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on Technology and Industrial Dynamics |
| By: | Jennifer Hunt; Iain M. Cockburn; James Bessen |
| Abstract: | Using our own data on Artificial Intelligence publications merged with Burning Glass vacancy data for 2007-2019, we investigate whether online vacancies for jobs requiring AI skills grow more slowly in U.S. locations farther from pre-2007 AI innovation hotspots. We find that a commuting zone which is an additional 200km (125 miles) from the closest AI hotspot has 17% lower growth in AI jobs' share of vacancies. This is driven by distance from AI papers rather than AI patents. Distance reduces growth in AI research jobs as well as in jobs adapting AI to new industries, as evidenced by strong effects for computer and mathematical researchers, developers of software applications, and the finance and insurance industry. 20% of the effect is explained by the presence of state borders between some commuting zones and their closest hotspot. This could reflect state borders impeding migration and thus flows of tacit knowledge. Distance does not capture difficulty of in-person or remote collaboration nor knowledge and personnel flows within multi-establishment firms hiring in computer occupations. |
| JEL: | O33 R12 |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33022 |
| By: | Draca, Mirko (CEP, London School of Economics ; Warwick University and CAGE Research Centre); Nathan, Max (CEP, London School of Economics ; Warwick University and CAGE Research Centre ; University College London); Nguyen-Tien, Viet (CEP, London School of Economics ; POID, London School of Economics); Oliveira-Cunha, Juliana (CEP, London School of Economics ; IGC, London School of Economics); Rosso, Anna (CEP, London School of Economics ; University of Insubria); Valero, Anna (CEP, London School of Economics ; POID, London School of Economics) |
| 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 Codes: D22 ; J24 ; O33 ; R11 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:wrk:warwec:1521 |
| By: | Koen Frenken; Frank Neffke; ; ; |
| Abstract: | The global economy operates as a complex system that allocates resources in a decentralized way across myriad agents. Over time, it exhibits an impressive rate of collective learning as evidenced by its growing productivity and the expanding variety of output it generates. However, growth, productivity and learning are not distributed equally across locations. On the contrary, wealth, opportunity, economic activity and innovation tend to all concentrate in a relatively small number of affluent places. Various strands of complexity Science have contributed to our understanding of these phenomena. However, they have done so in disconnected debates and communities. In this chapter, we use the framework of Economic Complexity to synthesize insights derived from three distinct literatures: urban scaling, evolutionary economic geography and global production networks. Economic complexity proposes that production requires access to capabilities, such that increasing the variety of economic production requires acquiring or accessing new capabilities. From this synthesis, we derive a research agenda that aims to understand how local economies develop, not only as individual units exploring their adjacent possible, but as parts of a system that allows local economies to mix their capabilities with those of distant counterparts by relying on the interplay of multinational corporations, global value chains and institutions to coordinate interactions at the local and global scale. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:egu:wpaper:2431 |
| By: | Bernardo Caldarola; Luca Fontanelli |
| Abstract: | Recent empirical evidence finds positive associations between digitalisation and industry concentration. However, ICT may not be all alike. We investigate the effect of the purchase of cloud services on the long run size growth rate of French firms. Our findings suggest that cloud services positively impact firm growth rates, with smaller firms experiencing more significant benefits compared to larger firms. This evidence suggests that the diffusion of cloud technologies may help mitigate concentration in the era of the digital transition by favouring the digitalisation and growth of smaller firms, especially when the cloud services provided are more advanced. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.17035 |
| By: | Eduardo Hernandez-Rodriguez; Ron Boschma; Andrea Morrison; Xianjia Ye; |
| Abstract: | This paper studies the role of complementary interregional value chain linkages in functional upgrading and downgrading in global value chains in EU regions. It adopts an evolutionary approach to assess functional upgrading and downgrading using relatedness and economic complexity metrics. The empirical analysis of 199 EU regions between the years 2000-2010 shows that, while local capabilities remain crucial, complementary interregional value chain linkages increase (decrease) the likelihood of functional upgrading (downgrading) in GVCs. Regions engaged in GVCs in which partner regions offer complementary capabilities are more likely to specialise in more complex functions and to retain such specialisations over time. By doing so, this paper proposes a new theoretical-analytical framework to identify partner regions that can offer complementary capabilities in GVCs. |
| Keywords: | Global value chains, upgrading/downgrading, interregional linkages, complementary capabilities, EU regions |
| JEL: | F14 F63 O19 R11 R12 |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:egu:wpaper:2432 |
| By: | Joonkyu Choi; Nathan Goldschlag; John Haltiwanger; J. Daniel Kim |
| Abstract: | Using administrative data from the U.S. Census Bureau, we introduce a new public-use database that tracks activities across firm growth distributions over time. With these new data, we uncover several key trends for high-growth firms---critical engines of innovation and economic growth. First, the share of firms that are high-growth has steadily decreased over the past four decades, driven not only by falling rates of entrepreneurship but also languishing growth among existing firms. Second, this decline is particularly pronounced among young and small firms, while the share of high-growth firms has been relatively stable among large and old firms. We also find rich variation across states and sectors. To facilitate future research, we highlight how these data can be used to address various research questions. |
| Keywords: | Organizational Growth; Entrepreneurship; High-Growth Firms; Business Dynamism; Publicly Available Dataset |
| JEL: | L11 L25 L26 O30 O40 |
| Date: | 2024–09–20 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfe:2024-74 |
| By: | Nicola Gagliardi (CEBRIG and DULBEA, Solvay Brussels School of Economics and Management, Université Libre de Bruxelles); Elena Grinza (Department of Economics, Social Studies, Applied Mathematics and Statistics, University of Turin); François Rycx (CEBRIG and DULBEA, Solvay Brussels School of Economics and Management, Université Libre de Bruxelles. IRES (UCLouvain)) |
| Abstract: | In this paper, we investigate the impact of rising temperatures on firm productivity using longitudinal firm-level balance-sheet data from private sector firms in 14 European countries, combined with detailed weather data, including temperature. We begin by estimating firms’ total factor productivity (TFP) using control-function techniques. We then apply multiple-way fixed-effects regressions to assess how higher temperature anomalies affect firm productivity – measured via TFP, labor productivity, and capital productivity. Our findings reveal that global warming significantly and negatively impacts firms’ TFP, with the most adverse effects occurring at higher anomaly levels. Labor productivity declines markedly as temperatures rise, while capital productivity remains unaffected – indicating that TFP is primarily affected through the labor input channel. Our moderating analyses show that firms involved in outdoor activities, such as agriculture and construction, are more adversely impacted by increased warming. Manufacturing, capital-intensive, and blue-collar-intensive firms, compatible with assembly-line production settings, also experience significant productivity declines. Geographically, the negative impact is most pronounced in temperate and mediterranean climate areas, calling for widespread adaptation solutions to climate change across Europe. |
| Keywords: | Climate change, Global warming, Firm productivity, Total factor productivity (TFP), Semiparametric methods to estimate production functions, Longitudinal firm-level data |
| JEL: | D24 J24 Q54 |
| Date: | 2024–08–21 |
| URL: | https://d.repec.org/n?u=RePEc:ctl:louvir:2024010 |
| By: | Klein, Michael |
| Abstract: | I develop an endogenous growth model that separates firm decisions to invent, patent, and commercialize new innovations. I use the model to examine how multiple dimensions of patent policy impact economic growth by shaping these relative incentives. I pay particular attention to the role of patenting requirements that dictate how far along the development process an inventor must progress to obtain a patent. The model formalizes how strengthening such requirements generates competing effects on economic growth; stronger requirements reduce ex ante research incentives by increasing the expected cost of patenting, but increase ex post incentives to fully develop patented inventions into commercial innovations by decreasing the additional cost associated with commercialization. Overall, my analysis supports the use of patenting requirements as an effective policy tool to improve economic outcomes by shifting incentives away from invention in the pursuit of patents and towards the development of commercial innovations. |
| Keywords: | Patent policy; Patenting requirements; Invention; Innovation; Economic growth |
| JEL: | O31 O34 O43 |
| Date: | 2024–09–16 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:122283 |
| By: | Andrea Coveri (Department of Economics, Society & Politics, Università di Urbino Carlo Bo); Elena Paglialunga (Department of Economics, Roma Tre University, Italy); Antoenllo Zanfei (Department of Economics, Society & Politics, Università di Urbino Carlo Bo) |
| Abstract: | The geographic dispersion of production activities has led regions to increasingly specialize in specific value chain functions, giving rise to a finer spatial division of labour. In this work, we use georeferenced FDI data to investigate the geography of functions and the interdependencies between functional and sectoral specialization of European regions. We show that the most intangible-intensive functions at the upper ends of value chains are concentrated in few advanced regions, while lower-income ones are largely and persistently specialized in production operations. Moreover, we find that regions locked-into low value-adding functions are the least likely to upgrade towards more knowledge-intensive industries. By contrast, only the few regions which experienced functional upgrading trajectories have been able to diversify towards more innovative industries. Accordingly, regional policies should aim at favouring functional upgrading of laggard regions as a key vehicle for economic and spatial convergence in Europe. |
| Keywords: | Geography of functions; Inter-regional inequality; Foreign direct investment; Global value chains; European regions |
| JEL: | F21 F23 L23 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:urb:wpaper:24_01 |
| By: | HARIT, ADITYA |
| Abstract: | This paper develops a dynamic general equilibrium (DGE) model to assess the impact of AI-driven automation on labor and capital allocation in an economy. The model considers the endogenous response of firms to task automation and labor substitution, showing how the increasing use of AI affects total output (GDP), wages, and capital returns. By introducing task complementarity and dynamic capital accumulation, the paper explores how automation impacts labor dynamics and capital accumulation. Key results show that while AI enhances productivity and GDP, it can also reduce wages and increase income inequality, with long-run effects that depend on the elasticity of substitution between labor and capital. |
| Keywords: | AI-driven Automation, Dynamic General Equilibrium, Labor Markets, Capital Accumulation, Income Distribution, Technological Change, Task Automation, Economic Inequality, Labor Demand, Capital Returns, Economic Policy, Neoclassical Growth Theory, Labor-Capital Dynamics. |
| JEL: | A10 A11 C0 C02 E1 E13 E6 E60 J3 J31 J4 J40 N3 P4 P48 |
| Date: | 2024–10–01 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:122244 |
| By: | Fangchen Song; Ashish Agarwal; Wen Wen |
| Abstract: | Generative artificial intelligence (AI) has opened the possibility of automated content production, including coding in software development, which can significantly influence the participation and performance of software developers. To explore this impact, we investigate the role of GitHub Copilot, a generative AI pair programmer, on software development in open-source community, where multiple developers voluntarily collaborate on software projects. Using GitHub's dataset for open-source repositories and a generalized synthetic control method, we find that Copilot significantly enhances project-level productivity by 6.5%. Delving deeper, we dissect the key mechanisms driving this improvement. Our findings reveal a 5.5% increase in individual productivity and a 5.4% increase in participation. However, this is accompanied with a 41.6% increase in integration time, potentially due to higher coordination costs. Interestingly, we also observe the differential effects among developers. We discover that core developers achieve greater project-level productivity gains from using Copilot, benefiting more in terms of individual productivity and participation compared to peripheral developers, plausibly due to their deeper familiarity with software projects. We also find that the increase in project-level productivity is accompanied with no change in code quality. We conclude that AI pair programmers bring benefits to developers to automate and augment their code, but human developers' knowledge of software projects can enhance the benefits. In summary, our research underscores the role of AI pair programmers in impacting project-level productivity within the open-source community and suggests potential implications for the structure of open-source software projects. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.02091 |
| By: | Wei Cheng; Bruce A. Weinberg |
| Abstract: | The adoption of new ideas is critical for realizing their full potential and for advancing the knowledge frontier but it involves analyzing innovators, potential adopters, and the networks that connect them. This paper applies natural language processing, network analysis, and a novel fixed effects strategy to study how the aging of the biomedical research workforce affects idea adoption. We show that the relationship between adoption and innovator career age varies with network distance. Specifically, at short distances, young innovators’ ideas are adopted the most, while at greater network distances, mid-career innovators’ ideas have the highest adoption. The main reason for this contrast is that young innovators are close to young potential adopters who are more open to new ideas, but mid-career innovators are more central in networks. Overall adoption is hump-shaped in the career age of innovators. Simulations show that the aging of innovators and of potential adopters have comparable effects on the adoption of important new ideas. |
| JEL: | D85 J11 O33 |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33030 |