3. Measuring digital skills and anticipating future needs

The advent of new technologies and their rapid spread across economies and societies have been accompanied by an increasing demand for digital skills. As the nature of jobs is swiftly changing and skills needs are evolving: recent LinkedIn data estimated the skillset for jobs to change by approximately 50% by 2027, with some competences becoming redundant in the age of automation and others, particularly digital ones, gaining traction (Roslansky, 2022[1]). These trends have resulted in skills shortages in many countries and point to the need for policies to adapt: the World Economic Forum, for instance, estimated that over 1 billion people would need to be reskilled by 2030, especially in the areas of ICT and interpersonal skills (Zahidi, 2020[2]). In that regard, systems to assess the current and future supply of and demand for skills, and to identify and anticipate skills shortages, are a crucial tool to help countries best tailor skills policies to evolving needs and tackle skills mismatches. They can also help firms better identify workforce and training needs (OECD, 2021[3]). The following section will therefore look at 1) digital skills measurement and 2) digital skills needs anticipation.

Data and data governance are critical for evidence-based policymaking. In the case of digitalisation, measuring digital skills, i.e. producing and collecting information on the level of digital skills among citizens and firms, is essential in identifying new demand for skills, designing adequate policies, monitoring the implementation of measures and programmes, and adjusting them as needed (OECD, 2019[4]). It can benefit several policy domains, from employment to education and migration policies. Many countries have implemented a wide range of tools to this end, although these vary in scope, definitions used, frequency, and methods (OECD, 2016[5]).

Moldova has reported an increasing ICT skills gap and low levels of digital literacy in previous policy documents, such as the NDS, but data on digital skills among population and firms, as well as data on uptake and use of technology, remain quite scarce. The National Bureau of Statistics (NBS), the body responsible for data collection, issues information on legal entities that have personal computers or web pages. However, this information does not give a precise understanding of businesses’ usage, as the datasets include public institutions but exclude micro firms, and there is no breakdown by type of entity. The NBS also started collecting data on the number of entities using ERP and CRM software, as well as social media, but these lack breakdown by type of entity (legal form, size). Some additional insights on the use of the Internet in the general population are provided by international databases such as that of the ITU, but, unlike Azerbaijan, Belarus and Georgia, Moldova is not included in the dataset on ICT skills by skills level (ITU, 2022[6]). There is no information available on the use of advanced digital tools by firms (e.g. AI and big data), or on the level of digital skills across the private sector’s workforce. However, a study was recently conducted for the EBRD.

The Digital Moldova 2020 Strategy had envisaged additional efforts in the area of skills assessment, setting it as a policy objective (Government of Moldova, 2013[7]). To this end, the Ministry of Education developed digital competence standards for students and teachers inspired by international standards such as that of the EU, as well as a methodology to evaluate teachers’ digital skills. Assessments of teachers’ digital skills can be conducted upon request; however, they have not been carried out so far. According to information provided by the Ministry of Education, students’ digital skills were not yet assessed either1.

Moreover, the EU4Digital initiative2 has worked on a Methodology for Measuring and Forecasting Digital Skills Gaps in the EaP Countries and published a dedicated study in June 2020 (EU4Digital, 2020[8]). The latter includes stocktaking of digital skills indicators available in each EaP country across several dimensions, covering the use of Internet services and the purpose thereof, the adoption of digital technologies and ICT in education, and ICT use and skills in the workforce. The results show that Moldova is already gathering most indicators in ICT in education (4 out of the 6 benchmarked), but that the country is, overall, lagging its regional peers, collecting only 8 indicators out of the 56 included in the study.

In order to refine the analysis of Moldova’s approach to digital skills measurement and better understand where the country stands in comparison to EU and OECD peers, the OECD has carried out a gap analysis against the indicators included in the OECD’s Going Digital Framework. The framework was launched in 2017 to help governments and stakeholders develop a comprehensive approach to navigate the digital age (see Box 3.1). It assesses the digital transformation through seven interrelated policy dimensions, each of them associated with a set of indicators.

Moldova’s available data were benchmarked against the three dimensions of the Framework covering digital skills indicators, i.e. Jobs, Use, and Society. The exercise revealed that Moldova is already collecting equivalent or similar data for more than half of the indicators included in these three dimensions. Slightly more than half of the indicators available are recent, i.e. available for the last two years, and regularly collected (Figure 3.1).

Regarding the Jobs dimension, Moldova offers insights on ICT specialists by tracking the number of STEM graduates, but data on the labour market remain scarcer and rather limited to the ICT sector (Table 3.1). While Moldova gathers data on legal entities’ expenditures on staff IT training, there is no information on the number of firms providing training or on the beneficiaries. Moreover, Moldova is close to OECD standards in the Society dimension, as it already collects more than half its indicators, thereby providing intelligence on the use of Internet by age, sex and income. The country’s lower score on the Use dimension reveals the lack of information on digital tools’ usage beyond the Internet, especially with regard to firms. However, ODA’s self-assessment questionnaire for firms provides some additional insights on businesses’ use of digital technologies (see below).

The gap analysis above shows that the benchmarked indicators collected by Moldova mostly come from international sources, which allows for comparability between countries. Yet beyond these selected examples, data collected by the National Bureau of Statistics on digitalisation lack a common methodology, not only with the OECD and EU, but also with EaP peers. For instance, Moldova does not have a definition of “digital-intensive sectors” like the OECD but publishes information on employment in the ICT sector. As for comparability with EaP neighbours, EU4Digital highlighted the differences in data collection methodologies used for digital skills related statistics (EU4Digital, 2020[8]). Common definitions and methodologies would help provide better insights into the state of play in Moldova and hence greater evidence for policy-making, in light of other countries’ situation.

More generally, one of the main challenges to skills measurement is the lack of clear, agreed-upon definition of the different job-relevant competencies. This renders the assessment more difficult and costly, and it also creates a potential gap between the skills provided by the formal education system and actual labour market requirements. This issue is widespread and also has an adverse impact on the assessment of skills needs: over three quarters of OECD countries use qualification levels or fields of study to measure skill needs, while only about a third look at specific skills (OECD, 2016[5]). Qualification levels and fields of study can be helpful in estimating skills needs, but they are most relevant where a qualifications framework and/or well-defined and regularly updated occupational standards are in place, offering a clear link between formal qualifications and occupational needs. The ANOFM publishes statistics on job vacancies on a regular basis, which helps with monitoring the demand for IT specialists, for instance. However, no description of the job positions is mentioned; the insights this monitoring exercise provides into the labour market, including the need for generic digital skills, could be refined by defining and analysing the specific qualifications and competencies required for a given position, including digital ones.

Additional data sources exist to provide insight into digital skills needs, such as self-assessment tools. The latter are designed to help individuals evaluate their competences, thereby informing their choices e.g., to undertake additional training and/or relevant steps for career advancement. They can be useful for graduates and the unemployed to help them transition to new jobs, as well as for SME managers and employees to understand the skills they need to go digital. These questionnaires, often offered by employment or SME support agencies, also enable providers to gather data from the respondents’ answers. In the case of Moldova, ODA (ODIMM at that time) has implemented in 2020 a self-assessment tool for SMEs to help them understand their level of digital maturity via questions on different aspects (online presence, e-commerce, client services, digitalisation of processes, and transports and logistics) and identify their needs for training and advisory services. The monitoring of the participants’ results offers valuable insights into Moldovan firms’ uptake of digital tools (e.g. CRM, website) as well as their Internet usage (e.g. use of online marketing in business promotion, customer strategy). However, there is no such tool to help citizens, including SME managers and employees, evaluate their digital skills.

Skills assessment and anticipation exercises can be defined as activities to estimate future skills needs in the labour market “in a strategic way, using consistent and systematic methods” (ILO, 2015[11]). They are a useful practice for policy makers to understand the evolution of skills demand and supply and to implement adequate measures to prevent and/or tackle skills shortages and mismatches, both in terms of formal education, life-long learning and support services to firms (OECD, 2016[5]). These exercises are particularly relevant with regard to the digital transformation, given the rapid pace of technology developments and their impact on economies and societies.

In practice, these tools to anticipate skills needs are manifold, ranging from simple surveys (among employers and/or school/training graduates) to quantitative projections based on macroeconomic modelling. Table 3.2 below provides an overview of the methods that can be used and their respective advantages and disadvantages. A combination of different approaches, sometimes both quantitative and qualitative, is recommended to get a thorough understanding of current and upcoming trends (ILO, 2015[11]). Most OECD countries have more than one type of exercise in place (OECD, 2016[5]). Forecasting exercises, i.e. using the information available to estimate future trends, including skills needs, mismatches or shortages (CEDEFOP, 2008[12]), are particularly widespread, being used in about 90% of OECD countries surveyed in 2016. Foresight exercises, which take a qualitative approach gathering relevant stakeholders to come up with future scenarios, identify priorities and imagine policy actions in response (ILO, OECD, 2018[13]) (ETF, 2014[14]), are less common, with only about half of OECD respondent countries reporting their use (OECD, 2016[5]).

Moldova has successfully implemented some of these anticipation initiatives, although they remain rather scattered, lack consistency, and rarely focus on digital skills.

Although Moldova, like other EaP countries, does not have a specific digital skills forecasting system, it benefits from a labour market forecasting system, appearing as the main anticipation tool implemented so far. The latter is conducted by the Labour Market Observatory (LMO), which is part of the ANOFM and was created in 2018 in an effort to improve analyses and forecasts (ETF, 2021[15]). The LMO prepares yearly short-term labour market forecasts to anticipate the requirements of the labour market and to reduce the gap between the training offered by education institutions and other service providers and the actual needs of economic agents. These analyses are based on a survey of 3000 economic agents, including SMEs and large firms, consisting of 20-30 questions on the evolution of workforce needs, economic activity and investment priorities. They provide valuable information on labour market trends, broken down by region, highlighting obstacles to doing business and upcoming priorities. The LMO also produces a Barometer for Employment Opportunities, which offers forecasts on upcoming surplus and shortage occupations at national and regional level.

However, the forecasting system focuses on professions/occupations and does not provide insights into future needs for specific skills. The data collected lack the detail and reliability needed to allow for well-grounded projections (ETF, 2021[16]). With regard to digital skills, the system in place warned of the lack of IT specialists (software engineers, programmers), reflecting the increasing shortage of advanced digital skills: programmers were among the three fastest-growing occupations in 2021, and their profession is expected to experience the second largest labour shortage in the short-term (Labour Market Observatory, 2022[17]). Yet, the study does not give insights on firms’ need for generic and complementary digital skills. The introduction of a few questions on which skills employers deemed most important in the last edition is a welcome improvement in that sense. Moving forward, this new section should be further developed and forward-looking, to allow for reliable projections.

More generally, while the production of these forecasts is a considerable achievement, the analysis could be improved in several ways. In terms of methodology, the forecasts are confined to the short-term (i.e. the next 12 months), and the sample of enterprises surveyed only includes a limited number of small and medium enterprises; it excludes micro firms – although these account for the majority of Moldovan businesses (see Figure 1.1). The methodology varies from year to year in terms of number of questions and areas covered: the 2021 edition entailed a section on the impact of COVID-19 on economic activity, while the 2022 publication introduced the above-mentioned section on specific skills needed by enterprises (Labour Market Observatory, 2021[18]) (Labour Market Observatory, 2022[17]). With regard to content, the forecasting system could benefit from more detail – e.g., on in-house trainings provided and planned, or on foreseen investments (those in technologies are considered together with equipment and space).

Moldova has also implemented several surveys, run by its National Bureau of Statistics. The latter conducts annual labour force, job vacancy and enterprise surveys, but these lack indicators on digital skills. These surveys occasionally included a graduate tracer study called “School-to-Work Transition Survey”, supported by the ILO Youth Employment project (National Bureau of Statistics, 2015[19]). It was, however, discontinued in 2015. Ad hoc surveys focusing specifically on skills have been occasionally conducted by international organisations such as the World Bank in 2017, which offered a detailed overview of skills gaps (Rutkowski, Levin and Bargu, 2017[20]). ATIC has also implemented surveys on digital skills needs a couple of times over the past ten years, in an effort to anticipate and map skills demand. One of the studies, conducted in 2021 and focusing on ICT students and graduates, highlighted the critical shortage of ICT specialists and the mismatch between the skills taught in the university curriculum and employers’ needs (both ICT companies and firms in “traditional” sectors). Over 80% of ICT graduates applying for junior positions or internships cannot pass the qualification tests, and complementary skills such as problem-solving are widely overlooked (FIDD, ATIC, 2021[21]). The report also underlines the lack of monitoring of graduates’ employment and career development, as well as of assessment of the quality of ICT studies.

Although no standardised and regular assessment is yet in place, these achievements and the stakeholders’ willingness, including from the private sector, to move forward on these topics provide a good basis to build on.

Finally, skills assessment and anticipation tools can be used not only to inform policy making and help design support programmes, such as trainings; they can also be highly relevant for firms themselves. Indeed, one of the main obstacles to businesses’ digitalisation is their lack of awareness of their needs, both in terms of digital equipment and skills for managers and employees (OECD, 2017[22]). Simple tools, such as online self-assessment questionnaires, can help them carry out a diagnosis of their immediate needs, but additional options exist to further support executives in managing their workforces effectively and anticipating upcoming skills shortages. These remain rarer than other above-mentioned tools, especially for SMEs (OECD, 2016[5]); but some initiatives are increasingly being developed, e.g., by the European Digital Innovation Hubs (EDIHs) at the EU level. However, such exercises have not been implemented in Moldova, and ODA’s 2020 self-assessment tool has been discontinued.


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[14] ETF (2014), Frame: Skills for the future. Foresight, https://www.etf.europa.eu/sites/default/files/m/7836A795A2AD6DB8C1257D90003E99BC_Foresight%20guide.pdf.

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[11] ILO (2015), Anticipating and matching skills and jobs, https://www.ilo.org/wcmsp5/groups/public/---ed_emp/---ifp_skills/documents/publication/wcms_534307.pdf (accessed on 26 April 2022).

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[3] OECD (2021), Incentives for SMEs to Invest in Skills: Lessons from European Good Practices, Getting Skills Right, OECD Publishing, Paris, https://doi.org/10.1787/1eb16dc7-en.

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[1] Roslansky, R. (2022), “Here’s why the world of work urgently needs to put skills first”, Centre for the New Economy and Society, https://www.weforum.org/agenda/2022/03/work-skills-first/ (accessed on 20 June 2022).

[20] Rutkowski, J., V. Levin and A. Bargu (2017), Missing Skills: Results of the Skills Module of the Moldovan Labor Market Forecast Survey, The World Bank Group, https://documents1.worldbank.org/curated/en/524971511793797679/pdf/121666-WP-P154573-PUBLIC-Missing-Skills-Moldova-Rutkowski-Bargu-2017-09-21.pdf.

[2] Zahidi, S. (2020), We need a global reskilling revolution – here’s why, https://www.weforum.org/agenda/2020/01/reskilling-revolution-jobs-future-skills/ (accessed on 15 June 2022).


← 1. Information collected through OECD fact-finding exercise carried out in Q2 2022.

← 2. The EU4Digital initiative aims at helping EaP countries tap into the potential of the EU Digital Single Market and accelerate their digital transformation. Launched by the European Commission in 2016, this umbrella initiative carries out a number of projects on different policy aspects such as telecom, trust and security, e-trade, digital security, and eSkills. For more information, see https://eufordigital.eu/.

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