Finds documents with both search terms in any word order, permitting "n" words as a maximum distance between them. Best choose between 15 and 30 (e.g. NEAR(recruit, professionals, 20)).
Finds documents with the search term in word versions or composites. The asterisk * marks whether you wish them BEFORE, BEHIND, or BEFORE and BEHIND the search term (e.g. lightweight*, *lightweight, *lightweight*).
This article delves into the growing importance of intangible capital, such as software and organizational capital, in modern economies, with a focus on France and Germany. The analysis reveals significant differences in investment patterns between the two countries, with France investing more heavily in these areas. The article identifies potential measurement issues, suggesting that Germany's investments in software and organizational capital may be underestimated, while France's investments appear slightly overestimated. The comparison highlights stark contrasts in software and organizational capital investments across various sectors, with France consistently investing more. The article also explores differences in national accounting practices and measurement methodologies, which may contribute to these discrepancies. It concludes with a call for harmonization of data measurement at both national and EU levels, emphasizing the need for further research and reform in accounting practices to accurately reflect intangible investments. By reading the full article, professionals will gain insights into the complexities of measuring intangible capital, the potential impact of measurement issues on economic growth, and the importance of harmonizing accounting practices across Europe.
AI Generated
This summary of the content was generated with the help of AI.
Abstract
In this article, we highlight important differences in capital investment and capital stock in intangible assets between France and Germany, which we attribute to potential measurement issues between the two countries. Using data from the latest 2025 EUKLEMS/INTANProd release for the period 1995 to 2021, we identify investment in software and databases, along with investment in organizational capital, as key drivers of these differences. Investment in software appears to be three times higher in France than in Germany, while organizational capital is about two and a half times larger in France. Given the comparable economic growth patterns of these two countries in recent decades, we believe these measurement discrepancies could have significant implications for understanding both past growth trends and prospects for future growth.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
1 Introduction
Intangible capital is becoming increasingly important as a productive factor in modern economies, due to their transition towards becoming increasingly knowledge-based and because its inherent nature generates spillover effects that are not typical of traditional inputs (Audretsch and Belitski 2020; Nonnis et al. 2023; Roth 2022, 2024). The rise of artificial intelligence (AI) and the complementary intangible investments it requires are accelerating the shift towards intangible capital, such as R&D and software, which are gradually replacing tangible components as a major input in production in the most advanced economies (Roth and Mitra 2024). The recent reports from Mario Draghi (Draghi 2024a, b) support this trend, encouraging European firms and policymakers to foster investment in advanced technologies to catch up with major players, such as the US and China.
Moreover, in their attempt to predict the impact of this new generation of investments, researchers emphasize the delayed effect of disruptive and general-purpose technologies. These technologies typically show positive effects on productivity only with the passage of a certain period of time. Economists often refer to this phenomenon as the J‑curve effect (Brynjolfsson et al. 2021), where the adjusted productivity curve initially declines, reflecting a decrease in total factor productivity (TFP), followed by a subsequent increase. In other words, mismeasurement of intangible capital may partially explain low productivity in many countries, particularly in the US (Brynjolfsson et al. 2021) and in Scandinavia (Fernald et al. 2025); and less so in major Western European economies and Japan (Bounfour et al. 2024). Given this, it is essential to accurately assess the level of intangible capital investments made by businesses and countries to evaluate their potential impact on labor productivity.
Advertisement
However, this task is challenging, as intangible capital is by nature difficult to measure and quantify compared to other types of capital investments (Corrado et al. 2022; Bavdaž et al. 2023). Furthermore, to a certain extent, national and business accounting practices remain anchored to traditional models centered on tangible assets such as buildings and machinery. Nevertheless, considerable progress has been made in recent years towards harmonizing the classification of intangible capital and incorporating it into business and national accounts, particularly with the seminal classification proposed by Corrado et al. (2005). Despite these advancements, however, many intangible expenditures are still not properly capitalized and treated as investments, even though their effects span multiple years, much like any other type of investment.
In this research, we compare intangible investments in France and Germany, two leading economies in Europe, and outline the key characteristics of each country’s approach to these investments. This comparison is motivated by the centrality of the two economies within the EU, the similar LPG trends highlighted in Fig. 1, and their comparable industrial structures, as elaborated by Delbecque and Bounfour (2011) and shown in Figs. 9, 10 and 11 in the Appendix. Our analysis allows us to identify potential measurement issues, with our findings suggesting that Germany’s investments in software and organizational capital are likely too low (underestimated), while France’s investments in these assets, in comparison, appear as slightly too high (overestimated).
Fig. 1
Labor Productivity Growth (in %) in Germany and France, 1996–2021 Notes: Labor productivity is computed as gross value added divided by the total hours worked by employees, with both measured in real terms using chained linked volumes (2020). (Source: Authors’ own calculations based on the EUKLEMS/INTANProd 2025 database (Bontadini et al. 2023))
Our research includes a call for harmonization of the measurement of software and organizational capital at the national and European levels, which is necessary to allow EUy countries to face the unprecedented challenges the AI revolution presents.
This paper is structured as follows. The next section presents some key stylized facts about intangible capital stock and investment in France and Germany. Section 3 provides a more detailed comparison focused on software and organizational capital, while Sect. 4 discusses potential issues in the measurement of the two assets in the two countries. Finally, Sect. 5 concludes.
Advertisement
2 Intangible capital stock and investment in France and Germany
France and Germany have exhibited contrasting trends in investment in intangible capital over the period 1995–2021.1 As shown in Table 1, which lists real2 average intangible investment as a percentage of real gross value added (adjusted for intangibles)3 during this period, France has been one of the top investors among the 20 European countries and the US included in the sample. France ranked fourth, with 16% of value-added, trailing only Sweden, Finland and the US. In contrast, Germany invested only 10.7% of value-added, placing it among the countries with only mid-level intangible investment in the sample. This difference stems from both: intangibles included in national accounts (such as computer software and databases, R&D, and other intellectual property products4) and those not included in national accounts (such as organizational capital, brand, design and training).
Table 1
National and non-national account real intangible capital investment as a percentage of real gross value added corrected for intangibles, average values 1995–2021.
Nat. Acc. Intangible Capital
Non-Nat. Acc. Intangible Capital
Total Intangible Capital
Sweden
8.1
9.7
17.8
Finland
6.4
10.6
16.9
United States
6.4
9.9
16.3
France
5.3
10.7
16.0
United Kingdom
4.5
10.0
14.5
Netherlands
4.3
9.0
13.3
Denmark
5.3
8.0
13.3
Slovenia
3.6
8.9
12.5
Latvia
1.9
9.9
11.8
Czech Republic
4.2
7.3
11.5
Germany
4.0
6.7
10.7
Slovakia
1.9
8.2
10.1
Estonia
2.6
7.4
10.0
Hungary
3.5
6.0
9.5
Italy
3.6
5.7
9.3
Austria
4.8
4.5
9.3
Romania
1.7
6.8
8.5
Spain
3.1
5.0
8.0
Lithuania
1.7
6.3
8.0
Luxembourg
1.4
6.2
7.6
Greece
1.5
5.9
7.4
Notes: As in Corrado et al. (2005), national account intangibles are software and databases, R&D and other intellectual property products, while non-national account intangibles are organizational capital, brand, design, and training. Non-national account intangibles also include new financial products in the EUKLEMS/INTANProd database. Following the existing literature, we exclude this variable for theoretical reasons (see footnote 14 in Gros and Roth 2012).) The figures are average values over the entire sample period (1995–2021) and express the ratios of investment in intangible and tangible capital to gross value-added, all measured in real terms using chained linked volumes (2020). Nat. Acc. National Accounts
Source: Authors’ own calculations based on the EUKLEMS/INTANProd 2025 database (Bontadini et al. 2023)
The different investment patterns are further illustrated in Fig. 2, which tracks tangible and intangible real capital investment over time. While tangible capital investments were comparable between the two countries, with Germany slightly ahead, France invested significantly more in intangibles, consistently above the EU15 average, while Germany remained below.
Fig. 2
Tangible and intangible real capital investment as a percentage of gross value added over time, 1995–2021. Notes: The figures represent the ratios of investment in tangible (a) and intangible (b) capital to gross value-added over time, all measured in real terms using chained linked volumes (2020). (Source: Authors’ own calculations based on the EUKLEMS/INTANProd 2025 database (Bontadini et al. 2023))
What drives these differences? Fig. 3 breaks down the various types of intangible capital, revealing that the largest disparities come from organizational capital, in which France invested 5.3% compared to Germany’s 2%, and software, in which France invested 2.4%, while Germany invested only 0.7%.
Fig. 3
Real average intangible capital investment, expressed as a percentage of real gross value added for the period 1995–2021. Notes: The bars in the figure are average values over the entire sample period (1995–2021) and represent the percentage of investment in each intangible asset relative to gross value-added, all measured in real terms using chained linked volumes (2020). Ratios denoted as “FR/DE ratio” above the bars indicate the ratio between the French and German values for each intangible capital investment type. OIPP other intellectual property products. Org. Cap. Organizational Capital. Nat. Acc. National Accounts. (Source: Authors’ own calculations based on the EUKLEMS/INTANProd 2025 database (Bontadini et al. 2023))
These country differences are very similar when looking at capital stock. Figure 4 shows real average capital stock as a percentage of real gross value added (corrected for intangibles) for the period 1995–2021. In France, organizational capital reached nearly 13% of value added, while in Germany, it was less than 5%. Similarly, software accounted for over 5.5% of value added in France, compared to 1.9% in Germany.
Fig. 4
Real average intangible capital stock, expressed as a percentage of real gross value added for the period 1995–2021. Breakdown of single intangible capital types. Notes: The bars in the figure are average values over the entire sample period (1995–2021) and represent the percentage of each intangible asset’s capital stock relative to gross value added, all measured in real terms using chained linked volumes (2020). Ratios denoted as “FR/DE ratio” indicate the ratio between the French and German values for each intangible capital stock type. OIPP other intellectual property products. Org. Cap. Organizational Capital. Nat. Acc. National Accounts. (Source: Authors’ own calculations based on the EUKLEMS/INTANProd 2025 database (Bontadini et al. 2023))
Figure 5 breaks down real intangible capital stock as a percentage of real gross value added over time for four types of intangible capital: software, R&D, organizational capital and training, across the market economy (first row), the Goods sector (second row), and the Services sector (third row).5 The major differences in software and organizational capital in Germany and France are confirmed, with these gaps remaining consistent over time and even widening over the last decade in the case of software, particularly in the services sector.
Fig. 5
Real intangible capital stock, expressed as a percentage of real gross value added over time (1995–2021). Notes: Breakdown of single intangible capital types. Market economy, Goods and Services sectors. The figures represent the ratios of capital stock in four types of intangible assets to gross value-added over time, all measured in real terms using chained linked volumes (2020). Sector K was excluded from the R&D capital stock series due to apparent irregularities in the French data of the variable. To ensure comparability, the sector was excluded for both France and Germany. (Source: Authors’ own calculations based on the EUKLEMS/INTANProd 2025 database (Bontadini et al. 2023))
3 Understanding the discrepancy: is there a measurement issue?
France and Germany have experienced similar labor productivity growth rates in recent decades (Guillou et al. 2018; Nonnis et al. 2024), and the discrepancies in investment levels between the two countries cannot be solely explained by differences in efficiency. A more plausible explanation may lie in measurement issues concerning investment in software and organizational capital in the two countries.
We begin by examining the measurement of software investments in France and Germany in more detail. Table 26 shows the average software investment rates as a percentage of value added for selected industries in both countries, including total manufacturing (C), manufacture of machinery and equipment (C28), manufacture of motor vehicles (C29–C30), information and communication (J), financial and insurance activities (K), and professional, scientific and technical activities (M). As mentioned above, the ratio (FR/DE) for the market economy is 3.21 (2.38/0.74), indicating that France invested, on average, 2.38% of its value added in software compared to Germany’s 0.74% over the period from 1995 to 2021. While this difference is already puzzling for two equally advanced and deeply integrated economies, it becomes even more compelling when analyzing single sectors and sub-sectors. For instance, within manufacturing, France invested almost five times more in software. Looking more closely at specific sub-sectors associated with Germany’s strong industrial performance, such as the manufacture of motor vehicles or the manufacture of machinery and equipment, we find that France invested 3.54 and 6.47 times more in software per unit of value-added, respectively. A similar picture emerges in business services sectors K and M, in which France invested up to almost six times (Sector M) more than Germany.
Table 2
Investment in software and organizational capital as percentage of gross value added in selected sectors, France and Germany, average values 1995–2021.
Industry
Industry Code
Software
Organizational Capital
DE
FR
FR/DE
DE
FR
FR/DE
Manufacturing
C
0.51
2.46
4.78
2.08
4.83
2.32
Manufacture of machinery and equipment
C28
0.33
2.11
6.47
2.20
4.57
2.08
Manufacture of motor vehicles
C29–C30
1.11
3.95
3.54
2.08
3.28
1.57
Information and communication
J
4.32
7.58
1.75
2.92
6.88
2.36
Financial and insurance activities
K
0.92
4.13
4.49
3.41
10.70
3.14
Professional, scientific, and technical activities
M
0.67
3.84
5.74
4.00
7.74
1.93
Market economy
MARKT
0.74
2.38
3.21
2.01
5.29
2.63
Notes: Sectors are selected for their relative importance and comparability in the two countries. Numbers refer to average values as a percentage of gross value added over the period 1995–2021. All variables are measured in real terms using chained linked volumes (2020)
Source: Authors’ own calculations based on the EUKLEMS/INTANProd 2025 database (Bontadini et al. 2023)
In Table 2, differences in organizational capital are also considerable. France invested more than three times as much in sector K and more than twice as much in almost all sectors, except manufacturing of motor vehicles, where the ratio is still above 1.5. These huge differences are also evident over time, as shown in Fig. 6, which displays the same sectors as Table 2, but over the whole period in our sample.
Fig. 6
Investments in software and organizational capital in selected sectors over time, France and Germany, 1995–2021. Notes: The figures represent the percentage of investment in software and organizational capital relative to gross value-added over time in selected sectors, all measured in real terms using chained linked volumes (2020). We refer to Table 2 for the full names of the sectors. (Source: Authors’ own calculations based on the EUKLEMS/INTANProd 2025 database (Bontadini et al. 2023))
These large discrepancies in intangible investments between France and Germany might be partly due to an under-reporting of intangibles in Germany (Roth et al. 2023) and a potential slight over-reporting in France (Guillou et al. 2018). Germany’s investment in software appears to be unusually low, with France investing more than three times more on average over the period considered (2.4% in France vs 0.7% in Germany) and significantly less than all other EU countries in our sample. Moreover, the low investment levels in Germany, as highlighted by official sources and well-established databases based on them, such as the EUKLEMS/INTANProd database, which we utilize in this study, are at odds with the actual recorded industrial and firm performances in Germany. Our results indicate that Germany’s investment in software should be higher than currently depicted in the German national accounts.
We want to highlight at this instance that addressing these measurement issues related to investments in software would increase software and, consequently, intangible capital investments in Germany. Given that software investments are incorporated in the official German GDP calculation, this would result in an increase of Germany’s official GDP, which would have important effects on past and future growth trends and perspectives.
Moreover, possible errors may also affect the measurement of organizational capital. The large differences between the two countries reflect differences in the number of managers between France and Germany, as we will explain in detail in the next section. We suggest, however, that Germany’s management share is likely too low, whereas France’s share seems to be disproportionately high.
To further corroborate the somewhat puzzling figures presented above, it is useful to look at several international firm-level surveys conducted in recent years. For example, Fig. 7 shows the percentage of investment relative to total investment7 according to the 2023 European Investment Bank (EIB) Investment Survey.8 The figure reveals that investment in organizational and business process improvements has been comparable in the two countries, with German firms actually investing more (6%) than French firms (4%). Regarding software, data, IT networks, and website activities, it is again German businesses (17%) that invested considerably more than their French counterparts (10%). This result is entirely at odds with what is shown in the EUKLEMS/INTANProd data. Moreover, another international firm-level survey, shown in Fig. 8, which was conducted under the H2020 project GlobalInto, confirms that German firms invested much more than French firms in software in 2019 (1.9% vs. 0.5% of total turnover), while the two invested similar amounts in organizational capital (1.2% Germany vs. 1.4% France).9
Fig. 7
Business investment as a percentage of total investment in the EIB investment survey. Notes: The figures represent average values of firms’ responses to the question in the EIBIS survey: “In the last financial year, how much did your business invest in each of the following with the intention of maintaining or increasing your company’s future earnings?”. Ratios denoted as “FR/DE ratio” above the bars indicate the ratio between the French and German values for each intangible capital investment type. Nat. Acc. National Accounts. (Source: Authors’ own calculations based on data from the EIB Investment Survey 2023)
Business investment as a percentage of firm turnover according to the GlobalInto survey. Notes: The figures represent average values of firms’ responses to the question in the GlobalInto survey: “In 2019, which percentage of the enterprise’s turnover was spent on [each category]?”. Ratios denoted as “FR/DE ratio” above the bars indicate the ratio between the French and German values for each intangible capital investment type. Org. Cap. Organizational Capital. Nat. Acc. National Accounts. (Source. Authors’ own calculations based on the GlobalInto survey (Bloch et al. 2024))
4 Comparing measurement and accounting practices in France and Germany
Given the pronounced differences between France and Germany highlighted in the previous sections and their close economic integration within the EU and eurozone, the similar LPG patterns of the two countries shown in Fig. 1 suggest that differences in investment efficiency alone cannot fully explain these trends. Instead, they are more likely the result of disharmonized accounting of software investment and measurement of investments in organizational capital in the two countries. In this section, we highlight some of the main differences in software accounting practices and in the measurement of organizational capital between the two countries.
4.1 Software
We begin by comparing the national accounting practices from INSEE and Destatis. Interestingly, the differences were even more staggering before the recent revision by INSEE (2024), which corrected some of these discrepancies related to software. The first major difference lies in how own-account software is calculated, which already accounts for a very large portion of total differences. To give an idea, after the 2024 INSEE correction, own account software in France amounted to € 32.3 billion in 2019 (INSEE 2024, p. 3). In comparison, own account software amounted to only € 11.5 billion in Germany in 2016 (Destatis 2022, p. 337, par. 5452). To compute own account software, Germany’s Destatis uses employment data derived from the annual micro censuses, applying a percentage of time spent on software production to workers whose main activity is software development (Destatis 2022, p. 332, par. 5445). In contrast, France’s INSEE applies shares directly to employees’ wages, which, from 2024, are computed directly from the nominative social declaration (INSEE 2024, p. 2).
A second relevant difference consists in how Cloud Computing and Software-as-a-Service (SaaS) expenses are treated. Germany explicitly excludes those expenses from capitalization, whereas France included them until 2024. With the new INSEE correction, the two methodologies now seem to be aligned, except for the way in which the two institutes refer to them.10 Similarly, consulting services are not included as Gross Fixed Capital Formation (GFCF) in Germany, while they were in France until the 2024 revision. The French document, however, does not explicitly mention maintenance and repair expenses, suggesting that, unless they are implicitly included in the expression, they may still be treated as GFCF, unlike in Germany.
Importantly, the German methodological report highlighted difficulties in measuring purchased software from survey data and separating the above-mentioned categories11, as well as a potential issue due to the lack of survey data for specific industries.12 It is not clear from Destatis reports (2016, 2022) to what extent these issues might lead statistical offices to rely on estimates that could be biased or incorrect.
A final potential issue relates to the distinction between R&D and software costs, which can be difficult to disentangle in practice, although the National Accounts treat them as distinct capital assets. According to ESA guidelines, R&D on software products should be recorded as investment in software, not R&D.13 However, in Germany, large corporations such as Bosch, Siemens, and car manufacturers employ a large number of software developers in their R&D departments. For example, in its 2022 annual report (Bosch 2022, p. 7), Bosch indicates that 44,000 of its 85,500 R&D employees are software developers. At Siemens, software revenues in industrial process technology account for approximately one-third of the company’s total revenues (Siemens 2024, p. 7), suggesting that software development is a large component of their innovation activity. These figures indicate that, in practice, drawing a strict line between R&D and software activities may be challenging, and such classification issues could contribute to differences at national level.
4.2 Organizational capital
Regarding the measurement issues related to organizational capital, the latest 2025 version of the EUKLEMS/INTANProd data release (Bontadini et al. 2023) measures investments in own account organizational capital—following Corrado et al. (2005, p. 29)—by attributing 20% of manager salaries as investment in organizational capital.14 The EUKLEMS/INTANProd data release (Bontadini et al. 2023) uses the Structure of Earnings Survey,15 which provides information on the annual earnings and number of employees by occupation.16 The crucial question here pertains to the quantity of managers in France and Germany. Might it be that France has a significantly higher proportion of managers, compared to other positions, than does Germany? And might this discrepancy be attributed to a lack of harmonization in the ISCO classification systems in France and Germany? And finally, might this also explain the larger investment rates, as depicted in Table 2, in organizational capital for France compared to Germany?
Our research seems to suggest the validity of this interpretation. Whereas in Germany official statistics indicate a management share of 5% within the active population in 2017 (Schuster and Strahl 2019), in France, we observe a four-fold higher proportion of 21.7% in 2022, which represents a significant increase from 8% in 1982 (Brillet 2024). It is likely that such strong differences in the share of managers in France and Germany do not reflect differences in business and organizational models but rather point to a lack of harmonization in the ISCO classifications between the two countries. This discrepancy may explain a large portion of the significant variance in investments in organizational capital between France and Germany.
Regarding the specific measurement of investments in own-account organizational capital and considering our research results in line with the recommendations of Stehrer et al. (2019), who constructed the first harmonized intangible EUKLEMS dataset,17 own-account organizational capital investment data should be handled with great care, considering potential measurement issues in the ISCO classifications. Future research efforts would need to refine both the conceptual and the empirical parts of the survey for organizational capital.
With this end in mind, one possible approach would be to adopt the method devised by Squicciarini and Le Mouel (2012) and Le Mouel and Squicciarini (2015), which relies on tasks from the OECD Programme for the International Assessment of Adult Competencies (PIAAC), rather than occupational titles. Their method identifies organizational capital-intensive occupations for each country and applies the 20% wage share to them, rather than limiting it to managers. As a result, this method avoids possible discrepancies in country-specific ISCO occupational definitions. One further option might be to examine organizational processes and identify the resources allocated to areas such as R&D, procurement, production, service-delivery, etc. (Bounfour 2011).
5 Conclusions
We find that the strong difference in intangible investments in software and organizational capital in France and Germany is partially attributable to measurement problems. Our analysis suggests that software and organizational capital investments in Germany are likely to be under-reported in national accounts and databases. Recent firm-level surveys, such as the European Investment Bank (EIB) Investment survey and the GlobalInto intangibles survey, which report higher investment levels in software in Germany vis-à-vis France and similar investment levels in organizational capital between the two countries, lend support to this hypothesis.
As a result, the first policy implication to draw from our findings is the need for further harmonization of data measurement at both bilateral national and EU levels. This requires a more detailed exploration of the way in which national accounts are calculated in the case of software and calls for greater harmonization at the European level. Achieving this goal might require a collaborative effort involving national statistical agencies, national ministries of economics, Eurostat, and other relevant stakeholders. This also applies to the harmonization of investments in organizational capital, particularly regarding ISCO classifications. What is clearly needed is reevaluation of how investment in organizational capital is measured, encompassing both conceptualization and survey dimensions. In line with earlier work by Stehrer et al. (2019), we opt for a cautious usage of the own-account data of organizational capital, due to potential biases in the ISCO classification, and recommend further harmonization between Germany and France as well as across the EU.
More broadly, we call for a prompt and straightforward reform of firm accounting practices with a view to disclosing the key components of intangibles, making this information accessible to the various stakeholders. Future research efforts should give priority to addressing these measurement issues in order to ensure accuracy in the harmonization of investment data for France and Germany.
Furthermore, due to the importance of intangibles for firms’ performance and economic growth, urgent attention should be given to the way in which they account for their investments, particularly under current accounting rules such as IAS 38. The present IFRS rules for capitalizing intangibles emphasize separability, control, and certainty of future benefits and diverge from the intrinsic nature of intangible investments, such as complementarity, commonality and spillover effects, and uncertainty. There is a need to revisit these rules to align European firms’ accounting practices in order to allow them to disclose their levels of investment in intangible assets in a straightforward and easy-to-implement way.
Acknowledgements
We would like to thank Christoph Maier, Alessio Mitra, Christian Rammer, Julien Ravet, Peter Voigt, Grégory Claeys, Rémi Lallement and all the participants at the research seminar at France Stratégie on January 7, 2025 in Paris, and two anonymous reviewers for their excellent comments.
Conflict of interest
A. Nonnis, F. Roth and A. Bounfour declare that they have no competing interests.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Sectoral value added as a share of total value-added (in %). Notes: The figure represents average values over the entire sample period (1995–2021) and shows the ratio of gross value added in each sector to total value-added for the whole market economy, all measured in real terms using chained-linked volumes (2020). (Source: Authors’ own calculations based on the EUKLEMS/INTANProd 2025 database (Bontadini et al. 2023))
Labor compensation per hour worked by sector. Notes: The figure represents average values over the entire sample period (1995–2021) and shows the ratio of compensation of employees, measured at current prices, to the total number of hours worked in each sector. (Source: Authors’ own calculations based on the EUKLEMS/INTANProd 2025 database (Bontadini et al. 2023))
Sectoral employment as a share of total employment (in %). Notes: The figure represents average values over the entire sample period (1995–2021) and shows the ratio of total number of workers in each sector to the total number of workers in the entire market economy. (Source: Authors’ own calculations based on the EUKLEMS/INTANProd 2025 database (Bontadini et al. 2023))
Investment in software and organizational capital as percentage of gross value added across sectors, France and Germany, average values 1995–2021.
Industry Name
Industry Code
Software
Organizational Capital
DE
FR
FR/DE
DE
FR
FR/DE
Agriculture, forestry and fishing
A
0.13
0.06
0.46
0.09
0.76
8.26
Mining and quarrying
B
0.50
1.89
3.81
2.32
3.02
1.30
Manufacturing
C
0.51
2.46
4.78
2.08
4.83
2.32
Manufacture of food products; beverages and tobacco products
C10–C12
0.05
0.87
16.99
2.39
5.02
2.10
Manufacture of textiles, wearing apparel, leather and related products
C13–C15
0.15
1.42
9.63
1.71
4.75
2.77
Manufacture of wood, paper, printing and reproduction
C16–C18
0.08
1.32
17.09
1.99
6.50
3.27
Manufacture of coke and refined petroleum products
C19
0.11
2.91
25.99
2.66
48.94
18.43
Manufacture of chemicals and chemical products
C20
0.53
1.67
3.15
1.84
4.89
2.65
Chemicals; basic pharmaceutical products
C20–C21
0.88
1.66
1.88
1.71
4.63
2.71
Manufacture of basic pharmaceutical products and pharmaceutical preparations
C21
1.70
1.61
0.95
1.38
4.00
2.90
Manufacture of rubber and plastic products and other non-metallic mineral products
C22–C23
0.16
1.01
6.13
1.93
5.55
2.87
Manufacture of basic metals and fabricated metal products, except machinery & equipment
C24–C25
0.13
1.07
8.44
1.86
5.25
2.82
Manufacture of computer, electronic and optical products
C26
2.33
26.48
11.39
3.16
7.38
2.34
Computer, electronic, optical products and electrical equipment
C26–C27
0.95
12.59
13.21
2.56
5.22
2.04
Manufacture of electrical equipment
C27
0.33
2.05
6.27
2.19
3.49
1.59
Manufacture of machinery and equipment n.e.c.
C28
0.33
2.11
6.47
2.20
4.57
2.08
Manufacture of motor vehicles, trailers, semi-trailers and of other transport equipment
C29–C30
1.11
3.95
3.54
2.08
3.28
1.57
Manufacture of furniture; jewelry, musical instruments, toys
C31–C33
0.21
1.48
7.08
1.84
3.62
1.96
Electricity, gas, steam and air conditioning supply
D
0.80
2.62
3.27
1.35
2.44
1.80
Electricity, gas, steam; water supply, sewerage, waste management
D‑E
0.56
2.14
3.80
1.34
2.68
2.01
Water supply; sewerage, waste management and remediation activities
E
0.22
0.93
4.21
1.26
3.37
2.67
Construction
F
0.23
0.25
1.07
1.05
3.80
3.61
Wholesale and retail trade; repair of motor vehicles and motorcycles
G
0.69
1.61
2.32
1.38
5.80
4.19
Wholesale and retail trade and repair of motor vehicles and motorcycles
G45
0.46
–
–
1.09
4.06
3.71
Wholesale trade, except motor vehicles and motorcycles
G46
0.87
–
–
1.36
7.22
5.31
Retail trade, except motor vehicles and motorcycles
G47
0.58
–
–
1.57
4.93
3.14
Transportation and storage
H
0.41
0.77
1.89
1.12
3.76
3.37
Land transport and transport via pipelines
H49
0.29
–
–
1.16
4.04
3.49
Water transport
H50
0.36
–
–
0.49
11.33
23.12
Air transport
H51
0.23
–
–
0.33
5.29
16.15
Warehousing and support activities for transportation
H52
0.56
–
–
1.13
2.79
2.48
Postal and courier activities
H53
0.42
–
–
1.46
3.14
2.15
Accommodation and food service activities
I
0.34
0.23
0.69
0.35
2.22
6.28
Information and communication
J
4.32
7.58
1.75
2.92
6.88
2.36
Publishing, motion picture, video, television program production; sound recordingsprogramming and broadcasting activities
J58–J60
5.63
4.00
0.71
2.24
6.94
3.10
Telecommunications
J61
3.03
16.02
5.28
2.98
7.67
2.58
Computer programming, consultancy, and information service activities
J62–J63
3.73
6.25
1.67
3.30
6.31
1.91
Financial and insurance activities
K
0.92
4.13
4.49
3.41
10.70
3.14
Real estate activities
L
0.03
0.08
2.38
–
0.82
–
Professional, scientific and technical activities
M
0.67
3.84
5.74
4.00
7.74
1.93
Professional, scientific and technical activities; administrative and support service activities
M‑N
0.59
2.99
5.10
3.10
6.16
1.99
Administrative and support service activities
N
0.46
1.73
3.75
1.68
3.84
2.28
Public administration and defense; compulsory social security
O
0.50
2.11
4.18
1.20
1.20
1.01
Public administration, defence, education, human health and social work activities
O‑Q
0.37
0.95
2.57
1.01
1.12
1.11
Education
P
0.28
0.49
1.79
0.90
1.40
1.56
Human health and social work activities
Q
0.33
0.18
0.53
0.92
0.84
0.91
Human health activities
Q86
0.46
0.17
0.38
0.89
0.75
0.84
Residential care activities and social work activities without accommodation
Q87–Q88
0.07
0.18
2.53
0.98
1.02
1.04
Arts, entertainment and recreation
R
0.35
1.06
3.05
0.90
3.48
3.89
Arts, entertainment, recreation; other services and service activities, etc.
R‑S
0.39
2.09
5.39
1.13
3.88
3.44
Other service activities
S
0.41
3.24
7.97
1.23
4.25
3.46
Market economy (all industries excluding L, O, P, Q)
MARKT
0.74
2.38
3.21
2.01
5.29
2.63
Notes: Numbers refer to average values as percentage of value added over the period 1995–2021. All variables are measured in real terms using chained linked volumes (2020)
Source: Authors’ own calculations based on the EUKLEMS/INTANProd 2025 database (Bontadini et al. 2023)
The data used in this study are sourced from the latest 2025 release of the EUKLEMS/INTANProd database (Bontadini et al. 2023). It follows the classification of intangible assets proposed by Corrado et al. (2005), which divides them into seven types: computer software and databases (referred to as “software” for simplicity in this paper), research and development, other intellectual property products, design, brand, training, and organizational capital.
All intangible capital and value-added variables in this study are real values, even when not explicitly stated, expressed in chained-linked volumes (2020).
Value added is corrected for intangibles through the capitalization of intangible assets that are otherwise not included in national accounts. This adjustment is calculated by the authors of the database and is directly available from its source (Bontadini et al. 2023).
Goods industries include: A—Agriculture, forestry and fishing, B—Mining and quarrying, C—Manufacturing, D—Electricity, gas, steam and air conditioning supply, E—Water supply, sewerage, waste management and remediation activities, F—Construction. Services industries include: G—Wholesale and retail trade; repair of motor vehicles and motorcycles, H—Transportation and storage, I—Accommodation and food service activities, J—Information and communication, K—Financial and insurance activities, M—Professional, scientific and technical activities, N—Administrative and support service activities, R—Arts, entertainment and recreation, and S—Other services activities. The market economy includes both goods and services industries.
Table 3 in the Appendix presents the same information for all sectors in our data and highlights that large differences in favor of France are present in almost all sectors.
Of course, these data refer to shares of total investment and are therefore not directly comparable with the shares of value added used throughout the paper. However, for the two to be compatible, France would need to invest more in all intangible categories. Yet Figs. 2 and 3 show similar investment levels in all assets, except for software and organizational capital. In other words, Fig. 7 suggests a similar composition of investment across asset types in the two countries, while is at odds with what is shown in Figs. 2 and 3.
Estimates from this survey are lower than those reported in our paper. This is a common finding in the literature, as firm-level surveys on intangibles typically produce lower estimates (e.g. Awano et al. 2010; Bavdaž et al. 2023; Martin 2024).
INSEE refers to these expenses as “data processing and hosting services” (INSEE 2024, Pag. 3), while Destatis is more specific and describes them as “software acquired together with ICT hardware, or software that is permanently integrated in machines and equipment”, “training, consulting or other services”, and “software-as-a-service modes (saas)”, that is “software [that] is sold not only once, but [that] is billed based on usage” (Destatis 2022, p. 329, par. 5434).
In 2016, Destatis stated that “recent survey data for the manufacturing industry were previously incomplete and were too contradictory as a basis of calculation” (Destatis 2016, p. 436, par. 5332) and that “non-official analyses and studies conducted by market-research institutes and trade associations as well as by specialized journals on software, computer, IT and multimedia markets, etc. do provide some clues, but in many cases the categories to which their data relate are not clearly defined, and the figures are scarcely comparable with each other” (Destatis 2016, p. 436, par. 5332). However, the issue is not mentioned in the 2022 report.
From Destatis (2016): “most market assessments of this kind are based on figures and estimates relating to sales in the relevant industries or subclasses of industries. Their main shortcoming lies in the mixing, whether complete or partial, of software sales, licencing revenues, training, advisory or other services and hardware sales—in other words, they combine elements of fixed capital formation with elements of intermediate consumption” (Destatis 2016, p. 436, par. 5332). In the 2022 report, this issue is tackled with “a so-called mixed model based on both survey and estimated data” (Destatis 2022, p. 329, par. 5437), so that “industries that are not covered by surveys are estimated using information on ratios of purchased software for recognised intangible assets or intellectual property products as well as ratios in three alternative, potential expansion factors” (Destatis 2022, p. 330, par. 5438).
Moreover, Destatis (2022) highlights that “In the case of own-account software, developments intended for internal use for a period of more than one year or internal developments of service providers for the market are considered capital formation in intellectual property products, while basic research in the software segment is not” (Destatis 2022, pag. 329, par. 5435).
We assume that the 20% figure was applied as the methodological background document (Bontadini et al. 2023) and did not specify detailed calculation for the own-account estimate for organizational capital, although it clearly mentioned its embeddedness in the CHS 2005 framework (Corrado et al. 2005, p. 29; Roth and Thum 2013, p. 491).
See Roth (2025) and Fernald et al. (2025) for an overview. In particular, Fernald et al. (2025) highlight potential inconsistencies across the different releases of the EU KLEMS data, which may result from revisions in national accounting methods and changes in the database’s computational methods, given that multiple scholars have been involved in its compilation over time.
Audretsch DB, Belitski M (2020) The role of R&D and knowledge spillovers in innovation and productivity. Eur Econ Rev 123:10339
Awano G, Franklin M, Haskel J, Kastrinaki Z (2010) Measuring investment in intangible assets in the UK: results from a new survey. Econ Labour Mark Rev 4(7):66–71. https://doi.org/10.1057/elmr.2010.98CrossRef
Bavdaž M, Bounfour A, Martin J, Nonnis A, Perani G, Redek T (2023) Measuring investment in intangible assets. In: Snijkers G, Bavdaž M, Bender S, Jones J, MacFeely S, Sakshaug JW, Thompson KJ, Delden AV (eds) Advances in business statistics, methods and data collection, 1st edn. Wiley, pp 79–103 https://doi.org/10.1002/9781119672333.ch5CrossRef
Bloch C, Protogerou A, Vonortas NS (2024) Intangible assets, productivity and economic growth: micro, meso and macro perspectives. Routledge studies in the economics of business and industry. Routledge, Taylor & Francis, London, New York, p 1 https://doi.org/10.4324/9781003324225CrossRef
Bontadini F, Corrado C, Haskel J, Iommi M, Jona-Lasinio C (2023) EUKLEMS & INTANProd: industry productivity accounts with intangibles. Sources of growth and productivity trends: methods and main measurement challenges. Luiss Lab of European Economics, Rome
Bounfour A, Edamura K, Ishikawa T, Miyagawa T, Nonnis A, Tonogi K (2024) The Productivity J‑curve from an International Perspective: Is the US a unique case? RIETI Discussion Paper Series 24-E-079. https://www.rieti.go.jp/en/publications/summary/24120002.html
Brynjolfsson E, Roc D, Syverson C (2021) The productivity J‑curve: how intangibles complement general purpose technologies. Am Econ J Macroecon 13(1):333–372. https://doi.org/10.1257/mac.20180386CrossRef
Corrado C, Hulten C, Sichel D (2005) Measuring capital and technology: an expanded framework. In: Corrado C, Haltiwanger J, Sichel D (eds) Measuring capital in the new economy, pp 11–46CrossRef
Delbecque V, Bounfour A (2011) Intangible investment: contribution to growth and innovation policy issues. A Franco-German comparison. The European chair on intellectual capital management Working Paper Series, No. 2011–1A
Fernald J, Inklaar R, Ruzic D (2025) The productivity slowdown in advanced economies: common shocks or common trends? Rev Income Wealth 71:e12690. https://doi.org/10.1111/roiw.12690CrossRef
Gros D, Roth F (2012) The Europe 2020 strategy: can it maintain the EU’s competitiveness in the world? Centre for European Policy Studies, Brussels
Le Mouel M, Squicciarini M (2015) Cross-country estimates of employment and investment in organisational capital: a task-based methodology using Piaac data. OECD Science, Technology and Industry Working Papers, No. 2015/08. OECD Publishing, Paris https://doi.org/10.1787/5jrs3smfgcjb-enCrossRef
Nonnis A, Bounfour A, Kim K (2023) Knowledge spillovers and intangible complementarities: empirical case of European countries. Res Policy 52(1):104611CrossRef
Nonnis A, Roth F, Bounfour A (2024) Is the EU ready for the next generation of investment? The case of France and Germany. Hamburg Discussion Papers in International Economics. https://www.econstor.eu/handle/10419/298791
Roth F (2025) Intangible capital and labor productivity growth at the macro, meso, and micro levels: A review of the literature and insights for public policies. Sci Public Policy (Forthcoming)
Roth F (2022) Intangible capital and growth. Springer, ChamCrossRef
Roth F, Mitra A (2024) EU competitiveness: the critical role of intangible assets in EU labour productivity growth. Hamburg Discussion Papers in International Economics, 19. University of Hamburg
Roth F, Thum AE (2013) Intangible capital and labor productivity growth: panel evidence for the EU from 1998–2005. Rev Income Wealth 59(3):486–508. https://doi.org/10.1111/roiw.12009CrossRef
Schuster E, Strahl J (2019) Statistik der Bundesagentur für Arbeit, Grundlagen: Methodenbericht—Beschäftigte mit Leitungsfunktion
Squicciarini M, Le Mouel M (2012) Defining and measuring investment in Organisational capital: using US microdata to develop a task-based approach. OECD Science, Technology and Industry Working Papers, No. 2012/05. OECD Publishing, Paris https://doi.org/10.1787/5k92n2t3045b-enCrossRef
Stehrer R, Bykova A, Jäger K, Reiter O, Schwarzhappel M (2019) Industry level growth and productivity data with special focus on intangible assets. Vienna Institute for International Economic Studies Statistical Report, 8
