WORLD TRADE  
ORGANIZATION   
WT/WGTDF/W/24 
5 May 2004  
 
(04-1996) 
Working Group on Trade, 
Debt and Finance Original:   English 
 
 
 
 
EXCHANGE RATE VOLATILITY AND TRADE FLOWS – SOME NEW EVIDENCE 
 
Paper prepared by the staff of the International Monetary Fund1 
  
  
  
_______________ 
 
 
 
 
                                                      
1 This paper is being circulated with the permission of the International Monetary Fund for the 
information of delegations. WT/WGTDF/W/24 
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INTERNATIONAL MONETARY FUND 
 
Exchange Rate Volatility and Tr ade Flows - Some New Evidence  
 
Prepared by Peter Clark, Natalia Tamirisa, and Shang-Jin Wei,  
with Azim Sadikov, and Li Zeng 
 
Approved by Raghuram Rajan 
 
April 8, 2004 
 
 
 
CONTENTS 
 
 
PAGE 
 
PREFACE........................................................................................................................ .......................5 
 EXECUTIVE SUMMARY.............................................................................................................. .....5 
 
I.
 INTRODUCTION AND OVERVIEW ................................................................................... 6  
II. BRIEF REVIEW OF THE THEORETICAL AND EMPIRICAL 
LITERATURE .................................................................................................................... ...... 8  
A. THEORETICAL ASPECTS OF THE RELATIONSHIP BETWEEN EXCHANGE RATE AND 
VOLATILITY AND TRADE  ............................................................................................................ 9  
B. EMPIRICAL RESULTS ON THE RELATIONSHIP BETWEEN EXCHANGE RATE 
VOLATILITY AND TRADE  .......................................................................................................... 11  
III. RECENT HISTORY AND GEOGRAPHY OF EXCHANGE RATE 
VOLATILITY .................................................................................................................... ..... 14  
A. MEASURING EXCHANGE RATE VOLATILITY  ............................................................................ 14  
B. COMPARISONS USING THE BENCHMARK MEASURE OF VOLATILITY  ....................................... 16  
C. ALTERNATIVE MEASURES OF VOLATILITY  .............................................................................. 18  
IV. NEW EVIDENCE ON THE EFFECT OF EXCHANGE RATE VOLATILITY 
ON TRADE ...................................................................................................................... ....... 20  
A. AGGREGATE VOLATILITY AND AGGREGATE TRADE – A FIRST LOOK ..................................... 21  
B. THE CONCEPTUAL FRAMEWORK FOR THE ANALYSIS OF THE VOLATILITY EFFECT IN 
TRADE  .............................................................................................................................. ......... 21  
C. WHAT DO THE DATA TELL US? ................................................................................................ 23  
V. SUMMARY AND CONCLUDI NG REMARKS ................................................................. 26  
VI. APPENDIX ...................................................................................................................... ........ 32  
A. DO COUNTRIES WITH STABLE EXCHANGE RATES AND COMMON CURRENCY TRADE 
MORE? AN EMPIRICAL ANALYSIS  ............................................................................................ 32  
1. The Gravity Model ............................................................................................................. ..... 32  
(a) Aggregate Trade................................................................................................................ ........ 33   WT/WGTDF/W/24 
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(b) Disaggregated Trade ........................................................................................................... ...... 33  
2. Data and Sources .............................................................................................................. ...... 34  
(a) Aggregate Trade................................................................................................................ ........ 34  
(b) Disaggregated Trade ........................................................................................................... ...... 36  
3. Key Findings .................................................................................................................. .......... 36  
(a) The Effect of Exchange Rate Volatility on Trade – Main Results ........................................... 37  
(b) Do Members of Currency Unions Trade More? ....................................................................... 41  
(c) Disaggregated Trade:  Does Exchange Ra te Volatility Have a Different Effect on 
Trade in Differentiated Vers us Homogeneous Products? ......................................................... 42  
 
Text Tables 
3.1A Short-Run Effective Volatility of Real I.F.S. Exchange Rates in G-7 Countries   
 by Major Country Groups:  Decomposition of Volatility .......................................................43   3.1B Short-Run Effective Volatility of Real  I.F.S. Exchange Rates in G-7 Countries  
 by Major Country Groups:  Volatility within Groups .............................................................44 
3.2 Average Effective Volatility Ranking (1970-2002) ................................................................45   
3.3 Frequency Counting (f) for Top 5 (High Vol) and Bottom 5 (Low Vol)  Lists (1970-2002) ............................................................................................................ ........46  
3.4 Real Effective Volatility Across Country Groups by Type of Exchange  Rate Regime .................................................................................................................. ..........47   
3.5 Real Effective Volatility Across Regimes and Time ..............................................................47  
4.1 Effect of Long-Run Real Exchange Rate  Volatility on Aggregate Trade ..............................23  
4.2 Alternative Measures of Exchange Ra te Volatility ................. ............................ ...................24 
4.3 Effect of Exchange Rate Volatility on Trade in Different Types of Products ........................25   References  ...................... ........................ .............................. ...................... ................. ........................27   
 
Figures 3.1 Short-Run Effective Volatility of the Real Exchange Rate by Country  Groups, 1970-2002 ............................................................................................................ .....48  
3.2 Short-Run Effective Volatility of the Real Exchange Rate for the G-7 Countries ................49 
3.3 Short-Run Effective Volatility of the Real  Exchange Rate in Developing Countries 
 Grouped by Geographic Region .............................................................................................50 
3.4 Short-Run Effective Volatility of the Real Exchange Rate in Two Developing 
 Country Groups by Source of Export Earnings ......................................................................51 3.5 Short-Run Effective Volatility of the Nominal Exchange Rate by Major Country 
 Groups, 1970-2002 ............................................................................................................ .....51  
3.6 Long-Run Effective Volatility of the Real Exchange Rate by Major Country 
 Groups ....................................................................................................................... .............52  
3.7 Long-Run Effective Conditional Volatility of the Real Exchange Rate by Major 
 Country Groups ............................................................................................................... .......52  
3.8 Long-Run Effective Volatility of the Real Ex change Rate in the G-7 Countries ..................53  
4.1 Effective Volatility of the Real Exchange Rate and World Trade .........................................53  
4.2 Effective Volatility of the Real Exchange Ra te & Trade:  Major County Groups .................54 
4.3 Effective Volatility of the Real Exchange  Rate & Trade:  Developing Countries  
 by Region .................................................................................................................... ............55  
4.4 Effective Volatility of the Real Exchange  Rate and Trade:  Developing Countries 
 by Type of Export ............................................................................................................ .......56  
 WT/WGTDF/W/24 
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Appendix Tables III.1 List of Countries in Major Country Groupings ...............................................................57  III.2 List of Countries in Regional Groups of Developing Countries and by Source  of Export Earnings ........................................................................................................... 60  
III.3 List of Countries in the Real Parallel Exchange Rate Dataset .........................................61  
III.4 Short-Run Effective Volatility of Real I. F.S. Exchange Rate:  Smaller Sample of  
 Major Country Groups .....................................................................................................63  III.5 Short-Run EffectiveVolatility of Real Pa rallel Exchange Rate:  Smaller Sample of 
 Major Country Groups .....................................................................................................64  III.6 Correspondence Between the Offi cial/IMF and Natural Regime 
 Classification 1970-2001 ........................ .............................. ............................ ................65  
III.7 Distribution of Exchange Rate Regime s Across Country Groups 1970-2001 .................66  
III.8 Distribution of Exchange Rate Regime s Across Country Groups 1970-2001 .................67  
IV.1 Summary Statistics and Correlations  ...............................................................................68  
IV.2 The Role of Exchange Rate Volatility in  Trade:  Main Results ......................................70  
IV.3 Alternative Measures of Volatility:  Short-Run, Parallel Market, Nominal and 
 Conditional .................................................................................................................. .....71  
IV.4 Country-Pair Fixed Effects ..............................................................................................7 2  
IV.5 Time-Varying Country Effects ........................................................................................73  IV.6A Controlling for Endogeneity of Exch ange Rate Volatility:  Relative Money 
 Supply as IV ................................................................................................................. ...74  
IV.6B Controlling for Endogeneity of Exch ange Rate Volatility:  Common Anchor 
 Dummy as IV .................................................................................................................. .75  
IV.6C Controlling for Endogeneity of Exchange  Rate Volatility:  Propensity to Share 
 a Common Anchor as IV .................................................................................................76  IV.7 Differentiation by Country Type .....................................................................................77  
IV.8 Differentiation by product Type (SUR) .......................................................................... 78  
IV.9 List of Countries in the Regressions w ith Disaggregated Trad e ........................... ...........79  
IV.10 Classification of Products into Homoge neous and Differentiate d Categories .................80  
  WT/WGTDF/W/24 
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PREFACE 
 
 This paper examines the effect of exchange ra te volatility on trade, prepared in response to a 
request from the Director General of the World Tr ade Organization to the IMF. The IMF produced a 
study in 1984 for the General Agreement on Tariffs and Trade (GATT) on this subject. Since then, 
there have been major developments in the world economy, some perhaps having exacerbated 
fluctuations in exchange rates whereas others perh aps having reduced the impact of volatility on trade. 
It is therefore appropriate to revisit the issue some 20 years later.    This paper was prepared by a team led by Peter Clark and Shang-Ji n Wei and consisted of 
Natalia Tamirisa, Azim Sadikov (summer intern), a nd Li Zeng (research assistant). It has benefited 
from comments from Mary Amiti, Giovanni Dell’Ari ccia, Raghuram Rajan, Stephen Tokarick, the 
Management and various departments of the IM F as well as from Marc Auboin, Richard Eglin, and 
other staff of the WTO. The views expressed are t hose of the authors and do not necessarily reflect the 
views of national authorities or IMF Executive Directors. 
 
 Raghuram Rajan Economic Counsellor and Director IMF Research Department  
 
EXECUTIVE SUMMARY 
 
 In 1984 the IMF produced a study for the General Agreement on Tariffs and Trade (GATT) on the impact of exchange rate volatility on world tr ade. As there have been major developments in 
the world economy since then, it is appropriate to revisit the issue some 20 years later. 
  Some of the developments would appear to ha ve exacerbated fluctuations in exchange rates. 
The liberalization of capital flows in the last tw o decades and the enormous increase in the scale of 
cross-border financial transactions have increased  exchange rate movements. Currency crises in 
emerging market economies are special examples of high exchange rate volatility. In addition, the 
transition to a market-based system in Central a nd Eastern Europe often involves major adjustments 
in the international value of these economies’ currencies.   Other changes in the world economy may have re duced the impact of exchange rate volatility. 
The proliferation of financial hedging instruments over the last 20 years could reduce firms’ 
vulnerability to risks arising from volatile currency movements. In addition, for multinational firms, 
fluctuations in different exchange rates may have offsetting effects on their profitability. As a growing 
fraction of international transactions is undertak en by these multinational firms, exchange rate 
volatility may have a declining impact on world trade.   As these different developments in the world economy may have opposing effects in altering 
the impact of exchange rate fluctuations on trade ove r the last two decades, it is not clear what the net 
effect is without undertaking a careful empirical study.    The review of the theoretical literature on th is topic indicates that there is no clear-cut 
relationship between exchange rate volatility and trad e flows. The presumption that trade is adversely 
affected by exchange rate volatility depends on a number of specific assumptions and does not 
necessarily hold in all cases, especially in a gene ral equilibrium setting where other variables change 
along with exchange rates. The ambiguity of the th eoretical predictions reinforces the importance of 
investigating the issue empirically.  WT/WGTDF/W/24 
Page 6  
 
 The empirical research in this study differs from the earlier one (IMF, 1984) in a number of dimensions. First, the country coverage is cons iderably broader. While the earlier study focused 
exclusively on the G-7 countries, this study covers  all Fund members for which data are available. 
Second, this study explores a range of different ex change rate volatility measures. Third, in addition 
to examining aggregate trade, the study also divi des all products into two groups—differentiated and 
homogeneous products—and tests whether volatility h as a differential effect on them. Fourth, the 
estimation techniques are also quite different, as rece nt theoretical advances in the specification of a 
gravity equation are incorporated to assess the im pact of exchange rate volatility on trade.  
  The main empirical findings of the study can be summarized as follows. First, while exchange 
rate fluctuations have increased in times of curre ncy and balance of payments crises during the 1980s 
and 1990s, there has not been any increase, on av erage, in such volatility between the 1970s and 
the 1990s. It is also noteworthy that an exchange rate regime that is classified as “pegged ” does not 
necessarily have lower overall exchange rate volatility  than an arrangement that permits some degree 
of exchange rate flexibility. Pegging to an an chor currency still leaves a country exposed to 
fluctuations in the anchor against other curre ncies, and a peg that becomes misaligned can 
subsequently generate exchange market pressures a nd large, discrete changes in currency values, and 
hence volatility.   Second, a negative relationship between excha nge rate volatility and trade is borne out by 
some of the empirical evidence in this study. Howe ver, such a negative relationship is not robust to 
reasonable perturbations of the specification linking bilateral trade to its determinants. Overall, if 
exchange rate volatility has a negative effect on trade, this effect would appear to be fairly small and 
is by no means a robust, universal finding.   These results suggest that, from the perspective of  enhancing trade, exchange rate volatility is 
probably not a major policy concern.  This does not necessarily rule out the possibility that a large 
exchange rate volatility could affect  an economy through other channels. 
 
I. INTRODUCTION AND OVERVIEW 
1. In 1984 the IMF (1984) produced a study for the General Agreement on Tariffs and Trade 
(GATT) on the impact of exchange rate volatility on  world trade. That study was motivated by an 
increase in protectionist pressures, large exchange rate movements among the major currencies, and a 
significant slowdown in world trade. Some of these developments have reappeared. For example, the 
growth in world exports of goods and services declined sharply in 2001 and 2002 from the double-
digit pace in 2000, and the exchange value of the U.S.  dollar has fluctuated fairly sharply in the last 
year. The 1984 study also reflected a desire to ta ke stock of the implications for currency volatility 
and trade of the shift from the largely fixed rat es among the major currencies to floating after the 
breakdown of the Bretton Woods system in 1971–1973. As there have been other major developments 
in the international monetary system since then, it is  appropriate to revisit th e issues addressed in that 
study some 20 years later. 
2. Some of these developments would appear to ha ve exacerbated fluctuations in exchange rates. 
The liberalization of capital flows in the last 30 years and the enormous increase in the scale and 
variety of cross-border financial transactions have clearly increased the magnitude of exchange rate 
movements in those countries with underdeveloped cap ital markets and where there is not yet a track 
record of consistently stable economic policies.
2 Currency crises in emerging markets, which have 
become more frequent in the last two decades, ar e especially notable cases of large exchange rate 
                                                      
2 Some aspects of this development have recently been analyzed in Prasad , Rogoff, Wei and Kose 
(2003).  WT/WGTDF/W/24 
 Page 7 
 
 
volatility.3 This has been of particular concern to  developing countries and emerging market 
economies. In addition, the transition to a market-b ased system in Central and Eastern Europe often 
involves major adjustments in the interna tional value of these economies’ currencies. 
3. Other changes in the world economy may have redu ced the impact of exchange rate volatility. 
The proliferation of financial hedging instruments over the last 20 years could reduce firms’ 
vulnerability to the risks arising from volatile cu rrency movements. In addition, for multinational 
firms, fluctuations in different exchange rates ma y have offsetting effects on their profitability. As a 
growing fraction of international transactions is undertaken by th ese multinational firms, exchange 
rate volatility may have a declining impact on world trade. 
4. On balance, it is not clear whether the major changes in the world economy over the past two 
decades have operated to reduce or increase the exte nt to which international trade is adversely 
affected by fluctuations in excha nge rates. One aspect of this i ssue is the extent to which such 
volatility itself has changed, and another is the degr ee to which firms are sensi tive to exchange rate 
risk and can take steps to mitigate it at low cost. It is therefore necessary to examine new empirical 
evidence at this issue.  
5. There are a number of differences between the current study and the earlier one. Most 
importantly, the country coverage is considerably broader. In IM F (1984) the analysis was almost 
exclusively focused on the G-7 countries. This reflected the view that the fluctuations in the major currencies were the most important factor for the “environment” within which other countries have to 
plan their policies.
4 While these currencies are the most im portant for the functioning of the 
international monetary system, fluctu ations in other exchange rates are now also relevant for systemic 
reasons as well as for their implications for other countries themselves. Therefore this study takes a 
more comprehensive view of the subject and cove rs the exchange rates of all Fund members for 
which data are available. 
6. This study also explores a range of different  exchange rate volatilit y measures. Moreover, 
aside from examining aggregate trade, the study divides all products into two groups—differentiated 
and homogeneous products—and tests whether vola tility has a differential effect on them. 
7. Given the large number of countries in the data set, it is possible to estimate the degree to 
which volatility has a differential effect depending on whether the country is a dvanced or developing. 
The estimation techniques are also quite different, as recent theoretical advan ces in gravity-equation 
specification are employed to assess the impact of exchange rate volatility on trade.  
8. Finally, following the work of Rose (2000), the study looks at the effect of common currency 
arrangements on trade. This is a related yet distin ct issue from the impact of exchange rate volatility, 
as a currency union is more than just an elimina tion of exchange rate volatility among members. It 
reduces other transactions costs relevant fo r trade and provides a commitment device for 
macroeconomic policies. 
9. Anticipating some of the findings below, th is study shows that while exchange rate 
fluctuations have increased in times of currency and balance of payments crises during the 1980s 
and 1990s, there does not appear to have been an y increase, on average, in such volatility between 
the 1970s and the 1990s. It is also noteworthy that an exchange rate regime that is classified as 
“pegged” does not necessarily have lower overall ex change rate volatility than an arrangement that 
                                                      
3 Issues related to balance of payments adjustment in response to capital account crises were discussed 
in a recent note to the WTO prep ared by staff at the IMF. See World Trade Organization (2003). 
4 For an analysis of the impact of G-3 exchange rate volatility on developing countries, see the essay, 
“How Concerned Should Developing Countries be About G-3 Exchange Rates,” in the Chapter III of the World 
Economic Outlook (2003). WT/WGTDF/W/24 
Page 8  
 
permits some degree of rate flexibility. Pegging to an anchor currency still leaves a country exposed 
to fluctuations in the anchor against other cu rrencies, and a peg that becomes misaligned can 
subsequently generate exchange market pressures a nd large, discrete changes in currency values, and 
hence volatility. 
10. The review of the theoretical literature since the 1984 study has, if anything, reinforced the 
conclusion there that there is no unambiguous relationship between ex change rate volatility and trade 
flows. The general presumption that trade is adver sely affected by an increase in the exchange rate 
fluctuations depends on a number of specific assu mptions and does not necessarily hold in all cases, 
especially in general equilibrium models where othe r variables change along with exchange rates. 
These models show that exchange rate volatility is the result of the volatility in underlying shocks to 
the economy and the policy regime which determines how the shocks are reflected in exchange rates 
and other variables.  
11. For the world as a whole, there is no obvious association between periods of low exchange 
rate volatility and periods of fast growth in trade. In  other words, at an aggregate level, there is no 
evidence of a negative effect of exchange rate on world trade. Once one goes to trade and exchange 
rate volatility at a bilateral level, a negative relati onship between the two is borne out by some of the 
empirical evidence in this study. However, this ne gative relationship is not robust to a more general 
specification of the equation linking bilateral tr ade to its determinants that embodies the recent 
theoretical advances in a gravity model. Thus, if ther e is a negative impact of exchange rate volatility 
on trade, it is not likely to be quantitatively large and the effect is not robust. 
12. These findings suggest that, from the perspective of world trade, exchange rate volatility is 
probably not a major policy concern. Note that th is does not imply necessarily that exchange rate 
fluctuations should be viewed with equanimity . For example, currency crises – special cases of 
exchange rate volatility - have re quired painful adjustments in output and consumption. However, in 
this case, what is important is not that measures n eed to be taken to modera te currency fluctuations 
directly, but that appropriate policies need to be  pursued in order to avoid the underlying causes of 
large, unpredictable and damaging movements in exchange rates. 
13. There are a number of aspects related to exchange rate volatility that are not covered in this 
study. First, it does not deal with the determination of the level of exchange rates. Second, it does not 
deal with the optimal choice of exchange rate arrangement, e.g., fixed versus floating.
5  
14. Part II of the paper reviews the relevant theore tical and empirical literature over the last two 
decades. Part III describes the recent hi story of exchange rate  volatility in different parts of the world. 
Part IV presents some new evidence on the effect of exchange rate volatility on trade. Finally, Part V 
offers concluding remarks. 
II. BRIEF REVIEW OF THE THEORETICAL AND EMPIRICAL LITERATURE 
15. Since the appearance of the IMF (1984) study of the effects of exchange rate volatility on 
trade, two survey papers of the literature on th e topic have appeared: Cote (1994) and McKenzie 
(1999). In addition, the U.K. Treasury (2003) recently  commissioned a number of studies, and invited 
submissions from numerous academics, to inform th eir assessment of the desirability of joining EMU. 
Therefore it is not necessary to present a comprehens ive discussion of the many contributions to the 
field. Rather, the focus here will be on certain key issues which highlight why it has been difficult to 
reach clear-cut conclusions on the impact of excha nge rate variability on trade flows, as well as on 
some of the more recent work in the area. These  two surveys conclude that from a theoretical 
perspective there is no unambiguous response in the level of trade to an increase in exchange rate 
                                                      
5 For an extensive analysis of the performance of a lternative exchange rate regimes, see the recent 
Board paper, “Evolution and Performance of Exchange Rate Regimes.”  WT/WGTDF/W/24 
 Page 9 
 
 
volatility, as differing results can arise from plausible alternative assumptions and modeling 
strategies. The same ambiguity pervades much of th e empirical literature, which may reflect the lack 
of clear-cut theoretical results as well as the difficulty  in arriving at an appropriate proxy for exchange 
rate risk. Nonetheless, some recent studies as well as some of the evidence presented here appear to 
suggest that a negative relationshi p has some support from the data. 
A. THEORETICAL ASPECTS OF THE RELATIONSHIP BETWEEN EXCHANGE RATE AND VOLATILITY 
AND TRADE  
16. It is useful to begin with the example of a rudimentary exporting firm to illustrate how (real) 
exchange rate volatility can affect the level of th e firm’s exports. The simplest case described by 
Clark (1973), for example, considers a competitive firm with no market pow er producing only one 
commodity which is sold entirely to one foreign ma rket and does not import any intermediate inputs. 
The firm is paid in foreign currency and converts th e proceeds of its exports at the current exchange 
rate, which varies in an unpredictable fashion, as there are assumed to be no hedging possibilities, 
such as through forward sales of the foreign curre ncy export sales. Moreover, because of costs in 
adjusting the scale of production, the firm makes its  production decision in advance of the realization 
of the exchange rate and therefore cannot alter its output in response to  favorable or unfavorable shifts 
in the profitability of its exports arising from moveme nts in the exchange rate. In this situation the 
variability in the firm’s profits arises solely from  the exchange rate, and where the managers of the 
firm are adversely affected by risk, greater volatilit y in the exchange rate – with no change in its 
average level - leads to a reduction in output, and he nce in exports, in order to reduce the exposure to 
risk. This basic model has been elaborated by a number of authors, e.g., Hooper and Kohlhagen 
(1978), who reach the same conclusion of a clea r negative relationship between exchange rate 
volatility and the level of trade.  
17. However, this strong conclusion rests on a number of simplifying assumptions. First, it is 
assumed that there are no hedging possibilities either th rough the forward exchange market or through 
offsetting transactions. For advanced economies wh ere there are well developed forward markets, 
specific transactions can be easily hedged, thus  reducing exposure to unforeseen movements in 
exchange rates.6 But it needs to be recognized that such markets do not exist for the currencies of 
most developing countries. Moreover, even in advan ced economies the decision to continue to export 
or import would appear to reflect a series of transactions over time where both the amount of foreign currency receipts and payments, as well are the forward rate, are not known with certainty.  
18. Moreover, there are numerous possibilities for reducing exposure to the risk of adverse 
exchange rate fluctuations other than forward currency markets. The key point is that for a 
multinational firm engaged in a wide variety of tr ade and financial transactions across a large number 
of countries, there are manifold opportunities to e xploit offsetting movements in currencies and other 
variables. For example, there is a clear tendency for ex change rates to adjust to differences in inflation 
rates, and recent evidence suggests that such adju stment may be quicker than indicated by earlier 
studies. Thus, if exports are priced in a foreign cu rrency that is depreciating, the loss to the exporter 
from the declining exchange rate is at least partly  offset by the higher fore ign-currency export price 
(Cushman, 1983 and 1986). In a similar vein, as noted by Clark (1973), to the extent that an exporter 
imports intermediate inputs from a country whose curre ncy is depreciating, there will be some offset 
to declining export revenue in the form of lower i nput costs. In addition, when a firm trades with a 
large number of countries, the tendency for some exch ange rates to move in offsetting directions will 
provide a degree of protection to its overall exposur e to currency risk. Finally, as analyzed by Makin 
(1978), a finance perspective suggests that there ar e many possibilities for a multinational corporation 
                                                      
6 For an analysis of the effects of forward cover on  the level of trade, see Ethier (1973),  Kawai and 
Zilcha (1986), and Viaene and de Vries (1992). However, Wei (1999) does not find empirical support for the 
hypothesis that the availability of hedging instrument reduces the impact of exchange rate volatility on trade. WT/WGTDF/W/24 
Page 10  
 
to hedge foreign currency risks arising from exports and imports by holding a portfolio of assets and 
liabilities in different currencies.  
19. One reason why trade may be adversely affected  by exchange rate volatility stems from the 
assumption that the firm cannot alter factor inputs in order to adjust optimally to take account of movements in exchange rates. When this assumpti on is relaxed and firms can adjust one or more 
factors of production in response to movements in ex change rates, increased variability can in fact 
create profit opportunities. This situation has been analyzed by Canzoneri, et al. (1984), De Grauwe (1992), and Gros (1987), for example. The effect of  such volatility depends on the interaction of two 
forces at work. On the one hand, if the firm can ad just inputs to both high and low prices, its expected 
or average profits will be larger with greater exchange rate variab ility, as it will sell more when the 
price is high, and vice versa. On the other hand, to the extent that there is risk aversion, the higher 
variance of profits has an adverse effect on the fi rm and constitutes a disincentive to produce and to 
export. If risk aversion is relatively low, the positive effect of greater price variability on expected profits outweighs the negative impact of the higher variability of profits, and the firm will raise the 
average capital stock and the level of output and exports. In a more general setting analyzing the 
behavior of a firm under uncertainty, Pindyck (198 2) has also shown that under certain conditions, 
increased price variability can result in increased average investment and output as the firm adjusts to 
take advantage of high prices and to minimize the impact of low prices. 
20. One aspect of the relationship between trade a nd exchange rate volatility that needs to be 
mentioned is the role of “sunk costs.” Much of international trade consists of differentiated 
manufactured goods that typically require significant investment by firms to adapt their products to 
foreign markets, to set up marketing and distri bution networks, and to set up production facilities 
specifically designed for export markets. These sunk  costs would tend to make firms less responsive 
to short-run movements in the exchange rate, as th ey would tend to adopt a “wait and see” approach 
and stay in the export market as long as they can recover their variable costs and wait for a turnaround in the exchange rate to recoup their sunk costs. Following the finance literature on real options (e.g., 
McDonald and Segel, 1986), Dixit (1989) and Krugm an (1989) have explored the implications of 
sunk costs in the context of an “options” approach , which has been applied by Franke (1991) and 
Sercu and Vanhulle (1992). The key idea is that an  exporting firm can be viewed as owning an option 
to leave the export market, and a firm not currently exporting can be regarded as owning an option to enter the foreign market in the future. The deci sion to enter or exit the export market involves 
considering explicit fixed and variable costs, but al so the cost of exercising the option to enter or 
leave the market. The greater the volatility in exchange rates, the greater the value of keeping the 
option, and hence the greater the range of exchange  rates within which the firm stays in the export 
market, or stays out if it has not yet entered. Th is suggests that increased exchange rate volatility 
would increase the inertia in entry and exit decisions.  
21. It is useful to note that in most theoretical m odels, what is being studied is the volatility of the 
real exchange rate as opposed to the nominal exchange  rate. The two are distinct conceptually but do 
not differ much in reality: prices of goods tend to be “sticky” in local currency in the short-to-medium 
run. In this case, real and nominal exchange ra te volatilities are virtually the same for practical 
purposes. For this reason, after reviewing the literature  on the effect of real exchange rate volatility, 
we do not present a separate discussion on the eff ect of nominal exchange rate volatility. The 
exceptions are episodes of high infl ation when nominal exchange ra te volatility tends to be bigger 
than real exchange rate volatility. Fo r this reason, in the empirical anal ysis that will be presented later, 
we examine explicitly whether real versus nominal exchange rate volatilities have different effects on 
trade or not.  
22. Up to this point the discussion of the impact of  volatility on trade has been within a partial 
equilibrium framework, i.e., the only variable that ch anges is some measure of the variability of the 
exchange rate, and all other factors that may have an influence on the level of trade are assumed to 
remain unchanged. However, those developments th at are generating the exchange rate movements  WT/WGTDF/W/24 
 Page 11 
 
 
are likely to affect other aspects of the economic en vironment which will in turn have an effect on 
trade flows. Thus it is important to take account in  a general equilibrium framework the interaction of 
all the major macroeconomic variables to get a mo re complete picture of the relationship between 
exchange rate variability and trade.  
23. Such an analysis has recent ly been provided by Bacchetta and Van Wincoop (2000). They 
develop a simple, two-country, general equilibrium  model where uncertainty arises from monetary, 
fiscal, and technology shocks, and they compare the level of trade and welfare for fixed and floating 
exchange rate arrangements. They reach two main conclusions. First, there is no clear relationship 
between the level of trade and the type of exch ange rate arrangement. De pending on the preferences 
of consumers regarding the tradeoff between consum ption and leisure, as well as the monetary policy 
rules followed in each system, trade can be higher or lower under either exchange rate arrangement. 
As an example of the ambiguity of the relati onship between volatility and trade in a general 
equilibrium environment, a monetary expansion in the foreign country would depreciate its exchange 
rate, causing it to reduce its imports, but the increas ed demand generated by the monetary expansion 
could offset part or all of the exchange rate ef fect. Thus the nature of the shock that causes the 
exchange rate change can lead to changes in other macroeconomic vari ables that offset the impact of 
the movement in the exchange rate. Second, the level of trade does not provide a good index of the 
level of welfare in a country, and thus there is no one-to-one relations hip between levels of trade and 
welfare in comparing exchange rate systems. In th eir model, trade is determined by the certainty 
equivalent of a firm’s revenue a nd costs in the home market relativ e to the foreign market, whereas 
the welfare of the country is determined by  the volatility of consumption and leisure. 
24. Obstfeld and Rogoff (1998) also provide an anal ysis of the welfare costs of exchange rate 
volatility. They extend the “new open economy macr oeconomic model” to an explicitly stochastic 
environment where risk has an impact on the price-setting decisions of firms, and hence on output and international trade flows. They provide an illust rative example whereby reducing the variance of the 
exchange rate to zero by pegging the exchange rate could result in a welfare gain of up to one percent 
of GDP. Bergin and Tchakarov (2003) provide an exte nsion of this type of model to more realistic 
situations involving incomp lete asset markets and investment by fi rms. They are able to calculate the 
effects of exchange rate uncertainty for a wide range of cases and find that the welfare costs are 
generally quite small, on the order of one tenth of one percent of consumption. However, they explore 
the implications of two cases where risk does matter quantitatively, on the order of the effect in the 
example cited above by Obstfeld and Rogoff (1998):  first, where consumers exhibit considerable 
persistence in their pattern of cons umption, such that welfare is adversely affected by sudden changes 
in consumption, and second, where asset market s are asymmetric in that there is only one 
international bond, such that the country w ithout its own bond is adversely affected. 
25. Finally, Koren and Szeidl (2003) develop a mode l which brings out clearly the interactions 
among macroeconomic variables. They show that wh at matters is not the unconditional volatility of 
the exchange rate as a proxy for risk, as used in ma ny empirical papers in the literature, but rather that 
exchange rate uncertainty should influence trad e volumes and prices through the covariances of the 
exchange rate with the other key variables in the model. In this general equilibrium context, they 
stress that it is not uncertainty per se in the exch ange rate that matters, but rather whether this 
uncertainty magnifies or reduces the firm’s other ri sks on the cost and demand side, and ultimately 
whether it exacerbates or moderates the risk faced by consumers. In addition, they analyze the extent 
to which local currency vs. producer currency pricin g by exporters affects the risks facing the firm; 
their empirical evidence suggests that risk is higher with the former pricing rule. 
B. EMPIRICAL RESULTS ON THE RELATIONSHIP BETWEEN EXCHANGE RATE VOLATILITY AND 
TRADE  
26. The early empirical work on the effect of excha nge rate variability and trade surveyed in the 
IMF (1984) study did not yield consistent results, with many studies yielding little or no support for a WT/WGTDF/W/24 
Page 12  
 
negative effect. For example, the early work by Hooper and Kohlhagen (1978 ) utilized the model of 
Ethier (1973) for traded goods and derived equatio ns for export prices and quantities in terms of the 
costs of production reflecting both domestic and imported inputs, other dom estic prices, domestic 
income, and capacity utilization. Exchange rate risk  was measured by the aver age absolute difference 
between the current period spot exchange rate and th e forward rate last period, as well as the variance 
of the nominal spot rate and the current forward ra te. They examined the impact of exchange rate 
volatility on aggregate and bilateral trade flow data for all G-7 countries except Italy. In terms of the 
effect of volatility on trade flows, they found essentia lly no evidence of any negative effect. Cushman 
(1983) used a model similar to that of Hooper and Kohlhagen (1978) but extended the sample size and 
used real as opposed to nominal ex change rates. Of four teen sets of bilateral trade flows between 
industrial countries, he found a negative and signifi cant effect of volatility for six cases. Finally, the 
IMF (1984) used a simplified version of Cushman’ s model to estimate bilateral exports between the 
G-7 countries from the first quarter of 1969 to the fourth quarter of 1982, with real GNP, the real 
bilateral exchange rate, relative capacity utilization,  and variability measured as the standard deviation 
of the percentage changes in the exchange rate over the preceding five quarters. In only two cases did 
variability have a significantly negative coefficient,  while positive coefficients were significant in 
several cases.  
27. A number of factors may have contributed to the lack of robust findings in this early work. 
First, as noted above, theoretical considerations do not provide clear support for the conventional 
assumption that exchange rate volatility has a nega tive impact on the level of trade. Second, the 
sample period over which exchange rates showed significant variation was relatively short. Finally, 
the specification of the estimating equations was ty pically rather crude, consisting of a few macro 
variables from standard trade equations in use at the time.  
28. McKenzie (1999) surveys a large number of empirical papers on the topic, most of which 
appeared after the IMF study. He stresses the point ma de above that at a theore tical level, models have 
been constructed which lead to negative or positive effects of variability on trade, and that a priori 
there is no clear case that one model is superior to another. His survey of the empirical work leads to 
the same mixed picture of results, with many studies finding no significant effect, or where significant, no systematic effect in one direction or  the other. He finds, however, that the most recent 
contributions to the literature have been more successful in obtaining a statistically significant 
relationship between volatility and trade, which he  attributes to more careful attention to the 
specification of the estimation technique and the measure of volatility used. Similarly, the U.K. 
Treasury (2003) cites (Box 4.1, p. 29) a number of recent studies (De Grauwe (1987), Rose (2000), 
Dell’Ariccia (1999), Anderton and Skudelny (2001) , Arize (1998) and Fountas and Aristotelous 
(1999)) which find a negative link, but these effects are not very large: complete elimination of volatility would raise trade by a maximum of 15 per cent, compared to the consensus estimate of the 
effect as typically less than ten percent. 
29. Recent work on this topic employing the gravit y model has found some significant evidence 
of a negative relationship between ex change rate variability and trade.
7 The gravity equation has been 
widely used in empirical work in interna tional economics and has been highly successful in 
explaining trade flows.8 In its basic form, the gravity model explains bilateral trade flows between 
countries as depending positively on  the product of their GDPs and negatively on their geographical 
distance from each other. Countries with larger economi es tend to trade more in absolute terms, while 
distance can be viewed as a proxy for transportation costs which act as an impediment to trade. In 
addition, population is often included as an explan atory variable as an additional measure of country 
size. In many applications a host of dummy variab les are added to account fo r shared characteristics 
                                                      
7 See Frankel and Wei (1993), Wei (1999), Dell’Ariccia (1999), Rose (2000), and Tenreyro (2003).  
8 See, for example, McCallum (1995 ) and Coe, Subramanian, and Tamirisa (2002). For discussions of 
the gravity equation, see Deardorff (1998), Anderson and van Wincoop (2003), and  Annex D in U.K. Treasury 
(2003).  WT/WGTDF/W/24 
 Page 13 
 
 
which would increase the likelihood of trade be tween two countries, such as common borders, 
common language, and a membership in a free trade association. To this basic equation researchers 
add some measure of exchange ra te variability to see if this prox y for exchange rate risk has a 
separate, identifiable effect on trade flows after all other major factors have been taken into account. 
30. The work by Dell’Ariccia (1999) provides a system atic analysis of exchange rate volatility on 
the bilateral trade of the 15 EU members and Sw itzerland over the 20 years from 1975 to 1994, using 
four different measures of exchange rate uncertainty : the standard deviation of the first difference of 
the logarithm of the monthly bilateral nominal and re al (CPI) exchange rate, the sum of the squares of 
the forward errors, and the percentage differen ce between the maximum and the minimum of the 
nominal spot rate. In the basic regressions, exch ange rate volatility has a small but significantly 
negative impact on trade: elimina ting volatility to zero in 1994 would have increased trade by an 
amount ranging from 10 to 13 per cent, depending on the particular measure of variability.9 The results 
for both nominal and real variability are very close, which is not surprising, given that in the sample 
the two exchange rate measures are highly correlated. 
31. Dell’Ariccia then goes on to take account of the simultaneity bias that can result from central 
banks trying to stabilize their exchange rates with their main trading partners. If they were successful, 
there would be a negative association between exchange  rate variability and the level of trade, but it 
would not reflect causation from the former to the la tter. He first uses an instrument (the sum of 
squares of the three-month logarithmic forward erro r) for the measures of exchange rate volatility to 
account for possible endogeneity in this variable . The results confirm the negative relationship 
between volatility and trade, with the magnitude of the effect about the same as before. In addition, he 
uses both fixed effects and random effects estimation methods to account for the simultaneity bias. In 
this case the effect is still significant, but the magnitude is much smaller: total elimination of 
exchange rate volatility in 1994 would have increased trade by only 3 – 4 percent. 
32. Rose (2000) also employs the gravity approach and uses a very large data set involving 186 
countries for the five years 1970, 1975, 1980, 1985, and 1990. His main objective in the paper is to 
measure the effect of currency unions on members’ trade – an issue which is dealt with at length 
below – but he also uses his model to test for th e effects of exchange rate volatility on trade. His 
primary measure of volatility is the standard devia tion of the first difference of the monthly logarithm 
of the bilateral nominal exchange rate, which is computed over the five years preceding the year of 
estimation. In his benchmark results using the pool ed data, he finds a small but significant negative 
effect: reducing volatility by one standard deviation (7 percent) around the mean (5 percent) would 
increase bilateral trade by about 13 percent, which is similar to the finding of Dell’Ariccia described 
above.10 This result is robust when using three alte rnative measures of volatility, but not when the 
standard deviation over the previous five years of the level of the exchange rate is used. However, 
when random effects are incorporated in the estimat ion, the magnitude of the effect of volatility on 
trade is reduced to about a third of the benchmark estimate, or roughly 4 percent. Thus the estimation 
results of Rose and Dell’Ariccia appear to be quite consistent. 
33. However, a recent paper by Tenreyro (2003) casts some doubt on the robustness of these 
results. She utilizes a gravity equation similar to that  of Rose for a broad sample of countries using 
annual data from 1970 to 1997. The measure of vola tility is the same as that employed by Rose, 
except that the standard deviation of the log change  in monthly exchange rates is measured only over 
the current year. Her main objective is to address several estimation problems in previous studies of 
                                                      
9 In 1994, the average standard deviation of the monthly nominal exchange rate change was roughly 
5.5 percent, and over the sample period the annual average bilateral trade growth was 3.5 percent.  
10 Parsley and Wei (2001) look at the effect of reducing nominal exchange rate variability on relative 
price variability across countries, and find that reducing the former diminishes the latter. However, they also 
find a much stronger effect arising from participating in a hard peg, such as a currency union, which is 
consistent with Rose’s finding of a larg e impact of a currency union on trade. WT/WGTDF/W/24 
Page 14  
 
the effect of volatility on trade. When these problem s are not addressed and ordinary least squares is 
used, she finds a small effect: reducing volatility fr om its sample mean of about 5 percent to zero 
results in an increase in trade of only 2 percent.  When the more appropriate method is used, but 
without taking account of endogeneity, eliminating ex change rate uncertainty lead to an estimated 
4 percent increase in trade. However, when endoge neity is taken into acc ount through the use of 
instruments, volatility has an insignificant effect on trade, a result that is robust on the choice of 
instruments.  
34. Finally, it should be noted that there has been  some recent work looking at the effects of 
exchange rate volatility on disaggregated trade fl ows. Broda and Romalis (2003) find that volatility 
decreases trade in differentiated pr oducts relative to trade in commoditi es, although the effect is rather 
small: eliminating all real excha nge rate volatility would increase trade in manufactures by less than 
5 percent and total trade by less than 3 percent.  They note, however, that some countries with 
particularly volatile exchange rates, especially developing countries, would experience a more 
pronounced increase in trade. Koren and Szeidl ( 2003) also use disaggregated data and find small 
effects: eliminating exchange rate variability w ould result in a change in export prices of only a 
few percentage points. 
III. RECENT HISTORY AND GEOGRAPHY OF EXCHANGE RATE VOLATILITY 
A. M
EASURING EXCHANGE RATE VOLATILITY  
35. In the voluminous literature on ex change rate volatility and trade,  there is no consensus on the 
appropriate method for measuring such volatility. This lack of agreement reflects a number of factors. 
As noted in the section below, there is no generall y accepted model of firm behavior subject to risk 
arising from fluctuations in exchange rates and ot her variables. Consequently theory cannot provide 
definitive guidance as to which measure is most suit able. Moreover, the scope of the analysis will to 
some extent dictate the type of measure used. If the focus is on advanced countries, then one could 
take into account forward markets for the assessment of exchange ra te volatility on trade, whereas this 
would not be possible if the analysis extended to a large number of developing countries. In addition, 
one needs to consider the time horizon over which variability is to be measured, as well as whether it 
is unconditional volatility or the unexpected movement in the exchange rate relative to its predicted 
value, that is the relevant measure. Finally, the le vel of aggregation of trade flows being considered 
will also play a role in determining the appropria te measure of the exchange rate to be used. 
36. This study provides a comprehensive picture of  volatility in exchange rates across the entire 
Fund membership for which data ar e available. In the empirical analysis, the paper starts with an 
examination of the relationship between aggregat e exchange rate volatility and aggregate trade. 
Recognizing the limitations of looking at the aggreg ate data, the paper then turns to analyzing the 
effect of exchange rate volatility on trade acr oss different country pairs and over time. 
Methodologically, the switch to bilateral trade and volatility allows one to better control a variety of 
other factors that could affect trade other than vol atility. As a consequence, the chance to detect an 
effect of exchange rate volatility on trade improve s. Given this methodological approach, the basic 
building block in the analys is is the volatility in the exchange ra te between the currencies of each pair 
of countries in the sample. For the descriptive part of the study below, which looks at the exchange 
rate volatility facing a country as a whole, it is n ecessary to aggregate the bilateral volatilities using 
trade shares as weights to obtain what is referre d to as the “effective volatility” of a country’s 
exchange rates. This ensures that the measures of  volatility in the descriptive and econometric parts of 
the study are fully consistent. 
37. Such a measure of “effective volatility” presuppo ses that the exchange rate uncertainty facing 
an individual firm is an average of the variability of individual bilateral exchange rates (Lanyi and 
Suss, 1982). However, if a trading firm engages in international transactions with a wide range of 
countries, any tendency for exchange rates to move  in offsetting directions would reduce the overall  WT/WGTDF/W/24 
 Page 15 
 
 
exposure of the firm to exchange rate risk. This  would argue for using the volatility of a country’s 
effective exchange rate as the measure of the exch ange rate uncertainty facing a country. This would 
seem particularly appropriate for advanced economies  where much trade is undertaken by diversified 
multinational corporations. This was the approach taken in the original IMF (1984) study, which 
focused almost exclusively on the G-7 countries.  However, the present study covers nearly all 
developing countries, where the role of diversified firms is less pronounced. For this reason, as well as 
to have consistency with the econometric analysis below, effective volatility is used in the descriptive 
part of the study. 
38. It is important to realize that the degree of exch ange rate variability a country is exposed to is 
not necessarily closely related to the type of excha nge rate regime it has adopted. A country may peg 
its currency to an anchor currency, but it will floa t against all other currencies if the anchor does as 
well. Thus, as with effective exchange rates, effective volatility is a multi-dimensional concept 
(Polak, 1988). Pegging can reduce nominal exchange ra te volatility vis-à-vis one trading partner, but 
it can by no means eliminate overall exchange rate  variability. This is shown below, where measured 
volatility is related to two different classificati ons of a country’s exch ange rate arrangement. 
39. The choice between using nominal and real ex change rates depends in part on the time 
dimension that is relevant for th e economic decision being taken. In the short- run where costs of 
production are known and export and im port prices have been determined , the exchange rate exposure 
of a firm is a function of the nominal exchange rate . However, the decision to engage in international 
transactions stretches over a longer period of time during which production costs and export and 
import prices in foreign currency will vary. From this  perspective, exchange rates measured in real 
terms are appropriate. Nonetheless, as nominal and real exchange rates tend to move closely together, 
given the stickiness of domestic prices, the choice of which one to use is not likely to affect 
significantly measured volatility or the econometric r esults. Nonetheless, real rates are preferable on 
theoretical grounds and are used in the benchmark measures of volatility below. Consumer prices are 
used to construct the real rates, as they are the mo st widely available measures of domestic prices. As 
a robustness check, results using nominal exchange rates are also reported. 
40. While exchange rates are often highly volatile, the extent to which they are a source of 
uncertainty and risk depends on the degree to whic h exchange rate movements are foreseen. When 
hedging instruments are available, the predicted part  of exchange rate volatility can be hedged away 
and hence may not have much effect on trade. This  suggests that the appropriate measure of risk 
should be related to deviations between actual and predicted excha nge rates. One possibility along 
these lines would be to use the forward rate as a pr ediction of the future spot rate, and to use the 
difference between the current spot rate and the pr evious period forward rate as an indicator of 
exchange rate risk. One problem with  this approach is that the forw ard rate is not a good predictor of 
future exchange rates. In add ition, quotations are available only for the major currencies. More 
generally, there are a wide variety of methods—r anging from structural models to time series 
equations using ARCH/GARCH approaches, for exampl e—that could be used to generate predicted 
values of exchange rates (McKenzie, 1999). Howe ver, as pointed out by Meese and Rogoff (1983), 
there are inherent difficulties in predicting exchange  rates. Therefore this study adopts the approach 
followed in much of the work on the topic and uses a measure of the observed variability of exchange 
rates as the benchmark. GARCH estimates are in cluded as an alternative measure of volatility. 
41. The most widely used measure of exchange rate volatility is the standard deviation of the first 
difference of logarithms of the exchange rate.11 This measure has the property that it will equal zero if 
the exchange rate follows a constant trend, wh ich presumably could be anticipated and therefore 
would not be a source of uncertainty. Following the practice in most other studies, the change in the 
exchange rate is computed over one month, us ing end-of-month data. The standard deviation is 
                                                      
11 See, for example, Brodsky (1984), Kenen and Rodrick (1986), Frankel and Wei (1993), Dell’Ariccia 
(1999), Rose (2000), and Tenreyro (2003). WT/WGTDF/W/24 
Page 16  
 
calculated over a one-year period, as an indicator of short-run volatility, as well as over a five-year 
period to capture long-run variability. 
42. Finally, it is useful to take note of the ro le of currency invoicing here. Very often trade 
between a pair of countries, especially between two developing countries, is not invoiced in the 
currency of either country. Instead, a major currency,  especially the U.S. dollar, is often used as the 
invoicing currency. It might appear to be the case that the volatility of the exchange rate between the 
currencies of the two trading partners is not the rele vant volatility to consider. For example, if Chinese 
exports to India are invoiced in U.S. dollars, it might seem that the Chinese exporters would only care 
about the fluctuations between the U.S. dollar and the Chinese yuan, but not between the Indian rupee 
and the Chinese yuan. However, this view is not correct. Any fluctuation between the Chinese yuan 
and the Indian rupee holding constant the Chinese yuan /U.S. dollar rate, must reflect fluctuations in 
the Indian rupee/U.S. dollar rate. As the latter could affect the Indian demand for Chinese exports, 
fluctuations in the Chinese yuan/Ind ian rupee exchange rate would also affect the Chinese exports to 
India even if the trade is invoiced in the U.S. dollar. Generally speaking, the choice of invoicing 
currency does not alter the effect of exchange rate volatility on trade. 
B. C
OMPARISONS USING THE BENCHMARK MEASURE OF VOLATILITY  
43. It is useful to begin the analysis of excha nge rate volatility over time and across countries by 
examining the evolution of fluctuations in exch ange rates for broad groups  of countries shown in 
Figure 3.1.12 This shows the short-run effective volatility since 1970 of exchange rates reported in the 
IMF’s International Financial Statistics (IFS) , converted to real terms using consumer prices, for 
advanced, transition, emerging market, and developing economies.13 As noted in the Introduction, 
there were several developments in the international monetary system over this period, including 
crises in emerging market economies, capital account liberalization, and the breakup of the former Soviet Union, all of which tended to be associat ed with an increase in exchange rate volatility. 
44. First, looking at the how variability has changed over the sample period, it is noteworthy that 
there is no obvious trend increase over time. In the first three years of the sample period, 1970–1972, 
lower-than-average effective volatility is evident for the advanced economies, which reflects the 
fixed-rate system of most of these countries. Sin ce then, the exchange rates of these countries have 
exhibited greater volatility, but not markedly so. In fact, the average effective volatility from 1991–
2002 is about the same as in 1970–1980. There is  also no clear upward trend in exchange rate 
volatility in emerging market economies and devel oping countries over the entire period. While 
transition economy exchange rates exhibited much greater variability, on average, in the 1990–2002 
period, this reflects the very large change in exch ange rates associated with the breakup of the former 
Soviet Union and the shift to market economies from 1989 to 1993. The unprecedented high level of 
volatility during these years was a refl ection by and large of adjustments in real exchange rates that 
were needed to accommodate the structural tr ansformation of these economies. These adjustments 
now appear to be essentially complete and in recen t years (1999–2002) the effective volatility in their 
real exchange rates has been less than that of emerging and developing countries. 
45. Second, looking across the major country groupings, it is not surprising that measured 
volatility is lowest for the advanced economies. Th is reflects both that these countries trade relatively 
                                                      
12 Figure 3.1 shows equal-weighted averages of th e effective volatilities of the exchange rates of the 
countries in each group, as each in dividual country is viewed as the unit of interest. Alternatively, one could 
weight the effective volatility of each country by its trad e share. This weighted av erage volatility was computed 
for each group, and the results are not markedly different from what is shown in Figure 3.1. 
13 The list of countries in each group is given in Ap pendix Table III.1. The lis t of advanced countries 
follows that in the World Economic Outlook , Table A in the Statistical Appendix, except that the four newly 
industrialized Asian economies are included in the gr oup of emerging markets. The transition economies 
comprise the countries in transition in Table A. The gro up of emerging market economies is a fairly narrow list 
of 20 countries. All other countries are included in the list of developing countries.  WT/WGTDF/W/24 
 Page 17 
 
 
more with each other and that their bilateral exch ange rates with each other tend to exhibit smaller 
fluctuations than with other countries. (See the discussion below.) The lower volatility within the 
group presumably arises from the greater stability in economic policies in the advanced economies, as 
well as their ability to adjust relatively smoothly to shocks. In addition, the foreign exchange markets 
in which these currencies are traded are very large and liquid, with instruments available to hedge 
volatility, which enables these markets to clear quick ly, dampening potentially large fluctuations in 
exchange rates.  
46. Figure 3.2 shows the same measure of volatility for the G-7 countries individually, as well as 
for the group as a whole. While variability is, on aver age, very similar to that for advanced countries 
as a whole, there are notable differences. The high average volatility for Japan, at 3.50 percent, is 
double that of Canada, at 1.75 percent; this low figure would appear to reflect the close integration of 
the Canadian and U.S. economies, as well the strate gy of the Canadian authorities to avoid large 
swings in the Canadian-U.S. dollar exchange rate over part of the sample period. Also noteworthy is 
the increased volatility for France, Germany, and Italy surrounding the ERM turmoil in 1991–1993 
(which also affected the United Kingdom in 1992 ), as well as noticeable reduction in effective 
volatility in the exchange rates of these three count ries with the introduction of the euro in 1999. 
47. To illustrate the reasons for the relatively low effective volatility of the advanced economies, 
it is useful to decompose the variability in their ex change rates into the contributions of each of the 
major country groups. This is done in Table 3.1 for the G-7 countries. Part A of Table 3.1 decomposes 
the effective volatility of each of the G-7 into the share of volatility from each group for each year 1970, 1980, 1990, and 2000, so that the row sum equals the overall effective volatility for that 
country shown in the last row. It is clear that  with two exceptions (Jap an and the United States 
in 1970) the largest component of volatility is th at arising from the exchange rates of the other 
advanced economies. This reflects, in part, the fact  that the trade weights of the industrial countries 
are very high as well as the lower volatility of th e individual bilateral ex change rates among the 
advanced countries. This is shown in Part B of Ta ble 3.1, which gives the volatility of the exchange 
rates of the G-7 with each of the major country gr oupings, computed with the trade weights summing 
to unity for each group. It shows that with only a few exceptions, the volatility of the exchange rates 
of the G-7 with other advanced economies was less th an that of their exchange rates with the other 
country groups. 
48. As noted above, of the major country groups, the transition economies have had the highest 
level of exchange rate variability,  which was associated with the breakup of the former Soviet Union. 
Data for this group are shown only starting in 1988, as most countries did not exist in the 1970s 
and 1980s. Only starting in 1995 are data availabl e for all 22 transition countries, and over the 
period 1995–2002 the exchange rate  volatility of this group was comparable to that of emerging 
market economies and developing countries. Vo latility in these two groups, while on average not 
quite double that of the advanced countries for the period as a whole, nonetheless declined between 
the 1980s and 1990s, especially for the emerging market economies. 
49. Some additional detail is shown in Figure 3.3, which gives a geographic breakdown of 
developing countries (WEO classification), and in Fi gure 3.4 for two analytical groups (fuel exporters 
and exporters of non-fuel primary products.)
14 Among the geographic regi ons, sub-Saharan Africa 
(excluding South Africa and Nigeria) shows the high est average level of volatility of real exchange 
rates over the sample period, altho ugh this may reflect the unusually large 14.5 percent figure in 1994, 
which is related in large part to the dramatic devaluation of the CFA franc that year. By contrast, the 
developing countries in Asia have fairly consistently exhibited below-average volatility, especially if 
one excludes the exceptionally high degree of variab ility associated with the Asian crisis in 1997–
1998. For the developing countries in the Western Hemi sphere, exchange rate fl uctuations have been 
below-average, except for the tur bulence associated with the “lost decade” of the 1980s. Regarding 
                                                      
14 The list of countries in each group is given in Appendix Table III.2. WT/WGTDF/W/24 
Page 18  
 
the analytic groupings shown in Figure 3.4, fuel e xporters have experienced increasing exchange rate 
volatility over the sample period, and exporters of  non-fuel primary products have had the highest 
average level of real exchange volatility over the entire period, which likely reflects the effects of 
movements in the terms of trade of these countries. 
50. The average figures for the country groups embod y wide variation in the level of exchange 
rate volatility of the individual countries in each group.  It is therefore useful to examine the variation 
across the members in each group. This is done in  Table 3.2, which presents figures for the average 
effective volatility of real exchange rates over the entire sample period 1970–2002 for the five 
countries with the highest and lowest volatilities.
15 As expected, the dispersion of exchange rate 
volatility across the advanced economies is quite lo w, compared with the other groups. It is 
noteworthy, however, that Japan has the highest me asured volatility in this group, with another G-7 
country, the United Kingdom, ranking fifth. The dispersion is much higher within the other country groups. 
51. Table 3.3 provides information on the frequency (number of years) each country appeared in 
the top five or bottom five in terms of effective real exchange rate volatility. This indicates which 
countries exhibited persistently high or low variab ility over the sample period. The results are often 
similar to what is shown in Table 3.2; for example,  Japan is in the top five advanced economies in 30 
out of the 33 years in the sample. Similarly, in the emerging markets group, Argentina is in the top 
five in 20 of the 33 years.
16 
C. ALTERNATIVE MEASURES OF VOLATILITY  
52. It is useful to compare the benchmark measure  of volatility with a number of alternatives. 
Figure 3.5 provides figures for the short-run effective volatility of the nominal  official exchange rate. 
A comparison with Figure 3.1 shows that while there are no major differences between these two measures, it is generally the case that real excha nge rate volatility is somewhat higher than nominal 
volatility. This is particularly the case in 1970, wh en fixed nominal rates were more widespread and 
inflation differentials generated larger movements in real rates.
17 Lower volatility in nominal 
exchange rates is also more pronounced among de veloping countries over the entire sample period, 
which would appear to reflect the “fear of fl oating” described by Reinhart and Rogoff (2002). 
53. Figure 3.6 shows the longer-run measure of exch ange rate volatility, namely, the standard 
deviation of monthly log differences in exchange rates calculated over the five years preceding the 
year in question. As one would expect, the measured volatility is larger than the average of the short-
run volatilities over the same years. Figure 3.7 show s a measure of conditional volatility, namely, that 
estimated for each currency assuming it follows a GARCH  process. The underlying idea is that part of 
the volatility can be forecasted, based  on past values of the exchange rate, and firms engaged in trade 
would naturally make an effort to develop su ch a forecast. Figure 3.7 plots the conditional—or 
forecasted—exchange rate volatility (for a description of this methodology, see the Appendix). A comparison with Figure 3.6 shows that this measure is in general somewhat lower than the standard 
                                                      
15 As noted above, the group of transition countries onl y attained its full complement of 22 in 1995, and 
so the ranking is only relevant for the 1990s. 
16 The results for Myanmar in Table 3.3 need to be interpreted with caution, given that the bulk of trade 
appears to occur at the unofficial parallel market rate . Only public sector ente rprises, accounting for about 
30 percent of reported trade, conduct transactions at the official rate. However, the parallel market rate, as 
reported by Reinhart and Rogoff (2002), exhibits somewhat greater volatility than the official rate. 
17 It is also interesting to note that the introduction of EMU in 1999 reduced, but by no means 
eliminated, effective nominal exchange rate volatility of the three G-7 members of EMU. Average nominal 
effective volatility from 1995–1998 before EMU was 1.91, 2.07, 2.34 percent, in France, Germany and Italy, 
respectively, whereas in 1999–2002, average effective nominal volatility was 1.41, 1.68, and 1.63 percent, 
respectively.  WT/WGTDF/W/24 
 Page 19 
 
 
measure, particularly the case for the transition economies in 1995. Figure 3.8 gives the long-run volatility for the G-7 countries. 
54. Up to this point, exchange rates in IFS, i.e., those compiled and reported by the authorities to 
the IMF, have been used in the analysis. Recently, however, attention has focused on the classification 
of exchange rate regimes and the appropriateness of  using these exchange rat es as the basis for such a 
classification. In particular, Reinhart and Rogoff (200 2) have put together an extensive dataset for 153 
countries of monthly market-determined parallel ex change rates going back to 1946. They find 
striking and widespread differences between the official de jure  regime, as reported in the IMF’s 
Annual Report on Exchange Rate Arr angements and Exchange Restrictions  (AREAER ), and that 
implied by the information they gathered on actual de facto  exchange rate practices.
18 As the 
exchange rates reported by Reinhart and Rogoff ma y be more representative of the price of foreign 
exchange at which international trade transactions we re conducted, it would also  appear worthwhile to 
calculate exchange rate volatility us ing these market-determined rates. 
55. In order to compare the volatility implied by both IFS and market-determined rates, it is 
necessary to use the same set of countries. As the usable data for real market-determined rates is 
significantly smaller (107 countries) than what is available for real IFS rates (172), the benchmark 
measure of volatility for the latter had to be recomputed.19 This is shown in Appendix Table III.4, 
where the sample period extends only through 1998 because of data limitations. Comparing the 
benchmark measure of exchange rate  volatility with the same measure, but using the larger sample of 
countries, the evolution of exchange rate volatilit y over time and between major country groupings is 
quite similar. The difference in measured vol atility for the same country group reflects only the 
difference in the sample of countri es and the fact that the variability of the currencies of the countries 
included in the larger sample is not the same as that of the currencies in the smaller sample. 
56. Appendix Table III.5 shows the benchmark me asure of volatility using parallel market 
exchange rates, which can be compared directly with Appendix Table III.4, as both utilize the same 
list of countries. It is immediately clear that, in al most all cases, the volatility of parallel market rates 
is larger than that of IFS rates.20 This is true for advanced countries as well. Even though there are 
unlikely to be significant differences between IFS and market quotations for the bilateral rates 
between advanced countries, there would tend to be much larger differences for the bilateral rates 
between the advanced economies and countries in other groups. The only exceptions occur in 1991, 1992, 1997 and 1998 for transition economies, when movements in IFS rates exceeded those 
in parallel market rates. However, it should be not ed that the difference between the two measures of 
volatility declined from the 1970s to the 1990s for all the country groups except emerging markets, 
where there was a slight increase. This largely re flects the fact that, except for transition economies, 
the effective volatility of the market exchange rate declined between the 1970s and the 1990s, 
whereas the volatility of the IFS rate increased for transition an d developing economies, was about 
unchanged for advanced countries, but d ecreased for emerging market economies. 
57. In comparing the volatility of currencies across count ries, it is relevant to consider the type of 
exchange rate regime, as this w ould likely have a bearing on the degree of variability of a country’s 
                                                      
18 The correspondence between th e official IMF and the Reinha rt/Rogoff “Natural” regime 
classifications is shown in Appendix Table III.6. Also shown are the distributions of the major country groups 
by type of exchange rate regime for the IMF classification (Appendix Table III.7) and for the Natural 
classification (Appendix Table III.8). It should be noted that since 1998, the IMF’s AREAER  reports exchange 
rate classifications that are based on de facto , rather than de jure  exchange rate arrangements. For an analysis 
that applies retroactively to the de facto  classification back to 1990, s ee Bubula and Ötker-Robe (2002). 
19 The list of countries in each group is given in Appendix Table III.3. 
20 The behavior of the two measures of volatility is qu ite different; the average of the simple correlation 
coefficient between the official and th e parallel real exchange rate volatilit y measure for each bilateral exchange 
rate over the entire sample was 0.58 . The correlation coefficient between the two measures of one-year volatility 
in the nominal exchange rate was even lower at 0.45. WT/WGTDF/W/24 
Page 20  
 
currency against other currencies. This is done in Ta ble 3.4, which shows the real effective exchange 
rate volatility across country groupings in terms of both the official IMF exchange rate classification 
as well as the Reinhart-Rogoff Natural classification. It is noteworthy that a currency that is classified 
as “pegged” is by no means insulated from exchange  rate fluctuations. Indeed, the average effective 
volatility of “freely floating” advanced countri es (2.94 percent with the IMF classification and 
3.09 percent with the Natural classification, respectively) is less than  the average volatility of 
“pegged” currencies of other country groups, except for the emerging market countries in the Natural 
classification. Also, looking across types of currency regimes within country groupings, “limited flexibility” confers less exchange rate volatility than “pegged,” except for the advanced countries 
under the Natural classification; and “managed floati ng” is not associated with a great deal more 
volatility than “pegged” regimes. Only “freely floa ting” and “freely falling” regimes have distinctly 
greater average volatility; the latter category in the Natural classification includes those countries that 
had annual inflation rates exceeding 30 percent, wh ich not surprisingly caused considerable exchange 
rate volatility. 
58. Table 3.5 shows how effective volatility has varied  over time by exchange rate regime. Again, 
“limited flexibility” is associated with less variability than a “pegged” regime. If one ignores 
the 1970s, when the major industrial countries were  pegged early in the decade, volatility declined 
from between the 1980s to the 1990s, except in the category “freely floating” in the Natural 
classification. 
IV. NEW EVIDENCE ON THE EFFECT OF EXCHANGE RATE VOLATILITY ON 
TRADE 
59. As discussed in Part II, theoretical models do not point unambiguously to a negative effect of 
exchange rate volatility on trade. Moreover, empi rical analysis in the existing literature has not 
uncovered a strong, causal, and consis tently negative impact. In the empirical analysis we report in 
this section, there is no obvious negative relations hip between aggregate exchange rate volatility and 
aggregate trade. When we turn to bilateral trad e, we do find some evidence that exchange rate 
volatility tends to reduce trade. However, this nega tive effect is not robust to alternative ways of 
controlling for factors that could affect trade. The key findings of our empirical analysis are summarized below, and an Appendix describes the statistical results in more detail. 
60. The objective of our empirical analysis is to ex amine the role of exch ange rate volatility in 
trade in a comprehensive manner. Compared to the existing academic literature and the Fund (1984) 
paper on the topic, the contribution of our analysis  lies in exploring the effect of exchange rate 
volatility on trade along several dimensions: 
 By the type of exchange rate volatility : We examine a range of different exchange rate 
volatility measures – short- and long-run, real and nominal, official, IFS-based and parallel 
market-based, and conditio nal and unconditional.  
 
 By country group : We test if the impact of exchange rate volatility differs across country 
groupings, including industrial a nd developing countries.  
 
 By the type of trade : We examine the impact not only on aggregate but also on sectoral trade, 
which allows us to test if the effect of ex change rate volatility varies in direction and 
magnitude across different types of goods. The role of exchange rate volatility has not yet 
been explored extensively us ing disaggregated trade data. 
 
61. 
In addition to the disaggregation of the volatilit y effect, we test its robustness to alternative 
ways of controlling for joint causality between trade and exchange rates and fo r trade-related factors 
other than exchange rate volatility. Finally, while our focus is on excha nge rate volatility, we take this  WT/WGTDF/W/24 
 Page 21 
 
 
opportunity to revisit a related topic – the role of  a common currency in enhancing trade flows – and 
explore the robustness of the finding by Rose (2002) that this positive effect is very large. 
A. AGGREGATE VOLATILITY AND AGGREGATE TRADE – A FIRST LOOK 
62. It is instructive to look at the time paths of  world trade and exchange rate volatility, and 
examine if there is any obvious negative associati on between the two. Figure 4.1 shows the evolution 
of world trade since 1970 together with the averag e real effective volatility for all countries in the 
sample. There is a clear bulge in exchange rate  volatility from 1989 to 1993, which reflects the large 
fluctuations in the currencies of a number of tr ansition economies during this  period in the aftermath 
of the breakup of the Soviet Union.21 If one excludes transition econom ies from the measure of world 
currency volatility, the bulge disappears. What one then sees is an upward trend in average volatility 
from the early 1970s through the end of the 1980s, but  a general moderation in the overall level of 
currency volatility since then.  
63. In comparison, the world trade has increased steadily since 1970 and the growth rate is much 
more smooth than that of the exchange rate vola tility. Looking at the movement of world trade and 
aggregate volatility over time, there does not appear  to be any clear relationship between them. 
Therefore, at the aggregate level, there is no evid ence of a negative effect of real exchange rate 
volatility on trade. 
64. It may be useful to examine the relationship between the two by breaking down the sample by 
major country groups (Figure 4.2) and developing countries by geographic region (Figure 4.3) and by 
type of export (Figure 4.4). In some of the sub-samp les and for some of the y ears, there appears to be 
a negative association between exchange rate vola tility and the level of trade in certain country 
groupings. This is most evident in the case of tr ansition economies in 1990-1994 (lower left graph of 
Figure 4.2), the Asian crisis in 1997-1998 (upper right graph of Figure 4.3), and non-fuel primary 
product exporting economies in the early 1980s (lower  graph of Figure 4.4). However, this negative 
association may not reflect a causal relationship, but rather is a manifestation of the effects of a 
common set of factors that both raises currency vol atility and reduces trade. For example, the Asian 
crisis led to a large decline in the imports of the affected countries and major movements in their 
exchange rates, but the fall in domestic demand was the most important factor reducing import 
volumes, not currency volatility. Similarly, the br eakup of the Soviet Union caused widespread 
dislocations in many transition economies, resulting in  substantial falls in output and trade, and huge 
changes in many exchange rates that were part and parcel of the transition process. 
65. In order to estimate the specific impact of ex change rate volatility on trade flows, it is 
necessary to take account of the separate effects of th e myriad of factors that determine the level of 
exports and imports. In what follows, we move aw ay from aggregate trade and discuss a methodology 
that exploits the much richer variations in the data  on bilateral trade and bilateral exchange rates that 
permits identifying the distinct contribution of volatility on trade. 
B. THE CONCEPTUAL FRAMEWORK FOR THE ANALYSIS OF THE VOLATILITY EFFECT IN TRADE  
66. To investigate the effect of exchange rate volatility on trade, there are several “building 
blocks” to consider. First, there are factors other than  exchange rate volatility that affect trade and it is 
important to account for them in a way that is cons istent with economic theory. Otherwise, one runs 
the risk of mis-attributing the effect of these othe r factors to exchange rate volatility. Second, the 
measure of exchange rate volatilit y should be conceptually reasonable. Third, it may be useful to 
                                                      
21 Data for transition countries ar e not reported before 1988 because most of these countries did not 
exist before 1991. Data are available only for Yugoslavia from 1970 and for Hungary beginning in 1976. 
However, the effective volatilities for the major country  groupings shown in Figures 4.2-4.4 do include the 
available bilateral exchange rate data for transiti on countries, weighted by appropriate trade shares. WT/WGTDF/W/24 
Page 22  
 
allow exchange rate volatility to have different effect s on different types of trade or trade in different 
country groupings. We explain these building blocks in turn. 
67. As part of the first building block, we seek to  account for the determinants of trade patterns 
other than exchange rate volatility in a modified gravity model. This model relates trade between a 
given pair of countries to characteristics of each of  these two countries and the characteristics of their 
relationship. The characteristics that are most important—which the model owes its name to—are the 
economic mass (i.e., GDP) and the distance between  the countries. In addition, the empirical 
specifications of the gravity model typically contro l for other factors augmenting or reducing trade, 
such as land areas, cultural similarity, geographical position, historical links, and preferential trading 
arrangements, all of which tend to affect the transa ction costs relevant for bilateral trade and have 
been found to be statistically significant determinan ts of trade in various empirical applications. The 
model also typically controls for the level of ec onomic development, which is expected to have a 
positive effect on trade, as more developed countries tend to specialize and trade more (e.g., Frankel 
and Wei, 1993). The gravity model has been empirica lly successful in terms of its ability to explain a 
large part of the variations in the observed trade pa tterns. It also has the merit of being grounded in 
international trade theories, ranging from those ba sed on country differences in factor endowments or 
technology to models of increasing returns to scale and monopolistic competition. 
68. A relatively recent development in the theoreti cal foundation of the gravity model emphasizes 
“remoteness” or “multilateral resi stance” effects. The se effects were proposed by Anderson and van 
Wincoop (2003) and are defined as a function of unobservable equilibrium price indices, which 
depend on bilateral trade barriers and income shares of all the trading partners. In other words, the 
“multilateral resistance” effects are catch-all e xpressions that summarize the effects on a given 
bilateral trade from differential, possibly unobserved,  trade costs between this country pair and all 
other trading partners. The gravity equation can then  be interpreted as indicating that bilateral trade 
depends on the bilateral trade barrier between th e two countries in question, relative to the two 
countries’ multilateral resistance indices: for a given bilateral trade barrier between the two countries, 
higher barriers between them and their other tradi ng partners would reduce the relative price of goods 
traded between them, raising bilateral trade. In empirical applications, the multilateral resistance 
indices can be conveniently proxied by country effect s (fixed or time varying). We also include time 
effects in the model to control for time-specific factors such as world business cycles, global shocks, 
etc.  
69. The second building block is the measure of exch ange rate volatility. In the benchmark model 
we focus on the long-run measure of IFS-based real exchange rate vola tility. Its value in any given 
year t is calculated as the standard deviation of the first-difference of the monthly natural logarithm of 
the bilateral real exchange rate in the five years preceding year t, which is a conventional measure 
most commonly used in the current literature on the subject. To check the robustness of results, we 
examine alternative, yet analogously calculated measures of exchange rate volatility: long-run IFS-
based nominal exchange rate vola tility, short-run, contemporaneous IFS-based real and nominal 
exchange rate volatility, and the short- and long-run volatility of real parallel market rates using data 
from Reinhart and Rogoff (2002). As  additional robustness analysis, we also consider the conditional 
volatilities of real exchange rates estimated using a GARCH (1, 1) model. To ensure the stationarity 
of the GARCH model, we exclude countries with hyper-inflation episodes, extreme exchange rate 
fluctuations and/or incomplete data, focusing our  estimation on 124 industrial, developing, emerging 
and transition economies. 
70. The third building block for the model is the c onsideration of different country groups and 
different types of trade. We analyze the exchange  rate volatility effect separately for industrial 
countries and developing countries. We also examine how the exchange rate volatility effect depends 
on the type of product trade – differentiate d or homogeneous. In classifying products into 
differentiated and homogenous varie ties, we follow the strategy in Rauch (1999). Conceptually, 
Rauch first identifies two types of homogenous pr oducts: those traded on an organized exchange  WT/WGTDF/W/24 
 Page 23 
 
 
(“commodities”), and those whose prices are reporte d regularly in a professional trade publication 
(“referenced price products”). All other products are then defined as differentiated products. 
C. WHAT DO THE DATA TELL US? 
71. The gravity model performs well empirically , yielding precise and generally reasonable 
estimates. The coefficient on distance is negative and statistically significant, while the coefficient on 
the economic mass is positive and statistically significa nt. Most other control variables are also mostly 
significant and have the expected signs.  
72. Does exchange rate volatility hamper trade?  As a benchmark specification using country 
and time fixed effects, we find that the long-run real exchange rate volatility has a statistically 
significant negative effect on trade (Table 4.1, Colu mn 1 Row 1). If exchange rate volatility were to 
rise by one standard deviation (from 0.12 to 0.15, fo r example, in our sample), trade would fall by 
about 7 percent (Table 4.1, Column 2 Row 1).22 This effect is comparable to the estimates found by 
previous studies, e.g., Rose (2000) and Tenreyro (2003).  
Table 4.1 Effect of Long-Run Real Exchange Rate Volatility on Aggregate Trade1/ 
 
 
 
Model specification Not Controlling for 
Joint Causality 
between Trade and 
Exchange Rates Implied Percentage 
Change in Trade by 
One Standard 
Deviation Increase in 
Volatility Controlling for Joint 
Causality between 
Trade and Exchange 
Rates Implied Percentage 
Change in Trade by 
One Standard 
Deviation Increase in 
Volatility 
 (1) (2) (3) (4) 
With Country and Time Fixed 
Effects -2.37* 
(0.67) 
 -6.64% -22.64* 
(12.50) -63.39% 
 
With Country Pair and Time Fixed Effects 
 -2.40* 
(0.47) -6.72% -6.49 
(6.24) -18.17% 
With Time-Varying Country 
Effects 
 2.89* 
(1.78) 8.09% -23.82 
(28.87) -66.70% 
With Country and Time Fixed 
Effects on the Full Sample 
 -1.16* 
(0.22) -8.82% - - 
 
1/Standard errors are indicated in parentheses.  An asterisk de notes significance at the 90 per cent level or higher.  For details , see Appendix 
Tables IV.2 and IV.6A 
 
73. Is the negative effect on trade robust to alternative ways of controlling for factors other 
than bilateral exchange rate volatility?  The answer is “No.” On the one hand, a negative effect is 
still observed when we control for unobservable cultu ral, economic, historical , geographical and other 
factors specific to a given pair of countries rather than individual countries (Table 4.1, Column 1 
Row 2). On the other hand, no negative effect is f ound when we allow country-specific effects to vary 
over time, as appears justified theoretically, give n the dynamic nature of “multilateral resistance.” 
Indeed, this specification could result in some cases  in even a positive coefficient (e.g., Table 4.1, 
Column 1 Row 3) . While this does not necessarily  imply that volatility promotes trade, it suggests 
that the finding of a negative effect of ex change rate volatility on trade is not robust. 
74. A note of caution is in order here. Recent de velopments in the theoretical foundation of a 
gravity specification suggest that it is important to include time-varying country fixed effects in order 
to fully absorb the “multilateral re sistance” effects. Otherwise, one might mis-attribute to exchange 
rate volatility those effects on bilateral trade that shoul d have been attributed to other factors. At the 
same time, we note that part of the forces underlinin g bilateral exchange rate  volatility is time-varying 
                                                      
22 This impact is computed as th e estimated coefficient in the regression equation multiplied by one 
standard deviation of the volatility measure, multiplied by 100 to convert to percent. WT/WGTDF/W/24 
Page 24  
 
and country-specific. The inclusion of the time-varyi ng country fixed effects could also “overcorrect.” 
For example, an unexpected increase in one country 's money supply could raise all the bilateral 
exchange rate volatility involving that country. Even if this increase in vola tility depresses all bilateral 
trade involving that country, a specification that cont rols for that country's time-varying fixed effects 
would not be able to reveal a negative effect of exch ange rate volatility on trade. We have to keep this 
qualification in mind in interpreting the result. 
75. Sorting out causality . To the extent that countries implement policies aimed at lowering 
exchange rate volatility in order to  increase bilateral trade, the mode l considered so far would suffer 
from an endogeneity bias. We control for this possib ility using two instrumental variable approaches: 
(i) that proposed by Frankel and Wei (1993), whereb y the volatility in the relative quantity of money 
is used as an instrumental variable  for exchange rate volatility, and (ii) that implemented by Tenreyro 
(2003) which relates the exchange rate volatility to  the propensity of countries to adopt a common 
currency anchor. Neither of these approaches is perf ect and each has its advantages: the Frankel-Wei 
approach appeals to the monetary theory of exchange rate determination, and is simple and easy to 
implement, while the Tenreyro approach appeals to  the optimal currency framework as described in 
Alesina, Barro, and Tenreyro (2002).  There is no significant effect of exchange rate volatility on trade 
in the models with country-pair and time-varying country effects (Table 4.1, Column 3 Row 2 and 
Column 3 Row 3). However, the negative volatility effect found in the model with constant country 
effects survives (Table 4.1, Column 3 Row 1). 
76. Would the conclusion change when one employs alternative measures of volatility?  The 
short answer is “no.” Table 4.2 reports results from the same regression which includes our standard 
long-run measure together with all three altern ative measures of exchange rate volatility (as 
differences from the long-run real IFS-based measure). The short-run vol atility in the real exchange 
rate appears to discourage trade, albeit to a smalle r extent than the long-run volatility. The volatility in 
the parallel market exchange rates has a si milar effect on trade as the volatility in the IFS-reported 
exchange rates, but only in the long run. As s hown in Appendix Tables IV.3  and IV.4 the volatilities 
of the nominal and real exchange rates are highly co rrelated and thus have similar effects on trade. In 
addition, conditioning the measure of  exchange rate volatility on historical information using the 
GARCH approach, instead of using the simple st atistical measure of volatility, also preserves the 
negative relationship with trade. As in Table 4. 1, when time-varying country fixed effects are 
controlled for, there is no longer evidence of a ne gative and significant association between volatility 
and trade. 
Table 4.2 Alternative Measures of Exchange Rate Volatility
1/ 
 
 
 
Model specification Long-Run Real 
Exchange Rate 
Volatility Short-Run Real 
Exchange Rate 
Volatility2/ Long-Run Real Parallel 
Market Exchange Rate 
Volatility2/ Short-Run Real Parallel 
Market Exchange Rate  
Volatility2/ 
 (1) (2) (3) (4) 
With Country and Time Fixed Effects 
 -3.92* 
(1.3) -2.72* 
(1.04) -1.20* 
(0.63) -0.55 
(0.73) 
Implied Percentage Change in Trade by One 
Standard Deviation Increase in Volatility -10.98% 
  -6.80% -6.48% -2.15% 
With Country Pair and Time Fixed Effects 
 
 -4.72* 
(0.76) -4.15* 
(0.55) -0.42 
(0.34) -1.14* 
(0.41) 
Implied Percentage Change in Trade by One 
Standard Deviation Increase in Volatility  -13.22% -10.38% -2.27% -4.45% 
With Time-Varying Country Effects 
 
 7.52* 
(3.89) 6.70* 
(3.24) -2.20 
(2.71) -1.55 
(2.8) 
Implied Percentage Change in Trade by One Standard Deviation Increase in Volatility  21.06% 16.75% -11.88% -6.05% 
1/Standard errors are indicated in parenthese s.  An asterisk denotes significance at th e 90 percent level or higher.  For details , refer to 
Appendix Tables IV.3, IV.4 and IV.5. 
2/In excess of long-run real offi cial exchange rate volatility.  WT/WGTDF/W/24 
 Page 25 
 
 
 
77. Does exchange rate volatility have a different effect on trade in differentiated or 
homogeneous products?  Recent developments in the economics of trade suggest that a given 
increase in transaction costs (of which exchange rate  volatility is a component) could have a larger, 
negative effect on trade in differentiated products than on trade in homogenous products. But, as with 
aggregate trade, the estimation results show that this  theoretical prior is not robust. When we control 
for country and time effects separately, exchange ra te volatility indeed has a negative effect on trade 
in differentiated products, but not on trade in homoge nous products (Table 4.3, Column 1). However, 
when we include time-varying country fixed effects (Table 4.3, Column 3), the conclusion is overturned, as in the aggregate trade model.  
 
Table 4.3 Effect of Exchange  Rate Volatility on Trade in  Different Types of Products1/ 
 
 
Model specification With Country and Time 
Effects Implied Percentage 
Change in Trade 
by One Standard 
Deviation Increase 
in Volatility With Time-Varying 
Country Effects Implied Percentage 
Change in Trade by 
One Standard Deviation 
Increase in Volatility 
 (1) (2) (3) (4) 
 
Trade in Homogeneous Products 
  
-0.59 
(2.12)  
-1.65% 
  
-2.98 
(4.39)  
-8.32% 
 Trade in Differentiated 
Products  
-2.89* 
(1.66)  
-8.09%  
0.98 
(3.06)  
2.74% 
 
 
 
1/Standard errors are indicated in parenthese s.  An asterisk denotes significance at th e 90 percent level or higher.  For details , refer to 
Appendix Table IV.8  
 
78. Do members in currency unions trade more?  Our core results confirm Rose’s (2000) 
finding that common currency arrangements triple tr ade. The trade-enhancing benefits of currency 
unions apparently by far exceed gains from a reduc tion in exchange rate volatility and are preserved 
over time (Appendix Tables IV.2 and IV.3). They are also robust to controlling for time-varying 
country effects, but break down in a model with country-pair fixed effects (Appendix Table IV.4). 
This suggests that currency union membership may be correlated with other country-pair 
characteristics. Once these characteristics are contro lled for by the inclusion of country-pair fixed 
effects, there is no additional trade-promoting effect from currency unions.  
79. Does the volatility effect differ across country groups? In principle, the effects could be 
different. Foreign exchange markets are typically  less developed and less liquid in developing 
countries, limiting firms’ opportunities for hedging fo reign exchange risk. Indeed, we do find that 
volatile exchange rates are more likely to be associ ated with smaller trade of developing countries 
than for trade among advanced economies in the speci fication with country fixed effects. However, 
the negative effect disappears for both country groups when county effects are time varying 
(Appendix Table IV.7). As hedging instruments are more readily available for the currencies of 
industrial countries, one might expect that their trade would be less affected by exchange rate 
volatility. However, Wei (1999) finds  little support for the hypothesis that the growing availability of 
hedging instruments is responsible for th e small impact of volatility on trade. 
80. On balance, for both aggregate and disaggregated  trade, there is empirical evidence pointing 
to a generally small negative effect of exchange rate volatility on trade. But this evidence is not 
overwhelming and not robust across different empirical specifications. WT/WGTDF/W/24 
Page 26  
 
V. SUMMARY AND CONCLUDING REMARKS 
81. This study has provided a much more comprehens ive analysis of exchange rate volatility and 
trade than the previous IMF (1984) analysis. It has examined exchange rate variability over the past 
thirty years for all countries for wh ich data are available and has employed state-of the-art statistical 
techniques to test the natural presumption that volatility in exchange ra tes reduces the level of 
international trade.  
82. In terms of observed variability, the analysis here  shows that while exchange rate fluctuations 
have increased in times of currency and balance of payments crises, there has been no clear increase 
in exchange rate volatility, on average, between th e 1970s and the 1990s. It is not surprising that the 
currencies of the advanced economies have had lo wer average volatility than other country groups. 
Nonetheless, many transition, emerging market, an d developing countries have recently exhibited 
exchange rate variability on a par, or clo se to that of many advanced economies.  
83. In terms of the impact of exchange rate vola tility on trade flows, the current study does not 
find a robustly negative effect. To be more precise, th e study reports some evidence that is consistent 
with a negative effect of volatility on trade. Howe ver, such a relationship is not robust to certain 
reasonable perturbation of the specification. Specifi cally, when time-varying country fixed effects are 
allowed, which are suggested by recent theoretical  work on the gravity model specification, the 
analysis does not reveal a negative asso ciation between volatility and trade. 
 84. The lack of a robustly negative impact of ex change rate volatility on trade may well reflect 
the ambiguity of the theoretical results in the general equilibrium  models. These models show that 
exchange rate variability is the result of the volatility of the underlying shocks to technology, 
preferences, and policies, for example, as well as the overall policy regime. Changes in the volatility 
of the exchange rate may reflect changes in the volatility of the unde rlying shocks and/or changes in 
the policy regime. For example, trade liberalization undertaken together with a move to greater exchange rate flexibility could well be associated  with increased trade flows as well as increased 
exchange rate volatility. This possi bility is a reason for the ambiguity of the theoretical results as well 
as the difficulty in finding consistent and robust empi rical results regarding the impact of volatility on 
trade. An additional implication is that the empi rical results do not provide clear policy guidance. 
Even if it were the case that such volatility is asso ciated with reduced trade flows, this does not 
necessarily mean that trade would expand if the author ities stabilized the exchange rate in the face of 
shocks that occur. 
85. These considerations suggest that there do not app ear to be strong grounds to take measures to 
reduce exchange rate movements from the perspective of promoting trade flows. Note that this does 
not rule out the possibility that exchange rate fluctuations can affect an economy through other 
channels. For example, currency crises – special cases of exchange rate volatility - have required 
painful adjustments in output and consumption. Howe ver, in this case, appropriate policies are those 
that help to avoid the underlying causes of large and unpredictable movements in exchange rates, 
rather than measures to moderate currency fluctua tions directly for the purpose of enhancing trade.  WT/WGTDF/W/24 
 Page 27 
 
 
REFERENCES 
 
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VI. APPENDIX 
A. D
O COUNTRIES WITH STABLE EXCHANGE RATES AND COMMON CURRENCY TRADE MORE? 
AN EMPIRICAL ANALYSIS  
1. While there is some evidence of a negative effect  of exchange rate volatility on trade, it is not 
robust to certain specifications. This is true both for aggregate trade as well as for trade in 
homogeneous and differentia ted products separately. Therefore, the overall message from the 
empirical analysis is that, if the exchange rate vol atility depresses trade, the effect is unlikely to be 
quantitatively large. Below we discuss the b asis for these conclusions in more detail. 
1. The Gravity Model 
2. The empirical analysis in this study is based  on the standard “gravity” framework, whereby 
trade between two countries is modeled as a functio n of incomes (economic mass) of these countries 
and distance between them. It has proved to be robu st and successful in a wide variety of empirical 
applications. Moreover, the gravity model has strong foundations in international trade theories, from 
those based on country differences in factor e ndowments or technology to models of increasing 
returns to scale and monopolistic competition. Enteri ng incomes in the product form is theoretically 
well-established in the trade literature.23 
3. Besides the economic mass and distance, the empirical specifications of the gravity model 
typically control for other factors augmenting or redu cing trade, such as land areas, cultural similarity, 
geographical position, historical links, and preferen tial trading arrangements, all of which tend to 
affect the transaction costs relevant for bilatera l trade and have been found to be statistically 
significant determinants of trade in various empirica l applications. The model also typically controls 
for the level of economic development, which is ex pected to have a positive effect on trade, as more 
developed countries tend to specialize and trade more . With all of these by now fairly standard 
explanatory variables included in the gravity equatio n as controls, the focus of interest here is on the 
introduction of alternative measures of exchange rate variability to see to what extent this particular 
variable may affect transaction costs and thereby aff ect the level of bilateral trade between two trading 
partners. 
4. To control for “remoteness” or “multilateral r esistance” effects, we include country-specific 
fixed effects in the model. The concept of “mu ltilateral resistance” was proposed by Anderson and 
Van Wincoop (2003) and is defined as a function of unobservable equilibrium price indices, which 
depend on all bilateral trade barriers and income sh ares of the trading partners. The gravity equation 
can then be interpreted as indicating that bilatera l trade depends on the bilate ral trade barriers between 
the two countries in question, relative to the product of their multilateral resistance indices: for a given 
bilateral trade barrier between the two countries, hi gher barriers between them and their other trading 
partners would reduce the relative price of goods tr aded between them, raising bilateral trade. In 
empirical applications, the multilateral resistance  indices can be conveniently proxied by country-
specific fixed effects. We also include time effects in the model to control for time-specific factors 
such as global business cycles, oil price shocks, etc., so  that the intercept in our model is allowed to 
change both across countries and over time. In add ition, we experiment with including time-varying 
country fixed effects, which are more general th an including time dummies and country fixed effects 
separately. The time-varying country fixed effects ar e arguably more consistent with the notion of a 
time-varying multilateral resistance emphasized in r ecent trade theories. At the same time, we note 
that part of the forces underlying bilateral exch ange rate volatility is time-varying and country-
specific. The inclusion of the time-varying country  fixed effects could also “overcorrect.” For 
example, an unexpected increase in one country's m oney supply could raise all the bilateral exchange 
                                                      
23 See Anderson (1979), Helpman and Krugman (1985), Bergstrand (1985), Deardorff (1998), and 
Anderson and van Wincoop (2003).   WT/WGTDF/W/24 
 Page 33 
 
 
rate volatility involving that country. Even if th is increase in volatility depresses all bilateral trade 
involving that country, a specification that controls for that country's time-varying fixed effects would not be able to capture a negative effect of excha nge rate volatility on trade. We have to keep this 
qualification in mind in interpreting the result. 
5. It might be useful to note that up to very r ecently, the literature that fits a gravity model to 
trade data seldom included any type of country fixed effects. It is still rarer to include time-varying 
country fixed effects. Augmenting the empirical tr ade equation with various kinds of fixed effects 
may be considered one of the value-added of this paper from a methodological point of view. 
(a) Aggregate Trade 
6. The benchmark panel specification for the analysis  of aggregate trade is similar to that used 
by Rose (2002). We estimate the model using ordinary  least squares with robust standard errors, based 
on the log-linear transformation: 
,18 17 16 15 1413 12 11 10 9 8 76 5 4 3 2 1 0
ijt ijt ijt ijt ijt ijij ijt ij ij ij ij ijijt ijt ij ij ijt ijt ijt
te fe bothin onein gsp fta comctrycolony curcol comcol border landl island comlangcustrict lreal ldist lareap lrgdppc lrgdp ltrade
              
              
 
 
where ltrade ijt denotes the logarithm of the real value of aggregate bilateral trade between country i 
and j at time t; lrgdp ijt is the logarithm of the product of real GDPs of countries i and j at time t; 
lrgdppc ijt is the logarithm of the product of real GDP per capita of countries i and j at time t; lareap ij is 
the logarithm of the product of the land areas of countries i and j; ldist ij is the logarithm of distance 
between i and j; lreal ijt is the long-run real IFS-based measure of volatility in the bilateral exchange 
rate of countries i and j at time  t; and custrict ijt is a dummy variable taking the value of 1 if countries i 
and j share a common currency at time t, and zero otherwise. The coefficients of interest are those on 
the measure of exchange rate volatility, lreal ijt, and the currency union dummy, custrict ijt. 
 7. Other variables control for various cultural, geographical, and historical factors: comlang
ij is a 
dummy taking the value of 1 if i and j have a common language; island ij is the number of islands and 
landl ij is the number of landlocked countries in the country pair; border ij is a dummy taking a value of 
1 when  i and j share a common border; comcol ij is a dummy taking a value of 1 if after 1945 i and j 
were colonies with the same colonizer; curcol ijt is a dummy taking the value of 1 if  i was a colony of j 
at time t, or vice versa; colony ijt is a dummy taking a value of 1 if  i ever colonized j, or vice versa; and 
comctry ijt is a dummy taking a value of 1 if  i and j belong to the same nation. 
8. There are also several controls for trade policy factors:24 ftaijt is a dummy variable if i and j 
are members in the same regional trading arrangement; gsp ijt is a dummy taking the value of 1 if  i 
were a Generalized System of Preferences beneficiary of j or vice versa at time t.; and onein ijt and 
bothin ijt are dummies taking a value of 1 if either i or j, or both were members of GATT/WTO at time 
t, respectively. Finally, the vectors fe and te denote country- and year-specific dummies. The error 
term εijt is assumed to be well-behaved. 
(b) Disaggregated Trade 
9. For the analysis of disaggregated trade, we c onsider a system of two equations separately for 
trade in differentiated products and in homogenous products, which ar e estimated by the Seemingly 
Unrelated Regressions (SUR) technique. This specification allows the parameters on the same 
                                                      
24 Bilateral tariff and non-tariff barriers are excluded from the model due to unavailability of data. WT/WGTDF/W/24 
Page 34  
 
variables to be different for different types of trad e, while the error terms for a given country pair are 
correlated in the two equations.  
10. A few comments are need ed to motivate the less familiar an alysis of disaggregated trade. 
Higher exchange rate volatility can be viewed as an  increase in a type of transaction costs in 
international trade. More concretely, it may add noise to the price signal and hence make it more 
difficult and more costly for buyers and sellers in th e international market to find the right match for 
trading goods. However, a given increase in search costs could play a different role in the overall 
transaction costs for trade in homogeneous pro ducts versus in differentiated products. For 
homogeneous products such as wheat, an importer is not concerned with who the producer is, as the 
products are easily comparable, and price is the primary decision factor. On the other hand, 
heterogeneous products, such as digital cameras or tennis shoes tend to be “branded,” as there are 
additional characteristics other than price that woul d affect importer’s purchase decision. For even 
more differentiated products, such as machine tools, price would also not necessarily be the key factor 
affecting the purchase decision. 
11. Noting the difference in search costs in intern ational trade in these two types of goods, Rauch 
(1999) presented some evidence suggesting that a given increase in transaction costs has a bigger 
negative effect on the volume of trade in diffe rentiated products than in homogeneous products. 
However, he did not look into the effect of excha nge rate volatility on trade. Extending his logic, one 
might hypothesize that a given increment in excha nge rate volatility would also dampen trade in 
differentiated products more than trade in homogenous products. A recent paper by Broda and 
Romalis (2003) contains a theoretical model that assumes  (as opposed to derives ) this difference in 
the effects of exchange rate volatility. The author s also report some empirical evidence demonstrating 
that exchange rate volatility deters trade in diffe rentiated products more th an trade in homogenous 
products. However, their regression specification does not include as control variables most of the 
usual country-pair characteristics described above. Given that many developing countries are striving 
to move towards producing and exporting more differe ntiated products, it is interesting to test this 
hypothesis using a regression specification similar to th at used for the analysis of aggregate trade. 
2. Data and Sources 
(a) Aggregate Trade 
12. Estimating the aggregate trade model requires data on bilateral total trade, incomes, 
population, distance, as well as geographical, cultural, and historical information. The study uses a panel data set which covers 178 Fund member  countries every fifth year from 1975 to 2000.
25 
Summary statistics and correlations for the datase t are presented in Appendix Table IV.1A and B. The 
list of countries in the sample is presented in Appendix Table III.1.  
13. Our data set is an updated version of Rose’s (2002) data set.26 To extend the data to 2000, we 
construct bilateral trade data series exactly follo wing Rose’s (2002) study: bilateral merchandise trade 
data are from the IMF’s Direction of Trade Statistics . Bilateral trade is measured in U.S. dollars, as 
total trade (exports plus imports) between the two countries in question, deflated by U.S. CPI (1982-
                                                      
25 A key regressor in our analysis is long-run exchange rate volatility, which is constructed from five-
year intervals.  When trade is sampled every fifth year , exchange rate volatility can then be constructed from 
non-overlapping five-year periods. 
26 We thank Andy Rose for making his data set available on his web-site. Rose (2002) describes the 
data set in detail, and we will only highlight a few pertinent data issues here.  WT/WGTDF/W/24 
 Page 35 
 
 
1983 prices) for urban areas (available from www. freelunch.com). Real GDP and population data 
come exclusively from the World Bank's World Development Indicators  (WDI). 27 
14. In the benchmark model we fo cus on the long-run measure of IFS-based real exchange rate 
volatility calculated as the standard deviation of the first-difference of the monthly natural logarithm 
of the bilateral real exchange rate in the five years preceding year t. The monthly bilateral exchange 
rates are obtained from the IFS. To obtain exchange rates for each EMU member currency for the 
years 1999-2000, the euro exchange rates were conve rted using the irrevocably fixed conversion rates 
obtained from the official ECB website. Real exchange  rates are constructed by  using consumer prices 
from IFS. 
15. To check the robustness of results, we examin e alternative, yet analogously calculated 
measures of exchange rate volatility: long-run IFS-based nominal exchange ra te volatility; short-run, 
contemporaneous IFS-based real and nominal exchange rate  volatility, and short-run and long-run 
volatility of real parallel market rates, data fo r which come from Reinhart and Rogoff (2002). For 
more details on these measures of exchange rate volatility, see Part III of the study.  
16. As part of the robustness analysis, we also consider the conditional volatilities of the 
exchange rates estimated using a GARCH (1, 1).28 The underlying equation for the model is an 
ARIMA(0, 1, 0) process of the exchange rates (in the logarithmic form), which implies that the log 
difference of the exchange rates is a random walk with drift. This model yields an estimate of 
volatility which is the standard deviation of the e rror term in the underlyi ng equation conditional upon 
historical information from all previous months in  the five-year period. We use the last estimated 
conditional standard deviation of each country pair  as the approximation of the conditional volatility 
at the beginning of next period. For example, the conditional volatility of 1975 equals the estimated 
conditional standard deviation for December 1974 in the GARCH regressions. 
17. We run the GARCH regressions on the monthly ex change rates for six fi ve-year panels, with 
the first one being 1970-1974 and th e last one being 1995-1999. In each five-year panel, exchange 
rate data are further grouped into three categories,  those of developed country  pairs, of developing 
country pairs, and of country pairs between devel oped and developing countries, which bring the total 
number of GARCH regressions to 15. To ensure that the estimated coefficients satisfy the stationarity 
conditions,29 we exclude countries with hyper-inflation ep isodes and countries with extreme exchange 
rate fluctuations, defined as the change in the l og exchange rate in absolute value in any month 
exceeding a threshold of 1, or 1|)] log([| t exrt d . The threshold amounts to a monthly appreciation 
over 170 percent or a monthly depreciation over 60 percent.30 In addition, we require that in each 
                                                      
27 In contrast, Andrew Rose used several sources: WDI, Penn World Tables, and International 
Financial Statistics . WTO and FTA dummies for 2000 are extended based on the information available from the 
WTO official web site (www.wto.org). 
28 As an alternative to estimating volatility usi ng parametric models su ch as GARCH, Andersen, et al. 
(2001) propose examining the realized  volatility directly, which has the advantage of being model-independent. 
This approach, however, is very data intensive and thus cannot be implemented in our study. 
29 The estimated coefficients of the regressi ons ensure that all time-varying variance ( t2 ) processes 
are stable.  Furthermore, the results of nine regression s satisfy the sufficient conditions that will guarantee the 
GARCH processes to be covariance stationary (see Greene 2000, p. 802).   
30 The following countries are ex cluded from all regressions: Angola, Argentina, Bolivia, Brazil, 
Nicaragua, Congo, Dem Rep of, Congo, Republic of, Armenia, Azerbaijan, Tajikistan, Ukraine, Turkmenistan, Yugoslavia, Israel, Chile, Mexico, Ghana, Uganda, Peru, Dominican Republic, Zambia, Honduras, Suriname, 
Iran, Sudan, Bulgaria, Lithuania, Romania, Nigeria. For regressions using 1995-1999 data, five more countries 
are excluded.  These countries are: Sri Lanka, Indonesia, Belarus, Sierra Leone, Venezuela. WT/WGTDF/W/24 
Page 36  
 
panel the series length of the exchange rate for each c ountry pair to be greater or equal than 30. This 
process renders 124 countries in total for the estim ation sample, which we use throughout the study.
31  
(b) Disaggregated Trade 
18. For disaggregated trade, data on the value of bilateral imports for 98 industries are obtained 
from the United Nations’ COMTRADE  database and cover 39  (Appendix Table IV.9.)  countries 
during the period from  1975 to 2000.32 Import data are disaggregated at the SITC-4 level (rev. 1) and 
are deflated by US urban CPI (1982-1984 prices).  
19. In classifying products into differentiated and ho mogenous varieties, we follow the strategy in 
Rauch (1999). Conceptually, Rauch first identifi es two types of homogenous products: those traded 
on an organized exchange (“co mmodities”), and those whose prices are reported regularly in a 
professional trade publication (“referenced price pr oducts”). All other products are then defined as 
differentiated products. Rauch im plemented the classification on SITC rev. 2 industries.  
20. There were instances when th e classification for a given product was ambiguous. Hence, 
Rauch produced two separate classification systems, one (“conservative aggregation”, in his words) 
attributed all ambiguous products to  the homogeneous category, and the other (“liberal aggregation”) 
attributed all ambiguous products to the differen tiated category. Rauch (1999) provides an appendix 
that lists the classification results of all SITC rev. 2 industries at the 4-digit level. We use a 
concordance from SITC rev1. to SITC rev. 2 (a vailable from www.nber.org) and then apply the 
Rauch classification to our data. To minimize the impact of mis-classified products on our 
conclusions, we exclude all products whose classi fication is ambiguous and only work with those 
products whose degree of classification is relativel y clear. That leaves 81 industries for which the 
classification is relatively unambiguous, 22 of them being classified as homogeneous and the 
remaining 59 as differentiated products. Th e classification lists are presented in 
Appendix Table IV.10.  
21. To obtain the series for bilateral imports of homogeneous products, we  sum sectoral import 
data across all sectors classified as homogeneous for a given country pair in a given year. Bilateral imports of differentiated products are constructed similarly. GDP  and GDP per capita data are from 
the World Bank Indicators (WDI) database. All other variables are from our aggregate trade data set described above. 
22. Appendix Table IV.1C presents some summary statistics on the two types of products in our 
sample over the years. As one can see, the total va lue of trade in differentiated products has been 
more than twice that of trade in homogeneous produc ts in our sample. There is a modest increase in 
the share of trade in differentiated products in to tal trade in our sample, from 75 percent in 1975 to 
83 percent in 2000. 
3. Key Findings 
23. The gravity model performs well empirically , yielding precise and generally reasonable 
estimates (Appendix Table IV.2), which are broadly consistent with the results of other papers 
employing a gravity model using trade data. The co efficient on distance is negative and statistically 
significant, ranging around -1.50 across the different variations of the model. The coefficient on the 
economic mass is positive and generall y statistically significant, ranging from 0.83 in the model with 
time-varying fixed effects estimated on a sample ex cluding high-inflation countries, to 0.06 in the 
same model including such countries. The high sensitivity of the coefficient for the economic mass to 
                                                      
31 Owing to missing data, the number of countries in the estimation samples for different years is less 
than 124. 
32 We thank M. Koren and A. Szeidl for sharing the disaggregated trade data set with us.  WT/WGTDF/W/24 
 Page 37 
 
 
the inclusion of high-inflation countries suggests that high-inflation episodes tend to distort economic relations between trade and other be havioral and policy variables, indirectly justifying the exclusion 
of such countries from the sample on which base line regressions are estimated in this study. 
24. Other control variables are also mostly signifi cant and have the expected signs. For example, 
a common language, FTA membership, GSP preferences, being a colonizer and a colony, and the 
colonization by the same country all have a positive and statistically significant effect on trade. The 
role of some controls, however, is sensitive to the specification of the model. For example, the level of 
economic development, as measured by the real GDP per capita, has a positive and statistically 
significant effect on trade only in the model which includes high-inflation countries and time-varying 
country effects (Appendix Table IV.2, column 5). WTO membership is positive and statistically 
significant in most specifications, on balance s uggesting that the WTO membership has a trade-
enhancing effect over a nd above other factors. 
(a) The Effect of Exchange Rate Volatility on Trade – Main Results 
25. Some benchmark results of using the gravity equa tion to estimate the effect of exchange rate 
variability on aggregate trade are given in Appe ndix Table IV.2. These equations use the standard 
measure of volatility, i.e., that of the long-run real IFS exchange rate, the coefficient of which is 
shown in the first row of the table.
33 As shown in column (1), which uses both time and country fixed 
effects, there is a statistically signifi cant negative impact on the level of trade.34 This impact can be 
computed as the effect of increasing volatility by one standard deviation around its mean, which 
implies a reduction in trade flows of almost 7 percent. 35 Employing this same specification but using 
the full sample of countries, as shown in column (4), the estimated reduction in trade generated by a 
one standard deviation increase in volatility ge nerates a reduction in trade of somewhat over 
9 percent.36 These estimates are comparable to those found by other authors using the same 
methodology, e.g., Rose (2000), who estimates a reduc tion of 13 percent, and Tenreyro (2003) with 
estimates ranging from 4 –8 percent. 
26. An alternative specification is used in the results reported in column (2) of 
Appendix Table IV.2, where individual country fixed effects are replaced with country pair fixed 
effects. The main advantage of this approach is that it allows one to cont rol for unobserved cultural, 
economic, historical, geographical and other factors which are specific to a given pair of countries.37 
Omitting such factors may bias the estimation results if  they are correlated with other regressors in the 
model. An F-test indicates that the estimated coeffi cients for the country pair fixed effects are jointly 
significant. It turns out that there is very little e ffect on the coefficient of exchange rate volatility, 
which is essentially the same as in column (1) of the same table.38  
27. However, the finding of a negative impact of exch ange rate volatility is not evident in a more 
general specification in which country and time fi xed effects are replaced with time-varying fixed 
                                                      
33 In the next table (Appendix Table IV.3) the eff ects of alternative measures of exchange rate 
variability are reported. 
34 This equation was also estimated without country fixed effects, an d an F-test confirmed that the 
inclusion of such effects is warranted. 
35 This impact is computed as th e estimated coefficient in the regression equation multiplied by one 
standard deviation of the volatility measure, multiplied by 100 to convert to percent. 
36 While the coefficient of volatility in this regression is about one half that in column (1), the standard 
deviation of volatility is over twice as large, as shown in Appendix Table IV.1, with the overall result being a 
somewhat bigger trade impact. Given that the larger samp le includes countries with substantial exchange rate 
changes, this is not at all surprising. 
37 In this specification, distance, land area, and other time-invariant bilateral variables become 
redundant in regressions with country pair fixed eff ects and therefore are exclud ed from the regression. 
38 However, as discussed below, this is not true for all of the other estimated coefficients, and in 
particular, for the dummy variable for a common currency union. WT/WGTDF/W/24 
Page 38  
 
effects. Allowing for time variation in country fixed effects is more consistent with the theoretical 
concept of “multilateral resist ance” proposed by Anderson and van Wincoop (2003), as such 
multilateral resistance indices are likely to vary over time. Moreover, an F-test comparing the two specifications indicates that the latter is pr eferred on statistical grounds. As shown in 
Appendix Table IV.2 column (3), this modification of the model results in a positive estimated impact 
of exchange rate volatility on trade ( but not signifi cant at the 90 percent confidence level). Using the 
time-varying country effects approach for the full sa mple, as reported in column (5), the estimated 
effect of volatility is negative and the same size as in column (4), but not statistically different from 
zero. 
28. What may account for the difference in the resu lts? One possible explanation runs as follows: 
time-varying country fixed effects in principle control for all unidentified country-specific time-
varying factors, including the effective, i.e., over all exchange rate volatility for each of the trading 
partners in question. Indeed, when we include th e measure of effective volatility (at the country as 
opposed to bilateral level) in the basic model with time-invariant country effects,
39 the coefficient on 
this measure of effective volatility is negative and st atistically significant, while the coefficient on the 
bilateral measure of exchange rate volatility become s positive and similar in magnitude to that in the 
model with time-varying fixed effects. This shows th at the negative effect of the bilateral volatility on 
trade is not robust to controlling for broader aspects of exchange rate volatility, and more generally, 
for all aspects of multilateral resistance.  
29. These benchmark results show that there is evid ence of a negative effect of exchange rate 
volatility on the level of trade, but the magnitude of the estimated impact appears to be small. 
However, this finding is not robust to the choice of  estimation technique. In particular, the negative 
effect disappears in a general model which controls for time-varying country-specific factors and is in 
line with the most recent theoretical work on the grav ity model of trade. This disparity in the findings 
characterizes not only the results above using our be nchmark measure of exchange rate volatility for 
aggregate trade, but also for the results reported be low that use alternative measures of volatility, that 
look at different country groupings and different types of traded goods, and that use alternative 
estimation techniques which attempt to control for th e possibility that exchange rate volatility is not 
exogenous. Thus to anticipate our ov erall conclusion, while there is evidence that increased exchange 
rate variability reduces the volume of internationa l trade, this finding depends on the particular 
estimation technique employed, so that it cannot be  considered an overwhe lmingly robust empirical 
result.  
Alternative Measures of Volatility 
 
30. The estimated impact of alternative measures of volatility on trade using time and individual 
country fixed effects are reported in Appendix Table IV.3. When we include in column (1) a measure 
of the short-run real exchange rate volatility in the model as the difference from the long-run real 
volatility,40 we find that short-run volatility has an a dditional dampening effect on trade over and 
above the negative effect arising from the long-run vo latility. The magnitude of this additional effect 
is about one half of the long-run volatility effect. Th is finding could be interpreted as indicating that 
trading firms form their expectations of the future  exchange rate volatility b ased on both historical and 
contemporaneous volatility.  
31. The volatility in the parallel market exchange ra te has a broadly similar effect on trade as the 
volatility in the official, IFS-based exchange rate. Appendix Table IV.3, columns 2-3 report 
regressions where the volatility measures based on the parallel market rates are included as 
differences from the IFS-based volatility measures, in addition to the  official volatility measures. The 
                                                      
39 See Appendix Table IV.5, column 4. 
40 Short-run volatility is not strongly correlated with long-run volatility in our sample: the correlation 
coefficient is 0.38.  WT/WGTDF/W/24 
 Page 39 
 
 
coefficient on the long-run parallel market volatility is  negative and statistically significant, about the 
same as the coefficient on the official exchange rate volatility in column 2, but about one third the size 
in column 3. However, in the short-run, the vola tility of the parallel market exchange rate does not 
appear to affect trade.41 These results suggest that parallel mark et rates are also relevant for trade 
transactions in addition to the official exchange rates.42  
32. Given that nominal and real exchange rates are highly correlated, it is probably not too 
surprising that their volatilities have similar effect s on trade. In Appendix Table IV.3, column 4, the 
coefficient on the nominal exchange rate volatility (–2. 60) is close to that on the real exchange rate 
volatility (–2.37) in Appendix Table IV.2, column 1.  
33. So far we have considered a simple statistical  measure of exchange rate volatility. We now 
replace it with a conditional measure of exchange rate volatility as estimated from a GARCH model 
(Appendix Table IV.3, column 5). The coefficient of the conditional volatilit y measure is virtually 
identical to that of the unconditional measure (–2.20 versus –2.37). Irrespective of whether one 
assumes that trading firms condition their expecta tions on the available historical information 
(GARCH) or that they project volatility using a simp le statistical approach, exchange rate volatility 
has a statistically significant negative effect on trad e, of a broadly similar magnitude. In terms of the 
impact of exchange rate volatility on the level of  trade, the estimates are comparable to those 
discussed above in connection with the benchmark re sults. They range from a low of about 5 percent 
for the GARCH estimate of volatility (column 5), to 25 percent for the combined effect of short- and long-run real official volatilities. 
34. These results using various measu res of exchange rate volatilit y are broadly robust to an 
alternative model specification where country fixed effects are replaced with country pair effects 
(Appendix Table IV.4). The estimated coefficient of volatility is consistently negative, in nearly all 
cases statistically significant, and tends to be somewhat higher.
43 As a consequence, the impact of 
higher exchange rate volatility on trade is also la rger, ranging from a reduction of 8 percent for the 
long-run nominal exchange rate, to a decline of 26 percent for the combined impact of short- and 
long-run real official rates. 
35. However, when country fixed effects and time effects are replaced with time-varying country 
effects (Appendix Table IV.5), this modification of the model reverses the impact of our standard 
measures of bilateral exchange rate volatility on trade in the long and short run – it becomes positive 
and statistically significant – and the effect of the parallel market volatility becomes insignificant. 
This lack of robustness to alternative specifications is in line with that discussed above in connection 
with the results for the benchmark measure of vol atility. With this particular model, increased 
exchange rate variability now has an estimated positive  impact on trade, ranging from 10 percent 
(column 4) to 34 percent (column 3). 
                                                      
41 When the alternative measures of exchange rate volatility are included as separate regressors in the 
gravity equation, the estimated coefficients are similar to  those reported above and ar e statistically significant, 
except for the volatility of the short-run parallel rate. 
42 It is also worth noting that the IFS-based exchange rates we use in the benchmark regressions are not 
only official rates but also include market and principal rates. The exchange rates in IFS are classified into three 
broad categories, reflecting the role of the authorities in  the determination of the exchange rates and/or the 
multiplicity of exchange rates in a country. The market rate is used to describe exchange rates determined 
largely by market forces: the official rate is used to describe an exchange rate determined by the authorities, sometimes in a flexible manner. For countries maintaini ng multiple exchange arrangements, the rates are labeled 
principal rate, secondary rate, and tertiary rate. The offici al rate is included in the series only if neither the 
market nor principal rate is available. The IFS-based measures are thus reasonab ly well correlated with parallel 
market rates, with the coeffi cient of correlation of 0.65. 
43 Again, when the alternative measures of volatility are entered in the equation by themselves, the 
coefficients are similar to those reported in Appendix Table IV.4 and are statistically significant. WT/WGTDF/W/24 
Page 40  
 
36. However, it should be noted that the e quation results reported in column (4) of 
Appendix Table IV.5 include the effective or overall ex change rate volatility of a country that is used 
in Part III. As bilateral trade flows are the depende nt variable, the sum of the effective volatilities of 
the country pairs is used as the regressor. The idea in this specification is to examine the effect of 
bilateral exchange rate volatility relative to  the aggregate measure of volatility, which is a component 
of “multilateral resistance” to trade mentioned ab ove. One would expect that an increase in the 
variability of the bilateral exchange rate between two countries would have a negative effect on their 
bilateral trade, and that an increase in the variab ility of all other exchange rates would tend to raise 
trade between the two countries in question, as such trade would become relatively less risky. In fact, 
the empirical results are counter to this expectati on. Nonetheless, the net impact of a one standard 
deviation increase in volatility is a reduction in trade of about 13 percent, as the negative effect of the 
higher effective volatility more than offsets the positive effect of the rise in bilateral volatility.
44  
Controlling for Endogeneity of Exchange Rate Volatility 
 
37. So far we have assumed that exchange rate vol atility is exogenous to trade. This assumption, 
however, may not be warranted: to the extent th at countries implement policies aimed to lower 
exchange rate volatility in order to increase thei r trade, the baseline equation would suffer from 
endogeneity bias. We control for this possibility using two instru mental variable (IV) approaches:45 (i) 
that proposed by Frankel and Wei (1993), whereby th e volatility in the relative quantity of money is 
an instrumental variable for exchange rate volatility,  and (ii) that proposed by Tenreyro (2003) which 
relates exchange rate volatility to the incidence a nd the propensity of countries’ to share a common 
anchor.46 Neither of these instruments is perfect, but each has its advantages: the Frankel-Wei 
approach is simple and easy to implement, while that  of Tenreyro’s instrumental variable (IV) appeals 
to the modern optimal currency framework  of Alesina, Barro, and Tenreyro (2002). 
38. Controlling for endogeneity using the Franke l-Wei instrumental-variable approach 
(Appendix Table IV.6A) modifies the basic results of the role of exchange rate volatility. While the 
coefficient on exchange rate volatility remains ne gative in every specification, it is statistically 
significant only in the equation with country and ti me effects for both real and nominal exchange 
rates. In these two cases, the estim ated coefficients are much larger than those reported above. The 
negative trade effects are also cons iderably larger: about 90 percent for the real rate and 125 percent 
for the nominal exchange rate, which seem implau sible compared to the findings described above.  
39. The results using the Tenreyro instrument al variable approach are reported in 
Appendix Tables IV.6.B and IV.6.C. When the IV is the dummy for a common anchor, the 
coefficients on the exchange rate volatility meas ure become statistically insignificant across all 
specifications. When the propensity to share a common anchor is used as an IV, these coefficients are 
negative in all specifications, and statistically signi ficant in the specifications with both country and 
time fixed effects and with time-varying fixed effe cts. In both cases the estimated coefficients are 
extremely large, implying reductions in trade rangi ng from 115 percent to 265 percent (column 1) for 
a one standard deviation increase in volatility. These are far beyond any other estimates in this paper 
or in the literature, and as such should probably be viewed as outliers.  
                                                      
44 In estimating the equation in column (4), country fixed effects are included in the model instead of 
time-varying country effects, on the assumption that the impact of effective volatility would be largely absorbed 
by the time-varying country dummies. However, when this same equation was estimated with time-varying 
country effects instead of  the time and fixed country effects used in column (4), the results were very similar, 
with a significant negative impact on trad e somewhat higher at 19 percent.  
45 The third alternative instrumental variable appr oach, based on Devereaux and Lane (2002), combines 
the factors underlying the optimal currency area theory with the factors underlying financial links to explain the 
volatility of the bilateral exchange rate for trading partners. While appealing conceptually, this approach is highly data intensive and was not implemented due to data unavailability.  
46 We thank S. Tenreyro for sharing her data on the dummy variable for countries’ sharing a common 
anchor.  WT/WGTDF/W/24 
 Page 41 
 
 
40. Several reasons may account for differences in th e magnitude and sign of our coefficients on 
the volatility measure and those obtai ned by Tenreyro (2003). The most important one is that in 
Tenreyro’s regressions volatility appears as log(1 + st andard deviation of the exchange rate) whereas 
our regressions include just the volatility or the standard deviation of the exchange rate for 
consistency with our benchmark OL S estimations and IV regressions a la Frankel and Wei (1993). 
Our specification differs from Tenreyro (2003) in other respects as well: (i) we include the common 
language and border dummies, which we find signifi cant, in the logit regressions estimating the 
propensity to adopt a common anchor; (ii) we contro l for whether the trading partners are related as a 
colony and a colonizer, as well as the WTO me mbership, common colonizer, and for whether the 
trading partners are island economies, and (iii) we u se trade between the two countries rather than just 
bilateral exports as the left-hand side variable.  
Differentiation Across Country Groups 
 
41. Across countries, we find that the impact of exchange rate volatility on trade is not uniform. 
In particular, volatile exchange rates appear to be more damaging fo r trade among developing 
countries than for trade among advanced economies. As shown in Appendix Table IV.7, column 1, 
the coefficients on the exchange rate volatility measures interacted with dummies for trade among 
advanced and developing countries (denoted by NS) and among developing countries (denoted by SS) 
are negative and statistically significant; their net magnitude is –2.23 and –3.22, respectively. This is 
consistent with the possibility that developing coun tries are less able to manage currency risks. 
Foreign exchange markets are typically nascent and less liquid in developing countries, limiting 
firms’ opportunities for hedging foreign exchange ri sk. However, with time-varying fixed effects, 
shown in column (2), there is essentially no imp act of volatility on trade flows among NS and SS. 
(b) Do Members of Currency Unions Trade More? 
42. Our main results confirm Ro se’s (2000) remarkable findi ng that common currency 
arrangements triple trade (Appendix Table IV.2, colu mn 1), as the coefficient on the currency union 
dummy is comparable to that found in his paper.  The trade-enhancing bene fits of currency unions 
appear to exceed by far the gains from a substantia l reduction in exchange ra te volatility, although as 
discussed above, the IV estimation results also indicat e a very large benefit in trade gains arising from 
a decline in volatility. 
43. However, while the trade-enhancing effect of a common currency is robust to controlling for 
time-varying fixed effects (Appendix Table IV.5, columns (1) and (5)), it breaks down in a model 
with country-pair fixed effects (Appendix Table IV .2, columns 2 and Appendix Table IV.5, columns 
1, 4, and 5), in line with the findings in Pakko and Wall (2001). The statistical insignificance of the currency union dummy in the model with country pa ir effects (which Rose (2002) did not utilize in 
his analysis) suggests that the trade-enhancing e ffect of a common currency found in specifications 
omitting country pair effects reflects an estimati on bias, as the omitted factors apparently are 
correlated with trade volume and with the like lihood that countries use a common currency (for 
example, common history or institutional and regu latory similarities between countries members in 
the currency union). Of course, currency unions are c ountry-pair characteristics, which evolve slowly 
over time. It thus appears that the power of the t est for an effect of currency unions on trade becomes 
much weaker when country-pair fixed effects are included. 
44. Moreover, the beneficial effect of a common currency on trade is also not uniform across 
country groupings, as it appears to be limited to currency unions between developing countries. When 
the currency union dummy is interacted with the dummy for the developing country pairs, the coefficient on this product term is positive and significant, while the general currency union dummy 
becomes negative and statistically significant, s uggesting that currency unions other than between 
developing countries impair trade between them (A ppendix Table IV.7, columns 1 and 2). This result 
suggests that currency unions may have a larg e positive impact on trade only in cases where WT/WGTDF/W/24 
Page 42  
 
transaction costs are high, where policy credibility problems are acute, or where hedging opportunities 
are limited.  
(c) Disaggregated Trade:  Does Exchange Rate Vo latility Have a Different Effect on Trade in 
Differentiated Versus Homogeneous Products? 
45. The finding that the negative effect of exchange  rate volatility on trade is not robust also 
carries over when we look at disaggregated trade. As discussed in the text, recent development in the 
economics of trade suggests that a given increase in transaction costs (of which exchange rate 
volatility is a component) could have a larger, negativ e effect on trade in differentiated products than 
on trade in homogenous products. We examine the ev idence for this possibility in this sub-section. 
46. In the first specification (reported in the first two columns of Appendix Table IV.8), we 
estimate a system of two equations by the Seem ingly Unrelated Regression (SUR) technique with 
time and country fixed effects. In this case, the co efficients on exchange rate volatility are negative in 
both equations, the volatility effect is statistica lly significant only in the equation on trade in 
differentiated products. In other wo rds, consistent with the conject ure above, exchange rate volatility 
has a negative effect on trade in  differentiated products, but not  on trade in homogenous products. 
However, this conclusion is not robust. In the l ast two columns of Appendix Table IV.8, when we 
include time-varying country fixed effects (which  are more general than the inclusion of a 
combination of time and country fixed effects and are dictated by the recent theory underlying the 
gravity specification used here), the conclusion is  overturned. More precisely, the coefficients on 
exchange rate volatility are not statistically differe nt from zero for trade both in differentiated and in 
homogenous products. As extens ions, we have also examined the e ffects of short-run exchange rate 
volatility and volatility of parallel market exchange  rate, the results are qualitatively the same as 
described above. 
47. Thus, the overall conclusion on disaggregated tr ade is the same as that for aggregated trade, 
namely, the evidence is not overwhelmingly robust th at exchange rate volatility has a negative effect 
on trade. Using an array of alternative formulati ons involving different measures of exchange rate 
volatility, estimation techniques, different country groupings and disaggregation by type of product, 
one does find fairly systematic evidence of a negativ e effect of volatility on trade. However, once one 
takes into account other factors that would affect trade in a more general model involving the time-varying “multilateral resistance” as emphasized by recen t trade theory, this negative effect disappears. 
Thus whether one finds evidence that exchange ra te volatility depresses the volume of trade, and in 
particular, has a larger negative effect on differe ntiated than homogeneous products, depends on the 
particular methodology that is employed in the estimation.  WT/WGTDF/W/24 
 Page 43 
 
 
Table 3.1A Short-Run Effective Volatility of Real I.F.S. Exchange Rates in G-7 Countries by Major 
Country Groups:  Decomposition of Volatility 
 
Country Year Advanced 
Economies Transition 
Economies Emerging 
Economies Rest Countries 
of the World Total Effective 
Volatility 
United States 1970 0.616 0.005 1.808 0.101 2.529 
United States 1980 1.751 0.021 0.364 0.321 2.457 
United States 1990 1.397 0.045 0.648 0.283 2.372 
United States 2000 1.385 0.028 0.811 0.111 2.335 
       
United Kingdom 1970 0.602 0.006 0.321 0.176 1.105 
United Kingdom 1980 1.957 0.029 0.251 0.415 2.651 
United Kingdom 1990 1.827 0.061 0.301 0.208 2.396 
United Kingdom 2000 2.569 0.085 0.334 0.132 3.119 
 
France 1970 0.427 0.008 0.263 0.200 0.899 
France 1980 1.213 0.049 0.239 0.520 2.020 
France 1990 0.951 0.109 0.254 0.244 1.558 
France 2000 0.998 0.090 0.302 0.224 1.614 
 
Germany 1970 0.523 0.022 0.360 0.115 1.019 
Germany 1980 1.353 0.143 0.347 0.365 2.209 
Germany 1990 0.964 0.283 0.347 0.130 1.724 
Germany 2000 1.102 0.228 0.401 0.117 1.847 
 
Italy 1970 0.624 0.044 0.407 0.180 1.255 
Italy 1980 1.355 0.136 0.355 0.575 2.421 
Italy 1990 0.950 0.258 0.361 0.334 1.903 
Italy 2000 0.908 0.218 0.386 0.260 1.772 
 
Canada 1970 1.144 0.002 0.261 0.041 1.448 
Canada 1980 1.706 0.006 0.140 0.090 1.943 
Canada 1990 1.384 0.022 0.199 0.054 1.660 
Canada 2000 1.443 0.008 0.197 0.039 1.687 
 
Japan 1970 0.663 0.002 1.315 0.170 2.150 
Japan 1980 1.885 0.009 1.173 0.898 3.966 
Japan 1990 2.354 0.029 1.343 0.351 4.077 
Japan 2000 1.789 0.036 1.465 0.229 3.519 
 
 WT/WGTDF/W/24 
Page 44  
 
Table 3.1B Short-Run Effective Volatility of Real I.F.S. Exchange Rates in G-7 Countries by Major 
Country Groups:  Volatility within Groups 
 
Country Year Advanced 
Economies Transition 
Economies Emerging 
Economies Rest Countries 
of the World Total Effective 
Volatility 
United States 1970 0.806 1.358 11.589 1.318 2.529 
United States 1980 2.913 6.231 1.565 1.972 2.457 
United States 1990 2.134 11.444 2.409 3.882 2.372 
United States 2000 2.478 2.346 2.236 1.670 2.335 
       
United Kingdom 1970 0.767 1.200 3.286 1.570 1.105 
United Kingdom 1980 2.442 6.656 3.739 3.256 2.651 
United Kingdom 1990 2.137 8.954 3.660 3.709 2.396 
United Kingdom 2000 3.162 3.079 2.909 2.905 3.119 
 
France 1970 0.496 1.525 6.524 2.164 0.899 
France 1980 1.571 7.338 4.467 3.094 2.020 
France 1990 1.130 10.462 4.268  2.764 1.558 
France 2000 1.220 2.444 3.979 3.237 1.614 
 
Germany 1970 0.606 1.414 5.756 1.921 1.019 
Germany 1980 1.644 7.666 5.095 4.048 2.209 
Germany 1990 1.141 8.243 4.228 3.331 1.724 
Germany 2000 1.453 2.059 3.938 3.989 1.847 
 
Italy 1970 0.758 1.574 6.205 2.169 1.255 
Italy 1980 1.786 7.381 4.836 3.844 2.421 
Italy 1990 1.152 8.950 4.969 4.532 1.903 
Italy 2000 1.220 2.516 3.893 3.721 1.772 
 
Canada 1970 1.224 2.058 6.704 1.622 1.448 
Canada 1980 1.907 6.210 2.208 2.215 1.943 
Canada 1990 1.514 14.301 3.018 3.009 1.660 
Canada 2000 1.597 2.342 2.496 2.721 1.687 
 
Japan 1970 0.988 1.68O 5.766 1.699 2.150 
Japan 1980 3.748 6.892 4.227 4.119 3.966 
Japan 1990 3.852 10.566 4.320 4.663 4.077 
Japan 2000 3.502 3.389 3.552 3.455 3.519 
 
  WT/WGTDF/W/24 
 Page 45 
 
 
Table 3.2 Average Effective Volatility Ranking (1970-2002) 
 
Advanced (Avg =  2.42) Emerging (Avg = 4.43) Other (Avg = 4.59) 
Top 5 (Avg = 3.21) Top 5 (Avg = 6.70) Top 5 (Avg = 16.05) 
Japan 3.50 Argentina 9.36 Angola 27.32 
Israel 3.48 Chile 6.52 Zambia 15.59 
Australia 3.23 Mexico 5.92 Nicaragua 13.51 
New Zealand 3.03 Peru 5.89 Congo, Dem. Rep. of 13.07 
United Kingdom 2.81  Uruguay 5.80 Uganda 10.77 
Bottom 5 (Avg = 1.78) Bottom 5 (Avg = 2.33) Bottom 5 (Avg = 1.30) 
Austria 1.64 Panama 1.89 Réunion 1.08 
Canada 1.76 Singapore 2.17 Martinique 1.20 
Bel_Lux 1.77 China, PR: Hong Kong 2.38 Aruba 1.33 
Netherlands 1.81 Malaysia 2.40 Guiana, French 1.34 
Denmark 1.91 Thailand 2.80 China, PR: Macao 1.55 
      
  
 
 
Sub Sahara 
(Avg = 5.89) Developing Asia 
(Avg = 3.66) Middle East and Turkey 
Avg = 4.28) 
Top 5 (Avg = 15.46) Top 5 (Avg = 5.44) Top 5 (Avg = 6.65) 
Angola 27.32 Afghanistan, I.S. of 6.82 Iran, I.R. of 8.39 
Zambia 15.59 Maldives 5.73 Lebanon 8.27 
Congo, Dem. Rep. of 13.07 Lao People's Dem. Rep. 5.43 Yemen, Republic of  6.07 
Uganda 10.77 Indonesia 4.87 Syrian Arab Republic 5.48 
Sudan 10.53 Sri Lanka 4.37 Turkey 5.04 
Bottom 5 (Avg = 2.60) Bottom 5 (Avg = 2.43) Bottom 5 (Avg = 2.46) 
Mali 2.16 Tonga 2.19 Malta 2.15 
Liberia 2.63 Bangladesh 2.23 Bahrain, Kingdom of 2.22 
Cape Verde 2.72 Malaysia 2.40 Kuwait 2.51 
Mauritius 2.72 Fiji 2.55 Saudi Arabia 2.58 
Cameroon 2.75 Thailand 2.80 Yemen, P.D. Rep. 2.85 
   
Western Hemisphere  
(Avg = 4.54) Fuel Export  
(Avg = 6.18) Nonfuel Export 
(Avg = 6.15) 
Top 5 (Avg = 9.55) Top 5 (Avg = 11.25) Top 5 (Avg = 11.65) 
Nicaragua 13.51 Angola 27.32 Zambia 15.59 
Bolivia 10.26 Iran, I.R. of 8.39 Congo, Dem. Rep. of 13.07 
Argentina 9.36 Equatorial Guinea 7.86 Uganda 10.77 
Suriname 8.11 Nigeria 6.61 Bolivia 10.26 
Chile 6.52 Yemen, Republic of  6.07 Ghana 8.56 
Bottom 5 (Avg = 2.11) Bottom 5 (Avg = 2.67) Bottom 5 (Avg = 2.90) 
Panama 1.89 Bahrain, Kingdom of 2.22 Mali 2.16 
Netherlands Antilles 2.13 Kuwait 2.51 Liberia 2.63 
Bahamas, The 2.14 Saudi Arabia 2.58 Solomon Islands 3.10 
Barbados 2.14 Oman 2.93 Guyana 3.30 
Dominica 2.24 Gabon 3.13 Côte d'Ivoire 3.31 
 WT/WGTDF/W/24 
Page 46  
 
Table 3.3 Frequency  Counting (f) for Top 5 (High Vol) and Bottom 5 (Low Vol) Lists (1970-2002) 
 
Advanced Emerging Developing Countries 
Frequency in Top 5 Frequency in Top 5 Frequency in Top 5 
Japan 28 Argentina 21 Congo, Dem. Rep. of  17 
Australia 21 Uruguay 21 Sudan 13 
Israel 18 Turkey 14 Angola 9 
New Zealand 16 Chile 14 Bolivia 9 
United Kingdom 15 Indonesia 13 Ghana 8 
Frequency in bottom 5 Frequency in bottom 5 Frequency in bottom 5 
Austria 28 Panama 22 Martinique 25 
Bel_Lux 28 Singapore  21 Guiana, French 22 
Canada 19 Malaysia 18 Réunion 20 
Netherlands 17 Venezuela Rep. Bol. 18 Netherlands Antilles 14 
Denmark 13 Mexico 17 Bahamas, The 9 
 
 
 
Sub-sahara Developing Asia  Middle East and Turkey 
Frequency in Top 5 Frequency in Top 5 Frequency in Top 5 
Congo, Dem. Rep. of 23 Sri Lanka 22 Turkey 30 
Sudan 21 Myanmar 21 Syrian Arab Republic 29 
Ghana 11 Pakistan 19 Egypt 24 
Angola 10 Samoa 17 Iran, I.R. of 19 
Uganda 9 Indonesia 16 Jordan 17 
Frequency in bottom 5 Frequency in bottom 5 Frequency in bottom 5 
Cameroon 24 Malaysia 28 Malta 32 
Gabon 16 Thailand 23 Bahrain, Kingdom of 26 
Côte d'Ivoire 13 Fiji 22 Kuwait 23 
Madagascar 12 Philippines 13 Saudi Arabia 21 
Mauritius 12 Samoa 12 Egypt 12 
 
 
 
Western Hemisphere Fuel-Export Nonfuel-Export 
Frequency in Top 5 Frequency in Top 5 Frequency in Top 5 
Argentina 19 Nigeria 25 Congo, Dem. Rep. of 24 
Paraguay 15 Iran, I.R. of 20 Bolivia 12 
Chile 14 Algeria 19 Ghana 12 
Haiti 11 Venezuela, Rep. Bol. 16 Sierra Leone 11 
Bolivia 11 Angola 12 Burkina Faso 11 
Frequency in bottom 5 Frequency in bottom 5 Frequency in bottom 5 
Netherlands Antilles 20 Kuwait 25 Côte d'Ivoire 19 
Panama 16 Bahrain, Kingdom of 24 Rwanda 17 
Bahamas, The 13 Gabon 23 Togo 15 
Mexico 13 Venezuela, Rep. Bol. 22 Liberia 15 
Trinidad and Tobago 12 Saudi Arabia 21 Bolivia 15 
  WT/WGTDF/W/24 
 Page 47 
 
 
Table 3.4 Real Effective Volatility Across Country  Groups by Type of Exchange Rate Regime 
 
 
OFFICIAL IMF CLASSIFICATION /1 
 
Country Groups Peg Limited Flexibilit y Managed Floating Freely Floating 
Advanced 2.14 2.07 2.81 2.94 
Emerging 3.74 2.28 4.30 6.90 
Transition 5.73  4.56 6.37 
Developing 4.35 2.94 4.95 6.47 
 
NATURAL CLASSIFICATION /2 
 
Country Groups Peg Limited 
Flexibility Managed 
Floating Freely Floating Freely Falling 
Advanced 1.81 2.37 2.81 3.09 4.76 
Emerging 2.98 2.81 4.02 4.66 8.31 
Transition 3.75 3.11 3.48 11.15 9.95 
Developing 3.28 3.16 4.53 5.26 13.47 
 
 
 
Table 3.5 Real Effective Volatility Across Regimes and Time 
 
 OFFICIAL IMF CLASSIFICATION /1 
 
 1970-1980 1981-1990 1991-2001 1970-2001 
Peg 3.12 4.96 4.11 3.99 
Limited Flexibility 2.13 2.20 2.13 2.15 
Managed Floating 4.93 4.75 4.18 4.43 
Freely Floating  3.05 6.95 5.01 5.22 
 
NATURAL CLASSIFICATION /2  
 1970-1980 1981-1990 1991-2001 1970-2001 
Peg 2.80 3.17 3.03 2.98 
Limited Flexibility 2.58 2.97 2.88 2.83 
Managed Floating 3.48 4.27 4.16 4.02 
Freely Floating  3.32 4.11 4.64 4.26 
Freely Falling 7.99 13.04 9.31 10.56 
 
Note:  Based on a sample of 150 countries for the period of 1970-2001. 1/ Based on the IMF's annual publication Exchange Arrangements and Exchange Restrictions 
2/Based on Reinhart and Rogoff (2003) 
 WT/WGTDF/W/24 
Page 48  
 
Figure 3.1  Short-Run Effective Volatility of th e Real Exchange Rate by Country Groups, 1970-
2002 
02468101214161820
1970
1972
1974
1976
1978 1980
1982
1984 1986
1988 1990
1992
1994 1996
1998
2000
2002ADVANCED
TRANSITION
EMERGING
OTHER DEVELOPING
 
 
  WT/WGTDF/W/24 
 Page 49 
 
 
Figure 3.2 Short-Run Effective Volatility of the Real Exchange Rate for the G-7 Countries 
Canada
02468
1970 1974 1978 1982 1986 1990 1994 1998 2002France
02468
1970 1974 1978 1982 1986 1990 1994 1998 2002
Germany
02468
1970 1974 1978 1982 1986 1990 1994 1998 2002Italy
02468
1970 1974 1978 1982 1986 1990 1994 1998 2002
Japan
02468
1970 1974 1978 1982 1986 1990 1994 1998 2002United Kingdom
02468
1970 1974 1978 1982 1986 1990 1994 1998 2002
United States
02468
1970 1974 1978 1982 1986 1990 1994 1998 2002G7 average
02468
1970 1974 1978 1982 1986 1990 1994 1998 2002
 WT/WGTDF/W/24 
Page 50  
 
Figure 3.3 Short-Run Effective Volatility of the Real Exchange Rate in Developing Countries 
Grouped by Geographic Region 
13579111315
1970
1972
1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002Sub-Saharan Africa
Developing Asia
Middle East
Western Hemisphere
  WT/WGTDF/W/24 
 Page 51 
 
 
Figure 3.4  Short-Run Effective Volatility of the Real  Exchange Rate in Two Developing Country Groups 
by Source of Export Earnings 
159131721
1970
1972
1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002Fuel Exporters
Non-Fuel Primary Products Exporters
 
Figure 3.5  Short-Run Effective Volatility of th e Nominal Exchange Rate by Major Country 
Groups 1970-2002 
0510152025
1970
1972
1974
1976 1978 1980
1982
1984 1986 1988 1990 1992
1994
1996 1998 2000 2002Advanced
Transition
Emerging
Other Developing
 WT/WGTDF/W/24 
Page 52  
 
Figure 3.6  Long-Run Effective Volatility of the Real Exchange Rate by Major Country Groups 
02468101214161820
1975 1980 1985 1990 1995 2000ADVANCED
EMERGING
OTHER DEVELOPING
TRANSITION
 
 
Figure 3.7 Long-Run Effective Conditional Volatility of the Real Exchange Rate by Major 
Country Groups 
01234567
1 9 7 51 9 8 01 9 8 51 9 9 01 9 9 52 0 0 0ADVANCED
EMERGING
OTHER DEVELOPING
TRANSITION
  WT/WGTDF/W/24 
 Page 53 
 
 
Figure 3.8  Long-Run Effective Volatility of th e Real Exchange Rate in the G-7 Countries 
0123456
1975 1980 1985 1990 1995 2000
JAPAN US UK ITALY GERMANY FRANCE CANADA
 
Figure 4.1  Effective Volatility of the Real Exchange Rate and World Trade 
012345678910
1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002020004000600080001000012000World Volatility including transition economies
World Volatility excluding transition economies
World Trade
Note: World trade is measured as the average of the volume of world exports and imports in b illions of 1995 US dollars (right) scale. Volat ility is measures as the 
unweighted average of the volatility of the real exchange rate of the countries in the sample. The dashed line above includes t he volatility of the transition economies.
 WT/WGTDF/W/24 
Page 54  
 
Figure 4.2  Effective Volatility of the Real Exch ange Rate and Trade:  Major Country Groups 
 
ADVANCED ECONOMIES
01000200030004000500060007000
1970 1978 1986 1994 200200.511.522.533.54EMERGING ECONOMIES
05001000150020002500
1970 1978 1986 1994 20020123456789TRADE
EFFECTIVE VOLATILITY
TRANSITION ECONOMIES
050100150200250300350400450
1970 1978 1986 1994 2002051015202530OTHER DEVELOPING ECONOMIES
0100200300400500600
1970 1978 1986 1994 2002012345678910
  WT/WGTDF/W/24 
 Page 55 
 
 
Figure 4.3  Effective Volatility of the Real Exchange Rate and Trade:   
Developing Countries by Region 
 SUB-SAHARAN AFRICA
01020304050607080
1970 1978 1986 1994 20020246810121416 DEVELOPING ASIA
050100150200250300350400450500
1970 1978 1986 1994 2002012345678910TRADE
EFFECTIVE VOLATILITY
 MIDDLE EAST AND NORTH AFRICA
050100150200250300
1970 1978 1986 1994 200202468101214 WESTERN HEMISPHERE
050100150200250300350400
1970 1978 1986 1994 2002024681012
 WT/WGTDF/W/24 
Page 56  
 
Figure 4.4.  Effective Volatility of the Real Exch ange Rate and Trade:  Developing Countries by 
Type of Export 
 FUEL EXPORTING ECONOMIES
050100150200250
1970 1974 1978 1982 1986 1990 1994 1998 200202468101214161820
TRADE
EFFECTIVE VOLATILITY
 NON-FUEL PRIMARY PRODUCTS EXPORTING ECONOMIES
0102030405060
1970 1974 1978 1982 1986 1990 1994 1998 200202468101214TRADE
EFFECTIVE VOLATILITY
  WT/WGTDF/W/24 
 Page 57 
 
 
Appendix Table III.1  List of Count ries in Major Country Groupings 
Advanced Economies Transition Ec onomies Emerging Economies ROW 
Australia Albania Argentin a Afghanistan, I.S. of 
Austria Armenia Brazil Algeria 
Belgium Azerbaijan Chile Angola 
Canada Belarus China, P.R.:  Mainland Antigua and Barbuda 
Cyprus Bulgaria China, P.R.:  Hong Kong Aruba 
Denmark Croatia Colo mbia Bahamas, The 
Finland Czech Republic Ecuador Bahrain, Kingdom of 
France Czechoslovakia Indonesia Bangladesh 
Germany Estonia Korea Barbados 
Greece Georgia Malaysia Belize 
Iceland Hungary Mexico Benin 
Ireland Kazakhstan Panama Bolivia 
Israel Kyrgyz Republic Peru Burkina Faso Italy Latvia Philippines Burundi 
Japan Lithuania Singapore Cambodia 
Luxembourg Macedonia, FYR South Africa Cameroon 
Netherlands Moldova Th ailand Cape Verde 
New Zealand Mongolia Turkey  Central African Rep. 
Norway Poland Uruguay Chad 
Portugal Romania Venezuela, Rep. Bol. China, P.R.:  Macao 
Spain Russia  Congo, Dem. Rep. of 
Sweden Slovak Republic  Congo, Republic of 
Switzerland Slovenia  Costa Rica 
United Kingdom Ukraine  Côte d'Ivoire 
United States Yugoslavia, SFR  Djibouti 
   Dominica 
   Dominican Republic 
   E g y p t  
   El Salvador 
   Equatorial Guinea 
   Ethiopia 
   Fiji 
   Gabon 
   G a m b i a ,  T h e  
   G h a n a  
   Grenada 
   Guatemala 
   Guiana, French 
   Guinea-Bissau 
   G u y a n a  
   Haiti 
   Honduras    India WT/WGTDF/W/24 
Page 58  
 
 
Appendix Table III.1  List of Count ries in Major Country Groupings 
Advanced Economies Transition Ec onomies Emerging Economies ROW 
   Iran, I.R. of 
   Iraq 
   J a m a i c a     J o r d a n  
   K e n y a  
   Kuwait 
   Lao People's Dem. Rep. 
   Lebanon 
   L i b e r i a  
   L i b y a  
   Madagascar 
   M a l a w i  
   Maldives 
   M a l i  
   Malta 
   Martinique 
   Mauritania 
   Mauritius 
   Morocco 
   Mozambique 
   M y a n m a r  
   N e p a l  
   Netherlands Antilles 
   Nicaragua 
   N i g e r  
   N i g e r i a  
   O m a n  
   Pakistan 
   Papua New Guinea 
   Paragua 
   Q a t a r  
   Rwanda    Réunion 
   S a m o a  
   Saudi Arabia 
   Senegal 
   S e y c h e l l e s  
   Sierra Leone 
   Solomon Islands 
   S r i  L a n k a  
  WT/WGTDF/W/24 
 Page 59 
 
 
 
Appendix Table III.1  List of Count ries in Major Country Groupings 
Advanced Economies Transition Ec onomies Emerging Economies ROW 
   St. Kitts and Nevis 
   S t .  L u c i a  
   St. Vincent & Grens. 
   Sudan 
   Suriname 
   Syrian Arab Republic    Tanzania 
   Togo 
   Trinidad and Tobago 
   Tunisia 
   Uganda 
   Vanuatu 
   V i e t n a m  
   Yemen Arab Rep. 
   Yemen, P.D. Rep. 
   Yemen, Republic of 
   Z a m b i a  
   Z i m b a b w e  
 WT/WGTDF/W/24 
Page 60  
 
 
Appendix Table III.2 List of Countries in Regional Groups of Developing Countries and by  Source of Export Earnings 
 
Sub Saharan Africa 1/  
Developing Asia  
Middle East and Turkey  
Western Hemisphere  
Fuel Exporters  
Nonfuel Primary 
Product Exporters 
Angola Afghanistan, I.S. of Bahr ain, Kingdom of Antigua and Bar buda Algeria Afghanistan, I.S. of 
Benin Bangladesh Egypt Argentina Angola Bolivia 
Burkina Faso Cambodia Iran, I.R. of Bahamas , The Bahrain, Kingdom of Burkina Faso 
Burundi Fiji Iraq Barbados Congo, Rep. of Burundi Cameroon Indonesia Jordan Belize Equatorial Guinea Chad Cape Verde Lao People's Dem. Rep. Kuwait Bolivia Gabon Chile Central African Rep Malaysia Lebanon Brazil Iran, I.R. of Congo, Dem. Rep. of Chad Maldives Libya Chile Iraq Côte d'Ivoire Congo, Dem. Rep. of Myanmar Malta Colombia Kuwait Ethiopia Congo, Republic of Nepal Oman Costa Rica Libya Ghana Côte d'Ivoire Pakistan Qatar Dominica Nigeria  Guinea-Bissau 
Djibouti Papua New Guinea Saudi Arabi a Dominican Republic Oman Guyana 
Equatorial Guinea Philippines Syrian Arab Republic Ecuador Qatar Liberia 
Ethiopia Samoa Turkey El Sa lvador Saudi Arabia Malawi 
Gabon Solomon Islands Yemen Arab Rep. Grenada Venezuela, Rep. Bol. Mali Gambia, The  Sri Lanka Yemen, P.D. Rep Guatemala Yemen, Republic of Mauritania Ghana Thailand Yemen, Rep. of Guyana  Niger Guinea-Bissau Tonga  Haiti  Papua New Guinea Kenya Vanuatu  Honduras  Rwanda Liberia Vietnam  Jamaica  Sierra Leone Madagascar   Mexico  Solomon Islands Malawi   Netherlands Antilles  Togo Mali   Nicaragua  Uganda Mauritania   Panama  Zambia Mauritius   Paraguay  Zimbabwe Mozambique   Peru   Niger   St. Kitts and Nevis   
Rwanda   St Lucia   
Senegal   St. Vincent & Grens.   Seychelles   Suriname   Sierra Leone   Trinidad & Tobago   Sudan   Uruguay   Tanzania   Venezuela, Rep. Bol.   Togo      Uganda      Zambia      Zimbabwe      
 
1/ Excludes Nigeria and South Africa.  WT/WGTDF/W/24 
 Page 61 
 
 
 
Appendix Table III.3 List of Countries in the Real Parallel Exchange Rate Dataset 
Advanced Economies Transition Ec onomies Emerging Economies ROW 
Australia Belarus Argentin a Afghanistan, I.S. of 
Austria Bulgaria Brazil Algeria 
Belgium Czech Republic Chile Bangladesh 
Canada Estonia China, P.R.:  Mainland Benin 
Cyprus Hungary China, P.R.:  Hong Kong Bolivia 
Denmark Latvia Colombia Burundi Finland Lithuania Ecuador  Congo, Dem. Rep. of 
France Poland Indonesia Costa Rica 
Germany Romania Korea Dominican Republic 
Greece Russia Malaysia Egypt 
Iceland Ukraine Mexico El Salvador 
Ireland Yugoslavia, SFR Peru Ethiopia 
Israel  Philippines Gambia, The 
Italy  Singapore Ghana 
Japan  South Africa Guatemala 
Netherlands  Thailand Guyana 
New Zealand  Turkey Haiti 
Norway  Uruguay Honduras 
Portugal  Venezuela, Rep. Bol. India 
Spain   Iran, I.R. of 
Sweden   Iraq 
Switzerland   Jamaica 
United Kingdom   Jordan 
United States   Kenya 
   Kuwait 
   Lao People's Dem. Rep. 
   Lebanon 
   L i b e r i a  
   L i b y a  
   Madagascar 
   M a l a w i  
   Malta 
   Mauritania 
   Mauritius 
   Morocco    M y a n m a r  
   N e p a l  
   Nicaragua 
   N i g e r i a  
   Pakistan 
   Paraguay 
   Saudi Arabia WT/WGTDF/W/24 
Page 62  
 
 
Appendix Table III.3 List of Countries in the Real Parallel Exchange Rate Dataset 
Advanced Economies Transition Ec onomies Emerging Economies ROW 
   Sierra Leone 
   S r i  L a n k a  
   Suriname 
   Syrian Arab Republic 
   Tanzania 
   Tunisia    Uganda 
   V i e t n a m  
   Z a m b i a  
   Z i m b a b w e  
  WT/WGTDF/W/24 
 Page 63 
 
 
Appendix Table III.4  Shor t-Run Effective Volat ility of Real I.F.S. 
Exchange Rate:  Smaller Samp le of Major County Groups  
 
Year G-7 Advanced 
Economies Transition 
Economies Emerging Market 
Economies Rest Countries of 
the World 
1970 1.39 1.17 1.56 6.15 1.82 
1971 1.75 1.93 6.34 1.99 2.33 
1972 1.76 1.63 2.41 4.17 3.68 
1973 2.84 2.73 3.26 6.82 4.17 
1974 2.26 2.35 3.05 3.89 3.08 
1975 1.71 1.77 1.94 4.89 3.21 
1976 2.38 2.25 2.42 4.70 2.86 
1977 1.98 2.40 1.86 4.97 2.74 
1978 2.41 2.20 3.21 2.81 3.47 
1979 1.94 1.78 2.31 2.18 4.12 
1980 2.01 1.84 5.83 2.71 2.73 
1981 2.28 2.23 3.89 3.01 5.47 
1982 2.67 2.61 4.33 6.46 4.04 
1983 1.97 2.05 3.34 3.65 5.68 
1984 1.79 1.96 3.61 3.84 4.16 
1985 2.47 2.38 4.23 4.24 7.52 
1986 2.49 2.39 4.48 4.49 6.61 
1987 1.96 1.90 6.49 3.03 5.75 
1988 1.96 1.90 4.60 5.27 8.56 
1989 2.12 2.19 11.71 7.37 5.18 
1990 2.10 1.81 13.55 4.68 5.04 
1991 2.03 2.03 22.04 3.45 4.89 
1992 3.02 2.84 27.35 2.78 5.17 
1993 2.49 2.41 6.52 2.48 6.29 
1994 1.73 1.62 7.23 3.60 7.22 
1995 2.66 2.23 4.53 3.54 3.15 
1996 1.33 1.33 3.56 1.87 2.25 
1997 2.16 2.00 5.78 3.55 3.19 
1998 2.55 2.29 8.43 5.02 3.74 
      
1970-1980 2.04 2.01 3.11 4.11 3.11 
1981-1990 2.18 2.14 6.83 4.62 5.87 
1991-1998 2.25 2.09 8.83 3.29 4.47 
1970-1998 2.14 2.08 7.46 4.04 4.55 
 WT/WGTDF/W/24 
Page 64  
 
Appendix Table III.5  Shor t-Run Effective Volatility of Real Parallel 
Exchange Rate:  Smaller Samp le of Major Country Groups 
 
Year G-7 Advanced 
Economies Transition 
Economies Emerging Market 
Economies Rest Countries of 
the World 
1970 2.27 1.88 4.87 8.47 4.15 
1971 2.01 2.43 5.58 4.75 4.93 
1972 2.32 2.27 3.97 4.98 5.57 
1973 4.02 4.06 4.48 7.51 6.95 
1974 6.00 6.18 6.71 5.74 8.10 
1975 2.78 4.14 4.81 4.87 6.57 
1976 3.95 4.26 5.58 7.15 6.01 
1977 2.90 3.83 6.83 6.31 6.49 
1978 4.29 4.58 7.88 4.54 6.44 
1979 3.34 3.59 9.47 3.17 6.96 
1980 3.37 3.55 9.86 4.14 8.50 
1981 4.39 4.83 7.32 6.28 10.01 
1982 4.24 4.67 7.13 9.61 9.65 
1983 3.71 4.70 6.59 9.42 7.87 
1984 3.88 4.49 7.82 6.60 7.65 
1985 3.58 3.69 8.30 8.14 10.15 
1986 3.53 3.72 10.67 6.60 8.52 
1987 2.89 2.94 7.53 7.52 9.09 
1988 2.78 2.79 8.48 7.27 12.42 
1989 3.17 3.35 16.16 9.64 8.47 
1990 2.83 2.71 13.76 10.88 6.32 
1991 2.71 2.91 10.16 4.92 6.71 
1992 3.64 3.66 15.18 4.84 6.40 
1993 3.71 4.65 8.15 3.94 6.80 
1994 2.09 1.89 6.04 4.43 6.33 
1995 3.13 2.74 6.23 3.57 4.49 
1996 1.89 1.83 4.57 2.50 3.58 
1997 2.63 2.51 8.57 3.87 4.79 
1998 3.03 2.70 6.96 5.26 3.98 
      
1970-1980 3.39 3.71 6.85 5.59 6.50 
1981-1990 3.50 3.79 9.99 8.22 9.02 
1991-1998 2.86 2.86 7.86 4.17 5.37 
1970-1998 3.28 3.49 8.23 6.08 7.02 
  WT/WGTDF/W/24 
 Page 65 
 
 
Appendix Table III.6  Correspondence Between the Official/IMF and Natural Regime 
Classification 1970-2001 
 
  
 
NATURAL 
CLASSIFICATION /2OFFICIAL IMF CLASSIFICATION /1 
Peg Limited 
Flexibility Managed 
Floating Freely 
Floating  
Total
Peg 84.29 
53.53 8.17 
46.61 5 
8.48 2.54 
5.18 100 
35.33
Limited Flexibility 34.86 
16.13 8.82 
36.65 35.84 
44.28 20.48 
30.42 100 
25.74
Managed Floating 55.1 
22.58 1.23 
4.52 24.6 
26.92 19.07 
25.08 100 
22.8
Freely Floating 12.58 
1.01 16.98 
12.22 1.26 
0.27 69.18 
17.8 100 
4.46
Freely Falling 31.21 
6.75 0 
0 35.82 
20.05 31.97 
21.52 100 
11.67
Total 55.64 
100 6.2 
100 20.84 
100 17.33 
100 100 
100
 
Note:  The first entry in every cell is the row percentage, while the second  is the column  percentage. 
1/ Based on the IMF's annual publication Exchange Arrangements and Exchange Restrictions  
2/ Based on Reinhart and Rogoff (2003) The above statistics are derived for a sample of 150 countries. WT/WGTDF/W/24 
Page 66  
 
Appendix Table III.7 Distribution of Exchange Rate Regimes across Country Groups 1970-2001 
 
 
 
 
Country Groups OFFICIAL IMF CLASSIFICATION /1 
Peg Limited 
Flexibility Managed 
Floating Freely 
Floating  
Total
Advanced 32.32 
11.31 25.46 
87.33 13.72 
13.27 28.5 
33.08 100 
19.82
Emerging 39.52 
11.5 0.63 
1.81 43.97 
35.33 15.87 
15.31 100 
16.47
Transition 35.06 
4.39 0 
0 34.32 
11.86 
 30.63 
12.71 100 
7.09
Developing 72.84 
72.81 1.11 
10.86 14.32 
39.54 11.73 
38.9 100 
56.62
Total 56.64 
100 5.78 
100 20.5 
100 17.08 
100 100 
100
 
Note:  The first entry in every cell is the row percentage, while the second  is the column  percentage. 
1/ Based on the IMF's annual publication Exchange Arrangements and Exchange Restrictions.  
The above statistics are derived for a sample of 150 countries.  WT/WGTDF/W/24 
 Page 67 
 
 
Appendix Table III.8  Distribution of Exchan ge Rate Regimes across Country Groups  
1970-2001 
 
  
 
Country 
Groups OFFICIAL IMF CLASSIFICATION /1 
Peg Limited 
Flexibility Managed 
Floating Freely 
Floating Freely 
Falling  Total
Advanced 26.82 
15.16 40.63 
33.3 17.71 
16.31 11.98 
57.14 2.86 
5.14 100 
20.65
Emerging 26.41 
12.44 24.84 
16.97 24.53 
18.82 2.97 
11.8 21.25 
31.78 100 
17.21
Transition 13.81 
2.72 25 
7.15 19.03 
6.12 3.73 
6.21 38.43 
24.07 100 
7.21
Developing 46.35 
69.68 19.53 
42.58 23.98 
58.75 1.96 
24.84 8.17 
39.02 100 
54.93
Total 36.54 
100 25.19 
100 22.43 
100 4.33 
100 11.51 
100 100 
100
 
Note:  The first entry in every cell is the row percentage, while the second  is the column  percentage. 
1/ Based on Reinhart and Rogoff (2003) 
The above statistics are derived for a sample of 150 countries.  WT/WGTDF/W/24 
Page 68  
 
Appendix Table IV.1 Summary Statistics and Correlations 
 
 
A. Summary Statistics of Main Variables for Aggregate Trade Data 
 
Full Sample: 
Variable  
Code in 
Dataset  
Num of Obs  
Mean  
St. 
Dev.  
Min  
Max 
L-R Official Real Vol vol_lor 26395 0.066 0.076 0.003 1.405 
L-R Official Nominal Vol vol_lon 37254 0.062 0.081 0 0.979 L-R Parallel Real Vol vol_lpr 11844 0.090 0.074 0.007 0.684 
L-R Parallel Nominal Vol vol_lpn 16302 0.089 0.073 0 0.596 
S-R Official Real Vol vol_sor 26444 0.046 0.065 0.001 0.836 
S-R Official Nominal Vol vol_son 37416 0.041 0.069 0 0.900 
S-R Parallel Real Vol vol_spr 12575 0.073 0.075 0.000 0.664 
S-R Parallel Nominal Vol vol_spn 16753 0.073 0.069 0 0.561 
Log of Bilateral Trade ltrade 37443 9.983 3.541 -16.090 20.890 Log of Real GDP Product lrgdp 37593 47.973 2.765 36.128 58.356 
Log of Real Per Capita GDP 
Product lrgdppc 37593 16.047 1.662 9.160 20.850 
 
 
Sample for Regressions: 
Variable  
Code in 
Dataset  
Num of Obs  
Mean  
St. 
Dev.  
Min  
Max 
L-R Official Real Vol vol_lor 16303 0.043 0.028 0.003 0.293 
L-R Official Nominal Vol vol_lon 16303 0.039 0.031 0 0.271 
L-R Parallel Real Vol vol_lpr 6988 0.066 0.054 0.007 0.541 
L-R Parallel Nominal Vol vol_lpn 6988 0.063 0.052 0 0.517 
S-R Official Real Vol vol_sor 16149 0.034 0.025 0.002 0.254 
S-R Official Nominal Vol vol_son 16303 0.028 0.027 0 0.267 
S-R Parallel Real Vol vol_spr 7190 0.052 0.039 0.000 0.393 
S-R Parallel Nominal Vol vol_spn 7229 0.049 0.040 0 0.387 
Conditional Vol cvol 16303 0.047 0.021 0.003 0.191 
Log of Bilateral Trade ltrade 16238 10.458 3.573 -8.076 20.890 
Log of Real GDP Product lrgdp 16303 48.296 2.852 37.368 58.356 
Log of Real Per Capita GDP 
Product lrgdppc 16303 16.264 1.733 9.160 20.850 
  WT/WGTDF/W/24 
 Page 69 
 
 
B. Correlation Matrix of Main Va riables for Aggregate Trade Data 
 
  (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) 
(1) L-R Official Real Vol 1 . 0 0             
(2) L-R Official Nominal Vol 0.96 1.00           
(3) L-R Parallel Real Vol 0.44 0.52 1.00          
(4) L-R Parallel Nominal Vol 0.42 0.51 0.99 1.00         
(5) S-R Official Real Vol 0.38 0.40 0.37  0.36 1.00        
(6) S-R Official Nominal Vol 0.35 0.40 0.38 0.37 0.97 1.00       
(7) S-R Parallel Real Vol 0.15 0.12 0.24 0.24 0.31 0.27 1.00      
(8) S-R Parallel Nominal Vol 0.12 0.10 0.21 0.21 0.29 0.25 0.99 1.00     
(9) Log of Bilateral Trade -0.32 -0.28 -0.31 -0.31 -0.19 -0.13 -0.22 -0.20 1.00    
(10) Log of Real GDP Product -0.20 -0.17 -0.23 -0.23 -0.08 -0.04 -0.16 -0.14 0.76 1.00   
(11) Log of Real Per Capita GDP 
Product -0.35 -0.27 -0.34 -0.33 -0.19 -0.11 -0.30 -0.26 0.61 0.44 1.00  
(12) Conditional Vol 0.56 0.57 0.33 0.32 0.32 0.31 0.09 0.07 -0.35 -0.22 -0.36 1.00 
 
 
 
 
C. Summary Statistics of Main Variables for Disaggregate Trade Data 
 
 Num of Obs Mean Std.Dev Min Max 
Log Imports of Differentiated Goods (Real  Value) 4667 10.54 3.23 -2-62 17.84 
Log Imports of Homogeneous Goods (Real Value) 4473 9.29 3.08 -04.05 17.08 
Log Total Imports (Real Value) 9140 9.93 3.22 -4.05 17.84 
 
 
1975 1980 1985 1990 1995 2000 
 
Total Imports of Differentiated Goods 
 (million real US Dollars)    
187 
  
290  
300  
538  
788  
946 
Total Imports of Homogenous Goods 
(million real US dollars)  70 138 114 148 165 185 
 
Share of Differentiated Products  in Total Trade  
0.73  
0.68  
0.72  
0.78  
.83  
0.84 
 
 WT/WGTDF/W/24 
Page 70  
 
Appendix Table IV.2 The Role of Exchange Rate Volatility in Trade:  Main Results 
 
Variable  Country  FE 
+ Time FE Country 
Pair FE + 
Time FE Time Varying 
Country 
Effects  Country FE + 
Time FE with 
Full Sample Time-Varying 
Country Effects 
with full Sample 
  (1) (2) (3)  (4) (5) 
Long-Run Volatility of Real 
Official Exchange Rate   -2.37 
(0.67) -2.40 
(0.47) 2.89 
(1.78)  -1-16 
(0.22) -1.17 
(0.83) 
Dummy for Common Currency Union   1.35 
(0.14) 0.25 
(0.38) 1.43 
(0.14)  1.35 
(0.12) 1.30 
(0.12) 
Log of Real GDP Product  0.20 
(0.13) 0.49 
(0.10) 0.83 
(0.19)  0.10 
(0.11) 0.06 
(0.27) 
 
Log of Real Per Capita GDP 
Product  -0.06 
(0.12) -0.25 
(0.09) 0.07 
(0.22)  0.00 
(0.10) 0.83 
(0.28) 
 
Log of Distance   
-1.52 
(0.02)  
  
-1.54 
(0.02) 
   
-1.50 
(0.02) 
  
-1.50 
(0.02) 
 
Common Language Dummy  0.25 
(0.05)  0.26 
(0.05) 
  0.36 
(0.04) 
 0.36 
(0.04) 
 
Common Border Dummy  -0.12 
(0.10)  -0.11 
(0.10)  0.36 
(0.08) 0.35 
(0.08) 
 
Number of Landlocked Countries in the Country Pair   
0.11 
(0.50)  
  
-1.37 
(0.39) 
   
0.28 
(0.32) 
  
-0.22 
(0.47) 
 
Number of Island Countries in the 
Country Pair 
  2.54 
(0.36)  0.86 
(0.77)  2.12 
(0.27) -0.11 
(0.4) 
Log of Area Product  0.52 
(0.08)  -0.03 
(0.18) 
  0.56 
(0.07) 0.16 
(0.14) 
Dummy for Same Colonizer after 
1945  0.70 
(0.07)  0.72 
(0.07)  0.59 
(0.06) 0.61 
(0.06) 
 
Dummy for both Currently Being Colonies  0.31 
(0.47) 0.21 
(0.98) -1.12 
(0.16)  0.59 
(0.44) -1.05 
(0.14) 
 
Dummy for Common Nation  0.83 
(0.57)  2.33 
(0.37)  0.57 
(0.53) 2.29 
(0.33) 
 
Dummy for Being Colonizer and 
Colony to Each Other 
  1.46 
(0.07)  1.45 
(0.07)  1.31 
(0.06) 1.29 
(0.06) 
Dummy for Common FTA 
Membership  0.28 
(0.08) 0.25 
(0.10) 0.27 
(0.08)  0.39 
(0.06) 0.40 
(0.07) 
 
Dummy for One in WTO  0.26 
(0.12) 0.10 
(0.10) 1.80 
(0.55)  0.22 
(0.09) 2.03 
(0.49) 
 
Dummy for Both in WTO  0.43 
(0.14) 0.22 
(0.10) 3.52 
(1.10)  0.41 
(0.11) 4.01 
(0.97) 
 
Dummy for GSP  0.63 
(0.03) 0.45 
(0.09) 0.59 
(0.03)  0.71 
(0.03) 0.67 
(0.03) 
 
Time Fixed Effects  
Country Fixed Effects Country Pair Fixed Effects Time-Varying Country Effects  Yes 
Yes 
No No Yes 
No 
Yes 
No No 
No No 
Yes  Yes 
Yes 
No No No 
No No 
Yes 
Number of  Observations R-squared RMSE  16238 
0.79 1.64 16238 
0.49 16238 
0.81 1.60  
 26267 
0.75 1.76 26267 
0.77 1.71  WT/WGTDF/W/24 
 Page 71 
 
 
 
Appendix Table IV.3 Alternative Meas ures of Volatility: Short-Run, Parall el Market, Nominal and Conditional 
 
Variable  LR & SR, 
Official, Real LR, Official & 
Parallel, Real LR & SR, Official & 
Parallel, Real  LR, Official, 
Nominal Conditional, 
Official, Real 
  (1) (2) (3)  (4) (5) 
L-R Official Real Vol)   -3.86 
(0.88) -1.86 
(0.98) -3.92 
(1.30)    
(S-R Official Real Vol) - 
(L-R Official Real Vol)   
-1.87 
(0.71)  
-2.72 
(1.04)    
(L-R Parallel Real Vol) - 
(L-R Official Real Vol)    
-1.55 
(0.60) -1.20 
(0.63)    
(S-R Parallel Real Vol)  - 
(S-R Official Real Vol)    -0.55 
(0.73)    
L-R Official Nominal Vol      
-2.60 
(0.60)  
Volatility Conditional upon 
Historical Information       
-2.20 
(0.92) 
Dummy for Common Currency 
Union  
1.37 
(0.14)    
1.30 
(0.14) 1.41 
(0.14) 
Log of Real GDP Product  
0.18 
(0.31) 0.21 
(0.19) 0.21 
(0.19)  
0.22 
(0.13) 0.18 
(0.13) 
Log of Real Per Capita GDP 
Product  
-0.05 
(0.12) 1.04 
(0.21) 1.03 
(0.21)  
-0.09 
(0.12) -0.04 
(0.12) 
Log of Distance  
-1.51 
(0.02) -1.40 
(0.03) -1.39 
(0.03)  
-1.51 
(0.02) -1.52 
(0.02) 
Common Language Dummy  
0.25 
(0.05) 0.30 
(0.06) 0.30 
(0.06)  
0.25 
(0.05) 0.25 
(0.05) 
Common Border Dummy  
-0.12 
(0.09) -0.26 
(0.12) -0.22 
(0.12)  
-0.12 
(0.10) -0.12 
(0.10) 
Number of Landlocked Countries in 
the Country Pair  
-3.12 
(0.56) -3.06 
(0.69) -3.33 
(0.55)  
-0.01 
(0.50) 0.10 
(0.50) 
Number of Island Countries in the 
Country Pair  
0.56 
(0.54) -3.08 
(0.72) -5.11 
(1.11)  
2.51 
(0.36) 2.58 
(0.36) 
Log of Area Product  
0.53 
(0.09) 0.27 
(0.10) 0.23 
(0.12)  
0.50 
(0.08) 0.52 
(0.08) 
Dummy for Same Colonizer after 
1945  
0.68 
(0.07) 0.70 
(0.11) 0.70 
(0.11)  
0.70 
(0.07) 0.70 
(0.07) 
Dummy for Both Currently being 
Colonies  
0.31 
(0.48) -0.81 
(0.16) -0.82 
(0.16)  
0.28 
(0.46) 0.29 
(0.47) 
Dummy for Common Nation  
0.80 
(0.58)    
0.91 
(0.57) 0.84 
(0.57) 
Dummy for Being Colonizer and 
Colony to Each Other  
1.44 
(0.07) 1.01 
(0.09) 1.00 
(0.09)  
1.45 
(0.07) 1.46 
(0.07) 
Dummy for Common FTA 
Membership  
0.27 
(0.08) -0.49 
(0.11) -0.50 
(0.11)  
0.28 
(0.08) 0.28 
(0.08) 
Dummy for One in WTO  
0.27 
(0.12) -0.14 
(0.18) -0.18 
(0.18)  
0.26 
(0.12) 0.29 
(0.12) 
Dummy for Both in WTO  
0.43 
(0.14) -0.15 
(0.22) -0.18 
(0.22)  
0.43 
(0.14) 0.49 
(0.14) 
Dummy for GSP  
0.63 
(0.03) 0.59 
(0.04) 0.60 
(0.04)  
0.63 
(0.03) 0.63 
(0.03) 
Time Fixed Effects  
Country Fixed Effects  Yes 
Yes Yes 
Yes Yes 
Yes  Yes 
Yes Yes 
Yes 
Number of  Observations 
R-squared RMSE  16085 
0.79 
1.64 6988 
0.82 
1.44 6949 
0.83 
1.44  16238 
0.79 
1.64 16238 
0.79 
1.65 WT/WGTDF/W/24 
Page 72  
 
Appendix Table IV.4 Country-Pair fixed Effects 
 
 
Variable  LR & SR, 
Official, Real LR, Official & 
Parallel, Real LR & SR, Official & 
Parallel, Real  LR, Official, 
Nominal Conditional, 
Official, Real 
  (1) (2)  (3)  (4) (5)  
L-R Official Real Vol)   
   -4.84 
(0.61) -1.43 
(0.60) -4.72 
(0.76)    
(S-R Official Real Vol) - (L-R Official Real Vol)    -2.97 
(0.44)  -4.15  
(0.55)    
(L-R Parallel Real Vol) - (L-R Official Real Vol)     -0.94  
(0.34) -0.42  
(0.34)    
(S-R Parallel Real Vol)  - 
(S-R Official Real Vol)      -1.14  
(0.41)    
L-R Official Nominal Vol 
      -2-51 
(0.43)  
Volatility Conditional upon Historical Information        -4.12  
(0.70) 
Dummy for Common 
Currency Union   0.22 
(0.38)    0.20 
(0.38) 0.28 
(0.38) 
Log of Real GDP Product 
  0.50 
(0.10) 0.61 
(0.13) 0.65 
(0.13)  0.51 
(0.10) 0.50 
(0.10) 
Log of Real Per Capita 
GDP Product 
  -0.25 
(0.09) 0.52 
(0.14) 0.47 
(0.13)  -0.27 
(0.09) -0.24  
(0.09) 
Dummy for Both Currently 
Being Colonies   0.17 
(0.98)    0.15 
(0.98) 0.16 
(0.98) 
Dummy for Common FTA 
Membership   0.25 
(0.10) 0.29 
(0.13) 0.33 
(0.13)  0.26 
(0.10) 0.24 
(0.10) 
Dummy for One in WTO 
  0.10 
(0.10) -0.24  
(0.13) -0.25  
(0.13)  0.10 
(0.10) 0.13 
(0.10) 
Dummy for Both in WTO 
  0.24 
(0.10) -0.20  
(0.14) -0.20  
(0.14)  0.22 
(0.10) 0.29 
(0.10) 
Dummy for GSP 
  0.43 
(0.10) 0.03 
(0.14) 0.00 
(0.14)  0.45 
(0.09) 0.46 
(0.09) 
Time Fixed Effects  
Country Pair Fixed Effects  Yes 
Yes Yes 
Yes Yes 
Yes  Yes 
Yes Yes 
Yes 
Number of  Observations R-squared  16085 
0.49 6988 
0.69 6949 
0.69  16238 
0.48 16238 
0.49 
  WT/WGTDF/W/24 
 Page 73 
 
 
Appendix Table IV.5 Time-Varying Country Effects  
 
 
Variable  LR & SR, Official, 
Real LR, Official & 
Parallel, Real LR & SR, Official & 
Parallel, Real  LR, Official, 
Nominal Conditional, Official, 
Real 
  (1) (2) (3)  (4) (5) 
L-R Official Real Vol)   
   5.99 
(2.14) 3.34 
(3.25) 7.52 
(3.89)  3.64 
(1.58)  
(S-R Official Real Vol) - 
(L-R Official Real Vol)    4.19 
(1.88)  6.70 
(3.24)    
(L-R Parallel Real Vol) - 
(L-R Official Real Vol)     -2.01 
(2.69) -2.20 
(2.71)    
(S-R Parallel Real Vol)  - 
(S-R Official Real Vol)  
    -1.55 
(2.80)    
Sum of Effective L-R Official Real Vol of 
Country Pairs       -6.20 
(1.52)  
Volatility Conditional upon Historical 
Information        0.97 
(1.50) 
Dummy for Common Currency Union 
  1.42 
(0.14) 
    1.49 
(0.15) 1.36 
(0.14) 
Log of Real GDP Product 
  0.81 
(0.18) 0.97 
(0.09) 0.99 
(0.09)  0.24 
(0.14) 0.72 
(0.17) 
Log of Real Per Capita GDP Product 
  0.12 
(0.22) 
 0.69 
(0.09) 0.83 
(0.13)  -0.08 
(0.12) 0.19 
(0.21) 
Log of Distance 
  -1.54 
(0.02) -1.42 
(0.03) -1.43 
(0.03)  -1.53 
(0.03) -1.53 
(0.02) 
Common Language Dummy 
  0.27 
(0.05) 0.30 
(0.06) 0.31 
(0.06)  0.26 
(0.05) 0.25 
(0.05) 
Common Border Dummy 
  -0.11 
(0.09) -0.22 
(0.12) -0.17 
(0.12)  -0.13) 
(0.09) -0.12 
(0.10) 
Number of Landlocked Countries in the 
Country Pair   -1.27 
(0.39) -0.19 
(0.25) -0.75 
(0.29)  -2.97 
(0.59) -1.26 
(0.39) 
Number of Island Countries in the 
Country Pair   0.97 
(0.75) 1.16 
(0.26) 0.77 
(0.26)  0.52 
(0.52) 1.02 
(0.76) 
Log of Area Product 
  0.01 
(0.18) -0.20 
(0.04) -0.21 
(0.04)  0.52 
(0.08) 0.10 
(0.16) 
Dummy for Same Colonizer after 1945 
  0.71 
(0.07) 0.72 
(0.11) 0.70 
(0.11)  0.73 
(0.07) 0.72 
(0.07) 
Dummy for Both Currently Being Colonies 
  -1.14 
(0.16) -0.75 
(0.18) -0.70 
(0.19)  0.00 
(0.57) -1.11 
(0.16) 
Dummy for Common Nation 
  2.42 
(0.38)    1.21 
(0.66) 2.33 
(0.37) 
Dummy for Being Colonizer and Colony 
to Each Other 
  1.45 
(0.07) 1.00 
(0.09) 1.00 
(0.09)  1.47 
(0.07) 1.45 
(0.07) 
Dummy for Common FTA Membership 
  0.26 
(0.08) 
 -0.57 
(0.11) -0.56 
(0.11)  0.30 
(0.08) 0.26 
(0.08) 
Dummy for One in WTO 
  1.78 
(0.54) 0.64 
(0.35) 0.10 
(0.41)  0.26 
(0.13) 1.78 
(0.55) 
Dummy for Both in WTO 
  3.49 
(1.06) 1.40 
(0.64) 0.38 
(0.75)  0.41 
(0.15) 3.46 
(1.09) 
Dummy for GSP 
  0.58 
(0.03) 0.57 
(0.04) 0.56 
(0.04)  0.65 
(0.03) 0.59 
(0.03) 
Time Fixed Effects  
Country Fixed Effects Time-Varying Country Effects  No 
No 
Yes No 
No 
Yes No 
No 
Yes  Yes 
Yes 
No No 
No 
Yes 
Number of  Observations 
R-squared 
RMSE  16085 
0.81 
1.59 6988 
0.83 
1.43 6949 
0.84 
1.42  15157 
0.79 
1.62 16238 
0.81 
1.60 
 WT/WGTDF/W/24 
Page 74  
 
Appendix Table IV.6A. Controlling for Endogeneity of Exchange Rate Volatility:  Relative Money Supply as IV1/ 
 
 
Variable  Real Exchange Rate  Nominal Exchange Rate 
  (1) (2) (3)  (4) (5) (6) 
Long-run Volatility of  Real Official 
Exchange Rate 
  -22.64 
(12.5) -6.49 
(6.24) -23.82 
(28.87)     
Long-run Volatility of Nominal Official 
Exchange Rate    
   -25.98  
(14.50) -7.78 
(7.52) -17.92  
(21.72) 
Log of Real GDP Product 
  0.37 
(0.2) 
 0.52 
(0.12) 0.44 
(0.41)  0.62 
(0.31) 0.60 
(0.16) 0.45 
(0.40) 
Log of Real Per Capita GDP Product  
  -0.25 
(0.19) 
 -0.28  
(0.12) 0.35 
(0.35)  -0.52  
(0.32) -0.36 
(0.17) 0.41 
(0.41) 
Log of Distance 
  -1.52 
(0.04) 
  -1.52  
(0.09)  -1.49  
(0.06)  -1.52  
(0.08) 
Common Language Dummy 
  0.27 
(0.06) 
  0.27 
(0.10)  0.22 
(0.08)  0.26  
(0.10) 
Common Border Dummy 
  -0.08 
(0.12) 
  -0.12  
(0.14)  -0.10  
(0.12)  -0.12  
(0.14) 
Number of Landlocked Countries in 
the Country Pair   -1.41 
(0.56) 
  -1.38  
(0.39)  -1.86  
(0.72)  -1.16  
(0.51) 
Number of Island Countries in the 
Country Pair   1.76 
(0.70)  -1.64  
(2.79)  1.36 
(0.91)  -0.87  
(1.87) 
Log of Area Product 
  0.44 
(0.11)  0.54 
(0.60)  0.31 
(0.16)  0.43  
(0.46) 
Dummy for Same Colonizer after 
1945   0.78 
(0.09)  0.79 
(0.13)  0.70 
(0.12)  0.76  
(0.16) 
Dummy for Being Colonizer and 
Colony to Each Other   1.33 
(0.08)  1.33 
(0.08)  1.32 
(0.09)  1.31  
(0.08) 
Dummy for Common FTA 
Membership   0.27 
(0.09) 0.23
 
(0.11) 0.26 
(0.13)  0.30 
(0.09) 0.24 
(0.11) 0.29 
(0.10) 
Dummy for One in WTO 
   0.14 
(0.16) 
 -0.02
 
(0.11) -1.77  
(3.83)  0.16 
(0.16) -0.02 
(0.11) -0.65  
(2.48) 
Dummy for Both in WTO 
 
  0.11 
(0.29) 0.12 
(0.15) -3.63  
(7.69)  0.10 
(0.30) 0.11 
(0.16) -1.41  
(5.01) 
Dummy for GSP 
  0.66 
(0.03) 
 0.44 
(0.11) 0.63 
(0.04)  0.65 
(0.03) 0.43 
(0.11) 0.63 
(0.03) 
Time Fixed Effects  
Country Fixed Effects Country Pair Fixed Effects 
Time-Varying Country Effects  Yes 
Yes 
No 
No Yes 
No 
Yes 
No No 
No No 
Yes  Yes 
Yes 
No 
No Yes 
No 
Yes 
No No 
No No 
Yes 
Number of  Observations R-squared 
RMSE  14343 
0.77 
1.71 14343 
0.48 14343 
0.80 
1.62  14343 
0.76 
1.74 14343 
0.46 14343 
0.80 
1.62 
 
1/The instrumental variable is t he long-run volatility of the relative money suppl y, constructed similarly to Frankel and Wei  (1 993).  WT/WGTDF/W/24 
 Page 75 
 
 
Appendix Table IV.6B. Controlling for Endogeneity of Exchange Rate Volatility:  Common Anchor Dummy as IV1/ 
 
 
Variable  Real Exchange Rate  Nominal Exchange Rate 
  (1) (2) (3)  (4) (5) (6) 
Long-run Volatility of  Real Official 
Exchange Rate 
  -10.62 
(7.45) 29.06 
(17.79) -7.23 
(7.73)     
Long-run Volatility of Nominal Official 
Exchange Rate    
   -7.06  
(4.92) 27.75 
(17.17) -4.37  
(4.67) 
Log of Real GDP Product 
 
  0.66 
(0.20) 1.08 
(0.18) 1.06 
(0.12)  0.73 
(0.20) 0.83 
(0.21) 1.02 
(0.1) 
Log of Real Per Capita GDP Product  
  0.31 
(0.26) 1.07 
(0.48) 0.36 
(0.16)  0.31 
(0.26) 1.41 
(0.69) 0.45 
(0.11) 
 
Log of Distance 
  -1.45 
(0.04) 
  -1.46  
(0.04)  -1.45  
(0.04)  -1.46  
(0.04) 
Common Language Dummy 
   0.35 
(0.07)  0.36 
(0.07)  0.36 
(0.07)  0.36  
(0.07) 
Common Border Dummy 
   0.24 
(0.14)  0.25 
(0.14)  0.24 
(0.14)  0.25  
(0.14) 
Number of Landlocked Countries in 
the Country Pair   -2.19 
(1.44)  -0.78  
(0.47)  -2.43  
(1.36)  -0.81  
(0.47) 
Number of Island Countries in the 
Country Pair   -2.39 
(1.02)  0.17 
(0.33)  -2.00  
(0.93)  0.13  
(0.32) 
Log of Area Product 
  0.08 
(0.14)  -0.17  
(0.07)  0.02 
(0.12)  -0.16  
(0.06) 
Dummy for Same Colonizer after 
1945   1.00 
(0.10)  1.02 
(0.10)  0.99 
(0.10)  1.01  
(0.10) 
Dummy for Being Colonizer and 
Colony to Each Other   0.95 
(0.20)  0.95 
(0.20)  0.95 
(0.20)  0.94  
(0.20) 
Dummy for Common FTA 
Membership   0.81 
(0.17) 0.74
 
(0.31) 0.69 
(0.18)  0.82 
(0.17) 0.77 
(0.33) 0.70 
(0.18) 
Dummy for One in WTO 
   0.02 
(0.16) -0.05
 
(0.16) 0.70 
(0.39)  0.04 
(0.15) -0.09 
(0.15) 0.84 
(0.34) 
Dummy for Both in WTO 
   0.01 
(0.22) 0.41
 
(0.29) 1.40 
(0.73)  0.06 
(0.20) 0.38 
(0.28) 1.66 
(0.63) 
Dummy for GSP 
  0.66 
(0.04) 0.20 
(0.20) 0.64 
(0.04)  0.66 
(0.04) 0.20 
(0.20) 0.64 
(0.04) 
Time Fixed Effects  
Country Fixed Effects Country Pair Fixed Effects 
Time-Varying Country Effects  Yes 
Yes 
No 
No Yes 
No 
Yes 
No No 
No No 
Yes  Yes 
Yes 
No 
No Yes 
No 
Yes 
No No 
No No 
Yes 
Number of  Observations R-squared 
RMSE  8531 
0.75 
1.72 8531 
0.52 8531 
0.77 
1.68  8531 
0.76 
1.71 8531 
0.49 8531 
0.77 
1.68 
 
1/The instrumental variable is a common anc hor dummy, constructed by Tenreyro (2003) WT/WGTDF/W/24 
Page 76  
 
Appendix Table IV.6C. Controlling for Endogeneity of Exchange Rate Volatility:  Propensity to Share a Common Anchor as IV1/ 
 
 
Variable  Real Exchange Rate  Nominal Exchange Rate 
  (1) (2) (3)  (4) (5) (6) 
Long-run Volatility of  Real Official 
Exchange Rate 
  -46.60 
(12.99) -19.76 
(20.98) -34.99 
(8.86)     
Long-run Volatility of Nominal Official 
Exchange Rate    
   -32.91  
(8.77) -11.48 
(11.44) -24.65  
(6.14) 
Log of Real GDP Product 
 
  1.87 
(0.37) 1.53 
(0.29) 1.19 
(0.09)  2.03 
(0.35) 1.55 
(0.29) 1.19 
(0.09) 
Log of Real Per Capita GDP Product  
  -1.89 
(0.67) 
 -0.80  
(1.10) 0.47 
(0.15)  -1.70  
(0.59) -0.53 
(0.77) 0.60 
(0.14) 
Log of Distance 
   -1.18 
(0.07)  -1.22  
(0.05)  -1.21  
(0.06)  -1.24  
(0.05) 
Common Language Dummy 
   0.51 
(0.10)  0.48 
(0.09)  0.49 
(0.09)  0.48  
(0.09) 
Common Border Dummy 
 
  -0.17 
(0.21)  -0.09  
(0.18)  -0.21  
(0.20)  -0.13  
(0.18) 
Number of Landlocked Countries in 
the Country Pair   -6.01 
(1.86)  -0.34  
(0.31)  -4.73  
(1.49)  -0.38  
(0.30) 
Number of Island Countries in the 
Country Pair   0.36 
(1.55)  -0.20  
(0.34)  1.34 
(1.42)  -0.12  
(0.34) 
Log of Area Product 
  -0.37 
(0.22)  -0.06  
(0.07)  -0.49  
(0.20)  -0.07  
(0.07) 
Dummy for Same Colonizer after 
1945   0.55 
(0.19)  0.66 
(0.15)  0.53 
(0.18)  0.64  
(0.15) 
Dummy for Being Colonizer and 
Colony to Each Other   0.80 
(0.24)  0.89 
(0.21)  0.81 
(0.22)  0.87  
(0.20) 
Dummy for Common FTA 
Membership   0.03 
(0.22) 0.25
 
(0.26) 0.00 
(0.21)  0.11 
(0.21) 0.31 
(0.22) 0.05 
(0.20) 
Dummy for One in WTO 
   -0.75 
(0.31) -0.18
 
(0.25) -1.60  
(0.63)  -0.53  
(0.26) -0.07 
(0.18) -0.49  
(0.48) 
Dummy for Both in WTO 
   -1.62 
(0.52) -0.53
 
(0.58) -3.06  
(1.22)  -1.16  
(0.40) -0.27 
(0.31) -0.87  
(0.91) 
Dummy for GSP 
  0.62 
(0.06) -0.22  
(0.32) 0.63 
(0.05)  0.60 
(0.05) -0.27 
(0.28) 0.61 
(0.05) 
Time Fixed Effects  
Country Fixed Effects 
Country Pair Fixed Effects Time-Varying Country Effects  Yes 
Yes 
No No Yes 
No 
Yes 
No No 
No 
No 
Yes  Yes 
Yes 
No No Yes 
No 
Yes 
No No 
No 
No 
Yes 
Number of  Observations R-squared 
RMSE  4801 
0.73 
1.78 4801 
0.44 4801 
0.81 
1.53  4801 
0.76 
1.68 4801 
0.49 4801 
0.81 
1.52 
 
1/The instrumental variable is a propensity to share a common anchor,  estimated based on Tenreyro (2003)  WT/WGTDF/W/24 
 Page 77 
 
 
Appendix Table IV.7  Differentiation by Country  Type 1/ 
 
 
Variable  Developed & Developing Countries 
  (1) (2) 
 
L-R Official Real Vol     
43.47 
(3.54)  
47.90 
(3.85) 
(L-R Official Real Vol) * (NS Dummy) 
  -45.70 
(3.60) -47.59 
(3.90) 
(L-R Official Real Vol) * (SS 
Dummy) 
  -46.69 
(3.64) -47.92 
(4.00) 
Dummy for Common Currency 
Union 
  -0.62 
(0.23) -0.60 
(0.21) 
(Currency Union Dummy) * (SS 
Dummy) 
  2.11 
(0.27) 2.12 
(0.25) 
Dummy for North-South 
Country Pair 
  -0.49 
(0.28) 1.11 
(0.45) 
Dummy for South-South 
Country Pair 
  -2.49 
(0.52) 0.56 
(0.87) 
Time Fixed Effects 
Country Fixed Effects 
Time-Varying Country Effects  Yes 
Yes 
No No 
No 
Yes 
Number of Observations R-squared RMSE  16238 
0.79 1.64 16238 
0.81 1.59 
 
1/  The estimated coefficients for standard c ontrol dummies in the baseline specifications are not 
reported. WT/WGTDF/W/24 
Page 78  
 
Appendix Table IV.8  Differentiation by Product Type (SUR) 
 
 
Variable  With Country Fixed Effect   With Time- Varying Country Effect 
  Homogeneous  Differentiated  Homoge neous Differentiated 
 
L-R Official Real Vol     
-0.59 
(2.12)  
-2.89 
(1.66)   
-2.97 
(4.39)  
0.98 
(3.06) 
Log of real GDP Product    2.29 
(0.28) 3.18
 
(0.22)  0.84 
(0.09) 0.23 
(0.12) 
Log of Real Per Capita GDP Product  -2.16 
(0.28) -3.24  
(0.22)  -0.79  
(0.20) 0.49 
(0.24) 
 
Log of Distance   -2.06 
(0.04) -1.01
 
(0.03)  -2.11  
(0.04) -1.08 
(0.03) 
Common Language Dummy 
  0.05 
(0.09) 0.30 
(0.07)  0.04 
(0.08) 0.33 
(0.06) 
Common Border Dummy  -0.95 
(0.14) -0.23  
(0.11)  -0.99  
(0.12) 
 -0.28 
(0.08) 
 
Number of Landlocked Countries in the 
Country Pair  -1.99 
(0.46) -6.41  
(1.22)  1.28 
(0.41) 
 0.62 
(0.48) 
 
Number of Island Countries in the 
Country Pair 
  -6.82 
(1.59) -12.11  
(1.25)  4.68 
(0.51) -3.97 
(0.63) 
Log of Area Product  -2.12 
(0.34) -3.36  
(0.26)  -0.29  
(0.08) 
 -0.16 
(0.11) 
 
Dummy for Same Colonizer after 1945  
  0.43 
(0.27) 
 0.35 
(0.21)  0.47 
(0.24) 0.26 
(0.17) 
Dummy for both Currently Being 
Colonies 
  0.49 
(1.16) 0.12 
(0.91)  0.13 
(1.05) 0.04 
(0.73) 
Dummy for Being Colonizer and Colony 
to Each Other 
  0.89 
(0.16) 0.79 
(0.12)  0.89 
(0.14) 0.81 
(0.10) 
 
Dummy for Common FTA Membership   -0.34 
(0.11) 0.05 
(0.08)  -0.51  
(0.11) 0.06 
(0.08) 
 
Dummy for One in WTO  0.67 
(1.16) 1.31 
(0.90)  -0.02  
(0.54) -0.12 
(0.38) 
 
Dummy for Both in WTO  0.99 
(1.16) 0.96 
(0.91)    
  
Dummy for GSP  -0.51 
(0.09) 0.51
 
(0.07)  -0.41  
(0.08) 0.55 
(0.06) 
 
Time Fixed Effects  
Country Fixed Effects 
Time-Varying Country Effect  Yes 
Yes 
No Yes 
Yes 
No  No 
No 
Yes No 
No 
Yes 
Number of  Observations R-squared 
RMSE  4370 
0.72 
1.61 4370 
0.82 
1.26  
 4370 
0.78 
1.43 4370 
0.89 
0.99 
  WT/WGTDF/W/24 
 Page 79 
 
 
Appendix IV.9. List of Countries in the Regressions with Disaggregated Trade 
 ____________________________________________________________________  
ARGENTINA AUSTRALIA 
AUSTRIA BELGIUM BRAZIL CANADA 
CHILE 
COLOMBIA DENMARK FINLAND FRANCE 
GERMANY 
GREECE HONG KONG INDIA INDONESIA IRELAND 
ITALY 
JAPAN KOREA, SOUTH (R) MALAYSIA MEXICO 
NETHERLANDS 
NEW ZEALAND NORWAY PERU PHILIPPINES PORTUGAL 
SINGAPORE 
SOUTH AFRICA SPAIN SWEDEN SWITZERLAND 
THAILAND 
TURKEY UNITED KINGDOM UNITED STATES CHINA POLAND 
_____________________________________________________________________ 
 WT/WGTDF/W/24 
Page 80  
 
Appendix IV.10.  Classification of Products into Homogeneous and Differentiated Categories 
 
A.  Homogeneous products 
 
SITC 4 (rev. 1) Name 
  
1121 Wine of fresh grapes including grape must 
1124 Distilled alcoholic beverages 1222 Cigarettes 2517 Sulphate wood pulp 3214 Coal /anthracite, bituminous/ 
3310 Petroleum, crude & partly refined 
3321 Motor spirit, gasolene and other light oils 3411 Gas, natural 5121 Hydrocarbons and their derivatives 5122 Alcohols, phenols, phenol-alcohols, glycerine 5811 Prods of condensation, polycond. & polyaddition 
6411 Newsprint paper 
6412 Other printing and writin g paper, machine-made 
6415 Machine-made paper & paperboard, simply fnshd 6516 Yarn and thread of synthetic fibres 6732 Bars and rods of iron or steel, ex wire rod 
6748 Oth. coated iron or steel plates etc under 3 mm 
6821 Copper and alloys, unwrought 6822 Copper and alloys of copper, worked 6841 Aluminium and aluminium alloys, unwrought 6842 Aluminium and aluminium alloys, worked 
7291 Batteries and accumulators 
 
 
B.  Differentiated products 
 
SITC 4 (rev. 1) Name 
  
2432 Lumber, sawn, planed, etc. - conifer 
5417 Medicaments 5530 Perfumery & cosmetics, dentifrices etc. 5999 Chemical products and preparations, nes 
6291 Rubber tyres & tubes for vehicles and aircraft 
6429 Art. of paper pulp, paper or paperboard 6522 Cotton fabrics, woven, other than grey 6537 Knitted or crochd fabrics not elast nor rubberd 6554 Coated or impregnate d textile fabrics & prod. 
6942 Nuts, bolts, screws, rivets, washers, etc. 
6952 Other tools for use in the hand or in machines 
6981 Locksmiths wares 6989 Articles of base metals, nes 7114 Aircraft – incl jet propulsion – engines 7115 Internal combustion engines, not for aircraft 
7143 Statistical machines-cards or tapes- 
7149 office machines, nes 7151 Machine-tools for working metals  WT/WGTDF/W/24 
 Page 81 
 
 
7171 Textile machinery 7182 Printing and bookbinding machinery 7184 Construction and mining machinery, nes 7191 Heating and cooling equipment 7192 Pumps and centrifuges 
7193 Mechanical handling equipment 
7195 Powered-tools, nes 7196 Other non-electrical machines 7197 Ball, roller or needle-roller bearings 7199 Parts and accessories of machinery, nes 
7222 Apparatus for electrical circuits 
7231 Insulated wire and cable 7241 Television broadcast receivers 7242 Radio broadcast receivers  7249 Telecommunications equipment nes 
7250 Domestic electrical equipment 
7293 Thermionic valves and tubes, transistors, etc. 7294 Automotive electrical equipment 7295 Electrical measuring & controlling instruments 7299 Electrical machinery and apparatus, nes 7321 Passenger motor cars, other than buses 
7323 Lorries and trucks, including ambulances, etc. 
7328 Bodies & parts motor vehicles ex motorcycles 7331 Bicycles & other cycles, not motorized, & parts 7341 Aircraft, heavier-than-air 7349 Parts of aircraft, balloons airships 
7353 Ships and boats, other than warships 
8210 Furniture 8310 Travel goods, handbags & similar articles 8411 Clothing of text fabr ic, not knitted crocheted 
8414 Clothing and accessories, knitted or crocheted 8510 Footwear 
8616 Photographic & cinematographic equipment nes 
8617 Medical instruments, nes 8619 Measuring, controlling & scientific instruments 8624 Photo. film etc & develpd film other than cine. 8911 Phonographs, tape & other sound recorders etc. 
8912 Phonograph records, recorded tapes, oth.sound rec 
8921 Books and pamphlets, printed 8942 Children toys, indoor games, etc. 8944 Other sporting goods 
 
 
 
 
 
__________  