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on Technology and Industrial Dynamics |
| By: | Lukas Rosenberger; W. Walker Hanlon; Carl Hallmann |
| Abstract: | How did Britain sustain faster rates of economic growth than comparable European countries, such as France, during the Industrial Revolution? We argue that Britain possessed an important but underappreciated innovation advantage: British inventors worked in technologies that were more central within the innovation network. We offer a new approach for measuring the innovation network using patent data from Britain and France in the late-18th and early-19th century. We show that the network influenced innovation outcomes and demonstrate that British inventors worked in more central technologies within the innovation network than French inventors. Drawing on recently developed theoretical tools, and using a novel estimation strategy, we quantify the implications for technology growth rates in Britain compared to France. Our results indicate that the shape of the innovation network, and the location of British inventors within it, explains an important share of the more rapid technological change and industrial growth in Britain during the Industrial Revolution. |
| JEL: | N13 O30 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:32875 |
| By: | Giacomo Damioli; Vincent Van Roy; Daniel Vertesy; Marco Vivarelli |
| Abstract: | Artificial intelligence (AI) is emerging as a transformative innovation with the potential to drive significant economic growth and productivity gains. This study examines whether AI is initiating a technological revolution, signifying a new technological paradigm, using the perspective of evolutionary neo-Schumpeterian economics. Using a global dataset combining information on AI patenting activities and their applicants between 2000 and 2016, our analysis reveals that AI patenting has accelerated and substantially evolved in terms of its pervasiveness, with AI innovators shifting from the ICT core industries to non-ICT service industries over the investigated period. Moreover, there has been a decrease in concentration of innovation activities and a reshuffling in the innovative hierarchies, with innovative entries and young and smaller applicants driving this change. Finally, we find that AI technologies play a role in generating and accelerating further innovations (so revealing to be “enabling technologies”, a distinctive feature of GPTs). All these features have characterised the emergence of major technological paradigms in the past and suggest that AI technologies may indeed generate a paradigmatic shift. |
| Keywords: | Artificial Intelligence, Patents, Structural Change, Technological Paradigm |
| Date: | 2024–08–14 |
| URL: | https://d.repec.org/n?u=RePEc:ete:msiper:746877 |
| By: | D’Alessandro, Francesco (Università Cattolica del Sacro Cuore); Santarelli, Enrico (University of Bologna); Vivarelli, Marco (Università Cattolica del Sacro Cuore) |
| 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: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp17206 |
| By: | Natália Barbosa (School of Economics and Management, University of Minho) |
| Abstract: | The adoption of new digital technologies offers new opportunities and has the scope to engender positive effects on firms’ expansion and success in international markets. This paper examines the main factors driving the adoption of Artificial Intelligence (AI) and AI-related digital technologies that enable the Industry 4.0 transformation and whether these new generation of digital technologies affect exporting performance at firm level. Using a rich and representative sample of Portuguese firms over the period 2014-2020, the estimated results suggest that firm’s ex-ante performance, digital infrastructures and in-house ICT skills are the main drivers of digitalisation. However, conditional to ex-ante firm’s performance, there are heterogenous effects on exporting performance across digital technologies and across industries. Moreover, there is evidence of positive selection towards large firms, casting doubts on the inclusiveness of the adoption process and the performance effects of AI and AI-related technologies. |
| Keywords: | Artificial Intelligence, Industry 4.0 enabling digital technologies, firms’ exporting performance |
| JEL: | L20 H81 L25 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:mde:wpaper:183 |
| By: | Dirk Czarnitzki; Malte Prüfer |
| Abstract: | This paper investigates the impact of Public Procurement of Innovation (PPI) and Research and Development (R&D) grants on firms' R&D investment using data from Belgian R&D-active firms over the past decade. Our empirical analysis robustly reveals a non-negligible crowding-out effect between the two instruments, suggesting a substitutive relationship. While each policy individually positively influences R&D investment, their combined implementation diminishes their effectiveness. These results challenge prevailing evidence and emphasize the need for a careful policy implementation, raising policymakers’ awareness against a blanket increase in innovation policies without considering potential interactions. |
| Keywords: | Public procurement of innovation, Research and Development, Econometric policy evaluation, Crowding-out |
| Date: | 2024–08–14 |
| URL: | https://d.repec.org/n?u=RePEc:ete:msiper:746875 |
| By: | Raphael Auer; David Köpfer; Josef Sveda |
| 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 J24 M48 O30 O33 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:bis:biswps:1207 |
| By: | Joana Elisa Maldonado; Anneleen Vandeplas; Istvan Vanyolos; Mauro Vigani; Alessandro Turrini |
| Abstract: | As the green transition is set to accelerate swiftly over the next decades, its implications for labour markets and workers are of key concern to policymakers. The aim of this paper is to review different methodologies to identify green jobs in cross-country comparable data that are regularly and timely available for EU Member States and assess their usefulness for policy-relevant labour market analysis. Three different methodologies are compared, of which one draws on Eurostat’s environmental accounts (EGSS) data. The two other methodologies use EU Labour Force Survey (LFS) data to implement taskbased approaches. The first task-based approach uses information on occupational task profiles from O*NET data, in line with several other existing studies. The second task-based approach uses a more novel source of information on occupational skills profiles, notably the European Classification of Occupations, Skills and Competences (ESCO). Two out of the three indicators show a rising trend in green jobs over recent years. Sectors such as industry, construction and agriculture account for the bulk of the green jobs; even if the proportion of service jobs among green jobs is on the rise. Green jobs are more likely to be taken up by men than non-green jobs. The geographical and skills distributions of green jobs depend on the methodology used. Based on the presented analysis, the national accounts (EGSS)-based approach seems the most reliable. Nevertheless, given its constraints in terms of opportunities for socioeconomic analysis, it still seems useful to consider other approaches to get at richer insights, while consistently verifying the robustness of results across different methodologies. |
| JEL: | J23 J24 L52 Q28 |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:euf:dispap:206 |
| By: | Frattini, Federico Fabio; Vona, Francesco; Bontadini, Filippo |
| Abstract: | What are the consequences of green industrialization on the labour market and industry dynamics? This paper tackles and quantifies this question by employing observable and reliable data on green manufacturing production for an extensive set of EU countries and 4-digit manufacturing industries for over a decade. First, at a descriptive level, this paper documents that potentially green industries outperform the others in terms of employment, average wages, value added and productivity, net of controlling for other drivers of the labour market and industry dynamics. Second, employing a shiftshare instrument to purge the analysis from possible endogeneity within green potential industries, this paper finds that an expansion of green production implies an increase in employment and value added. In contrast, average wages and labour productivity remain unchanged. These results hold in the short and long term, are heterogeneous depending on the countries considered, and are amplified by existing industry specialization and by accounting for input-output linkages. |
| Keywords: | Climate Change, Environmental Economics and Policy, Political Economy, Sustainability |
| Date: | 2024–08–27 |
| URL: | https://d.repec.org/n?u=RePEc:ags:feemwp:344791 |
| By: | Grant Goehring; Filippo Mezzanotti; S. Abraham (Avri) Ravid |
| Abstract: | This paper studies the impact of career concerns on technological change by analyzing the adoption of digital cinematography in the US motion picture industry. This setting allows us to collect rich data on the adoption of this new technology at the project-level (i.e., movie) as well as on the career of the main decision maker (i.e., director). We find that early career directors played a leading role in the adoption of digital technology and that this effect appears to be explained by career concerns, rather than alternative motives we consider and analyze. Technological savviness also plays a role. |
| JEL: | D22 L2 M19 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:32844 |
| By: | Machteld Hoskens; Koenraad Debackere |
| Abstract: | Given the importance of innovation for economic growth, many countries conduct innovation surveys. International guidelines for such measurement have been established (OECD/Eurostat, 2018). The European Commission has made the measurement of innovation mandatory for EU member states. Many differences remain, however, between countries in the practical implementation of measuring innovation at the firm level, which complicates cross-country comparability. We conducted a randomized experiment in which we randomly assigned enterprises a long or a short form for measuring their innovation activities. We found clear differences between the two types of forms. We discuss implications of this work and put this in the broader perspective of other work done investigating questionnaire design issues in innovation surveys. |
| Keywords: | questionnaire design, innovation survey, randomized experiment, questionnaire length, shortened survey form, nonresponse survey |
| Date: | 2024–08–19 |
| URL: | https://d.repec.org/n?u=RePEc:ete:msiper:747238 |
| By: | Kossi Messanh Agbekponou (SMART - Structures et Marché Agricoles, Ressources et Territoires - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - Institut Agro Rennes Angers - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement); Angela Cheptea (SMART - Structures et Marché Agricoles, Ressources et Territoires - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - Institut Agro Rennes Angers - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement); Karine Latouche (SMART - Structures et Marché Agricoles, Ressources et Territoires - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - Institut Agro Rennes Angers - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement) |
| Abstract: | This paper analyses how the quality of produced goods affects firms' position in global value chains (GVCs). Extending the theoretical framework of Chor et al. (2021), we find that quality upgrading increases the span of production stages performed by the firm: it imports more upstream (less transformed) intermediate products and exports more downstream (more highly processed) products. Expansion along GVCs through quality upgrading is accompanied by an increase in input purchases, assets, value added, and profits. These theoretical predictions are tested using 2004-2017 firm-level data on French agri-food industries (from French customs and the AMADEUS database). In line with recent work, we identify firms that participate in GVCs with those that jointly import and export, and measure firms' position in value chains through the level of transformation (upstreamness) of goods they use and produce. We use several ways to measure product quality at firm level, all inspired by the commonly accepted assumption that, at equal prices, higher quality products are sold in larger quantities. Our findings confirm the prediction that higher-quality firms use more upstream inputs produced by other firms to produce more transformed outputs, and perform a larger span of intermediate production stages in-house. We find limited empirical evidence in support of other predictions. |
| Keywords: | Global value chains, Production line position, Quality upgrading, Upstreamness, Agri-food industry |
| Date: | 2024–05–15 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-04666099 |