PROCEEDINGS OF JUNE 2005 WORKSHOP ON WO R K I N G PA P E R S E R I E S

WHAT EFFECTS IS EMU HAVING ON THE EURO

AREA AND ITS MEMBER COUNTRIES? NO 594 / MARCH 2006

THE EURO’S
TRADE EFFECTS

ISSN 1561081-0
by Richard Baldwin
comments by Jeffrey A. Frankel
9 771561 081005 and Jacques Melitz
 WO R K I N G PA P E R S E R I E S
NO 594 / MARCH 2006

PROCEEDINGS OF JUNE 2005 WORKSHOP ON

WHAT EFFECTS IS EMU HAVING ON THE EURO
AREA AND ITS MEMBER COUNTRIES?

THE EURO’S
TRADE EFFECTS 1

by Richard Baldwin 2

comments by Jeffrey A. Frankel

and Jacques Melitz

In 2006 all ECB

publications
will feature This paper can be downloaded without charge from
a motif taken http://www.ecb.int or from the Social Science Research Network
from the
€5 banknote. electronic library at http://ssrn.com/abstract_id=886260

1 First draft 8 May 2005; Second draft 29 May 2005; Third draft June 2005. Prepared for the ECB Workshop “What effects is EMU
having on the euro area and its member countries?” Frankfurt, 16 June 2005. I would like to thank Nadia Rocha for assistance
with data-wrestling. Andy Rose, Volker Nitsch, Howard Wall, Alejandro Micco and Hakan Nordstrom provided excellent comments
and answered my many questions about their data and regressions. They also saw early drafts of this paper and eliminated several
mistakes but there may be still some left in this version. Special thanks to Francesco Mongelli who carefully read the first complete
draft and caught many typos, thinkos and omissions.
2 Graduate Institute of International Studies, 11a, avenue de la Paix, CH-1202 Geneva, Switzerland; e-mail: Baldwin@hei.unige.ch
 PREFACE

On 16 and 17 June 2005, the ECB has hosted a Conference on “What Effects is EMU Having on the Euro Area and its
Member Countries?” One and a half decade after the start of the European Economic and Monetary Union (EMU) and more
than six years after the launch of the euro, the aim of the conference was to assess what can be learned about the impact of
economic and monetary integration and how it has benefited the euro area and its member countries.

The conference brought together academics, central bankers and policy makers to discuss the existing empirical evidence on
changes brought about, either directly or indirectly, by EMU and, in particular, the introduction of the euro in five main areas:
Area 1. Trade integration;
Area 2. Structural reforms in product and labour markets;
Area 3. Financial integration;
Area 4. Business cycles synchronisation and economic specialisation; and
Area 5. Inflation persistence and inflation differentials.

Lead presenters for each of the aforementioned areas had been asked to put together - and interpret - all the available
information, flag any open questions, and also discuss the implications in their respective field of expertise. With the benefit of
hindsight, lead presenters and discussants have also addressed some initial presumptions with the evidence that has
accumulated thus far.

In order to exchange information and ideas on the above effects, and increase mutual awareness of ongoing work in the diverse
areas, we deemed it useful to issue the five leading presentations, together with the accompanying discussions, in the ECB
Working Paper Series.

Otmar Issing Francesco Paolo Mongelli Juan Luis Vega

Member of the Executive Board Conference Organiser Conference Organiser

© European Central Bank, 2006

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The views expressed in this paper do not

necessarily reflect those of the European
Central Bank.

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Working Paper Series is available from
the ECB website, http://www.ecb.int.

ISSN 1561-0810 (print)

ISSN 1725-2806 (online)
CONTENTS
Abstract 4
Non-technical summary 5
1. Introduction 6
2. The Rose vine: review of the pre-euro literature 7
2.1. Roots: the world through Rose coloured
glasses 7
2.2. Garden pests: biases in gravity model
estimations 11
2.3. Rose branch #1: Rose and van Wincoop
(2001) 20
2.4. Rose branch #2: omitted variables 21
2.5. Rose branch #3:
complicated mis-specification 28
2.6. Rose branch #4:
roster-makes-the-sun-rise reasoning 32
2.7. Meta-Analysis: a rose is a rose is a
rose 35
2.8. Lessons for the eurozone from
non-European experience 36
3. Empirical findings on the eurozone 36
3.1 MSO (2003) 37
3.2 Berger and Nitsch (2005) 41
3.3 Flam and Nordstrom (2003) 42
3.4 Other studies 47
4 Collection of clues 48
4.1. Spatial variation of the Eurozone Rose
effect 48
4.2. Timing of the Eurozone Rose effect 52
4.3. Sectoral variation in the Eurozone Rose
effect 54
5. What could it be? 56
5.1. Spurious results 56
5.2. Microeconomic changes that might
produce a Rose effect 61
6. Battery of diagnostics 66
6.1. Checking for spurious results 66
6.2. Real changes 67
7. Concluding Remarks 67
References 68
8. Appendix on methods 71
Comments by Jeffrey A. Frankel 76
References 87
Comments by Jacques Melitz 91
References cited 98
European Central Bank Working Paper Series 99

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March 2006 3
 Abstract

This paper reviews reassesses the methodology and principal findings of the
“Rose effect”, i.e. the trade effects of currency union, looking at both EMU and non-
EMU currency unions. The consensus estimate suggests that the euro has already
boosted intra-euro area trade by five to ten percent. The paper discusses a gamut of
models that might explain the Rose effect in Europe and suggests a series of empirical
test that could help identify the economic mechanisms involved.

Key words: Rose effect, exchange rate volatility, monetary union, gravity model.

JEL codes: F12, C33, E0

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 Non-Technical Summary

The paper is articulated in three main parts. The first part reviews the origins,
methodology and principal findings of the empirical literature that has looked at
currency unions preceding EMU. The specification of the gravity model and
estimation strategies are newly reassessed. As a result the trade effects of currency
unions for non-European cases are completely recalibrated (i.e., the trade effects are
still important but less sizeable than in early estimates by Rose and others). One needs
to keep in mind that cases of pre-euro currency unions usually pertain to small (and
often poor) countries adopting the currency of a larger partner country. I.e., such
studies do not carry direct policy implications for the euro area.

The second part of the paper reviews the trade effects of currency unions –
i.e., the euro -- for the European Economic and Monetary Union thus far. The bottom
line of this literature is that the euro probably did boost intra-Eurozone trade by
something like five to ten percent on average, although the estimated size of this
effect is likely to change as new years of data emerge.

The third part of the paper investigates the economic mechanisms that might
be driving the euro’s trade effects. A theoretical model is presented. Diverse
competing hypotheses are examined and a battery of diagnostic tests -- that could help
reject some or all of the theoretical explanations – are lined up.

The way forward needs to be guided by detailed theoretical hypothesis as to

HOW the euro affects trade. As there is not enough data yet to answer the question
“How much did the euro boost trade?” one needs to tackle the question “If the euro
boosted trade by sharpening competition, then in which dataset should we find the
footprints?” and some footprints will have nothing to do with trade. In a nutshell,
studies of trade effects need to come out of their infancy and move beyond the
question of ‘how big is the magic.’

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 1. INTRODUCTION
The euro must be the world’s largest economic policy experiment. Six years ago, European nations
accounting for 20% of world output, 30% of world trade and 300 million people found themselves
using the same currency. Given the importance that monetary regimes have on economies, switching to
the euro should have had effects all across the board – changing everything from union’s wage
bargaining to educational exchanges and corporate investment strategies. Every problem looks like a
nail when you have a hammer in your hand, so being a trade economist I am naturally drawn to the
euro’s trade effects.
In this paper, I review the empirical literature on the trade effects of currency unions for non-European
and European cases. This is done in sections 2 and 3. My bottom line summary of this literature is that
the euro probably did boost intra-Eurozone trade by something like five to ten percent on average,
although the estimated size of this effect is likely to change as new years of data emerge. Then I collect
together the clues in section 4 and use them in section 5 to speculate on the sorts of economic
mechanisms that might be driving the euro’s trade effects. I come up with a set of competing
hypotheses and, in Section 6, propose a battery of diagnostic tests that could help reject some or all of
the theoretical explanations. The final section contains my concluding remarks.
Right up front I should apologise to the reader for the length of the paper. The ECB asked me to write a
short paper on the subject but I didn’t have time for that so I wrote a long paper instead.

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2. THE ROSE VINE: REVIEW OF THE PRE-EURO LITERATURE
Andy Rose of the University of California-Berkeley opened a new chapter in international economics
research with an Economic Policy article that was published in 2000. Rose (2000) asked a simple
question and got a simple answer:
“What is the effect of a common currency on international trade? Answer: Large.”
Rose’s paper generated a huge response. It has been cited over 550 times according to Google Scholar
as of May 2005. (Barro’s ‘Growth in a Cross-Section of Countries’ paper, for comparison, has been
cited two thousand times.) Much of the response has been critical with many authors trying to reduce
the size of the impact. This section reviews the evidence on the trade effects of currency unions – what
I’ll call the Rose effect for brevity’s sake – on the pre-euro data. The next section reviews the evidence
on the trade effects of the euro itself.
As an aside, I want to thank Andy for introducing a tradition of jocular writing into this literature. The
final version that Andy turned in to Charles Wyplosz (the Managing Editor of Economic Policy who did
the final editing; David Begg handled the manuscript in its early stages) was chock-block full of
exuberant usages of the English language. Charles tempered some of the most avant-garde
constructions, but the published version of Rose (2000) is still a lot of fun to read. Volker Nitsch
seconded this with his papers entitled: “Honey I shrunk the currency union effect” and “Have a Break”;
I shall struggle to uphold the tradition in this paper.

Scientific rectitude
Andy Rose is also responsible for another remarkable feature of this literature – transparency and
scientific rectitude. All of Rose’s data sets and regressions are posted on his web site. This has
permitted scholars from around the world to check his data and results, tinker with specifications and
challenge his findings. Subsequent contributors to this literature have generally followed this stellar
example.

Organisation of the pre-euro literature review

This section provides a ‘guided tour’ of the origins, core methodologies and principle findings of the
empirical literature on pre-euro Rose effects. It is arranged in approximate chronological order since
hindsight allows an easy way of illustrating how various corrections of the shortcomings in Rose’s
seminal contribution affect the estimates.

2.1. Roots: the world through Rose coloured glasses

Rose (2000) started the debate with his finding that countries in a currency union traded 3 times more
with each other than one would expect. He arrived at that astonishing result using a gravity-equation
approach on data for bilateral trade among 186 nations.2 His cross-section regression was:
ln(RVod)=a0+β1ln(RYoRYd)+β2ln(Distanceod)+β3(CUod)+controls
where RV is the real value of bilateral trade, the RY’s are real GDPs of the origin nation (‘o’ is a
mnemonic for origin) and destination nation (‘d’ is a mnemonic for origin), and CU is a dummy that
switches on when nations o and d share a common currency. In his favourite regression, β3=1.21 which
implies trade between common-currency pairs was e1.21=3.35 times larger than the baseline model
would suggest, i.e. a common currency boost trade by over 200%.
The size of this common-currency effect was just far too large to be believed and the profession’s
assault on this claim began even before he presented it at the October 1999 Economic Policy Panel
hosted by the Bank of Finland. There were three main themes in these critiques:

2
Standard references for the gravity model are Tinbergen (1962), Pöyhönen (1963), Linnemann (1966), Anderson (1979), Bergstrand (1985) and
Helpman and Krugman (1985). It was re-introduced to US academic circles by McCallum (1995) and Frankel, Stein, and Wei (1995, 1998).

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 Figure 1: Schematic depiction of hub and spoke common currency arrangement

Porto
Rico

American Turks and

Samoa Caicos
Islands

Guam Bahamas
USA

US Bermuda
Virgin
US
Islands
Virgin
Islands

• Omitted variables (omitting variables that are pro-trade and correlated with the CU dummy biases
the estimate upwards);
• Reverse causality (big bilateral trade flows cause a common currency rather than vice versa); and
• Model misspecification.
Most critiques turned on the fact that most of the common currency pairs involved nations that were
very small and very poor. A highly readable early presentation of such critiques can be found in Nitsch
(2002).
In his revisions, Rose produced a battery of robustness checks that he claimed had repulsed each of
these critiques, leaving his central result essentially unaltered. As the Editors’ Introduction to the issue
in which Rose (2000) appears says: “The Panel admired the paper and the author’s thoroughness but
retained an uneasy feeling that something had eluded them.”
Much of the subsequent literature on the Rose effect can be thought of as a search for that elusive
something. Before reviewing the ‘rose vine’ that has grown from Rose’s roots, it is critical to have an
idea of the currency unions that this literature investigated.

2.1.1. Pre-Eurozone currency unions

Rose (2000) lists all the currency unions (CU) and CU-like monetary arrangements from 1970 onwards.
This is reproduced in Table 1. There are three types of CUs in his table. The first two columns show the
hub-and-spoke CU arrangements. As Figure 1 shows with a schematic diagram for the US; hub and
spoke CUs involve small nations (the spokes) adopting the currency of their dominate trade partner (the
hub). The hubs are the USA, France, Britain, Australia and New Zealand.
There are two types of bilateral trade flows in hub and spoke arrangements, flows between the hub and
a spoke and flows between the spokes. Most hub-spoke trade flows involve the exchange of extremely
different goods (so-called Heckscher-Ohlin trade). For example, the US sells machinery to Barbados
while Barbados sells rum to the US. The spoke-spoke flows are typically very small, as is true of trade
among most poor nations.
The third column of the table lists the second type of CU, namely multilateral currency unions. The two
major multilateral currency unions that existed before the euro are the West African CFA arrangement
and the Caribbean arrangement, the ECCA. These CUs are among nations that are tiny economically by
world standards. The fourth column lists a series of highly idiosyncratic CU pairs often involving a very
local hegemony, like Switzerland and Liechtenstein, or Italy and San Marino.

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 Rose (2000) does not have data for all these. I have put checks against the ones in the table that he
includes in his study.
Another way to look at the oddness of the non-European currency union pairs is to plot their openness
ratios. The openness ratio is just the sum of trade divided by real GDP, were the trade is the bilateral
trade data from Rose (2000) summed across all of each nation’s trade partners. The results are displayed
in Figure 2. The top panel shows all 141 nations with data. The bottom panel includes only nations that
have openness ratios of less that 2.0 (i.e. trade which is less than 200% of GDP).

Table 1: The Rose Garden, currency unions considered in Rose (2000)

Hub and Spoke arrangements Multilateral currency unions Misc.
√ Australia √ USA CFA √ India
Christmas Island American Samoa √ Benin √ Bhutan
Cocos (Keeling) Islands Guam √ Burkina Faso √ Denmark
Norfolk Island √ US Virgin Islands √ Cameroon Faroe Islands
√ Kiribati Puerto Rico √ Central African Republic √ Greenland
√ Nauru Northern Mariana Islands √ Chad Turkey
√ Tuvalu √ British Virgin Islands Comoros N. Cyprus
Tonga (pre ’75) √ Turks & Caicos √ Congo Singapore
√ France √ Bahamas √ Cote d’Ivoire Brunei
√ French Guyana (OD) Bermuda Equatorial Guinea (post '84) Norway
√ French Polynesia √ Liberia √ Gabon Svalbard
√ Guadeloupe (OD) Marshall Islands Guinea-Bissau South Africa
Martinique (OD) Micronesia √ Mali (post '84) Lesotho
Mayotte Palau √ Niger Namibia
√ New Caledonia (OT) √ Panama √ Senegal Swaziland
√ Reunion (OD) √ Barbados (? 2:1) √ Togo Switzerland
Andorra √ Belize (? 2:1) ECCA Liechtenstein
√ St.Pierre & Miquelon √ Britain √ Anguilla Spain
Wallis & Futuna Islands √ Falkland Islands √ Antigua and Barbuda Andorra
Monaco √ Gibraltar √ Dominica Singapore
√ New Zealand Guernsey √ Grenada Brunei
√ Cook Islands Jersey √ Montserrat Italy
√ Niue Isle of Man √ St. Kitts and Nevis San Marino
Pitcairn Islands √ Saint Helena √ St. Lucia Vatican
Tokelau Scotland √ St.Vincent Morocco
√ Ireland (pre '79) Western Sahara
Notes: This lists all the pre-Eurozone currency unions and CU-like monetary arrangements from 1970 onwards. A ‘check’ sign
indicates that the nation is included in the sample of Rose (2000).
Source: Rose (2000) appendix table and footnotes.

The top panel shows that there are some extremely open nations that also share a currency with some
other nation.3 These nations’ openness is so unusual that it is hard to see what is going on with the rest.
There are 6 nations with openness above 200%, Bahamas (1400%), Singapore (750%), Liberia (600%),
Bahrain (400%), Kiribati (370%) and Belgium-Luxembourg (320%). All but one of these are involved
in a currency union. Eyeballing this list we see that many of these nations are known centres of transit
trade.
The bottom panel excludes these nations so as to better see the others. Here we clearly see the spokes
(the circles for poor nations to the left) and the hubs (the circles for rich nations to the right). The nine
rich nations participating in CUs are (by declining order of GDP per capita) US, Bermuda, Australia,
Norway, France, Denmark, New Zealand, Italy and UK. Note that Rose (2000) does not use data for all

3
I believe the Rose trade data has a systematic bias in it, what I call the silver-medal mistake below.

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March 2006 9
 of these. For example, Bermuda, Denmark, Italy and Norway have no trade data with their CU partners
so they are not included.

1600%

1400%

1200%
Openness, (X+M)/GDP

1000%

800%

600%

400%

200%

0%
0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000
Real GDP per capita, 1980

200%

180%

160%

140%
Openness, (X+M)/GDP

120%

100%

80%

60%

40%

20%

0%
0 5,000 10,000 15,000 20,000
Real GDP per capita

Notes: Real GDP per capita on horizontal axis (USD); total trade to real GDP on vertical axis (%).
Source: My calculations on the Rose (2000) data for 1980.

Figure 2: CU nations tend to be very small, poor and open.

Given simple combinatorics, there are many, many more spoke-spoke pairs than hub-spoke pairs (e.g.
Rose (2000) lists 16 nations using the US dollar, which implies 162/2=128 spoke-spoke bilateral flows
and 16 hub-spoke flows). Thus most of the CU pairs in the data will be between the nations with circles
in the left part of the bottom panel.
The main point of these graphics is that nations involved in currency unions are a long way from
average nations.

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2.2. Garden pests: biases in gravity model estimations
“Without theory, practice is but routine born of habit.” ~ Louis Pasteur
Rose (2000) employed a naïve version of the gravity model for his preferred specification, a version
that had been widely used by policy analysts in the 1980s and 1990s (including by me in my 1994 book
on Eastern EU enlargement). The naïve version omits two variables that severely bias results.
To see this point, however, we need to work through a bit of theory. Although the theory does involve a
small amount of ‘heavy lifting’, the work is handsomely rewarded. It helps us understand all the
mistakes in Rose (2000) and the subsequent literature, and why only a handful of the hundreds of
estimates of the Rose effect are worth paying attention to for policy purposes. In any case, who ever
said studying the Rose effect would be a bed of rose?

2.2.1. Gravity for dummies and dummies for gravity (equation) 4

Which falls faster, a cannonball or a feather? Every high school student has been taught the Galilean
lesson that they fall at the same rate in a vacuum. To practical, empirically oriented people, however,
this has got to be the silliest bit of knowledge tucked into humankind’s collective wisdom. There is no
perfect vacuum on earth, and where there is a perfect vacuum (deep space) there is no gravity.
Nevertheless, the thought experiment is useful since it allows students to get their minds around the
‘headline news’ when it comes to falling bodies – gravity – without confusing it with secondary factors,
like air resistance. Eventually, the students find out what happens when one lets some air into the
vacuum chamber, but only after that they are really convinced that gravity affects all objects equally,
ceteris paribus.5 This section works through the gravity model ‘without air resistance’ so as to illustrate
the common mistakes involved in gravity model estimation.

The value version of the gravity equation: a demand equation with social pretensions
The gravity model has more theoretical foundations than any other trade model I can think of, but these
foundations are usually ignored (Jim Anderson published the first theoretical foundation in 1979 and
despite it appearing in the AER, economists continue to invent new ones, claiming that the model has
none).6 This failure to consider its theoretical foundation has produced a string of errors – errors that
have been repeated so often that they have become accepted practice.
Most researchers estimate what I call the ‘value version’ of the model – i.e. the dependent variable is
the value of bilateral trade deflated by a common price index, so that’s where we’ll start.

Step 1: The expenditure share identity

The first step is the expenditure share identity:

(1) p od x od ≡ shareod E d ;
where xod is the quantity of bilateral exports of a single variety from nation ‘o’ to nation ‘d’ (the ‘o’ is a
mnemonic for ‘origin’ and ‘d’ for ‘destination’), pod is the price of the good inside the importing nation
also called the ‘landed price.’7 Of course, this makes xodpod the value of the trade flow measured in
terms of the numeraire. Ed is the destination nation’s nominal expenditure (again measured in terms of
the numeraire). By definition, shareod is the share of one good nation-o good share of expenditure in
nation d.

4
For more on this, see Baldwin and Taglioni (2004).
5
Little remembered fact: Commander David Scott tried Galileo’s experiment on the moon with a hammer and a feather, 2 August 1971. It worked.
6
For example, Deardoff refers to the gravity model in his 1984 Handbook of International Economics chapter as having “somewhat dubious
theoretical heritage” (p. 503) (despite Anderson having published his foundations 5 years before) and then ten years late saying, “It is certainly no
longer true that the gravity equation is without theoretical foundations, since several of the same authors who noted its absence went on to provide
one,” in his 1997 paper “Determinants of bilateral trade.”
7
Roughly speaking this would be the cif price not the fob price (cif stands for cost, insurance and freight, while fob stands for free on board, i.e. on
the boat in the exporting nation’s port).

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 Step 2: The expenditure function: shares depend on relative prices
Basic microeconomics tells us that expenditure shares depend upon relative prices and income levels,
but we postpone consideration of the income elasticity; here expenditure share is assumed to depend
only on relative prices. Adopting the CES demand function and assuming that all goods are traded, the
imported good’s expenditure share is linked to its relative price by:
1−σ
⎛p ⎞
= ∆ d , ∆ d ≡ ∑k =1 nk ( p kd )
1−σ m 1−σ
(2) shareod ≡ ⎜⎜ od ⎟⎟ , Pd ,σ >1
⎝ Pd ⎠
where pod/Pd is the relative price, Pd is nation-d’s CES price index for all goods that compete with the
imported good, ‘m’ is the number of nations from which nation-d buys things (this includes itself), and
σ is the elasticity of substitution among all varieties (all varieties from each nation are assumed to be
symmetric for simplicity); nk is the number of varieties exported from nation k. The symbol ∆ is a
mnemonic for ‘denominator’ of the CES demand function. Combining (2) and (1) yields a product-
specific import expenditure equation that could be estimated directly if we had the data. Lacking data
on the landed prices of individual goods, we compensate by putting more structure on the problem.

Step 3: Adding the simplest pass-through equation

Assuming full pass-through (this is consistent with both Dixit-Stiglitz monopolistic competition and
perfect competition) all manner of trade costs – tariffs, transport costs, foreign exchange costs, etc. – are
passed on to the consumer, so the landed price in nation-d is linked to the producer price in nation-o and
the bilateral trade costs via:

(3) pod = p oτ od
where po is the producer price of nation-o exports, τod reflects all trade costs, and we have used the
symmetry of varieties to drop the variety index.

Step 4: Aggregating across individual goods

So far we’ve focused on per-variety exports. To get aggregate bilateral exports from ‘o’ to ‘d’, we
multiply the expenditure share function by the number of varieties nation ‘o’ has to offer, denoted as no.
Using upper case V to indicate to total value of trade, Vod≡nosodEd, i.e.:

1−σ τ od 1−σ
(4) Vod = no po Ed
∆d
We could estimate this, and maybe one day when governments spend more on gathering trade data, we
shall.8 For now, however, we continue substituting assumptions for information.

Step 5: Using general equilibrium in the exporting nation to eliminate the nominal price
Lacking data on the number of varieties no and producer prices po, we compensate by turning to nation-
o’s general equilibrium condition. This is the only part that is even slightly tricky, so I’ll illustrate with
a diagram. The producer price in the exporting nation, nation-o, must be such that it can sell all its
output, either at home or abroad. Taking its nation-o’s output as given, we can use the CES expenditure
share function for all of nation-o’s destination markets to work out what nation-o’s producer price must
be.
Nation-o’s expenditure must equal the total value of its output (ignoring current account imbalances), so
Eo is the amount that nation-o must sell. To make this happen, nation-o’s producer prices must adjust to
ensure that:

8
The data that is most difficult to get is the landed price of imports that are partner specific.

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 (5) E o = no ∑k ( s ok E k )
where the summation is over all markets (including o’s) and the producer prices are hidden in the ‘sok’
terms.
Figure 3 shows this general equilibrium condition for the exporting nation diagrammatically; the right-
hand side is the downward sloped line; it is downward sloped since expenditure on nation-o’s exports in
every market fall with a ceteris paribus rise in nation-o producer prices. Using the CES expenditure
share function in (2), and solving for nopo1-σ:

Eo Ek
Ω o = ∑k τ ok
1−σ 1−σ
(6) no p o = ;
Ωo ∆k
Here Ωo can be thought of as the de facto openness of the world to nation-o’s exports, or, in other words
as nation-o’s market access.

Figure 3: Determination of the exporter’s producer price.

Step 6: Twelve times twenty-eight equals 300 – a ‘new maths’ gravity equation9
Substituting (6) into (4), we get our first-pass, or new-maths version of the gravity equation:

τ od ,t1−σ
(7) Vod ,t = ( ) Eo ,t Ed ,t ;
Ωo,t ∆ d ,t
This is a microfounded gravity equation. I have added time subscripts to stress the point that all of the
variables are time varying.

Multilateral resistance, remoteness, bilateral openness, relative-prices-matter

The novel term here (at least they were novel in 1979 when Jim Anderson first derived a similar, but
more general, expression) is the 1/∆Ω term. Of course (7) cannot be directly estimated since we do not

9
In the ‘old maths’ one worked out, e.g. 12 times 28 by direct calculation, while in the ‘new math’ one says it’s about 10 times 30=300, and then
sets about refining the estimate if need be.

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 have data on ∆ or Ω, nor do we have data on the total trade cost between nations, but this is what we
should estimate.10
As mentioned, Ω can be thought of as a measure of the exporting nation’s market access. The ∆ term is
the denominator of the demand function, but in the international context it can be thought of as a
measure of the importing nation’s openness – greater openness lowers the landed prices of imports in
nation d and thus raises ∆d since σ>1.
Some authors call the new term the ‘remoteness’ term; Anderson and van Wincoop (2003) call it the
‘multilateral trade resistance’ term.11 But from a purely description perspective, it should be called the
relative-prices-matter term – that phrase doesn’t exactly roll off the tongue but it says what it means.
After all, the ∆ is there to reflect the relative price of nation-o’s exports to nation-d and Eo/Ωo is there
since it affects nation-o’s producer prices. Yet another way to view the term in parentheses is to think of
it as bilateral relative openness. The numerator reflects direct openness of the bilateral relationship
(trade costs are raised to a negative power so the term gets larger as bilateral trade gets cheaper, i.e.
more open). The denominator reflects both the openness of the importing nation to all goods (recall ∆
rises when the landed price of imports from anywhere falls) and the openness of the world to the
exporter’s goods (recall Ω rises when the exporter’s share of any market rises).

Why the relative-prices-matter term affects statistical inferences, 2 examples

A classic example of the importance of relative-prices-matter terms is the trade between New Zealand
and Australia. Although these nations are far apart (it takes a jet airliner 3 hours to get from Sydney to
Wellington) both nations are very far from other industrial nations (the Sydney flight to Tokyo, Los
Angles and London take 9, 13 and 23 hours respectively). Thus the naïve gravity model’s prediction is
terrible. It under-predicts the bilateral trade. The reason is that it ignores the fact that the bilateral
Australian-New Zealand trade costs are not high relative to the trade costs facing exporters from the rest
of the world. As an aside, this is one of the reasons that the ANZ preferential trade agreement looks like
it has such a big effect in naïve gravity models; the ANZ dummy picks up the big error term.
In Europe, where distances between major industrial nations are extremely small by world standards,
the point works in reverse. Frankfurt is only an hour flying time from Amsterdam, but the Dutch-
German bilateral trade costs are not particularly low compared to the trade costs faced by other
exporters to the Netherlands. This is probably why new naïve gravity model estimates tend to show that
the EU has no impact on trade. In the old days, when it was hard to manipulate large data sets, the naïve
equation was estimated on bilateral flows for European trade only so the distance coefficient was
typically estimated as -0.7 and this left some room for the EU to matter. In broader data sets, the
distance coefficient is estimated to be something like-1.0, so the predicted intra-EU flows are expected
to be very high and this leaves nothing for the EU dummy.

Why it fits so well

Having slogged it through till here, I hope you’ll agree that the gravity model can be thought of as a
demand curve with ‘social ambitions.’ This is, of course, why it fits so well. Demand functions
generally do well in the data, and nations’ output and expenditure generally match. This perspective
also makes it clear that it is not a model in the traditional sense of the word. It is a regression of
endogenous variables on endogenous variables.

10
Anderson and van Wincoop (2001), a classic article, make a big deal of the fact that the ∆ and Ω are not price indices. This is true, but just barely.
The CES price index that is relative to the import demand equation must be ∆1-σ. For example, if home-bias (what Anderson-Wincoop call ‘non-
pecuniary’ effects) is important, then a properly constructed CES price index – for example, one that uses expenditure shares as weights – would
pick this up. What is true is that one needs to assume the range of goods with which imports compete in order to know which price index to use. My
guess is that an index of import prices would do a very good job of standing in for the ideal ∆. It would be important, however, to enter it separately
rather than, e.g., divide the importer’s nominal GDP by it and then putting it in. The reason is that P=∆1-σ should get a different coefficient in a log-
log regression than does nominal GDP, E. The issue is more complex for Ω, but this is proportional to the E-weighted sum of nation-o’s market
shares in each market (including its own). Nitsch (2000) implements this.
11
Anderson and van Wincoop 2001 assume each nation produces the same number of varieties (i.e. only one variety), and then one can show that
Ωi=∆i for each nation. Take a two country case and write down what all the Ω’s look like; then write down the ∆s. You’ll see that the two are
isomorphic so ∆=Ω is a solution; see Anderson and Wincoop for why it is the only solution.

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2.2.2. The biases in what Rose estimates
All well trained economists know they must use real variables, so when most hear about the gravity
model, they presume that the model must be about real GDPs and real trade. This is what occurred to
Rose and most of the international macroeconomists who used the model before him, although there are
some exceptions (de Sousa and Lochard 2003). This response, however, is incorrect. The V and E’s in
(7) are in nominal terms. Just to be clear, there is no money illusion in (7). The E’s and V must be, and
are, measured in terms of a common numeraire, but US dollars would do just fine. For example, if the
prices were suddenly switched from dollars to cents, nothing would change in (7) – it is homogeneous
of degree zero in nominal prices. Of course, the econometrician would have to worry about regressing
trends on trends but trends can be eliminated in other ways.
Be that as it may, Rose (2000)’s preferred regression estimates (simplifying for clarity’s sake):

Vod 1−σ E E
(8) = τ od ( o ) d ; τ od = f (dist od , other stuff )
PUSA Po Pd
where distod is the distance between o and d. In words, he deflates the bilateral trade value with the
United States’ GDP deflator, and uses real expenditure rather than nominal expenditure deflated by ∆ or
Ω. Rose follows a long tradition of modelling τ as depending upon natural barriers (bilateral distance,
adjacency, land border, etc.), various measures of manmade trade costs (free trade agreements, etc.),
and cultural barriers (common language, religion, etc.). His original contribution was to add a common
currency dummy to the list – hard to imagine that no one had thought of it before 2000, but that’s
always the case with truly brilliant research.
Rose (2000) estimates this on various cross-sections of his data as well as the full panel. This procedure
is a mistake, a mistake that would get the gold medal in the race for the most frequent mistake in gravity
equation estimations.12
Now if one estimates (8) when (7) is correct, what one is really doing is estimating:

Vod 1−σ E E ⎧P P 1 ⎫
(9) = τ od ( o )( d )⎨ d o ⎬; τ od = f (dist od , other stuff )
PUSA Po Pd ⎩ ∆ d Ω o PUSA ⎭
but omitting the terms in brackets.
What is wrong with this? One big problem – the gold medal of classic gravity model mistakes – and one
small problem – the bronze medal winner in the mistake race. The big problem is that the omitted terms
are correlated with the trade-cost term, since τod enters Ω and ∆ directly (see (2) and (6)). This
correlation biases the estimate of trade costs and all its determinants including, the currency union
dummy.
The small problem – what might be called the bronze-medal mistake – is that the inappropriate deflation
of nominal trade values by the US’ aggregate price index. Since there are global trends in inflation
rates, inclusion of this term probably creates a spurious correlation. Fortunately, Rose (2000) and other
papers reviewed below offset this error by including time dummies. Since every bilateral trade flow is
divided by the same price index, a time dummy corrects the mistaken deflation procedure. Note that
when Glick and Rose (2002) run their regression without the time dummies, their estimated coefficient
on the CU dummy is one standard deviation larger than it is with time dummies, so it can be important
to correct the small problem.

12
See Anderson and van Wincoop (2001) for the original and a more detailed version of this critique.

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 2.2.3. The rose has thorns only for those that would gather it: the gold-medal of gravity mistakes
"He who loves practice without theory is like the sailor who boards ship without a rudder
and compass and never knows where he may cast." ~ Leonardo da Vinci
Time for more heavy lifting. Coefficients estimated by regressing (8) on pooled data contain all the
biases that the subsequent literature has sought to correct. It is impossible to fully understand the
sequence of biases in the subsequent literature, including studies on the euro, without thinking a bit
more precisely about the source of the biases.

Gravity model with common kluges13

What Rose (2000) estimates is a bit more complex than (9). Before getting to estimation, he uses two
fixes that are common in the literature. First, he does not work with bilateral trade, exports from France
to Germany for instance, but rather the average of bilateral trade, namely the average of French exports
to Germany and German exports to France. Second, he does not estimate separate parameters for the
real GDP figures, he constructs the product of the GDPs and uses this in his regression. Using our new-
math version of the gravity equation (assuming trade costs are bilaterally symmetric), he is estimating:

1−σ Eo Ed ⎧⎪ Po Pd ⎫⎪
(10) Vod Vdo = τ od ⎨ ⎬; τ od = f (dist od , other stuff )
Po Pd ⎪⎩ Ω o ∆ o ∆ d Ω d ⎪⎭
Here and subsequently, I assume that the bronze-medal mistake – deflation by US price index – has
been offset by time dummies so we can ignore PUSA. Again simplifying for the sake of illustration,
suppose the true model of bilateral trade costs is:

τ od = distod b CU od ,t − b Z t b ;
1 2 3
b1 , b2 > 0
where CU is the currency union dummy and Z is the other (omitted) determinant of bilateral trade costs
(suppose there is only one for simplicity’s sake). Then the true gravity model (in logs) is:

y = X 1β1 + X 2 β 2 + ε
where y is the trade flow, X1 includes the product of the real GDPs, bilateral distance and the CU
dummy, and X2 includes the relative-prices-matter terms, ∆ and Ω, as well as that omitted determinant
of trade costs Z. Rose, however, estimates:

y = X 1 β1 + ut
where

⎧⎪ Po ,t ⎫⎪ ⎧⎪ Pd ,t ⎫⎪
u t = ln ⎨ ⎬ + ln ⎨ ⎬ + ln{Z od ,t } + ε t
⎪⎩ Ω o ,t ∆ o ,t ⎪⎭ ⎪⎩ ∆ d ,t Ω d ,t ⎪⎭
The biases from OLS on pooled data will be:

⎛ 1 ⎞ ⎡( RYo ,t RYd ,t ) X 21t ( RYo ,t RYd ,t ) ⋅ Z t ⎤

⎜ ⎟ ⎥⎛⎜ 1 ⎞⎟
(11) βˆ1 = ⎜ − b1 (σ − 1) ⎟ + ( X 1 ' X 1 ) ⎢⎢ dist ⋅ X 21t
−1
dist ⋅ Z t ⎥⎜ b ⎟
⎜ b (σ − 1) ⎟
⎝ 2 ⎠ ⎢⎣ CU t ⋅ X 21t CU t ⋅ Z t ⎥⎦⎝ 3 ⎠
where the RY’s are shorthand for the log of the E/P’s, and

13
Google definition: A kludge (or kluge) is a 'solution' for accomplishing a task, originally a mechanical one and usually an engineering one, which
consists of various otherwise unrelated parts and mechanisms, cobbled together in an untidy or downright messy manner.

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 Po Pd
X 21 ≡ ln( )
Ωo∆o∆d Ωd
Here we assume the true error ε is uncorrelated with anything.

The biases
Many researchers call the first two rows ‘nuisance’ parameters. That is a strange name, but I guess that
means they don’t want you to ask too many questions. Adopting that attitude for a moment, I focus
solely on the bottom row of the matrix, the one dealing with the ‘variable of interest’, i.e. the currency
union dummy.

The currency union dummy coefficient is biased: Part 1

We can be absolutely sure that CU and X21 are correlated since X21 contains CU and all other
determinants of bilateral trade costs. In short, omission of the relative-price-matters term produces
biased results. But what is the likely direction of the bias?
Stepping outside the model for a moment, suppose that not all goods are traded, so the GDP price
deflators include non-traded goods prices. Since the ∆ and Ω include only traded goods prices, X21 is
proportional to the ratio of non-traded prices to traded prices in the trading nations. If non-traded goods
are idiosyncratically high in these nations, they will be idiosyncratically open (consumers substitute
away from nontraded goods). Next, suppose that nations that are idiosyncratically open are more likely
to engage in pro-trade policies, like a currency union or FTA. If both of these conjectures are true, there
will be a positive correlation between CU and the relative-prices-matter term. In this case, the
coefficient on the CU dummy is upward biased.
As a matter of fact, most estimates of the Rose effect that ignore the relative-prices-matter term estimate
the pro-trade effects of a currency union to be amazingly high. For example, the pooled and cross-
section estimates in Rose (2000) ignore that relative-prices-matter term and these suggest that a
currency union boosts bilateral trade by 235%. When he roughly corrects for this in his difference-in-
differences regression in the same paper, he finds that the coefficient is about 8 standard deviations
lower, namely that the Rose effect boosts trade by 17% instead of 235%.

The currency union dummy coefficient is biased: Part 2

Expression (11) shows a second bias in the CU dummy estimator, the CU⋅Z term. Bilateral trade costs
are determined by many factors, ranging from personal relationships among business leaders that were
developed as school children on cultural exchange programmes to convenient flight schedules. David
Hummels has a series of papers pushing forward the range of these factors that can be measured, but
there will always be omitted variables in the regression. This is not a problem unless the omitted
variables are correlated with X1 variables. However, it is very likely that the omitted pro-bilateral trade
variables are positively correlated with the ‘variable of interest’, i.e. the CU dummy in this case. The
point is that the formation of currency unions is not random but rather driven by many factors, including
many of the factors omitted from the gravity regression. Indeed, below we discuss a number of papers
on the determinants of CUs.
If the omitted variables Z and CU are positively correlated, the estimated trade impact will be upward
biased for this second independent reason. The size of this bias is quite difficult to judge since it stems
from factors that are unobservable to the econometrician.

Nuisances with nuisance variables

Many researchers have found that the ‘nuisance’ variables in the gravity model are indeed a huge
nuisance. The new-math version of the theory tells us the GDP variables should have coefficients of
unity, and while slightly more sophisticated theory explains why the elasticities may deviate from unity,
most people get suspicious when the point estimates on GDP fall below, say 0.5. It is plain from the
reasoning above that we should expect the GDP elasticities to be biased downward, since X21 contains

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 the price index that is used to deflate nominal GDP. The correlation is not -1, however, since X21
includes the ∆ and Ω terms as well. Since the ratio of traded to nontraded goods will vary across
country samples and time periods, the biases on the GDP coefficient need not be systematic.

2.2.4. More thorns: the silver-medal of gravity mistakes

The basic theory tells us that the true relationship is for bilateral exports. Many gravity models –
including most in the Rose effect literature – are estimated on ‘bilateral trade’ namely, the average of
the two-way exports. There is nothing intrinsically wrong with this, but since it was done without
reference to theory, most researchers commit a simple, but grave error. They mistake the log of the
average for the average of the logs.

In his chapter on the gravity model, Feenstra (2004) shows an equation with the log of the sums,
rather than the sum of the logs. The theory leading up to this, however, is developed in the context
of the simplest trade model – i.e. the Krugman trade model without trade costs. In this model, all
bilateral flows are identical – exactly because there are no trade costs – so the sum of the logs
does equal the log of the sums. However, when trade costs are introduced, the theory does not
predict balance trade bilaterally, and indeed real-world trade flows are often very unbalanced.

Box 1: Log of the sums

This can seriously bias the results. The sum of the logs – the right way – is approximately the log of the
sums, but the approximation gets worse as the two flows summed become increasingly different.14 In
plain English, the error will not be too bad for nations that have bilaterally balanced trade, but it can be
truly horrendous for nations with very unbalanced trade. In fact, unbalanced trade is a huge issue. The
biggest exporters, German, Japan and the US, for example, sell something to most nations around the
world. However, many small nations sell nothing in return, at least not to all of the big-3. Thus the
problem is systematically worse for North-South trade than it is for North-North trade. Note that this
mistake is quite systemic, it is even in one of the most common references for the gravity model,
Chapter 5 in Feenstra (2003); see Box 1.

14
If x=yδ, ln[(x+y)/2]=lnx+ln(1+δ)-ln2, while ln(xyδ)/2=ln(x)+ln(δ)/2. The wrong way minus the right way is ln(1+δ)-lnδ/2-ln2.

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 Figure 4: Log averaging mistake for Germany, 2000, IMF DOTS data.

Note: The error is the incorrect (log of average)

minus the correct (average of logs), for Germany’s 200 or so trade partners in the IMF DOT data for the year 2000; trade gap is
the absolute value of difference in bilateral exports (to and from Germany) as percent of the smaller of the two flows. The two
extreme outliers are nations with extremely unbalanced trade, West Bank/Gaza Strip and Niger. The incorrect averaging makes
Germany-Niger bilateral trade about 150% higher than it actually is when averaged correctly.

To see the sorts of bias this mistake can induce, look at what the mistake does to Germany’s bilateral
trade data (IMF DOTS data for the year 2000). For nations with which Germany has perfect bilateral
trade balance, the log of the sums is exactly equal to the sum of the logs. But when the two flows to be
averaged are quite different, then the approximation becomes very wrong as Figure 4 shows. The
extreme outlier in the figure is German-West Bank trade. The proper measure is 1.2 in logs, while the
mistaken calculation yields 2.7 in logs. In short, the mistaken measure is always bigger and the mistake
is extra big for unbalanced bilateral trade relations. I also calculated this for Germany’s trade with
EU15 and other OECD partners for which bilateral trade is more balanced, but still I find errors on the
order of 15% even for these fairly similar nations.
By the way, the error always makes the bilateral trade look bigger (Jensen’s inequality).

The difference between theory and practice is different in theory than it is in practice
Of course, this silver medal mistake only matters if the error is especially bad for currency union trade
flows. To look at this quickly, I calculated the bilateral imbalance for all the hub and spoke CU pairs
around the US dollar. I used IMF DOTS data for 2000, so not all of the islands in the Rose (2000) list
are present.
Table 2 shows that most of the spoke-spoke trade flows are zero and the non-zero entries all have
imbalances on the order of 100%, so the trade flow will be severely upward biased. The hub-spoke
flows are less likely to be zero, and the trade imbalances are less severe, but in most cases they are over
50% and so also severely overestimated due to the silver medal mistake. Indeed, only one of the ten
non-zero pairs has less than a 50% imbalance.
Plainly, someone needs to re-do the Rose effect estimates on data that is correctly averaged and see
whether this really matters.

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 Table 2: Bilateral imbalance as % of 1-way flow, US dollar currency pairs
Am. Samoa Bahamas Belize Bermuda Guam Liberia Palau Panama USA
Am. Samoa
Bahamas
Belize -1420%
Bermuda -120%
Guam
Liberia 100%
Palau
Panama 100% 89%
USA 76% 52% 91% 12% 78%
Source: My calculations on IMF DOTS for year 2000, export data.

2.2.5. Summing up on Rose (2000)

Just to sum up, Rose (2000) is a great, path-breaking paper. But this section explains why the pooled
estimates in Rose (2000) – the most famous of which is the +200% estimate – should be ignored for
policy purposes. They are based on an estimation technique that has subsequently been proved to be
wrong by several authors, including Andy Rose himself. More on this below.
Thinking of the Rose effect literature as a climbing rose springing from the Rose (2000) roots, I turn
now to the first of the three main branches of the ‘rose vine.’

2.3. Rose Branch #1: Rose and van Wincoop (2001)

"I pass with relief from the tossing sea of Cause and Theory to the firm ground of Result
and Fact." ~ Winston Churchill
Once the gold-medal mistake became clear (Jim Anderson’s work was rediscovered once again), Andy
Rose immediately teamed up with Eric van Wincoop to try to correct it. Rose and van Wincoop (2001)
was the result and it shows that forgetting about the relative-prices-matter term – as Rose (2000) did –
leads to a severe upward bias in the Rose effect.
Rose and van Wincoop address the model misspecification issue in two ways. The simplest is to include
origin-nation and destination-nation dummies in a cross section regression. They continue Rose’s
practice of using real GDPs and US price deflators for trade flows. Thus they estimate:

Vod E E PP
(12) ln( ) = ln f (dist od , CU od , other stuff )1−σ + β 1 ln( o d ) + β 2 ( d o ) + u
PUS Po Pd ∆d Ωo
on panel data using the usual proxies for trade costs – most notably the common currency dummy, CU,
that equals one if nation-o and nation-d use the same currency. Of course they do not have data for the
terms involving ∆ and Ω, but they use country-specific dummies instead.
With these country dummies, the estimated Rose effect is radically lowered; it falls by 2.7 standard
deviations. However, this diminished Rose effect is still mighty; without the country dummies a
common currency is estimated to boost trade by 3.97 times; with them by 2.48 times.

2.3.1. A mistaken correction of the mistake

Putting in time-invariant country-specific fixed effects is wrong, as the simple theory laid out above
shows clearly. The omitted terms reflect factors that vary every year, so the country dummies need to be
time varying. If the researcher forgets about this and includes time-invariant country dummies, as Rose

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 and van Wincoop do, part of the bias may be eliminated. But since there will be a time varying residual
in the error, the results will still be biased to the extent that the trade costs are also time varying. This
problem may be relatively minor in the Rose-VanWincoop data since there is very little time variation
in the CU dummy (more on this below).
Just to be perfectly clear, the Rose-van Wincoop country dummy procedure ameliorates what was
called “the currency union dummy coefficient is biased: Part 1” above. The offending correlation was
CU⋅X21. This has a cross-section element to it, and the time-invariant dummies eliminate this. However,
it also has a time-series element to it and this is not eliminated.
This point probably explains why the second, harder way of correcting for the relative-prices-matter
effect in Rose-Van Wincoop yields such a different result. Given all the structure imposed on the
demand system – I mean the gravity equation – the econometrician can actually generate data for the ∆s
(Anderson-Wincoop makes assumptions that imply that a nation’s openness to imports equals the
world’s openness to its exports, so the Ω goes away). When they do, the estimated Rose effect is again
radically reduced. Doing some rough calculations on the numbers in the paper suggests the coefficient
on the common currency dummy fell to 0.65, or about one more standard deviation; the Rose effect is
then that a common currency boosts trade by 1.91 times.

2.3.2. Lessons: Still a rosy scenario

What are the lessons?
1) The estimates in the preferred regression in Rose (2000) are just plain wrong. They are
overestimates. They are overestimated because the naïve gravity model is mis-specified and
this mis-specification matters hugely in the dataset of Rose (2000).15
History divides neatly into two parts: pre Rose-van Wincoop and post Rose-van Wincoop. Pre-RvW,
we believed the 200% currency-union effect might have been correct. Post-RvW, we know better. More
generally, one should never pay attention to estimates of the Rose effect that come from the naïve
gravity model, i.e. one without fixed effects à la Anderson and van Wincoop or an equivalent fix (see
below).
2) The Rose effect was still blooming after this correction; the best estimate is that it boosts trade
by 1.9 times.
3) We can probably deduce something about the nature of bilateral trade flows that get the
currency union dummy.
4) The omitted variable bias discussed above as ‘The currency union dummy coefficient is
biased: Part 2’ is still in the Rose-van Wincoop numbers, so they are still too high.

2.4. Rose branch #2: Omitted variables

If there were such a thing as the ‘Gravity model for fun and profit handbook,’ page one would give this
advice: “To amaze your friends with another important trade effect, develop a new proxy for trade costs
and use a really big dataset; success is not guaranteed, but you’re likely to find significance (standard
errors involve the inverse of the square root of number of observation) and you’ll have loads of fun in
any case.” This is too cynical, but the basic point is that the gravity model omits an incredible range of
factors that are likely to affect bilateral trade – I’ve seen people get statistically significant coefficients
on time zones, language proximity, membership in the Austro-Hungarian Empire, presence of a
Chinatown in the two capitals just to name a few. More to the point, consider the trade among the
nations listed in Table 1 and ask yourself: “Can we be sure that Rose has not left out some key trade-
boosting factor that operates between many CU pairs?”

15
Formally, if one asks the statistics whether the country dummies should be excluded from Rose-Wincoop, the answer is no. They belong.
Therefore, the Rose effect estimates performed without them are null and void.

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 This matters for the Rose effect estimate since many of those omitted factors may be correlated with the
CU dummy in a way that boosts the estimated coefficient. There are ways of addressing this
econometrically, and we’ll get to them soon. It is useful, however, to get an idea of just what sort of
omitted variables we are talking about.

2.4.1. Exceptions that prove the rose16

When the Economic Policy Panellists at the presentation of the original Rose paper said they had “an
uneasy feeling that something had eluded them”, one of the many things that bothered them was that
they did not know enough about the particulars of the CU pairs that drove Rose’s results. Maybe if one
were an expert on West African trade, one Panellist suggested to me, one would know exactly what
omitted variable explained the overestimate of the Rose effect. The literature has followed up on this in
two ways. The first is to look at particular cases where we do really understand what was going on. The
second was to play with the CU dummy. I address these approaches in order.

A parable
Imagine an economist asserted that the growth of the money supply was the main cause of long-run
inflation and estimated the link using a huge international dataset. Using money supply growth and a
handful of other variables that were available for 150 nations, she estimated the money-prices elasticity
to be unity and every other explanatory variable had a negligible effect on inflation. Then suppose
another economist showed that Ireland’s money supply grew at 300% for decades but its inflation rate
was zero. This would make one pause. It would make one think that maybe something else was going
on. That maybe the original regression had omitted an important variable.
Of course, a sample of one has infinite standard errors, but the counter-example investigator can
consider a much more subtle model of the phenomenon since much more information is available than
is the case for a sample of 150 nations.
Importantly, the sorts of variables that are available for 150 nations are the sorts of variables that matter
for average nations. But Rose looked at a phenomenon that – until the Eurozone – was limited to
distinctly non-average nations. Thus, maybe using the 150 nation dataset approach guarantees that no
one can find the ‘silver bullet’ pro-trade variable that would make the Rose effect disappear because no
one bothered to gather internationally comparable information on a factor that matters only for a couple
dozen very unusual nations. This is why I think the counter examples considered below must be taken
very seriously.

Revolutions, economic chaos and asymmetric inflation lead to CU dissolution

Many currency union break ups are done in the context of, or as the result of massive social, economic
and/or political turmoil, many in the context of revolutions. As Thom and Walsh (2002) write:
“[m]any of these unions ended as part of a bloody decolonising process followed by the
adoption of Marxist/autarkic policies, bilateral trade deals with the Soviet Union or China,
and a descent into economic chaos – France and Algeria, whose independence was granted
only after a bitter struggle; India and Pakistan, who ended their currency union after the war
of 1965; Pakistan and Bangladesh, who split up after the war of 1971; South Africa and
Southern Rhodesia (Zimbabwe) who were ejected from the Commonwealth and had trade
sanctions imposed as they broke with sterling; the five Portuguese colonies in Africa, that
broke with Portugal after wars of liberation followed by civil wars. In all these cases and
many others it is very likely that trade between the former currency union partners would
have collapsed regardless of the currency regime in force.”
If this is right, the silver bullet is the mysterious third-cause that drove the revolution, currency union
break up and decline in trade. The same is true of currency union joiners. The decision to adopt a
currency – for example, to dollarise – takes place in the context of big political and economic changes,

16
Bill Bryson in his book “Mother Tongue” claims that this aphorism is ancient, so that we should read ‘proves’ using its archaic meaning, ‘tests’,
as in proving grounds.

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 many of which could be expected to affect trade. Plainly no one has good data on such factors – oh sure
there are proxies to be found in hyperspace – but such things really cannot be accurately measured with
a one-dimensional variable; if it could, we wouldn’t need historians and political scientists. Be that as it
may, my point is that one cannot see whether the Rose effect survives the inclusion of this sort of
mysterious third effect in the Rose, Rose-Wincoop or Glick-Rose dataset. We also need case studies.

Thom and Walsh (2002): The bloom on ‘my wild Irish rose’ is not for the taking
Ireland used the British pound before its independence. After independence and the introduction of the
Irish pound in 1927, the pound-punt exchange rate was held at 1-to-1 with no margins. Talks leading up
the European Monetary System suggested that this peg would remain in the context of the ERM since
everyone initially expected Britain to join. When Thatcher said no in 1979, Ireland was forced to choose
between ‘Europe’ (as they call it in Britain) and the ERM on one hand, and Britain and sterling on the
other. Ireland chose Europe and the 1-to-1 peg was abandoned. Market forces lifted the rate rapidly
away from the level it had been at for 50 years. What happened to Anglo-Irish trade?

Figure 5: UK’s share of Irish trade, 1924-98 (Thom and Walsh 2002).
Since Ireland and the UK were both embedded in the EEC, the termination of the currency union did
not and could not raise bilateral trade barriers. Moreover, both nations were run by stable, predictable
governments and although there certainly were a number of idiosyncratic factors affecting bilateral
trade, one has a very good idea of what they were and very good data that allows one to control for
them. In short, we should be able to learn a lot about the Rose effect by studying the Irish case. One
recent investigation of this example, Thom and Walsh (2002), finds no evidence from time series or
panel regressions that the change of the exchange rate regime had a significant effect on Anglo-Irish
trade. Should we be shocked?
Let’s set out the priors. If the Rose effect discussed in Rose (2000) is roughly right – the currency
regime switch should have reduced Anglo-Irish trade to about a third of its initial level. The impact on
Ireland should have been massive since the UK absorbed about half Ireland’s exports at the time. Even
if there were countervailing forces generated by the break up, it is hard to imagine any such forces that
would – all else equal – raise Anglo-Irish trade by enough to substantially offset a Rose effect of -
200%. By contrast, if the lower ranges of the Rose effect are right – say the effect is 15%, then we
might miss the Rose effect in the Irish experience – especially if one thinks the 15% would take a
number of years to be realised. My point here is that the Irish experience might help us reject a big Rose
effect, but not a modest one.

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 Inspection of Figure 5 shows that the initial Rose effect just could not have been right. OK, one should
run some regressions and talk about the standard errors (Thom and Walsh do), but really, would you
ever believe a regression that said the data in this figure was generated by a model where trade would
have dropped by 200% in 1979 were it not for some offsetting effect?17
I think there are other lessons in Figure 5.
The gradual decline of Anglo-Irish trade was due to structural changes, in my opinion, mainly in
changes in the Irish economy. As Ireland developed from a potato-exporting agrarian economy into the
Celtic Tiger it is today, its trade pattern naturally eroded from its historical overdependence on its
nearest market. This sort of thing is not in any version of the gravity model. The closest would be to
allow for a separate GDP per capita variable for exporter and importer nations (for the exporter it would
reflect structural shifts, for the importer an income elasticity – see Anderson 1979), but Rose only
includes the product of the two. Now suppose one threw into the gravity equation the 1965 Anglo-UK
free trade agreement, the 1974 adhesion to the EEC and a CU dummy. Moreover, suppose one did this
in a panel where it is not really possible to check for serial correlation in the errors. Plainly, the CU
dummy would pick up most of the action of the omitted variables that explained Ireland’s historic over
dependency on the British economy. One could throw in proxies for colonial relations in various guises,
but none of this would pick up the structural transformation of the Irish economy. Moreover, the history
related by Thom and Walsh makes it clear that the reduced dependency on the British market – which
was driven by factors that are unobservable to the gravity model – is one of the factors that caused the
Currency Union to break up (more on reverse causality below).

Fidrmuc and Fidrmuc (2001): Central and Eastern European break-ups

The eyeball evidence for a big Rose effect looks much better for the recent break-ups of currency
unions in Central and Eastern Europe. Figure 6, taken from Fidrmuc and Fidrmuc (2003), shows that
the break-ups were followed by dramatic drops in bilateral trade.
The top left panel makes that best case for a big, negative Rose effect due to a currency union break-up.
In 1993, Czechoslovakia went through a ‘velvet divorce’ just a few years after its ‘velvet revolution.’
The two parts of the nation separated into the Czech Republic and the Slovak Republic. They
maintained a customs union (no tariffs between them and a common external tariff) until they
simultaneously joined the EU’s customs union. On the face of it, this is just the sort of natural
experiment one should study. The figure plots year-by-year estimates of the above normal level of trade
between the partners (e.g. this is the exponent of the pair dummy, e.g. the Czech and Slovak dummy in
the Czechoslovak case). But even here one must raise a note of caution. As Fidrmuc-Fidrmuc’s paper
concludes:
“Our findings are broadly consistent with earlier findings on currency unions. In particular,
Rose (2000) shows that a common currency increases bilateral trade flows approximately
three times. Indeed, we found a decline of bilateral trade intensity by about this factor during
the first years of independence. However, we cannot separate the effect of the currency
separation from that of the political disintegration as both effects occurred (more or less)
simultaneously in the countries under scrutiny.”

Implications for the size of the Rose effect

The total drop was less than the size of Rose’s first estimate of 200%, with the size of the pair dummy
falling 100% from about 4.0 to about 2.0. At one extreme, we could claim that the only thing affecting
this trade was the loss of a common currency. This is rather naïve, but it gives a Rose effect of 2.0,
which is similar to many estimates. Yet one suspects that political and economic disintegration also
lowered trade. This means that a 100% currency union trade effect is too high; the Rose effect in
isolation would be smaller. To explore this conjecture, it would be interesting to revisit the Fidrmuc-
Fidrmuc data using some of the more sophisticated methods discussed above to sort out the two effects.

17
Glick and Rose (2003) show a figure for Anglo-Irish that looks quite different; they use the level of trade which drops due the second oil-shock
recession. Thom and Walsh look at the bilateral trade as a share of all Irish exports and thus control somewhat for the global recession.

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 Further observations follow from this work. Fidrmuc and Fidrmuc provide a qualitative discussion of
the changes that accompanied the currency union dissolutions. Their discussion makes it clear that
many time-varying, pair-specific omitted variables that affect trade yet were spawned by the same
forces that lead to the CU break-up. To list just one of a dozen stories, the Czechs and Slovaks
maintained free trade after the currency split, but they set up border controls that some businesses
claimed acted as a trade barrier. None of these stories could be included in regressions like Rose
estimates since there would be no way to gather such data for 100+ nations.

Figure 6: Trade collapses in Central and Eastern Europe.

The lessons from these two cases are unclear in terms of specifics, but crystal clear in terms of
generalities – lots of other complicated stuff matters. And it is the sort of factors on which we will never
have good, internationally comparable data. In short, gravity equations will always have omitted
variables. (Thank goodness for that; think how boring international trade economics would be
otherwise.)

2.4.2. Pair dummies: Glicks ‘N Roses

Andy Rose was, of course, well aware of the omitted variable bias critique even before it was echoed
many times by Economic Policy referees and panellists in Helsinki. He was also well aware that using
pair-specific dummies would wipe out ALL idiosyncratic level effects between ALL pairs of nations.
The only sticking point is that this tends to throw the roses out with the vase water. It eliminates all
cross-section variation from the residual, so the identification comes solely from time series variation.18
In plain English, we need lots of data to do this. As he explains it, he didn’t do it in Rose (2000) since
there was too little time variation in his original dataset. In Rose (2001) he shows what this means.
Using pair fixed effects on his original dataset, the Rose effect wilts (the raw estimate on the CU
dummy is minus 0.38 and the standard error is 0.67).

18
Note that Rose (2000) does roughly this with his difference-in-difference regression that is reported in the text but not in a table; the Rose effect
this yields is only 17% more trade.

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 2.4.3. Pakko and Wall (2001)
Pakko and Wall (2001) independently obtain the same results using a more general approach in terms of
fixed effects and data. They use the Rose (2000) data set but instead of averaging the two-way bilateral
flows (i.e. Germany’s exports to Denmark and Denmark’s exports to Germany), they preserve the
directional flows. This allows them to impose direction-specific pair dummies, i.e. two different
dummies per bilateral flow – a technique that is more general than in Rose (2001). Although they get
Rose-like estimates of the Rose effect without pair dummies, they find that the Rose effect droops and
withers away completely with pair dummies. The point estimate is negative and not significantly
different than zero.
Rather than pushing quickly on to the next dataset and empirical technique as does Rose (2001), Pakko
and Wall take the time to crush the rose petals one-by-one. Here is how they put it:
“Independently, Rose (2001) obtains these same results using the general fixed-effects
model. However, he rejects the findings on the grounds that the statistical insignificance of
the common-currency dummy is due to a small number of switches in common-currency
status. While it may well be true that the statistical insignificance of the common currency
dummy should not be taken to mean that the effect is not positive, this misses the point. A
comparison of the two sets of results suggests that pooled cross-section estimates are not
reliable because they are biased by the exclusion or mismeasurement of trading pair–specific
variables. This is evident in the dramatically different coefficients on the GDP and per capita
GDP variables that are found when using the two methods. In other words, the restrictions
necessary to obtain the pooled cross-section specification from the fixed-effects specification
are rejected, indicating that the fixed-effects specification is preferred.
The difference between the two methods in their estimates of the trade-creating effect of a
common currency is a separate issue. The proper conclusion to draw is that, when the
statistically preferred fixed-effects specification is used, there is no statistically significant
evidence of large trade effects (positive or negative). Although this means that Rose’s results
cannot be supported statistically, the small number of switches precludes us from saying
much about the effects of common currencies on trade, although the tripling of trade found
by Rose is well outside of a 95 percent confidence interval.”
This is a critical point that should not be overlooked by researchers. If you can show that the pooling
assumptions are false, then you should ignore all pooled estimates for policy purposes.

2.4.4. Rose revival

O MY Luve 's like a red, red rose/ …/ And I will luve thee still, my dear/ Till a' the seas gang
dry/ …/ And I will come again, my Luve,/ Tho' it were ten thousand mile. - Robert Burns, ‘A
Red, Red Rose’
Andy Rose is not a man to shy from a challenge. He saw the wilting of the Rose effect as a lack of data,
so he set about collecting an enormous panel dataset. He was, so to speak, trying to graft the old
flowering stem on to a healthy new dataset, and guess what? The flower continued to blossom. The
massive dataset he collected included annual data from 1948 to 1997 on bilateral trade between 217
countries. Theoretically, that’s 50(2172)/2=2,354,450 data points, but with missing observations and
zero flows (lots of little nations sell nothing to each other), the new Rose dataset has 219,558
observations.
Glick and Rose (2002) exploit this data in a number of ways. They throw in pair-specific dummies that
soak up any sort of idiosyncratic omitted variables that do not vary between 1948 and 1997. This, of
course, mimics the impact of country-dummies as in Rose-van Wincoop, but it goes further. The result
was, as we should have expected in the post-RvW world, that the size coefficient drops dramatically –
about 5 standard deviations from an estimated coefficient of 1.3 to 0.65. This brings down the Rose
effect from 3.7 to 1.9 times more trade among CU pairs (both estimates are statistically significant at
any conceivable level of confidence).

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What is going on here? The estimates are still biased
Pretend, for a moment, that Glick and Rose did their regression in two stages. First, they regressed the
left-hand side variable on the time-invariant pair dummies. Second, they regressed the residuals from
that regression on the main right-hand side variables, distance, CU and the joint real GDP variable. This
procedure is terribly inefficient in the econometric and practical sense, but it is very efficient from an
intuitive stand point.
The first stage strips out all time-invariant features of each bilateral trade flow. This completely
removes the bias stemming from the cross-section correlation between CU and Z. It does the same for
the cross-section correlation between what we called X21 (the relative-prices-matter term) and the CU
dummy. However, there is almost surely a bias left. We know that the relative-prices-matter term varies
over time, so there will still be a correlation with CU, after all the theory tells us that the relative-prices-
matter term contains CU and CU itself is time varying.
Second, there may still be a bias stemming from time-series correlation with omitted variables. For
example, if currency union encourages nations to deepen other forms of integration that are
unobservable to the econometrician, then CU and Z could be correlated over time as well as in a cross-
section sense. This is especially true since each pair gets only one dummy for the full 1948-1997 data
period.

Stop and smell the roses

What do we learn from this?
First, the impact of allowing for country-specific idiosyncrasies (either via the Rose-Wincoop procedure
or by the Glick-Rose technique of pair dummies) reduces the Rose effect massively. This confirms yet
again that the original +200% Rose effect was over-estimated. Of course, 90% more trade is still a huge
number, but 200% is huge-er.
Second, the Glick-Rose result should have a large ‘caveat emptor’ stamped across its forehead. The pair
dummies mean that ALL the identification is coming from the way in which trade between CU pairs
changes over the sample, compared to the way it changes for the non CU pairs, controlling for other
factors. The Glick-Rose data has lots of pairs that leave monetary or currency unions, but very few that
join (16 joiners and 130 leavers, with almost all of them having happened before the post-war
independence wave ended around 1970). This means that the results are being driven by how much
trade DROPPED after a nation leaves a monetary union, not by how much trade is created by a
currency union. Nitsch (2005) estimates these effects of currency union joiners separately and finds the
point estimate is small (about +8%) and statistically indistinguishable from zero.
To put it differently, if you want to know how much a small nation’s trade might drop if something
happens such that it has to, or wants to, leave its currency union, the then Glick-Rose numbers are what
you need. If you want to know how much trade a small nation would gain from abandoning its own
currency and adopting someone else’s – the Glick-Rose numbers are not what you need. I think we have
to admit that there just haven’t been enough new currency unions to answer the question. Or at least not
until the Eurozone came along, but I’m saving that part of the story for later.

Why are the country and pair dummy results so similar?

Third, the point estimates from the Rose-Wincoop and Glick-Rose approaches are amazingly similar.
With the naïve gravity model the Rose effect is 3.97 in the Rose-Wincoop dataset and it is 3.66 in the
Glick-Rose data set. Allowing country-specific idiosyncrasies drops the estimate to 1.9 times more
trade among CU pairs in both datasets.
This, I believe, is reassuring on the one hand, but worrying on the other. The reassurance is obvious;
different datasets, same results. The worry is that it seems to make no difference whether one controls
only for country-specific idiosyncrasies or one controls for country-specific idiosyncrasies and all other
pair-specific idiosyncrasies. Why worry?
My priors are that there are omitted variables correlated with the CU dummy – e.g. the quality of FTAs,
informal ethnic networks, foreign direct investment flows (trade and FDI are complements empirically),

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 and many more – for which the data is non-existent or too poor to use in a regression. If my priors are
right, the pair dummies are not doing their job properly. The why-part is easy.
Many of the pair-specific omitted variables probably varied over the five decades in the Glick-Rose
data set. Thus, putting in a time-invariant pair dummy leaves a times-series trace in the residual and this
trace is probably still correlated with the CU dummy. In particular, there are probably pair-specific
factors that caused nations to leave currency unions and these are probably time-varying. This is
certainly the lesson to draw from the case studies. More rigorously, Nitsch (2004) uses a large panel of
currency union pairs to identify factors involved in the break-up. Inter alia, he finds that departures from
currency unions tend to occur when there are large inflation differences among member countries, and
when there is a change in the political status of a member.19
Glick and Rose try out an admirable range of robustness checks, but they obviate most of the merit of
the exercise by trying them one by one. For example, they use data from tiny nations in 1950 in the
same regression as data from the United States in 1995. It would take a brave soul to assert that the
income elasticity of imports was the same number in these two cases. Tenreyro (2004) is particularly
strong on this idea that one must address all the problems together. Sure, that’s a lot of regressions to
try, but you have to water the thorn to harvest the rose.

2.5. Rose branch #3: Complicated mis-specification

There are two ways of correcting for omitted variable biases. The Glick-Rose approach works by
throwing lots of dummies into the regression. The alternative works by throwing lots of observations
out of the regression. That sounds strange, but it has many merits. Torsten Persson’s 2001 paper in
Economic Policy introduced this technique into the Rose effect literature. The technique is subtle and
complex; Persson explains it in technical terms (and you should have seen how technical it was before
Giuseppe Bertola rewrote it in his role as Managing Editor of Persson’s paper).20 Allow me to relate a
parable that may make the nature and intractability of the problem clearer.

A parable
A few years ago, middle-age surprised a ‘friend of mine’ and it chose to focus on his middle; he
developed a little belly. He decided to do something about it and, being an egghead, he started reading
studies on the effectiveness of dieting. One study found that a week’s worth of dieting was astoundingly
effective. I have plotted the data in Figure 7 (at least the data as my friend remembers it). Crucial
background: People tend to gain weight as they get older (their metabolism slows), so there is an
empirical link between weight and gaining weight. A proper account of the effectiveness of dieting
must take account of this. Assuming the link is linear, the study fitted the curve shown with the dashed
line. However, medical science (as my friend remembers it) tells us that the true weight-weight gain
link is bell shaped. (Once you reach middle age you pile on an extra 10 kilos and this rapidly pushes
you just beyond the normal Body Mass Index, or BMI, range but then the process slows down.)
With this background we can see how the study overestimated the dieting effect. The solid dots are the
weight gain of dieters and you can plainly see that they are below the linear dashed line. The study
claimed therefore that dieting was very effective, controlling for other factors. Obviously, this is a
spurious finding since the dieters’ weight gain is white noise around the true-model prediction without
dieting. Why the incorrect inference? The subtle interaction between nonlinearity and self-selection.

19
By the way, these suspicions of mine were raised by the similarity of the two different techniques applied on two separate dataset. It would be
interesting to make a more direct comparison, to see what the Rose-Wincoop country-dummy technique would yield on the Glick-Rose dataset, and
what the Glick-Rose pair dummy technique would yield on the Rose-Wincoop dataset. Such comparisons would help us to judge the importance of
the omitted variable critique, and the validity of the Glick-Rose solution of throwing in one pair dummy for the whole period.
20
Actually you can see it, since Economic Policy posts the Panel drafts on its web site www.economic–policy.org.

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 Figure 7: Model mis-specification and overestimation of treatment effect

Kilos lost
during
Fitted assuming
weeklong
linear model
diet

True model
without dieting

BMI
0

Normal range
First, if the study had estimated the correct nonlinear model, it would have found that dieting was
useless. Second, if the true relationship had been linear, then the deduction would have been valid.
Finally, if dieting were randomly distributed across all weight classes, the model mis-specification
would not have mattered since there would have been an equal number of dieters above and below the
fitted line (that’s what OLS does). But, dieting is self-selected. The people who are most likely to start a
diet are ones, like my friend, who have just crossed into the ‘jolly but not yet jelly’ category.

2.5.2. War of Roses

The Persson critique was presented at the Paris meeting of the Economic Policy Panel, which was, by
the way, hosted by Jean-Claude Trichet in his role of Governor of the Bank of France.
Persson (2001) employs a matching technique that can control for this sort of nonlinearity-with-self-
selection to the Rose (2000) dataset and finds that the point estimate for the Rose effect is much lower –
Persson’s estimates of the Rose effect range from 1.13 to 1.66 – and they are not significant statistically.
Kenen (2001) confirms part of the basic result using a different matching technique, but obtains very
different results in the regression analysis.
What’s going on here? It is useful to think about matching in terms of the parable. The matching
technique would throw out most of the non-dieter observations since they do not match those of the
dieters. If one compares the mean weight-loss of dieters and non-dieters in the narrow range just to the
right of normal, there is no difference in mean, so in the case of the parable, matching would yield the
correct inference. In this way, matching automatically eliminates the impact and any sort of non-
linearity by neutralising the interaction between self-selection and non-linearity.
What is the nature of the nonlinearity-with-self-selection in Rose’s study? Persson rightly points out
that while there is only one way to be linear, there are an uncountable infinity of ways to be nonlinear.
One cannot check them all, but Persson thinks he may have found one important nonlinearity – a
nonlinearity that concerns the openness and output link.21 Figure 8 shows the suggestion.
The figure plots all residuals from Rose’s preferred linear regression with the residual plotted against
their corresponding log of GDP (pair product as usual). Non-CU observations are shown with black
dots, CU observations with circles. The straight line shows the estimated linear relation between
bilateral trade and output – i.e. the linear model imposed by Rose. The curved line shows the best fit

21
By the way, this nonlinearity is consistent with Krugman’s famous Home Market Effect whereby a nation’s exports are affected by its size.

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 allowing for a nonlinear relationship between openness and output. Just as in the parable above, the
non-random distribution of CU pairs team up with the ‘true’ model’s nonlinearity to produce an
overestimation of the effect. The point is that if one compares the positions of the circles to the straight
line, it looks like they have far greater trade than they should have had. If one compares them to the
curved line, the circles are, on average, above the predicted relationship, but much less so than if one
takes the straight line as the true model. Thus, the linear regression substantially overestimates the
impact of a common currency on trade because it underestimates how much trade they would have had
without a common currency.

Figure 8: Persson’s hypothesis for why the Rose effect is overestimated.

Persson’s punchline
In short, Persson asserts that Rose (2000) overestimated the effect since he was comparing the actual
trade to a mis-specified model of what trade should have been absent the common currency.
Further evidence comes from the fact that allowing a quadratic term in Rose’s regression (i.e. pooled
cross-section without country or pair dummies), the Rose effect estimate drops radically. Rose (2000)
included a squared output and per-capita output terms in one of his dozens of regressions. When he
does, he finds that the Rose effect drops dramatically, from 3.39 times more trade to 1.95 times more;
this is a four standard deviation drop in the coefficient.
Further evidence for this interpretation – albeit very indirect evidence – can be found in Glick and Rose
(2002). Glick and Rose (2001) estimate the naïve gravity model on cross-section data for a handful of
years reaching back to 1950. The estimated Rose effects from a selection of years are plotted in Figure
9. It is interesting that the size of the effect rises over time. What could this mean? One cannot know for
sure, but the Persson-Kenen finding suggests a story. In 1950, many nations participated in currency
unions. Most nations were still colonies and many of these used the currency of the coloniser. Or, to put
it differently, the group of nations sharing common currencies was much more randomly spread. As the
decade of independence arrived, many nations adopted their own currency as a symbol of sovereignty.

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 In the Glick-Rose data there are 130 CU leavers but only 16 entries.22 The roll-call of CU dummy pairs
thinned out, but the decision to quit the coloniser’s currency was surely not random. Really tiny, really
open economies like New Caledonia decided they could not afford their own currency, while nations
like Algeria went their own way. In this chronicle, the self-selection part of the nonlinearity-with-self-
selection bias gets more severe as time passes. Thus, if you believe the Persson-Kenen account, the
rising Rose effect is completely in line with your priors.

Figure 9: The Rose effect over time.

2.5.3. The bloom is off the rose, or is it? Rose redux

Andy Rose is not one to let a new econometric technique lay in bed till noon. When Economic Policy
invited Andy to present a live rejoinder to Persson at the April 2001 Panel in Paris, he leapt to the wall
with bow strung and arrow nocked. He used a new, bigger data set and applied Persson’s matching
technique. Guess what? Rose (2001) confirms that matching lowers the point estimate on the CU
dummy (the Rose effect drops from 3.39 to 1.21 in the strictest match, to 1.43 in the most relaxed; that
is 21% and 43% more trade predicted for CU pairs). However, and critically, he finds that the Rose
effect is statistically significant in the bigger dataset even with matching. In the end, the rose was still
blooming. 20%, after all, is a pretty big number, certainly far bigger than prior in the minds of most
international economists in 2001.

2.5.4. Lessons: take nonlinearity and selection seriously

Decades of gravity model research tell us that the naïve model does pretty well in most cases. The vast
majority of the thousands of gravity equations estimated over the past 40 years assumed linearity
without objection. But in the old days, we could not handle large datasets, so most gravity estimators,
including me in 1993, used data from nations that were pretty homogeneous, like European nations plus
the USA, Canada and Japan. Or, all Latin American nations. In these cases, linearity – even if it is
wrong – is not a big deal. After all, any continuous model is approximately linear.
But the linear approximation gets worse the further one is from the point of approximation. Since
estimation is, in effect, approximating the functional relationships for the average country in the sample,

22
It would be interesting to see the share of trade pairs with common currencies by year, but this is not reported in Glick-Rose, only the full panel
average is reported.

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 the problem gets worse as the sample includes more extremely big, small, open or closed nations. The
problem is extra severe in the hunt for the Rose effect since nations that are members of monetary or
currency unions are extremely far from average; see Figure 2. One way of thinking about what Persson
and Kenen did is to say that they were trying to get a more homogeneous sample so that whatever
nonlinear does exist is not a big deal.
I believe it is extremely important to take seriously the Persson-Kenen lesson in any gravity equation
study that uses data from a very heterogeneous group of nations. The econometric theory tells us that if
the true model is nonlinear, yet a linear model is estimated, then the estimated coefficients are biased if
the policy under consideration is not randomly distributed across all observations. Both of these
premises hold for the gravity model on the Rose (2000), Rose and Van Wincoop (2001) and Glick and
Rose (2002) datasets, so we know the standard gravity-model estimate of the Rose effect is biased. To
wit:
− We know that CU pairs are not random. The first-stage matching regressions confirm the suspicion
raised by Figure 1: Schematic depiction of hub and spoke common currency arrangement
− and this has been confirmed many times over by authors such as Alesina, Barro and Tenreyro
(2002), and Nitsch (2004).
− We know that the true gravity is nonlinear (Rose 2000 finds a t-statistic on 24 on the GDP squared
term and there may be many other nonlinearities).
Again, history bifurcates. Before the 2001 Persson-Kenen-Rose papers, we didn’t think nonlinear was
an issue. Now we know it is. We are not exactly sure how best to address the nonlinearity, but we know
it is a problem. Two more lessons:
− For policy purposes, we should ignore all Rose effect estimates on large datasets that do not
address the nonlinearity-cum-selection issue. Researchers would be wise to address it in both ways:
1) try out various nonlinearities. In the context of Rose effect regressions, be sure to try a quadratic
terms for GDP and GDP per capita; 2) try matching procedures like those suggested by Persson or
Kenen (Honohan 2001 suggests another in his discussion of Persson).
− The Rose effect on multilateral data is about on the order of 20% to 40%, but this figure basically
reflects the extent to which bilateral trade dropped between nations when a currency union pair
involving a small poor nation is dissolved.

2.6. Rose branch #4: Roster-makes-the-sun-rise reasoning

While Andy Rose was declaring ‘Eureka’ for having shown that a common currency bumps up bilateral
trade, other researchers were declaring a Eureka for showing the reverse. Devereaux and Lane (2002),
inter alia, showed that nations tend to stabilise their bilateral exchange rates against nations with whom
they trade a lot, with a common currency being an extreme form of stabilisation. There are many
sophisticated reasons for this reverse causality (‘reverse’ that is from the Rose effect perspective), but
my favourite is a political economy story. If a nation’s currency depreciates against its major trade
partner, cheers arise from exporting firms but screams are heard from firms that import components and
materials. The roles are reversed for appreciations. Since losers lobby harder23 there is strong political
pressure on a Central Bank to keep the exchange rate steady against the currency of its major trading
partner, especially in very small nations where the importers and exporters dominate local politics. In
extreme cases, this means adopting the partner’s currency unilaterally or multilaterally.
The reverse causality problem is a thorn in the side of would-be Rose-effect estimators. Although one
could invent elaborate stories for why the simultaneity bias might be negative, common sense tells us
that the bias should lead to an upper bias. That is, at least part of the reason the circles in Persson’s
diagram are above the predicted trade line is because unusually open nations, especially tiny ones
located near big markets, are more likely to join currency unions. In this case the conditional correlation

23
See Baldwin and Robert-Nicoud (2002) for an explanation based on sunk costs.

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 between a common currency and trade is boosted in part by the impact of currency on trade and part by
the impact of trade on currency. This leads me to believe that all of the Rose effects discussed up to this
point are too large.
Of course, this possibility occurred to Andy and in his Economic Policy article, he tries to control for
this with instrumental variable techniques. His choice of instruments (inflation rates), however, was
regrettable and forgettable. When he instruments for the CU dummy, the Rose effect becomes “wildly
and implausibly bigger” in Andy’s own words; to wit, the Rose effect is 1.1 times 1036. To put that in
context, it implies that Fiji’s adoption of the Australian dollar would raise its bilateral exports to a level
that is several times larger than the value of world trade. I think we can agree that his instrumental
variables strategy was not successful; he abandoned the effort in subsequent papers.

2.6.1. Other instrumenting strategies

Three other instrumenting strategies have been mentioned in the Rose-effect literature and two have
been implemented: one is based on money supplies along the lines suggested by Frankel and co-authors,
and one by Tenreyro (2003). The final one, by Devereaux and Lane (2002), has not been tried to my
knowledge.
Tenreyro (2001) estimates the likelihood of a nation joining a hub-and-spoke currency arrangement
with the US dollar, British pound, French Franc or Australian dollar. She explains this decision using a
dozen of so variables that are very closely related to right-hand side variables in the gravity equation.
She finds that the probability is increasing in common language, common border, former colonial status
and the smallness and poorness of the nation. The fit of this first stage regression is not very good; the
pseudo-R2 is only 0.473, so roughly speaking she only explains about half of hub-and-spoke currency
pairs in her data. Since CU pairs account for less than 1% of all pairs in her data, this could be a real
problem in the sense of amplifying the nonlinear-and-selection biases. Her first-stage explanatory
variables are a sub-set of the second-stage explanatory variables, so the probability itself cannot be used
as an instrument. To get around this, she constructs an artificial probability of any two spokes sharing
the same currency by multiplying each spoke’s probabilities of adopting each anchor currency and
summing over the four products.
This seems a clever idea, but there are two problems. First, I would like to see what this procedure
yields. For example, how well does her newly-minted fitted CU variable line up with the real CU
variable? I would bet that this procedure invents lots of CU ties between very small, very open
economies and thus exacerbates the Persson-Kenen problem.
Second, she writes in Alesina, Barro and Tenreyro (2002), “The underlying assumption for the validity
of this instrument is that the bilateral trade between countries i and j depends on bilateral gravity
variables for i and j but not on gravity variables involving third countries.” As the simple gravity model
theory laid out above shows, this identifying assumption is false. All you need to do is remember that
the gravity model is essentially a demand equation and you know that each bilateral flow is affected by
the trade costs of every partner of the importing nation – via ∆ -- and every partner of the exporting
nation, via Ω. The Tenreyro identifying assumption is essentially saying that the trade between two
nations depends only upon the nominal price, not the nominal price divided by an index of prices from
all sources. In fact, this third country dependence is exactly what the whole Anderson-Wincoop paper is
about. Curiously, she includes Anderson-Wincoop dummies in some of her regressions and thus
implicitly admits that her identification strategy is based on a false premise. But maybe I missed
something. Other things seem strangely upside down in that paper; adding country dummies increases
the OLS estimate of the Rose effect, in contrast to what many others have found.
In any case, she finds that instrumenting raises the Rose effect to levels that would make Andy blush as
red as a rose – e.g. she gets a Rose effect of 6.77 in her paper with Barro and 14.87 in her paper with
Alesina and Barro (for the record, that means joining a currency union would increase a nation’s trade
with its CU partners by 577% and 1387% respectively). If this is right, then Finland with its 5 million
people will export more to Germany than the United States exports to the whole world; since Finland
has 10 Eurozone partners and the effect applies to each, Finland’s decision to join Euroland will double

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 world trade – according to Tenreyro’s upper estimate. Her lowest IV estimate predicts that formation of
the Eurozone will more than double world trade.
Of course, you may think that it is inappropriate, maybe even unfair, to extrapolate from the results of
small nations. But if you think that, then you don’t believe the linear gravity model works well for
nations that are extremely different from the average nation. In other words, you don’t believe what one
must believe to think the Tenreyro identification strategy makes sense.24
Given these problems, I think we can conclude that Tenreyro’s procedure failed. Probably prudent to
consign it to the regrettable and forgettable bin along with Rose’s instrumenting strategy.
I hasten to note that the theoretical points in Alesina, Barro and Tenreyro (2002) are interesting and
useful. The empirical implementation, however, is a failure in my humble opinion. .

2.6.2. Aphides: All instruments will be bad instruments

It is hard to know exactly how it all went so wrong with the Rose and Tenreyro approaches. It helps,
however, to think about the instrumenting technique. Basically, the econometrician has to invent a new
data series that looks something like the CU dummy but isn’t the CU dummy. This newly invented
variable is thrown into the gravity model and the coefficient on the new invented variable is taken to be
the currency union effect. There are several problems with this procedure in my opinion:
• Schrodinger's cat and amplified nonlinearity-selection problems. The CU dummy is digital by
the nature of the policy under investigation. The instrument, by construction, will be a continuous
variable. What this means is that the Rose effect estimate will be influenced by the sample
covariance between the invented variable and the bilateral trade of all pairs. If the invented variable
doesn’t resemble the CU dummy very closely, it is likely to display many features that the CU
dummy does not and may therefore generate an estimated coefficient that has nothing to do with the
Rose effect. This point is amplified if nonlinearities are important, as I believe they are (at least at
the extremes of country size). As discussed above, part of the overestimate in Rose (2000) stemmed
from self-selection and nonlinearities. The first stage of the instrumenting process guarantees non-
random selection – in fact it probably makes it much worse. Just take a look at Persson’s diagram
and imagine what the invented variable would look like. I haven’t seen the data, but my bet is that
Tenreyro’s invented CU variable places lots more circles (in a probabilistic sense) far above the
straight line in Figure 8.
• All instruments will be bad instruments. A good instrument is correlated with the offending
explanatory variable, but uncollected with the regression error. I believe all CU instruments will fail
on both criteria. No instrument is going to fit well. Having a common currency is a really unusual
outcome that is in reality governed by factors that can never be quantified in variables that exist for
150+ nations. This means a bad fit, but things are worse. No instrument will be uncorrelated with
the error due to ‘double omitted variables’. The first stage of fitting a currency-union model will, of
course, omit many variables given the fuzzy political, social and cultural factors involved in such a
choice. The instrument will not be orthogonal to these factors. Thus, if some of the variables omitted
in the first-stage variables are also omitted variables in the gravity equation, then the instrument will
not serve its purpose. Lots of things on which we have no reliable data could promote both CU
membership and bilateral trade among CU members. Here are couple of ones that occurred to me:
personal ties developed while studying in the ‘hub’ nation, and greater than usual mutual
understanding of each others’ legal systems, but the list is almost endless.

24
Tenreyro has a recent paper on the impact of exchange volatility on trade that applies the same IV strategy. Her conclusion is that volatility has no
effect on trade which is strange given her early findings on the CU dummy. Strangely, this paper, Tenreyro (2004), excludes the common currency
variable altogether and indeed never mentions it. Maybe it would have been too jarring to have a common currency boosting trade many times over,
but lower volatility having no impact. Or, maybe she, like Rose, decided that IV estimation of the Rose effect was a dead end.

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2.6.3. Lessons: Briar Rose
The Brothers Grimm tell the tale of a beautiful princess who came to be known as Briar Rose since she
slept a hundred years in a castle surrounded by an impenetrable hedge of briars (thorns). Princes from
far and wide sought to claim the great prize that lay within, but all found it impossible to get through the
thorny hedge, “for the thorns held fast together, as if they had hands, and the youths were caught in
them, could not get loose again, and died a miserable death.” Then one day a prince came near to the
thorn-hedge and “it was nothing but large and beautiful flowers, which parted from each other of their
own accord, and let him pass unhurt.”
Plainly, instrumental variable techniques are what we need to properly control for the reverse causality
that must be biasing the Rose effect upward. Maybe there is a beautiful instrument lying inside the thorn
bush of practical problems. Up to now, however, attempts to find that very instrument have failed
miserably. What might we do better? Well, surely, the idea would be to get some financial variables à la
Devereaux and Lane since these may influence the CU decision without influencing bilateral trade, at
least at trade data at 5 year intervals.

Table 3: Rose effect estimates arranged by estimator.

Country
Fixed
AUTHOR Effect
OLS Time-
(Pooled) Invariant Pair Specific Matching
Non Non Non
Linear linear Linear Linear linear Linear linear
1.21 0.77 -0.38
Rose (2000)a) (0.14) (0.16) (0.6)
Rose & van 1.38 0.91
Wincoop a) (0.19) (0.18)
1.30 0.65 0.61
Glick & Rose b) (0.13) (0.05) (0.05)
0.09
Tenreyro (0.14)
0.937 ; 0.69
0.52 0.37
Persson a) (0.15);
(0.320) (0.320)
(0.15)
1.47 – 2.19 1.4 – 2.1
Rose 0.74 0.66
(0.09) – (0.09) –
response b) (0.052) (0.05)
(0.14) (0.14)
Pakko 1.17 -0.378
& Wall a) (0.143) (0.529)
1.2 ; 1.4
1.7
Kenen (0.310)
(0.30) ;
(0.32)
Notes: a) Rose’s 5 year dataset. 1970-1990. UN data. b) Glick-Rose dataset. 1948-1997. IMF data.
To find Rose effect in terms of % increase in trade, take exponent of coefficient and subject 1.

2.7. Meta-Analysis: A rose is a rose is a rose

Readers who have made it this far may have a muddled impression of the many estimates discussed
above. Wouldn’t it be great to have one summary statistic, the number as it were? There is such a
number but I do not believe it is useful for policy purposes.

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 2.7.1. Weighted average of all point estimates
Rose and Stanley (2004) perform a sophisticated analysis on thirty-four studies of the Rose effect that
yield 754 point estimates. They reject the hypothesis that the true number is zero. The range they arrive
at is 30% and 90%. Surely, this is taking things too literally. Or more precisely, it throws away too
much information by treating all estimates as having been generated by the same process. As the
authors note: “While we have strong views about the quality of some of these estimates, each estimate
is weighted equally; alternative weighting schemes might be regarded as suspect.” Please, suspect!
That’s what empirical researchers get paid for. All the estimates in Rose (2000), for example, should be
ignored except the difference-in-difference estimator that roughly controls for the gold-medal mistake
of gravity models. Andy Rose himself showed that all of them were incorrect since the pooling
assumptions necessary for them to make sense have been rejected by his papers with van Wincoop and
Glick.
Moreover, the patently incorrect pooled estimates of the Rose effect – all of which are at least twice too
big – are generally repeated in the literature as a way of showing that the author’s dataset is sound in
that it can reproduce the mistaken estimates in Rose (2000). In other words, authors repeat them as a
form of benchmarking, not for policy relevance. The meta-analysis statistical techniques are fascinating,
but I don’t believe it adds to our knowledge since deep down they are basically a weighted average of
all point estimates. As we have seen, many of the published estimates are patently overestimated for
reasons that are quite clear.

2.8. Lessons for the Eurozone from non-European experiences

I believe the cleanest estimates we have on non-European currency unions are those that use matching
since these go a long way towards controlling for the omitted variable bias, and any bias from model
mis-specification. All the other estimates seem to have serious flaws that would bias them upward. On
the original Rose dataset, Persson (2001) found the effect to be between 15% and 66%. Rose (2001)
found it to be between 21% and 43% on a much larger dataset. An informal meta-analysis on these four
estimates would suggest a number like 30%. And we can be pretty sure that this is an overestimate since
the matching procedure cannot control for reverse causality, and it seems extremely likely that the je-
ne-sais-quoi factors that lead nations to adopt each other’s currency also tend to promote bilateral trade.
I believe, however, that the non-European evidence has essentially zero informational content for the
Eurozone – apart from the fact that it is worth looking for a Rose effect in Europe. The basic problem is
that the non-European data are driven by nations that are very small, very poor and very open. Exactly
because the currency union pairs in the data are so strange, we cannot use the 30% to predict the
currency union affect for any nation that is not strange in the same way.25 I guess this falls into the
category of common sense. If you study the trade effects of a currency union on very small, very poor
and very open nations, then what you learn is how much currency unions affect the trade of very small,
very poor and very open nations. Did I repeat myself? Well, I guess that is why they call it common
sense.

3. EMPIRICAL FINDINGS ON THE EUROZONE

"What matters is not the length of the wand, but the magic in the stick" ~ Hagrid to Harry
Potter
Most of the Rose effect literature treats currency unions as magic wands – one touch and intra-currency-
union trade flows rise between 5% and 1400%. The only question is: “How big is the magic?” This
approach was understandable when the literature was dealing with hundreds of pairs of trade among CU
members. Given the amazing range of peculiar situations under study – everything from France’s trade

25
Actually, since France includes some small, poor, open and remote islands (Outré-Mers), we could test whether the euro boosted trade between
these islands and, the nations that were not in the DM bloc-franc fort complex, say, Greece, Portugal, Spain, Finland and Ireland.

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 with its overseas departments to trade between two tiny Pacific islands Nauru and Tuvalu – one was
naturally attracted by generalisations.
In Europe, however, the big-magic approach is most definitely not good enough. We know an awful lot
about the affected countries, far too much to pretend that the euro will affect all their trade flows in the
same way. Moreover, the euro matters far too much for easy generalisations to be appropriate. 290
million people use the euro, and euro monetary policy quite directly touches the lives of another 200
million people living in the non-Euroland EU nations and near neighbours.

Empirical studies of the euro’s trade impact26

Given the roaring interest sparked by Rose’s papers, it was inevitable that someone would try to
estimate the Rose effect for the Eurozone. In April 2002, Andy Rose alerted the Managing Editors of
Economic Policy of what appears to be the first paper on the subject.27 When I got this message, I
looked up the paper and saw it was very much in the how-big-is-the-magic line – not at all what we
thought the world needed, but we asked the authors to write a paper that would go much further and
after some iteration, commissioned what was eventually published as Micco, Stein and Ordnez (2003),
or MSO for short. After a massive first revision between the first draft and the Panel draft, the authors
presented their paper at the October 2000 panel meeting, which was hosted by the Bank of Greece.
By the way, Lucas Papademos attended the Panel, and gave us an excellent dinner speech28, but I am
not sure he sat through this particular paper. The Economic Policy Panellists were quite positive
towards the paper and its main conclusion that the euro had already boosted trade, but they had many
questions and suggestions. In their second revision, the authors addressed almost all these concerns.
After a thorough edit, MSO was published in the April 2003 issue of Economic Policy – an issue in
which all articles dealt with the euro’s impact.29

3.1. MSO (2003)

The estimates in the earliest version of MSO presented estimates of the Rose effect on the order of
about 25%. These were so big that the referees and editors asked them to plot the data and see if it
appeared even without conditioning for other trade-enhancing factors. The result is shown in Figure 10.
It is somewhat hard to see what is going on when the data is plotted from 1980 to 2002 (the latest data
point MSO had), but in the right panel, the same data is plotted from 1993 to 2002. Note that there was
a major change in the way the EU collected its trade data from 1993, so it is difficult to compare pre and
post 1993 figures. (Much more on this below.) In the right panel, there does indeed seem to be some
sort of break in the data. But again, it is not that the touch of the euro’s magic wand made trade jump up
by 25%. What happened was that the Eurozone’s trade with everyone fell – as did trade among other
developing nations at the time – but the intra-Eurozone trade fell by less. Gomes et al (2004) show a
very similar graph, adding the data for 2003, and find similar results. Flam and Nordstrom (2003) plot
more detailed data, showing the Eurozone’s exports to other nations (8 other OECD nations) separately
from the other-8’s exports to the Eurozone. The results in Figure 10 confirm the basic message but rely
solely on export data.
The time-series are suggestive, but since bilateral trade is influenced by many things that vary over
time, especially incomes, so MSO estimate a gravity model on data from 1992 to 2002 for two samples,
one that is quite homogeneous (only the 15 EU nations) and one with 22 developed countries that is less
so.

26
This literature review draws on Gomes et al (2004).
27
Here is Andy’s email: “Respected Editors, Ernesto Stein (and co-authors) at the IADB has just started to circulate a short paper which analyzes the
effect of EMU on intra-EMU trade using data from the first couple of years of EMU. He shows the effect is significant (about 15-25% after just two
years), using only data from the EU-15 and also a larger sample of developed countries. I'm obviously biased (though I should say that I'm trying to
escape this particular sub-literature). But it's of obvious policy relevance for Europe and the ancestors if his work are in Economic Policy, so I think
it's of potential interest to you. Anyway, now that I've alerted you to it, I've done my duty to God and the Queen. “
28
See chapter 1 of Baldwin, Bertola and Seabright (2003).
29
Full disclosure: I was the Managing Editor who did the rewriting.

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 Figure 10: Intra-EZ trade, EZ trade with others, and trade among others, 1980-2002.

Source: MSO (2003) with some re-calculations by me (100=1999)

Notes: The series (1997=100) show the trade evolution between classified country pairs. Specifically, for every country in the sample, we
calculate a trade index with Eurozone (EZ) countries and one with non-EZ countries. The EZ-EZ series is the un-weighted average of the
EZ country’s EZ trade indices. The non-EZ-non-EZ series is the un-weighted average of the non-EZ country’s non-EZ trade indices. The
EZ-non-EZ series is the average of all ‘cross group’ indices. Nations in the sample: Australia, Austria, Belgium-Luxembourg, Canada,
Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Japan, New Zealand, Netherlands, Norway, Portugal, Spain, Sweden,
Switzerland, Britain, and the United States.

3.1.1. Rose effect estimates for the Eurozone, 6% more trade

The cleanest results in the MSO paper are estimates done with pair fixed effects on the EU15 sample for
the 1992 to 2002 period. Using this technique, MSO find that the Rose effect induced 6% more trade
among Eurozone members.30 This estimation strategy can be thought of as a difference-in-differences
estimate. Using terminology that comes from medical studies, one group of trade pairs gets ‘treated’,
while the control group of pairs gets a placebo; here, the treatment is Eurozone membership and the
placebo is non-membership. Using the gravity model to control for observable differences between the
control group and the treatment group, the estimate tells us how much bilateral trade rose in the
‘treatment’ group relative to the rise in the ‘control’ group. This is called difference in differences, since
it compares the before-and-after difference for the treatment group to the before-and-after difference for
the control group.
In doing this sort of exercise, it is important to get a control group that is as comparable as possible to
the treatment group as far as unobservable factors are concerned (any observable factors can be
controlled for via regression analysis). Given that EU membership is an extremely complex thing – one
that involves literally thousands of laws, regulations and practices that affect trade within the EU and
with third nations, most of which are unobservable to the econometrician since they are difficult or
impossible to quantify31 – limiting the control group to EU members is very useful. Moreover, limiting
the data to post 1992 data is useful since the EU changed the way it collects trade statistics in 1993.32

30
MSO, like many authors in this literature, use EMU to stand for European monetary union; unfortunately, EMU stands for Economic and
Monetary Union – at least since the Maastricht Treaty that implemented EMU in both senses. Since all EU members are part of EMU, writers who
are familiar with European integration use the terms Eurozone or Euroland to refer to those EU members who have adopted the euro. Also EZ is
shorter than EMU.
31
Just to take one example, the EU signed dozens of preferential trade agreements during the 1992-2002 period. Since each of these erodes the
preference margin of EU members, they should alter intra-EU trade flows.
32
In fact, MSO should probably have used 1993-2002 data since the new data collection systems started with 1993 data.

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 Figure 11: Intra- and extra-Eurozone trade, 1989 to 2002.

Source: Flam and Nordstrom (2003).

The difference in difference estimator on the EU sample takes seriously the lessons of Persson-Kenen
(use a sample where the treatment and control groups are as homogeneous as possible), and the lessons
of Anderson-Van Wincoop (control for omitted variables and model misspecification with dummies).33
Finally, the intra-EU trade data, but especially the intra-Eurozone trade data, may have some serious
data problems – intra-EU imports are under-reported and intra-EU exports may be over-reported due to
VAT fraud; there is some hope that MSO’s averaging the two-way flows helps with this.
What does this technique not control for? Probably the main thing is differences between Eurozone and
non-Eurozone members’ implementation of EU-wide reform. The EU is continuously ‘deepening’ its
integration, removing various barriers to the free movement of goods, people, capital and services. All
EU members must adopt these measures, but many EU members delay – sometimes for years – and so
the Single Market is not really a single market at any given moment. If the delays are systematically
more important for the ‘outs’, i.e. non-Eurozone members than they are for the ‘ins’, then the Eurozone
dummy may be biased upwards. In fact, the fastest implementers include all three of the outs (Britain,
Denmark and Sweden) while the three laggards (Italy, Portugal and Ireland) are ins, so the MSO 6%
may be an underestimate due to this point.34

Estimates with other control groups

MSO also perform the difference-in-difference technique using a broader sample that includes 8 extra
rich nations (Iceland, Norway, Switzerland, Australia, Canada, Japan, New Zealand and the US). Trying
to control for other forms of EU integration with dummies and proxies, they find the Rose effect is 8%.
This result is likely to be subject to more biases than the EU sample since many omitted factors affect
trade with and among these extra nations. Just to take one example, the US-Mexico free trade
agreement (in the guise of NAFTA) was phased in slowly during the MSO period. Classical trade
theory tells us that this preferential liberalisation should have reduced all third nation exports to the US
and Canada. If NAFTA were a one-time thing, the pair dummies would control for this, but NAFTA
was phased in slowly, so the trade-diversion effect is not fully controlled for. This matters since all non-
Eurozone flows are used to establish a basis for what intra-Eurozone trade would have done were it not
for the monetary union. Similarly, New Zealand and Austria deepened their trade ties during this period

33
The pair dummies are time-invariant and thus miss part of the Anderson-Van Wincoop point of using time-varying country dummies, but given
the short period, one can hope that the omission is not too important. Moreover, someone should redo MSO’s estimates with time-varying country
dummies (obviously in this case one cannot also include pair dummies).
34
Note that MSO try to control for the observable part of this, but the measure they use is extremely crude and so surely fails to fully control for this.
See http://europa.eu.int/comm/internal_market/score/index_en.htm.

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 and the EU signed many free trade deals with third nations – some of which were shadowed by Iceland,
Norway and Switzerland, but not the others. Given this, it is easy to see why limiting the sample to the
EU is a useful way to control for an abundance of unobserved factors.

Biased estimates and exchange rates

Additionally, MSO estimate the Rose effect without country or pair dummies, for comparison with the
early literature. They get a bigger estimate, 28%, but we know that we should ignore this for policy
purposes since it is biased upwards – for the same reason most of the Rose (2000) numbers were biased,
namely omitted variables and model misspecification.35
The authors also do the regressions including real exchange rate variables between the US dollar and
the origin nation, and the US dollar and the destination nation. The inclusion of exchange rate variables
is fairly rare in gravity equations. MSO justify it on the basis of a ‘valuation bias’.36 Their specification
seems quite wrong to me – I’ll explain in depth when discussing the Flam-Nordstrom paper below – but
in any case, it doesn’t make much difference even though these variables turn up as highly significant.
My guess is that the statistical significance of these variables arises from a correlation between their real
exchange rate variables and the time residual for the relative-prices-matter term.37

3.1.2. Trade diversion?

The simplest stories behind the Rose effect are that they reduce bilateral trade costs – transaction costs
are a standard suspect. If this is the case, then the euro’s introduction is like a discriminatory trade
liberalisation among Eurozone members and this should lead to supply switching from non-Eurozone to
Eurozone suppliers. Thus, if the simple transaction cost story is correct, all other bilateral trade flows
should be reduced by anything that boosts intra-Eurozone trade.
MSO look for trade diversion by including a dummy that switches on when either partner is in the
Eurozone in addition to the standard currency union dummy. They call this the EMU1 dummy, but it
should have been called the EZ1 dummy (see footnote 30). In any case, they find no evidence of trade
diversion. Indeed, in the developed country sample, they estimate a positive impact on the Eurozone’s
trade with the rest of the world. On the EU sample, the point estimate is bigger, but it is not
significantly different from zero. Moreover, the Rose effect jumps up somewhat from 4% to 13% in the
big sample and from 6% to 9% in the EU sample. Alho (2002) confirms the basic finding of no trade
diversion and a positive Rose effect.
Why does the Rose effect rise when they include a dummy for trade between Eurozone nations and
third nations? Recalling the difference-in-difference interpretation, we know that the size of the Rose
effect depends on what happened to intra-Eurozone trade compared to what would have happened
without the euro. By including their EZ1 dummy, they essentially take trade among non-Eurozone
nations as their control group, instead of all non-intra-Eurozone trade flows. Since MSO do not properly
control for free trade agreements among the third nations (e.g. they lump NAFTA, ANZCER, and EER
all together in one dummy called FTA), it is difficult to know what is really happening. It would be
useful to redo the MSO with more attention paid to time-varying trade arrangements among third
nations.

35
More formally, MSO statistically reject the pooling hypothesis that would be necessary for the estimates without dummies to make sense.
36
They write: “If dollar prices of goods produced in the euro zone fall as a result of depreciation, the value of trade between two EMU countries will
fall as well, relative to trade between other countries, and the EMU effect on trade could potentially be underestimated. One way to deal with this
issue would be to control for bilateral unit value indices in order to capture the change in import and export prices. Unfortunately, these indices are
not available. For this reason, in order to control for these valuation effects we include in most regressions an index of the real exchange rate for
each of the countries in the pair (the index is the ratio between the nominal exchange rate of each country vis-à-vis the US dollar and the country’s
GDP deflator). Reassuringly, the inclusion of these indices does not change the results significantly.”
37
Their pair dummies correct for the relative-prices-matter term on average but Anderson-VanWinccop showed us that this term should vary over
time; moreover, the relative-prices-matter term definitely includes bilateral exchange rates between the US and each importing nation so a spurious
correlation is assured.

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3.1.3. Timing is everything
MSO also study the timing of the Rose effect. What they do is interact the CU dummy with year
dummies, so they can estimate the Rose effect year by year. Of course, this means they identify the
Rose effect of the cross-section variation – and we know this results in point estimates that are too high
due to the ‘gold-medal’ mistake discussed above – but it is nonetheless instructive to look at what they
find. The Rose effect first appears in 1998 and increases with the introduction of notes in 2001. Finally,
they use dynamic panel techniques and find the short-run Rose effect is 9% to 12%, with the long-run
effect ranging from 21% to 34%.
Although it is useful to see the dynamic panel technique, the actual numbers should be ignored as far as
policy is concerned. We know from the Melitz-Levy-Yeyati results that the trade effects of monetary
union (what happened in 1999) may be very different than the trade effects of currency union (what
happened in 2002). The dynamic panel technique, however, views them as the same so the deepening of
integration that came with physical euros is confounded with the delayed effect of the monetary union.
The dynamic panel technique won’t tell us anything sensible until we have at least a few years of post-
2002 data.

Average bilateral trade instead of bilateral exports

If one adds the logs of exports from o to d together with the exports for d to o and divides by two – as
MSO and most papers in the literature do – the bilateral real exchange rate term drops out, but then one
must include the effective real exchange rate for both nations, or correct with time-varying country
dummies. Note that MSO include the silver-medal mistake and it would be useful to see what MSO’s
estimation techniques yield when the log of the sums and the sums of the logs are interchanged.

3.2. Berger and Nitsch (2005)

MSO (2003) and Rose (2000) are seminal work; they used the best available data and econometrics to
investigate an important policy-relevant issue. Even if subsequent data shows that all their conclusions
were wrong, they will remain great papers.
To many economists, the MSO paper was important in that it raised the possibility that a Rose effect
was happening in Europe’s monetary union. What was particularly striking was that they found an
effect on just four years of data. The fact that the size of the effect was small just made their findings
more believable. MSO, however, was never going to be the final word – we just cannot get a reasonable
estimate of the euro’s trade effect with just four years of data.
MSO became an instant target for the shrink-the-Rose-effect brigade, and the brigade’s informal captain
– Volker Nitsch – was in the lead. Berger and Nitsch note four crazy things in MSO’s findings, or as
they put it politely, things that ‘invite further study’:
1) The trade effect of the euro is too large relative to the trade effect of EU membership. Their
findings imply that the adoption of the euro has, in 4 years, had almost the same impact as the
radical liberalisation of the Single Market Programme that has been gradually phased in since
1986, with much of it completed by 1992. Anyone who follows European integration knows
this is just crazy;
2) Trade among EMU members seems to jump up in 1998, i.e. one year before the euro’s launch
as an electronic currency. Indeed, the Rose effect estimate seems to climb gradually over
MSO’s data period and this suggests that maybe something other than the euro was affecting
intra-Eurozone trade. More to the point, it suggests that MSO may be having trouble sorting
out the effects of monetary integration among Eurozone nations and non-monetary integration;
3) The size of the Rose effect is quite sensitive to disaggregation by country. MSO find that the
euro had the largest trade effect for DM-bloc countries, but it is negative for Greece and
Portugal (although only significantly so for Greece). It is positive but insignificantly different
from zero for Finland. That means the ‘magic wand’ is not working correctly for 4 of the 11
Eurozone members (Belgium and Luxembourg are treated as one); and

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 4) When the DM bloc is dropped from the sample, the Rose effect disappears. This seems strange
since – again referring to medical statistics – the dosage effect is all wrong (in showing that a
drug helps, medical studies try to establish that the size of the benefit is sensitive to dosage as
evidence that the result is not due to unobservable characteristics of the patient). The euro was
a far, far bigger policy change for Greece than it was for Germany, yet Germany seems to get a
significant positive Rose effect while Greece gets a significant negative effect whose
magnitude is almost twice that of Germany’s.
Shifting from critique to contribution, Berger and Nitsch add a fifth year of data (namely 2003), and re-
estimate MSO using recently revised trade data. Interestingly, both the data revision and extra year
seem to greatly increase the Rose effect. (Below, I’ll argue that this is a sign that, as Marcellus said so
eloquently when Hamlet slipped off for a tête-à-tête with a ghost, “Something is rotten in the state of
Denmark.”)
They also put the adoption of the euro in historical perspective, viewing the Eurozone as “a
continuation, or culmination, of a series of policy changes that have led over the last five decades to
greater economic integration among the countries that now constitute the [Eurozone].” Specifically,
they use data for MSO’s developed country sample of the EU15 plus 8 reaching back to 1948! Their
bottom line is that throwing in a time-trend-dummy for trade among the 11 Eurozone members wipes
out the Rose effect completely. There is surely something to Eurozone-as-a-continuum idea – see
Mongelli, Dorrucci and Agur (2005) for a more elaborate formalisation of the idea that European trade
and policy integration are a dialectic process – and this surely makes it hard to separate the Rose effect
from the effects of other integration initiatives. However, I think it is too blunt to throw in a time trend
for the Euro Area 12. European integration has affected all EU members equally. In future drafts, I hope
the authors will repeat more of the MSO robustness checks with their updated data, and redo the time
trend exercise, but with a trend for EU membership as a whole. It would also be interesting to see if
they could develop a data-based index of extraordinarily close integration among the DM bloc, rather
than the EZ11. For example, one might take estimates of bilateral pass-through elasticities as proxies
for pair-specific trade integration, the notion being that pass through would be bigger between more
tightly integrated partners.38

3.3. Flam and Nordstrom (2003)

This is probably the best paper in the field to date. It avoids the gold, silver and bronze medal mistakes
that plague the rest of the papers in this literature. Moreover, they use a data set that probably has far
fewer data issues than those used by MSO and its followers, namely they use bilateral exports rather
than an average of bilateral exports and imports. The use of direction-specific bilateral trade flows is
what the basic gravity theory suggests should be used. Moreover, it allows them to look at an issue that
concerns all the non-Eurozone nations, whether the euro puts their exporters at a disadvantage in
Euroland. Additionally, they also alert the reader to the problems with European trade data collection
(much more on this below). Finally, they perform their regressions on sector data as well as aggregate
data.
Their basic findings on the aggregate data are in line with MSO, both in terms of size and timing. Their
preferred estimate uses the three non-Eurozone and eight extra rich nations as the control group and
using this they find the Rose effect implies about 15% higher trade; Eurozone trade with other nations
(in either direction) is boosted by about half that. When they use the cleanest definition of the control
group – other EU nations – the Rose effect is only 8%. Their findings on the sectoral data suggest that
the Rose effect is only present in sectors marked by differentiated products, confirming the earlier
results of Taglioni (2002), and Baldwin, Skudelny and Taglioni (2005).
There are a few puzzling findings in Flam-Nordstrom. As in MSO, they find that the Single Market has
about the same magnitude effect on trade as the euro. Also, they find that the Rose effect is larger in the

38
Forcing the pair dummy to be the same for a half century is a bit strained, although all authors using the Glick-Rose data do this. For example,
surely the Franco-German dummy was strongly negative in the first part of the sample and strongly positive in the last part. It would be interesting
to see what happens if they allow, e.g., decadal pair dummies.

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broader sample of nations. When they run their preferred estimate on EU nations only, the Rose effect
drops more than two standard deviations to about 8%. Moreover, the estimate for the exports of non-
Eurozone nations to Eurozone nations is almost identical to the intra-Eurozone effect, however, the
Eurozone’s exports to non-Eurozone nations seem to be unaffected by the new currency.
This finding is both intriguing and suspicious. The intriguing part is that if it is really coming from the
euro’s introduction, then the euro must be making it easy, cheaper and/or safer to sell to Eurozone
nations. Or to put it differently, the euro makes the Eurozone members extraordinarily good importers,
rather than extraordinarily good exporters. It is suspicious since it suggests that it is not really the euro
that is behind it all, but rather something that the Eurozone nations, or a subset of them, did around the
time of the euro – something that made their markets more open to imports from all other EU nations.
Another hint that it may not have been the euro causing the big trade effect comes from the authors’
experiments with the sample. The estimate of the intra-Eurozone dummy jumps up by about one
standard deviation when the sample includes Norway and Switzerland in addition to the EU14 (Flam
and Nordstrom seem to exclude Greece from their aggregate regressions since they lacked Greek data
for the sector regressions). Moreover, the dummy on Eurozone exports to non-EZ nations also jumps
up, by two standard deviations. What could this mean?
Recall the difference-in-difference interpretation of the model estimated with pair dummies (a separate
one for each direction-specific bilateral export). The treatment group in all cases are intra-EZ trade
flows. What changes with the sample is the control group. In the EU sample, the control group is the 6
trade flows among the 3 ‘outs’, i.e. non-EZ members, Britain, Denmark and Sweden, since Flam and
Nordstrom include dummies for all trade flows between the ins and outs. Adding in Norway and
Switzerland brings the total up to 20 control pairs. The fact that the EZ dummy estimate rises so much
reflects the fact that trade between Norway and Switzerland and between these nations and the outs did
not rise as much as trade among the outs did during this period. That in itself suggests that some
strongly pro-trade factor was stimulating trade among EU members but omitted from the regression,
regardless of the euro usage. Now the authors include an EEA dummy (that is the EU15 plus Norway in
their sample), so this should have been controlled for, but they may still be missing something. The
leftmost bar shows the year by year EZ dummy; the other bar in each pair of bars shows the estimated
EU dummy (actually it is the EEA dummy even though they call it the EU dummy). What we see is that
just as the Rose effect is estimated to be increasing sharply, in the 1999 to 2001 period, the
effectiveness of the single market is estimated as diminishing by almost as much. The line shows the
sum of the two. This also ‘explains’ why De Souza (2002) finds no Rose effect when he includes a time
trend for EU integration, and why Berger and Nitsch (2005) are able to shrink the Rose effect to nothing
by including a time trend for integration among the eventual Eurozone members.

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 Figure 12: Flam-Nordstrom estimates of Single Market and Eurozone dummies

0.4
EEA dummy
0.35 EZ-EZ dummy
0.3 Sum
Dummy coefficients

0.25

0.2

0.15

0.1

0.05
0

-0.05

-0.1
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
All this invites further study, as Berger and Nitsch would say. In particular, it would be interesting to
see the Flam-Nordstrom robust procedures done on the EU sample alone, along the lines of MSO. It
might also be interesting to interact an estimated EU integration trend with individual members’
transposition deficits (i.e. the extent to which they are behind in implementing EU directives). It is
worrying that the outs and the ins are so different when it comes to transposition in the face of rising
overall integration. It would also be interesting to see the sensitivity to period with the EU sample
alone.

Exchange rates and gravity. But does it work in theory?

One of the big methodological innovations in Flam and Nordstrom is their treatment of exchange rates.
I believe this is extremely important and should become the norm in gravity studies, so it is worth
taking some time to think through why and how exchange rates should matter. It also allows us to think
more clearly about the biases remaining in Flam and Nordstrom and how we might correct them.
The inclusion of exchange rates is important to control one of the potential sources of spurious Rose
effects in the euro data. As the euro dropped sharply at birth, the intra-Eurozone goods came to look
cheap compared to third nation goods, US goods in particular. In other words, the exchange rate altered
relative prices inside the Eurozone in a way that would boost intra-Eurozone trade. If one fails to
control for this properly, the coefficient on the EZ dummy will be biased by the omitted variable.

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 Figure 13: Euro against the dollar, 1999-2005.

3.3.2. The ‘volume’ version of the gravity equation and exchange rates
Although the value version of the gravity equation is popular (since one does not need to find price
deflators for trade flows), it is equally simple to specify a volume version. This is what Flam and
Nordstrom (2003) do, implicitly (they don’t include any theory in the paper). We start with the CES
demand function rather than the expenditure function. Multiply the demand function for a single variety
exported from nation-o to nation-d by the number of varieties produced in nation-o, no, the aggregate
export volume is:

−σ

(13) X od
⎛p ⎞ E
= no ⎜⎜ od ⎟⎟ α d d ; Pd ≡ (∑ m
n ( p kd )
k =1 k
1−σ
)
1 /(1−σ )

⎝ Pd ⎠ Pd
where x is the quantity of the good exported from o to d, and αd is nation-d’s expenditure share on trade
goods. Note that Pd here is not the GDP deflator; it is the price index for goods that compete with
imports. I guess something like the importing nation’s producer price index for goods would be a
reasonable proxy.
Finally, we specify the exporting nation’s general equilibrium condition as usual. The precise
determinants of the allocation of a nation’s productive factors in an open economy are extremely
complex. It is called trade theory with trade cost; see Markusen and Venables (2000) for the latest
evolutions. Indeed, most of the complexity in microfounding the gravity equation stems from these
considerations (see Anderson 1979, Bergstand 1985, etc.). To make some headway without these
complexities, I’ll make a bold simplifying assumption. The number of varieties exported to nation-o is
proportional to its real GDP:

Eo Eo
(14) no = χ o ; χo = f [ ]
PoGDP PoGDP
Here the price index should be nation-o’s GDP deflator since we want a measure of the size of nation-
o’s stock of factors of production. In the simplest Helpman-Krugman trade model with no trade costs
(and homothetic cost functions), no is exactly proportional to nation-o’s supply of factors. However, in
slightly more sophisticated models with trade costs, the famous Home Market Effect will be in
operation so the number of varieties increases more than proportionally with nation-o’s real GDP. To

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 put that differently, χ may itself be a function of o’s real GDP, so it is important to allow the exporter’s
real GDP to have a different coefficient in the regression.
Using (14) in the aggregated demand function (13), and assuming the ‘f’ in (14) is a power function:

−σ
⎛ ⎞ ηo

(15) X od =⎜
p od ⎟ α E d ⎛⎜ E o ⎞
⎟
⎜ ( m n ( p )1−σ )1 /(1−σ ) ⎟ Pd ⎜⎝ PoGDP ⎟
⎝ ∑k =1 k kd
d
⎠ ⎠
where ηo is the exporter’s variety elasticity. To get the volume of imports as a function of real GDP and
real exchange rates, we define nation-o’s landed price in terms of its price-cost markup, bilateral trade
costs, and marginal cost measured in nation-d’s currency:

(16) p od ≡ ( µ od )(τ od )(eod )mco

where µ is the pair-specific markup (which may change with the exchange rate and thus result in less
than perfect pass-through), τ is our usual all-inclusive bilateral trade cost and e is the nominal bilateral
exchange rate that converts nation-o’s currency to nation-d’s; mco is nation-o’s marginal production
cost (this too may change with the level of sales and thus dampen pass-through). Plugging this into the
demand curve (15) and dividing top and bottom by Pd to get real exchange rates:

−σ
⎛ ⎞ ηo

(17) X od =⎜
τ od µ od (eod mco / Pd ) ⎟ α E d ⎛⎜ E o ⎞
⎟
⎜ ( m n (τ (e mc / P ) )1−σ )1 /(1−σ ) ⎟ Pd ⎜⎝ PoGDP ⎟
⎝ ∑k =1 k kd kd k d
d
⎠ ⎠
Assuming that α is a function of the importing nation’s per capita GDP. This can be written as:

(18)
−σ
⎛ ⎞ ηd ηo
⎜ τ od µ od RERod ⎟ ⎛ Eo ⎞ Ed ⎛ Eo ⎞ e mc
X od =⎜ ⎜⎜ GDP ⎟⎟ ⎜⎜ GDP ⎟⎟ ; RERkd ≡ kd k
⎜
⎝ (∑ n τ
k k
1−σ
kd (RERkd )1−σ )
1 /(1−σ ) ⎟
⎟
⎠
⎝ Po N o ⎠ Pd ⎝ Po ⎠ Pd

Where ηd is the importing nation’s income elasticity for imports. To estimate this, one would need a
proxy for each exporting nation’s marginal cost; its producer price index might serve well. Note that the
bilateral real exchange rate is nation-d’s effective real exchange rate (i.e. the weighted sum of bilateral
real exchange rates, where the right weights are approximately the importing nation’s import shares).
There are a couple of important points here.
- First, the denominator is time-varying but the same for all exports to nation-d in a given year, thus
a time-varying dummy for each importing nation could take care of this, thus alleviating the need to
determine the appropriate weights on the RERkd’s.
- Second, the price index for the importing nation in (19), i.e. Pd, is not the GDP deflator since not all
goods are traded.
- Third, one rarely has perfect price deflators for bilateral trade. Flam and Nordstrom, for example,
use nation-o’s producer price index instead of the perfect export price index Pod.
- Fourth, population in most rich nations is flat over short periods, so the real GDP per capita and
real GDP terms get conflated.
Thus one is estimating:

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 −σ
⎛ ⎞ ηd ηo
V ⎜ τ od µ od RERod ⎟ ⎛ Ed ⎞ ⎛ Eo ⎞ ⎧ 1 Pod PdGDP ⎫
(19) odP = ⎜ ⎜⎜ GDP ⎟⎟ ⎜⎜ GDP ⎟⎟ ⎨ ⎬
Po ⎜
⎝ (∑ m
k =1
1−σ
nkτ kd (RERkd ) 1−σ
)
1 /(1−σ ) ⎟
⎟
⎠
⎝ Pd ⎠ ⎝ Po
P
⎠ ⎩ N o Po Pd ⎭

but omitting the terms in curly brackets.

3.3.3. Potential econometric problems in Flam and Nordstrom

Note that the omitted terms in (19) are time-varying. The pair-specific dummies that Flam and
Nordstrom use control for this residual on average, but leave a time-varying residual that will be
correlated with all time varying trade costs measures – including the EZ dummy (by construction, Pd
includes τod). In short time samples, this problem may not be too bad, but in longer samples the bias
may be important. Evidence for this can be found in Flam-Nordstrom sensitivity checks. They find that
the size of the Rose effect increases with the length of the sample (a finding confirmed by others
including Berger and Nitsch 2005). Of course, one can directly test for serial correlation in the errors
and one should. It is particularly important to check this for the Eurozone trade flows.

3.4. Other studies

3.4.1. Barr, Breedon and Miles (2003)

Barr, Breedon and Miles (2003) is another paper that was presented at the same Economic Policy Panel
as MSO was. Estimating the Rose effect was not the central axis of investigation in this paper; they try
to systematically compare EU members inside and outside the Eurozone. Their early drafts study a wide
variety of issues, but comments from referees, Managing Editors and – above all – the Panellists led
them to pare down the paper to a focus on trade. For comparative purposes, they also make preliminary
estimates of the effect of monetary union on three other dimensions of economic performance: foreign
direct investment, the development of financial markets and overall macroeconomic performance,
though they recognise that their ability to control for other factors is more limited for these other
indicators. 39 These authors make a much more thorough attempt than MSO to correct for reverse
causality. Even after this correction, however, they note that the Rose effect is positive and significant.

3.4.2. Bun and Klaassen (2002)

One of the other papers in line for the I-did-it-first prize is Bun and Klaassen (2002), since their first
draft was circulated in 2001. This paper employs a dynamic fixed effects estimator. The results they
obtain are quite similar to those reported for MSO using a similar estimator.

3.4.3. De Souza (2002), Piscitelli (2003) and De Nardis and Vicarelli (2003)
De Souza (2002) estimates the basic gravity model for the EU15 countries with the addition of a time
trend. He finds no evidence for a significant Rose effect unless he removes the trend. This result is
interesting, maybe even important, but throwing linear terms can do lots of things to a regression that
gets most of its traction from the time-variation of the policy variable of interest. MSO’s experiments
with time trends (in the first draft) and a direct measure of EU integration in the published version do
not line up with De Souza’s findings.
Piscitelli (2003), following the 2001 draft of MSO, finds that lengthening the sample back to 1980
reduces the Rose effect estimates. The paper also finds that the size of the Rose effect changes with the

39
This idea was to figure out how much of Britain’s superior macro performance was due to their decision to stay out of the Eurozone, but with so
few data points this proved elusive.

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 data used. OECD trade data uses the “cost, insurance and freight” (cif) methodology while the IMF
trade data used in MSO (2003) takes the “free on board” (fob) approach. I’ll have much more to say
about this result, but I note here that fob is what you get when you rely on the exporter’s data and cif
when you rely on the importer’s data (for most nations, the UN’s ComTrade data base – the fount of all
trade data – has four observations on bilateral trade, e.g. France to Germany as reported by the French
and Germans, and Germany to France again by both nations; MSO average all of these to get their one
estimate of bilateral trade).
De Nardis and Vicarelli (2003) was also one of the early papers on this. (One of the reasons Economic
Policy decided to commission MSO was that others had found similar results.) They take a different
tack at controlling for reverse causality, but get about the same answer as MSO; 10% in the short run
and 20% in the long run.

3.4.4. Anderton, Baltagi, Skudelny and De Sousa (2002)

This paper uses more sophisticated econometrics – three-stage least squares – to estimate import
demand functions. They find no direct evidence of a Rose effect, but given the relatively small number
of post-euro observations, it is hard to know what to make of this. Given the good data on Europe,
however, this more direct approach to estimating the euro’s impact should be tried again with the longer
time series we have now.

4. COLLECTION OF CLUES40
I believe that we can be fairly sure that some form of Rose effect is occurring in the Eurozone. The
cleanest test by a long shot is the Flam and Nordstrom (2003) estimate using only EU members on data
from 1989 to 2002. Since they put in pair dummies using direction-specific exports, they have
controlled for all time-invariant idiosyncratic relationships among the EU15, and reduced the risk of
biases from the underreporting of imports. Because the time period is relatively short, the serial
correlation that we know must be in their residuals should not pose too much of a problem in terms of
biasing the point estimate of the Rose effect. And most importantly, because they only use EU members
that have not joined the Eurozone, they have controlled for most of the bias that might emerge from
unobserved pro- or anti-trade policies adopted by the EU in tandem with the euro’s introduction. It
would be useful to see a few more sensitive tests, but this result, combined with similar findings by
MSO, Berger-Nitsch and many others, leads me to believe that the Rose effect is for real in Euroland.
If I had to provide ‘the’ number, I would – after plenty of provisos about the Rose effect not being a
magic wand – say the number is between 5% and 10% to date. Most of the evidence suggests that this
number may grow as time passes, maybe even doubling.
This section attempts to draw critical clues from the empirical literature, that is to say, to stylise the
facts in a way that helps us think about the causes of the Rose effect. I organise the clues into spatial
clues, timing clues and sector clues.

4.1. Spatial variation of the Eurozone Rose effect

MSO (2003) and Flam and Nordstrom (2003) report extensive robustness checks. Deep within the MSO
paper are nation-by-nation estimates of the Rose effect for each Eurozone nations. Figure 14 (taken
from Baldwin and Taglioni 2004) converts MSO’s raw coefficients into percent increases in trade and

40
This section draws heavily on Baldwin and Taglioni (2004).

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plotted the results by nation. The nations are ordered by decreasing Rose effect. Three features are
particularly relevant.41
1) Apart from Spain, the nations with the highest Rose effects are those that are already the most
tightly integrated: the Benelux nations and Germany. Two aspects of this group may be
relevant in our search for positive OCA criteria.

Figure 14: The euro’s trade effect by nation

Spain
Netherlands
Belgium-Lux.
Germany
France
Austria
% boost to intra-Eurozone
Italy trade
Ireland
% boost to Eurozon trade with
Finland others
Portugal
Greece

-5% 0% 5% 10% 15% 20% 25% 30%

Source: Baldwin and Taglioni (2004), based on Micco, Stein and Ordenez (2003), Table 8.

These nations have been in an informal, but very tight exchange rate arrangement called the DM-bloc
for decades. Intra-DM bloc volatility was very low, so the euro had only a very small impact on the
bilateral exchange rate variability among these nations. This is a bit puzzling since one might have
thought that the trade effects would have been largest among nations that had the largest, pre-euro
bilateral volatility.
2) These nations are geographically proximate, so we suppose that the natural trade costs among
these nations are quite low; gravity model estimates in Europe suggest that each doubling of
the distance between capitals lowers trade by 70%. Moreover, these nations are among the
most avid integrationists in the EU and thus have embraced the EU’s deep trade integration
even more tightly than other members.
For example, the Benelux nations formed a customs union even before the EU was founded in 1958,
and Belgium and Luxembourg have shared a common currency since just after the war. As part of this
distance-Rose-effect nexus, we note that the size of the euro’s trade impact is lowest in the
geographically peripheral Euroland nations: Greece, Portugal, Finland and Ireland. Again this suggests
a negative relationship between trade costs and the Rose effect.
3) Berger and Nitsch (2005) point out that estimates of the Rose effect on an EU sample that
excludes the DM bloc turn out to be insignificant. In other words, the effect is not just strong in
these countries; the aggregate numbers like 5% to 10% are driven by these nations.
The fact can be read in two ways. Pessimistically, it says that it was not the euro, but some unobserved
policy adopted by DM bloc nations that is driving the results. But what could it be; general product and
labour market reforms that Britain, Denmark and Sweden had already undertaken? Optimistically, it
could be that exactly because these nations had such low exchange rate volatility for so long, their firms
were in a good position to profit from the removal of small costs. If this is right, we should see the Rose

41
The numbers for Greece, Portugal and Finland are not significantly different than zero, except Greece’s EZ1 estimate which is significant at the
5% level of confidence.

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 effect appearing in the non-DM Eurozone members, but more slowly. Glick and Rose (2002) present
some evidence that the currency-trade link can take 30 years to fully work its magic.

4.1.1. Trade with non-Eurozone nations

Intriguingly, MSO (2003) find that trade between Eurozone nations and other nations rose with the
euro’s introduction, but not quite as much. Specifically, they estimate what might be called a one-sided
euro dummy; its value is unity for any trading pair that involves only one Eurozone member (the
regular euro dummy, or two-sided dummy, is one only for trading pairs where both nations are in the
Eurozone). The results, again translated into percent increase in trade, are shown as the light bars in
Figure 14. Roughly speaking, the one-sided impact is lower than the two-sided effect, but the nations
with large two-sided effects also seem to have large one-sided effects.
This result is intriguing. It provides a very significant hint as to the microeconomics of the Rose effect,
or at least as to what it is not. Informal discussion of the trade effects of a monetary union typically
refer to ‘transaction costs’ of having different currencies. In standard trade policy terminology, having a
common currency is like reducing bilateral, non-tariff barriers. The evidence on the one-sided dummy
tends to reject this view. If one could model the trade-reducing effects of volatility as a frictional trade
barrier, the one-sided dummy should have been negative. The euro would have been akin to a
discriminatory liberalisation and this should have reduced the exports of non-euro nations to Euroland.

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 Figure 15: The euro’s trade effect over time

0.350
0.300
DC
0.250
EU
0.200
0.150
0.100
0.050
0.000
-0.050
1996

2000
1993

1994

1995

1997

1998

1999

2001

2002
Source: Micco, Stein and Ordenez (2003).

\
Notes: Top panel: The first bar is intra-Eurozone, the second is Eurozone exports to others, the third is Eurozone imports from others.
Bottom panel: Source: Flam and Nordstrom (2004).

Flam and Nordstrom (2003) refine this clue by estimating direction-specific trade flows. In their
cleanest regression – the one that only includes EU members – they find that EZ members have higher
than expected imports from non-EZ members, but not higher exports. Indeed, the rise in exports from
non-EZ members is statistically identical to the rise in exports between EZ members. If one averaged
the EZ imports with non-EZ members and EZ-exports to non-members, as MSO do, then it would seem
that having one half of a trade pair inside the Eurozone increased trade by one half the amount that it
would if both partners were inside the Eurozone.
This is a powerful clue, if it is true. It suggests that the euro has acted more like a unilateral trade
liberalisation than a preferential trade liberalisation.
If it is true, it also has some very important implications for the politics of Eurozone enlargement. I’ll
have a lot more to say about this below because it reverses some of the underpinnings of OCA theory.
In basic OCA theory, you have to give up your monetary autonomy to get the benefits of reduced

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 transaction costs. If this result is right, it suggests that Britain, Denmark and Sweden were the clever
ones from a mercantilist perspective – they got the better market access without sacrificing their main
marco-policy tool.

4.2. Timing of the Eurozone Rose effect

Monetary union in Europe was never a sure thing until it actually happened. Although the treaty that
laid out the path to the euro was signed in 1992, the Treaty had several major difficulties in becoming
law. Moreover, the treaty laid down a series of conditions – the famous Maastricht conditions – for
membership in the monetary union, and most European nations had trouble meeting these. Right up to
the announcement of the names of the inaugural members in March 1998, sceptics doubted that the
monetary union would ever become a reality.

4.2.1. The effect appears in 1998

Given this, the speed with which the euro’s trade impact appeared is striking. Evidence for this comes
from the MSO and Flam-Nordstrom estimate. The results are illustrated in Figure 15, which shows the
estimated year-by-year dummies for intra-Eurozone trade; the dark bars show the estimates for the
sample that includes only EU nations and the light bars show the estimates for the sample that includes
all industrialised nations. The main points are that the Rose effect jumps up and becomes statistically
significant in 1998, the year before the monetary union was formed. It jumps up again in 2001,
especially for the EU sample, the year before the monetary union became a currency union.
The rapid reaction of trade flows is quite remarkable since the MSO and Flam-Nordstrom controls for
the main determinants of bilateral trade. The speed also provides us with an important hint as to what is
not going on here. Such a rapid increase in trade would be very hard to explain if, for example, it was
driven by the construction of new plants related to the unwinding of hedging-related foreign direct
investment. The Flam-Nordstrom results show a longer time horizon.

4.2.2. Sensitivity to the period of estimation

Another clue relates to the nexus between the size of the estimated Rose effect and the sample period.
The original draft MSO sent to Economic Policy six months before they presented the paper in Athens
used two data samples, one from 1980 to 2002 and one from 1992 to 2002. Although the regressions
have some serious problems that may vitiate the results, the early MSO seems to find that the Rose
effect is bigger when a longer dataset is used. Berger and Nitsch (2005) confirm this result when they
extend the period back to 1948 and push it forward one more year to include 2003.
The finding that the Rose effect was bigger for samples starting further back is highly suspicious. It
suggests that the various dummies for EU integration are not really removing everything-but-the-euro
from the data. Figure 16 shows that European economic integration has been an ongoing process for the
last 50 years. The top panel is from Berger and Nitsch (2005) and the bottom panel is from Mongelli et
al (2005). Both show that economic integration was rising steeply just before the introduction of the
euro. If pro-trade adjustments to pre-Eurozone integration take time, it could very well be that the
lagged effects of Single Market measures are showing up in the post-1999 data and being confused with
the trade effects of the euro.
At the heart of this suspicion is a misspecification of the lags and the role of European integration. In
principle this is testable and correctable with proper econometrics. For example, Flam and Nordstrom
(2003) allow for a time-varying EU dummy and they find that their Rose effect estimate is affected very
little by a change in their sample period. In any case, the diagram showing the Flam-Nordstrom results
for the EZ dummy and EU dummy suggests that it may be quite difficult to tease apart the effects of
general European integration and the euro per se. Indeed, Berger and Nitsch (2005) go so far as to argue
that since a time trend for integration among the Eurozone members wipes out the Rose effect, the MSO
estimates are due to a misspecification.

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4.2.3. Sensitivity to data revisions
Berger and Nitsch (2005) re-run the MSO regressions using updated data and adding an extra year.
Surprisingly, running the same regression as MSO on the same years with the same nations, the Rose
effect jumps up with the updated trade data. This, I believe, provides a clue as to the importance of data
problems arising from VAT fraud. I’ll discuss this more below, but the basic point is that VAT-cheats
are systematically under-reporting imports. The data revisions are probably raising the level of intra-EU
imports to more closely match the intra-EU export data. Since the MSO procedure, followed by Berger-
Nitsch, involves averaging import and export data, such revisions would naturally boost the Rose effect.

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 Figure 16: Indices of European integration over time.

Sources: Top panel from Berger and Nitsch (2005), bottom from Mongelli, Dorrucci and Agur (2005)

4.3. Sectoral variation in the Eurozone Rose effect

While most studies of the euro’s impact have focused on aggregate trade data, Taglioni (2002) and
Baldwin, Skudelny and Taglioni (2005) run the standard gravity model using sectoral data. In addition
to confirming the general findings of the aggregate studies when all the sectors are pooled, this paper
also provides sector-specific estimates of the Rose effect. The results are shown in Table 4. Flam and
Nordstrom (2003) find a similar pattern.

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 Table 4: Rose effect and volatility impact by sector.

Rose
isic industry effect t-stat Volatility t-stat
40-41 electricity, gas and water supply 1.64 4.47 -15.78 -1.87
351 ……building and repairing of ships and boats 0.57 2.00 -15.87 -2.42
15-16 food products, beverages and tobacco 0.40 2.64 -7.78 -2.23
25 ….rubber and plastics products 0.35 2.25 -10.73 -3.04
35 ….other transport equipment 0.34 1.84 -17.72 -4.23
30 ……office, accounting and computing machinery 0.32 1.91 -5.77 -1.50
34 ….motor vehicles, trailers and semi-trailers 0.31 1.81 -13.78 -3.53
32 ……radio, television and communication equipment 0.27 1.68 -14.06 -3.74
36-37 manufacturing nec; recycling 0.27 1.76 -6.25 -1.76
353 ……aircraft and spacecraft 0.27 1.09 -16.89 -2.98
33 ……medical, precision and optical instruments 0.27 1.76 -7.75 -2.22
31 ……electrical machinery and apparatus, nec 0.26 1.64 -14.13 -3.94
28 ….fabricated metal products 0.25 1.66 -9.78 -2.85
17-19 textiles, textile products, leather and footwear 0.25 1.54 -12.00 -3.25
24 ….chemicals and chemical products 0.25 1.52 -8.80 -2.38
20 wood and products of wood and cork 0.23 1.41 -7.78 -2.08
29 ….machinery and equipment, n.e.c. 0.23 1.44 -9.29 -2.54
27 ….basic metals 0.19 1.16 -14.23 -3.70
26 other non-metallic mineral products 0.19 1.24 -10.29 -2.91
271+2731 ……iron and steel 0.14 0.74 -13.25 -3.08
2423 ……pharmaceuticals 0.13 0.70 -8.04 -1.90
272+2732 ……non-ferrous metals 0.12 0.63 -20.52 -4.72
01-05 agriculture, hunting, forestry and fishing 0.09 0.50 -7.59 -1.91
23 ….coke, refined petroleum products and nuclear fuel 0.03 0.12 -7.83 -1.33
352+359 ……railroad equipment and transport equipment n.e.c. -0.05 -0.23 -14.09 -2.96
10-14 mining and quarrying -0.21 -1.15 -9.84 -2.37
Source: Adapted from Baldwin, Skudelny and Taglioni (2003).

What these results show is a rough correlation between the size of the Rose effect and what we loosely
call ICIR sectors (imperfect competition and increasing return sectors). At the bottom of the list, we
have agriculture as well as mining and quarrying, while near the top, we have various types of
machinery and highly differentiated consumer goods such as food products, beverages and tobacco.
This finding opens the door to the possibility that ICIR like effects – for example, the impact of
uncertainty on market structure – may be part of the story.
The Flam-Nordstrom paper also provides sector results, which are reproduced in Table 5. These are
broadly in line with the earlier estimates in Table 4. The sectors without a Rose effect tend to be those
marked by fairly homogeneous products. Recall that trade inside Europe in agricultural goods is not free
trade. Although there are no formal barriers, market intervention is pervasive.

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 Table 5: Flam-Nordstrom sectoral Rose effects.

5. WHAT COULD IT BE?

“This is indeed a mystery. What do you imagine that it means?” Watson remarked. “I have
no data yet. It is a capital mistake to theorize before one has data. Insensibly one begins to
twist facts to suit theories, instead of theories to suit facts.” ~ Sherlock Holmes, A Scandal in
Bohemia
In this section I engage in a little Sherlock-ing about what might be going on. I’ll entertain two broad
sets of hypotheses.
1. The whole thing is down to spurious results.
2. Something real has changed trade flows.
In the next section, we’ll suggest a battery of diagnostic tests that should help us work out the true
answer, or answers.

5.1. Spurious results

While the size of the estimated Eurozone Rose effects does not strain credibility the way the bigger
estimates in the earlier literature did, many questions remain. The speed with which the effect appears is
suspicious, as is the fact that it appeared before 1999. The lack of a trade diversion effect also raises
questions, especially given that the cleanest estimates in the literature suggest that the ins and the outs
gained equally in terms of selling more to Eurozone nations.
It is always exciting to find something new and real, but one must also consider the possibility that the
effect is spurious. I believe there to be three hypotheses that need to be eliminated before we can be sure
that the estimates are telling us anything real about the economy.

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 • The measured trade does not reflect true flows and VAT fraud creates a spurious Rose effect.
• Delayed effects of the euro depreciation create a spurious Rose effect.
• Effects of Eurozone implementation of Internal Market measures create a spurious Rose effect.
We discuss these in turn.

5.1.1. Lies, damned lies and statistics

This section considers that very real possibility that the Rose effect in the Eurozone is a statistical
illusion stemming from the way trade figures are gathered. This is headache material, but I believe it is
necessary since existing estimates of the effects are of the same order of magnitude as the estimated
Rose effects in the Eurozone.

VAT fraud
One of the great coups of the 1986 Single European Act was to remove Europe’s internal borders, at
least as far as trade is concerned. This happened in 1993 and changed the way trade statistics were
gathered on intra-EU trade. Data on intra-EU trade from 1993 onwards was collected by VAT
authorities rather than customs officers.
The problem is that this creates a direct link between trade date and tax avoidance and evasion. Worse
still, tax enforcement changes – and anticipation of the same – can create reactions that distort EU trade
flows. These distortions can vary across time, across trade pairs and commodities.
Why would VAT authorities produce trade statistics? EU nations have VAT systems that are based on
the so-called destination principle, i.e. a good pays the VAT rate of the nation where it is sold, not
where it is made. Practically speaking, this means that the exporting EU nation rebates its VAT to the
exporting firm and the importing EU member imposes its own VAT rate on the importing firm. This is
why VAT authorities have always kept track of imports and exports.
Although the VAT system was massively reformed in anticipation of the suppression of border controls
– a major part of this being a narrowing of differences in VAT rates – the 1993 system was susceptible
to fraud.

Box 2: Acquisition and Carousel fraud

The easiest fraud is called ‘acquisition fraud’. Criminals set up a company in, say, the UK and import
goods from Germany at a zero-VAT price (the selling company gets the Germany VAT rebated). The
importing firm sells the goods in Britain at something like the with-VAT price (since that is what honest
importers have to charge) but they never pay the VAT; they go out of business before the VAT
authorities can get them.
The Carousel fraud takes this one step further, but understanding this requires some background on why
VAT is usually impervious to fraud. An EU firm that sells a good is liable for the VAT on the full sales
price, unless they can prove that they bought inputs to make the good, in which case they only pay the
VAT on their value added. The point is that the VAT has already been paid on the inputs. How do we
know the VAT’s been paid on inputs? Well, whether it was made locally or imported, the local VAT
rate was paid. How do we know that the firm won’t exaggerate its purchases of inputs? Counteracting
incentives is the answer. The input seller would like to under-report its sales to reduce its VAT bill, but
the input buyer would like to over-report its purchases to reduce its VAT bill. The size of the gain and
loss are identical so there is no reason to suspect an upward or downward bias. In short, buyers and
sellers become informants on each other as far as VAT payments are concerned. Now, how does this
work with exports? Suppose the export shipment is to Belgium and is worth 100,000 pounds. If the UK
VAT rate is 20%, the British VAT authority pays the exporting firm 20,000 pounds – which is the
amount of VAT that has been paid on the good in Britain.
But what if the VAT was never paid on the import into Britain because of an Acquisition fraud? In this
case, the criminals pocket 20,000 pounds having paid very little, or maybe no VAT, in Britain. Since it

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 worked once, they may be tempted to put the same goods through the same cycle again. The goods turn
around and around like a carousel, each time showing up twice as an export and once or never as an
import.

Fraud and intra-EU trade figures

As early as the mid-1990s, problems of VAT fraud were recognised, for example, by the European
Parliament’s first Temporary Enquiry Committee in 1997. Measures were taken to improve the system,
but things were still problematic when the euro was launched. A European Commission report to the
Council and European Parliament in 2000 used usually blunt language:
“The transitional VAT arrangements have been in place for more than 6 years. During this
period, one would have expected that the implementing problems should have been solved
and that the system should be running smoothly. However, this does not appear to be the
case. The 6 years appear to have given the fraudsters time to appreciate the possibilities
offered by the transitional VAT arrangements to make money, while, generally speaking,
Member States have not met the challenge posed by fraud. …There are indications that the
level of serious fraud in intra-Community trade is growing.”
The exact nature of such fraud is not easy to ascertain. Typically, it creates a gap between export
statistics (every exporter wants the VAT rebate) and import statistics (some have an incentive to avoid
paying the importing nation’s VAT). But, it can also inflate the trade statistics as in the case of the so-
called ‘carousel fraud’, see Box 2.

Figure 17: Difference between intra-EU exports and imports.

The effect is huge and anti-fraud activity differs across time and country pairs.
The effect of this fraud is so large that the UK had to restate its national accounts (see Ruffles et al
2003). The revisions involve upward adjustments to imports of £1.7 billion in 1999, £2.8 billion in
2000, £7.1 billion in 2001 and £11.1 billion in 2002. Unadjusted imports in 2002 were £220 billion, so

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 the effect is about 5%. The problem is not limited to the UK as Figure 17 shows. The problem is large,
on the order of 5%, and it varies over time. As inspection of the figure shows, the problem appears to
increase substantially in the run up to the euro’s introduction.
Policy reaction is correlated with the timing of the euro’s introduction. The European Commission has
been coordinating the implementation of anti-fraud activities since the 1990s, and these are still not
complete. The rate of implementation varies across nations and across types of trade. I presume the
criminals involved in this fraud follow the process carefully, so it is entirely possible that they alter their
behaviour in anticipation of changes.
If this fraud were simple – e.g. it consisted entirely of Acquisition Fraud, simple fixes might work. For
example, researchers could use export data. But as the Carousel Fraud suggests, even these data may be
exaggerated in ways that vary across time, country pairs and commodities.

The Rotterdam effect

A huge fraction of Germany’s imports from non-EU nations arrive via Rotterdam. Some of this trade is
recorded as a Dutch import from, say, New Zealand and then recorded as a German import from the
Netherlands. Some of it, however, is recorded as a German import from New Zealand since it is subject
to a ‘transit’ regime by which the tariff and VAT are not paid until the good arrives in Germany. One
such system is called the TIR system (‘Transports Internationaux Routiers’) and it involves transferring
sealed containers from ships to trucks (Rotterdam and Antwerp are the big centre for this, but it happens
in other ports as well).
The removal of fiscal border checks within the EU, teamed with the rapid rise in the volume of trade,
made fraud a big problem in the EU. Anti-fraud measures lead to a reaction that resulted in a rise in the
amount of third nation goods being declared twice, once as an import into Holland and once as an
export from Holland to the true destination. There are a million stories, but here is one that gives an
inkling of the problem: As a European Commission pamphlet on the EU’s transit system tells it:
“In the early 1990s, the TIR system also began to experience a significant increase in fraud
leading to large losses of duties and charges. Much of the fraud concerned tobacco and
alcohol, both subject to high rates of duties and charges. In those cases the USD 50 000 limit
of the guarantee was often inadequate to meet claims made by customs. A special
‘tobacco/alcohol’ guarantee of USD 200 000 was therefore introduced on 1 January 1994.
The situation was so bad that, with effect from 30 November 1994, the central pool of
insurers were forced to withdraw their insurance cover for all guarantees for tobacco and
alcohol. This meant it was no longer possible to move tobacco and alcohol under TIR.
Furthermore, with effect from 1 April 1996, the national associations of some Community
Member States withdrew their TIR guarantees for those sensitive goods that were banned
from using the comprehensive guarantee in Community transit, for example beef, milk, cream
and butter. As a result it is impossible for these goods to move under TIR into or out of the
Community.”
Of course, if these goods don’t use the TIR, the intra-EU trade rises relative to the extra-EU trade. The
point is that before and after the good is counted as being imported once from the third nation, but
afterwards it is also counted as trade between Holland and Germany.
As with the VAT fraud, one could image simple fixes if the problem were simple, but unfortunately the
magnitude of the problem varies over time and by member state. The worst part is that attempts to deal
with these problems are phased in tandem with the euro as Figure 18 shows.

PECS: Woes with ROOs

The next problem with the statistics comes from another highly technical consideration – Rules of
Origin, or ROOs as they are known to cognoscenti.
The EU is the world champion when it comes to preferential trade agreements. These cover not only
intra-EU trade but also a very large share of EU imports from third nations ranging from Switzerland to
Mexico. Preferential trade agreements, however, only cut tariffs on goods originating in nations that

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 have signed the agreement. To establish which goods get the tariff preference these agreements need
‘rules of origin.’

Figure 18: The reform of EU transit regimes line up with the euro’s introduction.

Source: European Commission.

Throughout my career as a trade economist, I’ve tried to ignore ROOs for two good reasons: they are
dauntingly complex and mind-numbingly dull. My third reason for ignoring them – they don’t matter
much – turns out to be wrong. A string of recent papers demonstrates that they do affect trade flows, i.e.
they are non-tariff barriers. A paper that will appear in the next issue of Economic Policy, Augier,
Gasiorek and Lai Tong (2005), studies the impact of ROOs on European trade. In particular, they study
the impact of a change in which the EU applies its ROOs. This change, known as the Pan-European
Cumulation System (PECS), was implemented in 1997.
The system is complex, but it was set up at the request of EU industry to reduce the existing
complexity. Here’s how. Staying competitive requires firms to set up a complex supply chain in which
components were shipped among many nations. In the mid 1990s, there were something like 60
bilateral FTAs in Europe, each with its own complex set of origin rules. Such complexity made it
difficult for firms to optimise manufacturing structures since it could be difficult if not impossible for a
firm to be absolutely sure how the outsourcing of one of its intermediate goods would affect the origin
status of its final-good exports.
The PECS simplified this in two ways: 1) it imposed uniform rules of origin in the EU15, EFTA nations
and the ten nations that joined the EU in 2004, and 2) it allowed firms to count goods from any of these
nations as originating in the EU.
Theoretically, the biggest impact on trade flows is between ‘spoke’ economies that had FTAs with the
EU, but it could also encourage or discourage EU imports from non-EU nations both those that are part
of PECS and those who aren’t (see Augier, Gasiorek and Lai Tong 2005).
The relevance here is that this could alter trade flows in the EU just about the time the euro was
introduced. Augier, Gasiorek and Lai Tong (2005), for example, found that it had a statistically

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 significant impact on trade flows between the EU and non-EU PECS nations (both ways), and as well as
boosting trade among the spokes. It is also not exactly clear whether PECS could warp the way in
which EU imports are allocated across third nations. This is something that should be checked with
ROO experts at the Commission.

5.1.2. Euro depreciation and appreciation

Another suspect for spurious results is the sharp depreciation of the euro at its birth. Here is the basic
story. The gravity equation is a fancy demand curve. The sudden and sharp depreciation of the euro
between 1999-2001 would make intra-Eurozone goods look cheaper relative to the extra-Eurozone
goods. If this is not properly controlled for, the EZ dummy would pick up the expenditure-shifting
effect and report it as a Rose effect.
Flam-Nordstrom attempt to control for this, but they have not addressed to problem of lags. Given the
usual lags involved in trade, the impact on trade flows could last for a couple of years and thus still be
biasing the results. Another point concerns the differential external exposures of Eurozone nations to
external trade. For example, Greece does much less trade with the dollar zone than Ireland, so the euro
depreciation could be behind part of the differential effects that MSO find.

5.1.3. Delayed Single Market effects

As the papers by Berger and Nitsch (2005) and Mongelli, Dorrucci and Agur (2005) show, European
integration is a work in progress. The doorstep to the euro, the 1992 to 1998 period, witnessed a
particularly intense burst of deeper integration (see Figure 16). This would not be a problem for the
Flam and Nordstrom or MSO estimates done on the EU sample, if only all EU members introduced
these Single Market measures at the same time. But ‘if only’ is the bane of empirical economics. EU
members differ widely on their pace of implementing EU Directives. Worse still, most of the tortoises
are inside the Eurozone and most of the hares are outside. If you combine this with the likelihood that
the pro-trade effects of many Directives may take a couple of years or more to be fully realised, it is
easy to see that there is a real problem. It is possible that the ‘euro effect’ is nothing more than the
delayed and differential effect of pro-trade directives.

5.1.4. Bottom line

Here I end my ‘Doubting Thomas’ digression. All three of the sources for spurious results should be
taken seriously by empirical researchers, but I believe a Rose effect happened. Just tell any group of
European businesspeople – especially ones who own small or medium enterprises – that the euro had no
impact on trade inside Europe. After a guffaw, you will almost surely hear ‘of course, it did.’ Now it is
time to think about the microeconomics of why.

5.2. Microeconomic changes that might produce a Rose effect

In science, "fact" can only mean "confirmed to such a degree that it would be perverse to
withhold provisional assent." I suppose that apples might start to rise tomorrow, but the
possibility does not merit equal time in physics classrooms. -- Stephen Jay Gould
When I first started working on exchange rate changes and trade in the mid-1980s, the US deficit was
the real puzzle. The dollar was falling like mad but the trade deficit wouldn’t budge. Various
economists had estimated the import demand equations and found nothing wrong, but others estimated
the import pricing equations and found structural breaks. This puzzled many international economists
since most had not heard of the ‘new trade theory’ with its increasing returns and imperfect competition
– for them the J-curve was the nec-plus-ultra. Having taken my trade course from Helpman and
Krugman (Helpman was visiting MIT that year), imperfect competition was the first thing I thought of,

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 and I invented the Hysteresis in Trade effect – also known as the Beachhead Effect – to explain why we
might see a structural break with the falling dollar that we had not seen during the rising dollar.42 In the
euro’s case, the facts seem to be reversed. We seem to have reasonably firm evidence of a structural
break in the volume of trade, but not in the pricing. This makes me think that the real story will require
the ‘new, new’ trade theory of Marc Melitz – but to start with, I want to consider a broader range of
alternatives.

Theoretical literature on the Rose effect

I should have a theory review to match the empirical review. That is easy. The only theoretical models
of the Rose effect that I know of are the ones I have done in Baldwin, Skudelny and Taglioni (2005)
and Baldwin and Taglioni (2004). Both turn on changes in the Beachhead costs and/or in uncertainty
facing small firms using the heterogeneous-firms model of Melitz (2003). More on this below.

5.2.2. An organising framework

By my count, there are four ways in which trade flows can change in the time framework discussed
here, namely 5 years or so. If the Rose effect was for real, it is probably due on one or more of these.
The best way to organise the discussion is to start with a bit of theory that just has to be right, the
demand function. Taking the CES demand function from my discussion of the Flam-Nordstrom paper:

−σ
⎛ ⎞
⎜ RERod ⎟ Ed ekd mc k
X od = noτ od µ od ⎜ ; RERkd ≡
(20)
(
⎜ ∑ nkτ kd
⎝ k
1−σ
(RERkd )1−σ )1 /(1−σ ) ⎟
⎟
⎠
Pd Pd

p od = ( µ od )(τ od )(eod )mco

Recall that αd is the share of expenditure on imports in the destination nation, µod is the price-marginal
cost markup on sales from nation-o to nation-d, eod is the bilateral exchange rate and mco is the marginal
production cost in the exporting nation.
We have reasonably good data on expenditures, bilateral exchange rates and aggregate price indices.
We have very poor data on trade costs, markups, the number of exported varieties, n, and marginal cost,
mco. Thus, if we get a structure break when the euro is introduced (significance of the euro dummy is
just a way of testing for structural breaks), then we can be reasonably sure that it is coming from a
change in:
• The trade bilateral costs, τ
• the markup, µ
• the marginal costs, mco
• the number of varieties traded, n
• one of more of the above changes for third nation exports to the Eurozone.

5.2.3. Trade costs

This is the traditional optimal currency area story. If two nations share the same money, transaction
costs are lower. The evidence on the Eurozone, however, suggests that this is a one-way trade cost
reduction. In other words, it seems that the euro makes Eurozone nations exceptionally good at
importing, rather than importing and exporting. Or, to put it differently, adoption of the euro was akin to
unilateral trade liberalisation.

42
The paper I gave at the NBER’s 1985 Summer Institute when I was a third year grad student was eventually published in Baldwin (1990), My
papers with Krugman, Baldwin and Krugman (1988) was written later but published earlier.

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 The problem with this one is that it is fairly hard to imagine the sorts of trade costs that could account
for the magnitude of the Rose effect we seem to have seen. Even a 5% increase in aggregate trade, if it
were due solely to lower trade costs, would require trade cost reduction of at least 5%. Such changes
would be plainly obvious to practitioners. Since the sectoral evidence tells us that the Rose effect
appeared only in some sectors, it would seem that we cannot search for macro changes, like a reduction
in the cost of changing monies, or more intense banking competition driving down financial fees on
international transactions.

A more subtle possibility

Flam and Nordstrom (2004) suggest that even small trade cost reductions could have large effects. It is
based on work by Yi (2003) that shows how important the international fragmentation of trade is these
days. The use of imported inputs in goods that are exported grew by almost 30 per cent in the OECD
between 1970 and 1990. In other words, goods – understanding a good to be the sum of its components
– cross borders many times these days. Thus a small change in trade cost can, via a cumulative impact
on costs – have big impact on trade volumes. Moreover, if the lower trade costs foster greater
fragmentation, the process can be highly nonlinear.

5.2.4. The markup

A good way of thinking about the markup is as a measure of the degree of competition. Changes in
market structure can change the markup and thereby change the volume of trade. A very simple
example is illustrated in Box 3. The critical point here is that if such a thing occurred, and it occurred
due to the monetary union, it would show up in a gravity equation as a positive Rose effect.
One very attractive feature of the markup hypothesis is that it is sufficiently flexible to account for the
national and sector diversity in the Rose effect estimates. One could expect that the euro would increase
competition among Eurozone companies, and indeed this is what many trade practitioners will tell you.
However, it is quite likely that the effect would interact in a complex manner with various nation-
specific and sector-specific features. For example, if the euro lead to greater price transparency for big-
ticket items like cars and trucks, the impact would be very different in, say, Greece than it would be in
Germany. The two nations differ greatly in terms of local producers and geographic distance to
alternative suppliers. Likewise, the extent of the change in the markup could easily vary by sector. The
obvious point is that the markup is pretty small already in some sectors and so unlikely to fall further.
The more subtle point is that the pro-competitive effects of the euro could interact in complex ways
with national regulations, a good example is banking. One would have thought that the elimination of
currency risk would have allowed German homebuilders to get a mortgage in Luxembourg, but
domestic regulations and practices effectively prevents this for now.

Box 3: Market structure and trade volumes

Consider the classic Brander-Krugman trade model of reciprocal dumping wherein there is one firm per
nation and two nations. The standard presentation assumes the firms play Cournot in each market and
so with trade costs, each sells some in the other’s market, but has a bigger market share in its local
market. One could, however, do the same assuming that the two firms act less competitively. For
example, if they can collude perfectly, they arrange things such that the sum of their sales in each
market equals the monopoly level, and somehow they agree to divide up the markets.
Both outcomes are shown in the diagram. The BRF lines are best response functions, one for each firm
in each nation. The intersections show the Nash equilibrium (NE), the other dots show one example of
perfect collusion. If we start from perfect collusion and somehow the euro creates more competition,
say it breaks the collusion, then we get more trade. It is easy to see this since the NE points are further
from the origin and thus involve higher joint sales. Indeed, in this example both firms sell more in both
markets. As the exhaustive and exhausting new trade literature showed, there is absolutely nothing
robust in this example. Nevertheless, it illustrates to point that a sudden change in the degree of
competition can lead to a sudden increase in the volume of trade – i.e. it is a contender for the
microfoundations of the Rose effect.

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 Home Foreign
XHH XHF

BRF_FH BRF_FF
1:1 1:1

NE
NE
BRF_HH BRF_HF

Perfect collusion XFH Perfect collusion XFF

5.2.5. Marginal costs

If trading firms in the Eurozone experienced a drop in their marginal costs, then there would appear to
be a structural break in the import demand equation that did not use real import price data. That is to
say, it would seem that these nations were trading more than they should be given the observables and
this would make the Rose effect dummy positive and significant.
What could cause the marginal costs of Eurozone companies to drop with the euro introduction? I can
only guess and as Sherlock Holmes said in The Sign of Four, “I never guess. It is a shocking habit –
destructive to the logical faculty.” Well, perhaps I can make an exception. One guess would be that the
euro led to a change in labour union behaviour in sectors where trade competition is particularly fierce.
Another guess arises from the subtle point raised by Flam and Nordstrom. If goods are made up of parts
that cross borders many times, then the total change in production cost can be many times the change in
the once-across-the-border cost that most of us have in mind.
The problem with the marginal cost explanation – regardless of what is driving it – lies in the trade
diversion facts. If the euro is lowering manufacturing costs in the Eurozone more than that of nations
outside the Eurozone, then the Eurozone exports to the rest of the EU should have flourished. Flam and
Nordstrom find the opposite, at least on their cleanest set of results – those involving only the other EU
members.

5.2.6. Intensive and extensive margins – magic with the Melitz model
A fascinating paper that I recommend as ‘mind candy’ to all international economists is Bernard and
Jensen (2004). Using real data – and here I am talking about data from individual plants for the entire
US manufacturing sector – they decompose sources of the US export boom in the late 1980s and early
1990s. They find that the preponderance of the increase in exports came from increasing export
intensity at firms that were already exporting, but a non-negligible share came from firms that switched
between only selling locally to selling locally and abroad. (Little known fact: most firms in most nations
do not export even when they are in so-called traded goods sectors.)

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 The relevance here is that a change in ‘n’ – the number of varieties produced in nation-o and sold in
nation-d – could produce a Rose effect since we do not have data on the n’s. There are many things to
recommend this idea, so many that I actually wrote a simple theory model of how it might work
(Baldwin and Taglioni 2004). Here it is in a nutshell.

The Baldwin-Taglioni story

The basic intuition is simple. Most European firms are not engaged in trade; they sell only in their local
markets due to a variety of reasons – one of which is aversion to exchange rate uncertainty. Such
uncertainty is a nuisance to giant companies like Nestle and Fiat, but to small and medium firms it is a
very real barrier. Our story is that monetary union eliminated this uncertainty and thus increased the
number of firms in the Eurozone that are engaged in exporting to other Eurozone markets. A sudden
and permanent reduction of bilateral volatility within the Eurozone thus led to an increase in exports
with little change in the basic production structure.43 This story rests on the Melitz model – that’s the
young Melitz (Melitz 2003) rather than his father, my esteemed discussant – where the range of firms
that export is endogenously determined and related to native firm-level productivity so that large firms
export while small firms do not.
Note that this story is flexible enough to account for sector variation and nation-specific variation. In
particular, the Melitz model works in industries marked by imperfect competition and increasing
returns, but not in Walrasian sectors – just the pattern that the sector studies suggest. It is also flexible
enough to allow for geographic variation if one goes into some of the craftier bits of the theory. To wit,
there is a non-intuitive interaction between trade costs and the impact of exchange rate uncertainty in
our model that predicts that the Rose effect should have been greatest in the nations that were already
most tightly integrated.

Do the facts fit?

Inspector Gregory: "Is there any other point to which you would wish to draw my attention?"
Holmes: "To the curious incident of the dog in the night-time."
"The dog did nothing in the night time"
"That was the curious incident," remarked Sherlock Holmes.
From "The Adventure of Silver Blaze" by Arthur Conan Doyle
Consider the clues that Sherlock would have before him: The effect happened very quickly, far too
quickly for the new trade to be explained by important changes in production structures. Moreover,
given the very small size of the likely transaction cost reductions entailed in monetary union (even
smaller than the one linked to currency union), the size of the effect seems to be too large to be
explained by a standard export supply curve model using a reasonable export supply elasticity.
In poorly written murder mysteries, the critical clue remains obscure to the end of the novel. So it is
with my paper. The keystone clue is that the scant evidence we have on the pricing equation in post-
euro Europe – e.g. the Economic Policy article by Engles and Rogers (2004) – suggest that there has not
been a break in the pricing relationships. Indeed, a more systemic study the ECB finds that although
there are structural breaks in inflation relationships, they do not seem to be tied closely to the euro’s
introduction. The pricing equations are the dog that did not bark.
All of the other explanations – unobservable trade cost changes, unobservable changes in markups and
unobservable changes in marginal cost – should have also shown up as structural breaks in the pricing
equation. But as even the most casual glance at (20) reveals, the number of exported varieties does not
enter the pricing equation. A ‘structural break’ in ‘n’ would thus create a structural break in the trade
volume equation without creating one in the trade price equation.

43
The link is extremely non-linear, so throwing in a linear volatility term would not eliminate it. In Baldwin and Taglioni (2004), we find evidence
that the Rose effect is actually a highly nonlinear effect of exchange rate volatility.

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 6. BATTERY OF DIAGNOSTICS
This section discusses a battery of diagnostics that should give a better idea as to what is really going
on.

6.1. Checking for spurious results

VAT fraud and the Rose effect

This is a very difficult one to check for. The approach by Flam-Nordstrom is a good start – they throw
in various dummies – but as I pointed out above, the problem is likely to vary by trade pair, by time and
by commodity. Surely the best place to start is with the extensive work done by Eurostat and member
states’ tax authorities to investigate this phenomenon. It is costing them billions, so they have surely put
serious resources into working out how much fraud is occurring. In all likelihood, the problem is worse
for some trade pairs, e.g. cross channel trade with Britain, and for some goods, e.g. tobacco and alcohol.
Indeed, the largest Rose effect in the Flam-Nordstrom paper is for the Tobacco and Beverage sector
(since there are high excise taxes in addition to the VAT in some member states, fraud in this sector is
particularly profitable). Direct evidence from anti-fraud studies would help us understand which data
are particularly unreliable. It might also provide ways of adjusting the data for underreporting. Note
however, that the VAT authorities are not interested in working out how much of the reported trade is
‘for real’ and how much is carousel trade.
One brave approach would be to estimate a model of the fraud – maybe based on VAT rate difference
by commodity and by trade pair – and use it to instrument the trade data. Or at least to tell us where to
put in fraud dummies akin the Flam-Nordstrom Rotterdam dummies.

The Rotterdam effect

The Rotterdam effect whereby external trade gets mis-represented as being imported into the wrong EU
nation and then generating false intra-EU trade is easier to deal with. Probably the best place to start is
with data reported by third nations. For example, Australia has engaged in a correction exercise since
many of its exports do not get reported in the nations it believes they are going to. Also, Switzerland
participates in most of the European schemes and yet imports a lot of its goods via Rotterdam and
Antwerp, so its trade may provide some sort of yardstick for separating the Rotterdam effect from the
Eurozone effect.

PECS and ROOs

Here the challenge is to find out exactly how PECS affected intra-Eurozone trade by finding out if and
how the system altered reporting of the country of origin. Also, since PECS affected all EU members
and many other nations, there is hope that there is sufficient variation in the data to sort out the effects
econometrically.

Euro depreciation
The obvious thing to do here is to use some real exchange rate indices that are specific to each
importing nation. The gravity equation theory suggests the way forward, but I believe it is important to
derive that proper control variable from the theory rather than just throwing in various real exchange
rate indices. One will also have to throw in some lags to soak up delayed effects that can take years to
show up.

Single Market initiatives

The first thing to do would be to use the Berger-Nitsch and Mongelli et al indices of European
integration interacted with the Commission’s implementation deficit scoreboard figures and see what
this does in the Flam-Nordstrom set up. Ultimately, however, the only thing to do is wait until we have
enough data to allow this sort of thing to average out. Alternatively, we could pursue a sectoral

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 approach and introduce sector-specific indicators for Internal Market implementation by year and by
nation.

6.2. Real changes

If I had a large research budget at my disposal to investigate the Rose effect in the Eurozone, one of the
first things I’d do would be to survey business people, asking them how they think the euro has changed
the trading environment in their businesses. (The Commission did this for both the One Market One
Money study and the Cecchini Report.) The next thing would be to shift the focus away from aggregate
trade data studies and towards more specific effects and sectors.
The key, I believe, is to use an empirical methodology that combines the estimation of both price and
quantity equations. If the unobservable trade costs, markup, or marginal cost stories are right, we should
find breaks in the price equations that are congruous with the Rose effect by nation, by sector and by
year. It can be hard to get bilateral price data, but even without this, one should be able to detect a break
in EU nations for whom the Eurozone is the dominant trade partner. The examples of Belgium and
Austria come to mind. Moreover, it seems that the ECB has access to some excellent price data for their
inflation persistence project. From the public description, these data would be extremely useful in
pinning down the source of the Rose effect.
The Flam-Nordstrom-Yi story could be tested directly by looking at trade in intermediates. There are by
now several classifications of goods into final and components. If this story is right, much of the Rose
effect should be coming through trade in components.
Changes in markups would also show up in the pricing equations. More directly, changes in market
structure leave footprints in many datasets. For example, a rapid change in competitiveness on the scale
necessary to produce a Rose effect should show up in the financial performance and thus stock market
performance of the companies involved. Changes in the marginal cost of trading firms could also be
tested directly by studying the financial performance of exporting firms.

7. CONCLUDING REMARKS
Fifteen years ago, Michael Emerson – the real author of the Cecchini report and high-ranking Eurocrat
at the time – asked me to write a paper called “On the Microeconomics of the European Monetary
Union”. In that paper I engaged in a practice that would have shocked Sherlock Holmes and Andy Rose
– I theorised before I had the facts. I am grateful to the ECB for giving me this opportunity to reverse
that error and to Andy Rose for stimulating an empirical debate that has provided many facts.
Although we are a long way from really knowing how the euro affected trade, it is now clear that the
way forward needs to be guided by detailed theoretical hypothesis as to HOW the euro affects trade.
There really is not yet enough information in the aggregate trade data to answer the question: “How
much did the euro boost trade?” Indeed, the question itself is probably as unanswerable in the
aggregate. Any conceivable theoretical accounting for the Rose effect would suggest that it should
apply in different ways to different goods and different countries. What we need to ask is questions like:
“If the euro boosted trade by sharpening competition, then in which dataset should we find the
footprints?” And then go and check for footprints in the appropriate datasets, many of which will have
nothing to do with trade.
In many ways, the whole Rose literature reminds me of the pass-through literature in the 1980s. It
started with really bad econometrics on aggregate data. I myself estimated aggregate import pricing
relationships for the US. Despite the manifest shortcomings of such an endeavour, it published in the
AER because, like Samuel Johnson quipped about a dog walking on its hind legs, the interest lay not in
the fact that it was done so well, but rather that it was done at all. Nowadays, researchers studying the
pass through of exchange rate changes on prices use firm-level, product-specific data and take account
of all the unusual features of market under study. In the same way, it is now time to move beyond
studies of ‘how big is the magic.’

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8. APPENDIX ON METHODS

8.1. A direct comparison of gravity estimators

Here I go through a very interesting paper that looks at various estimators side-by-side. I believe that
the Rose effect literature needs much more of this sort of methodical, methodological studies.
A wide range of dummies have been thrown into the various regressions we have discussed, creating a
bit of a muddle. It is worth sorting out the methodologies employed. Here I rely on a paper by I-Hui
Cheng and Howard Wall. Cheng and Wall (2005) take a single dataset – a dataset that involves
relatively homogeneous nations (OECD) and observations every five years from 1982 to 1997 and
estimates the same gravity equation using most combinations of dummies. Here is the list, working
down from least restrictive to most restrictive:
- Unidirectional pair dummies plus time dummies (FE). As I noted in my theory aside, the gravity
model should be thought of as ‘a demand equation with social ambitions’. Since the exports of
Canada to the US face a different demand function than do US exports into Canada and US export
prices are determined by different factor than Canadian export prices, one should include the two
bilateral trade flows separately. If one is willing to make somewhat stronger theoretical restrictions,
one may pool these flows but such assumptions are testable and in any case why bother? It just
means you have to throw out half your data points. (Rose and most of the Rose Effect Hunters add
up the two flows and divide by two before putting the values into the regression.) This specification
is attributed by Cheng and Wall (2005) to Cheng (1999) and Wall (1999).
- Symmetric pair dummies plus time dummies (SFE). In their data they force the unidirectional
dummies to be the same, i.e. the Canada-to-US dummy to equal the US-to-Canada dummy. This is
basically what Glick-Rose does.
- Dual country dummies plus time dummies (XFE). As Anderson-Wincoop recommend, they put
in a dummy for each nation as an importer and for each nation as an exporter. Of course, if one has
reduced the information in the dataset by averaging a nation’s bilateral exporters and importers, this
is not possible in a balanced panel. In that case, one can only put in one dummy per nation.
Unfortunately, Cheng and Wall (2005) don’t compare these two, reporting numbers only for the
dual country pair dummies case.
- Single country dummies plus time dummies. This is like the previous one, but there is only one
dummy per nation.
- Time-varying Country dummies. A balanced dataset has m time m-1 observations per year where
m is the number of nations. If the directional bilateral flows are averaged first, the number is m(m-
1)/2 times the number of years. This is a big number. The Anderson-Wincoop theory tells us that
the country dummies SHOULD vary by year since they included, inter alia, GDP of various
nations. One can include year-by-year country-specific fixed effects, i.e. T times 2 times m
dummies. This is the correct way to control for the real-prices-matter terms, but it excludes pair
dummies, so the researcher faces a trade-off. Unfortunately, Cheng and Wall (2005) don’t look at
this, but I hope they will in a future draft. Pooled Cross Section with time dummies (PCS). This
is the preferred technique in Rose (2000). One estimates the model on the whole panel allowing
only the intercepts to vary across years.

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 8.1.1. Comparing dummies
What do they find? Well, unfortunately they haven’t done this exercise on the Rose effect question, but
they do estimate a stripped-down gravity model involving only GDPs and populations of origin and
destination nations (the GDPs are real for unexplained reasons and they deflate the value of trade flows
by the US CPI). They also estimate a gravity model with a few regional trade agreement dummies.

Table 6: Comparing various FE estimators for the gravity model (Cheng and Wall 2005).
Pooled Cross- Unrestricted Restricted FE
Section FE Model Models
PCS FE SFE XFE
intercept 6.852* - - -
(0.546)
origin GDP 0.617* 0.122* 0.213* 0.122*
(0.038) (0.023) (0.025) (0.055)
destination GDP 0.511* 0.208* 0.117* 0.208*
(0.035) (0.027) (0.024) (0.054)
origin population 0.141* -0.390 0.935* -0.390
(0.038) (0.298) (0.268) (0.565)
destination population 0.214* 2.313* 0.989* 2.313*
(0.038) (0.319) (0.268) (0.584)
distance -1.025*
(0.023)
contiguity -0.125
(0.085)
common language 1.075*
(0.072)
1987 0.077 0.199* 0.199* 0.199*
(0.067) (0.029) (0.038) (0.063)
1992 0.014 0.357* 0.357* 0.357*
(0.068) (0.043) (0.053) (0.093)
1997 0.051 0.482* 0.481* 0.482*
(0.064) (0.058) (0.070) (0.122)
observations 3188 3188 3188 3188
parameters 11 804 408 63
log-likelihood -5163.27 -1663.07 -2863.46 -4704.08
R2 0.690 0.954 0.916 0.768
Notes: White-errors in parentheses; * denotes 5% significance level. FE is Cheng-Wall fixed effects.

In terms of priors on point estimates, the pooled cross section does best, with the distance elasticity
about -1.0 as usual, the GDP elasticities closest to the expected unit (but still more than ten standard
deviations away from unity), and the population variables positive but less than the GDP variables (so
GDP/population is pro-trade). The other estimators yield GDP elasticities that are hard to believe given
that trade has expanded faster than output in almost every year since the war. By the way, the hard-to-
believe GDP elasticities tend to go away when one uses the product of GDPs, although the authors
don’t look at this.44
I should also note that population in these regressions is probably acting a bit like a time-invariant
country dummy since population in the OECD nations has varied little in this time frame. Note how
much the population point estimates change with country dummies (XFE) and without (SFE), for
example. The theory suggests population should have a negative coefficient if import income elasticities
exceed unity and nations tend to produce more traded goods as they develop (or vice versa).
Seeing this, it is somewhat easier to understand why so few people have used sophisticated fixed effects
estimator. But which method is better econometrically?

44
One thing I don’t understand is why the point estimates for the FE and XFE models are exactly the same bare-bones gravity model, but different
when one throws in other proxies for trade cost (see their Table 2).

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8.1.2. Checking residuals
One thing that Cheng-Wall do, which I think should be standard practice for all Rose-effect hunters, is
to plot the residuals. The left panel of Figure 19 displays the residuals for the pooled cross-section
model for the 797 unidirectional country pairs in their data set. These are ordered by the average of
pairs residuals. Remember we have to assume that these residuals are white noise if the estimator is
valid, but this is plainly not the case. Thus although one might prefer the PCS model based on the
numbers in Table 6, a quick eyeballing of the residual tells us that not everything is coming up roses.
The right panel is for the Cheng-Wall direction-specific pair dummies. These look much better.

Figure 19: Residuals from PCS and FE models.

Residual claimants
When a model is mis-specified, there is a residual variation in the trade data that needs a home. If this
residual variation and some included explanatory variable find that they have something in common in
a correlation sense, an unholy alliance can emerge. Thus it is useful to run a simple model and inspect
the residuals. In the old days, when ‘lots of data’ meant more than one hundred observations, a
researcher could have undertaken this sort of inspection pretty easily – most of the old econometric
packages let you plot actual and fitted values for this task. Things are harder today with big datasets, but
the need has not disappeared.
Howard Wall kindly sent me the residuals from the pooled cross-section regression he ran on the
OECD. This is not directly related to the Rose effect since no one in his data had a currency union, but
it is useful in thinking about the sorts of nonlinearities that could arise even among OECD nations.
What I did was to order the residuals according to various criteria and look for patterns. Of course, since
these are regression residuals, they are supposed to be white noise and we know they are orthogonal to
the regressors. But what about nonlinear combinations of the regressors? Especially ones that theory
suggests might matter.
The new trade theory, which is a quarter of a century old this year, tells us to expect that north-north
trade and south-south trade should be different than north-south trade. There aren’t any really poor
nations in the dataset, but I labelled Argentina, Brazil and Mexico as developing nations (the south)
leaving Australia, Austria, Belgium-Luxembourg, Canada, Denmark, Finland, France, Germany,
Greece, Hong Kong, Ireland, Israel, Italy, Japan, the Korean Republic, the Netherlands, New Zealand,
Norway, Portugal, Singapore, Spain, Sweden, Switzerland, the United Kingdom, Uruguay, and the
United States as developed nations (the north). Then I lined up all the residuals with those
corresponding to north-north and south-south trade flows first and north-south second (the order within
these groups is pretty random – alphabetic by destination nation).

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 PCS residuals ranked abs(DCo-DCd)

0
1
136
271
406
541
676
811
946
1081
1216
1351
1486
1621
1756
1891
2026
2161
2296
2431
2566
2701
2836
2971
3106
-2

-4

-6
y = 1E-13x 4 - 1E-09x 3 + 2E-06x 2 - 0.0017x + 0.5265
-8 R2 = 0.0785

-10

PCS residuals ranked by min(Yi,Yj)*dist

0
1
127
253
379
505
631
757
883
1009
1135
1261
1387
1513
1639
1765
1891
2017
2143
2269
2395
2521
2647
2773
2899
3025
3151

-2

-4

-6

-8
y = 4E-13x 4 - 2E-09x 3 + 6E-06x 2 - 0.0049x + 1.1882
R2 = 0.042
-10

Figure 20: Residuals from PCS and FE models.

The result is shown in the top panel of Figure 19, with an Excel trendline (4th order polynomial) added
for emphasis. The North-South flows are at the right and they are systematically negative. In other
words, the gravity model works systematically worse for North-South trade, a point that has been
known to gravit-istas for decades.
The second ranking is a bit more subtle. It ranks the residuals by the minimum of the real GDP of the
smaller partner times the bilateral distance. The idea is that made trade with small distant nations is
more influenced by unusual, unquantifiable factors. The result, plotted in the bottom panel suggests
deviations on both ends of the spectrum. The observation further to the right is US-Japan (the smallest
is big and they are far apart), the leftmost observation is Netherlands-Belgium.
Honestly, I can’t say exactly what all this means apart from the fact that we should be looking at
residuals instead of relying purely on summary statistics.

8.1.3. Mistaken policy inferences

Cheng and Wall do the same exercise throwing in fairly standard dummies for preferential trade
agreements. I won’t go through their policy results in detail since these are not directly relevant, but one

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point jumps out. They provide a useful example of how one can be misled by the pooled cross section
(PCS) estimator employed by Rose (2000). Cheng-Wall estimate the impact of the Israel-U.S. FTA
using the various estimators. Plainly, there is something special about this trade relationship that would
be impossible to quantify accurately as a specific variable that was valid for a panel of 100+ nations.
Allowing for fixed effects, this je-ne-sais-quoi is swept away and the Israel-U.S. FTA is found to have
no significant impact on trade, a sensible result in my opinion. With the PCS method, however, the FTA
picks up all the je-ne-sais-quoi; the estimates suggest that the FTA boosts bilateral trade by an
unbelievable 5 times. That’s larger than all but the largest estimate of the Rose effect! Interestingly, the
Rose-Wincoop method also finds a significant but much smaller effect, while the other methods find the
FTA to be insignificant.

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 June 13; rev Aug. 13, 2005

Comments on Richard Baldwin’s, “The Euro’s Trade Effects,”

for
What effects is EMU having on the euro area and its member countries?
European Central Bank, Frankfurt, 16 June, 2005.

Jeffrey A. Frankel, Harpel Professor

Kennedy School of Government, Harvard University

Richard Baldwin’s survey of the evidence on how the volume of trade is affected
by currency unions in general and the euro in particular is excellent and extensive. As he
himself might say, it is not only exhaustive but even exhausting. But I take it that a
comprehensive and detailed survey of the literature is what was desired, and Richard is to
be congratulated for delivering it – especially as the prose is very clear and very lively,
and he does not hesitate to let us know his evaluation is of each contribution.

Baldwin correctly places Andrew Rose’s research at the center of this survey.
Indeed it could be titled The Rose Effect and the Reaction to It. This is appropriate. I
consider Rose’s 2000 Economic Policy paper, “One Money, One Market…” to be the
most influential international economics paper of the last ten years.

Baldwin goes into details of the intellectual history of this new sub-field. He
remarks that Rose’s “original contribution was to add a common currency dummy to the
list [of variables in the gravity model of bilateral trade] – hard to imagine that no one had
thought of it before 2000” (2.2.3). I think I can answer the question “why did nobody
think of it before?” Some of us had earlier added bilateral exchange rate variability to the
gravity model, to see whether a reduction in variability encouraged trade between pairs of
countries: Thursby and Thursby (1987), DeGrauwe (1988), Brada and Mendez (1988),
and Frankel and Wei (1993, 1994, 1995, 1997). The first data sets were limited to
European or OECD countries. But they were progressively broadened in coverage. My
data set generally used a set of 63 countries. A big advantage of these gravity models,
whether used to evaluate FTAs, or exchange rate regimes, or other issues, is that a large
amount of data could be applied: 63x62=3906 observations of bilateral exports per year
(or half as many, in the case – which Richard abhors -- where bilateral exports and
imports were aggregated together). We found some evidence that exchange rate
variability had a small negative effect on trade. The evidence was limited, and tended to
diminish in the 1980s. But it was stronger than had been found in the traditional time
series studies of non-bilateral trade by industrialized countries.1 I have always said that
having the much larger data set, and the wider variety of countries, is what made the
difference.

This fits in with a larger long-held belief of mine that lots of the important
questions in macroeconomics and international economics can only be satisfactorily

1
Surveys of this earlier time series literature were included in Edison and Melvin (1990) and Goldstein (1995).

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answered with large data sets – either long historical time series, or wide cross-sections,
or panel studies that combine the two. Many of our colleagues have wasted a lot of time
applying ever-more refined techniques to the same 30 annual observations that are
insufficient to yield sharp estimates or a powerful test no matter how sophisticated the
techniques. (Tests of PPP are one good example.2)

I haven’t yet answered why none of us thought to add to our gravity equation a
dummy variable for a currency union. That question is easy to answer: there were few
currency unions, if any, in the data set. Why then did we not extend the data set to
include all the smaller countries and dependencies, as Rose did in 2000, to include
enough cases of currency unions? Two answers. First were the technical limitations. The
data source at the time was the Direction of Trade Statistics books of the IMF. I don’t
think comprehensive bilateral trade data was available for most of these smaller countries
and regions. Furthermore this is another case where it is easy, today, to forget how
different computer technology was as recently as the early 1990s. Often I had to construct
the data set by having a research assistant type in all 3906 observations by hand. And that
was just one year of observations on one variable, bilateral trade. To do 200x199 (=
39,800) observations would not then have been practical.

The other reason is that there was not much interest until the late 1990s in the
specific question of currency unions, as distinct from fixed exchange rates more
generally. Most writings did not even make the distinction. Rather, the battle line was
drawn between fixed and floating. Furthermore, a majority of economists were skeptical
that exchange rate variability had a substantial effect on trade. (One of their major
arguments was that importers and exporters could hedge on the foreign market, which
neglected that there was a price for hedging.3 Another of their arguments was that
exchange rate variability under floating was merely the symptom of fundamental causes
that if suppressed under a regime of fixed rates would just show up somewhere else –
specifically in nominal prices.) I don’t think anybody –even those of us who took the
effect of exchange rate risk and transactions costs more seriously -- was thinking that a
currency union would have a substantially different effect than reducing exchange rate
variability to zero through a conventional peg.
The 1990s brought far more interest in what are now called “hard pegs,” for a
variety of reasons: (1) the realization that conventional pegs are in practice all changed
sooner or later, e.g., the devaluation of the CFA Franc countries against the French franc
in 1994); (2) the resurrection of currency boards, especially by Argentina in 1991; (3) the
much discussed option of dollarization, e.g., Ecuador in 2000; and (4) most of all, of
course, the achievement of the common currency in euroland in 1999. While monetary
theorists continued to focus on the touted dynamic consistency of hard pegs as the key
difference, rather than anything to do with effects on trade, the door was nevertheless

2
E.g., Frankel (1990).
3
The problem with the theoretical argument is that forward and futures markets (1) do not exist for most
countries and for most longer-term horizons, (2) come with transactions costs when they do exist, and (3)
come also with risk premiums, which can drive a wedge between the forward rate and the expected future
spot rate

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 open to consider hard pegs as a valid topic in its own right, distinct from fixed exchange
rates more generally.

Andy Rose deserves every bit as much credit as Baldwin gives him, for adopting
the larger data set, and asking the currency union question, which was made possible by
the inclusion of plenty of currency union members, in his 2000 paper. Even if he had
not included the currency union dummy, this paper would still have been important,
because he had bilateral exchange rate variability in there, and it was highly significant
statistically, which I, again, attribute to the much larger set of bilateral observations. But
the attention-grabber, of course, was that the currency union dummy had a far larger, and
highly significant effect – the famous tripling estimate -- above and beyond the effect of
bilateral variability per se.

I have relatively little to say about the possible microeconomic explanation for
such a finding, which Baldwin discusses at the end. I like the story about fragmentation
of stages of production in Baldwin-Taglioni (2004). I do have a few thoughts of my own,
as to why a common currency could have a discretely larger effect on trade than simply
reducing exchange rate volatility to zero. My understanding is that when the EU went
beyond an FTA with zero tariffs internally, to a full customs union in which the trucks no
longer had to stop at the border and fill out forms, this provided a boost to trade. I think
there is a big boost to simplicity and convenience from abolishing the distinction between
currencies or customs areas completely, which goes beyond reducing the measured price
costs to zero, and from an institutional commitment that the new regime is permanent. Or
consider the even closer analogy of the fundamental reason for the existence of money. A
money economy is more efficient than a barter economy, because of convenience and
transactions costs. It would be very inefficient if each individual had his or her own
money (“IOUs”), and had to evaluate those of others every time they wanted to do
business. The same is true, qualitatively, across countries.

Andy also deserves a lot of credit for two more things, two of his usual habits in
all his work. First, he tried out many extensions and robustness checks – often
anticipating ex ante the corresponding critiques from discussants or others, perhaps to a
greater extent than Baldwin allows, rather than merely reacting ex post to their trenchant
insights. Second, he posted the data and regressions on his website, making it easy for
others to replicate his results and try variations. This completed the process whereby a
gravity regression that used to take a year of hard data work, from beginning to end, can
now be done by a novice in a matter of minutes.

Rose’s remarkable tripling estimate has by now been replicated in various forms
many times. But no sooner had he written his paper than the brigade to “shrink the Rose
effect” (Baldwin’s phrase) – or to make it disappear altogether -- descended en masse.
These critiques often read to me as “guilty until proven innocent.” Until I got toward the
end of Baldwin’s paper, I had suspected that he might be a member of this group. I am
pleased to see that his bottom line is that there is a Rose effect, but that it is probably
substantially smaller than a tripling. That is fine with me. If Rose had come up with a

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30% effect on trade from the beginning, everyone would have considered that large and
important.

But I intend to offer a stronger defense of the Rose findings -- stronger not only
than Baldwin’s bottom line, but perhaps stronger than Rose himself claims. If one got the
impression from Baldwin’s paper that Rose has tended to slant the case in favor of large
and significant estimates and to downplay qualifications, I think this would be incorrect.
To the contrary, he has sometimes acted embarrassed and apologetic about the magnitude
of his estimates, and when possible has chosen to emphasize the smaller ones.

It is entirely understandable that a threefold effect would be greeted with a lot of

skepticism, as this is a very large number. There are four grounds for skepticism, as I
classify them. First, one cannot infer from cross-section evidence what would be the
effect in real time of countries adopting a common currency. Second, the statistical
association between currency links and trade links might not be the result of causation
running from currencies to trade but might arise instead because both sorts of links are
caused by a third factor, such as colonial history, remaining political links,
complementarity of endowments, or accidents of history. I would also include here many
of the other allegations of missing variables, misspecification of the equation, or
endogeneity of the currency decision. Third, the estimated effect on trade [and on
income, in subsequent work simply seems too big to be believable. Fourth, Rose’s
evidence came entirely from countries that were either small (e.g., Ireland, Panama) or
very small (e.g., Kiribati, Greenland, Mayotte), and so it was not clear that the estimates
could be extended to larger countries.
While each of these four arguments has some validity, to each there is a better
response than one might expect.

Times series dimension

First, regarding the time dimension, subsequent research on time-series data finds
that a substantial share of the tripling that Rose had estimated from the cross-section data
(which is presumably the long-run effect) shows up within a few decades of a change.
Using a 1948–97 sample that includes a number of countries which left currency unions
during that period, Glick and Rose (2002) find that trade among the members was twice
as high in the currency union period as afterward. This suggests that roughly two-thirds
of the tripling effect may be reached within three decades of a change in regime. (This
reasoning assumes symmetry with respect to entry into and exit from currency unions.)
Baldwin thinks of this doubling as an estimate of the long-run effect. (He also
views it as a likely overestimate, due to the usual claims of omitted variables and
simultaneity. This could well be right, but we will get to the issue next.) Regardless, even
30 years may not be the long run effect. The effect may keep rising for a long time. We
know that other gravity influences leave an effect on bilateral trade many decades after
the cause has been removed. One piece of evidence is the slow speed of adjustment
estimated in models with lags.4 Another is the effect that colonial relationships have even
decades after independence, and even after controlling for continuing linguistic, political,
or other links. Consider as an illustration a trivia question: what is Congo’s largest
4
Eichengreen and Irwin (1998) .

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 trading partner? Not one of its neighbors, nor a large country, as the simple gravity model
would lead you to expect; it is Belgium, the old colonial master, with whom ties were
abruptly severed 35 years ago.5 Even when the original reason for a high level of bilateral
trade has disappeared, the stock of capital that firms have invested in the form of
marketing and distribution networks, brand-name loyalty among customers, and so forth,
lives on for many years thereafter. The word hysteresis is sometimes applied to this
phenomenon, suggesting that the effect is considered to be permanent. I think Baldwin
may be right that there is an analogy with his early work on hysteresis in exchange rate
pass-through. Why should not the same long-lagged effects not also be true of
currencies? Panama is reported to send more than half its exports to the United States;
perhaps one reason is that it has been on the US dollar for over a hundred years.
Furthermore it is likely that currency unions, like FTAs, can start to have
substantial effects on trade patterns even before they have formally gone into effect. This
is the pattern in the data.6 The most obvious interpretation is that once the negotiations,
which typically have been going on for many years, are far enough along that the union
appears likely to take place, businessmen move quickly to try to establish a position in
what is expected to be a large new market opportunity, perhaps to get a “first mover
advantage.” (This works theoretically only in the case of markets not destined for perfect
competition.) Baldwin (section 5.1) makes much of the striking fact that the estimated
Rose effect in euroland appears suddenly in 1998, even though EMU did not take effect
until January 1999. He regards this as suspicious. Even allowing the principle that
perceptions of imminent monetary union can set the date, rather than waiting for 1999, he
claims “right up to March 1998, skeptics doubted that monetary union would be a
reality.” I am the one who lives far from Europe, so he probably knows better. But I have
statistics from financial markets that identify June 1997 as the breakpoint in perceptions.
On June 15, 1997, implied probabilities of joining Germany in EMU in 1999 were 100%
for Belgium and France and over 70% for Finland, Spain and Portugal (calculations from
JP Morgan based on spreads in the interest rate swap market). A similar statistic from
Goldman Sachs on the probability of EMU taking place on January 1, 1999, shot up
above 75% after the Stability Pact was agreed in June 1997. So I find it plausible that
businesses had started reacting in a measurable way by 1998.

Omitted variables, misspecification, and endogeneity

The second objection concerns the possible influence of third factors. Rose did a
thorough job of controlling for common languages, colonial history, and remaining
political links.7 The large estimated effect of a common currency remains. It seems very
possible that there remain other third factors (e.g., accidents of history) that influence
both currency choices and trade links. Nevertheless, Rose’s various extensions of the
original research—these robustness tests together with the time-series results—reduce
some of the force of this critique.
5
Kleiman (1976) finds that about one-quarter of the (2- to 4-fold) bias of colonial times remains for
countries that have been independent for two decades. Wang and Winters (1991) and Hamilton and Winters
(1992) find significant effects for UK Ex-colonial relationships (though not French) as late as 1984-86.
6
E.g., Frankel (1997).
7
While it is admirable how many factors Rose controls for, I agree with Baldwin and also Melitz (2001) in
regarding as a “nuisance” Rose’s persistent habit of calling these “nuisance parameters.” These coefficients
are of interest in their own right, and also help gauge the persuasiveness of the overall model.

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 Many of the critiques of the Rose results, after pointing out a problem of omitted
variables or endogeneity or one of the other legitimate problems, offer an alleged way to
address it and then report that the currency union effect disappears.8 My own view is that
many of these responses in effect throw out most of the data in the name of addressing
the (correctly emphasized) issues of endogeneity or country size. Or they do something
similar: put in a large number of dummy variables or fixed effects, in some cases one for
every pair of countries. This approach seems these days to be considered not just good
econometric practice, but essential; we are told definitively that we are not allowed so
much as a peek at evil studies that neglect to do this. But my view is that since the
finding of statistical significance arose only when Rose put together a large enough data
set for it to show up, there is little information gained in reducing the data set sharply and
then noticing the loss in statistical significance. The statistical power lies in the cross-
country variation. Throw that out, and you are left with little.
As Baldwin relates (2.5.2), Rose (2001) himself tried adding pair fixed effects to
his original data set, and found that the currency union dummy lost all significance, while
pointing out that it is hard to see how it could have been otherwise, since all the action is
in the bilateral cross-section. Ditto Pakko and Wall (2001). Klein (2002), who
deliberately focuses on US bilateral data alone, is one of many examples of throwing out
enough data until the results become insignificant. Persson (2001) is another, despite the
virtues of the matching estimator. When Rose tries the matching estimator on a larger
data set, he found a significant (though smaller) effect (2.6.3).
Then there are the uni-observational case studies. Thom and Walsh (2002), which
focuses on Ireland’s abandonment of pound sterling in 1979, is an example. In fact, a
strong downward trend in the UK’s share of Irish trade accelerates at just about the right
time. But when the authors try to control for Ireland’s accession to the EC, no
significance remains. This is what one would expect, with so few degrees of freedom. I
think it is misleading to summarize the paper as “finds no evidence from time series or
panel regressions that the change of exchange rate regime had a significant effect on
Anglo-Irish trade” (Baldwin in 2.5.1). The evidence is about as supportive as could be
expected, given how close together were the EC accession and the currency change. 9 The
example of the Czech-Slovak breakup of 1993, which had a substantial negative effect on
bilateral trade,10 is viewed as more supportive of the Rose effect, apparently because a
customs union was retained. But we know that political borders such as the one that
divided the new Czech and Slovak Republics at the same time that the two adopted
separate currencies have effects at least as large as conventional trade barriers.
Glick and Rose (2002) put together a huge data set covering the entire postwar
period, which includes enough additional examples like the breaking of the Irish-pound
link and Czech-Slovak link to get statistical significance out of the time series dimension.
Indeed, they are able to do so even when including pair-specific dummies, thereby giving

8
See Rose (2001) for a reply to one, and his Web site
(http://faculty.haas.berkeley.edu/arose/RecRes.htm#CUTrade) for more.
9
In fairness, this is similar to how Thom and Walsh themselves summarize their own finding. I just
disagree with their interpretation. Except for that, I do like the paper, which appeared in the same issue as
Glick and Rose (2002), and which I commissioned and edited. The case examined is potentially one of the
more important ones, as Ireland is one of the largest countries in the sample of countries that entered or left
a currency union in the postwar period.
10
Frankel (1997, 121-122) and Fidrmuc and Fidrmuc (2001).

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 up the power in the cross-section variation. It is true that the beauty of fixed effects is that
they take account of time-invariant facts, observed or unobserved; so Glick and Rose’s
still significant results are very persuasive. This should have been enough to satisfy the
hard-line gold-medal skeptics. As usual, the authors try lots of robustness checks. I don’t
agree with the admonishment (e.g., Tenreyo, 2004) that they should try all the robustness
checks together, rather than one by one. One-by-one is the way to keep the volume of
output manageable. Furthermore I don’t see as interesting an algorithm that checks
whether trying every possible permutation can eventually produce some equation in
which the currency union coefficient loses significance.
The omitted variable that is probably of greatest concern to the critics comes from
the influential Anderson-VanWincoop paper, and is variously called “remoteness,”
“multilateral resistance term,” or what Baldwin wants to call the “relative prices matter”
term. Baldwin is as fanatic on this point as VanWincoop: anyone who omits the relevant
terms is not fit to be received in polite society. As it happens, I am one of those who long
ago included remoteness in some of my gravity estimates (though not all). I devoted two
pages to the subject in Frankel (1997, 143-144), and noted that it sometimes makes a
difference to the results.11 I even gave the same intuitive example that Baldwin gives, of
how the remoteness of Australia and New Zealand makes each of them somewhat
dependent on the other for trade.12

I think the Anderson (1979) and Anderson-VanWincoop (2001) model is an

important contribution, both as theoretical foundations for the gravity model and in
offering an argument that some of the border effects may have been quantitatively over-
estimated. But its insistence on the role of trade-diversion may be too doctrinaire. If I
understand correctly the aspect of the Anderson-van Wincoop theory that leads to
numerical estimates of the effects of borders and currencies that are sharply reduced
(though still significant), it is the property that the elimination of borders or currency
differences entails substantial diversion of trade from the rest of the world to within the
union. But it seems to me that such trade-diversion from currency unions is not in the
data, by and large.13
Baldwin, after citing the gold medal to signal the primacy of the missing-variable
critique, cites the silver medal for what he sees as a serious misspecification problem:
aggregating exports and imports together. I won’t take issue with his declaration that the
correct specification is to use estimate exports and imports separately, except to make
two notes. Firstly, one advantage of adding them together is that exchange rate
11
The resistance to Canadian-U.S. trade is An example where it makes a difference; Wei (1996 ) found that
controlling for remoteness helped knock down the home country bias from around 10 to around 3. Another
may by the finding of a huge apparent effect of Pacific Islanders adopting the Australian dollar, in Nitsch
(2001).
12
Also it is just barely possible that I am responsible for coining “remoteness,” in defining it explicitly as a
term representing distance from everybody, expected to have a positive sign on bilateral trade, when
estimated alongside bilateral distance, expected to have a negative sign. But I don’t know for sure.
13
For example, the UK does not appear to have lost trade to euroland as a result of the euro. E.g., Begg, et
al (2003), Frankel and Rose (2002), Frankel (2003), and Micco, Stein and Ordoñez (2003). Baldwin also
mentions trade-diversion in 4.1.1, perhaps making an improper analogy: going from trade theory, where
trade barriers are distortions that deliver some revenue to somebody, to monetary theory, where it could be
argued that currency barriers are dead-weight losses more akin to transport costs, whose elimination can
potentially benefit all, even non-member countries.

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competitiveness effects (which worry him elsewhere) roughly wash out this way. And
secondly, many of us have tried it both ways – aggregating exports and imports together,
or not – and have found that it usually does not make much difference.
Rose (2004) performed a “meta-analysis,” combining estimates from 34 other
studies, which yielded a central tendency of 30% to 90% for the currency union effect.
Baldwin is dismissive of this meta-analysis, as others have been. He thinks he knows
which of the studies are good and which are bad (these are the ones that win the Olympic
medals14), and wants only to count the good ones. The problem with this is that other
authors have other opinions as to what is good and what is bad. I don’t suggest that the
meta-analysis should be viewed as the optimal estimator that pools all available
information, as it might be in the medical research field. For one thing, these studies
mostly use the same data set over and over again. But I don’t buy that the researchers
have all sought to mimic the same results “as a way of showing that the author’s data set
is sound in that it can reproduce the mistaken estimates in Rose (2000)” (2.8.1). To the
contrary, I think a majority of them have from Day One been looking for a way to
overthrow the new reigning champion. One might view the meta-analysis as a sort of poll
of the estimates of the experts who have worked in the field. As such its verdict carries
some information. I wouldn’t choose it as one of Rose’s most important contributions to
the debate, perhaps, but not to be ignored either.

The high magnitude of the estimates

The third objection is the surprisingly large magnitude of the estimates. In this
regard, it is important to consider something else that we have learned in recent years:
there is a strong home-country bias in trade, which is especially surprising in light of all
one hears about globalization. Many studies have found that people trade with their
fellow citizens far more easily than with those living in other countries. This finding
emerges whether one looks at the volume of trade flows between locations or at the
ability of arbitrage to keep prices in line across locations. It holds even when one controls
for the effects of distance; trade barriers; and linguistic, social, and historical differences.
It holds even between the United States and Canada. The best-known finding is that
Canadian provinces are 20 times more prone to trade with each other than with U.S.
states (McCallum, 1995). This estimate was cut very roughly in half after the Canada-
U.S. FTA went into effect (Helliwell, 1998) and was cut further when a few more factors
were controlled for (Wei, 1996). Nevertheless, a substantial bias—roughly threefold—
remains even in this case. The home-country bias must certainly be higher for most other
country pairs. Using the same gravity methodology, Nitsch (1990, 1991) finds that
intranational trade within European countries is about seven times as high as trade with
EU partner countries of similar size and similar distance away.
Similarly, studies of the ability of arbitrage to narrow price differentials find that
crossing the U.S.-Canada border discourages trade more than does traveling the entire
length of Canada (Engel and Rogers, 1996) and that the barrier is even greater for other
pairs of countries (Engel and Rogers, 1998; Parsley and Wei, 2001a, 2001b). What can
explain these remarkable findings of home bias in both quantity data and price data? The
difference in currencies is not an implausible explanation, given the paucity of alternative
candidates. This point -- that one needs to take the existence of home bias as the
14
This paper doesn’t just mix metaphors; it puts them through the blender.

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 benchmark, not the presumption of perfect integration -- seems to be one of the few
things missing from the Baldwin survey.
I have characterized the “get Rose” literature as an attitude of “guilty unless
proven innocent:” put in enough dummy variables or fixed effects, or throw out enough
data, until the estimates lose their significance. It might seem that to place the burden of
proof on the proponents of a currency union effect follows naturally from the surprising
magnitude and is consistent with the usual econometrics approach of requiring high
significance levels – 5% or 1 % -- before asserting that a relationship is supported. But
given the existence of home country bias, and the paucity of other candidates to explain
it, I don’t see why the burden of proof should be entirely on one side.
The estimated currency union coefficient is in magnitude and explanatory power
roughly the same as the FTA variable, ranking behind the colonial relationship, and
ahead of common language and the residual political union effect.15 Baldwin takes it as
self-evident that FTAs and customs unions have a big effect on trade. Indeed, he cites
approvingly the assertion of Berger and Nitsch (2005) that it is implausible, even crazy,
on the face of it to think that the trade effect of the euro could be as large as the trade
effect of the EU. But this sort of finding is in fact common. If he and other critics of the
currency union literature were to apply the same tough standards to both customs unions
and currency unions, I think he might find that the estimated magnitudes, significance
levels, and necessary methodological qualifications are comparable across the two kinds
of unions.

Further thoughts on small countries

Regarding the applicability of the results to large countries, we will not know for
sure until the EMU experiment is older. It would seem plausible that very small
geographical units (the Kiribatis) are so dependent on international trade—due either to
inadequate scale of the domestic market or to insufficiently diversified factors of
production—that strategies such as currency unions or free trade areas would have a
larger payoff for them than for larger, more self-sufficient, economies. But there are two
counterarguments. First, Rose tested whether there are any nonlinearities among his
currency union sample (e.g., any difference between the effects among units that are
merely small and those that are very small). He found no significant difference. Second, I
don’t see the evidence that the home-country bias departs from linearity, regardless of the
size of the country, as being as strong as Persson and Baldwin seem to (though I must
admit that my priors have shifted a bit and are further shiftable). That is, if two small
units join together, thereby doubling the size of the economy, the ratio of trade to gross
domestic product (GDP) falls—that is, home-country bias increases—roughly as much
(by 0.2, in log form) as when two large units join together.16 To the extent that currencies
explain this, the effect does not seem to be limited to small countries.

15
This claim is confirmed by Rose and van Wincoop (2001), who estimate that half the typical border
barrier is due to different sovereign monies.
16
One would expect that below some size threshold, a unit like New Caledonia is so small – lacking a large
enough internal market to sustain any scale economies and lacking a diversity of endowments – that it is
highly dependent on trade to survive, and that currency unions and FTAs are alternatives to political unions
to boost that trade. But the long-run effect of increased trade on income seems to be the same for large and
small countries (Frankel and Rose, 2002).

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 I agree that reverse causality of the decision to form a currency union is a big
problem – endogeneity -- which could bias the standard Rose results. It is certainly true
that small countries are more likely to adopt currency unions – after all, trade shares are
one of the classic optimum currency area criteria. But Germany and California are not
small units, and they have joined currency unions. Baldwin mentions my attempt to
address the endogeneity of exchange rate variability by using variability of money
supplies as an IV for exchange rate variability (Frankel and Wei). He doesn’t say what he
thinks of this idea. Probably he saw that I was going to be the other discussant at the ECB
conference, and so decided it would be prudent to withhold his true feelings. Basically I
agree with him that “all instruments will be bad instruments” (2.7.2). But we are hard up
for an instrumental variable to correct for the endogeneity of the exchange rate regime
decision. The only clearly better proposal for an exogenous means of identification that I
have encountered are the rare discrete experiments like EMU itself (and even here, of
course, there is an element of endogeneity with respect to trade patterns).
One aspect of selection bias can go in Rose’s direction: bilateral flows that are not
recorded do not always reflect the absence of integration, but sometimes reflect a level of
integration that is so high that the customs authorities can’t measure all the cross-border
flows (e.g., Belgium-Luxembourg).

Experience with the euro

Finally, we now have four to six years of data since the EMU went into effect in
January 1999. Econometricians have updated the gravity estimates to see what can be
learned from the record so far. Micco, Stein, and Ordoñez (2003) find that for pairs of the
12 countries that joined the EMU, trade has increased significantly. The estimated effect
is about 15 percent beyond what could be explained by growth and other factors—
actually a range of 6 to 26 percent, depending on the use of country and year dummies,
with a larger set of 22 industrialized countries. The preferred Micco, Stein, and Ordoñez
(2002) estimates of “differences in differences” reveal that between 1992 and 2001 the
boost to intra-EMU trade was about 18 to 35 percent, depending on whether one uses
country-pair dummies, or conditions on the standard gravity variables. These magnitudes
are less than in the Rose studies. As the authors quite reasonably point out, however, the
effects are not only statistically significant but also economically important, especially
considering that the sample covers only the first four years of the EMU, a period in which
the euro did not even circulate. Recall the arguments above that it might take many
decades to reach the peak effects on bilateral trade. The estimated effect on bilateral trade
among euro countries has already reached levels that most economists would not have
predicted ten years ago.
Other evidence confirms the finding. Bun and Klaassen (2002, p.1) also update
gravity estimates and find that “the euro has significantly increased trade, with an effect
of 4% in the first year” and a long-run effect projected to be about 40 percent. Berger and
Nitsch (2005) and De Nardis and Vicarelli (2003) report similarly positive results.
Baldwin’s favorite paper methodologically is Flam and Nordstrom (2003); but he is
bothered by the implication of their estimated effects in that the “euro must be making it
easy, cheaper and/or safer to sell to EuroZone nations.” Well, why not? Multinationals
now find it more convenient to sell into one and all, because they share a common
“currency platform.” A firm that might not have found it worth its while to export solely

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 to, say, Ireland, now finds it can reach a much larger market; the marginal costs of
expanding into additional countries are lower than they were in the days of multiple
currencies.
Studies with price data have tended to be more mixed, but some confirm that the
euro is facilitating arbitrage among the markets of member countries.17 It seems that the
trade effects of monetary union are not, after all, limited to small countries.

17
Looking at price data across pairs of European cities, Rogers (2001, 2002) finds evidence of convergence
in the 1990s. In the European auto market, Goldberg, Koujianou, and Verboven (2001) find gradual
convergence over the period 1970–2000. Goldberg and Verboven (2004) nail down EMU, per se, as a
significant determinant of this convergence.

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 Jacques Melitz
University of Strathclyde

This instructive, highly readable monograph has two fundamental aspects. First, it is by far
the most in-depth and comprehensive analytical survey of the Rose (and rosy) literature on
the impact of EMU on bilateral trade among the members. Second, it contains an effort to
explain this impact. Notably, Baldwin argues that small as the impact of EMU on bilateral
trade is, it is too large to result from usual thinking and requires special explication.

No one who followed the Rose discussion ever doubted that the author’s initial assessment
that EMU might triple or quadruple trade among the members was exaggerated. Still, based
on all of the subsequent evidence, many of us felt that the effect was larger than could ever
have been guessed beforehand: probably well over 20%. Few of Baldwin’s readers will come
away still cherishing this belief. According to Baldwin’s “bottom line,” the true numbers are
about 5 to 10%. Most important in destroying confidence in big numbers, I believe, is Bald-
win’s telling argument that one does not need to choose between the major criticisms of
Rose’s initial assessment. All of them are correct. There is the issue of omitted variables. In
addition, there is that of reverse causality. Further, there is the problem of model misspecifi-
cation (which Baldwin expresses with particular force and clarity). As a matter of fact, I find
the part of the monograph requiring most development to be the support for the 5 to 10% fig-
ures themselves following the important section on Carousel, fraud, ROOs and PECS where
Baldwin casts doubts upon them. There must be better reason to adhere to those numbers than
businessmen’s confidence that “of course, EMU increased trade.” In my following remarks, I
will turn a blind eye to this problem (reparable, I believe).

I have three main points to make about the 5 to 10 % figures, and then a few lesser remarks to
add concerning Baldwin’s criticisms of earlier contributors. In the first place, there is no mys-
tery about a 5-10% impact of EMU on trade. That is roughly what earlier empirical work to-
gether with the evidence of the European Commission in One Market One Money would have
led us to guess had Rose never written. If those are the right figures, then there is no bloom to
the Rose at all. Baldwin had no cause to search around for new theoretical foundations. Sec-
ond, the alternative, more traditional explanation I support is equally consistent with the de-
tailed data that Baldwin marshals in favor of his more novel reasoning. Third, based on the
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 two empirical studies on which Baldwin mainly rests his 5-10% assessment – the Micco-
Stein-Ordoñez (2003) and Flam-Nordstrom (2003) ones – the true figure is closer to 15%. As
for my lesser points, I would like to defend Rose (and most of his followers including myself)
against Baldwin’s criticisms for converting all the series into constant dollars and for focusing
on total trade rather than either exports or imports. In addition, in any choice between import
and export data, if one is needed, it is also not clear to me that the export data is preferable.

I. The size of the Rose effect

a. With regard to the 5-10% figures, Baldwin’s micro-founded gravity equation for bilateral
trade is a good start. Suppose we add Anderson and van Wincoop’s (2003) simplification τod
= τdo, saying that the ratio of trade costs to output price is the same for goods moving in either
direction (if only because this makes everything more transparent). Consequently, in terms of
Baldwin’s notation, we get the well-known specification:
1− σ 1/(1− σ)
⎛ τ ⎞ EoEd ⎡ ⎤
Vod = ⎜⎜ od ⎟⎟ Pi = ⎢∑ (β k p k τ ki )1− σ ⎥ i = d, o ∈k
⎝ Po Pd ⎠ EW ⎣k ⎦

EW refers to world spending on current output, βk is the ratio of country k output to world
output, Po and Pd are the Dixit-Stiglitz utility-based price indices. As distinct from Baldwin,
my stress will be on σ. According to the equation, the very sign of the impact of trade costs
τod on trade Vod depends on σ, the intra-temporal elasticity of substitution between goods o
and d. Trade would fall with a fall in trade costs if σ were zero, it would stay constant with
unitary elasticity, and it only rises because σ is greater than one. Usual estimates of σ are of
the order of 6 to 8. Obstfeld-Rogoff (2000) use 6 and Anderson-van Wincoop (2004) use 8.
Consequently, a fall in relative prices of 1% will suffice to yield a 5 to 7% rise in bilateral
trade between o and d and therefore to bring us into Baldwin’s 5-10% range. A fall in relative
prices of 1% is easy to defend.

In its well-known report One Market One Money, the European Commission (1990) calcu-
lates that eliminating costs of conversion of currencies and costs of cover for exchange risk
will reduce costs by .25 to .5 of 1 percent of total output in the EU. The report also stresses
additional gains that would come from the elimination of “in-house costs” associated with
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multiple units of account. These are reductions in costs connected with record keeping, ac-
counting and decision-making. The previous figures of .25 to .5 of 1% are therefore lows, and
those lows are relative to GDP. In order to relate the figures to trade we must multiply by 3 to
4, since trade in the average EU member is about a quarter to a third of GDP. Assume then a
fall in trade costs of about 2% relative to trade. Next, it is necessary to correct this figure
downward in light of the fact that EMU must lower the product PoPd as well. (Here I am
avoiding the “gold medal” mistake.) For example, the increase in bilateral trade between
France and Italy will be mitigated by the fact that both countries’ trade costs will also fall
relative to Germany, the Benelux and Spain. The EMU embraces roughly 50% of the trade of
the members. Therefore, based on the earlier 2% figure, the adoption of monetary union will
lower Po and Pd, respectively, by about 1%. The net result will be a fall in τod/PoPd of roughly
1% – the figure we were looking for. So much then for “the dog that didn’t bark” or the fact
EMU did not produce a sharp break in equations for relative prices. It did not need to do so in
order to account for an effect of 5 to 10% on Vod.

b. Based on this line of support for the 5-10% figure, there is also no difficulty explaining the
detailed evidence Baldwin summons. The impact of EMU on trade was particularly high for the
DM-bloc, consisting of Germany, the Netherlands and Austria, and low for Portugal and
Greece. Baldwin draws from Marc Melitz (2003) for a new and sophisticated explanation for
this evidence. According to Melitz’s model, even a minor change in costs, coming from reduc-
tions in exchange rate uncertainty, could lead more efficient firms to move significantly into the
export of differentiated goods. However, the same evidence is entirely consistent with the sim-
pler and more traditional explanation I propose. The elasticity of substitution between goods
produced by countries that are already closely integrated through trade will be particularly high.
This can only mean more trade between Germany and the Netherlands, for example, than be-
tween Germany and Greece. (Of course, the rise in German trade with Greece may nevertheless
raise welfare more than the rise in its trade with the Netherlands since it refers more largely to
greater heterogeneity in trade as opposed to greater variety: but that’s a different story.) As fur-
ther evidence on behalf of his theory, Baldwin also cites the relatively strong impact of EMU on
trade in the industries producing differentiated goods. But that too fits neatly into my simpler
interpretation. ρ must be higher between varieties than between totally different goods.
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 c. Finally, as regards numbers, according to Baldwin’s favorite study, by Flam and Nord-
strom, the figure is higher than 5-10% – at least if we base ourselves on these two authors’
widest control group consisting of 11 rich countries. In this case, their results show 15% more
trade from EMU. To justify his lower 5-10% figures, Baldwin relies on the 8% result that
Flam and Nordstrom get when they narrow their control group to include only the 3 EU
members outside the euro zone – their “cleanest definition of the control group,” he thinks.
However, it is easy to argue that the 15% figure is better.

The EMU members could get 15% more trade among themselves as opposed to everyone else
on average, while they only get 8% more trade among themselves as compared with the UK,
Denmark and Sweden. This could be because EMU also increased their trade with the UK,
Denmark and Sweden by 7%. In fact, this last result fits nicely with the evidence from Micco-
Stein-Ordoñez – the other study on which Baldwin relies highly – and it would be easy to
explain. In principle, outsiders ought to reap some monetary benefits of fewer moneys and
fewer units of account from EMU, and the UK, Denmark and Sweden should do so more than
most countries on average since they trade far more with the EMU members than the average
non-member does. To illustrate, consider the situation of Canada if there were 50 different
state monies in the U.S. Evidently Canadians benefit greatly from reductions in transaction
costs and units of account from a single U.S. currency, and they do so more than the average
country outside North America. Generally, introducing a common currency should be seen as
a graded reduction in trade barriers applying mostly to the members but extending to every-
body else to a degree depending on how much trade they do with two or more of the mem-
bers. The 15% figure is then preferable.

II. The criticisms of the tests in the Rose literature

Baldwin offers some interesting criticisms of the empirical use of the gravity model in the
Rose literature. But I take exception to a couple of them. In his engaging and inoffensive
manner, he sprinkles Olympic medals around in the Rose garden to indicate bad herbs. There
is no question about the “gold medal” mistake. But what about the “bronze” and the “silver”?

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a. The “bronze medal” mistake refers to the practice of converting trade and output figures
into real values, as Rose and many of his followers do. Baldwin observes that the model ap-
plies to nominal values not real ones. But he disregards the fact that Rose does not simply
convert nominal values into constant dollars. Rose also uses output values from the Penn
World Table. Those values are then PPP-adjusted and their use therefore may reflect a desire
to avoid distortions that come from converting undervalued currencies into dollars. Using
undervalued currencies for conversion can mean grossly undervaluing non-traded relative to
traded output. Think of Russian wages relative to Russian oil following the disintegration of
the Soviet Union. Interestingly, therefore, the resort to real values by Rose and others can be
seen as an effort to avoid precisely the error that Baldwin attributes to them: that of introduc-
ing distortions in relative values of traded and non-traded goods.1

In general, the gravity model says nothing about multiple currencies. It follows easily under
barter. The only necessity is a common unit of account. Consequently, when taking the theory
to the data, we face the problem of dealing with the presence of multiple monies. Relying on
nominal exchange rates for converting into a single unit becomes sheer necessity. But nomi-
nal exchange rates are also high frequency variables whose movements can produce serious
distortions of relative prices of traded and non-traded goods, as the previous example of Rus-
sia was meant to signify. Hence, it may be right to supplement the conversions with PPP-
adjustments. Of course, that is not necessarily the case. If exchange rates are well aligned,
sticking to the nominal values may be best. But the principle of uniformly sticking to the
nominal values looks dubious. Perhaps the best thing to do is to try both conversion based on
nominal exchange rates alone and the use of PPP-adjusted real values and then to choose the
alternative that yields the closest approximation to the theoretical implication of the gravity
model of a unitary elasticity of trade, Vod, with respect to output, YoYd.

b. The distance between France and the UK is the same as the distance between the UK and
France. The absence of a common language and a common money between the two countries
1
Baldwin also suggests that, in theory, countries belonging to monetary unions ought to be unusually open, and
therefore should exhibit exceptionally low values of traded goods relative to non-traded goods. The logic is
plain (see section 2.3.3); yet it is odd. Rose’s many studies concern monetary unions consisting mostly of tiny
principalities with low wages. Should we then not expect those countries to feature just the opposite – unusually
low relative prices of non-traded goods relative to traded goods – because of Balassa-Samuelson?

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 is also identical from either side. According to the gravity model, the bilateral trade between
the two depends on their respective outputs. But the model also says that the relevant variable
is the product of the outputs, and this variable too is identical on both sides. Moreover,
Anderson and van Wincoop (2003) show that under all the usual assumptions of the model, if
we also suppose that trade costs both ways are the same (τod = τdo), bilateral trade between all
countries must balance. In consequence, many researchers take the view that the model, in its
simplest form, says nothing about trade imbalances and applies strictly to total bilateral trade,
as measured by the average (or the total) of the movement both ways. (Of course, on this in-
terpretation, the proper variable to consider when converting into logs is the log of the aver-
age rather than the average of the logs of trade one way and the other [compare Baldwin’s
section 2.3.4].) In Baldwin’s eyes, this is all wrong; it is the “silver medal” mistake.

There is no question that it would be better to explain the movement of goods in both direc-
tions, as Baldwin enjoins. Suppose then that we try to do so. In that case, do we not simply
need a better model or a more sophisticated version of the one we have? It cannot suffice
merely to keep the identical model, use separate figures for the movement of trade both ways,
and simply add a relative price term concerning the exchange rate. Baldwin may well agree
on this last point, since he refers approvingly to a working paper by Helpman, (Marc) Melitz
and Rubinstein (2004) that adds productivity differences between firms in different countries
in order to explain imbalances in bilateral trade. But is that enough? Think of bilateral trade
between China and the U.S. If we employ the usual gravity variables to explain Chinese ex-
ports to the U.S. and U.S. exports to China, then merely add an exchange rate and an indicator
of productivity differences in order to take account of trade imbalances, will we not still make
wild mistakes in predicting the flow of trade either one way or the other or both? Surely we
must somehow take into account the U.S. willingness to borrow and the Chinese willingness
to lend. Generally, in order to cope with bilateral trade imbalances we must recognize differ-
ences in desired intertemporal substitution between countries and deviate from the assumption
that all countries wish to maintain balanced trade in the aggregate. Thus, it is not really clear
that the way ahead is well paved.

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My main point is that the usual tendency to apply the gravity model to aggregate bilateral
trade data may reflect a desire not to take the model further than its simplified structure will
allow.

c. A final issue, if exports both ways are to enter, is which data to use: those for the shipments
or the arrivals? Baldwin recommends the shipments. He has a good argument with respect to
the EU: the destination principle applying to VAT makes export figures more reliable since
the exporters want to be reimbursed for the VAT they pay at home. But the situation concern-
ing VAT in the EU is not universal. Tax considerations often lead to the opposite result: more
honest reporting of expenses (or imports) than receipts (or exports). In fact, to many minds,
underreporting of exports is big in explaining the massive trade deficit on a world level. In
addition, the gravity model is simply a demand equation in Baldwin’s neat formulation. If so,
it is difficult to see how export figures can be ideal. What matters to the buyer are clearly the
landed goods rather than those shipped. According to numerous presentations, some of the
goods even “melt” along the way. Finally, if we look at export flows in both directions, there
is also an issue of identification concerning the sign of the exchange rate. Based on these dif-
ficulties, are shipments really to be generally preferred to arrivals in testing the gravity
model?

III. Coda

Let me repeat my initial assessment that Baldwin’s piece is “must” reading for any one inter-
ested in the Rose debate. His paper has permanently affected my own views. Fireworks may
continue. So much is at stake. But with this essay, the War of the Roses may be passing into
history at last.

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 References cited
Anderson, James and Eric van Wincoop (2003). “Gravity with gravitas: A solution to the bor-
der problem,” American Economic Review 93, 170-192.
Anderson, James and Eric van Wincoop (2004). “Trade costs,” Journal of Economic Perspec-
tives 52, 691-751.
European Commission (1990). “One market, one money,” European Economy, 44.
Flam, Harry and Hakan Nordstrom (2003). “Trade volume effects of the euro: Aggregate and
sector estimates,” Institute for International Economic Studies, Stockholm University.
Helpman, Elhanan, Marc Melitz and Yona Rubinstein (2004). “Trading partners and trading
volumes,” Harvard and Tel Aviv Universities Working Paper.
Melitz, Marc (2003). “The impact of trade on intra-industry reallocations and aggregate in-
dustry productivity,” Econometrica, 71, 1695-1725.
Micco, Alejandro, Ernesto Stein and Guillermo Ordoñez (2003). “The currency union effect
on trade: Early evidence from EMU,” Economic Policy, 37, 316-356.
Obstfeld, Maurice and Kenneth Rogoff (2000). “The six major puzzles in international mac-
roeconomics: Is there a common cause?” NBER Macroeconomics Annual 2000, 339-
390.

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European Central Bank Working Paper Series

For a complete list of Working Papers published by the ECB, please visit the ECB’s website
(http://www.ecb.int)

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594 “The euro’s trade effects” by R. Baldwin, comments by J. A. Frankel and J. Melitz, March 2006.

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