Wage Level as One of the Most Important Indicators of the Quantitative Aspect of the Standard of Living of the Population and Selected Indicators of Economic Maturity in OECD Member Countries

The present paper focuses on the comparison of wage levels across OECD countries, the research data coming from an official OECD website. The following eight variables are employed in this study – the average wage, minimum wage, GDP per capita, tertiary education attainment, employment ratio, trade...

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Main Author: Bílková, Diana
Format: Article in Journal/Newspaper
Language:English
Published: Kaunas University of Technology 2020
Subjects:
Online Access:https://inzeko.ktu.lt/index.php/EE/article/view/23441
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spelling ftkaunastunivojs:oai:ktu.lt:article/23441 2023-05-15T16:52:26+02:00 Wage Level as One of the Most Important Indicators of the Quantitative Aspect of the Standard of Living of the Population and Selected Indicators of Economic Maturity in OECD Member Countries Bílková, Diana 2020-06-29 application/pdf https://inzeko.ktu.lt/index.php/EE/article/view/23441 eng eng Kaunas University of Technology https://inzeko.ktu.lt/index.php/EE/article/view/23441/13930 https://inzeko.ktu.lt/index.php/EE/article/view/23441 Copyright (c) 2020 Engineering Economics Engineering Economics Vol. 31 No. 3 (2020); 334-344 2029-5839 1392-2785 average wage GDP per capita employment ratio labour productivity purchasing power parity regression analysis cluster analysis Ward's method Euclidean distance Dunn's index time-series analysis exponential smoothing info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Articles 2020 ftkaunastunivojs 2022-02-14T11:34:02Z The present paper focuses on the comparison of wage levels across OECD countries, the research data coming from an official OECD website. The following eight variables are employed in this study – the average wage, minimum wage, GDP per capita, tertiary education attainment, employment ratio, trade unions, labour productivity and inflation rate. The average wage represents the main explained variable in regression and correlation analysis, the remaining seven variables being used as potential explanatory ones. In order to compare living standards in different countries, average and minimum wages as well as per capita GDP data were adjusted to relative purchasing power parity. The principal objective was to identify which explanatory variables statistically significantly affect the average wage. The analysis showed that only three of them – namely the employment ratio, GDP per capita and labour productivity – have a significant effect at a 5% statistical level. The regression hyperplane with a forward stepwise selection was applied. Nine clusters of OECD countries were created based on both all the eight variables and four of them selected in regression analysis (the average wage and three explanatory ones) with the aim to identify the countries that coexist in the same cluster. Ward's method and Euclidean distance are utilized in cluster analysis, the number of clusters being determined with the use of the Dunn index. The study also aims at the prediction of the average wage by 2022, which was made via exponential smoothing of time series. (The greatest purchasing power is reported by Luxembourg, Switzerland, Iceland, the U.S., the Netherlands, Denmark, Norway and Austria, the highest average wage growth rate by 2022 being expected in the Baltic and some other post-communist countries.) Article in Journal/Newspaper Iceland KTU Open Journal Systems (Kaunas University of Technology) Norway
institution Open Polar
collection KTU Open Journal Systems (Kaunas University of Technology)
op_collection_id ftkaunastunivojs
language English
topic average wage
GDP per capita
employment ratio
labour productivity
purchasing power parity
regression analysis
cluster analysis
Ward's method
Euclidean distance
Dunn's index
time-series analysis
exponential smoothing
spellingShingle average wage
GDP per capita
employment ratio
labour productivity
purchasing power parity
regression analysis
cluster analysis
Ward's method
Euclidean distance
Dunn's index
time-series analysis
exponential smoothing
Bílková, Diana
Wage Level as One of the Most Important Indicators of the Quantitative Aspect of the Standard of Living of the Population and Selected Indicators of Economic Maturity in OECD Member Countries
topic_facet average wage
GDP per capita
employment ratio
labour productivity
purchasing power parity
regression analysis
cluster analysis
Ward's method
Euclidean distance
Dunn's index
time-series analysis
exponential smoothing
description The present paper focuses on the comparison of wage levels across OECD countries, the research data coming from an official OECD website. The following eight variables are employed in this study – the average wage, minimum wage, GDP per capita, tertiary education attainment, employment ratio, trade unions, labour productivity and inflation rate. The average wage represents the main explained variable in regression and correlation analysis, the remaining seven variables being used as potential explanatory ones. In order to compare living standards in different countries, average and minimum wages as well as per capita GDP data were adjusted to relative purchasing power parity. The principal objective was to identify which explanatory variables statistically significantly affect the average wage. The analysis showed that only three of them – namely the employment ratio, GDP per capita and labour productivity – have a significant effect at a 5% statistical level. The regression hyperplane with a forward stepwise selection was applied. Nine clusters of OECD countries were created based on both all the eight variables and four of them selected in regression analysis (the average wage and three explanatory ones) with the aim to identify the countries that coexist in the same cluster. Ward's method and Euclidean distance are utilized in cluster analysis, the number of clusters being determined with the use of the Dunn index. The study also aims at the prediction of the average wage by 2022, which was made via exponential smoothing of time series. (The greatest purchasing power is reported by Luxembourg, Switzerland, Iceland, the U.S., the Netherlands, Denmark, Norway and Austria, the highest average wage growth rate by 2022 being expected in the Baltic and some other post-communist countries.)
format Article in Journal/Newspaper
author Bílková, Diana
author_facet Bílková, Diana
author_sort Bílková, Diana
title Wage Level as One of the Most Important Indicators of the Quantitative Aspect of the Standard of Living of the Population and Selected Indicators of Economic Maturity in OECD Member Countries
title_short Wage Level as One of the Most Important Indicators of the Quantitative Aspect of the Standard of Living of the Population and Selected Indicators of Economic Maturity in OECD Member Countries
title_full Wage Level as One of the Most Important Indicators of the Quantitative Aspect of the Standard of Living of the Population and Selected Indicators of Economic Maturity in OECD Member Countries
title_fullStr Wage Level as One of the Most Important Indicators of the Quantitative Aspect of the Standard of Living of the Population and Selected Indicators of Economic Maturity in OECD Member Countries
title_full_unstemmed Wage Level as One of the Most Important Indicators of the Quantitative Aspect of the Standard of Living of the Population and Selected Indicators of Economic Maturity in OECD Member Countries
title_sort wage level as one of the most important indicators of the quantitative aspect of the standard of living of the population and selected indicators of economic maturity in oecd member countries
publisher Kaunas University of Technology
publishDate 2020
url https://inzeko.ktu.lt/index.php/EE/article/view/23441
geographic Norway
geographic_facet Norway
genre Iceland
genre_facet Iceland
op_source Engineering Economics
Vol. 31 No. 3 (2020); 334-344
2029-5839
1392-2785
op_relation https://inzeko.ktu.lt/index.php/EE/article/view/23441/13930
https://inzeko.ktu.lt/index.php/EE/article/view/23441
op_rights Copyright (c) 2020 Engineering Economics
_version_ 1766042686560665600