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International capital flows: has it been a
bless or a curse for the Central-Eastern
European economies?
Master thesis
Msc International Economics and Business
Zsófia Jackli
S377421
Supervisor: Jakob de Haan
Co-accessor: Judit dr.Burucs
University of Groningen
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Content
Abstract ... 3
Introduction ... 4
Stylized facts on GDP per capita and FDI ... 6
The growth of GDP per capita ... 6
Characteristics of foreign direct investment in the Central Eastern
European region ... 8
Literature Review... 11
Methodology ... 18
Model ... 18
Data ... 19
Empirical Results ... 22
Comparison of regressions and the interpretation of results ... 22
Marginal effects analysis of capital flows ont he impact of EU
membership ... 26
Examination of marginal effects of capital flows together with a
sensitivity analysis... 30
Conclusion ... 33
Bibliography ... 35
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Abstract
I have analyzed the convergence process in the Central Eastern European region. As a part of my hypothesis I investigated the effect of capital inflows and the EU membership on income gap between the EU15 and CEE countries’. I found, that FDI stocks and flows together with the EU membership significantly decreasing the income per capita gap between the regions . The increased volume of inward FDI enhanced better productivity and innovation, supported higher consumption, expanded the markets and export sector of the CEE countries and resulted declining rate of unemployment. However, due to the strong credit growth, the CEE countries got highly dependent on procyclical foreign investment, which made them highly vulnerable and exposed to the global financial cycle. Regarding their future growth, the main question is, whether the CEE countries will be able to attract value-added FDI inflows or get stuck as the cheap-labor supplier of Western Europe?
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Introduction
More than 25 years have passed since the countries of the Central Eastern European1 (CEE) region went through a political and economic transformation and moved from a Communist ideology towards a democratic political system and a market economy. They needed to reconstruct their economy and implement structural reforms to get over the disparities of their original state. These reforms have affected the countries differently, and consequently their level of economic development is diverse. The first country-group of the region -consisting Slovenia, the Slovak Republic, Poland, the Czech Republic, Latvia, Lithuania, Estonia and Hungary- joined to the European Union (EU) in 2004, while Bulgaria and Romania joined in 2007, and finally Croatia became EU member in 2013. A few of the countries -Latvia, Estonia, Slovenia, and the Slovak Republic- even introduced the euro as official money and became the member of the European Economic and Monetary Union.
In my thesis I aim to examine the existence of convergence within the European Union. I investigate whether the Central Eastern European region has converged to the EU15 countries2 in terms of GDP per capita (expressed in purchasing power units). To measure the convergence, I use the gap between the EU15 countries’ GDP per capita average and the individual CEE countries’ income per capita. I define convergence if this gap has decreased in the examine d period.
Even though I use many variables in my econometric model I put special attention on the free movement of capital, and capital flows. Namely, I assume that foreign direct investment (FDI) and capital flows, where FDI is excluded3 have a central role in the CEE region’s convergence. Since FDI is a rather long-term project unlike to portfolio investment, its impact on convergence will be longer as well.
My hypothesis is that convergence has occurred between the regions, fueled mainly by capital flows. FDI and the other forms of capital flows are hypothesized to have a significant and positive effect on the convergence of countries of the CEE region, so the distance in terms
1 I investigated the convergence of the following country from the region: Bulgaria, Croatia, Czech Republic,
Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia and Slovenia.
2 In the EU15 country group I collected data on the following countries: Austria, Belgium, Denmark, Finland,
France, Greece, Ireland, Italy, Luxembourg, The Netherlands, Portugal, Spain, Sweden and the United Kingdom.
3 Capital flows, excluded FDI is mainly consisted of private foreign debt, loans and debt securities, since the
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of GDP per capita has decreased over the years. Moreover, I assume that FDI, and more precisely the FDI stocks have stronger impact on convergence, than other forms of the capital flows. I further assume that, this convergence was accelerated by EU membership, and the countries of the CEE region received higher level of investment, which decreased the gap between them and the EU15 countries even more. To support my assumption, I examine the impact of EU membership together with the impact of capital flows on convergence.
Since the countries of the CEE region had gained independence in 1989 and in the beginning of the 1990s, data was not always available from the beginning of my research period. Therefore, I aimed to collect variables and data, which was reliable, and substitute the variables where I could not get a sufficient amount of data.
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Stylized facts on GDP per capita and FDI
The growth of GDP per capita in the Central Eastern European
region
Central Eastern European countries share many characteristics, as history, geographic location, culture, similar way of economic transition, similar time and path in case of becoming an EU member etc. Although, they converge towards the same steady state, and their growth and convergence will be fueled mainly by factors, which improve their structural competitiveness, the level of institutional environment and productivity, the pace of their convergence and economic development is not entirely identical. Therefore, it is worth to examine their development and economic growth not only at the regional, but at the country-level as well.
7 Table 1.: GDP growth of the CEE countries individually, and at the regional level too.
Country Growth rate Average
Average before 2004 Average after 2004 CEE average CEE average before 2004 CEE average after 2004 Bulgaria 2,92% 3,36% 2,23% 3,31% 2,84% 3,96% Croatia 2,11% 1,22% 3,50% Czech Republic 2,73% 2,81% 2,60% Estonia 4,07% 3,06% 5,64% Hungary 2,33% 1,78% 3,19% Latvia 3,97% 3,10% 5,32% Lithuania 4,13% 3,40% 5,27% Poland 4,16% 3,97% 4,46% Romania 3,41% 2,54% 3,85% Slovak Republic 4,03% 3,98% 4,10% Slovenia 2,56% 1,99% 3,44%
Source: World Bank, 2019.
8 Source: World Bank Group database, 2019.
defined as the ability of the implementation of the Acquis Communautaire4, thus the ability to take the obligations of membership (Copenhagen Criteria, Treaty on European Union, 1993.). Since structural changes played an essential role in convergence, I will include variables in my econometric model, which reflect the previously mentioned three criteria, such as institutiona l quality, trade openness etc.
Characteristics of foreign direct investment in the Central Eastern
European region
I examine the effect of a set of variables on the convergence, although as a I have mentioned beforehand, I pay special attention to the role of capital flows, especially to the role of FDI. Therefore, in connection with convergence and economic growth, it is worth to talk shortly about the potential reasons and impacts of foreign direct investment. By removing trade barriers and opening up the market for foreign investors, both foreign investors and the host
4 The Acquis Communautaire is a collecting term for all the legal acts, court decisions and the treaties of the
European Union which consist the European law.
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0 5000 10000 15000 20000 25000 30000 35000 40000 1 99 5 1 99 6 1 99 7 1 99 8 1 99 9 2 00 0 2 00 1 2 00 2 2 00 3 2 00 4 2 00 5 2 00 6 2 00 7 2 00 8 2 00 9 2 01 0 2 01 1 2 01 2 2 01 3 2 01 4 2 01 5 2 01 6 2 01 7 Te n ge ly cím G DP p er c a p it a (P PP ) Years
Graph 1.: Convergence of the CEE region toward the EU15
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countries gain. By letting multinational corporations and foreign investors in the countries, FDI was able to finance short-term needs, support government budgets and adjust balance of payment disparities (Radosevic et al., 2003). From the point of the foreign investor, FDI occurs because of cost-effectiveness, like the availability of cheap human and physical capital; environmental characteristics, such as the political, financial, economic and commercial situation in the host country; or strategic objectives, such as market domination or avoidance of market restrictions (Marinova, and Marinov, 2016.). Within FDI we can distinguish two types: vertical and horizontal. In case of vertical FDI, foreign investors are mainly interested in the cheaper and more abundant inputs available in the host country. Due to low cost of transportation they may locate the production in the host country, but they do not produce for the market of it but transport it back to the home market. While, in case of horizontal FDI, foreign investors are producing for the market of the host country, primarily because of high transportation costs of the products, like in the manufacturing sector or the high demand of the host country’s market (Barba-Navaretti and Venables, 2004). Concerning horizontal FDI, the size of the host country’s market is another determining feature. The size of the market and its market potential, and more importantly the amount of demand is positively correlated with the volume of inward FDI (Carril-Caccia and Pavlova, 2018).
Although, FDI moves to countries where the cost of resources, such as labor or physic al capital is relatively low, the host countries can gain as well. Besides, the fact, that investing in a country, new jobs are created, the country obtains several benefits, such as receiving technological knowledge; wider experience of investors on international markets; the amount of taxes paid by corporations provides as income for the state; the country can benefit from the infrastructure that investors may set up in order to facilitate the production; stocks of foreign direct investment make the host country more attractive for further investments, which may have a positive effect on the currency of the country as well. Besides, all the mentioned factor, the CEE countries hoped that direct investment would have spill-over effects on other sectors and industries, where the domestically owned companies were located, therefore countries privatized their previously state-owned companies, although its impact varied among the countries (Szent-Iványi, 2017).
10 Source: World Bank Group database, 2019.
countries (Benacek et al., 2000). After the 1990s the amount of inward FDI has significant ly increased. Governance and institutional quality had major impact on the increase, which supported the competitiveness of the countries too (Lipsey, 2006). Examining the pattern of FDI flows as a percentage of GDP in the region between 1995 and 2017, we can see a unitary tendency, but different distribution as in case of the GDP per capita. Besides the unequal distribution, not all of the countries received direct investment immediately. Poland, the Czech Republic and Hungary were the first receivers, while the rest of the countries opened up their economies around the 2000s. The share of the CEE countries’ in the global FDI stocks has increased from 0,6% to 3,5% between 1993 and 2007 (Szent-Iványi, 2017). Over the years, it got clear, that Hungary enjoys the highest level of FDI flows as a percentage of GDP in the region, while Estonia and for a short period Bulgaria receives relatively high FDI as well. According to the OECD, the return on foreign direct investment in Hungary was one of the highest within the organization, and it took approximately 10.6 billion USD in 2015, whic h equaled approximately the 10.6% of the GDP (OECD, 2017.). Besides the previously mentioned institutional quality, the advancement of infrastructure, and the low cost of labor, this high, inequal distribution of FDI in favor of Hungary can be explained by the favorable tax system and low corporate taxes in the beginning of the 2000s.
0 100 200 300 400 500 600 -20 -10 0 10 20 30 40 50 60 1 99 5 1 99 6 1 99 7 1 99 8 1 99 9 2 00 0 2 00 1 2 00 2 2 00 3 2 00 4 2 00 5 2 00 6 2 00 7 2 00 8 2 00 9 2 01 0 2 01 1 2 01 2 2 01 3 2 01 4 2 01 5 2 01 6 2 01 7 FDI s to ck s, a gg re ag te v a lu e o f e ve ry fi ve y ea rs ,re gio n al l ev el Ex p re ss ed a s a % o f G DP FDI fl o w s in th e C EE c o u n tr ie s, c ou n tr y le ve l Ex p re ss ed a s % o f G DP Years
Graph 2.: Distribution of FDI flows in the CEE region at the
country and regional level
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Literature review
Convergence has different interpretations, and definitions, different economic schools approach the issue from different point of views. The two most acknowledged forms of convergence are beta- and sigma-convergence. Beta-convergence refers to the effect when poor nations grow faster than rich nations, therefore they are able to catch up on them. Beta-convergence has direct connections to the neo-classical Solow model and the Cobb Douglas production function. According to them, convergence is resulting from capital deepening or better technology. Within beta-convergence we can distinguish two types: absolute and conditional. Absolute convergence refers to cases when all the examined nations are converging to the same steady-state in terms of GDP per capita and growth rate; while conditional beta-convergence occurs when the so called steady-state is determined by several factors (e.g. institutional quality, resources etc.), occurs in different time period (some achieve their goal level earlier than others), therefore it is different and unique for every examined country (Monfort, 2008.). In my thesis, I will use conditional beta-convergence, and try to predict the effects of my independent variables on convergence. Regarding the structure of my literature review, I will discuss previous studies on the topic to see how economists interpreted the changes in the CEE region over the last decades.
Kaitila (2004) analyzed the convergence of real GDP per capita levels (in purchasing power units) in the period of 1990-2001 within the European Union to use it in the examination of the catch-up effect in the Central Eastern European region. For that, she firstly analyzed the structural developments in the 1960-2001 period in the cohesion countries in terms of trade, fixed and foreign direct investment. Based on that, she estimated a model for the convergence of the CEE countries toward the EU15 countries5 to see, how trade liberalization and terminating trade barriers have affected their convergence prospects. She separated two different country groups within her methodology, the CEE4 and the CEE7, where the first group consists of the Visegrad countries: the Czech Republic, Poland, the Slovak Republic, and Hungary, while second one also includes the Baltic countries: Latvia, Lithuania and Estonia.
She measured the catching-up effect through sigma- and beta-convergence. In the sigma-convergence she measured the level of dispersion of real per capita income, for which she used the standard deviation of the GDP per capita convergence between the original founder
5 They excluded Luxembourg in the 1993-2001 period due to its relatively high GDP per capita and with the
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countries of the European Economic Community (EEC) and the cohesion ones, where the level of income of the original member countries has been set as the benchmark; and the convergence of the CEE7 countries towards the EU15 countries. She found that convergence firstly occurred between the 1960s and 1973, then after 1986 when Ireland, Greece, Spain and Portugal experienced increasing economic growth and their income converged towards the benchmark level until, the first oil crisis. In connection with my thesis topic, it is interesting to see, that the CEE7 countries enjoyed a significant increase in investment after 1993, although according to her the aggregate value of investment has declined around 2001-2, but compared to the EU-member countries, the CEE7 region experienced a relatively higher investment rate.
In the beta-convergence model, she measured the trend, that poorer countries catch up with wealthier ones. She found a linear trend between the initial level of GDP capita from the early 1990s of the CEE7 countries and the EU15. Where she found, that the CEE7 had experienced a similar convergence trend to what the cohesion countries enjoyed in the second half of the 20th century.
Lastly, she measured an unconditional convergence of the CEE region, where she run a pooled least squares model. As a dependent variable she used the difference of the logarithm of GDP per capita in country i at time t and t-1. She found that there has been a 3.4%, decrease in the gap, resulting that convergence has been occurred between 1995 and 2001. She also found that CEE7 grew faster in the given period that the EU15 nations did after the 1960s. Moreover, the total factor productivity (TFP) as part of the economic growth contributed strongly to certain countries’ GDP growth, but it varied significantly among the countries. TFP contributed a 122% increase in GDP growth in Hungary, 82% in Slovenia, 51% in the Czech Republic, 44% in Poland and 9% in Slovakia. Besides TFP, she discussed the role of gross-capital formation, trade, foreign direct investment and the convergence of GDP per capita before the 1990s period as well, detailing the later joining countries’ slopes of GDP per capita levels toward the founding six members of the EU.
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effect in the Central Eastern European region, and it played a significantly important role in the rapid economic growth in terms of GDP per capita.
In their panel data analysis, they measured convergence through economic growth, and used it as their dependent variable. They run their regression with the following explanatory variables: initial level of GDP per capita, investment, trade openness, population growth rate, secondary school enrollment rate, and inflation. They run several versions of their model, including different forms of current account balances, such as solely the current account balance; the difference of the current account balance and the initial level of GDP; the aggregate value of current and capital account balances and their interactions with the initial level of GDP. They used lagged values, arguing that current account balances and capital inflows do not have an immediate effect on GDP growth, but they influence the economy in the long term.
Even though they found support for convergence, the interaction between the current account balance and the initial level of GDP was only significant at the 10% confidence level. Although, they provided evidence on additional increase in the current account deficits in the CEE region, which further supported convergence. However, they tested the transformed versions of their original model with the lagged values of current account balances too, and even though the coefficients of the variables were similar to the one in the original model they were not statistically significant. Based on their results they concluded that the current account deficit ranges between -4% and 0% of the GDP and results a convergence ranging from 1,1% to 1,8% per year.
Vamvakidis (2008) examined the sustainability of the Emerging Europe’s6 convergence in 2001-2007 period through a cross-country analysis to see, whether they suffer from imbalances or not, and if so, how does it affect their convergence and economic growth. He estimated three indicators to see how fragile they are, looking at convergence from different angles, which gave me the idea to measure convergence in more than one way too, and examine how it changes. Firstly, he estimated the potential future growth of the region. Secondly, he aimed to measure the effect of external imbalances on these economies. Finally, he used debt-accounting framework from another study to investigate the role of external debt to financial
6 The working paper defined a larger sample of countries and analyzed their convergence: Albania, Belarus,
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shocks. This last measurement is especially interesting due to the fact, that the paper was written right before the 2007-8 financial crisis.
Firstly, he estimated a model, where he defined convergence as the average of real GDP per capita growth, while the variables which may have impact on it are: the logarithm of the initial value of GDP per capita from 19967; age dependency ratio; gross fixed capital formation as a percentage of the GDP; foreign direct investment as percentage of the GDP; university enrollment rate as the measurement for human capital; CPI inflation rate; index of economic freedom from the year 1995 and its change between 1995 and 2005; and a regional dummy for the examined countries from Emerging Europe.
He found that keeping everything constant, where the country has a relatively low initial income level, high level of foreign direct investment, low dependency ratio and inflation, and high level of education, the country will grow faster, furthermore transition countries enjoy an even higher level of economic growth. Moreover, he discovered that, except Hungary, all the countries from the Central Eastern region grew faster that it was predicted, and their aggregate growth rate exceeded the predicted one by 2%. Connecting my thesis to their research, it is interesting to see, that even though the growth effect is uniquely high in the region and the effect of FDI was positive, it was not statistically significant on the growth of GDP per capita. Altogether they found, that convergence occurred due to structural reforms, although it slowed down by the end of the examined period, which might have been a warning sign of the upcoming financial crisis.
The exposure to global financial cycle and economic imbalances is estimated by a model, suggesting that less developed regions are converging towards the more developed ones. Similarly, to my research he assumed that international capital has a significant effect on convergence. He measured the current account balances of these economies, arguing that it is effected by the business cycle, the relative per capita income and the location, the demographic features of the countries (such as whether they are transition countries or not, whether they are landlocked or coastal countries etc.). He estimated a model, where the dependent variable is the current account balance of a country, while the explanatory variables consisted the difference between the logarithm of a country’s GDP per capita at time t and the logarithm of
7 They use the initial value of GDP per capita to avoid endogeneity problem between the dependent variable
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the average value of GDP per capita of the region; and the control variables. He found that current account deficits were excessive and have been resulted by regional convergence.
Emerging countries borrowed more from external sources, therefore external indebtedness may be resulted rollover difficulties, immediate changes in the interest rate and significant effects on the exchange rate of the country. Therefore, investments are highly affected by the level of external debt. He estimated a model, where the dependent variable is the growth rate of external debt ratio to GDP; while the independent variables were the effective nominal interest rate on external debt; the rate of real GDP growth; the noninterest current account balance as a percentage of the GDP; and the change in the domestic GDP deflator in euros, where he used the domestic GDP deflator inflation and rate of nominal appreciation. He concluded, that shocks have an increasing effect on the debt level of the countries in medium term. Additionally, they argued that high external debt and balance sheet exposure can lead to high exposure to the global financial cycle, which makes these economies highly vulnerable and their convergence less sustainable.
Nenovsky and Tochkov (2014) examined convergence of the CEE region on a larger sample similar to Vamvakidis (2008), since they included 19 countries, expanding the sample of some of the previous papers’ by Eastern European countries over the period of 1990 and 2012. 8 Their analysis contains the examination of the distribution and shape of convergence tendencies in the region, thus they estimated the per capita convergence by several factors, not solely focusing on one or two determents, but investigate the combined effect of them.
They run a panel data analysis with country fixed effects on GDP per capita growth. They estimated a model, where they measure convergence as the logarithm of the annual growth in real GDP per capita of the CEE as a percentage of the EU average in time t in country i of the region. Similarly, to Miron and Alexe (2014) they used the annual average of 3-year periods to prevent short term fluctuations. As explanatory variables they used physical and human capital, innovation in form of the amount of investment in research and development, trade openness, foreign direct investment, fiscal and governmental policy variables, indicators on the exchange rate regimes and financial deepening, thus variables representing the progress made in economic reforms. Moreover, they included two dummy variables, representing whether country i is a member of the community at time t; and another for the years of the global
8 They included the following Eastern European countries to their sample: Moldova, Belarus and Ukraine; and
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Literature Hypothesis and research question Econometric model Measurement of convergence Determinants of convergence Results, and drivers of convergence Kaitila (2004)
Convergence between the CEE7 and EU15 based
on the pattern of the cohesion countries in the
1960s-1990s period. Significant effect of trade
liberalization and termination of trade
barriers.
Sigma-, beta- and unconditional
convergence.
The logarithm of GDP per capita in country i at time t and
t-1. Productivity, employment, capital stock, EU membership, investment Found significant evidence on convergence, fueled by the increased level of investment, and total factor productivity.
Miron and Alexe (2013)
Statistical relationship for conditional convergence
and current account imbalances, thus convergence within the
Community between 1995-2007.
Panel data
analysis. Economic growth.
Initial level of GDP per capita, investment, trade openness, population growth rate, secondary school enrollment rate, inflation, current account balance. Evidence on convergence, ranges between 1,1%-1,8%, driven by capital
inflows, and the current account
balance.
Vamvakidis (2008)
Sustainability of convergence and the
countries’ economic growth. Three types of
measurement of sustainability:1. future growth rate; 2. effect of international imbalances;
3. role of external debt.
Cross-country analysis. 1.Average of real GDP per capita growth. 2.Current account balance. 3. Growth rate of external debt to GDP ratio. 1. Low initial income level, high level of FDI,
low dependency ratio, low inflation, high level of education. 2. Investment, regional convergence. 3. Nominal interest rate, GDP growth, current account balance, GDP deflator. Evidence on convergence, where transition countries grow faster. No statistically significant evidence on the role of FDI. Evidence on decreasing sustainability of convergence. Nenovsky and Tochkov (2014) Existence of convergence of the CEE region, and the distribution and shape
of convergence tendencies between 1990-2012. Panel data analysis with country fixed effects.
The logarithm of the annual growth in real GDP per capita of the CEE as a percentage of the EU average in time t in country i of the region. Structural reforms, which improve the competitiveness of the country. Evidence on strong convergence trends in the early
1990s with a slowing down tendency toward the years of 2000. They found evidence, that certain countries’ GDP per capita achieved 60-70% of the EU average by 2000.
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Methodology
Model
As we saw, convergence can be defined in different ways. As a part of my own research, I collected all the variables form the literatures, they used. Using these variables, I examined, which appear on in more than one study. Then, I started to estimate my model with all the collected variables, and systematically excluded the least significant variables. I measure convergence as the gap of income per capita between the regions. As my thesis has special focus on the role of capital flows, I measure the effects of capital flows in different ways. I performed the Hausman test on my models, which confirmed my intention to use fixed effect estimators.
convergenceit=α+β*CAPITALFLOWSit+γXit+ci+εit
I estimate a panel data regression, where the dependent variable is measuring the convergence effect, CAPITALFLOWS refers to the three differently measured form of capital flows, which I use separately in my models, X captures all the control variables, the c stands for country fixed effects to control the unobserved differences acrosscountries9, while the ε is the error term.10 The time period is between 1995 and 2007, which gives opportunity to observe the performance of the countries before and after entering the EU with the help of the dummy variable on EU membership.
As I am much interested in the impact of capital flows, I have collected data separately on foreign direct investment and on capital flows, where FDI is not included. I estimated the effect of FDI flows, and stocks, using the aggregate value of every five year.11 As I have collected data on the import and export of the countries separately, and then calculated the trade openness, the sum of these two as a percentage of the GDP, I have run my model with the variable trade openness, and independently with the variables on export and import. After examining the results, based on the coefficients and their significance level, I decided to use the
9 I use country fixed effects for controlling the outliers too in my data, since all the different outliers are
connected to one of countries.
10 Estimating the regression with country fixed effects allows me to measure the change in the different
variables on convergence within countries.
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trade openness variable (Appendix 3). The final version of my econometric model is the following:
Δln(𝑦̅𝑡𝐸𝑈− 𝑦
𝑖𝑡𝐶𝐸𝐸)=α+ β1SAVE it +β2CAPITALFLOWS it +β3ENTRY it +β4INSTITUTIONS it +β5PRIVATEDEBT it +β6FD it +β7FB it + β8TO it + uit+eit and
Δln(𝑦̅𝑡𝐸𝑈− 𝑦
𝑖𝑡𝐶𝐸𝐸), the dependent variable is calculated as the log of the difference between the
average GDP per capita of the EU15 countries and GDP per capita of individual countries from the CEE region. Regarding the independent variables: SAVEit is gross savings ; CAPITALFLOWSit measures the three types of capital flows separately: FDI flows, FDI stocks those capital flows where FDI is excluded; ENTRYit is a dummy variable on EU membership; INSTITUTIONSit is measuring institutional quality; PRIVATEDEBTit is the private debt level; FDit is financial development index of IMF; FBit is fiscal balance; TOit is trade openness.
I ran a joint hypothesis test using the F-statistics on my variables, where I could reject that my variables are jointly insignificant. I tested my models for serial correlation, and unfortunately the test confirmed the presence of it, therefore I created the lag of all the variables to decrease the impact of it. Since, I have discovered changes in the coefficients of my variables, I used and analyzed my model with the lagged variables. Besides autocorrelation, I have seen precedent in the literature for lagging the variables as well.12 Furthermore, I tested my models for heteroskedasticity, where the test confirmed that I need to deal with it. To correct the presence of it I used robust standard errors in my models.
Besides the lagged values of my variables, I included interaction terms between the dummy variable on EU membership and the three different forms of capital flows. I assume, that by including the interaction terms, the impact of capital flows will change significant ly , and their effect will decrease the gap between the countries.
Data
I have collected cross-sectional and time series annual data from the World Development Indicators, the Worldwide Governance Indicators and the Global Financial Development Dataset of World Bank Group; Debt, Capital Flows in Developing Economies, and Financial Development Dataset of the IMF; the United Nations Development Programme;
12 I run a joint hypothesis test using the F statistics, where I could reject that my variables are jointly
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and the OECD database. All my variables are collected from 1995 to 2017. However, since the CEE countries gained back their independence only in 1989 and in the beginning of the 1990s , my dataset contains missing values. I have started my research by collecting all the different definitions and explanatory variables used in previous studies13 and compared the different samples as well. My thesis gives additional value to the earlier analyzed literature, based on the different set of countries I use as a sample, the more recent data as the core of my research, and the marginal effects and sensitivity analyses I run on my results. Regarding my sample, my thesis is partially focusing on the effect of becoming an EU member and its combined impact with the three types of capital flows (separately) on the gap between the EU15 and the individual CEE countries’ GDP per capita. Hence, I have decided to narrow down my sample to those Central Eastern European countries, which became EU members. Therefore, I have included eleven countries in my research: Bulgaria, Croatia, the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, the Slovak Republic and Slovenia.
Variable Description Database
lngdp
Log of the difference between the EU15 GDP per capita and individual CEE countries’ income
per capita (constant 2010 US$)
World Bank Database
to Trade openness (% of the GDP) World Bank Database
fdi Foreign direct investment flows (% of the GDP) World Bank Database fdi_aggr Foreign direct investment stocks, five-year
aggregate (% of the GDP) World Bank Database
fb Fiscal balance (% of the GDP) World Bank Database
save Gross savings (% of the GDP) World Bank Database
fd Financial Development Index Financial Development Dataset of the IMF
institutions Institutional quality Worldwide Governance Indicators of World Bank
cap_inf Capital inflows excluding FDI Capital Flows in Developing Economies Dataset of the IMF Privatedebt Gross private debt (% of the GDP) Debt Dataset of the IMF
13 The summary table I have included in my Literature review contains the definitions of my main literatures.
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To the better comparability I have collected everything as a percentage of the GDP expressed in decimal fraction. GDP per capita is measured according to purchasing power parity in constant 2010 US dollars. I seek to find negative and significant results to prove my hypothesis on the existence of convergence. I took the logarithm of my dependent variable for three reasons: firstly, I saw precedent for taking the logarithm in the literature; secondly, I tried to reduce the influence of the outliers within my independent variables; finally, I would like to interpret the marginal changes in the independent variables in terms of percentage changes in the dependent variable. Regarding the independent variables: the level of gross savings, all forms of the capital flows and the fiscal balance is expressed as the share of GDP; private debt level is calculated as nonfinancial corporate debt, including all debt instruments, expressed as a percentage of GDP too; the dummy variable on EU membership is showing whether country i was a member of the European Union in time t; the institutional quality index ranges between 0 and 1, expressed as the average value of six indicators on institutional quality, which are Voice and Accountability, Political Stability and Absence of Violence, Government Effectiveness, Regulatory Quality, Rule of Law and Control of Corruption; the financial development index of IMF also ranges between 0 and 1, measuring level of development of the financial institutions and the financial markets of country i in time t; the trade openness is calculated as the sum of export and import, expressed as a percentage of the GDP of country i in time t.; the interaction term between capital flows and the dummy variable is showing the combined effect of inflows and the EU membership on the dependent variables.
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Empirical results
Comparison of regressions and the interpretation of results
Table 5 contains the estimates of my six regressions with the lagged values, showing the impact of entering the EU on convergence separately. The regressions, where I use one of the two forms of direct investment have 196 observations, while the ones, where those capital flows are presented, where FDI is excluded have 174 observations out of the total of 253. Looking at the R2 of each of the regressions, I can conclude, that the fittingness of my data ranges between 49% and 56%.
According to my previously determined definition of convergence, I seek negative and significant coefficients in the models. However, my main focus is on the impact of capital flows on convergence, thus their specific role after the countries have become EU members. Looking at the data, we can see that the mean value of FDI flows is 5.1%, for FDI stocks it is 23.7%, and for capital flows, excluded FDI it is 4.8% of the GDP. Their standard deviation is 6.6%, 20% and 6.5%. The value of FDI flows ranges between -15.98% and 54.83%, while the value range of FDI stocks is between -5.2% and 126%, and for the rest of the capital flows it changes between -14.86% and 37.43%. Interesting, that in case of FDI flows and stocks, both the minimum and maximum value is generated in Hungary, while in case of capital flows, excluded FDI both extreme values come from Latvia. Hungary not only enjoyed the highest FDI volume as a share of GDP, but it was in the top receivers too, when FDI is measured as per labor force (Hlavacek, Bal-Domanska, 2016). Therefore, as part of my analysis I will run several sensitivity tests on my models, by excluding the countries one by one to examine, how the impact of capital flows in general differs by changing the sample. I assume, that the exclusion of Hungary out of the sample will have a crucial impact on my results. Regarding, the results of the regression, I will interpret my results as a one-unit change in the standard deviation.14
14 For calculating standard deviation, I will use the standard errors presented in parentheses multiplied by the
23 Table 4.: Summary table of the variable lagfdi, lagfdiA and
lagcapflows.
Variable Obs Mean Std. Dev. Min Max lagfdi 242 .0507416 .066844 -.1598922 .5483506 lagfdiA 242 .2365385 .2000301 -.0528283 1.260679 lagcapflows 220 .0483245 .0648272 -.1486 .3743
Looking at the results of Model 1, the sole impact of FDI flows (lagfdi) is not satisfying. It suggests, that increasing the standard deviation by one unit, the gap between the countries will increase by 4.5% at a 5% confidence level. Analyzing the impact of FDI stocks (lagfdiA), the estimate of FDI stocks is extremely small, thus the coefficient is not statistically significant. Regarding the effect of capital flows, where FDI is excluded (lagcapflows), the sign of the coefficient is fulfilling my criteria, however the result is not significant.
Examining the dummy variable on EU membership, a steady, positive tendency is shown. In all the six models, entry tends to have an increasing impact on the gap, suggesting that by entering the EU, the countries of the CEE region are getting further from the EU15 countries in terms of GDP per capita. A potential explanation can lead back to the expectations and limits set by the EU before the CEE countries’ accession. Firstly, by implementing all the criteria, the candidate countries had to spend much more and expand their budget to meet the expectations. Secondly, as members of the EU, the CEE countries had to contribute to the common budget too and face a ten-year long period, before they were allowed to receive as much regional and cohesion funds as the older member states were (Kalotay, 2006). Moreover, the new members had to reshape their trading relationships according to the laws of the EU, losing certain export sectors, where they had gained income from before (Szent-Iványi, 2017).
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accession to the EU not only created better environment for foreign investors, but it also provided the free flow of goods, services, labor and capital, it transferred political stability, and financially integrated the CEE countries into the community (Hlavacek, Bal-Domanska, 2016).
The region’s unemployment rate peaked at 16% in 2001 after continuously rising. Estrin (2017) showed in one diagram, how FDI and unemployment changed between 1990 and 2015 (Appendix 5).15 As we can see on his graph as well, FDI inflows significantly increased after the accession of the CEE countries, not only supporting my findings, but showing too, that FDI started to impact crucially the level of unemployment from then on. Besides its impact on employment, FDI channels technological knowledge into the host countries as well. According to the endogenous growth theory, FDI affects economic growth through technology in four channels. Firstly, FDI provides technological knowledge and managerial skills for domestic firms and subsidiaries; secondly, they integrate domestic workers into the production process, and share the technological knowledge and managerial skills to the domestic labor; thirdly, the increased level of FDI creates competition among domestic and foreign firms, therefore, corporations are forced to increase their cost-effectiveness, productivity and managerial skills; finally, the knowledge spillover, namely that the already received knowledge is shared and further developed among firms within the same sector (Kornecki and Raghavan, 2011). Although, the impact of FDI is argued to depend on the industry. Koko (1996) claimed that there is higher likelihood for positive spillovers in sectors, where products are less differentiated, and their economies of scale is relatively small.
Capital inflows had an essential role in the region’s economic growth supported by credit increase too. FDI is decreasing the present account deficit and therefore reduce the volatility in the balance of payments. Although, we saw that the increased level of investment is beneficial in a way for the countries, the higher level of credit made the CEE countries more vulnerable, built up large external imbalances and exposed to shocks such as in case of the sovereign debt crisis. Not only the GDP per capita converged toward the EU15 level, but the credit to GDP ratio too, and since the majority of the FDI flows came from Western European countries, they not only supported the income convergence of the CEE countries, but played a crucial role in their financial exposure as well (Popescu, 2014). Moreover, the majority of the credit landed in the private sector, together with the continuous privatization, which made the
15 Estrin used a much wider sample of countries, including the former Soviet bloc too. He categorized the
25
CEE countries attract more investment, the exposure of the countries further increased. As a part of the process the previously state-owned financial institutions have been privatized or merged by Western banks and branches, and the assets of foreign owned banks were continuously increasing in the CEE countries after 1995 (Naaborg, 2007). Although, due to the financial crisis, it got clear, that the before-2008 growth policy of the CEE countries is not sustainable, mainly because the countries got highly dependent on external funding sources, and at the same time they didn’t increase their level of domestic savings (Bijsterbosch and Kolasa, 2010). This tendency can be shown through my analysis as well. The 66.7% decrease in the gap suggests that CEE countries got highly dependent on foreign investment, along with the fact, the FDI flows fueled the convergence of the region. The exposure to global financial cycle and the excessively high level of external debt appears in Vamvakidis’s (2008) analysis too. She argues, that the existing imbalances within the CEE countries hamper convergence. Countries of the region aims to attract higher and higher level of foreign capital, which puts them in a dangerous position. As a consequence of the increased level of capital inflow, they had to face huge current account deficits, which exceeds the level of national income. She voiced her concerns, that while the level of direct investment is increasing in the region, saving is declining, putting the economy of the country in more danger (Vamvakidis, 2008).
26 Table 5.: Panel data regression on the examined variables with their lagged value between the period of 1995-2017.
(1) (2) (3) (4) (5) (6) VARIABLES lngdp lngdp lngdp lngdp lngdp lngdp lagsave 0.0286 -0.0882 -0.411 0.0921 0.0195 -0.422 (0.372) (0.342) (0.362) (0.384) (0.375) (0.367) lagfdi 0.189** 0.942*** (0.0725) (0.250) entry 0.103*** 0.100*** 0.0739** 0.131*** 0.141*** 0.0789** (0.0248) (0.0249) (0.0292) (0.0249) (0.0140) (0.0263) laginstitutions -0.249 -0.274 -0.184 -0.326 -0.514 -0.218 (0.465) (0.475) (0.505) (0.446) (0.416) (0.502) lagpdebt 0.183** 0.178** 0.179** 0.191** 0.196** 0.183** (0.0748) (0.0763) (0.0654) (0.0740) (0.0682) (0.0657) lagfd -0.363 -0.307 -0.0762 -0.333 -0.224 -0.0426 (0.368) (0.373) (0.345) (0.349) (0.339) (0.338) lagfb -0.198 -0.228 -0.495* -0.126 -0.181 -0.487* (0.196) (0.196) (0.241) (0.188) (0.177) (0.236) lagto 0.0755 0.0819 0.0588 0.0872 0.109 0.0635 (0.0817) (0.0829) (0.0756) (0.0847) (0.0870) (0.0742) lagfdiA 0.000166 0.307*** (0.0355) (0.0671) lagcapflows -0.0247 0.221 (0.0896) (0.269) lfdientry -0.814*** (0.246) lfdiAentry -0.331*** (0.0707) lcapflowsentry -0.273 (0.241) Constant 9.798*** 9.828*** 9.780*** 9.779*** 9.854*** 9.779*** (0.395) (0.396) (0.391) (0.380) (0.349) (0.379) Observations 196 196 174 196 196 174 R-squared 0.511 0.500 0.492 0.539 0.559 0.496 Number of countries 11 11 11 11 11 11
Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
Marginal effects analysis of capital flows on the impact of EU
membership
27
significantly, such as EU membership, private debt level, or trade openness. However, there are coefficients which decreased by two-third, like institutional quality, or multiplied to their double, like FDI flows. Interesting change in connection with the capital flows variables, that only lagfdi, the variable on FDI flows changed majorly and its impact got insignificant, proving that direct invest in Hungary has crucial role in the convergence process. The other two variables on capital flows didn’t change much, neither their interaction terms. As second country, I dropped Bulgaria, which resulted that the value of the coefficients of FDI flows and its interaction term went up by approximately 40%, which can be explained by the low level of development of the Bulgarian economy. Not only the impact of FDI flows increased, but the effect of FDI stocks too, although the results became insignificant. The value of the other variables didn’t change much. Excluding the rest of the countries didn’t imply big changes on the coefficients.
To measure the impact of extremely high and extremely low level of direct investment on the whole set of countries I estimated the marginal effects of the variables of capital flows. After that, I run the sensitivity analyses here as well. I found significant results in case of FDI stocks and flows. Examining the impact of the FDI variables, there are 196 observations regardless of the variable. The mean of FDI flows is 5.3%, while 25.7% in case of stocks. The standard deviation is 7.3% estimating the model with investment flows and 21.1% with the aggregate value of FDI. Remembering, the impact of EU membership increased the gap between the regions in my regression, meaning, that instead of enhancing convergence, it hampered it. Looking at the impact of entry now, when FDI is at its minimum level, there is a 4.4% increase in the gap, while when FDI flows are at their maximum level, convergence occurs, and the gap between the regions’ income per capita is shrinking by -13.8%. In case of FDI stocks, there is a similar tendency, providing proof of convergence. The impact of minimum level of FDI stocks increases the gap between the countries with 0.7%, while using the maximum level of direct investment stocks, the gap is decreasing by -8.4%. The estimated results support my hypothesis regarding the positive impact of EU membership on convergence, thus, it supports the extension of my hypothesis as well, that the increased level of capital flows, namely FDI flows and stocks have a stronger impact on convergence. The rest of the results were not statistically significant.
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debt, the capital inflows to the CEE countries intensified a boom-bust cycle and amplified the inequalities within them due to the high presence of corruption (Jimborean and Kelber, 2014). Although, as the level of development of the economies, thus their share of FDI flows differs significantly, the impact of FDI inflows will differ too. Well-developed financial markets attract higher level of FDI. The reason for that is that many forms of direct investment are intangible, like focused solely on intellectual property or simply capital investment, which don’t require specified resources affected by natural endowments (Lipsey, 2006) This raises the question, whether being a market-based or bank-based economy has any impact on the speed of convergence. Demirgüc-Kunt and Detragiache (1998) find no evidence that the structure of financial system would influence the convergence and the economic growth of countries. Another paper believes that financial sector FDI (FSFDI) results more efficient credit allocation. It gets even more profitable if there are favored sectors, supported by the central government. In supporting a few, chosen sectors, banks have a crucial role and a bank-based system can be more appropriate, therefore direct investment in the financial system of a country can have long-term impact on the economic growth and convergence of the countries (La Porta, Lopez de Silanes and Shleifer, 2002).
29
30 Source: World Bank Group database, 2019.
Examination of marginal effects of capital flows together with a
sensitivity analysis
Running the marginal effects analysis after excluding Hungary of the sample16, the impact of entry when FDI flows are at their minimum is smaller than before, and with the maximum level of FDI flows the result gets insignificant. Without Hungary, EU membership has an only -5.5% decreasing impact on the gap, when FDI stocks are at their maximum level. The results prove that Hungary has a central role in the region’s convergence, thus, that the if I exclude the country receiving the highest level of inward FDI, the impact of EU membership is decreasing. Even more interesting, that by excluding Bulgaria from the sample, the convergence process gets stronger and the gap between the regions is decreasing by -19.9% in case of
16 As before, for calculating standard deviation, I will use the standard errors presented in parentheses
multiplied by the square root of my sample size. Only, that by excluding Hungary of my sample, the size of my sample changes too.
0 20 40 60 80 100 120
Graph 3.: Domestic credit to the private sector, expressed as a
% of the GDP
31
maximum level of FDI flows, and by -10% in case of the maximum level of FDI stocks. These results suggest, that although Bulgaria enjoys relatively high FDI inflow, by leaving it out of the analysis, the convergence of the region is much stronger. The results reinforce the previously mentioned feature of the region, that the level of development is highly heterogenic. Regarding the rest of the countries, the results didn’t change much.
During the last nearly 30 years, since direct investment has started to flow to the CEE region, the composition of the most preferred sectors has changed. Started with traditional manufacturing sector, green- and brownfield investments, through the service industries, like telecommunications, IT and financial services. The FDI flows in the last five years are still located in the service sector, and a specific industry of manufacturing, the automotive industry. Regarding the automotive industry, the most preferred host countries are Slovakia, the Czech Republic and Hungary. The first company, who announced opening a subsidiary in the CEE region was the Italian Fiat company in 1987 and started production in 1991 in Poland. From then on, several other car manufacturers moved part of their production to the CEE region. The majority of these newly launched subsidiaries was owned by the German Volkswagen Group, who firstly moved to East Germany from the former Soviet bloc, then Czechoslovakia (factories were planted at the territory of the now existing Slovakia and the Czech Republic as well) and Poland. Renault opened a factory in Yugoslavia, the Suzuki and Audi in Hungary and the Fiat another factory in Poland in the 1990s (Jacobs, 2017). By 2003 the value of the FDI stocks in the automobile industry increased to €10.4billion, and by 2014 to €30.8billion excluding the Baltic nations. However, the predictions say, that number of newly opened car manufacturing plants is going to stay decreasing, especially since there is a declining trend since 2013 (Pavlínek, 2017)
The future prospects of the region are not promising. Damijan, Kostevc and Rojec (2015) argues, that the success of the region laid in their improved supply capacity, but since it is declining, the CEE will need to face some challenges in their export performance. Szent-Iványi (2017) believes, that the region will face several struggles in the future as well, but his main argument in not on the export performance of the countries, but the increasing tendency of populist ideas, who prefer national form of capitalism and attacks foreign investors, who are led by market-seeking desires. They offer nine potential scenarios regarding the future of the region:
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2. The future cannot be predicted due to many external factors, such as undecided future of Russian sanctions, the situation in Ukraine etc.
3. The possibilities of the CEE countries as a cheap labor providing region is getting closer to its end, therefore they should focus on attracting direct investment in sectors with more value-added, where the danger of moving the production process to an even cheaper location is not frightening.
4. The CEE countries should introduce their own value-chain, enhance education, R&D, therefore they could decrease the dependency on foreign investors.
5. There might be a transformation in the top destinations of FDI. The increasing right-wing, nationalist policies in Hungary might worsens the country’s future prospects on direct investment inflows. Although, according to Szent-Iványi (2017) Estonia, Poland and Slovakia have great prospects to lead the way in the future.
6. The differences in their development level and the speed of their economic growth would remain regardless of the level of FDI.
7. The region might break into two sub-regions, one with providing value-added activities, where the knowledge-based jobs will be located, and the other one is stagnant with providing cheap, unskilled labor.
8. Similarly, to the external factor (scenario no.2) the constant reorganization of the production process makes the future unpredictable.
9. Until today, the majority of inward FDI to the CEE region came from within the EU. Due to higher level of integration in global value chains, the sources of FDI might change in the future (Szent-Iványi, 2017, p.43-45.).
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Conclusion
In my thesis I hypothesized that there is a convergence process between the Central Eastern European region and the EU15 countries. As a part of my hypothesis I assumed that FDI flows, stocks and other forms of capital flows together with the EU membership have a significant impact on the level of convergence. I further assumed that the higher the level of the capital flows are, the stronger the convergence process will be. I have estimated a panel data regression with country fixed effects to control the unobserved differences among the countries between the period of 1995 and 2017.
I aimed to collect a diversified literature on the topic to show how previous studies measured convergence, what were the main determinants in their models, and what kind of results they found. All the literature, I have presented in my Literature review, found evidence on convergence in a way or another, although not all of them could prove the positive impact of direct investment.
In my models, I have found evidence on convergence. Based on the statistical tests I have run on the models, I realized that I face with autocorrelation and heteroskedasticity. Therefore, I lagged all of my variables and used robust standard errors to correct for them, although I am well aware that these statistical conditions are part of the limitations of my thesis. As I was interested in the role of the EU membership together with the increased level of capital flows, I interacted the capital flows variables with the dummy variable. I have found that the stocks and flows of FDI, and EU membership separately are increasing the gap between the countries, but after interacting them, the combined impact is negative and significant, supporting my hypothesis. The interaction between the capital flows, excluding FDI and the EU membership was statistically insignificant, suggesting its role is much smaller than the direct investment’s.
I have observed outliers in my data, although these outliers belonged to specific countries. Due to the presence of them, I decided to run marginal effects and sensitivity analyses with the extreme values of capital flows on the EU membership variable. I believed that estimating my models with the minimum and maximum value of the capital flows does not only give a wider picture on convergence, but it makes me able to demonstrate the existing differences among countries. As I have assumed, the impact of the dummy variable with highest level of FDI flows and stocks supported my hypothesis. Since outliers weren’t spread across countries, but they concentrated in certain ones, I also ran a sensitivity analysis on my model. I excluded countries one by one to get a more complex view on the distribution of convergence. I found evidence on the major role of Hungary in the convergence process. After I have excluded the highest level of FDI stocks the speed of convergence has decreased, while in case of the maximum level of FDI flows, the results became insignificant. After that, I excluded Bulgaria, and I observed that the speed of convergence fastened up crucially, suggesting that Bulgaria is slowing down the region. After excluding the rest of the countries, the results didn’t change significantly, implying that their effect doesn’t have significant impact on the convergence of the region.
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raised the level of productivity and innovation, expanded the countries market and export sector, decreased the level of unemployment, and increased real wages and consumption.
Even though, the CEE countries benefitted from the credit growth in the region, high dependency on inward FDI resulted procyclicality, frightening the financial stability of the countries, thus made them more vulnerable and got them more exposed to global financial shocks, along with enlargement of external imbalances. The deeper examination of these factors could provide a good base for future research.
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