Economic and Mathematical Models for Assessing and Forecasting the Dynamics of Labour Potential of the Border Regions of Siberia and Far East

Journal of Environmental Treatment Techniques

2019, Special Issue on Environment, Management and Economy, Pages: 1224-1233

J. Environ. Treat. Tech.

ISSN: 2309-1185

Journal web link: http://www.jett.dormaj.com

Economic and Mathematical Models for Assessing

and Forecasting the Dynamics of Labour Potential

of the Border Regions of Siberia and Far East

3,4

Vadim A. Bezverbny , Sergey V. Pronichkin

¹Institute for Socio-Political Research, Russian Academy of Science, Moscow, Russia

Department of Demographic and Migration Policy, MGIMO-University, Moscow, Russia

Federal Research Center «Computer Science and Control», Russian Academy of Science, Moscow, Russia

Institute for Socio-Political Research, Russian Academy of Science, Moscow, Russia

Received: 13/09/2019

Accepted: 22/11/2019

Published: 20/12/2019

Abstract

The article is dedicated to assessment and forecasting of Gross Regional Product, employment, labour force and unemployment by

sectors of the economy in the border regions of Siberia and Far East until 2025. For this purpose, there are developed economic and

mathematical models considering the parameters of social and economic development. The social component is based on systematic

approach to forecasting employment depending on the anthropogenic burden index, which takes into account duration and standard of

living, population literacy, crime rate, environmental status and other indicators of the border regions of Siberia. The economic

component uses econometric analysis of the types of economic activity in the border regions of Siberia and Far East, as well as time

series analysis to forecast employment both in the medium and short term. In the labour market part, the labour force is projected

considering the socio-economic effect of hidden unemployment.

Keywords: Anthropogenic burden, Economic and mathematical models, Value added, Labour force participation rate, Unemployment,

Employment

Federal District economy for qualified personnel can be fully

met at the expense of scientific, educational and technical

potential of the regions, included in the district.

Introduction

The Siberian Federal District is the region with one of the

highest unemployment rates (4). At the same time, Siberia is

the region with the lowest rate of employment growth. Over

the past 10 years, employment has increased at a compound

annual rate of 1%. Over the same period, the unemployment

rate fell from 8.7% to 7.3%. All that is caused by the very slow

growth of labour force due to the demographic factors. The

level women participation in the labour force is not high

enough as well, comparing to the average values of

participation in the regions, since its growth is constrained by

the negative effect of financial and economic crisis in Russia

The Siberian Federal District can be described as a

territory with uneven regional development. Within the

district, subsidized regions border on regions with a high

potential for innovative development (5). Thus, there are

demographic differences within the Siberian region. In

particular, the subsidized Republic of Tuva is the border region

with the highest unemployment rate of 18.3%. Krasnoyarsk -

the inland region, is economically developed; the employment

rate and economically active population there grow faster than

in the rest of Siberian Federal District.

(

8). The recovery process that has begun in recent years (1)

The birth rate in Siberia is higher than the national average.

According to this indicator, the Siberian Federal District is in

third place after the North Caucasus and Ural Federal Districts.

The Omsk Region demonstrates the highest level of labour

force participation (about 69.5%). In 2005, the figure was

does not provide an increase in labour force participation of

previously disillusioned people, especially women.

It is important to note the significant innovative potential

of Siberian Federal District and the innovative infrastructure

created in it. All that leads to significant opportunities for

sustainable and long-term development. The need of Siberian

64.9%. As a result of job placement (18) the level of labour

force participation in the following years increased by almost

Corresponding author: Vadim A. Bezverbny, (a) Institute for

Socio-Political Research, Russian Academy of Science,

Moscow, Russia. (b) Department of Demographic and

Migration Policy, MGIMO-University, Moscow, Russia. E-

mail: vadim_ispr@mail.ru.

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2019, Special Issue on Environment, Management and Economy, Pages: 1224-1233

%, with high sensitivity to economic cycles (17). As a

manufacturing and services. The value added block (1)

consists of industry demand equations that explain the

corresponding value added using economic indicators at the

Federal and regional levels.

consequence of this change, the unemployment rate continued

to fall despite lower employment growth. The border region

with the lowest number of unemployed is the Altai Republic.

This figure in the Altai Republic is six times less than in the

Omsk Region. However, since 2005, the labour force

participation rate for the region has hovered at around 67%,

and as employment continues to grow, the unemployment rate

has fallen from 9.4% to 2.5%.

V ^0i

^1i NP ^2i NV ^3i^Vit1

it it

(1)

Vit is added value of the sector i in the region in the period t;

The border regions today are largely less developed and

economically less prosperous than the deep territories, which

are similar in terms of development (3, 7, 9, 12). The

hypothesis of the study formulated in this article is as follows:

the actual conditions in the labour market of Siberia and Far

East contribute to the reduction of unemployment in the short

and medium term. Thus, this article is devoted to forecasting

the Gross Regional Product, employment, labour force and

unemployment rates in the border regions of Siberia until

NPit is the price level of the sector i in the Federal District in

the period t; NVit is added value of the sector i in the Federal

District in the period t; and Vit-1- added value of the sector i in

the region in the period t-1. Employment block (2) consists of

dynamic sectoral demand equations that relate value added,

socio-economic factors and employment in previous periods.

Cobb-Douglas production functions are used as well.

LnE  ₀_i

 ₁_iLnV  ₂_iLnNR  ₃_iLnS  ₁₁_iLnE_i_t_₁

it it t

it is average annual number of employees in the period t by

2025. The article is structured as follows: first of all, there are

(

considered some economic and mathematical models, then the

results of assessment and forecasts. After all, there follow

conclusions.

type of economic activity i; Vit is added value of the sector i in

the region in the period t; NRit is the level of wages by type of

economic activity in the region i in the period t; S is index of

Research Methodology

Economic and mathematical models consist of two basic

anthropogenic load in the period t; and Eit-1 is the average

annual number of employees in the period t-1 by type of

economic activity i. The developed models make it possible to

predict the labour force, depending on demographic and socio-

economic factors, taking into account fluctuations in demand

for labour.

The level of labour force participation (3) depends on two

variables representing demographic and socio-economic

factors. The demographic variable is those aged 15 to 72 years.

The variable representing socio-economic factors is labour

demand (gross employment). The latter is measured in natural

logarithms to account for the disproportionate effect of

exogenous variables. Using the partial adjustment approach, it

is possible to formalize the level of labour force participation

as the following dynamic equation.

sub-models that take into account both social and economic

factors. The social sub-model follows cohort-based

forecasting method. They investigate the relationships

between demographic indicators and the index of

anthropogenic load (13, 14, 15, 16), which takes into account

life expectancy, standard of living, literacy, health and culture

of the population. It also takes into account the level of crime

and the state of environment.

Thus, the labour market is explained by socio-

demographic factors when modelling the relevant trends. Any

attempts to predict the employment level using social factors

cause difficulties because the adequate forms of equations are

not defined. The accuracy of existing models, based solely on

demographic indicators, is low (2, 10, 11), so we propose a

systematic approach that links socio-demographic and

economic variables.

The economic sub-model uses econometric analysis and

time series analysis to predict value added and employment in

the short to medium term. In addition, the socio-demographic

unit provides forecasts depending on the cyclical factors of the

region's economic sectors. Unemployment is then defined as

the balance between employment and economically active

population. The stimulating and restraining influence of short

and medium-term economic fluctuations on employees is

taken into account as well: the labour force is explained

considering both demographic growth and the sensitivity of

labour force participation rates to cyclical fluctuations in

labour demand.

The developed models consist of four recursive equations

that explain the following groups of variables: population

structure of the Siberian regions, value added, employment and

labour force. Value added and employment are projected in the

medium term using regional econometric models. These

models predict value added and employment for the larger

regional sectors of the economy, namely agriculture,

PL   PO  LnTE  PL 

t1

(3)

is level of labour force participation in the period t; PO

푚

persons aged 15 to 72 years; 푇퐸 = ∑ 퐸_푖_푡is gross number

푡

푖ꢀ1

of employees in the period t; and ɛ

is correction factor. As a

complement to the medium-term forecasts based on the

developed econometric models, we use multidimensional time

series models for short-term quarterly employment forecasts

and exogenous variables in models (1) and (2). For this

purpose, they use autoregressive modelling on the basis of

moving averages (6), which allows the use of regional series

of indicators as predictive variables of short-term employment

in the border regions of Siberia and Far East. Time series

models are more efficient for short-term forecasting than

cause-and-effect econometric models. They meet or even

exceed the accuracy of short-term forecasting with the help of

econometric models. When we forecast short-term regional

employment using econometric models, hypotheses are

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Journal of Environmental Treatment Techniques

2019, Special Issue on Environment, Management and Economy, Pages: 1224-1233

introduced in the process of model specification and selection.

This is a source of predictive inaccuracy that is difficult to

assess and control. Confidence intervals and various scenarios

for short-term forecasting are ineffective as well. Our short -

and medium-term employment models provide a forecasting

horizon for testing the sensitivity of labour force participation

and unemployment forecasts to different data sources and

hypotheses.

Federation. They need the data from 2004 to 2018 to work with

the value added and employment blocks. The labour force unit

and the short-term model were estimated from quarterly data.

For the short-term model, the sampling period is 2004-2019.2

(2019, the second quarter). Table 1 presents the results of

evaluation of econometric models of the added value V of the

i economic sector, where i = {1-agriculture; 2-manufactured

goods and services}, for seven border regions of Siberia. Table

also presents the advising determination coefficient R of

econometric models. Table 2 presents the results of evaluation

of the dynamic sectoral equations of the employed number in

the period t by the i type of economic activity, where i = {1-

agriculture; 2-manufactured goods and services}, for seven

border regions of Siberia and Far East. Table 2 also presents

the advising coefficient of determination.

Results and Discussion

.1 Estimates of Economic and Mathematical Models

The main data sources used to evaluate the models in this

article are the reports of the Federal State Statistics Service and

the Governmental Analytical Center of the Russian

Table 1: Estimates of Econometric Added Value Models of Border Regions of Siberia.

Equations

The Republic of Altai

V  0,29NP  0,53NV  0,57V 0,27V₂_t

0,79

0,96

1t1

V  0,23NP 0,47NV 0,2V  0,56V

2t1

The Republic of Buryatia

V  0,23NP 0,05NV  0,82V 0,08V₂_t

0,95

0,8

1t1

V  0,53NP 1,11NV  0,14V  0,15V

2t1

The Republic of Tuva

V  0,2NP  0,23NV  0,83V  0,06V

0,93

0,94

1t1

V  0,62NP  0,02NV 0,36V  0,65V

2t1

The Altai Territory (Krai)

V  0,07NP  0,66NV  0,43V 0,73V

0,86

0,97

1t1

V  0,07NP  0,73NV  0,32V  0,42V

2t1

The Zabaikalye Territory (Krai)

V  0,57NP  0,13NV  0,26V  0,02V₂_t

0,93

0,95

1t1

V  0,48NP  0,98NV  0,59V 0,13V

2t1

The Novosibirsk Region

V  0,27NP  0,35NV  0,73V 0,06V

0,96

0,79

1t1

V  0,14NP  0,06NV  0,27V 1,06V

The Omsk Region

V  0,32NP  0,85NV  0,06V  0,17V

0,61

0,9

1t1

V  0,26NP  0,43NV 0,03V 1,05V

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2019, Special Issue on Environment, Management and Economy, Pages: 1224-1233

Table 2: Estimates of Forecasting Models for the Number of Employed in the Border Regions of Siberia and Far East.

Equations

The Republic of Altai

LnE  0,37LnV 1,51LnNR 1,6LnS  0,93LnE

1t1

0,9

LnE  0,87LnV  2,27LnNR 1,79LnS 0,11LnE

2t1

0,92

The Republic of Buryatia

LnE  0,78LnV 0,95LnNR 0,25LnS 1,17LnE

1t1

0,96

0,92

LnE  0,34LnV  0,24LnNR 0,35LnS  0,78LnE

2t1

The Republic of Tuva

LnE  1,03LnV 1,44LnNR  0,08LnS 0,01LnE

1t1

0,73

0,66

LnE  0,73LnV  0,41LnNR 0,26LnS  0,25LnE

2t1

The Altai Territory (Krai)

LnE  0,26LnV 0,69LnNR  0,2LnS  0,32LnE

1t1

0,95

0,96

LnE  0,79LnV 0,03LnNR  0,25LnS  0,36LnE

2t1

The Zabaikalye Territory (Krai)

LnE  0,57LnV  LnNR  0,15LnS  0,62LnE

1t1

0,95

0,92

LnE  0,1LnV 0,91LnNR  0,67LnS  0,81LnE

2t1

The Novosibirsk Region

LnE  0,45LnV  0,15LnNR 0,35LnS  0,75LnE

0,98

0,96

1t1

LnE  0,04LnV  0,35LnNR 0,15LnS  0,75LnE

2t1

The Omsk Region

LnE  0,11LnV  0,16LnNR  0,43LnS  0,84LnE

0,94

0,97

1t1

LnE  0,64LnV 1,02LnNR 0,38LnS  0,41LnE

2t1

Table 3: Estimates of Forecasting Models for the Level of Labour Force Participation in the Border Regions of Siberia and Far East.

Equations

The Republic of Altai

PL  0,57PO  0,64LnTE  0,2PL

0,91

t1

The Republic of Buryatia

PL  0,09PO  0,41LnTE  0,59PL

0,87

0,41

0,55

0,91

0,83

0,95

t1

The Republic of Tuva

PL  0,35PO 0,38LnTE  0,22PL

t1

The Altai Territory (Krai)

PL  0,3PO  0,01LnTE  0,41PL

t1

The Zabaikalye Territory (Krai)

PL  0,32PO 0,17LnTE  0,62PL

t1

The Novosibirsk Region

PL  0,18PO  0,42LnTE  0,65PL

t1

The Omsk Region

PL  0,35PO  0,32LnTE  0,81PL

t1

Table 3 presents the results of assessment and forecasting

of the level of participation in the labour force depending on

the population from 15 to 72 years and employment in

agriculture and the sector of production of manufactured goods

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2019, Special Issue on Environment, Management and Economy, Pages: 1224-1233

and services. It can be seen from the estimates that the models

have high prognostic properties. The accuracy of the

constructed models, calculated on the basis of determination

coefficient averages 0.87. The labour force participation

equations include high-coefficient employment as an

explanatory variable (see Table 3). This means that labour

force participation in the border regions of Siberia depends

significantly on the economic cycle and social factors, but the

main effect on the labour force is produced by the demographic

factors in the Border Regions of Siberia and Far East.

in agriculture is expected to grow until 2022, but employment

in this sector of economy will decline. The manufactured

goods and services sector is growing in production by 2023,

there takes place expected employment growth. Figures 5 and

6 demonstrate the short-term projections of total employment

for the next four quarters. Compared to the results presented

by the econometric model (see Figure 3 and Figure 4), the most

significant discrepancy is observed in the manufactured goods

and services sector. Between 2020 and 2021, the econometric

model predicts a decline in employment of about 1%, as

opposed to the short-term model, which predicts an increase of

about 1.2%. As for agriculture, the short-term forecast is

higher than the medium-term one. The forecast of total

employment in agriculture, presented by the econometric

model, is 0.8% lower than the short-term forecast in 2020.

Figures 7 and 8 present, respectively, the medium-term

and short-term labour potential forecasts of the border regions

of Siberia and Far East. In the period of 2020-2021, the

econometric model predicts a 1.25% decrease in labour force

participation, as opposed to the short-term model, which

predicts an increase of about 1.35%.

3.2 Long-Term and Short-term Forecasts

Projections of value added (Gross Regional Product) differ

from sector to sector (see Figure 1 and Figure 2). While

agriculture is expected to grow up to 5% by 2022, production

of manufactured goods and provision of services will not

increase. Moderate growth in agriculture will give way to a fall

by 2023, while the manufactured goods and services sector

will see modest growth.

If we look at employment by sectors of the economy of the

border regions of Siberia (see Figure 3 and Figure 4), the

forecasts differ from the results on value added. Value added

Figure 1: Value Added of Agriculture, in Current Basic Prices; Percentage of the Total

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2019, Special Issue on Environment, Management and Economy, Pages: 1224-1233

Figure 2: Value Added of the Manufactured Goods and Services Sector, in Current Basic Prices; Percentage of the Total

Figure 3: Average Annual Number of People Employed in Agriculture (Thousands of People)

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2019, Special Issue on Environment, Management and Economy, Pages: 1224-1233

Figure 4: Average Annual Number of Employees in the Sector of Manufactured Goods and Services (Thousands of People)

Figure 5: Quarterly Number of People Employed in Agriculture (Thousands of People)

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2019, Special Issue on Environment, Management and Economy, Pages: 1224-1233

Figure 6: Quarterly Number of Employees in the Sector of Production of Manufactured Goods and Services (Thousands of People)

Figure 7: Labour Force Participation Rate, Percentage

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2019, Special Issue on Environment, Management and Economy, Pages: 1224-1233

Figure 8: Quarterly Labour Force Participation Forecast, Percentage.

creation of new jobs in accordance with national socio-

economic prospects. Thus, there should be emphasized the

importance of using some tools to support strategic decision -

related to promotion of employment in the region.

Conclusion

In this article, we have developed and applied economic

and mathematical models to predict the Gross Regional

Product, employment and labour force rates in the border

regions of Siberia in the Border Regions of Siberia and Far

East. According to the results of the study, there can be drawn

two main conclusions.

First, unemployment, defined as the difference between

labour and employment, depends on socio-economic factors in

two ways. The sensitivity of unemployment to employment

forecasts is lower than might be expected. Thus, despite the

difference between the two short-term and medium-term

employment forecasts, the hypothesis formulated in

Introduction can be answered as follows: both the short-term

and medium-term models predict an increase in

unemployment.

As a result of the expected reduction in labour force, the

unemployment rate will remain close to the current level in the

short term, despite the projected reduction in 2020

unemployment rate. In the medium term, however, we should

expect a slight increase in the unemployment rate in the border

regions of Siberia.

Secondly, taking into account the estimates of labour force

equations, they can conclude that the labour force is sensitive

to employment and socio-economic factors, but is mainly

determined by demographic determinants. Unemployment is

expected to remain a social problem for Siberia and in the

Border Regions of Siberia and Far East in the short term,

despite the expected significant increase in labour demand by

5 Acknowledgments

The reported study was funded by RFBR, project № 17-

06-00699 and the Grants Council of the President of the

Russian Federation, project № MK-5872.2018.6.

Ethical issue

Authors are aware of, and comply with, best practice in

publication ethics specifically with regard to authorship

(

avoidance of guest authorship), dual submission,

manipulation of figures, competing interests and compliance

with policies on research ethics. Authors adhere to publication

requirements that submitted work is original and has not been

published elsewhere in any language.

Competing interests

The authors declare that there is no conflict of interest that

would prejudice the impartiality of this scientific work.

Authors’ contribution

All authors of this study have a complete contribution for

data collection, data analyses and manuscript writing.

2022. The unemployment rate and its level can be reduced by

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2019, Special Issue on Environment, Management and Economy, Pages: 1224-1233

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