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Essays on business cycle volatility and global trade

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ESSAYS ON
BUSINESS CYCLE VOLATILITY AND GLOBAL TRADE
by
Arian Farshbaf Yazdandoust

A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(ECONOMICS)

August 2012

Copyright 2012 Arian Farshbaf Yazdandoust

ii

Dedication

To my parents
iii

Acknowledgments

I would like to express my sincere gratitude to the members of my Ph.D. Guidance and
Dissertation Committee: Professors Caroline Betts, Robert Dekle, Cheng Hsiao, John Matsusaka
and Vincenzo Quadrini. Their exceptional guidance, support, and advice throughout this research
process played a crucial role in shaping my dissertation and seeing it to this final stage.
In particular, I would like to thank my academic advisor Caroline Betts. Her insights, patience
and advice were essential to my ability to develop concepts into an academic thesis. She
supported my exploration of new ideas as part of the research, while helping me to keep the work
on track.
As well, I am thankful to Jeffrey Nugent for his incredible mentoring from the very beginning of
my Ph.D. studies at the University of Southern California (USC). His encouragement in research,
and emphasis on excellence in teaching, were crucial to my development as a scholar.
Guillaume Vandenbroucke provided generous feedback and advice during my final year, and I
am grateful for this. He, Caroline Betts, and Jeffrey Nugent also arranged various departmental
seminars and meetings, which provided a tremendous opportunity for me to develop and better
articulate my ideas, and to prepare for the job market.
Young Miller and Morgan Ponder offered continuous and invaluable help and advice throughout
my time at USC. They provided a friendly environment at the Department of Economics, making
the highly-demanding course of study more bearable!
Finally, I would like to thank the USC Family for the chance to conduct original research in such
a rich, inspiring learning environment, and for generously funding my Ph.D. studies.

iv

Table of Contents

Dedication _________________________________________________________________________________ ii
Acknowledgments ________________________________________________________________________ iii
List of Tables ______________________________________________________________________________ vi
List of Figures ____________________________________________________________________________ vii
Abstract __________________________________________________________________________________ viii

Chapter 1: Does Geographical Diversification in International Trade
Reduce Business Cycle Volatility? ________________________________________ 1
Chapter 1 Abstract _____________________________________________________________________________ 1
1.1 Introduction ________________________________________________________________________________ 1
1.2 Data and Variables _________________________________________________________________________ 6
1.2.1 Business Cycle Volatility ______________________________________________________________ 7
1.2.2 Herfindahl Index for Geographical Diversification in Trade _______________________ 8
1.2.3 Size of International Markets _________________________________________________________ 9
1.2.4 Development Level of Trading Partners _____________________________________________ 9
1.2.5 Economic Stability of Trading Partners ____________________________________________ 10
1.2.6 International Business Cycle Synchronization _____________________________________ 10
1.3 Descriptive Statistics _____________________________________________________________________ 11
1.4 Empirical Analyses _______________________________________________________________________ 19
1.4.1 Empirical Approach __________________________________________________________________ 19
1.4.2 Business Cycle Volatility and Geographical Diversification _______________________ 21
1.4.3 Mitigation of Shocks Through Geographical Diversification ______________________ 23
1.4.4 Economic Characteristics of International Trading Partners _____________________ 26
1.4.5 Internatonal Business Cycle Synchronization _____________________________________ 30
1.4.6 Robustness Check ____________________________________________________________________ 31
1.5 Summary and Conclusions ______________________________________________________________ 32

v

Chapter 2: Geographical Diversification in International Trade
and Its Determinants ______________________________________________________ 34
Chapter 2 Abstract ____________________________________________________________________________ 34
2.1 Introduction _______________________________________________________________________________ 34
2.2 Background and Literature Reivew ____________________________________________________ 36
2.3 Data, Variables and Statistics ___________________________________________________________ 41
2.3.1 Herfindahl Index of Geographical Diversification _________________________________ 42
2.3.2 Gravity to G-7 Counteries ____________________________________________________________ 44
2.3.3 Trade Shares with G-7 Countries ___________________________________________________ 44
2.3.4 Remoteness ___________________________________________________________________________ 45
2.3.5 Distance from Trade Partners _______________________________________________________ 45
2.4 Gravity Models’ Prediction of Geographical Diversification _______________________ 47
2.5 Empirical Analyses _______________________________________________________________________ 50
2.6 Summary and Conclusions ______________________________________________________________ 61

Chapter 3: Business Cycle Volatility and Economic Globalization_______________ 64
Chapter 3 Abstract ____________________________________________________________________________ 64
3.1 Introduction _______________________________________________________________________________ 64
3.2 Background and Literature Review ____________________________________________________ 67
3.3 Data and Variables ________________________________________________________________________ 69
3.4 Empirical Analyses _______________________________________________________________________ 71
3.4.1 Net Exports as a Venue for Smoothing Aggregate Fluctuations __________________ 71
3.4.2 Threshold and Complementary Effects _____________________________________________ 75
3.4.3 True Global Integration: Diversified Trade ________________________________________ 81
3.5 Summary and Conclusions ______________________________________________________________ 85

References ________________________________________________________________________________ 87
Data Sources ______________________________________________________________________________ 90
Appendix __________________________________________________________________________________ 92
List of Country Names and Groups _________________________________________________ 103
Quick Reference for Notations ______________________________________________________ 105

vi

List of Tables
1.1 and 1.2: Average Period Statistics for the Full Sample and OECD Countries ____ 14
1.3 and 1.4: Average Period Statistics for Latin America and “Other” Countries ___ 15
1.5: Business Cycle Volatility and Geographical Diversification ______________________ 22
1.6: Diversification and Mitigation of Shocks ___________________________________________ 24
1.7: BCV and Characteristics of Trading Partners ______________________________________ 27
1.8: BCV and Characteristics of Trading Partners, TPBCV Controlled ________________ 29
1.9: International Business Cycle Synchronization & BCV ____________________________ 30

2.1: Trade-Weighted Average Distance from Trade Partners, Latin America _______ 46
2.2: Simple Regression of Geographical Diversification on Single Regressors_______ 51
2.3: Panel Regression of Geographical Diversification on Country Size ______________ 52
2.4: Panel Regression on Country Size Controlling for Number of Neighbors _______ 53
2.5: Panel Regression on Gravity Towards G-7 Countries _____________________________ 54
2.6: Panel Regression on Remoteness from Centers of the World Trade ____________ 55
2.7: Panel Regression on Remoteness, Controlled for GDP per capita _______________ 55
2.8: Panel Regression on Real Effective Exchange Rate Volatility ____________________ 56
2.9: Fixed Effect Regression on Past Experiences of Output Volatility _______________ 57
2.10: Panel Regression on Language Dummies ________________________________________ 58
2.11: Panel Regression on Landlocked and Remoteness from G-7 ___________________ 59
2.12: Panel Regression on WTO Membership __________________________________________ 60

3.1: DA-NX relationship __________________________________________________________________ 73
3.2: Non-linearity of Trade Openness ___________________________________________________ 77
3.3: Complementary Effects of Financial Openness and Development _______________ 79
3.4: Complementary Effect of Democracy ______________________________________________ 80
3.5: Global Trade __________________________________________________________________________ 82
3.6: Global Trade and Consumption Smoothing ________________________________________ 84

A1: Correlations of Various Measures of BCV __________________________________________ 92
A2: Trade Shares with the U.S. and G-7 Countries _____________________________________ 93
A3: List of Countries in the Full Sample _______________________________________________ 103
vii

List of Figures

1.1 and 1.2: Scatter Diagram of Herfindahl Index for Geographical Diversification
in Imports against BCV and GDPPC, Period 1998-2006 _______________________________ 18
2.1: Period Averages of Geographical Diversification Measures for
the Full Sample and Sub-Groups ________________________________________________________ 43
2.2: Annual Time Series of Herfindahl Index for Geographical Diversification
for Full Sample (133) _____________________________________________________________________ 49
A1: Annual Time Series of Herfindahl Index for Geographical Diversification
for Select Countries __________________________________________________________________ 93-102

viii

Abstract

The main thesis of this dissertation is that geographical diversification in international trade is an
indispensable component of economic globalization, especially in the context of international
risk-sharing, the benefits of which can be assessed through smoother macroeconomic
fluctuations. The findings in this empirical work are based on a large amount of data analysis,
along with the construction and introduction of various trade variables that capture international
trade relations of an economy vis-à-vis its commercial partners and major players in the global
economy. For the most part, the compiled panel data used in various parts of this research consist
of 133 countries spanning sub-periods of different lengths, over 1960-2006.
The first chapter addresses the impact that more geographically diversified international trade
as well as trading partners with various economic characteristics have on business cycle
volatility. We suspect simultaneity between volatility and our trade variables, and use an
instrumental variable general method of moments estimation technique in most of our
econometric analyses. We find that, for a given level of openness to trade among other country-
specific characteristics, diversifying international trade among more trade partners with more
evenly distributed trade shares among them–captured by a Herfindahl index for either imports or
exports–is conducive to lower business cycle volatility. We use the standard deviation of the de-
trended output time series, or the standard deviation of the growth rates of output as measures of
business cycle volatility. We also find evidence that one of the likely stabilizing mechanisms of
diversification is the mitigation of various types of domestic and international shocks. Further,
we find that for the same set of controls, diversifying trade towards economies that are larger,
more developed, and more stable is associated with lower business cycle volatility. To make this
study even more similar to a portfolio view of international trade, we further demonstrate that
after controlling for a measure of external shocks, having a less synchronized business cycle with
those of the country’s trade partners is associated with smoother business cycles of major
macroeconomic aggregates at home.
ix

In the second chapter, we examine various economic and geographic variables that we suspect
to be associated with or influential to a country’s level of geographical diversification. Some of
the findings in this chapter are as follows. More advanced economies and nations that reach
beyond their bordering neighbors and establish trade with more distant countries tend to have
more geographically diversified trade. However, countries with a large share of established trade
with G-7 countries, or for which the official language is French or Spanish, or which are
landlocked, seem to be limited in the extent of their geographical diversification. Finally,
membership in the World Trade Organization or its predecessor has a positive impact on
geographical diversification, but only within the first five years of that membership. Although
we use gravity models as a guideline to construct multiple variables at a more aggregated level
here, the implied levels of geographical diversification based on predicted values of bilateral
trade flows from a Poisson pseudo-maximum likelihood estimation technique compare poorly
with the actual computed levels.
Chapter three explores the channels that challenge the widely-found empirical findings that
trade openness has a destabilizing impact on various macroeconomic aggregates. In particular,
using the same panel data and methodology applied in the first chapter, we document three main
findings. First, a non-linearity in the output volatility-trade openness relationship is shown, and
we estimate that beyond a certain threshold, de facto trade openness can turn into a stabilizing
factor for output fluctuations. Second, we find that certain institutional and economic factors are
capable of mitigating the destabilizing impact of exposure to international trade. Of these
complementary variables, we examine financial openness, financial system development, and
democracy. Finally, we find that international trade, when sufficiently diversified in geographical
terms, is capable of becoming a stabilizing factor in relation to business cycle volatility of major
macroeconomic aggregates such as output and consumption. More importantly, this type of
highly diversified or global trade, enjoyed by the countries in the top 25 percentile of
diversification, reduces the relative volatility of consumption to that of output, which is most
frequently used as a measure of consumption-smoothing in the empirical macroeconomic
literature and has been the most difficult to account for, especially by trade and finance variables.

x

One fundamental way open and closed economies differ [is that] with the aid
of loans from foreigners, an economy with a temporary income shortfall can
avoid a sharp contraction of consumption and investment. Similarly [to
smooth sharp expansions], a country with ample savings can lend overseas.
[These] exchanges across time are called intertemporal trade.
Obstfeld and Rogoff, Fundamentals of International Macroeconomics

1

Chapter 1:
Does Geographical Diversification
in International Trade
Reduce Business Cycle Volatility?

Abstract: This chapter 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.

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 chapter
tests the hypothesis that greater geographical diversification reduces business cycle volatility,
conditional on a country’s level of trade openness.
2

Business cycle volatility
1
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 time-
series 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.
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 chapter 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

1
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 1.2.
3

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 globalization
2

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
3
,
the capital account equivalent of trade openness, is realized only after a certain threshold of
financial openness is attained, which potentially explains why emerging markets have not yet
reaped benefits of financial liberalization. A similar analysis performed in Chapter 3 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

2
Chapter 3 challenges treating “de facto openness” alone as a proxy for economic globalization.
3
Financial openness is defined as gross stocks of capital flows to GDP, based on data constructed by Lane and
Milesi-Ferretti (2001). Gross stock is the accumulated sum of inflows and outflows in terms of foreign direct
investment (FDI), portfolio investment and bank lending.
4

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 reduced business
cycle volatility, which in turn depends on the diversification level of the portfolio, the
characteristics of the individual assets – trade partners in our case – and the correlation of
business cycles among them.
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
4
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

4
See Table A3 in the appendix for the full list of countries.
5

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
weighted
5
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
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

5
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.
6

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 chapter is organized as follows: Section 1.2 and 1.3 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 1.4 contains the empirical analyses that
examine three different relations between volatility and diversification variables. Section 1.5
summarizes our findings.

1.2 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 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.
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,
7

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 1950-
2007, 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 sub-
periods 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.
1.2.1 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
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
8

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
any macroeconomic aggregate (A) is defined as:

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
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 (M
j
/M) stands for the relative share of imports from country j to country i
for which, geographical diversity in imports measure is being constructed:

9

Here

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.
1.2.3 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.

Here S
j
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.
1.2.4 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
10

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.

In the above formulation, TPGDP
PC
is the weighted average GDP per capita of trade partners
where, as before, the weights S
j
'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 TPGDP
PC
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.
1.2.5 Economic Stability 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.

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).
1.2.6 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,
11

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.

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 1.4.5 the
preliminary empirical results are consistent with this hypothesis, only after controlling for one
source of volatility in net exports such as term of trade.

1.3 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 sub-
periods of 9 years: 1962-70, 1971-79, 1980-88, 1989-97 and 1998-2006. We also look at
different groups of countries separately. Tables 1.1-1.4 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. BCV
Y
and BCV
C
stand for the business cycle volatility
measures of output and consumption, respectively. Consumption volatility is studied to measure
12

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 1.1-1.4), including the full sample, the cross-country average BCV
Y
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 1.3),
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.
As Tables 1.1-1.4 suggest, the magnitude of the BCV of GDP
PC
declines after the 1970s sub-
period, and in three 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 1.1), and about 30% for the OECD group (Table 1.2), while the Latin American countries
(Table 1.3) settle at almost the same levels of output volatility that existed in 1960s, but are still
25% lower than levels 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 (BCV
C
/BCV
Y
), a measure of consumption smoothing, is below the
13

critical value of unity only for the most advanced (OECD) countries as shown in Table 1.2, on
average. The most anomalous case belongs to the Latin America group (Table 1.3) during 1989-
1997 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 1.1-1.4, 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 (BCV
C
/BCV
Y
) 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
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 1.1. 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 1.1 suggests,
exports have systematically enjoyed higher levels of diversification while we see frequent
instances of temporary increased concentration for imports 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
14

countries like the United States (USA) and the Great Britain (GBR) would not be insightful as,
for example, GBR's variations in HIM are within the lowest band of the diagram for USA.
Tables 1.1 and 1.2: Average Period Statistics for the Full Sample and OECD Countries

Full Sample of 133 Countries

1962-1970 1971-1979 1980-1988 1989-1997 1998-2006 Average
BCV
Y
0.047 0.063 0.056 0.059 0.043 0.054
BCV
C
0.058 0.077 0.075 0.093 0.063 0.074
BCV
C
/ BCV
Y
1.30 1.22 1.34 1.53 1.48 1.38
(X+M)/GDP .61 .65 .66 .71 .85 .70
HIM 0.220 0.183 0.173 0.173 0.181 0.185
HIX 0.195 0.153 0.145 0.140 0.126 0.151
TPGDP
PC
.m 22278 22707 22076 23868 23389 22894
TPGDP
PC
.x 22139 21845 21766 23449 22017 22257
TPGDP.m 2658 2767 2760 2801 3098 2824
TPGDP.x 2792 2524 2511 2651 2590 2610
TPBCV.m 0.027 0.043 0.032 0.027 0.025 0.031
TPBCV.x 0.026 0.044 0.033 0.027 0.027 0.031
IBCSync.m 0.042 0.135 0.138 0.142 0.207 0.138
IBCSync.x 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
BCV
Y
0.028 0.035 0.029 0.029 0.020 0.028
BCV
C
0.028 0.028 0.027 0.024 0.018 0.025
BCV
C
/ BCV
Y
0.95 0.84 0.95 0.83 0.91 0.89
(X+M)/GDP .32 .40 .46 .58 .80 .51
HIM 0.150 0.145 0.133 0.133 0.131 0.138
HIX 0.152 0.136 0.134 0.130 0.118 0.134
TPGDP
PC
.m 22539 21812 21629 25565 25932 23528
TPGDP
PC
.x 22108 21517 21569 25827 25379 23314
TPGDP.m 2471 2326 2364 2554 2758 2497
TPGDP.x 2516 2212 2274 2619 2554 2437
TPBCV.m 0.024 0.041 0.030 0.025 0.020 0.028
TPBCV.x 0.024 0.046 0.030 0.024 0.022 0.029
IBCSync.m 0.191 0.300 0.345 0.324 0.380 0.310
IBCSync.x 0.197 0.287 0.350 0.338 0.361 0.309

15

Tables 1.3 and 1.4: Average Period Statistics for Latin America and “Other” Countries

Sample of 33 Latin America Countries

1962-1970 1971-1979 1980-1988 1989-1997 1998-2006 Average
BCV
Y
0.037 0.054 0.057 0.047 0.039 0.047
BCV
C
0.053 0.063 0.076 0.104 0.061 0.072
BCV
C
/ BCV
Y
1.51 1.29 1.32 1.99 1.35 1.49
(X+M)/GDP .62 .66 .65 .75 .83 .70
HIM 0.241 0.235 0.247 0.240 0.255 0.244
HIX 0.218 0.175 0.191 0.206 0.179 0.194
TPGDP
PC
.m 26823 25964 25913 26746 25926 26260
TPGDP
PC
.x 26423 24272 24189 25936 23773 24879
TPGDP.m 4636 4526 4708 4661 4780 4664
TPGDP.x 4751 4001 4314 4781 4422 4449
TPBCV.m 0.025 0.034 0.033 0.025 0.023 0.028
TPBCV.x 0.025 0.042 0.036 0.026 0.027 0.031
IBCSync.m -0.057 0.232 0.102 0.027 0.363 0.141
IBCSync.x -0.055 0.174 0.128 0.047 0.382 0.144

Sample of 78 “Other Countries”
6

1962-1970 1971-1979 1980-1988 1989-1997 1998-2006 Average
BCV
Y
0.061 0.076 0.065 0.073 0.051 0.066
BCV
C
0.073 0.101 0.092 0.114 0.080 0.093
BCV
C
/ BCV
Y
1.37 1.34 1.50 1.61 1.73 1.52
(X+M)/GDP .71 .73 .74 .74 .87 .76
HIM 0.245 0.182 0.162 0.168 0.178 0.185
HIX 0.209 0.155 0.136 0.125 0.114 0.145
TPGDP
PC
.m 20532 21956 20925 22375 21731 21549
TPGDP
PC
.x 20643 21187 21062 21849 20316 21025
TPGDP.m 2039 2342 2229 2301 2696 2336
TPGDP.x 2223 2147 1980 1972 2013 2059
TPBCV.m 0.029 0.047 0.033 0.028 0.028 0.033
TPBCV.x 0.027 0.044 0.033 0.029 0.029 0.032
IBCSync.m 0.019 0.036 0.069 0.111 0.097 0.071
IBCSync.x 0.018 0.039 0.063 0.124 0.101 0.073

6
"Other countries" sample consists of the full sample countries excluding those in OECD or Latin America groups.
16

Next we study the variables that capture trade partner characteristics. Reported TPGDP is in
10
6
format for simplicity, and both TPGDP
PC
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 1.3) 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 countries
7
in the total trade of each country. As Table A2 in the
appendix 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 TPGDP
PC
do not seem to follow a clear trend when we consider both export and import
versions while a downward trend for HIM and especially HIX is more prominent, on average. In
addition, quantitatively, the variation coefficients for TPGDP and TPGDP
PC
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 1.1. 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.

7
G-7 countries include: Canada, France, Germany, Italy, Japan, United Kingdom, and United States.
17

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 1.1-1.4), 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 1.1)
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.
Finally, our measures of international business cycle synchronization (IBCSync) reported in
the last two rows of Tables 1.1-1.4 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 1.2, column 1), was at
the levels that the whole sample, on average, has reached only today (Table 1.1, 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 1.1 and 1.2. For the sake of brevity, only diversification in imports in the last sub-period
(1998-2006) is portrayed. Figure 1.1 depicts the scatter plot of the HIM-BCV relationship which
18

Figures 1.1 and 1.2: Scatter Diagram of Herfindahl Index for Geographical Diversification in Imports
against BCV and GDP
PC
. Period 1998-2006.

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.

0
.2 .4 .6 .8
HIM
0 .05 .1 .15 .2 .25
BCV of GDPpc
0
.2 .4 .6 .8
HIM
6 7 8 9 10 11
log of GDPpc
19

shows a positive unconditional correlation as confirmed by the simple regression line; countries
with less geographical diversification (or higher HIM) 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 1.2 shows, HIM-lnGDP
PC
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.

1.4 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?

1.4.1 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.

20

In practice, we estimate a two dimensional simultaneous equations model (SEM) of the
following form:

(1.7)
where:

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. LnGDP
PC
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
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 (1.7) 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,

, 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 possibility 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 Chapter 2.
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
21

Instrumental Variable General Method of Moments technique using the panel data described in
section 1.2, and although not reported, period dummies are included throughout all specifications.

1.4.2 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.
Table 1.5 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 1.5, column 1) is our benchmark model, and columns (2) and (3)
include the Herfindahl index of geographical diversification for imports and exports respectively.
Last two columns examine possible stabilizing effect of diversification on consumption volatility.
The estimation method is IV GMM as explained in section 1.4.1. 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 1.5 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
22

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%.
Table 1.5: Business Cycle Volatility and Geographical Diversification

D e p e n d e n t v a r i a b l e : B C V o f
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 0.00041 0.00036 0.00048 0.0009 0.0009

(0.00015)*** (0.00020)* (0.00016)*** (0.0003)*** (0.0003)***
σ(G) 0.071 0.069 0.072 0.167 0.167

(0.013)*** (0.014)*** (0.014)*** (0.028)*** (0.028)***
σ(M) 0.014 0.012 0.017 0.0221 0.0224

(0.006)** (0.006)* (0.006)*** (0.0123)* (0.0126)*
HIM

0.122

0.086

(0.057)**

(0.0854)
HIX

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.

Our measure of economic development level, lnGDP
PC
does not enter most of the
specifications in Table 1.5 significantly, due to its high correlation with other variables. In Table
1.5 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 (σ
G

and σ
M
), 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.
23

Finally, the statistically insignificant coefficient estimates for HIM and HIX in specifications
(4) and (5) in Table 1.5 illustrate that geographical diversification in trade does not smooth
consumption, which as we know from section 1.3, 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 Chapter 3, 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
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.

1.4.3 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 1.6, which as in Table 1.5 presents the results of IV GMM
estimation methodology with the same set of IV's described for Table 1.5. 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.

24

Table 1.6: Diversification and Mitigation of Shocks. Dependent variable: BCV of GDP per capita

(1) (2) (3) (4) (5)

lnGDP
PC
-0.0009 0.0009 0.0006 -0.0015 -0.0005

(0.0026) (0.0022) (0.0023) (0.0041) (0.0027)
Polity -0.0014 -0.0017 -0.0013 -0.0011 -0.0018

(0.0003)*** (0.0003)*** (0.0004)*** (0.0006)*** (0.0004)***
σ (G) 0.0756 -0.1628 0.0547 0.0612 0.1187

(0.0185)*** (0.1012)* (0.0207)*** (0.0216)*** (0.0395)***
TO 0.0005 0.0001 0.00009 0.0001 0.0002

(0.00015)*** (0.00006)** (0.00006) (0.00006)** (0.00005)***
HIM 0.1720 -0.0150 -0.0643 -0.0464 -0.0137

(0.0693)** (0.0567) (0.0711) (0.1137) (0.0474)
σ(G) x HIM

1.738

(0.535)***

σ(TOT)

-0.240

(0.178)

σ(TOT) x HIM

1.997

(1.053)**

σ(RER)

-0.040

(0.127)

σ(RER) x HIM

0.623

(1.044)

σ(r)

-0.0013

(0.0007)**
σ(r) x HIM

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.

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 1.6. 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 HIM enters the
regression with the opposite sign, with the latter being statistically significant even at 1% level.
25

The overall marginal effect of fiscal shocks using the point estimates from specification (2) can
be written as:

Quantitatively, for a country operating at the least level of geographical diversification or
HIM=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 HIM = 0.136, which interestingly is quite
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.
The volatility of the terms of trade is the most frequently used proxy for international shocks
in the literature and in specification (3) of Table 1.6 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 1.6, 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 (5) in Table 1.6 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)
26

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 HIM 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 1.6 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 Chapter 3 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
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.

1.4.4 Economic Characteristics of International Trading Partners
Next, we include newly constructed variables described in section 1.2 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 GDP
PC
, with larger economies as captured
by GDP and with more stable countries in terms of their BCV, all on a weighted average base,
27

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 1.7.

Table 1.7: BCV and Characteristics of Trading Partners. Dependent variable: BCV of GDP per capita

(1) (2) (3) (4) (5) (6) (7) (8)

lnGDPpc -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)
Polity -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)***
σ (G) 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)***
σ (M) 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)***
TO 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)***
HIM 0.122 0.151

0.141

0.139

(0.057)** (0.048)***

(0.053)***

(0.047)***

HIX

0.149 0.123

0.094

0.177

(0.058)** (0.053)**

(0.059)*

(0.070)**
TPGDP PC.m

-2.158

(0.696)***

TPGDP PC.x

-2.126

(0.724)***

TPGDP.m

-0.007

(0.003)***

TPGDP.x

-0.005

(0.002)*

TPBCV.m

0.292

(0.212)

TPBCV.x

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

Columns (1) and (3) in Table 1.7 are our benchmark models in which, the controls along with
openness to trade and Herfindahl measure of geographical diversification for imports and exports
(HIM and HIX) 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 1.7, 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 TPGDP
PC
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 TPGDP
PC
and TPGDP variables are
constructed using GDP and GDP
PC
values from the year 2000 to focus on the type of the country
with which, trading is 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.
One surprising result in Table 1.7 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 1.3. Another interesting result is that the
estimated coefficients of HIX lose some degree of magnitude and statistical power when
TPGDP
PC
and TPGDP variables are included in the regressions, nominating access to more
developed and larger international markets as stabilizing mechanisms for export diversification.
This loss in the explanatory power of the HIX coefficients is most prominent when the TPGDP
variable is included, as shown in Table 1.7 under specification (6), indicating that the stabilizing
mechanism of export 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
29

test this hypothesis, regression specifications in the previous table (Table 1.7) are augmented by
controlling for TPBCV or average output volatility of trading partners. Table 1.8 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.

Table1.8: BCV and Characteristics of Trading Partners, TPBCV Controlled. Dep.variable: BCV of GDP per capita

(1) (2) (3) (4) (5) (6) (7) (8)

TO 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)**
HIM 0.151 0.145

0.141 0.142

(0.048)*** (0.047)***

(0.053)*** (0.054)***

HIX

0.123 0.126

0.094 0.092

(0.053)** (0.059)**

(0.059)* (0.066)
TPGDP PC.m -2.158 -2.279

(0.696)*** (0.739)***

TPGDP PC.x

-2.126 -2.159

(0.724)*** (0.728)***

TPGDP.m

-0.007 -0.008

(0.003)*** (0.003)***

TPGDP.x

-0.005 -0.005

(0.002)* (0.003)*
TPBCV.m

-0.193

0.012

(0.208)

(0.177)

TPBCV.x

-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.

30

1.4.5 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 1.9 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.

Table 1.9: International Business Cycle Synchronization & BCV of Various Macroeconomic Aggregates
D e p e n d e n t v a r i a b l e : B C V o f

Y C I DA C/Y
†
Y

lnGDP
PC
0.0007 0.0094 -0.0110 0.0100 -0.0293 -0.0020

(0.002) (0.004)** (0.011) (0.003)*** (0.042) (0.003)
Polity -0.0013 -0.0028 -0.0016 -0.0016 -0.0173 -0.0012

(0.0003)*** (0.0005)*** (0.0016) (0.0004)*** (0.0062)*** (0.0004)***
σ (G) 0.12 0.23 0.59 0.18 0.48 0.08

(0.01)*** (0.02)*** (0.07)*** (0.02)*** (0.26)* (0.01)***
TO 0.0002 0.00023 .00032 .00024 0.00105 .00013

(.00005)*** (.00008)*** (.00024) (.00006)*** (.0009) (.00059)**
HIM 0.033 0.057 0.140 0.041 0.014 0.025

(0.016)** (0.027)** (0.077)* (0.022)* (0.303) (0.018)
IBCSync -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.
31

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 1.9 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
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 1.9, 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.

1.4.6 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
32

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
Chapter 3 – 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.

1.5 Summary and Conclusions
This chapter 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
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.
33

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 study 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.

34

Chapter 2:
Geographical Diversification
in International Trade and its Determinants

Abstract: Geographical diversification in international trade is an important topic
to study on its own and as a determinant of other variables, and it is an
indispensable component of economic globalization. This chapter follows gravity
models both as a benchmark for comparison and a guideline to construct
aggregate variables that might be associated with diversification. Calculated
diversification levels based on gravity model estimates do not fit well into the
actual data. We find the following variable movements to be associated with
higher geographical diversification in trade: higher GDP per capita; larger country
size, especially when proxied by population rather than land area, which seems to
help only by providing more neighbors; less established trade ties with G-7
economies; shorter distance to centers of global trade or G-7 countries; access to
open waters or not being landlocked, which seems to work as a discouraging and
deterring trade cost; ability to establish trade with more distant countries; and the
first five years of membership in the World Trade Organization or its predecessor.
Countries in which Spanish or French is the official language also tend to
experience restricted diversity of trade. Measure of output volatility only has a
negative and contemporaneous impact on diversification, and not beyond a five-
year sub-period. Finally, international relative-price volatility –in particular that
of real effective exchange rate– is higher for countries with more diversified trade.

2.1 Introduction
Most nations in the world are trading with more countries, and the volume of trade with each has
been on the rise, on average, at least over the second half of the last century. The interaction of
these two factors, namely the number of trade partners and their relative share in the total trade
of a country, determines how diversified its international trade is, in a geographical dimension.
In turn, this defines how complex the network of international commercial relations are, and it is
a good candidate to measure globalization beyond the two most frequently used ones –de facto
trade and financial openness– which measure international flows of goods and capital,
respectively, relative to the size of an economy. In a global economy, more diversified
international trade relations allow nations to share idiosyncratic risks and reduce macroeconomic
volatility.
35

If international trade flows and their derivatives
8
such as trade openness and geographical
diversification were determined solely by geographical factors, we would observe highly stable
paths for these variables. However, they are subject to various forces beyond a country's time-
invariant geographical and even cultural characteristics. Past experiences of volatility in
macroeconomic aggregates, standing of trade balances, movements in international relative
prices, and policy and institutional changes within a country and around the world as reflected in
various types of trade agreements are among the forces that shape these paths.
The actual time-series paths of our measure of geographical diversification for most countries
have been volatile; although the average, across the whole sample and various sub-samples, has
increased –has become more diversified− over time. Some countries, like Brazil and Korea, have
expanded their trade diversification by about 60 percentage points, as captured by a Herfindahl
index over a few decades, while Mexico's international trade has become more concentrated, in a
geographical sense, during the same period.
The following chapter is an empirical study of the behavior of geographical diversification
and its potential determinants or covariates, and in doing so resorts to data and regression
analyses. Since we know of no direct study on the subject, we rely on literature that relates to our
empirical investigation, namely that of gravity models. While gravity models use mutual
geographical, cultural and economic characteristics of two countries in relation to their impact on
the bilateral flow of trade, we use variables at a more aggregate level to study geographical
diversification, which itself is based on bilateral trade flows. For example, gravity models
consider the distance between two countries as a trade deterrent factor, while we construct and
use the average distance to world trade centers as a potential influencer of geographical
diversification for each country.
As the first study of geographical diversification, the aim of this work is not a comprehensive
study of the subject but rather a general overview of the relationships between trade
diversification and various economic and geographical variables that can be elaborated in future
research. The remainder of this chapter is organized as follows: In section 2.2 we explain why

8
By “derivatives” we mean variables that are calculated based on some other underlying variables. While de facto
trade openness is simply the sum of the international flows of goods and services normalized by the size of the
economy, geographical diversification used here is based on a Herfindahl index measure which is calculated using
bilateral trade flows between trading partners.
36

studying geographical diversification is important by referring to the literature and then briefly
report our findings. Section 2.3 explains the data and the variables that are constructed for our
analysis. Section 2.4 directly compares the predictions from a simple yet relatively more reliable
gravity estimation technique with actual data. Section 2.5 employs the constructed panel data to
identify relationships among diversification and various other variables. Section 2.6 concludes
the chapter with a discussion of future avenues for exploration.

2.2 Background and Literature Review
The macroeconomic significance of geographical diversification in international trade,
conditional on a general set of country-specific characteristics, is examined in Chapter 1. For a
given level of trade openness, measured by total volume of trade to national output, expanded
geographical diversification − which can be the result of either an increased number of trade
partners or more evenly distributed trade among them – is empirically found to lower real output
volatility in a large non-overlapping panel of 133 countries over five decades covering 1963-
2006. It is also shown that skewing diversification of trade towards economies that are more
advanced, larger, and more stable is associated with lower macroeconomic fluctuations for the
home country. The latter series of observations are made by constructing variables that capture
other aspects of diversification in international trade, namely the following trade-weighted
elements: output, output per capita, and output volatility of trade partners.
Bilateral trade is the basis of international trade in the domain of macroeconomics, and one
can consider it to be the outcome of decisions made at the firm-level, influenced by a country’s
economic and political environment and policies. Intensive and extensive margins in
international trade − the number of firms involved in trade and the volume of trade per firm,
respectively − as studied by Helpman, Melitz and Rubinstein (2007), for example, are partially
determined by regulatory factors such as the cost of starting a business, taxes on trade flows, and
trade barriers in general. The interaction of all factors at the micro level generates what we call
international trade flow to and from one country, the sum of which divided by the size of the
economy creates de facto trade openness variable.
37

The empirical study of bilateral trade flow has applications in many areas, and gained
momentum after the pioneering work of Tinbergen (1962), who proposed that the bilateral trade
flow between a pair of countries is proportional to the product of their national output and the
inverse of trade costs, which are proxied by the distance and commonalities such as language and
colonial ties between the two nations. Because it resembles Newton's gravity equation that
combines mass and distance between objects to find the gravity force between them, this is
called the gravity model, and has become popular in empirical studies due to its simplicity and
high explanatory power. Interestingly, the theoretical justification for the proposed relationship
came only after the model started appearing more frequently in empirical studies, although the
relationship is so intuitive that as Deardorff (1998) puts it, “I suspect that just about any plausible
model of trade would yield something very like the gravity equation, whose empirical success is
therefore not evidence of anything, but just a fact of life.”
Anderson (1979), Bergstrand (1990), Van Wincoop (2003) and Helpman et al (2006) are
among the major contributors to the theoretical aspects of the gravity model. On the empirical
frontier, the estimation method also has evolved and has been augmented from a simple, ordinary
least-squared of logarithmic form to more elaborate estimation techniques, in order to address
issues such as zero-trades (the observation that a pair of countries do not trade in either direction),
heterogeneity and asymmetries in trade flows from one country to another. We will discuss some
of these briefly in section 2.4.
In the absence of a theoretical framework, we rely on the implications of gravity models as a
guideline for empirical analysis. Our study is related to gravity models in two ways. First, we
apply a relatively advanced estimation technique from the gravity literature to a large sample of
annual bilateral trade data over 1960-2005. We use a Poisson pseudo-maximum-likelihood
(PPML) estimation technique, proposed by Silva and Tenreyro (2006), to address potential
heterogeneity and zero-trades that can lead to biases in the estimation. We then compute implied
geographical diversification levels for each country and each year to learn how much of the data
is explained by this model.
However, despite the gravity model’s success in explaining trade flows, the implied levels of
geographical diversification based on predicted bilateral trade values are not quite consistent
with the actual data. The correlation between fitted and actual diversification values are below 30
38

percent for the entire sample period and are extremely inhomogeneous across different countries,
with correlations ranging from zero for France to 75 percent for Canada. This is partially due to
the sensitivity of the Herfindahl index as a measure of diversification to the number of trade
partners, and also to the fact that gravity models predict more non-zero trade flows, on average,
than actual ones. This should be considered a caution in the application of gravity model
estimations where estimated bilateral trade flows are used to compute more complicated
aggregates or derivatives such as trade openness or geographical diversification. In instrumental
variable estimations, as Helpman, Melitz and Rubinstein (2007) emphasize, the accuracy of the
overall estimation depends on the results in the first stage, and therefore our results call for the
more reliable estimation methods of the gravity model.
Next, we investigate the relationship between geographical diversification and the kind of
variables that are used in gravity-type models, such as geographical and institutional variables,
but at a more aggregated level. For instance, for the aggregate counterpart of the bilateral
distance which proxies trade costs, we construct and examine a GDP-weighted distance from G-
7 countries to measure “remoteness” from world trade centers. As another example, while
gravity models use a binary variable to indicate whether the two countries in question belong to
the same free trade agreement at a point in time, we simply use a binary variable to indicate
membership in a major international trade organization such as the World Trade Organization
(WTO) or its predecessor, the General Agreement on Tariffs and Trade (GATT).
Based on economic intuition and using the gravity model as a guideline, we examine five sets
of variables. One is mostly geographic and includes the following: distance from G-7 countries,
being landlocked, number of neighboring countries, fuel exports or imports as proxies for
resourcefulness, being an island country, size of the country, and language dummies. The second
group includes general economic variables such as development level and size of the economy.
The third group captures the nature of a country’s international trade relations, such as trade
openness, tariffs, and trade shares with most advanced countries. Fourth, we examine certain
variables such as past volatilities in output, relative prices and other economic indicators and
changes in trade balance, to see if countries try to avoid the negative consequences of such
adversities by expanding their network of international trade and increasing trade diversification.
Finally, we construct a set of composite indicators that are a combination of economic and
39

geographic variables and are interesting to examine in the context of geographical diversification
of international trade. For example, a G7Gravity variable is the average GDP of G-7 countries
weighted by the inverse of their distance from the country in question. This is the aggregate
counterpart of the simple gravity relation. In other words, for a given level of economic size,
captured by its own GDP, a country is more likely to have larger trade shares with G-7 countries
if this variable is larger.
Our main empirical findings, using a panel of 133 countries over nine non-overlapping sub-
periods of five years each, covering 1960-2005, follow
9
. Larger countries enjoy more diversified
trade, although this seems to be related to them having a higher number of neighboring countries;
however more populated countries retain this diversity even after controlling for the number of
neighboring countries and level of economic development. Next, higher levels of established
trade with the most advanced economies reduces diversification, and higher G-7 gravity, or
distance-weighted average GDP of G-7 economies, is associated with lower geographical
diversification. The likely mechanism that makes this gravity force work is through pushing
countries to have more trade with the G-7 group, which in turn reduces diversification directly;
however this may not be true for diversification in exports.
Those countries that are more remote from centers of world trade have lower diversification,
and this is not due to remoteness’s impact on forging trade relations with countries that are main
players of world trade, the G-7 group. Also, our constructed “remoteness” variable is a dynamic
one compared to the one used in the existing literature such as in Andrew Rose's dataset. This
variable is the GDP-weighted distance from the seven most advanced economies in the globe,
and because of its being weighted by their corresponding GDPs, it takes into account the
evolution of their relative significance in the world economy. As explained in the next section,
this variable can be generalized to include emerging markets that are increasingly becoming
more important players in the world markets. Examining the role of this and other sets of
countries in international trade has potentially interesting implications for future research.
The nations that are landlocked or cut off from open waters have a lower level of
geographical diversification in international trade, on average. There are various reports and

9
The results should be interpreted as average across countries and over time, since the majority of estimations are
done by a random effects estimation technique.
40

studies, for example by the United Nations and the World Bank that refer to the adversities faced
by such developing countries. The latter two variables – remoteness and landlocked – are most
likely to affect diversification through increasing trade costs that are discussed in various studies
such as Shikher (2012) and the survey by World Bank in Doing Business Project (2010).
The volatility in relative international prices based on real effective exchange rates and terms
of trade is negatively correlated with the Herfindahl index measures of diversification. In other
words, higher volatility of international relative prices is associated with higher levels of
geographical diversification. Experiencing higher levels of volatility in output does not drive
countries to hedge against them via higher trade diversification in later periods. The relationship
between business cycle volatility and geographical diversification is already studied in Chapter 1.
Certain cultural factors such as the official language of the country being French or Spanish
discourage the extent of geographical diversification, even after controlling for level of economic
development and level of established trade shares with G-7 countries. On the other hand, having
more neighbors, either bordering or maritime, increases the chances of having a higher level of
geographical diversification in international trade. Figure series A1 in the appendix shows the
paths of diversification in imports and exports (HIM and HIX) compared to the implied levels of
diversification from the number of neighboring countries, had the country established trade only
with its neighbors and with equal trade shares among them. The emerging pictures are simply not
indicative of any recognizable pattern, so at least in the long run, geographical factors do not
seem to be determinative for the level of geographical diversification. Nonetheless, there are
some countries such as France, Italy, and Germany for which actual and gravity model-predicted
levels of diversification are indeed fluctuating around the neighbor-implied levels.
We find that while remoteness is discouraging for geographical diversification of international
trade, countries that have been able to establish trade with more distant economies across the
globe, on average, are those with higher diversification. We interpret this as an economic success
story which can be the result of various economic and political factors.
Joining the WTO or its predecessor, GATT, has only a contemporaneous positive effect on
geographical diversification. In other words, the diversification gains of joining such a large
41

international organization accrue only within five years, which is the length of sub-periods in our
panel analysis, and not longer.

2.3 Data, Variables and Statistics
This paper uses raw and compiled data from various sources for the construction of variables
used in our analyses. Bilateral international trade data is from the Dyadic Trade Dataset which
itself is mainly based on International Monetary Fund's Direction of Trade Statistics (DOTS).
The main advantage of this dataset compared with DOTS is its consistency in “integrating data
from previous trade data projects.” Barbieri, Keshk and Pollins (2008) ascertain that the reported
flow of trade between two countries is the same, while complementing some missing data by
resorting to appropriate sources such as national accounts.
National income variables along with other economic variables are from the Penn World
Table. Some of international trade variables such as exchange rates, terms of trade, tariffs and the
like are from the World Bank's World Development Index (WDI). Time-invariant geographical
and cultural variables, such as access to open waters and its official language, are from Andrew
Rose’s dataset. Bilateral geographical characteristics, such as the distance between two countries’
capital cities and whether they share a common language or border are borrowed from CEPII
dataset compiled by Head, Mayer and Ries (2010). Number of neighboring countries and year of
joining WTO/GATT are collected from Wikipedia and the organization’s official websites.
More than ninety variables were used in our analyses, either directly from the sources cited in
the preceding paragraph, or indirectly as composite variables computed based on the above. By
composite we mean variables that are constructed using a combination of geographic and
economic variables. The panel data constructed for the purpose of the regression analysis
consists of 133 countries over nine non-overlapping sub-periods of five years each, covering
1960-2005. The main reason for using periods rather than annual data is to enable us to examine
the effect of such volatility variables as terms of trade, exchange rates, and macroeconomic
aggregates, on the behavior of geographical diversification. While geographical variables
including landlocked status and total area are constant throughout for each country, period
42

averages are used for most variables. Meantime, by averaging geographical diversification levels,
we reduce the possible measurement errors.
The binary variable WTO was constructed in two stages: first, for each country and each year
we give a value 1 if the country was signed into GATT or WTO before the end of March in that
year and second, for each sub-period of five years, we assign a value 1 if the country was a
member of the organization for at least two years.
In what follows we explain the construction of the variables that one might expect to
influence or correlate with geographical diversification in international trade. Relevant statistics
and the expected impact of the variables on diversification will be briefly discussed alongside.
But before that, we turn to the main variable, that of diversification itself.

2.3.1 Herfindahl Index of Geographical Diversification in International Trade
In order to capture the level of trade diversification over international borders we use a
Herfindahl index, which is simply the sum of squares of trade shares with trade partners. In
particular, the Herfindahl index for imports and exports can be calculated using bilateral trade
data:

Here b
ij
is the value of bilateral trade flow from country i to country j. We call this variable HIM
when b
ij
equals imports of country i from j, and HIX when b
ij
equals the exports of country i to j.
This variable is constructed for each year separately and can vary between zero and unity, with a
smaller value indicating more diversified − or less concentrated − trade among other nations.
Obviously the value for this index depends on the number of trade partners and how evenly the
trade is distributed among them. As we will demonstrate, the general temporal pattern for this
variable is a more geographically diversified trade level for our entire sample of 133 countries
over five decades. Figure series A1 in the appendix show that individual countries can take quite
volatile paths. The following figures (2.1) illustrate the path of period averages for various
groupings of countries as well as the full sample.
43

Figure 2.1: Period Averages of Geographical Diversification Measures for the Full Sample and Sub-Groups

44

2.3.2 Gravity to G-7 Countries
The basic intuition derived from the gravity models tells us that for a given level of economy
size, trade flows with another country will be proportional to the size of that other country's
economy and the inverse of distance between them. As will be explained shortly, the group of G-
7 countries plays an important role in the international trade arena; therefore we construct a
variable that allows us to capture this fact in a gravity-type framework.

Naturally this variable takes on a bigger value if a country is located close to the largest
economies on the globe, on average. Originally this variable was developed after observing that
some countries that are located close to large economies have relatively less geographically
diversified trade. This is most prominent in the cases of Canada and Mexico, with levels of
geographical diversification that are about 50 percentage points above the sample average for the
post-1990 period.

2.3.3 Trade Shares with G-7 Countries
The important role played by the most developed countries in the global economy is shown by
computing trade shares with these countries at the beginning of each period, which we interpret
as established trade with G-7 countries. As Table A2 in the appendix shows, shares of the U.S.
alone in the imports of other countries for the entire sample of 133 countries has remained
relatively stable at around 16-18% over five decades. On the other hand, its shares in other
countries’ exports have fallen from about 22 to 13 percent. This statistic is more than double for
Latin America countries, almost consistently throughout all nine sub-periods.
On the other hand, Latin America’s trade shares with G-7 countries have shrunk from
approximately 66% from the 1960-64 sub-period to 50% in the first half of the new millennium.
The OECD's trade shares with the U.S. and the G-7 countries have been around 15% and 40% in
recent periods, respectively, which is a decline of about 20% from the 1960s, most likely due to
the rise of China and other emerging markets.
45

2.3.4 Remoteness
The cost of transportation in international trade is at the center of gravity models as a trade
discouraging and deterring factor. The distance between the capital cities of two countries is
usually used to proxy for this, along with other variables that they have in common, such as
language, borders, and colonial ties. The following variable is constructed to capture remoteness
from world trade centers and is simply a GDP-weighted average distance from G-7 countries. In
this equation variable d
ij
is the distance of the country in question from country j, which belongs
to the G-7 group. A simpler version of this variable can be found in Andrew Rose's dataset,
which takes the average distance of a country from three major cities around the world –namely
London, Rotterdam and Tokyo. Our measure has the advantage of taking into account changes in
the world economy, over time. In other words, the formulation gives more weight to larger
economies in calculating the distance from them. One might want to add China and emerging
markets to the summation:

We interpret a larger value for this variable for a country as its facing higher transportation
costs involved in global trade, which in turn leaves the country to exercise its “comparative
advantage locally”, as pointed out by Alan Deardorff (2004).
On the other hand, based on the gravity theory, a country’s proximity to G-7 countries (a
smaller value for REMOTE) can lead to establishing stronger trade ties with them, and leaves
little incentive for a country to diversify trade across other nations. Overall the impact of this
variable on diversification remains an empirical question. In gravity models in which bilateral
rather than multilateral trade flows are studied, being more remote from the rest of the world
strengthens trade relations between the pair of countries in question.

2.3.5 Distance from Trade Partners
While the previous variable captures average distance from “potentially” strong trade partners,
we have constructed a similar variable that measures average distance from “actual” trade
partners, weighted by their trade shares:
46

As before, b
ij
is the value of bilateral trade flow from country i to country j. An interpretation for
this measure is that a country with a relatively high value for TPDIST is one that has already
established trade ties with countries beyond its natural bordering neighbors and is likely to have
garnered a more diversified network of trade partners.
An interesting group of countries to examine here is Latin America. The following table (2.1)
shows them in descending order, based on distance from import partners in the year 2000. Chile
is at the top of table and Mexico is located almost at the bottom. Over time, we see a steady
increase and decrease in the level of geographical diversification for these two countries, as
shown in Figure series A1 in the appendix. Here, Chile ends up among the countries with the
most internationally diversified network of trading partners, while Mexico is among those with
the least.

Table 2.1: Trade-Weighted Average Distance from Trade Partners, Latin America Countries
rank Country* TPDist_m ▼ TPDist_x rank Country TPDist_m TPDist_x
1 CHL 11335 8803

18 CRI 5582 5334
2 PER 8815 7237

19 LCA 5076 5006
3 BRZ 8753 8877

20 KNA 4884 4289
4 ATG 8447 6970

21 PAN 4876 5542
5 BLZ 7585 4055

22 COL 4842 6156
6 CUB 7574 5991

23 GRD 4796 4390
7 ARG 7201 8716

24 VEN 4265 5651
8 URY 6733 6964

25 TTO 4048 4912
9 SUR 6652 6927

26 NIC 3986 3976
10 DMA 6430 4502

27 HND 3786 3978
11 ECU 6388 6350

28 PRY 3774 7119
12 VCT 6375 4150

29 DOM 3762 4263
13 GUY 5907 5312

30 GTM 3600 4096
14 BHS 5837 7443

31 HTI 3400 5020
15 BOL 5653 6082

32 MEX 3216 4434
16 BRB 5645 5230

33 SLV 3113 3790
17 JAM 5586 4542

Notes: Distances are in Kilometers. ▼ Ranked in Descending Order Based on Import Shares as
Weights (TPDist_m). * See the Appendix for the Full Name of the Countries.
47

2.4 Gravity Models’ Prediction of Geographical Diversification
As noted earlier, gravity models provide a natural starting point in the empirical investigation of
international trade relations. We rely on them for two purposes. First, we compare predicted
values of geographical diversification, from a simple gravity model, against actual values. This
leads to an understanding of the explanatory power of these models for more complicated
variables (such as diversification), which are based on predicted bilateral trade flows. Second,
variables analogous to those used in gravity models are constructed at a more aggregated level
and are employed to explain diversification paths of countries.
The issue of zero-trades is vital for determining geographical diversification measures since
this means less trading partners and Herfindahl index is highly sensitive to this change.
Therefore, a more recent and reliable estimation technique of the gravity model is applied to
predict bilateral trade flows. This is then used to calculate the implied value of geographical
diversification according to equation (2.1). Silva and Tenreyro (2006) provide the justification
for their proposed Poisson pseudo-maximum-likelihood (PPML) method that we apply here,
which simultaneously addresses two issues of heteroskedasticity and zero-trades. Further, Silva
and Tenreyro conduct comparisons with other popular estimation methodologies through Monte
Carlo simulations. The other popular techniques found in the literature are: (a) simple OLS,
which truncates the data due to the impossibility of including zero trades in a logarithmic
function, (b) augmented OLS, which adds a positive number to trade flows so that zero-trade
observations can be included in the regression analyses, (c) non-linear least squares, which can
be “very inefficient in the presence of heteroskedasticity that is characteristic of this type of data”
and finally (d) the tobit method based on the work of Eaton and Tamura (1994). Silva and
Tenreyro's simulations show significant biases as a result of ignoring heteroskedasticity, but not
zero-trades, in the aforementioned techniques and they recommend using PPML.
In our estimation of bilateral trade flows based on the PPML technique, we use a standard and
basic set of variables from the traditional gravity models. These include logarithm of the GDP of
exporter and importer countries, logarithm of the distance between them, dummies indicating if
there is a common border, a common language, and whether they belong to the same free trade
agreement at each point in time. The estimation is done for a large set of 224 countries over the
48

period 1960-2006 and the predicted values for bilateral trade flows are then used to compute the
Herfindahl index for geographical diversification for each individual country and each year.
The picture that emerges from the gravity-based model does not fit at all well into the actual
data for geographical diversification. The correlation between the fitted Herfindahl index and its
actual value for either imports or exports is less than 30 percent for our panel of 133 countries
over 1960-2005. The number is about 75 and 39 percents for OECD and Latin America groups,
respectively. The magnitude of the correlation is about 10 percent for the earlier period −before
1970− and slightly above 35 percent for the post-1985 period. This discrepancy, and its decline
over time, is likely due to the fact that geographical diversification is sensitive to the number of
trade partners, and over time countries have established commercial ties with a larger network of
countries; therefore, correlations are stronger towards the second half of the sample period.
However, even in recent years, the fitted values do not quite explain the reality, especially if we
assume that the reported international trade data have become more accurate and reliable over
time.
In general, our gravity model predicts much lower values for the Herfindahl index and
exaggerates the degree of diversification, despite the fact that our estimation technique generates
instances of no-trade among pairs of trade partners. The correlation for Canada, which has an
unusually concentrated geographical trade, even compared to an average country in the sample,
is about 75 percent, and it is close to zero for France. Figure 2.2 shows the movements of the
median and mean for actual and fitted values over the entire period.

49

Figure 2.2: Annual Time Series of Herfindahl Index for Geographical Diversification for Full Sample

50

2.5 Empirical Analyses
In this section we try to understand the relationships between our measures of geographical
diversification with various economic, geographical and composite variables that might be
considered as potential determinants or mere covariates of the diversification. Table 2.2 reports
the results from regressing geographical diversification in imports and exports (HIM and HIX)
on each variable of interest, individually, in a simple panel model where the estimation technique
is random effects and period dummies are included for each of nine sub-periods covering 1960-
2005. Due to the nature of the data, which contains many time-invariant variables, we eschew
fixed effects in favor of a random effects method for estimation of the parameters in most cases,
and try to capture country-specific characteristics through variables such as output per capita
when needed. In certain instances later on, in order to identify a relationship, we resort to
multiple regression analysis. The inclusion of all single regression results in one table is simply
for the sake of saving space and might also serve for comparison purposes.
The first row in Table 2.2 tells us that more advanced countries are those with significantly
higher levels of geographical diversification in both import and export dimensions, as the
negative coefficients
10
for log of GDP per capita indicate. No causal conclusion is drawn here,
since both variables are subject to a vast array of political, economic and geographical forces.
Next, economies that maintained a trade surplus, on average, over the sub-period of five years,
tend to have more geographically diversified exports, while trade openness per se does not show
any statistically significant relationship. The latter observation is worth further investigation
since de facto trade openness that we use here, namely the ratio of total volume of trade to output,
is one of the two most frequently used measures of economic globalization as discussed in
Chapter 3. Our observation here indicates that mere trade openness does not guarantee that a
country is involved in a more complex network of international relations as captured by
geographical diversification.

10
A positive coefficient in the table implies that a larger value of the explanatory variable is associated with an
increase in the value of the Herfindahl index (HIM or HIX), which implies lower geographical diversification.
51

Table 2.2: Simple Panel Regression of Geographical Diversification on Single Regressors

HIM

HIX

log GDPpc -0.01559

-0.00896

(0.00631)**

(0.00458)**

Trade Balance / GDP -0.00031

-0.000385

(0.000214)

(0.000156)**

Trade Openness 0.000012

0.00012

(0.00011)

(0.0000802)

log Population -0.0244

-0.02012

(0.00513)***

(0.0037)***

log Land Area -0.012

-0.00402

(0.0047)***

(0.0034)

Trade Share, U.S. 0.0023

0.0018

(0.00024)***

(0.000233)***

Trade Share, G7 0.001795

0.0015

(0.00019)***

(0.00018)***

G7 Gravity 0.005

0.007

(0.003)*

(0.002)***

Remoteness from G7 0.0080

0.0080

(0.0036)**

(0.0026)***

Agriculture Share 0.0011

0.0011

(0.0004)***

(0.0003)***

RE Exchange Rate Volat. -0.0452

-0.0078

(0.0143)***

(0.0081)

TOT Volatility -0.0542

-0.0195

(0.0357)

(0.0197)

Output Volatility 0.1732

0.1146

(0.0623)***

(0.0457)***

Fuel Exports 0.0006

-0.00024

(0.0002)***

(0.00014)*

Fuel Imports 0.0005

-0.00124

(0.0004)

(0.00028)***

English -.0031

-0.0041

(0.020382)

(0.0146)

French 0.057

0.047

(0.024)**

(0.0172)***

Spanish 0.0418

0.0348

(0.0265)*

(0.01913)**

Neighbors, no. of -0.0138

-0.0079

(0.0037)***

(0.0027)***

Landlocked 0.0382

0.0489

(0.0251)
0.12

(0.0178)***

Distance to Partners -0.0149

-0.0123

(0.0023)***

(0.0023)***

Tariffs -0.0001

-0.00016

(0.0003)

(0.00025)

GATT / WTO -0.0254

-0.01169

(0.0099)***

(0.00729)*

Notes: These are the results from regressing HIM and HIX on each explanatory variable,
separately, and are not from a multiple regression. Period dummies are included in each
regression. Estimation method is random effects. The numbers reported below each coefficient
in the parentheses are standard errors with *, ** and *** next to them indicating statistical
significance at 10, 5 and 1 percent respectively.

52

Larger countries, measured by either population or land area in Table 2.2, tend to have more
diversified exports and imports across the globe. The effect of country size on various economic
outcomes including growth and business cycle volatility is studied by Rose (2006), and Furceri
and Karras (2007), respectively. The latter study hypothesizes that larger countries tend to have
more sectors in their production structure, which in turn immunizes them against sector-specific
shocks and therefore they enjoy relatively smoother business cycles.
In our study, once both measures of country size are included together, population becomes a
much stronger influencer over diversification while land area ceases to increase diversity. This
result remains robust to controlling for per capita GDP as Table 2.3 below shows. This exercise
illustrates that in studying the impact of country size on geographical diversification, the
appropriate variable to pick is the population rather than land area.

Table 2.3: Panel Regression of Geographical Diversification on Country Size
HIM HIX
log GDP
PC
-0.021 -0.013
(0.0062)*** (0.0045)***
log Land Area 0.00397 0.0137
(0.006) (0.0043)***
log Population -0.029 -0.032
(0.0068)*** (0.00494)***
Obs. No. 1074 1074
R-sq 0.145 0.180
Notes: Dependent variables are Herfindahl indexes for geographical
diversification in imports and exports (HIM and HIX). The numbers reported
below each coefficient in the parentheses are standard errors with *, ** and ***
next to them indicating statistical significance at 10, 5 and 1 percent respectively.

The following supplementary exercise provides an additional insight on the possible
mechanisms at work behind the relationship. In Table 2.4 we control for the number of
neighboring countries and observe that larger country size, captured by land area, most likely
influences geographical diversification only through providing the country with larger number of
neighboring countries along its border. We make this observation by showing that the coefficient
of the land area variable has lost statistical strength or changed its sign, compared to the stand-
alone regressions reported in Table 2.2.
53

Table 2.4: Panel Regression on Country Size Controlling for Number of Neighbors
HIM HIX
log Land Area -0.00353 0.002102
(0.0055) (0.003989)
Neighbors, # -0.0123 -0.00884
(0.00442)*** (0.00319)***
Obs. No. 1107 1107
R-sq 0.09 0.12
Notes: Dependent variables are Herfindahl indexes for geographical diversification
in imports and exports (HIM and HIX). The numbers reported below each
coefficient in the parentheses are standard errors with *, ** and *** next to them
indicating statistical significance at 10, 5 and 1 percent respectively.

In this section we show that established trade relationships with the most advanced countries,
including those in G-7 and even the U.S. alone, seem to have a discouraging influence on
geographical diversification. This effect is captured by the coefficients of “Trade Share”
variables in Table 2.2. We call these established since they are based on trade carried out at the
beginning of each period and seem to be quite stable over time; the usage of lag of these
variables leads to the same results. Table A2, reported in appendix, summarizes the period
average statistics for these variables.
One should be careful in interpreting the above result since having a relatively high level of
established trade shares with G-7 countries naturally implies that the country in question is left
with fewer trade shares to diversify among other countries. Another interpretation is that such a
country is gaining a lot from trade and hence has less incentive to diversify. Chapter 1 showed
that trading with more developed economies is associated with lower business cycle volatility in
the home country, and Wagner and Zeckhauser (2006) document that “trading up” −having trade
relations with a more advanced country− is conducive to higher economic growth. Regardless of
the channel, either as a pure mathematical consequence or an economic justification, having
larger trade shares with the most advanced economies seems to reduce geographical
diversification of trade.
The next two variables in Table 2.2 are directly inspired by gravity-type models as explained
in section 2.4. Again, G7Gravity is the average GDP of G-7 countries, weighted by the inverse of
their distance from each country in the sample. Gravity models tell us that for a given size of
54

country's own GDP, trade relations are stronger and hence flows are higher if the GDP of the
potential trading partner relative to the distance from the partner is larger. Therefore a higher
G7Gravity is more likely to prompt a country to establish trade, and at a higher volume, with G-7
countries. In turn, through the mechanisms hypothesized above, the higher gravity will lead to
less geographical diversification. Indeed, once we include both G7Gravity and G7Share in the
same regression (Table 2.5), G7Gravity loses its explanatory power in the HIM regression.
However in export regression, even after controlling for the share of G-7 in exports, higher
“gravity” towards G-7 remains statistically strong and positively correlated with the
diversification in exports.
Table 2.5: Panel Regression on Gravity Towards G-7 Countries
HIM HIX
log GDP
PC
-0.026 -0.018
(0.0036)*** (0.0026)***
G7 Gravity -0.00071 0.0043
(0.00234) (0.00163)***
G7 Shares 0.0022 0.0017
(0.00018)*** (0.00017)***
Obs. No. 1043 1043
R-sq 0.33 0.34
Notes: Dependent variables are Herfindahl indexes for geographical
diversification in imports and exports (HIM and HIX). The numbers reported
below each coefficient in the parentheses are standard errors with *, ** and ***
next to them indicating statistical significance at 10, 5 and 1 percent respectively.

Our measure of distance from world trade centers, REMOTE, which is a dynamic and more
general version of the average air distance from three cities −London, Tokyo and Rotterdam− in
Andrew Rose's dataset, appears with a strong positive coefficient in a simple linear regression
that Table 2.2 reports, indicating that being farther from world trade centers, or G-7 countries on
average, is associated with lower geographical diversification. As such, this variable can be
thought of as a proxy for the cost of trade with the rest of the world discouraging and deterring
trade relations. Interestingly, Table 2.6 shows that this variable maintains its statistical strength
even after controlling for share of trade with G-7. In other words, its impact on the
diversification goes beyond forging trade relations only with the G-7 countries. Therefore our
55

remoteness variable appears as a good candidate to proxy overall transportation costs associated
with involvement in global trade.
Table 2.6: Panel Regression on Remoteness from Centers of the World Trade
HIM HIX
Remoteness 0.00589 0.00541
(0.0031)** (0.0023)***
Trade Shares, G7 0.00182 0.0015
(0.00019)*** (0.00018)***
Obs. No. 1070 1070
R-sq 0.18 0.19
Notes: Dependent variables are Herfindahl indexes for geographical
diversification in imports and exports (HIM and HIX). The numbers reported
below each coefficient in the parentheses are standard errors with *, ** and ***
next to them indicating statistical significance at 10, 5 and 1 percent respectively.

Furthermore, geographically remote countries seem to have a disadvantage when it comes to
expanding their trade even after controlling for their level of economic development.
Table 2.7: Panel Regression on Remoteness, controlled for GDP per capita
HIM HIX
log GDP
PC
-0.0123 -0.0045
(0.0066)* (0.0048)
Remoteness 0.005824 0.007677
(0.0036)* (0.0026)***
Obs. No. 1074 1074
R-sq 0.04 0.08
Notes: Dependent variables are Herfindahl indexes for geographical
diversification in imports and exports (HIM and HIX). The numbers reported
below each coefficient in the parentheses are standard errors with *, ** and ***
next to them indicating statistical significance at 10, 5 and 1 percent respectively.

Countries that have experienced higher volatility in their real effective exchange rates
11
or
terms of trade are the ones with more diversified international trade; however the relationship is
not statistically significant for most specifications, as Table 2.2 shows. Considering that these
relative price measures are constructed using trade shares as weights, as is the Herfindahl
measure of diversification, the results are understandable to some degree. Identifying this

11
This variable is from IMF’s IFS and is “effective” in the sense of being a weighted average of real exchange rates,
with weights being trade shares with trade partners.
56

relationship is beyond the scope of this paper and involves addressing the possible endogeneity.
However this interesting relationship remains quite robust to the various specifications that
control for the proxies of the level of economic development and economic stability, as
illustrated in Table 2.8.

Table 2.8: Panel Regression on Real Effective Exchange Rate Volatility, Random & Fixed Effects Techniques
HIM, RE HIM, FE HIX, RE HIX, FE
log GDP
PC
-0.02413 -0.03117 0.006651 0.015428
(0.00868)*** (0.0129)** (0.005186) (0.00719)*
Output Volat. 0.05337 0.03015 0.18338 0.178276
(0.089038) (0.09098) (0.04988) (0.05038)***
RER Volat. -0.04395 -0.04358 -0.01035 -0.0115278
(0.0142)*** (0.0145)*** (0.00794)
0.15
(0.00803)
0.15
Obs. No. 384 384 384 384
R-sq 0.03 0.06 0.02 0.01
Notes: Dependent variables are Herfindahl indexes for geographical diversification in imports and exports (HIM
and HIX). The numbers reported below each coefficient in the parentheses are standard errors with *, ** and
*** next to them indicating statistical significance at 10, 5 and 1 percent respectively. Superscripts indicate the
implied p-values.

We show that contemporaneous levels of higher output volatility are associated with less
diversification in Table 2.2. We identified this association through appropriate instrumental
variable techniques in Chapter 1 with the influence flowing from diversification towards output
volatility. In particular, for a given level of country-specific characteristics and openness to trade,
higher geographical diversification leads to lower output volatility.
Next, we include past levels of output volatility to examine whether such adversities would
lead a country to expand its network of trading partners in subsequent periods. The following
table (2.9) shows that our data does not support this conjecture; countries do not seem to be
learning from the past. Notation-wise, L1 is the first lag of output volatility, which corresponds
to the previous sub-period values. Our exercise with longer period lags gives the same results as
the first lag.

57

Table 2.9: Fixed Effect Regression on Past Experiences of Output Volatility
HIM HIX
L1. Output Volat. 0.0584 0.0259
(0.0561)
0.30
(0.039)
0.50
Obs. No. 838 962
R-sq 0.06 0.14
Notes: Dependent variables are Herfindahl indexes for geographical
diversification in imports and exports (HIM and HIX). Superscripts indicate the
implied p-values.

In terms of their association with diversification, shares of fuel in exports or imports have
asymmetric effects which are not easy to interpret. For example, based on Table 2.2, countries
exporting larger shares of fuel tend to have less diversified imports but more diversified exports,
as the positive and negative coefficients, respectively, illustrate. On the other hand, those
countries with larger shares of fuel imports, which is characteristic of countries with high
demands for energy, have more diversified exports.
As discussed, gravity models usually include a binary variable to indicate the incidence of a
common language as a driving force, and a facilitating factor that establishes and enhances
bilateral trade between a pair of countries. Many studies, most notably Head, Mayer and Ries
(2008), have examined the role of common language and colonial ties on bilateral trade
relationships. In our aggregated framework we posit that the official language of a country can
bias it to establish trade with a certain group of countries for cultural and historical reasons. As
Table 2.2 shows, countries with French or Spanish as their official language seem to have less
diversified trade. This is not the case with English speaking countries at all, either in terms of
sign or the significance of the coefficient estimates. To understand the mechanism, we
hypothesize that the channel through which a country’s official language affects trade relations is
by establishing trade with countries such as those in the G-7, which in turn limits trade
diversification even further. As the table below (2.10) demonstrates, the original result is robust
to the inclusion of GDP per capita and control of established trade with G-7 countries. In other
words, for a given level of economic development and established trade with the most advanced
countries, certain languages seem to be indeed limiting in terms of diversifying international
trade among other nations. We follow a random effects estimation technique, as most of the
variables of interest are time-invariant.
58

Table 2.10: Panel Regression on Language Dummies
HIM HIX
log GDP
PC
-0.0232 -0.0075
(0.0054)** (0.0041)*
Trade Shares, G7 0.0022 0.0017
(0.00019)*** (0.00018)***
English -0.0046 0.0018
(0.0172) (0.0136)
French 0.0455 0.0338
(0.0203)** (0.0161)**
Spanish 0.0401 0.0317
(0.0227)* (0.0179)*
Obs. No. 1042 1042
R-sq 0.29 0.30
Notes: Dependent variables are Herfindahl indexes for geographical
diversification in imports and exports (HIM and HIX). The numbers reported
below each coefficient in the parentheses are standard errors with *, ** and ***
next to them indicating statistical significance at 10, 5 and 1 percent respectively.

Here, two time-invariant variables illustrate the blessing of having more neighbors versus the
curse of being landlocked, in the context of geographical diversification. We use the number of
land neighbors for this, and the results are similar when we take maritime neighbors into account.
Table 2.2 shows that the number of neighboring countries is a strong covariate of geographical
diversification. Figure series A1 in the appendix of select countries portrays the movements of
actual geographical diversification indices, and compares them with the level implied by the total
number of neighboring countries. This is a benchmark number that equals the value of the
Herfindahl index for diversification, had the country established equally distributed trade
relations only with its neighboring nations. For example, the implied HI for Brazil with 10
neighboring countries is 0.10, while for Canada with only three neighbors it is equal to 0.33.
There are currently 48 recognized landlocked countries in the world that do not have
immediate access to open waters. In general, four “clusters” of countries fall within this category.
First is a group of Central Asian countries which contain former Soviet states plus Afghanistan;
these are not included in our sample. The three other clusters are in Central and Southern Africa,
Central Europe and South America. According to a report by the United Nations on Maritime
Transport (2010) the majority (31 out of 48) are landlocked developing countries or LLDCs. The
59

report highlights that “by definition, all LLDCs depend on their neighboring countries’ transit
systems, regulatory environment, and transport infrastructure in order to access seaports and
global markets.” International trade transaction costs are much higher for LLDCs compared to
non-landlocked developing countries. For example, according to a survey by the World Bank, for
Asian LLDCs this figure is 3 to 1. Our point estimates of the simple panel regressions reported in
Table 2.2 above show that being landlocked is associated with about 35% less geographical
diversification in international trade for the median observation. To examine if being landlocked
affects diversity of international trade through distance from world trade centers, we conduct the
following exercise to bring countries on par in terms of remoteness and development status. The
impact of being landlocked remains significant and discouraging for diversification in exports,
while it seems not to matter anymore for diversification in imports:
Table 2.11: Panel Regression on Landlocked and Remoteness from G-7
HIM HIX
log GDP
PC
-0.011 -0.0027
(0.00677)* (0.0049)
Remoteness 0.0058 0.0074
(0.00369)
0.12
(0.00267)***
Landlocked 0.0225 0.0366
(0.0254)
0.38
(0.0182)**
Obs. No. 1074 1074
R-sq 0.04 0.09
Notes: Dependent variables are Herfindahl indexes for geographical
diversification in imports and exports (HIM and HIX). The numbers reported
below each coefficient in the parentheses are standard errors with *, ** and ***
next to them indicating statistical significance at 10, 5 and 1 percent respectively.

An interesting variable that is constructed for this paper is average distance from trade
partners, weighted by their corresponding trade shares (TPDist). This variable should not be
confused with remoteness from G-7 countries (REMOTE), as the former is average distance
from existing trade partners while the latter should be interpreted as average distance from world
trade centers. Contrary to the remoteness case, countries which have established trade relations
with more distant nations are those with more diversified trade. Table 2.1 above (p.46) examines
the case for Latin American countries at a certain point in time, with Mexico located at the
bottom of the table (lowest average distance from trade partners) settling for proportionately
60

large trade relations with its immediate bordering neighbor, the U.S., and Chile appearing at the
top with quite impressive increases in its level of geographical diversification as shown in Figure
series A1 in the appendix. The negative coefficient for this variable in Table 2.2 might simply
convey a success story in the sense that those countries that are able to reach and establish trade
with countries at farther distances are also able to diversify trade among a larger network of
nations throughout the world. Therefore the strong and negative conditional correlation that we
observe might be the result of success in the international arena, in addition to other political and
economic factors.
Higher tariffs as a measure of barriers to trade do not seem to have a significant conditional
correlation with diversification, but it should be mentioned that due to lack of data our panel is
highly unbalanced in terms of this variable, and small sample-size might be driving this result. It
is a noteworthy observation that neither de facto nor this measure of trade restriction come out as
important covariates of geographical diversification in trade.

Table 2.12: Panel Regression on WTO Membership
HIM, RE HIM, RE

HIM, FE HIX, RE

HIX, RE

HIX, FE
WTO -0.0254 -0.0193 -0.0117 -0.0087
(0.0099)*** (0.0107)* (0.0072)* (0.0079)
L1. WTO -0.0121 -0.0027
(0.0092) (0.00617)

Obs. No. 1107 1005 1005 1107 1005 1107
R-sq 0.05 0.02 0.06 0.09 0.04 0.09
Notes: Dependent variables are Herfindahl indexes for geographical diversification in imports and exports (HIM and
HIX). The numbers reported below each coefficient in the parentheses are standard errors with *, ** and *** next to
them indicating statistical significance at 10, 5 and 1 percent respectively.

Finally, as replicated in Table 2.12 above in the first column from the left, the impact of
joining the World Trade Organization or its predecessor GATT, as defined in the section 2.3, is a
rise in the level of geographical diversification. Interestingly, this relationship holds only
contemporaneously. In other words, joining GATT/WTO is beneficial for geographical
diversification only in the same sub-period in which it occurs and not for subsequent periods, as
the lagged regressions in the table show. Obviously this result, which is based on a random
61

effects estimation, has a combined cross-section and time-series interpretation. The fixed effect
estimation outcome, which carries a purely time-series interpretation, is consistent with the
above random effects exercise only in import dimension. In other words, for a country, joining
this international organization is associated with higher diversification of only imports within
five years.

2.6 Summary and Conclusions
This chapter is an examination of the behavior and potentially important covariates in
geographical diversification in international trade, which by construction is based on bilateral
trade data. The paper was originally motivated by the observation in Chapter 1 that geography,
domestic and international policy, and macroeconomic volatility might influence how diversified
a country’s international economic relations are.
Due to the lack of previous studies in this area we use gravity models both as a benchmark
and guideline. Our analyses based on a relatively more reliable estimation technique display a
large discrepancy between the implied values of the diversification levels that are based on
predicted bilateral trade flows from a gravity model, and the actual time series of either import or
export diversification. Further, the correlation between fitted and actual data does not show much
improvement over time, illustrating that the underlying reason is not inaccuracy in the
international data.
We construct aggregate counterparts of the variables that are usually used in gravity models
and examine their relationship with actual levels of geographical diversification in both simple
regressions –as summarized in Table 2.2– and multiple regressions that followed, in order to
better identify relationships. Most of these results are based on our panel of 133 countries over
nine sub-periods covering 1960-2005.
More advanced countries have more complex networks of commercial partners, as measured
by a Herfindahl index of geographical diversification, in both imports and exports. Neither de
facto nor restrictive measures of trade openness –the latter being captured by average tariff rates–
are significant covariates of geographical diversification. Larger countries proxied by their
population or land size enjoy more diversified trade, while land size seems to influence the
62

diversification level only through providing a country with more neighboring countries. On the
other hand, more populated countries seem to demand more diversified international trade even
after controlling for the number of neighboring countries.
Those countries that have larger established trade ties with G-7 economies tend to have less
geographically diversified trade. This link calls for further examination of whether this result is
driven solely by a pure mathematical relation or by the author’s hypothesis that such trade
relations with the most advanced economies leaves little incentive for any country to seek
diversification of its remaining trade volume, due to the benefits that are already accrued from
this “high quality trade”. There are a few studies including Chapter 1 that discuss the advantages
of trading with larger or more-developed countries.
Remoteness from G-7 countries, weighted by their relative GDP, seems to be indicative of
large costs associated with being involved in global trade. Countries that are, on average, more
remote from these centers of world trade have less diversified international trade.
Current or same-period business cycle volatility of output, rather than past ones, is associated
with less diversification in a statistically significant manner. In other words, countries do not
learn from their past experiences of a volatile economy and therefore do not embark on
diversifying their trade. Higher volatilities in international relative prices captured by those in
terms of trade or real effective exchange rates are associated with higher geographical
diversification.
Certain cultural factors such as language display consistent results in terms of their impact on
the network of international economic relations. In particular, countries for which the official
languages are Spanish or French tend to have less diversified trade, while no statistically
consistent result is obtained for English-speaking nations.
Landlocked countries are dominated by developing rather than developed economies and are
less diversified in their commercial relations. This relationship seems to be working through
affecting transportation costs as is also pointed out in reports by the IMF and the World Bank.
Countries that have been able to establish trade ties with more distant nations enjoy more
diversified trade. This is evident in countries such as Brazil and Chile as shown in Table 2.1, and
63

is likely to be the result of a successful implementation of international policy. And finally,
joining WTO/GATT is conducive to higher diversification in trade, but only in the same sub-
period that the membership was initiated, and not beyond that five-year period.
The author hopes that this first attempt to study geographical diversification in international
trade, which has potentially important implications, spurs further discussions of the subject and
its application. One such application that has already been studied by the author (Chapter 1) is
the risk-sharing implication for more stable business cycles through mitigation of shocks. On the
other hand, since this variable captures complexities in international economic relations, it
should also be considered when studying economic globalization. As Farshbaf (Chapter 3)
proposes, a measure of true economic globalization should take diversity into account rather than
simply relying on de facto openness measures, in either trade or financial dimensions.
Although close to one hundred variables are studied and a dozen are constructed in this paper
to better understand the behavior of geographical diversification, there is a lot more that can be
explored and there are innumerous ways of combining these variables to better identify their
relationship with and to explain geographical diversification. For example, while membership in
the WTO is discussed here, there are many other free trade agreements, including regional ones,
which should have a significant impact on trade diversification, and probably in quite a different
direction from what we found with the WTO. Finally, the findings in this paper need to be
assessed using other sources of international data and other estimation methods.

64

Chapter 3:
Business Cycle Volatility and
Economic Globalization

Abstract: This chapter challenges current empirical findings in the literature
regarding the destabilizing impact of economic globalization, especially in the
dimension of trade, on macroeconomic volatility, and considers a potential non-
linearity in the relationship as well as the existence of complementary factors that
mitigate the volatility-trade openness link. More importantly, we argue that a
better measure of integration and globalization must take into account
diversification across international commercial partners. The main findings of this
chapter are as follows. The destabilizing impact of de facto trade openness
diminishes at higher levels, and this variable turns into a stabilizing factor for
output volatility beyond a relatively high threshold. Financial openness, domestic
financial system development, and democracy are among complementary factors,
the co-existence of which mitigate the volatility-openness relationship. Finally,
we show that a more comprehensive measure of economic globalization which
includes geographical diversification in international trade, namely global trade, is
capable of smoothing both output and consumption volatilities. Most importantly,
this highly diversified trade can reduce relative volatility of consumption to that
of output, in accordance with international risk-sharing theory. This corresponds
to levels of diversification enjoyed by the top 25 percentile of countries in our
panel of 133 countries over 1962-2006.

3.1 Introduction
The interaction between business cycle volatility and economic integration in trade and financial
dimensions has been the subject of a large and growing literature in empirical macroeconomics,
with rather puzzling results. Despite a general moderation
12
in the output volatility of the
majority of countries over last five decades up to the 2008 financial crisis, and the unprecedented
surge in international trade and financial flows relative to the size of national economies – also
known as economic globalization – increases in trade and financial integration have been found,

12
See Chapter 1, section 1.3 for quantitative explanation of this general moderation in the volatility of output.
65

at best, to not have a stabilizing impact on macroeconomic fluctuations, although evidence for
the destabilizing impact of trade has been almost unanimous. These findings usually hold for
panel data sets covering both developing and advanced countries over the last five decades and
are robust to various econometric settings and the inclusion of controls that account for country-
specific characteristics. More importantly, these results are at odds with theoretical predictions
that economic globalization should provide better international risk-sharing opportunities.
However, there are signs that these findings can be changed or even reversed once a certain
group of countries is excluded from the dataset, pointing to the potential underlying reasons
behind the puzzling volatility-globalization relationship. For this particular group of developing
countries, the globalization process either preceded certain institutional and economic reforms,
or has not reached integration levels that benefit the economy, or is implemented only in one
dimension rather than in both trade and financial aspects, or, finally, is not an exact integration
13

process in the sense that the country has not yet established international relations with a large
network of countries.
14

The objective of this chapter is to find support for the above explanations that seem to be
driving the puzzling results, which in turn will challenge the conventional finding that economic
globalization, especially in the trade dimension, is a destabilizing force. In doing so, first, we
investigate a non-linear relationship between international trade openness and output volatility
and find that, allowing for a given set of country-specific controls, trade openness appears to
increase business cycle volatility. However, this destabilizing effect is diminishing and at a
certain threshold level, it turns into a stabilizing factor.
Second, non-linearity directs us to search for possible complementary factors, ones which
either mitigate the destabilizing effect of openness, or ones which actually allow a country to
reach relatively high levels of openness in order to benefit from integration in terms of aggregate
volatility. The IMF’s World Economic Outlook report (2002) proposes the existence of potential

13
Although integration and globalization are different terms, and should be respectively used for the case of one
country versus many, the majority of the literature use them interchangeably, as is also pointed out by Prasad (2007).
14
It should be noted that the fact that developing countries are structurally different from advanced economies is
equally important. For example, financial system development, country size, and specialization patterns are some of
the factors that have important consequences for both globalization process and output volatility. This issue can be
addressed, to some degree, by including appropriate controls in econometric analyses.
66

complementary effects of institutional, macro-policy, and most importantly one type of
international integration on the others. In particular, a country can see positive results from rising
trade openness and financial integration if both are progressing together. In this chapter the
complementary effects of financial integration and domestic financial system development on
trade openness are documented in a panel framework. As a preliminary step to tackle
institutional factors, we also investigate the interaction of one type of political institution with
trade openness, namely democracy, which can host many fine institutions within a country, or
itself be the result of centuries of institutional evolutions. We find that the inclusion of such
factors does indeed mitigate the destabilizing impact of exposure to international trade in our
large panel data.
Third, as proposed through other chapters of this dissertation, the richness and complexity of
international economic relations captured by a geographical diversification index should be
considered as an important aspect of globalization. Without a complex network of international
trade partners, either the rise in the last two decades of international flow of goods and capital
would not be possible to realize in the first place, or it would simply imply international
dependence on very few economies. This in turn is unlikely to have stabilizing effects on
business cycles, as risk-sharing opportunities are scant. North Korea is a real world example of a
country which is highly dependent on international flow of goods, yet is highly isolated and
regardless of its de facto measures of openness
15
, it is not considered a globalized or integrated
economy. South Korea on the other hand, which has made it to the league of advanced countries
in only five decades, has shown one of the most impressive progressions in terms of
geographical diversification.
Chapter 2 is a preliminary attempt to examine potential determinants of geographical
diversification in international trade where a set of geographical, cultural and policy factors are
considered. Also, Chapter 1 shows that for a given level of trade openness, higher geographical
diversification reduces output volatility, and the link is robust to a specification that takes
potential endogeneity of trade variables into account. In this chapter, through examining the
interaction terms, we show that trade openness becomes less destabilizing as geographical

15
De facto measures of openness are the actual levels realized regardless of the de jure measures which capture
existence and sometimes magnitude of policy restrictions on either current or capital accounts.
67

diversification expands, and if international trade is diversified enough, it can actually turn into a
stabilizing factor on macroeconomic fluctuations. We believe that diversified integration can
better capture the true meaning of economic globalization, in both trade and financial dimensions,
and show that true economic globalization is potentially capable of reducing output and
consumption volatilities, as well as the relative volatility of consumption to that of output. The
latter, namely the consumption-smoothing effect, is one that has puzzled researchers for a long
time. Our findings show that, in order to benefit from the international risk-sharing opportunities
that economic globalization provides, an economy needs to have a sufficiently diversified
international trade network.
The remainder of this chapter is structured as follows: Section 3.2 contains a review of the
related literature. Section 3.3 describes the sources of data and construction of variables that lead
to the panel data used throughout. Section 3.4 includes empirical exercises that start with
addressing whether net exports, at least technically, provide a venue for smoothing gross
domestic output, and then we look at how and when trade openness is capable of smoothing
business cycle volatility by examining the proposed explanations such as non-linearity,
complementary effects, and the role of diversification. Section 3.5 concludes this chapter.

3.2 Background and Literature Review
The study of international factors that influence business cycle volatility (henceforth BCV) is
gaining momentum within a growing empirical literature that examines various factors that affect
macroeconomic fluctuations. Perhaps it all started with the traditional automatic stabilizers –
namely, progressive taxes and transfer payments that seem to smooth business cycles without
any deliberate actions.
Fatás and Mihov (2001) find that government size has a stabilizing effect on output volatility
beyond its role as automatic stabilizer, since in economies with larger governments, the extent to
which the government is ready and willing to step in, in the wake of any economic turbulence, is
higher. Furceria and Karras (2007) provide empirical evidence that the size of a country, proxied
by population, can be a stabilizing country-specific characteristic. They propose that larger
68

countries tend to have a production structure with a larger number of sectors, which makes them
less susceptible to either sector-specific or terms-of-trade shocks. The latter type of shock stands
at the center of explanations for a rather puzzling, yet almost-unanimous empirical finding that
de facto international trade openness is destabilizing for output growth.
The theory predicts that internationalization allows a country to share idiosyncratic domestic
risks with the world through inter-temporal trade, while exposing it to global systematic risks –
risks that cannot be shared – and also terms of trade shocks that can be more detrimental once a
country is at advanced stages of specialization. In an attempt to understand the mechanism
through which trade openness increases output volatility, Di Giovanni and Levchenko (2009)
examine three effects that connect trade openness and sectoral features, such as volatility and co-
movement with other sectors.
Cavallo (2008) on the other hand, shows that once the effect of terms-of-trade is separated
from the overall impact of trade openness, the latter turns into a stabilizing factor for output
fluctuations. This is highly related to Rodrik (1998), in which terms-of-trade volatility is
mentioned as the major cause of destabilization that comes with openness. This in turn calls for a
big government to stand ready to step in against such shocks. For political economy studies this
has important implications, since it renders the government size an endogenous variable,
especially in studies of international trade. Chapter 1 of this dissertation also documents that
higher trade openness – by itself – increases volatilities of various aggregates and confirms that
its effect becomes statistically insignificant, though not stabilizing, once terms-of-trade volatility
is taken into account.
While the majority of empirical work on BCV shows that higher trade openness increases
fluctuations of various macroeconomic aggregates, the studies on financial openness are mixed.
It is notable that the literature most frequently uses total exports and imports to output as a
measure of international trade openness, and does not include other definitions of trade openness
that take into account policy restrictions such as average tariff rates and other restrictions on
current accounts that were proposed by Jeffrey Sachs among others. Financial openness is
defined as the gross stock of capital flows relative to output, as will be explained in section 3.3.
69

We can attribute the preference for de facto measures of openness to other measures of trade
and financial openness in this and other empirical studies to availability of data for a large panel
analysis, and to the fact that effective cross-country comparisons based on a de jure variable
would not be appropriate. Prasad et al (2007) highlights this by contrasting two groups of
countries in the context of financial globalization in developing countries. On one hand, certain
countries, such as some in Latin America, can be considered closed, based on capital account
restrictions, even if actual capital flows are quite high relative to other developing countries. On
the other hand, certain African countries have almost no restrictions on cross-border capital
flows, while they are simply unable to attract capital inflows. Again, what matters are the actual
flows for the purpose of our analysis, and unless the goal is to examine the effectiveness of
restrictive policies, de facto measures of openness provide the appropriate measure.
Among other studies that have found that trade openness can be destabilizing are: Easterly,
Islam and Stiglitz (2001), Karras and Song (1996) and Mendoza (2000). Those that examine
financial openness in particular are: Buch, Döpke, and Pierdzioch (BDP 2005), and Kose, Prasad
and Terrones (KPT 2006) among others. The former study finds that the volatility-financial
openness relationship has not been stable over time and proposes either parameter instability or
the changing nature of the underlying shocks as possible explanations for the “missing link”. On
the other hand, the study by KPT (2006) examines the relationship among two broad
classifications of developing countries, namely the group of more- and that of less- financially
integrated economies, along with developed countries. The observation that even for the more
financially integrated economies (among the developing countries) the international risk-sharing
benefits are not realized directs them to investigate and document a threshold effect in the
volatility-financial openness relationship, which also motivated part of our work in this chapter.
KPT (2006) finds that financial openness turns into a stabilizing factor only beyond a threshold,
which is currently enjoyed only by developed economies.

3.3 Data and Variables
The panel data constructed and used throughout most of this and other chapters of this
dissertation contains 133 countries covering the period 1960-2006. The main criteria for
70

choosing these countries, the complete list of which is available in the appendix (Table A3), are
availability of bilateral trade data and a country’s independent status throughout the entire period.
For example, former Soviet Union countries and those that were separated like Czechoslovakia
and its emerging independent states are not included in the panel.
In most of the analyses, the entire period is divided into sub-periods, especially when dealing
with business cycle volatility measures, as these need to be defined over a certain period of time,
which is set at nine years for this chapter. We change the length of sub-periods throughout
various chapters of this dissertation, to test the robustness of our results.
The aggregate macroeconomic data are from the Penn World Table dataset, which includes
real and per capita components of aggregate demand along with their relative shares. We also
examined a large battery of controls, such as share of a certain sector in output and monetary
variables. These were collected from the World Bank’s World Development Indicators (WDI)
database and the International Monetary Fund’s International Finance Statistics (IFS).
Business cycle volatility for any macroeconomic aggregate is defined as the standard
deviation of either growth rates of per capita aggregate over the length of each sub-period, or of
the de-trended time series. The Hodrick-Prescott filter is used to de-trend time series with a
parameter of 100, as suggested for annual series, while the resulting cyclical data that belongs to
early or later years are discarded due to unusual behavior at each end as pointed out in some
empirical works. The two measures of business cycle volatility are highly correlated and most of
our regression results are similar and robust to both methodologies, as discussed in Chapter 1.
However, the former measure which is based on growth rates is the prevailing one in the
empirical literature and is the one reported in this paper. Table A1 in the appendix summarizes
the correlations for various measures of BCV for output and consumption per capita.
Bilateral trade data are borrowed from the Dyadics Trade Data, a Correlates of War (COW)
project which is a compilation of the IMF’s Direction of Trade Statistics (DOTS). The advantage
of this compiled data is that certain adjustments are made to make sure that the reported trade
flow from importer to exporter and vice versa match. Some of the missing data were also
completed using other sources.
71

The financial openness measure here is defined as the gross stock of capital flows relative to
gross domestic product, where the gross stock comprises accumulated inflow and outflow of
three types of foreign capital: foreign direct investments, portfolio investments and bank lending.
In calculating this variable, we follow Prasad et al (2007) and also use the data constructed by
Lane and Miles-Ferretti (2001). The main source of this data is the IMF’s IFS dataset. The terms
integration and openness are interchangeable in the literature for both trade and finance, while
one of the goals in this paper is to challenge this notion.
To measure geographical diversification, we use a Herfindahl-Hirschman index (henceforth
HI) which equals the sum of the squares of imports (exports) of each trade partner as a share of
total imports (exports) for each country. These indices can vary between zero and one, with a
lower value indicating more diversification and a higher value indicating a greater concentration
of international trade over trading partners. Section 1.2.2 in Chapter 1 presents the formula for
HI and Chapter 2 analyzes constructed HI data and examines potential determinants or covariates
of geographical diversification in imports and exports.

3.4 Empirical Analyses
3.4.1 Net Exports as a Venue for Smoothing Aggregate Fluctuations
In theory, international trade provides channels to alleviate the “abrupt” fluctuations in the
domestic absorption (DA ≡ C+I+G) portion of the GDP identity. Ideally, in times when domestic
production capacity falls short of domestic demand, imports provide a channel to satisfy this
need and when the opposite is the case, the excess production can be exported to generate
income for the whole economy. Therefore in principle, exports and imports with the rest of the
world can provide venues for smoothing overall GDP behavior in response to changes in DA, as
best exemplified in the opening quotation by Obstfeld and Rogoff (1996).
The purpose of this section is to examine our aggregate data to see whether it conforms to the
well-established fact that net exports are counter-cyclical, which answers the above question.
More importantly, since the main theme of this paper is business cycle volatility and
globalization, we divide the full sample period into two to check if the international risk-sharing
72

hypothesis holds stronger, if at all, for the period that coincides with the rise in economic
globalization.
The seminal work by Backus, Kehoe and Kydland (1992) builds a model that quantitatively
results in counter-cyclical net exports, consistent with the observed international data. The
driving force behind this result is capital formation, as the need to borrow for investment rises
during expansions. Raffo (2006) disputes this mechanism, proposing that consumption-
smoothing and not “dynamics of capital formation” should drive such results. In doing so, he
applies a preference introduced by Greenwood, Hercowitz and Huffman (1988) in their
international real business cycle model, which induces relatively larger consumption volatility
and derives quantitative counter-cyclical net exports that are driven by consumption-smoothing
behavior, rather than by capital.
Yet in reality, countries do not exchange in a vacuum nor do they have access to a perfectly
elastic supply of goods and services from the rest of the world. Chapter 1 documents that the
multilateral correlations of business cycles among trading partners, which are measures of
international business cycle synchronization, have been increasing significantly – by a factor of 4
over just the past five decades. The higher this synchronicity of business cycles among trading
partners, the less chance there will be for any individual trading nation to use international
channels to smooth domestic macroeconomic fluctuations. Further, it is shown that once an
external shock such as terms of trade volatility is accounted for, lower synchronization among
trade partners is associated with lower business cycle volatility of output, its major components,
and the domestic absorption.
To test the hypothesis, we take the following identity:

where DA stands for domestic absorption and NX for net exports. For a given level of cyclical
behavior in DA, which captures the response to and propagation of any type of shocks, total
output fluctuations will be smoother if NX is counter-cyclical with respect to DA. Since in
reality net exports are not exogenous, we should also think of the relationship running from NX
to DA – that is, for a given level of exposure to external shocks, an appropriate countercyclical
response by domestic demand will lead the GDP to follow a smoother path than without the
73

exposure. Our conjecture is easily tested by estimating the following simple model based on our
discussion:

In this equation, DA and NX stand for the cyclical components of these series, which are
obtained using an HP-filter, as explained above. Obviously this captures the simple correlation
coefficient between the two-time series across countries. Table 3.1 summarizes the results for
some simple regression settings using a random effects method applied over the panel data of
133 countries for the period 1950-2007.
Table 3.1: DA-NX relationship, Dependent variable: Domestic Absorption (DA)

(1) (2) (3) (4) (5)

1950-2007 1950-2007 1950-2007 1950-1979 1980-2007

NX -0.00002 0.00328 0.00328 0.02213 0.00247
(0.00013) (0.0012)*** (0.0012)*** (0.0094)** (0.00091)***
NX ∙ lnGDP
PC
-0.00037 -0.00037 -0.00249 -0.00029
(0.00014)*** (0.00014)*** (0.00115)** (0.0001)***
lnGDP
PC
0.00293 0.00289 0.00472 0.00418
(0.0012)** (0.001244)** (0.00436) (0.00109)***
Trade Opennes 0.00082 -0.00042 0.00132
(0.00288) (0.0087) (0.0027)
obs. # 6578 6578 6578 2896 3682
Notes: The numbers reported below each coefficient in the parentheses are standard errors
with *, ** and *** next to them indicating statistical significance at 10, 5 and 1 percent
respectively. R-Squared’s are not reported but are at levels of 1-2% throughout different
specifications. Estimation Method: Random Effect GLS.

The results of these simple exercises are actually quite interesting. The first three
specifications examine the entire 58-year period, while in the last two columns the entire
timeframe is divided into the periods before and after 1980, as that is when globalization started
accelerating, with both financial and trade openness picking up rapidly as many countries started
current and capital account liberalizations in many forms. The main findings follow.
First, the simple conditional correlation coefficient between domestic absorption and net
exports is negative, as expected, yet statistically insignificant at conventional levels. Further
examination of the data suggests that the relationship is stronger for more advanced countries
74

captured by per capita GDP; hence an interaction term with the NX variable is introduced in
specifications (2) and beyond, to capture this possible effect. As the second specification
suggests, once we take into account this possibility, the conditional correlation coefficients
become strongly significant, with different signs for NX and NX∙lnGDP
PC
, which can be
interpreted as follows: While for the initial levels of per capita GDP the cyclical pattern of net
exports are following those of domestic absorption – suggesting a destabilizing effect on the
overall behavior of GDP. As per capita income increases, the conditional correlation between
cyclical behavior of DA and NX turns negative, implying a smoothing effect on the overall GDP
fluctuations. The relationship is robust even after controlling for the levels of international trade
openness as specification (3) illustrates.
The turning point can also be calculated quantitatively from point estimates: From
specification (2) in Table 3.1 we find that the correlation coefficient between DA and NX
becomes negative and gets larger after the log of per capita GDP reaches a value of about 8.7
which, in our dataset, coincides with the approximate median value for this variable. In other
words, half of the countries in the sample are actually enjoying the aggregate smoothing effects
that are the consequence of a simple identity-type counter-cyclical relationship between domestic
absorption and net exports. It must be emphasized that this is a simple correlation analysis that
illustrates the possibility of the role that international trade can play in smoothing GDP, without
implying any causal relationship.
Next, the sample is divided into two periods, 1950-1979 and 1980-2007 (specifications 4 and
5 in Table 3.1), to see if the DA-NX relationship becomes any stronger in the age of economic
globalization. This is indeed the case as both the magnitude and statistical significance of the
estimated coefficients for the variables of interest are higher in absolute values in the latter
period. This outcome confirms our conjecture that expanding international linkages in both
commerce and finance strengthens the relationship between domestic demand and net exports,
the result of which can help to stabilize aggregate economic behavior.
To summarize, the counter-cyclicality of net exports that is suggested by international
business cycle theory and documented by international data is confirmed using our dataset.
Further, we find that a negative correlation between net exports and domestic absorption is more
likely for more developed countries. Finally, the relationship is strongest in the post-1980 period.
75

These two conditional correlations confirm that more advanced countries are benefitting from
economic integration in terms of international risk-sharing more than developing countries are,
especially during the surge in globalization over the past 30 years.
3.4.2 Threshold and Complementary Effects
In theory, the overall effect of de facto trade openness on business cycle volatility is considered
to be ambiguous. International trade provides venues for inter-temporal trade and international
risk-sharing which tend to reduce aggregate volatility. Openness impacts growth through
technology transfers and the “disciplinary effect” that it has on domestic policies can also
stabilize the economy. However, trade openness also exposes a country to an array of
international shocks.
Trade liberalization is usually associated with higher specialization in certain sectors,
although in this author’s opinion the exact sequence is not necessarily consistent. On the one
hand, by opening up to trade a country can gain the most only by exploiting its competitive
advantage through specializing, as the classical Ricardian trade theory suggests. On the other
hand, from a more strategic point of view, a country might be willing to open up to trade only
when it has found its competitive edge or excelled in a specific area, as supported by infant
industry theory. Therefore the interaction between openness and specialization is potentially
simultaneous, at least from an econometrics standpoint. Nonetheless, specialization is one
channel through which, once trade liberalization occurs, a country will be more vulnerable to
sector-specific shocks. It should be mentioned that sectoral concentration will not be explored in
this paper and it suffices to say that this type of concentration or lack of diversification is one
channel through which trade openness can destabilize business cycles.
16

Overall, economic theory is unable to tell us the direction towards which the trade-off might
tilt in terms of the effect of higher trade openness on business cycle volatility. The literature
unanimously calls the issue an empirical quest, and after exploring various empirical
methodologies, the general empirical conclusion is that higher openness is associated with higher
macroeconomic volatility. In this section we will empirically examine the non-linearity of the
BCV-trade openness relationship in order to check if there exists a threshold beyond which

16
See Di Giovanni and Levchenko (2009) for an interesting examination of the interaction between sectoral
specialization and business cycle volatility.
76

openness ceases to destabilize business cycles. Chances are that the reason increased trade
openness is found to be a destabilizing factor is simply that it has not yet reached an optimum
level in the context of BCV. This inquiry into the threshold effect is inspired by the work of
Kose, Prasad and Terrones (2006) who document a non-linearity in the BCV-financial openness
relationship. They observe a large gap in the stabilizing effects of financial openness between the
“most financially integrated” developing countries and the industrial nations. In particular they
find that financial integration is a stabilizing factor only for the latter group.
Perhaps a more interesting possibility is that, for countries to benefit from trade openness or
to be able to reach the threshold, other co-existing conditions are needed
17
. These include
appropriate institutions, a well-developed and integrated financial system, and “good”
macroeconomic policies as discussed in the IMF’s 2002 World Economic Outlook (2002) report.
Such complementary factors, here, can be defined as those that tend to move along with the
variable of interest – here trade openness – and help its augmentation. In turn, their interaction
has a positive impact on the ultimate dependent variable, defined here as smoothed business
cycle volatility. In the following empirical exercise we explore the complementary effect of three
such variables: financial openness, development and democracy.
Prasad et al (2007) examine the impact of financial globalization on the macroeconomic
volatility of developing countries, and suggest that irrespective of the degree of financial
integration, deficient legal systems along with weak protections for creditors will prevent
financial integration from having a positive influence on macroeconomic outcomes. In particular,
each type of capital flow, including FDI, portfolio investment, and bank lending, has its own
volatility attributes and is attracted to certain types of countries. Developing countries with
transparent governance and quality institutions, for example, tend to attract the bulk of FDI from
advanced countries. Structurally, this is the most stable type of capital flow. Thus, a more
thorough study of potential factors that might have complementary effects on the marginal
impact of financial and trade openness on volatility seems appropriate to better understand the
relationship between these broad indicators of globalization and any macroeconomic
performance measure such as BCV.

17
The author is aware of the importance of the sequence and speed of the institutional, policy, liberalization, and
integration reforms on the economic outcomes such as volatility and growth. However those are beyond the scope of
this paper.
77

The empirical results based on the panel data of 133 countries over five sub-periods covering
1962-2006 are reported in Table 3.2, and indicate that there indeed is some non-linearity in the
relationship between output volatility and trade openness. In this analysis, volatility of the per
capita GDP growth, our measure of business cycle volatility, is regressed on a measure of a
country's development index, captured by GDP per capita and measures of fiscal and monetary
shocks. The volatilities of fiscal and monetary policies are captured by σ(G) and σ(M), which are
calculated as the standard deviation of the log difference or the growth rate of government
expenditures and the M2 measure of money supply, respectively, for each sub-period. These
three variables are included to serve as controls that capture the general economic characteristics
of each country. Notation-wise, TO and FO are used for trade and financial openness for brevity.

Table 3.2: Non-linearity of Trade Openness, Dependent variable: BCV of GDP
PC

(1) (2) (3) (4)
RE RE RE IV

lnGDP
PC
-0.0024 -0.0042 -0.0024 -0.0008
(0.002) (0.002)* (0.002) (0.002)
σ (G) 0.110 0.069 0.110 0.105
(0.014)*** (0.015)*** (0.014)*** (0.031)***
σ (M) 0.079 0.109 0.081 0.11
(0.065) (0.059)* (0.0066) (0.068)*
TO 0.0002 0.0001 0.0003 0.0012
(0.0001)*** (0.0001)** (0.0001)** (0.0005)**
σ (TOT) 0.0897
(0.026)***
TO ^ 2 -0.0000013 -0.000006
(0.0000004) (0.000039)*
Between R
2
0.30 0.32 0.31
obs. # 533 277 533 478
Notes: The numbers reported below each coefficient in the parentheses are
standard errors with *, ** and *** next to them indicating statistical significance at
10, 5 and 1 percent respectively.

78

As the previous literature suggests, trade openness (TO) enters all regression settings in Table
3.2 with a positive sign and relatively high statistical significance in most cases, indicating a
destabilizing effect on output volatility. In specification (2), the volatility of the terms of trade,
σ(TOT), is included to examine its effect on the marginal impact of openness. Again, as expected
and suggested by many studies, the coefficient for trade openness loses magnitude and statistical
significance compared to the stand-alone case in (1), yet it continues to be a destabilizing factor.
As mentioned above, Cavallo (2008) tackles this issue more systematically to separate the effect
of terms of trade from that of openness. Our finding confirms that the volatility in terms of trade
is indeed one of the channels through which trade openness aggravates aggregate volatility.
The simplest case of the non-linear relationship between BCV and trade openness is examined
by embedding a quadratic form of trade openness in the regression. As reported in column (3) of
Table 3.2, the opposite signs on trade openness and its squared term suggest that there is a
threshold beyond which the impact of openness on output volatility turns into a negative one,
changing the current status of openness into a stabilizing factor.
In order to address the potential endogeneity of trade variables stemming from possible
reverse causality and measurement errors, trade openness is instrumented by a set of variables
that includes language dummies, elevation, average distance from the central cities in the world,
total area, and a landlocked dummy. These instrumental variables (IVs) are borrowed from
Andrew Rose’s dataset. The estimation methodology used here is IV General Method of Moment
(IV GMM) and includes period dummies. The results, shown in column (4) of Table 3.2, are
actually stronger than the GLS estimation in (3), confirming the existence of a non-linear,
threshold-type relationship between volatility and trade openness. Quantitatively, estimated
coefficients correspond to a turning point for trade openness ratio at 88%. As a point of reference,
this same ratio for OECD countries in the sub-period 1997-2006 was 79% on average.
Having confirmed the non-linear nature of the BCV-trade openness relationship, we can now
put the complementary effect of certain factors to the test. Obviously, the number of factors that
are complementary to international trade is enormous, and perhaps comparable to a country's
overall institutional, social and economic characteristics. Yet, since trade and financial openness
are, together, cited as the broadest measures of economic globalization, the complementary
effect of financial openness is examined here first. Column (2) in Table 3.3 shows the results and
79

confirms the hypothesis to some degree. Although openness to international trade is destabilizing
in the absence of financial openness (FO = 0) as shown by the coefficient of TO alone, this effect
diminishes as the country increases its financial integration, shown by the negative and
statistically significant coefficient of the interaction term (TO x FO). Column (1) is reported for
comparison purposes; for brevity, we do not show standard controls.

Table 3.3: Complementary Effects of Financial Openness and Development

(1) (2) (3)
TO 0.0002 0.00052 0.0007
(0.0001)*** (0.0001)*** (0.000319)**
FO 0.0545
(0.024)**
TO x FO -0.00049
(0.0002)**
FD 0.00037
(0.00019)**
TO x FD -0.0000072
(0.0000038)*
obs. # 533 404 381
Notes: The dependent variable is BCV of GDP
PC
for all specifications and
the following controls are not reported for brevity: lnGDP
PC
, σ(G) and σ(M).
The numbers reported below each coefficient in the parentheses are standard
errors with *, ** and *** next to them indicating statistical significance at
10, 5 and 1 percent respectively. Estimation method: IV GMM.

Next we briefly examine an important financial institution that is directly studied in relation to
its impact on business cycle volatility, namely financial system development. Ferreira da Silva
(2002) considers a few variables that might proxy for domestic financial development defined as
efficiency with which the information regarding financial intermediation is processed. Among
them, the ratio of liquid liabilities to GDP is the most widely available variable suitable for a
large panel data analyses such as ours, while other proxies such as the growth rate of financial
sector and the ratio of private credit to the total are mostly available for more developed
countries and more recent years. One would expect M2/GDP variable to be higher if the banking
system is better capable of creating loans beyond the monetary base. Column (3) in Table 3.3
80

reports the IV GMM estimation results for the trade openness (TO) and its interaction with this
proxy of financial development (FD) while control variables such as per capita GDP,
geographical diversification, fiscal and monetary policy volatilities are not reported for brevity.
The results from Table 3.3 confirm that our measure of financial development is capable of
mitigating the destabilizing effects of trade openness on business cycles of output per capita.
However, considering the range of M2/GDP variable in our dataset, this proxy of financial
development cannot reverse the adverse impacts and turn trade openness into a stabilizing factor
for business cycles.
Table 3.4: Complementary Effect of Democracy
TO 0.00042
(0.00019)**
Polity 0.00023
(0.00072)
TO x Polity -0.0000089
(0.013)***
Obs. No. 387
Notes: The dependent variable is BCV of GDP
PC
and the following
controls are not reported for brevity: lnGDP
PC
, σ(G) and σ(M). The
numbers reported below each coefficient in the parentheses are
standard errors with *, ** and *** next to them indicating
statistical significance at 10, 5 and 1 percent respectively.
Estimation method: IV GMM.

Finally, as a first attempt to check the complementary effect of a political institution variable
we examine the interaction of Polity with trade openness. This variable is an index ranging
between -10 and 10, indicating most autocrat to most democratic political regimes. Table 3.4
shows IV GMM estimation results, again without reporting the usual controls. The interaction
coefficient is negative, as before, and is statistically significant even at the one percent level.
This result, along with the stand-alone coefficient of trade openness, can be interpreted as
follows.
Trade openness (TO in Table 3.4) remains a destabilizing factor on output volatility, but for
more democratic nations this destabilizing effect diminishes and for less democratic systems the
effect aggravates. However, considering the range of the Polity variable, even at highest level of
democracy, openness remains destabilizing. We will show in the next section that among the
81

variables considered in this paper, only diversification of international trade across other
countries allows a country to turn trade openness into a stabilizing factor.
To summarize, in this section we showed that trade openness’s “well-established
destabilizing impact” on output volatility is subject to non-linearity, and it diminishes at higher
levels. Moreover, there is a threshold level beyond which trade openness has a stabilizing effect.
To reach very high levels of trade openness and to mitigate the destabilizing effect of trade
openness, a country must have the right economic conditions or institutions. We studied the
complementary effects of financial openness, financial development and democracy and found
that more financially integrated, financially developed and democratic countries – as measured
by their respective proxies – are less susceptible to the adverse effects that exposure to
international trade brings, in terms of business cycle volatility. Our analyses of such effects did
not take the possible endogeneity of complementary variables themselves, and that should be
addressed in future studies.

3.4.3 True Global Integration: Diversified Trade
It is difficult to regard any international integration as global without considering the diversity of
the international economic relations. In particular, for a given country, increased de facto
openness without expanding commercial relations to a larger network of countries, as captured
by geographical diversification, does not represent a true integration with the global economy.
Chapter 1 empirically confirms that geographical diversification has a stabilizing effect on
volatility of output at any given level of trade openness. This is realized through the mitigation of
various types of shock, including fiscal and monetary, international and financial. On the other
hand, Chapter 2 documents that higher trade openness, per se, does not guarantee a more
geographically diversified international trade.
In this section we demonstrate that a more relevant measure of economic globalization, one
that also takes into account a diversified network of trading partners, can smooth business cycle
volatility of output even without controlling for external shocks such as terms of trade volatility.
This is closely related to the non-linearity issue we found in the previous section, as it is hard to
imagine that a country could achieve and surpass the threshold level without having access to a
82

large international market. This access could be made possible or facilitated through a large
network of trading partners.
The results, shown in Table 3.5, follow same IV GMM estimation methodology described in
the previous sections. Throughout all specifications (1) - (2) certain control variables are
included, and are consistent with our work in Chapter 1. Column (1) is replication of the results
in that chapter, showing that for a given level of exposure to trade, higher trade diversification
reduces business cycle volatility of output as the positive and statistically significant coefficient
before the Herfindahl index for imports (HIM) indicates. The results for the export counterpart of
geographical diversification are very similar and hence not reported.

Table 3.5: Global Trade. Dependent variable: BCV of real GDP per capita
(1) (2)
lnGDP
PC
-0.0009 -0.0040
(0.0026) (0.0026)
Polity -0.0014 -0.0015
(0.0003)*** (0.0003)***
σ (G) 0.0756 0.0641
(0.0185)*** (0.0183)***
TO 0.0005 -0.0009
(0.00015)*** (0.00047)**
HIM 0.1720 -0.5547
(0.0693)** (0.1905)***
TO x HIM 0.00901
(0.00301)***
obs.# 442 437
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 volatility.

Column (2) in Table 3.5 addresses the main question of whether a more comprehensive
measure of trade integration or globalization is capable of smoothing output volatility, by
looking at the interaction between trade openness and the Herfindahl index of geographical
83

diversification for imports (TO x HIM). The overall marginal effect of trade openness using the
point estimates from specification (2) can be written as:

Quantitatively, if a country starts at the lowest level of geographical diversification or HIM=1,
the estimated marginal effect of trade openness on BCV is 0.0081. That is, openness is a
destabilizing factor when a country has only one trading partner, but as HIM decreases or,
equivalently, geographical diversification expands, the adverse effect diminishes. Therefore the
adverse impact of international trade openness is mitigated by greater diversification. More
importantly, the overall impact, on BCV, of our more comprehensive measure of economic
globalization becomes negative – that is, stabilizing – at geographical diversification levels equal
to 0.09 and beyond. This and higher levels of geographical diversification are enjoyed by
countries in the top 25 percentile of the diversification, throughout the whole sample period.
More generally, in a regression analysis that examines the effect of trade integration on output
volatility as in the following, the combined effect of trade openness (TO) and geographical
diversification (HI) should be considered as the true impact of global trade on volatility.

Furthermore, we repeat the same exercise with the volatility of real consumption per capita
(BCV
C
) and the relative volatility of consumption to that of output (BCV
C
/BCV
Y
) as the
dependent variables, as shown in columns (1) and (2) of Table 3.6. These are the aggregate
volatility measures that have been most difficult to curb by any kind of international integration
according to the majority of empirical studies. Even our separate study of geographical
diversification in Chapter 1 was unable to document a statistically significant consumption-
smoothing effect for a given level of de facto trade openness (Table 1.5). The results in Table 3.6
show that the combined effect of trade and diversification, namely global trade, is capable of
reducing both absolute and relative consumption volatilities. Although the coefficient of trade
openness alone is not statistically quite significant (p-value ≃ 15% for first specification), the
Overall impact of global trade on BCV
84

overall impact of trade and diversification is jointly significant at the 5% level. The results of our
business cycle smoothing effect extend to the volatility of consumption relative to that of output,
as shown in column (2). Based on the point estimates and the same line of reasoning as before,
the diversification level at which global trade turns into a stabilizing factor on consumption is
around HIM = 0.08. In other words, to benefit from the international risk-sharing opportunities
that global trade provides, a country needs to have a highly diversified international trade
structure, in geographical terms. A thorough examination of this topic in the context of
international business cycles is left for future research.
Table 3.6: Global Trade and Consumption Smoothing

(1)

BCV
C

(2)

BCV
C
/BCV
Y

TO -0.00106 -0.01418

(0.00076) (0.01047)
HIM -0.7176 -8.334
(0.30665)** (4.135)**
TO x HIM 0.01221 0.1387
(0.00501)** (0.06786)**
obs.# 437 437
Notes: Dependent variables are (1) the volatility of consumption (BCV
C
) and (2) the relative
volatility of consumption to that of output (BCV
C
/BCV
Y
). Basic controls such as GDP per
capita are included in regressions but not reported for brevity. 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 including period dummies.

To summarize this section, in order to grasp economic globalization one needs to take the
complexity of international economic relations into account and de facto measures of openness,
per se, are unable to do so. In an attempt to better proxy global integration in the trade realm, we
considered the combined effect of geographical diversification along with de facto trade
openness and showed that indeed, higher levels of trade integration are associated with lower
volatility of real output, and more importantly absolute and relative volatilities of consumption.
Notably, less than 25 percent of the countries in our sample of 133 had geographical
diversification levels that might allow them to reverse the adverse effects of international shocks
that come with being open to trade.
85

3.5 Summary and Conclusions
The main theme of this chapter is the examination of the impact of economic globalization,
especially in the dimension of trade, on output volatility. Currently the majority of the empirical
literature has found that trade and financial openness, at best, do not have a stabilizing impact on
business cycle volatility, despite the theoretical prediction that international integration could
potentially provide more channels for risk-sharing and thus alleviate some of the potential harm
from exposure to external shocks.
Despite the status quo of the relationship between openness and volatility found in the
international data, there are signs that studying openness in isolation and in linear form might not
capture its true impact, as a global factor, on macroeconomic fluctuations. This is because there
are many countries that have not reached the integration levels that many advanced countries are
now experiencing. Perhaps more importantly, many countries are opening up their current or
capital accounts without a strong enough presence of other determining factors, including
institutions that one might call “complementary” to international trade. Even more important, a
surge in openness relative to the size of a national economy, without expanding the network of
commercial partners, is hardly true globalization, but is, rather, an extreme version of
dependence.
In this chapter we explore the non-linearity in the volatility-trade openness relationship and
found evidence for a threshold effect parallel to the work of Kose et al (2006) on financial
openness. In particular, based on a panel data of 133 countries and five sub-periods covering
1962-2006, we find that the destabilizing impact of de facto measure of trade openness
diminishes at higher levels and turns into a stabilizing factor at levels around 88 percent of GDP.
The non-linear relationship we found warrants further exploration of the factors that actually
allow a country to reach that threshold of trade openness. These factors tend to either prevent
potential crises or mitigate the incoming adverse shocks associated with openness. We find
financial openness, domestic financial system development and democracy to be such
complementary factors. Countries that are integrated in both international dimensions – trade and
financial – tend to be enjoy relatively more stable business cycles. However, the complementary
variables studied only serve to reduce and not stop the destabilizing effect of trade openness.
86

Finally, true globalization must involve diversity in international economic relations. We
capture the richness of the network of commercial partners using the Herfindahl index of
geographical diversification as in the previous two chapters. Our more comprehensive measure
of trade integration that combines de facto trade openness and geographical diversification
proves that at high levels of international integration or economic globalization, countries can
indeed enjoy smoother business cycles. In other words, to reap the benefits of trade openness in
terms of business cycles, a country must expand and diversify its commercial relationships. In
particular, our econometric estimation points out that only the countries in the top 25 percentile
of geographical diversification distribution have reached a sufficient level of diversification that
guarantees trade to be stabilizing on output and consumption volatilities. Due to a lack of
sufficient data for financial flows, we focused on geographical diversification measures for trade
integration.
For future research, there exists a whole list of interesting complementary variables that can
potentially mitigate adverse shocks that come with any type of international liberalization and
integration. In doing so, the potential endogeneity of such variables should also be addressed for
an appropriate identification of the volatility-globalization relationship. Furthermore, we plan to
compare our empirical results with theoretical predictions by examining and extending open
economy macroeconomic models that allow heterogeneity in a multi-country model, and allow
openness and diversification to be choice variables for a structural analysis of the relationship.
Finally, to better grasp true financial globalization and assess its impact on macroeconomic
performance, similar research to that which we did for trade integration is called for. In doing so,
one challenge is to find the international data that will allow for the construction of geographical
diversification in financial relationships across borders. Only then, the true impact of financial
globalization on macroeconomic volatility can be assessed.

87

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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 (DOTS). 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
Address: 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.
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 (IFS) dataset
Description: Following Kose et al (2006) and Prasad et al (2007), and using the data constructed
by Lane & G.M. Miles-Ferretti based on IMF’s IFS, financial openness is defined as the gross
stock of capital flows to GDP. Gross stock is equal to the accumulated sum of capital inflows
and outflows, each including foreign direct investment, portfolio investment and bank lending.
91

Geographical Variables 1:
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.
Geographical Variables 2:
Source: CEPII
Web Link: http://www.cepii.fr/anglaisgraph/bdd/gravity.htm
Description: Dataset generated by Keith Head, Thierry Mayer & John Ries that includes basic
gravity model variables such as: GDP, bilateral distance, existence of conflict between two
countries and membership in a common free trade agreement. This dataset is used in the
following paper: “The erosion of colonial trade linkages after independence”, CEPR Discussion
Paper 6951, September 2008
Notes: The bilateral trade data used in our paper is still from Dyadic Trade Dataset.
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).

92

Appendix

Table A1: Correlations of Various Measures of Business Cycle Volatility

Correlations among Volatility Measures for Output
GDP.cyc GDP
PC
.cyc GDP.gr GDP
PC
.gr
GDP.cyc 1
GDP
PC
.cyc 0.963 1
GDP.gr 0.873 0.900 1
GDP
PC
.gr 0.871 0.906 0.993 1
Correlations among Volatility Measures for Consumption
Cons.cyc Cons
PC
.cyc Cons.gr Cons
PC
.gr
Cons.cyc 1
Cons
PC
.cyc 0.946 1
Cons.gr 0.884 0.877 1
Cons
PC
.gr 0.841 0.905 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.

93

Table A2: Trade Shares with the U.S. and G-7 Countries, five-year period averages

1 2 3 4 5 6 7 8 9

1960 1965 1970 1975 1980 1985 1990 1995 2000 Avg.

Full Sample (133)
US Share/ M 18.1 16.5 15.6 17.3 16.6 17.7 16.3 15.6 18.8 16.9
US Share/ X 22.7 17.6 15.9 15.6 14.5 14.7 14.3 14.4 13.5 15.6
G7 Share/ M 55.9 50.6 50.9 48.2 46.8 46.3 44.7 41.0 41.4 46.8
G7 Share/ X 57.5 52.9 51.0 47.6 44.7 43.9 43.7 40.1 36.9 45.8

OECD (28)
US Share/ M
14.6 14.6 16.0 12.9 12.3 16.6 13.4 13.0 15.4 14.3
US Share/ X
19.3 16.8 15.2 13.7 13.5 13.6 13.5 13.5 12.9 14.7
G7 Share/ M
48.3 45.7 46.1 40.9 40.7 45.0 44.8 41.0 42.5 43.9
G7 Share/ X
47.2 46.2 44.7 40.4 39.3 40.3 42.3 41.4 37.6 42.1

Latin America (33)
US Share/ M
42.6 37.3 35.1 35.4 33.9 39.5 35.6 34.4 39.5 36.9
US Share/ X
49.2 38.8 35.0 30.7 32.2 32.3 35.2 37.4 34.7 35.7
G7 Share/ M
66.0 63.5 62.4 57.1 54.8 58.9 56.2 52.7 54.8 58.1
G7 Share/ X
67.0 61.3 61.4 52.9 52.4 50.5 53.3 51.8 48.1 54.8

Other Countries (78)
US Share/ M
19.7 17.1 15.4 18.6 17.8 18.0 17.0 16.3 19.7 17.7
US Share/ X
24.1 17.9 16.2 16.1 14.8 15.0 14.6 14.6 13.7 15.9
G7 Share/ M
59.3 52.3 52.4 50.3 48.5 46.6 44.7 40.9 41.1 47.7
G7 Share/ X
62.1 55.2 53.0 49.7 46.3 44.9 44.1 39.8 36.7 46.9

Notes: Each sub-period stretches over 5 years following the starting year. Numbers in the parenthesis indicate the
number of countries that belong to each grouping. “Other Countries” excludes OECD & LA from the full sample.

Figure Series A1: Annual Time Series of Herfindahl Index for Geographical Diversification for Select Countries

Notes: The vertical bars show the timing of WTO/GATT or NAFTA memberships, when applied. The positioning
of the lighter gray bar on the extreme left indicates that WTO/GATT membership occurred before 1960. The thin
blue horizontal line points to the level of geographical diversification implied by the total number of neighboring
countries, with equal trade shares among them. Mathematically, this neighbor-implied level of diversification, which
serves as a benchmark, corresponds to HI = 1/N, where N is the total number of neighbors. HIM_Fitted and
HIX_Fitted show the geographical diversification time series computed based on the predicted levels from a simple
gravity model explained in section 2.4 of Chapter two. The full name of countries can be found in Table A-1 of the
Appendix.
94

Figure Series A1: Annual Time Series of Herfindahl Index for Geographical Diversification for Select Countries

95

96

97

98

99

100

101

102

103

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

104

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.
105

A Quick Reference for Variables and Notations used in this Paper

DA
Domestic Absorption ≡ C + I + G, which is the sum of consumption, investment and
government expenditures.
NX Net Exports = Exports - Imports, (in goods and services)
BCV
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.
TO Trade openness. Ratio of the sum of exports and imports (of goods and services) to GDP.
FO
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.
OECD Organization for Economic Cooperation and Development.
G-7 Canada, France, Germany, Italy, Japan, United Kingdom, and United States.
LA Latin America countries. See “List of Countries” for the full list.
WTO/GATT World Trade Organization/ General Agreement on Tariffs and Trade.
σ (G)
Fiscal policy volatility, calculated as the standard deviation of the growth rate of
government expenditures in each period.
σ (M)
Monetary policy volatility, calculated as the standard deviation of the growth rate of M2
measure of money in each period.
σ (TOT)
Volatility in terms of trade, calculated as the standard deviation of the terms of trade over
the length of each sub-period
σ (RER)
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.
σ (r)
Standard deviation of the real interest rate in each sub-period as a proxy for the volatility in
financial markets and or monetary policy.
.m
This suffix indicates that the variable has been constructed using trading partners' import
shares in total.
.x
This suffix indicates that the variable has been constructed using trading partners' import
shares in total.
HIM, HIX
Herfindahl index for geographical diversification. HIM and HIX are respectively
calculated based on shares of imports or exports in totals.
TP Variables that capture economic characteristics of trade partners. Including TPGDP, etc.
TPGDP Weighted average GDP of international trade partners. GDP's are from the year 2000.
TPGDP
PC
Weighted average GDP
PC
of international trade partners. GDP
PC
's are from the year 2000.
TPBCV Weighted average output volatilities of international trading partners.
IBCSync
Multilateral correlation of home country's business cycles with respect to those of its
international trading partners, weighted by trade shares.
G7Grav Average GDP of G-7 countries weighted by the inverse of their distance to a country.
REMOTE GDP-weighted average distance to G-7 countries.
TPDIST Trade-weighted average distance to actual trading partners
Air Distance Average air distance of the capital of a country to NYC, Tokyo & Rotterdam.
English,
Spanish, French
Language dummies which indicate the official language of a country.
Landlocked A binary variable that equal 1 if the country does not have access to open waters.
Elevation Average height of the country's surfaces above the Earth's sea level.
SEM Simultaneous equations models.

Abstract (if available)

Abstract The main thesis of this dissertation is that geographical diversification in international trade is an indispensable component of economic globalization, especially in the context of international risk-sharing, the benefits of which can be assessed through smoother macroeconomic fluctuations. The findings in this empirical work are based on a large amount of data analysis, along with the construction and introduction of various trade variables that capture international trade relations of an economy vis-à-vis its commercial partners and major players in the global economy. For the most part, the compiled panel data used in various parts of this research consist of 133 countries spanning sub-periods of different lengths, over 1960-2006. ❧ The first chapter addresses the impact that more geographically diversified international trade as well as trading partners with various economic characteristics have on business cycle volatility. We suspect simultaneity between volatility and our trade variables, and use an instrumental variable general method of moments estimation technique in most of our econometric analyses. We find that, for a given level of openness to trade among other country-specific characteristics, diversifying international trade among more trade partners with more evenly distributed trade shares among them–captured by a Herfindahl index for either imports or exports–is conducive to lower business cycle volatility. We use the standard deviation of the de-trended output time series, or the standard deviation of the growth rates of output as measures of business cycle volatility. We also find evidence that one of the likely stabilizing mechanisms of diversification is the mitigation of various types of domestic and international shocks. Further, we find that for the same set of controls, diversifying trade towards economies that are larger, more developed, and more stable is associated with lower business cycle volatility. To make this study even more similar to a portfolio view of international trade, we further demonstrate that after controlling for a measure of external shocks, having a less synchronized business cycle with those of the country’s trade partners is associated with smoother business cycles of major macroeconomic aggregates at home. ❧ In the second chapter, we examine various economic and geographic variables that we suspect to be associated with or influential to a country’s level of geographical diversification. Some of the findings in this chapter are as follows. More advanced economies and nations that reach beyond their bordering neighbors and establish trade with more distant countries tend to have more geographically diversified trade. However, countries with a large share of established trade with G-7 countries, or for which the official language is French or Spanish, or which are landlocked, seem to be limited in the extent of their geographical diversification. Finally, membership in the World Trade Organization or its predecessor has a positive impact on geographical diversification, but only within the first five years of that membership. Although we use gravity models as a guideline to construct multiple variables at a more aggregated level here, the implied levels of geographical diversification based on predicted values of bilateral trade flows from a Poisson pseudo-maximum likelihood estimation technique compare poorly with the actual computed levels. ❧ Chapter three explores the channels that challenge the widely-found empirical findings that trade openness has a destabilizing impact on various macroeconomic aggregates. In particular, using the same panel data and methodology applied in the first chapter, we document three main findings. First, a non-linearity in the output volatility-trade openness relationship is shown, and we estimate that beyond a certain threshold, de facto trade openness can turn into a stabilizing factor for output fluctuations. Second, we find that certain institutional and economic factors are capable of mitigating the destabilizing impact of exposure to international trade. Of these complementary variables, we examine financial openness, financial system development, and democracy. Finally, we find that international trade, when sufficiently diversified in geographical terms, is capable of becoming a stabilizing factor in relation to business cycle volatility of major macroeconomic aggregates such as output and consumption. More importantly, this type of highly diversified or global trade, enjoyed by the countries in the top 25 percentile of diversification, reduces the relative volatility of consumption to that of output, which is most frequently used as a measure of consumption-smoothing in the empirical macroeconomic literature and has been the most difficult to account for, especially by trade and finance variables.

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Creator Yazdandoust, Arian Farshbaf (author)

Core Title Essays on business cycle volatility and global trade

School College of Letters, Arts and Sciences

Degree Doctor of Philosophy

Degree Program Economics

Publication Date 08/03/2012

Defense Date 06/11/2012

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag auto-stabilizer,bcv,business cycle synchronization,business cycle volatility,characteristics of trade partners,diversified trade,geographical diversification,global trade,Globalization,herfindahl index,ibc,International trade,macroeconomic fluctuations,mitigation of shocks,OAI-PMH Harvest,portfolio view of international trade,smoothed business cycles,trade and financial openness complementarity,trade partners,trading partners

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Betts, Caroline (committee chair), Hsiao, Cheng (committee member), Matsusaka, John G. (committee member), Quadrini, Vincenzo (committee member)

Creator Email arnfrs@gmail.com,farshbaf@usc.edu

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