4. Building an effective skills information system in Kazakhstan
An effective skills information system can play a key role in reducing skills shortages and mismatches by providing information on current and future skills needs. Such information can inform individual choices as well as a broad range of policies, from employment to education and training, including policies that address the most vulnerable groups. Timely skills information is particularly important in times of economic crisis characterised by rapidly changing labour markets. This includes the coronavirus (COVID-19) crisis, which might entail heterogeneous effects on skills demand across sectors. This chapter examines the importance of a skills information system in strengthening skills development and use in Kazakhstan. Following an overview of current arrangements and recent performance, it explores three opportunity areas: Strengthening skills assessment and anticipation tools; creating an enabling environment for an effective skills information system; and enhancing the use of skills information to inform policy making and stakeholders’ choices.
A skills information system (SIS) is the set of fundamental arrangements, facilities and procedures that support the collection, processing and dissemination of skills and labour market information. It includes information on current and future labour market needs generated by skills assessment and anticipation (SAA) exercises, as well as information on current vacancies and education and training opportunities for individuals. An effective SIS can allow countries to improve the alignment between skills supply and skills demand, helping to reduce skill imbalances (OECD, 2016[1]).
Skills imbalances comprise skills shortages, skills surpluses and skills mismatches (see Box 4.1). These imbalances can exert a negative impact on overall economic growth, on firms and on individuals (OECD, 2016[1]). Skills shortages can lead to increased labour costs and lost production due to unfilled vacancies. Firms experiencing skills shortages also appear to have fewer opportunities to innovate and to adopt new technologies (OECD, 2016[1]). Skills mismatches can have negative impacts on individuals in terms of their wage, career development and work satisfaction. In addition, a mismatch can result in lower productivity and economic output, reflecting the inability to effectively use the skills that the labour force accumulates through education and work experience (OECD, 2016[1]; OECD, 2019[2]). The estimates available suggest that the aggregate costs of qualifications and field-of-study mismatch can exceed 1% of a country’s gross domestic product (GDP), reflecting the combination of productivity losses and the costs of developing skills that are not used (Mavromaras, McGuinness and Fok, 2009[3]; Montt, 2015[4]).
Skills shortages refer to a situation in which the demand for a specific type of skill exceeds its supply in the labour market at the prevailing market wage rate.
Skills surpluses arise when the supply of a specific type of skill exceeds its demand in the labour market.
Skills mismatches describe situations in which workers’ skills exceed or fall short of those required for the job under current market conditions. They can be measured along different dimensions:
Skills mismatch: When workers have higher or lower skills proficiency than what is required by their job. If their skills proficiency is higher, workers are classified as over-skilled; if lower, they are classified as under-skilled.
Qualifications mismatch: When workers have educational attainment that is higher or lower than what is required by their job. If their qualification level is higher, workers are classified as over-qualified; if lower, they are classified as under-qualified.
Field-of-study mismatch: When workers are employed in a different field from that in which they have specialised.
Source: OECD (2016[5]), Skills Matter: Further Results from the Survey of Adult Skills, https://doi.org/10.1787/9789264258051-en.
An effective SIS can help reduce skill imbalances by allowing sufficient production and dissemination of information on skills and labour market needs. Policy makers can rely on skills information to monitor the potential mismatch between educational outcomes and the demands of the labour market, and to design or revise employment, training and migration policies. Social partners can use this information to provide valuable input to governments on education and employment policy and the development of training programmes or provide advice to their members on skill development. Researchers and data analysts can use primary data to evaluate the impact of skills policies, produce evidence to inform policy design and generate SAA tools. Individuals (e.g. students, jobseekers and workers) can also benefit by being able to access the information needed to make informed career and educational choices (OECD, 2016[1]).
However, in Kazakhstan, just as in other emerging economies around the world, the development of a strong skills information system requires many constituent elements, none of which are readily available. At the technical level, these involve access to detailed skills and labour market databases, as well as of sophisticated analytical tools to elaborate the basic statistical information. At the communication and policy level, they include the capacity to disseminate the information to a wide range of interested stakeholders, which is essential, in turn, to nurture the policy debate. Many OECD countries report that poor statistical infrastructure and lack of knowledgeable human resources are key factors undermining the capacity to implement performing assessment systems (OECD, 2016[1]). In many countries, these difficulties go hand in hand with a lack of co-ordination among relevant agencies.
These challenges have been made even more urgent by the coronavirus (COVID-19) pandemic, which has resulted in rapidly changing work practices, including distancing measures and teleworking, for example. At the same time, the emergence of new employment needs in some services, notably the care sector, has been mirrored by a decline in other activities, such as recreation and hospitality, for example. Regular, up-to-date skills information will help track the impact of these diverse developments on the jobs on offer and the skills expected to match them.
This chapter begins with an overview of existing SAA exercises and the data collection and management system in Kazakhstan and an assessment of the overall performance of the SIS. Then, it identifies three opportunities to build an effective SIS in Kazakhstan, based on desk research and discussions with government and stakeholder representatives (participants) consulted during the OECD Skills Strategy project.
Current arrangements for the skills information system
Public and private institutions carrying out skills assessment and anticipation
In Kazakhstan, the Ministry of Labour and Social Protection of Population (MLSPP) and its research and policy analysis branch, the Workforce Development Centre (WDC), play a central role in generating, managing and disseminating information on labour market and skills needs. The skills analysis that they carry out is by industry and by occupations, and regards both national and regional levels.
The MLSPP is responsible for the formation of skills forecasting and anticipation within the country. It does so by relying on a set of guidelines for the formulation of the national labour force forecasting system. Notably, the recently created National System for Labour Force Forecasting (NSLFF) builds on guidelines updated in March 2019.
There are three main forecast terms in Kazakhstan’s SAA system:
short-term human resources forecast (forecast period: one year)
medium-term workforce forecast (forecast period: five years)
long-term human resources forecast (forecast period: up to 2050).
Several private institutions also carry out skills analysis and disseminate labour market and skills information to the public. For example, HeadHunter, a private online job-matching portal, provides information on changing demand and supply of work positions and average salaries. Some industry associations also analyse short-term skills shortages to inform and support the recruitment needs of their members.
The MLSPP within the framework of the World Bank project ‘Development of Labour Resources and Stimulation of Workplaces’, carries out qualitative skills technology foresight (STF) exercises for nine major industries in Kazakhstan to help disentangle possible future market trends and anticipate which professions and skills will be in increasing demand in coming decades. It provides one example of possible initiatives to assess and forecast skills demand.
Data collection and management to support an effective skills information system
The Bureau of National Statistics conducts surveys (e.g. the Labour Force Survey, Household Survey, census) to collect skills and labour market data. For example, the quarterly Labour Force Survey (LFS) collects some skills and labour market data, including the decompositions of salaries, working hours and unemployment duration by socio-demographic characteristics (e.g. age, region, education level, gender). In addition to the statistical office, the MLSPP and the WDC carry out specific thematic surveys to track the number of graduates or employment growth, for example.
Furthermore, in 2019, the National Chamber of Entrepreneurs (NCE or Atameken) conducted an employer survey at the national level to collect data on short-term skills needs. It was first conducted as a pilot in the Aktobe region in 2018, with the participation of over 5 000 employers. It was then scaled up nationally in 2019 and covered over 600 000 enterprises. The funding for the national survey was provided by the MLSPP. Pending the availability of additional funding, the NCE has plans to replicate the survey in the future. Other private research institutions and sector associations, such as KazEnergy and tourist sector associations, also conduct surveys, interviews and focus group discussions to collect data on current and future skills demands for particular industrial sectors, such as oil, gas, tourism and mining.
There are two main labour market databases in Kazakhstan: the Automated Labour Market Information System (ALMIS) and the Electronic Labour Market Exchange. The aim of ALMIS is to collect and manage regional data and to automate some employment services provided by the MLSPP, local government authorities and the employment centres. For instance, through ALMIS, the employment centres can access pension data to double-check the employment status of potential clients. For its part, the Electronic Labour Market Exchange provides comprehensive information on available job vacancies. This is mainly used by employers who have new vacancies to advertise and jobseekers searching for jobs on line.
Performance in building an effective skills information system
Assessment of the current skills information system
According to stakeholders consulted during the OECD Skills Strategy project, the existing skills assessment and anticipation tools have limitations and do not build a comprehensive picture of current and future labour market needs. Stakeholders expressed doubts about the results of the NSLFF carried out by the MLSPP and the WDC.
Efforts from the private sector are not entirely successful in compensating for these limitations. As foreshadowed in the section above, some sector associations analyse short-term skills shortages for specific sectors to inform industry representatives for hiring and qualifications upgrade purposes. However, the results of these exercises are mainly intended for dissemination among members of the given association, rather than the general public.
To some extent, limited quality and coverage of SAA exercises reflect obstacles to collect and manage high-quality data. In Kazakhstan, labour market and educational data are not collected systematically, or with sufficient frequency or breakdowns by regions and socio-demographic characteristics. For instance, three rounds of workforce data collections were carried out at the firm level between 2015 and 2017 by the MLSPP and WDC. Although these exercises allowed for some inference about the expected short-term changes in employers’ skills needs, and facilitated forecasts of occupations that will be in demand in the future, they have been discontinued since. Furthermore, collected data did not include information on disability status, participation in formal and informal training by adults. or adult learning opportunities, for example. These limitations hinder the production of granular analysis to understand labour market conditions for specific groups of populations or adult learning supply (see Chapter 3).
Efforts to disseminate available skills and labour market information is also limited. Several stakeholders consulted as part of the OECD Skills Strategy project reported that potential users, who could greatly benefit from the information, are not aware of the existence of the information due to lack of advertisement and promotion. Numerous training institutions, policy makers, jobseekers and students are willing to use this information and evidence but do not know where to find it.
Evidence on current skills imbalances
The limitations in domestic data sources and robust skills assessment and anticipation methodologies make it difficult to build a comprehensive picture of skill imbalances in Kazakhstan. The available evidence from international data sources suggests that Kazakhstan experiences substantial shortages and skills mismatches, with negative consequences for firms and individuals.
The recently released World Bank Enterprise Survey data show that more than 30% of firms in Kazakhstan believe that the low skill levels of the workforce is a major obstacle to the performance of their productive activities, which is relatively high by international comparison (see Figure 4.1).
According to the recent Survey of Adult Skills, a product of the Programme for the International Assessment of Adult Competencies (PIAAC) data, in Kazakhstan over 25% of workers feel that they are over-qualified for their tasks, while 10% feel they are under-qualified. Accordingly, almost 35% of workers feel that the tasks that they carry out do not match their level of qualification (see Figure 4.2, Panel A). In addition, the available data show that there are significant skills mismatches by field of study in Kazakhstan. About 40% of workers have a job that is not relevant to their education (see Figure 4.2, Panel B), which is relatively high, compared to the OECD average.
These mismatches result in substantial earning losses for individuals. On average, across the sample of OECD countries for which PIAAC data are available, over-qualified workers earn about 17% less than well-matched workers in the same field and who have the same qualification and proficiency levels. At 19%, in Kazakhstan, the wage penalty for over-qualified workers is somewhat higher. The equivalent wage penalty for OECD countries for over-skilling is 7% and that for field-of-study mismatch is 3%, compared to 4% and 5% for Kazakhstan, respectively (see Figure 4.3) (OECD, 2019[2]).
Building an effective skills information system is crucial for the development of a wide set of policies to help reduce the above-mentioned imbalances. Policy makers can use information generated by the SIS to improve the responsiveness of the education and training system to labour market needs, including by better tailoring the limited funding available. These efforts can be combined with better dissemination of information among students and jobseekers. Better exposure to information on labour market and skills needs can help steer individuals towards career and learning options in high demand in the labour market.
Note: Data for Italy, Kazakhstan, Kyrgyzstan, the Russian Federation (hereafter “Russia”), Turkey and Uzbekistan refer to 2019; Greece to 2018; Sweden to 2014; the Czech Republic, Estonia, Hungary, Poland, the Slovak Republic, Slovenia and Ukraine to 2013; the People’s Republic of China (hereafter “China”) to 2012; and Chile and Mexico to 2010.
The sample for the Russian Federation does not include the population of the Moscow municipal area. The data published, therefore, do not represent the entire resident population aged 16-65 in the Russian Federation but rather the population of the Russian Federation excluding the population residing in the Moscow municipal area.
Source: OECD calculations based on World Bank data (2019[6]), Enterprise Surveys, http://enterprisesurveys.org/en/enterprisesurveys.
Note: For PIAAC data from Russia see the note under Figure 4.1.
Source: OECD calculations based on data from OECD (2019[7]), Survey of Adult Skills (PIAAC, 2012, 2015, 2018), http://www.oecd.org/skills/piaac/data/.
Note: Coefficients from ordinary least squares regression of log hourly wages on mismatch directly interpreted as percentage effects on wages. Coefficients adjusted for years of education, age, gender, marital status, working experience, tenure, foreign-born status, establishment size, contract type, hours worked, public sector dummy, proficiency in numeracy and numeracy use at work. The wage distribution was trimmed to eliminate the 1st and 99th percentiles. The regression sample includes only employees. Statistically significant values (at the 5% level) are shown in a darker tone.
Source: OECD calculations based on data from OECD (2019[7]), Survey of Adult Skills (PIAAC) (2012, 2015, 2018), http://www.oecd.org/skills/piaac/data/.
This section describes three opportunities to strengthen the skills information system in Kazakhstan. The selection is based on input from literature, desk research, discussions with Kazakhstan’s national project team, discussions with stakeholders in workshops in Nur-Sultan and Almaty, as well as virtual meetings involving more than 100 stakeholders. In light of this evidence, the following opportunities are considered to be the most relevant for the specific context in Kazakhstan to build an effective skills information system:
Opportunity 1: Strengthening skills assessment and anticipation tools
Opportunity 2: Creating an enabling environment for an effective skills information system
Opportunity 3: Enhancing the use of skills information to inform policy making and stakeholders’ choices.
Opportunity 1: Strengthening skills assessment and anticipation tools
Rigorous analytical methodologies and instruments underpinning SAA tools are key to producing reliable, informative and relevant information on current and future skill needs. To maximise the utilisation of skills analysis, it is important to ensure that skills assessment and anticipation results are not only reliable and up-to-date but also relevant to the needs of different users (OECD, 2016[1]). However, as foreshadowed in the performance section, Kazakhstan has so far struggled in developing robust SAA tools. Building on these insights, this opportunity aims to provide recommendations on how to strengthen the skills assessment and anticipation exercises through two policy directions, as follows.
Strengthening skills assessment and anticipation methodologies to better identify and support immediate skills and job-matching needs
Both quantitative and qualitative SAA methodologies are used in Kazakhstan. While the MLSPP focusses on quantitative SAA methodologies through the NSLFF, the Atlas of New Profession includes a qualitative approach. The results of discussions with experts in the field are collected into surveys identifying labour market trends, pressing challenges and upcoming skills opportunities and future skills needs. The Atlas of New Profession’s foresight approach builds on the International Labour Organization (ILO)’s methodological framework. Groups of experts from different sectors (mining and metallurgy; oil and gas; energy; agriculture; construction; machine building; information technology; transport and logistics; tourism) engage in a dialogue and develop a conceptual vision of future skills needs. Stakeholders consulted during the OECD Skills Strategy project signalled that there are ongoing discussions within the MLSPP to improve SAA methodologies and further increase their coverage and frequency. Going forward, these discussions could benefit from redoubling efforts in two areas.
First, Kazakhstan should further refine and expand existing SAA tools. It is more important than ever to update skills information regularly and frequently to best respond to the current economic crisis, and track their responses to the ensuing recovery. As mentioned earlier, the COVID-19 pandemic, as well as ongoing digitalisation, have had a significant and rapid impact on skills demand and supply in the labour market (see Chapter 1). To make progress on tracking labour market and skills needs, Kazakhstan should consider reinforcing regional and sectoral skills analysis. This could involve mapping and consolidating existing SAA exercises and evidence in the country. For example, the MLSPP could consolidate evidence from sector associations, which, as discussed in the performance section, do not currently share their results publicly.
Reinforcing regional and sectoral SAA tools could also contribute to strengthening skills assessment and anticipation in the longer term. In effect, since labour market mobility often occurs within sectors or regions, mismatches and shortages observed in one region or level may not exist in others. Conversely, national-level assessments or aggregate data may sometimes overlook specific skills needs in a particular region or sector (Shah and Burke, 2005[8]). Breaking down skills analysis could be particularly important to countries with large regional differences and territorial coverages, such as Kazakhstan.
To strengthen its quantitative analytical tools, Kazakhstan could also take inspiration from the OECD Skills for Jobs indicators and methodology (see Box 4.2). These indicators have strengthened the evidence base on skills shortages and skills mismatches in numerous OECD countries and emerging economies (e.g. Malaysia and Thailand). They provide an overview of relative shortages and surpluses for skills and abilities in the labour market and measure skills mismatches through qualification and field-of-study mismatch indicators.
Furthermore, Kazakhstan could improve the robustness of the SAA results from the NSLFF by combining quantitative and qualitative approaches. Previous studies highlight the importance of adopting an integrated approach for measuring current or future skills needs in order to achieve robust and reliable skills analysis results. Countries generally apply a range of quantitative and qualitative methodologies to infer current or future skills needs, including (but not restricted to) macro-level forecasts, sectoral studies, questionnaires to employers and regional surveys on employment (CEDEFOP, 2008[9]). Making use of both quantitative and qualitative data helps to achieve robust and reliable results as each methodology has different advantages and disadvantages.
Qualitative information may help overcome certain shortcomings of quantitative approaches. For instance, quantitative forecast-based skills projections tend to be comprehensive, but they often fail to produce granular results to effectively inform the choices of employers, students and jobseekers. Furthermore, collecting detailed and reliable quantitative data could be demanding and costly. Quantitative data, such as wages and hours of work, could lack reliability as they tend to be under-reported for certain occupations/sectors with high levels of informality. Analysis based on employer surveys can be easier to carry out, but it implies higher risks of leading to partial results, reflecting subjective replies, selection biases and low response rates. Qualitative analysis methods based on focus group discussions, Delphi style methods, and scenario developments allow for the consideration of a broader range of factors than just economic factors, although results may again be subjective (OECD, 2016[1]).
To exploit complementarities, many countries use quantitative and qualitative approaches in combination, rather than as substitutes (OECD, 2016[1]). This seems to be particularly appealing to emerging economies, which, much like Kazakhstan, have limited quantitative data and rapidly changing labour markets (ILO, 2017[10]). However, the synergies between the quantitative and qualitative SAA methodologies remain largely unexploited. To make progress in this respect, Kazakhstan could learn from the experiences of several OECD countries, such as Sweden (see Box 4.2).
More broadly, Kazakhstan could also benefit from “big data” approaches to monitor changes to skills needs in real time. For instance, analysing changes in online vacancies can be useful to monitor labour market demand, especially in the case of unforeseen shocks, such as that induced by the COVID-19 pandemic.
OECD Skills for Jobs indicators: Shortages, surpluses and mismatches
To analyse the degree of skill shortages and surpluses in countries’ labour markets, the Skills for Jobs methodology uses five sub-indicators to detect occupational shortage/surplus pressure: 1) employment growth; 2) hours worked growth; 3) unemployment rate; 4) change in the share of under-qualified workers; and 5) hourly wage growth. For each occupation, the long-run trends of these indicators are measured relative to economy-wide trends. The five indicators are aggregated into a final occupational shortage index. To gain an understanding of the actual skills that are in shortage or surplus (rather than the occupations), the occupational shortage indicator is translated into a skills need indicator by using information on skills requirements by occupation (from the US Department of Labour’s O*NET database). The final skills needs indicator shows the degree of shortage or surplus for a wide range of skills, abilities and knowledge types.
Skills for Jobs also computes qualification and field-of-study mismatch indicators, measuring the misalignment between a worker’s occupation and his/her qualification level and field of study, respectively. Workers are said to be under-qualified when their highest educational attainment is below the usually observed qualification level in the worker’s occupation. In the opposite case, when a worker’s qualification level is above the standard qualification level in his/her occupation, the worker is over-qualified. Similarly, a worker is mismatched in terms of field of study when the field of study of his/her highest attained qualification does not match the field generally required in the worker’s occupation.
Combining quantitative and qualitative data sources in SAA tools
Several OECD countries have worked to combine quantitative and qualitative data sources, systemically, in the same exercise. Common quantitative sources of information include analyses of labour market information (e.g. flows in and out of employment by occupation and sector, trends in wages by occupation, trends in hours worked by occupation, etc.), vacancy surveys, employer surveys, surveys of recent graduates and administrative data (e.g. data on enrolments in, and graduation from, various levels of education). Qualitative inputs include the information provided by sector skills councils and sector studies in foresight exercises. They also include Delphi methods, which, through iterative and anonymous participation from experts, allow for reaching convergence on possible future scenarios.
In Sweden, enriching quantitative information with qualitative insights about trends and labour market dynamics is key to the Swedish National Institute of Economic Research’s (Konjunkturinstitutet, NIER) approach to skills anticipation. NIER conducts a monthly survey of more than 6 000 businesses to capture sentiments in the Swedish economy. Many questions are future-oriented, touching on softer concepts such as employment strategies or hiring expectations. The purpose is to provide policy makers and stakeholders with an early indication of future trends, some of which would probably not show up in more traditional econometric forecasting models. This information feeds into the more quantitative skills anticipation exercise.
Source: Adapted from OECD (2017[11]), Getting Skills Right: Skills for Jobs Indicators, https://dx.doi.org/10.1787/9789264277878-en; UKCES (2010[12]), Skills for Jobs: Today and Tomorrow, https://www.gov.uk/government/publications/skills-for-jobs-today-and-tomorrow; Konjunktur Institutet (2018[13]), User Guide to the Economic Tendency Survey, https://www.konj.se/download/18.5d694bf9162f539b6bb421ae/1524654654064/Konjunkturbarometern-april-2018.pdf.; CEDEFOP (2017[14]), Skills Anticipation in Sweden, https://skillspanorama.cedefop.europa.eu/en/analytical_highlights/skillsanticipation-sweden.
3.1. Carry out a stocktaking analysis of existing SAA tools. The MLSPP and WDC should build a comprehensive inventory of existing SAA tools, especially at the regional and sectoral level, and analyse advantages and disadvantages to identify gaps in knowledge, possible pitfalls in co-ordination and opportunities to strengthen their methodologies. Changes are being planned by the MLSPP and WDC in this direction. Building on the results of this inventory, the MLSPP and WDC could introduce additional regional/sectoral forecasts and quantitative SAA tools building on the OECD Skills for Jobs indicator. The stocktaking analysis could also benefit from the feedback of the inter-ministerial working group on skills and labour market information (see the following section).
3.2. Adopt an integrated approach by combining qualitative and quantitative methods to achieve robust skills analysis results. Building on the results of the stocktaking analysis, WDC and other skills research institutes could work to combine quantitative and qualitative methods in one exercise, for example by using macro-level forecasts, sectoral studies, job postings data, questionnaires to employers and regional surveys on employment. The MLSPP and WDC could analyse skills using diverse sources, including employer surveys and skills anticipation exercises, in a co-ordinated way. It is advisable to use both forecast and foresight techniques for skills anticipation (e.g. as in the case of Sweden’s NIER).
Producing skills assessment and anticipation results that are more relevant to end users
In many cases, information on skills needs fails to fulfil the key objective of informing choices for policy makers, individuals and social partners. Bringing anticipation exercises closer to the needs and requirements of the end users is difficult in any country (OECD, 2016[1]). Including the voices of all relevant stakeholders in the skills assessment and anticipation process is key to ensuring that SAA exercises define and measure skills in a way that can inform and influence the decision-making sphere. For example, for education and training providers, information about trends for specific occupations may not translate directly into the specific skills and courses or fields of study being promoted (OECD, 2016[1]).
Kazakhstan has considerable room for improvement on this front. For example, the MLSPP could conduct more consultations with the governmental and non-governmental stakeholders to understand their needs and align the design of SAA tools with the expected uses. Several participants in the workshops and meetings during the OECD Skills Strategy project noted that there is limited dialogue when it comes to the production of SAA tools among relevant governmental stakeholders, including the MLSPP, the Ministry of Education and Science (MOES) and regional authorities. Similarly limited is the dialogue among the MLSPP and non-governmental stakeholders, such as educational institutions and employer representatives. This is consistent with the findings regarding other policy areas addressed in this review, such as adult learning (see Chapter 3) and the assessment and monitoring of skills policies (see Chapter 5).
In addition to supporting the design and development of user-relevant SAA tools, engaging stakeholders could also contribute to increasing trust in the SAA results and relevant decision making. Stakeholders would be more likely to support the results of the analysis and their usefulness if they were involved in the process of developing the tools.
Chapter 5 discusses the possibility of establishing an inter-ministerial working group on skills and labour market information to promote co-operation and co-ordination in the production and dissemination of information on skills and the labour market. This body could play a prominent role in gathering feedback from relevant stakeholders on the existing skills assessment and anticipation tools to ensure that they are relevant to the needs of different users. In turn, this could also increase trust in the SAA results. Kazakhstan could take inspiration from OECD country examples, such as Estonia and France, to understand how to promote effective dialogue within the inter-ministerial working group (see Box 4.3).
Consolidation and dissemination of SAA tools: Examples from Estonia and France
A variety of mechanisms have proven successful in helping to produce SAA tools relevant to the needs of end users, including working groups (e.g. the inter-ministerial skills working groups in the United States) or roundtables with specific objectives and timelines (e.g. collaboration across regional/sub-regional administrative levels in the Netherlands). Sector skills councils (e.g. in Canada, the Czech Republic and the United Kingdom) and independent bodies such as national skills advisory groups (e.g. in Denmark, Finland and Germany) can also assist stakeholders in providing relevant information.
Estonia introduced two laws in 2015 to improve the governance of skills anticipation. The new arrangement established that the System of Labour Market Monitoring and Future Skills Forecasting (Oskuste Arendamise koordinatsioonisüsteem, OSKA) provides skill forecasts in five sectors every year. Based on quantitative forecasts and qualitative insights developed by sectoral expert panels, the OSKA Co-ordination Council publishes a yearly analysis of labour market trends and skills needs. Representatives from employers and trade unions are involved in the co-ordination council that oversees OSKA, alongside ministries and the Estonian unemployment insurance fund. Education providers are also involved in sectoral expert panels, while experts from universities and professional associations sit on the OSKA Panel of Advisers (a body guiding methodological discussion and reflection). The whole system is administered and developed by the Estonian Qualifications Authority (Kutsekoda) and is co-funded by the European Social Fund. The OSKA Co-ordination Council publishes the main findings by sector on a portal (https://oska.kutsekoda.ee/) and issues recommendations for policy makers, educational institutions and employers.
In France, Strategy France (France Stratégie), a government think-tank, is developing an Employment Skills Network (Réseau Emploi Compétences, REC) to better co-ordinate various skills analysis exercises results from different actors. It engages with a variety of stakeholders, including the Prime Minister’s Office and the research branch of the Ministry of Labour (France Stratégie, DARES); regional observatories (Observatoire Régional Emploi Formation, OREF); the Public Employment Service (Pôle Emploi); sectoral observatories; and employer organisations (e.g. Mouvement des Entreprises de France (MEDEF)’s Tendance Emploi Compétence).
Source: OECD (2016[1]), Getting Skills Right: Assessing and Anticipating Changing Skill Needs, https://doi.org/10.1787/9789264252073OECD; OECD (2019[15]), Getting Skills Right: Creating Responsive Adult Learning Systems, www.oecd.org/employment/emp/adult-learning-systems-2019.pdf; OECD (2020[16]), OECD Skills Strategy Slovak Republic: Assessment and Recommendations, https://doi.org/10.1787/bb688e68-en.
3.3. Promote dialogue among relevant ministries, sectoral and regional experts throughout the process of production of SAA tools. The MLSPP can continue to play a central role in producing skills analysis. The proposed inter-ministerial working group on skills and labour market information (see Chapter 5) could become a forum to promote dialogue on SAA tools, both between the MLSPP and MOES, as well as multiple stakeholders such as regional and sectoral experts. Through clear and pre-established procedures, the inter-ministerial working group on skills and labour market information could provide a platform to aggregate feedback from diverse stakeholders. It is important to ensure that sufficient region- or sector-specific input and feedback are received across relevant ministries and regional or sectoral experts to produce detailed and relevant skills analysis results.
Opportunity 2: Creating an enabling environment for an effective skills information system
Creating an enabling environment is crucial to establish and operate an effective skills information system. Importantly, this requires access to timely and high-quality labour market and skills data. Without prompt and secure access to such data, it becomes difficult for researchers and policy makers, for example, to evaluate the impact of skills policies, generate new evidence to inform policy development or carry out SAA exercises. However, data per se will not suffice. They need to be processed and analysed to generate meaningful insights. This depends on having human resources with abundant knowledge and skills in qualitative and quantitative data analysis. As foreshadowed in the performance section, Kazakhstan has so far struggled across both dimensions. To create an enabling environment for an effective SIS, Kazakhstan could therefore undertake two policy directions, as follows.
Improving the quality and accessibility of data on skills and the labour market
A wide range of data is important for research and analysis on skills policies. Useful data typically include skills, labour market and education data, such as, for example, flows in and out of employment by occupation and sector, trends in wages by occupation, information on career and learning opportunities (see Chapter 3) and enrolments in and graduation from various levels of education. These data can come in different types, each with different advantages and disadvantages (see Table 4.1). Reliable and consistent data series from household and business surveys and administrative records generally make an important contribution. Qualitative data can enrich the evidence base, for example, by addressing problems and concerns more subtly and in greater depth.
As described in the current arrangements section, in Kazakhstan, like in many other countries, the Bureau of National Statistics and the public employment services (PES) contribute through the compilation of regular labour force surveys, household and census statistical surveys, and vacancy and jobseeker data. The PES also oversee the Enbek digital platform, which is a rich database that houses data from the employers and jobseekers registered at employment centres. The Electronic Labour Market Exchange platform accumulates vacancy information from both state and private hiring agencies and online platforms. It has information about jobseekers, their qualifications, youth who are not in employment, education or training (NEET), members of young families, large families with multiple children close to poverty levels, people with disabilities and other vulnerable groups receiving financial assistance. There have been efforts to integrate the Enbek platform with other government platforms, such as the enterprises database and the pension system, for example. Although the benefits of these efforts are potentially important for long-term forecasting, which require longer data time series and microdata sources, the timeline for finalisation of the integration process remains unknown, at the time of writing.
More broadly, there is a general perception, including within the MLSPP and WDC, that insufficient coverage, frequency and continuity of data collection in Kazakhstan are key obstacles preventing research on skills and the labour market, including the realisation of robust SAA. It was noted that the skills, labour market and education data from statistical surveys are not collected systematically and do not include sufficient and in-depth information on the socio-demographic characteristics of respondents. For example, the current labour force survey or employer survey questionnaires do not include questions on disability status or work satisfaction, making it difficult to analyse labour conditions and develop evidence-based policies for specific groups of people. In addition, there are no comprehensive data on participation in formal and informal training by adults, which limits the capacity to assess gaps in the adult learning supply (see Chapter 3). The OECD requested data about adult learning opportunities in the country but did not receive them as they do not seem to be gathered at present.
The frequency and continuity of data collection could be further strengthened. Several surveys cannot be updated on a regular basis, reflecting a lack of funding, which undermines the relevance and continuity of information available. For example, the annual employer survey that the MLSPP and WDC launched in 2015 to collect data to assess short-term skills needs was discontinued after the third round (in 2017). Subsequently, NCE took over this responsibility and launched a large employer survey in 2019. However, it remains uncertain whether there will be other rounds in the future. Such discontinuity in data collection hampers building time-series data, which limits the available scope of the research to generate long-term skills forecasts and analyse labour market trends and prospects.
Once data are collected, it is crucial to facilitate access so that relevant research institutes and bodies can utilise them (see Box 4.4). Stakeholders consulted during the OECD Skills Strategy project stressed that in Kazakhstan, there is limited access to the microdata relevant to research on skills and labour market issues (e.g. LFS, business surveys and household surveys). The researchers who can apply to access the microdata must be affiliated to a limited number of eligible research organisations. Their special requests must undergo a cumbersome administrative acceptance process, which can take up to six months. Many countries face challenges to facilitate access to microdata, as they are often subject to a range of legal and technical restrictions: binding data user agreements, data confidentiality and non-disclosure rules, unavailability of “safe rooms” for hosting confidential data in user organisations, etc. In order to overcome these barriers and facilitate the use of large survey and administrative datasets administered by national authorities (i.e. national statistical offices, ministries, administrations), a number of OECD countries are reflecting on and implementing functional models to centralise the storage of large datasets, ensure data confidentiality and provide remote access to a large user community (see Box 4.4).
Labour market information collection: An example from the Netherlands
Skills assessment in the Netherlands includes various instruments and data sources, such as surveys from statistical offices, other surveys that are conducted with graduates entering the labour market, and administrative data from PES, the education ministry and social security insurance contributions. Various bodies and skills councils contribute qualitative (and additional quantitative) material to assist in understanding detailed problems. Long and medium-term forecasting models assess skills imbalances by detailed occupation and qualification (a combination of field and level) using survey data. There are also tracer studies of all major training programmes that elicit school-to-work transitions from recent graduates (one to two years after graduation). Several sector studies are also conducted within the same organisation. The PES generate several short-term, specific labour market forecasts and analyses, including vacancies. Within sector councils, ministries and tripartite policy groups, labour market or education and training system responses are discussed and incorporated into recommendations.
Improving access to research data: Examples from Sweden, the United Kingdom and France
In Sweden, digital services and application programming interfaces (APIs) facilitate access to labour market statistical information. The ambition of the Swedish Government’s digital agenda is to improve conditions for the reuse of public information from government agencies for both commercial and non-profit purposes. Companies and private individuals have access to Statistics Sweden’s statistics. A large part of this information is available for use in new products and digital services developed by third parties. Data from Statistics Sweden’s Statistical Database can be digitally transferred via an API, allowing the use of tables or selected information from Statistics Sweden’s Statistical Database to build applications for smartphones or new web services.
The UK Data Service is a national data service, hosted by the University of Essex in the United Kingdom (UK). It provides access for research to data collected by UK government departments and other agencies. It gives free access to all researchers and students, government analysts, business consultants and data analysts, non-governmental organisations, international organisations, think-tanks, and so on. There are currently more than 6 000 datasets available from a variety of sources, such as UK census data; government surveys such as the Labour Force Survey, the Annual Population Survey (APS), the Health Survey and the Time Use Survey; longitudinal datasets such as the British Household Panel Survey (BHPS) and Understanding Society; cross-national surveys such as the European Social Survey and Eurobarometer; business microdata, etc. Access to the data catalogue, documentation and guides are available free of charge. Registration is required to download data, and its use is subject to licensing requirements specified by data owners.
In France, the Centre d’Accès Sécurisé aux Données (CASD) functions as a trusted third party for the secure provision of data and for data matching. It is a public interest group composed of the National Statistical Office (INSEE), research and academic institutions. The main purpose of CASD is to organise and implement secure services to access confidential data for non-profit research, study, evaluation or innovation, activities described as “research services”, mainly public. Its mission is also to promote technological development to improve secure access to data in the private sector. Secure and equitable access is provided to accredited data users, allowing for advanced processing and analysis. An SD-Box with biometric access control provides remote access to the secure infrastructure where confidential data is stored in a so-called “secure bubble”. More than 336 data sources are available at CASD. They cover: 1) public data sources – surveys conducted by INSEE, research institutions, ministries, tax and administrative data; 2) health data on public and private hospital stays and health cohorts; 3) private sector data from partner companies looking to increase the security of their data through external access.
Source: ILO (2017[10]), Skills Needs Anticipation: System and Approaches, https://www.ilo.org/skills/areas/skills-training-for-poverty-reduction/WCMS_616207/lang--en/index.htm; ONS (2019[18]), Accessing secure research data as an accredited researcher, www.ons.gov.uk/aboutus/whatwedo/statistics/requestingstatistics/approvedresearcherscheme#becoming-an-approved-researcherthrough-the-ons-approved-researcher-scheme; CBS (2019[19]), Microdata: Conducting your own research, www.cbs.nl/en-gb/ourservices/customised-services-microdata/microdata-conducting-your-own-research.
3.4. Improve the frequency and coverage of quality data on skills and labour markets by strengthening statistical surveys and expanding administrative data collection. The Bureau of National Statistics should improve the level of detail and coverage of statistical surveys. This could be done by improving questionnaire designs in the surveys of evidence addressing employers and households, for example, asking questions about employment and pay levels, filled and unfilled vacancies, expanding the number of indicators, exploring new sampling criteria and classification and increasing frequency. To complement data from statistical surveys, the Bureau of National Statistics could also expand administrative data collection, such as tax records, pension fund data, and data on enrolment and graduation from various levels of education. The Bureau of National Statistics, in co-operation with the proposed inter-ministerial working group on skills and labour market information (see Chapter 5) could be responsible for monitoring that all organisations and institutions (e.g. providers of adult education and training) who receive public funding submit data to the government.
3.5. Strengthen access to microdata in a secure way, for instance, by gathering anonymised datasets in a centralised digital portal. The Bureau of National Statistics should simplify procedures for policy makers and external researchers who would like to access microdata in a secure way. It could consider developing a secure, centralised portal (e.g. as in the United Kingdom), where external researchers and policy makers can access anonymised data, pending a registration procedure. The Bureau of National Statistics could also establish an independent centre (e.g. as in France) to supervise the secure provision of data and for data matching.
Developing the human resources needed to build an effective skills information system
Developing human resources and technical capacity to understand, process and manage data is as important as improving the quality and accessibility of available datasets. Without competent human resources with sufficient knowledge in qualitative and quantitative data management, it is not possible to take full advantage of available data to exploit meaningful insights and information (ILO, 2017[10]).
In Kazakhstan, the lack of well-trained human capital stands out among the factors that prevent effective exploitation of available data on skills and the labour market (World Bank, 2015[20]). As discussed in the previous section, the Electronic Labour Market Exchange platform contains substantial information on vacancies and jobseeker characteristics. However, World Bank research suggests that such information is under-used and rarely analysed at the regional or national level, because of insufficient knowledge and skills among researchers and policy makers (World Bank, 2015[20]). This view appears to have been corroborated by remarks from several stakeholders consulted during the OECD Skills Strategy project who stressed that the capacity to manage granular skills and labour market data is limited in Kazakhstan. Many stakeholders considered that the lack of well-trained and experienced professionals is a significant barrier hindering the development of a reliable SIS. Moreover, according to stakeholders, not many students are aware of career and training opportunities to become data analysts, economists or statisticians, resulting in an insufficient number of professionals in the field.
Kazakhstan is not the only country in this situation. According to the ILO, the lack of human resources with relevant knowledge and expertise explains the bulk of the observed limited capacity of low- and middle-income countries to generate skills and labour market information – with more than three-quarters of the countries in the sample experiencing such a problem (ILO, 2017[10]). Several OECD countries, such as Estonia, Ireland, Portugal, the Slovak Republic, have also identified the lack of human resources as a main constraint to carrying out SAA exercises and analysis of skills and labour market policies (OECD, 2016[1]).
Going forward, Kazakhstan should provide adequate training opportunities to policy makers and researchers involved in processing and analysing data on the labour market and skills, for example, for producing skills assessment and anticipation tools. Kazakhstan could possibly design short courses targeting policy makers, as the London Schools of Economics has done in the United Kingdom (see Box 4.5). It could also do more to raise the awareness of young people of the importance of undertaking studies in fields relevant to the processing and analysis of data on labour market and skills, such as economics, mathematics or statistics. Kazakhstan might be inspired by France and Korea to make further progress in increasing the number of its skills analysis professionals (see Box 4.5).
Training courses to develop practical skills for analysing public policy: An example from the United Kingdom
In the United Kingdom, the London School of Economics and Political Science (LSE) provides a Public Policy Analysis online certificate course, which combines research, theory and practice. Developed by LSE’s School of Public Policy, which is the LSE’s centre for public policy training, it provides knowledge on core elements of the public policy process and analytical frameworks for analysing the success and failure of policy. Furthermore, the courses help develop practical skills and techniques and an understanding of the quantitative tools and research methodologies available to conduct evidence-based policy analysis.
Improving curricula to deliver new skills and technologies: Examples from France and Korea
In France, the implementation of a skills-based curriculum by the École Centrale de Lyon was initially challenged by existing conceptions about curricula in the sector. It is thanks to the support of other local engineering schools interested in their information technology (IT) skills referential that the model has gained ground. Similarly, the compatibility of the online laboratories of Amrita University with the Indian National Curriculum and the review of its contents by relevant authorities are a major factor supporting their large-scale adoption. One common barrier to the adoption of new teaching models or resources lies in the lack of professional development, including both formal teacher training and peer learning. Even at the development stage, providing professional development is a key factor for success for the successful implementation of a teaching model.
In Korea, the revised national curriculum reinforces software education to enhance students’ capacity in a creativity-based society. It emphasises the development of computational thinking, coding skills, and creative expression through software. To ensure that teachers can effectively deliver the revised curriculum a plan was developed to provide 60 000 elementary school teachers (30% of the total) with specialised training in software education. In addition, 1 800 middle school teachers who are certified to teach IT/computing will receive additional training.
Source: LSE (2020[21]), Public Policy Analysis: Online Certificate Course, https://cdn.www.getsmarter.com/uploads/course/info_pack/ 173/lse-public-policy-analysis-online-certificate-course-prospectus.pdf; OECD (2018[22]), “A brave new world: Technology and education”, https://doi.org/10.1787/9b181d3c-en.
3.6. Provide adequate training opportunities to key actors, including policy makers and researchers involved in processing and analysing data on skills and the labour market. The Government of Kazakhstan should ensure that policy makers and researchers have access to high-quality re-skilling and up-skilling opportunities relevant to processing and analysing data on labour market and skills, such as economics, mathematics, statistics and statistical modelling. The Government of Kazakhstan should support the development of up-to-date training materials, and ensure that training and educational environments are equipped with necessary information and information and communication technology (ICT) devices to effectively transfer knowledge and build necessary skills.
3.7. Raise the attractiveness of careers related to the analysis of labour market and skills data, by improving the quality of the learning environment and offering financial incentives to prospective tertiary students. The MOES should consider improving curricula in secondary schools (e.g. as in Korea) and universities (e.g. as in France) to raise the attractiveness of fields such as economics, mathematics and statistics. The MLSPP, in collaboration with the MOES, should also consider offering subsidies and scholarships to attract competent prospective tertiary students to such fields, while providing sufficient information about the potential and benefits of related careers. This could be done by increasing student access to career guidance (see Opportunity 3 below).
Opportunity 3: Enhancing the use of skills information to inform policy making and stakeholders’ choices
Although the information from a skills information system should be disseminated across a variety of users, reaching this broad audience requires adequate communication channels (e.g. an online portal and/or seminars) and tailoring the information to the target audience. Career guidance could play an important role in providing information on skills demands as well as on training and learning opportunities. It could help individuals make informed career decisions. However, according to stakeholders consulted during the OECD Skills Strategy project, Kazakhstan has so far struggled in both areas. To enhance the use of skills information in decision making, Kazakhstan could explore two policy directions, as follows.
Promoting sufficient dissemination of skills and labour market information among all relevant audiences
Users have different reasons and purposes for using skills information. For instance, policy makers might use skills and labour market information to design and evaluate policies, while education and training institutions can use them to better align their programmes. On the other hand, students, adult learners and career guidance services could use such information to ensure that choices about learning and careers are aligned with labour market demand (OECD, 2019[23]).
However, several participants in the workshops and meetings held during the OECD Skills Strategy project noted that the dissemination of skills information in Kazakhstan is fragmented and not always tailored to the needs of different users. To maximise its utilisation, skills and labour market information should be disseminated among potential users in a way that is relevant and easily understood. For example, in many cases, the presentation of skills information from skills assessment and anticipation exercises can be too technical and hard to understand for non-experts (OECD, 2016[1]).
Achieving these objectives requires the support of accessible channels of communication. Although the Electronic Labour Market Exchange platform, which connects private, online, job-matching portals, provides public access to the results of SAA exercises as well as information on job vacancies and learning opportunities, several stakeholders stressed that many potentially interested users are not aware of the existence of the platform and do not know where to find the information. This happens, reportedly, despite the fact that there is a strong consensus that skill, labour market and educational information play a positive role in informing policies, the identification of programmes and individual choices.
Accordingly, Kazakhstan could do more to reinforce dissemination to reach out to the public. In addition to the publishing of reports and posting the information on dedicated websites, efforts may be made to expand the recourse to public media (radio, TV, newspapers and magazines) and social platforms. Efforts might also include the expansion of online and offline channels, such as webinars, seminars and conferences. Kazakhstan can draw inspiration from the dissemination practices of OECD countries (see Box 4.6).
At the same time, there seems to be scope for ministries and social partners in Kazakhstan to play a more proactive role as facilitators. The proposed inter-ministerial working group on skills and labour market information discussed in Chapter 5 could play a key role in providing a relevant forum to empower the MLSPP and WDC to discuss how to develop diverse communication and awareness mechanisms to improve information dissemination.
Disseminating information to a wider audience: Examples from Poland, Bulgaria and Sweden
In Poland, the main tool for forecasting, the Study of Human Capital (BKL), includes a clear dissemination strategy, using structured steps and mechanisms, defined by a specific budget line. The annual reports are uploaded on the BKL website and are further supported by cycles of national conferences and regional seminars, which target multiple stakeholders. BKL experts participate in employment committees by playing an advisory role. PES officials are actively involved in the analysis that BKL carries out.
In Bulgaria, the new system for forecasting workforce demand encompasses a formal “Mechanism for including the results of forecasts in developing and implementing government policies”. It identifies competent institutions interested in the results of the forecasts and sets out clear and transparent guidelines for dissemination. Users and their institutions are encouraged to provide regular feedback and recommendations, taking into account their experience with using forecasting results for policy formulation and implementation. The Ministry of Labour uses this feedback to review and improve its forecasting methods.
In Sweden, both Statistics Sweden and public employment services are actively involved in the preparation and dissemination of SAA results. Statistics Sweden promotes regular publications and reports to keep the wider public and researchers up to date with developments in the Swedish labour and skills markets. Conferences and seminars are also organised to enhance the diffusion of skills assessment and anticipation information. The Prognosdag (Forecast Day) is an important event that takes place every third year that gathers a variety of stakeholders (e.g. national ministries, agencies and researchers) to discuss new forecasts on skills and the labour market. The PES is also in contact with the National Agency for Education to build bridges with education providers to disseminate SAA information.
Source: European Commission (2015[24]), Skills Governance in the EU Member States, https://ec.europa.eu/social/BlobServlet?docId=15587&langId=en; OECD (2016[25]), Getting Skills Right: Sweden, https://doi.org/10.1787/9789264265479-en.
3.8. Develop diverse communication and awareness mechanisms to reach out to all relevant users. The MLSPP and WDC could go beyond publishing reports and posting information on the Electronic Labour Market Exchange platform and vacancy banks, by promoting wider distribution through the use of public media (radio, TV, newspapers and magazines) and social platforms. To broaden the audience and ensure those with limited access to digital devices are also included, dissemination could be promoted via both online and offline channels, e.g. by organising webinars, conferences, seminars and workshops on a regular basis. The proposed inter-ministerial working group on skills and labour market information (see Chapter 5) could become a relevant forum for discussion to allow the MLSPP and WDC to gather feedback on how to develop diverse communication and awareness mechanisms across different groups.
3.9. Tailor the presentation of the information to the needs of a diverse audience in order to ensure relevance. The MLSPP and WDC should take an extra step to interpret the skills information and explain their implications in clear and simple terms that non-professionals can understand. Following these efforts, the MLSPP and WDC in collaboration with the inter-ministerial working group on skills and labour market information (see Chapter 5), could prepare a tailored dissemination strategy, mapping information (e.g. short-term information on labour market needs vs. longer-term trends) and channels (e.g. online portals vs. seminars) to different users (e.g. businesses, educational institutions, labour and policy leaders). Monitoring mechanisms are also required to ensure that the dissemination strategy produces desirable outcomes.
Using skills information to support career guidance services
Effective career guidance information could play an essential role in helping individuals make informed career and educational decisions that privilege skills and professions that are currently in high demand or are expected to emerge (European Commission, 2015[24]).
However, recent analysis by the Friedrich Ebert Foundation has stressed that career guidance is insufficient in Kazakhstan, which poses concerns, particularly with respect to the young generations. For example, in 2016, almost 75% of 19-year-olds and 60% of young people aged 24-25 years in Kazakhstan relied on their parents for their career decisions (Friedrich Ebert Foundation Kazakhstan, 2016[26]). As youth do not have sufficient information about the jobs that are in high demand and evolving skills demands, more generally, they have no choice but to rely on their close networks, mainly relatives and friends, whose perspectives are often based on anecdotal evidence and personal bias. Similarly, adults who have already joined the labour force find it difficult to access independent career advice in Kazakhstan, according to evidence gathered during the OECD Skills Strategy project.
Stakeholders consulted during the project also confirmed that in Kazakhstan, existing career guidance services do not take into account the findings of skills assessment and anticipation exercises. Although there is growing awareness across universities, colleges and individuals that many professions are declining, and possibly disappearing soon, few actions have been taken to introduce better career guidance.
In Kazakhstan, career guidance services are provided mainly by employment centres and private employment agencies. Although schools normally do not provide career guidance, Kazakhstan has several career guidance platforms in place, including one called Edunavigator (www.edunvavigator.kz), which helps high-school students discover their talents and choose their career paths. The platform includes a questionnaire to assist students in their efforts to narrow their areas of interest. Furthermore, it provides information on relevant training courses and internship opportunities in the professions that match students’ interests and competencies. However, the platform focuses. for the most part, on the student’s interests and capabilities, with little consideration given to evolving labour market trends, changing skills demand and availability of learning or education opportunities.
In OECD countries, it is relatively common to have information on study and training opportunities and skills and labour market trends on the same portal, to facilitate access to comprehensive information. This allows jobseekers, students and their families to more easily assess the advantages and disadvantages of different study and training options, along with career prospects, allowing them to make informed choices on their career and educational paths. Kazakhstan might be inspired by how a few OECD countries have designed information portals that rely on consolidated skills assessment and anticipation exercises (see Box 4.7).
The promotion of regular interactions between local authorities, chambers of commerce and schools would be important to ensure that the data related to skills demand and supply are used to develop more dynamic and up-to-date guidance tools that students and their families can trust (OECD, 2016[25]). This could materialise in the creation of stakeholder forums, of which Estonia provides an interesting example. Since 2010, Estonia uses a National Career Guidance Forum to gather together various stakeholders, including policy makers from relevant ministries (the Ministry of Social Affairs, the Ministry of Education and Research and the Ministry of Economic Affairs and Communications), practitioners and other target groups. The forum aims to ensure that career guidance services are provided in a co-operative and co-ordinated manner (European Commission, 2015[24]).
Informative career guidance systems for students and adults: Examples from Denmark, Canada, Poland, the United Kingdom and Lithuania
In Denmark, the Education Guide web portal offers a wealth of information to both youth and adults on education and training opportunities, jobs and professions, labour market conditions and statistics, and study programmes taught in English. The service also includes guidance materials and activities, which are accessible through a combination of channels, including online chat, phone, webinars, email services and social media interaction. The support of professional counsellors ensures that the services provided are personalised to take into account the profile and needs of each client. In addition, there is an online tool, Uddannelseszoom (education zoom) for students to make informed decisions about the course they choose to study at the tertiary education level. Uddannelseszoom links labour market outcomes to specific qualifications, allowing students to more actively consider career prospects when choosing where and what to study. The information provided to students includes unemployment levels, average pay, whether the qualification equipped previous graduates with the correct skills for their career, and how they got their first job after leaving education. The information is based on skills analysis results that monitor graduate outcomes. The Ministry of Education and Research has overseen the project since its launch in 2015, and the tool is continuously updated as new statistics become available. The Ministry of Higher Education and Science launched a media campaign just ahead of the deadline for higher education applications. It sought to encourage students to take labour market outcomes from Uddannelseszoom into consideration when making their choices.
In Canada, the Department of Employment and Social Development has centralised the dissemination of labour market information and skills assessment on a single platform (Job Bank). The users can access tailored information according to their interests and experience. The platform has been modernised to provide quality job-matching services along with detailed and timely labour market information. Job Bank has recently created a section with guidance on how to look for a job since the COVID-19 crisis. It also includes up-to-date sector-specific information about job vacancies during the pandemic.
In Poland, prospective tertiary graduates can use the ELA-Nauka portal to inform their university choice. This portal is the main tool to distribute key results from the Polish graduate tracking database. It provides infographics on what graduates are doing (further study, in work, unemployed, etc.) and can be sorted by subject area. It also ranks individual courses by area, such as total salary, the risk of being unemployed and time taken to find a job. Similar to the Danish Uddannelseszoom, students can directly compare specific programmes. Regular summaries are published to convey key trends and findings and, in order to increase engagement, the portal can be accessed both on line and via a dedicated app on the App Store and Google Play. A more detailed website – ELA Pro – provides statistical summaries, reports and the methodology behind the rankings.
In the United Kingdom, the online data portal, LMI for All, connects existing sources of high-quality, reliable labour market information (LMI) to individual clients with the aim to inform their career decisions. The information is freely available through a single portal or via an application (API). The UK Commission for Employment and Skills (UKCES) is the entity responsible for the portal.
In Lithuania, the main national web portal, AIKOS, provides information, guidance and counselling to students, employees and employers. It disseminates information on education and training programmes, as well as on education and employment statistics (vacancies, unemployed persons).
Source: CEDEFOP (2016[27]), Labour Market Information and Guidance, https://www.cedefop.europa.eu/files/5555_en.pdf; Government of Canada (2021[28]), Job Bank, www.jobbank.gc.ca/home; OECD (2016[1]), Getting Skills Right: Assessing and Anticipation Changing Skills Need, https://dx.doi.org/10.1787/9789264252073-en; European Commission (2015[24]), Skills Governance in the EU Member States, https://ec.europa.eu/social/BlobServlet?docId=15587&langId=en; OECD (2017[29]), In-Depth Analysis of the Labour Market Relevance and Outcomes of Higher Education Systems: Analytical Framework and Country Practices Report, oecd.org/education/skills-beyond-school/LMRO%20Report.pdf; Danish Ministry of Education and Research (2019[30]), Uddannelseszoom Portal, www.ug.dk/vaerktoej/uddannelseszoom; Polish Ministry of Science and Higher Education (2019[31]), ELA Portal, https://ela.nauka.gov.pl.
3.10. Introduce a consolidated portal to provide individuals with access to information on skills needs, labour market trends and the availability of study/work opportunities. The proposed inter-ministerial working group on skills and labour market information (see Chapter 5), in collaboration with the MLSPP, could consider creating a single portal that has comprehensive information on labour market demand as well as career and study opportunities. For example, the Electronic Labour Market Exchange platform could be interlinked with other career guidance platforms (e.g. Edunavigator) to facilitate collaboration and information exchange. This will allow students and jobseekers to access integrated information that they need to make informed and unbiased career decisions. The platform should include up-to-date information about relevant training programmes (e.g. as in Denmark and Portugal), labour market developments in the aftermath of the COVID-19 crisis (e.g. as in Canada) and recommendations for developing skills and looking for a job effectively.
3.11. Organise a career guidance forum to gather various stakeholders to ensure career guidance is provided in a co-operative manner, based on skills analysis of current and future skills needs. The proposed inter-ministerial working group on skills and labour market information (see Chapter 5), in collaboration with the MOES and the MLSPP, could guide the formation of a forum to promote regular interactions between local authorities, chambers of commerce and educational institutions. They could encourage policy makers from relevant ministries, practitioners and other target groups to participate in the forum, for example, by providing incentives to participate or raising awareness about its importance.
Table 4.2 summarises the recommendations for this chapter. Based on feedback from stakeholders and from the national project team, three recommendations have been selected that could be considered to have the highest priority based on potential impact and relevance in the current Kazakhstan context. To build an effective skills information system, the OECD recommends that Kazakhstan should:
Adopt an integrated approach by combining qualitative and quantitative methods to achieve robust skills analysis results (Recommendation 3.2).
Improve the frequency and coverage of quality data on skills and labour markets by strengthening statistical surveys and expanding administrative data collection (Recommendation 3.4).
Introduce a consolidated portal to provide all individuals with access to information on skills needs, labour market trends and the availability of study/work opportunities (Recommendation 3.10).
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