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on Technology and Industrial Dynamics |
| By: | Risi, Gianluca |
| Abstract: | This paper investigates the impact of digitalization and AI on wage inequality both between and within task-based groups of workers across Italian provinces. We contribute along two aspects: building a novel vertical categorization of occupational task dimensions designed to capture the distinct labor market implications of traditional digitalization and AI, and constructing the AI Occupational Catch-Up Index (AI-OCUI), the first empirical operationalization of the OECD AI Capability Indicators framework. Using a panel regression model for Italian NUTS3 regions over 2015-2018, we find that neither technology affects between-group inequality, while traditional digitalization reduces wage dispersion within the cognitive group and AI exposure compresses inequality within the non-routine group - a differentiation consistent with the distinct task profiles targeted by each technological wave. Both effects are attenuated in cities, where agglomeration dynamics moderates the equalizing potential of digital technologies. These findings contribute to the growing literature on the wage implications of digital transformation, with relevant implications for policy. |
| Keywords: | Digitalization, Artificial Intelligence, AI Capability Indicators, Task-based framework, Wage inequality, Italian provinces |
| JEL: | J24 O33 R11 R23 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:esprep:340911 |
| By: | Qiu, Xincheng; Yoshida, Masahiro |
| Abstract: | We study the impact of climate change on the labor share. Combining newly constructed US county-industry-level labor shares with climate variables, we find that extreme temperatures reduce the labor share, with stronger effects in industries with higher climate exposure and automation potential. Extreme temperatures also accelerate robot adoption. A back-of-the-envelope calculation suggests that the within-county-industry response to climate change accounts for 15% of the decline in labor share since 2000. Over the 20th century, however, the opposing effects of decreased cold days and increased hot days offset each other, consistent with the historical stability of labor share. |
| Keywords: | climate change, labor share, automation |
| JEL: | E25 Q54 O33 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:glodps:1764 |
| By: | Ouakil, Hicham; Liouaeddine, Mariem; Hosni, Mohamed; Saadi, Ayoub |
| Abstract: | The allocation and effectiveness of public subsidies for R&D and innovation are crucial issues for firms and policymakers. This study has two main objectives: first, to identify the determinants of access to public funding for R&D and innovation within firms; second, to quantitatively assess the causal impact of this support on firms’ R&D and innovation activities. We used data from the 2019 World Bank survey of 1, 096 Moroccan firms (www. enterprise surveys and applied two econometric approaches. For the first objective, we resorted to logistic regression based on a probit model. The results show that competitive firms, those investing in ICT, and those that employ graduates are more likely to receive public financial support. For the second objective, we used Propensity Score Matching (PSM) to control for selection bias and endogeneity. The results show that government financial support significantly favors the innovation inputs and outputs of Moroccan firms. |
| Keywords: | Public Support, Firm R&D and Innovation, Impact Assessment, Probit Model, Propensity Score Matching (PSM). |
| JEL: | D2 D22 O3 |
| Date: | 2026–01–30 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:128707 |
| By: | Leanne Cass (UK Health Security Agency); Federico Fabio Frattini (Fondazione Eni Enrico Mattei); Aurélien Saussay (London School of Economics and OFCE Sciences-Po); Misato Sato (London School of Economics); Francesco Vona (University of Milan and Fondazione Eni Enrico Mattei) |
| Abstract: | There is growing evidence that green jobs have higher skill requirements, but whether they offer sufficient wage incentives to encourage workers to acquire those skills remains unclear. We study the green wage premium and its drivers to isolate the average return to green tasks using online job vacancy (OJV) data for EU countries over the period 2018-2023. We develop a transparent LLM-based approach to classify job vacancies as green when they list at least one green task. Green jobs pay a premium of 5.5% relative to comparable postings within the same occupation, and this estimate is little changed when controlling for nonmonetary job attributes making these jobs more attractive. Roughly half of this premium is explained by firm fixed effects, consistent with an important role for firm rents. An Oaxaca-Blinder decomposition shows that the higher skill complexity explains a further one tenth of the premium, leaving a residual return to green tasks of around 2%. The green wage premium is higher outside the manufacturing sector, and for low-carbon roles. |
| Keywords: | Green wage premium, Skill gaps, Green tasks, LLM |
| JEL: | J24 J6 F64 |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:fem:femwpa:2026.17 |
| By: | Ursel Baumann; Zoë B. Cullen; Ester Faia; Annalisa Ferrando; Ricardo Perez-Truglia; Judit Rariga |
| 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 countries. We elicit firms' perceptions of the share of similar firms in their own country that had invested in artificial intelligence (AI), as well as the corresponding share among similar firms in Germany, France, and Italy. 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 AI investment in response to the information treatment. The treatment also causes a statistically significant increase in firms' own expected AI investment rate (p-value |
| JEL: | C93 D22 L21 O33 |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35314 |
| By: | Tugrul Temel, Tugrul |
| Abstract: | This paper develops a co-evolutionary network model to analyze how micro-level interactions among heterogeneous organizations generate macro-level structural patterns and performance outcomes in innovation systems. Organizations possess knowledge stocks, absorptive and distributive capacities, and adaptively rewire their connections. We integrate six key mechanisms---capacity-constrained knowledge flows, endogenous capacity accumulation, resource-based collaboration costs, innovation as a growth-structure interaction, strategic repositioning, and adaptive network rewiring---into a formal simulation framework. The model is calibrated using Approximate Bayesian Computation to match stylized facts from the innovation literature and employed in a structured scenario analysis spanning alternative policy-relevant regimes. Results reveal systematic trade-offs with important policy implications. Expanding connectivity without parallel capacity development yields limited gains; isolated capacity building amplifies inequality. In contrast, coordinated interventions targeting both network structure and organizational capabilities produce the most robust and equitable growth. Comparative analysis across four distinct economic environments demonstrates that intervention effectiveness is highly contingent on underlying frictions. The findings underscore the need for innovation policy that is explicitly network-aware and systemic, emphasizing bundled, context-sensitive interventions rather than isolated levers. The model provides a computational laboratory for exploring such policy design principles. |
| Keywords: | innovation systems; policy design; network analysis; graph-theoretic concepts; |
| JEL: | O31 O32 O38 |
| Date: | 2026–02–11 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:128018 |
| By: | Kopytov, Alexandr; Taschereau-Dumouchel, Mathieu; Xu, Zebang |
| Abstract: | We develop a general equilibrium model in which firms choose how scalable their production technologies are. More scalable technologies make it easier for firms to expand output but are less effective at small scale. In equilibrium, more productive firms adopt more scalable technologies and grow disproportionately large. As a result, the tail of the size distribution becomes thicker and, as resources reallocate to the most productive producers, GDP increases. Over the long-run, as aggregate productivity rises, firms adopt more scalable technologies, which lowers input prices, leading to further increases in scalability. Through this supply-chain amplification process, endogenous returns to scale raise the growth rate of GDP. A calibrated version of the model shows that these effects are quantitatively significant. We also document support for the model's predictions in firm-level data. |
| Keywords: | returns to scale, scalability, technology |
| JEL: | E23 D24 D57 O40 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:esprep:341038 |
| By: | Robert J. Gordon; Kenneth Ryu |
| Abstract: | Why did U.S. manufacturing productivity stop growing after 2010? Productivity growth disappeared, evaporating from an annual rate of +3.3 percent during 1987-2010 to -0.3 percent from 2010 to 2023. This paper shifts attention from 2010 as the start of the puzzle to a decade earlier when output stopped growing. This cessation of output growth in 2000 is attributed to the invasion of imports that closed domestic plants, destroyed jobs, and squeezed profits. Then followed a chain of causation that ultimately undermined productivity growth – from falling capacity utilization, to lower investment in fixed capital and R&D, and to an erosion of innovation. Beyond the import invasion, the paper identifies a set of handicaps ranging from self-inflicted wounds by private manufacturing firms to a marked reduction in government-funded R&D spending. Corporate funds were diverted from productive investment to share buybacks. Investment was distorted by environmental, health, safety, and fuel economy regulations. Innovation slowed not only because of diminishing returns to R&D, but also because of a decline in public R&D, and a diversion of private R&D from basic science and process improvements to product refinements and brand extensions. Skilled worker shortages have plagued manufacturing for decades in the absence of sufficient public and private investment in vocational training. |
| JEL: | O3 O4 |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35285 |
| By: | Mert Demirer; Leon Musolff; Liyuan Yang |
| Abstract: | How do the productivity effects of AI evolve across successive generations of tools, and to what extent do task-level gains ultimately translate into final output? We study these questions in the context of software development, using data on more than 100, 000 GitHub developers combined with their AI usage telemetry. In a matched event study design, we find that autocomplete, interactive coding agents, and autonomous coding agents each significantly increase coding activity (“commits”), with respective cumulative effects of 40%, 140%, and 180%. These gains, however, attenuate sharply across the production hierarchy: the 180% cumulative effect falls to 50% for the number of projects, and to 30% for actual releases. This pattern is consistent with the weak-link hypothesis: the strong productivity gains from AI are attenuated by human bottlenecks in the production chain, with an estimated elasticity of substitution of 0.25 between AI and human effort, which indicates strong complementarities. We further confirm these results across four major app marketplaces, finding a moderate increase in the number of new apps but no increase in total usage. Large task-level AI productivity gains have therefore translated only partially into shipped and used software thus far. |
| JEL: | D24 L86 O33 |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35275 |
| By: | Liao, Han-Teng; Ang, Karen |
| Abstract: | Global Business Services (GBS) have emerged as a "living laboratory" for the Twin Transition of Green and Digital Transformation, as multinational corporations (MNCs) face increasing pressure to harmonize digital efficiency with environmental stewardship. Aiming to derive a socio-technical framework, this paper synthesizes Technology Roadmapping (TRM) with the International Telecommunication Union (ITU) ICT-centric innovation ecosystem toolkit. A bibliometric analysis of research clusters reveals an evolutionary shift from basic process automation toward "Sustainable Intelligence, " identifying the GBS unit as a central "operational airlock" that mediates between landscape pressures—such as the EU’s dual mandate and Carbon Border Adjustment Mechanisms—and niche innovations in AI-native workflows. The study further maps these clusters onto a stakeholder engagement canvas, highlighting how resilient "Middle Power" hubs in Poland, Portugal, and Malaysia are bypassing the middle-income trap to provide a "third way" for global value chains amidst a bifurcated geopolitical cloud. The results offer a data-driven design approach for leaders and entrepreneurial support networks to orchestrate talent and supply chain flows, thereby enriching the conceptual understanding of Industry 5.0 and the role of GBS as a primary mechanism for navigating a volatile, multipolar digital economy. |
| Date: | 2026–06–05 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:ktcxd_v1 |
| By: | Ilse Lindenlaub; Ryungha Oh; Maria Alejandra Rodriguez; Laura Veldkamp |
| 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 (i.e., AI’s absolute advantage measured by exposure) but on profitability (i.e., 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 almost 60% of cross-occupation variation in observed AI adoption, compared to 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. |
| JEL: | E0 J2 J3 |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35271 |
| By: | Erik Bengtsson (Lund University); Jakob Molinder (Uppsala University); Svante Prado (University of Gothenburg) |
| Abstract: | We study the relationship between the structural transformation of the economy and the income distribution, focusing on the case of Sweden from 1870 to 1970, with extra attention paid to the 1870–1950 period, for which we produce extensive new data. Average income increased fivefold between 1870 and 1950, and the share employed in agriculture declined from 72 to 23 per cent. To study the evolution of the income distribution, we collected new data, including 232, 000 individual income tax returns, 13, 000 property tax returns, and a rich set of complementary sources. Using these micro data, we calculate Gini coefficients, top income shares, capital shares, skill premia, and occupation- and gender-specific income levels and ratios, providing new evidence on the long-run evolution of income and inequality. Our income data and decomposition analyses demonstrate that the movement out of agriculture, which was a severely unequal sector in Sweden in the late nineteenth and early twentieth centuries, accounts for much of the decline in income inequality, together with the expansion of more productive jobs in manufacturing and offices. This process was aided by the migration of labour out of agriculture, as well as by educational and other policies that facilitated structural transformation. Focusing on structural transformation can help explain two paradoxes in the literature on twentieth century income inequality: that much equalisation occurred before the growth of the welfare state, and that non-belligerents in the World Wars, like Sweden, saw similar levels of equalisation as belligerent countries. |
| Keywords: | incomes, inequality, Sweden, structural transformation, economic history |
| JEL: | D31 J30 N30 O52 |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:hes:wpaper:0305 |