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on Technology and Industrial Dynamics |
| By: | Gaia Dossi; Marta Morando |
| Abstract: | We link U.S. patent and inventor records to individual voter register files and map politically polarized policy issues to related technologies. Compared to Republicans, Democrats are one-third more likely to patent technologies addressing climate-change mitigation or women's reproductive health, and one-third less likely to patent weapons and related technologies. These gaps are not explained by differences in inventive ability or by sorting across organizations or teams. Party-technology alignment has strengthened over the past two decades, a period of rising political polarization in U.S. society. Technology diffusion is also politically polarized: Democrats are more likely than Republicans to cite aligned technologies and less likely to cite misaligned ones. Together, these findings are consistent with political polarization and societal views being important drivers of the direction and diffusion of technological change and operating, at least in part, through inventors' technology choices, with implications for innovation policy. |
| Keywords: | Diffusion, Innovation, Partisanship, Polarization, Technology |
| JEL: | D72 I10 J24 O31 O33 O44 P00 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:crm:wpaper:26064 |
| By: | Giuseppe Pulito; Mariola Pytlikova; Sarah Schroeder; Magnus Lodefalk |
| Abstract: | Using two waves of nationally representative Danish firm surveys linked to employer– employee administrative registers, we study how adoption varies across artificial intelligence (AI) and related advanced technologies. We show that AI adoption is highly technologyspecific. While firm size and digital infrastructure predict adoption broadly, workforce composition operates through distinct channels: STEM-educated workforces predict core AI adoption, whereas non-STEM university-educated workforces are associated with generative AI adoption, indicating different human capital complementarities. The factors associated with adoption differ from those predicting deployment breadth: firm size and digital maturity matter for both, whereas workforce composition primarily predicts adoption alone. Machine learning and natural language processing are deployed across multiple business functions, whereas other advanced technologies remain concentrated in specific operational domains. Individual-level evidence provides a foundation for these patterns, with awareness of workplace AI usage concentrated among managers and high-skilled workers. Self-reported AI knowledge is higher among younger and more educated individuals. Finally, commonly used occupational AI exposure measures vary substantially in their ability to predict observed adoption, with benchmark-based measures outperforming patent-based and LLM-focused alternatives. These findings show that treating AI as a monolithic category obscures economically meaningful variation in who adopts, what they deploy, and how well existing measures capture it. |
| Keywords: | Artificial Intelligence; Technology Adoption; Digitalisation; Human capital; AI Exposure Measures |
| JEL: | D24 J23 J62 O33 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:cer:papers:wp818 |
| By: | Lukas Freund; Lukas Mann |
| Abstract: | Who will gain and who will lose as AI automates tasks? While much of the discourse focuses on job displacement, we show that job transformation-a shift in the task content of jobs-creates large and heterogeneous earnings effects. We develop a quantitative, task-based model where occupations bundle multiple tasks and workers with heterogeneous portfolios of task-specific skills select into occupations by comparative advantage. Automation shifts the relative importance of tasks within each occupation, inducing wage effects that we characterize analytically. To quantify these effects, we measure the task content of jobs using natural language processing and estimate the distribution of task-specific skills. We construct projections of automation effects due to large language models (LLMs), exploiting a mapping between model tasks and automation exposure measures. Within highly exposed occupations, like office and administrative roles, workers specialized in information-processing tasks leave and suffer wage losses. By contrast, those specialized in customer-facing and coordination tasks stay and experience wage gains as work rebalances toward their strengths. Our findings challenge the common assumption that occupational automation exposure necessarily implies individual wage losses; and highlight that AI, through job transformation, may be disruptive even absent job displacement. |
| Keywords: | Artificial Intelligence, Labor Markets, Inequality, Skills, Tasks, Technological Change |
| JEL: | E24 J23 J24 J31 O33 |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:crm:wpaper:25117 |
| By: | Debi Prasad Mohapatra; Vatsala Shreeti |
| Abstract: | Why would a market leader choose not to patent an innovation? We study Samsung's decision to forgo patent protection for dual SIM technology in the Indian mobile handset market. Using a structural model of demand and supply estimated on quarterly product-level data from the Indian mobile handset industry, we document that rival firms' dual SIM products generated a preference discovery externality. Rival firms' widespread adoption of the dual SIM technology allowed consumers to discover the value of the technology, also benefiting Samsung itself. Counterfactual simulations show that a patent would have suppressed this externality, reducing Samsung's equilibrium profits despite holding monopoly rights. Voluntary non-patenting was therefore privately optimal. Our findings shed light on wider debates about open-sourcing in software and other markets. |
| Keywords: | innovation, patenting, telecom, preference discovery |
| JEL: | L13 O33 O34 L63 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:bis:biswps:1344 |
| By: | Martin Olsson; Fredrik Heyman |
| Abstract: | This paper examines how automation shapes intergenerational income mobility. Using Swedish register data on parents and children from 1985 to 2019, we study how parental exposure to robots at the occupational and industry level during the 1990s affected children's outcomes up to thirty years later. To address selection, we match parents on detailed worker, firm, and family characteristics and complement this with firm-level variation based on robot and broader automation imports. We also employ two IV strategies that leverage exogenous variation in automation adoption: one based on foreign industry-level robot adoption, and another exploiting differences in managerial education at the firm level. Our results show that parental exposure to robotization and automation reduces children's income and upward mobility, and leads to worse long-run labor market and educational outcomes. These effects are concentrated among low-income families. Evidence suggests that parental labor market shocks and financial strain are key mechanisms. Taken together, the findings indicate that technological change can reduce intergenerational mobility and contribute to long-run inequality. |
| Keywords: | Intergenerational Mobility; Robots; Automation; Inequality |
| JEL: | J31 J62 O33 |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:crm:wpaper:26047 |
| By: | Pascuel Plotkin |
| Abstract: | This paper studies how firm adoption of digital technologies reshapes labor demand and worker earnings. Linking administrative employer-employee records to restaurants and workers from a major delivery platform and using a matched event-study, I show that adopting restaurants substitute in-house labor hours one-for-one with outsourced platform-worker hours. Earnings losses for incumbent workers are modest because displaced workers reallocate to new formal-sector jobs. Exposed non-adopting restaurants are more likely to close, and their workers experience larger losses. I quantify earnings effects across restaurant and platform workers, showing how platform adoption redistributes earnings across workers and creates income outside traditional restaurant employment. |
| Keywords: | Alternative work arrangements, gig economy, technological change, outsourcing, displacement, informality |
| JEL: | J23 J31 J46 O33 L86 J63 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:crm:wpaper:26067 |
| By: | Francesca Miserocchi; Savannah Noray; Alice Wu |
| Abstract: | The advances of artificial intelligence (AI) are built on the groundwork laid by researchers. We study the labor market competition between academia and industry for AI researchers and its consequences for public knowledge production. Using data on 150, 000 computer science researchers, we document a major reallocation of AI talent toward top technology firms between 2005 and 2020. Publications at AI conferences predict transitions to top firms more strongly than to academia. Exploiting acceptance decisions at a leading AI conference, we compare accepted authors with similar rejected authors and find that a publication increases the probability of moving to a top firm by 2-6 percentage points in the next 1-3 years. Sorting to top firms is stronger for male researchers, whereas female students and postdocs are more likely to get tenure-track positions following a publication. Researchers who move to top firms subsequently publish fewer papers, resulting in approximately 1, 000 fewer AI papers and 2, 000 fewer papers in other computer science areas per year in the public domain. |
| Keywords: | Sorting, Productivity Signals, Labor Market Concentration, Innovation |
| JEL: | J23 J24 O31 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:crm:wpaper:26106 |
| By: | Anders Humlum; Emilie Vestergaard |
| Abstract: | We study the early labor market impacts of AI chatbots by linking large-scale adoption surveys to administrative labor market records in Denmark. We document rapid currents: most employers in exposed occupations have adopted chatbot initiatives, workers report productivity benefits, and new AI-related tasks are widespread. Yet these currents have not broken the surface: using difference-in-differences, we estimate precise null effects on earnings and recorded hours at both the worker and workplace levels, ruling out effects larger than 2% two years after the launch of ChatGPT. What moves is the structure of work: employers absorb AI through task reorganization-including new tasks in content generation, AI oversight, and AI integration-and adopters transition into higher-paying occupations where AI chatbots are more relevant, though still too few to move average earnings. Technological change reshapes work well before it surfaces in earnings or hours. |
| Keywords: | Generative AI; Labor Markets |
| JEL: | J23 J24 J31 O33 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:crm:wpaper:26078 |
| By: | Sascha O. Becker; Christian Dustmann; Hyejin Ku |
| Abstract: | We examine the long-term labor market impact of the steam engine, an early general-purpose technology, by linking newly digitized 19th-century records from Prussia to modern German labor market data (1975-2019). Regions with a higher concentration of steam engines per worker in 1875 exhibit higher wages today, primarily because of higher firm productivity and a more skilled workforce. These regions also exhibited greater skill diversity in 1939 and generated more innovations between 1877 and 1918, a pattern that persists to this day. Our findings highlight a lasting, self-reinforcing cycle between technology and skills, set in motion by the steam engine, offering a novel explanation for regional income disparities and their persistence. |
| Keywords: | steam engine, technology adoption, diversity, innovation, human capital, productivity |
| JEL: | I24 J24 O14 O33 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:crm:wpaper:26013 |
| By: | Yang Li; Ketan Ahuja; Karan Daryanani (Harvard's Growth Lab); Ricardo Hausmann (Harvard's Growth Lab); Muhammed A. Yildirim (Center for International Development at Harvard University) |
| Abstract: | The energy transition offers countries that can manufacture clean energy technologies substantial opportunities for sustainable economic growth. This paper provides a framework for context-aware industrial policy by applying economic complexity theory to a newly constructed dataset of twelve key clean energy supply chains (CESCs). We find that CESCs are diverse but highly interdependent; they are also growing faster and are more concentrated than other industries. CESCs exhibit substantial entry, exit and competitive churn, and countries are more likely to enter CESC industries that are related to their existing productive capabilities. We also explore changing global competitiveness and country positioning in these industries, and draw out implications of these patterns for industrial policymakers. |
| Keywords: | clean energy, supply chain, principle of relatedness, economic complexity, industrial policy |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:glh:wpfacu:268 |
| By: | Fotis, Panagiotis; Polemis, Michael |
| Abstract: | In this study, we present the first systematic evidence of the impact of cartel recidivism on innovation. Combining data from an international price-fixing cartel database with the structural characteristics of the US manufacturing sectors at the six-digit NAICS level, we analyze how cartel recidivists influence subsequent innovation outcomes. Using a staggered difference-in-differences (DiD) framework for 110 US cartel cases over the period 1979-2016 and a novel heterogeneous estimator, we find that cartel recidivists lead to a significant and sustained decline in innovation progress. We argue that cartel recidivists, rather than single offenders, drive the negative impact of collusion on innovation. The results of this study are vigorous to several robustness tests, justifying the absence of pretreatment effects and endogeneity. |
| Keywords: | Cartel; Recidivism; Innovation; Antitrust; Difference in Differences |
| JEL: | D43 K21 L13 |
| Date: | 2026–02–20 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:128115 |
| By: | Eduardo Levy Yeyati |
| Abstract: | We study how the speed of AI adoption affects labor market outcomes during technological transitions. In a dynamic model where displaced routine workers enter a retraining pipeline with finite capacity, faster adoption compresses the displacement window without reducing total displacement, overwhelming the pipeline and generating permanent labor force exit through worker discouragement. The central result is that, even when two economies share the same long-run automation level, adoption speed alone determines transition welfare: faster adoption produces a larger discouraged stock, a steeper and more persistent decline in labor force participation, and a sustained compression of the labor share throughout the transition window. Non-routine employment and wages exhibit a crossing pattern — initially higher under fast adoption, then lower — so that faster adoption can simultaneously raise long-run wages for survivors while permanently reducing participation. Social welfare is strictly concave in adoption speed and maximized at an interior optimum below the market rate, because firms do not internalize the congestion externality they impose on the retraining queue, the irreversibility of permanent exit, or the wage depression borne by non-routine incumbents. The socially optimal speed and retraining capacity are complements: stronger institutions raise the optimal adoption speed. |
| Keywords: | Inteligencia Artificial, Innovación tecnológica, Mercado Laboral, Desarrollo de Habilidades, Artificial Intelligence, Technological innovation, Laboral Market, Skills Development |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:udt:wpgobi:wp_gob_2026_01 |
| By: | Jennie Bai; Luc Laeven; Yaojun Ke; Hong Ru |
| Abstract: | We study the global footprint and real effects of Chinese overseas corporate ownership. By assembling a comprehensive micro-level dataset of 161, 773 firms across 159 countries (2012–2021), we independently reconstruct multi-layered ownership chains to trace capital through offshore tax havens to its ultimate origin. This approach reveals a global footprint substantially broader than official FDI statistics. Chinese-controlled foreign assets expanded at 20% annually, reaching $2.1 trillion or roughly 3% of global corporate assets by 2021. Chinese investors—particularly state-owned enterprises (SOEs)—strategically target R&D-intensive and supply-chain-linked firms. Following acquisition, target firms increase capital stock and R&D expenditures, yet these inputs fail to generate higher patent output and are accompanied by a significant decline in profitability. We document a novel 'innovation spillback' mechanism: while target innovation remains stagnant, Chinese parent firms experience a sharp acceleration in granted patents following their first developed-economy acquisition. Furthermore, a greater Chinese presence crowds out R&D at non-target peer firms, though aggregate industry-level innovation remains unchanged. China thus represents a distinctively state-driven model of global ownership that accepts weaker near-term performance to internalize technological capacity at home. |
| JEL: | F3 G32 G34 O3 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35106 |
| By: | Felipe Aguilar; Roberto Alvarez |
| Abstract: | In this paper, we use data for more than 5, 000 Chilean companies to investigate whether participation in business association increases the probability of R&D investment. Dealing with the endogeneity of participation through a bivariate Probit model with an exclusion variable that captures the trust environment among firms, we find that this probability increases by about 27%. This effect is heterogeneous across firms. Participation increases the probability of R&D investment by 30.8% for SMEs and by 43.9% for those companies with severe financial constraints. Our evidence is consistent with the idea that associativity may help SMEs to close the innovation gap and/or to alleviate financial problems. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:chb:bcchwp:1064 |