Medical condition, population density, and residents’ savings in China’s contiguous destitute areas
Bibliographic Data
| ID | 15067238 |
|---|---|
| Authors | Xu Chunhua (0000-0001-5352-1553, International Service Economic Research Institute, Guangdong University of Foreign Studies, Guangzhou City, Guangdong Province, China), Weijin Gong (0000-0001-9720-1199, corresponding author), Gong Weijin (National Academy of Economic Strategy, Chinese Academy of Social Science, Beijing, China) |
| Year | 2020 |
| Volume | 33 |
| Issue | 1 |
| Pages | 652-671 |
| Publication date | 2020-01-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Economic Research-Ekonomska Istraživanja (JOURNAL) |
| Journal identifiers | ISSN: 1331-677X • E-ISSN: 1848-9664 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/1331677x.2019.1668284 |
| OpenAlex | W3013055994 |
| Language | EN |
| Citations received | 3 |
| References cited | 20 |
This paper uses the 2005–2012 spatial panel data of China’s 11 Contiguous Destitute Areas (CDAs) and different kinds of econometric regression models, we examines the implications of medical condition and population density for the residents’ savings in these 11 CDAs. We find that, the increase in population density not only would reduce residents’ savings through its own, but also has negative effect on residents’ savings through the way of medical condition, while medical condition has positively and significantly effect on the residents’ savings. This means that as a CDAs’ population density increases, the needs of medical condition will increase too, and then it will cause the medical condition to be deteriorating relatively, thereby reducing households’ precautionary savings. In most of the models, especially in the direct effects, indirect effects, and total effects, these results are roughly the same and robust. These findings mean that medical condition and population density not only have influence on residents’ savings on their own, but also will decrease the residents’ savings by their interaction
China · Demographic economics · Econometrics · Economics · Environmental health · Geography · Medical emergency · Medical treatment · Panel data · Population · Population density · Regression analysis · Statistics · Demography · Housing Market and Economics · Mathematics · Medicine · Urban and Rural Development Challenges · Urban, Neighborhood, and Segregation Studies
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| Citations per year | 0.75 |
|---|---|
| Citation span | 2022 - 2025 (4) |
| Highly cited | No |