Policy Research Working Paper 10491 The Economic Effects of Market Integration in the Western Balkans María Del Mar Gómez Román D. Zárate Daria Taglioni Development Economics Development Research Group June 2023 Policy Research Working Paper 10491 Abstract In the Western Balkans, trade and transport policy reforms of trade reforms and improvements in road infrastructure that reduce waiting time at the border by just three hours would be further amplified if Western Balkan economies are equivalent to removing a value-based tariff of 2 percent. belonged to the European Union, which would result in Reform gains are maximized when they are coordinated an additional 6 percent boost to welfare. Moreover, the across economies and implemented jointly: cross-border accession of Western Balkan economies to the European coordination in the implementation of the package of Union would have positive spillover welfare effects for coun- national single window and other trade facilitation reforms tries such as Croatia, Bulgaria, Romania, and Hungary, and would generate 8 percent higher gains than if each economy negligible effects for other EU members. were to carry out the reforms autonomously. The impacts This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at rzaratevasquez@worldbank.org. The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Produced by the Research Support Team The Economic Effects of Market Integration in the Western Balkans∗ Maria Del Mar Gómez† Daria Taglioni‡ Román D. Zárate§ Keywords : Quantitative Spatial Models, Western Balkans, European Union accession, Trade Facilitation, Regional Integration. JEL Codes : D51, E61, F15, O18, O52, R40. ∗ We thank Alberto Criscuolo and Satya Prasad Sahu for the insightful discussions on the trade facilitation program in the Western Balkans as well as participants at the Conference on "Regional Economic Integration in the Western Balkans" held in Vienna on October 25th, 2022. All remaining errors are ours. † World Bank, email: mgomezortiz@worldbank.org ‡ World Bank, email: dtaglioni@worldbank.org § World Bank, email: rzaratevasquez@worldbank.org 1 Introduction In recent years, the world has witnessed a series of events that have challenged the foun- dations of global trade, from Brexit in 2016 to the Russian invasion of Ukraine in 2022. During these same years, the Western Balkan economies have embarked on an ambitious regional integration agenda. This effort has included a focus on improving trade facilitation, upgrading transport infrastructure, and leveraging deep trade agreements within the region and with the European Union (EU). While progress has been made in some areas, the overall implementation program of these reforms has had alternating fortunes and remains incomplete. The uncertain macroeconomic and geopolitical situation facing these economies, notably reversals in trade integration on a global scale, even in areas where progress could until recently be taken for granted, and the uncertain path to EU accession might explain the lack of momentum in completing the regional trade integration agenda. However, this approach contradicts the evidence base in international trade, which has shown that trade and global integration are essential for sustainable development and poverty reduction, particularly for smaller and low-income nations. Trade and regional integration are especially important for the Balkan economies. The region comprises small and fragmented markets that cannot exploit economies of scale and agglomeration forces. This fragmentation limits their ability to attract foreign investment and compete with firms in EU markets, let alone in global scenarios. In that sense, poli- cies that promote economic integration can help these economies to pool their resources, knowledge, and markets to create a more attractive and competitive economic environment. The EU has made it clear that economic and institutional convergence inside the Western Balkans region is necessary for it to integrate with the EU. Through closer economic coop- eration among themselves, the Western Balkans can better align their policies with the EU and reduce trade frictions and barriers to the latter more steeply. However, accession to the EU may bring significantly more benefits than Western Balkan regional integration alone. The EU is the largest trading partner for the Western Balkan economies, with over 70% of their exports going to EU member states. The EU is also the biggest source of foreign direct investment (FDI) in the region, accounting for around 70% of total FDI. Moreover, the EU offers access to a market of more than 500 million consumers, which provides vastly greater opportunities for job creation and economic growth compared to a regional Western Balkans market of only 18.5 million people. Accession to the EU ensures adherence to EU regulations and standards, which can improve the quality of goods and services produced and facilitate the movement of people and capital. Finally, accession to 1 the EU can also provide small Balkan economies with greater stability and financial security, facilitating their ability to invest in long-term capital (i.e. investments in infrastructure, education, research and development, and other productive sectors of the economy) that ultimately promotes sustainable development and contributes to reducing poverty. Against this background, this paper quantifies the potential benefits of more international integration. It uses various sources of microdata and simulates different scenarios using recent advancements in quantitative spatial models. The paper is structured as follows. First, it describes the different data sources used in the analysis. The paper uses various sources of information, such as the I2D2 database, which reports employment, earnings, place of residency, industry variables, and aggregate information from the Working Time Statistics database held by the International Labour Or- ganization (ILO). In doing so, this paper combines datasets specific to the Western Balkans with other sources for the EU. Second, the paper presents a range of descriptive statistics relevant to the discussion. Finally, it uses a quantitative spatial model (QSM) to understand the impact of market integration policies in the region. Using this QSM model, the paper simulates different counterfactual analyses to understand the impact of interventions such as reducing transit time at border crossings, resulting from the implementation of the National Single Window for Trade, investments in border management systems, and upgrading of bor- der crossing points; accession of the Western Balkan economies to the EU; and the provision of improved road infrastructure. Section 4 describes the model and section 5 explains the counterfactual analyses. Overall, the paper finds that market integration policies can effectively generate economic gains in the region, especially if cross-border coordination exists in the policy reform agenda and across policies. For example, reducing transit times at border crossings generates gains in terms of real income of around 2.5% in the region. Both cross-border coordination in the trade facilitation reform agenda and EU accession amplify these gains significantly. Similarly, EU accession boosts the benefits of road infrastructure improvements. In conclusion, the main policy message from the paper is that cross-border and cross-reform coordination is necessary to take advantage of regional integration opportunities. 2 Data The study focuses on the six Western Balkan economies, but it also includes data for the 27 European Union countries, the most important economic neighbor of the Western Balkans region. Since we are interested in assessing how impacts differ within countries and/or 2 economies and across different social groups, the data cover sub-national units and distin- guish by gender and skill level. We divide the Western Balkans group into 54 sub-national regions corresponding to the ISO3166 international standard division at the two-digit level1 . For the European Union, we take each of the 27 countries as our unit of analysis. In total, our QSM has 81 spatial units that respond heterogeneously to the policies we simulate. To use the model and perform our counterfactual analysis over these locations, we con- struct a database with basic information on wages and employment for four groups of workers: high-skilled males, low-skilled males, high-skilled females, and low-skilled females, divided into three economic sectors: agriculture and mining, manufacturing, and services. We employ two international databases: the International Income Distribution Database (I2D2) and the Working Time Statistics (WTS) to compute the national and sub-national level measures needed. In addition, we use the Global Roads Inventory Project (GRIP) for information on road infrastructure. For EU countries, we use the WTS database to calculate the country-specific wage ratio across skill levels and the wage ratio across gender. Then, we multiply the wages in each country and sector by the skill and gender ratios to have data at the skills-gender level in each sector. We follow the same procedure to obtain employment levels. Meanwhile, we use the I2D2 database for the Western Balkans because it allows us to analyze the data at the sub-national geographical level. This database enables cross-Balkan comparisons by standardizing representative household surveys conducted separately in each economy. However, given the independence of the data sources, not all the variables are available for all the Western Balkan economies. For example, Serbia and North Macedonia do not collect information about the economic sector from the workers. To overcome this problem, we combine the I2D2 with the WTS database by calculating the aggregated salaries and employment levels for every region and multiplying them by the proportion of wages across the three economic industries calculated at the national level. Additionally, some of the regions in the Western Balkans lack information on the salaries for specific sectors. Therefore, to circumvent this second problem, we input them using a regression model based on the industry, education, and age variables. Finally, we use data from the GRIP to calculate the travel times across the Western Balkans region and the EU countries. This dataset gathers and harmonizes information on all the road infrastructure for over 222 countries worldwide. 1 Table 12 lists all the sub-national regions of the Western Balkans included in the analysis 3 3 Descriptive statistics The working-age population of the Western Balkan economies is around 10.5 million people and is unevenly distributed across the region. While Serbia has a working-age population of over 4.6 million people, Montenegro has around 385,000. These differences in the labor force, combined with the employment rate differences, make the region diverge in terms of its employed population. The distribution of employment across sectors also varies inside the Western Balkan economies. Regions like Bar and Budva in Montenegro concentrate their labor force in the service sector, while cities like Elbasan in Albania do so in agriculture. Moreover, the workers across the Western Balkans region vary in their skill levels. Some of the regions with more high-skilled workers are Belgrade in Serbia, Severoistocen in North Macedonia, and The Federation of Bosnia and Herzegovina in Bosnia and Herzegovina. Figures 14-17 plot the distribution of the employed population inside the Western Balkans region. Compared to the European Union, the Western Balkans region has fewer workers in all economic sectors. Despite the fact that the sector that employs the most population in both regions is services, the share of employment in services relative to total employment is 20% higher in the EU. Table 1 compares the distribution of employment across sectors and gender for the aggregated group of Western Balkan economies and the EU, and table 3 summarizes the distribution of workers across sectors and gender for each of these economies. Table 1: Employment across sectors per region and gender (in thousands) Agriculture & mining Manufacturing Services & others Mean SD Min Max Mean SD Min Max Mean SD Min Max Balkans-females 93.55 106.34 2.71 229.13 78.54 86.96 4.24 217.54 289.88 325.73 75.07 860.08 Balkans males 147.16 145.3 16.03 345.99 164.09 162.67 34.25 444.94 310.2 262.1 81.57 754.1 EU-females 140.94 203.03 2.1 785.9 408.86 571.98 5.3 2617.5 2793.67 3943.47 91.1 16705.2 EU-males 325.36 394.22 4.5 1319.5 1285.4 1856.04 24 8305.7 2329.31 3227.86 104.5 13300.9 The average daily wages also vary significantly across the Western Balkans region, ranging from $39 in Albania to $66 in Montenegro. Moreover, these wages can also differ within each industry. Overall, the highest wages are found in the services industry -for example, in the financial and IT sectors- and the lowest wages are found in the agriculture sector. There are also some disparities in wages inside the Western Balkans region. For example, in Albania, regions like Durres have average wages higher than other parts of the county, like Berat and Vlore. Figures 10-13 plot the distribution of daily wages across the region. 4 It is important to note that wages in the Western Balkans region are substantially lower than in the EU. Table 2 shows that the average wages for male and female workers are lower across the three main economic sectors in the Western Balkans compared to the EU, even after adjusting for nominal differences. These gaps are particularly marked in the manufacturing sector, where the average wage in the Western Balkans only represents 30% of the average wage in the EU. Table 4 disaggregates these differences for the Western Balkan economies and shows that, for example, Montenegro outperforms Albania in terms of wages. Table 2: Daily wages across sectors per region and gender (in international $) Agriculture & mining Manufacturing Services & others Mean SD Min Max Mean SD Min Max Mean SD Min Max Balkans-females 32.15 2.59 30.32 33.98 23.73 1.15 22.91 24.54 36.62 9.95 29.59 43.66 Balkans-males 38.91 7.09 33.9 43.92 29.44 .26 29.26 29.63 38 9.55 31.25 44.76 EU-females 75.16 42.12 7.5 155.24 78.27 42.9 7.58 139.66 80.34 40.2 9.35 143.17 EU-males 89.39 44.28 8.08 155.22 102.35 57.34 9.15 233.07 103.4 52.7 9.91 205.06 Another difference between the two regions is that the premium in salaries for high-skilled workers relative to low-skilled workers in the agriculture sector is higher in the EU than in the Western Balkans. Still, in the manufacturing sector, it is lower in the EU than in the Western Balkans. Finally, wage gaps across gender are much smaller in the EU than in the Western Balkans, especially in the service sector, where the EU has nearly achieved gender parity. Figures 1 and 2 illustrate these differences. Figure 1: Skills Wage Gap (a) Agriculture (b) Manufacturing (c) Services 5 Figure 2: Gender Wage Gap (a) Agriculture (b) Manufacturing (c) Services In terms of infrastructure for trade, the Western Balkans lag significantly behind the European Union. Figure 3 plots the quality of trade and transport-related infrastructure obtained from the Logistics Performance Index database, which evaluates the quality of infrastructure such as ports, railroads, and information technologies. Since 2007, on average, the performance of the Western Balkans has been 30% lower compared to the European Union. Figure 3: Quality of Trade and Transport-related Infrastructure Notes: This figure plots the quality of trade and transport-related infrastructure index obtained from the Logistics Performance Index database. Scores range from 1 to 5, with a higher score representing better performance. 6 4 Model In this section, we describe the quantitative spatial model to understand the market integra- tion effects in the Western Balkan economies. The model includes multiple groups of workers, sectors, and locations. Each location i is endowed with an exogenous level of amenities Bi and capital K ¯ i . There are three different sectors in the economy: agriculture and mining, manufacturing, and services, and four different groups: male and female workers, divided into high and low-skilled groups. The model is based on the new generation of quantitative spatial models (Allen et al., 2015; Galle et al., 2017; Redding and Rossi-Hansberg, 2017). Firms produce varieties in all sectors. Consumers have love of variety, and there are trade frictions. As in a multi-sector Armington trade model, consumption in each sector is a CES aggregate of consumption of the good of each of the i regions, with an elasticity of substitution σs > 1 in sector s. There is a mass of workers in each country L ¯ that can migrate across locations facing iceberg migration costs as in Monte et al. (2018); Tsivanidis (2019). Each worker has one unit of labor that is supplied inelastically. Workers decide their location based on the real wage and amenities in each location and on an idiosyncratic preference parameter. The indirect utility of worker ω from group g that lives in location i and work in sector s is: Big wisg ϵisgω Uisgω = Pig where Big is an amenity parameter, wisg is the wage per efficiency unit of workers from group g in location i and sector s, ϵisgω is the idiosyncratic shock. We assume that ϵisgω has an extreme-value distribution with shape parameter κ and η . Finally, Pig is the CES price index. Look that all workers from different groups have different preferences, since the price index varies by group. Given our Armington assumption, the price index is: 1−ξg ξ g −1 1−ξg Pig = αsg Pisg ∀i, g s 1−σs −σs Pis = Mjs p1 ijs ∀i, s j where pijs is price charged by firms in location j to consumers in i, αsg is a preference parameter that captures how much consumers from group g prefer sector s, ξg , is the elasticity of substitution across sectors, σs is the elasticity of substitution across varieties, and Mjs the total number of firms. 7 Labor supply Workers live in each location i and supply labor to each sector based on an idiosyncratic shock. Then, the labor supply function is: g κ Bisg wisg λisg|i = κg , (4.1) k Bikg wikg where λisg|i is the share of workers from group g that live in i and work in sector s, κg is the labor supply elasticity across sectors, Bisg is an amenity parameter, and wisg is the wage per efficiency unit for workers in group g . The income average in location i and wage index is: ¯ig = y λisg|i wisg s κ κ Wigg = Bikg wikg k ¯ i. On the other hand, the total amount of capital is fixed and given by K Labor demand In each sector, firms produce with a Cobb-Douglas technology using two inputs: labor and capital. The production function is: 1−βs Yjs = Ajs Lβ s js Kjs , (4.2) where Ljs is the CES composite input of workers. In particular: σL σL −1 σL −1 σL Ljs = δgs Ljsg , g where σL is the elasticity of substitution of workers. We assume monopolistic competition, then the price charged by firms in location j to consumers in i is: σs βs 1−βs pijs = τij wjs (1 + tjsL )βs rj (1 + tjsK )1−βs /Ajs , σs − 1 8 where τij is an iceberg trade cost, t′ s are taxes, rj is the price of capital in location j , and Ajs is TFP, and wjs is the CES labor input cost: 1−σL σL −1 1−σL wjs = δgs wjsg . g Similarly, the share of total labor payments that firms pay to group g is: σL −1 1−σL δsg wjsg ˜jsg = δ (4.3) σL −1 1−σL h δsh wjsh The free entry condition implies that the number of firms in each location and sector is: ˜ βs K 1−βs /σs Fs , Mjs = βL js js where Fs is a fixed cost of production. Expenditure shares Based on the CES properties, the expenditure share of variety j in sector s for consumers from group g in i is: 1−σs Mjs pijs πijs|s = 1−σs , (4.4) l Mls pils and the expenditure share in each sector is: g ξ −1 1−ξg αsg Pis πisg = g ξ −1 1−ξg , (4.5) k αkg Pik Capital owners The capital is owned by all workers. In particular, we have that each worker receives an additional per capita income because of capital. This means: ¯i ri K i = r Lig g 9 Then, the total income of workers from group g that live in location i is: ¯ig + r y ¯i Migration We assume that there can be migration across regions based on an idiosyncratic shock. Then, the share of workers that live in location i from group g that were living in j after a shock is given by: ηg −ηg ri Wig +¯ Big dji Pig λjig|j = ηg , −ηg rl Wlg +¯ l Blg djl Plg where ηg represents the migration elasticity for each group g , and dji is an iceberg migration cost. Then, the share of workers that live in i is: λig = λjig|i λ′jg , j where λ′jg correspond to the share of workers from group g living in j before the shock. Aggregate welfare Based on the properties of the extreme value type shocks, the ex-ante average welfare measure of group g is: 1 ηg ηg −ηg ¯l Wlg + r Ug = Blg djl . l,j Plg We will be able to analyze the distributional effects of any shock by comparing Ug across groups. 4.1 General equilibrium The general equilibrium is defined by the following conditions: 10 • Labor market clears in each sector and location: ˜jsg wjsg Ljsg = βs δ yig Lig + r πijs|s πisg (¯ ¯i Lig ) i,g ¯g Lisg = L i,s • Capital market clears in each location: ri Kis = (1 − βs ) ¯)(¯ πijs|s πisg (1 + t ¯i Lig ) yig Lig + r s i,g ¯i Kis = K s • Migration decisions: ηg −ηg ri Wig +¯ Big dji Pig λjig = ηg −ηg rl Wlg +¯ l Blg djl Plg 4.2 Model Calibration To calibrate the model, we take most of the parameters from the literature since we do not have enough power to estimate them. In particular, we need three different sources of data: • Employment and wages • Expenditure shares • Elasticities 4.3 Recovering Economic Fundamentals To recover the baseline equilibrium, we invert the model and find the economy fundamentals that match exactly the data with the model equations. We proceed in four different steps: 11 1. With data on wages and employment, we can recover the relative productivity δsg for each group of workers from the CES production function. In particular, we can solve the following system of equations up to scale for each location and sector: σL −1 1−σL wjsg Ljsg δsg wjsg = σL −1 1−σL h wjsh Ljsh h δsh wjsh 2. With data on wages and the number of workers, we can recover the amenity/produc- tivity distribution of the sectoral choices. In particular, we can recover the vector Bisg up to scale by matching the sectoral shares in each location that we observe in the data with the model: g κ Bisg wisg λdata isg |i = κg , (4.6) k Bikg wikg 3. After recovering δisg and Bisg , with data on wages, employment, and capital, we can construct the labor supply and recover the productivity distribution Ais up to scale by matching the labor supply with the labor demand. We can recover the productivity vector by matching the following system of equations: ˜jsg wjsg Ljsg = βs δ yig Lig + r πijs|s πisg (¯ ¯i Lig ) i,g 4. Finally, with the employment data, wages, sectoral amenities, aggregate price indices, migration costs, and the return to capital, we can recover the amenity distribution of each location and group Big by matching the population share we observe in the data with the ones implied by the model. We solve the following system of equations: ηg ri Wig +¯ Big Pig λdata ig = ηg , rl Wlg +¯ l Blg Plg After we recover these four vectors, we match the initial equilibrium from the model with the employment shares and wages that we observe in the data, and we can simu- late the policy counterfactuals by comparing the equilibrium after the shock with the baseline equilibrium. 12 5 Counterfactual analysis In order to carry out the counterfactual analysis, we use the quantitative spatial model explained in the previous section and apply it to market integration policies in the region. More specifically, we run different simulations to analyze three interventions: 1. A reduction in waiting time at the border of three hours, resulting from implementing the National Single Window and National Trade Certificates2 investments in border management systems, and upgrading of Border Crossing Points across the Western Balkans region 2. The accession of the Western Balkan economies to the EU 3. The provision of road infrastructure Improving the trade facilitation infrastructure of the Western Balkan economies is mod- eled as a reduction in iceberg trade and migration costs, as customary in trade models. The iceberg trade costs measure the number of units of goods that are shipped from a given loca- tion (region within a Western Balkan economy) so that exactly one unit of the shipped good arrives at the destination. With migration, they capture the cost of relocating to another city or region to receive one unit of utility at the destination. Before proceeding to the discussion of the results, the reader is warned that there are two main limitations to these models: they are static and, as such, do not consider forward-looking decisions or expectations, and they assume full employment. 5.1 Discussion of the counterfactual analysis simulating the effects of trade facilitation reforms The first set of counterfactuals simulates the effect of a decrease in waiting times at the border that results from introducing trade facilitation reforms such as the National Single Window and National Trade Certificates, investments in border management systems, and upgrading of Border Crossing Points. This intervention has the potential to significantly improve the efficiency at the borders by reducing administrative burdens and delays and improving the speed of customs clearance. Since it improves border efficiency, it can be modeled as a reduction in the iceberg trade costs, and thus, the associated welfare gains can be estimated. Our analysis focuses on three sets of scenarios: one in which each economy 2 These are trade facilitation measures that allow for the submission and processing of trade-related documents through a single entry point. 13 does the reforms independently of the others (independent policies), a second scenario in which all Western Balkan economies coordinate and implement the reforms at the same time (cross-economy coordination), and a third scenario in which economies also gain EU accession (cross-economy coordination + Full EU accession).3 According to the Trading across Borders indicators, measured by The World Bank as part of the Doing Business Report, the time in hours to import goods and fulfill the border and documentary compliance is three hours (28%) higher in the six Western Balkan economies than in the high-income OECD countries. Therefore, on the counterfactual, we assume that an economy that implements the package of trade facilitation reforms mentioned above will be able to reduce by three hours the waiting time at the border. As shown by the model estimates, this intervention would be similar to lowering tariffs by around 2%. Figure 4 plots the aggregate effects of reducing border-crossing times. On average, welfare (real income) increases between 1.5%-3.0% in the Western Balkans in the long run. The most significant result is that if there is coordination among them, welfare increases by 8% more relative to the counterfactual in which each economy implements the reforms independently. Similarly, if economies are part of the EU, the gains are amplified by an additional 6%. Thus, the simulation reveals three important key messages. Firstly, the impact of these trade facilitation reforms on welfare is positive and significant in all Western Balkan economies. Second, if economies adopt a coordinated approach to the trade facilitation re- forms, the benefits are 8% larger. Additionally, if the Western Balkan economies become EU members, and assuming that this leads to a reduction in trade costs of 4% and of migration costs of 3% as suggested by Caliendo et al. (2021), the gains associated with the reduction of waiting times at the border are even greater, leading to welfare gains approximately 6% larger than relative to the reforms implemented under a non-membership scenario. There- fore, a clear policy implication is that collaboration and coordination of the reform agenda and a clear path to EU accession will maximize the welfare gains from these interventions. 3 The results of the counterfactuals depend on the initial trade-linkages, and migration shares. As shown by Hulten (1978), the effect of a reduction in trade costs depends on the time-saving formula. In particular, it affects more the regions that have greater trade-linkages with the regions in which the trade cost decreaseS. In a series of recent papers, Baqaee and Farhi (2020) have looked at reallocation effects originating from distortions and second-order effects. 14 Figure 4: Aggregate Welfare Gains: Reducing Waiting Times at the Border by Three Hours Notes: This figure plots the distributional effects across regions in the Western Balkans after implementing the trade facilitation reforms that reduce border crossing times by three hours. The blue bar corresponds to the counterfactual in which each economy implements the reforms independently, the red bar when economies coordinate with each other, and the orange bar when economies coordinate with each other and are also part of the European Union. When we analyze the effects for each one of the economies, we find that Albania, North Macedonia, and Montenegro are the ones that gain the most. For instance, Albania would obtain gains of around 2.5%, and North Macedonia, and Montenegro would gain slightly below 3%. This result is a combination of two factors. On the one hand, these economies must cross more borders to reach their consumer markets. On the other hand, their ability to take advantage of larger economies of scale as a result of improving their cross-border integration is greater compared to larger economies such as Serbia. We also observe that despite posting lower gains than their southern neighbors, Serbia, Bosnia & Herzegovina, and Kosovo also post significant welfare gains from improving efficiency at the border. Serbia posts gains of around 2%, Bosnia & Herzegovina of around 1.5%, and Kosovo of around 2.1%. Recent economic literature has highlighted that gains differ across locations and social groups. To capture the implications of this insight for the reforms in the Western Balkans, 15 figure 5 analyzes the distributional impacts of the trade facilitation reforms discussed above across locations within each economy. Specifically, the figure presents a heat map that dis- plays the percentual change in welfare resulting from reduced border time. The left-side figure depicts the welfare effects when each economy implements the reforms independently. Conversely, the center and right-side figures show the impact of the reforms when economies coordinate with each other. Panel b shows the effect of the coordinated implementation of the trade facilitation reforms under the status-quo scenario where the Western Balkans are not fully integrated with the EU, while panel c shows the impacts that the trade facilitation reforms would have if the Western Balkans were integrated with the EU. The results demon- strate that coordinated policy implementation generates higher welfare gains for all regions than independent implementation. More importantly, implementing the trade facilitation reforms would have 6% higher returns if the Western Balkans were integrated into the EU. Across all scenarios, the gains are unevenly distributed. Unsurprisingly, border regions experience the highest gains, while interior regions obtain positive but less substantial gains. Overall, these results suggest that trade facilitation policies can have significant distributional impacts and that policymakers should consider accompanying measures, such as place-based policies, to ensure that the gains are fairly shared across locations within the economy. Figure 5: Distributional Effects Across Regions: Reducing Waiting Times at the Border by Three Hours (a) Independent policy (b) Cross-economy coordination (c) Cross-economy coordination + Full EU accession Notes: This figure plots the distributional effects across regions in the Western Balkans after implementing the trade facilitation reforms that reduce border crossing times by three hours. Panel a shows the sum of the percentual change in welfare when economies implement the reforms independently. Panel b plots the results when economies coordinate among themselves to implement the reforms jointly. Panel c shows the effect of the trade facilitation reforms when economies implement it jointly and are part of the EU. Border regions experience the largest gains of the trade facilitation reforms implementation. 16 5.2 Discussion of the counterfactual analysis simulating the effects of trade integration with the EU Our second set of counterfactuals involves simulating the integration process between the Western Balkans and the European Union. Since accession to the European Union involves benefiting from freedom of movement for goods, services, people, and capital, we can safely assume that EU accession will lead to a decrease in the costs of trade and migration for Western Balkan economies. Specifically, we reduce the iceberg trade costs between the Western Balkan regions and EU countries by 4%, and the iceberg migration costs by 3%.4 The assumptions on the size of the reduction in trade and migration costs are based on the work of Caliendo et al. (2021) who study the enlargement of the European Union in the 2000s using a dynamic trade model in which factors of production, goods, and services can move faster within and across national borders. We conduct three counterfactual simulations. The first two assume a reduction of migration costs and trade costs independently from one another. In the third simulation, both trade and migration costs are reduced together. This exercise allows quantifying the gains from coordinating trade and labor mobility policies during market integration. The main results of this simulation are shown in Figure 6. They indicate that the reduction in trade costs associated with EU accession generates relatively even gains across and within economies in the Western Balkans region. Table 7 in the Annex shows that all Western Balkan economies experience gains, ranging from around 2.8% to 5.04%. These gains from EU accession are largely attributable to reductions in the price index and increased access to new goods. This effect is consistent and evenly distributed not only across regions but also across income groups. By contrast, we observe more substantial heterogeneity in the gains from the migration channel, with Albania and Montenegro experiencing higher gains compared to Bosnia & Herzegovina and Serbia. Specifically, the gains of the former economies are larger than 1.5%, while in the case of the latter, the effects are close to 0%. When we consider the synergies of combining the two cost reductions -trade and migration- our findings suggest that implementing the policies simultaneously may be feasible, but the additional gains from the interaction of the policies are relatively small compared to implementing the policies independently since the gains only increase, on average, by 1% when the two policies are combined. Finally, in terms of the distributional effects across groups, we find very similar effects between the different groups in our analysis, suggesting that accession to the EU has the important advantage of spreading the gains equitably. 4 Given that tariffs between the European Union and the Balkans are already at zero, we modeled the impact of EU accession as a reduction in the iceberg trade cost. 17 Figure 6: Welfare Effects: Simulation of joining the EU Notes: This figure plots the aggregate welfare gains for each West Balkan economy of the counterfactual in which they join the European Union. We simulate an iceberg trade cost reduction of 4% between the West Balkan economies and the European Union and a 3% reduction in iceberg migration costs. One important issue when discussing EU accession is also the potential impacts on cur- rent EU countries. Table 8 in the Annex illustrates the outcomes of our analysis for all current EU member countries. When evaluating the effects on existing EU member states, we found mixed results for both the trade and migration cost reduction simulations. The migration channel resulted in negligible gains for all EU countries, as the Western Balkan economies are too small to impact the EU’s labor market outcomes significantly. Only Croatia would experience a slight decrease in wages due to an increase in the labor supply. Conversely, the trade channel would have a positive impact on some neighboring countries of the Western Balkans region. Countries such as Croatia, Bulgaria, Romania, and Hungary would experience gains of approximately 0.2% due to an expansion in the variety of goods sourced from the Western Balkans. This is because these countries already trade intensely with the Western Balkans, making them more exposed to the positive market integration shock stemming from the Western Balkans’ access to the EU. However, taken together, our 18 findings indicate that both the migration and trade channels would have minimal impact on EU countries, as the Western Balkan economies are too small to impact the EU’s partners significantly. 5.3 Discussion of the counterfactual analysis simulating the effects of improvements in primary roads infrastructure The final set of counterfactuals involves analyzing the impact of new primary roads that connect regions within and across economies. Our goal is to simulate travel times both with and without the roads that have been built over the last decade and to calculate the welfare and productivity gains of these roads relative to a situation where the government did not invest in infrastructure. Additionally, we investigate the impact of roads on the Western Balkans’ integration with the European Union to measure the interaction effects of infrastructure development and EU integration. Figure 7 shows the map of the various primary roads we study, highlighting how governments in this region have invested in primary roads over the past years to facilitate more efficient goods movement. Figure 7: Road Infrastructure Notes: This figure plots the roads that we study in our second counterfactual. We calculate travel times with and without the primary road network to estimate the welfare effects of new roads that connect regions within and across economies. Our main findings suggest that roads generate important welfare and productivity gains for most regions in the Western Balkans. Still, roads can have a higher economic impact if the 19 Western Balkan economies are part of the EU. Figure 8 presents the welfare impact across the Western Balkan economies. We find significant welfare impacts overall. For instance, in the current situation, roads have generated an increase in real income of around 5%. However, roads have a bigger impact when these economies are part of the EU, boosting the increase in real income by 7%. The intuition for this result is that if the Western Balkan economies were part of the EU, they could take advantage of export opportunities and increase their productivity due to external economies of scale. When we analyze the effect for each economy, we find that those who gained the most from the road infrastructure investments of the past decade are North Macedonia, Serbia, and Kosovo. The boost from joining the EU is small for them. Intuitively this is explained by the fact that roads are already generating very high positive welfare gains; in comparison joining the EU has a marginal but still statistically significant effect. Turning to the rest of the Western Balkans, Albania, and Bosnia & Herzegovina obtained gains above 5% from road infrastructure investments, and accession to the EU would amplify these positive effects very significantly: by more than 50%. Finally, in the case of Montenegro, the gains are slightly below 5% in terms of real income. Note that in all the cases, the effects of road infrastructure investments are amplified by EU membership since the expansion to other markets is positively associated with more trade participation with the EU. We also study the distributional effects of roads by analyzing the effects on welfare be- tween males and females and low and high-skilled workers. Overall, the high-skilled workers (both male and female workers) benefit slightly more than lower-skilled workers. This pattern is very consistent across economies. Figure 8 reports the welfare gains of the two counter- factuals by the different groups. We can observe from the figure that high-skilled workers would generally obtain higher gains. However, if these economies are part of the EU, the distributional impacts of road infrastructure are less detrimental for low-skilled workers. In particular, the ratio of gains of low-skilled workers relative to those of high-skilled workers increases by 20% when the Western Balkans are integrated into the EU. This result is be- cause economies of scale benefit all groups of workers, as they become more productive if the sector’s size increases. Hence, the results reinforce the importance of Western Balkan economies joining the EU. 20 Figure 8: Distributional Effects Across Regions: Infrastructure (a) Effect of roads (b) Effect of roads under the EU Notes: This figure plots the distributional effects across different skilled groups of road infrastructure. We calculate travel times with and without the primary roads of the last decade to understand the welfare effect. Panel a reports the results when we study the impact of roads under the current situation, and panel b in the case in which the Western Balkan economies join the EU. We find that the high-skilled workers would benefit more from roads. 6 Conclusion In times of geopolitical tensions and reduced trust in outward-looking growth, it is important to understand the potential benefits of future market integration policies in the Western Balkans. This paper quantifies the economic and social benefits of regional economic integration in the Western Balkans in the current context with a view to guiding decision makers in identifying policy options that can contribute to a sound and sustainable economic recovery. Specifically, we look at the economic impacts of different market integration interventions in the Western Balkans. We have developed a quantitative general equilibrium model with various features. For instance, the model has multiple locations and sectors, different groups of workers, trade linkages between regions and countries, and external economies of scale. To calibrate the model, we collected information from several sources in which we computed employment and average wages across the different regions in the Western Balkans and the European Union. This question is fundamental for the region as market integration and trade openness can spur economic growth and reduce the spatial mismatch in the area. Some of these fragmented 21 economies can take advantage of external economies of scale, increasing their productivity and providing better jobs to their populations. For instance, recent statistics, such as the road infrastructure index and the export market penetration index, suggest that the region is behind similar regions in other parts of the world. Then, it is critical to have a complete assessment of the aggregate and distributional impacts of these market integration policies. To do that, we match the model to the data by recovering economic fundamentals such as total factor productivity measures and amenities. Then, we simulated in the model three different interventions: 1. Implementation of trade facilitation reforms 2. Accession to the EU 3. New infrastructure The results suggest that coordination among economies is imperative for some of these policies to be successful. For example, if there is coordination, the trade facilitation reform can generate 8% additional gains relative to a counterfactual in which each economy does it independently. Similarly, if these economies are part of the EU, the gains are amplified by an additional 6%. Both results indicate the importance of synergies from combining different interventions. Moreover, accession of the Western Balkans to the EU can positively impact some neighboring countries due to trade linkages. Countries such as Romania, Hungary, or Bulgaria can experience gains of around 0.2% because they will benefit more from trade with the Western Balkans. Thus, Governments from these countries need to coordinate with the Western Balkans to take advantage of these potential opportunities. Finally, regarding the impact of infrastructure, we find that roads have a substantial effect on trade linkages and on welfare. However, roads seem to have a more significant impact when these economies are already part of the EU, suggesting that there are synergies in implementing the two interventions together and that governments can spur economic growth by combining the two policies. In conclusion, as the world navigates an increasingly uncertain trade landscape, it is more important than ever to preserve and nurture international integration as a key driver of economic growth, job creation, and poverty reduction. 22 References Allen, T., Arkolakis, C., and Li, X. (2015). On the Existence and Uniqueness of Trade Equilibria. Working Paper, Yale University. Baqaee, D. R. and Farhi, E. (2020). Productivity and Misallocation in General Equilibrium. The Quarterly Journal of Economics, 135(1):105–163. Caliendo, L., Opromolla, L. D., Parro, F., and Sforza, A. (2021). Goods and Factor Market Integration: A Quantitative Assessment of the EU Enlargement. Journal of Political Economy, 129(12):3491–3545. Galle, S., Rodríguez-Clare, A., and Yi, M. (2017). Slicing the Pie: Quantifying the Aggregate and Distributional Effects of Trade. NBER Working Papers 23737, National Bureau of Economic Research, Inc. Hulten, C. R. (1978). Growth Accounting with Intermediate Inputs. 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Working Paper, UC Berkeley. 23 Annex A Additional Tables Table 3: Employment Across Sectors and Gender at the Country and/or Economy Level (in thousands) Country and/or Economy Gender Agriculture & mining Manufacturing Services Albania Female 229.13 85.50 231.35 Albania Male 250.14 136.48 309.11 Kosovo Female 2.71 4.24 75.07 Kosovo Male 26.09 72.90 167.57 Montenegro Female 9.42 5.36 88.38 Montenegro Male 16.03 34.25 81.57 North Macedonia Female 39.95 80.03 194.51 North Macedonia Male 97.57 131.88 238.64 Serbia Female 186.53 217.54 860.08 Serbia Male 345.99 444.94 754.10 EU Female 140.94 408.86 2793.67 EU Male 325.36 1285.40 2329.31 Table 4: Wages Across Gender at the Country and/or Economy Level (in international $) Country and/or Economy Gender Wages Albania Female 37.2 Albania Male 41.04 Bosnia and Herzegovina Female 39.57 Bosnia and Herzegovina Male 45.08 Kosovo Total 40.73 Montenegro Female 63.76 Montenegro Male 70.12 North Macedonia Female 50.47 North Macedonia Male 55.74 Serbia Female 36.01 Serbia Male 40.27 EU Female 78.43 EU Male 99.48 24 Table 5: Welfare Effects of Reducing Waiting Times at the Border by Three Hours for Western Balkan economies (%) Cross-country coordination + Country and/or Economy Region Independent policy Cross-country coordination Full EU accession Albania Berat 1.60 1.73 1.89 Albania Diber 2.74 2.96 3.18 Albania Durres 2.69 2.91 3.13 Albania Elbasan 2.69 2.91 3.15 Albania Fier 1.70 1.84 2.01 Albania Gjirokaster 1.39 1.50 1.64 Albania Korce 2.71 2.93 3.15 Albania Kukes 2.30 2.48 2.68 Albania Lezhe 2.66 2.87 3.10 Albania Shkoder 2.45 2.65 2.86 Albania Tirane 2.71 2.93 3.17 Albania Vlore 1.25 1.35 1.49 Bosnia and Herzegovina Brcko 1.40 1.51 1.61 Bosnia and Herzegovina FBiH 1.13 1.22 1.30 Bosnia and Herzegovina Republika Srpska 2.05 2.21 2.34 Kosovo Ferizaj 1.85 2.00 2.15 Kosovo Gjakova 1.89 2.05 2.20 Kosovo Gjilan 1.95 2.11 2.26 Kosovo Mitrovica 1.43 1.54 1.66 Kosovo Peja 1.69 1.82 1.96 Kosovo Prishtina 1.65 1.78 1.92 Kosovo Prizren 2.03 2.19 2.36 Montenegro Andrijevica 2.22 2.40 2.57 Montenegro Bar 3.05 3.30 3.54 Montenegro Berane 2.31 2.50 2.68 Montenegro Bijelo Polje 2.40 2.59 2.79 Montenegro Budva 2.94 3.18 3.40 Montenegro Cetinje 2.86 3.10 3.32 Montenegro Danilovgrad 2.60 2.81 2.99 Montenegro Herceg Novi 3.07 3.32 3.56 Montenegro Kolasin 2.41 2.61 2.78 Montenegro Kotor 3.02 3.26 3.49 Montenegro Mojkovac 2.56 2.77 2.96 Montenegro Niksic 2.63 2.85 3.05 Montenegro Plav 2.14 2.32 2.48 Montenegro Pljevlja 3.18 3.43 3.63 Montenegro Pluzine 2.87 3.11 3.30 Montenegro Podgorica 2.59 2.80 3.02 Montenegro Rozaje 2.22 2.40 2.57 Montenegro Savnik 2.42 2.62 2.79 Montenegro Tivat 2.98 3.22 3.43 Montenegro Ulcinij 3.28 3.54 3.78 Montenegro Zabljak 2.50 2.70 2.86 North Macedonia Istocen 2.34 2.53 2.68 North Macedonia Jugoistocen 2.17 2.34 2.47 North Macedonia Jugozapaden 2.30 2.48 2.65 North Macedonia Pelagoniski 2.45 2.65 2.81 North Macedonia Poloski 2.50 2.71 2.89 North Macedonia Severoistocen 3.03 3.28 3.47 North Macedonia Skopski 3.05 3.30 3.48 North Macedonia Vardarski 2.79 3.02 3.16 Serbia Belgrade 2.06 2.23 2.38 Serbia Central Serbia 1.50 1.62 1.73 Serbia Vojvodina 2.23 2.41 2.57 25 Table 6: Welfare effects of Reducing Waiting Times at the Border by Three Hours for EU Countries (%) Cross-country coordination + Country and/or Economy Independent policy Cross-country coordination Full EU accession Austria 0.04 0.04 0.04 Belgium 0.09 0.10 0.10 Bulgaria 0.01 0.01 0.01 Croatia 1.13 1.22 1.23 Cyprus 0.00 0.00 0.00 Czechia 0.02 0.02 0.02 Denmark –0.00 –0.00 –0.00 Estonia 0.68 0.73 0.73 Finland 0.04 0.04 0.04 France 0.01 0.01 0.01 Germany 0.01 0.01 0.01 Greece 0.27 0.30 0.30 Hungary 0.26 0.28 0.28 Ireland 0.00 0.00 0.00 Italy 0.02 0.02 0.02 Latvia 0.28 0.31 0.31 Lithuania 0.11 0.12 0.12 Luxembourg 0.08 0.08 0.08 Malta 0.00 0.00 0.00 Netherlands 0.01 0.01 0.01 Poland 0.01 0.01 0.01 Portugal 0.10 0.11 0.11 Romania –0.03 –0.04 –0.04 Slovak Republic 0.07 0.07 0.08 Slovenia 0.13 0.14 0.14 Spain –0.00 –0.00 –0.00 Sweden 0.02 0.02 0.02 26 Table 7: Welfare Effects of EU Integration for Western Balkan Economies (%) Country and/or Economy Region Migration channel Trade channel Migration and trade channel Albania Berat 1.34 4.47 5.87 Albania Diber 1.28 3.98 5.31 Albania Durres 1.50 3.99 5.55 Albania Elbasan 1.54 4.27 5.87 Albania Fier 1.60 4.26 5.93 Albania Gjirokaster 1.29 4.68 6.02 Albania Korce 1.28 4.70 6.03 Albania Kukes 1.40 3.57 5.03 Albania Lezhe 1.60 3.86 5.53 Albania Shkoder 1.73 3.72 5.52 Albania Tirane 2.03 4.05 6.17 Albania Vlore 1.23 4.66 5.94 Bosnia and Herzegovina Brcko –0.13 4.34 4.19 Bosnia and Herzegovina FBiH 0.11 4.97 5.08 Bosnia and Herzegovina Republika Srpska 0.13 4.74 4.88 Kosovo Ferizaj 0.96 3.52 4.51 Kosovo Gjakova 1.22 3.27 4.54 Kosovo Gjilan 0.93 3.38 4.34 Kosovo Mitrovica 0.85 3.04 3.91 Kosovo Peja 1.30 3.15 4.49 Kosovo Prishtina 1.03 3.20 4.27 Kosovo Prizren 1.31 3.35 4.71 Montenegro Andrijevica 1.46 3.44 4.94 Montenegro Bar 2.06 3.76 5.90 Montenegro Berane 1.78 3.42 5.26 Montenegro Bijelo Polje 2.26 3.44 5.78 Montenegro Budva 1.55 3.78 5.40 Montenegro Cetinje 1.96 3.88 5.92 Montenegro Danilovgrad 1.31 3.79 5.14 Montenegro Herceg Novi 2.18 3.99 6.25 Montenegro Kolasin 1.40 3.58 5.04 Montenegro Kotor 1.72 3.92 5.70 Montenegro Mojkovac 1.70 3.56 5.32 Montenegro Niksic 2.10 3.84 6.01 Montenegro Plav 1.41 3.39 4.85 Montenegro Pljevlja 1.65 3.76 5.47 Montenegro Pluzine 1.06 3.85 4.96 Montenegro Podgorica 2.38 3.62 6.09 Montenegro Rozaje 1.45 3.27 4.77 Montenegro Savnik 1.51 3.70 5.26 Montenegro Tivat 1.52 3.90 5.48 Montenegro Ulcinij 1.39 3.82 5.27 Montenegro Zabljak 1.14 3.64 4.82 North Macedonia Istocen 0.65 4.72 5.39 North Macedonia Jugoistocen 0.53 5.05 5.60 North Macedonia Jugozapaden 0.93 4.44 5.40 North Macedonia Pelagoniski 0.93 4.67 5.65 North Macedonia Poloski 1.27 4.24 5.56 North Macedonia Severoistocen 1.05 4.41 5.51 North Macedonia Skopski 0.94 4.38 5.37 North Macedonia Vardarski –0.18 4.60 4.41 Serbia Belgrade 0.27 3.64 3.92 Serbia Central Serbia 0.22 2.86 3.09 Serbia Vojvodina 0.42 3.89 4.31 27 Table 8: Welfare Effects of EU Integration for EU Countries (%) Country and/or Economy Migration channel Trade channel Migration and trade channel Austria 0.00 0.02 0.02 Belgium 0.00 0.00 0.00 Bulgaria 0.00 0.14 0.14 Croatia –0.01 0.26 0.25 Cyprus 0.00 0.00 0.00 Czechia 0.00 0.01 0.01 Denmark 0.00 0.00 0.00 Estonia 0.00 0.00 0.00 Finland 0.00 0.00 0.00 France 0.00 0.00 0.00 Germany 0.00 0.00 0.00 Greece –0.00 0.09 0.09 Hungary –0.00 0.17 0.17 Ireland 0.00 0.00 0.00 Italy 0.00 0.00 0.00 Latvia 0.00 0.00 0.00 Lithuania 0.00 0.00 0.00 Luxembourg 0.00 0.00 0.00 Malta 0.00 0.00 0.00 Netherlands 0.00 0.00 0.00 Poland 0.00 0.00 0.00 Portugal 0.00 0.00 0.00 Romania 0.00 0.22 0.22 Slovak Republic 0.00 0.05 0.05 Slovenia 0.00 0.07 0.08 Spain 0.00 0.00 0.00 Sweden 0.00 0.00 0.00 28 Table 9: Welfare Effects of Roads Infrastructure Improvements for Western Balkan Economies (%) Country and/or Economy Region Roads Roads + Full EU accession Albania Berat 2.48 5.66 Albania Diber 3.40 6.05 Albania Durres 3.40 6.05 Albania Elbasan 2.95 5.89 Albania Fier 3.29 6.16 Albania Gjirokaster 1.94 5.39 Albania Korce 2.48 5.85 Albania Kukes 3.32 5.69 Albania Lezhe 2.92 5.57 Albania Shkoder 2.01 4.72 Albania Tirane 3.16 5.90 Albania Vlore 2.29 5.66 Bosnia and Herzegovina Brcko 8.53 10.51 Bosnia and Herzegovina Fbih 1.65 5.36 Bosnia and Herzegovina Republika Srpska 1.78 5.30 Kosovo Ferizaj 8.92 10.26 Kosovo Gjakova 6.26 7.89 Kosovo Gjilan 9.18 10.37 Kosovo Mitrovica 10.24 11.01 Kosovo Peja 7.51 8.84 Kosovo Prishtina 9.58 10.58 Kosovo Prizren 7.41 8.90 Montenegro Andrijevica 2.73 5.09 Montenegro Bar 1.33 4.19 Montenegro Berane 3.14 5.42 Montenegro Bijelo Polje 2.25 4.71 Montenegro Budva 1.06 3.99 Montenegro Cetinje 0.77 3.82 Montenegro Danilovgrad 0.77 3.75 Montenegro Herceg Novi 0.62 3.78 Montenegro Kolasin 1.33 4.05 Montenegro Kotor 0.72 3.81 Montenegro Mojkovac 1.34 4.04 Montenegro Niksic 0.44 3.52 Montenegro Plav 3.53 5.73 Montenegro Pljevlja 0.16 3.24 Montenegro Pluzine 1.26 4.20 Montenegro Podgorica 1.45 4.19 Montenegro Rozaje 4.41 6.36 Montenegro Savnik 0.81 3.71 Montenegro Tivat 0.76 3.82 Montenegro Ulcinij 1.42 4.31 Montenegro Zabljak 0.85 3.70 North Macedonia Istocen 15.57 16.47 North Macedonia Jugoistocen 12.94 14.57 North Macedonia Jugozapaden 9.47 11.35 North Macedonia Pelagoniski 11.86 13.43 North Macedonia Poloski 10.73 12.24 North Macedonia Severoistocen 19.51 19.41 North Macedonia Skopski 17.75 17.98 North Macedonia Vardarski 18.07 18.40 Serbia Belgrade 15.28 15.48 Serbia Central Serbia 15.30 15.00 29 Table 10: Welfare Effects of Roads Infrastructure Improvements for EU countries (%) Country and/or Economy Roads Roads + Full EU accession Austria 0.09 0.09 Belgium 0.00 0.00 Bulgaria 0.89 0.87 Croatia 1.16 1.20 Cyprus –0.00 –0.00 Czechia 0.08 0.08 Denmark 0.01 0.01 Estonia 0.00 0.00 Finland –0.00 –0.00 France 0.00 0.00 Germany 0.00 0.00 Greece 0.91 0.84 Hungary 0.83 0.85 Ireland –0.00 –0.00 Italy 0.03 0.03 Latvia 0.00 0.00 Lithuania 0.00 0.00 Luxembourg 0.00 0.00 Malta –0.00 –0.00 Netherlands 0.00 0.00 Poland 0.01 0.01 Portugal 0.00 0.00 Romania –0.11 0.09 Slovak Republic 0.22 0.23 Slovenia 0.48 0.47 Spain 0.00 0.00 Sweden –0.00 –0.00 30 Table 11: Effect of the Simulation on Real Income by Country and/or Economy (%) Country and/or Economy TFR TFR-Coord EU (mig channel) EU (trade channel) EU (mig+trade channel) Roads Roads+EU Albania 0.75 0.84 1.48 4.18 5.73 2.80 5.72 Bosnia and Herzegovina 0.37 0.42 0.04 4.68 4.72 3.99 7.06 Kosovo 0.40 0.45 0.77 4.56 5.36 8.44 9.70 Montenegro 1.23 1.39 1.67 3.68 5.41 1.48 4.26 North Macedonia 1.03 1.16 0.30 3.46 3.77 14.49 15.48 Serbia 0.30 0.34 1.08 3.27 4.40 13.28 13.76 31 Table 12: List of Regions in the Western Balkan Economies Region Country and/or Economy Berat Albania Diber Albania Durres Albania Elbasan Albania Fier Albania Gjirokaster Albania Korce Albania Kukes Albania Lezhe Albania Shkoder Albania Tirane Albania Vlore Albania Brcko Bosnia and Herzegovina FBiH Bosnia and Herzegovina Republika Srpska Bosnia and Herzegovina Ferizaj Kosovo Gjakova Kosovo Gjilan Kosovo Mitrovica Kosovo Peja Kosovo Prishtina Kosovo Prizren Kosovo Andrijevica Montenegro Bar Montenegro Berane Montenegro Bijelo Polje Montenegro Budva Montenegro Cetinje Montenegro Danilovgrad Montenegro Herceg Novi Montenegro Kolasin Montenegro Kotor Montenegro Mojkovac Montenegro Niksic Montenegro Plav Montenegro Pljevlja Montenegro Pluzine Montenegro Podgorica Montenegro Rozaje Montenegro Savnik Montenegro Tivat Montenegro Ulcinij Montenegro Zabljak Montenegro Istocen North Macedonia Jugoistocen North Macedonia Jugozapaden North Macedonia Pelagoniski North Macedonia Poloski North Macedonia Severoistocen North Macedonia Skopski North Macedonia Vardarski North Macedonia Belgrade Serbia Central Serbia Serbia Vojvodina Serbia 32 B Additional Figures Figure 9: Aggregate Welfare Gains: Economic Effects of Reducing Waiting Times at the Border by Three Hours and of Infrastrcuture Notes: This figure compares the aggregate welfare gains of the reduction in border crossing waiting times from trade facilitation measures - when they are implemented independently and coordinately - with the effect of improvements in the primary road infrastructure. Figure 10: Wages for Skilled Males (log) (a) Agriculture (b) Manufacturing (c) Services 33 Figure 11: Wages for Low-skilled Males (log) (a) Agriculture (b) Manufacturing (c) Services Figure 12: Wages for Skilled Females (log) (a) Agriculture (b) Manufacturing (c) Services Figure 13: Wages for Low-skilled Females (log) (a) Agriculture (b) Manufacturing (c) Services 34 Figure 14: Number of Skilled Male Workers (a) Agriculture (b) Manufacturing (c) Services Figure 15: Number of low-skilled male workers (a) Agriculture (b) Manufacturing (c) Services Figure 16: Number of Skilled Female Workers (a) Agriculture (b) Manufacturing (c) Services 35 Figure 17: Number of low-skilled female workers (a) Agriculture (b) Manufacturing (c) Services 36