most importantly, that skills form at several stages from early childhood to adulthood benefitting from a multiplier effect if early investments have been made (Cunha et al., 2006).<sup>35</sup> Given that we are interested in the skills of the labor force according to employers’ demand, we isolate “technical skills” by adding a third type of skills next to cognitive and non-cognitive skills (which we refer to as socio-emotional skills) (Table 1, layer 1).<sup>36</sup> Our technical skills category corresponds to skills built on a combination of cognitive and socio-emotional skills, taught or learned at least partly for labor market use, and developed later in life while at school (general or technical education), in dedicated trainings or on-the-job. By doing that, we also follow recent reports focusing on the use of skills at work, for example Pierre et al. (2014) which sets the taxonomy to analyze STEP surveys (in particular the Employer survey expressing employers’ skills requirements), and the World Bank Development Report on Learning (World Bank, 2018).

In addition, we disaggregate this framework into 15 skills categories (cognitive skills, 5 socio-emotional skills - agreeableness, conscientiousness, emotional stability, extraversion, openness to experience, and 9 technical skills) building on Deming and Kahn (2018) to perform a more detailed analysis on the demand for specific technical skills (Table 1, layer 2).<sup>37</sup> For socio-emotional skills categories, we mapped our granular skills to personality traits also referred to as “Big Five” in the psychometric literature (Goldberg 1993), based on Chernyshenko et al. (2018) and Kureková et al. (2016).

Finally, each of the 15 categories comprises a relatively large set of skills requirements (Table 1, layer 3).<sup>38</sup> This layer offers the highest level of granularity in our analysis of skills in demand and is a key contribution of this paper. For example, it enables us to construct word clouds to inform youth on the skills employers look for, and to inform curricula on the specific skills to be taught (e.g. which software or coding language, in the computer science field). It relies on 152 skills requirements, themselves defined by 764 underlying keys across three languages (Albanian, English and German). Details on the taxonomy and text analysis are provided in Appendix B.<sup>39</sup>

> 35 More precisely, the “complementarity” of skills including across cognitive / non-cognitive categories (i.e. early skills acquisition raises the productivity of investment in skills later) and their “self-productivity” (i.e. skills are self-reinforcing) are two key aspects of skills formation explaining the multiplier effect and justifying the investments in early childhood development programs (Cunha et al., 2006).

> 36 In Handel et al. (2016), such skills are considered as cognitive skills but distinguished from the cognitive foundation skills (such as writing, reading, problem solving). They are labeled “specific mid-level” and “specific high-level” knowledge/skills, and Table 1.1 in their paper shows the path of skills acquisition for these job-related skills. In the World Bank (2018) report on learning, the terminology “technical skills” is used.

> 37 Note that due to their rare occurrence certifications are excluded from the analysis below.

> 38 Recall that in order to identify whether a skill requirement is in demand within a job description, we use the occurrence of underlying keywords, key phrases or text patterns from the fourth layer of our skills taxonomy. See Appendix B for more details.

> 39 Note that for the “Big Five” socio-emotional skills we map granular skills requirements based on Chernyshenko et al. (2018) and Kureková et al. (2016).

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