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ECONOMIC AND STATISTICAL ANALYSIS OF EMPLOYMENT AND UNEMPLOYMENT AND EVALUATION OF THEIR IMPACT ON THE PREMEDITATED BANKRUPTCY OF ENTERPRISES IN THE REGIONS OF RUSSIA THROUGH BALANCED SCORECARD
A.I. Utkin, Graduate Student Ivanovo State University (Russia, Ivanovo)
DOI:10.24412/2411-0450-2021-4-2-212-219
Abstract. The article examines an economic and statistical analysis of employment and unemployment in the regions of Russia and identifies risk-forming factors that determine the occurrence of bankruptcy of enterprises with a damage in the labor potential of the population. An assessment of the impact of employment and unemployment factors on the premeditation of bankruptcy of commercial organizations at the regional level is carried out within the framework of the balanced scorecard concept. As a result of identification of regional trends of premeditated bankruptcy, the strategic prospects for maintaining the continuity of the enterprise's activities are determined based on the analysis of bankruptcy according to the model of R.S. Saifullin and G.G. Kadykov.
Keywords: employment, unemployment, risk-forming factors of bankruptcy, premeditation of bankruptcy, balanced scorecard, the model of R.S. Saifullin and G.G. Kadykov.
The emergence of structural shifts in employment and unemployment due to the accumulation of bad payables leads to the bankruptcy of a commercial organization and is often the result of premeditated actions of the head or founder of the enterprise in the abuse of official powers [1]. At the same time, premeditated bankruptcy provides taxing operations for the sale of the liquidation mass and the liquidation of the organization's property [1; 2]. Managers who intend to lead an enterprise to a state of bankruptcy and worsen the situation of employees of the organization proceed from the fact that operations of the sale of liquidated objects are subject to value added tax on a general basis and thereby cause huge financial damage to the property of the organization. Unambiguous identification and forecasting of risk-forming factors of deliberate bankruptcy of a commercial organization which arise when the labor potential deteriorates and the share of unemployment increases can be carried out by developing a process model within the balanced scorecard concept [2].
To find ways to identify the factors of unemployment, regional trends of premeditated bankruptcy and ways to comprehensively manage the financial well-being and overall
financial performance of the enterprise, it is necessary to improve the integrated methodology of economic and statistical analysis. The modification of this tools consists in the development of a unified list of criteria for selecting the key factors of premeditated bankruptcy, which comprehensively characterize the situation of the unemployed and the damage to the financial condition of a commercial organization, correlated with all the parameters of financial activity and forming projections of the balanced scorecard [3]. The economic and statistical analysis proposed for application in the framework of the criteria approach will allow to select risk-forming factors based on the determination of the compliance of the regional trends generated by them with the premeditation of bankruptcy and the economic meaning of the criteria. In this regard, based on the modified methodology, it will be possible to reduce the entire multi-faceted assessment of bankruptcy to the analysis of the key factors selected on the basis of criteria and using the analytical model of R.S. Saifullin and G.G. Kadykov to form strategic projections of the organization's activities within the framework of the balanced scorecard, comprehensively covering the damage from unemployment and the direc-
tions (prospects) of improving the financial and economic potential.
According to the conducted research, the economic activity of the population working
at enterprises aged 15-72 years in the federal districts and regions for the IV quarter of 2015 was heterogeneous (table 1).
Table 1. Economic activity of the population aged 15-72 years by federal districts and regions for the IV quarter of 2015 [4]
Subject of the Russian Federation Economically active population, thousand people Including The level of economic activity of the population, % Employme nt level, % Unemploym ent level, %
employ ment unemploy ment
1 2 3 4 5 6 7
Russian Federation 76701.5 72329.6 4371.9 69.2 65.3 5.7
Central Federal District 21182.1 20431.7 750.4 71.0 68.4 3.5
Belgorod region 815.5 783.4 32.0 69.3 66.5 3.9
Bryansk region 627.9 602.5 25.3 66.9 64.2 4.0
Vladimir region 766.6 714.2 52.4 71.4 66.6 6.8
Voronezh region 1160.0 1107.1 52.9 65.3 62.3 4.6
Ivanovo region 546.9 517.5 29.5 69.1 65.4 5.4
Kaluga region 544.2 521.0 23.2 70.9 67.9 4.3
Kostroma region 323.0 305.5 17.5 65.7 62.1 5.4
Kursk region 571.6 547.6 24.0 67.6 64.8 4.2
Lipetsk region 586.9 564.3 22.6 66.8 64.2 3.9
Moscow region 3911.0 3779.6 131.5 71.0 68.6 3.4
Oryol region 393.8 368.2 25.6 67.4 63.0 6.5
Ryazan region 528.8 502.8 26.0 61.0 58.0 4.9
Smolensk region 526.5 496.7 29.8 70.3 66.3 5.7
Tambov region 526.6 502.5 24.1 64.5 61.6 4.6
Tver region 705.8 665.0 40.8 70.6 66.5 5.8
Tula region 807.1 777.3 29.8 69.3 66.7 3.7
Yaroslavl region 699.8 660.9 38.9 72.8 68.7 5.6
Moscow 7140.2 7015.8 124.4 75.5 74.1 1.7
North-Western Federal District 7596.7 7230.4 366.4 71.2 67.8 4.8
Karelia Republic 327.2 299.1 28.1 67.8 62.0 8.6
Komi Republic 480.4 441.1 39.3 71.8 65.9 8.2
Arkhangelsk region 608.2 562.2 46.0 67.3 62.2 7.6
Vologda region 609.7 560.6 49.1 68.0 62.5 8.0
Kaliningrad region 533.0 504.5 28.6 71.7 67.9 5.4
Leningrad region 959.1 912.0 47.1 69.7 66.3 4.9
Murmansk region 457.2 420.8 36.4 75.5 69.5 8.0
Novgorod region 321.7 305.8 15.9 68.8 65.4 5.0
Pskov region 332.0 311.5 20.5 66.7 62.6 6.2
Saint-Petersburg 2968.2 2912.8 55.4 73.8 72.4 1.9
Southern Federal District 7111.5 6646.3 465.2 67.5 63.1 6.5
Adygea Republic 202.7 185.1 17.6 61.3 56.0 8.7
Kalmykia Republic 146.9 133.0 13.8 70.2 63.6 9.4
Krasnodar Krai 2735.1 2574.0 161.1 67.4 63.4 5.9
Astrakhan region. 526.9 486.2 40.7 69.4 64.0 7.7
Volgograd region 1330.7 1232.5 98.2 68.5 63.5 7.4
Rostov region 2169.2 2035.4 133.8 67.1 63.0 6.2
North Caucasus Federal District 4509.4 3989.1 520.4 65.5 58.0 11.5
Dagestan Republic 1310.5 1158.4 152.1 62.1 54.9 11.6
Economically Including The level of
Subject of the Russian Federation active population, thousand people employ ment unemploy ment economic activity of the population, % Employme nt level, % Unemploym ent level, %
1 2 3 4 5 6 7
Ingushetia Republic 223.4 154.6 68.7 72.4 50.1 30.8
Kabardino-Balkar
Republic 434.7 390.7 44.0 68.5 61.5 10.1
Karachay-Cherkess Republic 223.1 184.8 38.3 64.7 53.6 17.2
North Ossetia - Alania
Republic 325.9 289.9 36.0 63.8 56.7 11.0
Chechen Republic 621.0 517.5 103.5 71.3 59.4 16.7
Stavropol Krai 1370.9 1293.1 77.8 65.3 61.6 5.7
Volga Federal District 15463.5 14735.8 727.7 68.7 65.5 4.7
Bashkortostan Republic 2020.5 1896.7 123.8 66.8 62.7 6.1
Mari El Republic 358.0 339.3 18.7 68.6 65.0 5.2
Mordovia Republic 446.1 426.5 19.6 70.9 67.8 4.4
Tatarstan Republic 2073.3 2002.1 71.2 71.7 69.2 3.4
Udmurt Republic 808.4 760.5 47.8 71.1 66.9 5.9
Chuvash Republic 660.8 626.6 34.2 70.7 67.1 5.2
Perm Krai 1300.5 1224.0 76.5 65.8 61.9 5.9
Kirov region 677.9 641.0 36.9 68.5 64.8 5.4
Nizhny Novgorod region 1759.2 1685.2 74.0 70.3 67.4 4.2
Orenburg region 1009.2 960.9 48.3 67.4 64.2 4.8
Penza region 691.0 659.0 32.0 66.3 63.2 4.6
Samara region 1754.3 1695.8 58.5 70.9 68.5 3.3
Saratov region 1248.3 1190.5 57.8 65.5 62.5 4.6
Ulyanovsk region 656.1 627.7 28.3 67.1 64.2 4.3
Ural
Federal District 6478.6 6065.5 413.1 70.1 65.6 6.4
Kurgan region 424.9 390.0 34.9 65.7 60.3 8.2
Sverdlovsk region 2256.6 2104.1 152.5 69.2 64.5 6.8
Tyumen region 1935.7 1841.6 94.1 71.4 67.9 4.9
Yamalo-Nenets Aut.Okr. 322.6 312.8 9.8 77.0 74.6 3.0
Chelyabinsk region 1861.4 1729.9 131.5 70.9 65.9 7.1
Siberian Federal District 9840.5 9001.9 838.6 67.9 62.1 8.5
Altai Republic 100.3 88.9 11.4 68.1 60.4 11.4
Buryatia Republic 452.5 405.3 47.2 63.7 57.1 10.4
Tyva Republic 134.3 106.4 27.9 64.8 51.3 20.8
Khakassia Republic 257.7 239.5 18.2 64.8 60.2 7.1
Altai Krai 1178.8 1057.0 121.8 65.7 58.9 10.3
Trans-Baikal Krai 535.9 480.2 55.7 66.3 59.5 10.4
Krasnoyarsk Krai 1507.7 1407.2 100.5 69.2 64.6 6.7
Irkutsk region 1268.1 1150.5 117.6 70.4 63.9 9.3
Kemerovo region 1370.0 1264.7 105.3 66.7 61.6 7.7
Novosibirsk region 1466.9 1350.8 116.2 70.3 64.8 7.9
Omsk region 1036.5 962.0 74.5 69.3 64.3 7.2
Tomsk region 531.8 489.4 42.4 64.6 59.5 8.0
Far Eastern Federal
District 3358.2 3143.6 214.6 69.8 65.3 6.4
Sakha (Yakutia) Rep. 494.3 458.3 36.1 69.9 64.8 7.3
Kamchatka Krai 187.7 178.8 8.8 73.8 70.4 4.7
Primorsky Krai 1046.3 972.6 73.7 69.1 64.2 7.0
Khabarovsk Krai 723.4 679.1 44.3 69.2 65.0 6.1
Amur region 415.0 392.7 22.3 67.3 63.7 5.4
Magadan region 94.5 90.7 3.8 78.7 75.5 4.0
Sakhalin region 277.9 260.7 17.2 72.2 67.7 6.2
Subject of the Russian Federation Economically active population, thousand people Including The level of economic activity of the population, % Employme nt level, % Unemploym ent level, %
employ ment unemploy ment
1 2 3 4 5 6 7
Jewish auth. region 86.2 79.1 7.1 66.8 61.3 8.3
Chukotka auth. distr.. 32.8 31.5 1.3 83.3 80.0 3.9
Crimean Federal District 1160.8 1085.2 75.6 65.2 60.9 6.5
Crimea Republic 956.1 892.0 64.1 64.2 59.9 6.7
Sevastopol 204.7 193.2 11.5 69.8 65.9 5.6
As it can be seen from the table, in 2015, the lowest level of unemployment that meets the criteria (lack of work or income-generating occupation, job search and readiness to start it in the surveyed week), and the highest level of employment of the population were recorded in the Central Federal District, the highest level of unemployment and the lowest level of employment-in the North Caucasus Federal District. Moscow and St. Petersburg look the most advantageous. It has the highest employment rate and the lowest unemployment rate. The economically active population in these cities also has the largest share.
The most tense situation in terms of unemployment is in Ingushetia, where the unemployment rate is the highest among all regions - 30.8%. There is also the lowest level of employment in the country - 50.1%, despite the fact that the level of the economically active population is quite high - 72.4%. In other North Caucasian republics, especially in the Chechen Republic and Karachay-Cherkessia, the situation is also quite serious, but not as acute as in Ingushetia.
The second most disadvantaged region in terms of unemployment is the Republic of Tyva. The unemployment rate there is 20.8%, with a very low level of employment - 51.3% and a fairly low proportion of the economically active population.
The Chukotka Autonomous District and the Magadan region hold the palm in terms of
the economically active population and employment. The level of the economically active population in Chukotka is 83.3%, the employment rate is 80%, and unemployment is one of the lowest in the country - 3.9%. Slightly worse indicators for the Magadan region. The level of the economically active population there is 78.7%, the employment rate is 75.5%, and the unemployment rate is only 4%. The Yamalo-Nenets Autonomous District is very close to Chukotka and Magadan in terms of employment and unemployment. There is a high percentage of the economically active population of 77%, a solid level of employment - 74.6%, and unemployment is one of the lowest in the country, second only to Moscow and St. Petersburg.
It should be noted that in the Crimean Federal District and in the Republic of Crimea, a fairly low level of employment is recorded -60.9% and 59.9%, respectively, which is only slightly higher than in the North Caucasus Federal District. However, the unemployment rate here is average - 6.5-6.7 %, and in Sevastopol it is almost the same and even slightly lower than the national average - 5.6%.
We investigated the correlation relationships between employment and unemployment indicators in the regions of the Russian Federation and assessed the impact of risk-forming factors on the premeditation of bankruptcy by calculating the Pearson linear correlation coefficient using the STATISTICA 8.0 program (table 2).
Table 2. Correlation relationships between employment and unemployment indicators in the regions of the Russian Federation
Variable Correlations (Spreadsheet!) Marked correlations are significant at p < ,05000 N=87 (Casewise deletion of missing data)
Эк активн насе пение Занятые Безработные Уровень эк.активности Уровень занятости Уровень безработицы
Эк. активн. население 1.00 1,00 0.70 0.14 0.27 -0.29
Занятые 1.00 1.00 0.68 0.15 0.28 -0.30
Безработные 0,70 0,68 1.00 -0.10 -0.13 0.10
Уровень эк активности 0.14 0.15 -0,10 1.00 0,82 -0.20
Уровень занятости 0,27 0,28 -0.13 0,82 1.00 -0,72
Уровень безработицы -0.29 -0.30 0.10 -0.20 -0.72 1.00
Note: statistically significant linear correlation coefficients are identified at p<0.05
The closest correlations were found between the number of economically active population, the number of employed (r =1.0, at p<0.05) and the unemployed (r = 0.7, at p<0.05). The average correlation between the number of employed and unemployed was traced (r = 0.68, at p<0.05). The level of economic activity was closely correlated with the level of employment (r = 0.82, at p<0.05).And a negative correlation was established between the levels of employment and unemployment (r = -0.72, at p<0.05). A weak but statistically significant correlation was found between the number of economically active population and the level of employment (r = 0.27 at p<0.05). A similar relationship was recorded between the number of employed and the level of employment (r = 0.28 at p<0.05). However, a weak negative but significant relationship was found between the number of economically active population in Russian regions and the level of unemployment (r = -0.29 at p<0.05). A similar relationship was established between the number of employed and the level of unemployment (r = -0.30 at p<0.05).
The conducted correlation analysis showed that the majority of indicators of economic activity of the population, employment and unemployment are statistically significantly interrelated. In this regard, in Russia, among the 85 subjects of the Federation, a rather motley picture is obtained in terms of well-being or disadvantage in terms of the levels of the economically active population, employment and unemployment. Currently, the most prosperous cities are Moscow, St. Petersburg, the Chukotka Autonomous District, the Magadan region and the Yamalo-Nenets Au-
tonomous District. The Republic of Ingushetia and other North Caucasian republics, as well as the Republic of Tyva, should be considered the least prosperous. These subjects of the Russian Federation should be given the closest attention to organize work to combat unemployment and overcome the deterioration of the labor potential of enterprises, to create jobs and improve the economy. In this regard, the impact of employment and unemployment factors on the premeditation of bankruptcy should be assessed as harmful and fundamental.
As a result of the economic and statistical analysis, it is established that the typical risk-forming factors of employment and unemployment at the regional level, which in the long term are essential for the occurrence of deliberate bankruptcy, are:
- the desire to appropriate for further third-party use a part of the property or the undisclosed surplus of a particular product (material), not taken into account by the page and general results, concerning a significant part of the values and decisive positions (influence on the change in the coefficient of security of own funds);
- the desire to hide illegal discrepancies or corrections that have arisen as a result of the abuse of the company's management of its property, and to eliminate the circle of persons interested in disclosing illegal actions (influence on the change in the current liquidity ratio);
- the desire to hide the invalidity of the signatures of the materially responsible persons on the financial and economic documents (if it is established on the basis of documentary data that the materially responsible
person refused to sign the documents on the grounds of incorrect and incomplete verification of values and required rechecking them, and the administration did not take appropriate measures) (influence on the change in the turnover ratio of current assets);
- the desire to expand the scope of business and optimize the staff when implementing a merger of several companies (a bankrupt enterprise is included in an organization with a high level of financial efficiency) (impact on changes in the return on sales and return on equity).
When assessing the continuity of the activities of JSC "Polyot" Ivanovo Parachute Plant, the analysis of the premeditation of bankruptcy for 2018-2019 according to the model of R.S. Saifullin and G.G. Kadykov revealed a low probability of intentional bankruptcy of the organization (the probability of bankruptcy R significantly exceeds 1). The company is characterized by significant financial stability and a high level of financial stability and solvency and is not affected by the zone of risk-forming factors of employment and unemployment, which is directly related to the balance of organizational and managerial, accounting, financial and strategic processes of the enterprise. The reliability of the financial and economic potential of JSC "Polyot" is confirmed by the significant indicators of the turnover ratio of current as-
These factors of employment and unemployment generate regional trends of intentional bankruptcy [5]. On the example of JSC "Polyot" Ivanovo Parachute Plant, the strategic prospects for maintaining the continuity of the company's activities and its exposure to deliberate bankruptcy for 2018-2019 were determined using the model of R. S. Saifullin and G. G. Kadykov and the balanced score-card concept, taking into account the impact of risk-forming factors of employment and unemployment on the financial performance of the organization (table 3).
sets, return on sales and return on equity, as well as the values of the availability of own working capital and the current liquidity ratio, which fully meet the established standards. However, the negative dynamics of the financing ratio (the ratio of own and borrowed funds) (a decrease of 0.16), the probability of bankruptcy (a decrease of 20.43) and the current liquidity ratio (a decrease of 0.3) in 20182019 demonstrates a decrease in the efficiency of financial activities and a deterioration in financial well-being by the end of the reporting year, caused by the crisis manifestations of economic motives for bankruptcy. The observed negative trends associated with the instability of the financial condition are the only problem (risk) area of the organization's activity, and in all other aspects the company is
Table 3. Comparative assessment of the continuity of the activities of JSC "Polyot" Ivanovo Parachute Plant in 2018-2019, taking into account the influence of risk-forming factors of employment and unemployment on the premeditation of bankruptcy within the framework of the balanced scorecard concept
N Coefficient 2018 2019 Deviation
1. Ratio of own and borrowed funds 2.56 2.40 -0.16
2. The probability of bankruptcy according to the model of R. S. Saifullin and G. G. Kadvkov 53.70 33.27 -20.43
3. Coeffic ient of balance coverage (liquidity) i T 3.71 3.41 -0.30
Strategic projections of the Balanced Scorecard rroiection 1 r manee ^ ^C. Projection 2 "Clients" Positive impact
Negative | Projection 3 "Internal processes"
Negative impact j Projection 4 "Innovations" - ^ -1 ».
not affected by the factors of employment, unemployment and premeditated bankruptcy.
In this regard, the main direction of ensuring the protection of the financial activities of JSC "Polyot" from premeditated bankruptcy should be the constant regulation of the ratio of own and borrowed funds while stimulating the growth of equity capital (ensuring financial potential and independence) and preventing the accumulation of the most urgent obligations (accounts payable).
Thus, the identification of typical risk-forming factors of employment and unemployment, which determined the high fre-
quency of risks of a crisis financial condition, premeditated bankruptcy and the resulting increase in the share of damage to the labor potential of the population, made it possible to assess the continuity of financial activity and the susceptibility to premeditate bankruptcy of a particular enterprise of JSC "Polyot" Ivanovo Parachute Plant. The company has a reliable potential and is not significantly affected by negative factors, but it is necessary to stabilize the sources of financing in the transition to a multidisciplinary economic system based on the balanced score-card model.
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ЭКОНОМИКО-СТАТИСТИЧЕСКИЙ АНАЛИЗ ЗАНЯТОСТИ И БЕЗРАБОТИЦЫ И ОЦЕНКА ИХ ВЛИЯНИЯ НА ПРЕДНАМЕРЕННОЕ БАНКРОТСТВО ПРЕДПРИЯТИЙ В РЕГИОНАХ РОССИИ С ИСПОЛЬЗОВАНИЕМ СБАЛАНСИРОВАННОЙ СИСТЕМЫ ПОКАЗАТЕЛЕЙ
А.И. Уткин, магистрант
Ивановский государственный университет
(Россия, г. Иваново)
Аннотация. В статье проведен экономико-статистический анализ занятости и безработицы в регионах России и выявлены рискообразующие факторы, определяющие возникновение банкротства предприятий при ухудшении трудового потенциала населения. Проведена оценка влияния факторов занятости и безработицы на преднамеренность банкротства коммерческих организаций на региональном уровне в рамках концепции сбалансированной системы показателей. В результате идентификации региональных тенденций преднамеренности банкротства определены стратегические перспективы сохранения непрерывности деятельности предприятия на основе анализа банкротства по модели Р.С. Сайфуллина и Г.Г. Кадыкова.
Ключевые слова: занятость, безработица, рискообразующие факторы банкротства, преднамеренность банкротства, сбалансированная система показателей, модель Р.С. Сайфуллина и Г.Г. Кадыкова.
