Returns on skills of IT specialists in Russian cities based on job vacancy data
https://doi.org/10.32609/0042-8736-2026-3-106-128
Abstract
The article assesses the returns on skills of IT specialists in Russian cities of different sizes. The empirical basis of the study consists of open data on job vacancies published on the HeadHunter platform in March—April 2025. The paper describes the data processing procedures, including the unification of skill descriptions and their transformation into discrete variables. The main research method is hedonic wage modeling in the labor market, which allows for estimating the impact of various factors on the salaries of information technology professionals. The econometric modeling employs a two-step OLS approach with Heckman correction and sequential inclusion of variables into the baseline specification. The results show that professional IT skills, personal characteristics, managerial skills, and employment in Moscow, the Moscow region, St. Petersburg, and cities located outside the Russian Federation have a statistically significant positive effect on IT specialists’ wages. Employment in cities with populations between 250 thousand and one million people, as well as social skills, have a significant negative effect. For professional IT skills, significant positive effects were identified in Moscow, the Moscow region, and St. Petersburg, indicating that the city effect remains even after accounting for cross-city differences in skill valuation. The main findings of the study are relevant for the development and implementation of education, regional, and migration policies aimed at fostering the training and mobility of highly qualified employees. The proposed approach to working with relatively small but easily accessible datasets can also be applied in expert HR analytics, for example, to identify differences among local labor markets.
Keywords
JEL: J23, J24, J31, R12
About the Authors
M. A. GiltmanRussian Federation
Marina A. Giltman
Tyumen
S. V. Sharifulin
Russian Federation
Stepan V. Sharifulin
Tyumen
A. Y. Merzlyakova
Russian Federation
Anastasiya Y. Merzlyakova
Tyumen
R. F. Murzagulova
Regina F. Murzagulova
Tyumen
References
1. Volgin A. D., Gimpelson V. E. (2022). Demand for skills: Analysis using online vacancy data. HSE Economic Journal, Vol. 26, No. 3, pp. 343—374. (In Russian). https://doi.org/10.17323/1813-8691-2022-26-3-343-374
2. Zemtsov S. P. (ed.) (2020). High-tech business in the regions of Russia: National report. Moscow: RANEPA. (In Russian).
3. Giltman M. A. (2021). Do the best cities have the best workers? Theoretical models and empirical evidence. Universe of Russia, Vol. 30, No. 3, pp. 127—149. (In Russian). https://doi.org/10.17323/1811-038X-2021-30-3-127-149
4. Demyanova A. V., Zinina T. S., Rudnik P. B. (2024). Remote employment: the scale of distribution in companies (Monitoring of digital business transformation No. 5). Moscow: HSE University. (In Russian).
5. Kapelyuk S. D., Karelin I. N. (2023). Dynamics of digital skills demand in labor markets of Russian regions. π-Economy, Vol. 16, No. 1, pp. 51—61. (In Russian). https://doi.org/10.18721/JE.16104
6. Kapeliushnikov R. I., Zinchenko D. I. (2024). Digital forms of employment in the Russian labor market: Remote and platform-based. Moscow: HSE University. (In Russian).
7. Lishchuk E. N., Kapelyuk S. D. (2020). Analysis of demanded occupations: Regional issues. Region: Economics and Sociology, No. 1, pp. 119—152. (In Russian). https://doi.org/10.15372/REG20200106
8. Lishchuk E. N., Kapelyuk S. D. (2021). Transforming human capital requirements in the context of a pandemic. Russian Journal of Labour Economics, Vol. 8, No. 2, pp. 219—232. (In Russian). https://doi.org/10.18334/et.8.2.111644
9. Lishchuk E. N., Kapelyuk S. D. (2022). Is it easy to become unemployed? Russian Journal of Labour Economics, Vol. 9, No. 8, pp. 1263—1280. (In Russian). https://doi.org/10.18334/et.9.8.114905
10. Loginov D. M., Lopatina M. V. (2021). Remote employment in the corona-crisis period: Тhe extent of spread and effectiveness of introduction. Population, Vol. 24, No. 4, pp. 107—121. (In Russian). https://doi.org/10.19181/population.2021.24.4.9
11. Melnikova L. V. (2023). Efficiency of large cities: Theory and empirics. Voprosy Еkonomiki, No. 3, pp. 83—101. (In Russian). https://doi.org/10.32609/0042-8736-2023-3-83-101
12. Murzagulova R. F. (2024). Remote employment in large cities: Gain for workers in terms of wages. Perm University Herald. Economy, Vol. 19, No. 1, pp. 85—106. (In Russian). https://doi.org/10.17072/1994-9960-2024-1-85-106
13. Murzagulova R. F., Giltman M. A., Sharifulin S. V. (2025). Opportunities and limitations of open job data for analyzing labor demand for IT specialists. Tyumen State University Herald. Social, Economic, and Law Research, Vol. 11, No. 2, pp. 261—277. (In Russian). https://doi.org/10.21684/2411-7897-2025-11-2-261-277
14. Rozhkova K. V., Roshchin S. Y. (2021). Non-cognitive characteristics and higher education choices. Educational Studies Moscow, No. 4, pp. 35—73. (In Russian). https://doi.org/10.17323/1814-9545-2021-4-35-73
15. Ternikov A. A. (2023). Artificial intelligence and the demand for skills in Russia. Voprosy Ekonomiki, No. 11, pp. 65—80. (In Russian). https://doi.org/10.32609/0042-8736-2023-11-65-80
16. Ternikov A. A., Aleksandrova E. A. (2020). Demand for skills on the labor market in the IT sector. Business Informatics, Vol. 14, No. 2, pp. 64—83. (In Russian). https://doi.org/10.17323/2587-814X.2020.2.64.83
17. Florida R. (2007). The creative class: People who change the future. Moscow: Klassika-XXI. (In Russian).
18. Bauer T. K., Breidenbach P., Schmidt C. M. (2015). “Phantom of the opera” or “Sex and the city”? Historical amenities as sources of exogenous variation. Labour Economics, Vol. 37, pp. 93—98. https://doi.org/10.1016/j.labeco.2015.05.005
19. Combes P.-P., Duranton G., Gobillon L., Puga D., Roux S. (2012). The productivity advan‑ tages of large cities: Distinguishing agglomeration from firm selection. Econometrica, Vol. 80, No. 6, pp. 2543—2594. https://doi.org/10.3982/ECTA8442
20. Dauth W., Findeisen S., Moretti E., Suedekum J. (2018). Matching in cities. NBER Working Paper, No. 25227. https://doi.org/10.3386/w25227
21. Dickerson A., Green F. (2004). The growth and valuation of computing and other generic skills. Oxford Economic Papers, Vol. 56, No. 3, pp. 371—406. https://doi.org/10.1093/oep/gpf049
22. Duranton G., Puga D. (2003). Micro-foundations of urban agglomeration economies. NBER Working Paper, No. 9931. https://doi.org/10.3386/w9931
23. Falck O., Fritsch M., Heblich S. (2011). The phantom of the opera: Cultural amenities, human capital, and regional economic growth. Labour Economics, Vol. 18, No. 6, pp. 755—766. https://doi.org/10.1016/j.labeco.2011.06.004
24. Fareri S., Fantoni G., Chiarello F., Coli E., Binda A. (2020). Estimating Industry 4.0 impact on job profiles and skills using text mining. Computers in Industry, Vol. 118, article 103222. https://doi.org/10.1016/j.compind.2020.103222
25. Felstead A., Gallie D., Green F., Zhou Y. (2007). Skills at work, 1986 to 2006. Oxford: SKOPE.
26. Filippi E., Banno M., Trento S. (2023). Automation technologies and their impact on em‑ ployment: А review, synthesis and future research agenda. Technological Forecasting and Social Change, Vol. 191, article 122448. https://doi.org/10.1016/j.tech‑fore.2023.122448
27. Florida R. (2014). The rise of the creative class—revisited: Revised and expanded. New York: Basic Books.
28. Fujita M., Krugman P., Venables A. J. (1999). The spatial economy: Cities, regions and international trade. Cambridge, MA: MIT Press.
29. Giabelli A., Malandri L., Mercorio F., Mezzanzanica M. (2022). GraphLMI: А data driven system for exploring labor market information through graph databases. Multimedia Tools and Applications, Vol. 81, pp. 3061—3090. https://doi.org/10.1007/s11042-020-09115-x
30. Glaeser E. L., Mare D. C. (2001). Cities and skills. Journal of Labor Economics, Vol. 19, No. 2, pp. 316—342. https://doi.org/10.1086/319563
31. Green F. (1998). The value of skills (Department of Economics Discussion Paper No. 9819). Canterbury: University of Kent.
32. Hanushek E. A., Schwerdt G., Wiederhold S., Woessmann L. (2015). Returns to skills around the world: Еvidence from PIAAC. European Economic Review, Vol. 73, pp. 103—130. https://doi.org/10.1016/j.euroecorev.2014.10.006
33. Heckman J. J. (1979). Sample selection bias as a specification error. Econometrica, Vol. 47, No. 1, pp. 153—161. https://doi.org/10.2307/1912352
34. Heckman J. J., Kautz T. (2012). Hard evidence on soft skills. IZA Discussion Paper, No. 6580.
35. Herrmann A. M., Zaal P. M., Chappin M. M. H., Schemmann B., Lühmann A. (2023). “We don’t need no (higher) education” — how the gig economy challenges the edu‑ cation—income paradigm. Technological Forecasting and Social Change, Vol. 186, part A, article 122136. https://doi.org/10.1016/j.techfore.2022.122136
36. Hiranrat C., Harncharnchai A. (2018). Using text mining to discover skills demanded in software development jobs in Thailand. In: Proceedings of the 2nd International Conference on Education and Multimedia Technology (ICEMT 2018). New York: Association for Computing Machinery, pp. 112—116. https://doi.org/10.1145/3206129.3239426
37. Kappelman L., Jones M. C., Johnson V., McLean E. R., Boonme K. (2016). Skills for success at different stages of an IT professional’s career. Communications of the ACM, Vol. 59, No. 8, pp. 64—70. https://doi.org/10.1145/2888391
38. Khaouja I., Kassou I., Ghogho M. (2021). A survey on skill identification from online job ads. IEEE Access, Vol. 9, pp. 118 134—118 153. https://doi.org/10.1109/ACCESS.2021.3106120
39. Lee E. S. (1966). A theory of migration. Demography, Vol. 3, No. 1, pp. 47—57. https://doi.org/10.2307/2060063
40. Lucas R. E. B. (1977). Hedonic wage equations and psychic wages in the returns to schooling. American Economic Review, Vol. 67, No. 4, pp. 549—558.
41. McCormick B., Wahba J. (2005). Why do the young and educated in LDCs concen‑ trate in large cities? Evidence from migration data. Economica, Vol. 72, No. 285, pp. 39—67. https://doi.org/10.1111/j.0013-0427.2005.00401.x
42. Mion G., Opromolla L. D., Ottaviano G. I. P. (2020). Dream jobs. IZA Discussion Paper, No. 13471. https://doi.org/10.2139/ssrn.3648811
43. Moretti E. (2011). Local labour markets. In: O. Ashenfelter, D. Card (eds.). Handbook of labour economics, Vol. 4, Part B. Amsterdam: North Holland, pp. 1237—1313. https://doi.org/10.1016/S0169-7218(11)02412-9
44. Paklina S., Shakina E. (2021). Which professional skills value more under digital trans‑ formation? Journal of Economic Studies, Vol. 49, No. 8, pp. 1524—1547. https://doi.org/10.1108/JES-08-2021-0432
45. Pejic-Bach M., Bertoncel T., Meško M., Krstić Ž. (2020). Text mining of industry 4.0 job advertisements. International Journal of Information Management, Vol. 50, pp. 416—431. https://doi.org/10.1016/j.ijinfomgt.2019.07.014
46. Roback J. (1982). Wages, rents and the quality of life. Journal of Political Economy, Vol. 90, No. 6, pp. 1257—1278. https://doi.org/10.1086/261120
47. Radovilsky Z., Hegde V., Acharya A., Uma U. (2018). Skills requirements of business data analytics and data science jobs: А comparative analysis. Journal of Supply Chain and Operations Management, Vol. 16, No. 1, pp. 82—101.
48. Shakina E., Volkova N. V., Paklina S. (2024). Perceived worth of human capital across IT jobseekers in the digital era. Technological Forecasting and Social Change, Vol. 209, article 123819. https://doi.org/10.1016/j.techfore.2024.123819
49. Smirnykh L. (2024). Working from home and job satisfaction: evidence from Russia. International Journal of Manpower. Vol. 45, No. 3, pp. 539—561. https://doi.org/10.1108/IJM-02-2023-0089
50. Ternikov A. A. (2022a). Soft and hard skills identification: insights from IT job adver‑ tisements in the CIS region. PeerJ Computer Science, Vol. 8, article e946. https://doi.org/10.7717/peerj-cs.946
51. Ternikov A. A. (2022b). Wage premium for soft skills in IT sector. International Journal of Development Issues, Vol. 21, No. 2, pp. 237—248. https://doi.org/10.1108/IJDI-12-2021-0257
52. Ternikov A. A. (2023). Skill preferences in job postings. Economics Bulletin, Vol. 43, No. 4, pp. 1928—1943.
53. Verma A., Yurov K. M., Lane P. L., Yurova Y. V. (2019). An investigation of skill requirements for business and data analytics positions: А content analysis of job advertisements. Journal of Education for Business, Vol. 94, No. 4, pp. 243—250. https://doi.org/10.1080/08832323.2018.1520685
54. Wooldridge J. M. (2010). Econometric analysis of cross section and panel data. 2nd ed. Cambridge, MA: MIT Press.
Review
For citations:
Giltman M.A., Sharifulin S.V., Merzlyakova A.Y., Murzagulova R.F. Returns on skills of IT specialists in Russian cities based on job vacancy data. Voprosy Ekonomiki. 2026;(3):106-128. (In Russ.) https://doi.org/10.32609/0042-8736-2026-3-106-128
JATS XML













