The multivariate techniques in evaluation of unemployment analysis of Polish regions

Abstract

Research background: The labour market situation is considered to be the most widely discussed part of economic development. However, it should be noted that the unemployment situation of young people (aged 15?24 years) in Poland in general terms seems to be problematic. Overall, the unemployment rate among young people in Poland is significantly higher than the overall unemployment rate in the EU.  Moreover, the situation varies greatly across the regions.

Purpose of the article: Using multivariate techniques as a theoretical framework, the main goal of the paper is to identify groups of Polish regions that share similar patterns regarding unemployment among young people. To reach this goal, first a set of labour market indicators were selected. Next, the authors compared the labour market situation of young people between the Polish regions in 2005 and in 2014. Finally, the conclusions regarding the conducted analysis are explored.

Methods: The initial calculation is based on the concept of the taxonomic measure developed by Hellwig. The final method used to create clusters of objects (across 16 voivodeships of Poland) is cluster analysis. A segmentation of the voivodeships is observed for the years 2005 and 2014, based on selected indicators to determine the labour market situation. The data was gathered from the databases of the Central Statistical Office of Poland and Eurostat.

Findings & Value added: Through the exploration of the advantages of multivariate methods, the nature of youth unemployment is revealed in more detail. Indeed, dendrogram analysis divided the voivodeships into five groups, which are characterized by similar features associated with the labour market. It was found that the groups which emerged in 2005 have a different composition of regions than in 2014; this difference seems to be connected with the economic crisis.

Keywords

labour market, unemployment, young people, cluster analysis

References

1. Antczak, E. (2013). A spatial taxonomic measure of development. Wiadomości Statystyczne, 7.
   View in Google Scholar
2. Balcerzak, A. P. (2016). Multiple-criteria evaluation of quality of human capital in the European Union countries. Economics & Sociology, 9(2). doi: 10.14254/2071-789X.2016/9-2/1. DOI: https://doi.org/10.14254/2071-789X.2016/9-2/1
   View in Google Scholar
3. Balcerzak, A. P., & Pietrzak, M. B. (2016). Quality of institutions for knowledge-based economy within new institutional economics framework. Multiple criteria decision analysis for European countries in the years 2000–2013. Economics & Sociology, 9(4). doi:10.14254/2071-789X.2016/9-4/4. DOI: https://doi.org/10.14254/2071-789X.2016/9-4/4
   View in Google Scholar
4. Balcerzak, A. P., & Pietrzak, M. B. (2017). Digital economy in Visegrad coutnries. Multiple-criteria decision analysis at regional level in the years 2012 and 2015. Journal of Competitiveness, 9(2). doi:10.7441/joc.2017.02.01. DOI: https://doi.org/10.7441/joc.2017.02.01
   View in Google Scholar
5. Bąk, I. (2014). Influence of feature selection methods on classification sensitivity based on the example of a study of Polish voivodship tourist attractiveness. Folia Oeconomica Stetinensia, 13(2). doi: 10.2478/foli-2013-0017 . DOI: https://doi.org/10.2478/foli-2013-0017
   View in Google Scholar
6. Boeri, T., & Jimeno, J. F. (2016). Learning from the great ivergence in unemployment in Europe during the crisis. Labour Economics, 41. doi: 10.1016/j.labeco .2016.05.022. DOI: https://doi.org/10.1016/j.labeco.2016.05.022
   View in Google Scholar
7. Cárdenas Hurtado, C. A., Hernández Montes, M. A., Gorron, T., & Edwar, J. (2015). A statistical analysis of heterogeneity on labour markets and unemployment rates in Colombia. Desarrollo y Sociedad, 75. DOI: https://doi.org/10.13043/dys.75.4
   View in Google Scholar
8. Carlsson, F., Johansson, M., Petersson, L. O., & Tegsjö, B. (1993). Creating labour market areas and employment zones. Umeå University.
   View in Google Scholar
9. Cheba, K., & Szopik-Depczyńska, K. (2017). Multidimensional comparative analysis of the competitive capacity of the European Union countries and geographical regions. Oeconomia Copernicana, 8(4). doi: 10.24136/oc.v8i4.30. DOI: https://doi.org/10.24136/oc.v8i4.30
   View in Google Scholar
10. Ciżkowicz, P., Kowalczuk, M., & Rzońca, A. (2016). Heterogeneous determinants of local unemployment in Poland. Post-Communist Economies, 28(4). doi: 10.2139/ssrn.2646569 . DOI: https://doi.org/10.1080/14631377.2016.1226784
    View in Google Scholar
11. Drutarovská, J., Kovacova, J., Pechová, H., & Podmajerská, K. (2016). Analysis of women's status in the labor markets of countries in the European Union 1. Journal of International Women's Studies, 17(1).
    View in Google Scholar
12. Eamets, R. (2013). Labour market and labour market policies during great recession: the case of Estonia. IZA Journal of European Labor Studies, 2(1). DOI: https://doi.org/10.1186/2193-9012-2-4
    View in Google Scholar
13. Gawronski, K., Prus, B., & Soltysik, S. (2014). Analysis and evaluation of the conditions of socio-economic development of the Podkarpackie Voivodeship. Infrastruktura i Ekologia Terenów Wiejskich, (IV/2).
    View in Google Scholar
14. Hellwig, Z. (1968). Application of the taxonomic method to the typological division of countries due to the level of their development and the resources and structure of qualified personnel. Przegląd statystyczny, 4.
    View in Google Scholar
15. Henkens, K., Remery, C., & Schippers, J. (2005). Recruiting personnel in a tight labour market: an analysis of employers' behaviour. International Journal of Manpower, 26(5). doi:10.1108/01437720510615116. DOI: https://doi.org/10.1108/01437720510615116
    View in Google Scholar
16. Jindrová, A., & Vostrá Vydrová, H. (2013). Modelling dependence indicators of labor market using advanced statistical methods. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 60(4). doi: 10.11118/actaun20 1260040165 . DOI: https://doi.org/10.11118/actaun201260040165
    View in Google Scholar
17. Jurkowska, B. (2014). The federal states of Germany – analysis and measurement of development using taxonomic methods. Oeconomia Copernicana, 5(3). doi: 10.12775/oec.2014.019 . DOI: https://doi.org/10.12775/OeC.2014.019
    View in Google Scholar
18. Mačerinskienė, I., & Aleknavičiūtė, R. (2017). National intellectual capital influence on economic growth in the European Union countries. Equilibrium. Quarterly Journal of Economics and Economic Policy, 12(4). doi: 10.24136/eq. v12i4.30. DOI: https://doi.org/10.24136/eq.v12i4.30
    View in Google Scholar
19. Madianos, M. G., Alexiou, T., Patelakis, A., & Economou, M. (2014). Suicide, unemployment and other socioeconomic factors: evidence from the economic crisis in Greece. European Journal of Psychiatry, 28(1). doi: 10.4321/S0213-61632014000100004. DOI: https://doi.org/10.4321/S0213-61632014000100004
    View in Google Scholar
20. Masso, J., & Krillo, K. (2011). Mixed adjustment forms and inequality effects in Estonia, Latvia and Lithuania. Work Inequalities in the Crises, Edward Elgar, ILO. doi: 10.5848/ilo.978-9-221248-86-6_3 . DOI: https://doi.org/10.4337/9780857937513.00009
    View in Google Scholar
21. Miśkiewicz-Nawrocka, M., & Zeug-Żebro, K. (2015). Assessment of the risk of unemployment in Polish voivodships in 2005–2012. Zeszyty Naukowe Uniwersytetu Ekonomicznego w Krakowie, 940.
    View in Google Scholar
22. Nadiya, D. (2008). Econometric and cluster analysis of potential and regional features of the labour market of Poland. Ekonomia. Rynek, Gospodarka, Społeczeństwo, 21.
    View in Google Scholar
23. Pietrzak, M. B (2014). Taxonomic measure of development (TMD) taking into account spatial dependencies. Przegląd Statystyczny, 61(2).
    View in Google Scholar
24. Pietrzak, M. B., & Balcerzak, A. P. (2016). A spatial SAR model in evaluating influence of entrepreneurship and investments on unemployment in Poland. In M. Reiff & P. Gezik (Eds.). Proceedings of the International Scientific Conference Quantitative Methods in Economics Multiple Criteria Decision Making XVIII. Vratna: Letra Interactive.
    View in Google Scholar
25. Pietrzak, M. B., & Balcerzak, A. P. (2016). Assessment of socio-economic sustainability in new European Union members states in the years 2004–2012. In M. Papież & S. Śmiech (Eds.). The 10th Professor Aleksander Zelias International Conference on Modelling and Forecasting of Socio-Economic Phenomena. Conference Proceedings. Cracow: Foundation of the Cracow University of Economics.
    View in Google Scholar
26. Rollnik-Sadowska, E. (2016). Young people in the European Union labour market. Latgale National Economy Research, 1(8). doi: 10.17770/lner2016 vol1.8.1482 . DOI: https://doi.org/10.17770/lner2016vol1.8.1482
    View in Google Scholar
27. Rose, A. K., & Spiegel, M. M. (2011). Cross-country causes and consequences of the crisis: an update. European Economic Review, 55(3). doi:10.1016/j.euro ecorev.2010.12.006. DOI: https://doi.org/10.1016/j.euroecorev.2010.12.006
    View in Google Scholar
28. Saczyńska-Sokół, S. (2018). Supporting NEETs — challenges facing labor market institutions in Poland. Oeconomia Copernicana, 9(1). doi: 10.24136/oc. 2018.009. DOI: https://doi.org/10.24136/oc.2018.009
    View in Google Scholar
29. Stanny, M. (2010). Spatial Diversification of the Balance on the Labour Market in Rural areas in Poland. Bulletin of Geography. Socio-economic series, 14. doi: 10.2478/v10089-010-0018-x. DOI: https://doi.org/10.2478/v10089-010-0018-x
    View in Google Scholar
30. Trapczynski, P., Jankowska, B., Dzikowska, M., & Gorynia, M. (2016). Identification of linkages between the competitive potential and competitive position of SMEs related to their internationalization patterns shortly after the economic crisis. Entrepreneurial Business and Economics Review, 4(4). doi: 10.15678/ EBER.2016.040403. DOI: https://doi.org/10.15678/EBER.2016.040403
    View in Google Scholar
31. White, A. (2016). Informal practices, unemployment, and migration in small-town Poland. East European Politics and Societies, 30(2). doi: 10.1177/08883 25415602056 . DOI: https://doi.org/10.1177/0888325415602056
    View in Google Scholar
32. Wierzbicka, W. (2018). Information infrastructure as a pillar of the knowledge-based economy—an analysis of regional differentiation in Poland. Equilibrium. Quarterly Journal of Economics and Economic Policy, 13(1). doi: 10.24136/eq.2018.007. DOI: https://doi.org/10.24136/eq.2018.007
    View in Google Scholar
33. Wysocki, F. (2010). Taxonomic methods in recognizing economic types of agriculture and rural areas. Wydawnictwo Uniwersytetu Przyrodniczego w Poznaniu, Poznań, 108.
    View in Google Scholar