ECONOMIC DEPRESSION IN REGIONAL LABOR MARKETS AND SUBSIDY DEPENDENCE OF REGIONS

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1 International Journal of Civil Engineering and Technology (IJCIET) Volume 10, Issue 2, February 2019, pp , Article ID: IJCIET_10_02_180 Available online at ISSN Print: and ISSN Online: IAEME Publication Scopus Indexed ECONOMIC DEPRESSION IN REGIONAL LABOR MARKETS AND SUBSIDY DEPENDENCE OF REGIONS F.I. Mirzabalaeva, I.A. Shichkin, O.V. Neterebsky Plekhanov Russian University of Economics, Moscow, Russia ABSTRACT The article conducted clustering of regional labor markets applying the integral index of labor market depression, proposed by the authors in order to Russian regions ranking. In accordance with obtained clusters, the subsidies allocated by the government to equalize regional budgets have been distributed. The main research hypothesis is whether the extent of regional labor markets depression interrelated with subsidies aimed to balance out the budget provision of Russian regions. A substantial aspect in reducing regional economic depression is the formation of self-development mechanisms. Government capital injection cannot be effective without creating an appropriate manufacturing and social infrastructure. The research revealed obvious interrelation between subsidy dependence and depression in Russia's regional markets. As part of the study, on the basis of seven indicators, Russian regions have been ranked into five clusters by the degree of economic depression as of The authors evaluated the subsidy dependence as regions (per capita) and clusters as well [1]. Key words: Depressed Regional Labor Markets, Subsidy Dependence of Regions, Integral Index of Labor Market Depression, Clusterization of Regional Labor Markets. Cite this Article: F.I. Mirzabalaeva, I.A. Shichkin and O.V. Neterebsky, Economic Depression In Regional Labor Markets and Subsidy Dependence of Regions, International Journal of Civil Engineering and Technology (IJCIET), 10 (2), 2019, pp INTRODUCTION Over the past few years, Russia has expressed keener interest in research related to the notion of depression in various aspects and areas of life. From the economic viewpoint, it is recommended to regard as Russian depressive areas only those, in which a contraction in production, deteriorating living standards, mounting negative trends in employment and editor@iaeme.com

2 F.I. Mirzabalaeva, I.A. Shichkin and O.V. Neterebsky demographic conditions, the state of social infrastructure, etc. exceed both all-russian and macro-regional indicators. The extent of this deviation is a sign that the problem is extremely urgent and its resolution is a priority. In the modern conditions, it is very important to measure the degree of economic depression in regional labor markets in Russia and to reveal the correlation between their subsidy dependence and economic depression in regions when elaborating strategies, plans, and programs of socio-economic development. 2. LITERATURE REVIEW In scientific literature, the category of depressed labor market is a poorly explored phenomenon. This term has been separately mentioned in the contexts related to the period of the Great Depression or economic recession [2]. Meanwhile, there is no complete understanding of this phenomenon. Generally, the notion of an economically depressed region can be determined as a local territorial association, a distinctive feature of which is substantial and stable lagging in comparison to other regions [3]. The term economic depression was first widely applied in regard to a certain city, region or territory in the s during the global economic crisis [4]. Depression means a recession, repression (Latin depressio ), i.e. deterioration of the condition after a period of growth and stable performance. The term economically depressed region first appeared in Great Britain to characterize a number of the country s regions, which were affected most of all by the 1929 crisis (the Great Depression period) [5]. In accordance with Section of the Code of Federal Regulations (USA), for classifying territories as economically depressed regions, the Federal Deposit Insurance Corporation (FDIC) individually decides whether or not an organization will be classified as being located in an economically depressed region [6]. This definition is based on four criteria: (i) high rate of unemployment; (ii) substantial decline in non-agricultural employment; (iii) high-interest rates on foreclosed real estate at insured deposit institutions; and (iv) evidence showing a decrease in the value of real estate. According to the Internet resource Investor Glossary, a recession is often a sign of the future depressed market [7]. To substantiate empirically our method of assessing economic depression in regional labor markets and disclose interrelation with regions subsidy dependence, we will review some scientific approaches and their interpretation to define attributes of a depression in national labor markets. In the article Competing Theories of Unemployment and Economic Policy: Evidence from the US, Swedish and German Economies, an attempt was made to reveal key reasons for unemployment in the context of three alternative theoretical approaches [8]. Empiric analysis results show that the pace of economic growth is a major reason behind unemployment and, to a lesser extent, the labor market structure. These conclusions make it possible to vary approaches to fighting against unemployment and reducing economic depression in the labor market [8]. Ronnie Schöb s theory about life satisfaction shows that unemployment not only leads to higher economic depression in territories, financial difficulties of households but also to the loss of social identity [9]. European Union member states are currently focused on the measures to increase living standards of their citizens and national wealth by driving up national economies and reducing regional disproportions and amounts of regional subsidies [10]. In Romania, an applied economics group (GEA) proposed in 2007 a methodology to assess regional indices, in which two categories of indicators are used, namely (1) those taken from official statistical data (hard factors), and (2) those, which can be found by processing answers in questionnaires at the regional and local levels (soft factors) [11]. Applying the method of decomposing regional editor@iaeme.com

3 Economic Depression In Regional Labor Markets and Subsidy Dependence of Regions competitiveness by factors of influence, in the article Regional Competitiveness in Romania [12], the author considered differences between Romania s developing regions and compares the Romanian situation with the ones in the European Union and Hungary. The main recommendation is for a mid-term strategic target to strengthen national competitiveness and reduce regional disproportions. 3. MATERIALS AND METHODS Various methods are applied to assess the current condition of the labor market (for example, definitions of tension in the labor market, the labor market index, etc.) which, adequately in their own way, assess the situation surrounding regional labor markets. One of the approaches to the study of regional labor markets is the clusterization of regions by a number of indicators based on Kohonen maps [13]. However, we set another task, particularly, to try to tie labor market developments to socioeconomic conditions in the regions. During the analysis, we used data on 83 regions (excluding Sevastopol and Crimea), as we have analyzed this process in previous articles since 2010 and planned to review trends just in these regions. We believe that indicators of the Integral Depressed Labor Market Index (IDLMI) should consist of the following components. 1 the unemployment rate in the labor market of a Russian constituent entity, % (UR) (I empl ); 2 the overall unemployment rate in the labor market of a Russian constituent entity, % (OUR) (I ov ); 3 the average duration of job search in the labor market of a Russian constituent entity, months (ADJS) (I ads ); 4 the proportion of the unemployed who look for jobs 12 or more months in the labor market of a Russian constituent entity, % (UVB 12+) (I my ); 5 the tension ratio in the labor market of a Russian constituent entity, persons/vacancy (TRLM) (I lmt ); 6 average monthly wages accrued for employees across all organizations in the economy as a whole, rub (AMW) (I aw ); 7 the position of a constituent entity in the socio-economic ranking of Russian constituent entities, coefficient (PCE-SERRCE) (I pr ). All components were measured in various indicators, and they, therefore, need to be standardized. Separate indices were calculated for all IDLMI components using the single formula: In = Kfact Kmin (1) Kmax Kmin where In is the index of a component; K fact, K max, K min are factual, maximum and minimum values of each component. By calculating the index of each component from Table 1 using the above formula, we determine the integral index of labor potential development as a whole as the arithmetic average found by adding all particular indices and divided by their number. Reverse ratios are taken for four indicators (unemployment rate; average duration of job search; a proportion of the unemployed who look for jobs for 12+ months; tension ratio) because the growth of these indicators points to deterioration in the labor market. An increase in employment, average monthly wages and a region s position in the socio-economic development ranking demonstrates positive changes in the labor market. IDLMI = I empl +(1 I ov )+(1 I ads )+(1 I my )+(1 I lmt )+I aw +I pr (2) 7 The proposed methodology of calculating IDLMI is universal and makes it possible to analyze economic depression in labor markets at any level (among countries, regions, within Russian constituent entities, etc.) editor@iaeme.com

4 F.I. Mirzabalaeva, I.A. Shichkin and O.V. Neterebsky 4. RESULTS We used this methodology to calculate integral indices for all Russian constituent entities. The maximum index is recorded in the city of Moscow (0.797), and the minimum index in the Ingushetia Republic (0.166). When dividing regions into five clusters, we grouped them as follows: five clusters: 0.631:5= (a cluster range). Based on our study, the breakdown of the regions is given in Table 1. Table 1 Integral depressed labor market indices of the regions (2016) Cluster 1 federal subjects ( ) 7 regions The City of Moscow (0.797); the Yamal-Nenets Autonomous Okrug (0.787); the Chukotka Autonomous Okrug (0.740); the city of Saint Petersburg (0.731); the Khanty-Mansi Autonomous Okrug Yugra (0.728); the Sakhalin region (0.682); and the Magadan region (0.679). Cluster 2 federal subjects ( ) 42 regions Cluster 2 = (the Moscow region (0.663), the Kamchatka region (0.651), the Tatarstan Republic (Tatarstan) (0.649), the Nenets Autonomous Okrug (0.637), the Republic of Sakha (Yakutia) (0.629), the Leningrad region (0.628), the Murmansk region (0.623), the Khabarovsk Krai (0.619), the Nizhny Novgorod region (0.610), the Samara region (0.605), the Krasnoyarsk Krai (0.603), the Tyumen region (0.600), the Sverdlovsk region (0.598), the Primorie Krai (0.594), the Belgorod region (0.593), the Republic of Komi (0.585), the Republic of Bashkortostan (0.585), the Lipetsk region (0.584), the Tula region (0.582), the Chelyabinsk region (0.581), the Perm Krai (0.579), the Kaluga region (0.577), the Voronezh region (0.577), the Krasnodar Krai (0.572), the Udmurtia Republic (0.572), the Irkutsk region (0.571), the Arkhangelsk region (0.570), the Omsk region (0.568), the Kaliningrad region (0.568), the Rostov region (0.568), the Orenburg region (0.566), the Novosibirsk region (0.562), the Vologda region (0.561), the Novgorod region (0.561), the Tomsk region (0.560), the Kursk region (0.558), the Yaroslavl region (0.558), the Amur region (0.555), the Vladimir region (0.555), the Penza region (0.554), the Chuvash Republic (Chuvashia) (0.553), and the Mordovia Republic (0.553). Cluster 3 federal subjects ( ) 29 regions The Kirov region (0.544), the Ryazan region (0.543), the Bryansk region (0.538), the Kemerovo region (0.537), the Tver region (0.536), the Ulyanovsk region (0.533), the Smolensk region (0.532), the Volgograd region (0.532), the Tambov region (0.527), the Stavropol region (0.526), the Saratov region (0.525), the Astrakhan region (0.524), the Mari El Republic (0.513), the Ivanovo region (0.509), the Karelia Republic (0.501), the Orel region (0.498), the Kostroma region (0.497), the Altai region (0.491), the Khakassia Republic of (0.486), the Pskov region (0.486), the Zabaykalsky Krai (0.485), the Jewish Autonomous Region (0.481), the Buryatia Republic (0.480), the Kurgan region (0.471), the Kabardino-Balkaria Republic (0.454), the Kalmykia Republic (0.453), the Adygea Republic (Adygea) (0.446), the Altai Republic (0.437), and the North Ossetia Alania Republic (0.428). Cluster 4 federal subjects ( ) 4 regions The Chechen Republic (0.408), the Karachaevo-Cherkesskaya Republic (0.399), the Tyva Republic (0.391), and the Dagestan Republic (0.373) editor@iaeme.com

5 Economic Depression In Regional Labor Markets and Subsidy Dependence of Regions Cluster 5 federal subjects ( ) 1 region The Ingushetia Republic (0.166). In 2016, Cluster 1 comprised seven regions whose socio-economic indicators were as follows: the City of Moscow showed max PCE-SERRCE (80.89); the Yamal-Nenets Autonomous Okrug recorded min TRLM (0.7); the Chukotka Autonomous Okrug demonstrated min PCE-SERRCE (31.89), max AMW (86,647), max UR (79.6); the City of Saint Petersburg had min ADJS (4.6), min AMW (48,703), min OUR (1.65) and min UVB 12+ (7.7); the Khanty-Mansiysk Autonomous Okrug Yugra logged max TRLM (3.2); the Sakhalin region had min UR (68.6) and max OUR (6.34); the Magadan region boasted of max ADJS (7.8) and max UVB 12+ (30.0). The regions that constitute this cluster are self-sufficient and largely donor regions. This cluster includes regions with high wages, high employment, strong socio-economic development and low tension and overall favorable conditions in the labor market. Cluster 2 is the biggest cluster in which diametrically opposed indicators are seen in the following regions: the Moscow region has max PCE-SERRCE (68.6); the Kamchatka Krai shows min TRLM (1.4) and max UR (70.2); the Nenets Autonomous Okrug demonstrates max AMW (71,850); the Nizhny Novgorod region has min ADJS (4.6) and min UVB 12+ (8.9); the Chelyabinsk region sees max TRLM (6.1); the Irkutsk region has max OUR (8.83); the Novgorod region logs max ADJS (8.7), and max UVB 12+ (40.0); the Tomsk region shows min UR (62.0); the Amur region demonstrates max ADJS (8.7); the Chuvash Republic has min AMW (22,908); the Mordovia Republic shows min PCE-SERRCE (33.12). As we believe, this cluster can be marked as a cluster of regions with economic depression in the labor market because indicators vary considerably. For instance, the Komi Republic, which is part of the cluster, is named from time to time as a territory with high tension in the labor market. Cluster 3 is comprised of the regions that experience average economic depression in the labor market: the Kirov region has min ADJS (5.5), and min UVB 12+ (15.1); max UR (65.8); the Ryazan region shows min OUR (4.42); the Tver region has max UR (65.8); the Karelia Republic demonstrates max AMW (33,061), the Altai Krai has min AMW (21,202), the Jewish Autonomous Oblast has min TRLM (0.8), min PCE-SERRCE (13.14), the Kabardino-Balkaria Republic shows max ADJS (11.5), and max UVB 12+ (63.7); the Adygea Republic has min UR (55.5); the Altai Republic reports max OUR (12.04); the North Ossetia-Alania Republic has max TRLM (36.6); and the Saratov region has max PCE-SERRCE (45.18). Economic depression is evident in this cluster because it includes such labor excessive, subsidy-dependent regions as the Kabardino-Balkaria Republic and the Adygea Republic. Cluster 4 includes regions with high economic depression in the labor market: the Chechen Republic, the Karachaevo-Cherkessia Republic, the Tyva Republic, and the Dagestan Republic. This cluster has max ADJS (12.0) in the Tyva Republic, the Dagestan Republic shows min ADJS (8.4), the Tyva Republic has min TRLM (9.2); the Dagestan Republic has max TRLM (181.2); the Dagestan Republic demonstrates max PCE-SERRCE (42.56); the Tyva Republic is marked by min PCE-SERRCE (15.44), the Tyva Republic has max AMW (29,828), the Dagestan Republic shows min AMW (20,629); the Tyva Republic has max UVB 12+ (69.3), the Dagestan Republic shows min UVB 12+ (27.7); the Chechen Republic sees max UR (58.8), the Tyva Republic has min UR (52.0); the Tyva Republic shows max OUR (16.56), and the Tyva Republic has min OUR (10.95). Labor markets in the regions are under permanent pressure, with a focus on agriculture amid poorly developed infrastructure making things worse. This segment requires special programs to develop territories, legalization of a big segment of informal employment, programs to improve labor force mobility, etc. These regional labor markets will be unable to get out of depression on their own editor@iaeme.com

6 F.I. Mirzabalaeva, I.A. Shichkin and O.V. Neterebsky Cluster 5 is characterized by the deeply depressed labor market in the Ingushetia Republic (see data in Table 2 Cluster 5). When generalizing the study s results, we present average percluster indicators. Russia s constituent entities Table 2 Breakdown of average indicators by clusters ADJS TRL M PCE- SERRCE AMW UVB 12+ UR OUR Cluster , Cluster , Cluster , Cluster , Cluster , We see that ADJS in the labor market of Russia s constituent entities is minimum in Cluster 1 (6.5 months) and it steadily rises until Cluster 5 (13.1 months); the same pattern is seen in TRLM, ranging from 1.4 to persons/vacancy; UVB 12+ is also gradually on the rise (from 20.4 to 70.3) and OUR increases from Cluster 1 (3.6) to Cluster 5 (30.2). Naturally, the rest of the indicators are marked by a reverse tendency. Statistical data confirm our hypothesis about rising depression and related indicators from cluster to cluster. We assumed that investment from the federal budget to subsidized regions can contribute to balancing out labor market conditions, and, therefore, decided to determine the interrelation between subsidies and the economic condition of a region. It was established that five high-subsidy regions garner 30% of all subsidies. For the third consecutive year, the Dagestan Republic has seen the biggest amount of subsidies (59 billion rub in 2018), followed by the Sakha (Yakutia) Republic and the Kamchatka Krai (more than 39.3 billion rub). The amount of federal budget transfers increased for 47 regions and dropped for 26 regions. Substantial cuts are scheduled for the Perm Krai (2.6 billion rub), the Krasnoyarsk Krai (2.4 billion rub), and the Chelyabinsk region (1.4 billion rub). In addition, 12 regions will not get transfers to balance their budgets in 2018 (the Khanty-Mansi, Yamal-Nenets and Nenets Autonomous Okrugs, Tyumen, Samara, Sakhalin, Sverdlovsk, Moscow and Leningrad regions, Tatarstan, the city of Moscow and Saint Petersburg). These regions did not see any subsidies in 2017 either. As part of the problem under research, we think that it is necessary to review peculiarities of grouping regions into clusters, which were formed based on IDLMI. Overall, our calculations showed that per-capita subsidies in Russia totaled rub. Table 3 Main indicators that characterize the distribution of subsidies to balance out regional budgets in Russia s constituent entities by clusters (in line with the IDLMI methodology) Permanent population as of January 1, 2016, thousand Budget balance before distribution of subsidies Total subsidies in 2017, thousand rub Budget balance after distribution of subsidies Percapita subsidies, rub Cluster 1 2, ,218, Cluster 2 2, ,503, , Cluster 3 1, ,715, , Cluster 4 1, ,779, , Cluster ,642, ,279.3 Cluster 1 includes five donor regions but given two subsidy-dependent regions (the Chukotka Autonomous Okrug and the Magadan region) per-capita subsidies total rub on average across the cluster (Russia s national average is 4, rub). These numbers editor@iaeme.com

7 Economic Depression In Regional Labor Markets and Subsidy Dependence of Regions characterize the most favorable position among other clusters and correlation with the situation in regional labor markets of this cluster. Cluster 2 is the biggest cluster in terms of number (42 regions) with per-capita subsidies from the federal budget standing at 2, rub. Eight regions are donors (the Moscow region, the Tatarstan Republic, the Nenets Autonomous Okrug, and Leningrad, Tyumen, Sverdlovsk and Kaluga regions). Cluster 3 regions (Table 3) have on average per-capita subsidies of 6, rub, or above the national average. Per-capita subsidies varied from 2, rub in the Astrakhan region to 44, rub in the Altai Republic. The last two clusters heavily rely on subsidies (Table 3), as confirmed by the amounts of per-capita subsidies. The breakdown of the regions is in line with the breakdown of regional labor markets based on IDLMI. 5. DISCUSSION When studying the peculiarities related to the operation of subsidy-dependent regions, a group of experts came to the following conclusions. Fixed assets are concentrated most of all in subsidy-free regions (fixed asset value in 12 subsidy-free regions accounts for over 52% of the total fixed asset value of all Russian regions); most subsidy-dependent regions are characterized by low financial support, lack of possibilities to strengthen their economic base due to insufficient record of socio-economic conditions of regional development; sizeable subsidies are transferred to the regions with difficult natural climate conditions, etc. [14]. As long depression periods in the regions and labor markets lead to irreversible changes in the economy and heighten social tensions in the society, it is essential to determine measures that would contribute to reducing depression trends in the regional labor markets and, as a consequence, to balancing out socio-economic conditions in the regions [15]. 6. CONCLUSIONS Risks associated with socio-economic problems are high in subsidy-dependent regions that do not have or have insufficient resources to develop their economies. The development of regional labor markets substantially depends on a region s economic development as a whole. Bigger flows of investment into the regions do not always reduce economic depression in the regional labor markets, and, therefore, it is necessary to develop real sectors of the economy taking into account the regions competitive advantages to encourage businesses to create more jobs. Curbing economic depression in the regional labor markets in Russia is a priority government target. In this connection, the methodology, which we have proposed, makes it possible to define the interrelation between subsidy dependence of the regions and their economic depression. It is a tool that can be used to balance out socio-economic development in the country s territories. ACKNOWLEDGEMENTS The paper has been prepared as part of the scientific project Depressed labor markets: Criteria of assessment, factors of formation, areas for development supported by Plekhanov Russian University of Economics editor@iaeme.com

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