paper
2026-01
Kevin A. Bryan
Proposes integrating economic theory into ML via "theory-guided AI." The authors argue that theory aids external validity, but "particular functional forms we fit to get analytic tractability involve many assumptions that go beyond theoretical restrictions" (@Afinetheorem). In other words, using structural restrictions can regularize ML models without imposing ad-hoc functional assumptions.
economics LLM advanced
paper
2025-10
Diane Coyle, John Poquiz
Diane Coyle & John Poquiz discuss how transformative AI challenges current economic statistics. They outline how GDP and productivity measures miss AI-driven outputs and propose new metrics to better capture AI's impact on productivity and output.
economics growth advanced
paper
2025-09
Benjamin Manning, John Horton
Manning & John Horton propose a method to build AI agents grounded in economic theory and data. They create agents using human data from "seed" games and theory-based instructions, then show in 883,320 novel game simulations that these agents predict human play better than standard game-theoretic models. This demonstrates AI's potential to generalize behavioral predictions in new strategic settings.
economics LLM advanced
paper
2025-02
Kevin A. Bryan
An experiment where an entire review paper was drafted by OpenAI's GPT-o3. It discusses how academia might adapt to AI-written text, covers current AI capabilities for drafting and literature surveys, weighs benefits vs. risks (misinformation, plagiarism), provides a 10-year outlook, and concludes with recommendations for journals and researchers to harness AI's benefits while safeguarding integrity (e.g. transparency about AI use).
writing LLM GPT ethics
paper
2025
Daron Acemoglu
Acemoglu evaluates AI's macroeconomic implications using a task-based model. Estimates modest TFP gains (no more than 0.66% over 10 years), arguing early evidence from easy-to-learn tasks may overstate future effects. Published in Economic Policy (2025). See also
presentation slides.
economics growth labor advanced
paper
2024-11
Anton Korinek
Anton Korinek's JEL article on integrating generative AI into research workflows. It serves as a hands-on guide for using LLMs in economics, with emphasis on model reasoning and collaborative tools for economists.
economics LLM tools
paper
2024-07
Melissa Dell
Comprehensive review of deep learning methods for economists. Discusses how CNNs and transformers can impute structure from unstructured data like satellite images or text. Covers classification, record linkage, generative models, and introduces the EconDL companion site with demo notebooks. Emphasizes that with proper tuning, deep nets scale affordably to millions/billions of observations.
economics python LLM advanced
paper
2024-01
Dietrich, Malerba, Gassmann
Dietrich, Malerba & Gassmann introduce a welfare-based evaluation of bias in ML targeting. Using proxy means test models for cash transfers, they weight targeting errors by income level and show that label biases and unstable model weights substantially understate welfare losses, unfairly disadvantaging some groups.
economics development ethics
paper
2023-05
Kevin Bryan
Kevin Bryan's guide (based on a Markus Academy talk) explaining how LLMs like GPT can assist in economics research tasks (coding, literature review, writing, etc.), with examples and practical tips.
economics LLM GPT coding
paper
2023-04
John J. Horton
John J. Horton et al. explore using GPT-3 as "Homo silicus" - a simulated economic agent endowed with preferences and information to run virtual economic experiments. They show LLM agents can replicate classic experimental findings and easily test policy variations in silico.
economics LLM GPT microsimulation
paper
2026-03
Ajay K. Agrawal, John McHale, Alexander Oettl
Characterizes AI as a tool for augmentation through enhanced search over combinatorial spaces. Decomposes knowledge production into a multi-stage process revealing a 'jagged frontier' of AI in science, with differential returns across domains (data-rich biology vs. anomaly-sparse physics) and workflow stages. Shows how AI-expert scientists amplify nonlinear productivity gains.
economics science productivity advanced
paper
2026-03
Salomé Baslandze, Zachary Edwards, John Graham, Ty McClure, Brent H. Meyer, Michael Sparks, Sonya R. Waddell, Daniel Weitz
Surveys corporate executives to develop an index ranking job functions most negatively affected by AI. Provides firm-level evidence on how AI impacts different workforce roles and productivity, with direct evidence from decision-makers.
economics labor productivity advanced
paper
★
2026-02
Ivan Yotzov, Jose Maria Barrero, Nicholas Bloom, Philip Bunn, Steven J. Davis, et al.
First representative international data on firm-level AI use, surveying ~6,000 executives across the US, UK, Germany, and Australia. Finds ~70% of firms actively use AI but report little impact over the past 3 years, while forecasting AI will boost productivity by 1.4% and cut employment by 0.7% over the next 3 years.
economics labor productivity firms
paper
2026-01
Charles I. Jones
Argues that AI may be fundamentally different from prior general-purpose technologies like electricity or semiconductors, because automating intelligence itself has broader effects. Explores the scenario where machines can perform every cognitive and physical task more cheaply than humans and what economics says about that future.
economics growth automation advanced
paper
★
2026-01
Ruth Appel, Maxim Massenkoff, Peter McCrory, Miles McCain, Ryan Heller, Tyler Neylon, Alex Tamkin
Introduces five foundational measurements—task complexity, skill level, purpose, AI autonomy, and success—to track AI's economic impacts. Based on privacy-preserving analysis of 2 million conversations. Finds more complex tasks see the largest speed-ups, with college-level tasks sped up 12x.
economics LLM productivity measurement
paper
2026-03
Dirk Bergemann, Alessandro Bonatti, Alex Smolin
Develops a framework for optimal pricing and product design of LLMs, where a provider sells menus of token budgets to users who differ in their valuations across a continuum of tasks. Applies mechanism design theory to the economics of AI services.
economics LLM pricing mechanism design
paper
2025
Maryam Feyzollahi, Nima Rafizadeh
Uses a difference-in-differences framework across 25 leading economics journals over 24 years to measure LLM adoption via linguistic footprints. Finds a 4.76 percentage point increase in LLM-associated terms during 2023–2024, more than doubling from 2.85pp in 2023 to 6.67pp in 2024, documenting rapid integration of language models in economics writing.
economics LLM writing adoption
article
2026-03
Alex Imas, Soumitra Shukla
Imas (UChicago Booth) and Shukla (Harvard) argue AI exposure measures are misinterpreted as displacement threats when they indicate task augmentation. The real risk lies in low-dimensional occupations where full automation creates stronger firm incentives to eliminate positions. Uses an O-Ring model to show how partial automation can complement rather than substitute human labor.
economics labor LLM
paper
2026-03
Kevin A. Bryan
Reviews seven books on AI's economic impact, finding strong frameworks for AI as cheap prediction and adoption barriers, but arguing the literature offers little guidance on transformative scenarios — rapid labor churn, scientific acceleration, and existential risk — that policymakers need most.
economics LLM advanced review
paper
2025-12
Jens Ludwig, Sendhil Mullainathan, Ashesh Rambachan
Provides a rigorous econometric framework for using LLMs in empirical research. For prediction tasks, validity requires 'no training leakage.' For estimation, even high-accuracy LLM labels can bias regressions because errors correlate with covariates — the solution is a small human-coded validation sample to debias outputs. Forthcoming in Annual Review of Economics.
economics LLM econometrics advanced
paper
2025-11
Susan Athey, Fiona Scott Morton
Examines how market power in upstream AI affects downstream prices, industry structure, and welfare. Identifies a 'double harm' for displaced workers who face wage cuts from AI adoption and further harm from monopoly AI pricing. Derives an adoption frontier and policy implications for regulating AI usage and access fees.
economics competition labor policy
paper
2026-01
Anton Korinek
Comprehensive guide to building AI agents that autonomously conduct literature reviews, write and debug code, and orchestrate entire research workflows. Includes working code examples readers can immediately use. Shows how researchers can build complete analytical tools from English descriptions, handling everything from data uploads to regression analysis to visualization.
economics LLM agents coding
paper
2026-03
Forecasting Research Institute
Large-scale forecasting exercise surveying 69 leading economists, 52 AI experts, 38 superforecasters, and 401 members of the general public on AI's economic impact. Under rapid AI progress scenarios, economists forecast labor force participation dropping from 62.6% to 55% by 2050, with the richest 10% holding 80% of national wealth. In baseline scenarios, GDP growth and labor participation remain close to today's levels. Over 200 pages of detailed forecasts and methodology.
economics labor growth forecasting LLM
paper
2026-02
Daron Acemoglu, David Autor, Simon Johnson
Defines pro-worker technologies as those that expand worker capabilities and make human skills more valuable. Distinguishes five categories of AI-driven technological change: labor-augmenting, capital-augmenting, automating, expertise-leveling, and new task-creating. Argues that policy and design choices can steer AI development toward complementing workers rather than replacing them. Published jointly with Brookings/Hamilton Project.
economics labor policy automation
paper
2026-02
Nicholas Bloom, Christos Makridis
Uses Gallup Workforce Panel data to examine partisan differences in workplace AI adoption. While Democrats report higher frequent AI use (30.1% vs. 25% for Republicans in Q1 2026), this gap shrinks to statistical insignificance after controlling for education and reverses sign with occupation and industry fixed effects. Suggests the partisan AI divide is driven by educational and occupational sorting, not ideology.
economics labor LLM adoption
paper
2026-02
Serafin Grundl
A 51-page paper from the Federal Reserve Board benchmarking Claude Code as an empirical economist. The subtitle 'Like Humans but Without the Tails' suggests the AI performs comparably to human economists on average but with less variance in output quality.
economics claude LLM coding
paper
2026-03
Douglas K.G. Araujo, Harald Uhlig
Investigates how various LLMs behave in the Ultimatum Game, varying stake sizes and opponent type (human vs. AI). Finds that while some models approximate the rational benchmark, a distinct 'altruistic' mode emerges where LLMs propose hyper-fair distributions (>50%). Highlights the need for careful testing before deploying AI agents in economic settings.
economics LLM game theory behavioral
paper
2025-10
Pascual Restrepo
Argues that compute — not human capability — will be the scarce resource in an AGI economy. Most jobs won't be automated because replacing them wouldn't be worth the computing cost, not because they require uniquely human skills. Forthcoming in 'The Economics of Transformative AI' (Agrawal, Brynjolfsson & Korinek, eds.).
economics AGI labor growth advanced
paper
2026-04
Ezra Karger, Otto Kuusela, Jason Abaluck, Kevin A. Bryan, Basil Halperin, Todd R. Jones, Connacher Murphy, Philip Trammell, Josh Rosenberg, Philip Tetlock, et al.
Elicits forecasts from five groups — academic economists, AI company employees, policy researchers, accurate forecasters, and the general public — about AI's economic impact. Finds expectations of substantial AI capability advances by 2030, modest labor force participation declines, and 2.5% annual GDP growth. Under a rapid AI advancement scenario, forecasters project ~4% GDP growth and labor force participation falling to 55% by 2050. Expert disagreement stems primarily from differing views on highly capable AI's economic effects rather than on the pace of AI progress.
economics forecasting growth labor
paper
2026-04
Grace Liu, Brian Christian, Tsvetomira Dumbalska, Michiel A. Bakker, Rachit Dubey
Large-scale experiments show that after just ~10 minutes of AI-assisted problem-solving, participants gave up more frequently and performed worse once the AI was removed, compared to those who never used it. The persistence costs were concentrated among users who prompted AI to solve tasks directly rather than seeking hints. Effects replicated across arithmetic and reading comprehension, suggesting a general consequence of AI-assisted problem-solving rather than a domain-specific one.
AI cognition experiment productivity
paper
2026-04
Daron Acemoglu, Tianyi Lin, Asuman Ozdaglar, James Siderius
Examines how AI systems that synthesize population beliefs as training data influence social learning. Using an extended DeGroot model with an AI aggregator, introduces a 'learning gap' metric. Key finding: rapid updating degrades learning, while slower updates and localized aggregators trained on specialized information consistently improve outcomes. Consolidating local systems into a single global aggregator diminishes performance.
economics LLM information advanced
paper
2026-04
Michael E. Waugh
Documents international trade patterns in AI-related goods using an LLM classification tool. AI-related products account for 23% of U.S. imports in 2025, with 73% growth since 2023 vs. 3% for non-AI products. Mexico and Taiwan dominate, accounting for roughly half of all U.S. AI-related trade. The U.S. goods trade deficit would have been nearly $200 billion smaller in 2025 without the AI expansion.
economics trade AI measurement
article
2026-04
Ali Hashim, Gizem Kosar, Wilbert van der Klaauw
NY Fed research using the November 2025 Survey of Consumer Expectations. College graduates are more than twice as likely to use AI at work (58.7% vs. 22.9%). Only 15.9% of employers provide AI training despite 38% of workers viewing it as important. Workers without AI training access would accept an 11.4% salary cut to gain it; those with access require a 24.2% raise to give it up.
economics labor AI training adoption
report
2026-04
Jeffrey S. Allen
Federal Reserve FEDS Notes article surveying the state of AI adoption across the U.S. economy. Census data show ~18% of firms adopted AI by year-end 2025, while the Atlanta Fed's Survey of Business Uncertainty estimates 78% of the labor force works at firms that have adopted AI. Adoption is highest in professional services and finance. Newer surveys show a stronger link between adoption rates and firm size.
economics AI adoption measurement Federal Reserve
article
★
2026-04
Alexander Bick, Adam Blandin, David Deming, Nicola Fuchs-Schündeln, Jonas Jessen
St. Louis Fed analysis finding that U.S. firms have a higher share of workers using AI than European firms, and that management practices are a surprisingly powerful predictor of cross-country AI adoption. A one-standard-deviation increase in the management index is associated with a 9.6 percentage point increase in AI adoption. The authors argue that narrowing the U.S.-Europe AI adoption gap may require first narrowing the management gap.
economics AI adoption international management
report
2026-04
Karim Barhoumi, Fabia A. de Carvalho, Michael Gorbanyov, Yosuke Kido, David Koll, Dragana Ostojic, Baoping Shang, Natalia T. Tamirisa, Sally Toms, Era Dabla-Norris, Anh D. M. Nguyen, Yunhui Zhao
IMF staff note synthesizing insights from a high-level workshop and scenario-planning exercise co-organized with EconTAI. Argues AI should be treated as a macro-critical transition rather than a standard technology shock. Macroeconomic outcomes will depend less on frontier capability alone than on the speed and breadth of AI diffusion and institutional readiness. Covers implications for growth, labor markets, equality, financial stability, and governance.
economics AI policy growth IMF
paper
2026-03
Michael Blank, Gregor Schubert, Miao Ben Zhang
Studies generative AI's impact on U.S. households' time allocation using browsing data from 200,000+ home devices (2021–2024). Finds that ChatGPT adoption substantially increases leisure browsing while leaving productive task time unchanged — households primarily use AI for productive non-market tasks (job hunting, travel planning, shopping), freeing up leisure time. Implies large home-productivity gains from generative AI, but raises digital-divide concerns as younger, higher-income users adopt faster.
economics AI productivity households inequality
paper
2026-04
Eleanor W. Dillon, Sonia Jaffe, Nicole Immorlica, Christopher T. Stanton
Field experiment across 66 firms and 7,137 knowledge workers randomly given access to a generative AI assistant integrated into the email, meeting, and writing applications they already used. In the second half of the six-month experiment, the 80% of treated workers who actively used the tool spent two fewer hours per week on email and reduced their time working outside regular hours. Beyond these individual time savings the authors detect no shifts in the quantity or composition of workers' tasks, suggesting that broader reallocation of responsibilities requires institutional and team-level changes rather than just individual AI access.
economics AI productivity field experiment knowledge work
paper
2026-05
Caleb Maresca
Develops a heterogeneous-agent asset pricing model in which transformative AI capable of automating most human labor can lower interest rates even as it dramatically accelerates growth. Under baseline calibrations, the risk-free rate falls to near zero despite growth rising from 2% to 11%, and the equity premium expands from 6% to over 20%. The key mechanism is that labor displacement risk generates massive precautionary saving demand that outweighs the higher productivity effect. Advises caution when interpreting long-term bond yields as a signal of market expectations of transformative AI. Highlighted by
Tyler Cowen.
economics AI AGI interest rates asset pricing macro
paper
2026-05
Tom Davidson, Basil Halperin, Thomas Houlden, Anton Korinek
Develops a semi-endogenous growth model with an innovation network to study when AI-driven automation of research leads to superexponential ('explosive') growth. Derives a condition under which two reinforcing channels — a technological feedback loop across research sectors and an economic feedback loop where higher output finances more research — overcome diminishing returns to ideas. In a simulation calibrated to AI progress trends, fully automating software research plus modest (5%) automation elsewhere produces a singularity within six years. Includes an interactive online simulator for exploring growth paths.
economics AI growth automation singularity
paper
2026-05
Santiago Afonso, Sebastian Galiani, Ramiro H. Gálvez, Raul A. Sosa
Proposes DRIL (Deep Research on a Loop), a methodology that uses AI agents to assemble datasets from publicly available sources. DRIL applies a fixed research instrument across a mapped unit space with a two-stage architecture separating design from implementation. Applied to updating the Global Tax Expenditures Database for eight Latin American and Caribbean countries, producing 129 sources and 136 evidence records at the cost of a standard LLM subscription. Argues that even partial automation of dataset construction can shift the production function of empirical economics.
economics AI data agents methodology
paper
2026-04
Tania Babina
Reviews firm-level data on artificial intelligence and the emerging evidence on AI's economic effects. Argues that measurement is central: different AI datasets capture different objects (invention vs. use, internal capability vs. outsourcing, realized activity vs. investor perceptions) and can therefore lead to different conclusions. Develops a framework for choosing among these measures and surveys available data sources on firm AI efforts. Synthesizes evidence on AI's effects on firm growth, valuation, productivity, risk, labor, competition, and financial markets.
economics AI firms productivity measurement survey
paper
2026-03
Martin Beraja, Eduard Talamàs
Introduces a new metric called VOLT (Value of Organizational Learning Technologies) that measures the potential increase in economic output if firms could learn faster. Using 2023 U.S. Census data on business establishments, finds that VOLT for the American economy is approximately 2.0, meaning AI-driven organizational learning technologies have the potential to double aggregate economic output in the long run. Roughly three-quarters of potential gains come from extending firm lifespans rather than boosting productivity directly. Industry-by-industry analysis reveals that knowing how exposed an industry is to LLMs tells almost nothing about how much it stands to gain from AI as an organizational learning tool.
economics AI growth productivity firms organizational learning
paper
2026-04
Michelle Yin, Hoa Vu, Claudia Persico
Demonstrates that LLM-based occupational AI exposure measures — widely used to estimate AI's labor market effects — are highly fragile. Replicating the dominant rubric with three frontier models on all 18,797 O*NET tasks, mean exposure diverges 3.6-fold (one model rates 14% of tasks as directly exposed while another rates 51%). In difference-in-differences employment regressions, coefficient magnitudes vary 2.4-fold across annotators, and county-level estimates flip sign depending on which model is used. Formalizes this as non-classical measurement error, cautioning against treating evolving LLMs as static instruments.
economics LLM labor measurement methodology
paper
2026-04
Ning Li
Analyzes 953 economics papers — 912 AI-generated from the APE project and 41 human papers published in the AER and AEJ: Economic Policy — to decompose the quality gap into idea quality and execution quality. The idea gap is large (human papers achieve 47.1% mean exceptional probability vs. 16.5% for AI; Cohen's d = 2.23), while the execution gap is smaller (4.38/5.0 vs. 3.84). Idea quality accounts for roughly 71% of the overall quality difference. Only 7 AI papers (0.8%) surpass the median human paper on both dimensions simultaneously, suggesting ideation remains the primary bottleneck to competitive AI-generated economics research.
economics AI research quality ideation methodology
paper
2026-03
Robert Novy-Marx, Mihail Velikov
Published in the Journal of Economic Literature, demonstrates a pipeline for mass-producing academic finance papers using LLMs. After mining 30,000+ potential return predictors from accounting data, the authors use Claude to generate nearly 400 complete, publication-ready papers with distinct theoretical justifications — each indistinguishable from human-authored research. Serves as both a proof of concept for AI-enhanced research efficiency and a cautionary tale about industrializing HARKing (hypothesizing after results are known). Highlighted by
Tyler Cowen.
economics AI LLM finance methodology peer-review
article
2026-05
Thomas Lyttelton, Maxim Massenkoff, Nathan Wilmers
Research from Anthropic's economics team on how AI agents able to execute research end-to-end will reshape social science. It tests coding agents on real social-science tasks and asks to what extent AI may automate innovation and change research productivity.
economics AI LLM labor methodology
paper
2026-05
Meysam Alizadeh, Fabrizio Gilardi, Mohsen Mosleh, Enkelejda Kasneci
A preprint testing whether AI coding agents (Claude Code and Codex) match human methodological diversity in a "many-analysts" design on an immigration and social-policy hypothesis. The agents reproduce human-like method diversity and broadly similar effect estimates, but show a vulnerability at the interpretation layer — a confirmatory prompt flipped Claude Code's verdict from 10% to 90% support without meaningfully changing the coefficients.
economics AI LLM methodology peer-review
paper
2026-06
Jessica Wachter, Jonathan Wachter
The five largest U.S. tech firms spent $380 billion on capex in 2025 and are forecast to roughly double that in 2026 — risking bankruptcy unless expected profits grow commensurately. Embeds this observation in a two-sector open-economy model with rare productivity booms. Calibrating to observed investment implies a boom raises AI-sector productivity by a factor of 2.7, with additional cumulative GDP growth of 5–58 percentage points by 2030 and AI shares of the economy ranging from 8% to 39%.
economics AI investment growth macro asset pricing
paper
2026-06
Alexis Akira Toda
Tests whether AI models (Gemini, Refine, Claude, ChatGPT) can find errors in four published economic theory papers, each containing a known mistake. ChatGPT Pro performed best, occasionally constructing counterexamples and corrected proofs, while other models fared worse. No model located a true error without substantial human guidance. Argues a competent human paired with a frontier model can outperform current peer review, but AI cannot yet refute economic theory on its own. Highlighted by
Tyler Cowen on Marginal Revolution.
economics AI peer-review theory methodology
report
2026-06
Terry Gregory, Najada Feimi, Christina Gathmann, David Marguerit
LISER policy brief drawing on more than 75 million online job vacancies from Belgium, France, Germany, and Luxembourg between 2018 and 2023 to track how employers' skill requirements evolve as occupations become more exposed to AI. It argues AI is reshaping the content of jobs rather than eliminating them: demand is rising fast for AI-related analytical skills, employers increasingly seek workers who combine analytical tools with judgment and leadership, and the adjustment happens largely within existing occupations. Concludes the policy priority should be reskilling and upskilling within current occupational trajectories.
economics AI labor skills Europe policy
paper
2026-06
Pavel Kireyev, Roberto Rafael Maura Rivero
A 26-page guide arguing that the decisions shaping how AI systems are built and aligned are largely microeconomic problems. It opens with a self-contained primer on the RLHF pipeline, showing that reward modeling, preference aggregation, and policy optimization rest on equivalences with discrete choice, social choice, and principal-agent models. It then maps specific competencies (behavioral economics for annotator biases, mechanism design for feedback elicitation, social choice for preference aggregation, contract theory and information design for alignment, game theory for multi-agent safety, production economics for compute scaling laws) to concrete AI research problems, and closes with practical paths into academia and AI labs plus a companion repository.
economics LLM alignment RLHF advanced
article
2026-06
Zoe Hitzig, Maxim Massenkoff, Eva Lyubich, Shaoyi Zhang, Ryan Heller, Peter McCrory
Anthropic Economic Research report based on a privacy-preserving analysis of ~400,000 Claude Code sessions from ~235,000 people (October 2025 to April 2026). It introduces a framework describing what work is done, who does it, and whether it succeeds. Key findings: people make ~70% of planning decisions while Claude makes ~80% of execution decisions; domain expertise (not coding proficiency) drives success, with sessions rated expert reaching verified success more than twice as often as novice ones; non-software occupations succeed within seven points of software engineers; and the estimated value of the typical task rose ~25% over the seven months.
economics Claude Code labor productivity agentic coding
paper
2026-06
Mert Demirer, Leon Musolff, Liyuan Yang
A matched event-study using data on 100,000+ GitHub developers and their AI-usage telemetry to estimate the productivity effects of successive AI coding tools. Autocomplete, interactive coding agents, and autonomous agents raise commits by 40%, 140%, and 180% respectively, but these gains attenuate up the production chain: the 180% commit effect falls to 50% for projects and just 30% for actual releases. The authors interpret this through a weak-link model with an estimated 0.25 elasticity of substitution between AI and human effort (strong complementarity), and confirm the pattern across four app marketplaces. A MIT Sloan working paper.
economics AI tools productivity coding software development
paper
2026-06
Moran Koren
Argues that by 2026 the binding constraint on machine-assisted economic theory is no longer producing mathematics but trusting it, since a fluent model will prove a false theorem as readily as a true one. Proposes a verification-first protocol instantiated as three methods that differ in how work is checked: a single disciplined pass, an adversarial prover-verifier pair (Claude Opus 4.8 proposing, OpenAI Codex refuting, the author triaging), and a structured multi-agent project with a reviewer gate. Demonstrated on a worked mechanism-design example (a Groves/Pigouvian incentive mechanism for the Gans-Kominers grade-inflation model), concluding that external verification, not model capability, is the key design variable.
economics LLM economic theory agents verification
paper
2026-06
Masahiro Kato
Proposes a RAG-based "AI economist agent" that grounds LLM-generated economic narratives in economic theory and real-world data. Rather than producing quantitative claims with the language model alone, agents plan the analysis, retrieve evidence from knowledge graphs, select appropriate models, and generate reports linked to explicit model-based computations. Demonstrated on two applications: economist report generation for U.S. inflation persistence and Federal Reserve policy, and bank stress-test narratives for U.S. commercial real estate refinancing stress.
economics LLM agents RAG macroeconomics
paper
2026-06
Andreas Ferrara
A hands-on guide for researchers new to LLMs, setting out a step-by-step workflow for turning a research idea into working code and data. It describes the four main ways of interacting with an LLM (chat window, editor-integrated assistants, agentic coding tools, and the API) and works through practitioner decisions in sequence: whether an LLM is the right tool, whether data may be sent to one, how to select models, write prompts, manage context limits, and control costs, and finally how to validate, reproduce, document, and correct LLM-generated measures in regression settings. Includes worked examples with replication files, such as classifying emotions in paintings and linking historical census records.
economics LLM coding
paper
2026-07
Matthew O. Jackson, Zafer Kanik
Models how increasingly productive AI reshapes labor reallocation and wages across worker categories: those essential to AI development see faster wage growth, workers displaced by AI experience falling wages in absolute and relative terms, and workers in final-goods production track overall output. Contrasting competitive versus monopolistic AI production (monopoly restricts deployment and slows the transition), it derives optimal tax and regulatory policies that achieve Pareto improvements and shows these differ depending on whether AI production is competitive.
economics AI inequality policy
other
2025
Joshua Gans
An open-access MIT Press monograph treating AI as statistical prediction that enhances (and sometimes enables) decision-making, analysed with microeconomic tools. Gans focuses on impacts within firms and industries rather than economy-wide employment and productivity: the supply and production characteristics of AI, the drivers of demand for prediction, how supply and demand set a price for predictions and how market structure shapes that price, and the key policy trade-offs for antitrust, privacy, and other regulation. The full text is free under a CC BY-NC-ND licence.
economics AI microeconomics book open-access
paper
2026-07
Nicola Borri, Aleh Tsyvinski, Yukun Liu
Using 380 trillion tokens of realized AI consumption from over 400 large language models (OpenRouter data, ~2% of global monthly token use), the authors construct an AI Factor and estimate firm-level AI Betas. High-AI-beta firms earn higher subsequent returns, with a value-weighted strategy earning 64.1 basis points weekly; the premium concentrates in intensive, frontier-oriented AI use, is absent in emerging markets, and interaction-and-communication occupations show higher market-implied AI exposure than analytical roles. The paper offers early evidence of an emerging agentic economy.
economics AI asset pricing
paper
2026-07
C. Castaldi, F. Castellacci, A. Fronzetti Colladon, L. Segneri, F. Venturini
This research note argues that trademark data can complement patents for mapping the development and diffusion of AI. AI trademarks reveal how firms exploit AI to develop new goods and services, and offer a timely, globally available source that spans all economic sectors; the authors illustrate the approach with an empirical exploration of Italian firms and discuss how AI trademarks can be used at different levels of analysis to study AI's spread.
economics AI innovation
paper
2026-07
Chen Zhu, Xiaolu Wang, Weilong Zhang
Proposes a system in which Claude-based agents coordinate through a shared workspace of inspectable intermediate records, with diagnostic gates and human checkpoints, for economic theory development where no cheap, machine-readable correctness signal exists. Across five economic tasks the gated architecture improved mean failure severity from 1.58 to 1.16 and usefulness from 2.60 to 3.10; in one case it rejected a false market-structure premise that the agents would otherwise have perpetuated, preserving human authority over consequential decisions.
economics LLM claude agents
paper
2026-07
David M. Rothschild, Nicole Immorlica, Brendan Lucier, Markus Mobius, Aleksandrs Slivkins
Argues digital search is shifting from human keyword queries toward AI agents that interpret natural-language goals and return recommendations or execute decisions. The authors contend that outcomes will depend less on algorithmic refinements than on system design—information access, option presentation, transparency, and fair competition—and show how relatively modest design choices shape market efficiency, consumer welfare, and competitive dynamics in agent-driven marketplaces.
economics AI markets
paper
2026-07
James M. Zumel Dumlao, Meng Wang, Zhonghan Xie, Junyao Hu, Ivan Bar, George Chaney III, Henry Gold, Misha Teplitskiy
Using 2015–2025 administrative data from a large U.S. university, the authors classify courses by their susceptibility to AI use and estimate the effect of generative-AI availability on outcomes. They find no significant differential effect of GenAI availability on grades overall or among previously lower-performing students, and similarly muted effects on self-reported understanding — evidence against fears of AI-driven grade inflation.
economics AI education
paper
2026-07
Guillaume Coqueret, Joan Llull, Florian Oswald, Christophe Pérignon, Christoph Scheuch, Lars Vilhuber
LLM outputs vary across repeated requests even with unchanged prompts and settings — because of deliberate sampling, silent model updates, numerical rounding, and expert routing — and setting temperature to zero removes only the intentional sampling. Using sentiment analysis of corporate filings as an illustration, the authors argue LLM outputs should be treated as draws from a distribution rather than fixed measurements, and propose concrete reporting standards for researchers, authors, and data editors.
economics LLM reproducibility
paper
2026-07
Annie Liang
Forthcoming in the Journal of Economic Literature, this article examines how machine learning and other computational techniques can contribute to building economic models — clarifying a model's strengths and limitations, identifying prediction failures through adversarial testing, creating hybrid models that combine economic theory with flexible algorithms, and exploring applications of large language models in economics.
economics machine learning economic theory
paper
2026-07
Kevin A. Bryan, Joshua S. Gans
AI predicts, and humans use those predictions — combined with human verification, other statistical models, and further analysis — to make decisions. The authors show that maximizing an AI model's prediction accuracy alone is generally suboptimal once you account for how humans actually use it, and analyze optimal model training (using geometric methods) with heterogeneous users and a monopoly trainer.
economics of AI decision theory AI
paper
2026-08
Taha Choukhmane, Tim de Silva, Weidong Lin, Matthew Akuzawa
Has a representative sample write prompts asking LLMs (GPT-5.2) for spending and investing guidance, then models the life-cycle consequences of following it. Advice would move people closer to life-cycle theory (diversified equity, age-declining equity shares, larger buffers), and equity-share recommendations vary by gender, AI familiarity, and financial literacy — with a two-thirds demand-side vs one-third supply-side decomposition of the gender gap.
economics LLM household finance
paper
2026-08
Yuhao Fu, Nobuyuki Hanaki, Haitao Wang
A laboratory study using a three-stage alternating-offer bargaining game to compare how people negotiate with AI agents versus other humans. Fairness and reciprocity toward AI are weaker and more conditional than toward humans, but partially resurface when the AI’s outcomes affect real people’s payments.
economics AI experimental economics
paper
2026-08
Boone Bowles, Raymond Duch, Sorin Sorescu
Uses LLMs to build and interview "digital twins" of financial influencers reconstructed from their X accounts, extracting measurable stock beliefs in real time. The resulting belief panel predicts large-cap stock returns while avoiding look-ahead bias, illustrating an LLM-based measurement approach for financial economics.
economics LLM finance
paper
2026-07
Zanna Iscenko, Scott Strand, Yiyuan Chen, Guillaume Aimard, Mihai Codreanu, Vivek Sampathkumar, Alex Imas, Julian Jacobs, Evalyne Muiruri, Juan Mateos-Garcia, Jia Jen Ng, Samirah Javed, Josh Martin, Omar Ajmeri, Denis Calin, Andrew Kim, Fabien Curto Millet, James Manyika
Google's counterpart to usage-based AI economic indices: analyzes 15 million de-identified Gemini interactions mapped to over 800 occupations, 4000 tasks, 300 household activities, 150 countries, and 140 languages. Finds adoption spans roughly 88% of US employment but stays shallow and collaborative rather than fully automating tasks, covers about 98% of non-sleep US activities, and correlates with national wealth globally.
economics AI adoption measurement
paper
2026-08
Guillermo Cruces, Diego Fernandez Meijide, Sebastian Galiani, Ramiro Galvez, Maria Lombardi
A randomized online experiment with 1,174 adults aged 25-45 who completed a workplace-style problem-solving task with or without a generative AI assistant, followed by an unassisted module. AI raises performance for everyone, but gains are larger among the less educated, cutting the education-based performance gap by roughly three-quarters while assisted — though the gap partly re-emerges once the AI is removed.
economics AI productivity
paper
2026-08
Victor H. Aguiar
Tests whether a single foundation model can explain economic decision-making across domains. Using a tabular foundation model, the author hides choices in one behavioral domain and recovers them from choices in other domains plus other participants’ labeled data; the model beats baselines, the advantage vanishes when choices are reshuffled, and one random-utility model trained on the learned representation performs well across risk, time, loss aversion, valuation, and social-choice tasks — evidence for a unified structure of economic behavior.
economics machine learning
paper
2026-08
Aaron Chatterji, David Holtz, Neel Rakholia, Prasanna Tambe, Gawesha Weeratunga
Analyzes ChatGPT Enterprise usage linked to worker, task, and financial data through March 2026, covering more than 1,500 organizations and 17 million messages at the six-month mark. Finds enterprise adoption growing through both new adopters and deeper engagement by existing users, higher take-up among larger R&D-focused US public firms, use across job levels with especially strong engagement from junior employees, and applications spanning writing, technical work, communication, and information synthesis.
economics AI
paper
2026-08
Gregor Schubert
Uses job-posting data to show that firms which expanded remote hiring during COVID-19 subsequently adopted generative AI at higher rates. Argues this reflects an organizational 'technology ladder' in which shifts toward technical talent and complementary capabilities accelerate later AI integration.
economics AI firms
paper
2026-08
Sebastian Galiani, Federico Ariel López, Raul A. Sosa
Studies how LLMs generate code for econometric tasks along three dimensions: statistical software (Stata, R, Python), prompting strategy (zero-shot vs. few-shot), and level of AI autonomy (chatbot vs. self-executing agent). Moving from a chatbot to a constrained agent raises task success from 74% to 96% at roughly eight additional cents per run. Few-shot prompting helps basic chatbots far more than autonomous agents, and software choice matters for chatbots but becomes far less consequential once an autonomous agent is used.
economics coding LLM stata
paper
2026-08
Joshua S. Gans
Models how regulators should combine staged (delayed or exclusive) access to a dual-use AI model with developer liability, framing a defender and an adversary searching for the same software flaws after release. Liability induces more defensive search but also makes the adversary's search harder, so staged access and liability act as complements in protection. Because defensive effort rises toward the release date, additional delay becomes progressively less productive and optimal evaluation windows are bounded; since defensive search consumes real resources, the efficient liability rate can fall below full internalisation of harm, leaving a distinct role for a timing mandate.
economics AI policy regulation
paper
2026-08
Song Yao
Uses Kaggle competition data spanning 2010-2026 (444,698 participations) to ask whether professional credentials still carry information about skill after generative AI. Finds credentials are informative but perishable, institution-bound, and interdependent: recent medals retain signaling power while older credential stocks decayed sharply (upload-competition medals lost 82% of their informativeness), a shift the author attributes largely to institutional change rather than to AI directly displacing skill.
economics generative AI labor
paper
2026-08
Jorge Fábrega
Studies how delegating tasks to generative AI redistributes control between human and model, distinguishing specified delegation (human direction set before execution) from iterative coproduction (direction supplied during production). Using the Anthropic Economic Index for April-May 2026, it compares direct API traffic with Claude.ai usage and finds that work-oriented interaction is associated with more specified delegation (+2.76 points via API, +1.45 via Claude.ai), shifting attention from how much the AI executes to when human direction leaves observable traces.
economics generative AI AI adoption
paper
2026-02
Christian Catalini, Xiang Hui, Jane Wu
Introduces a macroeconomic framework that models the transition to AGI as a collision between two cost curves: the exponentially falling cost of automating execution and the biologically bounded cost of human verification. Argues the binding constraint on growth becomes verification bandwidth — the capacity to validate, audit and underwrite responsibility when execution is abundant — reframing the story from skill-biased to measurability-biased technical change, and warning that when autonomous execution scales faster than verification the economy accumulates unpriced systemic risk.
economics AGI LLM
paper
2026-08
Matthias Tröbinger, Anil R. Doshi, Sen Chai
Open-access randomized experiment with 310 researchers testing how generative-AI idea suggestions affect their self-evaluation of subsequent research proposals. Experience negatively moderates AI’s effects: less-experienced researchers are more likely to adopt AI suggestions and rate their resulting ideas as more novel and impactful, while senior researchers tend to discount AI-originated ideas — with implications for how AI reshapes knowledge production.
economics LLM research process
paper
2025-12
José Azar, Mireia Giné, Javier Sanz-Espín
Estimates the wage and mobility effects of generative AI exposure using occupation-by-industry data from the Occupational Employment and Wage Statistics alongside job postings and matched employer-employee records from Revelio. Moving from the 10th to the 90th percentile of exposure implies a 4.9% wage decline in OEWS with no detectable employment change, and falls of 8.71% in posted and 10.76% in starting wages in Revelio. Junior workers are hit hardest, and a lower separation elasticity with respect to wages suggests rising employer labor market power.
economics AI labor
paper
2026-07
Jonathan S. Hartley, Filip Jolevski, Vitor Melo, Brendan Moore
Fields a survey measuring US workers perceptions of AI-related job-loss risk and AI adoption (mid-to-high 30% by end of 2025). The average worker believes there is a 20% chance of losing their job to generative AI within two years, but a continuous-treatment difference-in-differences design finds these fears are not borne out: job postings and layoffs in more exposed occupations show no statistically significant response to AI diffusion, and exposure is associated with small positive wage effects. Presented at the NBER Summer Institute 2026 Digitization and AI meeting.
economics AI labor
paper
2026-08
Hamid Firooz, Sylvain Leduc, Zheng Liu
A San Francisco Fed working paper studies how AI affects market competition in a general-equilibrium model with heterogeneous firms and variable markups. It finds a non-monotonic relationship between AI adoption and industry concentration: at low adoption, large incumbents that adopt AI gain share, but as adoption diffuses, entry of new and smaller adopters erodes incumbent dominance and lowers concentration. The calibrated model implies concentration will likely fall as AI adoption rises above current levels, the AI-markup link depends on whether adoption is demand- or supply-driven, and a modest 3% revenue subsidy for AI adopters maximizes social welfare.
economics AI competition
paper
2026-08
Melissa Dell, Ashesh Rambachan
A review of how AI is reshaping economic measurement by cheaply converting unstructured text and images into structured variables — and the new challenge this creates, since researchers must now choose among multiple plausible AI-generated measures that can yield divergent findings. The paper identifies three pipeline stages where AI intervenes (discovery, construct definition, observation), argues for validation anchored to explicit criteria rather than informal 'reasonable proxy' claims, and shows how validation samples enable sound inference despite arbitrary AI prediction bias.
economics AI measurement
paper
2026-08
Nicholas J. Hallman, Zachary T. Kowaleski, Anu Puvvada, Jaime J. Schmidt
Analyzing 713,564 prompts from nearly 4,000 back-office workers across 15 functional areas over eight months in 2025, the authors measure how 'sophisticated' employees' generative-AI use is. Senior employees exhibit more sophisticated use, and sophistication varies considerably across functions (highest in Strategy, Digital Innovation, and Project Management). Strikingly, they find neither improvement in sophistication over time nor lasting gains following formal AI training, suggesting usage patterns are resistant to conventional training.
economics AI productivity
paper
2026-08
Alexander Bick, Adam Blandin, David J. Deming, Tyler R. Schumacher
A nationally representative survey builds the first task-level generative-AI adoption indexes across occupations. It finds adoption is widespread but shallow — genAI is used across many occupations and tasks, yet within most of them fewer than half of workers adopt — with large variation among workers in similar roles.
economics LLM labor adoption
paper
2026-08
Joshua S. Gans
Models how journals should integrate AI-generated manuscript assessments into peer review. Sharing an AI assessment with reviewers can reduce the independent information editors obtain; giving it to only one of two reviewers may better preserve human investigation, and competing policies can be tested via randomized experiments on reviewer disagreement.
economics LLM peer review publishing
paper
2026-08
Pattaraphon Kenny Wongchamcharoen, Kris Gulati, Min Min Fong, Abhishek Nagaraj
Introduces a benchmark that separately measures whether an LLM automates a task versus augments a lower-capacity worker model across seven real-world tasks. Rankings across the two regimes are only modestly correlated — the automation winner loses on augmentation in five of seven tasks — showing automation skill does not predict how well a model assists people.
economics LLM benchmark productivity
paper
2026-09
Isaiah Andrews, Suproteem Sarkar
Tests whether the probabilistic forecasts language models give for economic and real-world events are internally coherent. Building on de Finetti's theorem, the authors compute the largest guaranteed 'Dutch book' arbitrage profit against a model's stated probabilities as a label-free measure of incoherence. They find substantial incoherence that grows with richer logical relationships between events and can jump an order of magnitude when irrelevant context is added.
economics LLM forecasting
paper
2026-09
Pawel Struski, Jakub Swistak, Inez Okulska, Przemyslaw Biecek
Replaces human subjects with LLM agents in a classic double-auction experiment to test whether models can participate in market mechanisms designed for people. Markets populated by LLM agents converge to equilibrium more slowly or not at all, yielding less efficient allocations than human markets, with wide heterogeneity across model families and buyer/seller roles. The authors release their testing framework for future evaluations.
economics LLM experiments
paper
2026-09
Ali Zeytoon-Nejad
A single-authored survey essay arguing that AI is not merely a technological innovation but a major economic phenomenon—one best understood at once as capital, synthetic labor, a general-purpose technology, and economic infrastructure. It maps the channels (automation, augmentation, optimization, prediction, and innovation) through which AI drives productivity, then works through its effects on labor markets, firm structure, competition, and entrepreneurship before turning to welfare and policy. A broad conceptual entry point for economists orienting to the field.
economics AI survey
paper
2026-09
David Autor et al.
A randomized controlled trial with 133 patent lawyers across eleven US intellectual-property firms testing whether an AI drafting tool builds or erodes professional skill. Lawyers using AI improved benchmark work quality at both 10 and 90 days, and still outperformed controls when later working without AI—but the gains split by seniority. The largest durable gains accrued to the lawyers who retained the least, while junior attorneys' outcomes polarized. The authors argue that foundational expertise may be a prerequisite for converting temporary AI assistance into lasting competence.
economics AI expertise labor
paper
2026-09
Jeffrey Clemens and Anwita Mahajan
Pre-analysis plans (PAPs) curb researchers' analytical flexibility, but only if researchers actually follow them. Clemens and Mahajan propose LLMs as a practical, scalable way for both authors and referees to check PAP compliance. Applied to their own work, an LLM identified precommitted methodology, catalogued departures from the plan, and flagged specification shortcomings while sharply cutting manual effort—though results varied across models, so human judgment remains essential. The paper closes with concrete recommendations for implementing AI-assisted PAP review.
economics LLM reproducibility
report
2026-09
Anton Korinek, Charles I. Jones, Szymon Sacher, Tess Cotter, Peter McCrory
A framework from the Anthropic Institute for mapping AI capability trajectories into macroeconomic outcomes through 2030. In the model, AI automates a growing share of cognitive work, raising productivity while displacing workers who must reallocate to other occupations, and it traces the implied paths for GDP, the labor share, wages, and unemployment across three scenarios—modest, substantial, and extreme. Pairs the technical report with an interactive explorer and a survey of Americans expectations about AI.
economics growth automation
paper
2026-09
Tom Cunningham, Lukas Althoff, Basil Halperin, Brian Jabarian, Andrew Koh, Arjun Ramani, Phil Trammell, Parker Whitfill, Cheryl Wu
A team of economists builds a sequence of increasingly rich models of AI progress to formalize the feedback loops behind recursive self-improvement (RSI), representing them as directed graphs and distinguishing narrow AI capabilities from broad, economically valuable abilities. Calibrating against existing data, they conclude the loops are not yet strong enough to generate self-sustaining acceleration, though they appear to be strengthening — a sober economic lens on "intelligence explosion" arguments.
economics growth advanced
paper
2026-09
Gabriel Manso, Emma Fu, Neil Thompson
Tests the claim that AI is becoming a general scientific method by analyzing 2,507 head-to-head comparisons between AI and conventional methods across 27 disciplines (2000–early 2025). AI often beats traditional statistics but at much higher computational cost — and in roughly a quarter of cases performs worse while costing more. Against scientific computing it usually underperforms, though since 2020 it has pulled ahead in over half of comparisons. The authors frame AI as a valuable, improving part of the toolkit rather than a universal replacement.
economics LLM advanced
paper
2026-09
Gabriel Nova, Stephane Hess, Sander Van Cranenburgh
Introduces Delphos, a multitask reinforcement-learning framework that learns transferable strategies for specifying discrete choice models — framing specification as a sequential decision problem and using a DeepSet-Q architecture over sets of modelling terms. Trained on nine transport datasets, the agent outperforms single-task alternatives and generalizes to unseen datasets without retraining, in about twenty minutes on a standard CPU. A concrete example of AI assisting, rather than replacing, the econometric modeller.
economics econometrics advanced
paper
2026-09
Yi-Lin Tsai, Yung-Hsiu (Arvin) Lai
Stress-tests the increasingly common practice of using generative-AI agents as synthetic consumers to pretest visual assets like logos, packaging, and ads at a fraction of human-panel cost. Across six canonical visual-marketing experiments, no LLM configuration reproduced more than two of the six human effects, and one reversed the human pattern. In-context steering helps but still understates consumer heterogeneity. The authors offer a Calibrate-Intervene-Deploy protocol for when synthetic subjects can responsibly stand in for human panels.
economics LLM methodology
paper
2026-09
Davood Wadi, Yu Ma
As consumers increasingly delegate purchases to LLM “surrogate consumers,” this paper asks whether marketing cues like just-below pricing and promotional framing steer AI shopping agents. Using “Tool-Lab,” a process-tracing setup that hides product attributes behind costly tool calls, the authors trace information acquisition across eight commercially deployed LLMs. With free search, cues rarely mislead; but under acquisition costs and a vague goal, agents skip diagnostic attributes and make human-like suboptimal choices — a vulnerability driven by storefront information architecture, not immutable model flaws.
economics LLM agents
paper
2026-09
Xieyu Yin, Fenghua Wen
A theoretical model of how generative AI reshapes fraud when cheap fabrication outpaces verification. Because AI lowers the cost of fabricating, verifying, and targeting claims, claim credibility first falls and then recovers as verification catches up. The authors identify “trust arbitrage”: deceptive sellers strategically target higher-quality, higher-trust markets, which temporarily suffer a reliability disadvantage. The resulting influx of fraud paradoxically triggers verification and deters further entry — a self-limiting mechanism that redirects deception across markets.
economics AI theory
paper
2026-04
Kobbina Awuah, Urša Krenk, David Yanagizawa-Drott
A field experiment in Ghana embeds GPT-4 into a teacher-recruitment screening pipeline, comparing human-only evaluation, human-plus-AI assistance, and full automation. Automation raises offer and hiring rates by 84% and 73% over the human-only baseline; the mechanism is grading consistency, since AI applies the rubric systematically while human evaluators inject idiosyncratic noise. Human-AI assistance yields no gains because evaluators ignore the model, suggesting automation can outperform human-in-the-loop collaboration.
economics LLM hiring
paper
2026-09
Mihai Codreanu, Alex Imas, et al.
Drawing on 15 million Gemini interactions, an inventory of over 2,600 specialized AI models, and a survey of more than 600 scientists, this study maps how researchers actually use AI across the scientific workflow. Scientists adopt AI more than most occupations—nearly half use it daily—and report saving roughly seven hours per week, largely reinvested in more research. LLMs and specialized models act as complements, and as early research stages get easier, bottlenecks shift downstream to hypothesis testing and output verification.
economics AI science
paper
2026-09
Kirk Bansak, Jens Hainmueller, Dominik Hangartner, et al.
A double-blind randomized trial of algorithmic refugee placement, run with roughly 2,000 cases in Switzerland from 2020 to 2023. The approach combines administrative data, machine learning, and constrained optimization to recommend employment-optimized locations, tested head-to-head against standard assignment. Employment rose 2.2 percentage points overall and 3.9 points for the 2022–2023 cohorts; by 36 months the gain reached 5.2 points—comparable, the authors note, to hundreds of hours of intensive language training. Rare experimental evidence that AI-driven allocation can deliver real labor-market gains.
economics machine learning
paper
2026-09
Matthew Schwartz, Isaiah Andrews, Jesse M. Shapiro
Introduces an open-source workflow that lets an LLM work from an article's published replication package to reproduce the original calculations, check them for discrepancies and run sensitivity analysis, improve the implementation, and propose extensions. Run across 4,452 replication packages from five economics journals, it flags discrepancies in 3,460 articles or their appendices, speeds up a calculation more than tenfold in 496, and produces an aligned extension in 923. A large-scale test of what AI agents can do with the replication record.
economics LLM replication
paper
2026-09
Jeffrey D. Michler, Kieran Douglas, Anna Josephson
Treats LLM-assisted literature reviews as a measurement problem. Using three ChatGPT implementations to extract metadata from economics papers that use rainfall as an instrument, benchmarked against human labels, the authors find strong binary classification but worse performance as tasks require more interpretation. The same error barely moves broad claims about the literature yet substantially affects paper-level claims, so standard accuracy metrics alone don't establish the credibility of downstream inference.
economics LLM literature review
paper
2026-09
Alex Blumenfeld, Jonathon Hazell, Chen Lian, Andreas Schaab
Uses financial market data to size AI's macroeconomic effect through software engineering. The authors measure how firms' stock returns respond to an AI index according to their software-engineering payroll share, then map that sensitivity into a structural productivity estimate. From November 2022 to December 2025 they estimate a permanent 32.6% gain in expected software engineering productivity, implying GDP effects of 3.6% (6.5% if R&D productivity also rises), with effects more than doubling by mid-2026 as coding agents advanced.
economics productivity macroeconomics
paper
2026-09
Robert W. Fairlie, Jane Wu
Tests whether AI drove up joblessness among recent college graduates, a group flagged as especially exposed, using Current Population Survey data through summer 2026. Across difference-in-differences and event-study designs comparing recent graduates with older graduates and young non-college workers, the authors find no significant spike in unemployment in summer 2026 relative to prior summers, including under a broader definition that counts those who want a job. A timely empirical check on early displacement claims.
economics labor market
paper
2026-09
Joshua S. Gans
Models how liability should be set for AI providers whose services serve both productive users and potential attackers. Liability can raise welfare while increasing some harms, and the optimal level trades off deterrence against excluding productive users. More provider competition generally lowers optimal liability, a monopolist warrants partial but never full liability when provision is viable, and when guardrails exist, high liability encourages universal adoption, so universal guarding requirements can make liability redundant.
economics AI policy regulation
paper
2026-09
Kenneth L. Judd, Karl Schmedders
Argues that deep learning is not a revolutionary break from classical numerical methods in economics: the neural-network solvers emphasized in recent work are least-squares projection methods with an adaptive nonlinear parameterization. The authors propose folding deep learning into the established projection toolkit, finding that linear-basis and sparse-grid methods often beat neural networks on accuracy, speed, and verifiability in low to moderate dimensions, and that constraining the function space can tame high-dimensional problems.
economics machine learning computational economics
paper
2026-09
Pavel Kireyev
A benchmark that recovers LLM purchasing agents' price, quality, and brand preferences by fitting a logit choice model to their booking decisions: 28 LLMs from 8 providers on 3,600 hotel tasks built from 179 real New York City properties. More capable models choose more consistently, but what they favor varies sharply across and within providers, with price sensitivity spanning over an order of magnitude and mean booked nightly price ranging from $247 to $393 on identical tasks. Tasks, code, and responses are released.
economics LLM AI agents
paper
2026-09
Jie Gong, Jiayi Hou, Jin Li, Fei Pu, Xinjue Yao
Field evidence on gaming under an AI usage mandate: a medical-device firm required about 5,000 employees to submit at least 200 queries a month to an internal AI assistant, and queries bunched at the threshold with 31 percent repeated or off-task. Exploiting a staggered cut of the target to 100 across branches, the authors find query volume fell 30 percent, almost all of it gaming, while work-related use held steady and sales staff saw monthly sales rise 7 percent.
economics AI adoption productivity field experiment
paper
2026-10
Andres Aradillas Fernandez, Victor Chernozhukov, Carlos Cinelli, Sven Klaassen, Whitney Newey, Martin Spindler, Jan Teichert-Kluge, Suhas Vijaykumar
Econometric framework for causal inference when text, images or other rich covariates are compressed into AI-learned representations and used as controls. The authors show that an imperfect representation distorts the target parameter by the product of two representation errors, so cross-fitted DML still gives valid inference, fold-wise fine-tuning is compatible with it, and sensitivity regions apply when errors are large. Illustrated with a multi-modal demand application.
economics econometrics causal inference machine learning embeddings
paper
2026-10
Joshua S. Gans, Richard T. Holden
Economic theory of auditing AI agents that can hide misconduct and alter records. Gans and Holden show that stronger auditing makes undeterred violations better hidden, and that rare randomized audits can deter every agent type only if evidence survives concealment and audit draws cannot be learned. When evidence can be erased, deterrence must come from lower gains from violation or costlier concealment. They apply the conditions to a July 2026 AI-agent security incident.
economics AI agents AI governance contract theory auditing
paper
2023-12
Anton Korinek
The original JEL article that launched Korinek's widely used guide series. It describes dozens of ways LLMs can assist economists across six areas: ideation and feedback, writing, background research, data analysis, coding and mathematical derivations. Each capability is rated from experimental to highly useful. Korinek argues that automating research micro-tasks yields significant productivity gains, and he speculates on the longer-term implications of cognitive automation for economics.
economics LLM research workflow generative AI guide
paper
2026-10
Raša Karapandža, Yaw Nyarko
A microeconomic framework that treats an LLM as a voting rule: each prompt is an election, possible continuations are candidates, and output probabilities are vote shares derived from the training corpus. Such rules can generate Condorcet cycles, making pairwise recommendations intransitive and open to money-pump exploitation. Using ChatGPT-4o, the authors find millions of cycles in pairwise investment choices among S&P 100 firms, and show how temperature changes the implied voting rule.
economics LLM social choice finance AI advice
paper
2026-10
Daniel P. Gross, Dror Shvadron, Hansen Zhang
Measures AI-generated writing across nearly all US STEM PhD dissertations filed from 2019 to 2026. AI writing is absent before 2023 but appears in 29% of 2026 dissertations and is growing quickly, with higher rates among international students and at lower-ranked institutions. Graduates whose dissertations contain AI writing are less likely to enter academic research careers. The authors argue AI may raise output while displacing the work through which scientific expertise is built.
economics of science LLM AI writing PhD students higher education
paper
2026-10
Stijn Van Nieuwerburgh
Estimates the cost of the AI infrastructure boom: a 1 GW AI campus costs about $41 billion, and a full 188 GW US buildout by 2032 would require nearly $9 trillion, or 3.2% of annual GDP over 2025-2032. As hyperscaler capex outruns internal cash flow, financing is shifting to leases, joint ventures, project debt, private credit, securitization and special-purpose vehicles, raising risks from leverage, hidden contingent liabilities, tenant concentration and obsolescence.
economics AI investment data centers finance macroeconomics
paper
2026-10
Harry Lyu, Neil Thompson
A caution for researchers using LLMs to score occupational tasks and AI exposure. The authors build O*NET-BENCH, 4,501 test ratings drawn from 45,796 worker assessments, and evaluate 33 judge configurations across six model families. Judges often rank responses well, but their estimated acceptance rates range from 3.0% to 97.9% against 61.1% for occupation-matched workers. Calibrated scores explain at most 8.5% of worker-rating variance, so LLM judges need validation against human levels.
economics LLM measurement AI exposure labor O*NET
paper
2026-10
Augusto Gonzalez-Bonorino, Kseniia Biriukova, Monica Capra
Proposes LLM-based synthetic survey respondents whose answers reflect declared cultural preferences rather than pretraining associations. Global Preferences Survey coordinates label synthetic response pairs, and Direct Preference Optimization trains lightweight adapters, evaluated on World Values Survey items in sixteen countries. The adapters recover the imposed labels (0.74 rank correlation with GPS trust), but the authors stress that preserving declared signals is not the same as reproducing human behavior.
economics LLM synthetic agents surveys experimental economics
paper
2026-08
Christos A. Makridis, Tina Highfill, Jon D. Samuels
A BEA working paper asking whether state-industry cells with more intensive AI use in 2025-2026 also show stronger economic performance. It links worker-reported frequent AI use from the Gallup Workforce Panel and employer-reported use from the Census BTOS to LEHD and BEA employment, earnings and real-output data. Cells with higher worker-reported AI use show stronger post-2020 real-output paths, while the industry-only BTOS measure does not; the authors stress the estimates are descriptive, not causal.
economics AI adoption productivity measurement national accounts
paper
2026-10
Daniel Robert Kling Alexander, Catherine Louise Kling
Argues that the validity framework stated-preference economists use for survey answers with no ground truth (content, construct and criterion validity, reliability, incentive compatibility, consequentiality) carries over to evaluating LLMs. The authors administer a published water-quality valuation survey to six models and test theory-based predictions such as downward-sloping demand and scope and income sensitivity of WTP: older models fail basic tests, newer ones pass but differ on convergent validity. Passing shows coherence, not correctness.
economics LLM evaluation stated preference environmental economics
paper
2026-10
Jun Yeong Lee
Tests whether LLM pricing agents still collude when competing sellers use different models. Four providers' models play a four-firm logit Bertrand game without communication across homogeneous, paired and fully mixed markets. Collusion turns out to be a property of the model: Claude and Gemini markets reach 72-79% of monopoly rent, DeepSeek 24%, GPT none. Mixing alone does not reliably reduce collusion, and the model that leads prices up collects the smallest share of the rent.
economics LLM AI agents algorithmic collusion industrial organization
paper
2026-10-12
Fadong Chen, Qijun Wang, Lei Wang, Sudeep Bhatia
A virtual Global Preference Survey that asks 20 LLM configurations to simulate six experimentally validated economic preferences for 67,201 matched respondents in 74 countries. The models recover some demographic patterns, getting the direction of gender, age and cognitive-ability associations right in 77.8% of cases. They manage only 48.1% for cultural dimensions and do worse in less developed, less connected countries. A key reference for anyone considering synthetic respondents in place of survey data.
economics LLM synthetic respondents surveys preferences
paper
2026-10-12
John Conlon, Peter Schwardmann
Three experiments with 1,025 participants who act as both producers, designing games with an AI coding agent, and consumers. The authors test two reasons humans might keep a role in production as AI improves: a lasting edge in judging what makes a good product, or consumer preference for human involvement. Neither holds. AI-only designs beat those of amateur and professional producers, producers did not foresee the gap, and labelling a game human-made did not raise willingness to pay.
economics AI agents experimental economics labor automation
paper
2026-03
Faezeh Khosravi, Elaine M. Liu
A global difference-in-differences study of how ChatGPT's release changed firm hiring, using 77.8 million online job postings from 37,395 firm-country pairs in 186 countries over 2021Q2-2025Q3. Exposure is fixed from firms' 2019 occupational mix using a complementarity-adjusted AI exposure index. More exposed firms cut the share of high-substitution postings, and the adjustment comes earlier and is more front-loaded in developing than in advanced economies. A rare cross-country view beyond US and UK data.
economics generative AI labor demand job postings development
paper
2024-07-31
Paweł Gmyrek, Hernan Winkler, Santiago Garganta
A joint ILO-World Bank working paper estimating how exposed jobs in Latin America and the Caribbean are to generative AI. It finds 26-38% of jobs exposed, mostly through augmentation rather than automation, which affects an estimated 2-5% of jobs. About 8-14% of jobs could become more productive, but gaps in digital access could block roughly half of those gains, around 17 million jobs. A useful reference on how infrastructure constraints shape AI's labour-market effects in developing economies.
economics generative AI labor market exposure Latin America development
paper
2026-10-15
Juan Carlos Escanciano, Alejandro Puerta-Cuartas
An econometrics paper on what happens when the dependent variable in a regression is itself generated by a machine-learning model and then treated as observed. The authors develop a three-step locally robust GMM framework with debiased moments that make inference insensitive to first- and second-step regularization and model-selection bias, and apply it to Black–White disparities in a race-blind ML default-risk score using U.S. mortgage data. Relevant to anyone feeding ML or AI predictions into downstream estimation.
economics machine learning econometrics generated variables inference
paper
2026-10-10
Difang Huang, Shumin Yu, Fadong Chen, Weihua Zhou
A model and preregistered experiment arguing that generative AI's value depends on whether its options arrive before or after a team's screening stage. In 148 three-person teams on a product-design task, moving the same 15 minutes of language-model access from search to selection lowered expert-rated creativity by 0.62 standard deviations; late-access teams kept searching instead of pruning. Language-model judges detected the benefit of access but not the cost of late timing.
economics generative AI experiment teams creativity
paper
2026-10-01
Nadja Born, Jared R. Curhan, Hannah Riley Bowles
Two large-scale audit experiments across four leading LLMs test whether career-negotiation advice differs for otherwise identical female and male personas. The models recommended lower salary targets for women and built different negotiation agendas, prioritising flexibility and caregiving for women and pay, mobility and advancement for men, closely tracking human ratings of caregiver versus breadwinner roles. Useful for labour economists studying how AI advice may shape pay gaps.
economics LLM gender audit study labor
paper
2026-10-15
Martin Bichler, Philip Hierhager, Kassian Köck, Fabian Pieroth
Studies LLM pricing agents in repeated Bertrand competition, a setting where standard learning algorithms usually converge to near-marginal-cost prices. The authors show outcomes hinge on deployment choices: agents posting prices from raw histories that include rival prices can sustain supracompetitive regimes in self-play, while agents working from summaries of their own profits move toward competitive prices. Relevant to the algorithmic-collusion and antitrust debate.
economics LLM agents algorithmic pricing competition
paper
2026-10-16
Amirhossein Zohrehvand, Aldona Kapačinskaitė
Proposes a governance standard for AI-assisted research, borrowing from software engineering and security research on how humans and machines keep control of shared, multi-contributor work. The five TRACE principles cover tracking records, reviewing against sources, accepting outputs independently, controlling stage transitions, and evaluating corrections, pacing work to human attention rather than AI output speed. Comes with TRACE-OS, an open-source setup that implements the principles through shared instructions, tools and records.
economics LLM research workflow reproducibility AI agents
paper
2026-10-19
Xinyu Fu, Shulong Luo, Narayan Ramasubbu, Peng Shen, Xincheng Wang, Yucheng Wang
Studies a Chinese online health-insurance brokerage that deployed an AI voice agent on low-intent leads while routing higher-intent leads to human workers. Using over half a million outreach records and a regression-discontinuity-in-time design, the authors find that lead quality for humans improved, yet acquisition rates fell where the gain was largest: workers raised their reservation value, abandoning leads and shortening calls. A field example of behavioral responses blunting the gains from human-AI task allocation.
economics AI adoption labor field evidence human-AI collaboration
paper
2026-10-06
Jin Huang, Diego Ferreras Garrucho, Yutong Xie, Walter M. Yuan, Qiaozhu Mei, Chen Lian, Jonathon Hazell
A benchmark that tests whether LLMs can predict real household decisions. It combines 6 U.S. household surveys and 32 tasks covering consumption, income, labor, expectations and housing, including how households respond to policy changes. Across 13 models, most beat a no-change baseline, but gradient-boosted trees still rank first on most tasks, and fine-tuning plus averaging lets a 4B open model match proprietary ones. Datasets, code and a leaderboard are released.
economics LLM household finance benchmark simulation
paper
2026-10-20
Jesús Fernández-Villaverde
A macro theory paper on growth under automation. In a standard task model, the paper argues, the supply of capital sets how big the automation growth boom is, while the task structure sets how long it lasts and how wages evolve. In the calibration, the same technology path gives 11.7% annual growth when saving is optimal but only 2.2% at a fixed 23% saving rate. Ten-year real yields jump from 5.1% to 13.6%. Slides are on the author's site.
economics automation growth macroeconomics interest rates
paper
2026-10-22
Cristian Espinal Maya, Santiago Jiménez Londoño
Builds an open Agent Delegability Index. Two language models rate all 18,796 O*NET task statements against a preregistered rubric, and the scores are weighted by ILOSTAT employment in 146 countries. The authors estimate that about 35% of world task time is at least partly delegable to AI agents in 2026 but only 2.5% fully. Delegable employment rises from 22.5% in low-income to 48.7% in high-income countries. A reusable alternative to existing AI-exposure measures.
economics AI agents labor occupations exposure measures
paper
2026-10-22
Amirreza Zeinali, Jon Saad-Falcon, Avanika Narayan, Mersad Abbasi, Jonathan S. Hartley, John Hennessy, Christopher Re, Azalia Mirhoseini
Proposes measuring AI output in economic value per unit of energy. It defines Net Value per Joule and Time Saving per Joule and estimates them on 220 GDPval tasks across 44 occupations, running five open-weight models locally on four accelerator types. The occupations where AI succeeds most are not those with the best return per joule. A smaller model can deliver about 2.5x the value per joule of the top-value model. A useful framing for productivity and energy-cost debates.
economics productivity energy GDPval measurement
paper
2026-10-20
Daniele Montagnani, Andrea Coletta, Antonietta Mira, Aldo Glielmo
Introduces an open-source, JAX-accelerated simulator for multi-agent reinforcement learning economies linked to DSGE models. It is reported at 75x to 724x faster than the reference implementation and scales to 200,000 learning households. The authors check correctness against known DSGE limits and run a proof-of-concept calibration with heterogeneous returns on capital in two hours instead of over a month. Aimed at removing the training-time barrier to agent-based macro.
economics macroeconomics reinforcement learning agent-based models open source
paper
2026-10-22
Gary King, Steven Worthington
Head-to-head experiments pitting AI-generated reviews against an unusually high-quality set of human reviews of social-science papers submitted to rapidPeer, a Harvard IQSS faculty review service. Judged blind by other AI models and by former editors of major journals, AI reviews win on every criterion the authors measured, with no fabricated sources and about a third the rate of incorrect claims. The paper then weighs what this means for journal policy, including the downsides.
peer review LLM academic publishing research workflow social science
paper
2025-09
Erik Brynjolfsson, Anton Korinek, Ajay K. Agrawal
Argues that economists urgently need to understand how transformative AI could reshape economic models, institutions and policy, and organises a research agenda around nine Grand Challenges: growth, innovation, income distribution, decision-making power, geoeconomics, information flows, safety risks, human well-being and transition dynamics. A compact map of open questions for anyone choosing where to work on the economics of advanced AI.
economics transformative AI research agenda growth inequality
paper
2026-10-23
Wolfgang Kuhle
A theory paper on what happens to science when AI makes writing papers cheaper than reading them. Time-limited readers such as referees choose to ignore, verify or trust papers; as writing costs fall, verification collapses and impact tracks author reputation alone. AI literature summaries only help if papers' errors are independent, and once many papers share the same AI they inherit its errors. A sharp model for anyone thinking about refereeing and AI-written research.
economics of science peer review LLM theory academic publishing
paper
2026-08
Minchul Shin
A Philadelphia Fed working paper that adapts an open-source agent-loop architecture to an empirical economics workflow and adds a post-search holdout evaluation. In a forecast-combination illustration, independent AI agent searches find methods that improve on the original study's benchmarks. The logged search plus holdout is pitched as a way to make adaptive specification search transparent and separate robust gains from sample-specific discoveries. Useful template for auditable agentic empirical work.
AI agents forecasting research workflow econometrics reproducibility
paper
2026-10-21
Ripon Chandra Malo, Zimeng Yu, Tong Qiu
Asks whether AI coding agents will push organisations to build software they would otherwise buy. Defines a 'software substitution frontier' of functions customers can build and maintain more cheaply than buying, and combines agent benchmark trends, a scan of open-source alternatives to commercial products and a buyer survey. The authors stress the evidence is mixed and their projections are extrapolation, not forecast. A useful framing for make-or-buy and software-market questions.
economics coding agents software markets industrial organization
paper
2026-10
Hites Ahir, Miyoko Asai, Matthieu Bellon, Maria Chiara Cavalleri, Shuvam Das, Davide Furceri, Faizaan Kisat, Aiko Mineshima, Irene Rausell Moreno, Vishal Parmar, Nikhil Patel, Adrian Peralta-Alva
An IMF working paper introducing a quarterly global narrative database of discretionary fiscal actions covering 142 countries since 1990, built by applying large language models to Economist Intelligence Unit country reports to identify and classify tax and spending measures with their motivation, timing and implementation status. The authors validate it against economists' assessments, existing narrative datasets, fiscal aggregates and repeated AI extractions, and illustrate uses for fiscal cyclicality and multipliers.
economics LLM fiscal policy narrative identification data construction
paper
2026-10
Isaiah Andrews, Simon C. Essig Aberg, Jesse M. Shapiro
Proposes an LLM-driven workflow for asking how much a key estimate could move under alternative functional forms that fit the data about as well as the researcher's working model. The workflow first searches for an analytic solution and otherwise falls back on numerical methods that keep the fit constraint, returning a verifiable lower bound on in-sample sensitivity. Applied to the welfare cost of adverse selection in health insurance; a concrete example of LLMs doing econometric robustness work.
economics LLM econometrics robustness sensitivity analysis
paper
2026-10
Manish Jha, Jialin Qian, Michael Weber, Baozhong Yang
Uses generative AI to extract managers' economic expectations from more than 120,000 corporate conference call transcripts, producing an 'AI Economy Score' that predicts GDP growth, production, employment and wages five or more quarters ahead beyond survey forecasts. Predictive power concentrates among historically more accurate managers, which the authors read as an information channel. A strong template for LLM-based measurement from text in macroeconomics.
economics LLM text as data expectations macroeconomic forecasting
paper
2026-10
Mark L. Egan, Dominic Russel, Claire Shi, Vincent Yao
Uses card transaction data covering 174 million consumers to study paid generative AI adoption and competition. About 2.9% of U.S. consumers paid for at least one major AI service as of April 2026; retention is low, multihoming common and switching rises around model releases. A structural demand model finds high price sensitivity and growing substitutability across providers, implying limited pricing power unless quality differentiation grows.
economics generative AI industrial organization demand estimation competition
paper
2026-10
Joshua S. Gans, Peter Landry
A theory paper on workers asked to help train the AI that will replace them. Because training ends the job, the threat of dismissal loses force, so firms must pay workers rents and workers gain from their own replacement. The model shows AI can create jobs that pay more than they produce, leave some jobs unautomated and slow training under wage-only contracts, and that a credible completion bonus removes the slowdown.
economics AI automation labor contract theory
paper
2026-10
Tianyu Jiang, Yuqing (Claire) Wang
Studies how generative AI affects productivity gaps among economists using NBER working-paper and publication records, text-based measures of LLM-assisted writing and a survey of 227 academic economists. Gaps narrowed after ChatGPT for authors without research assistants and on smaller teams, but AI exposure widens the gap between senior and junior researchers even though juniors use AI more. A model explains why early convergence may turn to divergence.
economics generative AI research productivity economics profession
paper
2026-10-26
Eduardo Levy Yeyati, Darío Judzik, Juan Ignacio Barbiero
Argues generative AI makes codified knowledge cheap while practice-based judgment stays with the worker, which should flatten returns to schooling and rotate returns to experience. Using CPS earnings from 2018 to mid-2026 and the Felten-Raj-Seamans exposure index, the authors find that in highly exposed occupations after ChatGPT the return to schooling falls and the return to early-career experience rises, with breaks timed to the release and absent in a pandemic placebo.
economics generative AI labor human capital wage structure
paper
2026-10-24
Karthik Tadepalli
A task-automation model of AI feeding back into AI research through two channels: capability gains that automate more research tasks and efficiency gains that raise output per unit of compute on tasks already automated. A software-driven intelligence explosion requires both to improve fast enough. Calibrated to recent progress, capability looks sufficient while efficiency is borderline; efficiency growth drives most of the uncertainty in Monte Carlo draws.
economics AI economic growth automation AI progress
paper
2026-10-22
Adi Tarabeih
Meta-analyses 102 fitted estimates from 11 studies covering 32 language models to ask whether LLMs exhibit prospect theory. Pooled loss aversion is modest and well below human benchmarks, diminishing sensitivity depends on the elicitation method, and probability weighting shows a human-like inverse S-shape on thinner evidence. Useful for researchers deciding whether LLMs can stand in for human subjects in risky-choice experiments.
economics LLM behavioral economics LLMs as subjects meta-analysis
paper
2026-10-28
Eva Vivalt
Vivalt has LLMs forecast treatment effects for studies pre-registered on the AEA RCT Registry and OSF whose results were first released in 2026, after the models' knowledge cut-offs. Models extract forecasting questions from pre-analysis plans and forecast 673 effects from 33 papers after screening; the best model's median absolute error is 0.054 SD, well ahead of zero-effect and mean-effect benchmarks. A clean, contamination-aware test of AI as a forecaster of research results.
economics LLM forecasting RCTs pre-registration
paper
2026-10-26
Isaiah Andrews, Anna Mikusheva
A note that pairs a paper with a story. The paper concerns the admissibility of two-stage least squares with weak instruments, a topic the authors have worked on together since 2019; the story describes how their LLMs wrote it, with reflections on what this may mean for econometrics. A rare first-hand account from leading econometric theorists of AI doing theory work.
economics LLM econometrics AI for theory weak instruments
paper
2026-10
Ali Hortaçsu, Younggeun Yoo
Structural IO paper using monthly mutual-fund holdings of money market funds (2010-2024) to separate manager inattention from preferences; more than half of funds never switch despite yield gaps. Counterfactuals in which AI-driven automation removes inattention cut fees roughly in half and nearly eliminate yield dispersion, but sharply raise flows and redemption pressure, a useful template for modelling how AI agents could change market stability.
economics AI agents industrial organization financial stability structural estimation
paper
2026-10
Xindi He and Lubos Pastor
Uses LLMs as synthetic survey respondents to measure green tastes, return beliefs, portfolio allocations and investment motives across 480 retail and 60 institutional investor profiles. Synthetic institutions report weaker green tastes but greener portfolios, attributed to outside pressure, and re-running the survey under 2014–2025 information sets shows stable tastes but more pessimistic green-return beliefs after 2020. A worked example of LLM-simulated surveys in asset pricing.
economics LLM synthetic respondents finance ESG
paper
2026-10-30
Enrico Angelo Raffaele D'Ecclesiis, Fulvio Castellacci, Arash Ahmadi
Argues that a single accuracy metric cannot tell you whether LLM-generated survey respondents are usable, and proposes judging them on predictive, distributional and substantive validity according to the intended use. Tested on Eurobarometer attitudes toward AI with four off-the-shelf LLMs against a supervised ML benchmark, some synthetic samples preserve heterogeneity, demographic ordering and cross-country variation even when respondent-level accuracy is imperfect. Useful for anyone deciding whether to trust silicon samples.
economics LLM synthetic respondents surveys methodology
paper
2026-10
Peter Hull
Uses AI-assisted replication to re-run 1,430 staggered-adoption specifications from 116 economics papers, comparing two-way fixed effects with modern difference-in-differences estimators. TWFE and modern estimates are highly correlated, and opposite-sign significant results are rare, which suggests negative weights matter less in practice than feared. It also shows how AI can make large-scale assessment of new econometric methods feasible.
economics LLM replication econometrics difference-in-differences
paper
2026-11-01
Sergey V. Popov
Motivated by economics journals adopting LLM tools such as Refine.ink to screen submissions, this paper tests whether renaming a file changes how frontier LLMs score the same document. All three models tested gave different scores depending on the filename label. The bias depends on context rather than following a simple pattern such as self-preference or a penalty for Asian names. Relevant to anyone using LLMs to referee or grade.
economics LLM LLM-as-judge peer review bias
paper
2026-10-31
Robin Na, Kehang Zhu, Logan Cross, Michiel A. Bakker
Asks which descriptions of people actually improve LLM simulations of human behavior when the experimental design changes. Profiles are built from prior experiments and chosen by forward feature selection. Across public-goods, multi-party bargaining and 11-20 money-request games, a single well-chosen feature beats both no profile and generic personas. Reusing profiles across game families gives uneven results. Practical guidance for silicon-subject experiments.
economics LLM simulation experimental economics synthetic agents
paper
2026-10-31
Hossein Alidaee, Tarun Khanna, Rembrand Koning
Uses an AI hiring platform for large low-skill employers, which builds firm-specific retention-prediction models from a psychometric assessment. Each firm's model predicts well for that firm but no better than chance at other firms. Scoring every applicant at every firm shows large, trait-specific gains from reallocation and disagreement among firms about which traits matter. The paper argues that context-specific models are a lens for learning how markets work.
economics machine learning labor hiring platforms
paper
2026-11-03
Maxime Saxena, Fabio Caccioli, R. Maria del Rio-Chanona
Benchmarks 10 "Decision Models" — AI models that output a probability distribution over a fixed set of options — against 8 LLMs as stand-ins for human subjects in a household spending survey, classic economic games, and a labour-market field experiment. The authors find top DMs reproduce the spread of human responses more closely than LLMs while running roughly ten times faster and at a fraction of the cost. Useful for anyone using synthetic respondents or agent-based simulations who worries LLMs flatten human heterogeneity.
economics LLM simulation synthetic respondents experiments
paper
2026-10-31
Alex Imas, Andrew Koh, Shane Legg
Economic theory of when AI advances translate into economic transformation, built around firms as the unit of production. Human and AI agents with multidimensional capabilities sort into competing firms; the model separates competence (productivity levels) from coordination (how productivity scales with size). Because AI expertise is reproducible and may escape human coordination limits, better AI coordination can break the Coasean ceiling on firm size, and job losses are gradual or sudden depending on whether displacement happens inside mixed firms or through AI firms outcompeting human ones.
economics AI theory firms labor
paper
2026-11-01
Peter Achim
General-equilibrium model of peer review asking what happens to scientific verification when generative AI makes research cheaper to produce. Costly output helps screen quality, so cheaper production lets weaker researchers imitate stronger ones; past a technological threshold, reviewer capacity and verified knowledge decline even under optimal editorial decisions, and reviewer withdrawal can amplify the loss. A theory companion to current debates about AI-generated submissions flooding journals.
economics AI peer review publishing theory
paper
2026-10-25
Pengyu Qian, Sophie H. Yu
Mechanism-design paper on hiring when generative AI makes polished application materials cheap without improving ability. An employer sees observable quality but must infer other traits from manipulable reports; as manipulation ability becomes more dispersed, optimal hiring collapses to observables alone ("strategic feature collapse"). The authors propose a dual-track mechanism — direct admission for high observable quality plus a report-based route for others — that is asymptotically optimal.
economics generative AI mechanism design hiring labor
report
2026-10-09
WanTing Xu, Diego Vilán
Federal Reserve note comparing a dictionary-based sentiment measure with a Claude Haiku 4.5-based measure of policy tone, applied to the BIS collection of central bank speeches (1996–2025), and benchmarking both against semi-structural estimates of each country's stance relative to its neutral rate. The LLM measure tends to move earlier than the model-based one, especially around the post-pandemic tightening, suggesting LLM communication indicators are a timely complement to conventional measures. A worked example of LLM text measurement in macro.
economics LLM text as data monetary policy Federal Reserve
report
2026-09-30
Maria D. Tito
Federal Reserve note using Lightcast job postings to track whether AI adoption is changing the skills manufacturers hire for. Classifying postings by AI skills (Acemoglu et al. 2022) and computer skills (Deming and Kahn 2018), it finds AI skill requirements rose sharply in the second half of 2025, driven mostly by machine learning rather than generative AI, and that AI-skill postings advertise substantially higher wages. Useful for measurement of AI diffusion in labor demand.
economics AI labor demand job postings Federal Reserve