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Browsing by Subject "online job vacancy advertisements"

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  • Wrede, Iris (2020)
    Researchers debate the skills required in future jobs and which skills are of particular consequence for the mobility of labor. In researching this topic, many turn to online job vacancy advertisements as a source of abundant, naturally-occurring data. Despite the great interest, economics research has often overlooked the nature of job vacancy ads as a context-bound genre of text and the implications that has on the analysis. This thesis has two aims. First, the thesis critically considers the use of online job vacancy advertisements as data for research on labor markets, seeking to advance the methodological rigor in studies using this type of data, particularly that from a Finnish context. Second, to consider, on the basis of a two-stage mixed methods analysis of online job vacancy advertisements published on a Finnish online job board in 2017-2019, the type of skills that Finnish employers call for in successful applicants in professional private sector jobs. This thesis also elaborates on the language aspect of online job vacancy advertising in Finland. The descriptive statistics of the random sample would seem to confirm the repetitive nature of job vacancy ads and trends in employee ideals which have been discussed in the literature. For example, it would seem that there is a greater focus on interpersonal skills compared to intrapersonal skills. The increasingly globalized nature of the Finnish labor market and workplace is also reflected in the data. Although job advertisements are a tempting source of data, in their current free-form state, some doubt can be cast on their relevance as a source of meaningful data on skills. Historical, geographical and other contexts must be carefully considered in analysis in order to avoid overstating the implications of findings and to better situate and analyze the observed tendencies and trends found in the data. Europe-wide efforts to improve job matching may as a by-product, produce a more robust source of data, should it be readily adopted.