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The Analysis Of Government Efficiency And Influencing Factors Of Prefecture-level Cities In Liaoning Province Based On Stochastic Frontier Approach Model

Posted on:2018-05-13Degree:MasterType:Thesis
Country:ChinaCandidate:W R SongFull Text:PDF
GTID:2346330542988949Subject:Political economy
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Local government efficiency problem is not only a theory problem but also a practical problem.In theory,the local government efficiency has always been one of the most common used method which many scholars used to study government behavior,the study of the local government efficiency will promote the assessment method of the existing research,in this paper,this paper made a overall comparative analysis on government efficiency evaluation methods,also provide a reference for following study on government efficiency evaluation.In practice,our country is a developing socialist country,the market economy is still imperfect,low efficiency of the government will have a limitation on the development of local economy.As for Liaoning Province,during the process of transforming from planned economy to market economy did not make a brilliance progress,even got negative growth,one of the important reason is that the government efficiency is low.Therefore,it is important to analyze the influencing factors,and it is essential for the government to take effective measures to improve local government efficiency.Therefore,this paper use the SFA model of 14 cities of Liaoning province in the past 2003 to 2015 to examine the efficiency of government analysis,although the average government efficiency is rising year by year,the whole government efficiency is low,there is a big space for the government to improve efficiency,and observed the government efficiency and the gross domestic product,industrial structure,the network popularity and so on.There is a definite relation between economic and these factors.This article main research content are as follows:The first part of the thesis introduces the research background,research significance and literature review at home and abroad.The second part is the government efficiency related definition and related theories.The third part is the measure method of the local government efficiency and the comparison.The fourth part is through the differences and similarities comparison between the two commonly used model,SFA method is selected and introduced,statistical measurement data processing,through the empir:ical analysis we found that the Liaoning province government efficiency is still low at present stage,found only Anshan,Shenyang,Dalian municipal government efficiency is higher,the rest are low.With regional differences,the low efficiency of district cities show that there are a lot of room to improve.The fifth part of this paper is that influencing factors of the Liaoning province government efficiency analysis,which mainly divided into two big aspects,economic factors and social factors,use data of 14 cities in Liaoning province from 2003 to 2015,it is found that the these various influencing factors all have a positive effect,including the proportion of the added value of secondary industry in GDP influence coefficient is the largest,followed by the general budget revenue of local governments and,in turn,is a capital investment,total retail sales of social consumer goods,population density,the proportion of the added value of the tertiary industry to GDP.labor input,international Internet users,the last is foreign investment companies.Including the economic and social factors.The sixth part is the conclusion and suggestion.Main contribution in this paper:first,in terms of the government efficiency in Liaoning province research are so far less,therefore,this article will promote the research’s development.Second,in addition to the analysis about the present efficiency’s situation,analyzed the influencing factors of the efficiency of the government on a prefecture-city level,provides a certain idea to farther develop of Liaoning province.
Keywords/Search Tags:government efficiency, SFA model, influencing factors
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