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Proposal pack · RO-LD02-05 v3

How much of a firm's AI productivity gain reaches the wages of workers who stay?

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Read this first. This is a research hypothesis drafted by Claude (SocioLens Project Lead), AI-drafted from the LD-02 debate on 12 October 2026, not a finding. Checked by Claude (SocioLens Project Lead) with AI research agents, by web search; not a person and not a domain expert; an independent AI reviewer with fresh context (not a person), 12 October 2026: every nearest-work citation checked against its source page; corrections applied. Every check so far was done by AI; no person or domain expert has checked it yet. The literature screen below was done by AI web search and can miss relevant work. Version v3, permanent address https://sociolens-web.pages.dev/opportunities/ro-ld02-05/v3/.

Causal mechanism not isolated One PhD chapter or paper Low to medium cost Partly addressed

Research question

When a firm starts using generative AI, how much does its value added per worker change over the following two to three years, and what share of that change shows up in the wages of incumbent workers who stay, compared with profits and with the wages of new hires?

Why it matters

Task experiments show large productivity gains, and incumbent earnings look flat, but no study found connects the two inside the same firms. Whether workers share in the gains, or whether gains go to profits and lower hiring, is the distributional core of this debate and a central question for wage bargaining. A pass-through estimate, even an imprecise one, would replace speculation with a number that can be compared with known rent-sharing rates.

The debate lists this among what we still do not know: Will productivity gains pass through into wages, profits, prices or output?

Evidence that motivates it

  1. The current R05 evidence base does not establish a causal pass-through from AI-induced productivity gains into worker wages. (LD02-HQ-C17 v1.0)
    The current evidence does not establish causal pass-through from AI productivity gains to wages; it also does not license saying workers receive none of the gains, so a direct estimate is needed either way.
    Cites: Humlum & Vestergaard (2026), “Still Waters, Rapid Currents”; Aldasoro et al., BIS Working Paper 1325, January 2026; Danmarks Nationalbank Working Paper No. 223, “AI Adoption…”; Klein Teeselink (2025), “Generative AI and Labor Market…”
  2. Instrumented AI adoption among more than 12,000 firms raises labour productivity by about 4% with no adverse short-run firm-level employment effect. (LD02-HQ-C11 v1.0)
    Instrumented AI adoption in over 12,000 firms raises labour productivity by about 4% without short-run employment loss, but the claim is not genAI-specific and does not license wage pass-through, which this design targets.
    Cites: Aldasoro et al., BIS Working Paper 1325, January 2026
  3. In 11 AI-exposed Danish occupations, Humlum and Vestergaard estimate precise null effects on earnings and recorded hours two years after ChatGPT, ruling out effects larger than 2%. (LD02-HQ-C02 v1.1)
    Incumbent workers in 11 exposed Danish occupations show precise null earnings and hours effects two years after ChatGPT; the claim does not license a no-hiring-effect reading, which is why the design separates stayers from new hires.
    Cites: Humlum & Vestergaard (2026), “Still Waters, Rapid Currents”

Claims are shown as they read on the day this pack was built; claim pages keep every earlier version.

A possible design

Firm-level rent-sharing design in linked registers. Take firms whose answers to the national ICT-usage-in-enterprises survey show they began using generative AI (or AI for text generation) in a known year, matched to non-adopters on sector, size and pre-period trends. Estimate event-study changes in value added per worker, operating profit per worker, the wage bill, and the individual wage growth of workers employed before adoption who stay, separately from the starting wages of new hires. The pass-through elasticity is the change in stayers' wages divided by the change in value added per worker. Compare it with the same register's pre-AI rent-sharing estimates and across firms with and without collective-agreement coverage.

Data needed

Linked employer–employee registers with firm accounts (Denmark, Norway, Sweden, Finland, the Netherlands or France); the ICT-usage-in-enterprises survey linked by firm identifier (the harmonised EU survey asked about AI that generates written or spoken language in its 2023 and 2024 waves, covers enterprises with at least 10 employees, and national versions and earlier waves must be checked); collective-agreement coverage where recorded.

Feasibility

One PhD chapter or paper. Low to medium; register access fees and approval time. Adoption is self-reported and not randomly assigned, so the design identifies pass-through only under the matching and parallel-trend assumptions, and should say so. Firm value-added gains of a few percent mean wage effects will be small; power calculations should come before the analysis.

Has this been done?

Partly done already: related work covers some of this question (first-pass screen of 12 October 2026, checked again on 13 October 2026).

Danish worker-level work estimated 3-7% pass-through of time savings to earnings in its May 2025 version, mainly from self-reports, with the authors noting they lacked power in the administrative data; its March 2026 revision drops that estimate and reports self-perceived earnings effects of 0 to 0.5% for adopters, an order of magnitude below their reported time savings. A European firm study finds higher average wages at AI adopters, for AI and big-data analytics in general, not generative AI, and without a worker-level analysis. A UK matched employer-employee study finds positive pay effects of AI-enabled technologies, more equal where employees have a say in pay, but uses surveys, covers AI in general and does not link pay to firm value added. A Danish register analysis finds value added and employment growing faster (value added not significantly) at intensive AI users but reports no wage outcomes, and US executive surveys report modest productivity gains without wages. The authors of a US state-by-industry study calculate that labour captures about 29 cents of each dollar of AI-driven output growth, using exposure, not adoption. No study was found that estimates generative-AI pass-through from firm value added to stayers' wages in linked administrative data with observed adoption.

Literature check. No study was found that estimates the pass-through of generative-AI-driven value added to incumbent stayers' wages in linked administrative data with observed adoption. The closest new work is a UK matched employer-employee study (Schulz et al., BJIR 2026) finding positive pay effects of AI-enabled technologies, largest for lower-skilled workers and spread more equally where employees have a voice in pay, but it covers AI in general, uses survey data and (on the pages read) does not link pay to firm productivity or separate stayers. A US CFO survey (Baslandze et al., NBER w34984) reports modest revenue-productivity gains but no wage pass-through, and earlier Danish register and NBER review checks found no pass-through estimate either. Checked by Claude (SocioLens Project Lead) with AI research agents, by web search; not a person and not a domain expert.

Where the search could not reach. Screened by web search on the date shown; Google Scholar, SSRN and paywalled journal pages were not systematically searched, and one item was read through the authors' article rather than the full paper. Absence of a hit is not proof that the work does not exist. Literature check of 2026-10-13: The BJIR publisher page and the ResearchGate record of 'Who Adopts Matters: Generative AI and Short-Run Wage Compression' could not be fetched; the latter's authors, venue and design were not confirmed and it was not used as evidence, though its title suggests it may bear on wages and should be checked. Full texts of working papers were not read in full. Google Scholar and paywalled journals were not searched. The French-language query returned only grey literature and business press. Absence of a hit is not proof that the work does not exist.

Before building a project on it, run your own search in a scholarly database such as Scopus, Web of Science, Google Scholar or SSRN.

Nearest work found

References

Sources behind the motivating claims:

BibTeX

The first entry cites this opportunity; then its nearest work; then the sources above. Nearest work and sources are recorded as reference strings, so each goes in whole as the title; add authors and year from the original before you cite it.

@misc{sociolens_ro_ld02_05_v3,
  author = {{SocioLens project}},
  title = {How much of a firm's AI productivity gain reaches the wages of workers who stay?},
  year = {2026},
  date = {2026-10-14},
  howpublished = {\url{https://sociolens-web.pages.dev/opportunities/ro-ld02-05/v3/}},
  note = {Research opportunity RO-LD02-05, version v3}
}

@misc{ro_ld02_05_nearest_1,
  title = {Humlum \& Vestergaard, "Large Language Models, Small Labor Market Effects," Becker Friedman Institute Working Paper No. 2025-56 (May 2025)},
  url = {https://bfi.uchicago.edu/wp-content/uploads/2025/04/BFI_WP_2025-56-3.pdf},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{ro_ld02_05_nearest_2,
  title = {Aldasoro, Gambacorta, Pal, Revoltella, Weiss \& Wolski, "AI adoption, productivity and employment: evidence from European firms," BIS Working Paper No. 1325 (23 January 2026)},
  url = {https://www.bis.org/publ/work1325.htm},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{ro_ld02_05_nearest_3,
  title = {Makridis \& Johnston, "More AI-Exposed Industries and States Are Benefiting, But Results Are Heterogeneous," ProMarket (21 May 2026), the authors' summary of Johnston \& Makridis, "The Labor Market Effects of Generative AI: A Difference-in-Differences Analysis of AI Exposure," SSRN Working Paper No. 5375017},
  url = {https://www.promarket.org/2026/05/21/more-ai-exposed-industries-and-states-are-benefiting-but-results-are-heterogenous/},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated; Judged from an abstract, summary or search listing only}
}

@misc{ro_ld02_05_nearest_4,
  title = {Kastrup, "AI-virksomheder har øget både vækst og beskæftigelse," Dansk Industri analysis (September 2026; date inferred from the URL)},
  url = {https://www.danskindustri.dk/arkiv/analyser/2026/09/ai-virksomheder-har-oget-bade-vakst-og-beskaftigelse/},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{ro_ld02_05_nearest_5,
  title = {Babina, "Understanding Firms' AI Efforts and Their Economic Impact," NBER Working Paper No. 35123 (April 2026, revised August 2026)},
  url = {https://www.nber.org/system/files/working_papers/w35123/w35123.pdf},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{ro_ld02_05_nearest_6,
  title = {Schulz, Valizade, Stuart, Soffia \& Skordis, "Artificial Intelligence Technologies and Employee Pay in the United Kingdom: Evidence From Matched Employer-Employee Data," British Journal of Industrial Relations 64(1), 116-129 (2026; online 30 October 2025), doi:10.1111/bjir.70019},
  url = {https://doi.org/10.1111/bjir.70019},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated; Judged from an abstract, summary or search listing only}
}

@misc{ro_ld02_05_nearest_7,
  title = {Baslandze, Edwards, Graham, McClure, Meyer, Sparks, Waddell \& Weitz, "Artificial Intelligence, Productivity, and the Workforce: Evidence from Corporate Executives," NBER Working Paper No. 34984 (March 2026)},
  url = {https://www.nber.org/system/files/working_papers/w34984/w34984.pdf},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{src_ld02_humlum_vestergaard_nber_33777,
  title = {Humlum \& Vestergaard, “Still Waters, Rapid Currents,” NBER Working Paper 33777, revised March 2026},
  url = {https://doi.org/10.3386/w33777},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{src_ld02_aldasoro_bis_1325,
  title = {Aldasoro et al., BIS Working Paper 1325, January 2026},
  url = {https://www.bis.org/publ/work1325.htm},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{src_ld02_nationalbank_wp_223_2026,
  title = {Danmarks Nationalbank Working Paper No. 223, “AI Adoption and Firm Size: Employment Effects in Monthly Administrative Data,” 16 September 2026},
  url = {https://www.nationalbanken.dk/en/news-and-knowledge/publications-and-speeches/working-paper/2026/ai-adoption-and-firm-size-employment-effects-in-monthly-administrative-data},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{src_ld02_klein_teeselink_uk_2025,
  title = {Klein Teeselink, “Generative AI and Labor Market Outcomes: Evidence from the United Kingdom,” SSRN, revised December 2025},
  url = {https://doi.org/10.2139/ssrn.5516798},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

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