Modeling the impact of cluster policy and special economic zones on the intensity of technological borrowing and the creation of in-house developments in industrially advanced and lagging regions

Abstract

The article is devoted to modeling the impact of cluster policy and special economic zones (SEZs) on the structure of technological renewal in industrially advanced and lagging regions, with a focus on the balance between technological borrowing and the formation of in-house developments. The aim of the study is to quantitatively assess whether preferential regimes enhance technological autonomy or reinforce dependence on imported technologies in regional systems with different initial levels of innovation potential. The empirical base is formed by panel data for 85 federal subjects for the period 2012–2024, aggregated into 1,240 region–year observations and supplemented with SEZ resident reports and surveys of 350 enterprises; the variables include GRP-normalized expenditures on engineering and equipment procurement (proxy for technological borrowing) and domestic R&D expenditures (proxy for in-house developments), as well as indicators of cluster environment quality, human capital, financial depth, and institutional quality. The methodology relies on fixed effects models, dynamic panel GMM estimations (Arellano–Bond), spatial models with inverse distance matrices, and regression analysis of the elasticity of in-house developments with respect to technological imports. It is shown that in advanced regions, the presence of SEZs increases specific expenditures on engineering and R&D by 50.5% and 75.3%, respectively, and raises the coefficient of technological autonomy from 0.381 to 0.417, while technology imports after two years ensure an elasticity of in-house developments of about 0.415, indicating the complementarity of external and internal knowledge. In lagging regions, preferences cause a multiple rise in expenditures on imported equipment with a weak increase in R&D and a decrease in the autonomy coefficient to 0.125; the revenue multiplier and the growth of intangible assets per ruble of benefits are 2–4 times lower, and the discounted payback period is nearly three times longer, which indicates technological substitution and low capacity to convert borrowings into in-house developments. The conclusion emphasizes the need for differentiated cluster policy: for developed regions—strengthening the scale and depth of preferences, and for lagging regions—prioritizing investment in human capital and engineering infrastructure, as well as the strict linkage of tax incentives to obligations regarding the localization of R&D and the growth of technological autonomy.