Policy Research Working Paper 9791
A Simple Method to Quantify the ex-ante
Effects of “Deep” Trade Liberalization
and “Hard” Trade Protection
Mario Larch
Shawn W. Tan
Yoto V. Yotov
Finance, Competitiveness and Innovation Global Practice
October 2021
Policy Research Working Paper 9791
Abstract
This paper proposes a simple and flexible econometric policy application that has not been studied before due to
approach to quantify ex-ante the “deep” impact of trade lack of data. This analysis overcomes this challenge by utiliz-
liberalization and the “hard” effects of protection with the ing a new dataset on trade and production that covers all EU
empirical structural gravity model. Specifically, the paper countries and all CEFTA members (except for Kosovo). The
argues that the difference between the estimates of border partial equilibrium estimates that we obtain confirm the
indicator variables for affected and non-affected countries validity of our methods, while the corresponding general
can be used as a comprehensive measure of the change in equilibrium effects point to significant and heterogeneous
bilateral trade costs in response to a hypothetical policy potential gains for the CEFTA countries from joining the
change. To demonstrate the effectiveness of these methods, EU. The proposed methods can also be extended to ex-post
the paper focus on the integration between the countries analysis and are readily applicable to other applications, for
from the Central European Free Trade Agreement (CEFTA) example, “hard” Brexit.
and the European Union (EU), which is an important
This paper is a product of the Finance, Competitiveness and Innovation Global Practice. It is part of a larger effort by the
World Bank to provide open access to its research and make a contribution to development policy discussions around the
world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may
be contacted at swtan@worldbank.org.
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names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those
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A Simple Method to Quantify the ex-ante Effects of
“Deep” Trade Liberalization and “Hard” Trade
Protection∗
Mario Larch Shawn W. Tan
University of Bayreuth World Bank
Yoto V. Yotov
Drexel University
JEL Classification Codes: F1, F10, F13, F14.
Keywords: Trade Costs, Trade Policy, Structural Gravity, CEFTA, EU.
Acknowledgments and disclaimers to be added later. The views expressed herein are those of the authors
∗
and should not be attributed to the World Bank, their Executive Boards, or management.
Contact information: Larch—Faculty of Law, Business Management and Economics, University of
Bayreuth, CESifo, CEPII, GEP, and Ifo Institute. Universitatsstrase 30, 95447 Bayreuth, Germany. Phone:
+49 (0) 921 55 6240. Email: mario.larch@uni-bayreuth.de; Tan– World Bank, 1818 H St NW, Washington,
DC 20433. Phone: (202) 473-7621. Email: swtan@worldbank.org; Yotov—School of Economics, LeBow Col-
lege of Business, Drexel University, CESifo, Gerri. C. LeBow Hall, 1020, 3220 Market Street, Philadelphia,
PA 19104, USA. Phone: (215) 895-2572. Email: yotov@drexel.edu.
1 Introduction
Quantifying the ex-ante effects of trade liberalization, e.g., the impact of free trade agreements
(FTAs) and the expansion of the European Union (EU), or trade protection, e.g., Brexit or the
break up of the free trade agreement between Estonia and Ukraine due to Estonia’s accession to the
EU, are important but difficult tasks. The transmission channels for the impact of trade protection
and trade liberalization are clear and well understood from a theoretical perspective, c.f., Arkolakis,
Costinot and Rodriguez-Clare (2012). However, difficulties in such evaluation efforts arise in the
empirical implementation and, more specifically, with the definition and quantification of the initial
change in trade costs that triggers ripple effects in the global economy. While ex-post evaluation
analysis usually allows researchers to obtain estimates of the impact of liberalization or protection
efforts, e.g., the impact of NAFTA or the impact of applied tariffs, this is not the case with ex-ante
studies, e.g., the formation of a new trade agreement or Brexit. Therefore, researchers have adopted
one of two approaches to quantify the impact of “shallow” vs. “deep” trade liberalization efforts, e.g.,
the “shallow” vs. “deep” effects of FTAs, or the impact of “soft” vs. “hard” impact of protection, e.g.,
the “soft” vs. “hard” effects of Brexit.1
Policy makers and academics seem to agree on the modeling choice for the measurement of
the initial trade cost changes in the “shallow” and “soft” scenarios. Specifically, these changes are
captured through changes in observed trade policies, e.g., tariffs. However, it is more difficult to
find appropriate measures for the initial change in trade costs in the case of “deep” and “hard” trade
liberalization and protection scenarios, respectively. To this end, two popular approaches have been
adopted. The first approach is to rely on additional observable trade policy measures (e.g., non-
tariff trade measures, NTMs) and to add the changes in those measures to the changes in tariffs
from the “soft” scenario. This approach has two drawbacks. First, data on NTMs are often scarce
and patchy, and the measurement and aggregation of NTMs can be difficult, c.f., UNCTAD-WTO
(2012). In addition, even if NTM data were available, the combination of changes in observable
1
While “shallow” vs. “deep” FTAs can be distinguished by the (number) of provisions included, for ex-
ample, the concept of “soft” vs. “hard” effects is a bit vague. One way to think about the difference between
“soft” vs. “hard” trade protection, which is consistent with our approach in this project, is as the difference
between directly observable measures that can be included explicitly in quantitative analysis vs. obstacles
to trade that are not directly observable and for which data for the econometric analysis are not available.
1
NTMs and tariffs may still be viewed as a conservative (“soft”) measure of the possible benefits from
liberalization or the costs from protection.
The second approach to measure the initial trade cost change for “deep”/“hard” trade liber-
alization and protection scenarios has been to use estimates of the effects of existing free trade
agreements. While, by design, this approach is very appropriate for ex-post evaluation, a major
drawback when applied for ex-ante analysis is that the initial shock is constructed based on limited
information (often a single FTA estimate) and often based on external sample data, i.e., estimation
data that are different from the data used for the counterfactuals. Even if the sample used for the
counterfactuals and for the estimations were the same, and even if the researcher has the opportu-
nity to obtain a number of ex-post FTA estimates that can be used for the counterfactual analysis,
it is difficult to identify an existing FTA that would match closely to the exact FTA in question.2
We propose a simple and flexible econometric method that overcomes the aforementioned chal-
lenges to deliver comprehensive estimates of the ex-ante initial impact of “deep” trade liberalization
and “hard” protection. Specifically, we capitalize on and extend the latest developments in the
empirical structural gravity literature to estimate the effects of bilateral borders that act in addi-
tion to all other trade costs that are observable to the researcher. Thus, the border variables will
account for all forces that are unobservable (or observable but not controlled for) in the econometric
specification of trade costs. Then, in combination with the estimates of the observable trade policy
measures (e.g., tariffs, FTAs, etc.), the difference between the flexibly selected border estimates
for the affected group and the corresponding estimates for the properly selected non-affected group
would offer a comprehensive account for the potential trade costs that may be eliminated with trade
liberalization or generated in the case of protection.
We believe that the proposed method has four attractive features. First, the method is simple
because it does not require specific policy data but only the construction and estimation of the
effects of appropriate indicator variables, i.e., proper border variables, within a standard structural
gravity model. Second, the implementation is flexible for two reasons: (i) because it allows to select
2
Baier, Bergstrand and Clance (2018) and Baier, Yotov and Zylkin (2019) are examples providing evidence
of heterogeneous EIA trade elasticities. As will become clear below, the approach that we propose in this
paper has two advantages. First, it is significantly simpler to implement. Second, it can utilize much more
information for the group of interest.
2
both the groups of affected and non-affected countries for the analysis among any pair or group of
countries that appear in the sample subject to data availability; and (ii) because it allows for the re-
searcher to explicitly control for all observable bilateral determinants of trade. Third, the approach
is comprehensive because, by construction, the border estimates will account for all trade costs that
have not been captured explicitly by other control variables that are observable to the researcher.
Note that, in addition to the border variables, our method allows for the inclusion of tariffs, FTAs,
and all other variables for which data are available. Finally, the method is implemented within the
standard empirical structural gravity equation and, therefore, the partial equilibrium estimates that
it delivers can be integrated within a wide class of new quantitative trade models, c.f., Arkolakis,
Costinot and Rodriguez-Clare (2012); Costinot and Rodriguez-Clare (2014), or even complex com-
putational general equilibrium models, such as the standard GTAP model. We demonstrate that in
Section 4.2, where we obtain general equilibrium (GE) welfare effects corresponding to our partial
equilibrium estimates.
To demonstrate the effectiveness of our methods we study the integration between the countries
from the Central European Free Trade Agreement (CEFTA) and the European Union (EU). The
integration of the CEFTA countries has been an extremely important goal for the governments of
those nations but also from the perspective of the European Union, especially after the EU released
a new strategy for the Western Balkans countries.3 We offer further details on the background on
CEFTA and EU accession process and status in Section 3.1. Data limitations have been the main
obstacle to study the impact of the potential CEFTA integration with the EU. We overcome this
challenge by being the first to utilize the new International Trade and Production Database for
Estimation (ITPD-E), which was construced by Borchert et al. (2021a) for the U.S. International
Trade Commission. The two main advantages of ITPD-E for our purposes are that (i) it covers all
3
The economic integration process between EU and the CEFTA countries has been slowly progressing
for the last 15 years. Four CEFTA countries (Albania, Montenegro, North Macedonia and Serbia) are
EU candidate countries, while Bosnia and Herzegovina and Kosovo are potential candidate countries, and
Moldova recently signed a bilateral free trade agreement with the EU. The recent EU strategy in 2018
provided a target accession date of 2025 for Serbia and Montenegro, with the possibility for the other
Western Balkans countries to join them, but this target date may be delayed given the 2020 COVID-19
pandemic. A review of the status of accession process and challenges (Grieveson, Grubler and Holzner,
2018) finds that target date is ambitious and may be established more as an incentive for reforms in the
countries. Nonetheless, there is still a possibility that in five years or so, Serbia and Montenegro will be new
EU members, with the other countries to follow in the next 10 years.
3
CEFTA countries (except Kosovo) and all EU members as well, and (ii) that it includes consistently
constructed domestic trade flows, which are crucial for the implementation of our methods. We offer
further details on the dataset in Section 3.2.
Several main findings stand out from our partial equilibrium estimates. Without going into
details, we note that we obtain estimates of the effects of all standard gravity variables that are
readily comparable to corresponding estimates from the existing literature. This establishes the
representativeness of our sample. In addition to the standard gravity covariates, we use border
variables to allow for differential impact of borders on trade within EU vs. trade between EU and
CEFTA. We find that, without any exception, the additional barriers to trade between CEFTA and
EU are larger as compared to the barriers to trade within the EU in each of the four main sectors
in our sample, including Agriculture, Mining, Manufacturing and Services. We also document
significant variation in the border differential across sectors. The differences between the CEFTA-
EU border and the EU-EU border are the largest in Agriculture, followed by Services, Mining, and
Manufacturing.4
Capitalizing on the structural properties of the gravity model, we transform our border estimates
into tariff-equivalent effects to find that the border tariff-equivalents that we obtain are significantly
larger as compared to the existing differences in applied tariffs for each of the three goods sectors in
our sample. Specifically we obtain a border tariff equivalent of 19.65% vs. 11.58% actual weighted
average tariff difference in Agriculture, 10.62% border tariff equivalent vs. 1.68 actual weighted
average tariff difference in Mining, and 7.92% border tariff equivalent vs. 5.22 actual weighted
average tariff difference in Manufacturing. This result supports our assumption that, in addition to
tariffs, there are other barriers to trade between CEFTA and EU. Comparison between the tariff
data and our border differential estimates suggests that Mining and Agriculture are subject to more
significant NTMs as compared to Manufacturing, where the difference between the tariffs and the
4
We find this heterogeneity intuitive. The border between CEFTA and EU is likely to impose the most
trade costs on agricultural products for two reasons: (1) most tariffs between EU and CEFTA countries have
gone to zero for all industrial products, but for some agricultural products there are still import tariffs for
CEFTA products into the EU and vice versa, and (2) even with low tariff barriers, the EU imposes high SPS
conditions which results in traders facing long delays at the borders crossing into EU (for e.g. from Serbia
into Croatia). The large border differences in services trade can be due to various unified standards within
the EU, which impose barriers to services trade with outsiders. Another possible explanation is that trade
in services is highly localized consumption. Finally, a possible explanation for the relatively small borders
in Mining and Manufacturing is that the barriers to trade in those sectors have been decreased.
4
border differentials that we estimate is the smallest. In addition, we note that there is no tariff data
that corresponds to the large (16.87 percent) tariff equivalent border differential that we obtain for
services. This highlights another advantage of using our estimation structural gravity approach to
measure border differentials.
Stimulated by the importance of the manufacturing sector for the CEFTA economies and by
the potential for very heterogeneous differences in the impact of borders on trade between CEFTA
and EU vs. trade within the EU in the aggregate manufacturing sector, we also obtain estimates
for eleven manufacturing industries. The disaggregated estimates confirm our main finding that the
CEFTA countries face significantly larger barriers to trade with the EU as compared to the barriers
faced by the EU countries for trade with each other. In addition, they reveal that the differences
in the borders between CEFTA and EU vs. the borders within the EU are quite heterogeneous
across the eleven industries. The border gap for EU trade vs. trade between EU and CEFTA is the
widest in Food (about 22% tariff equivalent), followed by Minerals (about 15% tariff equivalent),
and Wood (about 10% tariff equivalent). Overall, our partial equilibrium estimates imply that
CEFTA members have the potential to face significantly lower barriers to trade upon accession to
the EU, which, in turn, may lead to large welfare gains from joining the EU. We explore those
possibilities in counterfactual analysis with the GE structural gravity model.
We perform two general equilibrium (GE) experiments.5 First, we simulate the impact on
exports of a hypothetical harmonization of MFN tariffs by the CEFTA countries, as a bloc, to
the corresponding EU rates. To perform this analysis, we use data on actual tariffs. Consistent
with the possible practical implementation of such policy, the EU MFN rates will not affect the
preferential tariff rates they accord to their preferential trade partners as they are not acceding into
the EU but rather just adopting the MFN rates. Instead of tariffs, the second GE experiment that
we perform employs our partial equilibrium border estimates to simulate a decrease of the trade
costs between CEFTA and EU to the level of within EU trade barriers. The counterfactual analysis
highlights the substantial difference between the “tariff” and “border” scenario. The predicted total
export changes for the CEFTA countries are substantially larger when using the border estimates
5
To perform the counterfactual analysis we rely on a standard GE gravity setting following Dekle, Eaton
and Kortum (2007, 2008). For details, please see part B of the Supplementary Appendix.
5
to quantify the potential trade cost changes. Additionally, we are also able to predict total export
changes in services in the border scenario, while without tariff data we can not obtain total export
changes based on tariffs for services.
The rest of the paper is organized as follows. Section 2 presents our estimation and identification
methods. Section 3 motivates the focus on the integration of the CEFTA countries in the EU (in
Subsection 3.1), and describes the data and sources that we employ to perform the empirical analysis
(in Subsection 3.2). Section 4 presents our empirical findings. Subsection 4.1 offers an analysis of
our partial equilibrium estimates, while Subsection 4.2 translates the partial equilibrium estimates
into GE effects and discusses our findings. Section 5 concludes. The Supplementary Appendix
includes additional empirical results and robustness estimates.
2 Estimating “Deep”/“Hard” Trade Cost Changes
This section presents our methods to estimate ex-ante comprehensive “deep”/“hard” trade cost
changes in response to a hypothetical policy change. For expositional simplicity, clarity, and to
facilitate the interpretation of our results in the following sections, we will focus on a specific appli-
cation, i.e., the integration of CEFTA and EU.6 Our departing point is the following econometric
gravity model, which we adapt to accommodate the specific goals of our study:7
Xij,t = exp[πi,t + χj,t + GRAVij,t η ] + ij,t . (1)
Here, as defined earlier, Xij,t denotes nominal trade flows from source/exporter i to destina-
tion/importer j at time t. An important feature of the dependent variable is that, consistent
with all of the underlying theoretical models that deliver the structural gravity equation, Xij,t in-
6
The CEFTA consists of seven countries: Albania, Bosnia and Herzegovina, Kosovo, Moldova, Montene-
gro, North Macedonia and Serbia. We offer further details on the history and relationship between CEFTA
and the EU in Section 3.1.
7
As famously demonstrated by Arkolakis, Costinot and Rodriguez-Clare (2012), this empirical equation
is representative of a very wide class of theoretical trade models. We refer the reader to Anderson (2011),
Costinot and Rodriguez-Clare (2014), Yotov et al. (2016), and Baier, Kerr and Yotov (2018) for reviews of
the theoretical foundations of the gravity model.
6
cludes international and intra-national trade flows.8 We also note that, due to separability of the
structural gravity model at the sectors level, c.f., Anderson and van Wincoop (2004) and Costinot,
Donaldson and Komunjer (2012), equation (1) can be estimated at any desired level of aggregation.
We capitalize on this property in the empirical analysis, where in addition to aggregate estimates
we also obtain sectoral results.9
The exponential function, exp[·], on the left-hand side of Equation (1) reflects the fact that, to
obtain our main estimates, we employ the Poisson Pseudo Maximum Likelihood (PPML) estima-
tor.10 We favor the PPML estimator because, as demonstrated by Santos Silva and Tenreyro (2006,
2011), (i) PPML accounts for heteroskedasticity that often plagues trade data, and (ii) because,
due to its multiplicative form, PPML utilizes the information contained in the zero trade flows.
The vector πi,t denotes the set of time-varying source-country dummies (i.e., exporter-time
fixed effects), while the term χj,t encompasses the set of time-varying destination-country dummy
variables. These directional (exporter and importer) fixed effects will control for the unobservable
multilateral resistances of Anderson and van Wincoop (2003) and for the country-specific size terms
in the structural gravity model. In addition, they will absorb any other observable and unobservable
characteristics that vary over time for each exporter and for each importer.
GRAVij,t denotes the vector of bilateral determinants of trade flows in our model. Following
the existing literature, we include in this vector the standard set of time-invariant covariates that
are used in gravity regressions (i.e., the log of bilateral distance (DISTij ), and a series of indicator
variables capturing whether or not two countries share a common border (CN T Gij ), a common
official language (LAN Gij ), and any colonial ties (CLN Yij )). In addition, we control for the
impact of free trade agreements (F T Aij,t ) as a representative and widely-used time-varying trade
policy covariate. To reflect the use of intra-national trade flows, we also use an indicator variable
8
More recent literature emphasizes the role to take into account domestic sales and frictions, see for
example Coşar and Fajgelbaum (2016), Allen and Arkolakis (2014), Fajgelbaum and Redding (2014), and
Ramondo, Rodríguez-Clare and Saborío-Rodríguez (2016). In structural gravity, the importance to also
include domestic sales was emphasized by Yotov (2012), Bergstrand, Larch and Yotov (2015), and Heid,
Larch and Yotov (2021), for example. Yotov (2021) provides a survey of the use of intra-national trade flows
for structural gravity estimations.
9
For a discussion of the challenges and approaches to estimate gravity with disaggregated data, we refer
the reader to Yotov et al. (2016) and Borchert et al. (2021b).
10
Sensitivity estimates, which are included in the Supplementary Appendix, demonstrate that our main
results are robust to the use of the OLS estimator.
7
(BRDRij,t ) that takes a value of one for international trade and it is equal to zero for domestic
sales. The subscript t captures the possibility that the impact of international borders can vary over
time. This variable has the advantage of being exogenous by construction, and it will capture the
effects of any other determinants of international relative to internal trade, which act in addition to
the covariates that we control for explicitly in our specification.
Finally, and most important for our purposes, we capitalize (i) on the fact that the border
variable is exogenous by construction and (ii) that it captures the impact of all possible observable
and unobservable factors that impact bilateral trade in addition to the standard covariates that we
already control for in order to allow for possible differential border effects within the EU vs. border
effects on trade between EU and CEFTA. Specifically, we introduce BRDR_EUij,t , which takes
a value of one for international trade within the EU, and it is set to zero otherwise. To interpret
the estimate on the new border variable as levels, we set BRDRij,t to zero when BRDR_EUij,t is
equal to one. Thus, the estimate on this variable would capture the impact of borders within the
EU. In addition we define BRDR_EU _CEF T Aij,t as an indicator variable that takes a value of
one for trade between the CEFTA members and the EU members, and it is set to zero otherwise.
Once again, we set BRDRij,t to zero when BRDR_EU _CEF T Aij,t is equal to one. Thus, the
estimate on BRDR_EU _CEF T Aij,t would capture the impact of borders on trade between the
CEFTA members and the EU. Taking into account these modeling choices, our main estimating
equation becomes:
Xij,t = exp[η1 DISTij + η2 CN T Gij + η3 LAN Gij + η4 CLN Yij + η6 F T Aij,t + η7 BRDRij,t ] ×
exp[η8 BRDR_EUij,t + η9 BRDR_EU _CEF T Aij,t + πi,t + χj,t ] + ij,t . (2)
Several features of specification (2) with respect to the definition and interpretation of the key
border variables and their relation to the other covariates in our estimating equation deserve further
discussion. First, we note that, by construction, the three border variables in specification (2) will
capture the impact of all unobserved trade barriers that act in addition to (i) geography, which
is controlled for by the distance and contiguity variables (DISTij and CN T Gij , respectively); (ii)
cultural ties, which are controlled for by the language and colonial ties covariates (LAN Gij and
8
CLN Yij , respectively); and (iii) the average impact of FTAs, as the most prominent trade policy
variable. Of course, one may include in the econometric model any additional determinants of
bilateral trade flows for which data are available. Then, the interpretation of the border estimates
would be as capturing the effects of any impediments to trade that are not explicitly accounted for
in the vector of bilateral trade cost covariates.
Second, even if the border variables and their estimates are capturing/reflecting trade costs and
preferences (e.g., home bias effects) that cannot be affected by trade policy, we note that what is
relevant for our analysis is not the estimate of the border per se, but rather the difference between
the estimates of the border effects for the group of interest, i.e., BRDR_EU _CEF T Aij,t in our
case, and for the EU as reference group, i.e., BRDR_EUij,t . Given the history of strong integration
within the EU, we would expect that the borders for trade within the EU will be significantly smaller
as compared to the borders between the EU and the CEFTA countries, and the object of interest
to us will be the difference between the two border estimates. Further capitalizing on the structural
properties of the gravity model, we can express this difference as a tariff-equivalent index:
1
ηBRDR_EU )
exp(ˆ 1−σ
%∆tCEF T A,EU = − 1 × 100, (3)
ηBRDR_EU _CEF T A )
exp(ˆ
ˆBRDR_EU and η
where, η ˆBRDR_EU _CEF T A are the estimates of the border variables BRDR_EUij,t
and BRDR_EU _CEF T Aij,t from Equation (2), respectively, and σ is the elasticity of substitution.
Finally, we note that our methods allow for very flexible definitions of the border barriers, both
within the group of interest and within the reference group. Therefore, these definitions can be
refined further depending on the goals and institutional background behind the policy change. For
example, in the case of EU-CEFTA integration, it may be appropriate to define the border for
the reference group as the border between the ‘old/initial’ EU members and the countries from
Eastern Europe (e.g., Romania and Bulgaria), which are similar to the CEFTA members across
many economic indicators and are among the countries that joined the EU most recently. With
respect to the treatment group of interest, one can estimate heterogeneous impact across the CEFTA
members. Note that, in the case of CEFTA-EU integration, identification of the country-specific
borders comes from two sources, i.e., the time dimension and the pair-dimension (due to the fact
9
that the EU includes many members). However, in principle, the panel dimension of the data is
sufficient to identify pair-specific borders too. In fact, the panel dimension would allow for the
estimation of time-varying (or based on intervals) border effects. Given the methodological purpose
of our paper we abstract from such refinements in our estimating specification and we focus on two
variables only, which are sufficient to prove the validity of our methods.
3 Application and Data
Subsection 3.1 of this section motivates the focus on the main application for our analysis, i.e., the
integration of the CEFTA countries in the EU, while Subsection 3.2 describes the data and sources
that we employ to perform the empirical analysis.
3.1 CEFTA-EU Integration: Background and Relevance
The purpose of this section is to describe the importance of the integration process between the
CEFTA countries and the EU and to offer some institutional background on CEFTA-EU accession
process and status. The EU has expressed a strong interest to integrate the CEFTA countries
with European markets and has used Union membership to encourage the integration process. The
CEFTA consists of seven countries: Albania, Bosnia and Herzegovina, Kosovo, Moldova, Montene-
gro, North Macedonia and Serbia. Most of the CEFTA countries (except Moldova) are geographi-
cally within the EU, surrounded by Italy, Croatia, Romania, Bulgaria and Greece. Any economic or
political instability in CEFTA will transmit to the EU, which is only a recent memory for the former
Yugoslav countries.11 The CEFTA countries are still recovering the aftermath of conflicts in the
1990s: the Bosnian (1992-95) and Kosovo (1998-99) wars and the skirmishes in Serbia (1999-2001)
and North Macedonia (2001). Even presently, Kosovo and Serbia still have a tense relationship as
Serbia does not formally recognize Kosovo’s independent status. Higher trade between the countries
through further integration can increase the opportunity costs of war and reduce the possibility of
conflict (see Martin, Mayer and Thoenig, 2008; Vicard, 2012, for examples).
Former Yugoslavia was made up of Bosnia and Herzegovina, Kosovo, Montenegro, North Macedonia and
11
Serbia, along with Croatia and Slovenia.
10
The CEFTA-EU integration process has been progressing slowly for the last 15 years and the
CEFTA countries are at different stages of integrating their institutions to European standards.
The accession process is governed by the Acquis Communitaire or EU acquis, which constitutes
the body of EU legislation and potential members have to transpose the EU legislations into their
national law. The EU acquis is composed of 35 chapters that deal with all aspects of legislation
from free movement of goods (chapter 1) and workers (chapter 2) to social policy and employment
(chapter 19) and science and research (chapter 25). Four CEFTA countries (Albania, Montenegro,
North Macedonia and Serbia) are EU candidate countries, which means that the EU officially
recognizes them as potential EU members and can start accession negotiations. Montenegro and
Serbia are further along the process than Albania and North Macedonia, having started negotiations
on 18 (Serbia) and 33 (Montenegro) chapters out of the 35 chapters in the EU acquis, but the
negotiations on only 2-3 chapters have provisionally closed. Albania and North Macedonia have
candidate status but have not started negotiations on the EU acquis. North Macedonia finally
resolved its long-standing name dispute in January 2019 with Greece, who was the main obstacle to
beginning negotiations. The country, however, faces new challenges in its accession with Bulgaria
related to issues of identity, language and history. The European Council decided in March 2020
to begin accession negotiations with Albania and North Macedonia, with Albania’s negotiations
pending the fulfillment of certain conditions. The negotiations have not yet started with the two
countries. Bosnia and Herzegovina and Kosovo are potential candidate countries, and their accession
negotiations are further along.
As the first step towards integration, the CEFTA countries have progressively bilateral trade
agreements with the EU. The bilateral free trade agreements, referred to as Association and Stabi-
lization Agreement, has been concluded with North Macedonia (2004), Albania (2009), Montenegro
(2010), Serbia (2013), Bosnia and Herzegovina (2015) and Kosovo (2016). The EU has also recently
concluded the Deep and Comprehensive Free Trade Area (DCFTA) with Moldova (2014). These
bilateral trade agreements grant tariff-free access to most exports into the EU, with some agricul-
tural goods still attracting either tariff rates or tariff-rate quotas. These trade agreements grant
tariff-free market access into the EU but exports from the CEFTA countries still encounter border
costs as they are subject to border inspections by Customs, food and health agencies and other
11
technical inspectorates.
The EU announced a new strategy for the region in 2018, which has heightened the need to
examine the effects of integration between CEFTA and the EU. The new EU strategy for the
Western Balkans countries (i.e. CEFTA countries minus Moldova) provided a target accession date
of 2025 for the next batch of EU expansion, but this target date may be delayed given the 2020
COVID-19 pandemic. Given the current accession status, it is likely Serbia and Montenegro will be
first two CEFTA countries to join the EU, with North Macedonia possibly being the next country.
There are, however, many challenges to the reforms needed in the EU acquis and Grieveson, Grubler
and Holzner (2018) concludes that the target date is ambitious and serves more as an incentive for
reforms.
The CEFTA countries also recognize that further integration amongst themselves can help their
integration with the EU. After the EU, other CEFTA countries represent the next largest market for
imports and exports for CEFTA countries. The CEFTA only covers market access for all industrial
goods and most agricultural goods, but has recently expanded to include trade facilitation and
services liberalization.12 In 2017, the CEFTA countries endorsed a regional action plan to promote
economic integration amongst themselves, with the view to encourage economic convergence with
the EU. For example, the trade measures in the action plan adopt trade facilitation measures and
data systems that conform to EU standards. Most relevant for our paper is an action to investigate
the impacts of harmonizing their MFN tariff regimes with the EU’s common external tariff regimes
as a method to encourage more integration between CEFTA and EU.
Despite the importance and need to evaluate the benefits of integration for CEFTA countries,
data availability has limited any analysis on the topic. Databases such as the WIOD or GTAP, which
are widely used for general equilibrium counterfactual analysis such as the one performed here, do
not contain data for the CEFTA countries. The databases generally list the CEFTA countries in
regional aggregates.13 Constructing a general equilibrium model of the CEFTA countries is also
12
The agreement was originally signed in 1992 but the current members joined between 2006-207 as
previous members (Poland, Hungary, Czech Republic, Slovakia, Slovenia, Romania and Bulgaria) left CEFTA
once they acceded into the EU. Croatia was still a member in 2006 but left in 2013 when it joined the EU.
13
The GTAP database only lists Albania as a separate country, while the rest are either in the “Rest
of Europe” or “Rest of Eastern Europe” (for Moldova) regional aggregates. The WIOD database lists the
CEFTA countries in the “Rest of the World” aggregate.
12
difficult as these countries (except Albania) do not have national input-output tables.
3.2 Data: Description and Sources
To perform the empirical analysis, we employ a newly constructed dataset, The International Trade
and Production Database for Estimation (ITPD-E), which is developed and maintained by the U.S.
International Trade Commission (ITC), c.f., Borchert et al. (2021a). The original ITPD-E consists
of inter- and intra-national trade flows for 243 countries and 170 industries for the years between
2000 and 2016.14 The inclusion of domestic trade flows in the ITPD-E is a crucial feature for the
implementation of our methods. Another very important feature of the ITPD-E with respect to our
purposes is that it covers all EU countries and all CEFTA economies (except for Kosovo) as well.
This is an advantage over other databases, e.g., WIOD and the GTAP datasets, which are widely
used for general equilibrium counterfactual analysis. However, neither WIOD nor GTAP covers the
CEFTA countries. Albania is an exception.
Given the methodological purpose of our paper, and for computational and expositional purposes
too, we make several choices regarding the dimensions of the ITPD subsample that we employ for our
analysis. First, we focus on 2013, which is the latest year that allows most comprehensive coverage
across most countries and across most sectors. In the robustness analysis, which we report in the
Supplementary Appendix, we reproduce our estimates with panel data over the period 2000-2016,
and the panel estimates confirm the robustness of our main findings. On the country dimension,
we limit the analysis to cover one hundred and four (104) economies, which include the fifty (50)
largest exporters in each ITPD-E industry as well as all EU economies and CEFTA countries.15
14
Of the 170 sectors in ITPD, 26 are in Agriculture, 7 are in Mining and Energy, 120 are in Manufacturing,
and 17 are in Services. For further details on ITPD, we refer the reader to Borchert et al. (2021a), and for
its use for gravity estimations see Borchert et al. (2021b).
15
The countries in our sample are: Angola (AGO), Albania (ALB), United Arab Emirates (ARE), Ar-
gentina (ARG), Australia (AUS), Austria (AUT), Azerbaijan (AZE), Belgium (BEL), Bangladesh (BGD),
Bulgaria (BGR), Bahrain (BHR), Bosnia and Herzegovina (BIH), Belarus (BLR), Bolivia (BOL), Brazil
(BRA), Brunei (BRN), Canada (CAN), Switzerland (CHE), Chile (CHL), China (CHN), the Republic of
Congo (COG), Colombia (COL), Costa Rica (CRI), Cyprus (CYP), Czech Republic (CZE), Germany (DEU),
Denmark (DNK), Algeria (DZA), Ecuador (ECU), the Arab Republic of Egypt (EGY), Spain (ESP), Esto-
nia (EST), Ethiopia (ETH), Finland (FIN), France (FRA), Gabon (GAB), United Kingdom (GBR), Ghana
(GHA), Equatorial Guinea (GNQ), Greece (GRC), Hong Kong SAR, China (HKG), Croatia (HRV), Hungary
(HUN), Indonesia (IDN), India (IND), Ireland (IRL), the Islamic Republic of Iran (IRN), Iraq (IRQ), Israel
(ISR), Italy (ITA), Jordan (JOR), Japan (JPN), Kazakhstan (KAZ), Kenya (KEN), Cambodia (KHM),
the Republic of Korea (KOR), Kuwait (KWT), Libya (LBY), Sri Lanka (LKA), Lithuania (LTU), Luxem-
13
Finally, on the sectoral dimension, we focus the analysis on the four major sectors, which comprise
each economy, including Agriculture, Mining, Manufacturing and Services. In addition, given the
importance of the manufacturing sector for the CEFTA economies and for robustness, we also obtain
partial estimates for eleven manufacturing sectors, which we label broadly as Chemicals, Electronics,
Food, Machines, Metals, Minerals, Rubber, Textiles, Transport, Wood, and Other.
We perform the estimation (partial equilibrium) analysis with the original ITPD-E data without
any adjustments.16 In order to perform the general equilibrium (GE) counterfactual analysis in
Section 4.2, we employ the original data for international trade flows from ITPD-E, and we impute
missing observations for domestic trade, which are needed to balance the data for the GE analysis.
To fill in the missing values for domestic trade, we proceed in three steps. First, we construct the
ratio of total exports to internal sales for all of the original industries in ITPD-E for which data
are available. Importantly, the original ITPD-E allows us to construct such ratios for each of the
CEFTA countries in our sample for at least some of the industries within each of the main 14 sectors
that we employ in our analysis (11 manufacturing sectors, plus Agriculture, Mining, and Services).
Second, we construct an average ratio for each of the main 14 sectors and we use these ratios in
combination with data on total exports to fill in the missing intra-national trade values at the most
disaggregated industry level. Third, we aggregate the resulting balanced dataset to the 14 sectors
that we employ in our analysis. Since data for services trade are very patchy in the latest years in
ITPD-E, to construct domestic trade for services, we employ average data over the years 2010 to
2015 and we repeat the above steps with these data for the services sector.
We also employ several other data sets in addition to ITPD-E. Data on the standard gravity
covariates for our gravity estimations come from the Dynamic Gravity Dataset (DGD) of the U.S.
International Trade Commission, and we refer the reader to Gurevich and Herman (2018) for further
bourg (LUX), Latvia (LVA), Morocco (MAR), Moldova (MDA), Mexico (MEX), Macedonia (MKD), Malta
(MLT), Myanmar (MMR), Montenegro (MNE), Malaysia (MYS), Nigeria (NGA), Netherlands (NLD), Nor-
way (NOR), New Zealand (NZL), Oman (OMN), Pakistan (PAK), Peru (PER), Philippines (PHL), Papua
New Guinea (PNG), Poland (POL), Portugal (PRT), Qatar (QAT), Romania (ROU), the Russian Federation
(RUS), Saudi Arabia (SAU), Singapore (SGP), El Salvador (SLV), Yugoslavia (SRB), the Slovak Repub-
lic (SVK), Slovenia (SVN), Sweden (SWE), Thailand (THA), Turkmenistan (TKM), Trinidad and Tobago
(TTO), Tunisia (TUN), Turkey (TUR), Taiwan, China (TWN), Ukraine (UKR), the United States (USA),
the República Bolivariana de Venezuela (VEN), Vietnam (VNM), the Republic of Yemen (YEM), South
Africa (ZAF), and Zambia (ZMB).
16
In the robustness analysis, we reproduce our main estimates after replacing all missing bilateral trade
flows with zeros. These results are very similar to our main estimates.
14
details on DGD. In order to investigate the effect of CEFTA countries adopting the EU common
external tariff, we make use of MFN tariff data that come from https://wits.worldbank.org/WITS/
WITS/ Restricted/Login.aspx. The original tariff data are at the HS17 8-digit level for Albania,
Bosnia and Herzegovina, Moldova, Montenegro, and Serbia, while the tariff data for Macedonia are
at the HS17 6-digit level. We aggregate all tariff data to the HS17 6-digit level, as correspondence
tables to HS07 and SITC Reve. 3 & 4 are only available at the 6-digit level. We aggregate the
tariff data in two different ways: (i) unweighted by taking simple averages, (ii) weighted by total
import shares of the respective sectors. We then merge SITC Rev. 3 & 4 correspondences and HS07
correspondences in order to be able to match with our mining, manufacturing, and agricultural trade
flows data.
4 Empirical Analysis and Findings
This section presents our empirical findings. Subsection 4.1 offers an analysis of our partial equilib-
rium estimates, while Subsection 4.2 describes the results from our general equilibrium analysis.
4.1 On the Uneven Impact of EU and CEFTA Borders
The estimation results that we report and analyze in this section are based on econometric speci-
fication (2). We start with a discussion of our findings across the four main sectors in the sample,
including Manufacturing, Agriculture, Mining, and Services. Then, we zoom in on the determinants
of trade flows across eleven manufacturing categories, as described in the data section. Following the
best estimation practices from the structural gravity literature, which we summarized in Section 2,
we obtain our main results with the PPML estimator. Estimates obtained with the OLS estimator
are also consistent with our main findings.17
Several findings stand out from the estimates for the four main sectors in our sample, which
appear in Table 1. First, overall, we note that the estimates of the effects of the standard gravity
The corresponding OLS results can be found in Tables 3 and 4 of the Supplementary Appendix, where,
17
as mentioned earlier, we also offer estimates that are obtained after replacing the missing values in the
ITPD-E with zeroes (see Tables 5 and 6) and estimates that are based on panel data (see Tables 7 and 8).
15
covariates are readily comparable with the corresponding values from the existing literature.18 This
establishes the representativeness of our sample. Turning to the specific covariates, we see that
distance is a very significant impediment to international trade. The estimates on DIST are large,
negative, and significant at any conventional level for each of the four sectors. According to our
results, Agriculture and Mining are the two sectors where the negative impact of distance is the
strongest, while the effect of distance on Services trade is the weakest. We find this heterogeneity
intuitive and we point to transportation costs as a natural explanation for it.
Consistent with most of the existing gravity literature, we obtain positive estimates of the effects
of contiguity and language. In each case, the impact of these determinants of trade is positive in
all sectors. However, it is not statistically significant for Mining. A possible explanation for this
result is specialization in this natural resource industry. We also obtain positive and statistically
significant estimates of the impact of FTAs in each sector, which attest to the important and
successful positive effect of trade policies in promoting bilateral trade among FTA members and
is consistent with findings from the extensive related literature. According to our estimates, FTAs
have been most effective in Mining and least effective, but still important, in Services.
Next, we turn to the estimates of the impact of international borders, which are of central
interest to us. We remind the reader that, by construction, our border variables are indicators
that are designed to capture the impact of all observable and unobservable barriers to trade that
act on international relative to internal trade, after controlling for all standard gravity covariates.
Four main results stand out. First, based on the estimates of BRDR from Table 1, we conclude
that the average impact of borders on international relative to internal trade is very large and
significant. This is consistent with the extensive literature that has studies the effects of borders
and home bias in trade.19 All BRDR estimates are negative and significant at any conventional
18
For a reference set of gravity estimates, we direct the reader to the meta-analysis indexes of Head and
Mayer (2014) and to the disaggregated gravity estimates of Borchert et al. (2021b).
19
For analysis of the effects of borders and ‘home bias’ in the United States see Wolf (2000), Mayer and
Head (2002), Hillberry and Hummels (2003), Millimet and Osang (2007), Head and Mayer (2010), Hillberry
and Hummels (2012), Yilmazkuday (2012); for the European Union see Nitsch (2000), Chen (2004), and
Head and Mayer (2010); for OECD countries Wei (1996); for China see Young (2000), Poncet (2003, 2005),
Holz (2009), and Hering and Poncet (2009); for Spain see Llano and Requena (2010); for France see Mayer
(2005); for Brazil see Fally, Paillacar and Terrac (2010); for Germany see Lameli et al. (2013) and Nitsch
and Wolf (2013); for Canada see Agnosteva, Anderson and Yotov (2019); and Anderson, Larch and Yotov
(2018) for the world.
16
Table 1: Sectoral Gravity Estimates CEFTA, 2013
(1) (2) (3) (4)
Manufacturing Agriculture Mining Services
A. Gravity Estimates
DIST -0.685 -0.826 -1.185 -0.322
(0.036)∗∗ (0.038)∗∗ (0.148)∗∗ (0.087)∗∗
CNTG 0.493 0.647 0.193 0.621
(0.060)∗∗ (0.070)∗∗ (0.210) (0.131)∗∗
LANG 0.221 0.356 0.187 0.601
(0.043)∗∗ (0.055)∗∗ (0.155) (0.083)∗∗
BRDR -3.544 -5.542 -3.765 -5.656
(0.115)∗∗ (0.114)∗∗ (0.464)∗∗ (0.271)∗∗
FTA 0.401 0.380 0.781 0.266
(0.052)∗∗ (0.059)∗∗ (0.182)∗∗ (0.119)∗
BRDR_EU_CEFTA -3.477 -5.905 -4.905 -6.591
(0.163)∗∗ (0.149)∗∗ (0.472)∗∗ (0.290)∗∗
BRDR_EU -2.982 -4.592 -4.231 -5.470
(0.111)∗∗ (0.107)∗∗ (0.427)∗∗ (0.266)∗∗
B. CEFTA vs. EU Border: BRDR_EU _CEF T A-BRDR_EU
CEFTA vs. EU -0.495 -1.313 -0.674 -1.108
(0.132)∗∗ (0.124)∗∗ (0.291)∗∗ (0.125)∗∗
C. CEFTA vs. EU Border: Tariff Equivalents
%∆tCEF T A,EU -7.923 -19.650 -10.619 -16.870
(2.020)∗∗ (1.665)∗∗ (4.349)∗∗ (1.736)∗∗
N 634838 117585 4698 29665
Notes: Panel A of this table reports gravity estimation results for the four main
sectors in the sample including Manufacturing, Agriculture, Mining, and Services.
All estimates are obtained with data for 2013. The data for each main sec-
tor is constructed by pooling (not summing) the data for all individual prod-
ucts within the corresponding main sector. The estimator is PPML and the de-
pendent variable is nominal bilateral trade. All estimations are obtained with
exporter-product and importer-product fixed effects, whose estimates are omitted
for brevity. Standard errors are clustered by country pair and are reported in
parentheses. + p < 0.10, ∗ p < .05, ∗∗ p < .01. Panel B of the table re-
ports the difference between the estimates of the border effects for trade within
the EU vs. trade between CEFTA members and the EU. Panel C transforms the
differences from Panel B into tariff-equivalent trade cost changes %∆tCEF T A,EU =
(exp(BRDR_EU )/exp(BRDR_EU _CEF T A))1/(1−σ) − 1) × 100, where we set
σ = 7. The standard errors in Panels B and C are constructed with the Delta
method. See text for further details.
17
level. Second, according to our results, the largest barriers to trade that are not captured by the
standard gravity covariates are in Agriculture and in Services. We find this variation to be intuitive
and it is consistent with localized consumption in the case of services, and more pronounced ‘home
bias’ effects in the case of agricultural products.
Third, turning to the estimates on BRDR_EU _CEF T A and BRDR_EU , we see that, with-
out any exception, the additional barriers to trade between CEFTA and EU are larger as compared
to the barriers to trade within the EU. This is reflected in the larger (in absolute value) estimates on
BRDR_EU _CEF T A as compared to the corresponding values for BRDR_EU , and is consistent
with our expectations. Finally, we find that the differential in the borders between CEFTA and EU
as compared to the borders within the EU varies across the four main sectors in our sample. In order
to see this clearly (and also to be able to gauge whether the differences are statistically significant)
in Panel B of Table 1 we report the difference between the estimates on BRDR_EU _CEF T A and
BRDR_EU . In addition, following (3), in Panel C of Table 1, we calculate tariff equivalents of the
border differentials.20 Standard errors in panels B and C are constructed with the Delta method.
The estimates from panels B and C reveal several interesting results. First, the border differences
for each sector are statistically significant at any conventional level. Second, the tariff equivalents of
the border difference are larger as compared to the existing tariff differences for the goods sectors.
This supports our assumption that, in addition to tariffs, there are other barriers to trade between
CEFTA and EU. For example, in the case of Agriculture, the trade weighted average tariff for the
CEFTA countries is 11.58 percent, which is significantly lower as compared to the 19.65 percent
border differential tariff equivalent that we obtain from the structural gravity regressions. The
corresponding numbers for Mining are 1.68 percent weighted average tariffs and 10.62 percent tariff
equivalent border differential. And for Manufacturing the numbers are 5.22 percent weighted average
tariffs and 7.92 percent tariff equivalent border differential. Importantly, there is no tariff data that
corresponds to the large (16.87 percent) tariff equivalent border differential that we obtain for
services. This highlights the advantages and importance of using our estimation structural gravity
approach to measure border differentials. Third, comparison between the tariff data and our border
20
Following the literature, we set σ = 7. This value is also close to the corresponding meta-analysis index
(σ = 6.13) from Head and Mayer (2014).
18
differential estimates suggests that Mining and Agriculture are subject to more significant NTMs
as compared to Manufacturing, where the difference between the tariffs and the border differentials
that we estimate is the smallest.
Fourth, the differences between the CEFTA-EU border and the EU-EU border are the largest
in Agriculture (about 20% tariff equivalent), followed by Services (about 17% tariff equivalent),
Mining (about 11% tariff equivalent), and Manufacturing (about 8% tariff equivalent). We find this
heterogeneity intuitive. The border between CEFTA and EU is likely to impose the most trade
costs on agricultural products for two reasons: (1) most tariffs between EU and CEFTA countries
have gone to zero for all industrial products, but for some agricultural products there are still
import tariffs for CEFTA products into the EU and vice versa, and (2) even with low tariff barriers,
the EU imposes high sanitary and phytosanitary (SPS) conditions which results in traders facing
long delays at the borders crossing into EU (for e.g. from Serbia into Croatia). The large border
differences in services trade can be due to various unified standards within the EU, which impose
barriers to services trade with outsiders. Another possible explanation is that trade in services is
highly localized consumption. Finally, a possible explanation for the relatively small borders in
Mining and Manufacturing is that the barriers to trade in those sectors have been decreased.
Stimulated by the importance of the manufacturing sector for the CEFTA economies and by the
potential for very heterogeneous differences in the impact of borders on trade between CEFTA and
EU vs. trade within the EU within the aggregate manufacturing sector, next we obtain and present
estimates for 11 disaggregated manufacturing industries. Our results appear in Table 2, where, as
before, we include the PPML gravity estimates in Panel A, the differences between the CEFTA-EU
and EU-EU borders in Panel B, and the tariff equivalents of the border differences are reported in
Panel C of Table 2. Standard errors in panels B and C are constructed with the Delta method.
Without going into details, we note that our conclusions regarding the impact of the standard
gravity variables for total manufacturing are supported by the disaggregated manufacturing esti-
mates from Table 2. More importantly, the results in panels B and C reveal that the differences in
the borders between CEFTA and EU vs. the borders within the EU are quite heterogeneous across
the eleven manufacturing sectors in our sample. Overall, we confirm the finding that the CEFTA
countries face larger barriers to trade with the EU as compared to the barriers faced by the EU
19
Table 2: Sectoral Gravity Estimates CEFTA, 2000-2016
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)
Chemicals Electronics Food Machines Metals Minerals Other Rubber Textiles Transport Wood
A. Gravity Estimates
DIST -0.826 -0.603 -0.663 -0.559 -0.731 -0.912 -0.671 -0.935 -0.773 -0.444 -0.873
(0.048)∗∗ (0.054)∗∗ (0.041)∗∗ (0.046)∗∗ (0.070)∗∗ (0.047)∗∗ (0.065)∗∗ (0.051)∗∗ (0.059)∗∗ (0.076)∗∗ (0.043)∗∗
CNTG 0.399 0.302 0.854 0.530 0.448 0.626 0.609 0.546 0.345 0.825 0.798
(0.082)∗∗ (0.080)∗∗ (0.082)∗∗ (0.085)∗∗ (0.102)∗∗ (0.084)∗∗ (0.128)∗∗ (0.094)∗∗ (0.086)∗∗ (0.109)∗∗ (0.077)∗∗
LANG 0.218 0.287 0.449 0.138 0.452 0.139 0.128 0.031 0.209 -0.080 0.248
(0.069)∗∗ (0.066)∗∗ (0.065)∗∗ (0.059)∗ (0.067)∗∗ (0.070)∗ (0.093) (0.068) (0.071)∗∗ (0.083) (0.068)∗∗
BRDR -3.087 -3.006 -5.272 -2.741 -3.534 -4.092 -3.869 -3.232 -3.242 -3.712 -4.230
(0.157)∗∗ (0.190)∗∗ (0.118)∗∗ (0.167)∗∗ (0.226)∗∗ (0.148)∗∗ (0.191)∗∗ (0.179)∗∗ (0.157)∗∗ (0.233)∗∗ (0.128)∗∗
FTA 0.348 0.271 0.393 0.458 0.561 0.345 0.496 0.520 0.315 0.705 0.555
(0.071)∗∗ (0.077)∗∗ (0.059)∗∗ (0.062)∗∗ (0.130)∗∗ (0.077)∗∗ (0.098)∗∗ (0.085)∗∗ (0.085)∗∗ (0.093)∗∗ (0.071)∗∗
BRDR_EU_CEFTA -2.854 -2.643 -5.488 -2.276 -3.284 -4.502 -3.333 -3.109 -2.641 -2.897 -4.339
(0.263)∗∗ (0.319)∗∗ (0.178)∗∗ (0.240)∗∗ (0.267)∗∗ (0.194)∗∗ (0.262)∗∗ (0.214)∗∗ (0.260)∗∗ (0.546)∗∗ (0.159)∗∗
BRDR_EU -2.982 -2.425 -3.982 -2.451 -2.767 -3.545 -3.200 -2.597 -2.585 -2.743 -3.737
(0.152)∗∗ (0.175)∗∗ (0.117)∗∗ (0.157)∗∗ (0.197)∗∗ (0.143)∗∗ (0.188)∗∗ (0.152)∗∗ (0.176)∗∗ (0.202)∗∗ (0.129)∗∗
20
B. CEFTA vs. EU Border: BRDR_EU _CEF T A-BRDR_EU
CEFTA vs. EU 0.128 -0.218 -1.506 0.175 -0.516 -0.957 -0.133 -0.512 -0.056 -0.153 -0.601
(0.229) (0.272) (0.153)∗∗ (0.199) (0.181)∗∗ (0.154)∗∗ (0.208) (0.165)∗∗ (0.223) (0.516) (0.117)∗∗
C. CEFTA vs. EU Border: Tariff Equivalents
CEFTA vs. EU 2.153 -3.570 -22.195 2.952 -8.248 -14.741 -2.191 -8.177 -0.922 -2.529 -9.539
( 3.905 ) (4.377) (1.987)∗∗ (3.411) (2.762)∗∗ (2.185)∗∗ (3.396) (2.527)∗∗ (3.681) (8.386) (1.763)∗∗
N 61024 100591 80999 77449 46539 37020 34915 20705 64522 43567 67507
Notes: Panel A of this table reports gravity estimation results for the 11 main sectors within Manufacturing in the sample, as they appear in the column names.
All estimates are obtained with data for 2013. The data for each main manufacturing sector is constructed by pooling (not summing) the data for all individual
manufacturing products within the corresponding main sector. The estimator is PPML and the dependent variable is nominal bilateral trade. All estimations are
obtained with exporter-product and importer-product fixed effects, whose estimates are omitted for brevity. Standard errors are clustered by country pair and
are reported in parentheses. + p < 0.10, ∗ p < .05, ∗∗ p < .01. Panel B of the table reports the difference between the estimates of the border effects for trade
within the EU vs. trade between CEFTA members and the EU for each sector. Panel C transforms the differences from Panel B into tariff-equivalent trade cost
changes %∆tCEF T A,EU = (exp(BRDR_EU )/exp(BRDR_EU _CEF T A))1/(1−σ) − 1) ∗ 100, where we set σ = 7. The standard errors in Panels B and C are
constructed with the Delta method. See text for further details.
countries for trade with each other. This is supported by the fact that nine of the eleven possible
estimates in panels B and C are negative and more than half of them are statistically significant.
The two positive estimates that we obtain are not statistically significant and are for Chemicals
and Machines. According to our estimates the border gap is the widest in Food (about 22% tariff
equivalent), Minerals (about 15% tariff equivalent), and Wood (about 10% tariff equivalent). Based
on the analysis in this section, we conclude that CEFTA members have the potential to face signifi-
cantly lower barriers to trade upon accession to the European Union, which may lead to significant
gains in terms of welfare. We use our structural gravity estimates to quantify the potential for such
gains in the next section.
4.2 On the GE Effects of CEFTA-EU Harmonization
We employ the standard structural gravity general equilibrium framework following Dekle, Eaton
and Kortum (2007, 2008) to perform two counterfactual experiments that quantify the total GE
impact on the exports of the countries in our sample.21 The first experiment simulates harmonization
of MFN tariffs by the CEFTA countries to the EU tariff rates. To perform these analyses, we use
data on actual tariffs, as described in the Data Section 3.2, in order to change the vector of trade
costs for the CEFTA members. As the tariff data were available at the HS17 8-digit classification,
we aggregated them up using trade shares as weights to match the level of aggregation for the GE
analysis. For the counterfactual analysis we set MFN tariffs to zero for trade between EU member
countries and CEFTA member countries and set the MFN import tariffs of the CEFTA members
for all their other trading partners besides the EU member countries to the MFN import tariff level
of the EU.
The second experiment simulates accession of the CEFTA countries to the EU in terms of the
trade costs that these countries face for their trade with the EU. To perform this analysis, we rely
on the estimates from Section 4.1. Specifically, we change the vector of trade costs that are faced
by the CEFTA members so that the trade costs that they face with the EU are the same as the
trade costs among the existing EU members. The second approach has two advantages. First, as
discussed in the previous section, the differences in the border effects that we obtain should capture
21
A description of the framework can be found in Appendix B of the Supplementary Appendix.
21
the impact of any existing observable and unobservable trade barriers that act differentially on
trade between CEFTA and EU vs. trade within EU. Second, our results indicate that the borders
are also different between the groups in the services sector, where tariffs are not applicable. Finally,
before we present and discuss our results, we note that in each of the two experiments we obtain ‘full’
general equilibrium effects on trade for all countries in our sample. We capitalize on the separability
of the structural gravity model to obtain results for each of the sectors in our sample.
We present results for the 6 CEFTA countries, the EU countries, plus one Rest of the World
aggregate (ROW). Column (1) of Table 3 gives the country abbreviation. Columns (2), (4), and (6)
report the percentage changes of total exports for the tariff scenario for manufacturing, agriculture,
and mining, respectively. Columns (3), (5), (7), and (8) report the percentage change of total
exports for the border liberalization scenario for manufacturing, agriculture, mining, and services,
respectively. The table consists of two panels: Panel A reports the results for the 6 CEFTA
countries, while Panel B reports the results for the EU countries and for the ROW aggregate. To
obtain the aggregated effects, we sum total trade flows over all ROW countries in the baseline
and counterfactual and calculate the changes from these totals. All results in Table 3 assume an
elasticity of 7, which is at the higher end of average estimates of elasticity of substitutions and
therefore leads to more conservative results (see for a meta-analysis Head and Mayer (2014)).22
The following results stand out from our estimates in the tariff-change scenario. First, the
trade effects for the CEFTA countries are typically positive. Often the trade effects are around
20-30%. Further, some CEFTA countries have lower average trade-weighted MFN tariffs as the EU
countries. Hence, their tariff level increases to the level of the MFN tariff rate against all non-EU
trade partners.23 However, most trade of CEFTA member countries occurs within EU. Hence,
this liberalization leads to positive total export changes. Comparing across sectors, we find quite
substantial heterogeneous effects. Agriculture still has comparable high MFN tariffs around 10%,
while they are below 5% in mining, and around 5% in total manufacturing. This also explains the
larger effects in total export changes in agriculture, as compared to manufacturing and mining.
22
In the Supplementary Appendix, we also provide results based on elasticity of 4, which is on the lower
end of the spectrum for the elasticity of substitution in the Armington model. Typically, trade effects are
not heavily influenced by changes in σ (see Anderson and van Wincoop, 2003; Yotov et al., 2016).
23
We provide information about the MFN tariffs and initial shares of spending on domestic goods for
manufacturing, agriculture, mining, and services in the Supplementary Appendix.
22
Table 3: Trade Effects of CEFTA (σ = 7)
Manufacturing Agriculture Mining Services
Country
Tariff Border Tariff Border Tariff Border Border
PANEL A: CEFTA countries
ALB 25.17 66.94 53.13 288.90 0.81 12.90 128.95
BIH 35.23 53.77 44.37 235.07 13.59 46.12 -4.40
MDA 15.51 43.44 25.64 67.95 1.33 90.21 -0.01
MKD 29.16 54.04 56.77 125.43 22.82 95.11 283.32
MNE 35.14 57.92 71.12 88.38 2.01 4.01 87.11
SRB 31.85 45.27 46.93 96.67 4.67 45.71 116.04
PANEL B: EU countries and ROW
AUT 0.22 0.39 1.17 3.46 -0.05 -0.22 6.80
BEL 0.02 0.04 0.17 0.45 0.00 0.01 0.06
BGR 0.92 1.70 1.90 5.27 0.44 8.50 0.30
CYP 0.12 0.21 0.37 1.01 0.00 -0.01 0.49
CZE 0.13 0.24 0.37 1.04 0.10 1.62 0.10
DEU 0.09 0.16 0.27 0.77 -0.01 0.01 0.05
DNK 0.04 0.07 0.04 0.16 0.01 0.04 0.01
ESP 0.03 0.05 0.13 0.34 0.00 0.07 0.01
EST 0.02 0.05 0.02 0.09 0.00 -0.01 0.02
FIN 0.02 0.03 0.06 0.24 0.00 0.00 0.00
FRA 0.03 0.05 0.12 0.34 0.01 0.31 0.03
GBR 0.04 0.07 0.08 0.25 0.00 0.00 0.06
GRC 0.96 1.95 2.53 7.03 0.10 2.49 0.86
HRV 4.35 7.72 19.20 62.27 1.29 13.40 0.66
HUN 0.32 0.55 1.53 4.13 0.42 9.91 0.15
IRL 0.01 0.02 0.01 0.05 0.00 0.00 0.02
ITA 0.24 0.45 0.43 1.36 -0.02 0.44 0.17
LTU 0.04 0.09 0.13 0.43 0.00 0.05 0.11
LUX 0.05 0.09 0.00 0.01 -0.03 -0.12 0.00
LVA 0.04 0.08 0.10 0.33 0.00 0.00 0.10
MLT 0.17 0.41 0.03 0.14 1.17 17.01 0.10
NLD 0.03 0.05 0.10 0.27 0.00 -0.01 0.06
POL 0.13 0.24 0.41 1.23 0.11 1.41 0.02
PRT 0.02 0.03 0.04 0.14 0.00 -0.05 0.00
ROU 0.59 1.19 1.33 3.96 1.39 28.39 -0.06
SVK 0.21 0.36 0.67 1.83 -0.03 -0.05 0.48
SVN 1.54 2.70 7.47 21.51 -0.04 1.14 3.11
SWE 0.03 0.06 0.17 0.52 0.00 0.01 0.05
ROW 0.00 -0.01 0.00 -0.03 0.00 0.00 -0.01
Notes: This table reports results for our CEFTA border and tariff sce-
nario assuming an elasticity of substitution of 7 (σ = 7). Column (1) gives
the country abbreviations, columns (2), (4), and (6) report the changes in
total exports from our tariff scenario for manufacturing, agriculture, and
mining, respectively. Columns (3), (5), (7), and (8) report the changes
in total exports from our border scenario for manufacturing, agriculture,
mining, and services, respectively.
23
Besides the substantial heterogeneity across sectors, we also find substantial, but also intuitive,
heterogeneity across countries. For the CEFTA countries, the initial MFN tariff rate and the initial
openness explain quite well the resulting trade effects. For the EU countries and the ROW, the
effects are small in all sectors. The obvious reason here is that for the EU countries trade with the
CEFTA countries is only a small fraction of their overall exports.
Let us next turn to our border scenario. Note that for the border scenario we also report results
for services, which we could not include in our tariff scenario, as tariff data for services are not
available. We find substantial increases of total exports for all 6 CEFTA countries in manufacturing,
agriculture, and mining, and these increases are larger than in the tariff scenario. This highlights
that only relying on tariff data to investigate the effects of the integration between the CEFTA
countries and the EU may miss a substantial part of potential effects from trade liberalization. The
comprehensive measure of the change in bilateral trade costs based on differences of the border
estimates can be useful to quantify these additional gains.
Comparing across sectors, we find the largest increases again in agriculture, followed by man-
ufacturing. Besides heterogeneity across sectors, we again find substantial heterogeneity among
countries. Albania, Bosnia and Herzegovina, and North Macedonia are predicted to be the coun-
tries with the largest gains. Again, gains for the EU countries and ROW are small.
For services, we also find for some CEFTA countries, namely Albania, North Macedonia, and
Serbia, substantial trade effects. The reason that we do not find substantial total export increases
for the other CEFTA countries is the very low level of services exports of these countries to the EU.
5 Conclusion
We introduced a simple econometric approach to quantify ex-ante the “deep” impact of trade lib-
eralization and the “hard” effects of protection with the empirical structural gravity model, and we
demonstrated the effectiveness of our methods by quantifying the partial and GE impact on trade
of the integration of the CEFTA countries with the EU. Our partial equilibrium estimates revealed
that, even after controlling for the impact of standard proxies for trade costs (e.g., distance, lan-
guage, etc.), trade borders within the EU are large and, more importantly for our purposes, that
24
the borders between the CEFTA and the EU countries are even larger. We also observed significant
heterogeneity in the border estimates across sectors. A byproduct of our analysis is that we were
able to obtain ad-valorem equivalents of the trade barriers for services, which are not subject to
tariffs. The GE experiments that we performed demonstrated that (i) integration with the EU will
have large positive impact on the exports of the CEFTA countries, and (ii) a simple elimination of
tariffs may heavily under-predict the impact of CEFTA integration with the EU.
We hope that the simplicity and flexibility of our methods, along with their compatibility with
the standard quantitative trade models, will make them useful for benchmark policy analysis. In
addition to offering a comprehensive account of the impact of non-tariff barriers to trade, which is
especially relevant for services trade, we see an important application of our methods for quantifying
the impact of regional integration and of the impact of “borders” within countries. While tariffs are
not imposed for domestic trade, there is plenty of evidence that domestic trade is not frictionless
and that proper quantification of domestic trade costs is important for quantifying the effects of
both international and domestic policies.
25
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29
SUPPLEMENTARY APPENDIX
A Robustness Checks and Additional Results
This appendix offers estimates that correspond to the results from Tables 1 and 2 of the
main text. However, the estimates in Tables 4 and 5 are obtained with the OLS estimator,
the estimates in Tables 6 and 7 are obtained after the missing values for bilateral trade
with zeros, and the estimates in Tables 8 and 9 are obtained with 4-year-interval panel data
over the whole period of investigation, 2000-2016. Overall, the results from this appendix
support the main estimates, which are obtained with the PPML estimator and without
replacing missing values with zeros.
Table 4: Sectoral Gravity Estimates CEFTA, 2013, OLS
(1) (2) (3) (4)
Manufacturing Agriculture Mining Services
DIST -1.414 -1.158 -1.301 -0.770
(0.029) ∗∗ (0.030) ∗∗ (0.129) ∗∗ (0.098)∗∗
CNTG 0.894 1.025 1.233 0.557
(0.091) ∗∗ (0.075) ∗∗ (0.220) ∗∗ (0.121)∗∗
LANG 0.832 0.708 0.532 0.647
(0.038)∗∗ (0.040)∗∗ (0.167)∗∗ (0.072)∗∗
BRDR -4.433 -6.742 -7.069 -6.985
(0.227)∗∗ (0.154)∗∗ (0.533)∗∗ (0.385)∗∗
FTA 0.359 0.386 0.507 0.621
(0.045) ∗∗ (0.044) ∗∗ (0.216) ∗ (0.151)∗∗
BRDR_EU_CEFTA -4.720 -6.805 -7.411 -7.018
(0.241) ∗∗ (0.176) ∗∗ (0.651) ∗∗ (0.384)∗∗
BRDR_EU -4.039 -5.741 -7.160 -7.004
(0.227)∗∗ (0.155)∗∗ (0.563)∗∗ (0.366)∗∗
_cons 27.610 14.160 29.717 14.517
(0.280)∗∗ (0.226)∗∗ (0.909)∗∗ (0.655)∗∗
N 634838 106895 4698 19001
R2 0.698 0.617 0.664 0.843
Notes: This table reports gravity estimation results for the four main sectors in
the sample including Manufacturing, Agriculture, Mining, and Services, which corre-
spond to the estimates from Table 1 of the main text. All estimates are obtained with
data for 2013. The data for each main sector is constructed by pooling (not sum-
ming) the data for all individual products within the corresponding main sector. The
estimator is OLS and the dependent variable is the log of nominal bilateral trade. All
estimations are obtained with exporter-product and importer-product fixed effects,
whose estimates are omitted for brevity. Standard errors are clustered by country
pair and are reported in parentheses. + p < 0.10, ∗ p < .05, ∗∗ p < .01. See main
text for further details.
30
Table 5: Sectoral Gravity Estimates CEFTA, 2013, OLS
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)
Chemicals Electronics Food Machines Metals Minerals Other Rubber Textiles Transport Wood
DIST -1.661 -1.289 -1.456 -1.293 -1.580 -1.487 -1.246 -1.478 -1.368 -1.151 -1.589
(0.035)∗∗ (0.033)∗∗ (0.039)∗∗ (0.029)∗∗ (0.038)∗∗ (0.043)∗∗ (0.036)∗∗ (0.038)∗∗ (0.034)∗∗ (0.036)∗∗ (0.041)∗∗
CNTG 0.675 0.800 1.073 0.600 0.764 1.431 0.952 0.926 0.866 0.829 1.028
(0.101)∗∗ (0.117)∗∗ (0.100)∗∗ (0.094)∗∗ (0.099)∗∗ (0.109)∗∗ (0.115)∗∗ (0.122)∗∗ (0.106)∗∗ (0.091)∗∗ (0.105)∗∗
LANG 0.823 0.759 0.975 0.738 0.886 0.853 0.838 0.898 0.736 0.686 1.027
(0.045)∗∗ (0.045)∗∗ (0.050)∗∗ (0.042)∗∗ (0.048)∗∗ (0.052)∗∗ (0.050)∗∗ (0.053)∗∗ (0.046)∗∗ (0.046)∗∗ (0.050)∗∗
BRDR -3.495 -3.929 -5.725 -2.989 -3.639 -5.077 -4.121 -3.368 -3.695 -4.532 -4.855
(0.255)∗∗ (0.317)∗∗ (0.245)∗∗ (0.321)∗∗ (0.277)∗∗ (0.260)∗∗ (0.330)∗∗ (0.329)∗∗ (0.277)∗∗ (0.277)∗∗ (0.241)∗∗
FTA 0.329 0.336 0.550 0.298 0.377 0.378 0.317 0.426 0.241 0.366 0.356
(0.054)∗∗ (0.054)∗∗ (0.058)∗∗ (0.049)∗∗ (0.056)∗∗ (0.066)∗∗ (0.059)∗∗ (0.063)∗∗ (0.055)∗∗ (0.054)∗∗ (0.059)∗∗
31
BRDR_EU_CEFTA -3.617 -4.369 -5.985 -3.397 -3.685 -5.793 -4.326 -3.470 -3.822 -4.720 -5.051
(0.277)∗∗ (0.328)∗∗ (0.269)∗∗ (0.336)∗∗ (0.297)∗∗ (0.286)∗∗ (0.342)∗∗ (0.347)∗∗ (0.290)∗∗ (0.295)∗∗ (0.260)∗∗
BRDR_EU -2.919 -3.681 -4.798 -3.123 -3.254 -4.989 -3.891 -3.118 -2.954 -4.268 -4.351
(0.255)∗∗ (0.316)∗∗ (0.246)∗∗ (0.322)∗∗ (0.278)∗∗ (0.258)∗∗ (0.326)∗∗ (0.330)∗∗ (0.277)∗∗ (0.279)∗∗ (0.242)∗∗
_cons 29.736 26.471 28.937 25.794 28.770 28.034 25.404 27.676 26.078 25.686 27.988
(0.324)∗∗ (0.349)∗∗ (0.336)∗∗ (0.352)∗∗ (0.378)∗∗ (0.359)∗∗ (0.353)∗∗ (0.365)∗∗ (0.318)∗∗ (0.384)∗∗ (0.386)∗∗
N 61024 100591 80999 77449 46539 37020 34915 20705 64522 43567 67507
R2 0.675 0.747 0.604 0.713 0.690 0.652 0.719 0.759 0.707 0.677 0.679
Notes: This table reports gravity estimation results for the 11 main sectors within Manufacturing in the sample, as they appear in the column names. The
estimates in this table correspond to the estimates from Table 2 of the main text. All estimates are obtained with data for 2013. The data for each main
manufacturing sector is constructed by pooling (not summing) the data for all individual manufacturing products within the corresponding main sector. The
estimator is OLS and the dependent variable is the log of nominal bilateral trade. All estimations are obtained with exporter-product and importer-product fixed
effects, whose estimates are omitted for brevity. Standard errors are clustered by country pair and are reported in parentheses. + p < 0.10, ∗ p < .05, ∗∗ p < .01.
See main text for further details.
Table 6: Sectoral Gravity Estimates CEFTA, 2013, OLS
(1) (2) (3) (4)
Manufacturing Agriculture Mining Services
DIST -0.491 -0.953 -1.406 -0.467
∗∗ ∗∗ ∗∗
(0.099) (0.040) (0.156) (0.098)∗∗
CNTG 0.774 0.612 0.386 0.540
∗∗ ∗∗
(0.185) (0.070) (0.256) (0.138)∗∗
LANG 0.307 0.403 0.333 0.623
∗∗ ∗∗ ∗
(0.066) (0.056) (0.156) (0.089)∗∗
BRDR -5.960 -5.926 -4.605 -5.808
(0.294)∗∗ (0.126)∗∗ (0.445)∗∗ (0.305)∗∗
FTA 0.365 0.495 1.020 0.254
(0.103)∗∗ (0.064)∗∗ (0.183)∗∗ (0.123)∗
BRDR_EU_CEFTA -6.880 -6.170 -5.737 -7.375
∗∗ ∗∗ ∗∗
(0.338) (0.162) (0.463) (0.351)∗∗
BRDR_EU -5.794 -4.777 -4.932 -5.321
∗∗ ∗∗ ∗∗
(0.321) (0.119) (0.445) (0.289)∗∗
_cons 29.618 16.903 36.036 15.667
(0.581)∗∗ (0.265)∗∗ (0.955)∗∗ (0.627)∗∗
N 1291073 365939 39085 97202
Notes: This table reports gravity estimation results for the four main sectors in
the sample including Manufacturing, Agriculture, Mining, and Services, which
correspond to the estimates from Table 1 of the main text. All estimates are
obtained with data for 2013. The data for each main sector is constructed
by pooling (not summing) the data for all individual products within the cor-
responding main sector. The estimator is PPML and the dependent variable
is the level nominal bilateral trade, where we have replaced all missing val-
ues for international trade flows with zeros. All estimations are obtained with
exporter-product and importer-product fixed effects, whose estimates are omit-
ted for brevity. Standard errors are clustered by country pair and are reported
in parentheses. + p < 0.10, ∗ p < .05, ∗∗ p < .01. See main text for further
details.
32
Table 7: Sectoral Gravity Estimates CEFTA, 2013, OLS
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)
Chemicals Electronics Food Machines Metals Minerals Other Rubber Textiles Transport Wood
DIST -0.857 -0.605 -0.721 -0.562 -0.747 -0.977 -0.670 -0.941 -0.782 -0.468 -0.904
(0.047)∗∗ (0.054)∗∗ (0.041)∗∗ (0.046)∗∗ (0.070)∗∗ (0.047)∗∗ (0.066)∗∗ (0.051)∗∗ (0.059)∗∗ (0.077)∗∗ (0.044)∗∗
CNTG 0.369 0.299 0.828 0.528 0.429 0.600 0.598 0.541 0.337 0.808 0.767
(0.083)∗∗ (0.080)∗∗ (0.082)∗∗ (0.086)∗∗ (0.102)∗∗ (0.085)∗∗ (0.130)∗∗ (0.094)∗∗ (0.086)∗∗ (0.110)∗∗ (0.077)∗∗
LANG 0.253 0.288 0.489 0.145 0.461 0.177 0.125 0.032 0.214 -0.065 0.265
(0.070)∗∗ (0.066)∗∗ (0.065)∗∗ (0.059)∗ (0.067)∗∗ (0.071)∗ (0.094) (0.068) (0.071)∗∗ (0.083) (0.068)∗∗
BRDR -3.121 -3.007 -5.342 -2.741 -3.523 -4.099 -3.898 -3.225 -3.240 -3.687 -4.214
(0.156)∗∗ (0.190)∗∗ (0.118)∗∗ (0.169)∗∗ (0.227)∗∗ (0.151)∗∗ (0.193)∗∗ (0.179)∗∗ (0.157)∗∗ (0.235)∗∗ (0.129)∗∗
FTA 0.365 0.275 0.432 0.466 0.571 0.396 0.535 0.524 0.325 0.723 0.576
(0.072)∗∗ (0.077)∗∗ (0.060)∗∗ (0.062)∗∗ (0.132)∗∗ (0.080)∗∗ (0.100)∗∗ (0.085)∗∗ (0.085)∗∗ (0.094)∗∗ (0.072)∗∗
33
BRDR_EU_CEFTA -3.010 -2.589 -5.481 -2.174 -3.180 -4.519 -3.358 -3.082 -2.591 -2.802 -4.287
(0.247)∗∗ (0.326)∗∗ (0.177)∗∗ (0.256)∗∗ (0.281)∗∗ (0.200)∗∗ (0.266)∗∗ (0.216)∗∗ (0.263)∗∗ (0.578)∗∗ (0.162)∗∗
BRDR_EU -3.013 -2.429 -3.973 -2.459 -2.755 -3.546 -3.242 -2.593 -2.590 -2.734 -3.720
(0.153)∗∗ (0.175)∗∗ (0.118)∗∗ (0.158)∗∗ (0.197)∗∗ (0.148)∗∗ (0.190)∗∗ (0.152)∗∗ (0.177)∗∗ (0.203)∗∗ (0.130)∗∗
_cons 29.495 28.071 27.803 25.848 28.655 28.393 28.205 29.077 28.525 27.375 28.054
(0.286)∗∗ (0.345)∗∗ (0.254)∗∗ (0.296)∗∗ (0.414)∗∗ (0.282)∗∗ (0.415)∗∗ (0.310)∗∗ (0.389)∗∗ (0.471)∗∗ (0.263)∗∗
N 125674 166082 194859 151612 86680 86371 65929 33276 119184 107056 141743
Notes: This table reports gravity estimation results for the 11 main sectors within Manufacturing in the sample, as they appear in the column names. The
estimates in this table correspond to the estimates from Table 2 of the main text. All estimates are obtained with data for 2013. The data for each main
manufacturing sector is constructed by pooling (not summing) the data for all individual manufacturing products within the corresponding main sector. The
estimator is PPML and the dependent variable is the level nominal bilateral trade, where we have replaced all missing values for international trade flows with
zeros. All estimations are obtained with exporter-product and importer-product fixed effects, whose estimates are omitted for brevity. Standard errors are
clustered by country pair and are reported in parentheses. + p < 0.10, ∗ p < .05, ∗∗ p < .01. See main text for further details.
Table 8: Sectoral Gravity Estimates CEFTA, 2000-2016
(1) (2) (3) (4)
Manufacturing Agriculture Mining Services
DIST -0.745 -0.773 -1.196 -0.456
(0.020)∗∗ (0.039)∗∗ (0.115)∗∗ (0.057)∗∗
CNTG 0.453 0.674 0.172 0.347
(0.032)∗∗ (0.069)∗∗ (0.196) (0.083)∗∗
LANG 0.240 0.352 0.147 0.502
∗∗ ∗∗
(0.027) (0.054) (0.137) (0.064)∗∗
BRDR -3.132 -5.710 -3.588 -5.337
∗∗ ∗∗ ∗∗
(0.064) (0.111) (0.332) (0.190)∗∗
FTA 0.364 0.485 0.565 0.272
∗∗ ∗∗ ∗∗
(0.031) (0.055) (0.154) (0.097)∗∗
BRDR_EU_CEFTA -3.257 -6.214 -4.563 -6.558
(0.079)∗∗ (0.143)∗∗ (0.353)∗∗ (0.201)∗∗
BRDR_EU -2.899 -4.848 -3.800 -5.216
∗∗ ∗∗ ∗∗
(0.058) (0.105) (0.384) (0.193)∗∗
N 2836146 523742 20180 116229
Notes: This table reports gravity estimation results for the four main sectors in
the sample including Manufacturing, Agriculture, Mining, and Services, which
correspond to the estimates from Table 1 of the main text. All estimates
are obtained with 4-year panel data for the period 2000-2016. The data for
each main sector is constructed by pooling (not summing) the data for all
individual products within the corresponding main sector. The estimator is
PPML and the dependent variable is the level nominal bilateral trade, where
we have replaced all missing values for international trade flows with zeros. All
estimations are obtained with exporter-product-year and importer-product-
year fixed effects, whose estimates are omitted for brevity. Standard errors
are clustered by country pair and are reported in parentheses. + p < 0.10, ∗
p < .05, ∗∗ p < .01. See main text for further details.
34
Table 9: Sectoral Gravity Estimates CEFTA, 2000-2016
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)
Chemicals Electronics Food Machines Metals Minerals Other Rubber Textiles Transport Wood
DIST -0.816 -0.679 -0.710 -0.619 -0.809 -0.888 -0.693 -0.901 -0.876 -0.583 -0.967
(0.039)∗∗ (0.039)∗∗ (0.040)∗∗ (0.037)∗∗ (0.043)∗∗ (0.042)∗∗ (0.055)∗∗ (0.047)∗∗ (0.059)∗∗ (0.069)∗∗ (0.046)∗∗
CNTG 0.291 0.289 0.790 0.453 0.503 0.697 0.613 0.581 0.360 0.699 0.734
(0.069)∗∗ (0.076)∗∗ (0.078)∗∗ (0.072)∗∗ (0.078)∗∗ (0.074)∗∗ (0.103)∗∗ (0.079)∗∗ (0.085)∗∗ (0.094)∗∗ (0.068)∗∗
LANG 0.240 0.255 0.476 0.207 0.365 0.188 0.123 0.163 0.251 0.088 0.307
(0.064)∗∗ (0.065)∗∗ (0.062)∗∗ (0.057)∗∗ (0.059)∗∗ (0.067)∗∗ (0.079) (0.064)∗ (0.063)∗∗ (0.077) (0.060)∗∗
FTA 0.288 0.272 0.391 0.468 0.372 0.252 0.426 0.523 0.364 0.884 0.503
(0.063)∗∗ (0.060)∗∗ (0.060)∗∗ (0.060)∗∗ (0.083)∗∗ (0.071)∗∗ (0.094)∗∗ (0.080)∗∗ (0.070)∗∗ (0.107)∗∗ (0.070)∗∗
BRDR -3.052 -2.156 -5.058 -2.347 -3.279 -3.998 -3.522 -3.110 -2.812 -2.988 -3.961
35
(0.117)∗∗ (0.131)∗∗ (0.116)∗∗ (0.117)∗∗ (0.122)∗∗ (0.127)∗∗ (0.168)∗∗ (0.148)∗∗ (0.153)∗∗ (0.212)∗∗ (0.140)∗∗
BRDR_EU_CEFTA -3.035 -2.106 -5.599 -2.135 -3.025 -4.512 -3.300 -3.132 -2.246 -2.699 -4.259
(0.202)∗∗ (0.269)∗∗ (0.157)∗∗ (0.193)∗∗ (0.179)∗∗ (0.164)∗∗ (0.237)∗∗ (0.201)∗∗ (0.212)∗∗ (0.354)∗∗ (0.157)∗∗
BRDR_EU -2.862 -2.075 -4.063 -2.473 -2.687 -3.667 -3.292 -2.787 -2.527 -2.799 -3.805
(0.112)∗∗ (0.135)∗∗ (0.114)∗∗ (0.118)∗∗ (0.121)∗∗ (0.128)∗∗ (0.163)∗∗ (0.151)∗∗ (0.139)∗∗ (0.170)∗∗ (0.121)∗∗
N 274083 448186 354770 343395 206826 166532 156181 92764 288498 191861 313050
Notes: This table reports gravity estimation results for the 11 main sectors within Manufacturing in the sample, as they appear in the column names. The
estimates in this table correspond to the estimates from Table 2 of the main text. All estimates are obtained with 4-year panel data for the period 2000-2016. The
data for each main manufacturing sector is constructed by pooling (not summing) the data for all individual manufacturing products within the corresponding
main sector. The estimator is PPML and the dependent variable is the level nominal bilateral trade, where we have replaced all missing values for international
trade flows with zeros. All estimations are obtained with exporter-product-year and importer-product-year fixed effects, whose estimates are omitted for brevity.
Standard errors are clustered by country pair and are reported in parentheses. + p < 0.10, ∗ p < .05, ∗∗ p < .01. See main text for further details.
Table 10: MFN Tariffs and Initial Shares of Spending on Domestic Goods
Manufacturing Agriculture Mining Services
Country MFN MFN MFN
πii πii πii πii
tariff tariff tariff
PANEL A: CEFTA countries
ALB 3.41 0.88 6.35 0.90 0.97 0.99 0.97
BIH 7.17 0.85 5.07 0.85 3.52 0.93 0.99
MDA 3.28 0.77 12.34 0.90 0.12 0.61 0.89
MKD 5.56 0.95 14.65 0.85 4.18 0.73 0.79
MNE 5.39 0.80 15.62 0.97 0.95 0.98 0.92
SRB 7.72 0.92 17.56 0.97 1.24 0.91 0.91
PANEL B: EU countries and ROW
AUT 3.95 0.85 9.49 0.85 0.78 0.94 0.87
BEL 3.95 0.95 9.49 0.94 0.78 0.96 0.84
BGR 3.95 0.89 9.49 0.88 0.78 0.60 0.94
CYP 3.95 0.75 9.49 0.86 0.78 1.00 0.83
CZE 3.95 0.94 9.49 0.86 0.78 0.86 0.91
DEU 3.95 0.82 9.49 0.89 0.78 0.84 0.91
DNK 3.95 0.92 9.49 0.97 0.78 0.86 0.87
ESP 3.95 0.68 9.49 0.91 0.78 0.71 0.97
EST 3.95 0.92 9.49 0.80 0.78 0.65 0.85
FIN 3.95 0.87 9.49 0.89 0.78 0.46 0.91
FRA 3.95 0.73 9.49 0.93 0.78 0.77 0.94
GBR 3.95 0.79 9.49 0.91 0.78 0.61 0.91
GRC 3.95 0.59 9.49 0.93 0.78 0.56 0.94
HRV 3.95 0.78 9.49 0.84 0.78 0.86 0.97
HUN 3.95 0.91 9.49 0.91 0.78 0.57 0.86
IRL 3.95 0.97 9.49 0.96 0.78 0.26 0.71
ITA 3.95 0.65 9.49 0.91 0.78 0.68 0.96
LTU 3.95 0.94 9.49 0.89 0.78 0.24 0.89
LUX 3.95 0.95 9.49 0.89 0.78 0.91 0.90
LVA 3.95 0.89 9.49 0.83 0.78 0.77 0.88
MLT 3.95 0.94 9.49 0.97 0.78 0.69 0.83
NLD 3.95 0.95 9.49 0.97 0.78 0.95 0.89
POL 3.95 0.80 9.49 0.93 0.78 0.64 0.94
PRT 3.95 0.82 9.49 0.88 0.78 0.75 0.94
ROU 3.95 0.84 9.49 0.90 0.78 0.82 0.93
SVK 3.95 0.92 9.49 0.77 0.78 0.79 0.89
SVN 3.95 0.90 9.49 0.77 0.78 0.81 0.89
SWE 3.95 0.87 9.49 0.83 0.78 0.62 0.90
ROW 0.87 0.90 0.73 0.87
Notes: This table reports trade-weighted averages of MFN tariffs as well
as initial shares of spending on domestic goods. Column (1) gives the coun-
try abbreviations, columns (2), (4), and (6) give the initial level of MFN
tariffs for manufacturing, agriculture, and mining, respectively. Columns
(3), (5), (7), and (8) give the initial share of spending on domestic goods
for manufacturing, agriculture, mining, and services, respectively.
36
Table 11: Trade Effects of CEFTA (σ = 4)
Manufacturing Agriculture Mining Services
Country
Tariff Border Tariff Border Tariff Border Border
PANEL A: CEFTA countries
ALB 24.58 65.04 51.40 271.47 0.84 14.26 133.31
BIH 33.71 53.24 43.95 226.50 12.43 46.82 -3.96
MDA 15.36 42.51 25.44 69.18 1.75 91.26 -0.01
MKD 28.25 53.41 54.68 125.42 20.42 94.42 274.55
MNE 32.45 55.28 61.75 78.47 1.77 4.66 88.18
SRB 30.41 44.68 45.39 97.23 4.36 46.65 116.98
PANEL B: EU countries and ROW
AUT 0.22 0.39 1.12 3.35 -0.05 -0.23 6.59
BEL 0.02 0.04 0.17 0.45 0.00 0.01 0.06
BGR 0.92 1.73 1.92 5.40 0.39 8.62 0.32
CYP 0.12 0.22 0.36 1.02 0.00 -0.01 0.50
CZE 0.13 0.24 0.37 1.04 0.09 1.64 0.11
DEU 0.09 0.16 0.27 0.77 -0.01 0.02 0.06
DNK 0.04 0.07 0.04 0.16 0.01 0.04 0.01
ESP 0.03 0.05 0.13 0.35 0.00 0.07 0.01
EST 0.02 0.05 0.01 0.08 0.00 -0.01 0.02
FIN 0.02 0.03 0.06 0.24 0.00 0.00 0.01
FRA 0.03 0.05 0.12 0.33 0.01 0.34 0.03
GBR 0.04 0.08 0.08 0.25 0.00 0.00 0.06
GRC 0.98 2.02 2.52 7.08 0.09 2.60 0.89
HRV 4.26 7.78 19.11 61.56 1.17 13.32 0.68
HUN 0.32 0.57 1.52 4.19 0.42 9.97 0.16
IRL 0.01 0.02 0.01 0.05 0.00 0.00 0.02
ITA 0.23 0.45 0.43 1.34 -0.02 0.44 0.18
LTU 0.04 0.09 0.13 0.43 0.00 0.06 0.11
LUX 0.05 0.09 0.00 0.00 -0.03 -0.12 0.00
LVA 0.04 0.08 0.10 0.32 0.00 0.01 0.10
MLT 0.17 0.42 0.03 0.13 1.06 15.91 0.10
NLD 0.03 0.05 0.10 0.28 0.00 -0.01 0.06
POL 0.14 0.25 0.42 1.27 0.10 1.44 0.02
PRT 0.02 0.03 0.04 0.13 0.00 -0.05 0.00
ROU 0.59 1.20 1.33 4.00 1.39 28.72 -0.05
SVK 0.20 0.36 0.65 1.81 -0.03 -0.05 0.50
SVN 1.51 2.71 7.23 21.05 -0.05 1.13 3.06
SWE 0.03 0.06 0.16 0.50 0.00 0.01 0.06
ROW 0.00 -0.01 0.00 -0.03 0.00 0.00 -0.01
Notes: This table reports results for our CEFTA border and tariff sce-
nario assuming an elasticity of substitution of 4 (σ = 4). Column (1) gives
the country abbreviations, columns (2), (4), and (6) report the changes in
total exports from our tariff scenario for manufacturing, agriculture, and
mining, respectively. Columns (3), (5), (7), and (8) report the changes
in total exports from our border scenario for manufacturing, agriculture,
mining, and services, respectively.
37
B Theoretical Framework for Counterfactual Analysis
For our counterfactual analysis we apply the structural gravity framework formulated in
changes (Dekle, Eaton and Kortum, 2007, 2008). Start with the following expression for
trade flows:
1−σ
γi pi tij
Xij = Ej .
Pj
The price index is given by:
Pj1−σ = (γi pi tij )1−σ .
i
Using this expression for Pj1−σ , we can re-write the expression for trade flows as follows:
(γi pi tij )1−σ
Xij = 1−σ Ej .
l (γl p l tlj )
Dekle, Eaton and Kortum (2007, 2008) use country i’s share in country j ’s spending
Xij (γi pi tij )1−σ
πij = = 1−σ .
Ej l (γl pl tlj )
Assuming that the γ ’s are constant, the change of πij is then given by:
1−σ
pi tij
πij = 1−σ .
l πlj pl tlj
Market clearance implies Yi = j Xij . Hence, we can write Yi as:
(γi pi tij )1−σ
Yi = 1−σ Ej = πij Ej .
j l (γl pl tlj ) j
The counterfactual value of Yi , YiCF L , can then be written as:
YiCF L = CF L CF L
πij Ej .
j
The change in Yi is then given by:
BLN 1−σ
πij pi tij
YiBLN Yi = BLN
1−σ Ej Ej .
BLN
j l πlj pl tlj
38
Further, due to the endowment economy, we have Ei = φi Yi = φi pi Qi . Hence, Yj = pj and
Ej = Yj Using this in the last expression, we end up with:
1−σ
BLN
πij Yi tij
YiBLN Yi = BLN
1−σ Ej Yj .
BLN
j
l πlj Yl tlj
This system needs only data on national outputs (Yi ), expenditures (Ei ) and trade shares
(πij ), and knowledge about σ . Or, one can use trade flows data (Xij ) only, and utilize the
relationships Yi = j Xij and Ej = i Xij to calculate outputs and expenditures. In any
case, knowledge about γj is not necessary. The change in tij , tij , are exogenous, i.e. they
form the basis of our counterfactual experiment. Hence, we are left with one equation and
the unknown Yi .
In order to implement it, we simplify a bit:
1−σ
BLN
πij Yi tij
YiBLN Yi = BLN
1−σ Ej Yj ⇒
BLN
l πlj Yl tlj
j
1−σ
BLN
πij Yi tij BLN
Ej Yj
1= 1−σ .
BLN YiBLN Yi
l πlj Yl tlj
j
Having Yi , we can calculate the remaining changes Ej , pj , πij , and Xij :
Ej = Yj ,
pj = Yj ,
1−σ
pi tij
πij = 1−σ ,
l πlj pl tlj
Xij = πij Ej .
Real GDP changes (welfare) are given by:
1
Wj = (πjj ) 1−σ .
39