uk-ai-study

When the shock hits the top: the fiscal and distributional incidence of AI in the United Kingdom. The UK pairs one of the most AI-exposed workforces in the advanced economies with a tax-benefit system designed to insure shocks at the bottom of the income distribution. This study traces AI's employment, wage and capital shocks through the full UK tax-benefit system with PolicyEngine UK, using the scenario architecture of Doorley, O'Connor, O'Shea & Tuda (2026), Artificial intelligence and income inequality in Ireland, ESRI/DoF Report 16 (PDF).

The study asks: if generative AI displaces some workers, raises the wages of the rest, and raises returns to capital, what happens to the Exchequer, poverty, and income inequality — and who bears it, by income decile and age?

Method — exactly how we do it

The pipeline is C-AIOE exposure → employment/wage/capital shocks (JR16 eqs 3.4/3.5) → tax-benefit microsimulation, with these UK substitutions:

JR16 (Ireland)	This study (UK)
SILC microdata	FRS 2024-25 (frs_2024_25.h5, PolicyEngine build)
SWITCH microsimulation	PolicyEngine UK (latest release)
ISCO occupations	SOC2020 major groups from raw FRS adult.tab
C-AIOE (Pizzinelli et al. 2023)	Same measure, via the populace PR #325 UK crosswalk

1. Exposure (uk_ai_study/exposure.py)

Each FRS adult carries a 1-digit SOC2020 major group in the raw UKDA adult.tab (SOC2020, coded 1000–9000). We join it onto the PolicyEngine person table with person_id = SERNUM*1000 + PERSON — the current policyengine-uk-data convention, verified to match 100% of FRS 2024-25 adults. Each major group then gets its C-AIOE score (Felten AIOE × Pizzinelli complementarity adjustment) and complementarity θ from the packaged crosswalk (uk_ai_study/data/uk_soc2020_major_group_ai_exposure.csv, derived in populace PR #325 from open-licensed sources: Felten et al. 2021, O*NET-reconstructed θ per IMF WP/23/216, ASHE 2025 Table 14 employment weights; OGL v3 / CC BY 4.0 / MIT). Persons without a SOC code (children, non-workers) receive the mean score — they never enter the employment shock, which conditions on positive earnings.

2. Shocks (uk_ai_study/shocks.py)

• Employment (eq 3.4): aggregate displaced = displacement_rate × employed (weighted); allocated across major groups ∝ employment × mean C-AIOE, realised by weighted random draws within each group (probability ∝ individual exposure × survey weight). Displaced workers get employment_income = 0 and employment_status = UNEMPLOYED.
• Wage (eq 3.5): an uplift pool of wage_uplift × surviving wage bill is distributed across surviving workers ∝ θ × earnings — AI complements high-θ occupations.
• Capital: interest and dividend income scaled by (1.005% + 0.4pp)/1.005% ≈ 1.398 (JR16's return-to-capital shock).

Scenario presets (all overridable literature anchors):

Preset	Displacement	Wage	Source
central	7%	+2.6%	Briggs & Kodnani (2023); wage figure is JR16's adopted median (fn.3, §3.2)
low	1%	0%	Acemoglu (2025, Economic Policy 40(121)), employment-only per JR16 fn.8
high	13%	+2.6%	Brynjolfsson, Chandar & Chen (2025) — cohort-specific, upper bound
central_youth_tilted	7%	+2.6%	+ Klein Teeselink (2025) junior/total ratio 5.8/4.5 tilting draws toward ages 16–24

The youth_displacement_multiplier extends JR16 (which draws randomly within groups) toward the seniority-biased evidence in Klein Teeselink (2025) and Hosseini & Lichtinger (2026).

3. Microsimulation (uk_ai_study/runner.py)

Baseline and shocked policyengine_uk.Microsimulation runs on the same dataset; the shocked run receives the modified employment_income, savings_interest_income, dividend_income and employment_status via set_input. Reported deltas (shocked − baseline):

• Exchequer cost — change in gov_balance
• Poverty — BHC and AHC person-weighted rates
• Gini — of equivalised household net income
• By baseline income decile and by age band (16-24 … 65+) — mean household-net-income change, plus each band's share of the displaced

Reproduce

conda create -n ukai python=3.13 -y && conda activate ukai
pip install -e .
export HUGGING_FACE_TOKEN=hf_...   # needs access to policyengine/policyengine-uk-data
python analysis/download_data.py    # FRS h5 + raw UKDA zip (adult.tab) -> data/
python analysis/run_all.py          # all presets -> results/*.json

Microdata is licensed (UKDS EUL) and never committed; data/ is gitignored. Results in results/ are aggregates only.

Known limitations (v0.1)

• 1-digit exposure only: within-major-group exposure variation is lost; JR16 uses finer occupations. A QRF imputation from 4-digit LFS SOC (as in populace PR #325) is the planned upgrade.
• Plain FRS weights, not the calibrated enhanced FRS (its household cloning breaks the adult.tab ID join; needs the SOC merge moved upstream of cloning).
• Displaced workers are current-period unemployed; JR16's "9+ months unemployed, contributory benefits exhausted" contract is not fully expressible in PolicyEngine UK inputs.
• Self-employed are outside all shocks (as in JR16). Decile figures average 50 seeded draws; the central preset is Monte-Carlo'd over 20 draws (analysis/robustness.py); grid cells are single-draw (seed=0).

References

• Doorley, O'Connor, O'Shea & Tuda (2026), ESRI/DoF Report No. 16.
• Pizzinelli et al. (2023), IMF WP/23/216 (C-AIOE, θ).
• Felten, Raj & Seamans (2021) (AIOE).
• Briggs & Kodnani (2023), Goldman Sachs.
• Acemoglu (2025), Economic Policy 40(121).
• Brynjolfsson, Chandar & Chen (2025), "Canaries in the Coal Mine?".
• Klein Teeselink (2025), SSRN 5516798.
• Hosseini & Lichtinger (2026), SSRN 5425555.