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on Technology and Industrial Dynamics |
| By: | Francesco D'Alessandro (Dipartimento di Politica Economica, DISCE, Università Cattolica del Sacro Cuore, Milano, Italy); Enrico Santarelli (, Department of Economics, University of Bologna, Italy - Global Labor Organization (GLO), Essen, Germany); Marco Vivarelli (Dipartimento di Politica Economica, DISCE, Università Cattolica del Sacro Cuore, Milano, Italy – UNU-MERIT, Maastricht, The Netherlands – IZA, Bonn, Germany) |
| Abstract: | In this paper we integrate the insights of the Knowledge Spillover Theory of Entrepreneurship and Innovation (KSTE+I) with Schumpeter's idea that innovative entrepreneurs creatively apply available local knowledge, possibly mediated by Marshallian, Jacobian and Porter spillovers. In more detail, in this study we assess the degree of pervasiveness and the level of opportunities brought about by AI technologies by testing the possible correlation between the regional AI knowledge stock and the number of new innovative ventures (that is startups patenting in any technological field in the year of their foundation). Empirically, by focusing on 287 Nuts-2 European regions, we test whether the local AI stock of knowledge exerts an enabling role in fostering innovative entry within AI-related local industries (AI technologies as focused enablers) and within non AI-related local industries, as well (AI technologies as generalised enablers). Results from Negative Binomial fixed-effect and Poisson fixed-effect regressions (controlled for a variety of concurrent drivers of entrepreneurship) reveal that the local AI knowledge stock does promote the spread of innovative startups, so supporting both the KSTE+I approach and the enabling role of AI technologies; however, this relationship is confirmed only with regard to the sole high-tech/AI-related industries. |
| Keywords: | KSTE+I, Artificial Intelligence, innovative entry, enabling technologies |
| JEL: | O33 L26 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:ctc:serie5:dipe0039 |
| By: | Sabien Dobbelaere (Vrije Universiteit Amsterdam); Michael D. König (Vrije Universiteit Amsterdam); Andrin Spescha (ETH Zurich); Martin Wörter (ETH Zurich) |
| Abstract: | The fraction of R&D active firms decreased in Switzerland but increased in the Netherlands from 2000-2016. This paper examines reasons for this divergence and its impact on productivity growth. Our micro-data reveal R&D concentration among high-productivity firms in Switzerland. Innovation support sustains firms’ R&D activities in both countries. Our structural growth model identifies the impact of innovation, imitation and R&D costs on firms’ R&D decisions. R&D costs gained importance in Switzerland but not in the Netherlands, explaining the diverging R&D trends. Yet, counterfactual analyses show that policies should prioritize enhancing innovation and imitation success over cost reduction to boost productivity growth. |
| Keywords: | R&D, innovation, imitation, R&D costs, policy, productivity growth, traveling wave. |
| Date: | 2023–12–22 |
| URL: | https://d.repec.org/n?u=RePEc:tin:wpaper:20230080 |
| By: | Piotr Lewandowski; Wojciech Szymczak |
| Abstract: | We study the effect of the adoption of automation technologies – industrial robots and software and databases – on the incidence of atypical employment in 13 E.U. countries between 2006 and 2018. We combine survey microdata with sectoral information on technology use and exploit the variation at the demographic group level. Using instrumental variables estimation, we find that industrial robots significantly increase atypical employment share, mostly through involuntary part-time and involuntary fixed-term work. We find no robust effect of software and databases. We also show that the higher trade union coverage mitigates the robots’ impact on atypical employment, while employment protection legislation appears to play no role. Using historical decompositions, we attribute about 1-2 percentage points of atypical employment shares to rising robot exposure, especially in Central and Eastern European countries with low unionisation. |
| Keywords: | robots, automation, atypical employment, trade unions |
| JEL: | J23 J51 O33 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:ibt:wpaper:wp022024 |
| By: | Alexander Bick; Adam Blandin; David Deming |
| Abstract: | Generative Artificial Intelligence (AI) is a potentially important new technology, but its impact on the economy depends on the speed and intensity of adoption. This paper reports results from the first nationally representative U.S. survey of generative AI adoption at work and at home. In August 2024, 39 percent of the U.S. population age 18-64 used generative AI. More than 24 percent of workers used it at least once in the week prior to being surveyed, and nearly one in nine used it every workday. Historical data on usage and mass-market product launches suggest that U.S. adoption of generative AI has been faster than adoption of the personal computer and the internet. Generative AI is a general purpose technology, in the sense that it is used in a wide range of occupations and job tasks at work and at home. |
| Keywords: | generative artificial intelligence (AI); technology adoption; employment |
| JEL: | J24 O33 |
| Date: | 2024–09–20 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedlwp:98805 |
| By: | James M. Brand; Mert Demirer; Connor Finucane; Avner A. Kreps |
| Abstract: | Computing technologies have become critical inputs to production in the modern firm. However, there is little large-scale evidence on how efficiently firms use these technologies. In this paper, we study firm productivity and learning in cloud computing by leveraging CPU utilization data from over one billion virtual machines used by nearly 100, 000 firms. We find large and persistent heterogeneity in compute productivity both across and within firms, similar to canonical results in the literature. More productive firms respond better to demand fluctuations, show higher attentiveness to resource utilization, and use a wider variety of specialized machines. Notably, productivity is dynamic as firms learn to be more productive over time. New cloud adopters improve their productivity by 33% in their first year and reach the productivity level of experienced firms within four years. In our counterfactual calculations, we estimate that raising all firms to the 80th percentile of productivity would reduce aggregate electricity usage by 17%. |
| JEL: | D24 L86 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:32938 |
| By: | Yueling Huang |
| Abstract: | This paper empirically investigates the impact of Artificial Intelligence (AI) on employment. Exploiting variation in AI adoption across US commuting zones using a shift-share approach, I find that during 2010-2021, commuting zones with higher AI adoption have experienced a stronger decline in the employment-to-population ratio. Moreover, this negative employment effect is primarily borne by the manufacturing and lowskill services sectors, middle-skill workers, non-STEM occupations, and individuals at the two ends of the age distribution. The adverse impact is also more pronounced on men than women. |
| Keywords: | Artificial intelligence; technology; labor; local labor markets; shift share |
| Date: | 2024–09–13 |
| URL: | https://d.repec.org/n?u=RePEc:imf:imfwpa:2024/199 |
| By: | Immaculada Martinez-Zarzoso; Ana Maria Santacreu |
| Abstract: | This paper investigates the impact of cross-border patenting on the margins of international trade using disaggregated data on international patenting and trade flows. We develop a theoretical framework of trade and firms' patenting decisions that motivates our empirical analysis. The main results reveal that cross-border patenting has a larger effect on the extensive margin of trade compared to the intensive margin. This finding suggests that firms tend to seek patent protection in international markets prior to entering those markets with new products, rather than with their existing products. |
| Keywords: | cross-border patents; gravity model; margins of trade; trade agreements |
| JEL: | F63 O14 O33 O34 |
| Date: | 2024–09–26 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedlwp:98864 |
| By: | Leonardo Gambacorta; Han Qiu; Shuo Shan; Daniel M Rees |
| Abstract: | In this paper we examine the effects of generative artificial intelligence (gen AI) on labour productivity. In September 2023, Ant Group introduced CodeFuse, a large language model (LLM) designed to assist programmer teams with coding. While one group of programmers used it, other programmer teams were not informed about this LLM. Leveraging this event, we conducted a field experiment on these two groups of programmers. We identified employees who used CodeFuse as the treatment group and paired them with comparable employees in the control group, to assess the impact of AI on their productivity. Our findings indicate that the use of gen AI increased code output by more than 50%. However, productivity gains are statistically significant only among entry-level or junior staff, while the impact on more senior employees is less pronounced. |
| Keywords: | artificial intelligence, productivity, field experiment, big tech |
| JEL: | D22 G31 R30 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:bis:biswps:1208 |
| By: | Cesar Barreto; Jonas Fluchtmann; Alexander Hijzen; Stefano Lombardi; Patrick Bennett; Antoine Bertheau; Winnie Chan; Andrei Gorshkov; Jonathan Hambur; Nick Johnstone; Benjamin Lochner; Jordy Meekes; Tahsin Mehdi; Balázs Muraközy; Gulnara Nolan; Kjell Salvanes; Oskar Nordström Skans; Rune Vejlin |
| 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 the 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, just transition, linked employer-employee data |
| JEL: | J31 J63 Q43 |
| Date: | 2024–09–27 |
| URL: | https://d.repec.org/n?u=RePEc:oec:elsaab:310-en |
| By: | Paliński, Michał (University of Warsaw); Aşık, Gunes A. (TOBB University of Economy and Technology); Gajderowicz, Tomasz (University of Warsaw); Jakubowski, Maciej (University of Warsaw); Nas Özen, Efşan (World Bank); Raju, Dhushyanth (World Bank) |
| Abstract: | This study expands the inventory of green job titles by incorporating a global perspective and using contemporary sources. It leverages natural language processing, specifically a retrieval-augmented generation model, to identify green job titles. The process began with a search of academic literature published after 2008 using the official APIs of Scopus and Web of Science. The search yielded 1, 067 articles, from which 695 unique potential green job titles were identified. The retrieval-augmented generation model used the advanced text analysis capabilities of Generative Pre-trained Transformer 4, providing a reproducible method to categorize jobs within various green economy sectors. The research clustered these job titles into 25 distinct sectors. This categorization aligns closely with established frameworks, such as the U.S. Department of Labor's Occupational Information Network, and suggests potential new categories like green human resources. The findings demonstrate the efficacy of advanced natural language processing models in identifying emerging green job roles, contributing significantly to the ongoing discourse on the green economy transition. |
| Keywords: | AI, text mining, occupational classification, green jobs, green economy |
| JEL: | J23 Q52 O14 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp17286 |