# Reassessing the benefits of European integration and the European Union's ability to achieve strategic autonomy

Lionel Fontagné, Yoto V Yotov

<figure><img src="image_1.png" /></figure>

## To cite this version:

Lionel Fontagné, Yoto V Yotov. Reassessing the benefits of European integration and the European Union's ability to achieve strategic autonomy. 2026. halshs-05512419

HAL Id: halshs-05512419

https://shs.hal.science/halshs-05512419v1

Preprint submitted on 16 Feb 2026

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
<figure><img src="image_3.png" /></figure>

PARIS SCHOOL OF ECONOMICS
ECOLE D'ECONOMIE DE PARIS

WORKING PAPER N° 2026-04

Reassessing the benefits of European integration and the European Union's ability to achieve strategic autonomy

Lionel Fontagné
Yoto V. Yotov

JEL Codes: F10, F14, F16

Keywords: European Integration, Single Market, Trade, Risky Suppliers, Strategic Autonomy

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>
# Reassessing the benefits of European integration and the European Union's ability to achieve strategic autonomy

Lionel Fontagné

Yoto V. Yotov*

Paris School of Economics

Drexel University

CGPA and ifo

February 4, 2026

## Abstract

European integration is now faced with the question of strategic autonomy. Against this backdrop, this paper has three objectives. First, it uses disaggregated trade data and established empirical methods to assess the benefits of European integration on trade among the members of the European Union (EU) as well as on trade between EU members and non-member countries, including non-members that are part of the Single Market. Second, it evaluates the costs of EU strategic autonomy – implying not trading with “riskier” partners. Third, it asks whether deeper integration within the EU can alleviate these costs. The paper shows that the gains from European integration are substantial, albeit heterogeneous across Member States, non-members, and sectors, and that the costs of strategic autonomy can be offset by deeper, but comparatively more modest, integration efforts within the European Union.

**JEL Classification Codes:** F10, F14, F16

**Keywords:** European Integration, Single Market, Trade, Risky Suppliers, Strategic Autonomy

<footnote>*We thank Roman Arjona-Garcia, Antoine Bouët, Kenan Huremovic, Josefina Monteagudo, Armando Rungi, Frank Vandermeeren, and participants to various seminars and conferences for helpful comments and suggestions. Contact information: Lionel Fontagné (Corresponding author)– Paris School of Economics, 48 boulevard Jourdan 75014 Paris, France. E-mail: lionel fontagne@psemail.eu. Yoto Yotov – School of Economics and Center for Global Policy Analysis, Drexel University, Philadelphia, PA 19104, USA; ifo Insitute, CESifo, Munich. E-mail: yotov@drexel.edu. This project has received support from and was developed under the 2023 DG GROW Fellowship Programme of the European Commission. Lionel Fontagné thanks the support of the EUR grant ANR-17-EURE-0001 and the i-MIP institute. The views expressed in this paper are the authors’ and do not necessarily reflect and should not be considered as representative of the official positions of the European Commission. An earlier version of this paper was circulated under the title ‘‘Reassessing the impact of the Single Market and its ability to help build strategic autonomy”</footnote>
# 1 Introduction

While economists systematically introduce substitution (between intermediate inputs, between capital or labor and intermediate inputs, and, more so, between origins for sourcing), recent crises, e.g., the COVID-19 pandemic and the war between Russia and Ukraine, have demonstrated that substitution is not always an option, at least in the short run. To some extent, the world has proven more “Leontief” than expected in troubled times.

Previous warnings about the risks of further deepening globalization have not been taken seriously by either firms or governments: the floods in Thailand and the Fukushima disaster induced major disruptions in certain value chains (Boehm et al. 2019), which today sound like an early alarm. After all, a prospective exercise of the DG research (Commission 2009) mentioned a pandemic and a war as major wild cards. Firms made location or sourcing decisions that did not internalize the macroeconomic externality of their joint choices in terms of reduced sourcing diversity and concentration on potentially “riskier” trading partners. Therefore, there is a need for re-evaluation of the balance between “cost reduction” vs. “risk reduction”.

Against this backdrop, this paper provides a conceptual framework to revisit the benefits of European integration with disaggregated data, evaluate the costs of strategic autonomy regarding weakening ties with riskier trading partners, and quantify the progress needed in terms of European interdependence to offset these costs. Two often overlooked arguments come into play here: the European Union is far from fully integrated, as evidenced by the persistence of border effects; however, at the same time, it is a sufficiently integrated and large economy that marginal improvements can have very significant effects. Can further integration of the EU members help alleviate the “Leontief nature” of economic activity and be a promising avenue for Open Strategic Autonomy?

The current policy debate about the lack of full integration that prevents the EU from realizing its full potential, however, often relies on aggregate and indirect evidence regarding remaining barriers. The first objective of this paper is, therefore, to go beyond such
aggregated evidence and provide a detailed assessment of the gains of EU integration so far and the prospects for future integration benefits based on disaggregated empirical evidence covering a large spectrum of economic activities, from agriculture to manufacturing and services. The trade impact of EU integration is estimated using the latest econometric techniques in a panel before implementing a counterfactual analysis of further intra-EU integration in general equilibrium. This counterfactual analysis demonstrates that a modest reduction in intra-EU costs could significantly boost EU output: a modest reduction (about 4%) in intra-EU trade costs, which indeed includes more than just tariffs and extends to broader trade frictions, could significantly boost output in the EU and therefore offset the cost of cutting ties with risky partners. How such a result can be obtained is the outcome of traditional quantitative trade model: the largest fraction of trade for most EU members is with other EU members who are geographically close. Thus, the base from which the EU can start liberalizing further is very large, and a small further reduction in trade costs within the EU would have a significantly larger impact than liberalizing with (or protecting against) non-EU countries, even if they are large in size (e.g., Russia, China, and the United States).

To simulate, in terms of impact on trade and real output, a move from the “riskier” to “less risky” (but “more costly”) alternatives, a New Quantitative Trade Model (NQTM) is the most widely used tool. General Equilibrium (GE) adjustments to a shock will inform us about the induced reshaping of the world matrix of trade flows. However, the need to trace very detailed products or sourcing decisions that are more granular compared to the classifications used by most Multi-Regional Input-Output (MRIO) tables makes the use of a model featuring input-output relationships rather problematic. An alternative consists of combining the general equilibrium properties of structural gravity with detailed trade and production data for a large sample of countries over a long period. Input-output relationships not documented at this level of detail will be mirrored in the detailed trade relationships used for the calibration of the model.
The recent literature in structural gravity, which shares the theoretical background of NQTMs, combines sound theoretical foundations with the possibility of modeling trade at a more granular level. This analysis comes with important caveats. Specifically, the general equilibrium model used has one important feature and one important limitation in relation to the findings presented in this paper. The important feature is that it is a long run static model: in the absence of dynamics, we will simply contrast short- and long term adjustments without properly modeling the transition path. The important limitation is that our model and analysis do not include input-output links that would magnify the gains of further integration but also the costs of decoupling. Solving a quantitative trade model with a 170-sector MRIO is out of reach, and we prioritized the sectoral detail of the analysis. This limitation must be kept in mind when reading about the moderate simulated impact on the German economy of the reduction of bilateral trade with Russia. This result indeed contrasts with the observed toll on economic activity in Germany from the reaction of energy markets to the reduction of imports of Russian gas.

The model assumes that the EU can substitute imports of energy from Russia with imports from other countries. Due to the way in which energy markets work, this is a strong assumption that is valid only in the long term. Here, again, using a static model that informs about long-term outcomes is a limitation. Adjustments to the energy shock take time, although micro-economic evidence has shown that firms adjust by combining several levers to changes in energy prices (Fontagné et al. 2024). To tentatively bridge the short- and long term analysis dimensions, a counterfactual analysis capitalizes on recent developments in the trade literature concerning trade elasticities. Specifically, Anderson & Yotov (2025) demonstrate that the trade elasticity varies over time from the short- to the long-run, and we use short-run trade elasticity values to obtain “reduced-form” estimates of the impact of derisking. This analysis complements our long-run results and, as expected and consistent with the presence of adjustment costs and delays, it generates significantly larger losses, more than four times larger on average.
Our approach comprises three distinct building blocks. The first involves the estimation of the effects of the EU on trade among members and between EU members and non-member states, using detailed industry-level trade data over the period 1986-2019. Our estimation results contribute to the literature with more disaggregated estimates, including domestic trade flows (important for our identification purposes) and, most importantly, by shedding light on the EU effects on extra-EU trade and on the specific effects of the Single Market,<fnref n="1" /> which is important and novel concerning recent findings and claims from the Draghi report and the debate surrounding the IMF claims (Adilbish et al. 2025, Head & Mayer 2025). Indeed, the effects of the single market could be otherwise misattributed, as estimating them by subtracting partial equilibrium estimates of the effects of EU membership from observed trade data would conflate the effects of EU membership with those of the single market. Accordingly, our estimations allow for separate effects of the EU vs. the Single Market, where the latter is defined as trade between the EU members and the four non-EU members that are part of the Single Market (i.e., Iceland, Liechtenstein, Norway). As Switzerland did not formally join the Single Market but had signed a free trade agreement with the European Economic Community, and another agreement with the EU that covered agricultural products in 1999, we adjust our treatment accordingly.

The second building block consists of a counterfactual *simulating* the economic consequences of further European integration in a quantitative trade model calibrated with our disaggregated estimations. The third building block relies on this quantitative trade model to simulate the impact of a move towards greater European autonomy and “derisking” of sourcing by combining trade and production data for the contemporary period. Our objective is to offer an extreme counterfactual scenario of cutting these trade ties almost completely, which helps us highlight an important point, i.e., that due to the size and connectedness of the EU, offsetting the possibly large negative consequences of cutting ties deeply with riskier partners would be within reach in the medium-to-long term.

<footnote n="1">¹We are indebted to an anonymous referee for suggesting this estimation strategy.</footnote>
Building on our estimates for 170 industries and considering the results of these two
counterfactual simulations, we finally show that reaping some of the untapped benefits of
the EU market would compensate for the losses induced by such “derisking”. This result is
illustrated by taking as an example the two most affected Member States from the reduction
of trade with ‘riskier’ partners –Cyprus and Estonia–, then extending this reasoning to a
large country –Germany– and, finally, extending it to all Member states.

An important contribution of this paper is the level of disaggregation in the empirical
analysis. To obtain our partial equilibrium estimates, we rely on established developments
from the structural gravity literature regarding estimation techniques (Larch et al. 2025).
Specifically, for each industry, and capitalizing on the properties of the Poisson Pseudo
Maximum Likelihood (PPML) estimator (Santos Silva & Tenreyro 2006), our econometric
model is estimated in levels using data that includes international as well as domestic trade
flows (Yotov 2022). Motivated by theory and aiming to comprehensively account for all
observable and unobservable factors that may affect our estimates but may be omitted
from the model, we use a rich structure of fixed effects, including exporter-industry-time,
importer-industry-time, and country-pair-industry fixed effects. In addition, we include a
set of international industry-border-time fixed effects. Section 2 offers further details on our
estimating specifications and econometric methods. Importantly, our estimating models are
based on, and therefore perfectly consistent with, the theoretical framework that will be
employed for the counterfactual analysis.<fnref n="2" />

The chosen framework requires observing trade flows for each industry and country, in-
cluding domestic trade flows. Accordingly, usual trade data at the product-origin-destination
level is not an option. A good compromise between the granularity of the data and the cov-
erage of domestic flows is provided by the *International Trade and Production Database*

<footnote n="2">²In the robustness analysis, we capitalize on recent developments in the econometric literature concerning possible biases in the presence of treatment effect heterogeneity (e.g. “forbidden comparisons” that misuse already-treated units in the control group) and obtain heterogeneity-robust DiD estimates of the EU effects on intra-EU trade (Borusyak & Jaravel 2017, de Chaisemartin & D’Haultfœuille 2020, Nagengast & Yotov 2025).</footnote>
for Estimation (ITPD-E) (Borchert et al. 2020, 2022).<fnref n="3" /> The ITPD-E covers international and domestic trade data for a large number of countries (more than 200), a large number of industries (170) for the whole economy (e.g., including Agriculture, Mining and Energy, Manufacturing, and Services) over a long period (1986-2019), which varies by industry depending on the raw data used. Important for our purposes, the ITPD-E is constructed from raw data without reliance on any statistical modeling. Thus, it is appropriate for estimations. In addition to the ITPD-E, we employ other datasets to construct a vector of policy variables, which are used as control covariates in our model. Section 3 offers further details on the data and the sources we use.

Several findings stand out from our disaggregated estimates of the effects of the EU on trade among its members. First, the EU has been extremely successful in promoting trade among its Member States. Most of our industry-level partial equilibrium EU estimates are positive, sizable, and statistically significant. On average, our preferred estimates imply that the EU has led to about an 92 percent increase in members' trade based on all estimates, and about 194 percent based on only the positive values.<fnref n="4" />

Second, we find that the effects of the EU have been very heterogeneous across industries and the broad sectors in our sample. The strongest EU effects have been in Agriculture, which is consistent with and can be interpreted through the lens of the EU Common Agricultural Policy. The estimates for Services are mostly positive and sizable, while the impact on Manufacturing trade has been positive but smaller, with half of our estimates for Mining and Energy not being statistically significant.

Third, we obtain estimates of the EU effects on trade between members and non-members, including separate estimates for trade between EU members and non-members that are part of the Single Market. We find that the Single Market has been successful in promoting trade between EU countries and other members of this market; however, the

<footnote n="3">³https://www.usitc.gov/data/gravity/itpde.htm</footnote>

<footnote n="4">⁴In robustness experiments, we confirm that the EU effects are positive, sizable, and statistically significant both for ‘old’ vs. ‘new’ EU members. In addition, we obtain heterogeneity-robust DiD estimates, which are highly correlated with our main estimates and larger on average.</footnote>
effects of the Single Market are smaller than the corresponding intra-EU effects. We also obtain positive and statistically significant estimates of the effects of the EU on trade between EU members and non-EU states (apart from those in the Single Market). This result is consistent with recent claims from Draghi (2025) and Adilbish et al. (2025). However, unlike these studies, we find that the EU effects on extra-EU trade are significantly smaller than the corresponding direct effects of the EU on intra-EU trade.

To translate our estimates into simulation effects on real output for each industry, we rely on three building blocks. First, we use our own partial equilibrium estimates of the impact of the European Union. Second, we rely on a standard NQTM, which is perfectly consistent with our estimating equation. Third, we utilize the first edition of the *International Trade and Production Database for Simulations* (ITPD-S), which is constructed and maintained by the US International Trade Commission (Borchert et al. 2024)<fnref n="5" />

Armed with our partial equilibrium estimates, the NQTM methods, and the ITPD-S data, we obtain simulation results from three counterfactual scenarios. First, we employ our own partial sectoral estimates of the EU effects to calculate the benefits of the European Union, measured in terms of real output changes per industry. Overall, we obtain large gains for all Member States, but we also observe intuitive heterogeneity; larger countries (e.g., Italy, UK, France, and Germany) gain relatively less, while smaller and more central Member States (e.g., Luxembourg and the Netherlands) as well as some more recent joiners (e.g., Hungary, Slovakia, and Slovenia) gain more. Across the industries, as expected, the largest gains in real output are in Agriculture. In summary, and similar to our partial equilibrium conclusions, the main implication from our first counterfactual analysis is that the impact of the EU on the member states has been positive, remarkably strong, and very heterogeneous, both across countries and across industries.

<footnote n="5">⁵This database has two main advantages for our purposes: (i) It is fully balanced and thus appropriate for simulations; and (ii) The ITPD-S corresponds in each dimension to the ITPD-E, and thus is perfectly consistent with our partial equilibrium analysis. As a result, by covering 170 sectors, the ITPD-S is the most disaggregated existing balanced database for simulation analysis. We offer further details on the ITPD-S in Section 3.</footnote>
In our second thought experiment, we increase the EU trade costs with some “riskier” partners, e.g., China and Russia, to the extent that we eliminate about 98 percent of trade with these two countries. We find that limiting trade with Russia would mainly affect the former Soviet republics (e.g., Estonia, Lithuania, and Latvia) as well as countries that are geographically close and economically tied to Russia (e.g., Finland, Bulgaria, and Cyprus). Perhaps not surprisingly, the most affected industries are in “Mining and Energy”.<fnref n="6" /> Limited trade with China has a larger negative impact on average, and it affects different EU members (e.g., Estonia, Czechia, Germany, Poland, and Denmark) and different industries (e.g., mostly in the Manufacturing sector).

Finally, in the third set of counterfactuals, we explore the possibility of further trade liberalization within the EU to offset the losses from limiting trade with ‘riskier’ partners: (i) by focusing on the two countries (Estonia and Cyprus) that were most affected by limiting trade with China and Russia; (ii) by focusing on the largest Member State; and (iii) by addressing the question of how to compensate for the losses of decoupling for the EU as a whole.

We find that a moderate tariff-equivalent decrease of about 4 percent in the trade costs between Estonia and the EU will be more than sufficient to compensate for the losses incurred by this country as a result of limiting its trade with Russia and China.<fnref n="7" /> In the case of Cyprus, we show that almost all losses for Cyprus from the lost trade with Russia and China are due to trade in services. As for the losses concerning Germany and the EU as a whole, the same experiment of a 4 percent reduction in the cost of trading within the EU is more than sufficient to offset the losses from decoupling. The main implications of these experiments are (i) that relatively small further integration efforts within the EU may compensate for

<footnote n="6">⁶Our model is static and calibrated with long term trade elasticities. This has two consequences: first, we miss the transitional dynamics that can be very costly when substitution imposes the establishment of new business relationships and investment in new infrastructures; this limitation is especially important for energy markets. Second, and relatedly, lower short term elasticities are associated with larger economic losses, as illustrated in our exercise below comparing short- and long term losses in Figure 4.</footnote>

<footnote n="7">⁷We classify a 4 percent decrease in tariffs as moderate, compared to the tariff decrease of 18 percent, which we estimate to be due to membership in the EU.</footnote>
the potential losses for the EU Member States, and (ii) that intra-EU trade liberalization in specific sectors may be particularly effective. We view these findings as key contributions of our analysis.

**Related Literature.** Our paper speaks to several strands of literature. Firstly, it provides furhter evidence on the impact of European integration (Mayer et al. 2019, Head & Mayer 2000, Felbermayr et al. 2022, Santamaría et al. 2023, Nagengast et al. 2025) by providing more detailed evidence at the sectoral level, differentiating between the effects on intra-EU and extra-EU trade, and identifying the role of Single market independently of EU membership.

Secondly, our paper is related to the literature on the consequences of the energy crisis, sanctions, and decoupling. Bachmann et al. (2022) use a Baqaae-Farhi model to study the economic effects of a potential cut-off of the German economy from Russian energy imports and find GDP losses below 1 percent, thanks to substitution possibilities. Mahlstein et al. (2022) use a computable general equilibrium model to simulate an embargo of allies against Russian exports and allies' exports to Russia by means of prohibitive tariffs in all sectors. Germany would record a 1.2% drop in GDP, above the allies average loss of 0.5%. Eppinger et al. (2021) use a NQTM to simulate the economic consequences of a decoupling of GVCs, defined as increased barriers to global input trade and the repatriation of the production of intermediate goods. Switching-off all intermediate imports by the US would reduce US GDP by 2.2%, while decoupling the US only from China would cost only 0.1%. Closer to our exercise, Felbermayr et al. (2021) simulate the impact of decoupling the EU from China (a doubling of trade costs in all industries) in a different NQTM framework. In the event of Chinese retaliation, the loss of GDP for the EU would amount to 1.0%. Javorcik et al. (2023) quantify with a Baqaae-Farhi model the economic cost of friendshoring, constructing blocs of like-minded countries based on their UN votes. They introduce a 20% additional tariff on all goods between blocs (of the magnitude of the trade war between the US and China), and, alternatively, a 20% increase in trade costs. In the latter case, the cost is close
to 1.5% for China, 1.0% for France or Germany; 0.5% for North America, but up to 3%
for South-East Asia. We contribute to this literature in two ways: we further disaggregate
industries, and we assess whether further integration among the group of countries would
compensate for the induced economic losses of strategic autonomy.

Lastly, an older literature addresses the choice of the optimal public policy in the presence
of non-cooperative policies of trading partners (Bhagwati & Srinivasan 1976, Mayer 1977,
Arad & Hillman 1979). The bottom line is that public intervention may alleviate the eco-
nomic costs of distortions due to externalities from optimal micro-economic choices or trade
restrictions in exporting or importing countries. Our contribution here is to confirm, in the
European case, that there is room for maneuver by leveraging further integration within the
EU to pursue strategic autonomy. Indeed, in contrast to the *de jure* situation, *de facto* bor-
der effects among Member States are still sizable: Santamaría et al. (2023) show, considering
a shipment originating from a randomly selected European region, that the probability for
it to reach another region outside the country of origin is only one-fifth.

The rest of the paper is organized as follows. Section 2 describes the econometric methods used to estimate the effects of the European Union on intra- and extra-EU trade. Section 3 offers a brief description of the datasets that we used to perform the analysis and their sources. Section 4 presents our partial equilibrium estimates of the effects of the EU on trade. Section 5 reviews the model that we use to perform the counterfactual analysis, and we discuss our main findings. Finally, Section 6 concludes with a summary of our main results and a discussion of some caveats and possible improvements to our analysis.

## 2 Econometric Methods

To obtain estimates of the effects of EU integration for each of the industries in our sample,
we rely on the workhorse model of trade – the gravity equation. Specifically, we estimate
alternative specifications of the following econometric model:

$$X_{ij,t}^k = \exp[\mathrm{INTRAEU}_{ij,t} \times \beta^k + \mathrm{SINGLMARKT}_{ij,t} \times \delta^k + \mathrm{EXTRAEU}_{ij,t} \times \gamma^k] \times \\ \exp[\mathrm{POLICY}_{ij,t} \times \alpha^k + \mathrm{GLOB}_{ij,t}^k + \overline{\mu}_{ij}^k + \pi_{i,t}^k + \chi_{j,t}^k] \times \epsilon_{ij,t}^k. \quad (1)$$

Before we continue to describe each of the elements in equation (1), we note that it is consistent with a wide class of theoretical foundations which, subject to parameter interpretation, lead to isomorphic gravity equations (Arkolakis et al. 2012). This is important for our purposes for at least two reasons. First, as will become clear below, theory will motivate some of the terms in equation (1) as well as our econometric methods. Second, since it is theory-consistent, equation (1) is part of, and can be nested structurally within, the new quantitative trade framework that will be used in the counterfactual analysis.

To specify and estimate equation (1), we capitalize on established developments from the related literature (Larch et al. 2025).<fnref n="8" /> We start with a discussion of the characteristics of the dependent variable, $X_{ij,t}^k$, which denotes nominal (Baldwin & Taglioni 2006) trade flows from exporter $i$ to importer $j$ in industry $k$ at time $t$, i.e., the dependent variable includes directional trade (exports and imports) at the industry level at each point in time. Based on the recent analysis of Egger et al. (2022), we will use data for consecutive years instead of data with intervals, as suggested by Cheng & Wall (2005). $X_{ij,t}^k$ includes both cross-border/international and domestic/internal trade flows, which is consistent with structural gravity theory (Yotov 2022). The inclusion of domestic trade flows is potentially important for estimating the effects of the EU because, as demonstrated in Dai et al. (2014), much of the increase in bilateral trade flows between FTA members is actually due to diversion from domestic sales. Thus, gravity specifications that do not include domestic trade flows may under-predict the effects of FTAs. Moreover, the use of domestic trade flows will enable us

<footnote n="8">⁸In the robustness analysis, we also implement recent methods that combine established practices for gravity estimations with recent developments from the difference-in-differences econometrics literature (Nagengast & Yotov 2025).</footnote>
to simultaneously identify the effects of the EU on intra-EU trade vs. extra-EU trade vs.
the impact of the Single Market.

Due to the separability of the theoretical gravity model (Anderson & van Wincoop 2004, Costinot et al. 2012), equation (1) can be estimated at any level of aggregation, e.g., product, industry, sector, or aggregate. This is important for our current purposes, as it would enable us to estimate the model separately, and consistent with theory, for each of the 170 industries that are available in our data,<fnref n="9" /> while still having corresponding data on domestic trade flows/production, which would enable us to perform counterfactual analysis. In addition, guided by theory, we will be able to obtain aggregate estimates at the desired level of aggregation by pooling (instead of aggregating) the industries in our data.<fnref n="10" />

Motivated by Santos Silva & Tenreyro (2006), we will use the Poisson Pseudo Maximum
Likelihood (PPML) as our preferred estimator. Due to its multiplicative form, the PPML
estimator enables us to include and take advantage of the information contained in the
zeros in our sample. In addition, and more importantly, the PPML estimator successfully
handles heteroskedasticity in trade flows data, which, due to Jensen's inequality, renders the
corresponding OLS estimates inconsistent. PPML also has the advantage of being consistent
with the structural gravity model (Fally 2015). We will estimate our model in Stata with the
**ppmlhdfe** command of Correia et al. (2020), and following the standard approach in the
gravity literature (e.g., Egger & Tarlea (2015), Pfaffermayr (2019), and Pfaffermayr (2022)),
we cluster the standard errors by country pair.

Turning to the policy covariates in our model, the most important term in equation (1) is *INTRA_EU*<sub>ij,t</sub>. We use vector notation to denote this term because we will experiment with several EU variables. In the simplest (and main) scenario, *INTRA_EU*<sub>ij,t</sub> will be defined as a single dummy variable that takes a value of one if two countries in our sample are members of the European Union at the same time. In addition, we will experiment by obtaining directional EU effects depending on when a country joined the EU. Specifically,

<footnote n="9">⁹See the Data Section 3.</footnote>

<footnote n="10">¹⁰Pooling industries improves estimation efficiency and offers benefits in terms of tractability of the results.</footnote>
in the current analysis, we define two groups of countries – ‘New’ members, which joined
the EU after 2000, and ‘Old’ members, including the founding members and the countries
that joined prior to 2000. Based on this definition, we define four EU indicator variables,
including for trade from ‘New’ to ‘Old’ members (EU_NEW_OLD), from ‘Old’ to ‘New’
members (EU_OLD_NEW), from ‘Old’ to ‘Old’ members (EU_OLD_Old), and from ‘New’
to ‘New’ members (EU_NEW_NEW). Finally, we will obtain 170 estimates of the EU
effects for each of the industries in our setting.

The next two covariates in our model are also related to the effects of the EU, however,
they capture the impact of the EU on trade with non-EU members. Specifically, moti-
vated by recent assessments that the EU has promoted trade with non-EU members more
strongly that intra-EU trade, e.g., Draghi (2025) and Adilbish et al. (2025), we introduce
SINGL MARKT<sub>ij,t</sub>, which is an indicator that takes a value of one for trade between the
EU members and four non-EU members that are part of the Single Market (i.e., Iceland,
Liechtenstein, Norway, and Switzerland),<sup>11</sup> and EXTRA_EU<sub>ij,t</sub>, which is an indicator that
is equal to one for trade between the EU members and all other non-EU members in our
sample. We remind the reader that, even in the presence of the full set of exporter-time and
importer-time fixed effects, it is possible to simultaneously identify the effects of all three
EU covariates (i.e., INTRA_EU<sub>ij,t</sub>, SINGL MARKT<sub>ij,t</sub>, and EXTRA_EU<sub>ij,t</sub>) because our
estimating sample includes domestic (in addition to international) trade flows.

The vector *POLICY*<sub>*ij,t*</sub> in equation (1) includes the following time-varying bilateral policy variables: membership in Regional Trade Agreements (RTA) *other than the EU*, membership in the World Trade Organization (WTO), complete trade sanctions (*COMPL SANCT*),

<footnote n="11">¹¹Specifically, SINGL MARKT<sub>ij,t</sub> takes a value of one for trade between the EU members and Iceland and Norway since 1994, when these countries joined the Single Market through the European Economic Area (EEA) Agreement, and since 1995 with Liechtenstein when it became an EEA member. Switzerland did not formally join the Single Market. However, it signed a free trade agreement with the European Economic Community (EEC) in 1972 and another agreement with the EU, which covered agricultural products, in 1999. Since 1972 is significantly before our sample period, the effects of the 1972 agreement will be absorbed by the pair fixed effects in our model. However, SINGL MARKT<sub>ij,t</sub> accounts for the 1999 agreement between the EU and Switzerland but only for agricultural products, i.e., the SINGL MARKT<sub>ij,t</sub> indicator variable that we use for the agricultural industries includes Switzerland post 1998, however, we do not include Switzerland in the Single Market indicator variable that is used in the regressions for the other industries in our sample.</footnote>
partial trade sanctions (*PARTL SANCT*), other sanctions (*OTHER SANCT*), and membership in the Euro Zone (EURO).<fnref n="12" /> and membership in other currency unions (*COMM CURR*).

In principle, some bilateral policies (e.g., tariffs) may vary at the industry-level. However, we do not have such policies in our specification. Note, however, that even when a policy is implemented at the country/aggregate level, e.g., a complete trade sanction, the impact of such a policy may be heterogeneous across industries and sectors. Therefore, we also allow for the estimates of the policy coefficients ($α^k$) to vary by industry/sector and type of policy.

Equation (1) includes four sets of fixed effects. $GLOB_{ij,t}^k$ denotes a vector of time-varying border indicators, one for each year in our sample, which take a value of one for international flows and are equal to zero for domestic trade flows for each year in our sample. The estimates on these dummy variables capture the impact of common (de-)globalization trends that have affected international relative to domestic trade, e.g., improvements in communication, transportation, global recessions, etc. Thus, our key EU variables capture EU effects that are in addition to any common globalization trends among the countries in our sample. Notice that, due to perfect collinearity with the pair fixed effects in our preferred specification, we cannot obtain estimates on the full set of border/globalization variables, and we need to drop one of them. This is irrelevant given our focus, i.e., the choice of reference group for the globalization dummies will not affect our EU estimates.

Equation (1) includes directional/asymmetric pair fixed effects, $\vec{\mu}_{ij}^k$. Motivated by Baier & Bergstrand (2007), and consistent with the average treatment effects methods of Wooldridge (2010), country-pair fixed effects are standardly used in trade gravity models to mitigate potential endogeneity concerns.<fnref n="13" /> Also important for the current purposes, the

<footnote n="12">¹²Thus, the estimates of the EU effects that we obtain would not include possible additional impacts from membership in the Euro Zone.</footnote>

<footnote n="13">¹³We are aware that the country-pair fixed effects in our model cannot fully eliminate all possible endogeneity concerns related to EU membership, e.g., reverse causality, self-selection bias, etc.. However, we believe that such concerns are further mitigated in our disaggregated analysis because it is less likely for trade in a particular industry to motivate EU membership. We also recognize that it is possible that the countries that benefit most from EU integration due to input-output linkages may be more likely to join the EU in the first place (self-selection bias). However, these concerns are addressed in our econometric model because the exporter-industry-time and the importer-industry-time fixed effects will absorb and fully control for all possible within and across industries IO links.</footnote>
country-pair fixed effects would absorb and control for all (observable and unobservable)
time-invariant bilateral trade costs.<fnref n="14" /> On a related note, the country-pair fixed effects will
also absorb the effects of any EU integration efforts that were conducted and entered into
force before the period of investigation. Instead, our estimates would capture the impact
of the EU due to the countries that joined during the period of investigation.<fnref n="15" /> Finally,
following Baier et al. (2019), in order to avoid attributing asymmetries in the underlying
time-invariant trade costs to the EU, we explicitly allow for the country-pair fixed effects to
vary depending on the direction of trade flows, i.e., from “i” to “j” vs. from “j” to “i”. This
directional nature motivates the arrow notation.

Guided by theory, equation (1) also includes source-industry-time and destination-industry-
time fixed effects ($\pi_{i,t}^k$ and $\chi_{j,t}^k$, respectively) to account for structural multilateral resistance
terms (Anderson & van Wincoop 2003) and for any country-time-specific determinants of
trade flows on the source and the destination side. When the model is estimated with
pooled data, the corresponding fixed effects would be at the source-industry-time and the
destination-industry-time dimensions, respectively. Thus, regardless of the level of aggre-
gation, the exporter and importer fixed effects would account for and absorb all country-
industry-time-specific characteristics that influence bilateral trade flows, thus allowing us to
isolate and focus on the remaining bilateral components and the EU effects in particular.

3 Data and Sources

To perform the empirical analysis, we compile datasets that include (i) trade data, (ii)
production data, and (iii) data on bilateral policy variables, which come from several sources.

<footnote n="14">¹⁴Egger & Nigai (2015) and Agnosteva et al. (2019) demonstrate that the standard gravity variables (e.g., distance, etc.) do well in predicting relative bilateral trade costs, however, they fail to capture the level of bilateral trade costs (e.g., they underpredict the bilateral trade costs for poor countries and overpredict them for more developed countries).</footnote>

<footnote n="15">¹⁵For example, our estimates would not reflect the impact of the EU on trade between France and Germany or between other founding members. However, they will capture the EU effects due to new joiners (e.g., Bulgaria) on their trade with existing members, i.e., those who joined before the period of investigation (e.g., on trade between France and Bulgaria) and with other new members who also joined during the period of investigation (e.g., on trade between Romania and Bulgaria).</footnote>
**Trade data.** The data on bilateral trade flows that we employ come from the *International Trade and Production Database for Estimation* (ITPD-E). The ITPD-E was originally developed by Borchert et al. (2020) and, for the current analysis, we use the latest edition of the data (Borchert et al. 2022).<fnref n="16" /> The ITPD-E has several advantages for our purposes. First, it covers a large number of countries (more than 200)<fnref n="17" /> a large number of industries (170) that add up to the whole economy (e.g., including the broad sectors of Agriculture, Mining and Energy, Manufacturing, and Services), and a long period of time (1986-2019), which varies by industry depending on the raw data used. Second, the ITPD-E includes domestic trade flows. As discussed earlier, this is important for identification purposes and for consistency with the counterfactual analysis that we plan to perform<fnref n="18" /> Finally, and most important for our purposes, the ITPD-E is constructed from raw data without reliance on any statistical modeling, which makes it appropriate for estimations<fnref n="19" />

**Production data.** Counterfactual trade policy analyses require balanced production and trade data or, equivalently, balanced data on international and domestic trade. Given our purposes, existing balanced datasets (e.g., WIOD or GTAP) have two disadvantages. First, they are relatively aggregated, e.g. 56 sectors and 43 countries in WIOD, or 65 sectors and 141 individual countries in GTAP. Second, they are *constructed* using statistical methods and

<footnote n="16">¹⁶https://www.usitc.gov/data/gravity/itpde.htm</footnote>

<footnote n="17">¹⁷The large number of countries in our sample is due to the fact that the ITPD-E includes data for a number of small countries, islands, and territories. The criteria for the inclusion of a country in the ITPD-E is that there are bilateral trade flows for this country in at least one of the 170 ITPD-E industries. Thus, not all countries are present in each industry. Another reason for the large number of countries is that the list of ITPD-E countries is “dynamic” as there were some country splits and unifications in the data.</footnote>

<footnote n="18">¹⁸Domestic trade flows data are not available for 14 of the 170 industries in the ITPD-E. These industries are marked with an '*' in Table A.1 of the Supplementary Appendix. The implication is that the intra-EU estimates for these 14 industries are obtained only from international trade data and, therefore, it is not possible to obtain EU effects on trade with non-EU members. Unlike the data used for the estimations, the simulation data is fully balanced and includes domestic trade/production for all industries.</footnote>

<footnote n="19">¹⁹The ITPD-E is constructed from four main different original sources. For Agriculture the trade and production data come from the Food and Agriculture Organization of the United Nations Statistics Division (FAOSTAT). Manufacturing and Mining and Energy trade data are obtained from the UN Commodity Trade Statistics Database (COMTRADE), while the production data are from the UNIDO United Nations Industrial Statistics (INDSTAT) Database. For services trade, ITPD-E uses information from the WTO-UNCTAD-ITC Annual Trade in Services Database and the UN Trade in Services Database (UN TSD). Services gross output data are from the UN System of National Accounts (UN SNA) Database. See Borchert et al. (2020) and (Borchert et al. 2022) for further details on the construction and features of the ITPD-E.</footnote>
they do not have corresponding datasets based on raw data appropriate for estimations.<fnref n="20" />

To overcome both of these challenges, we capitalize on a new dataset, the *International Trade and Production Database for Simulations* (ITPD-S), which is constructed and maintained by the US International Trade Commission (Borchert et al. 2024) and has three advantages for our purposes. First, the ITPD-S is fully balanced. Second, by covering 170 sectors, the ITPD-S is the most disaggregated existing balanced database for simulation/counterfactual analysis. Finally, by construction, the ITPD-S corresponds in each dimension (i.e., industries, countries, and years) to the ITPD-E. In fact, the latter database is the basis for the construction of the ITPD-S. Thus, our key estimates of the effects of the EU will be obtained from a dataset that is based on raw data, and which is consistent with the dataset that will be used for the counterfactual analysis.<fnref n="21" />

The disaggregated data that we will use for our analysis come with some caveats. For example, input-output data are absent at such a level of sectoral detail. Moreover, beyond intermediate consumption, other factors of production, such as embedded capital, are not documented either. This constrains us to perform a comparative static analysis in an endowment setting.

We take two final steps to prepare the data for the simulation analysis. First, following Borchert et al. (2024), we use averaged data from 2017 to 2019. This averaging increases

<footnote n="20">²⁰Three recent exceptions that are suitable for estimation and cover international and domestic trade flows are: (i) the WTO ‘Structural Gravity Database (SGD)’ of Larch et al. (2019, forthcoming at the Canadian Journal of Economics), which covers aggregate manufacturing (1986-2016); the CEPII ‘Tradeprod’ database of Mayer et al. (2023), which covers 9 industrial sectors (i.e., Food, Textiles, Wood-Paper, Chemicals, Minerals, Metals, Machines, Vehicles, Other) over a long time span (1966-2018); and (iii) the ‘Granular Trade and Production Activities’ (GRANTPA) database (Bradley et al. 2024), which is at the product level (1995-2019) but only for 35 European economies. The ITPD-S data, which will be used for our analysis, has the advantages of covering Agriculture, Mining and Energy, Manufacturing, and Services at a more disaggregated level than the SGD and the Tradeprod data, and covering many more countries than the GRANTPA data.</footnote>

<footnote n="21">²¹Without going into details, we note that the ITPD-S was constructed through a sequence of statistical and econometric methods (e.g., interpolation and structural gravity estimations). The ITPD-S is based on the ITPD-E dataset, which we described above, and will be used to obtain our partial estimates of the impact of the EU. As a result, the ITPD-S has the same dimensions as the ITPD-E in terms of sectors (170), countries (258), and years (1986-2019), however, it is perfectly balanced in each year. The only reason why the ITPD-S is not balanced in all years is due to the fact that some countries do not exist in all years, while new ones appear in some years. For further details on the construciton and features of the ITPD-S, we refer the reader to (Borchert et al. 2024).</footnote>
the number of non-zero observations by about 17%, which comes from cases where, for some
international trade flows, there are zero observations in some years and non-zero observations
in other years. An additional benefit of the averaging is that it diminishes the possibility of
our results being influenced by outliers.

Second, for computational ease and without impacting the results, we kept all EU coun-
tries and the countries that account for most of the trade and expenditure in each industry,
and we aggregated the rest of the countries from the full sample into a rest of the world
(ROW) aggregate region. The result is that the countries included separately, including
all EU countries, China and Russia, account on average for 99.9 percent of the data, and
the industry coverage varies between 90.6 and 100 percent. We also note that none of the
countries aggregated in ROW were subject to any of the initial shocks in our counterfactual
experiments. Finally, in a robustness analysis, we confirm that the results do not change if
we completely drop the ROW regions from our counterfactual analysis.

**Policy Gravity Variables.** The data for the policy variables are from Stefanova et al. (2025), and the original data sources are as follows: (i) The data on membership in the European Union (EU) and data on membership in the World Trade Organization (WTO) come from the *Dynamic Gravity Dataset* (DGD) of the USITC (Gurevich & Herman 2018)<fnref n="22" /> (ii) The data on other currency unions were constructed by Jose de Sousa<fnref n="23" /> (iii) The data on membership in Regional Trade Agreements (RTAs) are from Egger & Larch (2008) and comprise information on enforced RTAs until year 2021²⁴ (iv) The data on sanctions are from the latest edition of the *Global Sanctions Database* (Felbermayr et al. 2020, Syropoulos et al. 2024)<fnref n="25" /> Finally, (v) we construct our own indicator variable for countries that use the Euro based on data from the European Union<fnref n="26" />

<footnote n="22">²²https://www.usitc.gov/data/gravity/dgd.htm</footnote>

<footnote n="23">²³http://jdesousa.univ.free.fr/data.htm</footnote>

<footnote n="24">²⁴https://www.ewf.uni-bayreuth.de/en/research/RTA-data/index.html</footnote>

<footnote n="25">²⁵https://www.globalsanctionsdatabase.com/</footnote>

<footnote n="26">²⁶https://european-union.europa.eu/institutions-law-budget/euro/countries-using-euro_en</footnote>
# 4 Estimation Results and Analysis

We present and discuss our estimates of the effects of the EU on trade. We start with an analysis of the estimates of the disaggregated industry-level EU effects that we obtain from specification (1). Then, we discuss and compare the estimates of the EU effects on trade within the EU and with non-EU members. Finally, we summarize the results from several robustness experiments. Since we obtain a large number of estimates (i.e., one for each of the 170 industries in our sample), we rely on a mixture of tables and figures to present them.

Our main estimates of the EU effects on disaggregated intra-EU trade are visualized in Figure 1. The top panel of Figure 1 reports the estimates for all ITPD-E industries, while the four panels in the bottom of the figure report the estimates for each of the four broad sectors in the ITPD-E. For clarity, in each panel of Figure 1, we have dropped the top and bottom 5 percent of the estimates. The full set of estimates for the EU effects on intra-EU trade is included in Table A.1 of the Supplementary Appendix.

Several findings stand out from the estimates in the top panel of Figure 1. First, the EU has been extremely successful in promoting trade among the Member States.<fnref n="27" /> This is supported by the fact that the vast majority of the estimates (more than 3-quarters) are positive, and most of them are sizable and statistically significant. Following the Cecchini report tackling “the Cost of Non-Europe” (Cecchini et al. 1988), the benefits of the EU have been repeatedly documented in the literature (Fontagné et al. 1998, Mayer et al. 2019, Head & Mayer 2021). Our large estimates are obtained at a more disaggregated level, which sheds light on the heterogeneous effects of EU integration not only between Member states but also across industries.

Second, on average, our estimates of equation (1) imply that the partial equilibrium impact of the EU on member's trade has been an increase of about 87 percent, calculated as $(\exp(0.651) - 1) * 100$, where 0.651 is the mean of the EU estimates from Figure 1. The

<footnote n="27">²⁷We remind the reader that our EU estimates do not include the effects of the Eurozone. Thus, for the countries that have adopted the Euro, the impact of European integration on international trade should be even larger. See Berthou & Fontagné (2013) for a more refined analysis of the impact of the Euro.</footnote>
corresponding number based only on the positive values in Figure 1 is 194 percent. Using a
standard value for the trade elasticity of 5, our estimates suggest that EU membership has
led to direct trade volume gains that are equivalent to tariff reductions of 12 percent and
19 percent, respectively.<fnref n="28" /> We do obtain some negative (although mostly not statistically
significant) estimates. Some of the negative estimates may be explained by directional trade
cost asymmetries, others may point to anomalies in the data.

Finally, we find that the effects of the EU have been very heterogeneous across the industries and the broad sectors in our sample. The four bottom panels of Figure 1 visualize the estimates for each of the four broad sectors in the ITPD-E. These estimates reveal that the strongest EU effects have been in Agriculture, which is consistent with and can be interpreted through the lens of the EU Common Agricultural Policy (CAP). Even though the uniform effects of CAP (e.g., subsidies to farmers) are fully controlled for and absorbed by the export-time fixed effects in our model, the domestic trade dimension of our data enables us to capture possible diversion from domestic to international trade flows, i.e., the possibility that the CAP has promoted intra-EU trade disproportionately, in addition to the uniform (presumably positive effects) on domestic production. The estimates for Services are mostly positive and sizable, but we also obtain some negative estimates, which are not precisely estimated. Finally, we note that half of the estimates for Mining and Energy are not statistically significant.

The large estimates for Agriculture contrast with the relatively small estimates for Man-
ufacturing, which may have two non-exclusive explanations. One is the changing geography
of the world economy, e.g., some natural trading partners (e.g., China) are outside the EU.
Another possible explanation is the muted nature of European industrial policy over the
period considered here. Without attempting to unravel these explanations, what emerges is
that the trade potential in this broad sector is perhaps not being fully exploited and realized.

<footnote n="28">²⁸We employ a trade elasticity value that corresponds to a value of 6 for the elasticity of substitution, in line with the average elasticity estimates computed at the product level. See Fontagné et al. (2022). The tariff-equivalent across all estimates is calculated as (exp(0.627/(1 − σ)) − 1) * 100, where σ = 6.</footnote>
It is also true that the estimates for the manufacturing industries are quite heterogeneous, and we do see from Figure 1 that some of them are quite large.

Since we cannot identify the nature of the individual industries in Figure 1, we obtain more aggregate sectoral estimates. To do so, rather than aggregating the industry data, we pool together individual industries into 18 sectors, including Agriculture, Mining and Energy, Services, and 15 Manufacturing sectors. We also use the sectoral estimates to discuss and compare the effects of the EU on intra-EU trade vs. extra-EU trade vs. the Single Market. Guided by theory, each of the fixed effects from equation (1) now also varies across the industry dimension, and we obtain the intra-EU trade vs. extra-EU trade vs. the Single Market simultaneously within the same specification, which is estimated for each sector.

Our estimates appear in Table 1 and are visualized in Figure 2. They reveal several important patterns and relationships. All but one of the estimates of the effects of the EU on intra-EU trade in panel A of Table 1 and in the top panel of Figure 2 are positive, sizable, and statistically significant. The single exception, with a negative but economically small and not statistically significant estimate, is for ‘Apparel and Footwear’. Overall, the estimates from panel A of Table 1 confirm our earlier conclusion that the EU has been extremely successful in promoting trade among the Member States.

We also observe significant heterogeneity in the sectoral estimates of the impact of the EU on intra-EU trade. The estimate for Agriculture is among the largest, implying that the EU has led to an increase in trade volume in this sector of more than 200 percent. The other three sectors with particularly large estimates are ‘Beverages and Tobacco’, ‘Textiles’, and ‘Food’. The fifth largest estimate is for Services.<fnref n="29" /> Consistent with the results in Figure 1, the estimate for ‘Mining and Energy’ is not estimated precisely, but it is statistically significant.<fnref n="30" /> Also consistent with Figure 1, most of the estimates for the broad Manufacturing sector are positive and statistically significant, but they are relatively small.

<footnote n="29">²⁹We remind the reader that the Services data in the ITPD-E starts in 2000.</footnote>

<footnote n="30">³⁰When pooling the industries in the ‘Mining and Energy’ sector, we exclude industry ‘35: Gas production and distribution’ because we could not obtain convergence with it.</footnote>
Panel B of Table 1 reports our estimates of the specific effects of the Single Market. Consistent with our prior expectations, we find that the Single Market has indeed promoted external trade between EU countries and other members of the Single Market. This is confirmed by the fact that 14 of the 18 estimates of the Single Market effects in panel B of Table 1 are positive, and 10 of them are statistically significant. The rest of the estimates are economically small. With two exceptions ('Mining and Energy' and 'Metal Products'), the Single Market effects are smaller than the corresponding intra-EU effects for each of the broad sectors in our sample. This can also be seen from panel B of Figure 1. An important policy implication of this analysis is that the EU has been more effective in promoting trade among its members than the Single Market. Finally, we find that the impact of the Single Market is heterogeneous across sectors.

Panel C of Table 1 reports our estimates of the effects of the EU on trade with non-EU countries (apart from those that are in the Single Market). We find that the EU has promoted trade with these third countries: 11 out of the 18 sectoral estimates we obtain are positive and statistically significant. This is consistent with the claims from Draghi (2025) and Adilbish et al. (2025). However, unlike these studies, we find that the EU effects on extra-EU trade are significantly smaller than the corresponding direct effects of the EU on intra-EU trade, and that a significant fraction of them (e.g., 7 out of 18) are not statistically significant. This can also be seen in the bottom panel of Figure 2. Finally, the EU impact on external trade is also heterogeneous across sectors.

We conclude this section with three robustness experiments. For brevity, we summarize the main results here, and relegate full implementation details and results to the Supplementary Appendix. First, we compare the intra-EU estimates with the estimates for all other RTAs in our sample. We find that the intra-EU estimates are significantly larger than the RTA effects, and the fraction of positive and significant RTAs is much smaller too. Thus, it reinforces the perception of the EU as one of the most successful trade liberalization arrangements. Second, we obtain estimates of the EU effects depending on the direction of
trade flows and after splitting the EU countries into ‘Old’ vs. ‘New’ members, as defined in Section 2. The estimates of the directional EU effects are positive, large, and statistically significant. Importantly, they are similar across the four groups of EU members. Third, capitalizing on recent methods from Nagengast & Yotov (2025) and Nagengast et al. (2025), we obtain heterogeneity-robust DiD estimates of the EU effects on intra-EU trade. These estimates are also positive, statistically significant, and heterogeneous across the industries in our sample. The new intra-EU estimates are highly correlated with, but larger than, our main estimates from Figure 1. We refer the reader to the Supplementary Appendix for further details on the robustness experiments that we perform.

# 5 Counterfactual analysis

This section presents the results from three simulation scenarios. First, we use the partial equilibrium estimates of the EU effects on intra-EU trade that we obtained in the previous section (see Figure 1) to calculate the real-output benefits of the EU for its members. Then, we evaluate the impact of decreasing/eliminating trade with ‘riskier’ partners. Specifically, we simulate a decrease in trade between the EU and China and Russia by 98 percent, which is equivalent to the imposition of a uniform tariff of about 55 percent across all industries in our data. Finally, and most importantly, we demonstrate that, through comparatively small trade liberalization efforts within the EU, the EU can fully offset the losses due to the forgone trade with China and Russia from the previous simulation scenario. Subsection 5.1 reviews the theoretical framework that we employ. Then, in Subsection 5.2, we summarize the findings from the three counterfactual experiments.

## 5.1 Summary of the structural gravity system

Our counterfactual analysis relies on the structural gravity model, a.k.a. the new quantitative trade model (NQTM), because it has been recognized and widely accepted as the
benchmark/workhorse model for simulation analysis of the effects of trade policies. In addition to its predictive power and theoretical foundations, which are consistent with a large class of trade models, the structural gravity system has two important and specific advantages for our purposes. First, it nests a structural gravity equation that corresponds directly to our econometric specification. Thus, our partial equilibrium estimates are consistent with the simulation framework. Second, the gravity system is ‘separable’ at the product/industry/sector level, which will enable us to perform the counterfactual analysis for each individual industry in our data.<fnref n="31" />

Our approach also comes with two important caveats. First, we do not account for input-output (IO) linkages. This is due to two reasons: (i) data limitations, i.e., we are not aware of IO tables that match the level of disaggregation of our analysis; and (ii) computational limitations, i.e., we are not aware of an existing algorithm that can accommodate GE counterfactual analysis with IO links for 170 industries. Second, our analysis is static and long-run. By construction, this implies larger gains from trade liberalization and smaller losses from protection. Distinguishing between the short- and the long-run requires a dynamic framework, which is beyond the scope of our analysis. However, below we offer a robustness experiment that delivers ‘reduced-form’ short-run welfare effects.

Given that the gravity system is isomorphic to many different foundations (e.g., Arkolakis et al. (2012)), in the following presentation, we rely on the simplest gravity theory, which is built on the assumptions of an endowment economy on the supply side and globally common CES preferences on the demand side (Anderson 1979, Anderson & van Wincoop 2003, 2004). The solution for expenditures on goods shipped from country *i* to country *j* of the consumer's

<footnote n="31">³¹The rest of the exposition in this section follows Beverelli et al. (2023), who study the impact of institutional quality on international trade and, more recently, Borchert et al. (2024), who quantify the impact of globalization on trade and welfare in the world over the period 1986-2019. Using the newly-created ITPD-S, they employ a solution of the structural gravity model in changes, following Dekle et al. (2007) and Dekle et al. (2008).</footnote>
optimization problem leads to the following expression for bilateral trade flows:

$$X_{ij} = \left( \frac{\alpha_i p_i t_{ij}}{P_j} \right)^{1-\sigma} E_j. \qquad (2)$$

Although, for expositional simplicity, equation (2) does not include time and sector/product subscripts or superscripts, the gravity system holds at any desired level of aggregation (Anderson & van Wincoop 2004, Costinot et al. 2012). This is important for the remaining discussion in this subsection and for our simulation analysis in the next subsection, which will be conducted at the industry level.

Turning to the different terms in equation (2), $\alpha_i$ is the CES share preference parameter, $p_i$ denotes the price of the good in country $i$, $t_{ij}$ denotes any determinants of trade between countries $i$ and $j$, including time-varying bilateral trade barriers (e.g., EU membership) as well as time-invariant trade costs, which were accounted for with the country-pair fixed effects in our econometric model. The CES price aggregator $P_j$, which can be interpreted as an ideal price index that combines the prices from all countries, is given by $P_j^{1-\sigma} = \sum_l (\alpha_l p_l t_{lj})^{1-\sigma}$. $E_j$ is the expenditure in country $j$, which can be calculated for each country as sum across all bilateral imports, including the domestic sales in country $j$: $E_j = \sum_i X_{ij}$. Due to the assumption of an endowment economy, we have $E_i = Y_i + TI_i = p_i Q_i + TI_i$, where $Y_i$ denotes the total value of production of country $i$, which can be calculated as total sales at home and abroad: $Y_i = \sum_j X_{ij}$. $Q_i$ denotes the initial endowments in each country $i$, and $TI_i$ denotes the trade imbalances, which are held constant.32 Finally, $\sigma$ denotes the elasticity of substitution.

Based on the gravity model and following Arkolakis et al. (2012) we can define:

$$\hat{W}_i = (\hat{\pi}_{ii})^{1/(1-\sigma)}, \qquad (3)$$

<footnote>32There are various ways to deal with trade imbalances. Holding them constant between the baseline and counterfactual, as we do, ensures that world trade imbalances are zero in the baseline and counterfactual. See for a nice discussion Costinot & Rodriguez-Clare (2014).</footnote>
where $\hat{\pi}_{ii}$ denotes the changes in the share of expenditure on goods from country i. With aggregate data, $\hat{W}_j$ can be interpreted as the change in expenditure, real income, or welfare in response to a trade shock. However, since our counterfactual analysis will be performed separately for each industry, we can no longer interpret the corresponding sectoral indexes as welfare or real GDP effects. Strictly defined, for each industry, they will be constructed as the ratio of nominal income (e.g., wage) changes in the industry over the changes in consumer prices for the goods paid in this same industry. Thus, our ‘welfare-like’ ACR measures are closer to terms-of-trade (ToT) indexes or real output per industry. It may be possible to obtain complementary sectoral and aggregate welfare measures by aggregating the consumer prices across all industries, e.g., by nesting the CES preferences across varieties within a given industry into Cobb-Douglas preferences across the different industries. However, we do not do this in the current analysis because our primary objective is to obtain industry-specific effects from our counterfactual analysis.

The shocks in the counterfactual experiments that we will perform will be triggered by changes in trade costs ($\hat{t}_{ij}$), e.g., lower trade costs due to EU integration or higher trade costs with ‘riskier’ partners. These changes in trade costs will translate into ‘ToT/real output’ changes by affecting the share of expenditure on goods from country i. Following Dekle et al. (2007, 2008), country i’s share in country j’s spending is defined as $\pi_{ij} = X_{ij}/E_j$, and, using (2), we calculate the change of $\pi_{ij}$ between the baseline (denoted with superscript b) and the counterfactual (denoted with superscript c) as follows:

$$ \hat{\pi}_{ij} = \frac{\pi_{ij}^c}{\pi_{ij}^b} = \frac{(\hat{p}_i \hat{t}_{ij})^{1-\sigma}}{\sum_l \pi_{lj} (\hat{p}_l \hat{t}_{lj})^{1-\sigma}}. \quad (4) $$

Due to the assumption of an endowment economy, we can express $\hat{Y}_j = \hat{p}_j$ and $\hat{E}_i = E_i^c/E_i = (\hat{Y}_i Y_i + TI_i)/E_i$, and $\hat{Y}_i$ can be calculated from:

$$ Y_i \hat{Y}_i = \sum_j \frac{\pi_{ij} (\hat{Y}_i \hat{t}_{ij})^{1-\sigma}}{\sum_l \pi_{lj} (\hat{Y}_l \hat{t}_{lj})^{1-\sigma}} (\hat{Y}_j Y_j + TI_j). \quad (5) $$
One important advantage of solving the gravity system in changes is that it has minimum data requirements. Specifically, in addition to the changes in the trade cost vector, we only need data on trade flows (including domestic and international trade flows) and value(s) for σ. We make two decisions with respect to the trade data. First, we limit the simulation samples for each industry to include all EU members, Russia, China, and the US, plus the thirty largest additional exporters in the corresponding industry. We made this decision to ensure convergence due to cases involving small countries that do not trade certain products. On average, the resulting sample covers 98.7 percent of trade across all industries, which varies between 90.6% and 100% for individual industries.33 Thus, despite limiting the number of countries, we still cover almost all trade in the world for each industry. Second, even after limiting the number of countries, we still have a few cases when a country does not produce and trade at all in a given industry. Those cases were dropped from the simulations, but their number is really small, i.e., a few small countries in only some of the industries.

To obtain our counterfactual results, we chose a single/common value of 6 for the elasticity of substitution σ, which is standard in the gravity literature. The trade elasticity can vary by industry, both theoretically and empirically. There indeed exist very detailed disaggregated estimates of the trade elasticity, e.g., the product-level estimates of Fontagne et al. (2022) that are obtained at the 6-digit HS level from a gravity setting such as ours. Such heterogeneous elasticities across products could be obtained at the industry level of aggregation that we employ in this paper. However, our simulation results will be obtained with a constant value across the industries in our sample: using a common trade elasticity will enable us to better decompose the drivers of the heterogeneous response to the trade shocks that we introduce across the EU countries, with sectoral impacts driven by the structure of the matrices of trade flows and trade costs, and not by differences in sectoral trade elasticities.

Using the trade data, we can obtain/define $Y_i = \sum_j X_{ij}, E_j = \sum_i X_{ij}, TI_i = E_i - Y_i$, and

<footnote n="33">33 We also experimented by increasing the number of largest exporters to the top 50 and the top 70 and as expected, whenever convergence was achieved, the results were very similar.</footnote>
$\pi_{ij} = X_{ij}/E_j$. Then, with $\hat{Y}_i$, we can calculate the remaining changes: $\hat{E}_j = (\hat{Y}_j Y_j + TI_j)/E_j$, $\hat{p}_j = \hat{Y}_j$, $\hat{P}_j = (\sum_l \pi_{lj} (\hat{p}_l \hat{t}_{lj})^{1-\sigma})^{1/(1-\sigma)}$, and $\hat{\pi}_{ij} = (\hat{p}_i \hat{t}_{ij})^{1-\sigma}/(\sum_l \pi_{lj} (\hat{p}_l \hat{t}_{lj})^{1-\sigma})$. Finally, as with all NQTMs, solving our model requires the choice of a numéraire. Given the focus on the EU, we use the factory-gate prices, $p_i$, in Canada as our numéraire. The main reasons are that Canada has reliable data and that it is relatively “remote” from the counterfactuals that we will perform. It should also be noted that the choice of numéraire is inconsequential for our “welfare-like” real output indexes, and it would only be relevant if we reported and discussed any nominal indexes, e.g., prices, trade, etc.

## 5.2 Counterfactuals, Results, and Discussion

We use the structural gravity model that we presented in the previous section to perform three counterfactual experiments. In what follows, we describe the design for each of the simulations, and we discuss our findings. In each case, we obtain real output effects for each country-industry combination in our data. Due to the large number of indexes, we only focus on the EU countries, and we report summary/average indexes across the industries in our sample, which are obtained from the underlying country-industry indexes with output shares used as weights. The motivation for this choice is to attach larger weight to the more important industries in each country. We use a combination of tables and figures.

**Counterfactual 1: The impact of the EU.** Our first counterfactual analysis evaluates the impact of the European Union. To this end, we start with our real-world data as the baseline, and we use our own estimates of the partial intra-EU effects for each industry to simulate a scenario *without* the EU (e.g., similar to Brexit but for all members). In the few cases/industries where we obtained negative partial estimates intra-EU (see Table A.1), we set them to zero. Thus, real output in these industries is not affected by the EU and they do not contribute to the country-specific indexes that we report and analyze below. By construction, eliminating the EU results in losses for all members. However, to ease
interpretation, we instead report the indexes in absolute value, i.e., as gains.

Figure 3 plots the average, across all industries, of the real output gains from the EU for each Member State.<fnref n="34" /> Although we obtain large and heterogeneous gains for all Member States, larger countries (e.g., Italy, UK, France, and Germany) gain relatively less. By virtue of country size and market potential, in contrast, smaller, more central, and ‘older’ Member States (e.g., Luxembourg and the Netherlands) gain more. We also see relatively large gains for some of the recent and more centrally located EU members (e.g., Hungary, Slovenia, and Slovakia).

While existing estimates of the EU effects on real output and welfare vary considerably (e.g., depending on the data used, the control for domestic flows, the estimator, the reference situation, i.e., the associated controls in terms of RTA, WTO participation, etc., and the type of model used for computing the counterfactual of the integration), our estimates of the EU impact on real output, which vary between 1.1% (for Italy and the UK) and 4.7% (for Luxembourg), are comparable to the corresponding estimates from Mayer et al. (2019) who find an EU (weighted) average welfare gain ranging from 1.3% to 5.5% depending on assumptions. Our main contributions with respect to the real output EU effects are that (i) they are obtained from disaggregated data that are consistent for the estimation and the GE analysis, and (ii) they are based on partial equilibrium estimates obtained from an estimating sample that includes domestic trade flows, which enables us to obtain simultaneously EU effects on intra-EU trade vs. extra-EU trade vs. the impact of the Single Market. The overall policy implication of this analysis is that the General Equilibrium impact of the EU on the member states has been positive, remarkably strong, and very heterogeneous, both across countries and across industries. Geography, country size, sectoral specialization, and the strength of the bilateral trade ties of the economies within the EU, therefore, explain the observed differences.

<footnote n="34">³⁴The real-output indexes from this and all subsequent experiments that we perform in this section are reported in Table A.3 from the Supplementary Appendix.</footnote>
**Counterfactual 2: Eliminating trade with “riskier” partners.** In a second thought experiment, the counterfactual starts with the actual data for the baseline year, i.e., with the EU in place, and we increase the trade costs with some “riskier” partners. Specifically, we increase trade costs to eliminate about 98 percent of the EU trade with China and Russia, *and the two countries retali ate*, i.e., we limit both the exports to and the imports from Russia and China for each of the EU Member States. By construction, such a decrease in trade is equivalent to the imposition of a uniform (across sectors and countries) reciprocal tariff of about 55 percent between the EU Member States vs. China and Russia across all industries in our data. To decompose the effects of limited trade with Russia vs. China, we proceed in three steps.

First, we increase the trade costs between the EU Member States and Russia only. The results from this experiment are reported in the top-left panel of Figure 4, and we find them intuitive for the most part. Specifically, some of the countries that suffer the most are former Soviet republics, e.g., Estonia, Latvia, and Lithuania. The other countries that are affected the most are those that are geographically close and economically tied to Russia, e.g., Finland and Bulgaria. We also note that Greece, and especially Cyprus, lose a lot. The case of Cyprus is particularly interesting because of the historically strong trade ties to Russia, “particularly in services trade, notably tourism and professional business services, which contribute over 25 percent to Cyprus’ total exports of services – the highest among EU countries” (Sakkas & Mavrigiannakis 2024). On the other end of the spectrum, i.e., with very small losses, we see Spain, Portugal, France, the UK, and Luxembourg.

We also find the results intuitive when we consider the effects of limiting trade between the EU and Russia across industries. Panel A of Table 2 lists the fifteen industries, where the losses from cutting trade ties with Russia are the largest. Before we discuss our findings, we remind the reader that the shock to the trade costs with Russia was identical for all industries in our sample. Thus, the widely heterogeneous counterfactual results that we obtain are driven by heterogeneous bilateral dependence, size, and sectoral specialization,
rather than by heterogeneous shocks. As expected, we see from Table 2 that the most affected industries belong to the broad category of “Mining and Energy”. In addition, most of the industries that fall within the broader manufacturing sector are also resource-driven.

In the top-right panel of Figure 4, we report the results from an experiment in which we only increased the trade costs between the EU Member States and China. Comparison between the results in the top-right panel reveals that, on average, the losses from limiting trade with China are larger than those from limiting trade with Russia. The composition of affected countries is also very different, as expected. The five most negatively affected countries in this scenario are Estonia, Czechia, Germany, Poland, and Denmark. Large initial trade volumes with China are the natural explanation for these results. The five Member States that are affected the least in this scenario are Luxembourg, Latvia, Croatia, Belgium, and Lithuania. Notably, Luxembourg and Croatia were also among the countries that were less affected by the decrease in trade with Russia, while Latvia and Lithuania were among the most affected countries.

The industry-level results in response to limiting trade with China are also intuitive. Panel B of Table 2 lists the fifteen industries where the losses from cutting trade ties with China are the largest. These industries are exclusively in the broad Manufacturing sector, with many of them related to ‘Textile’ and ‘Apparel’, as expected.

The estimates in the bottom-left panel of Figure 4 are obtained from an experiment in which we simultaneously increased EU members’ trade costs with Russia and China. Due to the General Equilibrium adjustments and non-linear relationships in our model, these results are not a simple sum of the individual effects from the previous experiments. Nevertheless, there is a clear additive component in the relationship between the three graphs, and it is informative to keep the decomposed estimates in mind when interpreting the aggregate results in the bottom panel. For example, the two most negatively affected countries are Estonia and Cyprus. Based on the top two panels of Figure 4, we see that, in the case of Estonia, the large losses are due to cumulative effects from limiting trade with both Russia
and China, while in the case of Cyprus, the losses are mostly driven by limiting trade with Russia. Overall, as indicated before, the heterogeneity in the results is indeed driven by geography, country size, initial sectoral specialization, and economic ties between the EU members and China and Russia.

As discussed earlier, our analysis is static and long-run. This implies smaller losses from protection and, in particular, from the increase in trade costs between the EU and China and Russia in our counterfactual. Developing a dynamic framework that distinguishes between the short- and long-run effects on the EU from eliminating trade with China and Russia is beyond the scope of our analysis. However, trade elasticities differ in the short- and long-run. Consequently, we capitalize on recent work by Anderson & Yotov (2025), who propose a reduced-form transitional gravity model and implement it empirically to obtain elasticities of substitution that vary from 1.4 in the short run to 5.8 in the long run.<fnref n="35" />

Specifically, in an attempt to obtain ‘reduced-form’ short-run effects from the increase in trade costs with China and Russia, we use a short-run trade elasticity of 1 (instead of the long-run elasticity of 6). The intuition for this experiment is indeed that the smaller elasticity value we use can be interpreted as capturing limited bilateral trade capacities and slower adjustment. Our findings are reported in the bottom-right panel of Figure 4 and, as expected, we see that the losses for all EU members in this scenario are significantly larger (i.e., more than 4 times larger, on average, than the corresponding indexes in the bottom-left panel of Figure 4).

**Counterfactual 3: Compensating trade liberalization.** In the third set of counterfactuals, we explore the possibility of further trade liberalization within the EU to offset the losses from limiting trade with China and Russia. To this end, we first focus on the two countries (Estonia and Cyprus) that were most affected by limiting trade with China and Russia. Then, we obtain results for a large EU member (Germany), and we also sim-

<footnote n="35">³⁵Note that the long-run elasticity of substitution from Anderson & Yotov (2025) corresponds very closely to the elasticity of substitution of 6, which is used in our main counterfactual.</footnote>
ulate further liberalization within the whole European Union. In each case, we start with
the benchmark that includes the EU effects and higher trade costs with China and Russia,
which were implemented in the previous counterfactual. Then, we decrease the trade costs
between some of the EU Member States further, depending on the counterfactual.

The results in the top-left panel of Figure 5 are obtained from a scenario where we uni-
formly decrease the trade costs for all industries between Estonia and the EU by a symmetric
shock to the bilateral trade cost vector between Estonia and the EU that is equivalent to
a partial equilibrium gravity estimate of 0.2, i.e., a hypothetical tariff decrease of less than
4 percent. For comparison, we remind the reader that the corresponding tariff-equivalent
effect of the EU on intra-EU trade that has already taken place is about 19 percent (calcu-
lated as the average of our positive partial EU estimates). Thus, in combination with the
widely heterogeneous EU effects across industries and the fact that Estonia is a relatively
new member, we believe that the 4 percent tariff-equivalent decrease that we implement in
this scenario is plausible and feasible in the longer run.

Two main results stand out from the top-left panel of Figure 5. First, the proposed 4
percent tariff-equivalent decrease in trade costs between Estonia and the EU will be sufficient
to more than offset the losses for this country from limiting trade with Russia and China.
The fact that such a moderate decrease in intra-EU trade costs is more than enough to offset
the losses from no trade with two of the EU's largest trade partners is not surprising because
the EU market is large and close. Thus, even a small success in further integration within
the EU would have large effects.

The second notable finding from this experiment is that, in addition to Estonia, some
other EU countries will gain as well. Perhaps not surprisingly, due to their close geographical
location and strong economic ties to Estonia, these countries include Latvia, Lithuania,
and Finland. This experiment and the corresponding results have broader implications
because the same logic applies in the opposite direction, i.e., in the case of trade protection.
Specifically, if Estonia suffered from increased trade costs, then it would have diverted trade
to its closest trading partners, e.g., Latvia, Lithuania, and Finland.

In our next experiment, we focus on Cyprus, which is the country that has lost the most
from limited trade with Russia and China (see the bottom-left panel of Figure 4 or the
blue estimates in Figure 5). Previously, we have speculated that most of Cyprus’ losses are
probably due to lost services trade with Russia. We now test this hypothesis in the top-right
panel of Figure 5, where we report two sets of estimates. In blue color, we reproduce the
losses for all countries due to limited trade with Russia and China in all sectors. Then,
the estimates in red are obtained from the same scenario but without increasing the trade
costs in Services between the EU and Russia. In other words, we have increased all trade
costs between the EU and China, and all trade costs between Russia and the EU, except
for services trade. Consistent with our expectations, most of the losses for Cyprus are
eliminated, we also see significantly smaller losses for some other countries, e.g., Lithuania,
Estonia, and Latvia.

Given that Estonia and Cyprus are relatively small EU members, one may wonder what it
would take to compensate Germany, the largest EU member in terms of GDP and population,
for the losses from limited trade with Russia and China. We answer this question with the
results in the bottom-left panel of Figure 5, which were obtained from a scenario in which
we uniformly decreased the trade costs for all industries between Germany and the EU by
a symmetric shock to the bilateral trade cost vector between Germany and the EU that is
equivalent to a partial equilibrium gravity estimate of 0.2, corresponding to a hypothetical
tariff decrease of less than 4 percent, i.e., the same scenario that we implemented for Estonia
in the top panel of Figure 5.

Two main results stand out from the bottom-left panel of Figure 5. First, the proposed
4 percent tariff-equivalent decrease in trade costs between Germany and the EU will be
sufficient to offset the losses and also generate significant gains for Germany. While this
result may seem counterintuitive at first because of Germany’s relative size, the explanation
for it is that Germany is a very important trading partner for many/most EU countries.
Thus, when trade is uniformly liberalized with all EU partners, the gains for Germany will be large. The same intuition explains the second main finding from this experiment, which is that trade liberalization with Germany only will be sufficient to offset the losses for some other countries. Perhaps not surprisingly, due to their tight connections with Germany, the two countries that will end up with net gains in this scenario are Luxembourg and Austria.

Stimulated by our findings for Germany, we next ask what would happen if we liberalized trade among all EU members, i.e., as if there were a coordinated round of additional trade liberalization within the EU. Once again, starting with the limited trade with Russia and China, we liberalize trade through a symmetric shock that is equivalent to a partial equilibrium gravity estimate of 0.2 (i.e., a tariff decrease of less than 4 percent), but this time for the trade costs among all EU members. Our findings are reported in the bottom-right panel of Figure 5, and we find them encouraging and intuitive.

The main result from this analysis is that almost all EU members will end up with net gains, and these gains will be larger for the smaller EU members. The only three countries that still experience net losses are Cyprus, Estonia, and Finland. The explanation is that (i) these were the countries with some of the largest losses from limiting trade with Russia and China, and (ii) unlike our first counterfactual, where we only liberalized trade between Estonia and the EU, the 4 percent tariff-equivalent decrease is no longer enough to compensate for the losses for Estonia due to trade creation and trade diversion to and from other EU members that also enjoy trade liberalization within the EU in the new counterfactual, i.e., Estonia would gain more if it traded at lower costs with all EU members while they were not trading more with each other.

# 6 Conclusion

We used established methods and new disaggregated data to quantify the economic impact of the EU on international trade, and conducted stylized thought experiments on strategic
autonomy and offsetting versus deepening EU integration. In summary, we found that the impact of the EU on its Member States has been positive and remarkably strong, but also heterogeneous, both at the partial and at the General Equilibrium level. In addition, our estimates reveal that the EU had a positive impact on trade between EU members and non-members, which has been stronger for trade between Member states and the other countries participating in the Single Market. Importantly, and this clarifies the current debate on the effects of EU integration, we find that both the Single Market effects and the effects of the EU on trade with non-members (i.e., extra-EU trade) were significantly smaller than the corresponding EU effects on intra-EU trade.

We also obtained significant negative effects from eliminating trade with Russia and China, but we showed that these effects are relatively small compared to the gains already achieved from EU integration. Consistent with this, a simulation analysis revealed that even the largest losses from decreased trade with Russia and China can be offset by relatively small further/deeper integration within the EU. As a natural consequence of the usual gravity forces governing trade in goods and services, this result has been established for small countries that benefit greatly from an additional reduction in trade costs vis-a-vis a large European market, whether for goods or services, namely Estonia and Cyprus. Interestingly, this possible compensation also exists for the largest Member State – Germany, which benefits from being a major trading partner for other EU Member States. The same conclusion applies to the EU as a whole, although complex trade diversion and creation effects within the EU are conducive to heterogeneous offsetting effects across Member States.

While we believe that our main conclusions are robust, we see several possible extensions, and we acknowledge some caveats. First, on the estimation front, we could identify a number of other EU effects depending on interests and needs. For example, due to the use of domestic trade flows, we could obtain EU estimates per country and decompose those into trade with members vs. non-members. We could also obtain estimates per pair within the EU, e.g., EU effects on trade between France and Bulgaria. We could also go a step further and obtain
directional pair EU effects, e.g., exports from France to Bulgaria, and vice versa. Second, on
the counterfactuals front, using heterogeneous trade elasticity estimates would add another
source of variation in the outcomes. We decided not to do so here in order to isolate the
impact of the sectoral composition of production and trade in each Member State.

We finally acknowledge the main caveats of adopting the very granular approach chosen
here. We are aware that some of the counterfactual predictions may not be reached due to
constraints on the extensive margin (e.g., due to capacity and/or lack of natural resources).
Moreover, our simple endowment analysis does not explicitly include global value chains and
does not allow for structural dynamic forces that act through asset accumulation. Based
on findings from the existing literature, we expect that allowing for input-output links and
dynamics would actually reinforce most of our conclusions.
References

Adilbish, O. E., Cerdeiro, M. D. A., Duval, M. R. A., Hong, M. G. H., Mazzone, L., Rotunno, L., Toprak, H. H. & Vaziri, M. (2025), Europe's productivity weakness: Firm-level roots and remedies, IMF Working Papers 2025/040, International Monetary Fund.

Agnosteva, D. E., Anderson, J. E. & Yotov, Y. V. (2019), ‘Intra-national Trade Costs: Assaying Regional Frictions’, *European Economic Review* **112**(C), 32–50.

Anderson, J. (1979), ‘A theoretical foundation for the gravity equation’, *American Economic Review* **69**(1), 106–116.

Anderson, J. E. & Yotov, Y. V. (2025), ‘Gravity in transition’, *Review of International Economics* **33**(3), 616–630.

Anderson, J. & van Wincoop, E. (2003), ‘Gravity with gravitas: A solution to the border puzzle’, *American Economic Review* **93**(1), 170–192.

Anderson, J. & van Wincoop, E. (2004), ‘Trade costs’, *Journal of Economic Literature* **42**(3), 691–751.

Arad, R. W. & Hillman, A. L. (1979), ‘Embargo threat, learning and departure from comparative advantage’, *Journal of International Economics* **9**(2), 265–275.

Arkolakis, C., Costinot, A. & Rodriguez-Clare, A. (2012), ‘New trade models, same old gains?’, *American Economic Review* **102**(1), 94–130.

Bachmann, R., Baqaee, D., Bayer, C., Kuhn, M., Löschel, A., Moll, B., Peichl, A., Pittel, K. & Schularick, M. (2022), What if? the economic effects for Germany of a stop of energy imports from Russia, Technical report, ECONtribute Policy Brief.

Baier, S. & Bergstrand, J. (2007), ‘Do free trade agreements actually increase members’ international trade?’, *Journal of International Economics* **71**(1), 72–95.

Baier, S. L., Yotov, Y. V. & Zylkin, T. (2019), ‘On the widely differing effects of free trade agreements: Lessons from twenty years of trade integration’, *Journal of International Economics* **116**, 206–226.

Baldwin, R. & Taglioni, D. (2006), ‘Gravity for dummies and dummies for gravity equations’, *NBER Working Paper No. 12516*.

Berthou, A. & Fontagné, L. (2013), ‘How do Multiproduct Exporters React to a Change in Trade Costs?’, *Scandinavian Journal of Economics* **115**(2), 326–353.

Beverelli, C., Keck, A., Larch, M. & Yotov, Y. (2023), ‘Institutions, Trade and Development: A Quantitative Analysis’, *Oxford Economic Papers*.

Bhagwati, J. N. & Srinivasan, T. N. (1976), ‘Optimal trade policy and compensation under endogenous uncertainty: The phenomenon of market disruption’, *Journal of International Economics* **6**(4), 317–336.
Boehm, C. E., Flaaen, A. & Pandalai-Nayar, N. (2019), ‘Input linkages and the transmission of shocks: Firm-level evidence from the 2011 tōhoku earthquake’, *Review of Economics and Statistics* **101**(1), 60–75.

Borchert, I., Larch, M., Shikher, S. & Yotov, Y. (2020), ‘The International Trade and Production Database for Estimation (ITPD-E)’, *International Economics*, https://doi.org/10.1016/j.inteco.2020.08.001.

Borchert, I., Larch, M., Shikher, S. & Yotov, Y. (2022), ‘The International Trade and Production Database for Estimation (ITPD-E): An Update’, *Manuscript*.

Borchert, I., Larch, M., Shikher, S. & Yotov, Y. (2024), Globalization, Trade, and Inequality: Evidence from a New Database, School of Economics Working Paper Series 2024-6, LeBow College of Business, Drexel University.
URL: https://ideas.repec.org/p/ris/drxlwp/202406.html

Borusyak, K. & Jaravel, X. (2017), ‘Revisiting event study design’, *Harvard University Working Paper*.

Borusyak, K., Jaravel, X. & Spiess, J. (forthcoming), ‘Revisiting Event Study Designs: Robust and Efficient Estimation’, *The Review of Economic Studies*.

Bradley, S., Flórez, J., Larch, M. & Yotov, Y. (2024), The Granular Trade and Production Activities (GRANTPA) Database, School of Economics Working Paper Series 2024-1, LeBow College of Business, Drexel University.
URL: https://ideas.repec.org/p/ris/drxlwp/202401.html

Callaway, B., Goodman-Bacon, A. & Sant'Anna, P. H. C. (2024a), *Difference-in-differences with a continuous treatment, Working Paper 32117, National Bureau of Economic Research*.
URL: http://www.nber.org/papers/w32117

Callaway, B., Goodman-Bacon, A. & Sant'Anna, P. H. C. (2024b), *Event-studies with a continuous treatment, Working Paper 32118, National Bureau of Economic Research*.
URL: http://www.nber.org/papers/w32118

Callaway, B. & Sant'Anna, P. H. (2021), ‘Difference-in-differences with multiple time periods’, *Journal of Econometrics* **225**(2), 200–230. Themed Issue: Treatment Effect 1.
URL: https://www.sciencedirect.com/science/article/pii/S0304407620303948

Cecchini, P., Catinat, M. & Jacquemin, A. (1988), *1992-the European Challenge: The Benefits of a Single Market*, Wildwood House.

Cheng, I. & Wall, H. (2005), ‘Controlling for heterogeneity in gravity models of trade and integration’, *Federal Reserve Bank of St. Louis Review* **87**(1), 49–63.

Commission, E. (2009), The World in 2025. Rising Asia and socio-ecological transition, Eur 23921, Directorate General for Research Socio-Economic Sciences and Humanities.
Correia, S., Guimaraes, P. & Zylkin, T. (2020), ‘Fast Poisson estimation with high-dimensional fixed effects’, *Stata Journal* **20**(1), 95–115.

Costinot, A., Donaldson, D. & Komunjer, I. (2012), ‘What goods do countries trade? a quantitative exploration of ricardo’s ideas’, *Review of Economic Studies* **79**(2), 581–608.

Costinot, A. & Rodriguez-Clare, A. (2014), ‘Trade theory with numbers: Quantifying the consequences of globalization’, Chapter 4 in the Handbook of International Economics Vol. 4, eds. Gita Gopinath, Elhanan Helpman, and Kenneth S. Rogoff, Elsevier Ltd., Oxford.

Dai, M., Yotov, Y. V. & Zylkin, T. (2014), ‘On the Trade-diversion Effects of Free Trade Agreements’, *Economics Letters* **122**(2), 321–325.

de Chaisemartin, C., D’Haultfoeuille, X., Pasquier, F. & Vazquez-Bare, G. (2023), ‘Difference-in-differences estimators for treatments continuously distributed at every period’.

de Chaisemartin, C. & D’Haultfœuille, X. (2020), ‘Two-way fixed effects estimators with heterogeneous treatment effects’, *American Economic Review* **110**(9), 2964–2996.
URL: https://www.aeaweb.org/articles?id=10.1257/aer.20181169

de Chaisemartin, C. & D’Haultfœuille, X. (2023), ‘Two-way fixed effects and differences-in-differences estimators with several treatments’, *Journal of Econometrics* **236**(2), 105480.
URL: https://www.sciencedirect.com/science/article/pii/S0304407623001963

Dekle, R., Eaton, J. & Kortum, S. (2007), ‘Unbalanced Trade’, *American Economic Review: Papers and Proceedings* **97**, 351–355.

Dekle, R., Eaton, J. & Kortum, S. (2008), ‘Global Rebalancing with Gravity: Measuring the Burden of Adjustment’, *IMF Staff Papers* **55**(3), 511–540.
URL: http://www.palgrave-journals.com/doifinder/10.1057/imfsp.2008.17

Draghi, M. (2025), ‘Forget the us — europe has successfully put tariffs on itself, The financial times’.

Egger, P. H., Larch, M. & Yotov, Y. V. (2022), ‘Gravity Estimations with Interval Data: Revisiting the Impact of Free Trade Agreements’, *Economica* **89**(353), 44–61.

Egger, P. H. & Nigai, S. (2015), ‘Structural gravity with dummies only: Constrained ANOVA-type estimation of gravity models’, *Journal of International Economics* **97**(1), 86–99.

Egger, P. H. & Tarlea, F. (2015), ‘Multi-way clustering estimation of standard errors in gravity models’, *Economics Letters* **134**(C), 144–147.

Egger, P. & Larch, M. (2008), ‘Interdependent preferential trade agreement memberships: An empirical analysis’, *Journal of International Economics* **76**(2), 384–399.

Eppinger, P., Felbermayr, G. J., Krebs, O. & Kukharskyy, B. (2021), ‘Decoupling global value chains’, *CESifo Working Paper-9079*.
Fally, T. (2015), ‘Structural gravity and fixed effects’, *Journal of International Economics* **97**(1), 76–85.

Felbermayr, G., Gans, S., Mahlkow, H. & Sandkamp, A.-N. (2021), Decoupling europe, Technical report, Kiel Policy Brief.

Felbermayr, G., Groeschl, J. & Heiland, I. (2022), Complex europe: Quantifying the cost of disintegration, Technical report.

Felbermayr, G., Kirilakha, A., Syropoulos, C., Yalcin, E. & Yotov, Y. V. (2020), ‘The Global Sanctions Data Base’, *European Economic Review* **129**(C).

Fontagné, L., Freudenberg, M. & Péridy, N. (1998), ‘Intra-Industry Trade and the Single Market: Quality matters’, *CEPR Discussion Paper-1959*.

Fontagné, L., Guimbard, H. & Orefice, G. (2022), ‘Tariff-based product-level trade elasticities’, *Journal of International Economics* **137**(103593).

Fontagné, L., Martin, P. & Orefice, G. (2024), ‘The many channels of firm’s adjustment to energy shocks: evidence from france’, *Economic Policy* **39**(117), 5–43.

Goldsmith-Pinkham, P., Hull, P. & Kolesár, M. (2022), Contamination bias in linear regressions, Working Paper 30108, National Bureau of Economic Research.
URL: http://www.nber.org/papers/w30108

Goodman-Bacon, A. (2021), ‘Difference-in-differences with variation in treatment timing’, *Journal of Econometrics* **225**(2), 254–277. Themed Issue: Treatment Effect 1.
URL: https://www.sciencedirect.com/science/article/pii/S0304407621001445

Gurevich, T. & Herman, P. (2018), The dynamic gravity dataset: 1948-2016. USITC Working Paper 2018-02-A.

Head, K. & Mayer, T. (2000), ‘Non-Europe: The magnitude and causes of market fragmentation in the EU’, *Review of World Economics (Weltwirtschaftliches Archiv)* **136**(2), 284–314.

Head, K. & Mayer, T. (2021), ‘The united states of europe: a gravity model evaluation of the four freedoms’, *Journal of Economic Perspectives* **35**(2), 23–48.

Head, K. & Mayer, T. (2025), ‘No, the eu does not impose a 45% tariff on itself’, *Vox-EU*.

Hull, P. (2018), ‘Estimating treatment effects in mover designs’.

Javorcik, B., Kitzmueller, L., Schweiger, H. & Yıldırım, A. (2023), ‘Economic costs of friend-shoring’, *EBRD Working Paper*.

Larch, M., Monteiro, J.-A., Piermartini, R. & Yotov, Y. (2019, forthcoming at the Canadian Journal of Economics), On the Effects of GATT/WTO Membership on Trade: They are Positive and Large After All, School of Economics WP Series 2019-4, Drexel University,.
Larch, M., Shikher, S. & Yotov, Y. (2025), Estimating gravity equations: Theory im-
plications, econometric developments, and practical recommendations, Working Papers
2025001, Center for Global Policy Analysis, LeBow College of Business, Drexel University.

Mahlstein, K., McDaniel, C., Schropp, S. & Tsigas, M. (2022), ‘Estimating the economic effects of sanctions on russia: an allied trade embargo’, *The World Economy* **45**(11), 3344–3383.

Mayer, T., Santoni, G. & Vicard, V. (2023), The cepii trade and production database, Working Papers 2023-01, CEPII.

Mayer, T., Vicard, V. & Zignago, S. (2019), ‘The cost of non-europe, revisited’, *Economic Policy* **34**(98), 145–199.

Mayer, W. (1977), ‘The national defense tariff argument reconsidered’, *Journal of international economics* **7**(4), 363–377.

Nagengast, A. J. & Yotov, Y. V. (2025), ‘Staggered difference-in-differences in gravity settings: Revisiting the effects of trade agreements’, *American Economic Journal: Applied Economics* **17**(1), 271–296.

Nagengast, A., Rios-Avila, F. & Yotov, Y. (2025), The European Single Market and Intra-EU Trade: An Assessment with Heterogeneity-Robust Difference-in-Differences Methods, Technical Report https://doi.org/10.1111/ecca.70018.

Pfaffermayr, M. (2019), ‘Gravity models, PPML estimation and the bias of the robust standard errors’, *Applied Economics Letters* **26**(18), 1467–1471.

Pfaffermayr, M. (2022), ‘Cross-section gravity models, ppml estimation and the bias correction of the two-way cluster-robust standard error’, *unpublished manuscript* .

Sakkas, S. & Mavrigiannakis, K. (2024), Cyprus and the eu sanctions on russia: macroeco-
nomic impacts and policy recipes for a sustained recovery, Blog post, London School of
Economics.
URL: https://blogs.lse.ac.uk/greeceatlse/2024/04/18/cyprus-and-the-eu-sanctions-on-
russia-macroeconomic-impacts-and-policy-RECIPES-FOR-A-SUSTAINED-RECOVERY/

Santamaría, M., Ventura, J. & Yeşilbayraktar, U. (2023), ‘Exploring european regional trade’, *Journal of International Economics* p. 103747.

Santos Silva, J. & Tenreyro, S. (2006), ‘The log of gravity’, *Review of Economics and Statistics* **88**(4), 641–658.

Stefanova, S., Bandarkar, S. & Yotov, Y. V. (2025), ‘A reassessment of the impact of the organization for economic co-operation and development on international trade’, *Applied Economics* pp. 1–18.
URL: https://doi.org/10.1080/00036846.2025.2584710
Sun, L. & Abraham, S. (2021), ‘Estimating dynamic treatment effects in event studies with heterogeneous treatment effects’, *Journal of Econometrics* **225**(2), 175–199. Themed Issue: Treatment Effect 1.

URL: https://www.sciencedirect.com/science/article/pii/S030440762030378X

Syropoulos, C., Felbermayr, G., Kirilakha, A., Yalcin, E. & Yotov, Y. V. (2024), ‘The global sanctions data base–Release 3: COVID-19, Russia, and multilateral sanctions’, *Review of International Economics* **32**(1), 12–48.

Wooldridge, J. (2010), *Econometric Analysis of Cross Section and Panel Data*, 2nd edn, The MIT Press, Cambridge, Massachusetts.

Wooldridge, J. M. (2021), ‘Two-way fixed effects, the two-way mundlak regression, and difference-in-differences estimators’, *Available at SSRN 3906345*.

Wooldridge, J. M. (2023), ‘Simple approaches to nonlinear difference-in-differences with panel data’, *The Econometrics Journal*.

Yotov, Y. V. (2022), ‘On the role of domestic trade flows for estimating the gravity model of trade’, *Contemporary Economic Policy*.

URL: https://doi.org/10.1111/coep.12567
<figure><img src="image_6.png" /><figcaption>Figure 1: The European Union effects on intra-EU trade</figcaption></figure>

Note: This figure visualizes the industry-level estimates of the effects of the European Union (EU) on intra-EU trade. The top panel of the figure shows all industry-level estimates and their corresponding confidence intervals, while the bottom four panels report estimates and confidence intervals for the broad sectors of Agriculture, Mining and Energy, Manufacturing, and Services. For clarity, in each panel of the figure, we have dropped the top and bottom 5 percent of the estimates. The full set of estimates appears in Table A.1 of the Supplementary Appendix. See text for further details.
<table><caption>Table 1: The effects of European integration on sectoral trade</caption><thead><tr><th rowspan="2">Sector ID</th><th rowspan="2">Sector Description</th><th colspan="2">A. Intra-EU</th><th colspan="2">B. Single Market</th><th colspan="2">C. Extra-EU</th></tr><tr><th>Est.</th><th>Std.Err.</th><th>Est.</th><th>Std.Err.</th><th>Est.</th><th>Std.Err.</th></tr></thead><tbody><tr><td>1</td><td>Agriculture</td><td>1.267</td><td>(.088)****</td><td>0.430</td><td>(.084)****</td><td>0.159</td><td>(.051)**</td></tr><tr><td>2</td><td>Mining and Energy</td><td>0.471</td><td>(.169)**</td><td>0.910</td><td>(.215)****</td><td>0.050</td><td>(.115)</td></tr><tr><td>3</td><td>Food</td><td>1.044</td><td>(.057)****</td><td>-0.064</td><td>(.132)</td><td>-0.008</td><td>(.046)</td></tr><tr><td>4</td><td>Beverages and Tobacco</td><td>1.328</td><td>(.134)****</td><td>0.844</td><td>(.294)**</td><td>0.093</td><td>(.08)</td></tr><tr><td>5</td><td>Textiles</td><td>1.192</td><td>(.12)****</td><td>0.503</td><td>(.108)****</td><td>0.479</td><td>(.071)****</td></tr><tr><td>6</td><td>Apparel and Footwear</td><td>-0.095</td><td>(.162)</td><td>-0.281</td><td>(.169)</td><td>0.155</td><td>(.083)</td></tr><tr><td>7</td><td>Wood</td><td>0.570</td><td>(.087)****</td><td>0.382</td><td>(.11)***</td><td>0.412</td><td>(.074)****</td></tr><tr><td>8</td><td>Paper</td><td>0.379</td><td>(.093)****</td><td>0.253</td><td>(.131)</td><td>0.147</td><td>(.058)*</td></tr><tr><td>9</td><td>Chemicals</td><td>0.246</td><td>(.085)**</td><td>-0.085</td><td>(.098)</td><td>-0.077</td><td>(.046)</td></tr><tr><td>10</td><td>Rubber and Plastic</td><td>0.311</td><td>(.091)***</td><td>0.026</td><td>(.096)</td><td>-0.006</td><td>(.051)</td></tr><tr><td>11</td><td>Mineral Products</td><td>0.379</td><td>(.063)****</td><td>0.134</td><td>(.136)</td><td>0.132</td><td>(.039)***</td></tr><tr><td>12</td><td>Metal Products</td><td>0.735</td><td>(.093)****</td><td>0.743</td><td>(.134)****</td><td>0.177</td><td>(.044)****</td></tr><tr><td>13</td><td>Machines General</td><td>0.298</td><td>(.112)**</td><td>-0.030</td><td>(.094)</td><td>0.141</td><td>(.061)*</td></tr><tr><td>14</td><td>Machines Specific</td><td>0.813</td><td>(.136)****</td><td>0.396</td><td>(.118)***</td><td>0.363</td><td>(.071)****</td></tr><tr><td>15</td><td>Electronics</td><td>0.368</td><td>(.081)****</td><td>0.041</td><td>(.075)</td><td>0.179</td><td>(.043)****</td></tr><tr><td>16</td><td>Transportation</td><td>0.580</td><td>(.172)***</td><td>0.561</td><td>(.147)***</td><td>0.132</td><td>(.098)</td></tr><tr><td>17</td><td>Other Manufacturing</td><td>0.591</td><td>(.116)****</td><td>0.377</td><td>(.12)**</td><td>0.287</td><td>(.06)****</td></tr><tr><td>18</td><td>Services</td><td>0.909</td><td>(.098)****</td><td>0.869</td><td>(.15)****</td><td>0.359</td><td>(.075)****</td></tr></tbody></table>

Notes: This table reports the sectoral estimates of the effects of the European Union (EU) on intra-EU and extra-EU trade, as well as estimates of the impact of the Single Market. The estimates for each sector are obtained from a single specification after estimating a pooled version of equation (1) across the groups of industries that comprise each of the sectors in this table, e.g., the ‘Intra-EU’, ‘Single Market’, and ‘Extra-EU’ estimates for Agriculture are obtained from the same specification by pooling together the 28 agricultural industries. The dependent variable is trade in levels and the Poisson Pseudo Maximum Likelihood (PPML) estimates for each sector are obtained with exporter-industry-time fixed effects, importer-industry-time fixed effects, directional pair-industry fixed effects, time-industry-varying border indicators, and a series of policy controls, whose estimates are omitted for brevity. Panel A reports the EU effects on intra-EU trade. Panel B reports the effects of the Single Market on trade between the EU members and the non-EU members that are part of the Single Market. Panel C reports the EU effects on extra-EU trade. Standard errors are clustered by pair-industry. See text for further details.
<figure><img src="image_7.png" /><figcaption>Figure 2: The effects of European integration on sectoral trade</figcaption></figure>

**Note:** This figure visualizes the sectoral EU estimates from Table 1. The top panel of the figure reports the estimates of the EU effects on Intra-EU trade from panel A of Table 1. The middle panel compares the estimates of the EU effects on Intra-EU trade (from panel A of Table 1) vs. the corresponding effects of the Single Market (from panel B of Table 1). Finally, the bottom panel of the figure reports the estimates of the EU effects on Intra-EU trade (from panel A of Table 1) vs. the corresponding effects on extra-EU trade (from panel C of Table 1). See text for further details.
<figure><img src="image_8.png" /><figcaption>Figure 3: EU membership and real output</figcaption></figure>

**Note:** This figure visualizes the effects of European Union (EU) membership on real output for each of the EU Member States. The indexes are obtained as weighted averages from the country-industry counterfactual results that are based on our partial equilibrium estimates of the effects of the EU on intra-EU trade. See text for further details.
<figure><img src="image_9.png" /><figcaption>Figure 4: Limiting trade with China and Russia</figcaption></figure>

**Note:** This figure reports a series of simulation results that are generated from a counterfactual scenario that limits European Union (EU) trade with Russia and China. In each case, we increase bilateral trade costs between the EU countries and Russia and China so that about 98% of the trade flows between the Member States and Russia and China are eliminated in each direction of trade. The simulation analysis are performed at the industry level, and the indexes that we report here are obtained as weighted averages from the underlying country-industry counterfactual results. The bottom left panel of the figure reports estimates from a scenario where only trade between the EU and Russia is limited. The top right panel reports estimates from a scenario where only trade between the EU and China is limited. The bottom left panel reports estimates from a scenario where both trade with China and Russia is limited. Finally, contrasting with the long-run results reported in the three first panels, the bottom right panel reproduces the results from the bottom left panel but in a short term perspective. See text for further details.
<table><caption>Table 2: Eliminating trade with ‘riskier’ partners. Most affected industries.</caption><thead><tr><th>Industry Description</th><th>%Δ in Real output</th></tr></thead><tbody><tr><td colspan="2"><strong>A. Eliminating trade with Russia</strong></td></tr><tr><td>Processing of nuclear fuel</td><td>-2.002</td></tr><tr><td>Extraction crude petroleum and natural gas</td><td>-1.526</td></tr><tr><td>Fertilizers and nitrogen compounds</td><td>-1.251</td></tr><tr><td>Basic precious and non-ferrous metals</td><td>-0.949</td></tr><tr><td>Government goods and services n.i.e.</td><td>-0.910</td></tr><tr><td>Refined petroleum products</td><td>-0.824</td></tr><tr><td>Mining of hard coal</td><td>-0.771</td></tr><tr><td>Sawmilling and planing of wood</td><td>-0.677</td></tr><tr><td>Dressing and dyeing of fur; processing of fur</td><td>-0.634</td></tr><tr><td>Other mining and quarring</td><td>-0.567</td></tr><tr><td>Animal feed ingredients and pet foods</td><td>-0.536</td></tr><tr><td>Veneer sheets plywood particle board etc.</td><td>-0.480</td></tr><tr><td>Basic iron and steel</td><td>-0.449</td></tr><tr><td>Jewellery and related articles</td><td>-0.359</td></tr><tr><td>Rubber tyres and tubes</td><td>-0.350</td></tr><tr><td colspan="2"><strong>B. Eliminating trade with China</strong></td></tr><tr><td>Luggage handbags etc.; saddlery and harness</td><td>-6.249</td></tr><tr><td>Other manufacturing n.e.c.</td><td>-4.485</td></tr><tr><td>Optical instruments and photographic equipment</td><td>-3.920</td></tr><tr><td>Sports goods</td><td>-3.511</td></tr><tr><td>Domestic appliances n.e.c.</td><td>-3.436</td></tr><tr><td>Other transport equipment n.e.c.</td><td>-3.320</td></tr><tr><td>Musical instruments</td><td>-3.297</td></tr><tr><td>Office accounting and computing machinery</td><td>-3.066</td></tr><tr><td>Lighting equipment and electric lamps</td><td>-3.047</td></tr><tr><td>Games and toys</td><td>-2.999</td></tr><tr><td>TV/radio transmitters; line comm. apparatus</td><td>-2.591</td></tr><tr><td>Knitted and crocheted fabrics and articles</td><td>-2.579</td></tr><tr><td>Dressing and dyeing of fur; processing of fur</td><td>-2.527</td></tr><tr><td>Made-up textile articles except apparel</td><td>-2.525</td></tr><tr><td>TV and radio receivers and associated goods</td><td>-2.389</td></tr></tbody></table>

Notes: This table lists the 15 most affected industries from a scenario that eliminates trade with Russia (in panel A) and from a scenario that eliminates trade with China (in panel B). The rankings are based on the effects on real output per industry, which are obtained as weighted averages from the country-industry counterfactual results that are based on our partial equilibrium estimates of the effects of the European Union (EU) on intra-EU trade. See text for further details.
<figure><img src="image_10.png" /><figcaption>Figure 5: Compensating liberalization</figcaption></figure>

**Note:** This figure reports four sets of simulation results in red color. In each case, the simulation analysis are performed at the industry level, and the indexes that we report here are obtained as weighted averages from the underlying country-industry counterfactual results. For comparison, the estimates that appear in blue color in each panel are those from the bottom-left panel of Figure 5, i.e., when we limit all trade between the EU Member States and Russia and China. The estimates in red color in the top-left panel are obtained from a scenario where we limit all trade between the EU Member States and Russia and China but we liberalize trade between Estonia and the European Union (EU) using a shock that is equivalent to a partial equilibrium gravity estimate of 0.2. The results in red color in the top-right panel are from a scenario, where we limit all trade between all EU Member States and Russia and China, except for trade in Services between the EU countries and Russia. In red color, the bottom-left panel reports estimates from a scenario where we limit all trade between the EU Member States and Russia and China but we liberalize trade between Germany and the EU using a shock that is equivalent to a partial equilibrium gravity estimate of 0.2. Finally, the bottom-right panel of the figure reports estimates (in red color) from a scenario, where we limit all trade between the EU Member States and Russia and China but, at the same time, we liberalize trade among all EU members using a shock that is equivalent to a partial equilibrium gravity estimate of 0.2. See text for further details.
# Appendix

This Appendix includes estimation results that complement the main analysis from the paper, as well as findings from several robustness experiments.

**Industry-level estimates.** Table A.1 reports the full set of our estimates of the EU effects on intra-EU trade, extra-EU trade, and trade in the Single Market. The three estimates for each industry are obtained simultaneously after estimating specification (1) for the corresponding industry. The first two columns of Table A.1 list the industry IDs and the industry descriptions from the ITPD-E, respectively. Panel A reports the estimates of the EU effects on trade among the EU members (i.e., intra-EU trade), together with their corresponding standard errors. Panel B reports the estimates of the Single Market effects on trade, together with their corresponding standard errors. Finally, panel C reports the estimates of the EU effects on trade between the EU members and non-member countries that are not part of the Single Market (i.e., extra-EU trade), together with their corresponding standard errors. See the main text for further details.

**The impact of the EU vs. other RTAs.** To highlight the remarkable success of the EU to promote trade among its members, in Figure A.1 we reproduce the estimates from the top panel of Figure 1 and, in addition, we use a scatter plot to overlay the corresponding results for all RTAs other than the EU and the Single Market, which are obtained from the same specification. Consistent with our expectations (e.g., because the EU is considered to be one of the ‘deepest’ trade agreements), we see that most of the estimates of the effects of the RTAs are significantly smaller than the corresponding EU estimates. Moreover, the fraction of positive and significant RTAs is much smaller too. Thus, the results in Figure A.1 reinforce the perception of the EU as one of the most successful trade liberalization efforts in the world. A practical implication of this analysis is that the EU should be treated separately from other RTAs in trade gravity regressions.
**The EU impact for 'Old' vs. 'New' members.** In the next experiment, we obtain industry-level estimates of the EU effects depending on the direction of trade flows and after splitting the EU countries into 'Old' vs. 'New' members. To this end, we rely on our main estimating equation (1), however, instead of a single EU variable, we use four EU indicators. Specifically, we define two groups of countries – 'New' members, which joined the EU after 2000 and 'Old' members including the founding members and the countries that joined prior to 2000. Based on this definition, we define four EU indicator variables, including for trade from 'New' to 'Old' members (`EU_NEW_OLD`), from 'Old' to 'New' members (`EU_OLD_NEW`), from 'Old' to 'Old' members (`EU_OLD_OLD`), and from 'New' to 'New' members (`EU_NEW_NEW`). Due to collinearity concerns, we do not include the `EU_OUT` controls in this analysis. All other variables are the same as in equation (1).

The results from this experiment appear in Table A.2, and they reveal the following. First, similar to the single EU impact that we discuss in the main text, the estimates of the directional EU effects are positive, large, and statistically significant. Second, on average, the estimates of the EU effects are quite similar across the four groups of countries in our sample. Specifically, we obtain an average estimate of 0.565 for exports from New to New members, 0.626 for exports from New to Old members, 0.495 for exports from Old to New members, and 0.576 for exports from Old to Old members. Third, even though the EU effects are symmetric across the four groups on average, we do observe some heterogeneity across groups within some industries and broad sectors. For example, we find smaller effects on intra-EU trade in services between New members. We believe that such asymmetries may be important from a policy perspective and they point to a fruitful direction for future research. Finally, the directional estimates are heterogeneous across industries and broad sectors but confirm that the strongest EU effects are for Agriculture.<fnref n="36" />

<footnote n="36">³⁶Since the Services data in the ITPD-E starts in 2000, we cannot obtain estimates for the EU effects on trade among Old members.</footnote>
**Heterogeneity-robust EU effects.** In order to account for possible biases in the presence of treatment effect heterogeneity, e.g., due to ‘negative weights’ or the so-called ‘forbidden comparisons’ that (mis)use already-treated units in the control group (e.g., Borusyak & Jaravel 2017, de Chaisemartin & D’Haultfœuille 2020), we follow Nagengast & Yotov (2025) and Nagengast et al. (2025) to obtain estimates of the EU effects on intra-EU trade after combining the established practices for gravity estimations that we just discussed with recent developments from the difference-in-differences econometrics literature (e.g., Borusyak & Jaravel 2017, Hull 2018, de Chaisemartin & D’Haultfœuille 2020, Callaway & Sant’Anna 2021, Goodman-Bacon 2021, Sun & Abraham 2021, Wooldridge 2021, Goldsmith-Pinkham et al. 2022, de Chaisemartin & D’Haultfœuille 2023, Borusyak et al. forthcoming, Wooldridge 2023).

The new ‘extended two way fixed effects’ (ETWFE) econometric model, which corresponds to our ‘two way fixed effects’ (TWFE) gravity specification (1), becomes:

$$X_{ij,t}^k = \exp\left[\sum_{g=q}^{T} \sum_{s=g}^{T} \delta_{gs}^k D_{gs} + \vec{\mu}_{ij,t}^k + \pi_{i,t}^k + \chi_{j,t}^k\right] \times \epsilon_{ij,t}^k \quad (A.1)$$

All but one of the terms in equation (A.1) were defined earlier$^{37}$. The single, and most important for the current purposes, exception is the term $\sum_{g=q}^{T} \sum_{s=g}^{T} \delta_{gs}^k D_{gs}$. Following, Nagengast et al. (2025), this term is designed following the staggered difference-in-differences literature, and it would capture the EU effects. Specifically, $g$ denotes a treatment cohort if the condition that both countries $i$ and $j$ were EU members was for the first time fulfilled in year $g$, $q$ is the first year of the treatment of cohort $g$, $T$ is the last year of the panel,

<footnote>$^{37}$In principle, we can include in equation (A.1) the additional policy and globalization covariates from specification (1). However, the inclusion of multiple or continuous treatments is not advisable in equation (A.1). de Chaisemartin & D’Haultfœuille (2023) propose an approach to estimate heterogeneous treatment effects in the presence of multiple treatments, while de Chaisemartin et al. (2023) and Callaway et al. (2024a,b) discuss estimations with continuous treatments. In both cases, the focus is on linear settings. Given the important benefits of using the PPML estimator for trade gravity estimations, we decided to use the PPML estimates as our main results and to delegate the ETWFE estimations to the robustness analysis. We believe that extending the recent ETWFE methods to accommodate multiple (and continuous) treatments in non-linear settings will be a very valuable contribution to the trade literature and beyond.</footnote>
$D_{gs}$ is a time-varying treatment indicator equal to 1 for cohort g for $s = t$ in post-treatment years and 0 otherwise, and $\delta_{gs}^k$ captures the cohort-year specific treatment effects for each industry $k$ in our sample.<fnref n="38" /> For example, $D_{2007,2007}$ would be equal to one for the country pair France-Bulgaria in the year 2007, since Bulgaria joined the EU in 2007, and France had already been an EU member since 1957. $\delta_{2007,2007}$ would capture the EU effect on trade between all country pairs of the 2007 cohort in the first year. $\delta_{2007,2008}$ would capture the EU effect on trade between all country pairs of the 2007 cohort in the second year, etc.

Finally, we will estimate equation (A.1) in Stata with the **jwdid** command of Nagengast et al. (2025) and, following their analysis, we will assume an ‘onset’ of the EU effects two years before the official entry into force for each EU member. The motivation for this is that it is possible that the onset of the EU effects on trade would precede the date of the official EU accession, e.g., due to the multi-stage enlargement process, and/or due to adjustments in bilateral trade costs or firm behavior in anticipation of EU membership.

The heterogeneity-robust ETWFE estimates are visualized in the top panel Figure A.2.<fnref n="39" /> Similar to the corresponding TWFE gravity estimates from Figure 1, the ETWFE estimates in Figure A.2 confirm that the effects of the EU on intra-EU trade are economically large, statistically significant, and very heterogeneous across the industries in our sample. The correlation between the positive estimates from Figure A.2 and Figure 1 is relatively high but far from perfect (0.7).

To further compare the two sets of estimates, in the bottom panel of Figure A.2, we reproduce the ETWFE estimates and, in addition, we use a scatter plot to overlay the corresponding TWFE estimates from the main text after eliminating the bottom and top 5% of outliers. We see that, for most industries, the two sets of estimates are not statistically different from each other. However, we also see some differences. Overall, (i) a smaller fraction of the ETWFE estimates are negative, and (ii) the ETWFE estimates are a bit

<footnote n="38">³⁸To obtain our aggregate ATT estimates for each industry, we give every post-treatment observation the same weight.</footnote>

<footnote n="39">³⁹For clarity, we do not report the top and the bottom 5 percent of the estimates in Figure A.2.</footnote>
larger on average. This is consistent with the differences between TWFE vs. ETWFE trade policy estimates, in favor of the latter, as documented in Nagengast & Yotov (2025) and Nagengast et al. (2025).

**Counterfactual results.** Table A.3 reports the full set of general equilibrium effects on real output from the counterfactual experiments that we performed in Section 5.2 of the main text. The estimates in Panel A correspond to the results in Figure 3. The results in Panel B are used to construct the top two and the bottom-left panels of Figure 4. The results in the last column of panel C are used to construct the bottom-right panel of Figure 4. Finally, the estimates in panel D are used to construct the four panels in Figure 5. See the main text for further details.
<table><caption>Table A.1: Industry-level estimates of the EU effects on trade</caption><thead><tr><th rowspan="2">Industry ID</th><th rowspan="2">Industry Description</th><th colspan="2">A. Intra-EU</th><th colspan="2">B. Single Market</th><th colspan="2">C. Extra-EU</th></tr><tr><th>Estim.</th><th>Std.Err.</th><th>Estim.</th><th>Std.Err.</th><th>Estim.</th><th>Std.Err.</th></tr></thead><tbody><tr><td>1</td><td>Wheat</td><td>2.262</td><td>(.293)</td><td>1.558</td><td>(.374)</td><td>0.072</td><td>(.186)</td></tr><tr><td>2</td><td>Rice (raw)</td><td>3.233</td><td>(.529)</td><td>0.978</td><td>(.464)</td><td>-0.017</td><td>(.258)</td></tr><tr><td>3</td><td>Corn</td><td>2.261</td><td>(.332)</td><td>0.196</td><td>(.55)</td><td>0.141</td><td>(.199)</td></tr><tr><td>4</td><td>Other cereals</td><td>1.811</td><td>(.222)</td><td>1.387</td><td>(.423)</td><td>-0.031</td><td>(.146)</td></tr><tr><td>5</td><td>Cereal products</td><td>3.191</td><td>(.503)</td><td>-2.539</td><td>(.803)</td><td></td><td></td></tr><tr><td>6</td><td>Soybeans</td><td>2.606</td><td>(.534)</td><td>1.689</td><td>(.619)</td><td>0.497</td><td>(.321)</td></tr><tr><td>7</td><td>Other oilseeds (excluding peanuts)</td><td>0.770</td><td>(.219)</td><td>0.276</td><td>(.426)</td><td>-0.080</td><td>(.149)</td></tr><tr><td>8</td><td>Animal feed ingredients and pet foods</td><td>0.597</td><td>(.172)</td><td>0.249</td><td>(.259)</td><td></td><td></td></tr><tr><td>9</td><td>Raw and refined sugar and sugar crops</td><td>1.560</td><td>(.813)</td><td>-0.676</td><td>(.981)</td><td>-2.965</td><td>(.599)</td></tr><tr><td>10</td><td>Other sweeteners</td><td>1.724</td><td>(.46)</td><td>0.424</td><td>(.352)</td><td>0.406</td><td>(.276)</td></tr><tr><td>11</td><td>Pulses and legumes, dried, preserved</td><td>0.463</td><td>(.29)</td><td>0.170</td><td>(.49)</td><td>-0.030</td><td>(.209)</td></tr><tr><td>12</td><td>Fresh fruit</td><td>0.665</td><td>(.191)</td><td>0.056</td><td>(.203)</td><td>-0.310</td><td>(.12)</td></tr><tr><td>13</td><td>Fresh vegetables</td><td>1.629</td><td>(.175)</td><td>0.664</td><td>(.242)</td><td>0.426</td><td>(.149)</td></tr><tr><td>14</td><td>Prepared fruits and fruit juices</td><td>0.900</td><td>(.246)</td><td>0.298</td><td>(.25)</td><td></td><td></td></tr><tr><td>15</td><td>Prepared vegetables</td><td>5.866</td><td>(2.057)</td><td>3.085</td><td>(.933)</td><td></td><td></td></tr><tr><td>16</td><td>Nuts</td><td>1.291</td><td>(.327)</td><td>0.091</td><td>(.349)</td><td>0.089</td><td>(.169)</td></tr><tr><td>17</td><td>Live Cattle</td><td>0.697</td><td>(.402)</td><td>0.522</td><td>(.637)</td><td></td><td></td></tr><tr><td>18</td><td>Live Swine</td><td>4.571</td><td>(.427)</td><td>1.284</td><td>(.797)</td><td></td><td></td></tr><tr><td>19</td><td>Eggs</td><td>1.653</td><td>(.237)</td><td>-0.842</td><td>(.474)</td><td>-0.362</td><td>(.193)</td></tr><tr><td>20</td><td>Other meats, livestock products, and live animals</td><td>0.137</td><td>(.31)</td><td>0.561</td><td>(.267)</td><td>0.149</td><td>(.197)</td></tr><tr><td>21</td><td>Cocoa and cocoa products</td><td>2.464</td><td>(.822)</td><td>1.329</td><td>(.556)</td><td></td><td></td></tr><tr><td>22</td><td>Beverages, nec</td><td>2.603</td><td>(.298)</td><td>0.486</td><td>(.333)</td><td></td><td></td></tr><tr><td>23</td><td>Cotton</td><td>1.594</td><td>(.326)</td><td>-0.432</td><td>(.497)</td><td></td><td></td></tr><tr><td>24</td><td>Tobacco leaves and cigarettes</td><td>3.661</td><td>(.415)</td><td>1.015</td><td>(.35)</td><td>1.515</td><td>(.22)</td></tr><tr><td>25</td><td>Spices</td><td>-0.451</td><td>(.256)</td><td>-0.668</td><td>(.238)</td><td>-0.784</td><td>(.142)</td></tr><tr><td>26</td><td>Other agricultural products, nec</td><td>0.677</td><td>(.316)</td><td>0.408</td><td>(.214)</td><td>0.158</td><td>(.163)</td></tr><tr><td>27</td><td>Forestry</td><td>-0.159</td><td>(.168)</td><td>-0.104</td><td>(.213)</td><td></td><td></td></tr><tr><td>28</td><td>Fishing</td><td>-0.307</td><td>(.326)</td><td>0.013</td><td>(.241)</td><td></td><td></td></tr><tr><td>29</td><td>Mining of hard coal</td><td>-0.447</td><td>(.475)</td><td>1.206</td><td>(.491)</td><td>-0.773</td><td>(.317)</td></tr><tr><td>30</td><td>Mining of lignite</td><td>4.551</td><td>(1.84)</td><td>-0.932</td><td>(1.49)</td><td>1.365</td><td>(.91)</td></tr><tr><td>31</td><td>Extraction crude petroleum and natural gas</td><td>-0.122</td><td>(1.105)</td><td>0.857</td><td>(.593)</td><td>0.016</td><td>(.553)</td></tr><tr><td>32</td><td>Mining of iron ores</td><td>2.216</td><td>(1.677)</td><td>1.415</td><td>(1.057)</td><td>0.993</td><td>(.832)</td></tr><tr><td>33</td><td>Other mining and quarring</td><td>0.239</td><td>(.186)</td><td>-0.211</td><td>(.246)</td><td>-0.216</td><td>(.127)</td></tr><tr><td>34</td><td>Electricity production, collection, and distribution</td><td>1.213</td><td>(.319)</td><td>1.846</td><td>(.756)</td><td>0.091</td><td>(.222)</td></tr><tr><td>35</td><td>Gas production and distribution</td><td>-4.613</td><td>(2.106)</td><td>-2.574</td><td>(1.815)</td><td>-1.641</td><td>(1.365)</td></tr><tr><td>36</td><td>Processing/preserving of meat</td><td>1.157</td><td>(.157)</td><td>-0.066</td><td>(.216)</td><td>0.004</td><td>(.148)</td></tr><tr><td>37</td><td>Processing/preserving of fish</td><td>1.228</td><td>(.202)</td><td>-0.161</td><td>(.212)</td><td>0.181</td><td>(.135)</td></tr><tr><td>38</td><td>Processing/preserving of fruit and vegetables</td><td>0.595</td><td>(.123)</td><td>0.114</td><td>(.229)</td><td>-0.096</td><td>(.074)</td></tr><tr><td>39</td><td>Vegetable and animal oils and fats</td><td>1.083</td><td>(.235)</td><td>0.991</td><td>(.234)</td><td>0.081</td><td>(.213)</td></tr><tr><td>40</td><td>Dairy products</td><td>1.034</td><td>(.103)</td><td>-0.111</td><td>(.231)</td><td>-0.482</td><td>(.09)</td></tr><tr><td>41</td><td>Grain mill products</td><td>0.825</td><td>(.175)</td><td>-0.212</td><td>(.33)</td><td>-0.434</td><td>(.105)</td></tr><tr><td>42</td><td>Starches and starch products</td><td>1.047</td><td>(.217)</td><td>0.463</td><td>(.514)</td><td>-0.060</td><td>(.14)</td></tr><tr><td>43</td><td>Prepared animal feeds</td><td>1.343</td><td>(.164)</td><td>0.700</td><td>(.26)</td><td>0.439</td><td>(.104)</td></tr><tr><td>44</td><td>Bakery products</td><td>0.700</td><td>(.169)</td><td>-0.122</td><td>(.182)</td><td>-0.193</td><td>(.092)</td></tr><tr><td>45</td><td>Sugar</td><td>1.873</td><td>(.33)</td><td>-0.074</td><td>(.359)</td><td>0.503</td><td>(.209)</td></tr><tr><td>46</td><td>Cocoa chocolate and sugar confectionery</td><td>1.101</td><td>(.135)</td><td>0.202</td><td>(.172)</td><td>0.070</td><td>(.075)</td></tr><tr><td>47</td><td>Macaroni noodles and similar products</td><td>2.277</td><td>(.524)</td><td>1.424</td><td>(.426)</td><td>0.417</td><td>(.279)</td></tr><tr><td>48</td><td>Other food products n.e.c.</td><td>1.012</td><td>(.124)</td><td>0.043</td><td>(.412)</td><td>0.020</td><td>(.077)</td></tr><tr><td>49</td><td>Distilling rectifying and blending of spirits</td><td>1.677</td><td>(.202)</td><td>0.788</td><td>(.341)</td><td>0.220</td><td>(.121)</td></tr><tr><td>50</td><td>Wines</td><td>1.502</td><td>(.273)</td><td>1.303</td><td>(.528)</td><td>0.416</td><td>(.168)</td></tr><tr><td>51</td><td>Malt liquors and malt</td><td>0.627</td><td>(.182)</td><td>0.371</td><td>(.321)</td><td>0.110</td><td>(.12)</td></tr><tr><td>52</td><td>Soft drinks; mineral waters</td><td>0.958</td><td>(.179)</td><td>0.284</td><td>(.426)</td><td>0.359</td><td>(.157)</td></tr><tr><td>53</td><td>Tobacco products</td><td>1.532</td><td>(.306)</td><td>0.799</td><td>(.614)</td><td>-0.305</td><td>(.182)</td></tr><tr><td>54</td><td>Textile fibre preparation; textile weaving</td><td>1.338</td><td>(.16)</td><td>0.711</td><td>(.165)</td><td>0.497</td><td>(.101)</td></tr><tr><td>55</td><td>Made-up textile articles except apparel</td><td>0.798</td><td>(.204)</td><td>0.210</td><td>(.178)</td><td>0.484</td><td>(.106)</td></tr><tr><td>56</td><td>Carpets and rugs</td><td>1.692</td><td>(.301)</td><td>0.210</td><td>(.327)</td><td>0.461</td><td>(.186)</td></tr><tr><td>57</td><td>Cordage rope twine and netting</td><td>0.879</td><td>(.218)</td><td>0.525</td><td>(.229)</td><td>0.436</td><td>(.119)</td></tr><tr><td>58</td><td>Other textiles n.e.c.</td><td>0.681</td><td>(.165)</td><td>0.034</td><td>(.171)</td><td>0.308</td><td>(.093)</td></tr><tr><td>59</td><td>Knitted and crocheted fabrics and articles</td><td>-0.339</td><td>(.182)</td><td>-0.302</td><td>(.255)</td><td>-0.103</td><td>(.099)</td></tr><tr><td>60</td><td>Wearing apparel except fur apparel</td><td>-0.289</td><td>(.353)</td><td>-0.453</td><td>(.289)</td><td>0.132</td><td>(.18)</td></tr><tr><td>61</td><td>Dressing and dyeing of fur; processing of fur</td><td>0.740</td><td>(.37)</td><td>0.028</td><td>(.358)</td><td>0.429</td><td>(.205)</td></tr><tr><td>62</td><td>Tanning and dressing of leather</td><td>1.201</td><td>(.351)</td><td>0.284</td><td>(.383)</td><td>0.808</td><td>(.203)</td></tr><tr><td>63</td><td>Luggage handbags etc.; saddlery and harness</td><td>-0.642</td><td>(.311)</td><td>-0.538</td><td>(.278)</td><td>-0.125</td><td>(.154)</td></tr><tr><td>64</td><td>Footwear</td><td>0.324</td><td>(.205)</td><td>0.062</td><td>(.248)</td><td>0.302</td><td>(.101)</td></tr><tr><td>65</td><td>Sawmilling and planing of wood</td><td>0.762</td><td>(.168)</td><td>0.400</td><td>(.187)</td><td>0.652</td><td>(.135)</td></tr><tr><td>66</td><td>Veneer sheets plywood particle board etc.</td><td>0.298</td><td>(.137)</td><td>0.219</td><td>(.161)</td><td>0.084</td><td>(.072)</td></tr><tr><td>67</td><td>Builders' carpentry and joinery</td><td>0.677</td><td>(.155)</td><td>0.596</td><td>(.192)</td><td>0.486</td><td>(.119)</td></tr><tr><td>68</td><td>Wooden containers</td><td>-0.071</td><td>(.154)</td><td>0.445</td><td>(.264)</td><td>0.133</td><td>(.102)</td></tr><tr><td>69</td><td>Other wood products; articles of cork/straw</td><td>0.474</td><td>(.21)</td><td>0.304</td><td>(.182)</td><td>0.140</td><td>(.105)</td></tr><tr><td>70</td><td>Pulp paper and paperboard</td><td>0.756</td><td>(.127)</td><td>0.554</td><td>(.184)</td><td>0.363</td><td>(.081)</td></tr><tr><td>71</td><td>Corrugated paper and paperboard</td><td>-0.014</td><td>(.115)</td><td>-0.347</td><td>(.157)</td><td>-0.283</td><td>(.076)</td></tr><tr><td>72</td><td>Other articles of paper and paperboard</td><td>-0.497</td><td>(.194)</td><td>-0.528</td><td>(.164)</td><td>-0.325</td><td>(.11)</td></tr><tr><td>73</td><td>Publishing of books and other publications</td><td>-0.044</td><td>(.142)</td><td>-0.044</td><td>(.163)</td><td>-0.173</td><td>(.093)</td></tr><tr><td>74</td><td>Publishing of newspapers journals etc.</td><td>-0.472</td><td>(.211)</td><td>-0.068</td><td>(.283)</td><td>-0.310</td><td>(.131)</td></tr><tr><td>75</td><td>Publishing of recorded media</td><td>-0.819</td><td>(.371)</td><td>-0.550</td><td>(.263)</td><td>-0.314</td><td>(.183)</td></tr><tr><td>76</td><td>Other publishing</td><td>0.345</td><td>(.237)</td><td>0.179</td><td>(.223)</td><td>0.258</td><td>(.138)</td></tr><tr><td>77</td><td>Printing</td><td>0.247</td><td>(.146)</td><td>0.379</td><td>(.169)</td><td>0.028</td><td>(.099)</td></tr><tr><td>78</td><td>Service activities related to printing</td><td>0.646</td><td>(.275)</td><td>0.814</td><td>(.305)</td><td>0.508</td><td>(.187)</td></tr><tr><td>79</td><td>Coke oven products</td><td>-0.488</td><td>(.528)</td><td>0.762</td><td>(.501)</td><td>-0.185</td><td>(.295)</td></tr><tr><td>80</td><td>Refined petroleum products</td><td>-0.229</td><td>(.171)</td><td>-0.593</td><td>(.249)</td><td>-0.325</td><td>(.096)</td></tr><tr><td>81</td><td>Processing of nuclear fuel</td><td>-0.161</td><td>(.447)</td><td>-1.297</td><td>(.542)</td><td></td><td></td></tr><tr><td>82</td><td>Basic chemicals except fertilizers</td><td>0.396</td><td>(.172)</td><td>0.210</td><td>(.225)</td><td>0.149</td><td>(.093)</td></tr><tr><td>83</td><td>Fertilizers and nitrogen compounds</td><td>0.782</td><td>(.288)</td><td>0.017</td><td>(.312)</td><td>0.092</td><td>(.165)</td></tr><tr><td>84</td><td>Plastics in primary forms; synthetic rubber</td><td>-0.036</td><td>(.16)</td><td>0.122</td><td>(.216)</td><td>-0.255</td><td>(.089)</td></tr><tr><td>85</td><td>Pesticides and other agro-chemical products</td><td>1.197</td><td>(.333)</td><td>0.488</td><td>(.296)</td><td>0.229</td><td>(.181)</td></tr><tr><td>86</td><td>Paints varnishes printing ink and mastics</td><td>0.054</td><td>(.108)</td><td>-0.455</td><td>(.137)</td><td>-0.125</td><td>(.06)</td></tr><tr><td>87</td><td>Pharmaceuticals medicinal chemicals etc.</td><td>0.162</td><td>(.184)</td><td>-0.111</td><td>(.223)</td><td>-0.145</td><td>(.102)</td></tr></tbody></table>

Continued on next page
<table>
  <tbody>
    <tr>
      <td>88</td>
      <td>Soap cleaning and cosmetic preparations</td>
      <td>0.797</td>
      <td>(.16)</td>
      <td>-0.158</td>
      <td>(.2)</td>
      <td>-0.026</td>
      <td>(.094)</td>
    </tr>
    <tr>
      <td>89</td>
      <td>Other chemical products n.e.c.</td>
      <td>1.197</td>
      <td>(.172)</td>
      <td>0.597</td>
      <td>(.221)</td>
      <td>0.474</td>
      <td>(.101)</td>
    </tr>
    <tr>
      <td>90</td>
      <td>Man-made fibres</td>
      <td>2.149</td>
      <td>(.511)</td>
      <td>1.898</td>
      <td>(.347)</td>
      <td>0.831</td>
      <td>(.266)</td>
    </tr>
    <tr>
      <td>91</td>
      <td>Rubber tyres and tubes</td>
      <td>0.816</td>
      <td>(.204)</td>
      <td>0.280</td>
      <td>(.235)</td>
      <td>0.154</td>
      <td>(.118)</td>
    </tr>
    <tr>
      <td>92</td>
      <td>Other rubber products</td>
      <td>0.002</td>
      <td>(.205)</td>
      <td>-0.125</td>
      <td>(.155)</td>
      <td>-0.102</td>
      <td>(.111)</td>
    </tr>
    <tr>
      <td>93</td>
      <td>Plastic products</td>
      <td>0.269</td>
      <td>(.108)</td>
      <td>-0.004</td>
      <td>(.117)</td>
      <td>-0.005</td>
      <td>(.065)</td>
    </tr>
    <tr>
      <td>94</td>
      <td>Glass and glass products</td>
      <td>0.404</td>
      <td>(.108)</td>
      <td>0.164</td>
      <td>(.334)</td>
      <td>0.095</td>
      <td>(.066)</td>
    </tr>
    <tr>
      <td>95</td>
      <td>Pottery china and earthenware</td>
      <td>0.348</td>
      <td>(.17)</td>
      <td>0.261</td>
      <td>(.171)</td>
      <td>0.157</td>
      <td>(.091)</td>
    </tr>
    <tr>
      <td>96</td>
      <td>Refractory ceramic products</td>
      <td>0.583</td>
      <td>(.247)</td>
      <td>0.525</td>
      <td>(.527)</td>
      <td>0.302</td>
      <td>(.148)</td>
    </tr>
    <tr>
      <td>97</td>
      <td>Struct.non-refractory clay; ceramic products</td>
      <td>0.300</td>
      <td>(.137)</td>
      <td>0.684</td>
      <td>(.169)</td>
      <td>0.159</td>
      <td>(.097)</td>
    </tr>
    <tr>
      <td>98</td>
      <td>Cement lime and plaster</td>
      <td>-0.011</td>
      <td>(.218)</td>
      <td>1.605</td>
      <td>(.498)</td>
      <td>0.073</td>
      <td>(.128)</td>
    </tr>
    <tr>
      <td>99</td>
      <td>Articles of concrete cement and plaster</td>
      <td>-0.232</td>
      <td>(.132)</td>
      <td>-0.371</td>
      <td>(.156)</td>
      <td>-0.302</td>
      <td>(.083)</td>
    </tr>
    <tr>
      <td>100</td>
      <td>Cutting shaping and finishing of stone</td>
      <td>0.509</td>
      <td>(.239)</td>
      <td>1.424</td>
      <td>(.315)</td>
      <td>0.508</td>
      <td>(.127)</td>
    </tr>
    <tr>
      <td>101</td>
      <td>Other non-metallic mineral products n.e.c.</td>
      <td>0.785</td>
      <td>(.128)</td>
      <td>-0.233</td>
      <td>(.198)</td>
      <td>0.369</td>
      <td>(.08)</td>
    </tr>
    <tr>
      <td>102</td>
      <td>Basic iron and steel</td>
      <td>0.822</td>
      <td>(.096)</td>
      <td>0.796</td>
      <td>(.129)</td>
      <td>0.204</td>
      <td>(.062)</td>
    </tr>
    <tr>
      <td>103</td>
      <td>Basic precious and non-ferrous metals</td>
      <td>0.776</td>
      <td>(.267)</td>
      <td>0.826</td>
      <td>(.316)</td>
      <td>0.135</td>
      <td>(.142)</td>
    </tr>
    <tr>
      <td>104</td>
      <td>Structural metal products</td>
      <td>-0.144</td>
      <td>(.1)</td>
      <td>0.476</td>
      <td>(.209)</td>
      <td>-0.182</td>
      <td>(.062)</td>
    </tr>
    <tr>
      <td>105</td>
      <td>Tanks reservoirs and containers of metal</td>
      <td>-0.319</td>
      <td>(.151)</td>
      <td>0.016</td>
      <td>(.222)</td>
      <td>-0.212</td>
      <td>(.098)</td>
    </tr>
    <tr>
      <td>106</td>
      <td>Steam generators</td>
      <td>-0.074</td>
      <td>(.249)</td>
      <td>-0.056</td>
      <td>(.421)</td>
      <td>-0.102</td>
      <td>(.123)</td>
    </tr>
    <tr>
      <td>107</td>
      <td>Cutlery hand tools and general hardware</td>
      <td>0.673</td>
      <td>(.163)</td>
      <td>0.192</td>
      <td>(.142)</td>
      <td>0.320</td>
      <td>(.091)</td>
    </tr>
    <tr>
      <td>108</td>
      <td>Other fabricated metal products n.e.c.</td>
      <td>0.699</td>
      <td>(.181)</td>
      <td>0.551</td>
      <td>(.143)</td>
      <td>0.260</td>
      <td>(.089)</td>
    </tr>
    <tr>
      <td>109</td>
      <td>Engines and turbines (not for transport equipment)</td>
      <td>1.098</td>
      <td>(.279)</td>
      <td>0.316</td>
      <td>(.247)</td>
      <td>0.764</td>
      <td>(.163)</td>
    </tr>
    <tr>
      <td>110</td>
      <td>Pumps compressors taps and valves</td>
      <td>-0.460</td>
      <td>(.446)</td>
      <td>-0.541</td>
      <td>(.273)</td>
      <td>-0.201</td>
      <td>(.225)</td>
    </tr>
    <tr>
      <td>111</td>
      <td>Bearings gears gearing and driving elements</td>
      <td>-1.269</td>
      <td>(.413)</td>
      <td>-0.662</td>
      <td>(.249)</td>
      <td>-0.739</td>
      <td>(.205)</td>
    </tr>
    <tr>
      <td>112</td>
      <td>Ovens furnaces and furnace burners</td>
      <td>0.199</td>
      <td>(.252)</td>
      <td>0.360</td>
      <td>(.351)</td>
      <td>0.118</td>
      <td>(.133)</td>
    </tr>
    <tr>
      <td>113</td>
      <td>Lifting and handling equipment</td>
      <td>0.106</td>
      <td>(.196)</td>
      <td>-0.168</td>
      <td>(.201)</td>
      <td>-0.018</td>
      <td>(.111)</td>
    </tr>
    <tr>
      <td>114</td>
      <td>Other general purpose machinery</td>
      <td>0.253</td>
      <td>(.135)</td>
      <td>0.072</td>
      <td>(.144)</td>
      <td>0.086</td>
      <td>(.077)</td>
    </tr>
    <tr>
      <td>115</td>
      <td>Agricultural and forestry machinery</td>
      <td>0.627</td>
      <td>(.145)</td>
      <td>0.016</td>
      <td>(.168)</td>
      <td>0.071</td>
      <td>(.079)</td>
    </tr>
    <tr>
      <td>116</td>
      <td>Machine tools</td>
      <td>1.473</td>
      <td>(.365)</td>
      <td>0.667</td>
      <td>(.227)</td>
      <td>0.716</td>
      <td>(.184)</td>
    </tr>
    <tr>
      <td>117</td>
      <td>Machinery for metallurgy</td>
      <td>1.202</td>
      <td>(.285)</td>
      <td>0.596</td>
      <td>(.322)</td>
      <td>0.443</td>
      <td>(.141)</td>
    </tr>
    <tr>
      <td>118</td>
      <td>Machinery for mining and construction</td>
      <td>1.083</td>
      <td>(.189)</td>
      <td>0.428</td>
      <td>(.294)</td>
      <td>0.481</td>
      <td>(.112)</td>
    </tr>
    <tr>
      <td>119</td>
      <td>Food/beverage/tobacco processing machinery</td>
      <td>0.418</td>
      <td>(.126)</td>
      <td>-0.059</td>
      <td>(.22)</td>
      <td>0.097</td>
      <td>(.078)</td>
    </tr>
    <tr>
      <td>120</td>
      <td>Machinery for textile apparel and leather</td>
      <td>-0.447</td>
      <td>(.309)</td>
      <td>-0.164</td>
      <td>(.263)</td>
      <td>-0.243</td>
      <td>(.143)</td>
    </tr>
    <tr>
      <td>121</td>
      <td>Weapons and ammunition</td>
      <td>0.614</td>
      <td>(.24)</td>
      <td>1.150</td>
      <td>(.308)</td>
      <td>0.216</td>
      <td>(.133)</td>
    </tr>
    <tr>
      <td>122</td>
      <td>Other special purpose machinery</td>
      <td>-0.137</td>
      <td>(.293)</td>
      <td>0.060</td>
      <td>(.263)</td>
      <td>-0.059</td>
      <td>(.149)</td>
    </tr>
    <tr>
      <td>123</td>
      <td>Domestic appliances n.e.c.</td>
      <td>0.641</td>
      <td>(.209)</td>
      <td>0.298</td>
      <td>(.157)</td>
      <td>0.279</td>
      <td>(.103)</td>
    </tr>
    <tr>
      <td>124</td>
      <td>Office accounting and computing machinery</td>
      <td>0.481</td>
      <td>(.381)</td>
      <td>-0.038</td>
      <td>(.309)</td>
      <td>0.099</td>
      <td>(.194)</td>
    </tr>
    <tr>
      <td>125</td>
      <td>Electric motors generators and transformers</td>
      <td>0.336</td>
      <td>(.206)</td>
      <td>0.789</td>
      <td>(.198)</td>
      <td>0.307</td>
      <td>(.1)</td>
    </tr>
    <tr>
      <td>126</td>
      <td>Electricity distribution and control apparatus</td>
      <td>0.773</td>
      <td>(.262)</td>
      <td>0.647</td>
      <td>(.212)</td>
      <td>0.385</td>
      <td>(.137)</td>
    </tr>
    <tr>
      <td>127</td>
      <td>Insulated wire and cable</td>
      <td>1.209</td>
      <td>(.226)</td>
      <td>0.816</td>
      <td>(.182)</td>
      <td>0.643</td>
      <td>(.124)</td>
    </tr>
    <tr>
      <td>128</td>
      <td>Accumulators primary cells and batteries</td>
      <td>1.813</td>
      <td>(.281)</td>
      <td>0.570</td>
      <td>(.278)</td>
      <td>0.835</td>
      <td>(.161)</td>
    </tr>
    <tr>
      <td>129</td>
      <td>Lighting equipment and electric lamps</td>
      <td>-0.183</td>
      <td>(.353)</td>
      <td>-0.324</td>
      <td>(.22)</td>
      <td>0.005</td>
      <td>(.182)</td>
    </tr>
    <tr>
      <td>130</td>
      <td>Other electrical equipment n.e.c.</td>
      <td>0.083</td>
      <td>(.156)</td>
      <td>-0.282</td>
      <td>(.223)</td>
      <td>0.245</td>
      <td>(.084)</td>
    </tr>
    <tr>
      <td>131</td>
      <td>Electronic valves tubes etc.</td>
      <td>0.621</td>
      <td>(.297)</td>
      <td>0.269</td>
      <td>(.251)</td>
      <td>0.156</td>
      <td>(.155)</td>
    </tr>
    <tr>
      <td>132</td>
      <td>TV/radio transmitters; line comm. apparatus</td>
      <td>-0.210</td>
      <td>(.257)</td>
      <td>-0.561</td>
      <td>(.222)</td>
      <td>0.016</td>
      <td>(.138)</td>
    </tr>
    <tr>
      <td>133</td>
      <td>TV and radio receivers and associated goods</td>
      <td>0.107</td>
      <td>(.392)</td>
      <td>-0.411</td>
      <td>(.276)</td>
      <td>0.119</td>
      <td>(.205)</td>
    </tr>
    <tr>
      <td>134</td>
      <td>Medical surgical and orthopaedic equipment</td>
      <td>0.434</td>
      <td>(.225)</td>
      <td>0.187</td>
      <td>(.194)</td>
      <td>0.221</td>
      <td>(.124)</td>
    </tr>
    <tr>
      <td>135</td>
      <td>Measuring/testing/navigating appliances etc.</td>
      <td>0.576</td>
      <td>(.138)</td>
      <td>-0.078</td>
      <td>(.176)</td>
      <td>0.243</td>
      <td>(.082)</td>
    </tr>
    <tr>
      <td>136</td>
      <td>Optical instruments and photographic equipment</td>
      <td>1.061</td>
      <td>(.428)</td>
      <td>0.164</td>
      <td>(.326)</td>
      <td>0.581</td>
      <td>(.221)</td>
    </tr>
    <tr>
      <td>137</td>
      <td>Watches and clocks</td>
      <td>-1.296</td>
      <td>(.313)</td>
      <td>-0.495</td>
      <td>(.31)</td>
      <td>-0.785</td>
      <td>(.152)</td>
    </tr>
    <tr>
      <td>138</td>
      <td>Motor vehicles</td>
      <td>0.496</td>
      <td>(.33)</td>
      <td>0.762</td>
      <td>(.217)</td>
      <td>0.016</td>
      <td>(.186)</td>
    </tr>
    <tr>
      <td>139</td>
      <td>Automobile bodies trailers and semi-trailers</td>
      <td>-0.454</td>
      <td>(.368)</td>
      <td>-0.322</td>
      <td>(.287)</td>
      <td>-0.455</td>
      <td>(.215)</td>
    </tr>
    <tr>
      <td>140</td>
      <td>Parts/accessories for automobiles</td>
      <td>0.780</td>
      <td>(.273)</td>
      <td>0.406</td>
      <td>(.206)</td>
      <td>0.165</td>
      <td>(.153)</td>
    </tr>
    <tr>
      <td>141</td>
      <td>Building and repairing of ships</td>
      <td>0.566</td>
      <td>(.284)</td>
      <td>0.400</td>
      <td>(.253)</td>
      <td>0.630</td>
      <td>(.157)</td>
    </tr>
    <tr>
      <td>142</td>
      <td>Building/repairing of pleasure/sport. boats</td>
      <td>-1.258</td>
      <td>(.285)</td>
      <td>-0.204</td>
      <td>(.31)</td>
      <td>-0.513</td>
      <td>(.162)</td>
    </tr>
    <tr>
      <td>143</td>
      <td>Railway/tramway locomotives and rolling stock</td>
      <td>0.637</td>
      <td>(.225)</td>
      <td>1.267</td>
      <td>(.463)</td>
      <td>0.211</td>
      <td>(.114)</td>
    </tr>
    <tr>
      <td>144</td>
      <td>Aircraft and spacecraft</td>
      <td>0.789</td>
      <td>(.425)</td>
      <td>1.115</td>
      <td>(.451)</td>
      <td>0.331</td>
      <td>(.217)</td>
    </tr>
    <tr>
      <td>145</td>
      <td>Motorcycles</td>
      <td>2.705</td>
      <td>(.702)</td>
      <td>1.432</td>
      <td>(.481)</td>
      <td>1.061</td>
      <td>(.356)</td>
    </tr>
    <tr>
      <td>146</td>
      <td>Bicycles and invalid carriages</td>
      <td>-1.134</td>
      <td>(.278)</td>
      <td>-1.083</td>
      <td>(.321)</td>
      <td>-0.445</td>
      <td>(.161)</td>
    </tr>
    <tr>
      <td>147</td>
      <td>Other transport equipment n.e.c.</td>
      <td>0.580</td>
      <td>(.282)</td>
      <td>0.792</td>
      <td>(.206)</td>
      <td>0.427</td>
      <td>(.148)</td>
    </tr>
    <tr>
      <td>148</td>
      <td>Furniture</td>
      <td>0.745</td>
      <td>(.142)</td>
      <td>0.571</td>
      <td>(.149)</td>
      <td>0.357</td>
      <td>(.084)</td>
    </tr>
    <tr>
      <td>149</td>
      <td>Jewellery and related articles</td>
      <td>-0.214</td>
      <td>(.273)</td>
      <td>-0.318</td>
      <td>(.304)</td>
      <td>-0.375</td>
      <td>(.159)</td>
    </tr>
    <tr>
      <td>150</td>
      <td>Musical instruments</td>
      <td>-0.516</td>
      <td>(.209)</td>
      <td>-0.430</td>
      <td>(.206)</td>
      <td>-0.213</td>
      <td>(.105)</td>
    </tr>
    <tr>
      <td>151</td>
      <td>Sports goods</td>
      <td>0.239</td>
      <td>(.222)</td>
      <td>0.024</td>
      <td>(.189)</td>
      <td>0.087</td>
      <td>(.123)</td>
    </tr>
    <tr>
      <td>152</td>
      <td>Games and toys</td>
      <td>0.504</td>
      <td>(.466)</td>
      <td>-0.483</td>
      <td>(.436)</td>
      <td>0.407</td>
      <td>(.216)</td>
    </tr>
    <tr>
      <td>153</td>
      <td>Other manufacturing n.e.c.</td>
      <td>0.484</td>
      <td>(.165)</td>
      <td>0.297</td>
      <td>(.18)</td>
      <td>0.288</td>
      <td>(.094)</td>
    </tr>
    <tr>
      <td>154</td>
      <td>Manufacturing services on physical inputs</td>
      <td>-2.059</td>
      <td>(1.282)</td>
      <td>6.504</td>
      <td>(1.381)</td>
      <td></td>
      <td></td>
    </tr>
    <tr>
      <td>155</td>
      <td>Maintenance and repair services n.i.e.</td>
      <td>-0.683</td>
      <td>(.512)</td>
      <td>10.39</td>
      <td>(.546)</td>
      <td></td>
      <td></td>
    </tr>
    <tr>
      <td>156</td>
      <td>Transport</td>
      <td>0.779</td>
      <td>(.123)</td>
      <td>0.562</td>
      <td>(.276)</td>
      <td>0.358</td>
      <td>(.107)</td>
    </tr>
    <tr>
      <td>157</td>
      <td>Travel</td>
      <td>1.957</td>
      <td>(.353)</td>
      <td>1.270</td>
      <td>(.365)</td>
      <td>0.697</td>
      <td>(.174)</td>
    </tr>
    <tr>
      <td>158</td>
      <td>Construction</td>
      <td>0.254</td>
      <td>(.298)</td>
      <td>1.896</td>
      <td>(.575)</td>
      <td>-0.036</td>
      <td>(.191)</td>
    </tr>
    <tr>
      <td>159</td>
      <td>Insurance and pension services</td>
      <td>0.979</td>
      <td>(.321)</td>
      <td>0.116</td>
      <td>(.554)</td>
      <td>0.114</td>
      <td>(.214)</td>
    </tr>
    <tr>
      <td>160</td>
      <td>Financial services</td>
      <td>0.633</td>
      <td>(.28)</td>
      <td>0.266</td>
      <td>(.294)</td>
      <td>-0.300</td>
      <td>(.159)</td>
    </tr>
    <tr>
      <td>161</td>
      <td>Charges for use of intellectual property</td>
      <td>0.150</td>
      <td>(.258)</td>
      <td>0.640</td>
      <td>(.502)</td>
      <td></td>
      <td></td>
    </tr>
    <tr>
      <td>162</td>
      <td>Telecom, computer, information services</td>
      <td>0.423</td>
      <td>(.387)</td>
      <td>0.653</td>
      <td>(.324)</td>
      <td>-0.218</td>
      <td>(.364)</td>
    </tr>
    <tr>
      <td>163</td>
      <td>Other business services</td>
      <td>0.531</td>
      <td>(.237)</td>
      <td>0.608</td>
      <td>(.303)</td>
      <td>-0.160</td>
      <td>(.172)</td>
    </tr>
    <tr>
      <td>164</td>
      <td>Heritage and recreational services</td>
      <td>-2.159</td>
      <td>(.991)</td>
      <td>139.6</td>
      <td>(.137)</td>
      <td>-0.022</td>
      <td>(.354)</td>
    </tr>
    <tr>
      <td>165</td>
      <td>Health services</td>
      <td>-0.088</td>
      <td>(.51)</td>
      <td>1.908</td>
      <td>(.527)</td>
      <td>-0.395</td>
      <td>(.43)</td>
    </tr>
    <tr>
      <td>166</td>
      <td>Education services</td>
      <td>1.602</td>
      <td>(.348)</td>
      <td>2.430</td>
      <td>(.737)</td>
      <td>0.583</td>
      <td>(.229)</td>
    </tr>
    <tr>
      <td>167</td>
      <td>Government goods and services n.i.e.</td>
      <td>0.570</td>
      <td>(.229)</td>
      <td>0.534</td>
      <td>(.364)</td>
      <td></td>
      <td></td>
    </tr>
    <tr>
      <td>168</td>
      <td>Services not allocated</td>
      <td>-0.868</td>
      <td>(.211)</td>
      <td>0.211</td>
      <td>(.417)</td>
      <td></td>
      <td></td>
    </tr>
    <tr>
      <td>169</td>
      <td>Trade-related services</td>
      <td>2.003</td>
      <td>(.405)</td>
      <td>2.122</td>
      <td>(.435)</td>
      <td>0.932</td>
      <td>(.256)</td>
    </tr>
    <tr>
      <td>170</td>
      <td>Other personal services</td>
      <td>-2.847</td>
      <td>(.868)</td>
      <td>5.778</td>
      <td>(27.656)</td>
      <td>-0.712</td>
      <td>(.781)</td>
    </tr>
  </tbody>
</table>

Notes: This table reports industry-level estimates of the impact of the European Union (EU) on intra-EU and extra-EU trade, as well as estimates of the impact of the Single Market. The estimates for each industry are obtained from a single specification, i.e., equation (1). The dependent variable is trade in levels and the Poisson Pseudo Maximum Likelihood (PPML) estimates for each sector are obtained with exporter-time fixed effects, importer-time fixed effects, directional pair fixed effects, time-varying border indicators, and a series of policy controls, whose estimates are omitted for brevity. Panel A reports the EU effects on intra-EU trade. Panel B reports the effects of the Single Market on trade between the EU members and the non-EU members that are part of the Single Market. Panel C reports the EU effects on extra-EU trade. The standard errors are clustered by country pair. See text for further details.
<figure><img src="image_11.png" /><figcaption>Figure A.1: Intra-EU vs. RTA Estimates</figcaption></figure>

**Note:** This figure visualizes (in blue color) the industry-level estimates of the European Union (EU) effects on intra-EU trade, and their confidence intervals (in red and green) from panel A of Table A.1. In addition, the figure reports (in orange color, as a scatter plot) the estimates of the effects of regional trade agreements (RTAs), which are obtained from the same industry-level regressions based on specification (1). See text for further details.
<table><caption>Table A.2: Directional industry-Level intra-EU estimates</caption><thead><tr><th rowspan="2">ID</th><th colspan="2">EU_NEW_NEW</th><th colspan="2">EU_OLD_NEW</th><th colspan="2">EU_NEW_OLD</th><th colspan="2">EU_OLD_OLD</th></tr><tr><th>Estim.</th><th>Std.Err.</th><th>Estim.</th><th>Std.Err.</th><th>Estim.</th><th>Std.Err.</th><th>Estim.</th><th>Std.Err.</th></tr></thead><tbody><tr><td>1</td><td>2.710</td><td>(.29)</td><td>1.891</td><td>(.338)</td><td>1.513</td><td>(.284)</td><td>4.335</td><td>(.672)</td></tr><tr><td>2</td><td>4.428</td><td>(.553)</td><td>3.356</td><td>(.3)</td><td>3.396</td><td>(.732)</td><td>2.838</td><td>(.549)</td></tr><tr><td>1</td><td>2.769</td><td>(.293)</td><td>1.975</td><td>(.346)</td><td>1.547</td><td>(.287)</td><td>4.484</td><td>(.698)</td></tr><tr><td>2</td><td>4.603</td><td>(.552)</td><td>3.385</td><td>(.303)</td><td>3.561</td><td>(.748)</td><td>2.863</td><td>(.549)</td></tr><tr><td>3</td><td>2.050</td><td>(.34)</td><td>2.886</td><td>(.373)</td><td>1.482</td><td>(.359)</td><td>2.696</td><td>(.623)</td></tr><tr><td>4</td><td>1.986</td><td>(.257)</td><td>1.830</td><td>(.277)</td><td>1.176</td><td>(.31)</td><td>2.825</td><td>(.426)</td></tr><tr><td>5</td><td>2.032</td><td>(.634)</td><td>2.530</td><td>(.713)</td><td>2.874</td><td>(.623)</td><td>3.129</td><td>(.826)</td></tr><tr><td>6</td><td>2.252</td><td>(.823)</td><td>2.177</td><td>(.529)</td><td>2.090</td><td>(.675)</td><td>1.172</td><td>(.528)</td></tr><tr><td>7</td><td>1.007</td><td>(.281)</td><td>0.707</td><td>(.329)</td><td>0.785</td><td>(.244)</td><td>1.707</td><td>(.306)</td></tr><tr><td>8</td><td>0.948</td><td>(.329)</td><td>0.364</td><td>(.267)</td><td>0.850</td><td>(.268)</td><td>1.018</td><td>(.274)</td></tr><tr><td>9</td><td>2.826</td><td>(1.316)</td><td>9.185</td><td>(2.609)</td><td>1.839</td><td>(1.933)</td><td>4.065</td><td>(1.371)</td></tr><tr><td>10</td><td>1</td><td>(.361)</td><td>1.091</td><td>(.483)</td><td>0.928</td><td>(.378)</td><td>2.773</td><td>(.472)</td></tr><tr><td>11</td><td>0.477</td><td>(.323)</td><td>-0.451</td><td>(.296)</td><td>0.916</td><td>(.479)</td><td>0.845</td><td>(.406)</td></tr><tr><td>12</td><td>1.504</td><td>(.193)</td><td>1.438</td><td>(.168)</td><td>-0.178</td><td>(.222)</td><td>1.266</td><td>(.29)</td></tr><tr><td>13</td><td>1.429</td><td>(.177)</td><td>1.156</td><td>(.245)</td><td>1.261</td><td>(.274)</td><td>0.733</td><td>(.274)</td></tr><tr><td>14</td><td>1.606</td><td>(.46)</td><td>0.226</td><td>(.299)</td><td>1.673</td><td>(.355)</td><td>1.311</td><td>(.26)</td></tr><tr><td>15</td><td>12.02</td><td>(7.371)</td><td>7.390</td><td>(2.999)</td><td>13.26</td><td>(6.046)</td><td>2.880</td><td>(.831)</td></tr><tr><td>16</td><td>1.368</td><td>(.355)</td><td>0.791</td><td>(.22)</td><td>1.317</td><td>(.289)</td><td>1.604</td><td>(.295)</td></tr><tr><td>17</td><td>2.396</td><td>(.738)</td><td>2.486</td><td>(.717)</td><td>1.136</td><td>(.484)</td><td>-0.692</td><td>(.676)</td></tr><tr><td>18</td><td>4.044</td><td>(.66)</td><td>3.952</td><td>(.632)</td><td>4.536</td><td>(.479)</td><td>1.971</td><td>(.574)</td></tr><tr><td>19</td><td>1.997</td><td>(.251)</td><td>1.743</td><td>(.414)</td><td>2.295</td><td>(.355)</td><td>1.800</td><td>(.352)</td></tr><tr><td>20</td><td>0.761</td><td>(.232)</td><td>0.318</td><td>(.282)</td><td>-0.572</td><td>(.256)</td><td>0.103</td><td>(.207)</td></tr><tr><td>21</td><td>-0.070</td><td>(.947)</td><td>1.370</td><td>(.873)</td><td>2.141</td><td>(1.068)</td><td>7.129</td><td>(.552)</td></tr><tr><td>22</td><td>2.824</td><td>(.393)</td><td>2.947</td><td>(.375)</td><td>2.489</td><td>(.518)</td><td>2.055</td><td>(.448)</td></tr><tr><td>23</td><td>5.526</td><td>(.512)</td><td>0.998</td><td>(.406)</td><td>3.126</td><td>(.551)</td><td>1.647</td><td>(.407)</td></tr><tr><td>24</td><td>1.076</td><td>(.442)</td><td>0.503</td><td>(.276)</td><td>1.312</td><td>(.493)</td><td>0.533</td><td>(.252)</td></tr><tr><td>25</td><td>1.348</td><td>(.233)</td><td>1.390</td><td>(.226)</td><td>-0.265</td><td>(.191)</td><td>1.604</td><td>(.391)</td></tr><tr><td>26</td><td>0.459</td><td>(.196)</td><td>0.514</td><td>(.157)</td><td>-0.045</td><td>(.186)</td><td>0.441</td><td>(.198)</td></tr><tr><td>27</td><td>0.146</td><td>(.283)</td><td>-0.347</td><td>(.209)</td><td>0.005</td><td>(.246)</td><td>-0.083</td><td>(.224)</td></tr><tr><td>28</td><td>-0.177</td><td>(.625)</td><td>0.157</td><td>(.494)</td><td>-0.791</td><td>(.625)</td><td>-0.735</td><td>(.451)</td></tr><tr><td>29</td><td>1.137</td><td>(.626)</td><td>0.481</td><td>(.547)</td><td>0.503</td><td>(.522)</td><td>-0.650</td><td>(.742)</td></tr><tr><td>30</td><td>1.752</td><td>(.647)</td><td>1.465</td><td>(.689)</td><td>1.357</td><td>(.88)</td><td>0.898</td><td>(.879)</td></tr><tr><td>31</td><td>0.121</td><td>(.975)</td><td>-0.276</td><td>(.415)</td><td>-0.884</td><td>(.865)</td><td>-0.059</td><td>(.471)</td></tr><tr><td>32</td><td>2.145</td><td>(1.188)</td><td>0.846</td><td>(.573)</td><td>0.707</td><td>(1.185)</td><td>-0.023</td><td>(.459)</td></tr><tr><td>33</td><td>0.392</td><td>(.229)</td><td>1.437</td><td>(.453)</td><td>-0.358</td><td>(.204)</td><td>0.118</td><td>(.227)</td></tr><tr><td>34</td><td>1.426</td><td>(.326)</td><td>0.914</td><td>(.446)</td><td>0.528</td><td>(.504)</td><td>-0.231</td><td>(.505)</td></tr><tr><td>35</td><td>-4.748</td><td>(2.702)</td><td>-2.730</td><td>(1.674)</td><td>-2.411</td><td>(2.079)</td><td>-0.912</td><td>(1.795)</td></tr><tr><td>36</td><td>1.560</td><td>(.194)</td><td>1.530</td><td>(.213)</td><td>0.584</td><td>(.27)</td><td>1.583</td><td>(.158)</td></tr><tr><td>37</td><td>1.267</td><td>(.175)</td><td>0.650</td><td>(.154)</td><td>1.264</td><td>(.217)</td><td>0.877</td><td>(.276)</td></tr><tr><td>38</td><td>0.833</td><td>(.13)</td><td>0.495</td><td>(.121)</td><td>0.686</td><td>(.162)</td><td>1.055</td><td>(.2)</td></tr><tr><td>39</td><td>1.433</td><td>(.258)</td><td>0.540</td><td>(.385)</td><td>2.081</td><td>(.408)</td><td>1.053</td><td>(.27)</td></tr><tr><td>40</td><td>1.354</td><td>(.14)</td><td>1.165</td><td>(.132)</td><td>1.598</td><td>(.214)</td><td>1.869</td><td>(.188)</td></tr><tr><td>41</td><td>1.376</td><td>(.175)</td><td>1.611</td><td>(.195)</td><td>1.696</td><td>(.344)</td><td>0.864</td><td>(.289)</td></tr><tr><td>42</td><td>0.785</td><td>(.226)</td><td>0.504</td><td>(.221)</td><td>2.161</td><td>(.242)</td><td>0.906</td><td>(.197)</td></tr><tr><td>43</td><td>1.074</td><td>(.137)</td><td>0.595</td><td>(.175)</td><td>1.230</td><td>(.233)</td><td>0.948</td><td>(.241)</td></tr><tr><td>44</td><td>0.437</td><td>(.132)</td><td>1.058</td><td>(.169)</td><td>1.864</td><td>(.166)</td><td>0.313</td><td>(.117)</td></tr><tr><td>45</td><td>1.657</td><td>(.323)</td><td>1.349</td><td>(.347)</td><td>0.447</td><td>(.376)</td><td>2.399</td><td>(.408)</td></tr><tr><td>46</td><td>0.980</td><td>(.143)</td><td>1.182</td><td>(.132)</td><td>1.324</td><td>(.151)</td><td>0.509</td><td>(.18)</td></tr><tr><td>47</td><td>1.108</td><td>(.326)</td><td>0.470</td><td>(.287)</td><td>1.957</td><td>(.468)</td><td>2.495</td><td>(.39)</td></tr><tr><td>48</td><td>0.654</td><td>(.097)</td><td>0.723</td><td>(.114)</td><td>1.670</td><td>(.193)</td><td>1.062</td><td>(.191)</td></tr><tr><td>49</td><td>1.638</td><td>(.253)</td><td>0.574</td><td>(.189)</td><td>2.528</td><td>(.406)</td><td>1.622</td><td>(.204)</td></tr><tr><td>50</td><td>0.794</td><td>(.254)</td><td>0.308</td><td>(.215)</td><td>0.886</td><td>(.329)</td><td>1.267</td><td>(.371)</td></tr><tr><td>51</td><td>0.547</td><td>(.19)</td><td>0.238</td><td>(.22)</td><td>0.575</td><td>(.321)</td><td>0.442</td><td>(.275)</td></tr><tr><td>52</td><td>0.860</td><td>(.159)</td><td>0.131</td><td>(.195)</td><td>0.880</td><td>(.281)</td><td>0.565</td><td>(.327)</td></tr><tr><td>53</td><td>1.421</td><td>(.342)</td><td>1.431</td><td>(.329)</td><td>2.848</td><td>(.45)</td><td>2.981</td><td>(.772)</td></tr><tr><td>54</td><td>1.077</td><td>(.178)</td><td>0.241</td><td>(.144)</td><td>1.262</td><td>(.176)</td><td>0.615</td><td>(.133)</td></tr><tr><td>55</td><td>0.513</td><td>(.23)</td><td>-0.405</td><td>(.164)</td><td>0.033</td><td>(.132)</td><td>0.377</td><td>(.159)</td></tr><tr><td>56</td><td>1.191</td><td>(.284)</td><td>0.477</td><td>(.179)</td><td>1.242</td><td>(.227)</td><td>1.061</td><td>(.213)</td></tr><tr><td>57</td><td>0.013</td><td>(.249)</td><td>-0.073</td><td>(.16)</td><td>0.166</td><td>(.162)</td><td>0.783</td><td>(.211)</td></tr><tr><td>58</td><td>0.114</td><td>(.22)</td><td>-0.019</td><td>(.136)</td><td>0.519</td><td>(.167)</td><td>0.141</td><td>(.128)</td></tr><tr><td>59</td><td>-0.149</td><td>(.248)</td><td>-0.193</td><td>(.175)</td><td>-0.418</td><td>(.165)</td><td>0.065</td><td>(.185)</td></tr><tr><td>60</td><td>-0.128</td><td>(.307)</td><td>-0.055</td><td>(.23)</td><td>-1.032</td><td>(.172)</td><td>-0.057</td><td>(.177)</td></tr><tr><td>61</td><td>-0.092</td><td>(.287)</td><td>-0.286</td><td>(.247)</td><td>0.031</td><td>(.271)</td><td>0.065</td><td>(.24)</td></tr></tbody></table>

Continued on next page
<table><tbody><tr><td>62</td><td>0.074</td><td>(.364)</td><td>-0.017</td><td>(.237)</td><td>-0.226</td><td>(.324)</td><td>-0.501</td><td>(.256)</td></tr><tr><td>63</td><td>-0.254</td><td>(.337)</td><td>-0.082</td><td>(.233)</td><td>-0.880</td><td>(.222)</td><td>0.034</td><td>(.208)</td></tr><tr><td>64</td><td>0.885</td><td>(.269)</td><td>-0.089</td><td>(.211)</td><td>-0.217</td><td>(.196)</td><td>-0.047</td><td>(.22)</td></tr><tr><td>65</td><td>0.040</td><td>(.176)</td><td>0.710</td><td>(.187)</td><td>-0.247</td><td>(.19)</td><td>-0.517</td><td>(.297)</td></tr><tr><td>66</td><td>0.233</td><td>(.139)</td><td>0.211</td><td>(.139)</td><td>0.325</td><td>(.153)</td><td>-0.015</td><td>(.166)</td></tr><tr><td>67</td><td>0.254</td><td>(.151)</td><td>0.477</td><td>(.229)</td><td>-0.097</td><td>(.243)</td><td>0.138</td><td>(.194)</td></tr><tr><td>68</td><td>0</td><td>(.156)</td><td>-0.068</td><td>(.176)</td><td>-0.480</td><td>(.16)</td><td>0.162</td><td>(.203)</td></tr><tr><td>69</td><td>0.378</td><td>(.197)</td><td>0.315</td><td>(.172)</td><td>-0.036</td><td>(.155)</td><td>0.852</td><td>(.226)</td></tr><tr><td>70</td><td>0.216</td><td>(.133)</td><td>0.278</td><td>(.122)</td><td>0.566</td><td>(.136)</td><td>0.086</td><td>(.138)</td></tr><tr><td>71</td><td>0.061</td><td>(.132)</td><td>0.061</td><td>(.146)</td><td>0.970</td><td>(.173)</td><td>0.052</td><td>(.137)</td></tr><tr><td>72</td><td>-0.116</td><td>(.133)</td><td>0.057</td><td>(.124)</td><td>0.232</td><td>(.182)</td><td>-0.509</td><td>(.174)</td></tr><tr><td>73</td><td>-0.439</td><td>(.219)</td><td>0.293</td><td>(.137)</td><td>0.271</td><td>(.156)</td><td>-0.124</td><td>(.13)</td></tr><tr><td>74</td><td>-0.682</td><td>(.208)</td><td>-0.160</td><td>(.249)</td><td>0.597</td><td>(.228)</td><td>-0.559</td><td>(.226)</td></tr><tr><td>75</td><td>-0.483</td><td>(.439)</td><td>-0.358</td><td>(.226)</td><td>0.307</td><td>(.359)</td><td>-0.263</td><td>(.224)</td></tr><tr><td>76</td><td>-0.774</td><td>(.294)</td><td>0.032</td><td>(.224)</td><td>-0.224</td><td>(.274)</td><td>-0.041</td><td>(.228)</td></tr><tr><td>77</td><td>0.291</td><td>(.159)</td><td>0.279</td><td>(.161)</td><td>0.415</td><td>(.244)</td><td>0.036</td><td>(.113)</td></tr><tr><td>78</td><td>-0.096</td><td>(.274)</td><td>-0.634</td><td>(.286)</td><td>0.794</td><td>(.302)</td><td>0.401</td><td>(.264)</td></tr><tr><td>79</td><td>0.595</td><td>(.526)</td><td>1.190</td><td>(.607)</td><td>-0.830</td><td>(.625)</td><td>-0.157</td><td>(.424)</td></tr><tr><td>80</td><td>0.280</td><td>(.16)</td><td>0.218</td><td>(.19)</td><td>0.092</td><td>(.204)</td><td>0.349</td><td>(.226)</td></tr><tr><td>81</td><td>-1.925</td><td>(.503)</td><td>-1.332</td><td>(.378)</td><td>-1.294</td><td>(.561)</td><td>0.099</td><td>(.498)</td></tr><tr><td>82</td><td>0.240</td><td>(.167)</td><td>0.221</td><td>(.107)</td><td>0.078</td><td>(.183)</td><td>0.021</td><td>(.123)</td></tr><tr><td>83</td><td>1.298</td><td>(.195)</td><td>1.160</td><td>(.224)</td><td>0.273</td><td>(.174)</td><td>0.956</td><td>(.282)</td></tr><tr><td>84</td><td>0.362</td><td>(.148)</td><td>0.326</td><td>(.1)</td><td>0.544</td><td>(.128)</td><td>0.403</td><td>(.12)</td></tr><tr><td>85</td><td>1.311</td><td>(.225)</td><td>0.756</td><td>(.161)</td><td>1.352</td><td>(.203)</td><td>0.249</td><td>(.252)</td></tr><tr><td>86</td><td>0.331</td><td>(.134)</td><td>0.110</td><td>(.103)</td><td>0.839</td><td>(.24)</td><td>-0.062</td><td>(.119)</td></tr><tr><td>87</td><td>0.773</td><td>(.181)</td><td>0.323</td><td>(.126)</td><td>1.489</td><td>(.17)</td><td>-0.565</td><td>(.163)</td></tr><tr><td>88</td><td>0.337</td><td>(.142)</td><td>0.626</td><td>(.12)</td><td>1.560</td><td>(.154)</td><td>0.321</td><td>(.108)</td></tr><tr><td>89</td><td>0.764</td><td>(.189)</td><td>0.179</td><td>(.121)</td><td>0.948</td><td>(.149)</td><td>0.432</td><td>(.147)</td></tr><tr><td>90</td><td>0.430</td><td>(.278)</td><td>0.485</td><td>(.196)</td><td>1.311</td><td>(.255)</td><td>-0.018</td><td>(.257)</td></tr><tr><td>91</td><td>0.146</td><td>(.215)</td><td>0.428</td><td>(.155)</td><td>0.642</td><td>(.161)</td><td>0.364</td><td>(.157)</td></tr><tr><td>92</td><td>0.203</td><td>(.198)</td><td>0.243</td><td>(.103)</td><td>0.193</td><td>(.141)</td><td>0.018</td><td>(.126)</td></tr><tr><td>93</td><td>0.201</td><td>(.139)</td><td>0.157</td><td>(.09)</td><td>0.623</td><td>(.134)</td><td>-0.050</td><td>(.107)</td></tr><tr><td>94</td><td>0.372</td><td>(.123)</td><td>-0.015</td><td>(.108)</td><td>0.600</td><td>(.119)</td><td>0.038</td><td>(.14)</td></tr><tr><td>95</td><td>0.150</td><td>(.185)</td><td>0.256</td><td>(.161)</td><td>-0.007</td><td>(.21)</td><td>0.121</td><td>(.155)</td></tr><tr><td>96</td><td>-0.010</td><td>(.316)</td><td>0.338</td><td>(.216)</td><td>-0.069</td><td>(.204)</td><td>0.287</td><td>(.197)</td></tr><tr><td>97</td><td>0.383</td><td>(.152)</td><td>-0.349</td><td>(.141)</td><td>0.546</td><td>(.197)</td><td>0.796</td><td>(.196)</td></tr><tr><td>98</td><td>0.295</td><td>(.257)</td><td>-0.300</td><td>(.321)</td><td>-0.617</td><td>(.252)</td><td>0.967</td><td>(.305)</td></tr><tr><td>99</td><td>-0.034</td><td>(.162)</td><td>0.199</td><td>(.171)</td><td>-0.154</td><td>(.197)</td><td>0.106</td><td>(.16)</td></tr><tr><td>100</td><td>0.685</td><td>(.227)</td><td>-0.671</td><td>(.18)</td><td>-0.162</td><td>(.295)</td><td>0.630</td><td>(.288)</td></tr><tr><td>101</td><td>0.126</td><td>(.135)</td><td>0.141</td><td>(.118)</td><td>0.264</td><td>(.158)</td><td>0.731</td><td>(.136)</td></tr><tr><td>102</td><td>0.960</td><td>(.12)</td><td>0.423</td><td>(.106)</td><td>0.855</td><td>(.121)</td><td>0.281</td><td>(.092)</td></tr><tr><td>103</td><td>0.895</td><td>(.211)</td><td>0.670</td><td>(.216)</td><td>0.671</td><td>(.227)</td><td>0.442</td><td>(.229)</td></tr><tr><td>104</td><td>0.097</td><td>(.135)</td><td>-0.028</td><td>(.106)</td><td>-0.052</td><td>(.12)</td><td>0.438</td><td>(.125)</td></tr><tr><td>105</td><td>-0.509</td><td>(.189)</td><td>-0.130</td><td>(.167)</td><td>-0.072</td><td>(.158)</td><td>-0.042</td><td>(.164)</td></tr><tr><td>106</td><td>-0.159</td><td>(.265)</td><td>0.335</td><td>(.217)</td><td>-0.041</td><td>(.264)</td><td>-0.187</td><td>(.284)</td></tr><tr><td>107</td><td>0.429</td><td>(.209)</td><td>0.181</td><td>(.157)</td><td>0.435</td><td>(.205)</td><td>0.129</td><td>(.149)</td></tr><tr><td>108</td><td>0.267</td><td>(.176)</td><td>0.263</td><td>(.118)</td><td>0.609</td><td>(.144)</td><td>0.116</td><td>(.102)</td></tr><tr><td>109</td><td>-0.913</td><td>(.338)</td><td>-0.448</td><td>(.254)</td><td>-0.094</td><td>(.276)</td><td>0.551</td><td>(.205)</td></tr><tr><td>110</td><td>-0.336</td><td>(.218)</td><td>-0.245</td><td>(.12)</td><td>0.121</td><td>(.147)</td><td>-0.218</td><td>(.146)</td></tr><tr><td>111</td><td>-0.660</td><td>(.238)</td><td>0.060</td><td>(.117)</td><td>0.278</td><td>(.151)</td><td>-0.172</td><td>(.15)</td></tr><tr><td>112</td><td>-0.252</td><td>(.232)</td><td>-0.594</td><td>(.155)</td><td>0.440</td><td>(.202)</td><td>0.249</td><td>(.179)</td></tr><tr><td>113</td><td>-0.621</td><td>(.218)</td><td>0.229</td><td>(.144)</td><td>0.124</td><td>(.152)</td><td>0.094</td><td>(.118)</td></tr><tr><td>114</td><td>0.224</td><td>(.15)</td><td>0.038</td><td>(.088)</td><td>0.399</td><td>(.143)</td><td>-0.017</td><td>(.084)</td></tr><tr><td>115</td><td>0.863</td><td>(.154)</td><td>0.589</td><td>(.112)</td><td>0.607</td><td>(.156)</td><td>0.348</td><td>(.165)</td></tr><tr><td>116</td><td>0.339</td><td>(.177)</td><td>-0.050</td><td>(.13)</td><td>0.450</td><td>(.134)</td><td>0.631</td><td>(.24)</td></tr><tr><td>117</td><td>0.493</td><td>(.255)</td><td>0.609</td><td>(.221)</td><td>0.771</td><td>(.271)</td><td>0.246</td><td>(.202)</td></tr><tr><td>118</td><td>0.467</td><td>(.215)</td><td>0.385</td><td>(.13)</td><td>0.757</td><td>(.172)</td><td>-0.035</td><td>(.128)</td></tr><tr><td>119</td><td>0.339</td><td>(.161)</td><td>0.310</td><td>(.098)</td><td>0.584</td><td>(.211)</td><td>-0.158</td><td>(.165)</td></tr><tr><td>120</td><td>-0.215</td><td>(.272)</td><td>0.101</td><td>(.169)</td><td>-0.157</td><td>(.214)</td><td>0.081</td><td>(.175)</td></tr><tr><td>121</td><td>0.942</td><td>(.332)</td><td>0.580</td><td>(.266)</td><td>-0.039</td><td>(.24)</td><td>0.001</td><td>(.246)</td></tr><tr><td>122</td><td>-0.091</td><td>(.174)</td><td>0.024</td><td>(.131)</td><td>0.025</td><td>(.13)</td><td>-0.124</td><td>(.146)</td></tr><tr><td>123</td><td>0.196</td><td>(.171)</td><td>0.257</td><td>(.144)</td><td>0.389</td><td>(.146)</td><td>-0.121</td><td>(.146)</td></tr><tr><td>124</td><td>0.187</td><td>(.259)</td><td>0.382</td><td>(.181)</td><td>0.017</td><td>(.208)</td><td>0.442</td><td>(.281)</td></tr><tr><td>125</td><td>-0.046</td><td>(.165)</td><td>-0.261</td><td>(.149)</td><td>-0.120</td><td>(.159)</td><td>0.032</td><td>(.126)</td></tr><tr><td>126</td><td>0.271</td><td>(.236)</td><td>0.329</td><td>(.131)</td><td>-0.063</td><td>(.16)</td><td>0.067</td><td>(.127)</td></tr><tr><td>127</td><td>0.685</td><td>(.231)</td><td>0.469</td><td>(.16)</td><td>0.329</td><td>(.185)</td><td>-0.034</td><td>(.114)</td></tr><tr><td>128</td><td>0.401</td><td>(.248)</td><td>-0.003</td><td>(.237)</td><td>0.807</td><td>(.234)</td><td>0.991</td><td>(.227)</td></tr></tbody></table>

Continued on next page
<table><tr><td>129</td><td>-0.329</td><td>(.284)</td><td>-0.480</td><td>(.227)</td><td>-0.016</td><td>(.19)</td><td>-0.028</td><td>(.157)</td></tr><tr><td>130</td><td>-0.126</td><td>(.211)</td><td>-0.191</td><td>(.147)</td><td>-0.447</td><td>(.156)</td><td>-0.044</td><td>(.13)</td></tr><tr><td>131</td><td>0.291</td><td>(.263)</td><td>0.561</td><td>(.244)</td><td>-0.092</td><td>(.217)</td><td>0.305</td><td>(.189)</td></tr><tr><td>132</td><td>0.369</td><td>(.318)</td><td>-0.008</td><td>(.189)</td><td>-0.268</td><td>(.236)</td><td>-0.424</td><td>(.166)</td></tr><tr><td>133</td><td>0.174</td><td>(.286)</td><td>-0.474</td><td>(.177)</td><td>0.083</td><td>(.225)</td><td>0.239</td><td>(.172)</td></tr><tr><td>134</td><td>0.117</td><td>(.21)</td><td>0.121</td><td>(.128)</td><td>0.314</td><td>(.21)</td><td>-0.155</td><td>(.156)</td></tr><tr><td>135</td><td>0.020</td><td>(.199)</td><td>0.237</td><td>(.114)</td><td>0.446</td><td>(.145)</td><td>-0.047</td><td>(.12)</td></tr><tr><td>136</td><td>0.155</td><td>(.365)</td><td>0.084</td><td>(.325)</td><td>0.363</td><td>(.302)</td><td>-0.064</td><td>(.186)</td></tr><tr><td>137</td><td>1.107</td><td>(.344)</td><td>0.500</td><td>(.185)</td><td>-0.141</td><td>(.281)</td><td>0.274</td><td>(.208)</td></tr><tr><td>138</td><td>0.894</td><td>(.27)</td><td>0.732</td><td>(.159)</td><td>0.382</td><td>(.214)</td><td>0.257</td><td>(.237)</td></tr><tr><td>139</td><td>-0.364</td><td>(.506)</td><td>0.291</td><td>(.428)</td><td>-0.337</td><td>(.301)</td><td>0.684</td><td>(.288)</td></tr><tr><td>140</td><td>0.350</td><td>(.24)</td><td>0.427</td><td>(.203)</td><td>0.836</td><td>(.206)</td><td>0.404</td><td>(.152)</td></tr><tr><td>141</td><td>-1.117</td><td>(.278)</td><td>-0.566</td><td>(.273)</td><td>-0.628</td><td>(.236)</td><td>0.663</td><td>(.429)</td></tr><tr><td>142</td><td>0.190</td><td>(.288)</td><td>-0.091</td><td>(.221)</td><td>-0.417</td><td>(.267)</td><td>-0.513</td><td>(.258)</td></tr><tr><td>143</td><td>0.582</td><td>(.189)</td><td>0.518</td><td>(.228)</td><td>0.311</td><td>(.243)</td><td>0.336</td><td>(.281)</td></tr><tr><td>144</td><td>-0.807</td><td>(.442)</td><td>0.308</td><td>(.257)</td><td>0.440</td><td>(.365)</td><td>0.245</td><td>(.283)</td></tr><tr><td>145</td><td>-0.610</td><td>(.392)</td><td>0.059</td><td>(.146)</td><td>1.363</td><td>(.275)</td><td>0.741</td><td>(.255)</td></tr><tr><td>146</td><td>-0.832</td><td>(.321)</td><td>-0.308</td><td>(.225)</td><td>-0.631</td><td>(.239)</td><td>-0.273</td><td>(.206)</td></tr><tr><td>147</td><td>-0.526</td><td>(.218)</td><td>-0.491</td><td>(.174)</td><td>-0.444</td><td>(.174)</td><td>0.390</td><td>(.17)</td></tr><tr><td>148</td><td>0.735</td><td>(.14)</td><td>0.481</td><td>(.142)</td><td>0.264</td><td>(.124)</td><td>-0.112</td><td>(.128)</td></tr><tr><td>149</td><td>0.516</td><td>(.351)</td><td>-0.142</td><td>(.253)</td><td>1.617</td><td>(.486)</td><td>-0.040</td><td>(.263)</td></tr><tr><td>150</td><td>0.237</td><td>(.279)</td><td>0.058</td><td>(.247)</td><td>-0.427</td><td>(.219)</td><td>-0.259</td><td>(.186)</td></tr><tr><td>151</td><td>0.176</td><td>(.262)</td><td>0.328</td><td>(.163)</td><td>-0.328</td><td>(.214)</td><td>0.192</td><td>(.174)</td></tr><tr><td>152</td><td>0.244</td><td>(.382)</td><td>-0.269</td><td>(.348)</td><td>-0.469</td><td>(.347)</td><td>0.100</td><td>(.381)</td></tr><tr><td>153</td><td>0.429</td><td>(.173)</td><td>-0.113</td><td>(.126)</td><td>0.470</td><td>(.144)</td><td>-0.187</td><td>(.155)</td></tr><tr><td>154</td><td>-3.084</td><td>(1.34)</td><td>-2.415</td><td>(1.557)</td><td>-1.974</td><td>(1.304)</td><td>.</td><td>(.)</td></tr><tr><td>155</td><td>-0.822</td><td>(.55)</td><td>-0.812</td><td>(.642)</td><td>-0.456</td><td>(.575)</td><td>.</td><td>(.)</td></tr><tr><td>156</td><td>0.578</td><td>(.146)</td><td>0.356</td><td>(.14)</td><td>0.463</td><td>(.136)</td><td>.</td><td>(.)</td></tr><tr><td>157</td><td>0.859</td><td>(.163)</td><td>1.140</td><td>(.251)</td><td>0.959</td><td>(.165)</td><td>.</td><td>(.)</td></tr><tr><td>158</td><td>0.615</td><td>(.268)</td><td>0.286</td><td>(.381)</td><td>0.153</td><td>(.361)</td><td>.</td><td>(.)</td></tr><tr><td>159</td><td>1.243</td><td>(.305)</td><td>0.784</td><td>(.36)</td><td>0.825</td><td>(.387)</td><td>.</td><td>(.)</td></tr><tr><td>160</td><td>0.595</td><td>(.19)</td><td>0.725</td><td>(.265)</td><td>1.405</td><td>(.321)</td><td>.</td><td>(.)</td></tr><tr><td>161</td><td>0.647</td><td>(.422)</td><td>-0.018</td><td>(.276)</td><td>0.672</td><td>(.476)</td><td>.</td><td>(.)</td></tr><tr><td>162</td><td>0.299</td><td>(.159)</td><td>0.871</td><td>(.424)</td><td>0.509</td><td>(.423)</td><td>.</td><td>(.)</td></tr><tr><td>163</td><td>0.484</td><td>(.147)</td><td>0.964</td><td>(.29)</td><td>0.475</td><td>(.26)</td><td>.</td><td>(.)</td></tr><tr><td>164</td><td>-2.538</td><td>(.884)</td><td>-0.205</td><td>(1.356)</td><td>6.436</td><td>(24.244)</td><td>.</td><td>(.)</td></tr><tr><td>165</td><td>0.313</td><td>(.225)</td><td>-0.164</td><td>(.455)</td><td>0.658</td><td>(.325)</td><td>.</td><td>(.)</td></tr><tr><td>166</td><td>1.050</td><td>(.341)</td><td>1.432</td><td>(.307)</td><td>0.373</td><td>(.373)</td><td>.</td><td>(.)</td></tr><tr><td>167</td><td>-0.146</td><td>(.359)</td><td>0.990</td><td>(.321)</td><td>0.076</td><td>(.344)</td><td>.</td><td>(.)</td></tr><tr><td>168</td><td>-0.877</td><td>(.219)</td><td>-0.965</td><td>(.215)</td><td>-0.798</td><td>(.251)</td><td>.</td><td>(.)</td></tr><tr><td>169</td><td>1.227</td><td>(.341)</td><td>1.219</td><td>(.427)</td><td>0.960</td><td>(.346)</td><td>.</td><td>(.)</td></tr><tr><td>170</td><td>-2.188</td><td>(1.092)</td><td>-1.158</td><td>(.828)</td><td>-3.122</td><td>(.992)</td><td>.</td><td>(.)</td></tr></table>

Notes: This table reports estimates of the impact of the European Union (EU) on intra-EU trade depending on time of joining and depending on the direction of trade flows. The estimates are obtained from the same specification – equation (1), but after using interactions for each of the four groups, corresponding to the four panels in the table. The dependent variable is trade in levels and the Poisson Pseudo Maximum Likelihood (PPML) estimates for each industry are obtained with exporter-time fixed effects, importer-time fixed effects, directional pair fixed effects, time-varying border indicators, and a series of policy controls, whose estimates are omitted for brevity. The standard errors are clustered by country pair. See text for further details.
<figure><img src="image_12.png" /><figcaption>Figure A.2: TWFE vs. ETWFE intra-EU estimates</figcaption></figure>

**Note:** The top panel of this figure visualizes (in blue color) the industry-level estimates of the European Union (EU) on intra-EU trade, along with their confidence intervals (in red and green). These estimates are obtained from the 'extended two-way fixed effects' (ETWFE) specification (A.1). The dependent variable is trade in levels and the Poisson Pseudo Maximum Likelihood (PPML) estimates for each industry are obtained with exporter-time fixed effects, importer-time fixed effects, and directional pair fixed effects whose estimates are omitted for brevity. The standard errors are clustered by country pair. The bottom panel of the figure reproduces the results from the top panel and, in addition, it reports (in orange, and as a scatter plot) the estimates of the intra-EU effects from the main text. See text for further details.
<table><caption>Table A.3: The Effects of integration and protection in the European Union</caption><thead><tr><th rowspan="2">Country<br>ISO3</th><th rowspan="2">A. Integration<br>EU</th><th colspan="3">B. EU protection (σ = 6)</th><th colspan="3">C. EU protection (σ = 2)</th><th colspan="3">D. Compensating Liberalization</th></tr><tr><th>RUS</th><th>CHN</th><th>CHN/RUS</th><th>RUS</th><th>CHN</th><th>CHN/RUS</th><th>EST</th><th>SERV/CYP</th><th>DEU</th><th>EU</th></tr></thead><tbody><tr><td>AUT</td><td>3.076</td><td>-0.061</td><td>-0.224</td><td>-0.317</td><td>-0.333</td><td>-1.113</td><td>-1.632</td><td>-0.317</td><td>-0.251</td><td>0.200</td><td>0.930</td></tr><tr><td>BEL</td><td>3.479</td><td>-0.208</td><td>-0.141</td><td>-0.366</td><td>-0.728</td><td>-0.782</td><td>-1.673</td><td>-0.365</td><td>-0.315</td><td>-0.170</td><td>1.162</td></tr><tr><td>BGR</td><td>3.113</td><td>-0.410</td><td>-0.250</td><td>-0.679</td><td>-1.338</td><td>-1.211</td><td>-2.680</td><td>-0.681</td><td>-0.506</td><td>-0.490</td><td>0.502</td></tr><tr><td>CYP</td><td>3.469</td><td>-1.727</td><td>-0.155</td><td>-1.896</td><td>-7.032</td><td>-0.766</td><td>-7.898</td><td>-1.886</td><td>-0.178</td><td>-1.767</td><td>-0.670</td></tr><tr><td>CZE</td><td>3.283</td><td>-0.180</td><td>-0.599</td><td>-0.826</td><td>-0.743</td><td>-3.411</td><td>-4.491</td><td>-0.825</td><td>-0.713</td><td>-0.353</td><td>0.569</td></tr><tr><td>DEU</td><td>1.695</td><td>-0.081</td><td>-0.445</td><td>-0.582</td><td>-0.280</td><td>-2.028</td><td>-2.592</td><td>-0.581</td><td>-0.507</td><td>0.382</td><td>0.169</td></tr><tr><td>DNK</td><td>2.070</td><td>-0.045</td><td>-0.333</td><td>-0.438</td><td>-0.209</td><td>-1.600</td><td>-2.097</td><td>-0.436</td><td>-0.354</td><td>-0.247</td><td>0.504</td></tr><tr><td>ESP</td><td>1.355</td><td>-0.029</td><td>-0.192</td><td>-0.251</td><td>-0.083</td><td>-0.985</td><td>-1.243</td><td>-0.250</td><td>-0.211</td><td>-0.146</td><td>0.367</td></tr><tr><td>EST</td><td>3.014</td><td>-0.803</td><td>-0.689</td><td>-1.517</td><td>-2.834</td><td>-3.025</td><td>-6.016</td><td>0.188</td><td>-1.191</td><td>-1.419</td><td>-0.219</td></tr><tr><td>FIN</td><td>1.535</td><td>-0.450</td><td>-0.262</td><td>-0.723</td><td>-1.792</td><td>-1.240</td><td>-3.105</td><td>-0.697</td><td>-0.589</td><td>-0.615</td><td>-0.055</td></tr><tr><td>FRA</td><td>1.281</td><td>-0.045</td><td>-0.220</td><td>-0.290</td><td>-0.167</td><td>-1.089</td><td>-1.399</td><td>-0.290</td><td>-0.243</td><td>-0.177</td><td>0.277</td></tr><tr><td>GBR</td><td>1.096</td><td>-0.050</td><td>-0.186</td><td>-0.252</td><td>-0.133</td><td>-0.881</td><td>-1.095</td><td>-0.251</td><td>-0.207</td><td>-0.154</td><td>0.255</td></tr><tr><td>GRC</td><td>1.544</td><td>-0.340</td><td>-0.187</td><td>-0.565</td><td>-0.417</td><td>-0.941</td><td>-1.643</td><td>-0.564</td><td>-0.439</td><td>-0.484</td><td>0.051</td></tr><tr><td>HRV</td><td>4.521</td><td>-0.124</td><td>-0.079</td><td>-0.212</td><td>-0.352</td><td>-0.478</td><td>-0.901</td><td>-0.210</td><td>-0.140</td><td>0.013</td><td>1.138</td></tr><tr><td>HUN</td><td>4.186</td><td>-0.167</td><td>-0.292</td><td>-0.480</td><td>-0.559</td><td>-1.768</td><td>-2.553</td><td>-0.480</td><td>-0.411</td><td>0.059</td><td>1.294</td></tr><tr><td>IRL</td><td>2.279</td><td>-0.059</td><td>-0.291</td><td>-0.373</td><td>-0.274</td><td>-1.442</td><td>-1.831</td><td>-0.372</td><td>-0.306</td><td>-0.208</td><td>0.811</td></tr><tr><td>ITA</td><td>1.150</td><td>-0.059</td><td>-0.257</td><td>-0.351</td><td>-0.212</td><td>-1.228</td><td>-1.641</td><td>-0.351</td><td>-0.293</td><td>-0.241</td><td>0.193</td></tr><tr><td>LTU</td><td>3.231</td><td>-0.647</td><td>-0.153</td><td>-0.831</td><td>-2.684</td><td>-0.830</td><td>-3.695</td><td>-0.783</td><td>-0.501</td><td>-0.681</td><td>0.583</td></tr><tr><td>LUX</td><td>4.727</td><td>-0.054</td><td>-0.071</td><td>-0.129</td><td>-0.250</td><td>-0.315</td><td>-0.581</td><td>-0.128</td><td>-0.076</td><td>0.338</td><td>1.743</td></tr><tr><td>LVA</td><td>2.628</td><td>-0.559</td><td>-0.061</td><td>-0.611</td><td>-1.710</td><td>-0.342</td><td>-2.063</td><td>-0.548</td><td>-0.447</td><td>-0.557</td><td>0.129</td></tr><tr><td>MLT</td><td>2.254</td><td>-0.139</td><td>-0.215</td><td>-0.366</td><td>-0.404</td><td>-0.836</td><td>-1.318</td><td>-0.360</td><td>-0.288</td><td>-0.239</td><td>0.555</td></tr><tr><td>NLD</td><td>2.990</td><td>-0.140</td><td>-0.233</td><td>-0.402</td><td>-0.326</td><td>-1.750</td><td>-2.400</td><td>-0.401</td><td>-0.336</td><td>-0.088</td><td>0.940</td></tr><tr><td>POL</td><td>2.232</td><td>-0.144</td><td>-0.334</td><td>-0.519</td><td>-0.527</td><td>-1.911</td><td>-2.737</td><td>-0.518</td><td>-0.436</td><td>-0.258</td><td>0.332</td></tr><tr><td>PRT</td><td>2.159</td><td>-0.025</td><td>-0.170</td><td>-0.220</td><td>-0.049</td><td>-0.834</td><td>-1.036</td><td>-0.220</td><td>-0.189</td><td>-0.098</td><td>0.721</td></tr><tr><td>ROU</td><td>2.108</td><td>-0.132</td><td>-0.173</td><td>-0.335</td><td>-0.322</td><td>-0.987</td><td>-1.507</td><td>-0.334</td><td>-0.279</td><td>-0.107</td><td>0.636</td></tr><tr><td>SVK</td><td>3.967</td><td>-0.196</td><td>-0.280</td><td>-0.507</td><td>-0.771</td><td>-1.687</td><td>-2.672</td><td>-0.506</td><td>-0.421</td><td>-0.152</td><td>1.205</td></tr><tr><td>SVN</td><td>4.110</td><td>-0.190</td><td>-0.301</td><td>-0.506</td><td>-0.673</td><td>-1.655</td><td>-2.506</td><td>-0.505</td><td>-0.433</td><td>-0.211</td><td>1.024</td></tr><tr><td>SWE</td><td>1.869</td><td>-0.066</td><td>-0.227</td><td>-0.321</td><td>-0.199</td><td>-1.045</td><td>-1.396</td><td>-0.314</td><td>-0.270</td><td>-0.180</td><td>0.510</td></tr></tbody></table>

Notes: This table reports estimates of the real output effects that we obtained in Section 5.2 of the main text. The estimates in Panel A correspond to the results in Figure 3. The results in Panel B are used to construct the top two and the bottom-left panels of Figure 4. The results in the last column of panel C are used to construct the bottom-right panel of Figure 4. Finally, the estimates in panel D are used to construct the four panels in Figure 5. See the main text for further details.