Optimizing spatially differentiated support policies for technology-based SMEs in Hebei Province: A spatial regression modeling approach

Abstract

Under the dual backdrop of the innovation-driven development strategy and the coordinated development of the Beijing–Tianjin–Hebei region, the growth of county-level technology-based small and medium-sized enterprises (SMEs) in Hebei Province has been constrained by regional heterogeneity in resource endowments and industrial foundations, which conflicts with the uniform application of support policies. Accurately identifying spatial differentiation patterns in policy effectiveness has become essential for optimizing policy implementation. Existing studies have primarily employed traditional models such as ordinary least squares (OLS), overlooking the spatial correlation of county-level economic systems. Moreover, limited attention has been paid to the local spatial heterogeneity of policy variables and enterprise development indicators, the nonlinear characteristics of policy effects at the county scale, and the quantification of spatial spillover effects of support policies. To address these limitations, moderation models, threshold regression models, and the Spatial Durbin Model (SDM) were integrated in this study to construct a moderating effect measurement model of support policies. Through this approach, the spatially differentiated effects of support policies for technology-based SMEs across counties in Hebei Province were quantified. The nonlinear moderating mechanisms of policy instruments and their spatial transmission patterns were systematically examined. By integrating spatial econometric techniques with policy effect evaluation frameworks, this study proposes a novel paradigm for regional policy research and offers evidence-based guidance for designing differentiated policy measures aimed at enhancing the effectiveness of support initiatives in Hebei Province.