Real wage rigidity and inflation in Europe : the effect of joining the European Monetary Union

Bachelor thesis

Real wage rigidity and inflation in Europe

The Effect of Joining the European Monetary Union

Tessa Snels

6135552

07-07-2013

Contents

1. Introduction

3

2. Literature review

5

3. Data description

11

4. Results

14

5. Discussion and concluding remarks

17

6. References

19

7. Appendices

21

Introduction

The introduction of the Euro was a big step in European integration, but till this day, people still argue if it was a good decision or not. The countries had such different economies that a singular monetary and fiscal policy was necessary to bring them a little more close together. Rules about inflation, public debt and others were implemented and in 2001 the Euro

officially became the national currency in 12 countries.

Some people say that countries like Spain, Italy and Greece gain more from de European Monetary Union (EMU) than other countries do. They say that the stronger economy countries pay for the ones with a weaker economy. The consequences of the drastic changes in their economic and fiscal policies is something people don’t know as much about. Some of these peripheral countries, Spain, Portugal, Ireland, Italy and Greece, had inflation rates as high as 8% and these had to come down to German levels.

So what are the implications of lowering inflation rates so drastically? The difference between nominal and real value comes from inflation and the interpretation real value can be problematic in everyday life. For many people, it is easier to think in nominal terms and this is where money illusion comes from. (Shafir et al.,1997) A little inflation can be beneficial for the economy, because it allows real wage cuts without lowering the nominal wage of the employee if this necessary. So lowering inflation rates by joining the EMU could be harmful for an economy, because it could hamper real wage cuts. This is what I want to investigate in this piece. In other words, did joining the EMU cause real wage rigidity for the peripheral countries?

A model with a dummy variable for joining the EMU for the peripheral countries was used to see if it lowered the amount of real wage decline in the economy. The database was retrieved from Eurostat, where they keep track on the wages in the sectors of most European

economies. Missing data in this database proved to be harmful for the results of the investigation, so the outcomes are not always conclusive and significant.

An overview of the relevant literature can be found in the next paragraph. In

paragraph 3, the data and the model are further explained. Next, a summary of the results will be provided in paragraph 4 and these will be discussed in paragraph 5, together with some concluding remarks

Literature review

People tend to evaluate economic transactions nominally and this induces a bias in the assessment of real value. This phenomenon is what Shafir, Diamond and Tversky. (1997) call money illusion. Others say that you suffer from money illusion if your excess-demand function depends on other things besides relative prices and real wealth (Patinkin, 1965). Another definition comes from Fehr and Tyran (2004), who say that you can speak of it when identical situations lead to different outcomes if it is framed in nominal terms instead of real terms. Money Illusion thus refers to the fact that people tend to value money in nominal terms instead of real terms and to the wrong decisions and equilibriums that follow from this misinterpretation.

Shafir et al. (1997) investigate the implications of this phenomenon with several survey questions regarding inflation and prices. Three previous findings from Howitt (1987) are that prices and wages are sticky, indexing does not occur in the way you might expect it to and, in daily life, people are confused by nominal and real worth. Apart from sticky wages, there is another important implication of money illusion regarding wages. Consider two cases, one with 4 percent inflation and a nominal wage increase of 2 percent, and the other with no inflation and a nominal wage decrease of 2 percent. The real loss is approximately the same, but people prefer the first case over the other. This comes from loss aversion, the tendency to strongly prefer gains over losses (Kahneman and Tversky, 1984), combined with money illusion. Apparently people do not evaluate their salary solely on buying power, but also on the actual amount of currency they earn. The results of the investigation show, among other things, that people are able to distinguish between nominal and real value when they are asked to think in economic terms, but if this is not pointed out, their evaluations tend to get more biased by nominal value (Shafir et al, 1997). Two comparable cases were proposed to another group of respondents. They were told that a firm was located in a community in a recession with high unemployment. The firm was making a little profit and in

the first case, they decide to lower wages by 7 percent while there was no inflation, and in the other case they raise wages by 5 percent while the inflation was 12 percent. 62 percent of the respondents judged the action in the first case as unfair, compared to 22 percent in the second case. Apparently, fairness is largely based on nominal changes in income.

But why is this sense of fairness important? Does money illusion really matter? Bewley (2004) studies the importance and the consequences of fairness in relation to wage rigidities. The previous studies he used in his literature review have conflicting results, depending on the investigation method. Bewley (2004) finds that only 24 out of the 235 businesses he studied lowered nominal wages. He explains that the main source of these findings is that employers don’t want to damage morale by reducing wages. The upper management needs their employees to have good morale, because it determines their commitment, their productivity and the quality of their work. Moral is all about fairness, or at least about perceived fairness. Relative pay is an important factor and wage cuts will damage moral if they are not explained properly or if the reason is not understandable. A remarkable theory Bewley mentions is that employers rather lay off workers than reduce wages. They prefer the first option because it only lowers moral on the short term, whereas wage reductions lower moral on the long term, because the misery stays in the company. Also, in the case of firing people, the management can decide who leaves the company, but when you lower wages, you risk losing the best people and these are exactly the people you do not want to lose.

If downward rage rigidity exists in times of low inflation and if it has the negative implications mentioned above, moderate inflation should “grease the wheels” of the labour market, because it facilitates real wage cuts when the market faces a negative shock. This is exactly what Card and Hyslop (1996) wanted to investigate in their paper. Using individual wage change data, they see that nominal rigid wages are more common in lower inflation environments. They find a relationship between inflation, the fraction of workers with downward-rigid wages and the speed at which real wages are able to fall. They say that if inflation increases by 1 percent, this reduces the fraction of workers with downward-rigid 6

wages with 0,8 percent and allow real wages to fall 0,06 percent faster. On the market level, they look at state average wages, inflation levels and the change in real average wages. The cross sectional Philips curve that follows from the relationship between the real wage change and the unemployment rate should be flatter when inflation is low and steeper when it is high. The most important findings are that real wages fall when unemployment is high and they rise when unemployment is low. The effect of inflation is however less conclusive. There is little evidence that the rate of wage adjustment is faster when inflation is high.

Card and Hyslop find only weak evidence for the effect of inflation on real wage rigidity at the market level, but McLaughlin (1992) even questions the existence of such rigidity at the individual level. His study on wage rigidity at the individual level brought him to the conclusion that there is no such thing as downward wage rigidity and instead he found downward flexibility. The wage growth is not even skewed away from wage cuts if you exclude union workers and minimum-wage workers from the analysis. There is however also some evidence for wage rigidity. In the nominal wage distribution, you see a small spike at zero. Also, nominal wage indexation is incomplete, but only in the case of unexpected inflation.

It would be interesting to see if the theories and findings so far correspond with the European Monetary Union (EMU) today. However, a special feature of the EMU is their objective to maintain stable and low inflation around 2 percent. This is very different from the 10 and 15 percent inflation rates considered in Card and Hyslop. Holden (2002) looks at the consequences of price stability in Europe. He mentions that multiple economists have argued that a low inflation rate leads to downward rigidity of nominal wages and in turn, this leads to higher wage pressure and a higher unemployment equilibrium (e.g. Tobin, 1972, Holden, 1994, and Akerlof, Dickens and Perry, 1996, 2000). On the other side, others state that wage rigidity follows from an inflationary environment and that we are better off without inflation (Ball and Mankiw, 1994, Gordon, 1996). Another group of scientists says the influence of unions should not be underestimated in the case of wage rigidity, because of their strategic advantage in the wage-setting for contracts (MacLeod and Malcomson, 1993, Holden, 1994, 7

1999). This advantage comes from the legal requirement of mutual consent to change a nominal wage. Holden (2002) argues that this is the typical form of employment contracts in Europe, so this aspect should be taken account of when you analyse this topic in modern Europe. Furthermore he says that there are 3 factors, apart from the union effects, that determine nominal wage rigidity and to what extent low inflation lowers output and drives up unemployment and thus harms society. First is the coverage of collective agreements, second the legal framework at renegotiations of collective agreements, and third the strictness of employment protection legislation for non-union workers. Collective agreements protect the working conditions of union and non-union workers, including wages. These agreements could prevent wage cuts, even if this is necessary for a sector. When renegotiating these agreements, the legal framework determines how much power the employees have in the negotiations. Apart from collective agreements, the state protects non-union workers through legislation on employment protection. This implicates that the macro-economic consequences of aiming at low inflation and the resulting nominal rigid wages are less severe for countries where these forces are weak. In other words, in countries with low bargaining coverage and weak employment protection legislation, a low inflation policy will be less harmful. To simplify his model, Holden has left out the assumption of money illusion out of consideration. Later he acknowledges that money illusion can play a complementary role in explaining nominal wage rigidity. Just like individuals, unions can suffer from it and make suboptimal decisions. He argues that in many European countries, these forces are strong and therefore price stability may harm these countries more than others. Shafir et al.(1997) draw another conclusion on the welfare implications of . They say that money illusion may have an influence on the labour market. It effects the allocation of workers and the level of employment.

Akerlof, Dickens and Perry(1996) also investigate the macroeconomics of low inflation environments like Europe. They see that very low levels of inflation cause an unnecessarily high level of unemployment through money illusion. It is approvable if the benefits of lower inflation, for example the reduction in tax distortion, outweighs the 8

unemployment costs. However, they argue that this is not the case and disapprove of zero percent inflation. Other low rates of inflation can be desirable, because it provides both stability and some room to lower real wages. They also find that wage rigidity declines in times of duress like the great depression, because employees perceive a wage cut as less unfair when times are rough.

In many southern European countries inflation rates have dropped since they joined the EMU. Before the establishment of the euro-area, the states had to reach the euro convergence criteria. One of those criteria is that inflation should not exceed the average inflation of the best three countries by 1,5 percent points. This meant that the rates had to converge from up to 8 percent to German standards around 2 percent. According to the money illusion theory, a lower level of inflation will be accompanied by more downward wage rigidity. Also, since the establishment of the EMU, the member states are faced with a single monetary policy. Economists from the Centraal Plan Bureau (CPB) in the Netherlands (2011) have argued that this single monetary policy is problematic for the euro-area, because it is not an optimum currency area. The states are subject to asymmetric shocks, labour mobility is very low and the area lacks fiscal transfers. In these circumstances, not having your own currency and monetary policy could be harmful. (CPB, 2011) The Eurosystem Inflation Persistence Network (IPN) states that wage rigidities have implications for the European monetary policy. It was suggested that there is a substantial degree of inflation persistence and that this may originate from wage rigidity. As stated before, moderate levels of inflation facilitate real wage cuts if necessary. The persistence of inflation is important when determining monetary policy. However, the persistence of inflation and the downward nominal and real wage rigidity patterns are heterogenic across the member states and this further complicates the design of optimal monetary policy (Babecký et al, 2009). Carlsson and Westermark (2008) develop a New Keynesian model which includes the assumption of downward nominal wage rigidity and investigate the changes in welfare associated with asymmetric economic conditions. They actually find when downward nominal wage rigidity is present, the welfare loss of asymmetric economic conditions are slightly smaller. They

suggest that this is the case, because wage rigidity is not a constraint on monetary policy design, it even opens up for potential welfare gains due to lower wage variability

Cukierman and Lippi (2001) wrote an interesting and highly relevant article about the influence of a monetary union (MU) on the labour market. They are particularly interested in MUs with cross-country asymmetries and the role of unions. They say that unions become smaller relative to the monetary area when an MU is established. This decreases their perception of their own influence on the inflation rate or on relative competitiveness when determining their wage demands. This results in more aggressive wage demands from all unions, resulting in even higher inflation rates and higher unemployment rates. Another important conclusion is that the formation of a MU will definitely change real wages, unemployment. Finally, they include another assumption in their analysis which may be important for this piece as well. They say that several countries may have been committed to the German monetary policy prior to the introduction of the EMU, through the exchange rate mechanism with Germany as an anchor.

Although not every article agrees on this, we can conclude from the literature that there is a positive relation between inflation and real wage cuts. For example, higher inflation facilitate real wage cuts, and thus lowers real wage rigidity. Another important conclusion is that unions and employment legislation play a big role in wage rigidity, because these forces can prevent wage cuts. Because these forces are strong in Europe, this is something to keep in mind in the rest of this piece. When establishing a monetary union, this can become even more problematic, because unions underestimate their influence on the expanding macro-economic environment.

Data description and model specification

We look at 12 countries that joined the EMU in 2001: Belgium, Germany, the Netherlands, Austria, Luxembourg, France, Finland, Italy, Portugal, Spain, Ireland and Greece. The last five countries started off with the highest inflation rates and for them, joining the EMU had the biggest impact on national economic governance, we call them the peripheral countries. As stated before, higher inflation rates tend to facilitate real wage cuts. This new economic governance policy with lower inflation rates may have made it harder to cut real wages. This is the effect that will be tested in this piece. Because countries with high inflation rates differ from low inflation countries in many aspects, inflation is not appropriate to use in the model. Instead, joining the EMU is used as a variable to measure this effect for the peripheral countries. So the peripheral countries get a treatment dummy in the years 2001 and forward. The years considered are 1996 to 2005, five years before and five years after the introduction of the euro. In this way, it is possible to study the effect of the policy changes.

The database used is Average Annual Gross Earnings by Economic Activity from Eurostat. This database contains information on the wage developments in many sectors and in most European countries. By calculating what percentage of the sectors have real wage decline, you can measure real wage rigidity. For example, if fewer sectors have real wage decline, there is more evidence for real wage rigidity. 13 sectors will be used in the analysis: mining and quarrying; manufacturing; electricity, gas and water supply; construction; wholesale, repair and retail trade; hotels and restaurants; transport, storage and communication; financial intermediation; real estate, renting and business activities; public administration, defence and compulsory social security; education; health and social work; other community, social and personal service. These sectors represent the main components of an economy.

Unfortunately, there is some missing data. For Italy and Ireland, there is just too little to work with, so these countries are not included in this study. There is some missing data for the other countries too. This is why the dependent variable Y is the percentage of sectors with real wage decline instead of simply the amount of sectors with real wage decline. The missing data in Eurostat will most certainly affect the results of this piece. The treatment effect could be measured better if Italy and Ireland were also considered. Now we have three peripheral countries instead of five and the treatment will only be used for Spain, Portugal and Greece. For almost all other countries there is some missing data too. This will probably harm the significance of the results. I will discuss these possible biases in the results section.

The following equation (1) is used in this model:

Y

= C + µ

+ γ

+ βT

+ e

Y is the number of sectors with real wage decline in country i and year j as a percentage of the total sectors for which there is data. The value of Y is explained with the different effects from countries, years and of course, the treatment T. C is a constant,

µ

effect for country i, γj represents the fixed effect for year j, βT is the effect for the peripheral countries from joining the EMU, which is the effect that we are most interested in. To conclude, there is the e that stands for the error.

Inflation is used to see if joining the EMU actually lowered inflation for the peripheral countries. This is important, because results in the first model needs to be compared to the inflation rates. For example, if the treatment parameter in the first equation is negative, but joining the EMU did not result in lower inflation rates, there must be another factor that influences real wage decline. Inflation is measured in the same way, with de following equation (2):

I

= C + µ

+ γ

+ β

+ e

Both are tested in the same way, using dummies for the 10 countries and the 10 years and a dummy variable ‘treatment’ to separate the five peripheral countries from the rest after 2001. In that way, you can estimate the influence of the new policy on the five countries.

As mentioned before, a fixed effects model is used to estimate the model. By using this model, you can control for country or year specific effects. For example, Germany is known for its low inflation, and a high growth year may have caused demand-pull inflation for all countries. The treatment variable needs to display only the effect of joining the EMU for the three peripheral countries and nothing else.

The treatment variable deserves some extra attention. This variable will help us to answer the main question: Did joining the EMU have a significant effect on inflation and real wage decline for the three peripheral countries? To answer the question, some assumptions had to be made. For example, the treatment dummy variable needs to represent the effect of joining the EMU. I could have chosen another year to start measuring this effect, because it is possible that the economies started converging earlier, or that the effect was visible a few years later, but I assume here that the effect sets in in 2001. Also, I assume here that the model contains enough explanatory variables to measure the effect properly. It is a simple model, apart from the treatment variable only years and countries are used to explain their own fixed effects. I believe this is enough for now and that simplicity can be a good thing. Of course there are other factors that have an effect on inflation and real wage rigidity, which also occurred in the period from 1996 to 2005. Some possible changes that had some influence on the macro-economics in the EMU are the intensifying of trade between the participating states or for example changes in the three factors in the labour market considered by Holden (2002). In this piece however, the focus remains on inflation, real wage rigidity, the introduction of the euro, the different states and years and the difference between the five peripheral countries and the rest.

Results

The SPSS function General Linear Model (GLM) was used to estimate the parameters, with all variables treated as fixed. The dummies for Belgium and 1996 were left out, so Belgium in 1996 will be the basis for the intersect. T

First we look at the results for equation 2, because it is helpful to have some insight in the development of the inflation rates before we look at the percentage real wage decline. The table below summarizes the main results, the whole SPSS spreadsheet can be found in appendix 1.

Dependent variable Inflation Estimated parameter

βT

Standard Error 0,370

Significance 0,280

R Squared of the model 0,590

The treatment parameter is in the first row, which tells us what the effect of joining the EMU had on the inflation rates of the peripheral countries. The parameter is -0,402, so joining the EMU lowered the inflation rates of these countries by about 0,4 percent points. Unfortunately, the figure has a significance value of 0,280, which is insufficient to call this a significant parameter.

Below the average inflation rates of the two groups are summarized in a graph. The average inflation rates of the peripheral countries are always higher than the average inflation rates of the non-peripheral countries. This is in line with the expectations. Also, there is a similar flow in the two graphs. Apparently, the inflation rates rose around the time the EMU was established and then dropped again. Maybe there was a macro-economic event that caused the inflation rates to rise, despite the strict rules on monetary and fiscal policy in the EMU. Another interesting feature is that the graph of the peripheral countries “follows” the

other graph. If the non-peripheral inflation rates start to rise in one year, the inflation rates of the peripheral countries rise in the next year.

Then the other dependent variable, the percentage real wage decline. The main results are also summarized in the following table:

Dependent variable % RWD

Estimated parameter 10,647

Standard Error 12,939

Significance 0,414

R Squared of the model 0,589

The rest of the results can be found in appendix 2. The treatment parameter is 10,647. This suggests that joining the EMU actually led to more real wage cuts, while the inflation model showed that the inflation effect of joining the EMU was negative. This contradicts the expectation of the outcome of this model. Note that this outcome is not significant.

The figure below graphs the average percentages real wage decline for both groups. They don’t paint a clear picture like the inflation graphs, but it does show the increase in % RWD that the treatment parameter estimate predicts.

0,00 1,00 2,00 3,00 4,00 5,00 6,00 Non-peripheral countries Peripheral countries 15

Finally, for both inflation and %RWD, the error is relatively large in comparison to the corrected total, respectively 57,372 to 139,919 and 31460,135 to 76463,549. This results in R squared values of respectively 0,590 and 0,589. These last numbers seem quite alright, but most parameter estimates are not significant. With a fixed effects model, you control for so much that you automatically achieve an acceptable R squared.

0,00 10,00 20,00 30,00 40,00 50,00 60,00 70,00 80,00 90,00 100,00 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Non-peripheral countries Peripheral countries

Discussion and concluding remarks

The main focus of this study was of course the treatment variable. Did joining the EMU have a significant effect on inflation and real wage decline for the three peripheral countries? Unfortunately, both parameters for treatment were not significant. I will come back to that later on. First I would like to discuss the value of the treatment variable in the %RWD

equation, apart from its significance. The parameter estimate was positive, which implies that joining the EMU actually increased the amount of sectors with real wage decline. This is not what the theory predicted, so how can this be explained? One explanation can be found in macro-economic factors that have an impact on real wage decline, but were not included in the model, such as the influence of unions. If the power of the unions weakened in the period of joining the EMU, this facilitates real wage cuts and this can explain the positive parameter. (MacLeod and Malcomson, 1993, Holden, 1994, 1999) Another possible explanation is that there was a negative economic shock in the three countries after 2001, which made real wage cuts necessary. Akerlof, Dickens and Perry (1996) find that people and unions

perceive wage cuts as less unfair when times are rough and accept them. This clearly needs some further investigation, but again, the figure is not significant, so the positive parameter can still be a coincidence.

This could come from the lack of data in some countries, years and sectors in the economy. Also, Italy and Ireland were omitted from the model and this lowered the number of peripheral countries from five to three. Maybe the effect could have been measured more properly with five countries. If the other influential factors mentioned above were incorporated in the model, this could have increased the explanatory power of the treatment variable.

The treatment variable was negative and this matches with the expectation, though still not significant. The goal of the strict monetary and fiscal policy rules was among others to lower inflation rates. This contradicts the theory of Cukierman and Lippi (2001). They say that the establishment of a monetary union can go hand in hand with an increase in inflation rates, because unions underestimate their influence on these numbers in a bigger area.

Even more interesting is that multiple articles in the literature match lower inflation with less real wage decline. In this case, a decline in inflation rates is accompanied with a rise in real wage decline. But, because both the parameters are not significant, this does not have to mean anything.

I can conclude that this investigation needs some further research with different or more data and extra variables, if only it were to enhance the significance of the model. Only then we can conclude if joining the EMU made a difference for the peripheral countries. Furthermore, it would be interesting to see what effect this all had on the welfare in these countries, because that is what policy making is all about in the end.

References

Akerlof, G., Dickens, W., Perry, G., 1996, The Macroeconomics of Low Inflation. Brookings

Papers on Economic Activity, 1-76

Akerlof, G., Dickens, W., Perry, G., 2000. Near rational wage and price setting and the long run Phillips curve. Brookings Papers on Economic Activity 1, 1-60.

Babecký, J., Du Caju, P., Kosma, T., Lawless, M., Messina, J., Rõõm, T., 2009, Downward Nominal and Real Wage Rigidity: Survey Evidence from European Firms. Czech

National Bank

Ball, L., Mankiw, N. (1994). Asymmetric price adjustment and economic fluctuations. Economic Journal, 247-261.

Bewley, T., 2004, Fairness, Reciprocity, and Wage Rigidity, IZA Discussion Paper Card, D., Hyslop, D., 1996, Does Inflation “Grease the Wheels of the Labor Market”? ,

NBER

Carlsson, M., Westermark, A., 2008, Monetary Policy under Downward Nominal Wage Rigidity, The B.E. Journal of Macroeconomics. 8 (1), 1935-1690

CPB (2011) Europa in Crisis

Cukierman, A., Lippi, F. (2001) Labour markets and monetary union: a strategic analysis.

The Economic Journal. 111: (541-565)

Eatwell, J., Milgate, M., Newman, P. (1987) New York: W.W. Norton

Fehr, E., Tyran, J., 2001, Does Money Illusion Matter? The American Economic Review, 91(5), 1239-1260

Fehr, E., Tyran, J., 2004. Expectations and the Effects of Money Illusion. DNB Staff Reports 115, Netherlands Central Bank

Gordon, R. J (1996). Comment on Akerlof, Dickens and Perry, The macroeconomics of low inflation, Brookings Papers on Economic Activity 1, 61-66.

Holden, S. (1994). Wage bargaining and nominal rigidities, European Economic Review 38, 1994, 1021-1039.

Holden, S., 2002, The Costs of Price Stability: Downward Nominal Wage Rigidity in Europe. University of Oslo and Norges Bank

Howitt, P. (1987) Money Illusion, The New Palgrave: A Dictionary of Economics Kahn, S., 1997, Evidence of Nominal Wage Stickiness from Microdata, The American

Economic Review

Kahneman, D. Tversky, A. (1984) Choices, Values, and Frames. American Psychologist. 39(4): 341-350

Lebow, D., Stockton, D., Wascher, W., 1995, Inflation, Nominal Wage Rigidity, and the Efficiency of Labor Markets, Board of Governors of the Federal Reserve System,

Finance and Economic Discussion Series. 94-45.

MacLeod, W., Malcomson, J. (1993). Investment, holdup, and the form of market contracts.

American Economic Review 37, 343-354.

McLaughlin, K., 1992, Rigid Wages? University of Rochester

Shafir, E., Diamond, P., Tversky, A., 1997, Money Illusion, The Quarterly Journal of

Economics

Solow, R. (1977) Another possible source of wage stickiness, Journal of Macroeconomics Tobin, J. (1972). Inflation and unemployment. American Economic Review 62, 1-18.

Appendices

1. Inflation

Fixed-effects model using GLM

Tests of Between-Subjects Effects

Dependent Variable: Inflation Source Type III Sum

of Squares

df Mean Square F Sig. Noncent. Parameter Observed Powerb Corrected Model 82,547a 19 4,345 6,058 ,000 115,104 1,000 Intercept 20,767 1 20,767 28,957 ,000 28,957 1,000 DummyTreatment ,847 1 ,847 1,181 ,280 1,181 ,189 DummyGermany 1,026 1 1,026 1,431 ,235 1,431 ,219 DummyNetherlands 1,585 1 1,585 2,210 ,141 2,210 ,312 DummyAustria ,267 1 ,267 ,372 ,544 ,372 ,093 DummyLuxembourg ,977 1 ,977 1,362 ,247 1,362 ,211 DummyFrance ,092 1 ,092 ,129 ,720 ,129 ,065 DummyFinland ,496 1 ,496 ,692 ,408 ,692 ,130 DummyPortugal 5,930 1 5,930 8,269 ,005 8,269 ,811 DummySpain 6,802 1 6,802 9,485 ,003 9,485 ,861 DummyGreece 24,316 1 24,316 33,907 ,000 33,907 1,000 Dummy1997 1,653 1 1,653 2,305 ,133 2,305 ,323 Dummy1998 4,362 1 4,362 6,082 ,016 6,082 ,683 Dummy1999 4,522 1 4,522 6,306 ,014 6,306 ,699 Dummy2000 ,041 1 ,041 ,058 ,811 ,058 ,056 Dummy2001 1,214 1 1,214 1,693 ,197 1,693 ,251 Dummy2002 ,074 1 ,074 ,103 ,749 ,103 ,062 Dummy2003 ,402 1 ,402 ,561 ,456 ,561 ,115 Dummy2004 ,150 1 ,150 ,209 ,649 ,209 ,074 Dummy2005 ,004 1 ,004 ,005 ,943 ,005 ,051 Error 57,372 80 ,717 Total 630,143 100 Corrected Total 139,919 99 a. R Squared = ,590 (Adjusted R Squared = ,493) b. Computed using alpha = ,05

Parameter Estimates

Dependent Variable: Inflation

Parameter B Std. Error t Sig. 95% Confidence Interval Noncent. Parameter

Observed Powera Lower Bound Upper Bound

Intercept 2,009 ,373 5,381 ,000 1,266 2,751 5,381 1,000 DummyTreatment -,402 ,370 -1,087 ,280 -1,137 ,334 1,087 ,189 DummyGermany -,453 ,379 -1,196 ,235 -1,207 ,301 1,196 ,219 DummyNetherlands ,563 ,379 1,487 ,141 -,191 1,317 1,487 ,312 DummyAustria -,231 ,379 -,610 ,544 -,985 ,523 ,610 ,093 DummyLuxembourg ,442 ,379 1,167 ,247 -,312 1,196 1,167 ,211 DummyFrance -,136 ,379 -,359 ,720 -,890 ,618 ,359 ,065 DummyFinland -,315 ,379 -,832 ,408 -1,069 ,439 ,832 ,130 DummyPortugal 1,212 ,421 2,876 ,005 ,373 2,050 2,876 ,811 DummySpain 1,298 ,421 3,080 ,003 ,459 2,136 3,080 ,861 DummyGreece 2,454 ,421 5,823 ,000 1,615 3,292 5,823 1,000 Dummy1997 -,575 ,379 -1,518 ,133 -1,329 ,179 1,518 ,323 Dummy1998 -,934 ,379 -2,466 ,016 -1,688 -,180 2,466 ,683 Dummy1999 -,951 ,379 -2,511 ,014 -1,705 -,197 2,511 ,699 Dummy2000 ,091 ,379 ,240 ,811 -,663 ,845 ,240 ,056 Dummy2001 ,513 ,395 1,301 ,197 -,272 1,299 1,301 ,251 Dummy2002 ,126 ,395 ,321 ,749 -,659 ,912 ,321 ,062 Dummy2003 -,296 ,395 -,749 ,456 -1,081 ,490 ,749 ,115 Dummy2004 -,181 ,395 -,457 ,649 -,966 ,605 ,457 ,074 Dummy2005 ,028 ,395 ,072 ,943 -,757 ,814 ,072 ,051 a. Computed using alpha = ,05

2. %RWD

Fixed-effects model using GLM

Tests of Between-Subjects Effects

Dependent Variable: %RWD

Source Type III Sum of Squares

df Mean Square F Sig. Noncent. Parameter Observed Powerb Corrected Model 45003,413a 19 2368,601 4,517 ,000 85,829 1,000 Intercept 26806,408 1 26806,408 51,125 ,000 51,125 1,000 DummyTreatment 354,997 1 354,997 ,677 ,414 ,677 ,128 DummyGermany 670,250 1 670,250 1,278 ,263 1,278 ,199 DummyNetherlands 106,509 1 106,509 ,203 ,654 ,203 ,073 DummyAustria 5,018 1 5,018 ,010 ,922 ,010 ,051 DummyLuxembourg 497,235 1 497,235 ,948 ,334 ,948 ,160 DummyFrance 35,307 1 35,307 ,067 ,796 ,067 ,058 DummyFinland 665,680 1 665,680 1,270 ,264 1,270 ,198 DummyPortugal 709,540 1 709,540 1,353 ,249 1,353 ,208 DummySpain 431,875 1 431,875 ,824 ,368 ,824 ,145 DummyGreece 1243,680 1 1243,680 2,372 ,129 2,372 ,329 Dummy1997 142,578 1 142,578 ,272 ,604 ,272 ,081 Dummy1998 15302,271 1 15302,271 29,184 ,000 29,184 1,000 Dummy1999 16777,576 1 16777,576 31,998 ,000 31,998 1,000 Dummy2000 9881,818 1 9881,818 18,846 ,000 18,846 ,990 Dummy2001 16675,757 1 16675,757 31,804 ,000 31,804 1,000 Dummy2002 11782,927 1 11782,927 22,472 ,000 22,472 ,997 Dummy2003 11390,894 1 11390,894 21,724 ,000 21,724 ,996 Dummy2004 10364,793 1 10364,793 19,767 ,000 19,767 ,992 Dummy2005 8301,829 1 8301,829 15,833 ,000 15,833 ,975 Error 31460,135 60 524,336 Total 158645,825 80 Corrected Total 76463,549 79 a. R Squared = ,589 (Adjusted R Squared = ,458) b. Computed using alpha = ,05

Parameter Estimates

Dependent Variable: %RWD

Parameter B Std. Error t Sig. 95% Confidence Interval Noncent. Parameter

Observed Powera Lower Bound Upper Bound

Intercept 87,116 12,184 7,150 ,000 62,745 111,488 7,150 1,000 DummyTreatment 10,647 12,939 ,823 ,414 -15,235 36,529 ,823 ,128 DummyGermany 11,959 10,577 1,131 ,263 -9,199 33,116 1,131 ,199 DummyNetherlands -4,615 10,240 -,451 ,654 -25,099 15,869 ,451 ,073 DummyAustria -2,472 25,265 -,098 ,922 -53,010 48,067 ,098 ,051 DummyLuxembourg 10,682 10,969 ,974 ,334 -11,260 32,624 ,974 ,160 DummyFrance 2,657 10,240 ,259 ,796 -17,827 23,141 ,259 ,058 DummyFinland -11,538 10,240 -1,127 ,264 -32,022 8,946 1,127 ,198 DummyPortugal 20,055 17,240 1,163 ,249 -14,431 54,541 1,163 ,208 DummySpain -11,549 12,725 -,908 ,368 -37,002 13,905 ,908 ,145 DummyGreece -18,588 12,069 -1,540 ,129 -42,730 5,554 1,540 ,329 Dummy1997 -7,067 13,552 -,521 ,604 -34,175 20,041 ,521 ,081 Dummy1998 -71,509 13,237 -5,402 ,000 -97,987 -45,031 5,402 1,000 Dummy1999 -74,877 13,237 -5,657 ,000 -101,355 -48,399 5,657 1,000 Dummy2000 -57,465 13,237 -4,341 ,000 -83,942 -30,987 4,341 ,990 Dummy2001 -74,995 13,298 -5,639 ,000 -101,596 -48,395 5,639 1,000 Dummy2002 -63,040 13,298 -4,740 ,000 -89,640 -36,440 4,740 ,997 Dummy2003 -61,982 13,298 -4,661 ,000 -88,583 -35,382 4,661 ,996 Dummy2004 -60,198 13,540 -4,446 ,000 -87,281 -33,115 4,446 ,992 Dummy2005 -53,875 13,540 -3,979 ,000 -80,959 -26,792 3,979 ,975 a. Computed using alpha = ,05