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on Technology and Industrial Dynamics |
| By: | Taghizadeh, Rahim; Babazadeh Behestani, Salar; Arabsheibani, G. Reza |
| Abstract: | Despite extensive research, a consensus remains elusive regarding the optimal method for measuring the effects of technological change and innovation on employment. This study introduces a Technological Change Composite Indicator (TCI), constructed using Principal Component Analysis (PCA) to synthesize seven firm-level innovation metrics. This methodology mitigates issues associated with multicollinearity in regression analyses involving correlated variables. The proposed TCI serves as a proxy for technological change to examine its association with employment in manufacturing sectors across European Union countries. Applying the TCI to a pooled cross‑section of ten European countries and seventeen manufacturing sectors (170 observations) with country fixed effects and a one‑year time lag, we find that a one‑unit increase in the TCI corresponds to a 0.58% higher employment level. The association is positive and statistically significant, indicating that a multidimensional measure of technological change outperforms traditional single proxies such as R&D expenditure or patent counts. The TCI provides policymakers and industry stakeholders with a novel framework for assessing how a broad portfolio of innovation activities—including machinery acquisitions, external knowledge, intellectual property rights, and both product‑ and process‑oriented efforts—relates to manufacturing employment. By moving beyond narrow indicators, our approach offers a more reliable empirical basis for understanding the employment implications of technological change, including emerging technologies such as AI. |
| Keywords: | composite indicator;latent approach;technological change;employment;manufacturing industry;PCA analysis;regression method |
| JEL: | O33 J23 O14 |
| Date: | 2026–07–24 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:140361 |
| By: | Mar Carpanelli; Jedrzej Duszynski; Fabian Stephany |
| Abstract: | As artificial intelligence transforms labor markets, understanding what makes workers adaptable has become increasingly important. Existing approaches typically characterize human capital using occupations, educational credentials, or predefined skill taxonomies, providing limited insight into how the structure of workers' skill portfolios shapes resilience to technological change. We develop an agnostic network based framework that reconstructs the hierarchy and diversity of skills directly from observed patterns of skill co occurrence. Using longitudinal data on 2.4 million United States workers and 16, 753 distinct skills from LinkedIn, we introduce three complementary measures of skill complexity: specialisation, capturing productive depth; diversity, capturing adaptive breadth; and the diversity frontier, measuring the highest attainable diversity conditional on a worker's level of specialisation. We show that these dimensions predict distinct career outcomes. Specialisation is most strongly associated with sorting into higher wage occupations, whereas diversity is associated with broader skill accumulation and occupational mobility. Workers closest to the diversity frontier are significantly more likely to acquire new skills, receive promotions, transition into occupations with lower exposure to automation than workers with comparable levels of specialisation but narrower skill portfolios. These findings distinguish productive from adaptive capital and demonstrate that workers' adaptive capacity depends not simply on possessing specialised expertise or broad capabilities, but on combining both. More broadly, our framework provides a data driven approach for measuring workforce resilience and identifying reskilling pathways, offering new tools for understanding human capital in rapidly changing labor markets. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.02102 |
| By: | C. Castaldi; F. Castellacci; A. Fronzetti Colladon; L. Segneri; F. Venturini |
| Abstract: | Researchers, managers and policymakers are exploring different approaches and data sources to map the development and the diffusion of Artificial Intelligence (AI). In this research note, we illustrate the opportunities offered by trademark data. We argue that AI trademarks can complement AI patents to capture different dimensions of AI innovation. AI trademarks can reveal the extent and ways in which companies exploit AI technologies to develop new goods and services. Importantly, trademark data offer a timely and globally available data source that covers all economic sectors. We present insights from using AI trademarks in an empirical exploration of Italian firms. In our discussion, we reflect on how AI trademarks can be used at different levels of analysis to tackle emerging questions about the development and diffusion of AI. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.18795 |
| By: | Aaron Chatterji; David Holtz; Neel Rakholia; Prasanna Tambe; Gawesha Weeratunga |
| Abstract: | We study how organizations use frontier generative AI by linking ChatGPT Enterprise account records to usage, worker roles, task classifications, and public-company financial data through March 2026. These linked data enable a privacy-preserving analysis of adoption, worker roles, and message-level tasks at scale: for instance, the worker-level sample we analyze at the six-month adoption horizon includes over 1, 500 organizations and over 17 million messages. We document four facts about enterprise AI adoption and use. First, ChatGPT Enterprise usage has grown rapidly due to a combination of new firm adoption and growing intensity among existing adopters. Second, U.S.-based public company adoption is concentrated among larger, more valuable, and more R&D- and SG&A-intensive firms. Third, active use within adopting firms spans job functions and seniority levels, with especially high usage intensity among early-career workers. Fourth, ChatGPT Enterprise usage encompasses a broad range of knowledge work tasks, including writing, technical work, communication, and information synthesis. In aggregate, these results suggest that firms differ widely in the speed, breadth and purpose of their enterprise AI adoption, and that they are still actively learning how to integrate AI into organizational workflows. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.12236 |
| By: | Horng Chern Wong; Sampreet Goraya; Anders Akerman |
| Abstract: | Does skill-biased technological change benefit less-skilled workers? This paper shows that who gains or loses from SBTC depends on where it occurs across firms and how widely it diffuses across markets. Using Swedish administrative microdata, we document that large firms became increasingly important in the market for skilled labor between 1997 and 2018. Relative to smaller firms, they grew more skill intensive and paid rising skill premia; this steepening reflected a rising large-firm wage premium for college workers but not for non-college workers. We interpret these facts through a model of heterogeneous firms with wage- and price-setting power. The quantified model infers that SBTC became increasingly concentrated among large firms. This concentration raises productivity, but widens wage inequality within and between firms. It can also lower low-skill wages and employment at the firms where SBTC occurs, with negative spillovers to low-skilled workers at competitors. The mechanism is that large firms have weaker scale responses to skill-biased shocks, limiting the expansion that would otherwise offset substitution away from low-skilled labor. Removing firm market power mitigates these losses, but does not overturn them. By contrast, broader diffusion of SBTC across industries can turn those losses into gains for low-skilled workers. |
| Keywords: | Skill-biased technological change, skill premium, wage inequality, large firms |
| JEL: | J31 J24 O33 D24 D43 J42 |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:crm:wpaper:26183 |
| By: | James Driver |
| Abstract: | This project benefitted the U.S. Census Bureau by the creation of novel, dynamic and data-driven innovation-industry classification systems (i.e., IICSs). The goal of these classification systems is to track a firm’s endogenous choice of innovation and be able to assign it to one, or more, innovation-industries through time. To capture and measure a firm’s innovative output, it is proposed to categorize a firm by its portfolio of patent abstracts from granted, USPTO utility patents where the firm is the original assignee. The idea is to capture the firm responsible for the innovation and to observe the area(s) in which it is innovating. A benefit of utilizing granted patents is that it allows one to compare public and private firms, and their roles in innovation. |
| Keywords: | BERD, LBD, CMF, USPTO |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:cen:tnotes:26-29 |
| By: | Poornima Varma; Sunghun Lim; Drishti Sharma |
| Abstract: | Do global value chains (GVCs) make firms cleaner, and under what conditions? Leveraging rich firm-level data from India in the period of 2010–2024, this study investigates the impact of GVC participation on firms’ environmental performance. To address dynamics and heterogeneous responses, we estimate dynamic panel threshold regressions with firm growth as the regime variable. We find that GVC participation raises energy and carbon intensity for low-growth firms but reduces both—and increases renewable energy use—for high-growth firms. Channel decomposition shows that forward participation delivers the clearest efficiency and decarbonization gains in the high-growth regime, while backward participation improves energy efficiency at high growth yet exhibits mixed carbon effects; in the low-growth regime, forward linkages raise intensity whereas backward linkages are comparatively less harmful or even carbon-reducing. This paper underscores the capacity-dependence of trade integration’s environmental impact and shows that policy can shift the relevant margin through finance, R&D, and compliance infrastructure. |
| Date: | 2026–08–18 |
| URL: | https://d.repec.org/n?u=RePEc:iim:iimawp:14736 |
| By: | Hyejin Ku; Uta Schönberg; Ragnhild C. Schreiner |
| Abstract: | This paper provides new micro-level evidence on how labor taxation shapes firm behavior, exploiting an EU-mandated payroll tax reform in Norway. Combining administrative and survey data, we find that firms facing larger tax increases sharply cut employment but also increased R&D spending, implemented labor cost-saving innovations, and adopted more automation. While these responses led to improvements in labor and total factor productivity within the firm, the firm's labor share fell. These effects persisted even after the tax hike was unexpectedly reversed three years later, suggesting a lasting shift toward more capital-intensive production in response to higher labor costs. |
| Keywords: | payroll taxes, labor costs, firm behavior, technology adoption, inequality |
| JEL: | J23 J32 H25 H32 O31 O32 |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:crm:wpaper:26192 |
| By: | Colin Davis (Doshisha University); Ken-ichi Hashimoto (Kobe University); Ken Tabata (Kwansei Gakuin University) |
| Abstract: | This paper studies how environmental policy designed to reduce transboundary pollution affects long-run productivity growth through shifts in the geographic location of industry. We construct a two-country endogenous growth and endogenous market structure framework in which there is a positive link between the geographic concentration of industry and the strength of knowledge spillovers from production to innovation. Emissions are generated as a byproduct of production. We show that an increase in the emissions tax of the country with a larger (smaller) share of industry lowers the concentration of industry leading to weaker (stronger) knowledge spillovers and a slower (faster) rate of productivity growth. In addition, we identify cases where a rise in the emissions tax of the country with a smaller share of industry lowers emissions while increasing productivity growth. With endogenous emissions taxes, a numerical analysis shows that stronger knowledge diffusion leads to higher tax rates, faster productivity growth, and lower global emissions. In contrast, trade liberalization leads to lower tax rates and eventually raises global emissions despite faster productivity growth. Our results highlight that the relationship between productivity growth and global emissions depends critically on the form of economic integration. |
| Keywords: | Asset bubbles; Emissions Taxes, Industry Location, Knowledge Diffusion, Trade Liberalization, Productivity Growth, Global Emissions, Endogenous Market Structure, Endogenous Policy |
| JEL: | F12 O40 Q56 |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:kyo:wpaper:1129 |
| By: | Chanya Chawla; Crystal Arnburg |
| Abstract: | This paper examines the adoption of artificial intelligence (AI) among firms in Canada and its expected effects on employment and capital spending. The analysis relies on special questions included in the December 2025 Business Leaders’ Pulse (BLP). The results show that while personal use of AI among business leaders is widespread, adoption for production purposes remains limited. On balance, firms anticipate AI to have a positive impact on their capital expenditures over the next 12 months and a slightly more positive impact over the next 3 years. Firms anticipate limited impacts to employment over the next year but expect modest net negative impacts on employment over the next 3 years. Overall, the findings suggest that AI adoption among Canadian firms remains at an early stage, with more material economic impacts expected to emerge over time. |
| Keywords: | Structural challenges; Digitalization and productivity |
| JEL: | E22 E24 O33 |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:bca:bocsap:26-22 |
| By: | Michele Liberatore; Massimo Riccaboni |
| Abstract: | We study how licensing affects the allocation of innovation in pharmaceutical R&D. We develop a model in which projects differ in both quality and innovation regime, distinguishing between incremental and novel innovations. Information precision is higher for incremental projects and lower for novel ones, generating different equilibrium dynamics in the market for technology. The model predicts that licensing sustains positive selection and competitive return equalization for incremental innovation, while novel projects may exhibit weaker screening consistent with lemons-type frictions. Using product-level data and Double Machine Learning methods, we test these predictions across success probabilities and monetary returns. We find that licensing increases success probability overall, but return equalization holds primarily for incremental projects. For novel innovation, licensing does not exhibit the same equilibrium adjustment, suggesting residual market imperfections. Instrumenting for licensing using exogenous pipeline shocks confirms this pattern causally: the competitive risk-return trade-off is preserved for incremental 'rushed' licenses, but it breaks down for novel ones. Our results reconcile evidence on both competitive efficiency and information frictions in markets for technologies, showing that market performance depends systematically on the type of innovation being transacted. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.20365 |
| By: | Gergő Tóth; Zoltán Elekes; Rikard Eriksson |
| Abstract: | Understanding whether transitioning between occupations is constrained by workers’ skills or by social stratification remains an unresolved challenge across economics, sociology and human geography. Existing approaches offer only partial solutions. In this paper we overcome this limitation by introducing a network-based framework that independently measures occupational skill similarity and compares it directly with realised labour mobility. This allows us to quantify how closely the two align across social groups and local labour markets. Combining rich administrative and job advertisement data from Sweden, we map skill demands into a network where nodes represent occupations and links measure the overlap in skills demanded. We trace realised labour mobility along these pathways. With this analytical framework, we show that labour mobility strongly follows skill-related pathways. However, access to these pathways is uneven: occupational segregation along gender, education and country of birth shapes labour mobility beyond skill relatedness. For gender and education, occupational segregation affects mobility most where skills overlap the least. Crucially, local labour markets where labour mobility aligns more closely with skill similarity exhibit higher incomes, greater occupational diversity, faster re-employment, reduced risk of worker out- migration and smaller gender wage gaps. These findings suggest that labour-market adaptation depends not only on whether workers possess transferable skills, but also on whether social and regional contexts enable those skills to be redeployed. Our framework provides a common empirical basis for understanding how skill similarity, social stratification and local labour-market structure jointly shape labour-market adaptation. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:egu:wpaper:2615 |
| By: | Milad Abbasiharofteh; Hamid Bekamiri |
| Abstract: | This paper argues that generative artificial intelligence (GenAI) represents a methodological turning point in economic geography and regional studies and provides a proof of concept for this transition. Building on successive waves of data collection methods, from surveys and standardized secondary data to web-based data, we show how GenAI can complement existing methods by approximating firms’ product portfolios from unstructured information and linking them to standardized classifications. Moving beyond product mapping, we conclude by discussing a research agenda that addresses broader data and methodological challenges and outlines how GenAI can contribute to a new methodological frontier. |
| Keywords: | economic geography, regional studies; generative artificial intelligence; geography of products; web data |
| JEL: | C55 O33 R12 L26 |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:egu:wpaper:2616 |
| By: | Hamid Firooz; Sylvain Leduc; Zheng Liu |
| Abstract: | We study how AI affects market competition based on a general equilibrium framework with heterogeneous firms facing idiosyncratic productivity and variable markups. Firms choose the AI technology subject to fixed costs, where AI production requires data and energy inputs. Our model predicts a non-monotonic relation of AI diffusion with industry concentration. As AI usage rises from an initially low level, large incumbent users gain market share. When AI usage is sufficiently diffused, entry of new and smaller adopters erodes the market share of incumbents, reducing industry concentration. The non-monotonic relations are robust when firms can complement AI with their own data. Our calibrated model predicts that industry concentration is likely to fall if AI adoption increases relative to the current level. In comparison, the relation of AI with the average markup depends on whether increased AI usage is driven by demand or supply factors. Our model also predicts that a modest subsidy of about 3 percent for AI adopter revenues maximizes social welfare, reflecting a tradeoff between aggregate productivity and the average markup associated with AI usage. |
| Keywords: | artificial intelligence; data; heterogeneous firms; industry concentration; markup; productivity; welfare |
| JEL: | E24 L11 O33 |
| Date: | 2026–08–10 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedfwp:103630 |
| By: | Konrad Adler (University of St.Gallen & SFI); Oliver Rehbein (WU Vienna & VGSF); Matthias Reiner (WU Vienna & VGSF); Jing Zeng (University of Bonn & CEPR) |
| 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:ajk:ajkdps:421 |