Decomposing productivity growth in Kazakhstan’s regions: technological progress and resource-use efficiency

Kazakhstan’s regional innovation policy, including its Smart Specialisation and Quadruple Helix approaches, relies predominantly on static indicators, leaving the separate contributions of technological change and efficiency catch-up to productivity dynamics unexamined. This study decomposes total factor productivity (TFP) change in the innovation systems of Kazakhstan’s regions into efficiency-catch-up and technological-change components using a Malmquist-DEA framework, and identifies the spatial and structural determinants of this change. The research hypothesis is that the average decline in regional innovation productivity over 2014–2024 is driven predominantly by technological regress (a contracting production frontier) rather than efficiency catch-up, and that this process is institutional rather than geographic in nature. The study draws on an official panel from the Bureau of National Statistics of the Republic of Kazakhstan for 2014–2024 across 17 regions (187 observations), combining panel regression, Malmquist-DEA decomposition, spatial autocorrelation analysis, principal component and cluster analysis, and explainable machine learning (Random Forest, gradient boosting, CatBoost with SHAP). Results show that the geometric-mean TFP change between 2014–2016 and 2022–2024 was 0.662, driven predominantly by technological regress (0.543) and partly offset by positive efficiency catch-up (1.219). No significant global spatial autocorrelation of productivity change was detected (Moran’s I = 0.148; p = 0.436), confirming an institutional rather than geographic diffusion mechanism. The findings indicate that Kazakhstan’s Smart Specialisation and GovTech/AI Governance policies should be reoriented toward technological renewal rather than efficiency improvement alone.

1. Foray D., David P.A., Hall B.H. (2009) Smart specialisation – the concept // Knowledge Economists Policy Brief, no. 9, pp. 1–5. (In English)

2. McCann P., Ortega-Argiles R. (2015) Smart specialization, regional growth and applications to European Union cohesion policy // Regional Studies, vol. 49, no. 8, pp. 1291–1302. (In English)

3. Carayannis E.G., Campbell D.F.J. (2009) ‘Mode 3’ and ‘Quadruple Helix’: toward a 21st century fractal innovation ecosystem // International Journal of Technology Management, vol. 46, no. 3–4, pp. 201–234. (In English)

4. World Bank (2022) GovTech Maturity Index, 2022 Update: Trends in Public Sector Digital Transformation. Washington, D.C.: World Bank. URL: https://hdl.handle.net/10986/38499 (accessed: 23.09.2026) (In English)

5. OECD (2019) Recommendation of the Council on Artificial Intelligence, OECD/LEGAL/0449. Paris: OECD (updated 2024). URL: https://legalinstruments.oecd.org/en/instruments/OECD-LEGAL-0449 (accessed: 23.09.2026) (In English)

6. Fare R., Grosskopf S., Norris M., Zhang Z. (1994) Productivity growth, technical progress, and efficiency change in industrialized countries // American Economic Review, vol. 84, no. 1, pp. 66–83. (In English)

7. Dwivedi Y.K. et al. (2021) Artificial Intelligence (AI): multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy // International Journal of Information Management, vol. 57, article 101994. (In English)

8. Balland P.-A., Boschma R., Crespo J., Rigby D.L. (2019) Smart specialization policy in the European Union: relatedness, knowledge complexity and regional diversification, Regional Studies, vol. 53, no. 9, pp. 1252–1268. (In English)

9. OECD (2024) 2023 OECD Digital Government Index: Results and Key Findings // OECD Public Governance Policy Papers, no. 44. Paris: OECD Publishing. DOI: 10.1787/1a89ed5e-en (In English)

10. European Commission (2024) Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act) // Official Journal of the European Union, OJ L, 2024/1689, 12.7.2024, 144 p. URL: http://data.europa.eu/eli/reg/2024/1689/oj (accessed: 23.09.2026) (In English)

11. UNESCO (2021) Recommendation on the Ethics of Artificial Intelligence, SHS/BIO/PI/2021/1. Paris: UNESCO. URL: https://www.unesco.org/en/artificial-intelligence/recommendation-ethics (accessed: 23.09.2026) (In English)

12. Balland P.-A., Boschma R. (2021) Complementary interregional linkages and Smart Specialisation: an empirical study on European regions // Regional Studies, vol. 55, no. 6, pp. 1059–1070. (In English)

13. Auty R.M. (1993) Sustaining Development in Mineral Economies: The Resource Curse Thesis. London: Routledge, 272 p. (In English)

14. Kurmanov N., Beisengaliyev Y., Bayandin M., Syzdykova E., Tolysbayeva M. (2022) Innovative development of Kazakhstan’s raw material (oil and gas) regions: multifactorial model for empirical analysis // International Journal of Energy Economics and Policy, vol. 12, no. 4, pp. 131–140. DOI: 10.32479/ijeep.13201. (In English)

15. Slavova S., Rubalcaba L., Franco-Riquelme J.N. (2025) Understanding imbalanced transmission from R&D inputs into innovation outputs and impacts: evidence from Kazakhstan // Economies, vol. 13, no. 2, article 25. DOI: 10.3390/economies13020025. (In English)

16. Charnes A., Cooper W.W., Rhodes E. (1978) Measuring the efficiency of decision making units // European Journal of Operational Research, vol. 2, no. 6, pp. 429–444. (In English)

17. Lundberg S.M., Lee S.-I. (2017) A unified approach to interpreting model predictions // Advances in Neural Information Processing Systems, vol. 30, pp. 4765–4774. (In English)

18. Bjuro nacional’noj statistiki Agentstva po strategicheskomu planirovaniju i reformam Respubliki Kazahstan. Oficial’nye statisticheskie dannye. URL: https://stat.gov.kz/ru/ (data obrashhenija: 08.07.2026) (In Russian)

19. Breiman L. (2001) Random forests, Machine Learning, vol. 45, no. 1, pp. 5–32. (In English)

20. Chen T., Guestrin C. (2016) XGBoost: a scalable tree boosting system, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, pp. 785–794. (In English)

21. Prokhorenkova L., Gusev G., Vorobev A., Dorogush A.V., Gulin A. (2018) CatBoost: unbiased boosting with categorical features // Advances in Neural Information Processing Systems, vol. 31, pp. 6638–6648. (In English)

22. Hausman J.A. (1978) Specification tests in econometrics // Econometrica, vol. 46, no. 6, pp. 1251–1271. (In English)

23. Nickell S. (1981) Biases in dynamic models with fixed effects // Econometrica, vol. 49, no. 6, pp. 1417–1426. (In English)

24. Kaiser H.F. (1974) An index of factorial simplicity // Psychometrika, vol. 39, no. 1, pp. 31–36. (In English)

25. Hair J.F., Risher J.J., Sarstedt M., Ringle C.M. (2019) When to use and how to report the results of PLS-SEM // European Business Review, vol. 31, no. 1, pp. 2–24. (In English)

26. Zeleny M. (1982) Multiple Criteria Decision Making. New York: McGraw-Hill, 563 p. (In English)

27. Saaty T.L. (2008) Decision making with the analytic hierarchy process, International Journal of Services Sciences, vol. 1, no. 1, pp. 83–98. (In English)

28. Hwang C.-L., Yoon K. (1981) Multiple Attribute Decision Making: Methods and Applications. New York: Springer, 259 p. (In English)

29. Moran P.A.P. (1950) Notes on continuous stochastic phenomena // Biometrika, vol. 37, no. 1–2, pp. 17–23. (In English)

30. Anselin L. (1995) Local indicators of spatial association – LISA // Geographical Analysis, vol. 27, no. 2, pp. 93–115. (In English)