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on Technology and Industrial Dynamics |
| By: | Teresa Fort; Nathan Goldschlag; Jack Liang; Peter K. Schott; Nikolas Zolas |
| Abstract: | Relatively flat US productivity growth versus rising R&D expenditures is often interpreted as evidence that ideas are getting harder to find. We build a new 45-year panel tracking the universe of US firms' patenting to investigate the micro underpinnings of this conclusion, separately examining the relationships between research inputs and ideas (patents) versus ideas and growth. We find that average patents per R&D input are increasing, the elasticity of patents to R&D inputs is flat or rising, and there is not systematic evidence of a secular decline in patenting after controlling for research inputs. We then document a positive, significant, and fairly steady relationship between firms' patent and labor productivity growth rates. Average firm growth after controlling for patent growth, however, declines. Together, these results suggest that firms' innovative efforts play a key role in sustaining growth that has not diminished over the last four decades. |
| Keywords: | innovation, productivity, R&D, patents, firm growth |
| JEL: | O31 O32 O33 O47 D24 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12652 |
| By: | Mark Hellsten; Giuseppe Pulito; Sarah Schroeder |
| Abstract: | This paper provides new evidence on how automation reshapes firms' demand for skills, not only by changing the occupational composition, but also by reshaping what existing jobs require. Using matched data on firm-level automation investments and detailed job vacancy postings from Denmark, we extract multidimensional skill profiles through natural language processing and decompose changes in skill demand into within- and between-occupation components. Within-occupation adjustment is a quantitatively important margin, accounting for 14-39% of total skill demand change depending on skill type and occupational group. Drawing on a task-based framework that links automation to shifts in multiple skill types within occupations, we estimate the causal effect of automation using a staggered difference-in-differences design. The effects are heterogeneous across the occupational hierarchy: among managers and professionals, automation increases the demand for soft skills, shifting the within-occupation skill mix toward interpersonal and cognitive competencies; among production workers, adjustment operates primarily through reduced hiring rather than changes in skill requirements, while retraining intensity rises by 5 percentage points. Our findings highlight that automation operates through multiple adjustment margins, with implications for training policy and labour market resilience. |
| Keywords: | automation, skills, task content, labour demand, technological change, job vacancies, within-occupation adjustment |
| JEL: | J24 O33 M51 L23 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:crm:wpaper:26122 |
| By: | James Lennox; Janine Dixon |
| Abstract: | This paper develops a task-based computable general equilibrium model to analyse the long-run economic effects of generative AI (GenAI) on the Australian economy. Each occupation performs a continuum of tasks executed in three modes: with raw labour; with AI-augmented labour; or automated using equipment and AI services. Task-level productivities in AI-using modes are draws from correlated Frechet distributions, captur ing heterogeneous within-occupation exposure. The model covers 45 industries and 97 occupations, calibrated to occupation-level GenAI exposure scores. The reference simulation yields a 29.8% real GDP increase: roughly one third from task-level productivity gains, the rest from capital deepening and general equilibrium reallocation. Real consumption - our long-run welfare metric - rises by 16.2%, substantially less because additional investment is required to equip automated tasks. Augmentation accounts for more tasks than automation in nearly all industries and occupations. Labour-market adjustment is dominated by within-occupation change - extensive-margin task reallocation equivalent to two thirds of current work - rather than net employment shifts between occupations. Losses con centrate in clerical, administrative, and sales roles, while most blue-collar occupations gain. Real wage effects are weakly correlated with initial wages; the rising capital share of income may matter more for distribution. Sensitivity analysis shows aggregate outcomes hinge on the distribution of task-level productivity gains: fatter tails roughly double the GDP gain while preserving the adjustment pattern, whereas variation in the dependence parameter shifts the augmentation - automation balance and the incidence of adjustment. Conventional substitution elasticities matter less. |
| Keywords: | Generative artificial intelligence, Computable general equilibrium, Task-based production, Occupational reallocation, Augmentation, Automation |
| JEL: | C68 J23 J24 O33 |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:cop:wpaper:g-367 |
| By: | Kathryn Bonney; Cory Breaux; Emin Dinlersoz; Lucia Foster; John Haltiwanger; Aditya Pande |
| Abstract: | Using novel, nationally representative data from the 2026 AI supplement to the U.S. Census Bureau’s Business Trends and Outlook Survey (BTOS), we characterize AI diffusion across three interconnected layers: overall firm use, deployment across business functions, and worker-task use. This multi-layered approach provides a nuanced picture of business AI adoption. During the supplement reference period (Nov 2025-Jan 2026), 18% of firms used AI in a business function, rising to 32% on an employment-weighted basis; adoption is expected to reach 22% within six months. AI use is substantially higher in large firms and knowledge-intensive sectors, with use rates reaching 50%-60% (60%-70%, employment-weighted) for very large firms in the Information, Professional Services, and Finance sectors. Among adopting firms, the scope of use remains limited: 57% of users integrate AI in three or fewer business functions, most commonly Sales and Marketing (52%), Strategy and Business Development (45%), and IT (41%). In 23% (41%, employment-weighted) of firms, workers use AI in work-related tasks. Writing, document analysis, and information search are the leading Generative AI use in tasks, though 65% of firms limit use to three or fewer tasks. The evidence points to both top-down and bottom-up diffusion channels: worker task use sometimes occurs without formal firm-level adoption, and firm-level adoption sometimes occurs without worker task use. Most users (66%) rely on AI solely to augment tasks, while AI-related employment decreases are rare, occurring in only 2% of firms. Regression analysis shows a robust positive correlation between firm commercial performance and the breadth of AI integration, including functional deployment, task-level use, and operational investment. A distinct divergence emerges, however, with respect to labor outcomes. Functional breadth and operational investment are positively associated with employment decreases, whereas worker-task integration shows no significant link to headcount reduction once functional integration and operational investment are taken into account. |
| Keywords: | Artificial Intelligence, AI, Technology Diffusion, Generative AI |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:cen:wpaper:26-25 |
| By: | Liang, Yuqi; Meyerhoff-Liang, Jan |
| Abstract: | Innovation research frequently relies on patent data to study technological change, yet empirical coverage of cities in the Global South remains limited. Sequence analysis has gained increasing attention as a method for analysing categorical trajectories in social sciences, but its application to regional innovation studies is constrained by the lack of sequence-ready urban datasets. Moreover, integration of sequence analysis with network analysis is underexplored, despite its potential to jointly capture relational structures and trajectory patterns in innovation processes. This paper introduces a database of sequential patent data for the innovation trajectories of 4, 125 Global South cities. Derived from existing geocoded patent data, the database includes general and technology-specific datasets (computing, environmental technology, and medicine), each available in sequence, network, sequence–network, and panel formats. Spanning from 1980 to 2014 and covering cities from seven countries (Brazil, Chile, China, India, Mexico, South Africa, and Turkey), the database supports analyses of innovation dynamics and helps increase the representation of Global South cities in economic geography, development studies and innovation research. |
| Date: | 2026–05–05 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:9w3ec_v1 |
| By: | Cristelli, Gabriele; Lissoni, Francesco |
| Abstract: | We study the innovation effects of the Agreement on the Free Movement of Persons, signed by Switzerland and the European Union in 1999. We exploit a quasi-experimental setting created by Switzerland’s implementation of the treaty, which initially eased entry restrictions only for commuters from neighboring countries, thereby inducing a large inflow of “cross-border inventors” in regions close to the border. We find that the treaty increased patenting in such regions relative to comparable ones farther away from the border. We find no evidence indicating the displacement of native inventors or a reduction in the patenting activity of Switzerland’s neighboring countries. We also find that incumbent inventors in regions next to the border increased their productivity, thanks to patents in collaboration with cross-border inventors. We provide evidence suggesting that cross-border inventors contributed to Swiss patenting by enabling R&D laboratories to enlarge, albeit without increasing the productivity of local peers outside direct collaborations. |
| Keywords: | immigration; innovation; patents; inventors; free movement of persons |
| JEL: | F22 J61 O31 |
| Date: | 2026–04–10 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:137441 |
| By: | Richard Audoly; Miles Guerin; Giorgio Topa |
| Abstract: | As generative AI tools become more widely used, a key issue is the technology’s impact on labor demand. Where might we find evidence of that impact? In this post, we examine whether early evidence of AI’s effect on the labor market appears in firms’ job postings. We combine an occupational measure of AI exposure with detailed U.S. job-posting data from Lightcast, which aggregates listings from company career pages, national and local job boards, and job-listing aggregators. Using this data, we test whether postings for AI-exposed occupations declined disproportionately since the release of ChatGPT in late 2022. We find that, while overall hiring has slowed since then, the evidence from job postings provides little indication of a distinct AI-driven decline in labor demand. |
| Keywords: | artificial intelligence (AI); labor demand; job postings; task automation |
| JEL: | J23 O33 |
| Date: | 2026–05–14 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fednls:103247 |
| By: | Daiwei Chen; Pierre-Alexandre Balland |
| Abstract: | China’s high-speed rail (HSR) network, initiated in 2008, now covers nearly all regionsof the country. This paper analyzes the effect of HSR connection on inter-city scientific collaboration and examines whether this e!ect varies systematically with the complexity of scientific fields. Combining the universe of HSR openings between 2008 and 2020 with OpenAlex publication records, we construct a panel spanning 33, 793 Chinese city pairs. Using a staggered difference-in-differences estimator, we find that HSR increases co-publications among city-pairs with existing collaborative ties by 35.2 percent at the city-pair level. Disaggregating across twenty scientific fields, we show that this effect is quite heterogeneous. Field-level treatment e!ects range from 19.8 to 45.1 percent, and their magnitude is positively and significantly correlated with average team size -a proxy of the fields’ complexity. These results are consistent with the view that face-to-face interaction is still important for knowledge production requiring deep divisions of cognitive labour, and they carry direct implications for the design of transportation and innovation policy. |
| Keywords: | High-Speed Rail (HSR), Scientific Collaboration, Knowledge Complexity, Face-to-Face Interaction |
| JEL: | O33 O38 R11 R58 |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:egu:wpaper:2605 |
| By: | Nicola Cortinovis; ; |
| Abstract: | Crowdfunding (CF) has emerged as a novel source of entrepreneurial finance, yet its role in shaping regional industrial dynamics remains poorly understood. Adopting an evolutionary economic geography perspective and exploiting a newly developed database, this paper examines the relationship between crowdfunding activity and the emergence of new local industrial specializations. The analysis shows that industries receiving funds through CF are more likely to become part of local specialization patterns, especially when they are related to the existing industrial structure. Moreover, these associations are stronger in counties characterized by higher levels of credit insecurity. |
| Keywords: | Crowdfunding, industrial diversification, relatedness, credit insecurity, US |
| JEL: | O14 O31 |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:egu:wpaper:2607 |
| By: | Gábor Kátay; Pálma Mosberger; Francesco Tucci |
| Abstract: | The paper evaluates the impact of the European Commission’s Seventh Framework Pro-gramme (FP7) grants on profit-oriented firms’ post-treatment performance. Using a robust quasi-experimental design and a dataset covering applicants from 46 countries, we find that FP7 grants increase firms’ sales and labour productivity by about 18%. However, there is no significant impact on employment levels, pointing to potential growth barriers that prevent firms from scaling production despite improved productivity. The effectiveness of these grants varies significantly based on factors such as financial constraints, project risk profiles, market structure, and the innovation environment. Smaller, less productive firms with tighter financial constraints in technology-intensive sectors operating in concentrated markets and favourable innovation environments, particularly those undertaking longer and riskier projects, tend to benefit more. |
| JEL: | C31 G28 H57 O31 |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:euf:dispap:238 |
| By: | Joseph Emmens; Dennis C. Hutschenreiter; Stefano Manfredonia; Felix Noth; Tommaso Santini |
| Abstract: | We study whether common ownership affects the direction of technological change. We develop a task-based model with multiple local labor markets in which commonly owned firms internalize wage externalities from portfolio rivals when hiring from the same labor pool, increasing incentives to automate. We establish causality by exploiting institutional investor mergers in a dynamic DiD design, using U.S. data on institutional ownership, establishment level employment, and text-classified automation patents. Increases in common ownership among local labor-market rivals raise firms' automation propensity by 22.7 percentage points and reduce employment growth. The effect disappears when firms do not compete within labor markets. |
| Keywords: | Common Ownership, Automation, Local Labor Markets, Market Power |
| JEL: | O33 G23 J42 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:crm:wpaper:26113 |
| By: | Fernando E. Alvarez; David Argente; Joyce Chow; Diana Van Patten |
| Abstract: | Data centers are the physical infrastructure behind cloud computing, artificial intelligence, and enterprise software. The rapid diffusion of artificial intelligence (AI) is intensifying demand for compute, accelerating investment in data centers, and raising concerns about the local economic and environmental footprint of these facilities. Their expansion creates a local policy tradeoff. A data center can bring capital investment, construction activity, and specialized employment, but it can also increase demand for electricity, land, and grid capacity. This paper studies these effects at the U.S. county level. We assemble a facility-level panel of global data centers with precise coordinates, scale metrics, and annualized revenue. We map facilities to U.S. counties and combine them with County Business Patterns, county-level IRS income, county-level house prices, and electricity prices. To address endogenous siting, we instrument for data center growth using two shift-share instruments, which leverage pre-existing proximity to InterTubes long-haul fiber nodes and the 1980 county share of U.S. urban college population as shares, and both Chinese and rest-of-the-world data center revenue growth as shifts. The IV estimates show positive effects on total employment, data-processing employment, construction employment, establishments, house prices, and electricity prices at different horizons after data center growth. We also find positive effects on tax returns, adjusted gross income, and wages, while annual payroll responds less robustly. The results suggest that data centers create measurable local activity, increase house prices, and affect local electricity markets through higher prices. |
| JEL: | D8 O3 |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35194 |
| By: | Hinh T. Dinh |
| Abstract: | This paper revisits Big Push industrialization theory in the context of open economies deeply integrated into global value chains (GVCs). While classical Big Push models emphasize demand complementarities and coordination failures in largely closed economies, many middle-income countries now industrialize through foreign-owned, import-intensive production networks. We develop an extended Big Push framework that incorporates GVC integration and import leakage, and show how these features can prolong the middle-income trap, despite rapid manufacturing expansion. Importantly, the analysis shows that without careful and well-designed industrial policy, large-scale investment programs inspired by Big Push logic may unintentionally reinforce import leakage, rather than generate self-reinforcing domestic demand spillovers. In this case, a Big Push can prolong the middle-income trap and lead to adverse outcomes. We characterize the conditions under which the domestic modern industry is left unviable, derive the critical industrial-policy threshold required to redirect domestic demand toward local production, and establish welfare rankings across alternative development strategies. Using a panel of ten middle-income economies from 1989 to 2024, we provide empirical evidence consistent with the model’s predictions: greater trade openness and higher investment-income payments are associated with systematically larger GDP-GNI wedges, reflecting structural income leakage rather than transitory price effects. Distributed-lag estimates show that investment-income outflows affect the wedge immediately, while trade integration operates with longer lags. The results imply that GVC participation alone does not guarantee national income convergence, and that successful late industrialization requires deliberate policy sequencing to convert export-led growth into domestic value capture. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:ocp:rtrade:rp_01-26 |