Spatial Diffusion of Social Policy in China: Spatial Convergence and Neighborhood Interaction of Vocational Education

Abstract

There has been a vast amount of discussion about the positive and negative regional effect on policy diffusion. During this debate, the role of neighborhood structure is ignored and the linear assumption is still prevailing in this field. By analyzing the spatial convergence of local vocational education development with data of 31 provinces from 1995 to 2008 in China, we explore the effects of neighborhood interactions on policy diffusion, paying specific attention to the dynamical role of neighborhood structures in policy diffusion. The empirical results clearly indicate that the development of local vocational education systems in China is spatially autocorrelated to the neighboring provinces. Local vocational education systems converge more slowly if a spatially lagged dependent variable is introduced, while they converge faster if a spatially error variable is introduced. The policy transition between neighbors considering their local spatial context is analyzed with Spatial Markov Chain and a fundamental nonlinear connection between neighborhood structure and policy transition is unveiled. Using spatial econometric models, we also find that the sociospatial diffusion patterns with the social factors such as consumption, urban/rural distribution and occupation serve as barriers to and amplifiers of policy diffusion. These results not only resonate with conventional linear wisdom on policy diffusion but also offer a new nonlinear perspective on socio-spatial patterns of policy diffusion that are clearly embedded within the local neighborhood structures.

Peer influence, market power, and enterprises' green innovation: Evidence from Chinese listed firms

“…However, the regression coefficient of peer influence is positive and significant at the 1% level in Column (3), which indicates that peer influence still positively contributes to an enterprise's green innovation when market power is considered. It appears that the industry‐based peer effect will influence the green innovation of enterprises through the market mechanism, which extends the traditional theory of spatial spillover (Gu, 2016, 2022d; Zhu et al, 2023). The regression coefficients of peer influence in columns (1), (2), and (3) are significant, and the regression coefficient of market power in Column (3) is also significant, fulfilling all the conditions of the mediating variable (Baron & Kenny, 1986; Frazier et al, 2004; Gu, 2021).…”

“…However, the regression coefficient of peer influence is positive and significant at the 1% level in Column (3), which indicates that peer influence still positively contributes to an enterprise's green innovation when market power is considered. It appears that the industry-based peer effect will influence the green innovation of enterprises through the market mechanism, which extends the traditional theory of spatial spillover (Gu, 2016(Gu, , 2022dZhu et al, 2023).…”

Effects of Patent Policy on Outputs and Commercialization of Academic Patents in China: A Spatial Difference-in-Differences Analysis

“…Thus, Hypothesis 4 was confirmed. There are therefore strategic interactions between neighboring provinces; they are not independent of each other [71,72,75]. When a province implements a patent policy that promotes local academic patenting and the level of commercialization, neighboring provinces will imitate it and implement similar patent policies, which will promote local academic patenting and commercialization.…”

Equity versus efficiency: A spatial analysis of residential aged care resources in Beijing

“…Jia et al, 2017; Wang, 2017; Zhou and Zhang, 2010), treating space only as an exogenous variable and failing to pay attention to the spatial correlation between the distribution of public resources. As mentioned earlier, due to policy diffusion or other structural reasons, there may be spatial correlations in the distribution of public facilities that are difficult to ignore, and in fact the allocations of public resources in neighboring areas often affect each other (Gu, 2016; Schmitt, 2011; Schmitt and Obinger, 2013). From a methodological perspective, ignoring spatial proximity or interactions can easily lead to biased estimates (Darmofal, 2015: 33, 41).…”