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on Technology and Industrial Dynamics |
| By: | Gaetan de Rassenfosse (Ecole polytechnique federale de Lausanne); Gabriele Pellegrino (Catholic University of the Sacred Heart); Emilio Raiteri (Eindhoven University of Technology) |
| Abstract: | This paper provides empirical evidence suggesting that patents may facilitate knowledge disclosure. The analysis exploits the Invention Secrecy Act, which grants the U.S. Commissioner for Patents the right to prevent the disclosure of new inventions that represent a threat to national security. Using a two-level matching approach, we document a negative and large relationship between the enforcement of a secrecy order and follow-on inventions, as captured with patent citations and text-based measures of invention similarity. The effect carries over to after the lift of the secrecy period, suggesting a lost generation of inventions. The results bear implications for innovation and intellectual property policy. |
| Keywords: | disclosure; follow-on invention; knowledge diffusion; patent |
| JEL: | O31 O33 O34 |
| Date: | 2023–12 |
| URL: | https://d.repec.org/n?u=RePEc:iip:wpaper:26&r= |
| By: | Arntz, Melanie; Findeisen, Sebastian; Maurer, Stephan; Schlenker, Oliver |
| Abstract: | This study quantifies the relationship between workplace digitalization, i.e., the increasing use of frontier technologies, and workers' health outcomes using novel and representative German linked employer-employee data. Based on changes in individual-level use of technologies between 2011 and 2019, we find that digitalization induces similar shifts into more complex and service-oriented tasks across all workers, but exacerbates health inequality between cognitive and manual workers. Unlike more mature, computer-based technologies, frontier technologies of the recent technology wave substantially lower manual workers' subjective health and increase sick leave, while leaving cognitive workers unaffected. We provide evidence that the effects are mitigated in firms that provide training and assistance in the adjustment process for workers. |
| Keywords: | health, inequality, technology, machines, automation, tasks, capital-labor substitution |
| JEL: | I14 J21 J23 J24 O33 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:zewdip:300006&r= |
| By: | Anabela Marques Santos; Francesco Molica; Carlos Torrecilla Salinas (European Commission, Joint Research Centre, Sevilla, Spain; European Commission, Joint Research Centre, Brussels, Belgium; European Commission, Joint Research Centre, Sevilla, Spain) |
| Abstract: | Artificial Intelligence (AI) is seen as a disruptive and transformative technology with the potential to impact on all societal aspects, but particularly on competitiveness and growth. While its development and use has grown exponentially over the last decade, its uptake between and within countries is very heterogeneous. The paper assesses the geographical distribution at NUTS2-level of EU-funded investments related to AI during the programming period 2014-2020. It also examines the relationship between this specialization pattern and regional characteristics using a spatial autoregressive model. Such an analysis provides a first look at the geography of public investment in AI in Europe, which has never been done before. Results show that in the period 2014-2020, around 8 billion EUR of EU funds were targeted for AI investments in the European regions. More developed regions have a higher specialization in AI EU-funded investments. This specialization also generates spillover effects that enhance similar specialization patterns in neighboring regions. AI-related investments are more concentrated in regions with a higher concentration of ICT activities and that are more innovative, highlighting the importance of agglomeration effects. Regions that have selected AI as an innovation priority for their Smart Specialization Strategies are also more likely to have a higher funding specialization in AI. Such findings are very relevant for policymakers as they show that AI-related investments are already highly spatially concentrated. This highlights the importance for less-developed regions to keep accessing to sufficient amounts of pre-allocated cohesion funds and to devote them for AI-related opportunities in the future. |
| Keywords: | Artificial intelligence; Public subsidy; Territorial specialization; Europe |
| JEL: | O31 R58 R12 O52 |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:mde:wpaper:181&r= |
| By: | Angelino, Pierluigi; Czarnitzki, Dirk; Volckaert, Astrid |
| Abstract: | The Flemish government launched its Spearhead Cluster (SHC) policy in 2017. The aim is to boost strategic sectors by setting up cluster initiatives which coordinate collaborative R&D initiatives. In this paper, we analyze whether becoming a member of such a cluster initiative has an impact on the Total Factor Productivity (TFP) of the firm. We exploit firm-level data between 2013 and 2020 to estimate TFP and apply a difference-in-differences approach to assess the programs' treatment effects. We find that becoming a member of a cluster has an average positive impact on firmlevel TFP of between 1 to 4.4 percent, depending on the econometric specification. These results are the first to provide an insight into the impact of the Flemish SHC policy on productivity. |
| Keywords: | cluster associations, cluster policy, innovation policy, total factor productivity, conditional difference-in-difference |
| JEL: | D24 L25 L52 L53 O25 O38 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:zewdip:300010&r= |
| By: | Amaral, Pedro; Rivera-Padilla, Alberto |
| Abstract: | Countries with high levels of human capital also tend to be technologically advanced. We study whether modeling technology adoption can significantly amplify the importance of human capital differences in accounting for cross-country income gaps. We document that schooling is positively and robustly correlated with measures of technology adoption and usage, and negatively correlated with the prevalence of traditional forms of production, where technology adoption is limited, and productivity is lower. Motivated by this, we build a general equilibrium model with human capital investment, endogenous occupational choices, and technology adoption. Production takes place either in a traditional sector, where technology adoption is absent, or in a modern sector, where managers hire a workforce and optimally choose technology. Economies differ in terms of schooling levels by occupation and in the size of barriers to technology adoption. These differences, working together, result in a factor of 6 between US income and that of the bottom quartile of countries. Schooling differences on their own result in a factor of 3.5, compared to a factor of 2 in a one-sector version of the model where technology choices are absent. |
| Keywords: | Human capital; Technology adoption; Cross-country income differences |
| JEL: | J24 O11 O33 O41 |
| Date: | 2024–04 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:121157&r= |
| By: | Francesca Guadagno (The Vienna Institute for International Economic Studies, wiiw); Oliver Reiter (The Vienna Institute for International Economic Studies, wiiw); Robert Stehrer (The Vienna Institute for International Economic Studies, wiiw) |
| Abstract: | Increasing production of green technologies in the EU holds great potential for the European economy. This study uses trade data and input-output tables to estimate the impacts on GDP and employment of reshoring to the EU the production of five major green technologies photovoltaics, wind turbines, batteries, electric motors and electric vehicles. Our findings show that reshoring these five technologies would increase EU GDP by EUR 18.4 billion, or 0.13% of EU GDP, and create 242, 728 new jobs. The same shift of imports to EU production would have had roughly half of the impact in 2010. We also find significant spillover effects on other sectors of the economy, particularly for metal products, wholesale and retail, professional, scientific and technical activities, and administrative and support services. To make the most from the transition, we argue that EU green industrial policy should put more emphasis on manufacturing capacities and innovation to meet the targets of the Net Zero Industry Act, remain internationally competitive, and reduce strategic dependencies. |
| Keywords: | green transition; photovoltaics; batteries; electric vehicles; GDP; employment |
| JEL: | Q55 Q56 F14 O25 |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:wii:pnotes:pn:80&r= |
| By: | Rafael Lopes de Melo (University of Edinburgh); Theodore Papageorgiou (Boston College) |
| Abstract: | This paper introduces a model of worker matching at the occupation level. In our setup, young workers, while employed in an occupation, accumulate human capital and also learn about their underlying productivity in that occupation. Human capital is partially transferable to other occupations and similarly, the information acquired in one occupation is useful for the worker’s productivity elsewhere. Workers with low tenure levels, as well as low-paid workers, are the ones most likely to switch occupations, consistent with our empirical findings. Though the model is quite general, we show that Gittins indices can be used in this setup to preserve tractability. We discuss potential applications ranging from assessing the impact of AI and automation to the evaluation of policies such as unemployment benefits, sector-specific subsidies, or minimum wages. |
| Keywords: | Human Capital, Occupations, Multi-armed Bandits, Worker Mobility, Learning, Information and Human Capital Spillovers, Wage Inequality, Gittins Index. |
| JEL: | J24 J31 J62 |
| Date: | 2024–06–10 |
| URL: | https://d.repec.org/n?u=RePEc:boc:bocoec:1076&r= |
| By: | Sangmin Aum; Yongseok Shin |
| Abstract: | When explaining the declining labor income share in advanced economies, the macro literature finds that the elasticity of substitution between capital and labor is greater than one. However, the vast majority of micro-level estimates shows that capital and labor are complements (elasticity less than one). Using firm- and establishment-level data from Korea, we divide capital into equipment and software, as they may interact with labor in different ways. Our estimation shows that equipment and labor are complements (elasticity 0.6), consistent with other micro-level estimates, but software and labor are substitutes (1.6), a novel finding that helps reconcile the macro vs. micro-literature elasticity discord. As the quality of software improves, labor shares fall within firms because of factor substitution and endogenously rising markups. In addition, production reallocates toward firms that use software more intensively, as they become effectively more productive. Because in the data these firms have higher markups and lower labor shares, the reallocation further raises the aggregate markup and reduces the aggregate labor share. The rise of software accounts for two-thirds of the labor share decline in Korea between 1990 and 2018. The factor substitution and the markup channels are equally important. On the other hand, the falling equipment price plays a minor role, because the factor substitution and the markup channels offset each other. |
| JEL: | D22 D24 D33 E22 E25 L11 |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:32591&r= |
| By: | Gaétan de Rassenfosse (Ecole polytechnique federale de Lausanne); Adam Jaffe (Brandeis University); Joal Waldfogel (University of Minnesota) |
| Abstract: | The arrival of creative machines—software capable of producing human-like creative content—has triggered a series of legal challenges about intellectual property. The outcome of these legal challenges will shape the future of the creative industry in ways that could enhance or jeopardize welfare. Policymakers are already tasked with creating regulations for a post-generative AI creative industry. Economics may offer valuable insights, and this paper is our attempt to contribute to the discussion. We identify the main economic issues and propose a framework and some tools for thinking about them. |
| Keywords: | generative AI; machine learning; copyright; fair use |
| JEL: | O34 K20 |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:iip:wpaper:27&r= |