Vulnerabilidad laboral a la IA · España 2026

This deposit contains the complete dataset, methodology, and interactive visualisation tool for assessing the theoretical exposure of 502 Spanish occupations to artificial intelligence. The analysis covers 22.46 million workers (EPA Q4 2025, INE) and assigns each occupation a calibrated exposure score on a 0–10 scale, cross-referenced with salary data, EU AI Act risk classification, and impact typology. The interactive dashboard is available at: https://alvarodenicolas.com/interactive/empleos-ia/index.html Dataset The core dataset (spain_502_complete.json) contains 502 records corresponding to the complete CNO-11 occupational taxonomy (SEPE expansion). Each record includes 11 fields: Field Type Description cno string 4-digit CNO-11 occupation code nombre string Official occupation name (Spanish) sector string Assigned economic sector (12 categories) empleo integer Estimated employment (EPA Q4 2025, redistributed via Census 2021 weights) salario_medio_eur float Estimated mean gross annual salary (EUR), based on INE EES 2023 + educational premia + FR/PT proxies (121 unique values; MAPE 4.96% vs 16 INE reference groups) vulnerabilidad_ia_score float AI exposure score (0–10), calibrated with 5 Spain-specific structural factors eu_ai_act string EU AI Act risk classification: "Alto riesgo" (Annex III), "Riesgo limitado", or "Riesgo mínimo" tipo_impacto string Impact typology: "Sustitución", "Híbrido", or "Aumentación" justificacion string 3–4 sentence justification in Spanish explaining the automation vector and human-protective factors census_2021_employed float Census 2021 employment figure used for intra-group weighting employment_method string Employment estimation method identifier Key Findings Weighted mean exposure score: 4.0/10 (unweighted mean: 4.3/10) High-exposure occupations (score ≥7): 122 occupations representing 4,690,622 jobs (20.9% of total employment) Wage-exposure index: 274,100M EUR (formula: Employment × Salary × Score/10 — a weighted index, not a prediction of wage losses) Score range: 1.0 (e.g., hairdressers, cleaners, firefighters) to 9.0 (data-entry clerks) Salary range: 12,985–79,282 EUR/year (121 unique estimated values) Methodology Exposure scores were generated by Gemini 2.5 Pro (temperature 0.2, structured rubric prompts) following the methodological lineage of Brynjolfsson et al. (2018) and Eloundou et al. (2023), adapted to the Spanish CNO-11 taxonomy with structural calibration. Five Spain-specific calibration factors are applied: DESI digitalisation index (DESI 2023, 69.8 points — 3rd in EU; note: the European Commission no longer publishes the composite DESI score after 2023) Services sector weight (74% of GDP, tourism 12.4%) Employment protection (3rd strictest in OECD; 33 days/year severance) EU AI Act (Regulation 2024/1689, Annex III — classifies AI systems by use-case context, not occupations) AESIA supervision (Real Decreto 729/2023; first operational national AI supervisory agency in the EU) The combined calibration produces an 11–12% reduction from base LLM scores. Employment data: EPA Q4 2025 microdata (INE, published 27/01/2026), 22,463,286 total employed. EPA publishes employment at 1-digit CNO level only; 4-digit figures are proportional estimates redistributed using Census 2021 structural weights (145 subgroups at 3-digit CNO). 4-digit employment figures are estimates, not observed data. Salary data: Encuesta de Estructura Salarial 2023 (INE, table 28186) at 2-digit CNO level, adjusted with educational premia (INE) and intra-group variance proxies from France (INSEE) and Portugal (INE-PT). Post-correction MAPE: 4.96% against 16 INE reference groups. Self-employed workers (~3.3M) are excluded from the salary survey by design. Validation Stack Employment validation (1-digit): EPA Q4 2025 totals reproduced with ±0.00% deviation (API Tempus, table 65134). AI score validation (inter-model): 100 stratified occupations blind-rescored by GPT-4o without access to original Gemini scores. Results: Pearson correlation: r = 0.715 (good) Intraclass correlation: ICC(2,1) = 0.701 (good) Weighted kappa: κw = 0.667 (substantial agreement, Landis-Koch convention) Mean absolute deviation: 1.0 point Systematic bias (GPT − Gemini): +0.28 points Agreement within ±1.0 points: 61% of occupations Agreement within ±2.0 points: 84% of occupations Bland-Altman analysis: no proportional bias; 95% limits of agreement: −2.77 to +3.33 points Disagreement pattern: GPT compresses toward the centre, scoring manual/physical occupations higher (+1.07 in the 0–3 band) and knowledge occupations lower (−1.00 in the 7+ band). The 16 large disagreements (>2.0 points) cluster in two interpretable groups: (1) GPT overestimates industrial automation potential (CNO 81xx/82xx), (2) Gemini overestimates the digital component of niche professions (notaries, actors, oenologists). Salary validation: MAPE 4.96% against 16 INE EES 2023 groups (post-correction). All deviations under 10%. Validated by Manus AI. Adversarial multi-model review: The methodology document was subjected to adversarial review by 7 independent AI models (Grok ×2, Perplexity ×2, Manus ×2, Gemini). Consensus corrections incorporated: relabelling of wage-exposure index, DESI 2024→2023 correction, EU AI Act reclassification (elimination of "prohibited" occupation-level category), and sector corrections. Limitations Exposure scores are theoretical estimates, not predictions of job displacement. Empirical evidence (Anthropic Economic Index, February 2026) shows significant gaps between theoretical exposure (~94%) and observed adoption (~33%) in computer/mathematical occupations. 4-digit employment figures are proportional estimates, not observed data. Deviations at 2-digit level against EPA published totals range from ±0% to ±540% due to structural changes between Census 2021 and EPA 2025. Calibration factors are expert judgement without empirical back-testing. Sensitivity analysis (±20%) shows weighted mean exposure shifting from 3.3 to 4.9 and wage-exposure index from 219B to 329B EUR. France/Portugal salary proxies assume structural similarity among southern European economies. Not empirically validated at individual occupation level. MCVL (Muestra Continua de Vidas Laborales) is an identified but unused validation source. Scores were generated in a single pass per model. Estimated intra-model reproducibility: ±0.5 points. The analysis is static (March 2026 snapshot) and does not model job creation by AI, regional variation, or part-time/full-time distinctions. Interactive Dashboard The dashboard (single-page React application) provides four views: Treemap — sector-level aggregation with drill-down to individual occupations; rectangle area proportional to employment, colour indicates exposure score Detailed treemap — occupation-level rectangles nested within sector groups Scatter plot — salary (y-axis) vs. AI exposure (x-axis) with regression trend line; bubble size proportional to employment Sortable list — tabular view with score, employment, salary, sector, and EU AI Act classification Filters: sector selector, minimum/maximum score range sliders, sort by employment/salary/risk. Detail panel: click any occupation for full profile including justification text, EU AI Act classification, impact typology, and wage-exposure sub-index. Comparative Positioning Dimension This analysis willrobotstakemyjob.com OECD AI Exposure ILO GenAI Index Taxonomy CNO-11 (502, Spain) SOC/O*NET (702, US) ~400 ISCO (cross-country) ISCO (cross-country) Scoring LLM + 5 calibration factors Frey & Osborne (2013) Expert + O*NET tasks GPT-4 task scoring Inter-model validation r=0.715, κw=0.667, Bland-Altman None published Expert panel (no LLM cross-check) None published Regulatory mapping EU AI Act (3 risk levels) None None None Salary cross-reference Yes (121 values, MAPE 4.96%) Yes (BLS direct) No No Keywords artificial intelligence, labour market, employment, Spain, AI exposure, occupational risk, EU AI Act, CNO-11, EPA, automation, interactive dashboard, inter-model validation, Bland-Altman, treemap License Creative Commons Attribution 4.0 International (CC BY 4.0) Language Spanish (dataset, justifications, dashboard UI); English (this description, methodology notes bilingual) Resource Type Dataset + Interactive Visualisation + Methodology Document Related Identifiers https://alvarodenicolas.com/interactive/empleos-ia/index.html (IsSupplementedBy — interactive dashboard) Brynjolfsson, E., Mitchell, T. & Rock, D. (2018). "What Can Machines Learn, and What Does It Mean for Occupations and the Economy?" AEA Papers and Proceedings, 108:43-47. (References) Eloundou, T., Manning, S., Mishkin, P. & Rock, D. (2023). "GPTs are GPTs." OpenAI/UPenn. arXiv:2303.10130v5. (References) Frey, C. B. & Osborne, M. A. (2017). "The Future of Employment." Technological Forecasting and Social Change, 114:254-280. (References) Regulation (EU) 2024/1689 (EU AI Act). Annex III, Arts. 5 and 6. (References) Creator Álvaro de Nicolás ORCID: [add your ORCID if available] Affiliation: Revamp Advisors / Independent researcher Version v11a (March 2026) Date 2026-03-16 Files to Upload spain_502_complete.json — Core dataset (502 occupations, 11 fields per record) ia-empleo-espana-metodologia-v11a.pdf — Full methodology document (35 technical notes) inter_model_reliability.png — Inter-model validation chart (Gemini vs GPT-4o scatter + Bland-Altman) index.html — Interactive dashboard (self-contained React application)