Cover story: Alaska – Both Near and Far
Critical Analysis of the Rules Evaluating the Efficiency of SEZ Operation
Published 2025-10-06
Keywords
- special economic zones; SEZ; methodology; evaluation rules; evaluation of effectiveness
How to Cite
1.
Rostislav К. Critical Analysis of the Rules Evaluating the Efficiency of SEZ Operation. ECO [Internet]. 2025 Oct. 6 [cited 2025 Dec. 8];55(5):42-58. Available from: https://ecotrends.ru/index.php/eco/article/view/4897
Abstract
Among the various approaches to evaluate the efficiency of Special economic zones (SEZs), those applied by the authorized executive authority are in fact the most important. The paper examines in detail some options of the rules for evaluating the functioning of special economic zones based on federal law No. 116 dated 07/22/2005 that were in force from 2012 to 2024. Beside technical flaws the rules were found to include: biased methods of averaging individual indicators, obtaining an overall assessment of the effectiveness of various SEZs of the same type and all SEZs as a whole, the inconsistency of a number of indicators with the goals of their calculation, but also the weakness of the fundamental principles: comparing the fact with a plan or with a “zero value”. The best alternative (requiring the use of statistical training) is indicated. It is a comparison of the observed indicator with a possible outcome in a case when the enterprises are not residents of the SEZ.References
- Кузнецова О.В. Особые экономические зоны: эффективны или нет? // Пространственная экономика. 2016. № 4. С. 129–152. DOI: 10.14530/se.2016.4.129–152
- Павлов П.В. Оценка эффективности функционирования особых экономических зон: правовое регулирование и экономическое содержание // Административное и муниципальное право. 2014. № 6. С. 520–532. DOI: 10.7256/1999–2807.2014.6.12108
- Athey, S., Imbens, G. W. (2019). Machine Learning Methods That Economists Should Know About. Annual Review of Economics. Vol. 11. Pp. 685–725.
- Chernozhukov, V., Chetverikov D., Demirer M., Duflo E., Hansen C., Newey W., Robins J. (2018). «Double/Debiased Machine Learning for Treatment and Structural Parameters». The Econometrics Journal. Vol. 21. No. 1. Pp. C1–68.
- Hernán, M.A., Robins, J.M. (2020). Causal Inference: What If. Boca Raton: Chapman & Hall/CRC. 310 p.
- Laan, M. J. van der, Rose, S. (2011). Targeted learning: causal inference for observational and experimental data. New York: Springer. 626 p.
- Pearl, J., Glymour, M., Jewell, N.P. (2014). Causal Inference in Statistics: A Primer. John Wiley & Sons. 160 p.
- Roth, J., Sant’Anna, P.H.C., Bilinski A., Poe, J. (2023). «What’s trending in difference-in-differences? A synthesis of the recent econometrics literature». Journal of Econometrics. Vol. 235. No. 2. Pp. 2218–2244.