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on Technology and Industrial Dynamics |
| By: | Aniket Baksy; Daniel Chandler; Peter Lambert |
| Abstract: | Using a novel proprietary survey of UK manufacturing sites, we study the impact on employment of arguably the two most important industrial automation technologies of the past fifty years: computer numerical control (CNC) machine tools and industrial robots. First, we document the growing prevalence of both technologies across a wide range of industries between 2005 and 2023. Second, we use a local-projection difference-in-difference design to show that plants that adopt these technologies for the first time increase their employment by 6% to 9% compared to non-adopting plants in the same industry. Third, we find that for both technologies, automation is associated with an increase in employment among industry-competitor sites, and a positive overall impact on industry-level employment. |
| Keywords: | Automation, Manufacturing, Employment, Technology Adoption, Robots |
| Date: | 2025–10–24 |
| URL: | https://d.repec.org/n?u=RePEc:cep:cepdps:dp2131 |
| By: | Siavash Mohades; Maria Savona |
| Abstract: | This paper investigates whether investments in data affect firms’ R&D and whether the two are productivity-enhancing complements. We conceptualise and test whether investments in data reduce market uncertainty, thereby mitigating the inherent uncertainty of R&D and enhancing research and innovation investment. Using Italian firm-level data from 2002 to 2024 and exploiting the GDPR as an instrument, we identify a positive causal effect of data on R&D investment. Moreover, we find that data and R&D are complementary in enhancing both short- and long-term productivity. Our analyses also identify a positive role of R&D for productivity only when firms are data-intensive. |
| Keywords: | uncertainty, data, R&D, digitalisation, innovation, productivity |
| JEL: | D22 D25 D82 O31 O33 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12230 |
| By: | Loles Añón Higón (Department of Applied Economics II and ERICES, Faculty of Economics (Universitat de València), Avda. Tarongers, s/n, 46022 Valencia (Spain)); Juan A. Máñez (Department of Applied Economics II and ERICES, Faculty of Economics (Universitat de València), Avda. Tarongers, s/n, 46022 Valencia (Spain)); Amparo Sanchis (Department of Applied Economics II and ERICES, Faculty of Economics (Universitat de València), Avda. Tarongers, s/n, 46022 Valencia (Spain)); Juan A. Sanchis (Department of Applied Economics II and ERICES, Faculty of Economics (Universitat de València), Avda. Tarongers, s/n, 46022 Valencia (Spain)) |
| Abstract: | We examine the role of digitalisation in shaping innovation strategies. To capture the multidimensional nature of digital transformation, we construct a firm-level digitalisation index that incorporates four dimensions: technological infrastructure, digital human capital, automation and digital stakeholders’ interactions. Using data from Spanish manufacturing firms for the period 2007-2022, we assess the effects of digitalisation on both technological innovation (product and process) and non-technological (organisational and marketing) innovation. Our empirical strategy is based on a knowledge production function framework that jointly analyses firms' innovation decisions while accounting for unobserved heterogeneity and potential endogeneity of digitalisation. The results show that digitalisation is a key driver of innovation, in particular for SMEs, but also for firms without formal R&D activities. However, its impact varies across innovation types, with the strongest effects observed for process innovation. The analysis further reveals that the components of digitalisation affect innovation strategies in different ways, underscoring the heterogeneous nature of digital transformation. |
| Keywords: | Digital transformation, manufacturing firms, product innovation, process innovation, organisational innovation, marketing innovation. |
| JEL: | O33 O32 L60 C35 D22 |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:eec:wpaper:2512 |
| By: | Seth Benzell; Kyle Myers |
| Abstract: | An increasingly large number of experiments study the labor productivity effects of automation technologies such as generative algorithms. A popular question in these experiments relates to inequality: does the technology increase output more for high- or low-skill workers? The answer is often used to anticipate the distributional effects of the technology as it continues to improve. In this paper, we formalize the theoretical content of this empirical test, focusing on automation experiments as commonly designed. Worker-level output depends on a task-level production function, and workers are heterogeneous in their task-level skills. Workers perform a task themselves, or they delegate it to the automation technology. The inequality effect of improved automation depends on the interaction of two factors: ($i$) the correlation in task-level skills across workers, and ($ii$) workers' skills relative to the technology's capability. Importantly, the sign of the inequality effect is often non-monotonic -- as technologies improve, inequality may decrease then increase, or vice versa. Finally, we use data and theory to highlight cases when skills are likely to be positively or negatively correlated. The model generally suggests that the diversity of automation technologies will play an important role in the evolution of inequality. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.24923 |
| By: | Lewandowski, Piotr (Institute for Structural Research (IBS)); Madoń, Karol (Institute for Structural Research (IBS)); Park, Albert (Hong Kong University of Science & Technology) |
| Abstract: | This paper develops a task-adjusted, country-specific measure of workers’ exposure to Artificial Intelligence (AI) across 108 countries. Building on Felten et al. (2021), we adapt the Artificial Intelligence Occupational Exposure (AIOE) index to worker-level PIAAC data and extend it globally using comparable surveys and regression-based predictions, covering about 89% of global employment. Accounting for country-specific task structures reveals substantial cross-country heterogeneity: workers in low-income countries exhibit AI exposure levels roughly 0.8 U.S. standard deviations below those in high-income countries, largely due to differences in within-occupation task content. Regression decompositions attribute most cross-country variation to ICT intensity and human capital. High-income countries employ the majority of workers in highly AI-exposed occupations, while low-income countries concentrate in less exposed ones. Using two PIAAC cycles, we document rising AI exposure in high-income countries, driven by shifts in within-occupation tasks rather than employment structure. |
| Keywords: | AI, occupations, job tasks, technology, skills |
| JEL: | J21 J23 J24 |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18235 |
| By: | Brüll, Eduard (ZEW); Mäurer, Samuel (University of Mannheim); Rostam-Afschar, Davud (University of Mannheim) |
| Abstract: | We provide experimental evidence on how employers adjust expectations to automation risk in high-skill, white-collar work. Using a randomized information intervention among tax advisors in Germany, we show that firms systematically underestimate automatability. Information provision raises risk perceptions, especially for routine-intensive roles. Yet, it leaves short-run hiring plans unchanged. Instead, updated beliefs increase productivity and financial expectations with minor wage adjustments, implying within-firm inequality like limited rent-sharing. Employers also anticipate new tasks in legal tech, compliance, and AI interaction, and report higher training and adoption intentions. |
| Keywords: | belief updating, firm expectations, technology adoption, innovation, technological change, automation, artificial intelligence, expertise, labor demand, white collar jobs, training |
| JEL: | J23 J24 D22 D84 O33 C93 |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18225 |
| By: | Rongjun Ao; Ling Zhong; Jing Chen; Xiaojing Li; Xiaoqi Zhou |
| Abstract: | While prior research has emphasized the path-dependent nature of occupational systems, it has paid limited attention to how local industrial structures contribute to occupational change. To address this gap, this study examines how regional occupational evolution is shaped by two distinct mechanisms: (1) path-dependent skill and knowledge transfer, whereby new occupations emerge through the recombination of existing occupational structures; and (2) industry-driven task reconfiguration, through which industrial upgrading reshapes the demand for occupations. To operationalize these dynamics, the concept of industry–occupation cross-relatedness is introduced, capturing the proximity between new occupations and a region’s existing industrial portfolio. Drawing on panel data from 241 Chinese cities between 2000 and 2015, the study estimates the effects of both occupational relatedness and cross-relatedness on new occupation specialization. The results reveal that both mechanisms significantly promote occupational evolution, yet they tend to function as substitutes rather than complements. Furthermore, their effects differ across skill levels: high-skilled occupations are more responsive to industrial transformation, low-skilled occupations to occupational pathways, while medium-skilled occupations exhibit relatively weak responsiveness to both. These findings underscore the importance of structural conditions and skill heterogeneity in shaping regional patterns of occupational change. |
| Keywords: | Occupational Evolution; Path Dependence; Chinese Cities; Industry-Occupation Cross-Relatedness; Skill Heterogeneity |
| JEL: | R11 O14 N95 |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:egu:wpaper:2533 |
| By: | Xiaoning Wang; Chun Feng; Tianshu Sun |
| Abstract: | Labor mobility is a critical source of technology acquisition for firms. This paper examines how artificial intelligence (AI) knowledge is disseminated across firms through labor mobility and identifies the organizational conditions that facilitate productive spillovers. Using a comprehensive dataset of over 460 million job records from Revelio Labs (2010 to 2023), we construct an inter-firm mobility network of AI workers among over 16, 000 U.S. companies. Estimating a Cobb Douglas production function, we find that firms benefit substantially from the AI investments of other firms from which they hire AI talents, with productivity spillovers two to three times larger than those associated with traditional IT after accounting for labor scale. Importantly, these spillovers are contingent on organizational context: hiring from flatter and more lean startup method intensive firms generates significant productivity gains, whereas hiring from firms lacking these traits yields little benefit. Mechanism tests indicate that "flat and lean" organizations cultivate more versatile AI generalists who transfer richer knowledge across firms. These findings reveal that AI spillovers differ fundamentally from traditional IT spillovers: while IT spillovers primarily arise from scale and process standardization, AI spillovers critically depend on the experimental and integrative environments in which AI knowledge is produced. Together, these results underscore the importance of considering both labor mobility and organizational context in understanding the full impact of AI-driven productivity spillovers. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.02099 |
| By: | Peeyush Agarwal; Harsh Agarwal; Akshat Ranaa |
| Abstract: | Purpose: The rapid integration of artificial intelligence (AI) systems like ChatGPT, Claude AI, etc., has a deep impact on how work is done. Predicting how AI will reshape work requires understanding not just its capabilities, but how it is actually being adopted. This study investigates which intrinsic task characteristics drive users' decisions to delegate work to AI systems. Methodology: This study utilizes the Anthropic Economic Index dataset of four million Claude AI interactions mapped to O*NET tasks. We systematically scored each task across seven key dimensions: Routine, Cognitive, Social Intelligence, Creativity, Domain Knowledge, Complexity, and Decision Making using 35 parameters. We then employed multivariate techniques to identify latent task archetypes and analyzed their relationship with AI usage. Findings: Tasks requiring high creativity, complexity, and cognitive demand, but low routineness, attracted the most AI engagement. Furthermore, we identified three task archetypes: Dynamic Problem Solving, Procedural & Analytical Work, and Standardized Operational Tasks, demonstrating that AI applicability is best predicted by a combination of task characteristics, over individual factors. Our analysis revealed highly concentrated AI usage patterns, with just 5% of tasks accounting for 59% of all interactions. Originality: This research provides the first systematic evidence linking real-world generative AI usage to a comprehensive, multi-dimensional framework of intrinsic task characteristics. It introduces a data-driven classification of work archetypes that offers a new framework for analyzing the emerging human-AI division of labor. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.23669 |
| By: | Jacob Moscona |
| Abstract: | This paper investigates how innovation responded to and shaped the economic impact of the American Dust Bowl, an environmental catastrophe that led to widespread soil erosion on the US Plains during the 1930s. Combining data on county-level erosion, the historical geography of crop production, and crop-specific innovation, I document that in the wake of the environmental crisis, agricultural technology development was strongly and persistently re-directed toward more Dust Bowl-exposed crops and, within crops, toward bio-chemical and planting technologies that could directly mitigate economic losses from environmental distress. County-level exposure to Dust Bowl-induced innovation significantly dampened the effect of land erosion on agricultural land values and revenue. These results highlight the role of crises in spurring innovation and the importance of endogenous technological progress as an adaptive force in the face of disasters. |
| JEL: | O3 O31 O33 Q1 Q16 Q54 |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34438 |
| By: | Mariia Vasiakina (Max Planck Institute for Demographic Research, Rostock, Germany); Christian Dudel (Max Planck Institute for Demographic Research, Rostock, Germany) |
| Abstract: | The ongoing economic transformation driven by automation has significant social implications, particularly for the health and well-being of workers who face the risk of job displacement and the pressure to acquire new skills and qualifications. However, the specific pathways through which exposure to automation risk affects health outcomes remain poorly understood, and the relative contribution of each potential mechanism is still unclear. In this study, we examine the nature of the relationship between high workplace exposure to automation risk and a range of subjective health outcomes – including self-reported health, anxiety, and both physical and mental component summary scores from the SF-12 Health Survey – among workers in Germany. Using data from the German Socio-Economic Panel (SOEP) linked with administrative records from the Occupational Panel for Germany (2014–2022), we apply the Karlson-Holm-Breen (KHB) mediation analysis method to assess whether broader indicators of economic uncertainty, alongside automation-specific factors, mediate the relationship between high automation risk and workers’ health. Our results indicate that the negative impact of high automation risk on health in Germany primarily operates through indirect pathways (related to mediators) for both genders, with the exception of physical health among male workers, where a direct negative effect is also evident. Economic concerns – particularly job insecurity and worries about one’s future financial situation – emerge as more significant mediators than automation-specific factors. Overall, our findings suggest that the mechanisms linking high automation risk to health are gender- and context-sensitive, and are shaped by broader economic conditions and workplace environments. |
| Keywords: | Germany, automation, health, risk exposure, technological change |
| JEL: | J1 Z0 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:dem:wpaper:wp-2025-032 |
| By: | Tatsuru Kikuchi |
| Abstract: | This paper develops a dual-channel framework for analyzing technology diffusion that integrates spatial decay mechanisms from continuous functional analysis with network contagion dynamics from spectral graph theory. Building on our previous studies, which establish Navier-Stokes-based approaches to spatial treatment effects and financial network fragility, we demonstrate that technology adoption spreads simultaneously through both geographic proximity and supply chain connections. Using comprehensive data on six technologies adopted by 500 firms over 2010-2023, we document three key findings. First, technology adoption exhibits strong exponential geographic decay with spatial decay rate $\kappa \approx 0.043$ per kilometer, implying a spatial boundary of $d^* \approx 69$ kilometers beyond which spillovers are negligible (R-squared = 0.99). Second, supply chain connections create technology-specific networks whose algebraic connectivity ($\lambda_2$) increases 300-380 percent as adoption spreads, with correlation between $\lambda_2$ and adoption exceeding 0.95 across all technologies. Third, traditional difference-in-differences methods that ignore spatial and network structure exhibit 61 percent bias in estimated treatment effects. An event study around COVID-19 reveals that network fragility increased 24.5 percent post-shock, amplifying treatment effects through supply chain spillovers in a manner analogous to financial contagion documented in our recent study. Our framework provides micro-foundations for technology policy: interventions have spatial reach of 69 kilometers and network amplification factor of 10.8, requiring coordinated geographic and supply chain targeting for optimal effectiveness. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.24781 |
| By: | Stephen Ayerst; Duc Nguyen; Diego Restuccia |
| Abstract: | We examine micro and macro productivity differences across nations using cross-country firm-level data and a quantitative model of misallocation that integrates firms' operational (selection) and investment (technology) decisions. Empirically, less developed countries display greater distortions and wider dispersion in firm productivity, driven largely by the higher prevalence of low-productivity firms. Quantitatively, cross-country differences in measured distortions account for most of the observed micro-level patterns and over half of aggregate labor productivity gaps. Both selection and technology channels are essential to matching the data, while static misallocation also plays an important role, albeit smaller. |
| Keywords: | Firms, productivity, size, distortions, misallocation, selection, technology. |
| JEL: | O11 O14 O4 |
| Date: | 2025–10–30 |
| URL: | https://d.repec.org/n?u=RePEc:tor:tecipa:tecipa-806 |
| By: | Mahabubur Rahman (ESC [Rennes] - ESC Rennes School of Business); M Ángeles Rodríguez-Serrano (Universidad de Sevilla = University of Seville); Md Tareq Bin Hossain (TU - Thammasat University) |
| Abstract: | While prior studies broadly explored the consequences of environmental innovation, the implications of environmental product innovation for firm performance have received relatively scant research attention. Past studies theorizing that environmental product innovation has a linear effect on firm performance have reported mixed results, indicating that the association between the two is far more complex than conceptualized by earlier research. Drawing on the natural resource-based view of the firm and the resource dependence theory, this study theorizes that the impact of environmental product innovation on firm growth follows a curvilinear (inverted Ushaped) pattern. It is also posited that this curvilinear relationship is moderated by marketing intensity, sustainability disclosure strategy and a firm's propensity to engage in deviant corporate practices. Using a sample of U.S.-based firms and employing an endogeneity-robust econometric modelling technique, this study demonstrates that the effect of environmental product innovation on firm growth is initially positive but subsequently becomes negative. Further, this research shows that this curvilinear relationship between environmental product innovation and firm growth is moderated by a firm's sustainability disclosure strategy (the curve flattens), marketing intensity (the curve flattens) and by a firm's level of engagement in deviant corporate practices (the curve steepens). The results are robust to additional sensitivity analyses. |
| Keywords: | Sustainability disclosure, Deviant corporate practices, Marketing intensity, Firm growth, Environmental product innovation |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05280178 |
| By: | Jonathan Colmer; Eva Lyubich; John Voorheis |
| Abstract: | The transition to clean energy represents a fundamental and important shift in economic activity. We present new facts about workers in clean and legacy energy sectors between 2005 and 2019 using linked, administrative employer-employee data for all W-2 workers in the United States. We show that both clean and legacy energy establishments hire a disproportionate share of non-Hispanic White and male workers compared to the working population, that workers rarely move from legacy to clean firms, and that, conditional on education, workers do not earn more in clean firms than in legacy firms. The occupational categories of jobs at clean firms differ notably from occupations at legacy firms and, on average, tend to be performed by workers with higher levels of education. Regional overlap in employment opportunities is not sufficient to facilitate worker transitions from legacy to clean firms. Substantially lower earnings outside of the energy sector combined with low mobility between legacy and clean firms suggests that the costs of the clean transition on workers in legacy fossil fuel sectors may be substantial. At the same time workers moving into clean activities from outside of the energy sector experience significant increases in earnings and greater job stability, suggesting that clean jobs are "good jobs" for those who can access them. |
| Keywords: | Clean energy transition, earnings, employment, transitional costs, mobility |
| Date: | 2025–10–09 |
| URL: | https://d.repec.org/n?u=RePEc:cep:cepdps:dp2127 |