Proceedings of 21st International Business Research Conference
10 - 11 June, 2013, Ryerson University, Toronto, Canada, ISBN: 978-1-922069-25-2
Business Cycles and EU Integration for the Balkan Region
John Ebio*, Scott W. Hegerty*, Steven Kania*, Raidas Korsakas*,
Nicholas Malone*, Batkhuu Narankhuu*, and Michael Wenz*,+
The global financial crisis has exposed problems with
coordination of monetary policy around the globe and
especially throughout the Euro currency area. This paper
examines the correlations in business cycles through the
Balkan region to examine whether adopting the Euro is an
appropriate strategy. Results show that business cycles in
certain Balkan nations are in general not contemporaneously
correlated with each other or with comparison countries such
as Germany, Poland and the United States. Results do show
that Croatian business cycles are correlated with Russian
business cycles, but overall, integration of the Balkan
economies into the Euro zone is not consistent with the theory
of Optimal Currency Areas.
JEL Codes: E32, F42
1. Introduction
Over the past decade, the European Union has expanded to include the former
communist countries of Central and Southeast Europe. While the first ―new‖
members of this now-27-member body included more advanced economies such as
Poland, Estonia, and Slovenia, attention is now turning toward the Balkan nations
and the former Yugoslavia. On July 1, 2013, Croatia is set to join the European
Union, while Serbia is still negotiating its accession.
The Balkan nations are located in Southeastern Europe and are generally
considered to include the Balkan Peninsula and the nations of the Former
Yugoslavia (and exclude Greece). We focus our study on the nations of Croatia,
Serbia, Bulgaria and Romania in hopes of identifying their suitability for joining the
Euro common currency area. Croatia, formerly a part of Yugoslavia with historic ties
to Austria, is a nation with a highly developed infrastructure and many waterfront
resort areas that have made it a major tourist destination in Eastern Europe.
Another former Yugoslav territory included in this study is Serbia, whose abundance
of mineral resources and relative political stabilization have led to its steady
economic growth over the past decade. Bulgaria was included due to how similar its
economy is to the other subject nations, which is largely driven by its exports of
energy and timber, as well as geographic proximity. The final nation included is
Romania, whose sustained economic growth can be attributed to its vast natural
resources and strong agriculture and manufacturing industries. These latter two
countries joined the EU in 2007, though they have not adopted the Euro.
__________________________________________________________________
*Department of Economics, Northeastern Illinois University, Chicago, IL 60625.
+
Corresponding Author. E-mail: m-wenz@neiu.edu
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Proceedings of 21st International Business Research Conference
10 - 11 June, 2013, Ryerson University, Toronto, Canada, ISBN: 978-1-922069-25-2
The countries of the former Soviet bloc have economies that differ greatly from those
of Germany and Western Europe, and this is even truer for countries that have been
unable to join the EU until now. Economic theory suggests that countries in a
common currency area should have economies that behave similarly, however. We
examine time series correlations in GDP between the four Balkan nations identified
above and five comparison countries, including some within and some outside the
Euro area. The findings in this paper indicate that the Balkan nations have business
cycles that are not well synchronized with other key comparison countries in the
European Union. These nations should proceed with great care as they consider
and move toward adoption of the Euro as a common currency.
2. Literature Review
According to the Optimal Currency Area (OCA) theory of Mundell (1961),
policymakers will be unable to solve different regions’ problems using a single set of
policy tools if these economies do not move in a similar fashion. Mundell’s OCA
theory states that whenever a region such as the European Union shares a common
currency, the ability of each participant country to cope with international or
interregional pressures on their prices and output through monetary policy is
diminished. The Central Bank set up by the treaties of the union must address the
needs of each member country within its policies; this leaves the union and its
member countries with limited potential responses to economic turmoil. If member
countries are not within the same business cycle, policy developed to benefit one will
have negative effects on the others.
Suppose countries A and B share a common currency. When country B is afflicted
by a negative supply shock it will push a policy to relieve that economic stress. If
country A was intertwined with country B to the point they also experienced the
negative effects of the supply shock, country A would agree to the new policy. If
country A is unaffected by country B's supply shock they would not support the use
of expansionary monetary policy to fight the downturn and try to avoid the inflation
those policies would cause. Avoiding this conundrum is important for countries with a
common currency and this is why identification of member countries correlations with
each other is important. We thus endeavor to measure how similar these economies
are.
Most recent analyses of these interconnections have focused on the West (e.g.,
Backus et al., 1992; Imbs, 2004; Inagaki, 2006; and Furceri and Karras, 2008). Even
those studies that have focused on transition economies (such as Hegerty, 2010)
have focused on the more integrated countries to the north. Questions regarding the
Balkans have gone unanswered, in part because the former Yugoslavian countries
do not have a history long enough to perform reliable time series analysis. As
sufficient data becomes available, it is now possible to expand this research into a
study of Balkan countries to examine how compatible they really are with the Euro
area.
As noted by Hegerty (2010), early studies on business-cycle integration include
Horvath and Rátfai (2004), Babetskii (2005), Benczúr and Rátfai (2005), Fidrmuc
and Korhonen (2006), Artis et al. (2008), and Darvas and Szapáry (2008). These
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Proceedings of 21st International Business Research Conference
10 - 11 June, 2013, Ryerson University, Toronto, Canada, ISBN: 978-1-922069-25-2
studies not only examine time periods that end well before the recent crisis, they also
focus on various facets of integration rather than the relative strength of particular
regional interconnections. Some focus specifically on modeling a country’s businesscycle dynamics without estimating regional connections. An example is Šergo et al.
(2012)’s study of the case of Croatia and the resulting stylized facts. These authors
considered many economic parts, such as vacancy rates, tourism patterns, price and
productivity indices, trade flows, wages, exchange rates, and the money supply. By
calculating the strength of the correlation between variables of the economic
components or between economic components and the reference variables (such as
output), this study found that the strong co-movements were between the growth of
vacancy rates and tourist arrivals, and the growth of vacancy rate and exports.
While that study focused on internal Croatian metrics, the methodology may help
indicate whether the economy of a country is compatible for economic integration
into a currency area such as the European Union.
In one of the few multi-country analyses, Hegerty (2010) studied Poland, Hungary,
the Czech Republic, Estonia, Latvia, and Lithuania, and deconstructed each
economy into output (Y), consumption (C), government spending (G), investment (I),
exports (X), and imports (M). Using these components, the strength of cross
correlations were calculated between the Eastern European nations themselves,
with the European Union, and with global cycles. This paper found that the Baltics
were more closely linked to global cycles, and to each other, than with the European
Union. This sort of extensive volatility analysis helps identify whether a country’s
economy would be a good fit for a system where there is only one monetary policy
for all participating members. This has important implications for the Euro,
particularly since Estonia has since joined the common currency. We extend the
literature here to conduct a similar analysis for four countries in the Balkan region.
3. Methodology
Using data from the International Financial Statistics of the International Monetary
Fund, we measure real GDP as well as its components (C, I, G, X, and M) by
dividing nominal values by the Consumer Price Index or GDP deflator. Our data are
quarterly, generally beginning in the mid 1990s and ending in 2012. The available
countries for this analysis are Croatia, Bulgaria, and Romania for all GDP
components and Serbia for GDP only. Quarterly data are not readily available for
some countries of the former Yugoslavia (such as Macedonia) and are therefore not
included. We also look for connections among real output and consumption series
for five partner countries, listed below.
It is typical to view each of these time series as having three components, a
seasonal component, a cyclical component, and a trend. We first deseasonalize our
real variables to eliminate within-year seasonal fluctuations using the Census-X12
procedure. We then decompose each series into its trend and cyclical component
using the Hodrick-Prescott filter (lambda = 1600). The H-P filter uses the data to
estimate and separate out the trend component of the series. Applying the filter
allows us to isolate the cyclical fluctuations in the various time series. These cyclical
fluctuations, presented in Figure 1, are the focus of our study.
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Proceedings of 21st International Business Research Conference
10 - 11 June, 2013, Ryerson University, Toronto, Canada, ISBN: 978-1-922069-25-2
Figure 1: Business Cycles of Balkan and Partner Countries
Bulgaria
Croatia
Romania
Serbia
Germany
Turkey
Poland
Russia
U.S.
Next, we use these cycles to estimate cross-correlation functions between various
country pairs as follows:
 X t X Y t kY 
 t k 
(1)
2
2
 X t X   Y t kY 
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Proceedings of 21st International Business Research Conference
10 - 11 June, 2013, Ryerson University, Toronto, Canada, ISBN: 978-1-922069-25-2
Here, X and Y represent two comparison countries, t represents the time period in
country X, and k represents the lead or lag in country Y, where k = 0 for the
contemporaneous observation. These are measured over a span of four lead
quarters and four lag quarters. As estimated here, the correlation at ―+2‖ quarters
measures the relationship between the Balkan country’s current variable and the
partner country’s variable two quarters previous (the Balkan country ―lags‖ its
partner). At ―-2,‖ the Balkan country leads, with its variable tied to its partner’s future
value. In our study, we investigate synchronization—whether the highest correlation
occurs contemporaneously so that business cycles move together.
We aim to answer two key questions in our analysis. First, are Balkan business
cycles more closely integrated with the core of the Eurozone than with other potential
partner countries? According to OCA theory, these cycles must move in a similar
fashion between countries over time for the economies to be considered compatible
and amenable for economic integration. We use Germany to represent the ―core‖
country in the Eurozone, expecting that, if the Balkans are to share a monetary
policy with the Eurozone, these contemporaneous correlations must be highest with
Germany. For comparison, we also include neighboring countries Turkey, Poland,
and Russia; to capture global effects, we also include the United States.
In our second question, we address the well-known ―Consumption Correlations
Puzzle.‖ According to economic theory, increased integration creates the opportunity
for risk sharing among economies as many of the trade barriers begin to disappear,
factors of production become more mobile, and economic interdependence
increases, thus allowing the economies to maintain their level of consumption
regardless of output shocks. As a result, consumption correlations should be higher
between countries than are output correlations. But, as Obstfeld and Rogoff (2000)
point out, the theory has not been proven empirically by noting that consumption
levels in actuality are even less correlated than those of output, which suggests that
the risk sharing should be quite minimal. We compare these two components to see
if the ―puzzle‖ persists in the Balkans.
4. Results
Figure 1 shows our generated GDP cycles for each of the four Balkan and five
partner countries. We see similar patterns—particularly the ―boom‖ right before the
2008 crash—for many of them. But many other movements, such as around
Bulgaria’s 1997 hyperinflation and the 1999 war in Serbia—are particular to the
country in question. It is therefore up to empirical testing to uncover any common
patterns.
Our cross-correlation functions for each Balkan country’s GDP, compared with each
of the five partners’ GDP, are provided in Table 1. If business cycles are well
synchronized, two things should come out in this table. First, the highest correlations
within each country-pair should occur contemporaneously; that is, the highest value
in each row should appear at lag 0. The highest value in each row appears in bold in
Table 1. Second, the correlations should be high in absolute value and highest for
the best potential currency partners. We indicate in the table whether the
correlations between the Balkan countries and their potential pairs are statistically
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Proceedings of 21st International Business Research Conference
10 - 11 June, 2013, Ryerson University, Toronto, Canada, ISBN: 978-1-922069-25-2
significant. In general, the standard error of a correlation coefficient is calculated as
Table 1: Output Cross-Correlations
Bulgaria and -4
-3
-2
-1
Germany
0.037
0.069
0.166
0.2180* 0.3115*** 0.316*** 0.3168*** 0.271**
0.189
Poland
0.018
0.031
0.012
-0.020
-0.0554 0.042
0.132
0.205*
0.256**
Russia
0.125
0.133
0.111
0.122
0.082
0.101
0.130
0.148
0.153
Turkey
-0.179
-0.132
-0.091
-0.057
0.118
0.138
0.167
0.166
0.148
U.S.
Croatia and
0.010
-4
0.035
-3
0.084
-2
0.137
-1
0.214*
0
0.2688** 0.3190*** 0.350*** 0.357***
1
2
3
4
Germany
-0.194
-0.012
0.2299* 0.4760*** 0.6320*** 0.6715*** 0.6206*** 0.4870*** 0.365***
Poland
-0.138
-0.179
-0.163
Russia
-0.167
0.055
0.359*** 0.6029*** 0.769*** 0.740*** 0.603*** 0.4300*** 0.2140*
Turkey
-0.409*** -0.265**
-0.031
0.240*
U.S.
-0.355*** -0.205
0.005
0.2558** 0.407*** 0.536*** 0.564*** 0.5389*** 0.5075***
-2
-1
0
Romania and -4
-3
-0.122
0
1
-0.0147 0.142
2
0.231*
3
4
0.388*** 0.472***
0.4240*** 0.5286*** 0.468*** 0.3796*** 0.269**
1
2
3
4
Germany
-0.528*** -0.391*** -0.149
0.138
0.4015*** 0.552*** 0.623*** 0.624*** 0.568***
Poland
-0.145
-0.153
-0.112
0.005
Russia
-0.346*** -0.147
0.104
0.370*** 0.5965*** 0.641*** 0.570*** 0.4950*** 0.3926***
Turkey
-0.460*** -0.4860*** -0.342*** -0.136
0.155
0.355*** 0.465*** 0.4926*** 0.458***
U.S.
-0.516*** -0.421*** -0.2235* 0.012
Serbia and
-4
Germany
-0.150
0.066
0.2305* 0.386*** 0.5306***
0.221*
0.4219*** 0.5046*** 0.541*** 0.5405***
-2
-1
0
1
2
-0.305** -0.208*
-0.115
0.029
0.172
0.205
0.2220* 0.2417* 0.2236*
Poland
0.094
-0.004
-0.103
-0.187
0.2699** -0.2606** -0.2777** -0.254*
Russia
-0.2897** -0.193
-0.041
0.079
0.182
Turkey
-0.067
-0.083
-0.071
0-0.0199 0.084
U.S.
-0.2739** -0.2969** -0.2998** -0.2207* -0.1867 -0.088
-3
-0.124
0.237*
3
4
-0.184
0.262**
0.2779** 0.280**
0.085
0.105
0.075
-0.014
0.088
0.117
Bold = highest correlation for a particular country pair
***, **, * = significant at 1, 5, and 10 percent, respectively
1  2
; we then use the corresponding t-values to assign asterisks based on the
T 1
traditional significance levels.
Contemporaneous correlations are weak. In examining the business cycle
synchronizations between the Balkan nations and Germany, the highest correlations
appear with a lag. Bulgaria’s highest correlation coefficient (0.3168) lags Germany’s
business cycle by two quarters. Romania (0.624) and Serbia (0.2417) lag Germany
by three quarters at their highest correlations. Given the influence Germany has with
regards to monetary policy in the Euro zone, this does not bode well for currency
integration.
Comparatively speaking, only Bulgaria has the highest contemporaneous correlation
with Germany, compared to the other four comparison countries. Serbia, Romania,
and Croatia have stronger correlations with Russia than with Germany. This pattern
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Proceedings of 21st International Business Research Conference
10 - 11 June, 2013, Ryerson University, Toronto, Canada, ISBN: 978-1-922069-25-2
holds true for one-period leads and lags as well.
The one possible exception to the loose linkages is Croatia. Its business cycle is
more synchronized with Germany than that of the other Balkan countries, with a
relatively high correlation coefficient of 0.6715 at a lag of only one quarter and a
correlation coefficient of 0.6320 at zero lag. Croatia, however, shows an even
stronger, contemporaneous correlation with Russia (0.769). The link between
Croatia and Russia is the only example of 20 possible country-pairs that shows the
sort of contemporaneous business cycle synchronization that we are searching for
over the sample period, with relatively high correlation coefficients and the highest
correlation value occurring at zero lag.
The link between Croatia and Russia is explored in more detail in Table 2, which
shows all Croatian GDP components’ cross-correlations with Russian GDP. Croatian
consumption (correlation coefficient=0.6760), exports (0.8180), and imports (0.8308)
have high coefficients at zero lags, while Investment lags Russian GDP by only one
quarter. While these statistical tests provide evidence for Russian and Croatian
linkages, it is less clear what is driving the connection. Russia is not among the top
few countries for tourism and trade with Croatia, so this correlation may be spurious,
but this is a worthy subject for future research. Possible connections include
Croatia’s dependence on Russian energy imports, for example.
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Proceedings of 21st International Business Research Conference
10 - 11 June, 2013, Ryerson University, Toronto, Canada, ISBN: 978-1-922069-25-2
Table 2: Cross-Correlations of Croatian GDP Components with Various
Partners’ GDP
Germany -4
-3
-2
-1
0
1
2
3
4
-0.029
0.2148*
0.4291*** 0.5409*** 0.5959*** 0.5621*** 0.4568*** 0.3398***
C
-0.2301*
I
-0.4443*** -0.2898** -0.096
0.096
0.2847**
0.3841*** 0.4415*** 0.4888*** 0.4724***
G
0.099
0.063
0.056
0.038
0.044
0.112
X
-0.015
0.159
0.3647*** 0.6116*** 0.7430*** 0.7516*** 0.6239*** 0.375***
M
Russia
-0.2809**
-4
-0.049
-3
0.253**
-2
C
-0.206
0.070
0.3418*** 0.5487*** 0.6760*** 0.6417*** 0.5325*** 0.357***
I
-0.2708**
-0.123
0.132
0.302**
0.4308*** 0.499***
0.4749*** 0.454***
0.407***
G
0.184
0.189
0.114
0.071
0.047
0.105
0.094
X
0.150
0.315**
0.486***
0.6885*** 0.818***
M
Turkey
-0.088
-4
0.162
-3
0.4589*** 0.7106*** 0.8308*** 0.7405*** 0.5350*** 0.3015**
-2
-1
0
1
2
3
C
-0.4157*** -0.238*
0.2100*
0.309**
0.341***
I
-0.284**
-0.2727** -0.124
0.064
0.2459*
0.3805*** 0.4339*** 0.479***
G
-0.044
0.013
0.063
0.069
0.091
0.193
X
-0.142
-0.043
0.137
0.337***
0.5429*** 0.652***
M
U.S.
-0.234*
-4
-0.148
-3
0.054
-2
0.3319*** 0.534***
-1
0
0.6417*** 0.6089*** 0.469***
1
2
3
C
-0.4608*** -0.307**
-0.107
0.113
0.2469**
0.362***
0.402***
0.4018*** 0.3836***
I
-0.264**
-0.205
-0.053
0.110
0.221*
0.322**
0.388***
0.4155*** 0.404***
G
0.184
0.251**
0.267**
0.264**
0.2808**
0.303**
0.296**
0.2840**
0.239*
X
-0.105
0.058
0.2777**
0.453***
0.6230*** 0.6998*** 0.646***
0.513***
0.3356***
M
-0.245*
-0.114
0.116
0.347***
0.538***
-0.003
0.535***
-1
0.7036*** 0.724***
0
1
0.068
0.111
0.634***
2
0.136
0.484***
3
0.085
0.7098*** 0.4878*** 0.191
0.617***
0.2527**
0.285**
0.175
0.101
0.110
0.3198**
4
0.126
-0.110
0.064
4
0.077
0.4968***
0.3286*** 0.369***
0.6239*** 0.4710*** 0.2800**
0.3410***
4
0.5780*** 0.4930*** 0.377***
Table 3 shows that our analysis does not solve the ―Consumption Correlations
Puzzle.‖ Output correlations, although limited, are still stronger than consumption
correlations. For example, Croatia and Romania have an output correlation of
0.5830 but a consumption correlation of 0.3369. Romanian and Serbian outputs do
share a contemporaneous output correlation (albeit small) of 0.2485. Expected
pairs—Romania and Bulgaria (0.079), or Croatia and Serbia (0.3317)—do not show
strong linkages in consumption or output. This runs counter to Hegerty (2010), who
found that the Baltics had more interconnections as a region than with outside
partners. The Balkans do not follow suit.
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Proceedings of 21st International Business Research Conference
10 - 11 June, 2013, Ryerson University, Toronto, Canada, ISBN: 978-1-922069-25-2
Table 3: Output and Consumption Cross-Correlations, Country Pairs
1
2
3
0.2660** 0.196
0.171
0.104
0.067 0.021
Bulgaria and Romania 0.3070** 0.274** 0.2446* 0.2828** 0.079
0.055
0.115
-0.059 -0.072
Output
-4
-3
Bulgaria and Croatia 0.2140* 0.067
Bulgaria and Serbia
-0.101
-0.156
-2
-1
0.173
-0.205
-0.145
0
0-0.1221 -0.092
4
-0.100 -0.086 -0.064
Croatia and Romania 0.4550*** 0.533*** 0.6038*** 0.6189*** 0.5830*** 0.4225*** 0.196
0.111 -0.075
0.4676*** 0.343*** 0.344*** 0.3525*** 0.3317*** 0.2158* 0.049
-0.123 -0.184
Croatia and Serbia
Romania and Serbia -0.025
Consumption
-4
0.047
0.120
0.193
0.2485** 0.2190* 0.180
0.125 0.075
-3
-2
-1
0
1
2
3
0.090
0.138
0.140
0.138
0.052
-0.022 -0.051
0.173
0.2438* 0.2377* 0.103 -0.076
Bulgaria and Croatia 0.3385*** 0.205
Bulgaria and Romania 0.238*
0.321** 0.3118** 0.297**
Croatia and Romania 0.259**
0.3050** 0.3597*** 0.377*** 0.3369*** 0.2728** 0.143
4
0.025 -0.058
5. Conclusion
Clearly, our findings have implications for the future of the Euro. Should these countries
one day join the common currency, a single monetary policy will prove ineffective in
solving a set of dissimilar economic problems. The eurozone’s current problems—as is
evidenced by Greece and Cyprus—may play out in similar fashion with the Balkan
countries that display very little synchronization with German business cycles.
The only correlations pointing to a potentially beneficial currency union were those
between Croatia and Russia. While the results here suggest that these two economies
are well synchronized, it is not clear from our analysis what is driving the linkages, and
while we believe this merits further study, we do not rule out spurious correlations.
Additionally, our analysis supports the existence of a consumption correlations puzzle,
with output correlations appearing stronger than consumption correlations between the
three Balkan countries for which we had sufficient data to perform the analysis. In sum,
the results of this study suggest that any sort of integration by the Balkan nations into
the Eurozone should be approached with great caution.
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