Regional Development. Analysis and Forecasting of the Number of

Unemployed in the Central Federal District

Natalia A. Marchuk

and Anton L. Kulentsan

Department of Information Technology and Digital Economy, Ivanovo State University of Chemical and Technology, 7,

Sheremetevsky Avenue, Ivanovo, Russia

Keywords: Sustainable Development, Forecasting, Wages, Unemployment, Growth Rate, Growth Rate, Demographic

Situation, Population, Jobs.

Abstract: The market economy is characterized by a number of parameters. One of them is competition in various fields.

Which leads to restrictions in jobs and as a result to the appearance of unemployment. The unemployment

rate is a rather complex socio-economic phenomenon that forms the attitude not only to local authorities, but

also to the activities of the government of the Russian Federation as a whole. The economic aspect plays a

very important role in the study of unemployment. However, it is necessary not to forget the various social

factors that influence the sustainable development of the unemployment rate. This article is devoted to the

study of changes in the main indicators of the level of the unemployed population. The dynamics of the

officially registered number of unemployed people aged 15-72 in the entire Central Federal district is analyzed.

The article discusses the characteristics of basic and chain indicators, calculated and quantified factors (wages,

population, the increase in high-performance workplaces, the cost of a fixed set of consumer goods and

services, the age structure of the population above the working age, and net migration) on the basis of which

it is possible to investigate the dynamics of changes in the level of unemployed population. The analysis

allowed to construct a regression model that reveals the relationship between the wages of workers, population

growth in high-performance workplaces, with the cost of fixed set of consumer goods and services, the age

structure of population above the working age, migration population growth and growth in the number of

unemployed in all regions of the Central Federal district. The paper also makes a forecast of the distribution

of the number of unemployed in the district in question for 2021.

1 INTRODUCTION

Economic instability shall be characterized by such a

phenomenon as unemployment. Unemployment is a

complex, multidimensional socio-economic

phenomenon, when a part of the economically active

population is not employed in social production,

cannot realize their physical and mental abilities with

the help of the labor market (the latter is due to the

lack of suitable jobs) (Slyunyayeva, 2014). In Russia,

the concept of unemployment arose during the

transition to a market economy. There have been

global changes in the structure of the national

economy of the country, which entailed changes in

the use of labor resources. The bankruptcy of many

organizations led to deterioration of the socio-

https://orcid.org/0000-0002-2024-0920

https://orcid.org/0000-0002-4012-9218

economic situation in the country and had an impact

on the effectiveness of the usage accumulated

production potential, that caused a sharp rise in

unemployment among the population. The possibility

of population migration to far foreign countries that

arose in the new economic conditions led to the loss

of a large number of highly qualified specialists,

which led to a decrease in the quality of the domestic

labor force. While the human resources of the state

represent the base - the foundation for ensuring

internal political, economic, social and other stability

(Sadovnikova and Makhova, 2019). The employment

rate of the population and the unemployment rate in

the region are the main social factors that form the

attitude not only to local authorities, but also to the

activities of the federal government as a whole. High

102

Marchuk, N. and Kulentsan, A.

Regional Development. Analysis and Forecasting of the Number of Unemployed in the Central Federal District.

DOI: 10.5220/0010664600003223

In Proceedings of the 1st International Scientiﬁc Forum on Sustainable Development of Socio-economic Systems (WFSDS 2021), pages 102-108

ISBN: 978-989-758-597-5

unemployment rates lead to social tension in society

(Abramov and Gavrikov, 2019). Such eminent

scientists of economic science as Adam Smith, Karl

Marx, Arthur Pigou, David Reckardo, John Maynard

Keynes, Jean Baptiste Say, Alfred Marshall

(Ramazanova, 2017) were widely engaged in these

questions. At the present time, many works are

devoted to the study of the unemployment problem in

the Russian Federation, such economists as

Bogachenko E.D., Kuznetsova O.I., Brusyanina M.S.

(Kostikova et al., 2013). They identify the following

reasons for unemployment - low effective demand,

surplus population and the introduction of new

technologies. The authors of the works (Borzenkov,

2012; Bashkirova, 2016) highlight the following

reasons for unemployment: an increase in the

minimum wage, the introduction of new

technologies, an economic downturn and seasonal

changes in the level of production in certain sectors

of the economy. The author of the work

(Mikhal'kevich, 2018) notes that the main reason for

the large number of unemployed is high and

inflexible downward wages. Although this problem is

reflected in the works of foreign and domestic

scientists, various measures taken by the Russian

Government have not yet led to its solution

(Kostikova et al., 2013; Urusova, 2019).

The main types of unemployment include the

following:

- Structural - is when the unemployed do not have

the necessary qualifications, or live in places that do

not allow them to find vacancies. In addition, this type

of unemployment is characterized by a discrepancy

between the quality and quantity of labor force and

the quality and quantity of jobs existing in this state.

The main reason for the appearance of this type of

unemployment is the changing structure of

population consumption. This type of unemployment

is long-term.

- Frictional - is a result associated with finding a

job and is due to the fact that it takes time for an

unemployed person to find a job that he likes. Thus,

frictional unemployment characterizes the

employee's desire to improve his working conditions.

This type of unemployment is short-term.

- Cyclical - is a consequence of the lack of

aggregate demand in the phases of recession and

depression of the business cycle, when there is a

shortage of jobs. Moreover, it does not depend on the

qualifications of the employee (Mal'tseva, 2020;

Kurakov et al., 2017).

Although there are many types of unemployment,

various measures to reduce it need to be considered

(Sazhina and Serushkina, 2015):

1. Unemployment benefits payment;

2. Creation of additional jobs;

3. Improving information support of the labor

market;

4. Creation of state services for retraining and

requalification;

5. Creation of conditions for increasing demand

for goods and services;

6. Eliminating factors that reduce labor mobility

(Zaval'nyuk, 2016):

7. Creation of conditions for the employment of

disabled people, including creation of special jobs for

people with disabilities.

Despite significant elaboration of issues related to

unemployment, it should be noted that over time, the

factors affecting unemployment (Kulentsan and

Marchuk, 2019) are constantly changing, which

requires constant in-depth study and analysis of these

issues.

The relevance of this work lies in the fact that the

problem associated with the number of unemployed

is one of the main problems in the modern world.

Without solving this problem, it is impossible to

overcome the economic slack and establish effective

economic activity in our country. Therefore, the

analysis of such indicators as the wages of workers,

the population, the growth of highly productive jobs,

the cost of a fixed set of consumer goods and services,

the age composition of the population over the

working age and the migration growth of the

population affecting the unemployment rate is an

important and key task. As a result, it is necessary to

consider the correlation and regression model and the

listed factors.

The work analyzes modern approaches to

economic and mathematical modeling (Goncharenko,

2011).

Correlation and regression analyzes are among

the main statistical methods for processing

experimental data (Dolgov, 2013; Kulentsan and

Marchuk, 2020; Polozhentseva, 2017).

2 RESEARCH METHODOLOGY

Currently, the correlation analysis technique shall be

used to identify the relationship between variables

and assess the tightness of the relationship.

Regression analysis shall be used to establish the

shape and study the relationship between variables.

Consider a sample of two variables A and B. The

multivariate distribution of variables A and B can be

represented as the density of the 2nd normal law of

distribution (1):

Regional Development. Analysis and Forecasting of the Number of Unemployed in the Central Federal District

103

𝑓



𝐴,𝐵



















𝑒𝑥𝑝























2𝜌







































 (1)

In this case, the probability density is determined

by 5 parameters: mathematical expectation (MA =

μA, MB = μB), variance (DA = σ2A, DB = σ2B) and

pair correlation coefficient (ρ). This coefficient shows

the closeness of the linear relationship between the

sampled values of A and B.

𝜌𝑀









∙









 (2)

Pair correlation coefficient properties:

1. - 1 ≤𝜌 ≤ 1 - 𝜌 takes values on the segment [-1;

1], and the closer

𝜌

to 1, the closer the relationship

between features A and B;

2. at

𝜌

1, the correlation between signs A and

B is a linear dependence);

3. at

𝜌

0, there is no linear correlation

between A and B (but this does not mean the

impossibility of a nonlinear relationship between

them);

4. 𝜌0 indicates the presence of an inverse

relationship between variables A and B (as one

variable increases, the other decreases);

5. 𝜌0 indicates the presence of a direct

relationship between variables A and B (as one

variable increases (decreases) in one variable, the

other also increases (decreases));

6. If all feature values are increased

(decreased) by the same number or by the same

number of times, then the value of the correlation

coefficient will not change (Kulentsan and Marchuk,

2020; Polozhentseva, 2017).

Since it is assumed that the random variable B has

a normal distribution with a conditional mathematical

expectation 𝐵



𝜑𝐴



,𝐴



,…,𝐴



 , which is a

function of the arguments 𝐴



and a constant variance

that does not depend on the arguments 𝜎



A certain class of functions depending on

unknown parameters shall be chosen as the form of

dependence. The task of regression analysis is to

estimate parameters for a number of independent

observations and to test hypotheses for such unknown

parameters. The task of regression analysis is to

estimate parameters for a number of independent

observations and to test hypotheses for such unknown

parameters.

The main assumptions of regression analysis of

the linear pairwise regression model are as follows:

 the dependent variable 𝐵



is a random

variable, and the explanatory variable 𝐴



is not

random;

 the mathematical expectation of the

disturbance is zero (𝑀𝜀



0);

 the variance of the dependent variable 𝐵



constant (for all i) and equal to 𝐷𝜀



𝜎



;

 variables 𝐵



and 𝐵



are not correlated 𝑀𝜀



𝜀





0, ∀𝑖  𝑗;

 the dependent variable 𝐵



is a normally

distributed random variable.

3 RESEARCH RESULTS AND

DISCUSSION

To analyze in this work the number of unemployed

aged 15-72 years, we considered the following

regions - Belgorod, Bryansk, Vladimir, Voronezh,

Ivanovo, Kaluga, Kostroma, Kursk, Lipetsk,

Moscow, Oryol, Ryazan, Smolensk, Tambov,

Tverskaya , Tula, Yaroslavl and Moscow. The

considered areas differ in many respects, however, we

hope that the considered approach can be universal

when studying the problem of unemployment,

regardless of the region of the Russian Federation. In

our work, we analyzed the influence of employee

wages, population size, growth in highly productive

jobs, the cost of a fixed set of consumer goods and

services, the age composition of the population over

the working age and migration growth of the

population on the growth rate of the number of

unemployed in all regions of the Central Federal

District. Based on the assessment of the tightness of

the relationship between the studied parameters

(Table 1) and the rated values of the significance level

and the Student's test (𝑝 = 0.05; 𝑡

tab

= 2.045), it can be

argued that some coefficients were insignificant. As a

consequence, these factors must be excluded from the

list of dependent variables for the study areas.

An assessment of the data obtained indicates that in

the Belgorod region, among the parameters under

consideration, the largest influence on unemployment

is exerted by the population size and the increase in

highly productive jobs. For the Belgorod region, the

value of the correlation coefficient is r = 0.961, in the

case of paired regression, the coefficient of

determination is equal to the square of the correlation

coefficient, R

= 0.924. Its value suggests that 92.4%

of changes in wages of workers, population size,

increase in high-productivity jobs, the cost of a fixed

set of consumer goods and services, and migration

population growth are explained by regression, and

WFSDS 2021 - INTERNATIONAL SCIENTIFIC FORUM ON SUSTAINABLE DEVELOPMENT OF SOCIO-ECONOMIC SYSTEMS

104

7.6% - by the influence of other factors. The t-statistic

criterion used to assess the statistical significance

indicates that the equations obtained for the Central

Federal District in Table. 1 are highly significant. The

parameter - the age composition of the population

over the working age - turned out to be insignificant

for the Belgorod region. In the Bryansk region, the

results showed that unemployment is most influenced

by the size of the population and the age composition

of the population over the working age. For this

region, 93.4% of changes in workers' wages,

population size, the cost of a fixed set of consumer

goods and services, the age composition of the

population over the working age and migration

growth of the population is explained by regression,

and 6.6% - by the influence of other factors. The

parameter, the growth of highly productive jobs,

turned out to be insignificant for this area. In the

Vladimir and Voronezh regions, the situation is as

follows, unemployment is most influenced by the size

and age composition of the population. For the

Vladimir region, 79.4% of changes in the wages of

workers, the population size, the increase in high-

productivity jobs, the cost of a fixed set of consumer

goods and services, the age composition of the

population over the working age and migration

population growth are explained by regression, and

20.6% - by the influence of other factors. For the

Voronezh region, 76.0% of the change in wages of

workers, the population size, the cost of a fixed set of

consumer goods and services, the age composition of

the population over the working age and migration

growth of the population is explained by regression,

and 24.0% - by the influence of other factors. In the

Voronezh region, the parameter turned out to be

insignificant - the increase in highly productive jobs.

In the Ivanovo region, the results showed that the

number of unemployed is most influenced by the age

composition and population, as well as the increase in

highly productive jobs. For the this region, 72.5% of

changes in the wages of workers, the population size,

the increase in high-productivity jobs, the cost of a

fixed set of consumer goods and services, the age

composition of the population over the working age

and migration population growth are explained by

regression, and 17.5% - by the influence of other

factors. In the Kaluga region, the situation is as

follows, unemployment is mainly influenced by the

size and age composition of the population. In this

area, 80.7% of the change in the studied parameters is

explained by regression, and 19.3% - by the influence

of other factors. At the same time, despite the fact that

in the Kostroma and Kursk regions, the obtained

equations (Table 1) also have a high degree of

significance (p-level significance level <0.05), in this

case, the growth of workers' wages and population

growth is very will have little effect on reducing the

unemployment rate. In the studied regions, 87.2% and

88.1% of the changes in the studied parameters are

explained by regression, and 12.8% and 11.9% are

explained by the influence of other factors,

respectively. The parameters turned out to be

insignificant in these areas - the growth of highly

productive jobs and the cost of a fixed set of consumer

goods and services. In the Lipetsk region, the results

showed that the factors under consideration have a

weak influence on the number of unemployed ~

29.6%, and 70.4% are influenced by other factors. In

the Moscow, Oryol and Ryazan regions,

unemployment is most influenced by the size of the

population and the age composition of the population

over the working age. For these regions, 83.6%,

88.5%, 91.4% of changes in the wages of workers, the

population size, the cost of a fixed set of consumer

goods and services, the age composition of the

population over the working age and migration

population growth are explained by regression, and

12, 4%, 11.5%, 8.6% - by the influence of other

factors, respectively. In the Moscow and Ryazan

regions, the parameter turned out to be insignificant -

the increase in highly productive jobs, in the Oryol

region - the cost of a fixed set of consumer goods and

services. In the Smolensk and Tver regions, the data

obtained showed that the parameters under

consideration have a weak effect on the number of

unemployed ~ 39.1% and 28.6%, respectively, and

60.9% and 71.4% are influenced by other factors,

respectively. In the Tambov region, the number of

unemployed is mainly influenced by the following

factor - the age structure of the population over the

working age. In this area, 78.0% of the change in the

studied parameters is explained by regression, and

22.0% - by the influence of other factors. In the Tula

region, the results showed that unemployment is most

influenced by the size of the population and the age

composition of the population. For this region, 84.9%

of changes in workers' wages, population size, the

cost of a fixed set of consumer goods and services,

the age composition of the population over the

working age and migration growth of the population

is explained by regression, and 15.1% - by the

influence of other factors. The parameter, the growth

of highly productive jobs, turned out to be

insignificant for this area. For the Yaroslavl region

and Moscow, the data obtained showed that the

unemployment rate, changes of the wages of workers,

the population size, the growth of highly productive

jobs, the cost of a fixed set of consumer goods and

Regional Development. Analysis and Forecasting of the Number of Unemployed in the Central Federal District

105

services, the age composition of the population over

the working age and migration growth of the

population, has little effect.

Based on the data obtained over the past 30 years,

regression models were built for the Central Federal

District, revealing the relationship between the wages

of workers (X

), the size of the population (X

), the

increase in highly productive jobs (X

), the cost of a

fixed set of consumer goods and services (X

), the

age composition of the population over the working

age (X

), the migration growth of the population (X

)

and the growth rate of the number of unemployed (Y)

in all regions of the Central Federal District (Table

3).In general, the regression model can be written as

follows: Ŷ = b

+ b

ꞏX

+ b

ꞏX

+ b

ꞏX

+ b

ꞏX

+ b

ꞏX

+ b

ꞏX

Table 1: Regression model describing the relationship between the unemployment rate and the factors under consideration in

the Central federal district.

Belgorod region Y = -54.41+0.003ꞏX

+0.72ꞏX

-0.087ꞏX

-0.003ꞏX

-0.001ꞏX

Bryansk region Y = -597.676-0.01ꞏX

+0.489ꞏX

+0.008ꞏX

-0.275ꞏX

-0.002ꞏX

Vladimir region Y = -3971.02-0.001ꞏX

+2.07ꞏX

+0.2ꞏX

+0.01ꞏX

-1.4ꞏX

+0.01ꞏX

Voronezh region Y = 4301.6-0.009ꞏX

-2.753ꞏX

+0.002ꞏX

-4.668ꞏX

+0.003ꞏX

Ivanovo region Y = 46.247-0.001ꞏX

-0.251ꞏX

-0.033ꞏX

-0.002ꞏX

+1.149ꞏX

-0.008ꞏX

Kaluga region Y = -464.761-0.001ꞏX

+0.585ꞏX

-0.035ꞏX

+0.001ꞏX

+0.592ꞏX

-0.001ꞏX

Kostroma region Y = 205.949-0.003ꞏX

-0.068ꞏX

-0.399ꞏX

+0.003ꞏX

Kursk region Y = -133.01+0.001ꞏX

+0.225ꞏX

-0.458ꞏX

-0.001ꞏX

Lipetsk region Y = -126.992-0.002ꞏX

-0.001ꞏX

Moscow region Y = -2626.49+0.001ꞏX

+2.55ꞏX

+0.001ꞏX

+2.99ꞏX

-0.001ꞏX

Oryol region Y = -1091.05-0.008ꞏX

+0.83ꞏX

+0.05ꞏX

+0.59ꞏX

-0.007ꞏX

Ryazan region Y = -616.668-0.002ꞏX

+0.46ꞏX

+0.005ꞏX

+1.067ꞏX

-0.003ꞏX

Smolensk region Y = -244.868+0.001ꞏX

+0.257ꞏX

-0.161ꞏX

+0.003ꞏX

+0.009ꞏX

-0.002ꞏX

Tambov region Y = 1146.007-0.002ꞏX

-0.385ꞏX

-0.014ꞏX

+0.002ꞏX

-1.823ꞏX

+0.002ꞏX

Tver region Y = 18.979-0.002ꞏX

+0.002ꞏX

+0.027ꞏX

+0.004ꞏX

-0.025ꞏX

+0.001ꞏX

Tula region Y = -2777.26-0.01ꞏX

+1.65ꞏX

+0.007ꞏX

+2.16ꞏX

+0.008ꞏX

Yaroslavl region Y = 180.828+0.006ꞏX

+0.133ꞏX

-0.024ꞏX

+0.002ꞏX

-1.024ꞏX

-0.005ꞏX

Moscow Y = -559.799-0,001ꞏX

+0.101ꞏX

-0.01ꞏX

+0.014ꞏX

-0.225ꞏX

+0.001ꞏX

Economic sanctions, the crisis, restrictions on

access to the global market have led to the fact that

our country has already learned to live in difficult

economic conditions. However, new unfavorable

conditions that have seized the whole world, such as

the coronavirus infection COVID-2019 and the

collapsed oil price in the oil market, may lead to an

increase in the number of unemployed in the Russian

Federation and in particular in the Central Federal

District. Therefore, it is very important to make a

forecast of the distribution of the number of

unemployed in the considered district. Further,

applying the rated estimates, we obtained the final

forecast of the distribution of the number of

unemployed in the Central Federal District for 2020.

The obtained calculations (Table 2) showed that the

average error of the predicted data for the period -

2017 is 4.6% and for 2019 - 6.1%, such results

indicate that the considered correlation and regression

analysis, the number of unemployed, predicts well the

observed values.

Table 2: Results of the final forecast distribution of the number of unemployed in the Central federal district.

Region

Number of unemployed,

thousand

Forecast of the number of

unem

ed, thousand

Forecasted data error

2017 2019 2021 2017 2019 2021 2017 2019

Belgorod region 32.1 31.9 - 33.1 34.1 37.2 3.1 6.9

Bryansk region 27.0 22.5 - 28.5 22.9 26.3 5.6 1.8

Vladimir re

ion 35.3 29.0 - 34.4 30.8 33.4 2.5 6.2

Voronezh re

ion 51.2 41.9 - 53.4 42.8 50.1 4.3 2.1

Ivanovo re

ion 25.4 19.5 - 26.1 21.3 24.3 2.8 9.2

Kaluga region 21.6 19.8 - 23.5 20.6 22.6 8.8 4.0

Kostroma region 17.3 12.6 - 18.0 14.2 19.3 4.0 12.7

Kursk re

ion 23.5 22.5 - 25.0 23.2 24.2 6.4 3.1

WFSDS 2021 - INTERNATIONAL SCIENTIFIC FORUM ON SUSTAINABLE DEVELOPMENT OF SOCIO-ECONOMIC SYSTEMS

106

Table 2: Results of the final forecast distribution of the number of unemployed in the Central federal district (cont.).

Region

Number of unemployed,

thousand people

Forecast of the number of

unemployed, thousand

eople

Forecasted data error

2017 2019 2021 2017 2019 2021 2017 2019

Lipetsk region 23.1 22.3 - 22.6 23.9 24.9 2.2 7.2

Moscow re

ion 130.3 113.9 - 134.1 118.2 130.5 2.9 3.8

ol re

ion 24.6 18.5 - 25.6 20.3 24.5 4.1 9.7

azan re

ion 22.0 21.1 - 23.1 22.6 26.3 5.0 7.1

Smolensk region 29.8 25.3 - 31.2 26.8 32.5 4.7 5.9

Tambov region 22.5 19.6 - 23.6 21.3 23.9 4.9 8.7

Tver region 31.0 26.7 - 33.0 28.6 30.9 6.5 7.1

Tula re

ion 30.7 29.9 - 32.1 30.9 33.2 4.6 3.3

Yaroslavl re

ion 44.1 35.0 - 45.9 37.2 42.9 4.1 6.3

Moscow 99.6 99.5 - 106.1 104.1 110.4 6.5 4.6

mean error of forecasted data,% 4.6 6.1

4 CONCLUSIONS

Thus, in this work, a forecast of the distribution of the

number of unemployed in the regions of the Central

Federal District for 2021 is made and the average

error of the predicted data is rated. The obtained

results of the regression model showed that in these

regions there is a tendency for the disproportion of

regional development. These results indicate that a

new regional policy is needed in the Central Federal

District, which will make it possible to implement and

find solutions to reduce the number of unemployed.

This can be achieved by developing new socio-

economic mechanisms, thanks to which the

considered regions of the Central Federal District will

be able to more fully realize and sustainably develop

their economic, cultural, educational, industrial and

scientific and technical potential.

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