Abstract
Based on the diffusing theories of knowledge, this paper uses the data between 2006-2008 of the 21 cities in Guangdong province to conduct the exploratory data analysis, constructs the input-output model of innovation which take the informatization level of a region into account, and uses the common econometric model and spatial econometric models based on geographical adjacency between two regions, then compared the estimation results of these models. The systematic analysis shows that the innovation output of the 21cities is spatially correlated to each other, the result of the econometric model considered about the adjacency element is more precise than the common one, the accumulation of capital for innovation is the domain engine of innovation creating, the input of human and the enhancement of informatization level stimulate innovation weakly. However, when take the informatization level into account, the results of the new model are more precise. Based on the empirical research, this paper proposes several policy recommendations.
Highlights
When it comes to the relation between input and output of innovation, we don’t consider about spatial factors
As an early experimental area of the policy of reform and opening-up, Guangdong province presently confronts with both domestic challenge and international challenge: the latter refers to appearance of trade protectionism, the fierce competition of market, resource, talents, technology, standards etc.; the former is the special domestic environment which is characterized by the low level of industry system compared to the world industry chain, the lack of a strong self-innovation ability and the pressure of huge population and environment suffered from extensive economy, and calls for a transformation of development approach
When comparing the results of spatial lagged model (SLM) and spatial error model (SEM), it is apparent to see the coefficient of the spatial lagged variable in SLM is not significant, but the coefficient (LMABDA) of spatial correlation error is significant under a probability of 5%, which is consistent with the former descriptive statistical result
Summary
When it comes to the relation between input and output of innovation, we don’t consider about spatial factors. This paper applies Exploratory Data Analysis(EDA) to the relative data of 21 prefecture-level cities in Guangdong province to test whether there is spatial correlation about creative activities among different cities; by the comparison results between OLS model without spatial factors and spatial econometric model, we get the former test further; at last, we. Information Technology for Manufacturing Systems III introduced an indicator which reflects regional informatization into the common innovation input and output model level and make a comparison between the common spatial econometrical model and the adjusted spatial econometrical model, which helps to find how the informatization level affect the regional innovation producing
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