Near-shoring is gaining traction in the Western Balkans, driven by geopolitical shifts and regulatory pressures to reduce carbon emissions. Relocating factories from Asia to Eastern Europe remains unlikely, owing to high costs, but Western firms are increasingly opting for Eastern Europe over Asia for new investments. Interestingly, Asian companies are also investing in the region to position themselves closer to the EU market. The recent shift to the right in global politics is likely to influence these trends in complex ways, but the greatest challenge for the Western Balkans lies in addressing the growing risk of a shortage of skilled workers.
Introduction
The concept of near-shoring first gained attention during discussions of ‘slowbalisation’ following the global financial crisis of 2007-2008 (Bakas 2015; The Economist 2019). Its relevance grew with the supply-chain disruptions caused by the COVID-19 pandemic (McKinsey & Co. 2020) and, more recently, the war in Ukraine (Agnew 2022).
In 2021, we published a study exploring whether the Western Balkan economies could benefit from near-shoring trends in the wake of the pandemic (Jovanović et al. 2021). Our findings indicated that the region could indeed capitalise on these trends if it were to focus on upskilling its workforce, enhancing infrastructure and strengthening governance.
More recently, we conducted a follow-up study that revisits and expands on these questions (Jovanović et al. 2024). This new research evaluates whether near-shoring has actually occurred in the Western Balkans, identifies examples of companies that have near-shored to the region, examines these cases in detail and investigates the factors driving the decisions to near-shore.
Trends in FDI in the Western Balkans
Our analysis begins with a quantitative examination of recent trends in foreign direct investment (FDI) inflows across the six Western Balkan economies, aiming to determine whether macroeconomic data indicate an increase in foreign investment following the pandemic.
The approach is straightforward: we analyse pre-pandemic FDI trends, extrapolate them to simulate expected post-pandemic inflows, and compare these simulations with actual data. If actual FDI inflows exceed the simulated values, we interpret this as evidence of near-shoring. The underlying assumption is that pre-pandemic trends represent the level of investment expected under a ‘business-as-usual’ scenario. Any excess in actual FDI over these projections suggests new factors driving increased investment, which we attribute to near-shoring.
To conduct this analysis, we use two complementary simulation methods. The first applies a simple logarithmic trend to total FDI inflows for each economy. The second employs econometric modelling to analyse macroeconomic factors influencing FDI, using four different models with different combinations of the following explanatory variables: sovereign credit ratings, rule of law, nominal GDP, and general government revenues. The analysis is performed separately for each Western Balkan economy, with the pre-pandemic period defined as 2012-2019 and the post-pandemic period as 2020-2023.
Figure 1 illustrates FDI inflows derived from both simulation methods alongside actual recorded inflows. Simulated values are represented as an orange range (spanning the lowest to highest estimates), while actual FDI inflows are shown as dark grey lines. The post-pandemic period is shaded in light grey for context.
The data reveal contrasting trends across the region. In Albania and Serbia, actual FDI inflows from 2020 to 2023 consistently fall below the simulated range, indicating no signs of near-shoring. Conversely, Bosnia and Herzegovina, Kosovo, and North Macedonia show actual FDI inflows exceeding the simulated range over the last two to four years, which we interpret as evidence of near-shoring in the post-pandemic period.
Montenegro represents a borderline case. Although FDI inflows exceeded the simulated range from 2020 to 2022, they fell below it in 2023. This makes it challenging to determine whether Montenegro is experiencing near-shoring or simply reflecting typical cyclical fluctuations in FDI.
Insights from case studies and interviews with companies
We identified examples of near-shoring to the Western Balkans, with such cases found in all economies except Montenegro. Notably, many of these investments come from Asian companies strategically positioning themselves in the region to be closer to EU business partners or to facilitate exports to the European market. In contrast, Western European companies that have near-shored to the Western Balkans have typically done so in preference to investing in Asia, drawn by the region’s low production costs and proximity to the EU. However, we found no evidence of companies closing operations in Asia to relocate to the Western Balkans, probably because of the high costs associated with such moves.
Interviews with foreign investors and other stakeholders underscore that multinational companies are actively discussing and implementing near-shoring strategies. Geopolitical developments, such as the war in Ukraine and growing global polarisation, are accelerating this trend. One prominent strategy gaining traction is the ‘local-for-local’ approach, in which companies locate production and other activities closer to their final markets.
The interviews also highlight the increasing importance of environmental sustainability and decarbonisation in shaping investment decisions. Regulatory pressures and consumer expectations are driving companies to reduce carbon emissions and shorten supply chains, further reinforcing the case for near-shoring.
Some tentative lessons for Europe and the world as a whole
Companies are actively considering and implementing near-shoring strategies, driven by two main factors. First, geopolitical tensions and rising global uncertainty are prompting firms to de-risk their operations and reduce costs. Second, there is increasing pressure to shorten supply chains and lower carbon emissions, arising both from regulatory demands and from expectations from partners seeking to minimise their own environmental impact.
Traditional near-shoring (for example, closing factories in Asia and relocating them to Eastern Europe) remains unlikely, as the costs involved are prohibitively high. Instead, Western companies already operating in Eastern Europe are likely to expand their activities there rather than investing in Asia. Similarly, new Western firms are expected to choose Eastern Europe over Asia for future investments.
Another significant trend involves Asian companies investing in Eastern Europe to be closer to the EU market. This allows them to sell directly within the EU and/or support EU-based companies more efficiently.
Recent geopolitical developments, such as the rise of far-right movements in Europe and Donald Trump’s return to power in the US, are likely to influence near-shoring dynamics in complex ways. On one hand, rising geopolitical uncertainty and global polarisation may accelerate near-shoring. Trade wars and the imposition of tariffs could incentivise Chinese and other Asian companies to establish operations in Eastern Europe to bypass EU tariffs. On the other hand, the anticipated rollback of environmental policies could reduce the regulatory and partner-driven incentives to near-shore.
The greatest challenge for near-shoring and FDI in Eastern Europe lies in the region’s growing shortage of skilled workers. This issue risks deterring foreign companies from investing. Governments must address this by expanding the pool of skilled workers through upskilling and reskilling initiatives, attracting foreign talent, and embracing greater automation. Additionally, there should be a stronger focus on high-tech industries that are less labour-intensive.
Branimir Jovanović is Economist at wiiw and country expert for North Macedonia and Serbia. His current research interests lie mainly around economic inequality, poverty, fiscal policy, taxation, social policies, labour rights, as well as financial crises and post-crises recoveries. Previously, he has done research on monetary policy, credit activity, exchange rates, trade, FDI, remittances, current account sustainability, forecasting, house prices.
The interactive graphics were created byAlireza Sabouniha. He is university assistant and a PhD candidate at the Leopold-Franzens University Innsbruck
This article is based on a forthcoming research paper of the TWIN SEEDS Horizon Europe project, which explores the distribution of rents across business functions that are linked to (greenfield and brownfield) projects undertaken by subsidiaries of multinational enterprises (MNEs). We contrast the traditionally tested ‘Smile Curve’ that associates value added generation with different stages along global value chains (GVCs) with analysis of a wider set of distributional variables (mark-up rates as proxies of profit margins, wage rates, labour shares in value added and in turnover).
Introduction: The Significance of Rent Distribution in GVCs
The distribution of rents in global value chains (GVCs) has long fascinated economists and policy makers. The Smile Curve reveals that the highest value is generated at the pre-production and post-production stages, leaving the manufacturing segment with lower value-added activities. GVCs are undergoing a period of transformation, driven by technological advancements, geopolitical tensions and the transition to greener economies. These changes, coupled with the need for resilient supply chains, have reignited interest in understanding how value and rents are distributed across the different stages of production. ‘Rents’, in this context, refers to the economic gains from profit margins, wage levels and value-added shares.
The Smile Curve, a conceptual framework introduced by Mudambi (2008),[i] captures this distribution by illustrating that value creation is highest in upstream (R&D, design, headquarters and financing) and downstream (marketing, sales and logistics) activities; the production stage generates less value. By analysing the activities of the subsidiaries of multinational enterprises (MNEs), we can uncover the factors driving rent distribution across locations and functions. In this research, we contrast the traditionally tested Smile Curve that associates value added generation across the different stages along GVCs against a wider set of distributional variables (mark-up rates as proxies of profit margins, wage rates and labour shares in value added and in turnover). This article draws on a detailed analysis of Orbis data spanning over a decade to investigate the specialisation structures of MNE subsidiaries in their global operations and their distributional implications.
Understanding Functional Specialisation
MNEs play a pivotal role in shaping GVCs through their functional specialisation. They strategically allocate activities to locations that maximise efficiency and profitability. Our study categorises MNE business functions into five broad groups:
1. Headquarters – central decision-making and management.
2.R&D and ICT – innovation, technology development and digital infrastructure.
3. Finance and Business Services (FBS) – administrative and financial activities.
4. Production – core manufacturing activities.
5. Sales/Marketing/Logistics (SML) – distribution, marketing and customer engagement.
These categories allow us to explore how value and rents are distributed not only across functions but also across regions, distinguishing between global and European patterns.
Key Findings on Rent Distribution
1. The Smile Curve Holds for Value Added
Our analysis confirms the Smile Curve’s relevance. Value-added ratios (value added as a share of turnover) are consistently higher in headquarters, R&D and ICT, FBS, and SML than in production (Figure 1). This finding underscores the importance of high-skilled, knowledge-intensive activities in driving value creation.
2. Mark-ups (profits) are Lower in High-Value Functions
Contrary to traditional interpretations of the Smile Curve, which focus on value-added ratios, we find that profit margins tend to be lower in pre- and post-production functions than in production itself (Figure 1). This is partly a consequence of higher operating costs and greater competition in knowledge-intensive sectors. However, the broader economic gains in these segments often manifest through elevated wage rates and labour shares rather than through profits.
3. Labour Outcomes Reflect Functional Specialisation
Wage rates are significantly higher in pre- and post-production functions, reflecting a reliance on skilled labour. Labour shares in turnover and value added are also elevated, indicating stronger bargaining power and higher remuneration for skilled employees in these segments (Figure 2). In contrast, production functions are characterised by lower wages and labour shares, often relying on automation and cost-efficient practices.
Regional Patterns of Functional Specialisation
Our findings also reveal notable regional variations (Figure 3) in rent distribution across GVCs:
1. Global vs European Dynamics
At the global level, MNE subsidiaries exploit a wide range of wage differentials and market conditions, such as lower wage rates in lower-income countries and increased scope to achieve higher profit margins through the market power of MNEs. Within Europe, however, wage structures are less differentiated, owing to a generally more evenly distributed skilled labour force, and more harmonised labour market policies and regulations. European subsidiaries exhibit a smaller wage gap between production and non-production functions than their global counterparts.
2. The EU-CEE Advantage in R&D, ICT and SML
MNE operations (through their subsidiaries) in Central and Eastern European (CEE) member states show strong profit margins in R&D, ICT and SML functions. Value-added ratios in these functions surpass those in production, driven by relatively lower costs and a growing pool of skilled labour. These trends suggest that, from a cost perspective, EU-CEE economies are emerging as competitive hubs for innovation and logistics within GVCs.
Policy Implications
Attracting High-value Functions
Policy makers must prioritise attracting high-value activities such as R&D, ICT and headquarters. This can be achieved through targeted incentives, investments in education, and infrastructure development to support functional upgrading.
Enhancing Labour Market Outcomes
Efforts to employ skilled labour forces across different business functions reduce disparities in rent distribution across functions. Such policies promote equitable wage structures, which would be supported by collective bargaining, and upskilling opportunities.
Encouraging Transparency in Rent Distribution
Ensuring transparency in how rents are allocated across GVCs is critical. Tax policies and reporting standards should discourage practices that concentrate profits in low-tax jurisdictions while neglecting the contribution of other functions and regions.
Outlook: The Future of Rent Distribution in GVCs
As global economies transition towards sustainability and digitalisation, the distribution of rents in GVCs is likely to evolve. Future research should investigate how these trends reshape functional specialisation and rent allocation. Additionally, granular data on intra-MNE transactions could provide deeper insights into the interplay between pricing strategies, wage bargaining, upgrading of skills and rent distribution.
Conclusion The Smile Curve remains a powerful framework for understanding the distribution of rents in GVCs. Our findings highlight the crucial role of functional specialisation in shaping value creation and rent allocation. Policy makers must leverage these insights to enhance labour market outcomes, and position their economies as attractive hubs for high-value activities that also promote equitable growth.
[i] Mudambi, R. (2008). Location, control and innovation in knowledge-intensive industries. Journal of Economic Geography, 8(5), 699-725.
Authors:
Mahdi Ghodsi is an economist at the Vienna Institute of International Economic Studies, a lecturer at the Vienna University of Economics and Business, and Senior Fellow and Head of the Economy Unit at the Center for Middle East and Global Order.
Michael Landesmann is Senior Research Associate at wiiw and Professor of Economics at the Johannes Kepler University Linz.
The interactive graphics were created byAlireza Sabouniha. He is research assistant at wiiw and a PhD candidate at the Leopold-Franzens University Innsbruck.
The EU Carbon Border Adjustment Mechanism (CBAM) is now in its transitional phase and will fully enter into force from 1 January 2026. We assess the impacts of such tariffs on CO2-intensive imports on welfare, income and emissions, employing a general equilibrium framework. For the EU, we find an increase in the terms of trade and consequently small positive welfare effects, whereas there are tiny negative effects on real wages. Although global CO2 emissions have been reduced, specialisation effects have led to a slight increase in emissions in the EU.
Introduction
The EU Carbon Border Adjustment Mechanism (CBAM), which is now in its transitional phase, aims to establish a comparable carbon pricing level between goods of different origins, regardless of whether production takes place inside or outside the EU. The CBAM has two main goals. The first is to reduce the risk of carbon leakage, i.e. the relocation of production facilities to non-EU countries with less stringent climate regulations. The second is to create an incentive for producers in non-EU countries to reduce emissions in the manufacturing process. The CBAM has been applied since 1 October 2023; its transitional phase spans the period to the end of 2025. It should be fully implemented from 1 January 2026. The first reporting period ended on 31 January 2024. From 1 January 2025, each importer or their representative is obliged to apply for authorisation as a CBAM declarant before importing CBAM goods. In the first phase of the CBAM, the sectors covered are cement, iron and steel, aluminium, fertilisers, electricity, and hydrogen. From 2026, CBAM certificates must be purchased when importing certain goods whose production in third countries has resulted in greenhouse gas (GHG) emissions. The quantity of CBAM certificates to be purchased depends on the quantity of GHG emissions generated during production; the price of CBAM certificates is based on the price of EU emissions trading system (ETS) certificates at the time the goods are imported. The costs imposed by CBAM on imports therefore correspond to those that would have been incurred by the emission of GHGs and the associated purchase of EU ETS allowances in the case of production within the EU.
From an economic modelling point of view, the CBAM constitutes a tariff on imports in certain industries, focusing on CO2 emissions. In Flórez Mendoza et al. (2024), we assess the implications of such tariffs, employing a general equilibrium model following the approach of Caliendo and Parro (2015), using novel data OECD-ICIO 2023 multi-country input-output tables, which provide data for 76 countries and 45 industries combined with information on CO2 emissions at the same industry classification for the most recent available year (2020).
Welfare effects of the EU CBAM
The tariffs depend on the price of the EU ETS certificates, which we assume to be EUR 100 for CO2 emissions in our base scenario. Of the 45 industries in our data, nine are affected by such tariffs. The most important are energy (NACE D), petroleum (NACE C19), minerals (NACE C23) and metals (NACE C24), with tariff equivalents up to 10%, assuming a carbon price of EUR 100.
The economic implications of such tariffs imposed by the EU and EFTA countries are presented in Figure 1. First, the terms of trade are improving for the EU as the products are inelastically supplied and hence EU import prices net of tariffs are decreasing, whereas export prices are rising. Consequently, the EU experiences an increase in welfare, which according to the model amounts to 0.016% in the base scenario of a carbon price of EUR 100, whereas terms of trade and welfare decline by 0.005% in the other countries. Global welfare hence declines only marginally. Owing to overall lower demand, real wages decline in all country groups, but most strongly in the EU (by 0.025%). As shown in Figure 1, the higher the underlying carbon price, the larger these effects are.
Effects on global CO2 emissions
The change in CO2 emissions in this framework is solely driven by changes in specialisation patterns. Because of the tariffs, EU specialisation in CO2-intensive industries increases, and thus CO2 emissions rise by 0.72% in the EU in the base scenario of a carbon price of EUR 100, as indicated in Figure 2. The opposite occurs for the other countries, in which CO2 emissions decline by 0.143%. The global effect is ambiguous, depending on overall CO2 intensities in both country groups. However, as production in EU countries is in general less CO2-intensive than in the other countries and as production of CO2-intensive industries shifts towards the EU, global CO2 emissions are reduced by 0.08%. The higher the carbon price, the stronger the specialisation effects towards the EU, and therefore CO2 emissions in the EU increase more strongly and consequently decline more strongly globally (see Figure 2).
Conclusions
These results first indicate a small positive environmental impact at the global level, as the introduction of the EU CBAM reduces overall CO2 emissions by reducing production of goods in countries with high CO2 intensities and increasing production in countries with low CO2 intensities. The effect on global emissions is, however, relatively small. Second, our simulations show that welfare is increasing in those countries that are participating in the CBAM (EU and EFTA countries). Conversely, the other countries see their welfare decrease. As with the findings for CO2 emissions, the magnitude of these changes is relatively small. Third, the higher the underlying carbon price, the greater the impacts on all variables presented. Again, this is to be expected as a higher carbon price implies a higher applied tariff rate.
The CBAM, however, leads to an increase of CO2 emissions in the EU, owing to the resulting specialisation effects. It should be emphasised that this approach does not consider the effect that higher import costs (and eventually rising CO2 prices) are incentives for firms to use less CO2-intensive technologies. On top of that, sector-specific strategies within the EU’s broader climate policy framework could enhance such a technological shift (see Draghi, 2024, Chapter 3). Policy makers should focus on high-emitting sectors, such as energy and heavy industry, and provide incentives for climate-friendly technologies. Meanwhile, lower-emitting industries could benefit from policies that maintain progress and encourage further green innovation. Such an approach aligns with the EU’s climate agenda and ensures an effective transition to a low-carbon economy while strengthening the EU’s competitive position.
References:
Caliendo, L. & F. Parro (2015), Estimates of the trade and welfare effects of NAFTA, The Review of Economic Studies, Vol. 82(1), pp. 1-44.
Draghi, M. (2024), The future of European competitiveness – Part A: A competitiveness strategy for Europe, European Commission.
Flórez Mendoza, J., O. Reiter & R. Stehrer (2024), EU carbon border tax: General equilibrium effects on income and emissions, wiiw Working Paper, The Vienna Institute for International Economic Studies (wiiw), forthcoming.
Korpar, N., M. Larch & R. Stöllinger (2023), The European carbon border adjustment mechanism: a small step in the right direction, International Economics and Economic Policy, Vol. 20(1), pp. 95-138.
Robert Stehrer is Scientific Director at wiiw. His expertise covers a broad area of economic research, ranging from issues of international integration, trade and technological development to labour markets and applied econometrics. His most recent work focuses on the analysis and effects of the internationalisation of production and value-added trade. Other contributions relate to the connection between digitalisation, demographics, productivity and labour markets. He studied economics at the Johannes Kepler University Linz, Austria, and sociology at the Institute for Advanced Studies (IHS) in Vienna and is lecturer of economics at the University of Vienna.
Javier Flórez Mendoza is an economist at the Vienna Institute for International Economic Studies (wiiw). His research focuses on international trade, trade policy, European integration, environmental economics and regional economics. He is a PhD candidate at the Vienna University of Economics and Business.
The interactive graphics were created byAlireza Sabouniha. He is research assistant at wiiw and a PhD candidate at the Leopold-Franzens University Innsbruck.
The US presidential candidate has announced massive tariff increases on US imports in general and against Chinese imports in particular in the event of his re-election. Such measures would further destabilise the global trading system and would also have a direct negative impact on incomes in export markets such as the EU and China. However, the consequences would be even greater for the US itself, as our calculations using a multi-sectoral equilibrium model show.
Introduction
As unpredictable as the outcome of the US presidential election on 5 November is, the possible consequences for international economic policy and the global trading system are just as uncertain. However, the latter depend not only on whether Kamala Harris or Donald Trump wins, but also on the but also on the post-election composition of the US Congress and the ideologies of its members. Harris and Trump pursue some of the same goals, such as protecting domestic industry, securing jobs, relocating lost industries back to the US, maintaining the country’s technological leadership and reducing US dependence on international supply chains. Nevertheless, their approaches differ considerably in terms of the radical nature of the planned measures, and the speed and method of their implementation. One major substantive difference concerns climate and environmental policy, where only Harris can be expected to take a constructive approach (see Stehrer, 2024).
The proposed tariff increases
One of Trump’s most important threats is the announced tariff increases to 10% for all US imports and possibly to 60% (or more) for imports from China; Trump has floated even higher tariffs at his campaign rallies. To assess the impact of such rises, it is first necessary to examine the current tariff rates (see Figure 1). On average, the EU imposes tariffs of 5.2% on the US and China; the US imposes tariffs of 3.5% on the EU and 3.6% on China. China imposes higher average tariffs of 7.5% on the EU and 7.6% on the US. The announced increase in US import tariffs to 10% under Trump would therefore mean a near-threefold increase.
Effects of these tariffs in a general equilibrium model
The effects of such tariff increases can be estimated using general equilibrium models. Calculations based on the model using the approach taken by Caliendo and Parro (2015) – for details, see Flórez Mendoza et al. (2024) – show that if US import tariffs were to rise to at least 10% (assuming that higher tariffs remain unchanged), total income in the US, including tariff revenue, would rise by 0.08%. However, real income excluding tariff revenue would fall by around 0.14%, mainly because imports would become more expensive. Incomes in China would fall by around 0.02%, while EU countries would be slightly more affected, with a decline of 0.05%. If tariffs on imports from China were increased to 60%, US income (including customs revenue) would rise by 0.12%, but real income would fall even more sharply, by 0.33%. In China, income losses in this scenario would be slightly higher, at 0.15%. For the EU27, the fall in income would be roughly unchanged. Overall, the global trade volume would fall slightly.
Conclusions
Our estimates show that the announced tariff increases on US imports would hit real incomes in the US itself the hardest; Clausing and Lovely (2024) and Baldwin (2024) argue similarly. The planned tariff increases would also have a (relatively) minor negative impact on the incomes of US trading partners.
Overall, it should be emphasised that our calculations build on the assumption of full employment and do not take any other factors into account. Relevant factors would include retaliatory measures and thus tariff increases by other countries against US imports, or further negative growth effects due to uncertainties and a decline in global trade flows. Such developments would result in stronger negative overall effects.
Although the announced tariff increases would have manageable overall effects on incomes and global trade, it can be assumed that such unilateral measures would further destabilise the international trading system under a Trump presidency.
References
Baldwin, R. (2024), Will Trump’s tariffs on China harm US manufacturing?, Factful Friday (via LinkedIn).
Caliendo, L. & F. Parro (2015), Estimates of the trade and welfare effects of NAFTA, The Review of Economic Studies, Vol. 82(1), pp. 1-44.
Clausing, K.A. & M.E. Lovely (2024), Why Trump’s tariff proposals would harm working Americans, PIIE Policy Brief 24-1, Peterson Institute for International Economics, May.
Flórez Mendoza, J., O. Reiter & R. Stehrer (2024), EU carbon border tax: General equilibrium effects on income and emissions, wiiw Working Paper, The Vienna Institute for International Economic Studies (wiiw), forthcoming.
Robert Stehrer is Scientific Director at wiiw. His expertise covers a broad area of economic research, ranging from issues of international integration, trade and technological development to labour markets and applied econometrics. His most recent work focuses on the analysis and effects of the internationalisation of production and value-added trade. Other contributions relate to the connection between digitalisation, demographics, productivity and labour markets. He studied economics at the Johannes Kepler University Linz, Austria, and sociology at the Institute for Advanced Studies (IHS) in Vienna and is lecturer of economics at the University of Vienna.
Oliver Reiter is an economist and data scientist at the Vienna Institute for International Economic Studies (wiiw). His research focuses on international trade, non-tariff measures in trade, the creation/updating of a multi-regional input-output database (such as WIOD) and agent-based macroeconomic models. He holds a Bachelor’s and a Master’s degree in Economics, a Bachelor’s degree in Statistics and a Master’s degree in Computer Science, all from the University of Vienna. He is currently pursuing his doctorate at the Vienna University of Economics and Business.
The interactive graphics were created by Alireza Sabouniha. He is research assistant at wiiw and a PhD candidate at the Leopold-Franzens University Innsbruck.”