CC BY
8DOI: 10.29141/2658-5081-2023-24-3-6 EDN: XRNNNM JEL classification: J20, J21, J23
Ekaterina S. Dashkova Voronezh State University, Voronezh, Russia Natalia V. Dorokhova Voronezh State University, Voronezh, Russia
Regional labour market: A method for research
Abstract. Turbulent socioeconomic environment significantly affects the state and dynamics of the regional labour market. The paper develops and tests a methodological toolkit for assessing the state of a regional labour market allowing for the main socioeconomic trends - digitalisation and innovative development of the economy. Labour economics constitutes the methodological basis of the research. Methods of economic statistical and content analysis were used. The evidence is the 2021 data of the Federal State Statistics Service of the Russian Federation concerning the labour markets of the Central Black Earth economic region of Russia, which comprises Belgorod, Voronezh, Kursk, Lipetsk and Tambov oblasts. The suggested method for investigating the state of the regional labour market takes into account the impact of digitalisation and innovative development processes on the latter. Testing the method at the case of the Central Black Earth economic region revealed that the regions' labour markets appreciably lag behind other subjects of the Russian Federation in terms of wages and encounter labour shortages against rather low rates of digital transformation and innovative development in their economies. The paper formulates recommendations for all parties of the social partnership, which suggest boosting the investment attractiveness of regions; creating high-productive jobs; spurring the activities of trade unions and associations; retaining the youth in the regions; increasing the efficiency of career guidance; attracting labour migrants, first and foremost, from other Russian regions due to improvements in economic, social and household infrastructure; promoting competencies of citizens of pre-retirement age and retired citizens; creating conditions for acceleration of digital transformation as well as expanding regions' innovation activities.
Keywords: labour market; socioeconomic environment; regions; regional economy; labour economics; Central Black Earth economic region of Russia.
For citation: Dashkova E. S., Dorokhova N. V. (2023). Regional labour market: A method for research. Journal of New Economy, vol. 24, no. 3, pp. 119-135. DOI: 10.29141/2658-5081-2023-24-3-6. EDN: XRNNNM.
Article info: received March 1, 2023; received in revised form May 2, 2023; accepted May 25, 2023
Introduction
The labour market is the critical element of the socioeconomic system, since indicators of economic growth and the quality of life of the population largely depend on its effective functioning. Providing the economy with the necessary labour resources, it plays a significant role in the development of human potential and accelerates the progress of the educational system and society as a whole.
The labour market, which is currently characterised by fundamental changes in the technical and technological sphere, the transition to a post-industrial model of society and the increasing role of human and knowledge in the economy, is experiencing considerable shocks provoked by the instability and variability of the external environment. Its development was deeply affected by the COVID-19 pandemic and a severe geopolitical crisis. For instance, the International Labour Organisation (ILO) report "World Employment and Social Outlook: Trends 2023"1, which analyses the development of the global labour market in 2022 and assesses the reasonable prospects, emphasises that the situation in labour markets is deteriorating both globally and nationally. ILO experts acknowledge that labour market indicators in 2022 failed to recover to the 2019 level. Moreover, owing to geopolitical tensions that intensified in 2022, stagflation of the world economy, and changes in logistics chains, the situation in the social and labour area has worsened.
Thus, the topical challenges persisting in the global labour market are the following: shortage of jobs with decent working conditions, spread of unreported employment, gender discrimination, scaling up of the 'working poor' phenomenon, a slowdown in labour productivity growth, etc. The atmosphere of high uncertainty and instability prevailing in the world has an adverse impact on the investment activity of businesses, especially small and medium-sized ones, and on the dynamics of job creation. According to ILO experts, there is a notable slowdown in the poverty reduction indicator achieved over the previous decade, as well as in improving the quality of working life through the creation of high-tech jobs with decent working conditions.
Adaptation of the Russian labour market to the conditions of socioeconomic turbulence is accompanied by the development of multivariate transformation processes, namely the emergence and spread of new forms of employment, formation of the phenomenon of 'agency labour, promotion of remote work, precarisation of labour relations, creation of new professions, a sharp decline in the demand for industrial-era professions, high differentiation of wage rates, etc. However, manifestation and intensity of these transformations differ by regions. This fact significantly complicates the formulation and implementation of measures for stabilising and regulating both national and regional labour markets. In this regard, there is a need to propose
1 World Employment and Social Outlook: Trends 2023. https://www.ilo.org/global/research/global-reports/ weso/WCMS_865332/lang--en/index.htm.
a methodological toolkit for assessing the state of the labour market accounting for regional specifics, as well as the existing peculiarities of the functioning of the socioeconomic system.
The research aims to improve the methodological toolkit for assessing the state of the regional labour market taking into account the key socioeconomic trends and therefore, focuses on the following objectives:
- to perform a comparative analysis of the current methodological toolkit designed to assess the situation in regional labour markets;
- to identify the key trajectories of the socioeconomic system's development that have the most profound effect on the state and dynamics of the labour market;
- to propose and test a method for assessing the state of the regional labour market considering the key socioeconomic trends;
- to formulate and substantiate recommendations on improving the mechanism for regulating the regional labour market in today's conditions.
Theoretical approaches to studying the labour market and directions for its transformation at various levels of the economy
The labour market being in the midst of its transformation is the object of numerous studies by foreign scientists. One of the pioneering fundamental works in this field was the publication by Standing [1986], who analyses empirical data and provides arguments in favour of the labour market's increasing flexibility.
These views were elaborated by numerous other researchers. For example, Card et al. [2018] analysed wage inequality in the labour market. Fortin, Lemieux, and Lloyd [2021] established a relationship between shifts in minimum wage and gender inequality in the labour market and identified external effects produced by the development of the trade union movement. In addition, based on the analysis of data on the US labour market in 1979-2017, they concluded that transformations in this market provoked by the instability of external determinants are intensifying. Abowd, McKin-ney, and Zhao [2017] examined the role of employers in earnings distribution in the USA. Having analysed empirical and statistical data from 2004 to 2013, the authors concluded that earnings distribution has a significant impact on the dynamics of unemployment in the country, as well as on population mobility. Autor and Dorn [2013] scrutinised the effect of migration on the US labour market in order to put forward proposals to enhance the labour productivity of immigrants.
Okudaira, Takizawa and Yamanouchi [2019] focused their attention on exploring the labour markets of enterprises and the transformations happening in them. Based on data on the dynamics of labour productivity and wage rates of a number of German enterprises, Lochner and Schultz [2022] studied staff turnover and came to the
conclusion that at enterprises with higher labour productivity and wages the team is more stable than at medium-productivity enterprises.
A wide range of works investigates the role of scientific and technological progress and information and communication technologies (ICT) in transformation processes unfolding in the labour market. The impact of digitalisation, automation and globalisation on the transformation of this market is described in details in the book Jobs lost, jobs gained: Workforce transitions in a time of automation. The authors of the book, James Manyika et al. [2017], presented evidence that the formation of large-scale professional imbalance is influenced primarily by the automation of work activities and the development of artificial intelligence.
In their publication Technology and the labor market, Graetz, Restrepo and Skans [2022] evaluate how technology changes the nature of labour demand and supply and substantiate the role of the state in regulating the socio-labour sphere in today's conditions.
Fossen and Sorgner [2019] delved into the effect of new digital technologies upon occupations. The authors found that implications of digitalisation can be both destructive and transformative and distinguished between four groups of occupations differing in the impacts that digitalisation has on them. Frey and Osborne [2017] proposed a novel methodology to examine how susceptible different jobs are to computerisation. The authors examined possible impacts of computerisation on US labour market outcomes and analyse the number of jobs at risk. They also studied the relationship between an occupations probability of computerisation, wages and educational attainment [Ibid., p. 254].
I§gin and Sopher [2015] scrutinised the labour market functioning in the information society and justified that its efficiency is largely determined by the transparency of firms' productivity that ensures fairness and allows building long-term employment relationships. Goos, Manning and Salomons [2011] examined the impact of technological progress, primarily information technology, to explain changes in the occupational structure of employment and the labour market. Their work accentuates the effect of offshoring and wage-setting institutions that establish these transformational processes.
Thus, foreign authors have identified the impact of modern exogenous determinants on demand, supply, and wage rates on intra-firm, regional, national and global labour markets. In addition, significant attention is paid to the analysis of the influence that the listed factors exert on labour productivity.
Among Russian economic researchers investigating transformation processes in the national and global labour markets are Bobkov [2020], Gimpelson and Kapely-ushnikov [2020], Kashepov, Afonina and Golovachev [2021], Kolosova, Razumova and Artamonova [2019], Korovkin [2018], Kalabina and Shadrina [2022], et al. Their
studies aim to reveal the essence-related aspects of transformation processes in the Russian labour market triggered by the scientific and technical progress, digitalisa-tion, crisis phenomena in the economy, socioeconomic and political changes, as well as to determine the specifics and scale of these transformations in the RF constituent entities.
Despite the problems of the labour market functioning being highly relevant and comprehensively covered in modern research, many issues still remain unresolved, namely improving the methodology for studying changes in the labour market, producing theoretical ideas about their nature and implications, designing a methodological toolkit that shapes the empirical basis to use when projecting the labour market evolution and making appropriate managerial decisions at different levels of economic activity.
Existing approaches and research methods
In science and practice, there is an extensive set of tools for assessing the condition of the regional labour market and the potential for its development.
Among the most comprehensive foreign models are MONASH and ORANI economic mathematical models by Monash University that evaluate the equilibrium in the labour market according to industries. These models are predictive and based on an extensive array of data. Their main advantages are the system nature of forecasts produced, attention to social and technological shifts, objectivity, and informational value, whereas their main disadvantage is the complexity of practical implementation [Khrabrov, 2014, pp. 136-137].
The US Federal Reserve System has developed and tested an LMCI (Labor Market Conditions Index) methodology. This measurement tool relies on 19 labour market indicators. It is a dynamic factor model that is used to ensure the maximum employment in the economy. The advantage of the methodology is using broad data, as well as taking into account indicators of unemployment and underemployment; its central disadvantage is the low correlation between conditions in the labour market and wage growth [Orlov, Postnikov, 2022, pp. 7-8].
One of the first methods developed in Russia was the one based on the system approach by Olga Petrunina. According to the author, the creation of a method for assessing the state of the regional labour market should be preceded by the construction of a conceptual model of the object being evaluated, which allows considering the multi-level and hierarchical order of the labour market as a system [Petrunina, 2010]. Petrunina's method premised on the system approach consists of three stages: - assessing the macroeconomic environment of the market based on 16 relative statistical indicators;
- determining the position of the object being evaluated in the general population based on ranking techniques;
- applying quantitative and qualitative indicators to specify the development problems of the regional labour market under study.
The main advantages of Petrunina's methodological toolkit are:
- versatility provided by the use of relative indicators only;
- ability to assess not only quantitative, but also qualitative parameters of labour market development;
- applicability in the field of interregional and cross-country comparisons.
Fomina [2015] proposes assessing the state of the regional labour market based
on the official list of interrelated socioeconomic statistical indicators. They are categorised into three groups, these are indicators of (a) the state of the labour market, (b) employee turnover, and (c) labour market functioning. This method involves the use of a wide range of quantitative indicators that comprehensively characterise the state of the specified market. The major difficulty with its practical implementation lies in the instability of the system of statistical indicators and methods for calculating them, which makes it hard to determine the development dynamics of the object under study.
The method for assessing the state and development potential of the regional labour market proposed by Agafonov [2018] is also founded on statistical indicators. The author justifies the need to include the 'labour potential' component in the composite assessment indicator. To do so, he proposes to apply the human potential development index (HPDI). The method allows assessing precisely the potential of the labour market development, which is expedient when choosing strategic directions for its regulation. Among the method's shortcomings are, first, a limited range of indicators used to evaluate both the state of the labour market and its development potential, and second, insufficient consideration of the influence that external and internal environmental factors has on the labour market development potential.
At the same time, in scientific literature there is a large number of research methods that account for the influence of individual factors. For example, Pankin [2018] proposes a method for evaluating the regional labour market, which implies identifying not only its condition, but also the impact of the labour migration factor. In addition, this tool allows one to determine the existing potential for labour market regulation, taking into account changes in the migration flow [Ibid., pp. 16-19]. Tarasyev [2020] also substantiates the need to account for migration processes when assessing the labour market in the RF subjects. His method focuses on several components of this factor, such as migrants' age and skills.
Recently, scientists have been actively searching for tools to estimate the impact of a rapidly and spontaneously changing external environment on the labour market. Such studies concentrate on the impact of the digitalisation process, which is due to the fundamental nature and scale of its impact on all spheres of society. Filipova notes: "Modern society has entered the era of digital transformation changing everyday life, production, education, culture, and politics. Digital technologies are penetrating virtually all spheres of society, leaving fewer and fewer 'islands' not affected by them yet... Digitalisation of the economy is one of the main manifestations of the ongoing transformation. Labour relations as part of social relations cannot remain unaffected. Digital technologies are becoming increasingly commonplace, spreading in production, the service sector, agriculture, that is, in areas where wage labour is used. The transition from industrial society to information one is already happening, the digital future is coming" [Filipova, 2021, pp. 5-6].
In this regard, the following developments are of special interest.
Anisimova and Mitrofanova [2022] proposed a method for assessing the efficiency of digital labour, which includes five groups of evaluation indicators for ranking enterprises by the efficiency of digital labour. Moreover, they found a correlation between knowledge growth and digital labour efficiency. Their method expands the understanding of the influence of transformation processes in the content-related aspects of labour activity on the system of social-labour relations and the labour market.
Larionova, Yuryeva and Burganova [2022] conducted a structural analysis of employment and unemployment with an emphasis on the impact of digitalisation using economic and mathematical modelling. The method was tested in the context of the RF federal districts and showed that the COVID-19 pandemic acted as a catalyst for increasing the use of ICT by households, which affected the relationship between employment and unemployment.
Lukyanova [2021] proposes evaluating the impact of digitalisation on the gender pay gap by analysing data from the Russian Longitudinal Monitoring Survey by HSE University (RLMS-HSE) and O*NET for 2003-2018. The author proved that, first, the level and pace of digitalisation differed significantly in the professional context, and second, digitalisation caused an increase in gender inequality in employment and wages.
Bylkov [2021] developed a system of criteria for segmenting the regional labour market according to the level of involvement in the digital economy. Having tested the proposed methodological toolkit, the author revealed an intersectoral gap in the area in question.
The analysis of the methods focused on the impact of the digital factor demonstrates their fragmented nature in terms of characterising the labour market conditions. It is
also concluded that a limited range of indicators is used to assess this impact, which is due to the imperfection of statistical data, including their incompleteness at the level of RF subjects.
The foregoing allows us to state that since the transition to market relations, an extensive array of methodological approaches in Russian science has been formed aimed at a multifaceted assessment of the state of the regional labour market. Most of them are based on official statistical indicators. At that, the high pace of scientific and technological progress and the subsequent fundamental changes in the life of society that became especially obvious in the last decade contribute to the accelerated obsolescence of the methodological toolkit. The learned society reacts to this challenge by constantly searching and improving methods for evaluating the state of the regional labour market while taking into account the changes occurring in it. A significant obstacle in this process is the lack of adequate and comparable statistical data.
To eliminate some of the shortcomings identified, the authors have developed a method for assessing the regional labour market with the key socioeconomic trends taken into account, namely digitalisation and innovation as the crucial areas for enhancing the competitiveness of the Russian economy. The fundamental role of these areas has been repeatedly emphasised by the President of the Russian Federation: "At various stages, our country successfully solved ambitious tasks of technological and spatial development, built railways at a unique pace at the turn of the 19th and 20th centuries, and carried out electrification in the 20s and 30s of the last century. But our plans for the widespread introduction of artificial intelligence and digital transformation are unparalleled in terms of the depth of changes in all areas. They will truly affect every person, every family, every sector of the economy and social sphere, every organisation and every tier of authority, the entire system of public administration"1.
The proposed method is based on the index approach and includes three components that make it possible to assess the transformation of both the internal parameters of the labour market and the main factors of the external environment:
- a general index of the regional labour market (Icm), which implies the use of some indicators that reflect the objectives of Russia's national projects achieved and is calculated on the basis of individual indices;
- a general index of digitalisation (Id) that embraces four individual indices for the indicators that most characterise the impact of the socioeconomic environment's digital transformation on the regional labour market;
- a general index of innovation orientation (I), which is determined according to individual indices based on indicators characterising the innovation development of the economy (Table 1).
1 Putin announced the need for digital transformation of Russia. https://tass.ru/ekonomika/10172635. (In Russ.)
Table 1. A system of indicators for assessing the regional labour market considering key
socioeconomic trends
Denotation Indicators
To calculate the general index of the regional labour market (Idm)
P1(clm) Employment rate of the population aged 15 years and older, %
P2(clm) Labour force participation rate of the population aged 15 years and older, %
P3(clm) Unemployment rate of the population aged 15-72 years, %
P4(clm) Registered unemployment rate, %
P5(clm) Aggregate indicator of unemployment and potential labour force of the population aged 15 years and older, %
P6(clm) Tension coefficient
P7(clm) Poverty level, %
P8(clm) Share of unemployed looking for work for 12 months or more, %
P9(clm) Average monthly nominal wages of employees for a full range of organisations in the economy at large, rubles
P10(clm) Employment rate of women with preschool children, %
Pll(clm) Need for workers declared by employers to the employment service authorities, units
P12(clm) Share of the labour force aged 22 years and older with secondary vocational and higher education in the total labour force of the corresponding age, %
To calculate the general index of digitalisation (Id)
Pi(d) Share of households with broadband Internet access, %
P2(d) Organisations that used the Internet, % of the total number of organisations surveyed
P3(d) Number of PCs per 100 employees, units
P4(d) Organisations that used electronic data exchange between internal and external information systems, by exchange format, %
P5(d) Costs for the implementation and use of digital technologies, million rubles
P6(d) Organisations having own websites, %
To calculate the general index of innovation orientation (Ii)
Pi(0 Share of researchers under 39 years in the total number of researchers in Russia, %
P2(i) Internal costs for R&D from all sources, billion rubles
P3(i) Patent applications for inventions filed, units
P4(i) Advanced production technologies used, units
P5(i) Level of innovation activity of organisations, %
The indicators included in the method are presented in official statistics; their use allows adopting this tool for interregional assessments. To calculate individual indices of the indicators, we suggest using the min-max scaling method:
1) if there is a direct relationship between the variables, then the following formula is used:
X — X
T =__ii- (1)
X — X '
max mill
2) if the relationship between the variables is inverse, the calculation is carried out by formula:
X - X
T =_~_— (2)
X — X '
max
where Ixi is index of indicator X in the i-th region; Xi is value of indicator X in the z'-th region; Xmax is the maximum conditional value of indicator X; Xmin is the minimum conditional value of indicator X.
Next, the general indices of the regional labour market, digitalisation and innovation orientation are calculated using the arithmetic mean formula. Based on the general indices, it is proposed to calculate a composite index for assessing the state of the regional labour market and its development taking into account the key socioeconomic trends by formula:
_ Idm +0,5/, +0,51,
1tm— 2 ' (3)
The general index of the regional labour market directly characterises its situation and has the greatest weight in calculating the composite index. The general index value will range from 0 to 1. The closer it is to 1, the more the labour market corresponds to the main socioeconomic development trajectories.
Thus, the proposed method for assessing the regional labour market with the key socioeconomic trends taken into account reflects the realities of social development and allows for the strategic goals of state policy. It can be used both to assess the labour market of a particular region and to perform interregional comparisons.
Research results
To test the proposed method, we used statistical data characterising the labour markets, digitalisation process and innovation development of Belgorod, Voronezh, Kursk, Lipetsk and Tambov oblasts. These regions were selected for analysis as they have long been part of the Central Black Earth economic region (CBER) and still have similar characteristics and parameters of socioeconomic development.
Supporting the position of Lyakhova and Grigoryan [2017], we can note that it is expedient to perform a comparative analysis of these RF regions in order to formulate managerial decisions on their strategic development: "The Central Black Earth economic region... distinguishes itself as having a special resource potential of the territory, which is realised in the development of the agro-industrial and mining-metallurgical complex. The unique natural resources of the CBER - chernozem soils and iron ore deposits - require a specific economic strategy to be developed in order to optimally utilise these resources" [Lyakhova, Grigoryan, 2017, p. 4].
To assess the state of the labour market, individual indices were initially calculated for the regions under study based on statistical data (Tables 2, 3).
Table 2. Initial data for calculating individual indices for the regions under study, 2021
Indicators X max X
BO VO KO LO TO
To calculate the general index of the regional labour market (Idm)
P1(clm) 61.0 57.6 58.6 60.0 55.8 76.2 48.6
P2(clm) 63.7 59.9 61.1 62.6 58.1 78.7 53.6
P3(clm) 4.2 3.8 4.0 4.2 3.9 31.1 2.0
P4(clm) 0.5 1.1 0.6 0.4 0.7 14.9 0.3
P5(clm) 4.8 5.2 5.0 5.2 6.2 31.1 2.5
P6(clm) 1.5 2.0 1.6 2.0 2.0 109.6 0.4
P7(clm) 7.0 7.9 9.1 8.2 10.6 29.3 4.6
P8(clm) 13.0 21.8 33.1 18.5 26.0 76.3 5.1
P9(clm) 41,775 40,830 40,292 40,188 34,438 130,738 31,291
P10(clm) 75.2 66.2 76.4 78.7 71.1 81.3 43.4
Pll(clm) 21,074 21,375 13,683 13,468 9,292 138,813 604
P12(clm) 83.0 76.5 86.2 83.1 82.0 93.0 59.2
To calculate the general index of digitalisation (Id)
Pi(d) 78.5 86.3 82.7 76.5 81.2 98.4 69.5
P2(d) 94.1 85.3 81.4 88.5 88.7 94.1 68.7
P3(d) 50 61 49 54 48 96 35
P4(d) 63.7 58.2 57.9 61.4 63.7 67.3 40.1
P5(d) 8,211.7 10,246.6 4,760.9 8,674.1 3,603.9 2,284,939.1 490.7
P6(d) 56.2 44.7 43.7 47.5 55.1 56.8 35.5
Table 2 (concluded)
Indicators x max x ■A-min
BO VO KO LO TO
To calculate the general index of innovation orientation (I)
Pm 47.0 47.4 38.6 33.8 31.5 60.9 23.8
P2(i) 4.0 11.1 3.8 0.7 1.0 460.7 0
P3(i) 208 454 141 63 117 5163 2
P4(0 3,349 3,072 1,794 3,105 2,010 16,455 83
Ps(0 17.0 12.6 6.8 13.7 10.7 29.0 1.7
Source: Own compilation based on data from the Federal State Statistics Service. https://rosstat.gov. ru/folder/210/document/13204. (In Russ.)
Note: Hereinafter, BO is Belgorod oblast; VO is Voronezh oblast; KO is Kursk oblast; LO is Lipetsk oblast; TO is Tambov oblast.
Table 3. Values of individual indices (Ixi), 2021
Indicators BO VO KO LO TO
To calculate the general index of the regional labour market (Idm)
P1(clm) 0.4493 0.3261 0.3623 0.4130 0.2609
P2(clm) 0.4024 0.2510 0.2988 0.3586 0.1739
P3(clm) 0.9244 0.9381 0.9313 0.9244 0.9347
P4(clm) 0.9863 0.9452 0.9795 0.9932 0.9726
P5(clm) 0.9196 0.9056 0.9126 0.9056 0.8706
P6(clm) 0.9899 0.9853 0.9890 0.9853 0.9853
P7(clm) 0.9028 0.8664 0.8178 0.8543 0.7571
P8(clm) 0.8890 0.7654 0.6067 0.8118 0.7065
P9(clm) 0.1054 0.0959 0.0905 0.0895 0.0316
P10(clm) 0.8391 0.6016 0.8707 0.9314 0.7309
P11(clm) 0.1481 0.1503 0.0946 0.0931 0.0629
P12(clm) 0.7041 0.5118 0.7988 0.7071 0.6746
To calculate the general index of digitalisation (Id)
P1(d) 0.3114 0.5813 0.4567 0.2422 0.4048
P2(d) 1.0000 0.6535 0.5000 0.7795 0.7874
P3(d) 0.2459 0.4262 0.2295 0.3115 0.2131
P4(d) 0.8676 0.6654 0.6544 0.7831 0.8676
P5(d) 0.0034 0.0043 0.0019 0.0036 0.0014
P6(d) 0.9718 0.4319 0.3850 0.5634 0.9209
Table 3 (concluded)
Indicators BO VO KO LO TO
To calculate the general index of innovation orientation (I)
P1(i) 0.6253 0.6361 0.3989 0.2695 0.2075
P2(i) 0.0087 0.0241 0.0082 0.0015 0.0022
P3(i) 0.0399 0.0876 0.0269 0.0118 0.0223
P4(i) 0.1995 0.1826 0.1045 0.1846 0.1177
P5(i) 0.5604 0.3993 0.1868 0.4396 0.3297
Source: Own compilation based on data from the Federal State Statistics Service. https://rosstat.gov. ru/folder/210/document/13204. (In Russ.)
Next, the general indices of the regional labour market, digitalisation and innovation orientation were calculated, as well as the composite index for assessing the regional labour markets with the key socioeconomic trends taken into account (Table 4).
Table 4. Initial data for calculating the composite index, 2021
Indicators Indices values
BO VO KO LO TO
Iclm 0.6884 0.6119 0.6461 0.6723 0.5968
Id 0.5667 0.4604 0.3713 0.4472 0.5325
Ii 0.2868 0.2659 0.1451 0.1814 0.1359
Ilm 0.5576 0.4875 0.4522 0.4933 0.4656
The results of the method testing at the case of the CBER territories can be interpreted as follows:
- the difference in the composite indices values is insignificant, and only in Belgorod oblast this index is slightly higher (0.5576). Its least value was obtained for Kursk oblast (0.4522). Hence, the developed methodological toolkit revealed the similarities in the regional labour markets taking into account the key socioeconomic trends;
- the analysis of the main components of the composite index varying between 0.5968 and 0.6884 in the regions under study shows that the general index of the labour market is above average. At that, the highest values were recorded in Belgorod and Lipetsk oblasts - 0.6884 and 0.6723, respectively;
- the general index of digitalisation in these regions is noticeably differentiated. Its maximum value is recorded in Belgorod oblast (0.5667), the minimum - in Kursk
oblast (0.3713). Thus, the digitalisation process in these RF subjects is uneven, and only Belgorod and Tambov oblasts demonstrate the most positive results;
- the general index of innovation orientation in the CBER territories is very low. Only Belgorod and Voronezh oblasts are noticeably ahead of the others in terms of innovation.
Based on the data obtained, we can conclude that the state of labour markets in the regions in question can be described as good, since the values of the general indices exceed 0.5. At the same time, these regions lag behind in terms of digitalisation and innovation orientation. It is also worth noting that the process of innovative transformation in the CBER is significantly slowed down.
Conclusion
Testing the developed method at the case of the Central Black Earth economic region revealed the problem areas in the functioning of its labour markets. This data should be taken into account when working out strategic development plans for the CBER territories under study, using the following recommendations.
1. To address the problem of the RF subjects lagging behind other regions of the Russian Federation in terms of wages it is necessary to increase the number of highly productive jobs, attract potential investors to the regions, and spur the activities of trade unions and associations.
2. To eliminate the shortage of labour resources, it is important to implement a set of measures to retain young people in their home regions, in particular to provide for active career guidance, develop the institution of mentoring, ensure socioeconomic support for young families through the efforts of regional authorities, and advance the youth employment promotion programme. It is also of high importance to boost the attractiveness of the CBER territories for labour migrants by improving the economic, social and household infrastructure. In addition, considering the demographic situation in these subjects of the Russian Federation, close attention should be paid to measures on training and promoting the competencies of citizens of pre-retirement and retirement age.
3. To accelerate the process of digital transformation of the regions, it is reasonable to create additional instruments of financial support for organisations introducing ICT into their activities.
4. Increasing the innovation activity of these territories is possible through expanding the system of business incubators, venture organisations, development funds, as well as through strengthening the motivation of business entities to innovate by the use of financial instruments.
Implementation of the proposed measures will allow solving the identified problems in the functioning of the labour market in the territories of the Central Black
Earth economic region and increasing the extent to which these territories are in line with the key trajectories of socioeconomic development.
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Information about the authors
Ekaterina S. Dashkova, Dr. Sc. (Econ.), Associate Prof., Head of Labour Economics and Management Fundamentals Dept. Voronezh State University, Voronezh, Russia. E-mail: dashkova-82@mail.ru
Natalia V. Dorokhova, Dr. Sc. (Econ.), Associate Prof., Prof. of Labour Economics and Management Fundamentals Dept. Voronezh State University, Voronezh, Russia. E-mail: nv_dorohova@mail.ru
© Dashkova E. S., Dorokhova N. V., 2023
