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Satisfaction Evaluation Orientated Active Employment Policy Optimization

Posted on:2018-02-01Degree:MasterType:Thesis
Country:ChinaCandidate:R J ChangFull Text:PDF
GTID:2359330533470382Subject:National Economics
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Employment is the essential of people's livelihood,also the eternal theme of social and economic development.So the government proclaimed the Active Labor Market Programs(ALMPs)in 2002,including vocational training,employment agency,micro-finance,and many other projects,which made a crucial contribution to holding the bottom line of people's livelihood and maintaining social stability.Over the past decade more than tens of billions of employment expenditure per year was devoted into the labor market.Whether or not it achieves the expected result and benefits the target groups needs a scientific accurate evaluation of the policies.But in practice,for a long time our government focuses only on indicators such as employment rate and income,and the indicators of social benefits such as the degree of satisfaction lack of attention.In research,concern from scholars home and abroad in the area of active employment policy satisfaction evaluation is still rare,and they also failed to solve the problem of endogeneity which comes from the self-selection and regional heterogeneity.What's more,they have not been able to form a evaluation system for multi-project.Thus it cannot help individuals to make the optimal choice between projects.In view of this,under the framework of Rubin causal model,based on the human capital investment theory,self-employment theory and the economics of happiness,by aid of 2008 World Bank Active Labor Market Programs sample and 2014-2015 survey data added,this research carries out multi-project satisfaction evaluation,taking vocational training and micro-finance as an example.First,we use a multi-lever model and Propensity Score Matching(PSM)method to control self-selection and regional heterogeneity.Second,based on the balance sample after matching we integrate macro and micro information to evaluate the relative satisfaction performance of active employment projects.In the end we explore by which mechanism individual characteristics,regional features and two different projects' characteristics affect the satisfaction of the policies.Empirical study finds that: First,the implementation of different projects is not random,so self-selection and regional heterogeneity significantly influence the relative performance.On the individual level,people with characteristics such as working in the city,being a woman are more likely to participate in the training project,while people with characteristics such as elderly,healthy,residency,experienced,high education,and party membership are apt to take part in the micro-finance project.On the district level,areas with high unemployment rate or high employment expenditure rate are more likely to carry out micro-finance project.Self-selection and regional heterogeneity make training project's performance to be underestimated by 0.09 and 0.04 points respectively,and the real relative satisfaction performance of the two projects is nearly 0.24 points.Second,projects also produce difference from the reactions bias caused by the macro and micro factors.From the average relative performance view,micro and macro variables act on satisfaction degrees of the two different projects by different mechanism,and the direction and extent are also different.Micro features such as urban residence and non-party identity have a positive effect on the satisfaction degree of micro-finance and have a negative effect on the satisfaction degree of training project.Macro characteristics such as high economic growth rate,high unemployment rate and fair income distribution have a positive effect on the satisfaction degree of training project,but its coefficient of the effect on micro-finance is negative.High satisfaction degrees of micro-finance main come from the upgrade of individual's working confidence,while high satisfaction degrees of training program come from improvements in living standards.Neither come from the increase in income.From the marginal effects view,the various factors' effects on high satisfaction to low satisfaction show a decreasing trend.Third,age,gender,growth rate of employment expenditure and so on,are all significant factors in the program selection,but their effects on satisfaction degrees are not significantly.The other way round,the factors affect satisfaction degrees significantly such as economic growth rate and unfair income distribution,have only a weak effect on the procedure of project selection.This reflects that when the government chooses people for projects,they don't follow the standard of satisfaction oriented,but is more likely to pay close attention to the employment rate and income.Thus "reverse distribution" greatly weaken its role in maintaining social stability.Based on the conclusions above,this paper propose three policy suggestions: First is to establish professional qualified Third-Party Assessment Entity of active employment policy,and build a framework combined of macro and micro information.Second is to perfect the sifting mechanism of target population and allocate employment expenditure between regions scientifically.Establish a large database for the project objects' information,which may be a start of Precision Strategy.Third is to insist on the satisfaction oriented of the active employment policies to build "Responsive Government”.The government should shift to satisfaction standard which focuses on the quality of employment from efficiency standard which focuses on the employment volume solely,including smoothing channel for public opinions,establishing effective response mechanism and strengthening the commercialization of performance satisfaction evaluation.Strategies should be taken to optimize the allocation of employment policy resources,promote employment equity and thus maintain social stability.
Keywords/Search Tags:Active Employment Policies, Satisfaction Orientated, Macro and Micro Evaluation, Regional Heterogeneity, Multi-lever Model, Propensity Score Matching
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