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以教控人是否有效?——基于北京、上海两地抬高随迁子女入学门槛政策的比较分析 被引量:21

Is it an Efficient Way to Reduce Population by Restricting Children's Education?——A Comparative Analysis of the Raising the Enrollment Threshold for Migrant Children Policies in Beijing and Shanghai
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摘要 留守儿童一直是备受关注的话题,它是在中国户籍制度、城乡二元结构的背景下,家庭的迁移决策中产生的。2014年户籍改革严格控制特大城市人口规模使得北京、上海等地区不断提高流动子女的入学门槛,试图以教控人。本文选择了城市规模、人口背景相似的上海、北京两地区,通过DID模型和PSM-DID模型考察了教育政策对流动人口子女随迁、留守的影响,结果表明:(1)大城市对流动儿童较高的入学门槛显著阻碍了流动人口子女随迁,迫使其留在户籍原地成为留守儿童。(2)收紧的教育政策显著增加了流动人口子女单独留守的概率,随迁子女被迫返回原籍读书,父母却仍留在流入地,通过抬高入学门槛以控制人口的目的未实现;(3)政策有明显的技能偏向性。人力资本水平较低的家庭受教育政策的影响程度更大,这在一定程度上固化了人力资本的代际流动。 Left-behind children have always been a subject of great concern.It was generated in the family migration decision in the background of China s household registration system and the urban-rural dual structure.Beijing and Shanghai have continuously raised the admission threshold for migrant children since 2014 and try to control the population size.Based on the survey Data in Beijing and Shanghai which are similar in city scale and population background,this paper used DID and PSM-DID to investigate the effect of education policy on children left-behind or migration.The results show that:(1)the raised entrance threshold for migrant children in big cities significantly discourage the children s migration,forcing them to stay left-behind.(2)the raised entrance threshold significantly increase the probability of children left-behind alone.(3)Education policy has obvious skill bias.The children whose parents with lower education have higher probability to be influenced by education policy,which aggravates the intergeneration transmission of human capital to some extent.
作者 杨娟 宁静馨 Juan Yang;Jingxin Ning(School of Economics and Business Administration, Beijing Normal University, Beijing,100875;School of Economics and Management, Tsinghua University, Beijing, 100084)
出处 《教育与经济》 CSSCI 北大核心 2019年第1期65-74,共10页 Education & Economy
基金 北京市教育委员会共建项目专项资助"北京市区县间基础教育差异性研究"
关键词 留守儿童 流动儿童 教育政策 技能偏向 left-behind children migrant children education policy skill bias
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