Neumanné Virág Ildikó

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1 University of Pannonia Doctoral School of Management Sciences and Business Administration Neumanné Virág Ildikó Impacts of the integration on trade of EU members- a a gravity model approach PhD Thesis Summary Thesis Supervisor: Dr. Elekes Andrea Veszprém

2 Table of contents 1. Introduction Subjects and aims of the research Research questions, research model... 4 Structure of the dissertation Methodology of the research... 8 The gravity model... 8 Theory of gravity model in international trade... 8 Content of the model Structure of gravity model, data analysis Data Panels Difference in differences technique The main results of the research, theses Conclusion, further research Own Publications

3 1. Introduction The most expedient economic factor in pushing economies into integration is international trade. Significant changes in regional trade patterns have prompted economists to pay more attention to the development of theoretical considerations and empirical approaches which would enable them to explore international trade flows and the role played by regional integration in the development of bilateral trade relations between countries. The European Union now having 28 1 members has reached though its unified market and monetary union a high degree of economic integration. This is a unique process not only in Europe but also in the world, since there has not been any such integrative cooperation yet to go as far as that. New, common policies have been elaborated in the EU, which have gradually expanded along with the deepening of the integration. As a logical consequence of the integrative process the common trade policy has become one of the decisive policies of the integration, showing the uniform external economic attitude of the EU towards the countries outside its borders. The EU enlargement not only resulted in free trade between the EU-12 and the EU-15, it also changed the EU-12s trade policy in relation to the rest of the world. On accession to the EU the new member states were required to apply the common external tariff of the EU, including the preferential access to developing countries and other preferential trade partners which is part of the acquis communautaire. In most cases this represented a liberalisation of trade policy (Avery and Cameron, 1998 and Buch and Piazolo, 2001). This trade opening would in any event be expected to foster trade as its costs fall (Bchir et al., 2003). 2. Subjects and aims of the research The purpose of the thesis is to model the trade of the European Union, to analyse the effects of EU enlargement in the period between 2000 and 2010 by means of a gravitational model, as well as to estimate and measure the commercial growth as a consequence of the opening up of the trade in the EU. It was also the aim of the present dissertation to measure and quantify the extent of the commercial change during the European integration. I analysed the behaviour of bilateral trade flows rade flow between the EU-15 members and the EU-12 countries. International trade flows are often considered to be indicators of links between the economic centres of the region, thus representing links between the economic and spatial concepts. Therefore the approach based on implementing the law of gravity for the study of international trade flows has been widely used in recent years. The previous studies have shown that the gravity equation is the most successful model for explaining regional trade patterns. 1 Croatia joined in 2013,but does not take part in the analysis. 3

4 The gravity model is a mathematical model based on analogy with Newton gravitational law which has been used to analyse spatial interaction beteen two or more points like the gravity in phisycs. Gravity model based studies have achieved empirical success in explaining various types of flows, including migration, commuting, goods, money capital information and international trade. The model is convenient as an examination tool for many reasons such as simplicity, high explanatory ability and improved econometrics. The gravity model of international trade was developed independently by Jan Tinbergen (1962).It is a multivariate linear regression model accepted modeling bilateral and regional trade used for analysing cross section and panel data. 3. Research questions, research model This researchs intends to examine the following questions. K1: Is the gravity model well suited to the trade database of EU countries between 2000 and 2010, compiled by me? Can it be applied to predict the EU's trade? What is the explanatory power of the parameters, can they converge to the expected value, and are they significant or not? K2: Can the EU trade be analyzed by gravity model? Can the trade enhance, trade creation and trade aversion be outlined? K3: Can a sectoral database of trade flows between sectors be compiled which is suitable to build up a model? What can be concluded for the homogenous and differentiated goods? K4: Are there any trade barriers in EU trade? Could the impact of these barriers pointed out? My research hypotheses are the following: Figure1. Research model H1:. The impact of trade expansion of EU integration in the period of can be characterized with gravity model. The new entrant countries (EU-12) have increased trade not only with the EU member countries but also towards outsiders. H2: The trade diverting effect of EU integration can be measured as far as third countries are concerned. H3 : The positive infuence of high-quality institutions on bilateral trade flows can be detected. H4: The distance coefficient of homogenous goods and goods with higher shipping costs is greater within EU integration H5 : On basis of the database of trade flows between sectors the trade-reducing effect of the remaining obstacles (TBT) can be justified. H6:The European economic space has not changed as a result of the new members' accession. Source: Own compilation 4

5 H1: The impact of trade expansion of EU integration in the period of can be characterized with gravity model. The new entrant countries (EU-12) have increased trade not only with the EU member countries but also towards outsiders.i assumed the tradecreation effect. H2: The trade diverting effect of EU integration can be measured as far as third countries are concerned. I assume that estimating with gravity model the trade diverting effect of EU integration as far as third countries concerned can be measured. H3: The positive influence of high-quality institutions on bilateral trade flows can be detected. My thesis combines and extends the empirical literature on institutions, trade and European integration by asking if the adjustment of the EU-12's institutional framework to that of the EU-15 might give rise to a further potential in trade between the old and the new member countries. Using the Index of Economic Freedom (IEF), I employ a gravity model to estimate the impact of the index on bilateral trade. H4: The distance coefficient of homogenous goods andgoods with higher shipping costs is greater within EU integration. In order to evaluate the EU economic integration effects across sectors, I build up a sectoral gravity model which allows us to capture the implicit benefits of EU trade, by controlling for the explicit determinants of trade. H5: On basis of the database of trade flows between sectors the trade-reducing effect of the remaining obstacles (TBT) can be justified. The trade within Europe is still impeded by significant barriers to trade. In particular, there remained many non-tariff barriers (Török-Deli, 2004), including so-called "technical barriers to trade, such as health and safety requirements. These barriers result from regulations that affect the sale of goods in some markets.with intra-eu tariff barriers having been completely eliminated by 1968, technical barriers have become increasingly visible. H6: The European economic space has not changed as a result of the new members accession. Using parameters estimated by the gravity model I assumed and analyzed the potentials of EU forcefield reorganization after enlargement. Structure of the dissertation The organization of the article is as follows. The first chapter is the introduction and will present the hypotheses that are tested at a later stage in order to answer the research question. The second chapter deals with regionalism and multilateralism. The question is whether regionalism may be a faster way to reach multilateralism or, rather, hurt multilateral liberalization and tend to regard regionalism as a complement to multilateralism. I show studies, empirical literature focusing on the trade impacts of regional trade agreements and multilateral liberalization.the third chapter briefly reviews the different theoretical 5

6 foundations of the gravity equation and the data collecting and database compiling procedure. I will describe the data, the variables used and the specifications of the model I am adopting. The fourth chapter deals with the descriptives statistics of EU trade. In the fifth chapter I present an empirical analysis based on panel data of EU countries in order to check for the above discussed theories with a specific focus on the trade effects of EU membership and the predicted trade effects for the EU entrants with gravity model.my results will be presented in this section with regard to the typology of EU trade creations. In the sixth chapter I analyze the potentials of EU forcefield reorganization after enlargement using parameters value estimations by the cross section gravity model. In order to evaluate the EU economic integration effects across sectors, in the seventh chapter I build up a sectoral gravity model which allows us to capture the implicit benefits of EU trade and the trade reducing effect of TBT coefficient. In the final eigth chapter, the main results and consequences are summarized. 6

7 Impacts of the integration on trade of EU members: a gravity model approach Literature survey Data and methodology EU trade model between Multilateral liberalisation Regional liberalization (Theories of integrations) EU trade Non trade barriers, surveys on TBT estimations The gravity model Theory,the gravity model of international trade Data collection, panel data structure between Trade within EU 2.Export from EU to the world 3. Import from EU to the world 4. Trade within EU in sectors Cross section and panel estimations Estimation of the effects integration OLS,Fixed effect,random effects model Impacts of institutions on EU trad EU sector trade gravity model Estimation of Coefficients Estimation of TBT coefficient Potential space of EU (potential model) Source: Own compilation Figure 2. Structure of the dissertation 7

8 4. Methodology, data source of the research The gravity model With its origins in Isaac Newton s law of gravity, the model posits that trade between two countries is directly proportional to the gravitational pull of their respective national incomes (GDP), and inversely proportional to the distance between them. Newton s law states that the attraction force between two bodies is directly related to their size and inversely related to the distance between them. Thus, interaction (Fij ) between entities i and j is a function of repulsive forces at i and attractive forces at j, and an inverse function of distance (or friction) (dij) between i and j. Analytically, the basic equation that is used to express the gravity hypothesis on trade flows between origin i and destination j is: F ij f m m i j b dij in which: Fij represents exports from origin i to destination j, f is a constant of proportionality, mi and mj express the sizes of origin i and destination j, dij represents spatial separation between each origin i and each destination j and b is the so-called distance decay parameter, measuring the flow sensibility to spatial separation. The gravity law based approach has been widely used in the social sciences. Thus, a gravity model is a mathematical model based on analogy with Newton s gravitational law which has been used to account for aggregate human behaviour related to spatial interaction (see Send and Smith, 1995). Gravity model based studies have achieved empirical success in explaining various types of inter-regional and international flows, including labour migration, commuting, customers and international trade. The gravity model of trade in international economics, similar to other gravity models in social science, predicts bilateral trade flows based on the economic sizes(often using GDP measurements) and distance between two units. There are two basic areas of the application of gravitational models based on physical analogy: the spatial flow analysis, and the demarcation of catchment areas. Theory of gravity model in international trade The gravity model is an instrument which enables statistical analysis of flows and patterns with bilateral trade flow data. The theoretical considerations are mostly based on microeconomic foundations, trade theories and new economic geography The gravity model of international trade was developed independently by Jan Tinbergen (1962) and Pentti Pöyhönen (1963). The model is convenient as an examination tool for many reasons such as simplicity, high explanatory ability and improved econometrics. In this basic form of the gravity model, the amount of trade between two countries is assumed to be 8

9 increasing in their sizes, as measured by their national incomes, and decreasing in the cost of transport between them, as measured the distance between their economic centres. Following this work, Hans Linnemann (1966) included population as an additional measure of country s size. This model is sometimes called the augmented gravity model.it is also common to specify the augmented gravity model using per capita income (or per capita GDP). The population expresses the size of a country as well as the size of its economy. Per capita income expresses the level of economic development. Thus, the size of economy and level of economic development are the main attractive forces or pull factors of bilateral trade flows. The main push factor is the distance between the trading partner s countries. The theoretical considerations for using gravity models to explore international trade flows have been widely discussed and developed. Anderson and van Wincoop (2004) propose an augmented version of the Anderson (1979) model based on the assumption of differentiation of goods according to place of origin. Goods are differentiated by place of origin and each country is specialised in the production of only one good. Preferences are identical, homothetic and approximated by a constant elasticity of substitution (CES) function. Anderson (1979) derives a version of the gravity equation using a setup with trade costs and CES preferences. Bergstrand (1985) initially supported this hypothesis.helpman and Krugman (1985) also derived a foundation relying on the assumption of increasing returns to scale where products were differentiated by firms, not only by country, and firms were monopolistically competitive. The main contribution of Anderson and van Wincoop (2003) is the inclusion of multilateral resistance terms for the importer and the exporter that proxy for the existence of unobserved trade barriers.) They developed a very adaptable version of the gravity equation using the generalization with CES preferences. They show that exports in gravity equations do not only depend on bilateral trade costs but rather on a ratio of bilateral trade costs and the respective two countries' trade costs to all countries as well. The index that measures a country's overall resistance to trade is called multilateral resistance. These multilateral resistances are necessary to retrieve unbiased results from empirical gravity equations. They are defined as a weighted summation over all countries' trade costs from a certain country's view and can be interpreted as a country's (adjusted) trade costs with all other countries. The exclusion of the multilateral trade resistance terms leads to biased estimates due to the omission of variables and this misspecification can invalidate the estimation. Bergstrand (1990) provided a foundation based on Dixit and Stiglitz s monopolistic competition assumption. In addition, he generalised the model by introducing prices and incorporating the Linder hypothesis and by including monopolistic competition and specialization in the context of the Heckscher-Ohlin model. 9

10 Content of the model Thanks to various modelling refinements and their application to debates about theoretical foundation of the gravity equation, this model has established itself as a serious empirical tool for exploring regional trade patterns. The regional integration effects as the deviations from the volume of trade predicted by the baseline gravity model, which expresses the impact of traditional gravitational forces like size of economy, population (Head, 2003), level of economic development and distance, are captured by dummy variables.gravity models have been used extensively for the empirical analysis of a wide range of international economics topics, including FTAs. The multiplicative form of the gravity model (Anderson (1979), Bergstrand (1985, 1989), Anderson and van Wincoop (2003)) is the following: The traditional approach to estimating this equation consists in taking logs of both sides, leading to a log-log model of the form. In which FLOW ij is the trade between economy i and j (as reported by economy i);gdp i is GDP of economy i, as a proxy for the size of the reporting economy;gdp j is GDP of economy j, as a proxy for the size of the partner economy; dij is the distance between i and j, as a proxy of travel cost of trade(data are extracted from Lij; Li, Lj, are the predictors, independent variables, stand for other variables such as common language and historical bonds, population, size of the economy;ɛij is the residual of the regression; the term captures movements in the bilateral trade not explained by the factors listed earlier. The equation in log form can be estimated with linear regression techniques. Therefore, the model is actually a multivariate linear regression function, which is used for cross-sectional and panel data analysis. Structure of gravity model, data analysis Analyzing with gravity model, a database is required that integrates data describing country pairs and the countries. The essential elements of the database are spatial data (two different countries) point- pairs. 10

11 Figure 3. Structure of gravity database Spatial pointpairs Countr y ij Countr y Flow (export) ij Distance ij Variables Country 1 a 1, a 2 a m Variables Country 2 b 1, b 2 b n Variables Country pair ij i j Source: Own compilation 27 countries of the European Union (EU-15, EU-12) from 2000 to 2010 are examined with gravity model and changes in bilateral trade between the countries are observed. A panel database was compiled and a panel regression analysis was performed based on a gravity model often used in the empirical literature for estimating the trade value between countrypairs. Databases used in the dissertation are as follows: I have compiled a database containing panel of bilateral trade flows for the period , to compare coefficient estimates for the gravity model of trade to evaluate the effects of EU regional trade integration. 1.Exports from EU countries to EU countries (within EU): first database, the matrix includes bilateral export data, variables country and country pairs, total 7723 observations, data rows and 28 variables from the years 2000 to 2010 between EU Member States. One row of the matrix contains variables regarding one country pair. (86 cells per line). The matrix comprises a total of data cells. 2. Export from EU countries to the world's countries. The database contains variables relating country and country pairs: export data to the world's countries between the period 2000 to 2010 totally 63,262 observations (data rows) and 154,465 cells. The number of variables is 11pcs. The database has an extended version with the "Index of Economic Freedom index data, which includes 22 pieces of data variables and 63,262 observations (rows in the matrix) and 384,411 cells. 3. Import of EU countries from the world's countries. The database contains variables relating country and country pairs: import data between the periods 2000 to 2010, totally observations (data rows.) The number of variables is 11pcs. The database has an extended version with the Index of Economic Freedom " index data, which includes 22 pieces of data variables and observations (rows in the matrix) and 2,626,489 cells. 4. Sectoral export database from EU countries to EU countries between 2009 to 2000 period with bilateral export data between the EU Member States in sectoral breakdown. The country and country pair data are from the OECD STAN database. The data matrix contains 7723 observations (rows) by industry (88 cells in a row). The matrix has 679,681 data cells by sector and regarding 25 sectors it is totally 193,075 observations that means a total of 4,826,875 pieces of data cells (the 25 sector matrix data combined). 11

12 Data The data used cover a period of 11 years ( ) whereas the country sample contains all of the 27 EU member countries. The bilateral trade data (EXP ij ) are extracted from the United Nation ComTrade Database (UN ComTrade). Population and GDP data come from the World Bank Database (World Trade Indicators), the institutional Index of Economic Freedom, data are from Heritage Foundation, sectoral and production data are from OECD, STAN Database STAN 2 ISIC Rev. 3.The analysis was carried out with STATA 10 programme. Panel data analysis In statistics and econometrics, the term panel data refers to multi-dimensional data frequently involving measurements over time. Panel data contain observations of multiple phenomena obtained over multiple time periods for the same firms or individuals. Time series and crosssectional data are special cases of panel data that are in one dimension only (one panel member or individual for the former, one time point for the latter). Longitudinal and panel databases and models have taken an important role in the literature. They are widely used in the social science literature, where panel data are also known as pooled cross-sectional time series and in the natural sciences, where panel data are referred to as longitudinal data. Recently, it is criticised that the use of conventional cross-section estimation is misspecified since it is not able to deal with bilateral (exporter or importer) heterogeneity, which is extremely likely to be present in bilateral trade flows. In this regard a panel based approach will be desired because heterogeneity issues can be modelled by including country-pair individual effects. To be able to answer the basic question in this paper an empirical research is performed with the frequently used gravity model. The gravity equation is estimated through the OLS procedure. In aim of receiving the best regression results from the OLS an alternative version of the standard gravity equation, a fixed effect equation is calculated and run as well. The quantitative study is performed on panel data from 2000 to Although a number of panel estimation techniques such as the pooled OLS, the Fixed Effects Model, the Random Effects Model have been applied in various contexts, the assumption that unobserved individual effects are uncorrelated with all the regressors is convincingly rejected in almost all studies. Therefore, the Fixed Effects estimation has been the most preferred estimation method in order to avoid the potentially biased estimation. The FE (Fixed effects) model does not measure the actual between-country effects but rather controls and fixes them, because the individual country-specific variation which is stable over time, should not affect the conclusion of the research. Unfortunately this also means that 2 Internetes oldal: Bilateral Trade Database for industrial analysis 12

13 constant factors like distance, common language and common borders cannot be estimated for which the model will drop. However, the reasons for a performance of a country-pair fixed effect regression overshadow this shortcoming. A FE regression is therefore performed on the data simultaneous to the regular OLS. This is done through inclusion of individual countrypair dummies in the gravity model. When examining panel data with a FE model, inclusion of time dummies is considered to provide better results where complete panel dimensions are taken account to. After discussing the recent econometric developments in gravity modelling, a correctly specified fixed effects gravity model is proposed. Difference in differences technique Difference in differences is a technique used in econometrics that measures the effect of a treatment at a given period in time. It is often used to measure the change induced by a particular treatment or event. The treatment effect (that measures the difference in an outcome between the treatment and control groups), the DID estimator represents the difference between the pre-post, within-subjects differences of the treatment and control groups. The simplest set up is one where outcomes are observed for two groups for two time periods. One of the groups is exposed to a treatment in the second period but not in the first period. The second group is not exposed to the treatment during either period. In the case where the same units within a group are observed in each time period, the average gain in the second (control) group is substracted from the average gain in the first (treatment) group. DID requires a parallel trend assumption. The treatment effect is the difference between the observed value of y and what the value of y would have been with parallel trends, had there been no treatment. To guarantee the accuracy of the DID estimate, the composition of individuals of the two groups is assumed to remain unchanged over time. 5. The main results of the research, theses The gravity model was applied successfully for the EU regional trade. The quantitative study is performed on panel data from 2000 to 2010.the model suited well the database. The gravity model was applied with success for the EU regional trade. Based on the research, the following theses can be formulated. T1. I proved with gravity model the impact of trade expansion of EU integration in the period of as far as the new entrant and old members concerned. According to panel fixed effect estimation (On the basis of my own EU bilateral trade flows database) a pair of EU members have 54.5% larger exports than a pair of non-members in the long run. The panel data analyzes demonstrating the trade between the EU and the rest of the world resulted in the increasing impact of EU trade. On entering the EU, the export from entering countries to EU countries increases by 24.6% in the long run while the trade increases towards the outsiders at a growing pace as well. DID analysis supports my claims. 13

14 The Hypothesis 1 has been proved. The analysis was carried out as first step with cross section for the years The GDP variables in the gravity equation are used as representing the importer demand and exporter supply potential, which also indicates that the size of an economy has direct relation to the volume of imports and exports as indicated by the equation. Larger economies produce more goods and services which means they have more to sell in the export market. Larger economies also generate a higher income enabling a higher import level. The coefficients have positive signs, because these are the traditional propulsion (for origins) and attraction (for destinations) variables in the gravity model. The sign and the statistical significance of these coefficients will indicate how these factors affect bilateral trade between a pair of countries/economies. If a coefficient is statistically significant and it is positive, the factor it represents has a strong direct relationship with bilateral trade, i.e., the factor is deemed to promote bilateral trade. From the equation GDP and POP to be positive since the size of reporting economy and partner economy will directly affect the size of bilateral trade between the two economies. The economies/countries with bigger economic sizes (as proxy by GDP) have a larger capacity to trade. The equation based on cross section estimation from the year 2008 is the following: export 12 =g*gdp 1 0,7057 * GDP 2 0,8633 *D 12-1,0451 * Border 0,5726 *Language The quantitative study is performed on panel data from 2000 to Exporter GDP (0,705) exhibits a positive coefficient.the importer GDP coefficient (0, 8633) has the same positive sign. These results indicate a great impact from the EU countries. The coefficient estimate indicates that an increase of 1% in the EU GDP will increase the EU export to EU by 0, 7057%. The distance coefficient which is negative in the OLS results has the most significant impact on bilateral trade flows and this impact is as expected negative. The impact of the trading partners size and level of economic development is positive. The common border coefficient, while positive and significant in the OLS table, exp (0,57)=1,768.The coefficient of the border dummy is statistically significant and it indicates that bilateral trade flows between the border countries are 76,8 % times larger than trade flows between other countries. Figure 4. Shape of linear regression (cross section model 2008) Export Distance 14

15 Source: Own compilation Bilateral trade within the EU: Seven models were set up according to the panel analysis between 2000 and 2010 (OLS, Random effects (RE) fixed effects (FE)). The sign and the statistical significance of these coefficients will indicate how these factors affect bilateral trade between a pair of countries/economies. The coefficients are statistically significant, the factors they represent have a strong direct relationship with bilateral trade, i.e., the factors are deemed to promote bilateral trade. I estimate different panel models, namely the OLS, the fixed effect (FE) and the random effect (RE) models. The estimated coefficient of total export is positive and significant at the 5% significance level. According to the panel fixed effect estimation the exporter GDP and importer GDP are positive as expected and significant at 5% (any unit increase of a country s GDP raises, ceteris paribus, its exports to other EU countries by 1,535% more). When one country of the country pair is the member of EU and the other is not then the export raises with exp (0,23) =25,6%. When both the countries are members of the EU, the estimated coefficient EU membership is positive and has a very high estimated value of exp (0,38) =1,462. The coefficient is also statistically significant at the 5% level. The EU dummy according to the model between is 0,545. The panel data analyzes demonstrating the trade between the EU and the rest of the world resulted in the increasing impact of EU trade.the export in the direction of third-country grows by 30.9% with a country's entry into the EU. When a country enters the EU, the trade coming from the insider EU countries grows by 10.5% and the export from entering countries to EU countries increases by 24.6%. These results demonstrate the impact of the EU, which shows the growth in trade with insiders and outsiders as well. Analysis with eu entrant dummy corresponds DID technique. According to DID the control group is the EU- 15, a treated group is EU-12 countries. The entrant dummy means: Has been treated anytime? The dummy eu entrant signs the treatment in the given period. With this technique I estimate the impact of the treatment more precisely. The export in the direction of third-country grows by 27 % with a EU-12 country's entry into the EU. When a country enters the EU, the trade coming from the insider EU countries grows by 29 % with country-year fixed effect. This means trade creation. The Hypothesis 2 has been proved. T2. I proved with gravity model the trade diverting effect of EU integration as far as third countries concerned. The panel data analyses demonstrating the trade between the EU and the rest of the world between resulted in the decreasing trade flow from outsider (non member) countries. On entering the EU, the import of EU-12 country from an outsider (non member) country decreases with 54 % with country-year fixed effect estimation. 15

16 According to the panel data analysis based on the database of the import of EU countries from the world's countries it is outlined that on entering the EU, the import of EU-12 country with country-year fixed effect estimation from an outsider (not EU member country) decreases with 54 %.The insider (EU member) countries import 76 % more with country-year fixed effect estimation from the entering (EU -12) country. The results indicate that the import decreases from third country. T3. Using the Index of Economic Freedom (IEF), I employ a gravity model to estimate the impact of the high-quality institutional environments on bilateral trade flows. The positive influence of high-quality institutions on bilateral trade flows can be outlined. My results indicate that not only the membership in the integration (38, 3%) but also the Business Freedom Index (0,40 %) and the Investment Freedom Index 1,4 %%) have positive impact on bilateral trade. The Hypothesis 3 has been proved. T3. Using the Index of Economic Freedom (IEF), I employ a gravity model to estimate the impact of the high-quality institutional environments on bilateral trade flows. The positive influence of high-quality institutions on bilateral trade flows can be outlined. My results indicate that not only the membership in the integration (38, 3%) but also the Business Freedom Index (0,40 %) and the Investment Freedom Index ( 1,4 %) have positive impact on bilateral trade. The eastern enlargement of the European Union (EU) constitutes an outstanding event in European history and brings with it multiple implications for the old and new members' economic affairs. By the beginning of the twenty-first century, EU tariffs on imports from the Central and Eastern European countries were almost completely eliminated. In the beginning of this process, large unexploited potentials in the trade volume between the EU and the Central and Eastern European countries were estimated. In light of the rapid growth, more recent studies from Breuss and Egger (1999) and Piazolo (2001) argue that the trade potential might already be exploited by now. Yet there remain informal barriers to trade, resulting from still existing differences in the old and the new members' institutional environments. The role of institutions for economic growth and development has been widely acknowledged. The positive influence of high-quality institutions on bilateral trade flows is confirmed by Babetskaia-Kukharchuk and Maurel (2004) focusing on the Central and Eastern European countries.my thesis combines and extends the empirical literature on institutions, trade and European integration by asking if the adjustment of the EU-12's institutional framework to that of the EU-15 might give rise to a further potential in trade between the old and the new member countries I investigate the impact of economic freedom on the EU bilateral trade. I used a balanced panel dataset of total volume of trade between the EU countries and the world. Using the 16

17 Index of Economic Freedom (IEF), I employ a gravity model to estimate the impact of the index on bilateral trade. I find that improvement in both exporter and importer economic freedom tends to induce more trade. My results also indicate that the membership in the integration have a positive impact (38,3%) on intra-eu trade. The index provides a comprehensive system for comparing the strength of economic institutions across countries. This index, encompassing attributes of economic freedom such as freedom to trade, freedom from corruption, size of government, legal structures, and access to money; provides a good framework to assess how economic policies in EU promote or hinder bilateral trade. My empirical results indicate that the economic freedom of both exporting and importing countries positively affect the volume of trade. This finding suggests that improving the strength of economic institutions will remove the barriers to trade that inhibit EU trade. The Hypothesis 4 has been proved. T4. In order to evaluate the economic integration effects across sectors, I build up a sectoral gravity model. According to the value of the distance coefficients I proved with fixed effect panel estimation that the trade integration in high-tech industries is greater compared to the homogenous goods and those with high shipping costs because of their higher distance coefficients. The eastern enlargement of the European Union (EU) constitutes an outstanding event in In order to evaluate the EU economic integration effects across sectors, I build up a sectoral gravity model which allows us to capture the implicit benefits of EU trade, by controlling for the explicit determinants of trade. The database used in analysis covers the time between and contains bilateral export data between EU members. (EXP ij is the export from country i to country j in dollars, as dependent variable). Export and production data are from az OECD, STAN Database STAN 3 ISIC Rev. 3 4 database with 7723 observations by sector this means altogether observations for 25 sectors. The trade integration is low in the following sector (distance parameter) a Coke, Refined Petroleum products and nuclear fuel (-2.060) Extraction of crude petroleum and natural gas and related services (-1.827) and non ferros szektor esetén, These sectors can be characterised with high transport costs. The trade integration is low in Agriculture, Hunting, Forestry and Fishing (-1.163), Mining and Quarrying (-1.466), and in Food products (-1,106). In sectors which are well integrated including some high-tech industries, like Machinery and equipment (-0,646) Accounting and Computing Machinery (-0.496), Electrical 3 Internetes oldal: Bilateral Trade Database for industrial analysis 4 International Standard Industrial Classification, Revision 3 (ISIC Rev. 3) 17

18 machinery and apparatus (-0,353), Radio, Television and communication equipment (- 0,208) the distance coefficients are lower. The Hypothesis 5 has been proved. T5. The sectoral trade integration analysis was deepened with capturing TBT ("Technical Barriers to Trade coefficient compiled for separate sectors. The tradereducing effect of such remaining obstacles (TBT) in Iron and steel (-13, 08), Coke, refined petroleum products and nuclear fuel (-5,324), Food and beverages (-8, 72), Electrical and optical equipment (-4, 54) Fabricated metal products (-6.094) industries can be justified. The trade within Europe is still impeded by significant barriers to trade. In particular, there remained many non-tariff barriers, including so-called "technical barriers to trade.these barriers result from regulations by requiring specific product characteristics or production processes. The sectoral database was accomplished with TBT indicator. I used two sources. The European Commission s Eurobarometer reports on opinions and experiences of European managers about the Single Market. A total of 4,900 managers at companies were interviewed by telephone in early 2006, the sample of companies being selected according to the size of countries and of companies, and the industry of activity. I use the answer to the question: Could you tell me whether you consider that for your company it is very important, rather important, rather unimportant or not important at all that future Single Market Policy tackles the question of removing remaining technical barriers to trade in goods? For each country, I grouped the answers from all managers who replied that TBTs are indeed an important issue, and use the percentage so obtained as a country-specific indication on the relevance of TBTs. To capture the sectoral relevance of TBTs, industries are classified at the NACE70 level on a five-point scale according to the effectiveness of different measures undertaken by the Single Market Programme to eliminate TBTs. My industry-specific qualitative variable takes on values between 1 and 5, with larger values indicating a lack of market integration due to persisting TBTs. At the sector level, examples of industries where TBTs are successfully removed are Dressing and dyeing of fur, Electric domestic appliances, Motor vehicles or Aircraft and spacecraft while TBTs are still prevalent in Jewellery or Imitation jewellery, among others. Given that TBTs require specific product characteristics or production processes we would expect them to be stronger for differentiated than for homogeneous goods. TBT coefficient is computed according to country specific (TBT i ; TBT j) and sector specific (TBT t ) chararacteristics in CES formation is often used in the literature of international trade. TBT ijt = [TBT i 1/2 + TBT j 1/2 + TBT t 1/2 ] 2 18

19 TBT coefficient is high in sector Iron and steel (-13, 08), Coke, Refined Petroleum products and nuclear fuel (-5,324), Food and beverages (-8, 72), Electrical and optical equipment (-4,54) Fabricated metal products (-6.094) industries. According to the sectoral gravity estimation in these sectors the trade integration is impeded by technical barriers to trade. The Hypothesis 6 has not been proved. If there are several bodies, the forces among them build up a forcefield, the potential space, in which every single body has its effect on the others. The potential value of a body consists of the sum of these forces. This model can be applied on the interactions of social space as the bodies to spatial entities (for example countries, regions), and the masses to economic or social power can be matched. In this case we can calculate the potentials and can take into account those forces, which work within the spatial entities and form their masses. (Tagai, 2011) It measures how the other spatial entities influence a chosen entity, in other words it can describe the position of a region within a country by taking into account the other regions. The potential model is good method to analyse core and periphery relations as we consider that great disproportions can be noticed in the patterns of economy's spatial layout in the forcefield East-Central Europe can be regarded as the periphery of the western parts, which dominate the whole area.(tagai,2011) T6. On the basis of cross-sectional GDP-and distance parameter estimations obtained from gravity models, applying potential model analysis I proved that the European economic space has not changed significantly as a result of the new members accession. Applying the potential model the investigation covers those countries, which are in close relationship since centuries, and whose development can't be imagined without one another. They put the formation of spatial layout in a core periphery relation. For understanding their position in the potential space it is possible to give picture of the dynamic core area as the action centre of Central Europe.In East- Central Europe the potential values decrease with moving off the local cores of the area. The western parts dominate the potential field by their huge economic power. We can observe zonality in the total potentials within its belts a nearly uniform image with decreasing values of potentials moving off the core. The core can be indeed imagined as a centre(the most developed regions of Central Europe are French, Belgian and Dutch areas give so much economic power that it has an effect) from which field of different strength emerge one after the other. As a result, an extended periphery comes into being around the core area of Europe, composed by the members of the Mediterranean and Central-Eastern regions.(tagai,2011) Moving eastwards, away from the dynamic core area, the value of economic potential gradually decreases. The transformation of the Central-Eastern European structures affecting each other is indirectly governed by the processes in the neighbouring central areas. 19

20 As we saw the neighborhood effect is also an important factor. Regions with greater potentials can have positive effect on their neighbors, like Germany and Austria in the case of the Czech Republic, while an area with less potential values can influence its neighbour's position negatively. It can be concluded that according to the potential model analysis in the years of 2004, 2007 and 2010 on the basis of cross-sectional GDP-and distance parameter estimations obtained from gravity models, the European economic space has not changed as a result of the new members accession. Compared to 2004, the economic potential map has not changed significantly, but little alteration was observed in the order of the countries. The central location centers will continue to remain in the best relative spatial position. In the longer term potential shift is expected. 6. Conclusion, further research In 2004 eight Central and Eastern European (CEE) Countries, Estonia, Hungary, Latvia, Lithuania, Poland, Slovakia, Slovenia and the Czech Republic along with Cyprus and Malta in 2007 Romania and Bulgaria,in 2013 Croatia joined the European Union. The focus has shifted towards deeper economic integration, especially in recent years. The degree of the economic impact of enlargement depends crucially on the degree of economic integration. The countries joining the EU in 2004 entered the highest degree of economic integration: the single market with the eventual prospective of adopting a single currency, which some of them have done since. Theoretically, as borders open up, trade costs will fall making goods that had until than been unaffordable due to high trade costs, more appealing. This increased competitive pressure will make some importers switch suppliers, choosing the cheapest ones available under the new market conditions. In general, the intra-eu trade volumes were positively affected by the enlargement of the European Community, e.g. with the accession of new member states. This clearly suggests that one of main factors behind the increasing importance of intra-eu trade within the total EU trade is clearly the stronger link among member states over the last decade. The objective of this dissertation is to review the recent empirical literature on gravity models and provide an overview of EU integration effects on international trade as reported by relevant gravity model-based studies over the past decade. Examining the trade prospects for the new European Union (EU) member states is an important issue in the context of European eastward enlargement and greater economic integration. I use a gravity equation for a panel data set of bilateral export flows from EU-12, EU-15 over the period. The potential trade volumes are calculated from gravity model. I have made estimates with robust standard errors for the impact of EU integration with cross section and panel data analysis. Perfect estimation is, of course, not possible, but I will work on improving it in the future. 20

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