Replication of

In search of WTO trade effects: Preferential trade

agreements promote trade strongly, but unevenly*

Shahidur Rashid Talukdar

Abstract: This paper is a replication of Eicher and Henn (2011). Using the author provided dataset, I

reproduce results in four of the tables in the paper. The results are almost identical to those in the paper.

The data show that any effect of WTO membership depends on the coding convention of how membership

or accession to WTO is defined and accounting for the proper PTA effect is essential. Otherwise, the

contribution of PTAs in trade creation between their members gets wrongly attributed to the WTO.

Accounting for multilateral resistance control and observed and unobserved bilateral heterogeneity, the

study concludes that there is no statistically significant and/ robust evidence that the WTO creates trade

between its members. I extend the framework to the agricultural trade only to find that the conclusion

remains pretty much the same.

*I would like to thank Dr. Christian Henn of IMF, one of the co-authors for providing me

the dataset used for this paper. Also, I would like to thank Dr Jason Grant, department of

Agricultural and Applied Economics, Virginia Tech for providing me the dataset for

agricultural trade.

1

1. Introduction: This paper is a partial replication of the seminal paper by Eicher and Henn

(2011), In search of WTO trade effects: Preferential trade agreements promote trade strongly,

but unevenly. The original paper unifies previous works such Rose (2004, 2005), (TGR) Tomz

et al. (2007) and (SW) Subramanian and Wei (2007) to one comprehensive approach thereby

minimizing the omitted variable bias. Rose (2004) initially found the absence of WTO effects on

bilateral trade flows. Updating the Rose's dataset by including both de jure and de facto WTO

membership, Tomz et al. found positive WTO trade effects. Rose (2005) also produced a positive

WTO impact on trade flows, after accounting for distinct effects of individual preferential trade

agreements (PTAs). Subramanian and Wei (2007) split the full dataset sample to highlight that

WTO trade effects exist for industrialized but not developing nations.

This implies a great deal of diversity in the results depending on the coding and

econometric specifications. Hence, to provide a better clarity to the economic researchers as well

as policy makers, Eicher and Henn (2011) unify the above approaches to produce one consistent

result. Although there have been studies in the past analyzing the impact of GATT/WTO on

agricultural trade, this paper takes a unique approach to study the effect of WTO on bilateral

imports using a rigorous procedure to consider different ways of defining WTO membership such

as Roses method and SWs method while addressing the issues of bilateral heterogeneity.

Since this is a replication paper, using the same data as used by the authors and identical

empirical set up, I reproduce similar results. The results show that the WTO effects on trade flows

are not statistically significant, while PTAs produce strong but uneven trade effects. Extending the

gravity model to address specific ways in which WTO might affect trade, I find, as do the authors

Eicher and Henn (2011), that WTO membership boosts the trade prior to the formation of RTAs.

Further, analysis of WTO accession dynamics shows no specific effect of WTO membership on

bilateral trade growth either pre-accession or during WTO membership. I further extend this study

to agricultural trade only to find that WTO does not have any significant and robust effect on

bilateral agricultural trade among members either. The rest of the paper is organized as follows.

Section 2 briefly describes the empirical model, section 3 gives a description of the data, section

4 gives the results from replication, and section 5 details the results from extension. Concluding

remarks are in section 6.

2. Theory/Model: The study makes use of the gravity model of trade. To account for the bilateral

heterogeneity and multilateral resistance control, the authors use an extended version of the gravity

equation. The empirical models used in this paper are as follows:

where Dt is a time dummy, Dmt is a time varying importer dummy, Dxt is a time varying exporter

dummy, PTA is an indicator variable for Preferential Trade Agreement membership,

WTO_Industrialmxt and WTO_Developingmxt stand for the disaggregated WTO dummies for

indurstrialized and developing countries respectively, GSP is a dummy that represents

industrialized countries' unilateral trade concessions to developing trading partners under the

GATT/WTO's Generalized System of Preferences (GSP) from 1979 onwards. In addition, Zmxt

represents a list of common gravity controls and proxies for transport costs and geographic/

cultural proximity that are not absorbed by the fixed effects. The list includes the natural log of

bilateral distance, Distancemx, and dummies for common currency union (CurrencyUnionmxt),

contemporaneous or historical colonial relationships (CurrentColonymxt and EverColonymx),

common colonizer relationships post-1945 (CommonColonizermx), shared official languages

CommonLanguagemx), and territorial dependency/contingency (CommonNationmx/Bordermx). The

* superscripts indicate mutually exclusive coding. The original paper describes another set of

equations for mutually inclusive coding also.

3. Data: The dataset I have used for this replication is the same dataset as used by the authors. One

of the authors, Dr. Christian Henn, was kind enough to send me the dataset for the study. Before I

got the dataset from Dr. Henn, I had access to a partial dataset provided to me by Dr. Jason Grant.

Initially I tried to complete the dataset on my own and replicate the results. This resulted in similar

results but not identical to the original paper. I am providing those results in the appendix.

However, on receiving the original dataset from the author, I re-ran the regressions and the results,

as expected, are very similar to those of the authors. For the extension part, I keep all other

variables same and just replace the bilateral import data by bilateral agricultural trade data.

4. Results from Replication: The tables 1 through 3 record the results from replication of the

original results while table 4 gives results from the analysis of WTO-accession dynamics. Table 5

gives results from the extension of the empirical framework to agricultural trade data. Table 1 here

is the same as Table 1(b) of the original paper. Table 2 and Table 3 similar to the tables in the

original paper. Although my intention was to replicate all the results, given the limitation with the

data initially and because of the shortage of time, I could reproduce only four tables from the paper.

Table 1(a) in the paper can also be constructed from the Table 1(b). The difference between Table

1(a) and Table 1(b) is that the latter accounts for the PTAs formed among industrialized countries

and those formed among developing countries separately while the former takes a common PTA

dummy for all. So technically, four of the tables can be reproduced from the tables I present in this

replication.

The tables 1 through 3 record the results from 16 different regressions run using some

variants of the models 1 and 2 described in section 2. In these regressions, the dependent variable

is natural logarithm of bilateral imports. The regressors are respectively listed in the first column

of each table. Regression 1 takes into account the multilateral resistance control (MLR) and

regression 1(a) splits the PTA dummy into PTA for developing countries and industrialized

countries. Regression 2 allows for individual PTA trade effects for the industrialized countries

while regression 3 allows for individual trade effects from all the PTAs. The regressions 4, 5, and

6 besides controlling for the MLR, also take into account the country-pair fixed effects (CPFE).

The difference between Table 1 and Table 2 lies in their different coding conventions for

the WTO and PTA dummies. While table 1 uses SW mutually exclusive coding convention, Table

2 uses Roses inclusive coding convention. The details regarding these coding conventions are

available in the original paper. Table 3 checks for the robustness of trade effects. The authors use

alternative approaches such as considering first-differencing and AR(1) errors to control for

unobserved bilateral heterogeneity. Table 4 records the results from extending the above set up to

agricultural trade data for the same period. Rather than reproducing all the respective regressions,

I replicate regressions 1, 3, 6, and 12 for the agricultural trade data. I choose these particular

regressions because these give the major difference in different specifications.

Regression 1 shows that WTO_industrialized has a positive and statistically significant

coefficient. It means that the industrialized countries, on average, experience their imports increase

by 187% ( e1.053- 1) from WTO membership while the coefficient on WTO_developing is much

smaller and statistically insignificant. The PTA membership also increases bilateral imports, on

average, by 234% (( e1.205- 1) for all countries. In the next column I record the results from

regression 1(a) which disaggregates the PTA dummy to developing and industrialized dummies.

This produces a remarkable change in the coefficients. While the coefficient on the

WTO_developing does not change, the coefficient WTO_industrialized becomes negative, smaller

than the previous case, and statistical significance also suffers. The PTA_developing dummy

retains the entire coefficient previously associated with PTA. The PTA_industrialized is dropped

because of its collinearity with WTO_industrialized. The coefficient on WTO_industrialized is

negative and gives the difference between the coefficients on the PTA_indutstrialized and

WTO_industrialized if the former were not dropped (1.205 0.153 = 1.052). This shows that a

large part of the coefficient on WTA_industialized is wrongly attributed to WTO_industrialized.

The collinearity arises because of the fact that all the industrialized countries are part of PTAs.

They joined WTO earlier but later joined some PTAs. So the coefficient on the WTO variable

suffers from omitted variable bias is biased unless the PTA dummies are correctly specified.

Regression 3 and 4 account for individual PTAs. The individual PTAs all have positive

and statistically significant. They all create trade between members. The coefficient on

WTO_developing is negative and statistically insignificant while those on WTO_industrialized

changes in both maginitude and sign with the inclusion/exclusion of individual PTAs. That means

the coefficient of WTO is affected by the inclusion of PTAs. Regressions 4, 5 and 6 account for

country-pair fixed effects (CPFE). Accounting for CPFE, the coefficients of WTO dummies

change significantly. In 4 and 5 regressions, coefficients on WTO_developing become positive

ans statistically significant while those on WTO_industrialized become insignificant. In regression

6, again the coefficient of WTO_ind is statistically significant but negative while the coefficient

on WTO_dev is insignificant. This is shows that there is no definite effect of WTO creating trade

between member countries. The coefficient we sometime attribute to the WTO is largely

attributable to the respective PTAs. The coefficients on PTAs are mostly positive and statistically

significant, except AFTA.

Using Roses coding conventions for membership in WTO and PTAs, from Table 2 we see

that the WTO has little or negative effects on trade between industrialized countries. This

observation is consistent with Rose (2004) as well as our previous results. Although the WTO

coefficient for developing countries are positive but none of them are statistically significant. Thus

we conclude that WTO fails to create trade between members. The PTAs, on the other hand, with

the exception of AFTA, create trade between members. For robustness checks, the authors

consider first-differencing and AR (1) error terms. In Table 3 we see that WTO mostly has positive

coefficients. However, when we consider the MLR + first-difference, the coefficient of WTO_ind

vanishes. In PTAs mostly have positive effects on trade between members. The coefficients on

currency union are also positive and mostly statistically significant. Which means that being in the

same currency union helps countries foster their trade.

Table 4 examines whether most of the growth in bilateral import occurred prior to

countries joining the WTO. This might happen because countries with high trade growth were

more likely to enter the WTO, or because the simple announcement of future WTO accession

caused an increase in bilateral trade. Looking at Table 4, we can see that while country-pair fixed

effects leave the WTO effect largely intact, it is eliminated by the introduction of multilateral

resistance controls. Once again, the authors preferred three-way regression 21 shows no

significant effect of WTO membership on trade growth either pre-accession or during WTO

membership.

5. Results from Extension: For the extension part, I keep all other variables same and just

replace the bilateral import data by bilateral agricultural trade data. With the natural logarithm of

bilateral trade as my dependent variable, I run the regressions 1, 3, 6, and 12. These regressions

pretty much capture all the different specifications. Regression 1 uses one dummy for all the PTAs

combined, while regression 3 accounts for all the individual PTAs. Regression 6 takes into account

MLR+CPFE with SWs exclusive coding while regression 12 covers Roses inclusive coding

convention. From regression 1 in Table 5, we see that although all the coefficients are negative,

none of them are statistically significant. So WTO does not necessarily have any impact on

agricultural trade between members. However, from Table 1 we know that regression 1 may suffer

from omitted variable bias. So I consider regression 3 and 4. In regressions 3 and 4 the coefficients

on WTO are negative and statistically significant at 10% and 5% level respectively. This is

contrary to our expectation. While WTO may or may not increase members trade but there is no

reason to expect that WTO reduces members agricultural trade.

Coming to the last regression, regression 12, which the authors also consider as a preferred

regression, shows that all the coefficients are statistically not significant. So our initial conclusion

about whether WTO has any significant effect on members trade does not change. However, the

puzzling aspect of this table is that when the results are statistically significant almost all of them

are and when they are not significant, almost all of them are not. Then, contrary to our expectations,

in regressions 3 and 4 we find that WTO and PTA membership has negative correlation with

members agricultural trade. This calls for a further research and detailed attention to agricultural

trade.

6. Concluding Remarks: The paper does a very good job of accounting for possible

heterogeneity, multilateral resistance control and country-pair fixed effects and even it addresses

endogeneity by adopting the first-differencing and AR(1) error approaches. However, one thing is

missing in this paper is the missing trade. The paper does not address the issue of zero trade flow

which will be ignored in the logarithmic transformation. A second remark is that in Table 1, in the

regressions 4, 5, and 6 the coefficient estimates are significantly different from what I have found.

The reason for this difference is mainly because of technical issues involved in computing. The

authors cite the reason as: Results vary slightly from published version, because subsequently

redone on more powerful server that does not require stripping of fixed effects before the

regression. This difference, however, does not change the overall conclusion of the paper. Other

than these three regressions, I was able to reproduce all other regressions. The results are identical

in most cases. My third remark is about my extension part. From the extension to agricultural data,

although we see that there is not much evidence in favor or a positive or negative effect of WTO

on members trade, however, this is hardly conclusive. Because, if we disaggregate the agricultural

trade into primary and processed, or if we consider subsectors within primary and processed goods,

the results may change. Overall, from this study it is clear that the impact of WTO on members

trade is affected by coding conventions, and how multilateral heterogeneity is accounted for. In

most of the specification, the data show no evidence of any significant positive correlation between

WTO membership and bilateral trade or trade growth.

References

Eicher, T.S., Henn, C., 2011. Preferential Trade Agreements Promote Trade Strongly, But

Unevenly. Journal of International Economics, Vol 83, pages 137-153.

Rose, A.K., 2004. Do we really know that the WTO increases trade? American Economic

Review 13 (4), 682698.

Rose, A.K., 2005. Which international institutions promote trade? Review of International

Economics 13 (4), 682698.

Subramanian, A., Wei, S.J., 2007. The WTO promotes trade strongly, but unevenly. Journal of

International Economics 72 (1), 151175.

Tomz, M., Goldstein, J.L., Rivers, D., 2007. Do we really know that the WTO increases trade?

Comment. American Economic Review 97 (5), 20052018.

Table1

Dependent variable: limport

Regression Specification 1 1(a) 2 3 4 5 6

Indep Variables MLR MLR MLR MLR MLR,CPFE MLR,CPFE MLR,CPFE

Adj R2 0.741 0.741 0.742 0.743 0.875 0.875 0.876

GSP (mt) 0.844*** (0.156)

-0.360*** (0.070)

0.385** (0.179)

-0.237** (0.093)

0.431*** (0.166)

0.467*** (0.179)

-0.018 (0.092)

WTO_Industrial (mt) 1.053*** (0.155)

-0.152** (0.067)

0.588*** (0.179)

-0.235*** (0.077)

0.160 (0.152)

-0.160 (0.146)

-0.270*** (0.076)

WTO_Developing (mt) 0.062 (0.153)

0.062 (0.142)

-.045 (0.139)

-0.035 (0.091)

0.618*** (0.165)

0.652*** (0.180)

0.125 (0.089)

PTA (mxt) 1.205*** (0.143)

0.912*** (0.158)

PTA_Ind (mxt) Dropped

PTA_Dev (mxt) 1.205*** (0.143)

Bilateral_PTA (mxt) 0.656*** (0.170)

0.316*** (0.099)

0.281* (0.162)

0.022 (0.093)

NAFTA(mxt) 2.117*** (0.204)

1.241*** (0.111)

0.706*** (0.188)

0.246* (0.150)

EU(mxt) 0.652*** (0.188)

-0.174* (0.105)

1.124*** (0.190)

0.396*** (0.111)

CACM(mxt) 1.301*** (0.189)

1.274*** (0.191)

1.864*** (0.282)

1.815*** (0.274)

CARICOM(mxt) 1.391*** (0.217)

1.211*** (0.178)

0.998*** (0.261)

0.887*** (0.235)

MERCOSUR(mxt) 1.485*** (0.287)

1.317*** (0.242)

0.904*** (0.283)

0.886*** (0.246)

AFTA(mxt) 0.523*** (0.202)

0.156 (0.173)

-0.144 (0.250)

-0.239 (0.214)

ANZCERTA(mxt) 2.463*** (0.215)

1.611*** (0.130)

1.425*** (0.217)

0.822*** (0.133)

SPARTECA(mxt) 1.412*** (0.204)

1.203*** (0.188)

0.887*** (0.185)

0.689*** (0.161)

EFTA(mxt) 0.455*** (0.127)

0.301** (0.120)

EEA(mxt) 0.472*** (0.078)

0.315*** (0.070)

AP(mxt) 0.404** (0.196)

0.521** (0.238)

LAIA(mxt) 0.032 (0.116)

0.982*** (0.177)

APEC(mxt) 0.616*** (0.094)

0.161* (0.099)

Notes: *, **, *** are 10%, 5%, 1% significance levels. Standard errors in parentheses. GSP and PTA coefficients are net of

WTO effects, see footnote 10. All regressions include time fixed effects. Fixed effects results are not reported. MLR indicates

multilateral resistance controls, i.e. time-varying importer and exporter fixed effects. CPFE indicates unobserved bilateral

heterogeneity controls, i.e. country-pair fixed effects.

Table 2

Dependent variable: limport

Regression Specification 7 8 9 10 11 12

Indep Variables MLR MLR MLR MLR,CPFE MLR,CPFE MLR,CPFE

Adj R2 0.740 0.742 0.743 0.875 0.0.875 0.876

GSP (mt) -0.123*** (0.430)

-0.183*** (0.042)

-0.181*** (0.041)

-0.213*** (0.044)

-0.196*** (0.044)

-0.166*** (0.044)

WTO_Industrial (mt) -1.103 (0.069)

-0.118* (0.069)

-0.109 (0.094)

-0.082 (0.071)

-0.083 (0.071)

-0.062 (0.068)

WTO_Developing (mt) 0.058 (0.071)

0.067 (0.070)

0.071 (0.071)

0.034 (0.071)

0.033 (0.071)

0.054 (0.069)

PTA (mxt) 0.629*** (0.064)

0.500*** (0.049)

Bilateral_PTA (mxt) 0.453*** (0.079)

0.470*** (0.080)

0.136** (0.068)

0.122* (0.067)

NAFTA(mxt) 1.756*** (0.112)

1.194*** (0.128)

0.346** (0.178)

0.136 (0.120)

EU(mxt) 0.072 (0.069)

-0.146* (0.078)

0.502*** (0.064)

0.313*** (0.074)

CACM(mxt) 1.316*** (0.188)

1.372*** (0.188)

1.926*** (0.269)

1.919*** (0.269)

CARICOM(mxt) 1.445*** (0.162)

1.454*** (0.161)

1.143*** (0.226)

1.135*** (0.227)

MERCOSUR(mxt) 1.505*** (0.251)

1.402*** (0.242)

1.066*** (0.244)

0.955*** (0.250)

AFTA(mxt) 0.551*** (0.179)

0.189 (0.175)

0.005 (0.213)

-0.136 (0.213)

ANZCERTA(mxt) 1.877*** (0.123)

1.514*** (0.118)

0.779*** (0.124)

0.628*** (0.127)

SPARTECA(mxt) 1.311*** (0.187)

1.350*** (0.192)

0.769*** (0.162)

0.764*** (0.162)

EFTA(mxt) 0.473*** (0.102)

0.189** (0.084)

EEA(mxt) 0.489*** (0.076)

0.286*** (0.073)

AP(mxt) 0.385** (0.199)

0.513** (0.244)

LAIA(mxt) 0.189* (0.110)

1.159*** (0.167)

APEC(mxt) 0.798*** (0.072)

0.291*** (0.074)

Notes: *, **, *** are 10%, 5%, 1% significance levels. Standard errors in parentheses. GSP and PTA coefficients are net of WTO effects, see footnote 10. All regressions include time fixed effects. Fixed effects results are not reported. MLR indicates multilateral resistance controls, i.e. time-varying importer and exporter fixed effects. CPFE indicates unobserved bilateral heterogeneity controls, i.e. country-pair fixed effects.

Table 3 Dependent variable: limport

Regression Specification 13 14 15 16

Adj R2 0.798 0.109 0.277 0.638

Indep Variables CPFE FIRST-DIFF FIRST-DIFF, MLR AR(1)

GSP (mt) -0.189*** (0.033)

-0.039 (0.029)

0.178*** (0.033)

-0.030 (0.021)

WTO_Industrial (mt) 0.381*** (0.0361)

0.102*** (0.029)

0.009 (0.060)

0.387*** (0.022)

WTO_Developing (mt) 0.102*** (0.029)

0.120*** (0.024)

0.124** (0.063)

0.88*** (0.017)

PTA (mxt) 0.713*** (0.055)

0.244*** (0.030)

-0.052 (0.040)

0.569*** (0.035)

GDPmt 0.455*** (0.049)

0.313*** (0.073)

0.650*** (0.009)

GDPxt 0.489*** (0.049)

0.677*** (0.060)

0.616*** (0.009)

GDPpcmt 0.318*** (0.050)

0.031 (0.058)

0.163*** (0.014)

GDPpcxt 0.413*** (0.049)

0.444*** (0.074)

0.354*** (0.013)

Landlockedm -0.477*** (0.034)

Landlockedx -0.320*** (0.034)

Islandm 0.152*** (0.034)

Islandx 0.149*** (0.035)

Aream -0.06*** (0.007)

Areax 0.001 (0.007)

CUmxt 0.695*** (0.153)

0.244** (0.112)

0.0948 (0.103)

0.518*** (0.062)

CurrentColonymxt 0.517*** (0.156)

0.249*** (0.70)

0.069 (0.0631)

0.539*** (0.105)

Notes: *, **, *** are 10%, 5%, 1% significance levels. Standard errors in parentheses. GSP and PTA coefficients are net of WTO effects, see footnote 10. All regressions include time fixed effects. Fixed effects results are not reported. MLR indicates multilateral resistance controls, i.e. time-varying importer and exporter fixed effects. CPFE indicates unobserved bilateral heterogeneity controls, i.e. country-pair fixed effects.

Table 4

Dependent Variable: bilateral imports

Regression Specification

18 OLS

19 MLR

20 CPFE

21 MLR & CPFE

No. of Observation N 54389 55831 54389 55381

Adj. R2 0.6419 0.7400 0.7977 0.8808

PTAmxt 0.860*** (0.051)

0.658*** (0.062)

0.735*** (0.051)

0.533*** (0.049)

WTO/GSPmx, t-1 0.038 (0.026)

0.057* (0.032)

0.054** (0.024)

0.014 (0.033)

WTO/GSPmx, t 0.224*** (0.032)

0.058 (0.040)

0.125*** (0.027)

-0.009 (0.035)

WTO/GSPmx, [t+1, n) 0.281*** (0.031)

0.002 (0.055)

0.306*** (0.037)

-0.048 (0.053)

Notes: *, **, *** are 10%, 5%, 1% significance levels. Standard errors in parentheses. GSP and PTA coefficients are net of WTO effects, see footnote 10. All regressions include time fixed effects. Fixed effects results are not reported. MLR indicates multilateral resistance controls, i.e. time-varying importer and exporter fixed effects. CPFE indicates unobserved bilateral heterogeneity controls, i.e. country-pair fixed effects.

Table 5 Extension to Agricultural Data

Dependent Variable: ln(tradeflow)

Regression Specification 1 3 6 12

Indep Variables MLR MLR MLR,CPFE MLR,CPFE

Adj R2 0.547 0.463 0.761 0.605

GSP (mt) -0.606 (0.461)

-0.476* (0.248)

-0.548** (0.287)

0.057 (0.132)

WTO_Industrial (mt) -0.526 (0.430)

-0.426* (0.233)

-0.531** (0.263)

0.008 (0.157)

WTO_Developing (mt) -0.581 (0.464)

-0.453* (0.252)

-0.675** (0.286)

0.180 (0.157)

PTA (mxt) -0.356 (0.431)

Bilateral_PTA (mxt) -0.142 (0.249)

-0..392 (0.292)

0.055 (0.200)

NAFTA(mxt) 0.835 (0.637)

0.354 (1.295)

0.452 (1.243)

EU(mxt) -0.037 (0.303)

-0.384 (0.337)

0.094 (0.245)

CACM(mxt) -0.457 (0.491)

-0.625 (0.662)

-0.310 (0.641)

CARICOM(mxt) 0.032 (0.378)

-0.830 (0.595)

-0.379 (0.532)

MERCOSUR(mxt) -0.737 (0.583)

-0.930* (0.533)

-0.794 (0.541)

AFTA(mxt) -0.506 (0.435)

-0.541 (0.435)

-0.499 (0.434)

ANZCERTA(mxt) -0.491* (0.278)

-0.073 (0.361)

0.181 (0.326)

SPARTECA(mxt) -0.369 (0.413)

-0.073 (0.538)

0.139 (0.556)

EFTA(mxt) -0.716* (0.383)

-0.707** (0.331)

-0.088 (0.204)

EEA(mxt) -0.396 (0.258)

-0.400* (227)

-0.127 (0.217)

AP(mxt) -0.241 (0.369)

-0.614 (0.546)

-0.636 (0.531)

LAIA(mxt) -0.565* (0.305)

-0.531 (0.356)

-0.184 (0.310)

APEC(mxt) -0.391 (0.249)

-0.633** (0.281)

-0.117 (0.172)

Notes: *, **, *** are 10%, 5%, 1% significance levels. Standard errors in parentheses. GSP and PTA coefficients are net of WTO effects, see footnote 10. All regressions include time fixed effects. Fixed effects results are not reported. MLR indicates multilateral resistance controls, i.e. time-varying importer and exporter fixed effects. CPFE indicates unobserved bilateral heterogeneity controls, i.e. country-pair fixed effects.