Relationship between Supply and Demand Factors in Regional Labor
Markets
Alexander N. Tyrsin
1,3 a
and Elena V. Vasilyeva
2b
1
Ural Federal University named after the first President of Russia B. N. Yeltsin, Yekaterinburg, Russia
2
Institute of Economics, Ural Branch of Russian Academy of Sciences, Yekaterinburg, Russia
3
South-Ural State University, Chelyabinsk, Russia
Keywords: Labor Market, Demand, Supply, Labor Force, Region.
Abstract: The article presents the author's approach to the analysis of matching labor demand and supply. The essence
of the approach is to dynamically assess the closeness of the relationship between two sets of indicators
describing their factors. The proposed model for calculating the coefficient of the closeness of the relationship
made it possible to consider the factors of formation of demand for labor and its supply simultaneously, as
well as to make quantitative estimates. Approbation of the approach was carried out on the example of the
constituent entities of the Russian Federation included in the Ural Federal District. The estimation results
show that the tightness of the relationship between the indicators of labor market supply and demand factors
has increased over 2000-2019. The most significant contribution to the alignment of supply and demand in
regional labor markets is made by the demographic factor - the share of the population of working age.
1 INTRODUCTION
In the context of economic instability and decline in
the working-age population, the search for
fundamentally new approaches to more efficient use
of productive resources, the most important of which
is labor, is of particular importance. To improve the
policy in labor resources management, it is necessary
to study the mechanisms affecting the alignment of
labor demand and supply.
An obvious approach to analyzing the matching
of labor demand and supply is to compare them
(Korovkin et al., 2012). According to A.G. Korovin
(2011), labor supply and demand exist in satisfied and
unsatisfied (current) states. The number of employed
in the economy characterizes the satisfied, and the
number of vacancies characterizes the unsatisfied
demand for labor. As a whole, they constitute the
aggregate demand for labor. Similarly, in the
aggregate labor supply, there is a part identically
equal to satisfied demand for labor and a part
corresponding to unsatisfied (current) supply - the
number of unemployed population. Therefore, in
theory, if the number of the employed population and
a
https://orcid.org/0000-0002-2660-1221
b
https://orcid.org/0000-0002-0446-1555
the number of vacancies is equal to the number of the
employed and unemployed population, there is
equilibrium in the labor market. However, in practice,
when comparing these indicators of supply and
demand, there is a problem of accurately determining
their values - a problem of statistical accounting.
Demyanova A. and Ryzhikova Z. (2020) noted
that the problem of the validity of the adopted labor
market aggregate measures is not new and has
accompanied economists throughout the XX century.
CardD. (2011) describes approaches to the definition
of "unemployment" and searches for theoretical
justification of its measurement. At present, in
Russian statistics, the number of unemployed is
measured by the number of the population officially
registered with the employment services or calculated
according to the methodology of the International
Labour Organization. There are also two main
sources of data on the total number of employed
people in Russia: sample surveys of the population on
employment problems and estimates based on the
labor resources balance methodology. As a result,
there are rather serious discrepancies between such
alternative estimates of the number of the
Tyrsin, A. and Vasilyeva, E.
Relationship between Supply and Demand Factors in Regional Labor Markets.
DOI: 10.5220/0010667200003223
In Proceedings of the 1st International Scientific Forum on Sustainable Development of Socio-economic Systems (WFSDS 2021), pages 273-277
ISBN: 978-989-758-597-5
Copyright
c
2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
273
unemployed and employed population
(Kapelyushnikov and Oshchepkov, 2014). In
statistics, the number of vacancies is estimated
through the indicator "employers' declared need for
workers", which raises questions about the adequacy
of this measurement. Thus, Gimpelson V.E. (2004)
believes that the declared need for workers is far from
being identical to the solvent demand for labor and
job creation. Tkachenko A.A. and Ginoyan A.B.
(2017) express a more categorical point of view.
There is no statistical basis for forecasting the
economy's needs in qualified personnel and high-
demand professions in Russian reality. Moreover,
given hidden unemployment, shadow, and secondary
employment, the proposed indicators cannot be
adequate (accurate) measures of labor demand and
supply.
To get away from objectively existing limitations
in statistical accounting, in the framework of this
study, the demand for labor and its supply are
described not by separate indicators but by a set of
indicators - two sets of indicators (vectors) that
characterize the factors of their formation. The
proposed author's approach to the analysis of
matching between the demand for labor and its supply
consists of a dynamic assessment of the closeness of
the relationship between the two sets of indicators
describing their factors.
2 LABOUR DEMAND AND
SUPPLY FACTORS
The scientific and academic literature considers the
main macroeconomic conditions and trends as factors
shaping the demand for labor. The study by E.S.
Mironova (2010) describes this formation process as
follows: rather high economic growth rates determine
the expansion of demand for labor, and, conversely,
under the impact of production decline, a significant
part of the labor force is released. Moreover, the
investment component of economic growth matters
for the formation of demand for labor. In the textbook
prepared by the Institute of Economic Forecasting of
the Russian Academy of Sciences and the Moscow
School of Economics of MSU (Ivanter et al., 2007),
this is explained by the fact that growth of production
and investment creates new jobs. Uzyakova E.S.
(2011) also considers gross output and the amount of
capital employed as basic indicators that determine
the economy's demand for labor, as they reflect the
scale of production.
In addition, the demand for labor is determined by
the demand for goods in society (Ermolaeva, 2015),
which is consistent with the Marshall-Hicks laws -
laws of derived demand: the demand for factors of
production (including labor) is derived from the
demand for the final product. As noted by S.S.
Nosova (2001) and F.N. Mailyan (2009), the
following factors underlie the changes in demand for
labor:
changes in the demand for a product: all else
being equal, a change in the demand for a
product that is produced by a particular type of
labour will lead to a shift in the demand for
labour in the same direction;
change in labor productivity: if other factors
remain unchanged, a change in labor
productivity leads to a unidirectional change in
the labor demand curve
change in price of other resources: the change
in price of one resource on the demand of
another resource depends on the degree of
interchangeability or complementarity of these
resources.
The supply of labour is determined by the
availability of labour and its quality. Supply is
influenced by demographic and migration trends
(Ivanter et al., 2007). Demographic processes shape
the potential size of the labor force and its separate
age groups (Ahapkin, 2012), while immigration flows
are a resource for its increase (Uzyakova, 2011). The
determining factor of labor supply in terms of its
quality is the level of professional training.
The literature review showed that macroeconomic
trends (economic growth, scale of production,
investment, consumer demand, and labor
productivity) are the main drivers of labor demand.
The main factors of labor supply are demographic and
migration trends, as well as the level of professional
education of the population.
3 SURVEY DATA
The statistical base for the study was Rosstat data by
Russian regions, including the results of sample labor
force surveys. The period from 2000 to 2019 is
chosen as the analyzed time series because, on the one
hand, it allows us to estimate the indicators of
interrelation quite correctly, and, on the other hand, it
is still possible to study the dynamics. Following the
factors of formation of demand for labor and its
supply identified based on the literature review,
indicators have been selected. Indicators of labor
demand and supply factors are shown in Table 1.
WFSDS 2021 - INTERNATIONAL SCIENTIFIC FORUM ON SUSTAINABLE DEVELOPMENT OF SOCIO-ECONOMIC SYSTEMS
274
Table 1: Indicators of supply and demand factors in the
labor market.
Designation Indicator, unit of measurement
Demand factor indicators
X
1
Index of the physical volume of
gross regional product (GRP) in
constant prices, in percent to the
p
revious
y
ea
r
X
2
Index of physical volume of
investment in fixed capital in
comparable prices, as a percentage
of the
p
revious
y
ea
r
X
3
Growth rate of labor productivity
per employee (adjusted for
inflation
)
,
p
ercent
X
4
Index of physical volume of retail
trade turnover,
p
ercent
Indicators of su
pp
l
y
factors
Y
1
Proportion of working-age
population in the total population,
p
ercent
Y
2
Graduation of specialists in the total
population, persons per thousand
p
eople.
Y
3
Migration growth rate, persons per
10,000
o
ulation
The index of physical volume of GRP (X
1
), was
chosen as an indicator of economic growth and scale
of production. In contrast, the index of physical
volume of investment in fixed assets (X
2
) was chosen
as an indicator of investment volume. The index of
physical volume of retail trade turnover (X
4
) was used
as solvent consumer demand. The growth rate of
labor productivity (X
3
) is calculated as the growth rate
of the ratio of GRP volume to the number of
employed in the economy, adjusted for consumer
price indexes. To describe demographic and
migration factors of labor supply we used indicators
of the share of working-age population in the total
population (Y
1
) and migration growth rate (Y
3
). The
level of professional education of the population is
calculated as the aggregate of mid-level specialists,
bachelors, specialists and masters per 10,000
population (Y
2
).
4 MODEL
For small samples, the analysis does not reveal the
difference of indicators distributions from the normal
distribution law. Therefore, we consider the special
case when vectors X=(X
1
,X
2
,…,X
m
) и Y=(Y
1
,Y
2
,…,Y
l
)
have joint normal distributions, the coefficient of
closeness of interdependence between random
vectors X and Y is determined by the formula (Tyrsin,
2018)
YX
YX
RR
R
YX
1),(
e
D
(1)
where
X
R
,
Y
R
,
YX
R
determinants of
correlation matrices of random vectors X, Y,
Z = X Y = (X
1
, … , X
m
, Y
1
, … , Y
l
),
0 D
e
(X, Y) 1. In this case we have the vectors of
demand X = (X
1
, X
2
, X
3
, X
4
) and supply
Y = (Y
1
, Y
2
, Y
3
).
The higher the value of the coefficient D
e
(X, Y),
the closer the relationship between the random
vectors X и Y is. The value D
e
(X, Y)=1 indicates a
linear functional relationship between at least two
components of vectors X and Y. If D
e
(X, Y)=0, then
the random vectors X and Y are linearly independent.
The contribution of the demand factor X
i
to the
change in the closeness of interdependence between
the random vectors X and Ү is defined as
D
e
(
X
i
)=D
e
(X, Y)
–
D
e
(X
\
X
i
, Y),
where X\X
i
= (X
1
, … , X
i–1
, X
i+1
, … , X
m
), i.e. it is
equal to the difference between the coefficient of
close interdependence between all demand and
supply factors and the same coefficient without the
demand factor X
i
.
Similarly, the contribution of the supply factor Yj
to the change in the closeness of interdependence
between the random vectors X and Y is defined as
D
e
(Yj)=D
e
(X, Y)
–
D
e
(X, Y
\
Y
j
),
где Y\Y
j
= (Y
1
, … , Y
j–1
, Y
j+1
, … , Y
l
).
5 RESULTS AND DISCUSSION
The proposed approach to the analysis of matching
supply and demand has been tested on the example of
the RF subjects that are part of the Ural Federal
District. The obtained results of the assessment of the
dynamics of the coefficient of close interdependence
between the two vectors - two sets of indicators of
supply and demand factors in the labor market - are
presented in Table 2. The D
e
(X, Y) coefficient was
estimated for periods of 13 years. This period allows
us, on the one hand, to smooth out the random
component and, on the other hand, allows us to
highlight the main trends in the labor market of the
RF subjects.
Relationship between Supply and Demand Factors in Regional Labor Markets
275
Table 2: Dynamics of the coefficient of closeness of
interdependence between the indicators of supply and
demand factors on the labor market.
Period
Kurgan region
Sverdlovsk
region
Tyumen region
Chelyabinsk
region
2000-2012 0.622 0.851 0.798 0.692
2001-2013 0.652 0.804 0.812 0.678
2002-2014 0.717 0.847 0.844 0.778
2003-2015 0.744 0.903 0.904 0.908
2004-2016 0.750 0.983 0.936 0.945
2005-2017 0.762 0.976 0.957 0.952
2006-2018 0.908 0.890 0.967 0.911
2007-2019 0,863 0.882 0.921 0.933
Over the analyzed period, the tightness of the
relationship between the indicators of labor demand
and supply factors has increased for all the subjects of
the Russian Federation under consideration. The most
significant increase in interconnection is observed in
the Kurgan region, explained by the "low start effect".
Moreover, the differentiation (gap) between the RF
subjects by this coefficient decreased over 2000-
2019. But the trajectory of the interdependence curve
differs by constituent entities of the Russian
Federation. For example, Sverdlovsk region peaked
in 2004-2016, Tyumen and Kurgan regions in 2006-
2018, and the Chelyabinsk Oblast in 2005-2017.
Table 3 shows the values of D
e
(X, Y) coefficient
on the whole sample from 2000 to 2019. The
strongest correlation between the indicators of supply
and demand factors in the labor market is observed in
the Sverdlovsk region, where there is a relatively
favorable macroeconomic situation. Thus, GRP
growth rates in Sverdlovsk region are slightly higher,
with an annual growth rate of 104.9% over 2000-2019
(in other subjects of the Urals Federal District it varies
from 102.6% to 103.9%).
Table 3: Values of the coefficient of close interdependence
between the indicators of supply and demand factors in the
labor market in the subjects of the Ural Federal District in
2000-2019.
Period
Kurgan region
Sverdlovsk
region
Tyumen region
Chelyabinsk
region
2000-2012 0.675 0.874 0.746 0.742
Table 4 presents an estimate of the average
contribution over 2000-2019 of each analyzed supply
and demand factor to the consistency between them
in the labor market. In Kurgan, Sverdlovsk and
Chelyabinsk regions the determining factors on the
demand side are the volume of retail trade turnover
(X
4
), and on the supply side - the share of working-
age population (Y
1
). In addition to the demographic
factor of labor supply (Y
1
), the volume of investment
in fixed capital (X
2
) and the output of specialists (Y
2
)
have a significant impact on the labor market in
Tyumen regions.
Table 4: Average contribution of each of the factors to
changes in the tightness of the relationship between supply
and demand factors over the period 2000-2019.
Contribution of
factors
Kurgan region
Sverdlovsk region
Tyumen region
Chelyabinsk
region
X
1
0.013 0.062 0.061 0.043
X
2
0.085 0.007 0.104 0,067
X
3
0.014 0.100 0.010 0.041
X
4
0.233 0.229 0.069 0.411
Y
1
0.226 0.468 0.273 0.474
Y
2
0.104 0.121 0.221 0.058
Y
3
0.141 0.053 0.077 0.048
As the results of the assessment show, matching
labor demand and supply is weakly responsive to
changes in output, which is an important feature of
the Russian labor market. Researchers (Mironova,
2010; Gimpel'son et al., 2017) note that in a crisis
situation, adaptation in the labor market occurs not
through changes in employment and unemployment,
but the spread of various forms of underemployment.
Earlier research (Tyrsin and Vasil'eva, 2021) on the
Russian labor market showed similar trends.
6 CONCLUSIONS
This study presents the author's approach to the
analysis of matching labor demand and supply. The
essence of the approach is to dynamically assess the
closeness of the relationship between two sets of
indicators describing their factors. Using the
correlation coefficient made it possible to consider all
indicators of supply and demand factors in the labor
market simultaneously and make quantitative
estimates. The approach was tested on the example of
the constituent entities of the Russian Federation that
WFSDS 2021 - INTERNATIONAL SCIENTIFIC FORUM ON SUSTAINABLE DEVELOPMENT OF SOCIO-ECONOMIC SYSTEMS
276
make up the Ural Federal District. The estimation
results show that the tightness of the relationship
between the indicators of labor market supply and
demand factors has increased over 2000-2019. The
most significant contribution to the alignment of
supply and demand in regional labor markets is made
by the demographic factor - the share of the
population of working age. Besides, in some subjects
of the Russian Federation, the functioning of the labor
market is significantly influenced by the turnover of
retail trade, the volume of investments in fixed
capital, and the output of specialists. The proposed
indicators of supply and demand factors adequately
characterize the labor market and can be used in the
study of employment based on the coefficient.
ACKNOWLEDGEMENTS
The research was supported by a grant from the
Russian Fund of Fundamental Research, project no.
20-41-660008.
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