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1 Introduction

The High North of Russia (Fig. 18.1) is traditionally associated with an unfavourable climate, remoteness from the European part of the country and high wages, which should compensate for the uncomfortable living conditions. The High North can be characterized by a 5.5% share of the population of Russia and about 10% of the Gross Domestic Product. Most areas of the High North have a strictly oriented economic specialization: in value they produce more than 50% of the product of the extractive industry of Russia. In recent years, despite the multiple growth of real wages in the High North, the number of employees in the majority of cases has demonstrated a negative trend. Thus, according to the Federal State Statistics Service (Rosstat), wages in 2000–2013 in the High North were 1.6–2 times higher than the average national wage, but the resident population in the High North and equivalent areas in 1999–2014 decreased by 1.207 million people, i.e. by almost 11%. Bignebat (2006), Mkrtchyan and Karachurina (2015), and Sardadvar and Vakulenko (2016) associate out-migration from the High North regions of Russia with labour demand insufficiency.

Fig. 18.1
figure 1

High North Regions of Russia (Source: http://www.gks.ru/bgd/regl/b08_22/IssWWW.exe/Stg/kart.htm with corrections made by the author as of January the 1st, 2014)

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Demand for labour in the High North regions of Russia is more constrained compared with the rest of the country because of institutional, geographical and economic reasons (Giltman 2016). First of all, the High North region of Russia is unique because of the strongest employment protection legislation among all the regions of the country. Employment protection legislation in the High North regions of Russia is based on Chapter 50 of the Labour Code of the Russian Federation and also implies special consequences for retirement. The Labour Code of the Russian Federation provides benefits for employees in the High North regions such as a regional coefficient of individual wage from 1.15 to 2 times in addition to annual paid leave. Additional employment protection legislation increases the costs to the employer in official hiring, wages and lay-offs, and weakens the enforcement in the High North regions of Russia (Giltman 2016).

Another reason for labour demand insufficiency in the High North regions of Russia is that establishing new industrial production in the High North is extremely costly compared to any other country because of transportation, climate and poor infrastructure (Kryukov and Moe 2013; Pilyasov 2016). Finally, an additional explanation to the demand insufficiency could stem from the one-company towns being the prevalent form of settlements in the High North of Russia (World Bank 2010; Commander et al. 2011; Pilyasov 2016). One-company towns, for example Kirovsk, Onega and Pudozh, are very close to creating a monopsony in the labour market, which constrains labour demand significantly compared with a competitive labour market.

Labour supply also seems to have some special features in the High North regions of Russia. According to the general model of the local labour markets’ equilibrium by Rosen (1979) and Roback (1982), local labour markets within one country are connected by migration. An employee selects an area for employment based on nominal and real wages, the productivity of a local economy, local amenities, housing costs in the location and idiosyncratic preferences for the location (Moretti 2011). According to some empirical research (World Bank 2010; Kryukov and Moe 2013; Pilyasov 2016; Giltman 2016; Nalimov and Rudenko 2015; Nazarova 2016; Saxinger 2016; Saxinger et al. 2016), the High North regions of Russia represent themselves as the ‘amenity-poor’ regions (the term by Greenwood et al. 1991). This means that wages should compensate for poor living conditions and an extreme climate. Thus, in the High North regions of Russia wages should be higher for a worker with the same productivity than in the rest of the country (Coelho and Ghali 1971; Greenwood et al. 1991; Bignebat 2003; Oshchepkov 2015). Empirical evidence for the existence of compensative differentials in the regions of the Russian Federation can be found in the works of Berger et al. 2003; Bignebat 2003; Oshchepkov 2015. Following from this, the migration of employees from the High North to other regions of the country is usually based on the size of wages and not limited by regional amenities due to their absence. The hypothesis that higher wages increase the number of employees both by reducing unemployment and increasing net migration from other regions was tested in this study.

2 Results and Discussion

The estimations were based on the regional data provided by the Rosstat from the surveys “Russia’s Regions. Socio-economic indicators” and “Economic and social indicators in the High North and equivalent areas” from 2005 to 2013. The observations were taken only from those regions whose territories are entirely included into the High North: the Republic of Karelia, the Komi Republic, the Republic of Sakha (Yakutia), the Tuva Republic, Kamchatka Krai, Arkhangelsk Oblast, Magadan Oblast, Murmansk Oblast, Sakhalin Oblast, Yamalo-Nenets Autonomous Okrug, Khanty-Mansi Autonomous Okrug-Yugra, and Chukotka Autonomous Okrug (see Fig. 18.1).

The regression analyses for migration were conducted first, following the methodology of Vakulenko (2016) by applying a dynamic panel data fixed effects model with spatial effects to estimate net migration among Russian regions. Given that migration is connected with employment and unemployment, they were also estimated in the regressions with the same specifications as the independent variables. It was assumed that changes in wages also affect the number of employees and migrants in the High North with some time lag, because workers need time to internalize the reduction in real wages, to take a decision about leaving and migrating to another region, to organize the move, etc. Employers also do not respond immediately to a change of requirement in the number of employees. They need time to understand the dynamics of wages and labour force population in the region. Three equations for the number of employees, number of unemployed and net migration with the following specifications (18.1) were estimated:

$$ \begin{array}{c}{Y}_{it}={\beta}_0+{\beta}_1 ln{W}_{it}+{\beta}_2 ln{W}_{it-1}+{\beta}_3 ln{W}_{it-2}\\ {}+{\beta}_4 ln{W}_{it-3}+{\beta}_5 ln{W}_{it-4}+{\beta}_6 ln{W}_{it-5}\\ {}+{\beta}_7 ln{W}_{it-6}+{\beta}_8 Trad{e}_{it}+{\beta}_9 Cons{t}_{it}\\ {}+{\beta}_{10} Manu{f}_{it}+{\beta}_{11} Ag{e}_{it}+{\beta}_{12} lnWome{n}_{it}\\ {}+{\beta}_{13} GR{P}_{it}+{\beta}_{14} Lif{e}_{it}+{\beta}_{15} Searc{h}_{it}+{\varepsilon}_{it}\end{array} $$
(18.1)

where it represents region i in time t; Yit – dependent variables. In this case the employment, migration and unemployment are connected, because there are almost no any other reasons for individuals to go to the High North regions, except for work and wages. This means that immigrants and native inhabitants can be employed or unemployed. If individuals are unemployed in the long term or they are not satisfied with real wages, they prefer to leave the High North territories. Three dependent variables were estimated to follow this logic: Emp – number of employees (1000 people); Unemp – number of unemployed (1000 people); Migr – net migration (1000 people).

The independent variables were as follows: W – average wage per month in a particular region (rubles); Trade, Const, Manuf – share of employees in trade, construction and manufacturing with respect to all the employees in the region (%), as a proxy for the structure of the regional economy. The joint share of employment in trade, construction and manufacturing industries, with respect to all the employees in the High North regions, was between 13% and 38%. These industries are present in all the regions, and the share of employees involved in these industries is not correlated with wages and Gross Regional Product (GRP), but correlated with the total number of employees in the region. This is not the case for the extractive industries, which usually have a particular geographic (regional) connection. Age–share of population of working age with respect to the total number of employees in the region (%); Women – number of women with respect to 1000 men (persons). Age and gender were added because there are special conditions of employment and payment for young people and women working in the High North, and Russian legislation provides for a 5-years earlier retirement for employees in the North. As a rule, not employed pensioners leave the northern territory because of the absence of regional amenities in respect of residence; i.e. the age structure of the labour force population consists almost completely of individuals of working age. GRP – Gross Regional Product (1000 rubles), as a variable that describes development of the regional economy (productivity in the model of local labour markets’ equilibrium); Life – life expectancy at birth (years), as a proxy for regional amenities; Search – as the average time taken for job search by the unemployed (months), as a proxy for the search intensity of unemployed job seekers in the regional labour market; ε - normally distributed error term. GRP and wages were deflated by the consumer price index (CPI) for the particular region in the respective years. The number of lags was determined on the basis of the formal Akaike and Bayesian information criterion (AIC and BIC criteria) and tested for the normal distribution of the error term. Estimation was carried out in the Gretl econometric package; the results of the estimation are shown in Table 18.1.

Table 18.1 Estimated results of the regression analysis.
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The results showed that wage level is a significant factor that positively affects the involvement of migrants in the workforce in the northern regions. Employment and unemployment react to changes in wages with different lag time and, in both cases, negatively. The results indicate that an increase in wage level attracts new employees from other regions, and this growth of the labour force population leads to the wage declining. More concretely, the estimated coefficients reflect the following picture. A growth in wages of 1% reduces the number of unemployed in the same year to 0.89 thousand people and increases net migration in the same year to 451 people. The simultaneous growth of wages and reduction in unemployment in the region most likely reflects increasing labour demand. In 3 years, the growth in wages by 1% has a negative impact on the number of employees of 0.17%, which reflects the narrowing of labour demand. Also, this ratio can be interpreted as a reduction of real wages after hiring additional employees with a lag of 3 years. Both interpretations demonstrate a reduction in the need of the employer to hire new employees.

At the same time, 3 years after the 1% wage growth, net migration increases by 911 people. The lag looks quite understandable due to the period necessary for information dissemination and making decisions about moving to another region. But, as is shown by the reaction of employment, employers do not have a need for additional workers anymore. In this context, the total decline of unemployment over 5 years by 0.89 thousand people compared with 0.35 thousand people, which has been estimated for the first year, looks quite logical. This difference demonstrates the positive dynamics of the number of unemployed from the second to the fifth years, which affects the signs of the coefficients of the lagged wages, although they are statistically insignificant. Thus, the results of this study reflect a surplus of labour supply with respect to labour demand in the High North of Russia. The growth of the individual labour supply in the High North is too high and leads to a decrease in wages and an increase in unemployment in the northern regions. In contrast, the reaction of labour demand is moderate or at least not so flexible. The lack of flexibility of Russian employers (companies) in the hiring and firing process was also empirically proven by Gimpelson et al. (2010).

Some control variables also appeared to be significant. Firstly, an increase of 1% in the number of women per 1000 men raises the number of employees in a region by 1.23%. It can be explained with the assumption that usually women are paid less than men; therefore they are cheaper for the employer and it raises their employment numbers. Empirically, the lower wages of women were estimated by Arabsheibani and Lau (1999) and Oshchepkov (2006). The positive sign and significance of the variable of life expectancy at birth is also revealing. Its growth over a year increases employment by 1.37%. It reflects the impact of the quality of life in a particular region on the dynamics of the labour force population in the High North. A growth in life expectancy at birth increases the level of regional amenities, and employees can agree to work for lower wages without leaving the northern areas. Increasing the share of people of working age in the general population by 1% leads to a growth in net migration of 3765 people. This fact reflects a concentration of the working age population in areas with higher wages, which attract immigrants. The structure of the regional economy also appears to be significant – a growth in the share of people employed in trade of 1% leads to an increase in net migration of 2337 people. According to the model of Moretti (2011), employment in trade, which refers to the industries that produce so-called ‘non-tradable goods’ such as services, in the region, is secondary to that in the main industries. In other words, the main industries of the regional economy are the first to develop. They attract more employees, and this affects the development of the service sector, including trade, in order to serve the needs of employees of the other industries. Consequently, the significance and the positive sign of the structure of the regional economy can be explained as a reaction of immigrants not only to the development of the trade itself but also to the growth of employment in the basic industries of the regional economy.

3 Conclusions

In general, it can be concluded that the peculiarities of employment in the High North of Russia are based on the specifics of labour supply and labour demand in those regions. A dynamic fixed effects model estimated using the aggregated regional panel data for the High North regions of Russia from 2005 to 2013 demonstrated that wage significantly and positively affects interregional migration to the northern regions. The estimations showed that even in the case where there is a need for additional employees in the High North regions, such a need will be covered only partly by immigrants and partly by the unemployed already living in those regions. This finding indirectly tells us about the surplus of labour supply with respect to labour demand in the High North regions of Russia. The growth of wages attracts immigrants from other regions of the country and eventually leads to lower wages and higher unemployment in the northern territories. It can be assumed that the artificial suppression of emigration from the High North regions of Russia may strengthen these negative consequences.