Comparison of changes in the labour markets of post-communist countries with other EU member states
DOI:
https://doi.org/10.24136/eq.2021.027Keywords:
European labour market, linear ordering, Dynamic Time Warping, Ward’s method, cluster analysisAbstract
strengthen their international competitiveness. This was linked to the implementation of institutional and economic reforms, significant technological changes and improvements in the quality of human capital, as well as fiscal stabilisation policies. These changes affected their situation in the labour market.
Purpose of the article: The aim of the study is to assess changes in the situation in the labour market in the EU with particular emphasis on the post-communist countries in the period 2002? 2019.
Methods: The situation of countries in the European labour market was estimated using the TOPSIS method. A similarity matrix of changes in the composite variable for each country was then constructed using the Dynamic Time Warping method. On its basis, homogeneous clusters of countries were determined using the Ward?s method.
Findings & value added: Four homogenous clusters of countries were formed. The post-communist ones belonged to two groups. In one, there were two countries ? Croatia and Slovakia. The rest of the post-communist countries were in a large cluster, which also included Germany, Malta, Finland, Portugal, France and Belgium. Changes of the situation in the post-communist countries in this group improved very much during the analysed period (this was particularly evident for Czechia, Estonia and Poland). It is interesting to investigate whether the reaction of labour markets to changes in the global economic situation in post-communist countries is similar to that in the old EU countries. The similarity of changes can be measured using the DTW method. There is an empirical research gap in this respect. Therefore, the added value is the use of this method in assessing similarities of changes in the labour market situation in post-communist countries in comparison to the Western European ones.
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References
Balcerzak, A. P., & Pietrzak, M. B. (2016a). 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), 66?81. 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
Balcerzak, A. P., & Pietrzak, M. B. (2016b). Structural equation modeling in evaluation of technological potential of European Union countries in the years 2008-2012. In Proceedings of the 10th professor Aleksander Zelias international conference on modelling and forecasting of socio-economic phenomena. Kraków: Foundation of the Cracow University of Economics, 9?18.
View in Google Scholar
Balcerzak, A. P., Pietrzak, M. B., & Rogalska, E. (2016). Fiscal contractions in Eurozone in the years 1995?2012: can non-keynesian effects be helpful in future deleverage process? In M. H. Bilgin, H. Danis, E. Demir, & U. Can (Eds.). Business challenges in the changing economic landscape - Vol. 1. Eurasian Studies in business and economics. Cham: Springer, 483?496.
DOI: https://doi.org/10.1007/978-3-319-22596-8_35
View in Google Scholar
Batóg, J., & Batóg, B. (2016). Application of correspondence analysis to the identification of the influence of features of unemployed persons on the unemployment duration. Economics and Business Review, 2(16)(4), 25?44. doi: 10.1855 9/ebr.2016.4.2.
DOI: https://doi.org/10.18559/ebr.2016.4.2
View in Google Scholar
Beesley, C. (2020). The compensation hypothesis goes east: FDI and welfare state demand in postcommunist countries. European Journal of Political Research, 59(2), 354?375. doi: 10.1111/1475-6765.12349.
DOI: https://doi.org/10.1111/1475-6765.12349
View in Google Scholar
Bieszk-Stolorz, B., & Dmytrów, K. (2020). Influence of accession of the visegrad group countries to the EU on the situation in their labour markets. Sustainability, 12(16), 6694. doi: 10.3390/su12166694.
DOI: https://doi.org/10.3390/su12166694
View in Google Scholar
Crespo, N., & Fontoura, M. P. (2007). Integration of CEECs into EU market: structural change and convergence. Journal of Common Market Studies, 45(3), 611?632. doi: 10.1111/j.1468-5965.2007.00726.x.
DOI: https://doi.org/10.1111/j.1468-5965.2007.00726.x
View in Google Scholar
Dimian, G. C., Ileanu, B., Jablonsky, J., & Fabry, J. (2013). Analysis of European labour market in the crisis context. Prague Economic Papers, 1. doi: 10.18267/j .pep.440.
DOI: https://doi.org/10.18267/j.pep.440
View in Google Scholar
Dmytrów, K., & Bieszk-Stolorz, B. (2019). Mutual relationships between the unemployment rate and the unemployment duration in the Visegrad Group countries in years 2001?2017. Equilibrium. Quarterly Journal of Economics and Economic Policy, 14(1), 129?148. doi: 10.24136/eq.2019.006.
DOI: https://doi.org/10.24136/eq.2019.006
View in Google Scholar
Dmytrów, K., Gdakowicz, A., & Putek-Szeląg, E. (2019). Statistical relations of the qualitative attributes of real properties subject to mass appraisal. Folia Oeconomica Stetinensia, 19(2), 25?37. doi: 10.2478/foli-2019-0011.
DOI: https://doi.org/10.2478/foli-2019-0011
View in Google Scholar
Dmytrów, K., Landmesser, J., & Bieszk-Stolorz, B. (2021). The connections between COVID-19 and the energy commodities prices: evidence through the Dynamic Time Warping method. Energies, 14(13), 4024. doi: 10.3390/en1413 4024.
DOI: https://doi.org/10.3390/en14134024
View in Google Scholar
Dries, L., Reardon, T., & Swinnen, J. F. M. (2004). The rapid rise of supermarkets in Central and Eastern Europe: implications for the agrifood sector and rural development. Development Policy Review, 22(5), 525?556. doi: 10.1111/j.146 7-7679.2004.00264.x.
DOI: https://doi.org/10.1111/j.1467-7679.2004.00264.x
View in Google Scholar
Ehrke, M. (2007). The European Union and the post-communist sphere. Integration, European neighbourhood policy and strategic partnership. Berlin: Friedrich-Ebert-Stiftung.
View in Google Scholar
Enderveen, S., & Thissen, L. (2004). Can labour market institutions explain unemployment rates in new EU member states? CEPS ENEPRI Working Paper, 27/June.
View in Google Scholar
Gabrisch, H., & Buscher, H. (2006). The relationship between unemployment and output in post-communist countries. Post-Communist Economies, 18(3), 261?276. doi: 10.1080/14631370600881804.
DOI: https://doi.org/10.1080/14631370600881804
View in Google Scholar
Gajdos, A., Arendt, Ł., Balcerzak, A. P., Pietrzak, M. B. (2020). Future trends of labour market polarisation in Poland. The perspective of 2025. Transformations in Business & Economics, 19, 3(51), 114?135.
View in Google Scholar
Giorgino, T. (2009). Computing and visualizing dynamic time warping alignments in R: the dtw package. Journal of Statistical Software, 31(7), 1?24. doi: 10.186 37/jss.v031.i07.
DOI: https://doi.org/10.18637/jss.v031.i07
View in Google Scholar
Godlewska-Dzioboń, B. (2020). Sectoral employment structure in central and eastern European countries compared to highly developed countries in the European Union. In A. Ujwary-Gil & M. Gancarczyk (Eds.). New challenges in economic policy, business, and management. Warsaw: Institute of Economics, Polish Academy of Sciences, 85?105.
View in Google Scholar
Havlik, P. (2005). Structural change, productivity and employment in the New EU member states. Wiiw Research Reports, 313, January.
View in Google Scholar
Hellwig, Z. (1972). On optimal choice of predictors. In Z. Gostkowski (Ed.). Towards a system of human resources indicators for less developed countries. Wrocław: UNESCO - Ossolineum, 69?90.
View in Google Scholar
Hwang, C.-L., & Yoon, K. (1981). Multiple attribute decision making. Methods and applications. A state-of-the-art_survey. Berlin, Heidelberg: Springer-Verlag. doi: 10.1007/978-3-642-48318-9.
DOI: https://doi.org/10.1007/978-3-642-48318-9_3
View in Google Scholar
Jajuga, K., Walesiak, M., & Bak, A. (2003). On the general distance measure. In M. Schwaiger & O. Opitz (Eds.). Exploratory data analysis in empirical research. Studies in classification, data analysis, and knowledge organization. Berlin, Heidelberg: Springer, 104-109. doi: 10.1007/978-3-642-55721-712.
DOI: https://doi.org/10.1007/978-3-642-55721-7_12
View in Google Scholar
Jakubowski, A. (2018). Convergence or divergence? Multidimensional analysis of regional development in the new European Union member states. Barometr Regionalny, 16(1), 31?40.
DOI: https://doi.org/10.56583/br.384
View in Google Scholar
Jenkins, R. M. (2001). Labor markets and economic transformation in postcommunist Europe. In I. Berg & A. L. Kalleberg (Eds.). Sourcebook of labor markets. Plenum studies in work and industry. Boston, MA: Springer, 135?162. doi: 10.1007/978-1-4615-1225-7_6.
DOI: https://doi.org/10.1007/978-1-4615-1225-7_6
View in Google Scholar
Jianu, E., Pîrvu, R., Axinte, G., Toma, O., Cojocaru, A.V., & Murtaza, F. (2021). EU labor market inequalities and sustainable development goals. Sustainability, 13(5), 2675. doi: 10.3390/su13052675.
DOI: https://doi.org/10.3390/su13052675
View in Google Scholar
Landmesser, J. (2021a) Analysis of COVID-19 dynamics in EU countries using the Dynamic Time Warping method and ARIMA models. In K. Jajuga, K. Najman, & M. Walesiak (Eds.). Data analysis and classification. SKAD 2020. Studies in classification, data analysis, and knowledge organization. Cham: Springer, 337?352. doi: 10.1007/978-3-030-75190-6_19.
DOI: https://doi.org/10.1007/978-3-030-75190-6_19
View in Google Scholar
Landmesser, J. (2021b). The use of the dynamic time warping (DTW) method to de-scribe the COVID-19 dynamics in Poland. Oeconomia Copernicana, 12(3), 539?556. doi: 10.24136/oc.2021.018.
DOI: https://doi.org/10.24136/oc.2021.018
View in Google Scholar
Lemos, S., & Portes, J. (2008). New labour? The impact of migration from Central and Eastern European countries on the UK labour market. IZA Discussion Papers, 3756.
DOI: https://doi.org/10.2139/ssrn.1286694
View in Google Scholar
Lipták, K. (2013). The labour market situation in the Central-Eastern European region ? towards a new labour paradigm. Journal of Geography and Geology, 5(3). doi: 10.5539/jgg.v5n3p88.
DOI: https://doi.org/10.5539/jgg.v5n3p88
View in Google Scholar
Lotfi, F. H., & Fallahnejad, R. (2010). Imprecise Shannon?s entropy and multi attribute decision making. Entropy, 12(1), 53?62. doi: 10.3390/e12010053.
DOI: https://doi.org/10.3390/e12010053
View in Google Scholar
Monfort, M., Cuestas, J. C., & Ordó?ez, J. (2013). Real convergence in Europe: a cluster analysis. Economic Modelling, 33, 689?694. doi: 10.1016/j.econmod. 2013.05.015.
DOI: https://doi.org/10.1016/j.econmod.2013.05.015
View in Google Scholar
Müller, M. (2007). Information retrieval for music and motion. Berlin, Heidelberg: Springer. doi: 10.1007/978-3-540-74048-3.
DOI: https://doi.org/10.1007/978-3-540-74048-3
View in Google Scholar
Novák, Z. (2020). Structural change in Central and South Eastern Europe ? does technological efficiency harm the labour market? Sustainability, 12, 4704. doi: 10.3390/su12114704.
DOI: https://doi.org/10.3390/su12114704
View in Google Scholar
Obadić, A., Arčabić V., & Dumančić, L. R. (2021). Labor market institutions convergence in the European Union. EFZG Working Paper Series, 21-02.
View in Google Scholar
Pełka, M. (2019). Assessment of the development of the European OECD countries with the application of linear ordering and ensemble clustering of symbolic data. Folia Oeconomica Stetinensia, 19(2), 117?133. doi: 10.2478/foli-2019-0017.
DOI: https://doi.org/10.2478/foli-2019-0017
View in Google Scholar
Pilc, M. (2017). Cultural, political and economic roots of the labor market institutional framework in the OECD and post-socialist countries. Equilibrium. Quarterly Journal of Economics and Economic Policy, 12(4), 713?731. doi: 10.24136/eq.v12i4.37.
DOI: https://doi.org/10.24136/eq.v12i4.37
View in Google Scholar
Podvezko, V. (2011). The comparative analysis of MCDA methods SAW and COPRAS. Engineering Economics, 22(2), 134?146. doi: 10.5755/j01.ee.22.2 .310.
DOI: https://doi.org/10.5755/j01.ee.22.2.310
View in Google Scholar
Rollnik-Sadowska, E., & Dąbrowska, E. (2018). Cluster analysis of effectiveness of labour market policy in the European Union. Oeconomia Copernicana, 9(1), 143?158. doi: 10.24136/oc.2018.008.
DOI: https://doi.org/10.24136/oc.2018.008
View in Google Scholar
Rollnik-Sadowska, E., & Jarocka, M. (2021). CEE labour markets ? homogeneity or diversity? Technological and Economic Development of Economy, 27(5), 1142?1158. doi: 10.3846/tede.2021.15014.
DOI: https://doi.org/10.3846/tede.2021.15014
View in Google Scholar
Saaty, T. L. (1980). The analytic hierarchy process. New York: McGraw-Hill.
DOI: https://doi.org/10.21236/ADA214804
View in Google Scholar
Saaty, T. L., & Ergu, D. (2015). When is a decision-making method trustworthy? Criteria for evaluating multi-criteria decision-making methods. International Journal of Information Technology and Decision Making, 14(6), 1171?1187. doi: 10.1142/S021962201550025X.
DOI: https://doi.org/10.1142/S021962201550025X
View in Google Scholar
Salavecz, G., Chandola, T., Pikhart, H., Dragano, N., Siegrist, J., Jöckel, K.-H., Erbel, R., Pajak, A., Malyutina, S., Kubinova, R., Marmot, M., Bobak, M., & Kopp, M. (2010). Work stress and health in Western European and post-communist countries: an East-West comparison study. Journal of Epidemiological Community Health, 64(1), 57?62. doi: 10.1136/jech.2008.075 978.
DOI: https://doi.org/10.1136/jech.2008.075978
View in Google Scholar
Shleifer, A., & Treisman, D. (2014). Normal countries: the east 25 years after communism. Source: Foreign Affairs, 93(6).
View in Google Scholar
Stübinger, J. (2019). Statistical arbitrage with optimal causal paths on high-frequency data of the S&P 500. Quantitative Finance, 19(6), 921?935. doi: 10.1080/14697688.2018.1537503.
DOI: https://doi.org/10.1080/14697688.2018.1537503
View in Google Scholar
Svabova, L., Tesarova, E. N., Durica, M., & Strakova, L. (2021). Evaluation of the impacts of the COVID-19 pandemic on the development of the unemployment rate in Slovakia: counterfactual before-after comparison. Equilibrium. Quarterly Journal of Economics and Economic Policy, 16(2), 261?284. doi: 10.24136/eq.2021.010.
DOI: https://doi.org/10.24136/eq.2021.010
View in Google Scholar
Toots, A., & Bachmann, J. (2010). Contemporary welfare regimes in Baltic States: adapting post-communist conditions to post-modern challenges. Studies of Transition States and Societies, 2(2), 31?44.
View in Google Scholar
Wachowicz, T. (2011). Application of TOPSIS methodology to the scoring of negotiation issues measured on the ordinal scale. Multiple Criteria Decision Making, 6, 238?260.
View in Google Scholar
Ward, J. H. (1963). Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58(301), 236?244. doi: 10.108 0/01621459.1963.10500845.
DOI: https://doi.org/10.1080/01621459.1963.10500845
View in Google Scholar
Wawrzyniak, K., Bak, I., Cheba, K., & Oesterreich, M. (2020). The similarity of European Union countries in terms of the structure of the unemployed. European Research Studies Journal, 23(4), 416?429. doi: 10.35808/ersj/16 91.
DOI: https://doi.org/10.35808/ersj/1691
View in Google Scholar
Zieliński, M. (2020). The impact of the unemployment level on non-standard employment forms in the Visegrad Group countries. Ekonomia i Prawo. Economics and Law, 19(2), 393?404. doi: 10.12775/eip.2020.027.
DOI: https://doi.org/10.12775/EiP.2020.027
View in Google Scholar
Żuk, P., & Savelin, L. (2018). Real convergence in Central, Eastern and South-Eastern Europe. ECB Occasional Paper, 212.
DOI: https://doi.org/10.2139/ssrn.3215693
View in Google Scholar
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