Age Structure Change, Consumption Structure Optimization and the Upgrading of Industrial Structure——Empirical Evidence from Chinese Provincial Panel Data
Industrial restructuring and upgrading plays an important role in meeting the needs of expanding domestic demand and sustaining medium-high economic growth under the background of new normal. As demanders and consumers, different age structure leads to different consumption structure, and then this change at individual level will accumulate and cause the changes of the entire social demand structure, thereby having different effect in industrial restructuring and upgrading. Nowadays, China's population age structure is undergoing dramatic changes, marked by low fertility rates, demographic dividend fading: the double stacking phenomenon of "aging" and "fewer-children". Reversely, changes in population age structure may force industrial restructuring through demand mechanism, thus, providing a new impetus to expand domestic demand and adjust industrial structure under the new normal. Based on the above logic, this paper systematically analyzes the impact of population age structure on industrial structure through the channel of consumption structure in terms of both theoretical and empirical aspects, and reveals its mechanism of action and presents the chain transitive relation between the three. Theoretical analysis and China's experience during the transition period show that population age structure plays a certain role in the upgrading of industrial structure through the channel of consumption structure. Sub-regional panel regression model is established using the data set from China's 30 provinces(including municipalities and autonomous regions)during 1995-2013, and our empirical tests show that consumption structure is an important intermediate variable through which population age structure affect the industrial structure. It is also found that there is a positive relationship between consumption structure and industrial structure. The decreasing child dependency ratio promotes the upgrade of consumption structure, thereby benefiting the upgrade of industrial structure. The rising elderly dependency ratio also promotes the upgrade of consumption structure, and helps upgrade the industrial structure. In addition, the demand effect of population age structure and its mechanism and strength on industrial structure show significant differences among three regions. The conclusions not only provides empirical explanation for the low level of China's consumption structure, industrial structure and the existence of their regional differentiation, but also have some policy implications as to how to expand domestic demand and upgrade the industrial structure with the changes of population age structure. Firstly, make full use of the optimization effect of the decreasing child dependency ratio on consumption structure, to promote industrial upgrading. Enhance children's consumption demand in the process of economic development and urbanization, and make children's consumption more multi-layered and diverse, meanwhile be aware of adverse effects on the upgrade of consumption structure and industrial structure caused by changes in birth policy. Secondly, promote the development of silver-haired industry which relies on service consumption bonus released by the rising elderly dependency ratio. Fully develop the domestic market and diversify consumer needs of elderly population, facilitate the positive interaction between consumption and production, and then stimulate the development of service industry and the upgrade of industrial structure. Thirdly, optimize the industrial regional transfer and industrial structure adjustment based on regional differences regarding population age structure and consumption structure.
引用本文:
张忠根 何凌霄 南永清. 年龄结构变迁、消费结构优化与产业结构升级——基于中国省级面板数据的经验证据[J]. 浙江大学学报(人文社会科学版), 2016, 2(3): 81-.
Zhang Zhonggen He Lingxiao Nan Yongqing. Age Structure Change, Consumption Structure Optimization and the Upgrading of Industrial Structure——Empirical Evidence from Chinese Provincial Panel Data. JOURNAL OF ZHEJIANG UNIVERSITY, 2016, 2(3): 81-.