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MODELING CLUSTER DEVELOPMENT USING PROGRAMMING METHODS: CASE
OF RUSSIAN ARCTIC REGIONS
Article in Journal of Entrepreneurship and Sustainability Issues · September 2020
DOI: 10.9770/jesi.2020.8.1(10)
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MODELING CLUSTER DEVELOPMENT USING PROGRAMMING METHODS: CASE OF RUSSIAN
ARCTIC REGIONS*
Tatiana Kudryavtseva ¹, Angi Skhvediani ², Mohammed Ali Berawi 3
1,2
Peter the Great St. Petersburg Polytechnic University (SPbPU), Polytechnicheskaya, 29, St. Petersburg, 195251, Russia
3
University of Indonesia (UI), Kampus UI, Depok, 16424, Indonesia
E-mails:1 kudryavtseva_tyu@spbstu.ru; 2shvediani_ae@spbstu.ru; 3maberawi@eng.ui.ac.id
Received 18 December 2019; accepted 15 June 2020; published 30 December 2020
Abstract. The aim of this research is to show how the process of data analysis can be automated through development of an information
system. The information system can be used for the identification of economic clusters and analysis of the regional potential for economic
growth. The authors used data on the Russian Arctic regions with extreme social, geographical, and economic conditions collected from
2009 to 2016 as an example. The authors have designed a database using MS Access software. The authors used the methodology of the
European cluster observatory and the approach suggested by M. Porter to identify economic clusters. This methodology was complemented
by introduction parameters, which mirror the strength and employment dynamic of the clusters. Based on the employment data of 83
Russian regions during the period of 2009–2016 the authors have calculated cluster localization parameters for nine Russian regions, which
are partly or fully located in the Arctic zone. The authors suggest that the cluster structure in this area is weak and most of the significant
clusters are declining. The only significant cluster, which is growing in all regions, is the «Oil and Gas» cluster. In conclusion, the authors
state that the obtained results are vital for policy makers and can be used for elaborating the regional economic development strategy in
order to support regional diversification and specialization, which are closely related to positive spillovers.
Keywords: Arctic region; economic cluster; cluster identification; MS access; data processing; regional policy making
Reference to this paper should be made as follows: Kudryavtseva, T., Skhvediani, A., Ali, M. 2020. Modeling cluster development using
programming methods: case of Russian Arctic regions. Entrepreneurship and Sustainability Issues, 8(1), 150-176.
http://doi.org/10.9770/jesi.2020.8.1(10)
JEL Classifications: O1, O3
1. Introduction
Creating conditions for the economic development of regions is one of the most important tasks for regional
governments, who nowadays, in large part, are supported by informational systems (Morrissey, 2016; Rytova &
Gutman, 2019). During this process, a regional government should take into account social, economic, and
*
The reported study was funded by the Russian Foundation for Basic Research (research project No. 18-31020012).
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geographical factors, which can affect the development of each concrete territory (Andreyeva et al., 2018; Dvas et
al., 2018; Baltgailis, 2019; Petrenko et al., 2019).
A combination of these factors determines whether a certain region will or will not be capable of developing
industries which will be competitive at national and international scales. Consequently, analysts should process
multidimensional data which reflect the current situation. Based on such analyses, they should receive specific
results, which can be used for determining potential directions for development of the region (Degtereva et al.,
2018; Kichigin, 2017; Kozlov et al., 2017; Thill, 2019). Therefore, it is essential to develop informational systems
to support and enhance the processes of policy making and, consequently, positively affect regional economic
development (Chun et al., 2010; Höchtl et al., 2016; Velasquez & Hester, 2013; Prodani et al., 2019).
A cluster approach to regional economic development put forth by Porter (1998) and developed further by a
number of authors (Delgado et al., 2014, 2015; Tvaronavičienė, 2017; Tvaronavičienė & Razminienė, 2017;
Razminienė & Tvaronavičienė, 2018; Bublienė et al., 2019), is one of the most innovative and effective tools for
policy implementation. The results of applying a cluster approach in American (Gupta, et al., 2006; Guzman &
Stern, 2015; Peiró-Signes, et al., 2015; Porter et al., 2011), European (Crawley & Pickernell, 2012; Looijen &
Heijman, 2013; Sellar, et al., 2011) and Russian (Islankina & Thurner, 2018; Kutsenko et al., 2017; Rodionova et
al., 2017) territories are widely represented in scientific literature. However, these applications are lacking in two
main aspects which are essential for using this approach effectively in practice. The first aspect is that most of
them are focused on receiving results, rather than making the process reproducible and applicable for other
researches and practitioners. The second aspect is that they aim at finding global linkages between some factors
and the level of cluster development (Akpinar et al., 2017; MATICIUC, 2015), but do not focus on concrete
results for a concrete set of territories with extreme social, economic, and geographical conditions. This gap may
lead to the development of a «cure» which is suitable for all territories, but in some extreme cases is ineffective
and should be combined with some «additives». Therefore, it is necessary to describe how we can create an
information system which will provide an analytical background for the development of the cluster-based policy
and give examples of applying these results in territories with extreme social, economic, and geographical
conditions.
As an example of such territories, we have chosen Russian regions which are partly or fully located in the Arctic
zone (Leksin & Porfiryev, 2017). These are poorly developed territories which have a certain economic potential
(Borisov & Pochukaeva, 2016; Komkov, et al., 2017; Korovkin, 2016). Developing these territories is claimed to
be one of the top priorities for a balanced development of the Russian Federation (Gutman et al., 2018;
Romashkina et al., 2017; Tatarkin et al., 2017). Developing an effective cluster-based policy, which relies on the
results of comprehensive and multidimensional analysis, is key for long-term socioeconomic growth of the
Russian Arctic regions (Komkov et al., 2017; Rytova et al., 2017).
Therefore, the aim of this research is to show how, through development of an information system, the process of
data analysis can be automated, which is necessary for identifying and analyzing economic clusters. In addition,
we demonstrate a potential approach to cluster structure analysis of the Russian Arctic regions, which have both
extreme social, geographical, and economic conditions and a potential for economic growth, during 2009–2016.
2. Data and methods
2.1. Data and cluster identification methodology
In order to gather the information necessary for calculating the parameters of cluster localization, we obtained
detailed data on employment from three main sources: the joint economic and social data archive of the Higher
School of Economics (HSE, 2018), the Central Statistical Database of Russia (Federal State Statistics Service,
2019), and United Interdepartmental Information-Statistical Service (MinComSvyaz, 2019). These sources
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provide official data obtained from the Russian Federal State Statistics Service. We used data from united
interdepartmental information-statistical service as the main source of data, as it is better structured and contains
more information. In some cases, when there were not enough data for some of the regions, we used data from the
central statistical database of Russia and the joint economic and social data archive of the Higher School of
Economics. The data were organized in the form presented in Table 1. As a result, we received 28044
observations for calculating the localization parameters of 37 clusters for 83 regions of Russia for the period of
2009–2016.
Table 1. Specifying the data used for identifying economic clusters in Russia
Federal District
Region
Year
Cluster
OKVED codes
Number of the
employed
Each of the 37 clusters is
composed of several
For each code we
Identifying the
List of 83 Russian
OKVED codes.
filled the number of
time:
regions
Therefore, for each
people employed in
2009–2016
cluster, we detail its
the region
composition
Sources: Employment statistics by activity type were obtained from: (HSE, 2018), (Federal State Statistics Service, 2019), (MinComSvyaz,
2019). Authors composed clusters based on employment statistics of separate types of activities, presented in each region.
List of 8 Federal
Districts, which
include Russian
regions
List of 37 clusters,
identified according
to M. Porter’s
classification for
each region
We follow the methodology developed by Porter (1998), which is now used by the U.S. Mapping project and the
European Cluster Observatory for identifying and monitoring cluster development. In particular, we use three
coefficients which show the localization properties of each cluster: localization, focus, and size. This
methodology was presented in detail by both developers (Ketels & Protsiv, 2014), their followers (Kopczewska,
2018; Kopczewska et al., 2017) and the authors of this research study (Berawi, 2017; Berawi et. al., 2018;
Schepinin et. al., 2018) in earlier works. The European Cluster Observatory defined these three factors as the
«Localization coefficient» (1), «Size» (2), and «Focus» (3). The values of the factors, within the threshold values,
reflects whether the examined cluster has or has not achieved a sufficient «critical mass» to generate positive
external effects and relations. These indicators are calculated using employment statistics and are reflected in the
following formulae:
,
(1)
where LQ is the «Localization coefficient»;
is the number of people employed in cluster i in region g; is the
total number of people employed in region g; is the number of people employed in cluster i; and is the total
number of people employed.
,
(2)
where Size is the «Size» of cluster i;
is the number of people employed in cluster i in region g; and
number of people employed in cluster i.
,
where Focus is the «Focus» of cluster i;
number of people employed in region g.
is the
(3)
is the number of people employed in cluster i in region g; and
is the
G. Lindqvist, a Swedish economist from the European Cluster Observatory (Lindqvist, 2009), establishes the
following criteria as the threshold values, which mark significant cluster groups in a region:
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1) «Localization coefficient» ≥ 2;
2) the region should be included in top 10% in «Size»;
3) the region should be included in top 10% in «Focus».
In addition, a region cannot receive a star if critical mass of the cluster is less than 1000 employed people.
If a criterion is fulfilled, the cluster earns one «star». Thus, the maximum a cluster can receive is three «stars».
The number of «stars» determines the strength of the cluster group
Table 2. Level of region specialization in types of activities performed by cluster i in region g
Level of region specialization
Average number of stars, obtained by cluster i in region g
High
(2.3; 3]
Medium
(1.7; 2.4]
Low
[1; 1.7]
Region has no specialization in this type of activity
[0; 1)
Source: Compiled by aurhors
In order to systemize the results and present them more clearly, we also separate regions by two dimensions: the
level of specialization in types of activities, performed by cluster i (Table 2) in region g and the dynamic state of
employment of cluster i in region g (Table 3). We have built dimension «levels of region specialization» in types
of activities performed by cluster i in region g based on the average number of stars which cluster i in region g
receives for the analyzed period, while the second dimension is based on the employment dynamics, calculated
through the growth rate:
,
,
(4)
(5)
The growth rate allows estimating the change in clusters’ critical mass and reflecting the dynamic aspect of
cluster growth, where
is the number of people employed in cluster i in region g at the beginning (
) of
the analyzed period, and
is the number of people employed in cluster i in region g at the time
and
- at the time
.
is the measure for calculating long-term employment dynamics, while
is used for the
short-term. In Table 3 we propose a possible classification of dynamic states of the cluster depending on the
values of
and
at the end of the period and their overall dynamics. It complements the existing localization
measures, since the main problem of the «Size», «Focus», and «Localization coefficient» is their independence
from the time trend. It means that if employment of the cluster, employment of the whole cluster group, and total
employment are decreasing, the «Localization coefficient» remains stable, and vice versa, since it cannot catch up
with dynamic changes in employment in certain cases
Table 3. Types of dynamic state of employment of cluster i in region g
Interval for
%
Dynamic state
Characteristic
Strong growth
Moderate growth
Stable
Strong positive employment dynamics
Moderate positive employment dynamics
Stable employment dynamics with slight changes in employment
Unstable
Employment dynamics with rough positive and/or negative changes
at the beginning, in the middle or at the end of the period
Moderate decrease
Strong decrease
Moderate negative employment dynamics
Strong negative employment dynamics
Source: Compiled by aurhors
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and/or
and
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2.2. Description of information system used for automated cluster identification
The database «Clusters of Russia’s Regions» was developed and registered in 2017 in order to support research of
the cluster structure in Russia. During the development process, we wanted to achieve the following objectives:
structuring and rationalizing big data concerning employment in different clusters in the Russian regions;
creating a convenient system for data input and editing;
creating a computing mechanism for estimating the localization coefficients for clusters in a certain year;
creating a flexible system which can be modified in case some regions have to be added or new clusters have
to be defined;
automating the estimation results and converting them into analytical reports.
A user receives the results of analysis in the form of summary tables, where main results are given for each region
and each cluster. The results are calculated in accordance with the methodology discussed in Paragraph 2.1.
Based on the research of the data structure we created four entities: «Federal District», «Region», «Cluster», and
«Employment». These entities allow us to minimize input errors and provide integrity of data. The entity «Federal
District» has two attributes: an identifier (which is a primary key), and a label. This table is a glossary, which
provides secure and convenient input of data in interconnected objects and access to the groups of regions. The
entity «Region» belongs only to one Federal District and cannot exist independently. Therefore, apart from its
own primary key, it has a secondary key for connection with the entity «Federal District». The entity «Cluster»
has two main attributes: a short label and a named key. Additional attributes are used for interface organization,
because long labels take too much space and are not suitable for usage in headlines and summary tables. The
entity «Employment» contains two external keys for connection with «Region» and «Cluster» and a nested
primary key, which protects the table from data duplication since only one cluster i can be created for each region
in a certain time period. Therefore, each cluster can be uniquely determined through such attributes as year,
region, and cluster. For the sake of convenient data processing, we have also added a counter, which defines the
unique nested key. The database evaluates the following attributes: «Localization coefficient», «Size», «Focus»,
and «Number of stars» (Table 4).
Table 4. Attributes of entity «Employment»
Attribute title
Attribute label
Year
YearEpml
Region
IdRegion
Cluster
IdIndustry
Empig
Empig
Size
Esize
Focus
EFocus
LQ
ELQ
Stars
Estars
Source: Compiled by aurhors
In order to organize the data input and provide immediate access to the clusters, a temporary entity,
«Computation», with a varying number of attributes, has been introduced. It adapts for each region and cluster in
a specific time period.
The physical model is SQL-based and realized in DBMS MS Access 2007 (Figure 1).
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Source: Compiled by Authors
Figure 1. Physical model of «Clusters of Russia’s Regions».
The table «Employment» contains data, which is used for calculation and data processing. Other tables provide a
safe and convenient form for data input and make the main table free from redundant data. Using equations 1–3
the program calculates total employment by each region, each cluster, and each year. In order to implement
calculations, we developed a chain of query operators and the function CalcStars (Figure 2). The program
calculates the results and inputs them into the main table. The data from this table has to be analyzed and selected
for display. A chain of query operators for displaying the result is presented in Figure 3.
Source: Compiled by Authors
Figure 2. A calculation model of the database
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Figure 3. A chain of query operators for displaying the result
3. Results of database application
3.1. General information
In accordance with the methodology for cluster identification discussed in Section 2.1 and the database design
presented in section 2.2, we have received analytical results for all 83 Russian regions for the 2009–2016 period.
Here we discuss only the results obtained for the Russian regions, which are partly or fully located in the Arctic
zone. These regions are the following:
Murmansk Oblast;
Chukotka Autonomous Okrug;
Komi Republic;
Arkhangelsk Oblast including Nenets Autonomous Okrug;
Yamalo-Nenets Autonomous Okrug;
Sakha Republic;
Republic of Karelia;
Krasnoyarsk Krai;
Khanty-Mansi Autonomous Okrug.
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Source: Compiled by Authors
Figure 4. The map of Russian regions, which are fully or partly located in the Arctic zone
The geographical location of the regions we analyze is presented in Figure 4. Next, we present a detailed analysis
of cluster specialization of each Arctic region of Russia and, after that, aggregate the results for all arctic regions.
Komi Republic cluster specialization analysis
The overall employment dynamic in Komi Republic was negative. The total number of employed people
decreased by 13.97% or by 53,967 people over eight years. Analyzing the employment statistics in Komi
Republic during the period of 2009–2016, we have detected five clusters: Transportation and Logistics, Oil and
Gas, Paper Products, Business Services, and Construction, with all of them receiving at least one star. It means
that the level of localization of these clusters, at least in one year, was relatively high in accordance with the
values of the «Localization Coefficient», «Size», and «Focus». The detailed results are presented in Table 5.
Komi Republic had a medium specialization level in Transportation and Logistics and the critical mass of this
cluster was unstable during the analyzed period. After a decrease of the clusters’ employment by 1.07% in 2010,
there was a significant growth of the clusters’ critical mass from 36,403 up to the 43,756 people; that is, by 19.7%
in 2012. After that, there was a stable decrease in the Transportation and Logistics cluster’s critical mass: 19.35%
in 2016 compared to 2012. Nevertheless, the overall specialization of the region in Transportation and Logistics
activities remained at a medium level, since two localization measures out of three fulfilled the threshold
requirements.
Komi Republic had a high specialization level in Oil and Gas and the critical mass of this cluster grew
significantly during the analyzed period, despite some falls in 2011 and 2016. The overall increase of the cluster’s
critical mass was 25.76% over eight years. This resulted in a stronger specialization of the cluster and its
stabilization at the high level, since three out of three localization measures fulfilled the threshold requirements.
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Komi Republic had a high specialization level in Paper Products and the critical mass of its cluster substantially
decreased during the period of 2009-2016. The overall decrease of the clusters’ critical mass was 27.61% over
eight years. In addition, the decrease in the critical mass of the Paper Products cluster in Komi Republic was
significantly greater than the overall decrease in the critical mass of the Paper Products Cluster, being 27.61%
compared to 4.78%. It resulted in Komi Republic losing one star of cluster specialization in 2016, since one of the
three localization measures did not fulfill the threshold requirements.
Komi Republic lost specialization in Business Services in 2012, since the cluster’s critical mass decreased by
23.02% over eight years, while the cluster’s overall critical mass increased by 7.41%. The breakpoint was in
2011–2012, when two localization measures did not fulfill the threshold requirements.
Specialization of Komi Republic in Construction was detected in the period of 2012–2013, when a sudden
increase in employment levels brought about a fall in the construction cluster localization. However, it was a
short-term increase, which did not allow the regional specialization to strengthen in the long run. Therefore, the
long-term decrease of the cluster’s critical mass in Komi Republic was 21.80%.
Table 5. Employment-based parameters of significant clusters in Yamalo-Nenets AO
Year
2009
2010
Parameter
Common employment parameters
47427502
46719007
(people)
(people)
386402
382869
Transportation and Logistics cluster parameters
3489740
3370683
(people)
(people)
36797
36403
-1.07
-1.07
1
1
Number of stars
1.29
1.32
LQ
1.05
1.08
Size
9.52
9.51
Focus
Oil and Gas cluster parameters
504955
504478
(people)
(people)
14858
15782
6.22
6.22
3
3
Number of stars
3.61
3.82
LQ
2.94
3.13
Size
3.85
4.12
Focus
Paper Products cluster parameters
137015
136152
(people)
(people)
4810
4709
-2.10
-2.10
3
3
Number of stars
4.31
4.22
LQ
3.51
3.46
Size
1.24
1.23
Focus
Business services cluster parameters
2969478
2921201
(people)
(people)
32156
32050
-0.33
-0.33
2011
2012
2013
2014
2015
2016
45872388
383163
45898382
382155
45815640
373393
45486400
360442
45106533
347562
44446352
332435
3371228
41187
13.14
11.93
2
1.46
1.22
10.75
3400956
43756
6.24
18.91
2
1.55
1.29
11.45
3360962
41241
-5.75
12.08
2
1.51
1.23
11.04
3377649
39560
-4.08
7.51
2
1.48
1.17
10.98
3352174
37282
-5.76
1.32
2
1.44
1.11
10.73
3308218
35289
-5.35
-4.10
2
1.43
1.07
10.62
517301
15357
-2.69
3.36
3
3.55
2.97
4.01
536739
15699
2.23
5.66
3
3.51
2.92
4.11
556754
16624
5.89
11.89
3
3.66
2.99
4.45
578881
18676
12.34
25.70
3
4.07
3.23
5.18
594546
19911
6.61
34.01
3
4.35
3.35
5.73
606641
18685
-6.16
25.76
3
4.12
3.08
5.62
137499
4444
-5.63
-7.61
2
3.87
3.23
1.16
136273
4195
-5.60
-12.79
3
3.70
3.08
1.10
132216
4181
-0.33
-13.08
3
3.88
3.16
1.12
128119
3769
-9.85
-21.64
3
3.71
2.94
1.05
125839
3611
-4.19
-24.93
3
3.72
2.87
1.04
130471
3482
-3.57
-27.61
2
3.57
2.67
1.05
2880799
31026
-3.20
-3.51
3146204
29169
-5.99
-9.29
3237312
27946
-4.19
-13.09
3272631
26602
-4.81
-17.27
3257275
26282
-1.20
-18.27
3189467
24755
-5.81
-23.02
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2
Number of stars
1.33
LQ
1.08
Size
8.32
Focus
Construction cluster parameters
3425797
(people)
(people)
28568
2
1.34
1.10
8.37
1
1.29
1.08
8.10
0
1.11
0.93
7.63
0
1.06
0.86
7.48
0
1.03
0.81
7.38
0
1.05
0.81
7.56
0
1.04
0.78
7.45
3430749
3163493
3254308
3225983
3123938
2983398
2800194
28673
29713
35404
34969
29572
24566
22340
0.37
3.63
19.15
-1.23
-15.43
-16.93
-9.06
0.37
4.01
23.93
22.41
3.51
-14.01
-21.80
0
0
0
1
2
0
0
0
Number of stars
1.02
1.02
1.12
1.31
1.33
1.19
1.07
1.07
LQ
0.83
0.84
0.94
1.09
1.08
0.95
0.82
0.80
Size
7.39
7.49
7.75
9.26
9.37
8.20
7.07
6.72
Focus
Source: Employment statistics were obtained from: (HSE, 2018), (Federal State Statistics Service, 2019), (MinComSvyaz, 2019)
Calculations were performed by authors.
Out of the five clusters identified in Komi Republic during 2009–2016, only two clusters had a relatively high
critical mass, which was enough for the region to have specialization in these types of activities. One cluster was
decreasing—Paper Products—and one was growing—Oil and Gas. In addition, the region had medium
specialization in Transportation and Logistics, which had unstable growth rates. The Business Services cluster
was decreasing steadily, which resulted in Komi Republic losing specialization in this type of activity, and the
Construction Cluster showed unstable employment dynamics.
Yamalo-Nenets AO cluster specialization analysis
The overall employment dynamic in Yamalo-Nenets AO was positive. The total number of people employed
increased by 5.65%, or by 18,018 people over eight years. Analyzing Yamalo-Nenets AO employment statistics
during the period of 2009–2016, we detected five clusters: Transportation and Logistics, Maritime, Oil and Gas,
Business Services, and Construction, which have received at least one star. Detailed results are presented in Table
6
Table 6. Employment-based parameters of significant clusters in Yamalo-Nenets AO
Year
2009
2010
Parameter
Common employment parameters
(people)
47427502
46719007
(people)
319089
314503
Transportation and Logistics cluster parameters
(people)
39386
35633
(people)
-9.53
-9.53
2
2
1.68
1.57
Number of stars
1.13
1.06
LQ
Size
12.34
11.33
Focus
39386
35633
Maritime cluster parameters
(people)
148225
152423
(people)
2468
2267
-8.14
-8.14
1
1
Number of stars
2.47
2.21
LQ
Size
1.67
1.49
Focus
0.77
0.72
2011
2012
2013
2014
2015
2016
45872388
311693
45898382
328308
45815640
333527
45486400
329129
45106533
331108
44446352
337107
36513
2.47
-7.29
2
1.59
1.08
11.71
36513
40414
10.68
2.61
2
1.66
1.19
12.31
40414
41824
3.49
6.19
2
1.71
1.24
12.54
41824
37802
-9.62
-4.02
2
1.55
1.12
11.49
37802
34637
-8.37
-12.06
2
1.41
1.03
10.46
34637
34997
1.04
-11.14
2
1.39
1.06
10.38
34997
136905
2212
-2.43
-10.37
1
2.38
1.62
0.71
129441.6
2153
-2.67
-12.76
1
2.33
1.66
0.66
126963
2151
-0.09
-12.84
1
2.33
1.69
0.64
116436.8
2102
-2.28
-14.83
1
2.49
1.81
0.64
116557
2110
0.38
-14.51
1
2.47
1.81
0.64
114799
2093
-0.81
-15.19
1
2.40
1.82
0.62
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Oil and Gas cluster parameters
(people)
504955
(people)
31962
504478
31838
-0.39
-0.39
3
9.38
6.31
10.12
517301
33940
6.60
6.19
3
9.66
6.56
10.89
536739
35253
3.87
10.30
3
9.18
6.57
10.74
556754
37616
6.70
17.69
3
9.28
6.76
11.28
578881
39032
3.76
22.12
3
9.32
6.74
11.86
594546
40693
4.26
27.32
3
9.32
6.84
12.29
606641
41514
2.02
29.89
3
9.02
6.84
12.31
3
Number of stars
9.41
LQ
Size
6.33
Focus
10.02
Business Services cluster parameters
(people)
2969478
2921201
2880799
3146204
3237312
3272631
3257275
3189467
(people)
20241
23056
24426
27574
28247
28332
29160
31328
13.91
5.94
12.89
2.44
0.30
2.92
7.43
13.91
20.68
36.23
39.55
39.97
44.06
54.77
0
0
1
1
1
1
1
1
Number of stars
1.01
1.17
1.25
1.23
1.20
1.20
1.22
1.30
LQ
Size
0.68
0.79
0.85
0.88
0.87
0.87
0.90
0.98
Focus
6.34
7.33
7.84
8.40
8.47
8.61
8.81
9.29
Construction cluster parameters
(people)
3425797
3430749
3163493
3254308
3225983
3123938
2983398
2800194
(people)
49716
48086
44634
51707
52911
52487
53417
55937
-3.28
-7.18
15.85
2.33
-0.80
1.77
4.72
-3.28
-10.22
4.00
6.43
5.57
7.44
12.51
2
2
2
2
2
2
2
2
Number of stars
2.16
2.08
2.08
2.22
2.25
2.32
2.44
2.63
LQ
Size
1.45
1.40
1.41
1.59
1.64
1.68
1.79
2.00
Focus
15.58
15.29
14.32
15.75
15.86
15.95
16.13
16.59
Source: Employment statistics were obtained from: (HSE, 2018), (Federal State Statistics Service, 2019), (MinComSvyaz, 2019)
Calculations were performed by authors.
Yamalo-Nenets AO had a medium specialization level in Transportation and Logistics and the critical mass of
this cluster was unstable during the analyzed period. After a 9.53% decrease of the cluster’s employment in 2010,
there was a significant growth of the cluster’s critical mass, from 35,633 up to 41,824 people employed; that is, by
17.3% in 2013 compared to 2010. After that, there was a stable decrease of the Transportation and Logistics
cluster’s critical mass: 16.32% in 2016 compared to 2013. Nevertheless, the overall specialization of the region in
Transportation and Logistics activities remained at a medium level, since two localization measures out of three
fulfilled the threshold requirements.
Yamalo-Nenets AO had a low specialization in Maritime. However, the critical mass of this cluster decreased by
15.19% during the analyzed period. The region still has a certain margin of safety in relative terms, since the
overall employment in Maritime activities decreased by 22.55% over eight years. However, in terms of absolute
values, the region was continuously losing its specialization in this type of activity.
Yamalo-Nenets AO had a high specialization level in Oil and Gas, and the critical mass of this cluster was
growing significantly during the analyzed period. The overall increase of the cluster’s critical mass was 29.89%
over eight years. This resulted in a stronger specialization of the cluster and its stabilization at a high level, since
three localization measures out of three fulfilled the threshold requirements.
Yamalo-Nenets AO was strengthening its specialization in Business Services, since the cluster’s critical mass in
Yamalo-Nenets AO increased by 54.07% over eight years, while the cluster’s overall critical mass increased by
7.41%. The breakpoint was in 2011, when one localization measure fulfilled the threshold requirements.
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Yamalo-Nenets AO had a medium specialization level in Construction and the critical mass of this cluster was
unstable during the analyzed period. There was a 3.28% decrease in the cluster’s employment in 2010, and a
7.18% decrease in 2011. After that, there was a significant growth of the cluster’s critical mass, from 44,634 in
2011 up to 55,937 people; that is, by 25.32% in 2016. It resulted in a stronger specialization of the cluster and its
stabilization at a high level, since two localization measures out of three fulfilled the threshold requirements.
Yamalo-Nenets AO was strongly specialized in only one cluster, showing a steady growth of the critical mass—
the Oil and Gas cluster. In addition, the region had a medium specialization in the Transportation and Logistics
and Construction clusters, which had unstable growth rates. The Maritime cluster was decreasing considerably,
which resulted in Yamalo-Nenets AO losing specialization in this type of activity. The Business Services cluster
demonstrated an intensive growth, which resulted in a stronger specialization of the cluster, since one localization
measure out of three fulfilled the threshold requirements.
Republic of Karelia cluster specialization analysis
The overall employment dynamic in the Republic of Karelia was negative. The total number of people employed
decreased by 17.42%, or by 40,822 people over eight years. Analyzing employment statistics of the Republic of
Karelia during the period of 2009–2016, we detected four clusters: Transportation and Logistics, Maritime, Paper
Products, and Furniture, which received at least one star. Detailed results are presented in Table 7.
The Republic of Karelia had a low specialization level in Transportation and Logistics, and the critical mass of
this cluster was steadily decreasing during the analyzed period. After an 8.15% decrease of the cluster’s
employment in 2010–2011, there was a slight growth of the cluster’s critical mass from 23,972 up to 24,285
people employed; that is, by 1.31% in 2013 compared to 2012. After that, there was a stable decrease in the
Transportation and Logistics cluster’s critical mass: 18.04% in 2016 compared to 2012. Therefore, the long-term
decrease of the cluster’s critical mass in the Republic of Karelia was 23.74% over eight years. It resulted in the
Republic of Karelia losing one star of cluster specialization in 2013, since two of the three localization measures
did not fulfill the threshold requirements.
The Republic of Karelia had a low specialization in Maritime. However, the critical mass of this cluster was
unstable. The region still has a certain margin of safety in relative terms, since the overall employment in
Maritime activities decreased by 22.55% over eight years. However, in terms of absolute values, the region
demonstrated a cyclic growth and a decrease of the critical mass by 9.01% over eight years. Nevertheless, the
region gained one additional star in 2016, which can be attributed to the overall decrease of the Maritime critical
mass.
Table 7. Employment based parameters of significant clusters in the Republic of Karelia
Year
2009
2010
Parameter
General employment parameters
(people)
47427502
46719007
(people)
234310
228336
Transportation and Logistics cluster parameters
(people)
3489740
3370683
(people)
26100
24582
-5.82
-5.82
2
2
Number of stars
1.51
1.49
LQ
Size
0.75
0.73
Focus
11.14
10.77
Maritime cluster parameters
(people)
148225
152423
2011
2012
2013
2014
2015
2016
45872388
226165
45898382
225442
45815640
220074
45486400
211446
45106533
205299
44446352
193488
3371228
23972
-2.48
-8.15
2
1.44
0.71
10.60
3400956
24285
1.31
-6.95
2
1.45
0.71
10.77
3360962
23232
-4.34
-10.99
1
1.44
0.69
10.56
3377649
21923
-5.63
-16.00
1
1.40
0.65
10.37
3352174
21375
-2.50
-18.10
1
1.40
0.64
10.41
3308218
19903
-6.89
-23.74
1
1.38
0.60
10.29
136905
129442
126963
116437
116557
114799
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(people)
1731
1
Number of stars
2.36
LQ
Size
1.17
Focus
0.74
Paper Products cluster parameters
(people)
137015
(people)
7794
3
Number of stars
11.51
LQ
Size
5.69
Focus
3.33
Furniture cluster parameters
(people)
314686
(people)
2439
1590
-8.15
-8.15
1
2.13
1.04
0.70
1628
2.39
-5.95
1
2.41
1.19
0.72
1755
7.80
1.39
1
2.76
1.36
0.78
1811
3.19
4.62
1
2.97
1.43
0.82
1734
-4.25
0.17
1
3.20
1.49
0.82
1623
-6.40
-6.24
1
3.06
1.39
0.79
1575
-2.96
-9.01
2
3.15
1.37
0.81
136152
7279
-6.61
-6.61
3
10.94
5.35
3.19
137499
7156
-1.69
-8.19
3
10.56
5.20
3.16
136273
7067
-1.24
-9.33
3
10.56
5.19
3.13
132216
6501
-8.01
-16.59
3
10.24
4.92
2.95
128119
5910
-9.09
-24.17
3
9.92
4.61
2.80
125839
5604
-5.18
-28.10
3
9.78
4.45
2.73
130471
5583
-0.37
-28.37
3
9.83
4.28
2.89
316139
294371
298059
294375
278843
267375
259033
2329
1991
1809
1603
1418
1426
1431
-4.51
-14.51
-9.14
-11.39
-11.54
0.56
0.35
-4.51
-18.37
-25.83
-34.28
-41.86
-41.53
-41.33
1
1
1
0
0
0
0
0
Number of stars
1.57
1.51
1.37
1.24
1.13
1.09
1.17
1.27
LQ
Size
0.78
0.74
0.68
0.61
0.54
0.51
0.53
0.55
Focus
1.04
1.02
0.88
0.80
0.73
0.67
0.69
0.74
Source: Employment statistics were obtained from: (HSE, 2018), (Federal State Statistics Service, 2019), (MinComSvyaz, 2019)
Calculations were performed by authors.
The Republic of Karelia had a high specialization level in Paper Products and the critical mass of its cluster was
strongly decreasing during the period of 2009–2016. The overall decrease of the cluster’s critical mass was
28.37% over eight years. In addition, the decrease of the Paper Products cluster’s critical mass in the Republic of
Karelia was significantly higher than the overall decrease of the Paper Products cluster’s critical mass, being
27.61% compared to 4.78%. It led to a decrease in the cluster localization parameters, but it did not result in
losing the specialization, since three localization measures out of three fulfilled the threshold requirements.
The Republic of Karelia lost specialization in Furniture Production in 2012, since the cluster’s critical mass
decreased by 41.33% over eight years, while the cluster’s overall critical mass went down by only 17.69%. The
breakpoint was in 2011–2012, when LQ did not fulfill the threshold requirements, along with Focus and Size.
Therefore, the Republic of Karelia was highly specialized only in one type of activity—Paper Products. However,
the critical mass of this cluster greatly decreased during the analyzed period. In addition, the region had a low
specialization in two other types of activities: Transportation and Logistics, which showed a decrease of the
critical mass, and Maritime, the critical mass of which was unstable. In one type of activity, the region showed
lack of specialization due to the continuously steady decrease in its critical mass.
Krasnoyarsk Krai cluster specialization analysis
The overall employment dynamic in Krasnoyarsk Krai was negative. The total number of employed people
decreased by 6.15%, or by 64,833 people over eight years. Analyzing the employment statistics in Krasnoyarsk
Krai during the period of 2009–2016, we detected four clusters: Transportation and Logistics, Business Services,
and Entertainment and Production Technology, which received at least one star. Detailed results are presented in
Table 8.
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Table 8. Employment-based parameters of significant clusters in Krasnoyarsk Krai
Year
2009
2010
2011
2012
2013
2014
2015
2016
Parameter
General employment parameters
47427502
46719007
45872388
45898382
45815640
45486400
45106533
44446352
(people)
1054055
1056537
1049084
1056420
1042109
1046767
1021040
989222
(people)
Transportation and Logistics cluster parameters
3489740
3370683
3371228
3400956
3360962
3377649
3352174
3308218
(people)
89985
88687
89832
91984
91829
92266
91374
90767
(people)
-1.44
1.29
2.40
-0.17
0.48
-0.97
-0.66
-1.44
-0.17
2.22
2.05
2.53
1.54
0.87
1
1
1
1
1
1
1
1
Number of stars
1.16
1.16
1.17
1.18
1.20
1.19
1.20
1.23
LQ
2.58
2.63
2.66
2.70
2.73
2.73
2.73
2.74
Size
8.54
8.39
8.56
8.71
8.81
8.81
8.95
9.18
Focus
Business Services cluster parameters
2969478
2921201
2880799
3146203.9
3237312
3272631.1
3257275.3
3189467
(people)
74557
73045
75263
83302
83352
86755
81563
74253
(people)
-2.03
3.04
10.68
0.06
4.08
-5.98
-8.96
-2.03
0.95
11.73
11.80
16.36
9.40
-0.41
0
0
0
1
0
1
0
0
Number of stars
1.13
1.11
1.14
1.15
1.13
1.15
1.11
1.05
LQ
2.51
2.50
2.61
2.65
2.57
2.65
2.50
2.33
Size
7.07
6.91
7.17
7.89
8.00
8.29
7.99
7.51
Focus
Entertainment cluster parameters
1134931
1096820
1076443
1087827.8
1067113.6
1027259
1014388
1010873
(people)
28162
28338
29061
29185
29604
29723
29290
28870
(people)
0.62
2.55
0.43
1.44
0.40
-1.46
-1.43
0.62
3.19
3.63
5.12
5.54
4.01
2.51
1
1
1
1
1
1
2
2
Number of stars
1.12
1.14
1.18
1.17
1.22
1.26
1.28
1.28
LQ
2.48
2.58
2.70
2.68
2.77
2.89
2.89
2.86
Size
2.67
2.68
2.77
2.76
2.84
2.84
2.87
2.92
Focus
Production Technology cluster parameters
630556
608180
619596
614537
602202
587375.7
571254
545333
(people)
20539
20599
19981
20140
19771
19170
19031
19658
(people)
0.29
-3.00
0.80
-1.83
-3.04
-0.73
3.29
0.29
-2.72
-1.94
-3.74
-6.67
-7.34
-4.29
1
2
1
1
1
1
2
2
Number of stars
1.47
1.50
1.41
1.42
1.44
1.42
1.47
1.62
LQ
3.26
3.39
3.22
3.28
3.28
3.26
3.33
3.60
Size
1.95
1.95
1.90
1.91
1.90
1.83
1.86
1.99
Focus
Source: Employment statistics were obtained from: (HSE, 2018), (Federal State Statistics Service, 2019), (MinComSvyaz, 2019)
Calculations were performed by authors.
Krasnoyarsk Krai had a low specialization level in Transportation and Logistics. However, the critical mass of
this cluster was stable during the analyzed period. In the long-term, the critical mass of the cluster increased by
0.87%; that is, by 782 people employed. In addition, the overall employment in the Transportation and Logistics
cluster decreased by 5.2%. In total, it resulted in a slight increase of the relative localization measures of this
cluster. However, it was not enough for significant strengthening of the regional specialization in this type of
activity.
The specialization of Krasnoyarsk Krai in Business Services was detected in 2012 and 2014, when a sudden
increase in employment levels resulted in a growth of the Business Services cluster localization. However, it was
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a short-term increase which did not allow the region to strengthen its specialization over a long-term period.
Therefore, the long-term decrease of the cluster’s critical mass in Krasnoyarsk Krai was 0.41%.
Krasnoyarsk Krai had low specialization in Entertainment activities, which demonstrated a stable critical mass. In
the long term, the critical mass of the Entertainment cluster grew by 2.51%; that is, 708 people. However, during
the analyzed period there was a growth stage—from 2009 to 2014, the critical mass increased by 5.54%—and a
decrease stage—from 2014 to 2016, it decreased by 2.87%. In addition, the overall employment in the
Entertainment cluster decreased by 10.93%; that is, by 124,058 people employed. Due to this situation, the
relative specialization of the region in Entertainment increased during 2015–2016 from one to two stars, since two
of the three localization measures fulfilled the threshold requirements.
Krasnoyarsk Krai had low specialization in Production Technology, which was demonstrated by the stable state
of its critical mass. In the long term, the critical mass of the Production Technology cluster decreased by 4.29%;
that is, by 881 people employed. Nevertheless, with the overall employment of the Production Technology cluster
decreasing by 13.52% (i.e. by 85,223 people employed), the relative specialization of the region in this type of
activity grew in 2015, since two of three localization parameters fulfilled the threshold values.
Therefore, Krasnoyarsk Krai did not have high specialization in any type of activity. However, there are three
groups of activities in which this region had low specialization: Transportation and Logistics, Entertainment, and
Production Technology. All three clusters demonstrated a stable condition of their critical mass. In Business
Services, the region had no specialization, since the critical mass of this cluster was too low.
Arkhangelsk Oblast (including Nenets AO) cluster specialization analysis
The overall employment dynamic in Arkhangelsk Oblast was negative. The total number of people employed
decreased by 11.44%, or by 50,660 people over eight years. Analyzing Arkhangelsk Oblast employment statistics
during the period of 2009–2016, we detected four clusters: Transportation and Logistics, Maritime, Paper
Products, and Furniture, which received at least one star. Detailed results are presented in Table 9.
Arkhangelsk Oblast had a medium specialization level in Transportation and Logistics, and the critical mass of
this cluster was unstable during the analyzed period. The long-term decrease of the cluster’s critical mass over
eight years was 5.94%; that is, 4,392 people employed. However, the overall specialization of the region in this
type of activity increased, since the employment of the whole cluster also decreased by 5.2%, or by 181,522
people employed.
Arkhangelsk Oblast had low specialization in Maritime. However, the critical mass of this cluster decreased by
31.68%, or by 1,195 people during the analyzed period. The decline of this cluster was faster at the regional level
than at the country level, meaning that the region was losing both its relative and absolute specialization in this
type of activity.
Arkhangelsk Oblast had a high specialization level in Paper Products, and the critical mass of its cluster was
strongly decreasing during the period of 2009–2016. The overall decrease of the cluster’s critical mass was
24.81%; that is, by 2,268 people employed over eight years. In addition, the decrease of the critical mass of the
Paper Products cluster in Arkhangelsk Oblast was significantly higher than the overall decrease of the critical
mass of the Paper Products cluster, being 24.81% compared to 4.78%. It resulted in Arkhangelsk Oblast losing
specialization in this type of activity. However, it still had a certain margin of safety, since all three localization
parameters fulfilled the threshold conditions.
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Table 9. Employment-based parameters of significant clusters in Arkhangelsk Oblast (including Nenets AO)
Year
2009
2010
2011
2012
2013
2014
2015
2016
Parameter
General employment parameters
47427502
46719007
45872388
45898382
45815640
45486400
45106533
44446352
(people)
442903
433931
436355
418786.1
409795
405572.6
399017
392243.2
(people)
Transportation and Logistics cluster parameters
3489740
3370683
3371228
3400956
3360962
3377649
3352174
3308218
(people)
73878
71412
72084
68609
67490
67275
68010
69486
(people)
-3.34
0.94
-4.82
-1.63
-0.32
1.09
2.17
-3.34
-2.43
-7.13
-8.65
-8.94
-7.94
-5.94
2
2
2
2
2
2
2
2
Number of stars
2.27
2.28
2.25
2.21
2.25
2.23
2.29
2.38
LQ
2.12
2.12
2.14
2.02
2.01
1.99
2.03
2.10
Size
16.68
16.46
16.52
16.38
16.47
16.59
17.04
17.72
Focus
Maritime cluster parameters
148225
152423
136905
129441.6
126963
116436.8
116557
114799
(people)
3772
3802
3949
3701
3192
2568
2554
2577
(people)
0.80
3.87
-6.28
-13.75
-19.55
-0.55
0.90
0.80
4.69
-1.88
-15.38
-31.92
-32.29
-31.68
1
1
1
2
1
1
1
1
Number of stars
2.73
2.69
3.03
3.13
2.81
2.47
2.48
2.54
LQ
2.54
2.49
2.88
2.86
2.51
2.21
2.19
2.24
Size
0.85
0.88
0.90
0.88
0.78
0.63
0.64
0.66
Focus
Paper Products cluster parameters
137015
136152
137499
136273
132216
128119
125839
130471
(people)
9141
8578
8548
8308
7778
7448
7012
6873
(people)
-6.16
-0.35
-2.81
-6.38
-4.24
-5.85
-1.98
-6.16
-6.49
-9.11
-14.91
-18.52
-23.29
-24.81
3
3
3
3
3
3
3
3
Number of stars
7.14
6.78
6.54
6.68
6.58
6.52
6.30
5.97
LQ
6.67
6.30
6.22
6.10
5.88
5.81
5.57
5.27
Size
2.06
1.98
1.96
1.98
1.90
1.84
1.76
1.75
Focus
Furniture cluster parameters
314686
316139
294371
298059
294375
278843
267375
259033
(people)
5145
4776
4429
4122
3566
3450
3492
2935
(people)
-7.17
-7.27
-6.93
-13.49
-3.25
1.22
-15.95
-7.17
-13.92
-19.88
-30.69
-32.94
-32.13
-42.95
1
1
1
0
0
0
0
0
Number of stars
1.75
1.63
1.58
1.52
1.35
1.39
1.48
1.28
LQ
1.63
1.51
1.50
1.38
1.21
1.24
1.31
1.13
Size
1.16
1.10
1.01
0.98
0.87
0.85
0.88
0.75
Focus
Source: Employment statistics were obtained from: (HSE, 2018), (Federal State Statistics Service, 2019), (MinComSvyaz, 2019)
Calculations were performed by authors.
Therefore, Arkhangelsk Oblast, in total, had clusters with decreasing critical mass, which resulted, in some cases,
in a rise in relative specializations, but a decrease in absolute values.
Arkhangelsk Oblast lost specialization in Furniture Production in 2012, since the critical mass of the cluster in
Arkhangelsk Oblast decreased by 42.95%; that is, by 2,210 people over eight years. Meanwhile, the overall
critical mass of the cluster decreased by only 17.69%. The breakpoint was in 2011–2012, when LQ fulfilled
neither of the threshold requirements, nor did Focus or Size.
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Khanty-Mansi AO cluster specialization analysis
The overall employment dynamic in Khanty-Mansi AO was negative. The total number of people employed
decreased by 2.18%, or by 16,772 people over eight years. Analyzing employment statistics in Khanty-Mansi AO
during the period of 2009–2016, we detected three clusters: Transportation and Logistics, Oil and Gas, and
Construction, which received at least one star. Detailed results are presented in Table 10.
Khanty-Mansi AO lost specialization in Furniture Production in 2010, since the cluster’s critical mass decreased
by 13.93%; that is, by 9617 people over eight years. Meanwhile, the cluster’s overall critical mass decreased by
only 5.2%. Therefore, the region was steadily losing its specialization in this type of activity due to the decrease
of the cluster’s critical mass.
Table 10. Employment-based parameters of significant clusters in Khanty-Mansi AO
Year
2009
2010
2011
2012
2013
2014
2015
2016
Parameter
General employment parameters
(people)
47427502
46719007
45872388
45898382
45815640
45486400
45106533
44446352
(people)
770656
770048
771193
774807
771928
769370
761089
753884
Transportation and Logistics cluster parameters
(people)
3489740
3370683
3371228
3400956
3360962
3377649
3352174
3308218
(people)
69030
68126
65137
64990
64567
61782
59825
59413
-1.31
-4.39
-0.23
-0.65
-4.31
-3.17
-0.69
-1.31
-5.64
-5.85
-6.47
-10.50
-13.33
-13.93
1
0
0
0
0
0
0
Number of stars 0
1.22
1.23
1.15
1.13
1.14
1.08
1.06
1.06
LQ
Size
1.98
2.02
1.93
1.91
1.92
1.83
1.78
1.80
Focus
8.96
8.85
8.45
8.39
8.36
8.03
7.86
7.88
Oil and Gas cluster parameters
(people)
504955
504478
517301
536739
556754
578881
594546
606641
(people)
119572
121334
124170
129379
134175
139619
146402
150665
1.47
2.34
4.20
3.71
4.06
4.86
2.91
1.47
3.85
8.20
12.21
16.77
22.44
26.00
3
3
3
3
3
3
3
Number of stars 3
14.57
14.59
14.28
14.28
14.30
14.26
14.59
14.64
LQ
Size
23.68
24.05
24.00
24.10
24.10
24.12
24.62
24.84
Focus
15.52
15.76
16.10
16.70
17.38
18.15
19.24
19.99
Construction cluster parameters
(people)
3425797
3430749
3163493
3254308
3225983
3123938
2983398
2800194
93202
93124
87788
87179
80821
77105
72677
68966
(people)
-0.08
-5.73
-0.69
-7.29
-4.60
-5.74
-5.11
-0.08
-5.81
-6.46
-13.28
-17.27
-22.02
-26.00
3
3
2
2
2
2
2
Number of stars 2
1.67
1.65
1.65
1.59
1.49
1.46
1.44
1.45
LQ
Size
2.72
2.71
2.78
2.68
2.51
2.47
2.44
2.46
Focus
12.09
12.09
11.38
11.25
10.47
10.02
9.55
9.15
Source: Employment statistics were obtained from: (HSE, 2018), (Federal State Statistics Service, 2019), (MinComSvyaz, 2019)
Calculations were performed by authors.
Khanty-Mansi AO had a high specialization level in Oil and Gas, and the critical mass of this cluster was growing
significantly during the analyzed period. The overall increase of the cluster’s critical mass was 26% over eight
years. This resulted in the strengthening of the cluster’s specialization and its stabilization at a high level, since
three localization measures out of three fulfilled the threshold requirements.
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Khanty-Mansi AO had a medium specialization level in Construction and the critical mass of this cluster was
greatly decreasing during the analyzed period. The long-term decrease of the cluster’s critical mass was 26%, or
24,236 people employed. Nevertheless, the specialization of Khanty-Mansi AO in Construction remains at a high
level, despite the fact that it is constantly decreasing.
We identified three clusters in Khanty-Mansi AO: Transportation and Logistics, Oil and Gas, and Construction.
Only the Oil and Gas cluster showed strong growth of its critical mass, while the other two clusters were
decreasing in terms of the number of people employed.
Murmansk Oblast cluster specialization analysis
The overall employment dynamic in Murmansk Oblast was negative. The total number of people employed
decreased by 11.11%, or by 34,409 people employed over eight years. Analyzing employment statistics in
Murmansk Oblast during the period of 2009–2016, we detected two clusters: Transportation and Logistics and
Maritime, which have received at least one star. Detailed results are presented in Table 11.
Table 11. Employment-based parameters of significant clusters in Murmansk Oblast
Year
2009
2010
2011
2012
2013
2014
2015
2016
Parameter
General employment parameters
47427502
46719007
45872388
45898382
45815640
45486400
45106533
44446352
(people)
(people)
309727
301079
300264
300209
296615
288905
281950
275318
Transportation and logistics cluster parameters
3489740
3370683
3371228
3400956
3360962
3377649
3352174
3308218
(people)
(people)
47243
44929
42501
41274
40302
38585
37209
36936
-4.90
-5.40
-2.89
-2.35
-4.26
-3.57
-0.73
-4.90
-10.04
-12.63
-14.69
-18.33
-21.24
-21.82
2
2
2
2
2
2
2
2
Number of stars
2.07
2.07
1.93
1.86
1.85
1.80
1.78
1.80
LQ
1.35
1.33
1.26
1.21
1.20
1.14
1.11
1.12
Size
15.25
14.92
14.15
13.75
13.59
13.36
13.20
13.42
Focus
Maritime cluster parameters
148225
152423
136905
129441.6
126963
116436.8
116557
114799
(people)
(people)
8734
8016
7464
7834
7466
7170
6832
6321
-8.22
-6.89
4.96
-4.70
-3.96
-4.71
-7.48
-8.22
-14.54
-10.30
-14.52
-17.91
-21.78
-27.63
3
3
3
3
3
3
3
3
Number of stars
9.02
8.16
8.33
9.25
9.08
9.70
9.38
8.89
LQ
5.89
5.26
5.45
6.05
5.88
6.16
5.86
5.51
Size
2.82
2.66
2.49
2.61
2.52
2.48
2.42
2.30
Focus
Source: Employment statistics were obtained from: (HSE, 2018), (Federal State Statistics Service, 2019), (MinComSvyaz, 2019)
Calculations were performed by authors.
Murmansk Oblast had a medium specialization level in Transportation and Logistics, and the critical mass of this
cluster was steadily decreasing during the analyzed period. The overall decrease of the critical mass of the
Transportation and Logistics cluster located in Murmansk Oblast was 21.82%; that is, 10,307 people employed
over eight years. Therefore, all three localization parameters of the cluster decreased. Nevertheless, its
specialization remains at the level of two stars.
Murmansk Oblast had a high specialization level in Maritime, and the critical mass of its cluster was steadily
decreasing during the period of 2009–2016. The overall decrease of the cluster’s critical mass was 27.63% over
eight years. In addition, the decrease of the Maritime cluster’s critical mass in Murmansk Oblast was higher than
the overall decrease of the Maritime cluster’s critical mass, being 27.63% compared to 22.55%. It resulted in
Murmansk Oblast decreasing in overall specialization in this type of activity in the long run.
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Therefore, there are only two significant clusters in the Murmansk region: Transportation and Logistics and
Maritime. The critical masses of both clusters were steadily decreasing during the analyzed period. Consequently,
the region lost its specialization and should promote new core activities, which can be part of its long-term
development.
Sakha Republic cluster specialization analysis
The overall employment dynamic in Sakha Republic was negative. The total number of people employed
decreased by 6.22%, or by 22,722 people employed over eight years. Analyzing the employment statistics in
Sakha Republic during the period of 2009–2016, we detected two clusters: Entertainment and Oil and Gas, which
have received at least one star. Detailed results are presented in Table 12.
Sakha Republic had not had a specialization level in Oil and Gas until 2011. Due to a significant growth of the
cluster’s critical mass over a long-term period of 3,535 people employed, or 83.65%, one of the localization
parameters fulfilled the threshold requirement and the region received one star in this type of activity. Therefore,
the region has a potential for strengthening its specialization if the critical mass continues to grow.
Sakha Republic had a low specialization in Entertainment; the critical mass of this cluster was at a stable level.
The long-term change of the critical mass was negative. It declined by 3.49%, or 432 people over eight years.
Table 12. Employment-based parameters of significant clusters in Sakha Republic
Year
2009
2010
2011
2012
2013
2014
2015
2016
Parameter
General employment parameters
47427502
46719007
45872388
45898382
45815640
45486400
45106533
44446352
(people)
365340
353047
355669
354493
351108
348962
344686
342618
(people)
Oil and Gas cluster parameters
504955
504478
517301
536739
556754
578881
594546
606641
(people)
4226
3836
6529
7120
7043
7209
7313
7761
(people)
-9.23
70.20
9.05
-1.08
2.36
1.44
6.13
-9.23
54.50
68.48
66.66
70.59
73.05
83.65
0
0
1
1
1
1
1
1
Number of stars
1.09
1.01
1.63
1.72
1.65
1.62
1.61
1.66
LQ
0.84
0.76
1.26
1.33
1.27
1.25
1.23
1.28
Size
1.16
1.09
1.84
2.01
2.01
2.07
2.12
2.27
Focus
Entertainment cluster parameters
1134931
1096820
1076443
1087827.8
1067113.6
1027259
1014388
1010873
(people)
12374
12200
12150
12571.8
12340.6
12059
11995
11942
(people)
-1.41
-0.41
3.47
-1.84
-2.28
-0.53
-0.44
-1.41
-1.81
1.60
-0.27
-2.55
-3.06
-3.49
1
1
1
1
1
1
1
1
Number of stars
1.42
1.47
1.46
1.50
1.51
1.53
1.55
1.53
LQ
1.09
1.11
1.13
1.16
1.16
1.17
1.18
1.18
Size
3.39
3.46
3.42
3.55
3.51
3.46
3.48
3.49
Focus
Source: Employment statistics were obtained from: (HSE, 2018), (Federal State Statistics Service, 2019), (MinComSvyaz, 2019)
Calculations were performed by authors.
Therefore, Sakha Republic has a potential for strengthening its specialization in Oil and Gas and Entertainment
activities.
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Chukotka AO cluster specialization analysis
The overall employment dynamics in Chukotka AO was negative. The total number of people employed
decreased by 9.72%, or by 2,946 people employed over eight years. Analyzing the employment statistics in
Chukotka AO during the period of 2009–2016, we did not detected any clusters which could receive at least one
star. The general results of the employment dynamics are presented in Table 13.
Table 13. Employment-based parameters of significant clusters in Chukotka AO
Year
2009
2010
2011
2012
2013
2014
2015
Parameter
Genera employment parameters
(people)
47427502
46719007
45872388
45898382
45815640
45486400
45106533
30300
30055
29914
29494
28983
27902
27758
(people)
Employment statistics were obtained from: (HSE, 2018), (Federal State Statistics Service, 2019), (MinComSvyaz, 2019)
Source: Combined results of the Russian regions cluster parameters analysis
2016
44446352
27354
Table 14 gives an analytical interpretation of the computational results presented earlier. The table includes only
those clusters which were significant in at least in one Arctic region. Therefore, nine clusters out of 37 are
presented. Boxes with the symbol «-» in Table 14 refer to the unidentified (insignificant) clusters. We did not
mark them in order to make it clearer for analysis. Other boxes include the characteristic of the cluster in a
specific region in accordance with the classification, presented in Section 2.1.
Tables 14 and 15 provide some valuable insights concerning the overall situation in the Russian Arctic regions.
The first insight is that the overall state of the most typical significant clusters for these regions is not satisfactory,
since there is only one significant cluster which achieved a steady growth. We can see that, in general,
employment in such clusters as «Transportation and Logistics», «Maritime», «Paper Products», «Construction»,
«Entertainment», and «Furniture» was mostly either decreasing or unstable, which means that these clusters were
steadily declining in a long term perspective during the analyzed period. On the other hand, the only significant
cluster which achieved a steady growth in all regions where it was present was the «Oil and Gas» cluster. The
second insight refers to the overall cluster structure of the Russian Arctic region. A majority of clusters in Russian
Arctic regions are not significant, meaning that there are relatively too few employees. Therefore, the localization
of these clusters is slightly above average, which is not enough for generating positive spillovers or organizing
export activities. These two insights can potentially become a basis for elaborating a policy which will slow down
the decrease of the discussed clusters and, consequently, support diversification and specialization of the
economy, since it is associated with positive spillover effects.
Region
Cluster
Transportation
and Logistics
Maritime
Table 14. State of development of identified clusters in Russian arctic regions for 2009–2016
Komi
YamaloRepublic Krasnoyarsk Arkhangelsk
KhantyMurmansk
Sakha
Republic
Nenets
of
Krai
Oblast
Mansi
Oblast
Republic
AO
Karelia
including
AO
Nenets AO
Medium
spec.
Unstable
Medium
spec.
Unstable
Low
spec.
Strong
decrease
Low
spec.
Strong
decrease
Low
spec.
Unstable
Low spec.
Stable
Medium
spec.
Unstable
No spec.
Strong
decrease
-
Low spec.
Strong
decrease
-
169
Medium
spec.
Strong
decrease
High spec.
Strong
decrease
Chukotka
AO
-
-
-
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Oil and Gas
High spec
Strong
growth
Paper products
High
spec.
Strong
decrease
No spec.
Strong
decrease
No spec.
Unstable
Business
services
Construction
High
spec.
Strong
growth
-
No spec.
Strong
growth
Medium
spec.
Unstable
-
-
-
High
spec.
Strong
growth
-
-
No spec.
Strong
growth
-
High
spec.
Strong
decrease
-
-
High spec.
Strong
decrease
-
-
-
No spec.
Unstable
-
-
-
-
-
-
-
-
Medium
spec.
Strong
decrease
-
-
-
-
-
-
-
-
Low
spec.
Stable
-
Entertainment
-
-
Low spec.
Stable
-
Furniture
-
No spec.
Strong
decrease
-
-
No spec.
Strong
decrease
-
-
Information
Technologies
Tourism
Production
Low spec.
Technology
Stable
Source: The table is constructed based on the results presented in section 2 methodology implementation. Detailed results are presented in
Section 3. Abbreviation «Spec.» refers to the term «Specialization. Symbol «-» refers to the situation, when a cluster’s critical mass is too
low, i.e. it is now identified in the region. The first line each box presents the evaluation result of region specialization in types of activities
performed by a cluster i in the region g. (see Table 2 for more details).The second line refers to the type of dynamic state of employment of
cluster i in region g. (see Table 3 for more details).
Table 15. Cross-matrix of the state of development of the clusters in Russian Regions for 2009–2016
Level of region
specialization
High
Medium
No
Low specialization
specialization
specialization
specialization
Dynamic state
of employment
Oil and Gas (1)
Strong
employment
Oil and Gas (3)
Business Services (1)
growth
Moderate employment
growth
Transportation and Logistics
(1)
Stable employment level
Entertainment (2)
Production technology (1)
Transportation
and
Business Services (1)
Unstable
employment
Logistics (3)
Maritime (1)
Construction (1)
growth
Construction (1)
Moderate decrease in
employment
Transportation
Transportation
and Transportation and Logistics
Logistics (1)
Strong
decrease
in Paper products (3)
Logistics (1)
(1)
Maritime (1)
Business Services (1)
employment
Construction (1)
Maritime (2)
Furniture (2)
Numbers in brackets reflect the number of regions where the cluster is present.
Source: Compiled by Authors
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Discussion and conclusion
This research study provides several results, which contribute both to practical and theoretical fields.
First, we present the architecture of the database for automated identification of clusters in the Russian regions.
This architecture can be used for creating any other database to calculate cluster localization parameters in any
other country or region.
Secondly, we, in brief, present methodology for cluster identification and discuss how clusters can be identified
from the perspective of the European Cluster Observatory. We complement this methodology through presenting
two additional dimensions, which can be used for better interpretation and systematization of results. The
dimension «Level of region specialization» depends on the average number of stars obtained by a certain cluster
in a certain region. The dimension «Dynamic state of employment» represents the pattern of employment change
during the analyzed period.
Thirdly, we present the main results for cluster identification using the example of the Russian Arctic regions. It
is stated that most of the significant clusters are decreasing, while the only cluster which achieved steady growth
in terms of localization parameters was «Oil and Gas». The obtained results allowed us to conclude that the
cluster structure of the Russian Arctic regions is poor in the sense that there are few significant clusters and that
most of them are weak and decreasing. This result can be used as a basis for elaborating regional economic
policy to support regional diversification and specialization.
There are also several opportunities for further research. Firstly, the presented database can be modified in order
to provide results, which are more valuable. Currently it calculates only four parameters, which reflect
localization parameters and regional specialization. It can be expanded in order to calculate more metrics, which
are based not only on employment data, but also on salary and sales data of the clusters. In addition, functions
can be included to compose indexes based on several parameters. In addition, it could be interesting to tackle the
technical issues connected with data input. At the moment, before data are input to the database, a big job has to
be done, which is connected to acquiring and formatting data. If it were possible to connect the database directly
to the State Statistical Service systems, the time spent waiting to receive a result would significantly decrease.
Funding
The reported study was funded by the Russian Foundation for Basic Research (research project No. 18-31020012).
Author Contributions
Conceptualization, A.S., T.K., M.A.B.; Methodology, T.K.; Validation, A.S. and T.K.; Formal Analysis, A.S.;
Investigation, A.S. and T.K.; Data Curation, A.S.; Writing-Original Draft Preparation, A.S., T.K., M.A.B.;
Writing-Review & Editing, T.K., M.A.B.; Visualization, A.S.; Supervision, T.K.; Project Administration, T.K.;
Funding Acquisition, T.K.
Conflicts of Interest
The authors declare no conflict of interest.
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References
Akpinar, M., Can, Ö., & Mermercioglu, M. (2017). Assessing the sources of competitiveness of the US states. Competitiveness Review:
An International Business Journal, 27(2), 161–178. https://doi.org/10.1108/CR-02-2016-0014
Andreyeva, D. A., Irina, I. V. K., Dvas, G. V., Malinin, A. M., & Nadezhina, O. S. (2018). Factors of effective regional development and
labor market condition as indicator of state of the economy of the region. Proceedings of the 31st International Business Information
Management Association Conference, 5507–5513.
Baltgailis, J. (2019). The issues of increasing the effectiveness of teaching comparative economics. Insights into Regional Development,
1(3), 190-199. https://doi.org/10.9770/ird.2019.1.3(1)
Berawi, M. A. (2017). Empowering Added Value in Highway Project: A Strategy to Improve the Feasibility. Highway Engineering, 67.
https://doi.org/10.5772/intechopen.71778
Berawi, M. A., Susantono, B., Miraj, P., & Nurmadinah, F. (2018). Prioritizing airport development plan to optimize financial feasibility.
Aviation, 22(3), 115–128. https://doi.org/10.3846/aviation.2018.6589
Borisov, V. N., & Pochukaeva, O. V. (2016). Relationships between development factors of the Arctic Zone of the Russian Federation.
Studies on Russian Economic Development, 27(2), 159–165.
Bublienė, R., Vinogradova, I., Tvaronavičienė, M., Monni, S. (2019). Legal form determination for the development of clusters‘ activities.
Insights into Regional Development, 1(3), 244-258. https://doi.org/10.9770/ird.2019.1.3(5)
Chun, S., Shulman, S., Sandoval, R., & Hovy, E. (2010). Government 2.0: Making connections between citizens, data and government.
Information Polity, 15(1,2), 1–9. https://doi.org/10.3233/IP-2010-0205
Crawley, A., & Pickernell, D. (2012). An appraisal of the European cluster observatory. European Urban and Regional Studies, 19(2), 207–
211. https://doi.org/10.1177/0969776411427328
Degtereva, V. A., Zaborovskaia, O. V., & Sharafanova, E. E. (2018). Methodology of targeted support for service sector enterprises in
regional economic system. Proceedings of the 31st International Business Information Management Association Conference, 955–966.
Delgado, M., Porter, M. E., & Stern, S. (2014). Clusters, convergence, and economic performance. Research Policy, 43(10), 1785–1799.
https://doi.org/10.3386/w18250
Delgado, M., Porter, M. E., & Stern, S. (2015). Defining clusters of related industries. Journal of Economic Geography, 16(1), 1–38.
https://doi.org/10.1093/jeg/lbv017
Dvas, G., Lyukevich, I., & Kulagina, N. (2018). Optimization of administration decentralization as a key mechanism for implimentation of
regional politics. Proceedings of the 32nd International Business Information Management Association Conference, 3933–3949.
Federal State Statistics Service. (2019).
http://www.gks.ru/dbscripts/cbsd/dbinet.cgi#1
Central
statistical
database
of
Russia.
Retrieved
February
11,
2019,
from
Gupta, M., Jacobi, R., Jamet, J.-F., & Malik, L. (2006). The LA Motion Picture Industry Cluster. The Microeconomics of Competitiveness:
Firms, Clusters and Economic Development, Harvard Business School.
Gutman, S., Kozlov, A., & Teslya, A. (2018). Sustainable development of industrial enterprises in one-industry towns through
harmonization of main stakeholders’ interests: Case of Russian arctic zone. In Soliman & K.S. (Eds.), Proceedings of the 31st International
Business Information Management Association Conference (pp. 3014–3023). International Business Information Management Association,
IBIMA.
Guzman, J., & Stern, S. (2015). Where is Silicon Valley? Science, 347(6222), 606–609. https://doi.org/10.1126/science.aaa0201
Höchtl, J., Parycek, P., & Schöllhammer, R. (2016). Big data in the policy cycle: Policy decision making in the digital era. Journal of
Organizational Computing and Electronic Commerce, 26(1–2), 147–169. https://doi.org/10.1080/10919392.2015.1125187
HSE. (2018). Joint economic and social data archive of Higher School of Economics. Retrieved February 11, 2019, from
http://sophist.hse.ru/eng/
172
ENTREPRENEURSHIP AND SUSTAINABILITY ISSUES
ISSN 2345-0282 (online) http://jssidoi.org/jesi/
2020 Volume 8 Number 1 (September)
http://doi.org/10.9770/jesi.2020.8.1(10)
Islankina, E., & Thurner, T. W. (2018). Internationalization of cluster initiatives in Russia: empirical evidence. Entrepreneurship &
Regional Development, 1–24. https://doi.org/10.1080/08985626.2018.1457086
Ketels, C., & Protsiv, S. (2014). European cluster panorama 2014. Center for Strategy and Competitiveness, Stockholm School of
Economics, October.
Kichigin, O. E. (2017). Fossil Fuel Production Impact on Regional Eco-Economic Development. International Journal of Ecological
Economics and StatisticsTM, 38(4), 12–22.
Komkov, N. I., Selin, V. S., Tsukerman, V. A., & Goryachevskaya, E. S. (2017). Problems and perspectives of innovative development of
the industrial system in Russian Arctic regions. Studies on Russian Economic Development, 28(1), 31–38.
Kopczewska, K. (2018). Cluster-based measures of regional concentration. Critical overview. Spatial Statistics, 27(September), 31–57.
https://doi.org/10.1016/j.spasta.2018.07.008
Kopczewska, K., Churski, P., Ochojski, A., & Polko, A. (2017). Measuring Regional Specialisation. In Measuring Regional Specialisation:
A New Approach. https://doi.org/10.1007/978-3-319-51505-2
Korovkin, A. G. (2016). Macroeconomic assessment of the state of regional labor markets in the Asian part of the Russian Arctic. Studies
on Russian Economic Development, 27(2), 166–179.
Kozlov, A. V, Gutman, S. S., Rytova, E. V, & Zaychenko, I. M. (2017). The application of the fuzzy sets theory to valuing cumulative
labor potential of the region. Soft Computing and Measurements (SCM), 2017 XX IEEE International Conference On, 621–623. IEEE.
Kutsenko, E., Islankina, E., & Abashkin, V. (2017). The evolution of cluster initiatives in Russia: the impacts of policy, life-time,
proximity and innovative environment. Foresight, 19(2), 87–120. https://doi.org/10.1108/FS-07-2016-0030
Leksin, V., & Porfiryev, B. (2017). Socio-Economic Priorities for the Sustainable Development of Russian Arctic Macro-Region. Economy
of Region, 1(4), 985–1004. https://doi.org/10.17059/2017-4-2
Lindqvist, G. (2009). Disentangling clusters agglomeration and proximity effects. Elanders, Vällingby.
Looijen, A., & Heijman, W. (2013). European agricultural clusters: how can European agricultural clusters be measured and identified?
Ekonomika Poljoprivrede, 60(2), 337.
MATICIUC, M. (2015). The complex relation between clusters and innovation in European Union. Ecoforum Journal, 4(2).
MinComSvyaz. (2019). United interdepartmental information – statistical service. Retrieved February 11, 2019, from https://fedstat.ru/
Morrissey, K. (2016). A location quotient approach to producing regional production multipliers for the Irish economy. Papers in Regional
Science, 95(3), 491–506. https://doi.org/10.1111/pirs.12143
Peiró-Signes, A., Segarra-Oña, M. del V., Miret-Pastor, L., & Verma, R. (2015). The Effect of Tourism Clusters on U.S. Hotel
Performance. Cornell Hospitality Quarterly, 56(2), 155–167. https://doi.org/10.1177/1938965514557354
Petrenko, Y., Vechkinzova, E., Antonov, V. (2019). Transition from the industrial clusters to the smart specialization of the regions in
Kazakhstan. Insights into Regional Development, 1(2), 118-128. https://doi.org/10.9770/ird.2019.1.2(3)
Porter, Michael E., Ramirez-Vallejo, J., Puri, A., Demirsoy, I., Woods, L., Zhou, M., & Rattanaruengyot, T. (2011). The Minnesota
medical devices cluster. Microeconomics of Competitiveness, 1–36.
Porter, Michael E. (1998). Clusters and competition: New agendas for companies, governments, and institutions. In M.E. Porter (Ed.),
Governments and Institutions, in: Ibid., On Competition (pp. 197–299). Boston, MA: Harward Business School Press.
Prodani, R., Bushati, J., Andersons, A. (2019). An assessment of impact of information and communication technology in enterprızes of
Korça region. Insights into Regional Development, 1(4), 333-342. https://doi.org/10.9770/ird.2019.1.4(4)
Razminienė, K., Tvaronavičienė, M. (2018). Detecting the linkages between clusters and circular economy, Terra Economicus, 16(4), 5065. https://doi.org/10.23683/2073-6606-2018-16-4-50-65
173
ENTREPRENEURSHIP AND SUSTAINABILITY ISSUES
ISSN 2345-0282 (online) http://jssidoi.org/jesi/
2020 Volume 8 Number 1 (September)
http://doi.org/10.9770/jesi.2020.8.1(10)
Rodionova, E. A., Trifonova, N. V, Epstein, M. Z., & Shvetsova, O. A. (2017). Multicriterial approach to estimation of economic
efficiency based on regional innovative cluster. Soft Computing and Measurements (SCM), 2017 XX IEEE International Conference On,
743–745. IEEE.
Romashkina, G. F., Didenko, N. I., & Skripnuk, D. F. (2017). Socioeconomic modernization of Russia and its Arctic regions. Studies on
Russian Economic Development, 28(1), 22–30.
Rytova, E. V., Gutman, S. S., & Zaychenko, I. M. (2017). Classification of the Russian Arctic zone territories according to the level of
small business development. In K. S. Soliman (Ed.), Proceedings of the 30th International Business Information Management Association
Conference, IBIMA 2017 - Vision 2020: Sustainable Economic development, Innovation Management, and Global Growth (pp. 3218–
3223). International Business Information Management Association, IBIMA.
Rytova, E., & Gutman, S. (2019). Assessment of regional development strategy in the context of economy digitization on the basis of fuzzy
set method. IOP Conference Series: Materials Science and Engineering, 497(1). https://doi.org/10.1088/1757-899X/497/1/012060
Schepinin, V., Skhvediani, A., & Kudryavtseva, T. (2018). An empirical study of the production technology cluster and regional economic
growth in Russia. In M. P. C. Amorim, C. Costa, & M. Au-Yong-Oliveira (Eds.), Proceedings of the European Conference on Innovation
and
Entrepreneurship,
ECIE
(pp.
732–740).
Academic
Conferences
and
Publishing
International
Limited.
https://doi.org/10.1051/shsconf/20184400050
Sellar, C., Emilova, M., Petkova‐Tancheva, C. D., & McNeil, K. (2011). Cluster policies in Bulgaria: European integration, postsocialist
dynamics and local level initiatives. International Journal of Urban and Regional Research, 35(2), 358–378. https://doi.org/10.1111/j.14682427.2010.00959.x
Tatarkin, A. I., Loginov, V. G., & Zakharchuk, E. A. (2017). Socioeconomic problems in development of the Russian Arctic zone. Herald
of the Russian Academy of Sciences, 87(1), 12–21.
Thill, J.-C. (2019). Spatial multicriteria decision making and analysis: a geographic information sciences approach. Routledge.
Tvaronavičienė, M. (2017). Clusters, innovations and energy efficiency: if relantionship could be traced. Marketing and Management of
Innovations, 2, 382-391. http://doi.org/10.21272/mmi.2017.2-35
Tvaronavičienė, M., Razminienė K. (2017). Towards competitive regional development through clusters: approaches to their performance
evaluation, Journal of Competitiveness, 9(4), 133-147, https://doi.org/10.7441/joc.2017.04.09
Velasquez, M., & Hester, P. T. (2013). An analysis of multi-criteria decision making methods. International Journal of Operations
Research, 10(2), 56–66.
174
ENTREPRENEURSHIP AND SUSTAINABILITY ISSUES
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Tatiana KUDRYAVTSEVA
PhD, professor at Graduate School of Industrial Economics of Peter the Great St. Petersburg Polytechnic University. T. Kudryavtseva
conducts the following courses: financial management, economic analysis, financial analysis. Her main fields of research are regional
development, cluster–based industrial policy and evaluation of economic efficiency of industrial policy.
ORCID ID: orcid.org/0000-0003-1403-3447
Angi SKHVEDIANI
PhD student, assistant at Graduate School of Industrial Economics of Peter the Great St. Petersburg Polytechnic University. A. Skhvediani
specializes in regional development studies. He conducts course on applied econometrics.
ORCID ID: orcid.org/0000-0001-7171-7357
Mohammed Ali BERAWI
PhD, associate professor in the department of civil engineering, faculty of engineering, Universitas Indonesia. He has extensive research
experience in value engineering/value management and innovation in the context of infrastructure, construction, and manufacturing
industries.
ORCID ID: orcid.org/0000-0002-1580-6686
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