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on Technology and Industrial Dynamics |
| By: | RENTOCCHINI Francesco (European Commission - JRC); VEZZANI Antonio; MONTRESOR Sandro |
| Abstract: | We examine whether government sponsored R&D induces the development of clean technologies with a high impact on subsequent technological development. The analysis uses information on USPTO patents granted between 2005 and 2015 and combines different methods to control for possible sorting of projects into public funding and for non-random (public) treatment. We also assess the distributional effect of government sponsored R&D. Results show that patents from public funded projects have a significantly higher impact and that this is particularly true for highly cited patents, thus supporting a role for technology-push policies in determining a clean technological transition. |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:ipt:wpaper:202301 |
| By: | Stefano Basilico; Alberto Marzucchi; Sandro Montresor; ; |
| Abstract: | This paper focuses on the combination of green and digital technologies at the regional level. Using patent data, we put forward an original measurement of the regional speed of green-digital (i.e. twin) combination: the temporal distance between the time at which a combination is realised for the first time in the frontier region and the time at which this same combination is accomplished in the focal region. We proceed by investigating the drivers and the technological impact related to this speed. We find that the speed of combination is enhanced by dealing with broad and diverse twin technologies. The speed at which the gap is closed, also crucially depends on the interdependencies between green and digital domains, captured by the overlap in their knowledge bases. Counterintuitively, the longer the combination paths, the faster the region combines green and digital technologies. This finding is then rationalised further looking at the policy and network characteristics. Finally, we find that the earlier the combination happens, the greater is likely to be the impact on subsequent inventions, but only for granted patents. Overall, these results are discussed in terms of policy recommendations, given the high attention placed by policymakers on the twin transition. |
| Keywords: | Twin transition; Digital technologies; Green technologies; Regional knowledge base |
| JEL: | O31 O33 R11 R12 Q55 |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:egu:wpaper:2440 |
| By: | Mirko Draca; Max Nathan; Viet Nguyen-Tien; Juliana Oliveira-Cunha; Anna Rosso; Anna Valero |
| 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: | Technology Diffusion, ICT, Human Capital, STEM |
| JEL: | D22 J24 O33 R11 |
| Date: | 2024–10–20 |
| URL: | https://d.repec.org/n?u=RePEc:csl:devewp:495 |
| By: | Flavio Calvino; Chiara Criscuolo; Luca Fontanelli; Lionel Nesta; Elena Verdolini |
| Abstract: | We leverage a uniquely comprehensive combination of data sources to explore the enabling role of human capital in fostering the adoption of predictive AI systems in French firms. Using a causal estimation approach, we show that ICT engineers play a key role for AI adoption by firms. Our estimates indicate that raising the current average share of ICT engineers in firms not using AI (1.66%) to the level of AI users (6.7%) would increase their probability to adopt AI by 0.81 percentage points - equivalent to an 8.43 percent growth. However, this would imply substantial investments to fill the existing gap in ICT human capital, amounting to around 450.000 additional ICT engineers. We also explore potential mechanisms, showing that the relevance of ICT engineers for predictive AI is driven by the innovative nature of its use, make-vs-buy choices, large availability of data, ICT and R&D intensity. |
| Keywords: | artificial intelligence, human capital, technological diffusion |
| Date: | 2024–11–18 |
| URL: | https://d.repec.org/n?u=RePEc:cep:cepdps:dp2055 |
| By: | Colin Wessendorf |
| Abstract: | Key enabling technologies (KETs) have gained attention in science and policy due to their multidisciplinary nature and their ability to link distant knowledge fields, endowing them with a central role in recombinant innovation processes. However, it remains under-researched whether KETs generally have a higher influence on innovation processes than non-KETs. This study addresses the question by using propensity score matching and regression analysis. First, a balanced dataset is created through matching KET patents to non-KET patents that stem from a comparable context. Subsequently, it is analyzed whether KET patents are associated with higher forward citation frequencies than non-KETs. The results show that KETs receive more citations on average, but it appears that this effect is driven by a few very impactful patents. The results further show that not all KETs exert a measurable impact on forward citations and highlight the heterogeneities between the individual KETs. These findings call for a more critical assessment of the KET concept and for nuanced approaches in research and policy. |
| Keywords: | Key enabling technologies, general purpose technologies, recombinant novelty, technological impact, patent citations, propensity score matching |
| JEL: | O30 O31 O33 C21 |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:atv:wpaper:2403 |
| By: | Armin Schmutzler |
| Abstract: | By affecting prices and thereby market shares of green and brown firms, product innovations and process innovations influence industry emissions even when they do not directly affect the emission intensity of the innovating firm. Using a differentiated two-stage duopoly, this paper therefore analyzes the effects of environmental policy on such innovations, and it asks how these effects differ from each other and from those of environmental innovations that directly reduce the emission intensity. The paper investigates the determinants of R&D investments, showing in particular that incentives for certain types of potentially beneficial innovations may be negative. Moreover, it analyzes how suitable policies can foster green innovation. |
| Keywords: | Innovation, environmental policy, imperfect competition |
| JEL: | Q55 L13 |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:zur:econwp:462 |
| By: | Dawid, Herbert (Center for Mathematical Economics, Bielefeld University); Riedel, Frank (Center for Mathematical Economics, Bielefeld University); Steg, Jan-Henrik (Center for Mathematical Economics, Bielefeld University); Wen, Xingang (Center for Mathematical Economics, Bielefeld University) |
| Abstract: | We study endogenous, credit-financed innovation under uncertainty in dynamic con- texts. In our model, a firm with limited cash reserves decides how much to invest in an R&D project, potentially using external financing. Investing more increases the proba- bility of a sooner innovation, but higher repayment obligations also increase bankruptcy risk if the innovation takes longer. We show that the firm reduces its investment dis- continuously if the financing cost is not favorable enough, in order to avoid the need for external financing. This insight implies that policies reducing financing costs can have discontinuous positive effects on investment, innovation rate and welfare. How- ever, policy measures increasing the effectiveness of R&D might reduce the innovation rate and welfare due to a discontinuous reduction of R&D investment. Furthermore, we find that low financing costs can lead to over-investment. The welfare loss from cash constraints is more severe for radical innovations compared to incremental ones. |
| Keywords: | Innovation, R&D investment, Cash constraints, Bankruptcy risk |
| Date: | 2024–12–09 |
| URL: | https://d.repec.org/n?u=RePEc:bie:wpaper:699 |
| By: | Shohei Momoda (Hiroshima University); Takayuki Ogawa (Osaka University); Ryosuke Shimizu (Ehime University) |
| Abstract: | Recent data suggest that countries with a higher accumulation of robots achieve higher economic growth. This study analyzes the international growth patterns in a two-country economy with task-based automation technology. We show that whenever one country can achieve perpetual growth by fully automating all tasks, another country can not. Thus, automation widens the international disparities in output growth. Using panel data covering 62 countries from 1994 to 2019, we empirically find that countries with more industrial robots are associated with higher economic growth through the increased accumulation of robots. |
| Keywords: | Automation; Growth patterns; International trade. |
| JEL: | F43 F62 O33 |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:kyo:wpaper:1109 |
| By: | Beatrice Negro; Giovanni Dosi; Maria Enrica Virgillito |
| Abstract: | The adverse effects of the climate crisis call for a structural change in the economy toward less environmentally disruptive development pathways. To address decarbonisation, hydrogen seems to be the most promising element to complement renewable energy. However, the dominant technology for its production relies on hydrocarbons, while a radical transition would require the establishment of a green hydrogen technological paradigm. Green hydrogen production is also hampered by critical materials and geographic attributes that only some countries would meet. This may constitute a window of opportunity for latecomers' countries to pursue green industrialization or a condition for their exploitation. So, what are the drivers behind hydrogen technologies production? And, how do countries learn and consequently specialise? We tackle these questions investigating the technologies, products, and processes behind hydrogen production. Using trade data, we examine the pattern of countries' specialisation and dependence on raw materials. Our findings indicate that hydrogen technologies market is undergoing a transformation in their composition rather than expansion. Moreover, looking at the critical raw materials content of green hydrogen technology, we find a negative relationship between dependence on critical raw materials and the autonomous specialisation of countries in their related production. |
| Keywords: | ecological transition, hydrogen paradigms, specialisation, dependency, mission-oriented policies |
| Date: | 2024–12–18 |
| URL: | https://d.repec.org/n?u=RePEc:ssa:lemwps:2024/33 |
| By: | Roth, Felix; Mitra, Alessio |
| Abstract: | The European Union (EU) faces challenges such as an ageing population, migratory pressures, geopolitical vulnerabilities, and climate change, highlighting the need to enhance its ability to do more with less. This paper examines the drivers of EU labour productivity before and after the 2007 financial crisis, across goods and services sectors, tangible and intangible assets, and Information and Communication Technologies (ICT) and non-ICT tangibles. Using the EUKLEMS 2022 dataset for 14 EU countries and the UK from 1995-2019 and growth regression analysis, we find that Research & Innovation (R&I) is crucial for productivity growth. Labour productivity in the goods sector benefits most from non-ICT tangible assets, while in the service sector, it benefits more from the non-R&D intangibles software, training, and organisational capital. On the other hand, training and ICT tangibles became more important drivers of labor productivity growth after the economic crisis. We argue that the productivity gap between the EU and the United States is largely due to insufficient investment in non-R&D intangibles like software, training, and organizational capital. |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:uhhhdp:19 |
| By: | Joachim Hubmer (University of Pennsylvania); Mons Chan (Queen’s University); Serdar Ozkan (Federal Reserve Bank of St. Louis, University of Toronto); Sergio Salgado (University of Pennsylvania); Guangbin Hong (University of Chicago) |
| Abstract: | Do larger firms have more productive technologies, are their technologies more scalable, or both? We use administrative data on Canadian and US firms to estimate a joint distribution of output elasticities of capital, labor, and intermediate inputs—thus, returns to scale (RTS)—along with total factor productivity (TFP). We find significant heterogeneity in RTS across firms within industries. Furthermore, larger firms operate technologies with higher RTS, whereas the largest firms do not exhibit the highest TFP. Higher RTS for large firms are entirely driven by higher intermediate input elasticities. Descriptively, these align with higher intermediate input revenue shares. We also show that high-RTS firms grow faster, pay higher wages, and are owned by wealthier households. We then incorporate RTS heterogeneity into the workhorse model of endogenous entrepreneurship that matches the observed heterogeneity in TFP and RTS. We find that the efficiency losses from financial frictions are more than twice as large compared to a conventional calibration that attributes all heterogeneity to TFP and assumes a common RTS parameter. |
| Keywords: | Production function heterogeneity, returns to scale, misallocation |
| JEL: | E22 L11 L25 |
| Date: | 2024–11–21 |
| URL: | https://d.repec.org/n?u=RePEc:pen:papers:24-036 |
| By: | Yibo Qiao (Nanjing University); Andrea Ascani (Gran Sasso Science Institute) |
| Abstract: | This paper investigates the effect of High-speed Railway (HSR) on city industrial upgrading. Using the Annual Survey of Industrial Firms (1998-2015) and HSR opening information in China, we conduct a difference-in-differences-in-differences (DDD) analysis on 300 prefecture- and higher-level cities and 389 4-digit manufacturing industries. We find that HSR enables cities to enter more complex industries, and this result is robust under parallel trend test, placebo test, instrumental variable estimation, and other specifications. We contribute to Evolutionary Economic Geography by considering HSR as a regional external linkage and by integrating the causal analysis in the study of regional diversification. |
| Keywords: | High-speed railway, industrial upgrading, complexity, regional diversification, China |
| JEL: | H54 O18 R11 |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:ahy:wpaper:wp58 |
| By: | Kauhanen, Antti; Rouvinen, Petri |
| Abstract: | Abstract This study examines the short-term impact of generative artificial intelligence (GAI) on employment and wages using data covering all wage earners from Finland. Employing a synthetic difference-in-differences approach, we analyze how the launch of ChatGPT affected occupations with varying levels of exposure to GAI. Our findings reveal that wages increased more in highly GAI-exposed occupations compared to less exposed ones following ChatGPT’s introduction. However, we do not observe statistically significant changes in employment levels between more and less exposed occupations. Additional analyses comparing more- and less-exposed occupations within specific occupational groups yield qualitatively similar results. These findings contrast with some previous studies on online labor markets but align more closely with research using nationally representative data. The positive wage effect observed in AI-exposed occupations could indicate that GAI is primarily enhancing rather than replacing human labor. The lack of significant employment effects might suggest that the impact of GAI on job creation or destruction may take longer to materialize or might be offset by other factors in the labor market. |
| Keywords: | Generative artificial intelligence, Technological change, Employment, Wages, Occupations |
| JEL: | E24 J21 O33 |
| Date: | 2024–11–19 |
| URL: | https://d.repec.org/n?u=RePEc:rif:wpaper:121 |
| By: | Costas Cavounidis; Vittoria Dicandia; Kevin Lang; Raghav Malhotra |
| Abstract: | We present a unified technological explanation of both the movement of workers across jobs using different skills and the changes in skill use within jobs. An envelope-theorem approach allows us to estimate relative skill-productivity growth from worker mobility using OLS while making minimal assumptions on each occupation's production function. Using six decades of data, we conclude that routine-cognitive- and finger-dexterity-skill productivity grew rapidly and abstract- and social-skill productivity grew slowly - a form of "skill bias." These effects, along with our estimated relationships between skill inputs, also explain changes in skill use within occupations. |
| Keywords: | skills; technological change |
| JEL: | J24 O33 |
| Date: | 2024–11–26 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedcwq:99181 |
| By: | César Barreto (OECD); Jonas Fluchtmann (OECD); Alexander Hijzen (OECD); Stefano Lombardi (VATT); Patrick Bennett (University of Liverpool); Antoine Bertheau (NHH); Winnie Chan (Statistics Canada); Andrei Gorshkov (IFAU); Jonathan Hambur (Reserve Bank of Australia); Nick Johnstone (IEA); Benjamin Lochner (FAU); Jordy Meekes (Leiden University); Tahsin Mehdi (Statistics Canada); Balázs Muraközy (University of Liverpool); Gulnara Nolan (Reserve Bank of Australia); Kjell Salvanes (NHH); Oskar Nordström Skans (Uppsala University); Rune Vejlin (Aarhus University) |
| Abstract: | This paper provides a comprehensive analysis of the costs of job displacement in energy-intensive industries in selected OECD countries. Based on harmonised linked employer-employee data from 14 OECD countries, we estimate the effect of job displacement in three energy-intensive industries, namely energy supply, heavy manufacturing and transport, compared to other industries. We find that workers displaced from energy supply and heavy manufacturing, experience larger earnings losses compared with workers in non-energy-intensive and transport sectors. Larger earnings losses mainly result from weaker re-employment outcomes in terms of wages and job instability but also challenges with finding another job. They reflect significant differences in the composition of workers and firms in energy supply and heavy manufacturing and the rest of the economy. Displaced workers in these sectors tend to be older, are less skilled and more likely to be previously employed in high-wage firms. |
| Keywords: | dismissal; linked employer-employee data; just transition |
| JEL: | J31 J63 Q43 |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:rba:rbardp:rdp2024-09 |