3. Assessment of policies to address skill shortages and improve matching
The 2023 European Year of Skills (324) It promotes skills policies and investments to address labour shortages and facilitate a better-skilled and more adaptable workforce in the EU. It focuses on promoting investment in training and upskilling, improving skills mismatches, matching people’s preferences and skillsets with labour market demand, especially in the context of the green and digital transitions, and attracting skilled workers from outside the EU.
The 2023 European Year of Skills will give a new impetus to the European Skills Agenda. (325) This five-year framework for EU skills policy cooperation aims to help businesses and individuals to develop and apply skills for sustainable competitiveness, social fairness, and resilience. It foresees 12 actions to support upskilling and reskilling for jobs through the Pact for Skills, including strengthening skills intelligence and supporting individuals in lifelong learning pathways. It also sets objectives to be achieved by 2025 in adult participation in learning, (326) including low-qualified adults, improving access to learning experiences for the unemployed, and basic digital skills. The European Pillar of Social Rights action plan sets a target of at least 60% of all adults participating in training by 2030. (327) The European Skills Agenda is supported by EU funding, chiefly from the European Social Fund Plus (ESF+) (EUR 61.5 billion), the Recovery and Resilience Facility (RRF) (EUR 19 billion) Erasmus (EUR 16.2 billion), InvestEU (EUR 4.9 billion), and the European Globalisation Adjustment Fund (EUR 1.1 billion), as well as the European Solidarity Corps and Digital Europe.
Table 3.4
European Skills Agenda: actions
1. Pact for Skills |
2. Skills Intelligence |
3. EU support for strategic national upskilling action |
---|---|---|
4. Council Recommendation on vocational education and training |
5. European universities initiative and upskilling scientists |
6. Skills to support the green and digital transitions |
7.Increasing STEM graduates and fostering entrepreneurial and transversal skills |
8. Skills for Life |
9. Initiative on individual learning accounts |
10. A European approach to micro-credentials |
11. New Europa Platform |
12. Improving the enabling framework for Member States’ and private investment in skills |
Source: European Skills Agenda - Employment, Social Affairs & Inclusion - European Commission (europa.eu)
This section presents the analysis of the overall impact of improved skills matching, followed by an assessment of some specific policies contributing to better matching: vocational training and lifelong learning, and access to PES. This is complemented by the presentation of the Pact for Skills in key sectors experiencing shortages, skills intelligence, and skills governance arrangements across the EU, together covering four key actions of the European Skills Agenda (Table 3.4).
3.1. Macroeconomic effects of improvements in skills matching at regional level (328)
Skills mismatches vary considerably across European regions. The regional distribution of the indicator of mismatch between individuals available for work (unemployed people) and the available jobs (vacancies) in 2017, the base year for this analysis, is presented in Chart 3.5. (329) The macroeconomic skills mismatch indicators are relative dispersion measures of employment and unemployment rates across skills groups. If there is a high discrepancy between the employment and unemployment rates across skills groups in a given region, the indicator has a high value, suggesting a significant skills mismatch between labour supply and demand in that region. The median value for the skills mismatch indicator for the NUTS 2 regions of the EU was 9.63, with a maximum of 21.93 (Slovakia: SK04 – Východné Slovensko) and a minimum of 1.99 (Finland: FI20 – Åland). Skills mismatches are typically more pronounced in Central and Eastern European countries and less evident in southern Europe.
Chart 3.5
Reducing the skills mismatch indicator stimulates the economy
Macroeconomic skills mismatch indicator, 2017, NUTS 2 regions of the EU
Source: Updated macroeconomic skills mismatch indicator (based on Kiss and Vandeplas, 2015)..
Box 3.5: Simulation of the long-term impact of improvements in skills-matching on GDP in NUTS 2 regions
Based on an earlier analysis of different dimensions of skills mismatch and their theoretical and empirical relationship with productivity, the macroeconomic effects of reducing skills mismatch at regional level are simulated using the spatial dynamic Computable General Equilibrium (CGE) RHOMOLO model, (1) calibrated with 2017 data. (2)
This exercise uses the updated version of the skills mismatch indicators developed by the European Commission. (3) It focuses on the macroeconomic skills mismatch indicator, measuring skills mismatches between individuals available for work (unemployed people) and available jobs (vacancies). This indicator is available yearly for the 2012-2021 period and for most NUTS 2 regions; in the regions where no indicator is available, the country average is used.
The analysis uses a previously estimated non-causal relationship between the macroeconomic skills mismatch and indicators of productivity, including TFP. (4) The estimated elasticity allows the linking of improvements in skills-matching and macroeconomic performance in the RHOMOLO model, through its effect on TFP. The analysis assumes that the elasticity between the indicator and TFP is equal to -0.01, which corresponds to an intermediate value of the various specifications used in the earlier analysis (fixed effects vs random effects; full sample vs EU-15 vs EU-13; (5) and different dependent variables). As the regression is estimated in log-linear terms, this elasticity implies that a 1 pp reduction in the macroeconomic skills mismatch indicator would result in a 1% increase in TFP.
- 1. Lecca et al. (2020).
- 2. García-Rodríguez et al. (2023).
- 3. Kiss and Vandeplas (2015).
- 4. Thum-Thysen and Vandeplas (2019).
- 5. EU-15: Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, the Netherlands, Portugal, Spain, Sweden (and then-Member State, UK); EU-13: Bulgaria, Croatia, Cyprus, Czechia, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Romania, Slovakia, Slovenia.
Policies to reduce skills mismatches can have a positive impact on the economies of the regions in which they are applied. Drawing on the literature linking skills mismatches and productivity, the analysis simulated the impact of a policy that successfully reduces skills mismatches in a number of EU regions. (330) The first scenario assesses the macroeconomic impact of a permanent 1 pp reduction in the skills mismatch indicator for all EU regions. The territorial distribution of the long-term GDP impact related to this scenario is shown in Chart 3.6. This reduction in skills mismatch is assumed to produce a 1% permanent increase in total factor productivity (TFP) for all regions. (331) All regions benefit from the increase in TFP. The increase in productivity makes labour and capital more valuable, and firms gradually start to increase both production factors, leading to more investment and an improvement in the labour market. Rising demand for labour leads to higher wages, with now-richer households increasing their consumption, further stimulating the economy. In addition, the all-around improvements in technology enhance trade and lead to lower prices.
Chart 3.6
Policies to reduce skills mismatches can have a positive impact on the economies of the regions
Long-term impact of 1 pp improvement in skills-matching on GDP (%), NUTS 2 regions
Source: JRC calculations, based on RHOMOLO model.
Economic linkages mean that reducing skills mismatches in a subset of regions will also have a positive effect in other regions. This is illustrated in the second simulated scenario, which assesses the impact of a permanent 10% reduction in the macroeconomic skills mismatch indicator in regions where the indicator was above the EU median of 9.63 in 2017. Under this scenario, regions just above the median of 9.63 would see a permanent 0.96 pp reduction, on average. For example, the indicator in Slovakia (SK04 - Východné Slovensko) moves permanently from 21.93 in 2017 to 19.78 (Chart 3.7). The economic mechanisms activated by the policy intervention are the same as in the first scenario, with the shock distributed unevenly across the regions. The shocks are bigger in those regions with larger skills mismatches, which consequently enjoy greater economic benefits in the longer term. The regions targeted by the intervention (those with macroeconomic skills mismatch indicators initially above the median) see an average long-term GDP increase of 1.96%, ranging from +1.38% to +3.55%. Regions not targeted by the intervention would also see increases in their GDP, as the additional economic activity in the target regions increases the demand for intermediate outputs and final products from their trade partners. The average GDP increase in non-targeted regions is about 0.16%, with an overall increase in long-term EU GDP of 0.85%.
Chart 3.7
Regions not targeted by a skills mismatch intervention also see GDP growth
Long-term impact of 10% improvement in skills-matching on GDP (%), NUTS 2 regions
Source: JRC calculations, based on RHOMOLO model.
Notes
- 324. European Skills Agenda available here.
- 325. 2023 European Year of Skills available here.
- 326. The European Skills Agenda sets objectives to be achieved by 2025, based on well-established quantitative indicators: Participation of adults aged 25-64 in learning during the last 12 month: (in %): 50% Participation of low-qualified adults 25-64 in learning during the last 12 months: (in %): 30% Share of unemployed adults aged 25-64 with a recent learning experience: (in %): 20% Share of adults aged 16-74 with at least basic digital skills: (in %): 70%
- 327. European Pillar of Social Rights action plan available here.
- 328. This subsection presents JRC analysis. The main findings are also published in (Christou, 2023).
- 329. Original version of the mismatch indicators was proposed by (Kiss and Vandeplas, 2015). The updated indicators are available yearly for 2012-2021 and for most NUTS 2 regions; in the regions where no indicator is available, the country average is used; 2017 data are used for consistency with the RHOMOLO model used in the analysis.
- 330. The RHOMOLO model cannot provide an explicit assessment of a specific policy to reduce skill mismatches, thus the analysis shows the macroeconomic effects of any policy that successfully reduces skills mismatches in a number of European regions. The estimated economic impacts can serve as a reference point when analysing the cost-effectiveness of such a policy. See Chart 3.5 for the methodology.
- 331. (Thum-Thysen, 2019).