Do Countries of The Same Development Degree Move on The Same Wavelength?
Rosmy Jean Louis*
Daniel Simons**
Abstract
Research on business cycle linkages show a tendency to model countries of relatively the
same degree of economic development jointly. However, the issue of whether countries
with the same degree of development move along the same business cycles has not been
investigated formally. The recent push by regional/economic bloc of countries toward the
adoption of one currency tends to suggest an interdependence of macroeconomic
activities but not necessarily a common cycle. Using the World Development Indicators
classification of countries as a proxy for countries level of development and the MarkovSwitching Vector Autoregression technique, we examine the business cycles of each
category of countries to determine whether 1) each group of countries follows its own
dynamics and is therefore subjected to the same business cycle and 2) whether these
cycles are independent of each other across groups. The preliminary results indicate that
countries in the High Income per capita group tend to be guided by similar business
cycles while some countries in the Middle and Low income groups have varying cycles
within the groups. We also determined that across groups, the wavelength was common
in most of the countries lending support to the existence of a common world cycle.
Keywords: Economic Development, Business Cycles, Markov Switching Vector
Autoregression, economic integration and globalization
JEL Classification: C0, E0, F1, F3
_________________
*,** Preliminary: Please do not quote without authors’ permission. Authors’ corresponding
address: Department of Economics and Finance, Faculty of Management, Malaspina UniversityCollege, 900 Fifth Street, Nanaimo, BC V9R5S5. Emails: jeanlouir@mala.bc.ca and
simonsd@mala.bc.ca.
1
Introduction
The research on business cycle linkages that abounds in the literature shows a tendency to
model countries of relatively the same degree of economic development jointly. The
works of Mills and Holmes (1999), Lumsdaine and Prasad (2002); Backus and Kehoe
(1992), Artis, Krolzig, and Toro (1999) are examples of such practice. All these studies
used OECD countries in their search for an international business cycle.
There have also been some attempts to jointly model countries of different economic
backgrounds. For example, Jean Louis (2003) and Jean Louis and Simons (2005)
investigates the business cycle linkages between North American countries but could not
conclude that Mexico shares a common cycle with the United States and Canada
combined. Mejia-Reyes (1999) models the United States along with the major Latin
American economies and arrives at a similar conclusion. The business cycles of the
United States and most of these countries are idiosyncratic, on a pairwise comparison
basis. Focusing on Asia, Girardin (2002) found little evidence of symmetry between
Japan and other South East Asian countries’ business cycle in a comparison of univariate
results.
Although current studies on international business cycle linkages implicitly give
indication that there is a link between business cycle synchronization and levels of
economic development, this research question has not been formally addressed in the
literature. McAdam (2003) investigates the business cycle features of the US, Japan and
the Euro area using Markov-switching techniques and concluded that even though these
three areas have a high trade and financial integration, there was no synchronization of
their cycles. The study failed to uncover any commonality among the features of the
three cycles. Using annual data on per capita GDP for 76 countries, Kose et al (2003)
examined the effect of trade and financial integration on synchronization of business
cycles. They compared correlations of output growth rates in the individual countries
with a constructed composite world output. They concluded that industrial countries had
stronger correlation with the world output than developing economies. Their conclusion
lends limited support that idea that globalization synchronizes macroeconomic activities.
2
In response to studies on monetary cooperation in Asia, Girardin (2004) looked at growth
cycles between Japan and East Asia. He used both correlation and regime switching
techniques to determine if the synchronization is regime-dependent. Japan seemed to be
synchronized with 5 out of the 10 selected East Asian countries in both the growthrecession and rapid-growth regimes but diverge from this synchronization during the
normal-growth phase implying that the synchronization of the cycles was not complete.
Chauvet and Yu (2006) used a dynamic factor Markov-switching model to discover that
there was some commonality among OECD countries but could not conclude the same
for the G7 countries.
Almost invariably, studies on business cycle synchronization have grouped
countries in regional blocs rather than on levels of development. Our contribution to the
literature is that we specifically model these countries based on income levels to
determine if there is a common component in the cycles of countries with the same
income level. There are at least three reasons for exploring this issue. First, there is the
idea that an All Americas’ Monetary Union could be a stronger economic bloc to
compete with the European Union and other rising economic powers such as India and
China. Second, there is the debate of “One World, One Money?” without abolishing
national currencies, which revisits the idea of a global money proposed by Keynes in
1944 (Mundell, 1995; 2001; Friedman 2001; Starr 2004). There has been a proliferation
of trade agreements among countries around the globe. On all accounts, our research
contributes to the overall debate.
3
Be it because of competition at the world level that gives rise to the creation of economic
blocs or because of globalization of markets that might necessitate a world currency to
facilitate international transactions, a study on business cycle linkages that accounts for
the level of economic development within and across blocs is enriching for the ongoing
debate. Moreover, business cycle synchronization is a prerequisite, in line with Mundell
(1961), for countries that contemplate higher forms of economic integration beyond
customs union. Countries forming those blocs must be subjected to similar shocks, hence
common cycle, in order for a “one-size-fits-all” monetary policy to be effective for each
member of the group. The absence of a common cycle in these unions may lead to severe
complications from monetary policies for the member nations.
Although there have been some research (Alesina and Barro 2002; Alesina, Barro, and
Tenreyro 2003) in the literature that explores the benefits of currency unions for countries
of different sizes and degree of specialization in the production of goods and services, it
still remains a subject of contention whether industrialized and less-developed nations
could find a mutually beneficial agreement. Disparities between the two groups of
countries in America and other continents are very well pronounced. Using Markovswitching vector autoregression technique and data on World Development Indicators
that classify countries according to their levels of income per capita, our paper sets out as
objectives to empirically estimate whether 1) each group of countries follows its own
dynamics and is therefore subjected to the same business cycle, and 2) whether these
cycles are independent of each other across groups.
The results indicate that countries in the High Income per capita group tend to be guided
by similar business cycles while those in Middle and Low income group have varying
cycles within each group. We also determined that across groups, there seem to be
symmetry in the cycles but some with lagged effects. Overall, our research supports the
view that the relative degree of economic development of countries does not matter for
synchronization of business cycle. The rest of the paper is organized as follows. Section 1
presents the methodology. Section 2 deals with the data and results and Section 3
concludes the paper.
4
Section 1
Methodology
The methodology of this paper is based on the original contribution of Hamilton (1989),
who
considers
a
univariate
Markov
switching
univariate
Markov-Switching
Autoregression (MS-AR) model with two regimes: recession and expansion, which
captures the changes in time series that can occur due to continuous shocks. The
Hamilton model has become one of the standard tools in time-series econometrics and
has been widely used in the literature. This model has been extended to accommodate
duration dependence in the transition probabilities (Durland and McCurdy 1994; Pelagatti
2005), three regimes: recession, normal growth, and high growth, and regime shifts in
intercepts (I), autoregressive parameters, and covariance matrix (H) (see Hamilton and
Raj 2002) for more advances in the field]. A substantial contribution is the generalization
to multivariate analysis, namely, MS-VAR or MS-VECM, which includes the works of
Krolzig (1997a, 1997b, 1998, 2000), Kim and Piger (2000), Clements and Krolzig(2002,
2004), and Sichel (1994). This paper follows the MSVAR generalization by Krolzig
(1997) and Artis et al. (2003) by estimating the final MSVAR models.
In its broadest form, a Markov switching mean vector autoregression of order p with M
regimes [MSM(M) −VAR(p)] is given by:
yt − µ(st) = A1(st)(yt−1 − µ(st−1)) + ... + Ap(st)(yt−p − µ(st−p)) + ut
(1)
where ut ~ NID(0, Σ(st)) and µ(st) is K×1 dimensional mean of the k-th dimensional time
series vector yt, A1(st), ...,Ap(st) are slope coefficients, ut is a disturbance term whose
covariance matrix Σ(st) along with all parameters of the model is dependent on the
unobservable regime, st. For example, in a three-regime set up:
µ(st) = µ1 < 0, if st = 1 (’recession’) with σ2(st) = σ21
µ(st) = µ2 > 0, if st = 2 (’normal growth’) with σ2(st) = σ22
µ(st) = µ3 > 0, if st = 3 (’rapid growth’) with σ2(st) = σ23
It is also expected that σ22< σ21< σ23 because episodes of rapid growth are normally more
volatile than periods of recession, which in turn are more volatile than period of slow
growth (in the vicinity of the steady state). One characteristic of this modeling device,
5
however, is that it allows for an immediate one-time jump in the mean following a shift
in regime, which, according to Krolzig, does not account for the possibility that the mean
may smoothly approach a new level after the transition from one state to another. In order
to factor in this feature, Krolzig therefore suggests a regime-dependent intercept ν(st) that
can take into account the smooth transition of the mean:
yt = ν(st) + A1(st)yt−1 + ... + Ap(st)yt−p + ut
(2)
where ν(st) = µ(st)(I – ∑ j =1 Aj ( st ) )
p
As Krolzig (1997a, Ch.3; 1998) has demonstrated, the [MSM(M) − VAR(p)] and the
Markov switching intercept vector autoregression of order p with M states [MSI(M) −
VAR(p)] are two different models that imply two different dynamic adjustments of the
observed variables in response to a change in regime. The former implies that a
permanent regime shift leads to an immediate jump in the mean growth rate of the
process to its new level. For the latter, a once-and-for-all regime shift in the intercept
gives rise to a dynamic response of the growth rate of the observed variable that is
identical to an equivalent shock in the white noise series ut.
The unobservable regime, st, is generated by a first-order Markov chain defined by
transition probabilities:
M
∑
pij = Pr(st+1 = j|st = i)
j =1
p ij = 1
i,j = 1…M
(3)
Where pij is the probability of being in regime j given that regime i has already
materialized. The transition probability matrix contains all possible cases:
⎡ p11
⎢p
P = ⎢ 12
⎢ .
⎢
⎣⎢ p1M
p21
p22
.
p2 M
.
.
.
.
.
.
.
.
.
.
.
.
pM 1 ⎤
pM 2 ⎥⎥
. ⎥
⎥
pMM ⎦⎥
By using the MSVAR process, we are able to determine whether there is an underlying
common unobserved component that governs the dynamics of the mean growth rates of
output within and across groups of countries. But our focal point is on the impulse
6
response functions for non-linear models introduced by Krolzig and Toro (1998) and
employed by Artis et al. (2003). In their view: “if the unobservable variable is to be
interpreted as the state of the business cycle, an alternative procedure is to look at cyclical
fluctuations in terms of the response of the variables to changes in the regime of the state
variable.” Following these author, our paper will concentrate on the path followed by
each country’s or groups of countries’ output growth in response to changes in regime of
the state variable in order to establish whether there is a link between business cycles and
degree of economic development.
In summary, we decompose the vector of growth rate of output per capita for the
countries in each group and across groups as the sum of two terms: a non-Gaussian
component and a Gaussian component. The former would capture the contribution of a
specific group’s cycle to individual countries while the latter would reflect each country’s
specific shocks. Rewriting Equation 2:
∆yt = A( L) −1ν ( st ) + A( L) −1 ∑1/ 2 ( st )ut
(4)
The contribution of country specific shocks to the growth rate of output is given by:
∂Et (∆yt + j )
lim
∂ut
Where Et(∆yt+j) is the output forecasted at time t for j periods ahead. However, when
j →∞
there is a switch in regime from recession (st = 1) to expansion (st = 2) or vice versa, the
impact in the long-run future level of output is given by:
lim{E (∆y
j →∞
t
t+ j
| st = 2) − Et (∆yt + j | st = 1)}
Where Et(∆yt+j|st = i) is the output forecasted in time t for j periods ahead when the
economy is in state i. So the difference between the two terms reflect the impact on the
growth rate of output when there is a change in regimes, which can be interpreted as the
response of each country to the “group’s” recession or expansion.
7
Section 2
Data
Data and Results
We used the annual growth rate of per capita Gross National Income (GNI) over the
period 1961 - 2001.
This data is taken from the 2004 CD-ROM of the World
Development Indicators published by the World Bank, which classifies countries as High
Income OECD (HIC_OECD), High Income non-OECD (HIC_other), Upper MiddleIncome (UMC), Low Middle-Income (LMIC) and Low Income (LIC) as a proxy for the
relative degree of economic development of countries. Accordingly, countries are
grouped as low income if their income per capita is $825 or less; lower middle income,
$826–3,255; upper middle income, $3,256–10,065; and high income, $10,066 or more.
The World Bank views low-income and middle-income economies as developing
economies.1
After cleaning up the data for missing values, we end up with an unbalanced panel of 24,
6, 22, 28, and 36 countries in the respective categories. From a presentation perspective,
since we are interested in the impulse responses, we surmise that our models could run
properly when a balanced panel of 6 countries in each category is used. We did not rely
on any specific criterion to choose the final sample shown in Table 1. We simply picked
the first six countries in each category, a justification for the alphabetical order observed
in Table 1. Therefore, our results are not tainted by the sample selection process or data
mining.
Table 1
Country Sample
HIC_OECD HIC_other
UMC
LMIC
LIC
AUS-Australia
DZA-Algeria
BGDBangladesh
BHSBahamas,
The
ARGArgentina
1
The use of the term is convenient; it is not intended to imply that all economies in the group are
experiencing similar development or that other economies have reached a preferred or final stage of
development. Classification by income does not necessarily reflect development status
8
AUT-Austria
BRBBarbados
BWABotswana
BLZ-Belize
BEN-Benin
BEL-Belgium
HKG-Hong
Kong, China
BRA-Brazil
BOL-Bolivia
BFA-Burkina
Faso
CAN-Canada
ISR-Israel
CHL-Chile
CHN-China
BDI-Burundi
DNKDenmark
MLT-Malta
CRI-Costa
Rica
COLColombia
CMRCameroon
FIN-Finland
SGPSingapore
GAB-Gabon
DOMDominican
Republic
CAF-Central
African
Republic
Since the data set on GNI is already expressed in growth rates and it is well known that
output per capita is integrated of order 1, all our series are stationary. We rely on the
Akaike Information Criterion in choosing the appropriate MSVAR models to run for each
group of countries. A Markov-switching intercept heteroskedastic vector autoregression
(MSIH(2)-VAR(1)) with 2 states and 1 lag reveals to be the best model for HIC_OECD,
HIC_other, and LIC countries; while UMC and LMIC data are better suited for a similar
model (MSMH(2)-VAR(1)) but with mean (M) instead. The two states are recession or
Regime 1 and expansion or Regime 2. These terms are used interchangeably.
Results
With the exception of Gabon (YGAB) in the Middle-Income group; Algeria (YDZA) in
the Lower-Middle-Income group; and Cameroon (YCMR) in the Low-Income group,
there is an underlying stochastic unobservable component that drives the business cycle
of each group of countries.2 Figures 1, 2, 3, 4, and 5 display our findings. The path of
output per capita forecasted over time is similar within groups. Of importance are the
2
There might be many reasons why these three countries exhibit such wild behavior but there are only two
that seem plausible to us at this point. The first might have to do with the fact certain countries might be at
the borderline of two categories while the remaining countries are right down or in the middle of a given
category of GNI per capita. The second might be due to shock events (e.g. a war) that create huge spikes in
growth rate of output per capita.
9
northwestern (Quadrant 2) and southeastern portion (Quadrant 3) of each figure that
shows the response of each country to the overall group’s recession and expansion, what
economists refer to as “cyclical shocks” or changes in the phases of the cycles.3
Figure 1
High-Income OECD Countries – Impulse Responses
Move to regime 1
YAUS
YBEL
YDNK
YAUT
YCAN
YFIN
Transition regime 2 to 1
0.0
0
-0.5
-1
-1.0
YAUS
YBEL
YDNK
YAUT
YCAN
YFIN
10
15
20
YAUS
YBEL
YDNK
YAUT
YCAN
YFIN
10
15
-2
0
5
10
Transition regime 1 to 2
2
YAUS
YBEL
YDNK
15
20
1.0
YAUT
YCAN
YFIN
0
5
Move to regime 2
0.5
1
0.0
0
0
5
10
15
20
0
5
20
3
For example, Quadrant 2 of Figure 1 is the response of each HIC_OECD country to a recession proper to
the develop world.
10
Figure 2
0.0
High-Income Other Countries – Impulse Responses
Move to regime 1
YBHS (cum)
YHKG (cum)
YMLT (cum)
0
YBRB (cum)
YISR (cum)
YSGP (cum)
Transition regime 2 to 1
YBHS (cum)
YHKG (cum)
YMLT (cum)
-5
-2.5
YBRB (cum)
YISR (cum)
YSGP (cum)
-10
-5.0
-15
-20
-7.5
25
0
5
10
Transition regime 1 to 2
15
20
0
5
Move to regime 2
10
15
20
15
20
15
10
10
YBHS (cum)
YHKG (cum)
YMLT (cum)
5
0
5
Figure 3
10
YBRB (cum)
YISR (cum)
YSGP (cum)
15
5
YBHS (cum)
YHKG (cum)
YMLT (cum)
20
0
5
10
YBRB (cum)
YISR (cum)
YSGP (cum)
15
20
Upper-Middle-Income Countries – Impulse Responses
Move to regime 1
Transition regime 2 to 1
0
0.0
YARG (cum)
YBRA (cum)
YCRI (cum)
YBWA (cum)
YCHL (cum)
YGAB (cum)
-2.5
YARG (cum)
YBRA (cum)
YCRI (cum)
-5
YBWA (cum)
YCHL (cum)
YGAB (cum)
-10
-5.0
0
5
10
Transition regime 1 to 2
15
20
7.5
10
0
5
Move to regime 2
10
15
20
5.0
5
YARG (cum)
YBRA (cum)
YCRI (cum)
YBWA (cum)
YCHL (cum)
YGAB (cum)
0
2.5
YARG (cum)
YBRA (cum)
YCRI (cum)
YBWA (cum)
YCHL (cum)
YGAB (cum)
0.0
0
5
10
15
20
0
5
10
15
20
11
Figure 4
2
Lower-Middle-Income Countries – Impulse Responses
Move to regime 1
Transition regime 2 to 1
YDZA (cum)
YBOL (cum)
YCOL (cum)
2.5
YBLZ (cum)
YCHN (cum)
YDOM (cum)
YDZA (cum)
YBOL (cum)
YCOL (cum)
0.0
0
YBLZ (cum)
YCHN (cum)
YDOM (cum)
-2.5
-2
-5.0
-7.5
0
5
10
Transition regime 1 to 2
15
20
4
0
5
Move to regime 2
10
15
20
7.5
5.0
2
2.5
0
0.0
YDZA (cum)
YBOL (cum)
YCOL (cum)
-2.5
0
5
Figure 5
10
YDZA (cum)
YBOL (cum)
YCOL (cum)
YBLZ (cum)
YCHN (cum)
YDOM (cum)
15
20
0
5
YBLZ (cum)
YCHN (cum)
YDOM (cum)
10
15
20
Low-Income Countries – Impulse Responses
Move to regime 1
Transition regime 2 to 1
0.0
0
-2.5
-5
-5.0
YBGD (cum)
YBFA (cum)
YCMR (cum)
YBEN (cum)
YBDI (cum)
YCAF (cum)
YBGD (cum)
YBFA (cum)
YCMR (cum)
-10
YBEN (cum)
YBDI (cum)
YCAF (cum)
-7.5
-15
0
5
10
Transition regime 1 to 2
15
20
0
5
Move to regime 2
10
15
20
15
YBGD (cum)
YBFA (cum)
YCMR (cum)
10
YBEN (cum)
YBDI (cum)
YCAF (cum)
4
YBGD (cum)
YBFA (cum)
YCMR (cum)
YBEN (cum)
YBDI (cum)
YCAF (cum)
2
5
0
0
0
5
10
15
20
0
5
10
15
20
12
In order to determine whether the synchronization of cycles that we observe within
groups for countries of similar degree of economic development also extends across
groups, we construct the growth rate of GNI for a representative country of each group by
taking the mean values over time. In this set up, a switch from Regime 2 to Regime 1 can
be interpreted as the response of each group of countries to a worldwide recession, while
the opposite is a worldwide expansion. As can be seen from Figure 6, irrespective of the
state of the world economy, developed countries as well as developing countries are
driven by the same unobserved component, hence, subjected to the same cycle.4 This is
by all account a surprising result, as one would expect each group’s cycle to be
independent of each other due to the huge disparities in the level of income per capita
across groups. Therefore, this research provides no supports to the claim that the current
World order of rich, poor, and super poor or the dominance of certain economic blocs
over others mainly has to do with worldwide shocks or bad luck.
4
There are a number of procedures that could be used to test for a common cycle. These include the
comparison of Log likelihoods by Hamilton and Quiros (1996); Peersman and Smets (2001); the Markovswitching identified VAR by Ehrmann et al. (2003), and the usual decomposition methods (HodrickPrescott 1980; Baxter and King 1995). Since we have 30 countries in total, any of these procedures adds a
lot of work with not much gain in terms of insights.
13
Figure 6
Impulse Responses Based on Worldwide Shocks
Move to regime 1
Transition regime 2 to 1
-2.5e-11
-1
-5e-11
-2
HICO
UMC6
LIC6
-7.5e-11
HICO
UMC6
LIC6
HIC
LMC6
HIC
LMC6
-3
-1e-10
0
5
10
Transition regime 1 to 2
15
20
3
0
5
Move to regime 2
3
HICO
UMC6
LIC6
2
1
1
0
5
10
HICO
UMC6
LIC6
HIC
LMC6
2
15
20
10
0
5
10
15
20
HIC
LMC6
15
20
Further analysis of the link between business cycles across groups of countries of similar
degree of economic development leads us to exclude those countries that have displayed
independent cycles within groups (namely, outlier countries) from the re-estimation of
World MSVAR. The main purpose is to account for the possibilities that these countries’
cycles could influence the world business cycle in our model. Figure 7 displays the
results. The message remains the same: the relative degree of economic development is
irrelevant for business cycles synchronization.
14
Figure 7
Impulse Responses with Exclusion of Outlier Countries
Move to regime 1
Transition regime 2 to 1
0.0
0
-0.5
-1
HICO
UMC5
LIC5
-1.0
HIC
LMC5
HIC
LMC5
-3
-1.5
-4
-2.0
5
HICO
UMC5
LIC5
-2
0
5
10
Transition regime 1 to 2
4
HICO
UMC5
LIC5
3
15
20
HIC
LMC5
0
5
Move to regime 2
2
2
10
15
HICO
UMC5
LIC5
HIC
LMC5
10
15
20
1
1
0
0
0
5
10
15
20
0
5
20
What can possibly explain the synchronization of cycles across countries of different
degree of economic development? First, there is large country and small country
hypothesis, which could easily be translated into developed and developing countries
hypothesis. From this perspective, whatever shocks that originate in a high income
country will easily permeate through the economies of lower income countries but the
converse is not true. Wars in Africa, guerilla in Latin America and in Asia, disturbances
in other parts of the world are not strong enough to deviate the develop world economies
from their long-run path.
The second explanation is also related to the first in that the justification of a world
business cycle might have to do mostly with trade across nations rather than capital
markets integration, though we do not model trade explicitly in our research. This line of
reasoning is consistent with Frankel and Rose (1998) Wynne and Koo (2000) that
countries with closer trade links are likely to have their business cycles synchronized.
Economic theory suggests that trade effects across countries can synchronize cycles. An
increase in domestic consumption generates increases in imports which in turn expands
15
the foreign economy showing a correlation between economic activities across countries.
A quick look at the data used in this research and in all research that use Real GDP to
uncover commonality in international business cycles indicates that trade is the main
connection of the underlying data generating process.5 For example, a developing country
that is in the midst of a civil war may not have much to export but its imports will
continue to grow to make up for the shortfall in domestic production. Its volume of trade
may even increase, irrespective of how large or small the common shocks that come from
the world are relative to the country’s specific shocks.
Our findings do not necessarily lend support to a world currency because it is not a study
on the feasibility of a single currency or a monetary union. Nonetheless, it does
contribute to further debate on the subject. The most important contribution of this paper
to the existing literature is that it is a formal attempt to establish whether our observations
about the way studies on international business cycle synchronization were carried out
responded to certain criterion in terms of the relative degrees of economic development
of the countries. We believe trade is likely the factor that could explain the
synchronization of individual countries’ cycles. For there may not be much connection
between Benin and Canada that could explain a common cycle between the two
countries, but sure enough there is a huge connection between Africa and America in
terms of trade, which these two countries are part, respectively. Hence, the
synchronization between high income and low income countries business cycles found in
this research.
5
Domestic output = Domestic absorption + Trade
16
Section 3
Conclusion
This paper has investigated the commonality in the business cycles of a panel of countries
that are classified according to their relative degree of economic development. There are
five groups with six countries each: High Income OECD, High Income non-OECD,
Upper Middle Income, Lower Middle Income, and Low income. The Markov-switching
vector autoregression is used to determine whether i) each group of countries follows the
same business cycle and ii) whether these cycles are independent of each other. We focus
mainly on the impulse responses of each country within groups in order to uncover
whether there is a common unobserved component that drives their business cycles.
Our findings show that countries of the same degree of economic development tend to
move according to the same wavelength. We then tackled the next question as to whether
there is a common unobserved component at the world level that governs the path of
output per capita for each group of countries. The results show that a World recession or
expansion has similar effects on both developed and developing economies. Overall, our
research suggests that the disparity in the levels of economic development across
countries is irrelevant for business cycles synchronization. We therefore argue that trade
in goods and services among countries is the potential factor underlying the common
cycle observed at the world level. Integration of financial markets still has a long way to
go before it could potentially undermine the gap between developed and developing
countries. For both physical and financial capital are relatively immobile across nations.
Although this paper contains a clear contribution to the existing literature, it is imperative
to interpret the results carefully within contexts. The paper is not intended to justify
monetary union at the world level nor is it an argument in favor of a world currency. It
can only contribute to the ongoing debate on these subjects. The strongest message of this
paper so far is that researchers might want to explore international business cycle
synchronization by relying on industrial product rather than other measures of output. All
these measures of output contain a trade component through which business cycle
transmission seems directly to take place. Therefore, there is a potential to finding
17
synchronization of business cycle where there might not be one. Industrial production
indexes might be less prone to this type of biasness, despite of the fact that some inputs
used in the production process are imported.
18
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