What holds cycles together?
by Michael Artis, European University Institute and CEPR
and Peter Claeys, European University Institute
Introduction.
The aim of this paper is to try to find some answers to the question as to why it is that
some countries’ cycles seem to have a similar timing to each other, and to be distinct
from others. As explained below there is more than one way to seek answers to this
question and this paper attempts to do so by exploiting a specific methodology, viz
the use of panel data estimation techniques where the explicand is a measure of
business cycle association between countries. The relative advantages of this and
other approaches will be discussed below.
The topic of the paper is important from a number of points of view. Traditionally the
most salient has been that provided by the desire to quantify the criteria suggested by
optimal currency area (OCA) theory in contexts where the formation of a monetary or
currency union is politically “on the cards”. In addition, the coherence of countries’
business cycles is significant from the point of view of forecasting economic
developments, including the evolution of policy variables, with added relevance
where economies are operating in quasi-fixed exchange rate regimes when policy
divergences may lead to exchange rate crashes.
Because of the OCA connection the interaction between trade and business cycles has
been much studied, beginning with the seminal studies by Frankel and Rose (1997,
1998), but more recent workers have begun to adduce other issues (e.g., Fidrmuc
2004). In the first section of this paper immediately below we provide a selective
review of this previous work. In a subsequent section we look at the alternative
merits of different approaches to the substantive question – for example, those
provided by factor analysis and VAR estimation. In section 3 we then turn to a
necessary prolegomenon of our approach – the definition and estimation of business
cycles and measures of association between countries. In section 4 we discuss the
factors that we think should be important in the analysis and the measurement
1
problems that may exist. Then, in Section 5 we present some estimation results.
Section 6 concludes.
1. The broad setting of the problem and previous work in the field
The seminal work in this area – as already referred to – is that by Frankel and Rose
(FR, 1997 and 1998), but this represented an exploration of a particular part of the
problematique. FR were intrigued by the possibility that what is usually presented as
the two arms of OCA theory – the benefits represented by trade and the costs
represented by ill-fitting stabilization policy – may be an illusion. If trade could be
shown to underpin business cycle synchronicity, then the trade-off implied in the “two
arms” approach would disappear. Suitable candidates for currency unions could then
be identified by reference to trade alone (an insight subsequently exploited in a paper
by Artis, Kohler and Melitz 1998 ), and the trade-creating capacity of initially
suboptimal currency unions might be counted upon to create more output
convergence – hence rendering the optimal currency area criteria “endogenous” in a
virtuous sense. The framework in which it was sought to verify or disprove the
connection between trade and business cycle synchronization was that of a large panel
study (with relatively weak time dimension) in which measures of business cycle
synchronization could be regressed upon measures of bilateral trade intensity and a
number of control variables. The estimates from this framework confirmed a highly
significant coefficient on the bilateral trade intensity measure and a battery of
controls, mostly in the form of dummy variables (to ensure robustness FR
experimented with a variety of measures of the leading variables). Because of a
probable endogeneity issue, FR instrumented the trade intensity measures from
gravity equation estimates.
That the results showed that the optimal currency area criteria were endogenous in the
virtuous sense could be held to run ahead of the findings strictu senso since those
findings were based on a static panel with a very weak time dimension (only four time
points were available), and it could be argued that the type of trade that a currency
union would create might be rather different from trade created by the ordinary
processes of growth and liberalization. Indeed FR’s work was prompted in part by
Paul Krugman’s speculation (Krugman 1993) that monetary union in Europe could
lead to trade creation of a different type than had been witnessed earlier in response to
2
the EU’s creation of a Single Market. Specifically, the absence of the possibility of
exchange rate change could encourage the location of industry on more spatially
concentrated lines as the need to hedge against disruptive exchange rate change would
disappear: hence the newly created trade might have more of the character of intratrade than of inter-trade. But specialization could invite more asymmetric shocks endogeneity of the OCA criteria, but in this case of a perverse type. A number of
subsequent papers have qualitatively replicated FR’s results for different sampleperiods and institutional regimes.1 Some researchers (e.g. Gruben et al (2002) and
Fidrmuc (2004) have obtained results that support the view that it pays to distinguish
between intra-trade and total trade, albeit both coefficients may be positive.
The line of argument supporting a distinction between intra-trade and inter-trade is
not at all unambiguous however.
Kenen (2002) has criticised the implicit
presumption that exogenous shocks affect broad product groups rather than individual
products. A related issue is that intra-trade may itself be divided into “horizontal” and
“vertical” intra-trade, corresponding intuitively to the distinction, respectively,
between “trade in varieties” (Mercedes for Fiats) and “trade in components”
(gearboxes for transmissions). See Fontagné and Freudenberg (1999) and Fontagné et
al. (2005) on this point. The reader can imagine for herself the range of stochastic
disturbances that might impact differently on the two categories. Worse, some types
of inter-trade are very closely related to vertical intra-trade: for example rubber
imports may as well be treated as imports of gearboxes in relation to a shock in the
motor industry, even though rubber production is classified quite differently from that
of motor cars.
The devastation that the Great Depression caused to primary
producers is a reminder that inter-trade is no protection from shocks hitting the
customer country.
Whilst the role of trade in linking business cycles has taken centre-stage other factors
have not been completely neglected.
In particular, there has been a (somewhat
unfocussed) suggestion that common policies (e.g. a common monetary policy) lead
to common cycles. A source for this suggestion in the European context is the paper
by Artis and Zhang (1997) which was widely interpreted as showing that the common
policies induced by adherence to the ERM were associated with a convergence of
1
Examples are Frankel and Romer (1999); Glick and Rose (2002); Alesina et al (2002); Frankel and
Rose (2002); but for a criticism see Canova and Dellas (1993); Schmitt-Grohe (1998); Kose and Yi
(2002), Imbs (2003).
3
business cycles.
If this were reliable it would be another way to achieve an
endogeneity of the OCA criteria. But it is not reliable. Policies address problems,
with error.
Kontolemis and Samiei (2000) were able to make a case that the
stochastic component in UK monetary policies in the 80s was large enough to make
the case for acquiring a common monetary policy via membership of the EMU
attractive. However, whilst common policies may imply less asymmetric policy error,
the pre-union policies themselves are addressed to problems that may arise (for
example) from asymmetric business cycles. Thus it is entirely possible that more
common policies, whilst reducing asymmetric policy error, lead to less appropriate
policies, less stabilization and more asymmetry in the business cycle than otherwise.
Policy issues may in fact be subsumed in a more general framework. The modern
theory of the business cycle basically goes back to that of Frisch (1933). There is an
initiating shock and then there is a propagation mechanism. Together these produce
the business cycle. Frisch likens the propagation of the regularities of the business
cycle to the rolling motion of a well-sprung car which crosses some potholes in the
road (another analogy that is used in this context is the notion of hitting a rocking
horse with a hammer…). It follows that even for common shocks business cycles may
still be different because the propagation mechanisms are different in the different
countries. We should endeavour to characterize in a quantitative way the principal
elements which observers generally will characterise as factors in the propagation
mechanism: these should include a description of the critical features of the labour
market and the product market and of the financial structure, as well as, in principle,
the policy reaction of the economy’s stabilization authorities. Additionally we might
need to take account of elements of the economies’ production structure that will
mediate the effect of a shock (say, its dependence on oil; its reliance on
manufacturing with its in-built stock-cycle and so on). In recent work, some of these
elements have been taken account.Thus Fidrmuc (2004) has taken account of
differential labour market features in seeking to explain business cycle
synchronization whilst Traistaru (2004) has exploited industrial structure measures
with some success. As explained below (section 4) we propose to take this approach
further in this paper.
2. Alternative methodologies
4
The approach we intend to pursue in this paper takes its place alongside other
approaches that might have been followed, of which mention should particularly be
made of two: VAR analysis and factor analysis. A heuristic representation for the
VAR approach can be set as below:
 y1t   a 11 a 12   y1t −1   g1 
 y  =  a a   y  + g 
 2t   21 22   2t −1   2 
   h 11 h 12   H 1t 
G0 t  +  h h   H t 
   21 22   2 
This is a representation of a two country world in which y1 and y2 are the growth rates
of output in the two countries, and G and H represent respectively “world” or
common shocks and H idiosyncratic or country-specific shocks. It has been said that
“all shocks are idiosyncratic” since in general g1 ≠ g2, although it could equally be
maintained that said that “all shocks are common” since data are rarely of a high
enough frequency that h12=h21=0, so that to all intents and purposes idiosyncratic
shocks are shared from the start.
Although this framework can be difficult to
implement in a satisfactory manner, it can teach us some useful lessons: for example,
observing that over time (say) there is an increasing correlation between y1 and y2
might alert us to the possibility that the aij have changed – yet a change in the relative
variance of G and H shocks (for given gi, hi) might be the cause (see e.g. Hoffmann
(2004) for an insightful discussion of this case). In a recent example Giannone and
Reichlin (2005) find that European countries’ shocks are predominantly driven by
common shocks, which may be helpful (as explained below) for our approach. The
VAR framework is a very flexible and useful one. Nevertheless, from our point of
view it is deficient in that it does not seek to explain business cycle affiliations
directly, as we wish to do. Another approach that has been used to illuminate the
issues we are dealing with is that of factor analysis. In this approach (of which Forni
and Reichlin (1995, 1997) are prominent examples), the critical contribution is to
decompose the cycle into factors. Thus it may turn out that the cycle in a particular
region (say) can be decomposed into a factor related to the national cycle and a factor
related to a regional or idiosyncratic driver, and quantification can be directed at
determining the relative importance of the two. Once again, whilst this related to, it is
not the same thing, as seeking to explain business cycle affiliations directly as we
propose to do.
5
3. The identification of business cycles
A first necessity of the approach we are adopting is to identify business cycles in a
group of countries and apply some measures of affiliation to them. Considering that
there is a number of ways of defining the cycle, a number of alternative sets of
countries to examine and a number of measures of association between them, it may
be helpful to take the short route and at this stage simply explain what we have chosen
to do. Table 1 indicates the set of eleven countries which data availability allows us
to include in the present study.
Our objective is to concentrate on quarterly
(seasonally adjusted) measures of GDP and the availability of these series for a
reasonably long time scale is the principal determinant of the choice made. We have
been forced to eliminate from consideration some countries for which the available
series is too short to span more than a few cycles. The type of cycle on which we are
concentrating here is the so-called “deviation cycle”, which requires a filtering of the
data to isolate the cycle. We have followed the suggestion in Artis et al (2003, 2005)
to use a band-pass filter based on the Hodrick-Prescott filter. A first filtering is
carried out in which high frequency movements and outliers are removed; then the
cyclical deviates are identified with the difference between this series and a smoother
series obtained by applying a Hodrick-Prescott filter which effectively identifies low
frequency movements. The values of the damping coefficient, λ, in the HodrickPrescott filter are set at λ1 = 1.25 and λ2 = 677 , which can be shown to isolate
frequencies with a periodicity of 1.25 to 8 years, in line with the suggestion of Baxter
and King (1999). The normal method of applying the Baxter-King filter leads to a
large number of observations being “wasted” at the end of the sample, which does not
happen for the hp band pass filter.
The simplest and most straightforward measure of association between cycles derived
in this fashion is the contemporaneous cross correlation; this cross correlation
captures synchronicity quite well, but there are other business cycle descriptors that
might be used. In particular, a much-discussed phenomenon of recent years is a
decline in the amplitude of the cycle; such a decline can take place without
influencing the value of the cross-correlation. Composite measures might also be
used.
Harding and Pagan (2002) identify several different business cycle
characteristics and these could be combined together. However, at this point we prefer
to follow the tradition set in previous work and confine ourselves to focussing on the
6
cross-correlation of cyclical deviates as our measure of business cycle association.
Table 2 in fact lists the complete set of bilateral cross-correlations for our sample:
corresponding cross-correlograms for three main subperiods are shown in the
Appendix, Tables A.1-3. In order to appreciate the pattern of these bilateral cross
correlations and how it changes over time it may be useful to refer to the sequence of
Figures, Figures 1- 4, where hard clustering has been used to identify groups that
“hang together”.
The clustering is defined against the characteristic of “cross
correlation against all other countries” and the method associates countries which
have the least difference between them in respect of this characteristic.2
(Alternatively, the clustering could have been carried out – say, against the
characteristic “cross correlation against Germany”, where the method would identify
as groups those countries which are least different from each other in respect of this
characteristic – this is not necessarily, note, countries which have high cross
correlations against Germany).
Over the full sample (Figure 1), there seems to be a distinct core EU-group that is
clearly set apart from the cyclical correspondences between US and Canada. The
United Kingdom tends to the latter group, whereas a separate group of Scandinavian
countries and Japan rather tend to the former. This pattern mostly depends on cyclical
behaviour in the eighties (Figure 3) when cyclical similarities were generally much
more pronounced (the average dissimilarity is much smaller, as can be seen in the
graph). The United Kingdom belonged more closely to the EU-group, though. In the
70s, on the other hand (see Figure 2), there is no clear distinction between groups of
countries, as the oil shocks induced rather similar cycles. The 90s (Figure 4) are
characterised by the Japanese crisis that makes this country stand apart from the other
groups.
EU,
Scandinavian
and
the
US-group
are
still
distinct.
4. The explanatory variables
An economical explanation of what causes business cycles to hang together would be
one that proceeds “as if” all shocks are common shocks; then it is apparent that
country pairs that are strongly linked by “integrating variables” like trade are likely to
be more highly correlated in business cycle terms. In principle what goes for trade
2
The distance measure is Euclidean, and the association method agglomerative average linkage.
7
flows could go for financial flows also, though there are data problems in measuring
the latter and we are not able to deploy a financial flows measure in this paper other
than for FDI.3Also, as common shocks need to be filtered for different country
impacts, we shall need to employ variables that serve that role – for example intercountry differences in industrial structure, reliance on oil and the like. Finally, the
business cycle correlations are going to be affected by differences between countries
in their propagation mechanisms and we need to find summary variables that describe
aspects of these mechanisms.
Under the heading of integrating variables here we use only measures of overall trade
intensity. Whilst some authors, as discussed above, have found a particular role for
intra-trade we have not to date pursued this route and our trade data pertain to total
trade. Two alternatives are considered. The first indicator measures the intensity of
trade as total bilateral exports and imports, scaled by total trade of the domestic and
foreign country. As a second measure and following the endogeneity argument of
Frankel and Rose (1998), we also use as an instrument the fitted trade intensity of a
first stage gravity equation regression. The gravity variables include a distance
measure, an “economic mass” index provided by (the log-product of) the two
countries’ GDP, a common language and a common border dummy.
As for production structure we experiment with all of the following: the share of
manufacturing in GDP and the ratio of oil imports to GDP. We also introduce a
measure of industrial specialization. Factors bearing on the propagation mechanism
we take to include the following market structure variables: variables that describe
the rigidity or flexibility of the labour market; variables that describe the financial
structure, variables that describe product market structure. In addition to these we
believe that (in principle at least), policy reactions are part of the propagation
mechanism and that some measures of monetary and fiscal policy should therefore be
included. This may raise obvious problems of endogeneity and simultaneity. All
variables, their construction and source are detailed in Table 3.
3
These data come from Lane and Millesi-Ferreti (2001), which also gives data on net foreign asset
positions. Forbes and Chinn (2004) have examined the role of financial variables in linking the largest
five economies, but they find trade to be more important in their context. Imbs (2003) uses data on net
financial asset positions.
8
Some of these measures merit further discussion if only, in some cases, to note that
there are important problems of data (non-)availability. Let us start with the labour
market variables. The work of the OECD through its Employment Outlook and the
recent paper by Nickell and Nunziata (2000) provide us with a number of candidate
variables to describe “labour market rigidity”. Perhaps the most obvious candidate
variable as summary indicator would be the NAWRU as other candidate variables
seem to measure some particular aspect of a country’s labour market that contributes
towards its good (or bad) functioning. In this category the sources quoted allow us to
mention four indicators:
employment protection legislation (an ordinal index of
effective employment protection legislation), union density (proportion of the
workforce unionized), the benefit replacement ratio and a “tax wedge” measure.
The availability of a suitable indicator of product market regulation is in serious
question. There is an OECD-constructed indicator (Nicoletti et al., 2000), but this is
available only for a comparatively short period of time. In default of a longer series
we have experimented with some measures of product market competition suggested
by Przybyla and Roma (2005). An aggregate mark-up for the total economy is defined
as the inverse of the labour income share in the economy, following Gali (1995).
Alternative macro-economic measures are the profit margin and the profit rate, that
express the operating surplus of firms to total value added, and the capital stock
respectively.
As regards production structure, a crude measure is the share of manufacturing in total
production. Dependency on oil is quantified through its import share. To account for
the Krugman-argument, the bilateral sectoral specialisation of a country in 7 main
production sectors is calculated in three different ways: (a) as the sum of absolute
differences in sector shares between country pairs; (b) as the sum of squared
differences in sector shares between country pairs; and (c) as the cross-country
correlation between sector shares.
For measures of financial structure several are available which mimic or represent
more or less closely the indicator of a “market-based” financial structure suggested by
Allen and Gale (2000) in their influential book. They compare the main financial
systems by the importance of equity market capitalisation, the difference of bank
versus capital market financing, the importance of internal financing and the holdings
9
of financial assets. We draw on the much larger Financial Development and Structure
Database of the World Bank (1999) and approximate these characteristics by three
aggregate measures that reflect the bank assets to stock market channel, and the main
stock price index.
This brings us to the question of how to describe fiscal and monetary policy in a
suitable way. Policy reaction is clearly part of the propagation mechanism (this has
long been one of the features that proponents of using correlations of shocks rather
than of cyclical deviates have long stressed. What is needed is some summary
measure of the flexibility of policy; yet, a measure of similarity would be sufficient
since in estimation what we shall seek to explain is why cross correlations differ
between country-pairs. From this standpoint, the cross correlation between country
pairs of the cyclical deviate of the real interest rate might serve as an adequate proxy
for country similarity difference in monetary policy. Some alternatives suggest
themselves however. Two rough measures of differences in monetary policy are just
the inflation differential across countries (Imbs, 2001), or the spread between short
and long-term interest rates. More closely related to the OCA-literature is the
volatility of the bilateral exchange rate. This may either indicate strong shocks in
financial markets, or an effective shock absorber (Artis and Ehrmann, 2000). A priori,
the sign is therefore unclear. Finally, the residual of a simple monetary Taylor rule
was used in order to isolate discretionary monetary policy. The rule includes a
reaction to inflation and the output gap, and, except for the United States, Japan and
Germany, the exchange rate.
For fiscal policy quarterly data are in general insufficient or missing. Nevertheless, we
can consult the OECD’s annual estimates of the cyclically adjusted deficit (or its
movement over the period we are interested in). A first indicator is the primary
balance ratio that excludes interest payments on government debt. As built-in
stabilisers are rather important for all countries in the sample, the problem of
endogeneity with respect to the cycle is removed by cyclically adjusting the primary
balance. Finally, we specify a rule for fiscal policy that allows for reactions of the
primary surplus to the output gap and government debt, as in Gali and Perotti (2003).
As for monetary policy, the residual of this rule then reflects discretionary policy.
10
Finally, the economic size of the pair of countries may be a relevant variable and was
found to be so in Imbs (2001).
This brief account of the data requirements and the data available must suggest that
the estimation is likely to suffer from missing variables and/or misspecification. We
expect that “country dummies” (i.e. a dummy for a country in a pair) may have a role
to play in cleaning up for some of these missing or poorly stated variables, and we
have reason to think that dummies for the EU, or EMU might have a role to play – the
former as a proxying a weak border effect, the latter a stronger one, or as an indication
of a policy homogeneity condition.
The results described in the next section, are derived from a panel in which the
number of time points is 6. The explicand is the bilateral cross correlation calculated
over a rolling window of 5 years from the series of cyclical deviates initially
generated for the whole period. The explanatory terms are corresponding 5-year
averages of the variables in question. Unless variables are bilateral (exchange rate
volatility and sectoral specialisation), they are generally entered in absolute difference
form; that is, the maintained hypothesis is that the pair-wise cross correlation will be
decreased by dissimilarity between the countries in the variables measuring the
chosen aspects of the propagation mechanism.
The panel estimation strategy is random effects. This choice is given in by two
motivations, and there is little reason to believe estimation results would be very
different under fixed effects. First, fixed effects may be favoured on the grounds that
most of our explanatory variables are probably correlated with some country-specific
effect. Moreover, there are some factors we necessarily need to omit that are timeinvariant over our sample period, but still relevant in explaining business cycle
correlations (e.g. institutional factors). A fixed effects estimator is robust to this
omission. However, as part of these variables are measured rather coarsely –
especially so the indicator variables – it is likely that the cure is worse and results in a
very imprecise fixed effects estimate.4 Second, if some of the explanatory measures
are long term responses to average business cycle correlations, then this endogeneity
would further bias downwardly the estimates.
4
An objection to this point is that measurement error is less of a problem once subsample averages are
used.
11
5. Some estimation results
Table 4 presents an initial search for those variables that might be key to explaining
business cycle cross-correlations. As noted above the majority of these variables
enter the equation in absolute difference form. Trade intensity (instrumented via the
estimates of a gravity model as described above) is accepted as sufficiently important
to be taken as the conditioning variable in the series of equations in which each
candidate variable is entered, one-at-a-time.
The results are shown, in the first
column, for the trade measure, with the coefficient and its p-value (always highly
significant) and in the second column for the candidate variable. The relative paucity
of emboldened entries in this column indicates that not many other variables, taken on
their own, are significant once trade is accounted for. Of those that are, one – oil –
seems to have an “incorrect” sign. Several variables from the “monetary policy”
block appear significant and of the right sign, however, as are all three dummies
(ERM, EU and EMU) and a variable indicating “economic size”. So far, there is no
evidence that any of the variables measuring labour or product market structure or the
structure of the financial sector appear to be important.
The analysis extensively examines a comprehensive set of the explanatory variables,
adding these one by one to the trade variable. This is an important step for the
exploratory analysis, but there are various interplays between the explanatory
variables, as nicely illustrated in Imbs (2003). We do not go so far as to model a
complete system, but examine some hypotheses on combinations of variables that
have at least some theoretical background. The main result is the relevant impact of
policy variables, and the ambiguous effect of differences in market structure. All
results reported in Table 5 refer to the absolute difference of non-standardised
measures and always condition upon instrumented trade-intensity. A first hypothesis
is that there important relations between labour market institutions and fiscal policy,
an argument which rephrases and deepens Rodrik (2000). The fiscal policy measure is
the cyclically adjusted primary surplus ratio. Combining both measures, fiscal policy
indeed becomes significant for nearly all measures and offsets the negative effect of
differences in labour market structure. Among the latter, only the tax wedge is really
significant. Similarly, fiscal policy differences have a significant impact (at least at
10%) on cyclical correlation once the industrial structure is taken into account.
12
The same regression is repeated with a relevant monetary policy measure, taken to be
the residual of a monetary policy rule. As before, larger differences in monetary
policy reduce cyclical correlation, but the impact of sectoral specialisation may go in
both directions. Another interesting hypothesis is the interplay between financial
market structure and monetary policy: for all measures, discretionary monetary policy
reduces cyclical correlation, but the effects of market structure are again not clear.
We also consider controlling for trade and common shocks – as reflected in oil
imports and economic size – in examining each of the explanatory variables.
Surprisingly, the larger the difference in oil imports, the stronger the cyclical
correlation. Moreover, not only does economic size never matter, but hardly any of
the various measures explains anything of correlation, except for the negative effects
of monetary policy and stock market prices.
Finally, it is worth noting that the dummies for EU or EMU have a significantly
positive effect on business cycle correlations. And this is also an extremely robust
result that holds regardless of the specification. This may seem to confirm and extend
Rose’s (2000) findings.
In order to capture some of the non-linear effects that may be present, we alternatively
thought it worth coming up with a standardised measure for all variables. For those
measures that are not already scaled to an interval (sectoral specialisation, rules’
residual, mark-up, profit rate and margin, and dummy variables), scaling further
reduces the probability of the results being inadvertently influenced by some outliers.
After all, a 5% difference in absolute terms in, for example, manufacturing shares
should not be the same when those shares have declined over time to low levels. The
data are standardised over the entire sample, and averaged over the 6 subsamples
discussed before. The results in Table 6 are rather disappointing. It confirms the role
of industrial structure and (the negative effect of) monetary policy in influencing
business cycle correlations, and the scarce evidence of any other factors being
important, except the European dummies. But it also wipes out the significant
contribution of trade to cyclical correlation.
13
6. Conclusions
Relative to the anticipation that it should be possible to find differences in the
propagation mechanism capable of explaining cross correlations in countries’
business cycle experience, the results reported here may appear disappointing. We
would emphasize two points in this context. First, this work is still ongoing in ways
which we will comment upon below. Second, we have worked hard to find summary
measures of key aspects of the propagation mechanism and it is important to know
that such a detailed search does not readily support certain hypotheses; moreover, we
do find positively that relative monetary policy is a major explanatory of business
cycle differences.
There are three directions in which we think further work needs to proceed. One is to
refine the concept of trade to check the relative roles of inter- and intra-trade, taking
account of the distinction between the vertical and horizontal varieties of the latter.
This requires exploitation of a new database CHELEM, which is organized by CEPII.
A second important area is that of finance. Although comprehensive bilateral data on
financial flows (or stocks) appears to be lacking, these data do exist for some
components – for FDI and bank lending and in a future version of this paper these
data will be exploited. A third direction in which the paper may be developed
concerns the econometric specification of the model, and testing for its robustness.
A first item is definitely the heteroskedasticity and autocorrelation correction of the
panel estimates. It is not just sufficient to correct for heteroskedasticity, because of
sampling uncertainty in our estimated dependent variable. As pointed out by Clark
and Wincoop (2001), if we believe that global shocks are important, then sampling
uncertainty for one bilateral correlation is likely to be dependent on sampling
uncertainty in the other bilateral correlations as well. There has moreover been some
documentation on the changing nature of the cycle, not just with regard to the crosscountry correlations, but also on the reduction in absolute size of the cycle, reducing
the absolute “distance” between countries’ cycles. There is even the additional
complication that we try to exploit the time-variance of the panel. Not only are the
residuals of any block of panel regressions dependent across countries; we moreover
have time-dependence as well.
14
Another point is the rather loose specification of the main panel regression, where we
just explored the relevance of adding one by one a set of measures. Regardless of the
specific explanatory variables that may be included, there is no consensus on the exact
way to do so. Following Frankel and Rose (1998), it is common to instrument the
trade variable via a first stage gravity regression, and this is also the approach
followed here. However, many of the measures that we include in our specification
may actually be influenced by the same gravity variables (Gruben et al., 2003). We
would attribute to large a role to the trade variable then, and downplay the role of the
other variables if we do not instrument these as well. The main results seem rather
robust to the instrumenting of the trade variable (see Tables A.4-5), so the gain may
be small. Nevertheless, a formal test for overidentifying restrictions should shed some
light on that.
We may refer already to some partial results in two of these instances. Our choice of
exploiting the time-span in the panel by using 5-year averages can be criticized for
being too short remove cyclical movements in both average correlation and its
explanatory variables. This does not seem to be a serious problem in our case, as
correlation between our various measures and the cyclical deviate is low, and hardly
significant. We nevertheless reran similar panel regressions for 3 time-periods,
spanning the 70s, 1980:1 up to 1992:4 and 1993:1 to 2003:4. This basically confirms
the importance of trade and differences in monetary policy,5 although a significant
role is attributed to financial market structure and fiscal policy too in these results. A
related issue is whether there is significant room to vary the specification. In the paper
we have been primarily interested in explaining how pairwise differences in economic
structures and policies contribute to bilateral cyclical correlations. In Tables A.4-5 we
report for both trade measures that we examine, two alternatives to the absolute
difference between the various measures.6 In the first column “Country”, we explain
the bilateral correlation between home country i and foreign country j, by the relevant
measure in country j. The second column (“Difference”) repeats the estimates
reported above on the absolute difference. The third column (“Ratio”) concentrates on
deviations only, by scaling the foreign country variable to the home country variable.
Instrumenting the trade-intensity or not, does not matter a lot for the main results.
Considering absolute differences then, none of the variables for labour or financial
5
6
Tables are not reported, but available from the authors upon request.
Tables A.6-7 repeat the same analysis for standardized data.
15
markets, industrial structure, fiscal policy or economic size explains a significant part
of average cyclical correlation, as before. The product market competition has a
negative impact, via the mark-up. The consistent result is the negative effect of
monetary policy, and the positive one of EU or EMU. If we just explain cyclical
correlation by the foreign country’s characteristics, then competition in product
markets, the financial structure and fiscal policy all become significant. Only financial
structure has a negative impact on cyclical correlation. If we consider the ratios of
right hand side measures, hardly anything is significant, except for the financial
market structure, albeit with a positive sign.
These results may suggest that a
different form of representation of the variables we have been concerned with would
be more productive: some authors (Fidrmuc, 2004) suggest using an alternative trade
measure that corrects for the size of the pair of economies. The importance of trade in
synchronizing cycles completely depends on the major share of the trade structure for
both countries. That is, in order to explain correlation between Austria and Germany,
it is more important that Germany is Austria’s largest trading partner, even if German
trade with Austria is only a small part of the former country’s trade relations. Only the
maximum of both trade shares should be calculated.
The task we set for this paper is not finished; yet we hope to have made considerable
progress.
16
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20
Tables
Table 1. Data sample
Country
Sample
Austria
1970:1-2003:4
Finland
1970:1-2003:4
France
1970:1-2003:4
Germany
1970:1-2003:4
United States
1970:1-2003:4
Italy
1970:1-2003:4
Spain
1970:1-2003:4
Sweden
1970:1-2003:4
United Kingdom
1970:1-2003:4
Canada
1970:1-2003:4
Japan
1970:1-2003:4
21
Table 2. Bilateral cross correlation of cycle, full sample: 1970:1-2003:4.
Austria
Finland
France
Germany
Italy
Spain
Sweden
UK
USA
Canada
Japan
Austria
-
Finland
0.27
-
France
0.69
0.43
-
Germany
0.76
0.17
0.66
-
Italy
0.68
0.43
0.65
0.64
-
Spain
0.62
0.50
0.68
0.47
0.58
-
Sweden
0.14
0.57
0.17
0.05
0.31
0.10
-
UK
0.38
0.48
0.59
0.43
0.43
0.44
0.37
-
USA
0.34
0.23
0.43
0.56
0.37
0.27
0.13
0.65
-
Canada
0.25
0.49
0.38
0.35
0.48
0.28
0.45
0.49
0.71
-
22
Japan
0.35
0.30
0.50
0.61
0.30
0.40
-0.12
0.47
0.52
0.21
-
Table 3 Data description
Variable
correlation of real GDP business
cycle, using band-pass filter
Trade intensity =
(export A to B + import A from B)
/ (total trade of country A + total
trade of country B)
idem, but instrumented with the
gravity equation
Trade intensity =
½ *(export A to B + import A from
B)*GDPworld / (GDP of country
A*GDP of country B)
NAWRU (non-accelerating wage
rate of unemployment)
employment protection legislation,
union density, benefit replacement
ratio and tax wedge
net oil imports
share of manufacturing in GDP
sectorial specialization (in 7 main
economic sectors)
a) sum absolute difference of sector
shares
b) sum squared difference of sector
shares
c) correlation of sector shares
product market competition
a) mark-up
b) profit rate
c) profit margin
private credit by
deposit money banks
to GDP/ stock market
total value traded to
GDP
deposit money bank assets to
GDP/stock market capitalization to
GDP
Sample
Source
cyclical correlation
quarterly,
own calculations on
1970-2003
GDP index of IMF
trade-intensity
quarterly,
IMF, Direction of
1970-2003
Trade Statistics
annual,
1970-2003
Notes
GDP at current PPP,
base year 2000
labour market
annual,
OECD Economic
1970-2003
Outlook no. 76
annual,
1970-2001
Nickell and Nunziata
(2000)
data for 2002 and
2003 are from 2001;
for Japan and
Canada data since
1995 are not
available
industrial structure
annual,
IEA,
1970-2002
Energy statistics of
OECD countries
quarterly,
OECD MEI
1970-2003
annual,
OECD National
1970-2003
accounts
product market structure
annual,
OECD STAN data base
1970-2003
and OECD Economic
Outlook no. 76
financial markets
annual,
Financial Development
1975-2003
and Structure database World Bank (2003)
23
bank deposits / stock market total
value traded to GDP
share price index
cumulative current account
balance/ GDP
net FDI flow/ GDP
Bank lending / GDP
primary balance ratio to GDP
cyclically adjusted primary balance
ratio to potential GDP
Residual of fiscal policy rule
deviates from band-pass filtered
real interest rates
quarterly,
IMF/IFS
1970-2003
annual,
Database Lane and
1970-1997
Milesi-Ferretti (2001)
annual,
1970-1997
annual,
1970-1997
fiscal policy
quarterly,
OECD Economic
1970-2003
Outlook no. 76
monetary policy
quarterly,
OECD Economic
1970-2003
Outlook no. 76
CPI inflation
yield spread
(difference between long term
government bond yield and 3month rate)
Volatility of nominal exchange
rate,
versus a benchmark country
(Germany for European countries,
and USA for others)
or bilateral
Residual of Taylor monetary rule,
for Austria and
Spain, annual data
have been used
instead
for Austria annual
data are available
and the HP-filter
with Ravn-Uhlig
weight is applied
for Austria annual
data are available
only, and there is a
break in 1986
on annual data for
Austria and Spain
reaction of interest rate to output
gap, inflation and the exchange rate
(except for US, Germany and
Japan)
bilateral dummy for ERM, EU and
EMU membership
dummy variables
quarterly,
1970-2003
economic size (real GDP at current
PPP)
annual,
1970-2003
-
other
IMF/IFS
24
Table 4. Panel estimation results, basic measure.
Trade measure
/ p-value
X – variable
/ p-value
trade
1.64/0.00
-
labour market
NAWRU
1.63/0.00
-0.38/0.46
EPL
1.63/0.00
0.03/0.53
union
1.64/0.00
0.01/0.84
tax wedge
1.55/0.10
-0.25/0.09
benefit replacement ratio
1.64/0.00
-0.02/0.86
industrial structure
Manufacturing
1.63/0.00
0.19/0.65
Oil
1.61/0.00
3.29/0.00
Sectoral specialisation 1
1.64/0.00
-0.08/0.71
Sectoral specialisation 2
1.64/0.00
2.46/0.14
Sectoral correlation
1.64/0.00
1.08/0.14
product market competition
Mark-up
1.53/0.00
-0.02/0.46
Profit margin
1.56/0.00
0.54/0.20
Profit rate
1.53/0.00
0.06/0.80
financial structure
Private Credit / Stock market 1
1.47/0.00
-0.01/0.48
deposit money /Stock market 2
1.44/0.00
0.03/0.21
Bank deposits / Stock market
1.52/0.00
0.00/0.93
Stock market index
1.63/0.00
-0.00/0.90
Net external asset position
GDP
1.98/0.00
0.05/0.78
25
Net FDI flow / GDP
1.95/0.00
2.69/0.38
fiscal policy
Primary balance
1.49/0.00
0.01/0.34
Cyclically adjusted primary
balance
1.67/0.00
0.01/0.07
Residual of fiscal rule
1.55/0.00
0.02/0.09
monetary policy
Residual Taylor rule
1.55/0.00
-0.05/0.00
1.63/0.00
0.31/0.02
1.63/0.00
-0.00/0.58
Inflation differential
1.61/0.00
-0.02/0.06
Interest Spread
1.74/0.00
-0.06/0.00
BP filtered real interest rate
1.70/0.00
-0.03/0.05
Volatility exchange rate v USA
and DEU
Volatility exchange rate
(bilateral)
dummies
ERM
1.54/0.00
0.15/0.00
EU
1.57/0.00
0.18/0.00
EMU
1.62/0.00
0.17/0.00
other
economic size
1.64/0.00
0.00/0.82
Notes:
The dependent variable is the correlation between deviation cycles of each bilateral pair of
countries; averages over 6 time periods are considered, 1970-1974, 1975-1979, 19801986,1987-1992, 1993-1999 and 2000-2003; all right hand side variables are expressed as
the absolute difference for the pair of countries; for detailed definitions and sources of the
variables, see Table 3; estimation is random effects.
26
Table 5. Panel estimation results, combination of measures.
NAWRU + cyclically adjusted primary
balance
EPL + Cyclically adjusted primary
balance
Union + Cyclically adjusted primary
balance
Tax wedge + Cyclically adjusted
primary balance
Benefit replacement ratio + Cyclically
adjusted primary balance
Manufacturing + Cyclically adjusted
primary balance
Oil + Cyclically adjusted primary
balance
Sectoral specialisation 1 + Cyclically
adjusted primary balance
Sectoral specialisation 2 + Cyclically
adjusted primary balance
Sectoral correlation + Cyclically
adjusted primary balance
Manufacturing + residual monetary
Taylor rule
Oil + residual monetary Taylor rule
Trade
measure
/ p-value
X1 –
variable
/ p-value
X2–
variable
/ p-value
X3 –
variable
/ p-value
1.65/0.00
-0.49/0.34
0.01/0.05
-
1.67/0.00
0.02/0.53
0.01/0.08
-
1.67/0.00
-0.01/0.87
0.01/0.07
-
1.56/0.00
-0.33/0.03
0.01/0.02
-
1.67/0.00
-0.09/0.44
0.01/0.05
-
1.66/0.00
0.04/0.92
0.01/0.07
-
1.63/0.00
3.12/0.01
0.01/0.11
-
1.67/0.00
-0.08/0.71
0.01/0.07
-
1.67/0.00
2.63/0.12
0.01/0.06
-
1.67/0.00
1.16/0.11
0.01/0.06
-
1.55/0.00
0.08/0.87
-0.05/0.00
-
1.54/0.00
1.01/0.46
-0.05/0.00
-
1.56/0.00
-0.47/0.04
-0.05/0.00
-
1.56/0.00
6.08/0.00
-0.04/0.01
-
1.56/0.00
2.77/0.00
-0.04/0.00
-
Sectoral specialisation 1 + residual
monetary Taylor rule
Sectoral specialisation 2 + residual
monetary Taylor rule
Sectoral correlation + residual
monetary Taylor rule
Private Credit / Stock market 1+
residual monetary Taylor rule
deposit money /Stock market 2 +
residual monetary Taylor rule
Bank deposits / Stock market +
residual monetary Taylor rule
Stock market index + residual
monetary Taylor rule
Net external asset position GDP +
residual monetary Taylor rule
Net FDI flow / GDP + residual
monetary Taylor rule
1.49/0.00
-0.00/0.62
-0.03/0.05
-
1.47/0.00
0.04/0.04
-0.08/0.00
-
1.53/0.00
0.00/0.76
-0.05/0.00
-
1.52/0.00
-0.05/0.01
-0.03/0.08
-
1.94/0.00
0.06/0.74
-0.05/0.01
-
1.91/0.00
3.80/0.23
-0.05/0.00
-
Economic size + oil + NAWRU
1.60/0.00
0.00/0.75
3.36/0.00
-0.36/0.49
Economic size + oil + Manufacturing
1.60/0.00
0.00/0.74
3.36/0.00
0.17/0.69
27
Economic size + oil + sectoral
specialisation 1
Economic size + oil + sectoral
specialisation 2
Economic size + oil + sectoral
correlation
1.61/0.00
0.00/0.83
3.35/0.00
-0.03/0.89
1.61/0.00
0.00/0.96
3.24/0.01
2.27/0.18
1.61/0.00
0.00/0.97
3.24/0.00
1.01/0.17
Economic size + oil + markup
1.50/0.00
0.00/0.54
3.03/0.02
-0.02/0.45
Economic size + oil + profit margin
1.53/0.00
0.00/0.77
2.97/0.02
0.51/0.24
Economic size + oil + profit rate
1.52/0.00
0.00/0.37
1.51/0.41
-0.18/0.60
1.47/0.00
0.00/0.45
0.70/0.64
-0.01/0.41
1.46/0.00
0.00/0.31
-0.89/0.60
0.03/0.19
1.52/0.00
0.00/0.32
1.10/0.46
-0.00/0.82
1.57/0.00
0.00/0.85
5.20/0.00
-0.04/0.03
1.92/0.00
0.00/0.06
3.75/0.01
0.04/0.82
1.90/0.00
0.00/0.07
3.52/0.02
2.34/0.45
1.49/0.00
0.00/0.55
0.26/0.86
0.00/0.40
1.63/0.00
0.00/0.91
3.15/0.01
0.01/0.12
1.55/0.00
0.00/0.58
0.59/0.67
0.02/0.12
1.54/0.00
0.00/0.35
1.21/0.38
-0.05/0.00
Economic size + oil + EU
1.51/0.00
0.00/0.75
4.83/0.00
0.21/0.00
Economic size + oil + EMU
1.57/0.00
0.00/0.85
4.40/0.00
0.20/0.00
NAWRU + EU
1.56/0.00
-0.37/0.47
0.18/0.00
EPL + EU
1.54/0.00
0.07/0.10
0.20/0.00
Union + EU
1.56/0.00
-0.05/0.42
0.19/0.00
Tax wedge + EU
1.51/0.00
-0.17/0.25
0.18/0.00
Benefit replacement ratio + EU
1.57/0.00
0.02/0.85
0.18/0.00
Economic size + oil + Private Credit /
Stock market 1
Economic size + oil + deposit money
/Stock market 2
Economic size + oil + Bank deposits /
Stock market
Economic size + oil + Stock market
index
Economic size + oil + Net external
asset position GDP
Economic size + oil + Net FDI flow /
GDP
Economic size + oil + primary
balance ratio
Economic size + oil + cyclically
adjusted primary balance ratio
Economic size + oil + residual fiscal
rule
Economic size + oil + residual
monetary Taylor rule
Notes:
The dependent variable is the correlation between deviation cycles of each bilateral pair of
countries; averages over 6 time periods are considered, 1970-1974, 1975-1979, 19801986,1987-1992, 1993-1999 and 2000-2003; all right hand side variables are expressed as
the absolute difference for the pair of countries; for detailed definitions and sources of the
variables, see Table 3; estimation is random effects.
28
Table 6. Panel estimation results, standardised measure.
Trade measure
/ p-value
X – variable
/ p-value
trade
0.00/0.15
-
labour market
NAWRU
0.00/0.16
-0.47/0.41
EPL(a)
0.00/0.14
-0.11/0.12
union(a)
0.00/0.14
-0.23/0.06
tax wedge(a)
0.00/0.27
-0.41/0.14
benefit replacement ratio(a)
0.00/0.15
0.08/0.66
industrial structure
Manufacturing
0.00/0.13
0.13/0.81
Oil
0.00/0.14
-0.48/0.68
Sectoral specialisation 1(a)
0.00/0.13
-0.27/0.39
Sectoral specialisation 2(a)
0.00/0.13
-0.53/0.03
Sectoral correlation(a)
0.00/0.13
-0.48/0.05
product market competition
Mark-up
0.00/0.14
-0.00/0.57
Profit margin
0.00/0.18
0.01/0.50
Profit rate
0.00/0.94
-0.07/0.42
financial structure
Private Credit / Stock market 1
0.00/0.27
0.00/0.86
deposit money /Stock market 2
0.00/0.98
-0.05/0.18
Bank deposits / Stock market
0.00/0.30
0.00/0.70
Stock market index
0.00/0.13
-0.02/0.58
Net external asset position
GDP
0.00/0.28
0.02/0.06
29
Net FDI flow / GDP
0.00/0.37
0.02/0.74
fiscal policy
Primary balance
0.00/0.15
-0.00/0.84
Cyclically adjusted primary
balance
0.00/0.15
-0.00/0.62
Residual of fiscal rule
0.00/0.09
0.03/0.13
monetary policy
Residual Taylor rule
0.00/0.18
-0.06/0.03
0.00/0.13
0.12/0.12
0.00/0.15
-0.00/0.16
Inflation differential
0.00/0.15
-0.02/0.09
Interest Spread
0.00/0.13
-0.09/0.00
BP filtered real interest rate
0.00/0.14
-0.03/0.34
Volatility exchange rate v USA
and DEU
Volatility exchange rate
(bilateral)
dummies
ERM(a)
0.00/0.18
0.13/0.03
EU(a)
0.00/0.20
0.15/0.01
EMU(a)
0.00/0.16
0.10/0.17
other
economic size
0.00/0.14
0.00/0.37
Notes:
The dependent variable is the correlation between deviation cycles of each bilateral pair of
countries; averages over 6 time periods are considered, 1970-1974, 1975-1979, 19801986,1987-1992, 1993-1999 and 2000-2003; all right hand side variables are expressed as
the absolute difference of standardised values for the pair of countries; for detailed definitions
and sources of the variables, see Table 3; estimation is random effects; (a) variables are not
standardised.
30
Figures
Figure 1 Hierarchical cluster of all bilateral cross correlations, full sample 1970-2003, average
linkage, Euclidean distance.
Dendogram for dissimilarity analysis
L2 dissimilarity measure
1.29293
0
A
U
T
D
E
U
F
R
A
I
T
A
E
S
P
J
P
N
F
I
N
S
W
E
U
K
U
S
A
C
A
N
countries
Figure 2 Hierarchical cluster of all bilateral cross correlations, sample 1970-1979, average
linkage, Euclidean distance.
Dendogram for dissimilarity analysis
L2 dissimilarity measure
2.04509
0
A
U
T
I
T
A
E
S
P
F
R
A
D
E
U
U
K
C
A
N
U
S
A
J
P
N
F
I
N
S
W
E
countries
31
Figure 3 Hierarchical cluster of all bilateral cross correlations, sample 1980-1992, average
linkage, Euclidean distance.
Dendogram for dissimilarity analysis
L2 dissimilarity measure
1.62097
0
A
U
T
D
E
U
J
P
N
F
R
A
E
S
P
I
T
A
F
I
N
S
W
E
U
K
U
S
A
C
A
N
countries
Figure 4 Hierarchical cluster of all bilateral cross correlations, full sample 1993-2003, average
linkage, Euclidean distance.
Dendogram for dissimilarity analysis
L2 dissimilarity measure
2.14366
0
A
U
T
F
R
A
D
E
U
E
S
P
I
T
A
F
I
N
U
S
A
S
W
E
C
A
N
U
K
J
P
N
countries
32
Appendix
Table A. 1. Bilateral cross correlation of cycle, sample: 1970:1-1979:4.
Austria
Finland
France
Germany
Italy
Spain
Sweden
UK
USA
Canada
Austria
-
Finland
0.33
-
France
0.70
0.24
-
Germany
0.77
0.31
0.87
-
Italy
0.42
-0.01
0.72
0.82
-
Spain
0.77
0.52
0.74
0.76
0.41
-
Sweden
0.59
0.23
0.54
0.43
0.38
0.48
-
UK
0.02
0.64
0.01
0.04
-0.27
0.36
-0.17
-
USA
0.51
0.23
0.82
0.80
0.81
0.46
0.51
-0.05
-
Canada
0.57
0.37
0.64
0.81
0.74
0.70
0.37
0.05
0.55
-
Japan
0.30
0.18
0.61
0.69
0.81
0.30
0.18
-0.13
0.74
0.57
UK
0.17
0.59
0.18
0.11
0.55
0.41
0.25
-
USA
0.11
0.47
0.40
-0.16
0.47
0.37
0.41
0.71
-
Canada
-0.12
0.44
0.15
0.10
0.84
0.58
-0.05
0.60
0.50
-
Japan
0.37
0.33
0.53
0.50
0.10
0.45
0.23
0.05
-0.02
0.22
UK
0.61
0.23
0.54
0.33
0.39
0.35
0.35
-
USA
0.38
0.59
0.58
0.56
0.43
0.60
0.40
0.71
-
Canada
0.55
0.44
0.55
0.45
0.66
0.35
0.45
0.85
0.75
-
Japan
-0.12
0.26
0.02
0.33
-0.17
0.30
0.26
-0.32
0.12
-0.27
Table A. 2. Bilateral cross correlation of cycle, sample: 1980:1-1992:4.
Austria
Finland
France
Germany
Italy
Spain
Sweden
UK
USA
Canada
Austria
-
Finland
0.25
-
France
0.48
0.57
-
Germany
0.64
-0.03
0.14
-
Italy
-0.08
0.29
0.09
0.19
-
Spain
0.37
0.46
0.56
0.42
0.38
-
Sweden
0.47
0.52
0.58
0.03
-0.15
0.40
-
Table A. 3. Bilateral cross correlation of cycle, sample: 1993:1-2003:4.
Austria
Finland
France
Germany
Italy
Spain
Sweden
UK
USA
Canada
Austria
-
Finland
0.16
-
France
0.78
0.27
-
Germany
0.67
0.39
0.79
-
Italy
0.26
0.46
0.18
0.14
-
Spain
0.54
0.41
0.71
0.84
-0.10
-
Sweden
0.72
0.37
0.78
0.83
0.27
0.67
-
33
Table A. 4. Panel estimation results, trade-intensity is scaled by total trade (not instrumented).
Rhs
variable
Rhs
variable
trade
coeff
/ p’value
/ p’value
country
Rhs
variable
Rhs
variable
trade
coeff
/ p’value
/ p’value
absolute difference
Rhs
variable
Rhs
variable
trade
coeff
/ p’value
/ p’value
ratio
LABOUR MARKET
NAWRU
EPL
union
tax wedge
benefit
replacement
ratio
0.00/0.00
0.00/0.00
0.00/0.00
0.00/0.00
0.51/0.19
-0.01/0.71
-0.08/0.23
0.22/0.06
0.00/0.00
0.00/0.00
0.00/0.00
0.00/0.00
-0.55/0.30
0.03/0.57
-0.00/0.93
-0.51/0.00
0.00/0.00
0.0/0.00
0.00/0.00
0.00/0.00
-0.00/0.80
0.03/0.30
-0.04/0.30
0.13/0.07
0.00/0.00
0.03/0.69
0.00/0.00
-0.02/0.87
0.00/0.00
0.01/0.80
INDUSTRIAL STRUCTURE
Manufacturing
Oil
Sectoral
specialisation 1
Sectoral
specialisation 2
Sectoral
correlation
0.00/0.00
0.00/0.00
-0.26/0.57
-1.59/0.15
0.00/0.00
0.00/0.00
0.30/0.50
2.81/0.03
0.00/0.00
0.00/0.00
-0.13/0.03
-0.01/0.73
-
-
0.00/0.00
-0.11/0.63
-
-
-
-
0.00/0.00
2.64/0.13
-
-
-
-
0.00/0.00
1.11/0.15
-
-
0.00/0.00
0.00/0.00
0.00/0.00
0.16/0.00
-0.09/0.47
0.01/0.41
PRODUCT MARKETS
Mark-up
Profit margin
Profit rate
0.00/0.00
0.00/0.00
0.00/0.00
0.12/0.00
0.74/0.03
0.52/0.00
0.00/0.00
0.00/0.00
0.00/0.00
-0.10/0.00
0.73/0.10
-0.05/0.85
FINANCIAL MARKETS
Private Credit /
Stock market 1
deposit money /
Stock market 2
Bank deposits /
Stock market
Stock market
index
Net external
asset position /
GDP
Net FDI flow /
GDP
0.00/0.01
-0.04/0.00
0.00/0.01
-0.00/0.38
0.00/0.00
0.01/0.00
0.00/0.00
-0.03/0.05
0.00/0.00
0.03/0.19
0.00/0.06
0.02/0.00
0.00/0.00
-0.04/0.00
0.00/0.00
-0.00/0.91
0.00/0.00
0.00/0.00
0.00/0.00
0.04/0.00
0.00/0.00
-0.02/0.24
0.00/0.00
0.03/0.00
0.00/0.00
0.11/0.46
0.00/0.00
0.02/0.91
0.00/0.00
-0.02/0.43
0.00/0.00
-4.20/0.07
0.00/0.00
3.26/0.32
0.00/0.00
-0.00/0.45
FISCAL POLICY
Primary balance
Cyclically
adjusted
primary balance
0.00/0.00
0.01/0.02
0.00/0.00
0.00/0.59
0.00/0.00
-0.00/0.84
0.00/0.00
0.02/0.00
0.00/0.00
0.01/0.26
0.00/0.00
0.00/0.92
34
Residual of
fiscal rule
0.00/0.00
0.03/0.00
0.00/0.00
0.01/0.26
0.00/0.00
0.00/0.11
MONETARY POLICY
Residual Taylor
rule
Volatility
exchange rate v
USA and DEU
Volatility
exchange rate
(bilateral)
Inflation
differential
Interest Spread
BP filtered real
interest rate
0.00/0.00
-0.03/0.00
0.00/0.00
-0.05/0.00
0.00/0.00
-0.01/0.33
0.00/0.00
0.31/0.02
0.00/0.00
0.33/0.02
0.00/0.00
0.00/0.26
0.00/0.00
-0.00/0.01
-
-
-
0.00/0.00
-0.00/0.02
-
-
-
-
0.00/0.00
-0.02/0.01
0.00/0.00
0.01/0.76
0.00/0.00
0.02/0.14
0.00/0.00
-0.06/0.00
0.00/0.00
0.00/0.93
0.00/0.00
-0.02/0.11
0.00/0.00
-0.02/0.21
0.00/0.00
-0.00/0.50
DUMMIES
ERM
0.00/0.00
0.18/0.00
-
-
-
-
EU
0.00/0.00
0.21/0.00
-
-
-
-
EMU
0.00/0.00
0.18/0.00
-
-
-
-
-
-
-
OTHER
economic size
0.00/0.00
0.00/0.94
-
Notes:
The dependent variable is the correlation between deviation cycles of each bilateral pair of
countries; averages over 6 time periods are considered, 1970-1974, 1975-1979, 19801986,1987-1992, 1993-1999 and 2000-2003; all right hand side variables are expressed as
the absolute difference for the pair of countries; for detailed definitions and sources of the
variables, see Table 3; estimation is random effects; “Country” refers to the foreign country j
variable being used to explain correlation between home country i and foreign country j;
“absolute difference” refers to the absolute difference between foreign country j and base
country i; and “ratio” refers to the ratio of country j’s variable, relative to the base country i.
35
Table A. 5. Panel estimation results, trade-intensity is scaled by total trade (instrumented by
gravity equation).
Rhs
variable
Rhs
variable
trade
coeff
/ p’value
/ p’value
country
Rhs
variable
Rhs
variable
trade
coeff
/ p’value
/ p’value
absolute difference
Rhs
variable
Rhs
variable
trade
coeff
/ p’value
/ p’value
ratio
LABOUR MARKET
NAWRU
EPL
union
tax wedge
benefit
replacement
ratio
1.63/0.00
1.64/0.00
1.64/0.00
1.64/0.00
0.43/0.24
-0.01/0.68
-0.10/0.11
0,00/0,98
1.63/0.00
1.63/0.00
1.64/0.00
1,55/0.10
-0.38/0.46
0.03/0.53
0.01/0.84
-0.25/0.09
1.64/0.00
1.65/0.00
1.64/0.00
1.64/0.00
-0.01/0.63
0.02/0.39
-0.06/0.12
0.00/0.98
1.64/0.00
0.02/0.79
1.64/0.00
-0.02/0.86
1.64/0.00
0.01/0.70
INDUSTRIAL STRUCTURE
Manufacturing
Oil
Sectoral
specialisation 1
Sectoral
specialisation 2
Sectoral
correlation
1.64/0.00
1.62/0.00
-0.36/0.40
-1.65/0.11
1.63/0.00
1.61/0.00
0.19/0.65
3.29/.00
1.67/0.00
1.64/0.00
-0.12/0.03
-0.02/0.39
-
-
1.64/0.00
-0.08/0.71
-
-
-
-
1.64/0.00
2.46/0.14
-
-
-
-
1.64/0.00
1.08/0.14
-
-
1.51/0.00
1.58/0.00
1.52/0.00
0.05/0.26
0.01/0.89
0.00/0.79
PRODUCT MARKETS
Mark-up
Profit margin
Profit rate
1.49/0.00
1.69/0.00
1.56/0.00
0.05/0.06
0.87/0.00
0.32/0.03
1.53/0.00
1.56/0.00
1.53/0.00
-0.02/0.46
0.54/0.20
0.06/0.80
FINANCIAL MARKETS
Private Credit /
Stock market 1
deposit money /
Stock market 2
Bank deposits /
Stock market
Stock market
index
Net external
asset position /
GDP
Net FDI flow /
GDP
1.52/0.00
-0.04/0.00
1.47/0.00
-0.01/0.48
1.50/0.00
0.01/0.00
1.56/0.00
-0.03/0.02
1.44/0.00
0.03/0.21
1.40/0.00
0.02/0.00
1.56/0.00
-0.04/0.00
1.52/0.00
0.00/0.93
1.50/0.00
0.00/0.00
1.64/0.00
0.04/0.00
1.63/0.00
-0.00/0.90
1.65/0.00
0.04/0.00
1.98/0.00
0.09/0.52
1.98/0.00
0.05/0.78
1.98/0.00
-0.01/0.61
1.97/0.00
-4.88/0.03
1.95/0.00
2.69/0.38
1.96/0.00
-0.00/0.47
FISCAL POLICY
Primary balance
Cyclically
adjusted
1.60/0.00
0.01/0.05
1.49/0.00
0.01/0.34
1.48/0.00
-0.00/0.89
1.62/0.00
0.01/0.01
1.67/0.00
0.01/0.07
1.64/0.00
-0.00/0.99
36
primary balance
Residual of
fiscal rule
1.58/0.00
0.02/0.01
1.55/0.00
0.02/0.09
1.54/0.00
0.00/0.09
MONETARY POLICY
Residual Taylor
rule
Volatility
exchange rate v
USA and DEU
Volatility
exchange rate
(bilateral)
Inflation
differential
Interest Spread
BP filtered real
interest rate
1.66/0.00
-0.03/0.00
1.55/0.00
-0.05/0.00
1.53/0.00
-0.01/0.37
1.63/0.00
0.28/0.03
1.63/0.00
0.31/0.02
1.63/0.00
0.00/0.71
1.63/0.00
-0.00/0.58
-
-
-
1.60/0.00
0.00/0.62
-
-
-
-
1.61/0.00
-0.02/0.06
1.64/0.00
0.00/0.99
1.64/0.00
0.01/0.45
1.74/0.00
-0.06/0.00
1.66/0.00
0.00/0.79
1.68/0.00
-0.03/0.05
1.70/0.00
-0.03/0.05
1.66/0.00
-0.00/0.39
DUMMIES
ERM
1.54/0.00
0.15/0.00
-
-
-
-
EU
1.57/0.00
0.18/0.00
-
-
-
-
EMU
1.62/0.00
0.17/0.00
-
-
-
-
-
-
-
OTHER
economic size
1.64/0.00
0.00/0.82
-
Notes:
The dependent variable is the correlation between deviation cycles of each bilateral pair of
countries; averages over 6 time periods are considered, 1970-1974, 1975-1979, 19801986,1987-1992, 1993-1999 and 2000-2003; all right hand side variables are expressed as
the absolute difference for the pair of countries; for detailed definitions and sources of the
variables, see Table 3; estimation is random effects. “Country” refers to the foreign country j
variable being used to explain correlation between home country i and foreign country j;
“absolute difference” refers to the absolute difference between foreign country j and base
country i; and “ratio” refers to the ratio of country j’s variable, relative to the base country i.
37
Table A. 6. Panel estimation results, standardized data, trade-intensity is scaled by total trade
(not instrumented).
Rhs
variable
Rhs
variable
trade
coeff
/ p’value
/ p’value
country
Rhs
variable
Rhs
variable
trade
coeff
/ p’value
/ p’value
absolute difference
Rhs
variable
Rhs
variable
trade
coeff
/ p’value
/ p’value
ratio
LABOUR MARKET
NAWRU
EPL
union
tax wedge
benefit
replacement
ratio
0.00/0.15
0.00/0.15
0.00/0.10
0.00/0.21
0.14/0.66
-0.06/0.02
-0.22/0.00
0.11/0.36
0.00/0.18
0.00/0.18
0.00/0.24
0.00/0.26
-0.41/0.30
-0.02/0.59
-0.22/0.01
-0.43/0.01
0.00/0.21
0.00/0.18
0.00/0.14
0.00/0.24
-0.01/0.06
0.02/0.49
-0.04/0.39
0.09/0.21
0.00/0.16
0.05/0.58
0.00/0.17
-0.05/0.67
0.00/0.16
0.03/0.27
INDUSTRIAL STRUCTURE
Manufacturing
Oil
Sectoral
specialisation 1
Sectoral
specialisation 2
Sectoral
correlation
0.00/0.13
0.00/0.15
-0.30/0.40
-0.65/0.35
0.00/0.14
0.00/0.14
0.60/0.14
1.10/0.19
0.00/0.13
0.00/0.24
-0.04/0.24
-0.01/0.00
-
-
0.00/0.18
-0.04/0.85
-
-
-
-
0.00/0.16
-0.39/0.02
-
-
-
-
0.00/0.15
0.00/0.016
-
-
0.00/0.49
0.00/0.60
0.06/0.05
0.00/0.99
0.15/0.00
-0.24/0.18
PRODUCT MARKETS
Mark-up
Profit margin
Profit rate
0.00/0.50
0.00/0.61
0.00/0.12
0.00/0.63
-0.01/0.00
0.01/0.13
0.00/0.48
0.00/0.60
0.06/0.14
0.00/0.50
0.01/0.01
0.03/0.60
FINANCIAL MARKETS
Private Credit /
Stock market 1
deposit money /
Stock market 2
Bank deposits /
Stock market
Stock market
index
Net external
asset position /
GDP
Net FDI flow /
GDP
0.00/0.54
-0.02/0.00
0.00/0.86
-0.00/0.77
0.00/0.33
-0.00/0.57
0.00/0.41
-0.02/0.40
0.00/0.48
-0.06/0.02
0.00/0.16
-0.00/0.72
0.00/0.29
-0.03/0.00
0.00/0.44
0.00/0.82
0.00/0.17
-0.00/0.22
0.00/0.12
0.04/0.00
0.00/0.16
0.01/0.54
0.00/0.13
-0.01/0.00
0.00/0.02
-0.00/0.92
0.00/0.01
0.02/0.03
0.00/0.02
-0.00/0.83
0.00/0.02
0.02/0.50
0.00/0.02
0.00/0.79
0.00/0.02
0.00/0.80
FISCAL POLICY
Primary balance
Cyclically
adjusted
0.00/0.16
0.01/0.03
0.00/0.16
0.01/0.21
0.00/0.16
-0.00/0.99
0.00/0.17
0.02/0.00
0.00/0.16
0.01/0.19
0.00/0.16
-0.00/0.66
38
primary balance
Residual of
fiscal rule
0.00/0.17
0.03/0.01
0.00/0.16
0.02/0.13
0.00/0.14
0.00/0.00
MONETARY POLICY
Residual Taylor
rule
Volatility
exchange rate v
USA and DEU
Volatility
exchange rate
(bilateral)
Inflation
differential
Interest Spread
BP filtered real
interest rate
0.00/0.16
-0.03/0.00
0.00/0.20
-0.07/0.00
0.00/0.16
-0.00/0.82
0.00/0.15
0.03/0.35
0.00/0.16
0.11/0.01
0.00/0.15
-0.02/0.54
0.00/0.16
-0.00/0.20
-
-
-
-
-
-
0.00/0.17
-0.01/0.10
0.00/0.17
0.00/0.50
0.00/0.17
0.00/0.68
0.00/0.21
-0.08/0.00
0.00/0.16
-0.00/0.47
0.00/0.15
-0.07/0.00
0.00/0.15
-0.02/0.31
0.00/0.16
-0.00/0.61
ERM
0.00/0.22
0.15/0.00
-
-
-
-
EU
0.00/0.16
0.18/0.00
-
-
-
-
EMU
0.00/0.17
0.16/0.00
-
-
-
-
-
-
-
DUMMIES
OTHER
economic size
0.00/0.16
0.00/0.39
-
Notes:
The dependent variable is the correlation between deviation cycles of each bilateral pair of
countries; averages over 6 time periods are considered, 1970-1974, 1975-1979, 19801986,1987-1992, 1993-1999 and 2000-2003; all right hand side variables are expressed as
the absolute difference of standardised values for the pair of countries; for detailed definitions
and sources of the variables, see Table 3; estimation is random effects; “Country” refers to
the foreign country j variable being used to explain correlation between home country i and
foreign country j; “absolute difference” refers to the absolute difference between foreign
country j and base country i; and “ratio” refers to the ratio of country j’s variable, relative to the
base country i.; (a) variables are not standardised.
39
Table A. 7. Panel estimation results, standardized data, trade-intensity is scaled by total trade
(instrumented).
Rhs
variable
Rhs
variable
trade
coeff
/ p’value
/ p’value
country
Rhs
variable
Rhs
variable
Rhs
variable
Rhs
variable
trade
coeff
/ p’value
/ p’value
absolute difference
trade
/ p’value
coeff
/ p’value
ratio
LABOUR MARKET
NAWRU
EPL
union
tax wedge
benefit
replacement
ratio
0.00/0.16
0.00/0.14
0.00/0.14
0.00/0.24
0.30/0.47
-0.02/0.64
-0.12/0.25
0.19/0.27
0.00/0.16
0.00/0.14
0.00/0.14
0.00/0.27
-0.47/0.41
-0.11/0.12
-0.23/0.06
-0.41/0.14
0.00/0.13
0.00/0.17
0.00/0.15
0.00/0.19
-0.00/0.71
0.05/0.19
0.01/0.23
0.05/0.65
0.00/0.17
0.18/0.11
0.00/0.15
0.08/0.66
0.00/0.15
0.03/0.42
INDUSTRIAL STRUCTURE
Manufacturing
Oil
Sectoral
specialisation 1
Sectoral
specialisation 2
Sectoral
correlation
0.00/0.09
0.00/0.16
-0.85/0.07
-0.83/0.39
0.00/0.13
0.00/0.14
0.13/0.81
-0.48/0.68
0.00/0.10
0.00/0.14
-0.07/0.06
-0.01/0.02
-
-
0.00/0.13
-0.27/0.39
-
-
-
-
0.00/0.13
-0.53/0.03
-
-
-
-
0.00/0.13
-0.48/0.05
-
-
0.00/0.15
0.00/0.20
0.00/0.94
-0.02/0.61
0.08/0.31
0.47/0.42
PRODUCT MARKETS
Mark-up
Profit margin
Profit rate
0.00/0.18
0.00/0.25
0.00/0.37
0.00/0.28
-0.01/0.12
0.02/0.09
0.00/0.14
0.00/0.18
0.00/0.94
-0.00/0.57
0.01/0.50
-0.07/0.42
FINANCIAL MARKETS
Private Credit /
Stock market 1
deposit money /
Stock market 2
Bank deposits /
Stock market
Stock market
index
Net external
asset position /
GDP
Net FDI flow /
GDP
0.00/0.25
-0.02/0.27
0.00/0.27
0.00/0.86
0.00/0.12
0.07/0.01
0.00/0.24
0.03/0.33
0.00/0.98
-0.05/0.18
0.00/0.14
0.00/0.69
0.00/0.23
-0.03/0.06
0.00/0.30
0.00/0.70
0.00/0.15
0.06/0.04
0.00/0.05
0.06/0.00
0.00/0.13
-0.02/0.58
0.00/0.09
-0.01/0.00
0.00/0.38
0.00/0.54
0.00/0.28
0.02/0.06
0.00/0.38
0.01/0.61
0.00/0.45
0.03/0.41
0.00/0.37
0.02/0.74
0.00/0.38
0.00/0.89
FISCAL POLICY
Primary balance
Cyclically
adjusted
0.00/0.15
0.01/0.12
0.00/0.15
-0.00/0.84
0.00/0.13
-0.01/0.31
0.00/0.15
0.01/0.08
0.00/0.15
-0.00/0.62
0.00/0.13
0.00/0.89
40
primary balance
Residual of
fiscal rule
0.00/0.15
0.04/0.01
0.00/0.09
0.03/0.13
0.00/0.15
0.00/0.03
MONETARY POLICY
Residual Taylor
rule
Volatility
exchange rate v
USA and DEU
Volatility
exchange rate
(bilateral)
Inflation
differential
Interest Spread
BP filtered real
interest rate
0.00/0.14
-0.02/0.05
0.00/0.18
-0.06/0.03
0.00/0.18
-0.00/0.11
0.00/0.17
0.02/0.69
0.00/0.13
0.12/0.12
0.00/0.15
0.00/0.98
0.00/0.15
-0.00/0.16
-
-
-
-
-
-
0.00/0.15
-0.02/0.09
0.00/0.14
0.00/0.49
0.00/0.14
0.01/0.68
0.00/0.13
-0.09/0.00
0.00/0.14
-0.00/0.97
0.00/0.15
-0.02/0.97
0.00/0.14
-0.03/0.34
0.00/0.14
-0.00/0.96
ERM
0.00/0.18
0.13/0.03
-
-
-
-
EU
0.00/0.20
0.15/0.01
-
-
-
-
EMU
0.00/0.16
0.10/0.17
-
-
-
-
-
-
-
DUMMIES
OTHER
economic size
0.00/0.14
0.00/0.37
-
Notes:
The dependent variable is the correlation between deviation cycles of each bilateral pair of
countries; averages over 6 time periods are considered, 1970-1974, 1975-1979, 19801986,1987-1992, 1993-1999 and 2000-2003; all right hand side variables are expressed as
the absolute difference of standardised values for the pair of countries; for detailed definitions
and sources of the variables, see Table 3; estimation is random effects; “Country” refers to
the foreign country j variable being used to explain correlation between home country i and
foreign country j; “absolute difference” refers to the absolute difference between foreign
country j and base country i; and “ratio” refers to the ratio of country j’s variable, relative to the
base country i; (a) variables are not standardised.
41