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on Business Economics |
| By: | Pulito, Giuseppe (ROCKWOOL Foundation Berlin); Pytlikova, Mariola (CERGE-EI, Charles University and the Economics Institute of the Czech Academy of Sciences, and AIAS, Aarhus University); Schroede, Sarah (Aarhus University and Ratio Institute); Lodefalk, Magnus (Örebro University School of Business) |
| 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: | 2026–03–27 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:oruesi:2026_003 |
| By: | Erol Taymaz (Department of Economics, Middle East Technical University, Ankara, Turkey); Kamil Yılmaz (Department of Economics, Koç University, İstanbul, Turkiye) |
| Abstract: | This paper investigates the relationship between workforce age composition, prior experience, and firm survival using matched employer–employee data from Turkey spanning 2007 to 2023. Using the universe of Turkish firms from the Entrepreneur Information System (EIS), we estimate discrete-time hazard models on manufacturing corporations and document three main findings. First, the relationship between average employee age and exit risk is non-linear but not smoothly quadratic: exit hazards are significantly elevated only for firms with very young (15–20) or older (45+) workforces, while the 25–40 age range shows no meaningful differences. This challenges the standard inverted-U specification commonly adopted in the literature. Second, this age effect is entirely confined to micro-firms (1–10 employees); for larger firms, capital intensity, export status, and supply-chain linkages dominate survival prospects. Third, prior employment experience of the workforce—measured through sector-specific experience, former employer characteristics, and employment network concentration—significantly predicts survival, especially for smaller firms. The influence of both age and experience variables fades as firms age, consistent with the gradual replacement of entry conditions by accumulated organizational capital. Our results highlight the size-dependent nature of human capital’s role in firm survival and carry implications for policies aimed at supporting new-firm longevity in developing economies. |
| Keywords: | firm survival, startups, employee experience, employee age, human capital, matched employer-employee data |
| JEL: | L11 L26 L60 M13 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:met:wpaper:2601 |
| By: | Alexander Bick; Adam Blandin; David J. Deming; Nicola Fuchs-Schündeln; Jonas Jessen; David Deming |
| Abstract: | This paper combines international evidence from worker and firm surveys conducted in 2025 and 2026 to document large gaps in AI adoption, both between the US and Europe and across European countries. Cross-country differences in worker demographics and firm composition account for an important share of these gaps. AI adoption, within and across countries, is also closely linked to firm personnel management practices and whether firms actively encourage AI use by workers. Micro-level evidence suggests that AI generates meaningful time savings for many workers. At the macro level, in recent years industries with higher AI adoption rates have experienced faster productivity growth. While we do not establish causality, this relationship is statistically significant and similar in magnitude in Europe and the US. We do not find clear evidence that industry-level AI adoption is associated with employment changes. We discuss limitations of existing data and outline priorities for future data collection to better assess the productivity and labor market effects of AI. |
| Keywords: | generative AI, technology adoption, labor productivity |
| JEL: | J24 M16 O14 O33 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12584 |
| By: | Ryan Kim; Justin H. Leung; Ariel Weinberger |
| Abstract: | This paper investigates how immigration affects consumer prices. Using scanner data and instrumenting county-level immigration with historical ancestry patterns, we find that an inflow of 10, 000 immigrants lowers four-year price growth by 0.58 percentage points. Leveraging variation in firm exposure through sales versus production locations, we show price declines stem entirely from the product demand channel: firms lower prices in response to immigrants in sales markets, not production locations. Evidence suggests that immigrants search more intensively, exhibit higher demand elasticity, pay lower prices for identical products, and shift expenditure toward lower-appeal products — consistent with a model of heterogeneous price sensitivity. |
| Keywords: | immigration, consumer prices, search, demand elasticity |
| JEL: | F22 E31 L11 J61 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12588 |
| By: | Arellano-Bover, Jaime (Yale University); Bussotti, Carolina (Rome Economics Doctorate); Paradisi, Matteo (EIEF); Wu, Liangjie (EIEF) |
| Abstract: | Brand capital--an intangible asset that differentiates a firm's products--has grown in recent decades, alongside the rise of intangible investment and the decline in the labor share. Trademarks are legal claims on brand capital and are traded across firms, providing a setting to study how reallocating brand capital reshapes firm behavior and aggregate outcomes. Leveraging a novel link of Italian administrative data on trademark ownership, firms' financial statements, and employer–employee records, we exploit firm-to-firm trademark transactions to identify the effects of brand-capital investment. Guided by a model in which firms combine production and expansionary with brand capital, we use an event-study design to estimate firm-level and aggregate effects. Acquiring a trademark increases intangible assets by 19%, sales by 8%, and employment by 6%, while leaving weekly earnings unchanged and reducing the firm-level labor share. Employment gains are concentrated among marketing and sales workers. Trademark transactions reallocate brand capital toward larger firms, raising combined buyer-seller sales. Calibrating the model, we find this reallocation generates a one percentage-point long-run decline in the aggregate labor share. |
| Keywords: | brand capital, trademarks, labor share, labor demand, markups |
| JEL: | L25 O34 E25 J23 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18461 |
| By: | Qayoom Khachoo (Indian Institute of Foreign Trade); Ridwan Ah Sheikh (Indira Gandhi Institute of Development Research); Pritam Banerjee (Indian Institute of Foreign Trade) |
| Abstract: | This study leverages India's Patents (Amendment) Act, 2002, as a quasi-natural experiment within a difference-in-differences framework to examine how domestic reforms related to patents may affect firms' export behavior and their integration to the global value chains. Exploiting a detailed firm-level database covering the universe of Indian manufacturing firms, we find that heightened patent protection is associated with approximately a 18 increase in exports and a 12 increase in total imports among high-tech firms relative to low-tech firms, even including firm, year, and industry-by-year fixed effects. We further show that stronger enforcement of intellectual property rights (IPRs) has a positive impact on firms' imports of intermediate inputs. Specifically, high-tech firms experienced 20 increase in raw-material imports relative to their low-tech counterparts. In contrast, the reform was associated with a significant reduction in imports of spares and stores. While the average treatment effects on capital and final goods imports remain insignificant, event-study estimates suggest positive and statistically significant effects, albeit with a delay. This study provides policy-relevant evidence that stronger IPRs in emerging market economies such as India enhance firms' trade performance by stimulating innovation, promoting technology transfer and adoption, and enabling access to advanced global inputs. |
| Keywords: | IPRs, Exports, Imports, Global value chains, Difference-in-Differences |
| JEL: | F13 F14 O30 O33 O34 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:ind:igiwpp:2026-001 |
| By: | David Hummels; Jakob Munch; Huilin Zhang |
| Abstract: | We build a model of CEO compensation that unites principal-agent and assignment models in the face of trade shocks that interact with CEO effort. The model predicts that trade shocks change CEO compensation through scale, volatility, and ability-magnification channels. Using Danish matched worker-firm data, we find empirical support for these channels: (1) Exogenous shocks to trade increase the size and value of the firm and CEO compensation; (2) the share of firm value paid to the CEO is increasing in the size and value of the firm and increasing in the volatility induced by global shocks; (3) Higher-ability CEOs generate increases in firm value that are more than 100 times greater than their compensation, through a combination of mitigating losses and maximizing the return to positive shocks. |
| JEL: | F16 G30 J30 J31 M52 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35004 |
| By: | Brett Hemenway Falk; Gerry Tsoukalas |
| Abstract: | If AI displaces human workers faster than the economy can reabsorb them, it risks eroding the very consumer demand firms depend on. We show that knowing this is not enough for firms to stop it. In a competitive task-based model, demand externalities trap rational firms in an automation arms race, displacing workers well beyond what is collectively optimal. The resulting loss harms both workers and firm owners. More competition and "better" AI amplify the excess; wage adjustments and free entry cannot eliminate it. Neither can capital income taxes, worker equity participation, universal basic income, upskilling, or Coasian bargaining. Only a Pigouvian automation tax can. The results suggest that policy should address not only the aftermath of AI labor displacement but also the competitive incentives that drive it. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.20617 |
| By: | Lubos Pastor; Taisiya Sikorskaya; Jinrui Wang |
| Abstract: | As stock market concentration has risen, regulatory limits on fund portfolio concentration have become increasingly binding, especially for large-cap growth funds. When funds approach these limits, they trim their largest holdings and reduce equity exposure. Funds perform worse when constrained. A constraint-based ownership measure predicts stock returns, particularly among the largest firms. These findings suggest that high market concentration can distort stock prices by limiting the ability of optimistic investors to scale their positions. Just like short-sale constraints can produce overpricing by limiting pessimistic investors' views, constraints on long positions can generate underpricing by suppressing optimists' views. |
| JEL: | G12 G14 G23 G28 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35007 |
| By: | Mayara Felix |
| Abstract: | I estimate the effect of trade on local labor market concentration and its implications for wages using employer-employee linked data and tariff shocks from Brazil’s trade liberalization. Trade increased concentration by 7%, an effect driven by firm exit and worker flows to surviving import-competing firms. Increased concentration reduced wage take-home shares—estimated at 50 cents on the dollar pre-shock—enough to offset small wage gains from reallocation, but did not meaningfully reduce wages on net. Most of the wage declines attributed to Brazil's trade liberalization resulted instead from reductions in the marginal revenue product of labor. Incorporating informality reveals substantial regional heterogeneity. |
| JEL: | F16 O1 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35018 |
| By: | Susan Helper; Resem Makan; Daniel W. Shoag |
| Abstract: | We study the establishment of U.S. National Laboratories in the 1940s–1950s to estimate local spillovers from public research infrastructure. This setting allows us to causally identify such spillovers, for two reasons: 1) Lab sites were chosen largely for security and political reasons, rather than existing or potential innovative capability and 2) We identify runner-up locations using archival sources. We find several types of knowledge spillovers: Compared to control counties, Lab counties experience large and persistent increases in patenting by non-lab inventors; non-lab patents in the same county shift toward laboratories’ research fields and cite laboratory patents more frequently. Using newly digitized county data from 1936–1970, we find sustained increases in retail sales and household income. Linked 1940–1950 Census records show wage gains for pre-existing residents who remain in lab counties, with larger effects for college-educated workers. We find that cohorts exposed to laboratory establishment during school-age years attained more education, consistent with a human-capital channel. Spillovers arise despite extensive secrecy around early nuclear research, suggesting that co-location with public R&D can generate sizable local benefits even under restricted information flows. |
| JEL: | O31 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35011 |
| By: | Marina Gertsberg; Michaela Pagel; Ekaterina Volkova; Valeria Volkova |
| Abstract: | The Epstein files, released between September 2025 and January 2026, offer an unprecedented window into the social and professional network of a convicted sex offender whose ties extended deep into corporate America. We construct a comprehensive sample of all S&P 500 CEOs and board members serving between 2006 and 2026 - 52, 266 unique individuals - and search the 1, 293, 753 text-bearing documents for evidence of their contact with Jeffrey Epstein. Using large language model (LLM) classification of 117, 394 matched correspondences, we identify 67, 637 that indicate direct contact with 1, 179 S&P 500 CEOs or directors. We then document three main findings. First, firms whose CEOs or board members appeared in Epstein-related news coverage experienced significantly negative cumulative abnormal returns of up to -8.5% over a ten-day window following the January 30, 2026 DOJ release. Second, adding Epstein-mediated ties to the firm network increases overall density and reduces average path lengths significantly, meaning that Epstein effectively wired corporate America into a denser, more tightly interconnected governance network than would have existed otherwise. Third, we show that Epstein's network transmitted norm contagion through shared board connections. Firms with more Epstein-connected CEOs or directors exhibit significantly worse ESG outcomes: each additional connection is associated with approximately 2.3 more annual governance incidents and 4.0 more total incidents. |
| Keywords: | Epstein files, connections, networks, corporate governance |
| JEL: | G30 G34 G38 J16 K38 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12580 |
| By: | Ertian Chen; Lichao Chen; Lars Nesheim |
| Abstract: | The European Union Emissions Trading System is set to substantially increase the effective carbon price faced by airlines. To quantify the impact of this carbon regulation on the European airline industry, we estimate a two-stage model of airline competition with endogenous route entry, flight frequencies, and pricing using European data on market shares and prices. Counterfactual simulations reveal that the impacts of carbon pricing are highly asymmetric across carrier types and market segments. Consumer surplus declines by up to 25% overall, with medium-haul markets bearing the brunt at up to 90%, while short-haul markets experience positive net welfare gains (including carbon revenue and the social value of avoided emissions) as airlines reallocate capacity toward shorter routes. We find that airline profits decline by 8–45% across scenarios, while carbon tax revenue of $0.9–3.1 billion and a social value of avoided CO2 emissions of $0.5–1.4 billion partially offset the welfare losses. We also show that a hypothetical Wizz Air–Ryanair merger primarily benefits firm profits through network expansion synergies. |
| Date: | 2026–03–30 |
| URL: | https://d.repec.org/n?u=RePEc:azt:cemmap:04/26 |
| By: | Andrew Johnston; Christos A. Makridis |
| Abstract: | Does artificial intelligence (AI) increase productivity - and does it displace workers? We examine aggregate effects using administrative data covering essentially all U.S. employers in a difference-in-differences design exploiting occupational AI exposure across industries and states. A one standard deviation increase in exposure raises output by 7%, with effects emerging in 2021 when enterprise AI tools entered the market. Employment effects follow the same timing but diverge by exposure type: where AI likely requires human collaboration, employment rises 4%; where AI can perform tasks independently, we find no significant employment effect. Results are robust to state-by-year and industry-by-year fixed effects and suggest AI has caused a decrease in the labor share of income. |
| Keywords: | artificial intelligence, generative AI, aggregate productivity, labor market, technological change |
| JEL: | O33 J24 J23 E24 O47 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12579 |