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on Technology and Industrial Dynamics |
| By: | Kikuchi, Tatsuru |
| Abstract: | This paper investigates how executive demographics—particularly age and gender—influence artificial intelligence (AI) investment decisions and subsequent firm productivity using comprehensive data from over 500 Japanese enterprises spanning 2018-2023. Our central research question addresses the role of executive characteristics in technology adoption, finding that CEO age and technical background significantly predict AI investment propensity. Employing these demographic characteristics as instrumental variables to address endogeneity concerns, we identify a statistically significant 2.4\% increase in total factor productivity attributable to AI investment adoption. Our novel mechanism decomposition framework reveals that productivity gains operate through three distinct channels: cost reduction (40\% of total effect), revenue enhancement (35\%), and innovation acceleration (25\%). The results demonstrate that younger executives (below 50 years) are 23\% more likely to adopt AI technologies, while firm size significantly moderates this relationship. Aggregate projections suggest potential GDP impacts of ¥1.15 trillion from widespread AI adoption across the Japanese economy. These findings provide crucial empirical guidance for understanding the human factors driving digital transformation and inform both corporate governance and public policy regarding AI investment incentives. |
| Keywords: | Artificial Intelligence, Executive Demographics, Technology Adoption, Productivity, Digital Transformation |
| JEL: | D24 L25 M12 O33 O47 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:126734 |
| By: | Hellsten, Mark; Khanna, Shantanu; Lodefalk, Magnus; Yakymovych, Yaroslav |
| Abstract: | Artificial intelligence (AI) is expected to reshape labor markets, yet causal evidence remains scarce. We exploit a novel Swedish subsidy program that encouraged small and mid-sized firms to adopt AI. Using a synthetic difference-in-differences design comparing awarded and non-awarded firms, we find that AI subsidies led to a sustained increase in job postings over five years, but with no statistically detectable change in employment. This pattern reflects hiring signals concentrated in AI occupations and white-collar roles. Our findings align with task-based models of automation, in which AI adoption reconfigures work and spurs demand for new skills, but hiring frictions and the need for complementary investments delay workforce expansion. |
| Keywords: | Artificial intelligence, Labor markets, Hiring, Task content, Technological change |
| JEL: | J23 J24 O33 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:glodps:1692 |
| 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:2534 |
| By: | G. Jacob Blackwood; Cindy Cunningham; Matthew Dey; Lucia Foster; Cheryl Grim; John Haltiwanger; Rachel Nesbit; Sabrina Wulff Pabilonia; Jay Stewart; Cody Tuttle; Zoltan Wolf |
| Abstract: | We present new empirical evidence suggesting that we can better understand productivity dispersion across businesses by accounting for differences in how tasks, skills, and occupations are organized. This aligns with growing attention to the task content of production. We link establishment-level data from the Bureau of Labor Statistics Occupational Employment and Wage Statistics survey with productivity data from the Census Bureau’s manufacturing surveys. Our analysis reveals strong relationships between establishment productivity and task, skill, and occupation inputs. These relationships are highly nonlinear and vary by industry. When we account for these patterns, we can explain a substantial share of productivity dispersion across establishments. |
| Keywords: | productivity dispersion, tasks, skills, occupations |
| JEL: | D24 J24 |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:cen:wpaper:25-63 |
| By: | Salvatore Viola (AQR-IREA, University of Barcelona); Ernest Miguelez (AQR-IREA, University of Barcelona); Rosina Moreno (AQR-IREA, University of Barcelona); Davide Consoli (Universitat Politècnica de València - CSIC-UPV); François Perruchas (Universitat Politècnica de València) |
| Abstract: | One important factor in addressing climate change is the development and deployment of environmental-related, or green, technologies (GT). Environmental-related technologies are distinct, requiring specific conditions to be developed which vary depending on their relative level of technological maturity. Recent studies have focused on the role of migrant inventors in creating these conditions and spurring regional diversification into new technological domains. Regional diversification helps regions avoid lock-in and even escape fossil fuel dependencies. While the contribution of migrants to science and innovation is well documented, less attention has been given to migrants and diversification, especially in the case of GT and along the technological life cycle. In this study, we investigate the role of US-based migrant inventors in regional GT diversification using patent data from the USPTO between the year 1990 and 2012. We find that migrant inventors are positively associated with regional GT diversification, partly as a result of their previous patenting experience as well as the specializations of their countries of origin. With regard to the technological life cycle, while geographically diffused technologies rely on corresponding inventor experience, emergent technological diversification benefits from inventors from specialized countries. These findings highlight the bridging role that migrant inventors in international knowledge transfer and their importance in regional diversification in particular environmental-related technologies. |
| Keywords: | Regional Diversification; Green Technology; Immigration; Technological Life Cycle JEL classification:O33; Q55; J61; R11 |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:aqr:wpaper:202508 |
| By: | Kikuchi, Tatsuru |
| 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 \citet{kikuchi2024navier} and \citet{kikuchi2024dynamical}, 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 \citet{kikuchi2024dynamical}. 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. |
| Keywords: | Technology diffusion, Supply chain networks, Spatial treatment effects, Network contagion, Navier-Stokes dynamics, Spectral graph theory |
| JEL: | C31 D85 L14 O33 R11 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:126724 |
| By: | Lisa Keding (RWTH University); Marten C. Ritterrath (University of Cologne) |
| Abstract: | We show that personal experiences affect high-stakes economic decisions among inventors. Using matched patent and survey data from French and German inventors linked to natural disaster records, we exploit exogenous variation in disaster exposure. Inventors personally affected by natural disasters subsequently produce 8.2% more green patents, primarily driven by emission-reducing mitigation technologies, while non-green innovation remains unaffected. The absence of sizable spatial spillovers highlights the importance of personal experience. Disaster exposure shapes innovation choices by altering profitability expectations through shifting higher-order beliefs about consumer demand and anticipated regulation. Embedding this channel in a formal model, we disentangle the role of expectations and intrinsic motivation. The model predicts, and the data confirm, that effects are strongest in competitive markets, where profit incentives matter most. |
| Keywords: | Inventors, Personal Experiences, Green Innovation, Expectation Formation, Natural Disasters |
| JEL: | D9 D84 O31 O34 Q54 Q55 |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:ajk:ajkdps:380 |
| By: | Caiza-Guamán, Pamela; García-Suaza, Andrés; Sepúlveda Rico, Carlos |
| Abstract: | The green transition is expected to be one of the most significant forces shaping labor markets in the incoming years. As economies shift toward cleaner technologies, green jobs will expand, while employment in high-emission sectors will either decline or move into other sectors, depending on skill transferability and policy design. In this context, the ability of workers to transition between green and non-green jobs will be crucial to ensure a just labor market adjustment. Labor transitions into and out of green jobs remain understudied, particularly in developing economies where data constraints limit empirical analysis. This paper addresses this gap, using household survey data and a synthetic panel approach to estimate the probability of labor transitions employs a skills-based green index. The results reveal a high degree of labor market persistence, explained by the role of skills in shaping mobility, and show a wage premium of 10.6% for green occupations compared to their non-green counterparts. These findings have important policy implications for ensuring a just energy transition. Given the observed rigidities in green labor mobility, targeted upskilling and reskilling programs are important to enabling non-green workers to acquire the necessary skills for green jobs. |
| Keywords: | Green jobs, labor mobility, wage inequality, just transition, informality |
| JEL: | J21 J24 Q52 J62 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:glodps:1693 |
| By: | Alessio MITRA (European Commission); Konstantinos NIAKAROS (European Commission) |
| Abstract: | This paper evaluates the causal impact of the Horizon 2020 Framework Programme for Research and Innovation on financial firm-level outcomes using a Difference-in-Differences (DiD) approach. We use administrative data from CORDA and financial data from ORBIS spanning from 2010 to 2022, for a sample of approximately 40 thousand unique private companies that applied for Horizon 2020 funding. The findings suggest that firms receiving Horizon 2020 grants exhibit an average increase of 20% in employment and about 30% in total assets and revenues, compared to comparable companies in the control group, in the years after receiving their first grant. Positive effects persist even after 2.5 years, which is the average duration of a project in our sample. Companies in the “Information and communication” and “Professional, scientific and technical activities” NACE sectors are driving the results, while other sectors show insignificant effects. |
| Keywords: | Research and Innovation funding, impact assessment, econometric methods, spillover effects, mediation analysis, policy evaluation |
| JEL: | O32 O38 C18 |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:eug:wpaper:ki-bd-23-010-en-n |
| By: | Marco Amendola; Francesco Ruggeri |
| Abstract: | This paper empirically examines the relationship between functional income distribution and labor productivity. In particular, it tests the hypothesis that a higher wage share promotes productivity growth by pushing firms to invest and innovate in order to preserve profit margins. Using panel data for OECD countries, the results provide strong support for this mechanism: increases in the wage share are associated with significantly higher labor productivity growth. The magnitude of the effect suggests that the contraction of wage shares in many advanced economies may explain an important part of their recent productivity slowdown. The analysis further shows that this positive link operates primarily through capital deepening, consistent with the view that wage pressures incentivize investment in laborsaving technologies. By contrast, no robust relationship is found between the wage share and Total Factor Productivity. |
| Keywords: | Labor productivity; Wage share; Productivity slowdown; Capital deepening; Induced technical change Jel Classification: C23 E25 D33 O30 |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:usi:wpaper:935 |
| By: | Silvia Leoni; Marco Catola |
| Abstract: | The debate on environmental policy increasingly focuses on aligning private incentives with social objectives in imperfectly competitive markets. While traditional literature has centred on public-based mechanisms like taxes and subsidies, a growing strand emphasizes private-based mechanisms, particularly green consumerism, where consumer preferences can drive firms’ adoption of clean technologies. Recent game-theoretic analysis shows that consumers’ willingness-to-pay can lead to various market equilibria, from all-green to all-brown outcomes. This paper complements this analytical approach by developing an agent-based model (ABM) to study the dynamic evolution of a spatial market where firms, based on relative performance, decide whether to supply brown or green products to heterogeneous consumers. Our computational simulations confirm that all three market structures—all-brown, all-green, and mixed—can endogenously emerge depending on average green consumer preferences. Furthermore, we evaluate the effectiveness of three policy instruments: an environmental tax, a subsidy to green firms, and a subsidy to green consumers. We find that supply-side policies are more effective than demand-side subsidies. Specifically, an environmental tax ensures the fastest convergence to an all-green market, while a production subsidy is most effective at reducing the share of brown firms and consumers in mixed-market scenarios. By bridging game-theoretic insights with agent-based computational analysis, this paper provides a dynamic and policy-relevant perspective on the transition to sustainable markets. |
| Keywords: | agent-based modelling, pollution abatement, green technology, environmental policy |
| JEL: | C63 D43 H23 L13 L51 |
| Date: | 2025–11–01 |
| URL: | https://d.repec.org/n?u=RePEc:pie:dsedps:2025/326 |