When the Soviet Union collapsed, the Russian Far North began to decline. The state's interest in this area diminished and it became unattractive for permanent residence, as the extremely harsh natural and climatic conditions were accompanied by a very sharp and severe decline in the living standards and the breakdown of the economies of the northern regions. As a result, the population inflows within the framework of planned migration turned into chaotic outflows. Most northern regions are still losing people due to migration, though at a lower rate than in the 1990s. As a result, now 70 percent of the country's territory is inhabited by less than 10 percent of its population. Since the departure to the materik (“mainland,” meaning central and western Russia) of all those who wanted to and could afford it, the dominant form of the population's spatial movement in the Russian Far North has become intraregional (internal) migration, that is, movements over relatively short distances. The Republic of Sakha (Yakutia) is no exception (Sukneva 2008).
The large share of internal migration is not necessarily detrimental to regional development. However, given the considerable number of people living in the republic and the concentration of the population in the regional capital, which is the most developed municipal unit, this direction of population movement deserves special attention. The fact that demographic disparities within a Russian region can be significant (this is true for Yakutia as well) is another reason to focus on internal migration. Questions arise about their determinants and whether they can be influenced by public policies.
The main theories for the international migration prioritize economic factors over others (Harris and Todaro 1970; Lee 1966; Piore 1979; Sjaastad 1962; Stark 1978, 1991; Todaro 1969). But economic factors are also essential for internal migration, a fact confirmed by the Soviet experience (Buckley 1995; Grandstaff 1975; Mitchneck 1991), especially with regard to labor migration (Shubkin 1970; Zaslavskaia 1970; Zhuchenko and Steshenko 1972). The transition to a market economy has increased their importance (Andrienko and Guriev 2004; Brown 1997; Fidrmuc 2004; Gerber 2006; Vakulenko 2019; Vakulenko and Mkrtchyan 2020). Studies on Yakutia confirm these findings, especially for the Arctic municipalities (Barashkova et al. 2022; Mostakhova and Tumanova 2009; Sukneva and Laruelle 2019; Sukneva and Trubina 2009; Tomaska 2018).
Social factors are also important for internal migration, especially in the Far North. Thus, the reasons for population departures from small settlements in the Arctic include economic (“shutdown” and scarcity of jobs, high unemployment, the small size of local markets, low wages with high costs of goods and services), social (low standard of living, poor quality of social infrastructure, limited access to tertiary services), poor transport connections, and extremely harsh natural and climatic conditions (Bogdanova et al. 2022; Huskey 2009; Savvinova et al. 2021; Tomaska 2020). As early as the beginning of the 1970s, there was a clear understanding that it was unrealistic to attract a highly qualified workforce to Siberia and the Russian Far East solely through economic means (Zhuchenko and Steshenko 1972: 43). The large population outflow was caused primarily by the lack of social infrastructure, housing, and utilities (Gonina 2016; Kalemeneva 2017; Karpov and Iudakova 2015).
Among Russia's Arctic regions, the Republic of Sakha (Yakutia) is of considerable interest to researchers because of its vast size. Articles focusing on internal migration provide a detailed analysis of the existing trends but suffer from several shortcomings: They are not recent publications (Sukneva and Trubina 2009), are limited to a few years (Sukneva and Trubina 2011), or concern only Arctic municipal districts of the republic (Kovaleva 2021; Savvinova et al. 2021). However, the literature has not thoroughly discussed the recent trends in the rest of the region, except for Yakutsk, because these areas are not among the strategic territories mentioned in legislative acts. Therefore, this study aims to fill this gap by examining Yakutia as a whole, taking into account its unique economic and climatic characteristics.
Not surprisingly, the capital city Yakutsk receives particular attention. Since a large city has more opportunities for favorable development, the population tends to move to the capital within the region (Mkrtchian 2017). It has better infrastructure, higher living standards, and a larger labor market (Fedorova and Ponomareva 2014). But the case of Yakutsk is far more curious than it appears at first glance. It is the only large city in the Russian north (excluding the Yamalo-Nenetskii Autonomous Okrug) whose population has grown significantly in the post-Soviet period (Sukneva and Laruelle 2019). It has also avoided the risk of becoming a monocity because of its distance from major extractive centers and now has a fairly diversified economy with a developed service sector. However, the rest of the republic has been depopulated by mass migration to the capital. Knowing what influences this process, in addition to socioeconomic development, is therefore important for the authorities.
Climate patterns may be one such determinant. Today, the climate is changing rapidly, but the Arctic climate is warming more than twice as fast as the global average (Meredith et al. 2019). The Russian Arctic is no exception (Edel'geriev and Romanovskaya 2020), with the largest temperature anomalies (3–6°C of warming) occurring in Siberia (Revich et al. 2022). By the end of the twenty-first century, the average summer temperature is predicted to increase by 3–5°C in Arkhangelsk and by 2–3.3°C in Yakutsk (Revich et al. 2020). By 2050, permafrost degradation will directly affect 3.6 million Arctic residents (Nelson et al. 2001), mostly in the Russian Arctic, where average temperatures are predicted to rise by 1.3–2.3°C (Streletskiy et al. 2023).
Russia is the only country with numerous large cities located in the continuous permafrost zone. The stability of the permafrost is crucial for the functionality of all its infrastructure. Global warming-induced permafrost degradation is expected to cause significant infrastructure damage, including crumbling building foundations (Hjort et al. 2022; Streletskiy et al. 2023; Svinoboev and Neustroeva 2017), deteriorating transportation infrastructure (Porfiriev et al. 2019; Streletskiy et al. 2023), and inaccessibility of many settlements (Porfiriev et al. 2021b; Svinoboev and Neustroeva 2017). In addition, an increase in the number of heatwaves is likely to result in excess mortality (Revich et al. 2018; Revich et al. 2019; Revich et al. 2020). Up to 3 percent of the regional health budget may need to be allocated to restoring the health infrastructure (Porfiriev et al. 2021a). It is important to note that the Arctic coastal zone is highly vulnerable to climate change. Erosion rates in some parts of the coastline could reach 2–3 meters per year, potentially causing damage to ports and the pipeline infrastructure (Ogorodov et al 2023).
Researchers are trying to understand how this will affect different aspects of life. In particular, they are interested in how this affects migration. The national literature is very poor on the issue of the influence of climatic conditions on intraregional migration. Basically, researchers only mention the existence of such variations (sometimes considerable) between regions and macroregions; they do not conduct in-depth analysis and do not investigate climate-induced disparities within a region, for example, in the vast Tiumen’ Oblast’, the Republic of Sakha (Yakutia), Krasnoiarskii Krai, and Khabarovskii Krai (Riazantsev and Moiseeva 2022). On the other hand, sometimes only a single settlement is considered within a region (da Cunha et al. 2022). Moreover, studies of migration in relation to climatic changes outside of Russia (Backhaus et al. 2015; Beine and Parsons 2015; Cattaneo and Peri 2016; Wesselbaum and Aburn 2019) cannot be fully applied to Russian conditions, because in the case of international migration, a large number of countries with different climates are taken into account.
Due to the small size of the regions within a country, which makes it impossible to capture climatic differences, intraregional migration has often been overlooked by researchers. The huge size of Yakutia (surpassing that of Argentina, the eighth largest country in the world) makes it ideal for testing hypotheses regarding internal migration, but no such studies have been conducted. This article is one of the first attempts to shed light on these issues for the Russian Arctic in general, and for Yakutia in particular, through the use of quantitative indicators. The question I pose is whether the disparities in the intensity of migration in the Republic of Sakha (Yakutia) are due solely to economic and social reasons, or whether the geographical location and climatic characteristics of the municipal districts also play a role. The answer will clarify the peculiarities of the migration patterns in Russia's largest region in terms of area and will help to develop policies aimed at regulating migration.
Based on the above, I can formulate three hypotheses. First, I assume that in the Republic of Sakha, the more northerly location of a municipal district results in higher migration outflows, higher migration turnover, and higher migration effectiveness (net migration as a proportion of gross migration turnover for a territorial unit). This can be explained by higher unemployment, lower socioeconomic development of the territories, and difficulties in obtaining education. The southern regions are more economically developed, but they actively import their labor force from outside the region.1 Therefore, their migration indices and contribution to internal migration are much lower. Second, in areas with higher temperatures, compared to those with colder climates, migration turnover and migration effectiveness are much lower. Third, Yakutsk has a noticeable impact on internal migration throughout the republic. However, it is particularly strong for the districts located near Yakutsk. The Republic of Sakha (Yakutia) is well suited to testing such hypotheses because of its size and the high social and climatic disparities of the districts.
Methods of the Study
By considering the historical context, human capital, natural factors, spatial development of the economy, and other features, a much more complete picture of the development of a territory and society can be obtained than statistical analysis alone can provide (Capello 2009). That is why the current project is devoted to studying internal migration in the Republic of Sakha (Yakutia) using both statistical methods and descriptive or survey methods. The first methods (statistical analysis) give us an understanding of why things are happening this way rather than that, while the second methods (descriptive and survey) give us the answer to my basic question of “what exactly is happening.”
I try to identify modern trends in the spatial movement of the population. The article differs from previous works in that it considers all municipal districts over a relatively long period (2006–2020). The aim of this research is to identify key peculiarities of population migration within the Republic of Sakha (Yakutia), related to the climate and geographical location of the municipal districts. To better account for their impact, three classification approaches were implemented.
In the first case, a hierarchical clustering based on Ward's method was used (see Figure 1). Average January and July temperatures were chosen for classification. The result is four clusters. The first cluster includes Abyiskii, Eveno-Bytantaiskii national, Gornyi, Megino-Kangalasskii, Niurbinskii, Olenekskii national (Evenkiiskii), Srednekolymskii, Ust’-Ianskii, Verkhnekolymskii, Verkhneviliuiskii, Viliuiskii, and Zhiganskii national (Evenkiiskii) uluses, with an average January temperature of −35–36°C, and an average July temperature of +16–17°C. The second cluster is smaller. It consists of Aldanskii, Lenskii, Momskii, Olekminskii, and Suntarskii uluses, as well as Mirnenskii and Neriungrinskii municipal districts (henceforth, I consider the terms “municipal district” and “ulus” to be synonymous). The average temperature in this second cluster in January is −29–30°C, and in July it is +17–18°C. The third cluster is the smallest and contains Allaikhovskii, Anabarskii national (Dolgano-Evenkiiskii), Bulunskii, and Nizhnekolymskii uluses. The average temperature in January is −31–32°C, and in July it is +10–11°C. The fourth cluster is the same in size as the first one. It covers Amginskii, Churapchinskii, Khangalasskii, Kobiaiskii, Namskii, Oimiakonskii, Tattinskii, Tomponskii, Ust’-Aldanskii, Ust’-Maiskii, and Verkhoianskii uluses, as well as the city of Yakutsk. The average temperatures in these uluses in January and July are −40–41°C and +18–19°C, respectively. Since the average temperatures are statistically heterogeneous, the clustering is valid.
Distribution of the uluses in the Republic of Sakha Yakutia by temperature
Citation: Sibirica 23, 2; 10.3167/sib.2024.230202
The second approach used is based on the normative division of the municipal districts (see Figure 2). The first group includes Abyiskii, Allaikhovskii, Anabarskii, Bulunskii, Eveno-Bytantaiskii, Momskii, Nizhnekolymskii, Olenekskii, Srednekolymskii, Ust’-Ianskii, Verkhnekolymskii, Verkhoianskii, and Zhiganskii uluses. According to the Decree of the President of the Russian Federation dated 2 May 2014, No. 296, “On the Land Territories of the Arctic Zone of the Russian Federation,” and the Federal Law No. 193-FZ of 13.07.2020, “On the State Support of Entrepreneurial Activities in the Arctic Zone of the Russian Federation,” they are a part of the Arctic Zone of the Russian Federation. The remaining municipal districts form the other group.
Arctic and non-Arctic territories of the Republic of Sakha Yakutia
Citation: Sibirica 23, 2; 10.3167/sib.2024.230202
Finally, the third approach to classifying uluses is more tentative. It divides the uluses into three groups (see Figure 3). The uluses of the Arctic zone belong to the northern group. The second group (the southern districts) consists of Aldanskii, Amginskii, Khangalasskii, Lenskii, Olekminskii, and Ust’-Maiskii uluses, and Neriungrinskii municipal district. Other uluses form the third group (central districts). This division does not reveal climatic characteristics but makes it possible to study the dynamics of internal migration in relation to the geographical location of the districts.
Geographical zones of the Republic of Sakha Yakutia
Citation: Sibirica 23, 2; 10.3167/sib.2024.230202
To analyze migrational trends, I use six standard but informative indicators. They should be used with caution, as most of them are crude rates that depend on the age distribution of the population. However, the age distributions are quite similar for the different uluses and stable over time, making the variations negligible. I also use the Pokrovskii-Pearl vital index, which measures the results of natural population movements.
The following notations are used in the formulae below:
B – live births during a year
D – deaths during a year
P – the mid-year population
I – total in-migration during a year
E – total out-migration during a year
II – internal in-migration during a year
EI – internal out-migration during a year
The formulae are as follows for intra-regional migration:
Crude rate of in-migration (CIRI) =
(1)
Crude rate of out-migration (CORI) =
(2)
Crude rate of net migration (CNMRI) =
(3)
Crude rate of gross migration (CGMRI) =
(4)
Migration succession ratio (MSRI) =
(5)
Migration effectiveness ratio (MERI) =
(6)
Formula (5) takes the following form when the total migration is considered:
Migration succession ratio (MSR) =
(5a)
The following formula is used as a population reproduction measure:
Pokrovskii-Pearl vital index (VI) =
(7)
These ratios have long been known and are well documented in the literature. It is worth mentioning only the migration succession ratio and the migration effectiveness ratio, which are almost never used in the study of the Russian Arctic. The migration succession ratio is calculated by dividing departures by arrivals multiplied by 1,000. If the value is greater than 1,000, a municipal district has a population loss during the migration exchange, and if the value is less than 1,000, it has a population gain. An exchange is successful if the value is below 500.
The migration effectiveness ratio is the ratio of net migration to gross migration multiplied by 100. Low values of the ratio indicate balanced migration flows and counterflows, while high values indicate that migration is highly efficient as a population redistribution mechanism. The crude rate of net migration and the migration effectiveness ratio show the population's response to changes in the system of incentives and restrictions on migration and are therefore usually closely correlated (Plane 1984; Rogers and Raymer 1998). However, they do not measure the same thing. The former measures the extent to which migration influences the change in the size of the population, while the latter shows the extent to which migration flows are balanced. Finally, the crude rate of net migration is calculated using the size of the population as the denominator, and is therefore affected by previous demographic history, whereas the migration effectiveness ratio is a function of the population movements into and out of an area that have occurred over a given period of time, and it is more sensitive to temporal shifts and spatial changes in migration flows.
As well as looking at the migration indices and the trends they reflect, I also try to find out whether there are statistically significant disparities within the three above-mentioned groupings of municipalities. Their presence is the central hypothesis of the study, allowing for the existence of considerable heterogeneity in terms of the patterns of migratory movements. The uluses are not normally distributed by the indices of migration dynamics, and the number of districts is small. Therefore, the analysis is not based on the ANOVA method but on the nonparametric van der Waerden test, which is more powerful than the Kruskal-Wallis test and almost as powerful as the parametric F-test. The nonparametric Mann-Whitney U test is used in the case of two samples (Arctic and other uluses).
The period studied (2006 to 2020) is long enough to discuss the existence or absence of long-term trends. Data provided by Sakhastat (statistical agency of the Republic of Sakha (Yakutia)) formed the information base for the study. I consider only long-term migrants, leaving temporary migrants, especially those working on a rotational basis, outside the scope of the study. The two periods (2006–2010 and 2011–2020) are shown together on the graphs, but they are treated separately, because the migration data before and after 2011 are not comparable due to changes in the methodology of registering migrants.
The transition to the new registration methodology took into account specificities of Russian legislation. All persons residing in Russia, citizen or noncitizen, must be registered with a local authority. Russian citizens are entitled not to register at their place of sojourn or new residence for 90 days after arriving, but after those three months they are required to register. If their registration exceeds nine months, with the first three months their permission to reside in that place would add up to a year or more. This is in line with the UN's recommended criteria for recording long-term migration. Primary registration for most foreigners staying longer than 72 hours in Russia is usually for 90 days. In cases involving a work contract, education, and so on, it can be extended for a longer period. As a result, some migratory flows have been more fully captured and the number of registered events has greatly increased. A significant drawback is that, according to the registration methodology, a migrant is considered to have left after the registration period has expired, even if there has been no actual change of residence. That's why the actual number of departures is lower than the number of departures reported by the statistics.
The figures below present the results for each geographical area as a whole and not for the individual districts. The calculations in the tables are based on the values of the indices (1–6) for the districts, so uluses with different population sizes have the same weights. For that reason, there may be statistically significant disparities between the uluses even when the lines on the graphs are very close to each other. On the other hand, there may be no statistically significant disparities despite the large spread on the graphs. The graphs also show the dynamics without Yakutsk, as it is strongly influenced by the size of the population of the regional capital.
The Republic of Sakha (Yakutia): General Demographic and Migratory Trends
The most unique feature of the Republic of Sakha (Yakutia) is that its population, which is very dispersed and spread over a huge area, has grown strongly during the period under consideration. This growth has been driven by a natural increase, while the impact of migration has been negative.
Virtually all uluses experienced a natural increase in their population. In the period 2006–2020, there were a total of 228,300 births per 129,500 deaths, with an average annual excess of 6,600. The Pokrovskii-Pearl vital index (hereafter referred to as the VI value), calculated using Formula (7), varied between 150 and 190, but its dynamics were unfavorable. As a result of the decrease that started in 2014, in 2020 half of the uluses had the lowest values for the entire period. However, there were no uluses where the VI values were below 100 every year throughout the period. The lowest mean values (86–92) were in Aldanskii, Oimiakonskii, Ust’-Maiskii and Verkhnekolymskii uluses. The next lowest district, Lenskii, had a mean value of 117. Gornyi and Namskii uluses were the leaders with average scores above 250. Churapchinskii ulus was just below this group.
Geographically speaking, the south, with a declining population towards the end of the period, had the lowest intensity of natural population movement (see Figure 4). In the north, the birth rates were much higher than the death rates, but the VI values were lower compared to the whole region. The central uluses had the highest values. In the capital, the VI value was above the average for this group. Looking at the distribution of uluses by temperature, the cooler clusters had the higher VI values and the hotter clusters had the lower ones.
Pokrovskii-Pearl vital index (VI value) from 2006 to 2020
Citation: Sibirica 23, 2; 10.3167/sib.2024.230202
There are no statistically significant disparities between the clusters on the basis of temperature, not even at the 0.1 level of significance. Disparities at the 0.05 level between Arctic uluses and the rest of the republic were significant in 2006, and between 2008 and 2010. Disparities were significant at the 0.05 level when looking at the uluses by geographical location for all years except 2016, and they were significant at the 0.01 level in 2008, 2009, 2013, 2015, and from 2018 to 2020. Consequently, there was strong heterogeneity in terms of natural population movement and a noticeable influence of the geographical location of the uluses: In the south, the VI values were on average lower, while in the center they were the highest. This is an interesting issue, but beyond the scope of this article.
While the focus of this article is intraregional migration, the main form of spatial movement of the population in the Republic of Sakha (Yakutia), it is necessary to say a few words about migration in general. Let us take the example of the migration succession ratio (MSR), calculated according to Formula (5a), to illustrate its negative impact on the population dynamics.
For the period 2006–2010, total in-migration was 97.1 thousand and total out-migration 129.4 thousand. For the period 2011–2020, the numbers were 370.0 and 415.2 thousand, respectively. The population decreased due to migration by about 6,500 people per year in the first period and by 4,500 people per year in the second period. Figure 5 shows that during the period considered, the MSR values decreased in all groups of uluses and approached 1,000, implying that the outflows slowed. However, outflows exceeded inflows until 2020—that is, migration made a positive contribution to the demographic dynamics during one year out of 15.
Total migration succession ratios from 2006 to 2020
Citation: Sibirica 23, 2; 10.3167/sib.2024.230202
The only place where the MSR was consistently below 1,000 was Yakutsk, which attracted both internal and external migrants. Over time, however, Yakutsk has become increasingly closer to the rest of the republic, which, initially, was noticeably inferior to the regional capital. Since 2013, the coefficient of variation has been less than 30 percent, indicating a trend towards convergence of the uluses, and every set of districts was homogeneous even at the 0.1 significance level. The MSR values for the center were lower than those for the north, but if Yakutsk is excluded, the two groups were close. Temperature was more inconsistently linked to the MSR. Northern uluses (clusters 1 and 3) had heterogeneous temperatures but the highest MSR throughout the period, except for 2009, 2010, 2016, and 2020, when external factors had a strong influence. In contrast, the MSR tended to be low in the southern districts, which had the highest temperatures and the highest heterogeneity. The central districts with the lowest temperatures had the lowest MSR. However, the values increased dramatically and the disparities between clusters 1, 3, and 4 disappear when Yakutsk is excluded from cluster 4. It can, therefore, be assumed that geographical location and climate had some influence on total migration, but were not the main determinants.
Dynamics of the Intraregional Migration in the Republic of Sakha (Yakutia)
Socioeconomic transformations that took place in the 1990s made intraregional migration the main direction of the spatial movement in the Republic of Sakha (Yakutia). Before 2011, this form of migration accounted for 65–74 percent of all arrivals, and 62–67 percent afterwards. Its share was only 48 percent in 2020, however, due to the COVID-19 pandemic. Intraregional migration was less popular as an outgoing destination, accounting for 43–60 percent of all departures before 2011 and 52–60 percent since that year. In 2020, it was 55 percent.
Between 2006 and 2010, about 68.0 thousand people arrived within the framework of intraregional migration. From 2011 to 2020, they numbered approximately 229.1 thousand. Yakutia is a closed system for this type of spatial movement, so departures were equal. Churapchinskii, Khangalasskii, Megino-Kangalasskii, Mirnenskii, Namskii, and Neriungrinskii uluses (the central districts) and Yakutsk had the highest number of arrivals both before and after 2011. The remaining districts attracted far fewer migrants. Allaikhovskii, Anabarskii, Eveno-Bytantaiskii, Momskii, Nizhnekolymskii, Oimiakonskii and Vekhnekolymskii—predominantly Arctic uluses—had the fewest arrivals. The number of arrivals increased in both groups, but the increase was more visible in the second one. The northern districts received 5–9 percent of all in-migrants and another 12–17 percent went to the southern districts. The majority of migrants came to the central uluses, where the share was very stable at 76–78 percent.
The distribution of districts regarding departures has some peculiarities but is generally similar to that regarding arrivals. The principal discrepancy is that Yakutsk was less conspicuous (especially before 2013), while the districts had much higher numbers of people leaving than arriving. The main contributors to the outflows were Churapchinskii, Khangalasskii, Megino-Kangalasskii, Namskii, Niurbinskii, Suntarskii, and Ust’-Aldanskii uluses and the city of Yakutsk. The number of those who left them has increased greatly. Departures were the lowest in Allaikhovskii, Anabarskii, Eveno-Bytantaiskii, Momskii, Nizhnekolymskii, Olenekskii, Verkhnekolymskii and Zhiganskii uluses. The northern districts accounted for 10–12 percent of all departures, but their share has been decreasing since 2011. The southern districts, with 17–21 percent, also showed a decline. Therefore, more people left the central uluses. Their share increased from 66–68 percent to 71–73 percent.
The analysis of the raw numbers shows some patterns that are related to the climate and the geographical location of the municipalities. However, the main differences arise from the size of the ulus populations. To overcome this shortcoming, it is necessary to consider the dynamics of population movement using Formulae (1–6). The most basic of these are the crude rates of in-migration (CIRI) and out-migration (CORI), introduced by Formulae (1) and (2), which describe a population's “risk” of migration.
Figure 6 shows that the intensity of arrivals has increased, reflecting the general trend for the country. Socioeconomic factors were the main drivers of migration. Intraregional migration was very much wage driven. The global economic crisis can thus explain the decline in the CIRI in 2009–2010. Decreases in wages were accompanied by a decline in the number of those who gave “due to work” as the reason for their arrival. In the same years, the number of newcomers with a general secondary education decreased significantly throughout the republic: more than two times in 2009 and more than five times in 2010. This was mainly due to the regional capital, where travel and study costs had become prohibitive. Educational migration was also responsible for the peak in 2007. Despite changes in the methodology of migrant registration, the increase after 2010 was largely compensatory, primarily for educational and long-term labor migration. Declining student numbers were largely responsible for both the 2014 and post-2017 recessions. Yakutsk had a decisive impact in both cases. In the second case, however, it is not possible to be precise about the reasons, as the share of those who did not indicate the reason for their arrival began to account for a quarter to a third of all arrivals, and the share of the “returning after a temporary absence” reason also increased significantly.
Crude rates of in-migration from 2006 to 2020
Citation: Sibirica 23, 2; 10.3167/sib.2024.230202
The statistical forms for registering arrivals and departures only permit the selection of one reason for migration, which is considered the primary reason. However, it is not always possible to verify that this reason is the actual reason. In the following paragraphs, I demonstrate that the number of internal migrants departing due to the unsuitable climate is several times higher than the number arriving for the same reason. When migration is not related to work or study, it is often necessary to interview the migrant to understand the real reason. This helps to avoid subjective evaluations and provides a clear and objective understanding of the situation. However, conducting surveys of this nature can be challenging in practice. This is especially true in the case of the climate migration.
On average, 20–40 people across the republic mentioned the unsuitable climate in their previous place of residence as the reason for their arrival. But I doubt the sincerity of the answers. A quarter to a half of all those who gave this reason came to Yakutsk, not to any of the neighboring uluses. Almost all of the rest arrived in Aldanskii, Mirnenskii, and Neriungrinskii districts, which have both the highest temperature and the highest level of economic development.
The specifics of the Republic of Sakha (Yakutia) as a mining-oriented region should be highlighted. The dynamics described above are often determined not by the implementation of large business projects aimed at attracting shift workers, but by the general economic state of an ulus. For example, there was no noticeable increase in the number of internal migrants arriving in Neriungrinskii and Lenskii districts following the coming onstream of the Elga coalfield and the Chaianda oil and gas condensate field. This is true for Mirnenskii and Aldanskii districts, which also received significant external migration flows.
Geographically, the intensity of arrivals in the northern uluses was initially low, which was comparable to the rest of the republic except Yakutsk. After 2012, the CIRI increased most rapidly in these districts and was well above average at the end of the period. Dynamics were very flat and there was no growth in the southern districts, which had the lowest values. Central districts had high values, but excluding Yakutsk, they were only slightly ahead of the northern uluses in terms of arrival intensity until 2011. After 2011, the contribution of Yakutsk became less pronounced against the background of the overall increase in the CIRI, and from 2016 the central districts began to be noticeably inferior to the northern ones. As regards the distribution by temperature, the lowest CIRI values were found in the areas with the highest temperature (cluster 2). Disparities between clusters 1, 2, and 4 were not large enough to indicate a close relationship between temperature and the CIRI. It should also be mentioned that the republic was very heterogeneous. The average coefficient of variation was 45 percent, with no year falling below 37 percent. It was 32.3 percent only in 2020.
Before 2011, 42–45 percent of all intraregional migrants were attracted by Yakutsk, the largest municipality. Since 2012, its share has decreased to 35–40 percent, and its CIRI has become lower than that of many districts. Six major units (as well as Yakutsk, Aldanskii, Lenskii, Mirnenskii, Neriungrinskii, and Niurbinskii uluses) accounted for 57–70 percent of all intraregional migrants until 2013, and 50–60 percent in the following years, with a marked downward trend in their share.
Table 1 shows that the disparities between the Arctic zone and the rest of the republic were insignificant almost every year. However, considering the districts in terms of their geographical location, I can be sure that they were statistically different in arrival rates due to the very low CIRI values in the south. The clusters on the basis of temperature variation have been even more statistically different, but again at the expense of the variations between the south (cluster 2) and the rest of the republic.
Years with statistically significant disparities between the uluses regarding the CIRI
| Classification attribute | Significance level of 0.1 | Significance level of 0.05 | Significance level of 0.01 |
|---|---|---|---|
| Arctic zone | 2011 | 2020 | — |
| Geographical location | 2006, 2009, 2010, 2019 | 2007, 2014–2016, 2020 | — |
| Temperatures | — | 2009, 2013, 2019, 2020 | 2014–2018 |
The intensity of departures has also increased (see Figure 7). In addition to “study-related” and “work-related” reasons as the main determinants of peaks in the dynamics, “personal, family reasons” have been added. After a rather flat dynamic before, its contribution was noticeable between 2015 and 2017. Job cuts and falling disposable incomes in 2009–2010 hampered migration, even for employment purposes. For the same reasons, fewer people left after completing their education. The departure of those who had completed their education was the main reason for the surge in departures in 2013. It is difficult to analyze the reasons for departing from 2016. This is due to the emergence of reasons such as “return after temporary absence” and a very large increase in the number of people who did not give a reason for departing. The number of departures due to unsuitable natural and climatic conditions exceeds the number of arrivals (200–250 people per year). Some 70–80 percent of them left Mirnenskii and Neriungrinskii districts and Yakutsk. In some years, the contribution of Aldanskii and Lenskii uluses was noticeable. This probably indicates an insincerity in the responses. The increase in the crude rate of out-migration (CORI) in 2020 was much lower and was only observed in seven uluses, mainly in the north and south. Although the number of departures decreased by 8.6 percent compared to the previous year, intraregional migration maintained a share of more than 50 percent of all departures, and will remain a priority in the coming years.
Crude rates of out-migration from 2006 to 2020
Citation: Sibirica 23, 2; 10.3167/sib.2024.230202
Geographically, the CORI was highest in the northern districts. Since 2012, the southern districts have had a rather flat dynamic and low values. In contrast to the previous case, the central districts were closer to the southern districts, probably due to the lagging growth of outflows in Yakutsk as arrivals increased. If Yakutsk is not taken into account, the central districts are closer to the districts in the north. Clustering by temperature shows no clear trend. Cluster 2 (the cluster with the highest temperatures) has the lowest CORI values. However, if Yakutsk is excluded from cluster 4, the disparities in the CORI between the lowest temperature cluster (cluster 4) and the two northern clusters with intermediate temperature values were not so great. The degree of heterogeneity of municipalities according to the CORI was lower. The average value of the coefficient of variation was 37 percent, but the republic was homogeneous only in 2020, when it decreased to 30.8 percent.
Years with statistically significant disparities between uluses regarding the CORI
| Classification attribute | Significance level of 0.1 | Significance level of 0.05 | Significance level of 0.01 |
|---|---|---|---|
| Arctic zone | 2014 | — | — |
| Geographical location | 2006, 2013, 2020 | 2011, 2014–2016, 2018, 2019 | — |
| Temperatures | 2010, 2014, 2015 | 2006–2008, 2011, 2013, 2016, 2018, 2019 | 2017, 2020 |
Yakutsk's share of intraregional outflows was 17–22 percent in the early years of the period considered. In 2011–2012, the share was only 15 percent, but afterwards it increased to 27–28 percent and reached 32 percent in 2020. Before 2011, the six main territorial units accounted for 32–36 percent of the outflows, but the percentage was 40–42 percent after 2015. This dynamic is probably due to the increase in return migration as the inflows of migrants increased.
As before, disparities between the Arctic zone and the rest of the republic were insignificant for most of the period considered. At the expense of the higher values in the north and low values in the south, the uluses in the distribution based on their geographical location statistically differed and were slightly higher for departures than for arrivals. Clustering by temperature was the most heterogeneous (with insignificant results only for 2009 and 2012), indicating different patterns of migration between cluster 2 and other clusters.
Results of intraregional migration in the Republic of Sakha (Yakutia)
The number of arrivals was lower than the number of departures within intraregional migration for most uluses, which is why their migration balances were negative. Only Neriungrinskii district and Yakutsk had positive crude rates of net migration (CNMRI) throughout the period. This is particularly noticeable in the northern uluses (see Figure 8), where the rates improved significantly, but where the decline could be as high as 10–20 permille in some years even closer to the end of the period considered (e.g., in Zhiganskii ulus). In the southern, more industrially developed districts, the values fluctuated around zero, but in general there was also a population decline. Central uluses experienced multidirectional trends. The overall increase in population was at the expense of Yakutsk. The CNMRI there had decreased as a result of population out-migration, although it remained well above the average. Excluding the regional capital from the central districts, their CNMRI would be negative, close to the northern uluses. The same applies to cluster 4, where, excluding Yakutsk, the CNMRI values were close to those of cluster 1. Cluster 2, where the crude rates of in-migration and out-migration were the lowest, had CNMRI values close to zero, indicating balanced migratory movement.
Crude rates of net migration between 2006 and 2020
Citation: Sibirica 23, 2; 10.3167/sib.2024.230202
Notably, the figure shows very clearly the influence of economic crises on the migration flows. During the global financial crisis of 2009–2010 and the COVID-19 pandemic, migration flows decreased, and the CNMRI values were approaching zero for both donor and recipient districts.
With regard to the CNMRI, districts were homogeneous: The significance level was lower, and the number of years observing statistically significant disparities was much smaller (see Table 3). The most notable results were obtained for the year in which the variation range of the CNMRI was the largest (2013), but even here the level of significance is only 0.1. For 2014, the impact of Russia's annexation of Crimea can be assumed,2 and in 2020, there was a trend reversal due to the COVID-19 pandemic.
Years with statistically significant disparities between uluses regarding the CNMRI
| Classification attribute | Significance level of 0.1 | Significance level of 0.05 | Significance level of 0.01 |
|---|---|---|---|
| Arctic zone | 2013, 2014 | — | — |
| Geographical location | 2013, 2018 | — | — |
| Temperatures | 2020 | — | — |
The crude rates of gross migration (CGMRI) largely reflect the dynamics of in-migration and out-migration rates, on which they are based (see Figure 9). These features contributed to the growth after 2011 and the high values of the CGMRI in the northern uluses, where even at the end of the period, every ninth to twelfth inhabitant was involved in spatial movement (e.g., in Ust’-Ianskii ulus), indicating an intensive migration turnover. Southern districts, which differed markedly from the rest of the republic, as expected had the lowest values, caused by attracting a labor force from outside of the republic. This group was very heterogeneous, in contrast to the northern uluses, where the low variation decreased throughout the period. The central districts showed a moderate increase (with Yakutsk clearly below many districts). There, in Churapchinskii ulus, the maximum for the whole period was recorded in 2016 (137.5 permille). The southern clusters stand out among the clusters based on temperature. Clusters 1, 3, and 4 showed similar results, and if I exclude Yakutsk from cluster 4, the migration turnover in the central uluses was the highest for almost the entire period.
Crude rates of gross migration between 2006 and 2020
Citation: Sibirica 23, 2; 10.3167/sib.2024.230202
Heterogeneity was very strong for the CGMRI (see Table 4). The disparities between the Arctic part and the rest of the territory were slightly higher than in the previous cases, but they were generally not significant (significant results were obtained mainly for the crisis years). Due to the high variations between the northern and southern uluses, the disparities for the distribution based on geographical location were significant throughout the period, with the exception of the post-crisis years of 2012 and 2017–2018, when the CGMRI had the maximum values. The most heterogeneous migration (disparities were insignificant only in 2012) was in the clusters based on temperature.
Years with statistically significant disparities between uluses regarding the CGMRI
| Classification attribute | Significance level of 0.1 | Significance level of 0.05 | Significance level of 0.01 |
|---|---|---|---|
| Arctic zone | 2010, 2011, 2020 | — | — |
| Geographical location | 2006, 2008, 2009, 2013 | 2007, 2010, 2011, 2014-2016, 2019, 2020 | — |
| Temperatures | 2006, 2007, 2010 | 2008, 2009, 2013–2020 | 2011 |
As the outflows of population during intraregional migration exceeded the inflows, the migration succession ratios (MSRI) were higher than 1,000 for most of the uluses (see Figure 10). The only exceptions were Neriungrinskii district and the capital of the republic. Yakutsk deserves a special mention. Initially, the MSRI there was 300–400. It was only after 2014, when out-migration increased, that the values rose to 600–700. The northern uluses greatly improved their position during the period considered, and the out-migration there had decreased significantly by the end of the period, approaching the average values. However, the out-migration of these districts was the highest. Prior to 2011, the MSRI could exceed 3,000–3,500 in some years (three or more departures for every arrival). Even closer to the end of the period, the north of the republic was losing population quite intensively. In the south, as expected, the figures were lower. Excluding Yakutsk from the central districts, the southern uluses generally showed the best results, although this group was very heterogeneous. It included both districts where the MSRI was similar to that of Yakutsk and those with the highest values (about 4,000). Cluster 2 showed the most stable dynamics with the lowest migration losses in the distribution of the uluses by temperature. The northern and central districts (excluding Yakutsk) exhibited close MSRI values. Note that the variation within the groups of municipalities was initially between 40 percent and 60 percent, a high value. Even the northern uluses were heterogeneous. By the end of the period, it had diminished considerably and the republic had become homogeneous even in the south.
Migration succession ratios between 2006 and 2020
Citation: Sibirica 23, 2; 10.3167/sib.2024.230202
There were no significant disparities, even at the 0.1 level, for any single year for any set of uluses. The most likely reason for this, as in the case of the crude rates of net migration, is the high degree of heterogeneity of the southern uluses in terms of the MSRI.
For most of the uluses, migration was an effective tool for population redistribution, with negative migration effectiveness ratios (MERI) due to excessive out-migration (see Figure 11). Initially, the MERI values were the lowest in the northern uluses, where the population decreased by 40, 50, or more people for every 100 people involved in migration. However, these districts had the best dynamics. At the end of the period, their values were often closer to zero than the regional average. Excluding Yakutsk, the central uluses showed dynamics close to those of the northern uluses. The southern area therefore had the most balanced migratory flows. Throughout the period, the uluses with the highest and lowest migration effectiveness were fairly homogeneously distributed over the territory of the republic, resulting in high values of the coefficient of variation, which increased over time. In the areas with the highest temperatures (cluster 2), migratory movements were balanced, although there was still a slight outflow of population. The MERI values in the central districts, excluding Yakutsk, where temperatures were the lowest, were close to those of the northern uluses (clusters 1 and 3). Migration effectiveness was more a function of socioeconomic indices than temperature. The cases of Yakutsk and the southern regions illustrate this clearly.
Migration effectiveness ratios between 2006 and 2020
Citation: Sibirica 23, 2; 10.3167/sib.2024.230202
As in the cases of Figures 5 and 7, the homogeneity of the different sets of uluses by MERI was high, with disparities among districts not significant even at the 0.1 level. This also applies for the clusters based on temperature. It can be argued that the patterns of migration effectiveness were similar in different parts of the republic.
Discussion and Conclusion
Over the last forty years, the climate has been changing rapidly. In the last decade, however, detailed studies of climate-related migrations have begun to emerge, and it has become clear that the Arctic is likely to change significantly from 2035 onward. Permafrost degradation will lead to the degradation of the infrastructure that depends on it. In less than twenty years, climate change will be among Russia's most challenging issues. One of the consequences of this will be an increase in migration losses in the northern regions. This will exacerbate the negative demographic trends in the Russian Far North, which covers about 70 percent of Russia.
Since the collapse of the USSR, Russia has become an even more northern country because of the secession of the western and southern republics with more favorable climates. The demographic problems in the Russian Far North and the Arctic mean that the authorities have done too little to develop the areas where the main reserves of many mineral and biological resources are located. The Republic of Sakha (Yakutia) is endowed with vast natural resources, which, if exploited, will have a significant impact on the whole of Russia.3 The human capital of the population there is also well developed and is an important resource for improving the socioeconomic situation. However, like the rest of the northern regions, it faces many challenges, not least of which are demographic issues that are only growing in significance over time. This is the reason for the importance of the topic presented here. The issue of unbalanced migration flows is a significant concern, particularly in regard to internal migration, which comprises over 50 percent of all migration. In response to this concern, my main task was to analyze the patterns of intraregional migration in the Republic of Sakha (Yakutia), taking into account the geographical location and climatic characteristics of the municipal districts.
Despite the fact that migration in Yakutia is fairly well represented in the literature, it has not been studied in sufficient detail. This is due to the fact that most researchers usually focus on the Arctic municipal districts. Moreover, researchers used to estimate the migration intensity dynamics using only the number of movements or the crude rates of in-migration and out-migration, and sometimes the crude rates of net migration. In contrast to previous works, this study examines migration intensity on the basis of a broader set of indices. I have also considered the whole republic over a sufficiently long period.
My results both negate and support some of the hypotheses. Crude rates of in-migration, out-migration, and gross migration showed statistically significant disparities within the sets of municipalities. The presented sets of municipalities do not statistically differ by crude rates of net migration, migration succession, and migration effectiveness ratios. There are several interrelated explanations for this.
First, the main disparities are between the fairly homogeneous north, with its high population mobility, and the very heterogeneous south, where the first three rates are the lowest. But they are much smaller when comparing inflows and outflows between the north and the south for the other three indices. The same is true for the center and the south. This reflects greater homogeneity regarding the balance of flows and counterflows and considerable heterogeneity in terms of the impact of migration on population size.
Second, there are disparities between the least developed and most remote agricultural uluses of the north and the most economically developed districts of the south. This probably implies a predominant influence of the socioeconomic indices of the territorial development, with a certain influence of the natural and climatic conditions. This assumption is also supported by the fact that the central districts near the regional capital, Yakutsk, have in-migration and out-migration rates that exceed even the turnover and outflows in the northern districts. They are markedly different compared to the nearby southern districts.
Third, there are disparities between the highest and lowest temperature clusters (clusters 2 and 4), but also between areas with smaller temperature differences (clusters 1 and 2, and 2 and 3). Again, this suggests that migration is driven more by socioeconomic than natural-climatic factors.
Finally, the geographical parts of the republic and the clusters based on temperature are heterogeneous. The coefficient of variation consistently exceeded 33 percent throughout the period under consideration. In general, the south had the lowest migration turnover: The turnover rates did not exceed 10–15 percent and were often 5–7 percent in the most developed districts, while in the remaining districts they were at or above the regional average. Although the clusters and geographical parts of the republic differ in the graphs, the heterogeneity does not allow us to find statistically significant disparities. This is due to the influence of more populated municipalities, whereas in the van der Waerden test the weights were the same for all the uluses.
This article shows that, since 2014, intraregional in-migration rates have been catching up with out-migration rates. However, throughout the period under consideration, except for 2020, the vast majority of municipalities experienced population losses. The intensity of migration has generally increased and, despite a slight decrease since 2017, is expected to increase further after the end of the pandemic and as the armed conflict in Ukraine enters a new phase. It will likely take the form of educational migration and return migration, combined with an increase in labor migration. An increase in the mobility of the population in general, and of the labor force in particular, could be a positive result of social transformations. Nevertheless, the increase in the intensity of migration is more likely to be seen as a negative result of the development of the republic, as it points to the dissatisfaction of the population with living conditions and the constant search for a new place to live and work.
An important finding is the demonstration of the impact of external shocks on intraregional migration in the Republic of Yakutia. Figures 4–11 clearly show that the economic crisis of 2008–2009, and the COVID–19 pandemic that started in 2020, broke the previous migration dynamics.
The study confirms that, when modeling socioeconomic processes, it is necessary to consider center-periphery differences. The most economically developed municipal districts (Lenskii, Mirnenskii, and Neriungrinskii) showed relatively favorable dynamics. The others lagged behind significantly. Nevertheless, the regional capital (Yakutsk) outperformed even the Neriungrinskii district, which clearly stands out against the general background. The case of Yakutia confirms the thesis of the development and strengthening of the center at the expense of the periphery, especially the outlying areas.
My study shows that intraregional migration contributes significantly to the population growth of Yakutsk, which is one of the two territorial units with stable migration growth throughout the period considered. These findings are consistent with the results of Fedorova and Ponomareva (2014) and Sukneva and Laruelle (2019). I can assume that the reserves for population transfer to Yakutsk have not yet been exhausted, despite the decline of its share in internal migration observed in recent years. The outflows of population from the city as a part of intraregional migration are increasing, contributing to the convergence of the capital and the periphery. However, Yakutsk remains the most important center of population attraction, and no second-tier district or city can compete with it in the medium term.
The article has a number of shortcomings. In the analysis of natural and climatic conditions, the clusters were formed on the basis of temperature alone. If I were to add the data on humidity and wind, it would provide more climate-related explanations. The results would be more comprehensive, but it is unlikely that they would change dramatically: The intensity in the south would remain low and there would still be a significant outflow from the north. I have considered only long-term intraregional migration. Additional information on the impact of climate on migration in Yakutia can be obtained by analyzing the movements of shift workers as well as migratory exchanges with other regions of Russia and other countries. Finally, although surveys can provide important information on the reasons for migration and the migration intentions, I did not use such data.
The ongoing project does not plan to address the first two questions. As far as the survey is concerned, it is currently being carried out and its findings will allow us to refine my results. Panel data analysis based on socioeconomic indices will be undertaken. This will allow us to identify the main determinants of intraregional migration in the Republic of Sakha (Yakutia). Examining the differences between clusters 1 and 3 is another potentially interesting area for further work. For example, it is possible to check which factors are associated with less intensive migration in the less developed coastal cluster 3. The lack of financial means is supported by the theory (Docquier et al. 2014; de Haas et al. 2019). The author's conversations with the inhabitants during his visit to Yakutia confirm their reluctance to move due to their affection for their homeland.4 Recent work (Savvinova et al. 2021) does not deal with this issue in detail.
The results of the study can be used by authorities in Russia in the development and implementation of migration policies in such large regions of Russia as Krasnoiarskii Krai and Khabarovskii Krai, or in particular macroregions (federal districts). For example, my findings imply that the incentives for population attraction and retention in the north of Yakutia are low, and Yakutsk attracts people from all over the republic. Consequently, in order to reduce migration outflows, it is necessary to comprehensively develop the entire territory of the republic, rather than particular municipalities. Currently, the lack of political will is the most significant obstacle. All Russian regions are characterized by extremely poor development of territories outside the few areas of growth. Additionally, the spatial distribution of social infrastructure is worsening due to the “optimization” of infrastructure facilities, increasing the heterogeneity of municipal districts. Long distances between settlements, poor accessibility and quality of services, high dependence of municipal budgets on the budgets at higher levels, and underfunding of social infrastructure are some other reasons for the lack of spatial development. It is important to note that the climate at present has an influence on spatial development, but it is not the main driver. However, with permafrost degradation, the climate will become a major factor in the development of the Far North, especially the Arctic. Climate-induced infrastructure deterioration will decrease the attractiveness of northern regions to migrants. As a result, the state will no longer be able to rely on social infrastructure, territorial connectivity, and a high standard of living as effective tools for population attraction and retention. Meanwhile, “non-migration policies may be more powerful in shaping migration than migration policies” (Castles 2004: 871).
Outside of Russia, the results of this study will be of interest to regional authorities in Canada, which has vast underdeveloped Arctic territories; Scandinavian countries with their different northern and southern climates; and large countries stretching along the meridians, such as the USA, Chile, and Argentina.
To sum up, in the case of Yakutia, the climatic characteristics of the municipalities and their geographical location have some influence on migration within the republic, but the relationship between them is not always obvious. Socioeconomic determinants are probably the most important, which is in line with generally accepted theories of migration. However, the observed changes in climate make its links with migration trends very acute and attractive to the research community. Climate research in the Far North and the Arctic is even more important in Russia, where 70 percent of the country is in the permafrost zone. The opportunity to contribute to the study of how people migrate in response to natural and climatic conditions is a very exciting proposition for our future. This allows us to correct past mistakes, become more responsible toward nature, and develop a sustainable global society.
Acknowledgments
I thank the two anonymous journal reviewers and the handling editor, Jenanne Ferguson, for helpful comments on an earlier draft.
Notes
The southern areas are notable for several reasons. They are accessible by rail, making transportation of goods and people more convenient. Additionally, a more favorable climate and proximity to other regions of Russia make it easier to explore and exploit natural resources.
Since the imposition of sanctions against Russia, there has been a reduction in business activity, slower GDP growth, ruble depreciation, and lower living standards for public sector employees, primarily in education and public health.
To develop the particularly rich South Yakutia coal basin, the northern branch of the Baikal–Amur Railway (the Tynda–Berkakit line) was built. Nowadays, the 550-kilometer private Pacific Railroad is being constructed to exploit the Elga coalfields and is expected to be completed by the end of 2024. Due to significant proven reserves of the southern deposits of hydrocarbons of the Lena–Tunguska oil and gas province, the East Siberia–Pacific Ocean oil pipeline and the Power of Siberia gas pipeline both pass through Yakutia. In 2015, there were 1,823 known deposits of 58 types of minerals in Yakutia. However, over 16,000 potential deposits remained poorly explored due to the vastness of the region and the low population density.
English-speaking readers are practically unaware of the works in Russian devoted to the social space of Yakutia in the second half of the twentieth century. There has been some discussion of these issues in the Russian language. However, many of them have not been considered in sufficient detail, despite the existence of a considerable amount of field material. Lilia I. Vinokurova is notable among the authors. She has studied the cultural landscape and daily life of Yakutia for over three decades. Her works, including Vinokurova (2016; 2018) and Vinokurova and Grigoriev (2022), highlight the significance of ancestral lands for the indigenous peoples of Yakutia, for whom moving to a new place of residence means a tragic break with their cultural heritage and a loss of roots. The concept of ancestral lands holds considerable meaning for the population, and this should be considered when developing migration policies. Cultural landscape studies (for example, Vinokurova and Grigoriev 2023) will be useful in modeling the behavior of the population of Yakutia under the conditions of climate change and infrastructure deterioration.
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