Download Does Geographical Diversification in International Trade Reduce

Does Geographical Diversification
in International Trade
Reduce Business Cycle Volatility?
ARIAN FARSHBAF 1, MAY 2014
Abstract: This paper studies empirically the effect for business cycle volatility in open
economies of geographical diversification in international trade. Using a panel data of
133 countries over five sub-periods covering 1962-2006 and applying Instrumental
Variables Generalized Method of Moments estimation to address potential simultaneity,
it is shown that greater diversification of trade among trade partners, measured by the
size of a Herfindahl index, is associated with significantly lower domestic output
volatility, although the estimated impact for lower consumption volatility is not
statistically significant. We also find that diversified trade insulates output fluctuations
from various fiscal, financial, and international shocks. In addition, analyses of the newly
constructed trade indicators introduced in this paper demonstrate that trading with
economies that are larger, more developed, or with lower business cycle volatility
reduces domestic output volatility. Finally, home business cycles being less synchronized
with those of trading partners is associated with lower output volatility, once an external
shock is controlled for.
JEL Classifications: F15, F40, F44
Keywords: Business Cycle Volatility, International Trade, Geographical Diversification,
Trade Partners, International Business Cycle Synchronization
1
This paper is based on a chapter of my Ph.D. dissertation on “Business Cycles Volatility and Global Trade”
completed at the Department of Economics, University of Southern California. Contact: [email protected].
† I am grateful to all members of my Ph.D. Committee: Professors Caroline Betts, Robert Dekle, Cheng Hsiao,
John Matsusaka and Vincenzo Quadrini for their guidance, support and valuable advice throughout. I would like to
also thank professors Jeffrey Nugent and Guillaume Vandenbroucke for their helpful feedback and advice. All errors
and mistakes are mine.
1
1
Introduction
Economic globalization, as measured by significant increases in international flows of goods and
capital relative to the size of national economies, has introduced both challenges and
opportunities for individual nations by exposing them to an array of international intra-temporal
shocks, while providing for mitigation of idiosyncratic shocks through inter-temporal trade. The
thesis of this paper is that geographical diversification in trade ‒ trading with a larger number of
countries with relatively equal trade shares ‒ can reduce the domestic output risk of exposure to
specific intra-temporal shocks in trade partners, and reduces domestic consumption risk by
providing a more diversified set of lending and borrowing opportunities. In short, this paper tests
the hypothesis that greater geographical diversification reduces business cycle volatility,
conditional on a country’s level of trade openness.
Business cycle volatility2 is viewed as a major indicator of national economic performance,
perhaps mostly due to robust links with other macroeconomic performance indicators. Barlevy
(2004) and Mendoza (2000) show a significant welfare loss ‒ much higher than the “trivial”
amount argued by Lucas (1987) ‒ as a result of higher macroeconomic volatility, while Ramey
and Ramey (1995) among others show a strong negative correlation between business cycle
volatility and long-run growth. On the other hand, lower business cycle volatility can be regarded
as the result of successful stabilization policies pursued by policy makers.
Empirical work on the subject of business cycle volatility (hereafter “BCV”) falls into three
major groups. One strand studies BCV as a time series, with a focus on a describing the timeseries behavior for a specific country or group of countries. Blanchard and Simon (2002), is one
of the most frequently cited works of this kind, where various properties of the growth rate of the
U.S. output such as persistence and volatility are studied. They find that a decline in output
volatility, rather than lack of relatively large shocks, is behind the extended U.S. economic
expansion in the 1990s. Kose, Otrok and Whiteman (2003) decompose business cycles into
components attributable to domestic and international factors through an unobservable common
factor analysis. Usually time series studies are primarily descriptive and non-structural and no
conditional relationships between business cycle volatility and other variables are examined.
2
Business cycle volatility is defined as the standard deviation of a macroeconomic aggregate’s growth rate or its
cyclical volatility as will be explained in detail in Section 2.
2
A second line of literature tries to account for variations in volatility across nations and over
time, by considering unconditional correlations with factors proposed as potential sources of
macroeconomic fluctuations. Karras and Song (1996) is an example which finds volatilities in
exchange rates and money supply, the degree of openness to trade, and smaller government size
are positively correlated with higher business cycle volatility.
The third line of research, which the current paper falls within, focuses on measuring the
conditional correlation of business cycle volatility with a specific factor or related factors
believed to be potentially important in influencing business cycle behavior. Ferreira da Silva
(2002) for example examines econometrically the impact of financial development on BCV.
Similarly, in our analysis, we do not try to account for as much variation in the volatility of
macroeconomic fluctuations as possible ‒ although appropriate country specific characteristics
and major factors that contribute to aggregate volatility are included as controls. The goal, rather,
is to investigate the impact of an important aspect of international trade, namely geographical
diversification, on the volatility of fluctuations.
The relationship between trade openness as one of the proxies for economic globalization3
with business cycle volatility has been extensively investigated in the literature. Due to the
ambiguous stance of theory regarding the relationship, it has been tackled as an empirical task
with a close-to unanimous finding that trade openness is a destabilizing factor for business cycles
even after controlling for many country characteristics. Di Giovanni and Levchenko (2009),
Karras and Song (2003) and Easterly, Islam and Stiglitz (2001) are among many who reached
this conclusion. By contrast, Cavallo (2008) reaches his main finding ‒ namely trade openness
has a net stabilizing effect on macroeconomic fluctuations ‒ after separating the effect of terms
of trade (TOT) volatility from the overall impact of openness. Some studies have produced
caveats to the general conclusion that trade openness aggravates domestic output volatility. For
example, Kose, Prasad and Terrones (2003) investigate a potential non-linearity for financial
openness-output volatility relationship and find that the stabilizing effect of financial openness 4,
the capital account equivalent of trade openness, is realized only after a certain threshold of
3
Farshbaf (2012b) challenges treating “de facto openness” alone as a proxy for economic globalization.
Financial openness is defined as gross stocks of capital flows to GDP, based on data constructed by Lane and
Milesi-Ferretti (2001). Gross capital flows is the sum of inflows and outflows in terms of foreign direct investment
(FDI), portfolio investment and bank lending.
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3
financial openness is attained, which potentially explains why emerging markets have not yet
reaped benefits of financial liberalization. A similar analysis performed in Farshbaf (2012b)
shows that trade openness and output volatility also share a non-linear relationship; trade
openness increases output volatility up to a threshold level of openness, estimated at around
trade/GDP ratio of 88 percent, after which level increases in openness reduce output volatility. In
addition, it documents that countries which are more financially integrated are less susceptible to
the increased output volatility associated with greater trade openness. In the current paper, the
role of geographical diversification is studied as a potential counter to the adverse output
volatility impact of various shocks for a given level of trade openness and country-specific
characteristics.
Despite empirical investigation of the trade openness-volatility relationship, the literature has
been silent on the role of geographical diversification of trade. The only direct work we later
became aware of is a mimeograph by Bacchetta et al (2007) that examines the effect of export
diversification on output volatility. By contrast, there are numerous works studying the impact of
sectoral concentration in trade for economic outcomes such as growth and fluctuations. For
example, Di Giovanni and Levchenko (2009) find the specialization that emerges from opening
up to trade increases the volatility of output growth. Our paper attempts to bridge the existing
gap in the empirical literature with a comprehensive investigation of the impact of geographical
diversification in international trade on business cycle volatility.
An important contribution of this paper is the construction of four new international trade
indicators that capture various economic characteristics of trade partners for each country in the
dataset, using bilateral trade data. We examine how, on average, across nations and over time,
trading with more developed nations, larger economies, countries with more volatile business
cycles, and countries which share with the domestic economy higher degrees of correlation
among business cycle indicators influences the volatility of business cycles at home. We regard
this study as a portfolio view of international trade, where a country’s trade partners constitute
the assets in the portfolio and the performance of the portfolio is assessed by the reduction in
business cycle volatility, which in turn depends on the economic characteristics of the trade
partners and the correlation of business cycles among them.
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In addition, we account for the possibility that the level of geographical diversification is
endogenously determined as a policy choice outcome, perhaps selected to be a function of
current macroeconomic volatility. This naturally raises the issue of simultaneity and calls for an
appropriate identification strategy.
Using a panel of 133 countries 5 over 5 sub-periods covering 1962-2006 and following
instrumental variable General Method of Moments technique which addresses potential
simultaneity, the paper shows that for a given level of trade openness, more geographically
diversified international trade in either imports or exports, captured by a lower value for a
Herfindahl index (HI), reduces business cycle volatility. Further, for a given level of exposure to
international trade and geographical diversification, trading with economies that are more
developed, larger and with less volatile output fluctuations is also associated with lower output
volatility at home. Our measure of international business cycle synchronization, which is a
weighted6 multilateral correlation of a country's business cycles with those of its trading partners,
is also strongly associated with lower volatilities of major domestic macroeconomic aggregates
which is counter to the intuition of international business cycle theory. However, once we control
for some external shocks, like terms of trade volatility, results are reversed in favor of the theory.
In other words, countries are able to benefit from international risk sharing most ‒ in terms of
lower aggregate volatility at home ‒ when this measure of international synchronization is lowest,
on average and conditional on external shocks.
Another important question addressed in our analysis is whether geographical diversification
has any mitigating effect on shocks of various origins. The results indicate that at higher levels of
geographical diversification in trade, the adverse effects on output volatility of fiscal, financial
and international shocks are significantly reduced while there is no statistically significant impact
for money supply shocks.
One noteworthy observation from descriptive statistics section is that despite increased
geographical diversification measured by HI over the past 5 decades in our sample, countries, on
average, have maintained trade with relatively similar type of international partners in terms of
5
See Table A3 in the appendix for the full list of countries.
Throughout this paper, “weights” refers to using the relative share of imports or exports of the international trading
partners in the calculation of an indicator.
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their level of development and size of the economy; namely the highly advanced economies with
large shares in the trade volume of the rest of the world.
Findings in this paper have three main implications. One is that geographical diversification
of international flows of goods and services is an important measure of globalization, which has
not only expanded with the rise in trade openness but has a significant impact on macroeconomic
performance. Second, since the estimated impact of enhanced diversification by only 10
percentage points in the sample is a reduction in output volatility of about 30 percent, this
implies that trade diversification ‒ suitably conditioned ‒ could be a potentially important policy
goal to increase domestic welfare. Finally, for stabilizing purposes, economic characteristics of
trade partners, like the size of their GDP and development level, over which trade is diversified
are also important influencers and might be the key in understanding the mechanisms through
which geographical diversification smoothes business cycle volatility.
The remainder of this paper is organized as follows: Section 2 and 3 describe the data and
variables, and present some stylized facts about evolution of the main variables of interest over
time and across different country samples. Section 4 contains the empirical analyses that
examine three different relations between volatility and diversification variables. Section 5
summarizes our findings followed by references and appendix.
2
Data and Variables
The basis for our empirical analysis is a panel data set of annual observations for 133 countries
over 1962-2006. The entire sample period is then divided into 5 sub-periods with a length of 9
years each. For the variables representing each period, either the beginning of period values,
period averages or standard deviations of the log differences or de-trended series are used. For
aggregates such as GDP per capita, used to characterize individual countries, the beginning of
period value is used to represent a country's development status for example; for the Herfindahl
index that captures geographical diversification, period averages are taken; and for volatility
measures, we apply the standard deviation of the log differences, growth rates or de-trended
variables for each sub-period. In this, we follow conventions in the existing empirical literature.
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This paper relies heavily on bilateral international trade data for the construction of trade
variables, including the Herfindahl index (HI) for geographical diversification, and other
variables that capture various characteristics of international flows. The source for the bilateral
trade data is Dyadic Trade Data which is based on the International Monetary Fund's Direction
of Trade Statistics. The obtained raw trade flow data is organized into 58 annual matrices of
bilateral trade for the period 1950-2007 where rows and columns represent the flow of trade
from one country to another in current U.S. dollars. The bilateral trade matrices are used to find
the relative shares of trade ‒ either imports or exports ‒ with each other country that are applied
as weights for computing the HI and other trade indicators, in combination with national income
data from Penn World Table, as described below. We also use as control variables some series
from the World Bank's World Development Indicators database. For the identification process,
instrumental variables are from Andrew Rose's compiled dataset that contains many
geographical and cultural time invariant variables. A complete reference to data sources is
included in the appendix.
The panel data that is constructed consists of 133 countries and the main criteria in choosing
them were that a) a country maintain its independence throughout the sample period of 19502007, and b) bilateral trade data be available for the most part of the period. For example, the
states that became independent following the dissolution of former Soviet Union in 1991 are
excluded from the panel. Further, as it is customary in almost all empirical business cycle
volatility literature, with some degree of arbitrariness, the entire period is divided into 5 subperiods of 9 years each. While in most papers periods of 10 years' length are used, this would
lead to some loss in informative data in more recent years or in earlier periods. Our organization
of the data is such that the panel makes use of most of the available data and therefore led to the
following 5 sub-periods: 1) 1962-70, 2) 1971-79, 3) 1980-88 4) 1989-97 and 5) 1998-2006.
The construction of the main variables follows.
2.1
Business Cycle Volatility
There are two main approaches to the construction of BCV measures in the literature. In some
analyses, the volatility variable is calculated as the standard deviation of the cyclical component
of the time series or of its growth rate over past quarters or years. Therefore, the constructed
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variable is a continuous function, like a moving average, as in Blanchard & Simon (2002).
Elsewhere, which is the case for most cross-sectional and panel data analyses like ours, the
whole period is divided into sub-periods and the volatility is computed as the standard deviation
of the growth rate or of the de-trended time series in each period, hence allowing the
characterization of variations across periods. For example, the period covering 1971-79 in our
panel data has the most pronounced output volatility among the full sample period, likely due to
the contemporaneous oil supply shocks.
The latter approach has the advantage that the length of each period can be altered to check
the robustness of the results subject to the choice of sub-sample, or it can also be reduced to a
cross-sectional analysis by taking each sub-period as one and examine the evolution of the
relationship over time as is done in some papers. In most cases that we analyze, our estimated
relations are robust to such changes.
The de-trending method for the underlying macroeconomic data series, and choosing the
corresponding parameters, and whether to use per capita or total values are also among the
choices that must be made in constructing BCV variables. Dominant in the empirical literature is
use of the standard deviation of the growth rate of per capita output or its components, over each
sub-period. Alternatively, the standard deviation of the filtered time series, usually using
Hodrick-Prescott (HP) filter with a weighting parameter value of 100 recommended for annual
data, is used. In this paper, BCV is computed based on all four methodologies. Although the
regression results are sometimes sensitive to this choice, the overall pattern remains the same.
This paper follows the dominant approach of using the standard deviation of the growth rate of
per capita aggregate in the remainder of this paper. Table A1 in the appendix displays the
correlations among various BCV measures. Formally, for any country i at any period t, BCV of
GDP per capita is defined as:
𝐵𝐶𝑉𝑖,𝑡 ≜ 𝑆. 𝐷. {∆ 𝑙𝑜𝑔(GDP𝑃𝐶𝑖,𝑠 )}, 𝑠 ∈ {𝑡 − 8, 𝑡}, 𝑡 ∈ {1970, 1979, 1988, 1997, 2006} (1)
2.2
Herfindahl Index for Geographical Diversification in Trade
The Herfindahl Index for geographical diversification is defined as the sum of the squares of
imports (exports) of each trade partner as a share of total imports (exports) of the country in
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question. Obviously this and other trade based indices, all have an import and an export
counterpart. By definition, the Herfindahl index for imports and exports ‒ henceforth HIM and
HIX ‒ can vary between zero and unity with a larger number indicating less diversification. In
the following formula (Mj/M) stands for the relative share of imports from country j to country i
for which, geographical diversity in imports measure is being constructed:
𝑀j 2
𝐻𝐼𝑀𝑖 = ∑ ( ) ,
𝑀
𝑗 = 𝑖𝑛𝑑𝑒𝑥(𝑎𝑙𝑙 𝑡𝑟𝑎𝑑𝑒 𝑝𝑎𝑟𝑡𝑛𝑒𝑟𝑠)
(2)
𝑗
Here 𝑀(= ∑𝑗 𝑀𝑗 ) is total imports of country i from all trading partners. The Herfindahl index
for exports (HIX) is computed analogously. It should be noted that these measures of trade
diversification convey no information about the characteristics of trade partners. Two countries
might have same HI but that does not tell us anything about the type of countries they are trading
with, whether trade partners are relatively large or developed economies for example, although
these features might also be important influences on volatility. A country might gain more in
lower volatility by having commercial relations with a large and highly developed economy like
the U.S. rather than with a smaller and less developed country with the same trade share.
In terms of the expected impact of these measures of diversification on output volatility,
higher diversification means a more evenly distributed trade among a given number of
international partners, which we hypothesize would reduce the aggregate risk of specific
international shocks and permit more channels of borrowing and lending in the wake of a shock
of any specific origin.
2.3
Size of International Markets
We use the weighted average GDP of the economies that trade is conducted with as a proxy for
the size of international markets that a country has access to, with the weights being the shares in
total exports or imports.
TP𝐺𝐷𝑃𝑖 = ∑(𝑆𝑗 . 𝐺𝐷𝑃𝑗 ) ,
𝑗 = 𝑖𝑛𝑑𝑒𝑥(𝑎𝑙𝑙 𝑡𝑟𝑎𝑑𝑒 𝑝𝑎𝑟𝑡𝑛𝑒𝑟𝑠)
(3)
𝑗
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Here Sj is the import or export share of partner j in the total imports or exports of country i. In
theory, the higher the value of TPGDP, it is more likely that the domestic country is able to
import excess domestic demand from abroad or export excess supply to international markets
through international capital flows. Therefore, ceteris paribus, we expect this variable to have a
stabilizing effect on domestic aggregate fluctuations.
2.4
Development Level of Trading Partners
Trading with more developed countries can potentially be beneficial in various ways and is
indicative of certain administrative capabilities of a country; establishing and conducting
commercial relations with more advanced economies requires maintaining higher standards in
various stages of doing business. In the long-run, such trades can influence own growth through
technological transfers. Therefore, we expect this variable might proxy for a wide range of
institutional and administrative capacities associated with lower levels of output volatility at
home country.
TP𝐺𝐷𝑃𝑝𝑐𝑖 = ∑(𝑆𝑗 . 𝐺𝐷𝑃𝑝𝑐𝑗 ) ,
𝑗 = 𝑖𝑛𝑑𝑒𝑥(𝑎𝑙𝑙 𝑡𝑟𝑎𝑑𝑒 𝑝𝑎𝑟𝑡𝑛𝑒𝑟𝑠) (4)
𝑗
In the above formulation, TPGDPPC is the weighted average GDP per capita of trade partners
where, as before, the weights Sj's are their import or export shares in the totals of the country for
which the indicator is being constructed for.
It should be noted that in the computation of both TPGDPPC and TPGDP, international
partners' output and output per capita from the year 2000 are used in order to control for their
growth and focus on the type or size of the economies that trade is being conducted with.
2.5
Business Cycle Volatility of Trading Partners
This variable can be thought of as a measure of the magnitude of exposure to international
shocks that a country is facing as a result of trade, computed as the weighted average of trading
partners' business cycle volatilities. The latter variable is calculated by taking the standard
deviation of the growth rate of GDP per capita over the past 9 years, which is consistent with the
measure of BCV that we are using for the country itself in empirical analyses.
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TP𝐵𝐶𝑉𝑖 = ∑(𝑆𝑗 . 𝐵𝐶𝑉𝑗 ) ,
𝑗 = 𝑖𝑛𝑑𝑒𝑥(𝑎𝑙𝑙 𝑡𝑟𝑎𝑑𝑒 𝑝𝑎𝑟𝑡𝑛𝑒𝑟𝑠)
(5)
𝑗
A higher TPBCV for home country implies that it is trading with, on average, relatively more
economically volatile countries, potentially subjecting demands for its exports or supply of its
imports to higher fluctuations, which in turn may be reflected in higher GDP volatility at home.
A similar variable appears in Bacchetta et al (2007).
2.6
IBC Synchronization
The construction of this new variable is an attempt to measure the level of business cycle
synchronization of a country with respect to its international trading partners. International
business cycle synchronization or IBCSync, is basically a multilateral correlation variable that
describes how a country's business cycles co-move with those of its trading partners. Formally,
the construction of this variable involves first, computing the cyclical component of the real
output time series for each country using HP filter and then calculating the correlations between
the cyclical components of the country in question with those of all its trading partners. Next,
these bilateral correlations are averaged with weights according to their shares (Sj) in the total
volume of trade (sum of exports and imports or either of them) of country i.
𝐼𝐵𝐶𝑆𝑦𝑛𝑐𝑖 = ∑(𝑆𝑗 . 𝐶𝑜𝑟𝑟(𝐵𝐶𝑖 , 𝐵𝐶𝑗 ) ) ,
𝑗 = 𝑖𝑛𝑑𝑒𝑥(𝑎𝑙𝑙 𝑡𝑟𝑎𝑑𝑒 𝑝𝑎𝑟𝑡𝑛𝑒𝑟𝑠)
(6)
𝑗
A high value for IBCSync implies that the country's cyclical component of GDP is closely
following those of its partners or the country is, on average, trading with economies whose
cyclical behavior is similar to its own.
The idea for constructing this variable was that risk sharing and hence smoothing of business
cycles are more likely, the lower the correlation of a country's business cycles with those of its
trade partners. This is possible, at least in theory, by borrowing from booming trading partners
when the country itself is experiencing recession and on the other hand the domestic country's
boom phase impact for output volatility can be curbed by lower demand for its exports from the
rest of the world if they are in recession. Hence from this perspective, being less internationally
synchronized should help smooth business cycles at home. As will be shown in section 4.5 the
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preliminary empirical results are consistent with this hypothesis, only after controlling for one
source of volatility in net exports such as term of trade.
3
Descriptive Statistics
This section provides a descriptive summary of the main variables of interest based on sample
averages for trade diversification using our newly constructed trade variables. As discussed, the
panel data consists of 133 countries over the time span of 1962-2006 divided into five subperiods of 9 years: 1962-70, 1971-79, 1980-88, 1989-97 and 1998-2006. We also look at
different groups of countries separately. Tables 1-4 report sample averages for the full sample,
and also for the OECD, Latin America, and for “other countries” which excludes the latter two
groups from the full sample. BCVY and BCVC stand for the business cycle volatility measures of
output and consumption, respectively. Consumption volatility is studied to measure the degree to
which trade diversification improves consumption smoothing through inter-temporal trade
opportunities. Most trade variables have import and export versions and this is indicated by a
suffix “.m” or “.x” at the end of each variable.
The behavior of BCV statistics over alternative sub-periods may reflect the macroeconomic
experience of the world economy since the Second World War. For almost all four country
groupings (Tables 1-4), including the full sample, the cross-country average BCVY peaks during
the 1971-79 period, the era of oil supply shocks. This period also is characterized by a greater
measure of business cycle synchronization for countries in each grouping. For example, the
weighted average business cycle correlations across trading partners jumped from about 0.04 in
1960s to about 0.13 in 1970s for the entire sample and continued rising thereafter. This rise in
international synchronization during 1970s is visible across all four country groupings and even
within the highly inharmonious Latin America group, the only group with an average negative
IBC stance in the 1960s (a negative IBCSync variable means that the country's business cycle is
moving on the opposite phase of its trading partners, on average). For Latin America (Table 3),
the indicator rises to levels of synchronization during 1970s that are only seen towards the
beginning of 21st century in the rest of the world. Overall, these are all signs of a dominant
global shock.
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As Tables 1-4 suggest, the magnitude of the BCV of GDPPC declines after the 1970s subperiod, and in 3 cases it settles at levels lower than those that prevailed during the initial period
of 1960s. This decline relative to the initial period is nearly 9% for the entire sample (Table 1),
and about 30% for the OECD group (Table 2), while the Latin America countries (Table 3) settle
at almost the same levels of output volatility that existed in 1960s, but it still is 25% lower than
those in 1970s. This general moderation, registered by our relatively large panel dataset, is more
pronounced by the OECD group. Cross-grouping comparison reveals that, not surprisingly, the
OECD group has the lowest aggregate volatility during all sub-periods, with volatility levels
about half those of the full sample.
The following observations are worth making regarding consumption volatility: First,
contrary to the notion of consumption smoothing, for the entire sample the volatility of
consumption component is, on average, higher than that of output. The relative volatility of
consumption to that of output (BCVC/BCVY), a measure of consumption smoothing, is below the
critical value of unity only for the most advanced (OECD) countries as shown in Table 2, on
average. The most anomalous case belongs to the Latin America group (Table 3) during 19891997 sub-period, which follows the 1980s debt crises and the 1994 speculative crisis that hit
these economies. Interestingly, these crises seem to have the highest impact on consumption
rather than output volatility. To summarize, consumption smoothing is simply not occurring
according to the predictions of macroeconomic theory, for most of the countries in the sample.
In addition across Tables 1-4, except for the OECD group, the volatility of consumption
reaches its high-point during the period 1989-1997. The international supply shocks of 1970s
seem to be a potential reason for increased output volatility while the financial crises of 1980s
through 90s might account for the large volatility of consumption. This observation suggests that
in trying to explain the consumption smoothing anomaly one should look at financial factors that
affect households’ consumption-saving behavior.
Finally, for all four country groupings, the relative volatility measure (BCVC/BCVY) has
remained more or less at the same levels over time in the past 5 decades, with relatively little
intra-group variations. In other words, despite all innovations and developments in the financial
sector, domestic or international, and integration of international commercial and financial
markets that globalization has brought about, we do not see any obvious improvement for
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consumption smoothing. We leave further analysis of this feature of the data for future work, and
focus on output volatility as a measure of BCV in assessing the role of trade diversification.
Trade openness or the total volume of trade (X+M) to GDP has increased more than 40% over
the entire sample on average since 1960s as can be seen in Table 1. We also see steady declines
in Herfindahl indexes for imports and exports, indicating a general movement towards higher
geographical diversification for both import and export exchange. Yet, as Table 1 suggests,
exports have systematically enjoyed higher levels of diversification while we see frequent
instances of temporary increased concentration for exports in the averages over time. The
individual time series for both indexes can actually be quite volatile for specific countries in
some cases, as Figure series A1 in the appendix for a select group of countries illustrates. They
correspond to the annual, non-averaged HI time series for the entire period of 1960-2007. It
should be noted that portrayal of this variable on the same scale even for such advanced
countries like the United States (USA) and the Great Britain (GBR) would not be insightful as,
for example, GBR's variations in HIX are within the lowest band of the diagram for USA.
Next we study the variables that capture trade partner characteristics. Reported TPGDP is in
6
10 format for simplicity, and both TPGDPPC and TPGDP values here are based on output per
capita and output from the year 2000 for all periods. This is done to focus only on the type of
economies that each country is trading with; GDP per capita being a proxy for development level
and GDP being a measure of the size of the trading partner and proxy for the size of international
markets. Admittedly, this approach has the disadvantage of ignoring the big changes that some
countries have gone through over the last 5 decades. Nonetheless, our empirical results are robust
to the choice of year ‒ either current or year 2000 ‒ for the construction of these indicators.
Latin America (Table 3) seems to enjoy commercial relations with richer and larger countries,
on average, than OECD and the “other countries” group do. This is at least in part due to being
located in the same continent as the largest economy in the world (the United States) which has a
relatively high share of trade with these countries. We have constructed the relative shares of the
U.S. and also G-7 countries7 in the total trade of each country. As Table A2 in the appendix
7
G-7 countries include: Canada, France, Germany, Italy, Japan, United Kingdom, and United States.
14
shows, the average share of the U.S. in the total trade of Latin America countries is about 36% as
compared to 15% for the full sample.
A notable observation emerges after reviewing the last three variables. In general, TPGDP
and TPGDPPC do not seem to follow a clear trend when we consider both export and import
versions while a downward trend for HIX and especially HIM is more prominent, on average. In
addition, quantitatively, the variation coefficients for TPGDP and TPGDPPC are between 3-5%,
while the same normalized measures of variation for HIM and HIX are between 11-18%,
respectively, for the entire sample and over 5 sub-periods in Table 1. In other words, despite
measurable diversification increases by countries reflected in their Herfindahl indexes, average
characteristics of trading partners show higher stability and no clear trend. These observations
point to the possibility that international commercial relations have been maintained with
countries of relatively similar economic size and development level throughout the globe and
over 5 decades, and these trade partners are the most advanced and largest economies in the
world, on average.
The TPBCV variable averages suggest that the mean exposure to business cycle volatility
from trading partners does not show much variation over time and across different groups
(Tables 1-4), with the exception of the period 1971-79 in which it reaches the maximum.
Average output volatility of trade partners declined since the 1970s and has settled at levels that
are quite comparable to our sample's initial period in 1960s. Perhaps more importantly, this
variable is lower in value than the BCV level of a typical country during any period. More
specifically, while the average output volatility (BCV) of countries in the full sample (Table 1) is
about 5.4%, the TPBCV is only around 2.3%, indicating a diversification across economies that
are at least half as volatile as the country itself, on average.
15
Tables 1-2: Average Period Statistics
Full Sample of 133 Countries
1962-1970
1971-1979
1980-1988
1989-1997
1998-2006
Average
BCVY
0.047
0.063
0.056
0.059
0.043
0.054
BCVC
0.058
0.077
0.075
0.093
0.063
0.074
BCVC / BCVY
1.30
1.22
1.34
1.53
1.48
1.38
(X+M)/GDP
.61
.65
.66
.71
.85
.70
HIX
0.220
0.183
0.173
0.173
0.181
0.185
HIM
0.195
0.153
0.145
0.140
0.126
0.151
TPGDPPC.x
22278
22707
22076
23868
23389
22894
TPGDPPC.m
22139
21845
21766
23449
22017
22257
TPGDP.x
2658
2767
2760
2801
3098
2824
TPGDP.m
2792
2524
2511
2651
2590
2610
TPBCV.x
0.027
0.043
0.032
0.027
0.025
0.031
TPBCV.m
0.026
0.044
0.033
0.027
0.027
0.031
IBCSync.x
0.042
0.135
0.138
0.142
0.207
0.138
IBCSync.m
0.044
0.120
0.141
0.156
0.211
0.140
Sample of 28 OECD Countries
1962-1970
1971-1979
1980-1988
1989-1997
1998-2006
Average
BCVY
0.028
0.035
0.029
0.029
0.020
0.028
BCVC
0.028
0.028
0.027
0.024
0.018
0.025
BCVC / BCVY
0.95
0.84
0.95
0.83
0.91
0.89
(X+M)/GDP
.32
.40
.46
.58
.80
.51
HIX
0.150
0.145
0.133
0.133
0.131
0.138
HIM
0.152
0.136
0.134
0.130
0.118
0.134
TPGDPPC.x
22539
21812
21629
25565
25932
23528
TPGDPPC.m
22108
21517
21569
25827
25379
23314
TPGDP.x
2471
2326
2364
2554
2758
2497
TPGDP.m
2516
2212
2274
2619
2554
2437
TPBCV.x
0.024
0.041
0.030
0.025
0.020
0.028
TPBCV.m
0.024
0.046
0.030
0.024
0.022
0.029
IBCSync.x
0.191
0.300
0.345
0.324
0.380
0.310
IBCSync.m
0.197
0.287
0.350
0.338
0.361
0.309
16
Tables 3-4: Average Period Statistics (continued)
Sample of 33 Latin America Countries
1962-1970
1971-1979
1980-1988
1989-1997
1998-2006
Average
BCVY
0.037
0.054
0.057
0.047
0.039
0.047
BCVC
0.053
0.063
0.076
0.104
0.061
0.072
BCVC / BCVY
1.51
1.29
1.32
1.99
1.35
1.49
(X+M)/GDP
.62
.66
.65
.75
.83
.70
HIX
0.241
0.235
0.247
0.240
0.255
0.244
HIM
0.218
0.175
0.191
0.206
0.179
0.194
TPGDPPC.x
26823
25964
25913
26746
25926
26260
TPGDPPC.m
26423
24272
24189
25936
23773
24879
TPGDP.x
4636
4526
4708
4661
4780
4664
TPGDP.m
4751
4001
4314
4781
4422
4449
TPBCV.x
0.025
0.034
0.033
0.025
0.023
0.028
TPBCV.m
0.025
0.042
0.036
0.026
0.027
0.031
IBCSync.x
-0.057
0.232
0.102
0.027
0.363
0.141
IBCSync.m
-0.055
0.174
0.128
0.047
0.382
0.144
Sample of 78 “Other Countries”8
8
1962-1970
1971-1979
1980-1988
1989-1997
1998-2006
Average
BCVY
0.061
0.076
0.065
0.073
0.051
0.066
BCVC
0.073
0.101
0.092
0.114
0.080
0.093
BCVC / BCVY
1.37
1.34
1.50
1.61
1.73
1.52
(X+M)/GDP
.71
.73
.74
.74
.87
.76
HIX
0.245
0.182
0.162
0.168
0.178
0.185
HIM
0.209
0.155
0.136
0.125
0.114
0.145
TPGDPPC.x
20532
21956
20925
22375
21731
21549
TPGDPPC.m
20643
21187
21062
21849
20316
21025
TPGDP.x
2039
2342
2229
2301
2696
2336
TPGDP.m
2223
2147
1980
1972
2013
2059
TPBCV.x
0.029
0.047
0.033
0.028
0.028
0.033
TPBCV.m
0.027
0.044
0.033
0.029
0.029
0.032
IBCSync.x
0.019
0.036
0.069
0.111
0.097
0.071
IBCSync.m
0.018
0.039
0.063
0.124
0.101
0.073
“Other countries” sample consists of the full sample countries excluding those in OECD or Latin America groups.
17
Finally, our measures of international business cycle synchronization (IBCSync) reported in
the last two rows of Tables 1-4 show that the weighted average correlations of business cycles
among trading partners have risen to levels many folds those that prevailed in the 1960s.
Interestingly, the OECD countries' synchronization in the 1960s (Table 2, column 1), was at the
levels that the whole sample, on average, has reached only today (Table 1, column 5) and not
surprisingly they have been the most internationally synchronized group throughout all periods,
by this measure. Latin America on the other hand, is the only group that experienced a negative
IBCSync value in the 1960s, and ended up with levels comparable to those of OECD group.
To summarize, generally speaking, despite expansion of geographical diversifications in the
broadest sense as measured by HI, countries seem to be trading with relatively similar economies,
over time, as reflected in the average GDP, GDP per capita and volatility measures of their
trading partners. Trade partners are more developed, larger in size and more stable than the
country itself, on average. This observation is consistent with the fact that the highly
industrialized nations have retained rather large shares in the trade volume of other countries,
revealing the important role that these advanced nations consequently play in the domestic
economy of other nations.
We finally examine the unconditional relationship between our measure of geographical
diversification (HI) against business cycle volatility of output and also against GDP per capita in
Figures 1-2. For the sake of brevity, only diversification in exports in the last sub-period (19982006) is portrayed. Figure 1 depicts the scatter plot of the HIX-BCV relationship which shows a
positive unconditional correlation as confirmed by the simple regression line; countries with less
geographical diversification (or higher HIX) tend to have higher macroeconomic fluctuations. In
this diagram, OECD countries are denoted by black hollow squares and are clearly centered
towards the origin of the diagram, indicating that they enjoy relatively lower output volatility and
higher diversification in trade, on average. On the other hand dark circle and gray diamond
markers belong to Latin America and the remaining countries in the entire sample of 133
countries, respectively. Finally, as Figure 2 shows, HIX-lnGDPPC exhibits a negative relationship
for the last sub-period, as one might have expected, namely that more developed countries tend
to have higher geographical diversification.
18
Figures 1-2: Scatter Diagram of Herfindahl Index for Geographical Diversification in Exports
against BCV and GDPPC, Period 1998-2006.
.8
.6
HIX
.4
.2
0
0
.05
.1
.15
BCV of GDPpc
.2
.25
7
8
9
log of GDPpc
10
11
.8
.6
HIX
.4
.2
0
6
Notes: Black hollow square, dark circle and gray diamond markers denote OECD, Latin America and the
remaining countries in the full sample of 133 economies, respectively.
19
4
Empirical Analyses
The purpose of the analyses in the next sections is to answer the following questions:
Does geographical diversification in international trade reduce business cycle volatility of
macroeconomic aggregates? Diversifying trade with respect to what type of country
characteristics most promotes stabilization of business cycle fluctuations? Does geographical
diversification mitigate the adverse impact of external and internal idiosyncratic shocks, and if so
what type of shocks?
4.1
Empirical Approach
In examining the effect of geographical diversification on macroeconomic volatility, the most
important source of endogeneity, besides the measurement error, is likely to be simultaneity.
Specifically, it is possible that if a country experienced economic instability, policy makers
might embark on efforts to expand international trade diversification, through measures such as
engaging in bilateral, multilateral or regional trade agreements. Thus, the explanatory variable HI
should be instrumented by appropriate exogenous variables. Fortunately there are several
instrumental variable (IV) candidates for international trade variables that are frequently used in
the literature. In practice, we estimate a two dimensional simultaneous equations model (SEM)
of the following form:
{
𝐵𝐶𝑉𝑖,𝑡 = 𝛼1 + 𝛽1 . 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠1 + 𝛿1 . 𝐻𝐼𝑖,𝑡 + 𝛿2 . 𝑇𝑃𝑘 + 𝑢𝑖,𝑡
𝐻𝐼𝑖,𝑡 = 𝛼2 + 𝛽2 . 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠2 + 𝜆 . 𝐵𝐶𝑉𝑖,𝑡−1 + 𝑣𝑖,𝑡
(7)
where:
𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠1 = {𝑙𝑛𝐺𝐷𝑃𝑃𝐶, , 𝑃𝑜𝑙𝑖𝑡𝑦, 𝑇𝑟𝑎𝑑𝑒 𝑂𝑝𝑒𝑛𝑛𝑒𝑠𝑠, 𝜎𝐺 , 𝜎𝑀 , 𝜎𝑖𝑛𝑡𝑒𝑟𝑛𝑎𝑡𝑖𝑜𝑛𝑎𝑙 , … }
𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠2 = {𝐺𝑒𝑜𝑖 }
𝑇𝑃𝐾 = {𝑇𝑃𝐺𝐷𝑃, 𝑇𝑃𝐺𝐷𝑃𝑃𝐶 , 𝑇𝑃𝐵𝐶𝑉, 𝐼𝐵𝐶𝑆𝑦𝑛𝑐}
In the above equations, after considering a large set of potential control variables, some of
them are included in the BCV equation to capture the general economic and political
characteristics of a country that might be common influencers for both the dependent and
independent variables. LnGDPPC is the log of GDP per capita, and Polity is an index ranging
between -10 and 10, indicating most autocratic to most democratic political regimes. Trade
openness is the ratio of total volume of trade to gross domestic output and is included so that the
20
effect of diversification can be analyzed for a given level of exposure to trade. On the other hand,
σ's capture fiscal, monetary or external shocks, respectively, which are conventionally viewed as
major sources of business cycle volatility.
The HI equation in (7) is estimated by Geo, a set of cultural and geographical variables such
as language dummies that specify whether the official language of a country is Spanish or French,
by the average air distance of the capital city of the country from certain cities around the world
(NYC, Tokyo and Rotterdam), binaries for being a landlocked or island country, elevation and
total area of the country. The predetermined value of consumption volatility, 𝐵𝐶𝑉(𝑐𝑜𝑛𝑠)𝑖,𝑡−1 , is
also included as we need at least one time-variant instrumental variable for the panel data
estimation and is used instead of output volatility due to the suspicion of autocorrelation. A rich
set of potential determinants or covariates of geographical diversification in international trade,
their quantitative impact, and underlying mechanisms are discussed in Farshbaf (2012a).
Finally TP variables capture various characteristics of trading partners and together with
above variables enable us to examine various specifications to answer the research questions that
were posed in the beginning of this section. In most of the following sections we apply
Instrumental Variable General Method of Moments technique using the panel data described in
section 2, and although not reported, period dummies are included throughout all specifications.
4.2
Business Cycle Volatility and Geographical Diversification
Our conjecture is that for a given level of exposure to international trade as measured by total
volume of trade to gross output, higher diversification among trade partners reduces domestic
output volatility due to at least two reasons: First, enhanced geographical diversification means
that shares of trade are relatively equitably distributed among many trade partners, so that the
impact of disturbances from any individual commercial partner is reduced. In other words,
aggregate international risk is decreased. Obviously, this assumes that the shocks to trade
partners are in part idiosyncratic and not too highly correlated. Second, any given shock that hits
the economy can be mitigated through inter-temporal trade with a rather large network of trade
partners.
21
Table 5 summarizes the results, which allows us to address the following question: For given
general political and economic characteristics in a country and level of openness to trade, does
diversifying in international trade reduce business cycle volatility?
The first specification (Table 5, column 1) is our benchmark model, and columns (2) and (3)
include the Herfindahl index of geographical diversification for exports and imports respectively.
Last two columns examine possible stabilizing effect of diversification on consumption volatility.
The estimation method is IV GMM as explained in section 4.1. Although the implementation of
IV methodology is out of our concern for simultaneity based on economic reasoning, we have
also conducted the Hausman test for endogeneity of HI, which confirms our theoretical concern.
As the last two rows in Table 5 illustrate, a higher value for either of Herfindahl indexes,
which is equivalent to less geographical diversification across other countries, shows positive
and statistically significant association with business cycle volatility in output. A comparison
between the results in columns (2) and (3) indicates that the marginal effects of import and
export counterparts of Herfindahl index are close in magnitude. Our estimates are also
economically meaningful and might have important policy implications. For example if a
country raises its export diversification by 10 percentage points (∆HIX = -0.10), which is what
some countries in our sample accomplished during a decade or so, the implied reduction in
output volatility (using sample mean BCV = 0.045) solely as a result of such efforts is about
(0.015/0.045) ≈ 33%.
Our measure of economic development level, lnGDPPC does not enter most of the
specifications in Table 5 significantly, due to its high correlation with other variables. In Table 5
we also see that greater openness to trade, (X+M)/GDP, is associated with higher levels of output
and consumption volatility, confirming the frequently documented findings in the literature that
this variable is destabilizing. In addition, fiscal and monetary policy shocks (σG and σM),
respectively measured by the standard deviation of growth rate of government expenditures and
by the standard deviation of the growth rate in the M2 measure of money over each sub-period in
the sample, are consistently and significantly destabilizing for output and consumption growth.
22
Table 5: Dependent variable: Business Cycle Volatility of
GDP per capita
Consumption per capita
(1)
(2)
(3)
(4)
(5)
lnGDPpc
-0.0026
-0.0020
-0.0022
0.0037
0.0029
(0.0020)
(0.0027)
(0.0022)
(0.0042)
(0.0042)
Polity
-0.0014
-0.0012
-0.0014
-0.00283
-0.0028
(0.0003)***
(0.0004)***
(0.0003)***
(0.0007)***
(0.0007)***
(X+M)/GDP
σ(G)
σ(M)
HIX
HIM
0.00041
0.00036
0.00048
0.0009
0.0009
(0.00015)***
(0.00020)*
(0.00016)***
(0.0003)***
(0.0003)***
0.071
0.069
0.072
0.167
0.167
(0.013)***
(0.014)***
(0.014)***
(0.028)***
(0.028)***
0.014
0.012
0.017
0.0221
0.0224
(0.006)**
(0.006)*
(0.006)***
(0.0123)*
(0.0126)*
0.122
0.086
(0.057)**
(0.0854)
0.149
0.077
(0.058)**
(0.07736)
Notes: The numbers reported below each coefficient in the parentheses are robust standard errors with *, ** and *** next to
them indicating statistical significance at 10, 5 and 1 percent respectively. Estimation method: IV GMM regression
including period dummies. IV's used for trade variables are: dummies for Spanish, French, elevation, total land area,
landlocked dummy, distance from certain cities in the world and lag of consumption BCV. Obs. No. in each: 387.
Finally, the statistically insignificant coefficient estimates for HIX and HIM in specifications
(4) and (5) in Table 5 illustrate that geographical diversification in trade does not smooth
consumption, which as we know from section 3, is more volatile than output throughout most of
our sample, on average. However, in results not presented here, both HIM and HIX are highly
significant stabilizers for domestic absorption ‒ a measure of total consumption by households,
government and businesses. Moreover, as discussed in Farshbaf (2012b), once the interaction of
trade openness and diversification is introduced in the regressions, the combined impact of this
measure of global trade is capable of smoothing all major macroeconomic aggregates, including
consumption and its relative volatility to that of output.
To summarize this sub-section, our results confirm the empirically well-established adverse
effects of trade openness on BCV, as well as that of volatilities in domestic fiscal and monetary
policies. Most importantly our conjecture regarding the impact of higher trade diversification for
output volatility – but not for consumption volatility – is confirmed. At a given level of trade
23
openness, more geographically diversified trade is associated with lower domestic output
volatility which, is likely to be indicative of a causal relationship from HI to BCV.
4.3
Mitigation of Shocks through Geographical Diversification
In this section we study the possible mitigation effects of trade diversification on shocks of
various origins. In other words we want to see whether higher geographical diversification
allows the countries to smooth the impact of specific shocks they are subject to, shocks which
might be rooted in domestic policies or be internationally originated.
The results are shown in Table 6, which as in Table 5 presents the results of IV GMM
estimation methodology with the same set of IV's described for Table 5. Throughout all
specifications (1) - (5) some control variables are included. Our empirical strategy is to introduce
one type of shock at a time and examine its interaction with the diversification variable. As it
will be illustrated below, the estimated coefficients on the shock variable and the interaction term
along with the possible range of diversification variable (HI) will capture the mitigation effect.
We start by replicating the benchmark model from previous section in column (1). The
possible mitigation of geographical diversification for the volatility induced by domestic fiscal
policy shocks is examined in specification (2) of Table 6. In the benchmark model (1), the
coefficient estimate of σ(G) is positive, implying that higher volatility in fiscal policy increases
business cycle volatility. However as in specification (2), once we add the interaction term, the
coefficient estimate on σ(G) becomes negative while its interaction with HIX enters the
regression with the opposite sign, with the latter being statistically significant even at 1% level.
The overall marginal effect of fiscal shocks using the point estimates from specification (2) can
be written as:
𝜕 𝐵𝐶𝑉⁄
𝜕 𝜎(𝐺) ≅ −0.1628 + (1.738) ∙ 𝐻𝐼𝑋
, 𝐻𝐼𝑋 ∈ [0,1]
(8)
Quantitatively, for a country operating at the least level of geographical diversification or
HIX=1, the estimated marginal effect of fiscal shocks on BCV is 1.575. This destabilizing effect
falls to a magnitude of 0.073 for the median value of HIX = 0.136, which interestingly is quite
24
comparable to the estimated coefficient of σ(G) throughout other regression settings, where it
enters alone without any interaction. In this regard, our estimates seem to be quantitatively and
qualitatively consistent. In other words, at high levels of HI, that is lower geographical
diversification, fiscal policy volatility is associated with higher macroeconomic volatility, but
this impact is mitigated as diversification rises.
Table 6: Diversification and Mitigation of Shocks. Dependent variable: BCV of GDP per capita
lnGDPPC
Polity
σ (G)
TO
HIX
(1)
(2)
(3)
(4)
(5)
-0.0009
0.0009
0.0006
-0.0015
-0.0005
(0.0026)
(0.0022)
(0.0023)
(0.0041)
(0.0027)
-0.0014
-0.0017
-0.0013
-0.0011
-0.0018
(0.0003)***
(0.0003)***
(0.0004)***
(0.0006)***
(0.0004)***
0.0756
-0.1628
0.0547
0.0612
0.1187
(0.0185)***
(0.1012)*
(0.0207)***
(0.0216)***
(0.0395)***
0.0005
0.0001
0.00009
0.0001
0.0002
(0.00015)***
(0.00006)**
(0.00006)
(0.00006)**
(0.00005)***
0.1720
-0.0150
-0.0643
-0.0464
-0.0137
(0.0693)**
(0.0567)
(0.0711)
(0.1137)
(0.0474)
σ(G) x HIX
1.738
(0.535)***
σ(TOT)
-0.240
(0.178)
σ(TOT) x HIX
1.997
(1.053)**
σ(RER)
-0.040
(0.127)
σ(RER) x HIX
0.623
(1.044)
σ(r)
-0.0013
(0.0007)**
σ(r) x HIX
0.0104
(0.0054)*
obs.#
442
442
283
214
313
Notes: The numbers reported below each coefficient in the parentheses are robust standard errors with *, ** and *** next
to them indicating statistical significance at 10, 5 and 1 percent respectively. Estimation method: IV GMM regression
including period dummies. IV's used for trade variables are: dummies for Spanish, French, elevation, total land area,
landlocked dummy, distance from certain cities in the world and lag of consumption BCV.
25
The volatility of the terms of trade is the most frequently used proxy for international shocks
in the literature and in specification (3) we observe that its adverse effect on BCV can also be
mitigated through higher geographical diversification. It is worth mentioning that there is a
decline in the significance of trade openness, once we control for TOT volatility, which is in line
with Rodrik (1998) and Cavallo (2008)'s findings that the major reason behind international
trade's destabilizing effect is the terms of trade channel.
Another international shock is volatility in the real effective exchange rate, which weights
bilateral real exchange rates by the corresponding country shares in trade as constructed by IMF
for its IFS database. As reported in column (4) of Table 6, the results follow the same pattern as
the above cases. In other words, the destabilizing effect of volatility in real effective exchange
rate tends to be mitigated at higher levels of geographical diversification, except that the
estimated coefficients are not statistically significant at the conventional levels.
Finally, I use variations in the real interest rate to capture the uncertainty or volatility in the
financial markets or monetary policies. The last column in Table 6 confirms that, diversity in
international trade partners can lessen the adverse effects of this type of volatility on the
macroeconomic fluctuations. Again, at low levels of international diversification (HI close to 1)
the estimated marginal effect of σ(r) on BCV is a positive number but this destabilizing effect is
reduced as diversification expands, or in terms of our variables as HIX falls.
Although not reported, measures of nominal monetary policy shocks such as the standard
deviation of the growth rate of different measures of money supply do not exhibit statistically
significant interaction effects. It should be noted that as the last row of Table 6 shows, the
number of observations in the panel for specifications (3) and beyond are much less than the
ones we had for specifications (1) - (2) which, might be one reason we get higher standard errors
and hence lose some statistical significance for the coefficient estimates. The reason for this
decline in the number of observations is the unavailability of data in some periods for variables
such as the terms of trade, real effective exchange rate and real interest rates.
A similar exercise is conducted in Farshbaf (2012b) in which the interaction of geographical
diversification with trade openness is introduced to capture the overall impact of global trade on
business cycle volatility. Those results indicate that although the stand-alone impact of trade
26
openness is destabilizing, the overall effect of the diversified or global trade, on major
macroeconomic aggregates including consumption, can actually become stabilizing for high
levels of geographical diversification.
To summarize, the overall results conform with our expectation that higher levels of trade
diversification have a mitigating effect on the output volatility induced by various types of
domestic and international shocks. Although a structural model is beyond the scope of this paper,
the results suggest that higher diversification means there are a wider array of international
opportunities with which to manage risk.
4.4
Economic Characteristics of International Trading Partners
Next, we include newly constructed variables described in section 2 that capture characteristics
of international trading partners with which a country diversifies its trade. In particular, we
would like to learn how, for a given level of trade openness and geographical diversification,
trading with more developed countries as proxied by GDPPC, with larger economies as captured
by GDP and with more stable countries in terms of their BCV, all on a weighted average base,
affects macroeconomic fluctuations at home. We follow the same IV GMM estimation strategy
due to the potential simultaneity between BCV and trade variables. Estimation results are
reported in Table 7.
Columns (1) and (3) in Table 7 are our benchmark models in which, the controls along with
openness to trade and Herfindahl measure of geographical diversification for exports and imports
(HIX and HIM) are respectively included for the purpose of comparison with the specifications
that add economic characteristics of trading partners, one at a time.
To summarize the findings in Table 7, we observe that trading with more developed countries
or larger economies is associated with smoother business cycles at home as shown by the
negative and statistically significant coefficients for TPGDPPC and TPGDP, while trading with
more volatile economies, on average, has the opposite effect but is only marginally statistically
significant. It should be recalled that TPGDPPC and TPGDP variables are constructed using GDP
and GDPPC values from the year 2000 to focus on the type of the country with which, trading is
27
being conducted rather than allowing the fact that some countries have grown rich, affect our
results. Nonetheless, the conclusions do not change once we allow real growth in output and
output per capita of the trading partners; although the results are not reported they are available
upon request.
Table 7: BCV and Characteristics of Trading Partners. Dependent variable: BCV of GDP per capita
lnGDPpc
Polity
σ (G)
σ (M)
TO
HIX
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
-0.0020
0.0007
-0.0022
0.0017
0.0008
-0.0001
-0.0008
-0.0020
(0.0027)
(0.0023)
(0.0022)
(0.0023)
(0.0023)
(0.0021)
(0.0023)
(0.0022)
-0.0012
-0.0011
-0.0014
-0.0014
-0.0011
-0.0013
-0.0014
-0.0014
(0.0004)***
(0.0004)***
(0.0003)***
(0.0003)***
(0.0003)***
(0.0003)***
(0.0004)***
(0.0004)***
0.069
0.068
0.072
0.072
0.075
0.074
0.071
0.070
(0.014)***
(0.014)***
(0.014)***
(0.013)***
(0.013)***
(0.013)***
(0.015)***
(0.015)***
0.012
0.016
0.017
0.014
0.015
0.015
0.016
0.017
(0.006)*
(0.006)**
(0.006)***
(0.006)**
(0.006)**
(0.006)**
(0.007)**
(0.007)***
0.0004
0.0004
0.0005
0.0004
0.0002
0.0003
0.0004
0.0005
(0.0002)*
(0.0002)**
(0.0002)***
(0.0001)***
(0.0002)
(0.0001)**
(0.0002)**
(0.0002)***
0.122
0.151
0.141
0.139
(0.057)**
(0.048)***
(0.053)***
(0.047)***
HIM
TPGDPPC.x
0.149
0.123
0.094
0.177
(0.058)**
(0.053)**
(0.059)*
(0.070)**
-2.158
(0.696)***
TPGDPPC.m
-2.126
(0.724)***
TPGDP.x
-0.007
(0.003)***
TPGDP.m
-0.005
(0.002)*
TPBCV.x
0.292
(0.212)
TPBCV.m
0.397
(0.339)
Notes: The numbers reported below each coefficient in the parentheses are robust standard errors with *, ** and *** next to
them indicating statistical significance at 10, 5 and 1 percent respectively. Estimation method: IV GMM regression including
period dummies. IV's used for trade variables are: dummies for Spanish, French, elevation, total land area, landlocked dummy,
distance from certain cities in the world and lag of consumption BCV. Obs. No. in each: 387.
28
One surprising result in Table 7 is that, being exposed to more volatile countries on average
(TPBCV), is only slightly destabilizing in terms of domestic output, as judged by the statistical
significance. This is to a large extent explained by the fact that TPBCV does not vary much over
time or across countries, as discussed in section 3. Another interesting result is that the estimated
coefficients of HIM lose some degree of magnitude and statistical power when TPGDPPC and
TPGDP variables are included in the regressions, nominating access to more developed and
larger international markets as stabilizing mechanisms for import diversification. This loss in the
explanatory power of the HIM coefficients is most prominent when the TPGDP variable is
included, as shown in Table 7 under specification (6), indicating that the stabilizing mechanism
of import diversification at least partially works through access to larger markets. This is
consistent with one of the main conjectures posed in this paper that international trade
diversification across countries provides a wide array of international channels for domestic
absorption to smooth domestic output, and this result suggests that the larger these international
markets are, the higher are the chances of reducing BCV.
One compelling explanation for these results is that trading with a larger number of advanced
countries is stabilizing because these countries are the most economically stable countries. To
test this hypothesis, regression specifications in the previous table (Table 7) are augmented by
controlling for TPBCV or average output volatility of trading partners. Table 8 shows the
regression results where only the main variables of interest are reported. The estimated impact of
trading with more advanced and larger economies is not reduced in either magnitude or statistical
significance, after controlling for average output volatility of trading partners. In other words,
trading with economically larger and more advanced nations' strong association with higher
stability at home is due to reasons other than their being highly stable.
29
Table 8: BCV and Other Characteristics of Geographical Diversification. Dep.variable: BCV of GDP per capita
TO
HIX
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
0.0004
0.0004
0.0004
0.0004
0.0002
0.0002
0.0003
0.0003
(0.0002)**
(0.0002)**
(0.0001)***
(0.0001)***
(0.0002)
(0.0002)
(0.0001)**
(0.0001)**
0.151
0.145
0.141
0.142
(0.048)***
(0.047)***
(0.053)***
(0.054)***
HIM
TPGDPPC.x
-2.158
-2.279
(0.696)***
(0.739)***
TPGDPPC.m
0.123
0.126
0.094
0.092
(0.053)**
(0.059)**
(0.059)*
(0.066)
-2.126
-2.159
(0.724)***
(0.728)***
TPGDP.x
-0.007
-0.008
(0.003)***
(0.003)***
TPGDP.m
TPBCV.x
-0.193
0.012
(0.208)
(0.177)
TPBCV.m
-0.005
-0.005
(0.002)*
(0.003)*
-0.010
-0.009
(0.274)
(0.277)
Notes: The numbers reported below each coefficient in the parentheses are robust standard errors with *, ** and *** next to them
indicating statistical significance at 10, 5 and 1 percent respectively. Estimation method: IV GMM regression including period
dummies. IV's used for trade variables are: dummies for Spanish, French, elevation, total land area, landlocked dummy, distance
from certain cities in the world and lag of consumption BCV. Obs. no. in each: 387.
4.5
International Business Cycle Synchronization
In this section we touch on the issue of business cycle smoothing through the introduction of a
new index constructed to measure multilateral correlations of home BCs with those of
international trading partners or IBCSync. The idea for the construction of this variable is that the
chance of smoothing business cycles would be higher, if the country in question and its
commercial partners were at different phases of business cycles, so that correlations between
home and foreign business cycles are lower.
Table 9 summarizes our econometric results using only random effects estimation, as most of
the variations in the data seem to be coming from cross rather than within countries. The
IBCSync variable used here is based on total trade shares as weights, rather than import or export
shares alone.
30
Table 9: International Business Cycle Synchronization & BCV of Various Macroeconomic Aggregates
D e p e n d e n t
Y
lnGDPPC
Polity
σ (G)
TO
HI
IBCSync
v a r i a b l e :
B C V
o f
DA
C/Y†
Y
C
I
0.0007
0.0094
-0.0110
0.0100
-0.0293
-0.0020
(0.002)
(0.004)**
(0.011)
(0.003)***
(0.042)
(0.003)
-0.0013
-0.0028
-0.0016
-0.0016
-0.0173
-0.0012
(0.0003)***
(0.0005)***
(0.0016)
(0.0004)***
(0.0062)***
(0.0004)***
0.12
0.23
0.59
0.18
0.48
0.08
(0.01)***
(0.02)***
(0.07)***
(0.02)***
(0.26)*
(0.01)***
0.0002
0.00023
.00032
.00024
0.00105
.00013
(.00005)***
(.00008)***
(.00024)
(.00006)***
(.0009)
(.00059)**
0.033
0.057
0.140
0.041
0.014
0.025
(0.016)**
(0.027)**
(0.077)*
(0.022)*
(0.303)
(0.018)
-0.0124
-0.0223
-0.0741
-0.0172
-0.2429
0.0071
(0.007)*
(0.012)**
(0.033)**
(0.009)**
(0.131)*
(0.007)
σ (TOT)
0.081
(0.026)***
R-sq within
0.14
0.08
0.13
0.14
0.01
0.11
R-sq between
0.47
0.61
0.35
0.51
0.26
0.43
obs. #
570
570
570
570
570
291
Notes: The numbers reported below each coefficient in the parentheses are robust standard errors with *, **
and *** next to them indicating statistical significance at 10, 5 and 1 percent respectively. Estimation
method is Random Effect. † By C/Y notation we mean relative BCV of consumption to that of output.
We consider business cycle volatility measures of different aggregates, where all are on real
per capita bases and business cycle volatility is calculated as the standard deviation of the growth
rate of each aggregate over the sub-period of 9 years length. In particular, the dependent
variables are the business cycle volatilities of output, consumption, investment, domestic
absorption (C+I+G), the relative volatility of consumption to that of output and finally, output
again in a different specification, respectively from the left to right columns. Domestic
absorption is an important variable to consider since it measures the overall domestic
consumption which, domestic agents including households, government and businesses seek to
smooth via import and export channels.
The results in Table 9 appear to contradict the conjecture that countries can benefit most in
terms of business cycle smoothing if their business cycles are least correlated with their trading
31
partners. To put it another way, being synchronized with trading partners is seemingly associated
with less volatility in own business cycles for any component of aggregate demand and also the
relative volatility of consumption, after controlling for general country specific characteristics
and measures of international trade integration that includes trade openness and geographical
diversification. However, in the last column of Table 9, we include a measure of exposure to
international shocks, namely the volatility of the terms of trade and this reverses our results. Not
only does the IBCSync variable lose its stabilizing status, it now becomes relatively destabilizing
for the volatility of output per capita, although with relatively low levels of significance. Overall,
the results suggest that controlling for an external shock, it is optimum to trade with countries
that are different from us in terms of business cycle behavior. While this new variable captures
an important feature of international trade relations, its further study is left for future work.
4.6
Robustness Check
A number of alternative approaches were considered throughout our empirical work to make
sure the results are not driven by the methodologies applied here. Specifically, our main results,
namely geographical diversification's being an economically and statistically significant
stabilizing factor on business cycle fluctuations, proved to be robust to at least: 1. The measure
of BCV of output; whether being the standard deviation of the growth rate of output per capita or
that of the HP-filtered time-series. 2. The choice of the sub-period length. In particular, we
divided the whole sample period into two sub-periods and then nine sub-periods – as done in
Farshbaf (2012b) – instead of five, and the results follow the same pattern. 3. Control variables; a
large set of control and instrumental variables were considered in the regression analyses.
5
Summary and Conclusions
This paper empirically investigates the impact of geographical diversification in imports and
exports on business cycle volatility of output. Despite being subject to extensive examination in
the literature, the BCV-trade relation still has many unexplored gaps of which an analysis of
geographical diversification is one. Our empirical analysis follows an IV strategy mostly due to
the belief that there is a simultaneity between BCV and trade variables. We showed that for a
32
given level of trade openness, diversifying international trade more evenly across more countries
in the sense captured by Herfindahl index for either imports or exports, lowers output volatility
significantly, and lowers consumption volatility but this relation is not statistically significant.
Further, through the introduction of four new variables, we found that for a given level of trade
openness and geographical diversity, trading with economies that are more developed, larger and
to some degree more stable are consistently associated with smoother output fluctuations. Finally,
once a major external shock is controlled for, being less internationally synchronized, as
captured by lower IBCSync, is shown to be associated with lower macroeconomic volatility at
home.
One important observation from the stylized facts laid out in the Descriptive Statistics section
was that although the degree of diversification in international trade has, on average across
nations, expanded notably over the last five decades, countries seem to have maintained their
commercial relations with relatively similar economies in terms of their trading partners' level of
economic development, size of their economy and their stability, over time. This is further
confirmed by the fact that the most industrialized nations have maintained a rather high share in
the total trade volume of other nations.
In future research, we plan to study the structural mechanisms through which geographical
diversification per se and trading with more advanced, larger and more stable economies reduce
business cycle volatility by extending a theoretical framework for studying diversification and
macroeconomic volatility. In addition, we plan to examine the connection between geographical
diversification and sectoral concentration, for example to examine how trade patterns change
when a country diversifies trade in favor of more advanced countries as opposed to less
developed ones. Finally we plan to analyze the consumption smoothing anomaly found for most
countries in our sample; namely, that consumption volatility exceeds output volatility.
33
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Data Sources
Bilateral Trade Data:
Source: Dyadic Trade Dataset for the Correlates of War Project
Description: Integrating bilateral trade data from previous data projects and supplemented with additional
sources when missing.
Based on: Majority from IMF's Direction of Trade Statistics. Supplemented with data from Barbieri,
Keshk & Pollins (“Trading Data: Evaluating our Assumptions and Coding Rules.”) & Barbieri’s
International Trade Dataset.
Notes: All entries are in current U.S. Dollars. For detailed description of data construction procedures
refer to: Correlates of War Project Trade Data Set Codebook, Version 2.0.
Online: http://correlatesofwar.org.
Country-Specific Economic Data:
Source: Penn World Table (PWT) and World Bank's World Development Indicators (WDI)
Based on: World Bank's WDI
Description: PWT's real per capita GDP along with the shares of consumption, investment and
government expenditures in it were used to construct time series for each aggregate along with
the totals using population data from the same source.
Units and other observations: Base years are different for different nations.
36
Financial Openness:
Source: "The External Wealth of Nations (EWN) Mark II: Revised and extended estimates of
foreign assets and liabilities, 1970–2004" by P.R. Lane & G.M. Miles-Ferretti
Based on: IMF's International Financial Statistics dataset
Description: As defined by Prasad et al (2007), Financial openness is the gross stock of capital
flows to GDP. Gross stock being equal to the accumulated sum of capital inflows and outflows,
each including foreign direct investment (FDI), portfolio investment and bank lending –as
constructed by Lane & G.M. Miles-Ferretti.
Geographical Variables:
Source: Compiled by Andrew Rose, available at:
http://faculty.haas.berkeley.edu/arose/recres.htm
Description: The data contains many geographical and cultural variables and is frequently used
in the literature for instrumenting international trade variables, including the language dummies,
distance from open seas or rivers, distance from central world cities, landlocked binary variable
among others.
Polity:
Source: Polity IV Project: Political Regime Characteristics and Transitions
Description: The Polity variable captures the authority characteristics of a state and was initially
collected under direction of T.R. Gurr. This is a composite index constructed upon various
political characteristics like competitiveness of executive recruitment, openness of executive
recruitment, constraints on and competitiveness of the executive power among others.
Notes: This variable can take on values between -10 (full autocracy) and 10 (full democracy).
37
Appendix
Figure A1: Herfindahl Index of Exports for Geographical Diversification in International Trade. Select Countries:
Unites States, Great Britain, Italy, Mexico, Chile, Brazil, Japan, Korea, China, Kuwait, Sudan and South Africa.
USA
.07
GBR
.1
.08
x
hix
.09
ITA
.1
.06
.09
.05
.08
.07
.04
.06
.03
1960 1970 1980 1990 2000
.07
.06
1960 1970 1980 1990 2000
MEX
1960 1970 1980 1990 2000
CHL
BRZ
.25
.6
.2
.2
.15
.15
.4
.1
.1
.3
.05
.05
.5
x
.25
hix
.7
1960 1970 1980 1990 2000
1960 1970 1980 1990 2000
JPN
1960 1970 1980 1990 2000
KOR
CHN
.18
.5
.4
.16
.4
.3
.3
.2
.2
.1
x
hix
.14
.12
.1
.08
.1
1960 1970 1980 1990 2000
1960 1970 1980 1990 2000
KWT
.2
x
hix
.15
.1
.05
1960 1970 1980 1990 2000
year
0
1960 1970 1980 1990 2000
SDN
ZAR
.4
.5
.3
.4
.2
.3
.1
.2
0
.1
1960 1970 1980 1990 2000
year
1960 1970 1980 1990 2000
year
38
Table A1: Correlations of Various Measures of BCV
Correlations among Volatility Measures for Output
GDPPC.cyc
GDP.cyc
GDP.cyc
1
GDP.gr
GDPPC.cyc
0.963
1
GDP.gr
0.873
0.900
1
GDPPC.gr
0.871
0.906
0.993
GDPPC.gr
1
Correlations among Volatility Measures for Consumption
Cons.cyc
ConsPC.cyc
Cons.gr
ConsPC.gr
Cons.cyc
1
0.946
0.884
0.841
ConsPC.cyc
Cons.gr
ConsPC.gr
1
0.877
0.905
1
0.953
1
Notes: In terms of notations in this table, the ".cyc" suffix corresponds to volatility measures based on
the standard deviation of the HP-filtered time series and ".gr" corresponds to one based on the standard
deviation of the growth rates.
39
Table A2: Trade Shares with the U.S. and G-7 Countries, five-year period averages
Full Sample of 133 Countries - Trade Shares
1962-1970
1971-1979
1980-1988
1989-1997
1998-2006
Average
US share / EX
0.16
0.16
0.17
0.17
0.17
0.17
US share / IM
0.20
0.16
0.14
0.15
0.15
0.16
G7 share / EX
0.54
0.51
0.49
0.47
0.41
0.48
G7 share / IM
0.57
0.51
0.46
0.45
0.40
0.47
Sample of 33 Latin America Countries - Trade Shares
1962-1970
1971-1979
1980-1988
1989-1997
1998-2006
Average
US share / EX
0.39
0.35
0.36
0.36
0.36
0.36
US share / IM
0.45
0.35
0.31
0.35
0.40
0.37
G7 share / EX
0.65
0.62
0.58
0.58
0.53
0.59
G7 share / IM
0.66
0.61
0.51
0.54
0.54
0.57
Sample of 28 OECD Countries - Trade Shares
1962-1970
1971-1979
1980-1988
1989-1997
1998-2006
Average
US share / EX
0.14
0.16
0.13
0.14
0.14
0.14
US share / IM
0.19
0.15
0.13
0.13
0.14
0.15
G7 share / EX
0.48
0.46
0.43
0.46
0.40
0.45
G7 share / IM
0.47
0.45
0.40
0.42
0.41
0.43
Notes: Each sub-period stretches over 9 years following the starting year. IM and EX refer to shares in total
imports and exports, respectively.
40
Table A3: List of Countries in the Full Sample
country
code
country
code
country
code
1
Albania
ALB
28
China
CHN
55
Haiti
HTI
2
Algeria
DZA
29
Colombia
COL
56
Honduras
HND
3
Angola
AGO
30
Congo, Dem. Rep.
ZAR
57
Hungary
HUN
4
Argentina
ARG
31
Costa Rica
CRI
58
Iceland
ISL
5
Australia
AUS
32
Cote d`Ivoire
CIV
59
India
IND
6
Austria
AUT
33
Cuba
CUB
60
Indonesia
IDN
7
Bahamas
BHS
34
Denmark
DNK
61
Iran
IRN
8
Bahrain
BHR
35
Djibouti
DJI
62
Iraq
IRQ
9
Bangladesh
BGD
36
Dominican Rep.
DOM
63
Ireland
IRL
10
Barbados
BRB
37
Ecuador
ECU
64
Israel
ISR
11
Belgium
BEL
38
Egypt
EGY
65
Italy
ITA
12
Belize
BLZ
39
El Salvador
SLV
66
Jamaica
JAM
13
Benin
BEN
40
Equatorial Guinea
GNQ
67
Japan
JPN
14
Bhutan
BTN
41
Estonia
EST
68
Jordan
JOR
15
Bolivia
BOL
42
Ethiopia
ETH
69
Kenya
KEN
16
Botswana
BWA
43
Fiji
FJI
70
Korea
KOR
17
Brazil
BRZ
44
Finland
FIN
71
Kuwait
KWT
18
Brunei
BRN
45
France
FRA
72
Lebanon
LBN
19
Bulgaria
BGR
46
Gabon
GAB
73
Liberia
LBR
20
Burkina Faso
BFA
47
Gambia, The
GMB
74
Libya
LBY
21
Burundi
BDI
48
Germany
GER
75
Luxembourg
LUX
22
Cambodia
KHM
49
Ghana
GHA
76
Macedonia
MKD
23
Cameroon
CMR
50
Greece
GRC
77
Madagascar
MDG
24
Canada
CAN
51
Grenada
GRD
78
Malawi
MWI
25
Centr. African Rep.
CAF
52
Guatemala
GTM
79
Malaysia
MYS
26
Chad
TCD
53
Guinea-Bissau
GNB
80
Maldives
MDV
27
Chile
CHL
54
Guyana
GUY
81
Mali
MLI
41
country
code
country
code
82
Mexico
MEX
109
Singapore
SGP
83
Moldova
MDA
110
South Africa
ZAF
84
Mongolia
MNG
111
Spain
ESP
85
Morocco
MAR
112
Sri Lanka
LKA
86
Mozambique
MOZ
113
Sudan
SDN
87
Namibia
NAM
114
Suriname
SUR
88
Nepal
NPL
115
Sweden
SWE
89
Netherlands
NLD
116
Switzerland
CHE
90
New Zealand
NZL
117
Syria
SYR
91
Nicaragua
NIC
118
Tanzania
TZA
92
Niger
NER
119
Thailand
THA
93
Nigeria
NGA
120
Togo
TGO
94
Norway
NOR
121
Trinidad &Tobago
TTO
95
Oman
OMN
122
Tunisia
TUN
96
Pakistan
PAK
123
Turkey
TUR
97
Panama
PAN
124
Uganda
UGA
98
Paraguay
PRY
125
Untd. Arab Emir.
ARE
99
Peru
PER
126
United Kingdom
GBR
100
Philippines
PHL
127
United States
USA
101
Poland
POL
128
Uruguay
URY
102
Portugal
PRT
129
Venezuela
VEN
103
Qatar
QAT
130
Vietnam
VNM
104
Romania
ROM
131
Yemen
YEM
105
Rwanda
RWA
132
Zambia
ZMB
106
Saudi Arabia
SAU
133
Zimbabwe
ZWE
107
Senegal
SEN
108
Sierra Leone
SLE
Groups of countries as used in various part of this paper:
OECD: Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Greece,
Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, Mexico, Netherlands, New Zealand,
Norway, Poland, Portugal, Spain, Sweden, Switzerland, Turkey, United Kingdom, United States.
Latin America: Argentina, Bahamas, Barbados, Belize, Bolivia, Brazil, Chile, Colombia, Costa
Rica, Cuba, Dominican Republic, Ecuador, El Salvador, Grenada, Guatemala, Guyana, Haiti,
Honduras, Jamaica, Mexico, Nicaragua, Panama, Paraguay, Peru, Suriname, Trinidad & Tobago,
Uruguay, Venezuela.
G-7: Canada, France, Germany, Italy, Japan, United Kingdom, and United States.
42
A Quick Reference for Variables and Notations used in this Paper
DA
NX
BCV
TO
FO
OECD
G-7
LA
WTO/GATT
σ (G)
σ (M)
σ (TOT)
σ (RER)
σ (r)
.m
.x
HIM, HIX
TP
TPGDP
TPGDPPC
TPBCV
IBCSync
Air Distance
English,
Spanish, French
Landlocked
Elevation
SEM
Domestic Absorption ≡ C + I + G, which is the sum of consumption, investment and
government expenditures.
Net Exports = Exports - Imports, (in goods and services)
Business cycle volatility, of either components of output. Throughout this paper this
variable is constructed as the standard deviation of the growth rate of real aggregate (such
as GDP or consumption) per capita over the length of each sub-period.
Trade openness. Ratio of the sum of exports and imports (of goods and services) to GDP.
Financial openness, which equals the ratio of gross stocks of foreign capital assets and
liabilities to GDP. Foreign capital includes FDI, portfolio investment and bank lending.
Organization for Economic Cooperation and Development.
Canada, France, Germany, Italy, Japan, United Kingdom, and United States.
Latin America countries. See “List of Countries” for the full list.
World Trade Organization/ General Agreement on Tariffs and Trade.
Fiscal policy volatility, calculated as the standard deviation of the growth rate of
government expenditures in each period.
Monetary policy volatility, calculated as the standard deviation of the growth rate of M2
measure of money in each period.
Volatility in terms of trade, calculated as the standard deviation of the terms of trade over
the length of each sub-period
Volatility in real effective exchange rate. Real effective exchange rate (RER) is a “nominal
effective exchange rate (a measure of the value of a currency against a weighted average of
several foreign currencies) divided by a price deflator or index of costs.” (Source: WB)
Volatility is then calculated as the standard deviation of this variable in each sub-period.
Standard deviation of the real interest rate in each sub-period as a proxy for the volatility in
financial markets and or monetary policy.
This suffix indicates that the variable has been constructed using trading partners' import
shares in total.
This suffix indicates that the variable has been constructed using trading partners' import
shares in total.
Herfindahl index for geographical diversification. HIM and HIX are respectively
calculated based on shares of imports or exports in totals.
Variables that capture economic characteristics of trade partners. Including TPGDP, etc.
Weighted average GDP of international trade partners. GDP's are from the year 2000.
Weighted average GDPPC of international trade partners. GDPPC's are from the year 2000.
Weighted average output volatilities of international trading partners.
Multilateral correlation of home country's business cycles with respect to those of its
international trading partners, weighted by trade shares.
Average air distance of the capital of a country to NYC, Tokyo & Rotterdam.
Language dummies which indicate the official language of a country.
A binary variable that equal 1 if the country does not have access to open waters.
Average height of the country's surfaces above the Earth's sea level.
Simultaneous equations models.
43