On the Conformity of Business Cycles Across EU Countries

Transcription

1 On the Conformity of Business Cycles Across EU Countries Jesper Gregers Linaa University of Copenhagen and EPRU First Version: July 2002 This Version: September 2004 Abstract This paper analyses the conformity of the European business cycles. While previous papers have searched for the existence of synchronous business cycles in the euro-zone, this paper takes the synchronization as given and instead focuses on the growth pattern over the business cycle and deepness of the business cycles. Three interesting results emerge: First, the growth pattern appears to be almost identical across countries with the exception of the UK and Denmark. Second, the business cycle can best be characterized as consisting of two phases. Third, the deepness of the EU business cycle appears to be homogenous across countries. This result challenges Friedman s ceiling hypothesis. Keywords: Business Cycles, European Union, Monetary Policy JEL Classifications: E32, E52 Thanks to Michael Artis, Thomas Lehman Jensen, Katarina Juselius, Claus Thustrup Kreiner and Torben Mark Pedersen for useful comments on issues dealt with in the paper. Thanks also to Mark Watson for providing me with the GAUSS version of the Bry-Boschan algorithm making it much easier for me to implement this in MATLAB. Address for correspondence: Jesper Linaa, Institute of Economics, University of Copenhagen, Studiestraede 6, 1455 Copenhagen K, jesper.linaa@econ.ku.dk, phone: , fax: The activities of EPRU (Economic Policy Research Unit) are financed by a grant from The Danish National Research Foundation.

2 1 Introduction Several papers have analysed whether the turning points of the European business cycles occur at the same point in time. Obviously, this is an important question in consideration to the possibility of the ECB of successfully conducting a single monetary policy in the euro-zone. The overall conclusion to this question is confirmative; synchronicity is present in the European business cycles, eg Artis et al. (1997, 1999), Artis and Zhang (1997). However, there are matters not treated in these analyses. Roughly speaking, one can claim that while the timing of turning points in business cycles is important for the timing of interest rate changes decided by the ECB, the evolvement of the business cycles between the turning points, including the amplitude of the business cycle, is of importance when the ECB is to choose the magnitude of these interest rate changes. Thus, this paper provides a first attempt to analyse these matters; specifically, industrial production and unemployment is analysed. As a starting point the paper adopts the characterisation of the business cycle as proposed by Burns and Mitchell (1946), which is assuming that business cycles consist of eight different phases. This makes the analysis tractable and at the same time the approach is a natural extension of the work provided by Artis et al. (1997) that relies on the Bry and Boschan (1971) (BB, hereafter) algorithm to identify turning points. First, it is tested whether the growth rates in the various stages of the business cycles are significantly different from the corresponding EU aggregate. Afterwards, light is shed on the depth of the business cycle, where depth is defined as troughs being further below trend than peaks are above trend, following the work of Sichel (1993). Despite the simple descriptive approach, which might be characterised measurement without theory, the analysis comes up with a number of interesting conclusions: First, industrial production appears to show a high degree of conformity across countries with regards to the growth pattern. Second, the same degree of conformity cannot be observed when the analysis is done for the labour market. 1

3 More specifically, the developments in the British and Danish labour markets appear to stand out from the EU aggregate. Third, testing whether it is appropriate to distinguish between eight phases reveals that for most countries it is sufficient to consider just two phases, viz. a contraction and an expansion phase. Following this road, however, yields the same conclusion; no great differences with regards to the growth pattern of industrial production, but again the cyclical development of unemployment in the UK and Denmark differs from the European pattern. Turning the focus to the depth of the business cycle, not many signs of deep business cycles in any country are found. This result is interesting, however, since it challenges the ceiling hypothesis suggested by Friedman (1969) and the results obtained by Goodwin and Sweeney (1993). The ceiling hypothesis predicts that growth in real output shows peaks that are relatively homogenous over time due to capacity constraints (the ceiling), while the magnitude of troughs should be very volatile. Given this hypothesis we should expect to find business cycles that go further below trend during contractions than they rise above trend during expansions. But in fact we do not. Linking the present analysis up with existing literature, this paper can be seen as a natural extension of Artis et al. (1997) in which the authors propose business cycle turning points for the G7 and a number of European countries. That paper finds evidence supporting the existence of a core group of European countries that have synchronous business cycles. Hence, the present paper can be seen as accepting the conclusion of Artis et al. (1997) and hence it takes the implied synchronicity of the business cycles as given. Other papers strongly related to the present analysis are Artis et al. (1999), Artis and Zhang (1997) as well as Krolzig and Toro (2001). The main purpose of these papers is to determine whether there is a common European business cycle, but in the sense of synchronicity. Artis et al. (1999) use regime switching models to identify the common cycle in Europe and obtain results in favour of a common cycle. Artis and Zhang (1997) conclude that the establishment of the ERM has created a higher degree of business cycle conformity, while Artis et al. (1997) establish turning 2

4 points in industrial production and use a binary measure to obtain the conclusion that a core group of European countries related to each other exists, but that the UK, Spain and Luxembourg stand out of this group. The present paper therefore contributes to the existing literature in the following areas: First, it goes a step further into the analyses of Artis et al. (1997, 1999) and Artis and Zhang (1997) and investigates the behaviour of the business cycle between the turning points taking the synchronization as given. Second, data for unemployment are used and it is shown that matters are not the same if labour market data are taken into account. Third, evidence that the ceiling hypothesis does not hold for the European industrial sector and labour market is provided. Finally, it contributes to the literature since it provides testable conclusions. Most results in this strand of the literature relies on more subjective results based on whether a given correlation coefficient is considered high or low. The rest of the paper goes as follows: In Section 2 the methodology is described, data is presented in Section 3 and Section 4 presents the findings. Section 5 concludes. 2 Methodology Two features of the European business cycles are examined. First, the growth patterns of the business cycles in the various EU member countries are characterised and compared. The comparison is done to the reference business cycle which is chosen to be the EU-15 aggregate. Second, it is examined whether the severity of contractions differs across countries. For this purpose, we rely on the measure of deepness also used by Sichel (1993). Hence, we employ the coefficient of skewness to analyse this. The first part relies on the classical measure of the business cycle, that is business cycles identified in non-detrended time series. Filtering time series always give rise to much scepticism and in order to avoid this, it is natural not to detrend data when it is not strictly necessary. In the latter part, however, it is necessary to 3

5 detrend data. The measure of skewness that will be employed for this operation is not defined for non-stationary time series. Therefore, the modern definition that regards business cycles as being movements around a trend is employed in the latter part. 2.1 Pattern of the European business cycles In order to characterise the growth pattern of a given business cycle, turning points of the corresponding time series must be identified. Official US peaks and troughs are determined by the NBER Business Cycle Dating Committee. The methodology behind this follows the (Burns and Mitchell, 1946, p. 144) definition in the sense that business cycles are not determined from the development in a single time series. However, the NBER only dates the US turning points. For this reason, the literature has used a number of different definitions in order to identify turning points for other countries. In an attempt to stay close to the NBER when choosing the turning points Bry and Boschan (1971), who developed an algorithm aimed at mimicking the peak and trough dates found by the NBER, are followed. In short, the algorithm is a mechanical way of determining turning points in highly smoothed time series, but it breaks with the NBER and Burns and Mitchell (1946) methodology since it relies on a single time series. Here a slightly modified version of this algorithm is used to find troughs and peaks in industrial production and unemployment in the various countries. 1,2 This way of picking the peaks and troughs can be criticised as being measurement without theory, but on the other hand, the way of picking the turning points can be argued to be more robust and transparent, e.g. Harding and Pagan (2002, 2003) compared to, for example, regime swithing models in the 1 See Appendix A for an overview of details on the Bry & Boschan methodology. The algorithm is modified since here each phase is restricted to having a length of at least 6 months instead of the usual 5 months. If a phase, e.g. a contraction, consisting of only 5 months is observed, there would not be enough observations for reserving two observations for Phase 1, one observation for Phase 2, Phase 3 and Phase 4 each, and one observation for Phase 5 this requires at least 6 observations. Remember that the two months surrounding a turning point are included in that particular phase, cf. Figure 1. 2 The algorithm is written in MATLAB and can be obtained from the author upon request. 4

6 spirit of Hamilton (1989). 3 Having found the troughs and peaks of the time series, it is straightforward to split the business cycles into sub-phases in accordance with Burns and Mitchell (1946, p. 144). More specifically, the three months centred on a peak are denoted as Phase 1, while the three months centred on a trough are denoted Phase 5. Phase 2, Phase 3 and Phase 4 are phases of equal length covering the time from Phase 1 to Phase 5, while Phase 6, Phase 7 and Phase 8 cover the time from Phase 5 to Phase 1. 4 Basically, Balke and Wynne (1995) apply the same technique in a different context when analyzing whether an RBC model is capable of mimicking symmetries of the US business cycle by estimating average growth rates in each phase. This approach is followed in order to obtain estimates of the average growth rates in each phase using phase dummies. Chow tests are employed to compare growth rates in the different phases with the reference business cycle. That is, for each country, i, the model below is estimated. µ µ µ µ γeu XEU 0 βeu εeu = + γ i 0 X i β i ε i Here γ EU and γ i are vectors containing the monthly growth rates of industrial productionintheeuandincountryi, X i is a (n i 8) matrix containing dummy variables describing which phase the corresponding growth rate belongs to, n i is the number of observations for country i, andε i is the error term. The OLS estimates of β EU and β i will be identical to the average growth rates in the corresponding phase. However, OLS must be applied with caution; error terms are likely to be serially correlated and standard inferences cannot be made since standard deviations are incorrectly computed by standard formulas. In order to correct the standard 3 A discussion of this issue between James D. Hamilton and Don Harding & Adrian Pagan is to be found in Journal of Economic Dynamics and Control 27 (9), 2003, pp Symmetry is imposed in the sense that the length of Phase 2 is required to equal the length of Phase 4. Similarly Phase 6 and Phase 8 are required to be of equal length. Therefore the length of Phase 3 (Phase 7) may differ by 1 month from Phase 2 and Phase 4 (Phase 6 and Phase 8). This happens if the duration measured in months of an expansion/contraction phase divided by three is not an integer. 5 (1)

7 deviations the Newey-West covariance estimator is used. When constructing the heteroscedasticity and serial correlation consistent covariance matrix, Sichel (1993) and Newey and West (1987) are followed. This means allowing for a serial correlation up to order six, which results from taking the integer part of the sample size raised to the 1/3 power. On top of this, 15 comparisons are carried out; one between the business cycle in each member country, i, and the total EU-15 business cycle, by testing the hypotheses β j,eu = β j,i for j =1, 2,..., 8 where j is the number of the corresponding phase. 2.2 Deepness of the Business Cycles Measuring the severity of the typical contraction is done by constructing a measure of the deepness of the business cycle using the same method used by Sichel (1993) inhisstudyoftheusbusinesscycle. For this operation the modern definition of the business cycle is employed, and business cycles are considered to be movements around a trend. The reason is that the coefficient of skewness is not defined for non-stationary time series. Therefore the time series are filtered in order to separate the cycle from the trend. To ensure this distinction between a trend component and a cyclical component, the Hodrick-Prescott filter, cf. Hodrick and Prescott (1997), is used. The smoothing parameter is set equal to as suggested by Ravn and Uhlig (2002) when working with monthly observations, and the first and last three years of observations are eliminated due to the end-point problems related to the Hodrick-Prescott filter, cf. Baxter and King (1999) and Cogley and Nason (1995). The Hodrick-Prescott filter has been subject to much criticism, cf. Cogley and Nason (1995). However, it still appears to be widely used in this field of the literature due to the lack of definitively better alternatives, and an obvious advantage of applying this filter is therefore the possibility of comparing results with other studies. For the same reason the possibility of choosing the smoothing parameter in order to minimize the distortion of the filter, cf. Pedersen (2001), is abstained. 6

8 Deviating from commonly accepted values will make it less easy to compare with results obtained in other studies. Following Sichel (1993) the coefficient of skewness is used D (c) = 1 T ΣT t=1 (c t µ c ) 3 σ 3 c (2) where µ c is the mean of c t, σ c is the standard deviation of c t, which is the cyclical componentattimet, andt is the size of the sample. Figure 1 gives examples of three time series. The upper panel shows a time series, where the distance from the mean to the peak value is equal to the distance from the mean to the trough value. The middle panel shows the case where the distance from the mean to the peak is greater than the distance from the mean tothetrough,whiletheoppositecaseisillustratedinthelowerpanel. Thethree examples will provide values of D(c) equal to zero, greater than zero, and less that 7

9 zero. Figure 1 Symmetric, high and deep Business Cycles. Three examples 1 D(c)= D(c)> D(c)< An estimate of D (c) is obtained by regressing z t = (c t µ c ) 3 σ 3 c (3) on a constant. Regressing (3) on a constant reveals the average of {z 1,z 2,...,z T } as the parameter estimate, which is just equal to (2). However, the regression delivers standard deviations of the parameter estimates that can be used when testing hypotheses. Once again serial correlation is expected, and so the Newey- 8

10 West estimator is used. Analogous to (1) the model µ µ µ µ ZEU IEU 0 β d EU ε d = Z i 0 I Zi β d + EU i ε d (4) i is estimated. Z EU and Z i are vectors containing the values of z t,t=1, 2,..., T for the EU and country i, I EU and I i are dummy variables containing ones, while ε d EU and ε d i areerrorterms. AgainthecomparisonisdonebyapplyingtheChowtest, that is, testing the hypotheses that β d E15 = β d i. 3 Data Data for industrial production and unemployment in the EU countries in the period April l979 - December 2002, hereafter 1979:4-2002:12, is used. This beginning of the period is chosen to respect the findings of Artis and Zhang (1997) who conclude that the ERM has promoted the synchronisation of the European business cycles, and that the similarity of European business cycles is to be found in another regime after the introduction of the ERM. Data is drawn from OECD Main Economic Indicators provided by EcoWin, see Appendix B for specific codes and for mnemonics used in this paper. The time series for the EU-15 industrial production is a weighted average of the member countries industrial production, cf. Eurostat (2000). Looking at industrial production instead of GDP has two advantages. First, data for industrial production is released on a monthly basis while GDP is only released on a quarterly basis. Second, policy makers might at least in the short run pay more attention to this figure, since the release of industrial production leads the release of GDP. To some extent GDP figures can be said to contain a large amount of information already known before its release, and monetary policy may very well have been changed ahead of the release. This makes industrial production data interesting in a monetary policy context. The obvious disadvantage by using industrial production instead of GDP is that industrial production only covers the economic conditions in a part of the economy. Furthermore, the share of industrial output relative to overall output has, 9

11 in general, dropped over the last decades and therefore the aggregate business cycle may differ from the industrial cycle to a greater extent than years ago. In Appendix B the reader can verify the differences in growth between these two measures of the business cycle. Converting industrial production into quarterly observations (by averaging), taking logs to industrial production and GDP, differencing and calculating the correlation coefficient results in a correlation of Repeating this procedure, but using fourth differences, results in a correlation coefficient of Ideally, data for employment instead of unemployment should have been used. However, it is not possible to find employment data on a monthly basis for most of the European countries and for this reason unemployment, is used. If there were no supply effects in the labour market, unemployment would simply be the flip side of employment. However, over time the size of the labour force changes and that is not case. At same, unfortunately, it is not possible to find information on the number of persons unemployed in all of the 15 member countries, and no aggregate time series for EU-15 unemployment going back to April 1979 seems to exist. Data for Austria, Denmark, Finland, France, Germany, Ireland, Luxembourg, Portugal, Spain, Sweden and UK is available, and EU unemployment is constructed by simply summing over the figures reported in each of these countries. As a last word on the data employed here, it would have been natural to include consumer price measures in the present analysis. However, the upward trend in (seasonally adjusted) consumer prices has been so strong that in most countries not even one full cycle is observed and for that reason this focus is dropped. The analysis is restricted to the members of the EU, which are the 12 countries participating in the EMU s third stage as well as the UK, Sweden and Denmark whohavedecidedtooptout. Itwouldbeobvioustoextendtheanalysistoinclude the Eastern European countries that are to join the EU and who one day even might be members of the euro-zone. However, the data material makes it impossible to include these countries. 10

12 4 Results 4.1 Pattern of the European business cycles After having identified the turning points and splitting the industrial production into the 8 phases the results presented in Table 1 are obtained. Table 1 Industrial production, average growth rates Phase 1 Phase 2 Phase 3 Phase 4 Phase 5 Phase 6 Phase 7 Phase 8 E15 0,10-0,26-0,20-0,30-0,35 0,27 0,26 0,22 (0,18) (0,18) (0,13) (0,11) (0,18) (0,06) (0,06) (0,06) DEU 0,03-0,46-0,19-0,38-0,16 0,34 0,45 0,27 (0,27) (0,14) (0,22) (0,17) (0,23) (0,10) (0,15) (0,11) FRA 0,15-0,19-0,11-0,35-0,07 0,14 0,24 0,20 (0,12) (0,12) (0,38) (0,21) (0,24) (0,08) (0,08) (0,08) ITA 0,13-0,26-0,03-0,31-0,32 0,42 0,42 0,38 (0,26) (0,21) (0,11) (0,12) (0,11) (0,16) (0,12) (0,11) ESP 0,08-0,49-0,23-0,15 0,58 0,19 0,31 0,41 (0,24) (0,23) (0,25) (0,34) (0,50) (0,15) (0,14) (0,12) NLD 0,18 0,14-0,27-0,39 1,09 0,14 0,36 0,05 (0,64) (0,34) (0,30) (0,36) (0,83) (0,19) (0,18) (0,13) BEL 0,84-0,42-0,21-0,30 0,33 0,31 0,35 0,19 (0,36) (0,22) (0,32) (0,26) (0,36) (0,24) (0,17) (0,14) AUT 0,41 0,02-0,24-0,31 0,19 0,29 0,63 0,46 (0,29) (0,24) (0,29) (0,23) (0,23) (0,14) (0,15) (0,17) LUX 1,01-0,29-0,05 0,31-0,42 0,51 0,36 0,47 (0,82) (0,47) (0,25) (0,53) (0,87) (0,26) (0,34) (0,29) FIN 0,67-1,08 0,32-0,34-1,25 0,52 0,38 0,60 (1,12) (0,64) (0,57) (0,34) (0,83) (0,13) (0,17) (0,19) PRT 0,25-0,08-0,62-0,28 1,04 0,54 0,50 0,35 (0,54) (0,25) (0,72) (0,68) (1,02) (0,36) (0,35) (0,21) GRC 0,24 0,03 0,05-0,31-0,26 0,32 0,09 0,42 (0,38) (0,41) (0,29) (0,27) (0,35) (0,17) (0,14) (0,17) GBR 0,05-0,42-0,59-0,07-0,05 0,29 0,25 0,18 (0,19) (0,16) (0,29) (0,22) (0,21) (0,11) (0,07) (0,06) SWE 0,39-0,11-0,21-0,11 0,19 0,59 0,33 0,32 (0,43) (0,60) (0,23) (0,16) (0,45) (0,16) (0,14) (0,16) DNK 0,46-1,38 0,00 0,30-1,47 0,59 0,30 0,49 (0,53) (0,53) (0,81) (0,39) (1,21) (0,31) (0,20) (0,30) It reports estimates of β i for i = EU, GER, FRA, ITA, SPA, NET, BEL, AUT,LUX,FIN,POR,GRE,UK,SWE,DENfrom (1). Examining Table 1 reveals only a few wrong signs in the estimates. This is hardly surprising since the method of dividing the time series into various phases dependent on the relative position of the particular phase to the previous peak (trough) and the next trough (peak) nearly defines the sign of growth. In contrast, the signs of the growth rates in Phase 1 and in Phase 5 are not obvious. Apriorithese should be expected to lie close to zero since these phases cover the period where growth goes from being negative (positive) to being positive (negative). However, remember that these phases include not only the turning points of the time series, but also its 11

13 two surrounding observations, and if these are not numerically equally large, the estimate should differ from zero. Furthermore, growth in the turning point will, in general, not be zero. Another conclusion to be drawn from Table 1 is that for the bigger countries almost no growth rates in any phase differs significantly from the EU-15 business cycle. 5 This is hardly surprising neither since the EU-15 industrial production by definition is a weighted average of industrial production in the individual member countries. In a monetary policy context this is, however, the problem in a nutshell; when attaching the largest weights to the largest countries before aggregating and using this aggregate for designing monetary policy, the risk of ignoring the economic development in the smaller countries arises. This was the subject of much debate over the years before the launch of the EMU s third stage and still is. Of course, the risk cannot be rejected from this analysis since industrial production is just one measure of the conformity of the business cycles, and at the same time it is a rather volatile one. However, it indicates that the European industry has a relatively high level of conformity. The most significant deviations from the EU-15 industrial cycle are found in the Austrian Phase 7 and the Danish Phase 2 these deviations can be found at the 5% level of significance. At the 10% level of significance, however, a number of phases stand out. Phase 5 (the trough) shows significant deviations in Spain, the Netherlands, Belgium and Austria, while Phase 6 stands out in Finland and Denmark. Furthermore, Phase 1 stands out in Belgium and Phase 8 stands out in Finland. It appears that the significant deviations are concentrated around the trough. That is when business cycles go from being contractive to expansive. The results indicate that this phase of the cycle is not as conform across countries as the remaining part of the cycle. Drawing special attention to the three countries not participating in the third stage of the EMU the UK, Sweden and Denmark the analysis does not reveal 5 Probabilities of F-tests can be found in appendix D. 12

14 much information pointing towards any circumstances that would make an entry into the third stage harder than for any other participating countries as far as the industrial growth pattern is concerned. As the most extreme case, Denmark differs in two phases when allowing for a 10 percent significance level. The Swedish and British cycles do not differ in any phase. For the British case, however, is worth noting that Artis et al. (1997) and Artis and Zhang (1997) find that peaks and troughs in the British cycle are not highly synchronised with the peaks and troughs in the core countries. But since, the present analysis has taken this synchronization as given and solely focuses on the underlying growth pattern, the results obtained here do not contradict any conclusions in the above mentioned studies. Drawing attention to the corresponding results for unemployment as presented in Table 2, it appears that the labour markets in the EU seem to differ a lot more across countries than the output from the industrial sector does. In particular, the UK seems to differ substantially from the EU. All phases but Phase 1, Phase 5 and Phase 6 differ at the 1% level of significance and indicate that the conformity between the British and the EU-15 business cycle is a poor one when talking about the labour market. At the 1% significance level France also turns out to differ in two phases, 6 and 7, while Germany differsinphase8. Atthe10%significance level it is worth noting that the three EU-15 member countries not participating in the third stage, the UK, Sweden and Denmark, are the countries with the most phases that differ. Sweden and Denmark each have three phases to differ. The results obtained so far have shown that industrial output differs relatively less across the EU member countries than the labour markets do. However, industrial production is a highly volatile figure and it might be worth testing whether the apparent high degree of conformity in the industrial cycles is a result of a real conformity or whether it is a result of a misspecification of the model by allowing for too many phases that are not found to be significantly different from each other. 13

15 Table 2 Unemployment, average growth rates Phase 1 Phase 2 Phase 3 Phase 4 Phase 5 Phase 6 Phase 7 Phase 8 E15 0,15-0,32-0,59-0,58-0,25 1,78 0,78 0,35 (0,12) (0,13) (0,13) (0,10) (0,10) (0,24) (0,14) (0,15) DEU 0,11-0,55-0,52-1,05-0,20 1,36 1,35 1,27 (0,16) (0,30) (0,10) (0,31) (0,20) (0,33) (0,53) (0,31) FRA 0,29-0,36-0,52-0,78-0,17 0,64 0,18 0,42 (0,12) (0,05) (0,26) (0,28) (0,15) (0,17) (0,13) (0,19) IRL 0,14-0,57-0,77-1,06-0,23 1,66 1,13 0,41 (0,18) (0,13) (0,20) (0,33) (0,28) (0,36) (0,20) (0,10) ESP -0,03-0,72-0,77-0,41-0,22 1,21 1,10 0,65 (0,08) (0,07) (0,15) (0,13) (0,11) (0,13) (0,33) (0,25) AUT 0,87-0,86-0,24-0,91-0,21 2,17 0,45 0,94 (0,47) (0,17) (0,30) (0,27) (0,26) (0,62) (0,29) (0,22) LUX 0,92-0,79 0,10-0,79 1,42 1,92 2,36 0,97 (0,47) (0,31) (0,54) (0,35) (1,11) (0,46) (0,42) (0,23) FIN -1,00-0,42-0,86-1,11-0,54 2,90 2,31 1,70 (1,22) (0,42) (0,41) (0,35) (0,54) (1,22) (1,13) (0,60) PRT -1,52-0,47-0,80-0,36 0,09 1,15 0,57 0,81 (1,51) (0,30) (0,19) (0,24) (0,21) (0,19) (0,31) (0,20) GBR 0,55-1,37-1,91-1,57 0,01 3,08 2,12 1,17 (0,39) (0,19) (0,12) (0,25) (0,26) (0,58) (0,42) (0,08) SWE 2,97-0,85-0,68 0,07-4,41 1,90 3,56 2,56 (1,71) (0,44) (0,57) (0,99) (2,15) (0,89) (0,82) (0,98) DNK -0,09-0,91-1,03-1,00 0,00 1,61 0,63 0,60 (0,50) (0,28) (0,20) (0,21) (0,28) (0,62) (0,19) (0,16) Standard deviations in parantheses Different from EU-15 growth at a 10 percentage significance level 5 percentage significance level 1 percentage significance level Therefore, the cycles are collapsed into just two phases; contractions (Phase 1) and expansions (Phase 2). With regards to industrial production, it can be seen that collapsing the 8 phases into just 2 phases only appears to be a problem for Denmark, cf. Appendix E. For Denmark it is found that at the 2% level of significanceitisnot appropriate to distinguish between just two phases. With regards to unemployment, this two-phase distinction is seen not to be appropriate for the EU because of the developments in Phase 6, Phase 7 and Phase 8 in Ireland, Austria, Luxembourg and the UK. However, when redefining the phases for industrial production, the same operation for unemployment is nevertheless performed. The results found in Table 3 do not reveal much new information compared to the results obtained in 14

16 Table 1. Table 3 Industrial Production and Unemployment, average growth rates Industrial production Unemployment Phase 1 Phase 2 Phase 1 Phase 2 EU (0.08) (0.04) (0.08) (0.17) GER (0.10) (0.07) (0.14) (0.23) FRA (0.09) (0.04) (0.14) (0.11) ITA (0.08) (0.07) - - SPA (0.14) (0.07) (0.08) (0.26) NET (0.17) (0.09) - - BEL (0.12) (0.10) - - AUT (0.12) (0.08) (0.18) (0.27) LUX (0.25) (0.16) (0.25) (0.23) FIN (0.27) (0.10) (0.20) (0.60) POR (0.20) (0.18) (0.16) (0.14) GRE (0.16) (0.09) - - UK (0.10) (0.05) (0.24) (0.47) SWE (0.19) (0.09) (0.44) (0.66) DEN (0.26) (0.14) (0.14) (0.22) IRE (0.15) (0.18) Standard deviations in parantheses Different from EU-15 growth at the 10 percent significance level 5 percent significance level 1 percent significance level It still shows a great deal of conformity in the European industrial cycle. Looking at unemployment, the UK still stands out from the EU. Both phases differ significantly from the EU aggregate. Furthermore, the contraction phase in Denmark and the expansion phases in France and Luxembourg differ at the 1% significance level. 4.2 Deepness of the Business Cycles Table 4 shows the results of estimating (2) for industrial production and unemployment. It appears that no coefficient is computed to be significantly different from the corresponding coefficient of the EU-15. Nor are any coefficients significantly different from zero. 15

17 Table 4 Coefficient of skewness Industrial production Unemployment EU (0.02) (0.02) GER (0.07) (0.05) FRA (0.02) (0.00) ITA (0.04) - SPA (0.08) (0.01) NET (0.05) - BEL (0.05) - AUT (0.04) (0.03) LUX (0.24) (0.06) FIN (0.25) (0.98) POR (0.14) (0.05) GRE (0.11) - UK (0.02) (0.21) SWE (0.13) (0.69) DEN (0.23) (0.02) IRE (0.01) Standard deviations in parantheses Different from EU-15 growth at the 10 percent significance level 5 percent significance level 1 percent significance level This latter result is interesting since it challenges the ceiling hypothesis suggested by Friedman (1969) and examined for a number of countries by Goodwin and Sweeney (1993). The ceiling hypothesis suggests that at times the economy is to be found at the limit of its capacity (the ceiling) with no further, or at least very modest, possibilities of increasing production. In these periods production is determined by the supply side of the economy. Most of the time, however, the economy is away from its potential output ceiling due to demand disturbances, but equilibrating forces and expansionary economic policy push the economy back. This is also the explanation for the Friedman (1969) result that the magnitude of an expansion is related to the magnitude of the preceding contraction, but that contractions are unrelated to the preceding expansions. As shown by Friedman (1993) a feature of the data in the ceiling case will be that if one considers real output in growth rates, one should expect to find peaks of the series that are relatively homogenous while 16

18 the troughs are extremely volatile. Hence we should expect to find deep business cycles. Goodwin and Sweeney (1993) find strong support for the ceiling hypothesis, while the present study does not, since it finds no evidence pointing towards deep business cycles. Before jumping to any conclusions in relation to the results of Goodwin and Sweeney (1993), though, one must note that the techniques for obtaining the result are not identical. The present approach involves no model, but simply relies on the coefficient of skewness, while Goodwin and Sweeney (1993) rely on the possibility of identifying an asymmetric error for efficiency in using factors when producing output. However, the studies also differ inthewaythatthe cyclicalcomponent isiden- tified. Goodwin and Sweeney (1993) rely on annual growth rates in GDP, while the present paper has so far relied on a Hodrick-Prescott measure of the cycle. Therefore, annual growth rates for industrial production as well as annual growth rates in thenumberofunemployedarecalculated,andthesameoperationsareperformed with these measures. However, this does not change the result that no coefficient is significantly different from zero. The results of this estimation are to be found in Appendix F. The results obtained here therefore question the robustness of the results obtained by Goodwin and Sweeney (1993). 5 Concluding remarks The analysis carried out involves dating the turning points of the European business cycle defined as the development in industrial production and in the number of unemployed. Once the turning points are determined the business cycles are split into sub phases in accordance with Burns and Mitchell (1946). Growth rates in these phases are compared and tested against the corresponding EU-15 measure. The analysis does not point towards much idiosyncrasy across countries with regards to industrial production, but apparently the dynamics on the British and Danish labour market do not follow the dynamics of the EU-15 labour market. The 17

19 reason why labour market dynamics are different in the UK and Denmark is very likely to be explained by the strictness of employment protection. According to OECD (1994, Table 6.5) employment protection in the EU was lowest in Denmark and third lowest in the UK. This finding seems highly consistent with the results here, saying that recessions measured by rising unemployment are significantly more severe in the UK and Denmark than in the EU. At the same time, however, unemployment also drops more sharply in these countries once the economy again expands. This is exactly what should be expected in a labour market with fewer frictions. This observation might be a good reason to involve the labour market when analysing the conformity of the European business cycle. Data for industrial production that indicates that the business cycle has a relatively high degree of conformity might give the wrong impression; it is always possible for a producer to slow down production in times of low demand, but the possibilities for producers to adjust the capacity over time are not identical. There are two reasons for this; first, the unemployment rate varies substantially across countries and labour hoarding arguments might be an issue in some countries while not in others. Second, the institutional setup might differ in a way that makes it a very expensive act to dismiss workers in some countries, while it can be done at almost no cost in other countries. The analysis also focuses on the depth of the typical recession in the European countries. Two conclusions emerge from this. First, no evidence on any countries having deeper business cycles than the EU-15 is found. Second, no evidence of any countries having business cycles that drop further below trend during contractions than they rise above trend during expansions is found. This latter result contradicts the Friedman (1969) ceiling hypothesis and the results obtained by Goodwin and Sweeney (1993). Gaining further knowledge about the similarity of the European business cycles is an important topic for future research. In order to satisfy monetary policy needs of all its member countries at the same time, the ECB must face relatively similar European business cycles. With regards to the implications for monetary policy 18

20 one must be careful, though; although we should expect that conformity of business cycles across the counties in the euro-zone is fundamental for the ECB to conduct an appropriate monetary policy for all countries, one must keep in mind that monetary transmissions differ from country to country due to, eg. the institutional setup in that particular country. Therefore an output gap of a given size might not imply the same price pressure in two different countries. Two extensions of the present work seem obvious. First, since the primary target of the ECB is to keep inflation below 2% in the medium term, more attention should be paid to similarity of the movements in consumer prices. However, for the present analysis, identifying classical turning points in consumer prices is an impossible task due to a very strong non-reversing upward drift in prices. No full cycle is found for a number of countries. Second, data for the Eastern European countries should be included since the enlargement of the EU is also very likely to lead to an enlargement of the EMU members group in the years to come. 19

21 6 Appendix A The Bry and Boschan (1971) algorithm PROCEDURE FOR PROGRAMMED DETERMINATION OF TURNING POINTS I. Determination of extremes and substitution of values. II. Determination of cycles in 12-month moving average (extremes replaced). A. Identification of points higher (or lower) than 5 months on either side. B. Enforcement of alternation of turns by selecting highest of multiple peaks (or lowest of multiple troughs). III. Determination of corresponding turns in Spencer curve (extremes replaced). A. Identification of highest (or lowest) value within ± 5 months of selected turn in 12-month moving average. B. Enforcement of minimum cycle duration of 15 months by eliminating lower peaks and higher troughs of shorter cycles. IV. Determination of corresponding turns in short-term moving average of 3 to 6 months, depending on MCD (months of cyclical dominance). A. Identification of highest (or lowest) value within ±5 months of selected turn in Spencer curve. V. Determination of corresponding turns in unsmoothed series. A. Identification of highest (or lowest) value within ±4 months, or MCD term, whichever is larger, of selected turn in short-term moving average. B. Elimination of turns within 6 months of beginning and end of series. C. Elimination of peaks (or troughs) at both ends of series which are lower (or higher) than values closer to end. D. Elimination of cycles whose duration is less than 15 months E. Elimination of phases whose duration is less than 5 months VI. Statement of final turning points. Source: Bry & Boschan (1971, Table 1, pp. 21). Requirement V.E is modified in this study: Instead of requiring a given phase to have a length of at least 5 months, in this study it is required to have a length of at least 6 months. If a phase, i.e. a contraction, consisting of only 5 months is observed, there would not be enough observations for reserving two observations for Phase 1, one observation each for Phase 2, Phase 3 and Phase 4, and one observation for Phase 5 this would require at least 6 observations. Remember that the two months surrounding a turning point are included in that particular 20

22 phase, cf. Figure 1. B Data sources All data are from the OECD Main Economic Indicators database provided by EcoWin Industrial production Country Code AUT Austria aut_prpein01_ixobsam.v BEL Belgium bel_prpein01_ixobsam.v DEN Denmark dnk_prpemn01_ixobsam.v FIN Finland fin_prpein01_ixobsam.v FRA France fra_prpein01_ixobsam.v GER Germany deu_prpein01_ixobsam.v GRE Greece grc_prpein01_ixobsam.v IRE Ireland irl_prpein01_ixobsam.v Country Code ITA Italy ita_prpein01_ixobsam.v LUX Luxembourg lux_prpein01_ixobsam.v NET Netherlands nld_prpein01_ixobsam.v POR Portugal prt_prpein01_ixobsam.v SPA Spain esp_prpein01_ixobsam.v SWE Sweden swe_prpein01_ixobsam.v UK UK gbr_prpein01_ixobsam.v EU EU-15 e15_prpein01_ixobsam.v 21

23 Notes Unemployment Country Code AUT Austria aut_unlvrg01_stsam.v DEN Denmark dnk_unlvrg01_stsam.v FIN Finland fin_unlvsu01_stsam.v FRA France fra_unlvrg01_stsam.v GER Germany deu_unlvrg01_stsam.v* West Germany deu_unlvrg01_stsam.v IRE Ireland irl_unlvrg01_stsam.v Country Code LUX Luxembourg lux_unlvrg01_stsam.v POR Portugal prt_unlvrg01_stsam.v* SPA Spain esp_unlvrg01_stsam.v SWE Sweden swe_unlvsu01_stsam.v UK UK gbr_unlvrg01_stsam.v *) Series for unemployment in Germany and Portugal were not seasonally adjusted. This was done by the X11 (multiplicative) module in EcoWin. 22

24 B.1 Industrial Production and GDP First differences GDP Industrial Production Fourth differences GDP Industrial Production

25 C Turning Points C.1 Industrial production Month EU GER FRA ITA SPA NET BEL AUT LUX GRE FIN POR UK SWE DEN IRE 1979:10 Peak 1979:11 Peak 1979:12 Peak Peak Peak Peak 1980:3 Peak Peak 1980:4 Peak 1980:9 Trough 1980:10 Trough 1980:11 Trough Trough 1980:12 Trough Trough 1981:4 Trough 1981:5 Trough 1981:7 Trough Peak 1981:8 Peak 1981:10 Peak 1981:12 Peak 1982:1 Peak 1982:2 Peak 1982:4 Peak 1982:7 Trough 1982:8 Trough Trough 1982:11 Trough Trough Trough Trough 1982:12 Trough Trough 1983:1 Trough 1983:5 Trough Trough 1984:1 Peak 1984:8 Trough 1985:1 Peak 1985:9 Peak 1985:11 Peak 1986:3 Trough Peak 1986:4 Peak Trough 1986:7 Peak 1986:8 Peak 1986:11 Trough 1987:1 Trough Peak Trough 1987:6 Trough 1987:10 Trough 1988:4 Trough 1988:12 Peak 1989:7 Peak Peak Peak 1989:8 Trough 1989:12 Peak 1990:2 Peak 1990:4 Peak 1990:6 Peak Peak 1990:8 Peak 1990:12 Peak 1991:1 Peak 1991:3 Trough 1991:6 Trough 1991:8 Trough 1991:12 Peak Peak 1992:2 Peak 1992:6 Peak 1992:12 Trough 1993:4 Trough 1993:5 Trough 1993:6 Trough 1993:7 Trough Trough Trough Trough 1993:8 Trough Trough 1993:10 Trough 1993:11 Trough 1994:12 Peak 1995:5 Peak 1995:6 Peak 1995:8 Peak 1995:10 Trough 1995:12 Peak 1996:1 Trough 1996:2 Trough 1996:5 Trough 1996:12 Trough 1997:10 Peak 1998:6 Peak 1998:7 Peak Peak 1998:12 Trough 1999:2 Trough Trough Trough 1999:6 Peak 1999:8 Peak 2000:4 Trough 2000:6 Peak 2000:10 Trough 2000:11 Peak Peak Peak Peak 2000:12 Peak Peak Peak Peak Peak 2001:2 Peak 2001:9 Trough Trough 2001:10 Trough 2001:11 Trough Trough Trough 2001:12 Trough Trough 2002:1 Trough 2002:2 Trough 2002:3 Peak 2002:4 Peak Peak 2002:5 Peak 2002:6 Peak 24

26 C.2 Unemployment Month EU GER FRA SPA AUT LUX FIN POR UK SWE DEN 1979:10 Peak Peak Trough 1979:11 Trough 1979:12 Trough Trough 1980:5 Trough Trough 1980:6 Trough 1982:11 Trough 1983:4 Peak 1983:6 Peak Peak 1983:7 Peak 1984:1 Trough 1984:2 Peak 1985:1 Trough 1985:5 Peak 1986:7 Peak Peak 1986:10 Trough 1986:11 Trough 1987:3 Peak Peak Peak 1987:12 Peak Peak 1988:3 Peak 1989:3 Trough 1989:6 Trough 1990:1 Trough 1990:5 Trough Trough 1990:9 Trough Trough 1991:1 Trough 1991:5 Trough 1992:7 Trough 1992:12 Peak 1993:6 Peak 1993:7 Peak 1994:1 Peak 1994:2 Peak 1994:4 Peak 1994:5 Peak Peak Peak 1994:12 Trough 1995:2 Trough 1995:7 Trough 1995:8 Trough 1996:3 Peak 1996:4 Peak 1996:12 Trough Peak 1997:5 Peak 1997:6 Peak 1998:2 Peak 1998:6 Peak 2000:4 Trough 2000:9 Trough 2000:11 Trough Peak 2001:2 Trough 2001:5 Trough Trough Trough 2001:7 Trough 2001:8 Trough 2002:3 Trough 25

27 D Testing whether growth phases and coefficients of deepness are equal to the corresponding values for EU-15 D.1 Pattern of the European business cycles - 8 Phases Probabilities of F-values for tests of growth beingequaltoeu-15growth Industrial production Phase 1 Phase 2 Phase 3 Phase 4 Phase 5 Phase 6 Phase 7 Phase 8 EU GER FRA ITA SPA NET BEL AUT LUX FIN POR GRE UK SWE DEN Unemployment Phase 1 Phase 2 Phase 3 Phase 4 Phase 5 Phase 6 Phase 7 Phase 8 EU GER FRA ITA SPA NET BEL AUT LUX FIN POR GRE UK SWE DEN

28 D.2 Pattern of the European business cycles - 2 Phases Probabilities for F-values for tests of growth beingequaltoeu-15growth Industrial production D.3 coefficient of skewness Phase 1 Phase 2 P(Ph.1=Ph.2) EU 0.00 GER FRA ITA SPA NET BEL AUT LUX FIN POR GRE UK SWE DEN Unemployment Phase 1 Phase 2 P(Ph.1=Ph.2) EU 0.00 GER FRA SPA AUT LUX FIN POR UK SWE DEN IRE Probabilities of F-values for tests of deepness being equal to EU-15 deepness Industrial production Unemployment EU GER FRA ITA SPA NET BEL AUT LUX FIN POR GRE UK SWE DEN IRE

29 E Specification tests Probabilities that expansion and contraction phases can be collapsed into one phase Industrial production P(Ph.2=Ph.3=Ph.4) P(Ph.6=Ph.7=Ph.8) EU GER FRA ITA SPA NET BEL AUT LUX FIN POR GRE UK SWE DEN Unemployment P(Ph.2=Ph.3=Ph.4) P(Ph.6=Ph.7=Ph.8) EU GER FRA IRE SPA AUT LUX FIN POR UK SWE DEN Probabilities whether Phase 1 is different from Phase 2 Industrial production Unemployment EU GER FRA ITA 0.00 SPA NET 0.06 BEL 0.02 AUT LUX FIN POR GRE 0.17 UK SWE DEN IRE

30 F Coefficient of skewness based on year-on-year growth rates Industrial production Unemployment EU (0.01) (0.02) GER (0.06) (0.06) FRA (0.01) (0.01) ITA (0.03) - SPA (0.05) (0.01) NET (0.02) - BEL (0.04) - AUT (0.03) (0.08) LUX (0.29) (0.04) FIN (0.15) (1.00) POR (0.08) (0.06) GRE (0.07) - UK (0.05) (0.17) SWE (0.10) (0.36) DEN (0.20) (0.07) IRE (0.02) Standard deviations in parantheses Different from EU-15 growth at the 10 percent significance level 5 percent significance level 1 percent significance level 29

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