Euro introduction and company valuation in CEEC countries

Faculty of Economics and Business Department of Business Studies MSc International Financial Economics MSc Business and Economics

Euro introduction and

company valuation in

CEEC countries

Rutger Haarmans

Keywords: Tobin’s Q, Euro, CEEC Countries, Financial Crisis JFL codes: F1, F31, G32, G15

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1. Introduction

The introduction of the euro as a common currency in Europe can be seen as one of the most significant changes in international financial markets for the last quarter century. Never before surrendered so many independent nation states their national currencies to a common central bank, abstaining from monetary sovereignty (Jonung and Drea, 2009). The effects of the introduction of the euro on corporations in the eleven countries that directly adopted the euro are widely debated. Bris, Koskinen, and Nilsson (2003) argue that the introduction of the euro led to an increase in corporate valuations for firms in countries which introduced the euro compared to firms in countries that did not introduce the euro. Bris, Koskinen, and Nilsson (2009) indicate that the firm value increased 15,3% in weak euro countries and in strong euro countries 6.1%.

Although these influences are significant on corporate valuations, a lot of disbelievers blame the common currency for the disappointing macroeconomic performance in the eurozone (Bris et al., 2009). Countries such as the UK, argue that the costs outweigh benefits. Moreover, several countries worry about the fact that the eurozone recovers slowly from the current crisis. This may be the result of a fundamental disequilibrium within the single currency zone, which applies a single monetary policy and a single exchange rate to a diverse group of countries (Moravcsik, 2012).

The impact of the introduction of the euro is relevant for new members of the European Union, which consider joining the EMU. This research explores the influence of the introduction of the euro on firm value in countries which introduced the euro after the initial introduction in 1999. The aim of this study is to contribute to the discussion about the effects of the introduction of the euro on corporations within the eurozone. It could help countries in the European Union, who still doubt on whether or not to join, in making their decision.

Seven countries introduced the euro after the initial introduction; Greece (2001), Slovenia (2007), Cyprus (2008), Malta (2008), Slovakia (2009), Estonia (2011) and Latvia (2014). Since countries from Central and Eastern Europe are most likely to introduce the euro in the future, the focus of this paper is on: Slovenia, Slovakia and Estonia. Latvia introduced the euro at the beginning of this year (2014) and, consequently, I do not include Latvia in this study.

2 The main question of this paper is: What is the effect of the introduction of the euro on

firm value, measured by the Tobin’s Q in Central and Eastern European Countries?

The value of a firm can be calculated as the sum of expected future cash flows discounted at the cost of capital. The introduction of the euro can influence the firm value by increasing the expected future cash flows or by lowering the cost of capital. This aspect will be discussed further in the next section.

This study contributes to the current literature about introduction of the euro in several ways. First, I estimate the effect of the euro on countries which did not introduce the euro initially but introduce the euro several years afterwards. This might lead to different results, because markets might be able to predict the effects of introduction of a common currency.

Furthermore, this study includes three control groups to estimate the effect of the introduction of the euro in Slovenia, Slovakia and Estonia. The first control group consists of countries which adopted the euro in 1999. The introduction of the euro might also have a positive effect on the firm value in these euro countries, i.e. due to a lower cost of capital or a decrease in exchange rate risk. The second control group consists of countries that are part of the eurozone, but did not introduce the common currency. The third control group consists of countries that will introduce the euro in the future.

Furthermore, I evaluate a country on multiple aspects to separate between weak and strong countries within the eurozone. Prior studies split these two types of countries by assessing whether the currency devaluated during the EMU crisis in the early nineties. However, I argue that this aspect is less important nowadays because the currency crisis took place more than twenty years ago. This study uses three elements to make the split between weak and strong countries: (1) nominal convergence, which is based on the EMU criteria, (2) real convergence, which is based on the Optimal Currency Area Theory, (3) GDP growth rate. Finally, this research takes both the announcement date and the conversion date into account to estimate the announcement effect and the introduction effect.

The paper consists of six sections. The next section provides a review of the literature; the third section explains the methodology. The fourth section states the results of the analysis and the fifth section is a discussion of the results. The final section concludes.

2. Theoretical framework

3 firm first. Second, Bris, Koskinen, and Nilsson (2011) argue that the introduction of the euro can influence the Tobin’s Q of a firm by increasing the expected future cash flows or by lowering the cost of capital. Consequently, I also discuss cost of capital and expected future cash flows. Furthermore, I discuss the effect of market integration on the Tobin’s Q of a firm, since a common currency enhances economic integration (Rose and Engel, 2000). Then, I discuss the change in competition between investors. Finally, I separate strong and weak countries based on the EMU criteria, GDP growth and Optimal Currency Area Theory.

2.1 Tobin’s Q

According to Bris et al. (2009), Tobin’s Q is a sufficient way to measure firm value. The Tobin’s Q can be calculated as the book value of total assets minus the book value of the common equity plus the market value of common equity, divided by the book value of total assets.

2.2 Cost of capital

Cost of capital consists of the cost of debt and the cost of equity. Cost of equity, which consists of the risk-free rate and the risk premium of a firm has an impact on a firm’s Tobin’s Q since it influences the book value of a company. Hardouvelis, Malliaropulos, and Priestley (2006) show that the process towards EMU has reduced the cost of equity capital in nearly all sectors of the economy. They indicate that the average reduction in the cost of capital for countries that signed up for the euro is significant larger than for countries that did not introduce the euro.

Risk free-rate: The effort from countries to satisfy the Maastricht criteria for joining

the euro area led to the so-called nominal convergence, which means that the inflation and long-term interest had to move toward German levels. This resulted in a convergence of real risk-free rates (Hadrouvelous et al., 2006). Therefore, the introduction of the euro lowered the real interest rates in most EMU countries (Stulz, 1999). The effect is especially noteworthy in high inflation countries (Alesina and Barro, 2000).

Risk premium: Both the market risk premium and the currency risk premium

4 exposed to currency risk (Adler and Dumas, 1984). Although the premium for market risk is considerably higher than for currency risk, it cannot be concluded that currency risk is not an important pricing factor (De Santis and Gerard, 1998). Currency risk has been an important source of concern for firms (Jorion, 1991), since stocks have a lower return if they are more sensitive to currency risk (Kolari, Moorman, and Sorescu, 2008). Moreover, currency risk influences both the cash flows of the firm and the discount rate employed to value these cash flows (Muller and Verschoor, 2006). Through the introduction of the euro, this currency risk is eliminated for intra-EMU transactions (Hardouvelis et al., 2006). Therefore, especially for firms who interacts frequently in intra-EMU transactions, the risk premium could decrease

2.3 Expected future cash flows

The introduction of the euro could increase a firm’s Tobin’s Q by increasing the expected future cash flows. This can be the result of a decrease in the transaction costs or an increase in trade in goods and services within the euro area (Rose and Stanley, 2005; Bris et al., 2009).

An increase in trade can lead to higher expected cash flows (Bris et al., 2009). At the start of the wave of research on trade effects by currency unions, the outcome was extremely positive. Rose and Engel (2000) indicate that nations in currency unions trade 235% more than expected. However, Baldwin and Nino (2006) argue that the reason for this increase is that the firms in the dataset where very small and very poor, which is not true for countries in the eurozone. Recent research shows that the euro’s influence on bilateral trade flows where indeed positive, however not close to 235%. The first study, which investigates the influence of the euro as a common currency, indicates that the introduction of the euro would increase intra-European trade by 50% (Rose and Wincoop, 2001). However, later research indicates that the effect on trade is in the 5% to 20% range (Bun and Klaassen, 2007; Berger and Nitsch, 2005; Flam and Nordström, 2006). Alesina and Barro (2002) argue that the advantages of the euro on trade is especially noticeable for countries which are already trading together, this means that the effect per country.

2.4 Market integration

The Tobin’s Q of a firm can be influenced by market integration, since an increase in market integration could change a firm’s market value by affecting stock returns (Hardouvelis, Malliaropulos, and Priestly, 2007).

5 Hardouvelis et al. (2006) find that introduction of the euro led to an increase in stock market integration among countries within the eurozone, at the same time, the market integration for countries which did not participate in the common currency did not increase.

The EMU affects the level of European stock market integration, and thereby the cost of capital, in several manners. First of all, Licht (1998) argues that the euro integrated the Europe stock markets by eliminating barriers to intra-EU investments. Furthermore, the introduction of the euro leads to extra risk sharing opportunities, by allowing foreign investors to enter local securities with diversification potential (Bekaert and Harvey, 1995). Finally, regulatory harmonization and competitive pressure could lead to more developed home financial markets. These influences could lower cost of capital due to more market integration (Guiso, Jappeli, Padula, and Pagano, 2004).

2.5 Competition

Competition can influence a firm’s Tobin’s Q, since competition could change the cost of capital of a firm and therefore the book value (Bris et al., 2009).

The cost of capital for countries within the eurozone could decrease due to the increase in competition between investors in financial markets in the eurozone. Rajan and Zingales (2003) argue that the volume of corporate bond issuance tripled after introduction of the euro within the Eurozone. This indicates that bond markets have become a good substitute to borrowing from a bank.

2.6 Weak / strong countries

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2.7 EMU criteria and GDP growth

In order to determine how each country scores on the EMU criteria and the GDP growth, I take the year of introduction and the four years before into account. A score will be classified as sufficient or as not sufficient, If a score is not sufficient it will be grey. A country can be classified, based on the nominal convergence and the GDP growth, as being very strong if it has a maximum of five insufficient scores, as being strong if it has a maximum of eight insufficient scores and as being weak if it has more than twelve insufficient scores.

HICP inflation: The aim of the ECB is to keep the inflation rate stable in the EMU

countries around a 2%. Table 1 gives an overview of the rates for the investigated countries. If the inflation rate is around 2%-4% it is sufficient.

Table 1: Inflation rate per country

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Euro area 2.3% 2.1% 2.2% 2.2% 2.2% 2.1% 3.3% 0.3% 1.6% 2.7% 2.5% 1.4% Estonia 3.6% 1.4% 3.0% 4.1% 4.4% 6.7% 10.6% 0.2% 2.7% 5.1% 4.2% 3.2% Slovenia 7.5% 5.7% 3.7% 2.5% 2.5% 3.8% 5.5% 0.9% 2.1% 2.1% 2.8% 1.9% Slovakia 3.5% 8.4% 7.5% 2.8% 4.3% 1.9% 3.9% 0.9% 0.7% 4.1% 3.7% 1.5%

Government budget deficit: The ratio of the annual general government deficit relative to

GDP at market prices is not allowed to exceed the 3% level at the end of the previous fiscal year nor for two years in a row. Table 2 gives the rates for the investigated countries. For the government deficit, the score is sufficient if it is maximum 3.0%.

Table 2: General government deficit/surplus per country

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Euro area 3.1% 2.9% 2.5% -0.3% 0.7% 2.1% 6.4% -6.2% 4.1% -3.7% -3.1% Estonia 1.7% 1.6% 1.6% 2.5% 2.4% 3.0% 2.0% 20.0% 1.1% -0.2% -0.2% Slovenia 2.7% 2.3% 1.5% 1.4% 0.0% 1.9% 6.3% -5.9% 6.4% -4.0% 14.7% Slovakia 2.8% 2.4% 2.8% 3.2% 1.8% 2.1% 8.0% -7.5% 4.8% -4.5% -2.8%

Government debt-to-GDP ratio: The ratio of gross government debt, which is

measured at its nominal value outstanding at the end of the year, and consolidated between and within the sectors of general government relative to GDP at market prices, is not allowed to be above the 60% at the end of the preceding fiscal year. Table 3 gives the results of the investigated countries. If the government debt in % of GDP is below the 60% it is sufficient.

Table 3: Governmental debt in % of GDP per country

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Euro area - - - - - - - - - - -

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Exchange rate stability: Countries who would like to participate in the common

currency are not allowed to devaluate their currency in the two years before introduction. For this period the currency of those countries should have been stable without big tensions. For Slovenia, Slovakia and Estonia this is not a problem.

Long-term interest rates: average yields for 10 year government bonds in the past years: Not allowed to be 2.0% higher, than the unweighted arithmetic average of the similar

10-year government bond yields in the 3 EU member states with the lowest inflation rate, which are qualified as benchmark countries for the calculation of the inflation reference value. Table 4 provides the results. For Estonia, Slovenia and Slovakia the results are adequate if it is around 5%.

Table 4: 10 year government debt interest rates per country

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Estonia 3.2% 3.9% 4.3% 4.7% 5.3% 5.8% 4.2% 3.0% 3.45 3.7% 2.4% Slovenia 6.4% 4.7% 3.8% 3.9% 4.5% 4.6% 4.4% 3.8% 5.0% 5.8% 5.8% Slovakia 5.0% 5.0% 3.5% 4.4% 4.5% 4.7% 4.7% 3.9% 4.5% 4.6% 3.2%

GDP Growth: Besides the low stable inflation, the ECB predicts a low stable growth.

A growth rate between 2%-7% is classified as stable. If the real GDP growth rate is within this range the results will be considered as sufficient; the results can be seen in Table 5.

Table 5: Real GDP growth per country

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Euro area 0.7% 2.2% 1.7% 3.3% 3.0% 0.4% -4.4% 2.0% 1.6% -0.7% -0.4% Estonia 8.1% 6.2% 8.9% 10.2% 7.3% -4.1% -14.1% 3.3% 8.7% 4.5% 2.2% Slovenia 2.9% 4.4% 4.0% 5.8% 7.0% 3.4% -7.9% 1.3% 0.7% -2.5% -1.1% Slovakia 4.8% 5.1% 6.7% 8.3% 10.5% 5.8% -4.9% 4.4% 3.0% 1.8% 2.0%

Kozluk (2005) investigates the readiness to comply with Maastricht criteria several years before potential EMU accession. He argues that Slovakia was within the reach of meeting the requirements; however, Slovakia did not follow a steady convergence path. Besides that, he argues that Slovenia started off much closer than the other CEEC’s but doesn’t converge anymore. Furthermore, Kozluk (2005) argues that Estonia steadily qualified according to the Maastricht criteria.

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introduction from a nominal perspective and GDP growth rate. In contradiction with Kozluk (2005) it could be argued that Slovakia did follow a steady convergence path for the last three years. Estonia had problems with the inflation rate which dropped with more than 10% from 2009 till 2010. The real GDP growth rate was only stable in Slovenia in the years before the introduction of the euro. However, the effect of the mortgage crisis which started in 2007 can be a relevant reason for the destabilization.

As I mentioned before, I classify a country as being very strong if it has maximum five insufficient scores, as being strong if it has it has less than eight insufficient scores and a as being weak if it has more than twelve insufficient scores. This study argues, based on the nominal convergence and GDP growth rate, that Slovenia was better prepared for the introduction of the euro than Slovakia and Estonia. This is reflected in the results: Slovenia had just two insufficient scores, while Slovakia had seven insufficient scores and Estonia had ten insufficient scores.

2.8 Optimal Currency Area Theory

The OCA theory originates from Mundell (1961), who argues that if the business cycles of two countries are highly correlated, it would be beneficial to peg the external value of its currency to another countries currency. The OCA theory developed over time due to the additions of McKinnon (1963) and Kenen (1969). They argue that a monetary union could only be qualified as optimal if, and only if, countries that belong to these area score well on the following criteria; degree of openness, industrial diversification and the mobility of production factors. Rinaldi-Larribe (2008) argue that countries are ready to enter the currency

area if there is a clear convergence between the business cycles of countries that are willing to join the monetary union and the business cycle within the currency area.

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three. Therefore, based on the optimal currency theory it could be argued that Slovenia is a very strong country, Estonia is average and Slovakia is below average.

To conclude: I argue, based on the nominal convergence, the real convergence and the GDP growth, that Slovenia can be classified as being a strong country, whereas Estonia and Slovakia can be classified as being average countries.

3. Research method

The data used in this study is collected by Orbis and range from the first of January 2005 until the first of January 2013. The sample consist of 962 companies and is divided into four groups: one treatment group and three control groups. The treatment group includes the CEEC countries which adopted the euro after the initial introduction: Estonia, Slovakia and Slovenia. The first control group consists of countries which introduced the euro on the first of January 1999; Finland and Belgium. Both countries are chosen based on the size of their economy, which are comparable to the treatment group. The second control group consists of countries who are part of European Union, but decided to keep their home currency; Denmark and Sweden. The third control group consists of countries that have not introduced the euro; however, they will introduce the euro in the future. These countries areLatvia and Lithuania.

As is discussed in the introduction, this research takes into account both the announcement date and the conversion date. This is a new element compared to earlier studies that used 1998 as starting point (Bris et al., 2009). Taking both dates into account is relevant, because trade might increase directly after the announcement due to a decrease in exchange rate risk. Therefore, it could be that the average firm’s Tobin’s Q in Estonia, Slovakia and Slovenia directly increases after the announcement date. Furthermore, the fact that earlier research indicates that the effect of the EMU impact on trade was the largest in 1998 and not in 1999 makes it even more interesting to analyse both years (Micco, Stein, and Ordoñez, 2003). Since this research takes both dates into account, the individual effects can be compared. Therefore, this research could conclude which date has the biggest influence on firm value.

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3.1 Data description

Table 6 shows that the total sample consists of 962 companies which are investigated between January 2005 and January 2013. The treatment group includes Estonia, Slovakia and Slovenia and consists of 89 companies. Slovenia is represented by 54 companies, which means that Slovenia is responsible for more than 60% of the total treatment group. The average Tobin’s Q between 2005 and 2013 is the highest in Estonia and the lowest in Slovakia. Belgium and Finland represents the euro control group. The group consists of 252 firms and is well split between Belgium and Finland. More than 50% of the sample represents the EU control group, namely Denmark and Sweden. The Tobin’s Q of companies in these countries are the highest of the total group, especially companies in Sweden with an average Tobin’s Q over 2 are well above the averages in the other countries. Finally, Latvia and Lithuania represents the future euro control group and consists of 60 companies. The companies are well divided between Latvia and Lithuania.

Table 6: Descriptive statistics: 2005-2013

Countrty Firms Tobin's q Size Profitability Leverage TFA/FA CAPEX/TA

An. Conv. An. Conv. An. Conv. An. Conv. An. Conv. An. Conv.

Estonia 16 1.26 0.63 4.90 0.93 0.12 0.18 27.31 18.28 0.31 0.30 0.06 0.06 Slovakia 19 0.82 0.28 4.66 1.43 0.07 0.10 15.45 10.81 0.28 0.26 0.03 0.04 Slovenia 54 0.90 0.49 4.80 1.10 0.07 0.09 27.00 17.50 0.37 0.26 0.04 0.04 TG 89 0.95 0.47 4.78 1.14 0.08 0.11 24.59 16.21 0.34 0.27 0.04 0.04 Belgium 130 1.35 0.95 5.23 1.12 0.08 0.26 27.26 26.04 0.33 0.28 0.05 0.07 Finland 122 1.51 0.78 1.86 0.90 2.67 0.12 28.11 19.27 0.25 0.23 0.05 0.05 CG1: Euro 252 1.43 0.87 3.60 1.01 1.33 0.20 27.67 22.76 0.29 0.25 0.05 0.06 Denmark 160 1.61 1.90 4.93 1.07 0.05 0.26 29.69 26.05 0.27 0.28 0.05 0.07 Sweden 401 2.09 3.26 4.63 1.31 0.00 0.97 24.03 20.92 0.18 0.25 0.04 0.07 CG2: EU 561 1.95 2.87 4.71 1.24 0.00 0.77 25.64 22.38 0.20 0.25 0.04 0.07 Latvia 29 0.83 0.37 4.16 0.76 0.10 0.11 23.67 17.17 0.46 0.22 0.06 0.07 Lithuania 31 0.82 0.56 4.83 0.53 0.11 0.12 27.35 21.50 0.49 0.27 0.06 0.06 CG3: Fut 60 0.97 0.47 4.51 0.64 0.10 0.11 25.57 19.41 0.48 0.25 0.06 0.07 Note: TG = Treatment group, CG 1 = Euro control group 1, CG2 = EU control group 2 and CG3 = Future euro control group

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3.2 Estimation method

I use two methods to estimate the effect of the introduction of the euro on firm value in Estonia, Slovakia and Slovenia; both methods are based on research by Bris et al. (2003; 2009). Bris et al. (2003; 2009) study the effects of the introduction of the euro on firm value for countries which directly participated in the eurozone using a similar model. Both models are estimated by an ordinary-least-squares regression and are fixed-effects panel regressions with the logarithm of Tobin’s Q as the dependent variable. The single-differences estimation measures the percentage change in firm value for one country after the introduction of the euro.

Single-differences estimation:

log𝑄_𝑖𝑡 = 𝑌_𝑡+ 𝐹_𝑖 + 𝛽𝑿_𝑖𝑡+ 𝛾𝒁_𝑡+ 𝛿𝐸𝑈𝑅𝑂_𝑡+ 𝜀_𝑖𝑡 , (1) where 𝑄𝑖𝑡 is Tobin’s Q for firm 𝑖 at time 𝑡; 𝑌𝑡 is year; 𝐹𝑖 is firm; 𝑿𝑖𝑡 is a vector of firm

control variables, such as size, profitability, leverage and asset-ratio’s. 𝒁_𝑡 is a vector of country control variables, including GDP growth rate, annual change in exchange rate with respect to the US dollar and change in interest rate. 𝐸𝑈𝑅𝑂𝑡 is a dummy equal to one after

introduction of the euro. This dummy is based on the announcement date or on the conversation date.

The difference-in-differences estimation measures the effects of the introduction of the euro on a firm’s Tobin’s Q and test for differences in the growth of the average Tobin’s Q in the treatment group and the control groups. The estimator is defined as the difference in average outcome of the treatment group before and after the introduction of the euro minus the difference in average outcome in the control groups before and after introduction of the euro. In contrast to the single-differences estimation, the difference-in-differences estimates the effect of introduction of the euro by comparing the change in Tobin’s Q of the treatment group with the change in Tobin’s Q of a control group.

Difference-in-differences estimation:

log𝑄_𝑖𝑐𝑡= 𝑌_𝑡+ 𝐹_𝑖𝑐 + 𝛽𝑿_𝑖𝑐𝑡+ 𝛾𝒁_𝑐𝑡+ 𝛿𝐸𝑈𝑅𝑂_𝑐𝑡+ 𝜀_𝑖𝑐𝑡 , (2) where 𝑄𝑖𝑐𝑡 is Tobin’s Q for firm 𝑖 in country 𝑐 at time 𝑡; 𝑌𝑡 is year; 𝐹𝑖 is firm; 𝑿𝑖𝑐𝑡 is a

vector of firm control variables, such as size, profitability, leverage and asset-ratio’s. 𝒁𝑐𝑡 is a

12 rate with respect to the US dollar and change in interest rate. 𝐸𝑈𝑅𝑂_𝑐𝑡 is a dummy equal to one for treatment group countries after introduction of the euro. This is based on the announcement date or on the conversation date.

Both models take into account several independent variables. 𝑌𝑡 is the fixed time effect

for year t, which takes common time trends across both euro and non-euro firms into account. 𝐹_𝑖(𝑐) represents the firm-specific fixed effects, by using the firm-specific fixed effects the dependent variable is controlled for both constant country factors such as taxation, accounting rules and legal environment and for constant firm factors such as industry effects.

The 𝑿𝑖(𝑐)𝑡 represent the firm characteristics used as controls. These characteristics are:

size, which is measured as the log of the firm’s sales; profitability, which is measured as the ratio of earnings before interest, taxes, depreciation and amortization to total assets; leverage, which is measured as the book value of nonequity liabilities divided by total assets; the ratio of fixed tangible assets to total assets; the ratio of capital expenditures to total assets; and the ratio of research and development expenses to total assets. Firm size is included because smaller firms have larger growth opportunities than larger firms (Bris et al., 2009), tangibility of assets is included because previous research argues that it is negatively related to the investment opportunities of an firm (Aivazian, Ge, and Qiu, 2005). Research and development expenses are expected to have a positive relationship between a firm’s Tobin’s Q and its stock prices reaction to announcements of increases in research and development expenditures (Szewczyk, Tsetsekos, and Zantout, 1996). However, research and development expenses cannot be included since there is no data available. Capital expenditures are also positively related to growth opportunities (Chen and Ho, 1997). Leverage is included since it has a negative effect on both growth opportunities (Lang, Ofek, and Stulz, 1996) and on a firm’s Tobin’s Q (McConnell and Servaes 1990). Finally, profitability affects directly firm value.

The Z(c)t represents the country controls. Real gross domestic product (GDP) growth

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effects of the changes in interest rates due to the new monetary policy environment are measured.

Both regressions are executed twice. In the first regression, the dummy variable for the treatment country is the year during announcement and in the second regression it is the year after conversion.

In conclusion, I conduct several tests to determine if the introduction of the euro has a positive influence on the average firm’s Tobin’s Q. I do these tests on both the treatment group as a whole and on each individual country in the treatment group. This research starts with some basic tests in order to determine if the Tobin’s Q increases in the year during announcement and the year after conversion. Then, I compare the change in the average firm’s Tobin’s Q in the treatment group with the change in the average firm’s Tobin’s Q of the control groups. Finally, I execute a regression to investigate both the total effects of the treatment group and the individual effects of the countries in the treatment group.

3.3 Hypotheses

This research studies the effects of the introduction of the euro on a firm’s Tobin’s Q. Therefore, several hypotheses are challenged.

Hypothesis 1a: The introduction of the euro has a positive influence on a firm’s Tobin’s Q in Estonia, Slovakia and Slovenia .

I expect that the introduction of the euro has a positive influence on the average firm’s Tobin’s Q of a country, since the introduction of the euro could increase the expected future cash flows and could decrease the cost of capital. Equation 1 estimates the change in Tobin’s Q in a country.

Hypothesis 1b: The effect on a firm’s Tobin’s Q is larger during the year of the announcement date than in the year after conversion.

14 the regression will be executed both during the year of announcement and in the year after conversion. Equation 1 estimates the change in Tobin’s Q after the announcement year as well as after the conversion.

Hypothesis 2a: The average firm’s Tobin’s Q increases significantly more in Slovenia, Slovakia and Estonia than in the control groups.

Since the introduction of the euro occurs in Slovenia, Slovakia and Estonia, I expect that the Tobin’s Q in those countries increases more than in the control groups. This seems realistic, because these countries have the advantages of a common currency. Equation 2 estimates the change in Tobin’s Q in Slovenia, Slovakia and Estonia compared to three treatment groups. The first treatment group consists of euro countries, the second group consists of EU countries without the euro and the third group consists of countries which will adopt the Euro in the future.

Hypothesis 2b: The difference in the effect on the average firm’s Tobin’s Q in the investigated countries is smaller compared to the euro control group than compared to the other control groups.

Countries within the eurozone can also benefit from new members. For example, due to lower exchange rate risk. Furthermore, it could be that countries in the eurozone trade more because of the elimination of this risk between the countries. Therefore, the bilateral trade flows between countries that share the same currency may increase (Glick and Rose, 2002), which is also an advantage for countries which are already a member of the eurozone. Moreover, the introduction of the euro leads to an increase in competition among providers of finance, which leads to more liquid financial markets (Lane, 2006). This might lead to lower cost of capital in countries in the euro area as well. Therefore, I expect that the results are less noticeable in comparison with the eurozone control group than with the other control groups.

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Hypothesis 3: The effect on a firm’s Tobin’s Q is larger in Estonia and Slovakia than in Slovenia.

Bris et al. (2009) indicates that the increase in firm value after the introduction of the euro is larger in weak economies than in strong economies. As is discussed above, Estonia and Slovakia can be seen as average countries while Slovenia can be seen as a very strong country. Therefore, I expect that the positive influence of the introduction of the euro is larger in Estonia and Slovakia. Equation 1 as well as Equation 2 estimates the change in Tobin’s Q in Estonia, Slovakia and Slovenia.

4. Results

First, I test whether the average Tobin’s Q increases after the introduction of the euro. The average firm’s Tobin’s Q in the treatment group can be seen in Figure 1. It shows that in Estonia, which introduced the euro in 2011, the average firm’s Tobin’s Q increased after the conversion. However, during the announcement year there is a small decrease in the average Tobin’s Q. For Slovakia, which introduced the euro in 2009, the average Tobin’s Q of a firm increased during both the year of announcement and the year after conversion. For Slovenia, which introduced the euro in 2007, there is an increase during the year of announcement and a decrease in the year after conversion. It might be that the decrease in the average firm Tobin's Q occurred because of the financial crisis. This seems realistic, because the Tobin's Q of the whole treatment group and all the control groups did decrease during 2008.

Figure 1: The average Tobin’s Q in Estonia, Slovakia, Slovenia

16 Figure 2: Average Tobin’s Q in treatment group and control groups

Furthermore, Figure 2 shows that the average firm’s Tobin's Q increases more in Slovakia, Slovenia and Estonia than in the control groups. Moreover, Figure 2 shows that in control group 1, which is the euro control group, only a small decrease in the average firm’s Tobin's Q occurs. For control group 2 and control group 3 the decrease is much more visible. As is mentioned earlier, it might be that the decrease of the average firm’s Tobin’s Q exists due to financial crisis, which started in 2007 and lowered the Tobin's Q on average about 0.4 in both the treatment group and the control groups.

Table 7: Growth in Tobin’s Q in investigated country’s

Country An. / Conv. Date Coefficient St. dev P-value Estonia Announcement year 2010 0.152 0.097 0.119

Conversion year 2011 0.025 0.082 0.758

Slovakia Announcement year 2009 0.212 0.116 0.072 *

Conversion year 2010 0.089 0.085 0.301

Slovenia Announcement year 2006 0.080 0.062 0.198

Conversion year 2007 0.049 0.082 0.547

An. = Year of announcement, Conv. = Year after conversion

To measure the effects of the introduction of the euro in Estonia, Slovakia, and Slovenia, I estimate the effects per country separately. I use the logarithm of Tobin’s Q as the dependent variable, consequently, the coefficient shows the percentage change in Tobin’s Q during the year of announcement and after the year of conversion. Table 7 shows that the effect is positive during both the year of announcement and the year after conversion for all three countries, however the effect is only significant in Slovakia during the year of announcement. It shows an increase of 21.2%; the effect is marginally significant. Appendix A provides the regressions separately with the corresponding estimates.

17 Table 8: Difference-in-differences estimation: Treatment group versus control groups

TG= Treatment group, Euro CG = Euro control group, EU CG = EU control group and F. euro CG = Future euro control group

The difference-in-differences regression for the total treatment group in comparison with the control group does not give significant results (Table 8 and Appendix B). It shows that the average growth of a firm’s Tobin’s Q in the treatment group is only higher in the year after conversion, compared to the future euro control group. This outcomes is not significant. During the year of announcement the increase is higher compared to both the euro control group and the EU control group, but again this is not significant. On the other hand, the individual regressions provide some significant results. Table 9 and Appendix C show the results of Estonia in comparison with the three control groups. In comparison with the euro control group, the increase in Tobin’s Q is at a 5% significance level lower than it is in the euro control group in the year of announcement. In the year after conversion, Estonia has a not significantly higher increase in the average firm’s Tobin’s Q. In comparison with the second control group, the increase in the average Tobin’s Q is, at a 1% significance level, lower in the year of announcement in Estonia. In the year of conversion the growth of a firm’s Tobin’s Q is higher compared to the EU control group, however this is not significant. The comparison of the results with the future euro control group shows that the increase in the average firm’s Tobin’s Q is higher in Estonia during both the year of announcement and the year after the euro introduction, however these results are again not significant.

TG An. / Conv. Date Coefficient St. dev P-value

Euro CG Announcement year 2010 0.030 0.040 0.460

Conversion year 2011 -0.016 0.024 0.506

EU CG Announcement year 2008 0.020 0.055 0.718

Conversion year 2009 -0.037 0.034 0.274

F. euro CG Announcement year 2006 -0.016 0.044 0.710

18 Table 9: Difference-in-differences estimation. Estonia versus control groups

Estonia An. / Conv. Date Coefficient Std. dev P-value

Euro CG Announcement year 2010 -2.979 1.510 0.049**

Conversion year 2011 0.054 0.051 0.288

EU CG Announcement year 2010 -7.305 1.198 0.000***

Conversion year 2011 0.014 0.075 0.852

F. euro CG Announcement year 2010 3.331 2.947 0.259

Conversion year 2011 0.095 0.061 0.119

An. = Announcement year, Conv. = year after conversion, Euro CG = Euro control group, EU CG = EU control group and F. euro CG = future euro control group

The comparison between the results of Slovakia, in Table 10 and Appendix D, and the control groups give some interesting results. For example, in comparison with the EU control group there is a lower increase in the Tobin’s Q in the year during announcement at a 10% significance level. There is also a lower increase in the average firm’s Tobin’s q in the year after conversion, but at a 1% significance level. On the other hand, in comparison with the future euro control group it shows that there is a higher increase in the average Tobin’s Q in firms in Slovakia compared to this group, at a 5% significance level, in the year after conversion. However, during the announcement year there is a not significant smaller increase in the average Tobin’s Q .

Table 10: Difference-in-differences estimation. Slovakia versuscontrol groups

Slovakia An. / Conv. Date Coefficient Std. dev P-value Euro CG Announcement year 2008 -0.042 0.058 0.469

Conversion year 2009 -4.206 2.721 0.122

EU CG Announcement year 2008 -0.141 0.083 0.089* Conversion year 2009 -10.585 2.115 0.000*** F. euro CG Announcement year 2008 -0.040 0.063 0.531

Conversion year 2009 9.049 4.323 0.037**

An. = Announcement year, Conv. = year after conversion, Euro CG = euro control group, EU CG = EU control group and F. euro CG = future euro control group

19 future euro control group the results are also positive in the year after conversion, however the results are not significant.

Table 11: Difference-in-differences estimation. Slovakia vs control groups

Slovenia An. / Conv. Date Coefficient Std dev. P-value

Euro CG Announcement year 2006 0.070 0.032 0.029**

Conversion year 2007 0.007 0.033 0.837

EU CG Announcement year 2006 0.011 0.046 0.812

Conversion year 2007 -0.086 0.046 0.060*

F. euro CG Announcement year 2006 0.067 1.981 0.048**

Conversion year 2007 0.006 0.034 0.857

An. = Announcement year, Conv. = year after conversion, Euro CG = Euro control group, EU CG = Euro control group and F. euro CG = future euro control group

Furthermore, I discuss the effects of the control variables on the average firm’s Tobin’s Q. This research discusses the control variables on the individual countries of the treatment group, during announcement year and in the year after conversion.

Table 12: The effect of control variables on a firm’s Tobin’s Q in Estonia, Slovakia and Slovenia

Estonia Slovakia Slovenia

Announce Converse Announce Converse Announce Converse Coef. Sign. Coef. Sign. Coef. Sign. Coef. Sign. Coef. Sign. Coef Sign. Sample 0.00 *** 0.01 0.57 -0.01 0.01** -0.01 0.01** 0.00 0.00** 0.00 0.10 Year -0.01 0.00 *** -0.52 0.01** 0.27 0.01** 0.13 0.05** -0.01 -0.02 0.00 0.66 Size 0.00 0.43 -0.08 0.29 0.04 0.05 ** 0.04 0.04** 0.02 0.02** 0.00 0.08 Profitability 0.46 0.00 *** 0.06 0.78 -0.54 0.02** -0.53 0.02** 0.32 0.32 0.30 0.02** Leverage 0.00 0.38 -0.00 0.06* 0.00 0.27 0.00 0.26 0.00 0.01** FA / TA -0.08 0.00 *** 0.24 0.03** -0.18 0.03** -0.17 0.04** 0.00 0.00*** Cap exp / TA 0.33 0.00 *** 0.31 0.45 0.17 0.73 0.19 0.70 0.21 0.20 0.20 0.42 GDP PC 0.15 0.00 *** 4.32 0.00 -10.63 0.01*** -5.95 0.06* 0.81 0.37 0.40 0.90 Ch. Ex. Rate 0.00 0.51 0.01 0.13 0.01 0.05** 0.00*** 0.00 0.16 Term spread 0.00 0.33 -0.47 0.01*** 0.05 0.50 0.06 0.40 0.01 0.06* 0.00 0.75 3-months rf -0.01 0.01*** -0.48 0.01*** 0.27 0.03 ** 0.15 0.13 -0.04 0.31 0.10 0.83

20

that this is only true for Estonia in the year after conversion. However, this result is not significant. It might be that larger companies are better diversified and that these companies have therefore a lower cost of capital, which leads to a higher Tobin’s Q. Profitably has a direct influence on firm value. Therefore, this research expects that profitability has a positive influence on the average firm’s Tobin’s Q. This is in line with the results of Estonia at a 1% significance level. For Slovenia, there seems to be a positive relation as well, however this effect is not significant. Leverage is expected to have a negative influence, because it is has both a negative effect on growth opportunities and on a firm’s Tobin’s Q. This is true for Estonia in the year after conversion at a 1% significance level, however this is not true for Slovenia in the year after conversion at a 5% significance level. Furthermore, the ratio between fixed tangible assets to total assets is expected to have a negative influence on growth opportunities. This is correct for Estonia and Slovakia at a respectively 1% and 5% significance level. On the other hand, this is not in line with Slovenia at a 5% significance level. Since the capital expenditures are also positive related to growth opportunities, this research expects a positive effect. This is true for Estonia, Slovakia and Slovenia, however the results are only significant in Estonia. I expect that companies who are active in stable countries with a more stable GDP growth have in general a higher Tobin’s Q, because for those countries the cost of capital is general lower, since there is less country risk. In Table 6, the results are very clear. Since the average Tobin’s Q in Sweden and Denmark are higher than in the treatment group and the other control groups, I expect that all the country control variables are positive related to a firm’s Tobin’s Q. For companies listed in Estonia, this is the situation for the Log GDP per capita at a 1% significance level. the 3-months risk free rate and the term spread are also positive related to a firm’s Tobin’s Q at a 1% significance level.

For Slovakian firms, the The Log GDP per captita is significant at a 1% significance level. The change in exchange rate is positive at a 5% significance level. The term-spread is positive, however not significant. The 3-months risk free rate is positive at a 5% significance level. For Slovenian companies, the Log GDP per capita is positive, however not significant. The change in exchange rate is positive at a 1% significance level and the term spread is positive at a 10% significance level.

Turning to the hypotheses, the following can be said.

Hypothesis 1a: The introduction of the euro has a positive influence on a firm’s

Tobin’s Q in the investigated countries. This hypothesis cannot be rejected. Based on Figure 1

21

indeed an increase in firm value in the year after euro conversion. For the other countries, there is reason to believe that the introduction of the euro has indeed led to an increase in a firm’s Tobin’s Q, although the results are not significant.

Hypothesis 1b: The effect on a firm’s Tobin’s Q is larger during the year of the

announcement date than in the year after conversion. This hypothesis can be rejected. Table 7

shows that Slovakia has significant increase in the average firm’s Tobin’s Q during the year of announcement. Based on the difference-in-differences estimation the following can be concluded. Table 11 indicates that in comparison with the future euro control group, the increase in the average firm’s Tobin’s Q in Slovenia is higher during the year of announcement than in the year after conversion. Compared to the euro control group only Slovenia performed significantly better during the year of announcement, while Estonia performed significantly worse. Only compared to the future euro control group the results are better. Slovenia performed significantly better during year of announcement and Slovakia performed significantly better in the year after conversion. For Estonia, the results look positive however, these are not significant.

Hypothesis 2b: the difference in the effect on a firm’s Tobin’s Q in the investigated countries is smaller compared to the euro control group than to the other control groups.

This hypothesis can be rejected. Although Slovenia performed significantly better than the euro control group, Slovakia performed significantly worse. Furthermore, in comparison with the other control groups the results are mixed as well. Therefore, it is difficult to answer this issue.

Hypothesis 3: The effect on a firm’s Tobin’s Q is larger in Estonia and Slovakia than in Slovenia. This hypothesis can be rejected, Slovenia did actually perform better than

Slovakia and Estonia. In the year of announcement Slovenia performed significantly better than the EU control group and the future EU control group. On the other hand, both Slovakia and Estonia scored significantly lower than the EU control group during the year of announcement, besides that, Estonia performed significantly worse than the euro control group. Slovakia achieved also lower results than the euro control group, however this result is not significant.

5. Discussion

22

increase in the average firm’s Tobin’s Q during the year of announcement. Slovakia has a significant higher increase in the average firm’s Tobin’s Q in the year after conversion, while Estonian firms have a higher increase in the average firm’s Tobin’s Q in both years, of which the year after conversion is nearly significant. The firms in these countries operate in the same environment. Therefore, this indicates that there is indeed an increase in the average firm’s Tobin’s Q due to the introduction of the euro.

Second, the new separation between weak and strong countries leads to new insights. Previous research used the currency crisis in the early nineties for the separation between

weak and strong countries and argues that the introduction of a common currency is more useful in weak countries. This paper uses the nominal convergence, real convergence, and GDP growth to separate between weak and strong countries. Using this new distinction the results suggests that strong countries are better prepared for the introduction of a common currency. There are several arguments why this outcome differs from results in the previous studies. First of all, countries scoring higher on the Optimal Currency Area Theory have a higher business correlation with countries within the eurozone. Therefore, a common currency gives them an advantage compared to countries that don’t have such a correlation. Moreover, based on the EMU criteria, I suggest that strong countries are better prepared for the introduction of the euro, which is a good sign to investors. It could be argued that investors were more interested in companies which operate in Slovenia than in Estonia and Slovakia because of these stable results.

Having said this, the results in comparison with the euro and the EU control group are not as positive as expected. To address this issue, both the individual increase in Tobin’s Q and as compared to the euro control group and compared to the EU control group are taken into account.

Estonia introduced the euro in 2011, and is just as Slovenia and Slovakia a small and open economy. Also similar like Slovakia and Slovenia, Estonia was hit severely by the

financial crisis in 2008 and 2009 (Krugman, 2012). However, Estonia suffered more than other regions by the turmoil in global trade and financial markets (Purfield and Rosenberg, 2010). Table 13 shows that the export fell with 20% in 2009. The import fell with more than

23

crisis, and that the export of 2012 was almost 1.5 times as high as before the crisis. Furthermore, Table 11 indicates that Estonia performed better than the control groups in the year after conversion, although this is not significant. The recovery was strong in the export, but especially the labor productivity increased significantly. This was a major contribution to the increase of Estonia competitiveness after the crisis. It, amongst others, brought the GDP back to the pre-crisis level at the end of 2011 (Parts, 2013). Altogether, it is clear that the effects on firm value were a lot healthier in the year after conversion than during the announcement year. The fact that commercial banks in Estonia are owned by Swedish banking groups is another reason why Estonia responded well to the crisis. Throughout the boom years the government ran constant budget surpluses and built up emergency reserves. Therefore, the Estonian government still had more reserves than debt during the crisis years and Estonia did not need a bailout. This Estonian policy was healthier than in countries where governments had to help rescue the banking system (Parts, 2013).

Table 13: Export and Import in Estonia, Slovakia and Slovenia

Trade in US$ bn. 2005 2006 2007 2008 2009 2010 2011 2012 2013 Estonia export 7.72 9.72 11.00 12.46 9.08 11.61 16.71 16.08 16.38 Import 10.19 13.43 15.61 16.04 10.11 12.21 17.38 17.77 18.30 Slovakia Export 24.86 34.03 52.27 68.57 56.03 64.52 79.85 80.57 85.64 Import 26.91 36.41 54.05 71.14 55.52 64.70 79.74 77.28 81.51 Slovenia Export 19.23 23.30 30.13 34.05 26.14 29.11 34.81 32.23 34.01 Import 20.24 24.19 31.62 37.09 26.48 30.04 35.55 32.20 33.34 Source: OECD

Slovenia introduced the euro in 2007. Figure 2 indicates that the average Tobin’s Q in 2007 lowered with almost 0.4 on average in both the treatment group and the control groups.

24

the risk premium that investors would like to get for their investment, due to the extra crisis risk. Altogether, the results in Slovenia are positive; however without the start of the financial crisis the performance might have been even improved. The problems in Slovenia arose in 2008-2009, when the import and export fell significantly. The inflow of cheap credit from abroad grinded to a halt, and the bubbles in the construction and real estate sector burst. This occurred because of the weak link between banks. Consequently, governments ended up recapitalizing, at great expense and resulting in weak government finances. Between the middle of 2012 and 2013, the ratio of non-performing total loans rose from 13.2% to 17.4%. This was the highest in the eurozone after Greece and Ireland, and shows the excessive risk taking by banks in Slovenia.

Slovakia introduced the euro in 2009. It experienced a significant change in the macroeconomic framework for a minor upcoming economy in the years before the crisis. Borrowing costs fell, and the exchange rate risk disappeared (Huefner and Koske, 2008).

Together with earlier financial sector privatization and liberalization, the introduction of the euro reduced barriers for borrowers (Backé and Wójcik, 2008). However, two problems existed already before the crisis. First, the exchange rate was locked in at an excessively high level since the currency appreciated by nearly 30% in real terms between 2006 and 2009. Second, the focus of fiscal policy was on nominal targets to meet the Maastricht criteria. This meant that Slovakia possibly missed the opportunity to run a stricter fiscal course during boom years. This made fiscal expansion during the crisis costly and painful to reverse. Therefore, the financial crisis aborted the expected boom before it could take hold. Unemployment continued to rise, and the GDP growth fell significantly with 4.9%. Since Slovakia is highly dependent on export and the whole eurozone was affected, the trade outflows lowered in 2009 with 15% (Fidrmuc, Klein, Price, and Wörgötter, 2013). Besides that, Slovakia had tight trade linkages with Germany and other countries within the eurozone. Therefore all those shocks hit Slovakia as well. Moreover, fiscal consolidation exercised a negative short term effect on domestic demand. Therefore, it is understandable that a country like Slovakia could not benefit as was expected from the introduction of the euro.

25

during those years. For future research it would be interesting to investigate how the evaluation of the Tobin’s Q would be compared to the control groups from 2011 onwards.

6. Conclusion

This study evaluates the impact of the introduction of the euro on firm value in CEEC countries. Taken together, the introduction of the euro improves the average firm’s Tobin’s Q in CEEC countries. Compared to the future euro control group, the average firm’s Tobin’s Q improves after the introduction of the euro as expected. The performance of Slovakia increases significantly in the year after conversion, while Slovenia performs significantly better in the year after announcement. However, this is not true compared to other control groups. Especially the results in comparison with the EU control group are not as expected. The average increase in the Tobin’s Q of firms in Estonia is significantly lower during the year of the announcement, while the average growth of a firm’s Tobin’s Q in Slovenia is significantly lower in the year after conversion. The average growth of a firm’s Tobin’s Q in Slovakia is significantly lower during both the year of the announcement and the year after conversion. Compared to the euro control group, Slovenia has a significant higher increase in a firm’s Tobin’s Q during the year of announcement. Estonia on the other hand, has a significant lower increase in the firm’s Tobin’s Q during the year of announcement. The effects of the financial crisis cannot be ignored. Especially the eurozone debt crisis had an impact on Estonia, Slovenia and Slovakia. On the one hand, the results of Slovenia are healthy in comparison with the control groups. On the other hand Slovenia had the most difficulties to recover from the crisis. The EU countries Denmark and Sweden score significantly higher than the treatment group. It could be that the performance improved due to the fact that they are not dependent on countries within the eurozone like Greece, Portugal and Ireland. These countries have experienced major troubles during the crisis.

Due to these mixed results and the impact of the financial crisis, it is difficult to give a clear answer to the question what the effect is of introduction of the euro on CEEC countries. Still, the results in comparison with the future euro control group are positive. Therefore it seems likely that the whole effect is possible. Moreover, the fact that the Slovakia, Slovenia and Estonia recovered quite quickly from the crisis, seems to be a good signal. However, during years of crisis it seems to be more attractive to stand alone, because then one can fully control both the monetary and fiscal policy.

26 effect. Therefore, future research could investigate how the evaluation of the Tobin’s Q would be compared to the control groups from 2013 onwards.

Besides that, it would be interesting to see the effects of the introduction of the euro on countries like Latvia and Lithuania, because at that point the whole crisis effect is diminished.

An interesting outcome of this paper is definitely related to the new separation between weak and strong countries and the outcome of it. For further research, it would be a possibility to test if strong countries are indeed better prepared for the euro introduction.

Another interesting outcome is that all the control variables do have an influence on a firm’s Tobin’s Q. Besides the size, all the outcomes are as expected.

To conclude: based on this research, I argue that the introduction of the euro could lead to a higher average firm’s Tobin’s Q, especially if the country has a high business correlation with the eurozone.

6.1 Management implications

The role of banks in an economy cannot be underestimated. The amount of risky loans in Slovakia and Slovenia were very high during the crisis, hereby the amount of non-performing loans led to significant economic problems. This can be seen in this research as well, because in both countries the average banks Tobin’s Q decreased significantly over time. Estonian banks on the other hand were stable. The Estonian financial sector is a unbureaucratic cooperation between companies and authorities, which was an advantage during the crisis. Whereas the loan to GDP ratio was really high in Slovenia and Slovakia, in Estonia this was stable. Also, this study indicates that a more tight fiscal policy helps during crisis. Furthermore, if a country is doubting on joining the eurozone it is interesting to investigate how they score on the nominal convergence, the real convergence and the GDP growth. If they perform well on these aspects and do have a high business correlation with the eurozone, this could be an argument to join.

6.2 Limitations

28

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32 Appendix A Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate Estonia = 1.0 (Selected) 1 .534a .285 .199 .20775300842830 4 a. Predictors: (Constant). An. Date. Size. Leverage. Log GDP per capita. Cap exp / Total assets. 3-months risk free. Profitability. Year. Fixed assets / Total assets. Sample #. Term spread

ANOVAa.b

Model Sum of Squares df Mean Square F Sig.

1 Regression 1.565 11 .142 3.297 .001c

Residual 3.928 91 .043

Total 5.493 102

a. Dependent Variable: Log Tobin's Q b. Selecting only cases for which Estonia = 1.0

c. Predictors: (Constant). An. Date. Size. Leverage. Log GDP per capita. Cap exp / Total assets. 3-months risk free. Profitability. Year. Fixed assets / Total assets. Sample #. Term spread

Coefficientsa.b Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 86.627 34.948 2.479 .015 Sample # .005 .009 .088 .558 .578 Year -.050 .019 -.554 -2.683 .009 Size -.082 .075 -.166 -1.096 .276 Profitability .052 .197 .033 .263 .793 Leverage -.004 .002 -.281 -1.957 .053

Fixed assets / Total

assets .242 .111 .300 2.171 .033

Cap exp / Total assets .300 .405 .087 .741 .461

Log GDP per capita 4.313 1.216 .568 3.548 .001

Term spread -.455 .172 -4.205 -2.636 .010

3-months risk free -.469 .172 -4.463 -2.724 .008

Announcement date .152 .097 .212 1.576 .119

33 Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate Estonia = 1.0 (Selected) 1 .534a .285 .190 .20883570050431 2 a. Predictors: (Constant). Con. Date. Leverage. Size. Change exchange rate. Cap exp / Total assets. Log GDP per capita. 3-months risk free. Profitability. Year. Fixed assets / Total assets. Sample #. Term spread

ANOVAa.b

Model Sum of Squares df Mean Square F Sig.

1 Regression 1.568 12 .131 2.996 .001c

Residual 3.925 90 .044

Total 5.493 102

a. Dependent Variable: Log Tobin's Q b. Selecting only cases for which Estonia = 1.0

c. Predictors: (Constant). Con. Date. Leverage. Size. Change exchange rate. Cap exp / Total assets. Log GDP per capita. 3-months risk free. Profitability. Year. Fixed assets / Total assets. Sample #. Term spread

Coefficientsa.b Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 90.352 37.678 2.398 .019 Sample # .005 .009 .089 .564 .574 Year -.052 .020 -.575 -2.601 .011 Size -.080 .075 -.162 -1.062 .291 Profitability .056 .199 .036 .283 .778 Leverage -.004 .002 -.281 -1.945 .055

Fixed assets / Total assets .241 .112 .300 2.155 .034

Cap exp / Total assets .307 .408 .089 .754 .453

Log GDP per capita 4.319 1.220 .569 3.539 .001

Change exchange rate .011 .007 .229 1.540 .127

Term spread -.467 .179 -4.314 -2.609 .011

3-months risk free -.480 .178 -4.563 -2.701 .008

Conversion date .025 .082 .036 .309 .758