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中国代际收入传递趋势及教育在传递中的作用 被引量:33

The Intergenerational Income Transmission Trends in China and the Role of Education
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摘要 本文利用中国健康营养调查(CHNS)数据,实证检验了出生于1949—1990年的男性是否存在代际收入传递,并分析了其变化趋势和教育在代际收入传递中的作用机制。估计结果表明,1949—1990年代际收入传递程度总体上不断加强,其中20世纪60年代初期和80年代初期出现两次代际收入传递高峰。中等收入者受父辈的影响及加强趋势均最高,呈现双倒"U"型特征。城镇代际收入传递加强趋势约是农村2倍,城镇高收入者受父辈的影响更强,农村则低收入者所受影响更强。对教育作用机制的分解发现,教育对代际收入传递的解释程度在农村达到26%,城镇则达到36%。家庭环境导致的子女教育机会不平等是影响代际收入传递趋势变化的最主要原因。随着国民教育水平提高,教育的作用也出现了"组合效应"特征。 Using the data from the China Health Nutrition Survey (CHNS), this paper empirically tests the existence of intergenerational income transfiaission of male born in 1949-1990. Also, this paper analyzes the trends and the mechanism of how education works in intergenerational income transmission. According to the result, the level of intergenerational income transmission has been increased (tom 1949 to 1990. The intergenerational income transmission elasticity reaches the peak respectively in the early 60' s and early 80' s. The increasing trend and the effect of parents on the secondary income cohort is the largest, featuring the shape of the "doubule-inverted-U". The increasing trend of transmission in urban is twice as much as in rural, the effect of parents on high income cohort in urban area is higher and the effect of parents on low income cohort in rural area is higher. Decomposing the mechanism, it is verified that education count on 36% in the urban area and 26% in the rural area on how intergenerational income transmission. The inequality of educational opportunity caused by the family background is the main reason which influences the change trends of the intergenerational income transmission. However, with enhancing of education level, the role of education in the mechanism in China also shows the combination effect.
作者 亓寿伟
出处 《统计研究》 CSSCI 北大核心 2016年第5期77-86,共10页 Statistical Research
基金 教育部人文社会科学基金项目"贫困与富裕阶层代际传递路径及对策研究(13YJC840030)" 国家自然科学基金项目"收入不平等代际传递:估计偏误 传递机制与干预仿真(71403297)"资助
关键词 代际收入传递 教育扩展 分位数回归 趋势分解 Intergenerational Income Transmission Educational Expansion Quantile Regression Trends Decomposition
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