Improving Employability: The Role of Cross-Cutting Competences in Vocational Education Outcomes

Improving Employability: The Role of Cross-Cutting Competences in Vocational Education Outcomes | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Improving Employability: The Role of Cross-Cutting Competences in Vocational Education Outcomes Vladimír Baláž, Dušana Dokupilová This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5783632/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Vocational education and training (VET) is often seen as a pathway to faster and better employment outcomes in comparison to general secondary education. However, the school-to-job transition remains complex, influenced by regional-, institutional- and individual-level factors. This paper evaluates the effects of the Slovak ‘Linking Secondary Education and Practice’ scheme, designed to improve success rates in secondary VET by enhancing cross-cutting literacy competences such as reading and mathematical skills. The study covers 25,219 students from 74 schools, spanning diverse contexts including developed and underdeveloped regions, and lower versus upper secondary vocational education. Using a combination of difference-in-differences (DiD) and the synthetic control method (SCM)—a novel approach for evaluating individual VET school performance—this research examines the determinants of school-to-job transitions at both regional and study field levels. The findings reveal significant disparities in VET outcomes based on programme type, field of study, and level of regional development. Vocational schools in underdeveloped regions exhibit weaker outcomes in comparison to their developed counterparts, underlining the importance of regional contexts in VET policy effectiveness. The research results indicate that the intervention worked best for the service-oriented fields in the lower secondary programme and/or in the poorest Slovak regions. The study contributes to the literature by offering a granular analysis of vocational education outcomes, providing evidence at the regional, study field, and programme levels. vocational education cross-cutting literacy competences socio-economic background study program study field synthetic control method Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Vocational education and training (VET) students, in theory, should find work more quickly and obtain better jobs than graduates of general secondary programmes. Yet, many national and cross-national studies point to substantial differences in the ease of the school-to-job transition. The transition is often complicated and depends on a number of regional- and local-level factors. Individual secondary vocational schools offer subjects of heterogeneous quality and may differ in the quality of teaching vocational subjects. The quality, unsurprisingly, varies among developed versus less developed regions. In the countries of Central and Eastern Europe (CEE), for example, vocational schools in rural areas are generally of lower quality than are those located in cities (Tūtlys & Vaitkutė, 2022 ). What is more, each school has its own institutional culture. Some schools, for example, place more emphasis on the admissions process and student quality in comparison to others. The school’s reputation may inform the employer’s willingness to hire the graduates (Bruin et al. 2023 ). This paper examines the effects of the ‘Linking Secondary Education and Practice’ scheme. The scheme was managed by the Slovak Government and aimed at improving success rates in secondary vocational education. The scheme involved 25,219 students in 74 schools and supported cross-cutting literacy competences (OECD, 2019a , b ) such as reading and mathematical skills. The scheme activities were developed under different study programmes (lower versus upper secondary vocational education), study fields, and types of regions (prosperous versus underdeveloped). The diversity of contexts enables analysing multiple determinants of intervention outcomes. The remaining part of this section examines the theoretical foundation of the skill-based employability of the secondary graduates. It firstly discusses the importance of professional versus cross-cutting literacy competences in the school-to-job transition and then turns to individual- and school-level determinants of the transition. Chapter two presents the system of VET in Slovakia and examines key determinants and outcomes of school-to-job transitions. Furthermore, the chapter introduces policy interventions aimed at enhancing cross-cutting literacy competences among the VET students. Chapter three states the data sources and analytical methods employed in the scheme evaluation. Chapter four presents the results of the factor and regression analysis, as well as difference-in-differences (DiD) and the synthetic control method (SCM). The concluding part of the paper discusses major findings of the research and suggests some policy recommendations. Moreover, it states some limitations and suggests directions for further research. The research has some novel elements. Most previous studies examined school-to-job transitions at the national level. This one was performed at both regional and individual school levels. It also takes into account the type of educational programme (lower versus upper secondary) and the field of study. A higher level of detail enabled analysing some important correlates of the VET outcomes. While DiD is frequently applied in VET studies, the SCM was, to the authors’ best knowledge, used for the first time to analyse the performance of individual VET schools. 1.1 What education, what skills? Employability is defined as “a set of cognitive and non-cognitive competencies that are relevant across work areas and are considered a prerequisite for successful participation in modern labour markets and social life” (Rausch et al. 2024 : 419). Such concepts are also understood to be “basic skills”, “cross-cutting skills”, “transferable skills”, “key competences”, “soft skills”, or “21st century skills”. OECD studies (OECD, 2019a , b , 2021 ) have emphasised the following in particular: (i) problem-solving abilities; (ii) critical thinking; (iii) reading, math and ICT literacy; (iv) communication, cooperation in solving tasks; and (v) the ability to regulate emotions, and openness to new ideas. The work-related values and attitudes to work also are important for transition from the school to work transition (Tūtlys et al. 2024 ). The OECD working group (Rausch et al. 2024 ) selected literacy, problem solving, and work performance as key skills. These skills and competences also form the basis of the international assessment of professional skills of vocational education and training for employment (OECD 2024 ). The goals and contents of VET studies are important prerequisites for students’ employability. Contents of VET courses are subject to debate in professional and scientific literature (Hordern et al. 2022 ). The traditional view is that students should receive as much knowledge as possible in relation to their future profession. Systematically organised knowledge of a certain field, which is verified by practice (“know what”), is the basis for making correct judgments and decisions (Winch 2010 ; Shalem 2014 ). Work-based training may significantly enhance the extent and quality of professional knowledge. The newer view puts more emphasis on the worker’s personal experience of solving specific problems in specific situations (knowhow, “knowing how”). This type of knowledge is extremely important in a fast-changing world in which employers’ demands for the skills of their employees are also constantly changing. From this point of view, traditional secondary school curricula can quickly become outdated (Young and Hordern 2022 ). The teaching of systematically organised knowledge is undoubtedly important for the professionalism of future secondary school students. However, the dynamic development of society, the economy and technology increasingly favours the flexible and innovative application of skills to solve specific problems in specific contexts. A student’s ability to obtain a job is no longer based on a set of learned knowledge, but rather on a set of problem-solving competences. Hanushek (2013: 31) notes: “A successful system of vocational training cannot neglect developing strong cognitive skills in individuals.” Several large-scale evaluations based on the International Adult Literacy Survey (IALS) (Hanushek et al. 2017 ) and/or the Program for the International Assessment of Adult Competencies (PIAAC) (Brunello and Rocco 2017 , Choi et al. 2019 ) found that vocational track graduates have lower literacy competences than do general track graduates. Vocational track graduates with work-based training may find it easier to obtain their first job, but general track graduates with school-based training collect a higher employment premium over their lifetime. Brunetti and Corsini ( 2019 ) came to a similar conclusion after analysing European Labour Force data. The findings point to the importance of generic cross-cutting literacy competences. 1.2 What students, what transition? VET programmes tend to have a strong focus on labour market needs and the employability of graduates. A number of institutional factors may impact the differentiation of career paths of VET graduates (Boyadjieva and Ilieva-Trichkova 2019 ). The efficiency of the school-to-job transition may depend on the extent of the work-based training. The apprenticeship-oriented courses have actually been proven to be quite successful in facilitating young people’s transition into work across European countries (Markowitsch and Hefler 2019 : 7). Most European countries currently manage school-based VET systems rather than work-based ones (Markowitsch and Hefler 2019 ). Some countries combine high shares of VET students among total secondary students with high shares of work-based training (Germany, Austria, Switzerland, Norway; Annex, Fig. 2 ). Other countries are typical with a relatively low share of vocational students among the total population of secondary students with low intensity of the work-based training (CEDEFOP 2024). The Czech and Slovak Republics and Belgium belong to a cluster of countries combining a high share of VET students in overall secondary education with a relatively low intensity of work experience at the employer, i.e. most education takes place within the formal school system (Salas-Velasco 2024 ). Employer involvement in the definition and updates of VET curricula, however, remains important for a smooth education-to-employment transition in most European countries (Bolli et al. 2017). The demand for labour may vary considerably among less and more qualified young people. School leavers from lower secondary education, for example, may find it more difficult to find their first and, subsequently, stable employment than their better-qualified peers (Morris 2023 ). Secondary vocational education sometimes is considered to be an effective tool for promoting social inclusion, as it can facilitate the transition from studies to employment. Dual education was supposed to help apprentices to establish themselves with potential future employers (Nillson, 2010: 251). Career and technical education may provide students from disadvantaged backgrounds with the first step on the pathway towards the middle class (Haviland and Robbins 2021 ). This idea, however, is not always fulfilled. In CEE countries, for example, apprenticeships and vocational training used to have a less favourable image than did upper secondary schools and gymnasiums preparing students for university in general. This image has not yet been completely overcome and de facto causes a negative selection of students for lower secondary vocational education. In this educational programme, there are therefore proportionately more students at risk of social exclusion than in gymnasiums (Choi et al. 2019 ; Tūtlys et al. 2022 ; Bruin et al. 2023 ). Education and skills alone are not sufficient for students to be able to apply themselves in the labour market after graduation. The social inclusion of VET students is currently considered to be an equally important goal for their employment in the labour market (Nilsson 2010 ; Allan and Catts 2014 ; Salvà et al. 2019 ). The effectiveness of VET (the ease of the study-to-work transition) is subject to debate and may vary across countries and institutional contexts. As for CEE countries, the research tends to bring inconclusive and often contrasting results. Straková ( 2015 ) employed logistic regression to analyse IALS and PIAAC datasets. Her findings highlighted the limited effectiveness of the dual education system in facilitating the transition from education to employment in the Czech Republic. Similarly, Hoidn and Šťastný ( 2023 ) used linear regression to examine PIAAC data from the Czech Republic, Austria, and Germany. For the Czech Republic, no significant relationship was identified between participation in dual education and subsequent employability. Employers in the Czech Republic tended to value upper secondary education diplomas more highly than certificates of apprenticeship. In Austria, a statistically significant but modest positive effect of dual education on employability was observed. By contrast, in Germany, the completion of dual education significantly enhanced employment outcomes. Noelke and Horn ( 2014 ) examined the effects of training vocational education students at employers’ workplaces versus in schools in Hungary during the period 1993–1999. Their analysis revealed that the shift from workplace-based to school-based education was associated with higher unemployment rates and lower job quality among recent graduates. Using the DiD method, the authors found that these negative effects were more pronounced for men, with the risk of unemployment increasing by up to 10% within the first two years after graduation. Cabus and Nagy ( 2021 ) analysed the impact of on-the-job training for apprentices at employers’ workplaces on company productivity and apprentice employment in Hungary from 2003 to 2011. Their findings indicated that vocational training for apprentices negatively affected company productivity. Furthermore, the retention rate of apprentices with their employers was quite low and steadily declined over the course of the training period. Švábová et al. ( 2021 ) explored the effects of active labour market policies on the employment of young school graduates using a sample of 12,953 Slovak graduates from 2014–2015. Employing multiple analytical methods, including regression adjustment, propensity score matching, and instrumental variables, the authors concluded that graduates supported by the European Social Fund retained their jobs significantly longer than did those without such support. Building on this research, Gabríková and Švábová ( 2023 ) applied classification and regression tree methods to the same sample. They found that supported graduates were employed, on average, three months longer during the observed two-year period than were unsupported graduates. The inconclusive results may have been impacted by diverse socioeconomic and sociodemographic backgrounds, but also school-level variables. The education-to-work transition, for example, is easier to achieve during times of tight labour markets of the 2020s than during times of high unemployment in the early 2010s. The labour market for lower secondary students / apprentices is different from those for upper secondary graduates, who may have more transferable skills. The intensity of work-based training varies considerably among EU countries. Finally, the employability of VET students may vary with the study field (e.g. manufacturing versus service-based occupation) and situation in regional labour markets. It follows that an analysis of the school-to-job transition should be performed at the school level, rather than the national one, so as to account for local/regional socioeconomic contexts, as well as the types of educational programmes (lower versus upper secondary VET) and study fields. 2. Vocational education and training in Slovakia: the case for intervention Secondary vocational education in Slovakia has a long history exceeding one century. The industrialisation period of the 1950s and 1960s marked a pivotal change. The demand for skilled workers in the rapidly growing manufacturing sector led to significant expansion in vocational education. This era was characterised by population growth, improved living standards, and a cultural shift that prioritised professional education linked to secondary diplomas. The number of secondary vocational education students increased more than sevenfold between 1945 and 1989 (Annex, Fig. 3 ). The post-1989 demographic, economic and social transformation significantly impacted secondary vocational education in Slovakia, leading to a gradual decline in student enrolment and a reduction in the number of vocational schools. Initially, the number of students in secondary vocational schools peaked in 1995, driven by demographic cohorts born between 1978 and 1982 and the establishment of new, predominantly private vocational schools. From the mid-1990s onwards, the number of vocational students began to decline. This was due to a combination of demographic changes, such as smaller cohorts born in the 1980s and 1990s, and significant restructuring of the economic and social landscape. Firstly, the share of the 15–19 age group in the total population dropped from 8.9% in 1995 to 4.8% in 2023 (Annex, Fig. 3 ), shrinking the pool of potential vocational students. Secondly, the transition from central planning to a market economy in Slovakia, marked by the privatisation of state enterprises, significantly weakened secondary vocational education. Private business owners showed little interest in sustaining vocational training, a trend mirrored in other transitional economies such as Poland and Hungary. Neoliberal market reforms emphasised fixed-term contracts over lifelong employment, leading many employers to cancel in-house training programmes across Europe. New private business owners were generally not interested in maintaining in-house VET programmes (Kogan et al. 2012 ). Similar processes took place in other transitional economies, e.g. Poland (Kurek and Rachwał 2012 ) and Hungary (Benke and Rachwał 2022 ). Thirdly, vocational schools entered into competition with general (upper) secondary education. High school graduates faced high unemployment rates, while university graduates enjoyed better job prospects, lower unemployment, and higher salaries during the transitional period in the 1990s. Fourthly, the decline in numbers of VET students was exacerbated by the state’s lack of attention to vocational education. A renaissance of vocational education related to the arrival of foreign investors, particularly in the automotive industry. From the 2000s, the Czech and Slovak Republics and Hungary became major exporters of cars and car parts. The Slovak Government responded to the needs of industry and passed the 184/2009 Law on vocational training and education. The law provided for voluntary involvement of professional and/or employer organisations in the national, regional and sectoral VET councils (Šćepanović 2020 ). The VET programmes, however, were financed by the state only and training was performed in schools (rather than with employers). Since the mid-2010s manufacturing industries have coped with a significant lack of skilled labour. Demographic transitions and some negative social developments have been key factors behind the labour shortages. Specifically, some social groups became increasingly marginalised (the Roma communities in particular, Kahanec et al. 2020 ) and lost access to secondary education. There is ample evidence on the importance of socioeconomic background for success in studies and employment (Broer et al. 2019 ; Liu et al. 2022 ; Tūtlys et al. 2022 ). Academic outcomes for lower and upper secondary students in Slovakia (Fig. 1 ) are strongly influenced by socioeconomic and regional factors. Key determinants include parental education, employment status, poverty levels, family structure, and the socioeconomic characteristics of the region, such as the proportion of marginalised Roma communities. There are vast economic and social disparities between the relatively developed western and poor eastern regions of Slovakia (Fig. 1 ). The disparities transferred to student performance. The poorest Slovak districts (NUTS 4, Lau 1 levels) 1 , such as Revúca (RA) and Košice-okolie (KS), for example, accounted for the highest dropout rates in lower secondary vocational education (Fig. 1 ). Students from middle-class families self-selected for upper secondary schools with substantially lower dropout rates (Fig. 1 ). The Slovak labour market experienced a dramatic transition over a quarter of a century. The overall unemployment rate fell from 20% in 2000 to a mere 5% in 2024. The rate was close to 2% in developed regions in the western part of the country (Fig. 1 ). Employers found it difficult to hire any Slovak graduates and imported labour from non-European countries. The situation was different in less developed regions in the eastern part of Slovakia, with unemployment rates exceeding 10%. What is more, the unemployment rates for attainment level ISCED 0–2 and age group 15–24, already decreasing in the 2010s, resurged to 59%, by far the highest level in the EU27 in 2023 (Annex, Fig. 4 ). Any scheme supporting VET graduates therefore was likely to have more tangible effects in the lagging-behind regions than in the developed ones. Based on the literature review, the following hypotheses are stated: H1 Students in lower secondary vocational education benefit from the literacy-aimed scheme more than those in upper secondary vocational education. H2 Students from regions with higher unemployment and poverty rates benefit from the literacy-aimed scheme more than those from relatively prosperous regions. The Slovak Government used financial means from the European Social Fund and designed the ‘Linking Secondary Education and Practice’ scheme. The scheme was launched in 2019 and supported cross-cutting literacy competences (OECD, 2019a , b ), such as reading, mathematical, financial and ICT literacy (including basic entrepreneurship skills), and language competences. The skills acquired via the scheme were supposed to improve the school-to-job transition and, specifically, decrease (i) shares of dropouts and (ii) unemployment rates of graduates. The scheme was implemented via accredited courses performed in secondary vocational schools, rather than through work-based training. It is generally difficult to provide work-based training, particularly in underdeveloped regions with no large private employers. The scheme envisaged extra schooling for students from disadvantaged socioeconomic backgrounds, including marginalised Roma communities. The scheme allocated €30m, €28.5m of which to the non-Bratislava regions (NUTS 3 levels). Some 74 schools benefitted from the scheme. The average support per student ranged from €314 to €1,281, with the poorest districts in the Slovak East receiving somewhat more than relatively prosperous ones in the Slovak West (Annex, Fig. 5 ). 3. Data and methods 3.1 Data sources The data on the scheme and on the performance of particular secondary schools in the period 2019–2023 were provided by the Ministry of Investment and Regional Development and Information and the Ministry of Labour, Social Affairs and Family, respectively. The literature review established contrasting results of studies on the education-to-work transition. We decided to focus on school-level data, rather than on national-level data, so as to account for education-, industry- and regional-level correlates of the school-to-job transition. The school-level data enable distinguishing between students in lower versus upper secondary education. What is more, we distinguish between graduates in the manufacturing versus service-oriented study fields. Each school is analysed within its own regional socioeconomic setting. The Statistical Office of the Slovak Republic (SOSR 2024) provides quite detailed data on the socioeconomic performance of 79 Slovak districts. We use these data as background explanatory variables for the efficiency of the school-to-job transition. 3.2 Research design We start with computing the socioeconomic correlates of the study-to-job transition at the regional levels. Many socioeconomic variables are highly correlated. We perform factor analysis so as to merge the high number of independent variables into a smaller number of meaningful factors. The factor scores are firstly used as inputs into the linear regression and later in the DiD and SCM. 4. Results 4.1 Factor and regression analysis Nine variables were used to capture the socioeconomic background of the districts: (1) number of enterprises in the district (per 1,000 inhabitants); (2) share of the population aged 24–35 who have a university degree in the total population of the district aged 24–35 (%); (3) urban population, share of the population in settlements with more than 5,000 inhabitants in the total population of the district (%); (4) average wage in the district as a percentage of the average wage in Slovakia; (5) unemployment rate (%); (6) share of the population receiving material deprivation benefits in the total population of the district (%); (7) share of marginalised Roma communities (MRC) in the total population of the district (according to the 2022 Atlas of Roma communities); (8) gross divorce rate (per 1,000 inhabitants); and (9) road distance from Bratislava (km). To minimise the influence of random fluctuations in individual years, the analysed variables were calculated as averages for the period 2014–2023. The sole exception was the indicator of higher education attainment, which was available only for the years 2021–2023. To account for inflation, the district’s average wage was not represented by its nominal value over the last decade, but rather as a percentage of Slovakia’s average wage for each year. The original set of nine independent variables was transformed into three factors which collectively explained over 85.7% of the variance in the sample (Annex, Table 4): · Factor 1 (“prosperity”) explained 33.31% of the total variance and reflects the educational, business and economic development of the district. · Factor 2 (“poverty”) explained 31.30% of the total variance and captures the prevalence of poverty, unemployment, and the presence of marginalised Roma communities within the district. · Factor 3 (“distance”) explained 23.13% of the total variance and represents geographical and cultural disparities between the urbanised West with a relatively affluent and educated population on the one hand and the more traditional East with a conservative rural population on the other hand. The factor scores were used as inputs into ordinary least squares regression (Table 1). The regressions yielded the following results: · In both regressions, only Factor 2 (poverty), which combines the share of the population in material need in the total population, the rate of registered unemployment, and the share of MRC in the total population (%), proved to be statistically highly significant. Factors 1 and 3, which characterised relative prosperity or geographical and cultural disparities of Slovak regions, became insignificant at the 0.05 level in both regressions. There is one fundamental difference between the two regressions. · The regression that focused on the differences between districts in lower secondary studies explained up to 41.6% of all differences (adjusted R squared = 0.416). The remaining differences are attributable to other factors that most likely characterise the activities of individual schools. · The regression that focused on upper secondary studies explained only 11.7% of all differences (adjusted R squared = 0.117). The poverty factor was also the only statistically significant variable in this regression, but its influence on the study results was much lower than in the lower secondary studies. For example, the standardised regression beta coefficient (0.360) was almost half of that in the previous regression (0.668). This result suggests that success or failure in the upper secondary (vocational) programme is determined by reasons beyond students’ socioeconomic backgrounds. The reasons may include variations in school equipment quality, teaching methodologies, and/or the extent of collaboration with industry. Table 1: OLS regression – factors impacting success in lower and upper secondary education B Std. Error Beta t Sig. Tolerance VIF Lower secondary Constant 31.392 1.346 23.324 0.000 Factor 1: prosperity 1.366 1.642 0.076 0.832 0.408 0.985 1.016 Factor 2: poverty 9.835 1.345 0.668 7.312 0.000 0.986 1.014 Factor 3: distance -0.080 1.322 -0.005 -0.060 0.952 0.997 1.003 Adjusted R squared 0.416 Upper secondary Constant 8.231 0.851 9.670 0.000 Factor 1: prosperity -0.919 0.850 -0.118 -1.081 0.283 0.999 1.001 Factor 2: poverty 2.871 0.871 0.360 3.297 0.002 0.998 1.002 Factor 3: distance -0.828 0.867 -0.104 -0.955 0.343 1.000 1.000 Adjusted R squared 0.117 Source: authors’ computations The OLS findings suggest some important policy implications. The average dropout rate was 30.8% in lower secondary education, with Slovak district Košice-okolie (KS) exceeding 88% and Revúca (RA) 65%. Poverty was a key factor behind the extreme dropout rates (Figure 1). A significant reduction in the dropout rates is hardly possible without a reduction of poverty and social exclusion. The average dropout rate in upper secondary vocational education was only 6.6%, with the districts of Košice-okolie (KS), Revúca (RA) and Bytča (BY) reporting the highest rates (Figure 1). It follows that lower secondary education students may benefit from public intervention more than those studying upper secondary vocational courses. 4.2 The difference-in-differences (DiD) The difference-in-differences method (DiD) enables estimating the causal effects of specific policy interventions in pre- and post-intervention periods (Donald and Lang 2007). In this case, DiD compares four different groups of schools: treated versus untreated schools in pre-test versus post-test time periods. In our case, we analysed data on graduate performance in the period 2019–2023. To effectively evaluate the impact of the intervention, it is essential to compare groups of schools that were under similar conditions prior to the intervention. Each supported school was matched with a “twin”—a school that did not receive support but operated in a region with a comparable socioeconomic background and offered similar programmes and fields of study. The propensity score matching (PSM) method was utilised to establish the twin. The matching criteria included factor scores for regional economic background and variables for study programmes (lower versus upper secondary) and fields (manufacturing versus services). The scheme was launched in August 2019. The schools applied for support in the 2020/2021 school year. We can therefore observe the first results in 2021, which we have also determined as dividing the pre- and post-intervention periods. The DiD results are displayed in Table 2. The scheme impacts are expressed in percentage change. At the national level, for example, the scheme contributed to a 5.0% increase in the numbers of successful students and a 1.7% decrease in the numbers of unemployed students. As for the regional level, the scheme seemed to increase the shares of successful students in the relatively prosperous regions in Western Slovakia (Bratislavský Trenčiansky and Trnavský krajs ). The results for the poorest Slovak regions (Prešovský krajs ) were insignificant. To ensure that the results are statistically significant and reliable, a sufficiently large sample size is required. However, the total number of supported schools was rather limited (n = 74), with some regions having as few as two supported schools. Consequently, results significant at the 0.1 level were only observed at the national level, and in five out of eight NUTS 3 regions. We consider the results of the DiD analysis to be inconclusive and proceed to the SCM. Table 2: Impact of intervention on students on national and regional levels NUTS 3 regions Number of supported schools Impact on shares of successful students Impact on shares of unemployed students SK 010: Bratislavský 5 4.8* -2.8* SK 021: Trnavský 2 18.5* -3.0 SK 022: Trenčiansky 7 1.1* -6.0 SK 023: Nitriansky 10 -3.6* 1.8* SK 031: Žilinský 12 5.1 -1.0 SK 032: Banskobystrický 2 4.9 4.5 SK 041: Prešovský 19 -0.5 -1.0 SK 042: Košický 17 21.0 -8.6* Slovak Republic 74 5.0* -1.7* Notes: * significant at the 0.1 level 4.3 Synthetic control method The synthetic control method (SCM) allows for a more in-depth study of the intervention impact, as it studies the impacts of the intervention at the level of the individual school. The SCM creates a weighted average of untreated units (‘synthetic cohorts’) that best reproduces characteristics of the treated unit over time, prior to treatment (Abadie et al. 2010; Abadie and Cattaneo 2021). This synthetic school is made by combining non-supported schools with key features (region, socioeconomic background, type of study programme, field of study) similar to supported schools. The results of factor analysis guided this process in terms of socioeconomic background. Slovak secondary schools offer hundreds of study fields. Many study fields, however, account for very low numbers or no students. Because of the low number of supported schools, we had to group study fields into two major branches. The first branch includes NACE sections A–F. We denote the group with the simplified label ‘manufacturing’, although it also includes a limited number of study fields in agriculture and construction. The second group includes NACE sections G–U. This group is denoted as ‘services’. Furthermore, we distinguished between two programmes of vocational education: the lower secondary one (with no high school diploma) and the higher secondary one (with a high school diploma). Two to three schools with a strong influence were typically used to create a synthetic school. These schools usually informed 70–95% of contents of the new synthetic school (in terms of location, socioeconomic background, and form and field of study). An additional three to four schools contributed a smaller influence, ranging from 5% to 30%. If there was an abundant number of supported and unsupported schools, more than one twin was created. If there was a limited number of non-supported schools in a given form and/or field of study, the synthetic twins proved to be impossible to create. The impact of the scheme was examined on the proportions of successful graduates and those unemployed. When analysing the increase in the share of successful students—expressed in percentage points—it is important to note that the sum of the increase and the average value can exceed 100%. This is because the increase reflects the improvement in success rates in comparison to a synthetic counterpart school, rather than the average value. A similar principle applies when analysing negative outcomes such as unemployment rates (Table 3). The Slovak Republic accounts for vast regional socioeconomic disparities. The Bratislava region, for example, had a GDP per capita of 118% of the EU27 average and an unemployment rate of 3.2% in 2022. The Prešovský region had a GDP per capita of 35% of the EU27 average and an unemployment rate of 10.0% in the same year (Eurostat 2024). We report the SCM results for all eight NUTS 3 regions (‘ krajs ’) so as to examine the effects of the scheme in the relatively prosperous regions in Western Slovakia (Bratislavský and Trnavský krajs ) versus the poorest ones in the east of the country (Prešovský and Košický krajs ). Table 3 also displays contextual statistics on the per capita GDP as a percentage of the EU27 average (Eurostat 2024) and unemployment rates in 2022 (SOSR 2024). The SCM results indicate mostly positive impacts of the scheme in supported schools. Most schools saw a significant increase in their success rate—through either higher employment or more students continuing their studies—and decreases in shares of unemployed students. Moreover, the SCM results suggest that (with a few notable exceptions) the scheme operated well across study programmes, study fields, and regions. The most positive results were found for the service-oriented fields in the lower secondary programme (H1 confirmed) and/or in the poorest Slovak regions (H2 confirmed). The Prešov region, for example, seemed to benefit most from the intervention. As for the upper secondary studies, the success rate increased by 0% in manufacturing versus 25% in service-oriented study fields. The respective decreases in the shares of unemployed students were zero and seven percentage points. As for the lower secondary studies, one manufacturing study field accounted for a 0% increase in the success rate but a 10% decrease in unemployment. Two service-oriented study field schools accounted for 10% and 35% increases in the success rate respectively. During the same time, the unemployment rates decreased by 3% and 10% respectively. The intervention showed similar positive impacts in the Košický region too, except for a small increase in the unemployment of upper secondary graduates in service fields. In the Žilina region the scheme seemed to operate better for the upper secondary graduates. In one service-oriented lower secondary school the success rate declined by 15%, but there was no increase in the numbers of unemployed graduates. This school is located near a major marginalised Roma community (MRC) settlement. The school location may have informed the student structure and, consequently, study outcomes. The SCM results cannot capture all of the circumstances to which schools are exposed. When choosing a synthetic twin, we only consider the district-level socioeconomic background. We are unable to account for some school-level factors such as image and/or quality of teaching. The influence of a poor image on the choice of school and, thus, self-selection into the social class is relatively common in Central and Eastern Europe (Bruin et al. 2023). Some schools are considered to be ‘good’ and attract students from middle-class families. Some other schools may concentrate students from poor families and/or MRC. If this is the case and the share of unemployed graduates from a ‘poor image’ school is equal to that from a ‘good image’ synthetic school, the policy intervention may have worked quite well in this case. The scheme seemed to operate with limited efficiency in the more prosperous Slovak regions, except for Bratislava City. In the Trnava region (upper secondary programme), for example, the graduate success rate dropped by 15% in manufacturing, but remained unchanged in the service fields of study. The share of unemployed graduates increased by 5% in manufacturing, but decreased by 1% in the service fields of study. It was impossible to create synthetic twins in lower secondary studies. We report the results for the upper secondary studies only. Table 3: Results of the synthetic control method NUTS 3 region Study programme Field of study Share of successful graduates (difference to synthetic school) Average Share of unemployed graduates (difference to synthetic school) Average SK 010: Bratislavský (GDP p.c. 116%, unemp. rate 3.2%) upper secondary A–F 14.00% 86.00% -7.00% 5.80% upper secondary G–U 14.00% 85.00% -8.00% 5.90% lower secondary A–F n.a. 4.60% lower secondary G–U n.a. 3.70% SK 021: Trnavský (GDP p.c. 60%, emp. rate 3.6%) upper secondary A–F -15.00% 84.00% 5.00% 6.00% upper secondary G–U 0.00% 81.00% -1.00% 6.00% lower secondary A–F n.a. lower secondary G–U n.a. SK 022: Trenčiansky (GDP p.c. 49%, unemp. rate 3.7%) upper secondary A–F 5.00% 87.00% -3.00% 4.00% upper secondary G–U 20.00% 83.00% -9.00% 7.00% lower secondary A–F 15.00% 80.00% -5.00% 5.00% lower secondary G–U 0.00% 76.00% -6.00% 6.00% SK 023: Nitriansky (GDP p.c. 47%, unemp. rate 3.8%) upper secondary A–F 0.00% 84.00% -3.00% 5.10% upper secondary G–U 5.00% 79.00% -3.00% 7.30% lower secondary A–F 20.00% 81.00% -10.00% 4.90% lower secondary G–U -5.00% 79.00% 0.00% 4.50% SK 031: Žilinský (GDP p.c. 50%, unemp. rate 4.6%) upper secondary A–F 15.00% 83.50% -8.00% 6.50% upper secondary G–U 15.00% 81.00% -7.00% 6.90% lower secondary A–F 0.00% 82.00% -5.00% 5.70% lower secondary A–F 0.00% 82.00% 0.00% 5.70% lower secondary G–U -15.00% 79.00% 0.00% 6.00% SK 032: Banskobystrický (GDP p.c. 45%, unemp. rate 8.5%) upper secondary A–F n.a. 8.70% upper secondary G–U 0.00% 75.00% 2.00% 10.50% lower secondary A–F n.a. 9.40% lower secondary G–U 15.00% 71.50% -5.00% 9.70% SK 041: Prešovský (GDP p.c. 35%, unemp. rate 10.0%) upper secondary A–F 0.00% 79.00% 0.00% 9.60% upper secondary G–U 25.00% 72.00% -7.00% 10.90% lower secondary A–F 0.00% 60.00% -10.00% 17.50% lower secondary G–U 15.00% 66.00% -3.00% 12.20% lower secondary G–U 35.00% 66.00% -10.00% 12.20% SK 042: Košický (GDP p.c. 48%, unemp. rate 8.7%) upper secondary A–F 10.00% 77.00% 0.00% 10.00% upper secondary G–U 10.00% 74.00% 3.00% 11.30% lower secondary A–F 10.00% 52.00% -14.00% 15.80% lower secondary G–U 35.00% 57.00% -10.00% 14.60% Notes: study field by the NACE: A: Agriculture, Forestry and Fishing; B: Mining and Quarrying; C: Manufacturing; D: Electricity, Gas, Steam and Air Conditioning Supply; E: Water Supply; Sewerage, Waste Management and Remediation Activities; F: Construction; G–U: all other NACE sectors; n.a. – no supported school exists and/or no full-time series on graduates in the last five years are available; average – average values for the non-supported schools. The scheme seemed to work generally better for the service fields of study than for the manufacturing ones. An examination of the individual school’s curriculum suggests that the ‘manufacturing’ courses mostly related to rather narrowly defined professions in metal processing, the manufacture of machinery, consumer electronics, and chemistry. Slovakia is a small and extremely open economy, with exports of goods nearing 95% of the GDP. The aforementioned industries actually dominated Slovak exports and it is not surprising that there were plenty of respective study fields. The manufacturing industries, however, accounted for only about one quarter of the total employment, while the remainder was provided by the service sector. The export-oriented (often foreign-owned) manufacturers predominantly settled in a developed western part of the country. The poor eastern regions, on the other hand, experienced a period of substantial deindustrialisation in the 1990s. It was easier to find service-based jobs in these regions. Typical study fields involved rather generic cross-cutting literacy competences in ‘entrepreneurship’ and/or ‘business studies’. Different performances by the service versus manufacturing-oriented graduates may relate to the diverse structures of regional economies. The suboptimal performance of the scheme in relatively prosperous regions may refer to higher competences of students from middle-class families. They had less to gain from the scheme than did students from poorer backgrounds. Table 4: Factor analysis – rotated component matrix Factor 1 Factor 2 Factor 3 Number of enterprises per 1,000 population 0.859 -0.008 -0.200 Share of the population aged 24–35 who have a university degree in the total population of the district aged 24–35 0.836 -0.461 -0.016 Share of the population in settlements with more than 5,000 inhabitants 0.808 -0.279 -0.099 Average monthly wage in the district 0.802 -0.270 -0.353 Share of the population receiving material deprivation benefits -0.260 0.917 0.233 Rate of registered unemployment in the district -0.306 0.850 0.310 Share of the MRC in the total population -0.179 0.806 0.391 Gross divorce rate (per 1,000 inhabitants) 0.192 -0.253 -0.871 Road distance from Bratislava -0.188 0.421 0.815 Source: authors’ computations 5. Conclusions, limitations, and directions for further research This research examined the effects of the ‘Linking Secondary Education and Practice’ scheme. The scheme supported students in lower and upper secondary schools. It aimed at building cross-cutting literacy competences (rather than narrowly defined professional knowledge). The research results indicate that the scheme activities were correctly chosen and enhanced students’ competences demanded by employers. Specific competences (such as reading, mathematical, financial and ICT literacy (including basic entrepreneurship skills) and language competences) resonated with the principles of the international assessment of professional skills of vocational education and training for employment (OECD PISA‑VET). As shown by the DiD and SCM, graduates of supported secondary schools found it easier to obtain jobs than did those from unsupported schools. The most interesting results of the research relate to the type of study programme and the region of student origin: (a) the scheme seemed to work better for students taking lower (rather than upper) secondary vocational education—the average dropout rate used to be substantially higher in the former educational programme than in the latter one; (b) the scheme results (in terms of shares of successful and/or job-finding graduates) were generally better in the poorest Slovak NUTS 3 regions, including Košický, Prešovský and Banskobystrický kraj s. These regions account for high unemployment rates and high proportions of students from marginalised Roma communities. The regression analysis aligns with findings from OECD studies such as the PISA assessment (OECD, 2023 : 107). Consistent with prior research, our analysis identified students’ socioeconomic backgrounds to be the most critical determinants of educational outcomes, particularly in lower secondary education. Students from districts typically with high proportions of marginalised Roma populations, high unemployment rates, and a dependency on material deprivation benefits faced the greatest challenges and generated the highest dropout rates. These findings suggest several policy recommendations for secondary vocational education: (1) Poverty and marginalisation are key issues in lower secondary vocational education. Upper secondary vocational tracks predominantly involve students from middle-class families. The impact of the socioeconomic environment on academic success is notably smaller in these studies in comparison to lower secondary tracks. (2) Policies aimed at lower secondary vocational education should place greater emphasis on social inclusion as a fundamental condition for the study-to-job transition. This should be prioritised more than in the past. Labour market inclusion through cooperation with employers may offer an effective strategy to enhance students’ overall social integration. However, it is essential to engage more mentors and/or social workers with experience in marginalised communities. (3) Results in VET studies exhibit significant territorial variation. Consequently, policy interventions should consider this geographical dimension and tailor policies accordingly to address regional disparities effectively. Our research has some notable limitations. Some limitations relate to traditional data constraints. Data on student performance, for example, were available for a relatively short time period (2019–2023). Other limitations refer to analytical procedures. The SCM has several advantages over traditional DiD. For example, it enabled an assessment of performance by students from individual schools (rather than a pool of VET institutions). Unlike DiD, the SCM does not require parallel trends for treated and non-treated units in the pre-test period. The SCM, of course, has its own shortcomings. The quality of synthetic units depends on the size and structure of the donor pool. The SCM is a non-probabilistic method and there is no general agreement on the methods for measuring the quality of the model fit. The ‘Linking Secondary Education and Practice’ scheme aimed at building cross-cutting literacy competences (rather than narrowly defined professional knowledge). We assume that these skills were key to the success of graduates, particularly on the lower secondary track. The assumption, of course, needs further verification. The limitations suggest directions for further research. There is an opportunity to use longer datasets and follow student performance over the long term. Another research direction relates to the nature of cross-cutting literacy competences. What specific competences (reading, mathematics, financial and/or entrepreneurial) proved to be the most useful for the employment of graduates in lower versus upper secondary education? These issues are best explored using qualitative research methods. Declarations Author contributions Vladimír Baláž, Conceptualization, Methodology, Formal analysis, Writing–original draft, Writing–review and editing; Dušana Dokupilová, Conceptualization, Methodology, Data curation, Formal analysis, Mathematical computations, Writing–review and editing; Funding acknowledgement This research was supported by Slovak VEGA Grant No. 2/0001/22 Data availability The analyses are based on data provided by the Slovak Ministry of Investment and Regional Development and Information and the Ministry of Labour, Social Affairs and Family, respectively. The authors have no permission to distribute this data; however, the data is available for scientific use upon request. Competing interests: We have no known conflicts of interest to disclose. Ethics and Consent to Participate declaration: not applicable. References Abadie A, Diamond A, Hainmueller J (2010) Synthetic control methods for comparative case studies: Estimating the effect of California’s tobacco control program. J Am Stat Assoc 105(490):493–505 Abadie A, Cattaneo MD (2021) Introduction to the special section on synthetic control methods. J Am Stat Assoc 116(536):1713–1715 Allan J, Catts R (2014) Schools, Social Capital and Space. Camb J Educ 44(2):217–228 Benke M, Rachwał T (2022) The evolution of vocational education and training in Hungary and Poland 1989–2035. Hung Educational Res J 12(3):328–356 Bolli T, Caves KM, Renold U, Buergi J (2018) Beyond employer engagement: measuring education-employment linkage in vocational education and training programmes. J Vocat Educ Train 70(4):524–563 Brunello G, Rocco L (2017) The effects of vocational education on adult skills, employment and wages: What can we learn from PIAAC? SERIEs 8:315–343 Boyadjieva P, Ilieva-Trichkova P (2019) Horizontal differentiation matters: Moderating influence of the type of upper secondary education on students’ transitions. Eur Educ 51(1):32–50 Broer M, Bai Y, Fonseca F, Broer M, Bai Y, Fonseca F (2019) A review of the literature on socioeconomic status and educational achievement. Socioeconomic inequality and educational outcomes: Evidence from twenty years of TIMSS, 7–17 Bruin M, Tutlys V, Ümarik M, Loogma K, Kaminskiené L, Bentsalo I, Väljataga T, Sloka B, Buligina I (2023) Participation and learning in Vocational education and training-a cross-national analysis of the perspectives of youth at risk for social exclusion. Journal of Vocational Education & Training, pp 1–22 Brunetti I, Corsini L (2019) School-to-work transition and vocational education: a comparison across Europe. Int J Manpow 40(8):1411–1437 Cabus S, Nagy E (2021) On the productivity effects of training apprentices in Hungary: evidence from a unique matched employer–employee dataset. Empirical Economics 60(4):1685–1718 CEDEFOP, European Centre for Development of Vocational Training (2024) Choi SJ, Jeong JC, Kim SN (2019) Impact of vocational education and training on adult skills and employment: An applied multilevel analysis. Int J Educational Dev 66:129–138 Clarke L, Westerhuis A, Winch C (2021) Comparative VET European research since the 1980s: Accommodating changes in VET systems and labour markets. J Vocat Educ Train 73(2):295–315 Donald SG, Lang K (2007) Inference with Difference-in-Differences and Other Panel Data. Rev Econ Stat 89(2):221–233 Eurostat (2024) Gross domestic product (GDP) at current market prices by NUTS 3 region, Online data code: nama_10r_3gdp. 10.2908/nama_10r_3gdp Gabríková B, Švábová L (2023) Impact Evaluation of the Graduate Practice Intervention in Slovakia with the Application of the CART Method. TalTech J Eur Stud 13(1):177–200 Hanushek EA (2012) Dual education: Europe’s secret recipe? CESifo forum (Vol. 13, No. 3. ifo Institut-Leibniz-Institut für Wirtschaftsforschung an der Universität München, München, pp 29–34 Hanushek EA, Schwerdt G, Woessmann L, Zhang L (2017) General education, vocational education, and labor-market outcomes over the lifecycle. J Hum Resour 52(1):48–87 Haviland S, Robbins S (2021) Career and technical education as a conduit for skilled technical careers: A targeted research review and framework for future research. ETS Research Report Series, 2021(1), 1–42 Hoidn S, Šťastný V (2023) Labour market success of initial vocational education and training graduates: a comparative study of three education systems in Central Europe. J Vocat Educ Train 75(4):629–653 Hordern J, Shalem Y, Esmond B, Bishop D (2022) Editorial for JVET special issue on knowledge and expertise. J Vocat Educ Train 74(1):1–11 Kahanec M, Kovacova L, Polackova Z, Sedlakova M (2020) The social and employment situation of Roma communities in Slovakia. Study for the Committee on Employment and Social Affairs, Policy Department for Economic, Scientific and Quality of Life Policies, European Parliament Kogan I, Gebel M, Noelke C (2012) Educational systems and inequalities in educational attainment in Central and Eastern European countries. Stud Transition States Soc, 4(1) Kurek S, Rachwał T (2012) Vocational education and training in Poland during economic transition. In: Pilz M (ed) The Future of Vocational Education and Training in a Changing World. VS Verlag für Sozialwissenschaften, Springer, Wiesbaden, pp 321–340 Liu J, Peng P, Zhao B, Luo L (2022) Socioeconomic status and academic achievement in primary and secondary education: A meta-analytic review. Educational Psychol Rev 34(4):2867–2896 Markowitsch J, Hefler G (2019) Future developments in Vocational Education and Training in Europe: Report on reskilling and upskilling through formal and vocational education training (No. 2019/07). JRC Working Papers Series on Labour, Education and Technology Morris K (2023) Getting a foot in the door: local labour markets and the school-to-work transition. J Youth Stud, 1–21 Nilsson A (2010) Vocational Education and Training – an Engine for Economic Growth and a Vehicle for Social Inclusion? Int J Train Dev 14(4):215–272 Noelke C, Horn D (2014) Social transformation and the transition from vocational education to work in Hungary: a differences-in-differences approach. Eur Sociol Rev 30(4):431–443 OECD (2019a) PISA 2018 Results (Volume II): Where All Students Can Succeed, PISA. OECD Publishing, Paris. https://doi.org/10.1787/b5fd1b8f-en OECD (2019b) Skills Matter: Additional Results from the Survey of Adult Skills, OECD Skills Studies. OECD Publishing, Paris. https://doi.org/10.1787/1f029d8f-en OECD (2021) Beyond Academic Learning: First Results from the Survey of Social and Emotional Skills. OECD Publishing, Paris. https://doi.org/10.1787/92a11084-en OECD (2023) PISA 2022 Results (Volume I): The State of Learning and Equity in Education, PISA. OECD Publishing, Paris. https://doi.org/10.1787/53f23881-en OECD (2024) PISA Vocational Education and Training (VET): Assessment and Analytical Framework, PISA. OECD Publishing, Paris. https://doi.org/10.1787/b0d5aaf9-en Rausch A, Abele S, Deutscher V, Greiff S, Kis V, Messenger S, Winther E (2024) Designing an International Large-Scale Assessment of Professional Competencies and Employability Skills: Emerging Avenues and Challenges of OECD’s PISA-VET. Vocations Learn, 1–40 SOSR, Statistical Office of the Slovak Republic (2024) Demographic and Social Statistics, DATAcube online database, available at: https://datacube.statistics.sk/#!/lang/en Straková J (2015) Strong vocational education–a safe way to the labour market? A case study of the Czech Republic. Educational Res 57(2):168–181 Salas-Velasco M (2024) Vocational education and training systems in Europe: A cluster analysis. Eur Educational Res J 23(3):434–449 Salvà F, Pinya C, Álvarez N, Calvo A (2019) Dropout prevention in secondary VET from different learning spaces: A social discussion experience. Int J Res Vocat Educ Train 6(2):153–173 Šćepanović V (2020) Skills on wheels: Raising industry involvement in vocational training in the Czech Republic, Slovakia and Hungary, Chap. 16, pp. 401–428, in: A. Covarrubias, V. Sigfrido and M. Ramírez Perez (eds): New Frontiers of the Automobile Industry: Exploring Geographies, Technology, and Institutional Challenges, Palgrave Studies of Internationalization in Emerging Markets Shalem Y (2014) What Binds Professional Judgement - the Case of Teaching. In Knowledge, Expertise and the Professions, edited by M. Young and J. Muller, 93–105. Abingdon: Routledge Straková J (2015) Strong vocational education–a safe way to the labour market? A case study of the Czech Republic. Educational Res 57(2):168–181 Švábová L, Kramárová K, Ďurica M (2021) Evaluation of the effects of the graduate practice in slovakia: comparison of results of counterfactual methods. Cent Eur Bus Rev 10(4):1 Tūtlys V, Vaitkutė L (2022) Knowledge Formation Practices in the Context of the VET Curriculum Reform in Lithuania. J Vocat Educ Train 74(1):126–145 Tūtlys V, Buligina I, Dzelme J, Gedvilienė G, Loogma K, Sloka B, Tikkanen TI, Tora G, Vaitkutė L, Valjataga T, Ümarik M (2022) VET ecosystems and labour market integration of at-risk youth in the Baltic countries: implications of Baltic neoliberalism. Educ + Train 64(2):190–213 Tūtlys V, Daukilas S, Mičiulienė R, Čiučiulkienė N, Krikštolaitis R (2024) The competence-based VET curriculum and teaching of work values: the case of Lithuania. Eur J Train Dev 48(3/4):298–317UHP Winch C (2010) Dimensions of Expertise: A Conceptual Exploration of Vocational Knowledge. Continuum, London Young M, Hordern J (2022) Does the vocational curriculum have a future? J Vocat Educ Train 74(1):68–88 Footnotes A complete list of Slovak districts, their official codes, as well as information on their area and population can be found at: http://www.statoids.com/ysk.html . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5783632","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":403505995,"identity":"0ae62c2b-f667-4525-97af-f24edd5ec94f","order_by":0,"name":"Vladimír Baláž","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtklEQVRIiWNgGAWjYDACCQbGAwwMNiAG8VoYgFrSSNdymAQtBrebHxz48ed8Yv/s5oMPGGpsoglruXPM4GAPz+3EGXeOJRswHEvLbSCkRXJGgsEBHonbiQ03cswkGBsOE6Ml/cPBPwbnEucTrYVfIsfgME/CgcQNxGuROVNwWOZAsvHGG2nJBgnE+IVNun3jwzd/7GTn3Ug++OBDjQ1hLTDgCFaZQKxyELAnRfEoGAWjYBSMMAAAd7pFmh6sXX4AAAAASUVORK5CYII=","orcid":"","institution":"Institute for Forecasting","correspondingAuthor":true,"prefix":"","firstName":"Vladimír","middleName":"","lastName":"Baláž","suffix":""},{"id":403505996,"identity":"e5660603-a106-478a-a77d-eb818cef693c","order_by":1,"name":"Dušana Dokupilová","email":"","orcid":"","institution":"Institute for Forecasting","correspondingAuthor":false,"prefix":"","firstName":"Dušana","middleName":"","lastName":"Dokupilová","suffix":""}],"badges":[],"createdAt":"2025-01-07 18:23:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5783632/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5783632/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":74251200,"identity":"28b40d0a-bfc9-43f4-a44a-7f221f1aa335","added_by":"auto","created_at":"2025-01-20 10:31:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":333565,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelates and outcomes of secondary vocational education in Slovakia. Source: SOSR (2024). Note: created with Datawrapper software.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5783632/v1/18306c5ad0a43618353f0829.png"},{"id":74252761,"identity":"441f76d7-c26d-4072-a798-cebe8d0a5550","added_by":"auto","created_at":"2025-01-20 10:47:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":36202,"visible":true,"origin":"","legend":"\u003cp\u003eExtent and intensity of VET in Europe. Source: CEDEFOP 2024\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5783632/v1/07c662c2bfc9f5f6811884e2.png"},{"id":74251196,"identity":"1d0b3127-367b-4ae8-9893-96c5dadc1d81","added_by":"auto","created_at":"2025-01-20 10:31:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":47204,"visible":true,"origin":"","legend":"\u003cp\u003eSociodemographics of the Slovak VET. Sources: Slovak Centre for Scientific and Technology Information and authors’ computations\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5783632/v1/8d8fd381e76abfa35d6d375f.png"},{"id":74252055,"identity":"db7eb3a6-954a-4ccf-8aef-cff8d5832213","added_by":"auto","created_at":"2025-01-20 10:39:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":51979,"visible":true,"origin":"","legend":"\u003cp\u003eUnemployment rates by sex, age, and educational attainment level (%). Source: Eurostat 2024: DOI: 10.2908/lfsa_urgaed\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5783632/v1/84f01a53541d549ecd452c45.png"},{"id":74251202,"identity":"12bd3287-605d-413c-a68d-7b6d315cd073","added_by":"auto","created_at":"2025-01-20 10:31:01","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":251208,"visible":true,"origin":"","legend":"\u003cp\u003eSupport per student by district. Source: authors’ computations\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5783632/v1/2a9be9af86619d07875630d8.png"},{"id":77996811,"identity":"15860e10-02d1-4d73-a3e5-0451581c8abb","added_by":"auto","created_at":"2025-03-07 15:46:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1499827,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5783632/v1/dad1461f-391c-4dfb-b8c2-0287a8afbc80.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Improving Employability: The Role of Cross-Cutting Competences in Vocational Education Outcomes","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eVocational education and training (VET) students, in theory, should find work more quickly and obtain better jobs than graduates of general secondary programmes. Yet, many national and cross-national studies point to substantial differences in the ease of the school-to-job transition. The transition is often complicated and depends on a number of regional- and local-level factors. Individual secondary vocational schools offer subjects of heterogeneous quality and may differ in the quality of teaching vocational subjects. The quality, unsurprisingly, varies among developed versus less developed regions. In the countries of Central and Eastern Europe (CEE), for example, vocational schools in rural areas are generally of lower quality than are those located in cities (Tūtlys \u0026amp; Vaitkutė, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). What is more, each school has its own institutional culture. Some schools, for example, place more emphasis on the admissions process and student quality in comparison to others. The school\u0026rsquo;s reputation may inform the employer\u0026rsquo;s willingness to hire the graduates (Bruin et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis paper examines the effects of the \u0026lsquo;Linking Secondary Education and Practice\u0026rsquo; scheme. The scheme was managed by the Slovak Government and aimed at improving success rates in secondary vocational education. The scheme involved 25,219 students in 74 schools and supported cross-cutting literacy competences (OECD, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003eb\u003c/span\u003e) such as reading and mathematical skills. The scheme activities were developed under different study programmes (lower versus upper secondary vocational education), study fields, and types of regions (prosperous versus underdeveloped). The diversity of contexts enables analysing multiple determinants of intervention outcomes.\u003c/p\u003e \u003cp\u003eThe remaining part of this section examines the theoretical foundation of the skill-based employability of the secondary graduates. It firstly discusses the importance of professional versus cross-cutting literacy competences in the school-to-job transition and then turns to individual- and school-level determinants of the transition. Chapter two presents the system of VET in Slovakia and examines key determinants and outcomes of school-to-job transitions. Furthermore, the chapter introduces policy interventions aimed at enhancing cross-cutting literacy competences among the VET students. Chapter three states the data sources and analytical methods employed in the scheme evaluation. Chapter four presents the results of the factor and regression analysis, as well as difference-in-differences (DiD) and the synthetic control method (SCM). The concluding part of the paper discusses major findings of the research and suggests some policy recommendations. Moreover, it states some limitations and suggests directions for further research. The research has some novel elements. Most previous studies examined school-to-job transitions at the national level. This one was performed at both regional and individual school levels. It also takes into account the type of educational programme (lower versus upper secondary) and the field of study. A higher level of detail enabled analysing some important correlates of the VET outcomes. While DiD is frequently applied in VET studies, the SCM was, to the authors\u0026rsquo; best knowledge, used for the first time to analyse the performance of individual VET schools.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1 What education, what skills?\u003c/h2\u003e \u003cp\u003eEmployability is defined as \u0026ldquo;a set of cognitive and non-cognitive competencies that are relevant across work areas and are considered a prerequisite for successful participation in modern labour markets and social life\u0026rdquo; (Rausch et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e: 419). Such concepts are also understood to be \u0026ldquo;basic skills\u0026rdquo;, \u0026ldquo;cross-cutting skills\u0026rdquo;, \u0026ldquo;transferable skills\u0026rdquo;, \u0026ldquo;key competences\u0026rdquo;, \u0026ldquo;soft skills\u0026rdquo;, or \u0026ldquo;21st century skills\u0026rdquo;. OECD studies (OECD, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003eb\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) have emphasised the following in particular: (i) problem-solving abilities; (ii) critical thinking; (iii) reading, math and ICT literacy; (iv) communication, cooperation in solving tasks; and (v) the ability to regulate emotions, and openness to new ideas. The work-related values and attitudes to work also are important for transition from the school to work transition (Tūtlys et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The OECD working group (Rausch et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) selected literacy, problem solving, and work performance as key skills. These skills and competences also form the basis of the international assessment of professional skills of vocational education and training for employment (OECD \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe goals and contents of VET studies are important prerequisites for students\u0026rsquo; employability. Contents of VET courses are subject to debate in professional and scientific literature (Hordern et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The traditional view is that students should receive as much knowledge as possible in relation to their future profession. Systematically organised knowledge of a certain field, which is verified by practice (\u0026ldquo;know what\u0026rdquo;), is the basis for making correct judgments and decisions (Winch \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Shalem \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Work-based training may significantly enhance the extent and quality of professional knowledge. The newer view puts more emphasis on the worker\u0026rsquo;s personal experience of solving specific problems in specific situations (knowhow, \u0026ldquo;knowing how\u0026rdquo;). This type of knowledge is extremely important in a fast-changing world in which employers\u0026rsquo; demands for the skills of their employees are also constantly changing. From this point of view, traditional secondary school curricula can quickly become outdated (Young and Hordern \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The teaching of systematically organised knowledge is undoubtedly important for the professionalism of future secondary school students.\u003c/p\u003e \u003cp\u003eHowever, the dynamic development of society, the economy and technology increasingly favours the flexible and innovative application of skills to solve specific problems in specific contexts. A student\u0026rsquo;s ability to obtain a job is no longer based on a set of learned knowledge, but rather on a set of problem-solving competences. Hanushek (2013: 31) notes: \u0026ldquo;A successful system of vocational training cannot neglect developing strong cognitive skills in individuals.\u0026rdquo; Several large-scale evaluations based on the International Adult Literacy Survey (IALS) (Hanushek et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and/or the Program for the International Assessment of Adult Competencies (PIAAC) (Brunello and Rocco \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Choi et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) found that vocational track graduates have lower literacy competences than do general track graduates. Vocational track graduates with work-based training may find it easier to obtain their first job, but general track graduates with school-based training collect a higher employment premium over their lifetime. Brunetti and Corsini (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) came to a similar conclusion after analysing European Labour Force data. The findings point to the importance of generic cross-cutting literacy competences.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.2 What students, what transition?\u003c/h2\u003e \u003cp\u003eVET programmes tend to have a strong focus on labour market needs and the employability of graduates. A number of institutional factors may impact the differentiation of career paths of VET graduates (Boyadjieva and Ilieva-Trichkova \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe efficiency of the school-to-job transition may depend on the extent of the work-based training. The apprenticeship-oriented courses have actually been proven to be quite successful in facilitating young people\u0026rsquo;s transition into work across European countries (Markowitsch and Hefler \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e: 7). Most European countries currently manage school-based VET systems rather than work-based ones (Markowitsch and Hefler \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Some countries combine high shares of VET students among total secondary students with high shares of work-based training (Germany, Austria, Switzerland, Norway; Annex, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Other countries are typical with a relatively low share of vocational students among the total population of secondary students with low intensity of the work-based training (CEDEFOP 2024). The Czech and Slovak Republics and Belgium belong to a cluster of countries combining a high share of VET students in overall secondary education with a relatively low intensity of work experience at the employer, i.e. most education takes place within the formal school system (Salas-Velasco \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Employer involvement in the definition and updates of VET curricula, however, remains important for a smooth education-to-employment transition in most European countries (Bolli et al. 2017).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe demand for labour may vary considerably among less and more qualified young people. School leavers from lower secondary education, for example, may find it more difficult to find their first and, subsequently, stable employment than their better-qualified peers (Morris \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSecondary vocational education sometimes is considered to be an effective tool for promoting social inclusion, as it can facilitate the transition from studies to employment. Dual education was supposed to help apprentices to establish themselves with potential future employers (Nillson, 2010: 251). Career and technical education may provide students from disadvantaged backgrounds with the first step on the pathway towards the middle class (Haviland and Robbins \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This idea, however, is not always fulfilled. In CEE countries, for example, apprenticeships and vocational training used to have a less favourable image than did upper secondary schools and gymnasiums preparing students for university in general. This image has not yet been completely overcome and de facto causes a negative selection of students for lower secondary vocational education. In this educational programme, there are therefore proportionately more students at risk of social exclusion than in gymnasiums (Choi et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Tūtlys et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Bruin et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Education and skills alone are not sufficient for students to be able to apply themselves in the labour market after graduation. The social inclusion of VET students is currently considered to be an equally important goal for their employment in the labour market (Nilsson \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Allan and Catts \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Salv\u0026agrave; et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe effectiveness of VET (the ease of the study-to-work transition) is subject to debate and may vary across countries and institutional contexts. As for CEE countries, the research tends to bring inconclusive and often contrasting results.\u003c/p\u003e \u003cp\u003eStrakov\u0026aacute; (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) employed logistic regression to analyse IALS and PIAAC datasets. Her findings highlighted the limited effectiveness of the dual education system in facilitating the transition from education to employment in the Czech Republic. Similarly, Hoidn and Šťastn\u0026yacute; (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) used linear regression to examine PIAAC data from the Czech Republic, Austria, and Germany. For the Czech Republic, no significant relationship was identified between participation in dual education and subsequent employability. Employers in the Czech Republic tended to value upper secondary education diplomas more highly than certificates of apprenticeship. In Austria, a statistically significant but modest positive effect of dual education on employability was observed. By contrast, in Germany, the completion of dual education significantly enhanced employment outcomes.\u003c/p\u003e \u003cp\u003eNoelke and Horn (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) examined the effects of training vocational education students at employers\u0026rsquo; workplaces versus in schools in Hungary during the period 1993\u0026ndash;1999. Their analysis revealed that the shift from workplace-based to school-based education was associated with higher unemployment rates and lower job quality among recent graduates. Using the DiD method, the authors found that these negative effects were more pronounced for men, with the risk of unemployment increasing by up to 10% within the first two years after graduation.\u003c/p\u003e \u003cp\u003eCabus and Nagy (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) analysed the impact of on-the-job training for apprentices at employers\u0026rsquo; workplaces on company productivity and apprentice employment in Hungary from 2003 to 2011. Their findings indicated that vocational training for apprentices negatively affected company productivity. Furthermore, the retention rate of apprentices with their employers was quite low and steadily declined over the course of the training period.\u003c/p\u003e \u003cp\u003eŠv\u0026aacute;bov\u0026aacute; et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) explored the effects of active labour market policies on the employment of young school graduates using a sample of 12,953 Slovak graduates from 2014\u0026ndash;2015. Employing multiple analytical methods, including regression adjustment, propensity score matching, and instrumental variables, the authors concluded that graduates supported by the European Social Fund retained their jobs significantly longer than did those without such support. Building on this research, Gabr\u0026iacute;kov\u0026aacute; and Šv\u0026aacute;bov\u0026aacute; (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) applied classification and regression tree methods to the same sample. They found that supported graduates were employed, on average, three months longer during the observed two-year period than were unsupported graduates.\u003c/p\u003e \u003cp\u003eThe inconclusive results may have been impacted by diverse socioeconomic and sociodemographic backgrounds, but also school-level variables. The education-to-work transition, for example, is easier to achieve during times of tight labour markets of the 2020s than during times of high unemployment in the early 2010s. The labour market for lower secondary students / apprentices is different from those for upper secondary graduates, who may have more transferable skills. The intensity of work-based training varies considerably among EU countries. Finally, the employability of VET students may vary with the study field (e.g. manufacturing versus service-based occupation) and situation in regional labour markets. It follows that an analysis of the school-to-job transition should be performed at the school level, rather than the national one, so as to account for local/regional socioeconomic contexts, as well as the types of educational programmes (lower versus upper secondary VET) and study fields.\u003c/p\u003e \u003c/div\u003e"},{"header":"2. Vocational education and training in Slovakia: the case for intervention","content":"\u003cp\u003eSecondary vocational education in Slovakia has a long history exceeding one century. The industrialisation period of the 1950s and 1960s marked a pivotal change. The demand for skilled workers in the rapidly growing manufacturing sector led to significant expansion in vocational education. This era was characterised by population growth, improved living standards, and a cultural shift that prioritised professional education linked to secondary diplomas. The number of secondary vocational education students increased more than sevenfold between 1945 and 1989 (Annex, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe post-1989 demographic, economic and social transformation significantly impacted secondary vocational education in Slovakia, leading to a gradual decline in student enrolment and a reduction in the number of vocational schools. Initially, the number of students in secondary vocational schools peaked in 1995, driven by demographic cohorts born between 1978 and 1982 and the establishment of new, predominantly private vocational schools.\u003c/p\u003e \u003cp\u003eFrom the mid-1990s onwards, the number of vocational students began to decline. This was due to a combination of demographic changes, such as smaller cohorts born in the 1980s and 1990s, and significant restructuring of the economic and social landscape.\u003c/p\u003e \u003cp\u003eFirstly, the share of the 15\u0026ndash;19 age group in the total population dropped from 8.9% in 1995 to 4.8% in 2023 (Annex, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e), shrinking the pool of potential vocational students. Secondly, the transition from central planning to a market economy in Slovakia, marked by the privatisation of state enterprises, significantly weakened secondary vocational education. Private business owners showed little interest in sustaining vocational training, a trend mirrored in other transitional economies such as Poland and Hungary. Neoliberal market reforms emphasised fixed-term contracts over lifelong employment, leading many employers to cancel in-house training programmes across Europe. New private business owners were generally not interested in maintaining in-house VET programmes (Kogan et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Similar processes took place in other transitional economies, e.g. Poland (Kurek and Rachwał \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and Hungary (Benke and Rachwał \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Thirdly, vocational schools entered into competition with general (upper) secondary education. High school graduates faced high unemployment rates, while university graduates enjoyed better job prospects, lower unemployment, and higher salaries during the transitional period in the 1990s. Fourthly, the decline in numbers of VET students was exacerbated by the state\u0026rsquo;s lack of attention to vocational education.\u003c/p\u003e \u003cp\u003eA renaissance of vocational education related to the arrival of foreign investors, particularly in the automotive industry. From the 2000s, the Czech and Slovak Republics and Hungary became major exporters of cars and car parts. The Slovak Government responded to the needs of industry and passed the 184/2009 Law on vocational training and education. The law provided for voluntary involvement of professional and/or employer organisations in the national, regional and sectoral VET councils (Šćepanović \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The VET programmes, however, were financed by the state only and training was performed in schools (rather than with employers).\u003c/p\u003e \u003cp\u003eSince the mid-2010s manufacturing industries have coped with a significant lack of skilled labour. Demographic transitions and some negative social developments have been key factors behind the labour shortages. Specifically, some social groups became increasingly marginalised (the Roma communities in particular, Kahanec et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and lost access to secondary education. There is ample evidence on the importance of socioeconomic background for success in studies and employment (Broer et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Tūtlys et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Academic outcomes for lower and upper secondary students in Slovakia (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003e) are strongly influenced by socioeconomic and regional factors. Key determinants include parental education, employment status, poverty levels, family structure, and the socioeconomic characteristics of the region, such as the proportion of marginalised Roma communities. There are vast economic and social disparities between the relatively developed western and poor eastern regions of Slovakia (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The disparities transferred to student performance. The poorest Slovak districts (NUTS 4, Lau 1 levels)\u003csup\u003e1\u003c/sup\u003e, such as Rev\u0026uacute;ca (RA) and Košice-okolie (KS), for example, accounted for the highest dropout rates in lower secondary vocational education (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Students from middle-class families self-selected for upper secondary schools with substantially lower dropout rates (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Slovak labour market experienced a dramatic transition over a quarter of a century. The overall unemployment rate fell from 20% in 2000 to a mere 5% in 2024. The rate was close to 2% in developed regions in the western part of the country (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Employers found it difficult to hire any Slovak graduates and imported labour from non-European countries. The situation was different in less developed regions in the eastern part of Slovakia, with unemployment rates exceeding 10%. What is more, the unemployment rates for attainment level ISCED 0\u0026ndash;2 and age group 15\u0026ndash;24, already decreasing in the 2010s, resurged to 59%, by far the highest level in the EU27 in 2023 (Annex, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Any scheme supporting VET graduates therefore was likely to have more tangible effects in the lagging-behind regions than in the developed ones.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on the literature review, the following hypotheses are stated:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH1\u003c/strong\u003e Students in lower secondary vocational education benefit from the literacy-aimed scheme more than those in upper secondary vocational education.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH2\u003c/strong\u003e Students from regions with higher unemployment and poverty rates benefit from the literacy-aimed scheme more than those from relatively prosperous regions.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThe Slovak Government used financial means from the European Social Fund and designed the \u0026lsquo;Linking Secondary Education and Practice\u0026rsquo; scheme. The scheme was launched in 2019 and supported cross-cutting literacy competences (OECD, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003eb\u003c/span\u003e), such as reading, mathematical, financial and ICT literacy (including basic entrepreneurship skills), and language competences. The skills acquired via the scheme were supposed to improve the school-to-job transition and, specifically, decrease (i) shares of dropouts and (ii) unemployment rates of graduates. The scheme was implemented via accredited courses performed in secondary vocational schools, rather than through work-based training. It is generally difficult to provide work-based training, particularly in underdeveloped regions with no large private employers. The scheme envisaged extra schooling for students from disadvantaged socioeconomic backgrounds, including marginalised Roma communities.\u003c/p\u003e \u003cp\u003eThe scheme allocated \u0026euro;30m, \u0026euro;28.5m of which to the non-Bratislava regions (NUTS 3 levels). Some 74 schools benefitted from the scheme. The average support per student ranged from \u0026euro;314 to \u0026euro;1,281, with the poorest districts in the Slovak East receiving somewhat more than relatively prosperous ones in the Slovak West (Annex, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"3. Data and methods","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Data sources\u003c/h2\u003e \u003cp\u003eThe data on the scheme and on the performance of particular secondary schools in the period 2019\u0026ndash;2023 were provided by the Ministry of Investment and Regional Development and Information and the Ministry of Labour, Social Affairs and Family, respectively. The literature review established contrasting results of studies on the education-to-work transition. We decided to focus on school-level data, rather than on national-level data, so as to account for education-, industry- and regional-level correlates of the school-to-job transition. The school-level data enable distinguishing between students in lower versus upper secondary education. What is more, we distinguish between graduates in the manufacturing versus service-oriented study fields. Each school is analysed within its own regional socioeconomic setting. The Statistical Office of the Slovak Republic (SOSR 2024) provides quite detailed data on the socioeconomic performance of 79 Slovak districts. We use these data as background explanatory variables for the efficiency of the school-to-job transition.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Research design\u003c/h2\u003e \u003cp\u003eWe start with computing the socioeconomic correlates of the study-to-job transition at the regional levels. Many socioeconomic variables are highly correlated. We perform factor analysis so as to merge the high number of independent variables into a smaller number of meaningful factors. The factor scores are firstly used as inputs into the linear regression and later in the DiD and SCM.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003ch3\u003e4.1 Factor and regression analysis\u003c/h3\u003e\n\u003cp\u003eNine variables were used to capture the socioeconomic background of the districts: (1) number of enterprises in the district (per 1,000 inhabitants); (2) share of the population aged 24\u0026ndash;35 who have a university degree in the total population of the district aged 24\u0026ndash;35 (%); (3) urban population, share of the population in settlements with more than 5,000 inhabitants in the total population of the district (%); (4) average wage in the district as a percentage of the average wage in Slovakia; (5) unemployment rate (%); (6) share of the population receiving material deprivation benefits in the total population of the district (%); (7) share of marginalised Roma communities (MRC) in the total population of the district (according to the 2022 Atlas of Roma communities); (8) gross divorce rate (per 1,000 inhabitants); and (9) road distance from Bratislava (km).\u003c/p\u003e\n\u003cp\u003eTo minimise the influence of random fluctuations in individual years, the analysed variables were calculated as averages for the period 2014\u0026ndash;2023. The sole exception was the indicator of higher education attainment, which was available only for the years 2021\u0026ndash;2023. To account for inflation, the district\u0026rsquo;s average wage was not represented by its nominal value over the last decade, but rather as a percentage of Slovakia\u0026rsquo;s average wage for each year.\u003c/p\u003e\n\u003cp\u003eThe original set of nine independent variables was transformed into three factors which collectively explained over 85.7% of the variance in the sample (Annex, Table 4):\u003c/p\u003e\n\u003cp\u003e\u0026middot; Factor 1 (\u0026ldquo;prosperity\u0026rdquo;) explained 33.31% of the total variance and reflects the educational, business and economic development of the district.\u003c/p\u003e\n\u003cp\u003e\u0026middot; Factor 2 (\u0026ldquo;poverty\u0026rdquo;) explained 31.30% of the total variance and captures the prevalence of poverty, unemployment, and the presence of marginalised Roma communities within the district.\u003c/p\u003e\n\u003cp\u003e\u0026middot; Factor 3 (\u0026ldquo;distance\u0026rdquo;) explained 23.13% of the total variance and represents geographical and cultural disparities between the urbanised West with a relatively affluent and educated population on the one hand and the more traditional East with a conservative rural population on the other hand.\u003c/p\u003e\n\u003cp\u003eThe factor scores were used as inputs into ordinary least squares regression (Table 1). The regressions yielded the following results:\u003c/p\u003e\n\u003cp\u003e\u0026middot; In both regressions, only Factor 2 (poverty), which combines the share of the population in material need in the total population, the rate of registered unemployment, and the share of MRC in the total population (%), proved to be statistically highly significant. Factors 1 and 3, which characterised relative prosperity or geographical and cultural disparities of Slovak regions, became insignificant at the 0.05 level in both regressions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThere is one fundamental difference between the two regressions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026middot; The regression that focused on the differences between districts in lower secondary studies explained up to 41.6% of all differences (adjusted R squared = 0.416). The remaining differences are attributable to other factors that most likely characterise the activities of individual schools.\u003c/p\u003e\n\u003cp\u003e\u0026middot; The regression that focused on upper secondary studies explained only 11.7% of all differences (adjusted R squared = 0.117). The poverty factor was also the only statistically significant variable in this regression, but its influence on the study results was much lower than in the lower secondary studies. For example, the standardised regression beta coefficient (0.360) was almost half of that in the previous regression (0.668).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis result suggests that success or failure in the upper secondary (vocational) programme is determined by reasons beyond students\u0026rsquo; socioeconomic backgrounds. The reasons may include variations in school equipment quality, teaching methodologies, and/or the extent of collaboration with industry.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1:\u003c/strong\u003e OLS regression \u0026ndash; factors impacting success in lower and upper secondary education\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cem\u003eStd. Error\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cem\u003eBeta\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;t\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;Sig.\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cem\u003eTolerance\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cem\u003eVIF\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cem\u003eLower secondary\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e31.392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cem\u003e1.346\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e23.324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eFactor 1: prosperity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e1.366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1.642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.832\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e1.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eFactor 2: poverty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e9.835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1.345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e7.312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e1.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eFactor 3: distance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1.322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e1.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cem\u003eAdjusted R squared\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.416\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\" valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cem\u003eUpper secondary\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e8.231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e9.670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eFactor 1: prosperity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.919\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-1.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e1.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eFactor 2: poverty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e2.871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e0.360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e3.297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e1.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eFactor 3: distance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.828\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e-0.955\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cem\u003eAdjusted R squared\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: authors\u0026rsquo; computations\u003c/p\u003e\n\u003cp\u003eThe OLS findings suggest some important policy implications. The average dropout rate was 30.8% in lower secondary education, with Slovak district Ko\u0026scaron;ice-okolie (KS) exceeding 88% and Rev\u0026uacute;ca (RA) 65%. Poverty was a key factor behind the extreme dropout rates (Figure 1). A significant reduction in the dropout rates is hardly possible without a reduction of poverty and social exclusion. The average dropout rate in upper secondary vocational education was only 6.6%, with the districts of Ko\u0026scaron;ice-okolie (KS), Rev\u0026uacute;ca (RA) and Bytča (BY) reporting the highest rates (Figure 1). It follows that lower secondary education students may benefit from public intervention more than those studying upper secondary vocational courses.\u003c/p\u003e\n\u003ch3\u003e4.2 The difference-in-differences (DiD)\u003c/h3\u003e\n\u003cp\u003eThe difference-in-differences method (DiD) enables estimating the causal effects of specific policy interventions in pre- and post-intervention periods (Donald and Lang 2007). In this case, DiD compares four different groups of schools: treated versus untreated schools in pre-test versus post-test time periods. In our case, we analysed data on graduate performance in the period 2019\u0026ndash;2023.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo effectively evaluate the impact of the intervention, it is essential to compare groups of schools that were under similar conditions prior to the intervention. Each supported school was matched with a \u0026ldquo;twin\u0026rdquo;\u0026mdash;a school that did not receive support but operated in a region with a comparable socioeconomic background and offered similar programmes and fields of study. The propensity score matching (PSM) method was utilised to establish the twin. The matching criteria included factor scores for regional economic background and variables for study programmes (lower versus upper secondary) and fields (manufacturing versus services).\u003c/p\u003e\n\u003cp\u003eThe scheme was launched in August 2019. The schools applied for support in the 2020/2021 school year. We can therefore observe the first results in 2021, which we have also determined as dividing the pre- and post-intervention periods.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe DiD results are displayed in Table 2. The scheme impacts are expressed in percentage change. At the national level, for example, the scheme contributed to a 5.0% increase in the numbers of successful students and a 1.7% decrease in the numbers of unemployed students. As for the regional level, the scheme seemed to increase the shares of successful students in the relatively prosperous regions in Western Slovakia (Bratislavsk\u0026yacute; Trenčiansky and Trnavsk\u0026yacute; \u003cem\u003ekrajs\u003c/em\u003e). The results for the poorest Slovak regions (Pre\u0026scaron;ovsk\u0026yacute; \u003cem\u003ekrajs\u003c/em\u003e) were insignificant.\u003c/p\u003e\n\u003cp\u003eTo ensure that the results are statistically significant and reliable, a sufficiently large sample size is required. However, the total number of supported schools was rather limited (n = 74), with some regions having as few as two supported schools. Consequently, results significant at the 0.1 level were only observed at the national level, and in five out of eight NUTS 3 regions. We consider the results of the DiD analysis to be inconclusive and proceed to the SCM.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2:\u003c/strong\u003e Impact of intervention on students on national and regional levels\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eNUTS 3 regions\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eNumber of supported schools\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eImpact on shares of successful students\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eImpact on shares of unemployed students\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSK 010: Bratislavsk\u0026yacute;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.8*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-2.8*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSK 021: Trnavsk\u0026yacute;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18.5*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSK 022: Trenčiansky\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.1*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSK 023: Nitriansky\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-3.6*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.8*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSK 031: Žilinsk\u0026yacute;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSK 032: Banskobystrick\u0026yacute;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSK 041: Pre\u0026scaron;ovsk\u0026yacute;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSK 042: Ko\u0026scaron;ick\u0026yacute;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-8.6*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSlovak Republic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.0*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.7*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNotes: * significant at the 0.1 level\u003c/p\u003e\n\u003ch3\u003e4.3 Synthetic control method\u003c/h3\u003e\n\u003cp\u003eThe synthetic control method (SCM) allows for a more in-depth study of the intervention impact, as it studies the impacts of the intervention at the level of the individual school. The SCM creates a weighted average of untreated units (\u0026lsquo;synthetic cohorts\u0026rsquo;) that best reproduces characteristics of the treated unit over time, prior to treatment (Abadie et al. 2010; Abadie and Cattaneo 2021). This synthetic school is made by combining non-supported schools with key features (region, socioeconomic background, type of study programme, field of study) similar to supported schools. The results of factor analysis guided this process in terms of socioeconomic background. Slovak secondary schools offer hundreds of study fields. Many study fields, however, account for very low numbers or no students. Because of the low number of supported schools, we had to group study fields into two major branches. The first branch includes NACE sections A\u0026ndash;F. We denote the group with the simplified label \u0026lsquo;manufacturing\u0026rsquo;, although it also includes a limited number of study fields in agriculture and construction. The second group includes NACE sections G\u0026ndash;U. This group is denoted as \u0026lsquo;services\u0026rsquo;. Furthermore, we distinguished between two programmes of vocational education: the lower secondary one (with no high school diploma) and the higher secondary one (with a high school diploma).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTwo to three schools with a strong influence were typically used to create a synthetic school. These schools usually informed 70\u0026ndash;95% of contents of the new synthetic school (in terms of location, socioeconomic background, and form and field of study). An additional three to four schools contributed a smaller influence, ranging from 5% to 30%. If there was an abundant number of supported and unsupported schools, more than one twin was created. If there was a limited number of non-supported schools in a given form and/or field of study, the synthetic twins proved to be impossible to create.\u003c/p\u003e\n\u003cp\u003eThe impact of the scheme was examined on the proportions of successful graduates and those unemployed. When analysing the increase in the share of successful students\u0026mdash;expressed in percentage points\u0026mdash;it is important to note that the sum of the increase and the average value can exceed 100%. This is because the increase reflects the improvement in success rates in comparison to a synthetic counterpart school, rather than the average value. A similar principle applies when analysing negative outcomes such as unemployment rates (Table 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Slovak Republic accounts for vast regional socioeconomic disparities. The Bratislava region, for example, had a GDP per capita of 118% of the EU27 average and an unemployment rate of 3.2% in 2022. The Pre\u0026scaron;ovsk\u0026yacute; region had a GDP per capita of 35% of the EU27 average and an unemployment rate of 10.0% in the same year (Eurostat 2024). We report the SCM results for all eight NUTS 3 regions (\u0026lsquo;\u003cem\u003ekrajs\u003c/em\u003e\u0026rsquo;) so as to examine the effects of the scheme in the relatively prosperous regions in Western Slovakia (Bratislavsk\u0026yacute; and Trnavsk\u0026yacute; \u003cem\u003ekrajs\u003c/em\u003e) versus the poorest ones in the east of the country (Pre\u0026scaron;ovsk\u0026yacute; and Ko\u0026scaron;ick\u0026yacute; \u003cem\u003ekrajs\u003c/em\u003e). Table 3 also displays contextual statistics on the per capita GDP as a percentage of the EU27 average (Eurostat 2024) and unemployment rates in 2022 (SOSR 2024).\u003c/p\u003e\n\u003cp\u003eThe SCM results indicate mostly positive impacts of the scheme in supported schools. Most schools saw a significant increase in their success rate\u0026mdash;through either higher employment or more students continuing their studies\u0026mdash;and decreases in shares of unemployed students. Moreover, the SCM results suggest that (with a few notable exceptions) the scheme operated well across study programmes, study fields, and regions. The most positive results were found for the service-oriented fields in the lower secondary programme (H1 confirmed) and/or in the poorest Slovak regions (H2 confirmed).\u003c/p\u003e\n\u003cp\u003eThe Pre\u0026scaron;ov region, for example, seemed to benefit most from the intervention. As for the upper secondary studies, the success rate increased by 0% in manufacturing versus 25% in service-oriented study fields. The respective decreases in the shares of unemployed students were zero and seven percentage points. As for the lower secondary studies, one manufacturing study field accounted for a 0% increase in the success rate but a 10% decrease in unemployment. Two service-oriented study field schools accounted for 10% and 35% increases in the success rate respectively. During the same time, the unemployment rates decreased by 3% and 10% respectively. The intervention showed similar positive impacts in the Ko\u0026scaron;ick\u0026yacute; region too, except for a small increase in the unemployment of upper secondary graduates in service fields. In the Žilina region the scheme seemed to operate better for the upper secondary graduates. In one service-oriented lower secondary school the success rate declined by 15%, but there was no increase in the numbers of unemployed graduates. This school is located near a major marginalised Roma community (MRC) settlement. The school location may have informed the student structure and, consequently, study outcomes. The SCM results cannot capture all of the circumstances to which schools are exposed. When choosing a synthetic twin, we only consider the district-level socioeconomic background. We are unable to account for some school-level factors such as image and/or quality of teaching. The influence of a poor image on the choice of school and, thus, self-selection into the social class is relatively common in Central and Eastern Europe (Bruin et al. 2023). Some schools are considered to be \u0026lsquo;good\u0026rsquo; and attract students from middle-class families. Some other schools may concentrate students from poor families and/or MRC. If this is the case and the share of unemployed graduates from a \u0026lsquo;poor image\u0026rsquo; school is equal to that from a \u0026lsquo;good image\u0026rsquo; synthetic school, the policy intervention may have worked quite well in this case.\u003c/p\u003e\n\u003cp\u003eThe scheme seemed to operate with limited efficiency in the more prosperous Slovak regions, except for Bratislava City. In the Trnava region (upper secondary programme), for example, the graduate success rate dropped by 15% in manufacturing, but remained unchanged in the service fields of study. The share of unemployed graduates increased by 5% in manufacturing, but decreased by 1% in the service fields of study. It was impossible to create synthetic twins in lower secondary studies. We report the results for the upper secondary studies only.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3:\u003c/strong\u003e Results of the synthetic control method\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eNUTS 3 region\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eStudy programme\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eField of study\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eShare of successful graduates (difference to synthetic school)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eAverage\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eShare of unemployed graduates (difference to synthetic school)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eAverage\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\"\u003e\n \u003cp\u003eSK 010: Bratislavsk\u0026yacute;\u003c/p\u003e\n \u003cp\u003e(GDP p.c. 116%, unemp. rate 3.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eupper secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eA\u0026ndash;F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e86.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-7.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.80%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eupper secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eG\u0026ndash;U\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e85.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-8.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.90%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003elower secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eA\u0026ndash;F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003en.a.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.60%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003elower secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eG\u0026ndash;U\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003en.a.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.70%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\"\u003e\n \u003cp\u003eSK 021: Trnavsk\u0026yacute;\u003c/p\u003e\n \u003cp\u003e(GDP p.c. 60%, emp. rate 3.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eupper secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eA\u0026ndash;F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-15.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e84.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eupper secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eG\u0026ndash;U\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e81.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003elower secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eA\u0026ndash;F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 329px;\"\u003e\n \u003cp\u003en.a.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003elower secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eG\u0026ndash;U\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 329px;\"\u003e\n \u003cp\u003en.a.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\"\u003e\n \u003cp\u003eSK 022: Trenčiansky\u003c/p\u003e\n \u003cp\u003e(GDP p.c. 49%, unemp. rate 3.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eupper secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eA\u0026ndash;F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e87.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-3.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eupper secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eG\u0026ndash;U\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e83.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-9.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003elower secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eA\u0026ndash;F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e80.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-5.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003elower secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eG\u0026ndash;U\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e76.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-6.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\"\u003e\n \u003cp\u003eSK 023: Nitriansky\u003c/p\u003e\n \u003cp\u003e(GDP p.c. 47%, unemp. rate 3.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eupper secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eA\u0026ndash;F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e84.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-3.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eupper secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eG\u0026ndash;U\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e79.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-3.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.30%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003elower secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eA\u0026ndash;F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e81.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-10.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.90%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003elower secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eG\u0026ndash;U\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-5.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e79.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.50%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\"\u003e\n \u003cp\u003eSK 031: Žilinsk\u0026yacute;\u003c/p\u003e\n \u003cp\u003e(GDP p.c. 50%, unemp. rate 4.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eupper secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eA\u0026ndash;F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e83.50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-8.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.50%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eupper secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eG\u0026ndash;U\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e81.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-7.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.90%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003elower secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eA\u0026ndash;F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e82.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-5.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.70%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003elower secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eA\u0026ndash;F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e82.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.70%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003elower secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eG\u0026ndash;U\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-15.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e79.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\"\u003e\n \u003cp\u003eSK 032: Banskobystrick\u0026yacute;\u003c/p\u003e\n \u003cp\u003e(GDP p.c. 45%, unemp. rate 8.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eupper secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eA\u0026ndash;F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003en.a.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.70%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eupper secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eG\u0026ndash;U\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e75.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.50%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003elower secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eA\u0026ndash;F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003en.a.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.40%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003elower secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eG\u0026ndash;U\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e71.50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-5.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.70%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\"\u003e\n \u003cp\u003eSK 041: Pre\u0026scaron;ovsk\u0026yacute;\u003c/p\u003e\n \u003cp\u003e(GDP p.c. 35%, unemp. rate 10.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eupper secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eA\u0026ndash;F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e79.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.60%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eupper secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eG\u0026ndash;U\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e72.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-7.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.90%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003elower secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eA\u0026ndash;F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e60.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-10.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17.50%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003elower secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eG\u0026ndash;U\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e66.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-3.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.20%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003elower secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eG\u0026ndash;U\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e35.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e66.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-10.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.20%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\"\u003e\n \u003cp\u003eSK 042: Ko\u0026scaron;ick\u0026yacute; (GDP p.c. 48%, unemp. rate 8.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eupper secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eA\u0026ndash;F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e77.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eupper secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eG\u0026ndash;U\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e74.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.30%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003elower secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eA\u0026ndash;F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e52.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-14.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15.80%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003elower secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eG\u0026ndash;U\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e35.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e57.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-10.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14.60%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNotes: study field by the NACE: A: Agriculture, Forestry and Fishing; B: Mining and Quarrying; C: Manufacturing; D: Electricity, Gas, Steam and Air Conditioning Supply; E: Water Supply; Sewerage, Waste Management and Remediation Activities; F: Construction; G\u0026ndash;U: all other NACE sectors; n.a. \u0026ndash; no supported school exists and/or no full-time series on graduates in the last five years are available; average \u0026ndash; average values for the non-supported schools.\u003c/p\u003e\n\u003cp\u003eThe scheme seemed to work generally better for the service fields of study than for the manufacturing ones. An examination of the individual school\u0026rsquo;s curriculum suggests that the \u0026lsquo;manufacturing\u0026rsquo; courses mostly related to rather narrowly defined professions in metal processing, the manufacture of machinery, consumer electronics, and chemistry. Slovakia is a small and extremely open economy, with exports of goods nearing 95% of the GDP. The aforementioned industries actually dominated Slovak exports and it is not surprising that there were plenty of respective study fields. The manufacturing industries, however, accounted for only about one quarter of the total employment, while the remainder was provided by the service sector. The export-oriented (often foreign-owned) manufacturers predominantly settled in a developed western part of the country. The poor eastern regions, on the other hand, experienced a period of substantial deindustrialisation in the 1990s. It was easier to find service-based jobs in these regions. Typical study fields involved rather generic cross-cutting literacy competences in \u0026lsquo;entrepreneurship\u0026rsquo; and/or \u0026lsquo;business studies\u0026rsquo;. Different performances by the service versus manufacturing-oriented graduates may relate to the diverse structures of regional economies.\u003c/p\u003e\n\u003cp\u003eThe suboptimal performance of the scheme in relatively prosperous regions may refer to higher competences of students from middle-class families. They had less to gain from the scheme than did students from poorer backgrounds.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTable 4:\u0026nbsp;\u003c/strong\u003eFactor analysis \u0026ndash; rotated component matrix\u003c/p\u003e\n\u003ctable style=\"border: none;width:100.0%;border-collapse:collapse;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69.84%;border-width: 1.5pt 1pt 1pt 1.5pt;border-style: double solid solid double;border-color: windowtext;padding: 0in 3.5pt;height: 14.25pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:left;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\";'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.06%;border-top: 1.5pt double windowtext;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 3.5pt;height: 14.25pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cem\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\";'\u003eFactor 1\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.06%;border-top: 1.5pt double windowtext;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 3.5pt;height: 14.25pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cem\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\";'\u003eFactor 2\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.06%;border-top: 1.5pt double windowtext;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1.5pt double windowtext;padding: 0in 3.5pt;height: 14.25pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cem\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\";'\u003eFactor 3\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69.84%;border-top: none;border-left: 1.5pt double windowtext;border-bottom: 1pt solid rgb(51, 51, 51);border-right: 1pt solid windowtext;background: white;padding: 0in 3.5pt;height: 14.25pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:left;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\";color:black;'\u003eNumber of enterprises per 1,000 population\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.06%;border-top: none;border-left: none;border-bottom: 1pt solid rgb(51, 51, 51);border-right: 1pt solid windowtext;background: rgb(221, 235, 247);padding: 0in 3.5pt;height: 14.25pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\";color:black;'\u003e0.859\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.06%;border-top: none;border-left: none;border-bottom: 1pt solid rgb(51, 51, 51);border-right: 1pt solid windowtext;padding: 0in 3.5pt;height: 14.25pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\";'\u003e-0.008\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.06%;border-top: none;border-left: none;border-bottom: 1pt solid rgb(51, 51, 51);border-right: 1.5pt double windowtext;padding: 0in 3.5pt;height: 14.25pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\";'\u003e-0.200\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69.84%;border-top: none;border-left: 1.5pt double windowtext;border-bottom: 1pt solid rgb(51, 51, 51);border-right: 1pt solid windowtext;background: white;padding: 0in 3.5pt;height: 14.25pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:left;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\";color:black;'\u003eShare of the population aged 24\u0026ndash;35 who have a university degree in the total population of the district aged 24\u0026ndash;35\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.06%;border-top: none;border-left: none;border-bottom: 1pt solid rgb(51, 51, 51);border-right: 1pt solid windowtext;background: rgb(221, 235, 247);padding: 0in 3.5pt;height: 14.25pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\";color:black;'\u003e0.836\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.06%;border-top: none;border-left: none;border-bottom: 1pt solid rgb(51, 51, 51);border-right: 1pt solid windowtext;padding: 0in 3.5pt;height: 14.25pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\";'\u003e-0.461\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.06%;border-top: none;border-left: none;border-bottom: 1pt solid rgb(51, 51, 51);border-right: 1.5pt double windowtext;padding: 0in 3.5pt;height: 14.25pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\";'\u003e-0.016\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69.84%;border-top: none;border-left: 1.5pt double windowtext;border-bottom: 1pt solid rgb(51, 51, 51);border-right: 1pt solid windowtext;background: white;padding: 0in 3.5pt;height: 14.25pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:left;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\";color:black;'\u003eShare of the population in settlements with more than 5,000 inhabitants\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.06%;border-top: none;border-left: none;border-bottom: 1pt solid rgb(51, 51, 51);border-right: 1pt solid windowtext;background: rgb(221, 235, 247);padding: 0in 3.5pt;height: 14.25pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\";color:black;'\u003e0.808\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.06%;border-top: none;border-left: none;border-bottom: 1pt solid rgb(51, 51, 51);border-right: 1pt solid windowtext;padding: 0in 3.5pt;height: 14.25pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\";'\u003e-0.279\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.06%;border-top: none;border-left: none;border-bottom: 1pt solid rgb(51, 51, 51);border-right: 1.5pt double windowtext;padding: 0in 3.5pt;height: 14.25pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\";'\u003e-0.099\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69.84%;border-top: none;border-left: 1.5pt double windowtext;border-bottom: 1pt solid rgb(51, 51, 51);border-right: 1pt solid windowtext;background: white;padding: 0in 3.5pt;height: 14.25pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:left;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\";color:black;'\u003eAverage monthly wage in the district\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.06%;border-top: none;border-left: none;border-bottom: 1pt solid rgb(51, 51, 51);border-right: 1pt solid windowtext;background: rgb(221, 235, 247);padding: 0in 3.5pt;height: 14.25pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\";color:black;'\u003e0.802\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.06%;border-top: none;border-left: none;border-bottom: 1pt solid rgb(51, 51, 51);border-right: 1pt solid windowtext;padding: 0in 3.5pt;height: 14.25pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\";'\u003e-0.270\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.06%;border-top: none;border-left: none;border-bottom: 1pt solid rgb(51, 51, 51);border-right: 1.5pt double windowtext;padding: 0in 3.5pt;height: 14.25pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\";'\u003e-0.353\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69.84%;border-top: none;border-left: 1.5pt double windowtext;border-bottom: 1pt solid rgb(51, 51, 51);border-right: 1pt solid windowtext;background: white;padding: 0in 3.5pt;height: 14.25pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:left;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\";color:black;'\u003eShare of the population receiving material deprivation benefits\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.06%;border-top: none;border-left: none;border-bottom: 1pt solid rgb(51, 51, 51);border-right: 1pt solid windowtext;padding: 0in 3.5pt;height: 14.25pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\";'\u003e-0.260\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.06%;border-top: none;border-left: none;border-bottom: 1pt solid rgb(51, 51, 51);border-right: 1pt solid windowtext;background: rgb(252, 228, 214);padding: 0in 3.5pt;height: 14.25pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\";color:black;'\u003e0.917\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.06%;border-top: none;border-left: none;border-bottom: 1pt solid rgb(51, 51, 51);border-right: 1.5pt double windowtext;padding: 0in 3.5pt;height: 14.25pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\";'\u003e0.233\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69.84%;border-top: none;border-left: 1.5pt double windowtext;border-bottom: 1pt solid rgb(51, 51, 51);border-right: 1pt solid windowtext;background: white;padding: 0in 3.5pt;height: 14.25pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:left;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\";color:black;'\u003eRate of registered unemployment in the district\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.06%;border-top: none;border-left: none;border-bottom: 1pt solid rgb(51, 51, 51);border-right: 1pt solid windowtext;padding: 0in 3.5pt;height: 14.25pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\";'\u003e-0.306\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.06%;border-top: none;border-left: none;border-bottom: 1pt solid rgb(51, 51, 51);border-right: 1pt solid windowtext;background: rgb(252, 228, 214);padding: 0in 3.5pt;height: 14.25pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\";color:black;'\u003e0.850\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.06%;border-top: none;border-left: none;border-bottom: 1pt solid rgb(51, 51, 51);border-right: 1.5pt double windowtext;padding: 0in 3.5pt;height: 14.25pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\";'\u003e0.310\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69.84%;border-top: none;border-left: 1.5pt double windowtext;border-bottom: 1pt solid rgb(51, 51, 51);border-right: 1pt solid windowtext;background: white;padding: 0in 3.5pt;height: 14.25pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:left;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\";color:black;'\u003eShare of the MRC in the total population\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.06%;border-top: none;border-left: none;border-bottom: 1pt solid rgb(51, 51, 51);border-right: 1pt solid windowtext;padding: 0in 3.5pt;height: 14.25pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\";'\u003e-0.179\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.06%;border-top: none;border-left: none;border-bottom: 1pt solid rgb(51, 51, 51);border-right: 1pt solid windowtext;background: rgb(252, 228, 214);padding: 0in 3.5pt;height: 14.25pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\";color:black;'\u003e0.806\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.06%;border-top: none;border-left: none;border-bottom: 1pt solid rgb(51, 51, 51);border-right: 1.5pt double windowtext;padding: 0in 3.5pt;height: 14.25pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\";'\u003e0.391\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69.84%;border-top: none;border-left: 1.5pt double windowtext;border-bottom: 1pt solid rgb(51, 51, 51);border-right: 1pt solid windowtext;background: white;padding: 0in 3.5pt;height: 14.25pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:left;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\";color:black;'\u003eGross divorce rate (per 1,000 inhabitants)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.06%;border-top: none;border-left: none;border-bottom: 1pt solid rgb(51, 51, 51);border-right: 1pt solid windowtext;padding: 0in 3.5pt;height: 14.25pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\";'\u003e0.192\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.06%;border-top: none;border-left: none;border-bottom: 1pt solid rgb(51, 51, 51);border-right: 1pt solid windowtext;padding: 0in 3.5pt;height: 14.25pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\";'\u003e-0.253\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.06%;border-top: none;border-left: none;border-bottom: 1pt solid rgb(51, 51, 51);border-right: 1.5pt double windowtext;background: rgb(226, 239, 218);padding: 0in 3.5pt;height: 14.25pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\";color:black;'\u003e-0.871\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69.84%;border-top: none;border-left: 1.5pt double windowtext;border-bottom: 1.5pt double windowtext;border-right: 1pt solid windowtext;background: white;padding: 0in 3.5pt;height: 14.25pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:left;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\";color:black;'\u003eRoad distance from Bratislava\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.06%;border-top: none;border-left: none;border-bottom: 1.5pt double windowtext;border-right: 1pt solid windowtext;padding: 0in 3.5pt;height: 14.25pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\";'\u003e-0.188\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.06%;border-top: none;border-left: none;border-bottom: 1.5pt double windowtext;border-right: 1pt solid windowtext;padding: 0in 3.5pt;height: 14.25pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\";'\u003e0.421\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.06%;border-top: none;border-left: none;border-bottom: 1.5pt double windowtext;border-right: 1.5pt double windowtext;background: rgb(226, 239, 218);padding: 0in 3.5pt;height: 14.25pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;text-align:center;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\u003cstrong\u003e\u003cspan style='font-size:15px;font-family:\"Times New Roman\";color:black;'\u003e0.815\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: authors\u0026rsquo; computations\u003c/p\u003e"},{"header":"5. Conclusions, limitations, and directions for further research","content":"\u003cp\u003eThis research examined the effects of the \u0026lsquo;Linking Secondary Education and Practice\u0026rsquo; scheme. The scheme supported students in lower and upper secondary schools. It aimed at building cross-cutting literacy competences (rather than narrowly defined professional knowledge). The research results indicate that the scheme activities were correctly chosen and enhanced students\u0026rsquo; competences demanded by employers. Specific competences (such as reading, mathematical, financial and ICT literacy (including basic entrepreneurship skills) and language competences) resonated with the principles of the international assessment of professional skills of vocational education and training for employment (OECD PISA‑VET). As shown by the DiD and SCM, graduates of supported secondary schools found it easier to obtain jobs than did those from unsupported schools.\u003c/p\u003e \u003cp\u003eThe most interesting results of the research relate to the type of study programme and the region of student origin: (a) the scheme seemed to work better for students taking lower (rather than upper) secondary vocational education\u0026mdash;the average dropout rate used to be substantially higher in the former educational programme than in the latter one; (b) the scheme results (in terms of shares of successful and/or job-finding graduates) were generally better in the poorest Slovak NUTS 3 regions, including Košick\u0026yacute;, Prešovsk\u0026yacute; and Banskobystrick\u0026yacute; \u003cem\u003ekraj\u003c/em\u003es. These regions account for high unemployment rates and high proportions of students from marginalised Roma communities.\u003c/p\u003e \u003cp\u003eThe regression analysis aligns with findings from OECD studies such as the PISA assessment (OECD, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e: 107). Consistent with prior research, our analysis identified students\u0026rsquo; socioeconomic backgrounds to be the most critical determinants of educational outcomes, particularly in lower secondary education. Students from districts typically with high proportions of marginalised Roma populations, high unemployment rates, and a dependency on material deprivation benefits faced the greatest challenges and generated the highest dropout rates.\u003c/p\u003e \u003cp\u003eThese findings suggest several policy recommendations for secondary vocational education:\u003c/p\u003e \u003cp\u003e(1) Poverty and marginalisation are key issues in lower secondary vocational education. Upper secondary vocational tracks predominantly involve students from middle-class families. The impact of the socioeconomic environment on academic success is notably smaller in these studies in comparison to lower secondary tracks.\u003c/p\u003e \u003cp\u003e(2) Policies aimed at lower secondary vocational education should place greater emphasis on social inclusion as a fundamental condition for the study-to-job transition. This should be prioritised more than in the past. Labour market inclusion through cooperation with employers may offer an effective strategy to enhance students\u0026rsquo; overall social integration. However, it is essential to engage more mentors and/or social workers with experience in marginalised communities.\u003c/p\u003e \u003cp\u003e(3) Results in VET studies exhibit significant territorial variation. Consequently, policy interventions should consider this geographical dimension and tailor policies accordingly to address regional disparities effectively.\u003c/p\u003e \u003cp\u003eOur research has some notable limitations. Some limitations relate to traditional data constraints. Data on student performance, for example, were available for a relatively short time period (2019\u0026ndash;2023). Other limitations refer to analytical procedures. The SCM has several advantages over traditional DiD. For example, it enabled an assessment of performance by students from individual schools (rather than a pool of VET institutions). Unlike DiD, the SCM does not require parallel trends for treated and non-treated units in the pre-test period. The SCM, of course, has its own shortcomings. The quality of synthetic units depends on the size and structure of the donor pool. The SCM is a non-probabilistic method and there is no general agreement on the methods for measuring the quality of the model fit. The \u0026lsquo;Linking Secondary Education and Practice\u0026rsquo; scheme aimed at building cross-cutting literacy competences (rather than narrowly defined professional knowledge). We assume that these skills were key to the success of graduates, particularly on the lower secondary track. The assumption, of course, needs further verification.\u003c/p\u003e \u003cp\u003eThe limitations suggest directions for further research. There is an opportunity to use longer datasets and follow student performance over the long term. Another research direction relates to the nature of cross-cutting literacy competences. What specific competences (reading, mathematics, financial and/or entrepreneurial) proved to be the most useful for the employment of graduates in lower versus upper secondary education? These issues are best explored using qualitative research methods.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVladim\u0026iacute;r Bal\u0026aacute;ž, Conceptualization, Methodology, Formal analysis, Writing\u0026ndash;original draft, Writing\u0026ndash;review and editing;\u003c/p\u003e\n\u003cp\u003eDu\u0026scaron;ana Dokupilov\u0026aacute;, Conceptualization, Methodology, Data curation, Formal analysis, Mathematical computations, Writing\u0026ndash;review and editing;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding acknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by Slovak VEGA Grant No. 2/0001/22\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analyses are based on data provided by the Slovak Ministry of Investment and Regional Development and Information and the Ministry of Labour, Social Affairs and Family, respectively. The authors have no permission to distribute this data; however, the data is available for scientific use upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e We have no known conflicts of interest to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics and Consent to Participate declaration:\u003c/strong\u003e not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbadie A, Diamond A, Hainmueller J (2010) Synthetic control methods for comparative case studies: Estimating the effect of California\u0026rsquo;s tobacco control program. J Am Stat Assoc 105(490):493\u0026ndash;505\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbadie A, Cattaneo MD (2021) Introduction to the special section on synthetic control methods. J Am Stat Assoc 116(536):1713\u0026ndash;1715\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAllan J, Catts R (2014) Schools, Social Capital and Space. Camb J Educ 44(2):217\u0026ndash;228\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBenke M, Rachwał T (2022) The evolution of vocational education and training in Hungary and Poland 1989\u0026ndash;2035. Hung Educational Res J 12(3):328\u0026ndash;356\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBolli T, Caves KM, Renold U, Buergi J (2018) Beyond employer engagement: measuring education-employment linkage in vocational education and training programmes. J Vocat Educ Train 70(4):524\u0026ndash;563\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrunello G, Rocco L (2017) The effects of vocational education on adult skills, employment and wages: What can we learn from PIAAC? SERIEs 8:315\u0026ndash;343\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoyadjieva P, Ilieva-Trichkova P (2019) Horizontal differentiation matters: Moderating influence of the type of upper secondary education on students\u0026rsquo; transitions. Eur Educ 51(1):32\u0026ndash;50\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBroer M, Bai Y, Fonseca F, Broer M, Bai Y, Fonseca F (2019) A review of the literature on socioeconomic status and educational achievement. Socioeconomic inequality and educational outcomes: Evidence from twenty years of TIMSS, 7\u0026ndash;17\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBruin M, Tutlys V, \u0026Uuml;marik M, Loogma K, Kaminskien\u0026eacute; L, Bentsalo I, V\u0026auml;ljataga T, Sloka B, Buligina I (2023) Participation and learning in Vocational education and training-a cross-national analysis of the perspectives of youth at risk for social exclusion. Journal of Vocational Education \u0026amp; Training, pp 1\u0026ndash;22\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrunetti I, Corsini L (2019) School-to-work transition and vocational education: a comparison across Europe. Int J Manpow 40(8):1411\u0026ndash;1437\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCabus S, Nagy E (2021) On the productivity effects of training apprentices in Hungary: evidence from a unique matched employer\u0026ndash;employee dataset. Empirical Economics 60(4):1685\u0026ndash;1718\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCEDEFOP, European Centre for Development of Vocational Training (2024)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChoi SJ, Jeong JC, Kim SN (2019) Impact of vocational education and training on adult skills and employment: An applied multilevel analysis. Int J Educational Dev 66:129\u0026ndash;138\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClarke L, Westerhuis A, Winch C (2021) Comparative VET European research since the 1980s: Accommodating changes in VET systems and labour markets. J Vocat Educ Train 73(2):295\u0026ndash;315\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDonald SG, Lang K (2007) Inference with Difference-in-Differences and Other Panel Data. Rev Econ Stat 89(2):221\u0026ndash;233\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEurostat (2024) Gross domestic product (GDP) at current market prices by NUTS 3 region, Online data code: nama_10r_3gdp. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2908/nama_10r_3gdp\u003c/span\u003e\u003cspan address=\"10.2908/nama_10r_3gdp\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGabr\u0026iacute;kov\u0026aacute; B, Šv\u0026aacute;bov\u0026aacute; L (2023) Impact Evaluation of the Graduate Practice Intervention in Slovakia with the Application of the CART Method. TalTech J Eur Stud 13(1):177\u0026ndash;200\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHanushek EA (2012) Dual education: Europe\u0026rsquo;s secret recipe? CESifo forum (Vol. 13, No. 3. ifo Institut-Leibniz-Institut f\u0026uuml;r Wirtschaftsforschung an der Universit\u0026auml;t M\u0026uuml;nchen, M\u0026uuml;nchen, pp 29\u0026ndash;34\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHanushek EA, Schwerdt G, Woessmann L, Zhang L (2017) General education, vocational education, and labor-market outcomes over the lifecycle. J Hum Resour 52(1):48\u0026ndash;87\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHaviland S, Robbins S (2021) Career and technical education as a conduit for skilled technical careers: A targeted research review and framework for future research. ETS Research Report Series, 2021(1), 1\u0026ndash;42\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoidn S, Šťastn\u0026yacute; V (2023) Labour market success of initial vocational education and training graduates: a comparative study of three education systems in Central Europe. J Vocat Educ Train 75(4):629\u0026ndash;653\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHordern J, Shalem Y, Esmond B, Bishop D (2022) Editorial for JVET special issue on knowledge and expertise. J Vocat Educ Train 74(1):1\u0026ndash;11\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKahanec M, Kovacova L, Polackova Z, Sedlakova M (2020) The social and employment situation of Roma communities in Slovakia. Study for the Committee on Employment and Social Affairs, Policy Department for Economic, Scientific and Quality of Life Policies, European Parliament\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKogan I, Gebel M, Noelke C (2012) Educational systems and inequalities in educational attainment in Central and Eastern European countries. Stud Transition States Soc, 4(1)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKurek S, Rachwał T (2012) Vocational education and training in Poland during economic transition. In: Pilz M (ed) The Future of Vocational Education and Training in a Changing World. VS Verlag f\u0026uuml;r Sozialwissenschaften, Springer, Wiesbaden, pp 321\u0026ndash;340\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu J, Peng P, Zhao B, Luo L (2022) Socioeconomic status and academic achievement in primary and secondary education: A meta-analytic review. Educational Psychol Rev 34(4):2867\u0026ndash;2896\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarkowitsch J, Hefler G (2019) Future developments in Vocational Education and Training in Europe: Report on reskilling and upskilling through formal and vocational education training (No. 2019/07). JRC Working Papers Series on Labour, Education and Technology\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorris K (2023) Getting a foot in the door: local labour markets and the school-to-work transition. J Youth Stud, 1\u0026ndash;21\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNilsson A (2010) Vocational Education and Training \u0026ndash; an Engine for Economic Growth and a Vehicle for Social Inclusion? Int J Train Dev 14(4):215\u0026ndash;272\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNoelke C, Horn D (2014) Social transformation and the transition from vocational education to work in Hungary: a differences-in-differences approach. Eur Sociol Rev 30(4):431\u0026ndash;443\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOECD (2019a) PISA 2018 Results (Volume II): Where All Students Can Succeed, PISA. OECD Publishing, Paris. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1787/b5fd1b8f-en\u003c/span\u003e\u003cspan address=\"10.1787/b5fd1b8f-en\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOECD (2019b) Skills Matter: Additional Results from the Survey of Adult Skills, OECD Skills Studies. OECD Publishing, Paris. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1787/1f029d8f-en\u003c/span\u003e\u003cspan address=\"10.1787/1f029d8f-en\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOECD (2021) Beyond Academic Learning: First Results from the Survey of Social and Emotional Skills. OECD Publishing, Paris. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1787/92a11084-en\u003c/span\u003e\u003cspan address=\"10.1787/92a11084-en\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOECD (2023) PISA 2022 Results (Volume I): The State of Learning and Equity in Education, PISA. OECD Publishing, Paris. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1787/53f23881-en\u003c/span\u003e\u003cspan address=\"10.1787/53f23881-en\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOECD (2024) PISA Vocational Education and Training (VET): Assessment and Analytical Framework, PISA. OECD Publishing, Paris. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1787/b0d5aaf9-en\u003c/span\u003e\u003cspan address=\"10.1787/b0d5aaf9-en\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRausch A, Abele S, Deutscher V, Greiff S, Kis V, Messenger S, Winther E (2024) Designing an International Large-Scale Assessment of Professional Competencies and Employability Skills: Emerging Avenues and Challenges of OECD\u0026rsquo;s PISA-VET. Vocations Learn, 1\u0026ndash;40\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSOSR, Statistical Office of the Slovak Republic (2024) Demographic and Social Statistics, DATAcube online database, available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://datacube.statistics.sk/#!/lang/en\u003c/span\u003e\u003cspan address=\"https://datacube.statistics.sk/#!/lang/en\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStrakov\u0026aacute; J (2015) Strong vocational education\u0026ndash;a safe way to the labour market? A case study of the Czech Republic. Educational Res 57(2):168\u0026ndash;181\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalas-Velasco M (2024) Vocational education and training systems in Europe: A cluster analysis. Eur Educational Res J 23(3):434\u0026ndash;449\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalv\u0026agrave; F, Pinya C, \u0026Aacute;lvarez N, Calvo A (2019) Dropout prevention in secondary VET from different learning spaces: A social discussion experience. Int J Res Vocat Educ Train 6(2):153\u0026ndash;173\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eŠćepanović V (2020) Skills on wheels: Raising industry involvement in vocational training in the Czech Republic, Slovakia and Hungary, Chap. 16, pp. 401\u0026ndash;428, in: A. Covarrubias, V. Sigfrido and M. Ram\u0026iacute;rez Perez (eds): New Frontiers of the Automobile Industry: Exploring Geographies, Technology, and Institutional Challenges, Palgrave Studies of Internationalization in Emerging Markets\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShalem Y (2014) What Binds Professional Judgement - the Case of Teaching. In Knowledge, Expertise and the Professions, edited by M. Young and J. Muller, 93\u0026ndash;105. Abingdon: Routledge\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStrakov\u0026aacute; J (2015) Strong vocational education\u0026ndash;a safe way to the labour market? A case study of the Czech Republic. Educational Res 57(2):168\u0026ndash;181\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eŠv\u0026aacute;bov\u0026aacute; L, Kram\u0026aacute;rov\u0026aacute; K, Ďurica M (2021) Evaluation of the effects of the graduate practice in slovakia: comparison of results of counterfactual methods. Cent Eur Bus Rev 10(4):1\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTūtlys V, Vaitkutė L (2022) Knowledge Formation Practices in the Context of the VET Curriculum Reform in Lithuania. J Vocat Educ Train 74(1):126\u0026ndash;145\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTūtlys V, Buligina I, Dzelme J, Gedvilienė G, Loogma K, Sloka B, Tikkanen TI, Tora G, Vaitkutė L, Valjataga T, \u0026Uuml;marik M (2022) VET ecosystems and labour market integration of at-risk youth in the Baltic countries: implications of Baltic neoliberalism. Educ + Train 64(2):190\u0026ndash;213\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTūtlys V, Daukilas S, Mičiulienė R, Čiučiulkienė N, Krikštolaitis R (2024) The competence-based VET curriculum and teaching of work values: the case of Lithuania. Eur J Train Dev 48(3/4):298\u0026ndash;317UHP\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWinch C (2010) Dimensions of Expertise: A Conceptual Exploration of Vocational Knowledge. Continuum, London\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoung M, Hordern J (2022) Does the vocational curriculum have a future? J Vocat Educ Train 74(1):68\u0026ndash;88\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e A complete list of Slovak districts, their official codes, as well as information on their area and population can be found at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.statoids.com/ysk.html\u003c/span\u003e\u003cspan address=\"http://www.statoids.com/ysk.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"vocational education, cross-cutting literacy competences, socio-economic background, study program, study field, synthetic control method","lastPublishedDoi":"10.21203/rs.3.rs-5783632/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5783632/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eVocational education and training (VET) is often seen as a pathway to faster and better employment outcomes in comparison to general secondary education. However, the school-to-job transition remains complex, influenced by regional-, institutional- and individual-level factors. This paper evaluates the effects of the Slovak \u0026lsquo;Linking Secondary Education and Practice\u0026rsquo; scheme, designed to improve success rates in secondary VET by enhancing cross-cutting literacy competences such as reading and mathematical skills. The study covers 25,219 students from 74 schools, spanning diverse contexts including developed and underdeveloped regions, and lower versus upper secondary vocational education. Using a combination of difference-in-differences (DiD) and the synthetic control method (SCM)\u0026mdash;a novel approach for evaluating individual VET school performance\u0026mdash;this research examines the determinants of school-to-job transitions at both regional and study field levels. The findings reveal significant disparities in VET outcomes based on programme type, field of study, and level of regional development. Vocational schools in underdeveloped regions exhibit weaker outcomes in comparison to their developed counterparts, underlining the importance of regional contexts in VET policy effectiveness. The research results indicate that the intervention worked best for the service-oriented fields in the lower secondary programme and/or in the poorest Slovak regions. The study contributes to the literature by offering a granular analysis of vocational education outcomes, providing evidence at the regional, study field, and programme levels.\u003c/p\u003e","manuscriptTitle":"Improving Employability: The Role of Cross-Cutting Competences in Vocational Education Outcomes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-20 10:30:56","doi":"10.21203/rs.3.rs-5783632/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"39f18902-2bbe-4f2b-8e80-3df5d0a1933a","owner":[],"postedDate":"January 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-07T15:38:36+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-20 10:30:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5783632","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5783632","identity":"rs-5783632","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}