5. Strengthening the governance of skills data in Luxembourg

Strong skills data governance is essential for helping policy makers and stakeholders navigate the complexity and uncertainty associated with the design and implementation of skills policies. Skills policies are complex as they fall at the intersection of multiple policy fields, including education, labour market, innovation, industrial and migration policy. At the same time, skills policies are developed in the context of substantial uncertainty as they are significantly impacted by megatrends, such as globalisation, automation, digitalisation, demographic change and climate change (OECD, 2019[1]), many of the implications of which are being accelerated by the COVID-19 pandemic. In the context of uncertainty and rapid change, strong skills data governance facilitates the provision of timely and relevant information, which is necessary for ensuring that governments and stakeholders can effectively design and implement skills policies and make informed choices leading to better skills outcomes. Such evidence-based policy making can also help to strategically target investments and generate higher returns on skills investments.

In this chapter, skills data is understood as all data relevant for skills policy making, most importantly, education and training data and labour market data. Strong governance of skills data refers to: 1) collecting skills data effectively and efficiently (i.e. collecting high-quality data and coordinating within government and with non-governmental stakeholders in the data collection process, respectively); and 2) facilitating the analysis, exchange and co-ordination of skills data (e.g. via data interoperability, data exchange platforms, etc.). Using skills data (and information on education and training opportunities) effectively and efficiently for career guidance is explored in Chapter 3.

Strong skills data governance provides the foundation for the successful design and implementation of skills policies and programmes in all of Luxembourg’s skills policy priority areas described in this Skills Strategy. Skills data are important for better aligning the adult education and training offer to fast-changing labour market needs (see Chapter 2) and informing the design and implementation of guidance and financial incentives that help steer skills choices (see Chapter 3). In addition, skills data are necessary to generate information on current and future skills needs, which is key for recruiting the right foreign talent (see Chapter 4).

In the long run, strong skills data governance supports building integrated skills information systems (Figure 5.1),1as strong data governance helps mobilise data and enhance data management and evaluation processes. In Luxembourg, the importance of strong skills data governance is further underscored by the fact that relying solely on national skills data sources, in most cases, does not fully capture the complexity of Luxembourg’s skills system due to the high reliance on labour sourced from the Greater Region (see Chapter 4).

This chapter is structured as follows: the following section provides an overview of the current skills data governance practices in Luxembourg. The next section describes Luxembourg’s skills data governance performance. The last section conducts a detailed assessment and provides targeted policy recommendations in two opportunities for strengthening the governance of skills data in Luxembourg: improving the quality of Luxembourg’s skills data collection; and strengthening co-ordination of, and synergies between, skills data within and beyond Luxembourg.

In Luxembourg, a wide variety of data relevant for the analysis and research of skills policies is being collected by both governmental and non-governmental actors, including both administrative (Table 5.1) and survey data (Table 5.2).

The Joint Social Security Centre (CCSS) is Luxembourg’s key source of data for analysing the structure of its national labour market and its evolution. Employers are required to make a declaration to the Common Centre for Social Security when they wish to recruit a new employee. Data collected by CCSS include information on the employee’s occupation at the moment of hiring, sector, the type of contract and duration and the employee’s place of residence.

Luxembourg’s public employment service (ADEM) is its key data source on vacancies and job seekers. ADEM collects data on job vacancies by economic activity (NACE2) and occupation (ROME3), as well as on job seekers by gender, age, duration, resident status and education level. ADEM vacancy and job seeker data make it possible to analyse the jobs available and the occupations sought by job seekers, as well as the mismatch between the two. In addition, vacancy data are also collected by certain sectoral institutions in Luxembourg, such as the Luxembourg Banker’s Association (ABBL) (financial sector) and the HORESCA Federation, which run their own job boards. The job board of the HORESCA Federation also contains data on job seekers in the HORESCA (hospitality) sector. In addition, data on vacancies are contained on Luxembourg’s private job portals, which are web-scraped and centralised by the European Centre for the Development of Vocational Training (CEDEFOP)’s Skills-OVATE (online vacancy analysis tool for Europe) tool. Co-ordination between Luxembourg and CEDEFOP is facilitated by the Luxembourg Institute of Socio-Economic Research (LISER), serving as a national contact point for CEDEFOP.

Administrative data on participation in general education (ISCED 2011 Level 1-8) are collected centrally by the Ministry of Education, Children and Youth (MENJE) and the Ministry of Higher Education and Research (MESR). These data also cover enrolment, new entrants and graduates and are reported to international institutions for the purpose of tracking data and indicators on education systems (OECD, 2021[4]). Data on adult education and training participation are collected in a decentralised manner by key public providers (e.g. the National Centre for Continuing Vocational Training [CNFPC], University of Luxembourg Competence Centre [ULCC], etc.), sectoral institutions providing training (e.g. ABBL, Chamber of Commerce [CC], Chamber of Skilled Trades and Crafts [CdM], Chamber of Employees [CSL], etc.), as well as private training providers. In addition, ADEM collects data on job seekers’ training participation.

Administrative data on education and training outcomes in Luxembourg are produced and collected, respectively, by the National Institute for the Development of Continuing Vocational Training (INFPC) and the University of Luxembourg (UoL). In addition, the Training Observatory of the INFPC regularly collects data on outcomes of initial vocational education and training (IVET) as part of the School – Active Life Transition (TEVA) annual study by linking data from MENJE, MESR and CCSS and on companies’ training practices.

At the higher education (HE) level, the UoL carried out a one-off graduate tracking exercise in 2021, which collected information on graduates’ first and current employment via LinkedIn and through graduates’ academic supervisors.

Luxembourg’s collection of administrative skills data (Table 5.1) is further complemented by skills data collected through surveys (Table 5.2).

The National Institute for Statistics and Economic Studies (STATEC) is in charge of administering Luxembourg’s household surveys relevant for the analysis of skills policies. The European Union Labour Force Survey (LFS), implemented in Luxembourg, collects, among others, information on respondents' (residents only) employment status (employed/unemployed/inactive), occupation (ISCO code), as well as participation in formal and non-formal education (except for guided on-the-job training) in the last four weeks. With the European Union (EU) Adult Education Survey (AES) implemented in Luxembourg, STATEC collects, among others, information on public participation in formal and non-formal education and training and informal learning in the last 12 months. Information on obstacles to training participation and time spent on education and training is also available via AES. STATEC is equally in charge of carrying out the Continuing Vocational Training Survey (CVTS), which measures enterprises' activity in the provision of continuing vocational education and training (CVET), and the Structure of Earnings Survey (SES), which collects data on earnings of all firms with at least ten employees in all sectors by economic activity (NACE) and occupation (ISCO). Similarly to LFS and AES, both CVTS and SES are EU surveys, which STATEC implements in Luxembourg.

The Training Observatory of the INFPC collects data on the learning offer of adult learning providers through a regular survey and launched another survey in 2021 on the provision of CVET in small companies. In addition, the UoL conducts an annual survey of its graduates, collecting self-reported data on graduates’ education and training outcomes (e.g. employment status, earnings, perceived mismatch, use of skills on the job, etc.).

In addition to surveys administered by governmental actors, non-governmental and sectoral actors in Luxembourg actively collect data on workforce needs, recruitment challenges and firm-based training provision for some of the sectors of Luxembourg’s economy through their own surveys.

The ABBL conducted a one-off survey with member companies on the difficulties and duration of recruitment in the financial sector in 2018. The Competence Centre of the Building Services Engineering and Completion Engineering (GTB/PAR), which provides training to craft companies, conducts a survey twice a year to enquire about the skills/training needs among employers in the construction sector. The survey collects information on skills and training needs by level of education and by job type. In addition, the Business Federation Luxembourg (FEDIL) conducts a survey every two years on the manufacturing and information and communication technology (ICT) sectors’ future qualification needs.

The CC, in collaboration with the Luxembourg Institute for Social Research (Institut Luxembourgeois de Recherches Sociales, ILRES), a private survey company, carries out its annual Barometer of the Economy study that collects information on the key concerns among Luxembourgish employers. Each Barometer of the Economy has a different thematic edition. In 2019 and 2021, the thematic editions enquired about employers’ recruitment difficulties, and firm-based training provision, respectively. In addition, the CdM conducted a survey in 2019 on skilled labour needs and shortages in the crafts sector, supplemented by a qualitative analysis based on interviews with company managers from the various groups of craft activities. The survey was repeated in 2022.

Since 2013, the CSL, in collaboration with the UoL, has conducted the Quality of Work Index survey on an annual basis with questions on employees’ working conditions and the quality of work in Luxembourg. Topics cover work demands and workloads, working hours, co-operation between colleagues, possibilities for further training and advancement, participation in decision making in businesses, and more.

Several key institutions in Luxembourg engage in the analysis, exchange or co-ordination of the collected skills data (as mentioned above). These institutions include ADEM, the General Inspectorate of Social Security (IGSS), INFPC, STATEC and the Labour Market and Employment Research Network (Réseau d’études sur le marché du travail et de l’emploi, RETEL). In addition, the “Trends” working group, established between ADEM and the Union of Luxembourg Companies (UEL), helps increase the transparency of existing skills data and to further consolidate and improve them.

ADEM publishes labour market analyses regularly, including analyses of vacancies reported to, and job seekers registered with, ADEM (ADEM, 2022[15]). In 2022, ADEM also elaborated sectoral studies, which analyse labour market trends across seven sectors in Luxembourg. Based on ADEM vacancy data, the sectoral studies were developed in collaboration with employers to provide information about Luxembourg’s growing, declining and emerging occupations, as well as labour shortages and surpluses, among others (ADEM, 2022[16]).

IGSS engages in the analysis and publication of labour market data. On a regular basis, IGSS publishes data on labour market trends in Luxembourg (including net employment creation trends by sector, age, gender, nationality, and place of residence, among others) (IGSS, 2022[17]). Every month, IGSS also publishes a snapshot of Luxembourg’s labour market situation (including the size of the labour force and labour force growth comparison with the preceding year, among others) (IGSS, 2022[18]).

INFPC’s Training Observatory conducts research in the field of education and training to support policy makers, employers and education and training providers in improving education and training policies and practices. The Training Observatory analyses employees' access to employer-sponsored training, firms' training activities and the state's financial contribution to firms' training plans, among others. The Training Observatory is a member of ReferNet, the European network of reference and expertise on vocational education and training (INFPC, 2021[19]).

STATEC carries out and publishes relevant analyses often (but not only) on the basis of the surveys it administers (see Table 5.2). For example, relevant publications have presented the results from the AES (STATEC, 2018[20]) and the CVTS (STATEC, 2018[21]) for Luxembourg.

Data exchange and collaboration between IGSS, ADEM and STATEC are supported via RETEL. RETEL was established to commission new, and centralise existing, labour market research in Luxembourg. RETEL aims to support the development of synergies between the work of various governmental entities and stakeholders engaged in Luxembourg’s primary labour market data collection and the analysis of labour market data. Through its work, RETEL seeks to foster an efficient and transparent approach to data-driven production of labour market information and evaluation of labour market policies in Luxembourg (Government of Luxembourg, 2018[22]).

MESR launched an initiative to set up a National Data Exchange Platform (NDEP) in Luxembourg to facilitate smoother data exchanges, covering data in various fields beyond skills. The NDEP project is developed in co-operation with various other governmental institutions in Luxembourg (see more in Opportunity 2).

Finally, to increase the transparency of the existing skills data (as summarised in Table 5.1 and Table 5.2) and further consolidate and improve them, Luxembourg established the Trends working group within the framework of the long-term collaborative effort between ADEM and UEL (ADEM/UEL, 2021[23]). Apart from ADEM, the Trends working group includes various members of the UEL, including representatives of the ABBL, CC, CdM, Luxembourg Trade Confederation (CLC), Federation of Artisans (FDA), FEDIL, Competence Centre for Building Services Engineering (GTB) and the HORESCA Federation (ADEM/UEL, 2021[23]).

As foreshadowed at the beginning of this chapter, strong skills data governance requires that skills data collection processes are effective and efficient (i.e. skills data collection is well-coordinated, resulting in the generation of high-quality data) and that skills data exchanges are easily facilitated.

Skills data governance in Luxembourg could be strengthened on several fronts. First, there is room to improve the quality of Luxembourg’s skills data collection in terms of accuracy (i.e. the degree to which the data correctly estimate or describe the quantities or characteristics they are designed to measure) (OECD, 2011[24]), coverage (i.e. completeness of the data) and granularity (i.e. level of detail) (OECD, 2014[25]), as well as to expand the range of skills data collected.

Moreover, there is space to better facilitate skills data co-ordination and exchanges both within the government and with relevant stakeholders in Luxembourg. With the labour market extending beyond national borders, Luxembourg could equally benefit from building stronger synergies with international data sources, especially to better understand the changing skills supply and demand in the Greater Region (see Chapters 1 and 4 for a discussion of the strategic importance of the Greater Region for Luxembourg).

The cross-border nature of Luxembourg’s labour market further complicates skills data governance processes. As almost half of Luxembourg’s labour force is constituted by cross-border workers (see Chapter 4), Luxembourg does not participate in the OECD Survey of Adult Skills (Programme for the International Assessment of Adult Competencies, PIAAC). As a result, Luxembourg cannot benefit from the survey’s assessment of the literacy, numeracy and problem-solving skills possessed by adults or how adults use their skills at home, at work and in the wider community. Similarly, other international surveys that Luxembourg takes part in (e.g. the EU LFS, AES, etc.), which, among others, provide information on the occupational distribution of Luxembourg’s workforce or participation in adult education and training, cover residents only. It is possible to include cross-border workers in the analysis of Luxembourg’s occupational distribution after combining variables in the LFS, but only with certain caveats (e.g. limited disaggregation is possible). A similar exercise is not possible in the context of the AES.

Existing skills data governance challenges affect Luxembourg’s skills assessment and anticipation (SAA) exercises.4 For example, given that ADEM’s sectoral studies (see above) were based solely on ADEM vacancy data, which themselves face important coverage challenges (due to the incomplete declaration of job vacancies by employers; see more in Opportunity 1), the results of the sectoral studies have to be interpreted with caveats, bearing in mind the limitations of the data sources used.

In addition, Luxembourg’s SAA exercises tend to be restricted to identifying current skills needs (e.g. by analysing skills demanded in job vacancies) and changing labour market conditions (e.g. growth or decline of certain occupations). However, little is done to anticipate Luxembourg’s future skills needs. Stakeholders have indicated that, to a large extent, the absence of skills anticipation exercises is linked to the quality and co-ordination challenges of Luxembourg's existing skills data sources.

It is welcome that the recently established Trends working group led by ADEM and UEL (see above) has recognised many of the skills data challenges highlighted above and intends to work collaboratively on solving them. In addition, LISER has similarly made improving the “data infrastructure and, in particular, access to high-quality data in and for Luxembourg” one of its core objectives (LISER, 2020[26]).

The performance of Luxembourg’s skills data governance arrangements reflects many factors. These include individual and institutional factors and broader socio-economic factors (i.e. Luxembourg’s heavy reliance on cross-border labour). However, two key opportunities for improvement have been identified based on a literature review, desk analysis and data, and input from officials and stakeholders consulted in conducting this OECD Skills Strategy.

The OECD considers that Luxembourg’s main opportunities for improvement in the area of skills data governance are:

  1. 1. improving the quality of Luxembourg’s skills data collection

  2. 2. strengthening co-ordination of, and synergies between, skills data within and beyond Luxembourg.

As foreshadowed earlier in this chapter, Luxembourg collects a wide variety of quantitative and qualitative data, which can be used to inform the design of skills policies, including labour market data and education and training data. These data sources, and their combination, can provide different types of insights essential for skills policy making.

Labour market data (e.g. social security data, vacancy data, etc.) are commonly used as proxies for the analysis of evolving skills needs because they allow for the observation of growth or decline in employment in specific occupations (OECD, 2016[27]). Vacancy data collected by public employment services (PES) can also provide valuable information on skills needs and shortages by facilitating the analysis of the changes in the number of declared and (un)filled vacancies and the duration of vacancy filling, among others (McGrath and Behan, 2017[28]). In addition, insights from employer surveys can be used to complement administrative data to assess skills needs and shortages by providing information not available in administrative datasets.

Education and training data (e.g. on attainment, participation, outcomes, expenditure, curricula, etc.) provide important information on the access, quality and relevance of the supply of education and training opportunities. This information helps policy makers identify challenges and opportunities in the skills system and target programmes and funding where the expected impacts will be highest.

To improve its quality, Luxembourg’s skills data collection could benefit from:

  • improving the accuracy, coverage and granularity of Luxembourg’s labour market data

  • expanding the range and strengthening the granularity of Luxembourg’s education and training data.

As shown in Table 5.1 and Table 5.2, Luxembourg collects a wealth of labour market data, which provides valuable information on the evolving skills needs in Luxembourg’s economy. However, to allow data users (both in and outside of the government) to unlock the full potential of Luxembourg’s labour market data, Luxembourg should work on addressing the accuracy, coverage and granularity challenges of certain labour market data sources.

Luxembourg’s social security data, collected by the CCSS, are Luxembourg’s only source of occupational data covering both Luxembourgish residents and cross-border workers. Given that cross-border workers account for 46% of Luxembourg’s labour market (see Chapter 4), the importance of CCSS data cannot be overstated. CCSS data can inform the assessment of the occupational distribution in Luxembourg’s labour market and serve as a proxy for assessing current skills needs by making it possible to observe growth or decline in specific occupations. Should Luxembourg consider developing exercises for anticipating future skills needs (as supported by stakeholders consulted in the context of this project), such as Canada’s Occupational Projection System (COPS) (Government of Canada, 2017[29]) or the United Kingdom’s Working Futures projections (Warwick Institute for Employment Research, n.d.[30]), CCSS data would be valuable for producing occupational projections (i.e. projections showing the expected future number of workers to be employed in different occupations, thereby producing insights about the future occupational growth or decline).

However, CCSS occupational data face important accuracy challenges due to incorrectly declared occupational codes by employers or their service providers (e.g. payroll managers or outsourced accounting firms). As mentioned above, each newly hired employee has to be reported to the CCSS (Government of Luxembourg, 2021[31]). When making the declaration to CCSS, the occupation of the new employee needs to be specified via a code defined in the International Standard Classification of Occupations (ISCO). However, there is evidence suggesting that in many cases, companies’ payroll managers or accounting firms to whom payroll management can be outsourced input incorrect ISCO codes in the employment declarations (ADEM/UEL, 2021[23]), making CCSS data unusable for the analysis of Luxembourg’s labour market at the occupational level.

During a data check carried out in 2016, the IGSS found that the ISCO codes were entered correctly in about only 30% of cases. More specifically, IGSS found that a large share of occupations belonging to ISCO groups 1-3 (managers, professionals, and technicians and associate professionals, respectively) were reported under ISCO group 4 (clerical support workers). In part, the reason for inputting incorrect ISCO codes stems from the French translation of the ISCO classification, where the name of ISCO group 4 is translated as “administrative employees” (employés administratifs), which does not fully reflect the meaning of the original English name “clerical support workers”.

In order to support employers and their service providers in declaring the correct ISCO codes, IGSS published an online guide in French, where the distinction between the ISCO groups was clearly delineated. IGSS also sent out a letter to employers to highlight the existence of the problem, underscoring the importance of correctly declaring the ISCO codes. Moreover, CCSS developed an occupational code search engine on its website, accessible in French, aiming to help identify the correct ISCO codes more easily. However, it was discussed in stakeholder consultations that in a recent data check, IGSS found that these communication efforts did not yet have the desired effect, as the share of ISCO codes declared correctly had not improved.

Occupational data (declared via ISCO codes) is used for purely analytical purposes in Luxembourg, which means that employers do not have a strong incentive to ensure that the declared ISCO codes are correct. Going forward, Luxembourg could incentivise employers to declare correct occupational codes by designing practical tools, which would have clear value-added for employers without increasing employers’ administrative burden. For example, based on the CCSS data, CCSS and IGSS could design an online occupations dashboard, where employers would log in and track the occupational structure and evolution within their company, together with additional features (e.g. employee salary distribution, age profiles, etc.). Such a dashboard could bring significant benefits, especially to small and medium-sized enterprises (SMEs), which tend to have constrained administrative capacity for human resources (HR) management. Furthermore, to increase the value-added of the dashboard, employers in Luxembourg could be, in the long term, asked to regularly update the declared ISCO codes, given that the codes are currently not being updated at all.

To further facilitate ISCO code declarations, CCSS could improve the search functions of the existing occupational code search engine. For example, the occupational coding tool developed by the University of Warwick in the United Kingdom features probability scores reflecting the perceived match between the keywords input into the tool and the suggested occupational codes, as well as descriptions of tasks of the occupations suggested to match one’s search, entry routes, associated qualifications and related (similar) job titles (Box 5.1). To support the user-friendliness of the occupational coding tool, CCSS could develop a chatbot allowing real-time responses to queries of employers or their service providers who might be unsure of which occupational code to declare. CCSS could also consider making the search engine accessible in English in addition to French, given the challenges related to correctly translating the ISCO codes from English to French highlighted above. In the long run, making the search engine accessible in German and Luxembourgish could also be considered, given the variety of languages used in Luxembourg and its labour market (see Chapter 4).

While the existing awareness-raising activities mentioned above (i.e. developing an online guide, sending a letter to employers) undertaken by CCSS/IGSS are steps in the right direction, they could be further strengthened. Information on common mistakes in inputting ISCO codes, as well as an overview of the existing resources and tools for helping employers select the correct ISCO code (e.g. the proposed occupations dashboard, CCSS’ occupational code search engine, IGSS’ online guide, etc.) could be regularly distributed to employers and the existing guidance tools more prominently featured on IGSS/CCSS websites. CCSS/IGSS could expand their awareness-raising activities to include associations of human resources (HR) professionals (e.g. POG – HR Community of Luxembourg) and associations of payroll experts (e.g. Luxembourgish Association of Accounting and Tax Consultancies, ALCOMFI) to target actors directly in charge of employment entry declarations. Employer representatives (e.g. UEL) should equally play a key role, and promote the importance of, and existing support tools for, correctly declaring new hires to the CCSS among employers. In addition to undertaking efforts to strengthen the accuracy of CCSS data, IGSS and other stakeholders in Luxembourg should still make use of the data to the extent possible (e.g. for the analysis of general growth trends across occupations).

Beyond addressing accuracy issues with social security occupational data, there is space to improve the coverage and granularity of Luxembourg’s vacancy data. In Luxembourg, a legal obligation exists for employers to declare all job vacancies to ADEM. Employers can report vacancies online via guichet.lu (Luxembourg’s e-government platform) or by filling out a PDF form which needs to be sent to ADEM via email or post. A vacancy declaration can be completed in English, French or German (ADEM, 2021[34]). Despite these efforts, a comparison of the number of vacancies declared to ADEM to the number of actual recruitments (based on employment entry declarations to the CCSS) shows that the vacancies reported to ADEM cover less than 30% of all actual job creations in Luxembourg’s labour market (ADEM, 2021[35]). The number of declared vacancies also varies by sector. For example, while the estimated average share of job vacancies reported to ADEM was approximately 35% in the ICT sector between January 2020 and January 2021, the figure stood at 18% in the construction sector (ADEM, 2021[35]). While a certain degree of caution should be used when interpreting the results of the comparison of vacancy and recruitment data, since not every recruitment is preceded by a job posting, it can still provide a rough estimate of the coverage of the vacancy data. Given the relatively low number of vacancies reported to ADEM, it can be challenging to use ADEM vacancy data to reliably assess Luxembourg’s labour market needs and shortages.

Stakeholders have mentioned several potential reasons for the relatively low number of vacancies reported to ADEM. For example, employers, and especially SMEs, without a dedicated HR department might struggle to find the time and resources to report job vacancies. Stakeholders have also suggested that certain employers do not see great value in reporting vacancies to ADEM since the pool of candidates (registered job seekers) is often unlikely to match the exact requirements of the declared vacancy. Moreover, some recruitments are based on informal referrals rather than officially created and published job postings.

It should be noted that ADEM has taken a number of concrete steps to incentivise employers to improve job vacancy reporting rates. Penalties for enforcing the mandatory vacancy reporting obligation are not applied, and ADEM prefers to use positive incentives instead (Goffin, 2015[36]). For example, ADEM has simplified the vacancy reporting forms (Goffin, 2015[36]) and has been running awareness-raising campaigns for employers within the framework of the ADEM/UEL partnership, underscoring the fact that vacancy reporting is as important for analytical as for recruitment purposes. ADEM also sends its employer service staff to visit companies and highlight the added value of regular vacancy reporting (Goffin, 2015[36]). Moreover, ADEM has started offering the possibility to automatically import a vacancy from a company's job database to ADEM, which several large employers have already implemented. In addition, ADEM has begun creating partnerships with certain private local job portals to automatically import their online job advertisements (OJAs) into ADEM's job board. Finally, ADEM has been developing a range of new services, so it is more attractive for employers to approach ADEM. For example, in April 2021, ADEM’s job board was for the first time opened to job seekers who are not registered with ADEM (ADEM, 2021[37]). The results of such efforts are yet to be seen.

Data from OJAs, as already partly explored by ADEM, could serve as a useful additional source of vacancy data for Luxembourg, as OJA data capture even those vacancies not declared to ADEM, but that appear on private job portals. CEDEFOP’s Skills-OVATE tool uses web-scraping techniques to collect OJAs from private job portals, public employment service portals, recruitment agencies, online newspapers and corporate websites (CEDEFOP, 2021[38]). In addition, Skills-OVATE provides a granular view of skills most sought after by employers in their vacancies, classified according to the European Skills, Competences, Qualifications and Occupations (ESCO) classification (see more below). While Skills-OVATE data for Luxembourg cover online private job boards, it does not include ADEM’s vacancy data. This is because Skills-OVATE only sources information from publicly accessible job boards, and ADEM’s job board was not public when the Skills-OVATE tool was designed. Since then, however, ADEM has agreed to publish its vacancies (with employers’ permission) on the publicly accessible European Employment Services (EURES) job board, with the view of supporting EU job mobility. Therefore, a large share of ADEM’s vacancies is now accessible on the EURES public job board and can be scraped by Skills-OVATE.

LISER, the national contact point for CEDEFOP, should thus consider reopening conversations with ADEM about including their vacancy data in the Skills-OVATE tool. This would allow for more comprehensive information on both occupational and skills needs trends in Luxembourg by having access to data from vacancies declared both to ADEM and at Luxembourg’s private job portals. As Skills-OVATE is updated four times a year, skills needs information could be obtained regularly. Partnering with CEDEFOP would equally help ensure that the data does not contain duplicate vacancies (i.e. vacancies declared both to ADEM and on private job boards) using the tools CEDEFOP had developed. Making greater use of OJA data could help improve both the coverage and granularity of Luxembourg’s vacancy data. Nonetheless, ADEM should still strive to increase the share of job postings created and declared by employers. As noted above, since not every recruitment is preceded by a job posting, even greater use of OJA data might not provide a comprehensive picture of Luxembourg’s labour market needs.

Going forward, ADEM could consider further expanding the range of support tools and services for employers to strengthen the incentives for employers to approach ADEM to declare job postings. For example, ADEM could develop an online vacancy dashboard for employers, similar to the occupations dashboard (see above), based on vacancy data reported to ADEM. Upon logging in, the vacancy dashboard could give the employer an overview of the vacancies (open and filled) over time within the company and how quickly different vacancies are being filled. In the future, consideration could be given to further developing the technical functions of the vacancy dashboard, for example, to calculate the “attractiveness scores” (i.e. probabilities of finding a suitable candidate for each vacancy based on ADEM’s job seekers database) of each vacancy. Stakeholders consulted in the context of this project agreed that employers, especially SMEs, would benefit from such an online vacancy dashboard. In addition, CCSS and ADEM could work on creating links between CCSS employment entry declarations and ADEM vacancy declarations (see more on ADEM vacancy declarations below) to make the reporting process more straightforward for employers. For example, a vacancy reported by an employer to ADEM could be assigned a unique identifier, which, once a suitable candidate has been found for the advertised job posting, could be used to pre-fill the CCSS employment entry declaration.

Evidence shows that the capacities of small employers to engage in formal recruitment, and thereby vacancy reporting, tend to be restricted the most owing to resource constraints or organisational culture (European Commission, 2018[39]). Therefore, to incentivise SMEs to approach ADEM and encourage higher vacancy reporting rates, ADEM could consider developing dedicated support services for SMEs. For example, ADEM could provide support to SMEs in drafting job postings according to their needs, on top of providing additional services (e.g. sign-posting the available reskilling/upskilling opportunities for SME employees). Box 5.2 details the recruitment and support services provided to SMEs in Germany by way of example.

Employer representatives in Luxembourg should equally play a role in encouraging higher vacancy reporting rates via active awareness-raising. Members of the UEL (including associations, federations and confederations and chambers of commerce) could raise awareness about the importance of vacancy reporting in their engagement activities with employers (e.g. information sessions, newsletters, prominent featuring on their respective websites, etc.).

In order to further improve the assessment of skills needs, Luxembourg’s vacancy data could be further enriched by insights on skills and workforce needs collected by directly surveying employers. As outlined above, several surveys of employers’ current and future workforce needs have been carried out by several non-governmental stakeholders (e.g. ABBL, CC, CdM, FEDIL, etc.) in Luxembourg in recent years. CdM and FEDIL implement employer surveys regularly. However, due to the varying structure of questions, sector coverage, frequency of implementation and definition of skills/workforce needs, each survey provides only a piece-meal picture of employers’ skills needs. In addition, stakeholders for this review mentioned that too many surveys risk creating “survey fatigue” among employers in Luxembourg, given the size of the country and the administrative and time burden associated with completing each survey.

Going forward, Luxembourg should review the existing employer surveys, in tandem with administrative data on skills needs (e.g. ADEM and Skills-OVATE vacancy data; see Recommendation 4.2) to identify information that: 1) would add value to Luxembourg’s labour market data collection but is currently not available in administrative datasets; and 2) is collected by existing employer surveys irregularly, inconsistently or not at all. For example, while Luxembourg’s administrative datasets currently do not hold information on future skills needs, CdM and FEDIL surveys collect such insights but only cover three sectors (crafts, industry and ICT). The review of administrative data and existing employer surveys would help Luxembourg assess the need to introduce a national, cross-sectoral employer survey of skills needs and/or gaps. Such a national survey should reduce the need for multiple employer surveys in the long run and mitigate against the risk of “survey fatigue” in Luxembourg. Box 5.3 describes how the United Kingdom implements a nationwide Employer Skills Survey. In developing such a national employer survey, Luxembourg could follow CEDEFOP’s (2013[42]) “User guide to developing an employer survey on skill needs”, where relevant.

Finally, to further improve the granularity of Luxembourg’s labour market data collection, Luxembourg could benefit from tools that make it possible to link the information on occupations in vacancies declared by employers to specific skills. In the future, ADEM plans to move towards skills-based matching, where job seekers would be matched with occupations best suited to their skills, which could facilitate a closer match. However, ADEM’s current job-matching system does not yet facilitate skills-based matching. At present, ADEM matches job seekers to vacancies by matching the occupation (ROME) code associated with a specific vacancy, with the occupation (ROME) code indicated to be of interest by a job seeker, as well as according to language or education requirements, among other criteria.

Several OECD countries make it possible to link occupations and skills by using skills-based occupational classifications. For example, O*NET in the United States is a database containing detailed information about the knowledge requirements of more than 800 occupations. In Canada, the National Occupational Classification describes the world of work and occupations, including the skills required by each of the 500 occupational unit groups (OECD, 2016[27]). In 2021, Australia launched its own comprehensive skills classification, identifying around 600 skills profiles for occupations in the Australian labour market based on the O*NET database (National Skills Commission, 2021[45]). In 2017, the European Commission introduced the ESCO classification, which links occupations and skills relevant for the EU labour market and education and training (Box 5.4).

In Luxembourg, no common, comprehensive skills classification is currently used. Links between occupations and specific skills in Luxembourg's labour market have been defined only for occupations in certain sectors and a non-coordinated manner. In some sectors, the content of occupations is described in collective agreements. For example, the collective agreement covering the banking sector describes the tasks, knowledge and technical skills, among others, required for occupations (ABBL, ALEBA, OGBL and LCBG-SEFS, 2018[48]). In addition, some sectors, such as crafts (CdM, n.d.[49]), have developed more detailed skills profiles for their occupations based on research carried out in working groups or through labour market monitoring (ADEM/UEL, 2021[23]). The limitation of the skills classifications developed at the sectoral level is that they are not linked to a recognised national or international occupational classification (e.g. ISCO), which confines their potential use to the internal needs of the sector. Moreover, the definition of “skills” used by the different classifications varies between sectors, while most sectors in Luxembourg have not developed skills classifications at all (ADEM/UEL, 2021[23]).

Stakeholders in Luxembourg have agreed that rather than developing its own common skills classification, ADEM should consider leveraging and adapting existing and internationally interoperable skills classification for classifying its own vacancy data. The ESCO classification (Box 5.4), designed to reflect the specificities of the EU labour market, might be particularly well-suited to Luxembourg's needs. The ESCO skills classification permits establishing direct links with occupational information classified according to the ISCO classification or indirect links with classifications that map onto ISCO (such as ROME), which are used in many EU member states (European Commission, 2021[50]), including Luxembourg.

ADEM would not be the first public employment service to adopt ESCO. In 2018, Iceland became the first country to adopt ESCO on a national level when the public employment service started using ESCO to revamp its job board (Box 5.5). To facilitate skills-based matching, ESCO is now being used by PES in seven countries (Albania, Finland, Greece, Iceland, Ireland, Israel and Malaysia) (European Commission, n.d.[51]). Most recently, ESCO has been adopted by Greece (Box 5.5). Beyond adopting ESCO at ADEM to support skills-based matching, ESCO could be equally used to help systematically define learning outcomes of adult learning courses, which would have important benefits for Luxembourg’s education and training data collection (see the section below on education and training data; also see Recommendation 1.3 in Chapter 2).

However, certain caveats will need to be kept in mind. For example, stakeholders have expressed concerns about the extent to which ESCO accurately reflects the skills requirements of certain occupations (especially occupations heavily reliant on digital skills). Nonetheless, for comparison, the inclusion of digital skills in the O*NET classification is even more limited. In fact, digital skills covered by the O*NET classification are limited to: 1) knowledge of computers and electronics; and 2) programming skills (Lassébie et al., 2021[55]). Stakeholders in Luxembourg have broadly agreed that despite certain shortcomings, ESCO was the most suitable skills classification, which could be adapted for use in Luxembourg’s context.

Luxembourg’s skills data collection includes a variety of education and training data that provides valuable information on Luxembourg’s skills supply (see Table 5.1 and Table 5.2). Going forward, stakeholders consulted during this review agreed that Luxembourg’s education and training data collection could be further expanded, and the granularity of the existing data further strengthened.

Data on education and training outcomes help shed light on the alignment of the education and training offer and the demands of the labour market (OECD, 2016[27]), helping policy makers to better tailor the education and training offer to labour market needs while supporting individuals in making informed skills choices (see Chapter 3 for an extended discussion on guiding and incentivising skills choices in Luxembourg). The key opportunities for strengthening Luxembourg’s data on education and training outcomes exist at the levels of higher education (HE) and adult learning.

At the HE level, graduate tracking is overseen and implemented by the University of Luxembourg. UoL is Luxembourg’s only public HE institution, with the UoL student population representing 99% of the total student population in higher education in Luxembourg (Eurostat, n.d.[56]).5 Since 2018, UoL has been collecting data on graduates’ (former master's and bachelor’s students) outcomes. UoL uses a graduate survey, implemented six months after graduation, asking graduates about their employment status, earnings, perceived skills mismatch, and use of skills on the job, among others. The survey is extended to all graduates (former master's and bachelor’s students). Around 30% of contacted graduates typically respond. As of 2022, UoL planned to include PhD graduates in the survey as well.

In 2020, UoL expanded its graduate tracking exercise by conducting a one-off “employment study” of graduates’ employment pathways using LinkedIn data. Based on LinkedIn data and anecdotal evidence from graduates’ supervisors, UoL was able to collect information for approximately 55% of its graduates (former master's and PhD students) having graduated between 2015 and 2019. UoL assembled a team of consultants who manually checked graduates’ LinkedIn profiles, while academic supervisors provided insights on graduates' employment pathways based on their knowledge. The evidence from academic supervisors was checked via desk research and only used if validated. Information on bachelor’s students was not collected due to time and resource constraints.

While UoL’s graduate tracking efforts are steps in the right direction, more could be done. For example, current graduate tracking efforts do not make it possible to gather data on how long it takes HE students to find a job post-completion of their studies, which could be useful (not only) to better facilitate the transitions of former international students into the labour market (see Chapter 4). It is also important to note that UoL’s survey results rely on self-reported information by graduates, while the survey’s response rate could be further increased. Moreover, the use of LinkedIn data for the purposes of the employment study can impact the representativeness of the graduate tracking results. Evidence suggests that low-income individuals and those outside of the workforce tend to be under-represented on LinkedIn, while LinkedIn tends to be used frequently by individuals in knowledge-intensive sectors such as management, marketing, HE and consulting (Blank and Lutz, 2017[57]; van Dijck, 2013[58]). In addition, via the employment study, UoL gathered information on graduates’ first and current job titles and employers through LinkedIn, missing out on collecting other valuable data, such as information on earnings. As of early 2022, UoL does not plan to repeat the employment study.

Going forward, UoL could consider using administrative data for the purposes of graduate tracking and combining it with existing survey data (OECD, 2019[59]). Administrative data can provide a longitudinal view of graduates’ outcomes and could provide evidence on the time graduates take to transition into the labour market. Box 5.6 details how England (United Kingdom) combines administrative and survey data in its graduate tracking of HE graduates. In Luxembourg, administrative data (e.g. CCSS data) could provide comprehensive information (e.g. on the employment status, sector and earnings of graduates),6 at least for graduates who have remained in Luxembourg following the completion of their studies.7

Presently, the use of administrative data for graduate tracking in Luxembourg is precluded by the lack of necessary legal basis, which is required under the EU General Data Protection Regulation (GDPR). However, in the context of the development of the NDEP (see Opportunity 2), Luxembourg is aiming to grant the necessary legal basis to the NDEP, thereby making the use of administrative data possible for graduate tracking purposes.

Should NDEP be capable of establishing connections to administrative databases of other countries, especially in the Greater Region (see Recommendation 4.14), the collection of information about outcomes of HE students receiving financial aid from the Government of Luxembourg but studying outside Luxembourg8 or graduates who had studied in Luxembourg but left the country after the completion of their studies, could equally be facilitated.

To complement administrative data with information that is not available in administrative databases (e.g. how useful the university experience was in preparing graduates for the labour market, skills used on the job, etc.) (OECD, 2019[60]), UoL could link administrative data to its existing graduate survey by using unique identifiers. It could also consider expanding the survey to gather information from former international students about reasons for leaving Luxembourg post-completion of their studies in order to support efforts to better foster the transition of former international students into Luxembourg's labour market (see Chapter 4). LinkedIn data could be considered as an additional source of information to cross-check the UoL graduate survey and administrative data and fill potential gaps in the latter two.

While there is space for improving Luxembourg’s data on education and training outcomes of HE graduates (see above), there are no measures in place for tracking the outcomes of graduates from adult learning, either publicly or privately provided.

Tracking the outcomes of adult learners is a challenge in many OECD countries. Nonetheless, several relevant international examples could inspire Luxembourg. In Denmark (Box 5.7), data on all publicly provided adult learning courses are collected in a centralised course register and can be combined with administrative data thanks to a unique identifier number (similar to the identifier number of Luxembourg’s National Registry of Physical Persons), making it possible to gain detailed information on the course participants’ outcomes. In Portugal, accredited providers (both public and private) of adult learning courses delivered in the framework of Portugal’s National Qualifications System (SNQ) are required to register the training activities of individual learners in the Integrated Information and Management System for Educational and Training Supply (SIGO). SIGO issues a certificate upon training completion to the learner and automatically registers training information in the learner’s own educational and training record (the Qualifica Passport) (Box 5.7). Luxembourg could create a centralised training register similar to Denmark’s and Portugal’s. To obtain information on learners’ outcomes following the conclusion of training, information in Luxembourg’s centralised course register could be linked to administrative data (e.g. CCSS data on employment status, sector, earnings, etc.) through the NDEP (see Opportunity 2). When initiating the tracking of outcomes of adult learners, Luxembourg could restrict the exercise to collecting data on participants in continuous professional development (formation professionnelle continue) and professional retraining (reconversion professionnelle) courses, rather than personal development (développement personnel) courses, as the latter are not of direct labour market relevance.

After exploring the use of administrative data for the purposes of adult learning graduate tracking, should Luxembourg wish to collect additional information not available in administrative datasets (e.g. job progression, use of skills on the job, etc.), a survey of adult learning outcomes could be considered. For example, France implements a survey of adult learners’ outcomes every year (Box 5.7). In Luxembourg, INFPC and STATEC could collaborate on designing and implementing such a survey.

Finally, Luxembourg’s education and training data collection would benefit from more granular and structured information on skills developed in adult learning courses. Clearly outlining the skills that individuals can expect to gain from a specific course and defining the skills using a “common language of skills” (i.e. following a common skills classification) would facilitate the interoperability of modular training across adult learning providers (see Chapter 2); support adult learning providers in requesting the alignment of non-formal qualifications with one of the levels of the Luxembourg Qualifications Framework (Cadre luxembourgeois des qualifications, CLQ) (MESR/MENJE, 2020[67]); and help develop automatic tools for guiding individuals towards the training courses most suitable to their needs. For example, the Luxembourg Institute of Science and Technology (LIST) has developed a skills e-assessment tool, Cross-Skill, allowing individuals to identify the skills gaps they would have to fill to move between different jobs (LIST, 2022[68]). Should LIST have access to a catalogue of training courses with learning outcomes defined in terms of specific skills, the results of such an e-assessment could be used to suggest to individuals the training courses best matched to fill their skills gaps.

Luxembourg’s online portal for lifelong learning (lifelong-learning.lu at www.lifelong-learning.lu/Accueil/en), managed by the INFPC, offers adult learning providers the opportunity to register on the portal and present their courses to the public. All providers registered on the lifelong-learning.lu portal should define the “objectives” (i.e. learning outcomes) of their courses. However, the level of granularity with which adult learning providers describe the skills that individuals are expected to develop in their courses varies widely, as there is no common definition of skills that providers follow in their descriptions.

Going forward, Luxembourg’s adult learning providers should be incentivised to upload their training offer and describe its learning outcomes on the lifelong-learning.lu portal. Accreditation of adult learning courses could be made conditional on publicising the training offers on lifelong-learning.lu, and on describing the course learning outcomes in a non-structured manner (i.e. not having to follow a skills-based occupational classification) yet in sufficient detail (e.g. by setting a minimum word count requirement) (see Chapter 2). Luxembourg could then consider developing an information technology (IT) tool, integrated on lifelong-learning.lu, which could use text mining and natural language processing (NLP) techniques to help providers articulate the skills developed in their programmes in a structured manner. The IT tool would analyse the non-structured descriptions of course learning outcomes specified by providers and suggest skills best suited to reflect the course content, which the providers could check and validate. The generated skills suggestions would appear on the lifelong-learning.lu portal, together with the non-structured descriptions. The generated skills suggestions should follow a common, internationally recognised skills-based occupational classification. For example, should ADEM adopt the ESCO classification for classifying its own vacancy data (see Recommendation 4.5), ESCO would be well-suited to help adult learning providers annotate the learning outcomes of their courses with specific skills, too.

A strategic and co-ordinated approach to collecting and managing public and even private data are important for taking full advantage of the social, scientific, economic and commercial potential of data resources. Skills data (i.e. labour market and education and training data, among others) is no exception. All too often, skills data, whether public or private, are collected in an uncoordinated manner by the various actors gathering data for their own internal needs. However, data are of limited value if they cannot be cross-referenced with other data from different sources. The usefulness of collecting data could be further enhanced by integrating them with different types of data sources. If skills data are collected and used in silos because their existence is not known or their accessibility is limited, the return on investment made in generating these data is suboptimal.

Data producers, including governmental institutions, can foster synergies between existing skills data by making relevant datasets available for use within the government or for free use by non-governmental stakeholders through "open data" or existing data platforms. However, several barriers need to be addressed to facilitate effective data co-ordination and exchange. Such barriers include: the lack of data collected based on international data standards (semantic interoperability); the lack of standards and technological solutions for data interoperability (technical interoperability); the lack of data documentation; the lack of metadata; the lack of reusability; the lack of tools to assess and optimise the quality of available data; difficulties in assessing the value of datasets; the risk of losing competitive advantage; and intellectual property risk or risk of privacy violation (OECD, 2021[69]; European Environment Agency, 2019[70]).

Stakeholders in Luxembourg have highlighted that Luxembourg lacks a co-ordinated approach to skills data collection and management. There is low awareness of all the existing skills data sources, as well as their scope and accessibility criteria, which limits the extent to which they can be fully leveraged to inform the design and implementation of Luxembourg’s skills policies. Efforts for improving Luxembourg’s skills data collection and use similarly often occur in an uncoordinated fashion. In addition, skills data exchanges between government institutions and stakeholders remain limited, not least because laws and technical infrastructure allowing for such exchanges have yet to be introduced.

Moreover, Luxembourg is not taking full advantage of international skills data sources, especially those of neighbouring countries. However, exploring synergies with international data sources seems crucial for Luxembourg, given the cross-border nature of its labour market, and could help Luxembourg assess the potential skills supply and demand in the Greater Region.

Luxembourg could better foster co-ordination and synergies between skills data within and beyond its national borders by:

  • strengthening the co-ordination of labour market and education and training data flows within government and with stakeholders

  • building synergies between Luxembourg’s and neighbouring countries’ data sources to improve the skills data availability for the Greater Region.

Luxembourg does not currently have a single government body with a specific mandate to collect data on skills and co-ordinate all the actors that collect data on skills demand and supply. The institutions working on skills data currently operate with different working definitions, methods and practices that reflect their different mandates.

In Luxembourg, co-ordination can be improved between four existing “levels of institutions” involved in the skills data collection process: 1) the national statistical office (STATEC) that has the authority to access and co-ordinate all data in the country; 2) the institutions (ADEM, CCSS/IGSS and INFPC) that collect key data on the labour market and training (with limited interoperability); 3) RETEL, which commissions new, and centralises existing, labour market research in Luxembourg (with limited capacity to carry out its role and collaborate with other institutions); and 4) stakeholders (e.g. CC, CdM, federations, private training institutes, etc.) that are collecting information on training participation in their own training courses, or employers’ skills needs.

In Luxembourg, STATEC co-ordinates national reporting efforts with respect to labour market indicators (e.g. Eurostat, LFS) but does not have a specific mandate to collect data on existing skills in Luxembourg or the Greater Region area or companies’ skills needs. STATEC has the authority to access any government data sources within Luxembourg as long as it operates within GDPR boundaries. However, STATEC does not compile skills data collected by stakeholders (e.g. on training participation courses organised by stakeholder associations, on employers’ skills shortages, etc.). Such stakeholder data are not currently centralised nor accessible for use by government authorities or other stakeholders. Furthermore, there is no central strategy, mandate or tool to assemble different data sources, streamline different data flows or improve skills data interoperability. In view of this, Luxembourg may be inspired by Norway, whose Future Skills Needs Committee supports effective skills data co-ordination and analyses (see Box 5.8).

The ADEM/UEL Trends working group mentioned above is a promising attempt at establishing an informal platform to think collectively about ways to improve information on skills and labour market data through existing stakeholder data sources. This working group, which relies on stakeholder participation, aims to co-ordinate efforts to develop and use skills data (both government and stakeholder) in Luxembourg. It includes representatives from ADEM and the UEL covering several sectors (ABBL for finance, FEDIL for construction and ICT, etc.) and seeks to gain an overview of the various data sources that provide information on skills needs and identify ways to improve these data sources.

The partnership between ADEM and UEL involves stakeholders in developing data on skills demand and supply in Luxembourg. These institutions are mapping existing data sources but have not yet assigned specific responsibilities for data collection and do not yet have a common objective in this regard. In addition, relevant stakeholders like CCSS, MENJE, MESR and STATEC are not currently involved in this project, suggesting that a broader government approach would be desirable.

Similarly, Luxembourg would benefit from an increase of data interoperability and could clarify institutions’ roles in data collection. Currently, skills data exist and are collected in a decentralised way; thus, their potential is underutilised. A government mandate, objectives and other issues related to the co-ordination could be clarified and formalised with a skills data charter (or national skills data strategy), as in the cases of the United States Department of Education or the Worldwide Initiatives for Grantmaker Support (WINGS) philanthropic network (see Box 5.9). While many government services and stakeholders actively promote and develop data collection on skills demand and supply, co-ordination can be improved. This is due to the lack of a clear government mandate on skills data collection and some shared objectives for the relevant institutions.

A national skills data charter, intended as a formal strategic document able to support the national co-ordination of skills data collection, could clearly set out objectives to improve Luxembourg’s skills data governance going forward. The charter could allocate roles and responsibilities to the different key actors in Luxembourg’s skills data system for improving Luxembourg’s skills data governance while leveraging potential synergies. The charter could also be complemented by an action plan that covers specific time periods, sets out the necessary actions to be taken in the short term and envisages those to take in the medium term.

Data exchanges seem to take place only to comply with national and international reporting obligations and on a case-by-case basis to tackle specific coverage or granularity issues. For example, IGSS exchanged data with ADEM and STATEC to learn about their respective issues related to data coverage (see Opportunity 1). However, many data exchange exercises have to be supported by specific agreements (or memoranda of agreement, MOUs) to comply with GDPR restrictions, which limits the possibility of exchanging data even between government agencies. These rules, therefore, limit data exchange between public authorities. The matter could be simplified if data requests by the same agency could be grouped together or if the rights to access a certain type of data could be valid for a certain period of time or could be automatically granted to a requesting institution if the data request matches certain pre-specified requirements. This latter option to simplify bureaucracy would be equivalent to establishing “institutions’ data passports”.

Recently, the Ministry of Higher Education, together with five other ministries, launched a project initiative to set up a common National Data Exchange Platform (NDEP), covering all data sectors and going beyond skills. This project materialised in the form of an Economic Interest Group (EIG) in July 2022. The recruitment of human resources has started, and it is expected that the structure will become operational in the coming months, hence moving the co-ordination of national data production in the right direction.

The NDEP project involves six main government entities (MESR, Ministry of State’s Department of Media, Connectivity and Digital Policy, Ministry for Digitalisation [MinDigital], Ministry of Finance, Ministry of the Economy and Ministry of Social Security’s IGSS) as well as the Luxembourg Institute for Health (LIH) and LISER. It is also planned that UoL and LIST will join in the course of 2022. The NDEP project aims to set up a broad data exchange environment that provides the IT infrastructure and legal basis to facilitate data sharing between government agencies while complying with GDPR requirements. This intragovernmental partnership is currently working on refining the list of participating authorities, setting out the list of priority fields that would benefit from this platform (the top priority is health, followed by energy data) and developing the legal framework. The Ministry of State is involved, as it is responsible for the publication of the government’s open data, while the other government partners have transversal responsibilities related to data governance. Other stakeholders, such as STATEC, although not part of the governance structure, will be involved in the operational activities.

The NDEP is a promising project that could help facilitate Luxembourg’s skills data exchanges. The inclusion of skills as a priority for data exchange (in addition to health and energy), covering both government and stakeholder skills data on the NDEP, should thus be considered. A similar project in Estonia led to the harmonious integration of data sources of many government agencies. It could be examined as a possible model for Luxembourg to follow (see Box 5.10).

The authorities participating in the development of the NDEP should analyse the potential of working with guichet.lu, the official IT tool to carry out tasks with Luxembourgish administrations. The guichet.lu (https://guichet.public.lu/fr.html) platform is managed by the recently created MinDigital and the State Information Technology Centre (CTIE). It has the double function of providing information on public administration services to Luxembourgish citizens, residents and workers and of allowing them to carry out a number of administrative tasks. It is not used to collect data, except for the National Registry of Physical Persons, which is the only dataset kept by CTIE. The guichet.lu portal covers all adults resident in Luxembourg and the active population of the Luxembourgish labour market, including job seekers (who, for example, plan to move to Luxembourg). For this reason, for example, the government is using this IT tool to carry out the latest population census (2021). While the platform does not currently collect data, it has the potential and legal basis to access and connect all government data sources with unique identifiers. Therefore, the potential value-added of the guichet.lu platform in facilitating data exchange and interoperability should be considered in developing Luxembourg’s NDEP.

The value of skills data sources increases when they are made available to the broader public. Direct benefits of open skills data can include, for example, reduced costs for firms related to real-time information on the skills supply and faster, improved information on career guidance for students, apprentices, or job seekers, among others. Open data increase the performance and transparency of public sector organisations and can also be a driver for the demand for data and the development of existing and new data sources. Open data can contribute to the development of innovative services and new research models. Moreover, it can help the institutions responsible for data collection make more informed decisions based on existing resources (Carolan et al., 2016[75]; European Data Portal, 2020[76]).

Luxembourg’s Ministry of State is responsible for the publication of open government data. At least some labour market and skills data are already public. The data.public.lu portal (https://data.public.lu/) currently displays a number of datasets and data flows covering several domains, such as COVID-19, public transport, etc. ADEM also shares key labour market data updated each month through this portal. However, the user-friendliness of the data.public.lu could be improved to allow for better access and the analysis of complex statistics, as, at the moment, the portal is not meant for the general public but for specific users who are comfortable with this type of open data in conducting their own analysis.

While currently co-ordination between the relevant authorities seems to be the main priority, making part of the skills data exchanged via the NDEP available for open data publication for the broader public (e.g. skills shortages and mismatches) could increase the value of data collections. If the government were to make further efforts in this direction to increase the value of existing data on skills and the labour market – and collect new data – Luxembourg could envisage greater adherence to open data principles and support further open skills data publication. New Zealand is an example of a government that has embraced a cross-government open data approach and adopted an open data charter (see Box 5.11).

The economies of Luxembourg’s Greater Region are characterised by a high level of economic interdependence, including aspects relating to labour, employment, skills demand and supply. This applies to workers commuting between the four countries and businesses operating both nationally and across borders.

In the Greater Region, it is therefore important to make information on residents, workers and businesses more readily available to relevant government partners to perform routine checks, provide work permits and maintain labour market and skills data quality across borders. Furthermore, governments (and governments’ data flows) need to keep pace with the increasing economic and market interdependence of the Greater Region, aiming to better estimate skills outflows and inflows in addition to skills shortages, demand, supply and mismatches.

Similarly, given that Luxembourg draws its labour supply from the Greater Region, it is necessary to consider the skills supply and demand within the Greater Region and not only that of Luxembourg when designing and implementing Luxembourg’s skills policies. Although Luxembourg is a net labour importer, significant numbers of workers cross borders from Luxembourg and to Luxembourg every day (see Chapter 4). A large share of Luxembourg’s workforce and job seekers (potential workforce) resides in Lorraine, Wallonia, Rhineland-Palatinate and Saarland. Skills that are not available in the Luxembourgish labour market may be available beyond Luxembourg’s borders and within the Greater Region. Similarly, the Greater Region’s skills demand should be considered in addition to Luxembourg’s own. Skills that may be in demand in Luxembourg may also be in demand in its neighbouring regions, impacting the types of skills that Luxembourg can expect to draw upon in the future.

Luxembourg should thus further explore how it can draw upon data on skills and labour supply from Luxembourg’s Greater Region (Saarland, Lorraine, Luxembourg, Rhineland-Palatinate and Wallonia). For example, France’s Occupations’ Observatories (Observatoires des Métiers) and the Lorraine ALFOREAS-IRTS Observatory undertake work on skills data, skills shortages and skills needs anticipation in their respective regions. France’s Ministries of National Education, Early Childhood and Sports and of Higher Education and Research collect data on graduates’ profiles, including on their fields of study. In addition, the French Pôle Emploi Grand-Est collects data on job seekers, their skills and training. Similarly, Forem (Wallonia’s Employment Service) and the Brussels Employment Observatory regularly publish analyses of occupations and skills shortages and work on skills needs anticipation. Germany’s federal and state employment agencies collect and publish labour market indicators by qualifications. It would be important that Luxembourg, as well as its neighbours in the Greater Region, have ways of securely accessing and sharing such data with each other.

The Interregional Labour Market Observatory (IBA-OIE) regularly gathers already-existing information from the main data-collecting institutions in Belgium, France, Germany and Luxembourg and publishes indicators on the situation of the employment market in the Greater Region. IBA-OIE was created in 2001 as a network of institutes specialised in the field of employment: the INFO-Institut (advocacy and research institute), ADEM, LISER, France’s Cross-Border Mission of the Grand-Est Regional Council, Grand-Est EURES/Cross-border Resource and Documentation Center, Belgium’s Ostbelgien Statistics and Walloon Institute for Evaluation, Forecasting and Statistics. IBA-OIE is an institutional user of labour market data in the Greater Region. However, it does not have a specific mandate to work on skills data (e.g. sectoral shortages/excess studies) or to develop data collection. However, it represents a good example of an institution that exploits synergies between neighbouring countries’ institutions and whose focus is Luxembourg’s Greater Region rather than limiting its scope to the national borders.

In this context, a promising project has been developed in Luxembourg by LISER and its Data Centre, which are working on establishing a “safe room” to access Belgium, France, Germany and Luxembourg’s administrative data on workers and firms. The setup of a data room would consist of a secure space to streamline and simplify organisations’ workflows and to enable them to access each other’s data while meeting compliance standards and ensuring high levels of security. Another project will link administrative data from Germany and Luxembourg, which would enable the analysis of employment profiles of past and current cross-border workers in these two countries. Such efforts could be scaled up and further developed. For example, Estonia and Finland have established systems allowing to exchange data across borders (Box 5.12).

A joint data exchange platform for Luxembourg’s Greater Region could work similarly to how the NDEP plans to function at the national level and build on the existing initiatives like LISER’s “safe room” and IBA-OIE’s work. A cross-border data exchange platform could foster data exchange between each country’s public authorities, integrate the Greater Region’s stakeholders and simplify data requests in relation to GDPR compliance. As this work would involve public institutions from different countries, it would also be more difficult to create the political stimulus to carry out this project: it would therefore need to be led by a single body, corresponding to a public authority or a consortium of public authorities in order for it to function effectively.

Strong governance of skills data is essential to help policy makers and stakeholders navigate the complexity and uncertainty associated with the design and implementation of skills policies. Two opportunities have been selected indicating where the governance of skills data in Luxembourg can be strengthened:

  1. 1. improving the quality of Luxembourg’s skills data collection

  2. 2. strengthening co-ordination of, and synergies between, skills data within and beyond Luxembourg.

This chapter presented 15 recommendations to seize these opportunities in the area of skills data governance. A high-level overview of the recommendations can be found in Table 5.3. This selection is based on input from a literature review, desk research, discussions with the Luxembourg National Project Team and broad engagement with a large variety of stakeholders, including two workshops in Luxembourg and various related meetings and group discussions.

Two recommendations have been selected that could be considered to have the highest priority based on potential impact, relevance in the current context in Luxembourg, as well as overall support for implementation. To strengthen the governance of skills data, the OECD recommends that Luxembourg, bearing in mind its administrative capacity, should:

  • Improve the accuracy of occupational social security data by creating targeted incentives for employers, strengthening existing guidance tools for identifying the correct occupational codes, and conducting targeted awareness raising (Recommendation 4.1).

  • Develop a national skills data charter and an action plan with clear roles, responsibilities and procedures for government and stakeholders to co-ordinate improving the relevance and quality of skills data in Luxembourg in the short and medium term. (Recommendation 4.10).


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← 1. Integrated information systems are systems that collect and manage the data and information that governments and stakeholders produce, analyse and disseminate to ensure that policy makers, firms, individuals and others have access to accurate, timely, detailed and tailored information. Relevant data and information include, among others, the results of skills assessment and anticipation exercises, information on where to access learning opportunities, as well as information from evaluations of public policies (OECD, 2019[1]).

← 2. The Statistical Classification of Economic Activities in the European Community, abbreviated as NACE, is the classification of economic activities in the European Union (EU); NACE is a four-digit classification providing the framework for collecting and presenting a large range of statistical data according to economic activity in the fields of economic statistics.

← 3. The ROME is the "Operational Directory of Trades and Jobs", which was created in 1989 by the ANPE (French National Agency for Employment). It is mainly used for classification and identifying trades based on associated skills. The ROME code is often used by administrations, employment services to classify occupations, announcements and requests from employers.

← 4. Skills assessment and anticipation (SAA) exercises are tools to generate information about the current and future skills needs of the labour market (skills demand) and the available skills supply. SAA exercises include general labour market information systems, sectoral/occupational/regional studies and forecast-based projections, among others (OECD, 2016[27]).

← 5. In addition to the graduate tracking undertaken by the University of Luxembourg, certain private higher education institutions in Luxembourg undertake their own graduate tracking studies. In 2022, the Luxembourg National Research Fund also completed a one-off tracking study of PhD students in public-private partnership programmes.

← 6. CCSS occupational data could also be used for the purposes of graduate tracking, conditional upon improving the quality of the CCSS occupational data (see Recommendation 4.1).

← 7. The results of the “employment study” carried out by the University of Luxembourg (see more above) show that 53% of master’s graduates from Luxembourg, 52% of master’s graduates from the European Union (excluding Luxembourg) and 49% of master’s graduates from third (i.e. non-EU) countries have found employment in Luxembourg following graduation. The figure stands at 38% (Luxembourg), 31% (European Union excluding Luxembourg) and 36% (third countries) for PhD graduates during the same time period. Lower-bound estimates are used in both cases due to missing information in the UoL employment study (see more above).

← 8. In Luxembourg, both resident and non-resident students (full-time or part-time) in higher education (HE) are eligible for financial aid for higher education (AideFi), subject to certain conditions. For example, a non-resident student is eligible for AideFi as long as the student’s parent(s) has been working in Luxembourg for at least five years over a ten-year reference period preceding the AideFi application, or for at least ten years at the time of the application (Government of Luxembourg, 2021[80]). Stakeholders have indicated that tracking the outcomes of HE students receiving AideFi but studying outside of Luxembourg is a challenge.

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