摘要
人口结构变迁是全球各国共同面临的问题,势必将对以人为本的寿险市场产生深远影响。结合国内外相关文献,全面梳理了人口自然结构、人口社会结构和人口地域结构变迁对寿险消费的影响,并利用73个国家和地区2000—2018年的面板数据,分别对全样本和按人口老龄化程度划分的子样本进行实证检验,回归结果显示一旦进入老龄化社会,预期寿命、老年抚养比、少儿抚养比、女性占比和受教育水平对寿险消费的正向影响都比较显著,尤其在深度和超老龄化社会,但在非老龄化社会中影响并不显著,甚至还有抑制作用。而城镇化水平正好相反,在65岁以上人口占比低于14%的地区,对寿险消费的刺激作用非常显著,但随着老龄化程度加深,影响变得不甚明显。在此基础上,提出未来寿险公司应结合具体地区的老龄化程度制定差异化发展策略。
Population structure change is a common issue facing countries all around the world,which will definitely exert great influence on the people-oriented life insurance market.By reviewing relative literatures at home and abroad,the article analyses the impacts of the natural structure,social structure and regional structure of population on life insurance consumption.Panel data from 73 countries and regions from 2000 to 2018 are used to make empirical tests on the full sample and the subsample based on population aging respectively.Regression results show that once a country or region steps into aging society,the positive influence of life expectancy,old-age dependency ratio,child dependency ratio,proportion of women and education background on life insurance consumption are significant,especially in deep aging society and super aging society.However,the influence is not significant in non-population aging society,instead,it may even have inhibitory effect.Urbanization is right on the opposite side.Regions with the proportion of people over 65 lower than 14%has significant stimulation function on life insurance consumption.However,with the deepening of population aging,the influence becomes insignificant.Based on the analysis,the paper proposes that in the future life insurance companies should make differentiated development strategies in different regions by taking population aging level of the regions into consideration.
作者
袁成
崔慧
YUAN Cheng;CUI Hui(School of Economics,Nanjing University,Nanjing 210093,China)
出处
《云南财经大学学报》
CSSCI
北大核心
2020年第9期49-61,共13页
Journal of Yunnan University of Finance and Economics
基金
2019年南京大学人文社会科学“双一流”建设“百层次”项目“‘一带一路’建设与我国保险服务国际化”
关键词
人口结构
寿险消费
人口老龄化
Population Structure
Life Insurance Consumption
Population Aging