How to Evaluate the Effectiveness of Governance in the Regions of the Russian Federation in the Context of a Given Indicator of Socio-Economic Development?

Artur R. Nagapetyan, Polina S. Poplavko, Dmitry A. Subbotovsky

Far Eastern Federal University, Vladivostok, Russia

Abstract

Evaluating management effectiveness is critically important as it enables targeted decision-making, such as reshuffling management teams or scaling successful practices, which underscores the relevance of this research. The study aims to develop an economic-mathematical toolkit for assessing regional-level management effectiveness across the subjects (regions) of the Russian Federation. This assessment focuses on a designated indicator of socio-economic development, holding other factors constant (ceteris paribus), using the following exemplar indicators: total fertility rate, life expectancy, and mortality from cardiovascular diseases. According to the research hypotheses, estimates of management effectiveness derived using regional fixed effects will differ from those based on the direct ranking of observable statistical indicators. Furthermore, these estimates are hypothesized to display varying degrees of robustness when analyzed across different model specifications. The research procedure was implemented by isolating the influence of factors unrelated to specific managerial actions from observable objective statistical indicators. This isolation was achieved through the estimation of regional fixed effects using various models based on panel data and spatial econometrics techniques. The results confirm the research hypotheses. The developed methodology enables the construction of regional rankings for the target indicator with minimal data input requirements, and tools for assessing the reliability/confidence level of the resulting rankings. Furthermore, the methodology offers tools for for the generation of composite rankings based on a group of indicators, weighted using estimates of the value of statistical life within the context of the respective indicators. The theoretical significance of the findings lies in advancing approaches to modeling management effectiveness through the isolation of regional fixed effects. These effects are purged of the influence of local socio-economic, demographic, and other territorial characteristics, as well as the spillover effects from neighboring regions (spatial dependence). Practically, the study enahances the precision of approaches for evaluating the effectiveness of managerial interventions. Specifically, it improves the capability to account for the linkages between specific decisions made by managers and their resulting position in the performance rankings.

Keywords

management efficiency; rating; fixed effects; spatial econometrics; cost of living.

JEL classification

References

1. Krasilshchikov, G., Troitskaya, E. (2024). Assessment of regional governance efficiency using data envelopment analysis (based on the materials of Perm territory and competing regions). Ars Administrandi, Vol. 16, No. 3, 513–532. (In Russ.). https://doi.org/10.17072/2218-9173-2024-3-513-532

          2. Bevan, G., Wilson, D. (2013). Does “Naming and Shaming” Work for Schools and Hospitals? Lessons from Natural Experiments Following. Public Money & Management, Vol. 33, Issue 4, 245–252. https://doi.org/10.1080/09540962.2013.799801

          3. Heinrich, C. (2002). Outcomes-based performance management in the public sector: Implications for government accountability and effectiveness. Public Administration Review, Vol. 62, Issue 6, 712–725. https://doi.org/10.1111/1540-6210.00253

          4. Burns, J., Kurz, C., Laxy, M. (2021). Effectiveness of the German disease management programs: quasi-experimental analyses assessing the population-level health impact. BMC Public Health, Vol. 21, 2092. https://doi.org/10.1186/s12889-021-12050-7

          5. Restrepo, B., Rieger, M. (2016). Denmark's Policy on Artificial Trans Fat and Cardiovascular Disease. American Journal of Preventive Medicine, Vol. 50, Issue 1, 69–76. https://doi.org/10.1016/j.amepre.2015.06.018

          6. Mikkola, T., Gissler, M., Merikukka, M., Tuomikoski, P., Ylikorkala, O. (2013). Sex differences in age-related cardiovascular mortality. PLoS ONE, Vol. 8, Issue 5, e63347. https://doi.org/10.1371/journal.pone.0063347

          7. Ha, N., Hendrie, D., Moorin, R. (2014). Impact of population ageing on the costs of hospitalisations for cardiovascular disease: a population-based data linkage study. BMC Health Services Research, Vol. 14, 554. https://doi.org/10.1186/s12913-014-0554-9

          8. Espeland, W., Sauder, M. (2007). Rankings and reactivity: How public measures recreate social worlds. American Journal of Sociology, Vol. 113, No. 1, 1–40. https://doi.org/10.1086/517897

          9. Simar, L., Wilson, P.W. (1998). Sensitivity analysis of efficiency scores: How to bootstrap in nonparametric frontier models. Management Science, Vol. 44, No. 1, 49–61. https://doi.org/10.1287/mnsc.44.1.49

          10. Hollingsworth, B., Peacock, S.J. (2008). Efficiency Measurement in Health and Health Care. 1st Edition. London, Routledge, 176 p. https://doi.org/10.4324/9780203486566

          11. Aigner, D., Lovell, C., Schmidt, P. (1977). Formulation and estimation of stochastic frontier production function models. Journal of Econometrics, Vol. 6, Issue 1, 21–37. https://doi.org/10.1016/0304-4076(77)90052-5

          12. Jacobs, R., Smith, P.C., Street, A. (2006). Measuring Efficiency in Health Care: Analytic Techniques and Health Policy. Cambridge University Press. https://doi.org/10.1017/CBO9780511617492

          13. Decancq, K., Lugo, M. (2013). Weights in multidimensional indices of wellbeing: An overview. Econometric Reviews, Vol. 32, Issue 1, 7–34. https://doi.org/10.1080/07474938.2012.690641

          14. Munda, G., Nardo, M. (2009). Noncompensatory/nonlinear composite indicators for ranking countries: A defensible setting. Applied Economics, Vol. 41, Issue 12, 1513–1523. https://doi.org/10.1080/00036840601019364

          15. Makarov, I. (2024). Composite indices for governing digitalization: methods for construction. Public Administration Issues, No. 3, 81–109. (In Russ.). https://doi.org/10.17323/1999-5431-2024-0-3-81-109

          16. Hastie, T., Tibshirani, R., Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd Edition. New York, NY, Springer, 745 p. https://doi.org/10.1007/978-0-387-84858-7

          17. Mullainathan, S., Spiess, J. (2017). Machine learning: an applied econometric approach. Journal of Economic Perspectives, Vol. 31, No. 2, 87–106. https://doi.org/10.1257/jep.31.2.87

          18. Hsu, C., Sandford, B. (2007). The Delphi technique: Making sense of consensus. Practical Assessment, Research, and Evaluation, Vol. 12, No. 10, 1–8. https://doi.org/10.7275/pdz9-th90

          19. Landeta, J. (2006) Current validity of the Delphi method in social sciences. Technological Forecasting and Social Change, Vol. 73, Issue 5, 467–482. https://doi.org/10.1016/j.techfore.2005.09.002

          20. Voronov, A., Leontieva, L., Orlova, L., Sergeev, S. (2022). Assessment of human capital state at the stage of the sixth technological paradigm: Regional aspect. E-Journal Public Administration, Vol. 90, 108–125. https://doi.org/10.24412/2070-1381-2022-90-108-125

          21. Milligan, G., Cooper, M. (1985). An examination of procedures for determining the number of clusters in a data set. Psychometrika, Vol. 50, Issue 2, 159–179. https://doi.org/10.1007/BF02294245

          22. Kaufman, L., Rousseeuw, P. (1990). Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley & Sons Inc. https://doi.org/10.1002/9780470316801

          23. Zubarevich, N., Safronov, S. (2024). Interregional inequality in Russia and post-Soviet countries in the 21st century. Regional Research, No. 1, 4–18. (In Russ.). https://doi.org/10.5922/1994-5280-2024-1-1

          24. Safiullin, M., Gataullina, A., Ildarkhanova, A., Kuzmishin, I. (2023) Russian regions clustering by the level of higher education development and economic competitiveness. University Management: Practice and Analysis, Vol. 27, No. 4, 23–42. (In Russ.). https://doi.org/10.15826/umpa.2023.04.031

          25. Mundlak, Y. (1978). On the pooling of time series and cross section data. Econometrica, Vol. 46, No. 1, 69–85. https://doi.org/10.2307/1913646

          26. Schmidt, P., Sickles, R. (1984). Production frontiers and panel data. Journal of Business & Economic Statistics, Vol. 2, Issue 4, 367–374. https://doi.org/10.2307/1391278

          27. Greene, W. (2005) Fixed and random effects in stochastic frontier models. Journal of Productivity Analysis, Vol. 23, 7–32. https://doi.org/10.1007/s11123-004-8545-1

          28. Badunenko, O., Kumbhakar, S. (2016). When, where and how to estimate persistent and transient efficiency in stochastic frontier panel data models. European Journal of Operational Research, Vol. 255, Issue 1, 272–287. https://doi.org/10.1016/j.ejor.2016.04.049

          29. Wooldridge, J. (2021). Two-Way Fixed Effects, the Two-Way Mundlak Regression, and Difference-in-Differences Estimators. SSRN, 67 p. http://dx.doi.org/10.2139/ssrn.3906345

          30. Drozd, A., Nagapetyan, A. (2024). Development of applied analytical products for the "Digital East" platform by the example of the service for determining theoptimal number of cardiologists in the region. Scientific Works of the Free Economic Society of Russia, Vol. 249, No. 5, 421–442. (In Russ.). https://doi.org/10.38197/2072-2060-2024-249-5-421-442

          31. Afonso, A., Fernandes, S. (2008). Assessing and explaining the relative efficiency of local government. The Journal of Socio-Economics, Vol. 37, Issue 5, 1946–1979. https://doi.org/10.1016/j.socec.2007.03.007

          32. Afonso, A., Schuknecht, L., Tanzi, V. (2005). Public sector efficiency: An international comparison. Public Choice, Vol. 123, 321–347. https://doi.org/10.1007/s11127-005-7165-2

          33. Geys, B., Moesen, W. (2009). Measuring local government technical (in)efficiency: An application and comparison of different methodologies. Public Performance & Management Review, Vol. 32, Issue 4, 499–513. https://doi.org/10.2753/PMR1530-9576320401

          34. Nagapetyan, A., Drozd, A., Subbotovsky, D. (2023). How to Determine the Optimal Number of Cardiologists in a Region? Mathematics, Vol. 11, Issue 21, 4422. https://doi.org/10.3390/math11214422

          35. Drozd, A. Nagapetyan, A. (2024). Modelling the variation of the impact of medical personnel availability on mortality rates: cardiovascular disease case. Journal of Applied Economic Research, Vol. 23, No. 4, 1017–1054. (In Russ.). https://doi.org/10.15826/vestnik.2024.23.4.040

          36. Hammitt, J. (2000). Valuing mortality risk: Theory and practice. Environmental Science and Technology, Vol. 34, Issue 8, 1396–1400. https://doi.org/10.1021/es990733n

Acknowledgements

The study was funded by the Ministry of Science and Higher Education of the Russian Federation, project no FZNS-2023-0016 “Sustainable Regional Development: Efficient Economic Mechanisms for Organizing Markets and Entrepreneurial Competencies of the Population under Uncertainty (Balancing Security and Risk)”.

About Authors

Artur Rubikovich Nagapetyan

Candidate of Economic Sciences, Associate Professor, Department of Socio-Economic Research and Regional Development, School of Economics and Management, Head of the Laboratory of Data Analysis and Applied Econometric Research, Far Eastern Federal University, Vladivostok, Russia (690922, Primorsky Krai, Vladivostok, Russky Island, Ayaks settlement, 10); ORCID https://orcid.org/0000-0002-7885-2460 e-mail: nagapetyan_ar@dvfu.ru

Polina Sergeevna Poplavko

Post-Graduate Student, Assistant, Department of Socio-Economic Research and Regional Development, School of Economics and Management, Far Eastern Federal University, Vladivostok, Russia (690922, Primorsky Krai, Vladivostok, Russky Island, Ayaks settlement, 10); ORCID https://orcid.org/0009-0002-5520-4943 e-mail: poplavko.ps@dvfu.ru

Dmitry Andreevich Subbotovsky

Student, Laboratory Research Assistant of the Educational and Scientific Laboratory of Experimental Economics and Game Theory, Manager of the Laboratory of Data Analysis and Applied Econometric Research, Far Eastern Federal University, Vladivostok, Russia (690922, Primorsky Krai, Vladivostok, Russky Island, Ayaks settlement, 10); ORCID https://orcid.org/0009-0001-1421-2157 e-mail: subbotovskii.da@dvfu.ru

For citation

Nagapetyan, A.R., Poplavko, P.S., Subbotovsky, D.A. (2025). How to Evaluate the Effectiveness of Governance in the Regions of the Russian Federation in the Context of a Given Indicator of Socio-Economic Development? Journal of Applied Economic Research, Vol. 24, No. 4, 1280-1311. https://doi.org/10.15826/vestnik.2025.24.4.042

Article info

Received June 24, 2025; Revised August 7, 2025; Accepted August 14, 2025.

DOI: https://doi.org/10.15826/vestnik.2025.24.4.042

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