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on Technology and Industrial Dynamics |
| By: | Oleš, Tomáš; Roncone, Francesco |
| Abstract: | Does artificial intelligence automate work or augment workers' productive capabilities? Our results show that it can do both, through distinct channels that conventional exposure measures obscure. We distinguish these channels by linking U.S. patents to occupations' tasks and tools, with patent-task matches capturing automation and patent-tool matches capturing augmentation. Using the origins and diffusion of historical AI breakthroughs to predict contemporary local exposure, we find large but opposing labor-market effects. A one-standarddeviation increase in AI automation reduces occupational employment by 21 percent, with little effect on wages; AI augmentation instead raises wages by 8 percent, alongside some employment growth. We also find that a larger AI share within each channel strengthens labor displacement through automation while shifting augmentation's effects from employment growth toward wage gains. The labor-market consequences of AI therefore depend critically on the balance between automation and augmentation rather than overall exposure alone. |
| Keywords: | Artificial Intelligence, Automation, Augmentation, Local Labor Markets, Occupational Tasks, Occupational Tools |
| JEL: | J23 J24 J30 O33 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:glodps:1821 |
| By: | Çiğdem Ekiz; Eren Gürer; Erol Taymaz |
| Abstract: | We examine how innovation shapes firm-level workforce dynamics using rich administrative data from Türkiye, a developing economy characterized by medium-technology, incremental innovation. We find that patent-based innovation increases both firm size and average daily wages, with the largest employment gains among workers earning just above the median wage. To uncover the mechanisms behind these outcomes, we construct worker transition matrices. Relative to comparable non-innovative firms, innovative firms (i) retain incumbent workers at higher rates, particularly at the top of the wage distribution, (ii) promote retained workers into higher wage bins more frequently, and (iii) hire more new workers, disproportionately into higher wage bins. Finally, occupational composition analysis shows that employment growth is concentrated among technical and production occupations, consistent with the medium-technology, incremental nature of innovation in our sample. |
| Keywords: | innovation, patents, wages, employment, worker transitions |
| JEL: | J21 J31 O31 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12986 |
| By: | Andrea Gazzani (Bank of Italy); Filippo Natoli (Bank of Italy) |
| Abstract: | Using detailed data on the artificial intelligence (AI) content of U.S. patents from 1980 to 2019, we construct a novel monthly measure of AI intensity in innovation and identify AI related technology shocks. These shocks generate delayed increases in total factor productivity, output, employment, hours, and wages, alongside persistent declines in consumer prices, consistent with a positive supply shock. Although AI-intensive patents largely fall within the broader ICT and automation domains, they stand out as higher-quality innovations, attracting more citations and exhibiting greater technological and market value. AI shocks consistently have substantially larger aggregate effects than broader ICT or automation shocks, suggesting that AI has had a particularly high-impact role in the ICT revolution. Unlike general technology shocks, however, AI shocks reduce the labor share and increase wealth inequality, indicating that their gains are not distributed evenly across the economy. To connect these historical findings to the most recent wave of innovation, we develop a new time series of Generative-AI patents within the U.S. patent universe. A shock based exclusively on Gen-AI patents produces the same qualitative supply-side effects as broader AI shocks, linking the macroeconomic consequences of earlier AI innovation to those of the emerging Generative-AI era. |
| Keywords: | artificial intelligence, technology shocks, local projections, business cycle, patents |
| JEL: | E32 C32 C36 O33 O34 |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:bdi:wptemi:td_1542_26 |
| By: | Cigdem Ekiz (UNU-MERIT, Maastricht University , UNU-MERIT, Maastricht, the Netherlands); Eren Gurer (Department of Economics, Middle East Technical University, Ankara, Turkiye); Erol Taymaz (Department of Economics, Middle East Technical University, Ankara, Turkiye) |
| Abstract: | We examine how innovation shapes firm-level workforce dynamics using rich administrative data from Türkiye, a developing economy characterized by medium-technology, incremental innovation. We find that patent-based innovation increases both firm size and average daily wages, with the largest employment gains among workers earning just above the median wage. To uncover the mechanisms behind these outcomes, we construct worker transition matrices. Relative to comparable non-innovative firms, innovative firms (i) retain incumbent workers at higher rates, particularly at the top of the wage distribution, (ii) promote retained workers into higher wage bins more frequently, and (iii) hire more new workers, disproportionately into higher wage bins. Finally, occupational composition analysis shows that employment growth is concentrated among technical and production occupations, consistent with the medium-technology, incremental nature of innovation in our sample. |
| Keywords: | innovation, patents, wages, employment, worker transitions |
| JEL: | J21 J31 O31 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:met:wpaper:2604 |
| By: | Fabrizio Leone (Bank of Italy) |
| Abstract: | How does a domestic firm's production process change when it is taken over by a foreign multinational? Using a panel of Spanish manufacturing firms covering the 1990-2017 period, this paper shows that acquired firms become significantly more likely to adopt industrial robots and that this shift towards automation reduces their labour share. To isolate these effects from selection into foreign ownership and robot use, the analysis combines nearest-neighbour matching with a staggered event-study design. Following an acquisition, affiliates' labour share falls by 6.5 percentage points (15 per cent relative to the sample mean), while their probability of adopting robots rises by 11 percentage points (30 per cent relative to the sample mean); robot adoption alone accounts for roughly one third of the acquisition-induced labour share decline. These patterns are consistent with the predictions of a model in which heterogeneous firms choose both their ownership structure and whether to adopt robots. They also remain robust after controlling for alternative explanations for the decline in the labour share, including factor-biased technical change, intangible capital and market power. |
| Keywords: | foreign acquisitions, robots, automation, labour share |
| JEL: | F23 F66 O33 |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:bdi:wptemi:td_1539_26 |
| By: | Florian Ederer; Regina Seibel; Timothy Simcoe |
| Abstract: | This paper examines innovation outcomes before and after 1, 200 startup acquisitions by eight major technology firms. Linking patent and workforce data to these deals, we document four main findings. First, most acquired startups hold no patents, but those with patents tend to operate in technology areas where the acquirer already has a presence and that see further acquisition activity. Second, innovation typically rises before an acquisition, continues afterward only where further acquisitions follow, and falls back once acquisition activity ends. We propose a stylized model in which beliefs about commercial viability drive both startup entry and acquisitions to explain how these patterns arise through selection rather than effects of the deals. Third, acquired patents receive significantly more citations after the acquisition than comparable patents, not only from the acquirer but also from firms that hire the targets’ employees. Fourth, although 31% of employees and 23% of inventors depart within a year, inventors who stay go on to patent substantially more, while stayers and leavers were equally productive beforehand. In the aggregate, we find little evidence that acquisitions by digital incumbents suppress innovation, even though serial acquisitions go hand in hand with growing patent consolidation. |
| JEL: | L41 L63 L86 O31 O34 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35762 |
| By: | Wurster, Maximilian; Hellenkamp, Detlef |
| Abstract: | In knowledge-based economies, technological innovation capability, intangible assets, and intellectual property are becoming increasingly important for long-term economic performance. Patent data are widely used as indicators of innovation; however, simple patent counts do not adequately capture the quality, technological relevance, or international market coverage of individual patents. This paper therefore examines the extent to which key patents can serve as a quality-oriented indicator of technological innovation capability for analysing economic performance. Key patents are understood as particularly high-quality patents in relevant key technologies, whose patent strength derives from technological relevance and international market coverage. The paper combines a conceptual analysis of the key patent approach with literature from growth and innovation economics, as well as with an exploratory empirical plausibility assessment based on a global patent dataset on world-class patents in future technologies (Breitinger et al., 2020). Economic performance is operationalised empirically through per capita income. The empirical evidence is assessed from two complementary perspectives: an international cross-section of high-income economies and a dynamic analysis of the United States, which held a particularly prominent position in high-quality patenting across key future technologies during the period under observation. In both perspectives, the findings point to a positive association between key patents and per capita income. They should not, however, be interpreted as evidence of causality, but as empirically grounded support for the plausibility of this relationship. The contribution of this paper lies in positioning key patents as a differentiated innovation indicator that takes patent quality, technological relevance, and international reach more fully into account than mere patent volumes. The key patent approach thus offers a scientifically grounded perspective on innovation capability and economic performance in knowledge-based economies. |
| Keywords: | Key patents, Patent quality, Innovation indicators, Technological innovation capability, Knowledge-based economies, Economic performance, Intangible assets |
| JEL: | O31 O33 O34 O47 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:esprep:343662 |
| By: | Gabriela Galassi; Gaelan MacKenzie |
| Abstract: | We estimate the direct effects of robot adoption on the within-firm distribution of income between non-owner workers and individual owners in Canadian private corporations. We link firm-level data on industrial robot imports to matched owner-worker-firm data that identify owners of private corporations, their employment income, and firms’ dividend payments to owners and retained earnings. Robot adoption is associated with sizable increases in firm revenues, value added, and non-owner employment and payroll. Unlike several studies of robot adoption in other settings, we find little evidence that adoption reduces adopters’ conventionally measured labor share of value added; if anything, the non-owner labor share increases slightly, while the total owner income share does not rise. Owners nevertheless benefit from adoption, but primarily through increases in employment income. These results underscore the importance of distinguishing owner employment income from ordinary worker payroll for measuring how firm-level technology shocks affect workers, owners, and the distribution of income within firms. |
| Keywords: | Structural challenges; Digitalization and productivity |
| JEL: | D D2 D22 D3 D33 J J2 J23 J3 J31 O O3 O33 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:bca:bocawp:26-34 |
| By: | Brad Cannon; David Hirshleifer; Joshua Thornton |
| Abstract: | Using Facebook friendship data, we study how three aspects of social capital shape innovative activity. We find that the most important aspect of social capital in explaining innovation is Economic Connectedness (EC)--the share of high-income friendships. One standard deviation greater EC is associated with 97% more patents per capita among patenting ZIP Codes and 45% more breakthrough patents per capita among ZIP Codes with breakthrough patents. Reverse-causality tests, a within-inventor relocation design, and a quasi-experiment using fracking-driven economic shocks to non-local friends support a causal interpretation. Mechanism tests provide evidence consistent with a financing channel. |
| JEL: | D14 D25 D83 D85 D9 G32 G41 G51 O16 O3 O33 O35 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35732 |
| By: | Mahdi Ghodsi (The Vienna Institute for International Economic Studies, wiiw); Michael Landesmann (The Vienna Institute for International Economic Studies, wiiw); Robert Stehrer (The Vienna Institute for International Economic Studies, wiiw) |
| Abstract: | This paper develops a theoretical and empirical framework for analysing how technological proximity shapes bilateral trade flows. We model technological alignment as a source of reduced effective trade costs and embed it within a structural gravity setting. Using a novel dataset combining sector-level patent information and OECD trade data for 76 countries across 45 sectors over the 1996-2020 period, we construct a granular measure of technological proximity based on revealed technological advantage. Estimating high-dimensional fixed effects gravity models, we find that technological proximity has a positive and economically significant impact on bilateral exports. Instrumental-variable estimates based on shift-share global technology shocks indicate that baseline results are attenuated by endogeneity. We further show that technological proximity mitigates the trade-reducing effects of compliance-intensive regulatory non-tariff measures, particularly sanitary and phytosanitary measures, while having limited interaction with tariffs. In non-services sectors, a 0.1 increase in technological proximity has an export effect comparable to lowering the gross tariff factor by about 6%-7%. Finally, we document substantial regional and sectoral heterogeneity, showing that the trade-enhancing effects of technological proximity are strongest when countries are connected to technological frontier economies and vary markedly across industries. Partial-equilibrium welfare counterfactuals imply positive but modest gains from targeted technological-alignment shocks. The findings highlight the central role of technological compatibility in shaping global trade patterns. |
| Keywords: | technological proximity, international trade, structural gravity, revealed technological advantage, non-tariff measures, trade policy, patent data, global value chains |
| JEL: | F13 F14 O33 O14 C23 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:wii:wpaper:279 |
| By: | Knarik Poghosyan; Martin Kalthaus; Nadine Riedel; Michael Rothgang; Anne-Marie Scholz |
| Abstract: | We estimate the direct and indirect causal effects of Germany’s Leading-Edge Cluster Competition on firm size, inventive activity, and joint patenting. With €600 million allocated to 15 clusters, this program is one of the largest cluster policy programs in Europe. Using a staggered event-study design, we estimate the direct effect of the cluster policy on firms within the funded clusters. Additionally, we differentiate between funded and non-funded cluster members to assess the effects of the financial and the systemic components of the policy. Furthermore, we estimate the indirect spillover effect of this program on firms in close proximity to the funded clusters that are not associated with them. Our results show that the cluster policy has a direct overall positive effect on firm size and inventive activity, but a negative effect on collaboration in inventive activity. Differentiating between funded and nonfunded cluster members reveals that the latter benefit strongly from the systemic component of the cluster policy, increasing their inventive activity while having no significant effect on collaboration. In contrast, we do not observe any indirect effects on geographically proximate non-cluster firms. Our results provide causal evidence of the direct benefits of cluster policy at the firm level but call into question the anticipated indirect effects of cluster policy on neighboring non-cluster firms. |
| Keywords: | Cluster Policy, Spillover Effects, Policy Evaluation, Patent Data, Event Study |
| JEL: | O33 O38 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:diw:diwwpp:dp2181 |
| By: | Jacob Moscona |
| Abstract: | How do patent rights affect innovation and downstream production? Patent protection was introduced for plant biotechnology in the United States in 1985, and it affected crops differentially depending on their reproductive structures. Exploiting this unique feature of plant flower structure and a new dataset of crop-specific technology development, I find that the introduction of patent rights increased the development of novel plant varieties. Technology development was driven by a rapid increase in private sector research investment, was accompanied by positive spillover effects on innovation in certain non-biological agricultural technologies, and led to an increase in crop yields. Patent rights, however, may come with potentially significant costs to the consumers of technology and distortions to downstream production. Nevertheless, despite higher spending on seeds, US counties that were more exposed to the change in patent law because of their crop composition experienced an increase in agricultural land values and profits. Taken together, the results suggest that the prospect of patent protection spurred innovation and increased downstream productivity and profits. |
| JEL: | O3 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35735 |
| By: | Aaron P. Kaye; Kazimier Smith; Neil Thompson; Aaron Kaye |
| Abstract: | We characterize the lifecycles of open-weight AI models using a weekly panel of the 108, 074 most-downloaded models on Hugging Face, which together account for 99.5 percent of the platform's more than 53 billion downloads. We link these data to token usage from OpenRouter and performance rankings from Arena and Artificial Analysis. We establish key institutional details about the supply of and demand for AI models, and we organize our analysis into three phases: birth, life, and death. In the "birth" phase, we show that major developers often release models in batches, concurrently introducing models that are both vertically differentiated (e.g., by size, capability, and latency) and horizontally differentiated (e.g., by task specialization, hardware compatibility, and alignment). Because weights are open, developers can also build on one another's models. We document cumulative innovation through fine-tuned, quantized, merged and adapted descendants of source models. In the "life" phase, we quantify how demand evolves after release. Even within the sample of top models, demand is concentrated, and the concentration persists: the median model loses ∼68 percent of its release-week downloads within ten weeks, while the most popular models decline gradually; as a result, the top percentile's advantage over the median model is ∼600-fold at release, and ∼900-fold six months later. Further, we estimate spillover effects from new popular model releases on related models and find that the first popular third-party descendant is associated with a 76 to 99 percent increase in weekly downloads of its source model. In the "death" phase, we construct measures of technical obsolescence and usage obsolescence. We find that technical obsolescence is associated with a slow but persistent decrease in demand, while usage obsolescence coincides with an immediate but temporary drop in demand. Our findings characterize open-weight AI as a market of complementary model portfolios, one in which third-party developers building on a source model can raise demand for the original, and in which demand is slow to respond to technical obsolescence. |
| Keywords: | artificial intelligence, open-source, open-weights, technology diffusion, platform economics, cumulative innovation |
| JEL: | O33 L17 L86 O31 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_13004 |
| By: | Cirillo, Valeria; Divella, Marialuisa; Simone, Giuseppe |
| Abstract: | This paper examines the drivers of income inequality across Italian provinces, focusing on the interaction between digitalisation and labour market flexibility. Using a novel provincial panel that combines Italian Labour Force Survey (LFS) data with detailed information on tasks from the INAPP-ISTAT Survey on Italian Occupations (ICP) and tax-based income records from Italian Ministry of Economics and Finance (MEF), we show that a higher incidence of non-standard employment reduces the labour share, lowers average employment income, and increases the gap between high- and low-income earners. Digitalisation, measured through the digital task intensity of local occupations, is associated with higher average incomes but also greater income polarisation. However, the income gains from digitalisation weaken and eventually disappear in provinces characterised by more precarious employment. Overall, the findings suggest that the digital transition, in the absence of stronger labour market protections, may reinforce rather than reduce territorial disparities. |
| Keywords: | Non-standard work, Digitalisation, Wage inequality, Labour-market dualisation, Regional inequality, Italian provinces |
| JEL: | J31 J41 J42 O33 R23 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:glodps:1816 |
| By: | Ronit Mukherji (Ashoka University); Alejandro Rojas-Bernal (University of Hawaii) |
| Abstract: | How does productivity evolve at the microeconomic level, and how does that evolution shape aggregate risk? Using restricted-use plant--product data from India's Annual Survey of Industries (2010-11 to 2022-23), we construct annual physical-output Solow-residual growth accounting for markups, inventories, and heterogeneous input costs. Productivity growth is sharply peaked and fat-tailed, with nonlinear mean reversion that varies with producer size. Larger producers recover more strongly after adverse shocks and retain more of their favorable gains-the Daedalian level effect-yet their shocks extend farther into both tails conditional on entry. Smaller producers exhibit sharper reversals after large favorable shocks-Icarian fallout. We capture these patterns with a three-regime Markov normal mixture conditioned on productivity history and size. Our law of motion yields the distribution of aggregate technology growth. Relative to a size-dependent Gaussian distribution, our regime model has lower aggregate dispersion but substantially thicker standardized tails and raises the probability of an aggregate technology contraction from 0.05% to 0.31%. Removing size dependence while retaining three regimes instead lowers expected growth and raises contraction probability to 2.47%. These separately estimated alternatives show that nonnormality and size dependence have distinct implications for aggregate risk. Granularity is distributional: aggregation weights determine whose shocks matter; productivity dynamics determine the risks they carry. |
| Keywords: | Firm productivity dynamics, aggregate productivity risk, non-Gaussian productivity shocks, production networks, granular fluctuations, firm-size heterogeneity, physical-output productivity |
| JEL: | C46 D22 D24 D57 E23 E32 L11 L25 O47 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:hai:wpaper:202604 |
| By: | Luca Brugnara (Bank of Italy); Gianmarco Cariola (Bank of Italy); Pasquale Recchia (Bank of Italy); Simone Zuccolalà (Bank of Italy) |
| Abstract: | This paper develops a firm-level input–output framework to measure participation in production networks using transaction-level data and applies it to the automotive industry. Using a novel dataset of firm-to-firm transactions, we construct a firm-specific measure of supply chain exposure based on the Ghosh inverse, which is a sufficient statistic for the upstream propagation of demand shocks within a standard production network model. We use this framework to trace both direct and indirect sales linkages across firms. We find that around 8, 000 Italian firms are significantly exposed to the EU automotive industry, with indirect linkages accounting for more than half of the supply chain. Exposure is highly heterogeneous across firms, sectors, and regions, and simulated demand shocks generate sizeable upstream propagation effects: a 1 per cent decline in final demand for cars would reduce value added among final producers by 63 million euros, with an additional 93 million propagating upstream through the network. The framework is scalable and can be extended to other downstream industries with clearly identifiable final producers, providing a flexible tool for analysing production networks and the upstream transmission of demand shocks. |
| Keywords: | production networks, firm-to-firm transaction data, shock propagation, automotive industry |
| JEL: | D57 L14 L62 |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:bdi:opques:qef_1041_26 |