Assessing the differences in the levels and dynamics of economic development of Kazakhstani regions
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Author(s)Anel KireyevaLink to ORCID Index: https://orcid.org/0000-0003-3412-3706
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Nailya NurlanovaLink to ORCID Index: https://orcid.org/0000-0002-4210-3783
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Akan NurbatsinLink to ORCID Index: https://orcid.org/0000-0001-5390-5776
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Nurgul SaparbekLink to ORCID Index: https://orcid.org/0000-0002-0405-962X
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Farida AlzhanovaLink to ORCID Index: https://orcid.org/0000-0002-7451-7470
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DOIhttp://dx.doi.org/10.21511/ppm.20(3).2022.45
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Article InfoVolume 20 2022, Issue #3, pp. 577-587
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107 Downloads
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Creative Commons Attribution 4.0 International License
This study aims to analyze the level of differences based on effectiveness, statistical dependence, and mutual influence of economic indicators for ranking the regions of Kazakhstan. In the paper, a systematic algorithm of actions is used, ensuring the interconnectedness, the sequence of work, and the validity of the methods used. Several model specifications were formulated to identify interregional differences in economic development indicators: the operating data environment analysis model (DEA) and the random effects regression model (RE). The information database of the Bureau of National Statistics of the Republic of Kazakhstan for 2010–2020 was used. The construction of the RE model was carried out in the SPSS program. A regression model with fixed and random effects in panel data was employed to determine the impact of the selected indicators on gross regional product produced (GRP). Based on the results, the influence of the physical volume of industrial products on GRP per capita in the regions of Kazakhstan for 11 years was revealed in both models with a high significance of coefficients. The study results can be used by public administration bodies that carry out effective strategic management by smoothing interregional differences. Moreover, they can determine the prospects for studying other parameters based on efficiency, statistical dependence, and mutual influence of economic indicators for ranking regions.
Acknowledgment
This study has been funded by the Committee of Science of the Ministry of Science and Higher Education of the Republic of Kazakhstan (grant “Priorities and mechanisms of inclusive regional development of Kazakhstan in the context of overcoming the economic recession” AP09259004).
- Keywords
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JEL Classification (Paper profile tab)O47, R11, R12
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References32
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Tables3
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Figures1
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- Figure 1. Comparison of the dynamics of Kazakhstan’s performance indicator from 2010 to 2020
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- Table 1. Description of the selected factors
- Table 2. Performance evaluation result and benchmarkable regions for 2010 and 2020
- Table 3. Regression model of Kazakhstani regions for 2010–2020
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- Acemoglu, D., Johnson, S., & Robinson, J. (2005). Institutions as the Fundamental Cause of Long-Run Growth (NBER Working Paper No. 10481). Cambridge, MA: National Bureau of Economic Research.
- Aneiros-Pérez, G., & Vieu, P. (2006). Semi-functional partial linear regression. Statistics & Probability Letters, 76(11), 1102-1110.
- Araújo, I. F., Goncalves, E., & Almeida, E. (2019). Effects of dynamic and spatial externalities on local growth: Evidence from Brazil. Papers in Regional Science, 98(2), 1239-1259.
- Bagautdinova, N. G., Murtazina, G., Fazlieva, E. P., & Naida, A. M. (2013). Improvement of the Regional Management System Using the Labor Potential Index. World Applied Sciences Journal, 27(1), 107-111.
- Banker, R. D., Zheng, Z., & Natarajan, R. (2010). DEA-based hypothesis tests for comparing two groups of decision making units. European Journal of Operational Research, 206(1), 231-238.
- Barro, R. J. (1997). Determinants of Economic Growth: A Cross-Country Empirical Study. Cambridge, MA: MIT Press.
- Boudeville, J. R. (1970). Les espaces economiques. Paris: Puf. (In French).
- Boukebbab, S., & Boulahlib, M. S. B. (2015). The Spatial Interactions Using the Gravity Model: Application at the Evaluation of Transport Efficiency at Constantine City, Algeria. Advances in Intelligent Systems and Computing, 365, 305-318.
- Bureau of National Statistics. (n.d.). Statistics of national accounts. (In Kazakh).
- Cabus, P., & Vanhaverbeke, W. (2003). The economics of rural areas in the proximity of urban networks: Evidence from Flanders. Tijdschrift voor Economische en Sociale Geografie, 94(2), 230-245.
- Charnes, A., Cooper, W. W., & Rhodes, E. (1976). Measuring the Efficiency of Decision Making Units with Some New Production Functions and Estimation Methods (Report ADA049149). Texas university at Austin center for cybernetic studies.
- Chemak, F., Boussemart, J., & Jacquet, F. (2010). Farming system performance and water use efficiency in the Tunisian semi-arid region: data envelopment analysis approach. International Transactions in Operational Research, 17(3), 381-396.
- Czaplewski, M., & Klóska, R. (2020). Regional Policy as a Factor in Shaping Regional Development in Poland. South East European Journal of Economics and Business, 15(1), 93-104.
- Fingleton, B., & Lopez-Bazo, E. (2006). Empirical growth models with spatial effects. Papers in Regional Science, 85(2), 177-198.
- Fried, H., Lovell, C., Schmidt, S., & Yaisawarng, S. (2002). Accounting for environmental effects and statistical noise in data envelopment analysis. Journal of Productivity Analysis, 17, 157-174.
- Friedmann, J. (1966). Regional Development Policy: a Case Study of Venezuela. Massachusetts: MIT Press.
- Fujita, M., Krugman, P. R., & Venables, A. J. (1999). The Spatial Economy: Cities, Regions, and International Trade. Cambridge: MIT Press.
- Gallup, J., Gaviria, A., & Lora, E. (2003). Is Geography Destiny? Lessons from Latin America. Stanford: Stanford University Press.
- Hausmann, R. (2001). Prisoners of Geography. Foreign Policy, 122, 44-53.
- Heryanti, Y., Junaidi, J., & Yulmardi, Y. (2014). Interaksi Spasial Perekonomian dan Ketenagakerjaan Antar Kabupaten/Kota di Provinsi Jambi. Jurnal Perspektif Pembiayaan dan Pembangunan Daerah, 2(2), 99-106. (In Indonesian).
- Ibragimova, K. I., & Turysbekova, A. B. (2021). Study of Development Trends and Development of Measures to Overcome Depopulation of Strategically Important Regions of Kazakhstan. Economics: the strategy and practice, 16(4), 76-89. (In Kazakh).
- Krugman, P. R. (1991). Geography and Trade. London: MIT Press.
- Lichter, D., & Brown, D. (2011). Rural America in an urban society: Changing spatial and social boundaries. Annual Review of Sociology, 37, 565-592.
- Morrissey, K. (2016). A location quotient approach to producing regional production multipliers for the Irish economy. Papers in Regional Science, 95(3), 491-506.
- Nurlanova, N. K., Satybaldin, A. A., Bekturganova, M. A., & Kireyeva, A. (2018). Spatial Distribution of Economic Growth and Inequality: Kazakhstan’s Experience. The Journal of Asian Finance, Economics and Business, 5(3), 169-178.
- Orazymbetova, D. A. (2014). Present-Day Challenges for and Postulates of the Regional Policy of the Republic of Kazakhstan. European researcher, 79, 1351-1356.
- Pedroni, P. (1999). Critical values for cointegration tests in heterogeneous panels with multiple regressors. Oxford Bulletin of Economics and Statistics, 61, 653-670.
- Piekkari, R., Nell, P., & Ghauri, P. (2010). Regional Management as a System – A Longitudinal Case Study. Management International Review, 50(4), 513-532.
- Rodrik, D. (1998). Where Did All the Growth Go? External Shocks, Social Conflict, and Growth Collapses. Journal of Economic Growth, 4(4), 385-412.
- Rogers, M. (1998). The Definition and Measurement of Productivity (Working Paper No. 9/98). Parksville: Melbourne Institute of Applied Economic and Social Research.
- Satpayeva, Z. T., Kireyeva, A. A., Kenzhegulova, G., & Yermekbayeva, D. (2020). Gender equality and women business of framework 5Ms in Kazakhstan: Analysis and basic directions. Journal of Asian Finance, Economics and Business, 7(3), 253-263.
- Soares, L., Rustiadi, E., & Mulatsih, S. (2017). Analisis Disparitas dan Interaksi Spasial di Tomor-Leste. Journal of Regional and Rural Development Planning, 1(1), 74-86. (In Indonesian).
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doi: http://dx.doi.org/10.21511/ppm.15(1).2017.05
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