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on Technology and Industrial Dynamics |
| By: | Koski, Heli |
| Abstract: | Abstract The data on Finnish patent applications filed at the European Patent Office and the United States Patent and Trademark Office between 2011 and 2021 indicate that foreign inventors and international collaboration have been crucial to Finland’s technological development. Throughout the 2010s, Finnish patent applications have included inventors from 68 different countries, and the share of foreign inventors has steadily increased over this period. In 2021, 60 percent of the patent applications filed involved foreign inventors, with over one-fifth featuring collaboration between both Finnish and foreign inventors. The United States has been Finland’s most important innovation partner, while Germany, Sweden, and China have also played significant roles in the development of patented ideas. When analyzed by technology sector, foreign and internationally based inventors are particularly prominent in ICT-related patent applications. Immigrant inventors contributed to more than one-fifth of ICT patent filings during the review period. In other technology sectors, the involvement of immigrant inventors has also grown, with their share of USPTO patent applications rising from 9 percent to 17 percent, and from 18 percent to 23 percent in EPO applications. |
| Keywords: | Innovations, Patents, Innovation collaboration, Inventors, Immigration |
| JEL: | D23 F22 J61 O3 |
| Date: | 2024–09–12 |
| URL: | https://d.repec.org/n?u=RePEc:rif:briefs:138 |
| By: | Su Jung Jee; Kerstin H\"otte |
| Abstract: | Escaping the middle-income trap requires a country to develop indigenous technological capabilities for high value-added innovation. This study examines the role of second-tier patent systems, known as utility models (UMs), in promoting such capability acquisition in less developed countries. UMs are designed to incentivize incremental and adaptive innovation through lower novelty standards than patents, but their long-term impact on the capability acquisition process remains underexplored. Using South Korea as a case study and drawing on the characteristics of technological regimes in catching-up economies, we present three key findings: First, the country's post-catch-up frontier technologies (U.S. patents) are more impactful (highly cited) when they build on Korean domestic UMs. This suggests that UM-based imitative and adaptive learning laid the foundation for the country's globally competitive capabilities. Second, the impact of UM-based learning diminishes as the country's economy develops. Third, frontier technologies rooted in UMs contribute more to the country's own specialization than to follow-on innovations by foreign actors, compared to technologies without UM linkages. We discuss how technological regimes and industrial policies in catching-up economies interact with the UM system to bridge the catching-up (imitation- and adaptation-based) and post-catching-up (specialization- and creativity-based) phases. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.14205 |
| By: | Federico Fabio Frattini (Fondazione Eni Enrico Mattei); Francesco Vona (University of Milan and Fondazione Eni Enrico Mattei); Filippo Bontadini (Luiss University and SPRU – University of Sussex) |
| 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: | Green transition, Employment, Manufacturing, Shift-share |
| JEL: | J21 J31 L6 O14 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:fem:femwpa:2024.23 |
| By: | Parrado, Eric; Benítez, Miguel |
| Abstract: | This paper introduces the AI Generated Index of Occupational Exposure (GENOE), a novel measure quantifying the potential impact of artificial intelligence on occupations and their associated tasks. Our methodology employs synthetic AI surveys, leveraging large language models to conduct expert-like assessments. This approach allows for a more comprehensive evaluation of job replacement likelihood, minimizing human bias and reducing assumptions about the mechanisms through which AI innovations could replace job tasks and skills. The index not only considers task automation, but also contextual factors such as social and ethical considerations and regulatory constraints that may affect the likelihood of replacement. Our findings indicate that the average likelihood of job replacement is estimated at 0.28 in the next year, increasing to 0.38 and 0.44 over the next five and ten years, respectively. To validate our methodology, we successfully replicate other measures of occupational exposure that rely on human expert assessments, substituting these with AI-based evaluations. The GENOE index provides valuable insights for policymakers, employers, and workers, offering a data-driven foundation for strategic workforce planning and adaptation in the face of rapid technological change. |
| JEL: | C53 C81 J23 J24 O33 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:idb:brikps:13696 |
| By: | Bettiol, Marco; Capestro, Mauro; Di Maria, Eleonora; Ganau, Roberto |
| Abstract: | Does Industry 4.0 technology adoption push firms’ labor productivity? We contribute to the literature debate—mainly focused on robotics and large firms—by analyzing adopters’ labor productivity returns when micro, small, and medium enterprises (MSME) are concerned. We employ original survey data on Italian MSMEs’ adoption investments related to a multiplicity of technologies and rely on a difference-in-differences estimation strategy. Results highlight that Industry 4.0 technology adoption leads to a 7% increase in labor productivity. However, this effect decreases over time and is highly heterogeneous with respect to the type, the number, and the variety of technologies adopted. We also identify potential channels explaining the labor productivity returns of technology adoption: cost-related efficiency, new knowledge creation, and greater integration/collaboration both within the firm and with suppliers. |
| Keywords: | Industry 4.0; Italy; labor productivity; MSME; technology adoption |
| JEL: | R14 J01 J1 |
| Date: | 2024–04–01 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:124545 |
| By: | Emilio Colombo; Fabio Mercorio; Mario Mezzanzanica; Antonio Serino |
| Abstract: | There is no doubt that AI and AI-related technologies are reshaping jobs and related tasks, either by automating or by augmenting human skills in the workplace. Many researchers have tried to estimate if, and to what extent, jobs and tasks are exposed to the risk of being automatized by state-of-the-art AI-related technologies. Our work tackles this issue through a data-driven approach: (i) developing a reproducible framework that uses several open-source large language models to assess the current capabilities of AI and robotics in performing work-related tasks; (ii) formalising and computing a measure of AI exposure by occupation, namely the TEAI (Task Exposure to AI) index. Our TEAI index is positively correlated with cognitive, problem-solving and management skills, while is negatively correlated with social skills. Our results show that about one-third of U.S. employment is highly exposed to AI, primarily in high-skill jobs, requiring graduate or postgraduate level of education. Using 4-year rolling regressions, we also find that AI exposure is positively associated with both employment and wage growth in the period 2003-2023, suggesting that AI has an overall positive effect on productivity. |
| JEL: | J24 O33 O36 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:dis:wpaper:dis2401 |
| By: | Enrique Ide; Eduard Talam\`as |
| Abstract: | Do improvements in Artificial Intelligence (AI) benefit workers? We study how AI capabilities influence labor income in a competitive economy where production requires multidimensional knowledge, and firms organize production by matching humans and AI-powered machines in hierarchies designed to use knowledge efficiently. We show that advancements in AI in dimensions where machines underperform humans decrease total labor income, while advancements in dimensions where machines outperform humans increase it. Hence, if AI initially underperforms humans in all dimensions and improves gradually, total labor income initially declines before rising. We also characterize the AI that maximizes labor income. When humans are sufficiently weak in all knowledge dimensions, labor income is maximized when AI is as good as possible in all dimensions. Otherwise, labor income is maximized when AI simultaneously performs as poorly as possible in the dimensions where humans are relatively strong and as well as possible in the dimensions where humans are relatively weak. Our results suggest that choosing the direction of AI development can create significant divisions between the interests of labor and capital. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.16443 |
| By: | Hipólito, Inês |
| Abstract: | This paper applies complex systems theory to examine generative artificial intelligence (AI) as a contemporary wicked problem. Generative AI technologies, which autonomously create content like images and text, intersect with societal domains such as ethics, economics, and governance, exhibiting complex interdependencies and emergent behaviors. Using methodologies like network analysis and agent-based modeling, the paper maps these interactions and explores potential interventions. A mathematical model is developed to simulate the dynamics between key components of the AI-society system, including AI development, economic concentration, labor markets, regulatory frameworks, public trust, ethical implementation, global competition, and distributed AI ecosystems. The model demonstrates non-linear dynamics, feedback loops, and sensitivity to initial conditions characteristic of complex systems. By simulating various interventions, the study provides insights into strategies for steering AI development towards more positive societal outcomes. These include strengthening regulatory frameworks, enhancing ethical implementation, and promoting distributed AI ecosystems. The paper advocates for using this complex systems framework to inform inclusive policy and regulatory strategies that balance innovation with societal well-being. It concludes that embracing complexity enables stakeholders to better navigate the evolving challenges of generative AI, fostering more sustainable and equitable technological advancements. |
| Date: | 2024–08–29 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:aq4tw |
| By: | Gagliardi, Nicola; Grinza, Elena; Rycx, François |
| 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 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:glodps:1485 |
| By: | Andrew Koh; Sivakorn Sanguanmoo |
| Abstract: | We analyze how uncertain technologies should be robustly regulated. An agent develops a new technology and, while privately learning about its harms and benefits, continually chooses whether to continue development. A principal, uncertain about what the agent might learn, chooses among dynamic mechanisms (e.g., paths of taxes or subsidies) to influence the agent's choices in different states. We show that learning robust mechanisms -- those which deliver the highest payoff guarantee across all learning processes -- are simple and resemble `regulatory sandboxes' consisting of zero marginal tax on R&D which keeps the agent maximally sensitive to new information up to a hard quota, upon which the agent turns maximally insensitive. Robustness is important: we characterize the worst-case learning process under non-robust mechanisms and show that they induce growing but weak optimism which can deliver unboundedly poor principal payoffs; hard quotas safeguard against this. If the regulator also learns, adaptive hard quotas are robustly optimal which highlights the importance of expertise in regulation. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.17398 |
| By: | Hernan Winkler (World Bank Poverty and Equity Global Practice); Vincenzo Di Maro (World Bank Poverty and Equity Global Practice); Kelly Montoya (World Bank Poverty and Equity Global Practice); Sergio Olivieri (World Bank Poverty and Equity Global Practice); Emmanuel Vazquez (CEDLAS-IIE-FCE-UNLP) |
| Abstract: | A growing body of literature investigates the labor market implications of scaling up “green†policies. Since most of this literature is focused on developed economies, little is known about the labor market consequences for developing countries. This paper contributes to filling this gap by providing new stylized facts on the prevalence of green occupations and sectors across countries at varying levels of economic development. Green occupations are defined using the Occupational Information Network, and green sectors are those with relatively lower greenhouse gas emissions per worker. The paper offers an initial assessment of how the implementation of green policies—aimed at expanding green sectors and strengthening the relative demand for green skills—may affect workers in developing economies. It finds that the share of green jobs is strongly correlated with the level of gross domestic product per capita across countries. When controlling for unobserved heterogeneity, a 1 percent increase in gross domestic product per capita is associated with 0.4 and 4.1 percentage point increases in the shares of new and emerging, and enhanced skills green jobs, respectively. The paper then focuses on Latin America and finds that only 9 percent of workers have a green job with respect to both occupation and sector. The findings show that within countries, workers with low levels of income and education are more likely to be employed in non-green sectors and occupations, and to lack the skills for a greener economy. This evidence suggests that complementary policies are needed to mitigate the potential role of green policiesin widening income inequality between and within countries. |
| JEL: | Q5 Q52 Q56 J01 J21 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:dls:wpaper:0335 |
| By: | Marco Amendola; Marco Valente |
| Abstract: | The electricity generating sector is the single largest source of climate altering pollution. A country aiming to meet its targets for a net-zero economy needs therefore to radically reduce the emissions stemming from this sector. Charging carbon emissions is the preferred market-friendly policy to promote the diffusion of green technologies assuming that investors find more profitable to adopt technologies not burdened by the cost of carbon emissions. We study empirically the effectiveness of an increase in the cost of carbon emissions in order to favor the replacement of power plants burning fossil fuels with generators powered by renewable energy in Italy. Based on hourly data from the Italian electricity market we find that a policy increasing the cost of carbon emissions is less effective than expected in promoting clean energy investments. Indeed, increasing the cost of emissions actually increases the relative profitability of brown energy sources in respect of green ones in the most likely conditions. We conclude that increasing the cost of carbon emissions hinders the diffusion of technologies necessary for the green transition in the Italian electricity production sector. More in general, our results suggest that market friendly policies based on biasing incentives for profit-seeking operators need to carefully analyze the mechanisms underpinning the markets of interests to prevent policy failures. |
| Keywords: | Electricity market; Carbon pricing; Hourly-frequency model; Energy transition |
| Date: | 2024–09–10 |
| URL: | https://d.repec.org/n?u=RePEc:ssa:lemwps:2024/19 |
| By: | Jule Hodok; Tomasz Kozluk |
| Abstract: | This report reviews the literature on the distributional consequences of climate change and mitigation and transition pathways. The heterogeneous levels of exposure and vulnerability to climate change across countries, regions, households, and workers hint at the significant distributional costs of inaction. Climate policies will likely trigger a reallocation from “high-polluting” sectors to “green” sectors, disproportionately affecting certain regions and low-skilled workers. Price-based policies, such as carbon taxation, show varied effects across countries: they tend to be more regressive in developed countries and more progressive in developing countries where energy affordability and energy poverty are major concerns. Non-market-based policies are often regressive and can result in equity issues. Effective climate action requires balancing distributional outcomes, ensuring political acceptability, and understanding the link between policy perceptions and support. |
| Keywords: | climate change, distributional impacts, environmental policy, inequality |
| JEL: | D30 H23 J23 Q52 Q58 |
| Date: | 2024–09–05 |
| URL: | https://d.repec.org/n?u=RePEc:oec:ecoaaa:1820-en |