Uncategorised Hits: 15

ECONOMIC RESEARCH

TITLE

Using Open Data Online Vacancies in Comparison with Official Statistics to Monitor and Forecast Labor Market Dynamics

Vitalii V. Altukhov, Aleksei D. Kudryavtsev

Размер файла000
 Размер файла  2.828 MB Размер файла Full text

INDEX

RAR (Research Article Report)

JEL J22, J23

https://doi.org/10.52180/1999-9836_2025_21_2_5_233_244

AUTHORS

Vitalii V. Altukhov

Lomonosov Moscow State University, Moscow, Russia

Profilum, Moscow, Russia

e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

OCRID: https://orcid.org/0009-0000-9307-4276

Aleksei D. Kudryavtsev

Lomonosov Moscow State University, Moscow, Russia

Profilum, Moscow, Russia

e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

FOR CITATION

Altukhov V.V., Kudryavtsev A.D. Using Open Data Online Vacancies in Comparison with Official Statistics to Monitor and Forecast Labor Market Dynamics. Uroven' Zhizni Naseleniya Regionov Rossii=Living Standards of the Population in the Regions of Russia. 2025;21(2):233–244. https://doi.org/10.52180/1999-9836_2025_21_2_5_233_244 (In Russ.)

Abstract

Digitalization of labor processes and the growing popularity of online platforms open up new opportunities for monitoring and forecasting labor market dynamics. However, the issues related to the representativeness of online vacancies data, their timeliness and completeness remain unresolved. The scientific interest of the study lies in the development of approaches to the integration of data from online sources with official statistics, which will improve the accuracy of forecasting and promptness of labor market assessment. In traditional labor market analysis, vacancies are used to measure labor market tensions and can signal the presence of imbalances in the labor market, when supply and demand do not match each other (in terms of qualitative characteristics, geographically, etc.). The purpose of the article is to compare the data of online vacancies and official statistics to develop approaches to monitoring and forecasting labor market dynamics. The article gives an example of implementation of labor market monitoring based on big data and comparison of online vacancies data with the sources of official statistics. The main sources of data for comparison were Rosstat and hh.ru (open vacancy data). The author's methodology of aggregation of vacancy data into groups of professional spheres and professions based on official classifiers, as well as methods of calculation and estimation of salary levels were used in the comparison. As a result of the study, it was revealed that the obtained and aggregated data of the online job search portal hh.ru reliably correlates with the official quarterly and monthly statistics on the dynamics of the number of open vacancies and salaries. Finally, we discuss methods of forecasting labor market dynamics using machine learning methods based on open big data. According to the authors, the possibility of correlating the dynamics of the indicators of online portals with official statistics of enterprises could complement the methodology of labor market monitoring and increase the reliability of forecasts.

Keywords

regional labor market, online vacancies, vacancy dynamics, wage, estimation of demand in the labor market, economic sectors, big data

AUTHOR'S BIOGRAFY

Vitalii V. Altukhov

Junior Research Fellow, Laboratory of Social and Economic Research «Technologies for the Development of Human Capital and the Construction of Institutional and Competence-Based Models of Human Development» at the Department of Labor and Personnel Economics, Faculty of Economics, Lomonosov Moscow State University; Director of Development and Research, Profilum

Aleksei D. Kudryavtsev

Junior Research Fellow, Laboratory of Social and Economic Research «Technologies for the Development of Human Capital and the Construction of Institutional and Competence-Based Models of Human Development» at the Department of Labor and Personnel Economics, Faculty of Economics, Lomonosov Moscow State University; Data Scientist, Profilum

References

  1. Makshanov AV, Zhuravlev AE, Tyndykar’ LN. Bol'shie dannye. Big Data. Textbook for Universities. Peterburg; 2022. 188 p. ISBN 978-5-8114-9690-7 (In Russ.).
  2. Mikryukov АА, Granatov MG, Abdrakhmanova ZA. Big Data Analysis Methods. Obshchestvo, ekonomika, upravlenie=Society, economy, management. 2023;8(4):70-74. https://doi.org/10.47475/2618-9852-2023-8-4-70-74 (In Russ.)
  3. Napierala J., Kvetan V., Branka J. Assessing the Representativeness of Online Job Advertisements. Cedefop working paper. No. 17. Luxembourg: Publications Office of the European Union; 2022. 26 p. http://data.europa.eu/doi/10.2801/807500
  4. Vermeulen W., Gutierrez Amaros F. How Well do Online Job Postings Match National Sources in European Countries?: Benchmarking Lightcast Data against Statistical and Labour Agency Sources across Regions, Sectors and Occupation. OECD Local Economic and Employment Development (LEED) Papers. No. 2024/02. Paris: OECD Publishing; 2024. https://doi.org/10.1787/e1026d81-en
  5. De Pedraza P., Visintin S., Tijdens K., et al. Survey vs Scraped Data: Comparing Time Series Properties of Web and Survey Vacancy Data. IZA Journal of Labor Economics. 2019;8(1):1-23. https://doi.org/10.2478/izajole-2019-0004
  6. Lovaglio P.G., Mezzanzanica M., Colombo E. Comparing Time Series Characteristics of Official and Web Job Vacancy Data. Quality and Quantity. 2020;54(1):85-98. https://doi.org/10.1007/s11135-019-00940-3.
  7. Evans D., Mason C., Chen H., et al. An algorithm for Predicting Job Vacancies Using Online Job Postings in Australia. Humanities and Social Sciences Communications. 2023;10(102):1-9. https://doi.org/10.1057/s41599-023-01562-9
  8. Kureková L.M., Beblavý M., Thum-Thysen A. Using Online Vacancies and Web Surveys to Analyse the Labour Market: a Methodological Inquiry. IZA Journal of Labor Economics. 2015;4(18):1-20. https://doi.org/10.1186/s40172-015-0034-4
  9. Romanko O., O’Mahony M. The Use of Online Job Sites for Measuring Skills and Labour Market Trends: a Review. ESCoE Technical Report No. 2022-19. London; 2022. 48 p. ISSN 2515-4664
  10. Roshchin S., Solntsev S., Vasilyev D. Recruiting and Job Search Technologies in the Age of Internet. Foresight and STI Governance. 2017;11(4):33-43. https://doi.org/10.17323/2500-2597.2017.4.33.43.
  11. Cammeraat E., Squicciarini M. Burning Glass Technologies’ Data Use in Policy-relevant Analysis: an Occupation-level Assessment. OECD Science, Technology and Industry Working Papers. No. 2021/05. Paris: OECD Publishing; 2021. 69 p. https://doi.org/10.1787/cd75c3e7-en
  12. Davis S., Faberman R., Haltiwanger J. The Establishment-level behavior of Vacancies and Hiring. Quarterly Journal of Economics. 2013;128(2);581-622. https://doi.org/10.1093/qje/qjt002
  13. Carnevale A.P., Jayasundera T., Repnikov D. Understanding Online Job Ads Data: Technical Report. Georgetown: Center on Education and the Workforce; 2014. 28 p. http://dx.doi.org/10.13140/RG.2.2.13283.57124
  14. Kapeliushnikov R.I. The Russian Labor Market: a Statistical Portrait on the Crises Background. Voprosy Ekonomiki. 2023;(8):5-37. https://doi.org/10.32609/0042-8736-2023-8-5-37 (In Russ.)
  15. Gorina T., Natsak. O. Analyzing Supply and Demand in Tuva’s Labor Market: Professional and Gender Aspects Revisited. Byulleten' Kalmytskogo nauchnogo tsentra RAN=Bulletin of the Kalmyk Scientific Center of the RAS. 2022;(1):204–235. https://doi.org/10.22162/2587-6503-2022-1-21-204-235 (In Russ.)
  16. Ternikov A. A. Professional Standards and Demand in the Labor Market in the Sphere of Higher Management in Russia. Vestnik Sankt-Peterburgskogo universiteta. Menedzhment=Vestnik of Saint Petersburg University. Management. 2022;21(1);131–148. http://doi.org/10.21638/11701/spbu08.2022.106 (In Russ.)
  17. Aletdinova A., Bakaev M. Intelligent Data Analysis and Predictive Models for Regional Labor Markets. In: Digital Transformation and Global Society. 6th International Conference Proceedings; June 23–25, 2021; St. Petersburg, Russia. 2022:351-363. https://doi.org/10.1007/978-3-030-37858-5_29
  18. Dawson N., Rizoiu M. A., Johnston B., et al. Predicting Skill Shortages in Labor Markets: A Machine Learning IEEE International Conference on Big Data. Conference Proceedings; December 10-13, 2020; Atlanta, USA. 2020:3052-3061. https://doi.org/10.1109/BigData50022.2020.9377773