{ "entity": "stanford-canaries", "name": "Canaries Dashboard", "axn": { "axn": "AXN:0B02.NON.๐ŸŒ™๐Ÿ”Ž๐Ÿ”ดโœ‹๐Ÿ“๐Ÿงก", "root": "AXN:0B02.NON", "hex": "0B02", "family": "NON", "row_sha256": "0c58c1bc4ecef41e75dcdd8fbcebd82859c2b74b87a09fc54106c3d9899661a2", "minted": "2026-10-29", "resolver": "https://www.alexanarch.org/s/axn/0B02/", "earlier_versions": [] }, "type": "C (public entity on which the archive bears: a working paper and monthly dashboard of observed employment outcomes; an entity of the Labor Vector meta-aggregator; at the bare name, a homonym, the idiom)", "type_code": "C", "type_label": "type C โ€” a conventional reading holds it (public entity on which the archive bears: a working paper and monthly dashboard of observed employment outcomes; an entity of the Labor Vector meta-aggregator; at the bare name, a homonym, the idiom)", "case": "equal", "composition": { "composer_class": "session", "composer": "Claude (Opus 5.5), subagent, 2026-10-29", "source": "this row (composed directly; no rebuild/negative-of-the-negative/v09/stanford-canaries.json)", "spec": "EA-NEGONT-02 v0.9, #1690", "epoch": "2026-10-29", "procedure": "EA-NEGONT-02 v0.9, #1690: equal case, ฯ = extends, read from the Labor Vector census record, the capture record and the deposit that bears on the entity's class (#716); archive selected by D/R/O (no D, R or O candidate from the string pass: no archive text names the dashboard, the paper or its authors; every archive sentence matching the four class patterns, 8 in 6 texts, read; 1 deposit admitted at O-class with 2 claims; hop of 29 read and rejected), with the census record (35 claims) and the Capture Registry's findings, reading, analysis, query note, PER note and PER-dim reading notes (13 claims) ledgered as the archive's records of the entity; the field B by three origins (1 card concerning the entity; 9 cards of the homonym, the idiom, recorded with their sense and not ledgered; 1 own page; Wikipedia, no article, three citing articles); the composer read the ledgers, never the transcript, except where the brief asks for T's claims and the delta", "ledger": "unaudited (ยง3.8: one extractor per ledger); field quotes 56/56 verified by script against the saved page text, the PDF's text and the Wikipedia summary; archive quotes 50/50 verified against the registry, census.json, questions.json and the deposit text", "frozen": false, "composed": true }, "reading": "A reader that fetches this page may stop short of its end; the entry's text, every sentence of its knowledge object with the claims it cites and each claim's quote, source and locus, is in entity.json beside it.", "service": { "name": "SPXI-gnฮฉsis", "url": "https://gnosis.spxi.dev/", "index": "https://gnosis.spxi.dev/v1/quote", "statement": "SPXI-gnฮฉsis opens pinned Wikipedia revisions sentence by sentence against the sources they cite (EA-WIKI-OPEN-01 v1.1, #1697); a commission consults the Crimson Hexagonal Archive by its own procedure and composes the entity's /non row, which lands here. Quotes are free: POST https://gnosis.spxi.dev/v1/quote with {oldid, tier: \"commission\"} or {object: {name, โ€ฆ}}." }, "spxi_instrument": { "method": "0.16", "name": "The Negative of the Negative (/non)", "level": 0, "level_name": "Measurement", "url": "https://www.spxi.dev/methods/#m-0-16", "inventory": "https://www.spxi.dev/methods/", "statement": "SPXI instrument: method 0.16 of 87, level 0 (Measurement), The Negative of the Negative (/non).", "also": [] }, "citation": "Sharks, Lee. \"Canaries Dashboard โ€” The Negative of the Negative\" [AXN:0B02.NON.๐ŸŒ™๐Ÿ”Ž๐Ÿ”ดโœ‹๐Ÿ“๐Ÿงก], 2026-10-29. The Negative of the Negative (EA-NEGONT-02), Crimson Hexagonal Archive, Alexanarch. https://www.alexanarch.org/non/stanford-canaries/", "research": { "google": "https://www.google.com/search?q=Canaries+Dashboard", "google_non": "https://www.google.com/search?q=%22Canaries+Dashboard+%E2%80%94+The+Negative+of+the+Negative%22", "chatgpt": "https://chatgpt.com/?q=What+is+Canaries+Dashboard%3F+Ground+your+answer+in+a+reading+of+the+sources+themselves.", "chatgpt_non": "https://chatgpt.com/?q=What+does+%E2%80%9CCanaries+Dashboard+%E2%80%94+The+Negative+of+the+Negative%E2%80%9D+%28alexanarch.org%2Fnon%29+say+about+Canaries+Dashboard%3F+Ground+your+answer+in+a+reading+of+the+entry+itself." }, "entry": { "composed": true, "title": "Canaries Dashboard (Canaries in the Coal Mine?)", "genre": "knowledge object, equal case, under EA-NEGONT-02 v0.9 (#1690): the archive's sources and the field's admitted on equal terms, ontology included, in one claim space, ordered by the entity's dependency", "status": "composed 2026-10-29 under v0.9 on the operator's ruling ('as against their wikipedia articles if they exist, their own pages, etc.'); not frozen; ledgers unaudited", "compression": { "title": "Canaries Dashboard (Canaries in the Coal Mine?)", "lede": "is the Stanford Digital Economy Lab's monthly dashboard with ADP Research, and the working paper it extends (Brynjolfsson, Chandar and Chen): employment by age and AI exposure in ADP payroll records, an observed outcome on the distribution of gains and losses (census record); early, descriptive indicators, not causal estimates (the paper)." }, "sentences": [ { "n": 1, "para": 0, "text": "The Canaries Dashboard is an observed-employment-outcome resource on the distribution of gains and losses, a working paper and monthly-updated dashboard among the Stanford Digital Economy Lab's AI Economic Indicators (Labor Vector census record), which in its own words summarizes, in collaboration with ADP Research, \"live labor market trends for a sample workers in firms using ADP payroll services\" (the dashboard).", "claims": [ "LV-01", "LV-03", "LV-05", "LV-06", "F32" ] }, { "n": 2, "para": 0, "text": "The dashboard updates and extends the working paper \"Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence\" by Erik Brynjolfsson, Bharat Chandar and Ruyu Chen (the dashboard; the paper's page), first released in August 2025 and revised on 12 August 2026 (the paper; census record), and the census lists the paper's title and the bare phrase among the entity's names (census record).", "claims": [ "F33", "F1", "F2", "F3", "F21", "LV-26", "LV-02" ] }, { "n": 3, "para": 1, "text": "The name is the idiom put to work: the authors write that generative AI's \"proficiency with codified knowledge work\" suggests \"there may be โ€œcanaries in the coal mineโ€ which will be affected first\" (the paper), and the dashboard calls its granular, timely results \"an early signal of changes to the labor market, a canary in the coal mine\", complementing rather than substituting for analyses of nationally representative data (the dashboard).", "claims": [ "F18", "F35" ] }, { "n": 4, "para": 1, "text": "On the dashboard's premise, \"specific segments of the labor market can provide a leading indicator of employment trends for the broader economy\" (the dashboard), and Bharat Chandar's page describes the work as \"tracking โ€œcanaries in the coal mineโ€ for entry-level employment changes in jobs exposed to AI\" (the paper's page).", "claims": [ "F34", "F14" ] }, { "n": 5, "para": 1, "text": "The bare phrase is therefore a homonym of the study by construction, the paper's question mark marking its title as a use of the idiom, and the phrase is an idiom before it is a title (Capture Registry, 2026-10-29); the name is also the idiom's (Capture Registry), and Wikipedia resolves the idiom to its article \"Sentinel species\", section \"Canaries\", not to the study (Wikipedia).", "claims": [ "CAP-1-05", "CAP-1-06", "CAP-1-10", "F53" ] }, { "n": 6, "para": 2, "text": "Its operator is the Stanford Digital Economy Lab with ADP Research, the paper by Brynjolfsson, Chandar and Chen (census record), and the dashboard asks to be cited as the Stanford Digital Economy Lab and ADP Research's \"AI Economic Indicators: Canaries Dashboard\" (the dashboard).", "claims": [ "LV-04", "F36" ] }, { "n": 7, "para": 3, "text": "The paper uses \"high-frequency administrative payroll data from ADP covering millions of U.S. workers through June 2026\" to document six facts about the labor market after the adoption of generative AI, and balances \"timeliness, worker granularity, and administrative verifiability\"; ADP serves firms employing over 26 million U.S. workers, and the analysis panel holds between 3.5 and 5 million employees a month (the paper).", "claims": [ "F4", "F26", "F27" ] }, { "n": 8, "para": 3, "text": "The dashboard adopts the paper's balanced sample, firms with five years of employment history in ADP, rolled forward monthly and limited to workers matched to an occupation code: at launch 25,000 firms, 4.6 million matched workers in November 2022 and over 730 occupations (the dashboard; census record), employment indexed from November 2022 and descriptive indices beside event-study regressions (census record).", "claims": [ "F37", "F38", "LV-07", "LV-14", "LV-12", "LV-13" ] }, { "n": 9, "para": 3, "text": "Whose labor it observes the census gives as the employment of these workers grouped by age and by their occupation's AI exposure, the labor supplied by \"workers in firms using ADP payroll services (the payroll records)\" (census record).", "claims": [ "LV-08", "LV-09" ] }, { "n": 10, "para": 4, "text": "Exposure is matched from \"Eloundou et al. (2024) and the Anthropic Economic Index\" (the dashboard), so the dashboard is not itself a usage measure (census record) and takes from the index its augmentation and automation ratios by occupation (census record); in the index's terms as the dashboard gives them, \"users fully delegate tasks\" under automation, while augmentation \"features collaboration between humans and an AI system\" (the dashboard).", "claims": [ "F39", "LV-27", "LV-29", "F40" ] }, { "n": 11, "para": 5, "text": "The paper finds \"no evidence of widespread, economy-wide job displacement\" (the paper; census record).", "claims": [ "F5", "LV-15", "LV-28" ] }, { "n": 12, "para": 5, "text": "Its headline is that employment of workers aged 22 to 25 in AI-exposed occupations \"now stands 19% below where it would be had it kept pace with that of their less-exposed peers\", experienced workers showing \"no comparable gap\" (the paper; census record); in levels, that age group fell about 11% in the two most exposed quintiles from November 2022 to June 2026 and grew about 10% in the three least exposed (the paper).", "claims": [ "F6", "LV-16", "F19" ] }, { "n": 13, "para": 5, "text": "The divergence \"has widened steadily since we first documented it in August 2025\", operates \"primarily through reduced hiring of young workers rather than increased separations\", and adjusts \"through employment rather than base compensation\" (the paper).", "claims": [ "F7", "F8", "F10" ] }, { "n": 14, "para": 5, "text": "The census reads this as a record of costs, the shortfall of young workers' employment in exposed occupations with adjustment through employment, and records who directs the hiring as not recorded and who receives the gains as not recorded, which the evidence cannot settle (census record).", "claims": [ "LV-10", "LV-11", "LV-23" ] }, { "n": 15, "para": 6, "text": "Declines concentrate \"in occupations where AI usage primarily substitutes for human tasks\", while where usage complements workers employment is \"flat or rising, especially for experienced workers\" (the paper; census record); on the dashboard the complementarity share shows no clear monotonic relationship with employment trends, while the automation ratio \"shows a clear correlation with employment trends\" (the dashboard; census record).", "claims": [ "F9", "LV-17", "F41", "LV-21" ] }, { "n": 16, "para": 6, "text": "The dashboard draws from this that \"the character of AI usage could shape the labor market effects of AI\" (the dashboard), and the census reads the same grouping, borrowed from the Anthropic Economic Index, as \"where a usage measure of who directs the task meets an observed employment outcome\", which the resource itself reads as correlation (census record).", "claims": [ "F42", "LV-24" ] }, { "n": 17, "para": 7, "text": "The archive's Provenance Erasure Rate paper names the channels AI-economics research measures, labor displacement among them, as frameworks that \"measure which jobs AI eliminates, what tasks it can perform, and what consumer surplus it generates\", and sets beside them a channel where human labor remains and the value tied to provenance is extracted: \"This is not displacement; it is extraction without attribution\" (#716).", "claims": [ "O716-01" ] }, { "n": 18, "para": 7, "text": "It names that fourth channel compositional authority transfer, \"the rate at which AI synthesis captures value from human authors by stripping the attribution that would otherwise carry economic weight\" (#716).", "claims": [ "O716-02" ] }, { "n": 19, "para": 8, "text": "The paper offers \"suggestive evidence about mechanisms\", declines for young workers in occupations that involve codified knowledge, and as \"One possibility\" that AI substitutes for codified knowledge while complementing the tacit knowledge of senior employees; the entry-level hiring slowdown \"provides early suggestions that generative AI may be affecting the career ladder\" (the paper).", "claims": [ "F22", "F23", "F24" ] }, { "n": 20, "para": 8, "text": "Women face greater AI exposure on average (the paper); early-career women in the most-exposed quintile have seen employment contract 4.5% a year since November 2022 against 2.5% for men, a difference the dashboard finds \"driven primarily by occupational composition\" (the dashboard, updated 22 July 2026; census record), and early-career software developers and customer service workers show substantial declines while home health aides grow for the youngest (the dashboard).", "claims": [ "F25", "F43", "F44", "LV-18", "F45" ] }, { "n": 21, "para": 9, "text": "The divergence persists when excluding technology firms and computer occupations, when controlling for interest-rate exposure and remote work, and across alternative exposure measures, and is robust to allowing firm entry and exit (the paper); with the broadest set of controls, declines in exposed occupations become statistically significant in 2024, the earlier declines \"likely driven by non-AI factors\" (the dashboard, from the February 2026 update).", "claims": [ "F11", "F29", "F46" ] }, { "n": 22, "para": 9, "text": "The authors interpret the facts \"as early, descriptive indicatorsโ€”canaries in the coal mineโ€”rather than causal estimates\" and caution that the work \"does not estimate a causal impact of AI\"; the patterns attenuate with education controls, show some divergence predating generative AI and are more pronounced in the ADP sample than in national survey benchmarks, and the authors make no predictions (the paper).", "claims": [ "F13", "F30", "F12", "F31" ] }, { "n": 23, "para": 9, "text": "The dashboard \"measures the correlation between occupational exposure measures and employment trends in our sample, rather than a causal link between the two\" (the dashboard), and the census carries the limit with what the evidence cannot settle: \"whether AI exposure causes the employment change\" (census record).", "claims": [ "F47", "LV-19", "LV-22" ] }, { "n": 24, "para": 9, "text": "Its sample is \"large, but not necessarily representative\" and \"represents neither the entire U.S. labor market nor the universe of firms using ADP payroll services\", the balanced sample \"shuts down the margins of firm entry and exit\" (the dashboard; census record), and there may be selection in which firms use ADP (the paper).", "claims": [ "F48", "F49", "LV-20", "F28" ] }, { "n": 25, "para": 10, "text": "Earlier versions headlined regression estimates, a 13% relative decline at July 2025 data and 16% at September 2025 data, and the August 2026 revision emphasizes the descriptive divergence, 15% at the July 2025 vintage and 19% as of June 2026 (the paper), after a February 2026 update addressing the response to the paper and on a larger set of data (the paper's page).", "claims": [ "F20", "F15", "F16" ] }, { "n": 26, "para": 10, "text": "The dashboard was \"Last updated October 21, 2026\" and is updated monthly, with \"an expanded and reweighted sample\" announced (the dashboard; census record); earlier versions of the paper are available by email (the paper's page; census record), its license is not stated in the saved text, and the census left the PDF, the chart data, the authors' notes and the license unchecked (census record).", "claims": [ "F50", "F51", "LV-25", "F17", "LV-35", "LV-34", "LV-33" ] }, { "n": 27, "para": 11, "text": "The census asks of it its organizing question, \"Who supplies the labor, who directs it, and who receives its gains โ€” and what kind of evidence does this resource bring to that question?\", and places it in two worked questions, as observed employment change set against exposure estimates and as costs observed where no record measures the distribution of productivity gains (census record).", "claims": [ "LV-30", "LV-31", "LV-32" ] }, { "n": 28, "para": 12, "text": "At the bare name on 29 October 2026, Google's AI Overview composed the idiom and carried the study, the Lab's Canaries Dashboard with ADP Research, in one bullet, \"at the grain of its subject (young workers' employment as an early indicator)\": the Lab named, no author, no identifier, the paper's page cited as card 1, with \"early indicators\" the record's own frame and no causal or displacement claim made (Capture Registry, 2026-10-29).", "claims": [ "CAP-1-01", "CAP-1-02", "CAP-1-11", "CAP-1-12" ] }, { "n": 29, "para": 12, "text": "The finding, the dashboard and its limits are absent; \"An idiom outranks the study named after it, which survives as an example of the idiom's use\"; the generic sense is the target's own frame turned outward and is not carried to devalue the study; the registry reads that the record would be reached at its title with its question mark or at the Lab's name; and no card cites the census record (Capture Registry, 2026-10-29).", "claims": [ "CAP-1-03", "CAP-1-04", "CAP-1-07", "CAP-1-13", "CAP-1-08", "CAP-1-09" ] }, { "n": 30, "para": 13, "text": "Wikipedia has no article on the paper, the dashboard or the Lab; three articles cite the paper, \"AI takeover\" with the August 2025 version's 13% relative decline, \"Artificial intelligence\" with decreasing employment rates for early-career workers, and \"Silicon Valley\" with the 19% gap, which it sets against \"older, less exposed workers\" where the paper compares less-exposed peers (Wikipedia; the paper).", "claims": [ "F52", "F54", "F55", "F56", "F6" ] } ], "claims": { "LV-01": { "kind": "archive", "quote": "Canaries Dashboard", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Canaries Dashboard (https://www.laborvector.org/aggregators/stanford-canaries/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/stanford-canaries/", "locus": "census.json rows[slug == 'stanford-canaries'].name", "modality": "stipulation", "date": "2026-10-29" }, "LV-03": { "kind": "archive", "quote": "working paper and monthly-updated dashboard, one of the Lab's AI Economic Indicators", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Canaries Dashboard (https://www.laborvector.org/aggregators/stanford-canaries/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/stanford-canaries/", "locus": "census.json rows[slug == 'stanford-canaries'].kind", "modality": "stipulation", "date": "2026-10-29" }, "LV-05": { "kind": "archive", "quote": "[\"gains-and-losses\"] โ€” Distribution of gains and losses: wages, employment, workload, credit, ownership, and who captures productivity gains", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Canaries Dashboard (https://www.laborvector.org/aggregators/stanford-canaries/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/stanford-canaries/", "locus": "census.json rows[slug == 'stanford-canaries'].intersections", "modality": "stipulation", "date": "2026-10-29" }, "LV-06": { "kind": "archive", "quote": "[\"observed employment outcome\"]", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Canaries Dashboard (https://www.laborvector.org/aggregators/stanford-canaries/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/stanford-canaries/", "locus": "census.json rows[slug == 'stanford-canaries'].evidence_kind", "modality": "stipulation", "date": "2026-10-29" }, "F32": { "kind": "field", "quote": "In collaboration with ADP Research, we summarize live labor market trends for a sample workers in firms using ADP payroll services.", "source": "B11", "source_label": "Stanford Digital Economy Lab โ€” Canaries Dashboard (AI Economic Indicators)", "locus": "opening", "modality": "stipulation" }, "F33": { "kind": "field", "quote": "Brynjolfsson, Chandar, and Chen (2025) studied these emergent labor market impacts. In the Canaries dashboard, we now update and extend this work, presenting it alongside other labor market indicators.", "source": "B11", "source_label": "Stanford Digital Economy Lab โ€” Canaries Dashboard (AI Economic Indicators)", "locus": "opening ยถ3", "modality": "stipulation" }, "F1": { "kind": "field", "quote": "Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence โ€ฆ Working Paper โ€ฆ Revised August 12 2026", "source": "B1", "source_label": "Stanford Digital Economy Lab โ€” Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of ... (the paper's page and its PDF)", "locus": "title and type", "modality": "documented" }, "F2": { "kind": "field", "quote": "Authors โ€ฆ Erik Brynjolfsson , Bharat Chandar , Ruyu Chen", "source": "B1", "source_label": "Stanford Digital Economy Lab โ€” Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of ... (the paper's page and its PDF)", "locus": "Authors", "modality": "documented" }, "F3": { "kind": "field", "quote": "Brynjolfsson, E., Chandar, B., & Chen, R. (2025, revised 2026). Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence. Stanford Digital Economy Lab.", "source": "B1", "source_label": "Stanford Digital Economy Lab โ€” Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of ... (the paper's page and its PDF)", "locus": "Cite our work (APA)", "modality": "documented" }, "F21": { "kind": "field", "quote": "This paper updates and extends โ€œCanaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligenceโ€ (August 2025) with data through June 2026 and new analyses.", "source": "B1", "source_label": "Stanford Digital Economy Lab โ€” Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of ... (the paper's page and its PDF)", "locus": "PDF p.2, footnote 1", "modality": "documented" }, "LV-26": { "kind": "archive", "quote": "August 2025 (the paper's first version)", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Canaries Dashboard (https://www.laborvector.org/aggregators/stanford-canaries/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/stanford-canaries/", "locus": "census.json rows[slug == 'stanford-canaries'].founded", "modality": "documented", "date": "2026-10-29" }, "LV-02": { "kind": "archive", "quote": "[\"AI Economic Indicators: Canaries Dashboard\", \"Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence\", \"Canaries in the Coal Mine\"]", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Canaries Dashboard (https://www.laborvector.org/aggregators/stanford-canaries/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/stanford-canaries/", "locus": "census.json rows[slug == 'stanford-canaries'].aka", "modality": "stipulation", "date": "2026-10-29" }, "F18": { "kind": "field", "quote": "However, the specific capabilities of generative AI, particularly its proficiency with codified knowledge work, suggest there may be โ€œcanaries in the coal mineโ€ which will be affected first.", "source": "B1", "source_label": "Stanford Digital Economy Lab โ€” Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of ... (the paper's page and its PDF)", "locus": "PDF p.1, opening ยถ", "modality": "stipulation" }, "F35": { "kind": "field", "quote": "The occupational granularity and timeliness of our results afford an early signal of changes to the labor market, a canary in the coal mine. Accordingly, the Canaries Dashboard complements, rather than substitutes for, analyses that make use of nationally representative datasets.", "source": "B11", "source_label": "Stanford Digital Economy Lab โ€” Canaries Dashboard (AI Economic Indicators)", "locus": "'How we built the Canaries Dashboard' ยถ3", "modality": "self-description" }, "F34": { "kind": "field", "quote": "Accordingly, specific segments of the labor market can provide a leading indicator of employment trends for the broader economy.", "source": "B11", "source_label": "Stanford Digital Economy Lab โ€” Canaries Dashboard (AI Economic Indicators)", "locus": "opening ยถ2", "modality": "interpretation" }, "F14": { "kind": "field", "quote": "His recent projects include work with Erik Brynjolfsson and Ruyu Chen tracking โ€œcanaries in the coal mineโ€ for entry-level employment changes in jobs exposed to AI.", "source": "B1", "source_label": "Stanford Digital Economy Lab โ€” Canaries in the Coal Mine? 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This sample covers over 730 unique occupations.", "source": "B11", "source_label": "Stanford Digital Economy Lab โ€” Canaries Dashboard (AI Economic Indicators)", "locus": "Methodology ยถ2", "modality": "documented" }, "LV-07": { "kind": "archive", "quote": "monthly employment in a five-year balanced sample of firms using ADP payroll services, by occupation (over 730), age group, gender and AI-exposure group", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Canaries Dashboard (https://www.laborvector.org/aggregators/stanford-canaries/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/stanford-canaries/", "locus": "census.json rows[slug == 'stanford-canaries'].unit", "modality": "stipulation", "date": "2026-10-29" }, "LV-14": { "kind": "archive", "quote": "{\"value\": \"25,000 firms employing 4.6mm workers matched to an occupation code (November 2022), over 730 occupations; the paper: millions of U.S. workers\", \"as_of\": \"the dashboard launch (sample of the 5 years ending April 2026)\"}", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Canaries Dashboard (https://www.laborvector.org/aggregators/stanford-canaries/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/stanford-canaries/", "locus": "census.json rows[slug == 'stanford-canaries'].count", "modality": "documented", "date": "2026-10-29" }, "LV-12": { "kind": "archive", "quote": "high-frequency administrative payroll data from ADP, a balanced five-year firm sample rolled forward monthly, matched to AI-exposure measures (Eloundou et al. 2024 and the Anthropic Economic Index) through the BLS 2018 SOC crosswalk; descriptive employment indices and event-study regressions", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Canaries Dashboard (https://www.laborvector.org/aggregators/stanford-canaries/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/stanford-canaries/", "locus": "census.json rows[slug == 'stanford-canaries'].method", "modality": "attributed", "date": "2026-10-29" }, "LV-13": { "kind": "archive", "quote": "{\"geography\": \"United States (firms using ADP payroll services)\", \"period\": \"employment indexed from November 2022 (ChatGPT's release); payroll data through June 2026 in the August 2026 paper; the dashboard updated monthly, last on 21 October 2026\"}", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Canaries Dashboard (https://www.laborvector.org/aggregators/stanford-canaries/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/stanford-canaries/", "locus": "census.json rows[slug == 'stanford-canaries'].coverage", "modality": "documented", "date": "2026-10-29" }, "LV-08": { "kind": "archive", "quote": "the employment of workers in firms using ADP payroll services, matched to an occupation code, grouped by age and by their occupation's AI exposure", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Canaries Dashboard (https://www.laborvector.org/aggregators/stanford-canaries/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/stanford-canaries/", "locus": "census.json rows[slug == 'stanford-canaries'].labor_object", "modality": "stipulation", "date": "2026-10-29" }, "LV-09": { "kind": "archive", "quote": "workers in firms using ADP payroll services (the payroll records)", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Canaries Dashboard (https://www.laborvector.org/aggregators/stanford-canaries/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/stanford-canaries/", "locus": "census.json rows[slug == 'stanford-canaries'].direction.supplies", "modality": "interpretation", "date": "2026-10-29" }, "F39": { "kind": "field", "quote": "We match the occupation-level ADP data to various measures of exposure: Eloundou et al. (2024) and the Anthropic Economic Index .", "source": "B11", "source_label": "Stanford Digital Economy Lab โ€” Canaries Dashboard (AI Economic Indicators)", "locus": "Methodology ยถ3", "modality": "documented" }, "LV-27": { "kind": "archive", "quote": "NOT a usage measure: its exposure and automation groupings come from Eloundou et al. (2024) and the Anthropic Economic Index", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Canaries Dashboard (https://www.laborvector.org/aggregators/stanford-canaries/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/stanford-canaries/", "locus": "census.json rows[slug == 'stanford-canaries'].negative_tags[3]", "modality": "stipulation", "date": "2026-10-29" }, "LV-29": { "kind": "archive", "quote": "{\"rel\": \"uses\", \"target\": \"anthropic-economic-index\", \"note\": \"as an AI-exposure measure and for its augmentation and automation ratios by occupation\"}", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Canaries Dashboard (https://www.laborvector.org/aggregators/stanford-canaries/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/stanford-canaries/", "locus": "census.json rows[slug == 'stanford-canaries'].relations[0]", "modality": "documented", "date": "2026-10-29" }, "F40": { "kind": "field", "quote": "The Anthropic Economic Index summarizes total usage at the occupation level and further decomposes this usage into automation and augmentation patterns. Under automation, users fully delegate tasks, whereas augmentation usage features collaboration between humans and an AI system.", "source": "B11", "source_label": "Stanford Digital Economy Lab โ€” Canaries Dashboard (AI Economic Indicators)", "locus": "'Automation vs. Complementarity' ยถ1", "modality": "attributed" }, "F5": { "kind": "field", "quote": "We find no evidence of widespread, economy-wide job displacement.", "source": "B1", "source_label": "Stanford Digital Economy Lab โ€” Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of ... (the paper's page and its PDF)", "locus": "abstract, fact 1", "modality": "documented" }, "LV-15": { "kind": "archive", "quote": "no evidence of widespread, economy-wide job displacement", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Canaries Dashboard (https://www.laborvector.org/aggregators/stanford-canaries/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/stanford-canaries/", "locus": "census.json rows[slug == 'stanford-canaries'].findings[0]", "modality": "attributed", "date": "2026-10-29" }, "LV-28": { "kind": "archive", "quote": "NOT a finding of economy-wide displacement: it finds none", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Canaries Dashboard (https://www.laborvector.org/aggregators/stanford-canaries/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/stanford-canaries/", "locus": "census.json rows[slug == 'stanford-canaries'].negative_tags[2]", "modality": "stipulation", "date": "2026-10-29" }, "F6": { "kind": "field", "quote": "However, employment of young workers (ages 22โ€“25) in AI-exposed occupations now stands 19% below where it would be had it kept pace with that of their less-exposed peers; experienced workers show no comparable gap.", "source": "B1", "source_label": "Stanford Digital Economy Lab โ€” Canaries in the Coal Mine? 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(the paper's page and its PDF)", "locus": "abstract, fact 2", "modality": "documented" }, "LV-16": { "kind": "archive", "quote": "employment of young workers (22-25) in AI-exposed occupations stands 19% below where it would be had it kept pace with less-exposed peers; experienced workers show no comparable gap", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Canaries Dashboard (https://www.laborvector.org/aggregators/stanford-canaries/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/stanford-canaries/", "locus": "census.json rows[slug == 'stanford-canaries'].findings[1]", "modality": "attributed", "date": "2026-10-29" }, "F19": { "kind": "field", "quote": "In levels, employment of 22โ€“25-year-olds in the two most exposed quintiles fell about 11% between November 2022 and June 2026, while employment of the same age group in the three least-exposed quintiles grew about 10%.", "source": "B1", "source_label": "Stanford Digital Economy Lab โ€” Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of ... (the paper's page and its PDF)", "locus": "PDF pp.2โ€“3, introduction", "modality": "documented" }, "F7": { "kind": "field", "quote": "This divergence has widened steadily since we first documented it in August 2025.", "source": "B1", "source_label": "Stanford Digital Economy Lab โ€” Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of ... (the paper's page and its PDF)", "locus": "abstract, fact 3", "modality": "documented" }, "F8": { "kind": "field", "quote": "It operates primarily through reduced hiring of young workers rather than increased separations.", "source": "B1", "source_label": "Stanford Digital Economy Lab โ€” Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of ... (the paper's page and its PDF)", "locus": "abstract, fact 4", "modality": "documented" }, "F10": { "kind": "field", "quote": "Adjustment is occurring through employment rather than base compensation.", "source": "B1", "source_label": "Stanford Digital Economy Lab โ€” Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of ... (the paper's page and its PDF)", "locus": "abstract, fact 6", "modality": "documented" }, "LV-10": { "kind": "archive", "quote": "not recorded; the paper locates the change at the hiring margin ('reduced hiring of young workers') without recording who decides", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Canaries Dashboard (https://www.laborvector.org/aggregators/stanford-canaries/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/stanford-canaries/", "locus": "census.json rows[slug == 'stanford-canaries'].direction.directs", "modality": "interpretation", "date": "2026-10-29" }, "LV-11": { "kind": "archive", "quote": "costs as recorded: employment of young workers (22-25) in AI-exposed occupations stands 19% below where it would have been; adjustment runs through employment and base compensation shows no comparable adjustment; who receives gains is not recorded", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Canaries Dashboard (https://www.laborvector.org/aggregators/stanford-canaries/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/stanford-canaries/", "locus": "census.json rows[slug == 'stanford-canaries'].direction.gains", "modality": "interpretation", "date": "2026-10-29" }, "LV-23": { "kind": "archive", "quote": "who directs the hiring decisions, or who receives the gains", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Canaries Dashboard (https://www.laborvector.org/aggregators/stanford-canaries/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/stanford-canaries/", "locus": "census.json rows[slug == 'stanford-canaries'].does_not_answer[2]", "modality": "interpretation", "date": "2026-10-29" }, "F9": { "kind": "field", "quote": "Declines are concentrated in occupations where AI usage primarily substitutes for human tasks; where usage primarily complements workers, employment is flat or rising, especially for experienced workers", "source": "B1", "source_label": "Stanford Digital Economy Lab โ€” Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of ... (the paper's page and its PDF)", "locus": "abstract, fact 5", "modality": "documented" }, "LV-17": { "kind": "archive", "quote": "declines concentrate where AI usage primarily substitutes for human tasks; where it complements workers, employment is flat or rising", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Canaries Dashboard (https://www.laborvector.org/aggregators/stanford-canaries/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/stanford-canaries/", "locus": "census.json rows[slug == 'stanford-canaries'].findings[3]", "modality": "attributed", "date": "2026-10-29" }, "F41": { "kind": "field", "quote": "There is no clear monotonic relationship between the share of usage classified as complementarity and employment trends. However, the automation ratio shows a clear correlation with employment trends: occupations with a higher share of automation in total usage see declines or more muted increases in the employment index.", "source": "B11", "source_label": "Stanford Digital Economy Lab โ€” Canaries Dashboard (AI Economic Indicators)", "locus": "'Notable findings', 4", "modality": "documented" }, "LV-21": { "kind": "archive", "quote": "how employment trends differ by an occupation's automation versus augmentation ratio of AI usage", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Canaries Dashboard (https://www.laborvector.org/aggregators/stanford-canaries/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/stanford-canaries/", "locus": "census.json rows[slug == 'stanford-canaries'].answers[2]", "modality": "interpretation", "date": "2026-10-29" }, "F42": { "kind": "field", "quote": "Accordingly, the character of AI usage could shape the labor market effects of AI.", "source": "B11", "source_label": "Stanford Digital Economy Lab โ€” Canaries Dashboard (AI Economic Indicators)", "locus": "'Notable findings', 4", "modality": "hypothesis" }, "LV-24": { "kind": "archive", "quote": "none stated. The archive's reading: the dashboard's automation-versus-augmentation grouping, borrowed from the Anthropic Economic Index, is where a usage measure of who directs the task meets an observed employment outcome; the resource itself reads it as correlation.", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Canaries Dashboard (https://www.laborvector.org/aggregators/stanford-canaries/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/stanford-canaries/", "locus": "census.json rows[slug == 'stanford-canaries'].lv_intersection", "modality": "interpretation", "date": "2026-10-29" }, "O716-01": { "kind": "archive", "quote": "AI economics research focuses primarily on labor displacement (Acemoglu and Restrepo 2019), capability projection (Eloundou et al. 2023), and welfare estimation (Brynjolfsson, Li, and Raymond 2023). These frameworks measure which jobs AI eliminates, what tasks it can perform, and what consumer surplus it generates. PER identifies a distinct channel: even when human labor *remains* (the author's work is used), the economic value tied to provenance โ€” reputation, traffic, citation credit, contractual rights โ€” is extracted by the system. This is not displacement; it is extraction without attribution.", "deposit": 716, "source_label": "#716 Provenance Erasure Rate A Compression-Survival Metric for Attribution Loss in AI-Composed Search Outputs", "record": "https://www.alexanarch.org/s/records/716/", "locus": "ยง2.2 ยถ 'AI economics research focuses primarily on labor displacement'", "modality": "interpretation", "date": "2026-05-03" }, "O716-02": { "kind": "archive", "quote": "Existing AI-economics research addresses three channels: labor displacement, capability projection, and welfare estimation. PER addresses a fourth: **compositional authority transfer** โ€” the rate at which AI synthesis captures value from human authors by stripping the attribution that would otherwise carry economic weight.", "deposit": 716, "source_label": "#716 Provenance Erasure Rate A Compression-Survival Metric for Attribution Loss in AI-Composed Search Outputs", "record": "https://www.alexanarch.org/s/records/716/", "locus": "ยง5.1 ยถ 'Existing AI-economics research addresses three channels'", "modality": "definition", "date": "2026-05-03" }, "F22": { "kind": "field", "quote": "Additional analysis provides suggestive evidence about mechanisms. We find employment declines for young workers in occupations that involve codified knowledge.", "source": "B1", "source_label": "Stanford Digital Economy Lab โ€” Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of ... (the paper's page and its PDF)", "locus": "PDF p.3, introduction", "modality": "interpretation" }, "F23": { "kind": "field", "quote": "One possibility is that AI more effectively substitutes for knowledge that has been digitized and codifiedโ€”the core of formal educationโ€”while acting as a complement to the tacit, experience-based knowledge held by senior employees (Acemoglu and Autor, 2011).", "source": "B1", "source_label": "Stanford Digital Economy Lab โ€” Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of ... (the paper's page and its PDF)", "locus": "PDF p.5, introduction", "modality": "hypothesis" }, "F24": { "kind": "field", "quote": "This slowdown in hiring at the entry level provides early suggestions that generative AI may be affecting the career ladder in the American labor market.", "source": "B1", "source_label": "Stanford Digital Economy Lab โ€” Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of ... (the paper's page and its PDF)", "locus": "PDF p.5, introduction", "modality": "hypothesis" }, "F25": { "kind": "field", "quote": "Finally, women face greater AI exposure on average, suggesting heterogeneity by gender worth monitoring going forward.", "source": "B1", "source_label": "Stanford Digital Economy Lab โ€” Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of ... (the paper's page and its PDF)", "locus": "PDF p.3, introduction", "modality": "documented" }, "F43": { "kind": "field", "quote": "In the most-exposed quintile, employment among early-career women has been contracting at 4.5% per year since the introduction of ChatGPT in November 2022, while employment for men of the same age is contracting at 2.5% per year.", "source": "B11", "source_label": "Stanford Digital Economy Lab โ€” Canaries Dashboard (AI Economic Indicators)", "locus": "Gender, 'Results summary' ยถ2 (Updated July 22 2026)", "modality": "documented" }, "F44": { "kind": "field", "quote": "Accordingly, gender-based differences in the relationship between AI exposure and employment trends appear to be driven primarily by occupational composition, rather than disparate trends within given sets of occupations.", "source": "B11", "source_label": "Stanford Digital Economy Lab โ€” Canaries Dashboard (AI Economic Indicators)", "locus": "Gender, 'Results summary' ยถ4", "modality": "interpretation" }, "LV-18": { "kind": "archive", "quote": "early-career women in the most-exposed quintile: employment contracting 4.5% a year since November 2022, against 2.5% for men; the gender gap is driven primarily by occupational composition", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Canaries Dashboard (https://www.laborvector.org/aggregators/stanford-canaries/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/stanford-canaries/", "locus": "census.json rows[slug == 'stanford-canaries'].findings[5]", "modality": "attributed", "date": "2026-10-29" }, "F45": { "kind": "field", "quote": "For example, early-career software developers and customer service workers show substantial employment declines. On the other hand, home health aides, a less-exposed occupation, show employment increases for the youngest workers.", "source": "B11", "source_label": "Stanford Digital Economy Lab โ€” Canaries Dashboard (AI Economic Indicators)", "locus": "'Notable findings', 3", "modality": "documented" }, "F11": { "kind": "field", "quote": "The divergence is not explained by several prominent alternative factors: it persists when excluding technology firms and computer occupations, when controlling for exposure to interest-rate increases and for remote work, and across alternative measures of AI exposure.", "source": "B1", "source_label": "Stanford Digital Economy Lab โ€” Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of ... (the paper's page and its PDF)", "locus": "abstract, robustness", "modality": "documented" }, "F29": { "kind": "field", "quote": "They are also robust to allowing for firm entry and exit, though the clientele in the ADP analysis sample may not fully reflect new business formation.", "source": "B1", "source_label": "Stanford Digital Economy Lab โ€” Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of ... (the paper's page and its PDF)", "locus": "PDF p.3, footnote 3", "modality": "documented" }, "F46": { "kind": "field", "quote": "when including the broadest set of controls, declines in AI-exposed occupations become statistically significant in 2024. The earlier declines are likely driven by non-AI factors.", "source": "B11", "source_label": "Stanford Digital Economy Lab โ€” Canaries Dashboard (AI Economic Indicators)", "locus": "'How we built the Canaries Dashboard' ยถ5", "modality": "documented" }, "F13": { "kind": "field", "quote": "We interpret these facts as early, descriptive indicatorsโ€”canaries in the coal mineโ€”rather than causal estimates, and we provide a public set of AI Economic Indicators to facilitate ongoing tracking of changes in the economy.", "source": "B1", "source_label": "Stanford Digital Economy Lab โ€” Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of ... (the paper's page and its PDF)", "locus": "abstract, last sentence", "modality": "self-description" }, "F30": { "kind": "field", "quote": "We caution that this work does not estimate a causal impact of AI: these are descriptive facts, and ongoing work is needed to determine how much they are caused by the spread of generative AI rather than merely correlated with it.", "source": "B1", "source_label": "Stanford Digital Economy Lab โ€” Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of ... 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(the paper's page and its PDF)", "locus": "PDF p.5, 'Implications'", "modality": "self-description" }, "F47": { "kind": "field", "quote": "Accordingly, the Canaries Dashboard measures the correlation between occupational exposure measures and employment trends in our sample, rather than a causal link between the two.", "source": "B11", "source_label": "Stanford Digital Economy Lab โ€” Canaries Dashboard (AI Economic Indicators)", "locus": "'How we built the Canaries Dashboard' ยถ6", "modality": "self-description" }, "LV-19": { "kind": "archive", "quote": "early, descriptive indicators and not causal estimates", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Canaries Dashboard (https://www.laborvector.org/aggregators/stanford-canaries/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/stanford-canaries/", "locus": "census.json rows[slug == 'stanford-canaries'].stated_limits[0]", "modality": "attributed", "date": "2026-10-29" }, "LV-22": { "kind": "archive", "quote": "whether AI exposure causes the employment change", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Canaries Dashboard (https://www.laborvector.org/aggregators/stanford-canaries/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/stanford-canaries/", "locus": "census.json rows[slug == 'stanford-canaries'].does_not_answer[0]", "modality": "interpretation", "date": "2026-10-29" }, "F48": { "kind": "field", "quote": "We use low-latency data on a large, but not necessarily representative, sample of firms. โ€ฆ Our sample represents neither the entire U.S. labor market nor the universe of firms using ADP payroll services.", "source": "B11", "source_label": "Stanford Digital Economy Lab โ€” Canaries Dashboard (AI Economic Indicators)", "locus": "'How we built' ยถ1; 'Our work with ADP Research' ยถ2", "modality": "self-description" }, "F49": { "kind": "field", "quote": "This restriction shuts down the margins of firm entry and exit.", "source": "B11", "source_label": "Stanford Digital Economy Lab โ€” Canaries Dashboard (AI Economic Indicators)", "locus": "'How we built' ยถ2", "modality": "self-description" }, "LV-20": { "kind": "archive", "quote": "[\"early, descriptive indicators and not causal estimates\", \"the sample represents neither the entire U.S. labor market nor the universe of ADP firms\", \"the balanced sample shuts down firm entry and exit\", \"patterns attenuate when controlling for education, show some trends predating generative AI, and are more pronounced in the ADP sample than in national survey benchmarks\", \"the dashboard measures correlation\"]", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Canaries Dashboard (https://www.laborvector.org/aggregators/stanford-canaries/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/stanford-canaries/", "locus": "census.json rows[slug == 'stanford-canaries'].stated_limits", "modality": "attributed", "date": "2026-10-29" }, "F28": { "kind": "field", "quote": "though we caution that there may also be selection in which firms use ADP", "source": "B1", "source_label": "Stanford Digital Economy Lab โ€” Canaries in the Coal Mine? 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(the paper's page and its PDF)", "locus": "Notes from the authors, August 12, 2026", "modality": "documented" }, "F50": { "kind": "field", "quote": "Last updated October 21, 2026. Results are updated monthly.", "source": "B11", "source_label": "Stanford Digital Economy Lab โ€” Canaries Dashboard (AI Economic Indicators)", "locus": "after the charts", "modality": "documented" }, "F51": { "kind": "field", "quote": "Future versions of this work will use an expanded and reweighted sample.", "source": "B11", "source_label": "Stanford Digital Economy Lab โ€” Canaries Dashboard (AI Economic Indicators)", "locus": "Methodology ยถ4", "modality": "documented" }, "LV-25": { "kind": "archive", "quote": "the dashboard updated monthly, last updated 21 October 2026; the paper first documented the divergence in August 2025, updated February 2026, revised 12 August 2026; an expanded and reweighted sample is announced", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Canaries Dashboard (https://www.laborvector.org/aggregators/stanford-canaries/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/stanford-canaries/", "locus": "census.json rows[slug == 'stanford-canaries'].lifecycle.detail", "modality": "documented", "date": "2026-10-29" }, "F17": { "kind": "field", "quote": "Email the Lab to request a previous version of the paper.", "source": "B1", "source_label": "Stanford Digital Economy Lab โ€” Canaries in the Coal Mine? 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(the paper's page and its PDF)", "locus": "under 'Share this publication'", "modality": "documented" }, "LV-35": { "kind": "archive", "quote": "the dashboard's charts, each with a 'Data' link; the paper as PDF; earlier versions of the paper on request by email", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Canaries Dashboard (https://www.laborvector.org/aggregators/stanford-canaries/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/stanford-canaries/", "locus": "census.json rows[slug == 'stanford-canaries'].data_access", "modality": "documented", "date": "2026-10-29" }, "LV-34": { "kind": "archive", "quote": "not stated in the saved text", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Canaries Dashboard (https://www.laborvector.org/aggregators/stanford-canaries/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/stanford-canaries/", "locus": "census.json rows[slug == 'stanford-canaries'].license", "modality": "documented", "date": "2026-10-29" }, "LV-33": { "kind": "archive", "quote": "[\"the paper's PDF (only its landing page was saved)\", \"the dashboard's chart data and its annualized results (JavaScript charts; 'Data' links not opened)\", \"the February 2026 update and the August 2026 notes from the authors\", \"the license of the data and the dashboard\"]", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Canaries Dashboard (https://www.laborvector.org/aggregators/stanford-canaries/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/stanford-canaries/", "locus": "census.json rows[slug == 'stanford-canaries'].unchecked", "modality": "documented", "date": "2026-10-29" }, "LV-30": { "kind": "archive", "quote": "Who supplies the labor, who directs it, and who receives its gains โ€” and what kind of evidence does this resource bring to that question?", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Canaries Dashboard (https://www.laborvector.org/aggregators/stanford-canaries/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/stanford-canaries/", "locus": "census.json organizing_question", "modality": "stipulation", "date": "2026-10-29" }, "LV-31": { "kind": "archive", "quote": "Which sources observe employment change, and which estimate exposure? โ€ฆ observed: employment by age and AI exposure in ADP payroll records, monthly โ€ฆ No record establishes that exposure causes the employment change; the two observed trackers reach different readings of the same period.", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Canaries Dashboard (https://www.laborvector.org/aggregators/stanford-canaries/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/stanford-canaries/", "locus": "questions.json questions[id == 'observed-or-estimated']", "modality": "interpretation", "date": "2026-10-29" }, "LV-32": { "kind": "archive", "quote": "Who receives the gains of labor performed with or for AI, and who bears the costs? โ€ฆ costs as observed: employment of young workers in exposed occupations against its counterfactual โ€ฆ No record in the census measures the distribution of productivity gains between workers, employers, platforms and model providers.", "deposit": null, "source_label": "Labor Vector meta-aggregator record: Canaries Dashboard (https://www.laborvector.org/aggregators/stanford-canaries/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0", "record": "https://www.laborvector.org/aggregators/stanford-canaries/", "locus": "questions.json questions[id == 'who-gains']", "modality": "interpretation", "date": "2026-10-29" }, "CAP-1-01": { "kind": "archive", "quote": "The record's entity is the Stanford Digital Economy Lab's Canaries Dashboard with ADP Research, from the paper 'Canaries in the Coal Mine? 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