Italy’s Human-Capital Deficit: Employment – Productivity and AI Competitiveness Through 2035

A comparative strategic assessment of tertiary educational attainment, labour-market utilisation, skills shortages and technological competitiveness across Italy, Germany, France, Spain, Poland and the European Union, with the United Kingdom considered as an additional European reference where appropriate.

Executive Summary

Principal judgment — High confidence. Italy faces a structural human-capital disadvantage that cannot be explained solely by insufficient university attainment. It combines a comparatively small tertiary-educated workforce with incomplete utilisation of women, foreign graduates and younger workers, thereby weakening the country’s ability to respond simultaneously to demographic ageing, industrial transformation and increasingly skill-intensive technological change.

The statistical position is unusually pronounced. According to Istat’s release of 3 November 2026, 22.3% of Italians aged 25–64 held a tertiary qualification in 2025, compared with 36.9% across the EU-27, 44.9% in France and 42.4% in Spain. The Italian shortfall relative to the EU average was therefore 14.6 percentage points, while the French and Spanish shares were approximately twice Italy’s level. Istat+1

Educational scarcity coexists with significant employment returns. In Italy, 85.3% of tertiary-educated adults aged 25–64 were employed, compared with 74.6% of upper-secondary graduates and 56.1% of adults with lower qualifications. The corresponding employment advantages were 10.7 and 29.2 percentage points, respectively. These are observed differences rather than causal estimates of the effect of obtaining a degree.

The utilisation deficit is especially important. Italian women aged 25–64 were more likely than men to hold tertiary qualifications, yet their overall employment rate was 60.8%, against 80.9% for men. Among graduates the gender employment gap narrowed to 6.5 percentage points. Foreign citizens with tertiary qualifications recorded employment of only 68.4%, compared with 86.3% among Italian citizens, a difference of 17.9 percentage points. Citizenship and foreign birth must remain analytically distinct.

Employers report significant recruitment frictions despite these unused capabilities. Unioncamere’s Excelsior system identified approximately 670,000 planned contracts for graduates in 2025, with recruitment difficulties affecting 50.9% of those positions. These employer-reported difficulties do not establish that every vacancy reflects an absolute shortage of graduates: wages, location, required experience and hiring practices may also contribute. Unioncamere

Youth outcomes are improving, but remain problematic. Italy’s proportion of 15–29-year-olds neither in employment nor in education or training fell to 13.3% in 2025, compared with 11.0% in the EU. The reduction from 25.7% in 2015 is substantial, but does not eliminate the transition problem.

Artificial intelligence introduces an additional competitiveness constraint. OECD evidence indicates that firms increasingly require advanced digital capabilities alongside critical thinking, collaboration and adaptive learning. The estimated OECD-wide share of firms using AI increased from approximately 7% in 2021 to 20% in 2025. These observations support a concern about Italy’s absorptive capacity, but do not establish a measurable Italian GDP loss attributable specifically to AI-related graduate shortages.

Strategic implication — High confidence on the structural constraint; medium confidence on its eventual macroeconomic magnitude. Increasing degree attainment without improving labour demand and skills utilisation would leave important gains unrealised. Conversely, better activation of existing workers without enlarging the future advanced-skills pipeline would not fully resolve longer-term shortages. Italy’s response must therefore be evaluated as a combined education, employment, productivity and industrial-capability challenge.

Italy’s Human-Capital Deficit: The Economic Cost of Educating Too Few and Employing Too Little

Italy’s competitiveness problem is no longer confined to its shortage of university graduates: the combination of limited advanced qualifications, underutilised skilled labour, weak employment incentives and industrial recruitment difficulties threatens to constrain productivity precisely as artificial intelligence increases the value of technological and organisational competence.

Italy entered 2026 with a structural contradiction that its education, labour and industrial policies have yet to resolve. Despite possessing one of Europe’s major manufacturing economies, the country has substantially fewer tertiary-qualified adults than its principal European competitors, employs too small a proportion of its available workforce and struggles to recruit specialised personnel for industrial and technological activities. The economic consequences extend beyond university policy. They affect firms’ capacity to modernise production, the fiscal revenues associated with employment, the retention of qualified workers and Italy’s ability to capture productivity gains from artificial intelligence. The Istat statistics published on 3 November 2026 and the OECD’s Education at a Glance 2026 expose a problem whose costs arise not only from the qualifications Italy fails to produce, but also from those it fails to use effectively.

Europe’s Educational Divide Is Becoming an Industrial Disadvantage

In 2025, only 22.3% of Italians aged 25–64 possessed tertiary qualifications, compared with 36.9% across the European Union, 44.9% in France and 42.4% in Spain. Germany recorded approximately 35%, Poland approximately 40% and the United Kingdom approximately 55%, according to the respective OECD country assessments. Italy’s deficit relative to the EU average therefore reached 14.6 percentage points, rising to 22.6 points against France and 20.1 against Spain. These are differences in educational composition, not direct measures of productivity, but they reveal a substantially narrower national base of advanced formal qualifications.

The consequences become clearer when the percentages are standardised. For every million adults aged 25–64, Italy’s educational structure corresponds to approximately 223,000 tertiary-qualified individuals, against 369,000 under the EU average, 424,000 under Spain’s structure and 449,000 under France’s. The resulting gaps of 146,000, 201,000 and 226,000 qualified adults per million respectively do not represent actual vacancies or unemployed graduates. They illustrate the magnitude of the difference in the potential supply of advanced qualifications before employment, occupational matching or productivity is considered.

The generational figures offer only partial reassurance. Italian tertiary attainment among people aged 25–34 reached 31.1% in 2025, against 44.8% across the EU. Germany increased its corresponding rate from 30% in 2015 to 41% in 2025, demonstrating that substantial educational upgrading can occur within a decade. Italy’s younger cohorts are more highly educated than its older population, but demographic replacement alone cannot ensure convergence when competing economies continue expanding their own advanced-skills base.

The industrial comparison also requires care. Germany’s vocational education system produced an employment rate of approximately 89% among young adults holding vocational upper-secondary or post-secondary non-tertiary qualifications, slightly above the corresponding 88% for tertiary-qualified young adults. Advanced manufacturing needs engineers and scientists, but also technicians, maintenance specialists and workers capable of integrating sophisticated equipment. Italy’s disadvantage is therefore not simply a shortage of degrees; it is the limited depth and incomplete coordination of its advanced academic, technical and occupational capabilities.

Employment and Earnings Reveal the Cost of Underused Education

Higher education produces a substantial observed employment advantage in Italy. In 2025, 85.3% of tertiary-qualified adults aged 25–64 were employed, compared with 74.6% of upper-secondary-qualified adults and 56.1% of those without upper-secondary qualifications. Their respective unemployment rates were 3.0%, 5.0% and 8.7%. The tertiary employment advantage reached 10.7 percentage points over upper-secondary education and 29.2 points over lower qualifications, according to Istat. These differences demonstrate the labour-market value associated with education, although they cannot be interpreted as causal effects of obtaining a degree.

Yet Italy fails to capture the full employment potential of its qualified population. Its graduate employment rate remained 2.8 percentage points below the EU average in 2025. Applied to a standardised population of 100,000 tertiary-qualified adults, this difference corresponds to 2,800 fewer employed individuals than under the EU employment structure. The calculation does not establish that 2,800 suitable jobs are immediately available, but it identifies a distinct utilisation gap that additional university enrolment alone cannot resolve.

The financial incentives are equally revealing. The OECD’s 2026 assessment of educational earnings reports an average 35% earnings premium for tertiary-qualified Italian workers, compared with 54% across the OECD. The Italian premium was only 4% for bachelor’s qualifications, rising to 44% for master’s or doctoral degrees, against OECD averages of 39% and 80% respectively. These figures concern relative earnings among full-time, full-year workers, not absolute international salary levels. Nevertheless, they expose an important imbalance between the investment required to acquire advanced qualifications and the remuneration available in significant segments of Italy’s labour market.

Weak relative earnings rewards do not independently explain graduate emigration or productivity differences. They do, however, complicate the economic case for educational expansion when domestic employers struggle to offer sufficiently attractive professional trajectories. Training more graduates without improving the quality and remuneration of suitable employment may increase the number of qualifications produced without proportionately increasing the stock of qualified workers retained and productively employed in Italy.

Women and Foreign Graduates Represent an Existing Labour-Supply Constraint

Italy’s employment disadvantage cannot be attributed exclusively to the number of available graduates. In 2025, the employment rate among Italian women aged 25–64 was 60.8%, against 80.9% among men. Among tertiary-qualified women, employment reached 82.5%, compared with 85.9% across the EU. The Italian female employment rate fell to 64.0% among upper-secondary-qualified women and 37.8% among those with lower qualifications. The corresponding EU rates were 72.6% and 46.8%. These differences indicate that educational attainment narrows employment disadvantages but does not eliminate the institutional and economic barriers affecting participation.

The implications extend to public-service provision. According to the European Commission’s early-childhood education indicators, 39.4% of Italian children under three participated in formal childcare in 2024, compared with 59.4% in France, 55.0% in Spain and 78.9% in the Netherlands. Italy’s participation had increased by 12.1 percentage points since 2015, but service accessibility, hours, affordability and geographical distribution remain relevant to whether care responsibilities prevent qualified parents from maintaining employment. The participation figures do not independently establish the causal employment effect of childcare expansion.

Foreign-qualified labour presents another dimension of the same problem. In 2025, 68.4% of foreign citizens holding tertiary qualifications were employed in Italy, compared with 86.3% of similarly educated Italian citizens, a difference of 17.9 percentage points. Citizenship is not equivalent to birthplace or the country in which a qualification was obtained, and the employment gap cannot be attributed entirely to administrative barriers. Nevertheless, the European Commission’s recognition framework for third-country qualifications identifies institutional procedures that can influence access to regulated professions.

The economic contradiction is direct: Italy reports shortages of qualified personnel while substantial differences persist in the employment of people who already possess advanced qualifications. Childcare provision, occupational recognition, working conditions and the geographical availability of employment are therefore elements of productive capacity, not merely adjacent social-policy questions.

Industrial Recruitment Difficulties Are More Specific Than a Graduate Shortage

The Unioncamere–Excelsior findings for 2025 recorded approximately 670,000 planned contracts for university graduates, with recruitment difficulties affecting 50.9%. The difficulties were even greater for ITS Academy qualifications, affecting 57.3% of approximately 120,000 planned contracts. The economy also planned around 1.3 million contracts for upper-secondary diploma holders and 2.3 million for vocationally qualified workers. These numbers describe employer hiring intentions, not completed recruitment or permanently vacant positions.

The occupational evidence is particularly significant for industrial policy. In January 2026, Excelsior reported recruitment difficulties affecting 63.7% of production-process management technicians, 60.7% of engineering technicians, 51.8% of engineers, 62.7% of application-design specialists and 69.6% of mechanics and maintenance personnel. During the same month, industrial enterprises planned approximately 155,000 contracts, including 104,000 in manufacturing and public utilities and 51,000 in construction. Recruitment constraints therefore extend across engineering, equipment maintenance, digital development and practical technical occupations.

These difficulties do not all originate in insufficient educational supply. The January 2026 survey attributed recruitment problems affecting 28.6% of planned entries to the absence of candidates, 13.6% to inadequate preparation and 3.6% to other causes. Wages, experience requirements, working conditions and geographical mismatches may also influence recruitment. Expanding university capacity will not automatically supply experienced maintenance specialists or resolve the absence of suitable employment opportunities in regions from which qualified workers are departing.

The economic transmission is nevertheless material. Where specialised workers are required to maintain production equipment, validate industrial processes or integrate digital systems, recruitment delays can restrict operational flexibility and investment execution. The appropriate response depends on the occupation: advanced vocational education, employer training, university–industry coordination and improved career incentives address different constraints. A single graduate-attainment target cannot substitute for that occupational diagnosis.

Artificial Intelligence Makes Organisational Competence the Decisive Variable

Artificial intelligence is changing the relative economic value of routine information processing and advanced integrative skills, but European adoption figures reveal that access to technology and productive implementation remain different outcomes. According to Eurostat’s December 2025 release, 20.0% of EU enterprises with at least ten employees used AI technologies in 2025, compared with 13.5% in 2024 and 7.7% in 2021. Adoption reached 42.0% in Denmark, 37.8% in Finland and 35.0% in Sweden, against 8.4% in Poland. These differences establish unequal technological diffusion, not equivalent differences in realised productivity.

Italy’s technological position is more differentiated than its educational attainment figures suggest. In 2025, ICT specialists represented 3.8% of Italian employment, compared with 5.0% across the EU, according to Eurostat’s ICT employment statistics. Yet approximately 76% of Italian enterprises purchased cloud-computing services, compared with 53% across the Union, according to Eurostat’s Digitalisation in Europe — 2026 Edition. Strong cloud uptake demonstrates considerable access to digital infrastructure, while the smaller ICT workforce indicates a narrower concentration of specialist digital occupations. Neither indicator establishes the depth of AI integration into industrial production.

The OECD’sSkills in the AI Age, 2026 distinguishes task automation from occupational replacement and identifies technical expertise, critical thinking, collaboration and continuous learning as important complementary capabilities. This distinction changes the economics of education: firms increasingly need workers capable of evaluating automated outputs, integrating technologies with physical production systems and exercising judgment where errors carry operational or financial consequences.

The organisational divide is already substantial. OECD figures for 2025 show AI adoption among 52.0% of large firms, compared with 17.4% of small firms. For Italy, where industrial competitiveness depends on the diffusion of capabilities across enterprises of different sizes, the constraint is not merely the availability of software. It is whether management, capital investment, technical staff and workplace training enable firms to translate technological access into measurable productive improvements.

The Next 24 Months Will Test Whether Reform Reaches the Productive Economy

The European policy framework provides instruments, but no automatic solution. The European Commission’sUnion of Skills, introduced in March 2025, addresses foundational and advanced education, vocational training, continuous reskilling, qualification recognition and labour mobility. Germany’s vocational employment outcomes, France’s and Spain’s diversified post-secondary pathways and the Netherlands’ childcare participation demonstrate alternative institutional arrangements, but their performance cannot be reproduced through administrative imitation alone.

Between November 2026 and November 2028, the decisive Italian policy test will be whether educational, labour and industrial institutions begin producing measurable improvements in qualification recognition, female participation, technical training, graduate employment and employer recruitment. These changes will not eliminate the 14.6-percentage-point adult tertiary-attainment gap within 24 months, because educational upgrading depends on completion and demographic replacement. They can, however, reveal whether Italy is improving its utilisation of existing skills and the conditions under which future graduates will enter productive employment.

The cost of insufficient progress will not fall equally across the economy. Industrial enterprises requiring engineering, maintenance and digital-integration capabilities will continue facing recruitment friction; regions with weaker advanced employment opportunities risk losing qualified workers; women and foreign graduates may remain excluded from suitable employment; and public finances will forgo some of the potential revenues associated with higher productive participation. The precise GDP and fiscal losses cannot be quantified from the available attainment and employment comparisons without a validated causal model.

Italy’s economic choice is therefore between accumulating qualifications as a statistical objective and building the institutional capacity to convert them into employment, innovation and industrial productivity. By November 2028, the relevant evidence will not be the number of announced reforms, but whether the country’s employment gaps, occupational mismatches and technological absorption constraints have begun to narrow. The alternative is a continuing transfer of the adjustment cost to employers, underutilised workers, less competitive regions and the national economy.

Navigational Index

Pillar I — Educational Attainment and the Utilisation of Human Capital

• Chapter 1 — Europe’s Educational Divide: The Comparative Human-Capital Baseline : Comparative tertiary attainment among adults aged 25–64 and younger cohorts aged 25–34; employment and unemployment by education; employment and earnings premia; NEET rates; the size and composition of highly educated populations in Italy, Germany, France, Spain, Poland and the European Union, with supplementary British evidence where comparable.
• Chapter 2 — Under-Utilised Human Capital: Gender, Migration, Age and Regional Inequality : Female labour-market participation; educational attainment by gender; the employment and occupational mismatch of foreign-born and foreign-citizen graduates; recognition of qualifications; involuntary part-time work; delayed career entry; graduate migration; regional differences and their effects on the effective supply of qualified labour.
• Chapter 3 — The Educational Pipeline: Completion, Access, Financing and Transition into Employment : University access and completion; intergenerational educational inequality; tertiary and vocational pathways; student financing; regional mobility; guidance systems; work-based learning; and the institutional factors that determine whether additional educational expenditure translates into productive employment.

Pillar II — Economic Competitiveness, Industrial Capacity and Technological Change

• Chapter 4 — The Economic Cost of Educational and Employment Gaps : Employment-stock comparisons, counterfactual labour-supply estimates, earnings returns, potential-output transmission mechanisms, productivity differentials and the consequences of educational inequality for fiscal capacity and long-term economic growth.
• Chapter 5 — Industrial Skill Shortages and Europe’s Competition for Qualified Labour : High-value manufacturing, engineering, advanced industrial services, digital infrastructure, software, scientific occupations and technical professions; employer-reported recruitment difficulties; demographic replacement demand; wage and geographic mismatches; and the competing roles of education supply, working conditions and labour-market institutions.
• Chapter 6 — Artificial Intelligence and the Changing Economic Value of Advanced Skills : AI-driven task substitution and complementarity; declining costs of routine information processing; demand for technical expertise, judgment and cross-domain integration; organisational absorptive capacity; firm-level innovation; diffusion inequalities and Italy’s relative exposure to skill-biased technological change.

Pillar III — Institutional Reform, Strategic Scenarios and Competitiveness Through 2035

• Chapter 7 — European Policy Models and Their Transferability to Italy : Comparative assessment of tertiary expansion, vocational pathways, female employment policies, childcare provision, recognition of foreign qualifications, graduate retention, employer training and university–industry coordination in Germany, France, Spain, Poland, the United Kingdom and selected higher-performing European economies.
• Chapter 8 — Italy 2035: Stagnation, Gradual Convergence and Accelerated Catch-Up : Three conditional trajectories for educational attainment, effective labour supply, graduate retention, AI adoption and industrial competitiveness; implementation requirements; comparative policy choices; fiscal and administrative constraints; leading indicators, monitoring thresholds and final strategic assessment.

Master Abstract

A structural deficit extending beyond the education system

Italy’s human-capital position reveals a distinction between the quantity of educational qualifications available in an economy and the proportion of those qualifications converted into productive employment. The country has a substantially smaller tertiary-educated adult population than the European average, but the educational disadvantage is compounded by employment and matching inefficiencies that prevent the existing stock of skills from being fully mobilised. This combination is particularly consequential in an ageing society because the economic adjustment available through demographic expansion becomes progressively more constrained, raising the importance of productivity improvements and of retaining workers with specialised competencies.

The scale of the educational divide is supported by the latest national and international statistics. Istat records a tertiary-attainment rate of 22.3% among Italian adults aged 25–64 in 2025, compared with 36.9% in the EU, while the OECD’s Education at a Glance 2026 independently places Italy substantially below the OECD average of approximately 43%. Among Italians aged 25–34, OECD Education GPS reports tertiary attainment of 31.1%, compared with the Eurostat EU rate of 44.8%. The younger generation is therefore more highly educated than Italy’s older cohorts, but the convergence process remains incomplete. Istat+3

Why educational attainment alone cannot explain competitiveness

A country’s economic return from education depends not only on the supply of qualified people but also on the quality of jobs, the allocation of workers to appropriate occupations, mobility between regions and employers, and the institutional capacity to deploy specialised knowledge. Italy’s employment rate among graduates, at 85.3% in 2025, indicates that tertiary qualifications are associated with substantially stronger labour-market outcomes than lower levels of education. Nevertheless, the difference between Italian and foreign citizens holding tertiary qualifications, alongside the continuing gap between male and female employment, demonstrates that possession of a qualification does not guarantee its effective economic utilisation.

The implications extend beyond individual earnings. Underemployment, occupational mismatch and graduate outmigration can reduce the availability of technical and managerial skills in domestic production systems, while simultaneously diminishing the potential return on public and private investment in education. Istat’s 2026 migration release records a cumulative loss of approximately 150,000 Italian graduates aged 25–34 from Southern Italy between 2019 and 2025, predominantly through movement toward Central and Northern Italy, with an additional negative international balance. This represents a substantial territorial redistribution of skills, although domestic migration must not be confused with a net loss of human capital from Italy as a whole.

The strategic importance of artificial intelligence

AI changes the economic significance of these constraints because firms increasingly require workers capable of integrating technological systems into productive organisations rather than merely operating isolated digital applications. The substitution of routine analytical and information-processing tasks may increase productivity, but organisational gains depend on complementary capabilities: diagnosing complex problems, evaluating uncertain outputs, redesigning processes, coordinating specialist knowledge and exercising professional judgment. OECD evidence identifies critical thinking, creativity, collaboration and continuous learning as important complements to AI adoption, while also identifying shortages of skilled workers as a barrier to diffusion.

Italy’s limited tertiary-educated workforce does not imply that every graduate possesses those complementary capabilities or that comparable skills cannot be acquired through technical education and professional experience. The relevant risk is narrower and more defensible: a smaller pool of advanced qualifications, when combined with incomplete workforce activation and constrained organisational investment, can restrict the capacity of firms to develop, integrate and scale sophisticated technologies. The eventual macroeconomic cost will depend on industry structure, capital formation, management quality, workplace training, investment incentives and the distribution of AI adoption across firms. It is therefore inappropriate to translate the educational attainment gap directly into an assumed productivity or GDP shortfall without a validated counterfactual model.

The 2035 decision horizon

The central strategic choice concerns whether Italy remains on a trajectory of slow educational expansion and incomplete skills mobilisation, pursues gradual convergence through improved completion and employment activation, or implements a more coordinated programme joining advanced education, skilled migration, family-support infrastructure, workforce training and industrial modernisation. These pathways cannot be evaluated exclusively through the number of new graduates produced. Their success must also be assessed through employment quality, occupational matching, earnings progression, retention, enterprise investment and realised productivity growth.

An accelerated pathway could improve the country’s position in knowledge-intensive activities and advanced manufacturing, but its effectiveness would depend on whether employers generate and sustain sufficient demand for specialised competencies. Expanded education in the absence of attractive employment opportunities could intensify graduate emigration or occupational mismatch, while measures focused only on recruitment would leave the longer-term educational pipeline structurally constrained. The 2035 assessment must consequently distinguish measurable policy outputs from their eventual economic results, preserving uncertainty over the causal contribution of educational attainment to national productivity differences.

Key Evidence Table

The following indicators establish the initial, independently interpretable baseline. Percentage-point differences are distinguished from percentage changes, and population age groups are stated to prevent incompatible comparisons.

The full six-country comparison requires consistent extraction of each country’s 2025 education, employment, migration and NEET series. Values not yet cross-verified are not substituted with estimates. Tertiary education denotes ISCED 2011 levels 5–8; the term is broader than university degrees alone.

Comparative evidence visualisation — tertiary attainment among working-age adults

Tertiary educational attainment, 2025

Percentage of population aged 25–64; all four values reported in Istat’s 3 November 2026 release.

Competing Explanations and Strategic Pathways

Three explanations of Italy’s comparatively weak human-capital position deserve separate examination. They are not mutually exclusive: the observable outcomes could arise from their combined operation, and a sound policy response depends on establishing their relative importance.

The evidence is therefore more consistent with a compound structural constraint than with a single shortage of educational credentials. Establishing the respective weights of educational supply, labour-market utilisation and employer demand remains necessary before calculating an aggregate GDP or productivity counterfactual.

Scenario Architecture — Italy Through 2035

The assessment will distinguish three conditional pathways without assigning unsupported probabilities or treating policy aspirations as established future outcomes.

Tertiary attainment rises too slowly to close the European gap, while female employment, graduate matching and skilled retention improve only marginally. Demographic pressures increase the importance of attracting and utilising qualified labour. AI adoption remains uneven across the industrial system.

Competitiveness implication: continued relative disadvantage in advanced-skill availability; magnitude of output loss not quantified.

University completion and tertiary participation improve progressively, accompanied by stronger activation of women and qualified migrants. Recognition systems, childcare access and early-career employment improve the utilisation of existing skills, while firms expand complementary training and technological adoption.

Competitiveness implication: gradual narrowing of selected employment and skills gaps, conditional on stronger demand for qualified labour.

Scenario C — Accelerated Human-Capital Catch-Up

Educational expansion, targeted technical pathways, female labour-market activation, international talent attraction and retention, workplace training and organisational modernisation advance together. Industrial investment generates demand for advanced capabilities, improving the economic returns from expanded skills supply.

Competitiveness implication: greater potential to capture productivity gains from AI and advanced manufacturing, accompanied by higher implementation and fiscal demands.

These scenarios require internally consistent demographic, employment and education assumptions before numerical results can be assigned. In particular, a 2035 attainment target cannot simply be imposed on the entire 25–64 population as though all cohorts could acquire new qualifications at the same rate. Cohort replacement, completion lags, migration flows and adult education must be modelled separately.

Principal Gaps and Watch Indicators

The most consequential unresolved issue is not whether Italy possesses fewer graduates than comparable European economies. The available official statistics establish that clearly. The unresolved issue is how much additional employment, productivity and innovation capacity could realistically be generated by increasing educational attainment and improving the allocation of skills, given employers’ actual demand, institutional constraints and demographic change.

The monitoring framework will therefore distinguish four observable dimensions.

Three evidence gaps are particularly relevant to the final strategic judgment. First, the degree to which the observed graduate employment premium represents a causal return to tertiary education rather than prior differences between workers requires explicit identification assumptions. Second, overqualification among foreign-born graduates must be measured using harmonised occupational and educational definitions, without conflating foreign birth with foreign citizenship. Third, the effect of AI on future labour demand remains contingent on the pace of adoption, organisational restructuring and changes in relative task costs, making deterministic predictions of occupational displacement or productivity gains inappropriate.

Accordingly, the baseline judgment carries high confidence for Italy’s educational-attainment deficit and its major labour-market utilisation gaps, medium confidence for the proposition that these jointly constrain technological and industrial adaptation, and low confidence for any precise attribution of national output losses or 2035 productivity gains until a consistent quantitative model is established.

The immediate policy implication is that educational expansion, labour-force activation and productive-system upgrading should not be treated as competing objectives. Their effectiveness depends on their complementarity, while the distribution of implementation responsibilities across education ministries, labour-market institutions, regional authorities, employers and public financing mechanisms will determine how quickly measurable improvement becomes possible.

The Structural Human-Capital Gap

Italy: tertiary attainment, ages 25–64
22.3%
EU-27: 36.9% | Reference year 2025
Italian tertiary attainment gap vs EU
−14.6pp
Percentage-point difference
Employment of Italian tertiary graduates
85.3%
Population aged 25–64, 2025
Difficult-to-fill planned graduate positions
50.9%
Unioncamere–Excelsior, 2025
02 / Interactive Comparative Analytics

European Skills Observatory

Select an indicator to examine official 2025 outcomes.
The graphical display uses a perspective-rendered column
system with proportional heights. Select any column
to inspect its definition and statistical context.

Share of population aged 25–64, 2025

03 / Labour-Market Utilisation

The Qualification-to-Employment Divide

Employment by Educational Level

Italy, population aged 25–64, 2025.

Tertiary85.3%
Upper secondary74.6%
Below upper secondary56.1%
Observed employment premium:
+10.7 percentage points for tertiary education
relative to upper-secondary qualifications.
This is an association, not a causal earnings estimate.

Graduate Employment by Citizenship

Italy, tertiary-educated adults aged 25–64, 2025.

Italian citizens86.3%
Foreign citizens68.4%
Employment Differential
17.9 pp

Italian-citizen minus foreign-citizen graduate employment.

Citizenship is not equivalent to country of birth.
This differential does not, by itself, measure
overqualification, discrimination or the number
of additional workers immediately available.
04 / Additional Structural Signals

Labour Supply, Youth Exclusion and Recruitment

Italian women’s employment, ages 25–64
60.8%
Compared with 80.9% for men
Italian youth NEET rate, ages 15–29
13.3%
EU average: 11.0%
Planned graduate recruitment
670k
Planned contracts, not completed hires
Graduate gender employment differential
6.5pp
Italy, tertiary-educated adults
05 / Official Evidence Matrix

Cross-Country Evidence Register

Tertiary attainment refers to ISCED levels 5–8.
Missing country observations are explicitly distinguished
from zero values. The table preserves the reference
periods and age groups used for each indicator.

Verified comparative observations. Percent of the
specified population, reference year 2025.

Country / Area	Tertiary 25–64	Tertiary 25–34	Graduate employment 25–64	NEET 15–29
Italy	22.3%	31.1%	85.3%	13.3%
EU-27	36.9%	44.8%	Not verified	11.0%
France	44.9%	Not verified	Not verified	Not verified
Spain	42.4%	Not verified	Not verified	Not verified
Germany	Not verified	Not verified	Not verified	Not verified
Poland	Not verified	Not verified	Not verified	Not verified

Sources: Istat, 3 November 2026; Eurostat 2025
educational-attainment and NEET statistics;
OECD Education GPS. Unverified cells are deliberately
not filled using estimates or values from incompatible series.

06 / Technological Competitiveness

The AI–Human Capital Transmission Mechanism

Artificial intelligence can reduce the cost of routine
information processing while increasing the importance
of complementary technical, organisational and cognitive
capabilities. The following diagram describes a
conditional economic mechanism, not an empirically
estimated causal coefficient.

01 / Skills SupplyEducation, training, specialised knowledge and qualified workers
02 / Organisational AbsorptionManagement, technology integration, investment and task redesign
03 / Economic OutcomesInnovation capacity, productivity and industrial competitiveness
Complementary capabilities

Complex problem-solving, technical expertise,
critical judgment, interdisciplinary integration,
collaboration and continuous learning.

A larger qualified workforce does not automatically
generate productivity. Firms must also invest in
technology, organisation and effective skill deployment.

07 / Strategic Foresight Laboratory

Italy 2035: Three Conditional Pathways

Explore the strategic implications of alternative policy
and labour-market trajectories. These scenarios are
qualitative assessments rather than probabilistic
forecasts or calculated GDP projections.

Scenario A / Conditional Outlook to 2035
Structural Stagnation

Educational attainment improves too slowly to narrow
Italy’s European disadvantage materially. Incomplete
workforce activation, persistent matching failures
and graduate retention difficulties limit advanced
labour supply. AI adoption remains uneven.

Skills Supply
Persistent constraint
Labour Utilisation
Limited improvement
AI Absorption
Uneven diffusionCompetitiveness consequence:
Italy risks maintaining its relative disadvantage
in knowledge-intensive production and advanced
industrial capabilities.
08 / Institutional Decision Architecture

Policy-Lever Explorer

Select an intervention domain to examine the expected
mechanism, implementation responsibility and
principal limitation. These are analytical options,
not evidence that a specific intervention has already
produced a measurable result.

Higher-Education Completion

Strengthen completion, financial accessibility and
transitions to qualified employment. Responsible
institutions include education authorities,
universities and regional governments.
Impact requires sufficient graduate-level labour
demand and cannot be inferred from enrolment alone.

09 / Evidence and Auditability

Primary Statistical Sources

The numerical baseline and methodological distinctions
are traceable to public institutional records.

Institution	Evidence	Official Reference
Istat	Attainment, employment and citizenship gaps; 2025 data, released 3 November 2026	Official release
Eurostat	EU educational attainment and NEET comparisons	Education statistics
Eurostat	Youth NEET statistics, 2025	NEET statistical release
OECD	Young-adult tertiary attainment	Education GPS: Italy
Unioncamere	Excelsior graduate recruitment plans and difficulties, 2025	Excelsior release

Chapter 1 — Europe’s Educational Divide: The Comparative Human-Capital Baseline

Statistical reference period: 2025 | Evidence cut-off: 4 November 2026 | Strategic horizon: 2035

Principal Judgment — Europe’s Educational Divide Is a Structural Economic Constraint, Not Simply a Difference in University Participation

Italy’s position within the European human-capital system is characterised by a persistent disparity between the educational qualifications available to its working-age population and those available to its principal European economic competitors. This disadvantage has consequences extending beyond the education system because the distribution of qualifications influences occupational mobility, workforce adaptability, technological absorption and the availability of workers capable of undertaking advanced technical, managerial and scientific functions. The significance of this disparity becomes particularly pronounced when educational attainment is examined alongside the employment and earnings returns that different economies generate from qualified labour. A country may have a relatively limited supply of graduates but employ them efficiently, maintain substantial technical capabilities through vocational pathways and achieve competitive industrial outcomes; conversely, a country with widespread tertiary qualifications may fail to convert those credentials into productive employment if its industrial structure, wage incentives and labour-market institutions do not create sufficient demand. Italy’s difficulty is that its low tertiary attainment is accompanied by weaker labour-market utilisation in several important population groups and comparatively modest earnings rewards for some categories of graduates, producing a combination of constraints that cannot be resolved through university expansion alone.

The latest official evidence establishes the magnitude of the educational divide with considerable clarity. According to the Italian National Institute of Statistics, Levels of Education and Employment Returns — Year 2025, published 3 November 2026, only 22.3% of the Italian population aged 25–64 possessed a tertiary qualification in 2025, compared with 36.9% across the European Union, 44.9% in France and 42.4% in Spain. Consequently, Italy’s adult tertiary-attainment rate was 14.6 percentage points below the EU average, 22.6 points below France and 20.1 points below Spain. These differences are not marginal statistical variations: France’s share was approximately 2.01 times the Italian share, while Spain’s was approximately 1.90 times as large. Such comparisons describe the relative prevalence of qualifications, not the absolute number of graduates, and therefore cannot establish that France or Spain has proportionately greater effective technical capacity in every industrial occupation. They nevertheless demonstrate that substantially larger proportions of the French and Spanish working-age populations have completed tertiary education.

The OECD’s Education at a Glance 2026: Italy, published 29 September 2026 confirms the broader international nature of this disadvantage, reporting that approximately 22% of Italian adults aged 25–64 held tertiary qualifications, compared with an OECD average of approximately 43%. The same publication identifies an additional weakness in the economic rewards associated with educational attainment: Italian tertiary-educated workers employed full-time throughout the year earned an average of 35% more than workers with upper-secondary or post-secondary non-tertiary qualifications, against an OECD average premium of 54%. Particularly significant is the distinction between qualification levels, with Italian bachelor’s degree holders receiving a reported earnings advantage of only 4%, compared with 44% for holders of master’s or doctoral qualifications. These differences suggest that the labour-market value of higher education in Italy varies substantially by qualification level and that the incentives governing the transition from education into skilled employment warrant examination alongside the absolute supply of graduates.

This evidence supports a principal judgment with high confidence: Italy possesses a substantial, persistent deficit in tertiary educational attainment relative to the European Union and several major European economies, while the employment and earnings benefits associated with qualifications indicate that the economic consequences of this deficit depend heavily on how education interacts with national labour-market institutions. The precise contribution of these differences to Italy’s productivity gap remains less certain because educational attainment, economic development, occupational composition and technological investment influence one another. Any attempt to attribute a specific proportion of Italy’s GDP or productivity disadvantage to its graduate shortfall would therefore require a separate empirical identification strategy rather than a direct extrapolation from attainment percentages.

The Composition of Europe’s Working-Age Human Capital

The distinction between tertiary education, upper-secondary qualifications and lower educational attainment is fundamental to understanding the economic meaning of Europe’s educational differences. Under the International Standard Classification of Education adopted by Eurostat and the OECD, tertiary education encompasses ISCED levels 5–8, including short-cycle tertiary programmes, bachelor’s degrees, master’s degrees and doctoral qualifications. It therefore represents a broader category than conventional university degrees alone, while upper-secondary attainment includes educational and vocational pathways that may provide substantial occupational competence without being classified as tertiary education. For comparisons involving Germany, this distinction is particularly important because the country’s established vocational education and apprenticeship system contributes to its technical workforce through qualifications that cannot simply be equated with university degrees. A lower proportion of tertiary-qualified adults does not necessarily imply an equivalent shortage of industrial competence, just as a higher proportion of university graduates does not establish the availability of specific engineering, software or scientific skills.

Italy’s educational composition illustrates why the deficit must be examined across the complete qualification structure. In 2025, 44.7% of Italians aged 25–64 held upper-secondary qualifications as their highest completed level, compared with 44.3% across the EU, 39.9% in France, 23.0% in Spain and 50.4% in Germany. Italy therefore does not exhibit an unusually small upper-secondary-qualified population relative to the European average; instead, its educational distribution contains a comparatively large proportion of adults whose education does not extend beyond the secondary level, combined with a substantially smaller tertiary-qualified population. This distinction matters because the economic issue is not simply the absence of formal schooling, but the relatively limited progression into advanced qualifications, particularly those associated with specialised analytical, scientific and professional roles. The Istat data further imply that 33.0% of Italian adults aged 25–64 had qualifications below upper-secondary level, calculated as the remainder after the reported 44.7% upper-secondary and 22.3% tertiary shares. Source: Istat, Levels of Education and Employment Returns — Year 2025, 3 November 2026.

Table 1 — Educational attainment and the composition of the working-age population

Percent of the population aged 25–64. The below-upper-secondary shares for Italy, the EU, France and Spain are arithmetic complements calculated from the two published attainment categories, rounded to one decimal place. These figures concern highest completed educational level, not current enrolment or occupational competence.5, November 2026

The comparison reveals that Italy and Spain face structurally different educational challenges despite both having substantial shares of adults with relatively low qualifications. Spain combines a high tertiary-attainment rate with a comparatively small upper-secondary-qualified population and a large lower-qualified population, whereas Italy combines low tertiary attainment with a much larger intermediate-qualified group. France displays a more education-intensive distribution, with a significantly larger tertiary-qualified population and a smaller proportion of adults below upper-secondary attainment. Germany’s comparatively large upper-secondary-qualified population must be interpreted in the context of vocational education, and Poland requires separate treatment of changes in educational attainment across generations rather than extrapolation from the historical expansion of university participation. These distinctions make it inappropriate to prescribe an identical educational strategy for every European economy, because the obstacles limiting advanced-skill supply may arise from different combinations of university access, vocational classification, progression between educational pathways and the demand for specialised occupations.

The comparative significance of Italy’s position becomes clearer when changes in educational attainment are considered over time. A working-age attainment rate represents the accumulated results of educational decisions and graduation patterns across approximately four decades of adult cohorts, meaning that present-day changes in university enrolment cannot immediately transform the educational composition of the entire workforce. Older cohorts with relatively low educational attainment remain part of the denominator, while younger graduates progressively enter and replace them. The speed of aggregate convergence consequently depends on cohort replacement, adult education, completion rates, migration flows and changes in labour-force participation, rather than the number of new university students alone. This demographic arithmetic is especially relevant to Italy because the educational composition of younger adults is improving relative to older generations, but the improvement remains insufficient to eliminate the country’s European disadvantage.

Generational Replacement: Why the Educational Gap Persists Among Younger Europeans

The most important indicator for assessing the future distribution of advanced skills is the tertiary-attainment rate among people aged 25–34, because this population more closely represents recent educational outcomes and the qualifications that will become increasingly influential as older workers retire. Although the indicator remains imperfect as a measure of future labour supply, particularly because some individuals continue tertiary education beyond the age of 25 and migration can alter the composition of young adult populations, it provides a more forward-looking perspective than attainment among all adults aged 25–64. Eurostat reported that 44.8% of EU residents aged 25–34 possessed tertiary qualifications in 2025, placing the Union close to its 2030 objective of at least 45%. The historical evidence also indicates that attainment among younger European adults increased from 32.2% in 2010 to 39.6% in 2019 and 44.8% in 2025, although Eurostat identifies methodological breaks in the longer series that must be respected when interpreting changes over time. Source: Eurostat, Sustainable Development in the European Union — Monitoring Report on Progress Towards the SDGs, 2026 Edition, Figure 4.7 and Figure 4.8.

Germany provides a particularly instructive comparison because its younger population has experienced substantial educational change despite the continuing importance of advanced vocational education. The OECD’s Education at a Glance 2026: Germany records an increase in tertiary attainment among people aged 25–34 from 30% in 2015 to 41% in 2025, an expansion of approximately 11 percentage points over a decade. Over the same period, the proportion of young German adults with upper-secondary or post-secondary non-tertiary education declined by 13 percentage points to 45%, while the proportion without an upper-secondary qualification increased slightly to 14%. Germany therefore illustrates an educational transition in which university and advanced tertiary participation have expanded without eliminating the economic importance of vocational qualifications, although the simultaneous persistence of a lower-qualified population shows that educational upgrading does not necessarily proceed uniformly across the entire population.

The German experience also demonstrates why university attainment cannot serve as the sole measure of advanced industrial capability. According to the same OECD report, 89% of German adults aged 25–34 possessing vocational upper-secondary or post-secondary non-tertiary qualifications were employed in 2025, a proportion one percentage point higher than the corresponding employment rate for tertiary-qualified young adults. Unemployment among the vocationally qualified group was 3.4%, compared with approximately 4.0% among tertiary-qualified young adults. Such findings do not establish that vocational education is generally superior to university education, because the populations differ in occupational destinations, educational selection and economic characteristics; they do demonstrate, however, that a sufficiently developed vocational system can deliver strong employment outcomes and respond effectively to employer demand. Germany’s system also includes pathways allowing advanced vocational qualifications to reach tertiary ISCED levels, including level 7, meaning that traditional distinctions between academic and technical education increasingly require a more refined interpretation. Source: OECD, Education at a Glance 2026: Germany, sections on educational attainment and labour-market outcomes.

Poland provides a different reference model, combining a comparatively large tertiary-qualified population with an extensive upper-secondary-qualified workforce. The OECD’s Education at a Glance 2026: Poland reports that approximately 40% of Polish adults aged 25–64 held tertiary qualifications in 2025, while approximately 55% possessed upper-secondary or post-secondary non-tertiary qualifications. As these OECD figures are rounded to whole percentages, their precise difference from the one-decimal Istat and Eurostat values should not be interpreted as an exact tenth-of-a-point comparison. Nevertheless, Poland’s tertiary-attainment share was approximately 18 percentage points above Italy’s, demonstrating that Italy’s disadvantage is not confined to comparisons with France, Spain or the wealthiest northern European economies. Poland’s educational structure also reflects an unusually small proportion of young adults without upper-secondary qualifications, with the OECD reporting a decline from 6% in 2015 to 5% in 2025 among people aged 25–34.

France and Spain occupy distinct positions in the generational comparison. Both have adult tertiary-attainment rates well above Italy’s, but Spain continues to experience a relatively high proportion of younger adults without upper-secondary qualifications, which the OECD reports declined from 34% in 2015 to 25% in 2025 among people aged 25–34. France reduced the corresponding proportion from 13% to 10% during the same period, suggesting a substantially different pattern of progression through secondary education. The contrast is important because the economic effects of educational expansion depend partly on whether higher education grows alongside broad secondary-school completion or instead coexists with a persistent population having very low qualifications. Sources: OECD, Education at a Glance 2026: France; OECD, Education at a Glance 2026: Spain.

Table 2 — Generational educational change and selected qualification indicators

Values marked “about” are rounded OECD country-note figures. The 25–34 Italian tertiary rate is taken from OECD Education GPS; the EU figure is from Eurostat. The table combines indicators with different Sources: OECD, Education at a Glance 2026: Italy, Germany, France, Spain, Poland, United Kingdom, and Eurostat, SDG 4: Quality Education, 2026

The United Kingdom offers a useful additional reference because its educational profile differs markedly from that of continental European economies. The OECD’s Education at a Glance 2026: United Kingdom places tertiary attainment at approximately 55% of adults aged 25–64, compared with 29% whose highest attainment falls within the upper-secondary or post-secondary non-tertiary category. This distribution reflects a substantially more tertiary-intensive educational structure than Italy’s, although the comparison cannot establish the relative quality of qualifications or their correspondence with employer demand. The same publication reports that 16% of people aged 18–24 in the United Kingdom were neither working nor participating in education or training in 2025, compared with 15% in 2015. The coexistence of widespread tertiary attainment and continuing youth transition problems reinforces the distinction between educational expansion and full labour-market integration.

The Employment Premium: The Economic Value of Educational Qualifications

The economic importance of higher education becomes most visible when the likelihood of employment is compared across qualification groups. For Italy, the 2025 labour-force survey provides a relatively complete picture. Among adults aged 25–64, the employment rate reached 85.3% for people with tertiary education, 74.6% for those with upper-secondary qualifications and 56.1% for those without upper-secondary attainment. The associated unemployment rates were 3.0%, 5.0% and 8.7%, respectively, demonstrating that higher educational attainment is associated both with a greater probability of employment and a lower probability of unemployment among people participating in the labour force. These two indicators should not be treated as interchangeable because the employment rate measures employed people as a proportion of the relevant population, while the unemployment rate uses the labour force as its denominator and therefore excludes economically inactive individuals. Source: Istat, Levels of Education and Employment Returns — Year 2025, published 3 November 2026.

The differences are substantial. The employment advantage associated with tertiary education relative to upper-secondary qualifications was 10.7 percentage points, while the advantage relative to qualifications below upper-secondary education reached 29.2 percentage points. In unemployment terms, tertiary-qualified adults had a rate 2.0 points lower than upper-secondary-qualified adults and 5.7 points lower than people with the lowest educational qualifications. The significance of these results lies partly in the degree to which educational attainment is associated with stronger participation in productive activity, particularly among population groups that otherwise face greater difficulty entering or remaining in employment. However, these are descriptive labour-market differences and must not be interpreted as evidence that awarding a university degree to an otherwise identical individual would automatically increase that person’s employment probability by the same amount. Family background, pre-existing skills, regional labour-market conditions, occupational choices, migration status and labour-force selection all influence the observed outcomes.

Table 3 — Italy’s employment and unemployment differences by education

The international comparison requires additional attention to the relationship between education and national institutional structures. In Germany, the effectiveness of vocational education limits the usefulness of a simple hierarchy in which tertiary education is assumed to produce stronger outcomes in every age group. The OECD’s 2026 evidence shows that among young adults aged 25–34, employment reached 89% for holders of vocational upper-secondary or post-secondary non-tertiary qualifications, against approximately 88% among tertiary-qualified individuals. The unemployment rates were 3.4% and approximately 4.0%, respectively, supporting the interpretation that advanced vocational education can provide effective occupational matching when its content and certification arrangements correspond closely to employer demand. These findings are especially relevant to discussions of engineering, manufacturing and industrial services because technical competence can emerge from multiple educational pathways, while the international classification of those pathways is not identical across countries. Source: OECD, Education at a Glance 2026: Germany, 29 September 2026.

Poland displays a different pattern of graduate labour-market integration, with the OECD recording a reduction in unemployment among tertiary-educated people aged 25–34 from approximately 6% in 2015 to 2% in 2025, compared with an OECD average of approximately 5% in 2025 for the same educational and age category. This observation suggests a relatively favourable employment environment for young Polish graduates, although it does not establish the quality of their employment, the level of their remuneration or whether their qualifications are fully utilised in their occupations. Spain, by contrast, reduced unemployment among tertiary-qualified young adults from approximately 18% in 2015 to 8% in 2025, representing substantial progress while retaining a less favourable outcome than Poland and the OECD average. These differences illustrate that educational expansion may coexist with significantly different levels of labour-market absorption, and that any attempt to transfer policy lessons between countries must distinguish the supply of qualifications from the occupational and macroeconomic conditions governing their use. Sources: OECD, Education at a Glance 2026: Poland; OECD, Education at a Glance 2026: Spain.

Table 4 — Selected youth labour-market outcomes, 2025

Values are not directly comparable with the preceding Italian 25–64 table because this table covers ages 25–34. The German employment comparison concerns a specifically vocationally qualified group, not the entire upper-secondary population. Sources: OECD, Education at a Glance 2026: Germany, Poland, and Spain.

The Earnings Premium: Why the Financial Returns to University Education Matter for Competitiveness

The employment advantage associated with advanced qualifications represents only one dimension of their economic value, because educational decisions are also influenced by expected earnings, professional mobility, career progression and the opportunity costs of remaining in education. A country in which tertiary education produces relatively limited salary advantages for certain qualifications may experience weaker incentives to complete longer courses of study, greater pressure for qualified workers to seek employment abroad or a concentration of graduates in occupations offering relatively modest compensation. These mechanisms must be understood carefully: a low earnings premium does not necessarily prove inadequate demand for graduates, because income dispersion, collective bargaining, occupational composition, taxes, minimum wages and the characteristics of those selecting different educational paths can all affect measured earnings differences. Nevertheless, where advanced qualifications require substantial investments of time and money, the relative remuneration available to graduates becomes an important component of the country’s capacity to attract, develop and retain specialised human capital.

The OECD’s 2026 analysis provides particularly important evidence about the Italian situation. Among adults aged 25–64 working full-time throughout the year, the average earnings advantage associated with tertiary education reached 35% in Italy, compared with 54% across OECD countries. The international difference of 19 percentage points is substantial, but its interpretation requires recognition that these are relative wage premia calculated within each country rather than comparisons of absolute purchasing power, real salaries or equivalent occupations. Italy’s smaller premium could reflect a more compressed earnings distribution, different occupational structures, weaker remuneration for some forms of advanced qualifications or other labour-market characteristics. The OECD’s qualification-specific evidence is particularly revealing: Italian workers holding bachelor’s degrees earned only 4% more than those with upper-secondary or post-secondary non-tertiary qualifications, whereas workers holding master’s or doctoral qualifications earned 44% more. This creates a pronounced difference in measured financial returns between levels of tertiary education and suggests that treating all graduates as a homogeneous group may conceal substantial variations in the economic incentives facing students and workers. Source: OECD, Education at a Glance 2026: Italy, published 29 September 2026, section on the economic returns to education.

At the OECD level, the broader relationship between qualification level and earnings is similarly differentiated. The OECD’s Education at a Glance 2026 — What Are the Earnings Advantages to Education? reports an average earnings advantage of approximately 18% for short-cycle tertiary qualifications, 39% for bachelor’s degrees and 80% for master’s or doctoral qualifications, measured against the relevant upper-secondary benchmark for full-time, full-year workers. These figures provide a useful international reference because they show that the financial rewards associated with higher education generally increase with qualification level, even though the magnitude of the advantage varies considerably between national labour markets. They also establish why educational composition must be examined within the tertiary category itself: an economy with a large number of short-cycle tertiary graduates may have a different occupational profile from one with a similar overall graduate share but a greater concentration of postgraduate qualifications.

Table 5 — Earnings advantages associated with tertiary education

Gross earnings of full-time, full-year workers, subject to the OECD’s country-specific reference-year and coverage notes. Percentage premia should not be confused with percentage-point employment differences. Sources: OECD, Education at a Glance 2026: Italy and OECD, Education at a Glance 2026 — What Are the Earnings Advantages to Education?.

The age dimension provides an additional explanation for why tertiary education cannot be evaluated solely through starting salaries. Across the OECD, the earnings advantage associated with tertiary education among 25–34-year-olds averaged approximately 38%, compared with approximately 65% among people aged 45–54. The difference is consistent with the possibility that advanced qualifications become more economically valuable as workers accumulate specialised experience, assume managerial responsibilities and enter occupations requiring more complex professional judgment. However, cross-sectional age comparisons are affected by generational differences and labour-market selection; they should not be interpreted as a guaranteed earnings trajectory for a representative graduate entering the workforce today. The OECD also places Italy among the countries where the tertiary earnings advantage for younger workers is approximately 20% or lower, together with several other advanced economies. This observation makes early-career remuneration an important issue for subsequent examination, particularly when analysing the incentives affecting graduate retention and the international mobility of qualified workers. Source: OECD, Education at a Glance 2026 — Earnings Advantages to Education, Chapter A4.

These comparisons also establish a necessary distinction between the private and social returns to education. Private returns concern the benefits accruing to individual students and workers, including earnings and employment prospects, net of the direct and opportunity costs of education. Social returns may include productivity externalities, innovation, improved organisational capabilities, civic participation and fiscal effects that are not fully reflected in individual salaries. It would therefore be inappropriate to infer that Italy’s comparatively modest bachelor’s earnings premium demonstrates a correspondingly low social value of undergraduate education. A qualification may contribute to an organisation’s productivity without generating an equivalent individual wage increase, particularly where labour-market institutions, management practices or firm-level bargaining power affect the distribution of economic returns. Conversely, a substantial private earnings premium may reflect occupational scarcity or selection effects rather than a proportionate causal increase in national productivity.

Youth Exclusion and the Transition From Education to Productive Employment

The proportion of young people neither employed nor participating in education or training provides an additional dimension of the human-capital baseline because the completion of schooling does not necessarily result in immediate entry into productive employment. The NEET indicator captures young people who are outside both employment and formal or non-formal educational activity, including individuals who are unemployed and seeking work as well as people who are economically inactive for other reasons. It is therefore a wider measure of labour-market disengagement than youth unemployment, although it cannot automatically be interpreted as a measure of permanent exclusion or unused graduate skills because the population includes people with different educational qualifications and circumstances. The age range is also decisive: Eurostat’s principal European policy indicator covers 15–29-year-olds, while some OECD country notes provide a narrower comparison for people aged 18–24. The two series cannot be placed into a single ranking without explicitly identifying that difference.

The European trend has been favourable over the past decade. According to Eurostat, Fewer Young People Not in Work or Education in 2025, published 28 May 2026, the EU-27 NEET rate for people aged 15–29 declined from 15.2% in 2015 to 11.0% in 2025. The reduction of 4.2 percentage points represents an improvement in youth labour-market and educational engagement, although changes in the indicator can result from several underlying developments, including stronger employment, increased educational participation, demographic change and variations in labour-market conditions. Eurostat also reports substantial differences within the age range: in 2025 the NEET rate was 5.3% among 15–19-year-olds, 12.8% among 20–24-year-olds and 14.7% among 25–29-year-olds. This variation is consistent with the different educational and employment circumstances of these cohorts, as the youngest group is more likely to remain in secondary education while older individuals face a wider range of labour-market transitions.

Italy’s progress has been particularly significant in absolute terms. Its NEET rate declined from 25.7% in 2015 to 13.3% in 2025, a reduction of 12.4 percentage points, which Eurostat identifies as the largest improvement among EU member states over the period. Nevertheless, Italy remained 2.3 percentage points above the EU average in 2025, demonstrating that rapid improvement has not eliminated the relative disadvantage. The correct interpretation is consequently twofold: the Italian youth transition problem has diminished substantially, but the remaining level of exclusion continues to indicate underused labour-market potential. This cannot be converted directly into a count of unemployed graduates because the NEET population includes both graduates and non-graduates, as well as individuals whose circumstances may prevent immediate labour-market participation. Source: Eurostat, Fewer Young People Not in Work or Education in 2025, 28 May 2026.

Table 6 — Youth exclusion and educational transition indicators

Eurostat and OECD NEET indicators use different age ranges in the rows shown. Comparisons are valid within the same definition, not across the entire table. Sources: Eurostat, Fewer Young People Not in Work or Education in 2025, OECD, Germany, Spain, Poland, and United Kingdom.

The evidence shows why Italy’s youth transition problem must be separated analytically from its educational-attainment deficit. A country may produce a large number of graduates yet experience relatively high youth unemployment or NEET rates if labour-market conditions prevent their integration, while another country may combine comparatively modest tertiary participation with effective vocational transitions and low unemployment. Spain illustrates the first challenge through its relatively high tertiary-attainment rate and continuing graduate unemployment, while Germany illustrates the importance of vocational pathways that provide occupational qualifications recognised by employers. Poland’s low unemployment among tertiary-qualified young adults indicates a different form of labour-market absorption, although this observation alone cannot determine whether graduates are employed in positions matching their qualifications. Italy’s policy challenge is therefore not simply to increase the quantity of advanced educational qualifications, but to improve the relationship between educational progression and the availability of suitable employment across the national economy.

The Size of the Highly Educated Workforce and the Limits of Percentage-Based Comparisons

The implications of differences in tertiary attainment become economically significant when population shares are converted into the absolute stock of qualified workers available to an economy. A tertiary-attainment rate represents the proportion of a defined population holding qualifications, whereas the corresponding stock is determined by multiplying that proportion by the number of people in the relevant age group. These two dimensions must be analysed separately because countries with different population sizes may have substantially different numbers of graduates despite similar attainment percentages. The economically active graduate workforce represents a further distinction, requiring information on employment, unemployment and labour-force participation. Even this measure is insufficient to establish the volume of advanced skills effectively available to employers, because qualifications differ in specialisation, occupational relevance, proficiency and actual utilisation.

Italy’s 14.6-percentage-point tertiary-attainment shortfall relative to the EU average can be expressed transparently as a demographic accounting comparison. For every one million adults aged 25–64, the Italian attainment structure corresponds to approximately 223,000 tertiary-qualified people, compared with 369,000 under the EU average attainment structure, producing a difference of 146,000 tertiary-qualified adults per million. Applying the corresponding French and Spanish rates would produce approximately 449,000 and 424,000 tertiary-qualified adults per million, respectively, equivalent to differences of 226,000 and 201,000 relative to the Italian structure. These are standardised comparisons derived from the published attainment percentages, not estimates of actual national graduate counts, immediately available labour supply or the number of additional graduates that an education policy could produce. Source: Istat, Levels of Education and Employment Returns — Year 2025, 3 November 2026.

Table 7 — Standardised human-capital stock comparison

Illustrative standardisation to one million people, not actual population stocks. Figures are calculated from the cited Istat and rounded OECD attainment rates; observations based on rounded rates are explicitly approximate. Sources: Istat, 2025 attainment statistics; OECD, Education at a Glance 2026: Germany; Poland; United Kingdom.

The calculation demonstrates the importance of distinguishing educational composition from actual labour-market capacity. If Italy possessed the EU average tertiary-attainment structure, its working-age population would contain a substantially larger proportion of tertiary-qualified individuals, but the difference would not automatically represent additional employment or output. Some additional graduates would have pursued other forms of education or entered occupations not requiring tertiary qualifications; others might have been employed under either educational scenario. The economic effect would depend on whether the hypothetical additional qualifications improved productive capabilities, influenced labour-force participation or enabled firms to expand activities requiring specialised knowledge. A credible employment counterfactual must therefore account for the qualification distribution of workers who would otherwise have entered employment with secondary education, rather than assuming that every additional graduate would represent an additional employed person.

The same distinction applies to public finance and long-term growth. Higher educational attainment can affect fiscal capacity through employment, wages and taxable income, but it also requires expenditure by households, institutions and governments. The eventual net fiscal effect depends on completion, employment trajectories, earnings, migration, the length of working life and the cost of educational provision. Similarly, the contribution to productivity is determined partly by how well qualifications correspond to economically valuable tasks and whether firms possess sufficient organisational and technological capital to make effective use of them. Consequently, the attainment gap provides a strong basis for identifying a structural disadvantage but remains an incomplete measure of its economic cost.

Educational Qualifications, Advanced Skills and the Industrial Structure of European Economies

The differences between European economies become more consequential when the analysis moves beyond the distinction between graduates and non-graduates toward the composition of qualifications and their relevance to industrial activities. Advanced manufacturing, engineering, scientific research, digital services and professional management require different combinations of formal education, occupational experience and technical competence. Certain functions depend on extensive university-level scientific and mathematical training, while others rely on specialised vocational pathways, regulated professional qualifications or the cumulative acquisition of workplace expertise. The relationship between tertiary attainment and competitiveness therefore depends on whether a country’s educational system generates the specific capabilities demanded by its productive structure, rather than on attainment rates alone.

Germany offers a documented example of the importance of this distinction. The OECD reports that 34% of national tertiary graduates in Germany were qualified in science, technology, engineering and mathematics fields, while the corresponding share among international graduates reached 51% in the latest referenced figures. The 34% share was reported as the highest among OECD countries for national graduates. This observation demonstrates that the composition of tertiary qualifications can differ materially even when countries are assessed using the same overall attainment category. Germany’s capacity to combine a substantial vocational education system with specialised tertiary qualifications is particularly relevant for industrial sectors in which engineering, technical operations and applied scientific competence must function together. Source: OECD, Education at a Glance 2026: Germany, Figure 4 and associated 2024 graduate statistics.

The internationalisation of advanced education also affects the potential supply of qualified workers. The OECD reports that international students represented 13% of tertiary students in Germany in 2024, compared with 7% in 2015, while the OECD average increased from 8% to 12% over the same period. Germany’s leading international student origins included India, China, Türkiye, Austria and Iran, although student mobility should not be equated with permanent graduate retention or subsequent employment. Nevertheless, these figures demonstrate the potential importance of international educational participation as an additional channel through which an economy can expand its future skilled workforce. The effectiveness of that channel ultimately depends on graduation, work authorisation, qualification recognition, language proficiency, recruitment conditions and the capacity to retain graduates after completion of their studies. Source: OECD, Education at a Glance 2026: Germany, international student mobility indicators.

Poland presents another relevant characteristic of the educational transition. According to the OECD, the proportion of adults whose highest educational attainment was vocational upper secondary education was 59% among those aged 55–64, compared with 33% among those aged 25–34 in 2025. This substantial generational difference indicates a transformation in the distribution of educational qualifications, but it should not automatically be interpreted as a deterioration of vocational training because some younger adults may progress to higher qualifications, including tertiary education. The relevant question for competitiveness is whether the evolving distribution of qualifications corresponds to the requirements of Poland’s productive system, including its capacity to maintain technical occupations while expanding advanced professional and scientific qualifications. Source: OECD, Education at a Glance 2026: Poland, 29 September 2026.

Italy’s position consequently requires a more detailed assessment than the aggregate graduate shortfall alone can provide. Its comparatively low tertiary-attainment rate establishes a quantitative disadvantage in educational composition, but the industrial implications depend on the distribution of qualifications across engineering, mathematics, computing, natural sciences, health professions, economics and other fields, as well as the strength of advanced technical and vocational programmes. A smaller graduate population does not necessarily imply a proportionately smaller supply of every specialised occupation, while a shortage of technically qualified personnel cannot automatically be resolved by expanding undifferentiated university enrolment. The decisive evidence will concern field-specific graduate supply, actual occupational vacancies, qualification matching, employer training and the capacity of firms to retain workers with specialised experience.

Lifelong Learning and the Ability to Maintain Human-Capital Value

Educational attainment at the beginning of working life does not fully determine the future stock of usable capabilities because technological change, occupational restructuring and evolving production methods require repeated investments in learning. This distinction is important for interpreting the competitive position of economies with different age structures and educational traditions. Initial qualifications provide a foundation for professional development, but the value of that foundation depends partly on whether workers continue updating their knowledge and whether employers provide opportunities to apply new capabilities. Lifelong learning therefore represents a mechanism through which existing human capital can remain productive despite changes in technology, although participation in a training activity does not by itself demonstrate that economically valuable skills have been acquired.

The OECD’s 2026 country reports provide comparable evidence on participation in non-formal education and training among employed adults, drawing on the Survey of Adult Skills. In Italy, 34% of employed people aged 25–44 and 32% of those aged 45–64 reported participation in non-formal education or training during the preceding twelve months, compared with OECD averages of 52% and 45%, respectively. Germany recorded considerably higher participation of 57% and 49%, while France reported 45% and 40%. Poland recorded lower participation than Italy, at 28% among employed 25–44-year-olds and 24% among those aged 45–64. The United Kingdom’s country note reports 65% and 61% for England, which must not be presented as an identical geographic measure for the entire United Kingdom. Sources: OECD, Education at a Glance 2026: Italy, Germany, France, Poland, and United Kingdom.

Table 8 — Participation in non-formal education and training among employed adults

Share of employed adults reporting participation in non-formal education or training over the preceding twelve months. These figures derive from the OECD Survey of Adult Skills and should not be described as 2025 annual labour-force survey observations. The geographic coverage for the British observation is England. Sources: OECD, Education at a Glance 2026 country reports.

The Italian figures show that the challenge of human-capital formation extends beyond initial tertiary attainment. Even among existing employees, participation in training is substantially below the OECD average, suggesting comparatively limited exposure to formalised opportunities for continuous skill development. This pattern may constrain technological adaptability where production systems require workers to learn new digital tools, integrate increasingly complex information or move between related technical functions. However, the indicator measures participation rather than training intensity, quality or subsequent productivity gains, and it excludes some forms of informal workplace learning. It consequently supports concern about the institutional and organisational conditions for maintaining skills, but cannot determine the precise quantity of economically useful knowledge that workers acquire.

The Baseline for Technological Adaptability: Education, Training and Work Organisation

One further indicator helps distinguish formal qualifications from the organisational environment in which they are used. The OECD reports substantial differences between countries in the proportion of tertiary-qualified workers whose jobs permit working from home at least occasionally. In Italy, the share was 28% among tertiary-qualified workers, compared with 10% among workers holding upper-secondary or post-secondary non-tertiary qualifications. Germany recorded 45% and 16%, France 56% and 19%, and Poland 23% and 8%, respectively. These observations reflect differences in job characteristics, industrial composition, employer practices and potentially technological infrastructure, rather than a direct measure of digital proficiency or organisational productivity. Sources: OECD, Education at a Glance 2026: Italy, Germany, France, and Poland.

The relevance of these observations lies in the relationship between occupational characteristics and the productive utilisation of qualifications. Knowledge-intensive occupations frequently involve activities that can be performed through digital systems, while manufacturing operations, laboratory work, infrastructure maintenance and many technical services require physical presence. A higher incidence of remote work therefore does not establish greater technological sophistication, and it would be particularly misleading to interpret the lower remote-working share of a manufacturing-oriented economy as evidence of inferior technical competence. Nevertheless, cross-country differences in the organisation of graduate employment may provide additional information about the distribution of workers across digitally mediated services, administrative functions, scientific occupations and other knowledge-intensive activities.

Table 9 — Work-from-home incidence by educational attainment

Share of workers reporting that they sometimes or usually work from home. This is an occupational and organisational indicator, not a direct measure of AI adoption.ports

The broader implication for an increasingly AI-intensive economy is that education must be understood as one component of a larger system of productive capabilities. Workers require not only formal qualifications but also the opportunity to apply specialised knowledge within organisations capable of investing in complementary technologies, redesigning processes and evaluating complex operational decisions. The international evidence supports the need to examine this relationship, but the precise contribution of educational attainment to AI-related productivity cannot be established from the qualification statistics presented in this chapter. Such a conclusion would require firm-level evidence linking workforce qualifications, technology adoption, organisational investment and measured productivity, together with methods capable of separating the effect of education from the influence of industry structure and other determinants.

Key Judgments and Implications for European Competitiveness

The comparative evidence establishes that Italy’s deficit in tertiary educational attainment is substantial even when measured against economies with different industrial structures and educational traditions. The country’s 22.3% tertiary-attainment rate among adults aged 25–64 is below the EU average of 36.9%, the German OECD figure of approximately 35%, the Polish figure of approximately 40%, the Spanish figure of 42.4%, the French figure of 44.9% and the British figure of approximately 55%. The magnitude of these differences provides strong evidence of an unequal distribution of formal advanced qualifications across Europe, although it does not permit the inference that every country with a higher attainment rate necessarily has more effective industrial capabilities, stronger productivity or better employment matching. Confidence: High for the educational-attainment comparison; medium for its independent contribution to economic competitiveness. Sources: Istat, Levels of Education and Employment Returns — Year 2025; OECD, Education at a Glance 2026.

The second judgment concerns the economic value of qualifications and the different ways in which European labour markets convert education into employment. Italy exhibits a clear positive association between tertiary attainment and employment, while Germany demonstrates that vocational qualifications can produce equally strong or stronger employment outcomes within certain age and qualification groups. Poland and Spain further illustrate that the unemployment outcomes of young graduates can vary substantially even where countries have expanded tertiary participation. Educational attainment must therefore be examined together with qualification type, occupational matching and labour-market conditions before a reliable competitiveness assessment can be made. Confidence: High for the documented employment differences; medium for the interpretation of their underlying institutional causes.

The third judgment concerns the financial incentives associated with advanced education. Italy’s reported tertiary earnings premium of 35% remains below the OECD average of 54%, while the especially limited measured premium for bachelor’s-level attainment raises questions about the remuneration and occupational opportunities available to graduates entering the labour market. These observations provide evidence of differentiated earnings returns, but do not establish that Italy’s graduate shortfall is caused by weak salary incentives or that increasing wages alone would generate educational convergence. Confidence: High for the observed premium; medium to low for the causal contribution of remuneration to educational choices, graduate migration and future skills shortages without additional empirical evidence. Source: OECD, Education at a Glance 2026 — What Are the Earnings Advantages to Education?.

The fourth judgment concerns the dynamic nature of the European human-capital stock. Changes among younger cohorts indicate that educational upgrading is continuing, but their effects on the entire working-age population emerge gradually through cohort replacement and migration. Germany’s increase in young-adult tertiary attainment from 30% to 41% between 2015 and 2025 illustrates the capacity for substantial change within a decade, while Italy’s younger-cohort attainment remains significantly below the EU average. The persistence of these differences means that educational policies implemented during the second half of the 2020s must be assessed according to realistic demographic and completion timescales rather than expectations of immediate aggregate convergence. Confidence: High in the observed demographic mechanism; medium in the eventual speed of convergence, which depends on policy implementation and future population changes. Sources: OECD, Education at a Glance 2026: Germany; Eurostat, Educational Attainment Statistics.

The fifth judgment concerns the importance of maintaining capabilities after workers enter employment. Italy’s comparatively limited participation in non-formal education and training among employed adults indicates that the country’s human-capital challenge cannot be confined to initial qualification rates. The economic importance of this observation increases when technological change requires workers to update their knowledge repeatedly, although the available participation indicators do not independently measure the effectiveness of training or its productivity impact. Confidence: High for the comparative training-participation difference; medium for its contribution to technological adaptability. Source: OECD, Education at a Glance 2026: Italy.

What Would Change the Assessment

The principal assessment would need revision if harmonised data showed that Italy’s comparatively low formal attainment substantially understates its stock of advanced technical and occupational capabilities, particularly through professional training or qualifications classified differently across European systems. It would also require adjustment if evidence demonstrated substantially stronger productivity, innovation or employment outcomes among particular categories of Italian workers than aggregate educational statistics suggest. Conversely, persistent gaps in younger-cohort educational attainment, together with limited progress in training participation and graduate employment, would reinforce the judgment that the country’s human-capital disadvantage remains structural rather than temporary.

A further change in the assessment would follow from evidence of rapid and durable improvements in educational completion, the employment of qualified workers and the remuneration of advanced skills, particularly if supported by firm-level observations demonstrating stronger technological adoption and productivity. Such improvements would indicate that the economic value of the existing educational stock was increasing even before the aggregate graduate share had converged with the European average. The decisive test is therefore not whether the number of qualifications rises in isolation, but whether the economy develops a larger and more effectively employed stock of knowledge, technical competence and adaptive capabilities.

Open Official Record

The principal unresolved statistical requirement is the construction of a complete, harmonised country-level panel for Italy, Germany, France, Spain, Poland and the EU-27 covering tertiary attainment by age and sex, employment and unemployment by educational level, earnings premia, young-adult transition outcomes and absolute working-age population counts. Eurostat’s Persons Aged 25–34 With Tertiary Educational Attainment — Dataset sdg_04_20, the associated Educational Attainment Statistics and the OECD’s Education at a Glance 2026 indicators provide the primary framework for completing that comparison. The country observations presented above distinguish verified values from unresolved cells rather than replacing missing observations with extrapolations.

For an eventual quantitative assessment of the aggregate graduate-stock gap, the required additional records include the country-specific population counts for ages 25–64, the number of persons holding each tertiary qualification level, the educational composition of employment and inactivity, and consistent migration data that distinguish foreign citizenship, foreign birth and the country in which qualifications were obtained. These records would permit the calculation of actual graduate stocks, employment-adjusted human-capital differences and clearly defined counterfactuals without confusing population shares with the number of additional workers or treating educational qualifications as homogeneous productive inputs.

The resulting conclusion is that Italy’s educational deficit is established beyond reasonable statistical doubt, while its exact economic cost remains dependent on how qualifications are produced, rewarded and utilised. The next analytical requirement is to determine how much of the country’s existing stock of advanced human capital remains underemployed, occupationally mismatched or geographically displaced, particularly among women, foreign-qualified workers, younger adults and the populations of economically disadvantaged regions.

Europe’s Structural Qualification Gap

Italy: tertiary attainment, ages 25–64
22.3%
EU-27: 36.9% | 2025
Italy’s gap against the EU average
−14.6pp
Percentage-point difference
Italian graduate employment
85.3%
Ages 25–64, 2025
Difficult-to-fill planned graduate positions
50.9%
Unioncamere–Excelsior, 2025
02 / Comparative Analytics

Interactive European Skills Observatory

Select an indicator and inspect the comparable official observations.
Column height is linear and starts at zero. The three-dimensional
effect is visual only.

Employment and Under-Utilised Qualifications

Employment by educational attainment

Tertiary85.3%
Upper secondary74.6%
Below upper secondary56.1%
Tertiary education is associated with a 10.7 percentage-point
employment advantage over upper-secondary education.
This difference does not establish a causal effect.

Italian citizens86.3%
Foreign citizens68.4%
The 17.9-point employment difference concerns citizenship,
not country of birth. It does not independently measure
overqualification or discrimination.
04 / Evidence Matrix

Comparative Statistical Register

Tertiary qualifications correspond to ISCED levels 5–8.
Unverified values are identified explicitly and never treated as zero.

Official observations, reference period 2025.

Country / Area	Tertiary, ages 25–64	Tertiary, ages 25–34	Graduate employment	NEET, ages 15–29
Italy	22.3%	31.1%	85.3%	13.3%
EU-27	36.9%	44.8%	Not verified	11.0%
France	44.9%	Not verified	Not verified	Not verified
Spain	42.4%	Not verified	Not verified	Not verified
Germany	Not verified	Not verified	Not verified	Not verified
Poland	Not verified	Not verified	Not verified	Not verified

Country observations are preserved only where the relevant
indicator and reference period were established.

05 / AI and Productive Capabilities

Human Capital and Artificial Intelligence

Advanced education can support technological absorption,
but productivity gains depend on organisational investment,
management, technical integration and appropriate deployment.
The relationship is conditional rather than mechanically causal.

Education and specialised capabilities.

02 / Organisational absorption

Technology investment and process redesign.

Innovation, productivity and competitiveness.

A larger graduate population does not automatically produce
higher GDP. Realised gains depend on skills matching,
capital investment and productive demand.
06 / Strategic Foresight

Italy 2035: Scenario Explorer

Choose a conditional pathway to examine likely implications
for skills supply, labour-market utilisation and AI absorption.
These are qualitative scenarios, not quantified GDP forecasts.

07 / Institutional Policy Options

Human-Capital Policy Explorer

Select a policy domain to examine the intervention mechanism,
institutional responsibility and implementation limitation.

Official Evidence and Sources

The statistical baseline draws on official institutional
releases

Institution	Evidence	Official source
Istat	Education and employment returns, 2025	Official release
Eurostat	Educational attainment	Official statistics
Eurostat	Youth NEET, 2025	Official release
OECD	Education GPS, Italy	Country data
Unioncamere	Excelsior graduate recruitment	Official release

Chapter 4 — The Economic Cost of Educational and Employment Gaps

Principal Judgment — Italy’s Economic Loss Is Concentrated in the Failure to Convert Available and Potential Skills into Productive Capacity

Italy’s human-capital disadvantage cannot be measured adequately by comparing university attainment rates or attributing a hypothetical percentage of national income to missing graduates. The economic cost originates through several distinct but interconnected mechanisms: a smaller stock of advanced qualifications relative to comparable European economies; incomplete participation by individuals who already possess economically valuable skills; the allocation of qualified workers to occupations that do not fully exploit their capabilities; weaker financial incentives for certain specialised careers; and the reduced capacity of enterprises to combine scientific knowledge, technical expertise, organisational innovation and capital investment. These constraints influence economic performance through employment volumes, hours worked, labour productivity, total factor productivity, technological diffusion and the fiscal resources generated by productive activity. Their combined effect may be substantial, but the existing statistical record does not justify an unconditional numerical claim that Italy would achieve a particular increase in gross domestic product simply by reaching the European average for tertiary education.

The strongest available evidence supports a more precise diagnosis. The Italian National Institute of Statistics, Levels of Education and Employment Returns — 2025, published 3 November 2026 documents not only the educational composition of the Italian population but also considerable employment differences across qualification categories, sex, citizenship and geographical location. The OECD’s Education at a Glance 2026: Italy, published 29 September 2026 further establishes that the country’s employment and earnings returns to education coexist with profound regional disparities. These observations indicate that Italy’s competitive position is constrained by both the formation of human capital and its subsequent utilisation, a distinction that becomes critical when estimating the possible economic consequences of policy changes.

The assessment is therefore that Italy’s labour-market utilisation deficit represents a potentially recoverable economic disadvantage, while its tertiary-attainment deficit represents a slower-moving structural constraint on the composition of future labour supply. The first may respond to improvements in childcare, employment incentives, credential recognition, occupational mobility and hiring practices, provided that suitable jobs exist; the second requires sustained improvements in education and completion over multiple cohorts. The relationship between these processes determines the country’s ability to expand productive employment and maintain technologically sophisticated activities through 2035. Confidence is high that both constraints exist, medium that their combined reduction would materially improve potential output, and low for any exact GDP gain in the absence of an empirically calibrated counterfactual.

Labour Supply, Employment Intensity and the Economic Meaning of Unused Qualifications

The connection between educational attainment and aggregate production operates through more than the number of people formally classified as graduates. Productive labour input depends on the number of employed individuals, their hours of work and the effective contribution of those hours within specific occupations and organisations. An increase in the employment rate may raise output by mobilising previously inactive resources, while an improvement in occupational matching may increase output without changing the number of people employed. Additional education can improve task-specific competence, but its measured productivity contribution depends on whether workers perform activities that benefit from those capabilities and whether complementary physical, digital and organisational capital is available. Consequently, changes in graduate employment, working hours and productivity should be treated as separate economic effects rather than combined into a single assumed educational dividend.

Italy’s employment structure reveals an important example of this distinction. In 2025, the employment rate of women aged 25–64 was 60.8%, compared with 80.9% among men, generating a difference of 20.1 percentage points. Among women with tertiary qualifications, the employment rate reached 82.5%, compared with 85.9% among similarly educated women across the EU. The Italian employment disadvantage therefore narrowed markedly with higher education, but did not disappear. The corresponding female rates were 64.0% for upper-secondary qualifications and 37.8% for qualifications below upper secondary education, against EU averages of 72.6% and 46.8%, respectively. These findings demonstrate that a substantial proportion of Italy’s aggregate female employment gap is concentrated among workers with lower educational qualifications, while graduates still exhibit a measurable disadvantage. Source: Istat, Levels of Education and Employment Returns — 2025, section on gender and European employment differences. Istat

From the perspective of national production, the implication is not that all economically inactive women could or should be immediately incorporated into employment. Inactivity encompasses care responsibilities, education, health-related limitations, personal circumstances and other categories that cannot be reduced to an undifferentiated reserve of available labour. The relevant economic question is what proportion of individuals would choose and be able to work if suitable employment opportunities, childcare services, transport, flexible working arrangements and adequate financial incentives were available. This requires distinctions between desired additional employment, observable unemployment, inactivity, underemployment and actual labour-market matching. Without such distinctions, the entire population difference between female and male employment rates would be incorrectly represented as readily recoverable productive capacity.

Table 4.1 — Female employment gaps and the economic utilisation of education

The table identifies a significant economic distinction: the observed Italian disadvantage is proportionately less severe among tertiary-educated women, implying that the employment returns associated with advanced qualifications are relevant to female labour-market participation, but also that measures addressing education alone would leave substantial barriers affecting women with intermediate and lower qualifications. If public policy concentrated exclusively on increasing university graduation, the employment potential associated with childcare accessibility, regional employment opportunities and vocational skills among non-graduates could remain substantially underexploited. Conversely, measures increasing employment among women with advanced qualifications could produce additional economic benefits through the greater utilisation of existing specialised competencies, without requiring a new cohort of university graduates to enter the workforce.

The Graduate Employment Gap: A Counterfactual That Can Be Quantified Without Inventing GDP

A useful first-stage counterfactual can be constructed by comparing observed graduate employment rates in Italy with the corresponding EU average, while maintaining the existing Italian educational composition unchanged. Istat reports an Italian employment rate of 85.3% among tertiary-qualified adults aged 25–64, with a disadvantage of 2.8 percentage points relative to the EU-27 average in 2025. The corresponding implied EU employment rate is 88.1%, obtained by adding the reported difference to the Italian figure. For every 100,000 tertiary-qualified adults, the Italian employment structure therefore corresponds to approximately 85,300 employed individuals, while applying the EU average would produce 88,100, or 2,800 additional employed people per 100,000 tertiary-qualified adults. This is a transparent static accounting comparison, not a projection of the number of jobs that could actually be created. Source: Istat, Levels of Education and Employment Returns — 2025, comparison of employment outcomes with the EU-27. Istat

The distinction matters because the resulting 2,800-person difference cannot be interpreted as evidence that the Italian economy currently has enough available vacancies to absorb an equivalent number of graduates. Nor can it determine whether the additional individuals would work full-time, whether their qualifications would match the requirements of available positions, or whether their employment would replace other workers. A defensible economic estimate would require information about employment transitions, occupational demand, regional accessibility, working hours and the productivity of newly employed workers. Nevertheless, the calculation establishes the scale of the employment difference independently of the educational-attainment gap, showing that Italy could theoretically improve the productive utilisation of its existing graduate population even if its percentage of tertiary-qualified adults remained unchanged.

An analogous comparison can be constructed for Italian women with tertiary qualifications. Their employment rate of 82.5%, compared with the EU figure of 85.9%, corresponds to 3,400 additional employed women per 100,000 tertiary-qualified women under the EU employment structure. The calculation is a separate, overlapping comparison and must not be added to the overall graduate employment differential because tertiary-qualified women are already included within the total graduate population. Such double counting is a common source of exaggerated labour-supply estimates: differences by education, sex, citizenship and region frequently describe intersecting groups, and their potential employment gains must be estimated jointly using mutually exclusive population cells before they can be aggregated.

Table 4.2 — Static employment counterfactuals per 100,000 people

Calculated from Istat, Levels of Education and Employment Returns — 2025. Each row is a separate hypothetical population of 100,000 persons with the stated characteristics. Rows cannot be added together without actual group sizes and safeguards against overlapping populations. These results do not forecast actual additional employment.

The advantage of this presentation is that it establishes the magnitude of differences in an auditable form while avoiding assumptions about Italy’s unverified graduate stock or the productivity of additional workers. The weakness is that it remains a static comparison rather than a structural model of labour supply. In reality, closing an employment gap depends on the distribution of individuals by age, region, household structure and occupation, together with the availability of suitable employment. It also depends on the response of firms: if employers expand production, improved labour supply could facilitate additional hiring and investment, but if demand remains unchanged, some employment gains could be offset by substitution or wage adjustment. The counterfactual should therefore be interpreted as a measure of the employment-rate disparity that requires explanation, not a guaranteed employment gain.

The Economic Significance of Regional Differences

Italy’s geographical disparities introduce an additional dimension because a national graduate employment rate can conceal very different labour-market conditions within the country. In 2025, Istat reported that the employment disadvantage of Southern Italy relative to Northern Italy remained 10.2 percentage points among tertiary-qualified adults, compared with 19.2 points among upper-secondary-qualified adults and 23.4 points among those with lower qualifications. The same report indicated that unemployment rates were higher in the South by 2.5, 6.0 and 10.5 points, respectively, across these qualification categories. These differences suggest that tertiary education is associated with narrower geographical employment inequalities, yet also demonstrate that advanced qualifications do not remove the influence of regional labour demand and institutional conditions. Source: Istat, Levels of Education and Employment Returns — 2025, territorial differences in labour-market outcomes. Istat

The implications for national productivity are broader than the immediate employment difference. Where qualified workers cannot find suitable opportunities locally, they may relocate to stronger domestic labour markets, seek employment abroad, accept positions below their educational level or leave the labour force temporarily. Domestic mobility can improve national productivity if workers move to more productive activities, but it may simultaneously weaken the capacity of their regions of origin to develop sophisticated industries, maintain professional services and attract investment. International migration may benefit individual workers through improved employment and earnings, while reducing the domestic availability of certain skills unless compensated by inward migration, returning graduates or other adjustments. The resulting economic effects depend on whether the departure of qualified workers leads to persistent shortages, whether firms respond by investing elsewhere and whether the home region possesses complementary infrastructure and capital capable of supporting advanced production.

The OECD’s Education at a Glance 2026: Italy identifies a regional tertiary-attainment range extending from approximately 17% in Sicily to 29% in Lazio, while reporting that Italy has particularly large regional disparities in employment outcomes. This means that national human-capital policy cannot be evaluated entirely through average educational attainment or aggregate employment rates, because identical national expenditure could generate different results in regions with different productive structures. The effectiveness of expanding university capacity in a locality with limited demand for highly qualified workers would depend on the simultaneous development of suitable employment, research activity and enterprise investment; otherwise, educational expansion might principally increase outward migration rather than the local stock of employed graduates.

From Employment Gaps to Potential Output: Establishing the Causal Mechanism

Potential output refers to the level of economic production sustainable with the available labour, capital and technology without generating persistent inflationary pressure. Human capital may affect this capacity by raising the effectiveness of labour input, facilitating the use of sophisticated capital equipment, improving organisational decision-making and supporting innovation. The relevant transmission mechanism is therefore not confined to the conventional relationship between educational attainment and wages, because wages are an imperfect proxy for marginal productivity and can be influenced by bargaining institutions, market power, occupational composition and other factors. The macroeconomic question is whether additional or better-utilised skills increase the economy’s capacity to produce goods and services, rather than simply changing the distribution of existing employment or income.

An appropriate estimation framework would separate changes in labour participation, working hours, qualification-specific productivity and capital accumulation. The effect of increasing graduate employment would first depend on the number of additional hours supplied and on the value added generated in the relevant occupations. An improvement in occupational matching could raise productive efficiency without increasing total hours worked, while additional investment in machinery, software and research infrastructure could increase the productivity of both graduates and non-graduates. These channels interact, which means that estimates obtained by independently adding participation gains, educational earnings premia and technological productivity effects would risk counting the same improvement more than once.

Table 4.3 — Economic transmission channels and evidentiary requirements

The central evidentiary problem is that the relationship between education and productivity is bidirectional. More productive firms may recruit more graduates because their activities require advanced skills, while graduates may contribute to productivity by enabling investment and innovation. Regions with higher wages may attract qualified workers, creating correlations between graduate concentration and economic performance that partly reflect migration rather than the effect of education on productivity. Similarly, companies adopting AI may already possess stronger management and organisational capabilities, making it difficult to identify the independent contribution of formal educational attainment. A credible empirical assessment must therefore control for these characteristics, use longitudinal or appropriately identified evidence where possible and avoid interpreting cross-sectional comparisons as causal relationships.

Earnings Returns, Household Income and Fiscal Capacity

The relationship between educational attainment and public finances must be assessed through the combined effects of income, employment and expenditure. Higher employment among qualified workers can increase income-tax receipts and social contributions, while reducing certain employment-related transfers. Higher earnings may expand consumption and the corresponding indirect tax base, while longer working lives can alter pension contributions and liabilities. Yet producing additional tertiary graduates also generates costs for educational institutions, households and governments, while the benefits may be reduced if individuals migrate internationally or enter occupations that do not use their acquired qualifications. The fiscal effect must therefore be calculated over an appropriate horizon and from a defined perspective, distinguishing the central government, social insurance institutions, regional authorities and the broader public sector.

The OECD’s 2026 evidence gives particular importance to the remuneration structure. Italy’s measured 35% earnings premium for tertiary-qualified full-time, full-year workers, compared with 54% across the OECD, indicates that the relative private wage advantage of higher education is smaller than the international average, although this does not determine the absolute level of wages or the fiscal contribution of graduates. The same evidence identifies a notably limited premium for bachelor’s-level qualifications relative to master’s and doctoral attainment. For public policy, the implication is that expenditure intended to increase participation in tertiary education should be evaluated together with the availability and remuneration of qualified employment, because the social and fiscal returns depend on whether the resulting skills are used productively in the domestic economy. Source: OECD, Education at a Glance 2026: Italy, economic returns to education. OECD

A necessary qualification is that a smaller educational wage premium is not automatically evidence of an inefficient educational system. It may also reflect a more compressed national wage distribution, stronger remuneration among workers without tertiary qualifications, occupational composition or labour-market institutions that distribute productivity gains differently. The policy-relevant concern emerges when limited remuneration is combined with recruitment difficulties, graduate emigration and insufficient access to high-productivity occupations, because those outcomes may indicate that the economy is failing to create attractive opportunities for the advanced qualifications it produces. Establishing that mechanism requires detailed evidence on wages by field of study, occupation, experience, region and employer productivity, rather than comparison of national averages alone.

WordPress Analytical Scheme — Labour-Supply and Productivity Transmission

The following component is intended for insertion at this point in the WordPress document. It visualises the documented employment-rate counterfactuals, allowing the reader to distinguish observed employment differences from assumed increases in GDP. The figures are derived from the official Italian and EU employment rates used in this chapter; the visualisation deliberately avoids assigning productivity coefficients, fiscal multipliers or probabilities that have not been established.

THE EMPLOYMENT UTILISATION GAP

Italy versus the EU-27, 2025. Select a population group to compare
observed employment with a common European reference rate. The
counterfactual is standardised to 100,000 people and does not
represent a forecast of additional jobs or GDP.

All tertiary graduates

Common scale: 0–100% employment.

Static difference per 100,000 people
+2,800

Additional employed persons under the EU employment rate,
holding the group population fixed.

Interpretation: this is an accounting difference,
not an estimate of labour-market absorption, additional vacancies,
productivity or national GDP.

Key Judgments — Chapter 4

The economic consequences of Italy’s educational and employment differences are best understood as a combination of constraints affecting effective labour supply, occupational allocation and productivity rather than as a single measurable stock of missing graduates. The official employment comparisons provide a defensible indication of relative labour-market under-utilisation, while the earnings and regional statistics identify reasons why employment gains cannot be assumed to follow automatically from educational expansion. Confidence: High for the existence of the identified gaps; medium for their contribution to potential output; low for precise macroeconomic losses without a model incorporating actual population stocks, labour demand, hours, qualification matching and displacement effects.

A second judgment concerns the importance of sequencing. Better employment utilisation can potentially improve the economic contribution of existing qualifications before educational-attainment rates materially change, while education reform influences the composition of the workforce more gradually through graduation and demographic replacement. These processes should be assessed together but measured separately, because they require different implementing institutions and generate effects over different periods. The decisive monitoring evidence consists of employment transitions, hours worked, sectoral job creation, occupational matching, firm productivity and international graduate flows rather than graduate attainment alone.

Chapter 5 — Industrial Skill Shortages and Europe’s Competition for Qualified Labour

Principal Judgment — Industrial Recruitment Difficulties Reflect a Combination of Occupational Scarcity, Demographic Replacement, Employer Requirements and Labour-Market Frictions

European industrial competitiveness increasingly depends on the ability to assemble complementary groups of engineers, technicians, scientific specialists, software developers, production managers, maintenance professionals and workers capable of operating sophisticated manufacturing equipment. The availability of these capabilities cannot be inferred directly from the number of university graduates because technical labour demand extends across multiple educational pathways and occupational experience levels. A firm seeking an automation engineer with responsibility for production-line integration is not necessarily able to substitute a recently qualified computer scientist, while a manufacturing enterprise requiring specialised machining, industrial maintenance or quality-control competence may depend on advanced vocational expertise rather than conventional university training. Skill shortages must therefore be measured at the level of occupations, qualifications, experience and location, with particular attention to the conditions under which employers attempt to recruit.

Italy’s official employer evidence indicates a significant recruitment challenge, but one that requires careful interpretation. The Unioncamere–Ministry of Labour Excelsior survey, Half of Graduates and ITS Academy Diploma Holders Are Difficult to Find, published 26 November 2025 recorded approximately 670,000 planned contracts for university graduates, 120,000 for ITS Academy diploma holders, 1.3 million for upper-secondary diploma holders and 2.3 million for holders of vocational qualifications. Recruitment difficulties affected 50.9% of planned graduate contracts and 57.3% of those associated with ITS Academy qualifications, while the overall difficulty rate across planned recruitment was approximately 47%. These figures establish that recruitment friction extends beyond university occupations into technical training pathways that are particularly relevant to industrial operations. They do not establish that every difficult-to-recruit position remained vacant or that every reported difficulty resulted from an absolute shortage of suitable workers. Unioncamere

This distinction is central to policy. Recruitment difficulties may arise because candidates do not possess the necessary technical competencies, because there are insufficient appropriately qualified workers within commuting distance, because employers seek experience that recently trained candidates cannot yet offer, or because proposed working conditions fail to attract individuals who possess the required capabilities. The same employer may therefore report a shortage while qualified workers elsewhere remain unemployed or employed in unrelated activities. An economically coherent response requires identifying the source of the mismatch rather than treating every reported difficulty as evidence that the country must produce additional university graduates. Where the principal limitation is occupational competence, specialised education and employer training may be appropriate; where location is decisive, mobility and regional infrastructure become relevant; where compensation or career prospects are inadequate, additional training capacity may have limited impact unless employers adjust their employment conditions.

The Composition of Recruitment Pressure Across Qualifications

The Excelsior figures indicate that Italy’s recruitment system relies on several parallel sources of qualified labour. The planned 670,000 graduate contracts represent a substantial component of demand, but the 120,000 planned ITS Academy contracts identify a separate technical pathway linking post-secondary professional education to specific occupational functions. The 1.3 million planned contracts for upper-secondary diploma holders and 2.3 million for holders of vocational qualifications demonstrate that the economy’s recruitment requirements cannot be analysed solely through university-level education. These categories should not be converted directly into a distribution of unique workers because the survey concerns planned contracts rather than individuals, and a single person may enter more than one employment relationship over the relevant period. Nevertheless, the figures reveal that industrial and service-sector competitiveness depends on a broad qualification ecosystem rather than a single category of graduates.

Table 5.1 — Italy’s planned recruitment by qualification, 2025

The greater difficulty reported for ITS Academy qualifications compared with university degrees is particularly relevant to advanced manufacturing because technical production frequently requires workers capable of linking engineering specifications with practical equipment operation, maintenance, process control and industrial quality systems. However, the relative difficulty percentages do not independently establish the total economic importance of the two qualification categories, because the number of planned graduate contracts is much larger than the number of planned ITS Academy contracts. Nor should the different percentages automatically be interpreted as evidence that the educational system has produced the wrong distribution of qualifications. Different occupations have different geographical concentrations, training pathways, employer requirements and recruitment cycles, so the figures must be connected to more detailed evidence before a particular investment in educational capacity can be justified.

High-Value Manufacturing: Where Qualification Shortages Affect Industrial Operations

The industrial significance of recruitment difficulties depends on whether scarce competencies perform functions that constrain production, maintenance, quality assurance, development or technological upgrading. In high-value manufacturing, shortages of engineering and technical personnel may delay the commissioning of equipment, reduce maintenance availability, complicate production-process improvements or constrain the introduction of digitally integrated manufacturing systems. A relatively small number of specialised workers may therefore influence a much larger volume of industrial production when their knowledge is essential to the operation of capital-intensive systems. This does not imply that every shortage produces an equivalent financial loss, because firms may reorganise operations, subcontract activities or adjust investment schedules. It does establish why occupational shortages should be evaluated according to their operational position within productive processes rather than simply their numerical prevalence.

The Unioncamere–Excelsior January 2026 employment bulletin provides useful evidence of the occupations and industrial branches affected by recruitment difficulties. For January 2026, surveyed enterprises planned approximately 527,000 contracts, including 155,000 in industry, of which about 104,000 were associated with manufacturing and public utilities and 51,000 with construction. Within the manufacturing sector, demand included more than 27,000 planned contracts in mechanical and electronic industries, approximately 21,000 in metallurgy and metal products, and around 15,000 in food and beverage manufacturing. These were planned monthly employment entries, not estimates of persistent annual vacancies or the number of additional workers needed to expand production. Unioncamere

The same bulletin reported recruitment difficulty affecting 45.8% of January 2026 planned contracts, compared with 49.1% in January 2025. Employers identified the absence of candidates as the main reason for difficulties affecting 28.6% of planned entries, while inadequate candidate training accounted for 13.6% and other reasons for 3.6%. This decomposition is analytically important because the predominance of reported applicant scarcity does not necessarily mean that no qualified workers exist nationally; it may indicate local availability constraints, unattractive employment conditions or inadequate recruitment reach. At the same time, the explicitly reported training insufficiency identifies a separate channel in which educational content, specialist training or practical experience may contribute to the problem. These percentages describe the survey’s classification of planned recruitment difficulties and should not be interpreted as causal estimates obtained through independent observation of each employer’s recruitment process. Unioncamere

Table 5.2 — Industrial recruitment difficulties and selected technical occupations

The concentration of reported difficulties among production-process technicians, engineering technicians and maintenance workers illustrates the interaction between specialised knowledge and operational continuity. Industrial production depends on the availability of personnel capable of maintaining equipment reliability, diagnosing technical problems and implementing process improvements. Where these capabilities are scarce, firms may experience greater difficulty increasing capacity or adopting sophisticated production technologies, even if adequate demand and financing exist. However, the causal contribution of any specific occupational shortage to lost output cannot be inferred from the recruitment difficulty percentage alone, because the same percentage may describe very different operational situations depending on the number of affected firms, their production systems and the availability of alternative staffing arrangements.

Europe’s Digital Workforce and Italy’s Relative Position

The European digital labour market provides a more directly comparable quantitative indicator of technological occupations. According to Eurostat, Number of ICT Specialists in the EU Continues to Grow, published 27 May 2026, 10.45 million people were employed as information and communication technology specialists across the European Union in 2025, representing 5.0% of total employment. The corresponding share had increased by 1.5 percentage points since 2015, indicating a sustained structural expansion of ICT-related occupations. Growth nevertheless moderated in recent years, with the number of specialists increasing by 2.6% between 2024 and 2025, compared with 4.5% between 2023 and 2024. These findings establish the continued importance of digital occupations but do not, by themselves, determine whether the supply of specialists is adequate relative to employer demand. Eurostat

Italy’s position was comparatively weak. ICT specialists represented 3.8% of total Italian employment in 2025, against the EU average of 5.0%, while Sweden recorded 8.9%, Luxembourg 8.7% and Finland 7.8%. The Italian shortfall of 1.2 percentage points relative to the EU average appears modest when expressed as a simple percentage difference, but it represents an employment structure in which ICT occupations occupy a substantially smaller share. Italy’s ratio was approximately 76% of the EU average, indicating a relative shortfall of about 24% when measured against the EU share. This is a structural comparison, not evidence that Italy needs exactly 24% more ICT specialists or that its industrial productivity is reduced by a corresponding proportion. The appropriate number of ICT specialists depends on the economy’s industrial composition, the extent to which digital services are outsourced or imported, the organisation of enterprise IT functions and the technological intensity of production. Eurostat

Eurostat defines ICT specialists as workers who develop, operate and maintain ICT systems and whose main occupational activities involve information and communication technologies. This occupational definition is distinct from holding a university degree in computer science and includes people who acquired technical competencies through other educational or professional pathways. The comparison therefore provides evidence about the actual distribution of employment in digital occupations rather than merely the qualifications held by workers. Its significance for Italy lies in the interaction between this relatively small ICT workforce and the need to integrate software, data infrastructure, cybersecurity, industrial automation and AI systems into existing production activities. A firm may employ highly competent engineers and technicians while still lacking the digital specialists required to connect operational systems, manage industrial data or maintain secure digital infrastructure.

Table 5.3 — ICT specialists in employment, 2025

An additional consideration is the distribution of digital specialists by sex. Eurostat reports that 80.5% of ICT specialists employed across the EU in 2025 were men and 19.5% were women, despite the increase of approximately three percentage points in the female share since 2015. This composition suggests that the effective supply of digital expertise remains unevenly distributed across the population, which may constrain recruitment options and limit the diversity of available professional pathways. However, the figures do not establish the causes of the difference, which may involve education choices, career progression, hiring, working conditions and other factors. They are relevant because expanding participation among underrepresented groups could strengthen the talent pipeline without requiring reliance exclusively on demographic growth or international recruitment. Source: Eurostat, Number of ICT Specialists in the EU Continues to Grow, 2026. Eurostat

Recruitment Difficulties Versus Absolute Labour Scarcity

Employer-reported recruitment difficulties should be interpreted as evidence of friction between requested and available labour under prevailing conditions, not as a complete measure of economically unavoidable shortages. A shortage may be described as a situation in which the supply of workers possessing particular capabilities is insufficient relative to demand at the wages, working conditions, locations and timeframes offered. If employers can attract additional workers by improving remuneration or altering recruitment requirements, part of the reported shortage may represent a price or matching problem rather than an absolute absence of qualified people. If the competencies require extensive training or occupational certification, however, wage adjustment may not generate sufficient short-term supply because the relevant qualifications cannot be produced immediately.

The policy significance of this distinction is considerable. Expanding university places may increase the future number of qualified workers but has little immediate effect on an employer seeking experienced technical personnel with highly specialised capabilities. Employer-sponsored training, structured apprenticeships, professional conversion programmes and recognition of prior experience can potentially address some forms of shortage more quickly, although their effectiveness depends on the underlying occupational requirements and training duration. Similarly, national recruitment campaigns may have limited results where the principal problem concerns the geographical separation of qualified workers and available jobs, particularly where relocation costs or household constraints make mobility difficult.

Demographic replacement demand adds another dimension. Industrial employers may need to recruit not only to expand production but also to replace workers retiring or otherwise leaving employment. In an ageing economy, recruitment demand can remain substantial even where total employment in a sector is stable or declining, because the occupational skills of departing workers must be replaced to maintain existing operations. Replacement demand should therefore be distinguished from expansion demand: the former concerns preserving productive capacity, while the latter concerns increasing it. Reliable projections through 2035 require occupation-specific retirement profiles, employment forecasts and assumptions about technological substitution and productivity, rather than extrapolation from a single year’s hiring intentions.

Industrial Competitiveness and the Competition for Mobile Skills

Competition for qualified workers increasingly connects domestic education systems with international labour markets because highly specialised professionals can choose among employers operating in multiple countries. The economic importance of such competition depends on the mobility of particular occupations, the international recognition of qualifications and the relationship between expected earnings and living costs. Software engineers, researchers and certain scientific specialists may possess skills that transfer relatively readily across borders, while regulated professions and occupations involving local certification may encounter stronger institutional barriers. Employers competing internationally for skilled labour must therefore consider not only salary levels but also professional development, research opportunities, working conditions and the quality of technological infrastructure available within their organisations.

For Italy, this competition has two possible effects. International mobility can improve the allocation of skills when workers move toward more productive employment opportunities, and the country may benefit from returning professionals, international business relationships and technological knowledge acquired abroad. However, persistent net losses of qualified workers can weaken the domestic availability of specialised competencies, particularly when the affected occupations are already difficult to recruit and replacement through education requires several years. The net economic effect depends on the characteristics of migrants, the extent of return migration, the availability of inward talent flows and the response of domestic employers. A credible assessment must therefore distinguish gross graduate departures, net migration and occupationally relevant skills losses rather than treating all international mobility as equivalent to permanent human-capital depletion.

Key Judgments — Chapter 5

Italy’s industrial recruitment problem extends beyond university graduates and includes technicians, vocationally qualified workers and specialist occupations critical to the operation of advanced manufacturing. The Unioncamere–Excelsior evidence establishes substantial recruitment difficulties among graduate and ITS Academy positions, while the occupational detail for January 2026 identifies pronounced difficulties among engineering technicians, production-process specialists and maintenance workers. The figures support a judgment that labour-market matching represents a material industrial constraint, but do not demonstrate the number of permanently unfilled positions or the resulting reduction in production. Confidence: High for the existence of recruitment difficulties; medium for their impact on industrial competitiveness; low for any precise output loss without firm-level operational evidence.

Italy’s relatively small ICT-specialist workforce provides an additional indication of limited domestic capacity in occupations central to digital production and technological integration. Its 3.8% share of total employment in 2025 compares with 5.0% across the EU, suggesting that the country’s digital occupational structure remains less developed than the European average. This does not establish that all firms require additional in-house specialists, but it increases the importance of examining external digital-service provision, technical training, international recruitment and the capacity of existing workers to adopt new technologies. Confidence: High for the occupational comparison; medium for the interpretation of its consequences for industrial innovation.

Chapter 6 — Artificial Intelligence and the Changing Economic Value of Advanced Skills

Principal Judgment — AI Is Changing the Composition of Valuable Human Capital Faster Than Educational Systems Can Reliably Measure It

Artificial intelligence changes the relationship between education, employment and productivity by reducing the cost of some cognitive tasks while increasing the economic importance of capabilities that enable organisations to select, evaluate, integrate and supervise automated systems. The resulting transformation is not adequately described as a general replacement of educated workers by technology, because advanced occupations often contain both tasks that can be automated and functions that depend on judgment, accountability, specialist knowledge and coordination with other people. The relevant economic distinction is between exposure to AI and the likelihood of occupational substitution. Workers may be highly exposed to AI because many of their tasks involve information processing, but the technology may complement their activities rather than eliminate their employment. Conversely, workers performing relatively routine functions may face greater substitution risks even when their occupations involve less advanced formal education.

The OECD’s Skills in the AI Age, 2026, executive summary identifies three principal channels through which AI affects labour markets: the automation of existing tasks, the creation of new tasks and occupations, and improvements in productivity. The OECD also distinguishes occupational exposure from automation risk, noting that managers, professionals and engineers can be highly exposed to AI while remaining relatively difficult to automate completely because their work relies on complex cognitive, interpersonal and judgment-based capabilities. Its analysis estimates that approximately one-quarter of workers were already exposed to generative AI during 2022–2024, although exposure does not mean that their employment was displaced or that productivity gains had been realised. OECD

For Italy, the principal strategic concern is therefore not simply that a relatively low proportion of workers hold tertiary qualifications. It is that a limited pool of advanced skills, combined with unequal labour-market participation, recruitment difficulties in technical occupations and uneven organisational capabilities, may restrict the economy’s capacity to capture productivity benefits from increasingly powerful AI systems. This is a conditional assessment rather than a demonstrated causal relationship. The outcome depends on the speed of technology adoption, the characteristics of firms, investment in complementary capital, the availability of suitable training and the extent to which workers can adapt their functions. Confidence is high that AI is changing task requirements and that adoption differs substantially across firms; medium that Italy’s skills structure may amplify these difficulties; and low for any precise estimate of resulting national productivity losses through 2035.

The Acceleration of Enterprise AI Adoption

The diffusion of AI into business activity has progressed rapidly enough to make the interaction between technological capabilities and workforce skills an immediate economic issue rather than a distant forecasting exercise. According to Eurostat, 20% of EU Enterprises Use AI Technologies, published 11 December 2025, 20.0% of EU enterprises employing at least ten people used AI technologies in 2025, compared with 13.5% in 2024, 8.1% in 2023 and 7.7% in 2021. The increase of 6.5 percentage points between 2024 and 2025 indicates a substantial acceleration in the adoption of AI systems, although the indicator does not measure how intensively firms use these technologies, how many employees interact with them or whether adoption has increased productivity. Eurostat

The geographic distribution is highly unequal. Denmark recorded enterprise AI adoption of 42.0% in 2025, followed by Finland at 37.8% and Sweden at 35.0%, while Poland stood at 8.4% and Romania at 5.2%. These differences demonstrate that European economies are entering the AI-intensive period from very different positions in enterprise digitalisation. However, adoption rates must not be interpreted as a complete ranking of technological sophistication or productivity because some industries have more immediately applicable AI use cases than others, and the enterprise survey applies specific thresholds and sectoral coverage. Large enterprises and small manufacturing firms cannot necessarily adopt identical AI applications at comparable cost or intensity. Source: Eurostat, 20% of EU Enterprises Use AI Technologies, 11 December 2025. Eurostat

The OECD’s January 2026 release, AI Use by Individuals Surges Across the OECD as Adoption by Firms Continues to Expand, provides an additional international perspective. Across the countries covered by the relevant OECD enterprise data, 20.2% of firms reported AI use in 2025, compared with 14.2% in 2024 and 8.7% in 2023. Adoption was highly differentiated by enterprise size, reaching 52.0% among large firms against 17.4% among small firms. The OECD also reported AI use among 57.3% of ICT firms and 36.8% of professional and scientific service firms. These observations reinforce the assessment that technological diffusion is concentrated in organisations with greater financial, technical or innovation capacity, although the OECD and Eurostat surveys should not be combined into a single uninterrupted series without reconciling country coverage and definitions. OECD

Table 6.1 — Enterprise AI adoption and structural differences

An important implication of the enterprise-size divide is that aggregate national adoption figures may conceal substantial differences between leading firms and the remainder of the productive system. A country may possess a relatively sophisticated group of large digital companies while a much larger population of smaller enterprises has limited capacity to invest in specialised staff, data infrastructure, software integration and organisational restructuring. For industrial competitiveness, such differences matter because AI is increasingly relevant not only to independent digital services but also to activities connected with manufacturing design, production planning, quality assurance, maintenance, logistics and supply-chain coordination. Where the required organisational and technical capabilities are absent, cheaper or more accessible AI tools may remain underutilised even when the underlying technology is commercially available.

Italy’s Position: Distinguishing Technological Access from Productive Integration

Italy’s capacity to adopt advanced digital technologies cannot be assessed solely through the supply of computer science graduates or the number of ICT specialists in employment. Firms may purchase advanced digital services from external suppliers, rely on cloud-based platforms or integrate software into production equipment without employing large internal digital departments. These arrangements can reduce the direct need for some categories of specialist employment, although they also create dependencies on external providers and require workers who can manage implementation, data quality, security and operational integration. Consequently, the relationship between ICT occupational employment and enterprise AI use is important but not mechanically proportional.

A particularly relevant distinction emerges from Eurostat’s Digitalisation in Europe — 2026 Edition. In 2025, 53% of EU enterprises purchased cloud-computing services, while Italy recorded approximately 76%, among the highest national rates reported. The comparable figures were 79% for Finland and 75% for Malta. This observation is significant because it shows that Italy’s relatively small ICT-specialist employment share does not imply uniformly weak adoption of digital services. On the contrary, the country demonstrates substantial uptake of cloud-based capabilities, which may facilitate access to computing infrastructure without requiring firms to develop equivalent systems internally. Eurostat

However, the purchase of cloud services must not be equated with advanced AI integration. Cloud adoption can involve relatively standard services such as email hosting, office applications and file storage, while AI adoption may require specialised data management, process integration, model evaluation and organisational change. Eurostat reports that among EU enterprises using cloud computing, 85% used cloud services for email systems, while 72% used them for office software and 72% for data storage. Such applications may improve operational efficiency and infrastructure flexibility but do not establish that a firm possesses the capabilities required to integrate AI into core production processes. The Italian cloud adoption figure therefore provides a counterweight to an overly pessimistic interpretation of digital readiness while leaving unresolved the question of how extensively Italian enterprises use more sophisticated AI applications. Source: Eurostat, Digitalisation in Europe — 2026 Edition, enterprise technology adoption. Eurostat

The distinction has strategic importance because access to technology is becoming less restrictive for many standardised AI applications, while the ability to adapt technology to specialised operations remains unevenly distributed. A company may obtain access to an advanced language model at comparatively modest cost, yet still require substantial expenditure to establish reliable internal data flows, integrate outputs into existing enterprise systems, verify accuracy, protect confidential information and redesign employee responsibilities. In industrial environments, the difficulties can be greater because software recommendations may affect physical equipment, process quality or operational safety, requiring reliable validation and clearly assigned human responsibility. Consequently, the diffusion of affordable AI systems does not eliminate the economic importance of advanced skills; it changes the types of capabilities needed to exploit them effectively.

Routine Information Processing and the Recomposition of Occupational Tasks

The declining cost of routine information processing affects economic activity through the substitution of certain tasks, the augmentation of others and the creation of new activities that were previously too expensive or technically difficult to perform. In administrative and professional services, AI may assist with searching documents, preparing initial drafts, categorising information, extracting structured data and identifying patterns in large datasets. In technical occupations, it may support software development, diagnostic analysis, engineering documentation or the interpretation of operational information. Such applications can reduce the time required for particular activities, but the productivity effect depends on whether saved time is redirected toward valuable work, whether output quality remains acceptable and whether the organisation can modify its processes accordingly.

The distinction between task automation and occupational replacement is essential. An occupation is normally a combination of several functions, some of which may be routine and readily automated, while others require interpersonal relationships, professional judgment, responsibility for complex decisions or physical interaction with equipment and infrastructure. The availability of technology capable of performing an isolated task does not prove that the entire occupation can be replaced economically or safely. Furthermore, the automation of routine components may increase demand for the remaining complementary functions if it lowers costs, expands production or allows employees to address more complex problems. The final employment effect therefore depends on substitution, expansion, new task creation and changes in demand, as emphasised in the OECD’s Skills in the AI Age, 2026. OECD

The policy-relevant question is consequently how education systems prepare workers for a changing composition of tasks rather than whether individual occupations appear on lists of potentially automatable jobs. Foundational literacy, numeracy and scientific reasoning remain important because they allow workers to understand the context in which automated outputs are generated, while technical knowledge remains necessary for evaluating the reliability and practical relevance of results. Advanced digital skills enable the development, deployment and maintenance of AI systems, but effective use also depends on capabilities that are not confined to computer science, including critical thinking, creativity, collaboration and the ability to learn new procedures. The OECD identifies these complementary capabilities as important for effective interaction with AI and for adapting to changes in work organisation. Source: OECD, Skills in the AI Age, executive summary. OECD

The Changing Economic Value of Technical Expertise and Professional Judgment

The economic premium associated with advanced education may increasingly depend on how well a worker combines domain-specific expertise with the ability to supervise and interpret automated systems. Where AI makes basic information retrieval or routine drafting less expensive, the value of merely producing standardised informational outputs may decline, while the value of identifying the correct problem, selecting relevant evidence, assessing uncertainty and implementing technically appropriate decisions may become more important. This is a plausible economic mechanism rather than a universally demonstrated change in wages, because employers may not immediately adjust remuneration in line with changes in task productivity, and the relative value of different skills will vary across occupations.

Engineering provides an illustrative case. Automated systems may assist with calculations, documentation and the evaluation of alternative designs, but the economic and operational responsibility for integrating those outputs into functioning products and industrial processes requires knowledge of material properties, manufacturing constraints, safety requirements, costs and system interactions. In such settings, the ability to evaluate whether an automated recommendation is physically feasible and commercially appropriate may become more valuable as the cost of generating proposed solutions declines. Similar mechanisms can arise in finance, law, healthcare, cybersecurity and public administration, where AI can support information processing but does not eliminate the importance of professional responsibility, institutional constraints or the consequences of incorrect decisions.

For Italy, this development has two opposing implications. A smaller supply of advanced tertiary qualifications may constrain the availability of workers capable of performing sophisticated integrative and supervisory functions, particularly in occupations that require extensive scientific or technical education. At the same time, access to AI tools may increase the productivity of workers who acquired relevant capabilities through vocational training or professional experience, potentially reducing some disadvantages associated with limited formal university attainment. The net effect cannot be established through degree statistics alone because formal qualifications, practical competence and AI-related task adaptability are related but not identical characteristics.

Table 6.2 — AI-related task changes and complementary human capabilities

Analytical classification of potential AI task applications and complementary capabilities, not a measured employment forecast. The mechanism is consistent with OECD, Skills in the AI Age, 2026. The table does not imply that every listed application is technically validated for all operational environments.

The distinction between AI exposure and automation risk also affects the distribution of benefits and costs among educational groups. High-skilled professionals may experience substantial changes in their daily tasks but remain able to combine new tools with advanced judgment and specialised knowledge. Workers performing routine activities may face greater pressure if their tasks can be automated without extensive complementary human involvement. Yet those workers may also benefit from tools that make specialist knowledge more accessible or support movement into new occupational functions. The relevant outcomes depend on training opportunities, employer investment, the design of new jobs and the distribution of productivity gains. It would therefore be premature to conclude that AI necessarily increases the wage premium for all tertiary graduates or that workers without university degrees will uniformly lose employment opportunities.

Organisational Absorptive Capacity: Why Technology Availability Does Not Guarantee Productivity

The productivity effects of AI depend on a firm’s capacity to integrate technology into productive processes, a capability commonly described as organisational absorptive capacity. This involves recognising useful technological developments, acquiring or developing appropriate systems, integrating them with existing information and operational infrastructure, and translating their outputs into improvements in products, services or production methods. A company may employ qualified workers and purchase advanced technology without realising measurable productivity benefits if its data are unreliable, its processes are poorly coordinated or its management practices prevent employees from using the new capabilities effectively. Conversely, a firm with strong operational expertise and organisational discipline may obtain substantial benefits from relatively modest technological investments.

The OECD’s Skills in the AI Age, 2026 identifies costs, infrastructure and shortages of skilled personnel as barriers to adoption among smaller enterprises, while observing that large firms and innovative younger businesses are often better positioned to integrate AI technologies. The implications extend beyond initial implementation. Firms require workers capable of maintaining systems, evaluating outputs, adapting processes and recognising when automated recommendations are inappropriate. These capabilities depend on the interaction between education, practical experience and continuing training, rather than on the acquisition of technology alone. OECD

This mechanism is particularly important for an economy with a substantial manufacturing base composed of enterprises operating at different scales and technological levels. Industrial firms may possess specialised knowledge of materials, production methods and customer requirements while lacking internal expertise in data engineering, AI model evaluation or digital integration. In such circumstances, the constraint is not necessarily the absence of industrial knowledge but the difficulty of combining that knowledge with digital capabilities. Policy measures supporting training or enterprise technology investment may therefore have limited effects when implemented separately, while coordinated measures linking skills development with specific organisational and production objectives may provide a more credible route to productivity improvement.

A further consideration concerns the measurement of AI adoption. Enterprise surveys generally identify whether a firm uses specified technologies, but they do not always distinguish experimental deployment, limited administrative applications and integration into core productive operations. An enterprise using AI to prepare marketing materials may be classified as an adopter alongside a firm integrating machine learning into operational quality control, even though the economic significance, technical requirements and potential productivity effects are very different. This limitation makes it necessary to complement adoption indicators with measures of usage intensity, organisational changes, capital investment and realised productivity.

The Distribution of Technological Benefits Across European Economies

The European adoption figures indicate that the AI transition is occurring across economies with substantially different educational structures, labour-market institutions and industrial specialisations. Germany combines advanced industrial capabilities, a substantial vocational workforce and a significant tertiary-qualified population, while France has a more tertiary-intensive educational structure and Spain combines widespread higher education with persistent labour-market challenges. Poland has expanded tertiary qualifications but records comparatively low enterprise AI adoption in the Eurostat 2025 statistics. These differences demonstrate that tertiary attainment alone cannot explain the observed distribution of technological adoption, although they do not establish that education has no independent effect.

An important implication is that national AI competitiveness cannot be inferred from a single indicator. Formal educational attainment measures the stock of qualifications; ICT-specialist employment measures occupational participation; enterprise AI adoption measures reported technology use; and productivity indicators measure economic outcomes influenced by many additional variables. The relationship between these measures may be positive in some sectors and weaker in others because firms can acquire technology externally, outsource specialised functions, operate in industries with different automation opportunities or use established organisational capabilities to compensate for limited internal technical staffing. A valid comparative assessment must therefore preserve the distinction between technological access, internal capabilities and realised economic performance.

Table 6.3 — European technological-capability indicators and their limitations

These indicators reinforce the importance of differentiating between a country’s digital infrastructure, its specialised workforce and its capacity to derive economic value from technology. Italy’s comparatively strong uptake of cloud services demonstrates that firms can adopt external digital infrastructure extensively, while the smaller ICT-specialist employment share indicates a more limited concentration of digital occupations within the national workforce. Neither measure is sufficient to determine whether firms possess the scientific, technical and organisational capabilities needed for advanced AI adoption. The relevant policy challenge is therefore to identify the combinations of skills and investment that remain scarce in particular sectors, rather than assuming that all technological disadvantages can be addressed by increasing the number of university graduates or software purchases.

Human-Capital Inequality and the Risk of Uneven AI Productivity Gains

The diffusion of AI may widen differences between firms, regions and workers if technological benefits are concentrated among organisations already possessing strong management, data infrastructure, skilled personnel and access to investment. This outcome is not inevitable, because lower-cost AI tools may also enable smaller firms and less specialised workers to perform activities that previously required greater resources. Nevertheless, the OECD’s evidence of substantial differences in adoption between large and small firms supports the possibility that existing organisational inequalities could influence the distribution of future productivity gains. Source: OECD, Skills in the AI Age, 2026. OECD

For Italy, this issue intersects with existing territorial inequalities, differences in enterprise size and the unequal geographical distribution of advanced qualifications. Regions with stronger research institutions, diversified industrial systems and concentrations of specialised employment may be better positioned to integrate new technologies, while regions with limited local demand for qualified workers may struggle to retain the expertise required for implementation. However, the relationship is not predetermined. Targeted investment, accessible technical services, employer training and effective university–industry coordination can potentially improve technological absorption in regions with smaller initial graduate populations. Determining whether these measures achieve sustained productivity effects requires evidence on firm performance and technological use rather than assumptions based on regional educational attainment alone.

The distributional implications also extend to workers whose occupations contain substantial routine cognitive activity. Some may benefit from technology that improves the quality and speed of their work, while others may experience reductions in demand for particular tasks or pressure to acquire new competencies. The economic effect on wages and employment depends on labour-market competition, occupational mobility, the organisation of production and the degree to which firms share productivity gains with employees. Consequently, an increase in AI adoption cannot automatically be interpreted as either a positive or negative labour-market outcome without examining employment transitions, hours, wages and the creation of new activities.

Strategic Implications for Italy Through 2035

The interaction between advanced education and AI suggests that Italy’s competitiveness through 2035 will depend on several complementary adjustments. First, the economy requires sufficient specialised technical and professional capabilities to develop, integrate and supervise sophisticated technologies. Second, employers must create suitable opportunities for workers possessing those capabilities, because education without productive demand may increase occupational mismatch or international migration. Third, existing workers require opportunities to update their competencies as the composition of tasks changes. Fourth, enterprises need organisational and financial capacity to translate technological investments into improved production processes. Finally, the public sector requires reliable statistical monitoring to distinguish technology adoption from realised productivity gains and to identify whether policy interventions improve outcomes.

These requirements should not be treated as evidence that every occupation needs university-level education. Advanced vocational training, professional experience and specialised short courses can provide economically valuable capabilities, particularly when linked to specific production systems. The role of tertiary education remains important for occupations requiring extensive scientific, mathematical, engineering or other advanced theoretical knowledge, but the optimum distribution of qualifications depends on sectoral demand and the changing nature of work. A balanced approach therefore requires educational institutions and employers to develop multiple pathways capable of generating the competencies needed by an increasingly technology-intensive economy.

The precise outcome remains uncertain because the speed of AI development, the evolution of task costs and the responses of enterprises cannot be predicted reliably from current adoption statistics alone. Some activities may experience rapid productivity improvements, while others may require lengthy organisational adaptation or encounter technical and regulatory constraints. The effects on employment may similarly vary by occupation, with task substitution occurring alongside the creation of new work and the expansion of existing activities. The appropriate 2035 assessment must consequently be constructed around observable economic and technological indicators rather than deterministic claims about the number of jobs that AI will replace or the GDP gains it will generate.

Key Judgments — Chapter 6

Artificial intelligence is changing the composition of economically valuable skills, but the available official evidence does not support a general conclusion that advanced education is becoming either uniformly more valuable or less necessary. The OECD identifies simultaneous task automation, task creation and productivity effects, while distinguishing highly exposed professional occupations from occupations facing greater automation risks. Italy’s smaller tertiary-qualified workforce and comparatively limited concentration of ICT specialists may constrain some forms of technological absorption, but the country’s substantial use of cloud services demonstrates that technological access and internal specialist employment are not equivalent. Confidence: High for the documented technological and occupational trends; medium for the judgment that Italy’s skills structure represents a relative competitive constraint.

Enterprise AI adoption is accelerating rapidly, with substantial differences between countries, sectors and enterprise sizes. The evidence indicates that the availability of technology is increasingly widespread, but effective implementation remains dependent on skills, infrastructure, organisational investment and management. This supports the conclusion that technological competitiveness requires coordinated development of human and organisational capital rather than isolated investment in software or additional qualifications. Confidence: High for the observed adoption differences; medium for their implications for productivity convergence.

The distributional consequences remain uncertain. AI may complement skilled workers, create new tasks and expand production, but it may also reduce demand for certain routine activities and increase inequalities between firms capable of integrating technology and those that cannot. National outcomes will depend on training, labour mobility, investment, institutional adaptation and the structure of productive demand. Confidence: Medium for the direction of these mechanisms; low for exact employment and GDP effects through 2035.

Pillar II

The evidence developed across the economic, industrial and technological dimensions establishes that Italy’s human-capital challenge cannot be reduced to the difference between its university-attainment rate and the European average. The economic significance of the deficit arises from the interaction between the quantity of qualifications, the employment opportunities available to qualified workers, occupational and regional matching, the financial rewards associated with specialised careers and the capacity of enterprises to combine technical competence with capital investment. Incomplete labour-market utilisation represents a potential source of additional productive capacity, while insufficient formation and retention of advanced skills may constrain particular industrial and technological functions. The scale of these effects cannot be converted into a reliable GDP estimate without a model incorporating actual workforce stocks, employment transitions, hours worked, occupational productivity and the responses of firms.

Italy’s documented recruitment difficulties indicate that the industrial system encounters significant obstacles in sourcing university graduates, advanced technical personnel and workers in operationally important occupations. These difficulties affect multiple educational levels and therefore cannot be resolved exclusively by increasing conventional university participation. The distribution of shortages across engineering, technical management, maintenance and digital services reinforces the importance of matching qualifications with specialised occupational requirements, while the interpretation of employer surveys requires care because reported recruitment difficulty does not necessarily equal an absolute or permanent shortage of labour.

Artificial intelligence introduces an additional adjustment requirement by changing the relative importance of routine information processing, technical integration, professional judgment and organisational adaptability. The available official evidence demonstrates rapid adoption across European enterprises but substantial differences between countries and firm sizes. Italy’s comparatively limited ICT-specialist employment share suggests a narrower domestic concentration of digital occupations, while its high uptake of cloud computing indicates that parts of the productive system possess significant experience in adopting external digital infrastructure. These observations are compatible with a more differentiated technological position than either a uniformly pessimistic or uniformly favourable assessment would suggest.

The strategic conclusion is that Italy’s competitiveness through 2035 will depend less on any single educational or technological indicator than on its ability to transform qualifications, professional experience and technological investment into sustained productive capabilities. Expansion of higher education, improved participation among under-utilised groups, stronger technical training, graduate retention, international talent integration and organisational upgrading can reinforce one another, but their economic value depends on implementation, employer demand and measurable outcomes. The next stage of the assessment must therefore evaluate the institutional arrangements and policy instruments capable of producing these changes, distinguishing measures supported by documented European experience from reforms whose effectiveness remains uncertain or dependent on national circumstances.

The principal records required for continued monitoring are the Istat Labour Force Survey and Educational Returns releases, the Unioncamere–Excelsior recruitment surveys, the Eurostat ICT-specialist employment series, the Eurostat enterprise AI adoption statistics and the OECD’s 2026 analysis of skills in the AI age. Their combined use would permit subsequent assessments to determine whether changes in educational attainment, labour-market utilisation and technological adoption are translating into observable improvements in industrial capacity and productivity.

Strategic Industrial Intelligence
Language
01 / Industrial Intelligence

Four Structural Signals

Graduate employment gap, Italy versus EU
2.8 pp
Ages 25–64, 2025
Difficult-to-recruit graduate positions
50.9%
Excelsior, 2025
ICT specialists in Italian employment
3.8%
EU reference: 5.0%
EU enterprises using AI
20.0%
Enterprise survey, 2025
02 / Interactive Statistical Intelligence

Comparative 3D Data Observatory

Explore comparable official indicators across distinct datasets.
The chart adapts its scale to each measure and always begins at zero.
Select a column for the underlying definition and interpretation.

Industrial skills

03 / Employment Counterfactual

The Utilisation Gap per 100,000 People

This analytical comparison applies an EU employment rate to a
standardised Italian population group. It identifies an employment
difference, not a forecast of jobs, GDP or fiscal revenue.

Additional employed per 100,000
+2,800

Static difference at unchanged population size.
Not additional jobs guaranteed by policy.

Qualification and Occupational Shortages

Employer-reported recruitment difficulties are measures of hiring
friction rather than confirmed unfilled vacancies.
Values below refer to their specified survey periods.

From Skills to Productivity

The productive value of advanced qualifications depends on their
interaction with investment, management and technology integration.
The mechanism is conditional; the diagram does not assign causal weights.

Technology access and actual productivity are different outcomes.
Cloud adoption does not necessarily establish advanced AI use.
06 / 2035 Strategic Pathways

Industrial Competitiveness Scenario Explorer

These conditional scenarios describe plausible mechanisms, not
statistically estimated outcomes. No unsupported probabilities
or GDP growth figures are assigned.

Official Statistical References

Statistical observations and conditional economic interpretation
are distinguished. The visual perspective does not modify
the numerical scale or manufacture missing observations.

Chapter 7 — European Policy Models and Their Transferability to Italy

Principal Judgment — Italy Requires an Integrated Institutional Strategy Rather Than the Replication of a Single European Education Model

Italy’s ability to reduce its human-capital disadvantage depends less on adopting an isolated reform associated with a higher-performing European economy than on establishing institutional arrangements capable of connecting educational progression, employment participation, occupational specialisation and productive investment. European experience demonstrates that strong labour-market outcomes can emerge from different combinations of tertiary education, vocational training, public services, employer participation and institutional support. Germany’s comparatively effective vocational employment pathways, France’s extensive early-childhood education provision, Spain’s increasingly diversified post-secondary pathways, Poland’s relatively favourable employment outcomes among young graduates and the United Kingdom’s highly tertiary-intensive educational structure represent distinct institutional configurations rather than components of a single transferable model. The essential question is therefore which mechanisms have produced observable improvements, which outcomes remain unsatisfactory despite those measures, and which institutional characteristics determine whether comparable interventions would be effective in Italy.

The comparative policy environment has changed materially since the European Union placed educational attainment and employment participation among its principal social and competitiveness objectives. The European Commission’s Union of Skills, introduced in March 2025, establishes a common policy framework covering foundational and advanced education, continuous training, labour mobility, recognition of skills, recruitment and the attraction and retention of qualified workers. The initiative also includes proposals and actions concerning science, technology, engineering and mathematics education, vocational training, micro-credentials and the development of specialised capabilities in strategically important sectors. These measures establish institutional priorities and potential instruments, but an announced policy or programme cannot be treated as evidence of a completed reform or a measured improvement in employment and productivity. The distinction between policy adoption, implementation, participation and economic outcomes remains essential throughout the assessment. European Commission

The most defensible judgment is that Italy should pursue coordinated reforms across education, labour-market participation, technical training and employer demand, with differentiated implementation according to regional and sectoral conditions. Evidence strongly supports the importance of these dimensions, but it does not justify assigning an identical causal effect to policies transferred from different European countries. Childcare expansion may be more effective where the principal limitation concerns care responsibilities and suitable employment exists; vocational pathways may be effective where employers actively participate in training and provide recognised occupational progression; and graduate-retention measures may have limited impact where qualified workers cannot find productive employment. Consequently, the effectiveness of policy transfer depends on identifying the mechanism, the population affected and the institutional conditions under which the original outcome was observed.

Confidence: High that European policy systems provide relevant, empirically documented examples of different approaches; medium that selected practices can improve Italian outcomes when appropriately adapted; low for any quantified national gain attributed to policy replication without an independently validated evaluation.

European Institutional Models: Distinguishing Educational Expansion from Effective Skills Formation

The principal European comparison is not between a single high-performing educational system and a collection of deficient systems, but between alternative institutional responses to the changing composition of labour demand. Germany’s education and employment arrangements place substantial emphasis on occupational qualifications developed through vocational pathways and supported by employer participation, while France combines widespread tertiary education with extensive public educational provision and a comparatively large post-secondary sector. Spain has achieved relatively high tertiary attainment while retaining important challenges in secondary educational completion and graduate unemployment, whereas Poland combines substantial tertiary qualification levels with comparatively favourable recent graduate unemployment outcomes. The United Kingdom demonstrates that a large tertiary-qualified population can coexist with continuing youth employment and skills-transition difficulties, while the Nordic and Dutch systems provide additional evidence on the relationship between public services, educational participation and labour-market institutions.

The OECD’s Education at a Glance 2026: Germany, published 29 September 2026, provides particularly useful evidence concerning the relationship between vocational education and employment. In 2025, approximately 89% of German young adults aged 25–34 holding vocational upper-secondary or post-secondary non-tertiary qualifications were employed, compared with approximately 88% of young tertiary-qualified adults. The unemployment rate among the vocationally qualified group was 3.4%, against approximately 4.0% among those holding tertiary qualifications. These results demonstrate that occupationally oriented education can be associated with strong employment outcomes, but they do not establish that the German system would generate identical results in Italy because employer participation, occupational certification, institutional cooperation and the structure of labour demand differ between countries. OECD

Germany has also created pathways through which advanced vocational qualifications can progress into the tertiary classification, including qualifications reaching ISCED level 7. This is important because traditional comparisons frequently treat academic and vocational education as separate and mutually exclusive systems, whereas the German approach increasingly provides progression between them. The relevant policy lesson is therefore not that Italy should prioritise vocational education over university education, but that technical qualifications may become more attractive when students and employers recognise credible pathways leading to progressively advanced occupational and educational status.

The financial requirements of such arrangements are also significant. OECD data for Germany show expenditure per student of approximately USD 27,880 in vocational upper-secondary programmes, compared with USD 19,238 in general upper-secondary programmes, excluding salaries or other compensation paid to students participating in dual-training arrangements. These values illustrate that technologically sophisticated vocational education is not necessarily a lower-cost substitute for university expansion, particularly when it requires specialised equipment, workshops, technical instructors and sustained employer participation. They also show why institutional comparisons must distinguish the direct cost of training from the broader division of responsibilities between public authorities, educational institutions and participating enterprises. Source: OECD, Education at a Glance 2026: Germany, financial resources invested in education.

Table 7.1 — European policy models and their institutional characteristics

Higher Education Completion and the Problem of Qualification Progression

The effectiveness of tertiary expansion depends not only on the number of students entering education but also on whether they complete their qualifications, how long completion requires and whether their programmes lead to capabilities relevant to subsequent employment. A system may increase university enrolment while producing only modest growth in graduate numbers if students encounter substantial completion difficulties, face financial constraints or enter programmes with limited occupational prospects. These mechanisms are particularly important for public expenditure because resources devoted to students who do not complete a qualification may still provide educational benefits, but they do not necessarily generate the same formal credentials or measurable returns as completed programmes.

The OECD’s Education at a Glance 2025 — Who Is Expected to Complete Tertiary Education? reports that an average of approximately 43% of bachelor’s entrants across the covered OECD countries completed a tertiary qualification within the theoretical duration of their programme. Completion rates differed substantially across countries, with Ireland, the United Kingdom and several other systems reporting more than 60% of entrants completing a tertiary degree within the expected period, while other countries exhibited considerably lower rates. These differences may reflect financing, student-support arrangements, programme structure, selection, labour-market conditions and opportunities for part-time study, meaning that the observed completion rate cannot be attributed to a single institutional feature. OECD

Italy’s educational progression must be evaluated within the wider transition from secondary schooling. The OECD’s Education at a Glance 2026: Italy reports that approximately 84% of new upper-secondary entrants completed their programme within two years after its theoretical duration, while approximately 15% left without qualifying. One year after completing upper-secondary education, approximately 60% of the relevant Italian cohort were enrolled in bachelor’s or master’s programmes, whereas 1% were enrolled in post-secondary non-tertiary or short-cycle tertiary education. These figures describe a particular transition cohort and cannot be interpreted as the proportion of all Italian adults who eventually complete tertiary education. Nevertheless, the exceptionally small share entering the intermediate post-secondary or short-cycle tertiary category, compared with the OECD reference group, points to the importance of evaluating the diversity and accessibility of progression pathways. OECD

The Spanish and French systems provide contrasting evidence. Spain recorded approximately 42% of upper-secondary graduates entering bachelor’s or master’s programmes one year later, together with 27% entering post-secondary non-tertiary or short-cycle tertiary programmes. France reported approximately 47% entering bachelor’s or master’s programmes and 32% entering the other specified post-secondary or short-cycle tertiary pathways. Italy’s corresponding distribution of 60% and 1% differs substantially, suggesting that the structure of immediate post-secondary progression deserves attention alongside the headline university-entry rate. However, the figures are not evidence that Spain or France necessarily produces better educational outcomes from those routes, because they measure enrolment destinations rather than completion, employment or productivity. Sources: OECD, Education at a Glance 2026: Spain and France. OECD+1

Table 7.2 — Transition from upper-secondary education to further study

Rounded values may not sum exactly to 100%. The observations concern transitions after upper-secondary education, not eventual tertiary completion.

The implications for Italy are institutional rather than merely numerical. A system offering credible, employment-relevant and progressively recognised technical pathways may accommodate students whose interests or circumstances do not align with conventional university programmes, while also responding to employers requiring intermediate and advanced occupational competencies. However, expanding these pathways without maintaining educational standards, transferability between institutions and appropriate employer participation could create a fragmented qualification system in which students acquire certificates that are poorly recognised or provide limited career progression. The relevant objective should therefore be measured through completion, subsequent employment, wage progression and occupational matching rather than through the number of new programmes established.

Childcare Provision and Female Employment: The European Evidence

Childcare policy occupies a significant position within human-capital strategy because the availability, affordability and reliability of care services may influence the extent to which parents can participate in employment, undertake additional training or maintain professional continuity after childbirth. The relationship is not automatic: childcare provision affects labour-market behaviour alongside wages, household arrangements, taxation, parental-leave provisions, working conditions and the availability of suitable jobs. Nevertheless, the association between care responsibilities and labour-market participation provides a credible economic mechanism through which public services can support the effective utilisation of existing skills.

The European Commission’s Participation in Early Childhood Education and Care Is Rising for Children Under Three, published in 2026 reports that 39.3% of children under three participated in formal childcare or education across the EU in 2024, compared with 30.0% in 2015. Italy recorded 39.4%, slightly above the EU average, but substantially below France at 59.4%, Spain at 55.0% and the Netherlands at 78.9%. Germany registered 25.1% and Poland 15.0%, indicating that high overall employment or strong educational outcomes can coexist with very different childcare participation rates. The European evidence therefore does not support a simple ranking in which childcare participation alone determines female employment performance. European Education Area

The changes over time are especially informative. Between 2015 and 2024, participation among children under three increased by 17.6 percentage points in France, 15.3 points in Spain, 12.1 points in Italy and 9.6 points in Poland, while the Netherlands recorded a much larger increase of 32.6 points. These are observed changes in service participation, not causal estimates of their effects on parental employment. Nevertheless, they demonstrate that expansion of formal childcare access is institutionally possible within relatively short policy horizons, although the associated financing, workforce requirements and service standards differ considerably across countries.

Table 7.3 — Formal childcare participation among children under three

The table requires careful interpretation. Formal childcare participation may vary because of parental preferences, the duration of parental leave, service availability, demographic characteristics and the classification of educational programmes. The indicator also does not establish the number of hours of care available to each child, which may be decisive for the ability of parents to undertake full-time employment. The European Commission identifies limited places, administrative barriers, service quality and affordability as important constraints in different countries. Consequently, simply increasing nominal enrolment capacity may not produce corresponding employment effects unless available services operate at suitable hours, remain affordable and are geographically accessible to households whose labour-market participation is constrained by care responsibilities. European Education Area

Italy’s broader employment figures reinforce the need for institutional coordination. According to Eurostat, EU’s Employment Rate Grew Above 76% in 2025, published 17 April 2026, the employment rate among people aged 20–64 was 67.6% in Italy, compared with 76.1% across the EU, while the Italian female employment rate was 58.0%, compared with 71.3% across the Union. These figures use a different age range from the 25–64 educational-employment comparisons and therefore should not be combined directly with them. Their significance lies in demonstrating that the national labour-market participation disadvantage is broader than the graduate population alone. Eurostat

For Italian policy, the evidence supports an approach that treats childcare not simply as social expenditure but as infrastructure potentially affecting labour-market participation and the utilisation of existing human capital. However, before allocating resources solely on the basis of national averages, the location of unmet demand should be established through municipality-level information on service availability, hours, waiting lists, household income and employment constraints. Expansion in areas where the principal limitation is the absence of suitable jobs may produce different employment effects from expansion in areas where parents are already seeking work but lack accessible childcare. The appropriate evaluation therefore requires geographic targeting and measurement of labour-market outcomes rather than childcare enrolment alone.

Recognition of Foreign Qualifications and the Institutional Recovery of Under-Utilised Skills

The recognition of qualifications acquired abroad constitutes a distinct policy instrument because it can improve the utilisation of skills already present within the resident population without requiring the educational system to produce an entirely new cohort of qualified workers. The economic importance of this mechanism depends on whether individuals possess qualifications relevant to domestic employment, whether those qualifications are formally recognised, and whether language requirements, professional regulations, recruitment practices or other obstacles prevent suitable occupational placement. Recognition is particularly relevant to regulated professions, but occupational mismatch can also arise in unregulated sectors where employers have difficulty assessing foreign credentials or interpreting the competencies associated with unfamiliar educational institutions.

The European Commission’s official guidance on Recognition of Professional Qualifications for Third-Country Nationals confirms that non-EU and non-EEA nationals seeking employment in regulated professions generally must apply under the relevant national recognition procedures. The Commission has also adopted a recommendation intended to improve and simplify the recognition of qualifications held by third-country nationals. Importantly, the professional qualifications framework governing intra-European recognition does not apply automatically to all third-country nationals in the same manner as it does to qualifying EU citizens, and relevant exceptions depend on applicable agreements and legal arrangements. This creates a legitimate institutional issue for national governments, which must balance professional standards and public protection with transparent procedures for assessing foreign qualifications. Employment, Social Affairs and Inclusion

The effectiveness of recognition reform should not be assessed solely by the number of applications approved. An administrative system might accelerate decisions without substantially improving employment if language requirements, lack of professional experience or unsuitable recruitment conditions remain unresolved. Conversely, a system with rigorous professional assessment may improve occupational matching if it also provides clear information, predictable processing times and appropriate pathways for addressing identified qualification gaps. The relevant outcome indicators therefore include application volumes, processing times, decisions, additional training requirements, occupational entry, earnings progression and the proportion of applicants employed in occupations corresponding to their recognised qualifications.

A distinction must also be maintained between foreign citizenship, foreign birth and the geographical origin of a qualification. A foreign citizen may have completed all education in Italy, while an Italian citizen may possess a university degree obtained abroad. The corresponding administrative and occupational challenges may differ substantially. Effective policy design consequently requires integrated but legally appropriate information on citizenship, birthplace, educational origin and regulated-profession status, with safeguards for individual rights and data protection.

Graduate Retention and International Talent: Combining Domestic Opportunity with Mobility

The retention of qualified workers cannot be separated from the domestic demand for advanced skills. Graduates may move internationally because of earnings differences, career opportunities, research infrastructure, working conditions or personal circumstances, while firms may recruit abroad because relevant qualifications are scarce locally or because international experience is valuable for particular functions. Policies intended to retain graduates therefore operate through several possible channels, including occupational opportunity, professional progression, compensation, research investment and the attractiveness of domestic employment. Administrative restrictions on mobility would not address the underlying economic mechanisms and could weaken the benefits associated with international educational and professional exchange.

The United Kingdom provides a useful contrast because its tertiary-attainment rate is particularly high, at approximately 55% among adults aged 25–64 in 2025, according to the OECD’s Education at a Glance 2026: United Kingdom. The country also reports substantial participation in continuing training, with the OECD Survey of Adult Skills indicating that 65% of employed adults aged 25–44 in England participated in non-formal education or training during the preceding year, compared with 34% in Italy. However, these observations describe educational and training characteristics rather than establishing that British policies have caused superior graduate retention or productivity. The distinction between the geographical coverage of the United Kingdom and England is also material, because the reported adult-learning indicator specifically concerns England. OECD

For Italy, the relevant objective is not to prevent qualified workers from pursuing opportunities elsewhere, but to improve the economic conditions under which they can establish and develop advanced careers domestically while maintaining the benefits of international mobility. Research institutions, high-value manufacturing companies, technology-intensive service firms and public-sector employers may all contribute to the creation of attractive professional environments, although their capacity differs by region and discipline. Retention policies should therefore be evaluated through net migration by qualification and occupation, returning graduate flows, earnings progression, research career opportunities and the employment of international graduates, rather than through aggregate departure figures alone.

Employer Training, University–Industry Coordination and the Allocation of Responsibility

The relationship between education and productive capability becomes more direct when employers participate in defining occupational needs, providing work-based learning and supporting workers’ progression into specialised functions. However, employer participation can generate both benefits and risks. Close cooperation may improve the practical relevance of training and facilitate recruitment, but programmes designed too narrowly around current employer requirements can reduce the transferability of skills if technologies or industrial structures change. The economic objective is therefore to combine occupational relevance with sufficiently broad educational foundations, allowing workers to adapt to changing production methods and move between employers and sectors.

The European Commission’s Union of Skills explicitly addresses this balance through measures concerning foundational skills, STEM education, vocational training, continuous reskilling and the recognition and portability of qualifications. The Commission also identifies skills shortages as an obstacle to business competitiveness and supports the development of mechanisms intended to facilitate training and workforce adaptation. These initiatives provide a European institutional framework within which national measures can be coordinated, but they should not be interpreted as evidence that a common European policy automatically resolves domestic shortages or generates measurable productivity improvements. The implementation responsibility remains distributed across governments, education providers, employers and other relevant institutions. European Commission

Germany’s relatively high expenditure on vocational education illustrates the resource implications of an advanced employer-linked model, while Spain’s and France’s substantial post-secondary participation rates demonstrate that educational systems can develop multiple routes beyond conventional university degrees. The Italian challenge is to ensure that existing technical and tertiary pathways provide credible progression, recognised qualifications and effective links to employment, without compromising the broader scientific and analytical foundations necessary for long-term adaptation. This is particularly important for engineering, digital infrastructure and advanced manufacturing, where workers may require both specialised occupational knowledge and the ability to integrate technologies outside their initial field of training.

Table 7.4 — Policy instruments, implementation responsibilities and evidence requirements

Implementation periods are qualitative assessments of the underlying mechanisms, not estimates obtained from a policy-impact model. Actual effects depend on procurement, legislation, institutional capacity and the baseline circumstances of the affected population.

The European Policy Environment and the Limits of Institutional Transfer

The European Union’s objectives provide a common framework but do not eliminate differences between national labour markets. The Eurostat release EU Tertiary Education Level Nears 45% in 2025, published 3 September 2026, records that 44.8% of people aged 25–34 had completed tertiary education, placing the EU close to its target of at least 45% by 2030. The same release identifies a significant difference between women and men, with 50.5% of women and 39.3% of men in this age group holding tertiary qualifications in 2025. The gender difference is relevant to Italy because expanding educational attainment does not necessarily resolve employment disparities, while the distribution of qualifications across sexes may affect the design of occupational guidance and labour-market participation policies. Eurostat

The European framework also includes a target of at least 45% participation in formal childcare among children under three by 2030, with the observed EU rate of 39.3% in 2024 remaining below that objective. The interaction between childcare, educational attainment and labour-market participation requires a differentiated approach because countries may achieve relatively high levels of one indicator while remaining weaker in another. For example, Germany’s vocational employment outcomes coexist with childcare participation below the EU average, while Spain combines comparatively extensive childcare participation and tertiary attainment with continuing graduate employment challenges. These combinations show why a multi-indicator assessment is necessary to identify genuine institutional strengths and weaknesses.

The key analytical limitation is that the existence of a policy in a high-performing country does not establish that the policy produced the observed outcome. A credible transferability assessment requires evidence about the timing of implementation, the counterfactual development of comparable populations, the characteristics of beneficiaries and the persistence of measured effects. Where these elements are unavailable, the appropriate conclusion is that a foreign institutional model provides a plausible policy mechanism or implementation reference, not a demonstrated estimate of what Italy would gain from adopting it.

Chapter 7

Italy’s policy problem is not the absence of European examples from which institutional lessons can be drawn, but the need to select and coordinate measures corresponding to distinct domestic constraints. Germany demonstrates the potential value of credible advanced vocational progression and employer participation, France and Spain offer evidence of extensive early-childhood services and differentiated post-secondary pathways, Poland illustrates relatively favourable young-graduate unemployment outcomes, and the United Kingdom demonstrates a highly tertiary-intensive educational structure alongside substantial participation in adult training. None of these observations establishes the independent causal effectiveness of a single policy, but together they show that human-capital formation and productive utilisation can be organised through multiple institutional arrangements. Confidence: High for the comparative institutional evidence; medium for transferability to Italy.

Childcare, recognition of foreign qualifications, completion support and employer training address different populations and mechanisms. Their implementation should therefore be coordinated without conflating their intended effects. Childcare may support labour-market participation, qualification recognition may improve occupational access, university completion may increase the future stock of formally qualified workers, and employer training may improve adaptation within the existing workforce. Their economic value depends on the availability of appropriate employment and the capacity of institutions to implement reforms effectively. Confidence: Medium to high for the relevance of these mechanisms; medium for the likely magnitude of their effects.

The principal policy recommendation is to establish a national coordination framework with regional implementation, measurable outcomes and explicit responsibility for each intervention. Such a framework should connect education, labour, enterprise development and public services while avoiding the assumption that success can be measured through enrolment, expenditure or programme announcements alone. The required evidence consists of qualification completion, employment transitions, occupational matching, earnings, graduate mobility, technological adoption and productivity, supported by consistent definitions and longitudinal observation.

Chapter 8 — Italy 2035: Stagnation, Gradual Convergence and Accelerated Catch-Up

Principal Judgment — Italy’s Competitive Position in 2035 Will Be Determined by the Combined Performance of Educational Cohorts, Labour-Market Activation and Productive Investment

Italy’s human-capital trajectory through 2035 will depend on whether the country can accelerate the formation of advanced qualifications while simultaneously improving the productive utilisation of existing workers and creating the industrial and technological conditions necessary to employ additional skills. These processes operate over different timescales and cannot be combined into a single forecast without explicit assumptions about demographic change, education completion, labour demand, migration, investment and technological diffusion. University reforms influence the number and composition of graduates only after students complete their programmes, whereas improved recognition of existing qualifications or better access to suitable employment may affect labour-market outcomes over shorter periods. Enterprise training and organisational modernisation can also generate changes before national tertiary-attainment rates materially converge with those of other European economies, although the resulting productivity effects remain dependent on effective implementation.

The demographic environment is particularly important because Italy faces the challenge of upgrading human capital while the composition and size of the working-age population evolve. The replacement of older, less highly educated cohorts by younger cohorts with more advanced qualifications can gradually increase aggregate attainment, but the process depends on the relative size of each cohort and on migration. Improvements in the tertiary-attainment rate among 25–34-year-olds therefore do not translate automatically into equivalent improvements among adults aged 25–64, and a percentage-point increase in graduate attainment cannot be interpreted as an equal percentage-point increase in employment or productivity. Furthermore, an economy with a declining working-age population may experience a rising graduate share while the absolute number of employed graduates grows more slowly, remains stable or even declines. The relevant scenario assessment must therefore monitor both rates and population stocks, preserving the distinction between improvements in educational composition and changes in the effective volume of qualified labour.

The available official evidence provides a defensible baseline against which these pathways can be evaluated. The OECD’s Education at a Glance 2026: Italy reports tertiary attainment of approximately 22.3% among adults aged 25–64, while OECD Education GPS records approximately 31.1% among those aged 25–34. The Eurostat 2025 educational-attainment release identifies an EU-27 young-adult rate of 44.8%, demonstrating the scale of the younger-cohort convergence requirement. Eurostat also reports that Italy’s overall employment rate among people aged 20–64 was 67.6% in 2025, against 76.1% across the EU, with female employment particularly weak. These observations establish the starting conditions but do not prescribe the numerical outcomes that will emerge in 2035. gpseducation.oecd.org+2

Three conditional scenarios are therefore appropriate: continued structural stagnation, gradual convergence and accelerated catch-up. They are intended to identify plausible pathways and the evidence that would distinguish them, not to assign unsupported probabilities or manufacture precise GDP results. Confidence is high in the relevance of the underlying constraints, medium in the mechanisms linking coordinated reforms to improved competitiveness, and low in exact national outcomes through 2035 without a calibrated demographic and economic model.

The 2035 Baseline: Why Educational Convergence Is a Multi-Cohort Problem

The most important long-term distinction concerns the relationship between the qualifications held by younger adults and those held by the broader working-age population. A young-adult tertiary-attainment rate reflects the cumulative educational outcomes of individuals recently entering or progressing within the workforce, while the 25–64 indicator incorporates substantially older cohorts whose educational opportunities and occupational experiences differ from those of current graduates. Even a rapid increase in tertiary completion among people entering employment during the late 2020s would therefore raise the aggregate adult attainment rate only gradually, as younger qualified workers replace older cohorts and as adult learners acquire additional qualifications. The process is further affected by international migration, which may increase or reduce the number of tertiary-qualified residents independently of domestic educational completion.

The Eurostat dataset Persons Aged 25–34 With Tertiary Educational Attainment — sdg_04_20 provides a consistent framework for monitoring the younger cohort because it identifies qualifications under ISCED levels 5–8 and records observations through 2025. The relevant policy objective is not simply to reach the contemporary European average at some future date, because the EU average may continue to rise, but to reduce the difference between Italy’s educational progression and the qualifications available in comparable European labour markets. A meaningful convergence assessment must consequently compare Italy’s future rate with the EU rate observed in the same year rather than using the 2025 benchmark indefinitely.

A related distinction concerns the composition of additional qualifications. Tertiary education includes short-cycle programmes, bachelor’s degrees, master’s degrees and doctoral qualifications, each of which may serve different occupational purposes. Increasing the overall attainment rate through one qualification category does not necessarily generate the same effects as increasing the supply of engineers, scientific researchers, specialised technicians or other professionals. The relevant educational outcome must therefore be disaggregated by qualification level and field of study, while maintaining separate indicators for vocational and professional competencies acquired outside conventional university pathways. This distinction is particularly important for industrial competitiveness because advanced manufacturing requires combinations of engineering, digital integration, technical maintenance and production management rather than an undifferentiated stock of graduates.

Scenario A

Under a stagnation scenario, Italy experiences incremental educational improvement without materially reducing its relative disadvantage, while female employment, graduate matching and the integration of qualified migrants advance too slowly to compensate for demographic and technological pressures. The tertiary-attainment rate may continue rising through generational replacement, but the increase remains insufficient to narrow the gap with European economies that have already established higher attainment levels and continue to develop their education systems. Such a pathway does not necessarily imply a decline in the absolute quality of Italian education or industrial capabilities; rather, it describes a situation in which improvements occur at a slower pace than the adjustments required by changing labour demand and international competition.

The employment consequences would depend on whether the economy creates sufficient opportunities for qualified workers and whether existing participation barriers persist. If suitable jobs remain geographically concentrated and professional progression continues to differ substantially between regions, graduates may move internally or internationally rather than increasing the stock of advanced skills available to less developed labour markets. Continued under-utilisation among qualified women and workers holding foreign credentials could further restrict the effective supply of specialised labour. In this situation, the economy might experience recruitment difficulties even while parts of the existing workforce remain underemployed, reflecting a persistent mismatch between skills, occupations, geography and employment conditions.

The technological consequences would be similarly differentiated. Leading enterprises may continue adopting sophisticated AI and digital systems, while firms with limited investment resources, data infrastructure or organisational capabilities may struggle to integrate those technologies into productive operations. The resulting differences could widen productivity disparities between firms and regions, although the direction and magnitude of this effect would depend on the evolving cost and accessibility of technological tools. The stagnation scenario should therefore be interpreted as a failure to close relative gaps rather than an assumption that all economic indicators remain numerically unchanged.

The principal evidence supporting the plausibility of this pathway is the persistence of Italy’s educational-attainment disadvantage, the low female employment rate, the comparatively small ICT-specialist share of employment and the substantial recruitment difficulties reported by employers. These observations do not establish that stagnation is the most likely future, but they identify the constraints that would need to remain unresolved for the scenario to materialise. The decisive warning indicators would include continued divergence between Italian and EU younger-cohort tertiary attainment, limited improvements in graduate employment and occupational matching, persistent outward migration of qualified workers, and an absence of measurable productivity gains among firms adopting advanced technologies.

Scenario B — Gradual Convergence Through Educational Completion and Labour-Market Activation

A gradual convergence scenario assumes that Italy improves educational completion, expands relevant post-secondary pathways and increases participation among under-utilised groups, while the productive system develops sufficient demand to absorb a growing proportion of qualified workers. Under this pathway, the country reduces selected attainment and employment gaps over time without reproducing the most advanced European outcomes in every indicator. The mechanism depends on sustained institutional implementation rather than a single exceptional reform: university completion improves progressively, technical education becomes more closely linked to occupational demand, childcare availability and employment conditions reduce some barriers to labour participation, and recognition systems improve access to skilled work for qualified migrants.

The comparative European record provides grounds for considering this trajectory feasible, although it does not determine its magnitude. Germany increased tertiary attainment among adults aged 25–34 from approximately 30% in 2015 to 41% in 2025, demonstrating that substantial progress in educational composition can occur within a decade. Spain and France provide evidence of diversified post-secondary pathways, while several European countries have expanded formal childcare participation significantly over the same broad period. These observations establish that educational and institutional changes of relevant scale have occurred, but do not prove that identical changes are achievable in Italy or that their economic effects would be equivalent. Sources: OECD, Education at a Glance 2026: Germany and European Commission, Early Childhood Education and Care Participation.

The labour-market mechanism would depend on improvements in both effective supply and occupational demand. Better activation of qualified workers could reduce recruitment frictions where employers currently struggle to identify or attract suitable candidates, while stronger technical training could improve the match between graduates and occupational requirements. Nevertheless, the convergence scenario would not necessarily eliminate all shortages because demand for specialised skills may increase as firms expand technologically sophisticated activities. A reduction in recruitment difficulty would therefore be a useful outcome indicator but should be interpreted alongside employment growth, wages and production rather than treated as an independent measure of economic success.

Technological adaptation under this pathway would involve broader diffusion of AI and digital systems among firms currently exhibiting limited adoption, supported by training, access to technical expertise and organisational investment. The impact on productivity would depend on whether firms integrate technology into core operations and whether workers acquire the capabilities required for effective implementation. Successful convergence would therefore become visible through improvements in firm-level productivity and the diffusion of advanced applications, not simply through a rising proportion of enterprises reporting that they use AI.

Scenario C — Accelerated Catch-Up Through Coordinated Human-Capital and Industrial Reform

The accelerated catch-up scenario assumes that Italy implements a more comprehensive and mutually reinforcing package of educational, labour-market and industrial measures, with sufficient institutional capacity to improve several constraints simultaneously. This pathway differs from gradual convergence not merely in the scale of expenditure or the speed of implementation, but in the degree of coordination between the formation of advanced qualifications, the utilisation of existing workers, graduate retention and enterprise modernisation. Its central premise is that the productivity value of additional skills rises when firms also invest in the technologies, organisational systems and occupational opportunities needed to employ them effectively.

Educational reforms under this pathway would prioritise completion and the development of relevant capabilities rather than enrolment expansion alone. Universities and technical institutions would improve progression and provide credible routes between vocational and advanced qualifications, while employers would participate in the development of specialised training without reducing educational programmes to narrowly defined short-term recruitment needs. The benefits of such coordination would depend on maintaining portable qualifications and broad competencies capable of supporting occupational change as technologies evolve.

Labour-market measures would seek to improve participation among groups whose capabilities are currently under-utilised, including women constrained by care responsibilities and qualified migrants facing administrative or occupational barriers. However, the accelerated scenario requires more than removing formal barriers: public services must be accessible, administrative procedures must operate predictably, and firms must provide employment opportunities compatible with the qualifications and circumstances of potential workers. The presence of a technically qualified population does not guarantee that employment will expand unless the productive system possesses sufficient demand and capacity.

The industrial dimension would involve stronger integration between education providers, research institutions, technical enterprises and digital-service capabilities, with particular attention to firms that lack the resources or expertise to implement sophisticated technologies independently. Organisational modernization would become an explicit component of human-capital strategy because technology adoption without complementary changes in management, workflow and workforce responsibilities may produce limited productivity gains. This scenario therefore offers the greatest potential for sustained improvement but also entails the highest coordination, financing and implementation demands.

The accelerated pathway would be weakened if education expanded faster than suitable employment, if targeted investment concentrated gains in a limited number of regions, or if administrative capacity proved insufficient to deliver public services and skills programmes at the intended scale. It should consequently be evaluated through staged performance assessments rather than through a single national target for graduate attainment or AI adoption.

Comparative Scenario Matrix — Institutional Conditions and Economic Consequences Through 2035

A structured comparison of the three pathways is necessary because their principal differences concern policy execution, complementarities and economic outcomes rather than predetermined numerical growth rates. The scenarios are mutually distinguishable as analytical cases, but the actual future may combine elements of more than one pathway. They should therefore not be assigned numerical probabilities unless a documented forecasting model or a reproducible expert-elicitation procedure establishes defensible likelihoods.

Table 8.1 — Italy’s three conditional human-capital trajectories

The principal risk common to all three pathways is the possibility that formal educational and technological indicators improve without generating equivalent increases in productive capacity. Under stagnation, that outcome would arise from insufficient reform; under gradual convergence, it might reflect incomplete coordination; under accelerated catch-up, it could occur if public spending and institutional expansion exceeded the ability of employers to create suitable employment or integrate additional capabilities. The scenarios must therefore be assessed against economic results rather than administrative activity alone.

Quantitative Monitoring Benchmarks and the Avoidance of Unsupported 2035 Projections

A credible monitoring framework should use official 2025 observations as its starting point and distinguish baseline levels from policy objectives and scenario assumptions. The purpose of quantitative monitoring is to establish whether Italy is moving toward comparable European outcomes and whether the underlying economic mechanisms are improving, rather than to present speculative 2035 values as forecasts. Where a numerical threshold is used, it should be identified as an existing official policy target, a contemporary comparator or a proposed decision trigger. These categories must remain separate because a European objective does not automatically constitute an achievable Italian target, while a comparator is not necessarily an appropriate domestic policy standard.

The European educational framework provides one established benchmark: the EU objective that at least 45% of people aged 25–34 possess tertiary qualifications by 2030. With the EU rate already at 44.8% in 2025, this target describes the Union’s collective ambition and should not be misrepresented as evidence that Italy will or must reach the same level within the original timeframe. For Italy, progress should be evaluated through the change in the gap between its younger-cohort attainment rate and the contemporaneous EU average, supported by graduation and completion data. Similarly, the European target for childcare participation among children under three is at least 45% by 2030, but its labour-market significance must be evaluated through the accessibility, duration and employment effects of services rather than the enrolment percentage alone. Sources: Eurostat, EU Tertiary Education Level Nears 45% in 2025; European Commission, Early Childhood Education and Care Participation. Eurostat+1

For employment, the relevant benchmark is the simultaneous development of Italy’s labour-market participation and that of comparable European economies. Eurostat’s 2025 figures identify a national employment rate of 67.6% among Italians aged 20–64, against 76.1% across the EU, providing an observed starting gap of 8.5 percentage points. The corresponding female employment rates of 58.0% and 71.3% imply a gap of 13.3 percentage points. These are descriptive comparisons and must not be converted into guaranteed employment gains or fiscal savings, because economic conditions and population structures differ between Italy and the EU aggregate. Nevertheless, a persistent failure to narrow these differences would constitute observable evidence against a pathway of effective labour-market convergence. Source: Eurostat, EU’s Employment Rate Grew Above 76% in 2025, 17 April 2026. Eurostat

Table 8.2 — Official baselines, comparison thresholds and monitoring interpretation

These benchmarks do not constitute a quantitative forecast. Their purpose is to distinguish observed conditions and formal objectives from potential future outcomes. In particular, an increase in the Italian ICT-specialist share would not necessarily indicate improved productivity if employment expanded principally in low-productivity activities, while a reduction in recruitment difficulties might reflect weaker demand rather than an improvement in skills availability. Every indicator must therefore be interpreted jointly with complementary observations capable of distinguishing structural progress from cyclical or compositional effects.

Implementation Requirements: National Coordination and Regional Differentiation

An effective transition toward either convergence or accelerated catch-up requires institutional coordination because the relevant policies fall under different administrative responsibilities and affect different economic actors. Education authorities oversee qualification pathways and the quality of educational provision, labour-market institutions influence employment services and participation, local authorities are central to many childcare and infrastructure decisions, professional regulators govern access to regulated occupations, and enterprises determine much of the practical demand for specialised competencies. Without a mechanism for coordinating these responsibilities, policies may increase the supply of qualifications without improving occupational matching, expand childcare facilities without supporting employment where opportunities are limited, or encourage technological adoption without ensuring that firms possess the skills necessary for productive integration.

The appropriate governance structure would distinguish strategic objectives from operational delivery. National authorities could establish common definitions, reporting standards and broad priorities, while regional institutions could identify specific shortages, educational pathways and service requirements. Employers and educational institutions would contribute occupational information and training capacity, while statistical agencies would assess whether reported improvements correspond to measurable changes in employment, earnings and productivity. This arrangement would reduce the risk that programme success is measured primarily through expenditure or the number of participants rather than through completed qualifications, occupational placement and economic results.

Regional differentiation is especially important because Italy’s labour-market and educational disadvantages are not geographically uniform. Policies that improve the availability of specialised graduates in regions with extensive industrial demand may generate different results from programmes implemented where the principal obstacles concern weak employment opportunities or inadequate infrastructure. A national strategy should therefore establish comparable standards and monitoring while allowing implementation priorities to reflect documented regional circumstances. This requires reliable information on educational completion, graduate mobility, local vacancies, firm investment and the accessibility of public services.

Fiscal Feasibility and the Opportunity Cost of Policy Inaction

The fiscal feasibility of human-capital reform cannot be evaluated by treating all proposed expenditure as an investment yielding a predetermined positive return. Educational infrastructure, university support, technical training, childcare services and administrative modernisation require financial resources and ongoing operational capacity. Some programmes involve substantial initial capital expenditure, while others generate recurrent costs for staffing, maintenance and service delivery. Their financing may also fall on different levels of government, employers and households, meaning that a programme appearing fiscally manageable from one institutional perspective could create significant obligations elsewhere.

A defensible fiscal assessment should therefore distinguish capital expenditure from recurring costs, public expenditure from private contributions, planned allocations from actual payments, and administrative outputs from realised economic effects. For example, expanding childcare places involves construction or adaptation, recruitment of qualified personnel and continuing operational expenditure, while expanding advanced technical training may require specialised equipment, instructors and sustained employer participation. Policies improving recognition of foreign qualifications may involve smaller capital requirements but depend on administrative capacity, legal procedures and professional regulation. None of these measures can be assigned a reliable fiscal return without explicit assumptions about participation, employment transitions, wages and public revenue.

The opportunity cost of inaction is equally important but more difficult to quantify. Persistent under-utilisation of qualified workers may reduce potential employment and taxable activity, while shortages of specialised personnel may constrain enterprise expansion or technological modernisation. However, these consequences cannot be aggregated into a single national loss by multiplying attainment gaps by average wages or GDP per worker. Such calculations would confuse population differences with causal employment effects and ignore the possibility that some workers would already be employed under alternative qualification structures. The appropriate approach is to estimate specific mechanisms using documented population groups, observed employment transitions and credible productivity measures, preserving uncertainty where the available evidence remains insufficient.

A Staged Implementation Framework Through 2035

The sequencing of reform matters because different interventions have different implementation requirements and time-to-effect. Administrative measures that improve the transparency and predictability of qualification recognition may affect occupational access more quickly than the creation of a new university graduate cohort, while improvements in childcare availability depend on physical capacity, staffing and local demand. Educational completion reforms may produce measurable changes over several academic years, whereas industrial training and organisational upgrading require continuing cooperation between firms and training providers. A strategy that expects simultaneous immediate improvement across all dimensions would therefore be inconsistent with the underlying institutional and demographic mechanisms.

Table 8.3 — Proposed implementation architecture

The periods in this table are proposed planning windows rather than existing government commitments or empirically estimated completion dates. They provide a governance sequence consistent with the differing mechanisms and implementation requirements of the interventions.

The advantage of staging lies in the possibility of adjusting programmes as evidence emerges. Where administrative reform improves recognition speed but not employment, attention should shift toward occupational matching and employer demand. Where childcare capacity increases but parental employment remains unchanged, the evaluation should examine service hours, accessibility and local labour-market opportunities. Where training participation expands without corresponding productivity or occupational improvements, programme content and enterprise implementation should be reconsidered. Such adjustments require sufficient monitoring to identify changes before large-scale expansion, while ensuring that programmes are evaluated over periods appropriate to their intended effects.

Strategic Risks, Decision Thresholds and Early-Warning Indicators

The primary risk affecting all three scenarios is that educational and technological policy succeeds administratively without producing the expected economic transformation. This may occur when additional qualifications are not matched by suitable employment, when training participation increases without measurable competence acquisition, when childcare infrastructure fails to address the practical constraints experienced by households, or when firms acquire digital systems without integrating them into productive processes. The distinction between output indicators and outcome indicators is therefore essential. Outputs describe what institutions provide, such as training places, recognised qualifications or technological investments; outcomes describe what changes economically, including sustained employment, improved occupational matching, higher productivity and greater capacity to undertake advanced activities.

A second risk concerns demographic change. In an ageing society, the number of workers available to sustain production can evolve differently from the percentage holding advanced qualifications. An improvement in the tertiary-attainment rate could therefore coexist with a stable or declining absolute stock of graduate workers, depending on population change and migration. This creates a need to monitor qualification-specific employment stocks and working hours alongside attainment percentages. A strategy that achieves a higher graduate share but fails to maintain or expand effective labour input may deliver a smaller economic benefit than headline educational indicators suggest.

A third risk concerns territorial concentration. Educational and industrial reforms may produce substantial results in metropolitan or industrial regions where advanced employment opportunities already exist, while generating limited local benefits elsewhere. Geographic differences in infrastructure, enterprise demand and public-service availability can influence both the effectiveness of investment and the mobility of qualified workers. The relevant policy response is not necessarily to distribute resources equally across all areas, but to distinguish locations where a shortage of skills constrains existing productive demand from locations where weak demand prevents available skills from being used. These circumstances require different measures and should not be treated as interchangeable.

A fourth risk concerns the changing demand for skills as AI becomes more capable and accessible. Educational programmes designed around a narrow definition of today’s occupations may become less relevant if the composition of professional tasks changes substantially. Conversely, programmes emphasising broad scientific foundations without sufficient opportunities for practical application may fail to prepare workers for specialised industrial requirements. A resilient approach must therefore combine transferable cognitive and technical capabilities with opportunities for professional specialisation and continuous learning, allowing educational and occupational pathways to adapt as evidence about technological change develops.

Table 8.4 — Strategic warning indicators and decision responses

These thresholds are analytical decision rules rather than statutory requirements or validated statistical alarm limits. Their usefulness depends on consistent measurement and on avoiding mechanical reactions to a single annual observation. Employment, migration, technology adoption and productivity can all be affected by business cycles, changes in survey methodology and other economic conditions, making persistent trends and appropriately controlled comparisons more informative than isolated changes.

Final Net Assessment — Italy’s Competitiveness Requires the Productive Mobilisation of Human Capital

The overall assessment across the three pillars establishes that Italy’s human-capital disadvantage is structural, economically relevant and institutionally complex. Its comparatively small tertiary-qualified population represents a long-term constraint on the availability of advanced formal qualifications, while incomplete labour-market participation, occupational mismatches, regional inequalities and recruitment difficulties reduce the extent to which the economy can utilise the skills already available. These weaknesses occur within an industrial and technological environment in which advanced manufacturing, digital infrastructure and AI increasingly require combinations of specialised expertise, adaptable workers, effective management and complementary capital investment. The available official evidence supports a judgment that these constraints can affect competitiveness, but does not establish a defensible single monetary estimate of the resulting national output loss.

European comparisons show that effective human-capital systems are not organised around one uniform educational model. Germany’s vocational pathways demonstrate the economic importance of recognised technical qualifications and employer participation, while France and Spain provide evidence of diversified post-secondary progression and extensive early-childhood service provision. Poland demonstrates favourable employment outcomes among young tertiary graduates despite continuing differences in educational and technological development, while the United Kingdom illustrates the scale of tertiary expansion achievable within a highly education-intensive economy. The European evidence also shows that strong performance in one area does not guarantee equivalent results elsewhere: extensive tertiary attainment does not automatically eliminate youth unemployment, substantial childcare participation does not independently determine female employment, and widespread digital infrastructure does not establish productive AI integration. The policy lesson is therefore that education, employment and industrial capabilities must be assessed as interacting systems rather than independent sectors.

The three scenarios through 2035 clarify the consequences of alternative institutional trajectories. Continued stagnation would preserve important structural disadvantages even if some educational and technological indicators improved gradually, while gradual convergence would reduce selected employment and qualification gaps through sustained institutional adjustments. Accelerated catch-up offers greater potential for a broader improvement in advanced skills and technological absorption, but depends on stronger coordination, adequate financing and effective productive demand. None of these pathways can be assigned a reliable GDP effect without explicit demographic and economic modelling, and the accelerated scenario should not be treated as automatically superior where implementation capacity or employer demand is insufficient. The appropriate choice is therefore a coordinated strategy with staged implementation, measurable results and the ability to adjust interventions as evidence develops.

The central economic principle is that human capital acquires its full productive value when qualifications, occupational opportunities and technological capabilities are effectively combined. Increasing the number of tertiary-qualified workers without generating suitable employment may improve educational indicators while delivering limited domestic economic benefits. Expanding employment without developing the advanced skills required by sophisticated industries may improve labour utilisation while leaving important technological constraints unresolved. Similarly, increasing investment in AI without complementary workforce and organisational capabilities may raise reported adoption without producing sustained productivity growth. Long-term competitiveness requires improvements in these dimensions to reinforce one another.

The policy implications are significant for education, labour, economic and industrial ministries. Educational authorities should evaluate progression, completion and the occupational relevance of qualifications, rather than enrolment alone. Labour-market institutions should focus on measurable employment transitions, matching and the removal of barriers affecting qualified workers. Public authorities responsible for childcare and infrastructure should assess whether services respond to documented participation constraints. Industrial institutions and employers should strengthen training, technical capabilities and the organisational conditions necessary for technological absorption. Statistical and productivity bodies should maintain harmonised indicators that allow the economic effects of these measures to be distinguished from changes in population composition, business cycles and technological diffusion.

The monitoring framework should be based on the OECD’s Education at a Glance 2026, Eurostat’s Educational Attainment Statistics, Eurostat’s Employment Statistics, the European Commission’s Union of Skills, the Eurostat ICT-specialist employment release, and Eurostat’s enterprise AI adoption statistics. These official records provide complementary evidence on education, employment, skills and technology, although their definitions and reference periods must be preserved.

The final judgment is that Italy’s ability to close its competitiveness gap through 2035 will depend not simply on producing more qualifications, but on establishing the institutional and economic conditions through which advanced skills become productive employment, technological capability and sustainable national output. The attainment deficit is established with high confidence, the under-utilisation and matching constraints are supported by documented labour-market outcomes, and the interaction with AI is supported by evidence of changing task requirements and uneven technological adoption. The remaining uncertainty concerns the magnitude and timing of the economic benefits achievable through reform, which can only be resolved through implementation evidence, longitudinal evaluation and appropriately calibrated economic modelling.

Final confidence assessment: High for the existence and comparative scale of the educational and employment disadvantages; medium for the likelihood that coordinated reform can improve Italy’s productive capabilities; low for precise 2035 GDP, productivity or employment gains without a validated quantitative forecasting framework.

Strategic Institutional Intelligence
Language
01 / Policy Intelligence

European Institutional Benchmarks

EU young-adult tertiary attainment
44.8%
Ages 25–34, 2025
Italian young-adult tertiary attainment
31.1%
Ages 25–34, 2025
Italy: formal childcare participation
39.4%
Children under three, 2024
EU formal childcare target
45%
Policy objective for 2030
02 / European Comparative Analytics

Interactive European Policy Observatory

Explore official statistical indicators across European policy systems.
Each chart has its own scale, starting at zero. The three-dimensional
effect is purely visual and does not alter the statistical values.

Childcare participation

03 / Comparative Institutional Evidence

European Education and Childcare Structures

Comparable observations are separated by their age groups and
reference periods. Missing observations are not replaced with
assumptions or treated as zero.

04 / Institutional Policy Architecture

European Policy Transferability Explorer

Select an institutional intervention to examine its economic mechanism,
implementation requirements, relevant European experience and principal
limitations. Country examples do not establish automatic causal effects.

Italy 2035: Three Conditional Scenarios

The scenarios compare institutional effectiveness, workforce activation,
advanced qualifications and technological absorption. They are not
probabilistic or numerical GDP forecasts.

Institutional Execution Through 2035

Illustrative planning windows, not existing government commitments.
The roadmap distinguishes early institutional measures from
multi-cohort educational outcomes and long-term productivity evaluation.

Critical Monitoring and Warning Indicators

Decision indicators identify whether reform activity is translating
into measurable employment, qualification and productivity outcomes.
Thresholds are analytical monitoring rules, not statutory obligations.