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Is There a Political Dimension on Business
Cycle Synchronization?
Article in Kyklos · August 2011
DOI: 10.1111/j.1467-6435.2011.00509.x
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KYKLOS, Vol. 64 – August 2011 – No. 3, 329–341
Is There a Political Dimension on Business Cycle
Synchronization?
Pedro André Cerqueira and Rodrigo Martins
I. INTRODUCTION
There is a long-standing interest in understanding which variables can explain
why some countries have higher business cycle synchronization than others.
Until now different channels have been proposed with mixed results. Some of
the leading candidates are: trade, industrial structure and/or specialization and
financial integration.
However for most of these channels we are far from reaching a consensus. Regarding the trade channel Frankel and Rose (1998), Imbs (2004,
2006), Calderon et al. (2007) and Baxter and Koupiratsas (2005), provide
evidence that countries with higher levels of bilateral trade are more
synchronous. However, Fidrmuc (2004) does not find any correlation between
business cycles convergence and bilateral trade, arguing that this result is
related to intraindustrial trade. As for industrial structure and specialization Imbs (2004, 2006) finds that similar countries are more synchronous,
however for Baxter and Koupiratsas (2005) this hypothesis is not robust.
Concerning international financial integration, Imbs (2004, 2006) argues
that more financially integrated countries are more synchronous and Kose
et al. (2003) find evidence that financially open developing economies
have their cycles synchronized with the G7 countries. Nevertheless, Heathcote
and Perri (2003, 2004) found that the decrease in business cycle synchronization of the US with the rest of the world could only be replicated by a
model when international diversification of portfolios increases. Besides
these three channels other variables have been used with different degrees of
success, such as: belonging to a currency union or a trade agreement, distance
Cerqueira: Faculty of Economics and GEMF, University of Coimbra, Contact: pacerq@fe.uc.pt.
Martins: Faculty of Economics and GEMF, University of Coimbra, Contact: rodrigom@fe.uc.pt.
We are grateful to Elias Soukiazis and to the participants in the Annual Meeting of the European
Public Choice Society (Jena, March 2008) and the European Economic and Financial Society
Conference (Prague, May 2008) for useful comments. The usual disclaimer applies.
r 2011 Blackwell Publishing Ltd., 9600 Garsington Road,
Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA
329
PEDRO ANDRÉ CERQUEIRA/RODRIGO MARTINS
between two countries, degree of development, similarity of exports and
imports, etc.1.
However, the literature on political cycles shows that economic growth (see
for instance Alesina et al. (1997)) may be influenced by electoral and ideological
factors. Thus the previous literature of GDP synchronization may be missing a
channel that reflects this dimension. This paper purposes to fill in this gap using
a panel data framework to check if there is a political channel to business
cycle synchronization.
The structure of the paper is the following: section two gives a short insight
on political cycles theories, and puts forward the hypothesis of how the political
variables may interact with business cycle synchronization; the following
section describes the data details and the synchronization index used. The
fourth section presents and discusses the results and the final one concludes.
II. POLITICAL CHANNEL OF BUSINESS CYCLE
SYNCHRONIZATION
Akerman (1947) and Frey and Lau (1968) are among the first to study the role of
electoral considerations on economic cycles. Since then a significant number of
papers seek to answer whether electoral motives and government ideology
influence short term economic performance. The literature evolved into two main
theories, the political business cycle and the partisan theories, and also to a branch
that tried to combine the features of both (Frey and Schneider (1978)). Overall, the
research on political cycles consistently finds evidence of opportunistic or partisan
effects in the macroeconomic environment. For encompassing surveys of the
literature see, for instance, Schneider and Frey (1988), Alesina et al. (1997),
Franzese (2002) and Franzese and Jusko (2006). If national growth exhibits
political effects then, theoretically, growth synchronization between nations may
well be explained by them2. This means that the literature on business cycle
synchronization may be disregarding the existence of a political channel.
The political business cycle theories hypothesise that all politicians try to
create similar favorable economic conditions before elections in order to gain
voters support. With the seminal work of Nordhaus (1975) a first generation of
models relying on adaptative expectations emerged (see also Lindbeck (1976)
and Ben-Porath (1975)), and in the late 1980’s and 1990’s a second wave
approached opportunistic models in a rational expectations perspective (Rogoff and Sibert (1988); Rogoff (1990)). Empirically, Grier (2008) finds evidence
1. Other related studies are those by Clark and van Wincoop (2001), Otto et al. (2001), Rose and Engel
(2002) and Darvas et al. (2005).
2. Sapir and Sekkat (2002) found that German politics impacts the economy of other European
countries.
330
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IS THERE A POLITICAL DIMENSION ON BUSINESS CYCLE SYNCHRONIZATION?
of an electoral cycle when examining GDP growth in the United States,
however these theories seem to receive more empirical support in developing
than in developed countries (see Brender and Drazen (2009); Vergne (2009);
and Shi and Svensson (2006)).
The expected consequences of opportunistic behaviour to business cycle
synchronization depend on electoral synchrony. If two countries hold elections
at the same time their economies experience similar politically induced
economic fluctuations that increase cross country business cycle synchronization, but without electoral synchrony these fluctuations occur out of phase thus
decreasing cycle coordination. This paper also tests the linkage between
political instability and business cycle synchronization. Alesina et al. (1996)
show that GDP growth is significantly lower in countries and in time periods
that exhibit a high propensity for government collapse. In a recent paper Jonga-Pin (2009) also finds that higher degrees of political instability tend to lower
economic growth. If this scenario occurs in two countries simultaneously then
business cycle synchronization may increase whereas if only one country is
experiencing political instability GDP synchronization is expected to decrease.
The traditional partisan theory (Hibbs (1977)) and the rational partisan
models (Alesina (1987); Alesina and Sachs (1988)) assume that different parties
have different policy objectives that reflect the preferences of their partisans.
When in office left-wing parties are relatively more concerned with unemployment (growth) than with inflation whereas right-wing parties are especially
worried with inflation control. Hibbs (1987, 1992) and Alesina et al. (1997) find
partisan outcome cycles in OECD democracies.
The partisan theory focuses on the role of ideology therefore countries with
governments that share the same policy preferences are expected to generate
similar economic outcomes. Comparative studies show that left wing governments worsen nominal and improve real and distributional outcomes. As
business cycle synchronization deals with growth alignment the simultaneity of
left wing governments should increase cross-country correlation. In the case of
right wing governments as they are more concerned with inflation one should
expect an increase in cross-country alignment of inflation. Regarding growth
synchronization, the effect may depend on how each economy reacts to the
anti-inflationary measures taken.
III. DATA AND MEASUREMENT
The analysis of the political channel requires a synchronization indicator at all
points in time in order to estimate the following equation:
rij;t ¼ FðXij;t ; uij;t Þ
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ð1Þ
331
PEDRO ANDRÉ CERQUEIRA/RODRIGO MARTINS
where at time t, rij, t represents the GDP cross correlation index between
country i and j, Xij, t is the matrix of explanatory variables and uij, t are the
idiosyncratic shocks.
However previous studies usually resort to a cross-section specification such
as:
T
T
X
Xij;t X
uij;t
rij ¼ F
;
T
T
t¼1
t¼1
!
ð2Þ
removing the time variability by averaging equation (1). To overcome this
problem we use a period by period correlation index based on the distance
between standardized variables proposed by Cerqueira and Martins (2009) of
the two countries at any given date:
0
rij;t
12 1
B
B
CC
B
CC
dj;t d j
1B
ðd
d
Þ
B
i;t
i
CC
s
s
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
ffi
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
ffi
¼ B1 B
CC
B
C
2B
T
T
@
P
P
2
2A A
@
1
1
d d
d d
0
T
i;t
t¼1
i
T
j;t
ð3Þ
j
t¼1
where di, t and dj, t are the GDP growth rates of countries i and j.
Contrary to the correlation index computed over the entire period, this index
enables us to distinguish negative correlations related with specific years,
asynchronous behavior over unstable periods and synchronous behavior in a
stable economic environment. Although rolling windows is an alternative way
to capture the time variability, this correlation index has some advantages.
First there is no need to set a window span, second there is no loss of
observations, third it does not exhibit the so called ‘ghost features’, as the
impact of a major shock is not reflected in n consecutive periods, with n being
the window span. Moreover, the major disadvantage with the use of overlapping windows is that the resulting variables are heavily autocorrelated and,
thus, difficult to handle in econometric analysis.
The index was computed using GDP data from the OECD database 2006 for
twenty OECD countries3 from 1970 to 2002.
Concerning the economic explanatory variables we account for the most
commonly and robust channels found in the literature: trade, specialization
and financial openness.
3. The countries are Australia, Austria, Canada, Denmark, Finland, France, Germany, Greece, Iceland,
Ireland, Italy, Japan, the Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, the UK and
the USA. Data details are available from the authors upon request.
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IS THERE A POLITICAL DIMENSION ON BUSINESS CYCLE SYNCHRONIZATION?
As to the trade channel we use the bilateral trade intensity index based in the
Deardorff (1998) model, given by:
Bilateral Tradeij;t ¼
Expij;t þ Impij;t GDPw;t
GDPi;t GDPj;t
2
ð4Þ
where Expij, t are the exports from country i to j, Impij, t are the imports of country
i from j and GDPw, t is the world level of GDP4 at time t This measure does not
depend on country size as the one proposed by Frankel and Rose (1998).
To access the impact of the industrial structure we use a similarity index
given by:
n
X
k
6 j
ð5Þ
sij;t ¼
si;t skj;t ; i ¼
k¼1
ski;t
represents the weight of sector k in the GDP of country i at time t.
where
When interpreting the results of this industrial similarity measure one should
take into account that they vary between a minimum of 0 (complete similarity
between countries) and a maximum of 2 (completely different structures).
As for the financial channel we use the financial openness average indicator
given by:
Ai;t þ Li;t Aj;t þ Lj;t
ð6Þ
Financial Opennessij;t ¼
þ
GDPi;t
GDPj;t
where Ai, t and Li, t are total assets and liabilities of country i, at time t.
The billateral trade and the industrial structural similarity indexes were
computed using the GDP, total imports and exports and the value added at the
sectorial level5 from the OECD database 2006 and regarding the financial
openness indicator we used the Lane and Milesi-Ferretti (2007) data set.
In order to control for institutional factors affecting synchronization we add
dummy variables for simultaneous membership of the EEC (later EU) or
NAFTA.
Regarding the political and electoral environment, we establish two groups
of dichotomous variables using the data set and classification of Armingeon
et al. (2005). The first tries to capture partisan effects in the economy. To get a
clear ideological influence, we take in consideration two dummy variables,
assuming the value 1 if both countries have left or right wing governments,
4. As a proxy for the world GDP we use the GDP of the OECD.
5. We build the industrial structure similarity index using the following aggregation of the ISIC codes: 0
1 1 – Food and live animals 1 Beverages and Tobacco; 2 1 4 – Crude materials, except fuels, animal
and vegetal oils, fats and waxes 1 Animal and Vegetal oils, fats and waxes; 3 – Mineral fuels; 5 1 6 1 8
1 9 – Chemical and related 1 Manufactured goods 1 Miscellaneous manufactured 1 Commodities
and transactions articles and 7 – Machinery and transport equipment. We used this code aggregation
in order to have the maximum observations by country and time span.
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PEDRO ANDRÉ CERQUEIRA/RODRIGO MARTINS
respectively. The second group tries to capture the impact of national elections
and the effect of changes in the cabinet composition of national governments.
As there are elections without cabinet changes, cabinet changes without
elections and both at the same time we test opportunistic and instability effects
jointly. The set of dummy variables used covers the revelant scenarios,
distinguishing the simultaneity or not of elections and cabinet changes and if
these events occur simultaneously or not in both countries. The decomposition
is comprised of three variables: (1) Electoral year in at least one country with
government changes; (2) Electoral year without government changes; (3)
changes in government in at least one country without elections. We also
introduce aditional variables in order to test if the simultaneity of political
events is significant in each of the three previous scenarios.
IV. ESTIMATIONS AND RESULTS
This section presents the results. As our model includes autorregressive
components we use the Blundell and Bond (1998) GMM methodology with
the Windermeijer correction in order to solve the related endogeneity problem
and also potential ones concerning economic and political variables. We also
use the openness index as instrument for trade intensity6 between countries i and
j. When introducing the industrial similarity index we lose a significant amount
of observations. For this reason, in addition to the unbalanced panel regressions
using the raw data, we estimate the missing values using the multiple imputation
algorithm developed by Honaker and King (2010) enabling us to get a balanced
panel and to fully exploit the available data. Finally, all economic variables,
other than the synchronization index, are in logs.
Table 1 presents the two-step GMM estimation results using the GDP
synchronization index as the dependent variable. Like other correlations
measures, this index may suffer from measurement errors that lead to a larger
error variance. But, as it is reasonable to assume that this measurement error
and the explanatory variables are orthogonal, the assumptions needed to keep
the desirable large-sample proprieties are not altered.
Regarding the economic variables, some of the results are in line
with previous findings. Bilateral trade has the expected positive sign across
regressions showing that more trade between countries increases bilateral
6. We also tried the sum of the GDP per capita of each pair of countries as instrument for trade
intensity and industrial specialization
n i
oand the GDP per capita distance between them, given
GDP GDPj
by: GDP distanceij;t ¼ Max GDPjt ; GDPit as instrument for industrial specialization, without
t
t
significant changes in our results. Previous studies have used other instruments/variables, such as
common border, distance or common language. However these variables are invariant over time and
therefore captured by the constant term of the fixed-GMM estimator.
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IS THERE A POLITICAL DIMENSION ON BUSINESS CYCLE SYNCHRONIZATION?
Table 1
Business Cycle Synchronization Determinants: GMM Panel Estimations: 1970–2002
Unbalanced Panel
(1)
Constant
Bilateral trade intensity
Financial openness
Industrial similarity
(2)
Balanced Panel
(3)
(4)
3.676
2.189
4.017
3.295
(4.13)
(4.175)
(4.59)
(4.33)
0.073
0.0572
0.084
0.073
(1.65)
(1.96)
(1.91)
(2.00)
2 0.670 2 0.363 2 0.780 2 0.657
( 2 3.70)
( 2 3.37)
( 2 3.70)
( 2 4.20)
2 0.091
2 0.111
( 2 0.56)
( 2 0.80)
0.098
(1.53)
0.067
(0.59)
0.151
(2.74)
2 0.015
( 2 0.15)
-
2 0.098
( 2 1.81)
0.129
(2.32)
-
Election year with government change
0.028
(0.22)
Simult. election year with (at least one)
0.156
gov. change
(0.44)
Simult. gov. change with (at least one) 2 0.116
election
( 2 0.19)
Simult. election year; Simult.
2 1.384
gov. change
( 2 1.23)
-
2 0.194
( 2 1.07)
0.256
(0.63)
0.345
(0.52)
2 0.119
( 2 0.12)
-
Election year without government change 2 0.246
( 2 3.30)
Simult. election year without
0.074
gov. change
(0.32)
-
2 0.172
( 2 2.56)
2 0.249
( 2 0.85)
-
2 0.263
( 2 3.70)
0.428
(1.42)
-
2 0.313
( 2 2.51)
0.765
(1.63)
-
EU
NAFTA
Simultaneous right-wing governments
Simultaneous left-wing governments
Gov. change without elections
Simult. gov. change without elections
Observations
Arellano-Bond test for AR(1)
Arellano-Bond test for AR(2)
Groups / Instruments
Hansen test
0.116
(1.68)
0.115
(1.06)
2 0.076
( 2 1.27)
0.100
(1.78)
0.127
(2.70)
0.115
(1.64)
5700
5700
5890
5890
2 9.50 [0.00] 2 9.58 [0.00] 2 9.68 [0.00] 2 9.68 [0.00]
2 0.66 [0.51] 2 0.62 [0.54] 2 0.38 [0.87] 2 0.11 [0.91]
190 / 188
190 / 185
190 / 189
190 /185
161.5 [0.13] 167.1 [0.15] 162.44 [0.14] 167.1 [0.14]
,,- Significant at 10%, 5% and 1% respectively. Numbers in curved brackets are t-ratios and
in squared brackets p-values. Regressions include a complete set of time-dummies and lags of the
dependent variable (results not shown)
synchronization and exibhits point estimates close to the ones found by
Frankel and Rose (1998) and Clark and Van Wincoop (2001).
We find a negative effect of financial openness, which confirms the results of
Heathcote and Perri (2003, 2004). Our results support the international risk
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PEDRO ANDRÉ CERQUEIRA/RODRIGO MARTINS
sharing theory which states that agents from financially integrated countries can
reduce their income risk wether by diversifying their portfolio across countries or
through the international credit markets. Either way, if countries are able to
reduce their income risk they become free to specialize in sectors that exhibit
comparative advantages. With these different specialization patterns weaker
synchronization is expected. However, once endogeneity is controlled, we find
no significant evidence relating industrial similarity with cross-country synchronization. This result indicates that the transmission channel from financial
integration to synchronization might not be due to industrial specialization. A
more satisfactory explanation, would be that, if countries are more financially
integrated, investment flows faster between them as agents try to exploit short and
medium term advantages. These flows of capital, as predicted by the finance
literature, benefit countries, that due to transitory idiosyncratic shocks, exhibit
higher capital returns, boosting even more their economic performance at the
expense of investment and growth in other countries. This increases the investment
volatility relatively to the one of GDP and reduces cross-country synchronization.
A final point regarding financial openness is why ours and Heathcote and
Perri’s (2003, 2004) results differ from those of Imbs (2004, 2006). As Imbs
work highlights the cross-section dimension the positive effect of financial
openness can be seen as a proxy for long-run institutional factors favouring
both financial integration and real synchronization. Differently, Heathcote
and Perri’s framework focuses on the time-dimension, while we use a panel
where the long-run factors are captured by the fixed effects in the GMM
regressions. Therefore, it seems that alternative approaches unveil different
effects of financial integration on the business cycle synchronization.
The use of a synchronization index suitable for panel data enables us to
control for economic integration areas more accurately as we can pinpoint the
countries year of entrance. Only EU simultaneous membership has a positive
effect on cross-country synchronization, which can be due to increased trade or
to higher similarity of the legal economic environment.
Looking at the political variables, that as far as we know have not yet been
taken into account in the literature, reveals interesting results7.
Concerning the ruling party’s (or parties) ideological affiliation we find that
simultaneous left-wing governments increase business cycle synchronization.
This result is congruent with the partisan theory, because as growth is the
priority for these governments it is expected that both countries strive to
achieve higher output levels, thus increasing growth alignment.
7. The GMM methodology also controls for potential endogeneity bias in political variables, such as the
possibility that governments are more likely to be voted out of office when the economic performance
is poor.
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IS THERE A POLITICAL DIMENSION ON BUSINESS CYCLE SYNCHRONIZATION?
For simultaneous right-wing governments, although the results are statistically weak, they suggest a possible detrimental effect on business cycle
synchronization. As stated by the partisan theory, conservative parties use
monetary and fiscal policy with the prime objective of controlling inflation
levels. So when countries face inflationary shocks, they will control inflation at
the short run cost of increased unemployment and lower GDP as stated by the
New Keynesian Phillips curve8. However, as time elapses, economic actors can
agree to new price and wage contracts expecting lower inflation, so growth and
employment return to their natural rates, while inflation remains low. Therefore, when inflationary shocks have asymmetric effects across countries the
anti-inflationary measures taken by different right-wing governments will
differ leading to different adjustment processes of growth and unemployment.
Even with symmetric effects the adjustment rates across countries to the natural
equilibrium will be different because economies have different levels of price/
wage flexibility9. In either case with simultaneous right-wing governments
business cycle synchronization through growth will decrease (while inflation
synchronization is expected to increase) when comparing with a left-right
scenario. In this latter case the variability in growth rates is not has high because
only one country takes strong anti-inflationary measures.
As to the effect of the remaining political variables, the results show that
election years with government changes do not affect bussines cycle syncronization, not even when we account for country simultaneity. However, election
years without government changes is found to be statistically significant and
negatively affecting the business cycle syncronization index, but it seems to be
irrelevant whether elections occour simultaneously in both contries or not. One
possible explanation for this result is the existence of opportunism, although
the evidence found seems to be partial, as business cycle syncronization only
decreases across countries when elections do not imply government changes.
These results appear to be in line with the hypothesis of Frey and Schneider
(1978) that reelection chances dictates whether incumbents behave opportunistically or idelogically. It is possible that governments with litle chances of
winning elections chose to act ideologically rather than oportunisically near
elections (an effect accounted for by the idelological variables), while governments with a figthing chance at the ballots chose to expand the economy before
elections in order to gain voters support, thus decreasing cycle alignment.
Furthermore, as the results show a statistically significant effect related only to
governments that manage to remain in power, the ability to create the best
8. For details about the New Keynesian Phillips curve see Gali and Gertler (1999) or Sbordone (2002).
9. This asymmetry can be due to differences in the exposure to global price shocks as a result of
differences in the commodity intensity of production and consumption or differences in the
propagation of shocks due to differences in inflation and wage dynamics. See for instance Vogel
et al. (2009) or Galesi and Lombardi (2009).
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PEDRO ANDRÉ CERQUEIRA/RODRIGO MARTINS
favorable economic conditions in election years may be important, which
indicates that cycle alignment may decrease with competent oportunistic
governments.
The empirical results also suggest that government changes not related to
elections reduce business cycle synchronization, regardless of being simultaneous or not. This is in line with Alesina et al. (1996) and Jong-a-Pin (2009)
which show that unexpected political turmoil causes instability and uncertainty
among economic agents disrupting economic growth. Overall, our findings
suggest that cylce alignment might be affected by unexpected cabinet changes
and unexpected or, at least, uncertain electoral results.
Finally, when comparing the results between models with and without the
political channel we find no significant changes in the importance of the
traditional economic channels. This shows that political factors are a different
and autonomous channel, that improves our knowledge of business cycle
determinants.
V. CONCLUSION
This study started with the assumption that integrating political factors in
business cycle synchronization models is likely to improve the knowledge of the
phenomenon. To investigate this hypothesis, until now unexplored by the
literature, we use a synchronization index that gives us the time variability
needed to capture these specific effects.
The main conclusion to be drawn from the empirical analysis is that the
political environment is a relevant channel that works independently without
altering the role of traditional economic channels.
To control for the economic determinants, we included a set of variables that
have been considered relevant in the literature (trade measures, industrial
structure and measures of financial integration). The findings generally confirm
the previous research, however regarding financial openness they are coherent
with the theoretical expectations but not with the standard empirical literature
that uses cross-section analysis. The negative sign found gives us a different
perspective of the financial mechanism because one of the main features of the
proposed index is the capture of time variability.
To evaluate the importance of the political environment we used two groups
of variables. The first, measuring ideological effects, was found to be relevant in
explaining business cycle synchronization and in line with the partisan
hypothesis. Simultaneous left-wing governments improve business cycle
synchronization while the results for right-wing governments, although
statistically weak, suggest the opposite effect. Our explanation is based on
the consensus that left-wing parties are more concerned with growth than rightwing parties. The second group tried to capture the effect of elections and a
338
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IS THERE A POLITICAL DIMENSION ON BUSINESS CYCLE SYNCHRONIZATION?
broader spectrum of governmental instability. Although the evidence found
seems to be partial, both election years and cabinet changes have a negative
impact on business cycle synchronization in certain conditions. Opportunistic
behaviour and economic instability due to unexpected cabinet changes may
explain this effect on cycle alignment, but the results do not reveal differences
when either of these events ocour simultaneously or not in both countries.
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IS THERE A POLITICAL DIMENSION ON BUSINESS CYCLE SYNCHRONIZATION?
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SUMMARY
The study of the business cycle synchronization determinants has traditionally focused on economic
variables disregarding aspects such as politics and elections. This paper intends to fill in this gap and test
whether the political environment is also a relevant channel explaining the synchronization between
countries. Using a synchronization index for panel data we find that government ideology affects
synchronization. Simultaneous left-wing governments improve business cycle synchronization while the
results for right-wing governments, although statistically weak, suggest the opposite effect. In some
particular cases, also elections and cabinet changes are found to have a negative impact on synchronization. Furthermore, the role of the traditional economic variables is not altered by the inclusion of this new
channel.
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