Innovation and diffusion: the rise of the new administrative approval system in Chinese cities
(2.Department of political science, Texas A&M University)
【Abstract】The reform of the administrative approval system is the breakthrough point of the government’s function transformation and for adjusting the relationship between the government and the market. Since the establishment of the first city-level administrative approval center in 1997, more than 10 years have passed and China has now established administrative examination and approval centers in more than 260 cities. How did such a large-scale organizational reformation and diffusion of local governments happen? And what factors contributed to the establishment of the administrative examination and approval centers? This paper reveals the process of the rise of the new administrative approval system in Chinese cities from the perspective of local government innovation and diffusion. The author has built a database consisting of data of the social economic situations, administrative examination and approval centers and the careers of local officials of 281 prefecture-level and sub-provincial cities from 1997-2012. Based on the discrete time and time series EHA models, this paper proposes and testifies the impact of local economic conditions and administrative factors, the longitudinal and lateral diffusion mechanism as well as the political movements of local officials on the establishment of local administrative examination and approval centers. This research helps reveal the internal mechanism of the “Chinese Experience” that promotes local innovation, which has long been praised by the academic world.
【Keywords】 local governments; innovation diffusion; administrative approval; political movements;
(Translated by WU Yue)
. ① Even though some provincial capitals are still at provincial level, they enjoy the political and economic status that are higher than regular prefectural cities. And the political status of provincial city leaders are also higher than those of prefectural cities. Therefore, in this article, we palce these cities at an administratively higher level than regular prefectural cities. [^Back]
. ②In our description of the EHA method, even though some units are an organization, government or country, we still refer to them as an “individual.” [^Back]
. ③ Of course, the probit model is frequently used by scholars. The authors of this paper also adopt the probit model to carry out evaluation and the results is in accordance with the logit model. [^Back]
. ④ China's party leader and cadre database: http://cpc.people.com.cn/gbzl/index.htm; the political circle website: http://www.zt360.cn/jgzyl/dfld; http://www.hotelaah.com/liren/index.html [^Back]
. ⑤This paper does not take into consideration the officials with a tenure that is less than a year. [^Back]
. ⑥ In this study, Eastern regions include: Hebei, Liaoning, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, Haina (Beijing, Tianjin and Shanghai are not included; central regions include: Shanxi, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Hubei, Hunan; Western regions include: Inner Mongolia, Guangxi, Sichuan, Guizhou, Yunnan, Tibet, Shaanxi, Gansu, Qinghai, Ningxia, Xinjiang (Chongqing not included). [^Back]
. ⑦ Odds ratio is different from odds. Odds only refers to the ratio of the probability of event A happening to the probability of it not happening, odd(A) = p/1 - p; the odds ratio of event A to event B is the ratio of the odds of event A happening to the odds of event B happening, odd(A)/odd(B). [^Back]
. ⑧ Since this study adopts the EHA data, once the dependent variable is valued at 1, the observed data will be intercepted and the endogeneity problem therefore does not exist. [^Back]
. ⑨ For the U curve y = ax2 + l > x + c, its maximum value appears when x = -b/2a. in model 3, -(-0.32)/(2x0.04) = 4 years. [^Back]
. ⑩ The mean value of the variation inflation factor of the main variables of the model is 5.46, and none of the factors exceeds the value of 10 apart from the power variables. This means that in the regression models, there is no multicollinearity problem. In addition, collinearity problem caused by the power of an artificially designed variable will not pose threats to the robustness of the model. Reference: http :www.statisticalhorizons.com/multicollinearity [^Back]
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