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on Technology and Industrial Dynamics |
| By: | Eric Iversen; Arvid Raknerud (Statistics Norway); Marit Klemetsen; Brita Bye (Statistics Norway) |
| Abstract: | What difference does government support of business R&D make to the rate of innovation? Addressing this important question has deep theoretical roots and broadening practical applications in OECD countries. The analysis of output additionality has been hampered by incomplete data combined with adaption of problematic methodologies. In this light, we contribute to the formative literature in three main ways: we analyze comprehensive panel data of Norwegian enterprises over a 20-year period; we include trademarks and industrial designs as well as patents to broaden measures of innovation output; and we apply machine learning methods to estimate treatment effect functions, thereby addressing the problem of a practically unlimited number of potential confounding factors. Our findings support and elaborate earlier work that fiscal stimulus tends to have greatest impact on previously non-innovative firms. The impact of support measures, alone or in combination, is on the extensive rather than intensive margin. For previously R&D-active firms, our results indicate that public support has low additionality and even risks crowding-out private financing of R&D. |
| Keywords: | Innovation; R&D support; Output additionality; Intellectual property rights; Patents; Trademarks; Public policy instruments; Lasso; Double selection; Poisson regression |
| JEL: | C33 C52 O31 O34 O38 |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:ssb:dispap:1020 |
| By: | Davide M. Coluccia; Gaia Dossi |
| Abstract: | This paper documents that out-migration promotes the diffusion of innovation from the country of destination to the country of origin of migrants. Between 1870 and 1940, nearly four million British immigrants settled in the United States. We construct a novel individual-level dataset linking British immigrants in the US to the UK census, and we digitize the universe of UK patents from 1853 to 1899. Using a triple-differences design, we show that migration ties contribute to technology diffusion from the destination to the origin country. The text analysis of patents reveals that emigration promotes technology transfer and fosters the production of high-impact innovation. Return migration is an important driver of this "return innovation" effect. However, the interactions between emigrants and their origin communities - families and neighbors - promote technology diffusion even in the absence of migrants' physical return. |
| Keywords: | age of mass migration, innovation, networks, out-migration |
| Date: | 2025–01–27 |
| URL: | https://d.repec.org/n?u=RePEc:cep:cepdps:dp2069 |
| By: | Silvia Appelt; Matej Bajgar; Chiara Criscuolo; Fernando Galindo-Rueda |
| Abstract: | Recent firm-level studies find R&D tax incentives to be much more effective at stimulating firms' R&D investment than what aggregate analyses indicate. Based on a distributed analysis of official R&D survey and administrative tax relief micro-data for 19 OECD countries, we show that two factors can reconcile these contrasting results. Firstly, a limited uptake of R&D tax incentives in most countries makes aggregate studies underestimate the effectiveness of R&D tax incentives. Secondly, R&D tax incentives are (much) less effective for large and R&D-intensive firms, which account for a small share of R&D-performing firms but most aggregate R&D tax relief, making firm-level studies overstate the aggregate effectiveness of R&D tax incentives. |
| Keywords: | mental health, employment, earnings, policy evaluation, psychological therapies |
| Date: | 2025–01–29 |
| URL: | https://d.repec.org/n?u=RePEc:cep:cepdps:dp2071 |
| By: | Dirk Czarnitzki; Robin Lepers; Maikel Pellens |
| Abstract: | The circular economy represents a systematic shift in production and consumption, aimed at extending the life cycle of products and materials while minimizing resource use and waste. Achieving the goals of the circular economy presents firms with the challenge of innovating new products, technologies, and business models, however. This paper explores the role of artificial intelligence as an enabler of circular economy innovations. Through an empirical analysis of the German Community Innovation Survey, we show that firms investing in artificial intelligence are more likely to introduce circular economy innovations than those that do not. Additionally, the results indicate that the use of artificial intelligence enhances firms’ abilities to lower production externalities (for instance, reducing pollution) through these innovations. The findings of this paper underscore artificial intelligence’s potential to accelerate the transition to the circular economy. |
| Keywords: | Circular economy, Innovation, Artificial intelligence |
| Date: | 2025–01–23 |
| URL: | https://d.repec.org/n?u=RePEc:ete:msiper:758339 |
| By: | José Alberto Fuinhas (Faculty of Economics, and Centre for Business and Economics Research (CeBER), University of Coimbra); Asif Javed (School of Advanced Studies, University of G. D'Annunzio Chieti-Pescara); Dario Sciulli (Department of Economic Studies, University of G. D'Annunzio Chieti-Pescara); Edilio Valentini (Department of Economic Studies, University of G. D'Annunzio Chieti-Pescara) |
| Abstract: | Governments across the globe are implementing stricter environmental policies to combat climate change and promote sustainability. This study contributes to the growing literature exploring the influence of environmental policy on skill-biased employment across various occupations. Specifically, we examine the causal effect of the revised version of Environmental Policy Stringency Index (EPS) and its components on skill-biased employment, focusing on occupations such as managers, professionals, technicians, and manual workers across 21 European economies from 2008 to 2020. Using the Method of Moments Quantile Regression (MMQR), the findings reveal that stringent environmental policies affect employment shares across different occupational categories. Skilled workers tend to benefit more from such policies, with a notable increase in the employment of professionals across all policy measures and a more differentiated impact among technicians and managers. In contrast, manual workers are generally adversely affected by environmental policies. These asymmetric effects on occupations exacerbate labour market inequalities, including disparities in employment levels and potential earnings. This research highlights the importance of designing tailored policies to mitigate adverse labour market outcomes while facilitating a transition to sustainable economic practices. |
| Keywords: | Environmental policy stringency, Skilled workers, Employment, Method of Moments Quantile Regression |
| JEL: | Q58 J24 |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:fem:femwpa:2025.02 |
| By: | Patrick Llerena; Corentin Lobet; Andr\'e Lorentz |
| Abstract: | We propose a disaggregated representation of production using an agent-based fund-flow model that emphasizes inefficiencies, such as factor idleness and production instability, and allows us to explore their emergence through simulations. The model incorporates productivity dynamics (learning and depreciation) and is extended with time-saving process innovations. Specifically, we assume workers possess inherent creativity that flourishes during idle periods. The firm, rather than laying off idle workers, is assumed to harness this potential by involving them in the innovation process. Results show that a firm's organizational and managerial decisions, the temporal structure of the production system, the degree of workers' learning and forgetting, and the pace of innovation are critical factors influencing production efficiency in both the short and long term. The coevolution of production and innovation processes emerges in our model through the two-sided effects of idleness: the loss of skills through forgetting and the deflection of time from the production of goods to the production of ideas giving birth to idleness-driven innovations. In doing so, it allows us to question the status of labour as an adjustment variable in a productive organisation. The paper concludes by discussing potential solutions to this issue and suggesting avenues for future research. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.09778 |
| By: | Pouliakas, Konstantinos (European Centre for the Development of Vocational Training (Cedefop)); Santangelo, Giulia (European Centre for the Development of Vocational Training (Cedefop)) |
| Abstract: | Understanding the labour market impact of new, autonomous digital technologies, particularly generative or other forms of artificial intelligence (AI), is currently at the top of the research and policy agenda. Many initial studies, though not all, have shown that there is a wage premium to AI skills in labour markets. Such evidence tends to draw on data from web-based sources and typically deploys a keyword approach for identifying AI skills. This paper utilises representative adult workforce data from 29 European countries, the second European skills and jobs survey, to examine wage differentials of the AI developer workforce. The latter is uniquely identified as part of the workforce that writes programs using AI algorithms. The analysis shows that, on average, AI developers enjoy a significant wage premium relative to a comparably educated or skilled workforce, such as programmers who do not yet write code using AI at work. Wage decomposition analysis further illustrates that there is a large unexplained component of such wage differential. Part of AI programmers' larger wage variability can be attributed to a greater performance-based component in their wage schedules and higher job-skill requirements. |
| Keywords: | artificial intelligence, skills, wage differentials, performance-based pay |
| JEL: | J24 J31 J71 M52 |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp17607 |
| By: | Mahyar Habibi |
| Abstract: | This paper explores the economic underpinnings of open sourcing advanced large language models (LLMs) by for-profit companies. Empirical analysis reveals that: (1) LLMs are compatible with R&D portfolios of numerous technologically differentiated firms; (2) open-sourcing likelihood decreases with an LLM's performance edge over rivals, but increases for models from large tech companies; and (3) open-sourcing an advanced LLM led to an increase in research-related activities. Motivated by these findings, a theoretical framework is developed to examine factors influencing a profit-maximizing firm's open-sourcing decision. The analysis frames this decision as a trade-off between accelerating technology growth and securing immediate financial returns. A key prediction from the theoretical analysis is an inverted-U-shaped relationship between the owner's size, measured by its share of LLM-compatible applications, and its propensity to open source the LLM. This finding suggests that moderate market concentration may be beneficial to the open source ecosystems of multi-purpose software technologies. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.11581 |
| By: | Bajgar, Matej; Criscuolo, Chiara; Timmis, Jonathan |
| Abstract: | This paper presents new evidence on the growing scale of large businesses in the United States, Japan and 11 European countries. It documents a broad increase in industry concentration across the majority of countries and sectors over the period 2002–2017. The rising concentration is strongly linked to investment in intangibles—particularly innovative assets; and software and data—and this relationship is magnified in more globalised industries. The results are consistent with intangibles disproportionately benefiting large firms, enabling them to scale up and increase their market shares by leveraging intangibles across multiple markets. |
| Keywords: | intangible investment; business groups; concentration |
| JEL: | J1 C1 |
| Date: | 2025–01–27 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:126673 |
| By: | Kiet Tuan Duong (University of York); Steven Ongena (University of Zurich - Department Finance; Swiss Finance Institute; KU Leuven; NTNU Business School; Centre for Economic Policy Research (CEPR)); Nam T. Vu (Miami University of Ohio - Department of Economics); Luu Duc Toan Huynh (Queen Mary University of London) |
| Abstract: | Do international sanctions impact patenting? To answer this question, we study patent applications originating in Russia, currently one of the world's most heavily sanctioned countries. We find that Russian applications are subject to longer processing times in sanctioning countries and that filed Russian patents exhibit fewer forward citations. Interestingly, applicants with names similar to those in the Kremlin or in the top 20 of popular Russian first names, and applicants who have filed patents during the last three years receive faster processing and greater forward citations. Retaliatory "revenge" sanctions imposed by Russia have an opposite impact, but the impact is more robustly overturned in sanctioning countries. |
| Keywords: | sanction, patent, knowledge spillover, processing duration |
| JEL: | D02 D74 D83 |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:chf:rpseri:rp2501 |
| By: | Nicholas Bloom; Mihai A. Codreanu; Robert A. Fletcher |
| Abstract: | We partner with a large US payment-processing company to run a 5-year, 10 wave panel survey of incentivized quarterly sales forecasts on over 6, 000 firms. We match firm predictions to proprietary revenue data to assess accuracy. We find firms forecast poorly, with issues of inaccuracy, over-optimism, predictable errors and over-precision. To assess the causes of these forecasting issues we run experiments on: (i) data use, (ii) incentives, (iii) forecasting skill, and (iv) contingent thinking. We find greater data use primarily decreases noise and reduces over-precision, while higher incentives moderate over-optimism. Both moderately increase accuracy. The other two treatments have no impact. These results suggest forecasting biases can be reduced but are hard to eliminate. In a simple simulation model, we show these biases change firm responsiveness to changes in taxes and productivity, highlighting their macro importance. |
| JEL: | J0 |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33384 |
| By: | Niklas Amberg; Richard Friberg; Chad Syverson |
| Abstract: | Using quarterly micro data on capacity utilization among Swedish manufacturing firms, we show that idiosyncratic factors are much more important than aggregate influences in explaining variation in capacity utilization across firms and over time. Idiosyncratic does not mean unpredictable, however. A simple newsvendor model of optimally set capacity predicts that higher demand uncertainty lowers capacity utilization, especially for high-markup firms. We test these predictions using data that contain firm-specific, forward-looking measures of uncertainty. Firms in the top of the uncertainty distribution on average have seven percentage points lower capacity utilization than firms in the bottom. The fall in capacity utilization is more than double for firms in the top rather than bottom tercile of the markup distribution. |
| JEL: | D20 L11 L23 L60 |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33400 |