Across 56 economies and 16 industries, industries more exposed to generative AI grew faster in 2022–23 where countries were better prepared for AI, which the authors read as generative AI likely benefiting advanced economies more than emerging ones and widening global income disparities in the near term.

SocioLens project,

Claim LD03-SL-C21 (v1.0)

Provenance and status

Claim type: Estimate
Publication status: Eligible for publication, with qualifiers: working paper
Verification state: Checked against the primary source. Who checked this claim is not recorded for it; claim checks so far were AI-led, and no person has checked claims against their sources.
Does not license: Total factor productivity; observed adoption; long-run effects
Limitations: Associational, over one year, using predicted exposure benchmarked on the US; results in value added, and the authors leave conversion to productivity to future work; BIS is an international organization of central banks
Source (as cited): Gambacorta, Kharroubi, Mehrotra & Oliviero, “Artificial intelligence and growth in advanced and emerging economies: short-run impact,” BIS Working Papers No 1321, December 2025
Source type: Working paper (recorded as “Academic working paper”)
Institutional position: Mixed
Maturity of the evidence: Emerging evidence
Recorded date: 15 October 2026
Currentness / correction: This is the current version. No correction or concern is recorded on its sources.

Sources this version cites

Gambacorta, Kharroubi, Mehrotra & Oliviero, “Artificial intelligence and growth in advanced and emerging economies: short-run impact,” BIS Working Papers No 1321, December 2025 (Working paper)
No checked link to the original yet.

Version

This page shows version v1.0. Its record never changes, even when newer evidence arrives; the site may add a note to the page or withhold a figure not cleared for public use.

This pinned version is also the current version.

Versions: v1.0 · current

Raw record: every recorded field, with its coded values

All 25 canonical Claim fields, verbatim, with the coded values derived from them. The plain-language labels above are read from these.

Claim type (coded): ESTIMATE (as printed: “ESTIMATE”)
Publication status (coded): ELIGIBLE (WORKING_PAPER) (as printed: “ELIGIBLE WITH WORKING-PAPER QUALIFIER”)
Verification state (coded): CONTENT_VERIFIED_PRIMARY
Source type (coded): WORKING_PAPER
Maturity (coded): EMERGING

#	Canonical field	Canonical value (verbatim)	Normalized (Claim Value Vocabulary v1.0.2)
1	CLAIM ID	LD03-SL-C21	—
2	PUBLIC-FACING CLAIM	Across 56 economies and 16 industries, industries more exposed to generative AI grew faster in 2022–23 where countries were better prepared for AI, which the authors read as generative AI likely benefiting advanced economies more than emerging ones and widening global income disparities in the near term.	—
3	CLAIM TYPE	ESTIMATE	base_types: ESTIMATE
4	RESEARCH STRAND	D — Aggregate estimates and theory	—
5	MATURITY	EMERGING	states: EMERGING
6	SOURCE	Gambacorta, Kharroubi, Mehrotra & Oliviero, “Artificial intelligence and growth in advanced and emerging economies: short-run impact,” BIS Working Papers No 1321, December 2025	source_ids: SRC-SL-GAMBACORTA-KHARROUBI-MEHROTRA-OLIVIERO-BIS-1321
7	SOURCE TYPE	Academic working paper	classes: WORKING_PAPER
8	INSTITUTIONAL POSITION	Mixed	—
9	SCOPE	56 economies, 16 industries; value-added growth in 2022–23	—
10	DESIGN	Cross-country, cross-industry interaction design (Rajan–Zingales style) with country and industry fixed effects	—
11	SAMPLE / DATASET	Industry-country value-added data	—
12	MODEL / TOOL / VERSION	Generative AI (predicted exposure, not observed use)	—
13	COMPARATOR	Less exposed industries; less prepared countries	—
14	MAIN RESULT	Growth differential of about two percentage points between high- and low-exposure sectors for a one-standard-deviation increase in AI preparedness; benefits likely larger in advanced economies	—
15	INFERENTIAL TARGET	Whether early growth effects of generative AI differ by country readiness	—
16	DOES NOT LICENSE	Total factor productivity; observed adoption; long-run effects	—
17	VERIFICATION STATE	CONTENT VERIFIED	state: CONTENT_VERIFIED_PRIMARY · verified_by: UNKNOWN
18	LIMITATIONS	Associational, over one year, using predicted exposure benchmarked on the US; results in value added, and the authors leave conversion to productivity to future work; BIS is an international organization of central banks	—
19	CONTRADICTORY / SUPPORTING SOURCES	Consistent with distributional readings of AI gains	—
20	GEOGRAPHY / LANGUAGE	Multi-country	—
21	DISTRIBUTIONAL LEDGER	Advanced economies gain more than emerging economies	—
22	R01 / FIELD-MAP LINKS	LD-03, D2	—
23	WHAT WOULD CHANGE THIS CLAIM?	Later years or observed adoption data reversing the pattern	—
24	PUBLICATION STATUS	ELIGIBLE WITH WORKING-PAPER QUALIFIER	state: ELIGIBLE · qualifiers: WORKING_PAPER
25	RECORDED DATE	2026-10-15	—

Field 22 references resolved against Field Map v0.2: LD-03 (LIVING_DEBATE); D2 (DOMAIN)

The sources as recorded, with their record IDs and coded source types:

Pinned Sources (field 6, version-specific)

“Gambacorta, Kharroubi, Mehrotra & Oliviero, “Artificial intelligence and growth in advanced and emerging economies: short-run impact,” BIS Working Papers No 1321, December 2025” → Gambacorta et al. (2025), “Artificial intelligence and…” (SRC-SL-GAMBACORTA-KHARROUBI-MEHROTRA-OLIVIERO-BIS-1321--v1.0) — Gambacorta, Kharroubi, Mehrotra & Oliviero, “Artificial intelligence and growth in advanced and emerging economies: short-run impact,” BIS Working Papers No 1321, December 2025; class WORKING_PAPER (explicit public provenance pin)

Cited by Living Debate versions

Organizational absorption: Why micro gains do not become macro outcomes (v0.1) LD-03

Cite this page

SocioLens project. (2026, October 15). Across 56 economies and 16 industries, industries more exposed to generative AI grew faster in 2022–23 where countries were better prepared for AI, which the authors read as generative AI likely benefiting advanced economies more than emerging ones and widening global income disparities in the near term [Claim LD03-SL-C21, version v1.0]. SocioLens. https://sociolens-web.pages.dev/claims/ld03-sl-c21/v1.0/

@misc{sociolens_ld03_sl_c21_v1_0,
  author = {{SocioLens project}},
  title = {Across 56 economies and 16 industries, industries more exposed to generative AI grew faster in 2022–23 where countries were better prepared for AI, which the authors read as generative AI likely benefiting advanced economies more than emerging ones and widening global income disparities in the near term.},
  year = {2026},
  date = {2026-10-15},
  howpublished = {\url{https://sociolens-web.pages.dev/claims/ld03-sl-c21/v1.0/}},
  note = {Claim LD03-SL-C21, version v1.0}
}

The link points to this version, whose record never changes. For a finding, cite the original study as well: SocioLens summarizes research and does not replace it.

Research-of-record provenance

Materialized by sociolens-materializer 0.6.0 at 2026-09-28T20:00:00.000Z; normalization contract SocioLens — Claim Value Vocabulary v1.0.2 — Pre-Spike Materialization Contract (v1.0.2). Content seal SHA-256 9cdc729324371d4d5e6fb26ad6e6e2a1a3a05e3d9f50489e918305f3c5e4e8a6.

Repository-authored inputs (not Drive exports)

SocioLens claim — LD03-SL-C21 v1.0 (v1.0) — written in the SocioLens repository by Project Lead (Claude, AI) on 2026-10-15; no Drive identity exists. Authority: AGENTS.md "Authority" (the Project Lead decides research content within the research-integrity rules) and the Owner's choice in the SocioLens project chat on 15 October 2026 to build the LD-03 debate; a new Claim, first version, written from the project's LD-03 source notes, checked against full texts or official abstracts. Block “ENTRY 21 — LD03-SL-C21” SHA-256 f7fb59bbf219d16e7e15dcc35b25e5dab743ff284c707cab29cf36a597bcefb0; fixture fixtures/authored/claims/LD03-SL-C21--v1.0.

Builder decisions (not Drive data)

VERSION = v1.0 — basis: Claim version taken from the repository-authored claim fixture version (SocioLens claim — LD03-SL-C21 v1.0); the first version of a new Claim of the LD-03 debate
CURRENT_POINTER = true — basis: repository-authored claim "SocioLens claim — LD03-SL-C21 v1.0" (Project Lead (Claude, AI), 2026-10-15); basis: AGENTS.md "Authority" (the Project Lead decides research content within the research-integrity rules) and the Owner's choice in the SocioLens project chat on 15 October 2026 to build the LD-03 debate; a new Claim, first version, written from the project's LD-03 source notes, checked against full texts or official abstracts
FIELD_6_SOURCE_REFERENCE = SRC-SL-GAMBACORTA-KHARROUBI-MEHROTRA-OLIVIERO-BIS-1321--v1.0 — basis: field 6 of ENTRY 21 — LD03-SL-C21 cites "Gambacorta, Kharroubi, Mehrotra & Oliviero, “Artificial intelligence and growth in advanced and emerging economies: short-run impact,” BIS Working Papers No 1321, December 2025"; the only field-6 segment of this ClaimVersion's repository-authored ledger block.
SOURCE_PROVENANCE_PIN = SRC-SL-GAMBACORTA-KHARROUBI-MEHROTRA-OLIVIERO-BIS-1321--v1.0 — basis: explicit version-pinned public provenance of this ClaimVersion (Amendment v1.2 §6): the pinned field-6 Source record "Gambacorta, Kharroubi, Mehrotra & Oliviero, “Artificial intelligence and growth in advanced and emerging economies: short-run impact,” BIS Working Papers No 1321, December 2025".