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 by Alireza Sabouniha. He is research assistant at wiiw and a PhD candidate at the Leopold-Franzens University Innsbruck.