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Study On The Influencing Factors Of Employment Status And Industry Of Migrant Workers

Posted on:2019-04-20Degree:MasterType:Thesis
Country:ChinaCandidate:Y XingFull Text:PDF
GTID:2417330572455219Subject:Quantitative Economics
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With more and more migrant workers flowing into cities,the topic of “How to integrate migrant workers into cities” has always been a hot topic of study by the government and scholars.This paper focuses on the policy of “Migrant Workers Going Back to Home Business” and “Structural Reform on the Supply Side” proposed by the “19th Party Congress”,the research subjects are the employment status of out-migrant workers and their participation in the industry and correspond to the two policies.It reveals the main factors affecting the employment status and employment of the industry,provides micro-evidence for understanding the current employment situation of migrant workers,and provides a basis for the government to implement migrant workers' employment policies according to local conditions.Based on the dynamic monitoring survey data of the floating population in 2014,this article first studies the employment status of migrant workers and the influencing factors in the industry from the overall perspective of the country.The first step describes the employment status of the rural migrant workers and the status quo of the engaged industries.The second step establishes the Logit model of the employment status of migrant workers and the multiple Logit models of the industry,from the individual level,family level,community and social level.From these three levels,the relevant influencing factors were analyzed.Then,considering that China has a vast territory and different regions have different economic development status,the flow of migrant workers in each province is not the same.Therefore,through the use of the number of peasant workers in the provinces reported by the People's Daily,the top six provinces and cities for migrant worker problems were selected as typical provinces and cities for analysis.Among them,Beijing,Shanghai,and Guangdong provinces are the provinces and cities with high attention to "migrant workers inflows",and Anhui,Henan,and Sichuan are the provinces and cities with high concern for "migrant workers outflows".According to the data characteristics of six provinces and municipalities,taking into account the impact of migrant workers' mobility distances and floating areas on their employment status and industry involvement,the “migrant workers inflows” group will use the outflow regions to match the propensity scores,the “migrant workers outflows” group matched the propensity score with the flow range and the inflow area,and analyzed the average treatment effect of each group.The article concludes as follows:(1)With the exception of household level food expenditure ratios and community and social level urban endowment insurance variables,changes in the positive direction of other variables will increase the probability that migrant workers will become self-employed workers.The degree of migrant workers below the level of higher education has a greater difference in their employment status in different mobility areas and inflow places.(2)Factors affecting rural migrant workers engaged in manufacturing,construction,wholesale,retail,and service industries are heterogenous.Outgoing peasant unions with urban endowment insurance are less likely to engage in construction,wholesale,retail,and service industries.(3)In addition to peasant workers who have flowed into Beijing's central region,migrant workers from the eastern region are more likely to choose to become self-employed workers and engage in manufacturing;migrant workers who flow across provinces and migrant workers who flow into the eastern region more inclined to become employed and have a greater probability of engaging in manufacturing.Article features and innovations:(1)Adopting dynamic monitoring and survey data of population health and family planning for empirical analysis,the data volume is large and covers a wide range;(2)Selective biases that may be brought by migrant workers' mobility distance and flowing areas are taken into account.
Keywords/Search Tags:Outgoing migrant workers, Logit model, Multinomial Logit models, Propensity score matching method
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