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on Technology and Industrial Dynamics |
| By: | Alam, Afroza; Diegmann, André |
| Abstract: | This paper provides new causal evidence on how patent allowances affect firms and their employees based on quasi-random assignment of patent applications to examiners. Exploiting employer-employee records with newly linked German firm data and web-scraped patent documents, we show that patent-induced shocks reduce firm exit, improve productivity, and increase wages, with rent-sharing elasticities between 0.10 and 0.21. Wage gains are broadly observed across occupational tasks, with high heterogeneity: managers benefit disproportionately in publicly traded firms, whereas broader wage increases accrue to workers in non-traded firms. Our findings highlight the role of institutional features and firm organization in shaping how rents are shared. |
| Keywords: | firm performance, innovation, rent sharing, worker compensation |
| JEL: | D22 J31 O31 O34 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:iwhdps:341391 |
| By: | Krieger, Bastian; Prüfer, Malte; Strecke, Linus |
| Abstract: | Public procurement is widely regarded as an important instrument to foster innovation. We examine how additional award criteria beyond price relate to firms' realized innovation performance by combining representative firm-level data from the German Innovation Survey with official tender-level data from Tenders Electronic Daily and estimating two-way fixed effects models. The results suggest that winning price-based tenders is associated with lower product and service innovation and higher turnover from established products and services, while criteria-based procurement shows an inverse U-shaped relationship between the average length of criteria lists in won tenders and firms' innovation outcomes. Overall, the findings indicate that the innovation effects of public procurement depend not only on whether additional award criteria are used, but also on how extensively they are applied. |
| Keywords: | Public procurement, Firm innovation, Demand side |
| JEL: | O31 O32 O38 H57 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:zewdip:341408 |
| By: | Bertolotti, Fabio; Lanteri, Andrea; Yoon, Hyeonsik |
| Abstract: | We analyze optimal subsidies for the replacement of durable assets in a model with heterogeneous producers, endogenous capital-embodied innovation, and environmental externalities that depend on capital vintages. We characterize the constrained-efficient allocation assuming a planner chooses capital replacement subject to the equilibrium evolution of innovation. Optimal subsidies equal the sum of two terms: (i) the difference in present discounted value of damages associated with old vs. new capital and (ii) the social value of innovation induced by capital replacement, net of the associated markup distortion. We generalize this formula to the case of new technologies, such as electric vehicles. We calibrate the model using empirical evidence on several types of capital, including aircraft and vehicles, and simulate the optimal transition. Initially, optimal subsidies are steeply increasing in the age of the replaced asset. In the long run, they are determined by the trade-off between innovation and markups. |
| Keywords: | Optimal policy; Environmental externalities; Innovation |
| JEL: | O44 O33 Q55 E22 |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:21578 |
| By: | Maryam Farboodi; Andrew J. Koh; Anchi Xia |
| Abstract: | We build a dynamic model of data-driven automation in which data (i) is heterogeneous and task-specific; (ii) accumulates endogenously as a byproduct of economic activity; and (iii) exhibits spillovers such that data generated by one task can augment the productivity of another. Along the transition path of automation, data plays a dual role in simultaneously augmenting the productivity of already-automated tasks and expanding the automation frontier. We derive tight conditions for the economy to be partially versus fully automated in the long-run. In the latter case, automation exhibits rich short-run dynamics that depend on the pattern of data spillovers but is always slow in the long-run: the share of tasks produced by labor decays asymptotically as a power law in time. We show that the economy is generically inefficient and analyze how a planner optimally tilts the direction of data accumulation. With endogenous capital accumulation, data-driven automation generates explosive growth but stagnant long-run wages. |
| JEL: | J31 O33 O4 |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35320 |
| By: | Baumann, Ursel; Cullen, Zöe; Faia, Ester; Ferrando, Annalisa; Perez-Truglia, Ricardo; Rariga, Judith |
| Abstract: | How well does innovation diffuse across geographic boundaries? To shed light on this question, we present a large-scale field experiment involving 3, 300 firms across twelve European Union (EU) countries. We elicit firms' perceptions of the share of similar firms in their own country that had invested in AI, as well as the corresponding share among similar firms in the three largest EU economies. We randomly provide half of the sample with accurate information about both domestic and foreign AI investment. We show that firms substantially underestimate competitors' current AI investment, both domestically and abroad, and that they update their expectations about competitors' future adoption in response to the information treatment. The treatment also causes a statistically significant increase in firms' own expected AI investment rate (p-value |
| Keywords: | Innovation |
| JEL: | O33 D22 C93 L21 |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:21601 |
| By: | Allison Leanage; Tahsin Mehdi |
| Abstract: | Artificial intelligence (AI) and automation hold the potential to transform the nature of work, raising concerns about how different occupations may be affected (Frenette and Frank, 2020; Mehdi and Morissette, 2024; Mehdi and Frenette, 2024). Although often used interchangeably, AI and automation represent different concepts: AI encompasses technologies capable of performing complex, non-routine and cognitive tasks, whereas automation refers to systems or machines designed to perform simple, routine and non-cognitive tasks. Recent estimates suggested that approximately 60% of employees in Canada may be highly exposed to AI-related job transformations, with AI complementing rather than replacing the work of about half of these individuals (Mehdi and Morissette, 2024). By contrast, about 1 in 10 workers may face a high likelihood (70% probability or greater) of automation-related job transformation (Frenette and Frank, 2020). However, these rates may vary substantially across occupations. |
| Keywords: | potential occupational, artificial intelligence, certified journeypersons |
| JEL: | J23 M21 |
| Date: | 2026–01–28 |
| URL: | https://d.repec.org/n?u=RePEc:stc:stcp8e:202600100001e |
| By: | Krzywdzinski, Martin |
| Abstract: | This paper investigates how software developers perceive the current and future automation of their work in the context of rapidly advancing generative and agentic AI. While existing research has primarily focused on productivity effects of specific AI coding tools in experimental settings, less is known about the broader organization of software-development work, the limits of automation, and developers' own expectations regarding labor-market outcomes. The paper addresses four research questions: the current level of automation across software-development tasks and occupations; expectations regarding future automation and its drivers; structural limits to automation; and perceived implications for job security, employability, and income. The analysis draws on an original survey of 1, 731 software developers from eleven countries and six professional subgroups. The findings show that software development is currently characterized by moderate automation across all task domains, with humans still central to planning, coordination, and problem-solving. Respondents expect substantial increases in automation over the next five years, driven primarily by generative and agentic AI. However, the study also identifies important limits to automation: as automation increases, remaining tasks become less standardized, while problem-solving and collaboration demands persist. Finally, most developers remain cautiously optimistic about their labor-market prospects, although workers already operating in highly automated environments express significantly greater concerns about future job security. |
| Keywords: | automation, artificial intelligence, skills, work organization, software development, programming |
| JEL: | J22 J24 J44 L86 O33 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:wzbgwp:341630 |
| By: | Lepers, Robin; Krieger, Bastian; Pellens, Maikel; Prüfer, Malte |
| Abstract: | Policy makers increasingly recognize circular public procurement as a demand-pull instrument for stimulating the transition to a circular economy. However, empirical studies on circular public procurement have been hampered by a fundamental measurement challenge, as public procurement databases do not contain structured ways of identifying circular projects. This paper presents the first application of an LLM-based semantic similarity approach to identify circular procurement at scale. Adapting a bibliometric text-embedding approach from circular economy research, we show its application in comparing tender descriptions to a reference corpus of circular economy scientific abstracts, generating circularity scores for each award. We then apply the identified circular public procurement awards in an empirical study of firm-level adoption of circular economy innovation, matching the classified tenders to German data from the Community Innovation Survey. The results show that firms winning circular procurement are more likely to introduce circular economy innovation after three to five years, while no significant results are found at shorter or longer time horizons. Overall, this paper demonstrates the potential of using LLMs to identify circular public procurement and study its effectiveness in enabling the circular transition. |
| Keywords: | Circular economy, Innovation, Circular public procurement |
| JEL: | H57 O38 Q55 Q58 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:zewdip:341409 |
| By: | Lindenlaub, Ilse; Oh, Ryungha; Rodriguez, Maria Alejandra; Veldkamp, Laura |
| Abstract: | We document and explain the gap between measures of AI exposure and measures of AI adoption in the workplace. This leads us to propose a new AI adoption index based on comparative advantage. Using the representative German DiWaBe employee survey linked to worker and establishment information, we compare worker-reported AI use to prominent exposure measures and find that the relationship is weak. Motivated by this gap, we develop a framework in which adoption depends not only on technical feasibility—AI’s absolute advantage measured by exposure—but also on profitability—AI’s comparative (dis)advantage relative to a specific worker—balancing AI productivity against AI user costs and worker productivity against wages. We operationalize this framework at the task level by (i) estimating worker productivity relative to pay, (ii) mapping exposure indices into AI productivity, and (iii) inferring task-specific AI user costs from revealed-preference adoption. The resulting occupation-level index accounts for 60% of the cross-occupation variation in observed AI adoption, compared with 14% for an exposure-only model. The two approaches diverge substantially for approximately 30% of workers, highlighting that comparative advantage—not exposure alone—is crucial for assessing AI’s labor-market impact. |
| Keywords: | Artificial intelligence; Comparative advantage; Technology diffusion; Worker productivity |
| JEL: | E24 D24 J24 O33 |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:21589 |
| By: | Dario Guarascio; Mario Pianta |
| Abstract: | The article examines the evolution of the current technological paradigm, based on digital technologies, considering the interaction between civilian and military trajectories, with a focus on the US case. Building on an original political economy framework, the activities of corporations and the industrial and technology policies of the US government are examined. The evolution of digital technologies and the rise of major US corporations, Alphabet, Amazon, Apple, Meta, Microsoft, is investigated, showing that their platform business model is characterised by monopoly power, management of Big Data and major capabilities of control, surveillance and targeting. A civilian trajectory, with large commercial markets and a novel reach in several areas of social activities, has dominated the rise of digital technologies. Its key characteristics, however, have become of major interest for military priorities. The analysis of recent US industrial and technology policies for the military shows that they have expanded the involvement of US digital corporations in arms and security programmes, developed large defense R&D projects in digital areas, and shaped a new convergence between civilian and military trajectories. The outcome we are facing is therefore the emergence of a digital-military-industrial complex, a major and problematic novelty in a digital age that had grown out of a civilian trajectory. |
| Keywords: | digital technologies, technological trajectories, military programs |
| JEL: | O30 O33 O38 |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:ter:wpaper:00185 |
| By: | Daron Acemoglu; A. Arda Gitmez; Mehdi Shadmehr |
| Abstract: | We consider a model of automation embedded in a political environment where workers can undertake a revolt (modeled as a global game), and greater inequality between capital and labor increases the likelihood of a revolt. Decentralized automation decisions raise the share of capital in national income and increase the likelihood of a successful revolt. A capitalist state (representing capital-owners) prefers to regulate the level of automation to lessen the threat of a successful revolt. The capitalist state can also redistribute to workers via the tax system or repress political action, thus creating greater room for further automation. We characterize the trade-off between the regulation of automation, redistribution and repression. Our main result is a complementarity between automation and repression. Unless the threat of revolt is quite weak or the capital stock is very low, the capitalist state prefers repression. A higher capital stock in turn encourages more automation and thus more repression. In our full dynamic model with capital accumulation, in the long run the economy tends to repression (again unless the threat of revolt is very weak). We also prove that the same conclusions apply when firms can additionally invest in new labor-intensive tasks. Finally, we show that, starting in a democracy, capital accumulation and thus greater automation encourages the capitalists to support a coup against democracy and set up a repressive system. |
| JEL: | J23 O33 P10 P16 |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35336 |
| By: | Xiliu He; Haoxiang Zhao; Mingyi Ma; Edward Wen Chuan Lai; Koei Enomoto; Anni Hu; Jiatong Li; Lingyun Chu; Yuan Lai |
| Abstract: | Generative artificial intelligence (GenAI) is the first automation wave to reach high-cognitive tasks at scale, yet its effects on intra-urban inequality remain largely unknown. Using 5 million job postings from Beijing (2018--2024), we construct a neighborhood-level GenAI Exposure Index by aggregating task-level assessments from five leading large language models. We examine the spatial, structural and causal mechanisms of this shock. We find that GenAI exposure is highly concentrated in the city's core districts, deepening the intra-urban AI divide. Since 2023, high-exposure neighborhoods have experienced wage stagnation even as they continue to attract high-skilled workers -- a "high-skill trap." This wage penalty is driven by task de-skilling and intensified labor-market crowding. A difference-in-differences design centered on ChatGPT's release supports a causal interpretation. These findings challenge the prevailing theory of skill-biased technological change and provide a basis for inclusive AI governance in global technology hubs. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.25505 |