Labour market attractiveness in the EU. 2018 edition.

Autor/inn/en	Lopes, Joao Sollari; Quaresma, Sonia; Moura, Marco
Institution	Europäische Kommission / Statistisches Amt
Titel	Labour market attractiveness in the EU. 2018 edition. Gefälligkeitsübersetzung: Die Attraktivität des Arbeitsmarkts in der EU. Ausgabe 2018.
Quelle	Brüssel (2018), 27 S. PDF als Volltext kostenfreie Datei (1); PDF als Volltext kostenfreie Datei (2) Link als defekt meldenVerfügbarkeit
Reihe	Eurostat statistical working papers
Sprache	englisch
Dokumenttyp	online; Monographie
ISSN	2315-0807
ISBN	978-92-79-80731-2
DOI	10.2785/252238
Schlagwörter	Armut; Demografischer Wandel; Mismatch; Arbeitskräftebedarf; Arbeitslosigkeit; Arbeitsmarkt; Arbeitsmarktanalyse; Arbeitsmarktentwicklung; Arbeitsmigration; Arbeitsmobilität; Qualifikation; Fachkraft; Internationaler Vergleich; Indikatorenbildung; Europäische Union; Hoch Qualifizierter; Belgien; Bulgarien; Deutschland; Dänemark; Estland; Finnland; Frankreich; Griechenland; Großbritannien; Irland; Italien; Kroatien; Lettland; Litauen; Luxemburg; Malta; Niederlande; Polen; Portugal; Rumänien; Schweden; Slowakei; Slowenien; Spanien; Tschechische Republik; Ungarn; Zypern; Österreich
Abstract	"In this work we present a data product developed for the European Big Data Hackathon, a competition between 22 teams which took place in March 2017. This data product is divided in two parts: an exploration part, which is aimed to better understand the EU global labour market and to capture its heterogeneity; and an inferential part, whose goal is to establish associations between characteristics of the EU labour market and indicators designed to capture important aspects of the labour market (i.e. Skills mismatch, Mobility and Emigration). For the exploration part, we developed the concept of Labour Market Attractiveness, which consists of a combination of variables from 6 Eurostats datasets on different subjects (i.e. demographics; earnings structure; education and training; life conditions; employment and unemployment; and national accounts). Using data mining techniques, such as social networks and clustering analysis, we showed that this combined set consistently captured the country-level heterogeneity in the EU, forming well-defined clusters. For the inferential part, we used model selection analysis and weighted network correlation analyses to establish associations between the characteristics of the EU labour market and the labour market indicators. Using model selection, we showed that the Labour Market Attractiveness set was able to capture well the variations of these indicators across EU. We further showed that the Labour Market Attractiveness set can be summarized by 6 Eigenvariables (i.e. 'Unemployment', 'Poverty', 'Ageing Population', 'Education (Employed Adults)', 'Employment' and 'Earnings structure'), whose association with labour market indicators was also assessed. We argue that the combination of both exploration and inferential parts can shed some light on the complex dynamics of the EU labour market. In fact, the final goal of our developed product is to help setting effective policies to tackle typical problems of a fast-changing global labour market environment." (Author's abstract, IAB-Doku).
Erfasst von	Institut für Arbeitsmarkt- und Berufsforschung, Nürnberg
Update	2019/1