Forecasting the sectoral structure of population employment

All the labor market subjects that can influence the labor resources dynamics are interested in employment forecasts by labor market sectors. Such subjects are state employees and municipal employees, employers and workers. The statistical data aggregation degree affects the quality of the labor resources dynamics forecasting. Each labor market indicator combines a set of detailed indicators in a high degree of aggregation case. When building the trends it is impossibile to take into account information on the detailed indicators trends. The labor market indicators for each specific year don’t contain information about the interaction with each other. This fact also negatively affects the forecast quality. The article discusses the use of a balance mathematical model of the labor resources dynamics, which relates the labor market sectoral indicators, to define the intersectoral movements indicators. The authors consider a calculating labor market indicators method that uses only statistical data on sectoral employment and unemployment. Thus, the statistical data on the labor resources dynamics provided by the Federal State Statistics Service is a sufficient condition for the Russian Federation labor market detailing using intersectoral movements’ indicators. The paper shows how a set of intersectoral movements indicators allows building the forecast values of these indicators and using them to calculate the forecast values of labor market indicators. The article considers examples of building employment estimates by Russian Federation economy sectors for 2011–2016 and 2019. The entry into force of the All-Russian classifier of types of economic activity second edition in 2017 is the reason for choosing such research intervals. The purpose of these examples was to determine the impact of the detailed labor market indicators of the sectoral employment estimates reliability. The authors compared the forecast obtained directly from labor market indicators with the forecasts obtained from intersectoral movements indicators. Intersectoral movements indicators are the results of applying balance models with varying degrees of detail. The reliability tables presented in this work to assess the forecasting quality indicate that the detailing of the sectoral employment indicators by using the balance model can increase reliability of the forecast.

Keywords

balance model, labor force dynamics, intersectoral relations detailing, forecasting, labor market.

Received

15.03.2023

Publication date

29.03.2023

Number of characters

16157

Cite

GOST	Drobotenko M., Nevecherya A. Forecasting the sectoral structure of population employment // Ekonomika i matematicheskie metody – 2023. – V. 59. – No. 1 C. 22-29 [Electronic resource]. URL: https://emm.jes.su/S042473880024868-3-1 (circulation date: 18.04.2024). DOI: 10.31857/S042473880024868-3
MLA	Drobotenko, Michael, Nevecherya, Artyom "Forecasting the sectoral structure of population employment." Ekonomika i matematicheskie metody 59.1 (2023):22-29. DOI: 10.31857/S042473880024868-3
APA	Drobotenko M., Nevecherya A. (2023). Forecasting the sectoral structure of population employment. Ekonomika i matematicheskie metody 59(1), pp.22-29 DOI: 10.31857/S042473880024868-3

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1. Armstrong J.S. (1984). Forecasting by extrapolation: Conclusions from 25 years of research. Interfacess. No. 003. 20 p.

2. Bakens J., Fouarge D., Peeters T. (2018). Labour market forecasts by education and occupation up to 2022. ROA. ROA Technical Reports, 14, 6, 52–66.

3. Borghans L., De Grip A., Heijke H. (1996). Labor market information and the choice of vocational specialization. Economics of Education Review, 15 (1), 59–74.

4. Cörvers F., Heijke H. (2005). Forecasting the labour market by occupation and education: Some key issues. Maastricht: Research centrum voor Onderwijs en Arbeidsmarkt.

5. Drobotenko M.I., Nevecherya A.P. (2021). Forecasting the labour force dynamics in a multisectoral labor market. Computer Research and Modeling, 13, 1, 235–250 (in Russian).

6. Edinak E.A., Korovkin A.G. (2014). Construction of balance of territorial mobility of employed population: Case study of federal districts of the Russian federation. Studies on Russian Economic Development, 3 (144), 72–85 (in Russian).

7. Key indicators of the labour market. Ninth edition. (2016). Geneva: International labour office.

8. Knobel C., Kriechel B., Schmid A. (2008). Regional forecasting on labour markets. Munich: Rainer Hampp Verlag.

9. Korovkin A.G. (2001). Employment and labor market dynamics: Issues of macroeconomic analysis and forecasting. Moscow: MAКS Press (in Russian).

10. Korovkin A.G., Edinak E.A., Korolev I.B. (2017). The number and population structure forecasting on the base of the balance approach. In: Demographic potential of the EAEU countries. Yekaterinburg: Institute of Economics UrB RAS, 297–303 (in Russian).

11. Nevecherya A.P. (2016a). Analysis of labor force dynamics in intersectoral mathematical model of the labor market. Economics and Mathematical Methods, 52, 2, 129–140 (in Russian).

12. Nevecherya A.P. (2016b). A numerical algorithm in the problem of self-organization of labor resources. Polythematic network electronic scientific journal of the Kuban State Agrarian University (Scientific Journal of KubSAU), 04 (118), 1333–1349 (in Russian).

13. Nevecherya A.P. (2021). Labour resources dynamics forecasting problem. Science. A new generation. Success, 2, 24–26 (in Russian).

14. Russia in figures. 2021: Brief Statistical Compilation (2021). Moscow: Federal State Statistics Service (in Russian).

15. Scott J., Marshall G. (2009). A dictionary of sociology. Oxford: Oxford University Press.

16. Tikhonov A.N., Arsenin V.Ya. (1979). Methods for solving ill-posed problems. Moscow: Nauka (in Russian).

17. Tsakalozos N., Konstantinos D., Scott R. (2011). Signal extrapolationusing empirical mode decomposition with financial applications. CASSP, IEEE International Conference on Acoustics, Speech and Signal Processing, 5744–5747.

18. Wilkinson F. (1981). The dynamics of labour market segmentation. NYC: Academic press.