区域层面知识生产函数的另一种方法:在美国和俄罗斯的应用

J. Perret
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引用次数: 2

摘要

本研究在美国和俄罗斯联邦的背景下,对知识产生方面进行了研究,这是每个国家创新系统的关键部分。继Fritsch和Slavtchev(2006)之后,知识生产函数可以用来解释创新系统的效率。详细地说,本研究提供了知识生产函数的分位数回归估计,以解释知识输入和知识输出之间可能存在的非线性关系。利用研究人员的区域数据,美国和俄罗斯联邦的研发支出和专利授予-由核密度估计和转移矩阵的结果驱动-对基本知识生产函数设计进行了分位数回归;也为俄罗斯提供了扩展设计。结果表明,两国均存在规模较小的研究系统区域群,其动态和知识生产功能与规模较大的研究系统区域显著不同。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An alternative approach towards the knowledge production function on a regional level: Applications for the USA and Russia
The present study picks up on the aspect of knowledge generation - a key part of every national innovation system - in the context of the USA and the Russian Federation. Following Fritsch and Slavtchev (2006) a knowledge production function can be used to account for the efficiency of an innovation systems. In detail this study provides a quantile regression estimation of the knowledge production function to account for a possible non-linear relationship between knowledge inputs and knowledge output. Using regional data for researchers, expenditures on R\& D and patent grants for the USA and the Russian Federation - motivated by the results of a kernel density estimation and transition matrices - a quantile regression is performed for a basic knowledge production function design; for Russia as well for an extended design. The results show that in both countries there exist groups of regions with smaller sized research systems that report significantly different dynamics and thus knowledge production functions than regions with larger sized research systems.
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