AI Adoption and Youth Labour-Market Slack: Early Evidence from EU Countries

Javier G. Clemente; Isabel Reiné-Cortés

doi:10.2478/lel-2026-0003

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AI Adoption and Youth Labour-Market Slack: Early Evidence from EU Countries

Labour Economics Letters

Volume 2026 (2026): Issue 1 (June 2026)

By: Javier G. Clemente and Isabel Reiné-Cortés

Open Access

|Oct 2026

Table 1.

Descriptive statistics for labour-market slack and components, by age group

	Mean	SD	Min	Max	Missing country-years obs
Total slack, ages 15-24	28.53	10.24	10.70	52.00	0
Unemployment, ages 15-24	14.58	5.00	5.50	31.30	1
Underemployed part-time, ages 15-24	5.29	3.55	0.80	13.80	21
Seeking, not available, ages 15-24	3.56	3.16	0.30	13.80	39
Available, not seeking, ages 15-24	6.71	4.15	1.40	22.20	4
Total slack, ages 25-54	9.51	3.85	3.60	22.10	0
Unemployment, ages 25-54	5.20	2.16	2.20	13.80	0
Underemployed part-time, ages 25-54	1.95	1.23	0.20	5.10	0
Seeking, not available, ages 25-54	0.79	0.69	0.10	4.00	9
Available, not seeking, ages 25-54	1.62	1.39	0.20	10.20	2
Total slack, ages 55-74	9.38	3.97	2.90	22.00	0
Unemployment, ages 55-74	4.29	1.98	1.70	12.10	4
Underemployed part-time, ages 55-74	1.85	1.08	0.20	5.00	13
Seeking, not available, ages 55-74	0.66	0.34	0.10	1.60	45
Available, not seeking, ages 55-74	2.99	1.81	0.40	10.10	9

[i] Notes: Rows identify the outcome and age group. Mean, standard deviation, minimum and maximum use all available observations. Missing country-years counts missing observations among the 108 possible country-years for each age group. Outcomes are percentages of the extended labour force.

[ii] Source: Eurostat Labour Force Survey.

Table 2.

Descriptive statistics for AI adoption and country-year controls

	Mean	SD	Min	Max	Country-year obs
Total
Enterprises using at least one AI technology (%)	12.74	8.76	1.38	42.03	108
Real GDP growth	3.14	3.47	−2.70	16.30	108
HICP inflation	3.84	2.65	0.60	17.00	108
Population with tertiary education (%)	31.87	7.80	14.60	48.00	108
ICT specialists (% of employment)	5.13	1.45	2.40	8.90	108
Total population, millions	16.61	22.03	0.52	83.58	108
Employment share in knowledge-intensive services	13.29	3.72	6.55	27.10	108

[i] Notes: Each country-year appears once. AI adoption is the percentage of enterprises using at least one AI technology. Controls shown are real GDP growth, HICP inflation, tertiary education, ICT specialists, population and employment in knowledge-intensive services.

[ii] Source: Eurostat.

Table 3.

AI adoption and labour-market slack by age group

	(1)	(2)	(3)
	Macro controls	Full controls	Country-year FE
AI adoption (%)	0.143**	0.135**
	(0.0553)	(0.0652)
AI adoption × Youth 15-24	0.116***	0.116**	0.116***
	(0.0415)	(0.0426)	(0.0411)
AI adoption × Adult 25-54	0.0451***	0.0451***	0.0451***
	(0.0148)	(0.0152)	(0.0146)
Constant	12.94***	5.489	15.07***
	(0.630)	(34.46)	(0.220)
Controls	Macro	Full	Absorbed
Country-age FE	Yes	Yes	Yes
Year FE	Yes	Yes	No
Country-year FE	No	No	Yes
Countries	27	27	27
Observations	321	321	321

Notes: The dependent variable is total labour-market slack. Seniors aged 55-74 are the reference group. The main estimation sample is restricted to country-year observations with complete information for all controls used across specifications. Macro controls are GDP growth and HICP inflation; full controls add tertiary education, ICT specialists, population, knowledge-intensive services and detailed sector shares. The country-year fixed-effects model absorbs the AI main effect. Standard errors are clustered by country.

* p<0.10,

** p<0.05,

*** p<0.01.

Table 4.

AI adoption and components of labour-market slack

	(1)	(2)	(3)	(4)
	Unemployment	Underemployed PT	Seeking, not available	Available, not seeking
AI adoption × Youth 15-24	0.0199	0.0518***	0.0251**	0.0235**
	(0.0254)	(0.0173)	(0.00913)	(0.0107)
AI adoption × Adult 25-54	0.0117	0.00476	0.00957	0.0167*
	(0.00864)	(0.00584)	(0.00623)	(0.00937)
Constant	7.937***	2.789***	1.555***	3.620***
	(0.137)	(0.0888)	(0.0596)	(0.0585)
Controls	Absorbed	Absorbed	Absorbed	Absorbed
Country-age FE	Yes	Yes	Yes	Yes
Year FE	No	No	No	No
Country-year FE	Yes	Yes	Yes	Yes
Countries	27	25	20	27
Observations	317	276	204	307

Notes: Each column uses its outcome-specific sample and country-age and country-year fixed effects. Seniors aged 55-74 are the reference group. Standard errors are clustered by country.

* p<0.10,

** p<0.05,

*** p<0.01.

Table 5.

Population-weighted robustness: preferred specification for slack and its components

	(1)	(2)	(3)	(4)	(5)
	Total slack	Unemployment	Underemployed PT	Seeking, not available	Available, not seeking
AI adoption × Youth 15-24	0.0720	−0.00193	0.0260**	0.0183**	0.0278**
	(0.0459)	(0.0424)	(0.0111)	(0.00668)	(0.0135)
AI adoption × Adult 25-54	0.0457***	0.0132	−0.00145	0.00483	0.0266**
	(0.0133)	(0.0144)	(0.00325)	(0.00451)	(0.0102)
Constant	16.29***	8.232***	2.879***	1.341***	4.159***
	(0.227)	(0.221)	(0.0480)	(0.0293)	(0.0783)
Controls	Absorbed	Absorbed	Absorbed	Absorbed	Absorbed
Weights	Population	Population	Population	Population	Population
Country-age FE	Yes	Yes	Yes	Yes	Yes
Year FE	No	No	No	No	No
Country-year FE	Yes	Yes	Yes	Yes	Yes
Countries	27	27	25	20	27
Observations	324	317	276	204	307

Notes: Each column uses its outcome-specific sample and the preferred fixed-effects model. Analytic weights are total population. Seniors aged 55-74 are the reference group. Standard errors are clustered by country.

* p<0.10,

** p<0.05,

*** p<0.01.

Table 6.

Robustness: youth defined as 15-29 versus seniors aged 55-74

	(1)	(2)	(3)
	Macro controls	Full controls	Country-year FE
AI adoption (%)	0.160***	0.153**
	(0.0510)	(0.0618)
AI adoption × Youth 15-29	0.122***	0.122***	0.122***
	(0.0310)	(0.0323)	(0.0306)
Constant	11.97***	7.912	14.25***
	(0.636)	(35.85)	(0.192)
Controls	Macro	Full	Absorbed
Country-age FE	Yes	Yes	Yes
Year FE	Yes	Yes	No
Country-year FE	No	No	Yes
Countries	27	27	27
Observations	214	214	214

Notes: Youth are aged 15-29 and seniors aged 55-74 are the reference group. Macro and full controls are defined as in Table 3. Standard errors are clustered by country.

* p<0.10,

** p<0.05,

*** p<0.01.

Table 7.

Geographical heterogeneity across four EU regions

	(1)	(2)	(3)	(4)
	Northern EU	Southern EU	Western EU	Central/Eastern EU
AI adoption × Youth 15-24	0.199**	−0.235	0.124***	0.0105
	(0.0625)	(0.242)	(0.0147)	(0.0997)
AI adoption × Adult 25-54	0.0445	0.0113	0.0466**	0.0698
	(0.0274)	(0.0589)	(0.0142)	(0.0436)
Sample	Northern EU	Southern EU	Western EU	Central/Eastern EU
Controls	Absorbed	Absorbed	Absorbed	Absorbed
Country-age FE	Yes	Yes	Yes	Yes
Country-year FE	Yes	Yes	Yes	Yes
Countries	7	6	6	8
Observations	84	72	69	96

[i] Notes: All columns estimate total slack with country-age and country-year fixed effects. North: DK, EE, FI, IE, LT, LV, SE. South: CY, EL, ES, IT, MT, PT. West: AT, BE, DE, FR, LU, NL. Central/East: BG, CZ, HR, HU, PL, RO, SI, SK. Standard errors are clustered by country within each subsample; inference is exploratory because cluster counts are small.

Table 8.

Economic significance of the preferred estimates

	Beta	Wild p-value	Effect of 10 pp	Effect of observed rise	Mean age gap
Total slack: youth vs seniors	0.116	0.013	1.155	1.505	19.007
Total slack: adults vs seniors	0.045	0.003	0.451	0.588	0.131
Unemployment component: youth vs seniors	0.020	0.448	0.199	0.259	10.347
Unemployment component: adults vs seniors	0.012	0.203	0.117	0.152	0.945
Underemployed part-time: youth vs seniors	0.052	0.009	0.518	0.675	3.360
Underemployed part-time: adults vs seniors	0.005	0.448	0.048	0.062	0.249
Seeking, not available: youth vs seniors	0.025	0.007	0.251	0.327	2.859
Seeking, not available: adults vs seniors	0.010	0.102	0.096	0.125	0.300
Available, not seeking: youth vs seniors	0.023	0.050	0.235	0.306	3.784
Available, not seeking: adults vs seniors	0.017	0.096	0.167	0.217	−1.309

[i] Notes: Betas reproduce the preferred estimates in Tables 3 and 4. Wild p is based on 9,999 wild-cluster bootstrap replications clustered by country on the identical estimation sample. Effect 10pp and Effect observed scale Beta by a 10-point increase and by the EU mean 2021-2025 increase in AI adoption, respectively. Standard errors are omitted; inference in this table uses the reported wild-bootstrap p-values.

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DOI: https://doi.org/10.2478/lel-2026-0003 | Journal eISSN: 3071-8317 | Journal ISSN: 3071-8317

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Language: English

Page range: 21 - 31

Submitted on: Jun 30, 2026

Accepted on: Aug 25, 2026

Published on: Oct 14, 2026

Published by: Asociacion Española de Economia del Trabajo

In partnership with: Paradigm Publishing Services

Publication frequency: 1 issue per year

Keywords:

artificial intelligence,

youth unemployment,

labour-market slack,

technological change,

EU labour markets

JEL:

O33,

J21,

J23

Related subjects:

Business and economics,

Mathematics and statistics for economists,

Econometrics

© 2026 Javier G. Clemente, Isabel Reiné-Cortés, published by Asociacion Española de Economia del Trabajo
This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 License.

Volume 2026 (2026): Issue 1 (June 2026)

AI Adoption and Youth Labour-Market Slack: Early Evidence from EU Countries

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