{ "schema": "schym-economics-radar-data/v1", "month": "2026-09", "source_cutoff": "2026-09-30T23:59:59.999999+02:00", "papers": [ { "id": "P01", "source_id": "S01", "title": "Does AI Assistance Enhance or Erode Expertise? Evidence from a Three-Month Field Experiment in Patent Drafting", "authors": "David Autor, Tanya Rodchenko, Josh Martin, Zanna Iscenko, Scott Strand, David Pearl and Melissa Ferere", "publisher": "MIT Stone Center / NBER", "date": "2026-09", "url": "https://shapingwork.mit.edu/wp-content/uploads/2026/09/Autor-et-al-Sept-2026.pdf", "local": "patent-lawyers.pdf", "method": "Preregistered randomized field experiment", "maturity": "emerging", "confidence": "High for internal task estimates; moderate for learning and external validity", "rationale": "Random assignment and blinded expert ratings strengthen identification; attrition, small subgroup samples and a single professional setting constrain transportability. The withdrawal assessment makes the contribution unusually original.", "score_unrounded": 92.5, "score_display": 92.5, "score_components": { "rigor": 91, "originality": 95, "economic_significance": 92, "long_run_importance": 93, "policy_business_relevance": 93, "surprise": 91 }, "peer_review_status": "Working paper / preprint; no journal acceptance verified", "rank": 1 }, { "id": "P02", "source_id": "S02", "title": "The Early Impacts of AI on Employment among Recent College Graduates", "authors": "Robert Fairlie and Jane Wu", "publisher": "IZA@LISER", "date": "2026-09", "url": "https://docs.iza.org/dp18945.pdf", "local": "dp18945.pdf", "method": "CPS difference-in-differences and event studies", "maturity": "emerging", "confidence": "Moderate; useful null for defined populations and timing", "rationale": "Timely population-based microdata and multiple comparison groups address a consequential claim. Nonrandom exposure and uncertain treatment timing limit the interpretation as an AI causal effect.", "score_unrounded": 88.35, "score_display": 88.3, "score_components": { "rigor": 84, "originality": 84, "economic_significance": 94, "long_run_importance": 89, "policy_business_relevance": 94, "surprise": 90 }, "peer_review_status": "Working paper / preprint; no journal acceptance verified", "rank": 4 }, { "id": "P03", "source_id": "S03", "title": "Data Centers Lower Nearby Housing Values", "authors": "Garrison Schlauch and Simon Greenhill", "publisher": "UC Berkeley Energy Institute", "date": "2026-09-10", "url": "https://escholarship.org/content/qt2z83c21b/qt2z83c21b.pdf", "local": "housing.pdf", "method": "Matched stacked event study", "maturity": "emerging", "confidence": "Moderate-high for local capitalization; lower for welfare extrapolation", "rationale": "A carefully constructed announcement design measures an overlooked margin. Siting selection, spatial spillovers and capitalization assumptions prevent a complete welfare verdict.", "score_unrounded": 88.05, "score_display": 88.0, "score_components": { "rigor": 87, "originality": 89, "economic_significance": 88, "long_run_importance": 84, "policy_business_relevance": 95, "surprise": 88 }, "peer_review_status": "Working paper / preprint; no journal acceptance verified", "rank": 6 }, { "id": "P04", "source_id": "S04", "title": "The Measurement Revolution? Credible Measurement and Inference in the Age of AI", "authors": "Melissa Dell and Ashesh Rambachan", "publisher": "NBER", "date": "2026-09", "first_public": "2026-08-24", "url": "https://www.nber.org/system/files/working_papers/w35744/w35744.pdf", "local": "measurement.pdf", "method": "Methods review and research framework", "maturity": "established", "confidence": "High for the methodological distinction; no new average treatment effect", "rationale": "The framework joins construct validity, measurement error and inference. Its September series entry is useful, but the August preprint means it receives no discovery premium.", "score_unrounded": 88.2, "score_display": 88.2, "score_components": { "rigor": 93, "originality": 83, "economic_significance": 89, "long_run_importance": 95, "policy_business_relevance": 87, "surprise": 76 }, "peer_review_status": "Working paper / preprint; no journal acceptance verified", "rank": 5 }, { "id": "P05", "source_id": "S05", "title": "The Macroeconomic Effect of AI: Sizing the Software Engineering Channel", "authors": "Alex Blumenfeld, Jonathon Hazell, Chen Lian and Andreas Schaab", "publisher": "Author manuscript / CEPR", "date": "2026-09-14", "url": "https://alexblumenfeld.com/files/AI_Software_Main.pdf", "local": "market-software.pdf", "method": "Asset-price exposure estimates mapped through an economic model", "maturity": "emerging", "confidence": "Moderate for market-implied expectations; low as realized growth evidence", "rationale": "A novel forward-looking signal uses cross-firm software payroll variation. Identification of AI news and the valuation-to-productivity mapping carry substantial model dependence.", "score_unrounded": 89.35, "score_display": 89.3, "score_components": { "rigor": 82, "originality": 94, "economic_significance": 94, "long_run_importance": 91, "policy_business_relevance": 86, "surprise": 90 }, "peer_review_status": "Working paper / preprint; no journal acceptance verified", "rank": 2 }, { "id": "P06", "source_id": "S06", "title": "Economic Scenarios for Transformative AI", "authors": "Anton Korinek, Charles I. Jones, Szymon Sacher, Tess Cotter and Peter McCrory", "publisher": "Anthropic Institute / CEPR", "date": "2026-09", "url": "https://www-cdn.anthropic.com/files/4zrzovbb/website/cf58f84d46a4a76bf5a5b039ac695fba6b80041c.pdf", "local": null, "method": "Calibrated task model with labor-market frictions", "maturity": "speculation", "confidence": "High that assumptions generate the reported scenarios; low predictive confidence", "rationale": "Explicit adoption, substitution and reallocation assumptions make the scenarios inspectable. Neither probabilities nor causal macro validation are supplied; policy and demand responses remain outside the model.", "score_unrounded": 88.80000000000001, "score_display": 88.8, "score_components": { "rigor": 82, "originality": 88, "economic_significance": 96, "long_run_importance": 92, "policy_business_relevance": 94, "surprise": 83 }, "peer_review_status": "Working paper / preprint; no journal acceptance verified", "rank": 3 }, { "id": "P07", "source_id": "S07", "title": "Relative Development and the Intelligence Divide: Human Capital, Technology Diffusion, and AI", "authors": "Patrick A. Imam and Jonathan R. W. 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Historical regimes do not identify the causal effect of schooling or predict AI diffusion.", "score_unrounded": 86.8, "score_display": 86.8, "score_components": { "rigor": 82, "originality": 83, "economic_significance": 91, "long_run_importance": 94, "policy_business_relevance": 90, "surprise": 84 }, "peer_review_status": "Working paper / preprint; no journal acceptance verified", "rank": 8 }, { "id": "P08", "source_id": "S08", "title": "Artificial Intelligence-Related Digital Skills and Employment Outcomes for Underrepresented Women", "authors": "Nick Drydakis", "publisher": "IZA@LISER", "date": "2026-09", "url": "https://docs.iza.org/dp18964.pdf", "local": "iza-skills.pdf", "method": "Matched correspondence experiment in London job applications", "maturity": "emerging", "confidence": "Moderate-high for invitation responses; low for employment or productivity effects", "rationale": "Matched fictional applications isolate employer responses to a credential signal. The credential can also signal training effort; the study does not observe actual skill, accepted offers or pay.", "score_unrounded": 85.7, "score_display": 85.7, "score_components": { "rigor": 87, "originality": 84, "economic_significance": 86, "long_run_importance": 83, "policy_business_relevance": 91, "surprise": 84 }, "peer_review_status": "Working paper / preprint; no journal acceptance verified", "rank": 9 }, { "id": "P09", "source_id": "S09", "title": "AI and the Market for Signals", "authors": "Doruk Cetemen and Emre Ozdenoren", "publisher": "arXiv / CEPR", "date": "2026-09-15", "draft_date": "2026-08", "url": "https://arxiv.org/pdf/2609.17752", "local": "signals.pdf", "method": "Career-signaling theory", "maturity": "speculation", "confidence": "High for the stated theoretical mechanism; untested empirical magnitude", "rationale": "The distinction between productive output, signaling and learning clarifies apparently conflicting effects. Results depend on equilibrium and trade assumptions, with no empirical effect size.", "score_unrounded": 87.35, "score_display": 87.3, "score_components": { "rigor": 84, "originality": 91, "economic_significance": 86, "long_run_importance": 93, "policy_business_relevance": 78, "surprise": 92 }, "peer_review_status": "Working paper / preprint; no journal acceptance verified", "rank": 7 }, { "id": "P10", "source_id": "S10", "title": "Artificial Intelligence and Aggregate Labor Productivity: Evidence from Patent Data", "authors": "Kodjovi M. Eklou", "publisher": "IMF", "date": "2026-09-25", "url": "https://www.imf.org/-/media/files/publications/wp/2026/english/wpiea2026204-source-pdf.pdf", "local": "imf-patents.pdf", "method": "Cross-country production-function estimates using AI patents", "maturity": "emerging", "confidence": "Moderate-low for causal attribution; useful historical benchmark", "rationale": "Provides an aggregate complement to task experiments. Patents proxy innovation rather than adoption; reverse causality, national composition and the pre-LLM sample constrain extrapolation.", "score_unrounded": 79.7, "score_display": 79.7, "score_components": { "rigor": 75, "originality": 75, "economic_significance": 87, "long_run_importance": 85, "policy_business_relevance": 82, "surprise": 76 }, "peer_review_status": "Working paper / preprint; no journal acceptance verified", "rank": 10 } ], "candidates": [ { "candidate_id": "P01", "title": "Does AI Assistance Enhance or Erode Expertise? Evidence from a Three-Month Field Experiment in Patent Drafting", "date": "2026-09", "url": "https://shapingwork.mit.edu/wp-content/uploads/2026/09/Autor-et-al-Sept-2026.pdf", "decision": "selected", "reason": "Random assignment and blinded expert ratings strengthen identification; attrition, small subgroup samples and a single professional setting constrain transportability. The withdrawal assessment makes the contribution unusually original.", "evidence_review": "full text; selected tables checked", "score": 92.5 }, { "candidate_id": "P02", "title": "The Early Impacts of AI on Employment among Recent College Graduates", "date": "2026-09", "url": "https://docs.iza.org/dp18945.pdf", "decision": "selected", "reason": "Timely population-based microdata and multiple comparison groups address a consequential claim. Nonrandom exposure and uncertain treatment timing limit the interpretation as an AI causal effect.", "evidence_review": "full text; selected tables checked", "score": 88.35 }, { "candidate_id": "P03", "title": "Data Centers Lower Nearby Housing Values", "date": "2026-09-10", "url": "https://escholarship.org/content/qt2z83c21b/qt2z83c21b.pdf", "decision": "selected", "reason": "A carefully constructed announcement design measures an overlooked margin. 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Patents proxy innovation rather than adoption; reverse causality, national composition and the pre-LLM sample constrain extrapolation.", "evidence_review": "full text; selected tables checked", "score": 79.7 }, { "candidate_id": "P11", "title": "Data Centers and the Local Economies in the Age of AI", "date": "2026-05; revised 2026-09", "url": "https://www.nber.org/papers/w35194", "decision": "context", "reason": "County-level outcomes illuminate spatial incidence; September revision delta could not be established against an earlier PDF.", "evidence_review": "full text", "score": null }, { "candidate_id": "P12", "title": "Writing Code versus Shipping Code", "date": "2026-05; revised 2026-09", "url": "https://www.nber.org/papers/w35275", "decision": "context", "reason": "Distinguishes coding activity from completed production. Revision delta unverified; no fresh-discovery claim.", "evidence_review": "full text", "score": null }, { "candidate_id": "P13", "title": "Will AI Help or Hurt Work and the Economy? What People Around the World Expect", "date": "2026-09-29", "url": "https://cepr.org/publications/dp21994", "decision": "frontier", "reason": "Wide cross-country beliefs survey; expectations are not realized outcomes. Full accessible version not secured.", "evidence_review": "primary abstract", "score": null }, { "candidate_id": "P14", "title": "Agentic AI in Organisations", "date": "2026-09-16", "url": "https://www.oecd.org/en/publications/agentic-ai-in-organisations_1257a26f-en.html", "decision": "frontier", "reason": "Qualitative deployment and governance evidence complements the experiments; not an average productivity estimate.", "evidence_review": "official overview", "score": null }, { "candidate_id": "P15", "title": "Voice AI in Firms", "date": "2026-09 revision; earlier public versions", "url": "https://arxiv.org/abs/2607.28222", "decision": "context", "reason": "Relevant field experiment, but July arXiv and older SSRN lineage preclude calling it first September evidence.", "evidence_review": "full text", "score": null }, { "candidate_id": "P16", "title": "Technology and Retirement", "date": "2026-09", "url": "https://docs.iza.org/dp18938.pdf", "decision": "not_selected", "reason": "Self-reported work capacity and retirement preferences differ from actual retirement; lower direct relevance to the central mechanism.", "evidence_review": "full text", "score": null }, { "candidate_id": "P17", "title": "The Utility of AI in Auditing Pre-Analysis Plans", "date": "2026-09", "url": "https://www.nber.org/papers/w35719", "decision": "context", "reason": "Audit feasibility is useful methodological context, but model-dependent self-audits do not establish independent verification.", "evidence_review": "full text", "score": null }, { "candidate_id": "P18", "title": "Everyone Is a Manager: AI Use as an Act of Management", "date": "2026-09-30; page revised 2026-10-01", "url": "https://cepr.org/publications/dp22002", "decision": "cutoff_exclusion", "reason": "Current page carries an October revision; no pre-cutoff full text was verified. Revisit in October.", "evidence_review": "primary metadata", "score": null }, { "candidate_id": "P19", "title": "Subsidizing the Unknown: Europe’s AI Support Schemes", "date": "2026-09-15", "url": "https://cepr.org/publications/dp21942", "decision": "frontier", "reason": "Sequential investment theory is relevant; empirical policy magnitudes not independently established for this edition.", "evidence_review": "primary abstract", "score": null }, { "candidate_id": "P20", "title": "Staged Access and Liability for Dual-Use AI", "date": "2026-08; revised 2026-09", "url": "https://www.nber.org/papers/w35586", "decision": "carry_forward", "reason": "Covered in August. September revision delta not verified; preserve earlier interpretation.", "evidence_review": "primary metadata and August review", "score": null }, { "candidate_id": "P21", "title": "AI in a Fragmenting World", "date": "2026-08-21; September commentary", "url": "https://cepr.org/publications/dp21867", "decision": "date_exclusion", "reason": "September commentary does not make the August paper a new study.", "evidence_review": "primary metadata", "score": null }, { "candidate_id": "P22", "title": "AI Meets Trade", "date": "2026-03-18", "url": "https://www.oecd.org/en/publications.html", "decision": "date_exclusion", "reason": "Useful trade channel background, outside the monthly window.", "evidence_review": "search-level discovery; URL requires final resolution", "score": null }, { "candidate_id": "P23", "title": "Innovation and Trust: Why AI Needs Both", "date": "2026-09-25", "url": "https://www.oecd.org/en/blogs/2026/09/innovation-and-trust-why-ai-needs-both.html", "decision": "context", "reason": "Official descriptive adoption evidence, not a causal paper or September observation vintage.", "evidence_review": "official article", "score": null }, { "candidate_id": "P24", "title": "PISA 2025 findings on AI use and learning", "date": "2026-09-08", "url": "https://www.oecd.org/en/about/news/announcements/2026/09.html", "decision": "not_selected", "reason": "Associations between AI use and achievement cannot identify learning effects; release surfaced in search, item-level source not verified.", "evidence_review": "discovery only", "score": null }, { "candidate_id": "P25", "title": "New Zealand: AI and productivity, Selected Issues", "date": "2026-09-17", "url": "https://www.imf.org/en/publications/selected-issues-papers", "decision": "not_selected", "reason": "Country policy assessment has lower original identification value than selected papers; catalog-level discovery.", "evidence_review": "official catalog discovery", "score": null }, { "candidate_id": "P26", "title": "Jurisdictional Capital and AI Regulation", "date": "2026; issue 180", "url": "https://www.imf.org/en/publications/wp", "decision": "date_unverified", "reason": "Publication date not verified within September; no substantive claim carried into the article.", "evidence_review": "catalog discovery", "score": null }, { "candidate_id": "P27", "title": "Beyond Code Generation: Reliability and Verification Cost in the SDLC", "date": "2026-09-04", "url": "https://arxiv.org/abs/2609.04681", "decision": "not_selected", "reason": "Review rather than a new measured firm outcome.", "evidence_review": "primary abstract", "score": null }, { "candidate_id": "P28", "title": "Control the Harness, Control the Cost", "date": "2026-09-24", "url": "https://arxiv.org/abs/2609.28919", "decision": "not_selected", "reason": "Session repricing and emulation do not establish realized enterprise return on investment.", "evidence_review": "primary abstract", "score": null }, { "candidate_id": "P29", "title": "After the Award: The Authorization Gap in Academic Access to Frontier AI", "date": "2026-09-28", "url": "https://arxiv.org/abs/2609.36304", "decision": "not_selected", "reason": "Useful process trace, narrow institutional coverage and lower generalizability.", "evidence_review": "primary abstract", "score": null }, { "candidate_id": "P30", "title": "Matching Models, Recruiting Agents", "date": "2026-09-03", "url": "https://arxiv.org/abs/2609.04286", "decision": "not_selected", "reason": "Narrative review of recruiting systems; does not identify deployment prevalence or labor-market effects.", "evidence_review": "primary abstract", "score": null }, { "candidate_id": "P31", "title": "Data-Efficient Language Modeling", "date": "2026-09-09", "url": "https://arxiv.org/abs/2609.10702", "decision": "scope_exclusion", "reason": "Technical benchmark does not directly estimate economic costs, value or incidence.", "evidence_review": "primary abstract", "score": null }, { "candidate_id": "P32", "title": "Capital without Labor: Data Centers", "date": "2026-09-01 reported", "url": "https://papers.ssrn.com/", "decision": "access_gap", "reason": "Primary item and September version not verified; retained as a discovery lead only.", "evidence_review": "secondary discovery", "score": null }, { "candidate_id": "P33", "title": "Local Air Pollution and Data Centers", "date": "2026-09-17 reported", "url": "https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6992037", "decision": "access_gap", "reason": "Primary full text and local identification details not verified; no numerical claim used.", "evidence_review": "primary search metadata", "score": null }, { "candidate_id": "P34", "title": "Environmental Costs and Geography of US Data Center Expansion", "date": "2026-05", "url": "https://escholarship.org/uc/item/2ts9g6jj", "decision": "date_exclusion", "reason": "Earlier Berkeley paper; useful background, not a September contribution.", "evidence_review": "primary abstract", "score": null }, { "candidate_id": "P35", "title": "AI’s Use of Knowledge in Society", "date": "2026-09 book release; 2025 conference", "url": "https://www.nber.org/books-and-chapters/economics-transformative-ai", "decision": "context", "reason": "Book release differs from first circulation; no fresh causal result inferred.", "evidence_review": "publisher metadata", "score": null }, { "candidate_id": "P36", "title": "The Impact of AI and Digital Platforms on the Information Ecosystem", "date": "2026-09 book release", "url": "https://www.nber.org/books-and-chapters/economics-transformative-ai", "decision": "not_selected", "reason": "Relevant synthesis; overlaps earlier evidence and not selected as a new study.", "evidence_review": "publisher metadata", "score": null }, { "candidate_id": "P37", "title": "Making AI Count: The Next Measurement Frontier", "date": "2026-09 book release", "url": "https://www.nber.org/books-and-chapters/economics-transformative-ai", "decision": "context", "reason": "Measurement synthesis is covered more directly by the Dell–Rambachan methods paper.", "evidence_review": "publisher metadata", "score": null }, { "candidate_id": "P38", "title": "How Much Should We Spend to Reduce AI Existential Risk?", "date": "2026-09 book release", "url": "https://www.nber.org/books-and-chapters/economics-transformative-ai", "decision": "not_selected", "reason": "Normative model with distinct scope; no commensurable probability estimate adopted.", "evidence_review": "publisher metadata", "score": null }, { "candidate_id": "P39", "title": "Economic Perspectives on AI in China, conference papers", "date": "2026-09-23/24", "url": "https://www.nber.org/conferences/economic-perspectives-artificial-intelligence-china-fall-2026", "decision": "coverage_lead", "reason": "Conference listing is a search lead, not evidence that every underlying paper is new or verified.", "evidence_review": "publisher listing", "score": null }, { "candidate_id": "P40", "title": "BIS discussion of AI, growth and financial stability", "date": "2026-09-10", "url": "https://www.bis.org/speeches/", "decision": "not_selected", "reason": "Speech used only as a coverage lead; not original causal research.", "evidence_review": "official search discovery", "score": null } ], "claims": [ { "claim_id": "C01", "public_wording": "AI access improved patent drafting; the unassisted follow-up gains concentrated among senior lawyers.", "status": "estimated", "source_ids": [ "S01" ], "figure_ids": "fig-01", "unit": "SD of control-group ratings", "period": "Trial 2025–2026", "geography": "US patent lawyers", "uncertainty_or_limitation": "133 randomized; 98 assessed on day-90 drafting and 91 on redlining; approximate intervals from rounded SE; distinct tasks.", "calculation_or_code": "code/reproduce.py; data/radar-data.json", "review_status": "source-checked" }, { "claim_id": "C02", "public_wording": "CPS comparisons find no significant summer-2026 relative unemployment increase among recent graduates.", "status": "estimated", "source_ids": [ "S02" ], "figure_ids": "", "unit": "unemployment probability", "period": "June–August 2026", "geography": "United States", "uncertainty_or_limitation": "Null is specific to outcome, controls and timing; 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reference is majority women without signal; racial-gap narrowing not statistically established.", "calculation_or_code": "code/reproduce.py; data/radar-data.json", "review_status": "source-checked" }, { "claim_id": "C09", "public_wording": "AI-generated output can alter both career signaling and effort that builds skill.", "status": "modelled", "source_ids": [ "S09" ], "figure_ids": "", "unit": "theoretical mechanism", "period": "September posting of August draft", "geography": "Two-period model", "uncertainty_or_limitation": "Equilibrium and tradability assumptions matter; not evidence of observed universal deskilling.", "calculation_or_code": "code/reproduce.py; data/radar-data.json", "review_status": "source-checked" }, { "claim_id": "C10", "public_wording": "Historical OECD AI-patent estimates attribute a 0.8–1.2% productivity contribution over 2000–2017 under the production-function approach.", "status": "modelled", "source_ids": [ "S10" ], "figure_ids": "", "unit": "percent output per worker", "period": "2000–2017", "geography": "OECD", "uncertainty_or_limitation": "Patents proxy innovation, not adoption; pre-LLM data; causal attribution is conditional.", "calculation_or_code": "code/reproduce.py; data/radar-data.json", "review_status": "source-checked" }, { "claim_id": "C11", "public_wording": "August emphasized the interface between access and action; September adds an independent-capability test.", "status": "interpretation", "source_ids": [ "S11", "S01" ], "figure_ids": "", "unit": "editorial synthesis", "period": "August and September 2026", "geography": "Radar continuity", "uncertainty_or_limitation": "Previous scores retained; no quantitative month-to-month index constructed.", "calculation_or_code": "code/reproduce.py; data/radar-data.json", "review_status": "source-checked" }, { "claim_id": "C12", "public_wording": "County-wide benefits can coexist with losses for nearby homeowners because the geographic estimands differ.", "status": "interpretation", "source_ids": [ "S03", "S12" ], "figure_ids": "", "unit": "spatial incidence", "period": "Historical data-center studies", "geography": "United States", "uncertainty_or_limitation": "This is a reconciliation of distinct designs, not a net-welfare calculation.", "calculation_or_code": "code/reproduce.py; data/radar-data.json", "review_status": "source-checked" } ], "figures": [ { "id": "fig-01", "slug": "expertise", "title": "Better assisted work does not guarantee independent gains", "question": "Does an improvement with AI carry into a task without it?", "goal": "uncertainty,comparison", "data_shape": "estimates", "features": "uncertainty,negative,categorical", "chart": "dot-and-whisker-plot", "unit": "Treatment effect in control-group standard deviations", "data": [ { "task": "Assisted drafting", "group": "Junior", "estimate": 0.6, "se": 0.26, "ci_low": 0.0904, "ci_high": 1.1096, "unit": "control-group SD", "assessed_people": 98, "source": "S01", "locator": "Table 4, column 4", "interval": "Approximate 95% Wald; rounded source SE" }, { "task": "Assisted drafting", "group": "Senior", "estimate": 0.3, "se": 0.17, "ci_low": -0.0332, "ci_high": 0.6332, "unit": "control-group SD", "assessed_people": 98, "source": "S01", "locator": "Table 4, column 4", "interval": "Approximate 95% Wald; rounded source SE" }, { "task": "Unassisted redlining", "group": "Junior", "estimate": -0.03, "se": 0.2, "ci_low": -0.422, "ci_high": 0.362, "unit": "control-group SD", "assessed_people": 91, "source": "S01", "locator": "Table 6, column 4", "interval": "Approximate 95% Wald; rounded source SE" }, { "task": "Unassisted redlining", "group": "Senior", "estimate": 0.45, "se": 0.19, "ci_low": 0.0776, "ci_high": 0.8224, "unit": "control-group SD", "assessed_people": 91, "source": "S01", "locator": "Table 6, column 4", "interval": "Approximate 95% Wald; rounded source SE" } ], "source_ids": [ "S01" ], "reason": "Two task facets preserve distinct outcomes while common position scales and intervals expose uncertainty.", "alt": "At day 90, juniors gain 0.60 SD in assisted drafting and −0.03 SD in unassisted redlining; seniors gain 0.30 and 0.45 SD. Approximate 95% intervals cross zero for junior redlining. Tasks differ." }, { "id": "fig-02", "slug": "hiring-signal", "title": "A credential narrows several invitation gaps", "question": "Does an AI-skills signal change interview-invitation gaps?", "goal": "comparison,change", "data_shape": "paired values", "features": "paired,categorical,negative,uncertainty", "chart": "dot-and-whisker-plot", "unit": "Invitation gap versus majority women without signal, percentage points", "data": [ { "group": "Black British", "condition": "Without signal", "estimate": -10.2, "se": 2.0, "ci_low": -14.12, "ci_high": -6.28, "difference_test_p": 0.19, "unit": "percentage points", "source": "S08", "locator": "Table 2, models I–V", "reference": "Majority women without AI-skills signal in each experiment" }, { "group": "Black British", "condition": "With AI-skills signal", "estimate": -8.2, "se": 2.1, "ci_low": -12.316, "ci_high": -4.084, "difference_test_p": 0.19, "unit": "percentage points", "source": "S08", "locator": "Table 2, models I–V", "reference": "Majority women without AI-skills signal in each experiment" }, { "group": "Older", "condition": "Without signal", "estimate": -15.7, "se": 2.1, "ci_low": -19.816, "ci_high": -11.584, "difference_test_p": 0.033, "unit": "percentage points", "source": "S08", "locator": "Table 2, models I–V", "reference": "Majority women without AI-skills signal in each experiment" }, { "group": "Older", "condition": "With AI-skills signal", "estimate": -12.3, "se": 2.3, "ci_low": -16.808, "ci_high": -7.792, "difference_test_p": 0.033, "unit": "percentage points", "source": "S08", "locator": "Table 2, models I–V", "reference": "Majority women without AI-skills signal in each experiment" }, { "group": "Lesbian", "condition": "Without signal", "estimate": -13.5, "se": 2.2, "ci_low": -17.812, "ci_high": -9.188, "difference_test_p": 0.005, "unit": "percentage points", "source": "S08", "locator": "Table 2, models I–V", "reference": "Majority women without AI-skills signal in each experiment" }, { "group": "Lesbian", "condition": "With AI-skills signal", "estimate": -8.1, "se": 2.4, "ci_low": -12.804, "ci_high": -3.396, "difference_test_p": 0.005, "unit": "percentage points", "source": "S08", "locator": "Table 2, models I–V", "reference": "Majority women without AI-skills signal in each experiment" }, { "group": "Autism disclosure", "condition": "Without signal", "estimate": -16.2, "se": 2.1, "ci_low": -20.316, "ci_high": -12.084, "difference_test_p": 0.001, "unit": "percentage points", "source": "S08", "locator": "Table 2, models I–V", "reference": "Majority women without AI-skills signal in each experiment" }, { "group": "Autism disclosure", "condition": "With AI-skills signal", "estimate": -9.9, "se": 2.2, "ci_low": -14.212, "ci_high": -5.588, "difference_test_p": 0.001, "unit": "percentage points", "source": "S08", "locator": "Table 2, models I–V", "reference": "Majority women without AI-skills signal in each experiment" }, { "group": "Pooled", "condition": "Without signal", "estimate": -13.7, "se": 1.0, "ci_low": -15.66, "ci_high": -11.74, "difference_test_p": 0.001, "unit": "percentage points", "source": "S08", "locator": "Table 2, models I–V", "reference": "Majority women without AI-skills signal in each experiment" }, { "group": "Pooled", "condition": "With AI-skills signal", "estimate": -9.7, "se": 1.1, "ci_low": -11.856, "ci_high": -7.544, "difference_test_p": 0.001, "unit": "percentage points", "source": "S08", "locator": "Table 2, models I–V", "reference": "Majority women without AI-skills signal in each experiment" } ], "source_ids": [ "S08" ], "reason": "Paired experimental conditions remain on a common position scale with separate markers and intervals; gaps are not a time series.", "alt": "Pooled invitation disadvantage narrows from 13.7 to 9.7 percentage points. The Black British comparison is not statistically significant; the older, lesbian and autism-disclosure comparisons are. Intervals are approximate." }, { "id": "fig-03", "slug": "housing-incidence", "title": "The nearest homes bear a larger estimated loss", "question": "How does measured capitalization depend on the local radius?", "goal": "comparison,uncertainty", "data_shape": "estimates", "features": "categorical,negative,uncertainty", "chart": "dot-and-whisker-plot", "unit": "Estimated housing-price effect, percent", "data": [ { "radius": "Within 3 miles", "estimate": -2.98, "ci_low": -4.59, "ci_high": -1.38, "unit": "percent", "source": "S03", "locator": "Results, manuscript p.3", "interval": "Published 95% confidence interval" }, { "radius": "Within 1 mile", "estimate": -7.2, "ci_low": null, "ci_high": null, "unit": "percent", "source": "S03", "locator": "Results, manuscript p.4", "interval": "Not transcribed; point estimate only, no interval inference" } ], "source_ids": [ "S03" ], "reason": "Direct dots compare two nested geographic estimands; a published interval accompanies the pooled result and the nearest-mile point is explicitly incomplete.", "alt": "Housing values fall an estimated 2.98% within three miles, with a published 95% interval from −4.59% to −1.38%. The estimate within one mile is −7.2%; its interval was not transcribed. The radii overlap." }, { "id": "fig-04", "slug": "market-expectations", "title": "The R&D assumption changes the growth mapping", "question": "How much does the model mapping change when R&D responds?", "goal": "comparison", "data_shape": "categorical values", "features": "categorical,positive", "chart": "horizontal-bar-chart", "unit": "Market-implied effect on the GDP level, percent", "data": [ { "mapping": "Baseline", "estimate": 3.6, "unit": "percent GDP level", "source": "S05", "locator": "Abstract; rounded headline", "status": "Market-implied model mapping" }, { "mapping": "With R&D channel", "estimate": 6.5, "unit": "percent GDP level", "source": "S05", "locator": "Abstract", "status": "Market-implied model mapping" } ], "source_ids": [ "S05" ], "reason": "Two zero-based bars communicate model dependence without suggesting a time series, forecast probability or realized output.", "alt": "The end-2025 market signal maps to a 3.6% GDP-level effect in the baseline and 6.5% with an R&D channel. These are model-implied expectations, not observed GDP gains." }, { "id": "fig-05", "slug": "development-time", "title": "Historical mobility differs across capability regimes", "question": "How persistent is the lowest productivity state by human capital?", "goal": "comparison", "data_shape": "categorical values", "features": "categorical,positive", "chart": "cleveland-dot-plot", "unit": "Estimated mean years to leave the lowest relative-TFP state", "data": [ { "regime": "Below estimated HC threshold", "estimate": 64.8, "unit": "years", "transitions": 632, "source": "S07", "locator": "Table 5", "status": "Estimated Markov mean exit time" }, { "regime": "At/above estimated HC threshold", "estimate": 25.4, "unit": "years", "transitions": 476, "source": "S07", "locator": "Table 5", "status": "Estimated Markov mean exit time" } ], "source_ids": [ "S07" ], "reason": "Two position marks anchored at zero make duration legible; no trajectory or causal education effect is implied.", "alt": "Expected exit time is 64.8 years below the estimated human-capital threshold and 25.4 at or above it. These historical Markov-model durations are not forecasts for AI adoption." }, { "id": "fig-06", "slug": "adjustment-scenarios", "title": "Wage rigidity moves the burden of adjustment", "question": "Where does adjustment fall as the assumed wage rigidity changes?", "goal": "comparison,pattern", "data_shape": "matrix", "features": "matrix,ordered,negative", "chart": "exact-value-table", "unit": "Conditional extreme scenario in 2030; no probabilities assigned", "data": [ { "rigidity": 0, "cognitive_wage": -42.2, "unemployment_cognitive": 2.6, "gdp_level": 36.6, "cognitive_employment": -1.3, "source": "S06", "locator": "Table 6, extreme scenario", "status": "Conditional 2030 scenario" }, { "rigidity": 0.5, "cognitive_wage": -11.5, "unemployment_cognitive": 17.9, "gdp_level": 32.4, "cognitive_employment": -21.5, "source": "S06", "locator": "Table 6, extreme scenario", "status": "Conditional 2030 scenario" }, { "rigidity": 0.75, "cognitive_wage": -2.9, "unemployment_cognitive": 21.7, "gdp_level": 30.5, "cognitive_employment": -25.9, "source": "S06", "locator": "Table 6, extreme scenario", "status": "Conditional 2030 scenario" }, { "rigidity": 0.9, "cognitive_wage": 2.8, "unemployment_cognitive": 24.0, "gdp_level": 29.2, "cognitive_employment": -28.5, "source": "S06", "locator": "Table 6, extreme scenario", "status": "Conditional 2030 scenario" } ], "source_ids": [ "S06" ], "reason": "An exact-value table preserves the distinct units of wage changes and unemployment levels across four discrete assumptions; no color scale or probability meaning is imposed.", "alt": "In the extreme 2030 scenario, cognitive wage change ranges from −42.2% with flexible wages to +2.8% at rigidity 0.9, while cognitive unemployment rises from 2.6% to 24.0%. The model moves adjustment between wages and employment." } ], "score_weights": { "rigor": 0.25, "originality": 0.2, "economic_significance": 0.2, "long_run_importance": 0.15, "policy_business_relevance": 0.1, "surprise": 0.1 } }