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on Technology and Industrial Dynamics |
| By: | David Hémous; Simon Lepot; Thomas Sampson; Julian Schärer |
| Abstract: | Intellectual property rights are a recurrent source of tensions between developed and developing economies. This paper provides the first quantitative analysis of optimal patent policy in trading economies. We develop a new model of trade, growth and patenting in which patent protection affects both innovation and market power. The model is estimated using data on patent applications to calibrate patent protection by country and the geography of innovation. Counterfactual analysis yields three main results. First, the potential gains from international cooperation over patent policies are large. However, achieving these gains requires more innovative economies to offer stronger protection. Second, only a small share of these gains has been realized so far. And third, by pushing towards policy harmonization, the TRIPS agreement hurts developing countries without generating global welfare gains. Overall, there is substantial scope for policy reforms to increase efficiency. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:zur:econwp:456 |
| By: | Paolo Carioli; Dirk Czarnitzki; Christian Rammer |
| Abstract: | We investigate the effects of different channels of industry-science collaboration on new product sales at the firm-level and whether government subsidies for collaboration make a difference. We distinguish four collaboration channels: joint R&D, consulting/contract research, IP licensing, human resource transfer. Employing firm-level panel data from the German Community Innovation Survey and a conditional difference-in-differences methodology, we find a positive effect of industry-science collaboration on product innovation success only for joint R&D, but not for the other three channels. The positive effect is limited to subsidized collaboration. Our results suggest that government subsidies are required to bring firms and public science into forms of collaboration that are effective in producing higher innovation output. |
| Keywords: | Industry-science collaboration, transfer channels, product innovation, treatment effects analysis |
| Date: | 2024–10–23 |
| URL: | https://d.repec.org/n?u=RePEc:ete:msiper:751257 |
| By: | Draca, Mirko (London School of Economics); Nathan, Max (University College London); Nguyen-Tien, Viet (London School of Economics); Oliveira-Cunha, Juliana (London School of Economics); Rosso, Anna (University of Milan); Valero, Anna (London School of Economics) |
| Abstract: | Which types of human capital influence the adoption of advanced technologies? We study the skill-biased adoption of information and communication technologies (ICT) across two waves in the UK. Specifically, we compare the 'new wave' of cloud and machine learning / AI technologies during the 2010s - pre-LLM - with the previous wave of personal computer adoption in the 1990s and early 2000s. At the area-level we see the emergence of a distinct STEM-biased adoption effect for the second wave of cloud and machine learning / AI technologies (ML/AI), alongside a general skill-biased effect. A one-standard deviation increase in the baseline share of STEM workers in areas is associated with around 0.3 of a standard deviation higher adoption of cloud and ML/AI. We find similar effects at the firm level where we are able to test for the influence of a wide range of skills. In turn, this STEM-biased adoption pattern has encouraged the concentration of these technologies, leading to more acute differences between high-tech and low-tech areas and firms. In contrast with classical technology diffusion, recent cloud and ML/AI adoption in the UK seems more likely to widen inequalities than reduce them. |
| Keywords: | human capital, ICT, technology diffusion, STEM |
| JEL: | D22 J24 O33 R11 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp17329 |
| By: | Podrecca, Matteo (University of Bergamo); Culot, Giovanna (University of Udine); Tavassoli, Sam (Deakin University); Orzes, Guido (Free University of Bozen-Bolzano) |
| Abstract: | This study analyzes the current state of artificial intelligence (AI) technologies for addressing and mitigating climate change in the manufacturing sector and provides an outlook on future developments. The research is grounded in the concept of general-purpose technologies (GPTs), motivated by a still limited understanding of innovation patterns for this application context. To this end, we focus on global patenting activity between 2011 and 2023 (5, 919 granted patents classified for “mitigation or adaptation against climate change” in the “production or processing of goods”). We examined time trends, applicant characteristics, and underlying technologies. A topic modeling analysis was performed to identify emerging themes from the unstructured textual data of the patent abstracts. This allowed the identification of six AI application domains. For each of them, we built a network analysis and ran growth trend and forecasting models. Our results show that patenting activities are mostly oriented toward improving the efficiency and reliability of manufacturing processes in five out of six identified domains (“predictive analytics”, “material sorting”, “defect detection”, “advanced robotics”, and “scheduling”). Instead, AI within the “resource optimization” domain relates to energy management, showing an interplay with other climate-related technologies. Our results also highlight interdependent innovations peculiar to each domain around core AI technologies. Forecasts show that the more specific technologies are within domains, the longer it will take for them to mature. From a practical standpoint, the study sheds light on the role of AI within the broader cleantech innovation landscape and urges policymakers to consider synergies. Managers can find information to define technology portfolios and alliances considering technological co-evolution. |
| Keywords: | artificial intelligence; AI; climate change; sustainability; patent analysis; technology foresight |
| JEL: | O14 O31 O32 O33 O34 |
| Date: | 2024–10–21 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:lucirc:2024_012 |
| By: | Lena Abou El-Komboz; Thomas A. Fackler; Moritz Goldbeck; Thomas Fackler |
| Abstract: | Software engineering is prototypical of knowledge work in the digital economy and exhibits strong geographic concentration, with Silicon Valley as the epitome of a tech cluster. We investigate productivity effects of knowledge worker agglomeration. To overcome existing measurement challenges, we track individual contributions in software engineering projects between 2015 and 2021 on GitHub, the by far largest online code repository platform. Our findings demonstrate individual productivity increases by 2.8 percent with a ten percent increase in cluster size, the share of the software engineering community in a technology field located in the same city. Instrumental variable and dynamic estimation results suggest these productivity effects are causal. Productivity gains from cluster size growth are strongest for clusters hosting between 0.67 and 13.5% of a community. We observe a disproportionate activity increase in high-quality, large, and leisure projects and for co-located teams. Overall, software engineers benefit from productivity spillovers due to physical proximity to a large number of peers in their field. |
| Keywords: | high-skilled labor, geography, innovation, peer effects, collaboration |
| JEL: | D62 J24 O33 O36 R32 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_11277 |
| By: | Timothy DeStefano; Nick Johnstone; Richard Kneller; Jonathan Timmis |
| Abstract: | The arrival of cloud computing provides firms a new way to access digital technologies as digital services. Yet, capital incentive policies present in every OECD country are still targeted towards investments in information technology (IT) capital. If cloud services are partial substitutes for IT investments, the presence of capital incentive policies may unintentionally discourage the adoption of cloud and technologies that rely on the cloud, such as artificial intelligence (AI) and big data analytics. This paper exploits a tax incentive in the UK for capital investment as a quasi-natural experiment to examine the impact on firm adoption of cloud computing, big data analytics and AI. The empirical results find that the policy increased investment in IT capital as would be expected; but it slowed firm adoption of cloud, big data and AI. Matched employer-employee data shows that the policy also led firms to reduce their demand for workers that perform data analytics, but not other types of workers. |
| Keywords: | capital incentives, firms, cloud computing, artificial intelligence |
| JEL: | J21 J24 L20 O33 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_11369 |
| By: | Aakash Kalyani (Federal Reserve Bank of St. Louis); Nicholas Bloom (Stanford University); Marcela Carvalho (Harvard University); Tarek Hassan (Boston University); Josh Lerner (Harvard University); Ahmed Tahoun (London Business School) |
| Abstract: | We identify phrases associated with novel technologies using textual analysis of patents, job postings, and earnings calls, enabling us to identify four stylized facts on the diffusion of jobs relating to new technologies. First, the development of economically impactful new technologies is geographically highly concentrated, more so even than overall patenting: 56% of the most economically impactful technologies come from just two U.S. locations, Silicon Valley and the Northeast Corridor. Second, as the technologies mature and the number of related jobs grows, hiring spreads geographically. But this process is very slow, taking around 50 years to disperse fully. Third, while initial hiring in new technologies is highly skill biased, over time the mean skill level in new positions declines, drawing in an increasing number of lower-skilled workers. Finally, the geographic spread of hiring is slowest for higher-skilled positions, with the locations where new technologies were pioneered remaining the focus for the technology's high-skill jobs for decades. |
| Keywords: | Employment, Geography, Innovation, R and D |
| JEL: | O31 O32 |
| Date: | 2024–06–22 |
| URL: | https://d.repec.org/n?u=RePEc:thk:wpaper:inetwp222 |
| By: | Raphael Auer; David Köpfer; Josef Švéda; Raphael A. Auer |
| Abstract: | How exposed is the labour market to ever-advancing AI capabilities, to what extent does this substitute human labour, and how will it affect inequality? We address these questions in a simulation of 711 US occupations classified by the importance and level of cognitive skills. We base our simulations on the notion that AI can only perform skills that are within its capabilities and involve computer interaction. At low AI capabilities, 7% of skills are exposed to AI uniformly across the wage spectrum. At moderate and high AI capabilities, 17% and 36% of skills are exposed on average, and up to 45% in the highest wage quartile. Examining complementary versus substitution, we model the impact on side versus core occupational skills. For example, AI capable of bookkeeping helps doctors with administrative work, freeing up time for medical examinations, but risks the jobs of bookkeepers. We find that low AI capabilities complement all workers, as side skills are simpler than core skills. However, as AI capabilities advance, core skills in lower-wage jobs become exposed, threatening substitution and increased inequality. In contrast to the intuitive notion that the rise of AI may harm white-collar workers, we find that those remain safe longer as their core skills are hard to automate. |
| Keywords: | labour market, artificial intelligence, employment, inequality, automation, ChatGPT, GPT, LLM, wage, technology |
| JEL: | E24 E51 G21 G28 J23 M48 O30 O33 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_11410 |
| By: | Santiago Caicedo; Jeremy Pearce |
| Abstract: | This paper studies how the speed-quality tradeoff in innovation interacts with firm dynamics, concentration, and economic growth. Empirically, we document long-run trends in the increasing speed of innovation alongside declining quality at large firms. Leveraging variation from an exogenous policy change, we document the existence of the speed-quality tradeoff both at the firm and aggregate level. We develop an endogenous growth model that incorporates the speed-quality tradeoff and show that allocating less labor towards speed increases growth, particularly in the presence of private benefits to innovation and spillovers from heterogeneous innovations. We quantify the model to link firms’ decisions across speed and quality to aggregate outcomes. Quantitatively, the recent growth slowdown is mainly due to changes in the innovation production function, while the allocation of inventors between speed and quality within firms has a modest impact. When spillovers across firms are taken into account, the effect becomes significantly larger; the shift to speed over the last 30 years explains up to one-quarter of the decrease in growth. |
| Keywords: | innovation; economic growth; slowdown; inventors; firm dynamics |
| JEL: | J63 O30 O31 O33 |
| Date: | 2024–10–01 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fednsr:98928 |
| By: | Samuel Muehlemann |
| Abstract: | This paper investigates the impact of artificial intelligence (AI) adoption in production processes on workplace training practices, using firm-level data from the BIBB establishment panel on training and competence development (2019-2021). The findings reveal that AI adoption reduces the provision of continuing training for incumbent workers while increasing the share of high-skilled new hires and decreasing medium-skilled hires, thereby contributing to skill polarization. However, AI adoption also increases the number of apprenticeship contracts, particularly in small and medium-sized enterprises (SMEs), underscoring the ongoing importance of apprenticeships in preparing future workers with the skills needed to apply AI in production. |
| Keywords: | Artificial Intelligence, Technological Change, Automation, Apprenticeship Training, Human Capital |
| JEL: | J23 J24 M53 O33 |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:iso:educat:0232 |
| By: | MORIKAWA Masayuki |
| Abstract: | With the rapid diffusion of artificial intelligence (AI), its effects on economic growth and the labor market have attracted the attention of researchers. However, the lack of statistical data on the use of AI has restricted empirical research. Based on original surveys, this study provides an overview of the use of AI and other automation technologies in Japan, the characteristics of firms and workers who use AI, and their views on the impact of AI. According to the results, first, the number of firms using AI is increasing rapidly and firms with a larger share of highly educated workers have a greater tendency to use AI. Robot-using firms are also increasing, but the relationship between their use and workers’ education is weakly negative, suggesting that the impact on the labor market is different for each technology. Second, AI-using firms have higher productivity, wages, and medium-term growth expectations. Third, AI-using firms expect that while it will increase productivity and wages, it may decrease their employment. Fourth, at the worker level, more-educated workers are more likely to use AI, suggesting that AI and education are complementary. Currently, AI may favor high-skill workers in the labor market. Fifth, workers who use AI evaluate their work productivity to have increased by approximately 20% on average, suggesting that AI could potentially have a fairly large productivity enhancing effect. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:eti:dpaper:24074 |
| By: | Andreas Fridolin Buehler; Patrick Lehnert; Uschi Backes-Gellner |
| Abstract: | This paper analyzes how social gender norms affect the innovation gender gap, part of which stems from an underrepresentation of women in science, technology, engineering, and mathematics education. This underrepresentation is traceable to gender-biased educational and occupational choices. One determinant for such biased choices is social gender norms, which also directly affect the innovation gender gap. We disentangle the direct effect of social gender norms from their indirect effect via educational and occupational choices. Combining municipality-level voting data as a measure for social gender norms with patent data as a measure for innovation outcomes, we apply structural equation modeling. Our results show that more traditional gender norms are associated with a significantly lower number of patents filed by women and that the indirect effect via educational and occupational choices accounts for 5.5% of the total effect. These results are crucial for policymakers: while social gender norms are highly persistent and difficult to change in the short term, promoting greater gender equality in educational and occupational choices can be achieved more quickly and may therefore yield important short-term reductions in the innovation gender gap. |
| Keywords: | Gender, Education, Occupational Choices, Innovation |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:iso:educat:0230 |
| By: | Giulia Iannone (Gran Sasso Science Institute); Andrea Ascani (Gran Sasso Science Institute); Alessandra Faggian (Gran Sasso Science Institute); Alexandra Tsvetkova (OECD Trento Centre for Local Development) |
| Abstract: | There is an increasing need for today’s economies to be both productive and resilient, but the interplay between these two fundamental factors for economic growth has been neglected in the literature. This paper aims at filling this gap by adopting an evolutionary framework for the joint study of productivity and resilience and proposes a regional taxonomy based on characteristics of the industrial structure. Data on European regions at the NUTS2 level are used first to classify regions as productive and/or resilient and then to analyze how certain regional features, in particular related and unrelated variety, relate to a combined measure of productivity and resilience. Results show that the spatial distribution of productive and resilient regions follows a core– periphery pattern and that related and unrelated variety have significant but heterogeneous effects on regions’ economic performance. |
| Keywords: | productivity, regional resilience, industrial structure, relatedness |
| JEL: | B52 O4 R1 |
| Date: | 2023–12 |
| URL: | https://d.repec.org/n?u=RePEc:ahy:wpaper:wp44 |
| By: | Andres, Pia |
| Abstract: | Low cost solar energy is key to enabling the transition away from fossil fuels. Despite this, the European Union followed the United States’ example in imposing anti-dumping tariffs on solar panel imports from China in 2013, arguing that Chinese panels were unfairly subsidised and harmed its domestic industry. This paper examines the effects of Chinese import competition on firm-level innovation in solar photovoltaic technology by European firms using a sample of 10, 137 firms in 15 EU countries over the period 1999–2020. I show that firms which were exposed to higher import competition innovated more if they had a relatively small existing stock of innovation, but less if their historical knowledge stock fell within the top 10th percentile of firms in the sample. This suggests that newer firms were more able to respond to increased competition by innovating, while firms with a large historical stock of innovation may have been locked into old technological paradigms. As firms with a smaller knowledge stock tended to innovate more overall, trade with China appears to have been beneficial in encouraging innovation among the most innovative firms. However, I also find evidence that import competition increased the probability of exit among firms in the sample. |
| JEL: | R14 J01 |
| Date: | 2024–10–07 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:125801 |