摘要
在作用机理的基础上,构建面板数据向量自回归模型,利用2006—2018年东北地区34个地级市面板数据对人口迁移、劳动力供给和经济增长动态关系进行实证研究。研究发现:经济增长显著影响人口迁移,正向驱动作用具有滞后性,人口迁移对经济增长亦具有正向驱动作用,滞后期较长;经济增长对劳动力供给的影响显著为正,在滞后1期达到峰值,劳动力供给是经济增长的格兰杰原因,其作用在统计上不显著;劳动力供给对人口迁移的影响在短期显著为正,长期不显著,人口迁移对劳动力供给的正向驱动作用较小且具有滞后性。研究结论的政策含义在于,要以经济增长和就业为突破口,引导人口流动,减少人口流失,而非以人口迁移为抓手,去作用于经济增长和劳动力供给。
Based on function mechanism, this paper constructs a panel data vector autoregressive model, and makes an empirical study on the dynamic relationships among population migration, labor supply and economic growth by using the panel data of 34 prefecture level cities in northeast China from 2006 to 2018. The results show that economic growth has a significant impact on population migration, and the positive driving effect is lagged;population migration also has a positive driving effect on economic growth, and the lagging period is longer;economic growth has a significant positive impact on labor supply, which reaches its peak in the first lagging period, and labor supply is the Granger cause of economic growth, and its effect is not statistically significant;the effect of labor supply on population migration is significant positive in the short term, but insignificant in the long term. The positive driving effect of population migration on labor supply is small and has time-lagging effect. The policy implication of the research conclusion is that we should take economic growth and employment as the breakthrough points, guide population flow and reduce population loss, instead of taking population migration as the starting point to affect economic growth and labor supply.
作者
曹献雨
睢党臣
CAO Xian-yu;SUI Dang-chen(School of Economics,Xi'an University of Finance and Economics,Xi'an 710100,China;International Business School,Shanxi Normal University,Xi'an 710119,China)
出处
《云南财经大学学报》
CSSCI
北大核心
2021年第6期1-11,共11页
Journal of Yunnan University of Finance and Economics
基金
国家社会科学基金项目“‘互联网+’养老服务体系生成机制与培育路径选择研究”(16BSH131)。
关键词
东北地区
人口迁移
劳动力供给
经济增长
向量自回归模型
Northeast China
Population Migration
Labor Supply
Economic Growth
Vector Autoregressive Model