Re-entering Europe: Does European Union candidacy boost foreign direct investment?

Transcription

1 Economics of Transition Volume 13 (1) 2005, Re-entering Europe: Does Blackwell Oxford, ECOT The Original Clausing The Economics European UK Article Publishing, and Europe: Dorobantu of Bank Transition Ltd. Does for Reconstruction European Union and Development, candidacy boost 2004foreign direct investment? European Union candidacy boost foreign direct investment? Kimberly A. Clausing and Cosmina L. Dorobantu Dept. of Economics, Reed College, Portland, OR, USA. Abstract This study examines the ability of the Central and Eastern European countries to attract foreign direct investment during the first decade of transition. After considering a model of profit maximizing firms, we undertake an empirical investigation of the factors that determine multinational firms location decisions within Europe. We find empirical support for the traditional market size and cost factors. In addition, we examine the effect of key European Union announcements regarding the accession process. Results indicate that the announcements had statistically significant and quantitatively important effects on foreign direct investment in the Central and Eastern European candidate countries. JEL classifications: F23, P33. Keywords: Foreign direct investment, transition economies, Central and Eastern Europe, EU accession, EU candidacy. The European Bank for Reconstruction and Development, Published by Blackwell Publishing, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA.

2 78 Clausing and Dorobantu 1. Introduction This paper empirically investigates the factors that influenced foreign direct investment in the Central and Eastern European (CEE) countries during the first decade of transition, The study advances previous work by considering the entire first decade of transition, by setting the CEE countries in a context of other European investment locations, and by emphasizing the important role of the European Union (EU) accession process in encouraging foreign direct investment. In the ten CEE countries examined in this study, foreign direct investment (FDI) became an important part of the domestic economy over the period 1992 to In 1992, the FDI stock averaged just two percent of GDP for these economies, ranging from essentially zero in several countries to eight percent in Hungary. By 2001, the FDI stock averaged 40 percent of GDP, ranging from a minimum of 17 percent in Slovenia to a maximum of 61 percent in Estonia. Foreign direct investment has played a crucial role in Central and Eastern European countries transition from socialist to capitalist economies. FDI helped the process of restructuring the state-owned enterprises, created competitive pressures in the CEE economies, provided important inflows of capital, and brought in outside managerial and technical expertise. These developments improved the microeconomic environment within the transition countries. While many previous studies have examined the determinants of FDI, there are relatively few that consider foreign direct investment in the CEE region, and even fewer that consider the entire first decade of transition. Only one previous study has analysed the importance of the Central and Eastern European states potential accession to the European Union (EU) on inward FDI. 2 In the following analysis, we examine the ability of ten CEE transition countries to attract foreign direct investment in the context of European integration. After considering a model of profit maximizing multinational firms, the paper undertakes an empirical investigation of the factors that determine firms European investment location decisions. Traditional market size and cost factors are considered, including openness to trade, the size of the receiving country s economy, the corporate income tax rate, labour costs, and macroeconomic policy. This paper furthers the understanding of the factors determining multinationals location decision by investigating the effects of important EU announcements regarding CEE countries accession on FDI. The empirical results consistently indicate that the EU announcements had statistically significant and quantitatively important effects on foreign direct investment in the CEE states. This work has important policy implications regarding the determinants of foreign 1 This study examines foreign direct investment in Bulgaria, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovak Republic and Slovenia during the period This study has several limitations, discussed in the literature review below.

3 Re-entering Europe: Does European Union Candidacy Boost Foreign Direct Investment? 79 direct investment as well as the desirability of prospective European Union membership. 2. Background The theory of multinational firms suggests that firms will invest abroad only if they possess the three OLI types of advantages: ownership, location, and internalization (Dunning, 1992, 1977). In the past decade, this theory has advanced through the creation of several general equilibrium theoretical frameworks, including Brainard (1993), Markusen and Venables (1998), Dixit and Pindyck (1994), and Markusen (2002). In this analysis, we focus on the location decision of existing multinational firms; we do not address firms decisions regarding how to serve a given market. Previous empirical work on the determinants of multinational firms location decisions includes Wheeler and Mody (1992), Devereux and Griffith (1998), Wang and Swain (1995), and Billington (1999). These studies estimate the effect on foreign direct investment of factors such as market size, labour costs, tax rates, and the overall quality of the receiving countries investment climate. Wheeler and Mody (1992) emphasize that previous investment stocks predict future flows of foreign direct investment; they also find that market size is a more important determinant of foreign investment decisions than labour costs or tax rates. Devereux and Griffith (1998) find that market size and tax rates are important determinants of investment flows, while labour costs do not play an important role. Wang and Swain (1995) find that FDI is determined by market size, capital costs, and political stability. Billington (1999) considers foreign direct investment choices both among countries and among regions within the United Kingdom. He finds that market size, labour costs, and trade and tax policy variables affect country location decisions, while population density, labour costs, and unemployment affect regional location decisions. In the following brief meta-analysis, we review the relatively few studies on the determinants of foreign direct investment in Central and Eastern Europe. The existing studies can be categorized by the type of data used: survey data, aggregate data, or disaggregate data broken down by sector or region. Representative papers from each category are reviewed; a table detailing the findings of these studies is also provided. 3 Meyer (1995) and Lankes and Venables (1996) rely on survey data to examine the overall behaviour of multinational firms in the CEE region. Meyer (1995) finds 3 Several articles have been written on how beneficial foreign direct investment has been for the CEE economies. These studies will not be discussed here, but the interested reader is referred to Kinoshita (2001), Barrell and Holland (2000), Konings (2001), and United Nations Economic Commission for Europe (2000).

4 80 Clausing and Dorobantu that market size is the primary determinant of foreign direct investment in the CEE region and that labour costs play a statistically insignificant role. Lankes and Venables (1996) reinforce Meyer s findings, but also emphasize the importance of political and economic stability, as well as the level of perceived risk, in attracting foreign investors to Central and Eastern Europe. Lansbury et al. (1996), Holland and Pain (1998), Woodward et al. (2000), and Carstensen and Toubal (2003) use aggregate foreign direct investment data similar to that employed in this paper. These studies find evidence to support the hypothesis that transition specific factors, such as the process of privatization and the perceived level of risk, influence the amount of foreign direct investment received by the CEE countries. The findings of these studies also support the hypothesis that market-seeking and efficiency-seeking considerations affect foreign investors location decision. Lansbury et al. (1996) and Holland and Pain (1998) find the openness of the economy and the prevalent labour costs to statistically significantly influence FDI, while Woodward et al. (2000) and Carstensen and Toubal (2003) emphasize the importance of tax incentives, as well as market size and potential, in attracting FDI to Central and Eastern Europe. Altomonte (1998), Resmini (2000), Altomonte and Guagliano (2001), and Smarzynska (2002) use data broken down by sector of activity to examine how the determinants of FDI differ across industries. Altomonte (1998) finds that the relative cost of labour deters FDI only in sectors that have low or middle to low levels of sunk costs, while the level of risk affects all sub-sector specifications. Resmini (2000) finds that the level of labour costs and the perceived quality of the business environment are statistically significant determinants of FDI only for projects undertaken in scale-intensive and high-tech sectors. Market size and the existence of agglomeration economies are statistically significant only for projects undertaken in traditional sectors. Altomonte and Guagliano (2001) find that local demand and the quality of the labour force affect every sub-sector specification, while a healthy legal environment has a positive influence only on industries with high levels of sunk costs. Finally, Smarzynska (2002) finds that market size variables, the corporate tax rate, and several transition related factors affect all foreign investment projects, while the existence of well-established intellectual property rights affects FDI mainly in high-tech sectors. Brada et al. (2003) and Kinoshita and Campos (2003) seek to identify the factors that contribute to the uneven distribution of foreign direct investment among the ex-communist countries. Brada et al. (2003) find that besides the traditional FDI determinants, the amount of foreign direct investment received by the Central European countries and the Baltic states is also influenced by transition-specific factors, such as the advancement of privatization, price and trade liberalization, banking sector reform and the eradication of corruption. The amount of FDI received by the Balkan states is further influenced by the political instability in the region. Kinoshita and Campos (2003) find that the quality of the labour force, the infrastructure, the availability of natural resources and the openness of the

5 Re-entering Europe: Does European Union Candidacy Boost Foreign Direct Investment? 81 economy have a statistically significant effect on FDI only in the CIS countries. The existence of agglomeration economies and the distance from the core of the EU, on the other hand, have a positive and statistically significant effect only on the FDI received by non-cis countries. The extent of external liberalization, the removal of FDI restrictions, and the quality of the legal and bureaucratic environment are found to affect FDI in all the countries studied by Kinoshita and Campos (2003). These studies on the determinants of foreign direct investment in the transition countries are summarized in Table 1. For each study, the results of the preferred equation are reported. 4 The variables that are statistically insignificant are shown in italics. 2.1 The EU s enlargement process and FDI While the literature reviewed above addresses many aspects of the determinants of foreign direct investment in the CEE transition countries, it does not consider the role of the European Union accession process. The European Union s commitment to accept qualifying CEE states within its ranks, as well as the enlargement process that ensued, are likely to have influenced foreign direct investment in Central and Eastern Europe by reducing the perceived level of risk in the region. The prospect of accession to the EU provided foreign investors with a measure of the extent and success of each CEE country s reform policies. Further, the promise of admission into the Union raised the likelihood of important benefits: a guarantee of free trade with the EU member states, and the CEE countries adoption of a business and legal environment similar to that of EU member states. Because the European Union s enlargement process provided important inducements to foreign investors, it is important to consider it as one of the factors that influenced the amount of FDI received by the CEE states. In this section, we review the accession process and discuss the one previous study that has considered this question. The economic relationship between the European Union and the Central and Eastern European countries began soon after the collapse of communism, when the EU granted the CEE states the General System of Preferences (GSP). In the early 1990s, as the Union concluded Trade and Cooperation Agreements with several ex-communist states, discussions about the possibility of accepting the Central and Eastern European countries into the Union started to intensify. The EU committed to enlarge at the Copenhagen European Council in June 1993, when it officially declared that the associated countries in Central and Eastern Europe that so desire shall become members of the European Union. (European Council in Copenhagen (1993), p. 13) At the same time, a provision was 4 Where no preferred equation has been identified, the overall results of the study are presented.

6 Table 1. Literature on FDI determinants for transition countries Study Years Traditional FDI determinants Transition-specific factors Others Market-seeking considerations Efficiency-seeking considerations General Studies Lansbury et al. (1996) Holland and Pain (1998) Woodward et al. (2000) Carstensen and Toubal (2003) Sectoral Studies Altomonte (1998) extent of trade between home and host countries relative labour cost + private sector share of GDP risk + relative stock of patents granted to residents of the host country relative energy consumption extent of trade relative labour cost economic risk + proximity to the EU with the EU labour productivity method of privatization + Baltic dummy GDP per capita + tax incentives political risk + population labour cost market potential relative labour cost + private market share + previous FDI tariffs corporate tax political risk method + skilled labour of privatization + relative labour-capital endowment population (significant relative labour cost + investors perceptions + proximity to the for all sectors) (significant only for of the business climate EU (significant only GDP per capita sectors that have (significant only for sectors for sectors that have (insignificant low or middle to low that have very low or very middle to high levels for all sectors) levels of sunk costs) high levels of sunk costs) of sunk costs) uncertainty of the local environment (significant only for some sectors) 82 Clausing and Dorobantu

7 Resmini (2000) Sectoral Studies Altomonte and Guagliano (2001) Smarzynska (2002) Table 1. (cont) Literature on FDI determinants for transition countries Study Years Traditional FDI determinants Transition-specific factors Others Market-seeking considerations GDP per capita (significant only for scale intensive and high tech sectors) + population (significant only for scale intensive and traditional sectors) + extent of trade with the EU (significant only for traditional sectors) population (significant for all sectors) + market access (significant for all sectors) Efficiency-seeking considerations relative labour cost (significant only for scale intensive and high tech sectors) extent of trade with the + investors perceptions of the business climate (significant only for high tech and scale intensive sectors) EU (significant only for high tech sectors) + quality of the legal environment (significant only for industries with high level of sunk costs, such as manufacturing and economies of scale industries) population corporate tax + progress in transition + + GDP per capita extent of trade (significant only + effectiveness of legal regulations governing investment for high tech sectors) + advanced privatization + intellectual property protection (significant only for high tech sectors) level of corruption + size of the manufacturing sector (significant only for high tech sectors) size of the manufacturing sector (significant only for specialized products and traditional sectors) level of education (significant for all sectors) Re-entering Europe: Does European Union Candidacy Boost Foreign Direct Investment? 83

8 Study Years Traditional FDI determinants Transition-specific factors Others Regional Studies Brada et al. (2003) Kinoshita and Campos (2003) Market-seeking considerations GDP extent of trade extent of trade (significant only for CIS countries) GDP per capita Note: Variables in italics are not statistically significant. Table 1. (cont) Literature on FDI determinants for transition countries Efficiency-seeking considerations GDP per capita + foreign exchange and trade liberalization + + proximity to the EU number of phone lines + banking sector reform inflation price liberalization private sector share level of corruption political instability (significant only for the Balkans) method of privatization labour cost + extent of external + natural resources liberalization (significant only for + rule of law the CIS countries) + quality of bureaucracy + secondary education FDI restrictions enrollment rate inflation (significant only for the CIS) + previous FDI (significant only for non-cis countries) + number of phone lines (significant only for the CIS) proximity to the EU 84 Clausing and Dorobantu

9 Re-entering Europe: Does European Union Candidacy Boost Foreign Direct Investment? 85 made that the CEE countries accession was conditional on their compliance with a set of economic, political, and administrative criteria set by the Union. The economic criteria required the CEE countries to have a functioning market economy and to be able to face the competitive pressures within the Union. The political criteria required the ex-communist states to be stable democracies that are governed by the rule of law, respectful of human rights, and protective of their minorities. Finally, the administrative criteria required that the acceding countries have the proper institutions for adhering to the political, economic, and monetary rules of the Union. 5 In 1997, the European Commission evaluated for the first time the progress made by the Central and Eastern European countries in meeting the accession criteria. The Commission summarized its findings and recommendations in Agenda 2000, a document released in July Regarding enlargement, the Commission proposed that accession negotiations start with a group of first wave countries (Cyprus, the Czech Republic, Estonia, Hungary, Poland and Slovenia) and continue at a later date with a group of second wave countries (Bulgaria, Malta, Latvia, Lithuania, Romania and Slovakia). Based on the Commission s recommendations, the enlargement process was launched in December 1997, at the Luxembourg European Council. In October 2002, based on its evaluation of the applicant countries compliance with the Copenhagen criteria, the Commission recommended that the Council conclude the accession negotiations with ten countries (Cyprus, the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Slovakia and Slovenia). The Commission also suggested setting 2007 as the tentative date for the accession of Romania and Bulgaria. The European Council endorsed the Commission s recommendation in the winter of We are aware of only one study that undertakes an empirical investigation of the effect of the European Union s enlargement process on foreign direct investment in the CEE countries. 6 Bevan and Estrin (2000) examine FDI flows from 18 market economies to the ten CEE accession countries and Ukraine over the period 1994 to The authors analyse the impact on FDI of the announcements made by the European Union regarding CEE countries prospects of admission in June 1993 and July Surprisingly, Bevan and Estrin (2000) find that the June 1993 announcement had a statistically insignificant effect on FDI flows. The authors also find that the July 1997 announcement influenced FDI flows positively and significantly only in five of the ten accession countries. 8 5 An additional criterion, requiring the CEE countries to adjust their administrative structures, was added at the Madrid European Council in December A few authors looked at the policy changes that will occur in the CEE countries upon accession and made speculative predictions about the effect that these policy changes will have on FDI. See, for example, Hunya (2000), Baldwin, Francois and Portes (1997), and Kaminski (2001). 7 The ten CEE accession countries are Bulgaria, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovak Republic and Slovenia. Ukraine was included in the study as a control variable. 8 These countries are the Czech Republic, Estonia, Hungary, Poland and Slovenia.

10 86 Clausing and Dorobantu The finding that the prospect of EU membership did not have a notable impact on the FDI inflows of the CEE countries is surprising on theoretical grounds, and it is probably the result of the limitations of Bevan and Estrin s (2000) study. Most importantly, the time dimension of their work ( ) does not enable a longterm perspective in which to judge changes in foreign direct investment patterns. In addition, their dataset only includes accession countries (and Ukraine), neglecting to account for the larger context of other European location options. Further, some of the authors control variables, such as using a country s private sector share of GDP as a measure of transition progress, are problematic. 9 The existing literature does not clearly indicate whether the European Union s process of enlargement changed the amount of foreign direct investment received by the CEE countries. This study will undertake a thorough analysis of this issue, examining the question in the context of a model that considers the major determinants of multinational firms location decisions. 3. Model and specification We will start by examining the behaviour of a representative multinational firm deciding where to invest. As mentioned above, we are not concerned with a firm s decision to become multinational; we simply assume that the firm in question has chosen to invest abroad due to its OLI advantages. Our concern is with multinationals firms location decisions within Europe. Given that multinational firms choose to invest in Europe, what factors determine the scale of their investment in particular European countries? Consider a representative multinational firm deciding on the scale and location of its foreign operations. It seeks to maximize its global after-tax profits, Π g, the sum of after-tax profits in each country in which the firm operates. Π g = Σ i (1 Τ i )Π i (1) Here Τ i is the tax rate of country i, and Π i are profits in country i. Locations (indexed by i) are more attractive to the extent that they generate higher after-tax profits. 10 Profits in each location are a function of revenues and costs. 9 For example, Russia has one of the highest private sector shares of GDP, due to its mass privatization programme of , and yet it ranks behind most of the CEE countries in terms of progress in completing the transition process. 10 Countries are here assumed to tax foreign income on a territorial basis. As a practical matter, several countries (including the United States) tax income on a residence basis, taxing the foreign income of their resident firms while allowing tax credits for taxes paid abroad. Under this system, deferral is usually allowed until income is repatriated, leaving firms with a substantial incentive to invest in low tax countries.

11 Re-entering Europe: Does European Union Candidacy Boost Foreign Direct Investment? 87 Π i = (1 Τ i )[R i (Q i ) C i (Q i ) θ i ] (2) R i is the revenue generated in country i from selling output Q i, and C i are the costs of producing Q i. θ i is a term that captures the costs that firms incur due to the investment environment prevalent in the host countries. These costs arise partly as a result of the macroeconomic policies pursued by the host countries governments. Q i = f (S i, E i ) (3) Output in country i is a function of the country s market size, S i, as well as the countries export opportunities, E i. S i and E i in turn depend on the size of a country s economy, the purchasing power of its citizens, how integrated the local market is with markets in other countries, and the country s openness to trade. Output is produced incurring costs, C i. In order to produce a unit of output, a firm faces two costs: the purchase of imported intermediate inputs and local production costs. The cost of imported inputs, m i, depends on trade barriers, t i. 11 Local production costs, x i, depend on local factors including wage levels, w i. C i = m i (t i ) + x i (w i ) (4) Profits in a given country, therefore, can be expressed by the following equation, where P i is the market price of the firm s output. Π i = (1 Τ i )[Q i (S i, E i ) * (P i m i (t i ) x i (w i )) θ i ] (5) In order to operationalize this model, we find variables to proxy for the above determinants of a multinational firm s location decision. Table 2 indicates the relationship between the firm s profit maximization problem and the empirical specification. We estimate the following regression equation: ln fdi it = β 0 + β 1 ln gdp it + β 2 ln gdppc it + (6) β 3 ln compensation it + β 4 open it + β 5 tax it + β 6 ln misery it + β 7 eu it + β 8 copen it + β 9 firstw it + β 10 secw it + Σ γ k D ki + u it 11 This discussion implicitly assumes that the multinational firm is organized vertically, importing inputs from other countries. However, even for horizontally organized multinationals, trade costs may be important as goods are still exchanged with affiliates and non-affiliates in other countries.

12 88 Clausing and Dorobantu Table 2. Variables affecting a firm s location decision Variable Proxies Expected effect Τ i Statutory corporate income tax rate S i GDP + GDP per capita + E i EU membership + EU announcements + Openness + t i Openness + w i Average compensation rate θ i Misery index EU membership + EU announcements + Z i Other country or region specific factors depends where i = 1,..., 28 host countries and t = 1992,..., A constant elasticity model will be used. Natural logs narrow the range of the variables included in the model and allow the coefficients to be interpreted as elasticities. 12 The reader is referred to the data appendix for a discussion of the data sources. The dependent variable is the stock of foreign direct investment (fdi) prevalent in host country i at time t. As Table 2 indicates, market size is proxied by several variables. GDP (gdp) measures the absolute market size of the host countries considered, and it is expected to influence foreign direct investment positively. GDP per-capita (gdppc) captures the purchasing power of local consumers, while the average compensation rate (comp.) measures the labour costs prevalent in the recipient country. 13 The purchasing power of local consumers is expected to be positively correlated with foreign direct investment. Labour costs are expected to be negatively correlated with FDI. Trade costs are proxied by a variable we refer to as open. This variable is constructed by regressing host countries import to GDP ratios on population and population squared; the residuals obtained from this regression are the openness 12 Aside from dummy variables, the openness measure and the corporate income tax rate are the only variables in the model that will not appear in log form. The openness measure will be included in the level form because it takes on negative values. The corporate tax rate will be included in the level form, but implied elasticities will be calculated as the results are discussed. 13 The GDP per capita variable needs to be interpreted with care, as it is usually (and unsurprisingly) highly correlated with the variable on average compensation rates.

13 Re-entering Europe: Does European Union Candidacy Boost Foreign Direct Investment? 89 measure. This variable measures the extent of import penetration that is not explained by a country s population. 14 The expected sign for open is positive. 15 Countries statutory corporate income tax rates (tax) are used to measure the tax burden associated with investing in a particular country. 16 The expected sign for the tax variable is negative. Country-specific costs, θ i above, are captured by a number of variables that affect the investment environment. Macroeconomic stability is one component of country-specific risk. Here we will utilize a misery index (misery), constructed by adding each recipient country s inflation and unemployment rates. The expected sign for the misery index is negative. Countries that belong to the EU give multinational firms access to the entire common market, and hence the expected sign on the EU dummy variable (eu) is positive. This variable also captures reduced risk-related costs associated with investing in the EU. Membership in the European Union (either current or prospective) may indicate a favourable investment climate, as it provides investors with a guarantee of the country s adherence to certain economic standards, as well as the adoption of regulations designed to harmonize its business and legal environment with the other member states. European Union membership also opens the possibility of a country s adopting the euro, which further harmonizes the country s macroeconomic policies with those of the rest of Europe. 17 In this study, the European Union s announcements regarding enlargement are of central interest, and they indicate how external factors influence foreign investment in Central and Eastern Europe. In terms of the above model, such announcements affect both the market access of a candidate country, E i, and the perceived level of country specific costs, θ i. Three announcement dummy variables are included in the specification. The first (copen) measures the impact of the commitment that the European Council made in Copenhagen in June 1993 to grant the Central and Eastern European countries access to the Union. This dummy variable is one from 1994 onward for all the countries included in the announcement and zero otherwise. The second and third dummy variables measure the impact of the July 1997 release of EU s Agenda 2000, the document which identified two waves of accession. Firstw is a dummy variable equal to one from 1998 onward if the country is 14 More populous countries naturally have lower import/gdp ratios although this is not a linear relationship. 15 We experimented with other measures of openness, including the ones reported in the Penn World Table, which consider the amount of total trade relative to GDP. Using these other measures did not substantively change any of the results. 16 While this measure does not adequately capture the prevalence of special arrangements and provisions that govern the true tax treatment of firms, it is the best measure that we have available. See footnote 10 regarding the implication of residence based income taxation. 17 In particular, independent monetary policy is relinquished, and fiscal policy is constrained by the Growth and Stability Pact.

14 90 Clausing and Dorobantu part of the group identified as first wave (Czech Republic, Estonia, Hungary, Poland and Slovenia) and zero otherwise. Secw is a dummy equal to one from 1998 onward for the countries identified as second wave (Bulgaria, Latvia, Lithuania, Romania, and the Slovak Republic) and zero otherwise. All three dummy variables measuring the impact of the European Union announcements regarding enlargement are expected to have a positive sign. It is important to control for differences across the main European regions as such differences probably influence foreign direct investment. Regional dummies are included in the model; they are constructed as shown below. The Core region is the omitted group. Regional dummy variables Variable Region Countries in the region central Central Europe Czech Republic, Hungary, Poland, Slovak Republic balkan Balkan region Bulgaria, Romania, Slovenia turkey Turkey Turkey med Mediterranean region Greece, Italy, Portugal, Spain benelux Benelux countries Belgium, Luxembourg, Netherlands ukire Ireland and the UK Ireland, United Kingdom scand Scandinavian region Denmark, Finland, Norway, Sweden baltic Baltic states Estonia, Latvia, Lithuania core Core region Austria, France, Germany, Switzerland While we have controlled for all of the influences that our simple profit maximizing model describes, there remain some potentially important variables that are omitted from the analysis. For example, transportation costs to other central markets may be important. While the regional dummy variables may capture some of this influence, this is probably an incomplete solution. Second, measures of political or economic risk may also be important. The misery index and the EUrelated variables capture some of the risks associated with investing in the CEE countries, but they may not account for all the political and economic obstacles. Finally, privatization efforts are likely to have important influences on foreign direct investment. One way of capturing these effects is by including a variable describing the method of privatization. This is an imperfect measure, however. Some countries implemented several privatization methods, while others changed their privatization programmes over time, which makes it hard to build a convincing classification scheme. Another way of capturing privatization efforts is by including a variable measuring the private sector share of GDP. As noted earlier, measuring the success of privatization programmes in this way can be deceiving. Due to the limitations of the data measuring privatization efforts, we chose to exclude this variable from our analysis.

15 Re-entering Europe: Does European Union Candidacy Boost Foreign Direct Investment? 91 Table 3. Summary statistics Variable Units Obs. Mean Standard deviation fdi millions of 1995 US dollars , ,940 tax percent gdp millions of 1995 US dollars , ,833 gdppc 1995 US dollars ,978 13,070 comp US dollars eu dummy: member of EU open residuals from IM/GDP eqn misery index copen dummy: included in EU s 1993 announcement firstw dummy: part of first wave of accession secw dummy: part of second wave of accession Data Twenty-eight host countries are considered in the following analysis. They include eleven candidate countries 18 (Bulgaria, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovak Republic, Slovenia and Turkey), the fifteen European Union member states (Austria, Belgium and Luxembourg, 19 Denmark, Finland, France, Germany, Greece, Ireland, Italy, Netherlands, Portugal, Spain, Sweden and the United Kingdom), and the two major Western European countries that are not part of the EU (Norway and Switzerland). The time period considered in the study is 1992 to Foreign direct investment in Central and Eastern Europe was practically non-existent before 1992 and data are only available through The data are summarized in Table 3 below. Data sources are detailed in Appendix 1; a correlation table is provided in Appendix 2. The FDI stock relative to GDP has been increasing over time in the ten Central and Eastern European countries that applied for European Union membership. As Figure 1 shows, however, this increase has not been constant. The majority of the candidate countries experienced a significant increase in the stock of foreign direct 18 Cyprus and Malta are not included due to insufficient data. 19 The data for Belgium and Luxembourg are reported together, so the two countries will be included in the regression analysis as one unit.

16 92 Clausing and Dorobantu Figure 1. FDI stock/gdp in CEE Countries, investment per GDP in time periods that coincide with the key European Union announcements regarding the CEE countries accession. 5. Results Results for the standard specification (Equation (6) above) are shown in Table 4. First consider only Equation (1), the baseline specification. The standard errors shown in parentheses are robust to clustering; hence, they are robust to any type of correlation among the observations for each of the country panels in the sample. The reported coefficients have the expected signs. The market size of the host countries, proxied by their gross domestic product, has a positive and statistically significant effect on foreign direct investment. The coefficient indicates that a one percent increase in GDP is associated with a proportional increase in foreign direct investment. The GDP per-capita term is meant to capture market size effects (as a richer populous provides a more attractive market), while the comp. variable is meant to proxy for labour costs. The two measures are highly correlated, which confounds the interpretation of the results. Still, both variables are of borderline statistical significance, and their coefficients have the expected signs. The GDP per-capita coefficient is positively related to foreign direct investment, while average compensation rates are negatively related. The openness variable is statistically significant and positively associated with FDI, indicating that countries that are more open to trade receive a higher amount of foreign direct investment. Countries with higher corporate income tax rates receive less FDI. The tax variable in Equation (1) is statistically significant and its coefficient implies an elasticity of FDI with respect to the corporate income tax rate

17 Re-entering Europe: Does European Union Candidacy Boost Foreign Direct Investment? 93 Table 4. Regressions explaining ln(fdi), Equation (1) Equation (2) Equation (3) ln gdp 1.071** (0.121) 1.043** (0.108) 0.967** (0.093) ln gdppc 0.538* (0.303) 0.555* (0.333) 0.520** (0.212) ln comp (0.318) 0.540* (0.325) (0.140) open 1.933** (0.787) (0.906) 1.168** (0.413) tax 2.549** (0.858) 1.807** (0.800) (0.551) ln misery 0.376** (0.156) 0.211* (0.137) (0.062) eu 0.415** (0.174) 0.231* (0.138) 0.278** (0.102) copen 0.819** (0.274) 0.823** (0.251) 0.726** (0.103) firstw (0.288) (0.273) (0.101) secw 0.730** (0.359) 0.678* (0.404) 0.941** (0.120) centr (1.074) (0.959) balk (1.124) (0.973) turk (0.791) (0.693) med (0.469) (0.412) ukire (0.294) 0.455** (0.256) scan (0.251) (0.251) balt (1.194) (1.022) benelux 0.989** (0.312) 1.368** (0.360) year 0.093** (0.012) Obs.: R-squared: Note: The quantities in parentheses are standard errors. ** (*) indicates statistically significant at α = 0.05 (0.10) or better. of These results imply that countries trade and tax policies may have substantial effects on their inflows of foreign direct investment. This finding is consistent with our model of the location decisions of profit-maximizing multinational firms. 20 Interpreting the magnitude of the coefficient on tax is slightly complicated. One can make use of the fact that for a small t, ln(1 t) is approximately equal to t. The coefficient on tax, therefore, can be interpreted as the coefficient of ln(1 tax). The regression results show that the elasticity of foreign direct investment with respect to 1 tax is The summary statistics indicate that the mean of the corporate tax rates of the countries considered in this study is For this dataset, therefore, 1 tax equals, on average, This value is very close to twice (2.07) the mean of the corporate tax rates. Due to this, one can consider the elasticity of foreign direct investment with respect to tax to be approximately equal to half of the elasticity of FDI with respect to 1 tax.

18 94 Clausing and Dorobantu Several of the variables included in the regression proxy for the perceived level of risk associated with investing in a given country. The misery index, which measures the level of macroeconomic stability in the countries considered, influences foreign direct investment negatively and statistically significantly. The elasticity of FDI with respect to misery is EU members receive more foreign direct investment, controlling for the other variables in the model. The EU dummy captures both the increased market access available to the member countries, and the reduced risk associated with investing in an EU economy. Importantly, two of the three European Union announcements regarding the enlargement process had a positive and statistically significant effect on foreign direct investment. The Copenhagen announcement, which was made in June 1993, influenced positively the amount of FDI received by the CEE countries. In 1993, the level of risk associated with investing in Central and Eastern Europe was high, as all of the countries in the region were in the beginning stages of their transition process. The magnitude and statistical significance of copen suggests that European Union s commitment to enlarge lowered the perceived level of risk associated with investing in Central and Eastern Europe. Interestingly, the release of the Agenda 2000 in July 1997 affected foreign direct investment in the first wave and second wave countries differently. The release of this document had a large and statistically significant effect on FDI going to the countries identified as second wave; the coefficient on the second wave dummy is The coefficient on the first wave dummy, however, is smaller (0.38), and the variable is not statistically significant at conventional significance levels. The difference in the magnitude and statistical significance of the first and second wave dummy variables might be explained by the way in which the transition programmes adopted in the CEE countries influenced the perceived chances of accession. The success of the reform measures adopted in the first wave countries ensured that these states would be offered European Union membership. However, the second wave countries transition processes have been much slower, and their economies have not been performing as well as those of the countries identified as first wave. Consequently, there was considerable doubt prior to the release of the Agenda 2000 as to whether the countries identified as second wave would be offered European Union membership. The release of the Agenda 2000 reduced this doubt. The increase in foreign direct investment in the second wave countries, therefore, may be attributable to the reduced uncertainty surrounding these countries prospects of joining the European Union. The adjusted R-squared for Equation (1) indicates that the variables included explain approximately 94 percent of the variation in foreign direct investment. Equation (2) retains standard errors robust to clustering, and also includes year fixed effects; this slightly increases the explanatory power of the regression. The

19 Re-entering Europe: Does European Union Candidacy Boost Foreign Direct Investment? 95 other results are broadly consistent with those in Equation (1); in all cases, the coefficient confidence intervals overlap. 21 A few changes are noteworthy. The openness variable is no longer estimated precisely. The tax, misery, and EU variables are still statistically significant (at α = 0.10 in the case of the latter two), but are somewhat reduced in size. The magnitude and statistical significance of the Copenhagen announcement dummy variable, however, is almost identical to that of Equation (1). In addition, results for the first and second wave dummies are also similar. Equation (3) estimates a random country-effects specification. In this specification, we include a time trend, the variable labeled year, instead of year fixed-effects. Results are broadly consistent with those in Equation (1), although a few differences stand out. While the openness variable is again estimated to have a statistically significant and positive effect on foreign investment, the tax, comp, and misery variables are no longer estimated precisely. This is probably due to the fact that the country-level random effects are capturing much of the cross-sectional variation in these measures. The size and statistical significance of the EU accession variables remain similar to those obtained by estimating Equations (1) and (2). Table 5 considers the same three specifications as Table 4, including two new interaction terms. These terms investigate how the tax and labour cost effects differ for the countries included in the Copenhagen announcement (referred to below as the Copenhagen countries ). As noted in the literature review, some authors have hypothesized that the motivations for undertaking investment projects in the CEE countries may differ from the incentives for undertaking investment projects in Western Europe. In particular, foreign investors in the CEE region might be more sensitive to cost-minimizing considerations; this hypothesis is examined in the specifications of Table 5. We obtain some noteworthy results. Consider Equation (4). First, foreign investment in the Copenhagen countries is highly sensitive to tax differences, but investment more broadly is no longer statistically significantly related to tax rates. The elasticity of FDI with respect to the corporate tax rate in the Copenhagen countries is 1.89, a higher tax elasticity than found in Table Second, average compensation rates influence foreign investment negatively in the Copenhagen countries, but do not have a statistically significant effect in the sample as a whole. Finally, the magnitude of the coefficient on the Copenhagen dummy variable is now much larger. This is partly accounted for by the large negative tax and labour cost effects associated with the Copenhagen countries. Other results are quite similar to those of the previous table. 21 One can also perform a panel-correlated standard errors regression that allows for panel-level heteroskedasticity and contemporaneous correlation of observations between the panels. This generates coefficient estimates identical to those of Equation (2), with standard errors that are uniformly smaller. Consequently, the coefficients on gdppc, comp, misery, eu, firstw, and secw are now all statistically significant with 95 percent confidence. 22 Again, elasticities must be calculated by dividing the tax coefficient by 2.07, as explained in footnote 13.

20 96 Clausing and Dorobantu Table 5. Regressions explaining ln(fdi), (with interaction terms) Equation (4) Equation (5) Equation (6) ln gdp 1.019** (0.119) 1.015** (0.109) 0.955** (0.097) ln gdppc 0.762** (0.301) 0.707** (0.322) 0.421** (0.214) ln comp (0.364) (0.374) (0.140) open 1.862** (0.705) (0.836) 1.359** (0.418) tax (0.960) (0.918) 1.731** (0.750) ln misery 0.387** (0.142) 0.226* (0.129) (0.061) eu 0.344** (0.154) (0.120) 0.252** (0.100) copen 6.507** (2.257) 4.696** (2.251) 2.680** (1.175) copen* tax 3.915** (1.331) 3.347** (1.021) (1.000) copen* ln comp 0.549* (0.299) (0.292) 0.325** (0.136) firstw (0.307) (0.289) 0.277** (0.115) secw 0.614* (0.361) (0.406) 0.961** (0.119) centr (1.365) (1.267) balk (1.429) (1.306) turk (1.122) (1.074) med (0.595) (0.529) ukire (0.323) 0.677** (0.300) scan (0.267) (0.270) balt (1.515) (1.352) benelux 0.956** (0.306) 1.317** (0.350) year 0.093** (0.011) Obs.: R-squared: Note: The quantities in parentheses are standard errors. ** (*) indicates statistically significant at α = 0.05 (0.10) or better. The results for the two Copenhagen interaction terms are probably due to the fact that there were virtually no economic ties between Central and Eastern Europe and the rest of the world prior to the start of transition. This lack of economic ties may have made foreign investors in the CEE countries more sensitive to costminimizing considerations, rather than previous investment arrangements. The addition of year dummies in Equation (5) generates similar results, although the Copenhagen labour cost variable is no longer estimated precisely, and the second wave variable is only statistically significant with 84 percent confidence, not meeting the typical threshold. The random effects specification in Equation (6) generates similar results to Equation (4). All of the accession variables are statistically

21 Re-entering Europe: Does European Union Candidacy Boost Foreign Direct Investment? 97 significant and positive. The Copenhagen tax interaction term is the only EUrelated variable that is not estimated precisely. In general, the above results indicate that the European Union announcements had a positive and statistically significant effect on foreign direct investment. In constructing the three dummy variables designed to measure the effect of the EU announcements (copen, firstw and secw), one assumed that this effect was constant over time. The equations in Table 6 relax this assumption, allowing the impact of the European Union announcements to vary across time. The effect of the EU Table 6. Regressions explaining ln(fdi), (with time varying announcement effects) Equation (7) Equation (8) Equation (9) ln gdp 1.080** (0.120) 1.048** (0.110) 0.979** (0.101) ln gdppc 0.664** (0.312) 0.635* (0.348) 0.683** (0.219) ln comp 0.611* (0.334) 0.595* (0.346) (0.143) open 1.538* (0.902) (0.976) 0.523** (0.444) tax 2.382** (0.886) 1.700** (0.854) (0.610) ln misery 0.380** (0.139) 0.220* (0.130) (0.062) eu 0.437** (0.176) 0.268* (0.145) 0.313** (0.100) copen * (0.208) 0.458** (0.205) 0.553** (0.120) copen ** (0.227) 0.738** (0.221) 0.742** (0.129) copen ** (0.249) 0.938** (0.222) 0.878** (0.133) copen ** (0.473) 1.312** (0.393) 1.127** (0.143) firstw ** (0.457) 1.336** (0.404) 1.114** (0.176) firstw ** (0.516) 1.336** (0.454) 1.112** (0.182) firstw ** (0.563) 1.246** (0.502) 1.081** (0.185) firstw ** (0.565) 1.623** (0.531) 1.301** (0.315) secw ** (0.647) 1.567** (0.611) 1.734** (0.200) secw ** (0.590) 1.654** (0.611) 1.878** (0.200) secw ** (0.631) 1.489** (0.659) 1.885** (0.240) secw ** (0.579) 1.643** (0.586) 2.019** (0.232) region effects? yes yes year 0.097** (0.011) Obs.: R-squared: Note: The quantities in parentheses are standard errors. Region effects are included in Eqns (7) and (8) but not reported due to space constraints. ** (*) indicates statistically significant at α = 0.05 (0.10) or better.