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Forecasting labour productivity in the European Union member states: is labour productivity changing as expected? (CROSBI ID 646346)

Prilog sa skupa u zborniku | sažetak izlaganja sa skupa | međunarodna recenzija

Žmuk, Berislav ; Dumičić, Ksenija ; Palić, Irena Forecasting labour productivity in the European Union member states: is labour productivity changing as expected? // Book of Abstracts ICOS2017- The International Conference on Official Statistics: Challenges, Opportunities and Future Directions in Official Statistics, March 30-31, 2017 in Sarajevo, Bosnia and Herzegovina / Kremić, Emir ; Kozarić, Kemal (ur.). Sarajevo: Institute for Statistics of the Federation of Bosnia and Herzegovina, School of Economics and Business in Sarajevo, University of Sarajevo, 2017. str. 90-92

Podaci o odgovornosti

Žmuk, Berislav ; Dumičić, Ksenija ; Palić, Irena

engleski

Forecasting labour productivity in the European Union member states: is labour productivity changing as expected?

In order to be competitive and to ensure economic growth a country should take care about its labour productivity development. Not only the labour productivity must be observed in present time but the projections of future trends and its developments must be conducted. Consequently, the aim of the paper is to propose different ways of forecasting labour productivity developments by using different statistical forecasting methods and by applying different approaches of the most appropriate statistical forecasting method selection. In the paper are examined labour productivities, measured per employee and per hour worked, in the European Union (EU) member states in period from 1990 to 2016. In the forecasting analysis seven statistical forecasting methods have been used to forecast labour productivity for each EU member states separately and for the EU as whole. Overall three approaches to determine the forecast values of labour productivity have been used in the analysis. In the first approach the forecast values were determined by using statistical forecasting method with the lowest Mean Squared Error (MSE). In the second approach the forecasting was conducted based on data from 1990 to 2015. The statistical forecasting method with the closest forecasting value from 2016 to the real value from 2016 was used to determine labour productivity changes in the future. In the third approach, which was introduced in the paper, all seven statistical forecasting methods were used together to determine forecasts. The impact of each statistical forecasting method was determined by using MSE approach. The lower MSE of a statistical forecasting method is, the higher impact or the higher weight on the forecasts the statistical forecasting method will have. In order to make groups of the EU member states with different labour productivity level, statistical non- hierarchical clustering approach was used. According to the labour productivity per employee measure, at standard forecasting approach, forecasts labour productivity will increase in 14 EU member states in the future whereas according to the labour productivity per hour worked measure the labour productivity will increase in 18 countries. Similar, benchmark forecasting approach show labour productivity level increase in 18 EU member states. Weighting forecasting approach forecasted labour productivity decrease only in Luxembourg and in Spain. According to the weighting forecasting approach and the average labour productivity per employee trend value for low level labour productivity countries is 1, 221.03 US$, for medium level labour productivity countries is 1, 093.41 US$, and for high level labour productivity countries is –7.36 US$. These results are suggesting that countries with lower labour productivity level are going to have higher labour productivity increase per year than countries with higher labour productivity level. On that way, the differences in labour productivity between countries should be smaller. In the future research labour productivity level convergence in the EU should be investigated. Furthermore, the labour productivity should be examined more detailed, i.e. on monthly base, to get better insight into labour productivity trends.

competitiveness ; European Union member states ; labour productivity ; statistical forecasting methods ; weighting

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Podaci o prilogu

90-92.

2017.

objavljeno

Podaci o matičnoj publikaciji

Book of Abstracts ICOS2017- The International Conference on Official Statistics: Challenges, Opportunities and Future Directions in Official Statistics, March 30-31, 2017 in Sarajevo, Bosnia and Herzegovina

Kremić, Emir ; Kozarić, Kemal

Sarajevo: Institute for Statistics of the Federation of Bosnia and Herzegovina, School of Economics and Business in Sarajevo, University of Sarajevo

978-9958-25-123-8

Podaci o skupu

ICOS2017: Challenges, Opportunities and Future Directions in Official Statistics, March 30-31, 2017 in Sarajevo, Bosnia and Herzegovina

predavanje

30.03.2017-31.03.2017

Sarajevo, Bosna i Hercegovina

Povezanost rada

Ekonomija