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on Technology and Industrial Dynamics |
| By: | Ruben Gaetani; Alexander T. Whalley |
| Abstract: | The emergence of firms like SpaceX and Blue Origin has made space a leading example of how private enterprise drives innovation, marking what many see as a sharp break between Old Space and New Space. Yet little systematic evidence documents when the transition to this new phase of space innovation occurred and which firms drove it. We use patent data to provide this measurement and find that the largest surge in space innovation occurred in the 1990s, coinciding with demand-side market creation, and preceding the entry of high-profile startups after 2005. Throughout this period and since, incumbent aerospace firms account for most of the space-related patenting, with entrants contributing a growing but minority share. The same geographic regions that dominated space innovation during the post-Apollo era remain dominant today. These patterns are consistent with directed technical change: incumbents direct R&D toward policy-created markets accessible from existing capabilities, while entrants bring science-based insights into domains requiring new paradigms. Our findings suggest that New Space is more closely connected to Old Space than prevailing narratives imply, and that government's most consequential role in space innovation may lie in constructing appropriable markets. We make patent data on space-related technologies available for future research. |
| JEL: | L64 O31 O38 R48 |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35212 |
| By: | Nuriye Melisa Bilgin; Gianmarco Ottaviano |
| Abstract: | Do the determinants of technology adoption depend on technological architecture? Using administrative data on Turkish firms from 2021 to 2024, we compare the adoption of traditional and generative artificial intelligence (GenAI).We show that GenAI adoption is driven by workforce skill intensity and is not positively associated with firm size, whereas traditional AI depends on both scale and skills. Firms that adopt both technologies are distinct and represent the most persistent adoption mode. Conditional on adoption, the skill-to-size ratio governs technology choice, and transition dynamics indicate a sequential process in which firms adopt GenAI before expanding to hybrid use. Exploiting the release of ChatGPT as a quasi-experimental reduction in access costs, we find that high-skill firms differentially increased GenAI adoption, while firm size played a limited role. These results suggest that the canonical size-based diffusion pattern is not universal but depends on the cost structure of technologies, with implications for innovation policy and productivity dispersion. |
| Keywords: | artificial intelligence, generative AI, technology adoption, firm heterogeneity |
| Date: | 2026–05–21 |
| URL: | https://d.repec.org/n?u=RePEc:cep:cepdps:dp2184 |
| By: | Afroza Alam; André Diegmann |
| 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: | innovation, firm performance, worker compensation, rent sharing |
| JEL: | O31 O34 J31 D22 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12666 |
| By: | Prashant Garg; Tommaso Crosta; Jasmin Baier |
| Abstract: | Automation affects the labour content of work differently across different contexts. Yet, most existing exposure measures assign fixed scores to tasks or occupations, limiting comparisons of automation exposure across countries. We develop a task-based and country-specific approach to classify automation exposure across the world to disentangle labor-substituting from labor-augmenting automation, the relevant technology channel, and the material role of AI. Our measure spans 124 countries, generating an atlas of 2.33 million task-country labels for economies covering 99% of world population and GDP. We present five descriptive results. First, exposure is highly uneven, ranging from 3.3% of tasks in South Sudan to 61.6% in China, and rises strongly with income, although substantial variation remains within income groups. Second, across countries, exposed tasks are skewed towards substitution rather than augmentation, but low-income countries are disproportionately exposed to substitution, whereas middle-income countries are more heterogeneous. Third, less technologically advanced forms of automation account for more than half of exposed tasks in low-income countries but about one quarter in high-income countries; while other more complex channels generally rise with income levels. Fourth, AI tends to be less prevalent in simpler channels of automation, but also more prevalent in labour-substituting margins in lower income settings and to augment labour in higher income settings. Fifth, we find that females seem to be disproportionately more exposed to labour-substituting automation than males. Our methodology provides a basis for comparing automation exposure across development stages, linking it with cross-country data and allowing us to treat exposure levels, labour margins, technological channels and AI involvement as separate dimensions. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.17086 |
| By: | Hassan Afrouzi; Andres Blanco; Andres Drenik; Erik Hurst |
| Abstract: | We study how an automating technology affects career dynamics, human capital, and welfare in an economy where workers acquire skill through the tasks they perform. In a continuous-time general equilibrium model, learning-by-doing is determined jointly with the share of tasks automated, the frontier of tasks managers maintain, and the worker-to-manager career transition. Economies with high learning capacity admit pairs of stationary equilibria strictly ranked by the aggregate learning rate. Cheaper technology has opposite effects across the two: in the high-learning equilibrium, it raises welfare through the learning channel itself; in the low-learning equilibrium, it tips the economy into a human-capital trap. The planner's first-best combines a tax on automation profits with a subsidy on frontier maintenance expenditures at a common rate. |
| Keywords: | human capital; learning-by-doing; automation; AI |
| JEL: | E23 E24 J24 |
| Date: | 2026–05–14 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedawp:103253 |
| By: | Cass, Leanne; Frattini, Federico Fabi; Saussay, Aurelien; Sato, Misato; Vona, Francesco |
| 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 J60 |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:138454 |
| By: | Goldberg, Pinelopi; Juhász, Réka; Lane, Nathan; Lo Forte, Giulia; Thurk, Jeff |
| Abstract: | The resurgence of subsidies and industrial policies has raised concerns about their potential inefficiency and alignment with multilateral principles. Critics warn that such policies may divert resources to less efficient firms and provoke retaliatory measures from other countries, leading to a wasteful “subsidy race.” However, subsidies for sectors with inherent cross-border externalities can have positive global effects. This paper examines these issues within the semiconductor industry: a key driver of economic growth and innovation with potentially significant learning-by-doing and strategic importance due to its dual-use applications. Our study aims to: (1) document and quantify recent industrial policies in the global semiconductor sector, (2) explore the rationale behind these policies, and (3) evaluate their economic impacts, particularly their cross-border effects, and compatibility with multilateral principles. We employ historical analysis, natural language processing, and a model-based approach to measure government support and its impacts. Our findings indicate that government support has been vital for the industry’s growth, with subsidies being the primary form of support. They also highlight the importance of cross-border technology transfers through FDI, business and research collaborations, and technology licensing. China, despite significant subsidies, does not stand out as an outlier compared to other countries, given its market size. Model estimates suggest the presence of learning-by-doing at the firm-product level as well as economies of scope within a firm and substantial cross-border learning spillovers. These spillovers likely reflect cross-country technology transfers and the role of fabless clients and input suppliers in disseminating knowledge globally through their interactions with foundries. Such cross-border spillovers are not merely accidental but result from deliberate actions by market participants that cannot be taken for granted. Firms may choose to share knowledge across borders or restrict access to frontier technology, thereby excluding certain countries. Future research will use model estimates to simulate the quantitative implications of subsidies and to explore the dynamics of a “subsidy race” in the semiconductor industry. |
| Keywords: | semiconductors; industrial policy; subsidies; learning-by-doing; multilaterism |
| JEL: | F13 L63 N60 O38 |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:138525 |
| By: | Joshua S. Gans |
| Abstract: | Empirical measures of AI's wage effect typically hold fixed the bundle of activities a worker is paid for at its pre-AI shape. We argue that this assumption hides much of the action. When automation breaks a job apart, firms decide how to recombine the surviving activities; whether they rebundle them into one broad role or split them into specialist roles changes which surviving skills the labour market actually rewards. A skill that played no role in the pre-AI wage can become the dominant component of the post-AI wage, while a skill that anchored the pre-AI wage can disappear from the schedule. We develop an assignment model in which the priced human bundle is endogenous, and we use it to show that a fixed-bundle wage regression can mis-sign the effect of AI exposure. In general, the omitted-redesign bias has no unconditional sign: it is the residual covariance between exposure and role-specific redesign terms. Under explicit sufficient conditions, exposure-correlated unbundling loads specialist comparative-advantage premia onto the exposure coefficient, while exposure-correlated rebundling loads a different, often opposite, omitted term. The sign must therefore be measured from local post-AI partition changes rather than assumed from exposure alone. |
| JEL: | D23 J23 J24 J31 O33 |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35211 |
| By: | Brandon Pecoraro; Nicholas C. Hoffman; Martin Lopez-Daneri; Elena C. Derby; Rachel Moore; Shannon E. Sledz |
| Abstract: | Using a panel of confidential corporate tax returns, we provide the first direct estimates of the realized present value of corporate tax benefits from R&D credits and deductions in the United States. Realized tax benefits can deviate from statutory tax benefits because firms in loss status are typically unable to fully utilize credits and deductions to offset current-year taxes and instead must carry these attributes forward. We develop a novel procedure to track the intertemporal firm-level utilization of tax attributes generated by corporate R&D spending, and find that the present value of R&D tax benefits varies substantially with firms’ loss status, age, and size. Old and large firms typically use R&D tax benefits quickly, while young firms – especially those that are small – frequently operate in loss status and use tax attributes more slowly. From 2012–2016, the average firm generated $0.41 in statutory tax benefits per dollar of R&D investment, with a realized present value of $0.36. Young and small firms in a loss position realized only $0.23 per dollar, a 44% decrease relative to the statutory benchmark. |
| JEL: | D22 H25 O30 O38 |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35208 |