Why so dierent from other CEECs Poland's cyclical divergence from the euro area during the recent nancial crisis
Karolina Konopczak∗ , Krzysztof Marczewski†
Abstract The aim of the article is to provide a plausible explanation for the relatively good performance of the Polish economy and the resulting cyclical divergence from the euro area during the recent nancial crisis. The investigation of the factors which contributed to this divergence is particularly important in the light of Poland's prospective accession to the euro area, as it may indicate the problem of asymmetric shocks aecting both economies or asymmetric responses to shocks. The results point out to two reasons for the dierential output trajectory in Poland as compared to other CEECs: (i) lower exposure to foreign shocks being the result of a lower degree of economic openness, and (ii) resiliant internal activity, which may be the result of structural characteristics of the Polish economy. The recent cyclical decoupling might, however, contribute to the acceleration of Poland's real convergence to the euro area and consequently speed up the cyclical convergence process.
JEL classi cation codes: E32, C22 Keywords: business cycles synchronisation, propagation of shocks, transmission of cyclical uctuations, real convergence
1 Introduction
In the aftermath of the global nancial crisis, triggered by the collapse of the U.S. subprime mortgage market, the global economy was hit by severe adverse shocks and consequently experienced a sharp and protracted downturn. With 1.7% y/y Poland was the only EU member state to show real GDP growth in 2009. The resilience of the Polish economy was particularly conspicuous when compared to other Central and Eastern European countries (CEECs1), whose economies shrank to a similar extent as the EU-15. This was despite the relative underdevelopment of CEECs' nancial markets, i.e. lower exposure to the primary cause of the crisis. What is more, the dierential performance across the new EU member states during the recent slowdown was clearly at odds with the results of numerous previous studies (for the latest reference, see Fidrmuc and Korhonen, 2006, Adamowicz et al., 2009, Skrzypczy«ski, 2009, Konopczak, 2009), according to which it was Poland whose degree of business cycle synchrony with the euro area was the highest of all CEECs. In order to nd a plausible explanation for this phenomenon, we investigate into the following areas: (1) the degree
∗Institute for Market, Consumption and Business Cycles Research (e-mail address: [email protected]) †Institute for Market, Consumption and Business Cycles Research (e-mail address: [email protected]) 1For the purpose of the analysis the CEECs group consists of the so-called Visegrad countries: the Czech Republic (CZ), Hungary (HU), Poland (PL) and Slovakia (SK).
1 of symmetry of shocks aecting CEECs and the euro area, (2) the share of foreign impulses (originating from the euro area) in structural shocks determining the CEECs' economic activity, (3) the propagation mechanisms of foreign shocks into the real economy. In order to get a sectoral insight we also analyse the degree of co-movement of GDP supply and expenditure components of CEECs vis-a-vis the euro area, and relate the results to the structural characteristics of the analysed economies. The purpose of the paper is twofold. Firstly, we attemp to shed some light on economic developments in Poland and other new EU member states during the recent slowdown. Secondly, we disentangle the causes of variation in those developments within the group. We base our investigation on an eclectic approach by employing a number of tools for business cycle analysis which allows to extract and combine dierent kinds of information.Output gaps were extracted from the GDP and its supply and expenditure components by means of the Christiano-Fitzgerald lter (Christiano and Fitzgerald, 2003). Leads and lags of the cyclical uctuations were established on the basis of a dating algorithm developed by Harding and Pagan (2002). Structural shocks, impulse responses and variance decomposition were obtained from sVAR models on the basis of an identi cation scheme in the spirit of Blanchard-Quah (Blanchard and Quah, 1989) . The rest of the paper is organised as follows. In Section 2 we outline econometric tools for business cycle analysis that were applied in the paper and in Section 3 we summarise the empirical results of the analysis, giving answers to the questions posed.
2 Methodology and data
2.1 Extraction of cyclical components
In order to isolate cyclical uctuations from the GDP series and its supply and expenditure components we applied the asymmetric Christiano-Fitzgerald lter (Christiano and Fitzgerald, 2003). Its advantage over the most common alternative, i.e. the Baxter-King lter (Baxter and King, 1995), is that it uses the whole time series for the calculation of the cyclical component and therefore allows to extract it at each data point. For this reason there is no loss of data at the ends of the sample, which allows for the analysis of the recent developments. The ideal band-pass lter extracting a cyclical component c from the original series has the following yt yt structure:
∞ c X (1) yt = Bjyt−j t = 1, 2, .., T j=−∞ with a response function in frequency domain of:
∞ −iω X iωj B(e ) = Bje . (2) j=−∞
The weight sequence Bj that allows to isolate the oscillations of durations between Tmin and Tmax (or 2π 2π alternatively frequencies within the range [ωmin = ; ωmax = ]) takes the following form: Tmax Tmin
( sin( 2πj )−sin( 2πj ) Tmax Tmin , j 6= 0 Bj = πj (3) 2 − 2 , j = 0. Tmax Tmin
2 The approximation to the ideal band-pass lter proposed by Christiano and Fitzgerald (2003) can be written as:
t−1 c X ˆ (4) yˆt = Bj,tyt−j t = 1, 2, .., T j=−(T −t) with a frequency response function of:
t−1 −iω X iωj Bˆ(e ) = Bˆj,te . (5) j=−(T −t)
The lter weights Bˆj,t are obtained by minimising the following loss function:
c c 2 (6) Q = E[(yt − yˆt ) ] or in the frequency domain
π Z −iω −iω 2 Q = |B(e ) − Bˆ(e )| fy(ω)dω, (7) −π
where fy(ω) is a spectral density of yt at frequency ω. The solution to this maximisation problem depends on the characteristics of the series, i.e. its spectral density. Owing to the fact that the true representation of the process is unknown, Christiano and Fitzgerald suggest to solve the problem on the assumption that the data is generated by a random walk, which is in line with the fact that most macroeconomic series exhibit the so-called Granger spectral shape, i.e. low frequencies dominate the spectrum. In case of non-stationarity of the series the minimisation problem is solved under the restriction that:
t−1 X Bˆj,t = 0, t = 1, 2, ..., T. (8) j=−(T −t)
Due to the fact that the weights vary over time, the problem is solved for each sample observation. The optimal weights obtained from the above minimisation problem are given by the following formula:
1 B − Pj−1 B , j = t − 1 2 0 k=0 k Bˆj,t = Bj, j = t − 2, ..., t − T + 1 , t = 1, 2, ..., T (9) 1 P0 2 B0 − k=j+1 Bk, j = t − T
For the purpose of our analysis we chose the cut-o periodicities conforming to the Burns and Mitchell (1946) de nition, i.e. Tmin = 6 quarters and Tmax = 32 quarters. Prior to being ltered, the series were subjected to the demeaning procedure, so as to improve the comparability of the cyclical components.
3 2.2 Turning points detection
Leads and lags of the CEECs cyclical uctuations vis-a-vis the euro were established on the basis of a dating algorithm developed by Harding and Pagan (2002), building upon Bry and Boschan (1971). A turning point occurs at time tif the value of a cyclical component at t ( c) is a local extremum relative to yt the two quarters on either side. For peaks we get the following condition:
c c c c c (10) (yt−2, yt−1) < yt > (yt+1, yt+2) and similarly for troughs:
c c c c c (11) (yt−2, yt−1) > yt < (yt+1, yt+2).
Additionally, the algorithm enforces a minimum duration of each phase (two quarters) and a complete cycle ( ve quarters), and ensures that peaks and troughs alternate.
2.3 Extraction of structural shocks
2.3.1 Supply, demand and nominal shocks
In order to extract the underlying structural shocks aecting economies we follow the Clarida and Gali (1994) framework based, on the one hand, on the Obstfeld (1985) open macro model and, on the other hand, on the empirical approach pioneered by Blanchard and Quah (1989). The structure of the economy is represented by the following trivariate VAR(s) model:
Ps i Ps i Ps i ∆yt = −γ12∆qt − γ13∆pt + i=1 βyy∆yt−i + i=1 βyq∆qt−i + i=1 βyp∆pt−i + εyt Ps i Ps i Ps i (12) ∆qt = −γ21∆yt − γ23∆pt + i=1 βqy∆yt−i + i=1 βqq∆qt−i + i=1 βqp∆pt−i + εqt , Ps i Ps i Ps i ∆pt = −γ31∆yt − γ32∆qt + i=1 βpy∆yt−i + i=1 βpq∆qt−i + i=1 βpp∆pt−i + εpt where yt denotes real GDP, qt real eective exchange rate (REER), pt index of consumer prices (CPI) and εyt, εqt, εpt are interpreted as structural innovations (zero-mean and uncorrelated), supply, real demand and nominal demand, respectively. All variables are assumed to be generated by I(1) processes. The equation (12) takes the following matrix form:
1 γ12 γ13 ∆yt Byy(L) Byq(L) Byp(L) ∆yt εyt γ21 1 γ23 ∆qt = Bqy(L) Bqq(L) Bqp(L) ∆qt + εqt , (13) γ31 γ32 1 ∆pt Bpy(L) Bpq(L) Bpp(L) ∆pt εpt or
ΓYt = B(L)Yt + εt, (14) where B(L) represents a matrix of lag polynomials of order s. Owing to the simultaneity bias , the model is estimated in the reduced form and the structural parameters and shocks are recovered from these estimates on the basis of an identi cation scheme.
4 Assuming the invertibility of Γ matrix we can solve (14) for Yt:
−1 −1 Yt = Γ B(L)Yt + Γ εt, (15) which yields the reduced-form VAR model:
Yt = A(L)Yt + et, (16)
−1 −1 where A(L) = Γ B(L) and et = Γ εt. The moving average representation of the process can be derived as follows (stationarity condition satis ed owing to rst order integration of level variables):
(I − A(L))Yt = et −1 Yt = (I − A(L)) et (17) 2 Yt = (I + A(L) + A(L) + ...)et Yt = et + C1et−1 + C2et−2+...
−1 Reduced-form residuals are linear combinations of structural innovations, since et = Γ εt. By substituting this into the above we arrive at the structural moving average representation:
P∞ (18) Yt = i=0 Diεt−i,
−1 where Di = CiΓ . The elements of Di matrix represent impulse response parameters of the endogenous variables to structural innovations at lag i:
∆y ∞ d d d ε t X 11i 12i 13i yt−i ∆qt = d21i d22i d23i εqt−i . (19) ∆pt i=0 d31i d32i d33i εpt−i
The Blanchard-Quah approach consists in imposing restrictions on the long-run multipliers of the model (apart from orthogonality and unit-variance of structural innovations). Conforming to the implications of the Obstfeld model, Clarida and Gali (1994) propose the following identifying restrictions: (i) in the long run the real GDP can be aected only by supply shocks, (ii) in the long run REER can be in uenced by both supply and real demand shocks, but not by nominal demand shocks. This requires the following zero-constraints on the cumulated impact of shocks:
∞ d d d . 0 0 X 11 12 13 Di = d21 d22 d23 = .. 0 . (20) i=0 d31 d32 d33 ...
2.3.2 External and country-speci c shocks
In similar fashion, the internal and external shocks for each CEEC were derived from a four-variable VAR model with the growth rate of GDP and consumer price index, at both domestic and the euro area level, as endogenous variables. The structural moving average representation of the model takes the following form:
5 EA EA ∆yt d11i d12i d13i d14i εyt−i EA ∞ εEA ∆pt X d21i d22i d23i d24i pt−i (21) CEECi = CEECi . ∆y d31i d32i d33i d34i ε t i=0 yt−i CEECi CEECi ∆pt d41i d42i d43i d44i εpt−i
In the spirit of the Blanchard-Quah identi cation scheme it is assumed that demand shocks do not aect GDP in the long run andadditionally that and additionally that the CEECs' country-speci c cannot determine the euro area variables. This translates into the following zero-restrictions:
d11 d12 d13 d14 . 0 0 0 ∞ X d21 d22 d23 d24 .. 0 0 Di = = . (22) d31 d32 d33 d34 . 0 . 0 i=0 d41 d42 d43 d44 ....
2.4 Structural disparity index
As a measure of dierences in economic structures of the CEECs vis-a-vis the euro area we used the Landes- mann index (Landesmann and Székely, 1995). The index is calculated according to the following formula:
s n si X i i 2 CEECj SL = (s − s ) ( ) (23) CEECj EA 100 i=1 where si stands for the share of ith category in the structure. The index takes values between 0 and 1. The lower the value, the smaller the disparities in economic structures.
2.5 Data source
The data used in the analysis come from the Eurostat database. The sample covers the quarterly data from the rst quarter of 1995 (for the Czech Republic: from the beginning of 1998) to the rst quarter of 2010. All series were seasonally adjusted using the TRAMO/SEATS procedure. The economic structures were de ned on the basis of the following statistical classi cations: (i) NACE (6 branches) for value added, (ii) COICOP (10 groups of products and services) for private consumption, (iii) CPA (6 groups of xed assets) for investments, (iv) BEC (19 basic categories aggregated into capital, consumption and intermediate goods) for exports and imports.
6 3 Results
In this section we make a detailed insight into the output developments during the recent downturn, prompted by a global nancial shock, i.e. we isolate and dissect the business cycle and extract structural shocks aecting the analysed economies. On this basis we attempt to nd plausible explanations for the dierential performance across the new EU member states in that period.
3.1 Economic performance during the nancial crisis
Two potential reasons for the relative resilience of the Polish economy compared to other CEECs seem plausible: (i) the dierence in the propagation mechanism of foreign shocks into the domestic economy that would manifest itself in lags in the business cycle or (ii) internal factors, e.g. the policy mix or structural characteristics. The onset of the recent contraction phase of the Polish business cycle coincided with the euro area (Figure 1). The same situation was to be observed in the Czech Republic, whereas in Hungary and Slovakia the peak of the cycle appeared with a one-quarter lag. Therefore the relatively good performance of the Polish economy cannot to be attributed to lags in the transmission mechanism, i.e. to a phase shift compared to other CEECs. The depth, duration and consequently steepness of the downturn were the factors that distinguished the Polish cycle. The deviation from the trend in the case of Poland amounted to merely 1 percentage point, whereas in the other new EU member states it amounted to 3-4 percentage points even more than in the euro area, despite a lower exposure to nancial turmoil. What is more, the upturn of the Polish cycle appeared a quarter before it appeared in any of the other analysed countries.
Figure 1: Cyclical components of (log) GDP
.03 .04
.02 .03
.02 .01 .01 .00 .00 -.01 -.01
-.02 -.02
-.03 -.03 98 99 00 01 02 03 04 05 06 07 08 09 98 99 00 01 02 03 04 05 06 07 08 09
EA PL EA CZ
(a) Poland and the euro area (b) the Czech Repubic and the euro area
.04 .06
.03 .04 .02
.01 .02 .00
-.01 .00
-.02 -.02 -.03
-.04 -.04 98 99 00 01 02 03 04 05 06 07 08 09 98 99 00 01 02 03 04 05 06 07 08 09
EA HU EA SK
(c) Hungary and the euro area (d) Slovakia and the euro area
Note: Turning points are agged by vertical lines (navy blue for the euro area, orange for CEECs).
7 In order to nd an explanation for the relatively low susceptibility of the Polish output to the global crisis we proceed to analysing shocks that aected the economies in that period. In the second half of 2008 the economy of the euro area was hit by massive adverse shocks (amounting to 2 to 3 standard deviations) both supply, real demand and nominal demand (Figures 2, 3 and 4). Poland was also subject to negative supply shocks, but of a much smaller magnitude. In the case of real demand, i.e. preference shocks relative to the rest-of-the-world, Poland was hit to a similar extent as the euro area. Its manifestation was a massive real REER depreciation of the Polish zloty. As regards nominal demand shocks, the money supply both in Poland and in the euro area increased as a result of monetary policy loosening in response to the nancial turmoil. However, the outcome of this action in terms of the actual nominal shocks aecting both economies was dierent. Namely, the monetary easing translated into strongly negative shocks in the euro area, which could indicate that the euro area monetary transmission mechanism during the recent crisis proved to be insu cient in terms of output stabilisation. In Poland, in turn, nominal shocks in that period were highly positive. The observed divergence between Poland and the euro area in the case of supply and nominal shocks stands in stark contrast to the trajectory of these shocks prior to the crisis. Until the end of 2007 Poland exhibited the highest degree of symmetry in this respect with the euro area of all the Visegrad countries. Consistent with the above descriptive analysis, the recursive correlation coe cients of the Polish supply and nominal shocks vis-a-vis the euro area fell dramatically during the recent downturn, contrary to the situation in other CEECs. The degree of symmetry of supply shocks with the euro area in those countries increased considerably in the crisis period, which can clearly be attributed to the transmission eect. Both the Czech Republic and Slovakia also experienced negative nominal shocks. Hungary, in turn, exhibited a similar nominal demand trajectory as Poland during the monetary easing period. All analysed countries2were subject to adverse real demand shocks, which can be related to the global collapse of trade.
2The lower magnitude of real demand shocks in Slovakia can be explained by a relatively low nominal depreciation, being the result of xing of the koruna exchange rate to the euro (the euro area countries being Slovakia's largest trade partner).
8 Figure 2: Supply shocks (identi ed on the basis of a trivariate VAR (GDP, REER, CPI)
2 2
1 1
0 0 -1 -1 -2 -2 -3
-3 -4
-4 -5 00 01 02 03 04 05 06 07 08 09 00 01 02 03 04 05 06 07 08 09
EA PL EA CZ
(a) time series of identi ed shocks Poland (b) time series of identi ed shocks the Czech Republic and the euro area and the euro area
2 2
1 1
0 0
-1 -1
-2 -2
-3 -3
-4 -4 00 01 02 03 04 05 06 07 08 09 00 01 02 03 04 05 06 07 08 09
EA HU EA SK
(c) time series of identi ed shocks Hungary (d) time series of identi ed shocks Slovakia and the euro area and the euro area
.3 .8
.6 .2
.4 .1 .2 .0 .0
-.1 -.2
-.2 -.4 2004 2005 2006 2007 2008 2009
CZ HU PL SK CZ HU PL SK
(e) correlation coe cients of shocks with the euro area (f) recursive correlation coe cients with the euro area prior to the crises (over the period 1995-2007) ( xed-length rolling window of 8 years)
Note: Correlation coe cients were computed in rolling windows of xed length (9 years) starting from the rst quarter of 1995.
9 Figure 3: Real demand shocks (identi ed on the basis of a trivariate VAR (GDP, REER, CPI)
3 3
2 2
1 1
0 0
-1 -1
-2 -2
-3 -3 00 01 02 03 04 05 06 07 08 09 00 01 02 03 04 05 06 07 08 09
EA PL EA CZ
(a) time series of identi ed shocks Poland (b) time series of identi ed shocks the Czech Republic and the euro area and the euro area
3 3
2 2 1
1 0
-1 0
-2 -1 -3
-4 -2 00 01 02 03 04 05 06 07 08 09 00 01 02 03 04 05 06 07 08 09
EA HU EA SK
(c) time series of identi ed shocks Hungary (d) time series of identi ed shocks Slovakia and the euro area and the euro area
.2 .6
.4 .1
.2 .0 .0 -.1 -.2
-.2 -.4
-.3 -.6 2004 2005 2006 2007 2008 2009
CZ HU PL SK CZ HU PL SK
(e) correlation coe cients of shocks with the euro area (f) recursive correlation coe cients with the euro area prior to the crises (over the period 1995-2007) ( xed-length rolling window of 8 years)
Note: Correlation coe cients were computed in rolling windows of xed length (9 years) starting from the rst quarter of 1995.
10 Figure 4: Nominal demand shocks (identi ed on the basis of a trivariate VAR (GDP, REER, CPI)
2 3
2 1
1 0 0 -1 -1
-2 -2
-3 -3 00 01 02 03 04 05 06 07 08 09 00 01 02 03 04 05 06 07 08 09
EA PL EA CZ
(a) time series of identi ed shocks Poland (b) time series of identi ed shocks the Czech Republic and the euro area and the euro area
2 3
2 1
1 0 0 -1 -1
-2 -2
-3 -3 00 01 02 03 04 05 06 07 08 09 00 01 02 03 04 05 06 07 08 09
EA HU EA SK
(c) time series of identi ed shocks Hungary (d) time series of identi ed shocks Slovakia and the euro area and the euro area
.35 .4
.30 .3
.25 .2 .1 .20 .0 .15 -.1 .10 -.2 .05 -.3 .00 -.4 -.05 -.5 2004 2005 2006 2007 2008 2009
CZ HU PL SK CZ HU PL SK
(e) correlation coe cients of shocks with the euro area (f) recursive correlation coe cients with the euro area prior to the crises (over the period 1995-2007) ( xed-length rolling window of 8 years)
Note: Correlation coe cients were computed in rolling windows of xed length (9 years) starting from the rst quarter of 1995.
Negative structural shocks in Poland can be entirely attributed to the transmission mechanism internal shocks that aected the economy during the midst of the nancial crisis were highly positive, and negative impulses that occured later on were of a minor magnitude (Figure 5). This resulted in the decrease in correlation coe cients of internal shocks with the euro area overall impulses. Contrary to the case of Poland, negative overall impulses in other CEECs can be attributed not only to transmission mechanisms, but also to their internal activity developments.
11 Figure 5: Structural shocks (identi ed on the basis of a four-variable VAR (with GDP and CPI in the euro area and each country's counterparts)
2 3
1 2
0 1
-1 0
-2 -1
-3 -2
-4 -3 98 99 00 01 02 03 04 05 06 07 08 09 98 99 00 01 02 03 04 05 06 07 08 09
EA EA PL PL PL - country specific PL - country specific
(a) time series of supply shocks (the euro area, (b) time series of demand shocks (the euro area, Polish internal, Polish overall) Polish internal, Polish overall)
.6 .3
.4 .2 .2
.1 .0
-.2 .0
-.4 -.1 -.6
-.8 -.2 2004 2005 2006 2007 2008 2009 2005 2006 2007 2008 2009
CZ HU PL SK CZ HU PL SK
(c) recursive correlation coe cients of internal supply (d) recursive correlation coe cients of internal demand shocks with the euro area overall shocks ( xed-length shocks with the euro area overall shocks ( xed-length rolling window of 8 years) rolling window of 8 years)
Note: The overall shocks were extracted from a trivariate VAR (GDP, REER, CPI). Correlation coe cients were computed in rolling windows of xed length (9 years) starting from the rst quarter of 1995.
To sum up, the analysis of structural shocks that hit the considered economies in the aftermath of the recent nancial crisis revealed that the resilience of internal activity was the factor that distinguished Poland from other CEECs. Namely, country-speci c shocks were highly positive in Poland in that period, contrary to other new EU member states. On the other hand, Poland was hit to a similar extent by the slump in international trade. In the next subsection we try to pinpoint those characteristics of the Polish economy that might have cush- ioned the impact of adverse global shocks.
3.2 Plausible explanations
The severity of the recent downturn was in the case of Poland considerably lower - than in other Visegrad countries, which can be attributed to its relatively resilient internal activity. In this section we attempt to examine dierent characteristics of the considered analysed countries in order to nd possible explanations for this phenomenon.
12 First of all, it can be assumed that the recent crisis was exogenous from the perspective of developing countries, i.e.was transmitted from the advanced economies. That is why the exposure of an economy to foreign shocks should play a key role in determining the evolution of the business cycle in that period. The share of euro area shocks in structural impulses determining the output in Poland has been by far the lowest of all the CEECs (Figure 6). This applies in particular to the euro area demand shocks, and can be related to the degree of openness and the size of the domestic market of the analysed economies. What is more, the exposure of the Polish economy to shocks originating in the euro area did not change during the recent downturn, contrary to other Visegrad countries. The observed rise in susceptibility of the Czech Republic, Hungary and Slovakia to foreign shocks at the end of the sample might explain the considerable convergence of their business cycles vis-a-vis the euro area (as measured by recursive correlation coe cients) in that period. .
Figure 6: CEECs GDP forecast error variance decomposition (on the basis of a 4-variable VAR with GDP and CPI in the euro area and their CEECs counterparts)
100 100 90 80 80 70 60 60 50 40 40
30 20 20 10 0 2004 2005 2006 2007 2008 2009 2004 2005 2006 2007 2008 2009
supply - EA supply - EA demand - EA demand - EA supply - country-specific supply - country-specific demand - country-specific demand - country-specific
(a) Poland (b) Czech Republic
100 100
80 80
60 60
40 40
20 20
0 0 2004 2005 2006 2007 2008 2009 2004 2005 2006 2007 2008 2009
supply - EA supply - EA demand - EA demand - EA supply - country-specific supply - country-specific demand - country-specific demand - country-specific
(c) Hungary (d) Slovakia
Note: The decompositions were obtained from four-variable VAR models estimated in xed starting-point windows (the rst subsample being the rst quarter of 1995 to the rst quarter of 2004).
13 Apart from dierent exposure to foreign impulses, the propagation of those shocks into the domestic economy may also explain the variation of economic performance among the CEECs. The impulse response of GDP to euro area shocks is notably dierent in the considered economies (Figure 7). The reaction of the output in Hungary by far surpasses the reaction in other countries. Poland, on the other hand, seems to be least reactive to the developments in the euro area.
Figure 7: CEECs' GDP's cumulative response to euro area shocks (on the basis of a four-variable VAR with GDP and CPI in the euro area and their CEECs counterparts)
.030 .007
.025 .006 .005 .020 .004 .015 .003 .010 .002 .005 .001
.000 .000
-.005 -.001 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50
CZ HU PL SK CZ HU PL SK
(a) supply shocks (b) demand shocks
The investigation into the sectoral developments in the CEECs, compared to the euro area, might also shed some light on the reasons of dierential output trajectories. The evolution of Polish cyclical components in some sectors did not dier from other CEECs, nor the euro area; in some sectors, however, Poland exhibited a disparate behaviour (Figures 8, 11, 13, 14, 17, 18 and 19). It seems that this outcome was not a coincidence and can be related to structural characteristics of the analysed economies. On the expenditure side the relatively good performance of the Polish economy can be attributed to private consumption developments (Figure 8). Firstly, the onset of the contraction phase in Poland lagged behind the peak of the euro area cycle by four quarters. Secondly, the depth of the downturn was much lower: Poland did not experience negative deviations from the trend during the recent crisis. A mild slowdown could also be observed in the Czech Republic, however, similarly to Slovakia and Hungary and contrary to Poland, the synchrony of the cyclical component with the euro area increased considerably at the end of the sample (being the result of a quicker response to the euro area contraction than in the previous period), which points to the transmission of shocks. No sign of convergence of the Polish private consumption cycle vis-a-vis the euro area during the recent crisis suggest that the consumers' behaviour in Poland did not react to the global crisis. This result is also consistent with the trajectory of the extracted nominal demand shocks, which indicates that unlike in other countries (except for Hungary) the Polish internal demand (primarily consumption owing to CPI in ation being a proxy for price developments) reacted positively to monetary easing. This phenomenon can be contributed to the structure of the consumption expenditures in Poland, compared to other considered economies (Figure 9). Despite considerable convergence that has taken place in this respect since the 90s, Poland has the most dissimilar consumption patterns vis-a-vis the euro area, as measured by the Landesmann index (together with Hungary), despite considerable convergence that has taken place in this respect since the 90s. The share of inelastic expenditure (food and beverages, as well as housing, water, electricity and gas) in the overall consumption basket in Poland is the highest of all CEECs (approximately 5 percentage points higher than in other Visegrad countries, and as much as 12 percentage points higher than in the euro area). The share of relatively dispensable expenditure (on restaurants and hotels, as well as recreation and culture) is, on the other hand, the lowest (5 percentage points lower than on average in
14 other CEECs and 7 percentage points lower than in the euro area). Therefore, the structure of private consumption might have buered the impact of adverse global shocks in Poland on household demand. This interpretation seems to be supported by the evolution of household savings rate in the aftermath of the crisis, which experienced a considerable slump in Poland and an increase in the euro area (Figure 10).
Figure 8: Cyclical components of (log) private consumption
.015
.010
.005
.000
-.005
-.010 98 99 00 01 02 03 04 05 06 07 08 09
EA PL
(a) cyclical components of Poland and the euro area
.04 .03 .02 .01 .00 -.01 -.02 -.03 -.04 -.05 98 99 00 01 02 03 04 05 06 07 08 09
EA HU SK CZ PL
(b) cyclical components of the analysed economies and the euro area
.6
.4
.2
.0
-.2
-.4
-.6
-.8 2004 2005 2006 2007 2008 2009
CZ HU PL SK
(c) recursive correlation coe cients of cyclical components with the euro area (overlapping xed-length rolling window of 9 years)
15 Figure 9: Structure of private consumption
1.0 0.9 Food and beverages 0.8 Housing, water, electricity, gas 0.7 Recreation and culture Restaurants and hotels 0.6 Furnishings Clothing and footwear 0.5 Health 0.4 Education Transport, communications 0.3 Miscellanea 0.2 0.1 EA CZ HU PLSK
(a) structure in 2008
.20
.18
.16
.14
.12
.10
.08
.06 1996 1998 2000 2002 2004 2006 2008
CZ HU PL SK
(b) Landesmann index
Figure 10: Household savings rate in Poland and the euro area (four period moving average) in percentage points
15.2
14.8
14.4
14.0 11 13.6 10 9 13.2 8 7 6 5 2006 2007 2008 2009
EA PL
16 Figure 11: Cyclical components of (log) gross xed capital formation
.08
.06
.04
.02
.00
-.02
-.04
-.06
-.08 98 99 00 01 02 03 04 05 06 07 08 09
EA PL
(a) cyclical components of Poland and the euro area
.15
.10
.05
.00
-.05
-.10
-.15
-.20 98 99 00 01 02 03 04 05 06 07 08 09
EA HU SK CZ PL
(b) cyclical components of the analysed economies and the euro area
.8
.6
.4
.2
.0
-.2
-.4
-.6 2004 2005 2006 2007 2008 2009
CZ HU PL SK
(c) recursive correlation coe cients of cyclical components with the euro area (overlapping xed-length rolling window of 8 years)
Contrary to consumption developments, the investment demand in Poland fell to a similar extent as in the euro area (Figure 11). What is more, until the rst quarter of 2010 there were no signs of recovery in this respect. Oddly enough, this was despite the highest (together with Slovakia, which also experienced a sharp fall) share of the so-called other construction works, which comprises mainly public investment in infrastructure, fairly resilient to unfavourable macroeconomic conditions (Figure 12). This indicates a risk-
17 averse approach to investing exhibited by entrepreneurs in reaction to the nancial crisis. This could possibly be explained to some extent by a higher share of FDI in the aggregate investments in Poland, as compared to the euro area, and a sharp fall of FDI in ows during the nancial crisis.
Figure 12: Structure of gross xed capital formation
1.0
0.9
0.8
0.7 Metal products and machinery 0.6 Transport equipment Construction work: housing 0.5 Construction work: other constructions Other products 0.4
0.3
0.2 EA CZ HU PL SK
(a) structure in 2008
.30
.25
.20
.15
.10
.05
.00 1996 1998 2000 2002 2004 2006 2008
CZ HU PL SK
(b) Landesmann index
In reaction to the global collapse of trade in the aftermath of the crisis, Poland's exports fell to a similar extent as in the euro area (Figure 13). Owing to a high degree of their openness, other CEECs experienced an even deeper slump. Poland's imports, on the other hand, fell dramatically in 2009 (the maximum deviation from trend amounted to almost 15 percentage points, whereas in the euro area merely to 10 percentage points, Figure 14). This phenomenon can also be tracked down to the structure of trade in the analysed countries (Figure 15). In the case of the euro area and the Czech Republic the composition of exports and imports is fairly symmetrical. Consequently, the deviation of their imports and exports from the trend were of similar magnitude. In Poland and Slovakia, on the other hand, the share of capital goods in imports is much higher and of consumption goods much lower than in exports. Private consumption is in general much less volatile than investment (likewise, during the recent downturn the deviation from the trend in the analysed countries was much higher in the case of investment demand than consumption expenditure). Therefore the structural disparity of exports and imports could explain much higher depth of imports' slump in Poland. In Slovakia, however, such divergence did not take place. This outcome could be, in turn, explained by the composition of consumption goods exports in both countries (Figure 16). The share of durable consumer goods in Slovak exports is much higher than in Poland. In the case of non-durable goods the situation is the opposite. That is why the foreign demand for Polish consumer goods is much less susceptible to unfavourable economic developments than in Slovakia.
18 Figure 13: Cyclical components of (log) exports
.10
.05
.00
-.05
-.10 98 99 00 01 02 03 04 05 06 07 08 09
EA PL
(a) cyclical components of Poland and the euro area
.15
.10
.05
.00
-.05
-.10
-.15
-.20 98 99 00 01 02 03 04 05 06 07 08 09
EA HU SK CZ PL
(b) cyclical components of the analysed economies and the euro area
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2 2004 2005 2006 2007 2008 2009
CZ HU PL SK
(c) recursive correlation coe cients of cyclical components with the euro area (overlapping xed-length rolling window of 8 years)
19 Figure 14: Cyclical components of (log) imports
.10
.05
.00
-.05
-.10
-.15 98 99 00 01 02 03 04 05 06 07 08 09
EA PL
(a) cyclical components of Poland and the euro area
.15
.10
.05
.00
-.05
-.10
-.15
-.20 98 99 00 01 02 03 04 05 06 07 08 09
EA HU SK CZ PL
(b) cyclical components of the analysed economies and the euro area
1.0
0.8
0.6
0.4
0.2
0.0 2004 2005 2006 2007 2008 2009
CZ HU PL SK
(c) recursive correlation coe cients of cyclical components with the euro area (overlapping xed-length rolling window of 8 years)
20 Figure 15: The cyclical components of exports and imports and economic structures in 2008
1.0
.10 0.8
0.6 .05
0.4 .00
0.2 -.05
0.0 exports imports -.10 capital goods 98 99 00 01 02 03 04 05 06 07 08 09 intermediate goods consumption goods exports imports
(a) the euro area
1.0
.10 0.8
.05 0.6
.00 0.4
-.05 0.2
-.10 0.0 exports imports -.15 capital goods 98 99 00 01 02 03 04 05 06 07 08 09 intermediate goods consumption goods exports imports
(b) Poland
1.0
.12 0.8 .08 0.6 .04
0.4 .00
0.2 -.04
0.0 -.08 exports imports -.12 capital goods 98 99 00 01 02 03 04 05 06 07 08 09 intermediate consumption goods exports imports
(c) Czech Republic
1.0
.15 0.8 .10
0.6 .05
.00 0.4 -.05 0.2 -.10
0.0 -.15 exports imports -.20 capital goods 98 99 00 01 02 03 04 05 06 07 08 09 intermediate goods consumption goods exports imports
(d) Slovakia
Note: The structure for Hungary could not be computed due to incomplete data.
21 Figure 16: Composition of consumption goods exports in Poland and Slovakia
1.0
0.8
0.6
0.4
0.2
0.0 PL SK
non-durable consumer goods semi-durable consumer goods durable consumer goods
The trajectory of the supply side components of GDP are in line with the previous outcomes, according to which the relatively good performance of the Polish economy is the result of the dichotomy in the cyclical behaviour of externally-exposed (exports and investments) and internal (consumption) economic activity. Namely, the industrial sector seems to have been the only one severely aected by the recent global crisis (Figure 17). However the depth of the slump was much lower as compared to other analysed countries. Market services (Figure 18) and construction sector (19), on the other hand, proved to be fairly resilient to the crisis. This is the re ection of the fact that services account for a large proportion of private consumption, whereas the downturn in construction building was oset by a high proportion of public investments, fuelled by EU structural funds.
22 Figure 17: Cyclical components of (log) value added in industry
.08
.04
.00
-.04
-.08
-.12 98 99 00 01 02 03 04 05 06 07 08 09
EA PL
(a) Cyclical components of Poland and the euro area
.12
.08
.04
.00
-.04
-.08
-.12 98 99 00 01 02 03 04 05 06 07 08 09
EA HU SK CZ PL
(b) Cyclical components of the analysed economies and the euro area
1.2
0.8
0.4
0.0
-0.4
-0.8 2004 2005 2006 2007 2008 2009
CZ HU PL SK
(c) recursive correlation coe cients of cyclical components with the euro area (overlapping xed-length rolling window of 8 years)
23 Figure 18: Cyclical components of (log) value added in market services
.04
.03
.02
.01
.00
-.01
-.02
-.03
-.04 98 99 00 01 02 03 04 05 06 07 08 09
EA PL
(a) Cyclical components of Poland and the euro area
.12
.08
.04
.00
-.04
-.08
-.12 98 99 00 01 02 03 04 05 06 07 08 09
EA HU SK CZ PL
(b) Cyclical components of the analysed economies and the euro area
1.0
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
-0.6 2004 2005 2006 2007 2008 2009
CZ HU PL SK
(c) recursive correlation coe cients of cyclical components with the euro area (overlapping xed-length rolling window of 8 years)
24 Figure 19: Cyclical components of (log) value added in construction
.10
.08
.06
.04
.02
.00
-.02
-.04
-.06 98 99 00 01 02 03 04 05 06 07 08 09
EA PL
(a) Cyclical components of Poland and the euro area
.25
.20
.15
.10
.05
.00
-.05
-.10 01 02 03 04 05 06 07 08 09
EA HU SK CZ PL
(b) Cyclical components of the analysed economies and the euro area
1.0
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
-0.6 2004 2005 2006 2007 2008 2009
CZ HU PL SK
(c) recursive correlation coe cients of cyclical components with the euro area (overlapping xed-length rolling window of 8 years)
25 The composition of GDP (Figure 20) together with the sectoral evolution of the business cycle may also explain dierences in cyclical behaviour across CEECs. Namely, the share of industry in Poland is the lowest, wereas that of non- nancial market services (trade, hotels and restaurants, transport) the highest among the Visegrad countries. Taking into account the dierence in the susceptibility of these sectors to foreign shocks, structural disparities may to some extent stand behind the dierential performance of the analysed countries during the recent crisis.
Figure 20: Structure of GDP
1.0
0.8 Agriculture, hunting, forestry and fishing Manufacturing 0.6 Industry: other Construction Trade, hotels and restaurants, transport 0.4 Financial intermediation Public service
0.2
0.0 EA CZ HU PLSK
(a) structure in 2008
.16
.14
.12
.10
.08
.06
.04 1996 1998 2000 2002 2004 2006 2008
CZ HU PL SK
(b) Landesmann index
4 Conclusions
In conclusion, the dierential output trajectory in Poland and other CEECs can be ascribed to two major fac- tors: (i) lower share of internationally-driven components (manufacturing, exports) in GDP and consequently lower responsiveness of output to foreign shocks and lower share of those shocks in structural impulses, and (ii) the dichotomy in internal activity. The former factor has for long been recognised in the discussion on the sources of the variation of economic activity within the CEEC group. The latter one, however, is an important contribution of this paper. It seems, namely, that domestically-oriented sectors (market services and construction), as well as consumption demand in all CEECs except for Poland experienced a considerable convergence towards euro area developments in the aftermath of the crisis. This may indicate an increase in the interdependency (risk sharing) between those countries and the euro area. Poland, on the other hand, experienced a notably dierent trajectory of internal shocks and, consequently, internal economic activity. This outcome suggests that Poland's relatively high degree of synchronisation with the euro area may dimin- ish, which may negatively in uence the balance of costs and bene ts of euro adoption. On the other hand,
26 the relative structural charateristics of the Polish economy may change as a result of a disparate cyclical behaviour, as compared to the euro area . The consumption patterns are to a great extent the re ection of the level of disposible income of consumers. Therefore, in consequence of the acceleration of real convergence process (in terms of GDP per capita) of the Polish economy during the recent crisis, the disparity of con- sumption patterns relative to the euro area may also shrink. This could make Poland's output more volatile, but at the same time it may contribute to cyclical convergence and consequently allow Poland to fully enjoy the bene ts of the prospective accession to the euro area.
References
Adamowicz E., Dudek S., Pachucki D., and Walczyk K. (2009):Synchronizacja cyklu koniunktural- nego polskiej gospodarki z krajami strefy Euro w kontek±cie struktury tych gospodarek, Raport nt. peªnego uczestnictwa Rzeczypospolitej Polskiej w trzecim etapie Unii Gospodaczej i Walutowej. Projekty badawcze, www.nbp.pl, 1, 8-224. Baxter M., and King R. (1995):Measuring business-cycles: approximate band-pass lters for economic time series, NBER Working Paper 5022. Blanchard O., and Quah D. (1989): The dynamic eects of aggregate demand and supply disturbances, The American Economic Review 79 (4): 655-673. Bry G., and Boschan C. (1971):Cyclical analysis of economic time series: selected procedures and com- puter programs, NBER Technical Working Paper 20. Burns A., and Mitchell W., (1946):Measuring Business Cycles, NBER, New York. Christiano, L., and Fitzgerald T. (2003): The Band Pass Filter, International Economic Review 44, 435-465. Clarida, R., and Gali, J. (1994): Sources of Real Exchange Rate Fluctuations: How Important Are Nominal Shocks?, Carnegie Rochester Series on Public Policy 41. Fidrmuc J. and Korhonen I. (2006): Meta-analysis of the business cycle correlation between the euro area and the CEECs, CESifo Working Paper 1693. Harding, D., and Pagan A. (2002): Dissecting the cycle: a methodological investigation, Journal of Monetary Economics 49 (2): 365-381. Konopczak K. (2009): Analiza zbie»no±ci cyklu koniunkturalnego gospodarki polskiej ze stref¡ euro na tle krajów Europy rodkowo-Wschodniej oraz pa«stw czªonkowskich strefyeuro , Raport nt. peªnego uczestnictwa Rzeczypospolitej Polskiej w trzecim etapie Unii Gospodaczej i Walutowej. Projekty badawcze, www.nbp.pl, 1, 68-104. Landesmann, S., and Székely, I. (1995): Industrial Restructuring and Trade Reorientation in Eastern Europe, Cambridge University Press, Cambridge. Obstfeld, M. (1985): Floating Exchange Rates: Experience and Prospects, Brookings Papers on Economic Activity 2, 369 - 450. Skrzypczy«ski P. (2009): Wahania aktywno±ci gospodarczej w Polsce i stre e euro, Raport nt. peªnego uczestnictwa Rzeczypospolitej Polskiej w trzecim etapie Unii Gospodaczej i Walutowej. Projekty badawcze, www.nbp.pl, 1, 213-276.
27