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10
DATA ARTICLE
INTER-REGIONAL DISPARITIES IN AGRICULTURE AND RURAL POPULATION CHANGE IN RUSSIA
T. Yu. Kuznetsova D
Received 08.06.2022 doi: 10.5922/2079-8555-2022-4-10 © Kuznetsova, T. Yu., 2022
Immanuel Kant Baltic Federal University I 14, A. Nevskogo St, Kaliningrad, 236016, Russia
The article presents data reflecting the territorial peculiarities of rural population dynamics and shows their dependence on external factors (primarily, the development of agriculture). The database includes 14 indicators of the regional spatial differentiation of rural population development in Russia between 2010— 2020. A typology of regions based on eight economic and ecological parameters is provided. The dataset covers the statistical indicators of 85 Russian regions from 2010 to 2020, published by the Federal State Statistics Service and the Unified Interdepartmental Information and Statistics System. The results are presented in seven tables and six maps. The dataset can be used by federal and regional authorities elaborating science-based rural development programmes and strategies, as well as experts on rural development.
Keywords:
rural settlement, production dynamics, inter-regional disparities, typology of regions, Russian Federation
Data characteristics
Subject area Geography, planning and development
Data type Tables Figures
Data collection method The statistical data were obtained from the Unified Interdepartmental Statistical Information System (EMISS) and the Regions of Russia. Socio-economic Indicators official statistics publications, prepared by Russia's federal state statistics service
Data format Raw data Grouped data
Data collection process The data collected include key indicators of settlement, agricultural production and regional employment in Russia. The data were structured by collating statistical information and normalising it by 1,000 population. Changes in the measures were calculated
To cite this article: Kuznetsova, T. Yu. 2022, Inter-regional disparities in agriculture and rural population change in Russia, Balt. Reg., Vol. 14, № 4, p. 162-181. doi: 10.5922/2079-8555-2022-3-10.
BALTIC REGION ► 2022 ► Vol.14 ► №4
Data source location Central federal district (18 regions): Belgorod region, Bryansk region, Vladimir region, Voronezh region, Ivanovo region, Kaluga region, Kostroma region, Kursk region, Lipetsk region, Moscow region, Oryol region, Ryazan region, Smolensk region, Tambov region, Tver region, Tula region, Yaroslavl region, Moscow; Southern federal district (eight regions): Republic of Adygea, Republic of Kalmykia, Republic of Crimea, Krasnodar Krai, Astrakhan region, Volgograd region, Rostov region, Sevastopol; Northwestern federal district (11 regions): Republic of Karelia, Republic of Komi, Arkhangelsk region, Vologda region, Kaliningrad region, Leningrad region, Murmansk region, Novgorod region, Pskov region, Nenets Autonomous Okrug, St. Petersburg; Far Eastern federal district (nine regions): Republic of Sakha (Yakutia), Kamchatka Krai, Primorsky Krai, Khabarovsk Krai, Amur region, Magadan region, Sakhalin region, Jewish autonomous region, Chukot-ka Autonomous Okrug; Siberian federal district (12 regions): Republic of Altai, Republic of Buryatia, Republic of Tuva, Republic of Khakassia, Altai Krai, Transbaikal Krai, Krasnoyarsk Krai, Irkutsk region, Kemerovo region, Novosibirsk region, Omsk region, Tomsk region; Ural federal district (six regions): Kurgan region, Sverdlovsk region, Tyumen region, Chelyabinsk region, Khanty-Mansi Autonomous Okrug — Yugra, Yamal-Nenets Autonomous Okrug; Volga federal district (14 regions): Republic of Bashkortostan, Republic of Mari El, Republic of Mordovia, Republic of Tatarstan, Republic of Udmurtia, Republic of Chuvashia, Kirov region, Nizhny Novgorod region, Orenburg region, Penza region, Ulyanovsk region, Samara region, Saratov region, Perm Krai; North Caucasus federal district (seven regions): Republic of Dagestan, Republic of Ingushetia, Republic of Kabardino-Balkaria, Karachay-Cherkessia Republic, Republic of North Ossetia — Alania, Republic of Chechnya, Stavropol Krai
Data availability The data are also available on Mendeley Data: Kuznetsova, Tatyana (2022), A regional-level database of rural population and agriculture in Russia, Mendeley Data, Vol. 2, doi: 10.17632/t286xfwmj6.2
Value of data
Rural areas across the world develop at different speeds. This has been linked in the literature to the national economic and political transformations [1], the state of infrastructure and market accessibility [2], natural and migration population change [3; 4] and the principal economic activity in the study area [5].
In Russia, rural development disparities are enormous. There are significant differences in settlement characteristics: population density, the share of the rural population and the population per village ratio. The economic and social indicators of agricultural development vary by region. The size and geographical features of Russia's territory, and the history of its exploration and development also have a role here. Tatyana Nefedova has categorised the factors at play into seven groups: a vast territory, diverse natural conditions, a sparse city network,
incomplete urbanisation, the vagaries of history, a centralised economy and social inequality [6]. She concludes that the key to the spatial reformatting of rural areas is their position along the 'north-south' and 'suburb-periphery' axes [7, p. 52].
Since rural areas develop under disparate conditions, different approaches should be applied to their study and management [8; 9]. The database presented in the study covers a range of indicators for measuring disparities in the development of rural population at a regional level. Linking the inequalities to the peculiarities of agricultural production and employment, this database may benefit rural development experts and the authorities in devising science-based programmes and strategies for rural development.
Methods
Russian official statistics publications containing information on rural population density, rural population as per cent of the total national population, the average number of villages and agricultural output were used to create a list of statistical indicators of settlement and socio-economic development of rural areas [10]. The data on the rural population employed in agriculture were obtained from the Unified Interdepartmental Statistical Information System for agriculture (EMISS) [11]. Growth and correlation coefficients were calculated to track changes in settlement indicators occurring in response to rural socio-economic processes.
Data description
The data cover 85 Russian regions for 2020. When comparing the change between 2010 and 2020 values, the Republic of Crimea and Sevastopol were left out, as comparable data are unavailable.
Table 1 shows the data used in the database.
Table 1
Measures of rural population development by region
Measure Calculation method Data source
Annual average population, 1,000 people Raw data Annual average resident population, EMISS, 2022, URL: https:// www.fedstat.ru/indicator/31556
Annual average rural population, 1,000 people Raw data Annual average resident population, EMISS, 2022, URL: https:// www.fedstat.ru/indicator/31556
Rural population as % of the regional population, 2020 Calculated as the ratio between the annual average rural population and the total annual average population Annual average resident population, EMISS, 2022, URL: https:// www.fedstat.ru/indicator/31556
Rural population as % of the regional population, 2020 Calculated as the ratio between the annual average rural population and the total annual average population Annual average resident population, EMISS, 2022, URL: https:// www.fedstat.ru/indicator/31556
The continuation of the Table 1
Measure Calculation method Data source
Annual average number of people employed in agriculture, forestry, hunting, fishing and fishery, people Raw data Annual average employment (calculated based on data integration) since 2017, EMISS, 2022, URL: https://www.fedstat. ru/indicator/58994
Average rural population per village ratio, 2020, people Calculated as the ratio between the annual average rural population and the number of villages (national census) Number of municipalities, inner-city districts, city districts, inter-settlement territories and settlements, All-Russian Population Census 2020, Rosstat, 2022, URL: https://rosstat.gov. ru/vpn_popul; Annual average resident population, EMISS, 2022, URL: https:// wwwfedstat.ru/indicator/31556
Population change, 2020, % of the 2010 value (as of the beginning of the year) Calculated as the ratio between the annual average population in 2020 and the national population in 2010 Annual average resident population, EMISS, 2022, URL: https:// wwwfedstat.ru/indicator/31556
Rural population change, 2020, % of the 2010 value (as of the beginning of the year) Calculated as the ratio between the annual average rural population in 2020 and the rural population in 2010 Annual average resident population, EMISS, 2022, URL: https:// wwwfedstat.ru/indicator/31556
Value added in agriculture, 1,000 roubles Raw data Gross regional product in basic prices (OKVED 2) in agriculture, forestry, hunting, fishery and fishing, EMISS, 2022, URL: https://www.fedstat.ru/indica-tor/61497
Value added in agriculture per a rural resident, 2019 Calculated as the ratio of gross regional product in basic prices (OKVED 2) in agriculture, forestry, hunting, fishery and fishing to the annual average rural population Gross regional product in basic prices (OKVED 2) in agriculture, forestry, hunting, fishery and fishing, EMISS, 2022, URL: https://www.fedstat.ru/indica-tor/61497 Annual average resident population, EMISS, 2022, URL: https:// www.fedstat.ru/indicator/31556
Agricultural output per a rural resident, 2020 Calculated as the ratio between agricultural production across all categories in actual prices and the annual average rural population Agricultural output in actual prices (final data), EMISS, 2022, URL: https://www.fedstat.ru/in-dicator/43337; Annual average resident population, EMISS, 2022, URL: https:// www.fedstat.ru/indicator/31556
The end of Table 1
Measure
Calculation method
Data source
Agricultural output per person employed in agriculture, 2020
Calculated as the ratio between agricultural output across all categories in actual prices and the annual average number of those employed in agriculture, forestry, hunting, fishing and fishery
Agricultural output in actual prices (final data), EMISS, 2022, URL: https://www.fedstat.ru/in-dicator/43337;
Annual average employment (calculated based on data integration) since 2017, EMISS, 2022, URL: https://www.fedstat. ru/indicator/58994_
Contribution of the region to agricultural output, 2010, %
Calculated as the ratio between regional agricultural output across all categories in 2010 in actual prices and the national average
Agricultural output in actual prices (final data), EMISS, 2022, URL: https://www.fedstat.ru/inr dicator/43337_
Contribution of the region to agricultural output, 2020, %
Calculated as the ratio between agricultural output across all categories in 2020 in actual prices in the region and the national average_
Agricultural output in actual prices (final data), EMISS, 2022, URL: https://www.fedstat.ru/in-dicator/43337
Change in the regional contribution to the total agricultural output, 2010 — 2020, percentage points_
Calculated as the difference between regional contribution to the total agricultural output in 2020 and 2010
Agricultural output in actual prices (final data), EMISS, 2022, URL: https://www.fedstat.ru/in-dicator/43337
Agricultural output per person employed, % of the national average, 2020
Calculated as the ratio between agricultural output per person employed in the region and the national average
Agricultural output in actual prices (final data), EMISS, 2022, URL: https://www.fedstat.ru/in-dicator/43337
Annual average employment (calculated based on data integration) since 2017, EMISS, 2022, URL: https://www.fedstat. ru/indicator/58994_
Agricultural production per capita, 2020 % of the national average
Calculated as the ratio between agricultural output per capita in the region and the national average
Agricultural output in actual prices (final data), EMISS, 2022, URL: https://www.fedstat.ru/in-dicator/43337;
Annual average resident population, EMISS, 2022, URL: https:// www.fedstat.ru/indicator/31556
Those employed in agriculture as % of the total rural population, 2020,
Calculated as the ratio between those employed in agriculture, forestry, hunting, fishing and fishery and the total rural population
Annual average employment (calculated based on data integration) since 2017, EMISS, 2022, URL: https://www.fedstat. ru/indicator/58994; Annual average resident population, EMISS, 2022, URL: https:// www.fedstat.ru/indicator/31556
The Appendix contains a database of the absolute and relative measures regarding settlement, rural population employed in agriculture and agricultural output by Russian regions between 2010 and 2020. Fig. 1 shows key parameters of rural settlement as of 2020 are rural population density, rural population as per cent of the total population and the population per village ratio.
Fig. 1. Spatial features of rural settlement in Russia, 2020 Prepared based on data from [10].
The central factor in rural settlement is favourable farming conditions. Spearman's rank correlation coefficient between the annual average temperature in the administrative centre of a region and population density is 0.67; between the share of rural population and the average annual temperature, 0.51. The average population per village ratio is also affected by natural conditions: smaller settlements are usually found in non-Black Earth regions where croplands have sinuous contours and pre-Soviet settlement patterns dominate. The southern steppe regions of the country with regular cropland contours and the eastern territories, where villagers are often involved in non-agricultural pursuits, have larger settlements. In most of the northern and eastern regions, the proportion of rural population is low and so is its density (less than 1 person per km2). In the north of European Russia, the situation is further complicated by a sparse population of local settlements and the resultant inadequate transport and social infrastructure. Although in the east, the population per village ratio is relatively high, rural settlements are still not sufficiently large to provide services of a quality comparable to that available in usually remote cities. The correlation coefficient between average population density and mean annual temperature is 0.52, compared to 0.62 for regions with more than one inhabitant per km2.
Almost all non-Black Earth regions of Central Russia, as well as the southern territories of Western Siberia and the Far East, have a rural population density in the range of 1 — 10 people per km2. The proportion of rural population is either low or close to the national average, except in the Republics of Kalmykia, Altai
and Buryatia, where it is rather high. There is a preponderance of smaller rural settlements in European Russia (albeit medium size villages are prevalent along the Volga River) and larger ones in the Asian part of the country. A peculiar is the Leningrad region, which is technically independent of St. Petersburg, but comprises with it a single territorial system.
The regions with the highest rural population density (10—75 people per km2) are in Black-Earth Central Russia, the Middle Volga area, the North Caucasus region and the western part of the Southern federal district. Most of these territories have a high proportion of rural population. The exceptions are the highly urbanised Kaliningrad region, Moscow, Vladimir and Tula regions, the latter three strongly influenced by the Moscow agglomeration. The density of rural settlements in the southern regions is high and decreases northwards.
Rural population change
Russian regions also differ substantially in rural population change. In 2010— 2020, the Republic of Adygea witnessed a 16 % increase in the rural population; the Republic of Karelia and the Kirov region, a 27 % reduction. Fig. 2 shows population change in regions differing in rural population density. As can be seen from the figure, rural population grew in the metropolitan Moscow and Leningrad region, three rapidly developing highly urbanised regions in Central Russia (Kaliningrad, Kaluga and Samara), Krasnodar Krai, several North Caucasus republics (except North Ossetia, whose rural population diminished), the Republics of Altai and Sakha (Yakutia), Yamal-Nenets Autonomous Okrug. In the republics, the growth is due to natural increase and/or a continually high population replacement rate; in the other regions, to a positive net migration rate. The most rapid decline in rural population was taking place in the north of European Russia, as well as some regions of the country's Far East and Southern Ural,
■I .ft X.
I
1
A
p^v-J \
Fig. 2. Differences between Russian regions in rural population change and density Prepared based on [10; 11].
Fig. 3 shows that rural population declined in most regions that have a rural population density of about the national average, regardless of the degree of urbanisation.1 The reduction is due to migration from villages to towns, interregional population redistribution and age structure peculiarities.
80
70
60
50
Republic of Altai
Je 40
30
Republic of ^Chechnya
Republic of AT Dagestan A- Republic of Adygea,
20
ft Repulic of Tuva • ▲ A a Republicof" Al<abardi_noßalka Krasnodar Krai ria •
v. vV ▲
- 4 * ^ # ^ A Tula region ^ • A Kaliningead region
_ — odiiidi d i egiun f," ® Moscow region
^ Murmansk region
10 20 30 40 50 60
Rural population density, people/km2
70
90
Symbols Rural population, 2020, % of Symbols Rural population, 2020, % of 2010 values 2010 values
^ 105.0-119.9 ^ 95.0-99.9
0 100.0-104.9 # 80.0-94.9
+ 70.0-79.9
Fig. 3. The distribution of Russia's regions according to some measures of agricultural development pace and rates, 2015-2019 average, % of the national average
Prepared based on data from [10; 11].
1 There is a direct correlation between the density of a population and its contribution to the total national population, as the trend line in Fig. 3 demonstrates.
A higher proportion and density of rural population is associated with population growth, which is the case in Russia's southern regions (Fig. 3, top right). Amongst the regions that have a low density but a high proportion of rural population, the number of rural residents increased in the Republic of Altai and Sakha (Yakutia).
In highly urbanised and densely populated regions, such as the Moscow and Leningrad region, suburbanisation stimulates rural population growth. These processes were also taking place in the Kaliningrad and Samara regions, as well as Udmurtia.
Rural population change
and spatial features of agriculture
There is no apparent direct connection between the change in a region's contribution to agricultural output and rural population change (Fig. 4). Therefore, it would be false to claim that population drift from rural areas has a markedly negative effect on agricultural output. In other words, Russian regions with a similar population change rate can perform differently in terms of agricultural production.
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Change in the contribution to national agricultural output, 2010-2020, percentage points
Fig. 4. Distribution of Russia's regions according to rural population change and change in their
Table 3 shows Russian regions grouped according to the two measures. As can be seen, only some regions with a growing population became more visible in national agricultural output. And the contribution of economically prosperous territories with a growing population, such as the Moscow and Leningrad regions and Krasnodar Krai, diminished, the latter having extremely favourable conditions for agriculture. The opposite change occurred in the south-west of European Russia, i.e., in the regions located in the fertile Black Earth zone and outstripping other territories in agricultural output per rural resident against the background of a declining population (Fig. 5). The contribution of some agriculturally developed and densely populated republics of North Caucasus decreased.
Table 3
Russian regions grouped according to their contribution to agricultural output and rural population change
(1)* Rural population, 2020, % of 2010 values
70.0-79.9 80.0—89.9 90.0 — 99.9 100.0 — 115.9
0.50-1.39 — Kursk, Voronezh, Tambov, Penza, Oryol regions Belgorod, Lipetsk, Rostov regions —
0.20-0.49 — Ulyanovsk, Volgograd, Bryansk regions Ryazan, Orenburg, Saratov regions Republic of Dagestan, Tula, Samara regions
0.0-0.19 Chukotka Autonomous Okrug Republics of Mordovia, Mari El, Nenets Autonomous Okrug Republics of Ta-tarstan, Ingushetia; Astrakhan, Amur, Kaluga regions Republic of Chechnya, Republic of Ady-gea, Leningrad region
- 0.09- 0.01 Arkhangelsk, Magadan regions Nenets Autonomous Okrug, Tver, Sakhalin, regions, Republic of Karelia Khanty-Mansi Autonomous region — Yugra, Kamchatka Krai, Republics of Khakass-ia, Kalmykia, Novgorod region Yamal-Nenets Autonomous Okrug, Republic of Kabardino-Balkaria, Republics of Altai, Tuva
- 0.19- 0.10 Kirov region Pskov, Ivanovo, Vologda, Kurgan regions, Jewish autonomous region, Republic of Chuvashia, Republic of Komi Republics of Buryatia, North Ossetia — Ala-nia, Karachay-Cherkessia Republic, Primor-sky, Transbaikal Krai, Smolensk, Tomsk, Nizh-ny Novgorod, Vladimir regions Yaroslavl region
The end of Table 3
(1)* Rural population, 2020, % of 2010 values
70.0-79.9 80.0-89.9 90.0-99.9 100.0-115.9
- 1.3 — - 0.21 — Kostroma region, Altai Krai Kemerovo, Omsk, Sverdlovsk, Tyumen, Chelyabinsk, Novosibirsk regions, Republic of Bashkortostan, Perm, Krasnoyarsk, Khabarovsk, Stavropol Krais, Republic of Sakha (Yakutia) Murmansk, Moscow, Irkutsk, Kaliningrad regions, Republic of Udmurtia, Krasnodar Krai
Comment: (1*) change in the region's contribution to national agricultural output, 2010-2020.
Prepared based on data from [10; 11].
A common trend is the concentration of agricultural production in regions with a higher per capita output (Fig. 5).
Fig. 5. The distribution of regions according to per capita agricultural output and change therein.
Agricultural production grew most rapidly in central Black Earth regions with the highest per capita rates (top right in Fig. 5). This is explained by their contribution to the national output increasing faster than in other territories. In the bottom left, there are regions performing the most poorly on per capita output and production development. These are the Moscow region, where most of the population is engaged in industries other than agriculture, as well as Russia's northern and eastern territories.
As can be seen in Fig. 6, the contribution of a vast majority of Russia's northern and eastern regions to national agricultural output declined in 2010—2019. Most of these territories lag behind the national average as regards output per capita and per person employed. Yet, stronger performance on both indicators does not immediately translate into output growth above the national average. Rural population is declining everywhere in the north and east of Russia, except the Republics of Sakha (Yakutia) and Altai (due to a high birth rate) and Yamal-Nenets Autonomous Okrug (where those employed in agriculture account for only 9 % of the rural population, the lowest percentage across the country).
The contribution to agricultural output of some southern regions with a growing population decreased as well. In most non-Black Earth regions, this reduction occurs against the backdrop of a rapid decline in the rural population.
The contribution to agricultural output increased not only in Black Earth regions proper but also in some of the neighbouring ones. All these regions are leaders in per capita agricultural output, whilst their rural population is declining.
An increase in this measure was also observed in regions where conditions are relatively favourable for agriculture. These are territories in the Middle and Southern Volga area, Southern Ural, the south of Central Russia, and the Kaliningrad and Pskov regions.
□ 0-49.9
H 50.0-99.9 y
Fig. 6. Some indicators of agriculture development and rural population change n Russian regions
Russian regions were divided into seven groups according to the features of rural population change, rural settlement and agriculture development.
The first three groups bring together 15 Russian regions with a growing rural population (Table 4). The groups differ markedly in settlement indicators, characteristics of agriculture development and the role natural increase and migration have in population change. Let us now consider them in detail.
Table 4
Regions with a growing rural population (2010 — 2020)
Region (17) Measure*
1 2 3 4 5 6 7 8
National average 98.5 2.2 25.3 241.9 100 100 12.3 100
1
I.A. Moscow region 101.4 32.1 18.5 243.9 88.4 45.8 6.4 - 1.20
I.A. Leningrad region 105.2 7.4 32.7 214.9 108.3 90.2 10.2 - 0.46
1.B. Kaliningrad region 106.4 15.0 22.3 210.6 150.8 116.7 9.5 0.07
2.1
2.I.A. Tula region 115.3 14.3 25.2 106.6 171.9 144.8 10.4 0.43
2.I.B. Republic of Udmurtia 107.4 12.0 33.8 259.2 130.3 80.8 7.6 - 0.23
2.I.B. Yaroslavl region 100.9 6.4 18.4 38.2 68.3 98.1 17.7 - 0.10
2.2
Samara region 100.6 12.0 20.2 489.9 107.9 111.2 12.7 0.38
2.3
Krasnodar Krai 102.7 33.5 44.5 1465.4 136.4 98.1 8.8 - 0.78
Republic of Adygea 113.4 31.4 52.8 1073.3 131.9 74.5 6.9 0.04
3
Republic of Dagestan 107.4 33.9 54.7 1065.1 48.4 52.0 13.2 0.47
Republic of Kabardino-Balkaria 106.7 33.3 48.0 2422.6 58.6 81.4 17.1 - 0.04
Republic of Chechnya 114.9 59.7 62.5 2607.3 25.9 24.5 11.6 0.17
4.1
Republic of Altai 104.7 1.7 70.8 634.5 64.0 43.8 8.4 - 0.06
Republic of Tuva 103.4 0.9 45.7 1043.5 84.8 30.8 4.5 - 0.05
The end of Table 4
Region (17) Measure*
1 2 3 4 5 6 7 8
4.2
Yamal-Nenets Autonomous Okrug 109.4 0.1 16.1 1109.5 37.7 20.1 6.6 - 0.01
Irkutsk region 105.0 0.7 22.1 358.0 68.7 73.3 13.1 - 0.4
Murmansk region 100.6 0.4 7.8 530.6 11.9 16.9 17.5 - 0.42
Comment: *the key:
1 — rural population change, 2020, % of 2010;
2 — rural population density, people per km2, 2020;
3 — rural population as % the total national population, 2020;
4 — average population per village ratio, people, 2020;
5 — output per person employed in agriculture, % of the national average, 2020 average;
6 — agricultural production per capita, % of the national average, 2020 average;
7 — the ratio between the number of people employed in agriculture and rural population, %, 2020;
8 — change in the contribution to national agricultural output, 2010 — 2020, percentage points
Prepared based on data from [10; 11].
Type 1 is represented by two metropolitan regions (Moscow and Leningrad) and the Kaliningrad region, whose rural population increases due to suburbanisation, whilst the rate of growth in agriculture is either at or below the national average.
Type 2, represented by developed regions of Central Russia with a growing rural population, includes three subtypes:
2.1 — highly urbanised industrial-agrarian regions of non-Black Earth Russia where rural population increases due to the administrative transformation of urban settlements into rural ones with the rate of growth in agriculture above (2.1. A) and below (2.1 B) the national average;
2.2 — the Samara region, which, very much like southern Black Earth regions, has large rural settlements. Its agricultural output per person employed and per capita is above the national average. The region's contribution to agricultural output increased over the study period;
2.3 — Krasnodar Krai and the Republic of Adygea, which have large rural settlements, a high rural population density, a significant proportion of rural residents in the total population and an agricultural output per capita and per person employed above the average. The contribution of these regions to the total output either reduced (Krasnodar Krai) or remained unchanged (Republic of Adygea).
Type 3 is represented by the republics of North Caucasus with large rural settlements and a high proportion and density of rural population. Output per person employed is below the national average. An increase in the rural population is due to a high birth rate and a low mortality rate (life expectancy in the territories is above that of an average Russian region). The contribution of Dagestan and Chechnya to the national agricultural output grew over the study period.
Type 4 brings together sparsely populated eastern regions with large rural settlements and the proportion of the rural population either high (4.1) or low (4.2). These regions have poor conditions for agriculture; the ratio between the number of people employed in agriculture and the rural population is low (which is especially true of subtype 4.2). Agricultural output per person employed and per capita is below the national average.
Tables 5 — 7 describe the characteristics of regions with a falling rural population. The types and subtypes are identified based on the same measures as used in Table 4.
Table 5
Regions with a 10 % reduction in rural population (2010 — 2020)
Region (36) Measure*
1 2 3 4 5 6 7 8
5
Republic of North Ossetia — Alania 96.0 31.0 35.7 1153.6 92.3 74.4 9.9 - 0.19
Karachay-Cherkessia Republic 98.9 18.6 57.1 1940.2 96.1 68.3 8.7 - 0.12
Republic of Ingushetia 92.2 63.0 44.3 1937.0 26.5 29.4 13.7 0.05
6.1
Stavropol Krai 95.9 17.3 40.9 1558.7 71.4 91.9 15.8 - 0.45
Astrakhan region 99.3 6.8 33.4 797.6 66.3 95.1 17.6 0.05
6.2
Belgorod region 96.5 18.5 32.5 319.0 203.4 329.8 19.9 0.50
Lipetsk region 93.9 16.7 35.4 250.5 197.1 233.8 14.6 1.12
Republic of Tatar-stan 96.3 13.3 23.1 292.5 130.5 168.2 15.8 0.10
Rostov region 94.8 13.2 31.8 588.2 108.4 142.2 16.1 0.70
Ryazan region 90.8 7.7 27.8 111.6 229.2 160.7 8.6 0.28
Kaluga region 98.8 8.1 24.2 76.0 158.3 128.7 10.0 0.03
Saratov region 90.9 5.8 24.3 329.8 163.0 185.4 14.0 0.21
Orenburg region 92.9 6.2 39.3 448.2 89.0 106.0 14.6 0.25
7.1
Republic of Bashkortostan 93.7 10.6 37.5 332.7 123.5 72.8 7.2 - 0.53
Tyumen region without autonomous okrugs 93.6 3.1 32.4 405.1 129.2 85.1 8.1 - 0.46
The end of Table 5
Region (36) Measure*
1 2 3 4 5 6 7 8
7.2
Chelyabinsk region 95.1 6.8 17.3 479.6 91.4 108.7 14.6 - 0.62
Novosibirsk region 95.2 3.3 20.8 383.2 106.7 105.0 12.1 - 0.29
7.3
Vladimir region 90.7 10.1 21.8 118.0 76.4 64.0 10.3 - 0.19
Nizhny Novgorod region 92.1 8.4 20.3 135.7 99.2 74.5 9.2 - 0.17
Smolensk region 96.2 5.2 28.1 53.7 87.5 58.8 8.3 - 0.13
Novgorod region 90.2 3.1 28.4 45.6 91.4 87.2 11.7 - 0.08
8.1
Tomsk region 95.7 0.9 27.8 522.4 104.2 66.4 7.8 - 0.17
Kamchatka Krai 93.7 0.1 21.4 826.5 43.2 85.7 24.4 - 0.01
Krasnoyarsk Krai 95.2 0.3 22.4 384.3 78.0 94.6 14.9 - 0.65
Khanty-Mansi Autonomous Okrug 95.4 0.2 7.4 811.7 55.6 47.2 10.4 - 0.07
Republic of Yakutia (Sakha) 96.3 0.1 33.8 576.1 57.7 46.0 9.8 - 0.28
Khabarovsk Krai 95.5 0.3 17.9 568.5 45.8 44.4 11.9 - 0.32
Primorsky Krai 90.9 2.6 22.6 687.2 39.1 59.8 18.8 - 0.15
Amur region 91.4 0.7 32.2 420.5 167.4 122.0 9.0 0.03
8.2
Republic of Kha-kassia 93.0 2.6 30.1 592.3 78.0 50.8 8.0 - 0.07
Republic of Bury-atia 99.1 1.1 40.8 654.3 46.7 24.3 6.4 - 0.15
Perm Krai 94.1 3.9 24.1 174.4 74.1 45.8 7.6 - 0.37
Omsk region 91.4 3.7 27.1 352.5 69.1 111.7 19.9 - 0.59
Kemerovo region 90.8 3.9 13.9 346.3 132.6 94.1 8.7 - 0.36
Sverdlovsk region 92.2 3.3 14.9 362.9 123.7 83.4 8.3 - 0.36
Republic of Kalmykia 90.3 2.0 54.0 557.3 106.3 108.6 12.6 - 0.03
Comment: * see Table 4 for the key.
Prepared based on data from [10; 11].
Table 5 describes types 5 — 9, where rural population decreased by 10 % or less in 2010—2020.
The republics of North Caucasus (Type 5) are less urbanised than an average Russian region. They also stand out for a high density of rural population and large rural settlements. Output per person employed and per capita is above the national average.
Type 6 regions also have a high proportion and density of rural population. Rural settlements are rather large as well. Output per person employed is below the national average for type 6.1 and above it for type 6.2. Agricultural output per capita and production growth rate are above those in an average Russian region.
Type 7 regions, on the contrary, have lower rates of growth in agriculture: in 2010—2020, their contribution to the national output decreased. Type 7.1. territories are less urbanised; just like subtype 7.2 regions, they have larger-sized rural settlements. Agricultural output per capita, as well as production per person employed , is rather high. In subtype 7.2 and 7.3 regions, the degree of urbanisation is higher, and output per person employed and per capita is lower. In subtype 7.3 regions, rural settlements are usually small-sized.
Type 8 regions have a low population density; output per person employed and per capita is below the national average, with the exception of the Omsk and Kemerovo regions; production growth rates are below those in an average Russian region, the only exception being the Amur region.
Table 6 describes regions that experienced a 10-20 % population decline in 2010—2020. Amongst them, only the Republic of Chuvashia (type 10) is a developed agrarian region: its rural population density is above 24 people per km2, with rural residents accounting for 36.6 % of the total population. Yet, in the region, output per person employed and per capita is below the national average. The contribution to the national agricultural output decreased in Chuvashia over the study period, just as it did in type 12, 13 and 14 regions, with the exception of the Pskov region (subtype 13.1). The highest production growth rates are characteristic of subtype 11.1, whose regions are the most agriculturally developed, boasting an output per person employed and per capita above the national average. Yet, the ratio between the number of people employed in agriculture and the rural population is higher for subtype 11.2, which increased its contribution to the national output over the study period. At the same time, these regions lag behind subtype 11.1 and the national average in terms of output per person employed .
Table 6
Regions experiencing a 10—20 % reduction in the rural population (2010 — 2020)
Region (24) Measure*
1 2 3 4 5 6 7 8
10
Republic of Chuvashia 85.3 24.2 36.5 257.2 68.0 58.1 10.5 -0.16
11.1
Penza region 87.8 9.3 31.0 290.6 163.2 183.8 13.8 0.90
Kursk region 87.5 11.5 31.4 124.6 232.2 320.4 17.0 1.39
Oryol region 88.9 9.8 33.3 83.0 280.6 249.8 10.9 0.56
Voronezh region 87.8 14.2 32.0 436.5 141.6 202.6 17.6 1.30
Tambov region 85.1 11.2 38.5 248.0 124.2 253.7 25.1 1.27
Bryansk region 88.6 10.1 29.6 134.8 142.6 158.7 13.7 0.45
11.2
Republic of Mari El 86.1 9.5 32.7 138.9 116.9 114.4 12.0 0.05
The end of Table 5
Region (24) Measure*
1 2 3 4 5 6 7 8
Republic of Mordovia 85.2 10.8 36.1 228.7 77.4 157.2 24.9 0.15
Ulyanovsk region 85.5 7.9 24.0 303.0 103.9 110.7 13.1 0.21
Volgograd region 89.3 5.0 22.6 385.7 91.7 182.4 24.4 0.32
12.1
Altai Krai 89.9 5.9 42.9 624.1 102.9 94.4 11.3 - 0.67
Kurgan region 85.1 4.3 37.7 254.6 111.4 83.2 9.2 - 0.10
13.1
Pskov region 89.4 3.3 29.1 21.7 118.6 156.0 16.2 - 0.17
13.2
Kostroma region 84.5 2.8 27.1 49.6 88.0 61.2 8.6 - 0.22
Vologda region 88.6 2.2 27.3 40.2 71.0 65.3 11.3 - 0.19
Tver region 86.6 3.5 23.8 31.3 63.7 75.9 14.6 - 0.09
Ivanovo region 89.1 8.5 18.2 60.0 87.1 61.2 8.6 - 0.10
Kirov region 78.7 2.3 22.0 66.1 68.7 101.3 18.1 - 0.14
14
Transbaikal Krai 88.3 0.8 31.7 399.1 45.9 40.2 10.6 - 0.19
Sakhalin region 84.4 1.0 17.6 384.5 60.2 92.9 19.0 - 0.06
Jewish autonomous region 86.5 1.4 31.5 501.3 64.6 50.8 9.7 - 0.14
Republic of Komi 84.6 0.4 21.7 247.4 49.0 39.1 9.8 - 0.10
Republic of Karelia 81.1 0.6 18.9 140.8 27.6 25.8 11.5 - 0.08
Comment: * see Table 4 for the key.
Prepared based on data from [10; 11].
Table 7 includes northern and eastern regions with a low population density and agricultural production rates about the national average.
Table 7
Regions experiencing a 20-30 % reduction in the rural population (2010—2020)
Region (3) Measure*
1 2 3 4 5 6 7 8
15
Chukotka
Autonomous 79.9 0.1 28.7 376.3 61.9 64.5 12.8 0.00
Okrug
Nenets
Autonomous 84.0 0.2 26.0 280.7 43.1 38.6 11.0 - 0.01
Okrug
Magadan region 74.6 0.3 3.9 116.3 82.1 293.0 43.9 - 0.02
Comment: * see Table 4 for the key.
Conclusion
The rural population is declining in Russia. Economic realities and concentration effects cause agricultural production and the rural population to converge on southern and metropolitan regions, which have favourable natural and socioeconomic conditions. The patterns of settlement and spatial organisation of production change differently in regions of disparate socio-economic types having unique agrarian production and rural settlement features.
The research was carried out with the financial support of the RFBR grant №20-55-76003 "Social innovations and increasing the value of the area in rural regions".
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The author
Dr Tatyana Yu. Kuznetsova, Leading Research Fellow, Immanuel Kant Baltic Federal Univetsity, Russia.
E-mail: TIKuznetsova@kantiana.ru
https://orcid.org/0000-0002-1523-2280
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