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on Technology and Industrial Dynamics |
| By: | Alessandra Bonfiglioli; Rosario Crinò; Gino Gancia; Ioannis Papadakis |
| Abstract: | We study the effect of Artificial Intelligence (AI) on employment across US commuting zones over the period 2000-2020. A simple model shows that AI can automate jobs or complement workers, and illustrates how to estimate its effect by exploiting variation in a novel measure of local exposure to AI: job growth in AI-related professions built from detailed occupational data. Using a shift-share instrument that combines industry-level AI adoption with local industry employment, we estimate robust negative effects of AI exposure on employment across commuting zones and time. We find that AI’s impact is different from other capital and technologies, and that it works through services more than manufacturing. Moreover, the employment effect is especially negative for low-skill and production workers, while it turns positive for workers at the top of the wage distribution. These results are consistent with the view that AI has contributed to the automation of jobs and to widen inequality. |
| Keywords: | artificial intelligence, automation, displacement, labor |
| JEL: | J23 J24 O33 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_10685&r=tid |
| By: | Sebastian Heinrich (KOF Swiss Economic Institute, ETH Zurich, Switzerland) |
| Abstract: | This paper investigates the potential of indicators derived from corporate websites to measure technology related concepts. Using arti cial intelligence (AI) technology as a case in point, I construct a 24-year panel combining the texts of websites and patent portfolios for over 1, 000 large companies. By identifying AI exposure with a comprehensive keyword set, I show that website and patent data are strongly related, suggesting that corporate websites constitute a promising data source to trace AI technologies. |
| Keywords: | corporate website, patent portfolio, technology indicator, text data, artificial intelligence |
| JEL: | C81 O31 O33 |
| Date: | 2023–07 |
| URL: | http://d.repec.org/n?u=RePEc:kof:wpskof:22-512&r=tid |
| By: | Sebastian Heinrich (KOF Swiss Economic Institute, ETH Zurich, Switzerland); Samad Sarferaz (KOF Swiss Economic Institute, ETH Zurich, Switzerland); Martin Wörter (KOF Swiss Economic Institute, ETH Zurich, Switzerland) |
| Abstract: | This paper studies the global synchronicity of technology and its impact on the economy. We employ dynamic factor analysis to decompose patent data in different digital technologies for various countries into global and country-speciffc factors. Our findings confirm the existence of global and local technology cycles. We further find a significant positive correlation between the estimated global technology index and a country's economic performance. This positive effect is stronger in countries with broad tech-nological exposure. However, a concentration in only few dominant techno-logical fields seems to reduce the positive impact of the global technology cycle on a country's economic performance. |
| Keywords: | innovation index, dynamic factor model, patent data, produc- tivity growth, knowledge diffusion, digitalization, globalization |
| JEL: | O31 O33 O47 C38 L86 |
| Date: | 2023–05 |
| URL: | http://d.repec.org/n?u=RePEc:kof:wpskof:22-511&r=tid |
| By: | Laura Bisio; Angelo Cuzzola; Marco Grazzi; Daniele Moschella |
| Abstract: | We investigate the impact of investment in automation-related goods on adopting and non- adopting firms in the Italian economy during 2011-2019. We integrate datasets on trade activities, firms’, and workers’ characteristics for the population of Italian importing firms and estimate the effects on adopters’ outcomes within a difference-indifferences design exploiting import lumpiness in product categories linked to automation and AI technologies. We find a positive average adoption effect on the adopters’ employment and on the value-added and average wage, whereas sales and productivity increase after an initial drop with a net positive effect five years after adoption. Crucially, the employment effect is heterogeneous across firms: a positive scale effect is predominant among small firms, whereas a negative displacement effect is predominant among medium and large firms. We complete the framework with a 5-digit sector-level analysis showing that adopting automation technologies has an overall negative effect on aggregate employment. |
| Keywords: | automation, employment, firm heterogeneity, imports, technology adoption |
| JEL: | D24 J23 L25 O33 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_10697&r=tid |
| By: | Kristina McElheran; J. Frank Li; Erik Brynjolfsson; Zachary Kroff; Emin Dinlersoz; Lucia S. Foster; Nikolas Zolas |
| Abstract: | We study the early adoption and diffusion of five AI-related technologies (automated-guided vehicles, machine learning, machine vision, natural language processing, and voice recognition) as documented in the 2018 Annual Business Survey of 850, 000 firms across the United States. We find that fewer than 6% of firms used any of the AI-related technologies we measure, though most very large firms reported at least some AI use. Weighted by employment, average adoption was just over 18%. AI use in production, while varying considerably by industry, nevertheless was found in every sector of the economy and clustered with emerging technologies such as cloud computing and robotics. Among dynamic young firms, AI use was highest alongside more-educated, more-experienced, and younger owners, including owners motivated by bringing new ideas to market or helping the community. AI adoption was also more common alongside indicators of high-growth entrepreneurship, including venture capital funding, recent product and process innovation, and growth-oriented business strategies. Early adoption was far from evenly distributed: a handful of “superstar” cities and emerging hubs led startups’ adoption of AI. These patterns of early AI use foreshadow economic and social impacts far beyond this limited initial diffusion, with the possibility of a growing “AI divide” if early patterns persist. |
| JEL: | M15 O3 |
| Date: | 2023–10 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:31788&r=tid |
| By: | Roberto Alvarez; Miguel A. Gonzalez |
| Abstract: | In this paper, we explore the relevance of obstacles to green innovation in Chilean firms. We analyze differences in green innovation across firm size and industries and we explore which barriers have a greater impact on green innovators. We find that these innovators, in general, do face higher obstacles to innovation than similar but non-green firms. We conclude that, after controlling for other firm characteristics, the most relevant obstacles for green innovators are those associated with financial and knowledge aspects. This finding is relevant for the implementation of public policies aimed at enhancing green innovation in Chile. |
| Date: | 2023–10 |
| URL: | http://d.repec.org/n?u=RePEc:udc:wpaper:wp550&r=tid |
| By: | Sharma, Gautam (CIRCLE, Lund University); Haldar, Stuti (CIRCLE, Lund University) |
| Abstract: | Makerspaces and fabrication laboratories (fablabs) have received extensive attention recently due to their potential to drive innovation and entrepreneurship. These spaces provide access to high-tech tools to the people and encourage community building and collaborations around technology-oriented projects. This paper analyzes the existing research on the innovation dynamics within these spaces. Seventy peer-reviewed studies were selected and thematically analyzed from the Scopus and Web of Science databases. The results are analyzed and presented as descriptive statistics and thematic analysis. We found nine significant themes from the extant literature: 1) Collaborations, learning and sharing practices in makerspaces; 2) Motivations and spatial environment affecting makerspace innovations; 3) Physical Resources, experimentations, and knowledge dimension of makerspaces; 4) Diversity and inclusion aspects of makerspaces; 5) Social and economic impact of innovation in makerspaces; 6) Regional innovation policies and maker cultures; 7) Maker movement in different regions; 8) Maker movement and the city culture; and 9) Academic makerspaces and innovation. The paper contributes to the broader literature on innovation dynamics within informal spaces like makerspaces and fablabs. |
| Keywords: | makerspaces; fabrication laboratories; fab labs; creative open spaces; innovation; systematic literature review; thematic analysis |
| JEL: | O30 O32 |
| Date: | 2023–10–31 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:lucirc:2023_010&r=tid |
| By: | Luis Bauluz; Sebastien Breau; Pawel Bukowski; Mark Fransham; Annie Seong Lee; Neil Lee; Margarita Lopez Forero; Clement Malgouyres; Filip Novokmet; Moritz Schularick; Gregory Verdugo |
| Abstract: | The rise of economic inequalities in advanced economies has been often linked with the growth of spatial inequalities within countries, yet there is limited comparative research that studies the relationship between national and subnational economic inequality. This paper presents the first systematic attempt to create internationally comparable evidence showing how different countries perform in terms of geographic wage inequalities. We create cross-country comparable measures of spatial wage disparities between and within similarly-defined local labour market areas (LLMAs) for Canada, France, (West) Germany, the UK and the US since the 1970s, and assess their contribution to national inequality. By the end of the 2010s, spatial inequalities in LLMA mean wages are similar in Canada, France, Germany and the UK; the US exhibits the highest degree of spatial inequality. Over the study period, spatial inequalities have nearly doubled in all countries, except for France where spatial inequalities have fallen back to 1970s levels. Due to a concomitant increase in within-place inequality, the contribution of places in explaining national wage inequality has remained fairly constant over the 40-year study period, except in the UK where we document a significant increase. Whilst common global social, economic and technological shocks are important drivers of spatial inequality, this variation in levels and trends of spatial inequality opens the way to comparative research exploring the role of national institutions in mediating how global shocks translate into economic disparities between places. |
| Keywords: | regional inequality, wage inequality, local labour markets |
| Date: | 2023–08–16 |
| URL: | http://d.repec.org/n?u=RePEc:cep:cepdps:dp1941&r=tid |
| By: | Eugenie Dugoua; Todd D. Gerarden |
| Abstract: | We study how individual inventors respond to incentives to work on “clean” electricity technologies. Using natural gas price variation, we estimate output and entry elasticities of inventors and measure the medium-term impacts of a price increase mirroring the social cost of carbon. We find that the induced clean innovation response primarily comes from existing clean inventors. New inventors are less responsive on the margin than their average contribution to clean energy patenting would indicate. Our findings suggest a role for policy to increase the supply of clean inventors to help mitigate climate change. |
| Keywords: | inventors, energy technology, induced innovation |
| JEL: | O31 Q55 Q40 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_10700&r=tid |
| By: | Gaetan de Rassenfosse (Ecole polytechnique federale de Lausanne); Gabriele Pellegrino (Catholic University of Milan) |
| Abstract: | This paper assesses the extent to which the international migration of inventors affects innovation in the receiving country. Drawing on a novel database that maps the migratory patterns of inventors, we exploit the end of the Soviet Union and the consequent post-1992 influx of ex-Soviet inventors to the United States. Econometric analysis on a panel of U.S. cities and technological fields shows that the patenting activity of U.S. inventors increased significantly after the arrival of ex-Soviet Union inventors. |
| Keywords: | geographic mobility; innovation; inventors; patents |
| JEL: | O31 O34 O51 J61 |
| Date: | 2023–10 |
| URL: | http://d.repec.org/n?u=RePEc:iip:wpaper:23&r=tid |
| By: | Carlo Pizzinelli; Augustus J Panton; Ms. Marina Mendes Tavares; Mauro Cazzaniga; Longji Li |
| Abstract: | This paper examines the impact of Artificial Intelligence (AI) on labor markets in both Advanced Economies (AEs) and Emerging Markets (EMs). We propose an extension to a standard measure of AI exposure, accounting for AI's potential as either a complement or a substitute for labor, where complementarity reflects lower risks of job displacement. We analyze worker-level microdata from 2 AEs (US and UK) and 4 EMs (Brazil, Colombia, India, and South Africa), revealing substantial variations in unadjusted AI exposure across countries. AEs face higher exposure than EMs due to a higher employment share in professional and managerial occupations. However, when accounting for potential complementarity, differences in exposure across countries are more muted. Within countries, common patterns emerge in AEs and EMs. Women and highly educated workers face greater occupational exposure to AI, at both high and low complementarity. Workers in the upper tail of the earnings distribution are more likely to be in occupations with high exposure but also high potential complementarity. |
| Keywords: | Artificial intelligence; Employment; Occupations; Emerging Markets |
| Date: | 2023–10–04 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfwpa:2023/216&r=tid |
| By: | Marina Chugunova (Max Planck Institute for Innovation and Competition); Klaus Keller (Max Planck Institute for Innovation and Competition); Jose Azar (University of Navarra, School of Economics and Business and IESE Business School); Sampsa Samila (IESE Business School, University of Navarra.) |
| Abstract: | We examine the impact of labor market power on firms' adoption of automation technologies. We develop a model that incorporates labor market power into the task-based theory of automation. We show that, due to higher marginal cost of labor, monopsonistic firms have stronger incentives to automate than wage-taking firms, which could amplify or mitigate the negative employment effects of automation. Using data from US commuting zones, our results show that commuting zones that are more exposed to industrial robots exhibit considerably larger reductions in both employment and wages when their labor markets demonstrate higher levels of concentration. |
| Keywords: | automation; employment; labor market concentration; industrial robots; wage setting; |
| JEL: | J23 J30 J42 L11 O33 |
| Date: | 2023–10–17 |
| URL: | http://d.repec.org/n?u=RePEc:rco:dpaper:432&r=tid |
| By: | Moretti, Enrico; Steinwender, Claudia; Van Reenen, John |
| Abstract: | We examine the impact of government funding for R&D—and defense-related R&D in particular—on privately conducted R&D, and its ultimate effect on productivity growth. We estimate longitudinal models that relate privately funded R&D to lagged government-funded R&D using industry-country level data from OECD countries and firm level data from France. To deal with the potentially endogenous allocation of government R&D funds we use changes in predicted defense R&D as an instrumental variable. In many OECD countries, expenditures for defense-related R&D represent by far the most important form of public subsidies for innovation. In both datasets, we uncover evidence of “crowding in” rather than “crowding out, ” as increases in government-funded R&D for an industry or a firm result in significant increases in private sector R&D in that industry or firm. On average, a 10% increase in government-financed R&D generates a 5% to 6% additional increase in privately funded R&D. We also find evidence of international spillovers, as increases in government-funded R&D in a particular industry and country raise private R&D in the same industry in other countries. Finally, we find that increases in private R&D induced by increases in defense R&D result in productivity gains. |
| JEL: | J1 |
| Date: | 2023–02–06 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:119703&r=tid |
| By: | Michael Barnett; William Brock; Lars Peter Hansen; Ruimeng Hu; Joseph Huang |
| Abstract: | We study the implications of model uncertainty in a climate-economics framework with three types of capital: "dirty" capital that produces carbon emissions when used for production, "clean" capital that generates no emissions but is initially less productive than dirty capital, and knowledge capital that increases with R\&D investment and leads to technological innovation in green sector productivity. To solve our high-dimensional, non-linear model framework we implement a neural-network-based global solution method. We show there are first-order impacts of model uncertainty on optimal decisions and social valuations in our integrated climate-economic-innovation framework. Accounting for interconnected uncertainty over climate dynamics, economic damages from climate change, and the arrival of a green technological change leads to substantial adjustments to investment in the different capital types in anticipation of technological change and the revelation of climate damage severity. |
| Date: | 2023–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2310.13200&r=tid |
| By: | Christian Peukert; Margaritha Windisch |
| Abstract: | Intellectual property rights are fundamental to how economies organize innovation and steer the diffusion of knowledge. Copyright law, in particular, has developed constantly to keep up with emerging technologies and the interests of creators, consumers, and intermediaries of the different creative industries. We provide a synthesis of the literature on the law and economics of copyright in the digital age, with a particular focus on the available empirical evidence. First, we discuss the legal foundations of the copyright system and developments of length and scope throughout the era of digitization. Second, we review the literature on technological change with its opportunities and challenges for the stakeholders involved. We give special attention to empirical evidence on online copyright enforcement, changes in the supply of works due to digital technology, and the importance of creative re-use and new licensing and business models. We then set out avenues for further research identifying critical gaps in the literature regarding the scope of empirical copyright research, the effects of technology that enables algorithmic licensing, and copyright issues related to software, data and artificial intelligence. |
| Keywords: | copyright, digitization, technology, enforcement, licensing, business models, evidence |
| JEL: | K11 L82 L86 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_10687&r=tid |
| By: | Gaetan de Rassenfosse (Ecole polytechnique federale de Lausanne); Adam Jaffe (Brandeis University); Melissa Wasserman (The University of Texas at Austin - School of Law) |
| Abstract: | This symposium Article discusses issues raised for patent processes and policy created by inventions generated by artificial intelligence (AI). The Article begins by examining the normative desirability of allowing patents on AI-generated inventions. While it is unclear whether patent protection is needed to incentivize the creation of AI-generated inventions, a stronger case can be made that AI-generated inventions should be patent eligible to encourage the commercialization and technology transfer of AI-generated inventions. Next, the Article examines how the emergence of AI inventions will alter patentability standards, and whether a differentiated patent system that treats AI-generated inventions differently from hu-man-generated inventions is normatively desirable. This Article concludes by considering the larger implications of allowing patents on AI-generated inventions, including changes to the patent examination process, a possible increase in the concentration of patent ownership and patent thickets, and potentially unlimited inventions. |
| Keywords: | generative AI; patent; intellectual property; invention |
| JEL: | K20 D23 O34 |
| Date: | 2023–05 |
| URL: | http://d.repec.org/n?u=RePEc:iip:wpaper:22&r=tid |
| By: | Giulio Cornelli; Jon Frost; Saurabh Mishra |
| Abstract: | How does economic activity related to artificial intelligence (AI) impact the income of various groups in an economy? This study, using a panel of 86 countries over 2010–19, finds that investment in AI is associated with higher income inequality. In particular, AI investment is tied to higher real incomes and income shares for households in the top decile, while households in the fifth and bottom decile see a decline in their income shares. We also find a positive association with exports of modern services linked to AI. In labour markets, there is a contraction in overall employment, a shift from mid-skill to high-skill managerial roles and a reduced labour share of income. |
| Keywords: | artificial intelligence, automation, services, structural shifts, inequality |
| JEL: | D31 D63 O32 |
| Date: | 2023–10 |
| URL: | http://d.repec.org/n?u=RePEc:bis:biswps:1135&r=tid |
| By: | Jamie Fogel; Bernardo Modenesi |
| Abstract: | This paper develops a new data-driven approach to characterizing latent worker skill and job task heterogeneity by applying an empirical tool from network theory to large-scale Brazilian administrative data on worker--job matching. We microfound this tool using a standard equilibrium model of workers matching with jobs according to comparative advantage. Our classifications identify important dimensions of worker and job heterogeneity that standard classifications based on occupations and sectors miss. The equilibrium model based on our classifications more accurately predicts wage changes in response to the 2016 Olympics than a model based on occupations and sectors. Additionally, for a large simulated shock to demand for workers, we show that reduced form estimates of the effects of labor market shock exposure on workers' earnings are nearly 4 times larger when workers and jobs are classified using our classifications as opposed to occupations and sectors. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.00777&r=tid |