) between the variables, we also used the VEC model in order to perform a Granger causality test. The aim of this test was to determine weather FDI flows determine the movements in the real exchange rate or if the opposite is true. 4. Results The first step of our analysis was to determine whether there is a relationship between the FDI flows and the fundamental variables which we selected. Consequently, we performed Johansen cointegration tests for each of the six countries analysed and taking into account the three models described above. Table 1 presents the number of cointegration equations found in each case at 1% significance level. 214

Table 1. Number of cointegrating equations determined for each model and each country analysed Bulgaria Czech Rep. Hungary Poland Romania Slovakia Model 1 1 3 1 2 0 2 Model 2 1 1 1 1 0 1 Model 3 1 1 0 1 1 1 The results above show that model 1, the one that includes all the fundamental variables provides the most inconsistent results as the number of cointegration equations varies across countries. The results for model 2 and 3 show that there is a long term relationship between the FDI flows and the real exchange rate, productivity and the unemployment rate. In case of Romania, we found a long term relationship only between the FDI flows and the real exchange rate, while for Hungary we couldn t find a cointegration relationship only between the FDI flows and the real exchange rate. We next focused our attention on the analysis of the statistical significance and the sign of the coefficients in the cointegration equation. Table 2 presents the sign and the t-statistics of the coefficients corresponding to the fundamental variables included in model 2. Table 2. The sign and statistical significance of the coefficients in the cointegration equation for model 2 Bulgaria Czech Rep. Hungary Poland Romania Slovakia Real exchange rate Sign - - + - - + t-stat 3.14* 6.31* -5.84* 0.62 2.72* -2.85* Productivity Sign - + + - t-stat 5.65* -4.67* -2.76* 1.98* Unemployment Sign - + - - - - t-stat 2.1* -4.45* 4.96* 0.86 2.76* 1.6 * denotes statistical significant coefficients at 5% significance level The minus sign reflects an inverse relationship between the FDI flows and the fundamental variable while the plus sign reflects a direct relationship between the FDI 215

flows and the fundamental variable. As the results in table 1 show, there is a negative relationship between the real exchange rate and the FDI flows with two exceptions: Hungary and Slovakia. This means that a decrease in the real exchange rate (a real appreciation) will increase the amount of FDI flows, while an increase in the real exchange rate (a real depreciation) will decrease them. All the coefficients are statistically significant with the exception of Poland in which case the t-statistic leads to the impossibility of rejecting the null hypothesis of a coefficient equal to zero. The sign of the productivity coefficient is ambiguous: negative for the Czech Republic and Slovakia and positive for Hungary and Romania. A negative coefficient signifies that, contrary to economic theory, an increase in productivity leads to a decrease in the amount of FDI flows. The positive coefficient sustains the direct relationship between productivity and FDI flows. It is also worth noticing that all coefficients are statistically significant. In case of the relationship between the unemployment rate and FDI flows, this appears to be consistently negative with the exception of the Czech Republic. The negative sign implies that an increase in the unemployment rate will lead to a decrease in the amount of net FDI flows. The estimation of model 3 provides a clear result regarding the relation between FDI flows and the real exchange rate. Table 3 presents the sign and the statistical significance of the coefficients from the cointegrating equation. Table 3. The sign and statistical significance of the coefficients in the cointegration equation for model 3 Bulgaria Czech Rep. Hungary Poland Romania Slovakia Real exchange rate Sign - - - - - - t-stat 3.14* 0.43 0.30 1.05 5.54* 1.03 * denotes statistical significant coefficients at 5% significance level 216

The estimations clearly indicate a negative relationship between the real exchange rate and the FDI flows, a result which is consistent with the economic theory. We will also test for weak exogenity of the FDI flows or the real exchange rate within model 3.This implies to test the statistical significance of the speed of adjustment coefficient. If the speed of adjustment coefficient for a particular variable is statistically insignificant, we conclude that this variable has no role in the short term adjustment to long term equilibrium and is, hence, weakly exogenous. Table 4 presents the statistical significance of the speed of adjustment coefficients for each country. Table 4. The statistical significance of the speed of adjustment coefficients in the cointegration equation between FDI flows and the real exchange rate (Model 3) Bulgaria Czech Rep. Hungary Poland Romania Slovakia t-stats for: FDI flows -3.83* -4.62* -3.11* -3.99* -1.73-4.3* Real exchange rate -0.32 0.65 0.8-2.93* -2.5* -1.8 In four cases the real exchange rate is weakly exogenous which means that the short term correction to long term equilibrium is only performed by the FDI flows. In case of Poland, both variables take part in the error correction, while in case of Romania the FDI flows appear to be weakly exogenous. Although the econometric analysis achieved the establishment of a long term relationship between the real exchange rate and the FDI flows, the causality relationship is still unclear. In particular, we are interested to establish whether the increase in FDI flows causes the appreciation of the real exchange rate or the appreciation of the real exchange rate causes the increase in the amount of FDI flows. Because the two variables are nonstationary, we have to perform the Granger causality test in a Vector Error Correction framework. We used only one lag in the construction of the VEC and the model for each country is represented by the following relation: FDI t RERt α1 = α 2 FDI γ γ FDI ε t 1 11 12 t 1 1t ( β ) + + 1 β 2 RERt 1 γ 21 γ 22 RERt 1 ε 2t (3) 217

We will test two different null hypothesis: H γ 0 and H γ 0. If we 01 : 12 = 02 : 21 = reject H 01 then the real exchange rate Granger-causes FDI flows and if we reject H 02 then the FDI flows Granger-cause the real exchange rate. The p-values of the Wald test for each country are summarised in table 5. Table 5. The results of the VEC Granger causality/wald tests Bulgaria Czech Hungary Poland Romania Slovakia Rep. γ = 0 12 0.018* 0.069* 0.665 0.539 0.614 0.41 γ = 0 21 0.387 0.371 0.195 0.0143* 0.1593 0.41 * denotes statistical significant coefficients at 10% significance level The results show that there is no Granger causality between the real exchange rate and the FDI flows in case of Hungary, Romania and Slovakia. For Poland we reject H 02 but we cannot reject H 01 which means that FDI flows Granger-cause the real exchange rate. The opposite is true for Bulgaria and Czech Republic for which we reject H 01 but we cannot reject H02 which means that the real exchange rate Granger-causes the level of FDI flows. 5. Conclusions The aim of the present paper was to analyse the relationship between the amount of FDI flows and a number of fundamental variables in six Central and East European countries: Bulgaria, Czech Republic, Hungary, Poland, Romania and Slovakia. We chose two perform the analysis employing a number of three models: model 1 comprised the level of net FDI flows, the real exchange rate, the unemployment rate, labour productivity and the average wealth tax rate; model 2 was constructed from model 1 excluding the average wealth tax rate; model three comprised only the FDI flows and the real exchange rate. 218

Because the variables selected were nonstationary we used the Johansen cointegration test to check for a long term relationship between the variables and the VEC model to test for weak exogenity and Granger causality. The results show that there is a statistically significant negative relationship between FDI flows and the real exchange rate on one hand and between the FDI flows and the unemployment rate on the other hand. The relationship between the FDI flows and productivity is less clear cut as we obtained two positive signs and two negative signs. We found one cointegrating relationship in case of model 2 and for all countries except in case of Romania and we found one cointegrating relationship in case of model 3 and for all countries except in case of Hungary. The weak exogenity test proved that, in case of Romania, the level of FDI flows doesn t participate to the short term adjustment to long term equilibrium, in case of Poland, both FDI flows and the real exchange rate participate to the adjustment to equilibrium. In the other four countries the real exchange rate is weakly exogenous. Finally, the Granger causality test showed that there is no Granger causality between the real exchange rate and the FDI flows in case of Hungary, Romania and Slovakia. In case of Poland the FDI flows Granger-cause the real exchange rate, while the opposite is true for Bulgaria and Czech Republic for the real exchange rate Grangercauses the FDI flows. References [1] BENASSY QUERY QUERE, A., L. FONTAGNE, and A. LAHRECHE REVIL (2000), Foreign Direct Investment and the Prospects for Tax Co-Ordination in Europe", CEPII Working Paper No. 2000-06. [2] BLONIGEN, Bruce A. (1997) Firm-specific assets and the link between exchange rates and foreign direct investments, American Economic Review 87, 447 465. 219

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