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on Technology and Industrial Dynamics |
| 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: | 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 |
| 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: | 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: | 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: | Xiaolin Yu (Yonsei University); Jin Seo Cho (Yonsei University) |
| Abstract: | The current study examines whether government-led digital finance initiatives promote firm-level digital innovation by leveraging the staggered rollout of China’s Fintech pilot programs as quasi-natural experiments. Our dataset comprises 26, 746 firm-year observations of A-share listed companies from 2009 to 2023. To measure innovation, we develop a text-based indicator derived from the frequency of digital-related keywords in the annual reports of the listed firms. Employing a multi-period difference-in-differences design, we find that designation as a pilot zone increases digital innovation intensity by 0.8225 per thousand report words. These results remain robust across parallel, propensity score matching, placebo, and robustness tests. Mediation analysis reveals that the part of the effect is attributable to increased R&D intensity, with the program raising the average R&D-to-sales ratio by 0.24 percentage points. Moreover, program effect is stronger among high-tech firms and those located in Central and Western China, regions characterized by relatively weaker financial and digital infrastructure. |
| Keywords: | Difference-in-differences; Fintech pilot programs; digital innovation; R&D investments; firm heterogeneity |
| JEL: | G18 G28 G38 O31 O32 O38 O53 P42 |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:yon:wpaper:2025rwp-257 |
| 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: | 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: | Golo Henseke; Rhys Davies; Alan Felstead; Duncan Gallie; Francis Green; Ying Zhou |
| Abstract: | We introduce the Generative AI Susceptibility Index (GAISI), a task-based measure of UK job exposure to large language models (LLMs), such as ChatGPT. GAISI is derived from probabilistic task ratings by LLMs and linked to worker-reported task data from the Skills and Employment Surveys. It reflects the share of job activities where an LLM or LLM-powered system can reduce task completion time by at least 25 per cent beyond existing productivity tools. The index demonstrates high reliability, strong alignment with AI capabilities, and superior predictive power compared to existing exposure measures. By 2023-24, nearly all UK jobs exhibited some exposure, yet only a minority were heavily affected. Aggregate exposure has risen since 2017, primarily due to occupational shifts rather than changes in task profiles. The price premium for AI-exposed tasks declined relative to 2017, measuring approximately 11 per cent lower in 2023-24. Job postings in high-exposure roles also fell by 6.5 per cent following the release of ChatGPT. GAISI offers a robust framework for assessing generative AI's impact on work, providing early evidence that displacement effects may already outweigh productivity gains. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.22748 |
| By: | Jacob Dominski; Yong Suk Lee |
| Abstract: | This study investigates the labor market consequences of AI by analyzing near real-time changes in employment status and work hours across occupations in relation to advances in AI capabilities. We construct a dynamic Occupational AI Exposure Score based on a task-level assessment using state-of-the-art AI models, including ChatGPT 4o and Anthropic Claude 3.5 Sonnet. We introduce a five-stage framework that evaluates how AI's capability to perform tasks in occupations changes as technology advances from traditional machine learning to agentic AI. The Occupational AI Exposure Scores are then linked to the US Current Population Survey, allowing for near real-time analysis of employment, unemployment, work hours, and full-time status. We conduct a first-differenced analysis comparing the period from October 2022 to March 2023 with the period from October 2024 to March 2025. Higher exposure to AI is associated with reduced employment, higher unemployment rates, and shorter work hours. We also observe some evidence of increased secondary job holding and a decrease in full-time employment among certain demographics. These associations are more pronounced among older and younger workers, men, and college-educated individuals. College-educated workers tend to experience smaller declines in employment but are more likely to see changes in work intensity and job structure. In addition, occupations that rely heavily on complex reasoning and problem-solving tend to experience larger declines in full-time work and overall employment in association with rising AI exposure. In contrast, those involving manual physical tasks appear less affected. Overall, the results suggest that AI-driven shifts in labor are occurring along both the extensive margin (unemployment) and the intensive margin (work hours), with varying effects across occupational task content and demographics. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.08244 |
| By: | Cloé Garnache; Elisabeth Isaksen; Maria Nareklishvili |
| Abstract: | The transition to a low-carbon economy requires a contraction of fossil fuel sectors, raising questions about the labor market costs of reallocation. We study the 2014 oil price shock as a natural experiment to examine the contraction of Norway’s oil industry. Using matched employer–employee data, we estimate long-run effects on earnings and employment using two complementary approaches. A difference-in-differences design shows moderate losses for all oil workers, while an event study reveals substantially larger and more persistent losses among displaced workers—up to 10% in earnings and 5% in employment nine years after displacement, especially for those with lower educational attainment. Although few displaced workers transition into green jobs, they are equally likely to enter green and brown (non-oil) sectors when accounting for the size of each destination sector. Earnings losses are larger for those entering green jobs rather than brown (non-oil) jobs, but smaller than for those entering other sectors. Decomposition results indicate that differences in establishment wage premiums—rather than skill mismatch—explain most of the observed gaps. |
| Keywords: | green transition, oil industry, job displacement, distributional effects, establishment wage premium, skills mismatch |
| JEL: | Q32 Q52 J24 J63 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12057 |