A lose-lose game? The twin transition and the division of labor in the European automotive supplier industry

Abstract

The twin transition—automation/digitalization and the shift to electromobility—poses major challenges for the European automotive supplier industry. This article examines how these transformations reshape plant roles and labor-use strategies in Germany and Central and Eastern/South Eastern Europe (CEE/SEE), regions historically linked through an integrated division of labor. The paper addresses three questions: (1) How have lead plant functions evolved between 2016 and 2024? (2) What differences do we observe between German and CEE/SEE automotive supplier plants with regard to the twin transition? (3) What differences do we observe between Germany and CEE/SEE with regard to labor-use strategies? The analysis draws on two surveys of employee representatives in supplier plants (Germany and five CEE/SEE countries) conducted in 2016 and 2024. Findings indicate slight erosion of German lead plant roles without corresponding upgrade in CEE/SEE; limited but stronger efforts in Germany toward electromobility; and persistent national differences in automation and labor-use strategies, with Germany maintaining high-skill approaches and CEE/SEE relying primarily on semi- and low-skilled labor.

Keywords

automotive industry automation electromobility labor-use strategies skills

Introduction

The twin transition is a fundamental challenge for automotive locations throughout Europe but especially for the most successful automotive production network in Europe, with Germany at its core and the Central and Southern Eastern Europe (CEE/SEE) states in a closely linked semi-periphery (Domański et al., 2017; Pavlínek, 2025). Digitalization and automation, the first dimension of the twin transition, are casting doubt on jobs throughout the supply chain, but remain indispensable for high productivity. The transformation to electromobility, the second dimension, calls into question combustion engine technology, this network’s core competence. Since the automotive industry is one of the key employers in both Germany and CEE/SEE, how the twin transition transpires is paramount for the entire region.

Germany and CEE/SEE countries represent a closely linked production network, but they are shifting into the transformation with different prerequisites. The German automotive industry has successfully specialized in upmarket products and has been able to maintain production jobs despite high wages (Krzywdzinski, 2017; Schwarz-Kocher et al., 2019). The high proportion of production plants with so-called “lead plant” functions has played an important role here: the plants used their workforce’s high-skill level and their proximity to corporate R&D to take on a pioneering role in the industrialization of new products and the introduction of new technologies. However, these competencies were closely linked to the combustion engine and could be difficult to maintain if the technology changes fundamentally.

While the German plants are concerned with defending their position in the value chains, the question for CEE/SEE locations is whether the twin transition is repudiating the prior trajectory of upgrading. The CEE/SEE supplier locations popped up in the 1990s as outsourcing locations for labor-intensive processes, but by the mid-2010s they had undergone a process of upgrading products and process technologies, taking over lead plant functions on a small scale (Krzywdzinski, 2017; Meszmann et al., 2024; Pavlínek, 2025). The twin transition is a challenge for these sites. Upgrading to date has largely been based on labor cost advantages. In addition, CEE/SEE had become an important location for combustion engine technologies, so the transition to electromobility promises to be disruptive.

Against this background, we examine the effects of the twin transition on automotive supplier plants, their roles and the labor-use strategies in Germany and throughout CEE/SEE. The automotive supplier industry in focus differs significantly from the automotive manufacturers themselves. Automotive suppliers have a large number of locations ranging in size. On the one hand, they are dependent on automotive manufacturers; on the other hand, they have greater scope for adaptation. In the shift to electromobility, manufacturers of filters, metal parts or electronics for combustion engines, for example, can try to offer components for electric drives, but can also diversify into other areas within and outside the automotive value chain.

Our article uses a survey of employee representatives at automotive supplier locations in Germany and CEE/SEE (Poland, Czechia, Slovakia, Hungary, and Romania) conducted in 2016 and 2024 with mostly the same questions (the module on electromobility was only asked in 2024). The survey thus provides an excellent basis for examining developments between the peak of the automotive boom in the 2010s and the situation of the industry in the context of the twin transition.

The central questions we ask of the data for this article are as follows:

(1) How has the prevalence of lead plant roles in automotive supplier plants in Germany and CEE/SEE changed between 2016 and 2024?

(2) What differences do we observe between German and CEE/SEE automotive supplier plants with regard to the twin transition, that is, automation and the transition to electromobility?

(3) What differences do we observe between Germany and CEE/SEE with regard to labor-use strategies, and how can we explain them?

A central concept in our analysis is labor-use strategies. We focus on the production area and distinguish between labor-use strategies that rely on skilled labor, that is, a high proportion of workers with completed vocational training (VET), versus labor-use strategies that primarily recruit unskilled and semi-skilled blue-collar workers and rely on white-collar employees (engineers and technicians) for more complex tasks. We base our concept on approaches that attribute the development of labor-use strategies to the influence of country-specific institutions (Bosch, 2025; Jürgens and Krzywdzinski, 2016), but also to the intra-company division of labor and technology use (Krzywdzinski, 2017).

Our analysis is structured as follows. Section 2 presents the state of the research. We discuss the development of the division of labor between Germany and CEE/SEE in the automotive industry, the concept of labor-use strategies and the challenges of the twin transition. Section 3 presents the data and methods. Section 4 discusses the change in the prevalence of lead plant roles in Germany and CEE/SEE, the activities of the transition to electromobility, as well as changes in automation levels. In Section 5, we focus on labor-use strategies and examine their development in Germany and CEE/SEE. We sum up with a discussion of the findings in Section 6 and overarching conclusions.

State of the research and theoretical framework

Patterns of division of labor between Germany and CEE/SEE

Since the end of the 1990s, a closely integrated production network has developed between Germany and CEE/SEE. CEE/SEE has become a crucial location for automotive component production for the German automotive industry (Jürgens and Krzywdzinski, 2009; Pavlínek, 2025) and a number of automotive plants have also sprung up in the region. In the 2010s, imports from CEE/SEE already accounted for around half of the total value of automotive parts imported into Germany (Krzywdzinski, 2014)—a significantly higher level than in the French automotive industry, for example (Frigant and Jullien, 2018). What is remarkable, however, is that the relocation of component production to CEE/SEE did not result in the decline of German production sites. Employment in the German automotive industry peaked in 2019 at around 920,000 employees (Eurostat, 2025a), which differed significantly from the steep downturn in other traditional European car manufacturing countries such as France or Italy (Gaddi et al., 2025; Pardi, 2020).

The stability of production employment in the German automotive industry (at least until the COVID-19 pandemic) was the result of several factors. Firstly, German car manufacturers had managed to successfully position themselves in the lucrative upper market segments, which was referred to as a premium strategy (Krzywdzinski, 2014). As Sorge and Streeck (2018) have pointed out, this specialization was based on certain institutional foundations. First, they set up including the specific innovation system of close cooperation between car manufacturers, parts and equipment suppliers. Then, they leaned upon the German vocational training system and cooperative labor relations. Furthermore, many of the German supplier companies had developed a specific strategy to focus on so-called lead plant functions (Schwarz-Kocher et al., 2019). Lead plants specialize in the most innovation-intensive technologies, production activities and products. Their advantage lies in the ability to introduce new technologies and products quickly, that is, they rapidly upscale product industrialization. As soon as the process is stable, lead plants often support the production rollout at other locations. Although this creates constant pressure to reorganize, production jobs can be maintained despite high German wages.

Factories in CEE and SEE started out as “extended workbenches” with simple products and a low level of vertical integration. They only gradually acquired increasingly complex products and functions, incrementally modernized their production equipment, and recently began taking over product development tasks (Domański et al., 2017; Pavlínek, 2017). The investments of automotive suppliers in CEE/SEE were partially driven by follow-sourcing of car manufacturers, partially by the aim of benefitting from the low local wages. Jürgens and Krzywdzinski (2009) have shown that different patterns of division of labor emerged between German and CEE/SEE locations. In some cases, companies established parallel production of the same products, which was also accompanied by a harmonization of technologies. In other cases, CEE/SEE was used specifically for particularly price-sensitive products. This could take the form of highly automated production, but could also mean the manual production of small series, the automation of which would be too expensive, just like manual production in Germany. What has remained unchanged despite all the upgrading in CEE/SEE is the dependence on corporate headquarters located in Germany, Western Europe, the USA, and Asia, which is why authors such as Pavlínek (2022) or Nölke and Vliegenthart (2009) speak of a (semi-)periphery.

Labor-use strategies in Germany and CEE/SEE

A fundamental characteristic of the labor-use strategy in the German automotive industry is the great importance of skilled blue-collar work, that is, a high proportion of workers with multi-year vocation training (Krzywdzinski, 2021). This labor-use strategy has two elements. First, in-house vocational training (carried out in cooperation with vocational schools, see Bosch, 2025) serves the companies as an important source of recruitment for workers, not only in departments such as maintenance, but also in direct production. Secondly, this availability of skilled labor influences how companies design jobs. An important question in job design in production is to what extent the tasks of controlling automated equipment, troubleshooting and optimization can be entrusted to blue-collar workers. In a comparison of German and Korean approaches, Krzywdzinski and Jo (2022) have shown that production engineers and skilled workers cooperate on many of these tasks in Germany, while there is a strict separation of tasks between the two groups in Korean factories.

Two explanations have been proposed for the development of labor-use strategies in the German automotive industry. The first explanation focuses on the institutional framework (Bosch, 2025; Sorge and Streeck, 1988). Sorge and Streeck (1988, 2018) have argued that in the case of Germany, several factors reinforce each other: an industry focus on upmarket and high-quality products, occupational labor markets with long-term careers within companies, and a broad popular enrollment in VET institutions. The second explanation focuses on the plant level and emphasizes that the vocational skills of workers in Germany are particularly useful in the case of specialization on so-called lead plant roles (Schwarz-Kocher et al., 2019). In lead plants workers take on tasks of control, troubleshooting and optimization, and they can be involved in the organization of product ramp-ups and the introduction of new technologies in cooperation with engineers. This differs from pure production tasks, which can be performed by workers without vocational training, even with a high degree of automation.

Labor-use strategies in CEE/SEE have developed very differently from those in Germany. The institutional framework conditions were shaped by the collapse of the company-based vocational training that had existed in the socialist societies of CEE/SEE (Grootings, 1993). In the course of that transformation, most companies in CEE/SEE abandoned forms of multi-year VET. Still, the proportion of skilled workers remained high for some time due to the legacy of the socialist societies. Since the 1990s, however, industrial employment has been increasingly characterized by unskilled and semi-skilled work, by a large proportion of precarious employment, and by very high worker turnover (Krzywdzinski, 2017). Even when labor shortages began to affect the labor markets of the CEE/SEE countries in the 2010s, companies did not react by rebuilding VET, but by recruiting migrant workers (Meszmann and Fedyuk, 2020). The lack of institutional foundations for skilled blue-collar work also shaped job design. Industrial plants with a high level of automation relied on the use of semi-skilled labor (Pavlínek, 2022) and recruited engineers for control, troubleshooting and optimization tasks (Krzywdzinski, 2017). Following Finegold and Soskice (1988), it may be argued that the interaction of institutional conditions and corporate strategies in CEE/SEE represents a low-skill equilibrium that differs from the high-skill equilibrium in Germany. Since the end of 2010s, however, continuing labor force shortages have started to put pressure on the low-skill labor-use strategy. Meszmann et al. (2024) have shown that from 2012 to 2022 there was an increase in the share of white-collar labor in the CEE/SEE automotive industry. Other studies have argued that automotive companies in CEE/SEE are increasingly trying to upskill their workforce (Adăscăliței and Guga, 2020; Olejniczak et al., 2020). The differences between domestic productive models that automotive suppliers have certainly play a role here (Meardi et al., 2009), although we will not discuss these differences in our own analysis.

Challenges of the twin transition

The twin transition is changing the conditions for the division of labor between Germany and CEE/SEE. First, new players have begun to compete in the value chain. Chinese companies such as BYD have established themselves as successful manufacturers of electric vehicles, and moreover they (alongside South Korean and Japanese companies) are at the cutting edge in battery development and production (Lüthje and Zhao, 2025). The rise of Chinese companies is closely linked to the rise of the Chinese market as a leading market for electromobility (Altenburg et al., 2022). New products and technologies are increasingly being launched on the Chinese market first, and global automotive suppliers’ Chinese locations are increasingly taking on lead plant functions (Handelsblatt, 2023). At the same time, Chinese (as well as South Korean) manufacturers of batteries and battery components are building plants in CEE and SEE to secure access to the European market (Jia-Zheng and Binda, 2024; Šebeňa et al., 2025). These represent strong competition for European automotive suppliers.

Second, the twin transition presents plants in Germany and CEE/SEE with the challenge of changing their skill base. Skills in the area of the combustion engine are in danger of becoming completely obsolete, while new skills in the area of batteries and electric drives are gaining significance. In other areas, however, this change is also gradual: many components in the area of interior, chassis or electronics must be adapted to the new drive technology, but will not fundamentally lose their importance. A further challenge for companies is to manage the transformation to electromobility in parallel with technological change in the area of digitalization and automation, which is also affecting work content and skill requirements (Butollo et al., 2019; Jürgens, 2025; Russo et al., 2025).

In Germany, after a phase of neglecting investments in electric mobility, companies have started to accelerate the change (Krzywdzinski et al., 2023; Lechowski and Weis, 2025). German manufacturers now aim to transfer their product strategy (particularly the focus on the upper market segments) from internal combustion engine vehicles to electric vehicles. This provides opportunities for suppliers themselves to make the transition towards electromobility. The German government has offered support in number of different subsidy programs (Lechowski et al., 2023).

In the case of CEE/SEE, the combustion engine and its associated parts may be viable longer than in Germany (Pavlínek, 2023; Szalavetz, 2022). In fact, German companies are applying a familiar game plan to rapidly relocate their combustion engine and components production to CEE/SEE to reduce costs and make way for a speedy conversion of domestic plants to electric mobility. Policy frameworks for electromobility in CEE/SEE remain fragmented (Krzywdzinski et al., 2024). Some countries, such as Poland, have adopted initial dedicated measures to support the use and production of clean mobility technologies, whereas others, such as Hungary, have concentrated primarily on investment-oriented industrial policies aimed at attracting foreign capital (Gagyi and Gerőcs, 2025).

Third, the twin transition has been accompanied by a slump in overall demand throughout Europe since 2020. As a result, car production in Germany dropped from 5 to 6 million to around 3 million vehicles directly after the Covid-19 pandemic and has only recovered to a limited extent: 4.1 million vehicles in 2024 (Statista, 2024). This means that the industry is producing around 20% fewer vehicles, which also has an impact on automotive suppliers. Companies have to manage the technological transition under particularly difficult cost conditions (Stieler and Schwarz-Kocher, 2022). The search for cost savings may also lead companies to question lead plant functions in Germany and increase relocation to low-wage countries. However, plants in CEE and SEE are also confronted with relocation to North Africa (Morocco), for example (Altenburg et al., 2025).

The weak demand is having an impact on automation and productivity. The German automotive industry has long been characterized by a very high level of automation (Krzywdzinski, 2021). In the context of the discussions about Industry 4.0, the digital networking of production had been further increased; at the same time, the digitalization of the product had increased under the buzzword “connected car.” Both processes had further strengthened the German automotive industry’s focus on skilled labor (Lee and Pfeiffer, 2019; Windelband, 2014). Due to the slow increase in demand for electromobility, however, German plants are now also being forced to build up small series production of the new components in parallel to existing products: this results in a lower level of automation and productivity (Maier et al., 2024; Roland Berger, 2025). This situation is nothing new for CEE/SEE. Due to their low labor costs, CEE/SEE plants have often specialized in mixed production, combining highly automated high-volume production with manual production of smaller batches (Krzywdzinski, 2017).

The described changes mean that the lead plant roles in the German automotive industry, but also in the CEE/SEE automotive industry, might come under pressure in the twin transition. It is unclear whether the previous strengths in the introduction of new products and technologies will still apply when the products and production processes change.

Hypotheses

In our analysis, we will examine the impact of institutional and workplace-level factors on the development of labor-use strategies. At the company level, we focus on the lead plant roles as well as the transition to electromobility and automation as central elements of the twin transition. First, we expect to see a weakening of lead plant roles in Germany, but also in CEE/SEE, due to the rise of new competitors and the increasing importance of non-European (especially Asian) markets. In CEE/SEE, this would mean a slowdown in the previous upgrading path.

The prevalence of lead plant functions in the German and CEE/SEE automotive supply industry is decreasing.

We expect the efforts of automotive supplier plants in the transition to electromobility to be more pronounced in the German automotive supplier industry than in CEE/SEE. This is due to the fact that German plants continue to benefit from their lead plant roles. In addition, much of the production of combustion engine components has been relocated from Germany to CEE/SEE already due to cost pressure. This reduces transition demands in CEE/SEE in the short term, but it could indeed threaten the long-term development of the automotive supply industry there.

Efforts to transition to electromobility are more pronounced in the German automotive supply industry than in CEE/SEE.

We expect automation levels in Germany and CEE/SEE to converge. On the one hand, where production volumes are high, automotive suppliers are establishing comparable levels of automation in their global production networks. Due to the slow increase in demand for electromobility in Europe, however, German automotive supplier plants are only expanding small series production of new electromobility components with lower levels of automation, for now. This has similarities with production models in CEE/SEE.

We expect automation levels in Germany and CEE/SEE to converge towards an increasingly mixed production composed of automated and less automated lines.

With regard to labor-use strategies, we expect the institutional framework conditions to continue to support the existing low-skill (CEE/SEE) and high-skill (Germany) equilibria. At the same time, there is a risk that the erosion of lead plant roles in the German automotive supply industry will reduce the importance of skilled blue-collar work. Our fourth hypothesis is therefore:

We expect a decline in the importance of skilled blue-collar work in the German automotive supply industry but a simultaneous continuation of the low-skill approach in CEE/SEE.

Data and methods

The following analysis is based on a survey of employee representatives in Germany and five CEE/SEE countries: Poland, Czechia, Slovakia, Hungary, and Romania. The same questionnaire was used in each country, asking for basic information about the plant and its level of employment, role and functions of the plant within the company (e.g., regarding product development and the introduction of new products and technologies), relocation pressures and concession bargaining, and the involvement of labor representatives in managerial decisions. The survey was conducted in 2016 (excluding Romania that year) and in 2024. The survey in 2024 included an additional module on product strategies regarding electromobility. Table 1 provides an overview of both surveys.

The survey was completed by the chairpersons of the works councils (Germany) or trade unions (CEE/SEE) of the plants. In Germany, it was distributed by the IG Metall to all automotive supplier works councils in four core automotive regions (Nordrhein-Westfalen, Baden-Württemberg, Bayern, Niedersachsen/Sachsen-Anhalt) and completed online by the works councils, with a response rate of 40%. In CEE/SEE it was distributed by the authors directly to trade union organizations in automotive supplier plants and indirectly via trade union federations. The response rates in CEE/SEE varied by country between 30% (Poland) and 70% (Romania).

It is important to emphasize that the populations of automotive supplier plants analyzed here differ between Germany and CEE/SEE. In Germany, works councils are present in nearly all automotive supplier plants. In CEE/SEE, however, employee representatives are only present in a small proportion (estimated at 10–20%). While the CEE/SEE sample in our survey represents a large part of these unionized plants, we do not have enough data to assess whether products and production processes differ between unionized and not unionized plants. Given the low number of plants per CEE/SEE country and a similar position of CEE/SEE countries in the European automotive production networks, we treat CEE/SEE as one region and one case in the following analysis.

Table 1.

Sample.

	Germany		CEE/SEE
	2016	2024	2016	2024
All surveyed plants	142	182	125	100
Country of plant
Germany	100%	100%
Poland			14%	14%
Czechia			41%	24%
Slovakia			29%	24%
Hungary			16%	22%
Romania			0%	16%
Country of origin of parent company
Germany	63%	67%	35%	27%
Northern or Western Europe	15%	10%	18%	23%
Central or South Eastern Europe	3%	3%	18%	20%
North or South America	16%	14%	16%	11%
Asia	4%	5%	13%	17%
Other	0%	0%	0%	2%
Employment
<251	19%	23%	18%	21%
251–500	30%	22%	16%	22%
501–1000	23%	16%	27%	20%
1001–2000	16%	11%	18%	17%
>2000	13%	27%	21%	13%
No information	0%	0%	0%	7%
Products (several possible):
Exterior	24%	10%	24%	19%
Body structure	20%	10%	14%	18%
Chassis	29%	24%	22%	21%
Powertrain	27%	33%	26%	19%
Engine and components	49%	34%	39%	20%
Interior	22%	20%	29%	19%
Electronics	30%	32%	26%	23%
Other	9%	22%	18%	35%

To compare our sample with the total population, we can draw on data from Eurostat (2025a, 2025b), although only information on the employment size and ownership of companies is available here; to our knowledge, there are no databases on the composition of the supplier industry by product area. In this comparison, we exclude micro-enterprises with up to 10 employees, which are not represented in our sample. In terms of employment size, large companies are significantly overrepresented in our sample: while according to Eurostat’s enterprise statistics (2025a), companies with more than 250 employees account for between 22% (Germany) and 40% (Hungary and Slovakia) of companies in the automotive industry in the countries surveyed, the figure in our sample is 77–82%. This is because we interviewed labor representatives, who are significantly more common in larger companies. Another characteristic of our sample is that the proportion of foreign-owned plants is high, at 33–37% in Germany and 80–82% in CEE/SEE. If we look at this share in the total population and once again exclude micro-enterprises, it is around 20–22% in the German, Polish, and Slovak automotive industry and 55–60% in Hungary and Romania, with Czechia in the middle (39%). This overrepresentation of foreign plants in our sample may be because foreign-owned plants are often larger than domestic ones and offer better organizational conditions for unions.

However, we consider the sample to be meaningful for several reasons. It includes the entire spectrum of product areas that are relevant for the automotive supply industry, whereby the composition by product differs slightly between Germany and CEE/SEE. In the German sample, plants that produce parts and components for engines, powertrain, chassis, and electronics account for a larger share, while in the CEE/SEE sample, producers of exterior and body parts have a higher share. Our sample covers middle-sized and large plants of domestic and foreign companies.

Two shifts in the 2016 and 2024 samples are worth highlighting. First, Romania was added to the CEE countries in 2024. We conducted all the analyses in this paper with and without Romania. As the findings did not differ, we kept Romania in the sample. A second shift in the composition of the 2016 and 2024 samples results from a stronger increase in the proportion of larger companies in Germany, while the proportion of larger companies in the CEE/SEE sample had fallen considerably. We have checked all analyses to see whether plant size plays a role and indicate if this is the case.

Table A1 in the online appendix to this paper shows the descriptive statistics for the variables used. The questions used were identical in the 2016 and 2024 surveys.

Two items were used to operationalize the lead plant functions (“The latest generation products are launched first in our plant,” “New production technologies are introduced first in our plant”), for each of which there was a three-point scale (always/mostly (1), sometimes/partly (2), rarely/never (3)). A principal factor analysis was carried out to simplify the data. The analysis revealed a dominant 1-factor solution which is used as a lead plant index. More information on the factor analysis is provided in the online appendix in Table A3.

The development of employment in lead plants was measured for blue- and white-collar workers with the question “How has employment at your location developed over the last 5 years?”, using a five-point scale (sharply (>10%) decreased (1), slightly (<10%) decreased (2), remained the same (3), slightly (<10%) increased (4), sharply (>10%) increased (5)).

Adaptation to electromobility was examined with the help of three items: “What is the impact of the transition to electromobility on the demand for your plant’s products?” (increase/no change/decrease), “Does your plant develop or produce new products for electromobility?” (yes/no) and: “Does your plant diversify its product range independently of electromobility” (no; yes, within the automotive value chain; yes, in sectors outside the automotive value chain; yes, within and outside the automotive value chain).

Automation was measured with one item (“How highly automated is production in your plant?”), using a five-point scale (highly automated, predominantly automated, mixed (including automated and manual lines), predominantly manual, purely manual). This item can only capture the overarching automation strategy; it does not differentiate between various levels of complexity and how much automation is networked digitally. We cannot capture changes in the type of automation—for example, through the use of digital production planning software, the use of predictive analytics, and other approaches discussed in the context of Industry 4.0 concepts (Butollo et al., 2019). However, our item can measure fundamental shifts in the weighting of automated processes and manual work.

The analysis of labor-use strategies was based on four indicators: the proportion of blue-collar workers with a VET degree and the proportion of jobs in production that require either (a) a VET degree or (b) at least 6 months of training or (c) a maximum of 1 week of training. All four variables were measured using a ten-point scale (0–9%, 10–19%, …, 90–100%). While the first variable provides information about the recruitment of skilled workers, the three other variables reveal the job design strategy of the companies. In addition, the proportion of fixed-term contracts and temporary agency work (measured using the same scale) as well as labor turnover are used.

Two control variables supplemented the analysis of labor-use strategies. First, the unionization rate (proportion of union members in total employment in the plant) was used as an indicator of labor power. It is assumed that the stronger the union organizational in the plant, the more influence the union can exert on labor-use strategies. Second, the country (or region) of origin of the company was taken into account, as companies often try to transfer their training approaches to foreign locations. The data from the survey allows a distinction to be made between (a) Germany, (b) other countries in Western and Northern Europe, (c) the countries of Central Eastern and South Eastern Europe, (e) America, (f) Asia and (g) other countries.

Lead plant roles and the twin transition

Development of lead plant roles in Germany and CEE/SEE

A first core finding is a slight weakening of lead plant roles in Germany (Table 2). The proportion of plants responsible for the introduction of new products and new technologies as well as supporting other plants in product ramp-ups has decreased, but only slightly. The decrease is not statistically significant, so we should not over-interpret it: a high proportion of plants in Germany continue to take on lead plant functions.

Table 2.

Lead plant roles in Germany and CEE/SEE.

	2016	2024	Test (2024/2016)
Germany
Lead plant indicators:
Share of plants responsible for introducing new products	48.4%	41.5%	Chi² = 3.18 p = 0.204
Share of plants responsible for introducing new technologies	43.9%	36.0%	Chi² = 2.36 p = 0.307
Lead_plant_index	0.19 (s.e. 0.07)	0.05 (s.e. 0.07)	T-test, p = 0.176
CEE/SEE
Lead plant indicators:
Share of plants responsible for introducing new products	27.2%	29.0%	Chi² = 0.47 p = 0.790
Share of plants responsible for introducing new technologies	20.8%	24.0%	Chi² = 0.75 p = 0.685
Lead_plant_index	−0.18 (s.e. 0.07)	−0.18 (s.e. 0.08)	T-test, p = 0.999

Note. “Don’t know” answers excluded.

In CEE/SEE, the proportion of plants responsible for the introduction of new products and new production technologies has increased slightly, but the gap to Germany remains considerable. There is a stagnation of an upgrading trend which was very dynamic in the 2010s.

If we look at the indicator of responsibility for the introduction of new products, the changes in lead plant roles differ depending on the product area. In Germany and CEE/SEE, lead plant roles in the traditional areas of combustion engine technology (engine, powertrain, and chassis) remain relatively stable, but are decreasing in the areas of exterior, interior, and electronics components. Exterior and interior components tend to be low R&D products that are under strong pressure to relocate to low-wage countries. Electronics is an area in which there is strong competition from companies in China, South Korea, and Japan.

Lead plants show a more positive employment trend (positive correlation coefficients between lead plant roles and employment) than plants without lead plant roles, but this correlation changes over time (Table 3). In 2016, that is, in the period of a strong growth of automotive markets, employment growth was observed both in lead plants and in plants without lead plant roles. However, lead plants built up more employment in the white-collar sector than non-lead plants, both in Germany and in CEE/SEE. Apparently, lead plants required more highly qualified personnel, for example, in engineering. In 2024, the increase in white-collar employment came to an end, both at lead plants and plants without lead plant functions. There are employment losses in the blue-collar areas, although these are lower in German lead plants than in plants without lead plant functions.

Table 3.

Employment in plants with and without lead plant roles.

	2016	2024
Germany
Change in blue-collar employment (last 5 years)
Lead plants	3.1	2.8
No lead plants	3.0	2.4
Change in white-collar employment (last 5 years)
Lead plants	3.7	3.0
No lead plants	3.4	2.9
Correlation between lead_plant_index and change in blue-collar employment (last 5 years)	0.17 (p = 0.058)	0.17 (p = 0.034)
Correlation between lead_plant_index and change in white-collar employment (last 5 years)	0.32 (p = 0.000)	0.03 (p = 0.752)
CEE/SEE
Change in blue-collar employment (last 5 years)
Lead plants	4.4	2.7
No lead plants	3.6	2.4
Change in white-collar employment (last 5 years)
Lead plants	4.2	3.0
No lead plants	3.6	2.8
Correlation between lead_plant_index and change in blue-collar employment (last 5 years)	0.18 (p = 0.087)	0.09 (p = 0.401)
Correlation between lead_plant_index and change in white-collar employment (last 5 years)	0.18 (p = 0.101)	0.05 (p = 0.665)

Note. Change in employment measured by 5-item-scale (1 = strong decrease (>10%), 3 = no change, 5 = strong increase (>10%)). “Don’t know” answers excluded.

Transformation to electromobility in Germany and CEE/SEE

The electromobility as part of the twin transition affects plants with and without lead plant functions, but lead plants seem to enjoy certain advantage (Table 4). Both in Germany and CEE/SEE, lead plants report fewer negative effects of the transition on the demand for their products, as they are considerably more engaged in developing new products for electromobility.

Table 4.

Electromobility and product strategies, 2024.

	Germany	CEE
Electromobility is decreasing demand for our products
Lead plants	29.2%	28.0%
No lead plants	39.3%	53.3%
Correlation with lead_plant_index	0.03 (p = 0.662)	0.24 (0.027)
Plant started developing new products for electromobility
Lead plants	64.2%	54.2%
No lead plants	44.9%	37.1%
Correlation with lead_plant_index	0.21 (p = 0.010)	0.17 (0.126)

Note. “Don’t know” answers excluded.

Automation processes in Germany and CEE/SEE

An important finding is that our data shows no significant shifts in automation levels in the automotive supply industry (Table 5). The average level of automation remained much higher in Germany than in CEE/SEE (T-Test, p = 0.000). In CEE/SEE, the level of automation hardly changed between 2016 and 2024. The mixed production model remained dominant here: highly automated production of large series and manual production of small series. This model arose from the specific competitive advantages of the CEE/SEE locations. These locations adopted the automation technologies used in Germany and other high-wage locations for large series. Due to the quality requirements of automotive manufacturers and their audits and certifications of automotive supplier plants, automotive suppliers hardly differentiate between high-wage and low-wage locations with regard to automation technologies. However, in addition to high-volume production, many CEE/SEE locations also host manual production of small batches, which would not be profitable in Germany but is possible in CEE/SEE due to the lower labor costs.

Table 5.

Automation levels in Germany and CEE/SEE, 2016 and 2024.

	Germany		CEE/SEE
Production is:	2016	2024	2016	2024
Highly automated	30.7%	18.2%	6.4%	3.0%
Predominantly automated	22.9%	25.6%	13.6%	16.0%
Mixed	36.4%%	45.4%	59.2%	60.0%
Predominantly manual	10.0%	10.2%	20.8%	19.0%
Purely manual	0%	0.6%	0%	2.0%
T-test for 2016–2024 difference	p = 0.030		p = 0.519

Note. “Don’t know” answers excluded.

In Germany, we can observe a marginal shift towards this mixed production in the period between 2016 and 2024. The proportion of plants with exclusively high automation has decreased somewhat, while the proportion of mixed production (high automation of large series plus manual small series production) has increased slightly. This could be related to problems associated with the transition to electromobility. In some cases, automotive suppliers have had to build up electromobility component production in parallel with part production for combustion engine technology. Yet, the trend has been asymmetric: the quantities for electromobility are only increasing slowly. In addition, product diversification strategies are often linked to low unit volumes at the beginning.

Our findings reveal an aspect that has hardly been examined to date, namely, the persistence of manual forms of production both in Germany and in CEE/SEE. This finding does not contradict the trend towards digitalization and the implementation of Industry 4.0 concepts in the automotive industry that has been widely described in the literature both for Germany and for CEE/SEE (Krzywdzinski, 2021; Papulová et al., 2022; Szalavetz, 2019). They have documented a modernization of automation, increasing networking and data-based control of production, while radical changes in manual assembly areas have so far failed to materialize (Krzywdzinski, 2021). Our data shows that such manual production areas are kept where it is economical due to low quantities and/or low wages.

In Germany, lead plant roles are associated with significantly higher automation. Pearson’s correlation coefficient r between lead_plant_index and automation is 0.26 (2016) and 0.28 (2024), both with p < 0.01. In CEE/SEE, on the other hand, there is no statistically significant correlation between the two variables. Automation is not related to the employment numbers at the plant, but there are differences in automation levels by product groups.

Labor-use strategies between transformation and stability

A central question for us is how the changes in the lead plant roles and the twin transition relate to the labor-use strategies in Germany and CEE/SEE. We thus focus primarily on the role of skilled production work. We measure these on the one hand by the share of blue-collar workers with a VET degree, and on the other hand by variables that are oriented towards job design (share of jobs that require a completed VET; share of jobs that require a qualification of max. 6 months; share of jobs that require only max. 1 week of training).

Table 6 shows the development in Germany and in CEE/SEE between 2016 and 2024. In Germany, the work model based on skilled production work has remained stable. In the automotive supplier plants we analyzed, the share of blue-collar workers with a completed VET increased slightly from 50–59% to 60–69%. It is typical for the German automotive industry to recruit workers with vocational training, even if the jobs do not always require VET. However, the share of production jobs requiring VET has remained stable at 40–49% and unskilled jobs (max. 1 week of training) at 10–19%.

Table 6.

Labor-use strategies in Germany and CEE/SEE.

	Germany		CEE/SEE
	2016	2024	2016	2024
Share of production jobs with training requirements of:^#
Max. 1 week	2.4 (10–19%)	2.6 (10–19%)	4.3 (30–39%)	3.5 (20–29%)
Max. 6 months	3.2 (20–29%)	3.1 (20–29%)	2.9 (10–19%)	3.8 (20–29%)
Multi-year VET	5.5 (40–49%)	5.3 (40–49%)	3.9 (20–29%)	3.7 (20–29%)
Share of workers with VET^#	6.5 (50–59%)	7.0 (60–69%)	5.2 (40–49%)	4.0 (30–39%)
Share of precarious jobs:^#
Fixed-term contracts	1.2 (0–9%)	1.5 (0–9%)	2.0 (10–19%)	1.9 (0–9%)
Temp. Agency work	1.3 (0–9%)	1.4 (0–9%)	1.8 (0–9%)	1.4 (0–9%)
Labor turnover	3.1%	7.0%	12.4%	17.2%
T-test 2016/2024
Jobs max. 1 week	p = 0.453		p = 0.013
Jobs max. 6 months	p = 0.883		p = 0.006
Jobs multi-year VET	p = 0.497		p = 0.421
Workers VET	p = 0.060		p = 0.003
Fixed-term contracts	p = 0.000		p = 0.860
Agency work	p = 0.076		p = 0.003
Labor turnover	p = 0.000		p = 0.036

Note. # Scale 1 (0–9%) to 10 (90–100%).

In CEE/SEE, the proportion of workers with a VET qualification has fallen slightly from 40–49% to 30–39%. This may be related to demographic change. In the socialist states, there was often in-company vocational training, which collapsed after liberalization. In addition, the increased recruitment of migrant workers in the CEE/SEE automotive industry might have reduced this share. A small proportion of the measured change is attributable to Romania’s inclusion in 2024, as the proportion of workers with VET is particularly low there. However, the trend does not change when Romania is excluded from the calculation.

If we look at job design, the proportion of jobs that require a completed VET remains lower in CEE/SEE (20–29%) than in Germany (40–49%). We see the reason for this in the different patterns of division of labor between engineers and technicians on the one hand and workers on the other: In CEE/SEE, tasks of production line control, troubleshooting and optimization are largely performed by engineers and technicians, while in Germany skilled workers perform a significant share of these tasks. There are slight upgrading trends in CEE/SEE with regard to the (statistically significant) reduction of unskilled jobs (1 week of training) in favor of semi-skilled jobs (up to 6 months of training).

In the case of Germany, the marginal (but statistically significant) increases in the proportion of fixed-term contracts and in labor turnover documented in Table 6 can also be interpreted as signs of slight erosion tendencies in the high-skill labor-use strategy. In CEE/SEE, the share of fixed-term contracts remains largely stable and the use of temporary work is even decreasing somewhat. However, the average labor turnover increased from 12.4% to 17.2%, which certainly also contributes to the orientation towards the low-skill labor-use strategy.

What role do lead plant roles and the twin transition play in the development of labor-use strategies? We focus on three variables (Table 7): proportion of workers with a VET degree, proportion of jobs requiring a VET degree, and proportion of jobs requiring at most 1 week of training.

Table 7.

Labor-use strategies, OLS regression.

Dependent variable	Share of blue-collar workers with VET degree	Share of jobs requiring VET degree	Share of jobs requiring max. one week of training
Index_lead_plant	0.10 (0.06)	0.10 (0.06)	−0.07 (0.06)
Automation level	0.16 (0.05)^**	0.10 (0.05)^*	−0.15 (0.05)^**
CEE/SEE region (Germany as reference)	−0.17 (0.15)	−0.43 (0.14)^**	0.47 (0.14)^**
Year 2024 (2016 as reference)	0.22 (0.11)^*	0.01 (0.12)	−0.02 (0.11)
CEE/SEE#2024 interaction (Germany#2016 as reference)	−0.75 (0.19)^**	−0.11 (0.19)	−0.26 (0.19)
Unionization rate	0.03 (0.05)	−0.06 (0.05)	−0.02 (0.05)
Home country of company (Germany as reference)
North & West Europe	0.02 (0.13)	−0.05 (0.13)	0.09 (0.13)
CEE/SEE	0.32 (0.19)	−0.24 (0.20)	0.20 (0.20)
North & South America	0.43 (0.13)^**	0.31 (0.13)^*	−0.15 (0.14)
Asia	−0.17 (0.16)	−0.44 (0.17)^**	−0.05 (0.17)
Employment size	0.05 (0.05)	0.10 (0.05)^*	−0.03 (0.05)
Const	−0.01 (0.09)	0.22 (0.10)	−0.18 (0.10)
N	385	411	411
R²	0.24	0.16	0.11

Note. Model uses z-standardized values. **p < 0.01, *p < 0.05, (*) p < 0.1.

In the first step, we calculated a cross-sectional model with both years 2016 and 2024, in which we included the region (Germany and CEE/SEE), the lead plant index and the automation level as independent variables. As control variables, we included the unionization rate (as an indicator of labor power, which can affect the skill strategies of the plants), the employment size, the home country of the companies, the year, and the interaction between year and region. The home country of the companies is important because some companies try to implement their home country training systems globally, regardless of the institutional frameworks they find locally.

In the second step, we also used these variables to calculate a regression model for 2024 only, in which we included the variables for the development of new products for electromobility and for the diversification of the product range, which were only surveyed in this year. Both variables were far from reaching a statistically significant relation with the variables characterizing the labor-use strategies. For reasons of space, we omit these models here. Apparently, the efforts of the plants to adapt to electromobility have no effect on the labor-use strategies.

The central finding of the OLS analysis is the stability of the differences between Germany and CEE/SEE. The region remains the decisive variable with the largest coefficient and the highest statistical significance for the job design variables (jobs with VET, jobs with 1 week of training). Germany is characterized by a focus on skilled work, while the job design in CEE/SEE plants focuses on simple production jobs that can be filled with unskilled workers. This also applies to the proportion of workers with a VET degree, although the interaction between region and year is decisive here in the model: Germany has more workers with a VET degree, and the proportion increases slightly over time, while it decreases in CEE/SEE.

Lead plant roles and higher automation levels increase the reliance on skilled work, although the coefficients and (in the case of lead plant roles) significance levels are lower than for the country variable. It should be noted that there are some differences between Germany and CEE/SEE. The online appendix provides Table A2, in which the models were calculated separately for Germany and CEE/SEE. It shows that lead plant roles and automation are associated with greater use of skilled labor in Germany. Meanwhile, in CEE/SEE, the relationship between lead plant roles, automation and job design focusing on skilled work is weak, but at the same time automation in particular is leading to a shift from unskilled work to semi-skilled work with at least 6 months of training.

It is also worth mentioning that the degree of unionization as a proxy for labor power has no influence, neither in Germany nor in CEE/SEE.

Discussion

Our first finding is that lead plant roles in Germany have weakened slightly in the period between 2016 and 2024, from which, however, the CEE/SEE locations have not benefited. This confirms hypothesis H1, but with the restriction, that, despite the erosion trends, the proportion of German plants with lead plant functions remains high. We interpret this finding as the result of the rise of new competitors in the context of the twin transition, including Chinese companies and companies such as Tesla.

In CEE/SEE, it is clear that the upgrading momentum of the mid-2010s (Domański et al., 2017) has come to a halt. The proportion of locations with lead plant roles is no longer increasing. The whole region is thus at risk: the twin transition could jeopardize the automotive industry upgrading in Poland, Czechia, Slovakia, Hungary, and Romania.

Our second finding is that there are clear differences between Germany and CEE/SEE with regard to efforts to transition to electromobility. This confirms hypothesis H2. Our analysis shows that a significant proportion of automotive supplier locations in Germany are making efforts to develop new products for electromobility or to diversify their product range in order to compensate for the loss of demand for combustion engine components. Important reasons are the role of lead plants and the German government’s support for the transition. However, efforts to develop new products have so far hardly been able to compensate for employment losses due to the falling demand for cars. The innovation activities identified in our analysis will therefore remain precarious if European demand for electromobility does not increase significantly in the near future.

Compared to Germany, there is significantly less activity in CEE/SEE to adapt to electromobility. This is related to the lack of government policies supporting the transition and the continuing investments in combustion engine technologies due to the relocation of the production of combustion engine components and older products from Germany to CEE/SEE (Pavlínek, 2023; Szalavetz, 2022). In the medium term, however, the lack of innovation in the field of electromobility represents a looming problem for the CEE/SEE region.

Our third finding is a surprising development with regard to automation. Despite the continued relevance of Industry 4.0 concepts (Krzywdzinski, 2021), our data for the German automotive supplier industry shows an increase in mixed production models: highly automated high-volume production, but also an increase in low-automation small-volume production. This confirms hypothesis H3. We see this as a transitional phenomenon related to the fact that suppliers are forced to set up new production lines for electromobility in parallel to the ongoing production of combustion engine components. Due to low volume, however, electromobility production does not yet justify investment in automation. Nevertheless, the level of automation in Germany remains significantly higher than in CEE/SEE, where the mixed model (automated large-scale production plus manual small-scale production) has long been dominant due to low labor costs.

This development raises the question of labor-use strategies. Our fourth finding is a high degree of stability here, which means that we cannot confirm hypothesis H4. In Germany, the high proportion of skilled workers and a job design based on skilled labor remain central. Lead plant roles and automation have a slight supporting influence on the proportion of skilled labor, but the comparison of Germany with CEE/SEE in particular reveals how important the institutional framework is and the significance of its influence on corporate strategies (Bosch, 2025). So far, our data for Germany shows no impact of the transformation to electromobility on this labor-use strategy, but the marginal erosion of lead plant roles could have a long-term negative impact if it persists.

For CEE/SEE, our analysis also shows stability, albeit with ambiguous shifts. The labor-use strategy remains strongly focused on unskilled and semi-skilled work, even in automated work processes (Krzywdzinski, 2017; Pavlínek, 2017). This does not necessarily mean that all production workers are unskilled, although the proportion of workers with VET degrees decreased slightly between 2016 and 2024. Rather, it means that companies use forms of job design that emphasize standardization and narrow tasks and do not utilize the vocational skills that workers may have. As a slight upgrading trend, however, we see a shift in the job design from unskilled (max. one week of training) to semi-skilled work (max. six months of training). VET is not gaining in importance, but more restricted forms of company training and investments in skills are increasing (Olejniczak et al., 2020).

However, it is important to emphasize the limitations of our analysis. In our view, these lie in particular in the non-random sample, which means that we cannot generalize our findings beyond the cases we have analyzed. While we cover a large proportion of automotive suppliers in Germany, our sample in CEE/SEE is small.

Conclusions

In Europe, the twin transition is overlapping with a demand crisis in the automotive industry. This calls into question the existence of component plants, established labor-use strategies and also patterns of division of labor in the European production networks of automotive suppliers.

The overarching findings of this article include, firstly, the persistent difference between German and CEE/SEE locations of automotive suppliers and the end of the mid-2010s upgrading in CEE/SEE. Lead plant roles remain heavily concentrated in Germany; German locations are also more active in the transition to electromobility and have higher levels of automation than plants in CEE/SEE. In Germany, this is linked to a high-skilled labor-use strategy in the field of production work, while in CEE/SEE the focus remains on the use of unskilled and semi-skilled labor. Our concept of low-skill work in CEE/SEE does not contradict the high quality of university education or the pockets of vocational training that still exist in the region—rather, it refers to the fact that companies base the design of production jobs on narrow tasks and highly standardized content and make little use of potentially available vocational knowledge.

However, our analysis also shows a declining competitiveness and falling employment throughout the Germany-CEE/SEE production network. The trajectory of the twin transition is still unfolding, as new actors and technologies continue to reshape the market landscape.

Labor representatives face a dilemma in this context (Galgóczi, 2020). On the one hand, the long-term rational strategy seems to be to call for a strengthening of corporate activities for the transition to electromobility and for state support for these activities. Lead plants in particular have a good chance of successfully navigating this transition. On the other hand, at least some plants without lead plant functions and specializing in combustion engine technologies will have little chance of making the transition. In these cases, the labor representatives only have the option to “buy time,” that is, to accept concessions or force the management to slow down the transition in order to structure the reduction in employment in such a way that it can be managed socially. These are contradictory requirements that are very difficult to meet given the dynamic nature of developments.

Supplemental material

Supplemental Material - A lose-lose game? The twin transition and the division of labor in the European automotive supplier industry

Supplemental Material for A lose-lose game? The twin transition and the division of labor in the European automotive supplier industry by Martin Krzywdzinski, Monika Martišková, Patrik Gažo, Ştefan Guga, Tibor Meszmann, and Katarzyna Rakowska in European Journal of Industrial Relations.

Footnotes

ORCID iDs

Martin Krzywdzinski

Monika Martišková

Patrik Gažo

Ethical considerations

No ethical approval was required for this study. All respondents were fully informed about the study and confirmed their consent. No personal data was collected.

Funding

Martin Krzywdzinski disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: German Research Fund (DFG), grant no. 495011805. The open access publication was funded by the WZB Berlin Social Science Center.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

Data available from authors on good request.*

Supplemental material

Supplemental material for this article is available online.

Author biographies

Martin Krzywdzinski is head of the research group “Globalization, Work, and Production” at WZB Berlin Social Science Center, director at the Weizenbaum Institute for the Networked Society, and professor of international labor relations at Helmut Schmidt University Hamburg.

Monika Martišková is a researcher at Central European Labour Studies Institute (CELSI) Bratislava, Slovakia and at the department of Social Geography and Regional Development at Charles University in Prague, Czechia.

Patrik Gažo is a research fellow at the Institute for Sociology of the Slovak Academy of Sciences (SAS) and at the Central European Labour Studies Institute (CELSI).

Ștefan Guga is director at Syndex Romania, a labor expert, and a doctor of sociology and social anthropology at the Central European University (CEU).

Tibor Meszmann is researcher at the Central European Labour Studies Institute (CELSI) and labor activist.

Katarzyna Rakowska is researcher at the Centre of Migration Research, University of Warsaw.

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