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on Technology and Industrial Dynamics |
| By: | Hanming Fang; Xian Gu; Hanyin Yan; Wu Zhu |
| Abstract: | We develop a high-precision classifier to measure artificial intelligence (AI) patents by fine-tuning PatentSBERTa on manually labeled data from the USPTO’s AI Patent Dataset. Our classifier substantially improves the existing USPTO approach, achieving 97.0% precision, 91.3% recall, and a 94.0% F1 score, and it generalizes well to Chinese patents based on citation and lexical validation. Applying it to granted U.S. patents (1976–2023) and Chinese patents (2010–2023), we document rapid growth in AI patenting in both countries and broad convergence in AI patenting intensity and subfield composition, even as China surpasses the United States in recent annual patent counts. The organization of AI innovation nevertheless differs sharply: U.S. AI patenting is concentrated among large private incumbents and established hubs, whereas Chinese AI patenting is more geographically diffuse and institutionally diverse, with larger roles for universities and state-owned enterprises. For listed firms, AI patents command a robust market-value premium in both countries. Cross-border citations show continued technological interdependence rather than decoupling, with Chinese AI inventors relying more heavily on U.S. frontier knowledge than vice versa. |
| JEL: | C55 G14 O31 O33 O34 O57 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35022 |
| By: | Pulito, Giuseppe; Pytlikova, Mariola; Schroeder, Sarah; Lodefalk, Magnus |
| Abstract: | Using two waves of nationally representative Danish firm surveys linked to employer- employee administrative registers, we study how adoption varies across artificial intelligence (AI) and related advanced technologies. We show that AI adoption is highly technologyspecific. While firm size and digital infrastructure predict adoption broadly, workforce composition operates through distinct channels: STEM-educated workforces predict core AI adoption, whereas non-STEM university-educated workforces are associated with generative AI adoption, indicating different human capital complementarities. The factors associated with adoption differ from those predicting deployment breadth: firm size and digital maturity matter for both, whereas workforce composition primarily predicts adoption alone. Machine learning and natural language processing are deployed across multiple business functions, whereas other advanced technologies remain concentrated in specific operational domains. Individual-level evidence provides a foundation for these patterns, with awareness of workplace AI usage concentrated among managers and high-skilled workers. Self-reported AI knowledge is higher among younger and more educated individuals. Finally, commonly used occupational AI exposure measures vary substantially in their ability to predict observed adoption, with benchmark-based measures outperforming patent-based and LLM-focused alternatives. These findings show that treating AI as a monolithic category obscures economically meaningful variation in who adopts, what they deploy, and how well existing measures capture it. |
| Keywords: | Artificial Intelligence, Technology Adoption, Digitalisation, Human capital, AI Exposure Measures |
| JEL: | D24 J23 J62 O33 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:glodps:1732 |
| By: | Claudia Collodoro (Dipartimento di Politica Economica, DISCE, Università Cattolica del Sacro Cuore, Milano, Italy); Lucrezia Fanti (Dipartimento di Politica Economica, DISCE, Università Cattolica del Sacro Cuore, Milano, Italy - Instituto di Economia, Scuola Superiore Sant’Anna, Pisa, Italy); Jacopo Staccioli (Dipartimento di Politica Economica, DISCE, Università Cattolica del Sacro Cuore, Milano, Italy - Instituto di Economia, Scuola Superiore Sant’Anna, Pisa, Italy); Maria Enrica Virgillito (Dipartimento di Politica Economica, DISCE, Università Cattolica del Sacro Cuore, Milano, Italy - Instituto di Economia, Scuola Superiore Sant’Anna, Pisa, Italy) |
| Abstract: | This work provides a comprehensive large-scale analysis of artificial intelligence-based worker management (AIWM) systems from an industry-wide exposure perspective focusing on traditional industries. We begin by examining the knowledge production underlying these workforce management tools and leverage technology patent-classification to identify their dynamics and specific features. For this purpose, we use patent data retrieved from Orbis Intellectual Property covering the years 1975 to 2022, considering patents filed with both the EPO and the USPTO. Furthermore, to identify patents related to AIWM heuristics, we retrieve their full text from Google Patents and conduct a textual analysis using a dependency parsing algorithm. Finally, using the dictionary of human tasks provided by O*NET, we construct a measure of exposure to AIWM systems for individual human tasks and occupations. Linking the technological and labour market domains, we find that the professions most exposed to AIWM systems are those at the top of organisational hierarchies. |
| Keywords: | Artificial Intelligence Worker Management, Sector-level Analysis, Patenting Activity, Techno-organisational Change |
| JEL: | O14 O33 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:ctc:serie5:dipe0056 |
| By: | Wagner, Joachim (Leuphana University Lüneburg) |
| Abstract: | The use of advanced technologies like artificial intelligence, robotics, or smart devices will go hand in hand with, among others, higher productivity, higher product quality, more exports and better chances to survive any crisis. Better firms tend to use advanced technologies. Information on firm level determinants of adoption of these technologies, therefore, is important to inform industrial policies. This paper uses firm level data for manufacturing enterprises from 38 countries collected in 2025 to shed further light on this issue by investigating the link between the use of advanced technologies and firm characteristics. Applying a new machine-learning estimator, Kernel-Regularized Least Squares (KRLS), which does not impose any restrictive assumptions for the functional form of the relation between use of advanced technologies, firm characteristics and any control variables, we find that firms which use advanced technologies tend to be larger and more innovation orientated, while firm age does not matter. |
| Keywords: | advanced technologies, firm characteristics, Flash Eurobarometer 559, kernel-regularized least squares (KRLS) |
| JEL: | D22 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18499 |
| By: | Dugoua, Eugenie; Noailly, Joëlle |
| Abstract: | This paper examines the patterns and mechanisms of global clean technology diffusion over the last two decades. We document four stylized facts: uneven sectoral progress favoring power and light transport; China’s dominance in innovation and manufacturing; the role of modularity in driving cost declines; and limited adoption in developing economies. Through case studies of solar, electric vehicles, and hydrogen, we analyze how policy and infrastructure enable scale. Finally, we assess emerging challenges for the next phase of diffusion, including critical mineral constraints, artificial intelligence, and geopolitical fragmentation. |
| Keywords: | clean technology diffusion; climate change mitigation; renewable energy; industrial policy; solar photovoltaics; electric vehicles; hydrogen |
| JEL: | O33 Q55 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:137824 |
| By: | Andreas F. Buehler; Patrick Lehnert; Harald Pfeifer |
| Abstract: | We examine whether exposure to climate change-related natural disasters is associated with adolescents' aspirations to work in green occupation. Understanding adolescents' aspirations for such occupations is crucial for ensuring a workforce with green skills to contribute to the mitigation of climate change. Combining individual-level data on occupational aspirations, job task data for measuring the greenness of occupations, and administrative data on disaster events, we find that adolescents who were exposed to a natural disaster aspire to occupations with a higher percentage of green job tasks. The result of this exposure is stronger for individuals who are more environmentally aware or more likely to believe that environmental issues will improve. |
| Keywords: | Green Occupations, Natural Disasters, Aspirations, Occupational Choices |
| JEL: | J24 Q54 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:iso:educat:0254 |
| By: | Autor, David (MIT); Chin, Caroline (MIT); Salomons, Anna (Tilburg University and Utrecht University); Seegmiller, Bryan (Northwestern University) |
| Abstract: | We study the role of expertise in new work—novel occupational roles that emerge as technological and economic conditions evolve—using newly available 1940 and 1950 Census Complete Count files and confidential American Community Survey data from 2011–2023. We show that new work is systematically distinct from simply more work in existing occupations in four respects. First, it attracts workers with distinct characteristics: new work is disproportionately performed by younger and more educated workers, even within detailed occupation-industry cells. Second, new work commands wage premiums that persist beyond workers’ initial entry into new work, consistent with returns to scarce, specialized expertise rather than temporary market disequilibrium. Third, these premiums decline across vintages as expertise diffuses, with ‘newer’ new work commanding larger premiums. Fourth, the emergence of new work can be traced to regional demand shocks, suggesting that expertise formation responds to economic opportunities. These findings suggest that new work is a countervailing force to automation-driven job displacement not merely by creating additional employment, butby generating new domains of human expertise that command market premiums. |
| Keywords: | new work, technological change, occupations, tasks |
| JEL: | E24 J11 J23 J24 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18504 |
| By: | Wagner, Joachim (Leuphana University Lüneburg) |
| Abstract: | The use of advanced technologies like artificial intelligence, robotics, or smart devices will go hand in hand with higher productivity, higher product quality, and lower trade costs. Therefore, it can be expected to be positively related to export activities. This paper uses firm level data for manufacturing enterprises from the 27 member countries of the European Union collected in 2025 to shed further light on this issue by investigating the link between the use of advanced technologies and extensive margins of exports. Applying a new machine-learning estimator, Kernel-Regularized Least Squares (KRLS), which does not impose any restrictive assumptions for the functional form of the relation between margins of exports, use of advanced technologies, and any control variables, we find that firms which use more advanced technologies do more often export and do export to more different destinations. |
| Keywords: | advanced technologies, exports, firm level data, Flash Eurobarometer 559, kernel-regularized least squares (KRLS) |
| JEL: | D22 F14 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18496 |
| By: | NAGAMUNE, TAKESHI (Niimi University) |
| Abstract: | The traditional economic base model in regional science argues that tradable industries promote regional development by earning income from outside the region and generating multiplier effects within the local economy. Within this theoretical framework, manufacturing has long been considered the primary export base. However, following the influential work of Moretti and others, recent empirical analyses demonstrate that industries fostering innovation and creative activities also exhibit substantial employment multipliers. This suggests that industries and occupations engaged in creative and intellectual activities can serve as new drivers of regional growth. This study focuses on municipalities in Japan, where the tertiarization of industry has advanced. Using industry and occupation classifications from Census data, we define “creative industries and occupations” and estimate their local employment multiplier effects through regression analysis. The empirical results confirm that these creative sectors exert a positive and statistically significant multiplier effect on regional economies, indicating their potential contribution to regional economic development. These findings demonstrate that promoting creative industries can complement traditional manufacturing-oriented strategies. They also provide empirical evidence—based on Japanese municipal-level data—to support the international discourse that knowledge- and creativity-based industries drive regional transformation. |
| Date: | 2026–03–28 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:x2vcy_v1 |
| By: | Leland D. Crane; Paul E. Soto |
| Abstract: | We evaluate whether LLMs have had any discernible impact on the aggregate labor market so far. We focus on occupations that are computer programming-intensive, motivated by data showing that coding is one of the most LLM-exposed tasks. Linking O*NET to CPS we find that aggregate employment of coders has decelerated sharply since the introduction of ChatGPT. Using a novel control variable for industry-level shocks we show that the deceleration is not attributable to the exposure of coders to slowing industries, suggesting instead that coders experienced an occupation-specific shock around the introduction of ChatGPT. Coder employment has continued to grow in recent years, though much more slowly than it did pre-2022. We validate the industry-level control variable by examining historical examples of occupations that experienced either occupation-specific or industry-level shocks. We also provide statistics on the agreement rates between different measures of AI exposure. |
| Keywords: | Labor demand; Machine learning; Shocks |
| JEL: | J23 J24 O33 |
| Date: | 2026–03–23 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfe:102997 |
| By: | Vanessa Alviarez; Cheng Chen; Nitya Pandalai-Nayar; Liliana Varela; Kei-Mu Yi; Hongyong Zhang |
| Abstract: | We study how multinational corporations (MNCs) shape firm-level and aggregate structural transformation. Using confidential microdata from Japan and exploiting a quasi-exogenous reform that expanded foreign investment opportunities in China, we assess empirically how this reform affected employment at firms in both the host country (China) and the home country (Japan). In liberalized industries, Japanese manufacturing affiliates in China expanded employment, while parent firms in Japan shifted out of manufacturing and into higher-value service activities, including R&D. To assess the broader relevance of this mechanism, we use microdata from several advanced and middle-income economies, and show that MNCs account for the majority of the middle-income countries' reallocation to manufacturing. |
| Keywords: | multinational firms; manufacturing employment; services employment; foreign direct investment liberalization |
| JEL: | F23 F60 |
| Date: | 2026–03–30 |
| URL: | https://d.repec.org/n?u=RePEc:fip:feddwp:102970 |
| By: | Mr. Alberto Behar |
| Abstract: | Skilled wage premia in Latin American countries have continued declining, albeit more slowly and unevenly. Is the decline driven by demand or supply? This paper proposes a novel adaptation to the demand-supply decomposition framework by incorporating directed technical change (DTC), specifically supply-induced skill-biased technical change that acts to increase the wage premium. DTC counters the traditional substitution effect through which higher education wage attainment reduces the skill premium. Therefore, DTC makes adjusted inferred demand changes less skill biased than the standard framework’s traditional inferred demand changes. We apply the framework to ten Latin American countries over three periods, namely the length of the sample, the period between maximum wage premia and 2015, and since 2015. In our baseline results, DTC is quantitatively significant while the substitution effects remain important. Traditional demand shifts were skill biased over the length of the sample including since 2015 but our novel adjusted demand shifts were skill neutral. During the period between maximum premia and 2015, unadjusted demand shifts were skill-neutral and adjusted demand shifts favored unskilled workers. Equivalently, sizeable DTC effects imply wages would have fallen significantly faster in the absence of DTC. For an alternative elasticity of 1.25, DTC effects are smaller, supply effects are bigger, and adjustments to demand effects are smaller. For alternative supply measures, the results are relatively robust. |
| Keywords: | Skill-biased technical change; directed technical change; elasticity of substitution; schooling premium; wage premium; wage inequality. |
| Date: | 2026–03–27 |
| URL: | https://d.repec.org/n?u=RePEc:imf:imfwpa:2026/054 |
| By: | Daron Acemoglu; Tianyi Lin; Asuman Ozdaglar; James Siderius |
| Abstract: | Artificial intelligence (AI) changes social learning when aggregated outputs become training data for future predictions. To study this, we extend the DeGroot model by introducing an AI aggregator that trains on population beliefs and feeds synthesized signals back to agents. We define the learning gap as the deviation of long-run beliefs from the efficient benchmark, allowing us to capture how AI aggregation affects learning. Our main result identifies a threshold in the speed of updating: when the aggregator updates too quickly, there is no positive-measure set of training weights that robustly improves learning across a broad class of environments, whereas such weights exist when updating is sufficiently slow. We then compare global and local architectures. Local aggregators trained on proximate or topic-specific data robustly improve learning in all environments. Consequently, replacing specialized local aggregators with a single global aggregator worsens learning in at least one dimension of the state. |
| JEL: | D80 D83 D85 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35036 |
| By: | Wei Cai; Andrea Prat; Jiehang Yu |
| Abstract: | Prior research has pointed to differences in organizational capital as a reason for the persistent performance discrepancies among otherwise similar firms. In this paper, we develop and validate a new measure of organizational capital. Based on over a million crowd-sourced employee reviews scraped from Glassdoor, we construct the measure of organizational capital at the firm-year level using the word embedding model and ChatGPT-generated synthetic reviews. Our measure varies over time in accordance with macro trends, and differs both across and within firms, reflecting firm heterogeneity and major internal changes. We validate our measure by testing empirical predictions of the properties of organizational capital discussed in prior literature. Our findings suggest that this measure captures a slowly evolving intangible asset that is significantly associated with firm performance and top management’s influence, aligning with the conceptualization of organizational capital by Dessein and Prat (2022). We further showcase applications of our measure in accounting, economics, finance, and management literature. Taken together, the paper provides implications for various stakeholders who are interested in assessing and managing firms’ organizational capital. |
| JEL: | D22 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35039 |
| By: | Gereffi, Gary; Hamrick, Danny |
| Abstract: | This report examines the development and upgrading prospects of the medical device and aerospace clusters in Baja California, Mexico within the context of global value chains (GVCs). Prepared for the Economic Commission for Latin America and the Caribbean (ECLAC) and the Government of Baja California, the study applies two complementary analytical frameworks: cluster development, which highlights the differentiated roles of firms within local production systems, and GVC analysis, which emphasizes how global lead firms, governance structures, and standards shape upgrading trajectories. The analysis finds that Baja California has experienced a marked process of industrial transformation over the past two decades. Building on capabilities developed in consumer electronics and other export-oriented industries, the region has consolidated its position as a strategic nearshoring platform for advanced manufacturing serving North American markets. The medical devices cluster has reached significant scale, employing approximately 100, 000 workers and hosting a dense concentration of multinational firms. While production remains focused on manufacturing and assembly, evidence of process, product, and functional upgrading is observed, particularly in areas such as sterilization services, automation, and selected design and engineering activities. The aerospace cluster follows a distinct development path. Rather than being organized around a few flagship firms, it is shaped by multiple Tier-1 and Tier-2 companies operating under stringent certification and quality regimes. This distributed structure has supported diversification but also underscores the need for strong coordination mechanisms, including industry associations, logistics platforms, and workforce certification systems. Drawing on comparative international experiences, the report highlights the role of targeted public policies in supporting upgrading through supplier development, skills formation, institutional coordination, and environmental performance. The findings emphasize that sustained competitiveness depends on aligning economic upgrading with social upgrading—through improved job quality and skill development—and environmental upgrading, particularly in regulation-intensive sectors. The study concludes that an integrated productive development approach is essential for strengthening local capabilities, increasing value capture, and supporting inclusive and sustainable industrial development in Baja California, Mexico. |
| Date: | 2026–03–25 |
| URL: | https://d.repec.org/n?u=RePEc:ecr:col094:87055 |