Diagnosing Critical Slowing Down and Resilience in Macroeconomic Systems During Global Turbulence: Evidence From the Republic of Tajikistan
https://doi.org/10.18288/1994-5124-2026-3-6-25
Abstract
This paper utilizes nonlinear dynamics and complexity theory to analyze dynamic resilience of the Republic of Tajikistan from 2010 to 2025. Identification of tipping points in this study was carried out with nonlinear dynamics, which can detect Critical Slowing Down (CSD) signals before they manifest overtly in macroeconomic indicators. The authors reconstructed the phase change of Takikstan’s economy based on Takens’ Theorem with inputs from cyclical components of GDP data and industrial production indices. Particular emphasis was placed on the analysis of CSD indicators, such as autocorrelation and time-series variance. The study mathematically verified a phase change of the macrosystem that occurred in 2019. Prior to 2018, the economy exhibited “fragile” equilibrium characterized by high autocorrelation coefficients ( = 0.89) accompanied by a loss of resilience. The commissioning of the first units of the Rogun Hydropower Plant was identified as a change in the control parameter (an order parameter according to Haken), which triggered bifurcation and the system’s transition into a new “high-energy” source of attraction. The authors have provided econometric evidence that Tajikistan’s current leadership in industrial growth rates within the CIS (reaching a record 22.1% in 2025) and stable economic growth of 8.4% are not short-term outliers, but rather direct consequences of the formation of a new stable industrial-type attractor. The predictive toolkit developed here can be utilized by government authorities for monitoring systemic risks and preemptively mitigating macroeconomic instability when implementing national development strategies and medium-term development programs for the Republic of Tajikistan.
Keywords
phase transition,
order parameters,
nonlinear dynamics,
macroeconomic resilience,
econometric modeling,
Rogun HPP,
economy of Tajikistan
JEL: C1, С10, C22, C32, C54
About the Authors
M. F. Khakimova
Tajik National University
Russian Federation
Russian Federation
Maftuna F. Khakimova, Dr. Sci. (Econ.), Associate Professor, Professor of Cybernetics and Digital Economy Department of the Accounting and Digital Economy Faculty, Tajik National University
17, Rudaki pr., Dushanbe, 734025
S. M. Drobyshevsky
Russian Presidential Academy of National Economy and Public Administration;
Gaidar Institute for Economic Policy
Russian Federation
Russian Federation
Sergey M. Drobyshevsky, Dr. Sci. (Econ.), Associate Professor, Deputy Director for Research, Institute of Applied Economic Research, Russian Presidential Academy of National Economy and Public Administration; Scientific Director, Gaidar Institute for Economic Policy
82, Vernadskogo pr., Moscow, 119571
3–5, str. 1, Gazetnyy per., Moscow, 125009
L. Kh. Saidmurodzoda
National Academy of Sciences of Tajikistan;
International University of Tourism and Entrepreneurship of Tajikistan (IUTET)
Russian Federation
Russian Federation
Lutfullo Kh. Saidmurodzoda, Dr. Sci. (Econ.), Professor, Corresponding Member of the National Academy of Sciences of Tajikistan, Chief Researcher at the Institute of Economics and Demography, National Academy of Sciences of Tajikistan; Lead Researcher of the Research Institute of Tourism and Entrepreneurship, International University of Tourism and Entrepreneurship of Tajikistan (IUTET)
6a, Mirzo Rizo ul., Dushanbe, 734035
References
1. Bobylev G. V., Kuznetsov A. V. Nelineynye metody v analize makroekonomicheskikh protsessov [Nonlinear Methods in Analysis of Macroeconomic Processes]. Moscow, Nauka, 2015. (In Russ.)
2. Vakhrushev D. S. Sinergeticheskie aspekty issledovaniya makroekonomicheskoy dinamiki [Synergetic Aspects of the Study of Macroeconomic Dynamics]. Vestnik Kostromskogo gosudarstvennogo universiteta [Bulletin of Kostroma State University], 2004, no. 4, pp. 112-117. (In Russ.)
3. Zhang W. B. Synergetic Economics: Time and Change in Nonlinear Economic Theory. Berlin, Heidelberg, Springer-Verlag, 1991.
4. Lebedev V. V., Lebedev K. V. Komp’yuternoe modelirovanie rynochnykh mekhanizmov [Computer Modeling of Market Mechanisms]. Moscow, MSTU STANKIN, 2002. (In Russ.)
5. Lebedev V. V. Matematicheskoe modelirovanie sotsio-ekonomicheskikh protsessov [Mathematical Modeling of Socio-Economic Processes]. Moscow, Izograf, 1997. (In Russ.)
6. Prigogine I., Stengers I. Order Оut of Chaos: Man’s New Dialogue With Nature. New York, Bantam Books, 1984.
7. Saidmurodov L. Kh. Respublika Tadzhikistan v sisteme sovremennykh mirokhozyaystven-nykh svyazey: model’ maloy otkrytoy ekonomiki [The Republic of Tajikistan in the System of Modern World Economic Relations: A Model of a Small Open Economy]. Dushanbe, Donish, 2021. (In Russ.)
8. Haken H. Advanced Synergetics: Instability Hierarchies of Self-Organizing Systems and Devices. Berlin, Heidelberg, Springer-Verlag, 1983.
9. Haken H. Erfolgsgeheimnisse der Natur. Synergetik: Die Lehre vom Zusammenwirken. Stuttgart, Deutsche Verlags-Anstalt, 1995.
10. Khakimova M., Saidmurodov L. Modelirovanie ustoychivogo razvitiya natsyonal’noy ekonomiki Respubliki Tadzhikistan: nelineynaya dinamika [Modeling the Sustainable Development of the National Economy of the Republic of Tajikistan: Nonlinear Dynamics]. Dushanbe, Tajik National University, 2022. (In Russ.)
11. Barnett W. A. Deterministic Chaos and Fractal Geometry of Monetary Economics. Journal of Economic Theory, 1988, vol. 40, no. 1, pp. 165-185.
12. Dakos V., Carpenter S. R., Brock W. A. Methods for Detecting Early Warnings of Critical Transitions in Time Series Illustrated Using Simulated Ecological Data. PLoS ONE, 2012, vol. 7, no. 7, article e41010. DOI: 10.1371/journal.pone.0041010.
13. Dakos V., Scheffer M., Van Nes E. H. Slowing Down as an Early Warning Signal for Abrupt Climate Change. Proceedings of the National Academy of Sciences (PNAS), 2008, vol. 105, no. 38, pp. 14308-14312. DOI: 10.1073/pnas.0802430105.
14. Haken H. Synergetics: An Introduction. Nonequilibrium Phase Transitions and Self-Organization in Physics, Chemistry, and Biology. Berlin, Heidelberg, Springer-Verlag, 1983.
15. Hausmann R., Pritchett L., Rodrik D. Growth Accelerations. Journal of Economic Growth, 2005, vol. 10, no. 4, pp. 303-329. DOI: 10.1007/s10887-005-4712-0.
16. Kantz H., Schreiber T. Nonlinear Time Series Analysis. Cambridge, Cambridge University Press, 2004.
17. Pomfret R. Central Asia’s Economy: Mapping Resilience and Challenges. Comparative Economic Studies, 2012, vol. 54, no. 1, pp. 7-33.
18. Pomfret R. The Economies of Central Asia. Princeton, Princeton University Press, 2014.
19. Prigogine I., Nicolis G. Self-Organization in Non-Equilibrium Systems: From Dissipative Structures to Order Through Fluctuations. New York, Wiley, 1977.
20. Rodrik D. One Economics, Many Recipes: Globalization, Institutions, and Economic Growth. Princeton, Princeton University Press, 2007.
21. Scheffer M., Bascompte J., Brock W. A. Early-Warning Signals for Critical Transitions. Nature, 2009, vol. 461, no. 7260, pp. 53-59. DOI: 10.1038/nature08227.
22. Schreiber T. Measuring Information Transfer. Physical Review Letters, 2000, vol. 85, no. 2, pp. 461-464. DOI: 10.1103/PhysRevLett.85.461.
23. Takens F. Detecting Strange Attractors in Turbulence. Lecture Notes in Mathematics, 1981, vol. 898, pp. 366-381.
24. Zhang W. B. Economic Structural Change and Synergetic Dynamics. Structural Change and Economic Dynamics, 1990, vol. 1, no. 2, pp. 291-303.
Review
For citations:
Khakimova M.F., Drobyshevsky S.M., Saidmurodzoda L.Kh. Diagnosing Critical Slowing Down and Resilience in Macroeconomic Systems During Global Turbulence: Evidence From the Republic of Tajikistan. Economic Policy. 2026;21(3):6-25. (In Russ.) https://doi.org/10.18288/1994-5124-2026-3-6-25
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