1Are Free Trade Agreements
Contagious?

Richard Baldwin
Dany Jaimovich*
Graduate Institute, Geneva

Preliminary. Please do not cite.
This version: 14 November 2008

ABSTRACTThis paper presents empirical evidence on the extent to which FTAs are “contagious”,
using empirical techniques inspired by the study of contagion in exchange rate crises. Using
different econometric techniques, it tests the null hypothesis that the signing of an FTA
between one nation’s trade partners has no affect on the probability of the nation signing a
new FTA. The hypothesis is tested against other political, economical and geographicaldeterminants of the FTA formation previously stated in the literature, finding evidence thatthe contagion phenomenon is present in different specifications and samples.

*Corresponding author: Dany Jaimovich, Avenue de la Paix 11A, 1202 Geneva, Switzerland,dany.jaimovich@graduateinstitute.ch. We would like to thanks the useful comments andsuggestions we received during presentations at the Swiss Trade Economist Cooperative(Champex), The Graduate Institute, Geneva, and Geneva Trade and Development Workshop(WTO).

2

1. INTRODUCTION
Free trade agreements (FTA) are being signed at a rapid pace. A central question for the trade system
is: Why is bilateralism and regionalism spreading so rapidly? Some, starting with Krugman (1991a),link the spread of regionalism to slow progress in multilateral talks, a view recently embraced by
Bhagwati (2008).
While this correlation between slow multilateralism and fast regionalism is intuitively appealing inrecent years and for the years preceding the 1994 Uruguay Round agreement, it does not square wellwith data over a longer period. Over the past two decades, the pace of regionalism is not closely tied tothe ebb and flow of multilateral talks. As the raw numbers in Figure 1 show, it is not clear that the
number of deals accelerated when multilateral talks stalled between 1990 and 1994. Indeed it seems thatthe trend picked up just after the Uruguay Round agreement was struck in 1994. Likewise, the recent
problems with the on-going Doha talks coincide with a slight decline in the rate of growth.
Cumulative RTAs

200
180
160

North-North
North-SouthSouth-South

140
120
100
80
60
40
20

2007

2000

1993

1986

1979

1972

1965

1958

0

Figure 1: Explosion of FTAs, North-North, North-South and South-South, 1958-2007
Source: WTO RTA database, www.wto.org.
The Krugman-Bhagwati explanation also has little to say about the clear geographical pattern in newagreements. As Figure 2 shows, the spread of regionalism is far from homogenous across regions.
Before 1976, Europe dominated, in the 1976-1990 period, the Americas dominated and in 1991-1999,
the Former Soviet bloc nations dominated. Plainly, a global-wide explanation does not account for thisregional variation in an obvious manner. This regional pattern suggests contagion – much like thespread of financial crisis.

3

FTA regional waves: Distribution of new FTAs by region over time
100%
90%
80%
70%

Former Soviet bloc
Mideast and North Africa
EuropeEast Asia
Americas
Sub-Saharan Africa

60%
50%
40%
30%
20%
10%
0%
Before 1976

1976-1990

1991-1999

2000-2005

Figure 2: Geographic pattern of FTAs: Region-specific waves
This paper improve previous studies on the determinants of FTA introducing empirical techniques
similar to the used to identify the transmission channels of financial crisis contagion, finding that thereis evidence of “contagion effect” in the proliferation of FTA in recent years.
The rest of the paper is organized in three sections. The next section, Section 2, briefly reviews thetheoretical frameworks that inform our empirics and summarizes relevant literature. Section 3 describes
the unprecedented dataset of FTA that we will use and our measure of contagion. In Section 4 we
present the main results of the empirical analysis in both a cross sectional and a panel data frameworkand the respective robustness checks. The final section contains our concluding remarks.
2. THEORETICAL CONSIDERATIONS AND RELEVANT LITERATURE
2.1. Theoretical considerations
Our empirical analysis focuses on the domino theory of regionalism (Baldwin 1993, 1995, 1997). The
basic idea has two parts. First, trade diversion, or the threat of it, creates de novo political economy
urges in third nations. These new forces favour pro-FTA forces and thus may tilt the balance towards a“yes” decision in nations that previously found it politically optimal to abstain. This is the first-roundeffect. The second round comes if new nations actually sign new FTAs as this then increases the threatof trade diversion on third nations. That is, the signing of agreements creates a sort of political economy
momentum that can make it seem that FTAs are ‘contagious’, spreading like wildfire as it were.
The idea underlying the domino mechanism is an old one. A version can be distilled from the accountin Chapter 5 of Viner (1950) of how dozens of German principalities and city-states were cajoled andcoerced into joining Prussia's Zollverien. Whalley (1993) also views Western Hemispheric regionalism

4

as largely defensive (rather than US-led). He, however, focuses on fears of US protectionism instead oftrade diversion, and he does not posit a circular causality between bloc size and the strength ofinclusionary pressures. Hufbauer (1989) uses the term “FTA magnetism” which captures the first step(idiosyncratic deepening sparks membership requests) but does not relate the strength of the magnetismto the bloc size. The formal model of the domino theory of regionalism was first presented in Baldwin(1993a) which was published as Baldwin (1995)1. Winters (1996) and Lawrence (1996) surveys
regionalism and multilateralism models, putting the domino theory in perspective.
The main alternate hypotheses are: 1) the FTA-vs-MTN hypothesis; 2) the bandwagon effect; and 3)
the spread of democracy. The first suggests a complementarity between progress in multilateral tradetalks and progress in regional trade agreements. The second suggests a very general ‘demonstration
effect’ whereby nations sign FTAs because they see other nations signing them. The third stated that
democratic countries are prone to sign FTA. These hypotheses could explain the wave like spread ofregionalism. The key empirical lever that allows us to distinguish them from the contagion hypothesis is
the extent to which trade ties connect the new FTA signers. The contagion hypothesis works on trade
diversion, so the spread of FTAs should follow a pattern that is clearly related to the new signers’ tradepatterns. In particular, a pair of nations should be more likely to sign a new FTA, if either of them hasrecently signed FTAs with third nations that in which the pair’s exporters are rivals.
2.2. Literature: Empirical analysis of FTA formation
Even the discussion about the determinants of FTA can be track back to Viner’s contributions in the
mid XX century, is in the early 1990s when become an important issue in International Trade. The focal
question in this literature is: Why are countries eager to open markets bilaterally or regionally but
reluctant to do so multilaterally? While trade policy scholars have proposed a number of explanations2,
and some of these explanations have been formalized by theorists3, in the empirical side there is justfew contributions. The Membership to FTA was typically taken as exogenous in empirical
specifications, but recently researchers have started to view it as an endogenous phenomenon and havebegun to explore its determinants.
The first systematic empirical study on the determinants of a FTA is Baier and Bergstrand (2004),
which use worldwide data to estimate cross sectional linear probability models, stressing that economic
factors seems to be enough to predict must of the agreements. In particular, they find that the likelihoodof an FTA between a country pair is higher the closer they are, the more distant from the rest of theworld, the larger and more similar their economic size are and the more different their labour ratios are(Heckscher-Ohlin trade).
Some previous empirical studies have already put emphasis in the “domino effect” as determinant of
FTA, focusing in trade diversion as a key determinant in the willingness of membership to the European
Union. A first attempt is Sapir (1997), which estimates year-by-year cross-section gravity models andfinds that trade diversion tends to spike in a time pattern that explains tend to EU enlargements.
1

See Baldwin (1994, 1997, 2002, 2006) for applications of it to European, Western Hemisphere and Asian

domino effects.
2

Anderson and Blackhurst (1993), Krugman (1993), Bhagwati (1993), Whalley (1996), Lawrence (1996),

Bergsten (1996), Panagariya (1996), inter alia.
3

Grossman and Helpman (1995), Yi (1996), Ethier (1996), Bond and Syropoulos (1996), Yi (1996), Winters(1996), Levy (1997), Fernandez and Portes (1998), Krishna (1998), Freund (2000), Mclaren (2002), and Aghion,
Antras and Helpman (2004).

5

Baldwin and Rieder (2007) follow a similar strategy but estimating trade creation and diversion in apanel setting to then use the results for calculate the likelihood to become a EU member.
Some studies have focused in aspects beyond the economic determinants. Mansfield and Reinhardt(2003) offer a more political explanation, arguing that one of the main determinants of regional trade
agreements are developments at the multilateral level, with difficulties in GATT/WTO negotiations
prompt countries to engage in regionalism. Mansfield, Milner, and Rosendorff (2002) empirically findthat pairs of democratic countries are more likely to form a FTA, a result confirmed by Wu (2004) that
also claims that economic and trade uncertainty matter. After determining FTA that are effectively
implemented, Holmes (2005) show evidence of “mercantile interests” in assuring export market accessare a important determinants.
2.3. Literature: Contagious crisis and policies
Dungey et al. (2004) review the empirical models of contagion in macro crisis literature, classifying
the methodologies in: latent factor; correlation analysis; vector autoregression (VAR); co-exceedance
approach and probability models. Our work is mainly related with the latter approach, developed in thework by Eichengreen, Rose, and Wyplosz (1995, 1996), where a dichotomic measure of crisis is thedependent variable and the explanatory variable is the same measure for the rest of the countries, using
trade and macro weights in order to analyse the channels of contagion. Using probit estimations they
provide evidence of an increase in the probability of the occurrence of a crisis if there is a crisis
somewhere else.
This approach is expanded by Novo (2004) to a multivariate framework in which multiple countries
can be infected and their relative contagious influence is measured by a spatial weights matrix.
Some studies have extended the contagion analysis to economic policies. Elkins et al. (2004) argue
that the diffusion of Bilateral Investments Treaties (BIT) is driven by international competition for FDI
among potential host countries, then the likelihood of sign a BIT increase with the number of competing
countries that have done so. Gassebner et al. (2008) examines if country’s economic reforms areaffected by the reforms adopted in the rest of the world. They find that cultural and geographicalproximity tend to be factors in the contagion of reforms, but that trade links are not.
3. DATA DESCRIPTION: FTA AND CONTAGION INDEX
3.1. Free Trade Agreements
The phenomenon we are investigating is the signing of new FTA, so the ‘left hand variable’ is adigital variable reflecting it existence (unity if yes and zero if no). We use the recently available
Preferential Trade Agreements Database from the Peterson Institute for International Economics and theWorld Trade Institute (WTI), constructed using the historical notifications of the date the agreements
entered into force and their contemporary participants. This source is the most extensive and detailed
currently available; it is more comprehensive than the WTO database which is based only on
agreements notified to the institution. The database has 570 agreements recorded from 1948 to 2007; ofthese, 329 agreements were still force in 2007.
We limit the period of investigation to 2005 due to lack of comprehensive data on our ‘right handside’ variables.

6

Table 1 summarises the FTAs. Europe dominates the FTAs with a total of 224, almost half of themsigned in the 90s. The source of Europe’s dominance in these figures is well known. Starting from theformation of the EEC in the 1950s and EFTA in 1960, the engagement between these groups and theenlargement of the former created a large number of FTA as has the external FTA engagement of thetwo blocks. In the Americas, 154 agreements have been signed including NAFTA, MERCOSUR andnumerous bilateral FTAs among their countries. The Former Soviet Bloc nations, that we report
separately, subscribed to numerous agreements during the 90s reaching a total of 124 in 2005. The
Middle East and North Africa have 83 deals, while Asia has 54. The least dynamic region is SubSaharan Africa with ‘just’ 41 agreements in total, half of them coming from before 1976.
Table 1: Evolution of signing of FTAs by region and time
SubMideast andPeriodSaharanAmericas Asia Europe North Africa
AfricaFormerSovietbloc

World

Country
pairs

Before 1976
1976-1990
1991-1999
2000-2005

22
4
3
12

20
42
48
44

8
8
9
29

42
14
100
68

11
10
31
31

1
6
93
24

83
66
229
159

326
221
495
236

Total

41

154

54

224

83

124

570

1319

Source: WTI Database. Asia includes East and South Asia and Oceania. Europe includes Turkey. In the regional disaggregation theagreements are computed more than one time when the subscribers belong to different regions.

Our unit of study is the country-pair formation of FTA, a number which has evolved as shown in thenext-to-last column of the table. A cumulative total of 1319 country-pair trade deals are registered up to2005, but just 1134 are still in force or signed for later implementation. That means that around 17% of
the pairs of countries are covered by some sort of trade agreement in the year 2005. Among these
agreements, 65% are classified as pure Free Trade Agreements (FTAs), and the others are partial scopeagreements, currency unions and the like. Nevertheless, to avoid linguistic infelicities, we refer to allthem as FTAs.
For our analysis we will use all types of registered agreements, either in force or signed, since ourprior is that the contagion effect is produced at the moment the agreement is created. The idea is that thethreat of trade diversion is as important as actual trade diversion. Nevertheless, in the robustness checksection we show that the use of a stricter definition of FTA do not alter the main results. In Appendix 1
we present the total number of FTAs signed by each country in the sample used in our empirics. The
agreements are reduced to a total of 782 in 2005 when the data is cleaned.
3.2. Measure of contagion
Following the probability model of contagious currency crisis, a key element of our empiricalapproach is a measure of “infection”. But here is going to appear an important difference between
contagion episodes in macro crisis and in the FTA formation: while the former are temporary events,
the latter are, in most of the cases, a permanent condition. A consequence of this difference is that themeasure we have to use is not just an addition over the infected countries, but also an accumulation over
the time.
In the case of contagion come from the threat of trade diversion, or loss of export competitiveness,
two elements must be considered. Firstly, the number of FTAs signed by other countries (the more theinfected, the most likely to be tainted). Second, the relative distance to other countries subscribing FTAs

7(the closest to the infected, the most exposed to contagion). Considering this, we propose the following“Contagion Index”:

where:

andSo in a given year t the index for country i with respect to j is the accumulated sum of the FTAs
signed by j with the countries outside the pair in the past years, weighted by the commercial
importance of the third countries to i, measured as the share of total exports to this country4.
This one-way contagion index can be better understood with the example illustrated inFigure 3. In2005 Australia and Malaysia started the studies and negotiations to establish a FTA. Can this fact be
related with a contagion effect? The contagion from Malaysia to Australia is measured by the
cumulative of trade-weighted shares of Malaysia’s FTAs. The trade weights are Australia’s export
shares to the third-nation with whom Malaysia has a deal, and to reduce simultaneity, the share is fixedat the time of FTA was signed. Ignoring the evolution of trade shares, it measures the share ofAustralian exports that go to nations with which Malaysia has an FTA.

ASEAN
nations

US

China

ASEAN FTA
(1992), weighted byAustralia ASEAN
trade share

Australia

Contagionindex (Malaysia
on Australia)

EU

China-Malaysia FTA(2005), weighted byAustralia China trade
share

Malaysia

Figure 3: One-way contagion index

4

The data on export shares is taken from COMTRADE. One possible concern with the total export
shares as weight is the fact that some FTA cover just a number of industries, so only these sectors are
threaded by the potential trade diversion, and only the export share of these industries might be used as
the weight in the contagion indexes. Even this concern is true, our database do not have information on
the specific sectors covered by the FTA. Moreover, our prior is that even one sector is not included in
the original FTA, the fact that the agreement was signed create the threat that the sector can be includedin the future.

8

Up to 2005, Malaysia has signed three FTAs with countries in our sample: ASEAN Preferential
Trading Arrangements with Indonesia, Philippines, Singapore and Thailand in 19785; ASEAN
expansion including Burma, Cambodia, Laos, and Vietnam in 1992; ASEAN-China Agreement on
Trade in Goods in 2005. On the other hand, in the year the FTAs were established the export shares ofAustralia to the signers were 6%, 0.1% and 4%, respectively. Then, the Contagion Index of Australia
from Malaysia is the accumulation over the time of the shares, as shown in Figure 4.

Figure 4: Contagion Index of Australia from Malaysia
Of course, it takes two to dance, when it comes to signing FTAs, so we must also consider thecontagion of Australia on Malaysia. Considering the dyadic nature of the data, in the empirical
exercises we do this in two ways: some specifications are with directed dyads, so two observations per
country-pair are included for each year (one for the country as recipient and other as giver), and theContagion Index is directly plugged. In the rest of the specifications, one observation per dyad isincluded, implying that a symmetric measure should be used. In this case, we take the two one-way
Contagion Indexes for each country-pair and build three indicators6: (1) the arithmetic average of theindexes for ij and ji; (2) the maximum of these indexes, related with effect of the most infected country;(3) the minimum of these indexes, related with the decision of the less infected country. The average ofthe three contagion indicators by country in 2005 is summarized in Appendix 1.
There are some potential endogeneity problems since the index depends upon actual trade flows
which are influenced by the matrix of FTAs. We address this in our robustness checks, where we showthat using different samples and measures of export shares the results are unaffected.

5That actually was established as a Free Trade Area just in 1992, so in the stricter definition of therobustness check this year will be used instead of 1978.
6
In order to align the contagion indicators with the scale of other variables we will use in the
regressions, the indicators are multiplied by 100.

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4. RESULTS
4.1. Cross-Section
Our first approach is to study the cross sectional properties of the contagion indicators, that have tothought as the long-term or cumulative effect. Here we will start following the literature in the static
determinants of the formation of FTAs between pairs of countries. The most important contribution in
the area is the study by Baier and Bergstrand (2004, B&B henceforth) that amply a qualitative choicemodel to show the main economic variables underlying the FTAs accumulated up to a giving year. The
basic empirical model is:where the probability to sign a FTA will be determined by the standard normal cumulative
distribution function of a linear specification, G(*), that ensures the result lies between 0 and 1. Xij is avector of time invariant explanatory variables related to the country-pair and β the related coefficients.
We expand this specification by introducing the contagion indicators to estimate models for the year
2005. Then, our model is the following:where Contagionij2005 is the accumulated value of one of the contagion indicators up to 2005 and γ isthe parameter of interest.
For the sake of comparability, we first estimate the results in a reduced sample similar to the one used
by B&B (and later expand the analysis to the entire database). The reduced sample is composed by 52
countries and 1303 country-pairs, of which 372 have a FTA in force or signed in 2005.7 This subsample has important advantages. With long series of export data available to build the contagion
indexes for these countries, our results are not be driven by discontinuous declaration of data in exports
or the creation/extinction of a country. Almost the full range of possible pairings are covered (thepossible country-pairs are 52 time 51 divided by 2, i.e. 1326).
In the first panel of Table 2 we present the result of the probit estimation of the model. The firstcolumn shows a very important fact: when just one explaining variable is included, “natural partners”
measure with the inverse of the countries distance, a great deal of the FTAs can be predicted as showedin the Correct Classification Ratio (CCR)8, with 86% of accuracy and a pseudo-R2 of 0.456. Adding
more economic and cultural determinants do little to improve the efficiency of the estimations. In
column 2 a measure of remoteness from the rest of the world, the product and ratio of the logs of GDPs
(proxies for economic size and its differences respectively) and the ratio capital/labour are included9,
with just slightly improvements in accuracy. The determinants in column 2 are the same as in the final
empirical specification of B&B, and we obtain similar signs, magnitudes and significance than them for7

In B&B there are 54 countries and 1431 pairings, of which 286 have a PTA. The 2 countries that wemiss are Iraq and Nigeria due to lack of updated data availability. In Table Appendix 1 the countries
that integrate the reduced sample are identified.
8
The ratio is built assigning 1 to the predicted probabilities over one half and 0 below and thencomparing with the row data.
9
All the definitions and sources are in Appendix 2.

10

all the parameters, with the exception of the one related with the capital/labour ratio10. In column 3 we
explore some shared cultural characteristics between the countries in the pair that could be important todeterminant the existence FTAs, namely common language, a past colonial relationship and common
colonizer (dropped by colinearity in this specification). B&B disregard this type of determinants, but, in
opposition, we find that common language has a positive and significant effect, and that slightly
improve the efficiency of the model, a result confirmed in later exercises.
In the last columns of the first panel the values for the three contagion indicators for 2005 areincluded. In order to follow B&B original specification, undirected dyads (one observation per dyad)
are estimated, and the symmetric contagion indicators are used. Column 4 shows that the indicator that
averages the country-pair contagion indexes has a positive and significant impact in the probability tosign a FTA, with an improvement of five percentage points in the pseudo-R2. The coefficients forcontagion indicators for the maximum and minimum index, in columns 5 and 6 respectively, also arepositive and significant, and increase significantly the goodness of fit with respect to the first
specifications. Together, these results provide a first evidence of the relevance of contagion effect thatshould anyhow be taken with care, considering that the pseudo-R2 is a ratio of likelihoods with no directinterpretation, as also happen with the probit coefficients that just inform about sign and significance.
The second panel of the Table 2 shows the results of estimate the cross-sectional probability model
for FTAs in the whole sample. Even we tried to have a sample as large as possible, some depurations
must be performed. In order to properly compute the contagion indicators, enough information for theexport shares should be available, so we drop countries with information of exports for less than 45
destinations. Also some countries are eliminated for lack of information in some of the other variables
used in the estimation. After this cleaning process, we end up with a sample of 112 countries and 4661
pairings, of which 782 have a FTA. Column 7 shows that the sole variable “natural partners” is stillenough to have a good prediction for the FTAs, as measured by the CCR, but has a lower goodness of
fit that in the B&B sample. When the other economic and cultural determinants are included (column 8)
similar results that in the reduced sample are obtained, with the difference that now the capital/labour
ratio is negative and significant, and the dummy for common colonizer is positive and significant.
When contagion indicators are added (columns 9 to 11) a significant improvement in the goodness of fit
is achieved, particularly in the case of the indicator for the minimum contagion in the pair. The three
indicators are positive and significant.
In the last panel of the table we present results for the directed dyadic regression, implying that the
number of observations are the double that in the undirected dyads specification. The direct Contagion
Index is estimated, allowing for asymmetry in the effect. The structure of the errors in this specificationis very particular, with blocks created for the interaction of the different members of the dyad with therest of the countries that violate the homoskedastic assumption11. To obtain the correct specification for
the errors, we use the correction proposed by Fafchamps and Gubert (2007). The results are similar tothe obtained with the undirected dyads specification, with a positive and statistically significative
Contagion Index.

10

We use the same data of B&B to measure this ratio, coming from Baier et al. (2000). The results
related to this parameter are the only ones that change considerably in sign and significance with thedifferent sample and model specifications we tried; in particular, most specifications give us negativeand significant estimates for this variable.
11
For further details, Fafchamps and Gubert (2007).

11

Table 2: Cross Section Results
B&B SAMPLE
COEFFICIENTTOTAL SAMPLE

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

All FTAAll FTAAll FTAAll FTAAll FTAAll FTAAll FTAAll FTAAll FTAAll FTAAll FTA

contagion (average)

0.0371***

0.0227***(0.0044)contagion (max)

DYADICREGRESSION
IN TOTALSAMPLE(0.0020)0.0319***0.0158***(0.0028)
contagion (min)(0.0011)0.0315***0.0315***(0.0029)(0.0016)

Contagion Index

0.0283***(0.0015)

natural1.277***(0.0664)0.969***

0.997***

0.811***

0.710***

0.548***

1.113***(0.0385)

1.050***

0.923***

0.862***

0.697***

1.577***(0.0934)(0.0940)(0.0939)(0.0980)(0.0958)

remoteness0.0826***

0.0696***

0.0671***0.0739***0.0765***(0.0170)(0.0176)(0.0177)(0.0187)(0.0190)(0.0090)(0.0093)(0.0095)(0.0101)(0.0175)

product GDP

0.116***

0.143***

0.133***

0.0898***0.116***

0.159***

0.147***

0.134***

0.0947***0.242***(0.0187)(0.0209)(0.0221)(0.0238)(0.0221)(0.0118)(0.0126)(0.0129)(0.0141)(0.0237)

ratio GDP

-0.102***-0.106***-0.0272

0.0485-0.00284-0.0738***-0.0815***-0.0752***-0.0187-0.113***(0.0346)(0.0342)(0.0368)(0.0391)(0.0372)(0.0184)(0.0194)(0.0199)(0.0206)(0.0371)-0.0267-0.0143-0.105-0.0617-0.168**-0.0791***-0.101***-0.0926***-0.0808**-0.150***

ratio K/L

(0.0649)(0.0507)(0.0522)(0.0544)(0.0555)(0.1014)0.0333***0.0332***0.0427***0.0415***0.0698***(0.0629)(0.0690)(0.0753)(0.0765)(0.0266)(0.0286)(0.0296)(0.0326)(0.0562)

common language

0.430***

0.739***

0.984***

0.862***

0.807***

0.896***

0.956***

1.035***

1.750***(0.1335)(0.1374)(0.1696)(0.1307)(0.0753)(0.0762)(0.0786)(0.0755)(0.1361)

colony-0.00934-0.187-0.259-0.2880.008210.01070.0453-0.08400.0233(0.3081)(0.2845)(0.3248)(0.2797)(0.1896)(0.1938)(0.2033)(0.1904)(0.3527)

0.327***

0.413***

0.366***

0.260**

0.660***(0.1002)(0.1013)(0.1061)(0.1018)(0.1844)

common colonizer
Observations

1303

1303

1303

1303

1303

1303

4661

4661

4661

4661

4661

9322

Countries

52

52

52

52

52

52

112

112

112

112

112

112

CCR

0.857

0.869

0.881

0.881

0.867

0.916

0.869

0.889

0.889

0.891

0.918

Pseudo R2

0.456

0.485

0.492

0.536

0.612

0.588

0.349

0.427

0.456

0.478

0.559

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

124.2. Robustness checks for cross section
In the estimations presented in the last section, the contagion indexes are defined in the way showedin Section 2, using as weights the export shares with a third country in the year the FTA is signed with
it. Even this specification alleviates a great deal of the simultaneity problem presented in most of
empirical specification that relates FTAs and partner trading relevance, since the export share of thecountries in the pair is never present, still some endogeneity problems could be biasing the estimates.
The fact that a pair of countries has signed a FTA can affect the share of exports to third countries indifferent ways. The most obvious endogeneity is produced when a bloc is created, since in the same
year a pair signs the agreement some of the main trading partners will be doing the same, then theexports shares to the members in the bloc will increase and the contagion index will be endogenous. To
alleviate this problem we re-estimate the direct dyadic specifications in the last panel of Table 212
dropping the country-pairs formed by founder members of one of three blocs: EU9 (the EU members
before 1976), MERCOSUR and ASEAN. The first column of of Table 3 shows that basic results do notchange in the new sample (control’s parameters are not displayed).
Table 3: Robustness Checks for cross sectional results

Contagion Index No Blocks

(1)

(2)

(3)

(4)

(5)

FTA
0.0284***

FTA

FTA

FTA

Pure FTA(0.0015)0.0382***

Contagion Index Weights 1976

(0.0027)
0.0399***Contagion Index Weights 1962

(0.0037)
0.0330***

Contagion Index Gravity(0.0022)0.0375***

Contagion Index Pure FTA(0.0014)

Observations

9234

4360

1578

9274

9322

Countries

112

72

46

112

112

Dyadic adjusted standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1
Same controls as in the last panel of Table 2 are included but showed in the table.

But the effects of FTAs are more complex. As shown in the theoretical discussion of the domino
effect, trade creation matters as well as trade diversion. Thus even trade shares of third countries thatare not part of a bloc get affected when a FTA is signed, and, if eventually they joint later, the weights
in the contagion index will contain an endogenous component. In order to definitively deal with thispotential problem we take a more radical solution. We recalculate the contagion index, but using as
weights the trade shares in the first year of the time series, 1976. The backdrop is a decrease in the
dynamic nature of the indicators, but exogeneity is strengthened, especially since we drop all countrypairs with FTAs in this year. The regressions with is displayed in the second column of Table 3. This12

If same procedures are applied to the undirected dyads estimates, all the results of this subsection
are the same.

13

sample contains only 72 countries (for which we have data in the initial year) and 2180 pairs. The basic
results hold.
We can even go more back in time, trying to eliminate any doubt due to possible endogeneity relatedwith trade diversion of the FTAs of before 1976. We rebuild the indicators using the trade shares in thefirst year we could get data of exports, 1962, where the only agreement was the EU6, which pairings are
eliminated of the specification. Column 3 of Table 3 shows the results of estimations that now get
reduced to just 46 countries and 1578 observations. All main results hold.
A different approach to deal with the endogeneity is to estimate “potential” export shares instead of
the observed in reality, so the weights is the latent trade relevance of the partner due to geographical,
cultural and economic characteristics. The methodology to estimate the potential export shares is in twostages13: we first estimate a gravity equation where the dependent variable is the log of the actual
exports volumes (measured in real US dollars) regressed against the log of the product of GDPs of the
trade partners in a given year, including the dummy for FTA between them, country-pair fixed effects(that account for all common time invariant characteristics as distance, language, border, etc.) and year
fixed effects (capturing common shocks). The second stage is to estimate the fitted export values tobuild the potential shares. Column 4 of Table 3 shows the results, with a no significant change in thevalue of the Contagion Index. Even this approach has several advantages in term of removing sourcesof endogeneity, like controlling for the change in trade shares due to agreements, we think that the realvariable observed by the policy makers, actual trade shares, is a better weight to account for contagion
effect.
A final robustness is related with the definition of FTA. In all the later calculations we use the broader
definition of trade agreement available in our database, which include every kind of deals. But it’s
possible that the contagion just happen when the agreement is actually implemented and implies realconcessions. In Column 5 of Table 3 we present re-estimates that use the contagion index built just with
FTA in force and defined explicitly as Free Trade Agreements in the database, same thing for the
dependent variable. 35% of the agreements are eliminated in these estimates, but the basic results hold.
4.3. Panel specification
Even the cross sectional results of the last section are important in order to state that a contagious
effect is relevant in the determination of FTA formation, we now move to a panel specification were thedynamic nature of domino effect can be better analysed.
Estimate panel data with limited dependent variable represent several challenges. The problem ofincidental parameters implies that the inconsistency in the estimation of fixed effects will be“transmitted” to estimation of parameters14.The bias in the estimation is very severe when T is short, but
get smaller as T increases if the number of cross sectional observations (N) is sufficiently large (Greene2004). Since we have the advantage that trade panels have both big T and N, our first approach is toestimate the panel specification of the contagion hypothesis trough pooled regressions. We have T=30
for the oldest countries in our sample, and an N of around three thousands country pairs (more for thelatest years)15 . The specification is the following:

13

For more details of this approach, Frankel and Romer (1999) and others.
For more details, Baltagi (2005)
15
Greene (2004) shows that, using Monte Carlo simulations, the bias of imposing fixed effects bybrutal force in panels with limited dependent variable is around 7% when T=20 and N=1000.
14

14

With the dependent variable been now the probability that a pair of countries sign a trade agreementin period t, conditional to the fact that such agreement did not exist in the previous period. This implies
that the dyads with FTA before the beginning of the sample are not going to be included (notably EU
founding members) and that the year after signing an FTA the dyad will disappear of the sample16.
Both the controls and the main variable of interest -the contagion index or its symmetrical measuresare expressed as one period lag in order to avoid endogeneity problems. This specification has several
advantages since allows to include various political and economic controls that suffer of simultaneity inthe cross sectional framework. Even more important, the use of lag values of the contagion index
avoids the potential endogeneity problem that arises from the fact that the probability of signing an FTA
is not independent of the signatures of FTA anywhere else in the same period, an issue that just can beproperly addressed with spatial econometrics techniques in the cross-section17.
Since most of the specifications are going to be over the directed dyads (both observations in eachdyad-year will be included), the structure of the errors is not homoskedastic. In order to correct for thisfact, all the specification will cluster the standard errors at dyad level.
Another concern with this empirical specification is the related with the problem serial correlation:
since observations are likely to be temporally related due to duration dependence in the data, the use of
traditional probit or logit techniques represent problems. In order to solve this issue, we include natural
cubic spline function of the numbers of years a dyad have been without a FTA, a solution proposed by
Beck et al, (1998).
Some previous studies have already used similar panel specification to analyse the formation of FTA,
and among those the most important is Mansfield and Reinhardt (2003, M&R henceforth). As firstexercise, we will use the same dataset used by M&R to analyse how the contagion indexes behave in
it18. The main goal of M&R’s work is to empirically test the hypothesis that developments in themultilateral trade system encourage the formation of FTA among GATT/WTO members, an idea
supported by Krugman (1993) and Bhagwati (1993) among others. Particularly, they test that: (1) FTA
are used as a way to gain bargaining power during multilateral trade rounds; (2) as the number of
GATT/WTO members arise, the individual country power leverage within the system decrease inducing
FTA; (3) countries involved in a GATT/WTO dispute will be more willing to sign FTA in order toenhance their leverage in the quarrel; and (4) a country losing a dispute will be more willing to form
FTA that mitigate the negative impacts of the lost.
In the first column of Table 4 we reproduce M&R results for the period 1950-1998 for the estimationsin the directed dyads19. The parameters estimated for the four variables associated to the multilateralsystem events just described have all positive and significative signs, supporting the M&R claim:
16

One concern to this specification is that values different than zero are rare events in the dependent
variable, implying possible loss of power and bias in the estimation. In order to address this problem,
we re-estimate all the main regressions using the methodology proposed by King and Zeng (2001) tosolve the problem, and all the main results do not change. These results are available upon request.
17
See Egger and Larch (2007) for details in the solutions to the problem of interdependence of FTA
membership.
18
The dataset is available in Eric Reinhardt’s web page:
http://userwww.service.emory.edu/~erein/data/ and all the variables and sources are described in M&R.
19
When undirected dyads are estimated, the main results are unchanged. These results are available
upon request.

15

detrended number of GATT/WTO members; a dummy equal 1 if a trade round is ongoing in a given
year; a dummy equal 1 if one country in the pair lost a dispute and a dummy equal 1 if the country wasin a dispute.
Table 4: Dynamic Determinants of FTA in M&R Dataset
(1)

(2)

(3)

(4)

(5)

m&r fta

m&r fta

m&r fta

m&r fta

fta wti

fta wti

1950-98

1950-75

1976-98

1976-98

1976-98

1976-98

Contagion Index (t-1)

0.0289***0.0104***(0.0032)
WTO Members (t-1, detrended)(6)(0.0025)0.138***-0.0974***-0.134***-0.0724**-0.143***-0.144***(0.0109)(0.0269)(0.0459)(0.0351)(0.0438)(0.0454)

1.691***

0.0351-1.196**-0.971**-2.053***-2.119***(0.1577)(0.1915)(0.5808)(0.4936)(0.5447)(0.5687)

WTO Dispute Lost (t-3)

1.438***

1.944***

1.218***

1.198***

0.380**

0.407**(0.1210)(0.1621)(0.2155)(0.2137)(0.1752)(0.1768)

WTO Dispute With Third Party (t-1)

0.769***

0.345**

0.704***

0.287-0.304-0.295(0.1331)(0.1595)(0.2016)(0.2029)(0.1900)(0.1897)

Democracy (t-1)

0.0491***

0.0562***0.205***

0.168***

0.0278**0.0299***(0.0053)(0.0060)(0.0254)(0.0197)(0.0114)(0.0116)

Distance-0.674***-0.650***-1.039***-0.982***-1.155***-1.166***(0.0272)(0.0349)(0.0870)(0.0827)(0.0840)(0.0844)WTO Round (t-1)

GDP (t-1)-0.148***0.0170-0.0944-0.240**-0.0292-0.0380(0.0559)(0.0794)(0.0635)(0.1051)(0.0712)(0.0704)

GDP per capita (t-1)

0.0185***-0.0121**0.0514***0.0353***0.008620.00879(0.0042)(0.0056)(0.0076)(0.0067)(0.0055)(0.0054)

GDP growth (t-1)-0.0339***0.00515-0.0599***-0.0735***-0.0517***-0.0497***(0.0091)(0.0101)(0.0146)(0.0138)(0.0105)(0.0105)

FTA density (t-1, centered)-21.58***-127.5***-114.5-105.0-60.26***-61.16***(2.2825)(10.9597)(103.3323)(87.3383)(13.6951)(13.8966)

FTA density² (t-1, centered)-511.1***-64.44

-1803

-1528

425.3*

466.8**(67.0399)(66.0358)(3826.5012)(3478.6977)(226.3410)(228.7370)

3.073***

2.889***

4.319***(0.1445)(0.2061)(0.3129)-0.357***

1.218***

0.246***

0.06440.414***

0.413***(0.0230)(0.1501)(0.0892)(0.0731)(0.1291)(0.1343)24291762038

180879101433

96237

96237

108

74

108

94

94

94

Trade Partner Coverage (t-1)
Time Trend
Observations
Countries

0.944***(0.2053)

2

Pseudo R
0.452
0.538
0.586
0.504
0.419
0.418
Robust standard errors clustered over dyads in parentheses. Six duration dependence splines are included, but no showed in the table.
*** p<0.01, ** p<0.05, * p<0.1

Several other ideas related with the formation of FTA can be tested in this framework, including themain theories described in Section 2. The thesis that link the spread of democracy and free trade
agreements seems to have support since the variable Democracy (built using the POLITY IV index) is
positive and significative. The idea that smaller countries are more prone to FTA is stated by thenegative sign in the parameter associated to GDP and that richer counties are more active in that senseby the positive sign associated to GDP per capita. The notion that FTA formation is more likely in16

economic downturns is backed by the negative sign in the parameter for GDP growth. Bandwagon
effect is incorporated by M&R using the proportion of the 10 main trade partners that already have an
FTA (“Trade Partner Coverage” in Table 4), a variable that appear with a positive and significative sign.
More than a simple “monkeys see, monkeys do” we think that this variable represents preliminary
evidence of the contagion effect. Finally, FTA density (proportion of dyads with and FTA in a givenyear) and its squared value are incorporated, showing a decreasing return of the gains related to theagreements (the negative sign is because the variable is centered in 0).
In columns 2 and 3 of Table 4 we split the sample of the first column in two sub-periods: 1950-1975
and 1976-1998, in order to study if the results are not dependent to the chosen time span. The mainchanges are that now the parameter associated to WTO members is now negative and significative,
same for WTO round in the second period (in the first is non signicative). GDP is not significative
anymore, while GDP per capita is negative in the first period and growth is just statistically relevant in
the second. This is first evidence that M&R results are not robust to different specifications.
In column 4 we now introduce a lagged value of our contagion index in order to test the hypothesisthat FTA are contagious. As expected, the parameter associated with the index is positive andsignificative, providing preliminary evidence that in the dynamics of the FTA formation the influenceeffects of the domino theory are relevant. In this specification we have to drop the “Trade Partner
Coverage” variable, because of its high collinearity with the contagion index (the partial pairwise
correlation is around 80%), that demonstrate the previously stated idea that this variable must be
considered as an alternative proxy for contagion20.
Up to now, the M&R definition of FTA has been used as the dependent variable, a measure based in
the WTO record. We now will use our definition of FTA as dependent variable, based in the data
described in Section 3. While in M&R 238 new FTA are registered between 1976 and 1998, in ours thenumber rises until 382, with and overlap in half of the M&R registers (122).
In column5 of Table 4 we reproduce the regression of column 3, but now having our FTA data in theright hand side. The only important changes are that dispute with a third party is not significant
anymore (driven by the change in sample size) and that GDP per capita is not significant either.
In the last column we include again the contagion index, but now using our data for FTA formation as
dependent variable, and the results supporting contagion effect hold.
We now turn into the analysis of the dynamics of FTA formation in the database that we built for this
proposes, that covers the period 1976-2005 (The countries included in the sample are listed in Appendix
1). We updated the variables of the M&R specification and add several more controls (all the detailabout the variables and their sources are on Appendix 2. The variables related with trade disputes at
WTO will be updated for future versions of the paper).
In the first column of Table 5 we replicate the specification of the last column of Table 4, but now for
the new period and dataset. Is possible to observe that the main results hold, with the contagion index
been positive, same for democracy, GDP is insignificant, GDP per capita positive and GDP growth
negative. The multilateral variables are non significatives or with the wrong sign. At this respect, is
important to note that in our sample 64% of the agreements were signed when a multilateral round was
happening, a fact that give no indication of the multilateral hypothesis since the round cover 60% of the20

When the contagion index and “Trade Partner Coverage” variables are together in the regressions ofcolumn 4 and 6 of Table 4, just the former is significant, but just at 5%, while the variables are
separately significant at 1%. The reason why we think that “Trade Partner Coverage” is contagion andnot bandwagon effect is because there is an economic meaning (trade diversion) that drives the FTA
decision, over simple imitation.

17

period in the sample. More importantly, by far the years with more FTA are 1984, 1993, 1995, 1998
and 2000, of which just during the second a multilateral round was open.

Table 5: Dynamic Determinants of FTA
Contagion Index (t-1)(1)
new fta
0.0205***(0.0018)(2)
new fta
0.0183***(0.0021)

Contagion Index(weights '76, t-1)

(3)
new fta(4)
new fta(5)
new fta(6)
new pure fta

0.0197***(0.0029)

Contagion Index(weights '62, t-1)

0.0260***(0.0051)

Contagion Index(weights gravity, t-1)

0.0219***(0.0032)

Contagion Index (PureFTA, t-1)

0.0210***(0.0025)

Contagion Index (withshare of j and k, t-1)
WTO Members(detrended, t-1)
WTO Round (t-1)
Democracy (t-1)

0.0500***(0.0022)-0.104***(0.0265)-0.327(0.2086)
0.0381***(0.0108)

Political Distance (t-1)
Geographical Distance
GDP (t-1)
GDP per capita (t-1)
GDP Growth (t-1)
Trade with county
partner over GDP (t-1)(7)
new fta-1.045***(0.0752)-0.0665(0.0404)
0.0389***(0.0051)-0.0420***(0.0093)-0.113***(0.0279)-0.348(0.2173)-0.133***(0.0307)-0.382(0.2906)-0.0471(0.0401)-1.281*(0.6657)-0.115***(0.0280)-0.339(0.2179)-0.238***(0.0324)-0.437(0.3328)-0.128***(0.0273)-0.483**(0.2303)-0.0691***(0.0130)-1.092***(0.0955)-0.0823*(0.0500)
0.0332***(0.0054)-0.0441***(0.0107)-0.0676***(0.0161)-0.922***(0.1281)-0.147***(0.0525)
0.0372***(0.0060)-0.0381***(0.0118)-0.0138(0.0241)-0.953***(0.1998)-0.169*(0.0902)0.0443***(0.0098)
0.00170(0.0197)-0.0682***(0.0132)-1.162***(0.0957)-0.117**(0.0548)0.0319***(0.0054)-0.0473***(0.0110)-0.0668***(0.0140)-1.237***(0.1133)-0.0927(0.0572)
0.0418***(0.0060)-0.0644***(0.0130)-0.0686***(0.0140)-0.727***(0.1051)-0.00183(0.0470)-0.00445(0.0060)-0.0634***(0.0124)0.0659***0.0586***
0.0468***0.0861***
0.0562***0.0626***(0.0157)(0.0158)(0.0233)(0.0159)(0.0162)(0.0140)FTA Expertise
0.333***
0.316**
0.438
0.308**
0.812***-0.156(0.1230)(0.1289)(0.2739)(0.1211)(0.1574)(0.1375)
Observations
11579911579980079
28092
115273115799115799Countries
112
112
72
46
112
112
112
Pseudo R2
0.238
0.297
0.347
0.358
0.292
0.454
0.366
Robust standard errors clustered over dyads in parentheses. *** p<0.01, ** p<0.05, *p<0.1
Remoteness from rest of the world, product and difference of GDPs, six duration dependence splines, time trend, FTA density anddummies for transition from communism, military alliance, common language, common colonizer, former colonial relationship areincluded, but no showed in the table

In Table 2 we add several variables to the specification. First, we test the idea that in terms of politicalsystem, more than having a pure democratic regime, what matters in order to form an FTA with anothercountry is the “Political distance”, meaning the difference between the regimens in both countries, that
we measure as the absolute value of the difference in the POLITY IV index for the countries in the pair.
Some descriptive statistics can help to understand our prior. In the M&R database, for the period 19761998, just 4% of the countries signing FTA were autocracies (with POLITY IV measure less than zero),
while in our database the proportion rise to one quarter. The reasons that explain the disparity are18

related with sample composition (countries like Algeria, Jordan and Syria, that are classified as
autocracies at the moment of sign various FTA are not in M&R database); the lack of information in
M&R record for FTA (agreements signed the year countries like Morocco, Singapore and South Africa
were autocracies are not registered); some of these FTA involving autocracies were signed after 1998
(65 in total). At the same time, while the average political distance for the countries in our database is
7.64 (7.99 in M&R database), it get reduced to 4.4 (2.94 in M&R) for countries that sign an FTA.
In column 2 of Table 5 we show that the measure of political distance is negative and significative, aresult that hold for different specifications, and that gives support to our idea. The measure of
Democracy is deleted in this specification (and the following), because of it collinearity with measure ofpolitical distance, but when was included was usually insignificant (and the results for political distance
where unchanged). Finally, is worthy to note that eliminating the measure of democracy also alleviate
the collinearity issue of this variable and GDP per capita that comes from the “modernisation
hypothesis” (richer countries tend to be more democratic).
Another important variable that we add to the new specification is the bilateral trade between thesignings countries. This is with no doubt one of the main determinants of the FTA formation, but theobvious endogeneity in cross sectional specifications have implied that several studies do notincorporate it (some of the M&R specifications included trade, but the limited number of observations
was restrictive for their sample size). We incorporate this variable as the total bilateral trade (exports
plus imports) over recipient country GDP. As expected, the estimated parameter is positive andsignificative.
It is very likely that countries that already have signed several FTA will be keep doing that, bothbecause they have learn how to do it and because the threat of new FTA in terms of import completionare likely to be less. We include a variable denominated “FTA expertise”, a dummy takes value 1 if thecountry has signed more than 8 agreements21. As expected this variable is positive and significative.
Other controls that are included but not showed in Table 5 are: all the controls of B&B specification,
a dummy equal one if one of the countries in the dyad is on transition from communism, a dummy for
military alliance (that was never significative) and the FTA density.
In columns 3 to 6 we include the same robustness measures of contagion indexes, obtaining always
results that support the contagion hypothesis and with control variables that have same results andinterpretation as in the basic specification.
In column 7 we add an additional robustness measure. Up to now, in the indexes we were considering
the trade relevance of the countries in the dyad, but not the importance of the third member with which
the “contagiant” country (country j in the contagion index equation) is signing agreements. In order totake this in account, we re-estimate the indexes, but now weighting the agreement with the third country
by the exports shares of j to k. We can see that main results hold, and the parameter associated to theindex is bigger than in previous estimations, indicating that accounting for the commercial relevance ofthe third countries signing an FTA increase the level of contagion.
Up to now, in the analysis of dynamic of FTA some crucial time invariant controls have been used ascontrols in the panel regression -notably country’s distance-, but no dyad fixed effects have been used(the inclusion of fixed effects by “brutal force” in this case will imply the addition of around four
21

Our first approach was to include the sum of FTA that a country has signed up to a given year, andits quadratic value, but collinearity problems arose from this measure. The threshold of 8 FTA waschosen because this is the median value of the sample, and because taken values in its vicinity leaves
the results unchanged, while having values over 12 or 13 changed the other results in unexpected ways
(and increase the correlation of the dummy with GDP per capita, democracy and the time trend).

19

thousand dummies). One solution to alleviate the bias problem in panels with limited dependent
variable is to estimate conditional likelihood functions with minimal sufficient statistics for the fixed
effects. Chamberlain (1980) proposed a conditional logit model that in our case will take the following
form:

When the conditional probability is different than 1, the logit function do not involve the fixed effects
parameters and conventional Maximum Likelihood estimation can be perform. Then conditional logits
will provide unbiased estimations of the parameters, but just for the sub-sample of dyads that switch
status during the observed period, that is those that subscribe an FTA between 1977 and 2005.
Table 6: Dynamic Determinants of FTA, Conditional Logit Estimates (Dyads Fixed Effects)

Contagion (average, t-1)

(1)

(2)

(3)

(4)

(5)

(6)

All FTAAll FTAAll FTAAll FTAAll FTA

Pure FTA

0.257**(0.1027)

Contagion (max, t-1)

0.265***(0.0383)

Contagion (min, t-1)

0.102
(0.0657)

Contagion exports '76 (average, t-1)

0.198*(0.1154)

Contagion gravity (average, t-1)

0.353**(0.1755)

Contagion pure FTA (average, t-1)

0.222***(0.0316)

WTO Members (t-1, detrended)
WTO Round (t-1)
Political Distance (t-1)
GDP product (t-1)-0.0498**-0.0374-0.0322-0.0675**-0.0518**

0.0246(0.0250)(0.0257)(0.0248)(0.0281)(0.0247)(0.0162)-0.468**-0.273*-0.110-0.812***-0.347**-0.0597(0.1950)(0.1537)(0.1368)(0.1857)(0.1675)(0.1767)-0.0178-0.0743-0.0553

0.0130-0.0403

0.0566(0.0523)(0.0543)(0.0632)(0.0771)(0.0567)(0.0547)

0.0121*0.0143*0.0224**0.0200**0.0104*0.00196*(0.0067)(0.0083)(0.0093)(0.0092)(0.0059)(0.0010)Observations

7332

7332

7332

5847

7268

8580

Dyads

401

401

401

255

397

456

Pseudo R2
0.769
0.750
0.731
0.809
0.744
0.770
Standard errors clustered over dyads in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Six duration dependence splines and atime trend are included but no showed in the table.

20

The dependent variable is now again the FTA status binary variable, with value 0 before theagreement was registered and 1 afterwards, so some of the economic variables in the previous
specification are endogenous and will not be included. Here we will prefer a undirected dyads
specification, so the symmetric measures of contagion will be tested.
In the three first columns of Table 6 we show the results of the conditional logit estimates for theundirected dyads for the symmetric indicators of contagion. Of those, the average and maximum
indicator are positive and significative, but the one for minimum contagion is not significative. We
control for the multilateral variables, that again have the opposite sign that predicted; for politicaldistance, that in this case have the right sign but is not statically relevant; and for the product of the
GDP of countries in the dyad, that is positive and significative (at 5%).
The last three columns of Table 6 perform the same previous robustness checks -now just for the
averaged contagion indicator- and the basic results supporting contagion effect hold.
CONCLUDING REMARKSThis preliminary draft presents evidence that contagion may be important in explaining the spread ofregionalism. Our index of contagion is aimed at capturing the extent to which a nation can ‘catch’ the
FTA ‘bug’ by trading with nations that sign many FTAs, especially those with important third-country
trading partners. Two critiques of the current state of the work have been made clear to us. First, thecontagion index is somewhat ad hoc. The solutions to this are to provide a firmer theoretical basis for it,
and to try many other variants. Second, even we implemented several checks to solve potential
endogeneity problems with the weights in the indexes, we have not done the same for the FTAs signedby third countries, something that requires the use of recent developments in spatial econometrictechniques.
In a subsequent version, we plan to redress these shortcomings.
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Aghion, P., P. Antràs and E. Helpman (2004). Negotiating Free Trade. NBER Working Paper
no.10721, National Bureau of Economic Research, Cambridge, MA.
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Baier, S.L., and J.H. Bergstrand (2003). Economic Determinants of Free Trade Agreements. Journalof International Economics, vol. 64, pp. 29-63.
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24

APPENDIX 1: COUNTRIES IN THE SAMPLE
Obs.

Obs.

First

FTAs

Contagion Indicators (mean 2005)(2005)(panel)Obs.

(2005)

Average

Max

Min

TOTAL

4661

134733

Albania

57

484

3

18.03

45.07

1.27

Country782
1998

Algeria*

83

2492

1976

27

19.44

53.46

23.05

Argentina*

102

3287

1976

10

6.36

22.67

2.87

Armenia

55

723

1993

9

11.54

27.37

9.12

Australia*

106

3823

1976

7

11.27

25.76

6.82

Austria

111

3774

1976

34

26.69

57.65

22.23

Azerbaijan

68

585

1998

13

13.47

32.32

10.07

Bangladesh

98

3110

1976

6

7.81

21.83

2.2

Belarus

88

909

1997

11

11.44

31.18

7.85

Belgium*

111

1258

1997

36

23.56

50.36

1.94

Bolivia*

72

2147

1976

10

4.99

19.9

3.54

Brazil*

110

3794

1976

11

4.9

18.64

1.75

Bulgaria*

95

2386

1980

23

14.05

48.66

22.39

Burkina Faso

49

1637

1976

4

13.21

30.98

0.89

Cambodia

54

453

1998

6

8.98

23.16

3.53

Canada*

110

3775

1976

7

9.08

22.68

4.01

CAF

44

1563

1976

1

21.42

50.92

0.04

Chad

37

1083

1976

1

15.25

41.23

0.04

Chile*

92

2738

1976

30

27

65.29

20.59

China

111

3795

1976

12

4.15

15.33

3.18

Colombia*

92

3161

1976

19

6.6

21.25

3.21

Costa Rica*

73

2574

1976

12

6.23

22.51

3.27

Cyprus

94

2974

1976

22

20.71

49.22

24.2

Czech R.*

110

1551

1993

24

13.06

49.77

20.77

Denmark*

111

3921

1976

36

27.78

58.65

22.71

Ecuador*

79

2319

1976

10

4.37

16.05

3.55

Egypt*

100

3136

1976

33

21.57

51.23

20.81

El Salvador*

70

2060

1976

9

5.56

18.38

2.49

Estonia

87

1349

1993

22

20.44

57.19

30.21

Fiji

45

1861

1976

3

13.24

29.54

1.83

Finland

108

3736

1976

32

27.42

57.45

24.39

France*

111

3942

1976

36

24.47

52.51

20.86

Gabon

59

1835

1976

0

15.51

34.58

0.01

Gambia

53

1560

1976

3

17.69

42.82

0.46

Georgia

61

866

1992

11

19.59

49.63

8.89

Germany*

111

3960

1976

36

23.31

50.96

19.92

Ghana

73

2535

1976

6

10.72

29.64

0.32

25

Greece*

109

3693

1976

8

21.13

49.38

18.95

Guatemala*

71

2323

1976

8

5.64

17.93

2.1

Guinea

57

1640

1976

4

15.89

39.89

0.36

Guinea-Bissau

30

1027

1976

0

14.3

30.53

0

Guyana

50

1983

1976

4

9.84

26.27

1.7

Haiti

55

1937

1976

2

6.2

17.64

0.38

Honduras*

66

2223

1976

8

5.05

19.53

1.66

Hong Kong*

109

3768

1976

1

5.71

17.9

1.63

Hungary*

107

3071

1976

24

12.5

50

21.72

India

110

3748

1976

9

6.71

19.15

3.06

Indonesia*

110

3375

1976

6

4.11

11.7

2.58

Iran*

76

2449

1976

7

7.89

26.15

2.04

Ireland*

109

3783

1976

36

27.92

60.13

23.61

Israel

97

2754

1976

22

22.91

69.04

17.05

Italy*

111

3936

1976

36

25.05

53.52

21.17

Jamaica

69

2683

1976

6

7.29

23.73

1.27

Japan*

111

3917

1976

2

1.93

12.54

1.55

Jordan

86

2491

1976

24

21.25

66.3

16.98

Kazakhstan

77

998

1994

14

14.95

36.6

8.86

Obs.

Obs.

First

FTAs

Contagion Indicators (mean 2005)

Obs.

(2005)

AverageCountry(2005)(panel)

Kenya

74

2595

1976

6

9.18

Max
24.65

Min
1.74

Korea, South.*

109

3556

1976

5

4.85

13.95

1.98

Kyrqyz R.

59

730

1994

14

12.11

29.58

13.27

Latvia

83

1221

1992

22

20.37

55.9

31.44

Lithuania

83

1187

1992

22

20.77

55.18

33.13

Madagascar

61

2178

1976

2

12.58

34.71

1.31

Malawi

60

1851

1976

7

13.62

35.55

1.57

Malaysia

111

3655

1976

6

5.58

15.17

2.56

Mali

54

1632

1976

4

11.58

28.41

0.81

Mexico*

85

2884

1976

35

21.96

69.96

9.72

Moldova

59

750

1995

13

18.08

42.92

11.87

Morocco

77

2799

1976

21

19.68

53.04

27.83

Mozambique

52

1692

1976

4

15.8

36.12

1.3

Nepal

47

1320

1976

4

7.19

24.33

1.73

Netherlands*

111

3927

1976

36

26.11

57.33

22.53

New Zealand

96

3576

1976

6

5.29

17.49

2.83

Nicaragua*

66

1949

1976

8

7.22

17.95

2.37

Niger

55

1608

1976

4

17.53

44.52

0.36

Norway*

108

3790

1976

24

25.32

55.33

21.83

26

Pakistan

108

3554

1976

13

6.87

19.67

3.05

Panama*

79

2491

1976

7

5.19

20.72

1.06

Papua N. G.

48

1793

1976

3

10.32

22.89

1.81

Paraguay*

62

1969

1976

10

9.7

25.65

3.96

Peru*

86

2818

1976

10

5.91

20.44

2.71

Philippines*

95

3312

1976

6

5.94

16.7

2.69

Poland*

109

3342

1976

24

13.19

51.51

23.17

Portugal*

111

3719

1976

34

26.69

58.19

22.57

Romania

99

1534

1976

23

13.27

48.97

23.2

Russia

104

977

1998

11

8.99

22.89

2.49

Sierra Leone

47

1696

1976

3

15.53

38.85

0.42

Singapore*

98

3308

1976

14

8.22

33.59

9.31

Slovak R.

104

1572

1993

24

13.65

51.8

20.88

South Africa*

107

2837

1976

17

18.32

40.73

17.63

Spain*

111

3869

1976

34

25.03

54.65

21.26

Sri Lanka

86

2923

1976

6

7.67

21.5

2.53

Sweden*

110

3840

1976

34

26.22

55.82

21.43

Switzerland*

111

3858

1976

24

24.79

53.06

19.83

Syria

68

2145

1976

14

18.32

49.84

23.69

Tajikistan

53

436

1998

12

12.75

33.06

10.04

Tanzania

71

2246

1976

6

10.6

27.06

2.37

Thailand*

110

3640

1976

9

6.65

18.49

3.56

Togo

60

1915

1976

6

15.01

34.42

0.55

TrinidadTobago

74

2590

1976

6

4.42

14.02

1.42

Tunisia

92

2964

1976

22

14.96

54.32

22.39

Turkey*

110

3350

1976

27

24.62

52.87

22.07

Turkmenistan

60

503

1998

14

10.77

27.81

9.82

Uganda

53

1652

1976

5

11.85

34.74

1.97

Ukraine

79

1020

1993

11

14.87

34.9

7.98

UK*

111

3986

1976

36

23.59

50.21

17.97

USA*

111

1964

1976

16

2.38

17.66

3.31

Uruguay*

83

2721

1976

14

8.21

24.5

3.35

Uzbekistan

58

481

1998

15

12.71

30.78

11.76

Venezuela*

73

2520

1976

2

5.54

16.74

3.06

Vietnam

101

921

1998

3

5.66

15.08

0

Yemen

68

1136

1991

10

7.67

26.02

3.04

Zambia

62

2009

1976

0

12.83

28.54

1.82

*Countries that belong to the B&B sample.

27

APPENDIX 2: DATA DESCRIPTION

Distance

Rose (2004).
As defined by Baier and Bergstran (2004) :RemotenessWith DCONT dummy for i and j same continent, tanking from the World Bank’s regionsclassification.
To calculate the product and ratio of the GDP (logs) we use data from the World Bank's
GDP initial ratio World Development Indicators (WDI). For each country we use the oldest available data,
starting in 1960 for old countries and the year of creation for new countries (in someand productexceptions the data just start later).
capital/labour Taken from Baier, S.L., Dwyer, G., Tamura, R., 2000. We use the oldest data available for
ratio
each country.
Commonlanguagedummy for Common Language, Rose (2004).
Commoncolonizer
Dummy for Common Colonizer after 1945, Rose (2004).
ColonyDummy for pairs ever in Colonial Relationship, Rose (2004).
We use the newly built database available at the World Trade Institute (WTI). The data is
All FTAbased on historical notifications of the date the agreements entered into force and theircontemporary participants. For more details: Hufbauer and Schott (2007)
UN Comtrade database (using the World Integrated Trade Solution of the World Bank), at
Export Shares the 1-digit Standard International Trade Classification (SITC) level. Whenever a country
did not report data on exports, we use the pairid image of import from the partner.
GDP, GDP
growth, GDPper capita

The data is taken from the World Bank's World Development Indicators (WDI).

Democracy

Captures this regime authority spectrum on a 21-point scale ranging from -10 (hereditary
monarchy) to +10 (consolidated democracy). The data comes from the Polity IV project,which publicly available data can be found at
http://www.systemicpeace.org/polity/polity4.htm

Trade withcounty partner
over GDPUN Comtrade database (using the World Integrated Trade Solution of the World Bank), at
the 1-digit Standard International Trade Classification (SITC) level.

A dummy taken value one for any positive record in the variable “formal alliances” inCorrelates of War database: http://www.correlatesofwar.org Since the variable is justavailable up to 2000, we assumed that the values of the last year were the same for the
missing period 2001-2004.
Transition from We update the variable existent in M&R database, incorporating former Soviet republics
communismand other countries not included in their sample.
Military
Alliance