ASSESSMENT OF MIGRATION ATTRACTIVENESS OF RUSSIAN FEDERATION FEDERAL DISTRICTS

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International Journal of Civil Engineering and Technology (IJCIET)
Volume 9, Issue 12, December 201
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ISSN Print: 0976-6308 and ISSN Online: 0976
©IAEME Publication
ASSESSMENT OF MIGRAT
ATTRACTIVENESS OF RU
FEDERAL DISTRICTS
Elena Irekovna Beglova, Svetlana Irekovna Nasyrova, Azat Vazirovich Yangirov
Bashkir State University
ABSTRACT
Migration is playing increasingly more prominent role in ensuring a well
balanced spatial development of the Russian economy in the context of growing
globalization.
That is why solution of the problems related to ac
development from a migration perspective is supposed to address some issues of
assessing the migration attractiveness of the Russian Federation territories, which
will make it possible to shape a sound migration policy aimed at over
territorial disparities. The study provides the assessment of the migration
attractiveness of RF federal districts, which were chosen as research objects because
of the need for comprehensive coverage of the Russian Federation megaspace.
Ranking and classification of the federal districts have been designed according to the
degree of migration attractiveness, with account of its upward or downward trend in
the period under review; in addition, a cartographic profile of the Russian migration
space has been constructed. This analysis is based on absolute and relative
parameters of migration processes (indicators of arrival and departure of population,
turnover, migration balance, and their rates) drawing on the data of the official
statistics for 2014-2016. Rate of migration attractiveness was employed as one of the
analytical tools and it was possible to reveal its interrelation at regional level with a
number of indicators describing economic, social, demographic and ecological
factors of regional development. It has been found that the interrelation of migration
attractiveness is most closely traced to economic and social factors. It has been
determined that Central, Northwestern and Southern Federal Districts demonstrate
the highest migration attracti
possible to identify the places with the greatest migration appeal within the most
migratory attractive federal districts and to classify them into the following groups:
mono-factor (Krasnodar Krai)
the city of Moscow, Republic of Adygea) and multi
Regions and the city of St. Petersburg) centers.
IJCIET/index.asp 323 editor@iaeme.com
International Journal of Civil Engineering and Technology (IJCIET)
2018, pp. 323–338, Article ID: IJCIET_09_12_03
http://www.iaeme.com/ijciet/issues.asp?JType=IJCIET&VType=9&IType=12
nd ISSN Online: 0976-6316
Scopus Indexed
ASSESSMENT OF MIGRATION
ATTRACTIVENESS OF RUSSIAN FEDERATION
FEDERAL DISTRICTS
Bashkir State University, Str. Zaki Validi, 32, 450076, Ufa, Russia
Migration is playing increasingly more prominent role in ensuring a well
That is why solution of the problems related to achieving a well
will make it possible to shape a sound migration policy aimed at over
classification of the federal districts have been designed according to the
been constructed. This analysis is based on absolute and relative
016. Rate of migration attractiveness was employed as one of the
lopment. It has been found that the interrelation of migration
the highest migration attractiveness at the present stage. The results obtained made it
factor (Krasnodar Krai), duo-factor (Voronezh, Yaroslavl, Leningrad Regions,
the city of Moscow, Republic of Adygea) and multi-factor (Moscow and Kaliningrad
Regions and the city of St. Petersburg) centers.
editor@iaeme.com
036
ION
SSIAN FEDERATION
alidi, 32, 450076, Ufa, Russia
Migration is playing increasingly more prominent role in ensuring a well-
hieving a well-balanced
will make it possible to shape a sound migration policy aimed at overcoming
classification of the federal districts have been designed according to the
been constructed. This analysis is based on absolute and relative
016. Rate of migration attractiveness was employed as one of the
lopment. It has been found that the interrelation of migration
veness at the present stage. The results obtained made it
factor (Voronezh, Yaroslavl, Leningrad Regions,
factor (Moscow and Kaliningrad

Elena Irekovna Beglova, Svetlana Irekovna Nasyrova and Azat Vazirovich Yangirov
http://www.iaeme.com/IJCIET/index.asp 324 editor@iaeme.com
Keywords: population, federal districts, region, migration, migration attractiveness,
factors of migration attractiveness.
Cite this Article: Elena Irekovna Beglova, Svetlana Irekovna Nasyrova and Azat
Vazirovich Yangirov, Assessment of Migration Attractiveness of Russian Federation
Federal Districts, International Journal of Civil Engineering and Technology (IJCIET)
9(12), 2018, pp. 323–338.
1. INTRODUCTION
The study of migration processes of the population is becoming quite an up-to-date research
area, since migration acts as a catalyst for spatial redistribution of labor resources.
The theoretical foundations of the research into migration are laid down in the works
of E. Ravenstein [1], A. Lee [2], J. Friedmann и W. Alonso [3]. It is known that migration is
closely interrelated with the economic behavior of the population and is formed under the
influence of consumption, values and motives of people, their poverty and wealth, as was
noted in a number of papers [4-17].
At present there is a growing tendency for greater mobility of the population. In terms
of the whole country, high migration attractiveness of some territories may be found
alongside with low migration attractiveness of others. Due to this, growing importance is
attached to the issue of identifying attractive and unattractive territories in order to further
develop migration policy with a view to smoothing out migration differentiation of places.
As a rule, most statistical studies focus on inflow and outflow of the population in
their analytical papers, which results in classification of places according to migration
dynamics. Such classification includes the following categories:
• Territories with increased migration attractiveness, characterized by a strong excess of
migrant inflows over their outflow.
• Territories with low migration attractiveness, where the outflow rate significantly
exceeds the rate of residential flow to the district.
• Territories with high migration mobility and simultaneously high rates of inflow and
outflow of population, but negligeable value of the balance.
• Territories with low migration mobility and simultaneously low rates of influx and
outflow of migrants with insignificant balance.
• Well-balanced areas, where the influx and outflow of migration processes are
practically equal to each other.
However, the information content of these studies does not fully allow to provide an
insight into migration processes from the point of view of territories where the share of both
inflow and outflow of mobile population is high.
As a result of this, it is especially relevant to study the issues of migration
attractiveness in individual districts of the Russian Federation, taking into account the
dynamics of the reciprocal processes of population mobility.

Assessment of Migration Attractiveness of Russian Federation Federal Districts
2. METHODS
According The authors of the article proposed a number of indicators, based on statistical,
analytical and comparative methods, which are expected to identify the migration
attractiveness of the federal districts of the Russian Federation. The first point to make is that
absolute and relative indicators were used to provide general description of migration
processes in the federal districts.
The following absolute indicators have beeen applied:
• The migration turnover, which is calculated by determining the total scale of
migration, regardless of its direction in a definite time.
• The balance of migration, which characterizes the migration inflow / outflow in a
definite time.
The relative indicators used in the survey included:
• The arrival rate, indicating the number of arrivals per 1,000 people (per mille).
• The departure rate, which estimates the number of people leaving a place per 1,000
people (per mille).
• The rate of migration turnover which shows the migration turnover per 1,000 people
(per mille).
• The net migration rate, reflecting the migration growth per 1,000 people (per mille).
• The migration efficiency ratio, showing to what extent the migration turnover
promotes residential growth / decline (percentage).
In terms of analysis, arrival and departure rates are crucial for the purpose of a
comprehensive assessment of place attractiveness in the context of migration flows. In fact,
they reflect the intensity of migration processes to a certain extent. However, application of
two differently directed indicators introduces some element of confusion in the present
analysis, in particular, in obtaining a general picture of the residential mobility.
As a result, the ratio of migration attractiveness was used in the research, since high
intensity of arrival (as a factor of migratory attractiveness of the place) and departure does not
fully provide for an adequate assessment of the situation.
The following formula was used to calculate the migration attractiveness ratio:
MAR = AR
DR
where MAR is total migration attractiveness ratio;
AR – arrival rate;
DR – departure rate.
The data used to describe the migration attractiveness of the federal districts are obtained
from the official statistics of the Federal Service for Statistics for 2014-2016 related to
migration in the Russian Federation regions.
3. FINDINGS AND DISCUSSION
The starting point of our study included the assessment of "gross" migration indicators in the
context of federal districts. Examination of the data shown in Table 1 has revealed that the
turnover of migration in the Russian Federation on the whole has increased by 170 thousand
people. From the perspective of the districts, this growth was mainly accounted for by Central
(an increase of 88.3 thousand in 2016 as compared to 2014), Northwestern (the growth in

2016 as compared to 2014 was 65,3 thousand people) and Southern (an increase of 104.6
thousand people) federal districts. In other cases, the turnover for the period under review
either remained unchanged or slightly decreased.
Table 1 Migration turnover and balance in the districts of the Russian Federation for 2014-
2016
District Year
Arrivals,
persons
Departures,
persons
Migration turnover,
persons
Migration balance,
persons
1 2 3 4 5=3+4 6=3-4
Central Federal District
2014 1016899 897517 1914416 119382
2015 1084531 957797 2042328 126734
2016 1043998 958772 2002770 85226
Northwestern Federal
District
2014 497691 461920 959611 35771
2015 514102 475751 989853 38351
2016 531726 493189 1024915 38537
Southern Federal District
2014 375352 357118 732470 18234
2015 377736 357619 735355 20117
2016 437036 400047 837083 36989
North- Caucasian Federal
District
2014 190881 218908 409789 -28027
2015 185240 216789 402029 -31549
2016 177152 202563 379715 -25411
Volga (Privolzhsky)
Federal District
2014 794413 844871 1639284 -50458
2015 773956 834769 1608725 -60813
2016 780451 827225 1607676 -46774
Urals Federal District
2014 395635 409067 804702 -13432
2015 375355 395805 771160 -20450
2016 379624 395503 775127 -15879
Siberian Federal District
2014 565401 611196 1176597 -45795
2015 569520 618000 1187520 -48480
2016 565459 612879 1178338 -47420
Far Eastern Federal
District
2014 210081 245756 455837 -35675
2015 213261 246077 459338 -32816
2016 215807 241075 456882 -25268
Russian Federation
2014 4046353 4046353 8092706 0
2015 4135906 4135906 8271812 0
2016 4131253 4131253 8262506 0
Source: Based on the Regions of Russia. Socio-economic indicators. 2017
It In addition, as can be seen from the table, the main part of migration processes is
largely formed in three districts: Central, Volga and Siberian federal districts (the turnover of
migration flows in these territories exceeds 1 million people per year). As for migration
balance, certain population growth can be observed in the districts indicated earlier, with the
main increase found in Central Federal District (although CFD had significantly weaker
positions in 2016, compared to 2014, obviously, thanks to the decrease in the migration
balance by 34.2 thousand people).
Migratory decline is observed in the SibFD, VFD, NCFD, FEFD and UFD (the
districts are arranged in descending order according to the decrease in the migration outflow
for 2016).

Table 2 Arrival rate in the Russian Federation districts in 2014-2016
District Year Arrivals, persons
Mid-year population,
thousand people
Arrival rate
1 2 3 4 5=3/4
2014 1016899 38885,7 26,151
2015 1084531 39027,9 27,789
2016 1043998 39157,0 26,662
Northwestern Federal District
2014 497691 13822,1 36,007
2015 514102 13848,6 37,123
2016 531726 13876,5 38,318
2014 375352 13983,9 26,842
2015 377736 14024,2 26,935
2016 437036 16398,2 26,651
North-Caucasian Federal
District
2014 190881 9624,6 19,833
2015 185240 9688,5 19,120
2016 177152 9746,9 18,175
Volga Federal District
2014 794413 29727,1 26,724
2015 773956 29694,6 26,064
2016 780451 29655,1 26,318
2014 395635 12255,0 32,284
2015 375355 12292,0 30,537
2016 379624 12326,9 30,796
2014 565401 19302,5 29,292
2015 569520 19318,1 29,481
2016 565459 19325,1 29,260
Far Eastern Federal District
2014 210081 6218,8 33,782
2015 213261 6203,0 34,380
2016 215807 6188,8 34,871
Russian Federation
2014 4046353 143819,7 28,135
2015 4135906 144096,9 28,702
2016 4131253 146674,5 28,166
As regards the arrival rate (Table 2), it was revealed that its increase was only observed in
Northwestern (of 2,311 per mille) and Far Eastern (of 1,089 per mille) federal districts.
It should be mentioned that this indicator is quite stable in the other Russian Federation
areas, with only insignificant decrease noted in Urals (of 1.488 ppm) and North-Caucasian (of
1.658 ppm) federal districts in the period under review. No doubt, growing arrival rate
indicates higher migration attractiveness. At the same time, this conclusion can not be
absolutely unambiguous, since this indicator should be considered in combination with the
departure rate (Table 3).

Table 3 Departure rate in the Russian Federation districts in 2014-2016
District Year
Departures,
persons
Mid-year population,
thousand people
Departure rate
1 2 3 4 5(3/4)
2014 897517 38885,7 23,081
2015 957797 39027,9 24,541
2016 958772 39157,0 24,485
2014 461920 13822,1 33,419
2015 475751 13848,6 34,354
2016 493189 13876,5 35,541
2014 357118 13983,9 25,538
2015 357619 14024,2 25,500
2016 400047 16398,2 24,396
District
2014 218908 9624,6 22,745
2015 216789 9688,5 22,376
2016 202563 9746,9 20,782
2014 844871 29727,1 28,421
2015 834769 29694,6 28,112
2016 827225 29655,1 27,895
2014 409067 12255,0 33,380
2015 395805 12292,0 32,200
2016 395503 12326,9 32,085
2014 611196 19302,5 31,664
2015 618000 19318,1 31,991
2016 612879 19325,1 31,714
2014 245756 6218,8 39,518
2015 246077 6203,0 39,671
2016 241075 6188,8 38,953
Russian Federation
2014 4046353 143819,7 28,135
2015 4135906 144096,9 28,702
2016 4131253 146674,5 28,166
In this case, there was an insignificant increase in the departure rate in Central (by 1.404
ppm), Northwestern FD (2.112 ppm), Southern FD (at 1.142 ppm), North-Caucasian FD (at
1.963 ppm), Volga FD (0.526 ppm) and Urals FD (at 1,295 ppm). No significant structural
changes have been observed in the outflow of population in the RF districts during the period
under study.
With respect to the migration turnover rate (Table 4), a significant increase is observed
in Northwestern FD (by 4.434 ppm), a slight increase in Central FD (by 1.915 ppm), a
significant decrease in North-Caucasian FD (3.619 ppm), Urals FD (by 2.782 ppm), quite
insignificant drop - in Southern (1.333 ppm) and Volga (0.932 ppm) federal districts.

Table 4 Migration turnover rate and net migration rate in the Russian Federation districts in
2014-2016
District Year
Migration
turnover,
persons
Migration
balance,
persons
Mid-year
population,
thousand people
Migration
turnover rate
Net migration
rate
1 2 3 4 5 6=3/5 7=4/5
Central Federal
District
2014 1914416 119382 38885,7 49,232 3,070
2015 2042328 126734 39027,9 52,330 3,247
2016 2002770 85226 39157,0 51,147 2,177
Northwestern
Federal District
2014 959611 35771 13822,1 69,426 2,588
2015 989853 38351 13848,6 71,477 2,769
2016 1024915 38537 13876,5 73,860 2,777
Southern Federal
District
2014 732470 18234 13983,9 52,380 1,304
2015 735355 20117 14024,2 52,435 1,434
2016 837083 36989 16398,2 51,047 2,256
North-Caucasian
Federal District
2014 409789 -28027 9624,6 42,577 -2,912
2015 402029 -31549 9688,5 41,495 -3,256
2016 379715 -25411 9746,9 38,958 -2,607
Volga Federal
District
2014 1639284 -50458 29727,1 55,144 -1,697
2015 1608725 -60813 29694,6 54,176 -2,048
2016 1607676 -46774 29655,1 54,212 -1,577
Urals Federal
District
2014 804702 -13432 12255,0 65,663 -1,096
2015 771160 -20450 12292,0 62,737 -1,664
2016 775127 -15879 12326,9 62,881 -1,288
Siberian Federal
District
2014 1176597 -45795 19302,5 60,956 -2,372
2015 1187520 -48480 19318,1 61,472 -2,510
2016 1178338 -47420 19325,1 60,974 -2,454
Far Eastern Federal
District
2014 455837 -35675 6218,8 73,300 -5,737
2015 459338 -32816 6203,0 74,051 -5,290
2016 456882 -25268 6188,8 73,824 -4,083
Russian Federation
2014 8092706 0 143819,7 56,270 0
2015 8271812 0 144096,9 57,405 0
2016 8262506 0 146674,5 56,332 0
The figures show that migration growth rate is decreasing in Central FD (0.893 ppm),
Urals FD (0.192 ppm), Siberian FD (0.082 ppm), although its value is rising in Far Eastern
FD (1.654 ppm), Southern FD (0.952 ppm), North-Caucasian FD (0.305 ppm) and Volga FD
(0.120 ppm).
In the course of analysis of migration efficiency (Table 5), it has been found that
migration turnover brought about a decrease in the size of residential population in Central
(by 1.98%), Urals (0.38%) and Siberian (0.132%) federal districts, whereas its growth is
observed in Far Eastern (2.3%) and Southern (1.93%) federal districts.

Table 5 Migration efficiency ratio in the Russian Federation districts in 2014-2016
District Year Arrivals, persons
Departures,
persons
Migration efficiency ratio
1 2 3 4 5= (3-4)/(3+4)
2014 1016899 897517 6,24
2015 1084531 957797 6,21
2016 1043998 958772 4,26
2014 497691 461920 3,73
2015 514102 475751 3,87
2016 531726 493189 3,76
2014 375352 357118 2,49
2015 377736 357619 2,74
2016 437036 400047 4,42
District
2014 190881 218908 -6,84
2015 185240 216789 -7,85
2016 177152 202563 -6,69
2014 794413 844871 -3,08
2015 773956 834769 -3,78
2016 780451 827225 -2,91
2014 395635 409067 -1,67
2015 375355 395805 -2,65
2016 379624 395503 -2,05
2014 565401 611196 -3,89
2015 569520 618000 -4,08
2016 565459 612879 -4,02
2014 210081 245756 -7,83
2015 213261 246077 -7,14
2016 215807 241075 -5,53
Russian Federation
2014 4046353 4046353 0
2015 4135906 4135906 0
2016 4131253 4131253 0
It is important to take into account two considerations. First, mention should be made that
the districts where the value of migration attractiveness rate (Table 6) is more than one, which
indicates that the arrival rate exceeds the rate of departure, are found to attract a great deal of
migration to the territory. Among these territories, we should note Central, Northwestern and
Southern federal districts, although CFD had significantly weaker positions in 2016, whereas
SFD, on the contrary, reinforced its standing. Let's note here that the three top leaders have
maintained their positions for more than ten years.

Table 6 Migration attractiveness ratio in the Russian Federation districts in 2014-2016
District Year Arrival rate Departure rate Migration attractiveness ratio
1 2 3 4 5
2014 26,151 23,081 1,0644
2015 27,789 24,541 1,0641
2016 26,662 24,485 1,0435
2014 36,007 33,419 1,0380
2015 37,123 34,354 1,0395
2016 38,318 35,541 1,0383
2014 26,842 25,538 1,0252
2015 26,935 25,500 1,0278
2016 26,651 24,396 1,0452
District
2014 19,833 22,745 0,9338
2015 19,120 22,376 0,9244
2016 18,175 20,782 0,9352
2014 26,724 28,421 0,9697
2015 26,064 28,112 0,9629
2016 26,318 27,895 0,9713
2014 32,284 33,380 0,9834
2015 30,537 32,200 0,9738
2016 30,796 32,085 0,9797
2014 29,292 31,664 0,9618
2015 29,481 31,991 0,9600
2016 29,260 31,714 0,9605
2014 33,782 39,518 0,9246
2015 34,380 39,671 0,9309
2016 34,871 38,953 0,9462
Russian Federation
2014 28,135 28,135 1,0
2015 28,702 28,702 1,0
2016 28,166 28,166 1,0
Source: based on the Regions of Russia. Socio-economic indicators. 2017
Secondly, there are districts where the value of migration attractiveness rate is less than
one, which may suggest the deteriorating migration image of these territories (North-
Caucasian, Volga, Urals, Siberian and Far Eastern Federal Districts, although the situation in
VFD and FEFD improved significantly which led to higher values of the migration
attractiveness rate as compared to the rest).
Within the framework of this classification, two subgroups were distinguished in the first
group of districts according to the dynamics shown in the period under study:
– districts, where migration attractiveness ratio is rising, which indicates that these territories
are especially attractive for migrants (Northwestern and Southern Federal Districts);
– districts, where migration attractiveness ratio is declining; this subgroup includes only
Central Federal District which is gradually weakening its position, despite the fact that it
remains the absolute leader in terms of attractiveness for migrants in the country.
Second group contains two similarly defined subgroups:
– districts, where migration attractiveness ration is increasing, including North-Caucasian,
Volga, Far Eastern Federal Districts;
– districts with decreasing ratio of migration attractiveness (Urals and Siberian federal
districts).

It has been found that this trend shows stability in the entire period under review. A
conclusion was made that the Russian Federation can not be described as a highly
differentiated country with clearly distinguished donor territories and recipient areas.
Therefore, the results suggest that the top three leaders are as follows: Central,
Northwestern and Southern federal districts. The first of these districts accounts for the
essential part of the migration turnover. At the same time, only Northwestern FD
demonstrated the increase in migration turnover during the period under review, mainly due
to the growing arrival rate. With regard to Southern FD, which was found to be a migratory
attractive area, it was revealed that this resulted from a decreasing departure rate.
In addition to the leaders mentioned above, special mention should be made of Far Eastern
FD, since popularity of this territory with migrants is increasingly on the rise, and the
migration appeal of FEFD has gone up by 102,3% from 2014 to 2016. Moreover, Urals and
Volga federal districts are "catching up" with the leaders, however, the pace of their
development in this aspect is much lower. Consequently, the districts with extensive
migratory inflows of population in most cases are characterized by extensive outflows of
migrants.
Let us have a closer look at the top three leaders by migration attractiveness from the
perspective of the Russian Federation members (tables 7, 8, 9).
Table 7 Migration attractiveness ratio of constituent territories of Central Federal District in 2016
Constituent
territories of CFD
Arrivals, persons
Departures,
persons
Mid-year
population,
thousand people
Arrival
rate
Departure
rate
Migration
attractiveness
ratio
1 2 3 4 5 6 7
Belgorod Region 39 114 39 285 1 551,5 25,210 25,321 0,9978
Bryansk Region 38 905 40 726 1 223,1 31,809 33,297 0,9774
Vladimir Region 33 565 35 465 1 393,4 24,089 25,452 0,9728
Voronezh Region 63 628 60 610 2 334,4 27,257 25,964 1,0246
Ivanovo Region 28 976 31 259 1 026,5 28,228 30,452 0,9628
Kaluga Region 26 262 27 000 1 012,2 25,945 26,675 0,9862
Kostroma Region 25 090 26 219 649,8 38,612 40,349 0,9782
Kursk Region 29 831 31 760 1 121,5 26,599 28,319 0,9692
Lipetsk Region 31 460 33 081 1 156,2 27,210 28,612 0,9752
Moscow Region 299 119 215 787 7 371,1 40,580 29,275 1,1774
Oryol Region 18 368 20 588 757,3 24,255 27,186 0,9445
Ryazan Region 32 502 32 700 1 128,4 28,804 28,979 0,9970
Smolensk Region 27 477 28 869 955,9 28,745 30,201 0,9756
Tambov Region 28 328 30 734 1 045,3 27,100 29,402 0,9601
Тver Region 36 990 40 079 1 300,8 28,436 30,811 0,9607
Тula Region 36 212 39 674 1 502,9 24,095 26,398 0,9554
Yaroslavl Region 35 757 34 678 1 271,3 28,126 27,278 1,0154
city of Moscow 212 414 190 258 12 355,4 17,192 15,399 1,0566
Total 1 043 998 958 772 39157,0 26,662 24,485 1,0435
Drawing on figures in Table 7, it is possible to make a conclusion that only four of the
eighteen members contribute to the leading positions of the entire Central Federal District:
Moscow Region, the city of Moscow, Voronezh and Yaroslavl Regions (the regions are
arranged in decreasing order of migration attractiveness rate in 2016).

Table 8 Migration attractiveness ratio of constituent territories of Northwestern Federal
District in 2016
Constituent territories of
NWFD
Arrivals,
persons
Departures,
persons
Mid-year
population,
thousand people
Arrival rate
Departure
rate
Migration
attractiveness
ratio
1 2 3 4 5 6 7
Republic of Karelia 21 374 22 621 628,5 34,008 35,992 0,9720
Komi Republic 33 439 41 057 853,7 39,169 48,093 0,9025
Arkhangelsk Region 39 087 46 725 1 169,9 33,411 39,939 0,9146
including
Nenets Autonomous Area
1 938 2 331 43,9 44,146 53,098 0,9118
Arkhangelsk Region without
autonomous areas
37 149 44 394 1 126,0 32,992 39,426 0,9148
Vologda Region 34 898 37 271 1 185,8 29,430 31,431 0,9676
Kaliningrad Region 32 675 29 005 981,3 33,298 29,558 1,0614
Leningrad Region 78 934 61 398 1 785,4 44,211 34,389 1,1338
Murmansk Region 34 406 39 555 759,9 45,277 52,053 0,9326
Novgorod Region 21 846 23 086 614,1 35,574 37,593 0,9728
Pskov Region 24 795 25 957 644,3 38,484 40,287 0,9774
city of Saint Petersburg 210 272 166 514 5 253,6 40,024 31,695 1,1237
Total 531726 493189 13876,5 38,318 35,541 1,0383
If we examine Table 8, we can see that only three members of Northwestern FD play a
decisive role in shaping the overall appearance of the district in terms of migration
attractiveness: Leningrad Region, the city of St. Petersburg and Kaliningrad Region
(territories are arranged in the descending order of the migration attractiveness rate in 2016).
Table 9 Migration attractiveness ratio of constituent territories of Southern Federal District in
2016
Constituent
territories of SFD
Arrivals,
persons
Departures,
persons
Mid-year
population,
thousand people
Arrival rate
Departure
rate
Migration
attractiveness ratio
1 2 3 4 5 6 7
Republic of Adygea 16 155 15 007 452,4 35,710 33,172 1,0375
Republic of Kalmykia 13 707 15 397 278,3 49,253 55,325 0,9435
Republic of Crimea 30 192 29 061 1 909,6 15,811 15,218 1,0193
Krasnodar Krai 189 313 147 307 5 542,4 34,157 26,578 1,1336
Astrakhan Region 18 911 22 599 1 018,7 18,564 22,184 0,9148
Volgograd Region 51 424 58 862 2 540,6 20,241 23,169 0,9347
Rostov Region 100 134 102 302 4 233,7 23,652 24,164 0,9893
city of Sevastopol 17 200 9 512 422,5 40,710 22,514 1,3447
Total 437036 400047 16398,2 26,651 24,396 1,0452
Source: based on the Regions of Russia. Socio-economic indicators. 2017
The Drawing on data from Table 9 we can come to a conclusion that 50% of Southern FD
members determine the migration attractiveness of the whole territory: the city of Sevastopol,
Krasnodar Krai, Republic of Adygea and Crimea (the places are arranged in the descending
order of migration attractiveness rate in 2016). To sum up, the "centers of attraction" in the
three leading regions across the Russian Federation in terms of migration attractiveness are
formed by only eleven areas (34% of the total number of the constituent territories of CFD,
NWFD and SFD).

The next stage in the assessment of migration attractiveness of these territories was
identification of the key factors that determine the place attractiveness for migrants. For the
purpose of this study, four groups of factors have been selected, which, in the opinion of the
authors, play a crucial role in the choice of the place for migration: economic, social,
demographic and environmental. Table 10 presents the results of estimated correlation of
migration attractiveness rate with economic, social, demographic, and environmental factors
for 2005, 2010-2016, using the case of the Voronezh region.
Table 10 Assessment of interrelation between economic, social, demographic and environmental indicators with migration
attractiveness ratio for 2005, 2010-2016, using the case of Voronezh Region (based on correlation coefficient)
Indicator 2005 2010 2011 2012 2013 2014 2015 2016
Coefficient of
correlation of
indicators with
migration
attractiveness
ratio
Migration attractiveness ratio 1,005 1,042 1,026 1,014 1,012 1,031 1,025 1,025 –
1. Economic factors
Expenses of the consolidated budget per
capita,
thousand rubles
10,353 31,243 34,083 38,259 43,014 45,786 45,056 44,806 0,389
Unemployment rate, % 7,6 7,5 6,4 5,5 4,7 4,5 4,5 4,5 0,006
Real population income, as a percentage of
the previous year
116,1 108,9 106,0 114,1 108,4 106,2 100,8 92,7 -0,422
Average monthly nominal wages payable
to employees, rubles
5382 14337 16055 19538 21825 24001 24906 26335 0,309
Industrial production index, as a percentage
of the previous year
111,0 106,6 110,1 129,7 106,1 108,0 103,7 104,4 -0,378
Fixed capital expenditures per capita,
rubles
12126 53890 66539 78223 93139 103119 113475 116087 0,302
2. Social factors
Population per hospital bed, pers. 93,4 109,7 106,4 107,3 108,8 108,8 116,7 118,9 0,532
Number of nursing staff per 10 thousand
people, pers.
117,6 115,6 117,5 116,4 114,7 113,3 114 111,0 -0,323
Number of children registered for
preschool education per 1000 children
aged 1-6 years
68 308 304 312 317 289 281 258 0,541
Number of organizations engaged in
educational activities for programs of
primary, basic and secondary general
education (at the beginning of the academic
year), per 10,000 people
4,6 3,9 3,8 3,7 3,7 3,6 3,6 3,5 -0,401
Number of teachers in organizations that
carry out educational activities for
programs of primary, basic and secondary
general education, per 1,000 people, pers.
No data
7,3 7,1 6,9 6,9 6,8 6,8 6,9 0,505
3. Demographic factors
Population younger than working age, in %
of the total population
14,5 13,8 13,9 14,1 14,4 14,7 15,1 15,4 -0,126
Mortality of population in working age
(number of deaths per 100 thousand people
of the corresponding age)
787,5 617,0 592,2 573,8 576,3 597,7 561,5 537,2 -0,431
Morbidity per 1000 people (registered
cases of diseases diagnosed for the first
time in patients’ lives)
525,4 549,9 553,3 542,5 525,0 527,3 545,6 549,9 0,568
4. Ecological factors
Emissions of pollutants into the
atmospheric air from stationary sources,
1000 tons
52 77 72 79 76 68 69 73 0,434
Discharge of contaminated sewage into
surface water bodies, mln cubic meters
169 134 135 131 129 122 117 122 -0,531

Using the analogy with the results presented in Table 10, the authors undertook assessment of the
relationship between the migration attractiveness rate and economic, social, demographic, and
environmental factors for the remaining eight members (except the city of Sevastopol and Republic of
Crimea).
The results obtained for Central (Voronezh, Moscow, Yaroslavl regions, the city of Moscow),
Northwestern (Kaliningrad and Leningrad Regions, the city of St. Petersburg) and Southern (Republic of
Adygea, Krasnodar Krai) federal districts are summarized in table 11. Carrying out an assessment of such
correlation for two members (the city of Sevastopol and Republic of Crimea) is not possible in view of the lack
of statistical data for the period under study and significant influence of political factor.
Table 11 Evaluation of relationship between migration attractiveness ratio and economic, social, demographic and environmental factors in
Central FD, Northwestern FD and Southern FD in 2005, 2010-2016 (based on the correlation coefficient)
Indicator
Migration attractiveness ratio
CFD NWFD SFD
VoronezhRegion
MoscowRegion
YaroslavlRegion
cityofMoscow
Kaliningrad
Region
LeningradRegion
cityofSaint
Petersburg
Republicof
Adygea
KrasnodarKrai
1. Economic factors
Expenses of the consolidated budget per
capita,
thousand rubles
0,389 -0,826 -0,597 -0,817 0,116 -0,424 -0,168 0,491 -0,184
Uneployment rate, % 0,006 0,490 -0,662 -0,348 -0,470 0,201 0,640 0,661 0,276
Real population income, as a percentage of
the previous year
-0,422 0,726 0,433 0,799 -0,712 -0,213 0,290 -0,270 0,322
Average monthly nominal wages payable
to employees, rubles
0,309 -0,775 -0,603 -0,869 0,142 -0,443 -0,463 0,474 -0,365
Industrial production index, as a percentage
of the previous year
-0,378 0,483 -0,551 0,418 -0,419 0,415 0,573 0,129 -0,026
Fixed capital expenditures per capita,
rubles.
0,302 -0,776 -0,436 -0,872 0,324 -0,046 -0,256 0,664 0,142
2. Social factors
Population per hospital bed, pers. 0,532 -0,564 -0,572 -0,770 -0,487 -0,332 -0,345 0,447 -0,419
Number of nursing staff per 10 thousand
people, pers.
-0,323 0,193 0,684 0,384 0,705 -0,236 -0,562 -0,329 0,290
Number of children registered for preschool
education per 1000 children aged 1-6 years
0,541 -0,557 -0,274 -0,338 0,044 -0,217 -0,592 0,427 -0,331
Number of organizations engaged in
educational activities for programs of
primary, basic and secondary general
education (at the beginning of the academic
year), per 10,000 people
-0,401 0,660 0,609 0,822 -0,066 0,412 0,384 -0,559 0,227
Number of teachers in organizations that
carry out educational activities for
programs of primary, basic and secondary
general education, per 1,000 people, pers.
0,505 -0,626 -0,458 0,365 0,480 0,038 -0,924 -0,109 -0,271
3. Demographic factors
Population younger than working age, in %
of the total population
-0,126 -0,863 -0,558 -0,854 0,225 -0,376 -0,789 0,282 -0,566
Mortality of population in working age
(number of deaths per 100 thousand people
of the corresponding age)
-0,431 0,666 0,501 0,788 -0,140 0,358 0,197 -0,502 0,194
Morbidity per 1000 people (registered cases
of diseases diagnosed for the first time in
patients’ lives)
0,568 -0,608 0,161 0,514 0,286 -0,292 -0,326 0,214 -0,327
4. Ecological factors
Emissions of pollutants into the
atmospheric air from stationary sources,
1000 tons
0,434 -0,620 -0,078 0,490 -0,250 -0,555 -0,616 0,340 -0,376
Discharge of contaminated sewage into
surface water bodies, mln cubic meters
-0,531 -0,108 0,567 0,589 0,253 -0,096 0,894 0,025 0,246
Source: Authors ' calculations

Drawing on Table 11, it was concluded that predominant factors for choosing the direction of
migration in this group of territories are mainly socio-economic. When assessing the impact
of each group of factors presented, the authors were guided by the following considerations.
Economic and social factors are taken into account as long as one of the following
conditions is fulfilled:
– there are two or more high values of the correlation coefficient (more than 0.45);
– there are three or more low values of the correlation coefficient (less than 0.45);
– there are one high and one low value of the correlation coefficient or even more.
Demographic and environmental factors are taken into account when one of the following
conditions is fulfilled:
– there is one or more high values of the correlation coefficient;
– there is two or more low values of the correlation coefficient;.
Based on the above stated, the authors created a matrix characterizing the significance of
impact made by different groups of factors on the migration attractiveness of "centers of
attraction" for migrants (Fig. 1).
VoronezhRegion
MoscowRegion
YaroslavlRegion
cityofMoscow
KaliningradRegion
LeningradRegion
cityofSaint
Petersburg
RepublicofAdygea
KrasnodarKrai
Economic factors
Social factors
Demographic factors
Ecological factors
Figure1 Matrix showing the influence of factors on the migration attractiveness of the regions
- "centers of attraction" for migrants in CFD, NWFD and SFD (background fill indicate
considerable impact of factors) Source: Developed by the authors
As a result of the research into migration attractiveness of the RF territories, the
authors created a cartographic profile of the country's space (Figure 2).
Figure 2 Cartographic profile of migration attractiveness of the Russian Federation Federal
Districts.

The figure shows the regions under investigation which are classified into groups according to
the number of factors which make a significant impact on residential mobility:
– mono-factor center – the region is regarded as attractive due to the influence of
predominantly one factor (Krasnodar Krai);
– duo-factor center – attractiveness of the territory is primarily determined by the influence of
two factors (Voronezh, Yaroslavl, Leningrad Regions, the city of Moscow, Republic of
Adygea);
– multi-factor center – attractiveness of the territory is accounted for by the influence of three
and more factors (Moscowand Kaliningrad Regions and the city of St. Petersburg).
4. CONCLUSION
The findings of the research made it possible to draw a number of conclusions:
1. The most intensive migration processes are observed in Central, Volga and Siberian
Federal Districts, where the turnover of migration (the total value of arrivals and departures)
exceeds 1 million people per year. However, Siberian, Volga, North-Caucasian, Far-Eastern
and Urals Federal Districts continue to show the negative balance of population migration.
2. Districts with the highest migration attractiveness (which is determined on the basis of
migration attractiveness rate) are divided into two subgroups. The first subgroup with
increasing migration attractiveness comprises Northwestern and Southern Federal Districts.
The second subgroup with decreasing migration attractiveness includes Central Federal
District. Similar classification principle has been applied to districts with low migratory
attractiveness. Despite existing problems, migration attractiveness is on the rise in North
Caucasian, Volga and Far Eastern Federal Districts, but declining in Urals and Siberian
Federal Districts.
3. In migratory attractive regions there are the following "points" or "centers of attraction" for
migrants: 1) in Central FD - Voronezh, Moscow, Yaroslavl Regions, the city of Moscow; 2)
in Northwestern FD – Kaliningrad and Leningrad Regions, the city of St. Petersburg; 3) in
Southern FD - Republics of Crimea and Adygea, Krasnodar Krai and the city of Sevastopol.
4. Migration attractiveness of the regions primarily depends on economic and social factors,
with demographic and environmental factors being less evident.
5. Migration attractiveness in regions - "centers of attraction" for migrants may be created in
the following ways: 1) under the influence of one of the factors discussed in the article, such
regions are referred to as "mono-factor centers" of attraction for migrants; 2) under the
influence of two factors (these regions are referred to as "duo-factor centers"); 3) as a result of
the interaction of many factors (this group includes "multi-factor centers"). The typologies of
regions developed by the authors make it possible to design a selective migration policy at the
regional level and most effectively implement federal and regional migration programs with
due regard to the specific migration characteristics of the region.
ACKNOWLEDGEMENT
The article presents findings of research project No. 17-02-00425-OGN "Interregional
asymmetry of territories and migration mobility of the population in Russia" which received
support from the Russian Foundation for Basic Research as a result of the competitive
selection of scientific projects and the winner of the OGN-A competition - RFBR Main
Competition 2017.

REFERENCES
[1] Ravenstein, E. The Laws of migration, Journal of the Statistical Society, 46, 1885, pp.
167-235.
[2] Everett, S. and Lee, A. Theory of Migration, Demography, 3(1), 1966, pp. 47-57.
[3] Friedmann, J. and Alonso, W. Regional Development as a Policy Issue, Regional
Iwelopment and Planning, Cambridge (Mass.), 1964.
[4] Beglova, E.I., Nasyrova, S.I. and Yangirov, A.V. Factors of Economic Behavior of
Population in Regional Labor Market. European Research Studies Journal, 20(4B), 2017,
pp. 151-159.
[5] Allen, W. M., Hung, S. N. and Leiser, D. Adult economic model and values survey:
Cross-national differences in economic beliefs. Journal of Economic Psychology, 26(2),
2005, pp. 159–185.
[6] Balli, F. and Tiezzi, S. Equivalence scales, the cost of children and household
consumption patterns in Italy. Review of Economics of the Household Rev Econ
Household, 8(4), 2009, pp. 527-549.
[7] Bardi, А. and Schwartz, S. H. Values and behavior: Strength and structure of relations.
Personality and Social Psychology Bulletin, 29, 2003, pp. 1207–1220.
[8] Bonini, N. Introductory article: explaining economic decisions. Mind and society, 8, 2009,
pp. 1–6.
[9] Bönke, T. and Schröder, C. Poverty in Germany – Statistical Inference and
Decomposition. Jahrbücher Für Nationalökonomie Und Statistik, 231(2), 2011, pp. 178-
209.
[10] Bosch, K. V., Callan, T., Estivill, J. et al., A comparison of poverty in seven European
countries and regions using subjective and relative measures. Journal of Population
Economics J Popul Econ, 6(3), 1993, pp. 235-259.
[11] Corazzini, L., Esposito, L. and Majorano, F. Exploring the absolutist vs relativist
perception of poverty using a cross-country questionnaire survey, Journal of Economic
Psychology, Ibidem, November, 2009.
[12] Dalbert, C. and Umlauft, S. The role of the justice motive in economic decision making.
Journal of Economic Psychology, 30, 2009, pp. 172–180.
[13] Fisher, P. and Montalto, C. Effect of saving motives and horizon on saving behaviors.
Journal of Economic Psychology, 31(1), 2010, pp. 92–105.
[14] Hagenaars, A., Vos, K. de and Zaidi, M.A. Poverty Statistics in the Late 1980s: Research
Based on Micro-data, Office for Official Publications of the European Communities,
Luxembourg, 1994.
[15] Chen, Z. Measuring the poverty lines for urban households in China – an equivalence
scale method. China Economic Review, 17(3), 2006, 239-252.
[16] Leiser, D. Azar, O.H. Behavioral economics and decision making: Applying in-sights
from psychology to understand how people make economic decision. Journal of Eco-
nomic Psychology, 29, 2008, pp. 613–618.
[17] Morciano, M., Hancock, R. and Pudney, S. Disability Costs and Equivalence Scales in the
Older Population in Great Britain. Review of Income and Wealth, 61(3), 2014, pp. 494-
514.

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