|
on Technology and Industrial Dynamics |
| By: | D’Alessandro, Francesco (Department of Sociology and Business Law, University of Bologna); Santarelli, Enrico (Department of Economics, University of Bologna); Vivarelli, Marco (Università Cattolica del Sacro Cuore) |
| Abstract: | This study examines how regional technological relatedness and local AI knowledge influence regional innovative activity, as measured by patenting activity. Using a novel three-way longitudinal dataset and leveraging a deep learning-based identification of AI patents, we show that two broad mechanisms operate in parallel. First, in accordance with the extant literature, technologies that are cognitively close to a region’s existing patent portfolio enjoy higher patenting activity, confirming that relatedness remains a strong and persistent predictor of innovative output. Second, local AI endowments are positively associated with patenting across technological fields, even after conditioning on relatedness, indicating that AI plays an enabling and cross-cutting role in a given regional innovation system. Moreover, the interaction between relatedness and AI turns out to be negative and statistically significant, implying that AI attenuates the extent to which local innovative efforts depend on the technology’s proximity to the regional portfolio. In sum, AI appears to enhance overall local innovative activity while reducing its reliance on pre-existing regional knowledge structures. |
| Keywords: | Artificial Intelligence, AI, technological change, regional innovation, relatedness |
| JEL: | O31 R11 |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18817 |
| By: | Carioli, Paolo (KU Leuven, Dept. of Management, Strategy and Innovation); Czarnitzki, Dirk (KU Leuven, Dept. of Management, Strategy and Innovation; Center for R&D Monitoring (ECOOM) at KU Leuven, and Leibniz Centre for European Economic Research (ZEW), Mannheim); Fernández, Gastón P. (Luxembourg Institute of Socio-Economic Research (LISER)) |
| Abstract: | Artificial Intelligence (AI) is considered to be the next general-purpose technology, with the potential of performing tasks commonly requiring human capabilities. While it is commonly feared that AI replaces labor and disrupts jobs, we instead investigate the potential of AI for overcoming increasingly alarming skills shortages in firms. We exploit unique German survey data from the Mannheim Innovation Panel on both the adoption of AI and the extent to which firms experience scarcity of skills. We measure skills shortage by the number of job vacancies that could not be filled as planned by firms, distinguishing among different types of skills. To account for the potential endogeneity of skills shortage, we also implement instrumental variable estimators. Overall, we find a positive and significant effect of skills shortage on AI adoption, the breadth of AI methods, and the breadth of areas of application of AI. In addition, we find evidence that shortage on academic qualifications and STEM skills relates to firms adopting AI. |
| Keywords: | Artificial Intelligence, skills shortage, CIS data |
| JEL: | J23 J24 O33 |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18793 |
| By: | Erik Brynjolfsson; J. Frank Li; Javier Miranda; Robert Seamans; Andrew Wang |
| Abstract: | This paper studies how minimum wage policy affects firms’ adoption of automation technologies. Using both state-level measures of robot exposure and novel plant-level data on industrial robot imports linked to U.S. Census microdata from 1992–2021, we show that increases in minimum wages raise the likelihood of robot adoption in manufacturing. Our preferred identification exploits discontinuities at state borders, comparing otherwise similar firms exposed to different wage floors. Across specifications, a 10 percent increase in the minimum wage increases robot adoption by roughly 8 percent relative to the mean. |
| Keywords: | automation, minimum wage, manufacturing, robots |
| JEL: | J38 O33 |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:cen:wpaper:26-42 |
| By: | Vasco Carvalho; Lukas Freund; Lukas B. Freund |
| Abstract: | We develop a theory of growth in which firms forage in idea space. A firm exploits a patch of related ideas, gradually exhausting opportunities for quality improvement, and then searches for a new patch. We cast this explore-exploit tradeoff as a tractable optimal-stopping problem and embed it in an endogenous-growth model. The composition of innovation — improving existing ideas versus discovering new ground — emerges as an equilibrium object. To construct an empirical representation of the idea space, we apply natural language processing to patent text data. The data support the theory’s central premises: returns to local exploitation diminish; firms stay longer on richer patches; and entry into new patches yields more and better patents. We calibrate the model to U.S. data and establish two results, on the composition of growth and on its pace. First, at a twenty-year horizon, patenting in new clusters accounts for over half of growth from quality improvements: sustained growth rests on firms continually entering new territory. Second, the model sign-identifies the origins of the productivity slowdown of the last four decades: exploitation spells have not shortened, weighing against worsening exploitation and tentatively pointing to harder exploration. |
| Keywords: | innovation, growth, firm dynamics, foraging, exploration and exploitation, patents, natural language processing, artificial intelligence |
| JEL: | O31 O41 O33 O40 O47 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12841 |
| By: | Arias, Omar (World Bank); Cesar, Andres (CEDLAS-UNLP); Fukuzawa, Daisuke (World Bank); Le, Duong (World Bank) |
| Abstract: | We examine the labor market effects of industrial robot adoption in Viet Nam. Using differences between districts in their initial industrial structure and exposure to global robotization trends, we find that robot adoption between 2014 and 2020 increased employment, wages, and the inflow of skilled workers. More exposed districts also experienced faster growth in value added, labor productivity, and trade flows. Employment and wage gains were concentrated among middle and high-skilled workers, while low-skilled workers were driven into informal employment or out-migration. We also find positive spillovers to neighboring districts, consistent with production linkages and higher demand for non-tradable goods. |
| Keywords: | Industrial Robots, Local Labor Markets, Wages, Informality, Migration, Export-led Growth, Viet Nam |
| JEL: | J23 J24 J31 O12 O14 O17 O33 |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18845 |
| By: | Seo, Gangcheol; Paudel, Krishna P.; Nelson, Kelly |
| Abstract: | This study examines long-run convergence in U.S. state-level agricultural total factor productivity (TFP) and investigates the role of patent-based technological knowledge in explaining persistent productivity differences across states. Using annual agricultural TFP data for 48 contiguous U.S. states from 1960 to 2015, we assess convergence dynamics through σ-convergence tests and the club convergence approach proposed by Phillips and Sul (2007). We then use a two-way fixed effects (TWFE) panel framework to examine whether patent-based knowledge stocks are associated with state-level agricultural TFP. Patent stocks are constructed for six agricultural technology subsectors under alternative assumptions regarding knowledge depreciation and lag structures. The results indicate that U.S. agricultural TFP does not converge toward a common steady state but instead exhibits multiple convergence clubs, suggesting persistent heterogeneity in long-run productivity paths across states. Patent-based knowledge accumulation also displays substantial sectoral heterogeneity. Plants, research tools, animal health, and machinery patent stocks are positively associated with agricultural TFP across most specifications, whereas fertilizer-related patent stocks are negatively associated. These patterns remain broadly robust across alternative constructions of the patent stock. Overall, the findings highlight the importance of technological heterogeneity in long-run agricultural productivity and suggest that accumulated patent-based technological knowledge is associated with agricultural productivity in distinct ways across innovation sectors. |
| Keywords: | Agricultural and Food Policy |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea26:404391 |
| By: | Lall, Ranjit (University of Oxford) |
| Abstract: | While advances in artificial intelligence (AI) are feared to bring about widespread job losses, workers highly exposed to the technology tend to anticipate productivity-driven improvements in their earnings and employment prospects. I argue that this paradox has important political economy implications: if labor complementarities are expected to outweigh substitution effects—raising income without commensurately intensifying employment risks—exposure to AI should weaken rather than strengthen support for redistributive policies and their political advocates. I test this argument using a combination of observational and original experimental data from Western Europe, finding that occupation-level AI exposure is negatively associated with support for redistribution, the left, and the (increasingly pro-welfare) populist right but positively associated with support for the mainstream right. The results enhance our understanding of the political economy of digitalization, suggesting a discrepancy between the perceived distributional consequences of AI and earlier automation technologies that have primarily displaced labor. |
| Date: | 2026–07–21 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:vmgdj_v1 |
| By: | Konrad Adler; Oliver Rehbein; Matthias Reiner; Jing Zeng |
| Abstract: | We propose measuring firms’ exposure to climate risk via the market. We build a theoretical foundation and construct empirical market-based greenness measures based on abnormal stock returns around UN climate conferences. Our measures cover around 36, 000 international firms, tenfold the existing measures. Market-based greenness is associated with lower present and future carbon emissions, and provides explanatory power distinct from existing climate risk measures. Market-based green firms are more likely to file green patents, have lower stock price volatility, and are financially more robust. At the country level, market-based greenness correlates with lower emission intensity and larger shares of renewable energy. |
| Keywords: | Climate change, greenness, green firms, climate risk |
| JEL: | G14 G32 G38 Q54 |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:bon:boncrc:crctr224_2025_768 |
| By: | Kumar, Labesh; Neumann, Rebecca |
| Abstract: | Whether financial development promotes industrial innovation depends not just on how developed a country’s financial system is, but on which dimensions of that system are well developed. This paper examines how depth, access, and efficiency of both financial institutions and financial markets shape R&D investment across industries that differ in their reliance on external finance. Using industry-level data from the ISIC Rev. 4 classification across 18 OECD countries from 1995 to 2019, and drawing on the IMF’s multidimensional Financial Development Index, we analyze how country-level financial development measures interact with an industry-level external finance dependence measure to influence R&D intensity measured relative to output and value added. Our findings show that the overall level of financial development matters primarily through depth. In particular, the depth of financial institutions and, to a lesser extent, the depth of financial markets significantly raise R&D intensity in industries that depend more heavily on external funding. Measures of access and efficiency display little systematic effect. The results are strongest within manufacturing industries, where innovation activity is concentrated. These findings highlight the importance of financial structure and, in particular, the scale and capacity of financial intermediation, in shaping the allocation of innovative investment across industries. |
| Keywords: | R&D intensity, financial development, financial institution depth, external finance dependence, innovation |
| JEL: | G10 G20 O16 O30 |
| Date: | 2026–03–24 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:128447 |
| By: | Anna D’annunzio (UNINT - Università degli Studi Internazionali di Roma = University of International Studies of Rome); Yassine Lefouili (TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - Comue de Toulouse - Communauté d'universités et établissements de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement); Bruno Jullien (TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - Comue de Toulouse - Communauté d'universités et établissements de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement); Leonardo Madio (Unipd - Università degli Studi di Padova = University of Padua) |
| Abstract: | This paper examines how horizontal mergers affect firms' incentives to invest in R&D leading to the development of new products. We characterize the impact of a merger to monopoly and a 3-to-2 merger on equilibrium innovation efforts and consumer surplus, absent efficiency gains and spillovers. We show that a 3-to-2 merger directly alters the outsider's innovation incentives by shifting its best-response function upward, and we analyze how this mechanism affects merger outcomes for innovation and consumer surplus. Finally, we examine how efficiency gains and remedies modify post-merger innovation efforts. |
| Keywords: | R&D Investments, Amp, Product Innovation, Horizontal Mergers |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05680914 |
| By: | Wagenvoort, Benjamin (Institute for New Economic Thinking at the Oxford Martin School, University of Oxford (INET Oxford)); Lafond, François; Dyer, Joel; Farmer, J. Doyne |
| Abstract: | Many technologies grow along S-curves: diffusion is slow, then rapid, then levels off. Forecasting this growth is vital for renewable energy, AI and other technology transitions, but it has been unclear whether technologies follow a single universal process, and past forecasts have proved unreliable. We assemble a database of 120 mature technologies, from canals to mobile phones, and show their S-curve shapes are remarkably universal. Using Bayesian methods and extensive out-of-sample backtesting, we show that a Bertalanffy-Richards process does a good job of fitting the data and its forecasting outperforms popular alternatives. Its point forecasts are typically accurate to within a factor of two, even from a 5% diffusion origin and decades ahead. This gives a validated method to forecast any technology that follows an S-curve, with known accuracy. Our forecasts for solar PV and wind indicate that by 2050 they will supply approximately 18–290 and 4–17 PWh globally each year (90% prediction intervals). Our median estimate for solar in 2050 is about 85 PWh, similar to all useful energy consumed today. Even the most aggressive IPCC AR6, IEA and NGFS scenarios are too pessimistic about solar, implying that ambitious climate targets will likely be met faster than widely believed. |
| Keywords: | Technology diffusion, Bayesian forecasting, S-curves, Energy transition |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:amz:wpaper:2026-19 |