XXI. MEZINÁRODNÍ KOLOKVIUM O REGIONÁLNÍCH VĚDÁCH. SBORNÍK
PŘÍSPĚVKŮ.
21ST
INTERNATIONAL COLLOQUIUM ON REGIONAL SCIENCES.
CONFERENCE PROCEEDINGS.
Place: Kurdějov (Czech Republic)
June 13-15, 2018
Publisher: Masarykova univerzita, Brno
Edited by:
Viktorie KLÍMOVÁ
Vladimír ŽÍTEK
(Masarykova univerzita / Masaryk University, Czech Republic)
Vzor citace / Citation example:
AUTOR, A. Název článku. In Klímová, V., Žítek, V. (eds.) XXI. mezinárodní kolokvium
o regionálních vědách. Sborník příspěvků. Brno: Masarykova univerzita, 2018. s. 1–5.
ISBN 978-80-210-8969-3.
AUTHOR, A. Title of paper. In Klímová, V., Žítek, V. (eds.) 21st
International Colloquium
on Regional Sciences. Conference Proceedings. Brno: Masarykova univerzita, 2018. pp. 1–
5. ISBN 978-80-210-8969-3.
Publikace neprošla jazykovou úpravou. / Publication is not a subject of language check.
Za správnost obsahu a originalitu výzkumu zodpovídají autoři. / Authors are fully responsible for the
content and originality of the articles.
© 2018 Masarykova univerzita
ISBN 978-80-210-8969-3
ISBN 978-80-210-8970-9 (online : pdf)
Sborník příspěvků XXI. mezinárodní kolokvium o regionálních vědách Kurdějov 13.–15. 6. 2018
215
DOI: 10.5817/CZ.MUNI.P210-8970-2018-28
REGIONAL DISPARITIES IN LABOUR PRODUCTIVITY OF
SMALLAND MEDIUM-SIZED ENTERPRISES IN
MANUFACTURING
Regionální odlišnosti v produktivitě práce u malých a středních podniků ve
zpracovatelském průmyslu
MARTINA NOVOTNÁ
TOMÁŠ VOLEK
JAROSLAV VRCHOTA
Katedra ekonomiky
Ekonomická fakulta
Jihočeská univerzita v Českých Budějovicích
Department of Economics
Faculty of Economics
University of South Bohemia in České Budějovice
 Studentská 13,370 05 České Budějovice, Czech Republic
E-mail: novotna@ef.jcu.cz, volek@ef.jcu.cz, vrchota@ef.jcu.cz
Annotation
The article aims at regional differences in the efficiency of the use of human work in small and medium-sized
enterprises in manufacturing. The analysis was focused on differences in high technology and low technology
SMEs in regions of the Czech Republic. Regional disparities were assessed using measure of variability. An
analysis of 1,068 enterprises showed greater variability between regions in labour productivity of high technology
enterprises than in low technology enterprises but these regional disparities significantly decreased over the
monitored period. The level of labour productivity in high technology enterprises declined (except for 3 regions)
as a result of an inadequate growth in personal costs. Also, for low technology enterprises in almost all regions,
there was a decline in labour productivity and the corresponding steady state. High wage growth in the regions
can lead to higher regional disparities in labour productivity, and the risk of losing competitiveness for businesses
in the future.
Key words
labour productivity, SMEs, manufacturing, technological intensity
Anotace
Článek se zabývá regionálními rozdíly v efektivnosti využívání lidské práce v malých a středních podnicích ve
zpracovatelském průmyslu. Analýza byla zaměřena na odlišnosti u high-tech a low-tech malých a středních
podniků v regionech České republiky. Regionální disparity byly hodnoceny pomocí měr variability. Z provedené
analýzy 1068 podniků byla zjištěna větší variabilita mezi regiony u produktivity práce high-tech podniků než u
low-tech podniků, ale tyto regionální disparity se za sledované období významně snížily. Úroveň produktivity
práce u podniků high- tech klesla (vyjma 3 regionů) důsledkem neadekvátního růstu osobních nákladů. Rovněž u
podniků low-tech u téměř všech regionů byl zaznamenán pokles produktivity práce vlivem růstu osobních nákladů.
Vysoký růst mezd v regionech může vést k vyšším regionálním disparitám v produktivitě práce a pro podniky do
budoucna znamená riziko ve ztrátě konkurenceschopnosti.
Klíčová slova
produktivita práce, MSP, zpracovatelský průmysl, technologická intenzita
JEL classification: D24, M21, R11
Sborník příspěvků XXI. mezinárodní kolokvium o regionálních vědách Kurdějov 13.–15. 6. 2018
216
1. Introduction
Effectiveness of the use of human work is one of the major factors in the competitiveness of small and mediumsized
enterprises in manufacturing. This efficiency, measured by labour productivity, is not identical in the
individual regions and, on the contrary, may lead to an increase in regional disparities. At the same time an
important role in efficiency is played by technological intensity of enterprises. The aim of the paper is to evaluate
the regional differences in the efficiency of the use of human work by small and medium enterprises in the
processing industry, taking into account their technological intensity.
Regional disparities can be defined as inequalities in the economic or socio-economic growth of the regions
(Dusek, 2013). The economic growth of individual regions is clearly linked to the concept of competitiveness as
a basic indicator of long-term success in market economies. An important role have the factors (resources) of the
region's competitiveness, what this competitive advantage is predominantly based on. We can include in these
competitive advantages the technological level, innovation (Melecky, 2015) or the efficiency of using factors of
production (Gonzalez-Pernia, et al., 2012). The prerequisite of regional competitiveness is the competitiveness of
enterprises that are active in the region and create jobs. Small and medium-sized enterprises are the engine of
economy (Mura et. al., 2015) and generator of development regions. The efficiency (productivity) of SME is
influenced by many factors: human capital, organization capital (Leitao, Franco, 2011) or business process
(Hajduova, Andrejkovic, Mura, 2014). Kislingerová (2008) says that the competitiveness of enterprises as the
ability of firms to constantly increase productivity.
Measurement of regional disparities can be done on the basis of objective indicators. One of these indicators are
indicators of productivity (Filippetti, Peyrache, 2013). Productivity measures how efficiently production inputs
are being used in an economy to produce a given level of output. There are many different productivity measures.
The simplest and the most frequently-encountered measure is labour productivity. Labour productivity can be
measured at the firm, sector and regional or national level. Labour productivity is defined as value added per
worker (Broersma, Oosterhaven, 2009), worker-hour or personal cost. Using personal costs for measuring labour
productivity reflects the costs that enterprises has to spend on employees. Labour productivity is influenced by
many factors. The important factors of labour productivity are the flexibility of the labour market (Pavelka,
Loester, 2013) and business cycle (Mayer et al. 2016). The size and dynamics of labour productivity in the regions
is one of indicators of regional competitiveness.
2. Methodology
The aim of the paper was to assess the regional differences in the efficiency of utilization of the labour factor in
the processing industry for small and medium enterprises. The goal was also to find a change in this efficiency in
2016 compared to 2012 (change after five years) in regions of Czech Republic (NUTS2). The analysis was
performed in 1068 SMEs, through their financial statements drawn from the Albertina database. The same
enterprises were under review in both of the years. We used the classification by Commission Recommendation
2003/361/ESES based on number of employees, turnover and balance sheet total. Attention was focused on
enterprises in the manufacturing industry, which were divided according to economic activity or technological
demands. The enterprises were sorted into two categories. Eurostat uses the aggregation of the manufacturing
industry according to technological intensity and based on NACE Rev. 2 at 2-digit level. The first category of HT
includes high-technology and medium-high-technology, while the second category includes medium-lowtechnology
and low-tech economic activities.
HT category includes mainly the enterprises in the following fields: 21 Manufacture of basic pharmaceutical
products and pharmaceutical products; chemical products; 27 to 30 Manufacture of electrical equipment;
Manufacture of motor vehicles, trailers and semi-trailers, category LT includes in particular these fields of activity
(especially those related to industries): 19 Manufacture of coke and refined petroleum products; 22 to 25
Manufacture of rubber and plastic products; 33 Repair and installation of machinery and equipment; 10 to 18
Manufacture of food products, beverages, tobacco products, textile, wearing apparel, leather and related products,
wood and of products of wood, paper and paper products, printing and reproduction of recorded media; 31 to 32
Manufacture of furniture; Other manufacturing.
It was evaluated not only the efficiency of the labour factor but also the company's performance. Efficiency of
work was measured through indicators: Labour productivity (Sales - S/personal costs - PC), Labour intensity
(Personal costs -PC/ total costs - TC), Capital-Labour ratio (fixed assets/personal costs). Business performance
was evaluated using ROA, ie the ratio of EBIT and Assets a Return on Equity (ROE) – ratio EAT and Equity, at
the same time it was detected also Material and energy intensity (consumption of material and energy / firm
Sborník příspěvků XXI. mezinárodní kolokvium o regionálních vědách Kurdějov 13.–15. 6. 2018
217
performance). One way to increase business competitiveness is to increase labour productivity, which can be
formally registered as
1
1
0
0
PC
S
PC
S
 ,
where PC represents personal costs and sales revenue from goods, products and services
After simple algebraic treatment we get
0
1
0
1
S
S
PC
PC
 , tj. <
which can be interpreted as a requirement for a slower growth in personal costs compared to a change in sales for
goods, products and services. Therefore, it is actually a declaration of a requirement that average wages for
employees grow more slowly than average labour productivity.
All the indicators were first identified for the whole set of enterprises and subsequently the regional disparity of
selected indicators was determined. All indicators were first identified for a whole set of enterprises and then a
regional disparity in labour productivity was determined by means of standard deviation and variation coefficient,
with the simultaneous use of both variability measures being recommended. The standard deviation ( =
∑ ( − ) ) is not a dimensionless number and depends on the overall level of the phenomenon, on the
contrary the variation coefficient ( = ) is a dimensionless number and shows only the size of variability
(Kutscherauer et al., 2010).
3. Results
Small and medium-sized enterprises play an important role in the endogenous growth of individual regions in the
Czech Republic. The share of small and medium-sized enterprises in the total value added of the business sector
in the Czech Republic is around 54% (2015). In the case of economic problems or the economic slowdown of
small medium-sized enterprises, this effect is immediately reflected in the economic growth and development of
the regions. The slowing or acceleration of economic growth or productivity of SMEs can thus be a significant
factor in creating regional disparities. The analysis carried out first divided the small and medium-sized enterprises
(SMEs) according to their economic activity into high-technology and low-technology. The economic
performance of MSEs is illustrated in Table 1. The table shows the level of selected ratios between 2016 and 2012.
The level of indicators monitored does not show significant differences between HT and LT. The most striking
difference is between technology-intensive businesses and enterprises with less demanding ROA (Return on
Assets) and ROE (Return on Equity) indicators. Firms in the HT category achieve significantly lower return on
equity in 2012.
Tab. 1: Selected MSEs ratios
MSEs ratios
2012 2016
HT LT HT LT
C-L RATIO in CZK 1.365 1.381 1.350 1.467
Material and energy intensity in CZK 0.499 0.423 0.424 0.395
Labour intensity in CZK 0.207 0.243 0.256 0.259
Labour productivity in CZK 3.907 3.603 3.527 3.536
ROA v CZK 0.027 0.073 0.057 0.075
ROE v CZK 0.021 0.100 0.095 0.100
Number of enterprises 265 803 265 803
Source: authors’ calculation
The growth rate of the absolute and relative indicators in 2016 compared to 2012 is shown in Figure 1. It is clear
that the sales index for goods, products and services is growing more slowly than the index of personal costs, both
for HT and LT. These results in a decline in labour productivity, which is more pronounced in HT businesses. At
the same time, labour intensity increased significantly, but material and energy intensity decreased. This can be
caused (in keeping with competitiveness) with technologically more demanding investments, which also have an
impact on the cost increase. The increase in personal costs can be related both to a change in the structure of
Sborník příspěvků XXI. mezinárodní kolokvium o regionálních vědách Kurdějov 13.–15. 6. 2018
218
employees in favour of more qualified and to a lack of experts in selected professions, to the development of
unemployment and to the pressure of wage growth.
Fig. 1: Indexes of Selected Indicators MSEs in Manufacturing (Year 2016 / Year 2012)
Source: authors’ calculation
Next part of the article is devoted to the area of regional disparities in the field of efficiency of labour factor. For
the analysis of MSEs within the Cohesion Regions, the labour productivity indicator (Table 2) was selected.
Tab. 2: Labour productivity of MSEs in NUTS2 regions in 2012, 2016
NUTS 2
2012 2016 Number of enterprises
HT LT HT LT HT LT
Praha 4.46 4.04 3.19 3.71 29 56
Střední Čechy 3.78 3.89 3.91 3.90 21 66
Jihozápad 3.13 3.43 2.91 3.42 31 95
Severozápad 3.47 3.55 3.00 3.75 22 69
Severovýchod 3.37 3.53 3.65 3.44 46 146
Jihovýchod 3.68 3.86 3.87 3.84 46 175
Střední Morava 3.98 3.21 3.87 3.15 47 115
Moravskoslezsko 6.18 3.53 4.04 3.26 23 81
Total 3.91 3.60 3.53 3.54 265 803
Standard deviation 0.85 0.24 0.40 0.25
Coefficient of Variation in % 21.30 6.63 11.25 6.92
Source: authors’ calculation
Labour productivity is expected to be highest in Praha and Moravskoslezko, especially in 2012, in the HT category.
After five years (2016), in Praha, due to wage growth, it is even lower than other regions; in Moravskoslezsko
labour productivity remains still the highest in 2016 in the HT. We can conclude from the calculation of rate
variability that greater variability between regions is at the standard deviation of the labour productivity of HT
enterprises, but in five years these companies have significantly decreased. The labour productivity of LT
enterprises is low in both monitored periods (approximately CZK 0.25 of sales per CZK 1 of personnel costs,
which is less than 7%). Labour productivity in all regions and categories of the company is affected mainly by the
amount of labour costs i.e. personal costs or the share of these personal costs in the total costs of the enterprise
(Table 3).
0,00 0,50 1,00 1,50 2,00 2,50 3,00 3,50 4,00 4,50 5,00
Capital-Labour ratio
Material and energy intensity
Labour intensity
Labour productivity
ROA
ROE
Sales
Personal costs
LT HT
Sborník příspěvků XXI. mezinárodní kolokvium o regionálních vědách Kurdějov 13.–15. 6. 2018
219
Tab. 3: Labour intensity in NUTS2 regions in 2012, 2016
NUTS 2
2012 2016 Number of enterprises
HT LT HT LT HT LT
Praha 0.1966 0.2125 0.2471 0.2744 29 56
Střední Čechy 0.1845 0.2393 0.2059 0.2379 21 66
Jihozápad 0.3208 0.2759 0.3326 0.2846 31 95
Severozápad 0.2737 0.2245 0.3367 0.2305 22 69
Severovýchod 0.2475 0.2332 0.2493 0.2584 46 146
Jihovýchod 0.2331 0.2340 0.2411 0.2539 46 175
Střední Morava 0.2295 0.2657 0.2544 0.2810 47 115
Moravskoslezsko 0.1523 0.2495 0.2189 0.2706 23 81
Total 0.2073 0.2438 0.2564 0.2594 265 803
Standard deviation 0.0468 0.0185 0.0426 0.0172
Coefficient of Variation in % 20.36 7.66 16.13 6.65
Source: authors’ calculation
It is precisely in the regions where businesses have the lowest share of personal costs in total costs, but also the
highest level of labour productivity. Labour intensity is only in some regions dependent on the category of business
by technological intensity. Higher share of personal costs in total can be seen especially in the category of HT
enterprises in the following regions: Jihozápad, Severozápad (this share is more than 33% in 2016), LT category
is the highest proportion of personal costs in Jihozápad, Střední Morava and Moravskoslezsko (about 28%). On
the other hand, the lowest share of personal costs in total can be recorded in the category of HT enterprises in
2012, namely in Praha (11.6%) and Moravskoslezsko (15.2%).
If we focus on the growth rate of the monitored indicators (Figure 2), certain differences can be observed in the
case of businesses by technological intensity, especially the jump increase in labour intensity for HT enterprises
in Praha and Moravskoslezsko. For businesses, regardless of category by technological intensity, labour intensity
grow more quickly than labour productivity, except in HT in Severovýchod and LT in Severozápad.
Fig. 2: Development of selected indicators in 2016 compared to 2012 in enterprises by technological intensity
Source: authors’ calculation
From the above it can be concluded that in the monitored enterprises in 2016, compared with 2012, personal costs
grew faster than labour productivity. One reason for this may be the growth of the Czech economy, the influence
of low unemployment, the competition of large enterprises, both on the part of produced products and services
and on the demand side of employees (pressure to increase wages). Another reason can be the investment activity
of the enterprises, which will mean an increase in labour productivity in future years, or a change in the structure
of employees for the benefit of more skilled workers.
0
0,2
0,4
0,6
0,8
1
1,2
1,4
Labour productivity HT (index)
Labour intensity HT (index)
0
0,2
0,4
0,6
0,8
1
1,2
1,4
Labour productivity LT (index)
Labour intensity LT (index)
Sborník příspěvků XXI. mezinárodní kolokvium o regionálních vědách Kurdějov 13.–15. 6. 2018
220
Figure 3 illustrates the growth rate of the C-L ratio (share of fixed assets per 1 CZK personal cost) for the
monitored enterprises in 2016 compared to 2012. It can be estimated from the development indexes where the
regions are more invested in enterprises in the HT category (Jihozápad, Moravskoslezsko), and in enterprises in
category LT (Praha, Střední Čechy). In some regions, the differences in the investment activity of LT and HT are
not significant (Střední Morava, Jihovýchod, Severovýchod and Severozápad).
Fig. 3: C-L Ratio (index 2016/2012)
Source: authors’ calculation
The profitability of the enterprise was monitored through the ROA indicator for the same companies segmented
by technological intensity in 2012 and 2016 (Figure 4). Companies in HT category in Praha had the highest return
on assets in 2012, which declined significantly in 2016 and is the second lowest in NUTS2. In Střední Čechy, LT
firms are more profitable. The highest profitability in regions of the HT industry is achieved by the regions
Jihozápad, Severozápad and Jihovýchod. The greatest improvement occurred in Střední Morava and
Severovýchod regions. In the LT, most regions have improved or maintained the same state except in
Severovýchod region.
Fig. 4: ROA in the monitored enterprises would be technological intensities in 2012 and 2016
Source: authors’ calculation
Conclusion
The paper dealt with the regional differences in the efficiency of the use of human work in SMEs. The analysis
was focused on differences in high technology and low technology MSEs. From an analysis of 1068 enterprises
in two periods, there were no significant differences in labour productivity between high a low technology firms.
The growth rate of labour productivity and labour intensity indicators showed that businesses' sales grew slower
0
0,2
0,4
0,6
0,8
1
1,2
1,4
1,6
C-L Ratio HT (index) C-L Ratio LT (index)
0,0
2,0
4,0
6,0
8,0
10,0
12,0
HT (2012) HT (2016)
0,0
2,0
4,0
6,0
8,0
10,0
12,0
LT (2012) LT (2016)
Sborník příspěvků XXI. mezinárodní kolokvium o regionálních vědách Kurdějov 13.–15. 6. 2018
221
than wages. The increase in labour costs can be related to a change in the structure of employees in favour of more
skilled workers, with a shift to greater automation and robotization of production, a lack of free labour (low
unemployment) and a pressure to wage growth.
In HT, it was found that only three regions experienced a rise in labour productivity, while labour productivity
levels declined in other regions. The main reason is not a drop in overall corporate performance but an inadequate
growth in personal costs. This situation poses a risk of lower competitiveness in the future, which can be
compensate by higher investments (Dosi et al, 2015).. Higher investment activity was recorded in HT in Praha
and Moravskoslezsko. Higher regional differences in labour productivity levels were also found for HT enterprises
than LT, but these disparities significantly decreased over the projection horizon (the variation factor decreased
by about 10 percentage points).
LT enterprises show a slight decline or steady state of labour productivity in most regions, excluding Severozápad.
Even in these sectors, the impact of a high increase in personal costs over the performance of firms is reflected.
This situation did not return either high investment activity or the Capital-Labour ratio in some regions (the largest
growth was in Jihozápad). For LT, a low regional disparity rate (around 7%) has been demonstrated, which has
not changed over the 5 years.
Overall, the current high wage growth in the regions may pose a risk to businesses in the future for a loss of
competitiveness at national or international level.
Literature
[1] BROERSMA, L., OOSTERHAVEN, J., (2009). Regional labor productivity in the Netherlands: evidence of
agglomeration and congestion effects. Journal of Regional Science, vol.49, no.3, pp. 483-511. ISSN 0022-
4146. DOI: 10.1111/j.1467-9787.2008.00601.x.
[2] EUR- LEX, (2003) Commission Recommendation of 6 May 2003. Official Journal L 124, 20/05/2003 P. 0036
– 0041. [online]. [1. 3. 2018]. Retrieved from: http://eur-lex.europa.eu/eli/reco/2003/361/oj.
[3] DOSI, G., GRAZZI, M., MOSCHELLA, D., (2015). Technology and costs in international competitiveness:
From countries and sectors to firms. Research Policy, vol. 44, no. 10, pp. 1795-1814. ISSN 0048-7333. DOI:
10.1016/j.respol.2015.05.012
[4] DUŠEK, J., (2013). European grouping of territorial cooperation as a way of cross-border regional
cooperation within the European Union. In XVI. mezinárodní kolokvium o regionálních vědách. Sborník
příspěvků. Brno: Masarykova univerzita, pp. 329-336. ISBN 978-80-210-6257-3. DOI:
10.5817/CZ.MUNI.P210-6257-2013-40.
[5] EUROSTAT, (2008. )Eurostat indicators on High-tech industry and Knowledge – intensive services
Annex 3 - High-tech aggregation by NACE Rev.2. [online]. [20. 2. 2018]. Retrieved from
http://ec.europa.eu/eurostat/cache/metadata/Annexes/htec_esms_an3.pdf
[6] FILIPPETTI, A., PEYRACHE, A., (2013). Is the Convergence Party Over? Labour Productivity and the
Technology Gap in Europe. Jcms-Journal of Common Market Studies, vol. 51, no. 6, pp. 1006-1022. ISSN
0021-9886. DOI: 10.1111/jcms.12066.
[7] GONZALEZ-PERNIA, J. L., PENA-LEGAZKUE, I., & VENDRELL-HERRERO, F. (2012). Innovation,
entrepreneurial activity and competitiveness at a sub-national level. Small Business Economics, vol. 39, no.
3, pp. 561-574. ISSN 0921-898X. DOI: 10.1007/s11187-011-9330-y.
[8] HAJDUOVA, Z., ANDREJKOVIC, M., MURA, L., (2014). Utilizing experiments designed results during
error identification and improvement of business processes. Acta Polytechnica Hungarica, vol. 11, no. 2, pp.
149 -166. ISSN 1785-8860.
[9] KUTSCHERAUER, A. et al., (2010). Regionální disparity. Disparity v regionálním rozvoji země - pojetí,
teorie, identifikace a hodnocení. Ostrava: VŠB-Technická univerzita Ostrava. ISBN 978-80-248-2335-5.
[10]KISLINGEROVÁ, E., (2008). Inovace nástrojů ekonomiky a managementu organizací. Praha: C.H. Beck.
ISBN 978-80-7179-882-8.
[11]LEITAO, J., FRANCO, M., (2011). Individual entrepreneurship capacity and small and medium enterprises
(SME) performance: A human and organizational capital approach. African Journal of Business Management,
vol. 5, no. 15, pp. 6350-6365. ISSN 1993-8233.
[12]MAYER, E., RUTH, S., SCHARLER, J., (2016). Total factor productivity and the propagation of shocks:
Empirical evidence and implications for the business cycle. Journal of Macroeconomics, vol.50, pp. 335-346.
ISSN 0164-0704. DOI: 10.1016/j.jmacro.2016.11.001.
[13]MELECKY, L., (2015). Spatial Analysis of NUTS 2 Regions Based on Competitiveness Factors and Their
Regional Variability. In Cers 2014: 5th Central European Conference in Regional Science, International
Conference Proceedings. Kosice: Tech Univ Kosice, pp. 581-91. ISBN 978-80-553-2015-1. DOI
Sborník příspěvků XXI. mezinárodní kolokvium o regionálních vědách Kurdějov 13.–15. 6. 2018
222
[14]MURA, L., BULECA, J., HAJDUOVA, Z., ANDREJKOVIC, M., (2015). Quantitative financial analysis of
small and medium food enterprises in a developing country. Transformations in Business & Economics, vol.
14, no. 1, pp. 212-224. ISSN 1648-4460.
[15]PAVELKA, T., LOSTER, T. (2013). Flexibility of the Czech Labour Market from a Perspective of the
Employment Protection Index. In 7th International Days of Statistics and Economics. Slany: Melandrium,
pp.1090 – 1099. ISBN 978-80-86175-87-4.
This paper was supported by the Grant Agency of the University of South Bohemia GAJU č. GA JU 053/2016/S.