Single market enlargement and technical barriers to trade: Revisiting the evidence

Hagemejer, Jan; Matuszczak, Łukasz Article Single market enlargement and technical barriers to trade: Revisiting the evidence Central European Economic Journal (CEEJ) Provided in Cooperation with: Faculty of Economic Sciences, University of Warsaw Suggested Citation: Hagemejer, Jan; Matuszczak, Łukasz (2024) : Single market enlargement and technical barriers to trade: Revisiting the evidence, Central European Economic Journal (CEEJ), ISSN 2543-6821, Sciendo, Warsaw, Vol. 11, Iss. 58, pp. 79-96, https://doi.org/10.2478/ceej-2024-0008 This Version is available at: https://hdl.handle.net/10419/324605 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ ISSN: 2543-6821 (online) Journal homepage: http://ceej.wne.uw.edu.pl To cite this article Hagemejer, J., Matuszczak, Ł. (2024). Single Market Enlargement and Technical Barriers to Trade: Revisiting the Evidence. Central European Economic Journal, 11(58), 79-96. DOI: 10.2478/ceej-2024-0008 To link to this article: https://doi.org/10.2478/ceej-2024-0008 This article was edited by Associate Editor: Joanna Mackiewicz-Łyziak University of Warsaw, Poland as part of the Special Call to mark the 70th Birthday of Prof. Jan Jakub Michałek Single Market Enlargement and Technical Barriers to Trade: Revisiting the Evidence Jan Hagemejer, Łukasz Matuszczak Open Access. © 2024 J. Hagemejer, Ł. Matuszczak, published by Sciendo. This work is licensed under the Creative Commons Attribution 4.0 International License. Jan Hagemejer University of Warsaw, Faculty of Economic Sciences, Długa 44/50, 00-241 Warsaw, Poland; CASE Center for Social and Economic Research ul. Zamenhofa 5/1b, 00-165 Warsaw, Poland Łukasz Matuszczak University of Warsaw, Faculty of Economic Sciences, Długa 44/50, 00-241 Warsaw, Poland corresponding author: [email protected] Single Market Enlargement and Technical Barriers to Trade: Revisiting the Evidence Abstract EU enlargements have given new EU member states access to the European Single Market. While tariff liberalisation was already completed at the time of enlargement, technical regulations were subject to different sectoral approaches, including harmonisation and mutual recognition. We employ a structural gravity model estimated using sectoral trade data from 1987 to 2020 to assess the trade effects of these measures. We find that trade expansion, particularly exports of the NMS to the incumbent EU members, has been stronger in the sectors covered either by the Old Approach (full harmonisation) or the New Approach (essential requirements) than in sectors covered by mutual recognition. The New Approach has been more effective when coupled with mutual recognition at the sector level than with either approach alone. Our results imply that the TBT harmonisation has had a heterogenous impact on different sectors (the most important for low-tech industries was the Old Approach, while for high-tech, it was the New Approach). Keywords European integration | EU enlargement | gravity model | technical barriers to trade JEL Codes F13, F02, F52 1. Introduction In this study, we examine the impact of the European Union (EU) enlargement on trade, specifically in relation to technical barriers to trade (TBT). The enlargement ushered in a new era of economic integration, uniting diverse member states with unique economic structures and regulatory frameworks into a unified, cohesive market. For the new member states of the EU (NMS), joining the Single Market coincided with their economic transition and provided unprecedented opportunities for economic growth and prosperity (Hagemejer & Mućk, 2019). Although tariff barriers were largely eliminated by the time the NMS acceded, the removal of technical barriers to trade (TBT) through full membership in the Single Market was anticipated to yield additional trade benefits. Technical barriers to trade (TBT) include a broad array of regulatory measures that aim to protect public health, consumer safety, and the environment. These measures can manifest as either regulations or standards (Disdier et al., 2018). De jure technical regulations or de facto standards may necessitate product modifications for a producer to gain market access. Such regulations or standards are considered TBTs when they potentially restrict trade (e.g., Brenton & Manzohi, 2002; Fischer & Serra, 2000) and can serve as protectionist measures, as evidenced by recent research (Grundke & Moser, 2019). The associated costs can be variable—fluctuating with production volume and requiring adjustments to each unit sold—or fixed, involving sunk investments to align production processes with the importing country’s regulations (Yang, 2020; Fischer & Serra, 2000). While standards are intended to mitigate market failures and limit the consumption of harmful goods or CEEJ • 11(58) • 2024 • pp. 79-96 • ISSN 2543-6821 • DOI: 10.2478/ceej-2024-0008 81 information asymmetry about product characteristics (Fernandez, 2021), they must not create unnecessary obstacles to trade. The World Trade Organization’s (WTO) TBT Agreement stipulates that technical regulations should not be excessively trade-restrictive and allows members to raise specific trade concerns (STCs) related to TBTs. An increase in STCs suggests a trend of replacing declining tariffs with TBTs (Orefice, 2016). The EU has implemented various approaches to manage TBT to complete the internal market strategy. These include the full harmonisation of technical regulations or the so-called Old Approach, harmonisation of only the essential requirements (New Approach), and Mutual Recognition, where the national regulations of individual member states are recognised as equivalent to those in other member states. These approaches can have varying effects on trade; for example, full harmonisation is cumbersome but complete, and it fully eliminates barriers from the incompatibility of national regulations. At the same time, it may be preferential for internal union trade; that is, EU firms complying with those regulations operate in completely harmonised regulatory environments. However, third-country firms may need to adjust the products to be able to export to the Single Market. In the case of Mutual Recognition, there are no costs related to harmonisation. However, national regulations may differ; therefore, some internal market barriers may remain. Moreover, while mutual recognition can be regarded as the preferred approach because of its cost-effectiveness (Felbermayr & Jung, 2011), the practical implementation of mutual recognition in the EU is not as effective as initially perceived (Ilzkovitz et al., 2007). Harmonisation, or in other words, regulation unification within the EU, takes the form of a standardisation union that is preferential in nature; that is, the costs of adjustment to the Single Market Standards are lower for the members of the Union. Some theoretical insights on this issue are provided by Gandal and Shy (1996), who show that standardisation unions are trade-creating relative to the world with no mutual recognition of standards; however, full global mutual recognition of standards is preferred from a welfare standpoint. Hence, we could expect that the EU, in general, and the 1992 Single Market Programme should enhance the EU’s internal trade while the barriers toward third countries remain high. This study endeavours to revisit the topic of European integration by analysing the trade implications associated with the New Approach, Old Approach, and Mutual Recognition within the Single Market context (see Hagemejer & Michałek, 2007, for an early analysis that this paper revisits). We employ a modern structural gravity model and sectoral trade data from 1995 to 2020 to assess the trade effects of EU expansion over a long horizon. We examine the effectiveness of the aforementioned EU policies in expanding trade. This study contributes to the literature in two ways. First, it provides new insights into the process of EU integration in general and EU enlargement in particular. This is a gigantic strand, with early ex-ante papers relying on computable simulations, such as Harisson et al. (1996). Smith and Venables (1988) and newer ex-post papers using a more sophisticated methodology to inquire about the trade and welfare effects of integration (e.g., Felbermayr et al., 2022 using the structural gravity framework and Campos et al., 2016 using the synthetic control method as well as Spornberger, 2021 using a structural gravity model). The second strand is the literature on measuring TBTs and their effects on international trade nested within a broader strand of quantifying non-tariff measures (e.g., Ferrantino, 2006; Kee et al., 2009). A review of early work in that strand, together with a meta-analysis, is presented in Li and Beghin (2012), including mainly the papers that employ the gravity literature, while the framework for measurement is outlined in Maskus et al. (2000). The empirical models typically include some quantitative measures of TBT as explanatory variables, such as the regulation stringency, as in Otsuki et al. (2001), or a result of an earlier frequency analysis – a coverage ratio of TBT in trade or number of measures applied (see, e.g., Disdier et al., 2008), number of TBT notifications (e.g., Bao & Qiu, 2012), or the number of trade concerns (e.g., Ghodsi, 2016; Orefice, 2016). In our study, we attempt to identify the differences in the evolution of relative internal to external EU trade in sectors subject to different EU approaches to remove TBT around the periods of EU enlargement. The paper provides estimates of the trade expansion of merchandise trade in sectors covered by various EU approaches to TBT, therefore evaluating the performance of those approaches in removing the technical barriers to trade. The remainder of this paper is organised as follows. Section two describes the dataset, empirical model, and identification strategy. Section three presents our estimation results. Section four concludes. CEEJ • 11(58) • 2024 • pp. 79-96 • ISSN 2543-6821 • DOI: 10.2478/ceej-2024-0008 82 2. Data and methods The primary data source is UN Comtrade. The dataset contains information on bilateral merchandise trade expressed in thousands of dollars. The dataset covers sectoral bilateral trade between 198 countries over the period of 1988-2020. This resulted in 12,355,183 units of observations. Our variables of interest are discrete variables that describe EU approaches to TBT removal. There are four major TBT categories: Harmonization (HR), Mutual Recognition Arrangement (MRA), Mutual Recognition Principle (MRP), and New Approach (NA). These data are available in the three-digit NACE (activity) classification. In the case of some sectors, more than one TBT can be observed (e.g., HR+MRA, HR+MRP, NA+MRA, and NA+MRP). These particular cases appeared in 9.9% of the sample (the number of observations in mixed TBT cases increased with time and reached a maximum of 9.86% in 2017). For sectors in which no TBT was introduced, we created the bilateral variable “None”. Overall, this accounted for 24.6% of the global observations. These dummy variables are fixed over time, coming from the European Commission’s (1998) publication, and are based on a detailed sectoral survey at the 3-digit NACE rev. 1 classification level. Merging trade data with NACE-based indicators presents a challenge. Initially, bulk-extracted products in the UN Comtrade were grouped according to HS 1988/1992 (H0). We purposely maintain a fixed product concordance to eliminate problems related to HS classifications changing over time. We use the product concordance obtained from Worldbank’s WITS database to convert the trade flows from the H0 classification to the SITC3 classification. Finally, we transformed the observations from SITC3 to NACE rev. 1. For some observations, we could not link H0 and NACE rev. 1, which seems to be a standard challenge when attempting to match product classifications to activity classifications. Therefore, these observations were eliminated (such eliminations were concentrated in only a few product categories and had no significant impact on the coverage of sectors or trade value). In our empirical model, the level of bilateral trade value is the dependent variable in all estimations. The unit of observation is a country-pair, a 3-digit NACE sector observed in a single period of time. We follow Baier and Bergstrand (2007) and use panel data to estimate three-way gravity models, including origin-time-industry, destination-timeindustry, and pair-industry fixed effects. Our model is estimated with both time-varying exporter-sector and importer-sector fixed effects as well as exporterimporter-sector-pair fixed effects, and therefore, it can identify only the variables that are bilateral in nature and variable over time. This means that fixed effects are absorbed by the effects of all timevarying country-specific variables and time-invariant “gravity” variables. Therefore, the only gravity variable included in the estimations is the regional trade agreement (RTA) membership dummy, which we borrow from the CEPII gravity database (Conte, 2022). While we are unable to control for the level of MFN tariffs (they are absorbed by time-varying fixed effects), the RTA-related dummies control for preferential tariff liberalisation. We distinguish a few categories of RTA (RTA among CEE countries, RTA between CEE and EU MS, and other RTA’s). Our main variables of interest reflecting the EU approaches to TBT, which are initially time-invariant, interacted with the EU dummy to account for the time variation. Therefore, the estimates on those variables are going to reflect the within-variation of trade within the TBT categories post-EU accession. This makes our empirical approach similar to a differencein-differences framework where we additionally control for all the sector-specific, exporter and importer time-varying developments as well as pairspecific effects. We are interested not only in the impact of TBT on overall EU bilateral trade value but also in assessing the impact of trade liberalisation among country groups, specifically EU-15 (“old” EU member states) and NMS (“new” member states to which we classified: CYP, LVA, LTU, HUN, MLT, POL, SVK, SVN, EST from 2004, BGR, ROU from 2007 and HRV from 2013). For this purpose, instead of a common EU dummy for each of the TBT types, we have three different variants of the variables: 1) when both parties are members of the NMS, 2) when the exporter is NMS, and the importer is part of the EU-15, and 3) when the importer is NMS and exporter is part of the EU-15. The trade values are taken from the interval [0,+∞). The lower bound value, zero, is interpreted as a lack of exports in a given period t for exporter i of a good from sector k and cannot be removed from the dataset. Standard linear panel data estimators are inapplicable because the dependent variable is limited to the interval [0,+∞). Silva and Tenreyro (2006) showed that the estimator of choice is pseudo-maximum likelihood estimation (PPML). CEEJ • 11(58) • 2024 • pp. 79-96 • ISSN 2543-6821 • DOI: 10.2478/ceej-2024-0008 83 Although the dependent variable, the trade value, is a quasi-continuous variable, the application of count data regression enables consistent estimates. The significant advantage of the estimator is that it is still consistent under heteroscedasticity. It is a known fact that in the case of PPML, the estimator does not suffer from the incidental parameter problem (this concerns the fact that there is no possibility of finding consistent estimates when the number of parameters depends on the sample size, for example, (Lancaster, 2000) or for models with single fixed effect (Wooldridge, 1999). In the case of three-way gravity models, Weidner and Zylkin (2021) showed that PPML is the only member of a family of pseudo-maximum likelihood estimators that is robust to incidental parameter problems. The baseline model used in the analysis is as follows: (1) 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖,𝑡𝑡𝑡𝑡=α+𝑅𝑅𝑅𝑅𝑇𝑇𝑇𝑇𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖,𝑡𝑡𝑡𝑡 ′𝜷𝜷𝜷𝜷+𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖,𝑡𝑡𝑡𝑡 ′𝛾𝛾𝛾𝛾+ 𝜃𝜃𝜃𝜃𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖,𝑡𝑡𝑡𝑡+𝛿𝛿𝛿𝛿𝑖𝑖𝑖𝑖,𝑖𝑖𝑖𝑖,𝑡𝑡𝑡𝑡+π𝑖𝑖𝑖𝑖,𝑖𝑖𝑖𝑖,𝑡𝑡𝑡𝑡+𝜀𝜀𝜀𝜀𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖,𝑡𝑡𝑡𝑡, (1) 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖,𝑡𝑡𝑡𝑡=α+𝑅𝑅𝑅𝑅𝑇𝑇𝑇𝑇𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖,𝑡𝑡𝑡𝑡 ′𝜷𝜷𝜷𝜷+𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖,𝑡𝑡𝑡𝑡 ′𝛾𝛾𝛾𝛾+ 𝜃𝜃𝜃𝜃𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖,𝑡𝑡𝑡𝑡+𝛿𝛿𝛿𝛿𝑖𝑖𝑖𝑖,𝑖𝑖𝑖𝑖,𝑡𝑡𝑡𝑡+π𝑖𝑖𝑖𝑖,𝑖𝑖𝑖𝑖,𝑡𝑡𝑡𝑡+𝜀𝜀𝜀𝜀𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖,𝑡𝑡𝑡𝑡, (1) where: Tradeijk,t is the value of bilateral trade in goods of sector k at time t; RTA‘ijk,t stand for a vector of different types of relative trade agreement RTA dummies; TBTijk,t is a vector of TBT of interest interacted with EU15 or NMS participation; θ ijk,t; δ i,k,t; πj,k,t are fixed effects; and ε ijk,t is an error term. The overview of all the variables included in the regression is given in Table 1. 3. Results Table 2 presents the first set of empirical results. Our analysis covers several separate models to assess the effect of trade liberalisation on EU member states. In the first column, we estimate trade liberalisation among all EU member states with the distinction of all available TBT. The second specification assesses the impact of trade liberalisation in the context of TBT among the aforementioned groups of countries. In columns three to six, we provide results of a specific technological breakdown (high-technology, medium-hightechnology, medium-low-technology, low-technology). The biggest population in a tested sample represents low-tech industries. The last column contains the results for additional V4 countries breakdown (Poland, Czech Republic, Slovakia, Hungary). As reported by column (1), being in common RTA is statistically significant for our sample and augments the value of bilateral trade. The positive impact is estimated to be approximately 2% of the bilateral sectoral trade value. Surprisingly, the impact of RTA between CEE and EU MS is negative and approximated 8%, suggesting that the sizeable expansion of trade of the CEE in the pre-accession period was universal and not necessarily EU-focused (and in our regression captured by country-specific time-varying fixed-effects). This result was found in models reported in Columns (1) and (2), while the only visible trade expansion due to FTA between EU and CEE was found in the case of low-tech goods. The positive effect of the CEE EU RTA also emerges when we control for the heterogeneity of the effects of the EU accession (column 7). The New Approach (NA) and Harmonization (HR) were positively and statistically significantly related to sectoral bilateral trade (column 1). In the case of NA, the impact was estimated to be a 1.4% increase in bilateral goods trade between EU member states. Compared to harmonisation, the innovation of the New Approach lies in harmonising national regulations, which are limited to a product’s most critical requirements to be released for free trade in a Single Market. An essential aspect of the New Approach is that using harmonised standards is voluntary, as they are not technical regulations. Products manufactured following harmonised standards are assumed to meet the most critical requirements and are automatically allowed to trade in the common market. However, if manufacturers can demonstrate that their products meet the essential requirements of the directives, they do not have to demonstrate compliance with the harmonised standard. Harmonising national regulations with standards supported by the European Commission is one of the most effective ways to liberalise non-tariff trade barriers. This stems from the fact that if countries have uniform regulations and the product gains access to one market, access is granted automatically to all other markets. Our results suggest that the impact of accession in sectors covered by HR is statistically significant and the highest among the obtained estimates. Full implementation of harmonised relationships among EU member states leads to a 1.4% increase in sectoral goods trade. It has to be said that sectors where none of the EU approaches applied experienced an almost 1.5% increase in goods trade value among the EU member states. This may mean that, in those sectors, the levels of technical barriers to trade were initially low, and CEEJ • 11(58) • 2024 • pp. 79-96 • ISSN 2543-6821 • DOI: 10.2478/ceej-2024-0008 84 EU accession has automatically ensured market access to exporters in the single market. As mentioned before, due to the number of fixed effects, particularly the time-varying country-sectorspecific fixed effects, these should not be understood as absolute increases in trade but rather as an increase in trade relative to other (non-EU) trade flows. While we cannot say for certain that the level of TBT in extra-EU trade is the highest in the HR-covered sectors, the EU accession boosts relative intra-to extra-trade the most in these sectors, which shows that either the level of TBT outside the EU is very high or that accession reduces the TBTs most effectively for the acceding countries. Table 1. Variables used in the empirical analysis Variable Description Trade value The dependent variable. The value of bilateral trade in goods. The value is expressed in thousands of USD. RTA The vector of discrete variables takes the value of 1 when both trade partners are in the same RTA and 0 otherwise (e.g. RTA among CEE countries; RTA between CEE and EU; other RTA combinations). NA-EU Discrete variables take the value of 1 when a New Approach in sectorkoccurs in tradebetween countryjand countryiin timet.Both partner countries should be EU member states. Otherwise, the variable takes the value of 0. MR-EU Discrete variables take a value of 1 when there is a Mutual Recognition in sectorkoccurring in tradebetween countryjand countryiin timet.Both partner countries should be EU member states. Otherwise, the variable takes the value of 0. HR-EU Discrete variables take a value of 1 when there are Harmonization Regulations in sector k occurring in tradebetween countryjand countryiin timet.Both partner countries should be EU member states. Otherwise, the variable takes the value of 0. None-EU Discrete variables take the value of 1 when no TBT is imposed in sectorkin tradebetween countryjand countryiin timet.Both partner countries should be EU member states. Otherwise, the variable takes the value of 0. NA-EU15-NMS Discrete variables take a value of 1 when there is a New Approach in sectorkoccurring in tradebetween countryjand countryiin timet.The good originated from EU15, and the trading partner is a new EU member state. Otherwise, the variable takes the value of 0. NA-NMS-EU15 Discrete variables take the value of 1 when a New Approach in sectorkoccurs in tradebetween countryjand countryiin timet.The good originates from a New member state, and the trading partner is an EU15 member state. Otherwise, the variable takes the value of 0. NA-NMS-NMS Discrete variables take the value of 1 when a New Approach in sectorkoccurs in tradebetween countryjand countryiin timet.Otherwise, the variable is taking the value of 0. MR-EU15-NMS Discrete variables take a value of 1 when there is a Mutual Recognition in sectorkoccurring in tradebetween countryjand countryiin timet.The good originated from EU15, and the trading partner is a new EU member state. Otherwise, the variable takes the value of 0. MR-NMS-EU15 Discrete variables take a value of 1 when there is a Mutual Recognition in sector k occurring in trade between country j and country i in time t. The good originates from a New member state, and the trading partner is an EU15 member state. Otherwise, the variable takes the value of 0. MR-NMS-NMS Discrete variables take a value of 1 when there is a Mutual Recognition in sectorkoccurring in tradebetween countryjand countryiin timet.Both trade partners are New EU member states. Otherwise, the variable takes the value of 0. HR-EU15-NMS Discrete variables take a value of 1 when there are Harmonization Regulations in sectorkoccurring in tradebetween countryjand countryiin timet.The good originated from EU15, and the trading partner is a new EU member state. Otherwise, the variable takes the value of 0. HR-NMS-EU15 Discrete variables take a value of 1 when there are Harmonization Regulations in sector k occurring in trade between country j and country i in time t. The good originates from a New member state, and the trading partner is an EU15 member state. Otherwise, the variable takes the value of 0. HR-NMS-NMS Discrete variables take a value of 1 when there are Harmonization Regulations in sectorkoccurring in tradebetween countryjand countryiin timet.Both trade partners are New EU member states. Otherwise, the variable takes the value of 0. CEEJ • 11(58) • 2024 • pp. 79-96 • ISSN 2543-6821 • DOI: 10.2478/ceej-2024-0008 85 Column (2) of Table 2 shows that the above results must be cautiously considered. There is a great deal of heterogeneity in the effects of EU accession when the direction of trade is considered among different country groups (NMS versus the EU-15 and exports versus imports). The highest effects of EU accession are present in sectors covered by harmonisation. The most significant beneficiaries of HR were exporters Table 2. Estimates of trade liberalisation in TBT among EU Member States VARIABLES All sectors All sectors High-tech Medium-high tech Mediumlow tech Low tech All sectors (1) (2) (3) (4) (5) (6) (7) RTA 0.0188*** 0.0189*** -.012465 .0329128*** .0017943 .0252775*** 0.0188*** (0.00472) (0.00472) .0083309 .0107669 .0056227 .0084095 (0.00472) RTA CEE -0.0223 0.0783*** .0649999* .1408041*** .0972213*** -.0167929 -0.0379* (0.0146) (0.0199) .0342033 .0292653 .0358558 .0409532 (0.0195) RTA CEE UE -0.0735*** -0.0409*** -.1129603*** .0039924 -.117901*** .0503106*** -0.0386*** (0.00830) (0.0100) .0149379 .0157854 .0179346 .0187081 (0.00960) NA_EU 0.0136*** (0.00460) MR_EU -0.00768 (0.00816) HR_EU 0.0140*** (0.00543) none_EU 0.0148*** (0.00525) NA-EU15-NMS -0.0534*** .0526952 -.0269492 .0155141 -.1057659** 0.0831*** (0.0160) .0504126 .0199265 .0364956 .049518 (0.0190) NA-NMS-EU15 0.145*** .2508488*** .1775791*** .097095*** .2936307*** 0.0895*** (0.0188) .0413104 .0251493 .0453394 .0539502 (0.0287) NA-NMS-NMS 0.0942*** .3505328*** .200204*** -.1348145** -.022882 0.356*** (0.0248) .0632008 .0332285 .0598275 .0734733 (0.0356) MR-EU15-NMS 0.0651*** (omitted) .0071325 .0985143*** .0923532** 0.148*** (0.0234) .0496438 .0290378 .0399543 (0.0316) MR-NMS-EU15 0.0377 (omitted) -.0343424 .3994119*** -.1333244*** 0.0560 (0.0275) .0616616 .039404 .0400508 (0.0374) MR-NMS-NMS 0.145*** (omitted) .0789878 .2778632*** .0426703 -0.0996 (0.0338) .0579968 .046876 .0603844 (0.0608) HR-EU15-NMS 0.128*** .0170128 (omitted) .0411172 .2959768*** 0.257*** (0.0295) .0270469 .0499832 .0442682 (0.0318) HR-NMS-EU15 0.276*** .3145475*** (omitted) -.064045 .8265343*** 0.551*** (0.0367) .0311062 .059602 .0494348 (0.0401) HR-NMS-NMS 0.209*** -.0845362* (omitted) .0838884 .4971171*** 0.575*** (0.0371) .0438424 .0673421 .0511993 (0.0380) None-EU15-NMS 0.166*** -.1212058*** .1458626*** .2292322*** -.0202643 0.144* (0.0505) .0472334 .0405111 .076868 .0351835 (0.0834) None-NMS-EU15 -0.122*** -.316273*** .0968501*** -.2923275*** .3317987*** -0.211*** CEEJ • 11(58) • 2024 • pp. 79-96 • ISSN 2543-6821 • DOI: 10.2478/ceej-2024-0008 86 VARIABLES All sectors All sectors High-tech Medium-high tech Mediumlow tech Low tech All sectors (1) (2) (3) (4) (5) (6) (7) (0.0223) .0605635 .0360711 .0332407 .0500403 (0.0290) None-NMS-NMS 0.431*** -.1457483** .147424*** .6738975 .2335274*** 0.357*** (0.0587) .0711817 .052262 .0969544*** .0610534 (0.0721) NA-EU15-V4 -0.184*** (0.0207) NA-V4-EU15 0.133*** (0.0190) NA-V4-V4 -0.153*** (0.0315) NA-V4-NMS -0.113*** (0.0287) MR-EU15-V4 0.0136 (0.0278) MR-V4-EU15 0.0293 (0.0311) MR-V4-V4 0.0405 (0.0418) MR-V4-NMS 0.169*** (0.0393) HR-EU15-V4 -0.0323 (0.0397) HR-V4-EU15 0.160*** (0.0407) HR-V4-V4 -0.178*** (0.0613) HR-V4-NMS 0.146*** (0.0403) None-EU15-V4 0.115*** (0.0409) None-V4-EU15 -0.151*** (0.0260) None-V4-V4 0.125** (0.0614) None-V4-NMS 0.412*** Constant 13.52*** 13.51*** 11.84421*** 13.28023*** 13.63611*** 13.6876*** 13.51*** (0.00157) (0.00158) .0027644 .0034178 .0022599 .0028308 (0.00155) (0.102) N of obs. 12,355,183 12,355,183 1,271,213 2,834,200 3,376,816 4,872,954 Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1 ContinuedTable 2. Estimates of trade liberalisation in TBT among EU Member States CEEJ • 11(58) • 2024 • pp. 79-96 • ISSN 2543-6821 • DOI: 10.2478/ceej-2024-0008 93 Industry NACE Technology TBT Industry NACE Technology TBT Meat products 151 Lowtechnology HR Basic precious and non-ferrous metals 274 Medium-hightechnology NA Fish and fish products 152 Lowtechnology HR Structural metal products 281 Medium-hightechnology NA Fruits and vegetables 153 Lowtechnology HR Tanks, reservoirs, central heating radiators and boilers 282 Medium-hightechnology NA Vegetable and animal oils and fats 154 Lowtechnology HR+MRP Forging, pressing, stamping and roll forming of metal; powder 289x metallurgy 284 Medium-hightechnology None Dairy products; ice cream 155 Lowtechnology HR Treatment and coating of metals; general mechanical engineering 285 Medium-hightechnology None Grain mill products and starches 156 Lowtechnology HR Cutlery, tools and general hardware 286 Medium-hightechnology NA Prepared animal feeds 157 Lowtechnology HR Machinery for production, use of mech. power 291 Medium-hightechnology NA Other food products 158 Lowtechnology HR Other general purpose machinery 292 Medium-hightechnology NA Beverages 159 Lowtechnology MRP Agricultural and forestry machinery 293 Medium-hightechnology NA Tobacco products 160 Lowtechnology HR Machine-tools 294 Medium-hightechnology NA Textile fibres 171 Lowtechnology None Other special purpose machinery 295 Medium-hightechnology NA Textile weaving 172 Lowtechnology None Domestic appliances n. e. c. 297 Medium-hightechnology NA+MRA Finishing of textiles 173 Lowtechnology MRP Office machinery and computers 300 Medium-hightechnology MRA Made-up textile articles 174 Lowtechnology MRP Electric motors, generators and transformers 311 Low-technology NA Other textiles 175 Lowtechnology NA+MRA Electricity distribution and control apparatus 312 Low-technology MRP Knitted and crocheted fabrics 176 Lowtechnology MRP Isolated wire and cable 313 Low-technology MRA Knitted and crocheted articles 177 Lowtechnology MRP Lighting equipment and electric lamps 315 Low-technology NA+MRA Other wearing apparel and accessories 182 Medium-lowtechnology None Electronic valves and tubes, other electronic comp. 321 Low-technology NA+MRA Dressing and dyeing of fur; articles of fur 183 Lowtechnology MRP TV, radio and recording apparatus 323 Low-technology HR+MRA ContinuedTable 5. The list of TBT and industry CEEJ • 11(58) • 2024 • pp. 79-96 • ISSN 2543-6821 • DOI: 10.2478/ceej-2024-0008 94 Industry NACE Technology TBT Industry NACE Technology TBT Tanning and dressing of leather 191 Medium-lowtechnology None Medical equipment 331 Medium-lowtechnology NA Footwear 193 Medium-lowtechnology None Instruments for measuring, checking, testing, navigating 332 Medium-lowtechnology None Sawmilling, planing and impregnation of wood 201 Medium-hightechnology None Optical instruments and photographic equipment 334 Medium-lowtechnology None Panels and boards of wood 202 Medium-hightechnology NA Watches and clocks 335 Medium-lowtechnology None Builders’ carpentry and joinery 203 Medium-hightechnology NA Motor vehicles 341 Medium-lowtechnology HR Wooden containers 204 Medium-hightechnology None Bodies for motor vehicles, trailers 342 Medium-lowtechnology None Other products of wood; articles of cork, etc. 205 Medium-hightechnology None Parts and accessories for motor vehicles 343 Medium-lowtechnology HR Pulp, paper and paperboard 211 Hightechnology HR Ships and boats 351 Low-technology MRP Articles of paper and paperboard 212 Hightechnology HR Railway locomotives and rolling stock 352 Low-technology MRP Publishing 221 Hightechnology HR Aircraft and spacecraft 353 Low-technology MRP Printing 222 Medium-lowtechnology None Motorcycles and bicycles 354 Low-technology MRP Coke oven products 231 Medium-lowtechnology MRP Furniture 361 Low-technology MRP Refined petroleum and nuclear fuel 232 Medium-lowtechnology HR Jewellery and related articles 362 Low-technology NA Nuclear fuel 233 Medium-lowtechnology HR Musical instruments 363 Low-technology None Basic chemicals 241 Medium-lowtechnology MRP Sports goods 364 Low-technology NA Pesticides, other agrochemical products 242 Medium-lowtechnology HR+MRP Games and toys 365 Low-technology NA Paints, coatings, printing ink 243 Medium-lowtechnology MRP Miscellaneous manufacturing n. e. c. 366 Low-technology None Pharmaceuticals 244 Medium-lowtechnology MRA Production and distribution of electricity 401 Low-technology None Detergents, cleaning and polishing, perfumes 245 Medium-lowtechnology HR Manuacture of gas; distribution of gaseous fuels through mains 402 Low-technology None Other chemical products 246 Medium-lowtechnology MRP Steam and hot water supply 403 Low-technology None ContinuedTable 5. The list of TBT and industry CEEJ • 11(58) • 2024 • pp. 79-96 • ISSN 2543-6821 • DOI: 10.2478/ceej-2024-0008 95 Industry NACE Technology TBT Industry NACE Technology TBT Rubber products 251 Medium-lowtechnology None Collection, purification and distribution of water 410 Low-technology HR Plastic products 252 Medium-lowtechnology NA Motion picture and video activities 921 Low-technology None Glass and glass products 261 Hightechnology NA Source: European Commission (1997) Table 6. Summary statistics Variable Obs Mean Std. Dev. Min Max Year 12,652,175 2009.808 6.449645 1995 2020 Export value 12,652,175 19094.65 341852.5 1.15e-10 2.01e+08 RTA 12,652,175 .2013112 .4009801 0 1 RTA-CEE-UE 12,652,175 .0121949 .1097551 0 1 RTA-CEE 12,652,175 .002402 .0489509 0 1 NA 12,652,175 .3144369 .4642912 0 1 NA-EU 12,652,175 .0226739 .1488616 0 1 NA-EU15-NMS 12,652,175 .0055967 .0746018 0 1 NA-NMS-EU15 12,652,175 .0048632 .0695668 0 1 NA-NMS-NMS 12,652,175 .0036937 .0606633 0 1 NA-EU15-NMS(other) 12,652,175 .0037779 .0613486 0 1 NA-EU15-V4 12,652,175 .0024246 .0491801 0 1 NA-V4-EU15 12,652,175 .0030244 .0549112 0 1 NA-V4-V4 12,652,175 .0024284 .0492193 0 1 NA-V4-NMS 12,652,175 .0013491 .0367053 0 1 MR 12,652,175 .342041 .4743933 0 1 MR EU 12,652,175 .0252982 .1570292 0 1 MR-EU15-NMS 12,652,175 .0062408 .078752 0 1 MR-NMS-EU15 12,652,175 .0053653 .0730516 0 1 MR-NMS-NMS 12,652,175 .0040377 .0634147 0 1 MR-EU15-NMS(other) 12,652,175 .0041938 .0646238 0 1 MR-EU15-V4 12,652,175 .0027208 .0520903 0 1 MR-V4-EU15 12,652,175 .0033346 .0576497 0 1 MR-V4-V4 12,652,175 .0026562 .0514701 0 1 MR-V4-NMS 12,652,175 .0014717 .0383343 0 1 HR 12,652,175 .2246639 .4173608 0 1 ContinuedTable 5. The list of TBT and industry CEEJ • 11(58) • 2024 • pp. 79-96 • ISSN 2543-6821 • DOI: 10.2478/ceej-2024-0008 96 Variable Obs Mean Std. Dev. Min Max HR EU 12,652,175 .0167582 .1283643 0 1 HR-EU15-NMS 12,652,175 .0041769 .0644939 0 1 HR-NMS-EU15 12,652,175 .0034418 .0585656 0 1 HR-NMS-NMS 12,652,175 .0026463 .0513737 0 1 HR-EU15-NMS(other) 12,652,175 .0028076 .0529122 0 1 HR-EU15-V4 12,652,175 .0018074 .0424755 0 1 HR-V4-EU15 12,652,175 .0020909 .045679 0 1 HR-V4-V4 12,652,175 .0017252 .0414992 0 1 HR-V4-NMS 12,652,175 .0009456 .0307362 0 1 None 12,652,175 .2428346 .428796 0 1 None EU 12,652,175 .0193679 .1378143 0 1 None-EU15-NMS 12,652,175 .0047352 .0686499 0 1 None-NMS-EU15 12,652,175 .003963 .0628271 0 1 None-NMS-NMS 12,652,175 .0029981 .0546731 0 1 None-EU15-NMS(other) 12,652,175 .0031353 .0559056 0 1 None-EU15-V4 12,652,175 .0021295 .0460975 0 1 None-V4-EU15 12,652,175 .002423 .0491641 0 1 None-V4-V4 12,652,175 .0020193 .0448908 0 1 None-V4-NMS 12,652,175 .0010664 .032638 0 1 Source: Own calculations ContinuedTable 6. Summary statistics