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A Spatial Econometrics Analysis Of Impacts Of Socio-economic Factors On Crime Rate In China

Posted on:2019-06-03Degree:MasterType:Thesis
Country:ChinaCandidate:A ChangFull Text:PDF
GTID:2416330542999675Subject:Industrial Economics
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After 40-year’s implementation of the reform and opening-up policy,China has made significant achievements in the economic development.However,at the same time,the rapid increase in the crime rate has become a crucial issue which is affecting China’s economic development and social stability seriously.As a crossing field between law and economics,the exploration for the role of socio-economic factors in the crime rate,and the proposition of corresponding solutions have certain practical and guiding significance.Criminal economists claim that there is a rational comparison of criminal costs and expected crime proceeds before potential offenders determine their behaviors.This view has become a pillar in the economic research at crime theory.However,people always overlook the uneven development of many socio-economic factors between geographical units,and the difference in crime rates’ growth under the rapid development of China’s economic and social development.At the same time,according to the first law of geography,socio-economic factors in a province will not only impact the level of crime in this particular province but will also have a spatial impact on the level of crime in other provinces,which may lead to a spatial autocorrelation of crime rates between provinces.Furthermore,this kind of space impact has different power in different regions.Therefore,on the basis of the observation of the current situation of crime rates in China’s provinces,this paper conducted a spatial exploratory analysis.Under the different spatial weight matrix,the global Moran index has been used to verify the existence of spatial autocorrelation;and through the studying of the local Moran scatter plots and LISA cluster maps,different types of spatial autocorrelation of crime rates in different provinces have been studied.Secondly,this paper sorted out the effects of some socio-economic factors such as economic growth,income disparity,urbanization,and opening-up policy on the crime rate in theory,then discussed the spatial impact of these factors on the crime rate under the modelling analysis before putting forward empirical guesses.Finally,on the premise of the existence of spatial autocorrelation of criminal behavior,31 provinces of China,from 1998 to 2014,were sampled to establish provincial panel data.In the framework of different spatial weight matrix,the spatial lag model,the spatial Dubin model and the spatial error model were adopted respectively.The spatial impact of various socio-economic factors on the crime rates of different provisions in China has been analyzed with empirical results.In addition,the direct utility,indirect utility,and total utility of the model have been measured respectively.The empirical results illustrate that during the observation period,the economic development has a severe inhibitory effect on the crime rate;while the expansion of urban-rural income gap and the regional income gap will,to some extent,motivate the implementation of criminal activities.And the process of urbanization and opening-up policy will deepen this phenomenon furtherly.The education plays a crucial role in improving the quality of citizens and reducing the crime rate;The shocking policy is effective as well.However,the public welfare and the expenditure on public safety have not played their due role.Therefore,when focusing on the development of economic,China should also promote the common prosperity between urban and rural areas and between regions.Policies should be in favor of the people’s livelihood,especially the education,in order to promote the healthy and sustainable development of China’s society and economy.In the process of urbanization and the implementation of the opening-up policy,the government must take both the economic base and the superstructure into consideration,which is the policy implications of this paper.
Keywords/Search Tags:crime rate, spatial autocorrelation, economic growth, income disparity, space panel model
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