Useful Knowledge

Useful Knowledge Issue No. 009 - July 28, 2026

Tue, 28 Jul 2026 23:59:59 GMT

Issue No. 009

49 papers.

Business models for digital innovation intermediaries: evidence from the digital innovation hubs initiative
Federica Rossi, Ana Colovic, Simona Ciappei, Andrea Bonomi Savignon
Industrial and Corporate Change
How do digital innovation intermediaries organize themselves to support technology adoption? Drawing on interviews with 20 Digital Innovation Hubs across five European countries, the paper compares their value propositions and governance structures in policy-driven digital transition programs. It identifies four recurring business-model configurations linking sectoral or territorial focus to centralized or decentralized delivery, and argues that these organizational choices shape how digital intermediation works in practice.
Demand-oriented policy tools and industrial convergence: evidence from cross-regional government procurement in China
Jun Liu, Chang Cao, Wei Wu
Industrial and Corporate Change
Can cross-regional public procurement steer industrial convergence? Using city-pair panel data for China from 2015 to 2022, the paper estimates how procurement across jurisdictions changes similarity in cities' industrial structures. Cross-regional procurement increases industrial convergence by expanding investment links, strengthening innovation and R&D collaboration, and reinforcing complementary policy support, with larger effects in non-cluster cities and places with similar resource endowments.
Beyond Innovation-Speak: Swiss Telephone Workers and the Making of Technical Identity
Riccardo Ferrigato
Technology and Culture
How did telephone-maintenance workers resist the idea that repair work was peripheral to technological progress? Union magazines and unpublished sources trace Switzerland's Post, Telephone and Telegraph workforce from 1921 to 1959 as the network expanded, automated, and aged. Workers moved from identifying as infrastructure builders to claiming maintenance as specialized indispensable labor, making status, expertise, outsourcing, and training central conflicts in the politics of technical identity.
The Indirect Disclosure Effect: How Disclosing Generative AI Use Impacts Human Creative Collaboration with AI
Ekaterina Jussupow, Kevin Bauer, Rebecca Maria Heigl, Benjamin Vogt, Oliver Hinz
Information Systems Research
Do mandatory labels for generative-AI use change how creators collaborate with AI before audiences ever see the label? Two nested mixed-methods experiments vary disclosure conditions while participants work with a text-to-image model. Anticipated disclosure pushes many creators to withdraw from the creative process because they expect audiences to discount their agency, so outputs become more machine-driven even though the policy is intended to protect human authorship.
Automated private enforcement: evidence from the Google Fonts Case
Jakob Merane, Alexander Stremitzer
Journal of Legal Analysis
Difference-in-differences design using a difference-in-differences approach on a two-year sample of 1,517,429 websites across 32 countries, finds a significant compliance effect in Austria: non-compliance dropped by 22.7 percentage points within three months, a nearly 50% reduction. These findings suggest automated enforcement can be disruptive, pressuring policymakers to recalibrate legal rules. Plaintiffs often lack incentive to enforce low-value claims, weakening defendants' compliance incentives.
Context-aware multimodal AI navigates hidden pathways in five centuries of art evolution
Jin Kim, Byunghwee Lee, Taekho You, Jinhyuk Yun
Proceedings of the National Academy of Sciences
Historical analysis. Uses generative AI, specifically Stable Diffusion, to analyze 500 y of Western paintings by extracting two types of latent information with the model: formal aspects (e.g., colors) and contextual aspects (e.g., subjects). Also, shows how artistic expression aligns with historical shifts using contextual keywords extracted from paintings. The rise of multimodal generative AI transforms the intersection of technology and art, offering richer insights into large-scale artworks.
Skill Deprioritization: Reorganizing in the Age of Generative Artificial Intelligence
Matteo Devigili, Erdem Dogukan Yilmaz, Vibha Gaba, Henrich Greve
Management Science
Theoretical model. GenAI effectively retrieves data, performs analysis, and conveys information, so it can substitute for workers doing these activities and complement workers relying on them. Finds significant declines in demand for monitoring (reward distribution), operational exceptions, and task division skills, with information provision also showing declines. How does generative artificial intelligence (GenAI) reshape the skills that organizations seek as they adapt to a new general-purpose technology?
Beyond Price: Rights Trading in Strategic Factor Markets
Anparasan Mahalingam, Matthew J. Higgins
NBER Working Papers
How is value divided in technology licensing when price is only one part of the deal? Using 858 hand-coded university-industry licenses, the analysis studies how payment terms move together with future improvement rights and commercialization scope. Contracts that rely more heavily on royalties and milestones give licensees stronger claims on future improvements and broader commercialization rights, with the relationship strongest when university technology transfer offices have more contracting experience.
The Effects of U.S. Public R&D on Global Growth
Gustavo De Souza, Andrew J. Fieldhouse, Karel Mertens, Ishan B. Nath, Valerie A. Ramey
NBER Working Papers
How much of the payoff from U.S. public R&D leaks abroad rather than staying in the United States? A narrative identification strategy links exogenous shifts in U.S. R&D appropriations to total factor productivity in 69 foreign economies from 1980 to 2019. A shock equal to 1 percent of the federal R&D capital stock raises foreign TFP by about 1 percent after twelve years, with larger effects from nondefense R&D and in non-OECD economies, implying global returns about twice as large as the domestic returns captured at home.
Innovation Spillovers Within Firms and Within Cities
Zhao Jin, Amir Kermani, Timothy McQuade, Minghao Yang
NBER SI 2026 Urban Economics
Do innovation spillovers matter more inside firms or across cities? A mover design and a shift-share IV based on variation in the supply of future inventors across university programs identify the causal effect of inventor concentration on output. A 10 percent increase in inventors raises output by 5.4 percent, while city-level agglomeration effects largely disappear once firm composition is controlled for, pointing to economically large within-firm gains from clustering inventive talent.
Innovation Nation: The Impact of Broadening Access to Doctoral Education on Ph.D. and Patenting Outcomes
Francisca M. Antman, Kirk B. Doran, Xuechao Qian, Bruce A. Weinberg
NBER SI 2026 Development of the American Economy
Did the expansion of doctoral education widen access to invention or mainly reproduce existing advantage? Matching the universe of Ph.D. recipients to full-count U.S. censuses from 1850 to 1940, an event-study design tracks outcomes around the opening of new doctoral programs. New programs increased doctorate attainment and patenting, expanded access for minority, immigrant, rural, and lower-status students, and generated spillovers that improved patent quality and increased invention beyond doctorate holders themselves.
Managing Innovation in Firms
Ingrid Haegele, Isabela Manelici, Jose P. Vasquez, Francis Wong
NBER SI 2026 Personnel Economics
Do managers matter for innovation, or do inventive workers succeed regardless of who supervises them? Administrative data from a global manufacturing firm track employee ideas from invention through patenting and commercialization, while quasi-random manager switches create variation in exposure to different supervisors. Moving from an average manager with no invention experience to a highly experienced manager approximately doubles workers' patented inventions within four years, while also increasing patent citations and commercialization through hands-on know-how and closer collaboration.
Openness to AI in Higher Education
Fernando Saltiel
NBER SI 2026 Digital Economics and Artificial Intelligence
How open are universities to AI in practice, as opposed to on paper? A dataset of 148,200 syllabi from 25 public universities in four U.S. states links course policies to instructor characteristics in Spring 2026. About 60 percent of syllabi mention AI explicitly, but only 6 percent integrate it into classroom use, with adoption more common among assistant professors and instructors with more recent publication records.
Calibrated Coarsening: Designing Information for AI-Assisted Decisions
Bnaya Dreyfuss, Ruru Hoong
NBER SI 2026 Digital Economics and Artificial Intelligence
When AI assists human decisions, should the signal be made less precise rather than more precise? An information-design model and an experiment with 150 professional loan specialists compare human-only decisions, continuous AI risk scores, and theory-guided coarsened signals. Accuracy is highest when the AI signal is coarsened at the model's threshold, beating both the human-only benchmark and uncoarsened AI assistance, with additional gains available from personalization.
From Compressing Complexity to Accommodating Complexity: How AI Transforms Standardization and Individualization
Li Li, Yu Cao
arXiv Computers and Society,arXiv economics of AI
Why was standardization so central to industrial production, and what exactly does AI change? A conceptual framework built around limited information-processing capacity introduces cognitive fixed cost to explain when producers compress variety and when they can accommodate it. AI relaxes the old constraint by shifting adaptation from physical production to information processing, but standardization does not disappear; it moves into the rules and infrastructures that govern personalized output.
Tracked in arXiv Computers and Society, arXiv economics of AI
The AI Wave and the Reinvention of Game Discovery: Oversupply, Structural Correction, and Agentic Player-Game Matching
Brian Dean Madanamootoo
arXiv Computers and Society,arXiv economics of AI
Is the flood of AI-assisted game releases setting up a market crash or a new form of concentration? The paper combines Steam and itch.io catalog data, user-behavior data, and a historical comparison with the 1983 video-game crash, then studies subscription and curated platforms as natural experiments in discovery. It finds extreme attention concentration and argues that falling production costs are pushing the market toward concentration rather than collapse, increasing the importance of discovery systems and agentic player-game matching.
Tracked in arXiv Computers and Society, arXiv economics of AI
Generative Artificial Intelligence in Scientific Research: Individual Benefits, Collective Risks, and a Framework for Responsible Research with AI
Fulvio Castellacci, Tommaso Ciarli, Yuan Gao, Marianna Marino, Giacomo Marzi, Massimo Riccaboni, Maria Savona, Simone Vannuccini
arXiv economics of AI
Drawing on an academic roundtable held at the AI for Science and Innovation Workshop (Scuola IMT Alti Studi Lucca, April 2026) and on a fast-expanding empirical literature, it maps the disagreement within the research community across four stages of the research process: funding, research tasks, publication and peer review, and use and uptake. The picture changes once productivity is disaggregated: AI-assisted work shows measurable gains in publication volume and citation share, while the evidence on novelty, disruption, and breakthrough output remains ambiguous or negative.
Reality Monitoring in Large Language Models: Self-Knowledge That Transforms with Conversation Memory
Saurabh Ranjan, Konstantina Sokratous, Brian Odegaard
arXiv Computers and Society
A conversational AI that cannot tell its own output from what a user said will treat its own mistakes as user-provided facts. Here shows, across two experiments and six LLMs, that source attribution depends on how conversational memory is structured: ceiling accuracy for self-generated content under minimal memory demands reverses to a fragile external-item advantage once episodic delay removes that shortcut. In humans, this capacity is called reality monitoring, and its failures are linked to hallucinations, delusions, and confabulation, yet whether LLMs possess it remains untested.
Artificial Intelligence and Application Domains in FinTech
Suela Vasil, Armela Maxhelaku
RePEc NEP Artificial Intelligence
Using a systematic search strategy? The results show that machine learning? deep learning and natural language processing are the most frequently applied AI models and Random Forest? Since late 2022, the rapid evolution of generative artificial intelligence and large language models has significantly accelerated the integration of AI into FinTech services, including credit scoring, fraud detection, algorithmic trading, and regulatory compliance.
Artificial Intelligence and the Rents of Finance Workers
Jean-Edouard Colliard, Junli Zhao
RePEc NEP Artificial Intelligence
Can better AI raise, rather than erode, the rents and employment of finance workers? A model of investment projects in which AI and workers perform distinct tasks separates a freeriding channel from a capital-reallocation channel. AI can increase both worker rents and labor demand when improved success probabilities raise the payoff to shirking or redirect capital toward high-rent workers, overturning the usual prediction of unambiguously lower labor rents.
Data, Power and Emissions: The Environmental Cost of AI
Alessandra Bonfiglioli, Rosario Crinò, Mattia Filomena, Gino Gancia
RePEc NEP Artificial Intelligence
Examines the environmental impact of artificial intelligence (AI) using a novel dataset that links measures of AI penetration, the location of data centers and power plants, and CO2 emissions across US commuting zones between 2002 and 2022. First, exploiting a shift–share identification strategy, shows that localities more exposed to AI experience relatively faster emissions growth. Second, decomposition results indicate that scale effects dominate, while changes in industrial composition exert at most a weak mitigating effect; at the same time, electricity generation becomes more carbon intensive.
Demographic Change and Business Dynamics in the EU
Igor Fedotenkov, Anneleen Vandeplas
RePEc NEP Entrepreneurship
Will population ageing reduce the rate at which new firms enter European markets? Cross-country estimates relate EU demographic structure to firm entry using alternative specifications and controls. The 30-to-44 age group has the strongest positive association with entry, younger cohorts have a negative association, and rising educational attainment may offset part of the drag from demographic ageing.
European entrepreneurship: Trends in start-ups and scale-ups in France, Germany and the UK
Massimo G. Colombo, Lena Füner, Massimiliano Guerini, Hanna Hottenrott, Daniel Souza
RePEc NEP Entrepreneurship
This policy brief examines entrepreneurial ecosystems in France, Germany, and the United Kingdom using data on more than nine million firm births between 2009 and 2023. While many firms exhibit characteristics associated with future growth, the number that achieve large-scale expansion falls short of expectations. The study aims to assess both the quantity of start-ups and their quality, measured by their potential to become scale-ups.
Exploring Household Adoption and Usage of Generative AI: New Evidence from Italy
Leonardo Gambacorta, Tullio Jappelli, Tommaso Oliviero
RePEc NEP Artificial Intelligence
Who uses generative AI at home, and does use correlate with higher earnings? A 2024 representative module in the Italian Survey of Consumer Expectations measures awareness, use, and anticipated use among adults aged 18 to 75, then estimates a Mincer earnings regression. Awareness reached 75.6 percent, 36.7 percent had used generative AI in the prior year, and monthly users reported an earnings premium of about 2 percent, with use concentrated among men, younger adults, students, and college graduates.
Governing Generative AI Across Financial Institutions: An SR 26-2-Compatible Framework for Generative AI Risk Control
Yiqing Wang, Yixin Kang, Luyun Lin, Siqi Mao
RePEc NEP Artificial Intelligence
The release of SR 26-2 marks a significant modernization of U.S. model risk management by replacing SR 11-7 with a more risk-based and materiality-sensitive supervisory framework. Although generative AI may not directly estimate credit risk or make underwriting decisions, its outputs can materially affect the surrounding control environment through monitoring interpretation, policy analysis, or adverse-action language drafting. However, generative and agentic AI are excluded, creating an important governance challenge for banking organizations and other financial institutions.
Harnessing Artificial Intelligence for Monitoring Financial Markets
Matteo Aquilina, Douglas Araujo, Gaston Gelos, Taejin Park, Fernando Perez-Cruz
RePEc NEP Artificial Intelligence
Develops a new approach based on a combination of a recurrent neural network (RNN) and a large language model. Predicting financial market stress has long proven to be a largely elusive goal. Advances in artificial intelligence and machine learning offer new possibilities to tackle this problem, given their ability to handle large datasets and unearth hidden nonlinear patterns.
Innovation and Survival of Young French Firms
David DADAKPETE, Moïse DOSSA
RePEc NEP Entrepreneurship
Does innovation help young firms survive, or does it expose them to a liability of innovativeness? Estimates for French firms during their first five years distinguish process, product, organizational, and marketing innovation at entry. Most innovation types have no statistically significant survival effect, but marketing innovation raises the risk of closure by 11.3 percent.
Is AI Trained on Public Money? Evidence from U.S. Data Centers
Adam Feher, Emilia Garcia-Appendini, Roxana Mihet
RePEc NEP Artificial Intelligence
Does the expansion of AI data centers already raise local electricity prices or impose measurable economic and environmental spillovers? U.S. data on data-center energy loads, utility prices, establishments, and emissions from 2010 to 2023 are combined with an instrumental-variables continuous difference-in-differences design. The estimates find no detectable local spillovers at observed scale, although a regional model implies that larger shocks could raise household utility bills without regulation or additional supply.
Managing the Human Fallback: Skill Investment Under Improving AI and Worker Mobility
Simrita Singh, Naireet Ghosh, Tinglong Dai
RePEc NEP Artificial Intelligence
Develops a parsimonious two-period model in which AI may outperform the worker when it functions, but may fail with positive probability. Mobility also reshapes how AI progress affects engagement: greater capability raises engagement by increasing the value of the skill trajectory a firm offers, whereas greater reliability can raise or lower it because it reduces fallback need while also changing learning opportunities. When firms deploy autonomous AI, they must decide how much work to leave to the system and how much to keep workers engaged.
Optimal Integration: Human, Machine, and Generative AI
Hongda Zhong
RePEc NEP Artificial Intelligence
Applying the model to artificial intelligence (AI) reveals that AI's generative capabilities make it more likely to serve as the final decision-maker, reducing the need for costly human input, but underscoring the risks of AI hallucination. I study the optimal integration of humans and technologies in multi-layered decision-making processes. Each layer can correct existing errors but may also introduce new ones.
The Future of AI in Capital Markets
Tobias Adrian, Benjamin Mosk, Jason Wu
RePEc NEP Artificial Intelligence
The impact of Gen AI is characterized by its ability to process vast amounts of data, allowing market participants to translate new information quickly into price signals. Generative Artificial Intelligence is starting to more significantly impact capital market trading activities, on the heels of the broad deployment of machine learning that has already revolutionized trading over the past two decades. While many current changes appear more evolutionary, more revolutionary shifts may emerge, potentially catching policy makers off guard.
When Summaries Distort Decisions: Information Fidelity in LLM-Compressed Financial Analysis
Hoyoung Lee, Suhwan Park, Seunghan Lee, Jun Seo, Jaehoon Lee, Sungdong Yoo, Minjae Kim, CheolWon Na, Zhangyang Wang, Zach Golkhou, Minkyu Kim, Sotirios Sabanis, Alejandro Lopez-Lira, Dhagash Mehta, Soonyoung Lee, Chanyeol Choi, Wonbin Ahn, Yongjae Lee
RePEc NEP Artificial Intelligence
When LLMs summarize financial material, do they preserve the decision the underlying source supports? Evidence from financial filings and earnings-call transcripts evaluates compression by information fidelity: whether it changes the resulting investment judgment. Fluent, factually plausible summaries can still alter decisions by stripping caveats from evidence or exposing different views across models; generating multiple compressions and auditing their disagreements against the source mitigates those failures.
Visible to the Court: How AI Is (and Isn't) Litigated in U.S. Federal Court Opinions
Julie Yu, Rock Yuren Pang, Jevan Hutson, Katharina Reinecke
arXiv Computers and Society
How are U.S. federal courts actually governing AI-related harms in the absence of comprehensive federal regulation? A systematic review of 559 opinions classifies seven dispute areas, six categories of AI technology, four recurring litigant types, and the doctrines parties invoke. Courts largely apply pre-existing legal rules rather than AI-specific law, leaving the litigation record shaped by which harms existing statutes recognize rather than by the full distribution of AI harms.
AI Strategy: How to Choose What AI Product to Implement
Foster Provost, Panos Ipeirotis
arXiv Computers and Society,arXiv economics of AI
Firms struggle to choose AI projects that pay off: two projects can look equally promising to smart, motivated stakeholders and yet deserve opposite decisions. A simple ROI estimate could not distinguish the two. Separating the three breaks a common catch-22: teams cannot estimate ROI until they know whether a project will work, yet cannot know whether it will work without building it.
Tracked in arXiv Computers and Society, arXiv economics of AI
Auditing Alignment Controllability in LLMs via Political Axes
Bartol Bućan, Nikola Sočec, Sarah Isufi, Morena Granić, Luka Hobor, Agneza Krajna, Mihael Kovac, Mario Brcic
arXiv Computers and Society,arXiv economics of AI
But that resting point barely matters in deployment: a model must land somewhere, and what counts is how far, and in which directions, its answers can be steered. Political audits of large language models (LLMs) usually reduce each to one point on a political compass. Models do not shift alike: some move more, and some saturate under extreme framings.
Tracked in arXiv Computers and Society, arXiv economics of AI
Separating Capability from Permission: A Governance Framework for Agentic AI Autonomy Levels
Haining Zheng, Qian Dong, Rodolfo K. Depena, Jonathan D. Bhatia, Feng Xiao, Peng Xu
arXiv Computers and Society
To operationalize this framework, proposes a risk-aware decision process for assigning allowed autonomy, analyze how risk and accountability evolve across autonomy levels, and demonstrate its application through a deployed enterprise data engineering agent, illustrating how a system assessed at a high capability level can be deliberately constrained to a lower allowed autonomy based on risk, reversibility, and organizational readiness. As AI systems increasingly exhibit agentic behavior, discussions of autonomy often conflate what systems are technically capable of doing with what they should be permitted to do in practice.
Agentic AI Orchestration of Heterogeneous Economic Models for Rapid, Multi-scenario Analysis of Energy Crises
Dana Golden, Brett Indelicato, Lav R. Varshney, Carlos D. Messina, Suzanne Thornsbury
arXiv economics of AI
Theoretical model. Off-the-shelf models that already exist typically focus only on limited aspects of the system and are distributed across research groups, programming languages, software architectures not designed for model integration, and incompatible formats. Shows that large language models can perform the critical integration directly. The language model generates no quantitative results: every reported value is reproduced directly from an underlying model run, remains traceable to its source and is subject to analyst approval at each stage.
Accountable yet Anonymous AI Agents - Split-Knowledge Binding in National Agent-Identity Layer in China
Yifan He, Zhiguang Shan, Le Luo, Wei Wang
arXiv Computers and Society
The system is evidence of feasibility at national scale; the framework is the instrument by which any deployment -- including this one -- should be judged. Documents a national system in China -- built as national infrastructure and scheduled for public launch in Q3 2026 -- that occupies a different and underexplored point in the same design space: an agent is associated with a verified legal principal without that principal being disclosed to any business-layer participant.
Opaque Epistemic Mediation: How LLM Deployment Configurations Shape the Validation of Pseudo-Science
Davide Scarso, Hugo Noronha de Almeida, Joaquim Pina
arXiv Computers and Society
Three additional findings emerged: (1) a silent patch reversed Grok's behaviour from chaotic to stably high validation overnight, without any public documentation; (2) the same Grok model identifier produced radically divergent outputs via API (75) and an unstable, near-zero collapse via web (mean 5.5) three months later; (3) refusal to rate the pseudo-scientific claim, the most defensible response observed, appeared in two model families through different interfaces (Claude Opus 4.1 categorically via web, GPT-5.1 Chat intermittently via API) and eroded in the successor version of each.
Developing and Validating the Spanish Version of the Large Language Models Dependency Scale (LLM-D12-SP)
Tran Gia Bao, Mo El-Haj, Sameha Al-Shakhsi, Antonio Garcia-Cabot, Raian Ali, Ala Yankouskaya
arXiv Computers and Society
The present study reports the first validation of the Spanish version of the Large Language Model Dependency Scale (LLM-D12-SP), extending prior validations conducted in English- and Arabic-speaking samples. External validation showed that both dependency dimensions were positively associated with internet addiction and perceived trustworthiness of LLMs, while showing weak or no association with need for cognition.
Open Veins of Algorithmic Auditing: Why AI Assessment Lags Behind Its Deployment in the Global South
Gemma Galdon Clavell, Alexandra Magaard
arXiv Computers and Society
Why has independent evaluation of deployed AI lagged so far behind public investment in the Global South? A decade of audit practice across Latin America, Sub-Saharan Africa, and Asia Pacific covers published and unpublished public-sector audits, 13 Responsible AI Assessments, and a regional landscape analysis. Fewer than 20 published second- or third-party audits appear against hundreds of documented public-sector systems, with the central constraint identified as funding demand for independent evaluation rather than local technical capacity.
pAI-Econ-claude: A Gated Human-in-the-Loop Multi-Agent Architecture for AI-Assisted Economic Theory Development
Chen Zhu, Xiaolu Wang, Weilong Zhang
arXiv economics of AI
Theoretical model. In many social-science research tasks, such as economics, LLM-based agents must produce outputs for which no cheap, task-complete, machine-readable correctness signal exists. This creates a distinctive reliability problem for multi-agent systems: how should generation, critique, coordination, and human judgment be organized when no component can certify the final result? The negative case shows that scaffolding can also compress an economically important mechanism too aggressively.
Scientific exploration, collaboration and labor division in the large language model era
Xiang Zheng, Xi Hong, Jialin Liu, Chaoqun Ni
arXiv Computers and Society,arXiv economics of AI
How has the spread of LLMs coincided with changes in scientific exploration and the organization of research teams? Publication and collaboration histories for 775,323 scientists, linked to 137,120 CRediT contribution statements, trace patterns before and after 2022. Researchers increasingly crossed into distant and new fields, especially established and non-English-speaking researchers in low- and middle-income countries, while teams adopted more differentiated roles with more software and validation work and less overlapping responsibility.
Tracked in arXiv Computers and Society, arXiv economics of AI
The Human-AI Substitution Principle: When will you be replaced by AI in your organization?
Bonny Banerjee, Shreya Singh
arXiv Computers and Society,arXiv economics of AI
Theoretical model. Introduces an analytical model for studying Human--AI Task Allocation (HAT) in hierarchical organizations. A key result is the Human--AI Substitution Principle, which provides a precise condition --- grounded in the formal asymmetry assumption --- under which AI replaces human labor. Building on this result, shows that AI adoption can produce abrupt workforce transitions, hybrid human--AI organizations, including cases where risk heterogeneity sustains human and AI roles without requiring a minimum-human-fraction constraint, and flatter managerial hierarchies with wider spans of control.
Tracked in arXiv Computers and Society, arXiv economics of AI
Spillovers from Science
Dennis Verhoeven, Anna Valero, Ralf Martin, Arjun Shah
CEPR Discussion Papers
Introduces a new measure---Science Rank---that uses the combined patent and paper citation network to assign a share of the private value of patented inventions to the scientific papers they directly or indirectly rely on. Finds that a relatively large share of the total value generated by research in Lower and Middle Income Country (LMIC) feeds into climate change related innovation. Quantifying spillovers from scientific knowledge to technology is important for understanding the social returns to science and for designing policy.
Taxi Market Deregulation: Effects on Market Outcomes, Tax Evasion and Crime
Jarkko Harju, Ida Kankaanranta, Kaisa Kotakorpi
CEPR Discussion Papers
The reform led to a surge in firm entry and a modest increase in exit, indicating substantial changes in market structure. Examines the effects of taxi market deregulation in Finland, which removed price controls and lowered barriers to entry. Average taxi prices increased slightly according to price indices, while monthly firm-level reported sales and VAT declined by over 10 percent.
Licensing and Innovation Regimes in Pharmaceutical R&D
Michele Liberatore, Massimo Riccaboni
arXiv innovation and entrepreneurship
When does licensing allocate pharmaceutical R&D efficiently, and when do information frictions leave valuable projects poorly screened? A model distinguishes incremental from novel projects, then product-level estimates and double-machine-learning tests use exogenous pipeline shocks to identify licensing effects. Licensing raises success probability overall and equalizes risk-adjusted returns for incremental projects, but novel projects retain lemons-type frictions and do not show the same competitive adjustment.
Generative AI floods and dilutes the market for books
Tuhin Chakrabarty, Xinyue Liu, Jane C. Ginsburg, Paramveer Dhillon
arXiv Computers and Society,arXiv economics of AI
Measures whether undisclosed AI-generated fiction is commercially consequential rather than merely expanding low-quality supply. Full-text AI detection for 14,419 self-published Amazon genre-fiction books from 2023-2026 is matched to daily sales through June 2026. Books with more than 25% detected AI text take a growing sales share and scarce top-rank positions; the number of selling books rose 19.2-fold while revenue rose 8.9-fold, reducing revenue per selling title in most genres.
Tracked in arXiv Computers and Society, arXiv economics of AI
When Shippers Become Algorithms: Candidate Exposure, Information Design, and the Concentration of LLM-Mediated Freight Markets
Takahiro Ezaki, Naoto Imura, Katsuhiro Nishinari
arXiv Computers and Society,arXiv economics of AI
Examines how delegating carrier selection to LLM agents changes concentration and surplus in freight matching markets. Agent-based simulations send 50 shipper agents using GPT, Claude, or Gemini through 30 days of capacity-constrained waterfall tendering. A single carrier receives as much as 76% of first-choice requests; disclosing remaining carrier capacity cuts concentration by one-third and doubles shipper surplus, while vendor diversification, order randomization, and popularity displays have no detectable effect.
Tracked in arXiv Computers and Society, arXiv economics of AI

Useful Knowledge Issue No. 008 - July 21, 2026

Tue, 21 Jul 2026 23:59:59 GMT

Issue No. 008

51 papers.

Large Industrial Clusters in the Long Run: Evidence from Million-Rouble Plants in China
Stephan Heblich, Marlon Seror, Hao Xu, Yanos Zylberberg
Review of Economic Studies
Examines the impact of large, successful manufacturing plants on other local producers in China, focusing on “Million-Rouble Plants” built in the 1950s during a brief alliance with the U.S.S.R. Finds a boom-and-bust pattern: Counties hosting these plants were 80% more productive than control counties in 1982 but 20% less productive by 2010. The ephemeral geopolitical situation and the locations of allied and enemy airbases provide exogenous variation in plant siting.
Attrition and the Gender Patenting Gap
Abhay Aneja, Oren Reshef, Gauri Subramani
Review of Economics and Statistics
Does differential persistence after patent rejection contribute to the gender patenting gap? Prosecution histories for almost one million U.S. applications exploit quasirandom examiner assignment to compare responses to early rejection. Women are 3.6-6.9 percentage points less likely to continue, and this difference accounts for more than half of the conditional gender gap in issued patents. Suggestive evidence indicates that institutional support can reduce the attrition gap.
Knowledge Access: The Effects of Carnegie Libraries on Innovation
Enrico Berkes, Peter Nencka
Review of Economics and Statistics
Between 1883 and 1919, Andrew Carnegie funded the construction of more than 1,500 public libraries across the United States, reducing the costs of accessing knowledge for millions. Examines the effect of these libraries on innovation. Shows that access to scientific knowledge and increased collaboration opportunities are possible mechanisms.
The growing importance of universities for patenting and innovation
Todd Schoellman, Vladimir Smirnyagin
Economics Letters
Asks whether university research has become more important for local patenting and innovation. Combines university R&D spending with patenting activity across metropolitan statistical areas since 1980. The patenting gap between MSAs with and without a research university has doubled, and the elasticity of patents per capita with respect to university R&D has tripled; the pattern survives controls for MSA and university characteristics, is stronger in basic-research fields, and partly reflects improved university-firm knowledge flows after Bayh-Dole.
Venture capitalists versus deep-pocketed incumbents: startup financing strategies in the presence of competitive threats
Peter K Pham, Roham Rezaei, Jason Zein
Review of Finance
Examines how venture capitalists (VCs) adapt their financing strategies when investing in startups that compete against deep-pocketed incumbents. Employing textual analysis to identify a startup’s potential competitors, shows that when competitors are cash-rich, VCs deploy a financing strategy characterized by less conditionality, as observed through larger, less frequent funding rounds that are less contingent on short-term performance. The results highlight that product market competition plays an important role in explaining VC financial contracting choices.
Assessing the feasibility of collective licensing of in-copyright works as training data for generative AI systems
Pamela Samuelson
Proceedings of the National Academy of Sciences
Copyright owners have sued several developers of large-scale generative AI systems for copyright infringement because of their uses of massive quantities of in-copyright works as training data for building AI models. Fair use will be the main defense against these charges. If fair use defenses succeed, developers will be free to continue to commercially exploit models already built on copyrighted data as well as to use these data to train new models or fine-tune existing ones.
Legal infrastructure for transformative AI governance
Gillian K. Hadfield
Proceedings of the National Academy of Sciences
What legal infrastructure is needed to make substantive AI rules enforceable? A governance perspective compares registration regimes for frontier models, identification systems for autonomous agents, and regulatory markets in which private firms supply oversight services. The analysis shifts attention from choosing rules to building institutions that can generate, implement, and update them as AI capabilities change.
The backfiring effect of weak AI safety regulation
Benjamin Laufer, Jon Kleinberg, Hoda Heidari
Proceedings of the National Academy of Sciences
Can weak AI safety rules make systems less safe? A strategic model links a regulator, a general-purpose developer, and downstream specialists who jointly determine performance, safety, and market revenue. Rules aimed mainly at specialists can reduce safety across a broad parameter range, while sufficiently strong standards applied to both stages operate as a commitment device and improve safety and performance for all parties.
Ideology and the scope of judicial discretion: evidence from the federal district courts
Banks Miller, Brett Curry
Journal of Law, Economics, and Organization
Does greater judicial discretion amplify ideological differences in patent litigation? A difference-in-differences design uses two unanimous 2014 Supreme Court decisions that expanded district judges' authority to award attorney fees, with copyright cases as a comparison. Conservative judges became significantly more likely to award fees while liberal judges did not, especially when accused infringers prevailed against patent claims. Procedural expansions of discretion can therefore produce substantively asymmetric IP outcomes.
Tracked in Journal of Law, Economics, and Organization
Staying Alive! Entrepreneurial Human Capital and Resilience of New Ventures
Pontus Braunerhjelm, Emma Lappi
Organization Science
Asks whether entrepreneurial human capital among employees improves new-venture survival. Uses Swedish longitudinal register data from 1997 to 2016 to identify employees who previously started and managed firms, then links their presence to survival outcomes for new ventures. Ventures with a higher share of employees with entrepreneurial backgrounds are more likely to survive, with mechanisms running through a larger resource base, better organizational fit for absorbing entrepreneurial knowledge, and stronger alignment between entrepreneurial competencies and venture needs.
The Effect of University Science on Corporate Innovation
Ashish Arora, Sharon Belenzon, Larisa Cioaca, Lia Sheer, Hansen Zhang
Management Science
To measure the relevance of university science to corporate innovation activity, uses machine-learning algorithms and bibliometric data to link university publications, PhD dissertations, and university patents to corporate publications and patents. Finds that university science affects corporate innovation primarily through PhD graduates and university patents: PhD graduates raise firm innovation, whereas university patents substitute for corporate patents and lower the need for internal research.
Interpretable Recommendations and Parameter-Grounded LLM Explanations with Multigraph Attention
Yan Leng, Xiao Liu, Rodrigo Ruiz
Information Systems Research
Can recommender systems provide faithful explanations without sacrificing prediction quality? A multigraph attention model ties each generated explanation to the neighbors and attributes that produced a recommendation and is evaluated on Yelp data from Ontario and Pennsylvania. Predictive performance matches strong deep-learning baselines, while a randomized experiment finds greater trust, persuasiveness, satisfaction, and engagement than similarity, social, or SHAP explanations.
Migrating to a golden opportunity: The gold rush and the roots of persistence of interregional differences in entrepreneurship
Michael Stuetzer, Michael Wyrwich, Abel Brodeur, Martin Obschonka, David B. Audretsch, Peter J. Rentfrow, Jeff Potter, Samuel D. Gosling
Journal of Business Venturing
Why do regional differences in entrepreneurship persist for generations? Historical migration evidence traces US entrepreneurship to nineteenth-century gold rushes over roughly a century. Entrepreneurially inclined people selectively moved to gold regions, migrants became more entrepreneurial than comparable movers and nonmovers, and affected states later developed more supportive institutions and persistently higher entrepreneurship rates through the present.
How to Silence Researchers? Evidence from Illiberal Policies in Hungary
Luc Paluskiewicz, Raphaël Wargon
NBER SI 2026 Science of Science Funding
How do attacks on academic freedom alter scientific production and mobility? Hungarian research repositories and international bibliometric records compare researchers by perceived political alignment and sensitive-topic exposure. Targeted academics lose roughly one-quarter of their publications and collaborators; sensitive-topic researchers experience a 10 percent publication decline and a 30 percent decline in top-journal work. Researchers also shift toward lower-ranked national-language outlets or leave Hungary, indicating both suppressed output and international reallocation.
Patent Vouchers as Innovation Incentives: Theory and Evidence from Europe
Pierre Dubois, Paul-Henri Moisson, Jean Tirole
NBER SI 2026 Innovation
A patent voucher grants the innovator a transferable right to extend market exclusivity. Results show that in most countries, vouchers impose a lower cost in terms of social surplus than the incentive-equivalent cash transfers, largely because post-patent generic competition is imperfect. Overall, the findings suggest that, if carefully designed, patent vouchers can serve as a valuable complement to existing innovation incentives.
The Labor Market Effects of Generative Artificial Intelligence and Job Loss Fears
Jonathan S. Hartley, Filip Jolevski, Vitor C. Melo, Brendan Moore
NBER SI 2026 Digital Economics and Artificial Intelligence
How do worker beliefs about layoff risk due to generative artificial intelligence compare to the realized labor market impact of the technology, and what shapes those beliefs? Using a continuous-treatment difference-in-differences design, finds that these fears are not borne out, as job postings and layoffs in more exposed occupations show no statistically significant response to the diffusion of generative AI. These results suggest that workers learn about AI's capacity to substitute for their labor by using it and form beliefs about displacement well ahead of any realized job loss.
Spillover Effects in Complementary Markets: A Study of the Indian Cell Phone and Wireless Service Markets
Chirantan Chatterjee, Ying Fan, Debi Prasad Mohapatra
CEPR Discussion Papers,NBER Working Papers
Quantitative model. Examines how spillovers across complementary markets shape product variety and firm entry. Examines the Indian cell phone and wireless service markets during the 4G rollout and estimate a structural model of demand, pricing, carrier network expansion, and phone product choice. The estimation results support the economic forces through which complementarity generates spillovers.
Tracked in CEPR Discussion Papers, NBER Working Papers
Estimating the Economic Effects of Federally Funded R&D
Sheila Campbell, Jaeger Nelson, Eli A. Schrag, Heidi L. Williams, Caleb K. Wroblewski
NBER Working Papers
Explains how the Congressional Budget Office estimates the economic and budgetary effects of changes in federal R&D funding and tax policy. The paper compares an R&D-capital-stock approach with a components approach that models distinct channels through which research investment affects output and federal finances. It provides the agency's current framework for evaluating proposed changes rather than estimating a single policy effect.
Human Capital in Venture Capital: Evidence From 100,000 Venture Capitalists
Blake Jackson, Ilya A. Strebulaev
NBER Working Papers
Measures how individual human capital affects venture-capital careers and investment performance using a dataset of more than 100,000 professionals affiliated with U.S. VC firms. Fewer than 40% of investors with any deals are ever credited with a success, and 5% generate 90% of investment profits. Education, prior work, and demographics predict progression, while marginal inclusion on the Forbes Midas List increases access to highly valued startups, indicating persistent investor-specific skill reinforced by reputation.
Retreating from Science: The Long-Run Effects of the 1970s U.S. Military Disinvestment from University Research
Daniel P. Gross, Bhaven N. Sampat, Hansen Zhang
NBER Working Papers
Estimates the long-run effects of the two-thirds decline in U.S. Defense Department funding for university research during the 1970s. Exposure to the cuts reduced university scientists, PhD production, and research output; early-career scientists funded by Defense in 1970 disproportionately left academia for industry. Although some became more likely to patent, aggregate science-linked innovation and U.S. leadership declined in the affected technologies, followed eventually by weaker defense patenting and fewer PhDs entering the defense industrial base.
Training AI for When Humans Will Use It
Kevin A. Bryan, Joshua S. Gans
NBER Working Papers
AI predicts; humans use its predictions to make decisions. These predictions are combined with human verification and analysis, queries to other statistical models, and so on. The economic value of an AI, therefore, depends on how it interacts with the surrounding decision environment.
Elite Talent and Firm Productivity in the Age of AI
Sagar V. Baviskar, Lee G. Branstetter, Prasanna Tambe, Liujie Wu, Cameron Drayton, Eduard Hovy
NBER SI 2026 Digital Economics and Artificial Intelligence
Does elite AI talent raise firm productivity and invention? Publication records identify influential scientists in ten AI subfields, link them to graduate students and postdoctoral researchers, and follow those experts into U.S. firms matched to Compustat and patent data. Frontier-trained talent has only a modest association with conventional productivity measures but a stronger relationship with AI patenting and with translating recent scientific knowledge into inventions. The current evidence is correlational while causal and Census-based analyses remain in progress.
How Exposed is Higher Education to Artificial Intelligence?
Jacob D. Light
NBER SI 2026 Digital Economics and Artificial Intelligence
How exposed is college instruction to large language models, and how have institutions responded? Task-level measures of LLM capability are matched to course descriptions from more than 1,000 U.S. institutions and 1.1 million syllabi from 27 colleges. Exposure is highest in writing- and quantitative-intensive fields and lowest in physical, clinical, and interpersonal fields, with little change in average curricular exposure over 15 years. AI policies spread to most syllabi by fall 2025, but examination and grading practices changed only modestly.
Spreading Out Across Expanding Idea Space
Ina Ganguli, Jeffrey Lin, Vitaly Meursault, Nicholas F. Reynolds
NBER Working Papers,NBER SI 2026 Macroeconomics and Productivity
Documents this secular decline in similarity using validated neural language models applied to the full text of claims in over 11 million US patents (1836–2023), corroborated by a 98% decline in patent interference rates, a measure of independent simultaneous invention. Over nearly two centuries, US inventions have become increasingly dissimilar: not just fewer head-to-head collisions between inventors, but growing distance between neighboring inventions.
Tracked in NBER Working Papers, NBER SI 2026 Macroeconomics and Productivity
The Decline in the Transmission of Scientific Ideas
Enrico Berkes, Ruben Gaetani
NBER SI 2026 Innovation
Documents that the diffusion of new scientific ideas beyond their field of origin has declined substantially over the past four decades. Policy interventions that align scientists’ incentives can broaden adoption and increase the social value of scientific research. This contraction is closely linked to increasing specialization in scientific language: research that employs more technical terminology tends to be adopted less broadly.
Learning to Prompt: Human Adaptation in Production with Generative AI
Sijie Lin
NBER SI 2026 Digital Economics and Artificial Intelligence
How important is human adaptation in AI-assisted creative production? Prompt-level Midjourney data track responses to model upgrades and sequential revisions, while cross-version prompt experiments and a structural search model separate changes in users from changes in the AI. Prompt adaptation accounts for 73 percent of output shifts across upgrades, compared with 20 percent from the model itself. Without iterative human adaptation, users would need three times as many prompts to reproduce observed results.
The Impact of Patient Capital
Yingxiang Li, Tong Liu, Ting Xu
NBER SI 2026 Entrepreneurship
How does the patience of limited partners shape venture-capital strategy and performance? A 2014 Chinese reform admitting insurers to RMB funds permits comparisons with unaffected USD funds run by the same general partners, supplemented by a randomized experiment with Chinese fund managers. Insurer entry displaced shorter-horizon investors and shifted funds toward earlier-stage investments and longer holding periods, improving startup exits and innovation. Managers explicitly adjust fund and investment horizons to their perceived LP base, with stronger responses when bargaining power permits.
The Nature of Firm Financing and Growth
Martin Aragoneses, Chris Hansman, David Licher, Ramana Nanda, Christopher Hansman
CEPR Discussion Papers,NBER SI 2026 Entrepreneurship
Theoretical model using the universe of incorporated firms in the United Kingdom, documents that long term debt that is not secured by specific corporate assets (commonly referred to as cash-flow-based debt) is ubiquitous, even among very young firms. It also highlights how frictions to financing based on available collateral vs. future value lead to differential selection and hence very different distributions of firms that survive and grow. Consistent with cash-flow-based lending depending on firms' continuation value, both the timing and the amount of cash-flow-based debt at first financing are strongly related to future performance.
Tracked in CEPR Discussion Papers, NBER SI 2026 Entrepreneurship
Harnessing Academic Science for Corporate Technology: Interpersonal Networks and Absorptive Capacity
Sam Arts, Lee Fleming, Lena Veretennik
NBER SI 2026 Science of Science Funding
How do firms obtain useful academic knowledge while conducting less science internally? A near-population network of U.S. life-science inventors and academics links corporate patent citations to interpersonal connections, using independently produced 'paper twins' to hold discovery content and commercial potential nearly fixed. Academic findings are more likely to enter corporate inventions when scientists and inventors are within two network degrees. Firms benefit most when their inventors remain scientifically active and possess expertise aligned with the external discovery.
AI and Labor-Market Reallocation
Hie Joo Ahn, Nicholas A. Carollo
NBER SI 2026 Conference on Research in Income and Wealth
How has AI changed U.S. labor-market stocks, flows, and matching? CPS and JOLTS data are combined with OpenAI exposure and Lightcast adoption measures to construct worker flows, market tightness, hirability, and separability by AI intensity. Since LLM diffusion, highly exposed and adopting workers have experienced larger declines in job finding and switching alongside more within-job task changes. Dispersion in job-finding prospects has risen, and the estimated natural unemployment rate has increased by about 0.2 percentage point, although with considerable uncertainty.
Factories of Ideas? Big Business and the Golden Age of American Innovation
Pier Paolo Creanza
NBER SI 2026 Development of the American Economy
Did the Great Merger Wave of 1895-1904 help create the golden age of American innovation? Newly linked firm, inventor, and patent data compare completed consolidations with announced but abandoned mergers and examine the role of corporate R&D laboratories. Previously innovative firms gained about six patents and 0.6 breakthroughs per year, roughly fourfold and sixfold increases, while abandoned mergers showed no comparable change. Consolidation raised breakthroughs by an estimated 13 percent through 1940, with gains reaching 30 percent in science-based fields.
Top Researchers on Scientific Committees: Decision Outcomes, Peer Dynamics, and Opportunity Costs
Milan Makany, Natalia Zinovyeva
NBER SI 2026 Science of Science Funding
Science disproportionately relies on top researchers to evaluate the work of others, potentially diverting their scarce time from research, mentoring, and other service. Committees with better-published evaluators select candidates with stronger subsequent citation and career outcomes; they also place greater weight on publication impact rather than quantity. Examines this trade-off in Italy's national academic qualification system, where evaluators are randomly assigned to committees.
Why Don’t Old Firms Do New Things?
Nicolas Crouzet, Zhiguo He, Victor Lyonnet, Yueran Ma
NBER SI 2026 Entrepreneurship
Why do young firms outperform incumbents when industries adopt some new technologies but not others? O*NET workstyles measure how strongly new technologies shift employment toward occupations that solve problems differently from a firm's existing workforce. Young firms grow significantly faster where technologies require larger workstyle shifts, while the sheer number or importance of patents does not predict the same advantage. The pattern points to internal organizational conflict, rather than technology volume itself, as a constraint on incumbent adaptation.
Measuring AI innovation with trademark data
C. Castaldi, F. Castellacci, A. Fronzetti Colladon, L. Segneri, F. Venturini
arXiv economics of AI,arXiv innovation and entrepreneurship
Can trademarks complement patents in measuring the development and diffusion of artificial intelligence? The research note explains how AI-related marks reveal firms' commercialization of AI in new goods and services across sectors and countries, then illustrates the approach with Italian firms. Trademark records are timelier and broader in sector coverage than many patent measures. They capture market-facing dimensions of AI innovation that patent data alone miss.
Tracked in arXiv economics of AI, arXiv innovation and entrepreneurship
Measuring the Culture of Useful Knowledge and the Roots of Innovation
Marc Klemp
CEPR Discussion Papers
Does a local culture oriented toward useful knowledge predict later innovation and growth? A new text-embedding method scores places using language from 38.6 billion context-word occurrences in nearly 2.7 million works published from 1500 to 1899, without using outcome data. Within countries, a one-standard-deviation increase in the index predicts 0.105 standard deviations more log patenting during 1980-2014 and a 3.4 percentage-point higher probability of any patent. Pre-1750 language also predicts modern patenting, regional income, and stronger post-1750 city growth near coal.
Intelligent Cause Prioritisation? An Analysis of AI Policy Priorities and Governance in Africa
Osaremen Iluobe, Kisso Selvan
arXiv economics of AI,arXiv Computers and Society
The rapid improvement of AI systems has intensified debate about humanity's economic, political, social, and existential future. As AI reshapes expectations about what lies ahead, policy choices and institutional responses will play a crucial role in determining who benefits, who bears the costs, and whether the most serious risks can be mitigated. Africa remains relatively overlooked in these discussions, partly because it is largely a consumer rather than a producer of frontier AI systems, and also because many countries on the continent continue to face pressing development challenges.
Tracked in arXiv economics of AI, arXiv Computers and Society
An approach to systemic risks of AI through the lens of emergence, collective action problems, and externalities
Carsten Orwat, Lucas Staab, Alexandros Gazos
arXiv economics of AI,arXiv Computers and Society
The integration of general-purpose artificial intelligence models into downstream AI systems, among other developments, has given rise to new forms of risk that are more systemic in nature than conventional AI risks. To contribute to the understanding of systemic risks of AI, proposes a conceptualisation of systemic risks of AI that considers complex phenomena that lead to the emergence of harms at the societal or global level.
Tracked in arXiv economics of AI, arXiv Computers and Society
When Not to Automate: A Formal Protocol for Human Preservation in AI-Optimized Organizations
Jose Manuel de la Chica Rodriguez, Jairo Rodriguez Arias, Spyridon Chouliaras
arXiv Computers and Society
Standard automation ROI misses four categories of systemic risk -- tacit knowledge erosion, resilience reduction, regulatory exposure, and socio-institutional capital degradation -- that affect long-term organizational performance. PHP-AIO (Protocol for Human Preservation in AI-Optimized Organizations) is a five-gate sequential decision protocol with a final composite check that quantifies these unpriced systemic risks at the role level and produces auditable automation decisions.
Making Agent-Mediated Contributions Governable: A Project-Level Governance Manifest for Open-Source AI Collaboration
Jinjin Gao, Luyang Li, Shufen Guo, Ligang He, Xiaoning Sun
arXiv economics of AI,arXiv Computers and Society
Generative AI and coding agents are intensifying a central governance tension in open-source software (OSS): they scale contribution generation faster than maintainers can assess risk, evidence, and accountability. A diagnostic audit of 50 GitHub repositories finds widespread general governance artifacts, observable agent-readability, and fragmented AI-governance cues, but no project-wide arrangement that coordinates shared rules, preparation obligations, verification rights, and maintainer decision authority across AI-mediated contribution workflows.
Tracked in arXiv economics of AI, arXiv Computers and Society
Cheaper AI, More Informality? A Dual Labor Market Model for Developing Economies
Gabriel Montes-Rojas, Fernando Toledo, Juan Manuel Rodríguez Repeti
arXiv economics of AI
Does cheaper imported AI capital expand formal employment or push workers into informality in developing economies? A small-open-economy DSGE model combines a dual labor market, imported AI, and country risk in a calibration with pervasive informality. When AI substitutes for formal labor, falling prices weaken formal demand and expand informal employment as a buffer. When AI complements workers, the same price decline raises formal employment, output, wages, investment, and capital accumulation.
The Industrialization of Research ; On AI-Driven Science and Its Consequences
Emmanuel Jeannot
arXiv Computers and Society
Artificial intelligence is transforming scientific research -- not merely as a more powerful instrument, but as an autonomous participant in the research cycle itself. This transition constitutes, in the most precise sense of the term, the industrialization of research: a shift from a craft model, in which knowledge, method, and judgment are embedded in the researcher, to a pipeline model, in which these steps are decomposed, automated, and supervised. The US Department of Energy's Genesis Mission is the most ambitious current instantiation of this shift, but the fundamental questions it raises extend far beyond any single program.
Platform Choice, Trust, and Privacy in the Consumer AI Assistant Market
Jennifer Zou
arXiv economics of AI,arXiv Computers and Society
Examines how a representative sample of United States adult AI-assistant users (n=1,999; June 2026) choose among platforms, allocate tasks across them, evaluate provider trustworthiness, and value data-handling features. Estimates are weighted to the AI-user population using external adoption benchmarks. Trust is earned through use rather than reputation: Claude is ranked most trustworthy in every head-to-head among users of both platforms, and shows by far the largest gap between how its users and non-users rate it.
Tracked in arXiv economics of AI, arXiv Computers and Society
SCITUS: A Multi-Jurisdictional Framework for Adapting NIST AI RMF to the Canadian Regulatory Context
Mohammad Etemad
arXiv economics of AI,arXiv Computers and Society
The death of Bill C-27 (Artificial Intelligence and Data Act) in January 2025 - and the federal government's June 2026 confirmation that it will pursue targeted instruments rather than omnibus AI legislation - leaves organizations without unified compliance guidance. Canadian organizations deploying artificial intelligence systems face a fragmented regulatory landscape spanning federal requirements (the Treasury Board Directive on Automated Decision-Making) and divergent provincial regulations across Ontario, Quebec, Alberta, Manitoba, and British Columbia.
Tracked in arXiv economics of AI, arXiv Computers and Society
Innocuous-Seeming Data, Latent Ideology: Ideological Generalisation in Finetuned LLMs
Robert Graham, Edward Stevinson, Yariv Barsheshat
arXiv economics of AI,arXiv Computers and Society
Shows that finetuning on narrow, factually-defensible, moderation-passing data can cause broad ideological shifts across unrelated domains, while preserving general capabilities. The same effect appears with plausibly-deployed datasets such as workplace HR policy and practical finance queries, as well as on a science-pseudoscience axis where food-safety finetuning increases sycophantic agreement with users expressing false health beliefs.
Tracked in arXiv economics of AI, arXiv Computers and Society
Governing Artificial Intelligence: Public Preferences and Regulatory Options
Magnus Lundgren, Jonas Tallberg
arXiv economics of AI
How do citizens want artificial intelligence governed? A conjoint survey experiment across seven politically and economically diverse countries varies regulatory priorities, governing institutions, and geographic scope. Respondents strongly favor regulation, generally preferring safety to innovation, public oversight to private self-regulation, and international to national governance. Safety preferences are strongest among people who view AI as risky, unpredictable, and personally consequential, revealing a gap between public preferences and prevailing policy approaches.
Firms as Foragers
Vasco Carvalho, Lukas B. Freund
CEPR Discussion Papers
How do firms balance exploiting nearby ideas against searching for new technological territory? An endogenous-growth model treats this choice as an optimal-stopping problem and maps idea space using natural-language processing of U.S. patents. Returns within a local idea patch decline, richer patches retain firms longer, and entry into new patches produces more and better patents. At a 20-year horizon, new clusters account for more than half of quality-improvement growth, while the productivity slowdown points more toward harder exploration than worsening exploitation.
Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion
Fengzhuo Zhang, Zhuoran Yang, Dirk Bergemann
arXiv economics of AI
Asks when LLM users and platforms should rely on supervised fine-tuning rather than in-context learning under scarce compute. Develops an equilibrium model of LLM personalization in which users trade off statistical gains against congestion from shared computational resources, then validates predictions with GPT-2 linear-regression experiments. ICL and SFT dominate in different regimes depending on pretraining coverage and data signal-to-noise, and congestion can reverse their ranking; offering both methods never reduces the platform's maximum profits even when it increases computational load.
Unsafe at any AUC: Unlearned Lessons from Sociotechnical Disasters for Responsible AI
Joshua A. Kroll, Andrew Smart, R. Stuart Geiger, Abigail Z. Jacobs
arXiv economics of AI,arXiv Computers and Society
As automated decision-making and data-driven technologies pervade society and are used to manage consequential outcomes, understanding the technology's capabilities, limitations, and attendant risks in context requires analysis of full sociotechnical systems. Sociotechnical analysis of risks in highly complex systems provides clear lessons for the design and evaluation of AI systems, transcending a technical focus on reliable or "responsibly designed" components to understand risks at a systems level.
Tracked in arXiv economics of AI, arXiv Computers and Society
From Vector Autoregressions to AI-based Time Series Forecasting: A Review
Likai Chen, Weining Wang
arXiv economics of AI
Argues that modern methods make progress by expanding the classical forecasting template: they allow more flexible dynamics, use larger information sets and training corpora, and represent richer predictive distributions. Forecasting is a central goal of time-series analysis. This review centers on three major developments in recent AI-based time-series forecasting: transformers, large pretrained models for zero-shot forecasting, and diffusion-based generative forecasters.
AI-Augmented Human Resource Management? Insights from German companies
Yannick Kalff, Katharina Simbeck
arXiv Computers and Society
Asks whether AI augments human-resource management or primarily advances efficiency and rationalization. Interviews, group discussions, and a survey of 410 respondents document adoption in German companies. AI improves HR analytics, but its use remains concentrated on efficiency; digital infrastructure, co-determination, data governance, and algorithmic transparency shape whether resources shift toward strategic and employee-centered work.
Persona Migration and Expectation Recalibration in Generative AI Adoption: A Longitudinal Study at a State Department of Transportation
Omidreza Shoghli, Fatemeh Banani Ardecani, Amin Mohamadi Hezaveh
arXiv Computers and Society
How do workers' expectations of enterprise generative AI change after they use it? Matched two-wave surveys of 124 employees during an eight-week Microsoft 365 Copilot pilot at a state transportation department combine nonparametric tests with persona clustering and migration analysis. Perceived usefulness falls significantly after use, and 68 percent of baseline Champions move to less enthusiastic personas, while communication and summarization remain stable use cases but data, chart, and presentation tasks decline.

Useful Knowledge Issue No. 007 - July 14, 2026

Tue, 14 Jul 2026 23:59:59 GMT

Issue No. 007

66 papers.

Open user innovation, producer innovation and industrial dynamics: An ABM approach
Isabel Almudi, Francisco Fatas-Villafranca, Carlos M. Fernández-Márquez, Jason Potts, Francisco J. Vázquez, Eric von Hippel
Research Policy
Asks how user innovation and producer innovation interact over an industry's life cycle. Builds an agent-based model that integrates von Hippel's user-innovation framework with Schumpeterian industrial dynamics to trace the transition from community-phase experimentation to market-phase production. The model reproduces familiar stylized facts and shows that user and producer innovation can be either complements or substitutes, with market evolution depending on when producer-led commercialization amplifies rather than displaces open user experimentation.
Government financial support and innovation in firms: additionality or crowding-out?
Jihye Yeo, Alex Eapen, Rekha Krishnan
Industrial and Corporate Change
Asks whether government financial support raises firm innovation through true additionality or partly crowds out private effort. Uses a 2011-2016 Australian SME panel from the Business Longitudinal Database and an econometric design addressing endogeneity, firm heterogeneity, and selection. Public support is positively associated with innovation, but the effect is stronger among financially unconstrained firms than constrained firms, which points more toward crowding-out or partial additionality than a pure financing-constraint mechanism.
Outsourced R&D and declining R&D productivity
Anne Marie Knott
Industrial and Corporate Change
Asks whether the growth of outsourced R&D helps explain falling firm R&D productivity. Studies the six-fold rise in outsourced R&D alongside a 65 percent decline in firms' R&D productivity and estimates the output elasticity of outsourced versus internal R&D. Outsourced R&D is unproductive for the funding firm on average, with an estimated output elasticity near zero; the aggregate productivity decline appears to come from reallocating R&D away from internal execution rather than a broad deterioration in firms' research capability.
Generative AI and Data Quality: Implications for Productivity, Labor Displacement, and Policy
Zhifeng Cai
Review of Financial Studies
Asks how generative AI changes productivity, labor displacement, and policy when AI both consumes and produces data. Develops a calibrated social-learning model with a data-AI feedback loop in which data quality shapes AI productivity, AI adoption changes the mix of AI- and human-generated data, and that mix feeds back into future quality. The model predicts hump-shaped labor displacement that partially reverses as data deteriorates; because AI adopters free-ride on human-generated information, competitive markets call for AI taxation, while concentrated AI suppliers may overcorrect enough to make subsidies optimal.
Ratings design and barriers to entry
Nikhil Vellodi
Theoretical Economics
How do consumer-review systems affect entry and survival by firms of uncertain quality? A dynamic platform model combines endogenous firm entry and exit, directed consumer search, fixed prices, and ratings that reveal quality. Full transparency weakens participation because bad early reviews can strand promising firms; suppressing some reviews for highly rated firms strengthens entry incentives and improves consumer welfare.
The mother of innovation? A comparative analysis of export modes in both developed and emerging economies
Jesus Arteaga-Ortiz, Fernando Muñoz-Bullon, Maria J Sanchez-Bueno, Jose A Zuñiga-Vicente
Industrial and Corporate Change
Asks how export destination and export mode shape firms' innovation outcomes. Uses a representative panel of Spanish manufacturing firms from 1999 to 2022 to compare direct versus indirect exporting to developed and emerging markets. Firms exporting to developed markets introduce more technological and non-technological innovations, while innovation gains are weaker in emerging-market destinations; direct exporters consistently introduce more innovations than indirect exporters.
Many AI analysts, one dataset: Navigating the agentic data science multiverse
Martin Bertran, Riccardo Fogliato, Zhiwei Steven Wu
Proceedings of the National Academy of Sciences
Empirical conclusions depend not only on data but also on analytic decisions. Shows that fully autonomous AI analysts built on large language models (LLMs) can, cheaply and at scale, produce the analytic dispersion observed in human many-analyst studies. Across three datasets, AI analyst-produced analyses exhibit substantial dispersion in effect sizes, P -values, and conclusions.
Generative AI and Price Discrimination in the Housing Market
Jitsama Tanlamai, Warut Khern-am-nuai, Maxime Cohen, Maxime C. Cohen
Information Systems Research
Can generative AI reduce racial price discrimination in housing? AI-generated and human valuations are compared for 284,749 US properties, followed by mechanism tests across neighborhood composition. Generative-AI valuations reduce the premium attached to otherwise similar homes in predominantly White neighborhoods, suggesting that these models can attenuate rather than simply reproduce human pricing bias.
The Uneven Impact of Generative Artificial Intelligence on Entrepreneurial Performance: Evidence from a Field Experiment in Kenya
Nicholas G. Otis, Rowan Clarke, Solène Delecourt, David Holtz, Rembrand Koning
Management Science
Asks whether GPT-4 business advice improves the performance of entrepreneurs in an emerging-market setting. Runs a field experiment with Kenyan entrepreneurs by randomizing access to an AI business assistant and measuring revenues and profits. The average treatment effect is not distinguishable from zero, but effects are sharply heterogeneous: initially low-performing entrepreneurs do nearly 10 percent worse with the assistant, while high performers may gain more than 15 percent, apparently because entrepreneurs differ in which AI advice they choose to implement.
When a Mass Resignation Becomes a Merger: Rethinking Asset Acquisitions for the AI Era
Nina Fridman
University of Chicago Law Review
Asks whether reverse acquihires in AI should be treated as asset acquisitions for antitrust review. Uses the Microsoft-Inflection transaction, in which most employees moved to Microsoft while Inflection received $650 million and changed direction, as the motivating case. The argument is that absorbing an AI startup's personnel, mission, and competitive core can mimic a conventional acquisition while avoiding Hart-Scott-Rodino filing requirements, so regulators should clarify that these deals can constitute reviewable asset acquisitions.
Intergenerational mobility fosters innovation in Europe
Sarah McNamara, Guido Neidhöfer, Patrick Lehnert
Nature
Does greater intergenerational mobility increase regional innovation? The EUROPE-IGM-ATLAS converts cohort-linked education measures into regional time series and documents the changing geography of opportunity across Europe. Regions with more mobility subsequently produce more innovation, although the relationship is nonlinear and operates differently in major innovation hubs; gains in mobility mainly reflect improved attainment among people from less-educated families.
Courts of Tomorrow: Evidence from a Nationwide Rollout of Generative AI
Sultan Mehmood, Christoph Goessmann, Elliott Ash
NBER SI 2026 Digital Economics and Artificial Intelligence,CEPR Discussion Papers
Field experiment. Introduces the first large-scale field experiment evaluating the integration of generative AI into a national justice system. Their attitudes toward AI also shift: they expect the tool and the targeted training to increase their productivity. At median-district exposure, introducing AI with targeted training corresponds to 1,848 additional cases resolved per year, a 6.3 percent increase over the mean.
Tracked in NBER SI 2026 Digital Economics and Artificial Intelligence, CEPR Discussion Papers
Selective Participation in the AI Data Commons
Kai Zhu
NBER SI 2026 Digital Economics and Artificial Intelligence
A further finding is use-specific selectivity: higher-quality publishers are more likely to restrict training while preserving search-like access, consistent with training offering weaker attribution and referral benefits than search. Related composition results show that misinformation sources and outlets at the political extremes become relatively more accessible. The evidence shows that publishers are not rejecting AI access wholesale---they distinguish uses.
The Welfare Effects of Gender-Inclusive Innovation: Evidence from Patents and Academic Publications
Matt Marx, Frank Mueller-Langer, Joel Waldfogel
NBER SI 2026 Science of Science Funding
What welfare gains resulted from the post-1970 rise of women in patenting and academic science? Citation evidence and an equilibrium model of innovation demand and entry compare mostly female inventions with male inventions and simulate removal of the female influx. Female innovations attract nearly comparable citations and expand rather than merely divert demand, with little evidence of displacement. The influx raises estimated innovator surplus by 0.82 percent in patenting and 2.3 percent in academic publishing, with larger gains in several fields where female participation grew most.
Assessing the Benefits of Optimized Agentic AI Systems for Asset Pricing
Ralph S. J. Koijen, Bradford Levy, Ralph Koijen
NBER Working Papers,NBER SI 2026 Asset Pricing,Becker Friedman Institute Working Papers,RePEc NEP Artificial Intelligence
Asks how to evaluate agentic AI systems for asset pricing when training on historical data creates look-ahead bias and market adoption changes prices. Introduces a real-time, out-of-sample benchmark that measures how well AI systems explain stock returns around earnings announcements using only information available at the announcement. Optimized agentic systems more than double explained return variation relative to standard benchmarks, raising R-squared from about 8 percent to nearly 20 percent, while producing human-readable mechanisms that can be compared with existing asset-pricing models.
Tracked in NBER Working Papers, NBER SI 2026 Asset Pricing, Becker Friedman Institute Working Papers, RePEc NEP Artificial Intelligence
How Might Fiscal Policy Respond to the Rise of Artificial Intelligence?
Karen Dynan, Douglas Elmendorf, Louise Sheiner
NBER Working Papers
Asks how U.S. fiscal policy should respond to uncertain economic changes from artificial intelligence. Analyzes long-run scenarios combining faster productivity growth, greater inequality, job displacement, and a higher capital share of income, then maps each scenario to federal debt and policy choices. Because the direction and magnitude of AI's effects remain uncertain, the paper emphasizes robust fiscal responses across growth, redistribution, displaced-worker support, and capital taxation or ownership.
Organizational Incentives and the Returns to Technology Adoption
Achyuta Adhvaryu, Smit Gade, Piyush Gandhi, Teresa Molina, Anant Nyshadham
NBER Working Papers
Asks why firms may fail to benefit from productive technologies even after adoption. Runs a randomized controlled trial in Indian garment factories assigning units to an anonymous worker-management communication technology, the same technology paired with incentives for HR managers to communicate effectively, or control. The technology alone has no measurable effect, but pairing it with HR incentives raises productivity by 5 percent, reduces absenteeism by 13 percent, and increases worker earnings by 3 percent by improving HR responsiveness and worker reporting of production problems.
Risk Design: AI and Prediction Beyond Screening in Insurance Markets
Alex Chan
NBER Working Papers
Asks how AI prediction changes insurance markets when it can design residual risk rather than only classify fixed risk. Builds a market-design model comparing complete contracting with adverse selection when prevention technology is scalable, contractible, and differently useful for high- and low-risk consumers. When high-risk consumers are more AI-treatable, low-risk contracts face a trilemma: separate types, use efficient prevention, or avoid cross-subsidies, but not all three; the result extends Rothschild-Stiglitz from coverage distortions to risk-control technology.
Academic Bubbles
Michael Sockin, Richard Lowery, Matteo Tranchero
NBER SI 2026 Science of Science Funding
Can citation-based career incentives produce excessive concentration in fashionable research areas? A principal-agent model links unobservable research effort to peer recognition, then tests its predictions in work on genetic determinants of human disease. Because citations rise mechanically with the number of researchers on a topic, crowded areas can attract effort despite weak prospects for advancing knowledge. Empirical patterns show that crowding inflates citation impact and is associated with distorted topic choice.
Making Talk Cheap: Generative AI and Labor Market Signaling
Anais Galdin, Jesse Silbert
NBER SI 2026 Digital Economics and Artificial Intelligence
What happens when LLMs make customized job applications cheap and weaken their value as signals? Freelancer.com data support a new measure of application tailoring and a structural model of costly labor-market signaling. Employers paid a premium for customized applications before LLM diffusion but not afterward. In a counterfactual where writing no longer signals ability, top-quintile workers are hired 19 percent less often and bottom-quintile workers 14 percent more often, making allocation substantially less meritocratic.
Research Value-Added in Elite Economics PhD Programs
Joshua Angrist, Marc Diederichs, Glenn Ellison
NBER SI 2026 Science of Science Funding
How much research value do elite economics PhD programs add after accounting for student selection? Data on MIT Economics applicants use admissions rankings and application portfolios to compare eight elite programs with non-elite U.S. schools. Rank controls narrow the raw gap, but graduates of top-eight programs still produce 60-75 percent more impact-adjusted publications, with an especially large advantage in top-five journals. Differences within the elite tier are modest, and editorial connections or peer effects do not explain the overall premium.
Safety in Unemployment and Risky Experimentation of Young Firms
Renato Faccini, Seho Kim, Javier Miranda
NBER SI 2026 Macroeconomics and Productivity
Field experiment using Danish matched employer--employee data and regional labor-market variation, shows that higher job-finding rates are associated with lower wage differentials between experimenting and non-experimenting young firms, both across firms and within firms hiring across multiple areas. Develops a theory in which a lower cost of job loss reduces the compensating wage premium workers require to join risky young firms. By lowering labor costs relative to safer firms, this encourages entrants to choose high-upside experimentation, raising aggregate productivity.
The Life Cycle of Ideas
Philippe Aghion, Antonin Bergeaud, Gaétan de Rassenfosse, Luc Paluskiewicz, Raphaël Wargon
NBER SI 2026 Science of Science Funding
How has the production and turnover of ideas evolved within economics? Text analysis classifies more than 300,000 articles published from 1950 to 2020 into 90 coherent topics. Individual topics follow a rise-and-fall life cycle, but aggregate research activity continues to grow as new topics displace old ones. The evidence describes continual renewal in the composition of the field rather than a simple exhaustion of ideas.
Does Teaching Entrepreneurship Produce Entrepreneurs?
Elif Nisa Guler
NBER SI 2026 Entrepreneurship
Does undergraduate entrepreneurship education increase business formation? Staggered program introductions at more than 150 U.S. universities are matched to historical catalogs and LinkedIn careers for over 300,000 graduates in a within-university, adjacent-cohort difference-in-differences design. Exposure reduces immediate business formation by 10.7 percent, entirely through fewer small businesses, without changing growth-oriented entry, long-run entrepreneurship, or business continuation. Larger declines at public universities and among early adopters support a screening mechanism.
End of the Road? Autonomous Vehicles and Displacement Risk
Danial Salman
NBER SI 2026 Digital Economics and Artificial Intelligence
How does exposure to autonomous vehicles affect commercial drivers before large-scale displacement occurs? Geographic differences in AV exposure are linked to licensing, employment, hours, mortgage participation, and household spending. More-exposed areas see larger declines in commercial licenses and truck-driving employment; remaining drivers work longer hours and participate less in mortgage markets. Alcohol and tobacco spending also moves in a pattern consistent with automation anxiety, showing that perceived displacement risk changes behavior before realized job loss is complete.
Is R&D Rivalry Slowing the Growth of Productive Firms?
Yoshiki Ando, James Bessen, Xiupeng Wang
NBER SI 2026 Conference on Research in Income and Wealth
Does technological rivalry slow the expansion of productive firms even as creative destruction improves allocation? U.S. Census microdata measure firms' exposure to rival technologies and their responses to productivity shocks. Firms facing more rivalry react less and more slowly to favorable shocks. Counterfactual growth from 1997-2018 indicates that rising rivalry can account for the declining responsiveness of firms and the associated fall in job reallocation.
Marginal Ideas
John J. Horton, Amir Sariri
NBER SI 2026 Entrepreneurship
Develops an equilibrium model of technology entrepreneurship in which engineers choose between founding startups and employment, startups require venture capital, and entrepreneurs pursuing the same idea compete in winner-take-all product markets. The results provide a distinct market-failure justification for public R\&D as a mechanism that reduces crowding existing opportunities to exploring new ones. Because opportunities depend on publicly observable advances, many entrepreneurs target the same ideas simultaneously, and better ideas attract proportionally more entrants until per-firm success probabilities equalize.
The Skill Premium in Times of Rapid Technological Change
Tarek Alexander Hassan, Aakash Kalyani, Pascual Restrepo
NBER SI 2026 Macroeconomics and Productivity
It develops a model in which skilled workers have a comparative advantage in learning new technologies. Shows that the pace of technology creation is a key driver of the skill premium. A rapid pace of technology creation leads to a sustained increase in the skill premium.
Directional AI Advice: Experimental Evidence from Healthcare
Yuyu Chen, Hongbin Li, Lingsheng Meng, Xinyao Qiu, Qingxu Yang
arXiv economics of AI,NBER SI 2026 Digital Economics and Artificial Intelligence
How does directional advice from generative AI alter patient-physician decisions? A preregistered field experiment at a Chinese hospital randomized access to an AI chatbot before outpatient visits. The chatbot discouraged medications, especially Traditional Chinese Medicine and antibiotics, while encouraging diagnostic tests; those recommendations carried into care, lowering prescriptions and increasing testing. AI access also reduced patient compliance and satisfaction, shifting authority within the clinical relationship.
Tracked in arXiv economics of AI, NBER SI 2026 Digital Economics and Artificial Intelligence
The Value Added of Innovation Managers: Evidence from the National Institutes of Health
Christina Nguyen
NBER SI 2026 Science of Science Funding
How does managerial discretion affect the performance of publicly funded researchers? Value-added estimates for NIH program officers compare outcomes when officers have discretion with outcomes under more constrained assignments, separately for first-time and established grantees. Managerial differences matter most for first-time grantees' short-run publication counts, whereas discretion is less beneficial for long-run citations and established grantees. Program officers with doctorates generate higher publication value added.
When Does Automating AI Research Produce Explosive Growth? Feedback Loops in Innovation Networks
Tom Davidson, Thomas Houlden, Basil Halperin, Anton Korinek
NBER SI 2026 Economic Growth and Long-Run Macroeconomic Development
AI labs are increasingly using AI itself to accelerate AI research, creating a feedback loop that could lead to an intelligence explosion. Develops a general semi-endogenous growth model with an innovation network, where research and automation in one sector increase the productivity of research in other sectors, and derive a clean analytical condition under which growth becomes superexponential (“explosive”).
AI-Native Insurance for Agentic AI: Pricing, Underwriting, and End-to-End Automation
Quanyan Zhu
arXiv Computers and Society
Case-study design. A healthcare case study illustrates contract optimization, sensitivity analysis, and automated claims processing for agentic AI systems. Agentic AI introduces new insurance challenges because autonomous AI systems can make decisions, invoke tools, modify external environments, and interact with third-party services. Develops an AI-native mathematical framework for underwriting, pricing, and contract design for agentic AI deployments.
Underwriting the Agent Economy: The Blueprint for an AI Insurance Stack
Cristian Trout, Sanmi Koyejo, Sasha Romanosky, Giorgio Ripamonti, Lynn Thompson, Desiree Spain, Alex Taylor, Kevin Casey, Stephen Casper, Matthew Botvinick, Sean McGregor, Miles Brundage, A. Feder Cooper, Patricia Paskov, Adrien Ecoffet, Ben Bucknall, Kevin Wei, Markus Anderljung, Lukasz Szpruch, Bri Treece, Tom Zick, Gabriel Weil, Ugur Ozer, Kevin Kalinich, Jesus Gonzalez, Vitaly Baranov, Moran Koren, Guy Laban, Gil Arazi, Henri Winand, Derek Blum, Toby Clowes, Adam Kleinman, Anita Srinivasan, Tom Fehring, Rune Kvist, Rajiv Dattani
arXiv economics of AI,arXiv Computers and Society
Asks what insurance infrastructure would be needed for AI agents to operate at commercial scale. Synthesizes historical insurance precedents and current AI-risk bottlenecks to map an eight-part insurance stack covering incident data, catastrophe modeling, standards, contract design, pricing, monitoring, and claims management. The report argues that existing coverage leaves much AI-agent risk implicitly uninsured, that concentrated model providers and rapidly changing capabilities make losses harder to price, and that billion-dollar affirmative coverage is feasible only with coordinated industry institutions and public backstops for frontier tail risks.
Tracked in arXiv economics of AI, arXiv Computers and Society
Beyond AI-Generated Labels: Watermarking, Co-Creation, and Conflation of AI-Generation with Disinformation
Federico Germani, Giovanni Spitale
arXiv economics of AI,arXiv Computers and Society
Invisible watermarking encodes only model origin; when operationalized into visible AI-generated labels, it reduces complex creative processes to a misleading binary and provides no information about truthfulness. Watermarking is often presented as a straightforward solution for distinguishing AI-generated from human-generated content, enabling platforms and regulators to trace synthetic content and detect AI-generated outputs at scale.
Tracked in arXiv economics of AI, arXiv Computers and Society
A New Look at AI's Impact on Jobs: Firm-Level AI Spending and Workforce Adjustment
Ara Kharazian, Lisa Simon, Ryan Stevens
RePEc NEP Artificial Intelligence
Examines how employment changes when firms adopt generative AI using observed AI spending from Ramp card and bill pay data linked to Revelio Labs workforce records for 21,559 firms in the United States. Finds that companies that adopt AI tend to grow faster following adoption, but the relationship is driven almost entirely by high-intensity adopters. The results counter predictions that AI adoption will lead to broad job loss.
A heterogeneous-agent model of growth and inequality for the UK - what has caused the UK's growth collapse since 2008?
Patrick Minford, Zheyi Zhu
RePEc NEP Entrepreneurship
Can entrepreneurial incentives explain the UK's post-2008 growth collapse? A heterogeneous-agent growth model with entrepreneurship-driven individual productivity is estimated by indirect inference on data through 2024. The model attributes both the post-1970s acceleration and recent collapse to changing tax and regulatory disincentives facing entrepreneurs.
A potential boost from AI in ageing societies: Early insights
Christophe André, Matthias Schief
RePEc NEP Artificial Intelligence
Uses, using OECD Programme for the International Assessment of Adult Competencies (PIAAC) data, that workers’ overall exposure to AI (automation and augmentation) exhibits an inverted U-shaped pattern across age groups, albeit less pronounced when controlling for education, occupation and country. However, little is known about how exposure to AI varies over the life cycle and what this may imply for AI deployment in ageing societies. Demographic headwinds are set to weaken economic growth in OECD countries over the coming decades.
AI Unbound: Digital Infrastructure, AI Adoption, and Firm Performance
Nuriye Melisa Bilgin, Gianmarco Ottaviano
RePEc NEP Artificial Intelligence
Difference-in-differences design using administrative data and a nationally representative enterprise survey from Turkey (2021–2024), documents significant disparities in AI adoption. Examines how digital infrastructure relaxes constraints on the diffusion and economic impact of artificial intelligence (AI). Difference-in-differences estimates show that improved connectivity significantly increases AI adoption, particularly for software-intensive technologies and among small and medium-sized enterprises.
Artificial Intelligence and Monetary Policy: A Framework and Perspective on Cyclical Transmission, Structural Transition, and...
Simone Lenzu
RePEc NEP Artificial Intelligence
How does AI diffusion alter the environment for monetary policy? A framework separates short-run inflation transmission, shifts in potential output and the natural interest rate, and financial-stability effects through credit allocation, asset valuations, and model monocultures. Adoption frictions combined with elevated valuations can produce an AI-specific mix of cost-push inflation and financial fragility that interest-rate policy alone is poorly suited to manage.
Artificial Intelligence, Productivity, and the Workforce: Evidence from Corporate Executives
Salomé Baslandze, Zachary Edwards, John Graham, Ty McClure, Michael Sparks, Brent Meyer, Sonya Waddell, Daniel Weitz
RePEc NEP Artificial Intelligence
Uses novel data from a survey of nearly 750 corporate executives to study the effects of artificial intelligence (AI) on productivity and the workforce. These gains are not primarily driven by firms’ capital deepening but instead reflect increases in revenue-based total factor productivity, closely associated with innovation- and demand-oriented channels. In labor markets, finds little evidence of near-term aggregate employment declines due to AI, though larger companies anticipate AI-driven workforce reductions, while smaller firms expect modest gains.
Can Socialism Work? Of Course it Can’t! Schumpeter on Capitalism, Socialism and Economic Change
Guichardaz Remy, Pénin Julien
RePEc NEP Entrepreneurship
In Capitalism, Socialism and Democracy, Schumpeter famously predicts the likely replacement of capitalism by socialism and claims that a socialist economy could be perfectly workable. Drawing on concepts that lie at the heart of Schumpeter’s theoretical framework, notably the distinction between growth and development, the opposition between perfect competition and plausible capitalism, and the central role of the entrepreneur in the emergence of novelty, shows that Schumpeter could not consistently maintain that a socialist economy would be capable of reproducing the developmental performance of capitalism.
Credit Constraints among Unincorporated Enterprises in India: An Empirical Investigation Using Unit-Level ASUSE Data.
Shivani Badola, Sacchidananda Mukherjee
RePEc NEP Entrepreneurship
Which unincorporated Indian enterprises are credit constrained? Unit-level 2022-23 Annual Survey of Unincorporated Enterprises data classify firms as fully, partly, or unconstrained using loan sources and survey responses. Manufacturing firms, women entrepreneurs, and SC/ST or OBC owners face greater constraints, with location, scale, GST registration, and margins also predicting access.
Does Generative AI Narrow Education-Based Productivity Gaps? Evidence from a Randomized Experiment
Guillermo Cruces, Diego Fernandez Meijide, Sebastian Galiani, Ramiro Galvez, María Lombardi
RePEc NEP Artificial Intelligence
Does generative AI narrow productivity differences by education? A randomized experiment assigns 1,174 US and Dutch adults with varied schooling to an incentivized workplace-style task with or without an AI assistant. AI raises output for every group and reduces the higher- versus lower-education performance gap from 0.548 to 0.139 standard deviations, closing about three quarters of the initial difference while leaving underlying skill gaps intact.
Entrepreneurship in Quasi-Markets: An Institutional Analysis
Niklas Elert, Magnus Henrekson
RePEc NEP Entrepreneurship
Why do quasi-markets often fail to generate the expected entrepreneurship and innovation in welfare services? An institutional analysis applies Knightian, Kirznerian, and Schumpeterian conceptions of entrepreneurship to common quasi-market designs. Competition and for-profit entry are insufficient without complementary epistemic institutions that help providers discover opportunities and users evaluate quality.
From Innovation to Speculation: AI and the Magnificent Seven
Rerotlhe B. Basele, Peter C.B. Phillips, Shuping Shi
RePEc NEP Artificial Intelligence
The AI boom has driven the Nasdaq and the Magnificent Seven tech stocks to record highs. The evidence and analyses show clear signs of bubble exuberance in most of these stocks, concentrated in a few names like Nvidia, leading to latent risks for investors who assume their index funds are safely diversified and supported by wider economic fundamentals. But how much do these new records reflect underlying value, how much is speculation, and how vulnerable are these stocks and the wider market to a major downturn?
Institutional Context and the Employment Effects of Artificial Intelligence in European SMEs
Anabela Santos, Francesco Molica
RePEc NEP Entrepreneurship,RePEc NEP Artificial Intelligence
How does AI adoption affect employment growth in European SMEs, and when are gains largest? Survey on Access to Finance data for 12 EU countries and inverse-probability-weighted regression adjustment compare adopters and nonadopters. AI use raises the probability of employment growth without increasing decline risk; effects double for intensive users and are strongest in larger SMEs, services, and countries with stronger innovation, labor-market, governance, and decentralization institutions.
Tracked in RePEc NEP Entrepreneurship, RePEc NEP Artificial Intelligence
Intellectual Property Protection for AI-Generated Output
Robin Döttling, Logan P. Emery, Shuo Zhao
RePEc NEP Artificial Intelligence
Models a firm's choice of AI versus human-capital use when investing in innovation, with IP protection granted based on a noisy signal of human-capital use. Derives the IP policy's effect on incentives and characterize when the IP system can "kill" AI use. Alternatively, low AI costs can "kill" the IP system or shift its role to providing a human-capital subsidy, depending on signal noise and the social value of human-capital use in innovation.
Intellectual property in the context of firms’ exit strategies: The role of patents
Chahreddine Abbes, Amélie Lafrance-Cooke, Nicholas Johnston
RePEc NEP Entrepreneurship
It can be a valuable asset to attract investors, and secure financing, therefore improving firms’ odds of survival and delaying exit (patent survival effect). While most exits can be the direct result of small and medium-sized enterprises’ failure to compete in a private market for various reasons, when exits involve intellectual property (IP), the situation may require thorough analysis because IP may play a double role. On the other hand, IP can also be a very attractive asset for incumbents to acquire, accelerating exit from the market through mergers and acquisitions (patent trigger effect).
Mind the Gap: AI Adoption in Europe and the US
Alexander Bick, Adam Blandin, David Deming, Nicola Fuchs-Schündeln, Jonas Jessen
RePEc NEP Artificial Intelligence
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. Finds no clear evidence that industry-level AI adoption is associated with employment changes. Cross-country differences in worker demographics and firm composition account for an important share of these gaps.
Patent Valorization and Business Performance: Evidence from an Italian Public Policy
Paolo Castelnovo, Cinzia Lombardo, Valentina Morretta
RePEc NEP Entrepreneurship
Does Italy's patent-valorization program help SMEs move inventions toward the market? A 2025 survey compares beneficiaries from the 2020-2021 calls with similar nonbeneficiaries across patenting, commercialization strategy, constraints, and innovation outcomes. The program increases patenting and technological maturation, particularly among smaller, younger, and resource-constrained firms, without crowding out private investment, but produces limited short-run financial or internationalization gains.
Picking Winners or Marking Them? Timing-Based Evaluation of Innovation Certification
Angelo Leogrande, Mauro Di Molfetta, Valeria Nortarnicola, Maria Giovanna Trotta
RePEc NEP Entrepreneurship
Do voluntary innovation-certification programs create high-growth firms or merely label firms already expanding? An event-study, staggered difference-in-differences design, and hazard model compare roughly 2,900 certified and 1,200 noncertified Italian SMEs. About 81% of certified firms' revenue premium predates registration, and take-up follows recent growth rather than profitability, indicating dynamic selection rather than treatment.
Productivity Dynamics of Artificial Intelligence Adoption: An Analysis of the Machinery Industry
Masayuki Morikawa
RePEc NEP Artificial Intelligence
How widely is AI used in Japanese machinery production, and what does it contribute to productivity? A worker survey finds that 34 percent use AI by the end of 2025, but on only 12 percent of tasks; average task efficiency rises 20 percent, producing a 4 percent user-level productivity gain. Continued users gain more than new users, and projected diffusion adds 0.3-0.4 percentage points to annual industry labor-productivity growth.
Skills, Not Scale: GenAI and Technology Adoption
Nuriye Melisa Bilgin, Gianmarco Ottaviano
RePEc NEP Artificial Intelligence
Using administrative data on Turkish firms from 2021 to 2024, Compares the adoption of traditional and generative artificial intelligence (GenAI). Shows 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. 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.
Sorting into Entrepreneurial Teams
Edoardo Maria Acabbi, Andrea Alati, Luca Mazzone, Marta Morazzoni
RePEc NEP Entrepreneurship
Quantitative model using matched employer-employee and balance-sheet data from Portugal, shows that teams combining similar talent with diverse specializations create larger, more productive, and longer-lived firms. Meeting bias lowers average wages and output by 12% and 13% respectively by distributing activity towards a higher number of less productive firms, while search frictions per se reduce wages and aggregate output by 15% and 13% respectively by preventing highly diverse but specialized individuals from forming successful teams.
The Contribution of Foreign Master's Students to US Start-Ups
Michel Beine, Giovanni Peri, Morgan Raux
RePEc NEP Entrepreneurship
Do foreign master's students increase U.S. startup formation? University-cohort data link administrative enrollments to comprehensive startup records from 1999 to 2019, using tuition changes and pre-2004 enrollment networks as sources of plausibly exogenous variation. Higher international enrollment raises startup creation within five years of graduation, with a substantial share operating through spillovers to U.S.-born classmates.
The Heterogeneous Diffusion of AI: Individuals, Organisations, and Adoption Barriers
Hankui Wang, Jiachen Yi, Philipp Harting
RePEc NEP Artificial Intelligence
Why is AI adoption faster among individuals than organizations? An extended Bass model with heterogeneous groups, cross-group spillovers, barrier decay, and productivity feedback is estimated from 45-country surveys for 2020-2025 and simulated for ten years. Individual-to-firm spillovers dominate diffusion, firm types differ by 5.2 years in reaching 50 percent adoption, training produces the largest aggregate adoption gain, and subsidies narrow the large-small firm gap most effectively.
The Shift to Agentic AI: Evidence from Codex
Drew Johnston, David Holtz, Alex Martin Richmond, Christopher Ong, Prasanna Tambe, Aaron Chatterji
RePEc NEP Artificial Intelligence
Analyzes usage data from OpenAI's Codex tool to present large-scale evidence of how agentic AI technology, which can take actions on a user's behalf, changes how people work. Finds that agentic AI usage is growing rapidly: the number of active users has grown more than fivefold in the first half of 2026, with the most rapid increase occurring outside the initial audience of software developers. Documents a similar shift to agentic tooling outside OpenAI, particularly within organizations, although external adoption remains lower and more uneven.
Weak Bundle, Strong Bundle: How AI Redraws Job Boundaries
Luis Garicano, Jin Li, Yanhui Wu
RePEc NEP Artificial Intelligence
Develops a two-task model in which AI can either assist one task inside a bundled job or supply that task autonomously while a human supplies the residual task. Examines how the effect of AI on an occupation depends not just on which tasks AI can perform but also on how costly it is to unbundle those tasks from the job. Jobs bundle tasks together, and the effect of AI depends on how costly it is to break the bundle.
The Role of College Education in the Rise of "New Work": Measurement and Evidence
Gueyon Kim, Cassandra Merritt, Giovanni Peri
CEPR Discussion Papers
Asks where new types of work come from and whether college-educated labor helps create them. Develops a semantic-distance algorithm for job titles and applies it to U.S. occupation and location data from 1980 to 2010, with pre-1920 land-grant colleges as instruments for college-educated density. The college-educated share is the strongest predictor of new-work creation even after controlling for technology, trade, immigration, aging, and agglomeration, and matched treatment-control evidence points to a causal role for educated-worker density.
Where Do Technology Shocks Come From? Public Funding and Private Ownership
Andrea Gazzani, Joseba Martinez, Filippo Natoli, Paolo Surico
CEPR Discussion Papers
Asks which funding and ownership sources generate technology shocks with macroeconomic consequences. Constructs postwar U.S. patent series by funding source and ownership and uses local projections with macro controls, external instruments, and information-set tests. Government-funded but privately owned patents account for only about 2 percent of filings but explain roughly one-fifth of medium-run fluctuations in TFP and GDP, propagating through private R&D and investment; implied social returns to public R&D are about twice those to private R&D.
Return of the solo author: The changing division of labor in science in the age of generative AI
Akira Matsui
arXiv economics of AI,arXiv Computers and Society
Asks whether generative AI is reversing science's long shift from solo work toward team production. Analyzes more than 300 million works across 26 fields and focuses on the solo-authored tail of the author-count distribution around ChatGPT's public release. The decades-long decline in solo authorship halts and partly reverses after late 2022, especially in fields where coauthors' tasks are more readily substitutable by AI; the change appears among established authors and prior team-only authors, not only among new entrants.
Tracked in arXiv economics of AI, arXiv Computers and Society
AI Adoption in S&P 500 Firms
Yang Yu, Martin Fleming, Lucy Hampton, Christophe Combemale, Neil Thompson
arXiv economics of AI,RePEc NEP Artificial Intelligence
How deeply has AI entered the operating systems of large U.S. firms, rather than their investor-relations rhetoric? SEC 10-K filings measure enterprise-level AI adoption among S&P 500 firms from 2016 to 2025. By 2025, 11 percent had deeply integrated AI into business processes and another 10 percent used it in production or delivery; deep adoption had more than quadrupled from 5 percent in 2022, but it shows a profitability J-curve without detectable capex or productivity differences.
Tracked in arXiv economics of AI, RePEc NEP Artificial Intelligence
Supply Shocks in a Heterogeneous-Firm New Keynesian Model: The Entry Multiplier
Florin Bilbiie, Marc J Melitz
CEPR Discussion Papers
Asks how endogenous firm entry changes the transmission of productivity shocks in New Keynesian models. Builds a heterogeneous-firm model with entry, selection, and nominal rigidities to compare flexible- and sticky-price adjustment. With sticky prices, productivity shocks generate a large entry multiplier, causing firm entry and exit to respond much more strongly than under flexible prices; adding wage stickiness makes adverse productivity shocks reduce profits, trigger exit, and open a negative output gap while remaining inflationary.
Validating LLMs in social science: Epistemic threats and emerging norms
Meera Desai, Dallas Card, Abigail Z. Jacobs
arXiv Computers and Society
Researchers increasingly prompt language models to generate quantitative measurements of social concepts, for example labeling data or simulating survey responses. Finds that LLM-generated measurements frequently play a central role in empirical analyses, yet validation practices are inconsistent and limited. Large language models (LLMs) are reshaping social science methodology.
Answering Without Referring: How AI Search Rewrites the Web's Economic Bargain
Qiaoni Shi, Kai Zhu, Kai Gu
arXiv economics of AI,arXiv Computers and Society,RePEc NEP Artificial Intelligence
Will AI search preserve the referral flows that finance much of the open web? URL-level U.S. desktop clickstream data compare ChatGPT with Google and use expansions of ChatGPT Search access to estimate displacement of traditional search. ChatGPT generates outbound clicks in only 5.2 percent of conversation sessions, and wider access cuts search use by 9.4 percent, with the largest referral losses in informational categories.
Tracked in arXiv economics of AI, arXiv Computers and Society, RePEc NEP Artificial Intelligence
Memory Scarcity, Open Models, and the Restructuring of the AI Industry, 2026-2030 -- A quantitative scenario analysis of inference economics, training-cost divergence, and infrastructure solvency
Satoshi Matsuoka
arXiv economics of AI
Asks how memory scarcity, open models, inference efficiency, and compute resale could reshape AI industry economics from 2026 to 2030. Models inference costs in dollars per petabyte of bandwidth delivered and compares entrant and incumbent cost trajectories under DRAM/HBM price pressure and amortized compute fleets. The entrant-incumbent cost gap stays open, training splits into a luxury frontier tier and a cheap previous-frontier tier, and the announced infrastructure buildout is solvent only under a narrow corridor of roughly 2x annual token-demand growth with sticky premium pricing.

Useful Knowledge Issue No. 006 - July 7, 2026

Tue, 07 Jul 2026 23:59:59 GMT

Issue No. 006

47 papers.

Technology and Labor Markets: Past, Present, and Future; Evidence from Two Centuries of Innovation
Huben Liu, Dimitris Papanikolaou, Lawrence D. W. Schmidt, Bryan Seegmiller
Brookings Papers on Economic Activity
How has technological change shifted occupational labor demand, and how might AI differ? Text-based measures of worker exposure spanning nearly two centuries are linked to census employment and embedded in a calibrated model. Twentieth-century technologies favored higher-paid, more-educated, and more female occupations, while the model predicts that medium-run AI exposure reverses those patterns toward lower-paid, less-educated, and more male occupations.
Patent protection and software firm financing
Christian Helmers, Brian J. Love
Research Policy
Asks whether weaker software patent protection after the Supreme Court's 2014 Alice decision changed the financing and performance of software startups. Uses detailed data on venture-capital financing rounds and startup exits in a difference-in-differences design comparing firms with greater and lesser exposure to the ruling. Startups more exposed to Alice perform no better or worse on financing, growth, or exit outcomes, suggesting the contraction in software patentability had little measurable effect on venture-backed software firms.
What’s in a name? Dynasties, selection, and talent allocation among classical composers
Karol Jan Borowiecki, Martin Hørlyk Kristensen, Marc T. Law
Journal of Economic Growth
How do family ties and standardized training affect selection into elite creative careers? Biographical data on more than 16,000 classical composers since 450 CE identify dynasties and measure prominence, with archival manuscript records providing an independent check. Descendants of composers are 15-21 percent less prominent than comparable nondynasts, although dynasty founders are not; the disadvantage reverses in the twentieth century as conservatories expand. Standardized training appears to weaken inherited privilege and improve talent allocation.
Do public credit guarantees boost R&D and innovation?
Ahmet Deryol, Leone Leonida, Gulcin Ozkan
Research Policy
Asks whether public credit guarantees do more than ease financing constraints by raising firms' innovative activity. Uses a rich firm-level dataset from Turkey's Credit Guarantee Fund and compares guarantee recipients with nonrecipients using difference-in-differences and propensity score matching. Guaranteed loans raise both innovation inputs such as R&D spending and outcomes such as high-tech exports, with larger effects for firms that were already innovating or had clear ex ante potential to innovate.
Scaling innovation through public procurement: Market acceleration, building, and transformation
Maria Merisalo, Matti Pihlajamaa, Elvira Uyarra, Ville Valovirta, Juha Oksanen
Research Policy
Asks how public procurement can move innovations from isolated pilots into broader markets and system-level change. Develops a framework that combines innovation-scaling and market-shaping perspectives and applies it to twelve publicly funded public-procurement-of-innovation projects in Finland. The study identifies three scaling modes: market acceleration through support for development, adoption, and diffusion; market building through aggregation of public demand; and market transformation through institutional and socio-technical change, with buyers using information dissemination, demand coordination, and norm-shaping practices throughout the procurement lifecycle.
The café economy: structural transformation in Greece in the wake of austerity and “reforms”
Michalis Nikiforos, Vlassis Missos, Christos Pierros, Nikolaos Rodousakis
Industrial and Corporate Change
Asks how austerity-era reforms reshaped Greece's sectoral structure and productivity path. Studies the rising weight of accommodation and food services after the Greek crisis and relates sectoral reallocation to labor productivity and theories of technological change. Employment shifted toward low-productivity services while aggregate labor productivity fell, suggesting that reforms meant to raise efficiency instead helped produce a Lewis-style dual structure in which output growth and labor costs constrained productivity upgrading.
Childbirth and Firm Performance: Evidence from Norwegian Entrepreneurs
John Bonney, Luigi Pistaferri, Alessandra Voena
Journal of Labor Economics
Asks how childbirth changes the performance of entrepreneurial firms. Uses multiple Norwegian administrative datasets to track revenues, costs, and profits after business owners become parents. Female-owned firms experience a large and persistent child penalty, with profits about 30 percent below baseline ten years after childbirth, while male-owned firms show no comparable decline; the gap is largest for highly capable founders, majority owners, and women with working spouses, pointing to entrepreneurial time demands as the main mechanism.
The Sound We Haven’t Heard Before: A Commentary on “Fighting Fire with Fire: Infusing AI into Peer Review to Sustain Quality Scholarship”
Tinglong Dai
Management Science
Asks how journals should govern AI use in peer review without outsourcing judgment to automated filters. Responds to a proposal for AI-assisted peer review in Management Science and argues that the main safeguard is human assessment of novelty rather than AI detection. The commentary?s mechanism is reputational and editorial: AI can be a non-final input, but journals should design workflows that demand more human judgment over contribution and originality, not less.
Bank Run, Interrupted: Modeling Deposit Withdrawals with Generative AI
Sophia Kazinnik
Management Science
Asks how generative-AI survey simulations can test bank-run communication strategies. Builds a representative population of synthetic depositors, exposes them to a viral panic post, randomizes bank messages, validates responses against human benchmarks, and feeds withdrawal propensities into a contagion model. Direct, personalized messages with strong reassurances and explicit survival clauses substantially reduce withdrawal intent, offering a low-cost way to stress-test crisis communications before a run unfolds.
Ethnic integration and the value of innovation
Stefano Breschi, Luisa Gagliardi, Thomas Yoon
Strategic Management Journal
Asks how ethnic integration within inventor teams changes the economic value of corporate innovation. Uses patent and co-inventor data for 890 publicly traded U.S. firms from 1990 to 2015 to build a network measure of cross-ethnic collaboration and link it to stock-market reactions around patent grants. Ethnic integration raises patent value especially in firms that are both ethnically diverse and technologically complex, implying that the structure of collaboration matters in addition to workforce composition.
Putting a price on mission: Social responsibility orientation and startup employment
J. Daniel Kim, Matthew Lee
Strategic Management Journal
Asks whether socially responsible startups gain an advantage in full-time hiring. Uses microdata from a structured startup recruitment process to compare candidate interest, offer acceptance, retention, and job satisfaction at social-responsibility-oriented employers. Mission-oriented startups attract more initial interest, especially from female candidates, and are more likely to have offers accepted; candidates with multiple offers appear willing to give up 13 to 18.5 percent of annual salary, but the hiring advantage does not translate into higher retention or job satisfaction.
Artificial Intelligence, Alliances, and Innovation1
Bowen Lou, Evan Rawley
MIS Quarterly
How do firm-level AI capabilities change pharmaceutical R&D alliances? Patents and job postings proxy for AI resources and are linked to alliance activity and drug development. Firms with more AI shift innovation toward alliances and develop more alliance-sourced drugs because AI helps discover, evaluate, and interpret partners' information, reducing information asymmetry within collaborations.
Technology Diffusion, Human Capital and Employee Mobility
Milan Miric, Hakan Ozalp
MIS Quarterly
As new technologies diffuse within an industry, they impact the value of worker skills, and in turn may influence the mobility of workers between firms in that industry. This mobility may be particularly pronounced for individuals whose jobs are complementary to these new technologies and therefore increase demand for these individuals. Examines the impact of a small set of development tools diffusing within an industry and investigate whether this was associated with an increase in worker mobility for individuals with complementary skills, compared to those with (partly) substitutable ones.
The Role of Digital Platforms in Data Markets: How Platform-Shared Market Data Through Advanced Analytics Empower Firm Innovation
Yubo Chen, Xuebin Cui, Aishen Li, Banggang Wu, Liu Yang
Management Science
Asks whether digital platforms can help firms innovate by sharing market information through analytics rather than raw data. Uses a natural experiment with Alibaba's Taobao Marketplace to estimate how platform-provided advanced analytics affect retailers. Advanced analytics raise retailer sales by 31.8 percent beyond descriptive analytics, with larger and more persistent gains from platform-level market data; the mechanism is product-assortment innovation, including 6.8 percent broader category scope and 10.5 percent more new SKU introductions.
Platform Governance and Automated Enforcement: Evidence from YouTube Content ID
Sverrir Arnórsson, Stefan Bechtold, Christian Peukert, Catherine Tucker
NBER SI 2026 Digital Economics and Artificial Intelligence
How does automated copyright enforcement redistribute power on large digital platforms? The authors run a large-scale audit by uploading sound recordings and compositions to YouTube and measuring how Content ID responds across rights holders and jurisdictions. The matching technology is fairly accurate, but enforcement departs systematically from legal entitlements: major-label works are flagged much more often than works from smaller rights holders, many lawful uses are flagged, and some jurisdiction-specific restrictions are not implemented.
Tax Incentives and Venture Capital Risk-Taking: Evidence from the QSBS Program
Murillo Campello, Guilherme Junqueira
NBER Working Papers,RePEc NEP Entrepreneurship
Do capital-gains tax subsidies change venture capitalists' appetite for startup risk? Bunching, triple-difference, and matching designs analyze 158,000 investor-firm pairs around QSBS eligibility rules. Subsidized VCs move toward precommercial and indebted startups, provide initial capital more often, and syndicate less; portfolio failures rise, but so do exit valuations, unicorn outcomes, and patent impact.
Tracked in NBER Working Papers, RePEc NEP Entrepreneurship
A Theory of Firm Wage Dynamics
Marc de la Barrera, Masao Fukui
NBER SI 2026 The Micro and Macro Perspectives of the Aggregate Labor Market
Theoretical model. Develops a theory of firm wage dynamics that integrates the canonical wage-posting model a la Burdett and Mortensen (1998) with firm dynamics. Consistent with recent empirical evidence, the model implies that (i) firm wages are strongly linked to firm growth but not to firm size; (ii) young firms pay higher wages than old firms; and (iii) the pass-through of productivity shocks to wages is higher in the short run than in the long run. Firms offer dynamic wage contracts under an equal-treatment constraint in the presence of search frictions.
Forecasting Social Science: Evidence from 100 Projects
Stefano DellaVigna, Eva Vivalt
NBER SI 2026 Forecasting & Empirical Methods
How accurate are forecasts of social-science results, and how should experiments use them? Data cover 53,298 predictions for 100 projects posted on the Social Science Prediction Platform from 2020 to 2024, including realized results for 66 projects. Forecasters overestimate treatment effects on average, although mean forecasts predict actual effects; academics and motivated repeat forecasters are more accurate, while confidence is negatively associated with accuracy. Shrinking predictions and weighting consistently accurate forecasters can improve power in experimental design.
Forecasting the Economic Effects of AI
Ezra Karger, Otto Kuusela, Jason Abaluck, Kevin A. Bryan, Basil Halperin, Todd R. Jones, Connacher Murphy, Philip Trammell, Matt Reynolds, Dan Mayland, Kevin Bryan, Rebecca Ceppas de Castro, Todd Jones, Ananaya Mittal, Josh Rosenberg, Philip Tetlock, Phil Trammell, Ria Viswanathan
NBER SI 2026 Forecasting & Empirical Methods,RePEc NEP Artificial Intelligence
How do experts and the public expect AI to change the US economy? Forecasts are elicited from academic economists, AI-company employees, AI policy researchers, high-performing forecasters, and a representative public sample. Belief differences map into policy: experts favor targeted worker retraining, while the public also supports broad interventions such as a job guarantee and universal basic income.
Tracked in NBER SI 2026 Forecasting & Empirical Methods, RePEc NEP Artificial Intelligence
Leveraging to Lend: A Theory of Lax Credit
Ulf Axelson, Igor Makarov
NBER SI 2026 Corporate Finance
A pervasive feature of financial intermediation is the use of third-party deal-by-deal capital in addition to balance sheet capital to finance investment activity. Shows that third-party capital serves a novel economic function: it allows informed intermediaries to separate screening decisions from surplus sharing, thereby intensifying competition and reducing the cost of capital to firms. Consequently, although entrepreneurs benefit from intermediary third-party leverage, restricting third-party financing can raise social welfare.
Quantifying Knowledge Spillovers Using Firm and Product Dynamics
Mohamad Adhami
NBER SI 2026 Economic Growth and Long-Run Macroeconomic Development
How large are intertemporal knowledge spillovers when many innovations are not patented? A growth model uses product and firm turnover by age to distinguish spillover-driven displacement from idiosyncratic product volatility, estimated on all U.S. nonfarm private employers. Stronger spillovers accelerate improvement across product generations and the exit of incumbents. The estimates imply an 11-28 percentage-point wedge between the social and private returns to innovation under laissez-faire.
Growth at the Resource Frontier
Martin Beraja, Noam Yuchtman
NBER SI 2026 Digital Economics and Artificial Intelligence
In important historical cases, documents that innovators appropriated such resources in anticipation of extraordinary rents, triggering conflicts with counterparties. The model rationalizes how property rights emerge as a consequence of innovation, establishes that appropriation rents can incentivize innovation, and ultimately shows that economic growth at the resource frontier frequently comes together with appropriation and conflict. Much of human progress comes from innovations that expand the frontier of usable resources.
Innovation through Recombination
Songyuan Teng
NBER SI 2026 Innovation
How much innovation comes from recombining existing knowledge rather than discovering new building blocks? Pharmaceutical products provide a measurable distinction between novelty and recombination, which informs a calibrated model of firm knowledge stocks and product portfolios. Recombination is large and increasingly important, and firms move from accumulating knowledge toward deploying it in combinations over their life cycle. Subsidizing novelty raises short-run growth, while subsidizing recombination produces larger long-run gains with heterogeneous effects across firms.
World War II and the 20th-Century Transformation of Biomedicine
Daniel P. Gross, Bhaven N. Sampat
NBER SI 2026 Development of the American Economy
Using data on all CMR research contracts, shows that despite mixed results during the war, this effort laid the foundation for the postwar growth of U.S. biomedical innovation, including the expansion of biomedical science, new drug development, advances in clinical knowledge, and the postwar extramural research funding at the National Institutes of Health. During World War II, the U.S. Committee on Medical Research (CMR) coordinated and funded an integrated, cross-sectoral effort to develop medical science and technology for war, representing the U.S. government's first substantial investment in medical research.
Ports, Technology and Inter-City Trade: The Economics and Geopolitics of Evolving Maritime Transport Networks
Réka Juhász, Dávid Krisztián Nagy, Claudia Steinwender, Woan Foong Wong
CEPR Discussion Papers,NBER Working Papers
Asks how containerization and related transport technologies reshaped port networks, trade, and the economic importance of maritime nodes. Uses newly digitized shipping records, georeferenced ship movements, and shipment-level routing data to document five facts about the evolution of the global maritime network. Shipping remains highly concentrated among changing top ports while lower-ranked ports disperse; Chinese state-owned terminal operators increasingly handle global volumes and improve efficiency mainly for Chinese vessels, highlighting how transport technology deepens integration while raising the value of controlling critical nodes.
Tracked in CEPR Discussion Papers, NBER Working Papers
Large language models create an uneven informational layer over cities
Lin Chen, Guangyuan Weng, Esteban Moro
arXiv economics of AI,arXiv Computers and Society
Asks how large language models shape the visibility of urban businesses and neighborhoods. Audits restaurant recommendations from three major LLMs across 304 neighborhoods in five U.S. cities using 320 synthetic user profiles that vary income, age, sex, and residential status. LLMs fabricate venues and systematically overlook real ones; even with verified venue lists, 47.5 percent of establishments are never recommended, and recommendation patterns steer higher-income users and tourists toward different venues, implying uneven effects on local demand and urban inequality.
Tracked in arXiv economics of AI, arXiv Computers and Society
Macroprudential Policy and Corporate Innovation: Evidence from European Countries
Xuan Viet Ho, Brigitte Granville, Luu Duc Toan Huynh, Steven Ongena
CEPR Discussion Papers
Asks whether macroprudential tightening changes the quantity and quality of corporate innovation. Links 401,755 patents to 2,844 firms across 21 European countries from 1990 to 2021 and combines multi-way fixed effects, an instrumental-variable strategy, and a difference-in-differences design around the EU macroprudential single rulebook. A one-standard-deviation increase in the macroprudential policy index reduces patent counts by about 10.1 percent and adjusted forward citations by about 8.4 percent, with stronger effects for financially constrained firms through the credit channel.
Whose fairness? Structural concentration in AI bias research
Abhash Shrestha, Subigya Gautam, Anu Sapkota, Sanju Tiwari, Tek Raj Chhetri
arXiv Computers and Society
Artificial intelligence increasingly mediates consequential decisions in healthcare, law, and public services, and the field has responded with an extensive methodology for measuring and mitigating bias. Shows that the AI bias research are structurally concentrated, and that this concentration is greatest, geographically, in precisely the domain the rest of the field inherits from.
Rating the Pitch, Not the Product: User Evaluations of LLMs Reflect Expectations More Than Performance
Robert Morabito, Tyler McDonald, Charitra Viswanath, Angel Hsing-Chi Hwang, Susanne Gaube, Jad Kabbara, Ali Emami
arXiv economics of AI,arXiv Computers and Society
Asks whether user ratings of large language models reflect actual model performance or expectations created before use. Randomizes 162 participants across six LLMs and landing pages that accurately describe, overstate, or understate each model's capability before collaborative tasks. Framing changes perceived usefulness, intelligence ratings, and prompting style, while output quality depends on true model capability; oversold users rate identical systems more favorably and use more directive prompts.
Tracked in arXiv economics of AI, arXiv Computers and Society
The Geography of Private Sector Agricultural Innovation in the USA: Evidence from Patents
Matt Clancy
arXiv innovation and entrepreneurship
Asks how private agricultural R&D is distributed across U.S. states and subsectors. Allocates national private-sector agricultural R&D to states from 1976 to 2014 using inventor locations on contemporaneous patents across five agricultural subsectors. The resulting state-year series shows that private agricultural R&D is strongly tied to the size of the state agricultural economy, highly persistent over time, and less geographically concentrated than in the past, although the deconcentration trend has recently weakened.
Psychological features of dispute content and public acceptance of AI in legal adjudication: evidence for systematic variation beyond individual differences
Masahiro Fujita, Eiichiro Watamura
arXiv Computers and Society
Study 2 replicated this structure in an independent sample and demonstrated that experimentally manipulated contextual features - emotional involvement and prototypicality - systematically modulated acceptability judgments, with effects varying by dispositional trust, AI-specific attitudes, and gender. These findings suggest that the psychological features of dispute content constitute an overlooked dimension in AI acceptance research, extending beyond technology acceptance models to fundamental questions about how individuals construe social problems and allocate adjudicative authority.
Government AI Use as a Monitoring Primitive: A Public Document Pilot Study
David I. Atkinson, Joan Eleanor O'Bryan
arXiv Computers and Society
Develops a revealed-behavior measure of government AI adoption using traces of language-model assistance in public documents. A pilot analyzes ten document streams associated with U.S. and Chinese government bodies against 2021 baselines. Four streams show statistically significant AI-writing signals by 2026, concentrated downstream from policy work in the United States and closer to policy production in China.
Hybrid Algorithmic Governance in U.S. Welfare Administration: State- and County-Level AI as a Case of Support-Control Convergence
Maxim Dedyaev
arXiv Computers and Society
When do AI systems in welfare administration function as support rather than control? The paper uses process tracing across six U.S. state and county cases to study how institutional design allocates the costs of algorithmic error. It argues that welfare AI systems drift predictably toward control when those costs fall on claimants, while reversals toward supportive uses are rare, slow, and costly.
Remote Work: Driver or Deterrent of Digital Product Innovation
Fangchen Song, Yixuan Liu, Ashish Agarwal
arXiv innovation and entrepreneurship
Asks whether remote work helps or hinders collaborative digital product innovation. Uses mobile-application panel data, firm-level remote-work adoption from job postings, and a staggered difference-in-differences design to estimate effects on app releases and feature introductions. Remote work raises both major releases and new feature introductions without reducing originality, suggesting that flexible work can improve continuous digital innovation rather than merely shift firms toward imitation.
Macro-Prudential AI Governance: A Two-Layer Early Warning and Response System for Frontier AI
Pranav Mehta
arXiv economics of AI,arXiv Computers and Society
Asks how frontier-AI governance could move from model-by-model review toward systemwide early warning and response. Adapts macroprudential ideas from Basel III and U.S. financial-stability regulation to internal frontier-AI systems used by labs for research, testing, and production. The proposed two-layer framework routes capability, autonomy, and security signals through a clearinghouse and pairs early-warning indicators with response tools, aiming to manage correlated sectoral risk rather than isolated model failures.
Tracked in arXiv economics of AI, arXiv Computers and Society
Taxing Labor: Firm R&D, Automation and the Labor Share
Hyejin Ku, Uta Schönberg, Ragnhild Schreiner
CEPR Discussion Papers
Asks how higher labor taxes reshape firms' innovation choices and factor mix. Uses an EU-mandated payroll tax reform in Norway together with linked administrative and survey data on firms' employment, R&D, automation, and productivity responses. Firms facing larger tax increases cut employment, raise R&D, adopt more labor-saving innovations and automation, and become more productive, but those gains come with a persistently lower labor share even after the tax hike is later reversed.
Doom Researching: A Conceptual Framework for Repetitive AI-Assisted Information Seeking, Cognitive Offloading, and the Illusion of Knowing
Santosh Premi Adhikari
arXiv economics of AI,arXiv Computers and Society
Asks why generative AI can encourage repeated information seeking without durable synthesis or completed work. Builds a conceptual framework of 'doom researching' from research on doomscrolling, cognitive offloading, transactive memory, productivity loss, and the illusion of knowing. The mechanism is that fluent AI responses lower cognitive effort and inflate perceived understanding, encouraging more querying; the paper proposes a risk index and testable predictions for measuring the loop empirically.
Tracked in arXiv economics of AI, arXiv Computers and Society
Dynamic Capabilities for AI-Enabled Exploration: Antecedents, Mechanisms, and Innovation Outcomes
Thabit Atobishi, Saeed Nosratabadi
arXiv economics of AI,arXiv innovation and entrepreneurship
Asks how firms turn AI adoption into exploratory and radical innovation rather than routine operational gains. Uses survey data from 245 senior executives in Saudi Arabia and a PLS-SEM design combining technology-organization-environment factors with the dynamic-capabilities view. Organizational readiness and technology compatibility build sensing capability, which then supports AI-enabled exploration and higher innovation performance; competitive pressure strengthens the readiness-to-exploration link.
Tracked in arXiv economics of AI, arXiv innovation and entrepreneurship
The Economic Benefits and Costs of AI and Policies to Mitigate AI's Impact on Inequality
Matthew O. Jackson, Zafer Kanik
arXiv economics of AI
Asks how faster AI productivity growth changes wages, inequality, and the case for redistribution. Develops a model in which AI substitutes for some labor, complements other labor used to build AI, and can be supplied competitively or by a monopolist. Labor essential to building AI sees wages rise faster than GDP, labor displaced by AI loses in both absolute and relative terms, and monopoly slows diffusion enough to change which tax and regulatory policies can deliver Pareto improvements.
NATO and Emerging Technologies: The Alliance's Shifting Approach to Military Innovation
Stephen Herzog, Dominika Kunertova
arXiv Computers and Society
In the current era of great-power competition and the diffusion of emerging disruptive technologies on the battlefield, NATO's approach to coordinating the development, adoption, and standardization of new technologies is changing from its practices during the Cold War, but the nature of these technologies poses additional challenges for the alliance.
Applications of Artificial Intelligence in the Vietnamese Economy
IZA@LISER Discussion Papers
The country is moving beyond a growth model based on low-cost labour and export-led industries. AI is becoming a driving force in Vietnam’s economic transformation. Adoptions of the widespread generative AI and AI-powered assistants have accelerated the transformation.
Evidence on the Adoption of Artificial Intelligence: The Role of Skills Shortage
IZA@LISER Discussion Papers
Instrumental-variables design. Exploits unique German survey data from the Mannheim Innovation Panel on both the adoption of AI and the extent to which firms experience scarcity of skills. Overall, finds a positive and significant effect of skills shortage on AI adoption, the breadth of AI methods, and the breadth of areas of application of AI. In addition, finds evidence that shortage on academic qualifications and STEM skills relates to firms adopting AI.
Let Me Check on You: Job Quality Under AI and Human Oversight
IZA@LISER Discussion Papers
Uses a preregistered vignette experiment with a nationally representative sample of 2,172 Dutch adults who evaluated otherwise identical workplaces introducing one of three safety systems: human supervisors, AI-only monitoring, or hybrid AI-human supervision. These findings suggest that AI can influence work not only by improving safety but also by reducing important non-pecuniary dimensions of job quality, highlighting that the welfare consequences of workplace AI extend beyond productivity and accident prevention.
Measuring National Entrepreneurial Ecosystems in Africa: A Critical Response
IZA@LISER Discussion Papers
At the empirical level, the AEEI suffers from important representational limitations, including the exclusion of roughly half of African countries, the combination of indicators drawn from very different time windows, and reliance on data sources whose coverage may reflect platform visibility as much as underlying entrepreneurial activity. The data released by the authors raise questions about the reproducibility of the results, including issues with published rankings, variable construction and country coverage. and treatment of missing values.
The Impact of Technological Change on Employment: A Composite Indicator Approach
IZA@LISER Discussion Papers
Can a multidimensional measure of technological change explain manufacturing employment better than R&D spending or patent counts alone? Principal-components analysis combines seven firm-level innovation metrics into a composite indicator for 17 manufacturing sectors in 10 EU countries. A one-unit increase in the indicator is associated with 0.58 percent higher employment, suggesting that correlated innovation measures capture labor-market effects missed by single proxies.
The Interplay Between AI and Technological Relatedness in Shaping Regional Innovation in Europe
IZA@LISER Discussion Papers
Can regional AI capability broaden innovation beyond technologies already close to a region's existing knowledge base? A three-way longitudinal patent dataset uses deep-learning identification of AI patents to measure local AI endowments and technological relatedness. Related technologies remain more likely to generate patents, but AI knowledge raises patenting across fields and significantly weakens the dependence of innovative activity on prior regional technological proximity.
When AI Does the Work: Does Attribution Shape Meaning and Effort?
IZA@LISER Discussion Papers
Does labeling identical creative output as AI-generated change the meaning people attach to work? Preregistered experiments with representative samples of 1,511 Americans and 2,117 Dutch adults randomize whether a public-health slogan is attributed to AI or a professional. AI attribution modestly reduces perceived task meaning and makes participants 13 percent less likely to contribute their own slogan.

Useful Knowledge Issue No. 005 - June 30, 2026

Tue, 30 Jun 2026 23:59:59 GMT

Issue No. 005

34 papers.

The Aggregate Costs of Uninsurable Business Risk
Corina Boar, Denis Gorea, Virgiliu Midrigan
American Economic Review
Asks how uninsured entrepreneurial business risk affects aggregate outcomes. Uses firm-level data showing large, fat-tailed, transitory fluctuations in private-business profit shares, then interprets the evidence with a model of entrepreneurial dynamics. Because entrepreneurs reduce scale to limit exposure to uninsurable output risk, the implied macroeconomic losses are large and exceed the losses from credit constraints; self-financing can ease borrowing limits, but even wealthy entrepreneurs remain substantially exposed to risk.
The Effect of High-Tech Clusters on the Productivity of Top Inventors: Comment
Michael Wiebe
American Economic Review
Revisits whether larger high-tech clusters raise the productivity of top inventors. Re-examines Moretti (2021) with corrected event-study and instrumental-variables coding and focuses on the variation created by inventors moving across cities. Once the event study and instrument are fixed, the previously reported positive elasticity between cluster size and patenting disappears, implying the original estimate should not be interpreted as causal.
Expecting Floods: Firm Entry, Employment, and Aggregate Implications
Ruixue Jia, Xiao Ma, Victoria Wenxin Xie
American Economic Journal: Macroeconomics
Asks how chronic flood risk, distinct from individual flood events, reshapes firm location, employment, and aggregate output. Combines U.S. county-level and zip-code-level data from 1998 to 2018 with a quantitative spatial model of firm entry and employment. Higher flood risk lowers firm entry, employment, and output in the long run, while realized flood events mainly reduce short-run output; the model attributes a 0.53 percent reduction in U.S. aggregate output in 2018 to flood risk, with 77 percent coming from long-run reallocation by firms and workers rather than direct damage.
University Research and the Market for Higher Education
Titan Alon, Damien Capelle, Kazushige Matsuda
American Economic Journal: Macroeconomics
Asks why universities fund research internally despite weak direct returns from patenting. Develops a model in which competition for tuition revenue and talented students makes research spending endogenous, then disciplines it with causal evidence and microdata from higher education. Research rises when students are more stratified across colleges or tuition increases steeply with rank, and calibrated simulations imply tuition policies stimulate university R&D while research subsidies partly crowd it out.
An Acquisition by Another Name: Reverse Acquihires Under the Clayton Act
David T. Wong
Yale Law Journal
Do AI-company deals that hire a startup's key personnel and license its technology without acquiring the firm fall under merger law? The Comment analyzes reverse acquihires under the text and precedent of Section 7 of the Clayton Act, treating human capital as an intangible asset. It argues that these transactions are acquisitions subject to review when they may lessen competition, preventing firms from evading scrutiny through deal form.
Chronos meets Kairos: The strategic enactment of time in corporate entrepreneurship
Aracely Soto-Simeone
Journal of Business Venturing
How do corporate entrepreneurs manage conflicting forms of time inside established organizations? Evidence from corporate entrepreneurs in large Finnish firms develops a model of temporal work around chronological constraints and opportune moments. Entrepreneurs use distinct practices to leverage and reshape organizational timing, with consequences for both their ventures and their employers.
Bottoms Up: Micro Entry Nonmarket Strategies to Change State Policy
Eppa Rixey
Organization Science
Asks how small firms can loosen regulatory constraints when entrenched interests dominate state-level policymaking. Uses qualitative evidence from craft breweries to trace how entrepreneurs pursued local hearings, coalition-building, and repeated use of 'event' licenses to pressure regulators from below. These bottom-up nonmarket strategies helped cities normalize direct-to-consumer sales, built support across locales, and eventually pushed state authorities to relax restrictions that had favored larger incumbents.
Criminal Deception in Silicon Valley
Tim Weiss, Nevena Radoynovska
Organization Science
Asks how entrepreneurs carry out criminal fraud when venture performance falls short of audience expectations. Studies prosecuted Silicon Valley fraud cases from 2000 to 2023 to reconstruct how founders presented and defended misleading growth narratives. The paper identifies three escalating forms of facading, from surface to deep, and argues that larger expectation-reality gaps push entrepreneurs toward more elaborate efforts to detach a venture's public image from its underlying operations.
AI Premium
Nicola Borri, Yukun Liu, Aleh Tsyvinski
arXiv economics of AI,NBER Working Papers,arXiv Computers and Society,RePEc NEP Artificial Intelligence
Do investors price firms' exposure to AI adoption, and which forms of use command a premium? Data covering 380 trillion realized AI tokens across more than 400 models construct a high-frequency AI factor and firm-level AI betas from stock-return comovement. Firms with higher AI betas earn higher subsequent returns: a value-weighted long-short portfolio earns 64.1 basis points per week, with the premium concentrated in intensive, frontier-oriented use of closed models rather than casual or open-weight use.
Tracked in arXiv economics of AI, NBER Working Papers, arXiv Computers and Society, RePEc NEP Artificial Intelligence
AI and the Collapse of the www
Alex Chan
NBER Working Papers
Asks how generative-AI answer systems change the market design of web search and online publishing. Develops a model in which AI intermediation improves user experience while diverting visits that finance content production and generate source-level quality signals. If the AI platform underinternalizes future content reproduction, it sends too little referral traffic to publishers and can push costly open-web information below a sustainable threshold; visitor-replacement royalties, audited provenance, human-information audits, and keystone-topic compensation are proposed as repair mechanisms.
Immigration, Innovation, and the Geography of Growth
Costas Arkolakis, Sun Kyoung Lee, Michael Peters
NBER Working Papers
Asks how the 1880-1920 U.S. immigration wave affected innovation, regional growth, and aggregate income. Links individual census and historical immigration records to the universe of U.S. patents, then embeds the evidence in a spatial growth model where skilled workers have a comparative advantage in innovation and sort across cities. Immigrants disproportionately settled in urban innovation hubs; the model attributes an 8.2 percent increase in U.S. income per capita by 1940 to post-1880 arrivals, and estimates that removing the 1920s immigration restrictions would have raised income per capita by another 1.7 percent by 2000.
Translation Readiness Index: Measuring Patent-Paper Proximity from Scientific Publication Text
Paul X. McCarthy, Rasika Amarasiri, Xian Gong
arXiv innovation and entrepreneurship
Asks whether publication text can identify research that is close to commercialization before patents, licenses, or startups appear. Uses 20,610 OpenAlex papers, patent-paper pair data, SPECTER2 embeddings, and an XGBoost classifier to estimate each paper's semantic proximity to patented science. The resulting Translation Readiness Index predicts whether a paper resembles high-confidence patent-linked research, reaches an ROC-AUC of 0.77 in the training task, and is positively associated in external validation with independent translational indicators at UWA and leading universities.
The Organizational Behavior of Agentic AI: Collective Intelligence in Human-Agent Workflows
Canhui Liu
arXiv economics of AI,arXiv Computers and Society
Asks when agentic AI systems organized as teams, reviewers, managers, or workflows improve performance inside organizations. Combines computational theorizing, synthetic task simulations, real LLM agent traces, and robustness checks to compare different human-agent coordination structures. The main mechanism is contextual transaction cost: human-like structures underperform when they create lossy handoffs, correlated deliberation, and verification overhead, while shared-state and adaptive designs work better when context is durable, inspectable, and tailored to the task.
Tracked in arXiv economics of AI, arXiv Computers and Society
AI Adoption by Human Experts: Evidence from Primary Care Physicians
Shan Huang, Renke Schmacker, Hannes Ullrich
RePEc NEP Artificial Intelligence
Why do experts underuse equally accurate AI advice? A nationwide experiment with 372 Danish primary-care physicians randomizes whether a diagnostic signal is labeled as AI or as a familiar dipstick test. Physicians update beliefs 41 percent less from the AI signal and roughly one third ignore it; correlation neglect among users increases antibiotic prescribing, while clinic technology use predicts adoption.
Beyond Price: A Technical Quality Framework for AI Antitrust
Patrick Y. Wu
ACM Conference on Fairness, Accountability, and Transparency
How should antitrust assess AI markets when incumbents subsidize prices and competition occurs through model quality? The article develops a Technical Qualities and Metrics Vector that uses established benchmarks to define markets and evaluate transactions such as acquihires. Case studies show how quality measures can reveal competitive harms that price-centered tests miss.
Tracked in ACM Conference on Fairness, Accountability, and Transparency
Caught in the Loop: How Algorithmic Management Disempowers Frontline Supervisors
Furkan Oezdemir, Armin Alizadeh, Alexander Benlian, Martin Wiener
ACM Conference on Fairness, Accountability, and Transparency
How does algorithmic management change the authority of frontline supervisors who are formally responsible for oversight? Twelve interviews across industries identify surveillance, opacity, and datafication as mechanisms that erode supervisors' discretion, knowledge, and legitimacy. Supervisors retain accountability for decisions they cannot fully understand or contest, implying that meaningful human oversight requires procedural authority rather than merely a person in the loop.
Tracked in ACM Conference on Fairness, Accountability, and Transparency
Generative AI impacts on intra-urban inequality and skill premium in Beijing
Xiliu He, Haoxiang Zhao, Mingyi Ma, Edward Wen Chuan Lai, Koei Enomoto, Anni Hu, Jiatong Li, Lingyun Chu, Yuan Lai
RePEc NEP Artificial Intelligence
Difference-in-differences design using 5 million job postings from Beijing (2018--2024), the analysis constructs a neighborhood-level GenAI Exposure Index by aggregating task-level assessments from five leading large language models. Finds that GenAI exposure is highly concentrated in the city's core districts, deepening the intra-urban AI divide. Generative artificial intelligence (GenAI) is the first automation wave to reach high-cognitive tasks at scale, yet its effects on intra-urban inequality remain largely unknown.
Governing AI Companionship: Lessons from Social Media Regulation
Guan Yue (Yuma) Wu
ACM Conference on Fairness, Accountability, and Transparency
Which regulatory tools developed for social media can govern AI companion services? A comparative legal analysis evaluates content moderation, age restrictions, intermediary immunity, addictive-design rules, antitrust, and litigation. Existing tools can address some harmful content and design practices, but enforcement technology and legal uncertainty limit them, while the accumulation of social power by companion platforms requires new approaches.
Tracked in ACM Conference on Fairness, Accountability, and Transparency
Indigenous employment and income in Indigenous-owned businesses: A comparative analysis
Bassirou Gueye
RePEc NEP Entrepreneurship
Statistics Canada first helped close a critical data gap by developing a framework to identify Indigenous business owners and Indigenous-owned businesses (Gueye et al., 2022; Gueye, 2024). Indigenous-owned businesses are a growing and important component of Canada’s economic landscape. These businesses not only contribute to entrepreneurship and community development but also serve as important sources of employment for Indigenous people.
Interactions of Artificial Intelligence with India's Labour Market
Payal Malik, Nikita Jain
RePEc NEP Artificial Intelligence
Using Indian databases such as KLEMS, PLFS, and NCO classifications, the analysis groups the economy into four structural categories and applies the productivity, inclusivity, and entrepreneurship framework to assess sector-wise AI exposure and adjustment capacity. The findings suggest that AI diffusion in India will be uneven. In public and social sectors, AI may lead to huge productivity gains.
Internal Deployment Gaps in AI Regulation
Joe Kwon, Stephen Casper
ACM Conference on Fairness, Accountability, and Transparency
Do frontier-AI rules cover systems deployed only inside firms? A comparison of 2025 US and European Union regulation identifies scope ambiguity, one-time compliance assessments, and information asymmetries as three routes by which internal systems can escape oversight. The analysis links these gaps to measurement and incentive problems and evaluates policy options and their tradeoffs.
Tracked in ACM Conference on Fairness, Accountability, and Transparency
Labor Market Consequences of Generative AI: Early Evidence from Norway
Dennis Facius, Roberto Iacono
RePEc NEP Artificial Intelligence
Difference-in-differences design using the within-firm composition difference-in-differences employed in recent work, supplemented with a synthetic difference-in-differences at the occupation level and a firm-level shift-share design, finds no robust evidence of employment displacement among young workers in highly AI-exposed occupations, nor any robust response across other age cohorts or on incumbent labor-market outcomes. A backdating exercise on the synthetic difference-in-differences yields larger absolute estimates than the actual treatment date across most age bands. Does Generative AI displace early-career workers?
Open AI in the Wild: Adoption and Adaptation of Open Models on r/LocalLLaMA
Woohyeuk Lee, James Howison, Min Kyung Lee, Hanlin Li
ACM Conference on Fairness, Accountability, and Transparency
How do users adopt and adapt openly released AI models under real-world constraints? A thematic analysis of the r/LocalLLaMA community finds that users define openness through reliability, local control, privacy, and modifiability rather than release terms alone. Autonomy and experimentation encourage adoption, while compute costs, licensing, usability, and performance gaps deter it; shared datasets and tools sustain the wider ecosystem.
Tracked in ACM Conference on Fairness, Accountability, and Transparency
Productivity Dynamics of Mergers, Acquisitions and Restructuring
Natalia Kuosmanen, Timo Kuosmanen, Terhi Maczulskij
RePEc NEP Entrepreneurship
A substantial share of firm entry and exit observed in register-based data reflects mergers, acquisitions, spin-offs, and other forms of corporate restructuring, instead of genuinely new firms or firm closures. The results show that firms involved in restructuring events exhibit significantly higher productivity levels than genuinely entering or exiting firms. This distinction is important for productivity decompositions, which typically interpret market entry and exit as manifestations of the Schumpeterian creative destruction.
Templates in the EU Inc. Regulation Proposal
Luca Enriques, Casimiro A. Nigro, Tobias Tröger
RePEc NEP Entrepreneurship
A central instrument of the Proposal is the use of model articles of association to be adopted through future implementing acts. European debates on competitiveness increasingly treat corporate law as a lever to help innovative firms scale. The European Commission's Proposal for a new "28th regime" seeks to introduce an optional, EU-wide corporate legal form designed, inter alia, to facilitate the cross-border scaling of innovative firms.
The Smarter State? Artificial Intelligence and Modern State and Local Public Finance
David R. Agrawal, William F. Fox
RePEc NEP Artificial Intelligence
However, government AI use may advantage larger jurisdictions with greater data access, raising equity and transparency concerns and increasing the value of interstate cooperation to harness scale economies from more data. Examines how artificial intelligence (AI) reshapes subnational public finance, largely through familiar channels observed from prior technological change. Although some effects are novel, many issues surrounding the taxation of AI-related income and consumption parallel earlier challenges from e-commerce, digitalization, and remote work.
Tracking the Economy through Firm Creation:Evidence from Real-Time Administrative Data
Anthony Savagar, Yannis Galanakis
RePEc NEP Entrepreneurship
Can daily company registrations provide an earlier signal of economic activity? Companies House Real-Time covers the full population of UK firm creations and dissolutions and is compared with official business demography, employment, and output data. Incorporations lead taxable business births and predict employment and output growth; structural VAR estimates show that positive entry shocks generate persistent increases in both outcomes.
Two Means to an End Goal: Connecting Explainability and Contestability in the Regulation of Public Sector AI
Timothée Schmude, Mireia Yurrita, Kars Alfrink, Thomas Le Goff, Sebastian Tschiatschek, Tiphaine Viard
ACM Conference on Fairness, Accountability, and Transparency
How can explainability and contestability be made operational in public-sector AI regulation? Interviews with 14 experts distinguish descriptive from normative explanation, judicial from nonjudicial contestation, and individual from collective action. Friction arises from mismatched top-down and bottom-up rules, unclear responsibility, and disciplinary boundaries, motivating three regulation-by-design recommendations.
Tracked in ACM Conference on Fairness, Accountability, and Transparency
Will Unicorns be Born in Japan? A comparative study of private equity market development and policy reforms
Hajime TADOKORO, Yuji HONJO
RePEc NEP Entrepreneurship
Why does Japan produce relatively few unicorns despite abundant savings and entrepreneurial potential? Comparative institutional analysis examines small offerings, crowdfunding, private placements, and secondary trading in Japan and five peer systems. Regulatory entry barriers restrict issuers and investors; simplified disclosure, flexible crowdfunding and placement rules, broader qualified-investor access, and digital capital raising would improve risk-capital allocation.
Can LLMs Hire Fairly? Racial Bias in Resume Screening
Zhenyu Gao, Wenxi Jiang, Yutong Yan
arXiv Computers and Society
Tests whether successive LLM generations reproduce racial and gender discrimination in resume screening. A paired-resume audit applies 24,024 paired postings to each of 14 mainstream models. The 2023 model produces a 2.12 percentage-point pro-White callback gap, while every model released from 2024 onward produces either no significant gap or a pro-Black reversal of as much as 3.01 percentage points; gender results follow the same generational pattern.
The registrar's function in a hybrid society. AI value chain,smart data and the concept of property
Pompeu Casanovas, Carmen Pastor Sempere, Marina Echebarria Saenz
arXiv Computers and Society
Artificial intelligence reaches the land registry not as another tool but as a value chain that turns data into intelligence and intelligence into economic value. Control emerges as the operative concept for digital representations of real estate, whose proprietary effect depends on anchoring to the register. Argues that the decisive legal move is to place validity, a functional, second-order concept, at the centre of that chain.
The Tilted Playing Field for Women in Science
Casandra Rusti, Hussain Hussain, Kian Ahrabian, Jay Pujara, Allon G. Percus, Buddhika Nettasinghe, Kristina Lerman
arXiv Computers and Society
Measures whether institutional prestige produces equal scientific returns for women and men. Nearly five million papers by 6.5 million authors at more than 65,000 institutions are analyzed with a distribution-sensitive measure of collaboration and high-impact output. Women receive comparable prestige advantages only at the most elite institutions, whereas men benefit throughout the hierarchy; broader cross-institutional collaboration networks help account for the difference.
Financing Artificial Intelligence Infrastructure: Mapping AI Infrastructure Investment and Compute Governance Across Africa
Kai-Hsin Hung, Sumaya Nur Adan, Krupa Suchak, Armita Sadeghian Barzoki, Kofi Yeboah, Mohammad Amir Anwar
arXiv Computers and Society
Maps who finances and controls AI compute infrastructure in Africa. The study codes 46 publicly announced projects worth $12.7 billion from 2019-2025 and places them along the infrastructure value chain. Investment clusters in South Africa, Kenya, Nigeria, and Egypt; capital and physical infrastructure receive 73% of funding, while control of the compute layer remains concentrated among a small group of global technology firms.
How Large Language Models Source Brand Reputation Across Languages and Markets
Dmitrij Zatuchin
arXiv Computers and Society
Which web sources shape the brand reputation that LLMs present to users across languages and markets? Analysis of 167,551 URL-grounded citations for 128 brands in 12 home markets and 13 languages classifies the domains underlying AI-generated answers. Third-party sites supply 85.7 percent of citations, 80 percent of citations come from roughly 18 percent of domains, and Wikipedia is the most-cited source in 11 of 12 languages, although local market sources matter at the margin.

Useful Knowledge Issue No. 004 - June 23, 2026

Tue, 23 Jun 2026 23:59:59 GMT

Issue No. 004

55 papers.

Mergers, innovation, and merger policy
Justus Haucap, Joel Stiebale
International Journal of Industrial Organization
What does existing research imply about mergers, innovation, and merger enforcement? A review synthesizes theoretical mechanisms, empirical studies, and European Commission cases. Most ex post studies find large reductions in innovation inputs and outputs after horizontal mergers, while vertical and other nonhorizontal mergers are less consistently harmful, supporting merger rules that distinguish transaction type and innovation channel.
Do societal promises influence patent value? An analysis of inventions in artificial intelligence
Sergio Pelaez, Barbara Esteves-Ribeiro, Gaurav Verma, Philip Shapira
Research Policy
Do AI patent claims that invoke societal benefits have higher private value? Text-as-data study of 154,934 USPTO AI patent documents uses generative-model labeling, discriminative classification, count-data regressions, and interviews with patent attorneys, examiners, inventors, and a linguist. Patents with more public-value expressions have higher forward citations, family size, claim counts, and technology-class breadth, suggesting societal-value language also functions as a patent-value signal.
Innovation and startup acquisition
Marc Bourreau, Axel Gautier
International Journal of Industrial Organization
How do startup acquisitions affect platform innovation and market concentration? A model lets competing platforms develop technology internally, acquire a startup exclusively, or license its technology nonexclusively. Acquisitions insure platforms against failed internal R&D but reduce the returns to innovation through weaker competition; allowing them raises platform innovation at the cost of greater concentration.
Entrepreneurship and Financial Deregulation
Toshihiko Mukoyama, Gang Zhang
International Economic Review
Did 1980s U.S. banking deregulation actually raise entrepreneurship? Modern staggered-treatment evidence is paired with a general-equilibrium firm-dynamics model with imperfectly competitive banks and occupational choice. Deregulation raises output growth and firm entry, and the occupational-choice margin matters because higher incumbent-firm wages partly offset the pull into entrepreneurship.
Innovation, Patenting and Appropriability: Survey Evidence from a Nationally Representative Sample of U.S. Firms
Filippo Mezzanotti, Timothy Simcoe
Review of Economics and Statistics
How do U.S. firms protect the rents from innovation, and how representative are patent-based measures? A nationally representative survey of U.S. businesses from 2008 to 2015 measures patenting, R&D, and alternative appropriability tools. Only 1.4 percent of firms patent, but they account for 87 percent of R&D; firms rate trade secrets, trademarks, and copyrights above utility patents as protection mechanisms.
Investments and Innovation with Non-Rival Inputs: Evidence from Chinese Artificial Intelligence Startups
Jian Xie, Kang Zhou
Review of Economics and Statistics
Do investments by data-rich technology firms increase innovation by AI startups? A dataset of roughly 9,800 Chinese AI-inventing startups supports a triple-differences comparison of investments from large technology firms versus other investors. Startups backed by data-advantaged tech firms file more AI patents and develop more software products, with evidence pointing to non-rival data sharing as the mechanism.
Preemptive Entry and Technology Diffusion: The Market for Drive‐In Theaters
Ricard Gil, Jean‐François Houde, Shilong Sun, Yuya Takahashi
RAND Journal of Economics
Why do firms enter new local markets before demand is fully revealed? U.S. drive-in theater markets from 1945 to 1957 test the dynamic-entry prediction that preemption is strongest in intermediate-size markets, then estimate the entry game. Preemptive motives raise early entry by up to 40 percent in mid-size markets, increasing entry costs by 5 percent and reducing expected firm value by about 1 percent.
Modularity, architectural innovation, and new venture success
Likun Cao, Ziwen Chen, James Evans
Research Policy
Which innovation strategy gives startups the best chance of success: modular improvements, modular invention, or architectural recombination? Computational study of 298,915 U.S. venture-funded startups from 1976 to 2020 embeds company descriptions in a dynamic semantic space built from business and patent discourse, then estimates event-history models. Architectural innovation predicts IPOs and high-value acquisitions, while modular innovation and invention raise failure risk.
R&D accounting and innovation signaling: Insights from Japan's pre-regulation era
Kazuyuki Motohashi, Tomomi Takada, Ayung Tseng
Research Policy
Can R&D accounting choices signal innovation quality? Japan's pre-2000 elective regime, where firms could expense or capitalize R&D before mandatory expensing, links accounting choices to patent outcomes and financing. Capitalizing R&D predicts higher patent quality and stock returns, while expensing is associated with greater patent volume and debt financing; after the rule change, former capitalizers expand patenting volume as the signaling channel closes.
‘Systems of use’: A lens to more deeply study user innovation
Eric von Hippel
Research Policy
Conceptual article introducing systems of use as a lens for user innovation. Reframes user innovation around the broader configuration of artifacts, practices, and complementary components in which users adapt and create solutions.
Aspirations, performance feedback, and informal entrepreneurs' decision to formalize
Selorm Agbleze, Marcus M. Larsen
Journal of Business Venturing
When do informal entrepreneurs choose to formalize their businesses? A behavioral theory links formalization to performance relative to opportunity- and necessity-based aspirations and to institutional support. Growth-oriented entrepreneurs below opportunity aspirations and survival-oriented entrepreneurs above necessity aspirations are most likely to formalize, while support changes how strongly these performance gaps affect the decision.
Three centuries of technological innovation: Opportunity is the mother of invention
Jordan G. Okie, James H. Brown, Astrid Kodric-Brown, Joseph R. Burger, Tatiana P. Flanagan, Trevor S. Fristoe, Sean T. Hammond, Norman Mercado-Silva, Jeffrey C. Nekola, Jennifer Richter
Proceedings of the National Academy of Sciences
Compiles a dataset on more than 400 major technological inventions from 1690 to 1990 spanning seven categories (agriculture, armaments, information and communication, household, industry, medical, and transportation) and performed inductive macroecological analyses to address how attributes of inventors, teams, and their social and geographic environments contributed to the innovation of new technologies that have changed the way people live. What socioecological conditions nurture the ingenuity and collaborative interactions underlying transformative technological innovations?
From Founders’ Knowledge to Economic Value Capture in Academic and Employee Startups
Mahka Moeen
Organization Science
How does founders' prior knowledge shape whether startups capture value through product markets or technology markets? Study of U.S. medical device startups separates academic founders' scientific knowledge from industry employee founders' firm-specific commercial knowledge. Academic startups are more likely to capture value in markets for technology, employee-founded startups in product markets, and mixed knowledge histories can shift the pathway toward pioneering products.
Lemon Cycles
Feng Dong, Ernest Liu
Management Science
Asks whether adverse selection by itself can generate endogenous credit cycles. Builds a model in which financially constrained entrepreneurs trade productive assets in anonymous markets while rent-seekers create lemon assets that are indistinguishable ex ante from productive ones. High asset prices encourage lemon creation, which later lowers average asset quality, depresses reallocation and prices, and can produce recurrent freezes and thaws in credit markets; a planner would tax credit-market activity to curb lemon creation and eliminate the cycle.
Second-Tier Stock Exchanges and Growth of Entrepreneurship
Huasheng Gao, Po-Hsuan Hsu, Yuxi Wang
Management Science
Do second-tier stock exchanges increase entrepreneurship by improving VC exit options? A global country-industry-year panel of VC-backed startups uses staggered difference-in-differences and industry exposure to second-tier exchanges. Launches raise startup creation, VC investment, valuations, and successful exits, with larger effects where VC activity is already higher.
Business Forums and Industrial Policy: Toward a New High-Tech Economy in Late Twentieth-Century Europe
Rasmus Salén, Grace Ballor
Enterprise & Society
Asks how new forms of business coordination shaped European high-tech industrial policy in the 1970s and 1980s. Uses archival and historical evidence on the Big 12 Roundtable and the ESPRIT program to trace how firms and European policymakers built new business forums and funding mechanisms around information technology. The article shows that these forums helped define a coordinated vision of European competitiveness in high tech and that ESPRIT became an institutional innovation that laid groundwork for later EU research and development policy.
Information Provision and Demand-Side Barriers to Healthcare Innovation: Experimental Evidence on Shared Medical Appointments in Menopause Care
Soledad Giardili, Monika Heller, Sanjay Jain, Amalia R. Miller, Kamalini Ramdas
NBER Working Papers
Can simple information reduce demand-side barriers to a healthcare delivery innovation? A preregistered field experiment with more than 4,000 U.K. women aged 45 to 60 tests patient and clinician testimonials for virtual shared medical appointments in menopause care. Testimonials more than double uptake when waits are six to eight weeks, from 11 percent in control to 24-26 percent in treatment arms, and shorter waits lift take-up close to 40 percent in testimonial groups.
A Dual Role Collision: How Generative AI's Intertwining Productivity Support and Social Support Reshape Indie Game Developers' Creative Work
Ruchi Panchanadikar, Yang Hu, Guo Freeman
ACM Conference on Fairness, Accountability, and Transparency
How does generative AI's simultaneous role as a production tool and source of social support alter creative work? Interviews with 16 independent game developers identify a 'dual role collision' inside the same workflow. Task assistance and emotional support can weaken community learning, collective evaluation, skill formation, and career calibration, potentially deskilling developers and reducing long-run participation in creative labor markets.
Tracked in ACM Conference on Fairness, Accountability, and Transparency
Advancing Regulation in Artificial Intelligence: An Auction-Based Approach
Marco Bornstein, Zora Che, Suhas Julapalli, Abdirisak Mohamed, Amrit Singh Bedi, Furong Huang
ACM Conference on Fairness, Accountability, and Transparency
Can regulation induce AI firms both to participate and to exceed a minimum compliance standard? An all-pay auction model rewards relative compliance while enforcing a threshold, and the authors derive its Nash equilibria and test the mechanism empirically. The auction raises compliance by 20 percent and participation by 15 percent relative to baseline minimum-standard rules.
Tracked in ACM Conference on Fairness, Accountability, and Transparency
Designing with AI at Work: Designers' Expertise and Pragmatic Decision-Making in Workplace AI Transformation
Lu Xian, Huiran Yi, Yile Zhang, Jingyan Zeng, Zifan Zhang
ACM Conference on Fairness, Accountability, and Transparency
How do professional designers decide whether and how to use generative AI at work? Interviews with 23 designers show that adoption depends on aesthetic expertise, business objectives, production constraints, and coordination with colleagues rather than technical capability alone. This largely invisible decision work governs which AI outputs become usable products and points to a need for clearer organizational guidance and recognition of human expertise.
Tracked in ACM Conference on Fairness, Accountability, and Transparency
Guardrails versus Gatekeepers: Understanding Product Managers' Ethical Decision-Making in Generative AI
Genevieve Smith, Natalia Luka, Merrick Osborne, Brian Lattimore, Jessica Newman, Brent Mittelstadt, Brandie Nonnecke
ACM Conference on Fairness, Accountability, and Transparency
What enables product managers to act on responsible-AI commitments? Evidence from 25 interviews and a survey of more than 300 product professionals shows that uncertainty and diffused responsibility inhibit action, while leadership commitment and organizational principles make some responsible practices up to 14 times more likely. Individual reviews and privacy safeguards are feasible without major resources, but audits and delayed launches require collective authority and organizational incentives.
Tracked in ACM Conference on Fairness, Accountability, and Transparency
How Can AI Augment Access to Justice? Public Defenders' Perspectives on Responsible AI Adoption
Inyoung Cheong, Patty Liu, Dominik Stammbach, Peter Henderson
ACM Conference on Fairness, Accountability, and Transparency
Where can AI responsibly assist overburdened public defenders? Interviews with 17 US public-defense professionals map tasks across evidence investigation, legal research, client communication, courtroom representation, and strategy. Evidence review offers the greatest potential, while costs, confidentiality, office rules, tool quality, and the relational nature of representation sharply limit adoption elsewhere.
Tracked in ACM Conference on Fairness, Accountability, and Transparency
The LLM Mirage: Economic Interests and the Subversion of Weaponization Controls
Ritwik Gupta, Andrew W Reddie
ACM Conference on Fairness, Accountability, and Transparency
Are compute thresholds an adequate basis for AI weaponization controls? The policy analysis argues that specialized data, efficient algorithms, and widely available hardware let adversaries bypass a frontier-model perimeter, while shifting industrial interests make compute thresholds politically unstable. It proposes an intent-and-capability definition and live benchmarks spanning data, algorithms, and compute.
Tracked in ACM Conference on Fairness, Accountability, and Transparency
Globalization, economic growth, and innovation: A two-country two-period model
Cuong Le Van, Duc V. Le, Thanh Tam Nguyen-Huu
arXiv innovation and entrepreneurship
When can trade liberalization help a developing country whose productivity lags a richer partner? Two-country, two-period theory model lets the developing country choose innovation investment before moving from autarky to globalization. Globalization can hurt when the TFP gap is large, but pre-liberalization innovation investment raises domestic productivity enough to recover gains from openness.
World Artificial Intelligence Cooperation Organization (WAICO): Mapping an Emerging Institution in the Global AI Governance Regime Complex
William Guey, Pierrick Bougault, Wei Zhang, Vitor D. de Moura, José O. Gomes
arXiv Computers and Society
Asks where China's proposed World Artificial Intelligence Cooperation Organization would sit in the emerging international AI-governance regime. Fifteen institutions and instruments are coded by membership, organization, and policy priorities. The proposed body uniquely combines universal sovereign membership, no regime-type entry test, and a development-first agenda, creating a potential second institutional pole organized around sovereignty and capability gaps rather than rights and safety.
AI Exposure Scores: what they measure, what they miss, and what comes next
Campbell Lund, Thomas Euyang, Zanele Munyikwa, Marzieh Fadaee
arXiv economics of AI
What do 2023 GPT occupational-exposure scores actually measure, and where do they fail for policy? Methodological review uses the diffusion of the Eloundou et al. scores as a case to compare static task exposure with dynamic adoption, geography, task ontologies, worker metrics, and usage data. The paper argues that policy inference needs ensembles, benchmarks, expanded task frameworks, and realized adoption data rather than treating exposure as realized AI impact.
Hallucinations in Organization-backed AI advisors: Evidence about Skepticism, Verification, and Reliance in Goal-Directed Use
Simon J. Blanchard, Aaron M. Garvey, Laura O'Laughlin
arXiv economics of AI,arXiv Computers and Society
Asks whether users of organization-backed AI advisors recognize hallucinations, verify questionable answers, and change their reliance after checking. A review of empirical studies spanning product search, medicine, content generation, and chatbot-assisted tasks separates skepticism, verification attempts, verification success, and reliance. Most studies measure reliance but not the preceding verification process; commonly deployable warnings about hallucinations have the weakest and most mixed effects.
Tracked in arXiv economics of AI, arXiv Computers and Society
The Urban-Rural Divide in the Age of Artificial Intelligence: Assessing the Effects of Technology and Automation on Regional Labor Markets
Chau Tran Bao, Khoi Nguyen Dinh Nguyen, Ha Nguyen Manh, Ngan Nguyen Thi Thuy
arXiv economics of AI,RePEc NEP Artificial Intelligence
Using a region-by-year panel and shift-share measures of technological exposure built from baseline industry and occupation composition, estimates two-way fixed-effects and instrumental-variable models that interact exposure with an urban indicator. Estimates show automation exposure lowering employment and wages, with the employment loss cushioned in cities, while AI exposure raises wages and concentrates in urban regions. Automation and artificial intelligence (AI) are reshaping labor demand unevenly across space, creating an urgent imperative for place-sensitive education and workforce policy.
Tracked in arXiv economics of AI, RePEc NEP Artificial Intelligence
Measuring Behavior Portability in Large Language Models
Tianjia Dong, Nadav Kunievsky, James A. Evans
arXiv economics of AI,arXiv Computers and Society
How portable is LLM economic behavior across payoff-equivalent environments with different surface presentation? Measurement framework fits an interpretable behavioral model on pooled source environments and tests out-of-sample prediction in held-out target environments against a target-trained oracle. Low portability reveals fragility in benchmark suites: models can change behavior even when incentives are held fixed and only presentation changes.
Tracked in arXiv economics of AI, arXiv Computers and Society
Boom, Bubble, or Buildout? A Multi-Method Evaluation of Whether Artificial Intelligence Is in an Ongoing Financial Bubble
Qianan Wang, Zen Chen
RePEc NEP Artificial Intelligence
Are current AI valuations a speculative bubble or capitalization of a general-purpose technology? A diagnostic review combines fundamental valuation, explosive-root tests, price-pattern methods, sentiment and issuance measures, and capital-expenditure payback analysis using evidence available through May 2026. Revenue, adoption, and productivity support substantial fundamentals, but concentrated private valuations, spending ahead of monetization, and anticipatory narratives indicate localized bubble dynamics.
Breaking the Vicious Circle of Informality in Entrepreneurship: A Conceptual Framework and Policy Agenda
Arvind Ashta
RePEc NEP Entrepreneurship
Why do informal entrepreneurs remain informal despite large disadvantages? A conceptual model identifies reinforcing loops in finance, human capital, and legitimacy, including documentation, fixed-cost, and capability traps. Because fixing one barrier leaves the others active, the framework calls for coordinated finance, training, administrative simplification, network, and digital-access policies.
Endogenous Entry and Optimizing Creative Destruction
Ching-Chong Lai, Ting-Wei Lai, Po-yang Yu
RePEc NEP Entrepreneurship,RePEc NEP Growth
How do entrants optimally choose creative destruction rather than mechanically replacing incumbents? A Schumpeterian model endogenizes entrant R&D, then evaluates patent protection, profit taxes, and sunk entry costs. Higher taxes or marginal entry costs reduce entry, replacement probability, and balanced growth, whereas stronger patents increase them; entry costs also shift welfare-maximizing patent and tax policy.
Tracked in RePEc NEP Entrepreneurship, RePEc NEP Growth
Fertility in an Unequal, Innovative World
Monisankar Bishnu, Chakshu Jain
RePEc NEP Growth
How does technological progress interact with inequality to shape fertility and long-run growth? A model combines returns to education with technology-induced inequality and is evaluated against U.S. state data. Their interaction produces fertility increases in Malthusian conditions, decline through demographic transition, and possible increases at high incomes, with regime-dependent implications for sustained growth or stagnation.
Financial Globalization, Entrepreneurship, and Economic Growth: Evidence from Asian Countries
Amjad Ali, Wafaria Iram, Mehboob Alam
RePEc NEP Entrepreneurship
How do financial globalization and entrepreneurship interact to affect growth in Asia? Panel evidence from 18 countries between 2013 and 2024 links international financial integration, entrepreneurial activity, and GDP growth. Financial globalization is associated with more entrepreneurship through capital access, regulation, and financial literacy, while entrepreneurship contributes more to growth where technology adoption, skills, flexibility, and infrastructure are stronger.
Generative AI and the Reorganization of Labor Demand
Fangyan Wang, Zaiyan Wei, Yang Wang
RePEc NEP Artificial Intelligence
Using a nationwide dataset of job postings in the United States, covering all sectors of the economy, the analysis constructs a dynamic, posting-level measure of generative AI exposure with a two-stage large language model pipeline. A complementary Oaxaca-Blinder decomposition shows that shifts in occupational composition account for about 90% of the exposure change attributable to observable job characteristics. Generative artificial intelligence (AI) is expected to transform work, but less is known about how firms reorganize labor demand as the technology diffuses.
Human Trust in AI: Evidence from Experimental Economics
Bernd Irlenbusch
RePEc NEP Artificial Intelligence
When do people underuse or over-rely on artificial intelligence? A selective review synthesizes experimental economics evidence from 2020-2026 on privacy, transparency, accountability, fairness, and efficiency. Opacity, autonomy concerns, and institutional distrust can suppress beneficial use, while deficient systems also attract excessive reliance and data disclosure; effective governance therefore requires calibrated rather than maximal trust.
Preference for Explainable AI
Alex Chan
RePEc NEP Artificial Intelligence
Participants acted as loan officers deciding whether to approve real $10,000-loans issued by a private U.S. lender using an AI’s default-risk predictions. When their bonuses depended on repayment, however, they sought predictions but avoided explanations, consistent with willful ignorance; this effect faded when explanations were framed as purely financial or demographics were hidden. When explanations revealed that the AI penalized non-White or female borrowers, participants were more likely to override the AI’s profit-maximizing recommendation.
Sensemaking and AI: Unraveling individuals' reactions to the black box in a three-study investigation
Domenico Di Prisco, Silvia Dello Russo
RePEc NEP Artificial Intelligence
What determines whether workers scrutinize, accept, or reject an unexpected AI recommendation? Three experiments show that failure of a person's prior mental frame increases both blind acceptance and effort to rationalize the opaque suggestion. Qualitative evidence identifies 'problematization pivoting,' in which attention anchors on AI advice and ignores other cues, and a third study links that response to worse task performance.
The Effect of Risk Aversion and Cash Flow Risk on the Equity Share Distribution in the Entrepreneur and Venture Capital Contract
María Florencia Gabrielli, Marcos Vergara
RePEc NEP Entrepreneurship
How do risk preferences and cash-flow uncertainty determine equity splits between entrepreneurs and venture capitalists? A double-sided moral-hazard model with price and background risk is evaluated under alternative risk and effort-complementarity parameters. Relative risk aversion dominates effort productivity in determining optimal shares, while risk and complementarity alter the slope of those effects.
The Growth and Performance of Artificial Intelligence in Asset Management
Shuang Chen, Clemens Sialm, David X. Xu
RePEc NEP Artificial Intelligence
Using investment advisers' regulatory disclosures, labor market data, and fund strategy descriptions, documents that AI-driven investing has grown steadily since the early 2010s and is concentrated among hedge funds. Examines the growth and performance of AI-driven investing. AI hedge funds outperformed non-AI hedge funds in the early years, but this outperformance declined over time, even among early adopters.
The Impact of Innovation on Firm Performance in Peru
Lourdes Alvarez, Angel Bullón
RePEc NEP Entrepreneurship
Which Peruvian firms gain from innovation, and along which performance margins? Firm-level manufacturing and service data are analyzed with Fligner-Policello tests, matching, and unconditional quantile treatment effects. Innovation increases sales growth for large manufacturers and service-sector SMEs but has no immediate productivity effect; longevity gains appear only among large manufacturers.
Trade War and Technology Rivalry
Xiao Ma, Zi Wang, Xiaodong Zhu
RePEc NEP Growth
How do trade wars affect innovation, technology diffusion, and welfare? A dynamic multicountry trade model with endogenous R&D is estimated from trade and patent-citation data and validated against U.S. export controls on China. Export controls slow technological progress in both countries by reducing Chinese knowledge inflows and U.S. R&D, while trade-driven diffusion and endogenous innovation amplify the international welfare effects of tariffs.
Getting There and Getting In: How Mobility and Sorting Keep Women out of Top Startup Accelerators
Chuan Chen, Michele Fioretti, Junnan He, Yanrong Jia
CEPR Discussion Papers
Why do women remain underrepresented in top startup accelerators? A hand-collected census of U.S. accelerator startups from 2008 to 2011 is followed for five years and estimated with a two-sided matching model that separates mobility costs from sorting across accelerator tiers. Women raise about 60 percent less than men; the gap is concentrated among non-relocating women and disappears among relocators, but top-tier access also requires removing a sorting disadvantage.
Scholarly Production and Public Health Determinants in Context of Funding: The Case of IoMT Research:
Peter Kokol
arXiv Computers and Society
Case-study design. The Internet of Medical Things (IoMT) represents a transformative technology that connects medical devices, sensors, and healthcare systems to enable real-time monitoring, data sharing, and advanced decision-making in healthcare. The results reveal a positive trend IoMT in research literature produc-tion. Thematic analysis shows that both funded and non-funded are associated with similar themes; however, founded research is more focused on recent research trends like artificial in-telligence applications in healthcare.
Does the Import Invasion Explain the Mysterious Disappearance of Productivity Growth in U.S. Manufacturing?
Robert J. Gordon, Kenneth Ryu
CEPR Discussion Papers
Why did U.S. manufacturing productivity growth disappear after 2010? Empirical macro-industry analysis shifts attention to the earlier post-2000 output slowdown and traces import competition through plant closures, employment, profits, utilization, investment, R&D, and innovation. The result is a causal chain in which the import invasion helps explain the erosion of manufacturing productivity growth rather than only its visible post-2010 collapse.
Resume Screening, Fast and Slow: (Biased) AI Recommendations' Influence on Human Decision Making
Kyra Wilson, Mattea Sim, Anna-Maria Gueorguieva, Soham Chatterjee, Aylin Caliskan
ACM Conference on Fairness, Accountability, and Transparency,arXiv Computers and Society
Examines how biased AI recommendations affect human attention and selection in resume screening. An experiment measures time spent reviewing candidates under alternative recommendation conditions and links review time to hiring choices. Each additional period of attention raises selection probability by 3-4% for candidates the AI did not recommend, while removing AI recommendations increases review time by as much as 55.6%, indicating that human oversight changes substantially with interface guidance.
Tracked in ACM Conference on Fairness, Accountability, and Transparency, arXiv Computers and Society
Automation and Aging in General Equilibrium: AI Capital, Fertility, and the Return to Capital
James Wabenga Yango
arXiv economics of AI
How do AI capital and demographic aging jointly affect fertility, investment, and returns? General-equilibrium overlapping-generations model with endogenous fertility lets firms accumulate both physical and AI capital and compares AI-technology and longevity shocks. AI works like a capital-demand shock, raising returns and front-loading output growth while modestly raising fertility; longevity works like a saving-supply shock, lowering returns and fertility.
Human Capital, AI, and Labor Commoditization
Auyon Siddiq, Niuniu Zhang
arXiv economics of AI,arXiv Computers and Society,RePEc NEP Artificial Intelligence
Has generative AI changed whether online labor markets reward workers' human capital or their low prices? Upwork worker profiles are represented with text embeddings to measure the predictive value of human-capital information and price, then a difference-in-differences design compares job categories around ChatGPT's release. In more AI-exposed categories, human capital becomes less important to labor demand and price more important; demand shifts toward lower-priced workers and the premium for strong human capital declines.
Tracked in arXiv economics of AI, arXiv Computers and Society, RePEc NEP Artificial Intelligence
Human-AI Interaction Requirements in Public Sector Procurements
Mateen A. Abbasi, Tommi Mikkonen, Sinna Pirinen, Aapo Koski
arXiv Computers and Society
Public sector organizations increasingly procure AI-enabled ICT systems to support decision-making and service delivery. Although ethical AI frameworks emphasize transparency, accountability, and human oversight, these principles are rarely translated into explicit requirements in procurement processes. Consequently, human-AI interaction (HAI) is often left to vendor design choices.
Directors Duties in the Age of Agentic Artificial Intelligence
Deirdre Ahern
arXiv Computers and Society
How much room do directors' duties leave boards to account for workers when adopting agentic AI? The article compares four models of corporate purpose within directors' best-interests duty and applies them to decisions about automation and reskilling. It argues that existing doctrine gives directors enough discretion to consider employee welfare, but that meaningful protection depends less on formal legal constraint than on whether boards choose broader engagement and reskilling strategies.
Generative Engine Optimization at Scale: Measuring Brand Visibility Across AI Search Engines
Pratyush Kumar
arXiv economics of AI,arXiv Computers and Society
How visible are brands in AI search engines, and what content do those systems cite? Measurement study of more than 100,000 prompt responses across over 100 brands on Ranqo compares ChatGPT, Claude, Perplexity, Gemini, and related AI search systems. Visibility follows a steep brand-stature ladder, with household names appearing far more often than small brands; cited sources are dominated by corporate websites, making AI search a new distribution channel with strong incumbent advantages.
Tracked in arXiv economics of AI, arXiv Computers and Society
The Algorithmic-Human Manager: AI, Apps, and Workers in the Indian Gig Economy
Omir Kumar, Krishnan Narayanan
arXiv Computers and Society
Examines the impact of artificial intelligence and digital technologies on the blue collar gig economy in India, focusing on algorithmic management he use of automated systems to allocate, monitor, and evaluate work in location-based services such as ride sharing and delivery. Examines the impact of artificial intelligence and digital technologies on the blue-collar gig economy in India, focusing on algorithmic management.
What Capital After Labor? Forecasting the Talent ROI Transition in the Human-AI Era
Kwan Soo Shin, In Seok Kang
arXiv economics of AI
How should firms measure talent when AI weakens the link between labor hours and output? Forecasting framework for human-AI talent ROI is illustrated with a Korean DART panel around the 52-hour workweek, using fixed effects, event studies, and staggered DiD. SG&A pressure rises after work-hour limits, which the authors interpret as an early proxy for a shift from time-based overhead accounting toward output-based human-AI productivity measures.
Challenges to Grassroots Organization Engagement with AI Policy
Carter Buckner, Jennifer Mickel, Nandhini Swaminathan, William Agnew, Jacob Hobbs, Sarthak Arora, Michelle Lin, Yanan Long, B. V. Alaka
arXiv economics of AI,ACM Conference on Fairness, Accountability, and Transparency,arXiv Computers and Society
Why do grassroots and marginalized organizations struggle to influence AI policy? A case study follows participatory policy work with several US government bodies and the development of proposals for queer communities. Limited networks, lobbying capacity, and institutional power constrain participation, leading to practical recommendations for policymakers and community organizers.
Tracked in arXiv economics of AI, ACM Conference on Fairness, Accountability, and Transparency, arXiv Computers and Society
Forecasting AI-Era Productivity: The Intellectually Converged Human Framework and a Missing Cognitive Mediator in Production Function Theory
Kwan Soo Shin, In Seok Kang
arXiv economics of AI,arXiv Computers and Society
Why might large AI investment fail to generate proportional productivity gains? Conceptual production-function framework adds a cognitive mediator, convergence capacity, to AI utilization intensity and human capital. Descriptive evidence from 20 OECD economies is consistent with the model: the AI-by-convergence interaction explains more TFP variation than AI alone, implying that deployment without complementary human cognitive capacity can mute productivity returns.
Tracked in arXiv economics of AI, arXiv Computers and Society

Useful Knowledge Issue No. 003 - June 16, 2026

Tue, 16 Jun 2026 23:59:59 GMT

Issue No. 003

59 papers.

Customer Acquisition, Business Dynamism and Aggregate Growth
Marek Ignaszak, Petr Sedláček
Review of Economic Studies
How does customer acquisition shape business dynamism and aggregate growth? Endogenous-growth model estimated with aggregate and firm-level data requires innovating firms to acquire customers before selling new products. Expansions of customer bases raise innovation incentives and shift resources toward high-growth gazelles; together these mechanisms explain more than 40 percent of aggregate growth and alter the predicted payoff from growth policy.
The Micro and Macro Dynamics of Capital Flows
Felipe Saffie, Liliana Varela, Kei-Mu Yi
Review of Economic Studies
How do international capital flows reallocate resources inside firms and sectors? Universe-of-firms data from Hungary discipline a model in which capital-account liberalization lowers firm capital costs and changes household consumption. Consumption responses dominate: spending shifts toward high-expenditure-elasticity services, expanding incumbents and entry there and matching aggregate productivity dynamics after financial openness.
Angels, entrepreneurship, and employment dynamics: Evidence from investor accreditation rules
Laura Lindsey, Luke C.D. Stein
Journal of Financial Economics
Accreditation-rule quasi-experiment using Dodd-Frank's removal of home equity from U.S. investor eligibility, linked to Census measures of startups and jobs. Larger losses in the local angel pool reduce angel investment, firm entry, and employment at small entrants, while employment rises at small young incumbents as activity shifts.
Business model innovation pathways: Digital transformation after disruption at The New York Times and The Washington Post
Paolo Aversa, Alessio Cozzolino, Amy Basil
Research Policy
Comparative case study of The New York Times and The Washington Post from 2009 to 2021 after digital disruption to distribution and monetization complements. The Times moves toward business-to-consumer knowledge leadership, while the Post builds business-to-business technology leadership, yielding two distinct business-model innovation pathways.
Strategic Submissions: An Analysis of Supplemental Drug Approvals
Pierre Dubois, Elissa Philip Gentry, Tuba Tunçel
Journal of Economics & Management Strategy
How does weaker off-label promotion regulation change pharmaceutical firms' incentives to seek formal supplemental approvals? R&D project data around the 2012 U.S. court decision protecting truthful off-label promotion compare supplemental uses with original indications. Greater promotion freedom lowers the hazard of supplemental approval, with patent protection, market size, and competition also shaping whether firms pursue formal indications.
Problematic persistence of innovation: Breakdowns and sociotechnical maintenance of the world's most enduring herbicide technology
Shane Hamilton, Beatrice D'Ippolito
Research Policy
Historical case study of Monsanto's glyphosate herbicide using STS theories of maintenance. Breakdowns trigger repair of both technical malfunctions and strained sociopolitical relationships, helping a repeatedly contested technology persist through strategic maintenance.
At cross purposes? Housing markets and innovation in American Political Economy
James D.G. Wood
Research Policy
Error-correction models for all 50 U.S. states from 1975 to 2016, linking house prices, fiscal policy, government ideology, and innovation outputs. Rising house prices predict weaker long-run innovation performance, while expansionary state spending offsets part of the drag.
Discontinuous but not disruptive: Lessons from green steel on sustainability innovation pathways for foundation industries
Viktor M. Salenius, Richard Cuthbertson, Jennifer Howard-Grenville
Research Policy
Comparative case study of Stegra and HYBRIT in northern Sweden, two leading green-steel ventures launched respectively as a startup and an incumbent-led joint venture. Resource constraints and system-level enablers push both toward creative accumulation rather than disruptive displacement, yielding discontinuous change without classic creative destruction.
Lurking Patent Claims and Strategic Royalty Contracts
Jay Pil Choi
Journal of Industrial Economics
Licensing model with unknown future patent claimants and a monopolistic manufacturer. The threat of later infringement claims makes per-unit royalties optimal as a rent-protection device, trading off hold-up risk against allocative inefficiency.
Business model innovation for disruptive sustainability: A theorizing review on how interdependencies between business model components reshape the problem-solution nexus
Maria Schmidt, Aoife Brophy, Philipp Trotter
Research Policy
Theorizing review of last-mile business model innovation in low- and lower-middle-income countries. Interdependencies across business-model components can redefine the problem-solution nexus at shallow, medium, or deep levels, making business-model innovation itself a driver of disruptive sustainability.
Values work in early-stage venturing: How entrepreneurs make sustainability commitments actionable
Matthias Pepin, Luc K. Audebrand, Maripier Tremblay, Karine Laperrière
Journal of Business Venturing
How do sustainability-oriented founders turn personal values into operating decisions? Event-based interviews with 15 entrepreneurs cover 165 decisions during early venture development. Founders combine aspirational and operational values in seven recurring configurations tied to benchmark, proof, embodiment, embeddedness, cohesion, legitimacy, and market challenges.
Navigating moral boundaries: How entrepreneurs manage stigma premiums in morally contested markets
Aureliu Sindila, Nicolai J. Foss, Xueyong Zhan
Journal of Business Venturing
Why might entrepreneurs in morally contested markets resist greater legitimacy? A theory combining repugnant-markets economics with organizational stigma treats disapproval as both a demand penalty and an entry barrier. The resulting stigma premium produces an inverted-U relationship with profitability: entrepreneurs destigmatize when stigma threatens viability but preserve it when normalization would invite competition.
Anticipating the Digital: How Interpretive Debt and Layered Architectural Framing Shape Innovation Pathways
Virginia Leavell
Information Systems Research
Why can organizations facing the same digital technology pursue radically different innovation paths before they even procure it? A comparative ethnography follows two municipal water agencies introducing smart meters and develops the concepts of interpretive debt and layered architectural framing. Prior digital-metering experience led one agency to treat smart meters as hardware replacement, while the other built a data-driven organizational transformation around them, showing how anticipatory practices set a technology's eventual scope.
Fast and Simple Adaptive Elicitations
Nicolò Bertani, Enrico Diecidue, Patrice Perny, Paolo Viappiani
Management Science
Adaptive elicitation method using linear programming and approximating splines, tested in one simulation and two experiments. FSE recovers probability-weighting functions more accurately than standard alternatives and improves out-of-sample choice prediction in lab and online samples.
The Cybernetic Teammate: A Field Experiment on Generative AI and Teamwork
Fabrizio Dell’Acqua, Charles Ayoubi, Hila Lifshitz, Raffaella Sadun, Ethan Mollick, Lilach Mollick, Yi Han, Jeff Goldman, Hari Nair, Stew Taub, Karim R. Lakhani
Organization Science
Preregistered field experiment with 791 Procter & Gamble professionals solving real product-innovation problems individually or in pairs, with and without generative AI. AI lifts individual performance to the level of unaided teams, produces more balanced technical-commercial solutions across functions, and raises positive affect, while human judgment remains valuable in idea selection.
The Social Attribution of Innovation: Uncovering the Heads Behind the Guillotine
Paolo Aversa, Paul Gouvard, Maria A. Makarova
Academy of Management Journal
Microhistorical study of French Revolutionary debates over penal reform and the guillotine from 1788 to 1792. Repeated evaluations of involvement progressively tie an innovation to specific people, producing durable spirals of credit and blame.
Revisiting Stock Market Signals as a Lens for Patent Valuation
Ashish Arora, Sharon Belenzon, Elia Ferracuti, Jay Prakash Nagar
Organization Science
Methodological reassessment of patent values inferred from stock-market reactions in the KPSS framework. Measurement error is negatively correlated with true patent value, making group comparisons attenuated and internally inconsistent unless researchers use abnormal returns or a two-distribution extension.
Optimal Medical Liability for AI
Alex Chan
NBER Working Papers,RePEc NEP Artificial Intelligence
How should medical liability be designed when an AI system acts as a doctor rather than as passive decision support? A model treats the legally observable medical record as an imperfect signal and characterizes the incentives that courts, contracts, and insurers can implement. Depending on record quality and the underlying harm process, the optimal rule can be no liability, strict liability, negligence, a safe harbor, comparative fault, or a continuous warranty.
Tracked in NBER Working Papers, RePEc NEP Artificial Intelligence
AI Diffusion Gaps: Unequal Integration of AI Across K-12 Schools
Christopher Campos, John D. Singleton, John Singleton
NBER Working Papers,RePEc NEP Artificial Intelligence,Becker Friedman Institute Working Papers
Although use of generative AI tools has quickly become widespread in education settings, emerging evidence suggests that effects on learning will depend on how that use is supported and guided. Finds that AI use has spread rapidly across schools, largely as a productivity aid.
Tracked in NBER Working Papers, RePEc NEP Artificial Intelligence, Becker Friedman Institute Working Papers
Artificial Intelligence Asset Pricing Models
Bryan T. Kelly, Boris Kuznetsov, Semyon Malamud, Teng Andrea Xu
NBER-SAIF Conference on AI and Financial Markets, Spring 2026
Asset-pricing model that embeds a transformer network in the stochastic discount factor. Uses cross-asset information sharing and nonlinear conditional pricing to reduce pricing errors relative to earlier machine-learning models, with a linear-transformer surrogate used to decompose where the gains come from.
Tracked in NBER-SAIF Conference on AI and Financial Markets, Spring 2026
How AI Impacts the Quest for Knowledge: Evidence from AlphaFold2
Myra Mohnen, Joshua S. Gans
NBER SI 2026 Innovation
Develops a model in which AI can both replace some experiments and make other experiments easier. Using 530,495 proteins linked to the Protein Data Bank and differences in the quality of AF2 predictions after its release, finds that AF2 increased experimental activity but redirected it towards proteins closer to existing knowledge. Examines how artificial intelligence changes the direction of scientific research.
The Optimal Use of AI in Financial Regulation
Christopher Clayton, Antonio Coppola
NBER-SAIF Conference on AI and Financial Markets, Spring 2026,RePEc NEP Artificial Intelligence
Graph-based deep learning model on security-level holdings of non-bank financial intermediaries covering nearly $40 trillion in wealth. The model sharply improves out-of-sample forecasts of trading and stress-period returns, and its learned network representations identify fire-sale vulnerability that improves macroprudential targeting.
Tracked in NBER-SAIF Conference on AI and Financial Markets, Spring 2026, RePEc NEP Artificial Intelligence
AI (at Work) in Finance
Christos Makridis, Erik Brynjolfsson, Sophia Kazinnik
NBER-SAIF Conference on AI and Financial Markets, Spring 2026
Why has workplace AI adoption risen especially quickly in U.S. finance and insurance? Gallup Workplace Panel data from 2023Q2-2026Q1 track use, tasks, hours, worker adaptability, governance, and regulation. Frequent use climbed from about 8 to 45 percent in finance, versus 10 to 27 percent overall, and is associated with 5 percent fewer hours, although imprecisely estimated. Declining regulatory restrictions account for an estimated 4.7 percentage points of frequent-use growth, roughly half of finance's excess increase through 2025Q4.
Tracked in NBER-SAIF Conference on AI and Financial Markets, Spring 2026
LLM Consumer Behavior Theory: Foundations of a Novel Research Field
Manon Reusens, Sofie Goethals, David Martens
arXiv economics of AI
Conceptual economics framework for agentic markets in which LLMs make consumption decisions on users' behalf. Organizes preference representation, behavioral departures from rational choice, and the aggregation of agent-level decisions into market demand.
Beyond Citations: Comparing Scholarly, Policy, and Patent Impact Across the FT50 Journals
Arash Hajikhani, Yi Zhang, Mengjia Wu
arXiv Computers and Society
Tests whether journals grouped in the FT50 have comparable scholarly, policy, and technological influence. More than 60,000 publications from 53 current and former FT50 journals between 2005 and 2019 are compared using citations, policy uptake, and patent citations. The three measures are largely independent and nearly half of journals change quartile in a multidimensional ranking; economics leads policy influence, while information systems and marketing lead patent impact.
AI Transparency: Governance Compliance or Stakeholder Requirements?
Muneera Bano, Didar Zowghi
arXiv Computers and Society
Quantitative model. Transparency is increasingly mandated for public-sector AI systems, with organisations required to publish statements describing their AI use and oversight arrangements. The findings show that while structural compliance is widespread, transparency calibration is uneven. However, the existence of such artefacts is often treated as equivalent to transparency itself, despite limited evidence that they proportionately serve relevant stakeholder groups.
Incumbent Advantage: Brand Bias and Cognitive Manipulation Dynamics in LLM Recommendation Systems
Xi Chu, Yupeng Hou
arXiv economics of AI,arXiv Computers and Society
How do brands compete when product discovery moves into LLM recommendations? Three experiments across GPT-4o-mini, Claude Sonnet, and Gemini Flash use skincare prompts, with a search-goods robustness check, to vary brand reputation and product ratings. Familiar brands can dominate recommendations when quality signals are tied, but modest rating advantages can overturn that dominance, making brand visibility and rating manipulation central margins in AI search.
Tracked in arXiv economics of AI, arXiv Computers and Society
AI as a Partner in Learning about, Doing, and Engaging with Science: Vigilance as the Key to Productive Augmentation
Marcus Kubsch
arXiv economics of AI,arXiv Computers and Society
Conceptual synthesis across scientific research, public science use, and science education. Productive AI augmentation depends on epistemic vigilance: the same systems help when users actively evaluate outputs and widen error and inequality when trust substitutes for checking.
Tracked in arXiv economics of AI, arXiv Computers and Society
AI systems out-persuade expert humans
Kobi Hackenburg, Caroline Wagner, Luke Hewitt, Ben M. Tappin, Ed Saunders, Hannah Rose Kirk, Helen Margetts, Christopher Summerfield
arXiv economics of AI,arXiv Computers and Society
Four preregistered persuasion experiments with 18,978 conversations from 6,923 participants, comparing AI systems with laypeople, tournament winners, canvassers, and debaters. AI persuaders outperform human experts, including nearly tripling real-donation fundraising relative to professional canvassers in a field setting.
Tracked in arXiv economics of AI, arXiv Computers and Society
Whose hotel does the AI recommend? An algorithm audit of reputation signals in LLM-assisted hotel selection
Mirza Samad Ahmed Baig, Syeda Anshrah Gillani, Asher Ali
arXiv Computers and Society
Field experiment. Travelers increasingly ask large language model (LLM) assistants which hotel to book, making these systems gatekeepers of property visibility -- yet what moves their recommendations is undocumented. Estimates the average marginal component effect of each signal on the probability of recommendation.
Artificial intelligence (AI) innovation and economic growth: asymmetric analysis and role of stock market, financial stability and trade openness
Peterson K Ozili
RePEc NEP Innovation,RePEc NEP Growth,RePEc NEP Artificial Intelligence
Does AI innovation affect growth differently across countries and points in the growth distribution? Quantile regressions use a panel of 50 countries from 2000 to 2020 and interact AI innovation with stock markets, financial stability, and trade openness. AI innovation raises growth mainly in the low and middle tails; stock-market applications strengthen growth, whereas interactions with financial-stability and trade activities are negative in particular growth regimes.
Tracked in RePEc NEP Innovation, RePEc NEP Growth, RePEc NEP Artificial Intelligence
Deep-Tech Innovation: A Multi-Method Study toward a Conceptual Framework and Research Agenda
Johann Kortsch, Stefan Raff-Heinen, David Bendig, Martin Murmann, Colin Schulz, Fiona Murray
RePEc NEP Innovation
What distinguishes deep-tech innovation from adjacent forms of technology entrepreneurship? An integrative literature review and interviews with deep-tech founders identify twelve attributes across invention, venture, and ecosystem levels. The framework centers science-based inventions and their exposure profile, then links them to staged finance, parallel technical and commercial maturation, multidisciplinary teams, specialized incubation, and industrial scaling partnerships.
Digital Inclusion and Economic Empowerment of Women Entrepreneurs in Kassala State: Overcoming Barriers to Resilience and Growth
Abdulhameed Suliman, Samia Nihar, Zuhair Arabi, Namariq Omer
RePEc NEP Entrepreneurship
Case-study design. The study examines how digital inclusion shapes the economic empowerment and resilience of women entrepreneurs in Kassala State, Eastern Sudan, using a mixed‑methods design that combines SLMPS 2022 survey analysis with interviews and focus groups. Quantitative findings show that education and household wealth are positively associated with empowerment, while age and marriage correlate negatively, and that basic digital access indicators are not robust predictors of empowerment, suggesting that technology alone is insufficient in the absence of key conversion factors such as skills, affordability, and institutional support.
Digitalization, AI Capabilities, Tasks: Occupational AI Exposure and Wage Inequality across Italian Provinces
Gianluca Risi
RePEc NEP Artificial Intelligence
Using a panel regression model for Italian NUTS3 regions over 2015-2018, finds 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. Examines the impact of digitalization and AI on wage inequality both between and within task-based groups of workers across Italian provinces.
Disruption without Dividend ? How the Digital Divide and Task Differences Split GenAI’s Global Impact
Pawel Gmyrek, Mariana Viollaz, Hernan Winkler
RePEc NEP Artificial Intelligence
Using data from skills surveys, the article demonstrates that workers in developing countries perform substantially fewer non-routine analytical tasks—the primary targets of GenAI—even within occupations classified as highly exposed. Cross-country differences in occupational structure suggest that developing economies face lower aggregate automation exposure than advanced economies but comparable potential for task augmentation. At the same time, conventional occupational exposure measures systematically overestimate the impact of GenAI in developing countries by assuming uniform task content across economies.
INCITE Technical Report on Innovative Techniques (TRIT)
Aries Eric, Fereres Sonia, Bellomo Nicolas, Gonzalez Cuenca Jose, Ferreira De Almeida Vanessa, Tejedor Sanz Sara, Chronopoulos Georgios, Retsoulis Ioannis, Roudier Serge, Lambert Caroline
RePEc NEP Innovation
The report maps a comprehensive dataset of 563 demonstrator projects across Europe identified be-tween 2020 and 2025. The content focuses on energy-intensive industries (EIIs) and prioritises sectors with the highest environmental impact and strategic relevance for the Clean Industrial Deal. It was found that innovation in industry is highly concentrated in three ‘hard-to-abate’ sectors: Iron & Steel, Chemicals, and Cement, Lime, and Magnesia.
Inflation, Innovation, and Technology Transfer in an Open Economy with Variety Expansion
Hung-Ju Chen, Hao Guo, Chien-Yu Huang, Yibai Yang
RePEc NEP Innovation
How does inflation affect innovation and international technology transfer? A North-South variety-expansion model, calibrated to China and the United States, traces transitional and long-run responses. Inflation in either region temporarily reduces northern innovation; southern inflation permanently lowers technology transfer, while the transfer response to northern inflation changes sign with the South's population size.
Innovation Policy and AI-Enabled Transformation
Marc Robert
RePEc NEP Innovation
It reshapes production, coordination, learning, and competition, while also unsettling the conceptual boundaries that have long separated innovation policy from business model analysis. If AI is treated as a neutral productivity tool, policy remains trapped within a narrow market-failure logic. Artificial intelligence has moved from a specialist computational field into a general organisational, industrial and political question.
Investment composition and growth: the role of intangible and tangible ICT capital in the EU and other economies
Marinela-Daniela Filip, Ralph Setzer, Diego Peréz-González
RePEc NEP Growth
Can investment composition explain part of Europe's productivity gap with the United States? Panel fixed-effects and local-projection estimates use asset-level investment data for the EU and other advanced economies from 1996 to 2021. Intangible and tangible ICT investment, especially communications equipment, R&D, and intellectual property, is associated with stronger per-capita growth than other assets, with larger effects in richer and more human-capital-intensive countries.
Korea in the Global Innovation Network: Navigating Technological Interdependence
Jongduk KIM
RePEc NEP Innovation
How has intensifying U.S.-China competition changed Korea's position in global innovation networks? Network analysis traces cross-country and cross-industry knowledge relationships among Korea, the United States, China, Japan, and Europe from 2000 to 2020. Korea improved its capabilities and network position partly through foreign knowledge, but deeper interdependence also creates strategic vulnerability; the report recommends tracking frontier shifts, building leading technologies, and sustaining international cooperation.
Microfoundations Of Ai Orientation As Market Signal: Evidence On Startup Funding Performance
Mukunthan Nadarajah, Samuel Keil, Kevin Riehl, Jan Schuller, Carolin Bock, Dirk Schiereck
RePEc NEP Entrepreneurship
Do startup executives' AI literacy and strategic AI orientation improve fundraising? A dataset links PitchBook outcomes for 1,517 post-ChatGPT U.S. startups to biographies of 4,075 founders and public communications. Literate teams adopt stronger AI orientations but receive no direct funding premium; an AI orientation raises initial and cumulative funding yet lowers the probability of later rounds, consistent with technical signals decaying.
Public Support, R&D and Firm Innovation in Developing Countries: The Case of Morocco
Hicham Ouakil, Mariem Liouaeddine, Mohamed Hosni, Ayoub Saadi
RePEc NEP Innovation
Which Moroccan firms receive public R&D support, and does it increase innovation? Probit estimates and propensity-score matching use the 2019 World Bank survey of 1,096 firms. More competitive, ICT-investing, graduate-employing firms are more likely to receive support, and matched estimates find subsidies significantly increase both innovation inputs and outputs.
The Co-Evolution of Networks and Capabilities in Innovation Systems: Principles for Systemic Policy Design
Tugrul Temel
RePEc NEP Innovation
How should innovation policy jointly shape organizational capabilities and knowledge networks? A formal simulation model combines capacity-constrained knowledge flows, endogenous capability accumulation, collaboration costs, strategic repositioning, and adaptive network rewiring, with parameters calibrated by approximate Bayesian computation. Connectivity expansion alone produces limited gains and isolated capacity building increases inequality, whereas coordinated interventions produce more robust and equitable growth across four economic environments.
Writing Code vs. Shipping Code: Productivity Effects Across Generations of AI Coding Tools
Mert Demirer, Leon Musolff, Liyuan Yang
RePEc NEP Artificial Intelligence
Event-study design. Examines 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, finds 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.
How to Detect and Measure the AI Dangers to Democracy
Giulia Sandri, Claudio Novelli
arXiv Computers and Society
Frames AI's risks to democratic institutions as a principal-agent problem in which public authorities delegate functions to systems and vendors they cannot fully monitor. The framework combines delegation theory with seven NIST trustworthiness dimensions to define measurable accountability and assessability indicators across information systems, elections, and public administration. Its central implication is that private vendors can silently inherit value judgments when institutions cannot inspect delegated AI decisions.
U.S. Policies Unintentionally Accelerated China's Open AI Ecosystems
Wang Jin, Nadav Kunievsky, Bowen Lou, Tianshu Sun, James Evans
arXiv economics of AI,RePEc NEP Artificial Intelligence,arXiv Computers and Society
Moreover, Chinese developers increased engagement with open-source large language model repositories substantially more than U.S. developers did, consistent with a shift toward open infrastructure under geopolitical constraints. These findings suggest that technological containment policies may unintentionally accelerate open innovation ecosystems as a competitive response, with implications for global leadership in both academic and commercial artificial intelligence. Over the past decade, U.S.
Tracked in arXiv economics of AI, RePEc NEP Artificial Intelligence, arXiv Computers and Society
Chaining Tasks, Redefining Work: A Theory of AI Automation
Mert Demirer, John J. Horton, Nicole Immorlica, Brendan Lucier, Peyman Shahidi
arXiv economics of AI
Task-based theory of production in which AI can augment steps or execute contiguous chains of steps. AI chaining breaks simple comparative-advantage logic, creates nonlinear productivity gains from model improvements, and is more common when AI-exposed steps are adjacent rather than dispersed across jobs.
Towards a Theory of Modular Natives: Explaining Superscaling, China's Greatest Innovation Yet
Bent Flyvbjerg, Alexander Budzier, Maria Christodoulou
arXiv innovation and entrepreneurship
Theory plus large project-level dataset on modular natives such as solar cells. Modularity reduces complexity and produces faster scale-up under a more predictable risk regime than bespoke projects, which the paper uses to interpret China's advantage in renewables, batteries, EVs, and robotics.
The Digital Omnibus on AI, Legislative Legitimacy and the Dynamics of AI Regulation
Donal Casey, Liane Colonna
arXiv economics of AI,arXiv Computers and Society
Legal-political analysis of the EU's proposed Digital Omnibus on AI as an early revision to the AI Act, read through a legislative-legitimacy lens. Competition, geopolitical, and cross-regime coordination pressures create a legitimacy dilemma that the omnibus answers by shifting the Act toward political and operational rationalities over legal and cultural ones.
Tracked in arXiv economics of AI, arXiv Computers and Society
Commons-Governed Artificial Intelligence: A Taxonomy of Collective Governance
Eduardo C. Garrido-Merchán
arXiv economics of AI,arXiv Computers and Society
Institutional taxonomy of commons-governed AI across data, compute, models, knowledge, evaluation, and energy. Recasts collective stewardship arrangements as a coherent governance family and uses Ostrom-style design principles to compare archetypes, bottlenecks, and tradeoffs between scale and sustainability.
Tracked in arXiv economics of AI, arXiv Computers and Society
Beyond the Algorithm: Professional Experiences and Perceptions of AI Bias
Micarah Malone-Gawu
arXiv Computers and Society
Historical analysis. The purpose of this qualitative multi-case study was to examine how social bias emerges, is perceived, and can be mitigated within artificial intelligence and machine learning systems by practitioners directly involved in their design, development, and governance. Although examples from healthcare, criminal justice, employment, and education were used to illustrate domains where automated systems shape everyday life, the study focused on the lived experiences and professional insights of AI practitioners rather than sector-specific populations.
Financial Conditions and Green Innovation
Luca Fornaro, Veronica Guerrieri, Will Hotten, Lucrezia Reichlin
CEPR Discussion Papers
U.S. patent data matched to Compustat and firm-level impulse responses to exogenous shifts in broad financial conditions. Financial tightening cuts R&D much more sharply and persistently for specialized green innovators than for diversified innovators or non-innovators, pointing to a financing bias against upstream green technology.
Final Authority in AI Governance: Frontier-Provider Sovereignty and Action-Centered Deployer Governance
Zexun Wang
arXiv Computers and Society
The first, frontier-provider sovereignty, assigns privileged authority to the provider of the most capable models and is reflected in contemporary arguments for frontier-model testing, release gating, transparency duties, and compute-related controls. The second, action-centered deployer sovereignty, places final authority over high-impact actions with the organization that authorizes the action, embeds it in a business process, and bears the downstream legal, operational, and commercial consequences.
The Privilege of Exposure: Caste and Generative AI in India's Graduate Labour Market
Kaibalyapati Mishra
arXiv economics of AI
Occupational AI-exposure indices matched to India's 2025 Periodic Labour Force Survey for 83,000 employed graduates. Scheduled Castes and Scheduled Tribes graduates are 0.24-0.37 standard deviations less exposed than upper-caste peers within districts, and because exposure carries up to a 20% wage premium, generative AI threatens to widen caste earnings gaps.
Fault Lines: Navigating Ethics and Responsible AI Where National Policy Meets Local Practice in Public Sector Transformation
Sitong Lyu, Shabnam Taghiyeva, Mohit Kukadia, Denis Newman-Griffis
arXiv economics of AI,arXiv Computers and Society
Thematic analysis of 17 interviews on responsible AI in UK Special Educational Needs and Disabilities services across policymakers, local authorities, and third-sector actors. The national-local interface repeatedly breaks on shadow AI use, privacy, vendor-state asymmetry, weak workforce readiness, missing common standards, and unresolved human accountability.
Tracked in arXiv economics of AI, arXiv Computers and Society
(Human) Attention Is (Still) All You Need: Human oversight makes AI-assisted social science reliable
Chen Zhu, Xiaolu Wang, Weilong Zhang
arXiv economics of AI,RePEc NEP Artificial Intelligence
Argues that the reliability of AI-assisted research depends not only on model capability, but also on how cognitive labour is structured between humans and machines. Large language models (LLMs) are increasingly used for tasks once reserved for trained researchers, including hypothesis generation, specification choice, and drafting conclusions. Examines this problem through Human-in-the-Loop Economic Research (HLER), a decision architecture based on pre-commitment, decision sequencing, accountability, and attention allocation.
Tracked in arXiv economics of AI, RePEc NEP Artificial Intelligence
Federal Research Funding and STEM Education
Emily E. Cook, Devaki Ghose, Ekaterina Khmelnitskaya
World Bank Policy Research Working Papers
How does federal research funding affect STEM education as well as scientific output? A triple-difference design uses variation across universities, fields, and years from 1971 to 2016. Federal grants generate an estimated 27.4 percent of STEM doctorates, 14.7 percent of undergraduate STEM degrees, 6.3 percent of doctoral programs, and 3.7 percent of undergraduate programs across 200 US research universities, with effects concentrated in biology and engineering.
Revisiting the ABCs of Working with AI: A Replication with Radiologists
Daniel Martin
arXiv economics of AI,RePEc NEP Artificial Intelligence
Which professionals benefit most from AI assistance? A replication uses 11,420 paired radiologist-patient-pathology observations from 68 radiologists reading chest X-rays with machine-learning predictions. Lower baseline ability and better calibration predict larger gains from AI, extending prior evidence on ability and belief calibration to expert medical work.
Tracked in arXiv economics of AI, RePEc NEP Artificial Intelligence
Market Design for AI: Beyond the Copyright Binary
Yan Dai, Maryam Farboodi, Negin Golrezaei, Sepehr Shahshahani
arXiv economics of AI,RePEc NEP Artificial Intelligence
How can markets compensate human creators whose work trains AI while preserving incentives for original content? Static and dynamic Stackelberg models show that both free use and strong intellectual-property rights underreward especially original creators; growing reliance on AI also homogenizes future training data and degrades model performance. A data intermediary that internalizes cross-creator externalities and subsidizes innovative contributions restores efficiency.
Tracked in arXiv economics of AI, RePEc NEP Artificial Intelligence

Useful Knowledge Issue No. 002 - June 9, 2026

Tue, 09 Jun 2026 23:59:59 GMT

Issue No. 002

51 papers.

Worker Heterogeneity and the Effect of Noncompetes on Firm Performance
Zhaozhao He, Modupe Babajide Wintoki
Journal of Economics & Management Strategy
How does weaker noncompete enforcement change firm performance when knowledge workers can move more freely? Staggered state-level changes in enforceability are matched to firm, plant, and inventor outcomes. Profitability, valuation, productivity, and plant growth rise mainly at productive knowledge-worker-intensive firms, because productive inventors sort toward already productive firms.
Leaky mess or safety vaults? Team knowledge production and post-mobility knowledge spillover
Di Tong, Xiaoyu Zhang
Research Policy
Patent-based study of inventor mobility and team knowledge production. Larger teams reduce post-mobility spillovers of task-specific knowledge, while metaknowledge can still travel with movers and offset some of that protection.
Action perspective on technological change: A triply-nested framework and case study
Zhixiao Hong, Jinghua Xiao
Research Policy
Triply nested conceptual framework with a case study of technological change. Links situated action, organizational routines, and system-level dynamics to explain how local interventions propagate across technologies.
Connectivity infrastructure and innovation: The effects of headquarters versus subsidiary management
Catherine Magelssen, Luis Ballesteros, Casidhe Troyer
Research Policy
Empirical multinational-firm study exploiting broadband rollout with difference-in-differences and triple differences. Compares HQ-managed and managing-subsidiary-managed R&D subsidiaries. Broadband shifts innovation gains toward managing-subsidiary structures, with larger effects on quantity, quality, and market value only when both managing and managed units gain access, identifying communication as the channel.
Temporal dynamic effects of public innovation subsidies on firms’ significant innovation outcomes
Robert van der Have, Matthias Deschryvere
Industrial and Corporate Change
Do public RDI grants produce significant firm innovations, and how long do effects take? A 22-year Finnish enterprise panel uses literature-based innovation-output measures, with patent-count robustness checks, to follow firms after first grant receipt. Effects are modest but mostly positive, persistent, and rising over time, peaking about five to eight years after the first subsidy.
Advancing AI negotiations: A large-scale autonomous negotiation competition
Michelle Vaccaro, Michael Caosun, Harang Ju, Sinan Aral, Jared R. Curhan
Proceedings of the National Academy of Sciences
Theoretical model. Conducts an international AI negotiation competition in which participants designed and refined prompts for AI negotiation agents. Surprisingly, warmth—a traditionally human relationship-building trait—was consistently associated with superior outcomes across all key performance metrics.
Acquiring Firm Inventors’ Performance: Exploring the Alignment of Inventors’ Knowledge Base with Firms’ Innovation Trajectories
Shinjinee Chattopadhyay, Samina Karim, Laurence Capron
Strategy Science
How do innovation-sourcing acquisitions affect the inventors already inside the acquiring firm? Panel data on 334 pharmaceutical acquisitions from 1990 to 2007 are analyzed with inventor and year fixed effects, plus a selection check using state noncompete enforcement. Acquiring-firm inventors' performance falls on average; generalists fare relatively better in distant acquisitions, specialists fare relatively better in close acquisitions, and aligned inventors are more likely to keep patenting.
Consumer Impatience, Technological Innovation, and Market Structure
Chaewon Seol, Federico Rossi, Sara Valentini, Elisa Montaguti
Marketing Science
How does demand for rapid delivery alter online retail competition? Purchase data from pizza delivery markets show that impatient consumers substitute less, softening price competition and sustaining low-quality nearby sellers. Faster delivery initially lowers concentration by weakening downtown location advantages, but reductions beyond 75 percent concentrate demand among high-quality firms; a targeted premium-delivery service raises platform profit by almost 20 percent.
Separation of Powers or Division of Labor? Patent Interference Disputes, the Grand Narrative, and the History of the Administrative State, 1790-1940
Rebecca Eisenberg, Naomi R. Lamoreaux
NBER SI 2026 Development of the American Economy
Patent Interference Disputes, the Grand Narrative, and the History of the Administrative State, 1790-1940 uses the history of the Patent Office to challenge the Grand Narrative of separation of powers that the current Supreme Court is using to invalidate congressional designs for administrative agencies. Focusing on the adjudication of patent interference disputes—cases in which two or more inventors applied for patents for essentially the same technology—finds that the division of labor between the Patent Office and the courts shifted repeatedly and dramatically over the century and a half preceding the Administrative Procedure Act.
AI and Economic Growth
Jung-Wook Kim, Jonghwa Lee
NBER East Asian Seminar on Economics, 2026
Does AI innovation improve national economic performance? A country panel from 1985 to 2019 measures AI advancement with patents and estimates fixed-effects and generalized-method-of-moments models. AI innovation is positively associated with economic performance, although the estimates also indicate catch-up effects or diminishing marginal returns.
Tracked in NBER East Asian Seminar on Economics, 2026
AI Coding Agents in Social Science: Methodologically Diverse, Empirically Consistent, Interpretively Vulnerable
Meysam Alizadeh, Fabrizio Gilardi, Mohsen Mosleh, Enkelejda Kasneci
arXiv Computers and Society
At the design layer, Codex matches human methodological diversity and Claude Code produces nearly three times as many specifications; both agents' effect estimates remain broadly aligned with the human consensus, and no agent model exactly matches any human model. The deployment of LLM-based agents in scientific analysis raises opposing concerns: that agents may reduce methodological diversity, or that they may amplify the analytic flexibility through which researchers reach motivated conclusions.
Accounting for AI Inference in Corporate GHG Inventories: A Four-Tier Methodology for Scope 3 Category 1 Reporting
Guillermo Llopis
arXiv economics of AI,arXiv Computers and Society
Four-tier emissions-accounting methodology for AI inference under CSRD Scope 3 Category 1, combining token-level physical estimates, GPU energy benchmarks, grid carbon intensities, and EEIO fallback. Generic ICT input-output factors overstate inference emissions by 10-40x; applied to a 200-person European firm, the framework yields under 1 tCO2e and exposes a carbon-water trade-off across data-center locations.
Tracked in arXiv economics of AI, arXiv Computers and Society
From Stacks to Circuits: A Regenerative Socio-Technical Roadmap for AI Infrastructure within Planetary Boundaries
Han-Teng Liao, Karen Ang
arXiv economics of AI,arXiv Computers and Society
Current scaling trajectories for Generative AI, typified by linear supply-side "stacks," prioritize performance density while externalizing significant thermodynamic and material costs. As the "Twin Transition" of green and digital transformation accelerates, the industry faces technology gaps - including Scope 3 emissions and e-waste recycling - that impede sustainable scaling and lead to social tensions.
Tracked in arXiv economics of AI, arXiv Computers and Society
Agentomics: Economic Foundations for the Valuation, Attribution, and Pricing of AI Agents in Human-AI Workflows
Quanyan Zhu
arXiv economics of AI
Workflow-based economic framework that models human-AI systems as coalitions with value, cost, reliability, and failure risk at the workflow level. Uses coalition value and Shapley attribution to value and price AI agents by expected marginal contribution, illustrated with a security-operations case study.
Gaming AI-Assisted Peer Reviews Poses New Risks to the Scientific Community
Lin Li, Qi Zhang, Xander Davies, Jianing Qiu, Yarin Gal
arXiv Computers and Society
Without changing the underlying scientific content and communication, and even without knowledge of the reviewing model, adversarially rewritten abstracts substantially improve AI review outcomes. Although such systems promise to reduce reviewer burden and accelerate publication, their robustness to strategic manipulation remains poorly understood. Here shows that AI-mediated peer review is vulnerable to a simple, low-cost manipulation: superficial rephrasing of the manuscript abstract.
The Jagged Global Economy: Frontier AI Unevenly Exposes National Economies
Arul Murugan, Tomás Aguirre, Abhishek Nagaraj, Rishi Bommasani
arXiv Computers and Society
Measures cross-country differences in exposure to frontier AI by combining occupation-level scores with employment data for 141 countries. Europe and Central Asia are 50% more exposed than Sub-Saharan Africa, and women are more exposed than men in 91% of countries because of occupational sorting. Incorporating remittance links shows that low-direct-exposure economies can face substantial indirect exposure through highly exposed destination labor markets.
Powering the Future of AI: Navigating the Trade-offs for Europe's Energy Transition and Net-Zero Goals
Mohammad Hemmati, Gbemi Oluleye, Vassilis M. Charitopoulos
arXiv economics of AI,arXiv Computers and Society
Energy-system optimization model of Europe across 21 AI data-center growth scenarios. Estimates 73-723 TWh of extra AI electricity demand by 2050 and 67-181 MtCO2 of cumulative emissions overshoot risk from 2030-2050. After 2030, firm power and system flexibility, not clean-energy abundance alone, shape data-center siting; moderate scenarios add about 200 hours of firm generation and raise LCOE by EUR35/MWh in key hubs.
Tracked in arXiv economics of AI, arXiv Computers and Society
AI Adoption in Islamic Finance using Extended TAM Model with Moderation of Shariah Compliance Perception, Perceived Risk, Perceived Trust
Hamza M Abdul Mateen Khan, Danish Ahmed Siddiqui
RePEc NEP Artificial Intelligence
What predicts continued use of AI services in Islamic banking? A survey of 350 banking customers estimates an extended Technology Acceptance Model with ease of use, awareness, social norms, usefulness, risk, trust, and perceived Shariah compliance. Ease, awareness, norms, and usefulness predict attitudes and continued-use intentions, while the proposed moderation by risk, trust, and Shariah compliance is not supported.
AI Revolution and Crash Risks in Technology Stocks
Onur Polat, Oguzhan Cepni, Riza Demirer, Rangan Gupta
RePEc NEP Artificial Intelligence
Utilizing the recently developed AI indexes that capture general public attention towards AI-related developments through the newspaper coverage frequency of artificial intelligence and related topics like machine learning and high-frequency (5-minute interval intraday) data on technology stocks over the period from January 2015 to March 2026, Examines the predictive effect of AI sentiment and uncertainty proxies on crash risks in technology stocks that are directly associated with the emerging AI boom.
AI Sycophancy and Decisions
John Conlon, Peter Schwardmann
RePEc NEP Artificial Intelligence
The experiment involves 1,500 participants in 30 decision environments spanning core domains in economics and the social sciences. Contrary to the vast majority of predictions in an expert survey the study conducts, finds that AI advice depolarizes choices on average, moving participants away from their initial leanings. Finally, several results mitigate the concern that market forces will generate greater polarizing effects outside the experiment or in the future.
AI and Human Capital Accumulation: Aggregate and Distributional Implications
Yang K. Lu, Eunseong Ma
RePEc NEP Growth,RePEc NEP Artificial Intelligence
How do forward-looking education and saving responses alter AI's aggregate and distributional effects? An incomplete-markets general-equilibrium model has three skill sectors, endogenous human capital and assets, and an anticipated sector-biased AI shock. Adjustment polarizes jobs, amplifies output and consumption gains, and cushions employment losses; precautionary saving limits wealth inequality unless AI also raises entry requirements for high-skill work.
Tracked in RePEc NEP Growth, RePEc NEP Artificial Intelligence
Artificial Intelligence, Emotions and Belonging
Carlos Federico Obregon Diaz
RePEc NEP Growth,RePEc NEP Artificial Intelligence
How do institutions mediate AI's effects on labor markets, distribution, and social cohesion? A theoretical framework combines institutional economics with participation and belonging, treating AI as a technology without autonomous biological or emotional agency. AI can reinforce exclusion or support inclusive growth depending on institutional design and middle-class participation.
Tracked in RePEc NEP Growth, RePEc NEP Artificial Intelligence
Benchmarking Türkiye’s AI Workforce Readiness : A Multidimensional Global Comparison Using LinkedIn Data
Freeha Fatima, Efsan Nas Ozen, Dhushyanth Raju
RePEc NEP Artificial Intelligence
Benchmarks Türkiye’s AI workforce readiness using LinkedIn skill and hiring data within a consistent cross-country comparison framework. Artificial intelligence (AI) is reshaping labor markets, with countries increasingly differentiated by the depth, breadth, and distribution of AI-related capabilities. The analysis examines eight dimensions of readiness: AI engineering depth, AI literacy, foundational and disruptive digital skills, sectoral specialization, employer demand, hiring momentum, exposure to generative AI, and international mobility of AI professionals.
Defence innovation and procurement reform: an empirical evaluation of the US Defense Innovation Unit
Ethan Kapstein, Javier Ospital, Guntram Wolff
RePEc NEP Innovation
Did the U.S. Defense Innovation Unit broaden access to defense procurement? Administrative contracting data and a 2017-2025 firm panel are analyzed with propensity-score matching and staggered difference-in-differences. DIU participation increases both the probability of receiving a Department of Defense contract and contract size, expanding the supplier base by reducing entry barriers for commercial technology firms.
Divided We Fall Behind. Why a fragmented EU cannot compete in complex technologies
Pierre-Alexandre BALLAND, Valentina DI GIROLAMO, Florence BENOIT, Julien RAVET, Alexandr HOBZA
RePEc NEP Innovation
How costly is fragmentation in Europe's research and innovation networks, especially for complex technologies? Patent data from OECD REGPAT and publication data from OpenAlex map hub connectivity from 2000 to 2023 across spatial and technology domains. European hubs are less interconnected than U.S. hubs, and the efficiency gap is largest in complex strategic fields where connectivity matters most.
Eco-Innovation and Firm Performance: The Role of Circular Economy Investments and Human Capital among SMEs in Emilia-Romagna
Giulia Fontanelli
RePEc NEP Entrepreneurship
How are circular-economy investment and specialized human capital related to eco-innovation and performance among Italian SMEs? Data from the ECOSISTER Circular Economy and Blue Economy project examine firms in Emilia-Romagna. Firms increasingly combine eco-innovation with circular-economy investment and specialized skills, identifying these complementary capabilities as part of their competitiveness and development strategies.
Endogenous Technological Thresholds and Jobless Growth: A Schumpeterian Theory of Volatility Trap
Ali Chebbi
RePEc NEP Growth
Why can rapid frontier technology coexist with persistent stagnation in follower economies? A stochastic Schumpeterian growth model makes the human-capital threshold depend on frontier level and volatility. High volatility can create irreversible collapse, jobless recovery, and breakdowns in Okun and Beveridge relationships, yielding resilience-policy externalities beyond conventional growth maximization.
Financial risk and technology shifting: Firm-level evidence from the rise of AI
Andrea Bacchiocchi, Germana Giombini, Ludovica Segneri, Francesco Venturini
RePEc NEP Innovation,RePEc NEP Artificial Intelligence
Does financial risk affect firms' development of new technologies? Data on 28,020 Italian firms from 2012 to 2019 are matched with patent records and alternative measures of cash-flow volatility. Firms facing greater volatility are significantly more likely to patent in AI; the effect is weaker for Industry 4.0 and negligible for ICT, indicating that financial risk matters most for high-uncertainty, high-reward innovation.
Tracked in RePEc NEP Innovation, RePEc NEP Artificial Intelligence
Japan’s Innovation Challenge: Escaping the Middle-Technology Trap
Dany Bahar, Shreyas Gadgin Matha, Ricardo Hausmann, Santiago Segovia
RePEc NEP Innovation
Why has Japan's high R&D intensity not produced stronger productivity growth? OECD industry data, linked patents, and nine technology taxonomies show R&D concentrated in mid-technology incumbent manufacturing with weak spillovers, while frontier ICT, pharmaceutical, scientific, and digital sectors receive less investment. Tax-based support reinforces the pattern by favoring large incumbents and under-supporting SMEs.
Macroeconomic Policies for AI
Luca Fornaro, Martin Wolf
RePEc NEP Artificial Intelligence
Provides a macroeconomic framework to study monetary and fiscal policies for AI. Since workers have a high propensity to consume, advances in AI may depress aggregate demand and lead to a slump. Advances in AI expand firms' ability to automate production.
New Trends in Industrial Policies
Guy Lalanne, Antoine Dechezlepretre, Won Hee Cho
RePEc NEP Innovation
How large is the recent revival of industrial policy, and what forms has it taken? Semantic analysis of Global Trade Alert announcements is combined with OECD policy data for 17 economies from 2019 to 2023. Announced measures rose from 42 in 2010 to 1,483 in 2022, while grants and tax expenditures increased more modestly from 1.39% to 1.59% of GDP and financial instruments declined; support is persistent but has shifted toward capital investment, green transition, R&D, energy costs, and SMEs.
On humans and AI: A financial reporting dilemma
Jeremy Bertomeu, Edwige Cheynel, Radhika Lunawat, Mario Milone
RePEc NEP Artificial Intelligence
How do people and large language models resolve ethical financial-reporting dilemmas? Human participants acting as CFOs are compared with models deciding whether to reverse a known but investor-corrected reporting bias. Models favor truthful reporting more consistently and follow institutional guidance more closely; people accept model advice when it includes an explanation but discount or resist unexplained recommendations.
Picking Winners or Making Them? Evidence from the J-Startup program
Masatoshi KATO, Kenta IKEUCHI
RePEc NEP Entrepreneurship
Does Japan's J-Startup program select firms already likely to succeed or cause additional growth? Propensity-score matching on a longitudinal startup dataset estimates treatment effects on employment, sales, high-growth status, finance, and networks. Participation raises growth, especially for younger firms, primarily by improving financing capacity; expanded investor and customer networks have little independent association with growth.
Quality Adjustment in Industry Deflators Strengthens Estimated Innovation–Productivity Relationships
Enghin Atalay, Ali Hortacsu, Nicole Kimmel, Chad Syverson
RePEc NEP Innovation
How much does output-price mismeasurement obscure the relationship between innovation and productivity growth? Industry estimates replace producer-facing price indices with quality-adjusted deflators derived from gaps between consumer- and producer-facing prices. Conventional measures understate manufacturing productivity growth, especially in computers and electronics; correcting them roughly doubles the estimated relationship of R&D intensity and patents per employee with subsequent TFP growth.
Tax Cuts by Occupation: Entrepreneurs vs. Workers
Myeongju Kim, Eunseong Ma
RePEc NEP Entrepreneurship
Do tax cuts targeted at entrepreneurs have larger macroeconomic effects than equivalent cuts for workers? A state-level panel of occupation-specific federal tax shocks from 1981 to 2017 is paired with an incomplete-markets occupational-choice model. Entrepreneur-targeted cuts generate substantially larger gains in output, consumption, and employment by increasing business formation and firm expansion, with borrowing constraints and demand amplification producing the larger multiplier.
Taxing Entrepreneurs and Workers: A Linear Optimization Approach for Multidimensional Screening
Nicolo Ceneri, Giuseppe Lopomo, Alessandro Villa, Nicolas Werquin
RePEc NEP Entrepreneurship
How should governments tax people who can choose between wage work and risky entrepreneurship when skills and effort are private? A general-equilibrium multidimensional-screening model uses lottery-based linear optimization and is calibrated to the United States. Optimal policy gives tax breaks, sometimes net subsidies, to agents with intermediate entrepreneurial ability and sufficiently strong worker options; lower risk and fewer high-ability entrepreneurs increase those subsidies.
The Geography of the Startup Surge during the Pandemic
Bontu Ankit Patro, Hannah Rubinton
RePEc NEP Entrepreneurship
Was the pandemic startup surge confined to a few technology hubs? Business Formation Statistics show the startup rate rose 0.9 percentage points from 2019 to 2022 and remained elevated at 8.4% in 2023. The increase spanned regions and city sizes rather than concentrating only in coastal or very large metropolitan areas.
The Sum of All (Workplace) Fears: How Managers Mediate the Fear of AI Job Displacement
Christos Makridis, Christos A. Makridis
RePEc NEP Artificial Intelligence
Using longitudinal data from the Gallup Workforce Panel from 2023-2026, I examine whether managers and workplace practices shape employees’ fears that AI will eliminate their jobs. Stronger workplace practices are associated with lower displacement fear: a one-standard-deviation increase in workplace quality is associated with 13-24 percent lower odds of reporting greater displacement risk, and workers reporting the highest level of organizational wellbeing support are 6-6.8 percentage points less likely to say their job is somewhat or very likely to be displaced in cross-sectional specifications.
The role of AI use and AI training in school-to-work transitions
Roman Theiler, Patricia Palffy, Uschi Backes-Gellner
RePEc NEP Artificial Intelligence
How do job seekers respond when vacancies advertise AI use and employer-provided AI training? A randomized survey experiment varies vacancy language for 3,347 users of a Swiss apprenticeship platform across IT support, medical assistance, and office administration. AI use lowers application intentions in the first two occupations; training fully offsets the decline in IT, partly offsets it in medical assistance, and has no detectable effect in office work.
Werner Sombart and the Deep Origins of Creative Destruction
Claude Diebolt, Romain Diebolt, Tapas Mishra
RePEc NEP Entrepreneurship
Where did the concept of creative destruction originate? A page-by-page reading of Sombart's 1913 Krieg und Kapitalismus compares its destruction-creation mechanism with Schumpeter's later formulations. Sombart linked war, scarcity, institutional rupture, and substitution from wood to coal and coke to production-system change, anticipating the structural logic while grounding it more explicitly in history and material transitions.
Contemporary AI lacks the imagination to diverge or negate in science
Honglin Bao, Siyang Wu, Xiao Liu, Sida Li, Shiyun Cao, James A. Evans
arXiv Computers and Society
Tests whether AI can generate scientifically useful ideas that diverge from established research paths. Authors of 121,640 preprints were invited to evaluate model-generated ideas, producing 25,139 ratings from 6,749 scientists. Non-reasoning models converge on similar ideas, no model reliably proposes null hypotheses, and automated evaluators agree weakly with experts; a reward model trained on scientist ratings improves performance by as much as 27% but does not remove the need for expert grounding.
Stable Geometry, Reversing Poles: The Bipolar Structure of AI Occupational Substitutability and Its Decade-Scale Inversion
Shuyao Gao, Minghao Huang
arXiv economics of AI
Occupational-measurement paper using an LLM pipeline with human calibration to decompose 1,961 O*NET work activities into 15,817 micro-actions. Finds AI substitutability is bipolar rather than a smooth exposure gradient: tool-mediated physical tasks sit at one low-exposure pole and planning/design at the high-exposure pole. The geometry is robust across stress tests, but the high-risk pole has inverted relative to Frey-Osborne's 2013 computerization rankings.
The atomic structure of work: a micro-action instrument reveals two-pole AI occupational exposure and its decade-scale polar inversion
Shuyao Gao, Minghao Huang
arXiv Computers and Society
Develops an instrument to see what those scores average over: a decomposition of 1,961 O*NET work activities into 15,817 atomic micro-actions by a consensus multi-agent LLM pipeline, clustered from text alone into seven semantic classes. Research on artificial intelligence and work assigns each occupation a single exposure score.
How AI Agents Reshape Knowledge Work: Autonomy, Efficiency, and Scope
Jeremy Yang, Kate Zyskowski, Noah Yonack, Jerry Ma
arXiv economics of AI
Empirical product-data study of AI agents in knowledge work using Perplexity Search and Computer logs. Compares agentic autonomy, task efficiency, and task scope as systems move from chat assistants to end-to-end execution. Main object is how autonomy changes the production process, not just task speed.
The Rising Unsustainability of AI Graphics Cards Production
Clément Morand, Aurélie Névéol, Anne-Laure Ligozat
arXiv Computers and Society
Estimates the production-side environmental burden of AI graphics hardware rather than focusing only on electricity used in model operation. A new dataset tracks energy use, carbon emissions, and resource depletion for NVIDIA workstation graphics cards from 2013-2025. Production-related damage rises steadily despite operational-efficiency improvements, pointing to hardware longevity, lifecycle disclosure, and reduced replacement frequency as material policy margins.
AI Sovereignty: A Qualitative Model of Strategic Competition as AI Becomes an Instrument of National Power
Timothy Clancy, Asmeret Naugle
arXiv economics of AI,arXiv Computers and Society
Qualitative strategic-competition model of AI sovereignty. Defines sovereignty as national control over AI technologies and frames frontier AI as an instrument of economic and geopolitical power. Contribution is a model of how strategic competition changes as AI capability becomes national infrastructure.
Tracked in arXiv economics of AI, arXiv Computers and Society
The Geography of Algorithmic Judgment: LLM Intermediaries, Place Identity, and Racial Steering in Housing Search
Hana Samad, Trung Lam, Christoph Mügge-Durum, Michael Akinwumi
arXiv economics of AI,arXiv Computers and Society
Behavioral audit of seven open-source and proprietary LLMs used as housing-search intermediaries. Tests whether place identity and race-linked prompts steer recommendations across urban space. Result concerns algorithmic discrimination in a new search interface rather than traditional platform listings.
Tracked in arXiv economics of AI, arXiv Computers and Society
When the Scaffold Stays On: AI, Practice Style, and Screening in Elite Skill Formation
Song Yao
arXiv economics of AI,arXiv Computers and Society
Elite-programming study using Codeforces histories, ICPC/IOI screening, and an AI-prompt signature around recent model rollouts. AI-style practice predicts weaker open-contest rating gains outside screened elite pipelines but stronger unaided performance inside AI-prohibited ICPC environments, pointing to screening and practice design as the key margin.
Tracked in arXiv economics of AI, arXiv Computers and Society
Sustainability by Design in Decentralized Autonomous Organizations: An Empirical Review of Governance, Innovation, and Institutional Design
Yutian Wang, Luyao Zhang
arXiv innovation and entrepreneurship,arXiv Computers and Society
Empirical review of decentralized autonomous organizations as innovation ecosystems. Reframes governance and institutional design outside hierarchical firms and closed boundaries. Contribution is descriptive evidence on how innovation and sustainability operate in digitally native decentralized organizations.
Tracked in arXiv innovation and entrepreneurship, arXiv Computers and Society
Insurance of Agentic AI
Quanyan Zhu
arXiv economics of AI,RePEc NEP Artificial Intelligence
Analyzes major risk pathways, including hallucinations, prompt-injection attacks, autonomous decision errors, model drift, dependency failures, and cyber-physical harms, and evaluate how existing insurance products are adapting to address these exposures. Agentic artificial intelligence (AI) systems are transforming the risk landscape by extending beyond information generation to autonomous planning, tool invocation, decision execution, and persistent modification of digital and physical environments.
Tracked in arXiv economics of AI, RePEc NEP Artificial Intelligence
Does Artificial Intelligence Advance Science?
Liangping Ding, Cornelia Lawson, Philip Shapira
arXiv economics of AI,arXiv Computers and Society
Does AI adoption make science more creative or only faster? OpenAlex publication data covering more than one million papers measure novelty, recombination, and citation impact around AI use in research. AI publications are more likely to reach the top decile of measured creativity, with tool-oriented AI linked especially to recombinant novelty and adaptation-oriented AI linked to domain-specific advances.
Tracked in arXiv economics of AI, arXiv Computers and Society

Useful Knowledge Issue No. 001 - June 2, 2026

Tue, 02 Jun 2026 23:59:59 GMT

Issue No. 001

102 papers.

Are patents with female inventors under-cited? Evidence from text estimation
Yael V. Hochberg, Ali Kakhbod, Peiyao Li, Kunal Sachdeva
Journal of Financial Economics
Empirical patent-citation study using causal text estimation to compare patents with female inventors to text-implied counterfactual citation benchmarks. Finds that patents authored by female inventors are under-cited by roughly 10 percent relative to otherwise similar patent content.
From pitch to progress: The interplay of team reputation and governance in crowdfunded innovation
Xin Deng, Yen Teik Lee, Qi Sun, Yu Yan
Research Policy
Empirical crowdfunding study comparing blockchain projects with conventional Kickstarter campaigns. Stronger team reputation raises fundraising success, but in blockchain crowdfunding it is associated with weaker subsequent GitHub development activity and higher token turnover; Kickstarter shows a tighter alignment between reputation, funding, and delivery.
Patent pledging as collateral: Navigating the strategic dilemma from patents' private-market values
Yuandi Wang, Zi Li, Qianbin Dou, Jiashun Huang, Zhao Zhou, Toby Li
Research Policy
How can innovative firms pledge patents as collateral when lenders observe public market value more easily than firm-specific use value? Strategy article on patent collateralization distinguishes private technological value from lender-facing marketability. The proposed solution is a hierarchy of collateral: pledge core but generalizable patents and reinforce their value with signals such as external citations, converting internal innovation value into credible loan security.
Workers’ incentives and the optimal taxation of AI
Jakub Growiec, Klaus Prettner, Maciej Szkróbka
Economics Letters
Theoretical optimal-taxation model with manual labor, cognitive labor, physical capital, and AI capital. Derives an AI-tax threshold: taxing AI becomes optimal when AI is capable enough that cognitive workers begin considering a switch into manual work.
Intellectual property protection lost and competition: An examination using large language models
Utku U. Acikalin, Tolga Caskurlu, Gerard Hoberg, Gordon M. Phillips
Journal of Financial Economics
Empirical finance and innovation study using large language models to measure firms' exposure to lost patent eligibility after the Alice decision. Affected firms reduce patenting, but the competitive effects are uneven: large exposed firms gain sales and valuation, face fewer lawsuits, and make fewer acquisitions.
The evolution of the nuclear industry in Korea: A dynamic model of sectoral innovation system perspective
Jongyoun Lim, Sunyang Chung
Industrial and Corporate Change
Historical sectoral-innovation-system analysis of Korea's nuclear industry. Uses a dynamic model combining sectoral innovation systems and industrial dynamics. Identifies three development stages: imitation, specialization, and stabilization.
Adjustable Product Attributes, Indirect Network Effects, and Subsidy Design: The Case of Electric Vehicles
Kevin Remmy
American Economic Journal: Economic Policy
How should electric-vehicle subsidies account for manufacturers' endogenous choices of price and driving range? A structural model with indirect charging-network effects is estimated on the German EV market. The support program nearly doubled EV sales but induced substantial price and range adjustments: purchase subsidies maximize unit sales, while charging-station subsidies generate greater consumer and total surplus.
Medical Technology and Life Expectancy: Evidence from the Antitoxin Treatment of Diphtheria
Philipp Ager, Casper W. Hansen, Peter Z. Lin
American Economic Journal: Economic Policy
How much did the first effective infectious-disease treatment contribute to the historical rise in life expectancy? Newly collected municipality-level antitoxin-distribution data are linked to more than 1.5 million Massachusetts death certificates from 1880–1914. Free and rapid diffusion of diphtheria antitoxin significantly increased life expectancy at young ages, showing that medical innovation combined with public provision mattered more to the early twentieth-century health transition than previously estimated.
The Effect of Mergers on Innovation
Kaustav Das, Tatiana Mayskaya, Arina Nikandrova
American Economic Journal: Microeconomics
How does a merger change the timing and direction of R&D when innovation feasibility is uncertain? A dynamic model separates three forces: reduced follow-on innovation from cannibalization, greater appropriability of a breakthrough, and earlier discovery through information pooling. Mergers are more beneficial when R&D outcomes are highly uncertain and less beneficial when the innovation path is clear; their value can also rise when the initial and subsequent innovations are closer substitutes.
The Organization of Innovation: Incomplete Contracts and the Outsourcing Decision
Thomas Jungbauer, Sean Nicholson, June Pan, Michael Waldman, Lucy Xiaolu Wang
American Economic Journal: Microeconomics
Why do firms outsource some R&D projects while developing similar products internally? A model of incomplete contracts gives internal teams greater control over where a new product sits in product space, allowing firms to limit cannibalization of profitable existing products. Evidence from pharmaceutical patents, patent expirations, and outsourcing at different R&D stages supports the model's prediction that the value of protecting incumbent products shapes organizational boundaries for innovation.
Cassatts in the Attic: Is There a Gender Gap in the Commercialization of Science
Marlène Koffi, Matt Marx
American Economic Journal: Applied Economics
Is there a gender gap in the commercialization of scientific research? An analysis of nearly 70 million articles compares commercialization outcomes by author gender while accounting for scientific quality and commercial potential. Female-led papers are 14 percent less likely to be commercialized, with the largest gap for female last authors; the gap disappears when researchers commercialize through their own ventures and narrows with smaller firms or firms employing more female inventors.
Information Frictions and Employee Sorting between Start-ups
Kevin A. Bryan, Mitchell Hoffman, Amir Sariri
American Economic Journal: Applied Economics
Would better information about startup quality improve matching between young firms and workers? A custom job board for 26 science-based startups randomized whether business-school alumni saw expert ratings of each firm's science and business model. Showing ratings strongly shifted applications, including those from high-quality workers, toward better-rated firms by changing beliefs about upside potential, although applicants remained markedly overoptimistic about startup success overall.
Mean field game model of the impact of reductions in support on faculty research activity
Robert A. Brown
Proceedings of the National Academy of Sciences
Theoretical model. Mean field game (MFG) theory is used to model federal support of faculty research assuming that each researcher seeks to maximize their funding according to a specified utility function. Results representing the funding for faculty in colleges of engineering demonstrate that a decrease in overall federal support of the magnitude proposed by the Administration could lead to over half the faculty inadequately funded to support research. The results have important implications to the university research environment and to their strategies for maintaining the vibrancy of their programs.
Enhancing AI-Assisted Purchase Decisions: The Role of the Sense of Autonomy
Jinghui (Jove) Hou, Shuai Yang, Guiyang Xiong, Paul A. Pavlou
MIS Quarterly
Can interface design correct AI recommenders' tendency to overlook consumers' idiosyncratic motives? Five laboratory experiments and a field experiment in apparel purchasing test interventions that increase users' sense of autonomy. Greater autonomy raises purchase intentions and decision quality, and actual purchase and return data confirm that a smartphone-based design improves outcomes outside the laboratory.
Regulating AI: Lessons From Scientific Computing
Jonathan Wareham, Angelo Kenneth Romasanta, Laia Pujol Priego, David Osimo
MIS Quarterly
What can scientific computing teach regulators about demands for explainable AI? Historical and contemporary cases show that fields combine theory and computation in several configurations and often manage opaque methods by inserting theory only at critical stages. The comparison implies that universal explainability mandates should be supplemented with domain-specific oversight tailored to where theory is needed for reliability and accountability.
The Legal Environment of Side Project Ownership and IT Innovation: Evidence from the Alcatel v. Brown Case
Xi Wu, Charlotte R. Ren, Min-Seok Pang
MIS Quarterly
Engaging in side projects outside of regular employment has become a growing trend among knowledge workers, particularly information technology (IT) professionals. Finds that in states where firms gained greater contractual authority to claim ownership of employees’ side projects, the number of IT patents owned by firms decreased. Further analyses of the underlying mechanisms suggest that these contrasting findings likely stem from shifts in both employee innovation behaviors and firms’ innovation strategies, post-Alcatel v.
Nondeterministic Torts: A Technical Approach to AI Liability
Trent S. Kannegieter
Yale Law Journal
How should tort law assign responsibility when identical prompts can produce different LLM outputs? The Note connects in-production nondeterminism to negligence, product-liability, and causation doctrine, emphasizing that developers knowingly deploy systems with irreducible output variation. It argues that recognizing this technical feature can place liability on developers that omit sufficient guardrails, improving compensation and incentives for safer deployment.
Getting in the Door vs. Winning It All: How Gendered Outcomes Change Across Evaluation Stages in Entrepreneurship
Tristan L. Botelho, Ethan J. Poskanzer
Administrative Science Quarterly
How does gender inequality change across stages of entrepreneurial evaluation? Data from a large multistage startup competition separate shortlisting from final winner selection as stakes and commitment costs rise. Female-led startups are 18.7 percent more likely to be shortlisted but 30.7 percent less likely to win, consistent with higher early quality assessments giving way to risk aversion and gendered performance expectations at the decisive stage.
The diffusion of large language models in published academic articles
Kyle Siler
Proceedings of the National Academy of Sciences
Who is adopting large language models in academic publishing? Text analysis of 7.3 million articles from four major publishers tracks 228 words whose frequency rose sharply after 2022, supplemented by difference-in-differences comparisons across institutions, regions, disciplines, and journals. Estimated LLM influence rises from 12 percent of articles in 2023 to 57 percent in 2025 and is greater at lower-ranked institutions, newer for-profit publishers, and regions farther from English-language and economic centers.
AI-assisted teams outperform AI-led teams but not human-only teams in assessing research reproducibility in quantitative social science
Abel Brodeur, David Valenta, Alexandru Marcoci, Juan P. Aparicio, Derek Mikola, Bruno Barbarioli, Rohan Alexander, Lachlan Deer, Tom Stafford, Lars Vilhuber, Gunther Bensch, Fabio Motoki, Mohamed Abdelhady, Yousra Abdelmoula, Ghina Abdul Baki, Tomás Aguirre, Sriraj Aiyer, Shumi Akhtar, Farida Akhtar, Melle R. Albada, Micah Altman, David Angenendt, Zahra Arjmandi Lari, Jorge Armando De León Tejada, David Rodriguez Arana, Igor Asanov, Anastasiya-Mariya Noha, Rebecca Ashong, Tobias Auer, Francisco J. Bahamonde-Birke, Bradley J. Baker, Söhnke M. Bartram, Dongqi Bao, Lucija Batinovic, Tommaso Batistoni, Monica Beeder, Louis-Philippe Beland, Carsten Gero Bienz, Christ Billy Aryanto, Cylcia Bolibaugh, Carl Bonander, Ramiro Bravo, Egor Bronnikov, Stephan Bruns, Nino Buliskeria, Sara Caicedo-Silva, Andrea Calef, Juan Sebastian Cano Arias, Gustavo A. Castillo Alvarez, Solomon Caulker, Simonas Cepenas, Arthur Chatton, Zirou Chen, Ngozi Chioma Ewurum, Anda-Bianca Ciocîrlan, Felix J. Clouth, Jason Collins, Nikolai Cook, Cesar Cornejo, João Craveiro, Jonathan Créchet, Jing Cui, Niveditha Chalil Vayalabron, Christian Czymara, Carlos Daniel Bermúdez Jaramillo, Hannes Datta, Lien Denoo, Arshia Dhaliwal, Nency Dhameja, Elodie Djemai, Erwan Dujeancourt, Uǧurcan Dündar, Thibaut Duprey, Yasmine Eissa, Youssef El Fassi, Ismail El Fassi, Keaton Ellis, Ali Elminejad, Mahmoud Elsherif, Aysil Emirmahmutoglu, Giulian Etingin-Frati, Emeka Eze, Jan Fabian Dollbaum, Jan Feld, Andres Felipe Rengifo Jaramillo, Guidon Fenig, Victoria Fernandes, Lenka Fiala, Lukas Fink, Mojtaba Firouzjaeiangalougah, Sara Fish, Jack Fitzgerald, Rachel Forshaw, Alexandre Fortier-Chouinard, Louis Fréget, Joris Frese, Jacopo Gabani, Sebastian Gallegos, Max C. Gamill, Attila Gáspár, Romain Gauriot, Evelina Gavrilova, Diogo Geraldes, Giulio Giacomo Cantone, Grant Gibson, Dirk Goldschmitt, Amélie Gourdon-Kanhukamwe, Andrea Gregor de Varda, Idaliya Grigoryeva, Alexi Gugushvili, Aaron H. A. Fletcher, Florian Habermann, Márton Hablicsek, Joanne Haddad, Jonathan D. Hall, Olle Hammar, Malek Hassouneh, Carina I. Hausladen, Sophie C. F. Hendrikse, Matthew Hepplewhite, Anson T. Y. Ho, Senan Hogan-Hennessy, Elliot Howley, Gaoyang Huang, Héloïse Hulstaert, Zlatomira G. Ilchovska, Paola Jaimes Santamaria, Niklas Jakobsson, Joakim Jansson, Ewa Jarosz, Hossein Jebeli, Yanchen Jiang, Hiba Junaid, Rohan Kalluraya, Sunny Karim, Edmund Kelly, Eva Kimel, Sorravich Kingsuwankul, Valentin Klotzbücher, Daniel Krähmer, Pijus Krūminas, Nicholas Kruus, Essi Kujansuu, Christoph F. Kurz, Stephan Küster, Blake Lee-Whiting, Felix Lewandowski, Tongzhe Li, Ruoxi Li, Dan Liu, Jiacheng Liu, Helix Lo, Katharina Loter, Felipe Macedo Dias, Christopher R. Madan, Nicolas Mäder, Marco Mandas, Cesar Mantilla, Jan Marcus, Diego Marino Fages, Xavier Martin, Ryan McWay, Daniel Medina-Gaspar, Sisi Meng, Lingyu Meng, Simon Merz, Alex P. Miller, Thibault Mirabel, Dibya Deepta Mishra, Sumit Mishra, Belay W. Moges, Morteza Mohandes Mojarrad, Myra Mohnen, Louis-Philippe Morin, Lucija Muehlenbachs, Gastón Mullin, Andreea Musulan, Sara Muzzì, James A. C. Myers, Florian Neubauer, Tuan Nguyen, Ali Niazi, Ardyn Nordstrom, Bartłomiej Nowak, Daneal O’Habib, Tim Ölkers, Justin Ong, Valeria Orozco Castiblanco, Ömer Özak, Ali I. Ozkes, Mikael Paaso, Shubham Pandey, Varvara Papazoglou, Romeo Penheiro, Linh Pham, Ulrike Phieler, Peter Pütz, Quan Qi, Jingyi Qiu, Manuel T. Rein, David A. Reinstein, Juuso Repo, Nicolas Rudolf, Shree Saha, Orkun Saka, Chiara Saponaro, Georg Sator, Martijn Schoenmakers, Raffaello Seri, Meet Shah, Paul Sibille, Christoph Siemroth, Vladimir Skavysh, Ben Slater, Wenting Song, Stefan Staubli, Tobias Steindl, Nomwendé Steven Waongo, Paul Stott, Stephenson Strobel, Roshini Sudhaharan, Pu Sun, Scott D. Swain, Oleksandr Talavera, Hanz M. Tantiangco, Georgy Tarasenko, Boyd Tarlinton, Mariam Tarraf, Ken Teoh, Rémi Thériault, Bethan Thompson, Tonghui Tian, Wenjie Tian, Emmanuel Tolani, Nicolai Borgen, Solveig Topstad Borgen, Javier Torralba, Carolina Velez-Ospina, Man Wai Mak, Lukas Wallrich, Zeyang Wang, Leah Ward, Matthew D. Webb, Duncan Webb, Bryan S. Weber, Christoph Weber, Wei-Chien Weng, Christian Westheide, Tom Wilkinson, Kwong-Yu Wong, Marcin Wroński, Zhuangchen Wu, Qixia Wu, Victor Y. Wu, Bohan Xiao, Feihong Xu, Cong Xu, Pranav Yadav, Yu Yang Chou, Luther Yap, Myra Yazbeck, Bo Yao, Zuzanna Zagrodzka, Tahreen Zahra, Mirela Zaneva, Xiaomeng Zhang, Ziwei Zhao, Han Zhong, Aras Zirgulis, Jiacheng Zou, Floris Zoutman, Christelle Zozoungbo
Proceedings of the National Academy of Sciences
Randomly assigns 288 researchers to 103 teams working under three conditions: human-only, AI-assisted (using ChatGPT as a collaborative tool), or AI-led (ChatGPT operating with minimal human oversight). Teams reproduced published results from leading social science journals, detected coding errors, and proposed robustness checks. While AI assistance did not degrade most outcomes, it provided no measurable advantages and was associated with reduced detection of major errors.
Flying high or crashing down: Pre‐entry knowledge, post‐entry learning, and the distribution of startup performance
Rajshree Agarwal, Benjamin Campbell, Seth Carnahan, Joonkyu Choi
Strategic Management Journal
Formal model plus empirical test of high-technology startup performance using confidential U.S. employee-employer microdata. Compares founders with insider pre-entry knowledge to outsiders who learn after entry. Result concerns how pre-entry knowledge and post-entry learning shape the distribution of startup outcomes.
Cheap Stock Options: Antecedents and Outcomes
Brad A. Badertscher, Bjørn N. Jørgensen, Sharon P. Katz, Jeremy Michels
Management Science
Empirical corporate-finance and entrepreneurship article on cheap stock options before IPO. Studies prevalence, determinants, and consequences of equity compensation priced below the eventual IPO share price. Main object is how pre-IPO compensation design affects firms and employees.
AI at the Wheel: The Effectiveness of Advanced Driver-Assistance Systems
Vikram Maheshri, Clifford Winston, Yidi Wu
Journal of Law and Economics
Has automakers' use of AI in advanced driver-assistance systems improved safety in real driving? A trim-level dataset links the universe of registered vehicles to Texas crashes over nine years. Driver-assistance technologies reduce the probability of any accident by 11–14 percent and the risk of a single-vehicle fatal crash by roughly one-third, providing early revealed-use evidence on the benefits of vehicle automation.
AI and Doctrinal Collapse
Alicia Solow-Niederman
Stanford Law Review
How can an AI developer treat training data as public enough to scrape yet private enough to shield from oversight? Pending litigation, discovery disputes, and licensing agreements identify an inter-regime doctrinal collapse in which firms use business-to-business purchases or broad contractual consent to avoid privacy and copyright constraints. The resulting data-acquisition regime advantages established firms and weakens limits on private power, motivating legal responses drawn from conflict of laws and legal pluralism.
Beyond Diffusion: Maintenance, Craft, and the Rise of Technical Prestige in Colonial Lima
Ruggero Pace Gravina
Technology and Culture
Why did technological change in colonial Lima depend on repair work as much as on imported inventions? Municipal contracts and artisanal records trace the import and maintenance of mechanical clocks after Lima's 1535 founding. Sustained upkeep gave artisans, including enslaved workers, technical expertise, authority, and social mobility, making maintenance an urban process linking craft, governance, and technical prestige.
Constructing the State: Materiality, Imaginaries and the Politics of Chilean Electrification, 1939–43
José Soto Vejar, Cecilia V.Ibarra Mendoza
Technology and Culture
How did material implementation shape Chile's state-led electrification program? Evidence from construction of the first state hydroelectric plants in 1939–43 follows engineering choices and conflicts among engineers, public officials, and private actors. Construction decisions changed the program's sociotechnical goals and explain why realized infrastructure diverged from national plans, making implementation itself part of state formation.
Making Passengers Work: Infrastructural Labor and Exclusion in Mid-Twentieth-Century Stockholm's Public Transit
Elise Perrault, Martin Emanuel
Technology and Culture
How did mid-century transit rationalization redistribute operating work between employees and users? The history of Stockholm's fixed-conductor system in the 1940s traces changes in payment, boarding, and passenger circulation. The reform intensified conductors' work while requiring passengers to perform new routines, embedding unequal access into an efficiency program that excluded travelers unable to comply.
Predictive Numbers: Labor, Data, and Power in the U.S. Oil Industry
Sarah Stanford-Mcintyre
Technology and Culture
How did predictive data technologies reorganize knowledge and work in the U.S. oil industry? Archival labor and technology history traces subsurface analysis from embodied field expertise to numerical and computational methods between the late nineteenth and mid-twentieth centuries. Seismic techniques displaced experiential authority, altered workplace hierarchies, and expanded white-collar analytical labor, making extraction an early institutional base for computational decision-making.
Why Concorde Failed: Political Economy and the Limits of Techno-Nationalism
Tom Kelsey
Technology and Culture
Why did Britain's commitment to the Concorde supersonic jet collapse? Government, parliamentary, press, and opposition records show that economic and industrial objections inside and outside the state were more decisive than environmentalism or declining trust in expertise. Critics converged on the project's weak commercial prospects and on state secrecy and propaganda, exposing the limits of techno-nationalism.
Green AI in Industry Quasi-Experimental Evidence from the Water Treatment Sector
Mathias Abitbol, Philippe Aghion, Céline Antonin, Lint Barrage, Benjamin Lengereau
CEPR Discussion Papers,NBER SI 2026 Environment and Energy Economics
Can AI monitoring reduce industrial emissions rather than merely add server electricity demand? Quasi-experimental evidence from French wastewater treatment plants uses adoption timing, outages, and high-frequency operational data. AI monitoring cuts electricity use by 5.4 percent, carbon emissions by 6 percent, and electricity costs by 8.2 percent while improving effluent quality, with server electricity below 1 percent of the savings.
Tracked in CEPR Discussion Papers, NBER SI 2026 Environment and Energy Economics
Innovation without Borders? The Geography of Technological Diffusion
Ursel Baumann, Zoë B. Cullen, Ester Faia, Annalisa Ferrando, Ricardo Perez-Truglia, Judit Rariga, Zöe Cullen, Judith Rariga
NBER Working Papers,CEPR Discussion Papers,RePEc NEP Innovation,RePEc NEP Artificial Intelligence
How geographically broad are strategic complementarities in firms' AI investment? A field experiment with 3,300 firms in 12 EU countries randomized accurate information about domestic and foreign peers' AI adoption. Firms substantially underestimated both, but only domestic beliefs changed intended investment: a one-point rise in expected domestic peer adoption raised own expected AI investment 0.57 points, while foreign-peer expectations had no significant effect.
Tracked in NBER Working Papers, CEPR Discussion Papers, RePEc NEP Innovation, RePEc NEP Artificial Intelligence
What Investment Data Implies about the AI Transition
Jessica Wachter, Jonathan Wachter
NBER Working Papers
Quantitative macro and asset-pricing model calibrated to 2025-27 AI capital expenditure by the five largest U.S. technology firms. Interprets the investment boom as requiring rare AI productivity jumps: scenarios imply 5-58 percentage points of extra cumulative GDP growth by 2030 and AI shares of 8-39%. Higher growth risk raises the model risk-free rate by about 0.5 pp and the equity premium by about 3 pp.
Prompted to Start: How Generative AI is Transforming Entrepreneurship
Jan Bena, Bo Bian, Mariassunta Giannetti
NBER SI 2026 Entrepreneurship,NBER-SAIF Conference on AI and Financial Markets, Spring 2026
Shows that following the diffusion of GenAI, industries with higher task-level exposure to the new technology experience 20% more startup formation than less-exposed industries, and a growing share of these startups offer new AI products and services rather than merely automating operations. This entry extends beyond traditional innovation hubs into regions with limited venture capital and thin specialized labor markets. While individual entrants are smaller in scale, aggregate entry generates net employment and wage growth at the industry level and creates employment in the very occupations most exposed to GenAI.
Tracked in NBER SI 2026 Entrepreneurship, NBER-SAIF Conference on AI and Financial Markets, Spring 2026
Startups in Africa
Emanuele Colonnelli, Marcio Cruz, Mariana Pereira-Lopez, Tommaso Porzio, Chun Zhao
NBER SI 2026 Development Economics,CEPR Discussion Papers,RePEc NEP Entrepreneurship,Becker Friedman Institute Working Papers
What financing do African startups demand, and who receives it? New evidence combines a continent-wide founder survey, an incentive-compatible financing-preference experiment, and VC deal records matched to founders' education and work histories. Startups strongly prefer equity, but foreign investors supply most equity and disproportionately fund foreign-connected founders, reducing startup creation and tilting the sector's composition.
Tracked in NBER SI 2026 Development Economics, CEPR Discussion Papers, RePEc NEP Entrepreneurship, Becker Friedman Institute Working Papers
How Artificial Intelligence Shapes Science: Evidence from AlphaFold
Ryan R. Hill, Carolyn Stein
NBER Applications of Artificial Intelligence in Healthcare, Spring 2026,NBER SI 2026 Innovation
Examines how a frontier AI model affects scientific discovery by examining the release of the AlphaFold2 algorithm and its impact on structural biology and related fields of science. Yet, to date, finds that the rate of experimental structure determination has remained almost unchanged. Looking at downstream science that builds on protein structures, finds that basic research on proteins that had no structure information prior to AlphaFold increases by 15 to 40% relative to proteins that already had a structure, shifting the direction of research toward less-studied proteins.
Tracked in NBER Applications of Artificial Intelligence in Healthcare, Spring 2026, NBER SI 2026 Innovation
Old Space, New Space: A Commercial Revolution in Innovation?
Ruben Gaetani, Alexander T. Whalley
NBER Entrepreneurship and Innovation Policy and the Economy 2026,NBER Entrepreneurship and Innovation Policy and the Economy Conference, 2026
When did the commercial revolution in space innovation actually happen, and who drove it? Patent-data measurement of space technologies compares the Old Space/New Space narrative with the timing and ownership of inventive activity. The largest surge appears in the 1990s before the best-known startups, incumbents still account for most patenting, and government-created demand looks central to making private space innovation appropriable.
Tracked in NBER Entrepreneurship and Innovation Policy and the Economy 2026, NBER Entrepreneurship and Innovation Policy and the Economy Conference, 2026
Defining Innovatisation: The case of New Space and the Changing Space Sector
Marc-André Chavy-Macdonald, Benoit Cornet, Dominique Foray
NBER Entrepreneurship and Innovation Policy and the Economy Conference, 2026
How did the space sector shift from prestige-oriented technological achievement toward market-oriented innovation? Historical evidence from Apollo and 1980s commercialization is combined with recent quantitative evidence on costs, private entry, startups, and venture capital. The proposed concept of 'innovatisation' distinguishes projects judged mainly by technical performance from those disciplined by customer demand, commercial opportunity, and cost. Recent changes indicate that innovation logic now plays a much larger role in space activity.
Tracked in NBER Entrepreneurship and Innovation Policy and the Economy Conference, 2026
Artificial Intelligence in the Office and the Factory: Evidence from Administrative Software Registry Data
Gustavo De Souza
NBER AI and Economic Measurement, Spring 2026
How does commercial AI affect office and production employment in Brazil? Administrative records cover nearly all locally developed AI applications, and exogenous variation in development costs identifies labor-market effects. Office applications automate tasks, reduce employment, and hollow out wages; factory applications support optimization and quality control while increasing demand for young and low-skilled workers able to perform formerly expert tasks. Production gains exceed office losses, producing a net employment increase.
Tracked in NBER AI and Economic Measurement, Spring 2026
Two Centuries of U.S. Innovation: Firms' Internal Networks and Resilience to Disasters
Mathias Kruttli, Noah Stoffman, Sumudu W. Watugala
NBER Climate and Nature Finance, Spring 2026
Develops a comprehensive database of the universe of approximately 12 million U.S. patents from 1836 to 2023 and analyze the resilience of innovation to disaster shocks using hurricane landfalls. Major hurricanes reduce local innovation for up to a decade and lead to permanent losses. Shows that spatial spillovers along firms’ internal innovation networks can be negative (via the capital channel) or positive (via the innovation productivity channel).
Tracked in NBER Climate and Nature Finance, Spring 2026
The Household Impact of Generative AI: Evidence from Internet Browsing Behavior
Michael Blank, Gregor Schubert, Miao Ben Zhang
NBER AI and Economic Measurement, Spring 2026
Does generative AI save household time outside paid work? Home-device browsing data from 2021-2024 use pre-ChatGPT browsing patterns as an instrument for later adoption. Adoption substantially increases leisure browsing without reducing observed productive digital activity, and surrounding web activity indicates that households mainly use ChatGPT for productive nonmarket tasks. A standard time-allocation interpretation attributes the extra leisure to economically meaningful home-productivity gains.
Tracked in NBER AI and Economic Measurement, Spring 2026
The Microstructure of AI Diffusion: Evidence from Firms, Business Functions, and Worker Tasks
Kathryn Bonney, Cory L. Breaux, Emin Dinlersoz, Lucia S. Foster, John C. Haltiwanger, Aditya A. Pande
NBER AI and Economic Measurement, Spring 2026
How broadly has AI diffused across firms, business functions, and worker tasks? Nationally representative data from the 2026 AI supplement to the U.S. Census Bureau's Business Trends and Outlook Survey show that 18 percent of firms used AI in at least one function during November 2025-January 2026, rising to 32 percent when employment-weighted. Adoption remains concentrated in large, knowledge-intensive firms and usually spans no more than three functions. Broader integration is positively associated with performance, while functional deployment and operational investment, unlike worker-task use alone, are associated with employment declines.
Tracked in NBER AI and Economic Measurement, Spring 2026
What Work Does Generative AI Do?
Alexander Bick, Adam Blandin, David J. Deming, Tyler R. Schumacher
NBER AI and Economic Measurement, Spring 2026
Which tasks do workers actually perform with generative AI? A nationally representative survey links individual adoption to detailed occupational tasks and contrasts the results with exposure indexes and platform chat logs. At least one-fifth of workers use generative AI in 80 percent of occupations and 40 percent of tasks, but exposure measures explain only about half of worker-level variation. Chat logs overstate generic basic tasks relative to survey reports, while the expertise level of adopted tasks differs systematically across occupations.
Tracked in NBER AI and Economic Measurement, Spring 2026
Interdisciplinary Scientists Are More Innovative Than Interdisciplinary Teams
Yiling Lin, Zak Risha, Erin Leahey, Lingfei Wu
NBER Investments in Early Career Scientists, Spring 2026
Is interdisciplinarity more productive within individual scientists or across specialist teams? Records for 49 million publications by three million scientists from 1900-2020 compare researchers spanning multiple fields with teams covering the same breadth collectively. Individually interdisciplinary scientists produce more disruptive and atypical work after accounting for team size, year, and field. Such researchers are becoming rarer, publish more slowly, and work in smaller teams, suggesting that integrated knowledge within a person has distinctive innovative value.
Tracked in NBER Investments in Early Career Scientists, Spring 2026
AI Agents and Higher-Order Work
Suproteem K. Sarkar
NBER Productivity, Innovation, and Entrepreneurship, Spring 2026
How do AI agents reorganize software work and who benefits? Cursor platform data and feature-release timing measure delegation, planning, verification, output, and worker experience. Agents shift effort from manual implementation toward supervision and higher-order delegation, with more use where outputs are readily verified. They increase software output most for experienced workers and firms with verifiable work, suggesting complementarity between agentic AI and expertise.
Tracked in NBER Productivity, Innovation, and Entrepreneurship, Spring 2026
The Unintended Consequences of Carbon Markets
Lior Shabtai
NBER Climate and Nature Finance, Spring 2026
A directed technical change model of a carbon market with sectoral differences in abatement costs shows that carbon markets inefficiently direct most innovation efforts toward decarbonizing the easy-to-abate sector. Shows that carbon markets create inefficiently weak incentives for green innovation in hard-to-abate industries by allowing firms to rely on purchasing emission allowances rather than independently decarbonizing.
Tracked in NBER Climate and Nature Finance, Spring 2026
Digital (Killer?) Acquisitions
Florian Ederer, Regina Seibel, Timothy Simcoe
NBER Productivity, Innovation, and Entrepreneurship, Spring 2026
Do acquisitions by major digital firms suppress or spread acquired innovation? Patent and workforce data cover 1,200 technology acquisitions in event-study and difference-in-differences designs. Acquired patents receive more subsequent citations than matched patents, not solely from the acquirer, while innovation persists mainly in technology areas that attract further deals. Target-firm employment falls by more than 50 percent within three years, and lower employee retention accompanies larger citation spillovers, consistent with diffusion through worker mobility rather than systematic killing of technology.
Tracked in NBER Productivity, Innovation, and Entrepreneurship, Spring 2026
Generative AI and Entrepreneurship
Abhinav Gupta, Franklin Qian, Yifan Sun, Elena Simintzi
NBER Productivity, Innovation, and Entrepreneurship, Spring 2026
Venture capital shifted toward frequent, smaller investments, boosting new firm formation. Exploiting the release of ChatGPT in November 2022, shows that startups with greater pre-release Gen AI task exposure reduced employment within two quarters, primarily among junior and implementation roles. Examines how Generative AI (Gen AI) is reshaping the U.S. startup ecosystem.
Tracked in NBER Productivity, Innovation, and Entrepreneurship, Spring 2026
Increasing Scholarly Productivity for Earliest-Career Tenure-Track Faculty: A Cohort Analysis (2011–2021)
Jamie Powers, William Savage, Anthony Olejniczak
NBER Investments in Early Career Scientists, Spring 2026
Introduction: The pressure to "publish or perish" is well-established. Results: Scholarly production by earliest-career scholars rose significantly over the decade. Conversely, in Education and the Humanities, growth was driven by participation, with a ~15% increase in the proportion of candidates who are publishing any works.
Tracked in NBER Investments in Early Career Scientists, Spring 2026
Beyond the Lab: The Effect of PhD Programs on Innovation
Manfredi Aliberti
NBER Investments in Early Career Scientists, Spring 2026
Do university PhD programs increase local innovation? The centrally planned, staggered rollout of doctoral programs across Italian universities is linked to patents in a difference-in-differences design, with admission scores separately identifying graduate effects. Program openings raise patenting by 21 percent from 1986 to 2001; about 22 percent comes directly from graduates and most of the rest from spillovers to local firms. Estimated social returns exceed total costs by at least 46 percent, with implied GDP gains of 0.6-4.7 percent.
Tracked in NBER Investments in Early Career Scientists, Spring 2026
Finding Nemo, Abroad
Vastav Ratra
NBER Investments in Early Career Scientists, Spring 2026
How does international graduate enrollment redirect scientific research? The staggered adoption of English-taught master's programs in biological and agricultural sciences at 118 non-Anglophone universities is compared with matched Anglophone institutions. Publications are linked to the native habitats of studied species to measure geographic orientation. Program adoption changes both researcher composition and the regions studied, with preliminary evidence pointing to students' location-specific skills as the mechanism.
Tracked in NBER Investments in Early Career Scientists, Spring 2026
Lost in Transition: Financial Barriers to New Technologies
Philippe Aghion, Antonin Bergeaud, Maarten De Ridder, John Van Reenen
NBER SI 2026 Economic Growth and Long-Run Macroeconomic Development,NBER Climate and Nature Finance, Spring 2026
Why do financial crises disproportionately slow adoption of emerging technologies? A Schumpeterian growth model with financial frictions emphasizes path-dependent expertise and the tendency of constrained young firms to select into new technologies, then tests the mechanism using German green patenting around the Global Financial Crisis. Tighter finance depresses new-technology investment more than mature-technology investment. Model quantification attributes about half of the post-crisis stagnation in green patenting to slower sorting of young firms into green technologies.
Tracked in NBER SI 2026 Economic Growth and Long-Run Macroeconomic Development, NBER Climate and Nature Finance, Spring 2026
Pyramids, Diamonds, and Oscillations: AI and the Structure of Internal Labor Markets
Guido Friebel, Yao Huang, Jin Li, Soumitra Shukla, Andrew Zhang
NBER Organizational Economics Working Group, Spring 2026
How do AI-driven productivity and learning changes reshape firms' internal job hierarchies? A model distinguishes productivity shocks from faster worker learning in organizations with firm-specific human capital. Productivity gains preserve the long-run managerial span but cause temporary freezes in junior hiring, moving firms from pyramids toward diamonds and sometimes producing oscillations during adjustment. Faster learning can permanently narrow spans, while transition dynamics create inequality across junior cohorts.
Tracked in NBER Organizational Economics Working Group, Spring 2026
Growth is Getting Harder to Find, Not Ideas
Teresa C. Fort, Nathan Goldschlag, Jack Liang, Peter K. Schott, Nikolas Zolas
NBER Productivity, Innovation, and Entrepreneurship, Spring 2026
Are ideas becoming harder to produce, or are ideas translating less effectively into growth? A new 45-year panel covering the universe of U.S. firms separates the relationship between R&D and patents from the relationship between patents and productivity. Patents per research input rise, and the patent-R&D elasticity is flat or increasing, while patent growth retains a stable positive relationship with labor-productivity growth. The decline occurs in firm growth conditional on patenting, pointing to a weaker conversion of ideas into growth rather than a shortage of ideas.
Tracked in NBER Productivity, Innovation, and Entrepreneurship, Spring 2026
A Test of Lookahead Bias in LLM Forecasts
Zhenyu Gao, Wenxi Jiang, Yutong Yan
NBER AI and Economic Measurement, Spring 2026
How can users tell whether an LLM forecast benefits from information that was already in its training data? A pre-training-data detection method estimates each prompt's Lookahead Propensity, then formally links a positive correlation between that measure and forecast accuracy to the presence and magnitude of lookahead bias. Tests on news-headline stock-return forecasts and earnings-call capital-expenditure forecasts yield a low-cost diagnostic for deciding when apparent LLM predictive performance is credible.
Tracked in NBER AI and Economic Measurement, Spring 2026
Social Learning among Urban Manufacturing Firms: Energy-Efficient Motors in Bangladesh
Ritam Chaurey, Gaurav Nayyar, Siddharth Sharma, Eric Verhoogen
NBER Productivity, Innovation, and Entrepreneurship, Spring 2026
Do nearby firms learn from one another about energy-saving technology? A randomized allocation of efficient servo motors among leather-goods firms in Dhaka measures exposure relative to simulated random assignment and tracks information, beliefs, and adoption. Exposure to treated neighbors within 500 meters substantially increases information flows and adoption. Accounting for these spillovers raises the estimated value of adoption subsidies enough to make them cost-effective.
Tracked in NBER Productivity, Innovation, and Entrepreneurship, Spring 2026
Trade and Industrial Policy in Supply Chains: Directed Technological Change in Rare Earths
Laura Alfaro, Harald Fadinger, Jan S. Schymik, Gede Virananda
NBER International Trade and Investment Program Meeting, Spring 2026
A quantitative trade model with Heckscher-Ohlin-based comparative advantage, directed technological change and input-output linkages rationalizes how input-supply restrictions induce REE-enhancing innovation and expand REE-intensive industries abroad. Shows that China’s REE export restrictions in 2010 induced a surge in global innovation increasing REE input-efficiency and exports in REE-intensive industries. Trade and industrial policies restricting critical inputs can inadvertently promote foreign downstream industries via a directed technological response.
Tracked in NBER International Trade and Investment Program Meeting, Spring 2026
Job Transformation, Specialization, and the Labor Market Effects of AI
Lukas Freund, Lukas Friedrich Mann
NBER Organizational Economics Working Group, Spring 2026
Theoretical model. Develops a general-equilibrium model of this process. A central effect of automation is to transform jobs—shifting their task content. Estimates the distribution of task-specific skills and project individual-level wage effects of generativeAI automation.
Tracked in NBER Organizational Economics Working Group, Spring 2026
Ray of Hope? China and the Rise of Solar Energy
Ignacio Banares-Sanchez, Robin Burgess, Dávid László, Pol Simpson, John Van Reenen, Yifan Wang
NBER Chinese Economy Working Group Meeting, Spring 2026,NBER SI 2026 Macroeconomics and Productivity
Develops new city and firm panel data on solar policies, patenting and output. Examines the impact of Chinese solar subsidies whose implementation by city-regions went alongside massive expansion of the sector and a dramatic fall in global solar prices. Using synthetic-difference-in-differences 2004-2020, finds production and innovation subsidies were more effective than demand-side (installation) subsidies in generating large and persistent increases in local innovation, net entry, production and exports.
Tracked in NBER Chinese Economy Working Group Meeting, Spring 2026, NBER SI 2026 Macroeconomics and Productivity
Serial Patent Litigation: An Emerging Strategy to Delay Entry of Generic Competition
Timothy Bonis, Aaron Kesselheim, S. Sean Tu
NBER Assessing the U.S. Medical Innovation System, Spring 2026
The Hatch-Waxman Act of 1984 was designed to accelerate generic drug entry by establishing a framework for resolving patent disputes between brand-name and generic manufacturers. While the Act has facilitated competition and expanded the availability of affordable medicines, brand-name firms have increasingly exploited its procedural structure to delay or deter generic competition through “serial litigation.” This strategy involves filing successive, questionable lawsuits, often based on non-innovative continuation patents.
Tracked in NBER Assessing the U.S. Medical Innovation System, Spring 2026
Negative Product Disclosure and Innovation
Colleen M. Cunningham, Jennifer L. Kao
NBER Economics of Health Program Meeting, Spring 2026
Examines how negative product disclosure affects the rate and direction of subsequent innovation. Finds that innovation in affected device markets declined by 17 percent, primarily driven by firms' responses to information about competitors' products rather than by the loss of their own ability to withhold information. Taken together, these findings show how large-scale negative product disclosure can reshape the allocation of innovative effort towards safer and higher quality products.
Tracked in NBER Economics of Health Program Meeting, Spring 2026
Market Contraction and Innovation Divergence: The Impact of the US-China Trade War on Chinese Innovation
Xiao Ma, Yueyuan Ma, Hanyi Tao, Yiran Zhang
NBER Chinese Economy Working Group Meeting, Spring 2026
How did U.S. tariffs alter the intensity and direction of Chinese innovation during the trade war? Text analysis extracts technical terms from patents to measure China-U.S. similarity, while a quantitative model links firms' feature-level R&D and exporting decisions to tariff-induced demand changes. Greater tariff exposure reduces Chinese patenting and similarity to recent U.S. patents; the demand channel explains 21 percent of the similarity decline. Changes in innovation lower Chinese exports by 3.3 percent by 2021, with directional shifts accounting for 14 percent of that loss.
Tracked in NBER Chinese Economy Working Group Meeting, Spring 2026
Human Learning about AI
Raphael Raux, Bnaya Dreyfuss
NBER Digital Economics and AI Meeting, Spring 2026
How do people form beliefs about AI performance and adoption? Laboratory experiments test whether users project human task difficulty onto machines, and a field experiment observes responses to an AI parenting adviser. Failures on human-easy tasks and successes on human-hard tasks receive too much weight, distorting beliefs and adoption even when those outcomes are not informative about overall AI quality. Less human-like parenting answers reduce trust and future use, showing that anthropomorphic expectations can worsen alignment between perceived and actual capability.
Tracked in NBER Digital Economics and AI Meeting, Spring 2026
Aligning Large Language Model Agents with Rational and Moral Preferences
Daniel L. Chen, Christian Hansen, Wei Lu, Amit Dhanda
NBER Digital Economics and AI Meeting, Spring 2026
Theoretical model. Understanding how large language model (LLM) agents behave in strategic interactions is essential as these systems increasingly participate autonomously in economically and morally consequential decisions in multi-agent systems. Models like GPT-4o show excessive cooperation and limited incentive sensitivity, while reasoning models, such as o3-mini, align more consistently with payoff-maximizing strategies. Finds that fine-tuning based on small datasets shifts LLM agent behavior toward the corresponding economic agent.
Tracked in NBER Digital Economics and AI Meeting, Spring 2026
AI and the Quantity and Quality of Creative Products: The Case of Books
Joel Waldfogel, Imke Reimers
NBER Digital Economics and AI Meeting, Spring 2026
Quantitative model. With the diffusion of LLMs between 2022 and 2025, new book releases have tripled, raising a question of AI's impact on book quality. Develops a ratings-based usage measure that is comparable across book release vintages, and finds that the vintages from the AI influx period have lower average quality. Yet, the top 1,000 monthly releases per category -- albeit not the top 100 -- have higher quality than before; and the effect is larger in categories with faster growth in new titles.
Tracked in NBER Digital Economics and AI Meeting, Spring 2026
Plateau That Never Comes: When Efficiency Claims in Datacenters and AI Become Greenwashing
Harshit Gujral, Eshta Bhardwaj, Dushani Perera, Christoph Becker, Steve Easterbrook
arXiv economics of AI,arXiv Computers and Society
Policy and accounting critique of AI data-center sustainability claims. Argues efficiency improvements and clean-power procurement do not prove absolute reductions in electricity, water, materials, waste, or local burdens. Contribution is a framework for identifying when efficiency rhetoric becomes greenwashing.
Tracked in arXiv economics of AI, arXiv Computers and Society
Beyond Exposure: Predicting AI Adoption Based on Comparative Advantage
Ilse Lindenlaub, Ryungha Oh, Maria Alejandra Rodriguez, Laura Veldkamp
CEPR Discussion Papers,RePEc NEP Artificial Intelligence,Becker Friedman Institute Working Papers
Using the representative German DiWaBe employee survey linked to worker and establishment information, Compares worker-reported AI use to prominent exposure measures and find that the relationship is weak. This leads us to propose a new AI adoption index based on comparative advantage. 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.
Tracked in CEPR Discussion Papers, RePEc NEP Artificial Intelligence, Becker Friedman Institute Working Papers
Do Matching Mechanisms Work with LLM Agents?
Yukihiro Hoshino, Ayato Kitadai, Nariaki Nishino
arXiv economics of AI
Experimental market-design study using LLM agents as delegated decision-makers. Compares decentralized negotiation with centralized matching mechanisms. Main question is whether standard allocation mechanisms remain incentive-compatible or stable when agents are LLMs.
What Happens in Paris, Does Not Stay in Paris: Trade Fairs and Search and Matching Frictions
Gábor Békés, Matyas Molnar, Claudia Steinwender
CEPR Discussion Papers
Do subsidized trade fairs help firms overcome search and matching frictions in export markets? Historical study of Hungarian manufacturers around the 1900 Paris World Exhibition builds a firm panel and uses exhibition-selection rules to discipline counterfactuals. Fair participation creates new international trade linkages, suggesting trade promotion can generate matches rather than merely select firms already ready to export.
The Main Barrier to AI Adoption in the Public Sector Is Lack of Training: How a Structured Method Accompanied Productivity Gains in Two Brazilian Government Cases
Vinicius Santana Gomes
arXiv Computers and Society
The analysis is consistent with the hypothesis that the method is portable across agencies with distinct mandates, operates within protocols designed to comply with international and national data-protection law and with the principles of public administration, and is accessible to public entities under budget constraints, since it used free AI models.
The Main Barrier to AI Adoption in the Public Sector is Lack of Training: How a Structured Method Increased Productivity in Two Brazilian Government Cases Without Incidents
Vinicius Santana Gomes
arXiv economics of AI
Two-case public-sector study of generative AI adoption in Brazilian government. Argues the main bottleneck is structured training rather than model availability. Reports productivity gains from a structured method without incidents in auditable administrative settings.
AI and Labor Market Outcomes: Evidence from China
IZA@LISER Discussion Papers
Develops city–year measures of AI labor demand from 1.6 million online job postings between 2016 and 2024, and merge them with nationally representative microdata from the China Family Panel Studies (2016–2022). Fixed-effects estimates show that local AI labor demand has positive impacts on individual wages: a one-unit increase in AI demand (1,000 postings, firms, or job titles) raises wages by about 0.2–0.3 percent. Women experience stronger gains—about 0.5–0.7 percent per unit increase—while men show no measurable effect.
Becoming The Man Without Qualities? Deskilling in the Age of Artificial Intelligence
IZA@LISER Discussion Papers
Using longitudinal O*NET data for the U.S. labour market over 2011–2025, the analysis distinguishes among three types of human capabilities - abilities, skills, and knowledge - and construct two measures of human capabilities’ exposure to AI: one based on observed progress in Generative AI benchmark performance and one based on the broader evolution of AI-related scientific and public attention. Within occupations, greater AI exposure is associated with higher proficiency requirements for selected capabilities.
Childhood misbehavior, toxic personality and leadership
Thomas Buser
RePEc NEP Entrepreneurship,IZA@LISER Discussion Papers
Does childhood misbehavior predict selection into entrepreneurship and leadership, and at what cost? Retrospective behavior measures in a representative Dutch panel show misbehaving children are overrepresented among adult entrepreneurs and leaders despite worse schooling. They also exhibit more aggression, transgression, aversive personality traits, and lower wellbeing, patterns that persist among those reaching senior positions.
Tracked in RePEc NEP Entrepreneurship, IZA@LISER Discussion Papers
Generative AI and the Redefinition of Entry-Level Software Work
Samuel Westby, Alicia Modestino, Peiran Cheng
RePEc NEP Artificial Intelligence,IZA@LISER Discussion Papers
Difference-in-differences design using the near-universe U.S. online vacancy data from Lightcast, Examines how the public release of ChatGPT changed entry-level software hiring standards. Event-study and difference-in-differences estimates show a 14–15 percent relative decline in junior versus senior software developer vacancies, larger than in related technical occupations and absent in mechanical engineering. A shift-share decomposition shows that rising experience requirements were driven primarily by employers asking for more experience within the same job titles, not by asking for a different composition of titles.
Tracked in RePEc NEP Artificial Intelligence, IZA@LISER Discussion Papers
Has AI Widened Employment Gaps? Tracking Early-Career Employment by Occupational Exposure in Norway
IZA@LISER Discussion Papers
Has employment fallen faster in occupations more exposed to AI? Monthly administrative data cover the universe of Norwegian private-sector jobs through February 2026 and compare young workers across complete exposure quintiles. Since October 2022, employment grows by 0.1 percent in the most exposed occupations versus 0.3 percent in the least exposed, and the estimated relative decline for the top quintile is statistically indistinguishable from zero.
Human Capital in the Heartland: Evidence on Brain Drain
IZA@LISER Discussion Papers
The Midwestern United States is often called the Heartland of America. The Heartland was once an engine of American economic growth, but technological change and industrial restructuring have made it difficult for many places to thrive. Skilled workers are critical for regional economic prosperity.
Understanding Occupational Wage Growth
IZA@LISER Discussion Papers
Why did wage inequality between occupations rise in Sweden despite institutions designed to compress pay differences? Estimates of occupational wage premia and time-varying life-cycle profiles for Swedish workers from 1996 to 2013 are identified by re-centering profiles around their flat spot. Differential growth in occupational premia drove a substantial rise in between-occupation inequality, partly offset by changes in worker composition, while the positive link between premium growth and employment points to demand-side forces.
Measuring the AI Economy
Anton Korinek, Patrick McKelvey
CEPR Discussion Papers,RePEc NEP Artificial Intelligence
These growth rates reflect three compounding forces: expanding data-center capacity, continued improvements in chip efficiency, and rapid algorithmic progress. Develops a macroeconomic estimate of total AI production for the United States, combining inference and R&D/training activities and applying quality adjustments based on the evolution of API prices at fixed performance levels and the pace of algorithmic progress. Estimates that nominal AI compute spending grew over 140 percent per year each in 2024 and 2025, raw compute capacity grew over 200 percent per year, and quality-adjusted AI output grew over 2,000 percent per year.
Tracked in CEPR Discussion Papers, RePEc NEP Artificial Intelligence
Measuring the Occupation-Level Impact of AbbVie Intelligence: AI Applicability Analysis, 2024-2025
John Regan, Jon Stevens, Brian Martin
arXiv Computers and Society
Measures how an enterprise AI system changes the tasks it can assist or automate across 192 occupations at AbbVie. The analysis classifies 598,744 de-identified conversations using O*NET activities and compares 2024 with 2025, the August 2025 platform release, and a November training program. Applicability rises significantly in all comparisons: the platform release increases the score by 10.0%, and structured employee training adds 6.68%.
How Proposal Novelty, Topical Diversity, and Theory-Practice Balance Shape Scholarly Outcomes in Funded Education Research
Yunfeng Gao, Yuxuan Xiao, Jiaming Zhang, Yang Ding
arXiv Computers and Society
Which features of publicly funded education-research proposals predict later scholarly output and influence? Linking 8,715 NSF education awards from 1990 to 2020 to 84,519 publications by principal investigators measures proposal novelty, topical diversity, and the balance of theoretical and practical aims. Funding is associated with more publications but not stronger citation or journal-visibility outcomes; balanced proposals perform most favorably overall, while topical diversity raises output in some divisions but weakens citation-based performance in others.
Prompts for Public-Sector LLMs Should Be Governed as Commons
Rashid Mushkani
arXiv Computers and Society
Prompts encode role instructions, decision framings, and value claims; prompt choice can materially shift outputs even when model weights and input records are held fixed. Argues that prompts used to deploy large language models (LLMs) in public-sector settings should be treated as governed artefacts rather than private, transient inputs. Existing governance tools, including model and dataset documentation, organisation-level policies, and post-training alignment, rarely make the local prompt collections used in deployment transparent, contestable, or auditable.
Certificates without Electrons? Theory and Evidence on Impacts from AI-Driven Power Demand
Dana Golden, Aruna Balasubramanian, Niranjan Balasubramanian
arXiv economics of AI,RePEc NEP Artificial Intelligence
How do renewable-energy procurement and data-center location shape the grid costs of AI? A difference-in-differences design evaluates renewable-energy certificates and power-purchase agreements as data centers reach 4.4 percent of US electricity demand, with counterfactuals for inference location and storage. Edge inference, spatial reallocation, and colocated storage substantially reduce grid impacts, while certificate-only strategies do not.
Tracked in arXiv economics of AI, RePEc NEP Artificial Intelligence
Authenticity Debt and the Synthetic Content Threat Landscape: A Layered Framework for Trust, Provenance, and IP Governance in the Generative AI Era
Shubhashis Sengupta, Benjamin McCarty, Milind Savagaonkar, Rhine Andotra
arXiv economics of AI,arXiv Computers and Society
Framework paper on synthetic-content governance in generative AI. Defines authenticity debt created by near-zero-cost production and redistribution of high-fidelity text, image, audio, and video. Organizes trust, provenance, and IP risks into a layered threat landscape.
Tracked in arXiv economics of AI, arXiv Computers and Society
Coalition-Based Digital Governance in the Era of AI
Dennis Snower, Paul Twomey
CEPR Discussion Papers
Institutional-design article on AI governance. Argues incremental regulation is structurally misaligned with AI autonomy, compositionality, and concentration. Proposes coalition-based digital governance as a redesign of institutional authority.
Next-Billion AI Index: The compass for AI utility and adoption in the global majority
Ambrish Rawat, Jessica He, Subhabrata Majumdar, Claudio Pinhanez, Yann Le Beux, Satyapriya Krishna, Rahul Gupta, Rumman Chowdhury, Kush R. Varshney
arXiv economics of AI,arXiv Computers and Society
Measurement-framework paper for AI utility and adoption in the global majority. Introduces the Next Billion AI Index to evaluate deployability, local adaptation, trust, and infrastructure constraints rather than frontier capability alone. Contribution is a diagnostic index for adoption contexts.
Tracked in arXiv economics of AI, arXiv Computers and Society
Global Science Sustains U.S. Innovation
Christopher R. Esposito
arXiv innovation and entrepreneurship
Empirical innovation paper on global scientific inputs to U.S. technology. Treats science as an international supply chain for innovation, analogous to physical-goods inputs. Main object is U.S. innovation's dependence on foreign knowledge and its vulnerability to disruption.
Serial Innovators
David Galenson
Becker Friedman Institute Working Papers
A select group of great innovators have produced more than one major contribution. These exceptional individuals are known far beyond their own disciplines, and are routinely described as unique. Yet although their extraordinary accomplishments obviously make them outliers, the processes by which they arrive at their discoveries follow patterns that are shared by innovators more generally.
Attention Asymmetry in AI Layoff Discourse on X: A Computational Analysis of Capital vs Labour Amplification
Joy Bose
arXiv economics of AI,arXiv Computers and Society
Computational social-media analysis of AI layoff discourse on X. Compares amplification of capital-side narratives about productivity and transformation with labor-side narratives about job loss. Contribution is measurement of attention asymmetry in AI restructuring discourse.
Tracked in arXiv economics of AI, arXiv Computers and Society
From Augmentation to Reconstruction: Guiding the AI Disruption to the Good Place
David M. Rothschild, Jake M. Hofman, Markus Mobius, Brendan Lucier, Eleanor Dillon, Daniel G. Goldstein, Nicole Immorlica, Aleksandrs Slivkins
arXiv economics of AI
Policy framework on steering AI disruption from augmentation toward institutional reconstruction. Argues delayed disruption reflects adoption and organizational design, not only model capability. Contribution is a governance agenda for making AI productivity socially useful.
AI in the Workplace: The Impact of AI on Perceived Job Decency and Meaningfulness
Kuntal Ghosh, Marc Hassenzahl, Shadan Sadeghian
arXiv Computers and Society
Examines how workplace AI affects perceived job decency, meaningfulness, and satisfaction rather than task performance alone. Interviews with 24 employees in technology, services, and healthcare compare anticipated effects across occupations. Technology and healthcare workers expect better hours but weaker social meaning, while service workers expect little improvement in hours but a status gain from working with AI.
Human Decision-Making with AI Assistance under Correlated Features
Yanru Guan, Naveen Raman, Fei Fang
arXiv Computers and Society
Asks how an AI assistant should recommend information when it must balance current decision quality against human learning. A theoretical model with correlated diagnostic features proves that stationary recommendation policies can perform arbitrarily poorly and that optimal policies explore before committing. Computing the exact policy is NP-hard, but a finite-horizon dynamic program and shorter-horizon approximation achieve near-optimal performance; stronger correlation requires longer exploration.
Dual-Bid Corporate Charters, Entrepreneurial Incentives and Social Efficiency
Piet Sercu, Tom Vinaimont
RePEc NEP Entrepreneurship
The incumbent's option to block the takeover by buying just one class of shares, shows, generates better terms if and when the firm is taken over, which translates in a higher IPO or PE value and, thus, stronger entrepreneurial incentives. The value-boosting effect is quite powerful when the potential for value-improving takeovers is high, notably for entrepreneurs who do have bright ideas but are not good at organization, or for incumbents facing rivals with big toeholds. In the standard dual-class equity structure, the founder retains control via a blocking minority, typically achieved via multiple-vote share ownership.
Generative AI Fuels Solo Entrepreneurship, but Teams Still Lead at the Top
Hyunso Kim, Hyo Kang, Jaeyong Song
RePEc NEP Entrepreneurship
Has generative AI changed who enters entrepreneurship and who reaches the top? More than 160,000 Product Hunt launches show a sharp post-ChatGPT rise in entry led by solo founders, especially in categories previously dominated by teams. Much of the increase is low-commitment experimentation; teams remain increasingly dominant among top-ranked products.
Interfirm Networks and the Financing of Newly Established Enterprises (Japanese)
Koji INOUE, Yoshimi YAMADA, Hikaru FUKANUMA
RePEc NEP Entrepreneurship
While prior research has primarily emphasized entrepreneurs’ human capital and firm attributes, they have paid limited attention to the role of interfirm relationships. The results show that securing high-performing trading partners is unlikely to increase the probability of obtaining bank loans, but tends to reduce the loan amounts received among enterprises that obtain loans. Furthermore, when the CEO shares the same university affiliation as the trading partner’s CEO, the probability of borrowing decreases, whereas sharing the same prefecture of origin is associated with larger loan amounts.
Patents, firm rents, and worker compensation: Causal evidence from quasi-random patent allocation
Afroza Alam, André Diegmann
RePEc NEP Entrepreneurship
How are patent-created rents divided between firms and workers? Quasi-random examiner assignment is linked to German employer-employee records, firm data, and patent documents. Patent allowances reduce exit, increase productivity and wages, and imply rent-sharing elasticities of 0.10 to 0.21; managers gain most in listed firms, while wage gains are broader in private firms.
The expansion of basic education during ‘deskilling’ technological change in England and Wales, c. 1780–1830
Louis Henderson
RePEc NEP Growth
Did early English industrialization expand basic human capital despite deskilling? Age heaping replaces marriage signatures as a measure of numeracy and literacy in England and Wales from 1780 to 1830. Industrial districts show education gains because Sunday schools signaled low leisure preference among child workers; their focus on reading rather than writing explains why signature evidence missed the expansion.
Trade and Growth with Digital Data
Kyu Yub Lee
RePEc NEP Growth
How do cross-border data flows affect innovation-led growth and privacy? A dynamic general-equilibrium trade model makes consumer-generated data both an R&D input and a privacy externality. Freer data flows and knowledge spillovers raise long-run growth, while localization slows it; tighter controls can improve privacy welfare, motivating deep digital agreements rather than blanket localization.
Trade liberalization reduces entrepreneurship rate
Ali Ozdagli, Maddie Shaheen
RePEc NEP Entrepreneurship
How do trade costs affect entrepreneurship? Cross-country and cross-industry evidence is paired with China's WTO accession as a trade-liberalization shock and a model combining trade with occupational choice. Higher trade costs are associated with more entrepreneurship, while industries experiencing larger import-penetration increases after accession saw larger declines in entrepreneurship.
When development finance spurs entrepreneurship: New evidence from 5 million projects using a machine learning classifier
Sven Werner, Philipp Trotter
RePEc NEP Entrepreneurship
Entwicklungsfinanzierung richtet sich zunehmend auf die Förderung von Entrepreneurship im Globalen Süden. Die makroökonomische Evidenz zur Wirksamkeit dieser Förderung ist bislang jedoch uneinheitlich. Bisherige Studien greifen auf aggregierte Daten zur Entwicklungsfinanzierung zurück, da spezifische Daten zur Entrepreneurship-Förderung bislang nicht verfügbar waren.
Algorithm Aversion in Prosocial Tasks: Evidence from AI-Based Performance Evaluation
Martin Abel, Raghad Dawi, Tyler Lenk, Aidan Singer
RePEc NEP Artificial Intelligence,IZA@LISER Discussion Papers
How does replacing human evaluation with AI affect effort in prosocial work? A field experiment with 1,491 US volunteers writing fundraising messages cross-randomizes evaluator type and performance pay. AI evaluation lowers effort by 11-14 percent among volunteers weakly committed to the cause, performance pay does not offset the decline, and skepticism about AI's ability to judge subjective quality is the primary mechanism.
Tracked in RePEc NEP Artificial Intelligence, IZA@LISER Discussion Papers
Beyond Startups: Sweden’s Scale-Up Challenge
Magnus Henrekson
RePEc NEP Entrepreneurship,IZA@LISER Discussion Papers
Why does Sweden excel at startup formation but struggle to scale young firms globally? An institutional and evolutionary analysis uses the collaborative innovation-bloc framework to study coordination among entrepreneurs, investors, skilled workers, and lead customers. Tax, labor-market, and capital-allocation distortions bias firms toward early exit rather than sustained productivity-enhancing growth.
Tracked in RePEc NEP Entrepreneurship, IZA@LISER Discussion Papers
Equipment, Structures, and the Limits of Investment-Specific Technological Change
Dongkeun Choi, Munseob Lee
RePEc NEP Growth,IZA@LISER Discussion Papers
Quantitative model. KLEMS data attribute roughly half the post-1996 rise in construction prices, in the U.S. and abroad, to declining construction TFP. A nested CES extension finds structures-unskilled substitutability alongside equipment-skilled complementarity. The falling relative price of equipment, long viewed as the signature of investment-specific technological change (ISTC), has a countervailing force: rising structures prices.
Tracked in RePEc NEP Growth, IZA@LISER Discussion Papers