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on Technology and Industrial Dynamics |
| By: | Sara Amoroso; Randolph L. Bruno; Laura Magazzini |
| Abstract: | This paper identifies the dichotomous role (bright and dark sides) of Intellectual Property Rights (IPR) protection on labor productivity among highly innovative globalised firms. The role of appropriability conditions -such as IPR protection- as "Schumpeterian" incentive to innovation has been largely explored in the empirical literature. In this paper, we contribute to this strand explore the role of appropriability conditions on firm labor productivity under different configurations of R&D activities in highly globalized companies. In line with the literature, we show that labor, capital and R&D investments lead to productivity gains, and that the strength of the patent system the firm is embedded into is positively linked to the firm’s labor productivity too. We call this the 'bright side' of IPR. However, stronger intellectual property rights might have a detrimental effect on the R&D returns, which appear to be maximized around the median level of IPR protection. In other words, too much protection might actually reduce R&D returns, again in line with the "Schumpeterian prediction". Then, we call this the ‘dark side’ of IPR. To our knowledge, this is the first paper highlighting such dichotomy (bright and dark sides of IPR) on a purpose-built high-quality database of globalized firms, which tend to be the most innovative firms in the world. |
| Keywords: | panel data, appropriability, productivity |
| Date: | 2025–07–24 |
| URL: | https://d.repec.org/n?u=RePEc:ssa:lemwps:2025/26 |
| By: | Jan Malek; Jo Seldeslachts; Reinhilde Veugelers |
| Abstract: | This paper provides empirical evidence on which M&A deals spur innovation, and which stifle it. To do so, we consider not only the product market position of the acquiring firm, but also the position of both target and acquirer in the technology space. Focusing on the antidiabetic drugs market, our dataset tracks the lifecycle and patenting of all individual antidiabetic projects in development between 1997 and 2017. We show that most terminations of acquired projects occur while the projects are still far from product market entry. Nevertheless, a number of these early-stage acquisitions have a positive impact on innovation. These cases arise when incumbents acquire projects close to their own projects in product markets, but only if these projects are also close in technology markets. Those deals are associated with increased subsequent patenting, which is consistent with the exploitation of technological synergies. Our results point to the crucial role of combining both product market and technology market positions in assessing the innovation effects of pharmaceutical M&As. |
| Keywords: | M&As, innovation, R&D, pharmaceutics, technology, novelty, patents |
| JEL: | L41 L65 O31 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:diw:diwwpp:dp2128 |
| By: | Laurence Jacquet; Stéphane ROBIN (CY Cergy Paris Université, THEMA) |
| Abstract: | We re-examine the R&D - innovation - productivity nexus in 8 EU countries in the context of a possible EU-wide "super deduction" on R&D expenditures, using panels of industries with a long time dimension. We introduce dynamics in the innovation production function and extended production function models, taking the availability/unavailability of R&D tax credits (R&DTC) into account. Our benchmark estimates, obtained with panel ARDL models, yield positive longrun elasticities of innovation and productivity with respect to R&D intensity. R&D conducted under an R&DTC either reinforces an already-existing positive elasticity or makes it significantly positive if it was not before. Disentangling the respective effects of ’pure’ business R&D and of government-supported R&D reveals a wider diversity of situations, however. The effect of R&DTC is less often significant, sometimes superseded by other forms of public support to R&D. The main policy implication of these results is that a harmonized "super-deduction" on R&D at the EU level may be slightly premature. Complementary analyses suggest that targeting specific industries may make such a policy more effective and accurate. |
| Keywords: | Innovation, Productivity, Dynamic Panel Data Models, Public Support to R&D, European Science and Technology Policy |
| JEL: | O30 O38 H25 H54 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ema:worpap:2025-09 |
| By: | Martin Neil Baily; David M. Byrne; Aidan T. Kane; Paul E. Soto |
| Abstract: | With the advent of generative AI (genAI), the potential scope of artificial intelligence has increased dramatically, but the future effect of genAI on productivity remains uncertain. The effect of the technology on the innovation process is a crucial open question. Some inventions, such as the light bulb, temporarily raise productivity growth as adoption spreads, but the effect fades when the market is saturated; that is, the level of output per hour is permanently higher but the growth rate is not. In contrast, two types of technologies stand out as having longer-lived effects on productivity growth. First, there are technologies known as general-purpose technologies (GPTs). GPTs (1) are widely adopted, (2) spur abundant knock-on innovations (new goods and services, process efficiencies, and business reorganization), and (3) show continual improvement, refreshing this innovation cycle; the electric dynamo is an example. Second, there are inventions of methods of invention (IMIs). IMIs increase the efficiency of the research and development process via improvements to observation, analysis, communication, or organization; the compound microscope is an example. We show that GenAI has the characteristics of both a GPT and an IMI—an encouraging sign that genAI will raise the level of productivity. Even so, genAI’s contribution to productivity growth will depend on the speed with which that level is attained and, historically, the process for integrating revolutionary technologies into the economy is a protracted one. |
| Keywords: | Artificial Intelligence; Machine Learning; Productivity; Technological Growth |
| JEL: | C45 O31 O33 O40 |
| Date: | 2025–07–17 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfe:2025-53 |
| By: | Yankova Dima; Abbasiharofteh Milad |
| Abstract: | European innovation policy combines place-based and spatially blind instruments that operate under distinct logics. Building synergies between them requires not only regulatory alignment, but a better understanding of how economic actors interact across policy levels. This study examines how companies’ participation in the European Framework Programmes (FP) influences their propensity to engage in regional R&D partnerships, supported by Cohesion Policy. We analyse longitudinal data on Valencian firms using inferential network analysis (i.e., Temporal Exponential Random Graph Models). Results indicate that FP beneficiaries are more active in regional tie formation than non-FP firms, especially when academic intermediaries are involved. Yet, they also tend to collaborate with each other, limiting opportunities for knowledge diffusion among firms that do not benefit from the international collaboration premium. |
| Keywords: | rR&D network, innovation policy, intermediaries, TERGM |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:egu:wpaper:2520 |
| By: | Davide Consoli; Francesco Lelli; FSandro Montresor; Francois Perruchas; Francesco Rentocchini |
| Abstract: | Given the crucial role of Venture Capital (VC) in financing the green transition, and its uneven geographical distribution, we examine how the proximity of VC investors to green start-ups influences the success of their deals. Considering the intrinsically higher risk profile of start-ups in the greensector, we maintain that their spatial proximity to VC investors will have a larger effect here than in other sectors. Furthermore, considering recent advancements in the digitalization of VC, we also argue that a digital kind of proximity between investors and green investees in accessing digital technologies (platforms) could matter for that, by also reducing the binding effect of spatial proximity on the success of VC green deals. Using data from Dealroom, and combining them with the SpeedTest open dataset by Ookla, we test for these arguments with respect to a large sample of about 12, 000 green start-ups, originally identified by combining multiple methods (text scraping, topic modelling, and machine learning), located in 27 EU (+3) countries from 2000 to 2020. Econometric estimates at the level of realised vs. potential VC green deals confirm that spatial proximity is more relevant for green than for non-green start-ups. The new quasi- dyadic indicator of digital proximity that we propose does also significantly and positively correlates with the actual occurrence of green deals, and negatively moderate the effect of spatial proximity, supporting our argument of a substitution relationship between the two. Policy implications are drawn accordingly. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:egu:wpaper:2521 |
| By: | Gibson, Gráinne; Lenihan, Helena; Perez-Alaniz, Mauricio; Rammer, Christian |
| Abstract: | Climate change can cause major challenges for Small and Medium-sized Enterprises (SMEs). Responding and adapting to such challenges is crucial, as SMEs are vital for driving economic growth and employment in most countries. Investing in R&D is a key way in which SMEs can build the capacities required for responding and adapting to climate change-related challenges. However, the extent to which such challenges affect SMEs' R&D activities remains a critical gap in existing knowledge. Using detailed firm-level data on 1, 730 SMEs in Ireland, our study is the first to explore this issue. We achieve this, using information on SMEs' climate changerelated challenges, from a new module of the 2018-2020 wave of the Irish part of the Community Innovation Survey (CIS), the Innovation in Irish Enterprises Survey (IIE). By combining a matching approach with probit regression analysis, we find that climate changerelated challenges can increase the probability of SMEs investing in R&D. Such challenges can also increase the probability of SMEs engaging in continuous, as opposed to occasional R&D. Based on our findings, the above impacts are mainly driven by climate change, resulting in higher costs/input prices. Our study highlights the importance of R&D for SMEs to adapt and respond to climate change and provides critical insights for SMEs and policymakers alike. |
| Keywords: | Climate change-related challenges, small and medium sized enterprises, research and development, climate change adaptation, climate change mitigation |
| JEL: | Q54 Q55 O32 O33 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:zewdip:319901 |
| By: | Fabio Landini (University of Parma, Deptartment of Economics and Management); Davide Lunardon (Gran Sasso Science Institute); Alberto Marzucchi (Gran Sasso Science Institute) |
| Abstract: | We investigate the perceived meaning of green jobs. Theoretically, we extend the standard meaningful work framework, by introducing a social esteem component, which depends on both the green content of occupations and the socio-political awareness of environmental issues. To identify green jobs, we employ a task-based indicator based on ESCO data, which is then merged with individual-level data from the 2015 and 2021 waves of the European Working Conditions Survey. Moreover, we proxy the degree of environmental consciousness at the country level through the Environmental Policy Stringency index from the OECD. In line with our theoretical framework, we find that workers’ perceptions of meaningful work increase with the green content of their occupation and are amplified in countries exhibiting higher levels of environmental consciousness. These results highlight the role of social esteem, derived from the contribution to what is considered a socially valuable objective (i.e. the fight against climate change), in shaping the experience of meaningful work. To allow a more ‘causal’ interpretation of the results, we employ an instrumental variable approach which corroborates the main findings. |
| Keywords: | Meaningful work, Green jobs, Social esteem, EWCS, Green transition |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:srt:wpaper:0625 |
| By: | Mercure, Jean-Francois; Pollitt, Hector; Geels, Frank W.; Zenghelis, Dimitri |
| Abstract: | The low-carbon transition is generally portrayed as involving costs to the economy through lower productivity and generating benefits through avoided impacts of climate change. This mainstream economic narrative hinges on two critical assumptions that stem from an allocation perspective: that low-carbon technologies are more expensive than high-carbon ones, and that low-carbon investment displaces resources from their optimal allocation. However, evidence increasingly suggests that neither assumption may be true. Drawing on evolutionary and complexity economics and making different, empirically-supported, assumptions about innovation dynamics, structural change, and the endogenous creation of finance, this paper examines the impacts on UK labour productivity of a low-carbon transition in the power, transport and heat sectors using a coupled macro-econometric and technology model (E3ME-FTT). Using realistic assumptions, the model results show moderate but positive productivity increases in the transition that stem from technological learning-by-doing and productivity growth in specific sectors, which induces investments that ultimately lead to expanded economic capacity across the economy. |
| Keywords: | labour productivity; climate policy; economic growth; low-carbon transitions |
| JEL: | N0 R14 J01 |
| Date: | 2025–07–02 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:128830 |
| By: | Krieger, Bastian; Rainville, Anne Marie |
| Abstract: | Public procurement requirements and voluntary standards are increasingly used to foster environmental product innovations. However, quantitative evidence on their individual and joint effects is absent, and their conceptualization remains at an early stage. This paper makes two contributions. First, it introduces the distinction between rigid threshold and flexible benchmark uses of voluntary standards in public tenders, theorizing their opposing effects on environmental product innovations. Second, using data from 5, 127 firms in the 2021 German Innovation Survey and applying linear probability models, it provides the first quantitative analysis of their individual and joint effects across varying degrees of environmental significance. Results show that public procurement requirements and voluntary standards individually increase the probability of firms introducing environmental product innovations with high environmental significance. However, their interaction reveals a negative effect - discomplementarity - likely driven by rigid standard use, which offsets the effectiveness of procurement requirements. For environmental product innovations with low environmental significance, only voluntary standards exhibit a positive effect. These findings suggest that voluntary standards might limit the capacity of public procurement to foster more radical or disruptive environmental product innovations, while supporting more incremental innovations when used independently. |
| Keywords: | Public procurement, Voluntary standards, Environmental innovation |
| JEL: | O31 O38 Q55 Q58 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:zewdip:319896 |
| By: | Dahlke, Johannes; Schmidt, Sebastian; Lenz, David; Kinne, Jan; Dehghan, Robert; Abbasiharofteh, Milad; Schütz, Moritz; Kriesch, Lukas; Hottenrott, Hanna; Kanilmaz, Umut Nefta; Grashof, Nils; Hajikhani, Arash; Liu, Lingbo; Riccaboni, Massimo; Balland, Pierre-Alexandre; Wörter, Martin; Rammer, Christian |
| Abstract: | This paper introduces the WebAI paradigm as a promising approach for innovation studies, business analytics, and informed policymaking. By leveraging artificial intelligence to systematically analyze organizational web data, WebAI techniques can extract insights into organizational behavior, innovation activities, and inter-organizational networks. We identify five key properties of organizational web data (vastness, comprehensiveness, timeliness, liveliness, and relationality) that distinguish it from traditional innovation metrics, yet necessitate careful AI-based processing to extract scientific value. We propose methodological best practices for data collection, AI-driven text analysis, and hyperlink network modeling. Outlining several use cases, we demonstrate how WebAI can be applied in research on innovation at the micro-level, technology diffusion, sustainability transitions, regional development, institutions and innovation systems. By discussing current methodological and conceptual challenges, we offer several propositions to guide future research to better understand i) websites as representations of organizations, ii) the systemic nature of digital relations, and iii) how to integrate WebAI techniques with complementary data sources to capture interactions between technological, economic, societal, and ecological systems. |
| Keywords: | web data, artificial intelligence, innovation studies, research methods |
| JEL: | C81 C45 B4 O3 R1 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:zewdip:319890 |
| By: | Aaron Flaaen; Fariha Kamal; Eunhee Lee; Kei-Mu Yi |
| Abstract: | Global value chains (GVC) are a pervasive feature of modern production, but they are hard to measure. Using confidential microdata from the U.S. Census Bureau, we develop novel measures of the linkages between U.S. manufacturing establishments’ imports and exports. We find that for every dollar of exports, imported inputs represent 13 cents in 2002 and 20 cents by 2017. Examining GVC trade flows in a gravity framework, we find that these flows are higher within “round-trip” (input and output market is the same) linkages, regional trade agreements, and multinational firm boundaries. The strong complementarities between input and output markets are muted by the proportionality assumptions embedded in global input-output tables. Finally, with an off-the-shelf model, we show the round-trip results can be obtained when firm-specific sourcing and exporting fixed costs are linked. |
| Keywords: | global value chains, manufacturing, exports, imports, establishment, microdata |
| JEL: | F1 F14 O51 |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:cen:wpaper:25-44 |
| By: | Stéphane Auray (ENSAI - Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] - GENES - Groupe des Écoles Nationales d'Économie et Statistique, ESC [Rennes] - ESC Rennes School of Business); Aurélien Eyquem (UNIL - Université de Lausanne = University of Lausanne) |
| Abstract: | We develop an open-economy model of endogenous automation with heterogeneous firms and labor-market reallocation to quantify the contribution of various trends to the adoption of robots in the U.S. economy. The decline in the relative price of robots is the major trend leading to automation, but interacts with other trends that either hinder (rising entry costs, rising markups) or slightly foster (rising labor productivity, declining trade costs) the adoption of robots. Taken alone, the decline in the relative price of robots produces moderate welfare gains in the long run, but less than labor productivity growth. We then exploit our model to show that a decline in the relative price of robots (i) generates small positive cross-country automation spillovers and (ii) produces inefficient labor-market reallocation since a small subsidy on robots combined with a training subsidy can generate small welfare gains. Our main conclusion is that automation can not be simply modeled as an exogenous decline in the price of robots, and must be analyzed in a broader framework taking into account trends affecting firms, such as the decline in business dynamism and the rise in markups.✩ We would like to thank the editor B. Ravikumar and two referees for their invaluable comments. We also thank Basile Grassi, Sergio Rebelo, Ariel Resheff and Farid Toubal as well as participants in various seminars and conferences for interesting feedbacks. We acknowledge financial support from the French National Research Agency (ANR-20-CE26-0018-02). |
| Keywords: | Robots, Automation, Heterogeneous firms, Labor market, Open-economy |
| Date: | 2025–06–13 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05137753 |
| By: | Niccolò Murtas (University of Ferrara) |
| Abstract: | This study estimates an aggregate green knowledge production function (GKPF) for 19 OECD countries from 1981 to 2012, using panel-data econometric methods to address spatial spillovers and unobserved heterogeneity. Both Cobb-Douglas and translog functional forms are evaluated with multiple estimators, including standard fixed and random effects models, pooled and mean group common correlated effects (CCE) estimators, and random-trend models to account for shared upward trends among variables. The regression analysis examines the relationship between green patenting and key determinants such as R&D expenditure, human capital, and environmental policy indicators. The results consistently show a robust positive effect of domestic R&D, whereas the impacts of other factors exhibit greater variability. Methodologically, the findings highlight the sensitivity of coefficient estimates to unobserved heterogeneity and the choice of functional form. |
| Keywords: | Green innovation, knowledge production function, panel data, spatial spillovers |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:srt:wpaper:0725 |
| By: | Yoshiki Ando |
| Abstract: | Motivated by the substantial growth and upfront investments of venture capital (VC) backed firms observed in administrative US Census data, this paper develops a firm dynamics model over the life cycle. In the model, startups choose the source of financing from VC, Angel investors, or banks, depending on their growth potential, and invest in innovation. The calibrated model explains the life-cycle dynamics of firms with different sources of financing and implies that venture capitalists’ advice accounts for around 22% of the growth of VC-backed firms. A counterfactual economy without VC financing would lose aggregate consumption by around 0.4%. |
| Keywords: | Venture capital, firm dynamics, innovation, upfront investment, defaultable debt, endogenous sorting |
| JEL: | D22 D25 E22 G24 G30 O32 |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:cen:wpaper:25-38 |
| By: | Xian Gong; Paul X. McCarthy; Colin Griffith; Claire McFarland; Marian-Andrei Rizoiu |
| Abstract: | This paper describes a novel methodology to map the universe of emerging technologies, utilising various source data that contain a rich diversity and breadth of contemporary knowledge to create a new dataset and multiple indices that provide new insights into these technologies. The Cosmos 1.0 dataset is a comprehensive collection of 23, 544 technologies (ET23k) structured into a hierarchical model. Each technology is categorised into three meta clusters (ET3) and seven theme clusters (ET7) enhanced by 100-dimensional embedding vectors. Within the cosmos, we manually verify 100 emerging technologies called the ET100. This dataset is enriched with additional indices specifically developed to assess the landscape of emerging technologies, including the Technology Awareness Index, Generality Index, Deeptech, and Age of Tech Index. The dataset incorporates extensive metadata sourced from Wikipedia and linked data from third-party sources such as Crunchbase, Google Books, OpenAlex and Google Scholar, which are used to validate the relevance and accuracy of the constructed indices. Moreover, we trained a classifier to identify whether they are developed "technology" or technology-related "terms". |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.10591 |
| By: | Jun Cui |
| Abstract: | This study examines the relationship between AI-driven digital transformation and firm performance in Chinese industrial enterprises, with particular attention to the mediating role of green digital innovation and the moderating effects of human-AI collaboration. Using panel data from 6, 300 firm-year observations collected from CNRDS and CSMAR databases between 2015 and 2022, we employ multiple regression analysis and structural equation modeling to test our hypotheses. Our findings reveal that AI-driven digital transformation significantly enhances firm performance, with green digital innovation mediating this relationship. Furthermore, human-AI collaboration positively moderates both the direct relationship between digital transformation and firm performance and the mediating pathway through green digital innovation. The results provide valuable insights for management practice and policy formulation in the context of China's evolving industrial landscape and digital economy initiatives. This research contributes to the literature by integrating perspectives from technology management, environmental sustainability, and organizational theory to understand the complex interplay between technological adoption and business outcomes. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.11558 |
| By: | Hanol Lee; Jong-Wha Lee |
| Abstract: | This study develops a novel cross-country measure of higher education quality by leveraging the robust relationship between institution-level indicators--such as faculty-to-student ratios and global university rankings--and the earnings of graduates employed overseas. Using U.S. microdata, it shows that global rankings are strongly correlated with key quality dimensions, including research performance, teaching environment, enrollment size, international outlook, and student selectivity. Building on this relationship, a country-level index of college education quality is constructed for 98 countries, capturing variations in institutional characteristics weighted by their estimated effects on graduate earnings. To examine macroeconomic impacts, the study estimates cross-country regressions of GDP per worker, resident patenting, and R&D expenditures. An instrumental variable strategy--exploiting geographic proximity to global academic hubs--is used to address potential endogeneity. The results show that tertiary education quality has a large and statistically significant effect on all three outcomes, underscoring its role in long-run economic development and innovation capacity. |
| Keywords: | education quality, human capital, economic development, innovation, college education, university rankings |
| JEL: | I23 I25 J24 O15 |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:een:camaaa:2025-41 |
| By: | Gibbs, Michael (University of Chicago); Mengel, Friederike (University of Essex); Siemroth, Christoph (University of Essex) |
| Abstract: | Using data from over 28, 000 innovators within a firm, we study how network position affects innovation, measured by the quality of ideas proposed in a formal suggestion system. Network degree is associated with higher quality ideas. Bridging across structural holes is negatively related to idea quality in the short run, conditional on degree, but has positive effects in the medium run. Bridging also has positive and persisting effects on the quality of colleagues’ ideas, suggesting a positive externality from ‘brokers.’ Network size is not related to idea quality, after controlling for degree and bridging. Compared to working from the office, remote work leads to lower average network degree and bridging. This weakening of networks may explain the reduced quality of innovation during remote work found in prior literature. |
| Keywords: | working from home, network centrality, structural holes, innovation, networks, hybrid work |
| JEL: | D7 D8 O3 |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp17966 |