How entering the euro area affects trading volumes : a case study of Slovakia and Slovenia

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Bachelor’s Thesis

How entering the euro area affects trading volumes: a case

study of Slovakia and Slovenia

Academic year 2017 – 2018

Faculty of Economics and Business Borbála Bella Hegyi, 11052457 Supervisor: Péter Földvári

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Statement of originality

This document is written by Borbála Bella Hegyi, who declares to take full responsibility for the contents of this document.

I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it.

The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.

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Abstract

The main objective of this thesis is to present the influence the introduction of the euro had on trade volumes between member states of the Economic and Monetary Union (EMU).

Carrying out a case study of Slovenia and Slovakia – having entered in 2007 and 2009, respectively - enables the application of reliable data. A gravity model is used to assess the separate effects on exports and imports between the respective country and the other 18 euro area members. Performing a panel data analysis, data was collected four years preceding and four years after the point of joining the EMU, which allowed for confronting trade volumes effectively. Using fixed effects regressions, a slightly more positive significant effect was found on imports than on exports, even though both measures increased as of entering the eurozone.

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1. Introduction

New countries joining the European Union (EU) has been an ongoing phenomenon in the past decades and is expected to proceed. Whether these countries take integration further and join the euro area by adopting the euro as their common currency is a matter of the country’s economic stability, and consequently a question of fulfilling the convergence criteria. By doing so, countries lose autonomy over monetary policy which may – in several cases - help the economy overcome negative shocks, recessions. This can be a deterring factor when evaluating how beneficial it can be to join a currency union. On the other hand, trade creation, eliminated exchange rate barriers, reduced risk, and uncertainty involved in transactions and transfers are considered as advantages. The prospect of a positive balance between benefits and costs is the principal reason for forming a currency area.

This paper consists of a case study regarding Slovenia and Slovakia. With these two countries having joined the European Economic and Monetary Union (EMU) in 2007 and 2009 respectively, recent enough data is accessible for analysing trade volumes. Through a panel data analysis, trading activities will be analysed between Slovakia, Slovenia and all the 18 EMU members they trade with. Exploring the impact of integration on export and import fluctuation will be conducted distinctly, 4 years preceding and 4 years after the relevant country joined the eurozone. By studying exports and imports separately and not as bilateral trade volumes as a rather comprehensive measure, deductions regarding the Current Accounts (CA) of Slovakia and Slovenia will be feasible. I expect to see increased trading activity after joining in comparison with the situation before. Consequently, performing this research may be conducive to countries being already part of the EU but hesitant about joining the EMU.

The question of entering a currency union is undoubtedly a critical decision and is irreversible – at least in the short-run. Several arguments suggest joining as it creates more synchronized business cycles, reduces the need for hedging activities, creates unification, hence simplicity, for an everyday EU citizen and enhances trade among others. As had been remarked before and will be discussed further, there are also rational disadvantageous aspects of engaging in such monetary integration. These matters will be reviewed throughout this paper, however, the main focus will be on the effects of joining the EMU on trading.

A substantial amount of research has focused on quantifying the above-mentioned benefits and costs and supporting the hypothesis that joining a common currency union does indeed boost trade among its members (Rose, 2000, p. 8). Jagelka (2013) concluded that the

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improved trade volumes were mainly due to intra-euro area trade: trade between the new member countries and their euro area partners; and negligibly to extra-euro area trade (pp. 47-48). Empirical research on recently-joined members has also been done, but shortly after the countries regarded in the study joined the EMU and hence dependable data was not obtainable. In this paper, an analysis of Slovakia’s and Slovenia’s trading volumes will be conducted with only EMU members, to closely examine export and import fluctuations, independently. The two aforementioned having joined in the past ten years, factual - and not projected - data is available to deduct practical conclusions.

2. Theoretical Framework

What follows is an extensive description of how the EMU emerged and what tasks it performs. 2.1 EMU as a stage of economic integration

The European Union (EU) is a political and economic community currently formed by 28 countries in order to create better cooperation and trade between them. The union was established as an effort to improve relations after World War II between European countries. The six nations engaged in the initial Treaty of Paris in 1951 were Belgium, France, Germany, Italy, Luxembourg, and The Netherlands. They officially unified in the Council of Europe after which the cooperation was further expanded to the 28 members constituting the EU by today. Through several steps of economic amalgamation, the EU became a common market – that comprises all the aspects of a free trade agreement (FTA) and a customs union. Having zero internal and common external tariffs among the countries, trading activities have been present since the creation of the common market on a rather large scale.

Whether a country gets in the position of integrating further and joining the EMU is a matter of its economic stability. The Maastricht Treaty requires EU countries to satisfy several macroeconomic convergence criteria prior to admission to the EMU (Krugman, Obstfeld & Melitz, 1987, p. 595). Satisfying these conditions, on 1 January 1999, the Eurosystem was established by 11 EU Member States1_{introducing the euro as their new international currency}

(Mongelli, 2008, p. 15). Being part of the monetary union implies the coordination of economic and fiscal policies and a common monetary policy. Responsibility for the conduct of monetary policy is thus transferred to a new institution – the European Central Bank (ECB) (Bean, 1998,

1_{These 11 first wave countries to adopt the euro are: Austria, Belgium, Finland, France,}

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p. 41). The primary objectives for monetary policy of the ECB are price stability, low inflation expectations and low long-term interest rates (European Central Bank, 2018). These being in the main focus, the ECB also conducts foreign exchange operations; holds and manages the euro area’s foreign currency reserves and promotes the smooth operation of payment systems. By today 19 out of the 28 EU countries form the EMU2_{. Analysing trade volumes will}

be applicable to the 9 countries not part of the EMU3_{. Countries being hesitant about joining}

the currency union could make use of the results of this thesis to see how trade volumes - and more specifically exports and imports - have changed upon entry. Concentrating on 2 countries that have joined the eurozone in the past 10 years, variation of their trading activities will be in the focus of this case study.

A country’s costs and benefits from joining the currency union significantly depend on how integrated its economy is with those of its potential partners. The theory of Optimum Currency Areas (OCA) predicts that monetary unions are most appropriate for areas closely integrated through international trade and factor movements (Krugmann et. al, 1987, p. 597). On the other hand, as Rose (2000) shows in his research, two countries with the same currency will trade more than comparable countries with their own currencies (2000, p. 8). This finding accompanied with the OCA theory resembles the so-called simultaneous causality – an endogeneity problem prevailing in econometrics. On the one hand, the OCA theory suggests that countries join monetary unions provided their markets are already highly integrated and trading is present among them. On the other hand, though, previous studies verified that having one common currency leads to advanced trading. Hence, it can be concluded that integration - as an outcome of trade -, and trade - as an outcome of integration -, are highly correlated and tend to trigger one another.

While trade advances, sustainability of the currency union might destabilize. Business cycles of members can become more decoupled when they develop comparative advantages or more strongly coupled when monetary shocks affecting them are of a similar nature (Rose, 2000, p. 33). Depending on the economic shocks and the evolving economic structure of the countries involved, the relationship between trade and business cycle synchronisation can get

2_{The EMU comprises the following countries by 2018: Austria, Belgium, Cyprus, Estonia,}

Finland, France, Germany, Greece, Ireland Italy, Latvia, Lithuania, Luxembourg, Netherlands, Malta, Portugal, Slovakia, Slovenia, Spain.

3_{These countries are: Bulgaria, Croatia, Czech Republic, Denmark, Hungary, Poland,}

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stronger if both economies respond similarly to external shocks (Zeman, Šuster, Benčík & Nemec, 2006, p. 7). Historically, closer international trade between countries has been shown to induce firmly more synchronised business cycles. Consequently, an increase in intra-European trade – precipitated by the EMU – could create a more sustainable union.

The first to seriously explore this topic was Rose’s (2000) article on the effects of currency unions on trade. His findings of the famous "tripling effect" were controversial and hence created a large amount of research in response. The tripling effect's essence is that two countries sharing the same currency trade three times as much as they would with different currencies (Rose, 2000, p. 8). Ben Lockwood commented on Rose’s research by pointing out the fact that he restricts himself to volatility measures based on the nominal bilateral exchange rate between any pair of countries (Rose, 2000, p. 35). However, Lockwood asserts that bilateral trade flows are more negatively affected by volatility in real exchange rates, rather than nominal. Other criticism regarding Rose’s paper points to the fact that he used cross-sectional data in his research conducted in 2000. In response to this, Glick and Rose applied a time-series approach in their analysis in 2002 and still found a doubling of trade as a result of a common currency. This study was seen as a step forward compared to the one in 2000, although it used a very long time-period (1948-1997) during which many factors influencing trade were changing.

Micco, Stein, and Ordoñez (2003) examined the EMU's effect on trade among its member countries (p. 316). Using a sample from the period 1992-2002, they found the currency union effect to be between 4 and 16 percent. Furthermore, their findings suggest that joining the EMU not only does increase trade but merely the expectation of the countries' joining is able to produce some of the results. Jagelka (2013) researched variation in bilateral trade of the most recent eurozone members by that time (p. 48). He analysed trade volumes of Slovenia, Malta, Cyprus and Slovakia with their eurozone and non-eurozone partners from the EU. Complete data after joining was not accessible due to merely conducting his study only a few years after the countries joined. Jagelka (2013) concluded that the 9 percent increase in trade he found was chiefly a result of intra-euro area trade and hence was not due to extra-eurozone trade (p. 61).

2.2 Trade creation and diversion

Custom unions eliminate trade barriers among countries being part of it, as trading starts to take place at a zero internal tariff. Wilhelmsson (2006) claimed that the enlargement process of the

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EU has already resulted in significant gross trade creation (p. 26). However, this expanded to an even larger scale when countries entered the EMU - reducing transaction costs totally as of introducing a common currency, the euro. The use of the terms “trade creation” and “trade diversion” here requires some clarification, since it does not correspond exactly to the concept developed by Viner (1950). According to him, trade diversion refers to the decreased welfare effect when trade is diverted from a more efficient exporter towards a less efficient one by the formation of a free trade agreement. Trade creation is defined in the same manner, accompanied by a welfare effect increase. This thesis focuses on the effects stemming from entering a currency union, namely the EMU. Correspondingly, in the following discussion, the before-mentioned terms will be applied when indicating enhanced or reduced trade volumes being a result of the installation of the euro and hence not to eliminated tariffs.

Having the same money of account may create much trade, but certain trade-relations may be more detrimental than beneficial. A rise in trade among members of a currency union (intra-euro area trade) implies an analogous drop in trade with countries outside the union and within member countries (Rose & Van Wincoop, 2001, p. 388). Ergo, it implies trade diversion and creation as well. However, an overall positive welfare effect will be verifiable, since fewer resources are wasted on trade costs, such as exchanging currencies.

The hereby used trade diversion occurs when trade is diverted from lower-cost producers not being part of the euro area to less efficient members of the currency union (Rose, 2000, p. 33). For instance, trade diversion would occur in case after introducing the euro Slovakia would start to trade a certain good relatively more with The Netherlands, even though other countries outside the monetary union could produce the exact same product at lower resource costs than the Dutch economy. In this case, trade is diverted from the more efficient extra-euro area country to The Netherlands - deriving from the creation of the EMU.

Whether trade diversion actually materializes, hinges on the resource-allocation and efficiency of the countries the freshly joined member state is going to trade with and also of the countries it used to trade with. Although the present academic paper will not discuss the resource-allocation and efficiency of the countries under investigation, the magnitude of trade creation or diversion will be noted.

Having reviewed the most relevant theoretical considerations on the EMU’s present stage and its operations, the following sections will assess the most relevant benefits and threats of joining the euro area thoughtfully.

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2.3 Benefits of joining the euro area

The benefits of a euro area membership can be seen from different perspectives: the joining country’s perspective and the euro area’s as a whole.

Firstly, from the perspective of the euro area, the EMU can be seen as the realization of the Internal Market in the EU, bringing cost and price transparency for goods, services, labour, and capital. A common currency contributes to further assimilating these countries’ markets, and hence allows for a more efficient allocation of resources. Cooperating markets initiate business cycle synchronisation, which in turn will lead to rising trading activities. Most influential aspects of the relating OCA theory have been discussed above.

The list of benefits of joining the eurozone for the individual countries will be somewhat different. The primary advantage of having the euro for these countries – especially in the case of small open economies – lies in its potential to promote trade. Rose and Wincoop (2001) argue that national money is a significant trade barrier and hence the aforesaid countries will be able to benefit relatively more from this type of monetary unification. (p. 387). Also, the euro may provide stronger protection against international financial disturbances in the case of smaller economies. Bean (1998) acknowledged that such shocks had empirically disproportional effects, boosting the risks of external shocks (p. 48). Overall though, the earlier specified convergence process is aimed at ensuring that participation in the euro area is beneficial for both the euro area as a whole and the individual joining country.

The means through which monetary unification can possibly affect – and mainly enhance - trade are various. The most striking change is that common currency eliminates bilateral nominal exchange rates between countries, and thus reduces risk and uncertainty involved in transactions and transfers. Reduced exchange rate volatility makes calculations and pricing decisions of firms easier, decreases transaction costs arising from the need to operate with multiple currencies (Micco et. al, 2003, p.6).

The subject of the next section is the possibly most perceivable beneficial transformation: the vanished exchange rate barriers.

2.3.1 Eliminated exchange rate risk

Eichengreen (1990) argued that fluctuating exchange rates increase the variability of relative prices (p. 3). Variation of prices as a result of unstable exchange rates are likely to discourage

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international trade and, more generally, distort the operation of the price mechanism. Consequently, perverted supply and/or demand can provoke malfunctioning of markets. It can be seen that introducing common currency unions preclude these from happening and increases welfare for both the countries under question.

For everyday travellers and EU-citizens, the most compelling advantage is that they no longer need to exchange currencies when visiting or moving from one euro area country to another. Hence, they can enjoy the comfort created by higher degrees of integration and a larger Single Market. Furthermore, experiencing such consequences of the euro adoption, other countries like Sweden or Denmark in Europe, but also Argentina, Canada and others may find it more worthwhile to join a common currency area, leading to a further growth in global integration.

Moreover, having the euro and common monetary policy has brought about monetary stability. This has been demonstrated by more stable low inflation rates and the convergence of long-term interest rates to levels prevailing in EU countries with the highest monetary policy credibility.

Having no nominal exchange rate fluctuation as a result of the euro, investors could no longer speculate on the rise of one prior-euro currency and make a profit by selling it at the right moment. Let me now turn to the discussion of hedging activities.

2.3.2 Hedging

Hedging activities relevant to investing in foreign assets may be costly. As Kenen (2003) points out, it is not always feasible to fully hedge against large, long-lasting exchange rate changes (p. 52). This is due to uncertain producers about both the price they will receive for their exports as well as the future demand for their products – as a result of the aforementioned price mechanism. In case at the materialisation of monetary unification both the investor’s and the foreign country share one currency, hedging will not be essential anymore. In this scenario, there will be no need to protect an investment or portfolio against currency risk since the two countries will have identical currencies and hence the nominal exchange rate will fall down to 1. Undoubtedly, this does not imply that price differences will not be present between countries - the real exchange rate will not slump to 1. This paper will delineate this matter, subsequently. To conclude, the so-called exchange rate profits are eliminated and such a fairer Stock Market can be created.

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2.3.3 Competitiveness

By eliminating exchange rate volatility between the currency union partners for the foreseeable future, globalisation – defined as the increasing interdependence of economies via cross-border transactions in goods, services, capital, and labour – has evolved rapidly (Di Mauro & Foster, 2008, p. 7). This process altered radically the competitive environment euro area firms are facing, through testing the adjustment capabilities of the economies. In cases of small economies, the possibility of adopting more refined techniques, manners due to being exposed to a broadened, more integrated environment is plausible. Therefore, such pre-industrial economies can benefit on a large scale.

On the other hand, tougher competition due to trading in its essence leads to reallocation of resources from less to more efficient firms. Such facts have prompted the novel development of trade theories that show how international trade integration has a positive impact on aggregate productivity through the expansion of the most productive firms (Corporate Finance Institute, 2018). Fostering countries’ specialization in sectors where they have comparative advantages enables a richer product variety, enhances the exploitation of economies of scale and improves efficiency by causing the least efficient firms to exit the market.

2.4 Costs of joining the euro area

Similar to other monetary regimes, currency unions are also perfectly compatible with Mundell’s (1968) famous concept, the "Incompatible Trinity" (Rose, 2006, p. 2). Countries, in general, aim to possess three desirable objectives of monetary policy, namely: domestic monetary sovereignty, capital mobility, and exchange rate stability. As a founding of Mundell, these three turn out to be mutually exclusive, hence countries need to give up one of the aforementioned in order to possess the other two. The EMU and in general terms a currency union is a regime which sacrifices having monetary sovereignty in order to retain free capital mobility and exchange rate stability. In monetary unions, by definition, there are no exchange rates and free capital movement is one of the four so-called fundamental freedoms of a common market, such as the European Union.

2.4.1 Lost monetary independence

Member countries of the EMU lose autonomy over monetary policy by adopting the euro, as the execution of operations is transferred to a central institution, the ECB.

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Monetary policy of an independent country enables its central bank to respond to specific situations in the economy, to curb shocks (internal and external, demand and supply) and consequently to create an environment for sustainable price stability. To support the stability of the real economy, the loss of independent monetary policy due to the euro adoption may constitute a serious disadvantage.

In the special case of Slovakia and other highly open economies, however, there are indications suggesting that monetary policy has a limited scope for its operation (Zeman et. al, 2006, p.2). The loss of independent monetary policy will therefore be substantially less disadvantageous than for larger or less open economies. Losing the possibility to independently influence the economy in a recession is a risk for the country’s optimum functioning. In this case, the country at issue would need a more relaxed monetary policy, while the rest of the union is on an upswing (thus would need a restrictive monetary policy). Consequently, a problem appears in the regime of a single currency.

2.4.2 Lost interest rate policy

The ECB setting a common interest rate for the eurozone as a whole implies member countries losing the possible use of interest rate policy to achieve independent macroeconomic objectives. Countries being in different stages of a business cycle would benefit from having differing interest-rates that specifically support them. For instance, countries experiencing high growth levels would require higher interest rates (to control for inflation) than other countries, which is not possible if being part of the EMU. This shows that in the case that countries’ economies have not converged sufficiently yet, a single policy can be harmful.

2.4.3 Control over the currency’s value

Ability to depreciate a national currency can also help overcome negative economic shocks. Monetary expansion and currency depreciation are tools used to stimulate aggregate demand in response to a cyclical downturn. Currency devaluation increases the balance of payment (BoP) by increasing domestic prices and thereby reducing the real money supply (Abbas Ali, D., Johari, F., & Haji Alias, 2014, p. 5). This positive effect on the BoP can thus help countries cope with even severe recessions.

Small, weak countries with chronic current account deficits generally have depreciating currencies. Gradual, orderly currency depreciation improves a nation’s export competitiveness through lower prices charged for the products and hence higher sales volumes possibly attained.

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This could help countries overcome severe trade deficit over time (Abbas Ali, D., Johari, F., & Haji Alias, 2014, p. 3).

2.4.4 Monetary Sovereignty

After the creation of the EMU, controlling the supply of money on the Single Market became centralised and independent in the hands of the ECB. The ECB has the exclusive right to authorise the issue of notes within the eurozone, albeit most notes are actually issued by the National Central Banks (NCBs). Printing money is a source of government revenue. National governments treat monetary sovereignty a valuable tool before joining a currency area because it allows Central Banks to buy government debt by printing domestic currency (Economics Discussion, 2018). To deal with national debt, governments, in general, do prefer other ways of tackling the problem. The reason is that money financing of budget deficit does not increase economic output in any way. It merely increases money supply, which in turn causes inflation in most of the cases. In spite of the fact that issuing notes tend to solve the problem and reduce the budget deficit only in the short-run, entering the EMU limits the means through which national governments can obtain revenues.

From the foregoing, it can be seen, that losing the ability to control such instruments of monetary policy can be painful for countries and accordingly might be deterring them from joining the EMU.

3. Empirical Methodology: The Gravity Model

This research paper analyses the effect of joining the EMU on import and export fluctuations, by closely examining the cases of the lately joined Slovakia and Slovenia.

Classical models of international trade in the tradition of Heckscher, Ohlin, and Samuelson or of Ricardo, may perform inadequately when it comes to explaining patterns or volumes of trade. These models ignore the existence of two crucial determinants, which characterise New Trade Theory models, namely: economies of scale combined with product differentiation and transportation costs. Gravity models, originally proposed by Linder (1961) and Linnemann (1966), are the first to take account of the aforementioned. They have become one of the most frequently used workhorse models to analyse trend in international trade. In view of their simplicity and high explanatory power, gravity models have been applied to the particular case of Central and Eastern European Countries (CSEECs) in several studies (see Hamilton and Winters (1992) and Baldwin (1994)). Along the same lines with Newton’s theory

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of gravitation, these models express bilateral trade as a function of two key variables: the economic size of the two countries engaged in trade and the distance between them. Expressing an enhanced gravity model in general terms looks as follows (variables commonly defined in logarithms):

𝑇𝑖𝑗𝑡 = 𝛼𝑖𝑗+ 𝜃𝑡+ 𝛽1𝑦𝑖𝑡+ 𝛽2𝑦𝑗𝑡+ 𝛽3𝑑𝑖𝑗 + 𝛽4𝑞𝑖𝑗𝑡+ ∑𝐾𝑘=1𝛾𝑘𝑍𝑖𝑗𝑘𝑡+ 𝜀𝑖𝑗𝑡 (1)

Where 𝑇_𝑖𝑗𝑡 corresponds to the size of bilateral trade between country i and j at time t; 𝑦_𝑖𝑡 and 𝑦_𝑗𝑡 are GDP per capita values of country i and j at time t, respectively; 𝑑_𝑖𝑗 measures geographical distance between country i and j; 𝑞_𝑖𝑗𝑡 marks the real exchange rate between the countries4 and 𝑍

𝑘 reflects cultural and political factors affecting bilateral trade between the two

countries. The terms 𝛼_𝑖𝑗 are the country-pair individual effects covering all unobservable factors, as it is unlikely that 𝑍_𝑘 encompasses all cultural, historical and political factors, which are intrinsically difficult to measure in practice. 𝜃_𝑡 are the time-specific determinants and 𝜀_𝑖𝑗𝑡 is the error term.

In this research, I will be using panel data analysis, as it is more informative than cross-sections - reflecting dynamics and Granger causality5_{across variables. In a panel data setting}

the behaviour of several entities are observed across time. The entities relevant to this study are all euro area countries with a special focus on Slovakia and Slovenia. Instead of analysing bilateral trade volumes, export and import values are going to be regressed on variables affecting them (see above)6_{. This will produce two panel data charts, one regarding Slovakia -}

with both its export and import volumes - and one for Slovenia.

In terms of econometric methodology, I will estimate the regressions using the standard fixed-effects (FE) estimator. Fixed effects regression is a method controlling for omitted variables in panel data when these variables vary across entities but do not change over time. When using FE we assume that some of the country’s individual characteristics may bias the dependent or independent variables and hence we need to control for them. Having violated the assumption of zero correlation between the error term and the explanatory variables is the rationale behind it. Controlling for these differences by using the model removes the 'cross-sectional' (time-invariant) characteristics related to unobserved heterogeneity (like distance,

4_{Detailed explanation regarding the real exchange rate will follow.} 5_{For an elaborated approach on this, see Kónya (2006).}

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most importantly, or cultural differences, preferences, etc.). The remaining variation, or 'within' variation can then be used to identify the causal relationships I am interested in. Therefore, instead of estimating the above-stated gravity equation, my model will be reduced to a form where the fixed effects are “partialled out” and dummy variables are added. The eventually regressed two equations for the case of each country are the following:

𝑙𝑛𝐼𝑚𝑝𝑜𝑟𝑡𝑠𝑖𝑡 = 𝛼 + 𝛽1𝑙𝑛𝑦𝑖𝑡+ 𝛽2𝑙𝑛𝑦𝑗𝑡+ 𝛽3𝑞𝑖𝑗𝑡+ 𝛽4𝑑𝐸𝑀𝑈,𝑖 + 𝛽5𝑑𝐸𝑀𝑈,𝑗+ 𝜀𝑖𝑗𝑡 (2)

𝑙𝑛𝐸𝑥𝑝𝑜𝑟𝑡𝑠_𝑖𝑡= 𝛼 + 𝛽₁𝑙𝑛𝑦_𝑖𝑡+ 𝛽₂𝑙𝑛𝑦_𝑗𝑡+ 𝛽₃𝑞_𝑖𝑗𝑡+ 𝛽₄𝑑_{𝐸𝑀𝑈,𝑖} + 𝛽₅𝑑_{𝐸𝑀𝑈,𝑗} + 𝜀_𝑖𝑗𝑡 (3) The dependent variables (𝑙𝑛𝐼𝑚𝑝𝑜𝑟𝑡𝑠, 𝑙𝑛𝐸𝑥𝑝𝑜𝑟𝑡𝑠) represent the natural logarithms of imports and exports of Slovakia and Slovenia from country i at time t. The explanatory variables 𝑙𝑛𝑦_𝑖𝑡 and 𝑙𝑛𝑦_𝑗𝑡 denote the natural logarithm of the GDP per capita values of country i and that of country j (denoting either Slovakia or Slovenia). The real exchange rate is indicated by 𝑞_𝑖𝑗𝑡 between the countries under question at time t. By adding a euro area dummy (𝑑𝐸𝑀𝑈,𝑗), I am

aiming to grasp the effect of Slovakia and Slovenia entering the EMU on exports/imports. Furthermore, the euro area partner dummy (𝑑𝐸𝑀𝑈,𝑖) will display when a certain trading partner

(country i) entered the EMU. This dummy will take only the value 1 for those first wave countries that adopted the euro on 1 January 1999, since the time span investigated in this research paper begins with 2004 in case of Slovenia and 2006 in the case of Slovakia. Finally, 𝛼 represents the constant term and 𝜀_𝑖𝑗𝑡 is the error term.

As specified above, exports, imports and countries' GDP per capita values have been expressed in logarithmic forms. Doing so serves as an advantage and makes interpretation more straightforward. Firstly, logarithmic transformation eliminates any complication that could arise from having exports and imports expressed in a different currency than the GDP per capita values, since changes are directly interpretable as percentage changes. Secondly, the regression coefficients on the GDP per capita values will represent constant elasticities which is a great preference in economic deduction. I will return to this topic in the Results section. The expected sign of the coefficients on the GDP per capita variables (𝛽₁, 𝛽₂) are of a positive nature – as higher income should presumably enhance trading activities.

The use of FE regression by the way of binary explanatory variables is commonly established in gravity models. Pöyhönen (1963) was one of the firsts to control for country-specific effects in this way, but is viewed as an adequate procedure in modelling trade flows comprehensively (see Anderson and van Wincoop (2003), Egger and Larch (2012), Bergstrand,

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Egger, and Larch (2013)). One appeal of using FE is that the coefficients on the estimated regressors – which are not totally collinear with the fixed effects – can be predicted with less danger of an endogeneity bias. However, this advantage comes at a potentially high cost of efficiency loss which might induce low R2_{measures. This paper will return to this matter also}

in subsequent sections.

Having graphically plotted the export and import volumes of Slovakia and Slovenia in the specified periods (see Figures 1-4, Results section), it can be seen that their trading activity with certain countries is relatively low. Trading of Slovakia and Slovenia with countries such as Malta, Cyprus, Estonia or Ireland seem to be negligible. Therefore, a comparative analysis will be conducted, where the aforementioned four countries showing the least trading activities with Slovakia and Slovenia are omitted from the regressions, in order to study a more accurate model, a better fit.

4. Data

To explain trade volume fluctuations accurately, the dataset includes 4 observations per year on exports and imports for both countries under investigation - 4 years prior and 4 years following admission to the EMU. Data on export and import volumes originate from the IMF Direction of Trade Statistics (DOTS) from 2004-2011 in the case of Slovenia and from 2006-2013 in the case of Slovakia. Export volumes are FOB (Free on board), while import volumes are CIF (Cost, Insurance, Freight) – both expressed quarterly in US dollars.

Quarterly GDP per capita data was not available at any sources for all 19 euro area countries, hence GDP at market prices was collected from the European Commission (Eurostat) in million euros, quarterly. In turn, it had to be divided by population in order to obtain per capita values. Data on population of all 19 eurozone countries was also retrieved from Eurostat. However, having access to population data only at a yearly frequency it had to be interpolated assuming constant increasing scale between values to attain quarterly data. Executing such calculation should not generate any limitation to this thesis since statistics on population is an estimation, hence converting it onto a quarterly basis produces valid results. The method used for calculating quarterly population values for country i between year t and (t-1) was the following:

𝑃𝑜𝑝_𝑖𝑡_{= 𝑃𝑜𝑝}

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Solving this equation, the quarterly percentage change ‘r’ is derivable, hence interpolating yearly values is feasible. Dividing quarterly GDP at market prices by the such computed quarterly population, GDP per capita was forecasted - quarterly.

Likewise, quarterly real exchange rate among countries was not directly available at any data sources for all 19 countries under investigation. Therefore, nominal exchange rates were gathered quarterly from Eurostat. After the point where certain countries joined the euro area, nominal exchange rates were fixed at the value which was relevant at the latest point of time before joining (see Appendix, Table 5). In order to calculate real exchange rates, nominal ones had to be adjusted by consumer price indexes. In the euro area, consumer price inflation is measured by the Harmonised Index of Consumer Prices (HICP). The term “harmonised” denotes the fact that all the countries in the EU follow the same methodology. This ensures that the data for one country can precisely be compared with the data for another. HICPs for all 19 countries were gathered from Eurostat on a monthly basis. Averaging every 3 months' values, quarterly values were calculated. Subsequently, the real exchange rate between each pair of countries i and j at quarter t was possible to be determined by using the following formula:

𝑞_𝑖𝑗𝑡 = 𝑒_𝑖𝑗𝑡∗𝐻𝐼𝐶𝑃𝑗𝑡 𝐻𝐼𝐶𝑃_𝑖𝑡

Thusly, two panel charts were created, one for Slovakia and one for Slovenia – including both exports and imports. Each of these two charts sample four observations per year – making up 32 observations per country throughout the specified 8 years. Having 18 euro area trading partners in both the cases of Slovakia and Slovenia, 32 observations per country assemble 576 lines for each chart, yielding a total size of 2304 export, import data points in the examined sample. All else being equal, large sized sample leads to increased precision in estimates.

After importing the panel charts in STATA, running regressions was possible. The next section will analyse and interpret the results of the regression analyses illustrated above.

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5. Results

5.1. The case of Slovakia

The tables below demonstrate Slovakia’s import and export volumes, graphically plotted against all the euro area countries.

Figure 1. Imports of Slovakia, graphical representation

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Notably, from the tables above both imports and exports volumes depict a rising tendency – after a drop – starting from the second and third quarter of 2009, when Slovakia joined the EMU. The most striking development is detectable against Germany (purple line, well above the others). Germany being Europe's largest national economy, this should be of no surprise to the reader. Regarding import volumes, Austria's performance is also noteworthy, having it increased by 1 582 314 701 US dollars between the first quarter of 2009 and the fourth quarter of 20137_{. However, inspecting a large majority of the trading partners, prominent}

upswing is not noticeable. This might be a factor distorting the analysis and leading to a low coefficient of determination (R2_{), since a considerable share of the data might not explain trade}

volumes fluctuation precisely. Although, it can be verified that studying panels where the four countries showing the least trading activities are excluded does make sense.

The decline in trading volumes around 2008 is due to the Slovak economy being hit hard by the sharp contraction in foreign demand in the wake of the global financial and economic crisis. A similar trend was not observed for the hereinafter described economy of Slovenia. One might presume then, that Slovenia entering the euro area in 2007 - before the financial crisis -, brought about economic stability, which did not avail the Slovak economy.

The table displayed on the next page illustrates the results of model (2) (see Methodology section), the analysis of Slovak imports.

7_{Executing the subtraction of 2209869321 (2013 Q4) – 627554620 (2009 Q1) revealed this}

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Table 1. Fixed effects regressions of Slovak imports (1) (2) VARIABLES lnM lnM lnpercapitaSlovak 1.874*** 1.322*** (0.239) (0.189) lnpercapitapartner 1.306*** 1.207*** (0.293) (0.232) dslovak 0.124*** 0.164*** (0.0438) (0.0339) dpartner 0.541*** 0.798*** (0.0845) (0.145) rex -0.00262 -0.00224 (0.00339) (0.00239) Constant -8.310*** -2.632 (2.375) (1.885) Observations 576 448 R-squared 0.344 0.381 Number of countries 18 14

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Regression (1) exhibits the full model, incorporating all the 18 euro area members, while regression (2) shows the results of the model with the reduced number of 14 trading partner. Except for the real exchange rate variable (rex), it can be seen that all variables of interest are significant even at a 1% significance level.

The regressor ‘rex’ not being significant may be due to the manner it is defined. In Figure 5 (see Appendix), one can see that a certain country’s nominal exchange rate in the applied model has been fixed at the level that has been in effect at the latest point before joining the EMU. Thereafter these values have been corrected for inflation to obtain real exchange rate values. Hence, real exchange rate does variate in the dataset – even for those first wave countries that have adopted the euro in 1999 already -, but varies with a minimal deviation around the nominal rate – due to price level fluctuations. It can be concluded then, that the so-formed real exchange rate values in the dataset are unlikely to explain many trends in import fluctuation. Rather, dummy variables should.

The dummy variable labelled ‘dslovak’ is aimed at capturing the increased levels in imports, as taking the value 0 before Slovakia entered the EMU (2006-2008) and 1 after (2009-2013). The coefficient on ‘dslovak’ in regression 2 (0.164) being higher than in regression 1

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(0.124) is due to model 2 being more accurate – as excluding certain eurozone countries, not of great influencing relevance. These coefficients denote that entering the EMU, in general, is equivalent to a 13.2%8_{increase in imports in regression 1 and a 17.8% increase in the second}

regression. These findings can be interpreted as trade being created due to the introduction of the euro. The other dummy variable ‘dpartner' is also significant in both regressions, showing that having the trading partner a member of the EMU does also affect the import volumes of Slovakia, notedly positively. I will further elaborate on this topic in the next section regarding the exports of Slovakia.

The coefficients in a log-log model represent the elasticity of the dependent variable with respect to the independent variable under question. In other words, the coefficient is the estimated percentage change in the dependent variable for a percentage change in the independent variable. Coefficients on the GDP per capita variables are also significant in both regressions. In regression 1, a 1% increase in Slovakian GDP per capita is associated with a 1.86% increase in imports, while a 1% increase in the trading partners GDP per capita can be translated as 1.3 % increase in imports on average. These findings do sound reasonable as GDP per capita is a measurement of a country's economic output per person as well as a measurement of income. Therefore, a higher income of an average Slovakian may possibly be a reason for importing more at a national level. Similarly, the trading partner’s GDP per capita progress can affect their exporting – hence Slovakia’s importing – volumes.

The R2 _{measures justify the conjecture of having more accuracy if removing certain}

quite negligible countries. The supporting value of regression 1 being 0.344 is indeed lower than 0.381 – pertinent to regression 2. In general terms, these R2_{values are not extraordinarily}

high. We could say – based on definition – that the first model’s independent variables explain 34.4% of the variation in import volumes, while the second model explains 38.1%. This implies that a proportion of the variance of the import volumes is not punctually predictable from the independent variables. This could be due to having omitted some variables that could have explained variation better. Although, literature affirms that a low R2_{value is adequate,}

assuming that the regression has strong statistical significance (large sample size) and good diagnostics. Ultimately, this research has a highly frequent and rather large sample in line with significant coefficients on variables of interest.

8_{The interpretation of a coefficient on a dummy variable with a log dependent variable is as}

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The table below demonstrates the results of model (3) (see Methodology section), the analysis of Slovak exports.

Table 2. Fixed effects regressions of Slovak exports (1) (2) VARIABLES lnX lnX lnpercapitaSlovak 1.110*** 1.088*** (0.236) (0.218) lnpercapitapartner 1.592*** 1.506*** (0.289) (0.268) dslovak 0.0256 0.0522 (0.0432) (0.0392) dpartner 0.628*** 0.140 (0.0834) (0.167) rex -0.00388 -0.00243 (0.00335) (0.00276) Constant -4.090* -2.304 (2.343) (2.179) Observations 576 448 R-squared 0.264 0.208 Number of countries 18 14

The case of exports for Slovakia is different. As that could be already seen from the graph depicted at the beginning of the section (see Figure 2), exports did not show as strong of an increasing tendency as imports for the specified period. This is verified by not having a significant variable on ‘dslovak’ in neither of the two regressions. The coefficients on the per capita GDPs, however, are significant in both regressions, but of less of a magnitude than in the previous analysis regarding imports.

The explanation for the not significant variable ‘rex’ in both regressions is equivalent to the preceding. An interesting feature of analysing the exports of Slovakia is that the R2

measure of the regression not including the four countries showing the least trading activities (0.208) reveals a lower value than the R2 _{of the full model (0.264). This could be due to the fact}

that even though Slovakia traded the least with the countries taken out from regression 2 (Malta, Cyrus, Ireland, and Estonia), their trade pattern explained better the fluctuations and directions of export variation. This perception can be also supported by the presence of the significant variable ‘dpartner’ is the first regression. Let me now turn to this topic.

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The binary variable ‘dpartner’ takes the values 0 in quarters when trading partners of Slovakia are not members of the eurozone and the values 1, through the years they are. Having this dummy significant in regression (1) has some meaningful implications. The regression effectively does not display a significant response of Slovakia entering the EMU – as the coefficient ‘dslovak’ is insignificant -, however, it demonstrates a suggestive impact of the trading partner being part of the currency union. This finding, in effect, translates to having the partner countries’ presence in the EMU a more effective symptom on the export volumes of Slovakia than the actual entree of Slovakia. In other words, EU countries entering the euro area had relatively a bigger effect on exporting of the Slovak nation as a whole than introducing the euro effectively. This effect amounts to an 87.4% increase in exports on average once a trading partner entered the EMU.

A discussion on CA variation in general and the relative magnitude of imports/exports changes may be found at the end of the next section (5.2). The subsequent discussion will focus on Slovenia’s trading pattern.

5.2. The case of Slovenia

The single-line diagrams below illustrate Slovenia’s import and export volumes graphically. Figure 3. Imports of Slovenia, graphical representation

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Figure 4. Exports of Slovenia, graphical representation

The above two figures show a differing trend of fluctuations from Slovakia’s case. First of all, the sharp decline in Slovakia’s case around the 2008 global financial crisis regarding both export and import volumes is not accountable at this juncture. As stated above, one might speculate this being a consequence of having had Slovenia admitted to the euro area before the crisis hit the world as a whole. A slight drop in the third quarter of 2008 in exports towards Italy and a decline in Italian and German imports is detectable to a similar extent. Although, these variations are not of a great magnitude.

A general increasing tendency after the admission of Slovenia to the EMU in 2007 on both representations is noticeable, however, an unanticipated plunge around 2011 requires explanation. Upon this finding, literature has been looked up, which clarified the reasons for this unexpected drop in trading volumes. Slovenia's booming economy built up internal and external imbalances in the years before the global financial crisis and the internal rebalancing process has been proving problematic afterwards. Around 2010-2011, increasing national debt levels hit Slovenia’s economy, leading to serious macroeconomic imbalances (Ec.europa.eu, 2018). Depressed output has helped to close the current account deficit, but competitiveness losses triggered plummeting export and import levels. The fluctuations accounting for these imbalances may possibly perturb the regression results of the analysis and hence might produce lower R2_{values than would otherwise.}

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The table beneath displays the results of model (2) (see Methodology section), the output regarding import volumes of the economy of Slovenia.

Table 3. Fixed effects regressions of Slovenian imports

(1) (2) VARIABLES lnM lnM lnpercapitaSlovenia 1.751*** 2.413*** (0.573) (0.421) lnpercapitapartner -0.464 0.0183 (0.424) (0.317) dsloven 0.373*** 0.162*** (0.0635) (0.0452) dpartner 0.171 0.121 (0.118) (0.133) rex 0.00371 0.00137 (0.00252) (0.00162) Constant 5.636 -2.835 (3.995) (2.889) Observations 576 448 R-squared 0.191 0.287 Number of countries 18 14

First, studying the R2_{measures of the regressions, one can see that by excluding the}

aforementioned four countries from the analysis, the goodness of fit increased notably. While the first regression model explains 19.1% of the variation in imports, regression 2 advances this to 28.7%. An explanation and interpretation of these relatively low R2 _{measures has been}

provided above and applies to the present diagnosis as well. Moreover, the discussion on the insignificance of the variable ‘rex’ shall continue to hold with regards to the analysis of Slovenia's import and export volumes.

The dummy variable ‘dsloven’ - being in the main focus of this investigation -, is highly significant in the above analyses. This implies, that Slovenia introducing the euro in 2007 have increased their importing volumes from the euro area on average by 45.2%. This value amounts to a 17.6% increase when concentrating on the second regression and hence trade creation is present also in this analysis. Again, the difference in the coefficients’ magnitude on ‘dslovak’ may be due to the four countries’ - omitted from the second regression - lower explanatory power concerning the tendency of imports.

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The coefficients on the GDP per capita values demonstrate disparate results. While the variable ‘lnpercapitaSlovenia’ is highly significant in both regressions, ‘lnpercapitapartner' is not. Regarding the analysis of Slovenia's GDP per capita, the model with a reduced number of observations shows a higher impact on importing levels. A 1% increase in Slovenia's GDP per capita translates as a 2.4% increase in imports on average regarding the second regression, while the first one exhibits an equivalent 1.75% increase. The factor reflecting the effect of the partner country's per capita value on imports did not prove to be significant in the case above. Fluctuation in exporting and importing volumes as a consequence of the global financial crisis in 2008, had a substantial effect on other euro area members by analogy. This balking may have produced variation in their GDP per capita values as well, which in turn, can be the reason for insignificant coefficients on the relevant variables.

In both regressions, the dummy variable controlling for other euro area countries introducing the euro (‘dpartner’) turned out to be insignificant. This outcome can be explained by the following contexture. The fact that other EU countries introduce the euro mainly effects the underlying countries’ economy, but is not a must that imports of Slovenia will rise due to this conversion. For instance, when Malta or Cyprus entered the eurozone in 2006, Slovenia still operated with their national currency: the tolar. Hence, the aforesaid two countries may possibly have affected the import volumes of Slovakia, as they still had the need of exchanging currencies, hedging, etc.

Finally, this research paper has reached the analysis of the last regression output. The summary respecting Slovenia’s export volumes bears the most significant results, outlining the best fit among the others.

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Table 4. Fixed effects regressions of Slovenian exports (1) (2) VARIABLES lnX lnX lnpercapitaSlovenia 1.291*** 0.840*** (0.339) (0.287) lnpercapitapartner 0.971*** 1.500*** (0.251) (0.217) dsloven 0.278*** 0.237*** (0.0376) (0.0309) dpartner 0.586*** 0.478*** (0.0697) (0.0909) rex -0.000320 -0.00154 (0.00149) (0.00111) Constant -2.411 -2.232 (2.363) (1.973) Observations 576 448 R-squared 0.476 0.555 Number of countries 18 14

First of all, evaluating the R2_{measure of the two regressions outputs above, it can be}

seen that in regression 1, the independent variables can explain 47.6% of the variation in exports, while the second raises this value to 55.5%. Having a look at the graph of Sloven exports at the beginning of the section, we can – in fact – see a steadier enhancement throughout the analysed period compared with imports – except for the sharp drop in 2008 due to the financial crisis. Obtaining all variables of interest significant is also a signal of good regression diagnostics.

Interpreting the results, both Slovenia's and the partner countries' GDP per capita values have a significant effect on the export volumes. A 1% increase in GDP per capita of Slovenia contributes to a 1.3% increase in exports - if analysing the first regression - and to a 0.87% increase based on the second. Regarding the partner countries, a 1% increase in per capita amounts to 0.97% and to a 1.5% increase in exports on average, with regards to the first and second regressions, respectively. These percentage changes seem to make sense once again, since the per capita values can be accounted as a measure of income. Accordingly, higher income levels may possibly correspond to higher spending, hence enlarged trading volumes.

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The dummy variable ‘dpartner’ indicates a 79.7% increase in exports on average between Slovenia and the 18 euro area member states when the latter were engaging in such monetary unification. This amounts to a 62.7% rise if considering only 14 countries. Concerning the binary variable ‘dsloven’, I find highly significant results, even at a 1% significance level. These translate to a 32% and a 26.7% upsurge in exports after the point when Slovenia enters the EMU - referring to the full and reduced regression models, correspondingly. These positive measures stand for trade creation – as a result of entering the EMU.

Finally, when comparing the response of export and import volumes of both countries under investigation as of entering the eurozone, one can realise that the effect on imports is larger in case of both regressions for Slovakia – since the dummy ‘dslovak’ concerning exports is not significant -, and in the case of the first regression when studying Slovenia. In other words, imports increased relatively more than exports in the specified period for both countries. Therefore, it can be concluded that the current account of both countries - simply defined as net exports – has decreased. An explanation for this could be that at the point of joining the EMU, both countries were net importers (see Appendix, Figures 5 and 6). By introducing the euro, eliminated exchange rate barriers allowed the countries to import more than before as transaction costs resulting from exchanging currencies were not present. This was not the case regarding exports, at least not to the same extent. Another justification of these findings may be due to the data being defined. As that can be seen in the Methodology part (Section 4), data on import volumes have been collected under the incoterm: CIF. This incoterm stands for Cost, Insurance and Freight (CIF), which means that the seller pays costs, freight and insurance against the buyer's risk of loss or damage in transit to destination. Exports on the other hand are FOB (Free on Board), where the contracts relieve the seller of responsibility once the goods are shipped. This causes a systematic asymmetry as the value of imports should then be higher than the value of the mirror exports. Consequently, countries aiming to engage in more importing (CIF) and less exporting (FOB) deals seems reasonable, as imports being more advantageous – pushing a larger share of responsibility on the seller. Thus, another explanation regarding the relatively higher effect of joining the EMU on imports has been outlined.

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6. Conclusion

The main objective of this research paper was to demonstrate the effects of joining the EMU on trade, by analysing two lately joined countries: Slovakia and Slovenia.

This thesis offers a broad overview of the major findings of available literature addressing the effect of joining a currency union (EMU) on trade. As demonstrated by the pioneer Rose and several other authors, the effect of a common currency (euro) on international trade is relatively strong. Countries already being part of the European Union show substantial trading activities, however, joining the monetary union boosts this to an even higher scale. The euro’s introduction represents one of the last phases in the long-lasting process of European integration. Having it seen as involving structural changes in the expectations of markets, a common currency i.e. an irrevocable fixed exchange rate regime is durable. The obvious benefits involved are eliminated exchange rate barriers, less hedging activities needed on stock markets and increased competitiveness among firms. Nonetheless, these are accompanied by costs of losing autonomy over monetary independence. Not being able to depreciate a nation’s currency, to print money as a source of government revenue or to influence interest-rate policy are definitely deterring factors. At the same time, the prospect of a positive balance between benefits and costs is the principal reason for joining a currency area.

With previous research having found larger effects of joining the EMU on intra-euro area trade, this case study focused on euro area members as trading partners of the entering Slovakia and Slovenia. Analysing export and import fluctuations separately between the aforementioned two countries and their 18 eurozone members was the methodology used in this panel data analysis. It allowed for deducting more accurate conclusions also regarding the current accounts of the countries. Data was gathered quarterly over an eight-year time span regarding both countries. In this way, the direct effect of entering the monetary union was possible to measure by comparing trade volumes before and after joining. Due to having time-invariant variables in the gravity model, fixed effect regressions were used. The model identified causal relationships between exports/imports and GDP per capita, real exchange rate, and dummies grasping the effect of entering the EMU.

This paper has produced results that fall within the range of previous estimates for the effect of joining the eurozone on trade with other member countries. Concerning both countries, the effect of joining the EMU had a relatively larger positive effect on imports when compared with exports. This can be explained by both Slovakia and Slovenia being net importers at the

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point of joining the EMU, and by the channels through which imports and exports are defined in the dataset. Regarding Slovenia, imports increased by approximately 10% more than exports, while this number amounts to about 13% in case of Slovakia. Hence, it can be concluded that the current account of both countries has declined effectively. Some interesting findings suggest that in Slovakia’s case the effect of other member states entering the euro area had a stronger effect on export volumes than the country itself joining.

Finally, answering the main research question of this analysis, trade volumes of countries - in general terms - do increase as a result of entering the EMU. With trade possibly one economic criterion influencing those 9 EU countries’ decision whether to enter the euro area or not, this finding may contribute to their possible future decision. Further research may target analysing what additional implications economic integration entails. Having obtained results suggesting constructive effects on trade, the desirability of synchronized business cycles and fiscal coordination may be studied. This, ultimately, raises the question whether forming an economic union - seen as the final stage of European integration after having achieved a monetary union - should be or not be undertaken.

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Appendix

Table 5. Representation of fixing nominal exchange rates after joining the EMU for the period 2004Q1 – 2006Q1 CURRENCY/TIME 2004Q1 2004Q2 2004Q3 2004Q4 2005Q1 2005Q2 2005Q3 2005Q4 2006Q1 Austrian schilling 13,76 13,76 13,76 13,76 13,76 13,76 13,76 13,76 13,76 Belgian franc 40,34 40,34 40,34 40,34 40,34 40,34 40,34 40,34 40,34 Cyprus pound 0,59 0,58 0,58 0,58 0,58 0,58 0,57 0,57 0,57 German mark 1,96 1,96 1,96 1,96 1,96 1,96 1,96 1,96 1,96 Estonian Kroon 15,65 15,65 15,65 15,65 15,65 15,65 15,65 15,65 15,65 Spanish peseta 166,39 166,39 166,39 166,39 166,39 166,39 166,39 166,39 166,39 Finnish markka 5,95 5,95 5,95 5,95 5,95 5,95 5,95 5,95 5,95 French franc 6,56 6,56 6,56 6,56 6,56 6,56 6,56 6,56 6,56 Greek drachma 340,75 340,75 340,75 340,75 340,75 340,75 340,75 340,75 340,75 Irish pound 0,79 0,79 0,79 0,79 0,79 0,79 0,79 0,79 0,79 Italian lira 1936,27 1936,27 1936,27 1936,27 1936,27 1936,27 1936,27 1936,27 1936,27 Lithuanian litas 3,45 3,45 3,45 3,45 3,45 3,45 3,45 3,45 3,45 Luxembourg franc 40,34 40,34 40,34 40,34 40,34 40,34 40,34 40,34 40,34 Latvian lats 0,67 0,65 0,66 0,68 0,70 0,70 0,70 0,70 0,70 Maltese lira 0,43 0,43 0,43 0,43 0,43 0,43 0,43 0,43 0,43 Dutch guilder 2,20 2,20 2,20 2,20 2,20 2,20 2,20 2,20 2,20 Portuguese escudo 200,48 200,48 200,48 200,48 200,48 200,48 200,48 200,48 200,48 Slovenian tolar 237,65 238,87 239,95 239,83 239,74 239,54 239,49 239,51 239,51 Slovak koruna 40,56 40,08 40,02 39,45 38,29 38,92 38,67 38,49 37,46

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Figure 5. Slovakian trade balance, joined the EMU in 2009

How entering the euro area affects trading volumes : a case study of Slovakia and Slovenia

Bachelor’s Thesis