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on Innovation |
| By: | Diego R. Känzig; Maximilian Konradt; Lixing Wang; Donghai Zhang |
| Abstract: | This paper examines the relationship between green innovation and the business cycle, revealing that while non-green innovation is procyclical, green innovation is countercyclical. This pattern holds unconditionally over the business cycle and conditional on economic shocks. Motivated by these findings, we develop a business cycle model with endogenous green and non-green innovation to explain their distinct cyclical behavior. The key mechanism operates through a ‘green is in the future’ channel: green patents are expected to generate higher profits in the future, making green patenting less sensitive to short-term economic fluctuations. In general equilibrium, this channel is reinforced, making green and non-green innovation effective substitutes. We provide direct evidence supporting the model mechanism using data on market-implied values of green and non-green patents. |
| JEL: | E32 O31 Q55 Q58 |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34041 |
| By: | Fraske, Tim |
| Abstract: | The concept of Reallabore (real-world laboratories) has undergone remarkable semantic evolution in Germany - rooted in the experimental turn in social sciences, shaped in sustainability research, and culminating in national innovation policy. This paper frames Reallabore as a travelling concept: a term that shifts in meaning as it moves across institutional, disciplinary, and political contexts. Drawing on perspectives from economic geography, it traces four distinct phases in the evolution of the term, highlighting the tensions and strategic translations that have shaped its development. Understanding such conceptual trajectories is key to interpreting the performative power of innovation discourse in regional policymaking. |
| Keywords: | Economic geography, travelling concept, real-world laboratory, innovation policy, experimental governance |
| JEL: | O31 O38 R11 R58 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:esprep:322478 |
| By: | Luisa Corrado (DEF and CEIS, Università di Roma "Tor Vergata"); Stefano Grassi (DEF and CEIS, Università di Roma "Tor Vergata"); Aldo Paolillo (Università di Roma "Tor Vergata") |
| Abstract: | Recent studies suggest that space activities generate significant economic benefits. This paper attempts to quantify these effects by modeling both business cycle and long-run effects driven by space sector activities. We develop a model in which technologies are shaped by both a dedicated R&D sector and spillovers from space-sector innovations. Using U.S. data from the 1960s to the present day, we analyze patent grants to distinguish between space and core sector technologies. By leveraging the network of patent citations, we further examine the evolving dependence between space and core technologies over time. Our findings highlight the positive impact of the aerospace sector on technological innovation and economic growth, particularly during the 1960s and 1970s. |
| Keywords: | Aerospace, Space Economy, Growth |
| JEL: | A1 C5 E00 O10 |
| Date: | 2025–08–07 |
| URL: | https://d.repec.org/n?u=RePEc:rtv:ceisrp:609 |
| By: | Leonie P. Meissner |
| Abstract: | I study the role of green research and development (R&D) subsidies under different environmental policies. Using a stylized equilibrium model calibrated to the European electricity sector, I analyze the effects of R&D subsidies under (1) an emission tax, (2) an emission cap, and (3) no environmental policy, focusing on competitiveness, environmental outcomes, and welfare. I find that increasing R&D subsidies increases knowledge accumulation and clean-sector output, displacing dirty-sector production. This raises overall output, lowers production costs, and enhances sectoral competitiveness. However, environmental benefits from R&D subsidies occur only under an emission tax or in the absence of environmental policy. Under an emission cap, emission prices fall from an increase in the R&D subsidy, reducing compliance costs without lowering total emissions. Our calibration further reveals interaction effects between environmental policy stringency and the effectiveness of the R&D subsidy under an emission tax, emission cap, or in the absence of an environmental policy. |
| Keywords: | climate policy, R&D support, innovation policy, renewable energy, environmental innovation |
| JEL: | D50 H23 O38 Q55 Q58 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12002 |
| By: | Searle, Nicola; Ganglmair, Bernhard; Borghi, Maurizio |
| Abstract: | A robust Research & Innovation (R&I) ecosystem is essential for progress, economic resilience, and addressing complex challenges. At the heart of this ecosystem, knowledge fuels innovation and further discovery. However, knowledge leakage (the loss of valuable information) can disrupt this cycle. This poses a challenge for what is known as Trusted Research & Innovation (TRI), a framework designed to strengthen research security, protect national interests, and build resilient research systems. Despite its significance, the challenges of TRI remain poorly understood. This report investigates knowledge leakage. It begins with an overview of the TRI context, focusing on policymaking, and then reviews the literature on knowledge leakage and related concepts. An exploratory data analysis examines novel empirical data to better understand the extent of knowledge leakage and how it impacts economic areas of defence, economic, and national security importance. The data analysis finds that industries deemed important for economic and national security (the UK's 'sensitive economic areas') have an 18% higher incidence of leakage than those that are not. |
| Keywords: | knowledge leakage, research security, theft of IP, economic security, national security, Trusted Research & Innovation |
| JEL: | F52 O25 O33 O34 O38 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:zewdip:321868 |
| By: | Koski, Heli |
| Abstract: | Abstract This paper examines the effects of data privacy regulation on R&D investment in the pharmaceutical and biotechnology sectors. In these industries, access to personal health data is essential for innovation, particularly in clinical research. Leveraging a firm-level panel of the world’s top R&D investors from 2013 to 2023, we exploit the staggered implementation of major data protection regimes to estimate their causal impact. Using a dynamic event-study design, we find that stricter privacy regulation leads to a significant decline in R&D spending. By year four after implementation, treated firms reduced R&D investment by approximately 39 percent. The effects are heterogeneous: firms without foreign affiliates and small and medium-sized enterprises experience larger declines. Our findings suggest that privacy regulation may constrain the foundations of data-driven innovation and shape the geographic distribution of R&D activity. |
| Keywords: | Privacy regulation, R&D investment, Innovation, Pharmaceuticals, Biotechnology, Firm-level panel, GDPR, Compliance costs |
| JEL: | D22 K23 L65 O32 O38 |
| Date: | 2025–08–11 |
| URL: | https://d.repec.org/n?u=RePEc:rif:wpaper:130 |
| By: | Breithaupt, Patrick; Gottschalk, Sandra |
| Abstract: | Past studies have successfully shown that the level of anonymisation of scientific use files (SUF) is sufficiently high to protect against disclosure attacks that use data from traditional firm databases. However, with the increasing availability of online data about firms, new challenges for the provision of SUFs arise. In this paper, we therefore focus on a scenario, where an attack against the Mannheim Innovation Panel SUF is performed using data from the Mannheim Enterprise Panel and Mannheim Web Panel. We find that the disclosure risk of our attack is small and increases only slightly if data from the Mannheim Web Panel are considered. Data protection officers may use our findings when researchers want to publish SUFs. |
| Keywords: | matching, firms, text as data, scientific use files |
| JEL: | L00 C8 C55 C61 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:zewdip:321871 |
| By: | Kelchtermans Stijn; Goni Navarro Luis; Lindholm Dahlstrand Asa; Mifsud Solange (European Commission - JRC); Zacharewicz Thomas |
| Abstract: | "The Regional Innovation Valleys (RIV) initiative is a flagship action under the New European Innovation Agenda (NEIA), designed to strengthen innovation ecosystems across the European Union by fostering interregional collaboration and accelerating the deployment of innovation. The initiative supports partnerships between regions with varying levels of innovation performance to address key EU challenges such as energy resilience, food security, digital transformation, healthcare, and circularity.A total of 146 regions have been designated as RIVs through three mechanisms: a Call for Expression of Interest (CEI), the European Innovation Ecosystems (EIE) and the Interregional Innovation Investments (I3). The political commitment for the initiative totals €170 million, with initial funding of €116 million distributed through EIE and I3 calls.This Gap Analysis aims to (i) identify the motivations and obstacles influencing regional participation in RIV calls, particularly CEI and EIE; and (ii) provide evidence-based recommendations to enhance future calls and stimulate more effective interregional collaboration.To address these objectives, the study employed a mixed-methods approach, combining qualitative data from 30 regions (via interviews and focus groups) with survey responses from 89 regions across the EU and Horizon Europe Associated Countries. The analysis revealed four critical areas influencing regional participation. These refer to co-funding requirements, collaboration patterns, the RIV label and thematic focus areas. Based on these findings, the report proposes several policy recommendations, including simplifying co-funding mechanisms, enhancing matchmaking and capacity-building efforts, providing clearer guidance on the RIV label’s value, and improving alignment and continuity of thematic areas. By addressing these gaps, the RIV initiative can more effectively fulfil its dual mission of boosting innovation and reducing regional disparities across Europe." |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:ipt:iptwpa:jrc142545 |
| By: | Timothy F. Bresnahan; Shane Greenstein; Pai-Ling Yin |
| Abstract: | Advances in a general-purpose technology (GPT) enable many firms to invent complementary inventions, or co-inventions, making the GPT more valuable. This study examines the empirical implications of a straightforward model in which firms choose either incremental or novel co-invention. Incremental co-inventors aspire to small gains at low costs and with less uncertainty. Novel co-inventors introduce new products or services with the potential for large returns, but do so at high costs and with uncertain outcomes. Similar firms investing in incremental co-invention will create value proportional to their existing business, a benchmark we illustrate with the experiences at local radio and newspapers. The study then examines the value of co-inventions for the World Wide Web and mobile ecosystems, focusing on success in 2013, using data from many sources. This data supports analysis comparing the incremental and novel regimes. The latter should display a distinctly different upper tail of the distribution of returns. We show that the value distributions for incremental and novel co-invention are far apart. Incremental co-invention is more widely distributed across regions, industries, and firms. Success from novel co-invention is rare, challenging, and the source of the largest value. In the aggregate, novel co-invention creates the most value, so the overall value distribution remains concentrated in a few industries, regions, and firms. |
| JEL: | L80 L86 M15 O31 O33 |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34090 |
| By: | Taheri Hosseinkhani, Nima (Auburn University) |
| Abstract: | Purpose: This study synthesizes and evaluates the empirical evidence on the transfer and diffusion of artificial intelligence (AI) by analyzing whether its implementation delivers productivity gains that consistently exceed those of previous general-purpose technologies (GPTs), such as information and communication technology (ICT) and electricity. It aims to clarify the magnitude, mechanisms, and contextual dependencies of AI's impact, framing the issue as a challenge in technology transfer from development to widespread economic application. Methodology: A systematic literature review was conducted following the PRISMA 2020 framework. The search utilized the Consensus academic search engine, covering sources like Semantic Scholar and PubMed, with 22 targeted queries across seven thematic groups. The process involved identifying 1, 100 papers, screening 630, assessing 491 for eligibility, and conducting a full-text analysis and narrative synthesis of the 50 most relevant studies. Methodologies of the included papers range from large-scale panel data regressions and randomized controlled trials to systematic reviews and macroeconomic analyses. Findings: The evidence consistently shows that AI implementation delivers measurable productivity gains at the firm and process levels across various sectors. Key mechanisms for this value capture include cost reduction, process automation, skill-biased labor enhancement, and innovation acceleration. For instance, specific applications like generative AI have been shown to reduce task completion time by 40% and improve output quality by 18%. However, the evidence that these gains consistently surpass those of earlier GPTs is nuanced, revealing lags and barriers characteristic of historical technology transfers. The diffusion of benefits is uneven, disproportionately favoring larger, digitally mature firms with higher absorptive capacity. At the macroeconomic level, AI's contribution to aggregate productivity growth remains limited, echoing the "productivity paradox" observed during the initial transfer of ICT and electricity. Implications: The findings suggest that while AI is a potent productivity driver, realizing its full economic potential is contingent on overcoming key barriers to technology transfer, including the need for complementary investments, organizational restructuring, and workforce upskilling. For policymakers and technology managers, this underscores the need for strategic initiatives that address expertise gaps and integration challenges, thereby fostering more inclusive and widespread technology diffusion and productivity growth. The historical parallels with previous GPTs suggest that the transformative impact of AI may materialize over a longer time horizon than currently anticipated, dependent on the efficiency of these transfer mechanisms. |
| Date: | 2025–07–22 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:hqp28_v2 |
| By: | Garcia Calvo, Angela; Hancké, Bob |
| Abstract: | When does industrial policy succeed and fail in advanced economies? Most approaches to these questions concentrate on policy design and state power. Instead, we draw attention to the historical legacies, industrial structures, and institutional arrangements that shape industrial policy outcomes. We use insights from historical institutionalism and international business to develop a relational argument based on two first-order conditions: a critical mass of firms with sufficient capabilities to leverage the resources resulting from industrial policy, and the alignment between industrial policy goals and national institutional systems. Industrial policy could succeed, given the important second-order conditions that many have examined, when one of these conditions is present and public intervention produces the other. But industrial policy is certain to fail when both conditions are absent. Using a most different systems design, we assess our framework through short case-studies of industrial policy success and failure in Europe in the past 6 decades. |
| Keywords: | industrial policy; O43 Institutions and growth; growth; Europe; economic policy; high-tech; institutions |
| JEL: | O38 |
| Date: | 2025–07–28 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:128970 |
| By: | Landini, Fabio; Lunardon, Davide; Marzucchi, Alberto |
| 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, Green Transition, EWCS |
| JEL: | J24 J28 O31 O33 Q20 Q40 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:glodps:1639 |