Regional innovation efficiency in Kazakhstan: Evidence from stochastic frontier and cluster analysis

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Type of the article: Research Article

Abstract
Innovative and effective territorial development drives the country’s economic growth and its ability to compete on the world stage. The purpose of this paper is to quantify the effectiveness of regional innovative development and to use cluster analysis to identify typological groups of regions and pinpoint priority points of innovative growth. The study’s statistical database consists of official data from the Bureau of Statistics of Kazakhstan for the period 2003–2024. The study used econometric methods such as panel regression, stochastic, and cluster analysis. The results demonstrate that the innovative development of the regions of Kazakhstan is positively influenced by the human resource factor (β = 1.131, p < 0.001) and socio-economic development (β = 1.894, p < 0.001), and negatively influenced by the level of R&D costs (β = –0.473, p = 0.016). The results of the SFA analysis showed that regions with industrial specialization have the most effective innovative development (Pavlodar TE = 0.764, Kostanay TE = 0.751) in the production of innovative products, and that growth may be driven by investments in fixed assets (SFA (β =1.565, p < 0.001)). Cluster analysis showed that сluster 1 is characterized by a high level of commercialization, whereas сluster 4 demonstrates a high level of innovation activity; cluster 2 has a high level of knowledge intensity and return on science; cluster 3 has a good technological base. The results showed that a high level of financing does not always lead to breakthrough innovation.

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    • Figure 1. Innovation activity vs Efficiency matrix
    • Table 1. Main variables of econometric analysis
    • Table 2. Indicators to group regions into clusters
    • Table 3. Fixed effects panel regression
    • Table 4. Structural parameters of the stochastic frontier model
    • Table 5. Regional technical efficiency scores (Pooled estimates, 2003–2024)
    • Table 6. Elasticity of innovation output (SFA)
    • Table 7. Characteristics of regional innovation development clusters
    • Conceptualization
      Dinara Mukhiyayeva, Arystan Kabikenov, Assem Kaliyeva, Yerkezhan Moldakenova
    • Data curation
      Dinara Mukhiyayeva, Arystan Kabikenov
    • Formal Analysis
      Dinara Mukhiyayeva, Arystan Kabikenov
    • Funding acquisition
      Dinara Mukhiyayeva, Arystan Kabikenov
    • Investigation
      Dinara Mukhiyayeva, Assem Kaliyeva, Yerkezhan Moldakenova
    • Methodology
      Dinara Mukhiyayeva, Yerkezhan Moldakenova
    • Project administration
      Dinara Mukhiyayeva, Arystan Kabikenov
    • Software
      Dinara Mukhiyayeva, Arystan Kabikenov
    • Supervision
      Dinara Mukhiyayeva, Arystan Kabikenov
    • Validation
      Dinara Mukhiyayeva, Arystan Kabikenov, Assem Kaliyeva
    • Writing – original draft
      Dinara Mukhiyayeva, Arystan Kabikenov
    • Writing – review & editing
      Dinara Mukhiyayeva, Assem Kaliyeva, Yerkezhan Moldakenova
    • Resources
      Assem Kaliyeva, Yerkezhan Moldakenova
    • Visualization
      Assem Kaliyeva, Yerkezhan Moldakenova