The Application of Dynamic Relative Taxonomy to Study Professional Activity of Seniors in The Visegrad Group Regions In 2015-2019

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Cite this Article as: Grazyna DEHNEL, Marek OBREBALSKI and Marek WALESIAK “ The Application of

The Application of Dynamic Relative Taxonomy to Study Professional Activity of Seniors in The Visegrad Group

Regions In 2015-2019

Grazyna DEHNEL

Poznan University of Economics and Business, Poznan, Poland ORCID 0000-0002-0072-9681

e-mail: grazyna.dehnel@ue.poznan.pl

Marek OBREBALSKI

Wroclaw University of Economics and Business, Wroclaw, Poland ORCID 0000-0002-7030-7089

e-mail: marek.obrebalski@ue.wroc.pl

Marek WALESIAK

Wroclaw University of Economics and Business, Wroclaw, Poland ORCID 0000-0003-0922-2323

e-mail: marek.walesiak@ue.wroc.pl

Abstract

The purpose of the paper is to measure the scope and degree of differences in the level of seniors’ involvement in the labour market in the conditions of ageing population, covering the regions of Po- land, Czechia, Slovakia and Hungary in 2015-2019. The involvement of senior citizens in the labour market is described using 4 variables: economic activity rate of the population aged 65 or over, employment at the age of 65-74 per 1000 total employment, employment rate of the population aged 65 or over, average number of weekly working hours in the main job of the population aged 65-74. The study was based on the data from Eurostat REGIO database. A dynamic relative taxonomy was adopted for the purpose of the study. The level of professional activity of seniors on the labour markets in the examined countries and regions shows an upward tendency. The seniors in Czechia presented a relatively high economic activity and clearly a lower one was recorded in the surveyed regions of Hungary and Slovakia. In Polish regions, however, significant interregional disparities were revealed in this respect. The results of the study also confirmed a significant correlation between the professional activity of seniors and the level of unemployment in a given region. A lower overall unemployment rate level in a region resulted in higher economic activity of seniors.

Keywords: silver economy, labour market, relative taxonomy, composite indicators

Introduction Rationale for The Study and The Related Works

Population aging is a global process. A significant increase in the share of senior citizens has been recorded both in Europe and in other parts of the world. At the same time, the European society remains much older than the societies living on other continents and, moreover, major differences have been observed in this respect among the European countries. The statistical presentation of this process is shown in Table 1.

While, on a global scale, the share of the population aged 65 and over increased in 1950-2019 from the level of 5.1% to 9.1%, in Europe it went up from 8.0% to 18.8% in the aforementioned period. It means that approximately every fifth European is a senior citizen. Among the Visegrad Group coun-

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tries, the society of Slovakia remains relatively the youngest (people aged 65 and over constitute 16.2% of the total population), whereas the oldest ones are the populations of Czechia and Hungary.

Table 1: The share of population aged 65 and over worldwide, in Europe and in the Visegrad Group countries in 1950-2050

Year World Europe Poland Czechia Slovakia Hungary

1950 5.1 8.0 5.2 8.4 6.6 7.8

1970 5.3 10.5 8.2 12.1 9.2 11.6

1990 6.2 12.7 10.0 12.7 10.2 13.5

2000 6.9 14.8 12.0 13.8 11.3 15.1

2010 7.6 16.3 13.5 15.4 12.5 16.1

2019 9.1 18.8 18.1 19.8 16.2 19.7

2030 11.7 23.0 23.2 22.2 21.0 22.0

2050 15.9 28.1 31.1 28.9 28.9 28.0

Source: authors’ compilation based on the UN data (World Population Prospects 2019, Volume II: Demograph- ic Profiles).

In addition, the increasing old-age dependency ratio has been recorded, which reflects the growing number of people aged 65 and over per 100 population aged 15-64. This indicator values in both dynamic and spatial perspective are presented in Table 2.

Table 2: Old-age dependency ratio worldwide, in Europe and in the Visegrad Group countries in 1950-2050

Year World Europe Poland Czechia Slovakia Hungary

1950 12.0 16.5 11.3 15.9 14.1 15.4

1960 12.1 17.8 12.2 18.1 14.4 17.5

1970 13.6 21.7 18.1 24.7 20.3 22.7

1980 14.8 25.2 21.2 27.4 22.6 26.7

1990 14.6 24.3 19.6 24.8 20.8 25.9

2000 15.2 27.4 23.4 25.4 21.8 28.4

2010 15.8 29.5 23.5 26.6 21.5 28.4

2019 18.4 34.3 32.0 35.7 27.9 35.6

2030 23.5 45.0 44.3 42.6 39.0 41.0

2040 28.7 52.4 50.2 50.3 46.5 48.7

2050 32.3 59.4 66.2 61.8 60.3 58.2

Source: authors’ compilation based on the UN data (World Population Prospects 2019, Volume I: Comprehen- sive Tables).

The processes of population aging result in numerous and diverse consequences, both economic and social (Samorodov 1999; Krajňáková and Vojtovič 2017, p. 321). The scale of these consequences is reflected, e.g., in the level and systematic increase in old-age dependency ratio. Its level also varies in the Visegrad Group countries.

The economic implications of the population aging process are not only correlated with the growing costs of public services and benefits (primarily covering health and care) for seniors as well as high financial burden incurred by the budgets of states and local government units. They are also visible in the sphere of labour market functioning, changes in economic activity or transformations in the struc- ture of workforce.The social consequences of population aging are noticeable in the sphere of living conditions, attitudes and behaviours towards social and economic rearrangements, or intergeneration- al relationships.

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The range of needs reported by senior citizens remains abundant. Among them, the need for further professional activity despite reaching retirement age is becoming increasingly apparent. The seniors present on the labour market are subject to the same rules as other employees competing for work in the conditions of market economy (Schimanek 2012, pp. 83-84). Hence, the analysis of their professional activity on these markets should take into account both the supply and demand for their work, including the general trends on the labour markets, the level of socio-economic development in territorial terms, or the applicable labour law provisions.

The research focused on the situation of older adults on labour markets covered diverse populations and territorial cross-sections. The conducted analyses studying the problems of seniors functioning in the working environment included many European countries (Van Dalen et al. 2009; Heywood and Siebert 2009; Thalassinos et al. 2019; Aging Europe 2019, Working ... 2019; Wyszkowska 2020).

For this purpose various methods of multivariate comparative analysis were applied as well as numerous indicators and sources of information were used. The review of the existing indicators and possible sources of information about the situation on the labour market was performed by, e.g., the International Labour Organization (Decent ... 2018).An interesting research covered, e.g., the prob- lem of measuring the unemployment risk and the degree of stability level of employees by age on the labour market (Hijzen and Menyhert 2016), the probability of employment and professional deactiva- tion of older people (Magda and Ruzik-Sierdzińska 2012) and the participation of seniors in trainings dedicated to work environment (Canduela et al. 2012).Many studies were also carried out addressing the multifaceted situation of seniors on the labour market in regional cross-sections (e.g. Steiner 2017; Obadić 2004). The scope of these studies, however, is limited, mainly due to the unavailability of some data characterizing individual regions.

A dynamic relative taxonomy in positional version was used in the article to analyse senior citizens’

professional activity in the Visegrad Group regions in the period 2015-2019. Two research hypotheses were formulated in the study:

– professional activity of seniors in the regions is increasing and the disproportions between regions in this respect are declining.

– lower overall unemployment rate in the region enhances higher professional activity of seniors.

The second hypothesis was formulated based, e.g., on the results of the research carried out in Cana- da for 1991-2007 at the regional level (Galarneau et al. 2015; Bélanger et al. 2016). Galarneau et al.

(2015) examined the relationship between the expected retirement age and the unemployment rate of the economic region. The authors concluded that over the considered period, the workers in economic regions characterised by higher unemployment rates were expected to retire two years earlier, on average, than the workers in economic regions presenting lower unemployment rates.

Research Methodology – Relative Taxonomy Method in Dynamic Version The method of relative taxonomy in classical terms was proposed by Wydymus (2013) and its positional version was developed by Lira (2015). Both versions of the relative taxonomy method apply a static approach despite using it for panel data. The relative taxonomy method, although being com- paratively new, has already had many empirical applications (see Walesiak and Dehnel 2020). The applications of relative taxonomy method are presented in the following studies: Szopik-Depczyńska et al. (2017), Müller-Frączek and Muszyńska (2016), Głowicka-Wołoszyn et al. (2018), Cheba and Bąk (2019), Ziolo et al. (2019), Cheba (2020), Lira (2015), Lira et al. (2014).

The article applies a modification of relative taxonomy for the purposes of a dynamic approach presented in the study by Walesiak and Dehnel (2020).

In the static version (see Wydymus 2013, Lira 2015), the relativization in formula (4) is performed separately for each period of the study = 1, … , . In the dynamic version, the value of j-th variable is relativized jointly based on the data matrix from T periods. The relative taxonomy procedure in its

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dynamic version covers the following steps:

1. In the first step, the observations on m variables for n analysed objects and T periods are compiled into one data matrix:

∙ × =

⎣⎢

⎢⎢

⎡ ⋯

⋮ ⋮ ⋯ ⋮

⋯ ⋯ ⋯⋯ ⋯

⋮ ⋮ ⋯⋯ ⋮

⋯ ⎦⎥⎥⎥⎥⎥⎤

, (1)

where: = 1, … , – object number (e.g. country), = 1, … , – variable number, = 1, … , – period number (e.g. year).

2. In the second step, stimulants ! (where higher values are more preferred), destimulants " (where lower values are more preferred) and nominants # (where the nominal value is most preferred) are distinguished in the set of variables. Destimulants are transformed into stimulants using the quotient transformation (see e.g. Walesiak 2002, p. 18):

= $ ^% &^' (2)

Nominants are transformed into stimulants using the quotient transformation (see e.g. Walesiak 2002, p. 18):

=_{(45+ ,}^{()*+ ,} ^-^;/^0-1²³

-;/_0-1²3, (3)

where: 6 – nominal level of j-th variable.

3. In the third step, the values of j-th variable are relativized in accordance with the below formula:

7^/_/^8-8

0-1, … ,^/_/^9-:

0-1;, for = 1, … , and = 1, … , (4) resulting in the matrix of relativized values for j-th variable presenting ∙ × ∙ dimensions. As a result of relativization the values of variables are dimensionless.

4. In the fourth step, the average similarity (using the arithmetic mean or the median) of a given rela- tivized observation is calculated in relation to the other relativized observations of j-th variable:

a. In the classical approach (arithmetic mean):

< = = ^∙

∑ ∑ ^/_/⁸⁸⁸

? 081

? ⋯ _∙ ∑ ∑ ^/_/^8@8

? 0@1

?

⋮ ⋮ ⋮

∙ ∑ ∑ ^/_/^98:

? 081

? ⋯ _∙ ∑ ∑ ^/_/^9@:

? 0@1

?

A (5)

b. In the positional approach (median):

< =

⎣⎢

⎢⎡med E^//⁸⁸⁸888, … ,_/^/⁸⁸⁸

98:F ⋯ med E^/_/^8@8

8@8, … ,_/^/^8@8

9@:F

⋮ ⋮ ⋮

med E^/_/^98:₈₈₈, … ,^/_/^98:

98:F ⋯ med E^/_/^9@:

8@8, … ,^/_/^9@:

9@:F⎦⎥⎥⎤

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5. In the fifth step, the values of !G aggregate measure are calculated using one of the below for- mulas:

a. Classical approach:

!G = ∑ _H

? 0-1⁄ (7)

b. Positional approach:

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!G = med E_H₀₈₁, ⋯ ,_H

0@1F (8)

The values of !G measures presenting (7) and (8) form can be higher or lower than 1. The lower the value of !G measure the better the position of object in relation to the remaining objects in the time interval from = 1 to = . Contrary to the static approach, the dynamic approach not only shows the relationships between the objects in particular periods, but also the occurred changes in the level of the analysed phenomenon between the objects in the entire period covered by the study.The dynamic version allows for the presence of NA values in matrices. Such data are not included in the calculation of the aggregate measure !G presenting (7) and (8) form.

Empirical Results

A dynamic relative taxonomy in the positional version was used in the article to analyse professional activity of seniors in the Visegrad Group regions in the period 2015-2019. The following variables were applied in the study:

x1 – economic activity rate of the population aged 65 or over (%) x2 – employment at the age of 65-74 per 1000 total employment x3 – employment rate of the population aged 65 or over (%)

x4 – average number of weekly working hours in the main job of the population aged 65-74

Variables x1-x4 represent stimulants where higher values are more preferred. The study was based on the data from Eurostat REGIO database.

Table 3 presents !G aggregate measure values showing changes in the level of professional activity of seniors in the Visegrad Group regions in 2015-2019. The positional approach of !G measure was applied in the study, using the median presenting (8) form.The lower the !G measure value, the better the position of i-th object in relation to other objects in the individual analysed years and the entire 2015-2019 period.The dynamic approach shows not only the relationships between the objects in particular periods, but also the occurred changes in the level of the analysed phenomenon between the objects in the entire period under study.

Table 3: KL aggregate measure values of (8) form showing changes in the level of professional activity of seniors in the Visegrad Group regions in the years 2015-2019

Unit !G Unemployment Rate

2015 2016 2017 2018 2019 2015 2016 2017 2018 2019 European Union 0.899 0.867 0.838 0.797 0.760 9.4 8.6 7.6 6.9 6.3

Czechia 0.874 0.778 0.772 0.685 0.681 5.1 4 2.9 2.2 2

Jihovýchod 0.853 0.778 0.838 0.784 0.681 4.9 3.7 3.1 2.3 1.9 Jihozápad 0.942 0.778 0.700 0.778 0.731 3.9 3.1 2.1 1.5 1.5 Moravskoslezsko 1.114 1.169 1.000 0.797 0.980 8.2 6.9 4.7 3.7 3.7 Praha 0.554 0.533 0.522 0.426 0.373 2.8 2.2 1.7 1.3 1.3 Severovýchod 0.899 0.867 0.748 0.685 0.700 5.2 4 2.8 2 1.7 Severozápad 0.942 0.772 0.803 0.690 0.838 7.3 5.2 3.4 3.4 3 Strední Cechy 0.838 0.705 0.826 0.760 0.772 3.5 3.1 2.1 2 1.3 Strední Morava 1.410 0.990 1.043 0.852 0.845 5.3 3.9 3.3 2.2 2.2 Hungary 2.178 1.879 1.675 1.376 1.119 6.8 5.1 4.2 3.7 3.4 Budapest 1.309 1.147 1.521 1.205 0.860 5.1 4.3 2.9 3.1 2.5 Dél-Alföld 1.803 1.499 1.407 1.312 1.199 7.9 5.6 4.1 3.3 3.5 Dél-Dunántúl 2.722 1.565 1.924 1.416 1.343 8.1 6.2 6.3 5.6 4.8 Észak-Alföld 1.826 2.280 2.125 1.502 1.071 10.9 9.3 7.4 6.6 6.3

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Észak-Magyarország 1.797 1.656 1.741 1.361 1.493 8.7 6.3 5.8 4.7 4.5 Közép-Dunántúl 3.063 2.130 1.587 1.426 1.230 4.4 3 2.2 2.2 2 Nyugat-Dunántúl 2.681 3.267 1.489 1.495 1.244 3.8 2.7 2.4 2 1.8

Pest 2.569 2.404 1.925 1.672 1.114 5.7 3.1 2.6 2.2 2.4

Poland 1.043 1.000 0.891 0.907 0.891 7.5 6.2 4.9 3.9 3.3 Dolnośląskie 1.246 0.952 0.766 0.810 0.857 7 5.5 4.7 3.3 3.3 Kujawsko-Pomorskie 1.607 1.175 0.959 1.057 0.890 7.9 7.4 5.5 4.3 4 Łódzkie 1.011 0.907 0.907 0.899 0.916 7.7 5.6 4.6 4.4 3.7 Lubelskie 0.850 0.970 0.980 0.907 0.899 9.3 8 7.2 6.3 5.5 Lubuskie 0.982 0.918 0.912 0.942 0.875 6.4 4.7 3.7 3 2 Małopolskie 0.886 1.140 1.065 0.907 0.892 7.2 5.2 4.2 2.9 2.8 Mazowiecki regionalny 1.400 1.181 0.961 1.052 0.935 8.3 7.8 6.4 5.7 4.6 Opolskie 1.485 1.400 1.065 1.074 1.137 6.5 5 4.3 3.2 3.2 Podkarpackie 0.973 0.957 0.755 0.845 0.942 11.6 9.6 8.4 6.5 5.1 Podlaskie 1.256 1.531 1.262 1.365 1.164 7 6.7 4.7 3.2 3.1 Pomorskie 0.742 0.765 0.725 0.748 0.764 6.6 5.7 4.2 3 2.8 Śląskie 1.401 1.562 1.326 1.230 1.287 7.2 5.4 3.9 3.4 2.4 Świętokrzyskie 1.000 0.891 0.721 0.845 0.772 10.1 8.9 7 5.8 4.2 Warmińsko-Mazurskie 1.445 1.033 1.079 1.288 1.227 9.5 8.8 7.2 5.6 3.3 Warszawski stołeczny 0.797 0.629 0.531 0.544 0.507 4.9 3.7 3.5 2.4 2.1 Wielkopolskie 1.225 1.000 0.913 0.847 0.925 5.8 4.8 3.1 2.2 2.6 Zachodniopomorskie 1.133 1.101 1.051 0.998 0.931 7.5 7 4.7 3.8 3.2 Slovakia 1.922 1.850 1.381 1.225 1.065 11.5 9.7 8.1 6.5 5.8 Bratislavský kraj 0.721 0.740 0.589 0.461 0.390 5.7 5.1 4.2 2.9 2.4 Stredné Slovensko 2.722 2.882 2.179 2.001 1.225 12.8 10.8 9.4 7.5 6.1 Východné Slovensko 2.579 2.514 2.514 2.579 1.960 15 13.2 12 10.1 9.1 Západné Slovensko 2.882 2.333 1.343 1.256 1.523 9.7 7.7 5.5 4.3 4 Parameters (for regions only)

Mean 1.450 1.328 1.157 1.076 0.986

Standard deviation 0.698 0.664 0.488 0.424 0.310

Median 1.246 1.101 1.000 0.942 0.925

Median absolute deviation 0.533 0.480 0.389 0.374 0.281 Source: authors’ calculations using R.

The graphic presentation of changes in the level of seniors’ professional activity covering the Vise- grad Group regions in 2015-2019 is provided in the form of line charts for the regions of V4 group countries (Fig. 1 – Fig. 4) and also a line chart comparing V4 countries against the EU average (Fig.

5).

The verification of research hypothesis was carried out using the dynamic approach of relative taxonomy method (Table 3 and Fig. 1 – Fig. 5) and dispersion measures (standard deviation and median absolute deviation).

Throughout the entire analysed period, a systematic decrease in both the mean value of aggregate measure and its differentiation can be observed (see Table 3, Fig. 1 – Fig. 5). This proves that, firstly, a systematic improvement in the level of seniors’ professional activity in the Visegrad Group regions is recorded. Secondly, the differentiation between the regions of V4 group (standard deviation and median absolute deviation values in Table 3) and between the regions within each of the analysed countries (Fig. 1 – Fig. 4) is declining. research hypothesis has been verified positively. In addition, it is worth adding that the recorded higher professional activity of seniors occurred at a faster pace in Hungary (Fig. 2) and Slovakia (Fig. 4), i.e. in the countries where active presence of older people on the labour market was the lowest at the beginning of the analysed period.

The verification of research hypothesis was performed using the Pearson correlation coefficient.

The significance and strength of the correlation between !G aggregate measure (Table 3) measuring

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the economic activity of seniors (relative taxonomy method – dynamic version) and the overall unemployment rate in the regions (Table 3) were examined separately for each year. The results are presented in Table 4.

Table 4: Pearson correlation coefficients between MN aggregate measure

(measuring economic activity of seniors) and the overall unemployment rate in the regions of V4

Specification 2015 2016 2017 2018 2019

Pearson correlation coefficient 0.3160 0.2913 0.4437 0.5580 0.5486

O − QRSTU 0.0207 0.0306 0.0016 6.156e-05 8.433e-05

Source: authors’ calculations.

Fig. 1: Graphic presentation of changes in the level of professional activity of seniors for Czechia in 2015-2019

Source: authors’ compilation.

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Fig. 2: Graphic presentation of changes in the level of professional activity of seniors for Hungary in 2015-2019

Fig. 3: Graphic presentation of changes in the level of professional activity of seniors for Poland in 2015-2019

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Fig. 4: Graphic presentation of changes in the level of professional activity of seniors for Slovakia in 2015-2019

Fig. 5: Graphic presentation of changes in the level of professional activity of seniors in the Visegrad Group (V4) and European Union (EU-28) in 2015-2019 Source: authors’ compilation.

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research hypothesis has been verified positively. All Pearson correlation coefficients are statisti- cally significant with the declared significance level of V = 0.05. A lower overall level of unemployment rate in a region is conducive to greater economic activity of senior citizens. Moreover, a clearly stronger correlation takes place if the situation on the labour market is better – the unemployment rate is lower. It has been observed at the end of the analysed period, when the Pearson correlation coefficient level went up from 0.3160 in 2015 to 0.5486 in 2019.

Additional conclusions can be formulated on the basis of the conducted research after analysing the graphic presentation results of the changes in the level of professional activity of seniors in the Vise- grad Group regions in 2015-2019 (Fig. 1 – Fig. 5):

1. In the analysed period, the above average level of professional activity presented by seniors in the European Union can be observed for Czechia only (Fig. 5). The situation is much worse in this respect with reference to Hungary and Slovakia.

2. The level of professional activity of senior citizens is the highest in the capital regions of the Vis- egrad Group countries (Praha region for Czechia, Budapest region for Hungary, Warszawski stołeczny region for Poland and Bratislavský kraj for Slovakia).

3. The strongest differentiation between V4 group regions in 2015 was observed in Slovakia and Hungary. In 2019 only Slovakia recorded a strong differentiation.

4. In the analysed period, the level of professional activity presented by seniors was significantly higher for the regions of Czechia and Poland comparing to the regions of Hungary and Slovakia.

Concluding Remarks

The demographic processes taking place in recent years, referred to as Population ageing, have re- sulted in major changes on the labour market. A growing potential ingrained in senior citizens has been noticed. The development of economic conditions delaying the moment of retirement, and thus extending the working life of seniors, has become one of the essential goals to be followed by the regional social policy.It is, therefore, crucial to perform a multidimensional assessment of the trends and the situation in terms of seniors’ economic activity, taking into account such factors as, e.g., demographic variables or employment conditions in a given region.

The article presents an attempt of carrying out such an analysis. Its novelty consists in the application of a dynamic relative taxonomy following a positional approach to assess changes in the economic activity of older people at the regional level of V4 countries.As a result of the dynamic approach application it was possible not only to assess the correlations occurring in the phenomenon level between the regions in individual years of the analysed period, but also to assess mutual relationship between them in the entire period covered by the analysis. Thus, the application of a dynamic relative taxonomy allowed tracing the changes which took place both in the cross-sectional and longitudinal perspective.

The results of the study clearly indicate that between 2015 and 2019 the level of professional activity presented by seniors on the regional labour markets of V4 countries was systematically improving, although the rate of these changes was diversified. On average, it was faster in those countries where the activity of seniors was lower at the beginning of the reference period. The changes which occurred over the course of 5 years brought about a decrease in the number of variations between the regions in terms of professional activity presented by seniors. Regardless of the above, the highest level of seniors’ professional activity was recorded, in all analysed years, in the capital regions of the Visegrad Group countries. In addition, the results of the study confirmed a significant correlation between the professional activity of seniors and the unemployment rate in a given region. A lower overall level of unemployment rate in the region was conducive to greater economic activity of senior citizens. Along with an improving situation on the labour market the discussed relationship was becoming increasingly recognized.

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The calculations were performed using R scripts developed by one of the authors (R Core Team 2020).

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