Regional Innovation Disparities in Kazakhstan: Input–Output Misalignment in an Official Panel, 2014–2025

How are innovation inputs and measured outputs distributed across the regions of a resource-dependent post-Soviet economy, and how closely do they correspond? We use a balanced official panel of Kazakhstan’s 17 regions for 2014–2025 (204 region–year observations) covering four indicators: R&D expenditure, innovation-active enterprises, the innovation activity rate and innovative product output. Granted patents for inventions are available by region for 2021–2025 only; this series is shorter than the panel, so it is used in the 2025 cross-sectional and descriptive analyses and does not enter the panel or machine learning models. On the panel, we estimate dispersion measures, panel regression with region-clustered standard errors and tree-based machine learning interpreted with SHapley Additive exPlanations; on the 2025 cross-section, we compute correlations, clustering and spatial association statistics. Inputs are highly concentrated: Almaty and Astana together accounted for 65.1% of national R&D expenditure in 2025, and cross-regional dispersion did not narrow over the period. In the 2025 cross-section (n = 17), R&D expenditure, innovation-active enterprises and patent grants are strongly inter-correlated (r = 0.96–0.97), but none of the three is significantly correlated with innovative product output. This input–output misalignment is a pattern whose mechanisms we do not test. The panel specification has modest explanatory power (R2 = 0.188), which bounds the weight that can be placed on any single coefficient. Within it the innovation activity rate is the only significant predictor of output (β = 0.177, p < 0.001), an association that survives exclusion of the three regions affected by the 2022 administrative reform; because that indicator and the output measure are compiled within the same system of enterprise observation, it is reported as a within-dataset association requiring validation against independently measured indicators. Under region-grouped cross-validation, the machine learning models show no out-of-region predictive ability and are reported solely as in-sample interpretation. The contribution is a documented diagnosis of input–output misalignment in an understudied regional system, with an explicit account of what such data can and cannot support, and a prospective statement of what institutional readiness for algorithmic decision support in regional innovation policy would require.