A multi-commodity network flow and gravity model integration for analyzing impact of road transport quotas on international trade

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Research in Transportation Economics journal homepage: http://www.elsevier.com/locate/retrec

A multi-commodity network flow and gravity model integration for analyzing impact of road transport quotas on international trade € Bora Çekyay a, Ozgür Kabak b, Füsun Ülengin c, *, Burç Ulengin d, Peral Toktas¸ Palut a, a € € Ozay Ozaydın a

Dogus University, Department of Industrial Engineering, Istanbul, Turkey Istanbul Technical University, Department of Industrial Engineering, Istanbul, Turkey Sabanci University, School of Management, Istanbul, Turkey d Istanbul Technical University, Department of Management Engineering, Istanbul, Turkey b c

A R T I C L E I N F O

A B S T R A C T

JEL classification: C44 F17 L91

Although EU and Turkey are integrated by Customs Union since 1996, there are still quota limits to Turkish road transport in some European countries. These quotas cause concerns related to increased transportation costs, which will in turn increase the costs of export goods and create important barriers to international trade. In this study, the effects of quotas on Turkish foreign trade with EU countries are investigated using an integrated multicommodity flow model and a gravity model. The classical multi-commodity network flow model is innovatively used to predict the costs of transportation with and without the transit and bilateral quotas applied by the Eu­ ropean countries. As a result, the extra costs originating from the quotas are forecasted. These extra costs are used in a gravity model to analyze their effect on international trade. The gravity model is estimated with panel data related to 17 selected European countries between 2009 and 2015. The results indicate that the costs originating from the quotas have significant effects on Turkish total exports, and exports of food and beverages and the machinery and equipment sectors. The size of the losses in the total export and the selected sectors are also calculated by using a scenario analysis.

Keywords: Gravity model Multi-commodity network flow Quota Turkey Export EU

1. Introduction The current framework of interaction between Turkey and the EU countries has been in place with a customs union since 1995 and this arrangement has substantially contributed to the removal of several major trade barriers. The strategic importance of EU-Turkey relations has increased even further owing to changes in international and regional relations related to the Middle East and the refugee crisis. As a result; the EU received 44.5% of Turkey’s exports in 2015 and nearly 48% of Turkey’s exports in 2016. In 2018, Turkey was the fifth largest partner for EU exports of goods and the sixth largest partner for EU imports of goods. EU exports to Turkey were highest in 2017 (EUR 85 billion) and lowest in 2009 (EUR 44 billion). EU imports from Turkey were highest in 2018 (EUR 76 billion) and lowest in 2009 (EUR 36 billion) (Eurostat, 2019; World Trade Organisation, 2016). EU exports to Turkey are dominated by machinery and transport material, chemical products and manufactured goods. However, the transport activities

which play an important role in the bilateral trade between EU and Turkey face important issues related to the movement of goods. For instance, in many cases there are quota limits on the number of transport permits that may be issued to Turkish trucks and lorries by an EU member state, with demand exceeding the number of permits available. Both sides recognize that this arrangement requires updating to resolve the deficiencies in order to increase the bilateral trade. It is estimated that liberalization of transportation could make a significant increase in bilateral trade volumes (Hakura, 2018). As the EU is dependent on many imported products, to maintain its competitiveness and recover fully from the impact of the 2008 fiscal crisis, it has to reduce its protectionist measures. As is underlined by the World Bank (2014), the road transport quotas and transit permits negatively influence the free circulation of goods covered by the Cus­ toms Union. A 10% increase in trade restrictions could lead to a 4% loss in national income (European Commission, 2014). Greater cooperation is needed between the EU and Turkey for this purpose (Dawar & Togan,

* Corresponding author. € Kabak), [email protected] (F. Ülengin), [email protected] (B. Ulengin), E-mail addresses: [email protected] (B. Çekyay), [email protected] (O. € Ozaydın). € [email protected] (P. Toktas¸ Palut), [email protected] (O. https://doi.org/10.1016/j.retrec.2020.100816 Received 14 March 2019; Received in revised form 29 November 2019; Accepted 8 January 2020 0739-8859/© 2020 Published by Elsevier Ltd.

Please cite this article as: Bora Çekyay, Research in Transportation Economics, https://doi.org/10.1016/j.retrec.2020.100816

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2016). The Turkish authorities also agree that the road transport quota limits submitted by some European countries provide important barriers to an increase in the trade potential that could emerge if these limits were removed (Dawar & Togan, 2016). Such an approach should in­ crease competition and lead to better allocation of resources in both Turkey and the EU, as well as other benefits for both partners. From the viewpoint of Turkey, quota barrier elimination or reduction will in­ crease greater market access in the EU market. This study is the final phase of a comprehensive research project. The aim of the project was to analyze the validity of the hypothesis that trade volume between Turkey and EU is negatively influenced by the transport quotas imposed on Turkey, which have been implemented by 24 of the 27 EU member states. In the first phase of the project, the effect of bilateral quotas on the total Turkish export by road transportation and on the total export of the Turkish textile sector was shown to be sig­ nificant (Ülengin et al., 2015). In the second phase, in addition to bilateral quotas, transit quotas are considered. We show that both types of quotas have significant effects on the total Turkish export by road transportation (Çekyay et al., 2017). In the final phase that is presented in this paper, we develop a model to estimate the extra costs resulting from quotas and use these estimations to show that the bilateral and transport quotas have a significant negative effect on the total Turkish export (by all transport modes) to the selected countries. We also extend our analysis to the Turkish machinery and equipment sector (TMES), and the Turkish food and beverages sector (TFBS), both of which are shown to be negatively affected by the quotas as well. The main reason for choosing specifically these sectors is that the companies in these sectors heavily use road transportation. The percentage (in terms of US $) of exported goods by TMES and TFBS to the EU by using road transportation are 36% and 52% in 2015, respectively. In order to calculate the extra costs owing to quotas, it is necessary to obtain the transportation cost and frequency of use for every used route between Turkey and the European countries. Although we can obtain the details of the routes and their corresponding costs, any reliable in­ formation about the yearly usage frequencies of the routes is not avail­ able (which is a common problem in the literature as indicated in Section 2.2). To estimate the approximate frequencies, we use an approach based on the multi-commodity flow model. This approxima­ tion approach estimates the usage of all possible routes under the assumption that all transport activities are governed by a central deci­ sion maker. As the transport companies act independently in real life, our approximation approach will provide only a lower bound for the real export loss due to quotas. Therefore, our results underestimate the actual situation. All our previous papers on this subject (Çekyay et al., 2017; Ülengin et al., 2015) manage to show the negative effect of quotas either on Turkish total export by only road transportation or on the export of some specific sectors. In this study, we finally show that Turkish total export is negatively affected by the bilateral and transport quotas. This is mainly because this study incorporates actual transportation costs, which is made possible by using the approach based on the multi-commodity flow model. To the best of our knowledge, this is the first study in the literature that combines the gravity model and the multi-commodity flow model for analyzing the effect of quotas on the international trade. This paper is organized as follows. The second section provides a literature review on the application of gravity models in the analysis of international trade. The third section provides the general framework of the proposed model. In the fourth section, we estimate the increase in transportation costs due to quotas using a multi-commodity flow model. The fifth section analyzes the impact of extra cost due to quotas on Turkish export using gravity models. The sixth section aims to estimate the decrease in Turkish export to EU thorough scenario analyses. Finally, conclusions and further suggestions are presented.

2. Literature review of the gravity approach to trade The gravity model aims to analyze spatial interactions between different types of variables by using the general idea of the gravity theory in physics. The first application of this approach in the econo­ metric domain was the seminal paper of Tinbergen (1962) on interna­ tional trade relations (Tinbergen, 1962); and gravity equations have been used as a basic tool to model international trade for many years (Brun, Carr�ere, Guillaumont, & de Melo, 2005; Liu & Xin, 2011; Novy, 2013; Redding & Venables, 2004). The trade flow models aim to analyze the implications of distance, geography, free trade agreements, and border effects. These analyses are based on bilateral trade flows and the country characteristics (For de­ tails see Antonucci & Manzocchi, 2006; Baier & Bergstrand, 2007; Jayasinghe & Sarker, 2008; Okubo, 2004; Soloaga & Winters, 2001; Koch & LeSage, 2015; Anderson & Wincoop, 2004; Krisztin & Fischer, 2015; Rose, 2000). This section mainly focuses on the literature review of the gravity approach to trade in three aspects: The studies that analyze border ef­ fects; the studies that include transportation costs; and existing appli­ cations for Turkey. 2.1. Studies on border effects analysis To�sevska-Trp�cevska and Tevdovski (2014) used the augmented gravity model to measure the effects of certain customs and adminis­ trative procedures on trade between the countries of South-Eastern Europe in the period 2008–2012. They showed that the number of days spent at the border and costs paid in both importer and exporter countries have significant negative influence on the volume of trade. Ishise and Matsuo (2015) explored the evolution of the US–Canada border effect from 1993 to 2007. They estimated the gravity model without taking the logs of both sides. They used a model with quasi-maximum likelihood (QML) estimators and found that the ordi­ nary least squares with log-transformed models leads to considerable overestimation compared with the QML without log-transformation, in the border effect. Persyn and Torfs (2016) used a gravity model to identify the effect of regional borders on commuting. The model is estimated by applying a negative binomial regression method on Belgian inter-municipal commuting data. They showed that regional borders have an impor­ tant negative effect on commuting. 2.2. Studies considering transportation costs Deepening EU–Turkey integration would offer significant gains for the EU as well as for Turkey. Therefore, although there are many factors that affect trade growth, the impact of transportation costs is of primary interest in this research. Transport costs are generally shown to have a negative impact on trade volumes and recent studies suggest that improvement in transportation has a substantial impact on trade volume (Anderson & Wincoop, 2004; Evans & Harrigan, 2016; Liu & Xin, 2011; Rietveld & Vickerman, 2004). Miao and Fortanier (2017) mentioned that although the costs of the international transportation and insurance of merchandise trade have an important impact on the volume and geography of international trade, official data are lacking. In fact, those costs are not the only barrier to trade. Geographical distance to market, oil prices, poor quality of transport infrastructure etc. will continue to shape the international trade of the countries. However, owing to a lack of data on the size and trends in transport and insurance costs, studies that investigate this impact are rare. Overall, the literature on estimating transport and insurance costs on international trade can be divided into studies using explicit or implicit data on cost, insurance, and freight (CIF) – free on board (FOB) margins. The studies based on explicit data on transport costs use data published by statistical 2

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offices (Clark, Dollar, & Micco, 2004). However, the studies and, thus, the data used are focused on countries with trade relation with the United States. The others use the differences between imports (CIF) and the mirror export data (reported FOB) to estimate CIF-FOB margin. They are generally based on UN Comtrade data in order to implicitly derive transport costs (Gaulier and Zignago, 2010; Miao & Fortanier, 2017). However, OECD’s International Trade by Commodity Statistics (ITCS), which is fully synchronized with UN Comtrade database, reports only 16 countries’ detailed information and Turkey is not among them. That is why it was not possible to find out the data related to the average transportation cost between Turkey and the European countries. In addition to these facts, transport and insurance costs are known to vary by mode of transport. Therefore, in this study, a novel approach based on multi-commodity flow model is proposed.

the models used in this study is presented in Fig. 1. As given in Fig. 1, initially we use a multi-commodity network flow model to predict the extra costs of transit, quotas, bilateral quotas, transit network, transport costs, and customs costs. Then a gravity model is developed to forecast Turkey’s total export, Turkey’s machinery and equipment sector export, and Turkey’s food and beverages sector export. By this way, we can reveal the effect of quotas on Turkey’s export. In Fig. 1, SUMGDP, SIMSIZE, RELENDOW, and COST inc are the explan­ atory variables used in the gravity model. SUMGDP is a measure to indicate the size of economies of Turkey and exporting countries. RELENDOW is a measure to indicate the relative factor endowments. SIMSIZE is a measure to indicate the size similarity between Turkey and exporting countries. COST inc shows the extra cost due to the quotas. The dependent variables of the gravity models are Turkey’s total export to the related countries (ET), Turkey’s machinery and equipment sector export (EM), and Turkey’s food and beverages sector export (EF) to the related countries. The details related to the gravity model including the definitions of the variables are given in Section 5. In this study, we only consider road transportation (including transportation by Roll-on/roll-off – RORO ships) and develop gravity models to analyze the impact of the increase in average transportation costs due to quotas on Turkey’s export issued by European countries. Detailed information about the routes is taken from Directorate General of Road Transport Regulation in Turkey. In addition, the detailed transportation costs and the annual numbers of transit and bilateral quotas are obtained from the experts of the Association of International Freight Forwarders (AIFF). AIFF experts calculate route costs based on the average transportation cost of each transportation carrier being a member of AIFF. The calculations are made according to each route, country and transportation mode type. As AIFF is the biggest interna­ tional freight forwarder of Turkey, the data provided by them are accepted as reliable in this research. Unfortunately, no data related to the usage frequencies of the routes could be obtained. In order to find approximate values for the fre­ quencies, the multi-commodity flow model-based approach is used. The details of the multi-commodity flow and gravity models as well as their applications are given in the following sections.

2.3. Existing applications for Turkey When we focus on the existing applications for Turkey, one outstanding study is by Antonucci and Manzocchi (2006), where they analyzed the impact of Turkey’s EU membership on trade utilizing a gravity model. They found that Turkey’s trade patterns can be suc­ cessfully modelled using the gravity model and showed that the customs union launched in 1996 did not significantly increase the trade between Turkey and the EU. The World Bank (2014) estimated a gravity model using a dummy variable for the EU–Turkey Custom Union (CU), where the coefficient estimate suggested a 22% increase in bilateral trade owing to the CU. However, this effect was not statistically significant. Moreover, Magee (2015) underlined that although the CU has generated more than twice as much trade creation as trade diversion, its overall impact has been relatively small. The World Bank (2014) report also notes the more deeply integrated production networks between Turkish and EU firms, particularly in the automobiles and clothing sectors, also enhanced the intra-industry trade between Turkey and the EU. Ülengin et al. (2015) investigated the effect of road transport quotas on Turkish foreign trade with EU countries using a gravity model. They claimed that transit quotas applied by the EU countries to Turkish trucks may have effects on the export of the other countries. Çekyay et al. (2017) extended the work of Ülengin et al. (2015) and implemented an integrated maximum flow and gravity model approach to analyze the impact of bilateral and transit quotas on international trade. The results of the gravity model do not clearly indicate a loss in the total export; they show the negative impact of quotas on Turkey’s export via road transport. While these two studies analyzed the effect of quotas on foreign trade, they did not take into account the implication of transportation and related costs. In this study, the impact of the increase in average transportation costs due to quotas on Turkey’s export is analyzed through several gravity models. To the best of our knowledge, this is the first study to integrate the multi-commodity network problem with the gravity model to analyze the aggregate effect of both the transit and bilateral quotas. The classical multi-commodity flow model is used to predict the costs of transportation with and without the transit and bilateral quotas imposed by the European countries. As a result, the extra costs originating from the quotas are forecasted. These extra costs are subsequently used in a gravity model to analyze their effect on in­ ternational trade.

4. Analysis of extra costs due to quotas using the multicommodity flow model This section focuses on estimating the increases in the average transportation costs from Turkey to EU countries due to quotas. To calculate the average transportation cost between Turkey and a selected European country, it is necessary to obtain the transportation cost and frequency of use for every used route between Turkey and the European country, and the number of bilateral and transit quotas issued by Eu­ ropean countries. A detailed list of stops in every used route is found in Directorate General of Road Transport Regulation in Turkey. The detailed transportation costs associated with all segments of the used routes and the number of available transit and bilateral quotas are ob­ tained from the experts of the AIFF. However, it was not possible to find any reliable information about the yearly usage frequencies of the routes. In order to obtain at least an approximation of the related fre­ quencies and, consequently, the average transportation costs, an approach based on the multi-commodity flow model is used. The idea behind this approximation is quite simple. In a multicommodity flow problem, the demand nodes in a capacitated network are satisfied by the flows sent by the source nodes in the same network in such a way that the total cost of using the links is minimized. In our setting, the only source node is Turkey and the demand nodes are the European countries. The network is obtained by combining the used routes from Turkey to the considered European countries. The link costs and link capacities are estimated by using the route transportation costs and available quota values at hand, respectively. The flow on this network represents the Turkish trucks going from Turkey to the

3. Framework of the proposed model The basic objective of the study is to investigate the impact of extra costs originating from the quotas on international trade. Thus, the impact of the increase in average transportation costs due to quotas on Turkey’s export is analyzed through several gravity models. In order to calculate the average transportation cost between Turkey and a Euro­ pean country, a multi-commodity flow model is used. The framework of 3

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Fig. 1. Framework of the models used in this study.

European countries. It is assumed that the trucks going to different Eu­ ropean countries are represented as different commodities. We also know the number of trucks transporting goods from Turkey to the considered European countries in a given year, which gives the demand values of the demand nodes of the network. Then, the solution of the resulting multi-commodity flow problem gives the optimal utilizations of all used routes, which minimize the total country-wide transportation cost. By using this solution, it is trivial to calculate the average trans­ portation cost from Turkey to a given European country. It is necessary to underline that the average transportation costs obtained by the solution of the multi-commodity flow model can be significantly lower than the real average transportation costs. The reason is that, in practice, the transportation carriers act independently of each other in a way to minimize their individual costs. According to Turkish Road Transport Regulation that is included in Road Transport Law 4925 (www.resmigazete.gov.tr/eskiler/2018/01/20180108-1.ht m), in order to be able to make an international and national com­ modity transportation, each transport carrier should get the C certificate of authority from the Ministry of transportation, maritime affairs and communication. After receiving this certificate, the carriers realize their transportation activities independently of each other. However, the multi-commodity flow model specifies the routes in a centralized way minimizing the total cost of all carriers. Furthermore, this approximation error does not decrease the value of the results ob­ tained in this study, as the quota-driven increase in transportation costs obtained in this study constitute the lower bound of the real increase. In other words, the export loss of Turkey due to transportation quotas is more than what we estimate.

quota portfolio. However, the following principles are applied in general: i. Different transport modes (conventional and RORO) can be used for freights to enter a country. We define an arrival node for each possible transport mode and each EU country. ii. Each node in (i) is connected to a corresponding node by an arc whose capacity is a function of the quotas corresponding to the related transport mode. iii. Different transport modes can be used for freights leaving a country. We define a departure node for each possible transport mode and each country. iv. A demand node is defined for each EU country. v. The nodes in (ii) are connected to the possible nodes in (iii) by some arcs whose capacities are functions of the transit quotas issued by the country. vi. The possible nodes generated in (ii) are connected to the demand nodes, generated in (iv), by some arcs whose capacities are functions of the bilateral quotas issued by the country. vii. A general source node s is connected to all departure nodes of Turkey. To give the idea of how the network for the multi-commodity flow model is developed, in Fig. 2, the splitting process is illustrated for Hungary, Austria, Italy, Switzerland, and Spain using the quotas in 2011. Hungary, Austria, and Spain have unique arrival nodes (hu1 , at1 , and es1 , respectively). However, Italy and Switzerland have multiple arrival nodes. Switzerland has two arrival nodes, namely ch1 and ch3 both of which are for road transport. The first node is used for transit shipments; the second node is used for shipments whose destination is Switzerland. Italy has three arrival nodes for transit to road transport, towing, and port to port transit transport separately (it1 ; it5 ; it7 , respec­ tively). The transport modes that are used to travel to Italy by Turkish carriers are the main determinants of this situation. Similarly, there are single departure nodes for Hungary and Austria (hu4 and at3 , respec­ tively), but there are two departure nodes for Italy (it5 is for road transport and it4 is for ship transport). For Turkish trucks, the HungaryAustria border is one of the important gateways, which is depicted by the thick line joining the nodes hu4 and at1 . Note that the cost of crossing this border is €359 for every truck (This cost includes road tolls and fuel expenses. The details of specification of the parameters are given in Section 4.2). In 2011, the maximum number of Turkish trucks that could

4.1. The multi-commodity flow model In order to formulate the multi-commodity flow model, we first construct the transportation network by combining the used routes. In the first step of this construction, an initial network, which is called the base network, is developed where the nodes represent the countries and the arcs represent the possible road transportation links including ROROs. Then, we split the nodes representing countries issuing bilateral and transit quotas into multiple nodes. Afterwards, the new nodes are joined by arcs whose capacities are equal to bilateral or transit quotas issued by the corresponding country. The network obtained at the end of the splitting process is called the transportation network. The splitting process is not standard for all countries, as each country has a different 4

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Fig. 2. Part of the transportation network.

enter Austria was 18000, at most 15000 of which could transit Austria. The remaining 3000 quotas could be used as bilateral quotas. This is mainly because of the quota types issued by Austria in 2011; 3000 bilateral permits and 15000 permits that can be used either as transit or bilateral permits. Hungary also issues free or paid general permits that can be used either as bilateral or transit permits. For example, 21500 free general permits and 16400 paid general permits were issued by Hungary in 2011. This interesting quota structure is the reason of the links between hu1 and hu2 , and hu1 and hu3 . Moreover, the cost of using the paid road in Hungary is €460 for every truck. Italy issues four different types of quotas, namely bilateral, road transit, port-to-port transit, and towing quotas. In 2011, Italy issued 5000 permits for port to port transit (depicted by nodes it7 and it8 ) and 6000 permits for towing (depicted by nodes it5 and it6 ). In addition, in 2011, Italy issued 6000 permits for road transit, and 31000 permits that could be used as either road transit or bilateral permits. This situation is modelled by using the link capacities between nodes it1 and it2 , and between nodes it2 and itend . The cost of transporting a truck from Turkey to Italy by ship transport is €2565, which is depicted by the links emanating from node tr2 which is the departure node of Turkey corresponding to ship trans­ port including ROROs. Moreover, the costs of travelling by road from Italy to Austria, to France, and to Switzerland are €1459, €1443, and €476 per truck, respectively, which is depicted by the links emanating from node it3 . By using the transportation network obtained at the end of the splitting process, it is easy to define the set of nodes N, the set of links A, and the set of demand nodes C. We assume that each demand node has its own commodity. This is why C is also the set of commodities. The reason of this assumption is that we need to identify the routes used by the trucks travelling to each EU country. As a result of this assumption, it is sufficient to check the corresponding commodity flow levels on all links to find the routes used for a given EU country. The capacities of some links are defined during the splitting process. The capacities of all remaining links are assumed to be infinite. The transportation costs associated with the links are determined by using the cost data obtained from AIFF. In addition, the total export (in terms of number of trucks) from Turkey to a European country is assumed to be the demand value of the demand node corresponding to the European country. The mathe­ matical formulation of the multi-commodity flow model is given below:

XX cij xijc

Min

ði;jÞ2A c2C

s:t: X xijc j:ði;jÞ2A

0�

8 < bðcÞand i ¼ s xjic ¼ bðcÞand i ¼ c : j:ðj;iÞ2A 0 otherwise X

X xijc � uij

8i 2 N; 8c 2 C

8ði; jÞ 2 A

c2C

The model parameters are as follows. N : The set of all nodes in the transportation network. A : The set of all arcs in the transportation network. s : The source node corresponding to Turkey (s 2 N). C : The set of demand nodes (C⊂N) corresponding to the European countries considered in this paper. It is also the set of commodities. cij : The cost of sending one unit of flow on link ði; jÞ. uij : The capacity of link ði; jÞ. bðcÞ : The total export via road transport from Turkey to the Euro­ pean country corresponding to demand node c 2 C (demand of node c in terms of number of trucks). Decision Variables: xijc : The amount of commodity c flowing on link ði; jÞ (the number of trucks on link ði; jÞ, whose destination is the European country cor­ responding to demand node c). The average transportation cost from Turkey to a European country can be calculated by using the optimal solution of the above model. Let c be the demand node corresponding to the European country, and x*ijc be

the optimal value of the decision variable xijc . Then, the average trans­ portation cost is

1 X cij x* : bðcÞ ði;jÞ2A ijc

5

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4.2. Specification of the parameters of the multi-commodity flow model

encountered. Using the model, it is also possible to see the routes used by the trucks to reach a selected country. Table 4 shows the routes determined by the model.

In order to solve the multi-commodity flow model, we need to obtain the costs and capacities of all links and demand values of all nodes included in the model. Unfortunately, the link costs are not available. The only source that we can use is the costs related to some specific routes which are obtained from AIFF. Initially, the route costs of Ger­ many, Bulgaria, Italy, and Spain are obtained, and the approximate costs of the connections are calculated. Based on these costs, in the next step, the link costs are calculated and used as inputs in the multi-commodity flow models. For example, the cost of the connection by maritime transportation from Turkey to the port of Trieste in Italy is calculated as 2782 Euros, using the costs given in Table 1. Based on similar computations, costs are calculated for 134 links for the 2009–2015 period. All the calculated costs are given in Appendix 1. Table 2 shows the costs for a sample of 20 links. The route capacities are specified according to the quota levels and types imposed by the countries as explained in Section 4.1. Finally, the demand values (b(c)) of all nodes are specified according to the yearly export volumes from Turkey to the selected European countries. In our model, the only source node is Turkey. All the other selected EU coun­ tries are assumed to be demand nodes.

5. Gravity models After estimating the increase in transportation costs due to quotas using a multi-commodity flow model, we use gravity models to analyze the impact of extra costs due to quotas on export. We use models similar to those used in Ülengin et al. (2015) and Çekyay et al. (2017). Table 5 defines the variables used in the gravity models. The dependent variables used in the empirical estimation are ETit , EMit , and EFit . The details of the explanatory variables used in different specifications are given below where Turkey is denoted as country j. SUMGDPit measures the size of the economies of both Turkey and the importing country and is expected to have a positive effect on export. Its definition is � SUMGDPit ¼ ln GDPit þ GDPjt ; (1) where GDPit is the gross domestic product of country i in year t. RELENDOWit measures relative factor endowments. The proxy employed is the difference in capital stock per labor force as suggested by Wood (1994) and used in Ülengin et al. (2015). The impact of factor endowments might be both positive and negative; a negative coefficient is an indicator of an intra-industry trade structure referring to the ex­ change of similar goods produced in the same industry. A positive co­ efficient would mean that an inter-industry trade structure prevails (Antonucci & Manzocchi, 2006). A high volume of inter-industry trade between two countries indicates that the main cause of this significant trade between the countries is their difference in technology or factor endowments. The formula of RELENDOW measure is � � GCFit GCFjt RELENDOWit ¼ ln ; (2) ln LFit LFjt

4.3. Calculation of extra costs due to quotas based on the multicommodity flow model The basic difference between the models used in this study and previous models is inclusion of extra costs due to the quotas. For this, we introduce the variable COST incit , which shows the additional cost (%) due to quotas for country i at year t. To calculate this variable, the multicommodity flow models that are explained in the previous sections are used. For example, if the cost of transporting one truck to a country through a network without quotas is 1000 Euros and with quotas is 1100 Euros, then COST inc will be 10%. The hypothesis is that whenever this value increases, the impact of the quotas on cost will increase, and thus, the exports will be negatively affected. The developed model is solved for the selected countries for the period 2009–2015. The output of the model provides which routes are used at which percentages for exporting goods from Turkey to European countries on a yearly basis. By using these percentages, the average costs of sending one truck from Turkey to the selected European countries are calculated as given in Table 3 in parenthesis. Then, the minimum cost routes – assuming quota-free transportation – are obtained by solving the same multi-commodity flow models using infinite upper bounds (uij ¼ ∞). Finally, percentage increases in costs with respect to the minimum cost routes are calculated. For example, if there was no quota imposed by Germany in 2009, the cost would be 2612,8 Euros, but because the Turkish trucks have to use routes different than the mini­ mum cost route due to quotas, the cost is increased on average by 24,3% and becomes 3248,1 Euros. For Serbia, on the other hand, the trucks were able to use the minimum cost routes. That’s why, cost increment is 0% for Serbia for all years. Please see Table 3 for all cost increment percentages. According to the results given in Table 3, it can be concluded that, generally, if it is necessary to cross several countries in transit in order to arrive at the destination country, quota-driven extra costs are

where LFit is labor force of country i in year t, and GCFit is gross capital formation of country i in year t. SIMSIZEit is a measure of size similarity between Turkey and country i. It can take any real value between ∞ and 0.69 in general (Anto­ nucci & Manzocchi, 2006). An increase in this measure indicates that the two countries are becoming more similar in terms of GDP and that the share of intra-industry trade between the countries is increasing. Moreover, its coefficient in the gravity model should be negative if the trade between the countries is of an inter-industry nature (Antonucci & Manzocchi, 2006). It is defined as �2 � �2 � � � GDPit GDPjt SIMSIZEit ¼ ln 1 : (3) GDPit þ GDPjt GDPit þ GDPjt The final independent variable in the model is COST incit which is the percentage increase in the transportation cost from Turkey to country i in year t due to transportation quotas. The values of this var­ iable are calculated by using the multi-commodity flow model given in Section 4.3 and presented in Table 3. Our hypothesis is that whenever this variable increases, the impact of the quota on transportation cost will increase, and thus, the export will be negatively affected. Therefore, the impact of this variable on the dependent variable should be negative. The formulations of the gravity models are as follows:

Table 1 Cost calculation for the link: Turkey tr1 to Italy it5 . Name of the cost

Cost

Halkalı - Pendik cost Ro-Ro (fee) Ro-Ro (allowance) Diesel fuel Total

50 € 2220 € 450 € 62 € 2782 €

β

β

β

β

ETit ¼ Gi ⋅SUMGDPit1 ⋅SIMSIZEit2 ⋅RELENDOW it3 ⋅COST incit4 ⋅εit :

(4)

where Gi is the dummy variable for country i incorporating the unob­ servable effects specific to the country. The gravity models in this paper are estimated by using fixed effect (FEM) panel data regression. An alternative approach might have been using random effect regression (REM). It is known that if one is 6

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Research in Transportation Economics xxx (xxxx) xxx

Table 2 Link costs (Euros). From

To

2015

2014

2013

2012

2011

2010

2009

Germany - de2

Belgium - be1

Austria - at3

705

734

917

917

858

800

741

Switzerland - ch1

778

810

1012

1012

946

882

817

Slovakia - sk1

116

116

144

144

135

126

116

Bulgaria - bg2

Serbia - rs1

588

623

546

546

513

480

447

Chekia - cz2

333

347

433

433

405

378

350

France – f r2

Germany - de1

1019

996

983

983

904

826

748

France – f r2

Germany - de1

Switzerland - ch1

755

732

684

684

635

587

538

Croatia - hr2

Slovenia - si1

442

442

491

491

457

423

390

778

810

1012

1012

946

882

817

Italy - it3

Germany - de1 Austria - at1

1606

1606

1618

1618

1459

1300

1141

Slovenia - si1

942

942

949

949

856

762

669

Moldova - md2

Romania – ro1

347

347

411

411

378

345

312

685

685

811

811

746

681

616

Serbia - rs4

Ukraine - ua1

Hungary - hu1

578

578

625

625

590

555

520

Slovakia - sk3

Poland – pl1

449

449

513

513

461

410

359

Slovenia - si2

Croatia - hr1

100

100

114

114

103

91

80

961

961

997

997

932

868

803

Turkey – tr2

Greece - el1 Italy – it5

2782

2782

2768

2768

2565

2363

2160

Hungary - hu1

665

665

661

661

613

564

516

558

558

637

637

574

510

446

Germany - de2

Switzerland - ch2 Italy - it3 Romania - ro3

Turkey – tr1

Ukraine - ua2 Greece - el4

Bulgaria - bg1

Table 3 Increase in transportation cost due to quotas (Percentage). Germany Austria Belgium Bulgaria France Croatia Holland UK Spain Switzerland Italy Hungary Poland Serbia Slovakia Ukraine Greece

2009

2010

2011

2012

2013

2014

2015

%24 (3248) %12 (2124) %38 (3650) %0 (891) %10 (3203) %0 (1862) %50 (3785) %33 (3987) %0 (3802) %3 (2532) %0 (2074) %7 (1792) %0 (1613) %0 (1377) %7 (1829) %0 (1166) %0 (803)

%23 (3512) %11 (2301) %39 (4009) %0 (941) %14 (3622) %0 (1982) %51 (4169) %26 (4117) %0 (3869) %0 (2699) %0 (2275) %4 (1911) %33 (2364) %0 (1459) %4 (1954) %0 (1276) %0 (867)

%27 (3952) %8 (2454) %31 (4077) %0 (991) %6 (3667) %0 (2102) %44 (4304) %19 (4197) %0 (3935) %0 (2952) %0 (2476) %2 (2029) %1 (1940) %0 (1541) %2 (2079) %0 (1385) %0 (932)

%19 (3993) %0 (2452) %23 (4145) %0 (1041) %0 (3711) %0 (2222) %36 (4387) %12 (4278) %0 (4002) %0 (3205) %0 (2678) %0 (2148) %28 (2649) %0 (1623) %0 (2204) %0 (1495) %0 (997)

%20 (4007) %0 (2452) %23 (4145) %0 (1041) %0 (3711) %0 (2222) %36 (4387) %12 (4278) %0 (4002) %0 (3205) %0 (2678) %0 (2148) %24 (2581) %0 (1623) %2 (2204) %0 (1495) %0 (997)

%7 (3535) %2 (2633) %30 (4339) %0 (1089) %6 (3828) %0 (2240) %41 (4534) %21 (4462) %0 (4081) %0 (3103) %0 (2579) %1 (2255) %19 (2487) %0 (1694) %5 (2281) %0 (1503) %0 (961)

%0 (3304) %7 (2764) %8 (3549) %0 (1091) %8 (3866) %0 (2220) %22 (3901) %24 (4499) %0 (4117) %0 (3097) %0 (2579) %21 (2708) %0 (2079) %0 (1674) %26 (2734) %0 (1503) %0 (961)

interested in analyzing the trade relations among a predetermined subset of countries (as it is in this paper), FEM is the right choice (Antonucci & Manzocchi, 2006; Egger, 2000; Ülengin et al., 2015). Moreover, FEM is consistent with short study periods similar to the one used in this study (Angers, Desjardins, Dionne, & Guertin, 2018; Yaacob, Lazim, & Wah, 2011). It was also possible to use time-dependent dummy variables. How­ ever, as we observe that the models without time dummies outperform the models with time dummies (similar to the case in Ülengin et al. (2015)), we decide not to include time dummies in this study. Another issue with our data set is that some observations are zero, as Turkey and some countries do not trade at certain periods. This is why the re­ gressions are estimated via the Poisson Pseudo Maximum Likelihood (PPML) estimator (the details can be found in (Silva & Tenreyro, 2006).

use balanced panel data, and the number of observations in each regression is 119 (17 trading partners and 7 years). The output of the model is given in Table 6. Coefficients of the country dummies are given in Table 7. For representation brevity, natural log of the original values, lnðGi Þ, are given as the original values has so many digits. According to the results of the gravity model, the coefficients of the SUMGDP and COST inc are found to be significant at a 95% confidence level; however, the coefficients of SIMSIZE and RELENDOW are not (p < 0:05Þ. SIMSIZE measures the similarity and RELENDOW measures relative factor en­ dowments. As explained above, RELENDOW can take a positive or negative sign according to the type of exchange in trade (inter- or intraindustry). SIMSIZE is also related to the type of the exchange. If the trade is balanced in inter-industry and intra-industry, then the effect may disappear. As we analyze the total export in this model, that is the total of all different types of goods, the effect of RELENDOW and SIMSIZE can be accepted to disappear. As a result, we can conclude that there is strong evidence that extra cost resulting from the quotas has significantly negative effects on the total export of Turkey to the selected European countries. In the following section, we analyze the impact of quotas on the

5.1. Effect of quota costs on Turkish total export In this section, we analyze the effect of the extra costs arising from road transport quotas on Turkish total export, by using a gravity model. We use a fixed effect panel data regression model given in Eq. (4). We 7

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Table 4 Routes found by the multi-commodity flow model (2015).

Table 7 Coefficients of dummy variables in the gravity models.

Destination

Route

Germany

Turkey-road, Bulgaria, Serbia, Croatia, Slovenia, Austria, Germany Turkey-road, Bulgaria, Serbia, Hungary, Austria, Germany Turkey-road, Bulgaria, Serbia, Hungary, Slovakia, Czech Rep., Germany Turkey-road, Bulgaria, Serbia, Hungary, Austria Turkey-road, Bulgaria, Serbia, Hungary, Slovakia, Czech Rep., Germany, Belgium Turkey-maritime, Ukraine, Poland, Germany, Belgium Turkey-maritime, France, Belgium Turkey-road, Bulgaria Turkey-road, France Turkey-road, Bulgaria, Serbia, Croatia Turkey-road, Bulgaria, Serbia, Hungary, Slovakia, Czech Rep., Germany, Holland Turkey-maritime, France, United Kingdom Turkey-maritime, France, Spain Turkey-road, Greece, Italy, Switzerland Turkey-road, Greece, Italy Turkey-road, Bulgaria, Serbia, Hungary Turkey-maritime, Ukraine, Poland Turkey-road, Bulgaria, Serbia Turkey-road, Bulgaria, Serbia, Hungary, Slovakia Turkey-maritime, Ukraine Turkey-road, Greece

Austria Belgium

Bulgaria France Croatia Holland UK Spain Switzerland Italy Hungary Poland Serbia Slovakia Ukraine Greece

lnðGi Þ values in ET model Austria Belgium Bulgaria Switzerland Germany Spain France United Kingdom Greece Croatia Hungary Italy Netherlands Poland Serbia Slovak Republic Ukraine

Definition

COST incit

Extra average cost (%) due to quotas from Turkey to country ​ i in year t

ETit

Turkey’s total export to country i in year t (in US$)

EMit

Turkey’s machinery and equipment sector export to country i in year t (in US$)

EFit

Turkey’s food and beverages sector export to country i in year t (in US$)

SUMGDPit

A measure indicating the size of the economies of both Turkey and country i in year t

RELENDOWit

A measure indicating the relative factor endowments between Turkey and country i in year t

SIMSIZEit

A measure indicating the size similarity between Turkey and country i in year t

Coefficient

Std. Error

z-Statistic

Prob.

SUMGDP SIMSIZE RELENDOW COST inc

0.643 0.366 0.127 0.926

0.304 0.414 0.153 0.386

2.117 0.884 0.829 2.402

0.040 0.376 0.407 0.016

Pseudo R2 ¼ 0.936

β

β

3.695 2.378 3.017 4.634 4.504 3.838 2.842 2.868

3.298 3.559 3.165 2.571 2.888 2.713 3.405 3.214

27.067 26.804 26.033 29.358 29.092 27.376 27.408 26.439

3.705

3.170

27.830

β

β

β

β

(6)

where EFit denotes the monetary value (in US dollars) of Turkey’s food and beverages sector exports to country i in year t. According to the results of the gravity model given in Table 9 (Co­ efficients of the country dummies are given in Table 7), the coefficients of all the variables except SUMGDP are found to be significant (p < 0:05). The negative coefficient of RELENDOW and positive coeffi­ cient of SIMSIZE indicate that there is an intra-industry trade in food and beverages sector. Therefore, it can be concluded that there is sufficient statistical ev­ idence that the extra quota costs have a significant negative effect on TFBS exports. It is interesting that even though it is not statistically significant, the coefficient of SUMGDP is negative (as opposed to the positive values in the previous gravity models). To compensate the resulting decrease, the coefficients of the country dummy variables are Table 8 Results of the gravity model for Turkish machinery and equipment sector exports.

In this section, we analyze the effect of the extra costs arising from road transport quotas on TMES exports, by using a gravity model. For this purpose, the model given in Eq. (4) is modified as follows: β

27.500 28.073 28.803 27.485 30.665 27.570 29.318 29.245

EFit ¼ Gi ⋅SUMGDPit1 ⋅SIMSIZEit2 ⋅RELENDOW it3 ⋅COST incit4 ⋅εit :

5.2. Effect of quota costs on TMES exports

β

3.381 2.691 2.117 4.287 1.275 2.873 2.169 1.565

Finally, we analyze the effect of the extra costs arising from road transport quotas on TFBS exports, by using a gravity model. For this purpose, the model given in Eq. (4) is modified as follows.

exports of Turkish machinery and equipment (TMES), and Turkish Food and Beverages sectors (TFBS) to EU. We specifically choose these two sectors to analyze further because these sectors heavily use road transportation.

EMit ¼ Gi ⋅SUMGDPit1 ⋅SIMSIZEit2 ⋅RELENDOW it3 ⋅COST incit4 ⋅εit :

3.330 4.353 4.299 4.349 5.291 4.212 4.571 4.979

5.3. Effect of quota costs on TFBS exports

Table 6 Results of the gravity model for Turkish total exports. Variable

lnðGi Þ values in EF model

where EMit denotes the monetary value (in US dollars) of Turkey’s machinery and equipment sector exports to country i in year t. According to the results of the gravity model given in Table 8 (Co­ efficients of the country dummies are given in Table 7), the coefficients of all the variables except RELENDOW are found to be significant (p < 0:05). The almost significant positive coefficient of SIMSIZE (p ¼ 0:056) indicates that intra-industry trade exists for the machinery and equipment sector. The significant coefficient of COST inc shows that there is sufficient statistical evidence that the extra quota costs have a significant negative effect on TMES exports.

Table 5 Definitions of the variables used in gravity models. Variable

lnðGi Þ values in EM model

(5)

Variable

Coefficient

Std. Error

z-Statistic

Prob.

SUMGDP SIMSIZE RELENDOW COST inc

0.827 0.631 0.102 1.211

0.377 0.330 0.133 0.380

2.191 1.911 0.762 3.187

0.028 0.056 0.446 0.001

Pseudo R2 ¼ 0.979

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Table 9 Results of the gravity model for Turkish food and beverages sector exports.

⋅SIMSIZE0:631 ⋅RELENDOW it 0:102 ⋅COST incit 1:211 EMit ¼ Gi ⋅SUMGDP0:827 it it

Variable

Coefficient

Std. Error

z-Statistic

Prob.

SUMGDP SIMSIZE RELENDOW COST inc

0.266 1.266 0.306 1.287

0.410 0.260 0.145 0.349

0.648 4.878 2.107 3.690

0.517 0.000 0.035 0.000

(8)

get increased by the gravity model. This implies that we need additional variables to explain the trade behavior specific to this sector, which may be the subject of a further study.

where country specific fixed factors, natural logarithm of Gi values, are presented in Table 7. In the quota-free exports scenario, all COST incit values are taken as zero, and it is assumed that there are no extra costs arising from quotas. Based on the results presented in Table 11, Turkey’s expected export loss in the machinery and equipment sector (to the countries under consideration) in 2009–2015 is about US$ 11.8 billion, which is 17.5% of the realized machinery and equipment sector exports. The most important effect of the quotas is on exports to Germany, with an expected loss of US$ 4.6 billion.

6. Scenario analysis

6.3. Scenario analysis for TFBS exports

Based on the results of the gravity model, a forecast is performed to find the effect of quotas on the sector exports. For this purpose, the realized exports are compared with the estimated quota-free exports, where we assume that no quotas are applied to road transportation.

To predict the losses in the TFBS export to the selected European countries, by using the coefficients given in Table 9 in Eq. (6), the following model is developed:

Pseudo R2 ¼ 0.966

EFit ¼ Gi ⋅SUMGDPit 0:266 ⋅SIMSIZE1:266 ⋅RELENDOW it 0:306 ⋅COST incit 1:287 it

6.1. Scenario analysis for Turkish total export

where country specific fixed factors, natural logarithm of Gi values, are presented in Table 7. In the quota-free exports scenario, all COST incit values are taken as zero, and it is assumed that there are no extra costs arising from the quotas. Based on the results presented in Table 12, Turkey’s expected export loss in the food and beverages sector (to the countries under consideration) in 2009–2015 is about US$ 6.5 billion, which is 19% of the realized food and beverages sector exports. The most important effect of the quotas is on exports to Germany and Netherlands, with an expected loss of US$ 2.2 billion each.

To predict the losses in the Turkish total export to the selected Eu­ ropean Countries, by using the coefficients given in Table 6 in Eq. (4), the following model is developed: ETit ¼ Gi ⋅SUMGDP0:643 ⋅SIMSIZE0:366 ⋅RELENDOW it 0:127 ⋅COST incit 0:926 it it

(7)

where country specific fixed factors, natural logarithm of Gi values, are presented in Table 7. In the quota-free exports scenario, all COST incit values are taken as zero, and it is assumed that there are no extra costs arising from quotas. Based on the results presented in Table 10, Turkey’s expected export loss to the countries under consideration in 2009–2015 is about US$ 50.8 billion, which is 12% of the realized export. The most important effect of the quotas is on exports to Germany, with an ex­ pected loss of US$ 16.3 billion.

7. Conclusions This study proposes an approach based on multi-commodity flow model and gravity model to show the negative impact of extra costs due to transport and bilateral quotas imposed by EU countries on Turkish carriers on Turkish export. Estimation of the extra costs is only possible by using transportation costs and frequency-of-use of all routes between Turkey and the selected European countries. The main problem with this analysis is that we do not have reliable information on the route fre­ quencies. Therefore, we use a multi-commodity flow model to approx­ imate these frequencies, which leads to an impact analysis providing a lower bound on the real negative effect. Then, by using an econometric analysis based on gravity models, we show that there is sufficient

6.2. Scenario analysis for TMES exports To predict the losses in TMES export to the selected European countries, by using the coefficients given in Table 8 in Eq. (5), the following model is developed: Table 10 Turkey’s total exports in 2009–2015 (in US$).

Austria Belgium Bulgaria Switzerland Germany Spain France United Kingdom Greece Croatia Hungary Italy Netherlands Poland Serbia Slovak Republic Ukraine Grand Total

Realized exports (billion $)

Estimated quota-free exports (billion $)

Difference (billion $)

7.309 17.690 12.555 23.496 95.987 29.706 46.246 63.831

7.715 22.778 12.555 23.585 112.323 29.706 49.060 77.437

0.406 5.087 0.000 0.089 16.336 0.000 2.815 13.606

10.964 1.757 4.253 50.103 22.455 14.178 2.788 3.122

10.964 1.757 4.468 50.102 32.405 16.279 2.788 3.322

0.000 0.000 0.215 0.000 9.950 2.101 0.000 0.201

11.305 417.744

11.305 468.549

0.000 50.806

(9)

Table 11 TMES exports in 2009–2015 (in US$).

Austria Belgium Bulgaria Switzerland Germany Spain France United Kingdom Greece Croatia Hungary Italy Netherlands Poland Serbia Slovak Republic Ukraine Grand Total

9

Realized exports (billion $)

Estimated quota-free exports (billion $)

Difference (billion $)

1.211 2.039 1.779 0.620 20.330 3.828 8.526 13.618 1.249 0.415 0.938 5.845 1.860 2.675 0.437 0.706 1.231 67.306

1.299 2.833 1.779 0.623 24.904 3.828 9.201 17.496 1.249 0.415 0.999 5.845 3.000 3.197 0.437 0.765 1.231 79.100

0.088 0.794 0.000 0.003 4.575 0.000 0.675 3.878 0.000 0.000 0.061 0.000 1.140 0.522 0.000 0.059 0.000 11.795

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time is of crucial importance in food and beverages sector due to perishability problems. That’s why, in order to avoid the lead time in­ crease, the carrier will have to select other more costly routes because of quota limit. In such a situation, the exporting companies may decide to switch to sellers from other countries. On the other hand, in the ma­ chinery and equipment sector, the results reveal intra-industry trade, which indicates that Turkey exports mainly semi-products to machinery producing countries. Since there is a high competition in semi-product exports, the countries exporting these products may prefer sellers of other countries if the cost increases due to quotas. As a further study, the proposed methodology can be applied to other sectors having different characteristics. For example, it may be applied to textile sector to see the implication of quotas on high fashion prod­ ucts. In addition, our scenario analysis on TBFS shows that a more detailed analysis is required to understand the trade dynamics specific to this sector.

Table 12 TFBS exports in 2009–2015 (in US$).

Austria Belgium Bulgaria Switzerland Germany Spain France United Kingdom Greece Croatia Hungary Italy Netherlands Poland Serbia Slovak Republic Ukraine Grand Total

Realized exports (billion $)

Estimated quota-free exports (billion $)

Difference (billion $)

0.915 1.283 1.257 0.932 9.311 1.174 3.385 3.026 0.719 0.156 0.152 5.081 3.337 1.169 0.263 0.153 1.747 34.061

0.991 1.826 1.257 0.938 11.515 1.174 3.670 3.952 0.719 0.156 0.163 5.081 5.535 1.403 0.263 0.167 1.747 40.557

0.075 0.543 0.000 0.006 2.204 0.000 0.285 0.926 0.000 0.000 0.011 0.000 2.198 0.234 0.000 0.014 0.000 6.496

CRediT authorship contribution statement € Bora Çekyay: Conceptualization, Methodology, Software. Ozgür Kabak: Conceptualization, Methodology, Investigation. Füsun Ülen­ gin: Supervision, Writing - original draft, Conceptualization. Burç Ulengin: Formal analysis. Peral Toktas¸ Palut: Conceptualization, € € Writing - review & editing. Ozay Ozaydın: Data curation, Visualization, Writing - original draft.

statistical evidence that the quotas have a significant negative effect on the total Turkish export to EU countries. We also extend this result to the TMES, and the TFBS. Then, based on a scenario analysis, we estimate the losses in Turkish export due to quotas. The results show that the biggest loss occurs in export to Germany, which agrees with our expectation, as Germany is the biggest trade partner of Turkey in the EU. When the results are analyzed for TMES and TFBS, although the gravity model results are not very similar for all variables, it is found that cost increases originated from the quotas have negative impacts on the exports in both sectors. The reason behind the negative effect may be due to the different characteristics of these sectors. For example, the lead

Acknowledgement This study was supported by TÜBITAK, The Scientific and Techno­ logical Research Council of Turkey, Project No.: 114K582.

Appendix 1 The complete cost data are given in the table below.

From

To

2015

2014

2013

2012

2011

2010

2009

Germany - de1 Germany - de2 Germany - de2 Germany - de2 Germany - de2 Germany - de2 Germany - de2 Germany - de2 Germany - de2 Austria - at1 Austria - at2 Austria - at2 Austria - at3 Austria - at3 Austria - at3 Austria - at3 Belgium - be1 Belgium - be2 Belgium - be2 Belgium - be3 Belgium - be3 Bulgaria - bg1 Bulgaria - bg2 Bulgaria - bg2 Bulgaria - bg2 Bulgaria - bg3 Chekia - cz1 Chekia - cz2

Germany - de2 Germany - deend Belgium - be1 France - fr1 Netherlands - nl1 Switzerland - ch1 Switzerland - ch3 Poland - pl1 Poland - pl3 Austria - at2 Austria - atend Austria - at3 Germany - de1 Switzerland - ch1 Switzerland - ch3 Slovakia - sk1 Belgium - be2 Belgium - beend Belgium - be3 Netherlands - nl1 United Kingdom - uk1 Bulgaria - bg2 Macedonia - mk1 Romania - ro1 Serbia - rs1 Bulgaria - bgend Chekia - cz2 Germany - de1

0 692 705 973 604 778 778 530 530 0 0 0 0 1074 1074 116 0 0 0 186 340 0 367 540 588 187 0 333

0 721 734 1014 630 810 810 552 552 0 0 0 0 1074 1074 116 0 0 0 194 355 0 384 566 623 200 0 347

0 900 917 1266 786 1012 1012 689 689 0 0 0 0 1341 1341 144 0 0 0 242 443 0 337 496 546 154 0 433

0 900 917 1266 786 1012 1012 689 689 0 0 0 0 1341 1341 144 0 0 0 242 443 0 337 496 546 154 0 433

0 842 858 1184 735 946 946 644 644 0 0 0 0 1255 1255 135 0 0 0 227 414 0 316 466 513 148 0 405

0 785 800 1104 686 882 882 601 601 0 0 0 0 1168 1168 126 0 0 0 211 386 0 296 436 480 142 0 378

0 727 741 1023 635 817 817 556 556 0 0 0 0 1081 1081 116 0 0 0 196 358 0 276 406 447 137 0 350

(continued on next page)

10

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(continued ) From

To

2015

2014

2013

2012

2011

2010

2009

France - fr2 France - fr2 France - fr2 France - fr2 France - fr2 France - fr2 France - fr2 France - fr1 France - fr1 Croatia - hr1 Croatia - hr2 Croatia - hr2 Netherlands - nl1 United Kingdom - uk1 Spain - es1 Switzerland - ch1 Switzerland - ch2 Switzerland - ch3 Italy - it1 Italy - it2 Italy - it2 Italy - it2 Italy - it3 Italy - it3 Italy - it3 Italy - it3 Italy - it3 Italy - it4 Italy - it5 Italy - it6 Italy - it6 Italy - it7 Italy - it8 Hungary - hu1 Hungary - hu1 Hungary - hu2 Hungary - hu2 Hungary - hu3 Hungary - hu3 Hungary - hu4 Hungary - hu4 Macedonia - mk1 Macedonia - mk2 Moldova - md1 Moldova - md2 Poland - pl1 Poland - pl2 Poland - pl3 Romania - ro1 Romania - ro1 Romania - ro1 Romania - ro2 Romania - ro2 Romania - ro2 Romania - ro3 Romania - ro3 Romania - ro3 Serbia - rs1 Serbia - rs2 Serbia - rs3 Serbia - rs3 Serbia - rs4 Serbia - rs4 Serbia - rs4 Slovakia - sk1 Slovakia - sk2 Slovakia - sk2 Slovakia - sk3 Slovakia - sk3 Slovakia - sk3 Slovenia - si1 Slovenia - si2 Slovenia - si2

France - frend Germany - de1 Belgium - be1 Spain - es1 Switzerland - ch1 Switzerland - ch3 United Kingdom - uk1 France - fr2 Spain - es1 Croatia - hr2 Croatia - hrend Slovenia - si1 Netherlands - nlend United Kingdom - ukend Spain - esend Switzerland - ch2 Germany - de1 Switzerland - chend Italy - it2 Italy - itend Italy - it3 Italy - it4 Austria - at1 France - fr1 Switzerland - ch1 Switzerland - ch3 Slovenia - si1 Spain - es1 Italy - it6 Italy - itend Italy - it3 Italy - it8 Italy - it4 Hungary - hu2 Hungary - hu3 Hungary - huend Hungary - hu4 Hungary - huend Hungary - hu4 Austria - at1 Slovakia - sk1 Macedonia - mk2 Serbia - rs1 Moldova - md2 Romania - ro1 Poland - pl2 Germany - de1 Poland - plend Romania - roend Romania - ro2 Romania - ro3 Hungary - hu1 Serbia - rs1 Ukraine - ua1 Hungary - hu1 Serbia - rs1 Ukraine - ua1 Serbia - rs2 Serbia - rs3 Serbia - rsend Serbia - rs4 Croatia - hr1 Hungary - hu1 Romania - ro1 Slovakia - sk2 Slovakia - skend Slovakia - sk3 Chekia - cz1 Poland - pl1 Poland - pl3 Slovenia - si2 Austria - at1 Croatia - hr1

0 1019 535 958 755 755 633 843 794 0 127 442 0 0 300 0 778 0 0 0 0 0 1606 1589 519 519 942 3676 0 0 0 0 0 0 470 168 0 168 0 526 194 0 420 0 347 0 530 0 144 248 0 631 448 685 631 448 685 0 0 181 0 601 578 448 0 0 0 230 449 449 0 90 100

0 996 511 923 732 732 633 806 759 0 127 442 0 0 300 0 810 0 0 0 0 0 1606 1589 524 524 942 3676 0 0 0 0 0 0 470 168 0 168 0 526 194 0 420 0 347 0 552 0 144 248 0 631 448 685 631 448 685 0 0 181 0 601 578 448 0 0 0 230 449 449 0 90 100

0 983 434 821 684 684 567 678 639 0 138 491 0 0 330 0 1012 0 0 0 0 0 1618 1601 528 528 949 3703 0 0 0 0 0 0 480 89 0 89 0 393 145 0 313 0 411 0 689 0 179 248 0 747 530 811 747 530 811 0 0 189 0 650 625 530 0 0 0 262 513 513 0 97 114

0 983 434 821 684 684 567 678 639 0 138 491 0 0 330 0 1012 0 0 0 0 0 1618 1601 528 528 949 3703 0 0 0 0 0 0 480 89 0 89 0 393 145 0 313 0 411 0 689 0 179 248 0 747 530 811 747 530 811 0 0 189 0 650 625 530 0 0 0 262 513 513 0 97 114

0 904 410 770 635 635 530 642 605 0 132 457 0 0 306 0 946 0 0 0 0 0 1459 1443 476 476 856 3339 0 0 0 0 0 0 460 83 0 83 0 359 132 0 286 0 378 0 644 0 169 200 0 687 487 746 687 487 746 0 0 185 0 614 590 487 0 0 0 236 461 461 0 91 103

0 826 386 718 587 587 494 606 571 0 126 423 0 0 282 0 882 0 0 0 0 0 1300 1286 424 424 762 2976 0 0 0 0 0 0 440 77 0 77 0 325 120 0 259 0 345 0 601 0 158 200 0 627 445 681 627 445 681 0 0 180 0 578 555 445 0 0 0 210 410 410 0 86 91

0 748 363 666 538 538 458 570 537 0 120 390 0 0 257 0 817 0 0 0 0 0 1141 1129 372 372 669 2612 0 0 0 0 0 0 400 70 0 70 0 291 107 0 232 0 312 0 556 0 148 200 0 568 403 616 568 403 616 0 0 176 0 541 520 403 0 0 0 184 359 359 0 80 80

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(continued ) From

To

2015

2014

2013

2012

2011

2010

2009

Slovenia - si2 Slovenia - si2 Turkey - trs Turkey - trs Turkey - tr1 Turkey - tr1 Turkey - tr1 Turkey - tr2 Turkey - tr2 Turkey - tr2 Turkey - tr2 Turkey - tr2 Turkey - tr2 Ukraine - ua1 Ukraine - ua1 Ukraine - ua2 Ukraine - ua 2 Ukraine - ua2 Ukraine - ua2 Ukraine - ua2 Ukraine - ua2 Greece - el1 Greece - el1 Greece - el1 Greece - el2 Greece - el2 Greece - el3 Greece - el3 Greece - el4 Greece - el4 Greece - el4 Greece - el4 Greece - el4

Italy - it1 Hungary - hu1 Turkey - tr1 Turkey - tr2 Bulgaria - bg1 Bulgaria - bg3 Greece - el1 France - fr1 Italy - it1 Italy - it5 Italy - it7 Ukraine - ua1 Greece - el1 Ukraine - uaend Ukraine - ua2 Hungary - hu1 Moldova - md1 Poland - pl1 Poland - pl3 Romania - ro1 Slovakia - sk1 Greece - el2 Greece - el3 Greece - el4 Greece - elend Greece - el4 Greece - elend Greece - el4 Bulgaria - bg1 Italy - it1 Italy - it5 Italy - it7 Macedonia - mk1

354 323 0 0 905 905 961 3023 2782 2782 2782 1390 1608 113 0 665 97 689 689 303 772 0 0 0 0 0 0 0 558 1618 1618 1618 491

354 323 0 0 890 890 961 3023 2782 2782 2782 1390 1608 113 0 665 97 689 689 303 772 0 0 0 0 0 0 0 558 1618 1618 1618 491

404 370 0 0 888 888 997 3033 2768 2768 2768 1383 1600 112 0 661 97 686 686 302 768 0 0 0 0 0 0 0 637 1681 1681 1681 561

404 370 0 0 888 888 997 3033 2768 2768 2768 1383 1600 112 0 661 97 686 686 302 1234 0 0 0 0 0 0 0 637 1681 1681 1681 561

363 333 0 0 843 843 932 3025 2565 2565 2565 1282 1483 104 0 613 89 635 635 279 1111 0 0 0 0 0 0 0 574 1544 1544 1544 505

323 296 0 0 799 799 868 3017 2363 2363 2363 1180 1366 96 0 564 82 585 585 257 987 0 0 0 0 0 0 0 510 1408 1408 1408 449

283 259 0 0 754 754 803 3008 2160 2160 2160 1079 1249 88 0 516 75 535 535 235 863 0 0 0 0 0 0 0 446 1271 1271 1271 393

References

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