Optimization clustering techniques on register unemployment data

An important strategy for data classification consists in organising data points in clusters. The k-means is a traditional optimisation method applied to cluster data points. Using a labour market database, aiming the segmentation of this market taking into account the heterogeneity resulting from different unemployment characteristics observed along the Portuguese geographical space, we suggest the application of an alternative method based on the computation of the dominant eigenvalue of a matrix related with the distance among data points. This approach presents results consistent with the results obtained by the k-means.info:eu-repo/semantics/publishedVersio

The missing link between wages and labour productivity in tourism

The present article aims to analyse wage-labour productivity cau-salities in Croatia and Slovenia using cointegration methods basedon monthly time series data of variables for labour productivityand real gross wages in tourism industry during the periodDecember 1999%January 2020. The data vector is integrated bychain indices with the constant base January 2000%100. A sto-chastic trend and shocks are covered in the analysis. Shocks arelinked to the European Union accession, and economic crisis fol-lowing with overwhelmed tourist arrivals. The contribution of theresearch is two-fold. First, the equations for at most normal dis-tributed variables of labour productivity and real wages in tourismare exposed. Three spatial cointegration relations confirm labourproductivity integrity of the regional tourism market. Second,pair-wise causalities indicate one cointegrated vector for labourproductivity, which drives real gross wages in tourism sub-indus-tries. These results suggest that for a higher non-seasonal assess-ment of real gross wage, the labour productivity should rise, i.e.less workers, more robotization or more tourist arrivals with betterquality solutions. These findings are at most important to beimplemented after the COVID-19 infection crisis with expectedrestructurings and digital transformation in the tourism industry

The Structure of Subjective Well-Being and Its Relation to Objective Well-Being Indicators: Evidence from EU-SILC for Serbia

In this article, we examine the structure of the subjective well-being and its relation to objective well-being indicators using the data from the European Union’s Survey on Income and Living Conditions (EU-SILC) from Serbia. This is one of the first papers to analyze a new module on subjective well-being from EU-SILC micro-dataset (with over 20,000 respondents). We investigate the factor structure of the items and the differences in the association of subjective well-being dimensions with objective indicators of well-being within the Organisation for Economic Co-operation and Development Better Life Initiative framework. Three factors emerge from the principal components analysis: general life satisfaction, affective well-being, and satisfaction with the local environment. The analysis further reveals that life satisfaction is more related to the material living conditions, such as income, unemployment, and housing conditions, while affective well-being is more related to non-material indicators of well-being such as perceived health, personal security, and social connections. On the other hand, positive and negative affect within the affective well-being are not clearly separable, nor is the eudaimonic indicator from either life satisfaction or affective well-being