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.”