FOLIA POMERANAE UNIVERSITATIS TECHNOLOGIAE STETINENSIS Folia Pomer. Univ. Technol. Stetin., Oeconomica 2017, 339(89)4, 63–70
Adrianna MASTALERZ-KODZIS, Ewa POŚPIECH
METHODOLOGY OF MEASUREMENT OF SOCIO-ECONOMIC DEVELOPMENT IN THE EU MEMBER STATES
METODYKA POMIARU POZIOMU ROZWOJU SPOŁECZNO-GOSPODARCZEGO PAŃSTW UNII EUROPEJSKIEJ
Department of Statistics, Econometrics and Mathematics, University of Economics in Katowice 1 Maja 50, 40-287 Katowice, Poland, e-mail: adrianna.mastalerz-kodzis@ue.katowice.pl, e-mail: ewa.pospiech@ue.katowice.pl
Streszczenie. Celem artykułu jest porównanie metod pomiaru poziomu rozwoju społeczno- -gospodarczego państw należących do Unii Europejskiej. Wzięto pod uwagę znany z literatury wskaźnik HDI. Wskaźnik ten jest zależny od trzech charakterystyk: poziomu życia, zdrowia i edukacji. W artykule rozszerzono HDI o czwartą charakterystykę określającą rynek pracy. Następnie porównano otrzymane wyniki analiz z miarą syntetyczną Hellwiga obliczaną dla trzech oraz czterech rozważanych charakterystyk. Na podstawie analiz można stwierdzić, że wartość miary rozwoju społeczno-gospodarczego HDI jest silnie dodatnio skorelowana z charakterystykami rynku pracy. Istnieją niewielkie różnice pomiędzy wynikami, w zależności od zastosowanej metodyki badań. Dla państw UE wskaźniki rozwoju gospodarczego są zależne od wielkości demograficznych; ich wartości wolno zwiększają się w czasie.
Key words: HDI, labour market, multivariate comparative analysis, socio-economic development, synthetic measures.
Słowa kluczowe: HDI, rynek pracy, wielowymiarowa analiza porównawcza, rozwój społeczno- -gospodarczy, miary syntetyczne.
INTRODUCTION
The measurement of the countries' socio-economic development is important not only for academics and governments but also for ordinary citizens. The issue of measurement methods is then absolutely legitimate. The most commonly applied synthetic measure of countries' (as well as regions') development is the HDI (Human Development Index). It is dependent on three variables, namely: health, education and income per inhabitant. In the paper we have added a fourth variable describing the labour market. We also compare the HDI with the Hellwig's synthetic measure, a classic method of multivariate analysis.
One of the HDI components is health and in order to calculate the HDI we have to take into consideration average lifespan. In the EU a historically unprecedented aging of the population is the crucial challenge. In most of the European countries the post-working age population is increasing whilst the pre-working age population is decreasing, which results in many socio-economic changes.. Many studies foresee further aging of the EU population in the coming years. When looking at the HDI components and the value of the measure alone, dynamics of the changes is evident. For most of the EU states, all components of the HDI, as well as the synthetic measure, rise from year to year (Mastalerz-Kodzis 2016).
64 A. Mastalerz-Kodzis and E. Pośpiech
The economic as well as social welfare reference literature o includes works dedicated to research methodology (Rencher 2002; Giri 2004; Biernacki 2006; Stanton 2007), demographic theories (Duncan and Scott 1998; Fihel and Okólski 2012) and application of methodology in economic analyses (Jóźwiak 2013); Mastalerz-Kodzis 2015; Mastalerz- -Kodzis and Pośpiech 2015; Mastalerz-Kodzis and Pośpiech 2016).
BASIC RESEARCH METHODOLOGY
Human Development Index
For the purposes of this paper we have used the standard HDI measure and extended it to included another, fourth characteristic. We have also applied the classic methodology of multivariate comparative analysis.
A widely applied measure of socio-economic development is the HDI indicator. The way it is constructed and interpreted has been discussed in, inter alia, the works of (Duncan and Scott 1998; Biernacki 2006; Santon 2007; Fihel and Okólski 2012).
The HDI indicator is used worldwide to determine the level of a country’s development. HDI was constructed in the ‘90s of the 20th century by Amartya Sen and Mahbuba ul Haqa.
In the evaluation process, the synthetic indicator HDI takes into account three criteria: a long and healthy life, the level of education and the standard of living. The US programme concerning development recommends to derive the HDI indicator from the following component indices:
– Health Index, for LEi – average lifespan in country i according to the formula:
65 25 . = i − i LE Ind H (1)
– Education Index, for LIT.Indi – illiteracy indicator and ENR.Indi – schooling indicator
according to the formula:
(
i) (
i)
i LITInd ENRInd
Ind
E. . 31 .
3
2 +
= (2)
– Welfare Index, where y is the income per one inhabitant in a given country: i
(
)
(
( )
)
(
(
)
)
100 $ log 40000 $ log 100 $ log log . − − = i i y Ind Y (3)– The social development index per one inhabitant for a given country is calculated according to the formula:
3 . . . i i i i Ind Y Ind E Ind H HDI = + + (4)
Countries are classified in one of the four development categories by the HDI score: highly developed (0.801–1), medium developed (0.501–0.8), poorly developed (0–0.5).
In this paper we have added a labour market development index to the classic HDII formula.
It was calculated according to the formula:
. . (100 . ) (100 . )
.
4 100
i i i i
i
Emp Ind Lab Ind Un Ind Youth Ind
L Ind = + + − + −
Methodology of measurement of socio-economic… 65
where:
– Emp.Indi– employment to population ratio (% ages 15 and older),
– Lab.Indi – labour force participation rate (% ages 15 and older),
– Un.Indi– unemployment total (% of labour force),
– Youth.Indi– youth not in school and employment (% ages 15–24).
The labour market–adjusted HDI index per inhabitant for a given country was calculated according to the formula:
4 . . . . i i i i i Ind L Ind Y Ind E Ind H HDIL = + + + (6)
The HDIL is interpreted in the same way as the HDI.
Multivariate comparative analysis
Selected methods of data classification were used in the study. By characterizing n objects (EU states) using m variables (factors), the nature of the variables was defined (stimulants, destimulants) and their normalization was performed (Rencher 2002; Giri 2004). In multivariate methodology of comparative analysis historical data are taken into consideration. This method allows to compare different objects (e.g. EU states) described in terms of various features, for example general economic factors or labour market factors. The methodology is based on the construction of a taxonomic model measure.
We conducted the normalization of variables. For x j − arithmetic mean of feature j and
for sj standard deviation of feature j, standardization according to the following formula:
(
ij j)
j ij x x sz = - / .
A pattern z0=
[
z01,z02,…,z0m]
and anti-pattern for development[
]
m
z z
z
z−0 = −01, −02,…, −0
was defined for: − − = t destimulan z z stimulant z z z ij ij i ij ij i j min , , max 0 − − = − z z destimulant stimulant z z z ij ij i ij ij i j max , , min 0 (7)
The distance between the object and the pattern was calculated with the use of Euclidean distance:
(
)
∑
-m j j ij i z z d 1 2 0 0 = = (8)The obtained variable was not normalized. It was transformed using a formula
(
0/ 0)
1 d d
mi = - i , where mi is the taxonomic measure of the development of object i (EU
country), d is the distance between the pattern and the anti-pattern stated by formula 0
(
)
∑
-m j j j z z d 1 2 0 0 0 = −= . The higher the level of the phenomenon, the higher the value of the
measure, mi ∈
[ ]
0,1.In a classic perspective the values of the economic and financial indicators are averaged in time. However, taking into consideration the dynamic character of the variables, the values of taxonomic measures should be analysed in time (Mastalerz-Kodzis 2015, 2016; Mastalerz- -Kodzis and Pośpiech 2015).
66 A. Mastalerz-Kodzis and E. Pośpiech
SELECTED FINDINGS. SOCIAL AND ECONOMIC CONDITIONS OF THE EU COUNTRIES
The empirical analysis was conducted with the use of selected economic indicators for the EU states. Taking into consideration selected demographic values for the EU countries and economic components of the HDI indicator, a spatial analysis for the 2013 data (data concerning this year were complete) was conducted. The data were retrieved from the following websites: UNDP Human Development Report, Eurostat, UNESCO Institute for Statistics (access on 28.06.2016).
The following demographic and economic indices were used in the analysis:
– LEi – average lifespan,
– LIT.Indi – illiteracy indicator,
– ENR.Indi– schooling indicator,
– y – income per one inhabitant, i
– Emp.Indi– employment to population ratio (% ages 15 and older),
– Lab.Indi – labour force participation rate (% ages 15 and older),
– Un.Indi – unemployment total (% of labour force),
– Youth.Indi – youth not in school and employment (% ages 15–24). Upon analysis of the data for the EU states, we can affirm that:
– The lifespan throughout the EU is continually rising. The highest values of this variable were recorded in Italy, Spain, France and Sweden, the lowest in Lithuania, Latvia, Romania and Bulgaria.
– The value of schooling indicator is gradually increasing. It is the highest in Denmark and Ireland, the lowest in Luxembourg, Cyprus, Malta, Croatia, Romania and Bulgaria.
– The indicator of income per EU inhabitant shows large variability. Its highest level is reached in Luxembourg, lowest in Bulgaria.
– The situation on the labour market is also characterized by a very large variability amongst the EU countries.
– The employment rate differed in 2013 in EU countries by over 22%; in the Netherlands it was over 60%, whilst in Greece just under 39%.
– The unemployment rate was lowest in Austria (4.9%), highest in Greece (27.3%).
– The percentage of not studying and unemployed youth also showed large variations: it was the lowest in France (5%), the highest in Italy (22.2%).
– The GDI significantly differentiated the countries of the EU: the difference in this variable amounted to as much as 43 115$ per person.
In order to give an overall picture of the EU states, including al the aforementioned features, the HDI measure and Hellwig's synthetic measure was used. The 2nd and 3rd column of Table 1 comprise the values of HDI and HDIL calculated according to formulas (1) – (6) together with the EU countries rankings. Columns 4 and 5 show the values of Hellwig's measure calculated according to formulas (7) – (8) including, respectively, 3 and 4 indices (Fig. 1, 2).
Methodology of measurement of socio-economic… 67
Table 1 . Values of synthetic measure
Country HDI 1 characteristics HDIL 2 characteristics Hellwig’s Measure mi 3 characteristics Hellwig’s Measure mi 4 characteristics L.Ind Netherlands 0.852 (2) 0.834 (1) 0.826 (1) 0.850 (1) 0.781 Germany 0.823 (5) 0.808 (3) 0.680 (5) 0.720 (3) 0.763 Denmark 0.856 (1) 0.834 (1) 0.794 (2) 0.819 (2) 0.769 Ireland 0.852 (2) 0.817 (2) 0.779 (3) 0.714 (5) 0.710 Sweden 0.820 (6) 0.807 (4) 0.629 (8) 0.680 (6) 0.769 United Kingdom 0.811 (10) 0.795 (8) 0.622 (9) 0.659 (7) 0.746 France 0.813 (8) 0.798 (7) 0.613 (10) 0.657 (9) 0.753 Austria 0.812 (9) 0.801 (6) 0.604 (11) 0.658 (8) 0.768 Belgium 0.816 (7) 0.787 (10) 0.645 (7) 0.614 (10) 0.703 Luxembourg 0.800 (13) 0.788 (9) 0.419 (17) 0.494 (15) 0.753 Finland 0.826 (4) 0.805 (5) 0.698 (4) 0.717 (4) 0.743 Slovenia 0.800 (13) 0.782 (11) 0.555 (13) 0.583 (11) 0.726 Italy 0.810 (11) 0.769 (13) 0.577 (12) 0.451 (17) 0.645 Spain 0.828 (3) 0.782 (11) 0.666 (6) 0.498 (14) 0.644 Czech Republic 0.782 (15) 0.773 (12) 0.477 (16) 0.539 (12) 0.747 Greece 0.809 (12) 0.759 (16) 0.550 (14) 0.358 (23) 0.611 Cyprus 0.754 (18) 0.742 (19) 0.323 (21) 0.378 (20) 0.707 Estonia 0.771 (16) 0.765 (15) 0.414 (18) 0.487 (16) 0.747 Lithuania 0.750 (20) 0.745 (18) 0.278 (23) 0.361 (22) 0.730 Poland 0.753 (19) 0.743 (19) 0.341 (20) 0.397 (19) 0.712 Slovakia 0.747 (21) 0.737 (20) 0.315 (22) 0.371 (21) 0.707 Malta 0.761 (17) 0.749 (17) 0.361 (19) 0.413 (18) 0.711 Portugal 0.790 (14) 0.768 (14) 0.501 (15) 0.509 (13) 0.701 Hungary 0.739 (22) 0.725 (22) 0.270 (24) 0.306 (24) 0.682 Croatia 0.731 (23) 0.709 (23) 0.232 (25) 0.218 (25) 0.642 Latvia 0.729 (24) 0.728 (21) 0.217 (26) 0.306 (24) 0.724 Romania 0.704 (25) 0.705 (24) 0.104 (27) 0.203 (26) 0.711 Bulgaria 0.696 (26) 0.688 (25) 0.058 (28) 0.117 (27) 0.663 0.6 0.65 0.7 0.75 0.8 N e th e rl a n d s G e rm a n y D e n m a rk Ir e la n d S w e d e n U n it e d K in g d o m F ra n c e A u st ri a B e lg iu m L u x e m b o u rg F in la n d S lo v e n ia It a ly S p a in C z e c h R e p u b lic G re e c e C y p ru s E s to n ia L it h u a n ia P o la n d S lo va k ia M a lt a P o rt u g a l H u n g a ry C ro a ti a L a tv ia R o m a n ia B u lg a ri a L.Ind
68 A. Mastalerz-Kodzis and E. Pośpiech 0.6 0.65 0.7 0.75 0.8 0.85 0.9 N e th e rl a n d s G e rm a n y D e n m a rk Ir e la n d S w e d e n U n it e d K in g d o m F ra n c e A u s tr ia B e lg iu m L u x e m b o u rg F in la n d S lo v e n ia It a ly S p a in C z e c h R e p u b li c G re e c e C y p ru s E s to n ia L it h u a n ia P o la n d S lo v a k ia M a lt a P o rt u g a l H u n g a ry C ro a ti a L a tv ia R o m a n ia B u lg a ri a HDI HDIL
Fig. 2. The HDI and HDIL indicator for EU countries in year 2013
Based on the values provided above, it can be stated that:
– A very strong, positive linear relationship exists between the values of HDI for EU countries and the synthetic measure for 3 components (health, standard of living and education). The value of Pearson's linear correlation coefficient between the values was calculated (Pearson = 0.989).
– A significant dependence of the HDIL value and the synthetic measure calculated for the 4 components (health, standard of living, education, labour market) was found (Pearson = 0.989).
– Spearman's rank correlation coefficient demonstrated that strong correlation exists between 3 and 4-component based rankings components.
– There is also a strong, positive correlation between the HDI and HDIL values for the EU countries (Pearson = 0.963), as well as between Hellwig's synthetic measure defined for 3 and 4 of the socio-economic areas (Pearson = 0.937).
– As it comes to the three HDI components, the highest-ranking countries are: Denmark, the Netherlands and Ireland, the lowest: Bulgaria and Romania.
– In case of the labour market-adjusted HDI indicator the synthetic values change. Highest socio-economic development is recorded in the Netherlands, Denmark, Germany, the lowest in Bulgaria and Romania.
– The synthetic indicator of the labour market is positively correlated with socio-economic measures.
– As the findings show, the socio-economic development of the EU countries is highly dependent on the condition of the labour market.
Moreover, analysing the time series of historical data concerning the study variables, it can be stated that:
– From 1980 there has been a slow, though systematic, growth in the HDI for all EU countries. – Upon analyzing the average rate of historical changes of the HDI value it can be safely
predicted that overall upward trend in the HDI value will continue in most of the EU countries. – It has been proven that the HDI values for the EU countries depend on the demographic
Methodology of measurement of socio-economic… 69
– In many countries the demographic structure of the population is changing; the 65+ population showed an increase, , whilst the 0−14 age population showed a decline.
– Adverse demographic trends such as the aging of societies influenced the growth of the HDI indicator components, thereby its synthetic value (demographic trends concerning the populations’ aging are also forecasted for the future).
CONCLUSIONS
It can be stated that changes that occurred in the EU countries in the1980−2013 period had a positive impact on the socio-economic indicators and labour market indicators analyzed in the paper. However, considering the dynamics of changes, it is necessary to continually analyze and monitor the subject indicators in an effort to prevent unfavourable trends and maintain balanced economic growth in the EU.
The studied economic, demographic and labour market characteristics are correlated. The interdependences which took place in the past can be used to anticipate the values of selected characteristics in the future. Unfavourable demographic phenomena, decreasing population in many EU countries, as well as changes in population’s age structure in many cases affected the growth of HDI components and increased the value of synthetic measure for labour market variables. However, we must remember that, in the long run, aging of the population is a negative phenomenon and will, in all probability, at some time adversely affect the socio-economic welfare the EU.
A stochastic and dynamic nature of the changes in the socio-economic environment should always be included in economic analyses. The knowledge of the relation between the variables and effects of demographic changes is certainly very important for economists, sociologists, the EU countries' authorities and the Council of Europe.
REFERENCES
Biernacki M. 2006. Kilka uwag o pomiarze dobrobytu społecznego [A few remarks about the measurement of the social well-being]. Mathematic. Econom. Wroc. 3(10), 115−126. [in Polish] Duncan C.J., Scott S. 1998. Human demography and disease, Cambridge, University Press.
Fihel A., Okólski M. 2012. Demografia. Współczesne zjawiska i teorie. Warszawa, Wydaw. Nauk. Scholar. [in Polish]
Jóźwiak J. 2013. Demograficzne uwarunkowania rynku pracy w Polsce [Demographic conditions of labour market in Poland]. Zesz. Demograf. 1, 9−23. [in Polish]
Mastalerz-Kodzis A., Pośpiech E. 2015. Wielowymiarowa analiza porównawcza w ujęciu dynamicznym na przykładzie wybranych charakterystyk ekonomicznych [Dynamic multivariate comparative analysis for choosen demographic characteristics]. Met. Iloś. Bad. Ekon. 16(4), 24−33. [in Polish] Mastalerz-Kodzis A. 2015. Zastosowanie dynamicznej wielowymiarowej analizy porównawczej
w badaniach ekonomicznych [Analysis of chosen economical characteristics by dynamical multivariate comparative analysis]. Stud. Ekonom. UE Katow. 227, 31−40 [in Polish]
Mastalerz-Kodzis A. 2016. Dynamika przemian społeczno-ekonomicznych krajów Unii Europejskiej [Economic and social dynamical changes in European Union countries]. Stud. Ekonom. UE Katow. 265, 26−37.[in Polish]
70 A. Mastalerz-Kodzis and E. Pośpiech
Mastalerz-Kodzis A., Pośpiech E. 2016. Dynamic and spatial analysis of economic and labour market characteristics in European countries, in: Proceedings of 34th International Conference Mathematical Methods in Economics. Eds. A. Kocourek, M. Vavroušek. Liberec, Technical University of Liberec, 546−551.
Rencher A.C. 2002. Methods of multivariate analysis. [b.m.], John Wiley & Sons, USA.
Stanton E.A. 2007. The human development index: A history, political economy research. University of Massachusetts at Amherst, Wirking Papers 127.
Data base, www.hdr.undp.org, www.ec.europa.eu, access: 28.06.2016.
Summary. The purpose of the paper is to compare the methods of measuring socio-economic development in the European Union member states. The authors have focused on the HDI indicator which is the main reference used in welfare economics literature. HDI is a composite of three basic components of human development: the standard of living, health and education. In the paper, the indicator has been extended to include one more component, i.e. labour market factors. Next, the results were compared with Hellwig's synthetic measure calculated first for the three and next for the four components. Based on our findings we can argue that the value of HDI is strongly positively correlated with labour market characteristics. There are minor differences between the results depending on the research methodology applied. Economic development indicators throughout the EU depend on demography, and the pace of population growth is expected to slow even further.
