城乡居民社会保障性支出对消费水平影响比较研究——基于15个省市面板数据的实证分析

Comparative Study of Expenditure Impact on the Level of Consumption in Urban and Rural Social Security ——The empirical analysis of the 15 provinces Panel Data

本文以具有一定代表性的15个省市城乡居民2004-2010年7年的城乡居民人均社会保障性支出水平、人均工资收入水平、消费支出水平为例进行探讨,利用省际静态面板数据分析工具和模型对社会保障性年人均支出水平如何影响城乡居民年人均消费支出水平二者进行了面板实证分析并且做出了对比性研究。研究发现,近年来我国农村居民的社会保障支出总体水平较城镇居民依然差距较大,但是其对于消费水平的影响程度约为社会保障性支出对城镇居民消费水平影响程度的1.2倍,各地区之间、省市之间差异明显。该结论在有力模型论证的基础上较好地反映了近几年来城乡之间社会保障性支出水平发展的轨迹及在内需拉动方面的效用。

A long time, stimulating domestic demand in China has not been an effective solution, promoting the level of social security expenditure for the size of the contribution of domestic demand can not be underestimated, experts and scholars at home and abroad have had a variety of research and thesis on the relations between social security expenditure level and the consuming level. On the basis of the previous research, this paper is studied from per capita expenditure level of social security, the level of per capita wages, consumer spending level during urban and rural residents. It is discussed earlier by inter - provincial static panel data analysis tools and models of how does the social security annual per capita expenditure level panel affect the level of the annual per capita consumption expenditure of urban and rural residents both empirical analysis and make？ a comparative study. The conclusion is that the overall level of social security expenditure of rural residents in recent years compared with urban residents is still a big gap, but its consumption level is about 1.2 times the extent of the impact of social security expenditure of urban residents consumption level, a obvious difference is found between various regions, provinces. The conclusions reflect the trajectory of the level of social security expenditure and the utility in stimulating domestic demand between urban and rural development better in recent years. Based on the results of this empirical analysis, this paper proposes the corresponding countermeasures and suggestions.