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中国城市房价对生育率的影响——基于长三角地区41个城市的计量分析
引用本文:方慧芬,陈江龙,袁丰,高金龙. 中国城市房价对生育率的影响——基于长三角地区41个城市的计量分析[J]. 地理研究, 2021, 40(9): 2426-2441. DOI: 10.11821/dlyj020201069
作者姓名:方慧芬  陈江龙  袁丰  高金龙
作者单位:1. 中国科学院南京地理与湖泊研究所,南京 2100082. 中国科学院流域地理学重点实验室,南京 2100083. 中国科学院大学,北京 1000494. 江苏省区域现代农业与环境保护协同创新中心,淮安 2233005. 中国科学院地理科学与资源研究所,北京 100101
基金项目:国家自然科学基金面上项目(41771184)
摘    要:房价与人口的关系是人文经济地理学研究的热点领域,本文选取长江三角洲地区作为研究区域,利用区域内41个城市11年(2008—2018年)的数据,分别采用差分GMM模型及LSDVC法,从整体和分组的层面分析城市房价与生育率的关系。整体模拟结果表明,城市房价上涨的速度对于生育率有着显著的负向作用:房价上涨得越快,生育率下降得越快。同时,以房价收入比衡量的居民购房能力对生育率也有着显著的影响,居民购房能力下降得越快,生育率也下降得越快。分组研究的结果表明,不同房价水平的城市,生育率受购房能力影响程度不同,中等房价水平及较低房价水平的城市,其生育率更容易受到购房能力波动的影响。基于以上研究结果,提出严控房价涨幅速度、适当提高购房补贴、提高居民收入等政策建议,以期提高区域内居民整体生育意愿,促进人口增长。

关 键 词:房价  房价收入比  生育率  购房能力  
收稿时间:2020-11-04

The econometric analysis of the effect of city housing prices on fertility rates:A study from cities of the Yangtze River Delta in China
FANG Huifen,CHEN Jianglong,YUAN Feng,GAO Jinlong. The econometric analysis of the effect of city housing prices on fertility rates:A study from cities of the Yangtze River Delta in China[J]. Geographical Research, 2021, 40(9): 2426-2441. DOI: 10.11821/dlyj020201069
Authors:FANG Huifen  CHEN Jianglong  YUAN Feng  GAO Jinlong
Affiliation:1. Nanjing Institute of Geography and Limnology, CAS, Nanjing 210008, China2. Key Laboratory of Watershed Geographic Sciences, CAS, Nanjing 210008, China3. University of Chinese Academy of Sciences, Beijing 100049, China4. Collaborative Innovation Center of Modern Agriculture and Environmental Protection in Jiangsu Province, Huai'an 223300, Jiangsu, China5. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
Abstract:The nexus between housing prices and population has long been the hot topic for scholars in fields of humanities, economics, and geography. Taking 41 cities in the Yangtze River Delta as examples, this article explored the influence of housing prices on fertility rates with the panel data from 2008 to 2018. Employing the differential GMM (Dif-GMM) and Biased-corrected LSDV (LSDVC) approaches, we modeled the underlying mechanism at regional and sub-group levels. The result of Dif-GMM model indicated that the rocketing housing prices in the study area largely triggered the decline of fertility rates. The faster the housing price rose, the faster the fertility rate declined. Besides, by using the variable of housing price to income ratio, we measured the home affordability of residents in every city. The result indicated that the faster the home affordability decreased, the faster the fertility rate decreased. Furthermore, the result of LSDVC model shed further light on the heterogeneity of nexus across sub-groups. Specifically, the fertility rates in cities with medium or low housing prices were more vulnerable to the fluctuation of housing purchasing ability, while in cities with high or medium-high housing prices, the situation was opposite. Besides, different types of cities showed distinct variations in the fertility rates when facing changes of regional economic development, which depended on the income expectation and cost expectation brought by the economic development. Stemming from the aforementioned findings, we finally proposed some policy suggestions on how to increase people’ fertility intention. The primary one is to control the speed of housing prices rise. The second one is to improve the home affordability of residents through increasing housing subsidies or residents’ income. In addition, the regulation of rising housing price in cities with low or medium housing price should not be ignored. Instead, more targeted policies should be formulated in these regions, with a view to increasing residents’ overall willingness to have children and promoting sustainable population development in the whole region.
Keywords:housing prices  housing price to income ratio  fertility rates  home affordability  
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