City and Industry Network Impacts on Innovation by Chinese Manufacturing Firms: A Hierarchical Spatial-Interindustry Model

Yuxue Sheng and James LeSage

A chapter in Spatial Econometrics: Qualitative and Limited Dependent Variables, 2016, vol. 37, pp 343-386 from Emerald Group Publishing Limited

Abstract: We are interested in modeling the impact of spatial and interindustry dependence on firm-level innovation of Chinese firms The existence of network ties between cities imply that changes taking place in one city could influence innovation by firms in nearby cities (local spatial spillovers), or set in motion a series of spatial diffusion and feedback impacts across multiple cities (global spatial spillovers). We use the termlocalspatial spillovers to reflect a scenario where only immediately neighboring cities are impacted, whereas the termglobalspatial spillovers represent a situation where impacts fall on neighboring cities, as well as higher order neighbors (neighbors to the neighboring cities, neighbors to the neighbors of the neighbors, and so on). Global spatial spillovers also involve feedback impacts from neighboring cities, and imply the existence of a wider diffusion of impacts over space (higher order neighbors). Similarly, the existence of national interindustry input-output ties implies that changes occurring in one industry could influence innovation by firms operating in directly related industries (local interindustry spillovers), or set in motion a series of in interindustry diffusion and feedback impacts across multiple industries (global interindustry spillovers). Typical linear models of firm-level innovation based on knowledge production functions would rely on city- and industry-specific fixed effects to allow for differences in the level of innovation by firms located in different cities and operating in different industries. This approach however ignores the fact that, spatial dependence between cities and interindustry dependence arising from input-output relationships, may imply interaction, not simply heterogeneity across cities and industries. We construct a Bayesian hierarchical model that allows for both city- and industry-levelinteraction(global spillovers) and subsumes other innovation scenarios such as: (1)heterogeneitythat implies level differences (fixed effects) and (2)contextual effectsthat implylocal spilloversas special cases.

Keywords: Bayesian; random effects; cross-sectional dependence; C21; R12; R15 (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:eme:aecozz:s0731-905320160000037019

DOI: 10.1108/S0731-905320160000037019

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