Inteligencia artificial y empleo en Espa\~na: una aproximaci\'on territorial y de g\'enero a la exposici\'on laboral

Antoni Mestre; Manoli Albert; Vicente Pelechano; Xavier Naya

REVIEW 2 major objections 2 minor 30 references

A sector-based AI incidence matrix applied to Spanish provincial employment data from 2021-2023 shows higher exposure in metropolitan service regions and consistently higher exposure among female workers.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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T0 review · grok-4.3

2026-05-16 18:57 UTC

load-bearing objection A practical Spain-specific mapping of AI exposure by province and gender via sector-level CNAE matrix, but the proxy risks smoothing over real occupational differences inside sectors. the 2 major comments →

arxiv 2512.23059 v3 submitted 2025-12-28 cs.CY

Inteligencia artificial y empleo en Espa\~na: una aproximaci\'on territorial y de g\'enero a la exposici\'on laboral

Antoni Mestre , Xavier Naya , Manoli Albert , Vicente Pelechano This is my paper

classification cs.CY

keywords AI exposurelabor marketSpainterritorial analysisgender gapCNAE sectorsprovincial employmentgenerative AI

verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper builds a methodological framework using sector data to estimate AI exposure in the Spanish labor market, sidestepping the lack of detailed occupation statistics. It constructs an incidence matrix tied to CNAE industry codes and multiplies it by provincial employment figures broken down by gender. Results indicate stable patterns over the three years, with greater exposure in urban and service-heavy areas plus a persistent gap where women show higher exposure in every territory. This approach supplies a structural view of where AI is likely to alter skill requirements and work organization rather than forecasting outright job losses. Such mapping supports targeted policy responses for regional development and workforce adaptation.

Core claim

By constructing an AI CNAE incidence matrix and applying it to provincial employment data for the period 2021 to 2023, we provide a territorial and gender disaggregated assessment of AI exposure across Spain. The results reveal stable structural patterns, with higher exposure in metropolitan and service oriented regions and a consistent gender gap, as female employment exhibits higher exposure in all territories. Rather than predicting job displacement, the framework offers a structural perspective on where AI is most likely to reshape work and skill demands, supporting evidence based policy and strategic planning.

What carries the argument

The AI CNAE incidence matrix, which assigns exposure levels to economic sectors via CNAE codes and scales them by provincial employment counts to produce territorial and gender-specific exposure estimates.

Load-bearing premise

That a sector-level incidence matrix built from CNAE codes adequately captures AI exposure without occupation-level detail and works reliably in the Spanish labor market context.

What would settle it

Direct comparison of the sector-matrix exposure estimates against fine-grained occupation-level AI exposure data for the same provinces and years that produces large systematic differences.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

AI is expected to reshape work and skill demands most strongly in metropolitan and service-oriented regions.
Female employment faces higher exposure in every territory, suggesting needs for gender-aware training and reskilling programs.
The stable patterns over 2021-2023 indicate that exposure differences are structural rather than transitory.
The framework supplies evidence for strategic planning and policy without assuming widespread job displacement.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same sector-matrix method could be adapted to other countries that publish provincial employment by industry code to compare exposure geography.
High-exposure provinces might prioritize public investment in digital skills infrastructure to match the structural shifts identified.
Overlaying the exposure map with existing regional training budgets could test whether current programs align with the identified needs.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Desk Editor's Note

A practical Spain-specific mapping of AI exposure by province and gender via sector-level CNAE matrix, but the proxy risks smoothing over real occupational differences inside sectors.

read the letter

The main takeaway is that this paper adapts existing AI exposure ideas to Spanish data by building a sector incidence matrix on CNAE codes and layering it onto provincial employment stocks from 2021-2023, with a gender split. It produces maps showing higher exposure in metropolitan service areas and a steady female premium across all territories, framed as a structural view rather than job-loss forecasts. That combination of territorial and gender detail for one country is the concrete addition, and it fits local policy needs where occupation-level data may be patchy. The results look stable and the approach stays modest in its claims, which is a plus. The soft spot is the sector-level proxy itself. Exposure to AI tasks can vary sharply inside the same CNAE code, and provinces and genders differ in their internal job mixes, so the rankings could shift if the matrix entries were refined. The abstract gives no error bars, sensitivity runs, or cross-checks against occupation data, so the strength of the territorial and gender patterns rests on how well that homogeneity assumption holds. If the full methods section has any validation steps or robustness tables, they would matter a lot. This is aimed at labor economists and regional policymakers working on Spain or comparable settings who need quick disaggregated baselines. It has enough structure and policy relevance to go to referees, mainly to pressure the authors on the matrix construction and any checks they did run.

Referee Report

2 major / 2 minor

Summary. The paper proposes a sector-based methodological framework to estimate AI exposure in Spanish employment by constructing an AI incidence matrix using CNAE codes and applying it to provincial employment stocks for 2021–2023. It delivers territorial and gender-disaggregated results showing stable patterns of higher exposure in metropolitan and service-oriented regions together with a consistent gender gap in which female employment exhibits higher exposure across all territories.

Significance. If the sector-level proxy proves adequate, the work supplies a practical, data-accessible tool for mapping AI exposure where occupation-level detail is unavailable, yielding policy-relevant evidence on spatial concentration and gender differentials that can inform targeted reskilling and regional planning.

major comments (2)

[Methods] Methods section on matrix construction: the AI CNAE incidence matrix is applied directly to provincial employment data without reported validation against occupation-level benchmarks, sensitivity tests for intra-sector heterogeneity, or error bounds; because provincial and gender employment compositions differ, any within-CNAE variation (e.g., routine vs. non-routine roles) directly affects the reported territorial rankings and female exposure premium.
[Results] Results section: the claim of 'stable structural patterns' and a 'consistent gender gap' rests on point estimates from the incidence matrix; no robustness checks, alternative matrix specifications, or occupation-weighted sensitivity analyses are presented, leaving the load-bearing assumption that sector-level incidence adequately captures exposure untested.

minor comments (2)

[Methods] Clarify the exact procedure and data sources used to assign incidence values to each CNAE code; a table listing the matrix entries would improve transparency.
[Introduction] Add references to prior occupation-based AI exposure studies (e.g., Frey & Osborne, Felten et al.) to better justify the sector-level choice in the Spanish context.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on our manuscript. We address each major comment below and indicate the revisions we will make to strengthen the presentation of our sector-based framework.

read point-by-point responses

Referee: [Methods] Methods section on matrix construction: the AI CNAE incidence matrix is applied directly to provincial employment data without reported validation against occupation-level benchmarks, sensitivity tests for intra-sector heterogeneity, or error bounds; because provincial and gender employment compositions differ, any within-CNAE variation (e.g., routine vs. non-routine roles) directly affects the reported territorial rankings and female exposure premium.

Authors: We acknowledge that the current Methods section does not include explicit validation against occupation-level benchmarks or formal sensitivity tests for intra-sector heterogeneity. Our sector-based proxy was developed specifically because granular occupation-level AI exposure data linked to provincial employment stocks are not available in Spain. In the revision we will add a dedicated limitations subsection that discusses within-CNAE variation and its potential effects on rankings and the gender gap. We will also introduce sensitivity analyses that perturb incidence scores for the largest sectors using alternative literature-based weights and report bootstrap error bounds on the provincial exposure estimates. These changes will allow readers to evaluate the robustness of the reported patterns. revision: partial
Referee: [Results] Results section: the claim of 'stable structural patterns' and a 'consistent gender gap' rests on point estimates from the incidence matrix; no robustness checks, alternative matrix specifications, or occupation-weighted sensitivity analyses are presented, leaving the load-bearing assumption that sector-level incidence adequately captures exposure untested.

Authors: The stability claim is currently supported by the consistency of results across the three annual cross-sections (2021–2023). To address the referee’s concern directly, the revised Results section will include a new robustness subsection that presents (i) alternative matrix specifications drawn from different AI-exposure taxonomies in the literature and (ii) sensitivity checks that reallocate borderline sectors. We will also show how these variations affect both the territorial ordering and the female exposure premium. While full occupation-weighted re-estimation remains infeasible given data constraints, the added checks will test the sensitivity of the core findings to the sector-level assumption. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The derivation applies an independently constructed sector-level AI incidence matrix (built from CNAE codes) to external provincial employment stocks for 2021-2023. Exposure estimates are obtained via direct weighted aggregation of the matrix entries with observed employment counts; this is a linear calculation with no fitted parameters renamed as predictions, no self-definitional loops, and no load-bearing self-citations that reduce the central result to prior author work by construction. The framework remains self-contained against the cited data sources.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The framework rests on the domain assumption that sector codes can proxy occupational AI exposure and on the construction of an incidence matrix whose values are not derived from first principles.

axioms (1)

domain assumption Sector-based classification sufficiently proxies occupational exposure to AI in the Spanish context
Paper explicitly addresses limitations of occupation-centered approaches by shifting to sectors.

pith-pipeline@v0.9.0 · 5461 in / 1029 out tokens · 41970 ms · 2026-05-16T18:57:50.764422+00:00 · methodology

0 comments

read the original abstract

The diffusion of artificial intelligence, particularly generative models, is expected to transform labor markets in uneven ways across sectors, territories, and social groups. This paper proposes a methodological framework to estimate the potential exposure of employment to AI using sector based data, addressing the limitations of occupation centered approaches in the Spanish context. By constructing an AI CNAE incidence matrix and applying it to provincial employment data for the period 2021 to 2023, we provide a territorial and gender disaggregated assessment of AI exposure across Spain. The results reveal stable structural patterns, with higher exposure in metropolitan and service oriented regions and a consistent gender gap, as female employment exhibits higher exposure in all territories. Rather than predicting job displacement, the framework offers a structural perspective on where AI is most likely to reshape work and skill demands, supporting evidence based policy and strategic planning.

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

By constructing an AI CNAE incidence matrix and applying it to provincial employment data... factor de incidencia comprendido entre 0,06 y 0,30
IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

modelo de cálculo... IA_empleo(p,c) = empleo(p,c) * factorIA(c)

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

30 extracted references · 30 canonical work pages

[1]

Introducción ....................................................................... 3

work page
[2]

Estado del Arte ................................................................... 5 2.1. De la automatización clásica a la IA generativa................................................. 5 2.2. Medición de la exposición ocupacional a la IA .................................................. 6 2.3. IA generativa, productividad y desempeño laboral ............

work page
[3]

Metodología ....................................................................... 8 3.1. Modelo de cálculo ........................................................................................... 10 3.2. Limitaciones del modelo .................................................................................. 12

work page
[4]

Resultados ........................................................................ 13 4.1. Exposición agregada de España ....................................................................... 14 4.2. Distribución territorial de la exposición (2021–2022) ..................................... 14 4.3. Exposición por género: una brecha estructural y persist...

work page 2021
[5]

Conclusiones y trabajos futuros ........................................ 20 5.1. Conclusiones .................................................................................................... 20 5.1. Trabajos futuros ............................................................................................... 21 Bibliografía ............................

work page
[6]

factor de incidencia

Introducción La acelerada expansión de la inteligencia artificial (IA) en los últimos años ha reconfigurado de manera sustancial los debates sobre el futuro del trabajo, la estructura productiva y la cohesión social. Desde la irrupción de los modelos de aprendizaje profundo y, especialmente, de los modelos fundacionales y generativos, la IA ha pasado de s...

work page 2021
[7]

Proporciona una visión territorial comparada, mostrando qué provincias y comunidades autónomas presentan mayor exposición relativa a la IA en función de su composición sectorial

work page
[8]

Permite estudiar las diferencias de género en la exposición tecnológica, poniendo de manifiesto patrones de riesgo diferencial que pueden requerir políticas específicas

work page
[9]

Construye una herramienta metodológica trasladable a futuras ediciones del Censo de Población Ocupada, permitiendo comparar tendencias año a año

work page
[10]

Facilita el diálogo entre instituciones, organizaciones sindicales y agentes económicos, proporcionando un lenguaje común para discutir la transición tecnológica. En un momento en el que España está desplegando estrategias nacionales de digitalización e inteligencia artificial —como la Estrategia Nacional de IA (ENIA), el PERTE de la Nueva Economía de la ...

work page 2021
[11]

Este informe se apoya en estos cuatro pilares para construir una matriz de incidencia CNAE-IA aplicada al territorio y al género

Estado del Arte En los últimos años se ha consolidado una literatura amplia y heterogénea sobre el impacto de la inteligencia artificial (IA) en el trabajo, con cuatro líneas principales: (1) la medición de la exposición ocupacional a la IA, (2) el análisis específico de la IA generativa, (3) los impactos diferenciales por género, y (4) la dimensión terri...

work page
[12]

empleo equivalente

Metodología La metodología empleada en este informe nace de una necesidad evidente: mientras la mayor parte de la literatura internacional sobre inteligencia artificial se formula en términos ocupacionales —habitualmente a partir de las clasificaciones SOC, ISCO o ESCO—, la estadística oficial española ofrece una fotografía del mercado laboral basada fund...

work page 2021
[13]

Resultados La aplicación de la matriz CNAE–IA a los datos provinciales y autonómicos de empleo permite identificar una serie de patrones territoriales y de género que estructuran el impacto potencial de la inteligencia artificial en el mercado laboral español. Aunque este es un ejercicio preliminar —y así debe interpretarse—, los resultados muestran una c...

work page 2022
[14]

Conclusiones y trabajos futuros El análisis desarrollado en este informe ofrece una primera aproximación sistemática al impacto potencial de la inteligencia artificial sobre el mercado laboral español, integrando dimensiones territoriales y de género que suelen quedar relegadas en la literatura. El ejercicio permite visualizar con claridad cómo la estruct...

work page 2021
[15]

The Skill Content of Recent Technological Change,

D. Autor, F. Levy, and R. Murnane, “The Skill Content of Recent Technological Change,” Quarterly Journal of Economics, vol. 118, no. 4, pp. 1279–1333, 2003

work page 2003
[16]

What Can Machine Learning Do? Workforce Implications,

E. Brynjolfsson and T. Mitchell, “What Can Machine Learning Do? Workforce Implications,” Science, vol. 358, pp. 1530–1534, 2017

work page 2017
[17]

How Will Language Modelers Affect Occupations?,

E. Felten, M. Raj, and R. Seamans, “How Will Language Modelers Affect Occupations?,” Brookings Papers on Economic Activity, 2023

work page 2023
[18]

Working with AI: Measuring the Applicability of Generative AI to Occupations,

T. Tomlinson, J. Fernandez, S. Agarwal, et al., “Working with AI: Measuring the Applicability of Generative AI to Occupations,” 2025

work page 2025
[19]

arXiv preprint arXiv:2303.10130 , year=

T. Eloundou, S. Manning, P. Mishkin, and A. Rock, “GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models,” arXiv:2303.10130, 2023

work page arXiv 2023
[20]

The Exposure of EU Occupations to Generative AI,

L. Nurski, “The Exposure of EU Occupations to Generative AI,” Bruegel Working Paper, 2024

work page 2024
[21]

Generative AI at Work,

E. Brynjolfsson, Y. Li, and L. Raymond, “Generative AI at Work,” NBER Working Paper 31161, 2023

work page 2023
[22]

El impacto de la inteligencia artificial en las tareas laborales,

Banco de España, “El impacto de la inteligencia artificial en las tareas laborales,” Informe Analítico, 2024

work page 2024
[23]

Labour Market Effects of AI Adoption in Europe,

European Central Bank, “Labour Market Effects of AI Adoption in Europe,” ECB Working Paper, 2024

work page 2024
[24]

AI Exposure and Regional Labour Markets in Europe,

D. Guarascio, L. Marcolin, and M. Squicciarini, “AI Exposure and Regional Labour Markets in Europe,” Regional Studies, 2023

work page 2023
[25]

Local Employment Implications of AI Adoption,

OECD, “Local Employment Implications of AI Adoption,” OECD Local Economic and Employment Development (LEED), 2023

work page 2023
[26]

AI Ecosystems in Spain,

OECD, “AI Ecosystems in Spain,” OECD Digital Economy Papers, 2024

work page 2024
[27]

Algorithm and Eve: Gender and AI in Labour Markets,

OECD, “Algorithm and Eve: Gender and AI in Labour Markets,” 2023

work page 2023
[28]

AI Adoption and Female Employment Participation in Europe,

European Central Bank, “AI Adoption and Female Employment Participation in Europe,” ECB Working Paper, 2024

work page 2024
[29]

Generative AI and its Implications for Women’s Work,

International Labour Organization (ILO), “Generative AI and its Implications for Women’s Work,” ILO Report, 2023

work page 2023
[30]

Exposición tecnológica de las titulaciones universitarias en España,

Funcas, “Exposición tecnológica de las titulaciones universitarias en España,” Documento de Trabajo, 2024. 24 ANEXO Anexo I: Mapas de incidencia IA Incidencia de la IA en el trabajo en el 2022 (izquierda) y en el 2021 (derecha). Incidencia de la IA en el trabajo en el 2022 en las mujeres (izquierda) y en los hombres (derecha). 25 Incidencia de la IA en el...

work page 2024

[1] [1]

Introducción ....................................................................... 3

work page

[2] [2]

Estado del Arte ................................................................... 5 2.1. De la automatización clásica a la IA generativa................................................. 5 2.2. Medición de la exposición ocupacional a la IA .................................................. 6 2.3. IA generativa, productividad y desempeño laboral ............

work page

[3] [3]

Metodología ....................................................................... 8 3.1. Modelo de cálculo ........................................................................................... 10 3.2. Limitaciones del modelo .................................................................................. 12

work page

[4] [4]

Resultados ........................................................................ 13 4.1. Exposición agregada de España ....................................................................... 14 4.2. Distribución territorial de la exposición (2021–2022) ..................................... 14 4.3. Exposición por género: una brecha estructural y persist...

work page 2021

[5] [5]

Conclusiones y trabajos futuros ........................................ 20 5.1. Conclusiones .................................................................................................... 20 5.1. Trabajos futuros ............................................................................................... 21 Bibliografía ............................

work page

[6] [6]

factor de incidencia

Introducción La acelerada expansión de la inteligencia artificial (IA) en los últimos años ha reconfigurado de manera sustancial los debates sobre el futuro del trabajo, la estructura productiva y la cohesión social. Desde la irrupción de los modelos de aprendizaje profundo y, especialmente, de los modelos fundacionales y generativos, la IA ha pasado de s...

work page 2021

[7] [7]

Proporciona una visión territorial comparada, mostrando qué provincias y comunidades autónomas presentan mayor exposición relativa a la IA en función de su composición sectorial

work page

[8] [8]

Permite estudiar las diferencias de género en la exposición tecnológica, poniendo de manifiesto patrones de riesgo diferencial que pueden requerir políticas específicas

work page

[9] [9]

Construye una herramienta metodológica trasladable a futuras ediciones del Censo de Población Ocupada, permitiendo comparar tendencias año a año

work page

[10] [10]

Facilita el diálogo entre instituciones, organizaciones sindicales y agentes económicos, proporcionando un lenguaje común para discutir la transición tecnológica. En un momento en el que España está desplegando estrategias nacionales de digitalización e inteligencia artificial —como la Estrategia Nacional de IA (ENIA), el PERTE de la Nueva Economía de la ...

work page 2021

[11] [11]

Este informe se apoya en estos cuatro pilares para construir una matriz de incidencia CNAE-IA aplicada al territorio y al género

Estado del Arte En los últimos años se ha consolidado una literatura amplia y heterogénea sobre el impacto de la inteligencia artificial (IA) en el trabajo, con cuatro líneas principales: (1) la medición de la exposición ocupacional a la IA, (2) el análisis específico de la IA generativa, (3) los impactos diferenciales por género, y (4) la dimensión terri...

work page

[12] [12]

empleo equivalente

Metodología La metodología empleada en este informe nace de una necesidad evidente: mientras la mayor parte de la literatura internacional sobre inteligencia artificial se formula en términos ocupacionales —habitualmente a partir de las clasificaciones SOC, ISCO o ESCO—, la estadística oficial española ofrece una fotografía del mercado laboral basada fund...

work page 2021

[13] [13]

Resultados La aplicación de la matriz CNAE–IA a los datos provinciales y autonómicos de empleo permite identificar una serie de patrones territoriales y de género que estructuran el impacto potencial de la inteligencia artificial en el mercado laboral español. Aunque este es un ejercicio preliminar —y así debe interpretarse—, los resultados muestran una c...

work page 2022

[14] [14]

Conclusiones y trabajos futuros El análisis desarrollado en este informe ofrece una primera aproximación sistemática al impacto potencial de la inteligencia artificial sobre el mercado laboral español, integrando dimensiones territoriales y de género que suelen quedar relegadas en la literatura. El ejercicio permite visualizar con claridad cómo la estruct...

work page 2021

[15] [15]

The Skill Content of Recent Technological Change,

D. Autor, F. Levy, and R. Murnane, “The Skill Content of Recent Technological Change,” Quarterly Journal of Economics, vol. 118, no. 4, pp. 1279–1333, 2003

work page 2003

[16] [16]

What Can Machine Learning Do? Workforce Implications,

E. Brynjolfsson and T. Mitchell, “What Can Machine Learning Do? Workforce Implications,” Science, vol. 358, pp. 1530–1534, 2017

work page 2017

[17] [17]

How Will Language Modelers Affect Occupations?,

E. Felten, M. Raj, and R. Seamans, “How Will Language Modelers Affect Occupations?,” Brookings Papers on Economic Activity, 2023

work page 2023

[18] [18]

Working with AI: Measuring the Applicability of Generative AI to Occupations,

T. Tomlinson, J. Fernandez, S. Agarwal, et al., “Working with AI: Measuring the Applicability of Generative AI to Occupations,” 2025

work page 2025

[19] [19]

arXiv preprint arXiv:2303.10130 , year=

T. Eloundou, S. Manning, P. Mishkin, and A. Rock, “GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models,” arXiv:2303.10130, 2023

work page arXiv 2023

[20] [20]

The Exposure of EU Occupations to Generative AI,

L. Nurski, “The Exposure of EU Occupations to Generative AI,” Bruegel Working Paper, 2024

work page 2024

[21] [21]

Generative AI at Work,

E. Brynjolfsson, Y. Li, and L. Raymond, “Generative AI at Work,” NBER Working Paper 31161, 2023

work page 2023

[22] [22]

El impacto de la inteligencia artificial en las tareas laborales,

Banco de España, “El impacto de la inteligencia artificial en las tareas laborales,” Informe Analítico, 2024

work page 2024

[23] [23]

Labour Market Effects of AI Adoption in Europe,

European Central Bank, “Labour Market Effects of AI Adoption in Europe,” ECB Working Paper, 2024

work page 2024

[24] [24]

AI Exposure and Regional Labour Markets in Europe,

D. Guarascio, L. Marcolin, and M. Squicciarini, “AI Exposure and Regional Labour Markets in Europe,” Regional Studies, 2023

work page 2023

[25] [25]

Local Employment Implications of AI Adoption,

OECD, “Local Employment Implications of AI Adoption,” OECD Local Economic and Employment Development (LEED), 2023

work page 2023

[26] [26]

AI Ecosystems in Spain,

OECD, “AI Ecosystems in Spain,” OECD Digital Economy Papers, 2024

work page 2024

[27] [27]

Algorithm and Eve: Gender and AI in Labour Markets,

OECD, “Algorithm and Eve: Gender and AI in Labour Markets,” 2023

work page 2023

[28] [28]

AI Adoption and Female Employment Participation in Europe,

European Central Bank, “AI Adoption and Female Employment Participation in Europe,” ECB Working Paper, 2024

work page 2024

[29] [29]

Generative AI and its Implications for Women’s Work,

International Labour Organization (ILO), “Generative AI and its Implications for Women’s Work,” ILO Report, 2023

work page 2023

[30] [30]

Exposición tecnológica de las titulaciones universitarias en España,

Funcas, “Exposición tecnológica de las titulaciones universitarias en España,” Documento de Trabajo, 2024. 24 ANEXO Anexo I: Mapas de incidencia IA Incidencia de la IA en el trabajo en el 2022 (izquierda) y en el 2021 (derecha). Incidencia de la IA en el trabajo en el 2022 en las mujeres (izquierda) y en los hombres (derecha). 25 Incidencia de la IA en el...

work page 2024